Year 2000 Iowa Greenhouse Gas Emissions Inventory
Center for Energy & Environmental Education
University of Northern Iowa
Cedar Falls, Iowa
A report for the Iowa Department of Natural Resources
Funded by the U.S. Environmental Protection Agency
January, 2005
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Year 2000 Iowa Greenhouse Gas Emissions Inventory
Prepared by:
Tracy L. Wollin
William M. Stigliani
Center for Energy & Environmental Education
University of Northern Iowa
Cedar Falls, Iowa
A report for the Iowa Department of Natural Resources
Funded by the U.S. Environmental Protection Agency
January, 2005
Disclaimer
This document was prepared with a grant from the U.S. Environmental Protection
Agency (EPA). However, any opinions, findings, conclusions, or recommendations
expressed herein are those of the authors and do not necessarily reflect the views of the
EPA.
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Page numbers and some graphics in this PDF version may differ from the Microsoft Word
electronic version and printed copies of this document. Corrections have been made to
figure ES-8 on page 8 by lengthening bar for energy efficiency to represent an offset of 5.3
million MTCE; the executive summary description of potential emissions savings from
energy efficiency from 2.3 to 5.3 million MTCE on page 9; and figure 37 on page 91 by
lengthening bar for energy efficiency to represent an offset of 5.3 million MTCE.
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Table of Contents
Executive Summary 1
Balance between Emissions and Capture of Greenhouse Gases in
Iowa in Year 2000 1
Overview 1
Energy use 4
Agriculture 4
Industrial Processes 5
Waste Treatment 6
Forest management/ Land-use change 7
Solutions for Mitigating Greenhouse Gas Emissions 7
Wind 7
Biomass 8
Solar 8
Ethanol 9
Energy efficiency programs 9
Recycling and source reduction 9
Carbon sequestration 9
Conclusions 10
Chapter 1: Fundamentals 11
Comparing the 1990 and 2000 Inventories 11
The Natural Greenhouse Effect: Protector against "Iceball" Earth 12
Global Warming Potentials (GWP) 13
The Greenhouse Gases 15
Carbon dioxide (CO2) 15
Methane (CH4) 16
Nitrous Oxide (N2O) 16
Chlorofluorocarbons (CFCs), Hydrofluorocarbons (HFCs),
Perfluorocarbons (PFCs), and Sulfur hexaflouride (SF6) 17
Tropospheric ozone (O3) 17
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Photochemically active and indirect greenhouse gases 18
Units (MTCE) 18
Quality Assurance and Control: Data Attribute Ranking Systems (DARS) 19
Chapter 2: Summary of Findings 20
Net Greenhouse Gas Emissions in Iowa in Year 2000 and
Comparison with Year 1990 20
Emissions by Category: Overview 23
Chapter 3: Greenhouse Gases from Energy Related Emissions 25
Iowa's Energy Profile 25
Trends from 1990 to 2000 25
Iowa electricity production: sources and trends 26
Carbon intensity 29
Iowa end use sector energy analysis 30
Per capita indices 31
State comparisons of per capita carbon emissions from fossil fuel use 32
CO2 from the Combustion of Fossil Fuels 39
CH4 and N2O Emissions 43
Emissions from stationary and mobile combustion sources 43
Emissions from natural gas and oil systems 44
Chapter 4: Greenhouse Gas Emissions from Agriculture 45
N2O and CO2 from Agricultural Soils 45
CH4 from Domesticated Animals 47
CH4 and N2O from Manure Management Systems 49
CH4 and N20 from Burning of Agricultural Wastes 53
Chapter 5: Greenhouse Gas Emissions from Industrial Processes ..55
Inventory of Greenhouse Gas Emitting Processes 55
CO2 from cement clinker and masonry cement manufacture 55
N2O from nitric acid production 55
Substitutes for ozone depleting substances 56
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CO2 from limestone use 56
SF6 electricity transmission and distribution 56
CO2 from lime manufacture 56
CO2 emissions from CO2 manufacture 57
Other industries 57
Overall Industrial Emissions and Comparisons of the 1990 and 2000 Emission
Inventories 57
Chapter 6: Greenhouse Gas Emissions from Wastes 60
Overview 60
Greenhouse Gas Emissions and Carbon Capture from Solid Waste Disposal 61
Greenhouse Gas Emissions and Carbon Capture from Municipal Wastewater at
Sewage Treatment Plants 63
Chapter 7: Carbon Sinks from Forest Management and Land
Use Change 66
Carbon Flux in Forests 67
Carbon Flux in Agricultural Soils 68
Uncertainty in Forest Carbon Flux 69
Chapter 8: Potential to Offset Greenhouse Gas Emissions
through Renewable Energy, Energy Efficiency, Recycling, and
Carbon Sequestration 70
Progress to Date 70
Wind Energy 71
Biomass 72
Solar 76
Ethanol 78
Energy Efficiency 82
Recycling 88
Carbon Sequestration 88
Summary 90
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Chapter 9: Conclusions and Recommendations
Acknowledgements
Works Cited
Appendices
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Executive Summary
Balance between Emissions and Capture of Greenhouse Gases in Iowa in
Year 2000
Overview. Iowa was among the first states to accept the challenge of quantifying its
atmospheric burden of greenhouse gases, creating a baseline inventory for 1990 (Iowa
Department of Natural Resources, 1996). The intent of this 2000 inventory is to update
estimates of greenhouse gas emissions and gauge progress made toward greenhouse gas
mitigation in the decade of the 1990s. When possible, the previously reported 1990
emissions were recalculated to take into account new methodologies introduced in the 2000
inventory.
Overall, it appears that total greenhouse gas emissions have increased from 22.8
million MTCE1 in 1990 to 32.8 million MTCE in 2000. A precise comparison, however, is
difficult to make because of differences in methodology and data availability for the two
years in question, particularly with respect to emissions from agricultural soils. Offsetting
increases in emissions in the 1990s were increases in the amounts of carbon sequestered in
forests, croplands, and landfills, and recovered in landfills and wastewater treatment plants.
These increased carbon savings narrow the apparent increase in net emissions (emissions
minus sequestration and recovery) from 21.1 million MTCE in 1990 and 26.2 million MTCE
in 2000.
The overall analysis is summarized in Figure ES-1. As one can observe, fossil fuel
combustion was by far Iowa's greatest source of greenhouse gases in 2000, and growth in
their consumption, 26 percent between 1990 and 2000, was the major reason why emissions
increased in the 1990s. Most of the rise in demand was for coal and petroleum, which grew
by 28 and 37 percent, respectively.
Iowa's energy picture is diversifying with the development of new sources of energy
such as natural gas-fired power plants, wind power, and biomass-derived fuels. Even so,
fossil fuels still dominate the market, and energy forecasts indicate the transition away from
them will likely be slow. Despite spectacular recent development in wind-generated
electricity, it still comprises only 1 percent of total electricity generation in Iowa, while
nuclear power, another emissions-free source of electric power, has grown to provide one-
tenth of the total demand.
Energy use increased in all four economic end-use sectors — industrial, commercial,
transportation, and residential. The industrial sector experienced the greatest increase at 36
percent. The residential sector saw the smallest increase at 3 percent owing to energy
efficiency programs, improved building energy codes, and technological advances.
1 MTCE stands for "metric ton of carbon equivalents." It is the standard unit used in greenhouse gas
inventories, and applied in order to equate the impact of different greenhouse gases that vary in their
"greenhouse effect" potency and chemical composition (some do not even contain carbon). For further details,
see page 18.
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Figure ES-1. C02 from fossil fuel combustion and N20 from agricultural soils make up 84 percent of all
emissions. Agricultural soils and forests also sequestered large amounts of carbon.
2
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Slow population growth and fast energy growth drove up per capita energy use in
Iowa in the 1990s. This ratio started to flatten out in the late 1990s, however, because the
electric utilities began burning coal emitting less carbon per unit of energy produced. With a
decentralized farm economy and a strong dependence on coal, Iowa exceeded U.S. average
per capita carbon consumption. As shown in Figure ES-2, it was the 13th highest state in
overall carbon emissions per capita from fossil fuel consumption. Its best performance was
in the transportation sector, where it ranked the 31st from the top, thus placing Iowa's per
capita transportation energy consumption below the national average.
All End Sectors - Per Capita C02 Emissions from Fossil Fuel Combustion, Year 2000
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Figure ES-2. Iowa ranked as the 13th highest per capita carbon emitting state from fossil fuel combustion in all
end use sectors, possessing annual emissions of 6.74 MTCE per person. The national average was 5.59 MTCE
per person.
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Energy use. In this report, the fluxes of greenhouse gases are grouped into five categories;
energy, agriculture, industry, wastes, and forest management/land-use change. As shown in
Figure ES-3, energy-related activities contributed 67 percent of total greenhouse gas
emissions, the largest share by far of all the categories. Carbon dioxide (C02) emissions
from combustion of fossil fuels made up two-thirds of all greenhouse gas emissions in Iowa,
totaling 21.3 million MTCE. This amount was up from 17 million MTCE in 1990. The
largest fraction came from electricity generation. Fugitive emissions and storage leaks from
natural gas transmission and distribution systems comprised the second largest source in this
category, adding another 553,278 MTCE. Compared to carbon dioxide, emissions of
methane (CH4) and nitrous oxide (N2O) from mobile combustion were rather insignificant
(14,212 MTCE and 172,033 MTCE, respectively). Nitrous oxide emissions from stationary
combustion sources were estimated to be 61 MTCE.
Iowa Greenhouse Gas Emissions from Energy Related Sources, Year 2000
Fossil Fuel Combustion C02 |
67% of Iowa
Natural Gas Systems ] Emissions
Mobile Combustion j / \
V / )
Stationary Combustion
1 1 1 1 1
0 5,000 10,000 15,000 20,000 25,000
1,000 MTCE
Figure ES-3. The leading source of energy sector emissions was C02 generated from fossil fuel combustion.
Agriculture. Agricultural activities, with 27 percent of overall emissions, made up the
second largest category of greenhouse gas emissions. Figure ES-4 illustrates the contributing
sources, the largest of which is soil nitrogen amendment through cropping practices,
comprising 6.3 million MTCE (19 percent of total emissions). This activity includes
application of synthetic and organic fertilizers, incorporation of crop residues into the soil,
growth of nitrogen fixing crops and deposition of manure directly onto pastures. This source
is a factor of six higher than the estimates published in the 1990 inventory, but the dramatic
rise is likely an artifact owing to improvements in the method of estimation. Previously, only
the fraction of emissions from application of fertilizers was quantified. Also new to the
methodology is the estimate of carbon dioxide from field lime (CaC03) applications,
calculated to release 222,545 MTCE.
Emissions from manure management increased slightly from 1990, masking the large
shifts that occurred in animal populations. The substantial rise in numbers of swine and layer
chicken in the past decade pushed emissions upward, while the large drop in cattle
populations served to hold down the overall increase. Emissions of nitrous oxide rose from
854,941 MTCE in 1990 to 989,376 MTCE in 2000. Methane also increased from 220,845
MTCE to 235,916 MTCE. The lower ruminant cattle population decreased emissions from
enteric fermentation in domesticated animals from 1.2 million to 0.9 million MTCE.
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Iowa Greenhouse Gas Emissions from All Agricultural Sources, Year 2000
Ag Soils
Manure Management
27% of all Iowa
Emissions
Domesticated Animals
Burning Crop Waste
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1,000 2,000 3,000 4,000 5,000 6,000 7,000
1,000 MTCE
Figure ES-4. Emissions from agricultural soils dominated the agricultural source category. Burning crop
wastes was not a significant source of emissions.
Burning of agricultural crop residues was identified as a large emitter of carbon
dioxide in the 1990 inventory. However, full carbon cycle analysis indicates that biogenic
sources of carbon dioxide such as crop residue burning do not produce significant net
greenhouse gas emissions. Based on this reevaluation we calculated the 2000 emissions and
recalculated the 1990 emissions and found them to be of minimal importance. Moreover, the
method of calculating gross emissions from this source, based on average national values,
may be overestimated for Iowa because burning agricultural fields is rather uncommon in the
state. Total mass burned may have increased in 2000 relative to 1990 because of the increase
in crop yield, but the emissions are still rather miniscule. Methane and nitrous oxide are
estimated to have increased from 21,375 and 13,245 MTCE to 26,558 and 17,431 MTCE,
respectively. The total represents about 0.1 percent of Iowa's entire greenhouse gas burden.
Industrial processes. As shown in Figure ES-5, emissions from non-energy related
industrial processes are minor compared to the energy and agriculture categories, estimated
to contribute only about 0.9 million MTCE (3 percent) to the total emissions. Cement
clinker manufacture and limestone (CaC03) use in industry are the largest emitters of carbon
dioxide (total of over 400,000 MTCE). Lime (CaO) manufacture, reported to be a major
CO2 emitter in the 1990 inventory (over 500,000 MTCE), is estimated to be much smaller in
2000 (34,000 MTCE). The latter conclusion is tenuous, however, because the current
estimate came from an anonymous government source using proprietary data, and an
undisclosed method of calculation by the U.S. Environmental Protection Agency (EPA).
Nitric acid production, based on its synthesis in fertilizer production, is the largest
emitter of nitrous oxide, accounting for 229,000 MTCE. In the 1990 inventory, this gas was
unreported because of lack of activity data.
Substitutes for ozone depleting substances include hydrofluoro- and perfluorocarbons
(HFCs and PFCs). Emissions of HFCs and PFCs were estimated to have risen sharply
between 1990 and 2000 with the increase in their use as substitutes for ozone-depleting
chlorofluocarbons (CFCs), going from 2,740 MTCE to 163,916 MTCE in the ten-year
period. In contrast, emissions of sulfur hexafluoride (SF6) were estimated to be dropping in
its use in electrical transformers because of increased environmental awareness about its
potency as a greenhouse gas.
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Iowa Greenhouse Gas Emissions from Industrial Processes, Year 2000
Cement Clinker Capacity
Nitric Acid Production
Substitution for Ozone Depleting Substances
Limestone Use
Electrical Transmission & Distribution
Lime Manufacture
Masonry Cement Capacity
Carbon Dioxide Manufacture
3% of all Iowa
Emissions
50
100
150
1,000 MTCE
200
250
300
Figure ES-5. Industrial emissions were dominated by cement clinker production, and a few other processes.
Waste treatment. Sources of emissions from treatment of wastes are provided in Figure ES-
6. At about 1.1 million MTCE (3 percent of the total), they are comparable in magnitude to
industrial emissions, but net emissions are significantly lower at about 0.5 million MTCE
owing to methane recovery and carbon sequestration in landfills. Wastewater and sludge
treatment at sewage treatment plants release methane and nitrous oxide emissions totaling an
estimated 65,000 MCTE. Of this total about 23,000 MCTE are recovered as methane,
yeielding net emissions of about 41,000 MTCE. These numbers may underestimate
emissions somewhat because they are based on only treatment of human wastes, rather than
considering all organic matter removed during municipal wastewater treatment.
Methane emissions from solid waste disposal (landfills), the most significant source
by far, increased slightly between 1990 and 2000. Undoubtedly, they would have risen
higher had it not been for initiatives set in place to reduce landfill usage through source
reduction and recycling. The amount of carbon that remains buried and unoxidized in
landfills is considered to be sequestered, crediting Iowa with an annual savings of over
413,377 MTCE. The large expansion of methane recovery and flaring at landfills provided
Iowa with an additional 150,000 MTCE in carbon credits.
Iowa Greenhouse Gas Emissions from Wastes, Year 2000
Landfill CH4
Sludge and Waste Water Treatment
Solid Waste Incineration
Wastewater CH4 Recovery
Landfill CH4 Recovery
Landfill C Sequestration
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—' 3% of all Iowa
Emissions
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-600 -400 -200
200 400
1.000 MTCE
600 800 1,000 1,200
Figure ES-6. Landfills are the largest source of emissions, as well as the largest source of carbon capture
among waste management practices.
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Forest management/Land-use change. Figure ES-7 gives the magnitudes of carbon
sequestration in the category "forest management/land-use change." These agricultural and
forest "sinks" for carbon serve to offset emissions of greenhouse gases from the other four
categories. During the 1990s, forests (biomass plus soil) were estimated to be sinking 2.9
million MTCE per year. Using a different method of estimation for forest carbon flux, Ney
et al. (2001) reported that Iowa forests were sinking 1.2 million MTCE annually. The
expansion of forestland may account for some of the difference between estimates. With
regard to carbon sequestration in agricultural soils, the U.S. Department of
Agriculture/Natural Resource Conservation Service estimated that Iowa agricultural soils
absorb 3.1 million MTCE annually, mostly as a consequence of adopting conservation tillage
and to a lesser degree due to increased crop yields (Brenner et al., 2001).
Iowa Annual Carbon Sequestration through
Forest Management and Land Use Change
Agricultural Soils
Forests
-3,500 -3,000 -2,500 -2,000 -1,500 -1,000 -500 0
1,000 MTCE
Figure ES-7. Forests in Iowa cover a small part of the state but sequester almost as much carbon as
agricultural soils.
Solutions for Mitigating Greenhouse Gas Emissions
Iowa has numerous options to reduce emissions of greenhouse gases. These include
the development of renewable energies, enhancement of energy efficiency and recycling, and
carbon sequestration. It was determined that initiatives such as these currently offset 8.7
million MTCE annually. The potential impacts of these options are summarize in Figure ES-
8, and explored in more detail below.
Wind - annual emission reduction potential of 10 to 39 million MTCE. Jumping from a
nameplate capacity of less than 0.5 MW in 1998 to 194 MW in 1999 to 471 in 2004, wind is
the fastest growing renewable energy source in the state, with plans to expand capacity to
781 MW by 2006. At that point, Iowa wind will deliver nearly 2 million MWh of electricity
and avoid 541,000 MTCE in carbon emissions annually. Wind power appears to possess the
largest potential by far of any measure Iowa could undertake to offset greenhouse gas
emissions. Estimates of the state's remaining untapped wind potential are huge, suggesting
that Iowa could supply up to four times its electricity demand in 2000, with an offset in
emissions of between 9.5 to 38.7 million MTCE per year. Less conservative estimates
suggest the potential is 12 times the state's electricity demand, providing offsets up to 132.1
MTCE.
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Potential to Offset Greenhouse Gas Emissions in Iowa by Use of Renewable Energies,
Energy Efficiency, Recycling/Source Reduction, and Carbon Sequestration in Soils
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Figure ES-8. Wind and biomass energy, and carbon sequestration appear to offer the greatest potential for
offsetting greenhouse gas emissions. Gray area under wind signifies emissions offset if wind-generated
electricity were to equal state's total electricity consumption in 2000; combined gray/hatched area corresponds
to emission offsets if wind-generated electricity were to produce four times the state's year 2000 consumption.
Biomass - annual emission reduction potential of 12 million MTCE. Energy from
biomass represents a large potential for greenhouse gas emission reductions. The most
promising resources available for development are corn stover and dedicated energy crops.
Expansion of methane recovery from landfills, wastewater and livestock also represent
feasible options to capture energy from biomass and abate CH4 emissions. All together,
biomass resources can offset 12 million MTCE of carbon dioxide, primarily through
replacement of coal-fired electricity.
Solar - annual reduction potential of 3 million MTCE. Solar power in Iowa is a feasible
option as a clean, renewable energy resource. The fastest growing market for photovoltaic
(PV) technologies is in customer-sited, grid-connected, roof-mounted systems in the
residential and commercial sectors (National Renewable Energy Laboratory, 2003). The
more than 940,000 single family homes in Iowa provide up to 58.4 million square meters of
roof space for mounting PV panels with a generation potential of 13.6 million MWh of
electricity, or 22 percent of year 2000 electricity consumption. Avoided emissions would be
3 million MTCE, equivalent to 32 percent of emissions from year 2000 electricity
production. However over the short term, Iowa's achievable potential for PV may continue
to be modest when compared to wind and biomass development that currently show greater
balance between costs and benefits.
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Ethanol - annual emission reduction potential of 2.8 million MTCE. Iowa has embraced
the benefits of ethanol fuels; more than half of year 2000 motor fuel purchases were ethanol
blends of either E10 (10 percent ethanol) or E85 (85 percent ethanol) (Iowa Department of
Natural Resources, 2002a). Because the use of ethanol brings feedstock and fuel production
to the state, the result is higher in-state emissions from the agriculture and energy use
categories that must be balanced against gains from reduced fuel combustion emissions.
Consuming 93 million gallons of ethanol in 2000, the state offset gasoline combustion
emissions by 149,915 MTCE. There is great potential in Iowa to further replace gasoline
with ethanol. Analysis shows that if all gasoline sold in the state were blends of E10 or E85,
gasoline emission offsets would be 237,716 MTCE and 2,831,305 MTCE respectively.
Energy efficiency programs - annual emission reduction potential of 5.3 million MTCE.
Energy efficiency programs have focused on demand-side management, including residential
tune-ups and weatherization, energy-efficient equipment rebates, new construction, and
recycling. Since 1990, Iowa's programs are estimated to have offset more than 1 million
MWh of electricity and about 283,000 MTCE emissions. Great potential still exists to
enhance energy efficiency in the state, and Iowa is committed to expanding its progress. The
Iowa Department of Natural Resources (IDNR) is an active partner in programs that
encourage energy efficiency installments across all sectors. A study through the U.S.
Department of Energy's Oakridge National Laboratory examined the potential for demand-
side energy management to reduce consumption in the state by the year 2020 (Hadley, 2001).
Using an economic simulation model with a moderate energy efficiency policy scenario and
an industrial market assessment survey, the study found that Iowa could reduce projected
energy use across all energy sectors by 5 percent by 2020.
A detailed study commissioned by Iowa's four investor owned utilities (Global
Energy Partners, LLS, 2002) explored emission reductions through the implementation of
258 demand side management energy efficiency measures specific to 26 segments of Iowa's
end use sectors. Interestingly, the residential sector had the greatest technical savings
potential through lighting, cooling and water heating. Ideally, by year ten of a plan working
toward implementing these measures, Iowa could be saving up to 5.3 million MTCE
annually.
Recycling and source reduction - annual emission reduction potential of 979,731
MTCE. A report prepared by the Tellus Institute (1999) analyzed emission reductions due
to recycling and source reduction activities in the state. The analysis employed state landfill
use reduction goals as the basis for the avoided emissions from resource extraction and
manufacturing. In 1995, Iowa attained a 33 percent reduction in waste relative to the 1988
baseline, resulting in annual greenhouse gas emission reductions estimated at 575,589
MTCE. Projections for future scenarios show that if Iowa were to reach the 50 percent waste
diversion goal, it could avoid nearly 1 million MTCE annually. If diversions were to drop to
25 percent, avoided emissions would accordingly decline to 374,023 MTCE.
Carbon sequestration - annual emission reduction potential of 6.4 million MTCE.
Carbon sequestration is one of the most promising options to reduce net greenhouse gas
emissions. As a major agricultural state, Iowa can exploit the opportunity to reduce soil
carbon emissions and thus store more soil carbon through adoption of conservation tillage.
9
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The Iowa Carbon Storage Project has created a county level database that provides modeled
predictions of soil carbon storage resulting from changes in crop rotations, tillage regimes,
and enrollment in the Conservation Reserve Program (Brenner et al., 2001). This database
can help focus carbon sequestration initiatives by indicating where the greatest potential for
carbon storage exists. Most of the 4.3 million hectares (10.6 million acres) of agricultural
land are still managed by conventional tillage practices. If three-quarters of this area were to
convert to "no-tillage" management, 4.4 million MTCE could be returned to the soil
annually.
Reforestation was an option featured in the Iowa Greenhouse Gas Action Plan (Iowa
Department of Natural Resources, 1996). Converting between 200,000 and 1 million acres
of land to forest would store an estimated 224,000 to 1.1 million MTCE per year,
respectively. The inclusion of 200,000 acres of hybrid poplar tree as buffer strips could
sequester and estimated additional 817,000 MTCE in Iowa's riparian environments. An
initiative to establish switchgrass as a biofuel for cofired electricity generation would restore
carbon to the lands in which they are grown. Building 100 MW of capacity from switchgrass
could sequester an estimated 81,400 MTCE annually. Taken together, no-till management,
reforestation, planting of poplar buffer strips and development of bioenergy crops can restore
between 5.5 million and 6.4 million MTCE per year.
Conclusions. Any comprehensive solution for mitigation of greenhouse gas emissions in
Iowa must focus considerable effort on reductions in fossil-fuel derived combustion.
Generation of electricity by coal and gasoline consumption dominate fossil fuel use and
continue to grow. Although Iowa has made considerable improvements in energy efficiency
and enormous strides in development of renewable energies, efforts thus far have been
unable to keep pace with growth in fossil fuel use.
No one strategy will reverse this trend. Rather, an array of coordinated actions can
serve to reign in net greenhouse gas emissions. It is even possible that Iowa could one day
become a net "negative emissions" state, in which sequestration and capture of greenhouse
gases exceeds their in-state emissions. This would require far greater investment in
renewable sources of energy generation, implementation of more aggressive energy
efficiency measures, expanded recycling and source reduction programs, and continued
development of land-use activities that sequester/capture carbon in landfills, forests and
croplands.
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Chapter 1: Fundamentals
Comparing the 1990 and 2000 Inventories
Responding to mounting concern over global climate change, Iowa was among the
first states to complete an independent state level greenhouse gas inventory in 1992. Later,
fulfilling a need to provide a uniform methodology for comparison between states, the U.S.
Environmental Protection Agency (EPA) provided the first state workbook for estimating
emissions. The methodology was based on guidelines offered by the Intergovernmental
Panel on Climate Change (IPCC) for country level greenhouse gas analysis. In 1996, Iowa
completed a baseline inventory for activities and emissions for the year 1990, using the EPA
State Workbook (Iowa Department of Natural Resources, 1996).
Major findings of the inventory included:
• Iowa's net greenhouse gas emissions were estimated at 21.5 MTCE.2
• Fossil fuel combustion was the main source of greenhouse emissions,
contributing 70 percent of net emissions.
• Compared to other states, Iowa ranked 15th highest for carbon dioxide
emissions per capita from fossil fuel combustion.
• Methane from domesticated livestock was the second largest source (2.1
million MTCE).
• Fertilizer application was estimated to be only 4 percent of gross emissions
(1.1 million MTCE).
• Burning of agricultural crop wastes was estimated to be 7 percent of gross
emissions (since believed to be a vast overestimation).
• Forests were estimated to be sequestering roughly 6.9 million MTCE.
Since the 1990 inventory, the EPA has turned over the task of designing the state
level inventory methodology to the Emission Inventory Improvement Program (EIIP). The
program has developed volumes of methodologies to help states estimate emissions of
various air pollutants. Greenhouse gases are addressed in volume VIII (Emission Inventory
Improvement Program, 1999). The methods in volume VIII are based on IPCC guidelines as
well as methods from the EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-
1996 (Environmental Protection Agency, 1998b), and reflects improvements culled from
state energy and environmental officials. Because volume VIII represents the most current
methods available for state greenhouse gas emission inventories, it was the method of choice
for this project.
One goal of the year 2000 inventory is to compare emissions with the baseline
estimates provided for 1990. The inventory is divided into five categories that encompass
activities involved with energy, agriculture, industrial processes, waste management, and
forest and land-use management. Each category summarizes the results of analysis based on
activity data and emission factors provided by EIIP, volume VIII. An appendix is provided
for each category to provide detailed calculations for the derivation of the emission estimates
reported here.
There are a number of reasons why emission estimates change over time. A reported
2 See page 18, for meaning of "MTCE."
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reduction or increase does not necessarily mean the situation has improved or degraded.
Apparent changes in emissions can be brought on by changes in the methods and
assumptions in estimating emissions. In comparing the 1990 and 2000 inventories, the 1990
estimates were recalculated in those cases where methodologies had been updated. Such was
the case for Iowa's second largest source of emissions, agricultural soils. The previous
inventory quantified direct nitrous oxide emissions only from application of commercial
fertilizers. In EIIP, volume VIII, this represents just one-third of direct emissions from
agricultural soils. The current method also accounts for indirect emissions from nitrogen
application and emissions from animals allowed to graze on fields. Clearly, it is pertinent to
determine the comparability of methods of estimation.
On the other hand changes may reflect real changes in level of activity of a particular
source. Such was the case for industrial limestone consumption. The 1990 inventory
reported consumption of 23 million metric tons of calcite and dolomite. Sources for year
2000 indicate only one million metric tons were used by the industry. This reduction in
activity led to a very different estimate of emission.
The Natural Greenhouse Effect: Protector against "Iceball" Earth
The natural greenhouse effect is a well understood phenomenon. (For a detailed
explanation, see the IPCC's latest report Climate Change 2001 The Scientific Basis (Baede et
al., 2001). The sun emits radiation to Earth in the form of visible, near-infrared (IR) and
ultraviolet (UV) light with a flux rate of 342 Watts/m2. A portion of this radiation (107
Watts/m2) is reflected back to space by the clouds, atmosphere and Earth's surface; this
reflected light does not play a role in Earth's overall heat balance (Spiro and Stigliani, 2003).
Some of the solar energy not reflected is absorbed by the atmosphere (67 W/m2) and the
remainder by Earth's surface (168 W/m2). By the laws of physics Earth's heat balance must
be in steady state, i.e., the incoming solar radiation must be balanced by outgoing radiation
from Earth's surface and atmosphere, which in this report we shall name "earthshine."
While the incoming sunlight is mostly in the visible light range, outgoing earthshine is in the
IR range with a flux of 235 W/m2 to balance the sum of solar fluxes of 67 and 168 W/m2.
If sunlight and earthshine were the only factors at play in the heat balance, the global
average temperature at Earth's surface would be about -18 °C (0 °F). The result would be a
so-called "iceball" Earth, which would be inhospitable to most life on the planet. In
actuality, the global average surface temperature is much warmer at +14 °C (57 °F). We owe
this warming effect to the natural "greenhouse effect." It occurs because some of the
earthshine emitted to the atmosphere is intercepted by certain gases in the atmosphere (the
"greenhouse" gases) that first absorb the radiation and subsequently re-emit it. A portion of
the re-emitted radiation is reflected back to earth's surface, thereby heating it and raising its
temperature. The overall heat balance is maintained because the surface is heated at the
expense of the atmosphere, which is cooled by the greenhouse effect. The main greenhouse
gases are carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and water (H20).
Thus, the current debate about the greenhouse effect is not whether it is good or bad; clearly
we owe our existence to it. Rather, it is whether or not, by increasing the concentrations of
greenhouse gases in the atmosphere, we are creating too much of a good thing? Scientists
estimate that the increase in greenhouse gas concentrations in the atmosphere due to human
activities has raised the radiation flux back to Earth by 2.43 W/m2, resulting in an increase in
temperature of about 0.6 °C. The year to which temperature and flux increases are compared
12
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is 1750, which serves as a pre-industrial baseline when little anthropogenic radiative impact
is assumed. Though the current magnitude of radiative change is small in comparison to the
total radiation entering and leaving the atmosphere, growth in this imbalance could have
severe impacts on the Earth's climate systems.
Among the greenhouse gases cited above, water is not a major issue because its
concentration is regulated by the hydrological cycle and out of our control. The focus of
policy debate is on the other three natural greenhouse gases, and a few additional ones that
are not found in nature, but rather are synthetic industrial gases that by coincidence also
exhibit a greenhouse effect. In this latter category, the major ones are the
chlorofluorocarbons (CFCs) formerly used in foam insulations, aerosol sprays, and air
conditioners. They are being phased out due to strong international cooperation between
nations and industries that produce them. However, their long lifetimes in the atmosphere
(45 - 1,700 years) and their radiative efficiency mark them as important players in perturbing
the radiation imbalance long after we have ceased emitting them to the atmosphere. There
are other greenhouse-active, synthetic gases with very small low current emissions, that may
be more abundant in the future. Moreover, there are drivers of climate change other than
greenhouse gases that may serve to heat or cool the climate. Among these are trophospheric
aerosols, land-use change, solar irradiance and stratospheric aerosols (Ramaswamy, et al.
2001). Uncertainty over the net impact of all these agents is a favored argument among
critics of those advocating actions to mitigate climate change.
Global Warming Potentials (GWP)
Scientists have adopted the parameter global warming potential (GWP) as an index
for assessing the relative potency of well mixed greenhouse gases over a particular time
horizon. Specifically, it quantifies the capacity of a gas to trap IR radiation in comparison to
CO2, which has been designated as the reference gas and assigned a GWP value of 1.
Molecular radiative properties (i.e., radiative efficiency, abundance) control the absorption of
radiation per kilogram of gas at any instant, but the atmospheric lifetime determines the
duration over which this absorption can take place (Ramaswamy, et al., 2001). Both factors
are considered in the quantification of GWP. Values can be assigned only to those
greenhouse gases with atmospheric lifetimes long enough to allow homogeneous mixing in
the troposphere (so-called "well mixed" gases). This is not the case for gases with regionally
distinct patterns of accumulation such as water and tropospheric ozone.
As shown n Table 1, greenhouse gases have atmospheric lifetimes ranging from a few
years to millennia. On timescales important to humans, say one or two generations, gases
with extremely long lifetimes represent essentially irreversible emissions, having the
potential to build up in the atmosphere without an efficient removal mechanism. Fortunately,
the gases with the longest lifetimes are emitted in the lowest quantities and have minimal or
no natural sources. Halting the buildup of these essentially synthetic chemicals can be
accomplished through bans on production, as is the case currently with the CFCs. On the
other hand, more abundant gases with relatively shorter lifetimes and large human-derived
emissions are the most promising targets for quelling future greenhouse forcing, because
greater reductions by mass can be achieved.
13
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Table 1. Atmospheric lifetimes (years) and Global Warming Potentials of
well mixed greenhouse gases based on a 100-year time horizon.
Gas
Atmospheric
Lifetime
GWPa
Year 1996
GWPb
Year 2001
C02
50-200
1
1
o
X
-t.
o
12
21
23
n2o
114
310
296
CFC-11
45
4,000
4,600
HFC-23
260
11,700
12,000
HFC-125
29
2,800
3,400
HFC-134a
13.8
1,300
1,300
HFC-143a
52
3,800
4,300
HFC-152a
1.5
140
120
HFC-227ea
33
2,900
35,00
HFC-236fa
220
6,300
9,400
HFC-4310mee
15
1,300
1,500
cf4
50,000
6,500
5,700
c2f6
10,000
9,200
11,900
o
Jr|
o
2,600
7,000
8,600
CsFm
3,200
7,400
9,000
sf6
3,200
23,900
22,200
Source: Intergovernmental Panel on Climate Change, 2001.
a GWP used in this inventory represents figures from the second IPCC
assessment report in 1996.
b GWP was updated in Third Assessment Report of the 2001 IPCC.
0 Methane GWP includes direct effects and those indirect effects due to
the production of tropospheric ozone and stratospheric water vapor; the
indirect effect due to the production of C02 is not included.
Quantification of GWP for different time horizons (e.g., 20, 100, 500 years) is useful
for analysis of shorter term impacts, such as the response of cloud cover to surface
temperature change or longer term impacts such as sea level rises (Ramaswamy et al., 2001).
The United Nations Framework Convention on Climate Change (UNFCC) has established
the 100-year time horizon as the foremost measure for member countries in assessing global
warming impacts of greenhouse gases (Environmental Protection Agency, 2001). These
figures have been recently revised in the IPCC third assessment report on climate change
(Intergovernmental Panel on Climate Change, 2001). Table 1 shows the earlier and revised
GWPs and atmospheric lifetimes for a range of greenhouse gases. In this inventory, we
employ the GWPs cited in the second IPCC assessment report published in 1996. This was
done to conform to the 1996 values cited in the EPA national greenhouse gas inventory for
2000, in compliance with the UNFCC reporting guidelines. Supplemental analysis of the
U.S. inventory showed these revised values did not have a significant overall effect on U.S.
emission trends, showing only a 0.7 percent increase in year 2000 emission estimates when
new GWP values replaced the 1996 values (Environmental Protection Agency, 2001).
Some gases, namely methane, carbon monoxide, nitric oxides and CFCs, can exert an
indirect effect on the global radiative budget. This category consists of both greenhouse and
non-greenhouse gases that are chemically reactive in the atmosphere and in some measure
exert controls on the abundances of the direct greenhouse gases (Ehhalt et al., 2001).
14
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Methods for quantification of indirect GWPs are highly uncertain because such reactions can
be difficult to predict owing to a number of processes and conditions that contribute to the
indirect effects of various molecules. Complex and highly uncertain models have been
employed to estimate indirect effects of several gases. However, these values remain unused
for inventorying gases (Ramaswamy, et al., 2001).
The Greenhouse Gases
Carbon dioxide (C02). Atmospheric concentrations of carbon dioxide have risen by 32
percent since preindustrial times, from around 280 ppm by volume (ppmv) in 1750 to about
370 ppmv today (Albritton et al., 2001). The IPCC states that such a rapid increase has not
occurred for at least the past 20,000 years and is a consequence of anthropogenic carbon
dioxide emissions, mostly from burning fossil fuels and clearing of forested land. Radiative
forcing3 for this gas is estimated at 1.46 W/m2, corresponding to 60 percent of the total
forcing from anthropogenic changes in concentrations of all long lived and well mixed
greenhouse gases.
Carbon is naturally cycled through the terrestrial, ocean and atmospheric reservoirs as
organic matter, minerals and gases. Lifetimes in these environmental compartments range
from weeks to centuries and longer. The carbon cycle is of particular importance to life on
the planet. Carbon dioxide is the primary vehicle of carbon exchange between the
atmosphere and other reservoirs. Growth and decomposition of biomass represent a full
cycle of carbon exchanged between the atmosphere and biosphere. During photosynthesis
plants fix carbon dioxide to organic carbon, the building block for a vast array of vegetative
structures that comprise plant biomass. Eventually this biomass is transformed by direct
microbial decomposition on land or sea, by herbivore consumption, or by combustion (e.g.,
forest fires, burning wood for fuel). In each case the organic carbon is reoxidized to carbon
dioxide and returned to the atmosphere. In nature, this cycling is assumed to occur in a
steady state with no net increase or decrease in atmospheric carbon dioxide, and thus with no
net climate forcing. The aerobic decomposition of grass clippings, the harvesting and
consumption of crops, the burning of prairies and forests for maintenance, and even
sustainable harvest of wood and grasses for fuel and products are not sources of emissions
that need to be counted in inventories like this one.
In contrast, emissions that result in the loss of long-stored carbon from land and sea
to the atmosphere disrupt the natural equilibrium of carbon between its various reservoirs.
There are two major sources of such emissions — the clearing and conversion of forest and
grassland and the excavation and combustion of fossil fuels. Deforestation is occurring on a
large scale in developing countries, particularly in the rain forests of Brazil and Indonesia,
where demands for wood and clear cutting for agriculture are the driving forces. In other
parts of the world, particularly the Northern Hemisphere, reforestation is apparently a net
sink for carbon. Globally, however, the net loss of carbon from forests to the atmosphere is
estimated to be 1.6 ± 0.8 gigatons (109 tons) carbon per year. The IPCC reports that
regenerated forests store less carbon than natural forests, even at maturity (Prentice et al.,
2001). Thus, fully restoring the carbon lost during deforestation will be difficult to achieve.
Globally, fossil fuel combustion, estimated to release on the order of 5.8 x 109 metric
3 Radiative forcing corresponds to the calculated additional radiation that human-added greenhouse gases
radiate back to Earth's surface beyond that already radiated by the natural greenhouse effect.
15
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tons of carbon per year, is responsible for three quarters of human influenced greenhouse gas
emissions (Albritton et al., 2001). Nationally, CO2 from fossil fuel combustion comprised 81
percent of gross greenhouse gas emissions with 1.6 x 109 metric tons carbon in 2000
(Environmental Protection Agency, 2002).
Methane (CH4). Methane is the second most dominant greenhouse gas. By weight it
possesses about 21 times the heat trapping capacity of carbon dioxide. Almost 20 percent of
the increase (0.48 W/m2) in radiative forcing by anthropogenic greenhouse gas emissions
comes from methane (Ramaswamy, et al., 2001). Nationally, it accounted for 9 percent of
greenhouse gas emissions in 2000 with 167 million MTCE (Environmental Protection
Agency, 2002). Analysis of snow and ice cores has determined that the atmospheric
concentration of this gas has increased two-and-one half times since preindustrial times, from
about 700 parts per billion by volume (ppbv) to about 1750 ppbv, although the rate of
increase has been declining in recent years (Environmental Protection Agency, 2001; Ehhalt
et al., 2001). It is estimated that approximately 60 percent of this global increase originates
from anthropogenic activities, and natural sources such as wetlands, termites, oceans and
hydrates contribute the balance (Ehhalt et al., 2001).
Anaerobic decomposition of organic matter is the primary cause of human-derived
methane emissions. The main sources are disposal of waste in landfills, enteric fermentation
by domesticated animals, decomposition and treatment of organic wastes, and wetland rice
cultivation. Other sources of methane release come from the energy sector and include
emissions of unburned hydrocarbons from mobile and stationary combustion, coal mining,
and fugitive emissions from drilling and transporting petroleum and natural gas. Another
minor source comes from the burning of agricultural residues. In nature, methane is emitted
from wetlands, termites, ocean water and sediment, and methane hydrate deposits on the
ocean floor (Ehhalt et al, 2001; Environmental Protection Agency, 1999a).
The major removal mechanism of atmospheric methane is reaction with the hydroxyl
radical (OH*) in the troposphere, producing water and carbon monoxide, which is rapidly
oxidized to carbon dioxide (Environmental Protection Agency, 1999a).
Nitrous oxide (N2O). Nitrous oxide accounts for about 6 percent (0.15 Wm'2) of the
increased global radiative forcing from well-mixed greenhouse gases. Although its
concentration is about 1,000 times lower than carbon dioxide and 5.5 times lower than
methane, it exerts a disproportional influence on radiative forcing because of its high GWP
value of 310. Nationally, it is responsible for about 6 percent of gross greenhouse gas
emissions at 117 million MTCE (Environmental Protection Agency, 2002).
Globally, the greatest source of rising emissions of N20 is agricultural soils, caused
by the ever increasing use of synthetic nitrogen fertilizers since the end of World War II and
the increased production of legumes, which fix atmospheric nitrogen (i.e., convert N2 to
NH3). Excess nitrogen amendments in these environments that are not taken up by crops
undergo microbial conversion mostly to diatomic nitrogen (N2), but also to nitrous oxide as a
minor byproduct (Ehhalt et al, 2001).
Another major source of rising emissions is the combustion of fossil fuels by which
high ignition temperatures convert atmospheric nitrogen (N2) to nitric oxide (NO). The nitric
oxide is subsequently converted to nitric acid (HNO3) in the atmosphere and deposited on
land. Some of the excess not taken up by plants is microbially transformed to N2O,
16
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especially in phosphorus-limited ecosystems of the tropical southern hemisphere (Ehhalt et
al, 2001).
Chlorofluorocarbons (CFCs), Hydrofluorocarbons (HFCs), Perfluorocarbons (PFCs),
and Sulfur hexaflouride (SF6). Since international bans against the production of ozone
depleting CFCs began with the 1987 Montreal Protocol, chemical companies have been
producing and marketing substitutes. This switch has led to a peaking and subsequent slow
decline in atmospheric concentrations of CFCs. Although they were phased out because of
concern for their ability to deplete stratospheric ozone, some of the CFCs are also long-lived,
potent greenhouse gases. Currently among the well-mixed greenhouse gases CFCs as a
group exert the third highest radiative forcing at 0.34 W/m2, or 14 percent of the global total.
Although the substitute chemicals do not present a threat to stratospheric ozone,
some of them, particularly the hydrofluorocarbons, are powerful greenhouse gases. One such
species is HFC-134a, used as a refrigerant in car air conditioners. This gas has grown from
an atmospheric concentration of near zero in 1990 to 7.5 parts per trillion in 2000
(Ramaswamy, et al., 2001). Although emissions of HFC-134a are still extremely low
relative to other greenhouse gases, its high GWP value of 1,300 translates to a contribution in
emissions of 41.6 million MTCE per year. None of the HCF molecules have significant
natural sources, thus any accumulation in the atmosphere is due to human releases.
Although their atmospheric abundance has grown nearly exponentially in the past decade,
the gases still only represent a small part (0.003 W/m2) of radiative forcing from well mixed
gases. However, they are expected to have a greater influence in the future as a result of the
ban on ozone depleting substances.
Because perfluorocarbons (including CF4, C2F6, C4F10, CeFi4) and sulfur hexaflouride
(SF6) have atmospheric lifetimes of more than 1,000 years and absorb a large range of
infrared radiation, they are powerful greenhouse gases with GWPs ranging from 6,500 to
23,900 on a 100-year time horizon. They result mostly from industrial processes including
aluminum smelting, semiconductor manufacturing, electric power transmission and
distribution, and magnesium casting. Although their contribution to radiative forcing has
been small in the past (0.006 W/m2), it is expected to increase because of their extremely
long lifetimes, strong ability to trap gases, and (except for SF6) significant growth rates
(Environmental Protection Agency, 2002).
Tropospheric ozone (O3). From the time period extending from pre-industrial times to the
present, tropospheric ozone is estimated to be the third most influential greenhouse gas4 after
carbon dioxide and methane (Ehhalt et al., 2001). Current estimates of atmospheric
concentrations come only from direct monitoring and satellite data. This is because it is not
directly emitted from any source, rather it is the product of photochemical reactions of CH4,
NOx (i.e., NO and NO2), CO and volatile organic compounds (VOCs) that are emitted during
fossil fuel combustion, numerous industrial processes, and biomass burning (Environmental
Protection Agency, 2002). Ozone has a very short lifetime relative to other greenhouse
gases, ranging from days to months. With no agreed-upon value of GWP, and no method for
estimating the load in the atmosphere originating from any one source, ozone is not included
4 The class of CFC chemicals constitute the third most important greenhouse gas among well-mixed
atmospheric gases. 03 is the third most important overall, but it is not a well-mixed gas.
17
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in greenhouse gas inventories.
Photochemically active and indirect greenhouse gases. Apart from possessing direct heat
trapping ability, a gas may be a reagent in a chemical transformation leading to the
production of a direct greenhouse gas species. Alternatively, it may interact with other gases
to influence the radiative importance of direct greenhouse gases (Ehhalt et al., 2001). In
these ways a gas can have an indirect forcing effect. For example as discussed above, the
direct greenhouse gas ozone is formed by reactions of CH4, NOx, CO and VOCs. Because
reactions leading to ozone production are largely dependent on environmental conditions
(specifically sunlight), it is difficult to quantify the indirect GWP of these gases.
Chlorinated and brominated halocarbons have been found to exert a negative indirect
climate forcing through reactions that destroy stratospheric ozone. Carbon monoxide is
generated from incomplete combustion of fossil fuels and other organic materials. This gas
has two indirect mechanisms that impact greenhouse forcing. By engaging the hydroxyl
radical, the gas interferes with major pathways of removal of methane and tropospheric
ozone from the atmosphere, thus prolonging their effective atmospheric lifetimes. Carbon
monoxide is also involved in photochemical reactions that produce ozone, thus providing a
second mechanism that increases ozone concentrations.
Methane exerts a similar feedback on its own atmospheric lifetime. Increased
emissions of methane in past decades are thought to have reduced the availability of
methane-destroying hydroxyl radicals, thereby reducing a major sink for removal of methane
from the atmosphere (Ehhalt et al., 2001). The oxidation of methane by the hydroxyl radical
also produces stratospheric water vapor and carbon dioxide, both of which are direct
greenhouse gases. Besides the direct heat trapping capacity of methane, its indirect effect on
ozone and water vapor has been incorporated in the GWP reported above.
For most indirect greenhouse gases, spatial variability, short lifetimes and complex
interactions make it difficult to derive GWPs and include them in greenhouse gas
inventories. Models using multidimensional analysis have attempted to quantify a GWP for
carbon monoxide (CO). Although results are highly uncertain and not yet included in
greenhouse gas inventories, they estimate a CO GWP value with a 100-year time horizon to
be between 1 and 3 (Ramaswamy et al., 2001).
Units (MTCE)
The principal unit of measure in this inventory is "metric tons carbon equivalents"
(MTCE). This is the standard reporting unit for state inventories as indicated in EIIP,
volume VIII. The national inventory for 2000 as well as the 1990 Iowa inventory reported in
a weight of "carbon dioxide equivalents" (Environmental Protection Agency, 2001). Both
measures enable a weighted comparison between dissimilar greenhouse gases based on their
global warming potential. To convert an amount of greenhouse gas to MTCE units, the
following simple relationship holds:
MTCE = (weight of gas) x GWP x 12/44
where the fraction 12/44 is the ratio of the weight of atomic carbon (12) to molecular CO2
(44). Because the GWP for CO2 equals 1, when the gas is CO2 it only needs to be multiplied
by 12/44 to obtain the value of MTCE.
18
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Quality Assurance and Control: Data Attribute Ranking System (DARS)
Accompanying the EIIP methodology is a quality assurance/quality control system
that quantifies the level of reliability that one can expect from the emission estimation
methods. This system is called the Data Attribute Rating System (DARS) and works by
evaluating attributes of the two components of the estimation method, viz., emission factors
and activity data. After qualitative analysis, a score between 1 and 10 is assigned to each of
four attributes including method of measurement, source specificity, spatial congruity, and
temporal congruity.
Evaluation of the measurement method is based on the quality and accuracy of the
emission factor or the activity data, regardless of how appropriate it is for the estimation
method. It is generally presumed that direct measurements of emissions and activity will
yield more accurate data than indirect statistical models. Thus factors and activity data based
on direct measurements will yield higher scores than estimations from models. Source
specificity looks at the congruence of the component and the emission source. Emission
factors developed directly for the source category will receive greater scores than those
developed for surrogate sources.
Spatial congruity concerns the spatial scaling of components. Data measured at the
state level loses accuracy as it is scaled up or down; the same can be said for the application
of climate dependent emission factors as they are applied further away from the original
location of development. Data that is measured at the desired level of application and use of
geographically specific emission factors will receive relatively higher spatial congruity
scores.
Temporal congruity evaluates how appropriately emission factors or activity data are
applied regarding their temporal scale. For example, for the annual emission inventory the
most appropriate data would be based on yearly activity rather than extrapolating monthly
data to a year-long scale. This type of calculation would reduce a DARS score.
Each of the attribute scores is then divided by 10 and the scores for each of the two
components are multiplied. Each of the sum attribute scores are averaged to arrive at a
single composite score for the methodology. A composite score equal to one indicates
greater reliability of the estimate than a lower score. DARS scores are reported as fractions
but do not indicate a percentage of accuracy. They serve as a measure of merit that can be
usedfor comparison between estimates. In this inventory, no source scored higher than 0.90,
and the average score for all source categories was 0.58.
The DARS score for each emission source will be presented with the emission
methodology for each source category in the electronic versions of this document. A more
complete explanation of each score can be found at the end of each emission source chapter
in the EIIP, volume VIII document found at
http://www.epa.gov/ttn/chief/eiip/techreport/volume08/index.html. For further explanation
of the method of score assignment, see EIIP, volume VI (chapter 4 and appendix F).
19
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Chapter 2: Summary of Findings
Net Greenhouse Gas Emissions in Iowa in Year 2000 and Comparison with
Year 1990
Figure 1 provides an overview of the greenhouse gas emissions and sinks that will be
discussed in detail in this inventory. It shows the relative magnitude of 21 greenhouse gas
emission sources, and the five greenhouse gas sinks (far right of figure). It is apparent that
on the basis of MTCE units carbon dioxide emitted from fossil fuel combustion was the
largest source, followed by nitrous oxide emissions from agricultural soils. Methane from
manure management, domesticated animals, and solid waste were also significant sources of
greenhouse gases. Other sources exerted less impact on total emissions.
Annual Iowa Greenhouse Gas Emissions, Year 2000
25 t
20
15
10
5
0
-5
~ ~ ~ a
UTT
m °
£ o -S
D) to £
CO U
CO U
=
"jfl !£>
c 2
"jfl !£>
c 2
CO
o>
<
¦55 £
¦0
0 c
(D CD
% i
O (1>
s
PFCs
SF6
(C02)
(CH4)
Figure 1. C02 from fossil fuel combustion and N20 from agricultural soils make up 84 percent of all
emissions. Agricultural soils and forests also sequestered large amounts of carbon (shown as negative
emissions on far right).
Table 2 quantifies the amounts of the emissions for each of the sources depicted in
Figure l,5 and compares them with the findings of the Iowa 1990 inventory by IDNR (Iowa
Department of Natural Resources, 1996). The 1990 numbers are given as originally
reported, and recalculated by the authors of the current report using the new calculation
methods, where feasible, that were applied in the 2000 inventory. The total emissions are
disaggregated into twelve sources for five greenhouse gases, carbon dioxide (CO2), methane
(CH4), nitrous oxide (N2O), perfluorocarbons (PFCs), and sulfur hexaflouride (SF6).
5 Table 2 also lists three sources not shown in Figure 1, viz.: CH4 from coal mining, which gave no emissions in
2000 because there was no mining activity; C02 from crop burning, which yields no significant net C02
emissions; and CH4 from stationary sources, for which no data were available.
20
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Table 2. Summary of gross greenhouse emissions in Iowa in year 2000 and comparison with 1990
(MTCE).
Source
Gas
Reported
1990
Recalculated
1990a
2000
Fossil fuel combustion
C02
19,924,586
16,964,059
21,268,523
Industrial processes
co2
3,272,252
1,142,768
489,194
n2o
0
0
229,071
PFCs
NAb
2,740
163,916
sf6
NA
82,753
40,143
Natural gas systems
ch4
124,105
124,105
553,278
Coal mining
ch4
833
833
Not Applicable
Municipal solid waste disposal
ch4
942,027
966,669
1,044,619
co2
NA
2,507
2,505
n2o
NA
194
194
Domesticated animals
ch4
2,069,418
1,221,943
941,024
Manure management
ch4
641,949
854,941
989,376
n2o
0
220,845
235,916
Agricultural soils
co2
NA
NA
222,545
n2o
1,107,659
1,107,659°
6,277,356c
Burning of agricultural crop wastes
co2
1,929,437
0
0
ch4
46,305
21,375
26,558
n2o
43,890
13,245
17,431
Municipal wastewater treatment
(treatment of human wastes)
ch4
11,251
36,787
38,769
n2o
NA
23,138
26,064
Mobile combustion
ch4
NA
NA
14,212
n2o
NA
NA
172,033
Stationary combustion
n2o
NA
NA
61
ch4
NA
NA
NA
Total emissions per gas
(all sources, excluding biomass
fuels)
co2
25,126,275
18,109,334
21,982,767
ch4
3,835,888
3,226,653
3,607,836
n2o
1,151,549
1,365,081
6,958,126
PFCs
0
2,740
163,916
sf6
NA
82,753
40,143
Total gross emissions
30,113,712
22,786,561
32,752,788
a Emissions were recalculated for 1990 where possible using best available new data and year 2000
methodology. If new data were unavailable, the data reported in the original 1990 inventory was
retained. Total gross emissions are the sum of all sources, with emissions recalculated when
possible, and as previously reported when no recalculation was performed.
b NA = not available.
0 The large disparity between the 1990 and 2000 estimates is likely the result of an artifact in
method of calculation.
21
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It appears from the table that emissions have grown by about 10 million MTCE (30
percent) between 2000 and 1990, from 22.8 million MTCE (recalculated) per year to about
32.8 million MTCE per year. As will be discussed in more detail below, the biggest factor in
this increase is the rise in fossil fuel combustion. The other large difference in emissions is
nitrous oxide for agricultural soils, from about 1.1 million MTCE in 1990 to about 6.3
million MTCE in 2000. However, most of this disparity is undoubtedly an artifact in the
calculations, caused by a change in methodology for the 2000 inventory, which accounted for
more sources of emissions in the soil. The data for 1990 could not be recalculated using this
revision for lack of adequate soil data. It is likely that the soil emissions of N2O in the two
years did not vary so significantly, and assuming that they were approximately the same, the
overall rise in year 2000 emissions would only be 15 percent relative to 1990.
Table 3 provides an overview in the sequestration and recovery of carbon in Iowa in
years 1990 and 2000. The analysis shows that for cases where comparisons of values were
feasible, carbon capture appears to have increased. In forestlands the increase was estimated
to be about 1.7 million MTCE. The increase was due to the expansion of forestlands,
although the difference in part may be explained by the use of a more sophisticated forest
inventory in the year 2000. The amount of CH4 recovered from landfills and wastewater in
1990 was unable to be recalculated, but it was undoubtedly low compared to the amount
recovered in 2000 due to the great expansion of this activity in the 1990s. It is also likely
that sequestration in agricultural soils grew to some extent owing to implementation of
conservation tillage and inclusion of more land in the Conservation Reserve Program.
Table 3. Summary table reporting soil carbon sequestration and methane recovery in Iowa in years
1990 and 2000 (MTCE).
Source
Gas
Reported
1990
Recalculated
1990
2000
Agricultural Soils
(C02)
NAa
NA
-3,097,730
Forest Management and Land-Use
Change
(C02)
-6,946,265
-1,200,000
-2,894,429
Carbon Sequestration in Landfills
(C02)
NA
-413,719
-413,377
Landfill CH4 Recovery
(CH4)
- 54,997
- 54,997 b
-148,619
Wastewater CH4 Recovery
(Ch4)
-113
-113 b
-23,000
Total Sequestration
c
-7,001,375c
-1,668,829c
-6,577,155
a NA = not available
b Assumes same value (not recalculated) as originally reported in 1990.
°Does not include sequestration in agricultural soils, which may have been significant in 1990.
The net emissions of greenhouse gases are shown in Table 4. These numbers are the
differences between the gross emissions given in Table 2 and the carbon captured as reported
in Table 3. The difference in net emissions narrows to about 5 million MTCE per year, from
about 21.1 million in 1990 to about 26.2 million in 2000 (corresponding to a 19 percent rise).
If we assume that the nitrous oxide emissions from agricultural soils were comparable for the
two years, gross emissions in 1990 would have been on the order of 5 million MTCE higher,
making the apparent increase in net emissions in 2000 almost negligible. On the other hand,
22
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the recalculated 1990 carbon sequestration does not count carbon in agricultural soils, which
would lower net emissions on the order of 3 million MTCE. These two discrepancies tend to
cancel each other out to a large degree.
Table 4. Summary table for net greenhouse gas emissions in Iowa in year 2000
(MTCE).
Recalculated
1990
2000
Total Gross Emissions
22,786,561
32,752,788
Carbon sequestration and methane recovery
-1,668,829a
-6,577,155
Net Emissions
21,117,732 b
26,175,633
aDoes not include sequestration in agricultural soils, which may have been significant in
1990.
b Includes original (not recalculated) value of N20 emissions from agricultural soils,
which may result in an large under-estimation of net emissions in 1990.
Emissions by Category: Overview
Figures 2 through 4 disaggregate CO2, N2O and CH4 into their significant emission
sources. Carbon dioxide emissions (Figure 2) were dominated by combustion of fossil fuels
with much smaller contributions from industrial processes, agricultural soils and incineration
of municipal solid waste. In total carbon dioxide contributed 67 percent of gross emissions
or 22 million MTCE. Forests, agricultural soils and landfills are all sites of carbon
sequestration (not shown in Figure 2) that offset the gross carbon emissions to a net 15.6
million MTCE. Carbon uptake in agricultural soils has increased recently owing to less
intensive agricultural practices and greater yields. These trends enhance agricultural soils as
a net sink for carbon dioxide.
Year 2000 Iowa Gross C02 Emissions
Figure 2. The 22 million MTCE of gross C02 emissions were predominantly from fossil fuel combustion.
Activities leading to these emissions include combustion of coal for electricity generation and industrial
sources, gasoline for transportation, and natural gas for space heating. Cement manufacture (industrial
processes) and limestone use (agricultural soils) were the largest sources of non-energy related industrial
emissions.
23
-------
Nitrous oxide emissions (Figure 3) contributed about 7.0 MTCE (21 percent) of
Iowa's gross emissions in 2000. Like CO2, they are dominated by a single source, in this
case agricultural soils. Activities that add nitrogen to the soil result in emissions of this gas.
In total they emitted about 6.3 million MTCE in 2000, 90 percent of the overall share. Many
other sources of N2O are present in the state, but their contributions are much smaller.
Year 2000 Iowa N20 Emissions
Solid Waste Disposal
Agricultural Soils
90.2%
Burning Agricultural
Crop Wastes
0.3%
Mobile Combustion
2.5%
Industrial Processes
3.3%
Manure Management
3.4%
Figure 3. The major portion of Iowa's 7 million MTCE of N20 emissions came from agricultural soils.
