United States
Environmental Protection
** Agency
EPA/600/R-20/244
August 2020
www.epa.gov/ord
WMOST
^^atershed Managemeot^B^-
Sv>PP
ortTool^
Watershed Management
Optimization Support Tool
Benefits Module:
Theoretical Documentation
Office of Research and Development
Center for Environmental Measurement and Modeling

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EPA/600/R-20/244
August 2020
www.epa.gov/ord
Watershed Management Optimization
Support Tool Benefits Module:
Theoretical Documentation
by
Naomi Detenbeck
Atlantic Coastal Environmental Sciences Division
Center for Environmental Measurement and Modeling
Narragansett, Rl 02882
Jessica Balukas
Elena Besedin
Alyssa Le
ICF, Inc
Cambridge, MA 02140
Center for Environmental Measurement and Modeling
Office of Research and Development
U.S. Environmental Protection Agency
Narragansett, Rl 02882

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Notice and Disclaimer
The views expressed in this User Guide are those of the authors and do not necessarily reflect the views
or policies of the U.S. Environmental Protection Agency. This document was subjected to the Agency's
ORD review and approved for publication as an EPA document. Mention of trade names or commercial
products does not constitute endorsement.
Acknowledgements
Version 1 of this tool was supported through EPA Contract EP-C-13-039 to Abt Associates, with
contributions from Alyssa Le, Annie Brown, and Justin Stein. An early draft of Version 2 of this tool was
supported through EPA Contract EP-W-17-009 to Abt Associates, with contributions from Olivia Griot, Liz
Mettetal, R. "Karthi" Karthikeyan, and Pearl Zheng. This tool was finalized through EPA Contract
68HE0C18D0001 to ICF, Incorporated. Former ORISE participant Timothy Stagnitta also contributed to
conceptual planning.

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Contents
Notice and Disclaimer	ii
Acknowledgements	ii
List of Figures	iv
List of Tables	iv
Abstract	v
1	Introduction	1
2	Overview of Benefits and Co-benefits Calculated by the Module	2
2.1	Overview	2
2.2	Water Quality Benefits	2
2.2.1	Avoided drinking water treatment costs	2
2.2.2	Nonmarket value of water quality changes	2
2.3	Non Water Quality Co-benefits	5
2.3.1	Change in property values associated with green space	5
2.3.2	Canopy cover benefits	5
2.3.3	Green roofs	7
3	Calculation of Benefits	8
3.1	Stand-alone Spreadsheet	8
3.2	Calculating Benefits - Dollar Year	8
3.3	Water Quality Benefits	10
3.3.1	Changes in water treatment costs	10
3.3.2	Changes in total nonmarket benefits of water quality	11
3.4	Non Water Quality Co-benefits	14
3.4.1	Improved aesthetic quality of the landscape from increases in green space leading
to changes in property values	14
3.4.2	Canopy cover benefits	19
3.4.3	Green roofs	22
4	References	25
Appendix A. Illustrative calculations for cobenefits (link to spreadsheet)	27
Appendix B. Green space values database (shapefile and metadata for green values dataset)	27

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List of Figures
Figure 1. Benefit and co-benefit categories and valuation methodologies included
in the Benefits Module	3
List of Tables
Table 1. Canopy cover benefits and descriptions of the quantification/monetization sources	5
Table 2. Green roof benefits and descriptions of the quantification/monetization sources	8
Table 3. Cost data sources and associated dollar years	9
Table 4. Definition of variables in equation 3.1	10
Table 5. Definition of variables in equation 3.2	10
Table 6. Definition of variables in equation 3.3	11
Table 7. Parameter weights for WQI calculation	11
Table 8. Definition of variables in equation 3.5	12
Table 9. Definition of variables for equation 3.6	13
Table 10. Definition of variables for equation 3.11	15
Table 11. WMOST management practice and assumed distance from residences	16
Table 12. Green Space Values Database variable summary	17
Table 13. Default percent tree canopy values for each BMP associated with the increase
in green space benefit calculation	18
Table 14. Definition of variables in equation 3.12	19
Table 15. Definition of variables in equation 3.13	20
Table 16. Definition of variables in equation 3.14	20
Table 17. Definition of variables in equation 3.15	21
Table 18. Definition of variables in equation 3.16	21
Table 19. Definition of variables in equation 3.17	22
Table 20. Definition of variables in equation 3.18	23
Table 21. Definition of variables in equation 3.19	24

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Abstract
The Watershed Management Optimization Support Tool (WMOST) was developed by the United States
Environmental Protection Agency (US EPA) to facilitate integrated water resources management. The
new Benefits Module for WMOST enables stakeholders to calculate the value of additional water-quality
benefits associated with water resource management as well as additional co-benefits. Water quality
benefits (or costs) include both changes in costs of drinking water treatment and total nonmarket
benefits (i.e., use and nonuse) of water quality changes. Co-benefits valued include (1) change in
housing property value due to improved aesthetic quality of the landscape from increases in green
space, (2) air pollution removal and energy savings benefits related to canopy cover, and
(3) air pollution removal and energy savings benefits related to green roofs.

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1 Introduction
The Watershed Management Optimization Support Tool (WMOST) was developed by the United States
Environmental Protection Agency (US EPA) to facilitate integrated water resources management (IWRM;
Detenbeck et al. 2018 a,b,c). It enables users to find the least-cost solution to meet water quantity and
water quality related objectives, considering practices within stormwater, wastewater, drinking water,
and land conservation programs. By adopting an integrated water management approach, WMOST
promotes cost efficiencies and helps users to avoid unintended consequences, e.g., increased algal
blooms associated with increased retention time following decisions to maximize surface water supply
in reservoirs. In addition, the flooding module within previous versions of WMOST allowed users to
consider some ancillary benefits of management actions such as reductions in flood-related costs.
However, other ancillary water quality benefits and cobenefits of IWRM were not captured in previous
versions of WMOST. Increasingly, municipalities are adopting a more holistic approach to IWRM, and
doing comprehensive evaluations of environmental, social, and economic consequences of their
decisions (Stratus Consulting Inc. 2009).
The new Benefits Module for WMOST is designed to assess potential water quality-related benefits and
non-water quality co-benefits associated with IWRM decisions. Additional water quality-related
benefits include avoided costs for drinking water treatment to reduce suspended solids (US EPA 2009) as
well as values assigned by the general public to water quality improvements (U.S. EPA, 2015). The latter
values are calculated based on a meta-analysis of willingness-to-pay, using the Water Quality Index
(WQI) approach (U.S. EPA, 2015). Co-benefit values monetized within the Benefits Module include:
1)	changes in water treatment costs
2)	changes in total nonmarket benefits of water quality changes
3)	improved aesthetic quality of the landscape from increases in green space leading to
changes in property values (Mazzotta et al. 2014),
4)	avoided human health damages associated with reduced exposures to criteria air
pollutants related to canopy cover (Nowak et al. 2014),
5)	increased carbon sequestration related to canopy cover (IWGSCC 2016, U.S. EPA,
2019a),
6)	reduction in heating and cooling needs associated with canopy cover (Nowak et al.
2017) and green roofs (https://sustainabilitv.asu.edu/urban-climate/green-roof-
calculator/). and
7)	avoided emissions from power plants associated with lessened energy costs.
Although there are other potential benefits and co-benefits associated with watershed management
practices (including green infrastructure; Tzoulas et al. 2007, Meerow and Newell 2016, Environmental
Finance Center 2017), we chose to highlight this subset of benefits because of the availability of
published methods to assign monetary values to this specific set. The Benefits Module is built upon
a Microsoft Excel interface, using the skeleton of EPA's ScenCompare utility for WMOST to import
results log files from WMOST runs and extract decision variables of interest from the WMOST results
(US EPA 2020).
Instructions for running the WMOST Benefits Module in the ScenCompare interface are provided in a
separate User Guide (US EPA 2020). In the current document, we provide additional background on the
WMOST Benefits Module: Theoretical Documentation
1

