OKDES
AN ENERGY AND FUEL DEMAND MODEL
FOR THE OHIO RIVER BASIN ENERGY STUDY REGION
PHASE II
OHIO RIVER DASIK ENERGY STUDY
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November 1980
AN ENERGY AND FUEL DEMAND MODEL
FOR THE OHIO RIVER BASIN ENERGY STUDY REGION
by
Walter P. Page
West Virginia University
Doug Gilmore
University of Illinois at Urbana-Champaign
Geoffrey Hewings
University of Illinois at Urbana-Champaign
Prepared for
OHIO RIVER BASIN ENERGY STUDY (ORBES)
Grant No. EPA R805585 and
Subcontract under Prime Contract EPA R805588
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
WASHINGTON, D.C. 20460
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ACKNOWLEDGEMENTS
This is a report of work completed on developing an input-output model
of energy and fuel use for the Ohio River Basin Energy Study project. The
work has been funded under grant number EPA R805585 and subcontract under
prime contract EPA R805588 through the U.S. Environmental Protection Agency.
In addition to the authors listed on the title page, Mike Rieber, then of
the Center for Advanced Computation, University of Illinois, was responsible
for initiating the project some two-and-one-haIf years ago and was the
principal investigator on the initial subcontract which provided funding for
model development work.
Special thanks are also extended to Marilyn Rose for her very competent
typing services and assistance in preparing the final version of this report.
11
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PREFACE
The Ohio River Basin Energy Study (ORBES) is charged with assessing
"...the potential environmental, social, and economic impacts of the proposed
concentration of power plants in the lower Ohio River Basin." Phase II of
the project focuses on a regional analysis consistent with the above mandate.
The study boundaries for ORBES Phase II include all of Kentucky and portions
of Illinois, Indiana, Ohio, Pennsylvania, and West Virginia (see Figure 1).
The regional boundaries were determined in such a way as to include desired
portions of the Ohio River drainage basin as well as regional coal fields.
For ORBES Phase II, alternative regional characteristics for future years
are obtained through scenario and impact models. The input-output model of
economic activity and energy and fuel use falls into the scenario model clas-
sification. For the ORBES project, it was necessary to develop an input-
output model at the substate level, other than the case of Kentucky where the
entire state lies within the ORBES boundaries. Given parameters specified by
other ORBES researchers or the ORBES Core Team, the input-output model pro-
vides projections of economic activity as well as fuel and energy use by end
use sector for future time periods in the ORBES region. Alternative specifi-
cations of the various parameters define the scenarios of interest to the
ORBES Core Team.
Chapter I discusses the construction of the energy and fuel demand model.
Chapter II provides information on implementation of parameter values which
define the scenarios of interest to the Core Team. Scenario runs are analyzed,
compared, and general conclusions are drawn in Chapter III.
111
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FIGURE I
OHIO RIVER BASIN ENERGY STUDY REGION
PHASE II
I'-.. f\
Ohio River Drainage Basin
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CONTENTS
Acknowledgements ii
Preface iii
1. The ORBES Energy and Fuel Demand Model 1
1.1 The Input-Output Model 1
1.2 Construction of the Regional Model 7
1.3 The Energy Supply-Product Input-Output Model 12
1.4 Derivation of the Energy Consumption Baseline . . 17
1.5 Two Versions of the Model Used in the Scenario Runs 18
2. Scenario Assumptions 21
2.1 General Discussion 21
2.2 The Incorporation of Scenario Parameters in the
Energy Demand Model 26
2.3 Scenario Growth Assumptions 27
2.4 Scenario Fuel Mix Assumptions 29
2.5 Modification of Material Requirements in Nonenergy
Goods Production 31
2.6 A Sensitivity Example 32
3. Analysis and Interpretation of Scenario Runs and
General Conclusions 35
3.1 Introduction 35
3.2 Energy Consumption Patterns in Scenario 2 36
3.3 Energy Consumption in Scenario 1 39
3.4 Energy Consumption in Scenario 3 40
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3.5 Natural Gas Scenario 42
3.6 Alternative Growth Rate Scenarios 44
3.7 "Wheeling of Power" 46
3.8 Conservation Scenario 46
4. Some General Conclusions 55
Appendix A 57
Appendix B 63
Appendix C 101
References 115
VI
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TABLES
1. SIC Classification of ORBES I-O Model 5
2. . Control Total Data Sources 9
3. Scenario Assumptions Used in the ORBES Energy Demand Model 22
4. Average Growth Rates for the Final Demand Nonenergy Goods
in Scenarios 2, 6, 4, 5, and 5a 28
5. Fuel Splits for Satisfying BED in 1974 30
6. Elements of the Factor Matrices for Several Important
Product Mix Changes 33
7. Amount of Energy Product Demands Displaced by. Alternative
Technologies in Scenario 4 41
8. Regional Consumption of Energy and Fuel Use, by Scenario,
in the ORBES Region 53
B-l.l Baseline Data, 1974, and Scenario 1 Solutions to the
ORBES Energy Demand Model, 1985 and 2000 65
B-1.2 Baseline Data, 1974, and Scenario 1 Solutions to the
ORBES Energy Demand Model, 1985 and 2000: Associated
Transaction Matrices 67
B-1.3 Baseline Data, 1974, and Scenario 1 Solutions to the
ORBES Energy Demand Model, 1985 and 2000: Associated
Technical Coefficient Matrices 69
B-2.1 Baseline Data, 1974, and Scenario 2 Solutions to the
ORBES Energy Demand Model, 1985 and 2000 71
B-2.2 Baseline Data, 1974, and Scenario 2 Solutions to the
ORBES Energy Demand Model, 1985 and 2000: Associated
Transaction Matrices 73
B-2.3 Baseline Data, 1974, and Scenario 2 Solutions to the
ORBES Energy Demand Model, 1985 and 2000: Associated
Technical Coefficient Matrices 75
vn
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TABLES (continued)
B-3.1 Baseline Data, 1974, and Scenario 4 Solutions to the
ORBES Energy Demand Model, 1985 and 2000 77
B-3.2 Baseline Data, 1974, and Scenario 4 Solutions to the
ORBES Energy Demand Model, 1985 and 2000: Associated
Transaction Matrices 79
B-3.3 Baseline Data, 1974, and Scenario 4 Solutions to the
ORBES Energy Demand Model, 1985 and 2000: Associated
Technical Coefficient Matrices 81
B-4.1 Scenario 2, 2a, and 4 Solutions to the
ORBES Energy Demand Model, Year 2000 83
B-4.2 Scenario 2, 2a, and 4 Solutions to the
ORBES Energy Demand Model, Year 2000: Associated
Transaction Matrices . 85
B-4.3 Scenario 2, 2a, and 4 Solutions to the
ORBES Energy Demand Model, Year 2000: Associated
Technical Coefficient Matrices 87
B-5.1 Scenario 3, 5, and 5a Solutions to the
ORBES Energy Demand Model, Year 2000 89
B-5.2 Scenario 3, 5, and 5a Solutions to the
ORBES Energy Demand Model, Year 2000: Associated
Transaction Matrices 91
B-5.3 Scenario 3, 5, and 5a Solutions to the
ORBES Energy Demand Model, Year 2000: Associated
Technical Coefficient Matrices 93
B-6.1 Scenario 3, 5, and 6 Solutions to the
ORBES Energy Demand Model, Year 2000 95
B-6.2 Scenario 3, 5, and 6 Solutions to the
ORBES Energy Demand Model, Year 2000: Associated
Transaction Matrices 97
B-6.3 Scenario 3, 5, and 6 Solutions to the
ORBES Energy Demand Model, Year 2000: Associated
Technical Coefficient Matrices . 99
C-l Baseline Energy End Use, 1974 103
C-2 Energy End Use, 1985, Scenario 1 104
C-3 Energy End Use, 2000, Scenario 1 105
viii
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TABLES (continued)
C-4 Energy End Use, 1985, Scenario 2 106
C-5 Energy End Use, 2000, Scenario 2 107
C-6 Energy End Use, 2000, Scenario 2a 108
C-7 Energy End Use, 2000, Scenario 3 , 109
C-8 Energy End Use, 1985, Scenario 4 110
C-9 Energy End Use, 2000, Scenario 4 Ill
C-10 Energy End Use, 2000, Scenario 5 112
C-ll Energy End Use, 2000, Scenario 5a 113
C-12 Energy End Use, 2000, Scenario 6 114
IX
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FIGURES
1. Ohio River Basin Energy Study Region Phase II iv
2. Scenario and Impact Models: Sequential Steps in
ORBES Assessment 13
3. Energy Input-Output Model 16
4. Coal Use, by Scenario, in the ORBES Region, 1012 Btu 47
5. Refined Petroleum Use, by Scenario, in the ORBES
Region, 1012 Btu 48
6. Natural Gas Use, by Scenario, in the ORBES Region, 1012 Btu 49
7. Electric Use, by Scenario, in the ORBES Region, 1012 Btu 50
8. Total Fuel Use, by Scenario, in the ORBES Region, 1012 Btu 51
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1. The ORBES Energy and Fuel Demand Model
1.1 The Input-Output Model
Input-Output analysis provides an efficient and detailed accounting
scheme for tracing transactions between firms in a consistent manner (see,
for instance, 1). Consumption is divided .into two classes, intermediate and
final; the former characterizing transactions between firms and the latter
involving consumption by the components of final demand: consumers, govern-
ments, export markets, and investment. When modeling an economic system,
there always exists a problem of assigning firms to particular industry
groups. In this work, classification is according to the Standard Industrial
Classification (SIC) scheme. The SIC system aggregates groups according to
specified criteria: for example, SIC 20 (Food and Kindred Products) contains
all firms producing dairy and meat products while SIC 201 contains only firms
producing meat products and SIC 2011 contains firms which are meat packing
plants. The degree of specificity rises as one moves to larger digit numbers
in the SIC classification.
In an input-output model, flows between industries may be represented by
an n by n matrix of interindustry transactions. These flows represent raw
materials, semi-finished and finished products. Final consumption is then
shown as final demand: m sectors, for instance, include consumers, govern-
ments, foreign markets, investment and interregional exports. In a static
model, flows between industries are flows.on current account only. Goods
which are regarded as investment or capital goods are allocated to the invest-
ment portion of final demand.
It is a convention in input-output analysis that flows from firms to
final demand markets via wholesalers and retailers are shown as direct sales
from firms to final demand. The summation of sales from an industry to all
other industries and sales to final demand is total output. The vector of
total output is equal to the vector of total input and the accounting balance
is provided b$ the elements categorized as primary inputs. Included are the
various components of value added (wages and salaties, entrepreneurial income,
and undistributed dividends), and interregional and foreign imports.
In the discussion of the ORBES input-output model, it is useful to
establish an accounting system of notation.
Let x.. be the flow of goods from industry i to industry j, and let Y. be
the flow of goods from industry i to all final_demand sectors and let X. be
the total output (input) of industry i. Then the"following accounting balance
is true for all i industries:
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Zx.. + Y. = X. (1.1)
. ID i i
Let a. . represent the cents worth of the output of industry i used per dollar
of output of industry j. Then:
a. . = x. ./X .
iD JO D
and this may be rearranged to read
x. . = a . .X.
iD iD D
We may now substitute for x.. in equation (1.1):
Ea. .X. + Y. = X. (1.2)
j ID D i i
Equation (1.2) will be true for all i industries. It will be more convenient
to express the system in matrix form. Let X, Y represent n by 1 vectors of
total output (input) and final demand respectively and let A represent an n
by n matrix of input coefficients a typical element of which will be a...
Then: 1D
AX + Y = X
Rearranging and factoring out the X vector, we have:
X - AX = Y
[I - A] X = Y
X = [I - A]~1Y (1.3)
The matrix [i - A] is known as the Leontief Inverse matrix. It provides a
summary of the rounds of spending and respending that take place when an im-
pulse is injected into the economy by one of the components of final demand.
In essence, the Leontief Matrix in equation (1.3) is a summary version of the
following process:
2345
I+A+A + A + A + A . . .
If we let b.. represent an element of the Leontief Inverse Matrix, then
the column summations in this matrix provide us with the specific industry
multipliers relating a change in one unit of final demand to the direct and
indirect changes in output in all other industries. If m. represents the
multiplier of the j th industry, then:
m. = Zb. .
D ID
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In summary, the input-output model is driven by the final demand vector,
the latter initiating changes in activity levels in all industries. The level
of impact on any one industry from a change in final demand will, of course,
vary depending upon the production process involved and the nature of the
backward and forward linkages between this industry and all other industries.
It was noted earlier that the input-output model aggregates firm activity
into industrial sectors and presents an accounting of transactions between in-
dustrial sectors and groups of firms portraying transactions between the firms
per se. To accomplish this'task, a number of assumptions require elaboration.
Flows from firms to wholesalers to retailers to final demand consumers, if
shown in an input-output table, would result in a very sparse matrix with most
of the entries shown in the wholesale and retail trade sectors. In input-
output analysis, flows through the trade sector are shown via the mark-up
margins; in other words, a consumer purchasing an automobile in an input-
output model would involve two transactions. The first would be the purchase
of the automobile from the manufacturer and the second, the purchase of a
"mark-up" from the retailer.
Similar conventions apply to other sector purchases which involve the
trade account. The margin referred to above includes operating expense and
profit to the wholesaler or retailer. All transactions in the system are
measured in dollars: this obviates the problem of comparing, tons of coal with
rolls of finished steel. Two alternatives are available: (1) measurement in
producers' prices or (2) measurement in purchasers' prices. In the case of
the former, distribution costs are excluded: when these are added to pro-
ducers' prices, the resulting sum will be purchasers' prices. In the pro-
ducers' price system, distribution costs are allocated to the trade and trans-
portation sectors. It is assumed that purchasers of a good will also pay a
fixes proportion to the trade and transportation sectors to accomplish the
purchase of a good from another sector. The 1967 national model adopted for
this work shows transactions in producers' prices and, hence, the ORBES model
is similarly structured.
The problem of classification of industries at the regional level is not
a trivial one. Essentially, two problems are involved in classification. The
first is to obtain as much detail as possible to avoid illegitimate aggrega-
tions of firms within one sector. The second problem relates to the prior
identification of sectors which were heavy users of energy and for which
separation was absolutely essential. Even with 484 sectors at the national
level, all aggregation problems are not solved, since there will still be
sectors producing more than one type of commodity and there will still be
cases where the same commodity may be produced by more than one industry.
These problems cannot be solved by greater disaggregation. In fact, the
early input-output models developed by Stone (1) were really commodity-
industry input-output models rather than industry-industry models. The
Canadian accounting system follows Stone's idea with the added advantage that
one-to-one correspondence between industries and commodities need not be as-
sumed. Hence, re.ctangular matrices can result by allowing firms to produce
more than one commodity.
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In the national model, the relationships are strictly industry-industry,
with the caveat that, wherever possible, homogeneity of input patterns was
sought in assigning firms to sectors. The problem of secondary products was
treated in two different ways. The first method transfers the secondary pro-
duct to the sector in which the product is the major output. In addition, all
the various inputs and value added components which were needed to make the
secondary product would be transferred to the industry in which the good was
the primary product. Hence, the output of each industry would consist almost
exclusively of primary products and the input structure would most closely ap-
proximate a single product production function. On the other hand, there are
many problems involved in assembling an input-output table without the added
complication of requesting firms to distinguish input structures for different
products. Furthermore, impact analysis would be difficult to accomplish since
one could not very easily translate product impacts into industry impacts.
The alternative method has come to be known as the "transfer" method: this
approach leaves secondary production where it originates, but also adds it to
the output of the primary industry. Thus, secondary production is treated as
though it were sold to the primary industry and, hence, the distribution pat-
tern would resemble the product of the primary industry. This procedure
greatly simplifies the accounting system although it does result in the devel-
opment of production structures which include a mixture of primary and second-
ary production. This problem is dealt with in greater detail in Bureau of
Economic Analysis publications (2). In the national model, the transfer ap-
proach was used for the mining and manufacturing sectors, while the other ap-
proach was used in the trade, construction, and service sectors.
Imports were treated in roughly the same fashion as secondary products if
they were substitutable for domestic goods and services. The domestic values
of these goods and services were added to the output of the U.S. industry pro-
ducing similar goods and services. This was accomplished by showing the
domestic industry as making a purchase from the import row and from trade,
transportation, and insurance industries which were responsible for bringing
the imported goods and services into the country. For non-competitive goods
and services, the amounts were entered directly into the import row of the
industry making the purchase.
Earlier we noted that the choice of sectors for the ORBES region model
was premised on the need for as much detail as possible and, in particular,
for explicit identification of the energy supply sectors. Our choice of sec-
tors was made on the basis of matching the sectoring scheme with the 1967
national model, the energy models produced at the Center for Advanced Compu-
tation (see Bullard (3)) and the problems involved in explicit identification
of sectors at the substate level. In the final analysis, 48 sectors1 were
identified: these are shown in Table 1.
For the most part, these sectors are identified at the two-digit SIC
level. In some cases, for example sector 24 (Blast Furnaces), we were able to
The actual number of sectors in the model is 67. Twelve energy product
sectors, whose roles are discussed in the second half of this chapter, are
also included. Also there are several energy supply categories, such as shale
oil, that are not used.
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TABLE 1
SIC CLASSIFICATION OF ORBES 1-0 MODEL
1: 1100 1200 46: 3340 3350 3360
2: 1310 1320 47: 3400
6: 2910 2990 48: 3500
7: 4920 4932 49: 3600
9-12: 4910 4931 50: 3710
25: 01-- 51: 3700 -3710
26: 1000 1400 52: 3800
27: 15— 1380 53: 4740
28: 2010 54: 4100 4600 4700 -4740 4800
29: 2000 -2010 2100
30: 2200 55:
31: 2300 56: 4400
32: 2400 2500 57: **500
33: 2600 -2620 -2630 -2650 2700 58: 5°°°
34: 2620 59: 52" 7396
35. 2630 60: 6000 6100 6200 6400 6700
36: 2650 6l: 650° 660°
37. 281Q 62: 7000 8100 8900 -8920 7800
38: 2800 -2810 -2820 79°°
39. 2920 63: 720° 76°° 73°° "731° ~7396
40: 2950 6/4: 731°
41: 3000 3100 65: 750°
1^2- 3200 66: 800° 820°
67: 8400 8600 8920
44: 3320 3390
45: 3330
(continued)
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TABLE 1 (continued)
NOTE: A preceding minus sign indicates exclusion rather than inclusion.
* See the Standard Industrial Classification for detailed description of
sectors.
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obtain detail at the three-digit level. In the manufacturing sector, with
the exception of sectors 13, 22, and 33, we were able to avoid aggregating to-
gether more than one two-digit activity. The degree of detail that we have
been able to maintain compares favorably with most regional input-output
models that have been produced either from survey of non-survey data. With
the possible exception of the Philadelphia region model (which contains many
hundreds of sectors), most regional models detail anywhere from 30 to 70 sec-
tors.
In arriving at the sectoring scheme, use was made of the 368-sector U.S.
model and the 105-sector CAC model. A major constraint was imposed by the ab-
sence of data at the substate level to enable a large number of the sectors
to be retained in an unaggregated form. We attempted to retain, wherever pos-
sible, as much detail in those sectors whose input pattern was shown to be
energy sensitive. These sectors were identified by a criterion which was
similar to that developed by Bullard and Sebald (4). Because energy is embod-
ied in all goods in the economy in a very diffuse manner, an element-by-
element sensitivity criterion may not yield a satisfactory aggregation scheme.
Thus, an alternative criterion is derived which incorporates the concept that
an industry should be considered important if total energy use is relatively
sensitive to a uniform change in an industry's input technical coefficient,
even though there is no single input technical coefficient which has a rela-
tively large sensitivity coefficient. The sectoring scheme is discussed in
detail in Appendix A. Application of the scheme resulted in a total of 57
industries (aggregated and unaggregated).
If energy output is of primary concern, then no aggregation scheme could
be completely satisfactory. In the aggregated 57 order matrix there are
absolutely no unimportant sectors.2 All of the aggregated sectors have a sig-
nificant sensitivity. The implication of this analysis is clear: direct and
indirect energy use is somewhat evenly embodied in the goods produced in the
U.S. economy and, as a consequence, very broad conservation measures may be
necessary to appreciably reduce energy consumption.
1.2 Construction of the Regional Model
As observed earlier, the ORBES region includes parts of the states of Il-
linois, Indiana, Ohio, Pennsylvania, and West Virginia, and all of the state
of Kentucky. However, the parts of states which were included were aggrega-
tions of counties. This spatial consistency enabled the use of a number of
published documents in the process of estimation of the ORBES region control
totals for the 48 industries. With few exceptions (for example, Standard
Metropolitan Statistical Areas), data on dollar outputs for industry are not
available at the substate level in the degree of detail necessary for their
use in the input-output model. Consequently, a number of assumptions had to
be made to provide a consistent set of estimates of dollar outputs. These
assumptions required consideration of the proposition that the productivity
9
This number decreased to 43 nonenergy sectors because disaggregate data
were unavailable due to nondisclosure rule.
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differences between firms in the same industry at the state level and those
in the portion of the state within the ORBES region were not significant.
Hence, output within the ORBES region may be approximated using the following
employment-based ratio:
x.° = X.S.E.°/E.S
i 111
where X. and E. refer to output and employment and where the superscript o
refers to the ORBES region area within a state and the superscript s re-
fers to the state. As shown in Table 2, most of the output and employment
data may be obtained directly from census volumes at the state level. Employ-
ment at the county level was obtained for most sectors from County Business
Patterns (the major source of which is the record file provided by state Em-
ployment Security records). For a number of sectors where no state level out-
put data were available, employment estimates were made for the ORBES region
and these data were then used to apportion national output to the ORBES re-
gion. The underlying assumption in all of these adjustment processes was that
the mix of firms in the ORBES region mirrored that of the nation in two
senses: (1) in terms of the product mix in each sector and (2) in terms of
the mix of technologies utilized in the production of the mix of goods in each
sector.
The most commonly used technique for modifying national coefficients has
been to develop one or more quotients which may then be applied against the
individual national matrix of a.,'s. These quotients have been as simple as
the location quotient, which seeks to compare the relative importance of an
industry in the region with the similar industry at the national level, to
more sophisticated techniques, which involve comparison of relative supply and
demand in the region with that in the nation. A large number of these tech-
niques have been evaluated in cases where both survey and nonsurvey tables
could be compared for one region. The evidence is somewhat inconclusive: a
good summary is provided in Morrison and Smith (5).
The technique which has gained the widest recognition and acceptance is
the one originally developed by Stone et al. (6) for the purpose of updating
a base year matrix to some future date when only the marginal vectors are
known. In this case, a new matrix is developed from a base matrix through the
following manipulation:
^ = RAQS (1.4)
where A refers to the matrix of technical coefficients (the subscripts refer
to the base (0) and future (1) year), and R and S are two diagonalized ma-
trices which take into account the effects of fabrication and substitution,
respectively (see 11). These matrices are estimated iteratively by adjusting
the base year matrix in such a way that the row sum AX, where X is total out-
put in the projection year, is exactly equal to the intermediate output
(known) in the projection year, and, similarly, the base matrix is further ad-
justed to ensure that intermediate purchases, XA i are equal to those known
in the projection year. The advantage of the technique lies in the fact that
n coefficients are modified through the use of only 2n pieces of data. How-
ever, there remains a major problem—estimating those two-colume vectors, one
8
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TABLE 2
CONTROL TOTAL DATA SOURCES
Sector Source of Data
1: Mineral Yearbook 196?
2: Mineral Yearbook 196?
3: Census of Manufactures 1967
4: Prorated from EEI 1967 state output by ratio of plant locations in
and out of ORBES region
5: Prorated from 1967 National table by 1974 CBP employment data
6: Farm Income Situation: Fis 218 supplement/August 1971 prorated to
the county level using 1974 census of Agriculture
7: Census of Construction Industries 1967
8: Census of Manufactures 1967
9: Census of Manufactures 1967
10: Census of Manufactures 1967
11: Census of Manufactures 1967
12: Census of Manufactures 1967
13: Census of Manufactures 1967
14: Census of Manufactures 1967
15: Census of Manufactures 1967
16: Census of Manufactures 1967
17: Census of Manufactures 1967
18: Census of Manufactures 1967
19: Census of Manufactures 1967
20: Prorated from County Business Patterns 1974
21: Prorated from County Business Patterns 1974
22: Prorated from County Business Patterns 1974
23: Prorated from County Business Patterns 1974
(cont inued)
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TABLE 2 (continued)
Sector Source of Data
2k: Prorated from County Business Patterns 1974
25: Prorated from County Business Patterns 197**
26: Prorated from County Business Patterns 1974
27: Prorated from County Business Patterns 197**
28: Prorated from County Business Patterns 1974
29: Prorated from County Business Patterns 197**
30: Prorated from County Business Patterns 197**
31: Census of Mineral Industries 1967
32: Census of Manufactures 1967
33: Census of Manufactures 1967
3**: Census of Manufactures 1967
35: Census of Manufactures 1967
36: Census of Manufactures 1967
37: Census of Manufactures 1967
38: Census of Manufactures 1967
39: Census of Manufactures 1967
40: Census of Manufactures 1967
41: Census of Manufactures 1967
42: Census of Manufactures 1967
A3: Census of Manufactures 1967
44: Census of Manufactures 1967
45: Census of Manufactures 1967
46: Census of Manufactures 1967
47: Prorated from County Business Patterns 1974
48: Prorated from County Business Patterns 1974
10
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of total intermediate input and the other of total intermediate output. In
modifying a national coefficient matrix, the procedure would involve estima-
ting these two vectors for the region in question and then applying the
iterative algorithm to the national coefficient matrix to generate the region-
al coefficient matrix. Thus:
R = TAU (1.5)
where A is the national coefficient matrix and R is the regional coefficient
matrix. T and U are diagonalized matrices, analogous to R and S above: how-
ever, their economic interpretation is less clear. In a sense, they represent
constraints on the ability of the regional industries to supply all the neces-
sary inputs to produce the level of output required in the region for inter-
mediate demand.
In the final analysis, it was decided to adopt a procedure rather similar
to the one used by Moore and Peterson (7) in the development of their Utah
input-output model. The procedure also enable the use of the accounting
scheme adopted by the U.S. model and the subsequent adjustments to that model
performed by the CONAES group (8) in making projections of future technology
at the end of the present century. The approach involves the estimation of
net final demand for the region given the national coefficient matrix and a
vector of total outputs for the region. In notation, the procedure may be
summarized as follows:
A* = AD (1.6)
where A represents the national technical coefficient matrix and D represents
a diagonalized matrix, a typical entry of which, d.., is equal to.
ii 1 - m.
i
where m. is the aggregate imports from foreign sources by industrial sector i.
