1__'
I
VD,
t
MIT UEL SSTITUTON T ~ THE C NISTTON
Ti
OF ENERGY IN THE UNITED STATES
Part I: Residential and Commercial Sector
Martin L.
Report
aughman
Paul L. Joskow
in association with
Frederick S.Zerhoot
MIT-EL 74-002
May 25, 1974.
P
R
E F
This is the first in a
A
C
E
eries of reports presenting the results
of our research on energy demand and supply in the U.S. economy.
The
research has been funded by the National Science Foundation under
Grant GI-#39150.
This report presents our analysis of residential
and commercial consumption patterns in the United States for 1960-1972
with a set of projections to 1980.
We have attempted to set out in
detail our methodology, data availability, and most importantly,
problems
that we have run into
and how we have tried to deal
This report should be viewed as work in progress.
with them.
We welcome comments
and suggestions.
Future reports will deal with consumption patterns in the
industrial
and transportation sectors, our electricity supply model, and our fossil
fuel supply models.
Appendix A to this report discusses the direction
of our efforts in more detail.
Considerable time and
the research reported herein.
fort from a number of individuals went into
We would like
to thank especially Bruce
Stangle and Kevin Lloyd of the M.I.T. Gas Project and Mr. Walt Maling of
National Bureau of Economic Research for their assistance in the use of the
TROLL System.
HLB
PLJ
May 26, 1974.
ii
ABSTRACT
The effects of alternative public policies on the consumption and
prices of various forms of energy in the United States depends critically
on the nature of consumer demands for fuels and the supply characteristics
of these fuels. Previous work on energy demand has tended to concentrate
on the demand for a particular fuel as determined by standard economic
variables such as the price of the fuel, income levels, sometimes the
price of alternative fuels, and other demographic characteristics of the
consuming population. In this work the consumer decision making process
is viewed as being composed of two steps. First, the consumer decides
that he wants a particular service and, secondly, seeks to find the fuel
that will provide this service most cheaply. This view leads us to concentrate on substitution possibilities among fuels for particular services
rather than own-price elasticities for a particular fuel.
This paper presents results for the determinants of energy consumption in the residential and commercial sector in the United States.
First, a discussion of the conceptual model used for fuel choice decisions
is presented. Then, empirical results are given for appliance choices in
the residential sector for four selected appliances and for the "fuel-split"
of aggregate energy consumption among the three fuels used in the residential
and commercial sector. The own-price and cross-price elasticities are
estimated and discussed.
Next, the paper discusses the determinants of total energy demand
in the residential and commercial sector and presents empirical results
for a simple flow adjustment model. The long run price elasticity of total
demand in this sector is estimated to be about -0.5 while the short run
(one year) value is -0.15. Finally, the estimated relationships are used
to make projections to 1980 for alternative price scenarios. These results
show that significant consumption responses to changing fuel prices can be
expected and, further, that some states are much more dramatically impacted
than others.
iii
TABLE OF CONTENTS
page
Preface
i
Abstract ...............................................
ii
Table of Contents
iii
List
List
I.
........................................
......................................
of Tables ........................................
of Figures .........................................
vi
Introduction .........................................
1
Fuel Choice Decisions by Individual Decision-Makers ..
Example:
II.
III.
The Determinants of Fuel Choice
Residential Appliance Choices
.
.....
8
....................
13
...........
.............. 15
................................
17
.
........................................
23
The Model of Fuel Consumption in the Residential and
Commercial Sector - Fuel Split Equations .
IV.2
4
Fuel Consumption Shares in the Residential and Commercial
Sector
IV.1
.....................
Producer Choice ....
Estimation Results
IV.
v
Data Sources and Derivation
........
25
.............................
26
Natural Gas ........................................
Electricity
.
27
....................................... 27
Oil ...............................................
Miscellaneous
......................................
28
IV.3
Estimation Problems and Procedures ......................
30
IV.4
Estimated Results (Model # 1) ...........................
31
Model # 2 ..........................................
33
iv
TABLE OF CONTENTS
page
V.
Total Energy Consumption in the Residential and
Commercial Sector ..
Estimation Results
....................
..........
......
44
.................
47
2 .........................
Model
...
.
.......
VI. Simulations and Analysis
50
53
Analysis with Model (Predictions for 1970-1972) ........
53
Analysis with Model (Predictions to 1980) ..............
55
VII.
Conclusions
VIII. References
......
...............
........................
.................
64
-
66
A. APPENDIX A
Introduction .............
A.1
...............
Fossil Fuel Sector Economics ..
......
.......
Development Investment in Fossil Fuel Supply ....
Investment
A.2
inExploration
.......................
Investment and Pricing in Electricity Supply .......
The Load Duration Curve
.
Capacity Expansion .........
A.3
A-1
A-6
A-6
A-9
A-11
........................
A-12
.............
A-12
The Environmental Effects of Energy Supply and
Consumption ........................
A-19
V
I
LIST OF TABLES
I
I
I
III
.
page
TABLE 1:
Elasticities Evaluated at the Sample Means
........
21
TABLE 2:
Residential and Commercial Energy Consumption ......
24
TABLE 3:
Regions for Dummy Variables
.......................
34
TABLE 4:
Elasticities of Market Shares with Respect to
Price
* ......
.................................
Comparison of Actual vs. Predicted for Total
TABLE 5:
United States
U
43
........................
54
Simulation Results for 1980, Total United States
TABLE 6:
(x 1015 Btu's)
...............................
61
Simulations Results for 1980 (Selected States)
TABLE 7:
................................
1015 Btu's)
Btu's)........
..
(x (x
10
62
TABLE A.1:
Outputs of the Energy - Environmental Impact
Model (per 1012 Btu's input)
..................
A-20
.
vi
LIST OF FIGURES
V.
L
page
FIGURE l.a:
I
Probability of Choosing Fuel # 1 vs. Cost
of Fuel Alternatives
....................
FIGURE
1
:
Natural Gas 1969 - (Btu's x 1012)
FIGURE
2
:
Oil 1969 - (Btu's x 1015) .....................
FIGURE
3
:
Electricity 1969 (Btu's
FIGURE
4
:
Market Shares vs. Gas Price
FIGURE
5
FIGURE
6
FIGURE
7
1012)
.............
......
10
36
37
I
[
fl
38
...................
39
0
Market Shares vs. Electricity Price ...........
40
:
Market Shares vs. Oil Price
41
:
Time Response of Total Consumption
...................
............
52
U
U
FIGURE
8
:
Natural Gas 1972 - (Btu's - 1015)
FIGURE
9
:
Oil 1972 - (Btu's x 1015)
.............
56
.....................
57
FIGURE 10 :
Electricity 1972 - (Btu's x 1012) ..............
58
FIGURE 11 :
Total Energy 1972 - (Btu's x 1015) .............
59
U
Li
FIGURE
A.1:
Overall Model Structure - Interfuel Competition
Population, Macro-economic Inputs .........
A-4
FIGURE A.2:
Broad Energy Flow Diagram (Primary Fuel Suppliers) A-5
FIGURE A.3:
Returns to Exploration (Re ) ...................
A-10
FIGURE A.4:
Load Duration Data for 1971 (New England Power
Exchange) ................................
A-13
FIGURE A.5:
Average Costs of Production
..................
A-15
FIGURE A.6:
Optimum Capacity Mix
..........................
A-16
FIGURE A.7:
Interfuel Model with Regional Electricity
Capability
FIGURE A.8:
..............................
Energy-Environment Interactions Policy Model
...--
A-18
A-22
1
t.
0
-1-
INTERFUEL SUBSTITUTION IN THE CONSUMPTION OF
ENERGY IN THE UNITED STATES
INTRODUCTION
The effects of alternative public policies on the consumption and prices
of various forms of energy in the United States depends critically on the nature
of consumer demands for fuels and the supply characteristics of these fuels.
Even before current domestic and international energy problems emerged,a
substantial body of econometric work seeking to estimate the supply and demand
characteristics of different fuels had developed.
On the demand side, the
work has tended to concentrate on the demand for a particular fuel in terms of
consumption per capita as determined by standard economic variables such as
the price of the fuel, income levels, sometimes the price of alternative fuels,
2
and other demographic characteristics of the consuming population 2 .
Work on
the supply side, although far less extensive than the demand work, has tended
to follow a similar methodology, i.e., the estimation of functions based on own
prices and other economic and demographic variables 3
We believe that much of the available work on the consumption side,
while extremely useful, suffers from two important methodological problems.
First, individuals, whether they be final consumers or producers of goods
and services, do not desire to consume a particular energy source per se,
but rather
desire particular services requiring an energy input.
energy input can often be
supplied by several fuels.
This
We believe that it
is useful to characterize the consumer decision-making process as being
composed of two steps.
First, the consumer decides that he wants a
Fisher and Kaysen, Anderson, MacAvoy and Pindyck are examples.
2See especially Anderson.
3 Klazzoom is an example.
-2P t."
II
I
particular service (let's say that it is heating) which defines some level
of energy usage and then seeks to find the fuel that will provide this
service most cheaply (broadly defined).
This view leads us to concentrate
9
I
on substitution possibilities among fuels for particular services rather
than on own-price elasticities for a particular fuel as has generally
been the case4 .
The second methodological problem involves the nature of the fuel
usage itself.
The decision to provide a particular service with a particular
fuel is essentially a durable goods decision.
cannot burn oil or natural gas, an
An electric space heater
3
5
oil heater cannot burn electricity
and requires a conversion investment to burn natural gas; an
electric
generating plant that burns natural gas cannot burn coal without substantial
conversion investments, etc.
The crucial decision the
is with regard to
I
the choice of the "burning technique" which defines the type of fuel and
~~~~~~~4~~~~~~~~~~~~~~~~~~~~~I
For example, a rise in the price of electricity will bring forth two types
of responses by residential consumers (other things equal). First, other
energy sources will now look relatively more attractive and some consumers
will switch from electricity usage (for heating) to the use of some other
fuel. Second, consumers will adjust their demands for electricity so as
to use their remaining electric appliances at lower utilization rates.
(The first response is a long-run response, the second may be both a
CohU
LWLU
L-&ULL
ImIl
onLL
-lA Lg-LuLLLL_-LLLL
LJoLLZse
CLLU
.
JL.LmaLeLZ
2
_f &:LLL
U.
:L..
L.LL
y
-4 A----A
orJ
LUeLL&-LY L;
for electricity indicate that the overall response to a 10 percent increase
in the real prices of electricity will be a 10 percent reduction in consumption of electricity (this is a long-run response and takes other prices
and the level of consumer income as a constant). However, about 75 percent
of this reduction in electricity consumption is shifted to the consumption
of other fuels
that the effective energy consumption elasticity (in
terms of
.energy savings) is probably only about .25, rather than 1.
It is extremely important, therefore, that the price elasticity estimates
obtained from demand studies of individual energy markets be interpreted
very carefully. These estimates normally give us some feeling for the
response of demand for a particular fuel to a change in its price, but no
t
idea of how much of that consumption is "shifted"
to other energy sources.
r
L
i
.'~~~~~~~~~~
.
I
-3-
utilization pattern for that fuel.
Once a burning technique has been
chosen, consumption of the fuel is pretty much defined (subject to the
ability to vary utilization rates).
In short, important consumer
responses for many energy usage categories are determined by decisions
about "burning techniques".
Primary responses to changes in energy
prices involve changes in the appliance stock determined by new consumers
and conversions of the existing capital stock to burn different fuels.
While there is some ability to respond to changing prices with increases
or decreases in the use of particular appliances, we believe that much of
the response to changing relative fuel prices is through "fuel switching".
Our efforts over the past 6 months have been directed toward analyzing
many aspects of interfuel substitutability at several levels of aggregation,
including residential appliance decisions, relative fuel consumptions within
the residential, commercial,and industrial sectors, and fuel choice by
electric utilities.
In this paper we present results for the determinants of
the distribution of aggregate energy consumption among different fuels for
the residential and commercial sector
and for appliance choices in the
residential sector for four selected appliances5 .
Before proceeding with the
particular empirical results, the next section of the paper traces out more
formally the basic conceptual model for fuel choice decisions that we have
been using.
5
The larger interfuel competition model of which this work is a part,
is outlined in Appendix A of this paper.
r,,.,s*slrpl'riL·LLYI···I01·
i--y
Lj·;q_l*U·YlkU·L·PIIIIWC·-·
___
-4-
FUEL CHOICE DECISIONS BY INDIVIDUAL DECISION-MAKERS
In this section we present the basic conceptual model of fuel choice
that we have been working with. The discussion is presented in the context of
consumer choice regarding the selection of the fuel to provide a particular
type of service at a particular level of capacity.
For convenience we shall
refer to this abstract service as "heating" and take the quantity of the
service desired as given exogenously.
.~~~~~~~~~~~~~~~~~~~~~~~~~
The individual is assumed to face a set of k alternative techniques
for providing heating services.
L
Each technique is associated with a different
fuel input (although this assumption is not really necessary).