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calculation of benefits and cobenefits. In Section 2 of this document, we provide additional background
on the theoretical basis for each of these benefits and co-benefit categories. In Section 3, we introduce
a stand-alone spreadsheet that illustrates the behind-the-scene calculations included in the Benefits
Module, and then describe the underlying calculations and data sources in greater detail.
2 Overview of Benefits and Co-benefits Calculated by the Module
2.1	Overview
Figure 1 below summarizes the benefit calculations and associated WMOST management practices
included in the standalone spreadsheet (Appendix A).
2.2	Water Quality Benefits
2.2.1	Avoided drinking water treatment costs
We can calculate the benefit of improved source surface water quality1 by estimating the reduction in
water treatment costs associated with lower total suspended solids (TSS) concentrations. The
methodology for this is laid out in the documentation for the 2009 Final Effluent Limitation Guidelines
and Standards for the Construction and Development Industry (US EPA 2009). The treatment costs
depend on turbidity levels and the costs of coagulants as turbidity is treated with varying doses of
chemical coagulants.
2.2.2	Nonmarket value of water quality changes
The US EPA has used the Water Quality Index (WQI) as a water quality metric in benefit cost analysis for
several rulemakings (U.S. EPA 2009, 2015). The WQI was developed to communicate complex water
quality information in valuation exercises (McClelland 1974). The WQI is a composite indicator that
combines information from multiple water quality parameters into a single overall value expressed on a
0 -100 scale. Creating the WQI involves three main steps (US EPA 2009): (1) obtaining measurements
on individual water quality indicators, (2) transforming measurements into "subindex" values to
represent them on a common scale, and (3) aggregating the individual subindex values into an overall
WQI value (Walsh and Wheeler 2013). We base the use and non-use value of water quality
improvements on the methodology from the 2015 Steam Electric Rule (SE ELG) (U.S. EPA 2015), which
used a meta-regression model (MRM) of the public's willingness-to-pay (WTP) for water quality
improvements. The SE ELG analysis expressed water quality improvements in WQI terms, calculated
mean household annualized WTP for water quality improvements on an annual and census block group
(CBG) basis, and multiplied the household WTP by the number of households that value the affected
resources to estimate the total annualized benefits. Some methodological updates were incorporated
to address the following critical elements of the approach.
1 This benefit calculation is limited to source surface water quality (flows to the water treatment plant from the
surface water and reservoir). It will not account for improvements to groundwater water quality.
2

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Effect of Management

Environmental Outcome

Benefit Category

Valuation Approach
Options
H
H
^¦1
Meet water quality targets
based on chosen
management options
to



M—

•



"to

Z>

a

i_

QJ

4-»

ro

5

c

o

z

' Changes to TSS concentrations ¦

Changes in water treatment
costs

Avoided water treatment costs
W
w
Implementation of land
conservation, bioretention
basin, grass swale, gravel or
constructed wetland, riparian
buffers, or increases in tree
canopy cover
Implementation of land
conservation, riparian buffers,
or increases in tree canopy
cover or urban/community
trees
Green roof implementation
Reduced criteria air pollutant
-~ levels from increased tree
canopy cover
Carbon sequestration from
increased tree canopy cover
Reduced urban heat island
effect from increased urban/
community trees or green roofs
Changes to surface water
quality (TN, TP, and TSS
concentrations)

Changes in total nonmarket
benefits of water quality
changes

Willingness-to-pay for water
quality improvements










Improved aesthetic quality of
	>•
Change in property values
Increased green space
	~
the landscape

Avoided human health damages •
j Human health benefits per
->• hectare of tree cover
!	(canopy cover) 	 _|
Increased carbon sequestration

Social cost of carbon
(canopy cover & green roofs)

Avoided emissions from power

Human health benefits per ton

of pollutant avoided
(canopy cover & green roofs)
plants

t


Reduction in heating/cooling

Electricity savings
needs

(canopy cover & green roofs)
Figure 1. Benefit arid co-benefit categories and valuation methodologies included in the Benefits Module.

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2.2.2.1	Water quality changes
The WQI used in the benefits assessment for the Construction and Development Rule (US EPA 2009)
included six parameters (US EPA 2009): dissolved oxygen (DO), biochemical oxygen demand (BOD), fecal
coliform (FC), total nitrogen (TN), total phosphorus (TP), and total suspended solids (TSS). The WQI
aggregates these multiple parameters into a single index value expressed on a 0-100 scale. For use
within the Benefits Module, we chose to use this six-parameter WQI, because of the greater simplicity of
use than the seven-parameter WQI used in the SE ELG.
Of the six parameters used in the WQI calculation, WMOST is capable of modeling three: TN, TP, and
TSS. Users can access USGS's National Water Information System (NWIS)2 for concentration data for the
other three parameters (DO, BOD, and FC). WMOST is used to estimate changes in values of TN, TP and
TSS, with the other three parameters assumed to remain constant. For example, if a user is using
WMOST to determine management options for reducing TN loads, the user can then also run WMOST in
simulation mode to understand how the same chosen management option(s) affect TP and TSS
concentrations and use USGS's NWIS to obtain data for DO, BOD, and FC for the same time period.
Please see the Benefits Module User Guide for more details (US EPA 2020b).
2.2.2.2	Estimating per household WTP
Our chosen approach applies the estimate per household WTP to all households residing in the affected
watershed, like the approach taken for the C&D Rule (2009). WMOST does not have the capability to
model water quality changes outside of the model study area. This limitation affects the substitute sites
variable (see Section 3.3.2) and the number of households that value the affected resource. The
substitute site proportion variable is set to one to indicate no substitute sites as WMOST cannot
quantify water quality changes outside of the study area. In addition, the number of households that
value the affected resource will be limited to households within the watershed boundary. Setting the
substitute sites variable to one may overestimate the per household WTP estimate by not accounting
for effects of available substitutes outside the watershed. However, limiting the number of households
to those within the watershed boundary will likely underestimate total WTP since households from
surrounding watersheds may also value the water quality improvements. Since we expect the number
of households to have a greater effect on the total WTP estimate than the substitute sites variable, this
methodology will likely produce a conservative estimate.
Overall, we have not made any changes to the benefit function used to estimate total WTP for a change
in water quality. Instead, we have made changes to the data sources and hard-coded values for the
meta-analysis function input variables (as shown in the tables in Section 3.3.2).
2 USGS's NWIS dataset provides information on the occurrence, quantity, quality, distribution, and movement of
surface and underground waters based on data collected at approximately 1.5 million sites in all 50 States, the
District of Columbia, and U.S. territories. More information on NWIS can be found at
http://waterdata.usgs.gov/nwis/