Methane emissions (Figure 4) were quite small in comparison with only 3.6 million
MTCE or 9 percent of gross emissions. Unlike CO2 and N2O, methane emissions are spread
more evenly among several sources. Unaccounted for in the figure is methane recovery from
landfills and municipal wastewater treatment plants, which offset total gross emissions by
about 1.2 million MTCE.
Year 2000 Iowa Gross CH4 Emissions
Do masticated
Animals
26.1%
Manure
Management
27.4%
Mobile Combustion
0.4%
Burning of
Agricultural Crop
Wastes
0.7%
Wastew ater
Treatment
1.1%
Natural Gas
Systems
15.3%
Solid Waste
Disposal
29.0%
Figure 4. Most of the 3.6 million MTCE of CH4 emissions came from landfilling of solid waste, manure
management, and domesticated animals.
24
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Chapter 3: Greenhouse Gases from Energy-Related Emissions
Combustion of fossil fuels is the primary source of greenhouse gas emissions in the
United States, making carbon dioxide the principal contributor to total emissions. Nationally
in the year 2000, carbon dioxide from burning of coal, natural gas and petroleum constituted
80 percent of all greenhouse gas emissions on a carbon equivalent basis (Environmental
Protection Agency, 2002). In contrast, Iowa's fossil fuel combustion accounted for only 65
percent of the state total. The lower percentage for Iowa reflects the large contribution of
N20 from agricultural sources. As shown in Figure 5, there are smaller energy related
sources of emissions. These include nitrous oxide produced at high temperatures from
mobile and stationary fossil fuel combustion sources, and methane produced from
incomplete combustion of fossil fuels in mobile and stationary sources. An additional source
of methane is fugitive emissions from leaks during natural gas transmission and distribution.
Previously coal mining in southern Iowa released fugitive methane emissions, which was
accounted for in the 1990 inventory. By year 2000, however, all coal mining had ceased in
Iowa, and thus this source is not included in the 2000 analysis.
Iowa Greenhouse Gas Emissions from Energy Related Sources, Year 2000
Fossil Fuel Combustion C02
Natural Gas Systems
Mobile Combustion
Stationary Combustion
1
67% of Iowa
3
Emissions
I
(
\
I /
J
v
5,000 10,000 15,000
1.000 MTCE
20,000
25,000
Figure 5. Most energy sector emissions came from C02 generated from fossil fuel combustion. N20 and CH4
were emitted in small amounts from mobile and stationary combustion, and CH4 was emitted from leaks in
natural gas systems.
Iowa's Energy Profile
The following section provides a detailed analysis of energy use activities in Iowa.
Understanding energy use is important for targeting emission reduction strategies. The
analysis summarizes changes in total energy use and electricity generation, carbon intensity,
energy use by sector and per capita energy use. Also included is a comparison among states
of carbon dioxide emissions from fossil fuel consumption. Subsequent sections summarize
emission estimates from the energy related sources introduced above.
Trends from 1990 to 2000. As shown in Figure 6, total energy consumption of fossil fuels
in Iowa grew by 26 percent (222.5 Trillion Btu) in the 1990s. Consumption of fossil fuels is
the single greatest influence on the state's greenhouse gas emissions inventory. With the
inexpensive price of coal, continuously decreasing since 1983, Iowa remains heavily
dependent on this carbon-intense fuel source. It accounted for 39 percent of all energy Iowa
25
-------
produced in 2000. Most of that — 84 percent — was used for electricity generation by
investor-owned, public and cooperative utilities. The remainder went to the industrial and
commercial sectors, including chemical, metal and food manufacturers, government
institutions, as well as non-utility power producers.
Petroleum consumption increased 37 percent (111.4 Trillion Btu) mostly due to
increased use by the industrial sector, and, to a lesser degree, the transportation sector.
Natural gas consumption increased a moderate 7 percent (15.0 Trillion Btu), most of which
was in the industrial sector. The residential sector remains a small but important consumer
of natural gas for space heating and cooking. Nuclear and renewable energy sources,
including hydroelectric, wood and waste, geothermal, wind, photovoltaic, and solar thermal
energies, make up about 7 percent of the state's total energy use.
Iowa Total Energy Consumption by Energy Type (1990-2000)
200 -
0 -I , , , , , , , , ,
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
~ RENEWABLES DGAS ¦ PETROLEUM ¦COAL
Figure 6. Total state fossil fuel energy consumption grew by 26 percent in the 1990s. Coal, petroleum, and
natural gas continue to be the dominant fuel sources for the state, while renewable energy remains a very small
part of total energy consumption.
Fuel mix and efficiency are important issues affecting emissions. Among the three
major fossil fuels, coal is the most carbon intensive; it emits 56 pounds of carbon for every
million Btu of energy produced, while petroleum and natural gas emit 43.5 and 31.9 pounds,
respectively. Coal supplies 86 percent of energy inputs for electricity generated in Iowa.
Thus, energy consumption for the production of electricity has the largest impact on
greenhouse gas emissions. Nuclear, hydroelectric and wind energy sources produce no
greenhouse gas emissions and make up 12.7 percent of total inputs to Iowa's electric utility
generation.
Iowa electricity production: sources and trends. Figure 7 shows energy inputs and
outputs in the year 2000 for all Iowa electric utilities, including commercial and non-
commercial producers. The most notable feature is the 70 percent of the energy lost as waste
heat. A large part of the loss is due to the Second Law of Thermodynamics, which governs
the efficiency of a heat engine and dictates that all energy cannot be converted into work.
The theoretical efficiency depends on the difference in temperature between the condenser
26
-------
and the boiler, and for a typical coal-fired power plant is about 65 percent. This means that 3
Btus of energy from coal combustion can produce no more than 2 Btus of electrical work.
Owing to transmissions losses and other system inefficiencies, the actual efficiency for
conventional coal plants is typically about half of the theoretical prediction, so in practice
every 3 Btus of coal produces only about 1 Btu of electrical work. Measures that enhance
the energy efficiency of these systems can have large, direct positive effects on Iowa's
greenhouse gas burden.
Figure 7. Inputs and outputs of energy in Iowa for electricity generation in year 2000 (Trillion Btu). Source:
Energy Information Administration (2001b).
The industrial sector is the largest consumer of electricity, followed by the residential
sector and finally the commercial sector. At the time of this report, the Energy Information
Administration (EIA) reported electricity consumption for public street and highway
lighting, interdepartmental and intradepartmental sales, and agricultural and electrified rails
sales as "other" uses.
Like the rest of the country, Iowa has always relied heavily on coal for electricity
generation. In the 1960s, it supplied almost half of the electric utility inputs, with natural gas
providing slightly more. Even before the 1978 federal legislation banning the construction of
new large gas-fired boilers Iowa, chose to shift away from use of natural gas, which was then
thought to be in short supply. Since that time, the nation's abundant coal supply has been the
number one source of fuel for Iowa's electricity generation. With changing priorities and
concerns about air quality, however, some predict that in time electricity production will shift
back toward cleaner burning fuels (Iowa Department of Natural Resources, 1996), which is
already happening to a degree.
Table 5 shows the state's fuel sources for electricity generation in 2000 with
27
-------
comparison to 1990. In 2000, coal generated 84 percent (35.0 million MWh) of the state's
total electricity output, increasing by more than a third (8.9 million MWh) since 1990.
Installed nameplate coal capacity, however, decreased by 2 percent over this time. The drop
marks a loss in the share of coal in total generation, resulting from development of other
resources including the rapid increase in wind energy, which accounted for 2 percent of total
generation capacity by 2000.
Table 5. Electric power industry capacity and generation by source for 2000 in comparison with 1990.
Fuel Source
Electricity
Generation
Year 2000
(1,000 MWh)
Percent of
Generation
Year 2000
(%)
Change in
Generation
1990 - 2000
(%)
Nameplate
Capacity
Year 2000
(MW)
Percent of
Nameplate
Capacity
Year 2000
(%)
Change in
Nameplate
Capacity 1990
-2000
(%)
Coal
34,984
84
36
6,370
67
-2
Nuclear
4,453
11
48
597
6
0
Hydroelectric
906
2
4
131
1
0.6
Wind
572
1
NA
206
2
NAa
Natural Gas &
Dual Fired
468
1
40
1,271
14
3
Petroleum
136
< 1
150
994
10
77
Total
41,519
100
38
9,789
100
8
a NA - not applicable because there was no nameplate capacity for wind in 1990.
Source: Data derived from Energy Information Administration (2001b, 2003b).
Nuclear power and renewable energy in the form of hydropower have long been
sources of electricity in Iowa. Although hydropower has not grown much in the past ten
years, it has been a very stable source and continues to provide a small share of electricity
with carbon-free emissions. Nuclear energy generation has experienced substantial growth
since the completion in 1974 of the Duane Arnold Energy Center in Palo, Iowa. Now
generating more than three times as much electricity as it did at its opening, the state emitted
1.2 million MTCE less in 2000 than it would have if this electricity were generated by a coal-
fired plant. This nuclear plant is now a significant part of Iowa's energy portfolio,
accounting for more than one-tenth of total electricity generation.
While natural gas and petroleum comprise 24 percent of total installed nameplate
capacity, they currently provide less than 2 percent of generated power. They typically serve
as secondary sources, supplying additional power during times of peak generation or backup
for security against power outages. However, in 2003, one new gas-fired combined-cycle
plant came online as an intermediate generator providing electricity daily. These types of
generators may be dispatched ahead of certain coal fired units as their high efficiency makes
the cost of operation less expensive despite higher natural gas prices (Iowa Utilities Board,
2003). There are also two proposals for new natural gas fueled plants to be sited in Iowa for
out-of-state export of electricity (Jack Clark, Iowa Utility Association, personal contact,
March 17, 2004).
Wind generation has been the source experiencing the greatest rate of growth in Iowa
28
-------
since 1990. Capacity was nearly zero in 1990 and rose to 206 MW by 2000. The first
significant leap came in 1999 when several large wind farms were installed across the state,
bringing capacity up from 9 to 204 MW. Despite this dramatic expansion, wind power still
accounts for only about 1 percent of total electricity generation.
Advances in the development of biomass fuels provide another exciting option for
electricity generation from Iowa-grown renewable resources. The Chariton Valley Resource
Conservation and Development Organization has partnered with Alliant Energy's Ottumwa
power plant to test and develop a new market for switchgrass, an indigenous Iowa prairie
grass, as a fuel to burn with coal in electricity generation. Their goal is to co-fire enough
switchgrass to increase capacity by 35 MW. Other projects using biomass include a waste
biomass gasification system in Cedar Rapids, and biogas recovery systems at livestock
operations, landfills and wastewater treatment plants. However, biomass is currently an
extremely small contributor to Iowa's electricity generation portfolio.
Most energy forecasts, including those from the U.S. Department of Energy, indicate
that the country will continue to depend on fossil fuels for a long time even though the
diversity of the fuel base is increasing to include renewables.
Carbon intensity. Carbon intensity is a measure of the carbon content of emissions per unit
of energy generated. On a statewide basis, it is calculated by dividing total carbon in energy
sources consumed by the total energy produced within the state. The lower this ratio, the less
carbon dioxide is emitted per unit of energy generated. Figure 8 shows that carbon intensity
has remained approximately flat over the decade of the 1990s, with a slight overall decrease
of 2.8 percent. This trend is noteworthy because a large increase in coal consumption has
occurred over the same time period, indicating that others factors have offset the coal
increase. These include a slightly less carbon-intense coal mix combined with an increase in
renewable energy production, and the use of less carbon-intense petroleum products in the
industrial sector.
Iowa Carbon Intensity Measured from Energy Consumption,
(1990 - 2000)
« 19,000
18,500
18,000
17,500
17,000
16,500
16,000
15,500
15,000
1990
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Figure 8. Despite increasing coal consumption, the slight decrease in carbon intensity is attributed to a less
carbon-intense coal mix, more renewable energy use, and other factors.
Outlooks from the EIA (Energy Information Administration, 2004a) are optimistic
that the proportion of coal-generated electricity capacity will decline continuously on a
national scale. The major replacement fuel will be natural gas, which is expected to
29
-------
experience a two-thirds increase in demand for electricity generation by 2020. Any rise in
use of a fuel with relatively low carbon emission potential such as natural gas would
continue to hold the carbon intensity down.
Iowa end use sector energy analysis. Energy use in all economic sectors experienced
growth in varying degrees. One manifestation of this growth was the large increase in
electricity consumption, which reflected the growth in its demand among the individual
sectors. Energy demands other than for electricity also grew, and in this section we analyze
how the different forms of energy were distributed across the end use sectors.
As shown in Figure 9, a rapidly growing industrial sector saw the greatest rise in
energy use among end users with a 36 percent increase. This sector includes activities
related to manufacturing, agriculture, forestry, fisheries, mining and construction, and non-
utility power producers. Growth in the petrochemical industry in the late 1990s brought a
nearly 200 percent increase in feedstocks. Industrial liquid petroleum gas (LPG)
consumption increased by 330 percent. As a highly versatile fuel, LPG is employed in
numerous industrial applications, including drying of agricultural and chemical products,
processing of metals, chemical and food products, and agricultural space heating. LPG is
among the least carbon-intense fossil fuels used, second only to natural gas.
Total Energy Use by Sector 1990 vs 2000
500 -i
400 -
300 -
200 -
100 -
n _
u ¦
Commercial
Residential
Industrial
Transportation
~ 1990
137
202
326
231
~ 2000
153
208
443
266
Figure 9. All sectors saw growth in energy consumption in the past decade. Energy inputs at electric utilities
were distributed across end use sectors in proportion to electricity consumed in that sector.
The transportation sector saw a 15 percent rise in energy use, mainly due to increased
consumption of distillate fuels, lubricants, and motor gasoline. Motor vehicle gasoline
consumption was 13 percent higher in 2000 than in 1990. This trend reflects in part the
growing popularity of less efficient light duty vehicles. Since the late 1980s the market share
of light duty trucks, including small pickups, vans and sport utility vehicles, has been
growing nation-wide (Environmental Protection Agency, 2003). The increasing preference
for these relatively inefficient vehicles over conventional passenger cars has played a critical
role in increased fuel consumption and concomitant emissions.
The commercial sector includes businesses, federal, state, and local governments,
private and public organizations, and institutional living facilities. Energy consumption
among these enterprises rose by 12 percent compared to 1990. Electricity and natural gas are
the energy sources in largest demand, satisfying the need for space and water heating, air
30
-------
conditioning, lighting, refrigeration, cooking and running a variety of office equipment.
Energy consumption in the residential sector had the slightest increase of all end use
sectors, rising only 3 percent in 2000 relative to 1990. The EIA has monitored regional and
national trends showing decreasing energy inputs for residential space and water heating, two
needs drawing a large share of total home energy demand (Energy Information
Administration, 2001c). This improvement is a credit to the effectiveness of energy
efficiency programs, more stringent building energy codes, and technology advances that
have targeted the residential sector. Other energy consuming needs include air conditioning,
lighting, refrigeration, cooking and other appliances.
Per capita indices. Iowa's population growth was sluggish between 1990 and 2000,
increasing at less than 0.5 percent per year. In the first half of the decade, much higher
growth rates in energy use (3 percent per year) and energy-related carbon emissions (17
percent per year) drove up per capita energy consumption (Figure 10) and per capita carbon
emissions (Figure 11). By 1996, the rise in emissions began to slow down to less than 1
percent per year, flattening out the trend while per capita energy use continued to rise after
1997. As we saw with the case of slightly decreasing carbon intensity, this trend was
affected by a less carbon-intense coal mix, improved energy efficiency, and accelerated
development of renewable fuels. Continuation of these positive measures will tend to lower
per capita energy consumption and carbon emissions in the future.
Per Capita Energy Consumption (1990-2000)
425
400
c
o
(A
375
0)
Q.
350
"3
S
325
c
o
300
i
275
250
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Figure 10. Per capita energy consumption rose almost continuously between 1990 and 2000.
31
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Per Capita Carbon Emissions from Energy Use
(1990-2000)
1992 1993 1994 1995 1996 1997 1998
1999 2000
Figure 11. While per capita carbon emissions rose steadily in the first half of the decade, they began to flatten
out after 1996 despite rising energy use.
State comparisons of per capita carbon emissions from fossil fuel use. In order to
compare Iowa's energy-related emissions with the rest of the country, state comparisons of
per capita carbon dioxide emissions from fossil fuel consumption have been estimated for
each end use sector, and are summarized in Table 6 and Figures 12 through 16. Emissions
were calculated from fossil fuel consumption data reported by the Energy Information
Administration (EIA). Electric utility emissions have been distributed across end use sectors.
Carbon stored in non-energy products (i.e., plastics, fertilizers, chemicals) were credited to
each state's industrial and transportation sectors based on national estimates of "non-energy
uses" of fossil fuels (Energy Information Administration, 2001a). Emissions from states that
do not participate in activities that sequester fossil fuel carbon in non-energy products may
be assigned too much carbon credit and, therefore, could be underestimated.
Table 6. Summary by end use sector of per capita carbon emissions from combustion of fossil
fuels in Iowa, 2000
Sector
Iowa Per Capita
C02 Emissions
(MTCE/Person/Y ear)
Iowa State Ranking
(out of 51a)
U.S. Per Capita
C02 Emissions
(MTCE/Person/Y ear)
All Sectors
6.74
13
5.59
Commercial
1.00
18
0.83
Residential
1.33
10
0.98
Industrial
2.65
13
1.94
Transportation
1.75
32
1.84
a District of Columbia included in the ranking.
With the exception of transportation, Iowa emitted more carbon per capita than the
U.S. average, indicating that overall the state uses more fuels with higher carbon intensity.
This is not surprising given Iowa's decentralized population and robust agricultural
economy, as well as a strong dependence on coal. Based on year 2000 data from the EIA
(Energy Information Administration, 2003c) and U.S. Census Bureau (2001) , Iowa ranked
10th highest in per capita coal consumption, preceded by (from greatest to least) Wyoming,
32
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North Dakota, West Virginia, Indiana, Kentucky, Alabama, Montana, Utah, and New
Mexico. All of these states also precede Iowa in total per capita carbon emissions except for
Utah.
Iowa's per capita emissions for the transportation sector ranked relatively low among
the states. Thirty-one states had higher values, thus placing Iowa below the U.S. average.
Extensive use of ethanol blends has given Iowa an advantage over other states. Moreover,
Iowa's transportation sector uses minimal amounts of some carbon-intense fuels used
extensively in other states such as jet and residual fuels. For example, the state ranked 47th in
its per capita use of jet fuel.
33
-------
All End Sectors - Per Capita C02 Emissions from Fossil Fuel Combustion, Year 2000
T-
WY
CM
AK
CO
LA
ND
LO
KY
CD
MT
h-
IN
00
TX
CD
WV
O
DE
¦*"
OK
CM
AL
CO
IA
KS
LO
OH
CD
NM
h-
UT
00
PA
CD
MS
O
CM
TN
cm
NE
US
CM
CM
Wl
CO
CM
MO
CM
NV
LO
CM
GA
CD
CM
MN
h-
CM
DC
S Ml
CO
C\l
CO
O
co
VA
CO
IL
CM
CO
NC
CO
CO
SC
-s-
co
SD
L£>
CO
MD
CD
CO
AR
1"^
CO
NJ
oo
CO
FL
CO
CO
MA
o
ME
HI
CM
-S-
WA
CO
AZ
NH
L£>
ID
CD
NY
1"^
OR
00
CA
CO
-S-
VT
O
L£>
CT
IE
Rl
0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0
Metric Tons Carbon Equivalents Per Person
Figure 12. Iowa ranked as the 13th highest per capita carbon emitting state from fossil fuel combustion in all
end use sectors, possessing annual emissions of 6.74 MTCE per person. The national average was 5.59 MTCE
per person.
34
-------
Commercial Sector- Per Captia C02 Emissions from Fossil Fuel Combustion, Year 2000
1.0
1.5
2.0
2.5
3.0
3.5
4.0
Metric Tons Carbon Equivalents Per Person
Figure 13. Iowa's commercial sector ranked 18 highest per capita carbon emitting state from fossil fuel
combustion, possessing annual emissions of 1.00 MTCE per person. The national average was 0.83 MTCE per
person.
35
-------
Residential Sector- Per Capita C02 Emissions from Fossil Fuel Combustion, Year 2000
*
CD
.
in
CD
00
DE
oj
o
T—
T_
T—
(M
o
CO
T—
CO
-------
Industrial Sector- Per Capita C02 Emissions from Fossil Fuel Combustion, Year 2000
3.0 4.0 5.0 6.0
Metric Tons Carbon Equivalents Per Person
10.0
Figure 15. Iowa's industrial sector ranked 13 highest per capita carbon emitting state from fossil fuel
combustion, possessing annual emissions of 2.65 MTCE per person. The national average was 1.94 MTCE per
person.
37
-------
Transportation Sector - Per Capita C02 Emissions from Fossil Fuel Combustion, Year 2000
Metric Tons Carbon Equivalents Per Person
Figure 16. Iowa's transportation sector ranked 32nd highest per capita emitting state from fossil fuel
combustion, possessing annual emissions of 1.75 MTCE per person. The national average was 1.84 MTCE per
person.
38
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CO2 from the Combustion of Fossil Fuels
Carbon dioxide emissions increased concomitantly with energy use over the decade
of the 1990s. Estimates based on total energy consumption show that for all end use sectors,
Iowa emissions were 21.3 million MTCE in 2000 compared to 17.0 million MTCE
recalculated for 1990. One major source of this increase was the rising emissions from
electric power generation. Figure 17 shows the upward trend of carbon dioxide emissions
from the electric utilities. In 2000 they released 9.5 million MTCE, comprising about 47
percent of all sources of emissions.
C02 Emissions from Electric Utilities (1990- 2000)
Figure 17. Emissions from electricity generation were on an upward trend in the 1990s.
Electricity generation is driven by the demand of end users, and is distributed
according to demand across the commercial, residential, and industrial sectors. (Electricity
demand in the transportation sector in Iowa is minimal.) Quantities for each sector's
electricity consumption and associated energy losses were calculated (see details in
appendices worksheets for fossil fuel combustion). Carbon emissions were estimated and an
emission distribution factor was determined from the fraction of total utility emissions and
all energy inputs including nuclear, hydroelectric and other types of generation. Electricity
consumption and losses in the end use sectors were multiplied by the distribution factor. For
2000, the EIA reported that Iowa's net interstate flows of electricity and losses amounted to -
74.6 trillion Btu. This number indicates that more electricity (including losses) went out of
the state than entered it. Emissions from this interstate energy transfer make up the
difference between electric utility inputs and consumption with losses in the in-state end use
sectors. Figure 18 compares the emissions of carbon dioxide from fossil fuel combustion in
2000 with 1990 for all end use sectors by energy source. Clearly for the three electricity
consuming sectors, most emissions came from this activity.
Figures 18 and 19 show total net emissions from industry increased 27 percent over
the last decade, totaling 7.7 million MTCE in 2000. This figure does not include the almost
0.5 million MTCE that was estimated to be stored in non-energy products made from fossil
fuels (e.g., fertilizer from natural gas, asphalt from road oil). With 36 percent of the total, the
industrial sector was responsible for the largest share of emissions and experienced a 1
percent increase in share since 1990.
39
-------
Comparison of Iowa C02 Emissions from Combustion of Fossil Fuel by Sector
by Source, 1990 vs 2000
8,000
7,000
6,000
5,000
4,000
3,000
2,000
1,000
II
u -
1990
2000
1990
2000
1990
2000
1990
2000
Commercial
Residential
Industrial
T ransportation
~ Electricity
1,827
2,005
2,551
2,428
2,763
3,457
0
0
~ Natural Gas
638
659
1,035
1,068
1,200
1,374
132
119
0 Petroleum
109
167
261
383
746
1,229
4,272
4,967
ECoal
120
153
28
18
1,323
1,628
0
0
Figure 18. Most energy-related emissions came from electricity consumption for the commercial, residential
and industrial sectors.
Industrial Sector C02 Emissions (1990- 2000)
10,000
hi 8,000
o
H
^ 6,000
O
O
° 4,000
2,000
1990 1991
1992 1993
1994 1995
1996
1997
1998 1999
2000
Figure 19. The industrial sector's emissions have increased 27 percent since 1990. Late in the decade
emissions stabilized due to improved electricity transmission and reduced distribution losses, and an increase in
use of less carbon-intense LPG.
Although not apparent from Figure 18, natural gas is the dominant fossil fuel
consumed in the industrial sector, providing 22 percent of total energy. It is applied for
cogeneration of industrial electric power and as an industrial feedstock. Coal provides only
15 percent of fuel, but it generates 3 percent more net carbon emissions than natural gas
because, as noted above, it is a more carbon-intense fuel than natural gas. Industry fuel
choices follow price variations more closely than other sectors owing to greater flexibility of
fuel switching. With this ability to switch fuels, industry is an ideal sector for substitution by
less carbon-intense fuels.
As shown in Figure 20, carbon dioxide emissions from the transportation sector rose
to about 5 million MTCE over the decade and averaged about 17 percent higher in the last
half. In 2000, motor gas consumption was up 13 percent relative to 1990, reflecting reduced
energy efficiency owing to an increase in the number of light duty trucks including pick-ups,
40
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minivans and sport utility vehicles. The recent popularity of these relatively inefficient
vehicles compared to passenger cars has played a critical role in increased fuel consumption
and emissions.
Transportation Sector C02 Emissions (1990- 2000)
10,000
8,000
6,000
4,000
2,000
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Figure 20. Emissions from the transportation sector were 17 percent higher by the end of the decade.
As a consequence of less stringent corporate average fuel economy (CAFE) standards
for light duty trucks, total U.S. fleet fuel economy has been dropping since 1987 when it
peaked at 26.2 miles per gallon (mpg). At that time light duty vehicles made up only 28.1
percent of the market. By 2001, these vehicles held a 46.7 percent market share, and fleet
fuel economy had dropped to 24.4 mpg (U.S. Department of Commerce, 2004). Addressing
this issue, the National Highway Traffic Safety Administration has raised the new light truck
standard from the long maintained one of 20.7 mpg to 21.0 mpg for model year (MY) 2005,
21.6 mpg for MY 2006 and 22.2 mpg for MY 2007 (National Highway Traffic Safety
Administration, 2004). Currently the conventional passenger car standard is remaining at
27.5 mpg where it has been set since 1990. The effect of the new standards will accumulate
in time, hopefully bringing fleet fuel economy back up. Despite the increase in emissions,
Iowa's transportation sector in 2000 held steady at 26 percent of the share of total emissions
it had in 1990. With ethanol consumption more than doubling during the 1990s,
transportation's carbon intensity was down 1 percent by the year 2000.
As shown in Figure 21, the commercial sector's emissions rose 11 percent but
remained the smallest energy consuming and emitting sector, accounting for 3 million MTCE
in 2000. Rises in coal use and electricity consumption drove this increase.
Commercial Sector C02 Emissions (1990- 2000)
10000
HI
8000
o
H
S
6000
o
o
T"
4000
2000
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Figure 21. The commercial sector was the lowest emitter at 3 million MTCE in 2000.
Figure 22 indicates that in 2000 the residential sector emitted about 4 million MTCE
41
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with less than a 1 percent rise relative to 1990. A switch from carbon-intense fuel oil and
coal to less intense natural gas and LPG gas allowed energy consumption to increase by 3
percent with almost no rise in carbon emissions. By 2000 the residential sector had lowered
its share of total emissions by 3 percent, down to 20 percent of the total energy-derived
emissions from the four sectors.
Residential Sector C02 Emissions (1990- 2000)
10,000
Lll
8,000
o
H
2
6,000
o
o
4,000
2,000
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Figure 22. Emissions from the residential sector remained relatively steady with only a 1 percent rise since
1990 despite a 3 percent increase in energy consumption.
Figure 23 shows the percent shares of carbon dioxide emissions from the four end use
sectors, and how those shares changed from 1990 to 2000. Total emissions increased by 15.6
percent, from 17.0 million MTCE to 19.7 million MTCE. The industrial sector had the
highest increase at 27.5 percent while the residential sector had the lowest at 0.6 percent.
This resulted in the industrial sector's increased share of emissions by 3 percent to 39 percent
in 2000.
42
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Comparison of Iowa End-use Sector Share in Energy C02 Emissions, 1990 vs. 2000
1MB
2000
25%
38%
26%
15%
~ Cnmmercial
~ Residential
~ Industrial
~ Transportation
20%
Sector
C02 Emissions (MTCE)
Percent Increase
1990 ->2000
2000
1990
Commercial
2,984,428
2,694,463
10.8 %
Residential
3,897,621
3,874,428
0.6 %
Industrial
7,687,837
6,031,709
27.5 %
Transportation
5,087,277
4,404,861
15.5 %
Total
19,657,163
17,005,461
15.6 %
Figure 23. End sector shares of C02 emissions from energy use.
CH4 and N2O Emissions
Emissions from stationary and mobile combustion sources. The above discussion
focused on carbon dioxide as the primary greenhouse gas from fossil fuel combustion. In this
section, emissions of methane and nitrous oxide from the same activities are reported.
Methane is formed during incomplete combustion of fossil fuels; nitrous oxide forms when
atmospheric nitrogen or nitrogen embedded in the fossil fuel reacts and combines with
oxygen at high temperatures. Compared to carbon dioxide emissions, however, both gases
are minor contributors to Iowa's total greenhouse gas emission inventory, accounting for a
combined share of only 0.6 percent in 2000. The overall results are presented in Table 7.
Nitrous oxide from mobile combustion contributed more than 90 percent of these emissions.
Different methodologies were devised for estimating emissions for stationary and
mobile combustion sources. Stationary combustion includes the burning of fossil fuels in
non-moving equipment (boilers, furnaces, kilns, ovens, etc.) in the utility, industrial,
residential, and commercial sectors. Nitrous oxide emissions were estimated based on coal,
natural gas and oil consumption data, and emission factors were provided by the EIIP,
volume 8. Methane emissions were not estimated for stationary combustion due to the lack
of availability of good quality data.
43
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Table 7. Summary of CH4 and N20 emissions from mobile and stationary
combustion (MTCE).
Source
ch4
n2o
1990
2000
1990
2000
Mobile
Combustion
NAa
14,212
NA
172,033
Stationary
Combustion
NA
NA
44.1
61
Total
NA
14,212
44.1
172,094
a NA = not available
Sources of mobile combustion emissions are road vehicles, airplanes, boats, railroad
and farm equipment. Again because of a lack of data, in this inventory only emissions from
road vehicles were quantified. Estimates were based on unpublished vehicle registration data
for 2001 provided upon request from the Iowa Department of Motor Vehicles, Federal
Highway Administration state travel data, and EIIP emission factors for vehicle categories
(defined by vehicle size, fuel type, and emission control technology). A more thorough
discussion of the estimation methods is provided in the appendices for this section.
CH4 emissions from natural gas and oil systems. Iowa neither extracts nor processes
natural gas or oil. However, it is an active participant in the national natural gas system as
both a direct consumer and as a conduit for transmission from western Canada to Chicago.
Most gas consumed in Iowa has been transported from Oklahoma, Texas, Alberta, and
Western Canada (Iowa Utilities Board, 2000). A cross-country network of high-pressure,
large diameter pipelines transmits the gas. Stations are responsible for maintaining
operations along these pipelines by metering, sustaining pressure, and scrubbing the gas. For
local distribution and customer connections, the gas is transported via smaller diameter, low-
pressure pipelines. Chronic leaks, venting and mechanical mishaps result in the release of
natural gas from either the main transmission or local distribution pipeline systems. Iowa is
served by four natural gas pipelines and more than 50 local distribution companies. Another
pipeline, not catering to Iowa customers, runs across the eastern portion of the state.
In 2000, it was estimated that 553,000 MTCE of methane was released in Iowa
during natural gas transmission and distribution activities. Two-thirds of this methane is
attributed to gas transmission pipelines and storage leaks, the remaining third to distribution
pipeline and customer connection leaks. These releases are Iowa's sixth largest source of
greenhouse gas emissions, comprising 2 percent of the total. They are also the fourth largest
source of total methane emissions, with a share of 18.5 percent.
Comparison with past emissions as reported in the 1990 inventory is not appropriate
due to the enhanced sophistication of the newer method of calculation. Previously,
emissions were based solely on natural gas consumption in the state. That method does not
account for emissions from non-stop interstate transmission, which is likely to be a large
source given that the majority of methane emissions for 2000 came from natural gas
transmission. It is likely, though, that emissions from transmission have increased since
1990 because of the expansion of pipelines in the late 1990s (Tobin, 1997).
44
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Chapter 4: Greenhouse Gas Emissions from Agriculture
Agriculture is the second largest source category of greenhouse gas emissions after
energy use. More than 8.7 million MTCE in emissions were derived from agricultural
activities in 2000. Nitrogen and lime inputs to soils from agricultural practices contributed
the largest share with 6.5 million MTCE from N2O and CO2 emissions, while manure
management systems that handle waste from livestock contributed 1.2 million MTCE from
N2O and CH4 emissions. Enteric fermentation in the digestive systems of domesticated
livestock is another significant source of methane emissions, adding 941,000 MTCE to the
annual total. Burning agricultural crop wastes contributed only a very small amount of
methane and nitrous oxide emissions, estimated to be 44,000 MTCE. An overview of
emissions and the agricultural sectors contributing to them is summarized in Figure 24.
Iowa Greenhouse Gas Emissions from All Agricultural Sources, Year 2000
Ag Soils
Manure Management
Domesticated Animals
Burning Crop Waste j
"I 1 1 1 1 I 1 1
0 1,000 2,000 3,000 4,000 5,000 6,000 7,000
1,001 MTCE
Figure 24 Emissions from the agricultural source categories were dominated by those from agricultural soils.
Burning crop wastes was not a significant source of emissions.
N2O and CO2 from Agricultural Soils
Iowa's rich soils are its greatest natural resource. They also are one of the state's
largest sources of greenhouse gas emissions. According to the methodology applied in the
2000 inventory, agricultural soil emissions are disaggregated into four types as indicated in
the pie chart in Figure 25. Table 8 quantifies the emissions represented in the chart. Direct
emissions from cropping practices contribute the largest share, accounting for 4.6 million
MTCE from nitrous oxide, and 71 percent of all soil emissions. Contributing to N2O
emissions are soil nitrogen inputs via commercial fertilizer applications, production of
nitrogen fixing crops, incorporation of crop residues into the soil, managed manure
application and application of daily spread manure. Additional emissions occur from the
cultivation of highly organic histosol soils. After the burning of fossil fuels, the application
of commercial nitrogen fertilizer was the largest individual emitter of greenhouse gases in
2000, contributing about 4 percent of total state emissions from all sources.6 When
combined with other direct soil nitrogen sources such as manure, crop residues, nitrogen
6 This percent probably underestimates the impact of commercial nitrogen fertilizer on nitrous oxide emissions
since it does not count the fertilizer's nitrogen that ends up in crop residues, or in indirect emissions.
27% of all Iowa
Emissions
45
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fixing crops, and the cultivation of histosols, nitrous oxide from soils comprised 14.1 percent
of Iowa's total gross greenhouse gas emissions.
Distribution of Year 2000 Greenhouse Gas Emissions from Agricultural Soils
Liming of Soil
Figure 25. Emissions from agricultural soils were dominated by direct emissions from cropping practices that
involve amending the soil with nitrogen and the cutivation of highly organic histosols.
Table 8. Summary of greenhouse gas emissions from agricultural soils in year 2000.
Type of Emission
Gas
Emitted
Total Emissions
(kg /yr)
Total Emissions
(MTCE)
Direct emissions from cropping practices
N20
54,547,847
4,611,773
Direct emissions from animals on soil
n2o
3,895,373
329,336
Indirect emissions from nitrogen applied to soil
n2o
15,806,296
1,336,350
Total emissions from liming soil
co2
815,998,955
222,545
Total
890,248,471
6,500,004
Indirect emissions of nitrous oxide stem from volatilization and redeposition of
nitrogen compounds from fertilizers and livestock manure. Such sources are considered to
be indirect because nitrogen does not follow a direct pathway of denitrification from the
nitrogen source to the atmosphere. In addition, excess nitrogen from fertilizer and manure is
transported by leaching and runoff. Emissions of nitrous oxide occur during these processes,
which are also classified as indirect. These sources add an additional 1.3 million MTCE (21
percent) of N2O emitted from agricultural soils. Grazing animals are a significantly smaller
source of direct emissions, comprising just 5 percent of the total. The direct, indirect, and
grazing animal inputs combine to emit about 6.3 million MTCE in N2O emissions,
comprising about 19.2 percent of Iowa's total gross greenhouse gas emissions in 2000.
The only source of carbon dioxide emissions from agricultural soils comes from the
application of lime for controlling soil pH. When this mineral is added to the soil and it
reacts with acids, it dissolves and releases CO2. Lime application contributed additional
emissions, although rather minor at 0.2 million MTCE (3.4 percent).
One reviewer expressed concern about the potential for double emissions counting
from nitrogen fixing crops. The methodology requires the inclusion of nitrogen inputs from
46
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aboveground residues of all crops (including soybeans), as well as estimates of additional
inputs from cultivation of nitrogen fixing crops. At first glance, this may appear to duplicate
the accounting of nitrogen soil inputs from nitrogen fixing crops. However, the methodology
adjusted for this potential overestimation. Specifically for nitrogen fixing crops, the nitrogen
contained in the aboveground plant material (including the crop product removed from the
soil) was assumed only to be a reasonable proxy for the total amount of nitrogen (fixed plus
residue) left in the soil by the crop (Intergovernmental Panel on Climate Change, 2000). In
this way double counting of emissions from nitrogen fixing crops was avoided.
As shown in Table 2 (page 21), a cursory comparison between the 1990 and 2000
inventories would show a large apparent increase in emissions of N2O from agricultural soils
(from 1.1 million MTCE to 6.3 million MTCE). However, the increase is explained largely
by the difference in complexity and exhaustiveness of the calculations. Since the 1990
inventory was completed, the method for estimating greenhouse gas emissions from
agricultural soils has been expanded and refined. Previously, commercial fertilizer was the
only nitrogen source considered, and from that only direct emissions were taken into
account. The new method includes more nitrogen sources, direct and indirect nitrous oxide
emissions, and different emission factors. Results of this newer and more refined method of
calculation should be considered as a new baseline for comparison with future inventories.
Recalculations applying the new method to the previously reported 1990 commercial
fertilizer data indicate no significant difference in emissions from this one source. The new
value for 1990 is 1.4 million MTCE compared to 1.5 million MTCE for 2000. This 7 percent
increase may be due to statistical uncertainty of application data, or it may reflect a small
shift toward application of more concentrated nitrogen inputs. Other components of the N20
inventory could not be calculated for 1990 for lack of sufficient data.
CH4 from Domesticated Animals
Methane is produced by animals as a by-product of digestion. Microbes aid in the
breakdown of food material through the process of enteric fermentation, which produces
methane. Ruminant animals, especially cattle, produce most of the methane attributed to
domesticated animals because of their unique digestive systems. They possess a fore-
stomach where coarse plant material and other food is rigorously attacked by an abundance
of microbes. The methane that results is exhaled or eructated through the mouth. Other
animals such as pigs and horses produce much less methane per head, because fermentation
occurs to a much lesser extent in the large intestines. The relatively small amount of
methane that is created is excreted.
Figure 26 shows the methane emissions disaggregated by animal type and compared
for the years 2000 and 1990. In 2000, methane from domesticated animals accounted for 3
percent of Iowa's gross greenhouse gas emissions with a release of 941,000 MTCE. Because
these emissions are primarily affected by herd population, Iowa's shift away from cattle
production in the 1990s has significantly reduced emissions in recent years. Emissions
reported for 1990 were not derived from average animal populations. For this reason, it was
necessary to recalculate 1990 emissions for more accurate comparison. Total emissions from
domesticated animals have dropped 23 percent since 1990, from 1.2 million MTCE to 0.9
million MTCE. As shown in Figure 27, this drop was largely due to the decline in Iowa
cattle population. In the past decade, average populations of dairy and beef cattle decreased
by 19.7 percent and 44.5 percent respectively. Because these animals are the largest source
47
-------
of methane among farm animals, their population change has greatly reduced total emissions.
However, beef cattle still emit more as a single group than all other animal types combined.
CH4 Emissions from Domesticated Animals, 1990 vs 2000
1 ,000 n
800 -
600 -
400 -
200 -
I—I I
Dairy Cattle
Beef Cattle
Sw ine
Sheep
Goats
Equine
~ 1990
236
842
116
22
0.3
5
~ 2000
184
601
132
14
0.4
10
Figure 26. Emissions of methane from domesticated animals have declined 23 percent since 1990. The
biggest influence was the shift away from cattle production.
Domesticated Animals Average Populations, 1990 vs 2000
15,000 -
10,000 -
5,000 -
Dairy
Beef
Swine
Sheep
Goats
Equine
~ 1990
533
2,934
13,501
490
12
49
~ 2000
427
1,987
15,370
301
12
100
Figure 27. Iowa experienced a significant decline in cattle populations while the swine populations gained 1.6
million head.
Emissions from hogs have increased by 13.8 percent, concomitant with a 1.6 million
head increase in the average population. The impact of this trend, however, is somewhat
mitigated by the fact that hog methane production is the lowest per head of all the animal
types considered in this analysis. Despite the relatively small amount of emissions that each
hog produces, the immense population increase coupled with lower emissions from cattle has
raised the hog contribution to a 14 percent share of total emissions from domestic animals.
Sheep populations dropped by 39 percent in the 1990s. Their population remains low
enough that the total contribution is only 1 to 3 percent in each inventory. Emissions from
goats, mules/asses and horses continued to collectively represent less than 1 percent of the
total emissions in the category.
48
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CH4 and N2O from Manure Management Systems
Given Iowa's huge livestock population, manure management is a serious concern.
Manure that is not applied by daily spread operations to croplands or deposited on pasture,
range or paddock, is considered to be managed. Different management practices result in
different quantities of methane and nitrous oxide emissions. Decomposition reactions that
result in greenhouse gas emissions are very dependent upon environmental conditions
including oxygen availability, heat, moisture, nutrient content, and pH, as well as duration of
management. Because each management system and manure type has unique characteristics
that determine these conditions, the result is a unique emission potential for each animal and
system combination. Systems that result in largely anaerobic conditions, such as the
anaerobic lagoons and liquid systems, release greater amounts of methane. Nitrous oxide
emissions result from nitrification-denitrification reactions that require oxygen for initiation.
Therefore, systems that are more aerated, such as solid storage and drylot storage, will result
in greater nitrous oxide emissions. Table 9 provides estimates of emissions of these two
gases in 1990 and 2000.
Table 9. Emissions from manure management systems;
comparison between 1990 and 2000 (MTCE).
Greenhouse
Gas
1990
(recalculated)
2000
ch4
854,941
989,376
n2o
220,845
235,916
Total
1,075,786
1,225,292
As the decade progressed emissions increased by 14 percent, from 1.1 million MTCE
in 1990 to 1.2 million MTCE in 2000. The disaggregated data show a 16 percent increase in
methane and a 7 percent increase in nitrous oxide emissions from manure management in the
past decade. Methane from manure management alone represents the third leading emission
source in Iowa, with 3 percent of the state's emissions coming from anaerobic lagoons, pit
stores, drylots, liquid slurry systems and other methods of managing animal waste.
The changes observed reflect the interaction between changing animal population and
emission potential. Figure 28 is a logarithmic representation of the annual CH4 emission
potential by animal type and management system. Bars that are below the axis represent
emissions less than 1 kg of methane per head per year. Only three animal types, milk cows,
breeding swine and market swine, represent the top 12 combinations emitting the highest
amounts of CH4. Paired with anaerobic lagoons, pit storage for more than one month, solid
storage and other systems, 12 animals representing each of these configurations combine to
emit 478 kg of methane per year. The remaining 19 animal and system combinations
together produce only 16 kg of methane per year. Barring extraordinary changes in
population, animals in the latter group cannot exert a significant effect on total methane
emissions.
Milk cows combined with anaerobic lagoons produce far and away the most methane
emissions per head, 211 kilograms per year, followed by milk cows with a liquid slurry
system (79.5 kg/yr), breeding swine with an anaerobic lagoon system (42.6 kg/year), market
49
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swine with an anaerobic lagoon (40.5 kg/year), and milk cows in other systems not specified
(30.3 kg/year). After these combinations, emissions are equal to or below 16 kg/year and
drop off quickly to less than 1 kg/year for almost 40 percent of the categories.
SingleAnimalCH4Emission PotentialbyManure Management System
(kg CH4/ year)
1 ,000
100
ai
>-
o
10
16.0
15.2
11.6 .
8.0 '
n 20 1.7 1,7 1.6n^
i fl i n i fi i n i n i n i 11_°
" LJ u u I I I I I I I I
0 8 0.7 0.6 0.6 0 6 H ^
0.5 0.4 0.4 q 4
Animal /
System
J W J J
<;—!<<
O O
_ - . O (0 W (/) (0 to CO
CO CO
CO 5
CO CO
CO 5
CO
0
01
01
O
01
CO
•—.
Q_
Q_
.
Q_
I
O
0
CO
CO
I
CD
O
2
LL
O
CD
CO
LL
O
CD
CO
LL
o
i ir co co ,
CO ^ O
• 0.10.1
Q co co o: o Q J
o q ^ ^ a a
u 8
m
Figure 28. Milk cows, breeding swine and market swine have the highest CH4 emission potentials when
coupled with systems that lead to anaerobic conditions such as anaerobic lagoons (AL), liquid slurry (LS) and
pit storage (PS). Most other animal and manure management system combinations have much smaller
potentials. [Animal Types: MC (milk cow), BS (Breeding Swine), MS (Market Swine), H (Horse), BC (Beef
Cow), BB (Breeding Bull), OFSH (Steers and Heifers On Feed), D (Donkey), L (Layers: Chickens), NOFSH
(Steers and Heifers Not On Feed), GC (Growing Calves), S (Sheep), G (Goats), T (Turkeys), B (Broilers:
Chickens); Management Systems: AL (Anaerobic Lagoon), LS (Liquid Slurry), O (Other), PS>1 (Pit Storage >
1 month), PS<1 (Pit Storage < 1 month), SS (Solid Storage), P (Paddock), PR (Pasture/Range), D (Drylot), DS
(Daily Spread), LS (Liquid Slurry), L (Litter), DPS (Deep Pit Stacks).]
Figure 29 shows the emissions of nitrous oxide by animal type and management
system. Comparing it with Figure 28, it is obvious that the nitrous oxide emission potentials
per head are at least 2 orders of magnitude smaller than they are for methane on a per weight
basis. Similar emission potentials among some systems permitted their aggregation into two
groupings for calculation. The first group encompassed solid storage, drylot and other
undefined systems. This group accounts for the vast majority of nitrous oxide emissions
because of the availability of oxygen required for nitrification-denitrification reactions. Milk
cows were the highest emitting animal type in these systems with 2.4 kg N2O /head/year.
The second group included anaerobic lagoons and liquid slurry systems. Because of
the lack of oxygen in these systems, they constitute a minimal source of N2O emissions. A
maximum emission from milk cow manure in these systems is 0.12 kg N20/yr.
50
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Iowa Single Animal N20 Emission Potentials by Manure Management Systems
(kg NzO/ head/ yr)
2.5 -
1 20
1 1-5
at
5 10
a) 0.5
0.0
—
—
I-,
n
n
n
n
n
MC
BB
BC
ONS
H
NOF
SH
GC
BS
MS
S
T
L
B
G
E
~ Solid Storage, Drylot, Other
2.44
2.13
1.84
1.
16
1.05
0.44
0.44
0.23
0.10
0.05
0.01
0.01
0.00
0.00
H Anerobic Lagoon, Liq Slurry
0.12
0.00
0.00
0.00
0.00
0.00
0.02
0.01
0.00
0.00
0.00
0.00
0.00
0.00
Figure 29. Of the two groups of management systems, the "solid storage, drylot, other" group showed much
greater N20 emission potentials due to greater oxygen availability. [Animal Types: MC (milk cow), BB
(Breeding Bull), BC (Beef Cow), ONSH (Steers and Heifers On Feed), NOFSH (Steers and Heifers Not On
Feed), GC (Growing Calves), BS (Breeding Swine), MS (Market Swine), S (Sheep), T (Turkey), L (Layers:
Chickens). B (Broilers: Chickens), G (Goat), E (Equine).]
The driving factor for the change in emissions of nitrous oxide has been the change in
the makeup of livestock populations since 1990. In Iowa, all of the highest emitting animal
populations have decreased except market swine. Figure 30 shows that populations of all
cattle types have dropped, especially milk cows with a decrease of almost 25 percent. Also
breeding swine dropped by more than 30 percent. In contrast, market swine increased by 21
percent between 1990 and 2000, leading to an increase in emissions by nearly 131,000
MTCE methane and 20,000 MTCE nitrous oxide.
Figure 31 shows the changing trends in methane emissions. In 2000, market swine
accounted for 78 percent of total methane emissions from manure management, with most
(61 percent) of that total coming from market swine manure managed in pit storage for more
than one month. This combination has an emission potential of 15.2 kg CH4/head/year and it
is estimated that 39 percent of Iowa's market swine manure is channeled to this type of
system.
51
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Average Populations of Livestock, 1990 and 2000
¦o
0)
30,000
25,000
20,000
15,000
10,000
5,000
0
MC
BC
BB
JZfclL
GC
NOFS
H
OFSH
MS
Eh
BS
~ 1990
285
1,123
81
1,129
1,383
574
11,795
1,705
8,058
9,450
8,800
490
12
49
~ 2000
216
1,031
67
713
1,174
563
14,227
1,163
27,282
17,200
7,100
301
12
100
Figure 30. Iowa's livestock populations underwent large shifts during the 1990s. There were drops in all
cattle types and breeding swine, but market swine increased and the layer chicken population exploded, gaining
more than 19 million head. [Animal Types: MC (milk cow), BC (Beef Cow), BB (Breeding Bull), GC
(Growing Calves), NOFSH (Steers and Heifers Not On Feed), ONSH (Steers and Heifers On Feed), MS
(Market Swine), BS (Breeding Swine), L (Layers: Chickens). B (Broilers: Chickens), T (Turkey), S (Sheep), G
(Goat), H (Horses).]
Change in Manure Mangement CH4 Emissions from
150 -
w 100 -
-50 -
Figure 31. The largest change in CH4 emissions in the 1990s was due to an increase of 131,000 MTCE from
growth in the market swine population. Even larger growth in the layer chicken population caused a relatively
large increase in emissions of 50,000 MTCE. These increases overshadowed the 41,000 MTCE drop in
emissions from declining populations of milk cows and breeding swine.
There are other systems in use that yield fewer emissions. Pit storage for less than a
month, which is thought to divert 11 percent of market swine manure, emits only 7.6 kg
CH4/head/year. Drylot storage, the system managing 30 percent of the market swine manure,
generates the least emissions, about 6 kg CH4/head/year. Even though these two systems
handle more than 40 percent of market swine manure, they contribute only 6 percent of the
methane emissions. They offer management alternatives that could be expanded further to
produce less CH4 emissions.
Anaerobic lagoons, with the potential to produce the highest methane emission rates,
are used the least frequently in Iowa, accounting for only 3 percent of market swine manure
management. Nevertheless, they generate 13 percent of CH4 emissions from manure
Selected Animals, 1990-2000
I
I I I
I 1
Milk Cows Breeding Swine Market Swine Layers
52
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produced by market swine. For the sake of controlling methane emissions, use of lagoons
should be discouraged, unless they can be coupled with CH4 recovery systemss.
Even more noticeable was the change in chicken population, which increased by 239
percent in Iowa in the 1990s. Although their emission potential per head is among the lowest
of all farm animals, their extreme population expansion was responsible for a 48,000 MTCE
increase in methane emissions and a 15,700 MTCE increase in N2O emissions.
Overall there was an increase in net emissions from manure management. The 24
percent drop in the milk cow population held down the increase somewhat because of their
large per head emission potential. The increase of 2.4 million head of market swine, coupled
with their relatively high emission potential, was the greatest factor in the net rise in
emissions. An 8 million head increase in layer chicken population was the second greatest
driver in elevating emissions.
CH4 and N2O from Burning of Agricultural Wastes
After crops are harvested fields still hold the residues of the harvest, which include
substantial plant materials such as husks, stems, and leaves. This uncollected crop debris
needs to be managed to mitigate entanglement and obstruction of farm equipment during
successive plantings, or stifling the growth of emerging crops. There are different ways to
manage residues, employing distribution, burial, removal, and burning (Korucu et al. 1999).
Combustion of residue releases large amounts of carbon dioxide and relatively
smaller amounts of methane, nitrous oxide, carbon monoxide and nitric oxides. Because the
emissions of carbon dioxide are from biological sources, however, they do not increase the
burden of CO2 in the atmosphere since the emissions are balanced by the original uptake of
CO2 during photosynthesis. Although the carbon in methane is also derived from
atmospheric carbon dioxide during photosynthesis, methane emissions do represent a net
increase in greenhouse gas forcing because the methane molecule has a global warming
potential that is 21 times that of the original carbon dioxide molecule from which it was
derived. Carbon monoxide and nitric oxide emissions have indirect effects on greenhouse
forcing, although the GWPs for these gases have not been quantified.
Burning of residues is more common for some crops than others. According to
national inventories, rice crop residues are the most frequently burned of all crop types, and
they are considered in national emission assessments along with sugarcane, barley and
peanuts (Environmental Protection Agency, 2002). For all crops other than rice, it is
assumed that 3 percent of field crop residue is burned. For Iowa we only considered
emissions for corn, soybean, and wheat residues assuming that 3 percent were burned,
although it is believed that this activity occurs even less frequently in Iowa.
Emissions from this source are relatively minor compared to other agricultural
sources. Crop residue burning comprises 0.13 percent of total Iowa greenhouse gas
emissions at 44,000 MTCE. Sixty percent of this total is derived from methane and 40
percent from nitrous oxide. Table 10 disaggregates the emissions according to crop type.
Greenhouse gas emissions from burning of agricultural residues were estimated to have
increased by 27 percent between 1990 (recalculated) and 2000. Emissions were estimated
based on crop production in the state, which can fluctuate from one year to another. The
observed rise in the 2000 emissions is directly proportional to the rise in production relative
to 1990.
53
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Table 10. Methane and nitrous oxide emissions from residue burning by crop
type, with comparison between 1990 (recalculated) and 2000 (MTCE).
Crop Type
1990
(recalculated)
2000
ch4
n2o
ch4
n2o
Corn
14,346
4,526
16,808
5,303
Soybeans
6,994
8,707
9,739
12,125
Wheat
35
12
11
4
Total
21,375
13,245
26,558
17,431
It should be noted that the method used in the previous inventory assumed the
fraction of residue burned was 10 percent. This is believed to be a gross overestimation, and
has since been lowered to 3 percent. According to expert opinion, even this lower estimate is
thought to be too large in Iowa because burning is mostly a maintenance tool for
conservation plantings, which are not extensive (D. Christensen, Recycle Iowa, personal
communication, August 8, 2003; R. Robinson, Iowa Farm Bureau, personal communication,
August 11, 2003).
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Chapter 5: Greenhouse Gas Emissions from Industrial Processes
In this industrial sector analysis, non-energy related greenhouse gas emissions from
industrial activities are quantified. For practicality, only the largest emitting industries are
considered in the inventory. Other industrial activities that are known to emit greenhouse
gases, such as soda ash manufacture and consumption, primary aluminum production, adipic
acid production, HFC-23 and HCFC-22 production, and magnesium production and
processing, are not addressed here either because they do not exist in Iowa or data related to
their activities were unavailable. Nevertheless, emissions from industries not analyzed are
thought to be minimal in comparison to those investigated here. Figure 32 shows the
industries covered and the relative strengths of their emissions. Overall, emissions from
industrial processes are relatively small, generating about 922,000 MTCE (3 percent of
Iowa's year 2000 gross greenhouse gas emissions), released as carbon dioxide, nitrous oxide,
sulfur hexafluoride, hydrofluorocarbons and perfluorocarbons.
Iowa Greenhouse Gas Emissions from Industrial Processes, Year 2000
Cement Clinker Capacity
Nitric Acid Production
Substitution for Ozone Depleting Substances
Limestone Use
Electrical Transmission & Distribution
Lime Manufacture
Masonry Cement Capacity
Carbon Dioxide Manufacture
]
0
3% of all Iowa
Emissions
50
100
150
1,000 MTCE
200
250
300
Figure 32. Industrial emissions were dominated by cement clinker production, and a few other processes.
Inventory of Greenhouse Gas Emitting Processes
CO2 from cement clinker and masonry cement manufacture. Cement clinker
manufacture in Iowa was the largest source of non-energy carbon dioxide emissions in the
year 2000, yielding 296,770 MTCE. However, this source is a minor one overall as it only
accounted for 0.9 percent of total greenhouse gas emissions. The gas is released when
calcium carbonate is heated for the production of clinker, a cement precursor. Masonry
cement requires additives including lime, an additional source of carbon dioxide emissions.
Because detailed data were unavailable, production estimates were drawn from Iowa's
clinker capacity as reported by the Portland Cement Association (H. Van Oss, U.S.
Geological Survey, personal communication, September 24, 2002). A maximum emission
scenario was assumed where all clinker went to masonry cement production leading to an
additional 13,112 MTCE.
N2O from nitric acid production. The EPA estimates that 70 percent of nitric acid (HNO3)
produced is consumed as an intermediate for the production of ammonium nitrate (NH4NO3),
55
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a major component of commercial fertilizer (Environmental Protection Agency, 1998a).
Nitrous oxide is an unwanted by-product in the industrial process, formed from the oxidation
of ammonia (NH3). This source released an estimated 229,071 MTCE in 2000, and is the
third highest emission source of N2O in the state.
Substitutes for ozone depleting substances. HFCs and PFCs serve as substitutes for ozone
depleting CFCs in a variety of applications including refrigeration and air conditioning,
aerosols, solvent cleaning, fire extinguishing, foam blowing and sterilization. They were
introduced after the 1987 Montreal Protocol, which began the process of banning the
production and use of CFCs. Although these substitutes mitigate the degradation of Earth's
protective ozone layer, they are powerful greenhouse gases with global warming potentials
ranging from 140 to 11,700. Even with their extremely high GWPs, their releases were so
low (163,916 MTCE) that they accounted for only an estimated 0.5 percent of Iowa's total
greenhouse gas emissions.
CO2 from limestone use. Limestone is composed of calcium carbonate (CaCCh), which
gives off C02 when heated. It has many chemical and industrial applications, including
cement and lime (CaO) manufacture, and as a purifying flux in refining metals like iron. The
construction industry is a large consumer of limestone for building roadways, although in
this use it is not subjected to the same heat and chemical processes that cause emission of
C02.
Lime and limestone are also applied to agricultural fields to neutralize acidic soils.
This activity releases CO2 and was accounted for in the analysis of emissions from
agricultural soils. In this section, emissions are quantified only for limestone used in the
chemical and metallurgical industries; their total contribution was estimated at 141,251
MTCE.
SF6 electricity transmission and distribution. Sulfur hexaflouride is most commonly used
as an insulator in electricity transmission and distribution equipment. Emission is a result of
equipment leakage and to a lesser extent from the manufacture of this equipment. On a per
weight basis, it is the most powerful greenhouse gas considered in this analysis with a GWP
of 23,900. Nevertheless, emissions were too low (40,143 MTCE) to make it a major
contributor to Iowa's inventory, accounting for only 0.1 percent of total Iowa greenhouse gas
emissions.
CO2 from lime manufacture. In 2000, only one facility manufactured lime in Iowa. For
this reason, the US Geological Survey was unable to disclose proprietary production data
needed to estimate emissions. Rather than calculating emissions based on production data, a
U.S. government source, applying an undisclosed EPA methodology, estimated these
emissions to be 34,091 MTCE.
This estimate illustrates the stark contrasts that can occur between different
inventories. Our figure is a factor of five less than the emissions reported for the same
activity in the 1990 inventory. Because neither the data nor the methodology were disclosed
for the 2000 inventory, it is not possible to reliably compare the two emission figures or
explain the discrepancy. This inconsistency, however, does not introduce a significant
uncertainty in the overall inventory, as it only accounts for 0.1 percent of total emissions.
56
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CO2 emissions from CO2 manufacture. Carbon dioxide is manufactured for applications
in food processing, chemical production, carbonated beverages and enhanced oil recovery.