Each alter-
native has associated with it a vector of characteristics x (xi for the i'th
technique) upon which individual choices are based.
Individual preferences
regarding heating mode are represented as a choice index I of the following
form:
I -I
(x)
+ z (x)
W~~~~~~~~~~~~~
+
where the c(x) are random disturbances with a probability distribution F(u)
The probability that an individual decision maker chosen at random
Et
from the population and faced with this set of alternatives will in fact
J.
choose alternative i is given by
Pi = Prob [I(xi) +
= Prob [C(xJ) -
(xi) > I(xj) + c(xj)]
for all i
(xi) < I(xi) - I(xj)] for all i
j
Let F(e1 ...sk) represent the cumulative joint distribution function of
the disturbances and let F
the i'th argument.
i
denote the derivative of F with respect to
L
L
Then we may write
r
L,.
i..
S
]
l
~
,ds
-:tS
rP
o
As
-5-
Pi '
f Fi
[E + I(x
(xi)
- I(Xl), .... + I(x)
-
I(x)
de
By choosing a particular distribution function F we can obtain a set of
estimated choice probabilities for each technique.
These estimates will
depend on the parameters of F and the functional form and parameters of
I.
If we assume that the e(xi) are independently and identically dis-
tributed random variables with the Weibull distribution1
-£
-e
Prob
[e(x i ) <c] = ee
Then we may write the probability that alternative i will be chosen as
eI(X
)
e (i i)
eI (Xi)
1
and thus, the relative odds of any two particular choices can be represented
in the following way:
log (/pj)
I(xi)
]
- I(xj)
This conditional or multivariate logit model can be estimated by maximum
likelihood techniques yielding estimates with desirable large sample
properties.
Suppose that there are two modes of heating an individual's home at the
required level and we assume that each alternative has a set of attributes:
1.
Cost per BTU (Capital cost + operating cost)
2.
Cleanliness index = H i
3.
Availability index = A i
C1
1 This assumption is iff with the assumption of the independence of
irrelevant alternatives.
--
I·--
IL"UC~VU.C-.·rUI3·I·IU··liN·C
I1C
-6-
Assume that the choice index I is linear in the attributes.
Then we may
write the probability that a randomly chosen individual will choose technique
1 as:
P1=
e 1 +
1 1|e
e
2 C1
e
8
l+e
3H1 +
81 + 82C 1 +
~
311 +
+
+e 61 +
4 Al
p=
+e
1
2(C2 C1 ) +
+
3(H
4A 1
2
C2 + 83H2 +
^
4(A22 -1)
4 A2
)
|
~~~~~~~~~~~~~~(6)
.
which is the binomial logit response function.
One important consideration that this approach ignores is the
possibility that "household" or "individual" characteristics enter directly
into the choice index i.
Let us call the set of individual characteristics
a and redefine the choice index as:
I
.~~~~~
I(x,a) + (x)
As before we write,
(I,a)()
i
=
Xe I(x
(x7)
e(Xa
i
a)
j=l,k
However, if the choice index is linear in the attributes we may
re-rite the expression (7) above in exactly the same form as the probability
statement that preceeds it (6).
cancels
That is, the effect of individual attributes
ut" when the choice index is linear in the attributes.
individual
To keep
[
effects in the model, it therefore appears that either interaction
components are necessary or the weightings on individual characteristics are
"fuel specific".
C
An alternative is to partition the sample according to the
relevant household characteristics and estimate a function like (6) for
each household group.
Presumably this would lead to a different set of
"weightings" estimated for the linear choice index for each consuming fuel.
*.a
And this is a violation of the assumption of the independence of
irrelevant alternatives.
!.
-7-
It may be that there are "technique specific" attributes associated
with each of the alternative techniques.
Each individual technique may have
inherent characteristics that are unmeasurable.
through the use of dummy variables.
Let the characteristic set now include
entries on a number of dummy variables
is chosen and equal zero otherwise).
We might include this idea
( = 1 when a particular technique
In the case of two possible alternatives)
our characteristic vector now has an additional variable d that is equal to
unity for (say) gas and zero for (say) electricity.
The linear decision index
I now looks like the following:
2C +
1 +
I =
3H +
4A
+
5d
and the binomial response function now looks like
1
82 (C2 -C1 ) +
P1
3(H2-H1) +
4 (A 2 -A 1 ) + 85
l+ e
The coefficient
gas"effect.
5 is given the interpretation of the autonomous
Note that the use of this procedure makes it impossible to
predict what will happen if a previously unavailable technique is suddenly
introduced since the "technique specific" weighting of the new technique
is not known.
As a result, every effort should be made to completely specify
the relevant choice characteristics so that this procedure need not be appealed
to, especially if the effects of new techniques are to be estimated.
-8-
EXAMPLE:
PRODUCER CHOICE
Let us assume that our problem is to estimate the probability that an
electric utility will install a gas burning generator when faced with the
alternatives of using gas or oil.
Assume that the relevant characteristics
of the techniques are the following:
I
Bi = expected fuel cost per Kwh generated
.
= annualized capital cost divided by the expected
K
number of Kwh's to be generated per year.
(i = 1 gas)
(i
2 oil)
In addition assume that the individual decision makers are faced with
various sulfur restrictions and that a restriction index "s" can be constructed
(it might be the cost of meeting the requirements).
following decision
We may specify the
index:
I
1+
2Bi
+
a
3Ki
+
4 sBi
+ ~5 sKi
and the binomial response curve becomes
,C
1
P1
2 (B2 -B1 ) +
(K
2 -K1 ) + Bs(B2-B1
l+e 2
+
s(K2-K1 )
The expected signs of the coefficients are the following:
<2
<03
0
4 <0 ;
<
n
j,
U
-9-
Note
that the form of the logit specification (Figure
1 A) imposes
a non-linearity on the elasticities of probabilistic choice,
paribus,if
and B 1 are
B2
very "far from one another", changes in either
will have very little effect on the choice probabilities.
B2 and B
2
1
Ceteris
However, if
very close", smallchanges in either may have very large
are
effects on the choice probabilities.
This is also true of the "emission
If B2 is very small relative to B 1 an increase in s(by legislation,
index".
let'ssay) will tend to have a much smaller effect than if B 2 were larger
relative to B.
1'
APPLICATION OF THE BASIC MODEL OF INDIVIDUAL FUEL CHOICE
The foregoing model of individual fuel choice has provided an
important basis for our analysis of the consumption of alternative fuels.
Given the availabilities of data we have found it necessary to stick more
with the spirit than with the letter of the model presented here in many
interesting situations.
logit function
The basic structural form of the model -- the
-- is being applied to three types of data on fuel
utilization for the residential, commercial, industrial and electricity
generating sectors.
a)
Individual fuel choice data -
observations on individual
decision-makers who choose one fuel from a set of two or
**
more alternatives .
Application of a model exactly like this to electric utility fuel
choice decisions is forthcoming (Joskow and Mishkin), 1974.
Se**
oskow and
and
SceeJoskow
ishkin, forthcoming.
Mishkin,
forthcoming.
-10-
I
.
p1
I
I
I
I
I
C
0
(B2 -
red,
1)
PROBABILITY OF CHOOSING FUEL # 1 VS. COST OF
FUEL ALTERNATIVES
FIG URE
--
i
m
i
i
1A
I L
L
-11-
b)
Frequency data on appliance choices across 48 states.
For
example, the proportion of households choosing electricity,
gas or oil for space heating.
c)
Fuel consumption shares by sector across states and through
time.
For example, the proportions of total BTU consumption
in the residential sector accounted for by electricity, gas
and oil across states and through time.
In each case we have attempted to use the basic logit specification:
z eI(Xj)
I(1
Pi
where Pi is the probability of individual fuel choice, appliance saturation
level, or proportion of BTU consumption with respect to each fuel, depending
upon which data set we are using.
In the case of individual fuel choice
data (electricity generating fuel choice data primarily), we are using the
conditional logit model as outlined in the previous section and maximum
likelihood techniques for estimation.
For the appliance saturation level
and fuel consumption data we are using the "log-odds" specification (2)
and regression techniques for estimation
Fi
log (-i
EP
=
= I(xi ) - I(xj)
1
(2)
.(2a)
where Pi is the appliance saturation level for appliances burning fuel i
or the proportion of total fuel consumption accounted for by fuel i
a particular sector, again depending upon the data set employed.
in
-12-
*
We realize that jumping from the model of individual fuel choice to
~
aggregate appliance choice and consumption data involves a large number of
heroic assumptions.
We do believe, however, that this is'a useful way of
conceptualizing the problem of interfuel substitutability in consumption
and provides an important component for the larger multi-sector, multiregional supply and demand model that we are in the process of building.
The conceptual model also has provided an important framework for structuring the collection of new data sets, some of which will hopefully be
available in the near future.
The remainder of the paper proceeds in the following way.
In the
next section we outline the basic determinants of fuel consumption as they
appirL
U
ULLa
L
.LLU
dLLUL
UaU
LLIL&
dLU
.ULL
LUnbWUptLV1J.
and other data that we have been able to find and .make use of.
p.L.LL.
In the
following two sections we describe the techniques used and results for
residential appliances.
In the third section we present overall fuel con-
sumption equations for the residential and commercial sectors.
Finally,
we put the "fuel split" equations together with the aggregate consumption
equations and make projections to 1980 of residential and commercial consumption based on various possible patterns of fuel prices, aggregate
i
income, population,etc. for the U.S. as a whole and for some particular
states and regions.
The paper concludes by discussing how these results
F
fit in with the larger multi-fuel, multi-sector model that we are in the
process of putting together.
I-
r
n
L-
-13-
Section II.
THE DETERMINANTS OF FUEL CHOICE
The basic determinants of fuel choice that we have been working with
consist of the standard economic variables that affect consumption decisions,
plus institutional and climatic variables that impact fuel choices in
particular.
The vector of fuel characteristics and household characteristics that
we have been using consists of the following entries:
x1 : Fuel Prices
[
(two or three alternatives for residential
and commercial decisions, three or four
alternatives for electricity generation,
and four alternatives for industrial
fuel choice decisions).
X2 : Capital Costs
(the costs of the burning techniques
associated with each alternative fuel).
x3 : Household Income:
(in the residential and commercial sectors)
X4 : Climatic Variables
x5 : Other regional and institutional characteristics that appear
to affect fuel choice.
We will discuss the particular variables used, the data sources, and
expected effects in the following sections.
it should be pointed out here,
that because of different fuel burning efficiencies and the inability to
obtain data on "effective" prices per BTU we have been forced to modify the
basic logit model by allowing the weightings in the choice index to be
fuel specific, thus allowing for burning efficiencies of different fuels.
In the log-odds formulation, the model therefore becomes the following:
-14-
Pi
log (- p
E Pi
Ii(xi)
-
j(xj)
1
where the weightings are sometimes the same and sometimes different for each
1
fuel depending upon the particular characteristic that we are examining
The meaning and the use of this more general multinomial logit model will be
discussed more fully in the following sections.
iI
I
E
*
.
LA~~~~~~~~~~~~~~~~~
LA
1
A more formal discussion of this departure may be obtained from the authors.
~
-15-
RESIDENTIAL APPLIANCE CHOICES
Section III.
We now present results for the application of the logit model of fuel
choice to the appliance decisions of residential consumers for four types
We are concerned here with the effects of economic and
of appliances.
demographic variables on consumer decisions to choose one fuel from a set
of alternatives to provide a particular type of service.
The services examined
are house heating, water heating, cooking, and clothes drying.
Together
these four appliances account for over 80% of residential energy consumption.
The house heating alternatives are oil, gas, and electricity.
HOUSE HEATING:
We explain the proportion of households using each technique
as a function of fuel prices, household incomes and minimum
winter temperature.
The model that has been estimated is the
following:
S
log (S
)
logao
e
=
a ++
=
a1
+
Y
g+
2 P9g
J~P e
4
TEMP
S
log (-)
1 Pe++
Y2 P
+
+Y
Y3
Y4TEMP
e
S
WATER HEATING:
+ S
oge +
Se + 1
(variable definitions below)
The water heating alternatives are oil, gas, and electricity.
We explain the proportion of households using each technique
as a function of fuel prices and household incomes.
that has been estimated is the following:
S
log ( -S
1 Pe +
) = ao +
2
+3
+
4 TEMP
e
S
log ( Si
+
)= a
1 Pe +
e
S
+S
g
=1
e
2 Po+ 8 3Y+
4
TEMP
The model
..
ifI
-16-
CLOTHES DRYING:
The clothes drying alternatives are gas and electricity.
We explain the proportion of households using each technique
I'
A
by fuel prices, household incomes, and the degree of
urbanization of each reporting unit..
The model thas has
been estimated is the following:
I
S
log (
-)
=ao +
e
S
g
COOKING
:
1 Pe
+ S
e
+
2 Pg
+
3 RUR +
4
Y
=
The cooking alternatives are gas and electricity.