Hence, matrix A* is a normalized matrix which includes the production require-
ments of industry, irrespective of their origin. The solution procedure for
the ORBES region takes the following form:
A*X +F+M-E=X (1,7)
where F represents net final demand, M represents interregional imports, E
represents interregional exports, and X represents gross ORBES output. As F
could not be calculated from local data, it was approximated as:
F = Fn.Yr°/Yn
where Y represents income in the ORBES region (o) and the nation (n). Since M
and E, trade on interregional account, could not be observed separately, the
vector V = M - E was derived as a residual from the accounting balance perform-
ed on equation 1.7 above. All other values, save for V, are known. Hence,
some of the entries in this vector will be negative since it represents net
interregional trade.
11
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Baseline estimates of the net exports by industry are needed for deter-
mining a vector of parameters that are needed in solving the input-output
model. When solving the input-output model for levels of regional production,
some relationship between consumption and net imports must, be made. Ideally,
net imports should be determined from aggregate production cost curves in and
out of the region and from the cost of transport. The information for such an
analysis could not be collected for even a region of our own choosing, let
alone the one under study. The only approach available was to assume in the
scenario runs a constant proportion between net exports and production, which
is equivalent to assuming a constant proportion between net imports and con-
sumption. The general form of the input-output equation that is solved is
Ax + f + Cx = x , (1.8)
where C is the diagonal matrix whose elements are the ratio of net exports to
total regional production in the base period. Thus, when the model is solved
using scenario parameters, the term Cx represents net exports.
1.3 The Energy Supply-Product Input-Output Model
The second component of the ORBES Phase II fuel and energy use model
arises from the need to deal with interfuel substitution in scenario specifi-
cations. For that purpose we have used an extension of the classical input-
output model called the supply-product input-output model. The extension
is accomplished by adding another set of variables and then by partitioning
the input-output matrix as shown in Figure 2. All nonenergy sectors are
grouped together in one sub-vector. The energy sectors are divided into
two components—energy supply and energy products. The energy product sec-
tors represent various categories of energy use, such as air conditioning,
space heating, and automotive transport. These are artificial sectors that
are assumed to be implicit in any energy consuming activity. It follows that
energy product demands are not exported and, thus, the consumption of an en-
ergy product demand equals its production. Except for energy transformation,
such as converting coal into electricity, all energy demand is satisfied by
the energy product classifications. Thus, the energy supply categories have
no inputs to the nonenergy sectors. The consumption of an energy supply cate-
gory equals the amount consumed in energy transformation processes plus the
sum of the contribution that the energy supply category makes to the total
production of each energy product demand.
The distinguishing feature of the model is that the entries in the sub-
matrices are expressed variously in $/$, btu/$, $/btu, and btu/btu, rather
than in $/$ in the regular input-output matrix. The coefficients in the A ,
A , and A submatrices are expressed in btu/btu: in $/$ in the A matrix,
ana in btu^f and $/btu in the A and A matrices, respectively. Rs there
are no trading relations between energy supply and the nonenergy sectors, A
has no entries. Similarly, there is no trading among the energy product sec-
tors and between the nonenergy and energy product sectors. Note that the en-
ergy supply sectors can and do make purchases from nonenergy sectors.
12
-------
Figure 2
Scenario and Impact Models: Sequential Steps in ORBES Assessment
Economic
Growth
Energy and.
Fuel
Demand
Population
Projection
Coal Supply
and
Allocation
Siting for
Electric
Generating
Units
— 9
Utility Emissions
Nonutility Emissions
Labor Requirements
and Induced
Migration
Social Impacts
Land Use Impacts
Water Demand
in Electric Sector
Health Impacts :
Occupational
Hazards
Water
Pollutant
Transport
Air
Pollution
Transport
)
Aquatic
and
Terrestrial
Ecological
Impacts
Health
Impacts :
Airborne
Residuals
Eponomi c ...
-------
Fuel substitution takes place within the A and A submatrices. In the
63-sector ORBES model, 12 energy supply sectors are identified:
1) coal mining
2) crude petroleum, gas
3) shale oil
4) gasified coal
5) solvent-refined coal
6) refined petroleum products
7) natural gas utilities
8) coal combined cycle electric
9) fossil electric utilities
10) nuclear electric utilities
11) high-temp gas reactor
12) renewable electric utilities
Within the submatrices, many of the entries are zero, since very few physical
transfers take place. For example, almost all crude oil will go to a refinery
and, from the refinery, the energy form (refined oil) will be distributed to
the nonenergy sectors. Within the energy supply/product matrices, substitu-
tion can take place in response to changes in the availability of supply,
changes in relative prices, and as a result of the introduction of new tech-
nology. The process is described by Behling et al. (9) as follows:
"A relative increase in oil and gas fired electric generating
plants versus coal fired plants can be depicted by increasing the
input coefficients for the fossil fueled electrical column vector
in the refined oil and pipeline gas rows and decreasing the
coefficients in the coal row. Changes in the energy products
coefficients and nonenergy sector coefficients in the fossil
electricity column are not required. In like manner, a shift in
the way a specific energy product is produced can be depicted by a
coefficient change in the A sub-matrix. A change in fossil
electricity versus refined oil for space heating, for example,
changes only the fossil electric and refined oil coefficients
within the space heat column vector."
Energy use, therefore, appears as a flow from supply > to product
> to nonenergy sector. Energy consumed by final demand will be shown in
the Y vector (i.e., consumers purchasing oil for home heating) and not as a
direct purchase from the energy supply sectors. The entries in the Y vector
of final demand represent energy exports, imports, and net inventory changes.
The supply-product model used for the ORBES region embraces both charac-
teristics in that endogenous substitution is made possible but is limited to
fuels: ex ante changes are made in technological structure through the modif-
ication of many coefficients in the nonenergy sectors. These latter changes
were introduced through the work accomplished by the CONAES group (10). The
activities of this group, composed of industry experts from a variety of aca-
demic, government, and private sector backgrounds, involved the estimation of
technological change in production practices by the year 2000. In an analo-
gous fashion to the procedure developed by Miernyk (11) , it was assumed that
14
-------
changes would be embodied in the production function of the average firm in
the early part of the next century.
The methodology involved the identification of the most likely changes
in terms of the foreseen reduction in toto of energy inputs and the possibili-
ties of substitution by nonenergy inputs.J The estimates of response to like-
ly changes (increases) in energy prices was estimated by industry experts ra-
ther than through the familiar economists' price elasticity of demand models.
The judgmental approach may be justified on the grounds that the historical
data necessary to implement any model of this kind (involving energy-related
price changes) are inappropriate when viewed against the energy price changes
of the last several years.
In addition to the advantage of being able to analyze the effect of
energy substitution, the model is also useful for incorporating energy con-
servation considerations in a much more direct manner than that allowed by the
conventional input-output framework. For example, using the conventional
framework, it is difficult to examine the effect of the reduction of space
heat requirements due to increased building insulation. Because energy con-
sumption by each industry is not broken down by end uses, the determination of
energy input to each industry due to space heat requirement is a difficult
task. With the supply-product formulation, the space heat requirements for
each industry are parameters of the model. Thus, the change can be effected
simply by scaling the technical coefficients in the space heat row by the ap-
propriate scale factor.
Though arbitrary fuel substitution possibilities can be handled by the
supply-product input-output model, the elements of the fuel mix sub-matrix,
A , cannot be arbitrarily set. Each column of A , which represents the fuel
mix for a particular energy product, must satisfy an energy conversion con-
straint equation. For instance, for each btu of fuel oil that is burned to
produce space heating, approximately .7 btu of space heat is produced. Be-
cause the conversion of electricity into heat is essentially lossless, a btu
of electricity converts into a btu of space heat. If space heat is only pro-
duced by electric resistance heating, then fuel mix column for space heating
would consist of a unit vector with the component corresponding to electric
generation bring unity, If all space heating was produced from fuel oil, the
space heat column would have the fuel oil component being I/.7, while all
other components are zero. Finally, if x and y represent the fractions of
space heating supplied by electricity and fuel oil, respectively, then the
components for the space heating column of A would be x and y/.7 for elec-
tricity and fuel oil. Thus, if a is a specific column of the A sub-matrix
corresponding to a particular energy product and is the corresponding vector
It should be pointed out that the term "nonenergy" input does not
imply that energy is not utilized in its production: the distinction, in the
case of the ORBES model, would be between substitution among the first five
sectors as opposed to substitution among the remaining 43 sectors. The latter
comprise the nonenergy sectors but, as the matrix shown in Figure 5.1
demonstrates, these sectors do consume energy in the form of energy products
(shown as entries in the A matrix).
pn
15
-------
Figure 3
Energy Input-Output Model
Supply
(Btu)
Product
(Btu)
Non-Energy
(S)
Supply
A
ss
A
ps
A
ns
Product
A
sp
0
0
Non-
Energy
0
A
pn
A
nn
Final
Demand
Y
s
Y
P
Y
n
Total
Output
X
s
X
P
X
n
16
-------
of conversion efficiencies,14 then 'a=l.
The generation of A sub-matrices which meet the above constraint can be
done simply by specifying the proportion of demand for a particular energy
.product, sometimes called the basic energy demand (BED), that is satisfied by
each particular energy supply classification. Forming a column vector from
the proportions of all supply categories used in the production of a unit of
an energy product, and then arranging the column vectors corresponding to all
energy product classifications into a matrix, we form what shall be termed the
fuel mix matrix. The A sub-matrix is formed by dividing the fuel mix matrix
by the corresponding energy supply to energy product conversion efficiency.
It is easy to see that this A sub-matrix will satisfy the energy balance
constraints if all of. the coliimns of the fuel mix matrix sums to unity. It
was convenient to modify the fuel mix matrix rather than directly modifying
the A sub-matrix during the process of regionalization of the supply-product
input-output model and during the development of determining the scenario
specific A sub-matrices. In this way, fuel substitutions can be easily
specified tnat do not violate the energy balance constraints.
1.4 Derivation of the Energy Consumption Baseline
Although energy supply consumption by end sector in the supply-product
input-output model is not explicitly represented, once A has been specified,
energy consumption by fuel type by each industry can be determined. Energy
supply consumption by industry is determined by transforming the energy pro-
duct requirements of a particular industry by the A sub-matrix. Once this
transformation has been performed,5 the resulting equations which determine
the production levels for the energy supply and nonenergy industries can be
solved independent of energy product sectors. This set of equations have the
same form as the classical input-output model. Thus, the supply-product in-
put-output model can be viewed as a structural model that can be transformed
into a reduced form model which, in this case, is the classical input-output
formulation.
Given regional estimates of the production of goods and services, fuel
consumption by each sector can be determined in the reduced form model. The
cells in the resulting fuel consumption table then can be summed and an ag-
gregate total can be determined for the residential, commercial, and indus-
trial sectors. This allows the comparison of the input-output baseline with
other sources of baseline data.
Because of the disaggregate nature of the model, no direct fuel consump-
tion data could be collected for the ORBES region. Thus, the energy consump-
tion baseline is indirectly determined from other baseline data. The accuracy
of the input-output energy consumption baseline is dependent upon the accuracy
^ These efficiencies are dimensioned btu of energy supply per btu energy
product.
5 Also the demand for energy products by the final demand sector is
transformed into a demand for energy supplies by the same procedure.
17
-------
of several sources of data and the validity of basic assumptions that are
made in regional input-output modelling. One of the most important factors
that effect the accuracy of the fuel consumption estimate is the accuracy of
ORBES production of goods and services. These estimates were compiled by the
ORBES energy demand model research project. Another important factor is the
estimate of energy product requirements per unit output of goods and services.
These estimates would have been very difficult, if not impossible, to collect
at the regional level and, thus, national estimates were used. More precisely,
we assumed that the national A coefficients are applicable to the ORBES re-
gion. The compilation of these coefficients are found in Knecht and Bullard
(12). The use of national energy product requirements is a reasonable assump-
tion. This amounts to assuming that the amount of energy products required in
the production of a good is independent of region. We are still able to
specify the aggregate fuel mix of each energy product at the regional level.
Given good estimates of regional production of goods and services, and if
the assumpeion that national energy product requirement estimates apply to the
ORBES region is reasonable, then it would be possible to tune the implicit en-
ergy consumption baseline generated by the input-output model by only modify-
ing the fuel mix matrix. The discrepancies in fuel consumption by industry
was assumed to be due to errors in the specification of the fuel mix for pro-
cess heat. Because space heating is the most significant component of fuel
use by the residential and commercial sectors,6 the fuel mix for space heat
was modified to bring the usage of fuels by the commercial and residencial
sectors into conformance with Page's estimates (13). After these adjustments
were made to the fuel mix matrix, the input-output model projected a lower
electricity consumption demand than that estimated by Page. Because no other
data sources were available to clear up the discrepancy, miscellaneous elec-
tric inputs to all sectors were uniformly increased. Since the input-output
baseline was for the year 1967, and Page's energy consumption baseline was for
1974, the comparison of the energy consumption baselines involved prorating
the 1967 production by Page's estimates in the increase in gross regional pro-
duct. Thus, additional errors are introduced due to the balanced growth as-
sumption which is implicit when one prorates estimates. Thus, the discrepancy
in the estimates of electricity consumption may be due to unbalanced growth
that occurred between 1967 and 1974. Also, the substitution of electricity
due to technological change may be an important explanation of why this dis-
crepancy occurred. Unfortunately, no data were readily available which could
pinpoint where the discrepancy arose.
1.5 Two Versions of the Model Used in the Scenario Runs
There are two versions of the model which were used in this study. In
the version which was first developed, the form was very similar to the clas-
sical input-output model formulation, though some of the industries were
° Though air-conditioning is a major source of energy consumption in the
residential and commercial sectors, it poses little problem in terms of de-
termining fuel mixes, since the vast proportion is produced from electricity.
18
-------
assumed to have a fixed output level in some or all of the scenarios. The
equations were rewritten so that net exports of these sectors were determined
rather than the production levels. Most runs of this version of the model re-
quired two solutions. After the first run of total demand, electric genera-
tion was determined. Then, the fuel mix for electric inputs to energy pro-
ducts were reallocated so that the amount of nuclear and hydro-electric gener-
ation would be at the specified levels in the second solution.8 Another ver-
sion of the model was specifically designed to allow a simpler solution pro-
cedure when the production levels of electric generation and alternative en-
ergy technologies are specified. In this formulation, Von Neumann^ process
activity vectors (14) were defined for nuclear and hydro-electric power, coal-
steam electric plants which .cogenerate waste heat, total energy systems, wind
and photo-electric electricity generation which can be centralized or decen-
tralized. Some of these process, such as total energy systems, ° have mul-
tiple outputs. The energy demand equations are explicitly written to deter-
mine the amount of conventional fuels necessary to meet energy product demands
that are not satisfied by the alternative technology fuels. Also, the model
determines the level of fossil fuel electric production when nuclear and
hydro-electric production is given. This is done by defining a new variable
called central station electric generation. An equation in this model speci-
fies that total central station electric production equals total electric con-
sumption plus line losses and net exports. Finally, the amount of fossil
electric generation is determined by an equation which states that fossil
electric generation is equal to total central station electric demand minus
the prespecified levels of generation from the other central station tech-
nologies. Any decentralized production of electricity is assumed to satisfy
miscellaneous electric demand with no transmission lines. Because the cen-
tral station electric classification is the only electric generation variable
that appears in the fuel mixes for energy products, no modification of A is
necessary, and the model does not require iteration to find the solution.
7 Nuclear and hydro-electric power production were set in all scenarios.
Crude oil and well-head gas production were specified in scenario 5.
8 Given a reasonable first guess for the electric fuel mix, only one
iteration was required to have a small residual between the solution and the
specified levels.
9 The Von Neumann model is the theoretical precursor of all economic
activity analysis models. This model is also noteworthy for it was one of the
first mathematically formulated economic growth models. In the ORBES energy
demand model we have incorporated the concepts of the general Von Neumann
process. In this context a process can be loosely defined as a linear
transformation of inputs (goods, labor, and depreciated capital) into one or
more outputs. The columns in the technical coefficient matrix are special Von
Neumann processes with but one output.
It is assumed that the total energy systems consume refined petroleum
or natural gas to produce electricity and cogenerated heat which may be used
for process or space heating.
19
-------
2. Scenario Assumptions
2.1 General Discussion
The ORBES Core Team specified a number of scenarios to be run through the
energy and fuel demand model. Of interest here is the way these scenarios are
given a quantitative dimension which permits analysis using the demand model.
It is necessary, for instance, to specify how much conservation can be ex-
pected in the region by 1985 and 2000 and in what specific energy uses conser-
vation will occur.
Before discussing these matters, the meaning of enduse efficiency should
be clarified. An energy efficiency increase in the industrial and commercial
sector is defined as a reduction in the amount of energy product used per unit
of output produced. An enduse efficiency increase in space or water heat in
the residential sector is defined to be the reduction of energy consumed in
these activities on a per-capita basis. In the solution of the model, it is
irrelevant whether it is in part achieved by increased insulation, better heat
transfer, or by reduction of basic energy demand by, for instance, decreasing
thermostat settings.
Table 3 lists the scenario assumptions that are incorporated in the
runs of the demand model. Most of the data in Table 3 have been taken direct-
ly from the code that implements the scenario runs and many of the parameter
changes found in Table 3 can be extracted from the scenario solution tables.
Items in the table are organized so that efficiency assumptions are listed
first. Items concerning growth rates and fuel mix assumptions' for each par-
ticular energy use then follow. Finally, miscellaneous assumptions are
listed.
Except for scenario 5, the scenario population growth assumptions are
.uniform across scenarios. Milke's estimate for ORBES region population under
the assumption of higher in-migration is 26 million in the year 2000 (15)
(the 1970 census figure was 23.06 million). This corresponds to an average
per annum growth rate of 0.476 percent. We have assumed that the growth rate
up to 1985 is 0.52 percent per annum (the average growth rate from 1960 to
1970 was 0.57 percent per annum).
1 This is not to say that the above considerations should not be
quantified, for by determining the aggregate enduse efficiency increase in
terms of contributions from the above factors will result in a more valid
assumption. The inability to precisely specify the factors which determine
the aggregate enduse efficiency increases are one of the most disturbing
shortfalls of this research effort.
21
-------
Scenario 1
Qivironnental Controls strict
Enduse efficiency as-
sumptions in 2000.
TABLE 3
Scenario Assumptions Used in the CRBES Energy Demand Kodel
Scenario 2 Scenario 2a Scenario 3 Scenario 4
Process heat: 10% in-
crease, water heat 5%,
space heat 15%, air
cond.: 10%.
lax
lax
lax
lax
Same as in Sane as in Same as in Process heat: 5% in-
Scenar- Scenar- Scenar- crease, space heat 7%
io 1. io 1. io 1. increase, air ccnd.: 5%
increase.
Scpnario 5 Scenario 6
lax lax
Sane as in Process heat: 351 in-
Scenar- crease, water heat: 20%
io 1. increase, space heat:
45% increase, air condi-
tioning: 43* ir.crea-e.
Enduse efficiency as-
surptions in 1965.
Transportation efficien-
cy increases in 2000.
Tr exportation efficien-
cy increases in 1985.
Growth in per capita
perscrcl r.ctive power
cer^r.d.
Growth in the per capita
fir^l cenar.d for miscel-
laneous electric power.
Growth in the final
ccr^r.d for nonenergy
goods in 2000.
Conventional technology
Cuei irix a££U.T.pticns for
Process heat: 5% in-
crease, water heat 2%,
space heat 5%, air
cond.: 3%.
17% decrease for water,
air, truck, and rail
transport, auto fuel
eff . 28 nipq, (modelled
after 2000 'NEP.)
9* decrease for water,
air, truck, and rail
transport, auto fuel
eff. 20 mpg.
Average of 2.4% per an-
num.
20% increase.
Aggregate grows at an
average of 2.47% per an-
nuTi though different
growth rates for indivi-
dual industries are
used. The weigt.ts in
CCICAES scenario 2 are
used to determined the
growth rates.
55% of process heat
basic energy supplied by
Sair.e as in
Scenar-
io 1.
Same as in
Scenar-
io 1.
Same as in
Scenar-
io 1.
Same as in
Scenar-
io 1.
Same as in
Scenar-
io 1.
Same as in
Scenar-
io 1.
Same as in
Scenar-
t * i
N/A
Same as in
Scenar-
io 1.
N/A
Same as in
Scenar-
io 1.
Same as in
Scenar-
io 1.
S&r.e as in
Scenar-
io 1.
Sane as in
Scenar-
i ~ 1
N/A
Same as in
Scenar-
io 1.
N/A
Ssre as in
Scenar-
io 1.
Same as in
Scenar-
io 1.
Same as in
Scenar-
io 1.
Sane as in
Scenar-
i ~. i
Process heat: 2% in-
crease, space heat 3%
increase, air cond.: 2%
increase.
9% decrease for water,
sir, truck, and rail
transport, auto fuel
eff. 21 npg,
5% decrease for water.
air, truck, and rail
transport, auto fuel
eff. 17 mpg.
Same as in Scenario 1.
Sane as in Scenario 1.
Same as in Scenario 1
except CONAES Scenario 1
is used.
7% of process heat basic
energy supplied by coal,
N/A
Same as in
Scenar-
io 1.
N/A
Average of
2.1% per
annum.
Same as in
Scenar-
io 1.
Sane as in
Scenario 1
except the
average
growth
rate is
2.075% per
annum.
Sane as in
Scenar-
t _ i
No decrees-:- for water,
55% decrease for dir,
40% truck, 9% rail.
37 mpg autos, (modelled
after CCKAES scenar-
io 3.)
N/A
No increase ever 1574
level.
20% decrease.
G^ne as in Sce.-uric 1
except OCii*iS Scenario 3
is used.
oil: 6%, gas: 85%,
elec.: 2%.
basic er.c-rgy supplied by
coal, oil: 22%, gas:
38%, elec.: C%.
-------
t\j
OJ
Sepnario
Conventional technology 36% of process heat Same as in
fuel mix assumptions for basic energy supplied by Scenar-
process heat in 1985. coal, oil: 18%, gas: io 1.
37%, elec.: 9%.
TABLE 3 (continued)
Scenario Assumptions Used in the CSBES Energy Demand Model
Scenario 2 pcpnario 2a Scenario 3 Scenario 4
N/A N/A
21% of process heat
basic energy supplied by
coal, oil: 16%, gas:
56%, elec.: 7%.
Conventional technology Gas: 35%, elec.: 65%.
fuel mix assumptions for
water heat in 2000.
Conventional technology Oil: 4%, gas: 57%, and
fuel mix assumptions for elec.: 39%.
water heat in 1985.
Conventional technology
fuel mix assumptions for
space heat in 2000.
Conventional technology
fuel mix assunptions for
space heat in 1985.
Modifications of ncnen-
ergy to nonenergy tech.
coef.
Exports of Electricity
17% of space heat basic
energy supplied by oil,
gas: 46%, elec. (heat
pumps): 37%.
1% of space heat basic Same as in
energy supplied by coal, Scenar-
oil: 29%, gas: 58%, io 1.
elec. (heat pumps): 10%,
elec. (resistance): 2%.
Scenario 2 CCKAES, (dou-
bling of real energy
prices.)
Ratio of exports to
ORBES production does
not change from of
24.6%.
Same as in Same as in Same as in Gas: 85%, elec.: 15%.
Scenar- Scenar- Scenar-
io 1. io 1. io 1.
Oil: 3%, gas: 79%, and
elec.: 18%.
13% of space heat basic
energy supplied by oil,
gas: 87%, elec. (heat
pumps): 0%.
3% of space heat basic
energy supplied by coal,
oil: 34i, gas: 51%,
elec. (resistance): 2%.
Scenario 5 Scenario 6
N/A N/A
Same as in Oil: 13%, gas: 87%.
Scenar-
io 1.
Same as in
Scenar-
io 1.
Same as in
Scenar-
io 1.
N/A
Same as in
Scenar-
io 1.
N/A
Same as in
Scenar-
io 1.
N/A
N/A
N/A
Same as in
Scenar-
io 1.
N/A
Same as in
Scenar-
io 1.
Same as in
Scenar-
io 1.
Same as in
Scenar-
io 1.
20ktfew of
additional
coal fired
capacity
in addi-
tion to
that
determined
in Scenar-
io 2.
Same as in
Scenar-
io 1.
Same as in
Scenar-
io 1.
Scenario 1 CCMAES, (real
energy prices remain
constant.)
Sane as in Scenario 1.
Same as
Scenar-
io 1.
Same as
Scenar-
io 1.
in
in
N/A
26% of space heat basic
energy supplied by oil,
gas: 49%, elec. (heat
pumps): 25%.
N/A
Scenario 3 CCTJAES, (qua-
drupling of real energy
prices.)
Same as in Scenario 1.
Nuclear Power Capacity
No increases over 1986
estimates.
Nuclear-elec. power pro- 210 trillion btu.
duction.
Hydro-elec. power pro-
duction.
35 trillion btu.
Same as in Same as in Sar.e as in Same as in Scenario 1.
Scenar- Scenar- Scenar-
io 1. io 1. io 1.
Same as in Same as in Same as in Same as in Scenario 1.
Scenar- Scenar- Scenar-
io 1. io 1. io 1.
Same as in Sase as in Same as in Sane as in Scenario 1.
Scenar- Scenar- Scenar-
io 1. io 1. io 1.
Same as in Same as in Scenario 1.
Scenar-
io 1.
Same as in Same as in Scenario 1.
Scenar-
io 1.
Same as in Same as in Scenario 1.
Scenar-
io 1.
-------
TABLE 3 (continueO)
Scenario Assumptions Used in the ORDES Energy Demand Model
Scenario 1
Steel production assump-
tions in 2000.
Steel production assump-
tions in 1985.
Air conditioning market
penetration in 2000.
Air conditioning market
penetration in 1S85.
Same as in
Scenar-
io 1.
Penetration of electric
arc process: 26%, 14%
less energy input per
• unit output.
Penetration of electric
arc process: 21%, 4%
less energy input per
unit output.
Increases from 51% in
1974 to 90% in residen-
tial sector, while in-
dustrial/corraercial
penetration increases
from 65% to 90%.