This source was estimated to be very minor with an emission of only 3,970 MTCE.
Other industries. The production of adipic acid, primary aluminum, and HFC-23 from
HCFC-22 are all processes that generate greenhouse gases as a part of their manufacturing
processes. These industries are not found in Iowa. Soda ash manufacture and consumption
are known to occur in the state, but data on these activities were not available, nor was a
method of estimation. Thus, their emissions were not calculated.
Overall Industrial Emissions and Comparisons of the 1990 and 2000
Emission Inventories
Table 11 summarizes the emissions discussed above, and compares the 1990 and
2000 inventories. The original 1990 data were recalculated in two different ways to make a
more valid comparison with the 2000 data.
Table 11. Emissions from industrial processes in Iowa in 2000, and compared with recalculated values for
1990 (MTCE).
1990
Gas/Source
1990
(alternative
(recalculated)a
recalculated)
co2
3,612,452
1,142,768
489,194
Cement Clinker Manufacture
265,935
265,935
296,770
Masonry Cement Manufacture
83
83
13,112
Limestone Use
2,812,320
340,200
141,251
Lime Manufacture
534,114
534,114
34,091
C02 Manufacture
2,436
2,436
3,970
Soda Ash Manufacture and Use
UA
UA
UA
Primary Aluminum Production
NA
NA
NA
n2o
0
0
229,071
Nitric Acid Production
UA
UA
229,071
Adipic Acid Production
NA
NA
NA
HFCs, PFCs and SF6
85,493
85,493
204,059
Substitutes for Ozone Depleting Substances
2,740
2,740
163,916
Electrical Transmission and Distribution
82,753
82,753
40,143
HFC-23 Production
NA
NA
NA
Total for year
3,700,381
1,228,261
922,324
Difference from year 2000
2,778,057
305,937
UA = activity data was unavailable
NA = not applicable; activity does not occur in Iowa
a Emissions were recalculated with the data from the 1990 inventory, and the methodology from the 2000
inventory. Numbers in italics were not included in the original 1990 inventory.
b The alternative recalculation uses what is believed to be more realistic data for "limestone use.'
Consumption of limestone in 1990 was previously reported as 25 million short tons (22.7 million metric
tons). Instead, 3 million short tons (2.7 million metric tons) of limestone was used to recalculate
emissions.
57
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Although it appears from the "recalculated" 1990 estimates that emissions from
production processes in 2000 decreased significantly, the apparent overall decrease is largely
explained by questionable activity data, and the estimation methods for "limestone use" and
"lime manufacture," the two sources with the greatest inconsistency between inventories.
Limestone use accounted for 76 percent of recalculated emissions from industrial processes
in 1990, but in 2000 this share was only 15 percent. This radical change in emissions seems
unlikely given the fairly stable number of producers in the state. Consumption data from the
two periods shows a large discrepancy with 25 million short tons (22.7 million metric tons)
reported in the 1990 inventory (Iowa Department of Natural Resources, 1996) and 1.3
million short tons reported for 1999 according to the Iowa Minerals Yearbook for 2000
(United States Geological Survey, 2000). Data for 2000 was withheld to protect proprietary
information. No source was cited in the 1990 inventory, making comparison of data and
emissions difficult. However, past Mineral Yearbooks show consumption by industry was
on the order of 3 million short tons in the late 1980s and early 1990s (Harrison and McKay,
1991; Zelten and McKay, 1991). Data for 1990 was again unavailable.
Because activity data reported in the 1990 inventory is different by an order of
magnitude, it seems more likely that it came from a dissimilar source and represents an
inordinately large figure for limestone use. It is possible that the 1990 inventory
overestimated industrial emissions by counting total crushed stone consumption, which
includes use by agriculture and construction industries. Sources cite that figure to be in the
range of 28 to 31 million short tons between 1989 and 1991 (Harrison asd McKay, 1993;
Zelten and McKay, 1991). It thus seems most likely that emissions in actuality were much
smaller in 1990 than reported in the 1990 inventory.
An alternative recalculation was conducted for 1990 limestone use based on the data
from the Iowa Minerals Yearbooks. Consumption was set at 3 million short tons and
emissions were calculated to be 340,200 MTCE for limestone use (see Table 11). This figure
is 2.5 million MTCE lower than the first recalculated value. Using the alternative data brings
total industrial emissions in 1990 closer to the 2000 estimate, but still higher by 25 percent.
It may indeed reflect a true decrease in emissions from industrial limestone use in the 1990s.
The other large discrepancy in industrial emissions was from lime manufacture. For
1990, emissions were recalculated to be about 534,000 MTCE; emission estimates for year
2000 were 500,000 MTCE lower. As explained above, emissions from 2000 were provided
by a government source with undisclosed documentation in order to protect proprietary
information. Without the fundamental information about this source, a sound comparison
cannot be made.
In the 1990 inventory, emissions of HFCs, PFCs, and SF6 were unquantified. For
these chemicals, recalculated 1990 emissions were based on recent calculations with the
methods used in the 2000 inventory. Worksheets explaining the calculation methods can be
found in the appendix under "Industrial Processes." The differences in emissions of these
gases are a consequence of the changing times and technologies. As noted above, the
marked increase in use of HFCs and PFCs in the 1990s (subsumed in the table under
"substitutes for ozone depleting substances") is a result of the phase-out of CFCs that began
after the 1987 Montreal Protocol, and was accelerated in subsequent follow-up agreements.
The drop in SF6 emissions from electrical transmission and distribution equipment by 42,600
MTCE reflects a worldwide trend of declining use owing to the increased price of the gas,
and environmental awareness about its impact on the greenhouse effect.
58
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Overall, applying the "alternative recalculated" emissions for the 1990 inventory
given in Table 11 and comparing them to the emissions from the 2000 inventory, it is
estimated that emissions from industrial sources decreased by 25 percent in the 1990s. The
largest apparent reduction stemmed from a drop in lime manufacture emissions, although this
assumption is not verifiable because of proprietary production data. Secondly, limestone use
was apparently cut in half over the 1990s, thereby reducing emissions by nearly 200,000
MTCE. Somewhat offsetting the net downward trend in industrial emissions was the
increased production in CFC substitute gases, which increased emissions by 160,000 MTCE.
59
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Chapter 6: Greenhouse Gas Emissions from Wastes
Overview
The disposal and treatment of organic wastes generate continuous releases of
greenhouse gases to the atmosphere. Practices can be employed to offset these emissions
through carbon sequestration and recovery (collectively called "carbon capture"). Figure 33
summarizes the magnitudes of emissions and capture from waste management activities. As
shown, landfills that collect municipal solid wastes (MSW) are the dominant source of
methane emissions. Because minimization of space allocated for waste disposal is a common
aim of landfill managers, decomposition of the organic fraction in the waste typically occurs
in confined pockets, which are underexposed to oxygen. Inevitably, anaerobic microbial
assemblages decompose the organic carbon to fulfill their needs for metabolic energy, and in
the process release a mixture of carbon dioxide and methane. It is possible, however, to
capture the methane and burn it, either as a fuel or simply to flare it to CO2. The former
option is of course preferable, but even in the latter case there is a benefit since there is
conversion from CH4 with a high GWP (21) to C02 which, because it is from biogenic
organic wastes, has no net global warming impact. In addition to methane, nitrous oxide is
released via denitrification under aerobic conditions by the action of microbes on the
nitrogen contained in the wastes. Another option for treatment of MSW is incineration rather
than landfill. In this case, small amounts of N20 are generated during the combustion
process.
Liquid wastes containing organic carbon, commonly known as sewage, are collected
and treated at sewage treatment plants (STPs). Treatment of the wastewater involves several
steps involving microbial decomposition under alternate anaerobic and aerobic conditions.
In the process of reducing the organic burden in the wastewater, microbes emit CH4, CO2,
and N2O, just as they do in landfills.
MSW landfills, STPs, and to a very small degree incinerators together generate 1.0
million MTCE (3 percent) of the state's total greenhouse gas emissions. When factoring in
carbon capture, the net emissions decline by about 600,000 MCTE. Despite efforts for
methane recovery from landfills, they are still the dominant net source of greenhouse gases
from wastes.
Iowa Greenhouse Gas Emissions from Wastes, Year 2000
Landfill CH4
SI ucfge and Waste Wsier T reatm ent
Solid Waste Incineration
Wastewater CH4 Recovery
Lendfill CH4 Recovery
Landfill C Sequestration
-600
E
I
3 3% of all Iowa
Emissions
I
I
-400
-200
200 400
1,100 MTCE
600
800 1,000 1,200
Figure 33. Landfills are both the largest source of emissions, and the largest sink for carbon capture among
waste management practices.
60
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Greenhouse Gas Emissions and Carbon Capture from Solid Waste
Disposal
Landfill gas (biogas) generation is the product of microbial breakdown of organic
wastes buried under the land surface. In the landfill, new wastes near the surface are initially
exposed to atmospheric oxygen. Subsequently, as these recent wastes are covered by yet
newer wastes and sink lower into the landfill, they enter the anaerobic zone where
degradation switches from aerobic to anaerobic microbes. Ultimately, methanogenic bacteria
break down the waste residues into water and biogas containing approximately 45 percent
carbon dioxide and 55 percent methane. Only the methane portion of the biogas is counted
in the greenhouse gas inventory; the carbon dioxide vented to the atmosphere merely
replaces that which was taken from the air when the original organic material was
biosysnthesized (thus, net emissions equal zero). The production of methane from
degradation in the landfill is slow but steady. It may take up to 30 years for all the organic
matter deposited at a given time to dissipate into biogas. Thus, accounting for emissions
from landfills must incorporate a cumulative timescale factor that considers the ongoing,
continued emissions from wastes that were deposited some decades in the past.
When municipal wastes are incinerated they emit carbon dioxide and nitrous oxide.
Oxidation of carbon in synthetic wastes including plastics, rubber and other petroleum-based
products constitutes a net emission of the greenhouse gas C02 because the source of the
carbon is from petrochemicals rather than from biogenic photosynthesis of atmospheric
CO2.7 Nitrous oxide is produced by processes analogous to those creating N2O emissions
from stationary combustion of fossil fuels (see page 43).
Large capacity landfills are now required by regulation to flare biogas. The practice
oxidizes methane with a GWP of 21 to carbon dioxide and water with no net greenhouse gas
emissions. However, much of the deposited carbon never gets degraded to biogas, and thus
remains in the landfill rather than vents to the atmosphere. This remaining carbon is thus
sequestered in the earth. Landfilled plastics and other petroleum-based products resistant to
degradation do not count in the calculation of sequestered carbon, because they have been
merely transferred from one long-term sink (fossil fuels in the ground before mining) to
another (the landfill).
In 1989, an initiative by the Iowa Legislature, the Waste Reduction and Recycling
Act, mandated that statewide landfill deposits decrease by 50 percent from 1988 to 2000. As
a result of this initiative, the estimated rate of landfilling MSW has decreased significantly
since 1990. Figure 34 shows the actual tonnage of wastes landfilled in Iowa between 1960
and 2000 (solid line) versus the estimated tonnage that would have pertained had there been
no 1989 legislation (dotted line). The implementation of the regulation appears to have
caused a zero-growth trend in quantities of waste landfilled during the 1990s despite a 5
percent increase in population.
7 Carbon in synthetic polymers taken from a petrochemical and burned in an incinerator represents a net
transfer of carbon from long-term storage (buried in the earth as petroleum, natural gas, or coal) to the
atmosphere as C02.
61
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Iowa Estimated Landfill Use Over Time and Projected Usage Without the Waste
Reduction and Recycling Act of 1989
3500
„ 3000
§ -o 2500
H (1)
¦a = 2000
| ? 1500
I ™ 1000
>-
500 -
0 -I—T-
1960
1965
1970
1975
1980
1985
1990
1995
2000
——Actual Landfill Usage Projected Usage w ithout Act
Figure 34. As a result of the Waste Reduction and Recycling Act of 1989, landfill use has remained relatively
flat since 1990, even with a 5 percent rise in population.
Table 12 summarizes the data for greenhouse gas emissions and carbon capture from
MSW waste disposal, and compares the years 1990 and 2000. MSW was the fourth highest
source of Iowa's gross greenhouse gas emissions in 2000 and was responsible for 3 percent
of total emissions. Landfill methane emissions made up the largest part of gross emissions
from this source. Incineration makes up a very minimal part of total MSW treatment in the
state and less than 0.01 percent of the gross greenhouse gas emissions.
Carbon sequestered in landfills and methane recovered from release of biogas equaled
more than half of the estimated emissions, dropping the net emissions to 485,000 MTCE. In
2000, four Iowa landfills were known to be recovering or flaring methane. About 149,000
MTCE (29,000 tons methane) of methane were recovered for energy or flared by Des Moines
Metro, Bluestem and Scott County landfills. The Johnson County landfill is known to have a
flare, but no data were available for the site.
Table 12. Emissions and carbon capture from waste disposal (MTCE), years 2000 and 1990
(recalculated).
Source
Year 1990 Emissions „ „„„„ . .
, , , . ,, Year 2000 Emissions
(recalculated)
Landfill CH4 (before recovery)
Incineration C02
Incineration N20
966,669 1,044,619
2,507 2,505
194 194
Gross Total
969,370 1,047,318
Landfill Carbon Sequestration
CH4 Recovery
-413,719 -413,377
- 54,997 -148,619a
Net Total
500,654 485,322
a The actual amount of methane recovered in 2000 may exceed this value, as data were not available
for all landfills recovering methane.
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Emissions for 1990 were recalculated to ensure consistent comparison with the 2000
data. While landfill carbon sequestration remained stable, the recovery of methane increased
170 percent, from 55,000 MTCE (10,500 tons methane) to nearly 150,000 MTCE (28,600
tons methane). Overall, net emissions decreased 3 percent, from 500,654 MTCE in 1990 to
485,322 MTCE in 2000, while gross emissions increased by 8 percent.
In the future as landfills grow, the option of landfill gas recovery could prove to be
low-hanging fruit in the effort to minimize Iowa's greenhouse gas emissions. The
investment in these systems is already increasing. The combustion of gas for energy
provides economic and environmental incentives and provides a double credit toward
reducing greenhouse gas emissions by evading direct methane emissions from landfill gas,
and circumventing CO2 emissions from the conventional fossil fuels.
Greenhouse Gas Emissions and Carbon Capture from Municipal
Wastewater at Sewage Treatment Plants
The function of a municipal sewage treatment plant is to remove the nutrient rich
organic fraction from wastewater generated in the community and transported via sewage
pipes to the central plant. This function is especially important to maintain the water quality
of natural waterways into which the treated sewage effluent is released, and to protect the
public from waterborne bacterial and viral diseases. Organic matter sent to the STP from
households and commercial businesses include human excreta, food scraps, soaps, and dirt.
If storm drains are connected to the sewage lines, during heavy storm events STPs may
receive leaves and other plant debris, greases and oils contained in street dust, and runoff
from lawns and other green space. Sometimes industries such as food processing plants send
their nontoxic organic wastes to STPs. The treatment plant employs various aerobic and
anaerobic processes to reduce the organic load in the water; methane production occurs
under anaerobic conditions and nitrous oxide under aerobic conditions.
The potential methane and nitrous oxide production capacity of wastewater is
determined by the degradable organic fraction, expressed as the biological oxygen demand
(BOD). This variable quantifies the amount of oxygen required to degrade the organic
matter under ideal aerobic conditions. A higher BOD indicates a greater amount of organic
matter and greater CH4 and N2O production potential. The method used for this analysis is
based on an estimation of per capita BOD generation rate and population.
Anaerobic biological treatment of wastewater results in biogas emissions containing
approximately equal parts carbon dioxide and methane. Emissions of carbon dioxide from
organic sources are considered to be in equilibrium with the atmosphere (with certain
exceptions) producing no net global warming effect. Nitrous oxide also results from a
complicated process of microbial cycling of nitrogen contained in the organic fraction. The
content of nitrogen in human waste may be increasing since, according to the United Nations
Food and Agriculture Organization (2004), the amount of protein, the chief source of
nitrogen intake, has increased in the American diet in recent decades.
In the first treatment step at the STP, the mixture of water and organic matter sits
under quiescent conditions, which allow solids to settle out. This concentrated buildup of
organic matter, called sludge or biosolid, is skimmed from the settling tanks, stabilized and
disposed of separately. Anaerobic digestion is one of the most widely used practices to
stabilize the sludge, especially in larger treatment works because of its methane recovery
63
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potential (Environmental Protection Agency, 1999b). Ninety percent of all methane
generated at the STP derives from sludge stabilization. The remainder is generated from
wastewater treated in subsequent steps. In contrast to CH4 emissions, most of the N20 is
generated in the treatment of wastewater after sludge removal, with only a small portion
released from the sludge.
Table 13 provides estimates of the emissions of methane and nitrous oxide and
recovery of methane at STPs in Iowa in 2000, with comparison to 1990. The total gross
emissions only accounted for about 0.20 percent of Iowa's total greenhouse gas emissions in
2000, and net emissions through methane recovery reduced the share to 0.13 percent of total
emissions. Although the quantity of methane released from STPs is relatively small
compared to other sources such as landfills and manure management systems, its recovery as
an energy source is among the most amenable of all methane emission categories. As can be
observed from the table, about two-thirds of the methane generated from sludge treatment
(35,000 MTCE) was recovered (23,000 MTCE). In recent years, treatment plants have
begun to use biogas for process heat, and small scale space heating and electricity
production. These activities serve to convert methane to biomass-derived carbon dioxide,
which has no net global warming impact.
Table 13. Emissions and methane recovery from treatment of municipal wastewater at sewage
treatment plants in Iowa (MTCE), years 2000 and 1990 (recalculated).
Source
Year 1990 Emissions
(recalculated)
Year 2000 Emissions
CH4 from Wastewater Treatment
3,679
3,877
CH4 from Sludge Treatment
33,108
34,892
N20 from Wastewater and Sludge Treatment
23,138
26,064
Gross Total
59,925
64,833
CH4 Recovery
-113 a
- 23,000
Net Total
59,812
41,833
a Source: Iowa Department of Natural Resources (1996).
As noted above human biological wastes are but one of several sources of organic
matter that is treated at the STP. The method employed in our analysis, however, only
considers the human-derived source. This undoubtedly underestimates the emissions as it
ignores other waste contributions from soaps, domestic food scraps, industrial food
processing, and organics collected during storms. A full analysis of these omitted sources
would result in higher emissions values than those quoted in Table 13. The extensiveness of
recovery in Iowa was suggested by the IDNR, which estimated that more than 19 Iowa
municipal STPs capture and use more than 1.2 million cubic feet of biogas per day (Iowa
Department of Natural Resources, 1999).
The emissions estimation methods have changed between the 1990 and 2000
inventory years, including addition of a calculation estimating nitrous oxide emissions from
wastewater and sludge treatment, and a near tripling of the methane emission factor. In
addition, the national BOD generation rate increased by 5 percent, the fraction of waste
64
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treated anaerobically increased by 1.25 percent, and per capita American protein
consumption has risen from 31.2 to 41.9 kg per year. A further factor affecting the emission
rates is that the Iowa population increased by about 5 percent in the 1990s. All of these
factors lead to an estimated increase in gross emissions of about 8 percent, from 59,925
MTCE to 64,833 MTCE. Despite these notable changes that have tended to increase
emissions, this source was an even smaller net contributor to Iowa's total greenhouse gas
releases in 2000. Efforts to recover methane expanded dramatically throughout the decade,
reducing net emissions by 30 percent from 59,812 MTCE in 1990 to 41,833 MTCE in 2000.
65
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Chapter 7: Carbon Sinks from Forest Management and Land
Use Change
Soils and biomass can be thought of as large sinks or pools of stored organic carbon.
These pools lose or gain carbon by exchange with the atmosphere. Carbon, in the form of
carbon dioxide (C02), is taken out of the atmosphere during photosynthesis by plants
(biomass), which produce a simple organic carbon (commonly characterized by the monomer
unit CH20). Plants fashion these simple units into a myriad of complex carbohydrates that
include sugars, starches, and cellulose, the building blocks of plant material. When plants
die, microbes decompose the plant tissue back to simple units (CH20), which they react with
oxygen or other oxidants when 02 is unavailable. CH20 is the "fuel" of the biosphere.
Microbes oxidize it to generate the energy required to supply their metabolic needs, and C02
is released back to the atmosphere as a by-product, thus completing the cycling of carbon
between the atmosphere and the plant biomass.
Not all of the organic carbon is rapidly returned to the atmosphere. Under anaerobic
conditions microbes oxidize CH20 using other oxidants (e.g., NO3", Fe+3, SO4"2), but these
processes are typically slower than reaction with 02, allowing organic carbon to accumulate.
A good example of this occurrence is the large store of carbon found in the "muck" of
anaerobic wetlands and swamps. Another reason for carbon retention in the biosphere is the
inherent difficulty of decomposing some forms of biomass relative to others. This is
particularly important with regard to cellulose, which is much more resistant to microbial
decomposition in comparison to starch and sugars. Cellulose makes up the "woody" parts of
plants and constitutes up to 50 percent of the mass of plants. Cellulose is not typically
transformed completely into C02, but rather semi-decomposed into fragments named
"humus," a major component of stored organic matter in soils.
The size of the organic carbon pool in soils and biomass depends on the balance of
gains and losses (the flux) of carbon with the atmosphere. This balance is particularly
sensitive to anthropogenic activities that alter the composition of the land, and to how the
land is used and maintained. The two dominant land uses that affect carbon flux in Iowa are
croplands and forests. Forestlands cover 2.6 million acres of the state, and croplands are ten
times larger at 26 million acres (two-thirds of the state's total area). Our analysis employed a
stock approach to account for carbon flux in the state's forestlands. Estimates of carbon flux
from agricultural soils are reported from a study conducted by the US Department of
Agriculture (USDA). As shown in Figure 35, despite the tenfold advantage in spatial
coverage, Iowa's croplands were estimated to store only slightly more carbon annually than
the forestlands.
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Iowa Annual Carbon Sequestration through
Forest Management and Land Use Change
Agricultural Soils
Forests
-3,500 -3,000 -2,500 -2,000 -1,500 -1,000 -500 0
1,000 MTCE
Figure 35. Forests in Iowa cover only a small part of the state but sequester almost as much carbon as
agricultural soils.
Carbon Flux in Forests
Carbon in the forest environment is divisible into four pools: large trees store carbon
in their trunk, branches, and roots; understory vegetation is comprised of relatively short-
lived species such as bushes, shrubs and small trees; the forest floor is the layer of fallen
debris and litter that collects above the soil; and the forest soil contains the smallest broken
down pieces of litter, debris, and humus. Understory vegetation is the smallest pool, and
forest soils the largest. For our inventory, the stock approach was applied to the years 1990
and 20018 to estimate the change in carbon stores from Iowa forests during the interim
between the two dates. Each carbon pool was analyzed from data in the Iowa forest
inventories compiled by the USDA Forest Service, which are available at the online Forest
Inventory Analysis Database (http://ncrs2.fs.fed.us/4801/fiadb/index.htm ). For further
explanation of the methods of calculation, see the appendices for this section.
Figure 36 compares carbon pool sizes in 1990 and 2001. Overall, the forests in 2001
held 168.0 million MTCE compared to 136.2 million MTCE in 1990, for an overall increase
of 31.8 million MTCE (23 percent). This translates to an annual uptake of 2.9 million
MTCE, which constitutes about 10 percent of Iowa's gross 2000 greenhouse gas emissions.
The uptake rate has more than doubled since 1990 when annual forest sequestration was
estimated to be 1.2 million MTCE (Ney et al. 2001). Soil, already the largest carbon pool,
accumulated most of the additional carbon, accounting for 22.4 million MTCE or 70 percent
of the total increase. This is a direct result of the estimated increase in the area of land
reported as forestland.
8 Year 2001 was adopted rather than 2000 because of the higher quality of its inventory data. It is assumed that
the additional year of data will not greatly impact the outcome of the analysis.
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Comparison of Iowa Forest Carbon Stores 1990 vs 2001
£
o
n
rc
O
(/)
£
o
H
o
4—1
0)
s
c
o
120
100
80
60
40
20
i
o -
Timberland Trees
Forestland Understory
Forestland Soil
Forestland Floor
~ 1990
40.2
1.3
82.2
12.4
~ 2001
45.6
1.6
104.6
16.2
Figure 36. Carbon storage grew during the 1990s, with most storage occurring in forest soils.
There are several alternative explanations for the assumed increase in carbon storage.
One is the unconfirmed trend observed by natural resource managers of increased forestland
encroachment onto private lands. According to Vern Fish of the Black Hawk County
Conservation Board this factor could have increased the forestland area captured in the 2001
forest inventory (personal communication, September 17, 2003). Ney et al. (2001) noted that
increases in forestland between 1974 and 1990 were largely attributed to conversion of
pasture areas that were no longer grazed. This pattern may have continued into the following
decade, further expanding the forested areas. Another factor favoring forest expansion may
have been the continued rise in enrollments in the Conservation Reserve Program (CRP), in
which government subsidies are given to farmers to introduce measures such as planting
trees for reducing erosion on marginal croplands. Finally, as discussed in the section on
uncertainty, the apparent overall change in forestland area may be due in part to sampling
and statistical errors stemming from the yet to be completed 2001 forest inventory.
Carbon Flux in Agricultural Soils
A study conducted by the US Department of Agriculture/Natural Resources
Conservation Service (USDA/NRCS) adopted the Century EcoSystem Soil Organic Matter
Computer Model to estimate historical soil carbon dynamics of Iowa agricultural soils, and to
predict county level changes that resulted from crop management and tillage practices
(Brenner, 2001). The study was unique in that it collected data on historical land-use,
dominant management practices (drainage, irrigation, crop rotations, tillage, fertilization)
over time, and installation of conservation practices (e.g., CRP enrollment, grassed
waterways, buffers). Providing the data were local experts from the conservation districts
and local NRCS offices for every county in Iowa. The study concluded that Iowa
agricultural soils are currently a net sink for carbon, and estimated annual carbon uptake to
be 3.1 million MTCE. About 1.9 million MTCE of this amount is stored directly in
cropland. The remaining storage was attributed to increased CRP enrollment, grassland and
tree conversions, and wetland reversions. The increased adoption of moderate tillage and no
tillage systems for row crop production as well as activities involved within the CRP had the
greatest influence on storage trends. A smaller contributor was the impact of increased crop
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residue inputs from rising productivity, an ongoing development since the 1950s.
Uncertainty in Forest Carbon Flux
The 2001 Iowa Forest Inventory, the source of our estimation of carbon sequestered
in Iowa's forestlands, marks the beginning of a new standardized five-year cycle
inventorying system initiated in 1999. This is an alternative approach to the previous three
periodic inventories taken in 1954, 1974 and 1990. Under the new system, a full and precise
inventory will take 5 years to complete, as one fifth of the field plots in the state are
measured each year. Thus, the most recent inventory will not be complete until after 2004
data are compiled. Preliminary data were available, but it was cautioned that the error would
be minimized only when all the plots have been measured, which at the time of this analysis
included 60 percent of the total. This introduces some level of uncertainty relative to the
1990 inventory. To minimize error, the most recent data at the time of the analysis, the 2001
cycle, was used rather than 2000 data. The North Central Research Station (2000) reported
the sampling error for the 2000 update: 4.32 percent error for area of forestland; 4.72 percent
for area of timberland; 8.35 percent for number of growing stock trees on timberland; 7.78
percent for volume of growing stock on timberland; and 9.20 percent for volume of saw
timber on timberland. These errors are high in comparison to the 1990 inventory, which
cited errors ranging from 1.9 percent for timberland area to 3.2 percent for growing stock
volume. Error values for the 2001 cycle update were unavailable, but are expected to be
somewhat lower than the 2000 data with the additional 20 percent of plot samples measured.
Differences in the methods of data collection and analysis further impede comparison
between the 1990 and 2001 inventories. In 1990, manual interpretation of aerial photos was
applied to classify land area. By 2001 this was replaced by remote sensing technology.
Moreover, more field plots have been added and new field plot designs implemented to
improve classifications and estimates. New nationally consistent algorithms were applied
beginning with the 2000 inventory to assign forest type and stand-size class to each plot
condition. Consequently, changes have been made to the list, grouping, and names of
recognized forest types, the equations used to assign stocking values to individual trees, and
the definition of non-stocked forests. Considering these modifications, the NRCS advises
that comparisons between the early periodic and five-year cycle inventories should be made
with caution.
Other areas of uncertainty involve the assignment of forest type specific carbon
coefficients to incongruent forest types from survey data for calculating carbon in forest
floors. When possible, the most appropriate coefficient was assigned to a forest type, but for
five of the ten forest types examined in Iowa a state average, determined by Birdsey (1992),
had to be used for want of better values. This average is smaller than any of the forest
specific carbon coefficients, and likely introduces more uncertainty when comparing the two
inventories.
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Chapter 8: Potential to Offset Greenhouse Gas Emissions
through Renewable Energy, Energy Efficiency, Recycling, and
Carbon Sequestration
Progress to Date
As Iowa's greenhouse gas emissions increase, there is growing interest in the state in
promoting technologies and policies that hold promise for reducing or offsetting the
emissions. In fact, significant actions toward this end are already in progress. Table 14
summarizes the accomplishments in year 2000, showing reductions/offsets were in the range
of 8.1 million MTCE for the most significant measures in place at that time. They include
new energy technologies, biomass recovery, corn ethanol, energy efficiency and
recycling/waste reduction initiatives, and carbon sequestration in different land forms.
Table 14. Measures in place in Iowa by year 2000, which reduced or offset greenhouse
gas emissions.
Measure
Reduction/Offset
(MTCE)
Wind Energy3
137,732
Biomass Energy
Energy recovery from wood and waste b
CH4 recovery from landfill0
CH4 recovery from STPs d
CH i recovery from Livestock0
Total:
411,506
181,641
34,126
3,224
630,497
Ethanol
149,915
Energy Efficiency
279,656
Recycling/W aste Reductione
575,589
Carbon Sequestration
Agricultural land
Forest land
Municipal landfill
Total:
3,097,730
2,894,429
413.377
6,405,536
Grand Total:
8,178,925
a Assumes generated wind energy replaced electricity from coal-fired power plant.
b Based on Energy Information Administration (2003c) for data on energy from "wood
and waste."
0 Assumes all methane was burned for energy recovery, thus offsetting emissions from
electricity consumption. Methane flared but not burned for energy recovery would
constitute an overestimation of emission offsets: landfill sources may be overestimated
by as much as 33,000 MTCE; STP sources by as much as 11,126 MTCE; livestock
sources by up to 531 MTCE.
d STP = sewage treatment plant.
e Compared to baseline year of 1988.
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The values in the table provide a baseline for assessing the state's larger potential for
offsetting emissions by more intensive adoption of the cited measures as well as additional
actions not yet initiated. Our analysis shows the potential for greatly expanding these
technologies, as well as the development of new biomass resources such as corn stover
residues, and expansion of solar energy and carbon sequestration. The following sections
discuss options in Iowa's future for achieving reductions well beyond those quoted in the
table.
Wind Energy
Since 1998, Iowa electricity generation from wind has enjoyed phenomenal growth.
As noted earlier, nameplate capacity jumped from less than 0.5 MW in 1998 to 194 MW in
1999 to 318 MW in 2001, making wind the fastest growing renewable energy source in the
state. Entering 2004, Iowa's nameplate wind capacity was estimated to be 471 MW
(American Wind Energy Association, 2004). As shown in Table 15, however, the currently
installed capacity doesn't begin to tap the full potential of Iowa's wind resource. By 2006,
MidAmerican Energy plans to complete the world's largest land-based wind farm project to
date, which will increase nameplate capacity by 310 MW, to 781 MW (Reuters News
Service, 2003). At that time Iowa wind will deliver an estimated nearly 2 million MWh of
electricity each year, avoiding 541,116 MTCE in carbon emissions annually.
Table 15. Past, planned and potential Iowa electricity generated from wind energy, and emissions from
equivalent amount of coal-generated electricity in year 2000.
Timetable
&
Potential
Nameplate
Capacity
(MW)
Actual
Capacity
(MW)
Electricity
Generated
(MWh)
Electricity
Delivereda
(MWh)
Coal
Replacement
(metric tons)
Equivalent
Emissions in
Year 2000
(MTCE)
Past/Current
2000
197
66
574,665
499,958
240,031
137,732
2001
318
106
927,737
807,131
387,505
222,355
2004
471
155
1,361,567
1,184,563
568,710
333,123
Planned
2006
781
258
2,257,715
1,964,212
943,022
541,116
Potential
PNNLb
18,420
161,357,521
140,381,043
67,399,969
38,674,843
AWEA°
62,900
551,004,000
479,373,480
230,148,002
132,061,454
1 percent
629
5,510,040
4,793,735
2,301,480
1,320,615
2 percent
1,258
11,020,080
9,587,470
4,602,960
2,641,229
10 percent
6,290
55,106,690
47,942,820
23,017,428
13,207,653
a Transmission and other losses are estimated at 13 percent based on assumptions for a 500 MW array
(Factor and Wind, 2002).
bPNNL = Pacific Northwest National Laboratory
0 AWEA = American Wind Energy Association
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Estimates of Iowa's total wind potential vary, depending largely on the amount of
land considered available for development. They range from almost four to more than 12
times Iowa's electricity demand for all end use sectors in 2000 (Elliott and Schwartz, 1993b;
American Wind Energy Association, 2004). Land is typically considered feasible for wind
energy development when it can deliver 400 - 500 W/m2 at 50 meters above the ground.
Land with this potential is classified in the "number 4 wind power class." In Iowa, class 4
land is found only in the northwest corner of the state. A report from the Pacific Northwest
National Laboratory estimates Iowa's wind potential from this area alone can produce about
5 percent (approximately 140 million MWh per year) of the total 1990 electricity
consumption of the contiguous United States (Elliot and Schwartz, 1993b). This is based on
a moderate land exclusion scenario where no wind development occurs on urban or state-
and federally owned lands (including parks, monuments, wilderness areas, wildlife refuge,
and other protected areas). It also sets aside for non-development half of forestland, 30
percent of agricultural land and 10 percent of range land.
A much less restrictive scenario, reported from the American Wind Energy
Association suggests that Iowa could deliver nearly 480 million MWh of clean electricity
annually, supplying more than 12 times the amount consumed by Iowans in 2000. If an
equivalent amount of electricity were generated with Iowa's current utilities, emissions would
amount to more than 132 million MTCE. Of course, economic and technical feasibility are
necessary considerations that will limit the extent of potential that will be tapped.
Nevertheless, even if only 10 percent of the full potential is developed (about 50 million
MWh delivered per year) it will exceed Iowa's electricity demand by all end use sectors in
2000.
Biomass
Biomass, including wood, agricultural residues, switchgrass, and other forms of
vegetative material, offers another option as a major renewable energy resource in Iowa. As
reported in the EIA State Energy Data 2000 Consumption tables (Energy Information
Administration, 2003c), Iowa consumed 16,200 billion Btu of biomass energy from wood
and waste. The residential and commercial sectors contributed slightly more than a third of
this energy through wood burning for space heating. The industrial sector consumed the
remaining two-thirds of the biomass for electricity generation and process steam. As shown
in Table 14 (page 70), this total translates to a savings of as much as 411,506 MTCE in
electricity production emissions.
Use of biomass in the electric utilities sector is still under development for the most
part. The Renewable Energy Annual for 2001 (Energy Information Administration, 2002)
reports that Iowa power plants are currently generating electricity from biomass on small
scales. In 2000 they generated almost 18,000 MWh in total, consuming more than 500
billion Btu of biomass.9 The BFC Gas and Electric plant located in Cedar Rapids is fueled
mainly by light paper mill waste, but also utilizes construction and demolition wood,
unrecycled low-grade paper, tree trimmings, road brush, local sawmill wastes, agricultural
by-products (including crop residues such as corn stalks, corn cobs, and waste seed corn),
sweet sorghum, poplar trees and energy crops such as switchgrass (Iowa Energy Center,
9 This consumption may underestimate actual biomass utilization by utilities as some projects known to exist in
Iowa were not reported in the Renewable Energy Annual for 2001.
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1999). Each day the plant consumes approximately 150 tons of material (BFC Gas and
Electric, 2004). It gasifies the biomass into a low energy gas, which drives a 7.5 MW
turbine. All of the electricity produced is sold to Alliant Energy in Cedar Rapids (Iowa
Energy Center, 1999).
Another venture, under the auspices of Chariton Valley Resource Conservation and
Development, is a demonstration project for marketing switchgrass as a renewable fuel.
Tabbed the "Biomass Project" it focuses on generating the supply, demand and waste
disposal infrastructure necessary for creating switchgrass markets. Partnering with Alliant
Energy's Ottumwa generating station, the goal is to co-fire switchgrass with coal to generate
35 MW of biomass-derived electric power. If the goals of the project are met, 63,557 MTCE
of carbon dioxide emissions could be avoided each year by replacing coal in the combustion
mix (Ney et al., 2001).
A study from the Oak Ridge National Laboratory (ORNL) has estimated the national
availability of five biomass feedstocks at the state level for different prices ranging from $20
to $50 per delivered dry ton (Walsh et al, 1999). The energy equivalent from the maximum
available quantity in Iowa is shown in Table 16. Even after consideration of the quantity of
agricultural litter that must remain on the fields to maintain soil quality, the availability of
corn stover rivaled coal utility inputs, which were 375,000 billion Btu in 2000. Dedicated
energy crops, such as switchgrass also showed promise for development as a clean fuel, with
a delivery potential of 122,000 billion Btu.
Calculating only the carbon emissions avoided from combustion of biomass
resources, however, offers an incomplete picture of the overall effect on net carbon
emissions. In the case of dedicated energy crops such as switchgrass, emissions from
production and delivery of the fuel must be counted and subtracted from the emissions
avoided from burning the biomass. In contrast, for fuels such as corn stover that are by-
products of other processes, the emissions associated with their production can be assumed to
be minimal, as emissions are already accounted for in the life cycle of the original product
generated. (For example, in the case of corn stover, emissions are already counted under the
"energy generation" and "agriculture" categories for corn production.)
A study of switchgrass as a coal substitute estimated greenhouse gas emissions from
production and delivery to be 177.42 lbs carbon dioxide equivalents per million Btu (2.19
MTCE/billion Btu) (Ney et al., 2001). These emissions constitute 81 percent of the carbon
avoided from the coal substitution, thus significantly diminishing the net greenhouse gas
reductions benefit from co-firing with switchgrass in place of coal. Full lifecycle analyses of
the resources considered here are beyond the scope of this study. Nevertheless, such studies
will be essential to assess the complete benefits in reducing greenhouse gas emissions by
biomass fuel substitution.
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Table 16. Potential electricity generation, coal replacement, and carbon dioxide and methane emissions avoided
from biomass resources in Iowa.
Type of Biomass
Current
Annual
Generation
(BBtu)
Potential
Annual
Generation
(BBtu)
Potential Net
Electricity
Generationa
(MWh/yr)
Equivalent
Coal Energy
(metric tons)
Potential
Carbon
Avoided
(MTCE/yr)
Potential CH4
Emissions
Avoided
(MTCE/yr)
ORNL Study b
Forest Residue
NA
2,160
170,918
90,377
54,867
NA
Mill Residues
NA
2,530
200,038
105,774
64,266
NA
Corn Stover
NA
361,000
28,532,386
15,087,076
9,169,978
NA
Dedicated Energy Crops
NA
122,000
9,637,451
5,095,997
588,809c
NA
Urban Wood Waste d
NA
4,580
362,521
191,690
116,339
NA
Other Sources
MSW Waste-to-Energye
NA
32,200
2,199,258
1,163,180
706,164
NA
Livestock CH4 Recoveryf
23 g
3,200 h
253,152
133,891
81,285
374,715
STP CH4 Recovery1
438
3,000
237,330
125,523
76,205
351,295
Landfill CH4 Recoverye
1,300
9,524
751,545
397,489
241,315
1,115,434
Total Potential
16,200J
507,994 k
40,145,341k
21,227,817k
10,393,064k
1,838,443
NA = Data not available. Numerous current activities recover energy from these sources in small quantities within the
industrial sector, but disaggregated data for individual sources is unavailable.
a The systems of production and delivery are assumed to be 27 percent efficient.
b For each waste the maximum potential is not total waste produced, but maximum waste deliverable as fuel given
economically feasible delivery price. Forest residues, mill residues and dedicated energy crops yield a maximum quantity at
a delivered price of $50/ton/year. Corn stover's maximum amount sells at $40/ dry ton/year and urban wood waste is
$30/dry ton/year.
c Owing to emissions from production and delivery of dedicated energy crops, net carbon emissions avoided are assumed to
be only 19 percent of gross emissions saved from combustion of the energy crop (based on estimate of Ney and Shnoor,
2000).
d Urban wood waste is a subset of municipal solid waste.
e These scenarios represent mutually exclusive options. "MSW Waste-to-Energy" refers to combustion of municipal solid
waste; combustion precludes MSW from being landfilled with subsequent recovery of CH4. Conversely, MSW going to
landfill for CH4 recovery cannot be burned for energy.
f Source: Garrison and Richard (2001)
8 Source: Iowa Department of Natural Resources (2003)
h Assumes only 10 percent of potential estimated by Garrison and Richard (2001) to take into account production losses and
economic feasibility not considered in original estimate.
1 Source: Iowa Department of Natural Resources (2002b).
1 Total exceeds itemized entries in column. Source is Energy Information Administration (2003c), which provides total
biomass energy consumed in Iowa in 2000 based on comprehensive list of sources including garbage, bagasse, sewage gas,
and other industrial, agricultural and urban refuse, wood and wood products used as fuel including round wood (cord wood),
limb wood, wood chips, bark, sawdust, forest residues, charcoal, pulp waste, and spent pulping liquor. The report, however,
does not disaggregate overall total into individual components.
k Totals do not include values for "MSW Waste-to-Energy" because its application is mutually exclusive with "Landfill CELt
Recovery," and the latter option is considered far more likely given minimal waste incineration in the state and concerns
about dioxin emissions during MSW combustion.
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Currently, there are no large-scale projects in Iowa that combust municipal solid
waste for energy. However, there is considerable potential for energy recovery of this type.
The more than 2.5 million tons of MSW landfilled in Iowa in the year 2000 contained an
estimated 7.4 percent of the energy content of fossil fuel inputs to electric utilities. Because
of concerns over the toxic air emissions from incineration, however, this source of fuel will
likely remain largely untapped.
Biogas recovery systems are becoming more prevalent in Iowa. As shown in the
lower part of the last two columns on the right in Table 16, recovery offers two opportunities
for greenhouse gas emission reductions. First, whether the biogas is flared or recovered for
energy production, burning it eliminates a large source of Iowa's methane emissions.
Emissions from livestock, STP sludge, and landfills constituted 57 percent of all methane
emissions in Iowa in 2000. Iowa's full potential emissions savings from eliminating these
sources are shown in the far right column in the table, and amount to 1.8 million MTCE. The
second opportunity is to replace conventional fuel with the methane obtained from recovery.
Iowa's potential for this option is shown in the lower part of the second column to the right,
with emissions avoided totalling nearly 400,000 MTCE.
Currently, there are three livestock operations, four landfills, and at least 19
municipal wastewater treatment plants using biogas generated from the anaerobic
decomposition of organic wastes (Iowa Department of Natural Resources, 2002b).
Composed of methane and carbon dioxide in almost equal parts, it is the methane portion of
biogas that makes it a valuable and versatile energy resource that, like natural gas, can be
burned for space heating or electricity generation. As with other biomass resources,
combustion results in no net emissions of greenhouse gases because the carbon accounting is
balanced by photosynthesis.
Livestock operations have a large potential for biogas recovery. Garrison and
Richard (2001) estimated the expanded use of Iowa's massive manure resources, assuming
no losses and no regard for economic feasibility, would provide 32 trillion Btus annually for
energy production. However, the feasible amount of methane recovery is likely to be much
lower, in the range of 1.7 to 2.6 trillion Btu, with emission reductions ranging from 43,000 to
66,000 MTCE (Garrison and Richard, 2001). Economic feasibility and market stimulation
depend heavily on changes in electricity rates and interest rates.
As discussed earlier, methane from anaerobic treatment of wastewater and sludge is
already being efficiently collected at STPs in the state. The IDNR (Iowa Department of
Natural Resources, 1999) reported that more than 250,000 tons of municipal treated sludge
could be available each year for anaerobic digestion in Iowa with the potential to produce
three trillion Btus. This would replace 125,500 tons of coal and could avoid more than
76,000 MTCE in carbon dioxide emissions if utilized for electricity production. Also, more
than 350,000 MTCE of methane would be removed from Iowa's emissions in this way.
Most of the methane recovery in Iowa occurs at landfills, where in 2000 they
produced 1,300 billion Btus of biogas energy for heat and electricity production. As noted
earlier, waste deposited in a landfill will produce biogas for 30 years. In its lifetime, a pound
of organic waste is capable of generating two to six cubic feet of biogas (Iowa Department of
Natural Resources, 1999). According to Iowa's statewide solid waste characterization study,
organic matter accounts for about 63 percent of municipal solid waste entering landfills each
year (R.W. Beck Inc, 1998). This suggests that waste put into the landfill in 2000 is capable
of generating 323 billion Btus of energy from gas per year for 30 years, or 9,700 billion Btus
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over 30 years. The accumulated organic waste in place from the past 30 years is capable of
producing 9,524 billion Btus in biogas energy per year. This generation may decrease in the
future, however, as the amount of waste landfilled decreases from waste diversion.
Nevertheless, utilizing this resource could abate more than 1 million MTCE in methane
emissions and could offset 241,315 MTCE through replacement of conventional electricity
generation.
Energy from biomass clearly represents a large potential for reduction in net
greenhouse gas emissions. In Iowa, the most promising resources for further development
are corn stover and dedicated energy crops. Expansion of methane recovery from landfills,
wastewater and livestock also represent feasible options. Not all scenarios can be taken
together because some represent exclusive use of the resource. For instance, waste cannot be
placed in a landfill with subsequent CH4 generated or carbon sequestered, and at the same
time be available for energy recovery by incineration. Altogether, as shown in Table 16 the
full development of biomass resources can offset greenhouse gas emissions in the range of
12 million MTCE with most of the resource derived from corn stover.
Solar Energy
Solar radiation is the most abundant form of renewable energy on earth. Enough
sunlight falls yearly on each square meter of the U.S. to equal the energy content of 190
kilograms of coal, and the total solar resource is nearly 600 times more than total U.S. energy
consumption in 2000. It is also versatile in its end uses, among which are passive solar space
heating , daylighting and water heating, and direct and indirect conversions to electricity. Its
use for electricity is particularly valuable since it is most efficient on hot sunny summer
afternoons when the demand for electric power is at its peak. Also, the energy extracted is
pollution free and generates zero emissions of greenhouse gases. Despite its numerous
advantages, its implementation has been slow because of the expense of solar technologies
relative to the fossil fuels and wind. Another factor has been public and private sector apathy
with respect to promotion of building design that more fully exploits passive solar heating
and natural daylighting. This does not detract, however, from the potential that exists for
solar power to reduce the need to consume fossil fuels with their concomitant emissions of
greenhouse gases. Solar power is one of Iowa's most promising underdeveloped energy
resources.
Photovoltaic (PV) electricity generation is the primary solar technology in use in
Iowa. Silicon-based semiconductor cells clustered into arrays and panels directly generate an
electric current when illuminated with sunlight. Research and development are improving
the efficiency with which PVs convert sunlight to electricity, extending cell lifetimes and
streamlining manufacturing processes. All these advances continue to have significant
impacts on the economics of PV electricity, bringing down costs 15- to 20-fold since its early
development. Commercial modules are now available with conversion efficiencies in the
range of 13 to 15 percent (National Renewable Energy Laboratory, 2003). Innovative new
"thin film" semiconductor technology is a thriving area of research with promise to reduce
costs even further in the future. Prototype multijunction silicon-based systems having 15 to
17 percent efficiencies are being tested in the desert areas of the U.S., while PV units with
even greater efficiencies are on the drawing boards.
A great advantage to PV over other renewable energies is the ease with which
systems can be sited. They can be installed just about anywhere with a sunny open surface.
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Currently, major applications are in cell phone transmission towers, and in rural locations
where grid connections to centralized power plants are either unavailable or too expensive to
install. But the fastest growing market for PV technologies is in customer-sited, grid-
connected, roof-mounted systems in the residential and commercial sectors (National
Renewable Energy Laboratory, 2003). New architectural design is taking advantage of
building-integrated photovoltaic systems, particularly in Europe. They are now available in
fa9ade coverings, shingles, roofing tiles, and awnings. Semi-transparent modules can also be
mounted on windows, shading and skylights. When PV adaptations are included in the
planning of new construction, their high installation costs are offset in part by savings on
replacement costs of more conventional building materials.
Solar-powered applications have already proven their practicality in Iowa. The state
Department of Transportation (DOT) has 34 solar-powered, portable, programmable message
signs, 125 solar-powered automatic traffic recorders, and 14 solar-powered weight-in-motion
detectors. An Iowa-based study found solar powered barricade warning lights to be an
economically beneficial alternative to bulky battery-powered warning lights. The solar-
powered light emitting diodes (LEDs) provided consistent illumination with minimal
maintenance, driving down costs over the product's lifetime (Midwest States Smart Work
Zone Deployment Initiative, 2000). With this endorsement, it can be expected that the Iowa
DOT will further expand its use of solar-powered lights.
Another Iowa success story in solar power application is the PV mount on the
rooftops of the Indian Creek Nature Center in Cedar Rapids. After a five-year onsite
evaluation by Iowa State University demonstrated the technology's effectiveness, the Center
invested in a 960-watt PV grid-connected system that supplies 10 percent of its power needs
on weekdays, and sells power back to the grid on weekends (Shanks, 1999). Today in Iowa,
more than 41 PV systems are in place with a generation capacity of 35 kW (Iowa Department
of Natural Resources, 2002). These systems are estimated to avoid 15 MTCE of carbon
dioxide emissions annually by replacing conventional electricity generation.
That Iowa's solar potential is underdeveloped is obvious by comparison with other
states. The EIA State Energy Data 2000 Consumption tables (Energy Information
Administration, 2003c) showed that Iowans generated 8.5 billion Btu in solar energy in year
2000. Neighboring Illinois generated 232 billion Btu, 27 times greater than Iowa's output,
even though its annual solar irradiance is slightly less (National Renewable Energy
Laboratory, 1994). New York state, at a higher latitude and possessing a smaller solar
resource than Iowa, generated 562 billion Btus of solar energy (66 times as much) (National
Renewable Energy Laboratory, 1994; EIA 2003c). Sunny California and Texas lead the
country in solar electricity generation, but most locations in the U.S. and worldwide have
sufficient sunlight to sustain a PV system. It is estimated that a PV generation station with
an area 140 km by 140 km placed at a location with average solar irradiance in the U.S.
could generate all the electricity needs of the country (2.5 x 106 GWh/yr) assuming a system
efficiency of 10 percent and an area packing factor of 50 percent (to avoid self shading).
This is an area equal to only 0.3 percent of the U.S. land area (National Renewable Energy
Laboratory, 2003).
Iowa's solar resource appears ripe for development. A five-year study funded by the
Iowa Energy Center (Shanks, 1999) found that solar PV modules are highly reliable, and
durable enough to survive for extended periods in Iowa's environment. When solar
irradiance data were compared to utility demand, it was found that peak solar output
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coincided closely with times of peak energy demands for air conditioning on hot, sweltering
summer days.
Optimal siting for PV systems is important for gaining the most economic and
efficiency benefits. South-facing panels angled to accommodate the latitude of the location
(42 degrees in Iowa) receive the most sunlight and represent the best configuration. Panels
that face east or west, while capturing less sunlight, may nevertheless be cost-effective.
Devices that track the sun as it moves through the sky have been found to be impractical in
the northern part of the U.S. (National Renewable Energy Laboratory, 2003).
The U.S. Department of Energy's (DOE) "Million Solar Roofs" program encourages
the installment of rooftop solar-powered systems across the U.S. by creating partnerships that
aid in their marketing, financing and construction. The goal is to install 1 million rooftop
systems by 2010. The premise of the program is that PV rooftop systems are technologically
feasible but currently stymied by economic barriers.
Table 17 shows the potential for rooftop solar electricity generation in Iowa. Housing
data from the 2000 U.S. census indicates the state has more than 940,000 single family
homes, and it is estimated that total rooftop area is 116.8 million square meters. If one-half
this space were mounted with PV panels, the roofs could generate 13.6 million MWh of
electricity annually, corresponding to 22 percent of electricity consumption in the year 2000.
In this scenario, avoided emissions would amount to 3 million MTCE of carbon dioxide and
offset 32 percent of emissions from electricity production. Even if 10 percent of homes took
advantage of PV systems, 3 percent of power plant emissions could be offset, and 2 percent
of electricity needs could be met. When one considers exploiting the additional pool of roofs
in commercial and industrial buildings, and in multi-unit housing, potential for PV becomes
even greater. Despite dedicated efforts like those of the U.S. DOE, Iowa's achievable
potential for PV may continue to be modest when compared to renewable technologies such
as wind and biomass that currently show a greater balance between costs, benefits, and
economic interests.
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Table 17. Electricity generation and emission savings from installation of PV on single
family homes in Iowa.
Home
Type
Single Family
Homes
(housing units)a
Half Roof
Area per Home
(square meters)
Electricity
Generated
per Unit Area
(kWh/sq m/year)
Total
Electricity
Output
(kWh/yr)
1 story
470,053
82.85
233.6
9,097,292,949
2 story
470,053
41.43
233.6
4,545,901,365
Total
940,106
124.28
233.6
13,643,194,314
Home
Type
Total Energy Saved
(billion Btu/yr)b
Carbon Emissions Avoided
per Unit Energy Saved
(tons CI billion Btu)
Total Carbon Avoided
(MTCE/yr)
1 story
92,795
21.8
2,024,758
2 story
46,369
21.8
1,011,768
Total
139,164
21.8
3,036,526
a Assumes half of all homes are one story and half are two story.
b Total energy saved is approximately 3 times the kWh generated because a coal-fired power
plant releases approximately twice as much waste heat as the energy in the delivered electricity;
1 kWh = 3,413 Btu (delivered) + 6,826 Btu (loses) = 10,239 Btu (total energy expended).
Ethanol
With concerns over energy security and the environmental problems caused by
consumption of fossil fuels, ethanol has become a more viable alternative transportation fuel.
Because it is synthesized from domestic feedstocks, currently almost exclusively from corn
kernel, its development is a positive step toward energy independence. Another important
factor is the economic benefit to farmers. And because it is a form of biomass, combustion
produces no net carbon dioxide emissions. The cultivation and processing of the corn
feedstock, however, do consume fossil fuel energy, resulting in considerable greenhouse gas
emissions.
Iowa has embraced the benefits of ethanol fuels; more than half of motor fuel
purchases in 2000 were ethanol blends of either E10 (10 percent ethanol, 90 percent
gasoline) or E85 (85 percent ethanol, 15 percent gasoline) (Iowa Department of Natural
Resources, 2002a). Any vehicle that can run on gasoline can run on E10, which bodes well
for ethanol's widespread applicability. Because of recent tax credits offered to ethanol
retailers, nearly every gas station in the state offers E10 (Iowa Department of Natural
Resources, 2002b). However, because it takes a specific flex fuel vehicle (FFV) to operate
on E85, this blend is still sold in limited quantities. With only six stations in the state
currently selling E85 to the general public, the vast amount of ethanol consumed is in the
E10 formulation.
The Energy Information Administration (2003c) reported that in 2000 Iowa
consumed 7.8 trillion Btus of energy contained in 93 million gallons of ethanol. As shown in
Table 18, if gasoline had been burned instead of this quantity of ethanol, it would have
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produced 149,915 MTCE in CO2 emissions. This represents the state's annual greenhouse
gas emissions savings from ethanol replacement of gasoline in blended fuels. There is great
potential in Iowa to expand gasoline replacement with ethanol. If 100 percent of
transportation fuel sold were an E10 blend, 237,716 MTCE per year could be saved through
offset of gasoline emissions. And if all transportation fuel sold were an E85 blend, savings
would be 2,831,305 MTCE per year. These considerable savings, however, would be offset
somewhat by emissions from increased agricultural and industrial activities for feedstock
cultivation and ethanol production.
Table 18. Current and potential annual greenhouse gas
emissions savings from combustion of ethanol blended fuels in
place of 100 percent gasoline.
Scenario
MTCE/yr
Current use of ethanol blends
149,915
Potential
All motor fuel sold as E10
237,716
All motor fuel sold as E85
2,831,305
An important question has been the extent to which emissions generated from corn
production and ethanol processing diminish the emissions saved by replacing gasoline with
ethanol in the gas tank. In seeking an answer, Argonne National Laboratory (ANL) prepared
a full fuel cycle analysis for corn based ethanol blends produced in the Midwest (Wang,
1997). After thorough consideration of farming practices, transportation of corn feedstock,
dry and wet milling, ethanol production processes and final vehicle combustion, it was
concluded that in comparison to both conventional and reformulated gasoline each mile
traveled on E10 fuel reduced greenhouse gas emissions by about 2.4 percent and fossil fuel
energy inputs by 3.3 to 3.5 percent. Each mile traveled on E85 fuel reduced emissions by 31
percent and fossil fuel energy inputs by 42 to 44 percent.
Applying ethanol consumption information and findings from the ANL study,
emission savings for the entire fuel cycle (feedstock production, fuel production, end-use
combustion) were estimated from the replacement of 100 percent gasoline with E10. Table
19 shows the savings from Iowa's use of E10 fuel in year 2000. The left side of the table
yields the result that total emissions using the E10 fuel were 2,658,716 MTCE, and the right
side shows that if 100 percent gasoline had been used, emissions would have been 2,724,096
MTCE for a net emission savings of 65,380 MTCE. Further analysis shows that complete
replacement of Iowa's gasoline consumption with E10 and E85 blends could produce full
fuel cycle savings of about 100,000 and 1.2 million MTCE, respectively.
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Table 19. Estimated full cycle greenhouse gas emissions savings from use of E10 fuel in Iowa in
year 2000.a
Passenger Cars
with E10 Fuelb
Passenger Cars
with 100 % Gasolinec
Greenhouse
Gas
Unit Emission
(g C02 eq/mi)d
Total
Emission
(MTCE/yr)
Unit Emission
(g C02 eq/mi)
Total Emission
(MTCE/yr)
C02
358.6
1,930,408
370.9
1,996,621
ch4
8.4
45,219
8.9
47,910
n2o
6.5
34,991
2.9
15,611
Total
373.5
2,010,617
382.7
2,060,142
Light Duty Trucks
with E10 Fuele
Light Duty Trucks
with 100 % Gasoline'
Greenhouse
Gas
Unit Emission
(g C02 eq/mi)
Total
Emission
(MTCE/yr)
Unit Emission
(g C02 eq/mi)
Total Emission
(MTCE/yr)
C02
483.9
623,737
500.4
645,006
ch4
10.7
13,792
11.3
14,565
n2o
8.2
10,570
3.4
4,383
Total
502.8
648,099
515.1
663,954
Total (cars
+ trucks)
2,658,716
2,724,096
Difference
(Gas - E10)
65,380
a Based on consumption of 930 million gallons of E10 fuel.
bBased on 19,738,320,000 passenger car miles traveled using E10 fuel.
0 Compares emissions for 19.7 billion passenger miles traveled using 100 percent gasoline.
dUnit emissions account for emissions generated in production of corn and processing of ethanol.
e Based on 4,726,260,000 light duty truck miles traveled using E10 fuel.
f Compares emissions for 4.7 billion truck miles traveled using 100 percent gasoline.
The emissions savings and economics of ethanol production from corn could clearly
benefit if stover produced as a by-product of the corn harvest were converted into ethanol as
well as the kernel. We saw in the earlier section on biomass that the availability of stover in
the state is immense. In fact, the embedded energy in the stover is larger than the energy
consumed in gasoline in the state in 2000. The problem is that stover is composed of
cellulose while the kernel is starch. It is economically more cost effective and technically far
easier to convert starch to ethanol than to convert the tough, woody cellulose. If
breakthroughs in research lower the costs and complexity of producing ethanol from
cellulose, there could be perhaps a doubling in the net savings of greenhouse gas emissions
from replacement of gasoline with ethanol. Thus, ethanol fuel could make a far greater
positive impact on mitigating the state's greenhouse gas burden, but that potential depends
on future research developments.