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2.3 Non Water Quality Co-benefits
2.3.1	Change in property values associated with green space
The calculation of increased housing prices resulting from increased green space (both natural and
constructed green infrastructure) uses the coefficients from a meta-regression of results in existing
hedonic literature from Mazzotta et al. (2014) to estimate the anticipated percent change in housing
prices per HUC12 or HUC10. Following the methodology in Mazzotta et al. (2014), the percentage
change in annual rental value of a property is dependent on the percentage change in green space,
distance of green space from residences, characteristics of the changed green space, and population
density. We have adjusted in calculations and underlying data to account for differences in the effects
of changes to green space for land uses with varying population densities and within varying distances
from residences.
2.3.2	Canopy cover benefits
Canopy cover benefits are based on increased acres of overall canopy cover and whether this increase
includes urban/community trees. Increased acres of canopy cover results in increased carbon
sequestration and increased removal of criteria air pollutants3 that cause negative human health
impacts (N02, S02, 03, PM2.5). Increased acres of urban/community trees also result in energy cost
savings for nearby buildings and subsequent reductions in criteria air pollutants (NOx, S02, and PM2.5)
and carbon dioxide from avoided power plant emissions.4 Table 1 describes the canopy cover benefits
in more detail, along with the sources used to quantify and monetize each benefit and the region type of
each source (e.g., national, state-level, local).
Table 1. Canopy cover benefits and descriptions of the quantification/monetization sources
Environmental
Outcome
Benefit
Source(s)
Region Type
Increased acres
of canopy
cover
Increased carbon
sequestration
Social Cost of Carbon:
Global: IWGSCC (2016)
Domestic: U.S. EPA (2019a)
National
Avoided human health
damages resulting from tree
removal of air pollutants
(N02, S02, 03, PM2.5)
Nowak et al. (2014)
National
(regressions);
county-level
(population density)
Increased acres
of urban/
community
trees
Electricity savings
Nowak et al. (2017); personal
communication with authors
State-level
Avoided human health
damages from avoided NOx,
S02, and PM2.5 emissions
from power plants
Quantification: Nowak et al.
(2012); Nowak etal. (2017)
Monetization: U.S. EPA
(2018)
Quantification:
State-level
Monetization:
National
3	EPA has established national ambient air quality standards (NAAQS) for six of the most common air pollutants-
carbon monoxide (CO), lead (Pb), ground-level ozone (03), particulate matter (PM2.5), nitrogen dioxide (N02; NOx),
and sulfur dioxide (S02; SOx) — known as "criteria" air pollutants. The primary NAAQS are set to protect
public health.
4	Avoided human health damages for acres of tree canopy overall and acres of urban/community trees differ
because power plants do not emit ground-level ozone.
WMOST Benefits Module: Theoretical Documentation
5

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Table 1 (Continued)
Environmental



Outcome
Benefit
Source(s)
Region Type
Increased acres

Quantification: Nowak et al.
Quantification:
of urban/
Avoided C02 emissions from
(2012); Nowak etal. (2017)
State-level
community
power plants
Monetization: IWGSCC
Monetization:
trees

(2016), U.S. EPA (2019a)
National
2.3.2.1	Increased acres of canopy cover
Increased carbon sequestration
Management actions that will increase carbon sequestration in the landscape include riparian buffer
restoration, increasing canopy cover to developed land, and conserving forested land areas. While
other green infrastructure practices could also increase carbon sequestration, carbon sequestration
values for other constructed green infrastructure practices are not readily available and are likely of
smaller magnitude than those associated with changes in canopy cover. Calculating the value of annual
carbon sequestration is a two-step process: (1) multiplying the increase in tree cover by the rate of
carbon sequestration to obtain the annual amount of carbon sequestered by the additional canopy
cover; and (2) multiplying the annual amount of carbon sequestered by the social cost of carbon.
EPA is currently using interim values of the domestic social cost of carbon (SC-C02) to inform Federal
regulatory analyses (U.S. EPA, 2019). For this application, we are giving the WMOST user the option to
calculate co-benefits using either the interim domestic or global social cost of carbon or both. We
propose giving the user these options for several reasons. First, available domestic social cost of carbon
values are unpublished; interim values have been developed under Executive Order 13783 (82 FR 16093,
March 31, 2017) for use in regulatory analyses until an improved estimate of the impacts of climate
change to the United States can be developed based on the best available science and economics.
Second, Executive Order 13783 required the use of domestic SC-C02 in regulatory impact analyses only.
WMOST users may use the tool for non-regulatory purposes, so they can decide whether domestic or
global SC-C02 values are appropriate for their analysis.
Avoided human health damages resulting from tree removal of air pollutants
Increased canopy cover results in increased removal of pollutants by trees and other plants. To quantify
the reduction in air pollutants due to trees, the Benefits Module multiplies the number of increased
canopy cover acres by pollutant removal rates from Nowak et al. (2014) and determines the pollutant
reductions in metric tons. To monetize the reduction in air pollutants due to trees, the benefits module
applies regression equations from Nowak et al. (2014) that estimate dollars per metric ton based on
population density.
2.3.2.2	Increased acres of urban/community trees
Electricity savings
In Nowak et al. (2017), calculations for energy savings from canopy cover first divide estimated total
energy savings from urban/community trees (MWh) in the United States by the number of acres of
urban/community trees in the United States to determine average electricity savings per acre of trees.
The calculations then multiply the average nationwide price of a megawatt hour of residential electricity
by the electricity savings per acre of urban/community trees to determine the dollar value of electricity

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savings per acre. Finally, the dollar value of electricity savings per acre is multiplied by the number of
increased canopy cover acres expected under the optimization scenario to estimate the total value of
energy savings from canopy cover.
We substitute state-level per hectare values for residential energy conservation from Nowak et al.
(2017) instead of calculating a national-level per hectare value. Although the Nowak et al. (2017) paper
only provides average national per hectare benefit values in 2009 dollars ($455 for energy conservation,
$228 for avoided power plant emissions) and a range for the per hectare energy conservation values by
state (low of $123 in Montana to a high of $1,811 in Washington, DC),5 the authors provided state-level
values via personal communication.
Avoided human health damages from avoided emissions from power plants
We also add human health benefits from avoided power plant emissions related to reduced heating and
cooling energy requirements, using EPA-vetted benefit per ton values to monetize human health
benefits from avoided power plant emissions.6 Instead of using the state-level per hectare benefit
values for avoided power plant emissions provided by the authors of Nowak et al. (2017), which are
based on European willingness-to-pay values, we calculate state-level per hectare values using national-
level, EPA-vetted benefit per ton values. To develop state-specific per hectare values of avoided
emissions, we combine national-level benefits per ton estimates with the state-level estimates of
avoided emissions and hectares of urban/community trees.
Avoided C02 emissions from power plants
Reductions in energy use associated with heating and cooling costs also leads to reductions in C02
emissions from power plants. Residential building energy conservation and avoided power plant
emissions by urban and community trees in the United States are obtained from Nowak et al. (2017) and
the social cost of carbon is defined as described above (IWGSCC 2016, U.S. EPA 2019a).
2.3.3 Green roofs
Like canopy cover, green roofs provide value by reducing energy requirements associated with heating
and cooling. In turn, those reductions in energy use lead to avoided human health damages from
avoided NOx, S02, and PM2.5 emissions. C02 emissions from power plants are reduced as well (Table 2).
5	The large range is attributable to factors that can affect energy savings, including density of residential buildings,
energy usage between heating and cooling seasons, local energy costs, and presence (or density) of tree cover in
the vicinity of residential buildings (Nowak et al., 2017).
6	We note that Nowak et al. (2017) includes national level per hectare values of avoided power plant emissions.
The authors also provided state-level values via personal communication. However, both national-level and state-
level values are based on air pollution cost factors from Europe that may not be appropriate for the U.S. due to
differences in population characteristics (e.g., perceived values of the same benefit).
WMOST Benefits Module: Theoretical Documentation
7

