ENERGY CONSUMPTION PREFERENCES AND CHANGES IN HOUSEHOLD
STRUCTURE. THE CASE OF UNITED STATES
Michael Dalton, U.S. National Oceanic and Atmospheric Administration, Seattle WA USA, +1 206 526 655,
michael.dalton@noaa.gov
Brian C. O'Neill, National Center for Atmospheric Research, Boulder, CO USA, +1 303 497 8118, boneill@ucar.edu; and
International Institute for Applied Systems Analysis, Laxenburg Austria,
Katarína Zigová, International Institute for Applied Systems Analysis, Laxenburg Austria, +43 2236 807 394, kzigova@iiasa.ac.at
Overview
The contribution to climate change of emissions from the combustion of fossil fuels is a concern for scientists and policy makers.
The energy-economic growth models used to project future energy demand and emissions usually assume that populations consist
of a large number of identical (i.e. representative) households which consume a single good. These assumptions ignore potentially
important differences in the composition of consumption among various households, which could affect projections of the
aggregate demand for energy (Dalton et al. 2008). Household age and size appear to be the most important demographic factors to
consider for energy demand in developed countries (Jiang and O'Neill 2007). In this paper, we improve on this possibly important
drawback of these models using a so called population-environment-technology (PET) model which allows for broader household
structure and different consumption preferences of households. We accomodate to this model a specific demographic structure of
consumption preferences via substitution elasticities between energy-related and other consumer goods, which result from a
separate econometric model on household demand. There is more than just accounting for novel features in an energy-economic
model. Due to our approach we are able to see shifts in projections of future energy demand and emissions as an immediate
consequence of changing household structure which inherently carries changes in consumption preferences over time.
Methods
The energy-economic model we use in our analysis is a dynamic computable general equilibrium model designed to analyze
economic trade-offs associated with production use of fossil fuels and carbon dioxide emissions. It achieves equilibrium via
concept of perfect foresight. The model goes beyond representative household as it may accommodates various types of
households. Furthermore it has a big level of detail in energy sector of economy, and it describes patterns of technological change
that may differ across industries and inputs. The model was named thanks to these three distinct features as populationenvironment-technology (PET) model and is profoundly described in Dalton et al. 2008a.
Demographic factors of households and households' consumption patterns are central to our analysis, hence we briefly introduce the
underlying parts of the model here. The version of PET model used in this paper assumes several household types stratified by age
and size. Each household type consists of large number of households of the same type. This is a consistent extension of the
neoclassical growth theory. In the PET model each household type i is maximizing its infinite horizon utility choosing optimal
consumption c ijt and savings x it sequences, i.e
max U i cijt =
1
∞
∑
m
t
nit
t= 1
∑
ijt c ijt
i
i
,
j= 1
with respect to budget constraint
m
p jt c ijt=
∑
j= 1
and capital law of motion k it 1= 1−
1−
it
w t l it
1−
it
r t k it g it
x it . Household choses between m consumption goods, while its consumption is
limited by taxed labor and capital income and lump-sum government transfers. Note, that the PET utility functions U i contain
parameters
parameter
i
i
k it
, expressing substitution elasticity between consumption goods as
together with consumption shares
ij
1− / 1−
i
. The elasticity
, represent the consumption preferences of the household type i . In most
general equilibrium models the consumption preferences are expressed by a single value
reflecting the preferences of a
representative household. However, the choice of this value is important to the model results. In our paper, we use instead thorough
empirical estimates of i based on a system of households demand equations estimated using US consumption expenditure survey
data (CEX). For details of this approach see our previous study (Dalton et al. 2008b). There exists a minimal number of household
types where the i significantly differ from each other. These household types and their consumption preferences we used to fill
the PET model. The model is then calibrated using the CEX data, US production data and projected data of US population and
households. The PET model is solved numerically via simulation procedure coded in fortran language. The result of the PET model
can be presented either as projections of future energy demand or emissions.
Results
In our previous study (Dalton et al. 2008b) we estimated a broad spectrum of substitution elasticities for household types differing
by age and size. Further testing revealed that some of the elasticities are not significantly different from each other. We re-estimated
the model of household demand again but for smaller number of household types. The underlying set of elasticities kept the
structure observed by original 10 household types and at the same time smaller number of types simplifies the accommodation of
these parameters into PET model. The final set of distinguished household types consists of young (age 15-34) and small (size 1-2);
young and large (size 3+); middle aged (age 35-64) and small; middle aged and large; and finally, small old households (age 65+),
i.e altogether five types. The estimates of the substitution elasticity 1 / 1− i range from 0.19 to 0.58. In comparison, Goulder et
al. (1999) use a value of 0.85 for a representative household in the U.S., and Cremer et al. (2003) estimate a value of 0.27 for a
representative household in France. The PET model uses population projections and projected population distribution living in
households stratified by age and size groups. These projections serve us to construct series of household types distribution of the
above five types at each point in time during considered period. Given that the population distribution over the household types
changes over time, the consumption preferences embedded in
i
and consumption shares
ij
vary with this distribution.
The results of the PET model are in the form of energy demand and emission patterns over the 2000-2100 timespan. We run three
simulation variants of the PET model. First one is a benchmark model with a single representative household and no changes in age
or size structure over time. Second variant includes whole complexity of heterogeneous households what includes all five
household types with underlying heterogeneity in consumption patterns ( i , ij ), initial capital k i0 and labor supply l i . The
, j , while age-size specifications in
third variant is a simplification of the second, where single consumption pattern is assumed
initial capital and labor supply are kept over the household types. This variant is pursued to clear whether a simpler model would
not perform sufficiently well. Our results suggest that model with representative household overestimate the future emissions by 1337%, depending on the population projection used, as compared to the simpler variant of heterogeneous households model. When
accounting also for different consumption preferences, the overestimation of the future emissions is scaled down to 11-31%.
Conclusions
Trends in population aging and shrinking household size will likely continue over the next 40-50 years in developed countries such
as the U.S. Jiang and O’Neill (2007) project that the proportion of old households (65+) could reach as much as 25% of the U.S.
population by 2050, while small households (one or two members) will peak around 2040 with 42% of the total U.S. population.
These values compare with 12% and 35%, respectively, in 2000. Existing differences in consumption patterns of different
households may have an effect on future energy demand and carbon emissions. The presented version of the PET model has
capacity to find out the extend of these effects. Its results may be well applied to other societies where this kind of demographic
changes are to be expected in the coming 100 years.
References
Cremer, H., F. Gahvari and N. Ladoux (2003). Environmental taxes with heterogeneous consumers: an application to energy
consumption in France, Journal of Public Economics, 87, 2791-2815.
Dalton, M., B. C. O'Neill, A. Prskawetz, L. Jiang, J. Pitkin (2008a). Population aging and future carbon emissions in the United
States. Energy Economics, 30, 642-675.
Dalton, M., B. C. O'Neill, K. Zigová (2008b). Effects of Household Age and Size on the Elasticity of Energy Consumption,
Proceedings of the IAEE 2008 Conference.
Goulder, L.H., I.W.H. Parry, R.C. Williams and D. Burtraw (1999). The cost effectiveness of alternative instruments for
environmental protection in a second-best setting, Journal of Public Economics, 72, 329-360.
Jiang, L. and B. C. O'Neill (2007). Impacts of demographic trends on US household size and structure, Population and
Development Review, 33, 567-591.