I
I
We explain the proportion of households using each technique
by fuel prices, household incomes, and the degree of urbanization
of each reporting unit.
The model that has been estimated
I
is the following:
S
log ( -S
)
o +
e
1 Pe
S. + S
g
e
+
+
2+Pg+
2
33
4Y
1
f
THE DATA1
The data
S :
I
for a cross-section of 48 states for the year 1969 are:
Proportion of appliance usage accounted for by gas.
r,
U
gS: Proportion of appliance usage accounted for by oil.
S0 : Proportion of appliance usage accounted for by electricity.
Se Proportion of appliance usage accounted for by electricity.
P :
Pg
Pe:
0
Ii
Price of gas measured as the typical bill per 100 Therms.
C11
i
Price of electricity measured as the difference between a typical
electric bill for 1000 KWH and a TEB for 500 KWH per month (1970)
U
1 A more detailed description of the data is available from the authors.
Li
..'
-- -
J~·~·m~rLrruurLrw~~ri~~~O;C~·r~riL.;1l~iIL~ir
-.--
-.
.Ql~~k*
*'
~
~
-17-
P
o :
Price of oil measured as cents per gallon.
Y :
Effective household buying income per capita.
TEMP :
Mean January Temperature
RUR
Ratio of rural occupied housing units to total occupied
housing units.
:
We expect that the odds of choosing gas or oil relative to electricity
will decrease in response to an increase in the prices of gas or oil and
increase in response to increase in the price of electricity.
Higher mean
January temperatures are expected to imply lower utilization rates for heating
equipment which should favor electricity relative to gas or oil since
electric heating is generally thought to have lower capital costs than the
two alternatives.
Finally, we expect higher income families to favorgas
relative to electricity and electricity relative to oil (other things equal),
reflecting perceptions about the relative cleanliness, reliability, etc. of
these fuels.
ESTIMATION RESULTS
The relationships presented above were estimated by ordinary least
squares procedures initially.
An examination of the residual patterns
indicated that the error variance increased as the level of gas and/or oil
consumption in the state decreased.
We believe that this problem arises
oecause of the way in which the price data are collected for fuel prices.
The data on electricity prices represent data inputs from virtually all
producers in the individual states.
(supposedly) random samples.
The oil and gas prices are based on
As a result states with very small levels of
consumption of gas and/or oil may have only a small number of reporters
as inputs into the price indices, leading to a larger variance in the index
price relative to the actual price.
Put more simply, we have far more
confidence in the price data on gas and oil from the larger consuming states
than the smaller consuming states.
As a result we reran our regressions
weighting the observations by the square root of total gas or oil consumption
u
L
-18-
(deendin
on the eauation).-
This
led to a maor ioroveent
r~
in the estimates
A---
(depending on the equation). This led to a-maior improvementin the estimates
for the househeating and waterheating equations, but not for he clothes
dryer and cooling fuel decisions which do not contain oil as an alternative.
The results of this weighted least squares procedure for each of the four
i
appliance groups
-J---'-.----
4
is reported below.
*
HOUSEHEATING:
S
(1) In ()
Seg
=)2.11 + 0.298 P -30.95
e
(1.48)
(4.85)
+ 0.296 x 10 3-
P
(-5.69)
0.456 x 10 -
(2.71)
TEMP
(-4.62)
S~~~~~~~~~~
(2) in (
S
3
-)
]
12.85 + 0.298 P - 0.601 P - 0.152 x 10 y - 0.408 x 10
e
0
Se
(4.13)
(4.85)
R2
(-4.02)
(-0.97)
1
TEMP
(-2.51)
.913
-,
U~~f
*~*-A
(See footnote
r-
next page)
-
q
-19-
WATER HEATING
S
(1)
n (
S
e
) =
-2.82 + 0.415 P
(1.12)
g
(3.85)
(-4.48)
S
(2)jn ( S ) =
e
24.59 + 0.415P
e
(3.85)
(4.49)
R
2
=
+ 0.485 x 10- 3 Y - 0.113 x 10- 1 TEMP
- 43.96 P
e
- 1.73 P
(2.52)
(0.65)
10-3 xY +0.528
+ 0.528 x10
x 10- 1 TEMP
- 0.110xl0.110
o
(-6.57)
(-0.40)
(1.85)
0.655
We have constrained the coefficient of Pe to be the same in each equation.
In the most simple form, the model that we are working with has a decision
index of the following form:
I (X) = a + bX
where X is a vector of fuel characteristics. Let's say that the only
relevant characteristic is fuel price. Since the weightings in our
model are fuel specific:
I2 (Pg)
I1 (Pe) = al + bl
e
12 (Pg) = a2 + b2 Pg
g
E xi (i)
3
S =
0
(P
(P)o )
Ii (Pi)
I3 (Po) = a3 + b3 Po
Then,
S
Qn (S-)
S2
e
.S
n (
O
= (a 2 -al)
S- ) = (a3 - a)
e
+ b2 P
- b1 P
+ b3 PP
- b
Pe
i
L
-20-
r
r
COOKING FUEL (Unweighted)
S
n c
-L )= -2.67 + 0.30 Pe - 17.85 P
e g
e
(-2.22)
(5.02)
R
2
=
+
(-4.69)
.25 x 10- 3 Y (2.07)
0.72 x 10- 5 RUR
(0.11)
L
0.54
CLOTHES DRYERS
S
n (
S- )
e
e
-7.09 + 0.35 P
7
+ 0.54 x 10- 3 Y + (0.29
0.19 x 10- 4 RUR
(-5.62)
(4.39)
(0.29)
21.56 P
.09
(-5.74) (5.75)
g~~~~~~~~~~~~~~~~~~~~~~~~~~t
Se
R
e
e~~~~~~~~~~~~~~~r
= 0.67
The results are surprisingly good.
a
The signs of all of the price variables
are as expected a priori and they are all statistically significant at the 5%
level.
For househeating we observe that higher winter temperatures lead to
the favoring of electricity over gas and oil which is as expected.
In the
water heating equation, we would not expect temperature to be an important
variable and yet it is positive and significant in the oil-electricity equation.
This result appears to emerge because there is a lot of oil consumption in
the colder areas of the country where gas is not as readily available as
elsewhere, thereby biasing the decision relationships.
higher incomes
expected.
In all of the equations
generally lead to the favoring of gas over electricity as we
In the househeating and waterheating equation higher incomes lead
to a higher probability of choosing electricity over oil.
It is difficult to get a feel for the relative impacts of the different
price variables because the prices are not in comparable units in this data
set.
We can calculate "saturation
elasticities" evaluated at the sample
means, however, to get a feeling for how sensitive appliance choices are to
price changes.
The results of these calculations are shown on Table 1 for
each of the usage categories.
l
_
.. _I---^
-_
-21-
ELASTICITIES EVALUATED AT THE SAMPLE MEANS
HOUSE HEATING
Fuel Saturation elasticity with respect to:
P
~
P
e
'.l ~ ~
I
P
g
l[11
I
0
l[
1111
-2.08
2.12
3.30
Gas
.23
-1.48
3.30
Oil
.23
2.12
-7.21
Electricity
WATER HEATING
P
e
P
P
o
g
Electricity
2.08
2.87
2.91
Gas
1.14
-2.28
2.91
Oil
1.14
2.87
-2.74
COOKING FUEL
P
P
e
Electricity
Gas
g
-1.18
1.05
1.15
-1.03
CLOTHES DRYERS
P
P
e
g
Electricity
-.58
.53
Gas
2.05
-1.99
TABLE
1
_
._ rrrB9·iUIIPI·pbW,r,·
rfi;lirir.
-22-
The elasticity estimates are quite interesting.
Except for electric
clothes dryers, the "own-price" saturation elasticities are always greater
than unity and in several cases greater than two.
The "own-price"
elasticities for oil using appliances are very large, in fact so large that
they are very suspicious.
We will want to compare these values with the
associated values for total oil consumption in the residential and commercial
sector reported in the next section (estimated with a different set of
price data) to see if they hold up there.
The cross price elasticities are
also fairly high, often close to three, indicating that changes in the price
of a particular energy source are heavily reflected as a switch to appliances
using alternative energy sources.
The very low sensitivity of gas and oil
house heating appliance choices to changes in the price of electricity result from the very low mean saturation rates of electricity.
In this regard,
it is important to note that the elasticities depend on both prices and
saturation levels.
As any given saturation level increases (other things
the same), the own-price" elasticity decreases and the cross-elasticities
increase.
This is intuitively appealing, since as the market share approaches
unity we would expect the sensitivity to "own-price" changes to diminish.
B
In conclusion, we see that fuel prices play an important role in
appliance choices.
Relatively small changes in prices can profoundly affect
consumer appliance buying decisions in the short run and the fuel burning
capabilities of the appliance stock in the long run.
We turn next to an
investigation of the effects of prices and other variables on total energy
r
-
consumption in the residential and commercial sector by fuel type.
rL
___~~ h
~.j._
-lIf*;,~-
~
)
*-~
r
W
~
~
i.
.·
-23-
SECTION IV: FUEL CONSUMPTION SHARES IN THE RESIDENTIAL AND COMMERCIAL SECTOR
We now turn to fuel consumption in the residential and commercial
sector as a whole.
We are interested in specifying and estimating "fuel
split" equations which indicate the proportions of total energy consumption
in the residential and commercial markets associated with each of three
fuels - oil, gas, and electricity.
These equations are then used in our
overall model to determine energy consumption by fuel at the national, re-
1*1--t---1-A--4
r,.LLL..L
C.LLLL
LCL
L=
rC-:
c4-14-4arc-v
PU-c4mn
1- Th
=L0
.LLLr-
LVOL.
bJ.LrLLJ..L.LLdCLLLL
UMZ-
UL
fs
LC.L
o
LLLI
1
LLL=
-_
JL=
sidential and commercial sector for 1969 are presented in Table 2.
In 1969 the usage categories of space heating, water heating, cooking,
and clothes drying encompassed about 80% of total energy consumption in
the sector.
In these same categories, all the fuels to be considered in
the discussion that follows (natural gas, oil, and electricity) were used
in significant quantities.
(Coal only accounted for 3% of the fuels con-
sumption in the residential and commercial sectors in 1968.
Since this
share has been and continues to rapidly decline, it is neglected in the
model discussions that follow).
The configuration of fuels consumption in the residential and
commercial market in various regions of the country varies markedly.
For
example, the market shares of gas, oil, and electricity in 1969 in
Massachusetts were 16.2%, 76.2%, and 7.6% respectively, while in Texas for
the same year they were 66.3%, 2.6% and 31.1% respectively.
The prices of
these alternatives forms of energy also vary significantly as one moves
from state to state throughout the country.
In 1969, the average price
of natural gas consumed in the residential and commercial sector varied
between 58¢ and $1.89, for oil the price ranged from $1.06 to $1.47, and
for electricity from a low of $3.08 to a high $8.14 per million BTU's
in different parts of the country.
If consumers do utilize information on fuel prices when choosing
a fuel, and if fuel prices do really make a difference in the final de-
i
---
'-~s
·-
-24-
RESIDENTIAL AND COMMERCIAL ENERGY CONSUMPTION
1
9
6
a
8
(In per cent of total consumption)
I
I
PETROLEUM
TOTAL
COAL
NATURAL GAS
Space Heating
65.78
3.53
27.66
33.56
1.02
Water Heating
11.54
8.72
.91
1.91
3.70
2.75
.30
.65
.10
.36
.06
.32
Refrigeration
3.10
.03
3.08
Air Conditioning
3.88
.62
3.26
PRODUCTS
ELECTRICITY
I
U
Cooking
Clothes Drying
11.26
Other
TOTAL
Source:
100.00
40.14
3.53
6.12
5.13
40.95
15.36
I
Patterns of Energy Consumption in the United States,
Stanford Research Institute, January 1972
TABLE
2
I
-25-
cision, we felt that this information should be available in historical
data.
It was this hypothesis that we set out to test using a model
based on the logit formulation discussed in the previous section.
IV.1 THE MODEL OF FUEL CONSUMPTION IN THE RESIDENTIAL AND COMMERCIAL
SECTOR
-
FUEL SPLIT
EQUATIONS
The overall model used to describe the fuel consumption behavior
in the Residential and Commercial market consists of two parts.
The first
relationship describes the total residential and commercial energy consumption in each state and, given the exogenous
ariables through time,
simulates the growth of total demand in these states.
This total energy
consumption relationship is discussed in the next section.
The second part of the residential and commercial sector model
consists of a set of fuel split equations based on the multinomial logit
functional forms discussed in the previous sections.
Since in the re-
sidential and commercial sectors we are concerned with three fuel alternatives the basic fuel splitmodel becomes the following:
S
Zn (S.)
S3
I1 (X)
13
I3
(X3
3
S2
n (
2
2
S1 + S2 + S 3
3 (X3
=
1
where X represents fuel, household and demographic characteristics
associated with each observation; and where S1, S2 and S3 are the market
shares of gas, oil, and electricity respectively.