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Table 2. Green roof benefits and descriptions of the quantification/monetization sources
Benefit
Source(s)
Region Type
Electricity savings
State-level: U.S. EIA (2018)
State-level (you can provide
local values)
Avoided human health
damages from avoided N0X,
SO2, and PM2.5 emissions from
power plants
Quantification: U.S. EPA
(2019b)
Monetization: U.S. EPA (2018)
Quantification: Regional
(AVERT regions)
Monetization: National
Avoided CO2 emissions from
power plants
Quantification: U.S. EPA
(2019b)
Monetization: IWGSCC
(2016), U.S. EPA (2019a)
Quantification: Regional
(AVERT regions)
Monetization: National
To calculate green roof benefits, the user will determine green roof energy savings by inputting
information about potential green roofs into Arizona State University's Green Roof Energy Calculator
tool.7 The calculator tool requires green roof characteristics and location, using the closest available city
to the study watershed of interest.
To quantify reductions in criteria air pollutants (S02, NOx, PM2.5) and carbon dioxide (C02) from reduced
energy consumption due to the installation of green roofs, the Benefits Module applies regional AVERT8
emission rates (U.S. EPA, 2019b) to convert energy savings into avoided emission of criteria air
pollutants (in lbs of S02, NOx, and PM2.5) and carbon dioxide (in tons of C02). The contiguous United
States is divided into ten AVERT regions. Four different types of regional AVERT emission rates are
available: wind, utility-scale photovoltaic, portfolio energy efficiency, and uniform energy efficiency.
3 Calculation of Benefits
3.1	Stand-alone Spreadsheet
A subset of the calculations of co-benefits related to canopy cover and green roofs is illustrated in a
stand-alone spreadsheet (Appendix A). This resource is provided to make calculations and associated
data sources more transparent to the user and to enable exploration of scenarios outside of those
associated with WMOST optimizations. It could also be used in conjunction with other regional decision
support tools requiring access to these calculations and look-up tables.
3.2	Calculating Benefits - Dollar Year
Within the Benefits Module, benefits are calculated using various costs that are either defined by the
user, the literature, or a government data source. As a result, benefit values are calculated in a variety
of dollar years. The table below summarizes the various cost data sources and associated dollar year for
each of the benefit calculations.
7	https://sustainability.asu.edu/urban-climate/green-roof-calculator/
8	AVoided Emissions and geneRation Tool

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As shown, benefit values are in a variety of dollar years. To streamline the calculation of benefits for
users, ensure benefit calculation consistency, and confirm the accuracy with which the benefit values
can be compared to WMOST outputs, the following approach is adopted. Users have the option to
adjust the default dollar year value (2016 - 2019) on the Benefits Module interface. The specified dollar
year will then be used in ScenCompare's background programming. For any benefits that include costs
or data input by users (e.g., avoided water treatment costs or value of energy savings), the user will
specify the dollar year of their data and the appropriate conversion will be made by the module's
background programming.
Table 3. Cost data sources and associated dollar years.

Dollar

User
Literature
Government
Benefit Valuation
Year
Cost
Input
Value
Data Source
Avoided water treatment costs Set by user
Cost of alum
X


Use and non-use of water
2007$
Mean income (converted



quality improvements

to 2007$ from user-
specified year)
X

X
Green space
2013$
Median home value
(converted to 2013$
from 2017$)


X
Carbon sequestration benefits
2016$
Global social cost of
carbon


X
Carbon sequestration benefits
2017$
Domestic social cost


X


of carbon


Avoided C02 emissions from
2016$
Global social cost of


Y
avoided power plant emissions

carbon


A
Avoided CO emissions from
2017$
Domestic social cost


X
avoided power plant emissions

of carbon


Human health benefits from
2010$
Benefit per ton



increased canopy cover (tree

estimates, using a



uptake of pollutants)

regression equation
based on population
density

X

Human health benefits from
2015$
Benefit per ton



increased canopy cover

estimates for PM . ,


X
(avoided power plant

SO , NO.


emissions)





Human health benefits from
2015$
Benefit per ton



green roof implementation

estimates for PM2.5,


Y
(avoided power plant

S02, NOx


A
emissions)





Value of energy savings from
2009$
Energy savings per

y

increased canopy cover

hectare of tree cover

A

Value of energy savings from
Set by user
Price of a MWh of
V

V
green roof implementation

residential electricity
A

A
WMOST Benefits Module: Theoretical Documentation
9

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3.3 Water Quality Benefits
3.3.1 Changes in water treatment costs
This benefit requires a comparison of TSS concentrations before (i.e., baseline) and after the
implementation of management options. We can convert our pre- and post- management practice TSS
concentrations (mg/L) to turbidity (nephelometric turbidity units [NTU]) using the following relationship
from EPA (2009):
(^SwWtp,t+^ResWtp,t j	^ ^3.79X10
J, _ V	/ bQswWtp,t + bQResWtp,t) \9.07X10~ /	^ ^
where variables are defined as follows:
Table 4. Definition of variables in equation 3.1
Variable
Definition
Value
Source
T
turbidity (NTU)
>0
Calculated from WMOST results
Lswwtp.t
TSS loadings from surface water to
the water treatment plant (tons)
>0
WMOST results
LResWtp,t
TSS loadings from the reservoir to the
water treatment plant (tons)
>0
WMOST results
bQswWtp,t
Flows from surface water to the
water treatment plant (MG)
>0
WMOST results
bQuesWtpt
Flows from the reservoir to the water
treatment plant (MG)
>0
WMOST results
b
estimated ratio of turbidity to TSS
1.5 by default, can
be edited by user9
EPA (2009)
The values 3.79x10s and 9.07x10s correspond to the conversion factors of liters per million gallons and
milligrams per ton, respectively.
Once we have calculated baseline and post-management practice implementation levels of turbidity, we
can calculate the baseline and post-management practice implementation dosage of the coagulant used
to treat turbidity (alum). The dosage of alum used to treat water of a given turbidity can be
approximated by the following relationship from EPA (2009):
Al = 33 log(T) - 28	(3.2)
where variables are defined as follows:
Table 5. Definition of variables in equation 3.2
Variable
Definition
Value
Source
Al
alum dose (mg/L)
>0
Calculated from WMOST results
T
turbidity (NTU)
>0
Calculated from WMOST results
9 The 2009 C&D rule used 0.8,1.5, and 2.2 for low, midpoint, and high benefit estimates. We recommend using 1.5
as the default value but allow users to adjust it in the interface. As discussed in EPA (2009), "as the value of b
decreases, a given level of TSS generates more turbidity, leading to higher treatment costs".