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Energy Efficiency
The Iowa Utilities Board (IUB) is responsible for the review and approval of Iowa
utility-sponsored energy efficiency (EE) programs conducted by investor-owned utilities
(IOUs), and reviews and compiles data on the results of the programs. The IUB also
compiles data on programs voluntarily implemented by municipal utilities and rural electric
cooperatives, but does not verify the data.
Table 20 shows the annual energy and carbon emission savings in Iowa from
implemented efficiency programs in the decade of the 1990s. Since 1990, these programs
have offset 5 million MWh of electricity and about 1.5 million MTCE in emissions (Gordon
Dunn, Iowa Utilities Board, personal communication, August 2003). To put these numbers
in perspective, the EIA Electric Power Annual 2002 (Energy Information Administration,
2003a) reported that over the same period total fossil fuel electricity production in Iowa was
329 million MWh and carbon dioxide emissions were 110 million MTCE.
EE programs have focused on demand-side management including residential tune-
ups and weatherization, energy efficient equipment rebates and new construction. IOU
programs provide rebates for purchase of energy efficient equipment for residential,
commercial, industrial, and agricultural customers. The greatest potential for energy savings
lies with industrial customers, as they consume the greatest amount of energy. Often
programs are tailored to the needs of specific customers. In the residential sector, programs
for low-income customers primarily focus on weatherization and education on wise energy
use. They are delivered through community action agencies and combined with federal
weatherization grants to Iowa. The utility programs fill in where community and federal
funds cannot provide complete coverage of the homes.
EE programs and the savings they accrue will continue to grow in the future.
Between 1990 and 2001, IOUs filed periodic modifications of their original programs. In
2002, all four IOUs filed new five-year energy efficiency program plans developed in
collaboration with their customers and ratepayers. Following contested proceedings, the
plans were approved by the IUB and implemented in 2003. All utilities increased the level
of funding allocated for these programs. Additionally, the IUB ordered each of the utilities
to double the level of spending allocated to their low-income programs. Utilities are
developing expanded low-income programs in conjunction with the Iowa Department of
Human Rights (which contracts with the community action agencies to deliver programs),
the Iowa Finance Authority (which provides funding to multi-family low-income projects),
and the Office of Consumer Advocate (which represents the rate payers). They are working
together to carry out the request of the IUB to implement an accelerated 10-year program
focused on low-income customers.
As these programs expand, savings tend to be ongoing and accumulate each year
from past progress. On the other hand as the installed high efficiency equipment
deteriorates, energy savings will diminish. Fortunately, such equipment has typical lifetimes
on the order of 20 or more years, so any slowdown in savings accumulation will not be
imminent. And when old equipment is replaced the new purchases are likely to be even
more energy efficient, reflecting the ongoing trend toward greater focus on efficiency in new
product development.
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Table 20. Annual energy and emission savings through
Iowa energy efficiency programs from the Electric Utilities
Sector, 1990-2000.
Year
Electricity Savings
(MWh)
Emission Savings
(MTCE)
1990
42,590
12,022
1991
92,820
25,398
1992
141,340
39,411
1993
233,285
66,475
1994
365,111
100,479
1995
513,859
144,636
1996
639,879
179,554
1997
743,041
207,777
1998
811,196
223,908
1999
894,149
247,968
2000
1,015,200
279,656
Cumulative
5,492,470
1,527,284
Emission savings calculated from data retrieved from
Gordon Dunn, Iowa Utilities Board, personal contact,
August 2003.
Energy efficiency is a concern for all end use sectors statewide. Apart from
efficiency efforts initiated by the utilities, the IDNR is an active partner in state and federal
efficiency programs. It has been committed to improving Iowa's building energy use and
expenditures since 1986. Three building energy management programs save Iowa taxpayers
$22.7 million annually by implementing energy efficiency measures (Iowa Department of
Natural Resources, 2002c). Since 1989, the Iowa Energy Bank, the State of Iowa Facilities
Improvement Corporation, and Rebuild Iowa have implemented programs saving more than
$173 million in energy costs (Iowa Department of Natural Resources, 2002c). Improvements
have for the most part been related to mechanical issues, building envelope, lighting and
controls. Additionally, installation of renewable energy generation equipment has been
promoted by the IDNR. Each program provides energy audits, aids in identifying cost-
effective energy use improvements, and establishes financing for the implementation of
improvements. They have very successfully targeted projects for different entities that span
the array from state run facilities, schools, hospitals, non-profit organizations, local
governments, and private colleges to entire communities.
The IDNR also regularly encourages updates in the state energy building code, and
sponsors educational programs for code compliance and enforcement. The Iowa Energy
Center operates an EE initiative called the Energy Resource Center. Its mission is to
research and demonstrate the feasibility of energy efficient building technologies, as well as
heating ventilation and air conditioning, building control and daylighting systems.
The potential exists to achieve further large improvements in energy efficiency, and
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Iowa is committed to driving its progress. An ongoing effort includes participation in the
U.S. DOE Program, Industries of the Future, which seeks to improve industrial efficiency
and productivity by lowering raw material inputs and energy use per unit of output, improve
labor and capital productivity, and reduce the generation of wastes and pollutants. Through
this program, the IDNR and Iowa State University received grants to pursue efficiency
improvements in the agricultural and metal casting industries.
One study sponsored by the Iowa Energy Center and the U.S. DOE's Oakridge
National Laboratory examined the potential for demand side management methods to
improve energy efficiency and reduce energy consumption in Iowa by the year 2020
(Hadley, 2001). Specifically, the study examined the potential to replace less efficient
equipment in the residential, commercial and industrial sectors. Using an economic
simulation model, the National Energy Modeling System, with a moderate EE policy scenario
(borrowed from the U.S. DOE Scenarios for a Clean Energy Future), and an industrial
market assessment survey, the study found that Iowa sectors could reduce projected energy
use by 5 percent by 2020.
The results of the study are provided in Table 21. In the residential sector, minimum
equipment standards, and a lowered discount rate (a proxy representing market barriers,
customer resistance, real or perceived risks associated with buying a new technology,
transaction costs to customers, etc.) gave EE technologies greater market penetration. This
scenario stimulated savings of 2.9 trillion Btus (TBtu) of electricity (5.3 percent of total
projected sector use) and 2.1 TBtu of natural gas (2.4 percent of total projected sector use)
through equipment replacement. The majority of the energy offset came from replacement of
electric air conditioning and water heating equipment with more efficient models. These
electricity and natural gas savings translate to avoidance of more than 269,000 MTCE per
year carbon dioxide emissions, assuming coal-fired power plants generate the same share of
electricity in the future as they do currently.
The commercial sector has an estimated potential of reducing annual energy use by
2.1 TBtu of electricity (2.3 percent of total sector use), and 5.1 TBtu of natural gas (3.7
percent of total sector use) through lowered discount rates. The greatest savings would be
achieved in the areas of gas space heating and electric lighting. Total commercial sector
energy savings would avoid 255,000 MTCE per year carbon dioxide emissions.
The greatest draw of electricity in the U.S. comes from industrial drive and motor
systems, swallowing 23 percent of the nation's electricity consumption (Hadley, 2001). The
analysis estimated 3.2 TBtu of electricity (6 percent of total projected sector use) could be
saved in Iowa through installation of more efficient technologies. This would offset
emissions of 263,000 MTCE per year.
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Table 21. Potential savings in electricity and natural gas, and reductions in emissions through
installment of energy efficient equipment. Source: Hadley (2001).
Efficiency Savings in Electricity
Sector
Electricity
Savings
(TBtu/yr)
Energy
Savings
(TBtu/yr)a
Saved
Carbon Emissions
Unit Emission Factor
(tons C/TBtu)
Total
Emission Savings
(MTCE/yr)
Residential
2.9
10.7
22,064.4
236,089
Commercial
2.1
7.8
22,064.4
172,102
Industrial
3.2
11.9
22,064.4
262,566
Total
8.2
30.4
22,064.4
670,758
Efficiency Savings in Natural Gas
Sector
Natural Gas
Savings
(TBtu/yr)
Energy
Savings
(TBtu/yr)b
Saved
Carbon Emissions
Unit Emission Factor
(tons C/TBtu)
Total
Emission Savings
(MTCE/yr)
Residential
2.1
2.3
14,482.6
33,310
Commercial
5.1
5.7
14,482.6
82,551
Total
7.2
8.0
14,482.6
115,861
Total (electricity +
natural gas)
15.4
38.4
14,482.6
786,619
a Includes avoided electricity delivered, losses of waste heat at the power plant, and transmission
losses; power plant efficiency is assumed to be 27 percent.
b Included avoided natural gas use and losses during transmission; delivery efficiency is assumed
to be 90 percent.
The total potential for energy savings through EE improvements is likely to be much
greater than that calculated in Table 21. Because Hadley did not investigate building
envelope improvements, lighting in the residential sector, efficiency standards in commercial
equipment, or many of the other industrial sector energy issues, it only provides a glimpse of
what may be achieved by improving the market for commercial and residential energy-
consuming products and industrial motor and drive systems.
In 2001, the four Iowa IOUs commissioned a second, more in-depth analysis of
Iowa's energy use and EE potential (Global Energy Partners, LLC, 2002). The goal of the
study was to predict the technical potential for electricity and natural gas savings through the
implementation of 258 demand-side EE measures specific to 26 segments of Iowa's
residential, commercial and industrial/agricultural end users. These measures range from
equipment replacement to duct repair and insulation to digital controls for cooling equipment
to installation of window blinds that deflect sunlight in the summer. It is important to note
that technical potential can only give insight into actual achievable potential, which will
undoubtedly be lower owing to practical constraints (i.e., cost effectiveness, timing for
equipment replacement, etc.). With an understanding of where the maximum energy
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reductions may be achieved, however, each utility will be better equipped to develop
effective EE programs tailored to their particular circumstances. Table 22 summarizes some
of the findings of the joint utility assessment, as well as independently derived greenhouse
gas emission savings using the data of Global Energy Partners, LLC, and other sources.
While the utilities study gave only relative energy savings projections, this
information was combined with additional data to estimate the potential for greenhouse gas
emissions reductions. Year 2002 electricity sales and natural gas delivery data from EIA
were applied with the corresponding emission factors from this inventory to estimate
emission savings in the first year (Energy Information Administration, 2003a, Energy
Information Administration, 2004b). They totaled 486,000 MTCE after implementation of
the EE measures indicated in the study.
Interestingly, the residential sector has the greatest technical potential for EE
improvements. Lighting, cooling and water heating are the areas where most electricity
savings could be achieved with the greatest gas saving potential in furnaces and water
heaters. In the past, utility EE programs have had a smaller impact on the residential sector
than other sectors. This is likely because the latter experience greater turnover of equipment,
and are more inclined to invest capital upfront to reduce long-term energy costs to stay
competitive.
Ideally by year 10 of implementation, significant declines in projected electricity and
natural gas consumption could be achieved. Using the projections of the IDNR (Iowa
Department of Natural Resources, 2002a) for Iowa energy consumption in 2012 and sector
energy use patterns consistent with year 2000, it was estimated that more than 5.3 million
MTCE could be avoided, assuming the same electricity emission factors pertain in the future.
This amounts to 16 percent of year 2000 gross greenhouse gas emissions. Once again, this
estimate is only an indication of technical potential, and does not necessarily reflect feasible
achievable reductions. Nevertheless, the analysis sets limits to achievement and may provide
a roadmap to help target future EE projects with the biggest paybacks.
With a statewide collective effort, improvements in energy efficiency can make a
large contribution to reducing greenhouse gas emissions. However, for maximum impact it
is apparent that these improvements need to be coupled with other improvements in the
energy sector, particularly through expanded development of renewable energies.
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Table 22. Projected technical energy savings potential from 258 demand-side energy efficiency measures,
and calculated carbon emission reductions. Sources: Global Energy Partners, LLC, 2002; Iowa
Department of Natural Resources 2002a; Energy Information Administration 2003a and 2004b.
1st Year Technical Potential Energy Savings (2002)
Sector
Energy
Consumptiona
(TBtu)
Energy
Savings
(TBtu)
Percent Energy
Savings b
Emission
Reductions
(MTCE)
Electricity
Residential
44 (163)
9.8
6%
215,976
Commercial
30(111)
3.3
3%
73,575
Industrial
56 (207)
4.1
2%
92,201
Total
130 (481)
17.2
4%
381,752
Natural Gas
Residential
73 (81)
4.0
5%
58,771
Commercial
48 (53)
1.1
2%
15,248
Industrial
95 (106)
2.1
2%
30,303
Total
216 (240)
7.2
4%
104,322
Total (electricity
+ gas)
346 (721)
24.4
3 %
486,074
10th Year Technical Potential Energy Savings (2012)
Sector
Energy
Consumption
(TBtu)
Energy
Savings"
(TBtu)
Percent Energy
Savings
Emission
Reductions
(MTCE)
Electricity
Residential
49.1 (182)
87.3
48%
1,924,280
Commercial
40.6 (150)
42.1
28%
928,174
Industrial
70.0 (259)
49.3
19%
1,085,918
Total
159.7 (591)
178.7
30%
3,938,373
Natural Gas
Residential
100.8(112)
50.4
45%
729,280
Commercial
45.8 (51)
10.7
21 %
154,634
Industrial
137.0 (152)
35.0
23 %
506,605
Total
283.6 (315)
96.1
31 %
1,390,519
Total (electricity
+ gas)
443.3 (906)
274.8
30 %
5,328,892
a First number corresponds to electricity and natural gas delivered to customer; number in parentheses
corresponds to actual total energy expended, which includes production and transmission losses.
Efficiency for delivery of electricity and natural gas are assumed to be 27 and 90 percent, respectively.
b Obtained by dividing energy savings by total energy consumed (number in parentheses in first column).
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Recycling
A report prepared by the Tellus Institute (1999) analyzed emission reductions in
Iowa resulting from recycling and source reduction activities. The analysis used state landfill
use reduction goals as the basis for the avoided emissions from resource extraction and
manufacturing. The findings are shown in Table 23. In 1995, Iowa attained a 33 percent
reduction in landfill waste generation relative to the 1988 baseline, reducing annual
greenhouse gas emissions by an estimated at 576,000 MTCE. Projections for future
scenarios show that if Iowa reaches a 50 percent waste diversion goal, it could avoid nearly 1
million MTCE annually. If diversions were to drop to 25 percent, avoided annual emissions
would decline to 374,000 MTCE.
Table 23. Emission reductions from recycling and source reduction in Iowa for various
waste diversion scenarios (Tellus Institute, 1999).
Scenario
Recycled
(tons)
Source
Reductiona
(tons)
Diverted
Waste
(tons)
Avoided
Emissions
(MTCE)
25 percent diversion
447,227
527,142
974,369
374,023
33 percent diversion
600,000
707,221
1,307,221
575,589
50 percent diversion
894,454
1,054,285
1,948,739
979,731
a Source reduction is the best option in the "waste hierarchy," and refers to precluding
production of materials that would otherwise generate wastes. Examples are reducing
the amount of wrapping on consumer products, or the amount of plastic in a plastic
bottle, thus lowering the amount of wrapping or plastic needing to be recycled or
landfilled. It is the most energy saving of all options.
Carbon Sequestration
Another option to pursue in reducing Iowa's greenhouse gas burden is increasing
carbon sequestration in biomass and soils. As biomass grows, carbon dioxide is pulled from
the atmosphere and fixed into biological carbon that can be stored in living plant tissue and
subsequently as organic matter in the soil. Long-term storage accrues from the accumulation
of carbon in these pools.
As an agricultural state, Iowa is in an excellent position to sequester carbon through
more aggressive adoption of conservation tillage. Conventional tillage disaggregates
(breaks up) soil particles and the overturns the earth. Both actions serve to expose more of
the organic soil fraction to air (O2) leading to the reoxidation of soil carbon to atmospheric
CO2. Conservation tillage greatly reduces the disturbance of the soil, and in so doing inhibits
oxidation, thus allowing soil organic matter to accumulate. The Iowa Carbon Storage
Project, conducted through the USDA/NRCS, has created a county level database that
provides modeled predictions of soil carbon storage resulting from changes in crop rotations,
tillage regimes, and Conservation Reserve Program enrollment. This information allows
estimation of the efficacy of different agricultural policies coupled to carbon sequestration
planning projects (Brenner et al., 2001).
The study analyzed the impact of no-tillage farming on various soil types by
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simulating the interaction of key environmental factors including soil texture, drainage
characteristics, and climate. It found the greatest potential for carbon storage was in the
application of no tillage management in the lighter textured sandy soils in eastern Iowa.
Under this scenario, the soil would store an estimated 0.65 metric tons carbon/ hectare/year
(0.26 tonnes C/acre/yr). Most of the state's soils showed an intermediate response to the
simulation under no till management, sequestering carbon in the range of 0.50-0.55 metric
tons/ha/yr (0.20 - 0.23 tonne/acre/yr). The prospects are less promising in western Iowa
where crop productivity is limited by lower precipitation; in this region the potential for
carbon storage is estimated at < 0.5 metric tons/ha/yr. Overall, the model estimated that
Iowa's agricultural soils are currently a net sink for carbon, absorbing 3.1 million MTCE per
year. This figure includes land used to grow crops, land enrolled in the CRP, and
agricultural land converted to grassland, tree cover or wetland in the past 10 years. Cropland
accounts for 1.9 million MTCE of this total. With more than half of Iowa's 10.6 million
hectares of cropland still managed by conventional tillage practices, conversion to no-tillage
offers a large potential to increase carbon sequestration.10 If three-quarters of Iowa's
croplands were managed under a no-tillage regime assuming an intermediate level of carbon
storage, an estimated 4.4 million MTCE could be sequestered annually.
With regard to sequestration in forests, a report from the University of Iowa's Center
for Global and Regional Environmental Research (Ney, et al., 2001) updated the
effectiveness of carbon sequestration measures proposed in the Iowa Greenhouse Gas Action
Plan (Iowa Department of Natural Resources, 1996). The revised plan introduced two
options; a priority option and a maximum feasible option for reforesting of 200,000 and 1
million acres of land, respectively. The report quantified the carbon sequestered for each
option to be 224,000 and 1,120,000 MTCE per year. A third option explored planting
200,000 acres of riparian buffer strips with hybrid poplars. This action would sequester an
estimated 817,000 MTCE per year, with 86 percent in above ground biomass and 14 percent
stored in the soil.
Another option investigated was the potential for carbon sequestration from the
development of switchgrass as a biofuel. Organic matter can accumulate in the soil from the
grass' deep root system and litter that remain in the fields after harvest. Net sequestration
was estimated to be 0.54 MTCE/acre. It was estimated that 62,500 acres of switchgrass
would be required for a priority option of 35 MW of nameplate electricity capacity. In
addition to generating power with no greenhouse gas combustion emissions, the grass would
provide a net sequestration benefit of 33,750 MTCE per year. If one considers the offset
from coal combustion, the net greenhouse gas reduction benefit is raised to 106,389 MTCE
per year. A maximum feasible option of developing 100 MW of switchgrass-derived
electricity capacity would require cultivation of 150,000 acres, which would sequester
81,400 MTCE with a net greenhouse gas reduction benefit of 123,709 MTCE per year.
Table 24 summarizes the options discussed above for carbon sequestration in Iowa.
The information presented suggests the overall potential to sequester carbon as a means to
offset emissions is significant. If all the options given in the table were undertaken, carbon
storage would amount to between 5.4 million and 6.4 million MTCE per year. This would
10 It should be mentioned that the prospect of increasing no-tillage agriculture has other pluses and minuses. Its
implementation has played a large in role in reducing soil erosion in Iowa. On the negative side, it requires
large inputs of herbicides such as atrizine, which have caused considerable ground and surface water pollution
problems.
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offset between 17 to 19 percent of gross greenhouse gas emissions in Iowa in 2000.
Table 24. Summary of options to increase carbon sequestration in Iowa.
Management Action
Annual
Carbon Storage
(MTCE)
No-till management:
7.95 million acres of cropland with moderate potential for sequestration
a
4,372,500
Reforestation b:
Priority Option, addition of 200,000 acres of forestland
Maximum Feasible Option, addition of 1,000,000 acres of forestland
224,000
1,120,000
Hybrid poplar buffer strips b:
Addition of 200,000 acres of buffer strips
817,000
Bioenergy crops for electricity generationb
35 MW from switchgrass biomass
100 MW from switchgrass biomass
33,750c
81,400c
Total:
high-
low -
6,390,900
5,447,250
a Based on data from Brenner et al. 2001.
b Source: Ney et al 2001.
0 Represents net sequestration that accounts for the harvest of surface biomass.
Summary
Figure 37 summarizes the results of the discussion presented in this chapter. It shows
the potential of seven options for reducing net greenhouse gas emissions in Iowa by fuel
substitution (wind, biomass, solar, ethanol), reduction in demand for energy (energy
efficiency, recycling/reduction), and carbon sequestration. Wind and biomass energy, and
carbon sequestration appear to have the greatest potential to reduce net emissions, but a
combination of implementing all seven options would maximize the benefit.
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Potential to Offset Greenhouse Gas Emissions in Iowa by Use of Renewable Energies,
Energy Efficiency, Recycling/Source Reduction, and Carbon Sequestration in Soils
40.0 n
Figure 37. Potential to reduce net greenhouse gas emissions in Iowa for seven major options. Chart shows
annual reduction potentials in million MTCE. Gray area under wind signifies emissions offset if wind-
generated electricity were to equal state's total electricity consumption in 2000; combined gray/hatched area
corresponds to emission offsets if wind-generated electricity were to produce four times the state's year 2000
consumption.
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Chapter 9: Conclusions and Recommendations
Overall, it appears that total greenhouse gas emissions have increased from 22.8
million MTCE in 1990 to 32.8 million MTCE in 2000. A precise comparison, however, is
difficult to make because of differences in methodology and data availability for the two
years in question, particularly with respect to emissions from agricultural soils. Offsetting
increases in emissions in the 1990s were increases in the amounts of carbon sequestered in
forests, croplands, and landfills, and recovered in landfills and wastewater treatment plants.
These increased carbon savings narrow the apparent increase in net emissions (emissions
minus sequestration and recovery) from 21.1 million MTCE in 1990 and 26.2 million MTCE
in 2000.
The energy-use category was by far Iowa's greatest source of greenhouse gases in
2000, contributing 22.0 million MTCE (67 percent) to total emissions. Growth in the
consumption of fossil fuels, 26 percent between 1990 and 2000, was the major reason why
emissions increased in the 1990s. Most of the rise in demand was for coal and petroleum,
which grew by 28 and 37 percent, respectively.
The next largest emission source category in Iowa is agriculture, having contributed
27 percent to the total greenhouse gas inventory in year 2000. The major share of emissions
stems from nitrogen amendments to the soils (6.3 million MTCE) followed by manure
management. Emissions from the latter increased slightly from 1990 (1.1 million to 1.2
million MTCE), masking the large shifts that occurred in animal populations. The
substantial rise in numbers of swine and layer chicken in the past decade pushed emissions
upward, while the large drop in cattle populations served to hold down the overall increase.
The lower ruminant cattle population decreased emissions from enteric fermentation in
domesticated animals from 1.2 million to 0.9 million MTCE.
The contributions to greenhouse gas emissions from other source categories were far
less significant, with releases from industry and waste treatment each amounting to 3 percent
of total emissions.
Any comprehensive solution for mitigating greenhouse gas emissions in Iowa must
place a large focus on reducing fossil-fuel derived emissions. One way to do this is by fuel
substitution. Iowa is particularly rich in wind and biomass resources, and there appears to be
an enormous potential for reducing emissions when these and other renewable resources are
more fully exploited. The wind resource is so large that it can supply several times the
state's electricity demand without generation of greenhouse gases. Someday Iowa could
plausibly be a net exporter of emission-free wind-based electricity to other states. One of the
most promising biomass fuels appears to be agricultural residues like corn stover, because in
the energy/emissions balance they add significantly to the energy supply without a
concomitant rise in emissions. Expanded methane recovery at landfills, wastewater
treatment plants, and livestock farms will also reduce emissions and provide useful sources
of renewable fuel.
Reducing greenhouse gas emissions can be achieved also by reducing energy
demand. This has been accomplished thus far mainly through energy efficiency, and the
expansion of existing programs will be well worth the effort. Recycling/source reduction is a
less appreciated option for reducing energy demand and greenhouse gas emissions. Existing
programs are small in scope but further initiatives, particularly in untapped opportunities for
source reduction, could pay large dividends.
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Another strategy for reducing net greenhouse gas emissions is to offset them by
sequestering carbon in soils. Currently carbon sequestration in forest and agricultural lands
offsets more than 20 percent of total greenhouse gas emissions in the state. The percentage
can be even higher if the options recommended in the Iowa Greenhouse Gas Action Plan
(Iowa Department of Natural Resources, 1996) and Ney et al. (2001) are enacted that couple
reforestation and agroforestry to biofuel development. Extension of agricultural practices
that build up organic carbon in cropland soils will complement carbon gains in forest soils.
After fossil fuel combustion, the largest single contributing activity to greenhouse gas
emissions in Iowa is the addition of nitrogen amendments to agricultural soils. This results
in emissions of N2O (GWP = 296), which contributed 19 percent of total emissions in year
2000. Thus, any actions to reduce nitrogen inputs to the soil will have important benefits in
reducing Iowa's greenhouse gas burden. According to Ney et al. (2001) it has been
demonstrated that field nitrogen amendments can be cut substantially without loss of crop
yield. Reduced nitrogen applications also have the additional environmental benefit of
improving water quality, an issue that has the specific attention of the Governor in his plan
The New Face of Iowa for 2010. Thus, there seems to be compelling reasons for enacting
programs to reduce nitrogen inputs.
Combining the potential for renewable energies, energy efficiency, recycling/source
reduction, carbon sequestration, and more efficient agricultural nitrogen management, it is
conceivable that Iowa could one day become a net negative greenhouse gas emissions state.
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Acknowledgements
The authors wish to thank the following individuals, and the organizations to which they are
affiliated, for their generous donation of time in volunteering to participate in the working
group for this inventory. Their guidance was indispensable in preparation of this report.
Mike Triplett
Bill Ehm
Keith Kutz
Jack Clark
Bob Haug
Mark Landa
Gordon Dunn
Del Christiansen
Catherine Zeman
Rick Robinson
Rick Ney
Tim Parkin
Iowa Association of Business and Industry
Iowa Department of Agriculture and Land Stewardship
Iowa Energy Center
Iowa Utility Association
Iowa Association of Municipal Utilities
Iowa Association of Electric Cooperatives
Iowa Utilities Board
Iowa Renewable Energy Association
Recycling and Reuse Technology Transfer Center
Iowa Farm Bureau
Sebasta Blomberg
United States Department of Soil Tilth Laboratory
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http://www.census.gov/population/cen2000/phc-t2/tab01.pdf
United States Department of Commerce: International Trade Administration: Office of
Automotive Affairs. (2004). CAFE. Retrieved April 6, 2004, from
101
-------
http ://www. ita. doc.gov/td/auto/ start 1. html
United States Department of Energy, (n.d.) About Photovoltaics: Turning Sunlight Into
Electricity: How It All Works: About Conversion Efficiencies [website] Office of
Renewable Energy and Energy Efficiency: Photovoltaics program. Retrieved January
26, 2004, from
http://www.eere. energy. gov/pv/pvmenu.cgi?site=pv&idx=Q&bodv=video. html
United States Geological Survey. (2000a). Minerals Yearbook 2000: Nitrogen.
Kramer, D. Retrieved August 2003 from
http://minerals.usgs.gov/minerals/pubs/commodity/nitrogen/480400.pdf
United States Geological Survey. (2000b). Minerals Yearbook 2000: Stone, Crushed.
Tepordei, V. Retrieved August 2003 from
http://minerals.usgs.gov/minerals/pubs/commoditv/stone crushed/630400.pdf
United States Geological Survey. (2000). U.S. Geological Survey Minerals Yearbook —
2000 — Iowa [electronic version]. Retrieved September 23, 2002, from
http://minerals.usgs.gov/minerals/pubs/state/981901.pdf
Walsh, M. E., Perlack R.L., Turhollow A, de la Toree Ugarte, D., Becker, D.A.,
Graham, R.L., et al. (1999, updated 2000). Biomass feedstock availability in the
United States: 1999 state level analysis [electronic version]. Oak Ridge, TN:
Oakridge National Laboratory. Retrieved October 21, 2003, from
http ://bioenergy. ornl. gov/resourcedata/
Wang, M., C. Saricks, M. Wu. (1997). Fuel-cycle fossil fuel energy use and
greenhouse gas emissions of fuel ethanol produced from U.S. Midwest corn.
Argonne, IL: Argonne National Laboratory: Center for Transportation Research.
Retrieved January 27, 2004 from: http://www.transportation.anl.gov/pdfs/TA/141.pdf
Zelten, Jeanne E., Robert M. McKay. (1991). "Iowa Minerals Yearbook — 1991" Pages
206-207 in Bureau of Mines Minerals Yearbook, Vol. II.
Washington: Department of the Interior, Bureau of Mines.
102
-------
APPENDIX A
ENERGY RELATED ACTIVITES
I. CO2 from Combustion of Fossil Fuels
Energy use emissions were calculated based on energy consumption figures from the
Energy Information Administration (EIA) published as the State Energy Data 2000
Consumption Tables formerly the State Energy Data Report (SEDR) (EIA, 2003c).
Consumption estimates are reported for 5 economic sectors including the commercial,
residential, industrial, transportation and electric utilities. For each sector, emissions are
estimated by multiplying the fuel use by the established carbon content coefficient and the
assumed fraction of oxidization. Carbon stored in non-energy products namely from the
petrochemical industry, was credited to the state. National estimates of fossil fuel
consumption for non-fuel use are published in the EIA 2000 Annual Energy Review (2001a).
Estimates of the quantity of carbon stored in Iowa industrial products were made based on
the national fraction of non-fuel use to total U.S. industrial consumption. Seven fuels,
including asphalt and road oil, liquid petroleum gas, lubricants, natural gas, coal,
petrochemical feedstocks and other oils, were considered to store carbon in non-energy
products. Carbon stored in lubricants used by the transportation sector was also credited to
the state.
Emissions contributed from the electric utilities sector were distributed to each end
use sector based on its consumption and associated losses. A distribution factor was
calculated as the fraction of total emissions from electric utilities divided by the total energy
input at all electric utilities. Energy inputs into forms of generation that do not emit
greenhouse gases were also included in this total (i.e. nuclear, hydroelectric, wood, etc.). As
more generation comes from nuclear, hydroelectric, wind and other renewables, it will be
reflected in a smaller fraction of emissions per unit of electricity. It was assumed that
electricity consumption and losses equaled energy consumption by the electric utilities. The
difference between the two represents electricity that is exported from the state and is
accounted for as net interstate flow of electricity. The difference may also be the result of
statistical error from different methods of data collection. The emission distribution factor
served as any emission factor does. For each end use sector, the electricity consumption and
associated losses from transmission and distribution were multiplied by the electric
generation emission distribution factor. Therefore, emissions from electric utilities are
accounted for within the four end use sectors and as net interstate flows of electricity.
-------
Summary of Iowa CO? Emissions from Combustion of Fossil Fuels. 2000
Input
Input
A x B / 2000
Input
C- D
Input
E x F x 0.9072
A
B
C
D
E
F
G
Carbon Content
Stored
Net Carbon
Fraction
Net Carbon
Consumption
Coefficient
Total Carbon
Carbon
Potential
Oxidized
Emissions
1
(10A6 Btu)
(lbs C/10A6Btu)
(tons C)
(tons C)
(tons C)
(metric tons C)
Electric Utilities
Natural Gas
4,700,000
31.9
74,965
0
74,965
0.995
67,668
Coal
374,900,000
56.0
10,495,496
0
10,495,496
0.99
9,426,299
Light Oil
1,300,000
43.5
28,275
0
28,275
0.99
25,395
380,900,000
Total
NA
Net Interstate Flow of Electricity a
-74,600,000
48.1
1,794,130
0
1,794,130
0.99
1,611,358
-74,600,000
1,611,358
T r ans portation
Aviation Gasoline
400,000
41.6
8,320
0
8,320
0.99
7,472
Distillate Fuel Oil
73,000,000
44.0
1,606,000
0
1,606,000
0.99
1,442,394
Jet Fuel
4,400,000
43.5
95,700
0
95,700
0.99
85,951
Liquefied Petroleum G<
0
37.8
0
0
0
0.99
0
Lubricants
3,300,000
44.6
73,590
35,870.97
37,719
0.99
33,877
Motor Gasoline
176,800,000
42.8
3,783,520
0
3,783,520
0.99
3,398,085
Natural Gas
8,300,000
31.9
132,385
0
132,385
0.995
119,499
266,200,000
Total
5,087,278
Industrial
Asphalt & Road Oil
16,400,000
45.5
373,100
373,100
0
0.99
0
Distillate Fuel Oil
34,600,000
44.0
761,200
0
761,200
0.99
683,655
Kerosene
0
43.5
0
0
0
0.99
0
Liquefied Petroleum G<
48,200,000
37.8
910,980
584,128
326,852
0.99
293,555
Lubricants
1,200,000
44.6
26,760
13,044
13,716
0.99
12,319
Motor Gasoline
4,100,000
42.8
87,740
0
87,740
0.99
78,802
Residual Fuel Oil
1,100,000
44.0
24,200
0
24,200
0.99
21,735
Other Oil 2/
4,900,000
47.4
116,130
5,899
110,231
0.99
99,002
Natural Gas
100,900,000
31.9
1,609,355
87,631
1,521,724
0.995
1,373,605
Coal
64,200,000
56.6
1,818,319
5,518
1,812,801
0.99
1,628,127
Petrochemical Feedstoc
8,300,000
42.7
177,205
132,904
44,301
0.99
39,788
Electricity
58,400,000
48.1
1,403,257
0
1,403,257
NA
1,273,035
Electricial Losses
100,200,000
48.1
2,407,643
0
2,407,643
NA
2,184,214
442,500,000
Total
7,687,837
Residential
Distillate Fuel Oil
2,800,000
44.0
61,600
0
61,600
0.99
55,325
Kerosene
200,000
43.5
4,350
0
4,350
0.99
3,907
Liquefied Petroleum G<
19,100,000
37.8
360,990
0
360,990
0.99
324,215
Natural Gas
74,200,000
31.9
1,183,490
0
1,183,490
0.995
1,068,294
Coal
700,000
55.7
19,511
0
19,511
0.99
17,523
Electricity
41,000,000
48.1
985,163
0
985,163
NA
893,740
Electricial Losses
70,400,000
48.1
1,691,598
0
1,691,598
NA
1,534,617
208,400,000
Total
3,897,622
Commercial
Natural Gas
45,800,000
31.9
730,510
0
730,510
0.995
659,405
Coal
6,100,000
55.7
170,024
0
170,024
0.99
152,703
Distillate Fuel
2,800,000
44.0
61,600
0
61,600
0.99
55,325
Kerosene
0
43.5
0
0
0
0.99
0
Liquefied Petroleum G<
3,400,000
37.8
64,260
0
64,260
0.99
57,714
Motor Gasoline
2,800,000
42.8
59,920
0
59,920
0.99
53,816
Residual Fuel Oil
0
47.4
0
0
0
0.99
0
Electricity
33,900,000
48.1
814,562
0
814,562
NA
738,971
Electricial Losses
58,100,000
48.1
1,396,049
0
1,396,049
NA
1,266,495
152,900,000
Total
2,984,428
All Sectors
1,070,000,000
Total
21,268,523
a Net interstate flow of electricity is the difference between the amount of energy in the electricity sold within a state (including associated losses) and
the energy input at the electric utilities within the State. A positive number indicates that more electricity (and associated losses) came into the state
than went out of the State during the year, conversely, a negative number indicates the more electricity (including associated losses) went out of the
State than came into the State.
-------
Summary of Iowa CO> Emissions from Combustion of Fossil Fuels, recalculated 1990
Input
Input
A x B / 2000
Input
C- D
Input
E x F x 0.9072
A
B
C
D
E
F
G
Carbon Content
Stored
Net Carbon
Fraction
Net Carbon
Consumption
Coefficient
Total Carbon
Carbon
Potential
Oxidized
Emissions
1
(10A6 Btu)
(lbs C/10A6Btu)
(tons C)
(tons C)
(tons C)
(metric tons C)
Electric Utilities
Natural Gas
3,500,000
31.9
55,825
0
55,825
0.995
50,391
Coal
272,600,000
57.5
7,837,250
0
7,837,250
0.99
7,038,854
Light Oil
700,000
43.5
15,225
0
15,225
0.99
13,674
276,800,000
Total
NA
Net Interstate Flows of Electricity a
1,700,000
49.2
-41,820
0
-41,820
0.99
-41,402
1,700,000
-41,402
T ransportation
Aviation Gasoline
500,000
41.6
10,400
0
10,400
0.99
9,341
Distillate Fuel Oil
56,300,000
44.0
1,238,600
0
1,238,600
0.99
1,112,421
Jet Fuel
5,000,000
43.5
108,750
0
108,750
0.99
97,671
Liquefied Petroleum G
200,000
37.8
3,780
0
3,780
0.99
3,395
Lubricants
3,200,000
44.6
71,360
35,680
35,680
0.99
32,045
Motor Gasoline
157,000,000
42.8
3,359,800
0
3,359,800
0.99
3,017,530
Natural Gas
9,200,000
31.9
146,740
0
146,740
0.995
132,457
231,400,000
4,404,861
Industrial
Asphalt & Road Oil
10,200,000
45.5
232,050
232,050
0
0.99
0
Distillate Fuel Oil
24,100,000
44.0
530,200
0
530,200
0.99
476,187
Kerosene
100,000
43.5
2,175
0
2,175
0.99
1,953
Liquefied Petroleum G
11,200,000
37.8
211,680
126,388
85,292
0.99
76,603
Lubricants
1,200,000
44.6
26,760
13,380
13,380
0.99
12,017
Motor Gasoline
5,600,000
42.8
119,840
0
119,840
0.99
107,632
Residual Fuel Oil
600,000
44.0
13,200
0
13,200
0.99
11,855
Other Oil
2,300,000
47.4
54,510
2,728
51,782
0.99
46,507
Natural Gas
90,900,000
31.9
1,449,855
120,826
1,329,029
0.995
1,199,667
Coal
53,100,000
55.9
1,484,386
11,490
1,472,897
0.99
1,322,850
Petrochemical Feedsto>
2,800,000
42.7
59,780
44,835
14,945
0.99
13,423
Electricity
38,900,000
49.2
957,768
0
957,768
NA
868,887
Electrical Losses
84,800,000
49.2
2,087,884
0
2,087,884
NA
1,894,128
325,800,000
551,696
6,031,709
Residential
Distillate Fuel Oil
4,600,000
44.0
101,200
0
101,200
0.99
90,891
Kerosene
100,000
43.5
2,175
0
2,175
0.99
1,953
Liquefied Petroleum G
9,900,000
37.8
187,110
0
187,110
0.99
168,049
Natural Gas
71,900,000
31.9
1,146,805
0
1,146,805
0.995
1,035,180
Coal
1,100,000
55.7
30,660
0
30,660
0.99
27,537
Electricity
35,900,000
49.2
883,904
0
883,904
NA
801,877
Electrical Losses
78,300,000
49.2
1,927,846
0
1,927,846
NA
1,748,942
201,800,000
Total
3,874,428
Commercial
Natural Gas
44,300,000
31.9
706,585
0
706,585
0.995
637,809
Coal
4,800,000
55.7
133,680
0
133,680
0.99
120,062
Distillate Fuel
2,900,000
44.0
63,800
0
63,800
0.99
57,301
Kerosene
200,000
43.5
4,350
0
4,350
0.99
3,907
Liquefied Petroleum G
1,800,000
37.8
34,020
0
34,020
0.99
30,554
Motor Gasoline
700,000
42.8
14,980
0
14,980
0.99
13,454
Residual Fuel Oil
200,000
47.4
4,740
0
4,740
0.99
4,257
Electricity
25,700,000
49.2
632,767
0
632,767
NA
574,046
Electrical Losses
56,100,000
49.2
1,381,253
0
1,381,253
NA
1,253,073
136,700,000
Total
2,694,463
All Sectors
895,700,000
Total
16,964,059
a Net interstate flow of electricity is the difference between the amount of energy in the electricity sold within a state (including associated losses) and
the energy input at the electric utilities within the State. A positive number indicates that more electricity (and associated losses) came into the state
than went out of the State during the year, conversely, a negative number indicates the more electricity (including associated losses) went out of the
State than came into the State.
-------
Worksheet to Calculate Iowa CO, Emissions from Fossil Fuels. 1990- 2000
Commercial Sector
Input Input A x B / 2000 Input C x D x .9072
A
B
C
D
E
Carbon Content
Fraction
Net Carbon
Consumption
Coefficient 1/
Total Carbon
Oxidized
Emissions
(10A6 Btu)
(lbs CI 10A6 Btu)
(tons C)
(metric tons C)
2000
Natural Gas
45,800,000
31.9
730,510.00
0.995
659,405.08
Coal
6,100,000
55.7
170,023.64
0.99
152,702.99
Distillate Fuel
2,800,000
44.0
61,600.00
0.99
55,324.68
Kerosene
0
43.5
0.00
0.99
0.00
Liquefied Petroleum Gas
3,400,000
37.8
64,260.00
0.99
57,713.71
Motor Gasoline
2,800,000
42.8
59,920.00
0.99
53,815.83
Residual Fuel Oil
0
47.4
0.00
0.99
0.00
Electricity 2/
33,900,000
48.1
814,561.98
NA
738,970.63
Electrical System Losses 2/
58,100,000
48.1
1,396,048.70
NA
1,266,495.38
Total
152,900,000
2,984,428.29
1999
Natural Gas
45,800,000
31.9
730,510.00
0.995
659,405.08
Coal
8,200,000
55.7
228,556.36
0.99
205,272.87
Distillate Fuel
2,600,000
44.0
57,200.00
0.99
51,372.92
Kerosene
0
43.5
0.00
0.99
0.00
Liquefied Petroleum Gas
3,300,000
37.8
62,370.00
0.99
56,016.24
Motor Gasoline
2,300,000
42.8
49,220.00
0.99
44,205.86
Residual Fuel Oil
0
47.4
0.00
0.99
0.00
Electricity 2/
33,000,000
48.4
798,272.29
NA
724,192.62
Electrical System Losses 2/
64,100,000
48.4
1,550,583.45
NA
1,406,689.31
Total
159,300,000
3,147,154.90
1998
Natural Gas
43,500,000
31.9
693,825.00
0.995
626,290.85
Coal
5,900,000
55.9
164,770.91
0.99
147,985.37
Distillate Fuel
2,700,000
44.0
59,400.00
0.99
53,348.80
Kerosene
0
43.5
0.00
0.99
0.00
Liquefied Petroleum Gas
2,700,000
37.8
51,030.00
0.99
45,831.47
Motor Gasoline
2,400,000
42.8
51,360.00
0.99
46,127.85
Residual Fuel Oil
0
47.4
0.00
0.99
0.00
Electricity 2/
32,000,000
48.2
770,451.97
NA
698,954.03
Electrical System Losses 2/
65,700,000
48.2
1,581,834.20
NA
1,435,039.99
Total
154,900,000
3,053,578.36
1997
Natural Gas
50,600,000
31.9
807,070.00
0.995
728,513.03
Coal
7,800,000
55.6
216,875.45
0.99
194,781.92
Distillate Fuel
2,000,000
44.0
44,000.00
0.99
39,517.63
Kerosene
0
43.5
0.00
0.99
0.00
Liquefied Petroleum Gas
3,100,000
37.8
58,590.00
0.99
52,621.32
Motor Gasoline
2,300,000
42.8
49,220.00
0.99
44,205.86
Residual Fuel Oil
0
47.4
0.00
0.99
0.00
Electricity 2/
30,500,000
48.8
743,936.62
NA
674,899.30
Electrical System Losses 2/
63,100,000
48.8
1,539,095.11
NA
1,396,267.08
Total
159,400,000
3,130,806.15
1996
Natural Gas
54,900,000
31.9
875,655.00
0.995
790,422.24
Coal
4,800,000
55.7
133,745.45
0.99
120,120.54
Distillate Fuel
2,100,000
44.0
46,200.00
0.99
41,493.51
Kerosene
0
43.5
0.00
0.99
0.00
Liquefied Petroleum Gas
3,400,000
37.8
64,260.00
0.99
57,713.71
-------
Commercial Sector
Input
Input
A x B / 2000
Input
CxDx .9072
A
B
C
D
E
Carbon Content
Fraction
Net Carbon
Consumption
Coefficient 1/
Total Carbon
Oxidized
Emissions
(10A6 Btu)
(lbs CI 10A6 Btu)
(tons C)
(metric tons C)
Motor Gasoline
1,300,000
42.8
27,820.00
0.99
24,985.92
Residual Fuel Oil
0
47.4
0.00
0.99
0.00
Electricity 2/
29,600,000
49.0
724,504.17
NA
657,270.19
Electrical System Losses 2/
61,400,000
49.0
1,502,856.63
NA
1,363,391.53
Total
157,500,000
3,055,397.64
1995
Natural Gas
50,600,000
31.9
807,070.00
0.995
728,513.03
Coal
1,900,000
55.9
53,061.82
0.99
47,656.30
Distillate Fuel
2,600,000
44.0
57,200.00
0.99
51,372.92
Kerosene
0
43.5
0.00
0.99
0.00
Liquefied Petroleum Gas
2,500,000
37.8
47,250.00
0.99
42,436.55
Motor Gasoline
200,000
42.8
4,280.00
0.99
3,843.99
Residual Fuel Oil
0
47.4
0.00
0.99
0.00
Electricity 2/
30,300,000
49.1
743,919.88
NA
674,884.12
Electrical System Losses 2/
62,900,000
49.1
1,544,308.93
NA
1,400,997.06
Total
151,000,000
2,949,703.97
1994
Natural Gas
48,300,000
31.9
770,385.00
0.995
695,398.81
Coal
800,000
55.8
22,320.00
0.99
20,046.22
Distillate Fuel
2,300,000
44.0
50,600.00
0.99
45,445.28
Kerosene
100,000
43.5
2,175.00
0.99
1,953.43
Liquefied Petroleum Gas
2,500,000
37.8
47,250.00
0.99
42,436.55
Motor Gasoline
200,000
42.8
4,280.00
0.99
3,843.99
Residual Fuel Oil
0
47.4
0.00
0.99
0.00
Electricity 2/
29,900,000
48.0
717,751.50
NA
651,144.16
Electrical System Losses 2/
61,900,000
48.0
1,485,913.65
NA
1,348,020.86
Total
146,000,000
2,808,289.29
1993
Natural Gas
50,500,000
31.9
805,475.00
0.995
727,073.29
Coal
1,400,000
55.8
39,040.91
0.99
35,063.73
Distillate Fuel
2,100,000
44.0
46,200.00
0.99
41,493.51
Kerosene
0
43.5
0.00
0.99
0.00
Liquefied Petroleum Gas
2,500,000
37.8
47,250.00
0.99
42,436.55
Motor Gasoline
3,300,000
42.8
70,620.00
0.99
63,425.80
Residual Fuel Oil
0
47.4
0.00
0.99
0.00
Electricity 2/
29,100,000
49.7
723,298.95
NA
656,176.81
Electrical System Losses 2/
61,200,000
49.7
1,521,164.81
NA
1,380,000.72
Total
150,100,000
2,945,670.41
1992
Natural Gas
46,300,000
31.9
738,485.00
0.995
666,603.82
Coal
1,300,000
55.7
36,199.09
0.99
32,511.42
Distillate Fuel
2,800,000
44.0
61,600.00
0.99
55,324.68
Kerosene
0
43.5
0.00
0.99
0.00
Liquefied Petroleum Gas
2,200,000
37.8
41,580.00
0.99
37,344.16
Motor Gasoline
3,400,000
42.8
72,760.00
0.99
65,347.79
Residual Fuel Oil
200,000
47.4
4,740.00
0.99
4,257.13
Electricity 2/
26,600,000
48.6
646,973.37
NA
586,934.24
Electrical System Losses 2/
56,300,000
48.6
1,369,345.89
NA
1,242,270.59
Total
139,100,000
2,690,593.84
-------
Worksheet to Calculate Iowa CO, Emissions from Fossil Fuels. 1990- 2000
Commercial Sector
Input
Input
A x B / 2000
Input
CxDx .9072
A
B
C
D
E
Carbon Content
Fraction
Net Carbon
Consumption
Coefficient 1/
Total Carbon
Oxidized
Emissions
(10A6 Btu)
(lbs CI 10A6 Btu)
(tons C)
(metric tons C)
Natural Gas
47,000,000
31.9
749,650.00
0.995
676,682.07
Coal
4,500,000
55.6
124,997.73
0.99
112,263.96
Distillate Fuel
3,300,000
44.0
72,600.00
0.99
65,204.09
Kerosene
0
43.5
0.00
0.99
0.00
Liquefied Petroleum Gas
2,100,000
37.8
39,690.00
0.99
35,646.70
Motor Gasoline
3,800,000
42.8
81,320.00
0.99
73,035.77
Residual Fuel Oil
100,000
47.4
2,370.00
0.99
2,128.56
Electricity 2/
27,100,000
47.7
646,815.20
NA
586,790.75
Electrical System Losses 2/
58,400,000
All
1,393,874.82
NA
1,264,523.23
Total
146,300,000
2,816,275.13
1990
Natural Gas
44,300,000
31.9
706,585.00
0.995
637,808.84
Coal
4,800,000
55.7
133,592.73
0.99
119,983.37
Distillate Fuel
2,900,000
44.0
63,800.00
0.99
57,300.57
Kerosene
200,000
43.5
4,350.00
0.99
3,906.86
Liquefied Petroleum Gas
1,800,000
37.8
34,020.00
0.99
30,554.31
Motor Gasoline
700,000
42.8
14,980.00
0.99
13,453.96
Residual Fuel Oil
200,000
47.4
4,740.00
0.99
4,257.13
Electricity 2/
25,700,000
49.2
632,766.72
NA
574,045.97
Electrical System Losses 2/
56,100,000
49.2
1,381,253.43
NA
1,253,073.11
Total
136,700,000
2,694,384.12
1/ Coal Carbon Content is determined from EIA State Energy Data Report 2000 Appendix E Table El.
For 1996 figure is an average of those from 1990 to 1999
2/ Carbon Content Coefficient was determined from ratio of electric utilities carbon emissions to electric
utilities energy inputs
-------
Worksheet to Calculate Iowa CO> Emissions from Fossil Fuels.
1990-2000
Residential Sector
Input
Input
A x B / 2000
Input
C x D x 0.9072
A
B
C
D
E
Carbon Content
Fraction
Net Carbon
Consumption
Coefficient 1/
Total Carbon
Oxidized
Emissions
(10A6 Btu)
(lbs CI 10A6 Btu)
(tons C)
(metric tons C)
2000
Distillate Fuel Oil
2,800,000
44.0
61,600.00
0.99
55,324.68
Kerosene
200,000
43.5
4,350.00
0.99
3,906.86
Liquefied Petroleum Gas
19,100,000
37.8
360,990.00
0.99
324,215.23
Natural Gas
74,200,000
31.9
1,183,490.00
0.995
1,068,293.82
Coal
700,000
55.7
19,510.91
0.99
17,523.29
Electricity 21
41,000,000
48.1
985,163.45
NA
893,740.29
Electrical System Losses 21
70,400,000
48.1
1,691,597.74
NA
1,534,617.47
Total
208,400,000
3,897,621.63
1999
Distillate Fuel Oil
2,800,000
44.0
61,600.00
0.99
55,324.68
Kerosene
100,000
43.5
2,175.00
0.99
1,953.43
Liquefied Petroleum Gas
18,900,000
37.8
357,210.00
0.99
320,820.30
Natural Gas
72,800,000
31.9
1,161,160.00
0.995
1,048,137.33
Coal
1,100,000
55.7
30,660.00
0.99
27,536.60
Electricity 2/
40,500,000
48.4
979,697.81
NA
888,781.86
Electrical System Losses 21
78,700,000
48.4
1,903,758.46
NA
1,727,089.68
Total
214,900,000
4,069,643.88
1998
Distillate Fuel Oil
3,200,000
44.0
70,400.00
0.99
63,228.21
Kerosene
100,000
43.5
2,175.00
0.99
1,953.43
Liquefied Petroleum Gas
15,100,000
37.8
285,390.00
0.99
256,316.75
Natural Gas
69,700,000
31.9
1,111,715.00
0.995
1,003,505.11
Coal
700,000
55.9
19,549.09
0.99
17,557.59
Electricity 21
40,500,000
48.2
975,103.28
NA
884,613.69
Electrical System Losses 21
83,100,000
48.2
2,000,767.46
NA
1,815,096.24
Total
212,400,000
4,042,271.02
1997
Distillate Fuel Oil
4,500,000
44.0
99,000.00
0.99
88,914.67
Kerosene
200,000
43.5
4,350.00
0.99
3,906.86
Liquefied Petroleum Gas
17,800,000
37.8
336,420.00
0.99
302,148.22
Natural Gas
82,400,000
31.9
1,314,280.00
0.995
1,186,353.24
Coal
1,000,000
55.6
27,804.55
0.99
24,972.04
Electricity 21
39,800,000
48.8
970,776.31
NA
880,688.27
Electrical System Losses 21
82,300,000
48.8
2,007,409.31
NA
1,821,121.73
Total
228,000,000
4,308,105.03
1996
Distillate Fuel Oil
4,600,000
44.0
101,200.00
0.99
90,890.55
Kerosene
200,000
43.5
4,350.00
0.99
3,906.86
Liquefied Petroleum Gas
19,200,000
37.8
362,880.00
0.99
325,912.69
Natural Gas
88,600,000
31.9
1,413,170.00
0.995
1,275,617.68
Coal 1/
700,000
55.7
19,504.55
0.99
17,517.58
Electricity 21
39,400,000
49.0
964,373.80
NA
874,879.91
Electrical System Losses 21
81,700,000
49.0
1,999,729.42
NA
1,814,154.53
Total
234,400,000
4,402,879.81
1995
Distillate Fuel Oil
4,900,000
44.0
107,800.00
0.99
96,818.20
Kerosene
100,000
43.5
2,175.00
0.99
1,953.43
Liquefied Petroleum Gas
14,400,000
37.8
272,160.00
0.99
244,434.52
Natural Gas
82,600,000
31.9
1,317,470.00
0.995
1,189,232.74
Coal
300,000
55.9
8,378.18
0.99
7,524.68
Electricity 21
39,700,000
49.1
974,706.91
NA
884,254.11
Electrical System Losses 21
82,400,000
49.1
2,023,069.25
NA
1,835,328.42
Total
224,400,000
4,259,546.09
1994
Distillate Fuel Oil
5,700,000
44.0
125,400.00
0.99
112,625.25
Kerosene
100,000
43.5
2,175.00
0.99
1,953.43
-------
Worksheet to Calculate Iowa CO, Emissions from Fossil Fuels.
1990-2000
Residential Sector
Input
Input
A x B / 2000
Input
C x D x 0.9072
A
B
C
D
E
Carbon Content
Fraction
Net Carbon
Consumption
Coefficient 1/
Total Carbon
Oxidized
Emissions
(10A6 Btu)
(lbs CI 10A6 Btu)
(tons C)
(metric tons C)
Liquefied Petroleum Gas
14,300,000
37.8
270,270.00
0.99
242,737.05
Natural Gas
78,900,000
31.9
1,258,455.00
0.995
1,135,962.02
Coal
100,000
55.8
2,790.00
0.99
2,505.78
Electricity 21
37,700,000
48.0
904,991.02
NA
821,007.86
Electrical System Losses 21
78,200,000
48.0
1,877,196.24
NA
1,702,992.43
Total
215,000,000
4,019,783.82
1993
Distillate Fuel Oil
4,800,000
44.0
105,600.00
0.99
94,842.32
Kerosene
200,000
43.5
4,350.00
0.99
3,906.86
Liquefied Petroleum Gas
14,300,000
37.8
270,270.00
0.99
242,737.05
Natural Gas
83,700,000
31.9
1,335,015.00
0.995
1,205,069.98
Coal
300,000
55.8
8,365.91
0.99
7,513.66
Electricity 21
37,900,000
49.7
942,028.53
NA
854,608.29
Electrical System Losses 21
79,600,000
49.7
1,978,508.48
NA
1,794,902.89
Total
220,800,000
4,203,581.04
1992
Distillate Fuel Oil
4,500,000
44.0
99,000.00
0.99
88,914.67
Kerosene
100,000
43.5
2,175.00
0.99
1,953.43
Liquefied Petroleum Gas
12,300,000
37.8
232,470.00
0.99
208,787.82
Natural Gas
75,200,000
31.9
1,199,440.00
0.995
1,082,691.31
Coal
300,000
55.7
8,353.64
0.99
7,502.63
Electricity 21
35,100,000
48.6
853,712.98
NA
774,488.42
Electrical System Losses 21
74,400,000
48.6
1,809,579.65
NA
1,641,650.66
Total
201,900,000
3,805,988.93
1991
Distillate Fuel Oil
5,200,000
44.0
114,400.00
0.99
102,745.84
Kerosene
200,000
43.5
4,350.00
0.99
3,906.86
Liquefied Petroleum Gas
12,100,000
37.8
228,690.00
0.99
205,392.89
Natural Gas
79,400,000
31.9
1,266,430.00
0.995
1,143,160.77
Coal
900,000
55.6
24,999.55
0.99
22,452.79
Electricity 21
38,100,000
47.7
909,360.11
NA
824,971.49
Electrical System Losses 21
82,100,000
47.7
1,959,539.77
NA
1,777,694.48
Total
218,000,000
4,080,325.13
1990
Distillate Fuel Oil
4,600,000
44.0
101,200.00
0.99
90,890.55
Kerosene
100,000
43.5
2,175.00
0.99
1,953.43
Liquefied Petroleum Gas
9,900,000
37.8
187,110.00
0.99
168,048.73
Natural Gas
71,900,000
31.9
1,146,805.00
0.995
1,035,179.59
Coal
1,100,000
55.7
30,615.00
0.99
27,496.19
Electricity 21
35,900,000
49.2
883,903.71
NA
801,877.45
Electrical System Losses 21
78,300,000
49.2
1,927,845.70
NA
1,748,941.62
Total
201,800,000
3,874,387.55
1/ Coal Carbon Content is determined from EIA State Energy Data Report 2000 Appendix E Table El.