The basic variable
entries for the fuel and household characteristics
-26-
0
are similar to those used in the appliance analysis in the previous
section:
0
Q
Pg
=
Price of gas
P
=
Price of oil
=
Price of electricity
=
per capita income
0
Pe
Y
PCg=
capital cost of gas equipment
PCO=
capital cost of oil equipment
R
=
a set of regional dummies
T
=
temperature variables
Before reporting the estimation results for several combinations of these
[
explanatory variables, we present a brief discussion of the data that has
been employed.
IV.2 DATA SOURCES AND DERIVATION:
.LLneaala
Tas seties
use
o
sector
Lom run gLtLraLLy
mrG.
,-uU V
by state, i.e.,48 states and D.C., though occasionally, observations on
states are by necessity combined.
L
Specifically, there is no gas consumption
in Maine and Vermont until 1966, and even then their consumption and price
data is combined with that of New Hampshire.
In addition, both gas and
electricity data for Maryland and the District of Columbia are always combined.
Thus, because of the structure of the estimating equations, obser-
vations for the total energy demand equation and the gas half of the fuel
choice equations are combined for Maine, Vermont, and New Hampshire, as
are observations for Maryland and the District of Columbia.
In the oil
L
half of the fuel choice equations only observations foreMaryland and
District of Columbia are combined.
r
L
L
________
-27-
The price data (which is at the retail level) is in $/BTU;
the
consumption data is in BTU's; the surrogate capital cost data is in
$/unit; income per capita is
in $/person, and all other variables are
in similar singular units.
All variables involving dollar figures have been adjusted by
the cross-sectional time-series deflator later described.
NATURAL GAS
Natural Gas Price and consumption data is clearly the most reliable,
structurally, of our observations in the residential-commercial sector.
The
Bureau of Mines (Minerals Yearbook) provides information on sales and revenues by year by state for both the residential and commercial sectors.
The sales data, in MCF's, is converted to BTU's by the conversion factors
for electric utilities' fuel consumption found by state in the Edison
Electric Institute's Statistical Yearbook.
The prices result from dividing
revenues by sales, and the price for the residential
commercial sector is
an average of the prices weighted by each sector's consumption.
ELECTRICITY
,,
.
Electricity price and consumption data is readily derived from the
Edison Electric Institute's "Statistical Yearbook's" Sales and Revenues
sections.
The data is availz)le for the residential sector specifically,
but not for the commercial sector.
We have had to assume that the small
light and power figures are roughly equal to what would be actual
commercial sector figures, since no data source separates "commercial" from
industrial, but rather, only "small light and power" from "large light and
power".
The consumption data is converted to BTU's by 3412.8 BTU's/kwh,
and the price data, like that of gas, is an average of the residential and
small light and power prices weighted by each sector's consumption.
4
__1·
-28-
OIL
U
Oil data is by far the most unreliable of the three energy data sets.
If one looks at 13
ears of distillate and residual htin
for particular states, the series suspiciously cycles.
U
n-il consnumntion
This consumption
data is found in the Bureau of Mines' Mineral Industry Surveys, "Shipments
of Fuel Oil and Kerosine" (kerosine used for heating is not included in our
analysis), broken down by distillate grades one through four and residual
grades five and six.
A representative of this publication claims that heating
oil used industrially is not consistently included or excluded from the heating
oil figures from year to year; so, it is not even possible to explain this
noise with a level-of-economic activity regressor.
None of this data is broken down by sector, i.e.,residential or
commercial or industrial heating use.
D
It is assumed that numbers 1 through
6 distillate and residual heating oil at least exhaust residential and
commercial uses of oil substitutable with natural gas and electricity, and
is roughly proportional to what would be the actual consumption in these
sectors.
The raw data, in barrels, is converted by 5.825 x 106 BTU's per
barrel of distillate and by 6.287 x 106 BTU's per barrel of residual.
The only retail oil
oil.
rice found on the state lvel
i
for
This data was obtained from the American Gas Association.
2 fuel
We are well
L
aware of this regressor's unreliability as a distillate-residual oil price
in
1
'ie residential-commercial sector (though it is probably a reasonable
surrogate for a distillate oil price in these sectors), but there is nothing
more available.
LJ,
L
MISCELLANEOUS
The temperature variables used here are the average temperature of
U
the three warmest months and the average temperature of the three coldest
months in degrees Farhrenheit.
This information is from the Department of
Commerce's National Oceanic and Atmospheric Administration publications.
r
L
-29-
The adjustor used for all dollar figure variables is a time-series,
cross-sectional deflator constructed through the work of Kent Anderson
for 1970.
This 48 state deflator (Maryland and District of Columbia
consumer
combined) is adapted to 1960 through 1972 by the nationwide
price index.
This, of course, very strongly assumes that inflation
rates are uniform all over the United States, i.e. that the relative cost
of living in each state does not change over time.
It is thought that this
procedure is no worse than obtaining the cost-of-living studies done by
the Bureau of Labor Statistics for three of the thirteen years in question
and extrapolating and interpolating the other ten years, especially since
this cost of living index is not available by state.
Since our research
employs cross-sectional time series data and since there is not enough
variation in price or any explanatory variable over time to fit a demand
curve, it was assumed that a deflator oriented primarily to cross-
-
sectional variation would suffice.
The Anderson index for 1970 is constructed as follows:
" The 1970 BLS data for SMSA's on the relative living cost of a
family of four having an "intermediate" budget permitted construction of an index for state metropolitan areas.
Indices for
state non-metropolitan areas were set at 90/103 of the metropolitan
indices, based upon the U.S. averages for these two types of areas.."'
Every effort has been made to obtain the best data available
--
any
suggestions as to better sources of data series would be greatly appreciated.
1 si,.dential
Energy Use:
An Econometric Analysis (prepared for N.S.F.)
October, 1973, Kent P. Anderson, pp. 21-22.
C
-30-
IV.3
ESTIMATION PROBLEMS AND PROCEDURES
The estimation of the logit equations basically revolved around the
use of OLS techniques,
In all cases, since the estimated coefficients
were really the result of variations in space rather than time, serial
correlation of the errors was very high.
A much more significant and troublesome problem was the unreliability
of some of the data used, to some extent with gas but even more so with
the oil data.
x
In states where only a very small amount of consumption of
these fuels took place, it was found that very high variation existed in
the consumption trends over the decade.
This is not surprising since the
percentage error associated with any sampling process used to accumulate
the data would be magnified in states with small consumption.
For a
state with very few supply outlets, the occasional abscence of a report
from just one supplier, especially if large, would significantly vary
the consumption trend for that fuel.
.
0
0
Since the logit equations contain
no information about the size of the sample in each state (the dependent
variable is simply the ratio of market shares), it was necessary to
t
compensate for this disturbance term to reflect the reliability of the
data of each state.
If one assumes that the consumption of any fuel in a state reflects
L
the number of individual decisions made in favor of that fuel, then the
variance of the observed mean frequency (market share in our context) is
proportional to the reciprocal of the number of decisions (N).
that the residual
To assure
error terms of the estimated equations have constant
variance, each observation has to be multiplied by the square root of N
L
(in our case the square root of consumption).
r
This weighting procedure
yielded much more realistic estimated relationships and was used throughout
in the estimation of the fuel split equations
.
.
U
This same procedures has been used by Theil in another context. A further
discussion is given in Statistical Decomposition Analysis, by Henry Theil,
North Holland Publishing Company, 1972, pp. 174-177. We also estimated
the functions using no weights and weighting directly proportional to
consumption, but the results were less satisfactory.
U
i
··-i-- i..--·. .rrr---·3-· .-iuhVr=C*rliLIILrYC
h -- rir-rhll·al·illlllIsllmrranr(UI·l·aU-
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-31-
IV.4
ESTIMATED RESULTS
MODEL
1
The resulting fuel choice equations estimated without regional dummy
are 1 ' 2, 3
variables
log (
log (
)
log
-
9.45 x 10
(~)- 945
)=-4.34
logE(~)
=
P
105
(-11.8)
x 1 6P
0
- 106Po
4.34
x
(-14.4)
g
+ 2.86 x 10 P
e
(19.6)
p
- 0.627
(7.41)
+ 2.86 x 15P
e
(19.6)
0.85
R2 =0.85
+ 3.41 x 10-4Y
NOB =980
980
NOB
(-3.10)
- 3.34 x 10-Yy
p
+ 4.29
(-0.43)
(8.10)
= 901.8
FF =901.8
It can be seen that the price terms are all quite significant and
exhibit the proper signs. The signs of the income coefficients indicate
that states with higher income per capita prefer gas over electricity,
and electricity over oil, all else being equal. This was also observed
in the analysis of appliance choices presented in the previous section.
Again, we believe that this result emerges because gas and electricity
are probably preferred fuels on the basis of cleanliness and ease of
use. However, it is not clear why this effect is more important in the
gas-electricity equation.
The same effect also appears in the coefficients
of prices.
From the magnitude of the coefficients, it appears that
consumers are much more sensitive to oil price than gas, and least
sensitive to electricity prices.
There is some difference in efficiency
of consuming equipment, but this effect is more pronounced between
electricity and the fossil fuels, not between the fossil fuels themselves.
One would expect the coefficients of gas and oil prices to be nearly
equal.
In both halves of the equation, the income and price effects are
apparently present because other regional heterogeneities are forced to
be explained by the price and income variables. The adoption of regional
dummy variables changes the results significantly.
1,
2,
3:
(Footnotes on next page)
-32-
FOOTNOTES
Originally, these equations were estimated using data on costs of standard
size furnaces. Later, we found this data unreliable, having been constructed
for 10 vears and 49 states from a very small number of actual observations.
The costs of furnaces were then dropped from the estimation procedures.
Before eliminating the capital cost numbers, the estimation yielded:
in (--)
=-
E
10
1.25 x
(-18.9)
in
(E)
0
0
P + 2.30 x 105 P - 0.00908 G
+ 3.42
g
e
cap
(24.9)
6
- 3.78 x 106 P
0
(-13.0)
(-11.9)
+ 2.30 x 10
5
e
(15.2)
- 0.00608 0cap + 6.07
cap
P
(24.9)
(- 5.6)
(21.1)
2i
R
= .96
F = 4674
where Gcap is the cost of a gas furnace.
0
cap is
the cost of an oil furnace.
r
It is unfortunate that this data was unusable because these results do look
quite good. However, these equations were weighted by consumption, not
by the square root of consumption, so they are not directly comparable
to those given in the text.
The equations were also estimated using time dummies in place of the
constant, a different one for each year of the data. The results were very
stable as the constant varied only a few percent over the decade.
D
3Another form of the equation estimated utilized the temperature variables.
The results were:
log (+
) = 9.77 x 10
P
+ 2.52 x 10
P
3g
e
- 2.72 x 10- 4 Y
p
- 0.00425 Maxtemp
- 0.00618 Mintemp + 0.357
(2.96)
(1.01)
log
(
0)
) = 4.31
6
10
(14.7)
5
P
o
+ 2.52 x 10
P
e
-4
- 1.27 x 10 4 Y
(16.8)
(-1.7)
p
- 0.0103 Maxtemp
(-4.66)
- 0.02137 Mintemp + 6.08
(-5.63)
(10.9)
R
2
0.85
NOB = 980
F = 587.8
These results show that the higher the temperature, the more electricity is
favored, as expected. These results look quite good; however, when regional
dummy variables were used the temperature variables became insignificant.
Due to the collinearity with regional duTmies, the temperature variables
were dropped from the estimation procedures. The results when using
only the regional dummies are reported under Model # 2.
L
r
L
LJ
-33-
MODEL # 2
To further account for regional heterogeneities in supply and consumer
attitudes, the equations in the preceding section were estimated with regional
dummy variables.
The nation was divided into five regions.
A conscious
effort was made to keep states with significant indigenous supply separate
from primarily consuming states, to keep coastal states separate from inland
states, and northern states separate from southern states.
Compromises
had to be made, but the regions defined in Table 3 were used.
No doubt some
states could be placed in regions other than those designated, but these
seemed to work sufficiently without glaring inconsistencies.
The fuel choice equations estimated with regional dummies are:
log (
-) = - 1.25 x 106P
9~+
E
(-14.4)
~ ~ e+
1.68 x 105P
(12.3)
8.62 x 10- Y
p+
(2.14)
1.46Region 1
(5.87)
+ 1.33 Region 2 + 1.13 Region 3 + 0.95 Region 4 + 0.77Region 5
(6.13)
log (
)
(5.41)
- 1.63 x 106P + 1.68 x 15P
0
(-6.46)
(4.73)
e
(3.75)
- 3.08 x 10-4Y + 3.46Region 1
(12.3)
p
(-5.34)
(8.93)
+ 2.52 Region 2 + 1.84 Region 3 + 0.40 Region 4 + 2.69 Region 5
(6.32)
2
R
=
0.93
(4.64)
NOB = 980
(0.9o)
(6.58)
F = 880.1
Comparing these results with those derived without regional dummies, it can be
seen that there appear to be definite regional biases toward the fuels.