Residential: 71%, indus- Same as in
trial/ccnnvercial: 80%. Scenar-
io 1.
Scenario 2 Scenario 2a Scenario 3 Scenario 4
Sane as in Same as in Sane as in Sane as in Scenario 1.
Scenar- Scenar- Scenar-
io 1. io 1. io 1.
N/A
N/A
Same as in Scenario 1.
Same as in Sane as in Sane as in Same as in Scenario 1.
Scenar- Scenar- Scenar-
io 1. io 1. io 1.
N/A
N/A
Same as in Scenario 1.
Scenario § Scenario 6
Same as in Sane as in Scenario 1.
Scenar-
io 1.
N/A
N/A
Same as in No increase over 1974
Scenar- level.
io 1.
N/A
No increase over 1974
level.
K!
-------
Except for scenario 2a in 2000 and scenarios 1 and 2 in 1985, the ratio
of electricity exports to production remains at 24.2 percent, the estimated
1974 level. In all scenarios, total production of nuclear power for 1985 is
assumed to be 210 trillion btu. For the year 2000 in all scenarios except 2c,
no more additional nuclear capacity is assumed to be installed, thus total
production by nuclear electric remains at 210 trillion btu. For all scenarios
in both 1985 and 2000, total hydro-electric production is assumed to be 35
trillion btu (16). In all scenarios and from all central station electricity
production facilities, the assumed transmission loss factor was assumed to be
8.7 percent of total generation.
In the energy demand model, the demand for energy in the production of
steel is only affected by aggregate enduse efficiency increases and assump-
tions concerning the proportions of steel produced by two aggregated processes.
One of the two processes is electric arc. All other processes are aggregated
into one process loosely termed as blast furnaces. These assumptions do not
vary across scenarios. We assume that 28 percent of steel production is by
the electric arc process in 2000.12 The percentage for 1985 is over-
optimistically assumed to be 22 percent. The aggregate enduse efficiency in-
creases are assumed to be 4 percent in 1985 and 14 percent in 2000.
For all scenarios except 3 and 6, what could be viewed as optimistic in-
creases in the demand for the basic energy demand for air conditioning.
We assumed that the relation of demand for air conditioning is linear with the
market penetration of air conditioning. The national market penetration rate
for air conditioning was approximately 50 percent in 1974 in the residential
sector (18). This percentage is assumed to increase to 71 in 1985 and reach
90 in the year 2000. For the commercial sector, the percentages are 65, 80,
and 90 percent, respectively, for the years 1974, 1985, and 2000. With as-
sumed incomes being twice as large, such an increase in market penetration for
air conditioning units is quite reasonable. The problem is with the way the
assumption is implemented in the model in that with higher energy costs the
use patterns of these units will become more discretionary.
Table 3 provides a means for easy comparison of scenario dependent as-
sumptions. As is readily seen, detailed descriptions are only necessary for
scenarios 1, 3, and 6. In general, the assumptions of scenarios 2, 2a, 2n, 4,
5, and 5a correspond to those of scenario 1. In particular, scenarios 1 and 2
differ only in environmental controls. Scenario 2a differs from 2 only in
that 20,000 MWe of coal-fired and nuclear electric generation capacity are
added in the respective scenarios. The conventional technology assumptions
for scenario 3 are identical to those of scenario 2. Thus, a difference arises
only in that alternative technologies displace some energy demands satisfied
by conventional technologies. Scenario 5 is identical to scenario 2, except
that the growth for personal automotive transportation and the aggregate
growth rate for nonenergy goods by final demand are, respectively, 2.1 and
2.075 percent per annum. In scenario 5a, both growth rates are 3.1 percent.
This approximately corresponds to Hermelee's assumption in his refer-
ence energy system diagram (17).
25
-------
It is in scenarios 1, 5, and 6 that we find the largest variation in
scenario assumptions. An important scenario dependent assumption which dif-
ferentiates these scenarios is real energy prices. Being the most optimistic
scenario, scenario 4 assumes that real energy prices do not change from the
present value. In scenario 1, it is assumed that real energy prices double,
while scenario 6 assumes that real energy prices quadruple. Thus, in scenario
4, we find the lowest increases in enduse efficiency with energy use reduc-
tions on the order of 5 to 7 percent and average vehicle efficiency of 21
miles per gallon. In scenario 1, efficiency increases range from 5 to 15 per-
cent, while average vehicle efficiency is 28 miles per gallon. Scenario 6,
as the zero energy growth scenario, assumes enduse efficiency increases on
the order of 35 to 45 percent, with personal vehicle efficiencies averaging
37 miles per gallon.
2.2 The Incorporation of Scenario Parameters in the Energy Demand Model
The modification of the baseline data by the scenario dependent data and
the subsequent solution of the model is a straightforward process. The mod-
ification of the baseline data to incorporate the scenario assumptions in-
volve :
1. Generation of the fuel mix technical coefficient sub-matrices.
Also many of the enduse efficiency assumptions are incorporated
at this stage.
2. Modification of nonenergy to nonenergy technical coefficients.
These changes represented technical innovation assumptions and
energy price sensitivity assumptions incorporated in the scen-
arios of the CONAES study.
3. The specification of the residential demand for energy is made
by changing the corresponding elements of the final demand sub-
vector for energy products. Also the demand for personal auto-
motive transportation is specified at this stage.
4. Modification of the final demand for nonenergy goods was done by
prorating the corresponding final demand sub-vector by the
transformed CONAES weights13 and then all of the elements uni-
formly adjusted so that the aggregate growth rate corresponds to
the rate specified for each scenario.
13 The CONAES study integration model (19) used a different sectoring
scheme than that used in the ORBES Energy Demand Model. The procedure re-
quired for a transformation can be mechanized since both models can be ex-
pressed as aggregations of the 1967 national benchmark IO table. A program
was developed to use the best available national data to resolve sectoring
clashes whenever they occurred. Because the resolution of sectoring clashes
required base comparison vectors of order 357, regional data could not be
used in this program.
26
-------
Once the vector and matrices that have been mentioned above have been modified,
the actual system of equations to be solved is pieced together and then solved
by decomposition. As described earlier, net exports are determined by the mo-
del by assuming that the ratio of net exports to total output for each indus-
try remains the same as the 1974 baseline ratio. The model explicitly solves
for total ORBES production by industry. Once total production is known, net
exports and total ORBES consumption by industry can be determined. Once con-
sumption by industry has been determined, the solution is then aggregated into
what has been called the energy enduse tables. These have been calculated for
the purpose of comparison among scenarios. In general, it is difficult to re-
late changes in the enduse tables to changes in scenario assumptions. But, in
most cases, it is fairly easy to perform hand calculations to check the ef-
fect of a change in a scenario assumption given the solution to the input-
output model.
2.3 Scenario Growth Assumptions
In the growth for the final demand for goods, a general assumption has
been made that the growth in the demand for nonenergy goods would loosely be
tied to increases in gross regional product, while the growth in the demand
for energy products was in general tied to the growth in population. Table 4
contains the computed average growth rates in the final demand for nonenergy
goods for scenarios 1, 4, 5, 5a, and 6 in the year 2000.15 It is readily seen
that there is an explicit assumption across all scenarios that people spend an
increasing share of their income increase in the purchase of services. Also,
as one would expect, the lowest growth rates are associated with the purchase
of meats and other food products. Across the various scenarios, the most
visible difference is that air transportation grows twice as fast in scenario
4 as in scenarios 1 and 6. As it would be expected, the growth rates in
scenario 5 are smaller than those found in the other scenarios, usually by
0.3 to 0.4 percent per annum.
The residential demand for energy products is specified on a per capita
basis. Per capita miscellaneous electric use is assumed to increase by 20
percent in all scenarios except scenario 6, in which a 20 percent decrease is
assumed. This is motivated by the increased use of electric and electronic
appliances which may occur in all but the most pessimistic scenario assumption
concerning energy prices. Historically in the U.S., the number of vehicle
14 The important computations needed are the solution vectors, the trans-
actions matrix, and (for convenience) the technical coefficient matrix. These
vectors and the sub-matrices corresponding to the fuel-mix coefficients have
been printed for each scenario.
15 Scenarios 2, 2a, and 3 growth rates for the final demand for nonenergy
goods are assumed to be the same as in scenario 1.
27
-------
TABLE 4
Average Growth Rates for the Final Demand Nonenergy Goods
in Scenarios \
25 agriculture
26 mining
27 construction
28 meat products
29 food exc. meat products
30 apparel and misc text, prod
31 misc fabricated text, prod
32 logging and misc. wood prod
33 misc paper prod and publ.
34 paper mills
35 paperboard mills
36 paperboard containers
37 industrial org-inorg chem.
38 ag and misc chem.
39 plastic and synthetic resins
40 paving and asphalt
41 rubber and misc plastic prod
42 glass, stone, and clay prod
43 blast and basic steel prod
44 iron and steel found, and forging
46 nonfer. forge, cast, and rolling
47 fabricated metal containers
48 industrial and farm machinery
49 elec. equipment and components
50 truck, bus, and auto manufac.
51 misc transport, equipment
52 misc manufac.
53 railroads
>, 6, 4, 5, and 5a
s2
1.71
2.52
2.52
1.38
1.51
2.39
2.33
2.43
2.36
2.36
2.36
2.36
2.39
2.39
2.39
2.39
2.36
3.09
2.51
2.51
2.54
2.63
2.63
2.59
2.77
2.63
2.50
2.54
54 misc transport, and communication 2.77
55 motor freight transport.
56 water transport.
57 air transport.
58 wholesale trade
59 retail trade
60 finance and insurance
61 real estate
62 hotels, lodging, and amusements
63 misc business and personal serv.
64 advertising
65 auto repair
66 medical and educational serv.
67 nonprofit organizations
2.39
1.46
2.47
2.15
2.15
2.87
2.87
2.87
2.87
2.87
2.87
2.87
2.87
Source: differential growth rates are based on
adjusted (see text) to meet scenario
growth rate
s6
1.71
2.53
2.53
1.39
1.51
2.39
2.33
2.44
2.36
2.36
2.36
2.36
2.40
2.40
2.40
2.40
2.36
3.09
2.51
2.51
2.54
2.63
2.63
2.59
2.78
2.63
2.50
2.72
2.77
2.00
1.46
2.45
2.15
2.15
2.87
2.87
2.87
2.87
2.87
2.87
2.87
2.87
s4
1.70
2.51
2.51
1.37
1.50
2.37
2.31
2.42
2.34
2.34
2.34
2.34
2.38
2.38
2.38
2.38
2.34
3.07
2.50
2.49
2.52
2.62
2.62
2.57
2.76
2.62
2.48
2.30
2.75
2.76
1.44
4.58
2.13
2.13
2.85
2.85
2.85
2.85
2.85
2.85
2.85
2.85
s5
1.44
2.12
2.12
1.17
1.27
2.01
1.96
2.05
1.98
1.98
1.98
1.98
2.01
2.01
2.01
2.01
1.98
2.60
2.11
2.11
2.13
2.21
2.21
2.18
2.33
2.21
2.10
2.14
2.33
2.01
1.23
2.08
1.81
1.81
2.41
2.41
2.41
2.41
2.41
2.41
2.41
2.41
s5a
2.14
3.16
3.16
1.73
1.89
2.99
2.92
3.05
2.95
2.95
2.95
2.95
3.00
3.00
3.00
3.00
2.95
3.87
3.15
3.14
3.17
3.30
3.30
3.24
3.48
3.30
3.13
3.18
3.47
2.99
1.82
3.10
2.69
2.69
3.59
3.59
3.59
3.59
3.59
3.59
3.59
3.59
CONAES(1978) which are
assumptions.
28
-------
miles per capita has been increasing at a rate of acount 3 percent per annum.16
All of the scenarios except 3, 5, and 5a assume that the growth rate in per
capita final demand for automotive transportation (in vehicle-miles) is 2.4
percent per annum. In the low and high economic growth scenarios 5 and 5a,
the growth rate is assumed to be 2.1 and 3.1 percent, respectively. In the
zero energy growth scenario, this rate is assumed to be 2.4 percent per annum
until 1985. In 2000, it is assumed that per capita vehicle miles for personal
transportation falls back to the 1974 level.
2.4 Scenario Fuel Mix Assumptions
The scenario fuel mix assumptions, in general, have little impact upon
total energy consumption, even though the assumptions are the most important
determinates in individual fuel consumption. Table 5 contains the fuel-mix
coefficients for the 1974 baseline data.17 In all the scenarios except scen-
ario 4, the gas scenario, it is assumed that there is a significant incentive
to use coal instead of other fossil fuels. Because direct combustion of coal
for the production of space and water heat is, in general, not environmentally
sound, the switch to coal only has a major impact upon the production of in-
dustrial process heat.18 In scenarios 1, 2, 3, 5, and ta, it is assumed that
the percentage of process heat produced from coal rises from 28 percent in
1974 to 55 percent in 2000.19 In scenario 6, the fuel mix for process heat
does not significantly change from that in 1974 except that no process heat is
generated by the relatively energy inefficient method of resistance heating.
In the natural gas scenario, the greater amount of substitution for other
fuels by natural gas is assumed to occur between 1985 and 2000. In scenario
16 Total vehicle miles have been increasing at a rate between 4 and 4.7
percent per annum since 1960. Since the average growth rate of the U.S. pop-
ulation between 1960 and 1970 was around 1.25 percent per annum, per capita
vehicle miles have been increasing at a rate of around 3 percent per annum (20)
17 The derivation of the fuel mix baseline is discussed in Chapter 2.
18 Originally it was assumed that heat for clothes drying and the house-
hold preparation of food was included in the final demand for process heat.
These energy demands were reallocated to the final demand for space heat;
otherwise, a significant portion of clothes dryers would be assumed to be
coal-fired. This aggregation is more plausible, since a household that uses
natural gas for space heating will probably use gas for other heating appli-
cations in the household.
19 This approximately corresponds to the change assumed in the 2000 NEP
Referance Energy System diagram by Hermelee (21).
29
-------
TABLE 5
FUEL SPLITS FOR SATISFYING BED1 IN 197*t
Energy Product
Ore-Reduction Feedstock
Chemical Feedstocks
Process Heat
Water Heat
Space Heat
Air Conditioning
Coal
100
2
28
-
k
-
Fuel Type
Oil Gas
-
73 25
21 A2
5 75
37 57
5
Electric
-
-
9
20
2
95
1. Basic Energy Demands.
2. Percentage of BED satisfied by fuel type.
30
-------
5 for the year 2000, 7 percent of all process heat is produced from coal,
while 85 percent is produced from natural gas.20
The "push to coal" scenarios assume a widespread use of heat pumps for
space heating. Since the net energy cost of space heating via heat pumps is
approximately the same as the energy required in space heating by direct com-
bustion of fossil fuels, the use of heat pumps would promote energy efficiency
and would decrease dependency upon fuels that are more scarce. Since the vast
majority of this electric demand would be satisfied by coal-steam generators,
coal would indirectly displace the use of other fossil fuels. Also, the re-
sidual generation associated with the use of heat pumps will be less than that
generated by the direct combustion of coal for space heating, since coal fur-
naces most likely will be uncontrolled. It is assumed that in all of the
scenarios except 4 and 6 that heat pumps satisfy 37 percent of all space heat-
ing demand in the year 2000. Because the investment cost of space heating is
relatively expensive (22), a considerable government subsidy would be neces-
sary to encourage the installation of heat pumps. In the natural gas scenario,
the use of gas for home heating increases from 57 percent in 1974 to 87 per-
cent in the year 2000. After the fuel mix for space heat was specified, the
fuel mix for water was determined. Households burning gas for space heating
were assumed to use gas for water heat. Households with furnaces burning fuel
oil were assumed to use electric water heaters.
2.5 Modification of Material Requirements in Nonenergy Goods Production
To incorporate the changes in production techniques which may occur under
different real energy prices, the changes to the A technical coefficients21
of the CONAES study (23) have been transformed into the sectoring scheme of
the ORBES energy demand model. Most scenarios used the transformed factor
matrix22 of CONAES scenario III (24). ORBES scenarios 6 and 4, respectively,
use the transformed factor matrices of CONAES scenarios II and 1C (25).
Scenario IV assumes that overall real energy prices double and quadruple.
Table 6 lists several of the important elements of the factor matrices.
p A
It is assumed that the increased availability of natural gas begins
in the early 1980's. At that point, it is assumed that all new plants will
install equipment that burn natural gas to produce process heat. Essential-
ly, it is assumed that the use of coal, oil, and electricity for process heat
then declines at an average rate of depreciation of capital (assumed to be
5 percent per annum).
21 A represents the nonenergy inputs to the production of the non-
energy goods.
22 The factor matrix is a square matrix of order 43 (which is the same
as the order of A ). Each element of the factor matrix is multiplied by the
corresponding element of the base year A to yield the A sub-matrix used
in the scenario runs. As indicated in footnote 1, transformations which both
aggregate and disaggregate the CONAES factor matrices must be determined to
compute corresponding factor matrices using the ORBES sectoring scheme.
31
-------
Complete tables which list the three factor matrices used in the ORBES scen-
arios are available on request. In CONAES scenario II, it is assumed that
rail and motor freight transportation margins23 increase and decrease, re-
spectively, due to the price incentive to use more fuel economic forms of
transportation. In the same scenario, it is assumed that lighter automobiles
require more plastics and aluminum21* and less steel. In the highest energy
price scenario, it is assumed that recycling efforts will increase. Thus, it
is assumed that plastics to food products will decrease in that scenario.25
2-6 A Sensitivity Example
An inconsistency in the non-automotive transportation efficiency assump-
tions can be seen in Table 3 2^. In a manner similar to that used in the
Brookhaven NEP-RES (26) and Hermelee (27), decreases in energy use per ton mile
were assumed to be the same for water, air, truck, and rail transport.27
Scenario 3 transportation efficiency assumptions were taken from CONAES scen-
ario III (28). Thus, there is an inconsistency here because we assume a
higher efficiency for rail transport in a scenario with cheaper energy prices.
A more consistent assumption for scenario 1 would be to incorporate the non-
automotive transportation efficiency assumptions from CONAES scenario III.28
A first order approximation for a change in the transportation efficiency as-
sumptions can be found by performing the following calculations. The relevant
data can be found in the solution transactions matrices and technical coeffi-
cient matrices for scenario 1. Specifically, the data in row 6, columns
2 The transportation margins represent the average cost (per unit out-
put) of transporting all input goods to the producing industry by each trans-
portation mode. Wholesale and retail margins are defined in the same manner.
Thus, once all of these margin costs have been subtracted from the other
technical coefficients, these costs are represented in F.O.B. prices.
2t* Aluminum production is contained in the aggregate classification
"other primary metals." A separate sector is not possible, since aluminum
production in the ORBES region is so minute that production figures are not
disclosed in the census publications.
25 In Table 6, it is indicated that there would be a slight per unit
increase in the use of plastics in food product production in CONAES scenario
III. This erroneous figure was caused by a bug in the algorithm which trans-
formed the factor matrices. After this error was corrected, it was found
that only a few coefficients were affected by this defect in the algorithm.
ORBES scenario 2 was rerun and the largest difference in energy consumption
was an 0.08 percent decrease in coal consumption.
26 See item "Transportation Efficiency Increases in 2000" in Table 3.
27 The NEP-RES refers to the National Energy Plan Reference Energy Sys-
tem diagram. See the discussion in Chapter 5 on the relationship between the
supply-product input-output model and the reference energy system.
9 ft
CONAES scenario III assumes a doubling of real fuel prices.
32
-------
TABLE 6
ELEMENTS OF THE FACTOR MATRICES FOR SEVERAL IMPORTANT PRODUCT MIX CHANGES1
2
Motor freight margins to most industries
Rail margins to most industries
Plastic to truck, bus, an auto man.
Iron and steel to truck, bus, and auto man.
Other primary metals to truck, bus, and auto man.
Plastics to food products
CONAES
IV
1.18
1.01
2.10
0.87
1.70
1.25
SCENARI
1 1 1
1.03
1.09
3.20
0.6k
2.40
1.06
0
I I
0.09
1.16
4.20
0.41
3.00
0.87
1. These factors are represented in terms of the sectoring scheme used in
the ORBES energy demand model.
2. See text for the discussion of transportation and trade margins.
3. Includes aluminum production.
33
-------
15 through 18 of Tables A-1.2 and A-1.3 will be used. For air transportation,
the baseline technical coefficient was 5. In scenario 1, a 20 percent ef-
ficiency increase in air transportation was assumed. Therefore, the modified
technical coefficient becomes 5/1.2 = 4.16667. The energy intensity of rail
transportation in CONAES scenario III is 0.97. Under this assumption, the
technical coefficient becomes .97-5 = 4.85. In ORBES scenario 1, fuel con-
sumed by rail transportation is 136.2 trillion Btu of refined petroleum. If,
instead, the CONAES scenario III efficiency is used, then [ (0.97 -5) / (4.16667) ]
•136.2=158.5 trillion Btu would be consumed, or the increase in consumption
would be:
?
4. 16667
- 1 )«136.2 = 22.3 .
CONAES scenario III assumes energy intensity changes for water, air, and mo
tor freight transportation are 1, 0.48, and 0.8, respectively, we have
- 5 = 17- 5
,~
4.16667
- 1 )'332.2 = -13.3
Thus, the net effect of these four changes is a decrease in refined petroleum
consumption of 59.6 trillion Btu (or a 1.7 percent decrease in ORBES region
refined petroleum production). Though this is a first order approximation,
it is a good one, since running the model with these changes incorporated re-
sults reflecting a decrease of 60.2 trillion Btu. Such a close correspondence
indicates that this technique to determine the effect of a perturbation in
scenario parameters will be sufficient for our needs.
34
-------
3. Analysis and Interpretation of Scenario Runs and General Conclusions
3.1 Introduction
The discussion of the results of the scenario runs proceeds as follows.
The results of the runs for Scenario 2 for 1985 and 2000 are discussed in
depth. An important emphasis in this discussion is how the various growth
rates in production and income affects the demand for various forms of energy.
Because the effect of growth assumptions works essentially the same way in
all scenarios, this discussion is only necessary for Scenario 2. Because
most scenarios tend to be perturbations of Scenario 2, the analysis of
other scenarios are done by comparing the results with those of Scenario 2.
The measure of energy consumption which is used in the comparison of the
scenarios is
ph + on
te = cc + cr + eg + — *— (3.1)
where te = total energy
cc = consumption of coal
cr = consumption of refined petroleum
eg = consumption of natural gas
ph = production of hydro-electric power
pn = production of nuclear electric power
a = thermal conversion efficiency (assumed to be .34)
The term (ph + pn)/a represents the energy consumed in the production of
electricity by hydro and nuclear power in terms of the amount of fossil fuels
required to produce an equivalent amount of electric power. It should be
noted that this is not the only possible measure. Another approach would net
out interregional transfers in energy forms. In some comparisons such a
definition is more plausible for as we shall see some of the variations in
aggregate fuel consumption between scenarios can be explained by interregional
transfers of energy form (most importantly electricity). With the energy
supply-product model the most meaningful way of doing this is to determine
the amount of energy supply categories required to satisfy total regional
energy product demands.29 Once these energy supply demands are determined
a formula similar to (3.1) is used to determine total energy consumption.
The measure given in (3.1) is a more physical measure. Loosely it represents
the amount of energy that is actually burned (consumed) in a region.
29 Note that by definition energy product demands are not exported.
35
-------
Appendix tables B-l through B-6 contain the solutions vectors of the
scenario runs. Final demand, total production, and total consumption by in-
dustry are listed. Net exports can be found by subtracting total consumption
from total production. Also the energy supply to supply and energy supply to
energy product sub-matrices of the technical coefficient and transactions
matrices are printed. Appendix C contains summary tables of results by
fuel and end use.
3.2 Energy Consumption Patterns in Scenario 2
Using the measure of (3.1), the total energy consumption for scenario 2
is 12,781 trillion Btu in 1985 and 15.151 trillion Btu in 2000. Since total
energy consumption is 10,311 trillion Btu in 1974, the average total energy
growth rate31 is higher between 1974 and 1985 than over the 1985-2000 period.
The reduction in the growth in total energy consumption can best be explained
after some detailed discussion on the consumption of individual fuel forms.
In scenario 2, ORBES consumption of natural gas and refined petroleum in
1985 does not change substantially from the 1974 levels. For refined petro-
leum the small change in consumption can be explained as follows. Growth in
non-automotive transportation demand closely tracks the increase in GRP.
Since the assumed growth rate of 2.5 percent per annum translates into a 32
percent increase over a period of 11 years, a 9 percent increase in non-
automotive transportation fuel efficiency will reduce fuel consumption in
these sectors to an overall increase of around 20 percent. Because it is as-
sumed that the growth in per capita annual vehicle miles for personal (final
demand) automotive transportation rises at approximately the same rate as
GRP, with a 20 mph32 average fuel efficiency the net effect is an 8 percent
drop in refined petroleum consumed by automotive transportation. The con-
sumption of refined petroleum in space heating falls when oil space heating
furnaces are replaced by electric heat pumps. It is assumed that approxi-
mately 13 percent of all furnaces burning fuel oil are replaced by heat pumps.
It is assumed that a negligible amount of newly installed productive capacity
will consume refined petroleum to produce process heat. Finally, it is as-
sumed that only refined petroleum is used in the generation for peak electric
demand. Though there is a decrease in consumption of refined petroleum for
space heating and personal automotive transportation, the net result is that
30 In the notation of Chapter 1 these are, respectively, A , A , T ,
and T sub-matrices. In the corresponding tables, columns 1 tnrougn 12 con-
tain rne energy supply to supply sub-matrix while columns 13 through 24 con-
tain the energy supply to product sub-matrix.
31 We define the average growth rate a as the parameter fitted to ea = x.
32 The national average fuel efficiency for automobiles in the mid-1970's
was around 13.5 mpg.
33 We have assumed that peak electric demand that must be satisfied by gas
or oil turbine generators which amounts to 5 percent of total fossil generation.
36
-------
total refined petroleum consumption increases because of the increases in the
consumption by non-rautomotive transportation and by utilities for peak
generation.
Before discussing the 1985 consumption of natural gas, it is important
to note that transmission loss in the input-output model in 1974 has been al-
located to the gas utility industry, while in the 1985 and 2000 model, it is
allocated to the crude oil and gas sector. Thus, for the purpose of com-
parison, the 109 trillion Btu in 1974 should actually be subtracted from the
consumption of natural gas and added to the consumption of crude oil and
natural gas. After doing so, it is seen that the consumption of natural gas
increases by a slight amount. Although some gas water heaters have been re-
placed by electric, most of the expansion in the demand for space heating has
been met by natural gas. Also it is assumed that a small amount of new pro-
ductive capacity will burn natural gas to produce process heat.