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If the calculated alum dose is negative, the value is adjusted to 0.
Finally, the cost of the alum required before and after the TSS reduction can be calculated for a given
volume of intake flow using a calculation from EPA (2009):
TCAl = ZtriQswwtpt + QReswtp,t) X	X CAl	(3.3)
where variables are defined as follows:
Table 6. Definition of variables in equation 3.3.
Variable
Definition
Value
Source
tcai
total annual alum cost ($/year)
>0
Calculated from WMOST results
QswWtp,t
daily flow from surface water system
to water treatment plant (MG/day)
>0
WMOST results
QResWtp,t
flow from reservoir to water
treatment plant (MG/day)
>0
WMOST results
Al
alum dose (mg/L)
>0
Calculated from WMOST results
Cai
cost of alum ($/ton)
>0
Set by the user
ndays
number of days in the year
>0
WMOST results
The values 3.79x10s and 9.07x10s correspond to the conversion factors of liters per million gallons and
milligrams per ton, respectively.
By calculating this value before and after the implementation of any management practices, the
difference can be used to measure the benefit of reduced water treatment costs due to TSS reductions
within the watershed.
3.3.2 Changes in total nonmarket benefits of water quality
After obtaining water quality levels for each of the six parameters included in the WQI, WMOST
transforms the parameter measurements into subindex values expressed on a common scale and
aggregates the subindices to obtain a daily or monthly overall WQI value. Table 7 summarizes the
weights used to aggregate the subindices to an overall WQI value using a geometric mean function (see
equation below the table).
Table 7. Parameter weights for WQI calculation.
Parameters
Weights
Dissolved oxygen
0.24
Fecal coliform
0.22
Biochemical oxygen demand
0.15
Total nitrogen
0.14
Total phosphorus
0.14
Total suspended solids
0.11
WMOST Benefits Module: Theoretical Documentation
11

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wqi = u?=1q71
(3.4)
WQI	=	the multiplicative water quality index (from 0 to 100)
Qi	=	the water quality subindex measure for parameter i
Wi	=	the weight of the /-th parameter
n	=	the number of parameters (i.e., six)
The marginal household willingness to pay calculation is as follows:10
In(MWTP) = —2.30 + 1.18 x northeast + 0.561 x central + 1.4 x south + 0.333 x
In (income) — 0.827 x mult_type — 0.079 x river — 0.271 x In (agricultural) —
0.034 x In (ar_ratio) + 1.1 x substitutes — 0.015 x Q	(3.5)
where variables are defined as follows:
Table 8. Definition of variables in equation 3.5
Variable
Definition
Value
Source
northeast
binary variable indicating that the affected
population is in a Northeast U.S. state, defined
as ME, NH, VT, MA, Rl, CT, and NY.
0,1
Set by the user
central
binary variable indicating that the affected
population is in a Central U.S. state, defined as
OH, Ml, IN, IL, WI, MN, IA, MO, ND, SD, NE, KS,
MT, WY, UT, and CO.
0,1
Set by the user
south
binary variable indicating that the affected
population is in a Southern U.S. state, defined
as NC, SC, GA, FL, KY, TN, MS, AL, AR, LA, OK,
TX, and NM.
0,1
Set by the user
In (income)
natural log of median household income
values for the watershed area. ($/year)
> 0, calculated
by user
Set by the user;
2015 ACS 5-Year
Estimates11
multjtype
binary variable indicating that multiple
waterbody types are affected (e.g., river and
lakes).
0
Hard-coded12
river
binary variable indicating that rivers are
affected.
1
Hard-coded13
10	Meta-analysis documentation for the SE ELG analysis describes additional methodological variables that
characterize features of the source studies included in the meta-analysis, such as the year in which the study
was conducted, payment vehicle, elicitation format, WTP estimation method, and publication type. These
variables are included to explain differences in WTP across studies but are not expected to vary across different
management scenarios. The equation accounts for these hard-coded variables in the intercept coefficient.
11	https://factfinderxensus.gOv/bkmk/taj3le/l.0/en/ACS/15 5YR/B19001
12	WMOST is a lumped model so this variable will be set to zero.
13	WMOST can route flows through a surface water component and reservoir component. For the purposes of this
benefit calculation, the reservoir component is assumed to represent a dammed river so this variable will be set
to one.

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Table 8. (Continued)
Variable
Definition
Value
Source
In (agricultural)
natural log of the proportion of the watershed
area which is agricultural.
> 0, calculated
by user
Set by the user;
NLCD
In (ar_ratio)
ratio of the sampled area, in km2, relative to
the affected resource area. For WMOST
purposes, the sampled area is equal to the
affected resource area.
1
Hard-coded
substitutes
size of the affected resources relative to
available substitutes. Calculated as the
proportion of water bodies of the same
hydrological type affected by the water quality
change within the affected resource area
1
Hard-coded
Q
Water quality changes due to the
implementation of the management practice,
Q = WQIscenario " WQIsaseline-
0-100
Calculated from
WMOST results
After calculating marginal willingness-to-pay, we can calculate annual household WTP for the change in
water quality due to the implemented management practice(s):
HWTP = MWTP x Q	(3.6)
where variables are defined as follows:
Table 9. Definition of variables for equation 3.6
Variable
Definition
Value
Source
HWTP
annual household WTP for households located
within the WMOST watershed area ($/year)
>0
Calculated from WMOST
results
MWTP
marginal WTP for water quality estimated by
the meta-analysis function
>0
Calculated from WMOST
results (see above)
Q
estimated annual average water quality
change:
Q WQIscenario ^Q^Baseline
Where WQIM0 is the annual average water
quality index value resulting from the
management option implementation and
WQIbl is the annual average water quality
index value for the baseline
>0
Calculated from WMOST
results (see above)
Once household willingness-to-pay has been calculated, we can calculate total willingness-to-pay
(TWTP) for water quality improvements by multiplying the number of households (HH) within the
watershed by the household willingness-to-pay value (HWTP):
TWTP = HWTP x HH	(3.7)
WMOST Benefits Module: Theoretical Documentation
13

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3.4 Non Water Quality Co-benefits
3.4.1 Improved aesthetic quality of the landscape from increases in green space leading to
changes in property values
3.4.1.1 Benefit equations
Benefits for three different residential land use types (low, medium, and high density) are estimated
based on equations from Mazzotta et al. (2014). Changes in home price are calculated for two buffer
zones: (1) for increases in green space occurring within 250 meters of residences and (2) for increases in
green space occurring at distances of 250 - 500 meters. These estimates are then combined to estimate
the total benefits to affected homeowners:
%Ahomeprice25Qm = 0.039 + 0.169 x Pinc_250 — 0.063 x Ppopdens + 0.252 x riparian —
0.013 x wetland + 0.245 x trees + 0.392 x protected + 0.081 x recreational —
0.018 x In(lotsize) — 0.0009 x homeprice	(3.8)
%Ahomeprice5QQm = 0.039 + 0.102 x Pinc_500 — 0.063 x Ppopdens + 0.252 x riparian —
0.013 x wetland + 0.245 x trees + 0.392 x protected + 0.081 x recreational —
0.018 x In(lotsize) — 0.0009 x homeprice	(3.9)
Because long-term home price trends are difficult to predict, before applying the calculated percent
change in home price, we convert the overall present-day median housing values to annual rental-
equivalent home values to calculate an annualized value by multiplying median housing values by a
discount rate (3% in Mazzotta et al. (2014)):
median home value x .03 = annual rental equivalent value	(3.10)
The percentage change in home prices can be applied to this annual rental-equivalent value to estimate
the monetary benefits to homeowners from increased green space within the defined buffer distance.
%Ahomeprice250m x annual rental equivalent value x housing units250m
(3.11)