The
regional dummy variables are quite significant and the values vary markedly
over the regions, especially in the oil-electricity equation.
The values in
the oil equation signify that all else being equal, regions 1, 2, and 5
-34-
REGIONS
I
II
ME
PA
NH
OH
VT
IND
MA
ILL
RI
MICH
CONN
WIS
NY
MINN
NJ
IO
DEL
MO
MD
END
DC
SD
FOR DUMMY VARIABLES
III
IV
V
COL
I
NEB
KAN
VA
MONT
TENN
KEN
ID
ALA
WV
HISS
NC
WYO
UTAH
ARK
SC
WASH
LA
GA
I
ORE
OKLA
FLA
TEX
NM
ARI
NEV
CAL
r
TABLE
3
El
4
ri
~__ ~
..
,~~,_,,,,;~;·~j
~~
PLU~UM-rlllrl-I
-35-
(Northern states) are more inclined toward oil than regions 3 or 4, and
the predominant supply area, region 4, is less likely to use oil than
The dummy variables in the gas half of the
any of the other regions.
equations can be interpreted in a similar fashion, although the variation
there is not nearly so great.
These regional biases change the estimated coefficients of fuel
prices and income, but to relative values that seem more reasonable.
The coefficients of oil price and gas price are now much closer in
value and the income effect, though still the same in direction (from
oil to electricity to gas as incomes increase), is much more predominant
in the oil equation than in the gas equation.
To further demonstrate the quality of the fit of the equations
with regional dummies, Figures 1-3 shows plots of the actual vs. fitted
results for each of the fuels in 1969, the last year of the data base
to which the equations were fit.
From the estimated fuel split equations, the importance of prices
Figures 4-6 illustrate the
in fuel choice decisions is apparent.
responsiveness of market shares to changing relative prices for a hypothetical state in Region 2.
In each case, two of the prices are held
constant while the third is varied.
market shares as a function of price.
The plots show the equilibrium
Note that the system is most
sensitive to oil price changes and least sensitive to changes in electricity price.
This may be an indication of consumer attitudes toward
the fuels, although the absence of more substantial variation in our
oil price series may also be a contributing factor.
The range of our
price data is indicated on the axis of each plot.
The effect on the plotted results of different regional dummy variables
or of variations in income per capita is not to change the shape of the
curves, but merely to shift them left or right.
The effect of varying
the two prices held constant on each plot is to change the relative heights
of the plotted curves.
For example, if we increase the natural gas price,
-36-
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-42-
the effect is to reduce the natural gas market share, increase the oil
market share, and increase the electricity market share.
This corresponds
to moving along the curves on the plots in figure 4, and shifting the
L
L
curves in figures 5 and 6.
The matrix of elasticities and cross-elasticities can be computed
from the estimated relationships.
These are shown in Table 4.
shows the elasticities symbolically.
Table 4a
Table 4b shows the same matrix for
our estimated coefficients and mean values of the price and market share
variables.
The behavior of the elasticities and cross-elasticities is
most enlightening.
market shares.
Note that they are dependent both on the prices and
The relationships indicate that as any given market share
increases, the own-price elasticitiy decreases and the cross-elasticities
increase.
This is not unreasonable, for as the market share increases we
approach the saturation shown in figures 4, 5, and 6 and the own-price
elasticity should decrease.
At this same high market share, a shift of
consumption to another fuel with a low market share is a large percentage
increase, consequently the high cross-elasticities.
At the other extreme,
as the market share approaches zero, the cross-elasticities go to zero.
In this case,the impact of any shift on the market shares of competing
fuels is minimal.
U
U
The elasticities calculated in Table 4 are generally lower than those
presented previously for individual appliances in Table 1.
The probable
explanation for this is that data are included in the total sector for end
uses of fuel that are not substitutable.
These non-substitutable categories
L
bias downward the price responsiveness that was estimated for individual
end use categories in the previous section.
The elasticity matrix shown in Table 4
is obviously state specific,
depending on the prices and market shares for the state.
This is a very
useful property of the logit formulation, allowing one to identify the
r
geography of changes in consumption brought about by changes in prices.
We now turn to the specifications and estimations of our total energy
consumption equations for the residential and commercial sector.
U
.
.
-43-
( G )
a
O
a
P +a
P
Pg g
Pee
P +a
Po
o
ELASTICITIES
P
- a
0
a
P
-a
Pe (I-E)
eP
*
*
g
OF MARKET SHARES WITH RESPECT
e
a
G
P +
Poo
P
Po
E
a
E
a
0
P G
Pg
g
-aP9
P (1-0)
g Pg G'
P 0
pO
g
~------
_ Ir
o
p
Pe e
0
TO PRICE
a
o
P
(1-G)
Pg g
(a)
USING
P
---
MEAN VALUES OF PRICES
P
e
411
L·
E
- .950
0
.180
G
.180
·
AND NATIONAL MARKET SHARES
P
o
-·l·IL--__l-
---
.682
-
.643
1.21
.643
.682
IEAN VALUES:
E =.159,
g
-.698
0 = .361,
G
4r
c P
= 1.13,
p
ao
p
Pe
(b)
T A B L E
4
=
-
1.89,
.480
p = - 1.34
Pg g
-44-
Section V:
TOTAL ENERGY CONSUMPTION IN THE RESIDENTIAL AND COMMERCIAL SECTOR
Our basic model for the demand for energy in the residential and
commercial sector is a simple flow adjustment model.
depends upon the price of energy (Pt )
i (qit
energy at time t in state
The desired demand for
income per capita (Yit) and various demographic variables (Zit).
=tf f(Y
it
Z
it,
E)
it,t t
[et is a random disturbance term]
But since energy consumption depends on durable good stocks, actual consumption
I1 i
As a result we
(qit) may not be completely adjusted to desired consumption.
specify the following adjustment relationship.
(qith
qit - qi, t-l
qi,t-v )
If we make desired consumption linear in the independent variables
(1)
ai
qit
+
~1 Pit
+
~2 Yit
+
~3 Zit
+
:
,
~t
the demand relationship can be written in terms of observable variables.
(2)
tt
=
a
+
1Y
Pt
+
2 YYit +
3 YZit + (l-y)qi t
Several things should be noted about this model.
is serial correlation present in the disturbances,
be consistent.
1
+ Yet
First, if there
OLS estimates will not
Second, this estimating equation (2) is consistent with at
least two structural models of consumer behavior.
In one model, prices,
incomes, and other exogenous variables influence only appliance replacement
demand and-new demand, with the utilization rate for existing appliances
unaffected by these variables
(see Balestra and Nerlove).
This relationship
is also consistent with a second model that assumes that all demands are
affected by the exogenous variables.
Additional information
s needed to
L.
-
L'~~~~~~
-45-
identify one model from the other.
This identification problem can be
important for policy analysis since the two models imply different
speeds of adjustment.
In the presence of serial correlation, ordinary least squares estimation
of (2) will
yield inconsistent estimates because of the presence of a lagged
dependent variable appearing on the right hand side of the equation.
Even
without serial correlation alternative stochastic specifications from the
one made here would still lead to inconsistent OLS estimates.
Additional
problems may arise because of the use of cross-sectional data where there
are differences among states.
Perhaps the best way of handling this problem
is to use the error components technique of Balestra and Nerlove [5].
An
alternative technique for obtaining consistent estimates would be to use
separate state dummy variables and an instrumental variable estimating
technique.
We have decided to use the latter technique here, using regional
dummies instead of state dummies, primarily for reasons of simplicity.
report results both for equation ()(Model
We
# 1) and equation (2) (Model # 2)
with the variables as defined below and the regional dummies as defined in
the preceding section.
The variables used in the equation for total demand are (corresponding
to 1):
Tp(t)= f(Y ), N(t), Mintemp(t), Maxtemp(t), Pave(t), Region)
where
T = energy consumed per capita in the residential and commercial
p
sector
Yp= income per capita.
P
N = population1
Mintemp = average temperature of coldest three months of the year.
Maxtemp = average temperature of warmest three months of the year.
I
Pave
= weighted average price of fuels consumed in the sector.
Region
= set of regional dummies as defined previously in Table 3.
Population is used here as a surrogate for population density.
Density and Yp are extremely collinear.
-46-
A priori, one would expect that income per capita would be positively
correlated with energy consumption measuring the ability of consumers to
purchase and utilize energy consuming goods and equipment.
The temperature
variables are a surrogate measure for heating and air conditioning needs.
fl
One would expect that minimum temperature would be negatively related with
energy consumption.
heating demand.
The higher the minimum temperature the less the
On the other hand, the maximum temperature variable is
a surrogate measure
f air conditioning needs.
Since higher summer tem-
peratures reflect a greater need for air conditioning, one would expect
the sign of Maxtemp to be positive.
Finally, ceteris paribus, a higher
price for energy should discourage high consumption due to efforts to
more efficiently utilize the energy.
Therefore, one would expect the sign
of Pave to be negative.
.-
D
U
n
C
-
E
L
-47Estimation Results
MODEL # 1
The equations were estimated with and without regional dummy variables.
First, a discussion of the results without regional dummies will be given so
that the effects of inclusion of dummy variables will be evident.
For the total consumption equation without regional dummies the results
1
of the estimation process are:
log(T ) = 3.59x10-4Y
p
- 1.62x10
N - 0.00681Mintemp - 0.00876 Maxtemp - 3.26x1l Pave + 18.5
p
(20.5)
(-7.3)
EQUATION 1
(-7.1)
(-5.9)
(21.5)
2
R
=.80
NOB = 490
F = 397.3
1Two other forms of this equation were estimated. The first was just a
linear expression in place of the log linear formulation in the text.
T
=
4
4
- 1.31N + 6.88 x 10 Mintemp - 1.48 x 104Maxtemp
2.22 x 10 Y
P
p
(14.0)
(-5.80)
(0.67)
(-0.10)
- 1.55 x 1013Pave + 4.82 x 107 Region 1 + 3.79 x 107 Region 2
(10.9)
(3.76)
(2.84)
+ 2.97 x 107 Region 3 + 2.54 x 107 Region 4 + 3.60 x 107 Region 5
(2.20)
(1.86)
R
2
= 0.65
(2.85)
NOB = 490
F = 97.6
In this expression the temperature coefficients both have the wrong sign
and both are insignificant. The explained variation is also much lower
than the log-linear formulation given in the text.
The second form was a linear expression with total energy consumed (T),
not energy per capita (T ) as the dependent variable.
T
5.02 x 101 Y
+ 5.97 x 10 N - 2.59 x 10 12Mintemp p
(7.10)
(6.60)
(6.72)
- 1.67 x 1012Maxtemp - 5.50 x 10 19Pave + 1.76 x 1014
(-2.80)
(-8.95)
2
R
= 0.94
NOB = 490
(3.23)
F = 1342.0
However, the price elasticity of demand in each state in this form of the
equatioll is dependent
on the magnitude
is
this
no pysical
per capita
reason
formulation
should
slowl
of consumption
(T).
Since
there
be true, we use the log-linear energy
in the text.
(137.2)
U
-48-
The negative coefficient for population indicates that some economy
of scale is present in energy consumption.
This effect is probably due to
the higher percentage of multiple dwellings existent in the more populated
It can be seen that all the estimated coericients
states.
significant.
are quire
To
The only questionable term is the coefficient of Maxtemp.
the extent that this variable serves as a surrogate for energy used in air
Apparently,even in the
conditioning, one would expect it to be positive.
warmer regions of the country, heating requirements
conditioning (at least over the time
span
far outweighted air
of our data) so that Maxtemp
has a negative coefficient and helps explain part of the heating demand.
However, it will be seen that when regional dummy variables are employed,
this coefficient becomes very small and insignificant.
The results of the estimation process for the consumption equation with
regional dummy variables are:
log (Tp) = 3.05 x 10-4Y
- 1.20 x 10-8 N - 4.72 x 10-3 Mintemp -
- 5.44 x 10
(-4.51)
(-5.25)
(19.0)p
EQUATION 2
U
Maxtemp - 3.18 x 10. 5 Pave + 18.02 Region 1
4
(138.8)
(-22.1)
(-0.36)
+ 17.88 Region 2 + 17.75 Region 3 + 17.72 Region 4 + 17.92 Region 5
(130.1)
(132.9)
2
R
85
NOB = 490
(128.0)
(140.2)
F = 306.9
There exists a slight variation in the constant term for each region.
This variation is enough to remove the importance of Maxtemp completely.
The coefficient of Maxtemp reduces in magnitude by a factor
of over ten and becomes insignificant.
The values of the other
variables change slightly, but not enough to substantially change the
results.
U,
r
-49-
The price elasticity of total demand computed from this equation
is -0.63 for the mean state , but this holds only if'all prices increase
proportionally and no fuel switching takes place.