For 1996 figure is an average of those from 1990 to 1999
21 Carbon Content Coefficient was determined from ratio of electric utilities carbon emissions to electric utilities energy inputs
-------
Worksheet to Calculate Iowa CQ Emissions from Fossil Fuels. 1990- 2000
Industrial Sectoi
A
B
C
D E
G
H
Input
Input
Ax B/2000
input
[C-D]
input
E xG x .9072
Carbon
Net Potential
Content
Stored
Carbon
Fraction
Net Carbon
Consumption
Coefficient 11
Total Carbon
Carbon
Emissions
Oxidized
Emissions
(10A6 Btu)
(lbs C/10A6)
(tons C)
(tons C)
(tons C)
(metric tons C)
2000
Asphalt & Road Oil
16,400,000
45.5
373,100.00
373,100.00
0.00
0.99
0.00
Distillate Fuel Oil
34,600,000
44.0
761,200.00
761,200.00
0.99
683,655.03
Kerosene
0
43.5
0.00
0.00
0.99
0.00
Liquefied Petroleum Gas
48,200,000
37.8
910,980.00
584,127.66
326,852.34
0.99
293,555.23
Lubricants
1,200,000
44.6
26,760.00
13,043.99
13,716.01
0.99
12,318.73
Motor Gasoline
4,100,000
42.8
87,740.00
87,740.00
0.99
78,801.75
Residual Fuel Oil
1,100,000
44.0
24,200.00
24,200.00
0.99
21,734.70
Other Oil 2/
4,900,000
47.4
116,130.00
5,898.90
110,231.10
0.99
99,001.63
Natural Gas
100,900,000
31.9
1,609,355.00
87,630.99
1,521,724.01
0.995
1,373,605.48
Coal
64,200,000
56.6
1,818,319.09
5,518.10
1,812,800.99
0.99
1,628,127.33
Petrochemical Feedstock 3/
8,300,000
42.7
177,205.00
132,903.75
44,301.25
0.99
39,788.19
Electricty 4/
58,400,000
48.1
1,403,257.21
NA
NA
1,273,034.94
Electrical System Losses 4/
100,200,000
48.1
2,407,643.37
NA
NA
2,184,214.06
Total
442,500,000
7,687,837.10
1999
Asphalt & Road Oil
19,500,000
45.5
443,625.00
443,625.00
0.00
0.99
0.00
Distillate Fuel Oil
31,400,000
44.0
690,800.00
690,800.00
0.99
620,426.82
Kerosene
200,000
43.5
4,350.00
4,350.00
0.99
3,906.86
Liquefied Petroleum Gas
45,500,000
37.8
859,950.00
552,037.40
307,912.60
0.99
276,544.93
Lubricants
1,200,000
44.6
26,760.00
11,420.77
15,339.23
0.99
13,776.59
Motor Gasoline
4,600,000
42.8
98,440.00
98,440.00
0.99
88,411.72
Residual Fuel Oil
800,000
44.0
17,600.00
17,600.00
0.99
15,807.05
Other Oil
6,000,000
47.4
142,200.00
7,595.03
134,604.97
0.99
120,892.50
Natural Gas
103,900,000
31.9
1,657,205.00
94,111.86
1,563,093.14
0.995
1,410,947.91
Coal
66,600,000
56.6
1,886,293.64
8,869.55
1,877,424.09
0.99
1,686,167.14
Petrochemical Feedstock
8,100,000
42.7
172,935.00
129,701.25
43,233.75
0.99
38,829.44
Electricty 4/
56,300,000
48.4
1,361,900.91
NA
NA
1,235,516.50
Electrical System Losses 4/
109,500,000
48.4
2,648,812.60
NA
NA
2,403,002.79
Total
453,600,000
7,914,230.26
1998
Asphalt & Road Oil
14,300,000
45.5
325,325.00
325,325.00
0.00
0.99
0.00
Distillate Fuel Oil
37,700,000
44.0
829,400.00
829,400.00
0.99
744,907.36
Kerosene
200,000
43.5
4,350.00
4,350.00
0.99
3,906.86
Liquefied Petroleum Gas
35,900,000
37.8
678,510.00
424,010.13
254,499.87
0.99
228,573.46
Lubricants
1,200,000
44.6
26,760.00
11,900.89
14,859.11
0.99
13,345.38
Motor Gasoline
4,700,000
42.8
100,580.00
100,580.00
0.99
90,333.71
Residual Fuel Oil
600,000
44.0
13,200.00
13,200.00
0.99
11,855.29
Other Oil
4,900,000
47.4
116,130.00
4,897.51
111,232.49
0.99
99,901.01
Natural Gas
107,100,000
31.9
1,708,245.00
101,616.15
1,606,628.85
1.00
1,450,246.02
Coal
67,400,000
56.6
1,908,951.82
20,672.33
1,888,279.49
0.99
1,695,916.68
Petrochemical Feedstock
8,700,000
42.7
185,745.00
139,308.75
46,436.25
0.99
41,705.70
Electricty 4/
54,900,000
48.2
1,321,806.66
NA
NA
1,199,143.00
Electrical System Losses 4/
112,600,000
48.2
2,711,027.87
NA
NA
2,459,444.48
Total
450,200,000
8,039,278.96
1997
Asphalt & Road Oil
17,400,000
45.5
395,850.00
395,850.00
0.00
0.99
0.00
Distillate Fuel Oil
40,000,000
44.0
880,000.00
880,000.00
0.99
790,352.64
Kerosene
200,000
43.5
4,350.00
4,350.00
0.99
3,906.86
Liquefied Petroleum Gas
15,900,000
37.8
300,510.00
188,124.07
112,385.93
0.99
100,936.95
Lubricants
1,200,000
44.6
26,760.00
12,836.04
13,923.96
0.99
12,505.50
Motor Gasoline
5,700,000
42.8
121,980.00
121,980.00
0.99
109,553.65
Residual Fuel Oil
500,000
44.0
11,000.00
11,000.00
0.99
9,879.41
Other Oil
4,400,000
47.4
104,280.00
4,329.85
99,950.15
0.99
89,768.03
Natural Gas
108,400,000
31.9
1,728,980.00
101,041.09
1,627,938.91
1.00
1,469,481.85
Coal
68,900,000
56.8
1,957,073.18
15,849.00
1,941,224.19
0.99
1,743,467.80
Petrochemical Feedstock
8,700,000
42.7
185,745.00
139,308.75
46,436.25
0.99
41,705.70
Electricty 4/
53,000,000
48.8
1,292,742.33
NA
NA
1,172,775.84
Electrical System Losses 4/
109,600,000
48.8
2,673,293.57
NA
NA
2,425,211.93
Total
433,900,000
7,969,546.14
-------
Worksheet to Calculate Iowa CQ Emissions from Fossil Fuels. 1990- 2000
Industrial Sectoi
A
B
C
D E
G
H
Input
Input
Ax B/2000
input
[C-D]
input
E xG x .9072
Carbon
Net Potential
Content
Stored
Carbon
Fraction
Net Carbon
Consumption
Coefficient 11
Total Carbon
Carbon
Emissions
Oxidized
Emissions
(10A6 Btu)
(lbs C/10A6)
(tons C)
(tons C)
(tons C)
(metric tons C)
Asphalt & Road Oil
13,600,000
45.5
309,400.00
309,400.00
0.00
0.99
0.00
Distillate Fuel Oil
36,900,000
44.0
811,800.00
811,800.00
0.99
729,100.31
Kerosene
100,000
43.5
2,175.00
2,175.00
0.99
1,953.43
Liquefied Petroleum Gas
18,000,000
37.8
340,200.00
214,974.83
125,225.17
0.99
112,468.23
Lubricants
1,100,000
44.6
24,530.00
12,265.00
12,265.00
0.99
11,015.54
Motor Gasoline
5,800,000
42.8
124,120.00
124,120.00
0.99
111,475.65
Residual Fuel Oil
600,000
44.0
13,200.00
13,200.00
0.99
11,855.29
Other Oil
4,600,000
47.4
109,020.00
4,696.78
104,323.22
0.99
93,695.60
Natural Gas
114,700,000
31.9
1,829,465.00
120,845.48
1,708,619.52
1.00
1,542,309.33
Coal
68,700,000
55.7
1,914,856.36
15,048.51
1,899,807.85
0.99
1,706,270.62
Petrochemical Feedstock
7,500,000
42.7
160,125.00
120,093.75
40,031.25
0.99
35,953.19
Electricty 4/
50,500,000
49.0
1,236,062.86
NA
NA
1,121,356.23
Electrical System Losses 4/
104,800,000
49.0
2,565,136.40
NA
NA
2,327,091.74
Total
426,900,000
7,804,545.16
1995
Asphalt & Road Oil
10,900,000
45.5
247,975.00
247,975.00
0.00
0.99
0.00
Distillate Fuel Oil
35,500,000
44.0
781,000.00
781,000.00
0.99
701,437.97
Kerosene
200,000
43.5
4,350.00
4,350.00
0.99
3,906.86
Liquefied Petroleum Gas
44,400,000
37.8
839,160.00
528,576.14
310,583.86
0.99
278,944.06
Lubricants
1,100,000
44.6
24,530.00
12,265.00
12,265.00
0.99
11,015.54
Motor Gasoline
5,400,000
42.8
115,560.00
115,560.00
0.99
103,787.67
Residual Fuel Oil
600,000
44.0
13,200.00
13,200.00
0.99
11,855.29
Other Oil
2,200,000
47.4
52,140.00
2,445.90
49,694.10
0.99
44,631.67
Natural Gas
115,700,000
31.9
1,845,415.00
120,488.68
1,724,926.32
1.00
1,557,028.89
Coal
60,000,000
56.7
1,700,181.82
13,119.16
1,687,062.66
0.99
1,515,198.21
Petrochemical Feedstock
1,200,000
42.7
25,620.00
19,215.00
6,405.00
0.99
5,752.51
Electricty 4/
47,000,000
49.1
1,153,935.13
NA
NA
1,046,849.95
Electrical System Losses 4/
97,500,000
49.1
2,393,801.60
NA
NA
2,171,656.81
Total
421,700,000
7,452,065.43
1994
Asphalt & Road Oil
13,000,000
45.5
295,750.00
295,750.00
0.00
0.99
0.00
Distillate Fuel Oil
38,900,000
44.0
855,800.00
855,800.00
0.99
768,617.94
Kerosene
200,000
43.5
4,350.00
4,350.00
0.99
3,906.86
Liquefied Petroleum Gas
39,600,000
37.8
748,440.00
464,845.91
283,594.09
0.99
254,703.80
Lubricants
1,200,000
44.6
26,760.00
12,933.65
13,826.35
0.99
12,417.83
Motor Gasoline
5,800,000
42.8
124,120.00
124,120.00
0.99
111,475.65
Residual Fuel Oil
1,100,000
44.0
24,200.00
24,200.00
0.99
21,734.70
Other Oil
2,500,000
47.4
59,250.00
2,542.75
56,707.25
0.99
50,930.37
Natural Gas
109,600,000
31.9
1,748,120.00
122,128.70
1,625,991.30
0.995
1,467,723.81
Coal
57,600,000
56.9
1,638,458.18
12,705.77
1,625,752.42
0.99
1,460,133.77
Petrochemical Feedstock
1,200,000
42.7
25,620.00
19,215.00
6,405.00
0.99
5,752.51
Electricty 4/
45,100,000
48.0
1,082,628.52
NA
NA
982,160.59
Electrical System Losses 4/
93,500,000
48.0
2,244,473.76
NA
NA
2,036,186.60
Total
409,300,000
7,175,744.41
1993
Asphalt & Road Oil
9,000,000
45.5
204,750.00
204,750.00
0.00
0.99
0.00
Distillate Fuel Oil
35,800,000
44.0
787,600.00
787,600.00
0.99
707,365.61
Kerosene
200,000
43.5
4,350.00
4,350.00
0.99
3,906.86
Liquefied Petroleum Gas
39,500,000
37.8
746,550.00
449,339.49
297,210.51
0.99
266,933.08
Lubricants
1,100,000
44.6
24,530.00
12,265.00
12,265.00
0.99
11,015.54
Motor Gasoline
4,200,000
42.8
89,880.00
89,880.00
0.99
80,723.74
Residual Fuel Oil
1,000,000
44.0
22,000.00
22,000.00
0.99
19,758.82
Other Oil
2,800,000
47.4
66,360.00
3,136.15
63,223.85
0.99
56,783.11
Natural Gas
102,900,000
31.9
1,641,255.00
120,224.28
1,521,030.72
0.995
1,372,979.67
Coal
53,100,000
56.8
1,507,557.27
11,976.36
1,495,580.91
0.99
1,343,223.10
Petrochemical Feedstock
1,200,000
42.7
25,620.00
19,215.00
6,405.00
0.99
5,752.51
Electricty 4/
42,500,000
49.7
1,056,364.45
NA
NA
958,333.83
Electrical System Losses 4/
89,400,000
49.7
2,222,093.69
NA
NA
2,015,883.40
Total
382,700,000
6,842,659.27
1992
Asphalt & Road Oil
9,300,000
45.5
211,575.00
211,575.00
0.00
0.99
0.00
-------
Worksheet to Calculate Iowa CQ Emissions from Fossil Fuels. 1990- 2000
Industrial Sectoi
A
B
C
D E
G
H
Input
Input
Ax B/2000
input
[C-D]
input
E xG x .9072
Carbon
Net Potential
Content
Stored
Carbon
Fraction
Net Carbon
Consumption
Coefficient 11
Total Carbon
Carbon
Emissions
Oxidized
Emissions
(10A6 Btu)
(lbs C/10A6)
(tons C)
(tons C)
(tons C)
(metric tons C)
Distillate Fuel Oil
36,200,000
44.0
796,400.00
796,400.00
0.99
715,269.14
Kerosene
100,000
43.5
2,175.00
2,175.00
0.99
1,953.43
Liquefied Petroleum Gas
17,900,000
37.8
338,310.00 202,275.28
136,034.72
0.99
122,176.59
Lubricants
1,100,000
44.6
24,530.00
12,265.00
12,265.00
0.99
11,015.54
Motor Gasoline
5,500,000
42.8
117,700.00
117,700.00
0.99
105,709.67
Residual Fuel Oil
400,000
44.0
8,800.00
8,800.00
0.99
7,903.53
Other Oil
2,800,000
47.4
66,360.00
3,289.40
63,070.60
0.99
56,645.47
Natural Gas
101,200,000
31.9
1,614,140.00
127,390.37
1,486,749.63
0.995
1,342,035.37
Coal
56,000,000
56.8
1,590,654.55
12,787.17
1,577,867.38
0.99
1,417,126.87
Petrochemical Feedstock
1,200,000
42.7
25,620.00
19,215.00
6,405.00
0.99
5,752.51
Electricty 4/
41,400,000
48.6
1,006,943.52
NA
NA
913,499.16
Electrical System Losses 4/
87,700,000
48.6
2,133,066.34
NA
NA
1,935,117.78
Total
360,800,000
6,634,205.05
1991
Asphalt & Road Oil
10,400,000
45.5
236,600.00
236,600.00
0.00
0.99
0.00
Distillate Fuel Oil
26,800,000
44.0
589,600.00
589,600.00
0.99
529,536.27
Kerosene
100,000
43.5
2,175.00
2,175.00
0.99
1,953.43
Liquefied Petroleum Gas
11,800,000
37.8
223,020.00
140,750.06
82,269.94
0.99
73,888.93
Lubricants
1,100,000
44.6
24,530.00
12,265.00
12,265.00
0.99
11,015.54
Motor Gasoline
6,100,000
42.8
130,540.00
130,540.00
0.99
117,241.63
Residual Fuel Oil
500,000
44.0
11,000.00
11,000.00
0.99
9,879.41
Other Oil
2,500,000
47.4
59,250.00
4,072.49
55,177.51
0.99
49,556.46
Natural Gas
98,200,000
31.9
1,566,290.00
85,286.06
1,481,003.94
0.995
1,336,848.94
Coal
62,600,000
56.2
1,759,344.55
13,989.35
1,745,355.20
0.99
1,567,552.37
Petrochemical Feedstock
1,100,000
42.7
23,485.00
17,613.75
5,871.25
0.99
5,273.13
Electricty 4/
39,900,000
47.7
952,322.01
NA
NA
863,946.52
Electrical System Losses 4/
86,000,000
47.7
2,052,623.88
NA
NA
1,862,140.38
Total
347,100,000
6,428,833.03
1990
Asphalt & Road Oil
10,200,000
45.5
232,050.00
232,050.00
0.00
0.99
0.00
Distillate Fuel Oil
24,100,000
44.0
530,200.00
530,200.00
0.99
476,187.47
Kerosene
100,000
43.5
2,175.00
2,175.00
0.99
1,953.43
Liquefied Petroleum Gas
11,200,000
37.8
211,680.00
126,388.03
85,291.97
0.99
76,603.10
Lubricants
1,200,000
44.6
26,760.00
13,380.00
13,380.00
0.99
12,016.95
Motor Gasoline
5,600,000
42.8
119,840.00
119,840.00
0.99
107,631.66
Residual Fuel Oil
600,000
44.0
13,200.00
13,200.00
0.99
11,855.29
Other Oil
2,300,000
47.4
54,510.00
2,727.64
51,782.36
0.99
46,507.19
Natural Gas
90,900,000
31.9
1,449,855.00
120,825.88
1,329,029.12
0.995
1,199,666.74
Coal
53,100,000
55.9
1,484,386.36
11,489.70
1,472,896.67
0.99
1,322,849.74
Petrochemical Feedstock
2,800,000
42.7
59,780.00
44,835.00
14,945.00
0.99
13,422.52
Electricty 4/
38,900,000
49.2
957,767.53
NA
NA
868,886.70
Electrical System Losses 4/
84,800,000
49.2
2,087,883.98
NA
NA
1,894,128.34
Total
325,800,000
6,031,709.13
1/ Carbon content coefficient for coal was taken from Appdx F of SEDR '99
2/ EIA reports Other Oil as including 16 other petroleum products including petrochemical feedstocks- other oils equal to or greater
than 401 degrees F and still gas. Petrochemical feedstocks have been subtracted for later accounting
3/ Petrochemical Feedstock includes Napthas less than 401 degrees F
4/ Carbon Content Coefficient was determined from ratio of electric utilities carbon emissions to electric utilities energy inputs
-------
Worksheet to Calculate Iowa CO, Emissions from Fossil Fuels. 1990- 2000
Transportation Sector
A B C D E F G
Input Input A x B / 2000 Input [C - D] Input E x F x .9072
Carbon Content Stored Net Emissions Fraction Net Carbon
Consumption Coefficient Total Carbon Carbon Carbon Oxidized Emissions
(10A6 Btu) (lbs C/10A6 Btu) (tons C) (tons C) (tons C) (metric tons C)
2000
Aviation Gasoline
400,000
41.6
8,320
8,320
0.99
7,472.42
Distillate Fuel Oil
73,000,000
44.0
1,606,000
1,606,000
0.99
1,442,393.57
Jet Fuel
4,400,000
43.5
95,700
95,700
0.99
85,950.85
Liquefied Petroleum Gas
0
37.8
0
0
0.99
0.00
Lubricants
3,300,000
44.6
73,590
35,870.97
37,719
0.99
33,876.52
Motor Gasoline
176,800,000
42.8
3,783,520
3,783,520
0.99
3,398,085.25
Natural Gas
8,300,000
31.9
132,385
132,385
0.995
119,499.17
Total
266,200,000
5,087,277.79
1999
Aviation Gasoline
400,000
41.6
8,320
8,320
0.99
7,472.42
Distillate Fuel Oil
74,900,000
44.0
1,647,800
1,647,800
0.99
1,479,935.32
Jet Fuel
5,000,000
43.5
108,750
108,750
0.99
97,671.42
Liquefied Petroleum Gas
0
37.8
0
0
0.99
0.00
Lubricants
3,400,000
44.6
75,820
32,358.85
43,461
0.99
39,033.67
Motor Gasoline
179,200,000
42.8
3,834,880
3,834,880
0.99
3,444,213.10
Natural Gas
7,900,000
31.9
126,005
126,005
0.995
113,740.18
Total
270,800,000
5,182,066.12
1998
Aviation Gasoline
400,000
41.6
8,320
8,320
0.99
7,472.42
Distillate Fuel Oil
73,900,000
44.0
1,625,800
1,625,800
0.99
1,460,176.50
Jet Fuel
6,700,000
43.5
145,725
145,725
0.99
130,879.70
Liquefied Petroleum Gas
100,000
37.8
1,890
1,890
0.99
1,697.46
Lubricants
3,300,000
44.6
73,590
32,727.45
40,863
0.99
36,699.80
Motor Gasoline
179,400,000
42.8
3,839,160
3,839,160
0.99
3,448,057.09
Natural Gas
8,900,000
31.9
141,955
141,955
0.995
128,137.67
Total
272,700,000
5,213,120.65
1997
Aviation Gasoline
400,000
41.6
8,320
8,320
0.99
7,472.42
Distillate Fuel Oil
72,100,000
44.0
1,586,200
1,586,200
0.99
1,424,610.63
Jet Fuel
4,500,000
43.5
97,875
97,875
0.99
87,904.28
Liquefied Petroleum Gas
300,000
37.8
5,670
5,670
0.99
5,092.39
Lubricants
3,200,000
44.6
71,360
34,229.45
37,131
0.99
33,347.99
Motor Gasoline
176,900,000
42.8
3,785,660
3,785,660
0.99
3,400,007.24
Natural Gas
11,400,000
31.9
181,830
181,830
0.995
164,131.40
Total
268,800,000
5,122,566.35
1996
Aviation Gasoline
400,000
41.6
8,320
8,320
0.99
7,472.42
Distillate Fuel Oil
73,800,000
44.0
1,623,600
1,623,600
0.99
1,458,200.62
Jet Fuel
4,600,000
43.5
100,050
100,050
0.99
89,857.71
Liquefied Petroleum Gas
400,000
37.8
7,560
7,560
0.99
6,789.85
Lubricants
3,000,000
44.6
66,900
33,450.00
33,450
0.99
30,042.38
Motor Gasoline
176,200,000
42.8
3,770,680
3,770,680
0.99
3,386,553.29
Natural Gas
12,700,000
31.9
202,565
202,565
0.995
182,848.13
Total
271,100,000
5,161,764.40
1995
Aviation Gasoline
400,000
41.6
8,320
8,320
0.99
7,472.42
Distillate Fuel Oil
66,100,000
44.0
1,454,200
1,454,200
0.99
1,306,057.74
Jet Fuel
5,900,000
43.5
128,325
128,325
0.99
115,252.28
Liquefied Petroleum Gas
200,000
37.8
3,780
3,780
0.99
3,394.92
-------
Worksheet to Calculate Iowa CO, Emissions from Fossil Fuels. 1990- 2000
Transportation Sector
A B C D E F G
Input Input A x B / 2000 Input [C - D] Input E x F x .9072
Carbon Content Stored Net Emissions Fraction Net Carbon
Consumption Coefficient Total Carbon Carbon Carbon Oxidized Emissions
(10A6 Btu) (lbs C/10A6 Btu) (tons C) (tons C) (tons C) (metric tons C)
Lubricants
3,100,000
44.6
69,130
33,411.93
35,718
0.99
32,079.40
Motor Gasoline
167,500,000
42.8
3,584,500
3,584,500
0.99
3,219,339.82
Natural Gas
11,100,000
31.9
177,045
177,045
0.995
159,812.15
Total
254,300,000
4,843,408.72
1994
Aviation Gasoline
300,000
41.6
6,240
6,240
0.99
5,604.32
Distillate Fuel Oil
60,000,000
44.0
1,320,000
1,320,000
0.99
1,185,528.96
Jet Fuel
5,100,000
43.5
110,925
110,925
0.99
99,624.85
Liquefied Petroleum Gas
500,000
37.8
9,450
9,450
0.99
8,487.31
Lubricants
3,100,000
44.6
69,130
34,565.00
34,565
0.99
31,043.79
Motor Gasoline
164,700,000
42.8
3,524,580
3,524,580
0.99
3,165,523.99
Natural Gas
10,800,000
31.9
172,260
172,260
0.995
155,492.90
Total
244,500,000
4,651,306.12
1993
0
0
0.00
Aviation Gasoline
400,000
41.6
8,320
8,320
0.99
7,472.42
Distillate Fuel Oil
55,500,000
44.0
1,221,000
1,221,000
0.99
1,096,614.29
Jet Fuel
4,100,000
43.5
89,175
89,175
0.99
80,090.56
Liquefied Petroleum Gas
200,000
37.8
3,780
3,780
0.99
3,394.92
Lubricants
3,000,000
44.6
66,900
33,450.00
33,450
0.99
30,042.38
Motor Gasoline
158,500,000
42.8
3,391,900
3,391,900
0.99
3,046,360.36
Natural Gas
7,400,000
31.9
118,030
118,030
0.995
106,541.43
Total
229,100,000
4,370,516.38
1992
Aviation Gasoline
400,000
41.6
8,320
8,320
0.99
7,472.42
Distillate Fuel Oil
51,200,000
44.0
1,126,400
1,126,400
0.99
1,011,651.38
Jet Fuel
4,500,000
43.5
97,875
97,875
0.99
87,904.28
Liquefied Petroleum Gas
200,000
37.8
3,780
3,780
0.99
3,394.92
Lubricants
3,000,000
44.6
66,900
33,450.00
33,450
0.99
30,042.38
Motor Gasoline
152,900,000
42.8
3,272,060
3,272,060
0.99
2,938,728.70
Natural Gas
7,000,000
31.9
111,650
111,650
0.995
100,782.44
Total
219,200,000
4,179,976.53
1991
Aviation Gasoline
400,000
41.6
8,320
8,320
0.99
7,472.42
Distillate Fuel Oil
49,200,000
44.0
1,082,400
1,082,400
0.99
972,133.75
Jet Fuel
5,000,000
43.5
108,750
108,750
0.99
97,671.42
Liquefied Petroleum Gas
200,000
37.8
3,780
3,780
0.99
3,394.92
Lubricants
2,900,000
44.6
64,670
32,335.00
32,335
0.99
29,040.97
Motor Gasoline
156,800,000
42.8
3,355,520
3,355,520
0.99
3,013,686.47
Natural Gas
6,700,000
31.9
106,865
106,865
0.995
96,463.19
Total
221,200,000
4,219,863.14
1990
Aviation Gasoline
500,000
41.6
10,400
10,400
0.99
9,340.53
Distillate Fuel Oil
56,300,000
44.0
1,238,600
1,238,600
0.99
1,112,421.34
Jet Fuel
5,000,000
43.5
108,750
108,750
0.99
97,671.42
Liquefied Petroleum Gas
200,000
37.8
3,780
3,780
0.99
3,394.92
Lubricants
3,200,000
44.6
71,360
35,680.00
35,680
0.99
32,045.21
Motor Gasoline
157,000,000
42.8
3,359,800
3,359,800
0.99
3,017,530.45
Natural Gas
9,200,000
31.9
146,740
146,740
0.995
132,456.92
Total
231,400,000
4,404,860.79
*Motor gasoline as reported in the State Energy Data Report includes ethanol consumption.
Ethanol is a biofuel and as such is subtracted from the motor gasoline consumption in this spreadsheet.
-------
Worksheet to Calculate Iowa CO> Emissions from Fossil Fuels, 1990- 2000
Electric Utilities Sector
Input Input A x B / 2000 Input
A B CD
C x D x .9072
E
(Ex 2204.6)/A
F
2000
Natural Gas
Coal 1/
Petroleum
Nuclear
Hydroelectric
WoodAV aste
Other
Total
Input
Carbon Content
Coefficient
Fraction
Total Carbon Oxidized
la Electric Generation
Net Carbon Emission Distribution
Emissions Factor
(10A6 Btu)
(lbs C/10A6 Btu)
(tons C)
(metric tons C)
4,700,000
374,900,000
1,300,000
46,400,000
9,200,000
200,000
0
436,700,000
31.9
56.0
43.5
0.0
0.0
0.0
0.0
74,965.00
10,495,495.91
28,275.00
0.00
0.00
0.00
0.00
0.995
0.99
0.99
NA
NA
NA
NA
67,668.21
9,426,298.75
25,394.57
0.00
0.00
0.00
0.00
9,519,361.53
Lbs C/MBtu
48.06
1999
Natural Gas
Coal 1/
Petroleum
Nuclear
Hydroelectric
WoodAV aste
Other
Total
1998
Natural Gas
Coal 1/
Petroleum
Nuclear
Hydroelectric
WoodAV aste
Other
Total
1997
Natural Gas
Coal 1/
Petroleum
Nuclear
Hydroelectric
WoodAV aste
Other
Total
1996
Natural Gas
Coal 1/
Petroleum
Nuclear
Hydroelectric
WoodAV aste
Other
Total
5,300,000
344,500,000
1,700,000
38,000,000
10,000,000
200,000
0
399,700,000
6,000,000
346,000,000
1,600,000
39,500,000
9,800,000
200,000
0
403,100,000
4,100,000
315,200,000
1,200,000
43,500,000
9,200,000
200,000
0
373,400,000
3,400,000
309,300,000
800,000
41,200,000
9,500,000
200,000
0
364,400,000
31.9
56.0
43.5
0.0
0.0
0.0
0.0
31.9
55.9
43.5
0.0
0.0
0.0
0.0
31.9
57.8
43.5
0.0
0.0
0.0
0.0
31.9
57.8
43.5
0.0
0.0
0.0
0.0
84,535.00
9,644,434.09
36,975.00
0.00
0.00
0.00
0.00
95,700.00
9,672,272.73
34,800.00
0.00
0.00
0.00
0.00
65,395.00
9,107,847.27
26,100.00
0.00
0.00
0.00
0.00
54,230.00
8,937,364.09
17,400.00
0.00
0.00
0.00
0.00
0.995
0.99
0.99
NA
NA
NA
NA
0.995
0.99
0.99
NA
NA
NA
NA
0.995
0.99
0.99
NA
NA
NA
NA
0.995
0.99
0.99
NA
NA
NA
NA
76.306.70
8,661,936.30
33,208.28
0.00
0.00
0.00
0.00
8,771,451.29
86,384.94
8,686,938.96
31,254.85
0.00
0.00
0.00
0.00
8,804,578.76
59.029.71
8,180,012.66
23,441.14
0.00
0.00
0.00
0.00
8,262,483.51
48,951.47
8,026,896.94
15,627.43
0.00
0.00
0.00
0.00
8,091,475.83
48.38
48.15
48.78
48.95
1995
Natural Gas
Coal 1/
Petroleum
Nuclear
Hydroelectric
WoodAV aste
Other
Total
1994
Natural Gas
Coal 1/
Petroleum
Nuclear
Hydroelectric
3,600,000
308,700,000
900,000
39,200,000
10,200,000
200,000
0
362,800,000
2,700,000
291,000,000
1,100,000
42,900,000
10,900,000
31.9
57.8
43.5
0.0
0.0
0.0
0.0
31.9
57.7
43.5
0.0
0.0
57,420.00
8,920,026.82
19,575.00
0.00
0.00
0.00
0.00
43,065.00
8,392,704.55
23,925.00
0.00
0.00
0.995
0.99
0.99
NA
NA
NA
NA
0.995
0.99
0.99
NA
NA
51,830.97
8,011,325.85
17,580.86
0.00
0.00
0.00
0.00
8,080,737.67
38,873.23
7,537,722.95
21,487.71
0.00
0.00
49.10
-------
Worksheet to Calculate Iowa CO> Emissions from Fossil Fuels, 1990- 2000
Electric Utilities Sector
Input Input Ax B/2000 Input CxDx.9072 (Ex 2204.6)/A
A B C D E F
Input
Carbon Content
Coefficient
Fraction
Total Carbon Oxidized
la Electric Generation
Net Carbon Emission Distribution
Emissions Factor
(10A6 Btu)
(lbs C/10A6 Btu)
(tons C)
(metric tons C)
Lbs C/MBtu
WoodAV aste
Other
Total
1993
Natural Gas
Coal 1/
Petroleum
Nuclear
Hydroelectric
WoodAV aste
Other
Total
1992
Natural Gas
Coal 1/
Petroleum
Nuclear
Hydroelectric
WoodAV aste
Other
Total
1991
Natural Gas
Coal 1/
Petroleum
Nuclear
Hydroelectric
WoodAV aste
Other
Total
1990
Natural Gas
Coal 1/
Petroleum
Nuclear
Hydroelectric
WoodAV aste
Other
Total
300,000
0
348,900,000
4,300,000
287,900,000
700,000
34,000,000
7,600,000
200,000
0
334,700,000
2,300,000
272,300,000
500,000
35,700,000
10,100,000
100,000
0
321,000,000
3,700,000
281,800,000
600,000
43,500,000
9,200,000
200,000
0
339,000,000
3,500,000
272,600,000
700,000
31,900,000
8,900,000
200,000
0
317,800,000
0.0
0.0
31.9
57.8
43.5
0.0
0.0
0.0
0.0
31.9
57.6
43.5
0.0
0.0
0.0
0.0
31.9
57.5
43.5
0.0
0.0
0.0
0.0
31.9
57.5
43.5
0.0
0.0
0.0
0.0
0.00
0.00
68,585.00
8,319,001.36
15,225.00
0.00
0.00
0.00
0.00
36,685.00
7,838,526.82
10,875.00
0.00
0.00
0.00
0.00
59,015.00
8,100,469.09
13,050.00
0.00
0.00
0.00
0.00
55,825.00
7,832,293.64
15,225.00
0.00
0.00
0.00
0.00
NA
NA
0.995
0.99
0.99
NA
NA
NA
NA
0.995
0.99
0.99
NA
NA
NA
NA
0.995
0.99
0.99
NA
NA
NA
NA
0.995
0.99
0.99
NA
NA
NA
NA
0.00
0.00
7,598,083.89
61.909.21
7,471,528.06
13,674.00
0.00
0.00
0.00
0.00
7,547,111.27
33,114.23
7,040,000.41
9,767.14
0.00
0.00
0.00
0.00
7,082,881.79
53,270.72
7,275,258.10
11,720.57
0.00
0.00
0.00
0.00
7,340,249.39
50.391.22
7,034,402.22
13,674.00
0.00
0.00
0.00
0.00
7,098,467.44
48.01
49.71
48.64
47.74
49.24
NA = Not Applicable
1/ Emission Factor for coal taken from Appdx F of SEDR '99
2/ Interstate flows determined from the difference in Iowa retail sales and Iowa Net Generation and is
multiplied by (1/1- National Rate Loss). In years with net imports, a U.S. electric generation carbon content is
determined from EIA National Net Generation and National C02 Emissions from Electric Generation (including
losses due to inefficiencies) for each year
For years that electricity is imported, a national emission factor is used
For years that electricity is exported, a state emission factor is used
All Fuel Data refers to Higher Heat Value or Gross Caloric Value
-------
Worksheet to Calculate Iowa CO, Emissions from Fossil Fuels. 1990- 2000
Net Interstate Flow of Electricity
Input Input A x B / 2000 Input C x D x .9072
A B C D E
Carbon Content
Total
Fraction
Net Carbon
Input
Coefficient
Carbon
Oxidized
Emissions
(10A6 Btu)
(lbs C/10A6 Btu)
(tons C)
(metric tons C)
2000
-74,600,000
48.1
-1,794,130
0.99
-1,611,358
1999
-17,400,000
48.4
-421,080
0.99
-378,184
1998
-14,300,000
48.2
-344,630
0.99
-309,522
1997
4,200,000
48.8
102,480
0.99
92,040
1996
2,900,000
49
71,050
0.99
63,812
1995
-2,900,000
49.1
-71,195
0.99
-63,942
1994
-2,500,000
48
-60,000
0.99
-53,888
1993
4,900,000
49.7
121,765
0.99
109,361
1992
400,000
48.6
9,720
0.99
8,730
1991
-7,400,000
47.7
-176,490
0.99
-158,511
1990
1,700,000
49.2
41,820
0.99
37,560
-------
FOSSIL FUEL COMBUSTION PARS SCORES
DARS SCORES: C02 FROM GASOLINE COMBUSTION
Emission
Factor
Attribute
Explanation
Activity
Data
Attribute
Explanation
Emission
Score
Measurement
9
The emission factor is based on a precise
stoichiometric relationship.
9
Fuel purchases are measured using top-down statistics;
states may have better data from tax records.
0.81
Source Specificity
10
The emission factor was developed specifically for
gasoline combustion
9
Fuel purchases are very closely correlated to the
emissions process.
0.90
Spatial
Congruity
9
U.S. emission factors are used, but the carbon
coefficient for gasoline varies depending on its
source.
9
States use state-level activity data to estimate state- wide
emissions.
0.81
Temporal
Congruity
9
The emission factor is based on stoichiometry, not
on measured emissions over a particular time
frame. However, the emission factor should not
vary significantly over the course ofa year.
10
States use annual activity data to estimate annual
emissions.
0.90
Composite Score 0.86
Note 1: The DARS scores for gasoline are used as a benchmark for determining DARS scores for other fuels.
Note 2: This inventory estimates gasoline emissions from the point ofsale. The spacial DARS score would be lower if emissions
were estimated based on VMT.
-------
DARS SCORES: C02 FROM DISTILLATE FUEL OIL COMBUSTION
Emission
Factor
Attribute
Explanation
Activity
Data
.¦J (tribute
Explanation
Emission
Score
Measurement
9
The emission factor is based on a precise
stoichiometric relationship.
9
Fuel purchases are measured using top-down statistics.
0.81
Source Specificity
9
The emission factor was developed specifically for
distillate fuel oil combustion.
9
Fuel purchases are very closely correlated to the
emissions process.
0.81
Spatial
Congruity
9
U.S. emission factors are used, but the carbon
coefficientfor distillate fuel oil varies slightly
depending on its source.
8
States use state-level activity data to estimate state- wide
emissions, but there are minor cross-state sales by
retailers.
0.72
Temporal
Congruity
9
The emission factor is based on stoichiometry, not
on measured emissions over a particular time
frame. However, the emission factor should not
vary significantly over the course ofa year.
9
States use annual activity data to estimate annual
emissions.
0.81
Composite Score 0.79
-------
DARS SCORES: C02 FROM RESIDUAL FUEL OIL COMBUSTION
Emission
Factor
Attribute
Explanation
Activity
Data
.¦J (tribute
Explanation
Emission
Score
Measurement
9
The emission factor is based on a precise
stoichiometric relationship.
9
Fuel purchases are measured using top-down statistics.
0.81
Source Specificity
8
The emission factor was developed specifically for
residual fuel oil combustion, but residual fuel can
be more or less dense, depending on how the
refinery is run.
9
Fuel purchases are very closely correlated to the
emissions process.
0.72
Spatial
Congruity
8
U.S. emission factors are used, but the carbon
coefficient for residual fuel varies slightly
depending on its source.
9
States use state-level activity data to estimate state-wide
emissions.
0.72
Temporal
Congruity
8
i he emission factor is based on stoichiometry, not
on measured emissions over a particular time
frame. However, the emission factor may vary
over the course of a year.
9
States use annual activity data to estimate annual
emissions.
0.72
Composite Score 0.74
-------
DARS SCORES: C02 FROM COMBUSTION OF JET FUEL
Emission
Factor
Attribute
Explanation
Activity
Data
.¦J (tribute
Explanation
Emission
Score
Measurement
9
The emission factor is based on a precise
stoichiometric relationship.
9
Fuel purchases are measured using top-down statistics.
0.81
Source Specificity
10
The emission factor was developed specifically for
jet fuel combustion
9
Fuel purchases are very closely correlated to the
emissions process.
0.90
Spatial
Congruity
9
U.S. emission factors are used, but the carbon
coefficient for jet fuel varies slightly depending on
its source.
7
States use state-level activity data to estimate state- wide
emissions. However, jet fuel is generally not burned
where it is bought
0.63
Temporal
Congruity
10
T he emission factor is based on stoichiometry, not
on measured emissions over a particular time
frame. However, jet fuel is a relatively
homogenous product, and the emission factor
should not vary over the course ofa year.
10
States use annual activity data to estimate annual
emissions, and jet fuel is typically combusted in the year
in which it is purchased.
1.00
Composite Score 0.84
-------
DARS SCORES: C02 FROM KEROSENE COMBUSTION
Emission
Factor
Attribute
Explanation
Activity
Data
.¦J (tribute
Explanation
Emission
Score
Measurement
9
The emission factor is based on a precise
stoichiometric relationship.
9
Fuel purchases are measured using top-down statistics.
0.81
Source Specificity
9
The emission factor was developed specifically for
kerosene combustion.
9
Fuel purchases are very closely correlated to the
emissions process.
0.81
Spatial
Congruity
9
U.S. emission factors are used, but the carbon
coefficient for kerosene varies slightly depending
on its source.
9
States use state-level activity data to estimate state- wide
emissions.
0.81
Temporal
Congruity
9
The emission factor is based on stoichiometry, not
on measured emissions over a particular time
frame. However, the emission factor should not
vary significantly over the course ofa year.
9
States use annual activity data to estimate annual
emissions.
0.81
Composite Score 0.81
-------
DARS SCORES: C02 EMISSIONS FROM COMBUSTION OF LIQUIFIED PETROLEUM GAS
Emission
Factor
Attribute
Explanation
Activity
Data
.¦J (tribute
Explanation
Emission
Score
Measurement
9
The emission factor is based on a precise
stoichiometric relationship.
9
Fuel purchases are measured using top-down statistics.
0.81
Source Specificity
6
The emission factor is based on the emission
factors for the three products collectively known
as LPG—propane, butane and ethane—and the
national proportions of their use. In addition,
although the amount of propane used each year
for heating will vary, the emission factor is not
changed eachyear.
9
Fuel purchases are very closely correlated to the
emissions process.
0.54
Spatial
Congruity
9
U.S. emission factors are used, but the carbon
coefficient for each product in LPG varies slightly
depending on its source.
8
States use state-level activity data to estimate statewide
emissions.
0.72
Temporal
Congruity
9
The emission factor is based on stoichiometry, not
on measured emissions over a particular time
frame. However, the emission factor was assumed
not to vary significantly over the course ofa year.
8
States use annual activity data to estimate annual
emissions.
0.72
Composite Score
0.70
Note 1: Data on sales of propane, butane, and ethane (which make up LPG) are available from the American Petroleum Institute. Note 2:
Some ethane is used as a feedstock.
-------
DARS SCORES: C02 FROM NATURAL GAS COMBUSTION
Emission
Factor
Attribute
Explanation
Activity
Data
.¦J (tribute
Explanation
Emission
Score
Measurement
10
The emission factor is based on a precise
stoichiometric relationship.
8
Fuel purchases are measured using top-down
statistics.
0.80
Source Specificity
10
The emission factor was developed specifically
for natural gas combustion.
9
Fuel purchases are very closely correlated to the
emissions process.
0.90
Spatial
Congruity
10
Natural gas from different sources is very
homogenous in the amount of carbon per BTU.
8
States use state-level activity data to estimate state-
wide emissions.
0.80
Temporal
Congruity
10
The emission factor is based on stoichiometry,
not on measured emissions over a particular
time frame. However, natural gas produced at
different times is very homogenous in the
amount of carbon per BTU.
10
States use annual activity data to estimate annual
emissions, and natural gas is typically combusted in
the year in which it is purchased.
1.00
Composite Score 0.88
Note: The ratings shown here are for measurements ofnatural gas based on BTU content, not measurements based on volume.
-------
DARS SCORES: C02 FROM COAL COMBUSTION
Emission
Factor
Attribute
Explanation
Activity
Data
Attribute
Explanation
Emission
Score
Measurement
8
The emission factor is based on a stoichiometric
relationship, but a variety of coal types are used.
8
Fuel purchases are measured using top-down statistics.
0.64
Source Specificity
8
The emission factor was developed specifically for
coal combustion.
8
Fuel purchases are closely correlated to the emissions
process. However, data are not available for the
consumption of coal by rank for industrial, commercial,
or residential consumers.
0.64
Spatial
Congruity
8
U.S. emission factors are used, but the carbon
coefficient for coal varies depending on the source
of the coal.
8
States use state-level activity data to estimate state- wide
emissions.
0.64
Temporal
Congruity
8
1 he emission factor is based on stoichiometry, not
on measured emissions over a particular time
frame. The emission factor may vary over the
course of a year.
8
States use annual activity data to estimate annual
emissions.
0.64
Composite Score 0.64
Note: The emission factor scores are for state-specific emission factors (i.e., emission factors developedfor the state in which the coal was produced).
-------
DARS SCORES: C02 FROM OXIDATION OF LUBRICANTS
Emission
Factor
Attribute
Explanation
Activity
Data
.¦J (tribute
Explanation
Emission
Score
Measurement
7
The emission factor is based on a stoichiometric
relationshipfor one component of lubricants (i.e.,
motor oil).
4
Sales of lubricants in each state are based on national
sales and each state's 1977proportion of national sales.
Oxidation of lubricants is approximated as a percentcge
of lubricant sales.
0.28
Source Specificity
8
The emission factorfor oxidation of lubricants (a
category comprising motor oil and other products)
is based on the factorfor motor oil alone;
however, the range in emissionfactors for the
different products is small.
8
Lubricant purchases are correlated to the emissions
process.
0.64
Spatial
Congruity
9
U.S. emission factors are used, but the carbon
coefficientfor each product in the "lubricants"
category varies slightly depending on its source.
9
States use state-level activity data to estimate state- wide
emissions.
0.81
Temporal
Congruity
8
The emission factor is based on stoichiometry, not
on measured emissions over a particular time
frame. The emission factor may vary over the
course ofa year.
9
States use annual activity data to estimate annual
emissions.
0.72
Composite Score 0.61
-------
DARS SCORES: C02 FROM COMBUSTION OF MISCELLANEUS PETROL1
JEM PRODUCTS
Emission
Factor
Attribute
Explanation
Activity
Data
.¦J (tribute
Explanation
Emission
Score
Measurement
6
The emission factor is based on a stoichiometric
relationship. A number of products are included
in the "miscellaneouspetroleum products"
category. Moreover, the relationship is based on
highly uncertain storage factors for the various
products.
7
Fuel purchases are presumed to be measured using top-
down statistics.
0.42
Source Specificity
4
Because of the number of products in the
"miscellaneouspetroleum products" category, the
emission factor is not specific to any given
product. Storage is estimated for broad categories
ofproducts.
8
Fuel purchases are correlated to the emissions process.
0.32
Spatial
Congruity
6
U.S. emission factors are used, but the carbon
coefficient for each product in "miscellaneous
petroleum products" varies depending on its
source.
6
States use state-level activity data to estimate state- wide
emissions, but some products maybe used out of state.
0.36
Temporal
Congruity
7
The emission factor is based on stoichiometry, not
on measured emissions over a particular time
frame. The emission factor is expected to vary
over the course of a year.
8
States use annual activity data to estimate annual
emissions.
0.56
Composite Score 0.42
-------
II. CH4 and N20 from Stationary Combustion of Fossil Fuels
For nitrous oxide emissions from stationary combustion sources, the analysis is
largely similar to the methods in the previous section. Emissions are determined by
multiplying fuel use for each sector by an emission factor provided by the EIIP. The
transportation sector was excluded from this analysis because its emissions are estimated in
the method for mobile combustion. For this section, it was only necessary to disaggregate
fuel into three categories; coal, oil and natural gas as the small amount of nitrous oxide that is
emitted from stationary sources are similar within these categories. In order to be consistent
with internationally accepted methods, fuel consumption is considered as lower heating value
or net calorific value. EIA reports fuel consumption as gross calorific value or higher heating
value. The difference is the heat that is lost during the evaporation of moisture contained in
the fuel. It was necessary to make this conversion assuming that petroleum products and coal
have a lower heating value that is 95% of the higher heating value. For natural gas that
contains more moisture the net heating value is 90% of the higher heating value.
-------
Total N20 Emissions from Stationary Combustion by Sector
Industrial
Commercial
Residential
Utility Sector
Sector
Sector
Sector
Total
MTCE
MTCE
MTCE
MTCE
MTCE
2000
43.70
7.48
0.71
0.08
51.97
1999
40.16
7.76
0.96
0.13
49.00
1998
40.33
7.86
0.69
0.08
48.96
1997
36.74
8.03
0.91
0.12
45.80
1996
36.05
8.01
0.56
0.08
44.70
C3
O
r i
1995
35.98
6.99
0.22
0.03
43.23
1994
33.92
6.71
0.09
0.01
40.74
1993
33.56
6.19
0.16
0.03
39.95
1992
31.74
6.17
0.15
0.03
38.09
1991
32.85
6.53
0.52
0.10
40.00
1990
31.77
6.19
0.56
0.13
38.65
2000
0.07
6.06
0.46
1.13
7.71
1999
0.09
5.98
0.42
1.12
7.60
1998
0.08
5.52
0.40
0.94
6.94
1997
0.06
4.78
0.38
1.15
6.37
1996
0.04
4.50
0.35
1.22
6.11
5
1995
0.05
5.18
0.27
0.99
6.49
1994
0.06
5.28
0.26
1.02
6.61
1993
0.04
4.83
0.41
0.98
6.26
1992
0.03
3.80
0.44
0.87
5.14
1991
0.03
3.08
0.47
0.89
4.48
1990
0.04
2.96
0.30
0.75
4.04
2000
0.03
0.70
0.32
0.51
1.56
1999
0.04
0.72
0.32
0.50
1.57
1998
0.04
0.74
0.30
0.48
1.56
<91
1997
0.03
0.75
0.35
0.57
1.69
0
1996
0.02
0.79
0.38
0.61
1.81
-
1995
0.02
0.80
0.35
0.57
1.74
=
03
1994
0.02
0.76
0.33
0.54
1.65
z
1993
0.03
0.71
0.35
0.58
1.67
1992
0.02
0.70
0.32
0.52
1.55
1991
0.03
0.68
0.32
0.55
1.58
1990
0.02
0.63
0.31
0.50
1.45
-------
Commercial N20 Emissions from Stationary Combustion
A B C D E F G
input
input
Conversion
A x B
input
CxD
E/2205
Fx 310 x (12/44)
Coal
factor to Lower
Lower Heat
Consumption
Heat Value
Value
Emission Factor
Emissions N20
Emissions N20
Emissions N20
(Million Btu)
(Million Btu)
(lbs N20/106 Btu)
(lbs N20)
(Metric Tons N20)
(MTCE)
2000
6,100
0.95
5795
0.0032
18.54
0.01
0.71
1999
8,200
0.95
7790
0.0032
24.93
0.01
0.96
1998
5,900
0.95
5605
0.0032
17.94
0.01
0.69
1997
7,800
0.95
7410
0.0032
23.71
0.01
0.91
1996
4,800
0.95
4560
0.0032
14.59
0.01
0.56
1995
1,900
0.95
1805
0.0032
5.78
0.00
0.22
1994
800
0.95
760
0.0032
2.43
0.00
0.09
1993
1,400
0.95
1330
0.0032
4.26
0.00
0.16
1992
1,300
0.95
1235
0.0032
3.95
0.00
0.15
1991
4,500
0.95
4275
0.0032
13.68
0.01
0.52
1990
4,800
0.95
4560
0.0032
14.59
0.01
0.56
Conversion
factor to Lower
Lower Heat
Oil Consumption
Heat Value
Value
Emission Factor
Emissions N20
Emissions N20
Emissions N20
(Million Btu)
(Million Btu)
(lbs N20/106 Btu)
(lbs N20)
(Metric Tons N20)
(MTCE)
2000
9,000
0.95
8550
0.0014
11.97
0.01
0.46
1999
8,200
0.95
7790
0.0014
10.91
0.00
0.42
1998
7,800
0.95
7410
0.0014
10.37
0.00
0.40
1997
7,500
0.95
7125
0.0014
9.98
0.00
0.38
1996
6,800
0.95
6460
0.0014
9.04
0.00
0.35
1995
5,300
0.95
5035
0.0014
7.05
0.00
0.27
1994
5,100
0.95
4845
0.0014
6.78
0.00
0.26
1993
8,000
0.95
7600
0.0014
10.64
0.00
0.41
1992
8,700
0.95
8265
0.0014
11.57
0.01
0.44
1991
9,300
0.95
8835
0.0014
12.37
0.01
0.47
1990
5,800
0.95
5510
0.0014
7.71
0.00
0.30
Conversion
Natural Gas
factor to Lower
Lower Heat
Consumption
Heat Value
Value
Emission Factor
Emissions N20
Emissions N20
Emissions N20
(Million Btu)
(Million Btu)
(lbs N20/106 Btu)
(lbs N20)
(Metric Tons N20)
(MTCE)
2000
45,800
0.9
41220
0.0002
8.24
0.00
0.32
1999
45,800
0.9
41220
0.0002
8.24
0.00
0.32
1998
43,500
0.9
39150
0.0002
7.83
0.00
0.30
1997
50,600
0.9
45540
0.0002
9.11
0.00
0.35
1996
54,900
0.9
49410
0.0002
9.88
0.00
0.38
1995
50,600
0.9
45540
0.0002
9.11
0.00
0.35
1994
48,300
0.9
43470
0.0002
8.69
0.00
0.33
1993
50,500
0.9
45450
0.0002
9.09
0.00
0.35
1992
46,300
0.9
41670
0.0002
8.33
0.00
0.32
1991
47,000
0.9
42300
0.0002
8.46
0.00
0.32
1990
44,300
0.9
39870
0.0002
7.97
0.00
0.31
-------
Residential N20 Emissions from Stationary Combustion
A
B
C
D
E
F
G
input
input
A x B
input
CxD
E/2205
Fx 310x (12/44)
Conversion
Coal
factor to Lower
Lower Heat
Consumption
Heat Value
Value
Emission Factor
Emissions N20
Emissions N20
Emissions N20
(Million Btu)
(Million Btu)
(lbs N20/106 Btu)
(lbs N20)
(Metric Tons NzO)
(MTCE)
2000
700
0.95
665
0.0032
2.13
0.00
0.08
1999
1,100
0.95
1045
0.0032
3.34
0.00
0.13
1998
700
0.95
665
0.0032
2.13
0.00
0.08
1997
1,000
0.95
950
0.0032
3.04
0.00
0.12
1996
700
0.95
665
0.0032
2.13
0.00
0.08
1995
300
0.95
285
0.0032
0.91
0.00
0.03
1994
100
0.95
95
0.0032
0.30
0.00
0.01
1993
300
0.95
285
0.0032
0.91
0.00
0.03
1992
300
0.95
285
0.0032
0.91
0.00
0.03
1991
900
0.95
855
0.0032
2.74
0.00
0.10
1990
1,100
0.95
1045
0.0032
3.34
0.00
0.13
Conversion
factor to Lower
Lower Heat
Oil Consumption
Heat Value
Value
Emission Factor
Emissions N20
Emissions N20
Emissions N20
(Million Btu)
(Million Btu)
(lbs N20/106 Btu)
(lbs N20)
(Metric Tons N20)
(MTCE)
2000
22,100
0.95
20995
0.0014
29.39
0.01
1.13
1999
21,900
0.95
20805
0.0014
29.13
0.01
1.12
1998
18,400
0.95
17480
0.0014
24.47
0.01
0.94
1997
22,500
0.95
21375
0.0014
29.93
0.01
1.15
1996
24,000
0.95
22800
0.0014
31.92
0.01
1.22
1995
19,400
0.95
18430
0.0014
25.80
0.01
0.99
1994
20,000
0.95
19000
0.0014
26.60
0.01
1.02
1993
19,200
0.95
18240
0.0014
25.54
0.01
0.98
1992
17,000
0.95
16150
0.0014
22.61
0.01
0.87
1991
17,500
0.95
16625
0.0014
23.28
0.01
0.89
1990
14,700
0.95
13965
0.0014
19.55
0.01
0.75
Conversion
Natural Gas
factor to Lower
Lower Heat
Consumption
Heat Value
Value
Emission Factor
Emissions N20
Emissions N20
Emissions N20
(Million Btu)
(Million Btu)
(lbs N20/106 Btu)
(lbs N20)
(Metric Tons N20)
(MTCE)
2000
74,200
0.9
66780
0.0002
13.36
0.01
0.51
1999
72,800
0.9
65520
0.0002
13.10
0.01
0.50
1998
69,700
0.9
62730
0.0002
12.55
0.01
0.48
1997
82,400
0.9
74160
0.0002
14.83
0.01
0.57
1996
88,600
0.9
79740
0.0002
15.95
0.01
0.61
1995
82,600
0.9
74340
0.0002
14.87
0.01
0.57
1994
78,900
0.9
71010
0.0002
14.20
0.01
0.54
1993
83,700
0.9
75330
0.0002
15.07
0.01
0.58
1992
75,200
0.9
67680
0.0002
13.54
0.01
0.52
1991
79,400
0.9
71460
0.0002
14.29
0.01
0.55
1990
71,900
0.9
64710
0.0002
12.94
0.01
0.50
-------
Industrial N20 Emissions from Stationary Combustion
A
B
C
D
E
F
G
input
input
AxB
input
CxD
E/2205
Fx 310 x (12/44)
Conversion
Coal
factor to Lower
Lower Heat
Consumption
Heat Value
Value
Emission Factor
Emissions N20
Emissions N20
Emissions N20
(Million Btu)
(Million Btu)
(lbs N20/106 Btu)
(lbs N20)
(Metric Tons NzO)
(MTCE)
2000
64,200
0.95
60,990
0.0032
195.17
0.089
7.48
1999
66,600
0.95
63,270
0.0032
202.46
0.092
7.76
1998
67,400
0.95
64,030
0.0032
204.90
0.093
7.86
1997
68,900
0.95
65,455
0.0032
209.46
0.095
8.03
1996
68,700
0.95
65,265
0.0032
208.85
0.095
8.01
1995
60,000
0.95
57,000
0.0032
182.40
0.083
6.99
1994
57,600
0.95
54,720
0.0032
175.10
0.079
6.71
1993
53,100
0.95
50,445
0.0032
161.42
0.073
6.19
1992
52,900
0.95
50,255
0.0032
160.82
0.073
6.17
1991
56,000
0.95
53,200
0.0032
170.24
0.077
6.53
1990
53,100
0.95
50,445
0.0032
161.42
0.073
6.19
Conversion
factor to Lower
Lower Heat
Oil Consumption
Heat Value
Value
Emission Factor
Emissions N20
Emissions N20
Emissions N20
(Million Btu)
(Million Btu)
(lbs N20/106 Btu)
(lbs N20)
(Metric Tons N20)
(MTCE)
2000
118,800
0.95
112,860
0.0014
158.0
0.072
6.06
1999
117,300
0.95
111,435
0.0014
156.0
0.071
5.98
1998
108,300
0.95
102,885
0.0014
144.0
0.065
5.52
1997
93,800
0.95
89,110
0.0014
124.8
0.057
4.78
1996
88,200
0.95
83,790
0.0014
117.3
0.053
4.50
1995
101,600
0.95
96,520
0.0014
135.1
0.061
5.18
1994
103,500
0.95
98,325
0.0014
137.7
0.062
5.28
1993
94,800
0.95
90,060
0.0014
126.1
0.057
4.83
1992
74,600
0.95
70,870
0.0014
99.2
0.045
3.80
1991
60,400
0.95
57,380
0.0014
80.3
0.036
3.08
1990
58,100
0.95
55,195
0.0014
77.3
0.035
2.96
Conversion
Natural Gas
factor to Lower
Lower Heat
Consumption
Heat Value
Value
Emission Factor
Emissions N20
Emissions N20
Emissions N20
(Million Btu)
(Million Btu)
(lbs N20/106 Btu)
(lbs N20)
(Metric Tons N20)
(MTCE)
2000
100,900
0.9
90,810
0.0002
18.2
0.008
0.70
1999
103,900
0.9
93,510
0.0002
18.7
0.008
0.72
1998
107,100
0.9
96,390
0.0002
19.3
0.009
0.74
1997
108,400
0.9
97,560
0.0002
19.5
0.009
0.75
1996
114,700
0.9
103,230
0.0002
20.6
0.009
0.79
1995
115,700
0.9
104,130
0.0002
20.8
0.009
0.80
1994
109,600
0.9
98,640
0.0002
19.7
0.009
0.76
1993
102,900
0.9
92,610
0.0002
18.5
0.008
0.71
1992
101,200
0.9
91,080
0.0002
18.2
0.008
0.70
1991
98,200
0.9
88,380
0.0002
17.7
0.008
0.68
1990
90,900
0.9
81,810
0.0002
16.4
0.007
0.63
-------
Electric Utility N20 Emissions from Stationary Combustion
A
B
C
D
E
F
G
input
input
A x B
input
CxD
E/2205
Fx 310 x (12/44)
Conversion
Coal
factor to Lower
Lower Heat
Consumption
Heat Value
Value
Emission Factor
Emissions N20
Emissions N20
Emissions N20
(Million Btu)
(Million Btu)
(lbs N20/106 Btu)
(lbs N20)
(Metric Tons NzO)
(MTCE)
2000
374,900
0.95
356,155
0.0032
1,139.70
0.52
43.70
1999
344,500
0.95
327,275
0.0032
1,047.28
0.47
40.16
1998
346,000
0.95
328,700
0.0032
1,051.84
0.48
40.33
1997
315,200
0.95
299,440
0.0032
958.21
0.43
36.74
1996
309,300
0.95
293,835
0.0032
940.27
0.43
36.05
1995
308,700
0.95
293,265
0.0032
938.45
0.43
35.98
1994
291,000
0.95
276,450
0.0032
884.64
0.40
33.92
1993
287,900
0.95
273,505
0.0032
875.22
0.40
33.56
1992
272,300
0.95
258,685
0.0032
827.79
0.38
31.74
1991
281,800
0.95
267,710
0.0032
856.67
0.39
32.85
1990
272,600
0.95
258,970
0.0032
828.70
0.38
31.77
Conversion
factor to Lower
Lower Heat
Oil Consumption
Heat Value
Value
Emission Factor
Emissions N20
Emissions N20
Emissions N20
(Million Btu)
(Million Btu)
(lbs N20/106 Btu)
(lbs N20)
(Metric Tons N20)
(MTCE)
2000
1,300
0.95
1,235
0.0014
1.73
0.00
0.07
1999
1,700
0.95
1,615
0.0014
2.26
0.00
0.09
1998
1,600
0.95
1,520
0.0014
2.13
0.00
0.08
1997
1,200
0.95
1,140
0.0014
1.60
0.00
0.06
1996
800
0.95
760
0.0014
1.06
0.00
0.04
1995
900
0.95
855
0.0014
1.20
0.00
0.05
1994
1,100
0.95
1,045
0.0014
1.46
0.00
0.06
1993
700
0.95
665
0.0014
0.93
0.00
0.04
1992
500
0.95
475
0.0014
0.67
0.00
0.03
1991
600
0.95
570
0.0014
0.80
0.00
0.03
1990
700
0.95
665
0.0014
0.93
0.00
0.04
Conversion
Natural Gas
factor to Lower
Lower Heat
Consumption
Heat Value
Value
Emission Factor
Emissions N20
Emissions N20
Emissions N20
(Million Btu)
(Million Btu)
(lbs N20/106 Btu)
(lbs N20)
(Metric Tons N20)
(MTCE)
2000
4,700
0.90
4,230
0.0002
0.85
0.00
0.03
1999
5,300
0.90
4,770
0.0002
0.95
0.00
0.04
1998
6,000
0.90
5,400
0.0002
1.08
0.00
0.04
1997
4,100
0.90
3,690
0.0002
0.74
0.00
0.03
1996
3,400
0.90
3,060
0.0002
0.61
0.00
0.02
1995
3,600
0.90
3,240
0.0002
0.65
0.00
0.02
1994
2,700
0.90
2,430
0.0002
0.49
0.00
0.02
1993
4,300
0.90
3,870
0.0002
0.77
0.00
0.03
1992
2,300
0.90
2,070
0.0002
0.41
0.00
0.02
1991
3,700
0.90
3,330
0.0002
0.67
0.00
0.03
1990
3,500
0.90
3,150
0.0002
0.63
0.00
0.02
-------
STATIONARY COMBUSTION SOURCES PARS SCORES
DARS SCORES: N20 EMISSIONS FROM STATIONARY SOURCE COMBUSTION
Emission
Factor
Attribute
Explanation
Activity
Data
Attribute
Explanation
Emission
Score
Measurement
5
The emission factors are based on
measurements at a representative
sample of stationary source
combustion facilities, but have large
uncertainty ranges (Dc Soete, 1993)
9
Fuel purchases are measured using top-
down statistics.