Coal consumed in the production of electricity, process heat, and ore-
reduction feedstocks accounts for over 95 percent of total ORBES coal con-
sumption.. The largest percent increase in coal consumption is associated
with process heat requirements. This is close to the assumption that most
newly installed industrial capacity will burn coal. The second largest in-
crease is associated with electric generation,34 while coal used for ore-
reduction feedstocks rises only slightly. This is due in part to a 4 percent
enduse efficiency increase and the assumption that electric arc techniques
will replace blast furnace production. Coal consumption by the coal indus-
try35 increases because of the losses associated with beneficiation. This
loss is assumed to be 0.5 percent of total coal production.
The relatively high production of electricity is in part due to the as-
sumption that historic consumption patterns for the demand for miscellaneous
electric power use will prevail. Also, part of the increase is due to the
assumption that households and the commercial sector will begin to install
heat pumps to provide space heat. Most of the newly installed water heaters
are assumed to be electric. The demand for electricity for air conditioning
rises by 56 percent. Though the increase in air conditioning market penetra-
tion in the industrial and commercial sector is not as large as the increase
in the residential sector,36 approximately half of the growth in the air con-
ditioning demand occurs in the industrial and commercial sector because of
the effect of economic growth. The aggregate growth in the regional consump-
tion of electricity rises by 3.6 percent per annum from 1974 to 1985.
If the model is solved for fossil electric production given the same
ratio of exports to production used in other runs (24 percent), then fossil
electric production would be approximately 1,430 trillion Btu instead of
1,587 trillion Btu.
35 Some of the direct transactions from coal to coal represent trans-
portation loss.
36 See items concerning air conditioning market penetration in Table 2.1.
37
-------
In the 1985 runs of the ORBES energy demand model it was assumed that
each element of the final demand vector grew on the average at the same rate
as the growth in GRP (29). In the 2000 runs, the CONAES weights for differ-
ential growth rates were incorporated into the model. Thus, in the 2000 runs,
there is a relative increase in the production of services as compared to in-
dustrial consumption. Since on a dollar per dollar basis, the energy inten-
sity of a purchase of a service is, in general, less than the energy inten-
sity of a purchase of manufactured goods, we expect to find a decrease in
the growth rate of energy consumption over the 1985-2000 period. As pre-
viously noted, electricity exports are assumed to be relatively high in
1985.37 In 2000, the ratio of exports to production of electricity is as-
sumed to decrease to the 1974 ratio. Also the enduse efficiency increases
that are assumed to occur between 1985 and 2000 dampen energy consumption.
Gas consumption falls between 1985 and 2000. This is due in part to the
assumption that, after 1985, the use of heat pump technology for space heat-
ing becomes widespread. The majority of the new additions to the housing
stock are assumed to install heat pump technology to produce space heating.
Gas water heaters are also being displaced by electric units. Also, it is
assumed that the direct combustion of coal will continue to displace natural
gas in the production of process heat.
Total refined petroleum consumption rises slightly between 1985 and 2000.
Though automotive efficiencies are assumed to be 27 mpg in 2000, gasoline con-
sumption by automobiles increases slightly, due to the assumed 2.4 percent an-
nual growth rate in per capita vehicle miles. The demands for freight trans-
portation modes except water38 grow approximately at the same rate as GRP. A
7 percent increase in the fuel efficiency of these freight transportation
modes is assumed to occur between 1985 and 2000; fuel use by these transpor-
tation modes increases approximately 2 percent per annum between 1985 and
2000. After efficiency increases are netted out, approximately 20 percent of
the process heat produced by refined petroleum has been displaced by coal.
Also, approximately 30 percent of the oil space heat furnaces in 1985 have
been replaced by heat pumps in the year 2000. Most of the increase in the de-
mand for refined petroleum for the production of chemical feedstocks is due
to the CONAES assumption of an increase in the consumption of asphalt by the
construction industry. The net effect on ORBES consumption of refined petro-
leum is a 6 percent increase in refined petroleum consumption from 1985 to
2000.
In scenario 2, the consumption of natural gas decreases 9 percent in
2000 from the amount consumed in 1985. The decrease in consumption of natural
37 Fossil fuel consumption would be reduced by 462 trillion Btu if the
1985 fossil electric exports were assumed to be at "historic" levels. The
total energy consumption growth rate would then be 1,7 percent per annum.
38 The CONAES study (30) assumes that water transportaion margins are as-
sumed to be 0.72 of the 1974 values. This followed from the assumption that
the growth rates for water freight transport are 0.38 times the GNP growth
rate.
38
-------
gas for space heating is for the most part due to an increase in enduse ef-
ficiency, though a small portion of the reduction is due to the replacement
of gas furnaces by heat pumps. About 4 percent of the process heat that was
produced by natural gas in 1985 has been replaced by either coal or electric
resistance heating. Also, the trend of replacing old gas water heaters by
electric heaters is assumed to continue.
The significant rise in coal consumption from 1985 to 2000 is mostly due
to the assumption that 55 percent of process heat will be generated by direct
coal combustion in the year 2000. The consumption of coal in the production
of ore-reduction feedstocks rises only slightly, since it is assumed that most
new iron and steel production facilities will use the electric arc process.
Hydroelectric and nuclear electric power production is assumed not to in-
crease in scenario 2 from the amount produced in 1985. Thus, a greater share
of electricity must be produced by coal-fired plants. But this is checked by
the assumption that exports relative to production will decrease in the year
2000 as compared to 1985.
Total ORBES consumption of electric power increases approximately 50 per-
cent from 1985 to 2000. About half of this increase is due to the increase in
miscellaneous electric power demand, which grows at a rate of 2.1 percent per
annum39 between 1985 and 2000. Nearly 20 percent of this increase is due to
the substitution of electric heat pumps for the generation of space heat. The
rest of the increase is fairly evenly distributed among ore-reduction feed-
stocks, process heat, and water heat.
In terms of the parameter changes which implement the various scenario
descriptions in the energy demand model, scenario 2 can be considered as the
base case scenario, and scenarios 1, 2, 3, 5, and 5a are perturbations of
this scenario. Scenario 1 is different from scenario 2 only in environmental
controls. Scenarios 5 and 5a are different only in that different growth
rates for final demand are assumed. Scenario 3 assumes that alternative en-
ergy technologies are used to satisfy basic energy product demands. The con-
ventional technology fuel mix assumptions and growth rates are the same as
those in scenario 2.
3.3 Energy Consumption in Scenario 1
The fuel mix, enduse efficiency, and other economic assumptions in scen-
ario 1 are the same as those contained in scenario 2, except for the conver-
sion efficiencies for coal combustion. The environmental controls in scenario
1 are stricter than those of scenario 2. Thus, it is assumed in scenario 1
that the conversion efficiency of coal into electricity or process heat is
lower by 2 percent, due to the need to reheat flue gas after scrubbing. This
results, in an additional 166 trillion Btu of coal being consumed by the fos-
sil electric power sector in the year 2000. Similar efficiency reductions
39 Miscellaneous electric power demanded by the industrial and the com-
mercial sectors grows at a rate of 2.4 percent per annum between 1985 and
2000.
39
-------
are assumed for process heat produced from coal in scenario 1. Besides very
minor second order effects on fuel consumption, there are no other differences
in the runs for scenarios 1 and 2.
3.4 Energy Consumption in Scenario 3
The energy produced by the alternative technologies that are specified
in scenario 3 satisfy specific energy enduses. The input-output equations
are specifically written1*^ so that the consumption of conventional energy sup-
plies are determined as a residual in a set of simultaneous equations. That
is, given (1) a vector of final demand, (2) assumptions which determine net
exports, and (3) the specified levels of energy product demands that are
satisfied by alternative energy supplies, the set of simultaneous equations
determines the amount of conventional energy supplies that must be consumed
to satisfy the energy product demands that are not satisfied by the alterna-
tive energy technologies. Note that the A submatrix only specifies the
fuel mix for the energy product demands that are to be satisfied by conven-
tional fuels.
The amounts of the alternative technology energy sources that are to be
consumed were derived from estimates developed by the TASE project (31).
Schiffman (32) contains tables which disaggregate the national totals into
state shares. The state totals were then prorated by the most relevant com-
ponents of Page's (33) ORBES region GRP estimates. Table 7 contains the
ORBES totals for the various alternative energy supply technologies examined
in the TASE project. The centralized production of electricity from solar
energy is allocated to the central station electric power sector. This power
is supplied to the power grid and displaces fossil electric power production.
An 8.8 percent transmission loss is associated with all centralized electri-
city production. Decentralized electric generation by windmills is assumed
to satisfy miscellaneous electric power demand with no transmission loss. Ex-
cept for the assumed level of market penetration of air conditioning, the ef-
ficiency and conventional technology fuel mix assumptions are the same as
those in scenario 2. Only a 10 percent increase in the per capita demand for
air conditioning by households is assumed in scenario 3. Also, the amount of
air conditioning consumed per unit output in the industrial and commercial
sectors is assumed to increase by 5 percent in scenario 3.
The total consumption of conventional fuels in scenario 3 is 13,189 tril-
lion Btu, which is approximately 87 percent of total energy consumption in
1+0 See the last section of Chapter 1, which discusses the two forms of
the energy demand model that are used in this study.
1+1 Technology Assessment of Solar Energy (TASE). Several scenarios were
developed. We have used their "Maximum Practical" scenario. In this national
scenario, solar energy displaces 14 quads of fossil fuels in the nation.
40
-------
TABLE 7
AMOUNT OF ENERGY PRODUCT DEMANDS DISPLACED BY
ALTERNATIVE TECHNOLOGIES IN SCENARIO k
Technology
Electric Uti 1 ities
Biomass
Photovoltaic Conversion
Solar Thermal
Wind .
Decentralized Wind Elec. Gener.
Process Heat from Biomass
Solar Process Heat
Active Solar Space Heat
Passive Solar Space Heat
Space Heat from Wood
Solar Water Heat
BED1
22
1
7
57
16
381
118
98
^k
22
72
2
Percent
8.00
O.OA
0.30
2.00
0.60
20.00
6.00
10.00
1.00
2.00
33.00
1. All units are in trillion BTU's. Output of electric generation tech-
nologies are measured in electricity generated. All other figures represent
Basic Energy Demand (BED) satisfied.
2. Percentages for Electric Generations are relative to total ORBES produc-
tion of Electricity. All other percentages represent the portion of total
consumption of energy products which is satisfied by an alternative tech-
nology.
41
-------
scenario 2.'^ The most significant reduction in conventional fuel use is in
coal, which drops by approximately 1,500 trillion Btu. This is mostly due to
the reduction in fossil electric consumption1*3 although 400 trillion Btu of
coal are displaced in the production of process heat. Approximately 400 tril-
lion Btu of natural gas is displaced by alternative energy sources. More
specifically, 200, 40, and 160 trillion Btu of natural gas is displaced in the
production of process, water, and space heats. Refined petroleum consumption
is reduced by 200 trillion Btu. Most of the displacement occurs in the pro-
duction of process and space heating, where gas consumption falls by 80 and
150 trillion Btu, respectively. Due to the decrease in demand for conven-
tional electricity production, the oil consumed in peak electric generation
falls by 60 trillion Btu.4l+
3.5 Natural Gas Scenario
Scenario 4 could be viewed as the most optimistic scenario in terms of
energy prices. Because of the assumed relative abundance of natural gas in
this scenario, there is a significant incentive to substitute this fuel in all
energy uses except for electricity production. Being the most energy opti-
mistic scenario, the enduse efficiency increases were relatively small. As
compared to 1974 baseline, the total enduse efficiency increase for process
and space heating was assumed to be 5 and 7 percent, respectively.^ This
increase in the supply of natural gas is assumed to occur after 1985. Thus,
there are no major differences in the fuel mix for scenario 4 in 1985 and the
baseline fuel mix. After 1985 it is assumed that additions to the capital
stock will consume natural gas rather than other fuels. The consumption of
other fuels in the production of process and space heat is assumed to decline
with the depreciation and replacement of the capital stock that utilizes
these fuels. This rate of depreciation was assumed to be 5 percent per annum.
By this assumption, 85 percent of the basic energy demand for process heat
was satisfied by natural gas. The rest of the demand for process heat was
split between coal, refined petroleum, and electric with the respective per-
centages of 7, 6, and 2 percent. For space and water heating, it was assumed
that approximately 15 percent of the demand could not be satisfied by gas
utilities. In such cases it was assumed that heating oil was used for space
heating and hot water was supplied by electric heaters.
1+2 Using the market penetration assumptions for air conditioning of
scenario 2, total energy consumption would be 13,330 trillion Btu.
43 The reader should keep in mind that any drop in ORBES electric con-
sumption has a multiplier effect upon production since exports also drop.
44 Peak electric production has only been allocated in fossil electric
production. It would have been more satisfactory to allocate some natural gas
or refined petroleum consumption to all central station electric production
technologies, since large coal plants, nuclear plants, or the alternative
technology plants cannot economically provide peaking capacity.
1+5 Increases of 10 and 15 percent, respectively, in the average enduse
efficiency for process and space heat was assumed in scenario 2.
42
-------
In comparing the aggregate energy consumption of scenarios 2 and 4, the
most significant difference is, of course, in natural gas consumption. In
scenario 2, natural gas consumption is 1,960 trillion Btu, while it is 5,390
trillion Btu in scenario 4. The majority of the difference in natural gas
consumption in these two scenarios comes from the demand for natural gas in
the production of space heat. In scenario 4, 2,752 trillion Btu of natural
gas are consumed, while in scenario 2, only 762 trillion Btu are used. Most
of the rest of the difference can be accounted for in the production of pro-
cess heat. In scenario 4, 1,852 trillion Btu of natural gas is consumed, as
compared to 929 trillion Btu in scenario 2.
The largest change in fuel use in this scenario is coal consumption. Be-
cause of the reduction of electric power generation and the reduction of the
use of coal in the production of process heat, total coal consumption in scen-
ario 4 is 5,895 trillion Btu, as compared to 8,967 trillion Btu in scenario 2.
Of this 3,000 trillion Btu difference, approximately 1,700 trillion Btu is due
to the reduced consumption of coal in the generation of electricity, while the
rest is due to the reduction of the use of coal in the production of process
heat.
Vis a vis scenario 2, the consumption of refined petroleum increases from
3,503 trillion Btu to 3,632 trillion Btu in scenario 4. In several uses,
though, the consumption of refined petroleum did decline. Due to the substi-
tution by natural gas, refined petroleum consumption in the production of pro-
cess heat is 120 trillion Btu lower in scenario 4 than in scenario 2. Also,
372 trillion Btu is eliminated in peak electrical generation. These decreases
were offset by increased consumption in the transportation sectors. Much of
this increase in the consumption of refined petroleum in transportation uses
is attributed to lower efficiencies. Auto gasoline consumption is 1,716
trillion Btu in scenario 4, instead of 1,267 trillion Btu as in scenario 2.
Nearly all of this increase can be attributed to lower automotive efficien-
cies. The consumption of refined petroleum in air transportation in scenario
4 is 370 trillion Btu, as compared to 203 trillion Btu in scenario 2. This
increase is mostly due to an increase in the final demand for air transporta-
tion, though some is due to an increase in the freight margin that is appor-
tioned to air transport.
Total electricity consumption in scenario 4 is 1,409 trillion Btu, which
•is 25 percent lower than in scenario 2. The most significant factor that con-
tributes to this reduction is that plentiful gas supplies make electric heat-
ing, whether resistance or heat pump, noncompetitive. This factor accounts
for 179 trillion Btu of the difference. Because 15 percent of the water heat
46 The average auto efficiency is assumed to be 28 mpg in scenario 2,
while 21 mpg is assumed in scenario 4 in year 2000. For other transportation
modes, a 10 instead of 20 percent average efficiency increase is assumed in
scenario 4.
1+7 CONAES scenario C (34) was used to determine increases in the final
demand expenditures, along with nonenergy technical coefficient and transpor-
tation margin changes. CONAES scenario C was an optimistic energy scenario in
which no change in real energy prices was assumed to occur.
43
-------
in scenario 4 is assumed to be produced by electric heaters; in scenario 2,
the percentage is 65, or 107 trillion Btu less consumption of electricity.
The other major factor responsible for the lower consumption of electricity
is that 10 percent of process heat in scenario 2 is produced by electric,
while in scenario 4, the percentage is 2. This results in 147 trillion Btu
less consumption of electricity in the production of process heat.
3.6 Alternative Growth Rate Scenarios
Scenarios 5 and 5a provide a measure of the sensitivity of energy con-
sumption with respect to economic growth. The reader is warned that the mea-
sure is a crude one. Most important, this test should not be construed as an
answer to the question: what portion of economic growth can be attributed to
the use of energy? It is assumed in these scenarios that growth occurs only
by an increase in labor productivity. In scenarios 1 through 5, we have as-
sumed that labor productivity has increased in such a manner as to allow an
average annual growth rate of 2.47 percent per annum. Besides the fact that
the people are assumed to be better workers in the year 2000, we have essen-
tially assumed 4a that input material requirements are the same as that of
1974. Since, cateris paribus, if labor productivity increases x percent and
if leisure does not increase, GRP will increase by x percent. If material
input requirements do not change, then the consumption of all goods should
rise by the same percentage when the composition of final demand does not
change. Therefore, if nothing else changes, one just multiplies all consump-
tion figures by the growth in GRP.
The motivation behind the alternative growth rate scenarios is to provide
some insight into the relationship between increases in energy consumption and
personal income. In scenarios 5 and 5a, we have assumed that the demand for
space and water heat by households does not change with the growth rate as-
sumptions. That is, it is assumed that the increase in real energy prices is
large enough that the consumption of these goods becomes income inelastic.
The final demand for personal transportation is assumed to have a unitary in-
come elasticity and, thus, grows at the same rate as the increase in GRP.
Also, we have incorporated the CONAES differential growth rates for the final
demand for nonenergy goods and services. The growth in the individual ele-
ments of GRP are determined in a two-step process. In the first step, the
following formula is used to adjust the differential growth rates so that they
are approximately consistent with the overall growth in GRP.
InG «a
* - (3'2)
The term g is the growth rate of a specific element of a sector in the fi-
nal demand vector to be scaled in the second step,- G is the overall growth
in the corresponding element of the CONAES final demand vector from 1967 to
2010 (a period of 43 years) . The term 0.02 represents the growth rate in GNP
1+8 Except for some minor changes in input requirements which were in-
corporated from the CONAES integration model.
44
-------
assumed in the CONAES scenarios. The growth rate is a (in percent per annum)
of overall GRP. These growth rates are then applied to the individual ele-
ments of the final demand vector. The resulting vector is then scaled by a
constant so that the overall growth rate is consistent'with the assumed growth
rate of GRP.1*9 The intuition behind (3.2) is that the elasticity of the var-
ious components of final demand with respect to the growth in GRP can be de-
termined from the differential growth factors developed in the CONAES study;
(3.2) is an approximation50 to the growth rate of a specific element of final
demand given a different growth rate of income (or GRP).
The total energy consumption in scenarios 2, 5, and 5a, respectively, are
15,150, 13,970, and 17,360 trillion Btu. Since percentage decrease in energy
consumption between scenarios 5 and 2 is -7.8 percent, while the percentage
decrease in GRP is -9.4 percent, the elasticity of energy consumption with re-
spect to GRP is 0.83. The elasticity computed from the differences in scen-
ario 2 and 5a is 0.82.
For the most part, growth in the consumption of individual fuels in
scenarios 5 and 5a mirror the economic growth rates. There are some dis-
crepancies that do arise. One important factor which induces slight increase
in the consumption of coal is due to fixed output assumptions for nuclear and
hydroelectric power. The effect of this assumption is to increase the con-
sumption of coal in that a greater portion of electric demand is satis-
fied by coal steam generation at higher levels of electricity consumption.
Also, the residential consumption of fuels tends to be smaller in scenario 5
since the low population (due to inmigration assumptions) estimate of Milke
(35) was used. There is no similar effect in scenario 5a,. since Milke's base
case population growth estimate is used. Another assumption which tends to
increase refined petroleum consumption is that the per capita final de-
mand for automobile transportation grows at essentially the same rate as GRP.
With positive rates of population growth, this assumption increases petroleum
consumption. Compared with scenario 2, the consumption of individual fuel
types are, respectively, -8.3, -7.5, -6.8, and -7.1 percent lower in scenario
5 for coal, refined petroleum, natural gas, and electricity. For scenario 5a,
the respective increases are 15.3, 14.4, 16.7, 12.6, and 12.8 percent. Again,
the reader is cautioned not to read too much into the sensitivity analysis for
the experiment has assumed that differences in growth rates only occur by dif-
ferent labor productivities.
1+9 The vector resulting in the first step needs to be adjusted only by
two or three percent.
150 Actually (3.2) is a first order approximation because the actual
growth rate is time varying. The actual growth factor should be:
. at _ i
G = G where d> = ———-—— .
o c y 02-43
e - 1
45
-------
3.7 "Wheeling of Power"
The models for scenarios 2 and 2a are identical, except that in scenario
2a, electric power generation from coal plants is specified to be 2,546 tril-
lion Btu.51 This amount is equal to the total generation from coal plants in
scenario 2 plus 20,000 megawatts additional capacity operating at a 50 percent
plant factor. Almost all of the increase in electricity production is ex-
ported. 52 This results in 890 trillion Btu more coal consumption in scenario
2a as compared to scenario 2. Due to peaking capacity fuel requirements, re-
fined petroleum consumption in scenario 2a is 62 trillion Btu larger than in
scenario 2. The consumption of electric power produced by coal plants in-
creases by 50 trillion Btu though 25 trillion Btu of the increase is due to
line loss. Natural gas consumption is only 7 trillion Btu larger in scenario
2a.
3.8 Conservation Scenario
Scenario 6 was an attempt to determine total energy consumption when
maximum practical enduse efficiencies were assumed. The enduse efficiency
increases were assumed to be: 45 percent for space heat, 35 percent for pro-
cess heat, and 40 percent increase for air conditioning. Average automobile
fuel efficiencies were assumed to be 37 mpg. On a per capita basis, the fi-
nal demand for personal automobile transport was assumed to be equal to that
in 1974. Transportation efficiencies of 55 percent, 40 percent, and 9 per-
cent are assumed for air, truck, and rail transport, respectively.0 It is
assumed that 140 trillion Btu of electricity is generated from coal steam
plants that cogenerate the equivalent of 154 trillion Btu of basic energy de-
mand (BED) for process heat. Also, 70 trillion Btu of BED for process heat
and 28 trillion Btu of BED for space heat is satisfied by these total energy
systems. With the assumed increases in enduse efficiencies and the assumed
levels of production of cogenerated electric power, total energy consumption
in scenario 6 is 10,531 trillion Btu. This is only two percent larger than
the 1974 energy consumption baseline.
The above discussion has stressed the detailed way in which changes in
energy and fuel use occurred as well as in the magnitude of associated
changes. The interested reader is referred to Table 8 for a convenient sum-
mary of the 1974-2000 percent changes in energy and fuel use, by scenario, as
well as the average annual compounded growth rates, 1974-2000. Figures 4
The model in scenario 2a determined net exports of electric power from
coal plants rather than total regional production as in scenario 2.
c 9
ORBES-region electricity consumption does increase slightly due to
the increase in indirect demands required by the increase in electric power
production.
CONAES (1978) scenario II was used as a guide for determining enduse
efficiencies for the transportation sector.
46
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FIGURE 4
12
Coal Use, by Scenario, in the ORBES Region, 10 ^ Btu
Btu
10,000
9000
8000
7000
6000
5000
4000
3000
2000
1000
-
-
-
-
-
1974
SI
S2
S2a
S3
S4
S5
S5a
S6
1974 2000 2000 2000 2000 2000 2000 2000 2000 Scenario
and Year
-------
FIGURE 5
Refined Petroleum Use, by Scenario, in the ORBES Region, 1012 Btu
CD
Btu
5000-
4000 -
3000 -
2000
1000 -
-
1974
SI
52
S2a
S2b
S3
S4
S5
S5a
S6
1974 2000 2000 2000 2000 2000 2000 2000 2000 2000 Scenario
and Year
-------
Btu
FIGURE 6
Natural Gas Use, by Scenario, in the ORBES Region, 1012 Btu
VD
5000
4000
3000
2000
1000
1974
SI
S2
S2a
S2b
S3
S4
S5
1974
2000
2000
2000
2000
2000
2000
2000
S5a
S6
2000
2000
Scenario
and Year
-------
FIGURE 7
Electric Use, by Scenario, in the ORBES Region, 1012 Btu
Btu
5000
4000
3000
2000
1000
-
1974
SI
S2
S2a
S2b
S3
S4
S5
S5a
S6
1974 2000 2000 2000 2000 2000 2000 2000 2000 2000 Scenario
and Year
-------
FIGURE 8
Total Fuel Use, by Scenario, in the ORBES Region, 1012 Btu
Btu
20,000
18,000
16,000
14,000
12,000
10,000
8000
6000
4000
2000
-
1974
SI
S2
S2a
S2b
S3
S4
S5
S5a
S6
1974 2000 2000 2000 2000 2000 2000 2000 2000 2000 Scenario
and Year
-------
through 8 provide visual representation of the data contained in Table 8.
Figures 4 through 6 relate to, respectively, coal, refined petroleum products,
and natural gas use, while Figure 7 is for electric (fossil fuel equivalent),
and Figure 8 is for total energy and fuel use.
52
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TABLE 8
REGIONAL CONSUMPTION OF ENERGY AND FUEL USE, BY SCENARIO, IN THE ORBES REGION"'
Ln
1012 Btu
S1
S2
S2a
S3
S4
1974
1985
2000
1974
1985
2000
1974
1985
2000
1974
1985
2000
1974
1985
2000
Coal
4,842
6,736
9,133
4,842
6,617
8,967
4,842
9,857
4,842
7,567
4,842
5,158
5,865
% Change
1974-2000
88.6
(2.47)
85.2
(2.40)
103.6
(2.77)
56.3
(1.73)
21.1
(0.74)
Ref ined
Petroleum
3,186
3,287
3,504
3,186
3,287
3,503
3,186
3,565
3,186
3,312
3,186
3,556
3,632
% Change
1974-2000
10
(0.37)
10
(0.37)
11.9
(0.43)
4
(0.15)
14
(0.51)
Natura 1
Gas
2,176
2,157
1,961
2,176
2,156
1,960
2,176
1,967
2,176
1,589
2,176
2,769
5,390
% Change
1974-2000
-9-9
(-0.40)
-9.9
(-0.40)
-9.6
(-0.39)
-27
(-1.20)
147.7
(3.55)
Electric
842
1,250
1,878
842
1,250
1,878
842
1,910
842
1,680
842
1,099
1,409
% Change
1974-2000
123
(3.13)
123
(3.13)
126.8
(3.20)
99.5
(2.69)
67.3
(2.00)
Total
(fossil
fuel eq.)