Switching from a
higher to lower cost fuel can mitigate the effects of an across the
board increase in all fuel prices, thus, lowering the effective
elasticity.
The income elasticity of total demand indicated from the
the mean
mean state
state2 .
estimated
is
results
for the
is
++ 0.80
0.80 for
estimated results
Derived using mean value of price for all states ($1.99 x 10-6 /BTU)
2Derived using mean value of income per capita for all states
($2 6 39/person).
-50-
MODEL # 2
The estimated results using a lagged dependent variable are}:
log (Tp)t = 0.677 log (Tp)t-l
+
8.02 x 10-5Y
(22.5)
(5.26)
- 3.71 x 10 9Nt
(-.23)
I
- 0.00162 Mintempt - 6.44 x 10- 4 Maxtempt - 1.09 x 105pave
EQUATION 1
(-2.11)
(-0.61)
(-7.65)
+ 5.95 Region 1 + 5.91 Region 2 + 5.87 Region 3 +
(10.87)
(10.88)
(10.88)
+ 5.85 Region 4 + 5.92 Region 5
(10.86)
R2
= 0.93
(10.89)
NOB = 441
I
F = 579.8
From this equation it can be seen that the value of Y as defined in equation
(2) is 0.323.
Also, the values of the coefficients for T * from model # 2
p
and model # 1 are approximately the same when corrected by the value of
Using this value for
total consumption.
.
8
it is possible to derive a rate of adjustment for
Taking exponents of each side of the previous expression
yields.
TPt = Tpt_
yT
1
TPt
L
*Y
If
where Tpt
is given by model # 1 in the previous section.
If we assume
t~~~
for the moment Tp
remains constant, then the adjustment process operates
so that
Tp(t+n)= Tpt(Y)
Tp(-(-Y)n)
n = 1, 2, 3,
...
When deleting the regional dummies, the major change in the results is that
the coefficient of Maxtemp becomes quite large and significant, the same
effect as in Model # 1.
-51-
and as n goes to infinity,
(1 _-Y)n.
T(t+n)
For a y of approximately
TP(t+n)
T(2/3)
.
approaches T
Tpt fades away as
p T
t
)
n = 1, 2, 3,
1/3,
T(l
-(2/3)
n
...
From figure 7, it can be seen that after four years,consumption is 75%
adjusted, and after six years is 90% adjusted.
The adjustment time constant
is thereforeon the order of 3.0 years.
The short run (one year) price and income elasticities can be derived
by using these adjustment parameters.
After one year, total consumption
in the residential and commercial market is approximately 25% adjusted.
This
implies that the short run price elasticity of demand in this sector is about
- 0.16 while the short run income elasticity is + 0.2.
These are significantly
less than the long run elasticities calculated previously, as expected.
This final total energy consumption equation is used along with our
fuel split equations for various simulations to be discussed in the next
section.
-52-
1&
I
A
I-
T* - 2.0 i
Tpo
1.0,
v0
I
I
I
I
I
I
I
I
I
2
3
4
n
5
6
7
8
FIGURE 7
TIME RESPONSE
OF TOTAL CONSUMPTION
r
L.
[3
1,J
i
J
r
-53-
Section VI:
SIMULATIONS AND ANALYSIS
In this section,the equations estimated in te
used for two purposes.
previous sections are
First, the model is used to predict the total con-
sumption and market shares for 1970-1972; then, these results are compared
with actual data for these years.
Second,
the model is used to explore
the effects of various fuel price scenarios for the year 1980.
ANALYSIS WITH MODEL (Predictions for 1970-1972)
Here we discuss some initial tests performed to help assess the
predictive power of the estimated relationships.
estimates presented for the residential and
Recall that all the
ommercial sector were derived
from a data base spanning the years 1960-1969.
The first analysis done
with the model was to predict the consumption patterns for the years
immediately post-dating our data base and then to compare these predicted
values with the actual values.
The data for all the variables in the model
were collected for 1970-1972 and the model was simulated using actual
values for the independent variables over the time period.
The predicted
values for total consumption and consumption of each fuel derived from
the model were then compared to the actual values for 1970-1972.
The comparison of the actual vs. predicted for the total U.S. (sum
of 49 states) for the three years is shown in Table 5.
There it can be
seen that a slight upward bias exists in our predicted values for total
consumption.
The bias ranges from 5.9% to 7.3% over the three years.
However, this error arises due to errors in the market share calculations.
The average price used in the total consumption equation is endogenous
in these simulations.
The errors in the predicted market shares of gas
and electricity bias downward the weighted average price used in the
total consumption equation, thus resulting in a high predicted demand.
The error in the natural gas predictions ranges from 13.4% to 18.2%.
-54-
I
COMPARISON OF ACTUAL vs. PREDICTED FOR TOTAL UNITED STATES
GAS CONSUMPTION
TOTAL CONSUMPTION
YEAR
Actual
Predicted
Error
Actual
Predicted
Error
1970
1.36 x 1016
1.44 x 1016
+ 5.9%
6.82 x 10
8.06 x 10
1971
1.38 x 1016
1.48 x 1016
+ 7.3%
7.09 x 1015
8.04 x 1015
+ 13.4%
1972
1.46 x 1016
1.55 x 1016
+ 6.2%
7.42 x 1015
8.70 x 1015
+ 17.3%
19691
1.32 x 1016
1.41 x 1016
+ 6.8%
6.33 x 1015
7.83 x 1015
+ 20.5%
18.2%
YEAR
OIL CONSUMPTION
Actual
ELECTRICITY CONSUMPTION
Predicted
Error
Actual
Predicted
Error
1970
4.25 x 1015
4.04 x 1015
- 5.0%
2.59 x 1015
2.32 x 1015
- 10.4%
1971
3.99 x 105
4.32 x 1015
+ 8.3%
2.76 x 1015
2.40 x 1015
- 13.%
1972
4.26 x 1015
4.26 x 1015
0.0%
2.97 x 1015
2.56 x 10L5
- 13.8%
19691
4.75 x 1015
4.08 x 1015
-14.1%
2.09 x 1015
2f20 x 1015
+
The values for 1969 are fitted from the 1960-1969 dz a base and
are presented only to provide a benchmark for comparison.
The values for 1970-1972 are predicted with the equations
estimated from the 1960-1969 data base.
5.3%
'I
Li
r-1
-55-
Supply limitations may be responsible for this, but until the supply
functions are incorporated into our simulation framework little more can
be said.
Note, however, that this bias is down from 20.5% for 1969,
the last year used in deriving our estimated relationships.
The bias
in oil varies from -5.0% to +8.3%, but in 1972 the predicted and actual
match exactly.
For electricity, although there was upward bias in 1969 at
the start of the simulation, the predicted consumption was from 10.4%
to 13.8% low for the 1970-1972 period.
This may be due to the same
influences that affected the natural gas predictions.
To show how well the estimated relationships do on a sate by state
basis, Figures 8 to 11 show plots of the actual vs. predicted values of
total consumption and consumption of each fuel for 1972, three years
beyond the data base used in the estimation procedures.
It can be seen
that substantial error does exist in the predicted fuel mix of some
states, but the equation for total energy consumption does quite well for
all states.
Probably the most glaring error in consumption for any fuel
exists at the far right on the plot for gas consumption.
The predicted con-
sumption for this state (New York) is 0.95 x 1015 BTU's, while the actual
was about 0.5 x 1015 BTU's.
Whether curtailments can explain this great
a discrepancy is questionable.
Although less discernible, errors of
the same percentage magnitude exist near the low end of the plot for
other states.
Reestimation using the 1970-1972 data should improve
these results significantly.
ANALYSIS WITH THE MODEL (Prdictions
to 1980)
The final set of analyses performed with the model were simulations
to the year 1980 for a set of alternative future fuel prices.
For these
simulations it was assumed that population grows at 1.4% per year and real
incomes grow at 3.3% per year over the 1969 base year in each state.
using mean values for the temperature variables,
following price scenarios were performed:
simulations with the
Then
-56-
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-60-
Case I
:
All fuel prices remain at their 1969 values.
Case II
:
Oil prices increase by 50% to the final consumer,
gas and electricity remain at their 1969 values.
L
Case III : All fuel prices increase by 50% over their 1969 values.
Case
IV :
Oilprices increase by 100%, gas and electricity
prices increase by 50%.
Case
V
Gas prices increase by 100%, oil and electricity
prices increase by 50%.
:
I
All price changes are assumed to be after deflation (relative to 1969
dollars), and it is further assumed that the
adjusted in 1980.
are
markets
completely
The results of the simulations for the total U.S.
and four states (Massachusetts, New York, Texas and California) are
summarized.
For the total U.S., the smmary
output displayed in Table
6 clearly shows that total demand and the fuel mix is quite sensitive
to the price scenario being assumed.
In Case I (where 1969 prices are
assumed to hold to 1980) total consumption almost doubles, between 1969
and 1980.
However, in Case V (the most severe in terms of its impact
on demand) total consumption in 1980 increases by only 13% over the
1969 value.
The effect of the price variations on the fuel consumption
mix is quite evident.
In Case III where all fuel prices are increased by 50% over their
1969 values, the 1980 consumption is reduced by about 25% below what it
The indicated price
would be if prices remained at their 1969 values.
elasticity of total demand is therefore about -0.5.
Cases IV and V show
that an additional 50% increase in oil price or gas price over that of
Case III has a very strong effect on oil and gas consumption respectively,
reducing consumption of these fuels to half their value in Case III. Furthermore, since such a large fraction of consumption in Case III is natural gas,
the further natural gas price increase in Case V has a much morc dramatic
effect on total consumption than the oil price increase in Case IV.
The output for individual states in Table 7 demonstrates the appealing
properties of the logit formulation.
r
In cases where the oil price is increased
L
L
.
-61-
SIMULATION RESULTS FOR 1980, TOTAL UNITED STATES
(x 1015 BTU's)
TOTAL
1969
U. S.
ACTUAL
CASE I
CASE II
CASE III
CASE IV
CASE V
GAS
6.334
16.31
17.55
12.52
13.08
7.63
OIL
4.754
5.10
2.47
3.08
1.43
3.45
ELEC.
2.092
4.03
4.35
3.39
3.55
3.89
13.180
25.44
24.37
18.99
18.06
14.97
TOTAL
CASE
I
CASE II
:
1969 Prices
:
Oil price up 50%
CASE III:
All prices up 50%
CASE IV :
Gas price up 50%, elec. price up 50%, oil
price up 100%.
CASE
V :
Gas price up 100%, elec. price up 50%,
Oil price up 50%.
T
A
B L E
6
I
-62-
S}RTLATIONS RESULTS FOR 1980(SELECTED STATES)
x 15 BTU's)
BTUs)
.( x ( 10
L
1969
ACTUAL
CASE I
CASE II
Gas
.105
.322
.387
.213
.238
.095
Oil
.495
.413
.224
.283
.142
.315
Elec.
.050
.097
.116
.089
.099
.102
TOTAL
.650
.832
.727
.585
.479
.512
MASSACHUSETTS
CASE III
CASE IV
CASE V
a
NEW YORK
Gas
.365
1.408
1.644
1.070
1.188
.586
Oil
.913
1.070
.562
.714
.356
.831
Elec.
.158
.294
3.43
.264
.293
.320
TOTAL
1.436
2.772
2.550
2.048
1.837
1.737
U
U
TEXAS
Gas
.340
.830
.841
.642
.646
.404
Oil
.014
.035
.015
.019
.008
.020
Elec.
.161
.212
.214
.163
.165
.183
TOTAL
.515
1.077
1.070
.824
.819
.607
Gas
.795
1.658
1.675
1.284
1.291
.825
Oil
.023
0.052
.023
0.027
0.012
.030
Elec.
.243
.438
.443
.354
.356
.403
TOTAL
1.061
2.148
2.141
1.665
1.659
1.258
F
CALIFORNIA
L..
4
T A B L E
7
Lif1
u
i
-63-
beyond the norm, a significant impact on the total level of consumption and
fuel mix occurs for Massachusetts and New York (large oil consuming states),
but creates only a minor change in consumption of Texas and California
(which consume only a very small amount of oil).
changes in gas price.
The opposite is true for
This is a good example of how the saturation property
of our fuel choice equations allows one to identify the geography of states
responding to changes in prices.
in Table 7
our model.
Even though only four states are summarized
the same type of information is available for all states in
-64-
Section VII.
CONCLUSIONS
I
L
The purpose of this paper has been to report the conceptual design
and estimation results of models for appliance and aggregate fuel choice
decisions in the residential and commercial sector.
We started with the
view that fuel utilization decisions could be viewed as a two-level
decision process.
Then, the theory for individual fuel choice decisions
was used as a framework for structuring our models of aggregate appliance
choice and residential and commercial fuel consumption.
Though not
restricting ourselves to the conditional logit theory, we have utilized
the appealing functional properties of the theory in the construction of
our models.
9
The application of these functional forms to appliance saturation data
for the year 1968 was quite successful.