0.45
Source Specificity
7
The emission factors were developed
specifically for the intended source
category, but do not account for
different emission rates from various
combustion technologies.
5
Fuel purchases are somewhat correlated to
the emissions process.
0.35
Spatial
Congruity
9
The emission factors were developed
for global use, but spatial variability is
expected to be low.
8
States use state-level activity data to
estimate state-wide emissions, but there
are minor cross-state sales by retailers.
0.72
Temporal
Congruity
8
The emissions factors were derived
using sampling for only part of a year,
but temporal variability is expected to
be low.
9
States use annual activity data to estimate
annual emissions.
0.72
Composite Score 0.56
-------
III. CH4 and N2O from Mobile Combustion of Fossil Fuels
The processes that result in these gases are slightly less straightforward than in the
previous section. Each vehicle will emit different amounts of each gas based on fuel type,
engine design, age and emission control technologies. For instance, a motorcycle will not
produce the same emissions per mile as a tractor-trailer. Fundamentally, they have different
engines, different fuels, and different emission control technologies. For this reason, it was
necessary to define vehicle emission categories (VEC), which were used to sort the entire
vehicle fleet. The VECs were relatively congruent with vehicle type categories (VTC) that
are used by Departments of Transportation (DOT).
In this method there are seven VECs including motorcycles, light duty gas vehicles,
light duty gas trucks, light duty diesel vehicles, light duty diesel trucks, heavy-duty gas
vehicles, and heavy-duty diesel vehicles. These are not to be confused with vehicle type
categories (VTC), which are used by the Federal Highway Administration and DOT to
identify the difference in the type of vehicle (i.e. passenger car vs. bus). The table below
shows each VEC definition and the corresponding Iowa DOT VTC. It should be noted that
one VTC may fall under more than one VEC. For instance "Automobiles and Multipurpose
Vehicles" can fall under both "Light Duty Gas Vehicles" and "Light Duty Diesel Vehicles."
Recognizing this will make understanding of the estimation method easier later on.
Vehicle Emission
Categories
IA DOT Vehicle Type
Category (VTC)
Vehicle Emission Category
(VEC) Definition
Motorcycles
(MC)
Motorcycles, Motorized Bike
Light Duty Gas
Vehicles (LDGV)
Automobiles and Multipurpose
Vehicles
Powered by gasoline, rated gross vehicle weight
less than 8,500 lbs, designed for transport of 12
or fewer passengers, no 4-wheel drive, no off-
road abilities (ex: passenger cars)
Light Duty Gas
Trucks (LDGT)
Trucks (3 and 4 ton)
Powered by gasoline, single unit 2 axle, rated
gross vehicle weight less than 8,500 lbs,
designed for cargo or transport of more than 11
passengers, off-road abilities (ex: most pickup
trucks, passenger and cargo vans, and four-wheel
drive vehicles)
Light Duty Diesel
Vehicles (LDDV)
Automobiles and Multipurpose
Vehicles
Powered by diesel, rated gross vehicle weight
less than 8,500 lbs, designed for transport of 12
or fewer passengers, no 4-wheel drive, no off-
road abilities (ex: passenger cars)
Light Duty Diesel
Truck (LDDT)
Trucks (3 and 4 ton)
Powered by diesel, single unit 2 axle, rated gross
vehicle weight less than 8,500 lbs, designed for
cargo or transport of more than 11 passengers,
off-road abilities (ex: most pickup trucks,
passenger and cargo vans, and four-wheel drive
vehicles)
-------
Heavy Duty Gas
Vehicles (HDGV)
Trucks (5 tons and greater),
Truck Tractors, Tractors,
Buses, Motor homes
Powered by gasoline, single unit 2 axle truck
with 6 or more tires, rated gross vehicle weight
greater than 8,500 lbs., (ex: large pickups and
vans, specialized trucks using pickup and van
chassis, larger "true" heavy duty trucks with
gross vehicle weight of 8 tons or more)
Heavy Duty
Diesel Vehicles
(HGDV)
Trucks (5 tons and greater),
Truck Tractors, Tractors,
Buses, Motorhomes
Powered by diesel, buses and combination trucks
(with single or multiple trailers), rated gross
vehicle weight greater than 8,500 lbs., (ex: large
trucks with gross vehicle weight ratings of 10 to
40 tons)
Within each VEC there were between one and six types of emission control
technologies (CT) that were modeled while some older vehicles have no emission controls.
Depending on the CT device, vehicles will produce greater or lesser emission. Generally,
with the progress of technology, modern vehicles have more advanced CTs that prevent large
emissions and older vehicles tend to have greater emissions.
The emission estimation method involved two broad steps. The first step was to
determine the distance traveled in Iowa by vehicles in each VEC. The second step built on
the first and involved using Iowa vehicle registration data along with VEC travel data to
determine emissions.
The first step determines how much travel was done by each VEC and on what type
of road systems. Data from the Federal Highway Administration (FHA) which detailed
travel in Iowa by VTC, distance and road type (functional system) was used (Jeff Patten,
Federal Highway Administration, personal contact August 15, 2002). The EIIP also
provided state specific fractions of travel that facilitated translation of estimates of travel by
VTC to estimates of travel by VEC (Noam Glick, ICF Consulting, personal contact, August
19, 2002). These translation fractions break up the distance traveled by a single VTC into the
distance traveled by one or more VECs. For instance, the VTC "buses" can be broken into
two separate VECs, "HDGV" and "HDDV." The EPA data estimated that in Iowa 5.99% of
bus travel is carried out in vehicles falling under the category HDGV and 94.01% is in buses
that are HDDVs (see columns K and L on the
It is recommended that for ease of understanding, the reader refer to the worksheet
worksheet titled "Worksheet to Calculate Distance Traveled by Vehicle Emission Category."
Total distance traveled on each road type (column A) was multiplied by the vehicle type
travel fraction (columns B,E,I,M,Q) for each road type to result in the estimation of total
distance traveled by VTC (columns C, F, J, N, R). The translation fractions were applied to
the resulting distances to sort the VTC by VECs (columns D,G,H,K,L,0,P,S).
One unique case to note is that Iowa, unlike other states, aggregates passenger cars
and light trucks when reporting to the FHA. For this reason, LDGV and LDGT had to be
aggregated, as were the similar diesel categories. The translation fraction applied was the
average of the fractions for the combine categories. The categories were separated below
with the use of EPA MOBILE5 default fractions found in the table 13.4-1 of the Emission
Inventory Improvement Program Volume VIII method (1998).
The methodology provides MOBILE5 default fractions that estimate the fraction of
total travel that each VEC performs. Light duty gas vehicles that are shown account for
63.6% of the total travel in the United States. Light duty gas trucks account for 26% of total
distance traveled. Together these two categories or all light duty gas sources travel 89.6% of
-------
the total distance traveled. Of this aggregate distance LDGV travel 71% and LDGT travel
29%. These percentages were applied to the aggregation of light duty gas sources to separate
them into LDGV and LDGT. A similar procedure was performed to separate diesel sources.
Once the first step was complete travel distances were established for each VEC, the
second step proceeded by obtaining the number and VTC of vehicles in Iowa in order to sort
by emission control technology (CT) and estimate the distance traveled by vehicles with each
CT. From that distance, emissions were calculated.
Obtaining data vehicle counts for the inventory year was not possible. Instead Iowa
vehicle registration data was requested and granted from the Iowa DOT. The department
provided unpublished records of 2002 vehicle registration counts. Names and personal
information of registrants were not included in the data. The 6.2 million registration records
were counted and tallied by vehicle type codes, weight class for trucks, fuel type, and model
year. Fifteen percent of registrations could not be counted because they were extensively
incomplete. However, not all of the 5.3 million records that were tallied were complete
either. Three percent of the 5.3 million records were missing information for either or both
of the attributes, fuel type and/or weight class. For these records adjustments were made
based on assumptions drawn from the complete records so they could be counted.
For the 144,159 records missing only fuel type, a proxy count was taken from the
complete records of the same vehicle type, model year and weight class for trucks. The
fraction of each fuel type was applied to the incomplete records and they were included in the
final counts. For the 16,175 truck records missing only weight class a similar procedure was
followed. The proxy counts included both the complete records and the records adjusted
with fuel proxy. For the 4,240 truck records missing both fuel type and weight class, the
same procedure was followed to first determine the fuel type then to determine the weight
class. For these proxies the previously adjusted records were also included.
Once tallies were made by VTC, fuel type, weight class (for trucks) and model years,
emissions could be estimated. Counts by Iowa DOT VTCs were matched up to VECs. For
each VEC a worksheet was designed to aid in emission estimation. At this point, emission
estimation is based on 2 attributes. These are 1) the emission control technologies (CT)
installed in vehicles and 2) the distance that is traveled by vehicles with each CT. There
were six recognized CTs for the model that was used. Generally, the CTs installed in new
vehicles are improved with time. Though, the improvement is not necessarily seen in CH4 or
N2O emissions. Some newer technologies actually increase these emissions. So it is
important to sort the vehicles by CT. This was done by first sorting by model year, and then
applying the EIIP provided CT fractions to determine how many vehicles have each CT.
From here the fraction of the number of total vehicles with each CT is applied to the total
vehicle kilometers traveled (VKT) (which was determined on the worksheet to calculate
distance traveled by vehicle emission categories) to determine the VKT traveled by vehicles
with each CT. This distance is then multiplied by the provided emission factor to estimate
emissions.
-------
2002 Total Emissions From Iowa Mobile Sources
ch4
N20
VEC
(g)
(g)
LDGV
1,148,130,892
1,110,994,809
LDDV
4,716,671
4,716,671
LDGT
790,726,592
598,213,335
LDDT
2,354,790
4,709,581
HDGV
160,144,800
123,485,973
HDDV
285,959,338
190,820,809
MC
89,406,938
1,857,626
Totals
Grams
2,481,440,023
2,034,798,804
Metric Tons
2,481
2,035
MTCE
14,212
172,033
-------
Worksheet to Calculate Distance Traveled by Vehicle Emission Categories
Column ID
A
B
D
G
H
Calculation
input
input
A x B
Cx 1.0
input
AxE
F x .9822
Fx 0.0179
input
A x I
Motorcycles (MC)
Passenger Cars/ Light Duty Trucks (LDG, LDD)
Buses (HDGV
IA Total
Vehicle Type
Total
Distance
Vehicle Type Travel
Total Distance
Distance
Distance
Vehicle Type
Total Distance
Distance
Travel
Distance
Traveled by
Fraction for
Traveled by
Traveled by
Traveled by
Travel Fraction
Traveled by
Traveled by
Fraction for
Traveled by
MCc
Passenger Cars and
Passenger Cars
Passenger
Passenger
for Busesb
Buses
Functional
Motorcycles b
Motorcycles
Light Trucksb
and Light Trucks
Cars/Lgt
Cars/Lgt
System3
Trucks as
Trucks as
LDGC
LDDC
million km
million km
million km
million km
million km
million km
million km
Rural Interstate
7,112
0.005
36
36
0.596
4,239
4,163
76
0.004
28
Arterial
8,442
0.010
84
84
0.811
6,846
6,724
123
0.003
25
Rural Minor Arterial
4,366
0.011
48
48
0.879
3,838
3,770
69
0.002
9
Collector*
6,072
0.011
67
67
0.879
5,337
5,242
96
0.002
12
Collector*
1,446
0.011
16
16
0.879
1,271
1,249
23
0.002
3
Rural Local*
2,619
0.011
29
29
0.879
2,302
2,261
41
0.002
5
Urban Interstate
3,490
0.007
24
24
0.825
2,879
2,828
52
0.003
10
Urban Other
Freeways and
Expressways
0
0.000
0
0
0.000
0
0
0
0.000
0
Urban Other
Principal Arterial
4,739
0.007
33
33
0.895
4,242
4,166
76
0.003
14
Urban Minor Arterial
4,658
0.006
28
28
0.971
4,523
4,442
81
0.004
19
Urban Collector**
1,405
0.006
8
8
0.971
1,364
1,340
24
0.004
6
Urban Local**
2,744
0.006
16
16
0.971
2,664
2,617
48
0.004
11
Vehicle Emission Category Distance Total;
390
MC
38,802
LDG
707
LDD
27,550
11,253
467
235
LDGV LDGT LDDV LDDT
a Data from Federal Highway Statistics 2000 Table VM-2, Vehicle Miles of Travel by Functional System
b Source: Jeff Patten, Federal Highway Administration, personal contact, August 15, 2002
information was previously found in table VM-4 of theHighway Statistics from the Federal Highway Administration.
0 Source: Noam Glick, ICF Consulting, personal contact, August 19, 2002
-------
Column ID K L M N O P Q R S
Calculation
J x 0.0599
J x 0.9401
input
A x M
Nx 0.4214
Nx 0.5786
input
AxQ
Rx 1.0
, HDDV)
Single Unit Trucks (HDGV, HDDV)
Combination Trucks (HDDV)
Distance
Distance
Vehicle Type
Total Distance
Distance
Distance
Vehicle Type
Total Distance
Distance
Traveled by
Traveled by
Travel Fraction for
Traveled by
Traveled by
Traveled by
Travel Fraction
Traveled by
Traveled by
Buses as
Buses as
Single Unit 2 axle 6
Single Unit 2
Single Unit
Single Unit
for Combo
Combo Trucks
Combo Trucks
HDGV0
HDDV0
tire or more
axle 6 tire or
Trucks as
Trucks as
Trucksb
as HDDV0
Trucksb
more Trucks
HDGV0
HDDV0
million km
million km
million km
million km
million km
million km
million km
Rural Interstate
2
27
0.042
299
126
173
0.353
2,511
2,511
Arterial
2
24
0.055
464
196
269
0.121
1,021
1,021
Rural Minor Arterial
1
8
0.053
231
98
134
0.055
240
240
Collector*
1
11
0.053
322
136
186
0.055
334
334
Collector*
0
3
0.053
77
32
44
0.055
80
80
Rural Local*
0
5
0.053
139
58
80
0.055
144
144
Urban Interstate
1
10
0.028
98
41
57
0.137
478
478
Urban Other
Freeways and
Expressways
0
0
0.000
0
0
0
0.000
0
0
Urban Other
Principal Arterial
1
13
0.043
204
86
118
0.052
246
246
Urban Minor Arterial
1
18
0.015
70
29
40
0.004
19
19
Urban Collector**
0
5
0.015
21
9
12
0.004
6
6
Urban Local**
1
10
0.015
41
17
24
0.004
11
11
134
828
1,137
5,089
HDGV HDDV
HDGV HDDV
HDDV
-------
Demonstration of Calculations for Estimation of CH4 and N20 Emissions from Mobile Sources
Fraction of Vehicles with Control Technologies (CT)
Number of Vehicles with each CT
Column ID
A
B
C
D
E
calculation
input
input
input
A x B
A x C
Model Year
#-#
#-#
2000 Iowa
Distribution
Uncontrolled
Non-Catalyst
Uncontrolled
Non-Catalyst
(# vehicles)
#
#
#
#
#
#
Total # Vehicles SUM column A row ID calculation row ID calculation
# MC with each CT
F
SUM column D
G
SUM column E
Fraction of MC with each CT
H
F / SUM column A
I
G/ SUM column A
Vehicle Kilometers Traveled (VKT) by MC
J (input)
VKT by CT
K
Hx J
L
Ix J
CH4 Emission Factor (g CH4/VKT)
M
input
N
input
CH4 Emissions (g CH4)
O
KxM
P
LxN
N20 Emission Factor (g N20/VKT)
Q
input
R
input
N20 Emissions (g N20)
s
KxQ
T
LxR
Total Emissions from Vehicles
Column ID
U
V
W
calculation
Sum Emissions
U/1,000,000
conversion
(S)
(metric tons)
(MTCE)
Total CH4 Emissions
O + P
V x 21 x (12/44)
Total N20 Emissions
S + T
V x 310 x (12/44)
-------
2002 Emissions from Motorcycles (MC)
Fraction of MC with Control Technologies (CT)
Number of MC with each CT
2000 Iowa
Uncontrolled
Non-Catalyst
Uncontrolled
Non-Catalyst
Model Year
Distribution
(# vehicles)
1995 and before
154,840
1.00
154,840
1996 and after
47,928
1.00
47,928
Total # Vehicles 202,768
# MC with each CT
154,840
47,928
Fraction of MC with each CT
0.76
0.24
Vehicle Kilometers Traveled (VKT) by MC
389,960,000
VKT by CT
297,785,678
92,174,322
CH4 Emission Factor (g CH4/VKT)
0.260
0.130
CH4 Emissions (g CH4)
77,424,276
11,982,662
N20 Emission Factor (g N20/VKT)
0.005
0.004
N20 Emissions (g N20)
1,488,928
368,697
Total Emissions from Motorcycles
L
(g) (metric tons)
(MTCE)
Total CH4 Emissions
89,406,938 89
512
Total N20 Emissions
1,857,626 2
157
-------
2002 Emissions from Light Duty Gas Vehicles (LDGV)
Model Year
2002 Iowa
Distribution
Fraction of Vehicles with Control Technologies (CT)
Number of Vehicles with each CT
Uncontrolled
Non-catalysi
Control
Oxidation
Catalyst
Tier 0:
3-way
Catalyst
Tier 1:
3-way
Catalyst
Uncontrolled
Non-catalyst Control
Oxidation Catalyst
Tier 0: 3-way
Catalyst
Tier 1: 3-way
Catalyst
(# vehicles)
1972 and before
111,067
1.00
111,067
1973-1974
21,576
1.00
21,576
1975
10,716
0.20
0.80
2,143
8,573
1976-1977
45,545
0.15
0.85
6,832
38,713
1978-1979
82,946
0.10
0.90
8,295
74,651
1980
32,686
0.05
0.88
0.07
1,634
28,764
2,288
1981
36,049
0.15
0.85
5,407
30,642
1982
40,557
0.14
0.86
5,678
34,879
1983
62,426
0.12
0.88
7,491
54,935
1984-1993
1,666,775
1.00
1,666,775
1994
177,083
0.60
0.40
106,250
70,833
1995
193,392
0.20
0.80
38,678
154,714
1996 and after
1,066,980
1.00
1,066,980
Total # vehicles 3,547,798
Number of Vehicles with each CT
111,067
40,480
169,278
1,934,447
1,292,527
Fraction of Vehicles with each CT
0.03
0.01
0.05
0.55
0.36
Vehicle Kilometers Traveled (VKT) by HDGV
27,549,581,599
VKT by CT
862,464,885
314,337,309
1,314,484,916
15,021,487,720
10,036,806,769
CFfj Emission Factor (g CFt/VKT)
0.135
0.120
0.070
0.040
0.030
CFfj Emissions (g CFI4)
116,432,759
37,720,477
92,013,944
600,859,509
301,104,203
N20 Emission Factor (g lS^O/VKT)
0.010
0.010
0.032
0.051
0.029
N20 Emissions (g N2O)
8,624,649
3,143,373
42,063,517
766,095,874
291,067,396
Total Emissions from Light Duty Gas Vehicle:
1 (g)
(metric tons)
(MTCE)
Total CH4 Emissions
1,148,130,892
1,148
6,576
Total N20 Emissions
1,110,994,809
1,111
93,930
-------
2002 Emissions from Light Duty Gas Trucks (LDGT)
Model Year
2002 Iowa
Distribution
Fraction of Vehicles with Control Technolo
2,ies (CT)
Number of Vehicles with each CT
Uncontrolled
Non-catalyst
Control
Oxidation
Catalyst
Tier 0: 3-way
Catalyst
Tier 1: 3-way
Catalyst
Uncontrolled
Non-catalyst
Control
Oxidation
Catalyst
Tier 0: 3-way
Catalyst
Tier 1: 3-way
Catalyst
(# vehicles)
1972 and before
63,375
1
63,375
1973-1974
22,089
1
22,089
1975
13,834
0.3
0.7
4,150
9,684
1976
20,203
0.2
0.8
4,041
16,162
1977-1978
54,698
0.25
0.75
13,675
41,024
1979-1980
59,514
0.2
0.8
11,903
47,612
1981
18,857
0.95
0.05
17,914
943
1982
20,937
0.9
0.1
18,843
2,094
1983
27,306
0.8
0.2
21,845
5,461
1984
33,972
0.7
0.3
23,781
10,192
1985
35,264
0.6
0.4
21,158
14,106
1986
40,347
0.5
0.5
20,174
20,174
1987-1993
317,292
0.05
0.95
15,865
301,428
1994
51,386
0.6
0.4
30,832
20,555
1995
46,159
0.2
0.8
9,232
36,927
1996 and after
308,354
1
308,354
Total # Vehicles
1,133,589
Number of Vehicles with CT
63,375
55,857
254,061
394,460
365,835
Fraction of Vehicles with each CT
0.06
0.05
0.22
0.35
0.32
Total Vehicle Kilometers Traveled (VKT) by HDGV
11,252,646,005
VKT by CT
629,100,182
554,468,809
2,521,955,642
3,915,633,678
3,631,487,694
CH4 Emission Factor (g CLL/VKT)
0.135
0.140
0.090
0.070
0.035
CH4 Emissions (g CH4)
84,928,525
77,625,633
226,976,008
274,094,357
127,102,069
N20 Emission Factor (g N20/VKT)
0.012
0.012
0.042
0.085
0.040
N20 Emissions (g N20)
7,549,202
6,653,626
105,922,137
332,828,863
145,259,508
Total Emissions from Light Duty Gas Truck:
L
(g)
(metric tons)
(MTCE)
Total CH4 Emissions
790,726,592
791
4,529
Total N20 Emissions
598,213,335
598
50,576
-------
2002 Emissions from Light Duty Diesel Vehicles (LDDV)
Fraction of Vehicles with Control Technologies (CT)
Number of Vehicles with each CT
2002 Iowa
Uncontrolled
Moderate
Advanced
Uncontrolled
Moderate
Advanced
Model Year
Distribution
(# vehicles)
1982 and before
4,810
1.00
4,810
1983-1995
5,270
1.00
5,270
1996 and after
720
1.00
720
Total # vehicles 10,800
Number of Vehicles with each CT
4,810
5,270
720
Fraction of Vehicle with each CT
0.45
0.49
0.07
Vehicle Kilometers Traveled (VKT) by HDGV
471,667,111
VKT by CT
210,052,428
230,154,489
31,460,195
CH4 Emission Factor by CT (g/VKT)
0.010
0.010
0.010
CH4 Emissions (g CH4)
2,100,524
2,301,545
314,602
N20 Emission Factor by CT (g/VKT)
0.010
0.010
0.010
N20 Emissions (g N20)
2,100,524
2,301,545
314,602
Total Emissions from Light Duty Diesel Vehicles
| (g) (metric tons) (MTCE)
Total CH4 Emissions 4,716,671 5 27
Total N20 Emissions 4,716,671 5 27
Vehicle Kilometers for Iowa were estimated for an aggregate of Automobiles and Light Duty Trucks
so from VKT by LDD
-------
2002 Emissions from Light Duty Diesel Trucks (LDDT)
Fraction of vehicles with Control Technologies (CT)
Number of vehicles with each CT
2002 Iowa
Uncontrolled
Moderate
Advanced
Uncontrolled
Moderate
Advanced
Model Year
Distribution
(# vehicles)
1982 and before
5,063
1.00
5,063
1983-1995
21,852
1.00
21,852
1996 and after
12,453
1.00
12,453
Total # vehicles 39,369
Number of Vehicles with each CT
5,063
21,852
12,453
Fraction of Vehicles with each CT
0.13
0.56
0.32
Vehicle Kilometers Traveled (VKT) by LDDT
235,479,027
VKT by CT
30,285,344
130,705,497
74,488,186
CH4 Emission Factor (g CH4/VKT)
0.010
0.010
0.010
CH4 Emissions (g CH4)
302,853
1,307,055
744,882
N20 Emission Factor (g N20/VKT)
0.020
0.020
0.020
N20 Emissions (g N20)
605,707
2,614,110
1,489,764
Total Emissions from Light Duty Diesel Trucks
| (g) (metric tons) (MTCE)
Total CH4 Emissions 2,354,790 2 13
Total N20 Emissions 4,709,581 5 398
-------
2002 Emissions from Heavy Duty Gas Vehicles (HDGV)
Fraction of Vehicles with Control Technologies (CT)
Number of Vehicles with each CT
2002 Iowa
Uncontrolled
Non-catalyst
Oxidation
Tier 0
Uncontrolled
Non-catalyst
Oxidation Catalyst
Tier 0
Model Year
Distribution
Control
Catalyst
3-way
Control
3-way Catalyst
(# vehicles)
1981 before
70,482
1.00
70,482
1982-1984
10,377
0.95
0.05
9,858
519
1985-1986
7,527
0.95
0.05
7,151
376
1987
3,882
0.70
0.15
0.15
2,717
582
582
1988-1989
10,065
0.60
0.25
0.15
6,039
2,516
1,510
1990 and after
51,574
0.45
0.30
0.25
23,208
15,472
12,893
Total # vehicles 153,906
Number of Vehicles with each CT
80,339.70
39,115.41
19,465.88
14,985.45
Fraction of Vehicles with each CT
0.52
0.25
0.13
0.10
Vehicle Kilometers Traveled (VKT) by HDGV
836,722,359
VKT by CT
436,771,988
212,653,462
105,827,510
81,469,399
CH4 Emission Factor (g/VKT;
0.270
0.125
0.090
0.075
CH4 Emissions (g CH4)
117,928,437
26,581,683
9,524,476
6,110,205
N20 Emission Factor (g/VKT;
0.027
0.026
0.870
0.173
N20 Emissions (g N20)
11,792,844
5,528,990
92,069,933
14,094,206
Total Emissions from Heavy Duty Gas Vehicles
1 (g)
(metric tons)
(MTCE)
Total CH4 Emissions
160,144,800
160
917
Total N20 Emissions
123,485,973
123
10,440
-------
2002 Emissions from Heavy Duty Diesel Vehicles (HDDV)
Fraction of Vehicles with Control Technologies (CT
Number of Vehicles with each CT
2002 Iowa
Uncontrolled
Moderate
Advanced
Uncontrolled
Moderate
Advanced
Model Years
Distribution
(# vehicles)
1982 and before
19,150
1.00
19,150
1983-1990
44,396
1.00
44,396
1991 and after
103,274
1.00
103,274
Total # vehicles 166,820
Number of Vehicles with each CT
19,150
44,396
103,274
Fraction of Vehicles with each CT
0.11
0.27
0.62
Vehicle Kilometer Traveled (VKT) by HDDVs
6,360,693,641
VKT by CT
730,186,198
1,692,786,856
3,937,720,587
CH4 Emission Factor by CT (g/VKT)
0.060
0.050
0.040
CH4 Emissions (g CH4)
43,811,172
84,639,343
157,508,823
N20 Emission Factor by CT (g/VKT)
0.030
0.030
0.030
N20 Emissions (g N20)
21,905,586
50,783,606
118,131,618
Total Emissions from Heavy Duty Diesel Vehicles
1 (S)
(metric ton)
(MTCE)
Total CH4 Emissions
285,959,338
286
1,638
Total N20 Emissions
190,820,809
191
16,133
-------
MOBILE COMBUSTION SOURCES PARS SCORES
DARS SCORES: CH4 EMISSIONS FROM HIGHWAY VEHICL]
ES
Emission
Factor
Attribute
Explanation
Activity
Data
Attribute
Explanation
Emission
Score
Measurement
8
The emission factors are based on a
sophisticated model that uses measured inputs.
6
Vehicle miles traveled are estimated based on
sampling.
0.48
Source Specificity
10
The emission factors were developed
specifically for the various types ofvehicles
and their emission control technologies.
9
Vehicle miles traveled are very closely correlated to
the emission activity.
0.90
Spatial
Congruity
7
Emission factors were developed for the U.S.,
not for individual states; spatial variability is
expected to be moderate.
10
States use state-level data on vehicle miles traveled.
0.70
Temporal
Congruity
7
Emission factors were developed based on
assumptions reflecting conditions at one point
during the year; temporalvariability is
expected to be low to moderate.
7
As of late 1998, FHWA data on vehicle miles
traveled were available only for 1994; temporal
variability over a four-year period is expected to be
low to moderate.
0.49
Composite Score 0.64
-------
DARS SCORES: N20 EMISSIONS FROM GASOLINE FUELED HIGHWAY VEHICLES
Emission
Factor
Attribute
Explanation
Activity
Data
.¦J (tribute
Explanation
Emission
Score
Measurement
8
The emission factors arc based on
measurement of emissions from a small
sample of vehicles.
6
Vehicle miles traveled are estimated based on
sampling.
0.48
Source Specificity
10
The emission factors were developed
specifically for the various types of vehicles
and their emission control technologies.
9
Vehicle miles traveled are very closely correlated to
the emission activity.
0.90
Spatial
Congruity
7
Emission factors were developedfor the U.S.,
not for individual states; spatial variability is
expected to be moderate.
10
States use state-level data on vehicle miles traveled.
0.70
Temporal
Congruity
7
Emission factors were developed based on
testing over less than a full year; temporal
variability is expected to be low to moderate.
7
As of late 1998, FHWA data on vehicle miles
traveled were available only for 1994; temporal
variability over a four-year period is expected to be
low to moderate.
0.49
Composite Score 0.64
-------
DARS SCORES: N20 EMISSIONS FROM DIESEL FUELED HIGHWAY VEHICLES
Emission
Factor
Attribute
Explanation
Activity
Data
.¦J (tribute
Explanation
Emission
Score
Measurement
8
The emission factors are based on a
sophisticated model.
6
Vehicle miles traveled are estimated based on
sampling.
0.48
Source Specificity
7
The emission factors were developed
specifically for the various types of vehicles,
but assumed moderate control for all vehicles;
the expected variability is low to moderate.
9
Vehicle miles traveled are very closely correlated to
the emission activity.
0.63
Spatial
Congruity
7
Emission factors were developedfor Europe,
not for states in the U.S.; spatial variability is
expected to be moderate.
10
States use state-level data on vehicle miles traveled.
0.70
Temporal
Congruity
7
Emission factors were developed based on
assumptions reflecting conditions at one point
during the year; temporal variability is
expected to be low to moderate.
7
As of late 1998, FHWA data on vehicle miles
traveled were available only for 1994; temporal
variability over a four-year period is expected to be
low to moderate.
0.49
Composite Score 0.58
-------
IV. CH4 from Natural Gas Systems
The method used is a simplified version of a complex methodology used by the EPA
for the U.S. inventory. The complex method very accurately takes into account
approximately 100 components of the entire natural gas system including the areas of natural
gas production, processing, storage, transmission and distribution. The simplified method
aggregates these components into a few fundamental activities. In doing so, the accuracy of
the method is hindered. Because Iowa is a state that does not produce or process natural gas,
the method admittedly over estimates CH4 releases by assigning some emissions from
production and processing activities. It is uncertain to what degree the overestimation is and
there is no feasible method that can reduce this inaccuracy. At best, the method can deduce a
maximum emission scenario.
According to the American Gas Association, Iowa has 25,131 miles of transmission
and distribution pipelines. The Iowa Utilities Board provided reports showing that Iowa has
16,229 miles of main distribution pipeline broken down by the various fortifications (Cynthia
Munyon, Iowa Utilities Board, personal contact, November 8, 2002). State specific data was
unavailable on numbers of transmission and storage stations. These were estimated from the
methodology based on factors for the number of stations per mile of pipeline. The number of
customer connections was also provided by the IUB. These were broken down into protected
and unprotected steel connections based on EIIP fractions. It is estimated that about 13% of
customer connections are made of unprotected steel and about 47% are protected steel. The
remaining 40% are assumed to be plastic or copper. An EIIP provided emission factor was
applied to each of the variables listed in the worksheet to determine CH4 emissions.
-------
2000 CH4 Emissions from Natural Gas Transmission and Distribution
ABC D E F
input input AxB input (AorC)xD E x 21 x (12/44)
Method for
Estimated
Methane
Methane
Value a
Estimation
Value
Emission Factor
Emissions
Emissions
(units)
(units)
unit)
(metric tons CH4)
(MTCE)
Gas Transmission Emissions
Miles of Transmission Pipeline, L
8,902
0.68
6,053
34,669
Transmission Stations
L*.006
53.19
891
47,392
271,426
Storage Stations
L*.0014
12.08
914
11,041
63,236
LNG Storage Stations
914
0
0
Total
64,486
369,331
Gas Distribution Emissions
Miles of Distribution Pipeline, M
16,229
0.7
11,360
65,064
Miles Cast Iron Main Pipeline
41
4.63
190
1,087
Miles Unprotected steel main pipeline
256
2.16
553
3,167
Miles Protected steel main pipeline
M*.53
8,601.37
0.11
946
5,419
Miles Plastic main pipeline
M*.3
4,868.70
0.42
2,045
11,711
Customer Connections, H
862,331
0.014
12,073
69,143
Unprotected Steel Customer Connections
H*0.1246
107,446.44
0.033
3,546
20,307
Protected Steel Customer Connections
H*0.4656
401,501.31
0.0035
1,405
8,048
Total
32,118
183,947
Total
553,278
3 All values came from the Cynthia Munyon, Iowa Utilties Board, personal contact November 8, 2002
-------
NATURAL GAS SYSTEMS PARS SCORES
DARS SCORES: CH4 EMISSIONS FROM NATURAL GAS SYSTEMS
Emission
Factor
Attribute
Explanation
Activity
Data
Attribute
Explanation
Emission
Score
Measurement
7
The factors were based on
measurement of emissions from a
small sample of sources over
typical loads
9
Data on each activity are based on
intermittent measurement
0.63
Source
Specificity
10
An emission factor was developed
for each of approximately 100
components of natural gas systems
which were identified as methane
emission sources
9
The activities measured were
identified as methane emission
sources and thus are highly correlated
to emissions
0.90
Spatial
Congruity
8
The factors were developedfor the
entire U.S., not for any state.
Assuming variability within the
U.S. is low to moderate.
9
Activity Data are sometimes scaled
based on national ratios of one
activity to another; spatial variability
is expected to be low.
0.72
Temporal
Congmity
8
The emission factors are based on
measured emissions over a period
of less than a year. However,
temporal variability is expected to
be low.
10
States use activity data from a given
year to estimate emissions in that
year.
0.80
Composite Score
0.76
-------
DARS SCORES: C02 EMISSIONS FROM OIL SYSTEMS
Emission
Factor
Attribute
Explanation
Activity
Data
.¦J (tribute
Explanation
Emission
Score
Measurement
2
Because emissions are not measured,
the highest possible score is 5,
Because of the wide range for each
emission factor, we assigned a score
of 2.
10
Data on each activity are based on
continuous measurement.
0.20
Source Specificity
7
An emission factor was developed for
each activity (e.g., production,
refining, and distribution), but the
emission factor aggregates emissions
at a higher level than where they
occur.
7
The activities measured (production,
transportation, refining, and
consumption) are highly correlated to
emissions, but are aggregated at a higher
level than where emissions occur.
0.49
Spatial
Congruity
8
The factors were developed for the
entire U.S., not for any state.
Variability within the U.S. is
assumed to be low to moderate.
5
States use state-level activity data to
estimate statewide emissions, but these
data (e.g., oil refined) are poor proxies
for the desired activity level (e.g., oil
stored).
0.40
Temporal
Congruity
9
The emission factors are not based
on measured emissions over a
particular time frame. However, the
emission factors should not vary
significantly over the course of a
year, so the score is 9.
10
States use annual activity data to estimate
annual emissions.
0.90
Composite Score 0.50
-------
APPENDIX B
AGRICULTURE
I. N2O and CO2 from Agricultural Soils
N2O and CO2 from direct emissions from agricultural cropping practices
Application of synthetic and organic fertilizers. Emissions from fertilizer
application were estimated using data from the Iowa Fertilizer Distribution Reports from the
Iowa Department of Agriculture & Land Stewardship (IDALS). The semi annual reports
present quantities of commercial fertilizer sales and is based on inspection fees collected and
tonnage reports filed by licensees and therefore does not account for manure that is applied to
the farm where it was generated. The assumption was understood that data in these reports
are the best representation of quantities of commercial fertilizers applied in a given period.
Reported figures aggregate synthetic and organic fertilizers sold in Iowa by nutrient content.
Nitrogen application was calculated directly from the nutrient content and tonnage
distributed on the fertilizer worksheet provided below. It was not possible to segregate
figures for organic or synthetic fertilizer.
Before the emission calculation could be performed, the quantity of unvolatilized
nitrogen was determined. Organic fertilizers were assumed to volatilize 20% of the nitrogen
as NOx and NH3. For synthetics the volatilization was assumed to be 10%. It was assumed
that 100%) of fertilizer applied was synthetic with only 10%> loss of nitrogen to volatilization.
This is probably inaccurate, as a portion of the fertilizer is known to be organic. This
assumption will yield a maximum of direct N2O emissions. The emission factors provided
by the EIIP for synthetic and organic fertilizers were the same (0.0125 kg N2O-N/ kg
unvolatilized nitrogen). This factor was used to calculate emissions from total unvolatilized
nitrogen applied.
Field application of managed manure must account for only the nitrogen that remains
after emission and volatilization within the management system. In order to estimate the
nitrogen applied as managed manure, figures from the manure management section of this
inventory were used. In that section, it was calculated that a total of 2.1 x 108 kg of Kejdahl
nitrogen was excreted from the livestock populations in 2000. It is assumed that 20% of this
nitrogen was volatilized during management, leaving 1.7 x 108 kg nitrogen to be managed
and land applied. After application, it is assumed that another 20% volatilizes as NOx and
NH3 leaving 1.38 x 108 kg nitrogen that could be emitted. The emission factor used was that
for organic fertilizers (0.0125 kg N2O-N/ kg unvolatilized nitrogen).
Application of animal waste through daily spread operations. The EIIP estimates that 8%
of manure from dairy cows is applied through daily spread. Again, the unvolatilized nitrogen
must be determined and is based on population data, typical animal mass, Kjeldahl nitrogen
excretion factor, and a volatilization fraction. Population data was taken from the National
Agricultural Statistics Service (NASS) reports. Other parameters were provided by the EIIP.
From the sum of these parameters, N20 emissions were determined with an emission factor
also provided by EIIP for unvolatilized applied nitrogen from daily spread operations (0.0125
kg N20-N/ kg unvolatilized N).
-------
Incorporation of crop residues into the soil. N2O is emitted from the decomposition of crop
residues left in the fields. In order to estimate emissions from this source it was necessary to
estimate the quantity of residue nitrogen remaining after harvest. By using the annual
production of the specified crops quantity of crop residue biomass was determined. The mass
ratio of crop product to residue and a residue dry matter factor were used for this calculation.
Crop production data was taken from the NASS reports.
Some crop residues are burned, used in construction or as fodder. This is taken into
consideration by subtracting the fraction of biomass that is consumed for these "other uses."
Only 3% of corn, wheat, and soybean residues were assumed to be consumed by these other
activities. Factors for nitrogen content of residues were provided by the EIIP and are used to
determine the total nitrogen returned to the soil. Once this figure is known, emissions are
calculated using the emission factor 0.0125 kg N20-N/ kg N in crop residues.
Production of nitrogen-fixing crops. Cultivation of nitrogen fixing crops results in
emissions of N2O from the soil. To estimate soil nitrogen amendments from nitrogen
fixation, aboveground dry matter nitrogen was assumed to be a reasonable proxy. Legume
production data was taken from NASS reports. That data was multiplied by 1 + the mass
ratio of crop matter to residue matter, the fraction of dry matter in aboveground biomass, and
the fraction of nitrogen in nitrogen-fixing crops to yield subsurface nitrogen inputs. This
figure was then multiplied by the provided emission factor (0.0125 kg N20-N/ kg N inputs)
to calculate N2O emissions.
Cultivation of histosol soils. Histosols are rich and highly organic soils that become
mineralized releasing N20 as they are cultivated. According to figures from the Natural
Resources and Conservation Service (NRCS), Iowa has 64,477 acres of histosols and 95% of
these are in cultivation. The EIIP estimates 8 kg N20-N/ha-yr is released from the
cultivation of these soils.
Direct N2O emissions from animal production
Direct deposition of animal wastes onto agricultural soils. This section accounts for N2O
emissions from the direct deposition of animal waste onto pasture, range and paddock. Iowa
2000 average animal populations and state specific manure management fractions along with
typical animal mass and Kjeldahl nitrogen excretion figures were used to calculate the
quantity of nitrogen that was excreted directly to the land. From this, the 20% fraction of
nitrogen that volatilizes was discounted. Then emissions were calculated with the provided
emission factor (0.02 kg N20-N/kg N).
Indirect N2O emissions from nitrogen applied to agricultural soils
NOx and NH3 volatilization from fertilizer application. Volatilization of NOx and NH3 from
fertilizer and manure results in atmospheric redeposition of nitrogen onto the soil, followed
by denitrification and emissions of N2O. This section accounts for these indirect emissions.
First, the amount of nitrogen that volatilized from fertilizer applications was
determined with the same assumption that was used before to calculate direct emissions from
agricultural cropping practices. That is, 100% of the fertilizer applied is synthetic and from
-------
that 10% of the nitrogen volatilizes. This assumption could undercount as much as 45 tons of
nitrogen volatilizing per year. Organic fertilizers volatilize by a greater amount, 15% of
nitrogen is lost as NOx and NH3. As before, fertilizer data was taken from the Iowa Fertilizer
Distribution Report from IDALS.
The other source of nitrogen volatilization is manure. Year 2000 average animal
population data from the NASS, typical animal mass and a kjeldahl nitrogen excretion factor
were used to determine the total amount of nitrogen available for volatilization from
livestock manure. Of this total, 20% was considered to be volatilized.
The quantity of volatilized nitrogen from these two indirect sources was then
multiplied by an emission factor provided by EIIP. For every 1 kg of nitrogen volatilized as
NH3 and/or NOx, 01 kg of N2O-N are emitted. Later, the resulting N2O-N figure was
converted to kg N20 and MTCE.
Nitrogen leaching and runofffrom agricultural fields. Much of the nitrogen applied to
fields is lost to leaching and runoff. This addition of nitrogen to the ground and surface
water results in elevated N20 emissions from denitrifiying bacteria. The EIIP estimates that
30%) of the applied nitrogen is lost to leaching and runoff. This loss is calculated from the
unvolatilized applied nitrogen discussed above. The EIIP provides the same emission factor
(0.025 kg N20-N/kg N) for synthetic and organic fertilizers and manure nitrogen lost to
leaching and runoff. The sum of these parameters yields the total kg N20-N emissions.
Later, this total was converted to kg N2O emissions then MTCE N2O.
C02 from agricultural application of lime (CaC03)
Agricultural application of lime. Agricultural application of limestone and dolomite to the
soils results in a release of C02 over several years. Some of the carbon is also leached to the
groundwater, however, in this methodology that avenue is not addressed and it is assumed
that 100%) of the carbon is converted to CO2. It is also assumed that all of the carbon is
released in the year of application.
Total limestone and dolomite consumption data for 1997 through 2000 was taken
from the United States Geological Survey Minerals Yearbook" Stone, Crushed" Reports
(2000). Total consumption figures were used to estimate the portion of agricultural lime
consumption as limestone and dolomite. Because year 2000 data on dolomite consumption
was withheld, the average of the 3 previous years was used to determine year 2000 dolomite
consumption.
The EIIP methodology suggests that the USGS report does not report all agricultural
uses of lime specifically. Instead, there is a general category labeled "other uses" that
accounts for some unspecified agricultural lime uses. The quantity of these unspecified
agricultural uses for limestone and dolomite are estimated from the proportion of Iowa's
specified agricultural use to the U.S. total specified use. That ratio is then multiplied by the
U.S. unspecified use (from the "other uses" category). This calculation is performed
separately for each limestone and dolomite. This assumes that the fraction of U.S. specified
use that is consumed by Iowa specified agricultural uses equals the fraction of the U.S.
unspecified uses consumed by Iowa unspecified agricultural uses.
Once the total quantities for agricultural uses of limestone and dolomite were
determined, the totals were multiplied by the carbon to lime ratio, to determine the carbon
-------
available for emission as CO2. It was assumed that 12% of the composition of limestone is
carbon and 13% of dolomite is carbon. Since other inventories have reported in units of C02
equivalents, the carbon weight was multiplied by the ratio of CO2 to carbon (44/12) to report
consistent units with those reports. This inventory reports in the units of metric tons carbon
equivalents (MTCE), therefore the CO2 equivalents units were multiplied by the ratio of
carbon to C02 (12/44) to yield MTCE.
-------
2000 Summary of Greenhouse Gas Emissions from Agricultural Soils
Gas Total Emissions from
Type of Emission Emitted Total Gas Emissions Agricultural Soils
(kg gas/yr) (MTCE)
Direct Emissions from Ag
Cropping Practices N20 54,546,632 4,611,670
Direct Emissions from Animals
on Ag Soil N20 3,895,373 329,336
Indirect Emissions from
Nitrogen applied to Ag Soils N20 15,806,296 1,336,350
Total N20 6,277,356
Total Emission Liming Soils C02 815,998,955 222,545
Total C02 222,545
Total Greenhouse Gases
6,499,902
-------
2000 Direct N20 Emissions from Agricultural Cropping Practices
Application of Synthetic + Organic Fertilizers Organic Fertilizer Application (Manure)
A
B
C
D
E
F
input
input
AxB
input
input
DxE
Total Use
Fraction of Synth
(calculated on
and Org Fertilizer Unvolatilized Applied N
"fertilizer"
N not Emitted as
from Synthetic and Org.
Applied N from Animal
1- Fraction that
Unvolatilized Applied ]N
worksheet)
NOx and NH3
Fertilizer
Manure
Volatilizes
from Animal Manure
(kg N/yr)
(Kg N/ Kg N)
(kg N/yr)
| (kg N/yr) |
(kg N/yr)
897,772,281.43
0.9
807,995,053.29
173,005,002
0.8
138,404,002
G
H
J
K
L
M
input
input
Fraction of Manure
input
input
G x H x (1/1000) x J x 365
input
KxL
Unvolatilized Applied
IA Average
Managed as Daily
Typical Animal
Total Kjeldahl N
1- Fraction that
N from Daily Spread
Animal Type
Population 2000
Spread
Mass (TAM)
Kjeldahl Nitrogen Excretion Factor
Excreted
Volatilizes
Operations
(head)
(kg/head)
(kg/day/1000 kg TAM)
(kg/yr)
(kg N/yr)
Milk Cows
215,702
0.08
640
0.45
1,813,968
0.8
1,451,174
Incorporation of Crop Residue into the Soil
N
O
P
Q
R
S
T
U
input
N x conversion factor
input
Mass Ratio
input
OxPxQ
input
input
Rx (S-l) x T
Total N Returned to
Crop Residue
Production
Production
Crop/Residue
Residue Dry Matter
Crop Residue Biomass
Fraction for Other UsesN Content of Residue
Soil
(Bushels)
(kg)
(kg)
(kg)
(kg N/ kg dry biomass)
(kg N/yr)
Corn for Grain
1,728,000,000
43,891,200,000
1
0.4
17,556,480,000
0.03
0.0094
160,079,985
All Wheat
846,000
23,011,200
1.3
0.83
24,829,085
0.03
0.0058
139,688
Soybeans for Beans
464,580,000
12,636,576,000
2.1
0.86
22,821,656,256
0.03
0.03
664,110,197
Dry Edible Beans 1/
98,472
2,678,438
2.1
0.86
4,837,260
0
0.03
145,118
Barley
260,264
5,673,755
1.2
0.93
6,331,911
0
0.0077
48,756
Sorghum
84,584
2,148,434
1.4
0.91
2,737,104
0
0.0108
29,561
Oats
14,293,977
177,960,014
1.3
0.92
212,840,176
0
0.007
1,489,881
Rye
55,205
1,402,207
1.6
0.9
2,019,178
0
0.0048
9,692
Millet
1,602
39,249
1.4
0.89
48,904
0
0.007
342
Other Crops 2/ 0
| 826,053,219.971
1/ units are in hundredweight
21 Other crops are not reported in Iowa. Includes Peanuts and nuts, dry edible peas, austrian winter peas, lentils, wrinkled seed peas & rice.
3/ Fraction burned, used as fuel, fodder or construction
Production of N-fixing Crops
V W X Y Z A1 B1
input V x conversion factor Wx0.85 input input input XxYxZxAl
-------
N fixing crops
Soybean
Dry edible beans
Hay, All 5/
Other N fixing Crops 6/
Production
Units
464,580,000 Bushel
98,472 CWT
6,000,000 tons
0
Production
Production
1 + Mass Ratio Crop:
Residue
| kg/year | kg dry biomass/yr |
Fraction Dry Matter Fraction of N in N- N input from N-fixing
Aboveground Biomass fixing Crops Crops
12,636,576,000
2,678,438
5,443,108,800
10,741,089,600.00
2,276,672.64
4,626,642,480.00
3.1
3.1
1.0
5/ Includes Alfalfa, Red clover, Wht Clvr, Birdsfoot Trefoil, Arrowleaf Clvr, Crimson Clvr, Hairy Vetch
6/ Other N fixing Crops are not reported in Iowa. Includes Peanuts, dry edible peas, austrian winter peas, lentils, wrinkled seed peas.
0.87
0.86
0.85
I (kg N/ kg dry biomass) | (kg N/yr) |
0.03
0.03
0.03
869,061,559.54
182,088.28
117,979,383.24
987,223,03 L05"l
Direct N20 Emissions from Cultivated Histosols
El
CI D1
input input
ClxDl
Area of Cultivated
Histosols Emission Factor for Direct Soil Emissions
Direct Emissions from Histosols
(hectares) | (kg N20-N/ha/yr)
(kg N20-N/yr)
24,676.50 8
197,412.00
Summary of Direct NoO Emissions
F1 G1 HI II J1
input
input
F1 xGl
HI x (44/28)
(11/1000) x 310 x (12/44)
Emission Factor for
Amount of N input
Direct Emissions
Direct Soil Emissions
Direct Soil Emissions
Direct Soil Emissions
| (kg N/yr) |
(kg-N20-N/kg N) |
(kg N20-N/yr)
| (kg N20/yr)
| (MTCE) |
Unvolatilized Applied N from Synthetic Fertilizer & Organic
Fertilizer (column C)
807,995,053
0.0125
10,099,938
15,871,331
1,341,849
Unvolatilized Applied N from Manure (F)
138,404,002
0.0125
1,730,050
2,718,650
229,850
Unvolatilized Applied N Daily Spread Operations (M)
1,451,174
0.0125
18,140
28,505
2,410
N in Crop Residues Returned to Soils (U)
826,053,220
0.0125
10,325,665
16,226,045
1,371,838
N fixation from N-fixing Crops (Bl)
987,223,031
0.0125
12,340,288
19,391,881
1,639,495
Cultivation of Histosols (El)
197,412
310,219
26,228
|Subtotal
1
34,514,0811
54,546,632 |
4,585,442 |
-------
2000 Direct N20 Emissions from Animal Production on Agricultural Soil
A BCD E F
input
input
input
input
B x (C/1000) xD x
input
Manure
Typical
Fraction of
2000 Iowa
Deposited on
Animal
Kjeldahl N per Total Kjeldahl N Excreted N
Average
Pasture, Range
Mass
day per 1000
Excreted by
that
Animal Type
Population
and Paddock
(TAM)
kg mass
Animal Type
volatilizes
(head)
(kg/head)
(kg/day/1000 kg)
(kg/yr)
Beef Cows
1,030,817
0.87
500
0.34
55,647,096
0.2
Breeding Bulls
67,054
0.87
680
0.34
4,922,947
0.2
Growing Calves
712,984
0.87
181
0.34
13,933,151
0.2
Growing Steers&
1,173,609
0.87
387
0.34
49,037,174
0.2
Steers & Heifers On
563,345
0.87
387
0.34
23,538,373
0.2
Sheep
300,944
0.99
70
0.42
3,197,136
0.2
Goats 1/
12,275
1.0
64
0.42
120,432
0.2
Equine 1/ 2/
100,000
0.92
450
0.3
4,533,300
0.2
G
H
I
J
K
E x (1-F)
input
GxH
I x (44/28)
J x (12/44) x 310
Unvolatilized
Total
Applied N from
Direct N20
waste deposited
Emissions
on Pasture,
Emission factor
from
Total Direct
Range, and
for Direct
Animal
Emissions of
Total Direct
Paddock
Emissions
Production
N20
Emissions
(kg N)
(kg N20-N/kg N)
(kg N20-N)
(kg N20/yr)
MTCE
Beef Cows
44,517,677
0.02
890,354
1,399,127
118,290
Breeding Bulls
3,938,357
0.02
78,767
123,777
10,465
Growing Calves
11,146,521
0.02
222,930
350,319
29,618
Growing Steers& Heifers
39,229,739
0.02
784,595
1,232,935
104,239
Steers & Heifers On
18,830,699
0.02
376,614
591,822
50,036
Sheep
2,557,709
0.02
51,154
80,385
6,796
Goats 1/
96,346
0.02
1,927
3,028
256
Equine 1/ 2/
3,626,640
0.02
72,533
113,980
9,637
123,943,687
Total
3,895,373
329,336
1/ Not calculated off of an average population. Info unavailable for calc of avg.