10,311
1 2 , 900
15,317
10,311
12,781
15,151
10,311
16,109
10,311
13,190
10,311
12.203
15,609
% Change
1974-2000
48.6
(1.53)
46.9
(1.49)
56.2
(1.73)
27.9
(0.95)
51.4
(1.61)
(continued)
-------
TABLE 8 (continued)
S5
S5a
S6
1974
1985
2000
1974
1985
2000
197**
1985
2000
Coal
A, 842
8,214
4,842
10,343
4,842
5,097
% Change
1974-2000
69.6
(2.05)
113.6
(2.96)
' 5.3
(0.20)
Refined
Petroleum
3,186
3,215
3,186
4,090
3,186
2,767
% Change
1974-2000
1
(0.03)
28.4
(0.97)
-13.2
(-0.54)
Natural
Gas
2,176
1,825
2,176
2,208
2,176
2,006
% Change
1974-2000
(-0.67)
1.5
(0.01)
-7.8
(-0.3D
Electric
842
1,745
842
2,120
842
1,063
% Change
1974-2000
107-2
(2.84)
151.8
(3.62)
26.2
(0.90)
Total
(fossil
fuel eq.)
10,311
13,974
10,311
17,362
10,311
10,591
% Change
1974-2000
35.5
(1.18)
68.4
(2.02)
2.7
(0.10)
'Figures in ( ) are the average annual compounded growth rates.
-------
4. Some General Conclusions
The preceeding discussion of model results stressed the reasons why there
were observed differences as between scenario runs. In some cases, observed
differences were uniquely attributable to a single changed condition (export
of electricity, for example), while in other cases there were complex changes
in parameter values which accounted for differences in output (the conserva-
tion scenario contrasted with scenario 2). In terms of the orientation of
certain scenarios, it is also possible to draw some general conclusions with
respect to total energy and fuel use in the ORBES region. The reader must
bear in mind that for most such statements the reasons for large observed dif-
ferences are complex and relate to reasonably detailed changes in parameter
values of the model.
Contrasting total energy use in the region by scenario (Table 8 and Fig-
ure 8) supports the following general conclusions:
1. Strict pollution control standards are largely irrelevant with re-
spect to anticipated energy use. This conclusion follows from com-
paring scenarios 2 and 1.
2. A relatively strong conservation scenario (scenario 6) produces dra-
matically less energy use in the region as compared with the base
case scenario (scenario 2).
3. An alternative technology scenario (scenario 3) suggests substan-
tially less energy use in the region than under base case conditions,
but not as much less as the conservation scenario (scenario 6).
4. Total energy use in the region is relatively sensitive to alternative
specifications concerning economic growth rates (scenario 2 as com-
pared with scenarios 5 and 5a).
Other methods of implementing the scenarios might well produce different
results, although we believe the qualitative differences between scenarios
would probably remain unchanged.
Focusing on total energy use, of course, masks the differences discussed
earlier among particular fuels. Figures 4, 5, 6, and 7 and Table 8 support
the following general conclusions:
55
-------
1. Coal is the fuel most sensitive to alternative scenario specifica-
tions. As was the case with total regional consumption of fuels and
energy, the greatest degree of sensitivity is to conservation, al-
ternative technology, and assumptions about economic growth. Unlike
the case of total energy use, coal use in the region is highly sen-
sitive to assumptions in the natural gas scenario (scenario 4).
2. Natural gas use, except for the case of scenario 4, is largely in-
dependent of scenario specifications.
3. Refined petroleum use, like coal use, is most sensitive to conserva-
tion, alternative technology, and economic growth scenarios.
4. Electric use, for the most part, is sensitive to the following scen-
arios: conservation, natural gas, alternative technology, and al-
ternative growth rates.
As discussed earlier, many of these general statements concerning indi-
vidual fuels are a direct result of the way in which the scenarios were im-
plemented.
56
-------
Appendix A
Determining the Energy Demand Model's
Sectoring Scheme by Sensitivity Analysis
There is always a problem of dimensionality in modeling large scale sys-
tems. While it is usually useful to economize on the number of parameters
that need to be determined in a large scale model, severe aggregation may
hide some of the more important internal relationships. We discuss below a
technique which permits an economy of data collection and identifies those
parameters which must be determined with the greatest degree of precision.
Sensitivity analysis of linear equations can be subdivided into two gen-
eral procedures. The first, which would be termed tolerance analysis, is
used to determine the effect of random fluctuations of all of the parameters
on the solution of the system of equations. The analysis usually involves
Monte Carlo simulations which relate the level of disaggregation with the de-
gree of numerical instability (36). The second procedure involves the per-
turbation of each parameter in order to determine the effect on an element,
or on several elements, of the solution. This is the procedure that is par-
ticularly useful for determining the key parameters (37) in a system of equa-
tions, and, therefore, for determining the most important technical coeffi^-
cients in an input-output model with respect to energy consumption.
In the following discussion, the sensitivity equation is derived. The
result was originally deduced by Sherman and Morrison (38), though the paper
only verified the result for a change of a single element. A general dis-
cussion of the derivation is useful, for it will demonstrate a general tech-
nique that can easily be extended to the determination of the change in the
solution when several parameters are simultaneously modified.
Let B=(I-A) and 6B be nxn matrices whose elements are all zero except
for the element in position (i,j). This element is represented by 6b...
Suppose that x is the solution of ^
Bx = y (3.5)
and z is the solution of
(B+6B)z = y . (3.6)
Premultiplying (3.6) by B , we have
(I+B~ 6B)z = B~ y = x . (3.7)
57
-------
Written out explicitly, (I + B 6B) is of the form
1 0
0 1 0
0
0
b. .6b. .
Ij ij
b0.6b. .
2x i j
1+b. . 6b. .
b . 6b. .
ni i]
0
0
0
0 01
where (b ) = B~ . It follows directly that
K -L
z . =
D
x .
D
x .
D
1 + b..6b.. 1 - b..6a..
(3.8)
because 6b.. = -6a... For
b, . fib. .x.
z, = x, - b, .6b. .x. = x, -
k k ki 13 : k
1 + b. .6b. .
(3.9)
Because (3.8) can be rewritten as
z. = x. +
D D
b. .6a. .
3-D 13
1 - b..6a..
(3.10)
the perturbation in the j element of the solution vector is of the same
form as the perturbation of any other element (3.9). By approximating (3.9)
by the first two terms of the equivalent geometric series expansion,
b, . 6a. .x .
1 - b.. Sa..
- b .6a. .x. + b6a. ,x.b..6a. .
. ..
ID D
D DI
(3.11)
th
If the perturbation of a.. is small, the change in the k element of the
solution vector can be approximated by
b, . 6a. .x .
ki 1 3
(3.12)
As the present application is concerned only with changes in the total output
of the energy industries, only values of the index k that correspond to the
energy industries are of interest.
58
-------
th k
Using (3.12), we can define an n order sensitivity matrix S , whose
elements are used to approximate the change in the total output of the k
industry when the corresponding element in the A matrix is perturbed. S is
an n order square matrix whose element in position (i,j) represents the
factor by which the relative change in corresponding technical coefficient is
multiplied by to give the approximate change in total output of the k in-
dustry. If S.. is the element in the (i,j) position of the matrix S , then
Sk. = b, .a. .x. = b .t. . . (3.13)
ij ki 13 j ki 13
This definition of the sensitivity matrix is particularly useful in the iden-
tification of the key technical coefficients that must be determined with the
greatest precision.
Suppose that an input-output model is to be principally used to forecast
output of a small number of industries. Because input-output modeling re-
quires a relatively large amount of data collection, it is particularly worth-
while if an industrial classification scheme could be found that kept sectors
with relatively sensitive technical coefficients disaggregate while classi-
fying the relatively insensitive sectors in a more aggregate manner.
The approach which should be used to find such a classification is very
dependent on the industries of primary interest. If one is primarily inter-
ested in the total output of some capital goods industry, say laundry equip-
ment, a reasonable classification scheme can be found for an input-output
model of the order of ten to twenty sectors. In this model, all of the im-
portant sectors will be kept disaggregate, while the very appreciable aggre-
gation of the rest of the economy does not swamp the importance of the dis-
aggregate sectors. The aggregation can be found directly from the sort of
the elements of the sensitivity matrix.
In the case of energy production, the specification of the aggregation
is not as straightforward. The procedure used in the preceding example will
result in an aggregation in which the aggregate industries will become as im-
portant and sometimes much more important than those industries that have
been left disaggregate. It is also possible that an industry has no single
particularly sensitive technical coefficient, but has a large number of tech-
nical coefficients of moderate sensitivity. Therefore, the sensitivity
analysis of entire row and column changes are of considerable importance.
By perturbing each row and column of the A matrix, new sensitivity relations
can be deduced.
Support that the elements of the j column of the A matrix have been
multiplied by the constant (1+c). The exact solution to the new system of
equations can be found by the same procedure used in the derivation of the
sensitivity relation for a single element. If z and x are the solutions to
the perturbed and unperturbed systems of equations, respectively, then
x.
z. = -J (3.14)
3 1 - (b..-l)c
59
-------
and for
cb .x . _
z, -x, = "*—^ = cb, .x . . (3.15)
IT V —~ K"I "i
K K i - (b. .-DC D D
33
Note, though, that the column sum of the sensitivity matrix is
Eb a x — —
i ki ij k - b .x. + b x. =
Eb, .a. .x. + b, . (a. ,-l)x. + b, .x. = b. .x. (3.16)
j ki 13 3 ki 13 3 k3 3 k3 3
The second equality holds because the inner product between the j column of
(I-A) and the i row of the inverse is zero. Therefore, the column sum of
the sensitivity matrix provides a first-order approximation for the perturba-
tion of output due to a scalar change in the corresponding column of the A
matrix, though is is far more computationally efficient to use (3.15).
The sensitivity relation for the perturbation of the i row of the A
matrix can be found by an analogous solution perturbation technique (39). If
z and x are the perturbed and unperturbed solution vectors, respectively,
then
z = x + b .$ (3.17)
where
c(x - y )
= . (3.18)
Therefore,
- Xk =
Note that the row sum of the sensitivity matrix is
Eb. .t. . = b. . (x. - y.) . (3.20)
i ki 13 ki i *i
By the comparison of the row and column sensitivity relations, (3.19) and
(3.15), it is evident that output is always more sensitive to a column change
than the same size row change. This, of course, does not hold for the change
in x, when the k row is perturbed. Therefore, the column perturbation re-
lation b -x. is chosen to the sensitivity relation used in determing the ag-
gregation scheme.
60
-------
The 35751* terms, b .x., were ranked and sectors whose value of b x.
were less than 450 trillion Btu were left unaggregated05. There were 34 such
industries. The other sectors were then aggregated into major industrial
classes. The philosophy followed in the aggregation of the other sectors was
1. Similar industries should only be aggregated into the same industry.
2. The column sensitivity of the aggregated industries should be on the
order of the industries left unaggregated.
3. Energy output should not be sensitive to diagonal elements where it
is not expected that self-use in direct production is not important.
It was found that 23 aggregate industries were necessary if the column sensi-
tivity of the aggregate industries were to be on the same order as the un-
aggregated industries.
The base input-output matrix was the 357-order 1967 national energy
input-output matrix developed by the energy group at CAC. All energy trans-
actions are in trillion Btu, while all other transactions are in million
dollars.
55 b x. has the dimension of 10 BfH/ Because the total energy con-
sumption of the U.S. in 1967 was 5.8 x 10 Btu, the cutoff point of 4.5 x
10 implies that industries were considered important if their sensitivity
£elation was approximately 1 percent of total energy output. Note that
b x. approximates the change in the total output of the k industry when
the coefficients in the i column are doubled. Because the exact change is
found by (3.16),. b x. underapproximates the actual change. It was not ne-
cessary to rank the sensitivity relations corresponding to the refined petro-
leum, gas, and electric utilities. Because of the inefficiencies of the
energy conversion processes, the sensitivity with respect to crude petroleum
and gas total output was always larger than with respect to the product in-
dustries (refined petroleum, gas, and electric utilities).
61
-------
APPENDIX B
63
-------
TABLE B-l.l
Baseline Data, 1974, and Scenario 1 Solutions to the ORBES Energy Demand Model, 1985 and 2000*
final demand total production total consumption
1974 1985 2000 1974 1985 2000 1974 1985 2000
1 coal mining
2 crude petroleum, gas
3 shale oil
4 gasified coal
5 solvent-refined coal
6 ref'd petroleum products
7 natural gas utilities
8 coal combined cycle elec.
9 fossil electric utilities
10 nuclear elect, utilities
11 high-temp gas reactor
12 renewable elec. util's
13 ore-reduction feedstocks
14 chemical feedstocks
15 water transport
16 air transport
17 truck, bus transport
18 rail transport
19 auto transport
20 misc. thermal uses
21 water heat
22 space heat
23 air-conditioning
24 misc. elec. power uses
25 agriculture
26 mining
27 construction
28 meat products
29 food exc. meat products
30 apparel and misc text, prod
31 misc fabricated text, prod
32 logging and misc. wood prod
33 misc paper prod and publ.
34 paper mills
35 paperbbard mills
36 paperboard containers
37 industrial org-inorg chem.
38 ag and misc chem.
39 plastic and synthetic resins
40 paving and asphalt
41 rubber and misc plastic prod
42 glass, stone, and clay prod
43 blast and basic steel prod
44 iron and steel found, and forging
45 other primary metal manufac.
46 nonfer. forge, cast, and rolling
47 fabricated metal containers
48 industrial and farm machinery
0
0
0
0
0
0
0
0
0
0
0
0
0
12
0
0
0
0
240
0
113
486
69
163
827
14
11071
2260
6614
455
2329
885
937
37
1
22
180
1246
14
2
867
109
94
12
0
58
1005
3308
0
0
0
0
0
0
0
0
0
0
0
0
0
17
0
0
0
0
331
0
119
515
102
190
1085
18
14527
2965
8679
597
3056
1161
1230
48
1
28
236
1635
19
2
1138
142
123
16
0
77
1318
4341
0
0
0
0
0
0
0
0
0
0
0
0
0
26
0
0
0
0
506
0
127
550
139
221
1290
26
21335
3239
9800
846
4267
1666
1731
67
1
40
336
2322
26
3
1601
242
180
24
0
113
1992
6558
10964
683
0
0
0
2109
3302
0
1074
23
0
13
215
325
15
26
45
17
254
1190
166
787
126
543
8393
688
10562
2102
10603
302
1162
1875
3257
431
248
825
2644
3026
1286
177
4163
3586
10475
1727
657
3200
6510
10506
15254
722
0
0
0
2175
3273
0
1587
210
0
35
282
434
19
34
60
22
349
1542
190
912
195
692
11015
908
13897
2758
13915
397
1526
2465
4284
567
326
1083
3612
4019
1697
232
5475
4718
13786
2272
866
4209
8562
13815
20682
703
0
0
0
2319
2975
0
2233
210
0
35
390
704
21
49
80
33
532
2150
227
1117
289
940
13529
1292
20339
3080
16963
569
2137
3451
5984
794
436
1425
5306
6302
2410
542
8578
6736
19081
3099
1254
6187
12067
20302
4842
5429
0
0
0
3186
2176
0
814
18
0
10
215
325
15
26
45
17
254
1190
166
787
126
543
7312
1422
13051
2747
. 9384
1508
2783
2412
2961
732
391
851
2322
2743
954
129
2633
1996
5351
1304
1566
2129
5683
7026
6736
5738
0
0
0
3287
2157
0
1083
143
0
24
282
434
19
34
60
22
349
1542
190
912
195
692
9597
1876
17172
3605
12315
1982
3653
3171
3894
963
515
1118
3172
3643
1259
170
3463
2626
7043
1716
2063
2800
7475
9239
9133
5587
0
0
0
3504
1961
0
1692
159
0
27
390
704
21
49
80
33
532
2150
227
1117
289
940
11787
2669
25134
4026.
15013
2842
5115
4440
5439
1348
689
1471
4660
5711
1788
397
5426
3749
9748
2341
2988
4116
10535
13577
*Sectors 1-24 in trillions of Btu's; industries 25-67 in millions of 1967 dollars.
65
-------
TABLE B-l.l (continued)
final demand total production total consumption
1974 1985 2000 1974 1985 2000 1974 1985 2000
49 elec. equipment and components
50 truck, bus, and auto manufac.
51 misc transport, equipment
52 raise manufac.
53 railroads
54 misc transport, and communication
55 motor freight transport.
56 water transport.
57 air transport.
58 wholesale trade
59 retail trade
60 finance and insurance
61 real estate
62 hotels, lodging, and amusements
63 misc business and personal serv.
64 advertising
65 auto repair
66 medical and educational serv.
67 nonprofit organizations
3085
3403
3093
1382
406
1989
669
224
455
4269
11029
3329
9559
1751
2201
22
1072
4630
1298
4048
4465
4059
1813
533
2610
878
294
597
5602
14472
4368
12543
2298
2888
29
1407
6076
1703
6042
7001
6132
2643
786
4087
1246
327
866
7460
19273
7015
20143
3690
4639
47
2259
9757
2735
9635
6698
2957
1573
1874
3780
2387
566
527
7333
12470
5008
9487
2895
3712
1187
1606
4409
1684
12666
8792
3881
2066
2495
4950
3142
738
692
9639
16377
6580
12447
3802
4878
1562
2112
5786
2210
18565
13673
5808
3011
3695
7353
4441
819
1004
13146
21994
10139
19193
5754
7289
2177
3189
9280
3499
6605
5751
4004
2274
1874
3780
2387
566
527
8107
12563
5795
14106
3577
5015
2215
1879
4754
1493
8684
7550
5256
2986
2495
4950
3142
738
692
10656
16498
7615
18507
4699
6592
2915
2471
6238
1959
12728
11740
7866
4352
3695
7353
4441
819
1004
14533
22158
11733
28537
7111
9849
4061
3731
10005
3102
•Sectors 1-24 in trillions of Btu's; industries 25-67 in millions of 1967 dollars.
66
-------
TABLE B-1.2
Baseline Data, 1974, and scenario 1 Solutions to the CRBES Energy Demand Model, 1985 and 2000:
Associated Transaction Matrices (trillions of Btu's)
cn
10
11
12
1
8.6
76.0
103.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0 .
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
2
0.0
0.0
0.0
16.9
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
• 0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
6
0.0
0.0
0.0
2109.5
2364.5
2520.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
64.4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
7
0.0
0.0
0.0
3302.3
3373.7
3067.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
109.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
9
3358.5
4907.7
6312.8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
87.9
283.4
372.1
42.0
0.0
0.0
0.0
0.0
0.0
94.5
138.0
194.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
10
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0 .
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
2.0
18.3
18.3
0.0
o.c
0.0
0.0
0.0
0.0
11
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
12
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.2
3.0
3.0
-------
TABLE B-1.2 (continued)
CT>
00
10
12
13
922.7
928.4
1044.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
51.6
97.5
0.0
6.8
9.2
0.0
0.0
0.0
0.0
1.1
1.5
14
7.9
10.5
70.4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
238.8
314.7
478.6
81.3
108.3
154.8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
15
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
72.5
86.0
87.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
B.O
0.0
16
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
128.0
152.7
203.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
17
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
225.4
270.7
332.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
18
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
82.9
100.3
136.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
19
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1268.7
1163.1
1267.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
20
478.5
787.0
1601.9
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
373.6
382.0
289.5
788.3
851.5
759.3
0.0
0.0
0.0
100.4
116.6
175.9
2.2
15.4
16.5
0.0
0.0
0.0
1.2
2.6
2.8
21
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
12.9
12.4
0.0
198.0
169.0
120.2
0.0
0.0
0.0
32.5
62.1
126.7
0.7
8.2
11.9
0.0
0.0
0.0
0.4
1.4
2.0
22
65.2
26.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
631.3
521.9
337.3
950.6
1027.9
926.5
0.0
0.0
0.0
17.1
53.8
161.2
0.4
7.1
15.2
0.0
0.0
0.0
0.2
1.2
2.5
23
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
6.2
0.0
0.0
0.0
0.0
0.0
43.8
61.8
89.4
0.9
8.2
8.4
0.0
0.0
0.0
0.5
1.4
1.4
24
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
525.6
599.2
847.5
11.3
79.3
79.7
0.0
0.0
0.0
6.5
13.2
13.3
-------
TABLE B-1.3
Baseline Data, 1974, and Scenario 1 Solutions to the ORBES Energy Demand Model, 1985 arid 2000:
Associated Technical Coefficient Matrices (dimensioned Btu's input per Btu's output)
10
11
12
1
0.00079
0.00498
0.00498
0.00000
0.00000
C. 00000
o.oocoo
o.ocooo
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
o.ocooo
o.oooco
0.00000
o.oocoo
0.00000
o.cocco
o.coooo
o.oocoo
0.00000
0.00000
0.00000
0.00000
o.coooo
o.coooo
o.cooco
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
2
0.00000
o.ocooo
0.00000
0.02470
0.00000
o.oooco
0.00000
o.ocooo
o.coooo
0.00000
0.00000
o.coooo
0.00000
o.coooo
0.00000
o.oooco
o.cooco
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
o.oocoo
0.00000
o.oooco
o.occoo
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
3
0.00000
C. 00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
0.00640
0.00000
o.ccooo
o.ocooo
0.00000
0.00000
0.00000
o.coooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.doooo
0.00000
4
1.62000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.03319
o.coooo
0.00000
0.00000
0.00000
o.coooo
0.00000
o.oocoo
0.00000
O.OOQOO
o.oocoo
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
5
' 1.69700
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
6
0.00000
0.00000
0.00000
1.00040
1.08696
. 1.08696
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.03053
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
0.00000
o.oocoo
0.00000
o.oooco
0.00000
0.00000
0.00000
0.00000
0.00000
7
0.00000
0.00000
0.00000
1.00022
1.03093
1.03093
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.03319
o.oooco
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
8
2.10872
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
0.00000
0.00000
0.00000
0.14837
C. 00000
0.00000
C. 00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
9
3.12811
3.09245
2.82738
0.00000
0.00000
0.00000 '
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.08184
0.17857
0.16667
0.03909
0.00000
0.00000
0.00000
0.00000
0.00000
0.08800
0.08696
0.08696
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
0.00000
0.00000
0.00000
10
0.00000
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.08801
0.08696
0.08696
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
11
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
- 0.00000
0.00000
o.oocoo
o.coooo
o.oooco
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.090CO
0.00000
0.00000
0.00000
0.00000
0.00000
12
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.08815
0.08696
0.08696
-------
TOBLE B-1.3 (continued)
O
10
12
13
4.30000
3.28723
2.67685
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
O.POOOO
0.00000
o.oocoo
o.cocoo
0.00000
o.oocoo
0.00000
o.coooo
o.coooo
0.00000
0.00000
0.00000
0.18284
0.24984
0.00000
0.02419
0.02350
O.OOOOC
0.00000
0.00000
0.00000
0.00403
0.00392
14
0.02428
0.02428
0.10000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.73438
0.725S5
0.68000
0.24985
0.24986
0.22000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
o.coooo
o.cccoo
0.00000
o.oocoo
o.coooo
0.00000
15
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
5.00000
4.54545
4.16667
0.00000
0.00000
0.00000
0.00000
0.00000
O.OQOOO
0.00000
o.coooo
0.00000
o.oocoo
o.oooco
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
16
o.cocoo
0.00000
0.00000
0.00000
0.00000
o.oocco
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
5.00000
4.54545
4.16667
0.00000
0.00000
o.oooco
0.00000
o.oocoo
0.00000
o.oocco
0.00000
0.00000
0.00000
• 0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
17
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
5.00000
4.54545
4.16667
o.oocoo
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.00000
o.oocoo
0.00000
o.oooco
0.00000
o.ocooo
0.00000
0.00000
13
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
5.00000
4.54545
4.16667
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
19
0.00000
0.00000
0.00000
0.00000
0.00000
o.poooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
5.00000
3.33333
2.38095
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
20
0.40212
0.51051
0.74509
0.00000
0.00000
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.31393
0.24781
0.13465
0.66242
0.55234
0.35316
0.00000
0.00000
0.00000
O.C8437
0.07564
0.08132
0.00182
O.P1C01
0.00769
0.00000
0.00000
0.00000
0.00105
0.00167
0.00128
21
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.07789
0.06521
0.00000
1.19153
0.89084
0.52910
0.00000
0.00000
0.00000
0.19538
0.32753
0.55784
0.00422
C. 04334
0.05246
0.00000
O.GOOOO
0.00000
0.00242
0.00722
0.00874
22
0.08233
0.02848
O.OCOOO
0.00000
0.00000
O.COOOO
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.80215
0.57220
0.30197
1.20776
1.12704
0.82955
0.00000
0.00000
0.00000
0.02172
0.05895
0.14433
0.00047
O.OC'780
0.01357
0.00000
0.00000
0.00000
0.00027
0.00130
0.00226
23
0.00000
O.OOCOO
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.04906
0.00000
0.00000
0.00000
0.00000
0.00000
0.34611
0.31737
0.30913
0.00747
0.04200
0.02907
0.00000
0.00000
0.00000
0.00429
0.00700
0.00485
24
O.OCOOO
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.96712
0.86627
0.90112
0.02088
0.11463
0.08475
0.00000
0.00000
0.00000
0.01200
0.01910
0.01412
-------
TABLE B-2.1
Baseline Data, 1974, and Scenario 2 Solutions to the ORBES Energy Demand Model, 1985 and 2000*
final demand total production total consumption
1974 1985 2000 1974 1985 2000 1974 1985 2000
1 coal mining
2 crude petroleum, gas
3 shale oil
4 gasified coal
5 solvent-refined coal
6 ref'd petroleum products
7 natural gas utilities
8 coal combined cycle elec.