Fuel prices were quite significant
(and of the proper sign) for house heating, water heating, cooking fuel, and
clothes dryer decisions.
In addition, the price and cross-price elasticities
D
were quite large for all the above uses.
The same functional forms were then applied to data on total fuels
consumption in the residential and commercial sector.
Here aain
they
P
seemed appropriate, with all estimated terms yielding the expected sign
r
and quite significant.
r
The estimated long run price elasticities and
cross-elasticities of the market share for each fuel varied in magnitude
from 0.0 to 1.3 for electricity; 0.0 to 1.34 for natural gas, and 0.0 to
1.89 for oil.
The model used to explain total demand for energy in the residential
and commercial sector is a simple flow adjustment model.
The long run price
and income elasticities of demand in this sector were estimated to be about
-
- 0.6 and 0.8 respectively.
[1
The short run (one-year) elasticities were about
one quarter of these values.
*
t~~~~~
-65-
The model that was estimated using data for 1960-1969 was then
used to predict consumption patterns by state for 1970-1972.
Although
errors for some individual states were quite large, the discrepancies
between the actual and predicted values for the total U.S. were much
smaller and much less variable.
Considering that 1) supply functions
have not yet been incorporated into the estimation and simulation
procedures, and 2) reliable data on the costs of appropriate durable
goods
was not available, it was felt that the results were quite
encouraging.
Finally, a set of simulations to 1980 were performed using some
hypothesized price scenarios.
The results show that much conservation
can be expected to take place in the residential and commercial sector
as a result of price increases.
Also, the geographic shifts in consumption
are highly dependent on both the particular fuel in use and the cost of
competing fuels in the region.
The results reported in this paper
will be quite useful for determining the magnitude and geography of
these shifts.
-66VIII.
~R
E F E R E N C E S
L
[1]
Fisher, Franklin, M., and Kaysen, C., "A Study in Econometrics: The
Demand for Electricity in the United States", North Holland
Publishing Company, Amsterdam, 1962.
[2]
Anderson, Kent, "Residential Demand for Electricity: Econometric
Estimates for California and the United States", Rand
Corporation, January 1972.
[3]
MacAvoy, P. W. and Pindyck, R. S., "Alternative Regulatory Policies
for Dealing with the Natural Gas Shortage", The Bell Journal
of Economics and Management Science, Autumn 1973
[4]
Khazoom, J. D., "The F.P.C. Staff's Econometric Model of Natural Gas
Supply in the U.S.", Bell Journal of Economics and Management
Science, Spring 1971.
[5]
Balestra, P., and Nerlove, M., "Pooling Cross-Section and Time Series
in the Estimation of a Dynamic Model: The Demand for Natural
Gas", Econometrica,
[6]
:
Volume
i
34, No. 3 (July 1966).
Houthakker, H. S. and Taylor, L. O.,"Consumer Demand in the United
States 1929-1970", Cambridge, Harvard University Press,
1966.
[7] McFadden, D., "Conditional Logit Analysis of Qualitative Choice
Behavior", in P. Zarembka (ed.), Frontiers of Econometrics,
Academic Press, New York, 1974.
I8]
Theil, Henry, "Statistical Decomposition Analysis", North Holland
Publishing Company, 1972.
19] Anderson, Kent P., "Residential Energy Use:
An Econometric Analysis"
(prepared for N.S.F.), Rand Corporation, October 1973.
110]
F
r
Stanford Research Institute, "Patterns of Energy Consumption in the
United States", Ja:uary 1972.
I.
4~~~~~~~~~t
A-1
APPENDIX
A
INTRODUCTION
In this appendix we present a discussion of the larger "interfuel
competition model" of which the work presented here is an important part.
We present it so that readers of this manuscript can get a feeling for the
direction that our research is headed.
The emphasis of the appendix is
on the theory and structure of the supply models to be used along with the
demand models described in the body of this document.
Currently, the
fossil fuel supply models that we have are nationally aggregated and based
on engineering estimates of supply responsiveness.
The electricity supply
model is an engineering-economic cost minimization routine, matching
electricity supply to electric load patterns so as to minimize production
costs.
Electricity supply functions have been constructed for each of
nine EEI regions.
THE STRUCTURE OF THE MODEL
The interfuel competition model is an engineering-econometric
simulation model for the medium to long range (3-30 years) interactions
of the primary fuels and secondary energy sources (coal, oil, natural
gas, nuclear fuels, and electricity) in the U.S. energy consuming markets.
Total energy demand is subdi. ided into residential and commercial,
industrial process heating, transportation, and electricity (a consumer
as well as a supplier).
Structurally, the model consists of an economic
framework for matching consuming sector demands with energy supplies in
a way consistent with consumer preferences and relative fuel prices.
This is done via the following features:
q
A-2
a.
By combining coupled equations that connect all primary
fuels and secondary energy sources within a total energy
system framework, a picture of how the different fuels
interact with each other in the market place over time
is provided.
b.
By focusing on the market processes in supply and
demand, a method for examining the effects of regulatory
policies and technological change on the energy supply
and consumption patterns is provided.
c.
By considering the time delays involved in capital investments and changes in demand, the inertia of the system
is incorporated into the structure.
d.
By identification and incorporation of the principal
factors influencing marginal and average costs in the
models for each energy source, a basis for market ricing
is developed.
and
.
i
Basically, the model combines an engineering description of the state
of the system (the configuration and magnitude of the physical
plant for
production and consumption of energy at each point in time) with the
economic considerations that affect the changes in state.
The model is
dynamic in that consumption patterns and cost trends affect the rate of
development of new supplies available to the energy markets, which in turn
uu=uc
ruel
prices
anu
energy consumption
trends.
F
The model is applied to the U.S. energy system and contains the dynamic
relationships relevant on a 3-J
year time scale.
The seasonal fluctuations
of demand are not included and, for this reason, the effects of storage and
F '
processed good inventories in smoothing these variations are neglected.
The intent is to concentrate on those phenomena which have their effects
on prices for periods of years, such as resource depletion, persistent
shortages or excesses in production capacity, or lasting exploration
successes or failures.
ri
L
I
A-3
The overall model structure is depicted in Figure A.1.
The exogenous
inputs to the model are the macro-economic variables that drive demand
in the upper portion and the resource characterizations in the lower
portion.
As determined from the rates of growth of demand and the
fraction of consumers replacing their energy consumption equipment over
each interval of time, some portion of the total demand in each of the
consuming sectors goes to the market place to "bargain" for energy.
This
portion of the total demand is termed the market sensitive demand in Figure
A.1.
The aggregate of those consumers who continue utilizing the same
fuels from one time period to the next is termed the base or ("locked in")
demand.
Depending on the fuel price configurations, the market clearing
process matches up supplies of energy to meet the market sensitive demand.
Electricity, as a secondary supplier
which utilizes the primary
fuels and competes in the marketplace with the primary fuels is simultaneously a supplier and consumer.
The sales to the ultimate consumer are
determined in the marketplace, and simultaneously this places a demand on
the primary fuels commensurate with those sales.
The price of electricity
to the consumers is then related to the price that must be paid for the
primary fuels, along with the other fixed and variable costs pertinent to
that industry.
The environmental, regulatory, and technological affects are shown
crossing the boundaries of the model in Figure A.1.
The programmed model
does not explicitly incorporate these influences, rather the user must
translate these effects into parameters, inputs, and structural changes
for the issue of interest.
The model can then be used to simulate the
implications of these changes on the overall system behavior.
In this
interactive mode, it becomes a tool for aiding in the analysis of future
alternatives.
Figure A.2 depicts the levels of aggregation and interconnections
as they exist in the programmed model.
A-4
OVERALL
MODEL
STRUCTURE - INTERFUEL
POPULATION
N
r
/
I
ENVIRON
M E NTAL
EFFECT
ID
EMAND
j
BY
l
U
3
N
SECTORS
I
-Sf/
REGULATI ON
PO
MACROECONOMIC INPUTS
-
0- l
COMPETITION
N
N1%
NN~~~~~~~~~~~~~".
IN.
"N
I
N1%
N
MARKEr-T
blNS I IVt
I
DEMAND
\
I
MARKET
CLEARING
PROCESS
I
I
I
....
_=
I
*N
I
FUEL
i
----
I
QUANTITIES /
PRICE!
/ I
l
I
I
I
I
I
I
I
SUPPLY
CAPACITY /
I
/
COSTS
I
-
I
I
\
I
I
II
/
P
.f
A
.
I
I
L RESOURCES
ECONOM ICAL
TO DEVELOP
-m
F-
I
TECHNOLOGICAL.
I~~~~
+/ ~/
~~I
~~I
_-
/
/
/
/
/
/
J.~~~~
CHANGE
I
EXPLORATI ON
I
-
j
Resource
~~i I
·--
Charocterization
Figure
A. 1I
I
A-5
BROAD ENERGY FLOW DIAGRAM
PRIMARY CONSUMING
SECTORS
Energy
Flows
PRIMARY FUEL SUPPLIERS
F I GU RE
A.2
A-6
A.1.
FOSSIL FUEL SECTOR ECONOMICS
Prices in the fossil fuel sectors are assumed to be related to marginal
cost of development.
The marginal cost of development may be thought of
as the cost of the next increment in supply output.
As will be seen shortly, the lower the existing reserves, the higher the
marginal costs of development and the more incentive that exists to explore
for further reserves.
In other words, if prices remain equal to marginal
costs of development, a feedback mechanism is built into the market pricing
structure of each sector to provide the incentive for exploration to increase
or decrease, depending upon how successful the exploration process is.
The next section explains this process in more detail.
the economics of development.
First, we present
X
For ease of presentation the theory and
terminology will be applied only to the oil industry; however, it applies
equally well to natural gas and coal.
I
Development Investment in Oil Supply
Development investment is an exchange of dollars for producing capacity
on a stock of reserves.
Since these reserves exist in finite units, called
reservoirs, higher investment at a particular location leads to higher costs
per unit output.
Let us designate as N the number of wells drilled into a
particular reservoir and
q
as the average initial capacity per well.
The
present value of an investment in production facilities to provide an initial
output Nq
is made up of two components, the initial capital investment and
the discounted future operating costs.
The initial capital investment may
be considered to be proportional to the number of wells drilled in a
particular formation, whereas the operating costs for any given well can
be approximated by a constant per unit time.
Consequently, the present
value of the initial investment in a particular formation can be approximated by
r
A-7
I = N (b + k/r)'
where
N
=
number of wells
b
=
costs of drilling and equipping a well
k
=
operation and maintenance costs per
unit time per well
r
=
discount rate
This investment brings forth an output form the reservoir that
declines with time.
The output may be approximated by an exponential
decline where the total integrated output equals R,
reserves in the reservoir.
the initial
If future output is discounted at a rate
"r", then the discounted future output from a reservoir (termed present
barrel equivalents or PBE), assuming continuous discounting, can be
approximated by
Nq o
PBE -
EQUATION A.1
(Nqo/R
0 0o) + r
Investment in development is an exchange of dollars for future
output, which when discounted to the present equals the PBE's in the
reservoir.
The marginal cost of development equals the ratio of the
incremental change in investment to the incremental PBE's procured by
that investment.
Mathematically this can be expressed, since both I
and PBE are a function of
, as
~~I ~~aN
aN
Di
MDC =
a3PE
PBE
the differentiation yielding
2
=MDC
MDC
b + k/r
0o
o-
(Nqo + r R )
r R22
rR0
*-
..
.
I
A-8
With the exponential decline model for reservoir time performance,
the decline rate "a" is approximated by Nq 0 /R o.
C _
If we define
Q
b + k/r
as the average cost per unit capacity for the reservoir, the marginal
development and average development cost functions (MDC and ADC respectively)
I
N
L
U
may be expressed as:
___
(a )"
__
MDC
=
CPBE
(a + r)
i
EQUATION A.2
-
ADC =
PBE
=
c (a + r)
t
i
EQUATION A.3
These two functions are what are used in the interfuel model as a basis
for the market pricing of fossil fuels.
In the present nationally aggregated formulation, the U.S. is considered as one reservoir.
The decline rate used in the model is then the
U.S. production reserve ratio.
The cost per unit capacity "c" represents
the average cost per unit capacity of new development wells drilled in
any given year.
Future effort will be devoted to regionalizing and
estimation this model structure for the fossil fuels.
rL
I
L
I
A-9
Investment in Exploration
A second investment alternative in fossil fuel supply is for exploration.
Exploration itself does not result in more PBE's, for a reservoir must be
developed with some initial capacity before any of the oil in place in that
reservoir can be considered a PBE.
The significant returns for the explor-
ation effort do not accrue until the reservoir is fully developed or the
knowledge and rights to it are sold.
At this point, the rewards for finding
the reservoir materialize.
What are these rewards?
previous section.
Let's go back to our simple example in the
Suppose for some exploration expenditure (IE) we find
a reservoir with a fixed amount of recoverable oil "R
0
of
,cost per capacity
b", and operation and maintenance costs per year "k".