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Variables are defined as follows14:
Table 10. Definition of variables for equation 3.11
Variable
Definition
Value
Source - Riparian Buffer
BMP15
Source-All Other
Green Space BMPs
Pinc_2 50
Percentage increase in
open space within 250
meters of residences
(%).
0-100
WMOST results, (based
on Tables 11,12) WMOST
Green Space Values
Database (AD variable),
and riparian contribution
lookup table
WMOST results
(based on Table 12)
Pinc_ 500
Percentage increase in
open space between
250 and 500 meters of
residences (%).
0-100
WMOST results, (based
on Tables 11, 12)
WMOST Green Space
Values Database (AD
variable), and riparian
contribution lookup table
WMOST results
(based on Table 12)
Ppopdens
Percentage increase in
open space if watershed
had >800 people per
square mile in 2011;
zero if watershed has
<800 people per square
mile in 2011 (%).
0-100
WMOST Green Space
Values Database (based
on PD10p900 variable,
using WMOST results
value if watershed has
>800 people per square
mile)
WMOST Green Space
Values Database
(based on PD10p900
variable, using
WMOST results value
if watershed has
>800 people per
square mile)
riparian
One if riparian buffer
area is expected to
increase; zero
otherwise.
0,1
WMOST results
Hard-coded as 0 as
they are non-riparian
buffer BMPs
wetland
One if wetland area is
expected to increase;
zero otherwise.
0,1
Set by the user
Set by the user
trees
Percentage of the
increase in open space
that is tree cover
0-100
Default values set or
suggested by WMOST
(see Section 4.4.1.2), can
be adjusted by the user
Default values set or
suggested by
WMOST (see Section
4.4.1.2), can be
adjusted by the user
14	EPA developed a Green Space Values Database to aid users with the data input required for the calculation of
this co-benefit. Its implementation is discussed in more detail in Section 4.4.1.2.
15	The WMOST Green Space Values Database includes more detailed information on residential land uses within
the defined buffer distances (0-250 meters and 250-500 meters) from a riparian zone. As a result, the data
sources for riparian buffer BMPs is slightly different from the data sources for other green space BMPs (see
Table 13 for the additional green space BMPs).
WMOST Benefits Module: Theoretical Documentation
15

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Table 10. (Continued)



Source - Riparian Buffer
Source-All Other
Variable
Definition
Value
BMP16
Green Space BMPs
protected
One if open space
0,1
WMOST results;
WMOST results; 1 if

associated with

1 if land conservation
land conservation

development practice is

BMP implemented
BMP implemented

typically permanently




protected; zero




otherwise.



recreational
One if recreational
amenities are included
in development
practice; zero
otherwise.
0,1
Set by the user
Set by the user
In (lotsize)
Natural log of median
>0
WMOST Green Space
WMOST Green Space

lot size

Values Database
(hh variable)
Values Database
(haphulO variable)
homeprice
Median home value
>0
WMOST Green Space
WMOST Green Space


($thousands)
Values Database
(MHVK variable)
Values Database
(MHVK variable)
housing units
Number of housing
>0
WMOST Green Space
WMOST Green Space

units within defined

Values Database
Values Database

buffer distance

(m2 and hu variables)
(AREAM2 and
hulOpha variables)
The percentage increase in open space within the two defined buffers (0-250 m and 250-500 m from
residences) would be determined based on the scale of the implemented BMPs. Table 11 below
summarizes available WMOST management practices and their associated assumed distance from
residences. All other WMOST management practices either do not involve changes in green space or
would be expected to occur more than 500 meters from residences and therefore would have no
impacts on property values.
Table 11. WMOST management practice and assumed distance from residences
WMOST management practice
0-250 meter buffer
250-500 meter buffer
Land Conservation

X
Bioretention Basin

X
Grass Swale
X

Gravel Wetland

X
Direct Reduction Tree Canopy
X

Riparian Buffer
X
X
16 The WMOST Green Space Values Database includes more detailed information on residential land uses within
the defined buffer distances (0-250 meters and 250-500 meters) from a riparian zone. As a result, the data
sources for riparian buffer BMPs is slightly different from the data sources for other green space BMPs (see
Table 13 for the additional green space BMPs).

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Only riparian buffers are assumed to potentially affect values of properties within both 0-250 meters
and 250-500 meters. For other BMPs, implementation is assumed to occur within either the 0-250
meter or 250-500 meter buffer. For example, if WMOST chose to implement a grass swale BMP, the
percentage increase in open space within 250 meters from residences would be nonzero and used to
calculate the percentage change in home prices. The grass swale BMP would not increase the
percentage of open space within 250 to 500 meters from residences and WMOST would not calculate
associated benefits on that basis.
3.4.1.2 WMOST Green Space Values Database
EPA developed the WMOST Green Space Values Database to aid users with obtaining input data
required for the calculation of this co-benefit. This section describes how WMOST will use the database,
especially when applied to WMOST model runs that span multiple HUC12s. Table 12 summarizes the
relevant database variables and how WMOST will utilize them for the co-benefit calculation.
Table 12. Green Space Values Database variable summary
Variable Name17
Variable Description
WMOST Usage
MHVK
Median home value ($thousand)
in land use class in HUC12
Used directly; area-weighted average if
study area spans multiple HUC12s
PD10p900
Average population density per
900 m2 in land use class in
HUC12
Used to determine if variable should
be 0 or the percent increase in open
space; area-weighted average if study
area spans multiple HUC12s
AD
Average distance (m) of building
centroid from riparian zone for
each land use class if within
buffer distance of riparian zone
in HUC12
Used as a crosswalk with the riparian
contribution lookup table. The
distance will be rounded to the nearest
tenth to crosswalk with the lookup
table and identify the fraction of the
building radius that intersects with the
riparian zone; area-weighted average
distance if study area spans multiple
HUC12s
hulOpha
Housing units per hectare in land
use class in HUC12
Used directly; area-weighted average
if study area spans multiple HUC12s
haphulO
Average hectares per housing
unit in land use class in HUC12
Used directly; area-weighted average
if study area spans multiple HUC12s
17 The truncated variable name is used within this table. The full variable name includes indicators of the
associated land use area type or riparian zone buffer distance.
WMOST Benefits Module: Theoretical Documentation
17