9 fossil electric utilities
10 nuclear elect, utilities
11 high-temp gas reactor
12 renewable elec. util's
13 ore-reduction feedstocks
14 chemical feedstocks
15 water transport
16 air transport
17 truck, bus transport
18 rail transport
19 auto transport
20 rnisc. thermal uses
21 water heat
22 space heat
23 air-conditioning
24 misc. elec. power uses
25 agriculture
26 mining
27 construction
28 meat products
29 food exc. meat products
30 apparel and misc text, prod
31 misc fabricated text, prod
32 logging and misc. wood prod
33 misc paper prod and publ.
34 paper mills
35 paperboard mills
36 paperboard containers
37 industrial org-inorg chem.
38 ag and misc chem.
39 plastic and synthetic resins
40 paving and asphalt
41 rubber and misc plastic prod
42 glass, stone, and clay prod
43 blast and basic steel prod
44 iron and steel found, and forging
45 other primary metal manufac.
46 nonter. forge, cast, and rolling
47 fabricated metal containers
48 industrial and farm machinery
0
0
0
0
0
0
0
0
0
0
0
0
0
12
0
0
0
0
240
0
113
486
69
163
827
14
11071
2260
6614
455
2329
885
937
37
1
22
180
1246
14
2
867
109
94
12
0
58
1005
3308
0
0
0
0
0
0
0
0
0
0
0
0
0
17
0
0
0
0
331
0
119
515
102
190
1085
18
14527
2965
8679
597
3056
1161
1230
48
1
28
236
1635
19
2
1138
142
123
16
0
77
1318
4341
0
0
0
0
0
0
0
0
0
0
0
0
0
26
0
0
0
0
506
0
127
550
139
221
1290
26
21335
3239
9800
846
4267
1666
1731
67
1
40
336
2322
26
3
1601
242
180
24
0
113
1992
6558
10964
683
0
0
0
2109
3302
0
1074
23
0
13
215
325
15
26
45
17
254
1190
166
787
126
543
8393
688
10562
2102
10603
302
1162
1875
3257
431
248
825
2644
3026
1286
177
4163
3586
10475
1727
657
3200
6510
10506
14986
• 722
0
0
0
2175
3272
0
1587
210
0
35
282
434
19
34
60
22
349
1541
190
912
195
692
11015
908
13896
2758
13915
397
1526
2464
4284
567
326
1083
3610
4019
1697
232
5474
4717
13781
2271
865
4208
8560
13810
20307
703
0
0
0
2318
2975
0
2232
210
0
35
390
704
21
49
80
33
532
2149
227
1117
289
940
13529
1292
20338
3080
16963
569
2136
3450
5983
794
436
1425
5304
6301
2410
542
8576
6734
19074
3098
1254
6186
12064
20294
4842
5429
0
0
0
3186
2176
0
814
18
0
10
215
325
15
26
45
17
254
1190
166
787
126
543
7312
1422
13051
2747
9384
1508
2783
2412
2961
732
391
851
2322
2743
954
129
2633
1996
5351
1304
1566
2129
5683
7026
6617
5737
0
0
0
3287
2156
0
1083
143
0
24
282
434
19
34
60
22
349
1541
190
912
195
692
9597
1876
17171
3605
12315
1982
3653
3170
3894
963
515
1118
3171
3642
1259
170
3462
2625
7040
1716
2062
2799
7473
9236
8967
5587
0
0
0
3503
1960
0
1692
159
0
27
390
704
21
49
80
33
532
2149
227
1117
289
940
11787
2669
25132
4026
15013
2842
5115
4439
5439
1348
689
1471
4658
5711
1787
397
5424
3748
9744
2340
2987
4115
10532
13572
*Sectors 1-24 in trillions of Btu's; industries 25-67 in millions of 1967 dollars.
71
-------
TABLE B-2.1 (continued)
final demand total production total consumption
1974 1985 2000 1974 1985 2000 1974 1985 2000
49 elec. equipment and components
50 truck, bus, and auto manufac.
51 misc transport, equipment
52 misc manufac.
53 railroads
54 misc transport, and communication
55 motor freight transport.
56 water transport.
57 air transport.
58 wholesale trade
59 retail trade
60 finance and insurance
61 real estate
62 hotels, lodging, and amusements
63 misc business and personal serv.
64 advertising
65 auto repair
66 medical and educational serv.
67 nonprofit organizations
3085
3403
3093
1382
406
1989
669
224
455
4269
11029
3329
9559
1751
2201
22
1072
4630
1298
4048
4465
4059
1813
533
2610
878
294
597
5602
14472
4368
12543
2298
2888
29
1407
6076
1703
6042
7001
6132
2643
786
4087
1246
327
866
7460
19273
7015
20143
3690
4639
47
2259
9757
2735
9635
6698
2957
1573
1874
3780
2387
566
527
7333
12470
5008
9487
2895
3712
1187
1606
4409
1684
12665
8792
3881
2066
2494
4949
3141
738
692
9637
16376
6579
12445
3801
4878
1562
2112
5786
2210
18563
13673
5808
3011
3694
7353
4440
818
1004
13144
21993
10138
19190
5753
7288
2176
3189
9280
3499
6605
5751
4004
2274
1874
3780
2387
566
527
8107
12563
5795
14106
3577
5015
2215
1879
4754
1493
8683
7549
5256
2986
2494
4949
3141
738
692
10655
16497
7614
18503
4698
6591
2914
2471
6238
1959
12727
11740
7866
4352
3694
7353
4440
818
1004
14532
22156
11732
28532
7110
9847
4060
3731
10005
3102
*Sectors 1-24 in trillions of Btu's; industries 25-67 in millions of 1967 dollars.
72
-------
TABLE 8-2.2
Baseline Data, 1974, and Scenario 2 Solutions to the CRBES Energy Demand Model, 1985 and 2000:
Associated Transaction Matrices (trillions of Btu's)
10
11
12
1
8.6
74.6
101.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
2
0.0
0.0
0.0
16.9
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
6
0.0
0.0
0.0
2109.5
2364.2
2519.8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
64.4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
7
0.0
0.0
0.0
3302.3
3373.2
3066.8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
109.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
' 0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
9
3358.5
4807.6
6182.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
87.9
283.4
372.0
42.0
0.0
0.0
0.0
0.0
0.0
94.5
138.0
194.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
10
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
2.0
18.3
18.3
0.0
0.0
0.0
0.0
0.0
0.0
11
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
o.c
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
12
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.2
3.0
3.0
-------
TfiBLE &-2.2 (continued)
10
11
12
13
922.7
928.1
1044.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
51.6
97.5
0.0
6.8
9.2
0.0
0.0
0.0
0.0
1.1
1.5
14
7.9
10.5
70.4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
238.8
314.7
478.5
81.3
108.3
154.8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
15
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
72.5
86.0
87.4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
16
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
128.0
152.7
203.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
17
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
225.4
270.5
332.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
18
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
82.9
100.3
136.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
19
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1268.7
1163.1
1267.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
20
478.5
770.7
1568.7
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
373.6
381.9
289.4
788.3
851.2
759.0
0.0
0.0
0.0
100.4
116.6
175.8
2.2
15.4
16.5
0.0
0.0
0.0
1.2
2.6
2.8
21
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
12.9
12.4
0.0
198.0
169.0
120.2
0.0
0.0
0.0
32.5
62.1
126.7
0.7
8.2
11.9
0.0
0.0
0.0
0.4
1.4
2.0
22
65.2
26.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
631.3
521.8
337.2
950.6
1027.8
926.4
0.0
0.0
0.0
17.1
53.8
161.2
0.4
7.1
15.2
0.0
0.0
0.0
0.2
1.2
2.5
23
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
6.2
0.0
0.0
0.0
0.0
0.0
43.8
61.8
89.4
0.9
8.2
8.4
0.0
0.0
0.0
0.5
1.4
1.4
24
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
525.6
599.1
847.3
11.3
79.3
79.7
0.0
0.0
0.0
6.5
13.2
13.3
-------
TABLE B-2.3
Baseline Data, 1974, and Scenario 2 Solutions to the ORBES Energy Demand Model, 1985 and 2000-
Associated Technical Coefficient Matrices (dimensioned Btu's input per Btu's output)
10
11
12
1
0.00079
0.00498
0.00498
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.ocooo
o.coooo
0.00000
0.00000
o.oooco
o.coooo
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
2
o.coooo
0.00000
0.00000
0.02470
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.coooo
o.coooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.cocoo
o.oooco
0.00000
0.00000
0.00000
o.occoo
o.cccoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
3
0.00000
0.00000
o.doooo
0.00000
0.00000
o.coooo
o.ocooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00640
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.occoo
0.00000
0.00000
0.00000
4
1.62000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.03319
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.coooo
o.cooco
O.OOOOu
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
5
1.69700
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
6
0.00000
0.00000
0.00000
1.00040
1.08696
1.08696
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.03053
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
7
0.00000
0.00000
0.00000
1.00022
1.03093
1.03093
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.03319
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.ococo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
8
2.10872
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.14837
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
o.occoo
0.00000
0.00000
o.oocoo
o.oocoo
0.00000
0.00000
0.00000
9
3.12811
3.02934
2.76968
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.08184
0.17857
0.16667
0.03909
0.00000
0.00000
o.ooobo
0.00000
0.00000
0.08800
0.086S6
0.08696
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
10
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
0.00000
o.coooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.08801
0.08696
0.08696
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
11
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.09000
o.oooco
0.00000
0.00000
0.00000
0.00000
12
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
o.ocooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oooco
o.ocooo
0.00000
0.00000
o.oooco
0.00000
0.08815
0.08696
0.08696
-------
TABLE B^2.3 (continued)
cn
10
11
12
13
4.30000
3.26723
2.67685
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.cccoo
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.18284
0.24983
0.00000
0.02419
0.02350
0.00000
0.00000
0.00000
0.00000
0.00403
0.00392
14
0.02428
0.02428
0.10000
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
0.73438
0.72585
0.68000
0.24985
0.24966
0.22000
O.OOCOO
O.COOOO
0.00000
O.OCOOO
0.00000
o.oocoo
0.00000
0.00000
0.00000
o.oocoo
0.00000
o.oocoo
0.00000
0.00000
0.00000
15
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
O.COOOO
o.oocoo
0.00000
0.00000
0.00000
0.00000
5.00000
4.54545
4.16667
O.COOOO
o.oooco
0.00000
0.00000
o.oocoo
0.00000
0.00000
O.CCGOO
0.00000
0.00000
o.oooco
o.oocoo
0.00000
0.00000
0.00000
0.00000
o.ooood
0.00000
16
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.cooco
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
5.00000
4.54545
4.16667
0.00000
o.oooco
0.00000
0.00000
o.oooco
0.00000
0.00000
O.OGOOO
0.00000
0.00000
o.oocoo
0.00000
0.00000
O.OCOOO
0.00000
0.00000
o.oooco
0.00000
17
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.conoo
0.00000
0.00000
0.00000
0.00000
5.00000
4.54545
4.16667
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
O.PCOOO
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
o.oocoo
0.00000
0.00000
0.00000
o.oocoo
18
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.cocoo
O.OCOOO
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
5.00000
4.54545
4.16667
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
O.OCOOO
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
19
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
5.00000
3.33333
2.38095
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
20
0.40212
0.50009
0.72988
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.31393
0.24781
0.13465
0.66242
0.55234
0.35316
0.00000
0.00000
0.00000
O.OE437
0.07564
0.08182
0.00182
0.01001
0.00770
0.00000
0.00000
0.00000
0.00105
0.00167
0.00128
21
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.07789
0.06521
0.00000
1.19153
0.89084
0.52910
0.00000
0.00000
0.00000
0.19538
0.32753
0.55782
0.00422
0.04334
0.05247
O.OOOCO
O.OOCOO
0.00000
0.00242
0.00722
0.00875
22
0.08283
0.02848
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
0.00000
0.00000
0.00000
0.80215
0.57220
0.30197
1.20776
1.12704
0.82955
0.00000
0.00000
0.00000
O.C2172
0.05895
0.14432
0.00047
0.00780
0.01358
0.00000
0.00000
0.00000
0.00027
0.00130
0.00226
23
O.OOOCO
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.04906
0.00000
0.00000
0.00000
0.00000
o.ooooc
0.34611
0.31737
0.30913
0.00747
0.04200
0.02908
0.00000
0.00000
0.00000
0.00429
0.00700
0.00485
24
0.00000
0.00000
0.00000
0.00000
0.00000
O.OCOOO
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.96712
0.86627
0.90110
0.02088
0.11463
0.08476
O.COOOO
o.oocoo
0.00000
0.01200
0.01910
0.01413
-------
TABLE B-3.1
Baseline Data, 1974, and Scenario 4 Solutions to the QRBES Energy Demand Model, 1985 and 2000*
final demand total production total consumption
1974 1985 2000 1974 1985 2000 1974 1985 2000
1 coal mining
2 crude petroleum, gas
3 shale oil
4 gasified coal
5 solvent-refined coal
6 ref'd petroleum products
7 natural gas utilities
8 coal combined cycle elec.
9 fossil electric utilities
10 nuclear elect, utilities
11 high-temp gas reactor
12 renewable elec. util's
13 ore-reduction feedstocks
14 chemical feedstocks
15 water transport
16 air transport
17 truck, bus transport
18 rail transport
19 auto transport
20 misc. thermal uses
21 water heat
22 space heat
23 air-conditioning
24 misc. elec. power uses
25 agriculture
26 mining
27 construction
28 meat products
29 food exc. meat products
30 apparel and misc text, prod
31 misc fabricated text, prod
32 logging and misc. wood prod
33 misc paper prod and publ.
34 paper mills
35 paperboard mills
36 paperboard containers
37 industrial org-inorg chem.
38 ag and misc chem.
39 plastic and synthetic resins
40 .paving and asphalt
41 rubber and misc plastic prod
42 glass, stone, and clay prod
43 blast and basic steel prod
44 iron and steel found, and forging
45 other primary metal manufac.
46 nonfer. forge, cast, and rolling
47 fabricated metal containers
48 industrial and farm machinery
0
0
0<
0
0
0
0
0
0
0
0
0
0
12
0
0
0
0
240
0
113
486
69
163
827
14
11071
2260
6614
455
2329
885
937
37
1
22
180
1246
14
2
867
109
94
12
0
58
1005
3308
0
0
0
0
0
0
0
0
0
0
0
0
0
17
0
0
0
0
331
0
119
515
102
190
1085
18
14527
2965
8679
597
3056
1161
1230
48
1
28
236
1635
19
2
1138
142
123
16
0
77
1318
4341
0
. 0
0
0
0
0
0
0
0
0
0
0
0
26
0
0
0
0
506
0
127
550
139
221
1285
26
21240
3225
9756
842
4249
1659
1723
67
1
40
334
2311
26
3
1594
241
179
24
0
113
1983
6529
10964
683
0
0
0
2109
3302
0
1074
23
0
13
215
325
15
26
45
17
254
1190
166
787
126
543
8393
688
10562
2102
10603
302
1162
1875
3257
431
248
825
2644
3026
1286
177
4163
3586
10475
1727
657
3200
6510
10506
11682
700
0
0
0
2353
4201
0
1204
210
0
35
281
432
19
. 34
59
21
349
1541
189
909
194
688
11011
903
13824
2758
13913
395
1525
2454
4271
565
325
1081
3591
4010
1690
231
5449
4693
13692
2257
861
4187
8519
13731
13283
700
0
0
0
2404
8179
0
1613
210
0
35
408
• 703
21
81
81
31
532
2186
227
1116
289
948
13285
1324
20201
3075
16907
564
2134
3472
6023
799
447
1470
5317
6252
2464
535
8365
6803
19930
3289
1289
6325
12299
20313
4842
5429
0
0
0
3186
2176
0
814
18
0
10
215
325
15
26
45
17
254
1190
166
787
126
543
7312
1422
13051
2747
9384
1508
2783
2412
2961
732
391
851
2322
2743
954
129
2633
1996
5351
1304
1566
2129
5683
7026
5158
6889
0
0
0
3556
2769
0
913
159
0
27
281
432
19
34
59
21
349
1541
189
909
194
688
9593
1864
17083
3605
12314
1975
3651
3157
3882
959
513
1116
3154
3634
1254 .
169
3447
2612
6995
1705
2051
2786
7437
9183
5865
11044
0
0
0
3632
5390
• 0
1223
159
0
27
408
703
21
81
81
31
532
2186
227
1116
289
948
11574
2736
24962
4020
14963
2819
5110
4467
5475
1357
706
1517
4670
5667
1828
392
5291
3786
10181
2484
3072
4207
10737
13585
*Sectors 1-24 in trillions of Btu's; industries 25-67 in millions of 1967 dollars.
77
-------
TABLE B-3.1 (continued)
final demand total production total consumption
1974 1985 2000 1974 1985 2000 1974 1985 2000
49 elec. equipment and components
50 truck, bus, and auto manufac.
51 misc transport, equipment
52 misc manufac.
53 railroads
54 misc transport, and communication
55 motor freight transport.
56 water transport.
57 air transport.
58 wholesale trade
59 retail trade
60 finance and insurance
61 real estate
62 hotels, lodging, and amusements
63 misc business and personal serv.
64 advertising
65 auto repair
66 medical and educational serv.
67 nonprofit organizations
3085
3403
3093
1382
406
1989
669
224
455
4269
11029
3329
9559
1751
2201
22
1072
4630
1298
4048
4465
4059
1813
533
2610
878
294
597
5602
14472
4368
12543
2298
2888
29
1407
6076
1703
6015
6970
6105
2632
739
4069
1373
326
1498
7427
19183
6984
20054
3673
4618
47
2249
9714
2723
9635
6698
2957
1573
1874
3780
2387
566
527
7333
12470
5008
9487
2895
3712
1187
1606
4409
1684
12627
8787
3878
2063
2430
4939
3117
731
692
9611
16351
6558
12410
3788
4859
1554
2105
5785
2209
18590
13635
5824
3010
3456
7415
4907
812
1678
13185
21886
10128
19117
5760
7304
2189
3209
9238
3488
6605
5751
4004
2274
1874
3780
2387
566
527
8107
12563
5795
14106
3577
5015
2215
1879
4754
1493
8657
7545
5252
2982
2430
4939
3117
731
692
10626
16473
7589
18451
4681
6566
2899
2462
6237
1958
12745
11708
7887
4351
3456
7415
4907
812
1678
14577
22049
11721
28424
7118
9869
4086
3754
9960
3092
*Sectors 1-24 in trillions of Btu's; industries 25-67 in millions of 1967 dollars.
78
-------
TABLE B-3.2
Baseline Data, 1974, and Scenario 4 Solutions to the ORDES Energy Detrund Model, 1985 and 2000:
Associated Transaction Matrices (trillions of Btu's)
10
11
12
1
8.6
58.2
66.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
c.o
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
2
0.0
0.0
0.0
16.9
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0,0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
6
0.0
0.0
0.0
2109.5
2557.6
2612.8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
64.4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
o.c
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
7
0.0
0.0
0.0
3302.3
4331.1
8431.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
109.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
9
3358.5
3647.2
4467.4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
87.9
215.0
0.0
42.0
0.0
268.8
0.0
0.0
0.0
94.5
104.7
140.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
10
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
c.o
2.0
18.3
18.3
0.0
0.0
0.0
0.0
0.0
0.0
11
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
12
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.2
3.0
3.0
-------
TftBLE B-3.2 (continued)
00
o
10
11
12
13
922.7
922.1
1093.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
49.2
98.3
0.0
8.6
12.8
0.0
0.0
0.0
0.0
1.4
2.1
14
7.9
10.5
14.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
238.8
313.3
477.9
81.3
107.8
210.8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
15
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
72.5
89.2
94.7
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
16
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
128.0
159.9
370.4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
17
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
225.4
279.9
366.7
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
18
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
82.9
102.4
138.9
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
19
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1268.7
1395.6
1716.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
20
478.5
464.6
224.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
373.6
362.1
174.5
788.3
1319.4
2751.6
0.0
0.0
0.0
100.4
84.1
41.6
2.2
14.7
5.4
0.0
0.0
0.0
1.2
2.4
0.9
21
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
12.9
9.0
0.0
198.0
236.3
306.2
0.0
0.0
0.0
32.5
29.0
29.6
0.7
5.1
3.8
0.0
0.0
0.0
0.4
0.8
0.6
22
65.2
55.8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
631.3
629.2
292.7
950.6
1105.2
1852.6
0.0
0.0
0.0
17.1
13.9
0.0
0.4
2.4
0.0
0.0
0.0
0.0
0.2
0.4
0.0
23
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
6.2
0.0
0.0
0.0
0.0
0.0
43.8
59.7
90.3
0.9
10.4
11.8
0.0
0.0
0.0
0.5
1.7
2.0
24
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
525.6
572.0
822.7
11.3
99.8
107.1
0.0
0.0
0.0
6.5
16.6
17.9
-------
TABLE B-3.3
Baseline Data, 1974, and Scenario 4 Solutions to the ORBES Energy -Demand Model, 1985 and 2000:
Associated Technical Coefficient Matrices (dimensioned Btu's input per Btu's output)
00
10
11
12
1
0.00079
0.004S8
0.00498
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
2
0.00000
0.00000
0.00000
0.02470
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
o.coooo
o.ocooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
3
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00640
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0'. 00000
0.00000
4
1.62000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.03319
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
' 0.00000
0.00000
5
1.69700
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
o.ocooo
0.00000
0.00000
6
0.00000
0.00000
0.00000
1.00040
1.08696
1.08696
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.03053
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
7
0.00000
0.00000
0.00000
1.00022
1.03093
1.03093
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.03319
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
8
2.10872
0.00000
0.00000
' 0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.14837
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
9
3.12811
3.02934
2.76968
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.08184
0.17857
0.00000
0.03909
0.00000
0.16667
o.oooob
0.00000
0.00000
0.08800
0.08696
0.08696
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.00000
10
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.08801
0.08696
0.08696
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
11
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.09000
0.00000
0.00000
0.00000
0.00000
0.00000
12
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.08815
0.08696
0.08696
-------
TABLE B-3.3 (continued)
00
NJ
1
2
3
4
5
6
7
8
9
10
11
12
13
4.30000
3.28723
2.67685
0.00000
O.OOCOO
0.00000
0.00000
0.00000
0.00000
O.OOCOO
0.00000
0.00000
0.00000
0.00000
o.oooco
o.ocooo
0.00000
O.OOCOO
0.00000
o.coooo
o.cccco
c.coooo
O.OOCOO
0.00000
o.occoo
0.17538
0.24068
O.OOCOO
0.03059
0.03135
0.00000
o.cocoo
O.OOCOO
0.00000
0.00510
0.00522
14
0.02428
0.02428
0.02000
0.00000
0.00000
0.00000
0.00000
o.coooo
o.oooco
o.coooo
0.00000
0.00000
0.00000
o.ocooo
o.cccoo
0.73438
0.72585
0.68000
0.24985
0.249S6
0.3COOO
O.COCCO
O.COCOO
o.oooco
0.00000
o.coooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
15
0.00000
0.00000
0.00000
0.00000
o.cocoo
0.00000
0.00000
0.00000
O.OOCOO
o.oooco
0.00000
O.OOCOO
o.coooo
0.00000
0.00000
5.CCOOO
4.76190
4.54545
0.00000
o.oooco
O.OOC30
o.ccooo
o.ocooo
O.OOCOO
0.00000
o.cocoo
o.coooo
0.00000
0.00000
0.00000
0.00000
o.ocooo
o.oooco
0.00000
•Q. 00000
0.00000
16
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
O.OOCOO
5.00000
4.76190
4.54545
o.coooo
o.occoo
O.OQCCO
o.oooco
o.ocooo
o.coooo
0.00000
0.03000
0.00000
o.oooco
0.00000
0.00000
O.OOCOO
0.00000
0.00000
0.00000
0.00000
0.00000
17
0.00000
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.00000
O.OOCOO
0.00000
0.00000
0.00000
0.00000
0.00000
O.OOCOO
0.00000
5.00000
4.76190
4.54545
0.00000
o.coooo
0.00000
0.00000
0.00000
0.00000
0.00000
O.OOCOO
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.ocooo
18
0.00000
0.00000
0.00000
O.OOOGO
0.00000
0.00000
0.00000
O.OOCOO
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
5.00000
4.76190
4.54545
0.00000
O.OOCOO
o.coooo
0.00000
o.coooo
0.00000
o.oooco
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
0.00000
0.00000
0.00000
C. 00000
0.00000
19
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
O.OOCOO
0.00000
0.00000
0.00000
0.00000
0.00000
5.000CO
4.00000
3.22581
0.00000
0.00000
o.ocooo
o.oocco
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
o.occoo
o.oooco
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
20
0.40212
0.30155
0.10273
C. 00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.31393
0.23501
0.07983
0.66242
0.85631
1.25893
0.00000
o.oooco
0.00000
0.08437
0.05458
0.01902
0.00182
0.00952
0.00248
0.00000
c.oooco
0.00000
0.00105
0.00159
0.00041
a
0.00000
0.00000
0.00000
0.00000
O.COCCO
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
0.00000
0.00000
0.00000
0.07789
0.04762
0.00000
1.19153
1.24762
1.34921
0.00000
0.00000
0.00000
0.19538
0.15289
0.13021
0.00422
0.02667
0.01696
0.00000
0.00000
0.00000
0.00242
0.00444
0.00283
22
0.08283
0.06136
0.00000
O.OCOOO
O.OOOOC
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
0.00000
0.00000
o.coooo
0.80215
0.69233
0.26230
1.20776
1.21608
1.65998
0.00000
0.00000
0.00000
0.02172
0.01533
0.00000
0.00047
0.00267
0.00000
0.00000
0.00000
O.OOCOO
0.00027
0.00045
0.00000
23
0.00000
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.04906
o.oooco
0.00000
o.ocooo
o.oaooo
0.00000
0.34611
0.30740
0.31198
0.00747
0.05362
0.04063
0.00000
O.OOOCO
O.OOOCO
0.00429
0.00894
0.00677
24
O.OOOCO
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.96712
0.83091
0.86809
0.02088
0.14493
0.11306
O.OCOOO
o.ccooo
0.00000
0.01200
0.02416
0.01864
-------
TABLE B-4.1
Scenario 2, 2a, and 4 Solutions to the ORBES Energy Demand Model, Year 2000*
final demand
s2 s2a s4
total production
s2 s2a s4
total consumption
s2 s2a s4
1 coal mining
2 crude petroleum, gas
3 shale oil
4 gasified coal
5 solvent-refined coal
6 ref'd petroleum products
7 natural gas utilities
8 coal combined cycle elec.