2/ Category is comparable to "Horses/Mules" for 1990 inventory includes horses, ponies, mules, burros, and donkeys
-------
2000 Indirect Emissions from Nitrogen Applied to Agricultural Soils
MX and NFU Volatilization from Fertilizer Application
A B C
input input A x B
Total Application of Fraction of Fertilizer N applied Volatilized N from
Fertilizer in IA that Volatilizes Fertilizer
| (kg N/ yr) | | (kg N/yr) |
Synthetic + Organic 897,772,281 0.1 89,777,228
Total N excreted that volatilizes 89,777,228
MX and NFU Volatilization from Livestock Manure
- • •— """ - ' ¦- .1
D
E
F
G
input
input
input D x (E/1000) x F x 365
Typical Animal
Kjeldahl N per day per
Total Kjeldahl N
Animal Type
2000 Iowa Average Population
Mass (TAM)
1000 kg mass
Excreted by Animal
1
(head)
(kg/head) |
(kg/day/1000 kg mass)
(kg/yr)
Dairy Cattle
215,702
640
0.45
22,674,578
Beef Cows
1,030,817
500
0.34
63,962,195
Breeding Bulls
67,054
680
0.34
5,658,560
Growing Calves
712,984
181
0.34
16,015,117
Growing Steers & Heifers
1,173,609
387
0.34
56,364,567
On Feed Steers & Heifers
563,345
387
0.34
27,055,601
Market Swine
14,209,836
46
0.52
124,063,236
Breeding Swine
1,160,000
181
0.52
39,850,408
Chickens: Layers
27,282,126
1.6
0.84
13,383,520
Chickens: Broilers
17,200,000
0.7
1.1
4,834,060
Turkeys
7,100,000
3.4
0.62
5,462,882
Sheep
300,944
70
0.42
3,229,430
Goats
12,275
64
0.42
120,432
Horses/Mules
100,000
450
0.3
4,927,500
Total kg N excreted
387,602,087
Volatilization factor
0.2
Total
N excreted that volatilizes
77,520,417
Indirect MO Emissions Resultina from Atmospheric Redeposition of NCI and NFU
H
I
J
c
input
H x I
Source
Total Amount of N that Volatilizes
Emission Factor
N20 Emissions
(kg nh3
-N + NOx-N/kg N)
(kg N20-N/kg NH3-N + NOx-N)
(kg N20-N/yr)
Fertilizer
89,777,228
0.01
897,772
Livestock Excretion
77,520,417
0.01
775,204
Subtotal
1,672,976
Emissions from Leachina and Runoff of Mtroaen from Aaricultural Fields
K
L
M
N
O
(Ax 0.9) or (Gx 0.8)
input
KxL
input
MxN
Unvolatilized
Applied N from
Fraction of Unvolatilized N
N Leaching or
Total N20-N
Fertilizer
that Leaches or Runs off
Running off
Emission Factor
Emissions
(kgN/yr) |
(kg N/yr) |
(kgN20-N/kgN)
(kg N20-N/yr)
Synth + Organic
807,995,053
0.30
242,398,516
0.025
6,059,963
Manure
310,081,669
0.30
93,024,501
0.025
2,325,613
Subtotal
8,385,575
Total Indirect N20 Emissions from Aaricultural Soils
P
Q
R
P x (44/28)
Q/ 1000 x 310 x (12/44)
Total Indirect N20
Total Indirect N20
Source of Indirect Emission
Indirect Soil Emissions
Emissions
Emissions
(kg N20-N/yr)
(kg N20/yr) |
MTCE
Atmospheric Deposition (J)
1,672,976
2,628,963
222,267
Leaching/Runoff (O)
8,385,575
13,177,333
1,114,084
Subtotal
15,806,296
1,336,350
-------
2000 C02 Emissions from Agricultural Lime Application
A
B
C
D
E
F
G
H
input
input
A + B
A/C
B/C
input
FxD
FxE
Total IA
IA total Ag Use
IA total Ag
IA total Ag
IA Total
IA Total
Limestone &
Fraction of Total
Fraction of Total
of Limestone &
Use as
use of
Year
Limestone 1/
Dolomite 1/
Dolomite
as Limestone
as Dolomite
Dolomite 2/
Limestone
Dolomite
(103 m tons)
(103 m tons)
(103 m tons)
fraction
fraction
(103 m tons)
(103 m tons)
(103m tons)
2000
40,100
withheld
40,100 (avg '97-99) 0.999
0.001
997
996
1
1999
42,000
53
42,053
0.999
0.001
1,300
1,298
2
1998
41,700
72
41,772
0.998
0.002
1,010
1,008
2
1997
37,200
41
37,241
0.999
0.001
896
895
1
Estimation of Unspecified Agricultural Uses of Limestone and Dolomite
I
J
K
L
M
N
O
P
Q
input
input
input
G/J
H/J
KxL
KxM
N + G
H + I
U.S. Total
U.S. Total
Ratio of IA
Ratio of IA
IA Specified +
IA Specified +
Specified Use of
Unspecified Use
Specified Ag
Specified Ag
IA Limestone
IA Dolomite
Unspecified
Unspecified
Total U.S. Use
Limestone &
of Limestone &
Limestone Use to
Dolomite Use to US
Uspecified Use
Uspecified Use
Limestone Ag
Dolomite Ag
2/
Dolomite 2/
Dolomite 2/
US specified Use
specified Use
for Ag
for Ag
Use
Use
(103 m tons)
(103 m tons)
(103 m tons)
fraction
fraction
(103 m tons)
(103 m tons)
(103 m tons)
(103 m tons)
2000
1,100,000
601,000
499,000
0.0017
0.0000023
827
1
1,823
2
1999
1,080,000
615,000
465,000
0.0021
0.0000027
982
1
2,280
3
1998
1,060,000
604,000
456,000
0.0017
0.0000029
761
1
1,769
3
1997
1,010,000
589,000
421,000
0.0015
0.0000017
640
1
1,535
2
4 year Average
1,852
3
1,851,805
2,527
Summary COo Emissions from Limestone and Dolomite Application to Agricultural Soi
R
S
T
u
V
Q x 1000
input
RxS
T x (44/12)
U x (12/44)
IA Ag Use of Carbon to Lime
C available for
Lime
Ratio
emission
C02 Emissions
C02 Emissions
(m tons)
(m tons C)
(m tons COj Equiv)
(MTCE)
Limestone
1,851,805
0.12
222,217
814,794
222,217
Dolomite
2,527
0.13
329
1,205
329
222,545
1/ From "Stone, Crushed" Report from USGS Minerals Yearbook for approriate year, table 8
2/ From "Stone, Crushed" Report from USGS Minerals Yearbook for approriate year, table 15
-------
Iowa 2000 Commercial Fertilizer Application Worksheet
Anhydrous Ammonia
Ammonium Nitrate
Ammonium Sulfate
Ammonium Thio sulfate
Urea
Nitrogen Solution 28%
Nitrogen Solution 30%
Nitrogen Solution 32%
Ammonium Phosphates
08-24-00 liquid
10-30-00 liquid
10-34-00 liquid
11-37-00
11-52-00 dry
18-46-00 dry
Triple Super Phosphate
Phosphoric Acid
Muriate of Potash
Mixtures and Suspensions
02-06-35 suspens
03-09-27
03-10-30 suspens
04-10-10 liquid
06-18-06 liquid
07-21-07 liquid
07-23-05 liquid
09-18-09 liquid
05-20-35 dry
06-24-24 dry
08-32-16 dry
09-23-30 dry
10-20-20
10-26-26 dry
23-09-12 dry
20-10-10
Totals
A
input
Consumption 1/
7/99 to 6/00
B
input
Nitrogen
Content
C
A x (B/100)
Total
Nitrogen
tons
%
tons
644,389
20,577
11,635
10,086
189,146
544,933
0
367,971
82
33.5
21
14
46
28
30
32
528,399.0
6.893.3
2.443.4
1,412.0
87,007.2
152,581.2
0.0
117,750.7
7,755
7,815
48,500
0
149,997
372,588
13,011
72
650,918
10
10
11
11
18
0
0
0
620.4
781.5
4,850.0
0.0
16.499.7
67.065.8
0.0
0.0
0.0
20,653
0
24,455
5,051
1,567
10,375
1,016
3,384
736
2,217
765
5,238
0
483
108
0
2
3
3
4
6
7
7
9
5
6
8
9
10
10
23
20
413.1
0.0
733.7
202.0
94.0
726.3
71.1
304.6
36.8
133.0
61.2
471.4
0.0
48.3
24.8
0.0
989,624.5 Tons
1,979,248,950.0 Lbs
897,772,281.4 Kg
1/ Data from IDALS fertilizer Bureau, "Iowa Fertilizer Distribution"
-------
Iowa 1990 Commercial Fertilizer Application
A
B
C
input
input
A x (B/100)
Consumption
Nitrogen
Total
89-'91
Content
Nitrogen
Avg tons/yr
%
tons
Anhydrous Ammonia
634,870
82
520,593.4
Ammonium Nitrate
26,401
33.5
8,844.3
Ammonium Sulfate
8,906
21
1,870.3
Ammonium Thiosulfatt
8,449
14
1,182.9
Urea
176,747
46
81,303.6
Nitrogen Solution 28%
511,608
28
143,250.2
Nitrogen Solution 30%
1,544
30
463.2
Nitrogen Solution 32%
159,098
32
50,911.4
Ammonium Phosphate}
08-24-00 liquid
9,718
8
777.4
10-30-00 liquid
10,187
10
1,018.7
10-34-00 liquid
42,735
10
4,273.5
11-37-00
1,131
11
124.4
11-52-00 dry
87,474
11
9,622.1
18-46-00 dry
398,599
18
71,747.8
Triple Super Phosphate
36,380
0
0.0
Phosphoric Acid
1,541
0
0.0
Muriate of Potash
688,779
0
0.0
Mixtures and Suspensions
02-06-35 suspens
18,828
2
376.6
03-09-27
1,327
3
39.8
03-10-30 suspens
26,857
3
805.7
04-10-10 liquid
3,843
4
153.7
06-18-06 liquid
1,817
6
109.0
07-21-07 liquid
19,400
7
1,358.0
07-23-05 liquid
3,769
7
263.8
09-18-09 liquid
4,829
9
434.6
05-20-35 dry
1,618
5
80.9
06-24-24 dry
12,300
6
738.0
08-32-16 dry
4,088
8
327.0
09-23-30 dry
6,577
9
591.9
10-20-20
337
10
33.7
10-26-26 dry
1,744
10
174.4
23-09-12 dry
1,583
23
364.1
20-10-10
371
20
74.2
Totals
901,908.8 Tons
############ Lbs
818,197,958.9 Kg
-------
AGRICULTURAL SOILS PARS SCORES
DARS SCORES: DIRECT N20 EMISSIONS FROM AGRICUL1
rURAL SOILS
Emission
Factor
Attribute
Explanation
Activity
Data
Attribute
Explanation
Emission
Score
Measurement
3
Since the factor is derived from field
measurements, applying the DARS formula the
score would be 5. However, emissions vary
across soil types, climates, and management
practices.
6
The value of 6 is a composite value, based on use of
top-down statistics for fertilizer purchases; estimates
of manure use based on sample data; estimates of
nitrogen from crop residues based on crop
production; and estimates ofhistosol area cultivated.
0.18
Source
Specificity
7
The emission factor was developed specifically
for N20 from fertilizer use, but not for emissions
from legume cultivation, crop residue
incorporation, or manure application.
Variability is expected to be low to moderate.
9
Data on fertilizer purchases are used as a proxy for
fertilizer use; other data are source specific
estimates.
0.63
Spatial
Congruity
3
The emission factor is a global value. Because
the variance of emissions across regions and
across states is expected to be high.
10
States use state-level activity data to estimate
statewide emissions.
0.30
Temporal
Congmity
5
The emission factor is based on measured
emissions over a crop year or calendar year.
The emission factor is expected to vary
significantly over time.
10
States use annual activity data to estimate annual
emissions.
0.50
Composite Score 0.40
-------
DARS SCORES: DIRECT N20 EMISSIONS FROM ANIMAL PRODUCTION
Emission
Factor
Attribute
Explanation
Activity
Data
.¦J (tribute
Explanation
Emission
Score
Measurement
5
IPCC (1997) did not document the source of
the factor; it stated only that the factor was
"derived on the basis of a very limited amount
of information."
7
The value of 7 is a composite value, based on animal
populations, default values for nitrogen excretion,
and estimates of amount managed using daily
spread or equivalent, based on sampling.
0.35
Source
Specificity
10
The emission factor was developed specifically
for NjO emissions from animal production.
10
Data on animal populations are used to estimate
nitrogen excretion.
1.00
Spatial
Congruity
3
The emission factor is a global value. The
variance of emissions across regions is
expected to be high.
9
State values for animal populations are used; values
for nitrogen excretion are global average values, but
spatial variability is expected to be low.
0.27
Temporal
Congruity
5
It is unknown whether the emission factor is
base< on measured emissions over a
particular time frame. However, emissions are
expected to vary significantly over the course
of a year.
10
States use annual activity data to estimate annual
emissions.
0.50
Composite Score
O.J 3
-------
DARS SCORES: INDIRECT
r N20 EMISSIONS FROM MANURE AND FERT]
[LIZER
Emission
Factor
Attribute
Explanation
Activity
Data
.¦J (tribute
Explanation
Emission
Score
Measurement
3
The emission factor is based on reported N20
emissions from nitrogen deposited on soil (i.e., it
is based on direct emissions, not indirect
emissions).
7
1 he value ot/is a composite value, based on use of
top-down statistics forfertilizer purchases; estimates of
manure use based on sample data; and default values
for (1) NOx andNH3 volatilization, and (2) fraction of
nitrogen that leaches
0.21
Source Specificity
6
The emission factor is based on reported N20
emissions from nitrogen deposited on soil (i.e., it
is based on direct emissions, not indirect
emissions). Variability is expected to be moderate
to high.
9
Data on fertilizer purchases are used as a proxy for
fertilizer use; other data are source specific estimates.
0.54
Spatial
Congruity
3
The emission factors are global values. The
variance of emissions across regions is expected
to be high.
5
State values forfertilizer use are used; values for (1)
NOx and NH3 volatilization and (2) fraction of
nitrogen that leaches are global, and spatial variability
is expected to be moderate to high
0.15
Temporal
Congruity
3
It is unknown whether the emission factor is
based on measured emissions over a particular
time frame However, emissions are expected to
be highly varied over the course of a year.
7
States use annual activity data to estimate annual
emissions However, there is a lag time between
application of nitrogen and indirect emissions due to
leaching; temporal variability is expected to be low to
moderate
021
Composite Score 0.28
-------
DARS SCORES: C02 EMISSIONS FROM AGRICULTURAL USE OF LIMESTONE
Emission
Factor
Attribute
Explanation
Activity
Data
Attribute
Explanation
Emission
Score
Measurement
5
Because the emission factors for each type of
limestone are not based on measurement, the
highest possible score is 5. The emission
factors are based on mass balance.
8
Data on limestone purchases are used. Assuming use
of top-down statistics on limestone sales is assumed;
the breakdown between limestone and dolomite sales
must be estimated.
0.40
Source Specificity
10
The emission factor was developed specifically
for CO 2 from agricultural use of limestone.
9
The activity measured (limestone purchases) is very
closely correlated with the emissions process.
0.90
Spatial
Congruity
9
The emission factor is a global value. The
variance of emissions across regions is
expected to be low.
10
States use state-level activity data to estimate
statewide emissions.
0.90
Temporal
Congruity
9
The emission factor is not based on measured
emissions over a particular time frame.
However, the emission factor should not vary
significantly over the course of a year.
7
States use annual activity data to estimate annual
emissions. However, there is a lag time between
application of lime and CO 2 emissions; temporal
variability is expected to be low to moderate.
0.63
Composite Score 0.71
-------
II. CH4 from Enteric Fermentation in Domesticated Animals
The methods used for the current inventory are the same as those used for the
National EPA greenhouse gas inventory and were provided in the Volume VIIIEIIP
document. To calculate emissions from cattle, the EPA designed digestion model for feeding
systems in the United States, which generated emission factors based on feed input
characteristics (Emission Inventory Improvement Program, 1999). The emission factors used
for Iowa are specified to the feeding systems of the North Central region of the United States.
For non-cattle animals the quantity of CH4 produced likely varies across the country as well,
however, emissions factors for these animals are not geographically specific and were taken
from the scientific literature (Crutzen et al, 1986). This is because emissions from these
animals are relatively small in comparison to cattle, and models to specify geographic
variability among non-cattle have not been developed. Because of the vast quantity,
uncertainty surrounding emissions from cattle would dominate the domesticated animals
category.
Animal population data was taken from 1997 agricultural census and the USD A National
Agricultural Statistics Service reports found at http://www.usda.gov/nass/ and cited on the
Worksheet for Calculating 2000 Iowa Livestock Average Population (National Agriculture
Statistics Service, 1997). Because populations can significantly fluctuate depending on the
time of year, average animal populations were used for cattle, swine and sheep. The Iowa
average livestock populations were determined using the U.S. inventory populations for
January and July. The U.S. January populations were divided by the average of U.S. January
and U.S. July populations. The fraction was applied to Iowa January inventories to
determine an average population. This was then multiplied by the appropriate emission
factor to determine emissions. For weanling and yearling system steers/heifers the total
number animals slaughtered in 2000 was the population used. For other animals, the annual
population was used because further data was unavailable.
-------
Year 2000 CH4 Emissions from Enteric Fermentation in Domesticated Animals
ABC D
input
input
A x B /2000
C x.9072 x 2 lx( 12/44)
IA Average
North Central Emission
Livestock Type
Population 1990
Factor
CH4 Emissions
CH4 Emissions
head
lbs CH4/head/yr
tons
MTCE
Dairy Cattle
Replacements (0-12 mo)
105,875
41.60
2,202
11,442
Replacements (12-24 mo)
105,875
126.30
6,686
34,739
Mature Cows
215,702
246.50
26,585
138,131
Beef Cattle
Replacements (0-12 mo)
129,786
44.80
2,907
15,105
Replacements (12-24 mo)
129,786
133.80
8,683
45,113
Mature Cows
1,030,817
130.90
67,467
350,544
Weanling System Steers/ Heifers 1/
125,840
49.70
3,127
16,248
Yearling System Steers/ Heifers 1/
503,360
103.40
26,024
135,214
Bulls
67,054
220.00
7,376
38,324
Other Livestock
Swine
15,369,836
3.30
25,360
131,766
Sheep
300,944
17.60
2,648
13,760
Goats 2/
12,275
11.00
68
351
Equine 2/
100,000
39.60
1,980
10,288
Buffalo 3/
TOTAL 941,024
1/Number of head Slaughtered in 2000
2/ Not calculated off of an average population. Info unavailable for calc of avg.
3/ Data was not found for this animal
-------
Year 1990 CH4 Emissions from Enteric Fermentation in Domesticated Animals
ABC D
input input A x B /2000 C x .9072 x 2 lx (12/44)
Livestock Type
IA Average North Central Emission
Population 2000 Factor
CH4 Emissions
head
lbs CH4/head/yr
tons
CH4 Emissions
MTCE
Dairy Cattle
Replacements (0-12 mo)
Replacements (12-24 mo)
Mature Cows
Beef Cattle
Replacements (0-12 mo)
Replacements (12-24 mo)
Mature Cows
Weanling System Steers/ Heifers 1/
Yearling System Steers/ Heifers 1/
Bulls
Other Livestock
Swine
Sheep
Goats 2/
Equine 2/
Buffalo 3/
123,936
123,936
284,787
145,982
145,982
1,122,661
287,720
1,150,880
80,741
13,500,799
490,000
11,784
49,300
41.60
126.30
246.50
44.80
133.80
130.90
49.70
103.40
220.00
3.30
17.60
11.00
39.60
1/Number of head Slaughtered in 1990
2/ Not calculated off of an average population. Info unavailable for calc of avg.
3/ Data was not found for this animal
2,578
7,827
35,100
3,270
9,766
73,478
7,150
59,500
8,882
22,276
4,312
65
976
TOTAL
13,394
40,665
182,372
16,990
50,743
381,777
37,149
309,152
46,146
115,743
22,404
337
5,072
1,221,943
-------
Worksheet for Calculating 2000 Iowa Livestock Average Population
A B C D E F
input
input
input
B + C/2
D/B
ExA
2000 Iowa
Corresponding
IA January
US January
U.S. July
Average
Average
GHG Inventory
Population,
Data
Population,
Data
Population,
Data
U.S.
National
Population
Animal Type
Section'
2000
Source
2000
Source
2000
Source
Population
Fraction
(head)
Mature Milk Cows
DA, MM, AS
215,000
l
9,190,000
l
9,250,000
2
9,220,000
1.0033
215,702
Dairy Replacement (0-12 mos)
DA
110,000
l
4,000,000
l
3,700,000
2
3,850,000
0.9625
105,875
Dairy Replacement (12-24 mos)
DA
110,000
l
4,000,000
l
3,700,000
2
3,850,000
0.9625
105,875
Beef Cows
DA, MM, AS
1,025,000
l
33,569,000
l
33,950,000
2
33,759,500
1.0057
1,030,817
Beef Replacement (0-12 mos)
DA
140,000
l
5,503,000
l
4,700,000
5,101,500
0.9270
129,786
Beef Replacement (12-24 mos)
DA
140.000
l
5.503.000
l
4.700.000
2
5.101.500
0.9270
129.786
Breeding Bulls
DA. MM. AS
70.000
l
2.293.000
l
2.100.000
2
2.196.500
0.9579
67.054
Growing Calves
MM. AS
510.000
l
16.815.000
l
30.200.000
2
23.507.500
1.3980
712.984
Growing Heifers, Steers/Bullocks/
Bulls
MM, AS
1,289,366
1,2,3,4,5
24,917,000
3,5
20,443,000
4,2
22,680,000
0.9102
1,173,609
Feedlot-Fed Steers and Heifers on
High-Grain Diets
MM, AS
590,696
1,2,3,4,5
11,414,000
3,5
10,357,000
4,2
10,885,500
0.9537
563,345
Weanling System Steer/Heifercd
DA
125,840
12
125,840
Yearling System Steer/Heifercd
DA
503.360
12
503.360
Market Swine6
DA. MM. AS
14.240.000
53.109.000
13
52.884.000
13
52.996.500
0.9979
14.209.836
Breeding Swine6
DA. MM. AS
1.160.000
8
6.234.000
13
6.234.000
13
6.234.000
1.0000
1.160.000
Layers:Chicken
MM. AS
27.407.000
6
328.557.000
6
325.563.000
6
327.060.000
0.9954
27.282.126
Broilers: Chicken atl
MM, AS
17,200,000
7
6,665,000
7
17,200,000
Turkeys0
MM, AS
7,100,000
8
269,969,000
8
7,100,000
Sheep
DA, MM, AS
270,000
8
6,915,000
8
8,500,000
9
7,707,500
1.1146
300,944
Goats"
DA, MM, AS
12,275
10
12,275
Equine00
DA, MM, AS
100,000
11
100,000
a December 1, 1995 through November 30, 1996 d Not calculated off of an average population. Info unavailable for calc of avg.
b Equine includes horses, ponies, mules, burros, and donkeys e January data comes from Dec 1 1999, July data comes from June 1 2000
c Number of Head Slaughtered for all of 2000 f DA= Domesticated Animals, MM= Manure Management, AS= Agricultural Soils
Data Source Numbers
1 "Cattle" NASS released Jan 26, 2001
8 "2001 Iowa Agriculture Statistics" compiled by Iowa Agriculture Statistics"
2 "Cattle" NASS released My 20, 2001
9 "Sheep" NASS released July 21, 2000
3 "Cattle on Feed" NASS released Jan 18, 2002
10 "1997 Census of Agriculture" Volume 1, Geographic Area Series Part 51 USDA
4 "Cattle on Feed" NASS released July 20, 2002
11 "Equine" NASS released March 2,1999
5 "Cattle" NASS released Feb. 1, 2002
12 "Livestock Slaughter" NASS releases Feb 25, 2000 through Jan 19, 2001
6 "Chicken and Eggs 2000 Summary" NASS POU 2-4 (01)
13 "Quarterly Hogs and Pigs" NASS released June 29, 2001
7 "Poultry Production and Value 1997 Summary" NASS Pou 3-1(4-98) [1996 is
the final year that NASS reported Broiler populations from Iowa]
-------
Worksheet for Calculating 1990 Iowa Livestock Average Population
A B C D E F
input
input
input
B + C/2
D/B
ExA
2000 Iowa
Corresponding
IA January
US January
U.S. July
Average
Average
GHG Inventory
Population,
Data
Population,
Data
Population,
Data
U.S.
National
Population
Animal Type
Section8
1990
Source
1990
Source
1990
Source
Population
Fraction
(head)
Mature Milk Cows
DA, MM, AS
285,000
100
10,015,000
100
10,000,000
100
10,007,500
0.9993
284,787
Dairy Replacement (0-12 mos)
DA
125,000
100
4,171,000
100
4,100,000
100
4,135,500
0.9915
123,936
Dairy Replacement (12-24 mos)
DA
125,000
100
4,171,000
100
4,100,000
100
4,135,500
0.9915
123,936
Beef Cows
DA, MM, AS
1,115,000
100
32,454,000
100
32,900,000
100
32,677,000
1.0069
1,122,661
Beef Replacement (0-12 mos)
DA
150,000
100
5,283,000
100
5,000,000
100
5,141,500
0.9732
145,982
Beef Replacement (12-24 mos)
DA
150,000
100
5,283,000
100
5,000,000
100
5,141,500
0.9732
145,982
Breeding Bulls
DA, MM, AS
80,000
100
2,160,000
100
2,200,000
100
2,180,000
1.0093
80,741
Growing Calves
MM, AS
870,000
100
18,418,000
100
29,400,000
100
23,909,000
1.2981
1,129,375
Growing Heifers, Steers/Bullocks/
Bulls3
MM, AS
1,431,103
100
22,876,000
100
21,341,000
100
22,108,500
0.9664
1,383,089
Feedlot-Fed Steers and Heifers on
High-Grain Diets
MM, AS
618,897
100
2,986,708
100
2,548,840
100
2,767,774
0.9267
573,530
Weanling System Steer/Heiferbc
DA
287,720
101
287,720
Yearling System Steer/Heiferbc
DA
1,150,880
101
1,150,880
Market Swine"
DA, MM, AS
11,820,000
102
46,931,000
102
46,735,000
102
46,833,000
0.9979
11,795,318
Breeding Swine"
DA, MM, AS
1,680,000
102
6,857,000
102
7,065,000
102
6,961,000
1.0152
1,705,481
Layers: Chicken
MM, AS
8,140,000
103
272,979,000
103
267,499,000
103
270,239,000
0.9900
8,058,296
Broilers: Chicken06
MM, AS
9,450,000
104
9,450,000
Turkeys"
MM, AS
8,800,000
104
8,800,000
Sheep"
DA, MM, AS
490,000
105
490,000
Goats"
DA, MM, AS
11,784
106
11,784
Equine"1
DA, MM, AS
49,300
106
49,300
3 Populations are calculated on separate sheet from the sources indicated
b Number of Head Slaughtered for all of 1995 is earliest data available
c Not calculated off of an average population. Info unavailable for calc of avg.
d January data comes from Dec 1 1989, July data comes from June 1, 1990
e December 1, 1995 through November 30, 1996
f Equine includes horses, ponies, mules, burros, and donkeys
8 DA= Domesticated Animals, MM= Manure Management, AS= Agricultural Soils
Data Source Numbers
100 "Cattle Final Estimates 1989-93" NASS released Jan, 1995 104 "Poultry Production and Value Final Estimates 1988-93 " NASS Statistical Bulletin Number 910
101 "Livestock Slaughter" NASS releases Feb 24, 1995 through Jan 26, 1996 105 "Sheep and Goats Final Estimates 1989-93" NASS Statistical Bulletin Number 906
102"Hogs & Pigs Final Estimates 1988-92" NASS Statistical Bulletin Number 904 106 "Iowa Greenhouse Gas Action Plan"
103 "Chicken and Eggs Final Estimates 1988-93" NASS Statistical Bulletin Number 908
-------
DOMESTICATED ANIMALS PARS SCORES
DARS SCORES: CH4 EMISSIONS FROM CATTLE
Emission
Factor
Attribute
Explanation
Activity
Data
Attribute
Explanation
Emission
Score
Measurement
4
Because the emission factors are not
based on measurement, the highest
possible score is 5. Since the factors
are derived from a model, applying the
DARS formula the score would be 3;
however, the model is sophisticated
8
Data on annual average animal
populations are estimated based on state
and national data
0.32
Source Specificity
10
The emission factors were developed
specifically for the intended emission
source (i.e., eight categories of cattle
were modeled).
9
The activity measured, average animal
population, is very closely correlated to the
emissions activity.
0.90
Spatial
Congruity
7
The emission factor was developed for
five regions of the U.S. (each larger
than a state). However, spatial
variability for the emissions factor
within each region is assumed to be
moderate.
10
States use state-level activity data to
estimate state-wide emissions.
0.70
Temporal
Congruity
7
The emission factors are based on a
model, not on measured emissions
over a particular time frame.
Temporal variability is expected to be
low to moderate.
10
States use annual activity data to estimate
annual emissions.
0.70
Composite Score 0.66
-------
DARS SCORES: CH4 EMISSIONS FROM DOMESTICATED ANIMALS OT]
HER THAN CATTLE
Emission
Factor
Attribute
Explanation
Activity
Data
.¦J (tribute
Explanation
Emission
Score
Measurement
3
Because the emission factor is not
based on measurement, the highest
possible score is 5. Since the factor is
derived from a model, applying the
DARS formula the score would be 3.
The model uses only one emission
factor for each species (i.e., it does not
adjust for animal mass).
8
Data on annual average animal
populations are estimated based on state
and national data
0.24
Source
Specificity
10
The emission factors were developed
specifically for the intended emission
source (i.e., an emission factor was
developedfor each species).
9
The activity measured, average animal
population, is very closely correlated to
the emissions activity.
0.90
Spatial
Congruity
7
A single global emission factor was
developed for each species. Spatial
variability for the emission factors is
assumed to be moderate.
10
States use state-level activity data to
estimate state-wide emissions.
0.70
Temporal
Congruity
7
The emission factors are based on a
model, not on measured emissions
over a particular time frame.
Temporal variability is expected to be
low to moderate.
10
States use annual activity data to estimate
annual emissions.
0.70
Composite Score
0.64
-------
III. CH4 and N2O from Manure Management
Methane emissions from manure management were estimated for each animal type by
manure management system on the worksheet titled "Year 2000 CH4 Emissions from
Manure Management." Average animal populations in the inventory year were used. For an
explanation of "average population" see the method for enteric fermentation from
domesticated animals. A fractional breakdown was provided by the EIIP estimating the part
of animal manure that is handled in each manure system by animal type (Fraction of Waste
System Usage, column A). The EIIP determined these fractions by obtaining information
from staff of the USD A agricultural extension office. These fractions were multiplied by
average populations to give estimates of the number of animals with manure going into each
management system (Number of Animals using System, column C). Emissions were
estimated based on the typical animal mass, a volatile solid emission factor, the factor for the
maximum CH4 producing capacity of the manure, and a CH4 conversion factor for each
manure management system. The EIIP obtained these factors from the American Society of
Agricultural Engineers, literature review and EPA sponsored analysis.
Nitrous oxide emissions were estimated on the worksheet titled "2000 N2O Emissions
from Manure Management" by aggregating management systems into two groups. Solid
storage, drylot and other systems were aggregated into one group. The second group
consisted of anaerobic lagoons, and liquid systems. First the quantity of nitrogen that is
managed in all systems is estimated from average animal populations (column A), typical
animal mass estimates (column B), a Kjeldahl nitrogen excretion factor for each animal type
based on animal mass (column C), and the EIIP provide fraction for livestock manure that is
handled in all management systems (column D). That is, all manure that is not either applied
through daily spread or deposited directly to pasture, range, or paddock. Once the amount of
manure nitrogen excreted into all systems was known, a 20% volatilization correction factor
was applied to remove the N that escapes as NH3 and NOx from the stock that is being
managed.
At this point, the total managed manure is multiplied by the fraction representing the total
manure that is managed in one of the two groups of systems (columns H and N). The
managed N is multiplied by the N2O emission factor specific to the group (columns J and P).
These emission factors have been established by the IPCC for the Revised 1996 Guidelines
for National Greenhouse Gas Inventories. From here, emissions are converted to the
appropriate unit.
-------
Year 2000 N20 Emissions from Manure Management
column label
A
B
C
D
E
F
G
calculation
input
input
input
input
A x (B/1000) x C x D x 365
input
ExF
Animal Type
Iowa Average
Population 2000
Typical
Animal Mass,
TAM 1/
Kjeldahl N per
day per 1000
kg mass 1/
Fraction of
Manure
Managed
Total Kjeldahl N
Managed
Volatilization
Correction
Factor
Unvolatilized N
head
kg
kg/day
fraction
kg/yr
kg JN
Milk Cows
215,702
604.0
0.44
0.92
19,249,723
0.80
15,399,778
Beef Cows
1,030,817
590.0
0.34
0.13
9,811,801
0.80
7,849,441
Breeding Bulls
67,054
750.0
0.31
0.13
739,748
0.80
591,798
PreWeaned Calves
712,984
159.0
0.30
0.13
1,613,743
0.80
1,290,994
Heifers & Steers Not on Feed
117,609
369.0
0.31
0.13
638,359
0.80
510,687
Steers and Heifers On Feed
563,345
420.0
0.30
0.13
3,368,071
0.80
2,694,457
Market Swine
14,209,836
53.8
0.47
0.96
124,475,985
0.80
99,580,788
Breeding Swine
1,160,000
198.0
0.24
0.96
19,315,169
0.80
15,452,135
Layers
27,282,126
1.8
0.62
1.00
11,113,101
0.80
8,890,481
Broilers
17,200,000
0.9
1.10
1.00
6,215,220
0.80
4,972,176
Turkeys
7,100,000
6.8
0.74
1.00
13,040,428
0.80
10,432,342
Sheep
300,944
27.0
0.42
0.01
12,456
0.80
9,965
Goats
12,275
64.0
0.45
0.00
0
0.80
0
Horses/Mules
100,000
450.0
0.30
0.00
0
0.80
0
209,593,803.94 167,675,043
1/ Factor comes from USEPA Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2000 Table L-2
-------
column label
Emissions from Manure Managed in Solid Storage, Drylot and Other Undefined Systems & Litter, Deep P
H I J K L M
calculation
tiput
GxH
tiput
I X J
K x (44/28)
L/lOOOx 310 x (12/44)
Animal Type
Fraction of Manure
Managed in Solid
Storage Drylot &
Other Systems
fraction
Amount Manure
Nitrogen Managed in
These Systems
EgT5
N20 Emission
Factor
N20-N Emission
Total N20
Emissions
kg N2Q-N/kg N
kg N2Q-N
kg N2Q/yr
Emissions from Solid
Storage Drylot & Other
Systems
MTCE
Milk Cows
0.69
10,625,847
0.02
212,517
333,955
28,234
Beef Cows
0.13
1,020,427
0.02
20,409
32,071
2,711
Breeding Bulls
0.13
76,934
0.02
1,539
2,418
204
PreWeaned Calves
0.13
167,829
0.02
3,357
5,275
446
Heifers & Steers Not on Feed
0.13
66,389
0.02
1,328
2,087
176
Steers and Heifers On Feed
0.13
350,279
0.02
7,006
11,009
931
Market Swine
0.43
42,819,739
0.02
856,395
1,345,763
113,778
Breeding Swine
0.43
6,644,418
0.02
132,888
208,825
17,655
Layers
0.94
8,357,052
0.02
167,141
262,650
22,206
Broilers
1.00
4,972,176
0.02
99,444
156,268
13,212
Turkeys
1.00
10,432,342
0.02
208,647
327,874
27,720
Sheep
0.01
100
0.02
2
3
0
Goats
0.00
0
0.02
0
0
0
Horses/Mules
0.00
0
0.02
0
0
0
1,710,671
-------
Emissions from Manure Managed in Anaerobic Lagoons, Liquid Slurry, Pit Storag
column label
N
O
P
Q
R
S
calculation
input
GxN
input
OxP
Q x (44/28)
(R/1000)x 310 x (12/44)
Fraction Manure
Amount Manure
Emissions from
Anaerobic Lagoons & Liq
Villi 1»1»*7
Animal Type
Managed in Anaerobic
Lagoons and Liq
Nitrogen
Managed in
N20 Emission
Factor
N20-N Emission
Total N20
Emissions
Slurry
These Systems
fraction
(kg N)
(kg N20-N/kg N)
(kg N20-N)
(kg N20/yr)
(M'l'CE)
Milk Cows
0.23
3,541,949
0.001
3,542
5,566
471
Beef Cows
0.00
0
0.001
0
0
0
Breeding Bulls
0.00
0
0.001
0
0
0
PreWeaned Calves
0.00
0
0.001
0
0
0
Heifers & Steers Not on Feed
0.00
0
0.001
0
0
0
Steers and Heifers On Feed
0.00
0
0.001
0
0
0
Market Swine
0.53
52,777,818
0.001
52,778
82,937
7,012
Breeding Swine
0.53
8,189,632
0.001
8,190
12,869
1,088
Layers
0.06
533,429
0.001
533
838
71
Broilers
0.00
0
0.001
0
0
0
Turkeys
0.00
0
0.001
0
0
0
Sheep
0.00
0
0.001
0
0
0
Goats
0.00
0
0.001
0
0
0
Horses/Mules
0.00
0
0.001
0
0
0
65,043
-------
Summary of ISyi) Emissions from Manure Management, 2000
column label T U V
calculation
M
S
T+U
Emissions from
Emissions from
Total
Animal Type
Solid Storage
Drylot & Other
Anaerobic
Lagoons & Liq
Emissions
from All
Systems
(MTCE)
Slurry
(MTCE)
Systems
(MTCE)
Milk Cows
28,234
471
28,705
Beef Cows
2,711
0
2,711
Breeding Bulls
204
0
204
PreWeaned Calves
446
0
446
Heifers & Steers Not on Feed
176
0
176
Steers and Heifers On Feed
931
0
931
Market Swine
113,778
7,012
120,790
Breeding Swine
17,655
1,088
18,743
Layers
22,206
71
22,277
Broilers
13,212
0
13,212
Turkeys
27,720
0
27,720
Sheep
0
0
0
Goats
0
0
0
Horses/Mules
0
0
0
227,275
8,641
235,916 MTCE
-------
1990 N20 Emissions from Manure Management
column label A B C D E F G
A x (B/1000) x C x D
calculation
input
input
input
input
x 365
input
ExF
Typical
Animal
Kjeldahl N per
Fraction of
Volatilization
Iowa Average
Mass, TAM
day per 1000
Manure
Total Kjeldahl N
Correction
Animal Type
Population 2000
1/
kg mass 1/
Managed
Managed
Factor
Unvolatilized N
head
kg
kg/day
fraction
kg/yr
kg JN
Milk Cows
284,787
604.0
0.44
0.92
25,414,982
0.80
20,331,986
Beef Cows
1,122,661
590.0
0.34
0.13
10,686,015
0.80
8,548,812
Breeding Bulls
80,741
750.0
0.31
0.13
890,745
0.80
712,596
PreWeaned Calves
1,129,375
159.0
0.30
0.13
2,556,188
0.80
2,044,950
Heifers & Steers Not on Feed
1,383,088
369.0
0.31
0.13
7,507,135
0.80
6,005,708
Steers and Fleifers On Feed
573,530
420.0
0.30
0.13
3,428,967
0.80
2,743,173
Market Swine
11,795,318
53.8
0.47
0.96
103,325,177
0.80
82,660,142
Breeding Swine
1,705,481
198.0
0.24
0.96
28,397,978
0.80
22,718,382
Layers
8,058,296
1.8
0.62
1.00
3,282,466
0.80
2,625,973
Broilers
9,450,000
0.9
1.10
1.00
3,414,758
0.80
2,731,806
Turkeys
8,800,000
6.8
0.74
1.00
16,162,784
0.80
12,930,227
Sheep
490,000
27.0
0.42
0.01
20,282
0.80
16,225
Goats
11,784
64.0
0.45
0.00
0
0.80
0
FIorses/Mules
49,300
450.0
0.30
0.00
0
0.80
0
205,087,476.51 164,069,981
1/ Factor comes from USEPA Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2000 Fable F-2
-------
column label
H
Emissions from Manure Managed in Solid Storage, Drylot and Other Undefined Systems & Litter, Deep Pit
I J K L M
calculation
tiput
GxH
tiput
I X J
K x (44/28)
L/1000 x 310 x
(12/44)
Animal Type
Fraction of
Manure Managed
in Solid Storage
Drylot & Other
Systems
fraction
Amount Manure
Nitrogen Managed in
These Systems
EgT5
N20 Emission
Factor
N20-N Emission
Total N20
Emissions
Year 2000
Emissions from
Solid Storage Drylot
& Other Systems
kg N2Q-N/kg N
kg N2Q-N
kg N2Q/yr
MICE
Milk Cows
0.69
14,029,070
0.02
280,581
440,914
37,277
Beef Cows
0.13
1,111,346
0.02
22,227
34,928
2,953
Breeding Bulls
0.13
92,637
0.02
1,853
2,911
246
PreWeaned Calves
0.13
265,844
0.02
5,317
8,355
706
Heifers & Steers Not on Feed
0.13
780,742
0.02
15,615
24,538
2,075
Steers and Heifers On Feed
0.13
356,613
0.02
7,132
11,208
948
Market Swine
0.43
35,543,861
0.02
710,877
1,117,093
94,445
Breeding Swine
0.43
9,768,904
0.02
195,378
307,023
25,957
Layers
0.94
2,468,415
0.02
49,368
77,579
6,559
Broilers
1.00
2,731,806
0.02
54,636
85,857
7,259
Turkeys
1.00
12,930,227
0.02
258,605
406,379
34,357
Sheep
0.01
162
0.02
3
5
0
Goats
0.00
0
0.02
0
0
0
Horses/Mules
0.00
0
0.02
0
0
0
1,601,593
-------
column label
N
Emissions from Manure Managed in Anaerobic Lagoons, Liquid Slurry, Pit Storage
O P Q R S
calculation
tiput
GxN
tiput
OxP
Q x (44/28)
(R/1000) x 310 x
(12/44)
Animal Type
Fraction Manure
Managed in Amount Manure
Anaerobic Nitrogen
Lagoons and Liq Managed in
Slurry These Systems
fraction (kg JN)
N20 Emission
Factor
N20-N Emission
Total N20
Emissions
(kg N2Q-N/kg N) (kg N2Q-N)
(kg N2Q/yr)
Year 2000
Emissions from
Anaerobic Lagoons
& Liq Slurry
(MICE)
Milk Cows
0.23
4,676,357
0.001
4,676
7,349
621
Beef Cows
0.00
0
0.001
0
0
0
Breeding Bulls
0.00
0
0.001
0
0
0
PreWeaned Calves
0.00
0
0.001
0
0
0
Heifers & Steers Not on Feed
0.00
0
0.001
0
0
0
Steers and Heifers On Feed
0.00
0
0.001
0
0
0
Market Swine
0.53
43,809,875
0.001
43,810
68,844
5,820
Breeding Swine
0.53
12,040,743
0.001
12,041
18,921
1,600
Layers
0.06
157,558
0.001
158
248
21
Broilers
0.00
0
0.001
0
0
0
Turkeys
0.00
0
0.001
0
0
0
Sheep
0.00
0
0.001
0
0
0
Goats
0.00
0
0.001
0
0
0
Horses/Mules
0.00
0
0.001
0
0
0
60,685
-------
Summary of ISyi) Emissions from Manure Management, 1990
column label T U V
calculation
M
S
T+U
Year 2000
Year 2000
Emissions from
Emissions from
Total
Solid Storage
Anaerobic
Emissions
Drylot & Other
Lagoons & Liq
from All
Animal Type
Systems
Slurry
Systems
(MTCE)
(MTCE)
(MTCE)
Milk Cows
37,277
621
37,899
Beef Cows
2,953
0
2,953
Breeding Bulls
246
0
246
PreWeaned Calves
706
0
706
Heifers & Steers Not on Feed
2,075
0
2,075
Steers and Heifers On Feed
948
0
948
Market Swine
94,445
5,820
100,266
Breeding Swine
25,957
1,600
27,557
Layers
6,559
21
6,580
Broilers
7,259
0
7,259
Turkeys
34,357
0
34,357
Sheep
0
0
0
Goats
0
0
0
Horses/Mules
0
0
0
212,783 8,062 220,845 MTCE
780,204
29,562
809,766 C02 Equiv
-------
2000 CH4 Emissions from Manure Management
column label
A
B
C
D
E
F
G
H
J
K
calculation
input
input
Ax B
input
input
((C x D)/1000) x E x 365
input
Fx G
input
Hxl
J x 0.66
Fraction of
Typical
Max CH4
Maximum
CH4
Waste
Number of
Animal
Volatile Solids
producing
Potential
Conversion
Animal
Manure
System
2000IA Avg Animals using
Mass
Emission
Total Volatile
capacity of
Methane
Factor for
Methane
Type
System
Usage
Pop
System
(TAM) 4/
Factor 4/
Solids Produced
Manure (Bo) 4/
Emissions
System 5/
Emissions
Methane Emissions
fraction
(Total head)
(head)
ks
kg/day/ 1000 kg
mass
ks
(m3 CH4/kavs)
(m3 CH4)
fraction
(m3 CH4)
(ks)
Female Cattle
Milk Cows
Daily Spread
0.08
215,702
17,256.15
604
8.46
32,184,297
0.24
7,724,231
0.002
15,448.46
10,195.99
Soild Storage
0.65
215,702
140,206.20
604
8.46
261,497,411
0.24
62,759,379
0.009
564,834.41
372,790.71
Liquid/Slurry
0.20
215,702
43,140.37
604
8.46
80,460,742
0.24
19,310,578
0.2627
5,072,888.84
3,348,106.64
Anaerobic Lag(
0.03
215,702
6,471.06
604
8.46
12,069,111
0.24
2,896,587
0.6981
2,022,107.18
1,334,590.74
Other
0.04
215,702
8,628.07
604
8.46
16,092,148
0.24
3,862,116
0.1
386,211.56
Milk Cow Total
254,899.63
5,320,583.70
Beef Cows
Pasture/Range
0.87
1,030,817
896,810.57
590
6.04
1,166,494,067
0.17
198,303,991
0.009
1,784,735.92
1,177,925.71
Drylot
0.13
1,030,817
134,006.18
590
6.04
174,303,711
0.17
29,631,631
0.011
325,947.94
Beef Cow Total
215,125.64
1,393,051.35
Male Cattle
Breeding Bulls
Pasture/Range
0.87
67,054
58,337.05
750
6.04
96,457,391
0.17
16,397,757
0.009
147,579.81
97,402.67
Drylot
0.13
67,054
8,717.03
750
6.04
14,413,173
0.17
2,450,239
0.011
26,952.63
Breeding Bull Total
17,788.74
115,191.41
Younc Cattle
Growing Calves
Pasture/Range
0.87
712,984
620,296.03
159
6.41
230,752,822
0.17
39,227,980
0.009
353,051.82
233,014.20
Drylot
0.13
712,984
92,687.91
159
6.41
34,480,307
0.17
5,861,652
0.011
64,478.17
Growing Calves Total
42,555.59
275,569.79
Growing Heifers, Steers/Bullocks/ Bulls (Not on Feed)
Pasture/Range 0.87 1,173,609 1,021,039.83
Drylot 0.13 1,173,609 152,569.17
Feedlot-Fed Steers and Heifers on High-Grain Diets (on feed)
Pasture/Range 0.87 563,345 490,110.26
Drylot 0.13 563,345 73,234.87
Swine
Market
Breeding
369
369
420
420
Drylot
0.30
14,209,836
4,262,950.80
90.75
Anaerobic Lag(
0.03
14,209,836
426,295.08
90.75
Pit Storage < 1
0.11
14,209,836
1,563,081.96
90.75
Pit Storage > 1
0.39
14,209,836
5,541,836.04
90.75
Other
0.13
14,209,836
1,847,278.68
90.75
Drylot
0.30
1,160
000
348,000.00
Anaerobic Lag(
0.03
1,160
000
34,800.00
Pit Storage < 1
0.11
1,160
000
127,600.00
Pit Storage > 1
0.39
1,160
000
452,400.00
Other
0.13
1,160
000
150,800.00
Poultry
Chickens: Layers
Deep Pit Stacks
Liquid/Slurry
Anaerobic Lag(
Other
0.90 27,282,126
0.04 27,282,126
0.02 27,282,126
0.04 27,282,126
24,553,913.26
1,091,285.03
545,642.52
1,091,285.03
5.44
5.44
5.40
5.40
5.40
5.40
5.40
2.60
2.60
2.60
2.60
2.60
9.70
9.70
9.70
9.70
836,113,997
124,936,574
408,728,433
61,074,364
0.17
0.17
0.33
0.33
142,139,379 0.009 1,279,254.42
21,239,218 0.011 233,631.39
Growing Heifers, Steers/Bullocks/ Bulls (Not on Feed) Tota
134,880,383 0.009 1,213,923.45
20,154,540 0.011 221,699.94
Feedlot-Fed Steers and Heifers on High-Grain Diets (on feed) Total
762,506,549
0.48
76,250,655
0.48
279,585,735
0.48
991,258,514
0.48
330,419,505
0.48
65,389,896
0.48
6,538,990
0.48
23,976,295
0.48
85,006,865
0.48
28,335,622
0.48
156,479,634
0.39
6,954,650
0.39
3,477,325
0.39
6,954,650
0.39
366,003,144
36,600,314
134,201,153
475,804,087
158,601,362
31,387,150
3,138,715
11,508,622
40,803,295
13,601,098
61,027,057
2,712,314
1,356,157
2,712,314
0.011
0.6981
0.13135
0.2627
0.2
0.011
0.6981
0.13135
0.2627
0.2
0.2627
0.2627
0.6981
0.1
4,026,034.58
25,550,679.46
17,627,321.41
124,993,733.61
31,720,272.46
Market Swine Total
345,258.65
2,191,136.95
1,511,657.46
10,719,025.62
2,720,219.67
Breeding Swine Total
16,031,807.92
712,524.80
946,733.08
271,231.37
Chicken: Layers Total
844,307.91
154,196.72
998,504.63
801,189.48
146,321.96
947,511.44
2,657,182.82
16,863,448.45
11,634,032.13
82,495,864.19
20,935,379.82
134,585,907.40
227,870.71
1,446,150.39
997,693.92
7,074,556.91
1,795,344.98
11,541,616.91
10,580,993.23
470,266.37
624,843.83
179,012.70
11,855,116.13
-------
Fraction of Typical
Waste Number of Animal Volatile Solids
Animal Manure System 2000IA Avg Animals using Mass Emission
Type System Usage Pop System (TAM) 4/ Factor 4/
Max CH4 Maximum CH4
producing Potential Conversion
Total Volatile capacity of Methane Factor for
Solids Produced Manure (Bo) 4/ Emissions System 5/
Methane
Emissions
Methane Emissions
Chickens: Broilers 1/
Litter
Turkeys
fraction (Total head) (head)
kg
kg/day/ 1000 kg
mass
kg
(m3 CH4/kg vs)
(m3 CH4)
fraction
(m3 CH4)
(kg)
Other Animals
Sheep
Pasture/Range
Other
1.00 17,200,000 17,200,000.00
1.00 7,100,000 7,100,000.00
0.99
0.01
300,944 297,934.16
300,944 3,009.44
27
27
0.00
0.00
0.36
30,511,C
0.009
0.1
3,051,108.00
Chickens:Broilers Total
6,153,672.24
Turkeys Total
0.00
0.00
Sheep Total
2,013,731.28
2,013,731.28
4,061,423.68
4,061,423.68
0.00
0.00
0.00
Pasture/Range
Equine 2/ 3/
Pasture/Range
Paddock
0.92
0.08
100,000
100,000
92,000.00 450
8,000.00 450
0.00
0.00
0.009
0.009
0.00
Goats Total
0.00
0.00
Equine Total
0.00
0.00
0.00
0.00
0.00
1/ December 1, 1995 through November 30, 1996
2/ 2000 Populations are not averages
3/ Equine includes horses, ponies, mules, burros, and donkeys data from 1999 NASS "Equine" Report (1999 Data)
4/ Factor comes from USEPA Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2000 Table L-2
5/ Liquid/Slurry Deep Pit and Anaerobic Lagoon Factor comes from USEPA Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2000 Table L-4
Total Kg
Total lbs
Total tons
CQ2 Equiv.
IMTCE ~
173,108,208
380838057 00
190419 03
3,627,711
989,375.731
-------
1990 CH4 Emissions from Manure Management
column label
A
B
C
D
E
F
G
H
J
K
calculation
input
input
Ax B
input
input
((C x D)/1000) x E x 365
input
Fx G
input
Hxl
J x 0.66
Fraction of
Typical
Max CH4
Maximum
CH4
Waste
Number of
Animal
Volatile Solids
producing
Potential
Conversion
Animal
Manure
System
1990IA Avg
Animals
Mass
Emission
Total Volatile
capacity of
Methane
Factor for
Methane
Type
System
Usage
Pop
using System
(TAM) 4/
Factor 4/
Solids Produced
Manure (Bo) 4/
Emissions
System 5/
Emissions
Methane Emissions
fraction
(Total head)
(head)
kg
kg/day/1000 kg mass
kg
(m3 CH4/kg vs)
m3 cH4
fraction
(m3 CH4)
(kg)
Female Cattle
Milk Cows
Daily Spread
0.08
284,787
22,782.93
604
8.46
42,492,243
0.24
10,198,138
0.002
20,396.28
13,461.54
Soild Storage
0.65
284,787
185,111.27
604
8.46
345,249,476
0.24
82,859,874
0.009
745,738.87
492,187.65
Liquid/Slurry
0.20
284,787
56,957.31
604
8.46
106,230,608
0.24
25,495,346
0.2627
6,697,627.37
4,420,434.06
Anaerobic Lag(
0.03
284,787
8,543.60
604
8.46
15,934,591
0.24
3,824,302
0.6981
2,669,745.15
1,762,031.80
Other
0.04
284,787
11,391.46
604
8.46
21,246,122
0.24
5,099,069
0.1
509,906.92
Milk Cow Total
336,538.57
7,024,653.62
Beef Cows
Pasture/Range
0.87
1,122,661
976,715.47
590
6.04
1,270,427,485
0.17
215,972,673
0.009
1,943,754.05
1,282,877.67
Drylot
0.13
1,122,661
145,945.99
590
6.04
189,833,992
0.17
32,271,779
0.011
354,989.57
Beef Cow Total
234,293.11
1,517,170.79
Male Cattle
Breeding Bulls
Pasture/Range
0.87
80,741
70,244.44
750
6.04
116,145,677
0.17
19,744,765
0.009
177,702.89
117,283.90
Drylot
0.13
80,741
10,496.30
750
6.04
17,355,101
0.17
2,950,367
0.011
32,454.04
Breeding Bull Total
21,419.67
138,703.57
Younc Cattle
Growing Calves
Pasture/Range
0.87
1,129,375
982,556.31
159
6.41
365,515,221
0.17
62,137,588
0.009
559,238.29
369,097.27
Drylot
0.13
1,129,375
146,818.76
159
6.41
54,617,217
0.17
9,284,927
0.011
102,134.20
67,408.57
Growing Heifers, Steers/Bullocks/ Bulls (Not on Feed)
Pasture/Range 0.87 1,383,088
1,203,286.94
369
6.08
985,353,386
0.17
167,510,076
0.009
Growing Calves Total
1,507,590.68
436.505.84
995.009.85
Drylot
0.13
1,383,088
179,801.50
369
6.08
147,236,713
0.17
25,030,241
0.011
275,332.65
181,719.55
Feedlot-Fed Steers and Heifers on High-Grain Diets (on feed)
Growing Heifers, Steers/Bullocks/ Bulls (Not on Feed) Tota
1,176,729.40
Pasture/Range
0.87
573,530
498,971.52
420
5.44
416,118,296
0.33
137,319,038
0.009
1,235,871.34
815,675.08
Drylot
0.13
573,530
74,558.96
420
5.44
62,178,596
0.33
20,518,937
0.011
225,708.30
148,967.48
Drylot
0.30
11,795,318
3,538,595.40
90.75
5.40
632,942,367
0
48
Anaerobic Lag(
0.03
11,795,318
353,859.54
90.75
5.40
63,294,237
0
48
Pit Storage < 1
0.11
11,795,318
1,297,484.98
90.75
5.40
232,078,868
0
48
Pit Storage > 1
0.39
11,795,318
4,600,174.02
90.75
5.40
822,825,077
0
48
Other
0.13
11,795,318
1,533,391.34
90.75
5.40
274,275,026
0
48
Swine
Market
Breeding
Drylot
Anaerobic Lag(
Pit Storage < 1
Pit Storage > 1
Other
Poultry
Chickens: Layers
Deep Pit Stacks
Liquid/Slurry
Anaerobic Lag(
Other
Feedlot-Fed Steers and Heifers on High-Grain Diets (on feed) Total
303,812,336
30,381,234
111,397,857
394,956,037
131,652,012
0.011
0.6981
0.13135
0.2627
0.2
3,341,935.70
21,209,139.18
14,632,108.46
103,754,950.87
26,330,402.45
Market Swine Total
0.90
0.04
0.02
0.04
8,058,296
8,058,296
8,058,296
8,058,296
7,252,466.40
322,331.84
161,165.92
322,331.84
9.70
9.70
9.70
9.70
46,219,243
2,054,189
1,027,094
2,054,189
0.39
0.39
0.39
0.39
18,025,505
801,134
400,567
801,134
0.2627
0.2627
0.6981
0.1
Breeding Swine Total
4,735,300.12
210,457.78
279,635.66
80,113.35
Chicken: Layers Total
2,205,677.56
13,998,031.86
9,657,191.58
68,478,267.57
17,378,065.62
111,717,234.19
0.30
1,705,481
511,644.30 198
2.60
96,138,987
0.48
46,146,714
0.011
507,613.85
335,025.14
0.03
1,705,481
51,164.43 198
2.60
9,613,899
0.48
4,614,671
0.6981
3,221,502.10
2,126,191.38
0.11
1,705,481
187,602.91 198
2.60
35,250,962
0.48
16,920,462
0.13135
2,222,502.65
1,466,851.75
0.39
1,705,481
665,137.59 198
2.60
124,980,683
0.48
59,990,728
0.2627
15,759,564.26
10,401,312.41
0.13
1,705,481
221,712.53 198
2.60
41,660,228
0.48
19,996,909
0.2
3,999,381.87
2,639,592.03
16,968,972.72
3,125,298.08
138,902.14
184,559.54
52,874.81
3,501,634.57
-------
Turkeys
Other Animals
Sheep
column label
A
B
C
D
E
F
G
H
I
J
K
calculation
input
input
Ax B
input
input
((C x D)/1000) x E x 365
input
Fx G
input
Hxl
J x 0.66
Fraction of
Typical
Max CH4
Maximum
CH4
Waste
Number of
Animal
Volatile Solids
producing
Potential
Conversion
Manure
System
1990IA Avg
Animals
Mass
Emission
Total Volatile
capacity of
Methane
Factor for
Methane
System
Usage
Pop
using System
(TAM) 4/
Factor 4/
Solids Produced
Manure (Bo) 4/
Emissions
System 5/
Emissions
Methane Emissions
fraction
(Total head)
(head)
kg
kg/day/1000 kg mass
kg
(m3 CH4/kg vs)
m3 cH4
fraction
(m3 CH4)
(kg)
Broilers 1/
Litter
1.00
9,450,000
9,450,000.00
0.9
15.00
46,564,875
0.36
16,763,355
0.1
1,676,335.50
Chickens:Broilers Total
1,106,381.43
1,106,381.43
Litter
r
1.00
8,800,000
8,800,000.00
6.8
9.70
211,863,520
0.36
76,270,867
0.1
7,627,086.72
Turkeys Total
5,033,877.24
5,033,877.24
Pasture/Range
Other
0.99
0.01
490,000
490,000
485,100.00
4,900.00
27
27
0.00
0.00
0
0
0
0
0
0
0.009
0.1
0.00
0.00
Sheep Total
0.00
0.00
0.00
Pasture/Range
1.00
11,784
11,784.00
64
0.00
0
0
0
0.009
0.00
0.00
Equine 2/ 3/
Pasture/Range
Paddock
0.92
0.08
49,300 45,356.00 450
49,300 3,944.00 450
0.00
0.00
0.009
0.009
0.00
0.00
Equine Total
0.00
0.00
0.00
1/ December 1,1995 through November 30,1996
2/ 2000 Populations are not averages
3/ Equine includes horses, ponies, mules, burros, and donkeys
4/ Factor comes from USEPA Inventory ofU.S. Greenhouse Gas Emissions and Sinks: 1990-2000 Table L-2
5/ Liquid/Slurry Deep Pit and Anaerobic Lagoon Factor comes from USEPA Inventory ofU.S. Greenhouse Gas Emissions and Sinks: 1990-2000 Table L-4
Total lbs
Total tons
CQ2 Equiv.
IMTCE ~
149,586,506
329090313
1645451565
3,134,783
854,940.731
-------
MANURE MANAGEMENT PARS SCORES
DARS SCORES: CH4 EMISSIONS FROM MANURE MANAGEMENT
Emission
Factor
Attribute
Explanation
Activity
Data
Attribute
Explanation
Emission
Score
Measurement
3
Because the emission factor is not based on
measurement, the highest possible score is 5.
Since the factor is derived from laboratory and
field measurements, applying the DARS formula
the score would be 5. However, only a few
measurements have been taken.
8
Data on annual average animal populations are
estimated based on state and national data
0.24
Source
Specificity
10
The emission factors were developed
specifically for the intended emission source
(i.e., emission factors were developedfor each
manure management system).
7
The activity measured, average animal population,
is highly correlated to the emissions activity.
0.70
Spatial
Congruity
6
Methane conversion factors are developedfor
each type of manure management system in
each state, but the factors account in only a
rough way for the state-by-state variability in
average temperature. For lagoons, a single
factor is used that does not account for
temperature differences among states.
8
States use state-level activity data or proxy data for
similar states to estimate state-wide emissions;
spatial variability is expected to be low to moderate.
0.48
Temporal
Congmity
3
The emission factors are based on field and
laboratory tests that presumably did not cover
an entire year. The temporal variability over
the course of a year is expected to be high.
7
States use annual activity data to estimate annual
emissions, but the percentage breakdowns for
manure management systems are based on data
from the early 1990s.
0.21
Composite Score
0.41
-------
DARS SCORES: N20 EMISSIONS FROM MANURE MANAGEMENT
Emission
Factor
Attribute
Explanation
Activity
Data
.¦J (tribute
Explanation
Emission
Score
Measurement
3
IPCC 1997 states that the emission factors (kg
N20-Nper kg N excreted) were based on a
very limited amount of information; no further
information is provided regarding how the
emission factors were developed.
8
Data on annual average animal populations are
estimated based on state and national data
0.24
Source Specificity
10
Emission factors were developedfor each type
of manure management system.
7
The activities measured— average animal population
and percentage of manure managed using each
management system-are highly correlated to the
emissions activity.
0.70
Spatial
Congruity
7
Single, global emission factors were developed;
spatial variability is expected to be moderate.
8
States use state-level activity data or proxy data for
similar states to estimate state-wide emissions;
spatial variability is expected to be low to moderate.
0.56
Temporal
Congruity
7
Assuming that the limited amount of
information used was generated by less than
full-year measurements; temporal variability is
expected to be low to moderate.
7
States use annual activity data to estimate annual
emissions, but the percentage breakdowns for
manure management systems are based on data
from the early 1990s.
0.49
Composite Score 0.50
-------
IV. CH4 and N2O from Burning Agricultural Wastes
To find the amount of dry matter combusted (column G), the crop production data
(column A) was multiplied by the ratio of residue to crop biomass (column B), the fraction of
the residue that is burned in Iowa each year (column C) (assumed to be 3%), the fraction of
dry matter content of the residue (column D), the fraction of dry biomass that burns (column
E) (burning efficiency) and the fraction of carbon that is released (oxidized) during burning
(column F) (combustion efficiency). Each of these factors was provided by the EIIP
methodology and updated factors were found in the Inventory of U.S. Greenhouse Gas
Emissions and Sinks: 1990-2000. The product was then converted to short tons by division
by 2000 lbs/ ton.
From the quantity of dry matter combusted, CH4 and N20 emissions were calculated.
To calculate the total carbon released during combustion, the quantity of combusted dry
matter was multiplied by it's carbon content (column I). The total carbon released was
multiplied by a fraction representing the part of total released carbon that is in the CH4 form
(column J). For every 1000 atoms of carbon released, 5 carbon atoms are in a CH4 molecule.
The mass of CH4-C emissions were then expanded to establish the mass of CH4 emissions by
multiplying by the atomic weight ratio of CH4 and carbon (column L). Methane emissions
were converted to metric tons of carbon equivalents (column M). The same calculations
were used to estimate N20 emissions with numbers specific to N content, N20-N fraction of
total N released, and conversions to MTCE.