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Table 13. Default percent tree canopy values for each BMP associated with the increase in green space
benefit calculation.
Associated BMP
Default Percent
Tree Canopy
Rationale
Land conservation
Variable based on
conserved land use
This should vary based on the land use chosen to be
conserved. Default values for each NLCD land use/land
cover class are summarized in Appendix B of the
Benefits Module User Guide: Default Percent Tree
Canopy Values by Vegetated NLCD Land Cover Class
Bioretention basin
50%
According to North Carolina's Department of
Environmental Quality's (NCDEQ) Stormwater Design
Manual18, bioretention cells should be designed to have
a maximum tree or shrub canopy of 50 percent at five
years after planting.
Grass swale
0%
The assumed vegetation is grass.
Gravel wetland
0%
This should vary based on native wetland vegetation,
but the default value assumes that the implemented
gravel wetland is dominated by grasses and shrubs.
Constructed
wetland
30%
This should vary based on native wetland vegetation,
but the default value is based on a constructed forested
wetland. According to a U.S. Army Corps of Engineers
(USACE) performance standards for nontidal wetland
mitigation banks guide19, canopy cover should be at
least 30 percent. Users will be notified that they should
adjust this value if implementing constructed wetlands
dominated by grasses or shrubs.
Riparian buffer
implementation
100%
The default value assumes that the user will implement
forested riparian buffers20, but users will be notified that
they should adjust this value if implementing riparian
buffers dominated by grasses or shrubs.
Direct reduction
tree canopy
100%
This BMP is intended to represent increases in tree
canopy in urban areas.
18littpsi//files,nc,go¥/ncdeq/Etiergy+IVlineral+ancl+Land+R.esources/Stormwater/BIVlP+IVlanual/C-
2%2Q%20Bioretention%201~19~2018%20FINAL,pdf
19https://www. nab, usace. army, mil/Portals/63/doc5/Regulatorv/Mitigation/IRT NT. Wetland Buffer
otocol Bank 10 28 16.pdf?ver=201?-ll-13-163628-277.
20 Based on a U.S. Department of Agriculture Natural Resources Conservation Service (USDA-NRCS) riparian forest
buffer specification guide sheet (httpsi//efotg,sc,egov,ysda,gov/references/public/VT/VTSpec391-0109,pdf),
canopy density should be at least 80 percent coverage. We will include this lower bound in the theoretical
documentation.

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3.4.2 Canopy cover benefits
3.4.2.1 Increased acres of canopy cover
Increased carbon sequestration
Calculating the value of annual carbon sequestration is a two-step process (see example equation
below): (1) multiplying the increase in tree cover by the rate of carbon sequestration to obtain the
annual amount of carbon sequestered by the additional canopy cover; and (2) multiplying the annual
amount of carbon sequestered by the social cost of carbon.
Vtcs = Atc x Res x Ore x GPDIPD	(3.12)
where variables are defined as follows:
Table 14. Definition of variables in equation 3.12
Variable
Definition
Value
Source
Vtcs
total value of annual carbon sequestration ($/year)
> 0, calculated
by user
n/a
Atc
increase in tree cover (acres)
> 0, calculated
by WMOST
WMOST results
Res
rate of carbon sequestration (metric ton C acre1 year1)
0.77
US EPA (2019)21
Cscc
Domestic social cost of carbon (2016$/metric ton C) OR

WMOST look-up
table22
Cscc
Global social cost of carbon (2007$/metric ton C)

WMOST look-up
table23
GPDIPD
GPD Implicit Price Deflator

WMOST look-up table
Avoided human health damages resulting from tree removal of air pollutants
The Benefits Module uses both pollutant removal rates for criteria air pollutants (N02, 03, PM2.5, and
S02) and population density-based regression equations from Nowak et al. (2014), as shown in Equation
3-13 below. The Benefits Module performs the calculations for each of the air pollutants and sums the
values to obtain the total value of avoided human health damages resulting from tree removal of air
pollutants.
Vtahd = C<4re x PRx) x PDRX	(3.13)
where variables are defined as follows:
21	httpsi//www,epa,go¥/energv/greenlioyse-gases-eqyi¥alencies-calcylator-calcylations-an_d-references# ftnl
Please note that this is an estimate for "average" U.S. forests in 2017; i.e., for U.S. forests as a whole in 2017.
Significant geographical variations underlie the national estimates, and the values calculated here might not be
representative of individual regions, states, or changes in the species composition of additional acres of forest.
Nowak et al. (2013) also reports rates of carbon sequestration per unit of tree cover for specific cities and states.
22	https://www.epa.gov/sites/prodyction/files/2019-06/documents/ytilities ria final epp repeal and ace 2019
23	https://19ianyarv2017snapshqt.epa.gov/sites/prodyction/files/2016-
12/documents/sc co2 tsd august 2016.pdf
WMOST Benefits Module: Theoretical Documentation
19

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Table 15. Definition of variables in equation 3.13
Variable
Definition
Value
Source
Vtahd
total value of avoided human health damages from
increased tree removal of air pollutants ($/year)
> 0, calculated by
the Benefits Module
n/a
Atc
increase in tree cover (acres)
> 0, calculated by
WMOST
WMOST results
PRx
pollutant removal rate for N02, 03, PM2.5, and S02
(converted from g/m2 to metric tons/acre)
Varies by pollutant
Nowak et al. (2014)
PDRX
population density-based regressions estimating
avoided human health damages per metric ton
(population density is X variable)
Varies by pollutant
Nowak et al. (2014)
3.4.2.2 Increased acres of urban/community trees
Reduction in heating/cooling needs
The Benefits Module uses state-level per hectare energy conservation values to calculate total annual
energy savings due to increased acres of urban/community trees:
Vtes = ATc x C°nvha X VES	(3.14)
where variables are defined as follows:
Table 16. Definition of variables in equation 3.14
Variable
Definition
Value
Source
Vtes
total value of energy savings due to
increase in urban/community trees
($/year)
> 0, calculated by
the Benefits
Module
n/a
Atc
increase in urban/community trees (ac)
> 0, calculated by
WMOST
WMOST results
Ves
state-level value of energy savings ($/ha)
Varies by state
Nowak et al. (2017)
C°nvha
Converts acres to hectares
0.405
n/a
Avoided human health damages from avoided emissions from power plants
We quantify human health benefits from air pollution reduction related to reduced energy consumption
using estimates of national monetized benefits per ton (BPT) of avoided NOx, S02, and PM25for the
Electricity Generating Unit sector. Several adverse health effects have been associated with PM25and
its precursors (NOx and SOx), including premature mortality, non-fatal heart attacks, hospital admissions,
emergency department visits, upper and lower respiratory symptoms, acute bronchitis, aggravated
asthma, lost workdays and acute respiratory symptoms. All these health effects were included in the
estimation of benefits that went into the calculation of benefits per ton (U.S. EPA, 2018). A very large
percentage, 98 percent, of the total monetized benefits of reducing PM25 concentrations are
attributable to avoided premature mortality. U.S. EPA (2018) estimated two sets of BPT values from two
different epidemiology studies (Krewski et al., 2009; Lepeule et al., 2012). Using both values provides
low and high estimates for air pollution reduction benefits and informs the user of the potential range
for these benefit estimates.