9 fossil electric utilities
10 nuclear elect, utilities
11 high-temp gas reactor
12 renewable elec. util's
13 ore-reduction feedstocks
14 chemical feedstocks
15 water transport
16 air transport
17 truck, bus transport
18 rail transport
19 auto transport
20 misc. thermal uses
21 water heat
22 space heat
23 air-conditioning
24 misc. elec. power uses
25 agriculture
26 mining
27 construction
28 meat products
29 food exc. meat products
30 apparel and misc text, prod
31 misc fabricated text, prod
32 logging and misc. wood prod
33 misc paper prod and publ.
34 paper mills
35 paperboard mills
36 paperboard containers
37 industrial org-inorg chem.
38 ag and misc chem.
39 plastic and synthetic resins
40 paving and asphalt
41 rubber and misc plastic prod
42 glass, stone, and clay prod
43 blast and basic steel prod
44 iron and steel found, and forging
45 other primary metal manufac.
46 nonfer. forge, cast, and rolling
47 fabricated metal containers
48 industrial and farm machinery
0
0
0
0
0
0
0
0
0
0
0
0
0
26
0
0
0
0
506
0
127
550
139
221
1290
26
21335
3239
9800
846
4267
1666
1731
67
1
40
336
2322
26
3
1601
242
180
24
0
113
1992
6558
0
0
0
0
0
0
0
0
0
0
0
0
0
26
0
0
0
0
506
0
127
550
139
221
1290
26
21335
3239
9800
846
4267
1666
1731
67
1
40
336
2322
26
3
1601
242
180
24
0
113
1992
6558
0
0
0
0
0
0
0
0
0
0
0
0
0
26
0
0
0
0
506
0
127
550
139
221
1285
26
21240
3225
9756
842
4249
1659
1723
67
1
40
334
2311
26
3
1594
241
179
24
0
113
1983
6529
20307
703
0
0
0
2318
2975
0
2232
210
0
35
390
704
21
49
80
33
532
2149
227
1117
289
940
13529
1292
20338
3080
16963
569
2136
3450
5983
794
436
1425
5304
6301
2410
542
8576
6734
19074
3098
1254
6186
12064
20294
22322
709
0
0
0
2359
2984
0
2546
210
0
35
391
706
21
49
80
33
532
2158
227
1120
290
943
13533
1296
20408
3081
16965
570
2137
3458
5995
796
437
1428
5323
6311
2415
544
8595
6754
19137
3108
1257
6202
12095
20348
13283
700
0
0
0
2404
8179
0
1613
210
0
35
408
703
21
81
81
31
532
2186
227
1116
289
948
13285
1324
20201
3075
16907
564
2134
3472
6023
799
447
1470
5317
6252
2464
535
8365
6803
19930
3289
1289
6325
12299
20313
8967
5587
0
0
0
3503
1960
0
1692
159
0
27
390
704
21
49
80
33
532
2149
227
1117
289
940
11787
2669
25132
4026
15013
2842
5115
4439
5439
1348
689
1471
4658
5711
1787
397
5424
3748
9744
2340
2987
4115
10532
13572
9857
5641
0
0
0
3565
1967
0
1742
144
0
24
391
706
21
49
80
33
532
2158
227
1120
290
943
11790
2677
25218
4027
15014
2847
5117
4449
5450
1351
690
1473
4675
5720
1791
398
5436
3759
9776
2348
2995
4126
10559
13608
5865
11044
0
0
0
3632
5390
0
1223
159
0
27
408
703
21
81
81
31
532
2186
227
1116
289
948
11574
2736
24962
4020
14963
2819
5110
4467
5475
1357
706
1517
4670
5667
1828
392
5291
3786
10181
2484
3072
4207
10737
13585
*Sectors 1-24 in trillions of Btu's; industries 25-67 in millions of 1967 dollars.
83
-------
TABLE B-4.1 (continued)
final demand
s2 s2a s4
total production total consumption
s2 s2a s4 s2 s2a s4
49 elec. equipment and components
50 truck, bus, and auto manufac.
51 misc transport, equipment
52 misc manufac.
53 railroads
54 misc transport, and communication
55 motor freight transport.
56 water transport.
57 air transport.
58 wholesale trade
59 retail trade
60 finance and insurance
61 real estate
62 hotels, lodging, and amusements
63 misc business and personal serv.
64 advertising
65 auto repair
66 medical and educational serv.
67 nonprofit organizations
6042
7001
6132
2643
786
4087
1246
327
866
7460
19273
7015
20143
3690
4639
47
2259
9757
2735
6042
7001
6132
2643
786
4087
1246
327
866
7460
19273
7015
20143
3690
4639
47
2259
9757
2735
6015
6970
6105
2632
739
4069
1373
326
1498
7427
19188
6984
20054
3673
4618
47
2249
9714
2723
18563
13673
5808
3011
3694
7353
4440
818
1004
13144
21993
10138
19190
5753
7288
2176
3189
9280
3499
18593
13677
5810
3013
3746
7370
4462
827
1004
13166
22013
10158
19219
5767
7305
2184
3195
9281
3500
18590
13635
5824
3010
3456
7415
4907
812
1678
13185
21886
10128
19117
5760
7304
2189
3209
9238
3488
12727
11740
7866
4352
3694
7353
4440
818
1004
14532
22156
11732
28532
7110
9847
4060
3731
10005
3102
12747
11744
7869
4355
3746
7370
4462
827
1004
14555
22177
11755
28575
7127
9871
4075
3737
10006
3103
12745
11708
7887
4351
3456
7415
4907
812
1678
14577
22049
11721
28424
7118
9869
4086
3754
9960
3092
*Sectors 1-24 in trillions of Btu's; industries 25-67 in millions of 1967 dollars.
84
-------
TABLE B-4.2
Scenario 2, 2a, and 4 Solutions to the QRBES Energy Demand Model, Year 2000:
Associated Transaction Matrices (trillions of Btu's)
00
Ul
10
11
12
1
101.1
111.2
66.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
2
o.c
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0'
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
c.o
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
6
0.0
0.0
0.0
2519.8
2564.0
2612.8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
7
0.0
0.0
0.0
3066.8
3076.5
8431.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
. 0.0
0.0
0.0
0.0
0.0
0.0
9
6182.6
7052.4
4467.4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
372.0
424.4
0.0
0.0
0.0
268.8
0.0
0.0
0.0
194.1
221.4
140.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
10
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
18.3
18.3
18.3
0.0
0.0
0.0
0.0
0.0
0.0
11
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
o.c
0.0
o.n
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
12
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
3.0
3.0
3.0
-------
TftBLE B-4.2 (continued)
00
CTi
10
11
12
13
1044.2
1047.6
1093.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
97.5
99.0
98.3
9.2
8.2
12.8
0.0
0.0
0.0
1.5
1.4
2.1
14
70.4
70.6
14.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
478.5
480.0
477.9
154.8
155.3
210. 8
0.0
0.0
0.0
0.0
0.0
0.0
d.o
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
15
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
87.4
88.4
S4.7
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
16
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
203.1
203.2
370.4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
17
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
332.1
334.7
366.7
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
18
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
136.1
138.1
138.9
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
19
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1267.1
1267.2
1716.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
20
1568.7
1575.0
224.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
289.4
290.6
174.5
759.0
762.1
2751.6
0.0
0.0
0.0
175.8
178.7
41.6
16-5
14.7
5.4
0.0
0.0
0.0
2.8
2.5
0.9
21
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
120.2
120.4
306.2
0.0
0.0
0.0
126.7
128.5
29.6
11.9
10.6
3.8
0.0
0.0
0.0
2.0
1.8
0.6
22
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
337.2
338.2
292.7
926.4
929.0
1852.6
0.0
0.0
0.0
161.2
163.6
0.0
15.2
13.5
0.0
0.0
0.0
0.0
2.5
2.2
0.0
23
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
89.4
90.7
90.3
8.4
7.5
11.8
0.0
0.0
0.0
1.4
1.2
2.0
24
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
847.3
860.6
822.7
79.7
71.0
107.1
0.0
0.0
0.0
13.3
11.8
17.9
-------
TABLE B-4.3
Scenario 2, 2a, and 4 Solutions to the ORBES Energy Demand Model, Year 2000:
Associated Technical Coefficient Matrices (dimensioned Btu's input per Btu's output)
00
-j
10
11
12
1
0.00498
0.00498
0.00498
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
0.00000
0.00000
0.00 000
o.ccooo
0.00000
0.00000.
0.00000
0.00000 .
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
2
0.00000
0.00000 .
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
o.coooo
0.00000
0.00000
0.00000
0.00000
0.00000
3
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0:00000
0.00000
4
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
5
0.00000
0.00000
0.00000
. 0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.coooo
o.coooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
6
0.00000
0.00000
0.00000
1.08696
1.08696
1.08696
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.ooooo •
. 0.00000
0.00000
0.00000
0.00000
7
0.00000
0.00000
0.00000
1.03093
1.03093
1.03093
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.ocooo
8
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
9
2.76968
2.76968
2.76968
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.16667
0.16667
0.00000
0.00000
0.00000
0.16667
0.00000
0.00000
0.00000
0.08696
0.08696
0.08696
0.00000
0.00000
o.occoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
10
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.ooooo -
0.00000 .
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.08696
0.08696
0.08696
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
11
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
12
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
0.00000
0.08696
0.08696
0.08696
-------
TABLE B-4.3 (continued)
00
03
10
11
12
13
2.67685
2.67685
2.67685
O.OOCOO
o.oocoo
o.cocco
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
o.coooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.24983
0.25292
0.24C08
0.02350
0.020E6
0.03135
0.00000
O.OCCOC
o.oocco
O.OC392
0.00348
0.00522
14
0.10000
0.10000
0.02000
0.00000
o.cooco
o.oocoo
0.00000
O.GOOOO
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.68000
0.68000
0.68000
0.22000
0.22000
0.30000
O.OOCOO
o.ocooo
0.00000
o.oocoo
o.occoo
C. 00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
0.00000
0.00000
15
0.00000
o.coooo
0.00000
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
4.16667
4.16667
4.54545
o.oooco
O.OOOuO
0.00000
0.00000
o.oocoo
0.00000
0.00000
o.ccooo
o.oooco
o.ocooo
c.oocoo
0.00000
0.00000
o.cccoo
0.00000
o.occoo
o.coooo
0.00000
16
o.oocoo
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
0.00000
0.00000
o.oooco
o.oooco
0.00000
0.00000
0.00000
0.00000
4.16667
4.16667
4.54545
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
o.cocco
0.00000
0.00000
o.oooco
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
17
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
4.16667
4.16667
4.54545
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.00000
o.cooco
0.00000
0.00000
o.coooo
o.cocoo
0.00000
0.00000
0.00000
0.00000
18
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
4.16667
4.16667
4.54545
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
C. 00000
0.00000
o.oooco
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
19
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
2.38095
2.38095
3.22581
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
20
0.72988
0.72988
0.10273
0.00000
0.00000
o.oooco/
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.13465
0.13465
0.07983
0.35316
0.35316
1.25893
0.00000
0.00000
0.00000
0.08182
0.08283
0.01902
0.00770
0.00683
0.00248
O.COOOO
O.OOCOO
0.00000
0.00128
0.00114
0.00041
21
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.52910
0.52910
1.34921
0.00000
0.00000
0.00000
0.55782
0.56471
0.13021
0.05247
0.04657
0.01696
O.OOCOO
O.OOOCO
O.OOCOO
0.00875
0.00776
0.00283
22
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.30197
0.30197
0.26230
0.82955
0.82955
1.65998
0.00000
0.00000
0.00000
0.14432
0.14611
0.00000
0.01358
0.01205
0.00000
0.00000
0.00000
0.00000
0.00226
O.C0201
0.00000
23
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.30913
0.31294
0.31198
0.02908
0.02581
0.04063
0.00000
0.00000
o.cocoo
0.00485
0.00430
0.00677
24
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.90110
0.91223
0.86809
0.08476
0.07523
0.11306
O.OCOOO
o.oocoo
0.00000
0.01413
0.01254
0.01884
-------
TABLE B-5.1
Scenario 3, 5, and 5a Solutions to the ORBES Energy Demand Model, Year 2000*
1 coal mining
2 crude petroleum, gas
3 shale oil
4 gasified coal
5 solvent-refined coal
6 ref'd petroleum products
7 natural gas utilities
8 central station elec.
9 fossil electric utilities
10
11
12
13 ore-reduction feedstocks
14 chemical feedstocks
15 water transport
16 air transport
17 truck, bus transport
18 rail transport
19 auto transport
20 misc. thermal uses
21 water heat
22 space heat
23 air-conditioning
24 misc. elec. power uses
25 agriculture
26 mining
27 construction
28 meat products
29 food exc. meat products
30 apparel and misc text, prod •
31 misc fabricated text, prod
32 logging and misc. wood prod
33 misc paper prod and publ.
34 paper mills
35 paperboard mills
36 paperboard containers
37 industrial org-inorg chem.
38 ag and misc chem.
39 plastic and synthetic resins
40 paving and asphalt
41 rubber and misc plastic prod
42 glass, stone, and clay prod
43 blast and basic steel prod
44 iron and steel found, and forging
45 other primary metal manufac.
46 nonfer. forge, cast, and rolling
47 fabricated metal containers
48 industrial and farm machinery
final demand
s3 s5 s5a
total production total consumption
s3 s5 s5a s3 s5 s5a
0
0
0
o'
0
0
0
0
0
0
0
0
0
26
0
0
0
0
506
0
127
550
86
221
1290
26
21335
3239
9800
846
4267
1666
1731
67
1
40
336
2322
26
3
1601
242
180
24
0
113
1992
6558
0
0
0
0
0
0
0
0
0
0
0
0
0
24
0
0
0
0
465
0
127
550
139
221
1202
24
19225
3060
9209
766
3876
1507
1570
61
1
36
304
2103
24
3
1453
213
162
22
0
102
1786
5883
0
0
0
0
0
0
0
0
0
0
0
0
0
31
0
0
0
0
608
0
127
550
139
221
1443
31
25175
3544
10814
989
4972
1954
2020
79
2
47
393
2716
31
4
1869
297
212
28
0
133
2367
7795
17137
612
. 0
0
0
2192
2412
2216
1884
0
0
0
388
701
21
49
79
32
532
1583
151
960
200
920
13522
1286
20251
3080
16960
568
2135
34:9
5967
792
435
1422
5275
6286
2402
540
8551
6708
18986
3084
1249
6165
12020
20216
18601
650
0
0
0
2128
2769
2302
2057
0
0
0
351
637
19
44
73
30
489
1947
218
1063
275
871
12622
1165
18337
2901
15907
516
1939
3118
5433
721.
398
1302
4832
5724
2182
489
7756
6075
17170
2785
1128
5565
10874
18239
23422
805
0
0
0
2707
3351
2797
2552
0
0
0
461
826
24
57
92
38
638
2519
244
1215
315
1067
15112
1523
23984
3389
18787
666
2492
4053
6978
927
505
1648
6160
7346
2823
640
10068
7939
22554
3672
1483
7322
14236
24056
7567
4869
0
0
0
3312
1589
1680
2216
0
0
0
388
701
21
49
79
32
532
2132
227
1111
199
935
11781
2656
24992
4026
15010
2834
5112
4425
5422
1343
686
1467
4520
5651
1782
395
5407
3733
9698
2330
2976
4101
10490
13517
8214
5167
0
0
0
3215
1825
1745
2057
0
0
0
351
637
19
44
73
30
489
1947
218
1063
275
871
10997
2407
22660
3792
14078
2575
4643
4012
4939
1223
628
1343
4244
5188
1619
358
4905
3381
8771
2104
2689
3702
9493
12198
10343
6397
0
0
0
4090
2208
2120
2552
0
0
0
461
826
24
57
92
38
638
2519
244
1215
315
1067
13166
3145
29638
4430
16627
3324
5967
5215
6343
1572
798
1700
5411
6658
2094
468
6368
4418
11522
2774
3533
4871
12428
16088
*Sectors 1-24 in trillions of Btu's; industries 25-67 in millions of 1967 dollars.
89
-------
TABLE B-5.1 (continued)
final demand
s3 s5 s5a
total production total consumption'
s3 s5 s5a s3 s5 s5a
49 elec. equipment and components
50 truck, bus, and auto manufac.
51 misc transport, equipnent
52 misc manufac.
53 railroads
54 misc transport, and communication
55 motor freight transport.
56 water transport.
57 air transport.
58 wholesale trade
59 retail trade
60 finance and insurance
61 real estate
62 hotels, lodging, and amusements
63 misc business and personal serv.
64 advertising
65 auto repair
66 medical and educational serv.
67 nonprofit organizations
6042
7001
6132
2643
786
4087
1246
327
866
7460
19273
7015
20143
3690
4639
47
2259
9757
2735
5430
6243
5500
2385
708
3645
1129
308
782
6828
17640
6231
17893
3278
4120
42
2007
8667
2430
7159
8400
7288
3114
928
4902
1457
360
1019
8586
22182
8468
24317
4454
5600
. 57
2727
11779
3302
18528
13668
5806
3008
3633
7328
4414
807
1003
13116
21966
10108
19142
5733
7264
2165
3181
9279
3497
16690
12203
5213
2715
3346
6601
4033
758
906
11977
20102
9054
17144
5146
6538
1980
2854
8245
3114
21987
16384
6897
3551
4327
8734
5178
927
1181
15246
25374
12138
22967
6873
8667
2531
3807
11200
4212
12694
11735
7862
4347
3627
7323
4413
806
1003
14495
22121
11688
28458
7080
9804
4034
3719
10003
3100
11442
10478
7060
3924
3346
6601
4033
758
906
13241
20251
10478
25491
6359
8834
3694
3339
8889
2761
15074
14068
9341
5133
4327
8734
5178
927
1181
16855
25563
14047
34149
8493
11711
4723
4453
12075
3733
*Sectors 1-24 in trillions of Btu's; industries 25-67 in millions of 1967 dollars.
90
-------
TABLE B-5.2
Scenario 3, 5, and 5a Solutions to the ORBES Energy Demand Model, Year 2000:
Associated Transaction Matrices (trillions of Btu's)
10
11
12
1
85.3
92.6
116.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
6
0.0
0.0
0.0
2382.5
2312.8
2942.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
7
0.0
0.0
0.0
2486.3
2854.6
3454.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
192.7
200.2
243.3
2215.7
2301.8
2797.4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
9
5216.8
5696.6
7069.4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
313.9
342.8
425.4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
10
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
11
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
c.o
0.0
12
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
c.o
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-------
TABLE B-5.2 (continued)
10
11
12
13
1039.4
939.8
1235.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
107.7
97.3
127.9
c.o
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
14
70.1
63.7
62.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
476.5
433.5
561.4
154.2
140.2
181.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
15
0.0
c.o
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
86.2
80.9
99.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
16
0.0
0.0
0.0
0.0
0.0
0.0
0.0
o.n
0.0
0.0
0.0
0.0
0.0
0.0
0.0
203.0
183.3
239.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
17
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
328.6
304.5
381.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
c.o
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
18
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
c.o
0.0
0.0
133.9
123.3
159.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
19
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1267.0
1163.7
1518.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
20
1155.7
1421.0
1838.9
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
213.2
262.2
339.3
559.2
687.6
889.8
143.8
176.8
228.8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
21
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
c.o
0.0
0.0
0.0
0.0
80.0
115.2
129.2
93.6
134.8
151.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
O.C
0.0
0.0
0.0
22
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
289.8
321.0
366.8
796.1
881.8
1007.8
153.7
170.3
194.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
23
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
68.5
94.3
108.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
24
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
919.6
871.1
1066.5
0.0
0.0
0.0
O.C
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-------
TABLE B-5.3
Scenario 3, 5, and 5a Solutions to the CRBES Energy Demand Model, Year 2000:
Associated Technical Coefficient Matrices' (dimensioned Btu's input per Btu's output)
10
11
12
1
0.00498
0.00498
0.00498
0.00000
O.OOOCO
0.00000
0.00000
0.00000
0.00000
0.00000
O.OOOCO
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
c.coooo
o.coooo
o.oocoo
0.00000
o.oocoo
0.00000
0.00000
O.OOOCO
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
2
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
3
0.00000
0.00000
0.00000
0.00000
0.00000
0.00300
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
0.00000
O.OOOCO
O.OOOCO
0.00000
0.00000
0.00000
0.00000
o.oococ
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
4
0.00000
o.oocoo
0.00000
0.00000
0.00000
o.cccoo
0.00000
O.OOOCO
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
5
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
6
0.00000
0.00000
0.00000
1.08696
1.08696
1.08696
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
C. 00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
7
0.00000
0.00000
0.00000
1.03093
1.03093
1.03093
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
O.OOOCO
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
8
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.08696
0.08696
0.08696
1.00000
1.00000
1.00000
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.00000
O.OOOCO
0.00000
0.00000
9
2.76968
2.76968
2.76968
0.00000
0.00000
o.coooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.16667
0.16667
0.16667
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
10
0.00000
o.ooooc
0.00000
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
11
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
12
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
O.OOOCO
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
O.OOOCO
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
-------
TABLE B-5.3 (continued)
10
11
12
13
2.67685
2.67685
2.67685
0.00000
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocco
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
0.27725
0.27725
0.27725
0.00000
o.oooco
0.00000
0.00000
0.00000
o.ocooo
0.00000
o.oocoo
0.00000
0.00000
o.oocoo
0.00000
14
0.10000
0.10000
0.10000
0.00000
o.coooo
0.00000
o.oooco
0.00000
0.00000
0.00000
o.cocoo
0.00000
0.00000
0.00000
0.00000
0.68COO
0.68COO
0.68000
0.22COO
0.22000
0.22000
0.00000
0.00000
o.oocoo
0.00000
o.oocco
o.oocoo
0.00000
o.cocoo
o.coooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
15
0.00000
0.00000
0.00000
o.oocoo
o.oocco
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.cocoo
0.00000
4.16667
4.16667
4.16567
0.00000
0.00000
o.cccoo
0.00000
0.00000
o.coooo
o.ococo
o.ocooo
o.oocoo
o.oocoo
o.cooco
o.oocco
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
16
0.00000
o.ooooc
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
0.00000
o.oocoo
4.16667
4.16667
4.16667
0.00000
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
o.oocoo
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
17
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
4.16667
4.16667
4.16667
0.00000
0.00000
o.oooco
0.00000
o.oooco
0.00000
0.00000
o.oooco
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
18
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
4.16667
4.16667
4.16567
o.coooo
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
o.oooco
C 00000
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
0.00000
0.00000
0.00000
19
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
2.38095
2.38095
2.38095
0.00000
0.00000
0.00000
o.ooooc
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
20
0.72988
0.72988
0.72988
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.13465
0.13465
0.13465
0.35316
0.35316
0.35316
0.09080
0.09080
0.09030
0.00000
o.cocoo
0.00000
0.00000
o.oocoo
o.coooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
21
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.52910
0.52910
0.52910
0.61905
0.61905
0.61905
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
22
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.30197
0.30197
0.30197
0.82955
0.62955
0.82955
0.16017
0.16017
0.16017
O.OOCOO
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
23
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.34305
0.34305
0.34305
o.coooo
o.oooco
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
24
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
1.00000
1.00000
1.00000
o.oocoo
0.00000
0.00000
o.oocoo
0.00000
0.00000
o.coooo
o.coooo
0.00000
0.00000
0.00000
0.00000
-------
TABLE B-6.1
Scenario 3, 5, and 6 Solutions to the ORBES Energy Demand Model, Year 2000*
final demand
s3 s5 s6
total production
s3 s5 s6
total consumption
s3 s5 56
1 coal mining
2 crude petroleum, gas
3 shale oil
4 gasified coal
5 solvent-refined coal
6 ref'd petroleum products
7 natural gas utilities
8 central station elec.
9 fossil electric utilities
10
11
12
13 ore-reduction feedstocks
14 chemical feedstocks
15 water transport
16 air transport
17 truck, bus transport
18 rail transport
19 auto transport
20 misc. thermal uses
21 water heat
22 space heat
23 air-conditioning
24 misc. elec. power uses
25 agriculture
26 mining
27 construction
28 meat products
29 food exc. meat products
30 apparel and misc text, prod
31 misc fabricated text, prod
32 logging and misc. wood prod
33 misc paper prod and publ.
34 paper mills
35 paperboard mills
36 paperboard containers
37 industrial org-inorg chera.
38 ag and misc chem.
39 plastic and synthetic resins
40 paving and asphalt
41 rubber and misc plastic prod
42 glass, stone, and clay prod
43 blast and basic steel prod
44 iron and steel found, and forging
45 other primary metal manufac.
46 nonfer. forge, cast, and rolling
47 fabricated metal containers
48 industrial and farm machinery
0
0
0
0
0
0
0
0
0
0
0
0
0
26
0
0
0
0
506
0
127
550
86
221
1290
26
21335
3239
9800
846
4267
1666
1731
67
1
40
336
2322
26
3
1601
242
180
24
0
113
1992
6558
0
0
0
0
0
0
0
0
0
0
0
0
0
24
0
0
0
0
465
0
127
550
139
221
1202
24
19225
3060
9209
766
3876
1507
1570
61
1
36
304
2103
24
3
1453
213
162
22
0
102
1786
5883
0
0
0
0
0
0
0
0
0
0
0
0
0
14
0
0
0
0
271
0
127
550
78
148
1291
26
21346
3241
9805
846
4270
1667
1732
68
1
40
336
2323
26
3
1602
243
180
24
0
113
1993
6562
17137
612
0
0
0
2192
2412
2216
1884
0
0
0
388
701
21
49
79
32
532
1583
151
960
200
920
13522
1286
20251
3080
16960
568
2135
3439
5967
792
435
1422
5275
6286
2402
540
8551
6708
18986
3084
1249
6165
12020
20216
18601
650
0
0
0
2128
2769
2302
2057
0
0
0
351
637
19
44
73
30
489
1947
218
1063
275
871
12622
1165
18337
2901
15907
516
1939
3118
5433
721
398
1302
4832
5724
2182
489
7756
6075
17170
2785
1128
5565
10874
18239
11543
645
0
0
0
1831
3044
1403
1018
0
0
0
361
679
20
48
75
32
297
1716
226
1062
185
700
13696
1226
20072
3080
16959
565
2126
3367
5862
111
417
1356
5133
6255
2305
539
8613
6523
17707
2819
1189
5919
11578
19818
7567
4869
0
0
0
3312
1589
1680
2216
0.