-------
Year 2000 CH4 and N20 Emissions from Burning Agricultural Wastes
A
B
C
D
E
F
G
input
input
input
input
input
input AxBxCxDxExF/2000
Year 2000
Ratio
Fraction of
Fraction of
Burning
Combustion Dry Matter (DM)
Crop T ype
Crop Production3
Residue/Crop
Residue Burned
Dry Matter
Efficiency
Efficiency
Combusted
lbs
tons
All Cornb
96,999,552,000
1
0.03
0.91
0.93
0.88
1,083,597.52
Soybeans1"
27,858,881,312
2.1
0.03
0.87
0.93
0.88
624,826.01
Wheat
50,731,012
1.3
0.03
0.85
0.93
0.88
688.17
Rice
0
1.4
0.03
0.85
0.93
0.88
0
Sugar Cane
0
0.8
0.03
0.62
0.93
0.88
0
H
I
J
K
L
M
input
GxH
input
I x J
Kx (16/12)
Lx 0.9072x21 x
(12/44)
Total Carbon
CH4-C
CH4-C
ch4
Carbon Content
Released
Emission Ratio
Emission
Emissions
CH4 Emissions
tons C/ton DM
tons C
ton CH4-C /ton C
tons CH4-C
tons CH4
MICK
All Cornb
0.4478
485,235
0.005
2,426
3,235
16,808
Soybeans1"
0.4500
281,172
0.005
1,406
1,874
9,739
Wheat
0.4428
305
0.005
2
2
11
Rice
0.3806
0
0.005
0
0
0
Sugar Cane
0.4235
0
0.005
0
0
0
Total CH4
26,558
N
O
P
Q
R
S
input
GxN
input
OxP
Q x (44/28)
R x 0.9072 x 310 x (12/44)
Total Nitrogen
n2o-n
n2o-n
n2o
Nitrogen Content
Released
Emission Ratio
Emissions
Emissions
N20 Emissions
tons N/ton DM
tons N
ton N20-N /ton N
tons N20-N
tons N20
(MTCE)
All Cornb
0.0058
6,285
0.007
44
69
5,303
Soybeans1"
0.0230
14,371
0.007
101
158
12,125
Wheat
0.0062
4
0.007
0
0
4
Rice
0.0072
0
0.007
0
0
0
Sugar Cane
0.0040
0
0.007
0
0
0
Total N20 17,431
a Production Data is from NASS "Annual Crop Summary for 2001: Iowa", (January 11, 2002)
b Figures for Carbon and Nitrogen Contents taken from the EPA U.S. Inventory of
Greenhouse Gas Emissions and Sinks: 1990-2000
-------
Year 1990 CH4 and N20 Emissions from Burning Agricultural Wastes
A
input
Year 1990
Crop T ype Crop Production"
lbs
B
input
Ratio
Residue/Crop
C
input
Fraction of
Residue Burned
D
input
E
input
F
input
Fraction of Burning Combustion
Dry Matter Efficiency Efficiency
G
AxBxCxDxExF/2000
Dry Matter (DM)
Combusted
tons
All Corn
Soybeans1"
Wheat
Rice
Sugar Cane
82,792,266,648
20,005,699,980
167,299,980
0
0
1
2.1
1.3
1.4
0.8
0.03
0.03
0.03
0.03
0.03
0.91
0.87
0.85
0.85
0.62
0.93
0.93
0.93
0.93
0.93
0.88
0.88
0.88
0.88
0.88
924,885.66
448,692.88
2,269.42
0
0
H
input
Carbon Content
tons C/ton DM
All Corn
Soybeans'"
Wheat
Rice
Sugar Cane
0.4478
0.4500
0.4428
0.3806
0.4235
I
GxH
Total Carbon
Released
J
input
CH4-C
Emission Ratio
K
I x J
CH4-C
Emission
L
Kx (16/12)
CH4
Emissions
M
L x 0.9072 x 21 x (12/44)
CH4 Emissions
tons C
ton CH4-C /ton C tons CH4-C tons CH4
414,164
201,912
1,005
0
0
0.005
0.005
0.005
0.005
0.005
2,071
1,010
5
0
0
2,761
1,346
7
0
0
MICK
Total CH4
14,346
6,994
35
0
0
21,375
N
o
P
Q
R
S
input
GxN
input
OxP
Q x (44/28)
R x 0.9072 x 310 x (12/44)
Total Nitrogen
n2o-n
n2o-n
n2o
Nitrogen Content
Released
Emission Ratio
Emissions
Emissions
N20 Emissions
tons N/ton DM
tons N
ton N20-N /ton N
tons N20-N
tons N20
(MTCE)
All Cornb
0.0058
5,364
0.007
38
59
4,526
Soybeans1"
0.0230
10,320
0.007
72
114
8,707
Wheat
0.0062
14
0.007
0
0
12
Rice
0.0072
0
0.007
0
0
0
Sugar Cane
0.0040
0
0.007
0
0
0
Total N20 13,245
a Production Data is from NASS "Annual Crop Summary for 2001: Iowa", (January 11, 2002)
b Figures for Carbon and Nitrogen Contents taken from the EPA U.S. Inventory of
Greenhouse Gas Emissions and Sinks: 1990-2000
-------
BURNING AGRICULTURAL CROP WASTES PARS SCORE
DARS SCORES: GREENHOUSE GAS EMISSIONS FROM BURNING OF AGRICULTURAL CROP WASTES
Emission
Factor
Attribute
Explanation
Activity
Data
Attribute
Explanation
Emission
Score
Measurement
4
Because the emission factors for each crop are
not based on measurement, the highest possible
score is 5.
4
The amount of crop residues burned is estimated to
be three percent for each crop in each state (based
on state inventory data reports). The DARSformula
does not apply to this scenario.
0.16
Source Specificity
5
The emission factors were developedfor crop
residues in general; expected variability is
high.
5
The activity measured (crop production) is somewhat
correlated to the emission process (crop burning).
0.25
Spatial
Congruity
9
The emission factor was developedfor a region
larger than the one it is applied to; it is not
based on state-level crop burning and
emissions. However, spatial variability is
assumed to be low.
10
States use state-level activity data to estimate state-
wide emissions.
0.90
Temporal
Congruity
9
The emission factor is based on mass balance,
not on measured emissions over a particular
time frame. However, the emission factor
should not vary significantly over the course of
a year.
10
States use annual activity data to estimate annual
emissions.
0.90
Composite Score 0.55
-------
APPENDIX C
INDUSTRIAL PROCESSES
I. CO2 from Industrial Processes
Cement and masonry cement manufacture. Because detailed data was not available,
average production estimates were drawn from Iowa's clinker capacity as reported by the
Portland Cement Association (H. Van Oss, U.S. Geological Survey, personal
communication, September 24, 2002). An 85% capacity utilization rate was assumed. A
maximum emission scenario was assumed where all clinker went to masonry cement making.
Emissions were calculated from the EIIP provide factors.
Limestone use. The U. S. Geological Survey Minerals Yearbook reports that Iowa's
chemical and metallurgical industries consumed 1.3 million short tons of crushed limestone
and dolomite in 1999 (2000). Data for 2000 was withheld to protect proprietary information.
It is noted that this data includes consumption by cement manufacture, for which emissions
have already been quantified. It is not known how much of the total went to cement
manufacture or to what aspects of cement manufacture. The difference represents an
unavoidable double counting of CO2 from limestone used in cement production. Emission
estimation consisted of multiplying production data by an average of two emission factors
from EIIP for dolomite (0.13 tons C/ton dolomite limestone) and calcite (0.12 tons C/ ton
calcite limestone). This was done because consumption data for the different types of
limestone was unavailable.
Lime manufacture. Today, Iowa has only one manufacturer of lime. For this reason, the
USGS is unable to disclose proprietary production data that was needed to estimate
emissions. Instead of calculating emissions based on the production data, a U.S. government
source estimated these emissions with an undisclosed EPA methodology.
C02 manufacture. State specific data was unavailable. Instead, emissions were estimated
by applying a fraction of the state to national population to the reported U.S. emissions in the
National 2000 inventory(EPA, 2002). The national inventory assumes that 100% of the C02
used in all applications except for enhanced oil recovery is eventually released to the
atmosphere
II. N2O from Industrial Processes
Nitric acid production. Because state specific production data was unavailable, the figures
were estimated from the states anhydrous ammonia production. The EPA estimates that 70
percent of nitric acid (HNO3) produced is consumed as an intermediate for the production of
ammonium nitrate (NH4NO3), a major component of commercial fertilizer (Environmental
Protection Agency, 1998a). It was assumed that those plants that produced anhydrous
ammonia produced nitric acid as an early intermediate. We assumed that the fraction of the
national anhydrous ammonia capacity in Iowa can be applied to the national nitric acid
production (capacity). For 2000 Iowa had 4% of the national anhydrous ammonia capacity
(United States Geological Survey, 2000a). Iowa production of nitric acid was estimated to be
-------
338,681 metric tons. This assumption does not necessarily account for all nitric acid
production. The remaining 30% of nitric acid that is not associated with ammonium nitrate
production is not considered. The emission factor chosen came from the U.S. EPA national
inventory (Environmental Protection Agency, 2002). It assumes 20% of plants are equipped
with Non-Selective Catalytic Reduction, a technology that is known to destroy 80-90% of
N20 Emissions.
III. Hydrofluorocarbons, Perfluorocarbons and Sulfur Hexafluoride from
Industrial Processes
Substitutes for ozone depleting substances. Because state level data was unavailable,
national data from the EPA U.S. inventory for 1990-2000 was used as a proxy. National per
capita emissions were figured, then multiplied by the year 2000 population of Iowa.
Electricity transmission and distribution. State level data was not available for estimating
SF6 emission. Instead they were estimated by applying the fraction of U.S. electrical
generation in Iowa to the U.S. SF6 emissions (EPA, 2002). The emission estimates for Iowa
are highly uncertain especially because not all electric utilities use SF6. It is more common
in urban areas where space occupied by electrical distribution and transmission facilities are
more valuable
Other Industries. The production of adipic acid, primary aluminum, and HFC-23 from
HCFC-22 are all processes that generate greenhouse gases as a part of their manufacturing
processes. These industries are not found in Iowa. Soda ash manufacture and consumption
are known to occur in Iowa, however, data on these activities was not available, nor was a
method of estimation. C02 emissions for these activities were not calculated.
-------
2000 Greenhouse Gas Emissions from Industrial Processes
COt Emissions
C02 Emis
A
B
c
D
(MTCE)
input
input
A x B x (44/12)
Cx(l 2/44) x 0.9072
Activity
Production Unit
Emission Factor
C02 Emissions
Carbon Emissions
(short tons)
(ton C/ ton production)
(short tons CQ)
(MTCE) |
Cement Clinker Capacity 1/
2,365,811
0.138
1,199,466.18
296,770
296,770
Masonry Cement Capacity 1/
2,365,811
0.006
52,994.17
13,112
13,112
Limestone Use 2/
1,245,599
0.125
570,899.54
141,251
141,251
Lime Manufacture 3/
34,091
34,091
A
B
c
D
E
input
input
input
B/C
D x A
IA 2000
U.S. 2000
IA Fractional
Carbon
U.S. Emissions
Population
Population
Population
Emissions
(MTCE)
(people)
(people)
(fraction)
(MTCE)
C02 Manufacture
381,822.00
2,926,324
281,421,906
0.0104
3,970
3,970
Soda Ash
(3)
Manufacture
Primary Aluminum
*
Production
Total CO 2 Emissions
489,194
N^O Emissions NzO Emis
A B CD (MTCE)
input input A x B C x (12/44) x 310
Production Unit Emission Factor N20 Emissions NzO Emissions
I (metric ton) (metric ton N2Q/ metric ton) (llHirii' lulls Y()) (MTCE) |
Nitric Acid Production 338,680 0.008 2709 229,071 229,071
Adipic Acid Production
Total N 20 Emissions 229,071
Other Greenhouse Gas Emissions
Other Emis
A
B
C
D
E
(MTCE)
input
input
A/B
input
CxD
2000 U.S.
2000 U.S.
U.S. Per Capita
HFC and PFC
Emissions
Population
Emissions
IA Population
Emissions
(MTCE)
(people)
(MTCE/Person)
(people)
(MTCE)
HFC's and PFC's
from sbst ODS
15,763,636
281,421,906
0.0560
2,926,324
163,916
163,916
A
B
C
D
E
input
input
input
B/C
A x D
U.S. Emissions
IA Generation/
of SF6 from
U.S. Electricity
IA Electricity
U.S.
Utilities
Generation
Generation
Generation
SF6 Emissions
(MTCE)
(megawatt hours
(megawatt hours
(fraction)
(MTCE)
Electric Utilities SF6
3,927,273
3,800,000,000
38,842,106
0.010
40,143
40,143
HFC-23 Production
*
Total Emissions of Other Greenhouse Gases
204,059
I TOTAL EMISSIONS OF GREENHOUSE GASES FROM PRODUCTION PROCESSES | 922,3231
@ Activity is known to occur, but data was unavailable
# It is not known that activity occurs
* It is known that activity does not occur
1/ Cement production was estimated from Iowa's clincker capacity as reported by the Portland Cement Association.
This was upon direction by Hendrick Van Oss, cement specialist at the USGS
21 Limestone use data came from USGS Minerals Yearbook- 2000, table 4 "Iowa: Crushed stone sold or used by
producers in 1999, by use and district
3/ Emissions were estimated by an undisclosed EPA method from a government source
-------
1990 Greenhouse Gas Emissions from Industrial Processe
COt Emissions
C02 Emissions
A
B
C
D
MTCE
input
input
A x B x (44/12)
Cx (12/44) x 0.9072
Activity
Production Unit
Emission Factor
C02 Emissions
Carbon Emissions
short tons
ton C/ ton production
short tons C02
MTCE
Cement Clinker
2,120,000
0.138
1,074,840
265,935
265,935
Masonry Cement
15,000
0.006
336
83
83
Limestone Use
3,000,000
0.125
1,375,000
340,200
340,200
Lime Manufacture
2,750,000
0.214
2,158,750
534,114
534,114
A
B
C
D
E
input
input
IA 1990
input
U.S. 1990
B/C
IA Fractional
Dx A
U.S. Emissions
Population
Population
Population
Carbon Emissions
MTCE
people
people
fraction
MTCE
C02 Manufacture
218,184
2,776,755
248,709,873
0.0112
2,436
2,436
Soda Ash Manufacture
@
and Consumption
Primary Aluminum
Production
*
Total CO 2 Emissions
1,142,768
NiO Emissions
JN2
-------
INDUSTRIAL PROCESSES PARS SCORES
DARS SCORES: C02 EMISSIONS FROM CEMENT PRODUCTION
Emission
Factor
Attribute
Explanation
Activity
Data
Attribute
Explanation
Emission
Score
Measurement
9
Because the emission factor is not
based on measurement, the highest
possible score is 5. However, the
emission factor is based on a precise
stoichiometric relationship.
9
Data on clinker and cement production
(from which CC>2 is emitted as a by-
product) are aggregated from intermittent
measurements.
0.81
Source Specificity
10
The emission factor was developed
specifically for the intended emission
source.
10
The activity measured (clinker and cement
production) is the activity from which COj
is emitted
1.00
Spatial
Congruity
9
The emission factor was developed for
a region larger than the one it is
applied to; it is not based on state-
level production and emissions.
However, spatial variability for the
emissions factor is assumed to be low.
10
States use state-level activity data to
estimate statewide emissions.
0.90
Temporal
Congmity
9
The emission factor is based on mass
balance, not on measured emissions
over a particular time frame. However,
the emission factor should not vary
significantly over the course of a year.
10
States use annual activity (fata to estimate
annual emissions.
0.90
Composite Score 0.90
-------
DARS SCORES: C02 EMISSIONS FROM LIME PRODUCTION
Emission
Factor
Attribute
Explanation
Activity
Data
.¦J (tribute
Explanation
Emission
Score
Measurement
8
Because the emission factor is not
based on measurement, the highest
possible score is 5. The emission
factor is based on a precise
stoichiometric relationship. Applying
the DARS formula, the score would be
5. However, the relationship is precise,
although some carbon dioxide is
reabsorbed when lime is used for
certain purposes.
9
Data on lime production (pom which CO2 is
emitted as a by-product) are aggregated
from intermittent measurements.
0.72
Source Specificity
7
Although the emission factor was
developed specifically for the intended
emission source, the data source does
not account for all lime production.
Thus, the emission factor is based on a
subset of emission sources. Variability
in emissions across sources is assumed
to be low to moderate.
9
The data source for the activity measured
(lime production) does not account for all
lime production. Assuming the lime
production activity reported is very closely
correlated to all lime production activity,
the highest score possible is 9.
0.63
Spatial
Congruity
9
The emission factor was developed for
a region larger than the one it is
applied to; it is not based on state-level
production and emissions. However,
spatial variability for the emissions
factor is assumed to be low.
10
States use state-level activity data to
estimate statewide emissions.
0.90
Temporal
Congruity
7
The emission factor is based on mass
balance, not on measured emissions
over a particular time frame. The use
of pollution control equipment
introduces additional variability,
assumed to be low to moderate.
10
States use annual activity data to estimate
annual emissions.
0.70
Composite Score 0.74
-------
DARS SCORES: C02 EMISSIONS FROM LIMESTONE USE
Emission
Factor
Attribute
Explanation
Activity
Data
.¦J (tribute
Explanation
Emission
Score
Measurement
8
Because the emission factor is not
based on measurement, the highest
possible score is 5. The emission factor
is based on a precise stoichiome tric
relationship. Applying the DARS
formula, the score would be 5.
However, the relationship is precise,
although some carbon may not be
released as C02 when lime is used for
certain purposes.
6
Data for limestone consumption (from
which CO2 is emitted as a by-product) are
based on a proxy (limestone sales).
0.48
Source Specificity
10
The, emission foe,tor was developed
specifically for the, intended emission
source.
10
Limestone consumption - the activity
measured with a proxy - is the activity from
which CO2 is emitted
100
Spatial
Congruity
9
The emission factor was developed for
a region larger than the one it is
applied to; it is not based on state-level
production and emissions. However,
spatial variability for the emissions
factor is assumed to be low.
3
States may need to estimate the state-level
activity data based on national-level data;
in that case, spatial variability is expected
to be high
0.27
Temporal
Congruity
9
The emission factor is based on
stoichiometry, not on measured
emissions over a particular time frame.
However, the emission factor should
not vary significantly over the course
of a year.
10
States use annual activity data to estimate
annual emissions.
0.90
Composite Score 0.66
-------
DARS SCORES: C02 EMISSIONS FROM SODA ASH MANUFACTURE AN
D CONSUMPTION
Emission
Factor
Attribute
Explanation
Activity
Data
.¦J (tribute
Explanation
Emission
Score
Measurement
5
Because the emission factors are not
based on measurement, the highest
possible score is 5. The emission
factors are based on a stoichiometric
relationship. Applying the DARS
formula, the score would be 5.
However, the relationship is precise,
although C02 emissions from
consumption are less for some uses.
7.5
Data on soda ash manufacture are
aggregated from intermittent
measurements, suggesting a score of 9.
Data for soda ash consumption are based
on a proxy (sales), suggesting a score of 6.
The composite score is 7.5.
0.38
Source Specificity
10
The emission factor was developed
specifically for the intended emission
source.
10
The activities measured (either directly or
by proxy) are the activities from which CQj
is emitted
LOO
Spatial
Congruity
9
The emission factor was developed for
a region larger than the one it is
applied to; it is not based on state-level
production and emissions. However,
spatial variability for the emissions
factor is assumed to be low.
10
States use state-level activity data to
estimate statewide emissions.
0.90
Temporal
Congruity
9
The emission factor is based on mass
balance, not on measured emissions
over a particular time frame. However,
the emission factor should not vary
significantly over the course of a year.
10
States use annual activity data to estimate
annual emissions.
0.90
Composite Score 0.79
-------
DARS SCORES: C02 EMISSIONS FROM CARBON DIOXIDE
MANUFACT1
LIRE
Emission
Factor
Attribute
Explanation
Activity
Data
.¦J (tribute
Explanation
Emission
Score
Measurement
3
The U.S. GHGinventory emission
factor (C02 emitted equals 20 percent
of C02 consumed for uses other than
enhanced oil recovery) is based on an
estimate by the Freedonia Group that
20 percent of C02 is produced from
natural wells.
3
The Freedonia Group's method for
determining U.S. C02 consumption is not
described in the U.S. GHG inventory.
0.09
Source Specificity
10
The emission factor was developed
specifically for the intended source
category.
5
State population is somewhat correlated to
the emission process.
0.50
Spatial
Congruity
7
The emission factor was developed for
the U.S. as a whole; spatial variability
is expected to be moderate.
10
States use state population data to estimate
state emissions.
0.70
Temporal
Congruity
10
The emission factor was developed to
estimate annual emissions.
8
States may use population data from the
Census Bureau's most recent census data;
temporal variability is expected to be low.
0.80
Composite Score 0.52
-------
DARS SCORES: C02 EMISSIONS FROM NITRIC ACID PRODUCTION
Emission
Factor
Attribute
Explanation
Activity
Data
.¦J (tribute
Explanation
Emission
Score
Measurement
3
Because the emission factor is based
on measurement, the lowest possible
score is 5. However, the measurement
was from a single plant, and a large
range in emissions was measured at
that plant.
9
Data on nitric acid production (pom which
N20 is emitted as a by-product) are
aggregated from intermittent measurements.
0.27
Source Specificity
10
The, emission factor was developed
specifically for the, intended emission
source.
10
The activity measured (nitric acid
production), is the activity from which Nf )
is emitted
1.00
Spatial
Congruity
9
The emission factor was developed for
a region larger than the one it is
applied to; it is not based on state-
level production and emissions.
However, spatial variability for the
emissions factor is assumed to be low.
10
States use state-level activity data to
estimate statewide emissions.
0.90
Temporal
Congruity
$
Because the emission factor is based
on mass balance, not on measured
emissions over a particular time frame.
However, the emission factor should
not vary significantly over the course
of a year.
10
States use annual activity data to estimate
annual emissions.
0.90
Composite Score 0.77
-------
DARS SCORES: EMISSIONS OF HFC'S AND PFC'S FROM CONSUMPTION OF SUBSTITUTES FOR OZONE DEPLETING
SUBSTANCES
Emission
Factor
Attribute
Explanation
Activity
Data
Attribute
Explanation
Emission
Score
Measurement
3
National vintaging model estimate is
based on a crude mass balance
approach that estimates leak rates for
equipment containing ODS substitutes,
and release profiles for such uses as
solvents and sterilants.
3
Per-capita national estimate (based on a
vintaging model) and state population are
used to estimate state emissions.
0.09
Source Specificity
10
The emission factor was developed
specifically for the intended source
category.
5
State population is somewhat correlated to
the emission process.
0.50
Spatial
Congruity
9
The emission factor was developed for
the U.S. as a whole; spatial variability
is expected to be low.
5
States use state population data and
national consumption data to estimate state
emissions; spatial variability is expected to
be moderate to high.
0.45
Temporal
Congruity
10
The emission factor was developed to
estimate annual emissions.
8
States may use population data from the
Census Bureau's most recent census data;
temporal variability is expected to be low.
0.80
Composite Score 0.46
-------
DARS SCORES: EMISSIONS OF SF6 FROM ELECTRIC UTILITIES
Emission
Factor
Attribute
Explanation
Activity
Data
.¦J (tribute
Explanation
Emission
Score
Measurement
5
The emission factor used in the U.S.
greenhouse gas inventory, to estimate
U.S. emissions from this sector, was
based on mass balance.
6
Electricity consumption is used as a proxy
for the number of transformers from which
SF6 would leak.
0.30
Source Specificity
7
The emission factor was based on
atmospheric concentrations ofSF6, as
emitted from all sources. Expected
variability is low to moderate.
5
Electricity consumption is somewhat
correlated to the emissions process.
0.35
Spatial
Congruity
7
The emission factor was developed
based on global emissions, not U.S.
emissions. Spatial variability is
expected to be moderate.
10
States use state data on electricity
consumption to estimate state emissions.
0.70
Temporal
Congruity
7
The emission factor was based on total
emissions since 1950; temporal
variability is expected to be low to
moderate.
10
States use annual data on electricity
consumption to estimate annual emissions.
0.70
Composite Score 0.51
-------
APPENDIX D
WASTE
I. CH4, CO2 and N2O from Solid Waste Disposal
Waste is assumed to produce biogas for 30 years. For this reason, it is necessary to
know how much waste has been placed in landfills in the past. Because data on Iowa
landfilling quantities only began to be collected in 1989, waste in place (WIP) over the past
30 years was estimated on the worksheet titled "Estimation of Waste in Place from 1960 -
2000." This was done by back casting state population and EPA national per capita
landfilling rates to estimate waste quantities for 1970-1988. Historical Iowa population data
was taken from the U.S. census website for 1970, 1980, 1990 and 2000. Uniform population
growth was assumed across decades to estimate annual populations. Annual populations
were multiplied by the United States per capita municipal solid waste landfilling rates as
presented in the updated EIIP State Workbook for 2002. The same approach was taken to
estimate WIP for 1990.
The first step on worksheet "CH4, CO2 and N2O Emissions from Municipal Landfills,
Industrial Landfills and Waste Incineration" was to estimate the WIP in small vs. large MSW
landfills. As directed in the state workbook, it was assumed that 81% was in landfills
containing more than 1.1 million tons of WIP (column Bi) and 19% of the total waste was in
landfills with less than 1.1 millions tons WIP (column Ci).
High moisture content can affect the microbial environment and result in more biogas
production. Emission factors were provided for arid and non-arid states (column B2). Iowa is
considered a non-arid state with more than 25 inches of rain per year and therefore delivers
more moisture content to the landfill producing more biogas per ton of waste than arid states.
For lack of state specific data, emissions from industrial landfills were estimated
based on the assumption that they produce an additional 7% of CH4 emissions (column B4).
Of course, this is a very rough estimate that leaves much uncertainty about the amount of
emissions coming from industrial landfills.
Biogas can be flared, collected and burned for energy, or naturally oxidized. Each of
these processes converts CH4 to C02. These activities essentially cancel out the excess
global warming potential that is counted in the inventory from the anaerobic decomposition
of organic matter. Thus, for landfills that participate in flaring and energy recovery, there are
essentially no CH4 emissions to count. There are 4 landfills known to be collecting gas for
energy projects; Des Moines Metro, Cedar Rapids Bluestem, Scott County and Johnson
County. The 28,000 tons of CH4 recovered from these sites were credited to the inventory,
though data for CH4 recovery from Johnson County was not available (column E6). The EPA
assumes that 10% of the net CH4 emissions are oxidized in the landfill and surrounding soils.
Only the remaining 90% is available as CH4 emissions (column E7).
When the waste has decomposed to a point where no more biogas is produced, the
movement of carbon out of the landfill is assumed to have stopped. This results in the
sequestering the carbon in the landfill. Once the carbon is sequestered in the landfill, it can
no longer escape as a greenhouse gas. The EPA assumes that 0.18 tons of carbon for every
one ton of municipal solid waste is sequestered in this way. Thus 18% of the MSW is
considered to be carbon that is trapped in the landfill (column D8). This carbon is subtracted
from landfill emissions.
-------
Finally, waste incineration is a disposal method that produces CO2 and N2O
emissions. This source represents only a very small amount of emissions because
incineration is not common in Iowa. From their annual survey, Biocycle magazine estimates
that less than 1% of Iowa MSW is burned (Goldstein & Madates, 2001). In this inventory,
1% waste incineration is assumed (column B9). Emissions were determined by multiplying
1% of the waste in the inventory year by the appropriate emission factor provided by the EIIP
(columns D9 and F9).
-------
CH4, C02 and N20 Emissions from Municipal Landfills, Industrial Landfills and Waste Incineration
2000,1990 recalculated
Estimate Fraction of Waste in Place (WIP) in Large Versus Small MSW Landfills
Column Label A: B: C:
Calculation
D,
input
input
input
AjxBj
A1 x Ct
Year
2000
1990 recalc
Waste in Place (WIP)
Fraction of WIP in
Landfills w/ more than
1.1 million tons
Fraction of WIP in
Landfills w/ less than 1.1
millions tons
WIP in Large
Landfills
WIP in Small
Landfills
tons
tons
tons
73,831,353 0.81 0.19
67,406,850 0.81 0.19
Estimate Methane Generated from WIP at Small MSW Landfill
A, B, C,
59,803,396
54,599,548
14,027,957
12,807,301
input A2 x B2 C2 x .0077
WIP in Small
Landfills
Non Arid Emission
Factor
Methane Produced
Methane Produced
tons
ft"5 CHd/ton/clay
ftJ CHj/day
tons CH4/ yr
2000 14,027,957
1990 recalc 12,807,301
Estimate Methane Generated from WIP
As
0.35 4,909,785
0.35 4,482,556
at Large MSW Landfills
B3 C^
37,805
34,516
D3 e3 f3
Et input A3/B3 input B3 x (C3 x D3) E3 x .0077
WIP in Large
Landfills
Number of Large
Landfills
Avg WIP at Large
Landfills, Wavg
Non Arid Emission
Factor and
Correction Factor
Methane
Production
Methane Produced
tons
landfills
avs tons/ landfill
ftVday
tons CH4/yr
2000 59,803,396 15 3,986,893 417,957 + 0.26 21,818,238 168,000
1990 recalc 54,599,548 15 3,639,970 417,957 + 0.26 20,465,238 157,582
Estimate Methane Generated from Industrial Landfills
Ai Bi C4
F3 + D2 input A4 x B4
Total CH4 Produced
at Small and Large
Landfills
Fraction of CH4
generated in industrial
landfills
Total CH4 Produced at
Industrial Landfills
tons/year
tons/year
2000 205,806 0.07 14,406
1990 recalc 192,098 0.07 13,447
Gross CH4 from Landfills in 2000 unadjusted oxidation
A5 C^ Ds
F3 D2 C4 A5 + B5 + C5
CH4 from Large
Landfills
CH4 from Small
Landfills
CH4 from Industrial
Landfills
Unadjusted Gross
CH4 Emissions from
Landfills
tons CH4/yr
tons CH4/yr
tons CH4/yr
tons CH4/yr
2000 168,000 37,805 14,406 220,212
1990 recalc 157,582 34,516 13,447 205,545
Estimation of Methane Recovered at four Iowa Landfill Gas to Energy Projects
Ag Bg Cg Dg Eg
input input input input A6 + B6 + C6 + D6
Metro
Bluestem
Scott Co
Johnson Co
Total
tons CH4
tons CH4
tons CH4
tons CH4
tons CH4
2000 14,240 10,415 3,949 Not Available 28,604
1990 recalc 10,585 10,585
difference 18,019
-------
Adjustment for Oxidation of CH4
A7 B7 C7 D7 E7
D5 E6 A7 - B7 input C7 x D7
CH4 Emissions from
Landfills
CH4 Recovered from
Landfills
Net CH4 Emissions from
Landfills
Unoxidized Fraction
Unoxidized
Landfill CI^
tons CH4
tons CH4
tons CH4
tons CH4
2000 220,212 28,604 191,608 0.9 172,447
1990 recalc 205,545 10,585 194,960 0.9 175,464
Estimation Carbon Sequestration in Landfill
Ag Bo Co Do
input input A8 x B8 C8 x .9072
MSW Landfilled in
Inventory Year
Fraction of Carbon
Sequestered
Carbon Sequestered
Carbon Sequestered
tons
tons Ci ton MSW
tons
metric tons
2000 2,531,456 0.18 455,662 413,377
1990 recalc 2,533,551 0.18 456,039 413,719
Estimation Emissions from Incinerated Waste
a9 b9 c9 d9 e9 f9 g9
input input A9 x B9 input input C9 x D9 C9 x E9
MSW Landfilled in
Inventory Year
Fraction of Waste
Incinerated
MSW Incinerated
C02 Emission Factor
N20 Emission
Factor
Nonbiogenic C02
Emissions
N20 Emissions
tons
tons
ton COi / ton waste
ton NiO / ton waste
tons COi
tons NiO
2000 2,531,456 0.01 25,315 0.4 0.0001 10,126 3
1990 recalc 2,533,551 0.01 25,336 0.4 0.0001 10,134 3
Net Total Emissions from Solid Waste
Landfill CH, (E7)
C Sequestration (D8)
Incineration C02 (F9)
Incineration N20 (G9)
Net Total
199U Recalculated
1600
Tons
MTCE
Tons
MTCE
175,464
911,672
172,447
895,999
-456,039
-413,719
-455,662
-413,377
10,134
2,507
10,126
2,505
3
194
3
194
500,655
485,322
-------
A
B
c
D
input
input
A x B = C
C x 1.638
Estimated Iowa
MSW
Landfilling
Estimate of Waste
Adjusted Estimate nl
Iowa Waste
Population
Rate
Landfilled
Landfilled
(people)
(tons/ person)
(tons)
(tons)
1960
2,757,537
0.31
854,836
1,400,222
1961
2,764,221
0.32
884,551
1,448,894
1962
2,770,905
0.33
914,399
1,497,785
1963
2,777,589
0.34
944,380
1,546,895
1964
2,784,273
0.36
1,002,338
1,641,830
1965
2,790,957
0.37
1,032,654
1,691,487
1966
2,797,640
0.38
1,063,103
1,741,363
1967
2,804,324
0.39
1,093,686
1,791,458
1968
2,811,008
0.41
1,152,513
1,887,817
1969
2,817,692
0.42
1,183,431
1,938,459
1970
2,824,376
0.43
1,214,482
1,989,321
1971
2,833,319
0.44
1,246,660
2,042,030
1972
2,842,262
0.45
1,279,018
2,095,032
1973
2,851,206
0.47
1,340,067
2,195,029
1974
2,860,149
0.48
1,372,871
2,248,763
1975
2,869,092
0.49
1,405,855
2,302,791
1976
2,878,035
0.50
1,439,018
2,357,111
1977
2,886,978
0.51
1,472,359
2,411,724
1978
2,895,922
0.52
1,505,879
2,466,630
1979
2,904,865
0.53
1,539,578
2,521,829
1980
2,913,808
0.54
1,573,456
2,577,321
1981
2,900,103
0.55
1,595,056
2,612,703
1982
2,886,397
0.55
1,587,519
2,600,355
1983
2,872,692
0.55
1,579,981
2,588,008
1984
2,858,987
0.55
1,572,443
2,575,661
1985
2,845,282
0.55
1,564,905
2,563,314
1986
2,831,576
0.56
1,585,683
2,597,348
1987
2,817,871
0.56
1,578,008
2,584,777
1988
2,804,166
0.56
1,570,333
2,572,205
TOTAL (1970- 1988)
28,023,170
Wllllll 1 OIIIM^l'1
FY 1989
2,790,460
0.56
1,562,658
2.385.135
FY 1990
2,776,755
0.56
1,554,983
2.533,551
FY 1991
2,791,397
0.55
1,535,269
2.444.272
FY 1992
2,806,040
0.53
1,487,201
2.087.821
FY 1993
2,820,682
0.52
1,466,755
t-J
'vJ
FY 1994
2,835,325
0.50
1,417,662
2.187.859
FY 1995
2,849,967
0.46
1,310,985
2.351.130
FY 1996
2,864,609
0.46
1,317,720
2.360.704
FY 1997
2,879,252
0.47
1,353,248
2.262.906
FY 1998
2,893,894
0.47
1,360,130
2.244.634
FY 1999
2,908,537
0.48
1,396,098
t-J
t-J
'vJ
FY 2000
2,923,179
0.46
1,344,662
2.531,456
Total (1989- 2000)
17,107,371
27,929,400
Ratio of Estimated Tonnage to
Actual T onnage
1.5
AVG 1.638
2000 Total Waste in Place (estimated for 1970-1988
H actual for 1989-2000) (tons)
73,831,353
1990 Total Waste in Place (estimated for 1960-1988
H actual for 1989-1990) (tons)
67,406,850
a Source: Nina Kroger, Iowa Department of Natural Resources Waste Bureau, personal contact, October 29, 2002
-------
MUNICIPAL SOLID WASTE PARS SCORES
DARS SCORES: CH4 EMISSIONS FROM LANDFILLS
Emission
Factor
Attribute
Explanation
Activity
Data
Attribute
Explanation
Emission
Score
Measurement
3
The factors are derived from a model which in
turn draws from a limited set of measurements.
6
If a state uses the Workbook formula for waste in
place, the activity level is estimated based on state and
national data.
0.18
Source Specificity
10
The emission factors were developed
specifically for landfills.
7
The activity data are highly correlated to the
emissions process.
0.70
Spatial Congruity
5
Emission factors were developed for arid and
non-arid regions; but even within these regions,
spatial variability is probably moderate to high.
7
If a state uses the EIIP formula for waste in place, the
national average per capita waste landfilled is used
instead of state-specific data; spatial variability is
expected to be moderate.
0.35
Temporal
Congruity
8
The emission factor is based on a model that
estimates average annual emissions over a 30-
year time frame. Temporal variability is
expected to be low.
8
If a state uses the EIIP formula for waste in place, the
state's current population and population growth rate
is used to estimate waste placed over the past 30
years.
0.64
Composite Score 0.47
-------
DARS SCORES: LANDFIL]
L CARBON SEQUESTRATION
Emission
Factor
Attribute
Explanation
Activity
Data
.¦J (tribute
Explanation
Emission
Score
Measurement
5
The sequestration factor is based on
laboratory research data.
10
The amount of waste disposed is
measured by weighing garbage trucks
before and after they tip their waste at
the facility
0.50
Source Specificity
6
The sequestration factor was
developed for a subset of the source
category; expected variability is
moderate.
7
The activity data are highly correlated to
the sequestration process.
0.42
Spatial
Congruity
5
The sequestration factor was
developedfor waste from a North
Carolina community; spatial
variability is expected to be moderate
to high
10
States use state-level activity data to
estimate state-wide emissions.
0.50
Temporal
Congruity
5
The laboratory research was intended
to simulate long-term degradation of
organic wastes in a landfill; temporal
variability is expected to be moderate
to high.
10
States use activity data for a given year to
estimate carbon sequestration associated
with that year.
0.50
Composite Score 0.48
-------
DARS SCORES: C02 EMISSIONS FROM WASTE COMBUSTION
Emission
Factor
Attribute
Explanation
Activity
Data
.¦J (tribute
Explanation
Emission
Score
Measurement
5
The emission factor is based on an imprecise
mass balance relationship.
10
The amount of waste combusted is measured by
weighing garbage trucks before and after they tip
their waste at the combustor.
0.50
Source Specificity
10
The emission factor was developed specifically
for waste combustion.
9
The amount of waste combusted is very closely
correlated to the emissions process.
0.90
Spatial
Congruity
7
The emission factor is based on U.S. data, but
the content of nonbiogenic carbon in waste
varies depending on its source. Spatial
variability is expected to be moderate.
10
States use state-level activity data to estimate state-
wide emissions.
0.70
Temporal
Congruity
7
The emission factor is based on mass balance,
not on measured emissions over a particular
time frame. The variability of the emission
factor is expected to be low to moderate.
10
States use annual activity data to estimate annual
emissions.
0.70
Composite Score 0.70
-------
DARS SCORES: N20 EMISSIONS FROM WASTE COMBUSTION
Emission
Factor
Attribute
Explanation
Activity
Data
.¦J (tribute
Explanation
Emission
Score
Measurement
3
The emission factor was derived by averaging
widely-varying measurements made throughout
the world.
10
The amount of waste combusted is generally
measured accurately by weighing garbage trucks
before and after they tip their waste at the combustor.
0.30
Source Specificity
10
The emission factor was developed specifically
for waste combustion.
6
The amount of waste combusted is correlated to the
emissions process.
0.60
Spatial
Congruity
5
The emission factor is based on global, not
U.S., data; moreover, the emission level
depends on the nature of the waste combusted.
10
States use state-level activity data to estimate state-
wide emissions.
0.50
Temporal
Congruity
9
The emission factor is based on an average of
short-term measurements, not onyear-long
measurements. However, the emission factor is
not believed to vary significantly over the
course of a year.
10
States use annual activity data to estimate annual
emissions.
0.90
Composite Score 0.58
-------
II. CH4 and N2O from Municipal Wastewater Treatment
The estimation method is based on the default IPCC methodology also used by the
EPA for the national greenhouse gas inventory. The state population was multiplied by a per
capita BOD generation rate (column B) to determine the total BOD generated and delivered
to municipal treatment systems. It was assumed that 16.25% of wastewater and sludge was
treated anaerobically (column E) and with a methane emission factor 0.6 kg CH4/kg BOD5
(column G). To determine the nitrogen content of the wastes, the annual per capita
consumption of protein (column Q) was used from the online United Nations Food and
Agricultural Organization database (2004). For 2000, 41.9 kg of protein were consumed
annually by per capita in the United States. It was assumed that 16% of protein is nitrogen
(column R), thus 6.7 kg of nitrogen were consumed in 2000 per American. This number was
multiplied by the state population to determine the amount of nitrogen consumed and
released as waste (column U). With an emission factor of 0.01 kg N20-N/kg sewage-N, the
nitrous oxide emissions were determined. State population data came from the U.S. Census
Bureau 2000 census (2001). The remaining information was taken from the Inventory of
U.S. Greenhouse Gas Emissions and Sinks: 1990- 2000 (EPA, 2002).
-------
2000 CH4 and N20 Emissions from Municipal Wastewater Treatment
CELi Emissions from Municipal Wastewater Treatment
A B C D
input input A x B input
Cx(l-D)xEx365
G
input
H
FxG
I
H/2205 x 21 x (12/44)
Wastewater BOD
2000 IA Population Generation Rate
E
input
Fraction of
Fraction of BOD Wastewater BOD Quantity of BOD CH4 Emission
BOD Generated removed as Sludge Treated Anaerobically Treated Anaerobically Factor CH4 Emissions CH4 Emissions
people
kg BOD/capita/day
kg BOD/day
kg BOD/year
kg CKt/kg BOD
kgCK,
MTCE
2,926,324
0.065
190,211
0.90
0.1625
1,128,189
0.6
676,914
3,877
CEL Emissions from Municipal Sludge Treatment
J
C
BOD Generated
K
input
Fraction of BOD
removed as sludge
L
input
Fraction of Sludge
BOD Treated
Anaerobically
M
Jx KxL x 365
Quantity of BOD
Treated
Anaerobically
N
input
Methane Emission
Factor
O
MxN
CH4 Emissions
0/2205 x 21 x (12/44)
CH4 Emissions
kg BOD/day
kg BOD/yr
kg CH4/kg BOD
kgCH4
MTCE CH4
190,211
0.90
0.1625
10,153,704
0.6
6,092,222
34,892
N-.0 Emissions from Wastewater and Sludge Treatment
Q R S T
input input Q x R input
Annual Per Capita Annual per capita
Consumption of Fraction of nitrogen consumption of
Protein (2000) in protein nitrogen in protein State Population
U
SxT
State Annual
Consumption N in
protein
V W X Y
input UxV W/lOOOx(44/28) X x 310 x (12/44)
State Annual
Emissions of \,0
Emission Factor from wastewater N,0 Emissions
N20 Emissions
kg
kg
people
kg
kg N20-N/kg sewage-N
kg N20-N
metric tons
MTCE
41.90
0.16
6.70
2,926,324
19,618,076
0.01
196,181
308
26,064
Total CH4 (MTCE)
Total N20 (MTCE)
38,769
26,064
-------
1990 CH4 and N20 Emissions from Municipal Wastewater Treatment
CELi Emissions from Municipal Wastewater Treatment
A B C D
input input A x B input
Cx(l-D)xEx365
G
input
H
FxG
I
H/2205 x 21 x (12/44)
Wastewater BOD
2000 IA Population Generation Rate
E
input
Fraction of
Fraction of BOD Wastewater BOD Quantity of BOD CH4 Emission
BOD Generated removed as Sludge Treated Anaerobically Treated Anaerobically Factor CH4 Emissions CH4 Emissions
people
kg BOD/capita/day
kg BOD/day
kg BOD/year
kg CKt/kg BOD
kgCK,
MTCE
2,776,755
0.065
180,489
0.90
0.1625
1,070,526
0.6
642,315
3,679
CEL Emissions from Municipal Sludge Treatment
J
C
BOD Generated
K
input
Fraction of BOD
removed as sludge
L
input
Fraction of Sludge
BOD Treated
Anaerobically
M
Jx KxL x 365
Quantity of BOD
Treated
Anaerobically
N
input
Methane Emission
Factor
O
MxN
CH4 Emissions
0/2205 x 21 x (12/44)
CH4 Emissions
kg BOD/day
kg BOD/yr
kg CH4/kg BOD
kgCH4
MTCE CH4
180,489
0.90
0.1625
9,634,732
0.6
5,780,839
33,108
N-.0 Emissions from Wastewater and Sludge Treatment
Q R S T
input input Q x R input
Annual Per Capita Annual per capita
Consumption of Fraction of nitrogen consumption of
Protein (2000) in protein nitrogen in protein State Population
U
SxT
State Annual
Consumption N in
protein
V W X Y
input UxV W/lOOOx(44/28) X x 310 x (12/44)
State Annual
Emissions of \,0
Emission Factor from wastewater N,0 Emissions
N20 Emissions
kg
kg
people
kg
kg N20-N/kg sewage-N
kg N20-N
metric tons
MTCE
39.20
0.16
6.27
2,776,755
17,415,807
0.01
174,158
274
23,138
Total CH, Emissions
Total N20 Emissions
36,787
23,138
-------
MUNICIPAL WASTEWATER PARS SCORES
DARS SCORES: CH4 EMISSIONS FROM MUNICIPAL WASTEWATER AN
D SLUDGE
Emission
Factor
Attribute
Explanation
Activity
Data
Attribute
Explanation
Emission
Score
Measurement
5
Because the emission factor is not based on
measurement, the highest possible score is 5. It
is assumed that the MetcalfandEddy (1 972)
study used pilot study data.
4
Uncertainty arises if the state uses default values for
factors such as the fraction of wastewater treated
anaerobically, or the fraction of BOD removed as
sludge.
0.20
Source Specificity
6
The emission factor was developed for
wastewater treatment, with moderate to high
variability.
5
The "activity" measured (population) is somewhat
correlated to the emissions process.
0.30
Spatial
Congruity
5
The emission factor was developedfor the U.S.
as a whole, and spatial variability is probably
moderate to high, varying as a function of
severalfactors.
5
States use state-level activity data to estimate
statewide emissions, but variability exists at the
treatment system level.
0.25
Temporal
Congruity
5
Temporal variability is expected to be
moderate to high.
10
States use annual activity data to estimate annual
emissions.
0.50
Composite Score 0.31
-------
DARS SCORES: N20 EMISSIONS FROM MUNICIPAL WASTEWATER AN
D SLUDGE
Emission
Factor
Attribute
Explanation
Activity
Data
.¦J [tribute
Explanation
Emission
Score
Measurement
3
The emission factor is an estimate based
on available data.
3
Activity level is based on population, estimated
protein consumption, and estimated fraction of
nitrogen in protein
0.09
Source Specificity
8
The emission factor was developed for
agricultural use of nitrogen fertilizers
5
Activity data are somewhat correlated to the
emissions process
0.40
Spatial
Congruity
7
The emission factor is based on global, not
US, data. Spatial variability is expected to
be moderate.
7
States use state-level population data and
global estimates for protein consumption and
nitrogen fraction in protein, to estimate
statewide emissions. Spatial variability is
expected to be moderate.
0.49
Temporal
Congruity
5
The emission factor is based on an
assumption that all N20 emissions from
nitrogen fertilizers, wastewater, or sludge
are emitted in the same year the fertilizer
is applied or the wastewater or sludge is
generated.
6
States use population data from the most recent
census, and the most recent available global
estimates for protein consumption and nitrogen
fraction in protein, to estimate annual
emissions. Temporal variability is expected to
be moderate.
0.30
Composite Score 0.32
-------
APPENDIX E
LAND-USE AND FOREST MANAGEMENT
Carbon in Timberland Trees. To determine the carbon sink in Iowa's forest trees, dry
weight data of "all live biomass" by major species group on timberland was downloaded
from the 1990 and 2001 Iowa forest inventories. The data category "all live biomass" is
described the total above-ground biomass of a sample tree 1.0 inch in diameter or larger,
including all tops and limbs (but excluding foliage) (personal communication, Patrick Miles,
North Central Research Station).
The choice to use data for "timberland" instead of "forestland" was for the reason
of comparison between inventory years. Timberland is a subset of forestland that produces
or is capable of producing a commercial wood crop. Dry weight data for forestland was
unavailable for the 1990 inventory. This is due to the fact that prior to 1995 tree
measurements such as biomass dry weight, were usually only taken on timberland plots
(personal communication, Patrick Miles, North Central Research Station). After 1995 these
measurements were taken on all forestland.
It was decided that the use of timberland data would be acceptable for two reasons.
The first reason being that alternative methods of calculation allowing for comparisons using
"growing stock" on forestland would involve greater generalization and broader assumptions
and ultimately greater error. The second reason being that timberland consistently dominates
Iowa's forest landscape accounting for 90.5 percent and 92 percent of all forestland in 1990
and 2000 respectively. Thus comparison of timberland trees will introduce less error. For all
other forest carbon pools, forestland data was used.
In the worksheet titled "Carbon in Timberland Trees for Years 1990 and 2001"
order to estimate the below ground biomass, the dry weight of "all live biomass" (column A)
was multiplied by a belowground expansion factor (column B) specified for the major
species type (soft vs. hardwood). The total above and belowground dry biomass was
multiplied by a regional carbon content fraction from Birdsey (1992). The resulting mass of
carbon in pounds was converted to short tons of carbon. This value represents the total
amount of carbon that is stored above and below ground in Iowa's timberland trees.
Carbon on the Forest Floor. To estimate the carbon stored in Iowa's forest floors, the area
of forestland delineated by Resource Protection Act (RPA) forest type was downloaded for
both the 1990 and 2001 forest inventories. RPA Forest type is a category assigned to a plot
of land by the forest service based on the dominant tree species present. Within a category,
other characteristics are assumed to be similar such as understory species and floor contents.
The total forest area by type (column A) is multiplied by a regional carbon content figure
(column B) for the particular forest type and converted to short tons of carbon.
For forest types reported in forest inventories that did not have a related carbon
coefficient, an average carbon value for Iowa forest floors was used. The value is a direct
finding of the 1992 Birdsey study. To get this number, the area of each type of Iowa
forestland was multiplied by estimated carbon values pulled from the literature then
multiplied by a factor that represents the Iowa forest age distribution. The resulting value,
11,724 lbs/acre, was actually lower than any of the forest specific carbon factors.
-------
Carbon in Forest Understory and Soil The carbon estimation procedure was essentially the
same for forest understory and soil and can be found on the worksheet titled "Carbon in
Forest Understory and Soil for Years 1990 and 2001." The total area of 1990 and 2001
forestland was multiplied by the appropriate region specific carbon factor (column B) which
came from Birdsey (1992). The figure was then converted to short tons.
Birdsey developed a regression model to relate soil carbon in relatively undisturbed,
secondary forests to temperature and precipitation. Using published regional weather records
and Iowa average forest age distribution; Birdsey determined an average per acre estimate of
soil carbon in Iowa forestland.
For carbon storage in the understory, Birdsey assigned to each forest age class a
percentage of overstory carbon that is assumed to equal understory carbon. At age 0,
understory biomass equaled 0 and peaked at 5 years. For Iowa forests 55 years and older, it
was assumed that 2% of overstory carbon equals the amount of carbon found in the
understory in most forests. In douglas fir and red pine forests, 1% of overstory carbon is
used. Using Iowa forest age distribution by forest type a weighted average value of carbon
per acre was determined.
-------
Carbon in Forest Floor for Years 1990 and 2001
A
input
input
C
A x B
D
Dx .0005
Forest Type
1990
Loblolly-Shortleaf Pint
Oak-Pine
Oak-Hickory
Elm-Ash-Cottonwooc
Maple-Beech-Birch
Aspen-Birch
White-Red-Jack Pine
Non stocked
Total
Forest Area
(acres)
Carbon Coefficient
of Forest Floor 1/
23,700
26,300
957,600
519,100
507,200
7,300
6,300
2,700
2,047,500
(Lbs/ acre)
23,061
23,061
12,045
11,724
16,663
16,663
11,724
11,724
Carbon in Forest
Floor
(Lbs)
Carbon in
Forest Floor
546,545,700
606,504,300
11,534,292,000
6,085,928,400
8,451,473,600
121,639,900
73,861,200
31,654,800
(tons C)
273,273
303,252
5,767,146
3,042,964
4,225,737
60,820
36,931
15,827
13,710,123
2001
Loblolly-Shortleaf Pine 40,094
Oak-Pine 32,793
Oak-Hickory 988,693
Elm-Ash-Cottonwood 660,434
Maple-Beech-Birch 799,251
Aspen-Birch 11,749
Oak-Gum-Cypress 9,504
Nonstocked 49,594
Unknown 14,517
2,606,630
23,061 924,615,863 462,308
23,061 756,247,156 378,124
12,045 11,908,811,431 5,954,406
11,724 7,742,922,413 3,871,461
16,663 13,317,925,245 6,658,963
16,663 195,773,587 97,887
11,724 111,426,596 55,713
11,724 581,440,759 290,720
11,724 170,197,308 85,099
17,854,680.2
1/ 11,724 is an average carbon storage number determined by region (North Central/Central) and state (IA) and is from Table 2.2 of Birdsey (1992)
The following are from Table 1.4 of Birdsey (1992) which estimates carbon on forest floor by region (North Central/Central) and state (IA)
12,045 lbs/acre is assigned specifically to "Oak-hickory and bottomland hardwoods"
16,663 lbs/acre is assigned specifically to the "Maple-beech and Aspen-birch" forest types
23,061 lbs/acre is assigned specifically to "Pines" forest type
23,122 is assigned specifically to "Spruce-fir" forest type
-------
Carbon in Timberland Trees for years 1990 and 2001
A
input
Above Ground Dry
Matter 1/
B
input
Below Ground
Expansion Ratio 2/
c
A xB
Below Ground
Dry Matter
D
A + C
Above & Below
Ground Dry
Matter
E
input
Percent
Carbon 3/
F
input
Unit
Conversion
Factor
G
D x E x F
Carbon in
Trees
(dry pounds)
(dry pounds)
(dry pounds)
(short tons C)
1990
Softwood
Hardwood
2001
Softwood
Hardwood
2,029,420,858
151,815,112,737
2,141,657,318
194,713,077,849
0.17
0.155
0.17
0.155
345,001,545.90
23,531,342,474.20
426,864,592.42
26,663,399,175.79
2,374,422,404.17
175,346,455,210.95
0.521
0.498
0.0005
0.0005
2,937,832,783.10 0.521
198,685,329,342.18 0.498
618,537.04
43,661,267.35
Total tons C 44,279,804.38
0.0005
0.0005
Total tons C
765,305.44
49,472,647.01
50,237,952.45
Change over Decade 5,958,148.06
Percent change over Decade 13.46
Change per year 595,814.81
1/ Source: Forest Inventory Analysis Online Database
2/Found inBirdsey (1992) Table 1.1 Ratio of total volume to merchantable volume.
3/ Found in Birdsey (1992) Table 1.2 Factors to convert tree volume (cubic feet) to carbon (pounds).
-------
Carbon in Forest Understory and Soil for Years 1990 and 2001
Carbon in Forest Understory on Forestland
A B C
input input A x B
Forest Area Forest Carbon Coefficient3 Carbon in Understory
D
C x .0005
Carbon in Understory
Year
1990
2001
(acres)
(lbs C/acre)
(lbs)
(short tons)
2,050,200
2,606,630
1,391
1,391
2,851,828,200
3,625,822,330
1,425,914.10
1,812,911.17
A
input
Forest Area
Carbon in Soil on Forestland
B C
input A x B
Soil Carbon Coefficient 1/ Carbon in Soil
D
C x .0005
Carbon in Soil
Year
1990
2001
(acres)
(lbs C/acre)
(lbs)
(short tons)
2,050,200
2,606,630
88,442
88,442
181,323,788,400
230,535,570,460
90,661,894.20
115,267,785.23
3 Found inBirdsey (1992) Table 2.2 Average Storage of Carbon in the United States by region, State and
forest ecosystem component, 1987.
-------
Carbon in Forest Floor for Years 1990 and 2001
A
input
input
C
A x B
D
Dx .0005
Forest Type
1990
Loblolly-Shortleaf Pint
Oak-Pine
Oak-Hickory
Elm-Ash-Cottonwooc
Maple-Beech-Birch
Aspen-Birch
White-Red-Jack Pine
Non stocked
Total
Forest Area
(acres)
Carbon Coefficient
of Forest Floor 1/
23,700
26,300
957,600
519,100
507,200
7,300
6,300
2,700
2,047,500
(Lbs/ acre)
23,061
23,061
12,045
11,724
16,663
16,663
11,724
11,724
Carbon in Forest
Floor
(Lbs)
Carbon in
Forest Floor
546,545,700
606,504,300
11,534,292,000
6,085,928,400
8,451,473,600
121,639,900
73,861,200
31,654,800
(tons C)
273,273
303,252
5,767,146
3,042,964
4,225,737
60,820
36,931
15,827
13,710,123
2001
Loblolly-Shortleaf Pine 40,094
Oak-Pine 32,793
Oak-Hickory 988,693
Elm-Ash-Cottonwood 660,434
Maple-Beech-Birch 799,251
Aspen-Birch 11,749
Oak-Gum-Cypress 9,504
Nonstocked 49,594
Unknown 14,517
2,606,630
23,061 924,615,863 462,308
23,061 756,247,156 378,124
12,045 11,908,811,431 5,954,406
11,724 7,742,922,413 3,871,461
16,663 13,317,925,245 6,658,963
16,663 195,773,587 97,887
11,724 111,426,596 55,713
11,724 581,440,759 290,720
11,724 170,197,308 85,099
17,854,680.2
1/ 11,724 is an average carbon storage number determined by region (North Central/Central) and state (IA) and is from Table 2.2 of Birdsey (1992)
The following are from Table 1.4 of Birdsey (1992) which estimates carbon on forest floor by region (North Central/Central) and state (IA)
12,045 lbs/acre is assigned specifically to "Oak-hickory and bottomland hardwoods"
16,663 lbs/acre is assigned specifically to the "Maple-beech and Aspen-birch" forest types
23,061 lbs/acre is assigned specifically to "Pines" forest type
23,122 is assigned specifically to "Spruce-fir" forest type
-------
FOREST AND LAND-USE MANAGEMENT PARS SCORES
DARS SCORES: C02 Emissions from Forest Management and Landuse Change (Trees Only)
Emission
Factor
Attribute
Explanation
Activity
Data
Attribute
Explanation
Emission
Score
Measurement
8
The default sequestration factor (for tons of
carbon per ton of dry matter) is based on an
average of species-specific measurements.
6
The US Forest Service makes direct, periodic
measurements of forest timber stocks, using sampling.
However, the Forest Service does not estimate stocks of
non-forest trees (e.g., urban and suburban trees), and
excludes some forested land areas due to restricted
access.
0.48
Source Specificity
9
The default sequestration factor was developed
specifically for forest management and land use
change, but does not reflect differences among
tree species. (If Birdsey's species-specific values
were used, the score here would be ten.)
8
Activity data (change in forest stocks) are closely
correlated with the carbon sequestration and emission
process for trees.
0.72
Spatial Congruity
9
The default sequestration factor was developed
for all states, and spatial variability is low.
10
The US Forest Service measurements of forest stocks
are totaled by state.
0.90
Temporal
Congruity
9
The default sequestration factor is based on an
average of instantaneous measurements, not on
measured sequestration over a particular time
frame. However, the percentage of carbon in dry
matter should not vary over time.
6
Annual change in forest stocks is estimated based on net
change in forest stocks over several years. Year-to-year
variability in forest harvests within a given state is
expected to be moderate.
0.54
Composite Score 0.66
-------
DARS SCORES: C02 Emissions from Forest Management and Landuse Change (Understory, Forest Floor, and Soil Carbon)
Emission
Factor
Attribute
Explanation
Activity
Data
.¦J (tribute
Explanation
Emission
Score
Measurement
3
The default sequestration factors (pounds of
carbon stored, per forested acre, in the
understory, forest floor, and soils) are derived
from a model, and are based on forest timber
production and forest area for 1987; the
assumptions made in the model are not public.
1
The US Forest Service makes direct, periodic
measurements of forest area.
0.21
Source Specificity
10
The default sequestration factors were
developed specifically for forest management
and land use change.
4
Activity data (change in forest acreage) are
somewhat correlated with the carbon sequestration
and emission process for the understory and forest
floor, but not for soil carbon.
0.40
Spatial
Congruity
10
Separate default sequestration factors were
developed for each state.
10
The US Forest Service measurements of forest
acreage are totaled by state.
1.00
Temporal
Congruity
7
The default sequestration factors are based on
a model that uses 1987 data. Temporal
variability is expected to be low to moderate.
8
Annual change in forest acreage is estimated based
on net change in forest acreage over several years,
but temporal variability is expected to be low.
0.56
Composite Score 0.54
------- |