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The Benefits Module uses the following calculation for each of three air quality pollutants (PM2.5, S02,
NOx) and sums the values to calculate total avoided human health damages from avoided power plant
emissions:
Vtae = AE«<"°*Conv*™ x Atc X Convha X VBPT	(3.15)
ULstate
where variables are defined as follows:
Table 17. Definition of variables in equation 3.15
Variable
Definition
Value
Source
Vtae
total value of avoided criteria air pollutant
emissions due to increase in urban/community
trees ($/year)
> 0, calculated by the
Benefits Module
n/a
AEstate
Avoided emissions (metric tons of PM2.5, S02,
NOx) from power plants due to
urban/community trees
Varies by state
Nowak et al. (2017)
C°nvton
Converts metric tons (tonnes) to U.S. tons
(short tons)
1.1023
n/a
Upstate
urban/community tree cover (ha)
Varies by state
Nowak et al. (2012)
ATc
increase in urban/community tree cover (acres)
> 0, calculated by WMOST
WMOST results
C°nvha
Converts acres to hectares
0.405
n/a
^BPT
National-level benefit per ton values for PM2.5,
S02, NOx
Low and high estimates for
each pollutant
U.S. EPA (2018)
Increased carbon sequestration
To calculated benefits from avoided C02 emissions from power plants, the Benefits Module uses state-
level per hectare values of avoided C02 emissions and multiplies the value by the increased hectares of
urban/community trees associated with WMOST-prescribed management practices, including land
conservation, riparian buffer restoration, and selected green infrastructure practices. The Benefits
Module then uses either the global or domestic SC-C02 values (see Section 2.3.2.1) to monetize the
avoided C02 emissions from power plants, as shown in Equation 3.16.
Vtac = n^tate x Atc x Convha X Cscc	(3.16)
ULstate
where variables are defined as follows:
Table 18. Definition of variables in equation 3.16
Variable
Definition
Value
Source
Vtac
Total value of avoided C02 emissions due to
increase in urban/community trees ($/year)
> 0, calculated by the
Benefits Module
n/a
AEstate
Avoided metric tons of C02 from power plants due
to urban/community trees
Varies by state
Nowak et al.
(2017)
Upstate
urban/community tree cover (ha)
Varies by state
Nowak et al.
(2012)
ATc
increase in urban/community tree cover (acres)
> 0, calculated by WMOST
WMOST results
C°nvha
Converts acres to hectares
0.405
n/a
Cscc
Domestic social cost of carbon
($/metric ton C) OR

U.S. EPA
(2019a)
Cscc
Global social cost of carbon ($/metric ton C)

IWGSCC (2016)
WMOST Benefits Module: Theoretical Documentation
21

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3.4.3 Green roofs
3.4.3.1 Reduction in heating/cooling needs from green roofs
We add energy cost reductions by multiplying the megawatt hour electricity savings by the price of a
megawatt hour of residential electricity. Users can access state-level average prices for residential
electricity through the U.S. Energy Information Administration (2018)24, or they can use local values for
the price of a MWh of residential electricity, if available.:
Vtes = ESkWH x ConvMWH x VMWH	(3.17)
where variables are defined as follows:
Table 19. Definition of variables in equation 3.17
Variable
Definition
Value
Source
Vtes
total value of energy savings from green
roofs ($/year)
> 0, calculated by the
Benefits Module
n/a
E^kwH
annual electrical savings (kWh)
> 0, calculated by user
Green roofs calculator25
ConvMWH
Convert kilowatt hours to megawatt hours
0.001
n/a
^MWH
price of a MWh of residential electricity
Varies
Set by the user; U.S.
Energy Information
Administration (2018)26
3.4.3.2 Avoided human health damages from avoided N0X, SO2, and PM2.5 emissions from
power plants
To quantify reductions in criteria air pollutants (S02, NOx, PM2.5) from reduced energy consumption due
to the installation of green roofs, the Benefits Module applies regional AVERT emission rates (U.S. EPA,
2019b) to convert annual energy savings into avoided emission of criteria air pollutants and then
monetizes the reductions using benefit per ton estimates (U.S. EPA, 2018), as shown in Equation 3.18:
Vtahd = (ESkWH x C°nvMWH) X (AVERTX X Convto?IS) X VBPT	(3.18)
where variables are defined as follows:
24	If the study watershed spans more than one state, we propose averaging the appropriate state-level values
25	https://sustainability.asu.edu/urban-climate/green-roof-calculator/
26	Values provided in cents per kilowatt hour for years 2001 through 2018.

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Table 20. Definition of variables in equation 3.18
Variable
Definition
Value
Source
Vtahd
total value of avoided human health
damages from avoided power plant
emissions ($/year)
> 0, calculated by
the Benefits Module
n/a
E^kwH
annual electrical savings (kWh)
> 0, calculated by
user
WMOST results, green
roofs calculator27
ConvMWH
convert kilowatt hours to megawatt hours
0.001
n/a
AVERTX
AVERT regional emission rate for S02, NOx,
PM2.5 (Ibs/MWh)
Varies by pollutant,
region, and rate
type28
U.S. EPA (2019b)
COYlVions
convert pounds to tons
0.0005
n/a
^BPT
National-level benefit per ton values for
SO2, NOx, PM2.5
Low and high
estimates for each
pollutant
U.S. EPA (2018)
The Benefits Module uses the same methodology for applying the U.S. EPA (2018) benefit per ton
estimates as described in Section 3.4.4.2. U.S. EPA (2018) provides benefit per ton estimates for the
three pollutants for years 2016, 2020, 2025, and 2030. We use linear regression to calculate values for
the years in between the reported values. The Benefits Module then uses the value that corresponds to
the study period, averaging values if the study period is longer than one year, and then applies the value
corresponding to the year of the analysis.
3.4.3.3 Avoided CO2 emissions from power plants
To quantify reductions in carbon dioxide (C02) from reduced energy consumption due to the installation
of green roofs, the Benefits Module applies regional AVERT emission rates (U.S. EPA, 2019b) to convert
annual energy savings into avoided carbon dioxide emissions and then monetizes the reductions using
either global or domestic SC-CO2 values, as shown in Equation 3.19:
Vtac = (E$kWH x CotWmwh) x (AVERTx X Convtor[S) X Cscc	(3.19)
where variables are defined as follows:
27	https://sustainability.asu.edu/urban-climate/green-roof-calculator/
28	The contiguous United States is divided into ten AVERT regions. Four different types of regional AVERT emission
rates are available: wind, utility-scale photovoltaic, portfolio energy efficiency, and uniform energy efficiency.
WMOST Benefits Module: Theoretical Documentation
23

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Table 21. Definition of variables in equation 3.19
Variable
Definition
Value
Source
Vtac
total value of avoided C02 emissions from
avoided power plant emissions ($/year)
> 0, calculated by the
Benefits Module
n/a
E^kwH
electrical savings (kWh)
> 0, calculated by user
WMOST results, green
roofs calculator29
ConvMWH
convert kilowatt hours to megawatt hours
0.001
n/a
AVERTX
AVERT regional emission rate for C02
(tons/MWh)
Varies by pollutant,
region, and rate type30
U.S. EPA (2019b)
COYlVions
convert U.S. tons to metric tons
0.907185
n/a
Cscc
Domestic social cost of carbon
($/metric ton C) OR

U.S. EPA (2019a)
Cscc
Global social cost of carbon ($/metric ton C)

IWGSCC (2016)
29	https://sustainability.asu.edu/urban-climate/green-roof-calculator/
30	The contiguous United States is divided into ten AVERT regions. Four different types of regional AVERT emission
rates are available: wind, utility-scale photovoltaic, portfolio energy efficiency, and uniform energy efficiency.

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Appendix A. Illustrative calculations for cobenefits (link to spreadsheet)
Appendix B. Shapefile and metadata for green values dataset
WMOST Benefits Module: Theoretical Documentation
27

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