0
0
388
701
21
49
79
32
532
2132
227
1111
199
935
11781
2656
24992
4026
15010
2834
5112
4425
5422
1343
686
1467
4520
5651
1782
395
5407
3733
9698
2330
'2976
4101
10490
13517
8214
5167
0
0
0
3215
1825
1745
2057
0
0
0
351
637
19
44
73
30
489
1947
218
1063
275
871
10997
2407
22660
3792
14078
2575
4643
4012
4939
1223
628
1343
4244
5188
1619
358
4905
3381
8771
2104
2689
3702
9493
12198
5097
5129
0
0
0
2767
2006
1063
1403
0
0
0
361
679
20
48
75
32
297
2039
226
1102
185
769
11932
2533
24803
4025
15010
2819
5090
4332
5329
1318
659
1399
4508
5670
1710
395
5448
3630
9046
2130
2834
3937
10108
13254
*Sectors 1-24 in trillions of Btu's; industries 25-67 in millions of 1967 dollars.
95
-------
TABLE B-6.1 (continued)
final demand
s3 s5 s6
total production total consumption
s3 s5 s6 s3 s5 s6
49 elec. equipment and components
50 truck, bus, and auto manufac.
51 misc transport, equipment
52 misc manufac.
53 railroads
54 misc transport, and communication
55 motor freight transport.
56 water transport.
57 air transport.
58 wholesale trade
59 retail trade
60 finance and insurance
61 real estate
62 hotels, lodging, and amusements
63 misc business and personal serv.
64 advertising
65 auto repair
66 medical and educational serv.
67 nonprofit organizations
6042
7001
6132
2643
786
4087
1246
327
866
7460
19273
7015
20143
3690
4639
47
2259
9757
2735
5430
6243
5500
2385
708
3645
1129
308
782
6828
17640
6231
17893
3278
4120
42
2007
8667
2430
6045
7004
6135
2645
823
4089
1127
327
861
7464
19283
7018
20154
3692
4641
47
2260
9763
2737
18528
13668
5806
3008
3633
7328
4414
807
1003
13116
21966
10108
19142
5733
7264
2165
3181
9279
3497
16690
12203
5213
2715
3346
6601
4033
758
906
11977
20102
9054
17144
5146
6538
1980
2854
8245
3114
18304
13633
5792
2989
3594
7228
3903
772
995
12950
21902
10016
19025
5666
7157
2124
3124
9282
3492
12694
11735
7862
4347
3627
7323
4413
806
1003
14495
22121
11688
28458
7080
9804
4034
3719
10003
3100
11442
10478
7060
3924
3346
6601
4033
758
906
13241
20251
10478
25491
6359
8834
3694
3339
8889
2761
12549
11706
7844
4321
3594
7228
3903
772
995
14316
22065
11592
28287
7002
9671
3964
3655
10007
3096
*Sectors 1-24 in trillions of Btu's; industries 25-67 in millions of 1967 dollars.
96
-------
TABLE B-6.2
Scenario 3, 5, and 6 Solutions to the ORBES Energy Demand Model, Year 2000:
Associated-Transaction Matrices (trillions of Btu's)
10
11
12
1
85.3
92.6
57.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
6
0.0
0.0
0.0
2382.5
2312.8
1990.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
7
0.0
0.0
0.0
2486.3
2854.6
3138.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
192.7
200.2
122.0
2215.7
2301.8
1403.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
9
5216.8
5696.6
2819.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
313.9
342.8
169.7
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
10
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
11
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
12
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-------
TftBLE B-6.2 (continued)
vo
CO
10
11
12
13
1039.4
939.6
967.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
107.7
97.3
100.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
14
70.1
63.7
68.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
476.5
433.5
354.8
154.2
140.2
255.7
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
15
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
86.2
8C.9
99.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
16
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
203.0
183.3
108.7
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
17
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
328.6
304.5
226.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
18
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
133.9
123.3
144.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
19
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1267.0
1163.7
541.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
20
1155.7
1421.0
741.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
213.2
262.2
411.2
559.2
687.6
754.7
143.8
176.8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
21
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
39.7
80.0
115.2
258.9
93.6
134.8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
22
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
289.8
321.0
396.0
796.1
881.8
737.2
153.7
170.3
91.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
23
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
o.c
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
68.5
94.3
49.8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
24
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
919.6
871.1
700.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-------
10
10
12
TABLE B-6.3
Scenario 3, 5, and 6 Solutions to the ORBES Energy Demand Model, Year 2000:
Associated Technical Coefficient Matrices (dimensioned Btu's input per Btu's output)
1
0.00498
0.00498
0.00498
0.00000
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
C. 00000
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
2
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000.
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
3
o.ocooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
0.00000
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
4
o.oocoo
0.00000
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.onooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
5
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
6
0.00000
o.oooco
0.00000
1.08696
1.08696
1.08696
o.coooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
7
0.00000
0.00000
0.00000
1.03093
1.03093
1.03093
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
8
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.08696
0.08696
0.08696
1.00000
1.00000
1.00000
0.00000
0.00000
0.00000
0.00000
0.00000
O.OOGOO
0.00000
0.00000
0.00000
9
2.76968
2.76968
2.76968
o.oooco
o.oooco
0.00000
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
0.00000
o.coooo
0.16667
0.16667
0.16667
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
0.00000
0.00000
0.00000
o.onooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
10
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
11
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
12
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.cocoo
o.coooo
0.00000
0.00000
0.00000
o.cooco
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
-------
TABLE B-6.3 (continued)
O
o
10
11
12
13
2.67685
2.67685
2.S7685
0.00000
0.00000
0.00000
0.00000
o.occoo
o.oocco
0.00000
o.oooco
0.00000
o.oooco
o.coooc
o.ocooo
0.00000
o.oocco
0.00000
o.oocoo
o.oocoo
o.cooco
0.27725
0.27725
0.27725
O.CfiCOO
o.ocooo
o.ocooo
o.coooo
o.cocoo
o.cocco
o.oocoo
o.cocco
0.00000
0.00000
C. 00000
0.00000
14
0.10000
0.1COOO
0.10104
0.00000
0.00000
0.00000
0.00000
o.oocco
o.ocooo
0.0000.0
0.00000
0.00000
o.ocooo
o.oocoo
o.ocooc
0.68000
0.68000
0.52247
0.22000
0.22CCO
0.37649
O.OOOCO
0.00000
o.oooco
o.coooo
C.cncoo
C. 00000
o.oocoo
o.cooco
0.00000
0.00000
0.00000
o.coooo
0.00000
o.ococo
0.00000
15
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
4.16667
4.16667
5.00000
o.ocooo
o.coooo
o.coooo
o.oocoo
0.00000
0.00000
o.oocoo
o.cocco
o.oooco
0.00000
o.ocooo
o.oocco
o.coooo
0.00000
0.00000
0.00000
d. 00000
0.00000
16
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
4.16667
4.16667
2.25COO
0.00000
0.00000
o.oocco
o.oocoo
0.00000
o.occoo
c.cnooo
0.00000
o.oocoo
o.ocooo
0.00000
o.oocco
0.00000
0.00000
o.oooco
0.00000
o.ocooo
o.oocoo
17
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
4.16667
4.16667
3.00000
o.ocooo
o.oocoo
0.00000
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
18
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
0.00000
0.00000
0.00000
o.oocoo
o.oocoo
0.00000
0.00000
4.16667
4.16667
4.55000
0.00000
o.oocoo
0.00000
0.00000
0.00000
o.oocoo
o.coooo
o.ocooo
0.00000
o.cocoo
o.coooo
0.00000
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
19
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
c.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
2.38095
2.38095
1.82432
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
c.ooooc
0.00000
o.coooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
20
0.72988
0.72988
0.43192
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.ocooo
0.00000
o.oocoo
0.00000
0.13465
0.13465
0.23965
0.35316
0.35316
0.439S1
0.09080
0.09080
0.00000
0.00000
o.cooco
o.ocooo
o.coooo
0.00000
o.ococo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
21
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
o.oooco
0.00000
0.00000
0.17593
0.52910
0.52910
1.14683
0.61905
0.61905
0.00000
0.00000
o.oooco
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oooco
o.ocooo
22
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocco
o.oocoo
0.30197
0.30197
0.37279
0.82955
0.82955
0.69390
0.16017
0.16017
0.08621
o.cocoo
o.oocoo
o.oooco
0.00000
o.cooco
0.00000
o.ocooo
0.00000
0.00000
0.00000
0.00000
0.00000
23
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
o.oocco
0.00000
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
0.00000
0.00000
o.coooo
0.00000
o.ccooo
0.34305
0.34305
0.26954
o.oocco
o.occoc
o.ocooo
o.oooco
o.oocoo
o.oooco
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
24
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oocoo
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
0.00000
0.00000
o.cocoo
o.oocoo
0.00000
l.OOCOO
1.00000
1.00000
o.oooco
c.ooocc
0.00000
0.00000
0.00000
0.00000
o.oooco
0.00000
0.00000
o.oooco
0.00000
0.00000
-------
APPENDIX C
101
-------
TABLE C-l
Baseline Energy End Use, 1974
(in trillion Btu's)
1 Residential
2. Commercial
3 Industrial
4 Electric Power
5 Energy Conversion
6 Agriculture
7 Mining
8 Construction
9 Water transport
10 Air transport
11 Truck transport
12 Rail transport
13 Auto transport
14 Total Consumption
Coal
40.6
23.0
1344.1
3359.1
56.1
2.2
8.9
7.4
0.0
0.0
0.0
0.0
0.0
4841.5
Crude
Oil and
Gas
0.0
0.0
0.0
0.0
5428.7
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
5428.7
Refined
Petro-
leum
407.7
179.3
522.0
94.1
103.2
21.5
7.0
74.2
72.5
128.0
225.4
82.9
1268.7
3186.5
Natural
Gas
727.8
321.5
801.8
51.8
190.4
33.8
14.7
34.1
0.0
0.0
0.0
0.0
0.0
2175.9
Elec-
tricity
221.5
118.0
366.2
102.3
18.1
5.4
7.9
2.1
0.0
0.0
0.0
0.0
0.0
841.5
103
-------
TABLE C-2
Energy End Use, 1985, Scenario 1
(in trillion Btu's)
1 Residential
2 Commercial
3 Industrial
4 Electric Power
5 Energy Conversion
6 Agriculture
7 Mining
8 Construction
9 Water transport
10 Air transport
11 Truck transport
12 Rail transport
13 Auto transport
14 Total Consumption
Coal
15.1
17.3
1628.3
4908.1
139.3
1.0
15.0
11.6
0.0
0.0
0.0
0.0
0.0
6735.6
Crude
Oil and
Gas
0.0
0.0
0.0
0.0
5738.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
5738.2
Refined
Petro-
leum
314.5
170.2
583.7
290.7
32.1
20.4
7.3
95.4
86.0
152.7
270.7
100.3
1163.1
3287.2
Natural
Gas
690.6
377.0
903.7
14.9
71.1
40.8
16.2
42.4
0.0
0.0
0.0
0.0
0.0
2156.7
Elec-
tricity
307.3
183.0
550.1
167.8
19.6
9.1
10.5
3.1
0.0
0.0
0.0
0.0
0.0
1250.4
104
-------
TABLE C-3
Energy End Use, 2000, Scenario 1
(in trillion Btu's)
1 Residential
2 Connercial
3 Industrial
4 Electric Power
5 Energy Conversion
6 Agriculture
7 Mining
8 Construction
9 Water transport
10 Air transport
11 Truck transport
12 Rail transport
13 Auto transport
14 Total Consumption
Coal
2.6
19.9
2526.7
6312.8
202.7
0.2
31.1
36.6
0.0
0.0
0.0
0.0
0.0
9132.6
Crude
Oil and
Gas
0.0
0.0
0.0
0.0
5587.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
5587.5
Refined
Petro-
leum
183.7
129.9
620.2
377.3
18.8
13.6
5.6
128.2
87.5
203.1
332.2
136.2
1267.1
3503.6
Natural
Gas
529.6
390.1
875.4
14.7
49.1
36.5
14.8
50.6
0.0
0.0
0.0
0.0
0.0
1960.8
Elec-
tricity
436.3
327.1
827.7
228.8
22.1
15.8
15.0
5.3
0.0
0.0
0.0
0.0
0.0
1878.1
105
-------
TABLE C-4
Energy End Use, 1985, Scenario 2
(in trillion Btu's)
I Residential
2 Commercial
3 Industrial
4 Electric Power
5 Energy Conversion
6 Agriculture
7 Mining
8 Construction
9 Water transport
10 Air transport
11 Truck transport
12 Rail transport
13 Auto transport
14 Total Consumption
Coal
15.1
17.1
1613.7
4807.9
136.6
1.0
14.7
11.4
0.0
0.0
0.0
0.0
0.0
6617.4
Crude
Oil and
Gas
0.0
0.0
0.0
0.0
5737.4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
5737.4
Refined
Petro-
leum
314.5
170.2
583.5
290.7
32.1
20.4
7.3
95.4
86.0
152.7
270.5
100.3
1163.1
3286.8
Natural
Gas
690.6
376.9
903.5
14.9
71.0
40.8
16.2
42.4
0.0
0.0
0.0
0.0
0.0
2156.4
Elec-
tricity
307.3
183.0
549.9
167.8
19.6
9.1
10.5
3.1
0.0
0.0
0.0
0.0
0.0
1250.1
106
-------
TABLE C-5
Energy End Use, 2000, Scenario 2
(in trillion Btu's)
1 Residential
2 Commercial
3 Industrial
4 Electric Power
5 Energy Conversion
6 Agriculture
7 Mining
8 Construction
9 Water transport
10 Air transport
11 Truck transport
12 Rail transport
13 Auto transport
14 Total Consumption
Coal
2.6
19.5
2496.7
6182.6
198.7
0.2
30.5
36.3
0.0
0.0
0.0
0.0
0.0
8966.9
Crude
Oil and
Gas
0.0
0.0
0.0
0.0
5586.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
5586.6
Refined
Petro-
leum
183.7
129.9
620.1
377.3
18.8
13.6
5.6
128.2
87.4
203.1
332.1
136.1
1267.1
3503.1
Natural
Gas
529.6
390.0
875.2
14.7
49.0
36.5
14.8
50.6
0.0
0.0
0.0
0.0
0.0
1960.5
Elec-
tricity
436.3
327.1
827.5
228.7
22.1
15.8
15.0
5.3
0.0
0.0
0.0
0.0
0.0
1877.7
107
-------
TABLE C-6
Energy End Use, 2000, Scenario 2a
(in trillion Btu's)
1 Residential
2 Commercial
3 Industrial
4 Electric Power
5 Energy Conversion
6 Agriculture
7 Mining
8 Construction
9 Water transport
10 Air transport
11 Truck transport
12 Rail transport
13 Auto transport
14 Total Consumption
Coal
2.6
19.6
2504.0
7052.4
211.1
0.2
30.6
36.4
0.0
0.0
0.0
0.0
0.0
9856.8
Crude
Oil and
Gas
0.0
0.0
0.0
0.0
5640.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
5640.5
Refined
Petro-
leum
183.7
130.2
621.8
430.3
19.2
13.6
5.6
128.7
88.4
203.2
334.7
138.1
1267.2
3564.5
Natural
Gas
529.6
390.7
877.5
16.6
50.2
36.5
14.8
50.8
0.0
0.0
0.0
0.0
0.0
1966.7
Elec-
tricity
436.3
327.5
829.7
257.7
22.7
15.8
15.1
5.3
0.0
0.0
0.0
0.0
0.0
1910.1
108
-------
TABLE C-7
Energy End Use, 2000, Scenario 3
(in trillion Btu's)
1 Residential
2 Commercial
3 Industrial
4 Electric Power
5 Energy Conversion
6 Agriculture
7 Mining
8 Construction
9 Water transport
10 Air transport
11 Truck transport
12 Rail transport
13 Auto transport
14 Total Consumption
Coal
2.6
14.7
2126.3
5216.8
152.8
0.2
22.5
31.5
0.0
0.0
0.0
0.0
0.0
7567.4
Crude
Oil and
Gas
0.0
0.0
0.0
0.0
4868.8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
4868.8
Refined
Petro-
leum
160.8
112.1
546.8
317.8
13.1
11.9
4.2
126.8
86.2
203.0
328.6
133.9
1267.0
3312.2
Natural
Gas
444.3
325.6
685.1
10.8
33.9
31.2
10.9
47.7
0.0
0.0
0.0
0.0
0.0
1589.4
Elec-
tricity
375.7
288.0
763.7
203.3
17.0
14.0
13.8
4.0
0.0
0.0
0.0
0.0
0.0
1679.5
109
-------
TABLE C-8
Energy End Use, 1985, Scenario 4
(in trillion Btu's)
1 Residential
2 Commercial
3 Industrial
4 Electric Power
5 Energy Conversion
6 Agriculture
7 Mining
8 Construction
9 Water transport
10 Air transport
11 Truck transport
12 Rail transport
13 Auto transport
14 Total Consumption
Coal
32.0
22.5
1339.5
3647.8
97.6
2.1
8.8
8.1
0.0
0.0
0.0
0.0
0.0
5158.4
Crude
Oil and
Gas
0.0
0.0
0.0
0.0
6888.7
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
6888.7
Refined
Petro-
leum
374.3
201.3
572.4
222.0
32.6
24.4
6.9
94.7
89.2
159.9
279.9
102.4
1395.6
3555.6
Natural
Gas
779.0
428.0
1316.0
12.9
115.0
44.8
25.0
47.9
0.0
0.0
0.0
0.0
0.0
2768.7
Elec-
tricity
259.0
156.8
514.1
132.1
17.4
6.9
9.8
2.3
0.0
0.0
0.0
0.0
0.0
1098.3
110
-------
TABLE C-9
Energy End Use, 2000, Scenario 4
(in trillion Btu's)
1 Residential
2 Commercial
3 Industrial
4 Electric Power
5 Energy Conversion
6 Agriculture
7 Mining
8 Construction
9 Water transport
10 Air transport
11 Truck transport
12 Rail transport
13 Auto transport
14 Total Consumption
Coal
0.5
2.8
1304.1
4467.4
79.9
0.0
4.4
6.2
0.0
0.0
0.0
0.0
0.0
5865.4
Crude
Oil and
Gas
0.0
0.0
0.0
0.0
11044.4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
11044.4
Refined
Petro-
leum
161.8
113.9
512.6
3.4
12.2
11.8
3.4
126.0
94.7
370.4
366.7
138.9
1716.6
3632.4
Natural
Gas
1093.2
820.4
2792.7
291.1
176.8
72.5
53.9
89.3
0.0
0.0
0.0
0.0
0.0
5390.0
Elec-
tricity
290.6
221.2
692.0
169.2
13.4
7.4
12.4
2.2
0.0
0.0
0.0
0.0
0.0
1408.4
111
-------
TABLE O10
Energy End Use, 2000, Scenario 5
(in trillion Btu's)
1 Residential
2 Conmercial
3 Industrial
4 Electric Power
5 Energy Conversion
6 Agriculture
7 Mining
8 Construction
9 Water transport
10 Air transport
11 Truck transport
12 Rail transport
13 Auto transport
14 Total Consumption
Coal
2.4
17.7
2254.6
5696.6
182.2
0.2
27.5
32.7
0.0
0.0
0.0
0.0
0.0
8213.8
Crude
Oil and
Gas
0.0
0.0
0.0
0.0
5167.4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
5167.4
Refined
Petro-
leum
182.3
117.2
561.7
347.7
17.3
12.7
5.1
115.6
80.9
183.3
304.5
123.3
1163.7
3215.3
Natural
Gas
529.2
351.8
792.2
13.7
45.0
34.1
13.3
45.7
0.0
0.0
0.0
0.0
0.0
1824.9
Elec-
tricity
436.3
294.8
747.8
212.5
20.3
14.7
13.6
4.7
0.0
0.0
0.0
0.0
0.0
1744.8
112
-------
TABLE C-ll
Energy End Use, 2000, Scenario 5a
(in trillion Btu's)
1 Residential
2 Commercial
3 Industrial
4 Electric Power
5 Energy Conversion
6 Agriculture
7 Mining
%
8 Construction
9 Water transport
10 Air transport
11 Truck transport
12 Rail transport
13 Auto transport
14 Total Consumption
Coal
3.1
22.8
2938.2
7069.4
230.3
0.2
35.9
42.8
0.0
0.0
0.0
0.0
0.0
10342.6
Crude
Oil and
Gas
0.0
0.0
0.0
0.0
6396.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
6396.6
Refined
Petro-
leum
187.2
153.3
726.3
431.3
21.9
15.2
6.6
151.2
99.1
239.0
381.6
159.5
1518.1
4090.1
Natural
Gas
530.7
459.9
1026.2
16.6
57.0
40.8
17.4
59.7
0.0
0.0
0.0
0.0
0.0
2208.4
Elec-
tricity
436.3
386.1
972.7
258.3
25.6
17.6
17.7
6.2
0.0
0.0
0.0
0.0
0.0
2120.5
113
-------
TABLE C-12
Energy End Use, 2000, Scenario 6
(in trillion Btu's)
1 Residential
2 Commercial
3 Industrial
4 Electric Power
5 Energy Conversion
6 Agriculture
7 Mining
8 Construction
9 Water transport
10 Air transport
11 Truck transport
12 Rail transport
13 Auto transport
14 Total Consumption
Crude Refined
Oil and Petro- Natural
Coal Gas leum Gas
1.4
10.0
1685.8
3262.8
95.3
0.2
14.4
27.1
0.0
0.0
0.0
0.0
0.0
5097.1
0.0
0.0
0.0
0.0
5128.8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
5128.8
227.4
167.0
657.6
447.9
21.8
16.5
8.0
100.1
99.0
108.7
226.3
144.6
541.6
2766.8
519.6
377.9
934.6
6.5
40.1
32.7
14.7
80.5
0.0
0.0
0.0
0.0
0.0
2006.4
Elec-
tricity
201.1
192.0
518.4
126.9
6.0
9.3
8.7
1.0
0.0
0.0
0.0
0.0
0.0
1063.5
114
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REFERENCES
1. Stone, R. A., et al. A Programme for Growth, Part III: Input-Output
Relationships, 1954-66. Chapman and Hall, 1963.
2. Bureau of Economic Analysis. Definitions and Conventions of the 1967
Input-Output Study. U.S. Department of Commerce, Washington, D.C.,
1974.
3. Bullard, C.W., B. Hannon, and R. Herendeen. Energy Flow Through the
United States Economy. University of Illinois Press, Urbana, Illinois,
1974.
4. Bullard, C.W., and A. Sebald. "Effects of Parametric Uncertainty and
Technological Change in Input-Output Models," Review of Economics and
Statistics, 59: 75-81, 1977.
5. Morrison, W., and P. Smith, Simulating the Urban Economy: Experiments
with Input-Output Analysis. Pion, London, 1974.
6. Stone, R. A., et al., A Programme for Growth.
7. Moore, F. T., and J. W. Peterson. "Regional Analysis: An Interindustry
Model of Utah," Review of Economics and Statistics, 37:368-381, 1955.
8. CONAES. "U.S. Energy Demand: Some Low Energy Futures," Science, 200:
142-152, 1978.
9. Behling, D. J., et al. A Two-Level Iterative Model for Estimating
Inter-Fuel Substitution Effects. In: Proceedings of the 1975 Summer
Computer Simulation Conference, San Francisco, California, 1975.
10. CONAES, U.S. Energy Demand.
11. Miernyk, W. H., et al. Simulating Regional Economic Development. Re-
gional Research Institute, Morgantown, West Virginia, 1969.
12. Knecht, Ronald L., and Clark W. Bullard. End Uses of Energy in the U.S.
Economy, 1967. CAC Document No. 145, University of Illinois, Urbana,
Illinois, September 1975.
13. Von Neumann, J. "A Model of General Equilibrium," Review of Economic
Studies, 13(l):l-9, 1945.
115
-------
14. Von Neumann, J., A Model of General Equilibrium.
15. Memo to ORBES Core Team from Tom Milke, dated October 26, 1978.
16. Von Neumann, J., A Model of General Equilibrium.
17. Hermelee, A. Supporting Data: Analysis of the National Energy Plan
Through the Year 2000. ERDA, Office of the Assistand Director for Sys-
tem Analysis, Brookhaven National Laboratory, 1977.
18. Hermelee, A., Supporting Data.
19. CONAES, U.S. Energy Demand.
20. Statistical Abstract of the U.S., 1975.
21. Hermelee, A., Supporting Data.
22. Cherniavsky, E., et al. Brookhaven Energy System Optimization Model:
Methodology and Documentation. Version 2.1, Brookhaven National Labora-
tory, Upton, New York, 1978.
23. CONAES, U.S. Energy Demand.
24. CONAES, U.S. Energy Demand.
25. CONAES, U.S. Energy Demand.
26. Nagarvala, P. J., et al. Regional Energy System for Planning and Opti-
mization of National Energy Scenarios (RESPONS). Bechtel Corporation,
San Francisco, California, 1976.
27. Hermelee, A., Supporting Data.
28. CONAES, U.S. Energy Demand.
29. Von Neumann, J., A Model of General Equilibrium.
30. CONAES, U.S. Energy Demand.
31. CONAES, U.S. Energy Demand.
32. CONAES, U.S. Energy Demand.
33. Von Neumann, J., A Model of General Equilibrium.
34. CONAES, U.S. Energy Demand.
35. CONAES, U.S. Energy Demand.
116
-------
36. Bullard, C., D. Amado, D. Putnam, and A. Sebald. Stochastic Analysis of
Uncertainty in a U.S. Input-Output Model. CAC Document 208, University
of Illinois, Urbana, Illinois, 1976.
37. Sebald, A. An Analysis of the Sensitivity of Large Scale Input-Output
Models to Parametric Uncertainties. CAC Document 122, University of
Illinois, Urbana, Illinois, 1974.
38. Sherman, J., and W. Morrison. "Adjustment of an Inverse Matrix Corres-
ponding to a Change in One Element of a Given Matrix," Annals of Mathe-
matical Statistics, 21(1):124-126, March 1950.
39. Gilmore, D. J. An Efficient Algorithm for Solving the Linear Input-
Output Equation with an Extension to the Nonlinear Input-Output Model.
CAC Document 235, University of Illinois, Urbana, Illinois, 1977.
117
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