Then under
optimal development, we develop until marginal development cost equals
the price.
This is depicted in Figure A.3.
Also note that the price
received for the output of this reservoir is above the average costs.
The difference between the marginal costs (MDC) and the average (ADC)
costs represents the profit per PBE when undertaking this investment.
On a per year basis,the magnitude of the returns to exploration is
equal to production times the difference between price and the average
costs of development.
One would expect that investment in exploration
would be closely related to the historical trends in these returns.
The component model for petroleum exploration in the current working
nationally aggregated interfuel framework utilizes this relationship
to derive the levels of investment in exploration.
Engineering estimates
of the cost of drilling exploratory wells are then used to derive drilling
activity and reserve additions in the model.
· r.UU·IIIIII-·-----------
I
A-10
a
RETURNS TO EXPLORATION
.
(RE)
Cost/ PBE
E.
· J
l
i
Pric
, _
_
_m
_
ADC
I!
I
br+
qo
Number
of Wells
B
INDUSTRY COST CURVES ·
I
Cost/PBE
Industry
/ MDC
Pric
*1
m
Demand
Capacity
-
·
paocity
9
I G U R E
A.3
1
A-11
A.2
INVESTMENT AND PRICING IN ELECTRICITY SUPPLY
Electricity, as an energy supplier, is unique in that it has no energy
storage capability.
Because of this, the capacity levels required to
maintain a reliable supply are governed by the peak power requirements and
not the average output levels.
Consequently, the theory for investment
and pricing in electricity supply deviates from the primary fuels models
given previously.
This deviation is substantial in two respects.
The first of these
is that electrical output has generally been priced at average cost
rather than at the long run marginal cost level, and the two need not
be the same.
This is in reality what the regulation in electricity rate
structures attempts to achieve.
Secondly, the decision to built new
capacity is the result of trade-offs in economics and reliability.
To
supply electricity at lowest cost, it is desirable to keep reserve
capacity (excess capacity over and above peak power requirements) as
small as possible, so that at any given level of electricity demand the
average costs are minimum.
Counter to this, to reliably meet peak power
requirements, there is a desire to keep excess reserve capacity, which
increases the average costs of energy produced.
The optimum value of reserve capacity is the minimum needed to
reliably meet peak power requirements.
The investment decision in electricity
supply is basically governed by the projected load, or more precisely, the
load duration curve and the desired level of supply reliability.
Before
going into the capacity expansion logic, let's digress for a moment and
discuss what is meant by a load duration curve.
A-12
THE LOAD DURATION CURVE
This load duration curve characterizes the fraction of time that the
electrical load is equal to or greater than various output levels.
Figure A.4
is shown such a curve for New England for 1971.
In
For example,
the point at 50% on the abscissa indicates that the load for New England
was 7683 MW or higher for 50% of that year.
at 4322 MW and the maximum at 12,005 MW.
I
The minimum load is indicated
Since the load varies in such
extremes, and also because utilities are expected to supply the load at
all times, the economics of capacity expansion in electricity supply
U
are markedly different than for other energy suppliers.
CAPACITY EXPANSION
For convenience, let's divide the load duration curve of Figure
A.4 into three regions, say, at load durations of 80% (7007 hours) or
more, 20% to 80% (1751 to 7007 hours), and less than 20% (1751 hours).
Furthermore, let's designate these as base load, cycling load, and peak
r
load, respectively.
L
The most economical form of capacity for supplying
these loads is quite different.
r
The principal economic parameters of electrical generating units are
the capital costs, fuel costs, and heat rates (or conversion efficiencies).
In general, the higher the capital cost per kw. capacity, the more efficient
is the unit that can be purchased, and in turn, the lower the operating costs
that are incurred.
The average cost (in cents per kwh.) of a generating
unit with a capital cost of k1 dollars per kw., fuel cost of k 2 cents per
million BTU's, and heat rate H
BTU's per kwh. is given by
r
10
AC =
U
ka
I-+
k
H
2
j106
+ 0c
EQUATION A.4
A-11
A.2
INVESTMENT AND PRICING IN ELECTRICITY SUPPLY
Electricity, as an energy supplier, is unique in that it has no energy
storage capability.
Because of this, the capacity levels required to
maintain a reliable supply are governed by the peak power requirements and
not the average output levels.
Consequently, the theory for investment
and pricing in electricity supply deviates from the primary fuels models
given previously.
This deviation is substantial in two respects.
The first of these
is that electrical output has generally been priced at average cost
rather than at the long run marginal cost level, and the two need not
be the same.
This is in reality what the regulation in electricity rate
structures attempts to achieve.
Secondly, the decision to built new
capacity is the result of trade-offs in economics and reliability.
To
supply electricity at lowest cost, it is desirable to keep reserve
capacity (excess capacity over and above peak power requirements) as
small as possible, so that at any given level of electricity demand the
average costs are minimum.
Counter to this, to reliably meet peak power
requirements, there is a desire to keep excess reserve capacity, which
increases the average costs of energy produced.
The optimum value of reserve capacity is the minimum needed to
reliably meet peak power requirements.
supply is basically governed
The investment decision in electricity
y the projected load, or more precisely, the
load duration curve and the desired level of supply reliability.
Before
going into the capacity expansion logic, let's digress for a moment and
discuss what is meant by a load duration curve.
A-13
'-4
* a
u n o
0
30 0
0
00
OQ)
~0
a'O
r.%OC)
0
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%0
D
cvn
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r%
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co00 0
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r
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0
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cn
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cn
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A-14
where
U
AC
=
average costs in cents per kwh.
k
=
capital cost (dollars/kw.)
a
=
annual write-off rate (/year)
k2
=
fuel cost (cents/MMBTU's)
=
heat rate (BTU's/kwh.)
=
utilization factor (hours per year)
=
operation and maintenace costs (cents/kwh.)
rH
r
U
c
For the three different types of units, varying inversely in capital costs
and operating costs, the average cost per kwh produced as a function of
utilization is shown in Figure A.5.
The bottom profile of these curves
represents the minimum cost production profile.
For low utilization
factors (U< U c) the most economical type of unit to use is a peaking
unit (low capital cost, high operation costs).
For high utilization factors
(U >Ucb,) the most economical is a base load unit (high capital cost, low
F
operating costs).
t
The design of the most economical generation mix to meet the load
curve of Figure A.4 can be ascertained in Figure A.6., at least for this
simplified example.
Using the break points from Figure A.5 for the most
economical type of unit versus utilization, these can be superimposed on
the load curve to define capacity
requirements of each type of unit.
Neglecting forced outages and reserve requirements, the optimal generation
mix is as shown in Figure A.6.
In reality, due to complexities of equipment reliabilities and to
uncertainty in projected loads, the system planning operation for a utility
is much more complex than the previous example.
K
!
However, the same overall
logic applies, only uncertainty considerations are incorporated.
L
I
~L
A-15
c
CX
0
0.2
N
= o
U
CMI
I!
a
I
t.
!
I
I
.I I .
0
-
I'
Us
I
Pa
Up
CH
L
'o
C)
C0. u
6u
0
A-16
U
c~~~
*
*.
0
n
o
n
U
>o
M
u
-
H
>4ba
a
m
l.0 i~ '.U
0
_
H
E-4
0
_
'a
wo
r,
P4
izl
¢
ID
r9
Ps
.4
H
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Ok
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c
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UL
F
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~
A-17
A model for the electricity sector utilizing the aforementioned
This model is designed to
structure has recently been completed.
replicate from the fuels point of view the fuel choice and expansion
planning decisions of electric utilities for the nine EEI regions of
the U.S.
In a gross way, it duplicates
the techniques used by utilities
for expansion planning, generation scheduling, and production costing.
Alternative plants are assigned to nine categories on the basis
Then, as
of capital cost, heat rate, fuel capability, and duty cycle.
determined by these parameters,
the investment decision is modeled by
finding the optimum (minimum cost) configuration of plants to meet the
projected load duration curve.
Next, the model generates electricity for
each of nine regions by minimizing the cost of production.
Finally, elec-
tricity is costed out using the economic characteristics of the stock of
plants that exists for any given year.
Costs of electricity, nuclear and
fossil fuel requirements for generation and capital investments for the
regional growth patterns of electricity demand are computed by the model,
both regionally and nationally.
Figure A.7 shows how the electricity model interconnects with the
rest of the overall fuels modeling program.
L
A-18
INTERFUEL MODEL WITH REGIONAL
ELECTRICITY CAPABILITY
I
I
____
I
Electricity Price
DEMAND FUNCTIONS BY
SECTOR AND STATE
- 4i
Electricity Demand
~-
a
Direct Fuel
Consumptior
Fossil
e
Fuel
- -
Prices
. _
_
__
g
REGIONAL ELECTRICITY
MODEL
ENERGY MARKETS
'
Ir
F
I
I
I
1. Expansion Planning
-
2.
I
Producing Costing
__
.-
_.
I
a
i~~~~~~~
Electricity
fuel requirements
-
w
__
5f
i
FOSSIL FUEL SUPPLY
FUNCTIONS
I
Fos il Fuel
Prices
Fuel< Consumption
by Sector
F
I
G U R E
b
A.
7
P-1
A-19
A.3
THE ENVIRONMENTAL EFFECTS OF ENERGY SUPPLY AND CONSUMPTION
Energy production and utilization affect the environment in all
phases of its delivery and use:
extraction, transportation, processing,
conversion, storage, distribution, and utilization.
Different fuels have
different environmental effects, and alternative means of performing each
of the above activities have different characteristics in terms of costs,
efficiency, and use of the environment.
A first effort at quantification
1
of these characteristics for the energy system has been completed .
A
data base has been prepared that translates the level of activity by
process (e.g. coal extraction via surface mining, or electric power
production via oil) into the air and water effluents and the land and
occupational impacts, both for presently existing environmental controls
as well as a set of controls and technologies which could be economically
feasible in the decade ahead.
An effort is now ongoing to combine this
environmental data with the interfuel model of the energy system.
The
combined system will provide a framework for assessment of the environmental
impacts of alternative energy policies, as well as provide a tool for
evaluation of the effectiveness and costs of a wide variety of potential
controls in the environmental area.
Table A.1 summarizes the list of environmental effects assembled for
each activity in the supply and consumption chain for energy.
These
environmental coefficients are catalogued for both presently existing
controls and technology as well as a set of controls and te:chnologies
which would become economically feasible in the next ten to fifteen years.
The coefficients are all expressed on a commom base (per 1012 Btu's input
to the process), and accompanying the controlled coefficients in each case
is the change in cost incurred in performing the activity (whether extraction,
I
This project was done by Hittman Associates, Inc., under contract
with the Council on Environmental Quality EQC-308.
I
A-20
OUTPUTS OF THE ENERGY - ENVIRONMENTAL
I
IMPACT MODEL (PER 1012 BTU's INPUT)
I
1.
Total land impact (acres of land disturbed).
2.
Total tonnage of water pollutants.
3.
Total B.O.D. of water wastes
4.
Total suspendal solids tonnage in water wastes.
5.
Total acid and alkali tonnage in water wastes.
6.
All water wastes other than B.O.D., solids, and acids.
7.
Total thermal discharge to water (BTU's).
8.
Total radioactive release to water (curies).
9.
Total tonnage of air pollutants.
(tons).
10.
Total CO tonnage.
11.
Total NO
12.
Total SOx tonnage.
13.
Total particulate tonnage.
14.
Total HC tonnage.
15.
All air releases other than CO, NOx, SOx, HC, and particulates.
16.
Total radioactive release to air (curies).
17.
Total tonnage of solid waste.
18.
Deaths, injuries, and man days lost.
x
I
I
tonnage.
T A BL E
A.1
1
A-21
transportation, or whatever for the particular fuel) in a controlled
manner.
This environmental impact data will be used with the interfuel
model as depicted in Figure A.8.
The output of the interfuel model is
the fuel consumption configuration by sector for alternative fuels.
Depending upon the environmental controls being exercised or hypothesized,
the environmental coefficient matrix translates the fuels consumption
into the various effluents and impacts shown in Table A.1.
Note that
the controls used also affect the cost inputs in the interfuel model,
The interfuel
which in turn, affect prices and fuels consumption.
model will serve the function of translating the changes in costs due
This is needed so that
to the controls into changes in fuel consumption.
one can evaluate whether a control scenario, no matter what the goal,
does not simply create a problem in another sector.
4V
A-22
--
'ENERGY-ENVIRONMENT INTERACTIONS POLICY MODEL
Demand
-.
-
-1
I
I
I
I
I_
; Cosmto
Fuels Consumption
and Prices
I
I
Environmental
Coefficients
cccc
·I
iI
I
I
L
__
Ali
---
Costs
e
Controls
( Environmental
Policy)
4
I
Efficiency
-
____-
I
I
I
cc
Controlled Coefficients
ucc
Uncontrolled Coefficients
FIGURE
'
I
I
Effluents
L-- - LandRequirements
Occupational
Hazard
A.8
L