J. Munksgaard, K.A. Pedersen r Energy Economics 22 2000 423]440 424
In the present article we focus on CO emissions associated with private
2
consumption. Our main point is that changing consumer habits and household demand towards more environmental detrimental commodities represents the main
challenge with respect to CO emission reduction targets.
2
Using structural decomposition analysis, we have analysed the factors affecting the development in CO emissions from private consumption over the period
2
1966]1992, distinguishing between direct and indirect CO emissions. Direct CO
2 2
emissions are generated by direct household energy use whereas indirect CO
2
emissions are generated in the industrial sectors producing non-energy commodi- ties demanded by the households. The study thus examines the influence of both
consumer behaviour and behaviour of the firm. The article decomposes CO emissions from Danish household consumption at
2
the global level, i.e. emissions from imported commodities as well as commodities produced in Denmark are taken into account. The reason for doing so is that
Danish consumers are responsible for the global environmental consequences of their consumption.
1
Section 2 below describes the model and methodology while Section 3 describes the data applied and Section 4 reports the results of the analysis. Finally, Section 5
summarises the conclusions.
2. Model and methodology
Over the past decade, decomposition analysis has proved to be a useful tool for Ž
analysing changes in energy consumption e.g. Boyd et al., 1988; Chen and Rose, 1990; Li et al., 1990; Rose and Chen, 1991; Liu et al., 1992; Lin and Polenske, 1995;
. de Bruin et al., 1996 . A few studies have also decomposed changes in emissions
Že.g. Halvorsen et al., 1991; Common and Salma, 1992; Ang and Pandiyan, 1997; .
Chang et al., 1998 . With regard to Danish data, decomposition analysis has been applied to changes
Ž .
in energy consumption from production Howarth et al., 1993; Pløger, 1984 , Ž
. changes in CO emissions Torvanger, 1991 and changes in CO , SO and NO
2 2
2 x
Ž .
emissions from production Wier, 1998 . Finally, changes in nitrogen loading from agricultural and industrial production have been decomposed by Wier and Hasler
Ž .
1999 . In the present study, decomposition addresses the production sectors as well as
the household sector. Compared to the studies mentioned above, this study distinguishes itself by two important features: Firstly, it includes the households’
direct demand for energy and secondly, it handles changes in commodity mix in private consumption at a highly disaggregated level, encompassing 66 commodities.
1
Analysing the problem in this way implies that consumers are not accounted for changes in CO
2
emissions due to changes in international trade, i.e. reduced CO emissions from Danish household
2
consumption due to decreasing national production and increasing import of highly CO intensive
2
goods.
J. Munksgaard, K.A. Pedersen r Energy Economics 22 2000 423]440 425
In addition, the Danish data sets used are very comprehensive, covering 117 production sectors, 30 types of energy and 66 commodities, and the analysis period
is very long, namely from 1966 to 1992. The model applied is an extended input]output model based on Danish input-
output tables plus energy flow matrices and CO emission factors. These can be
2
linked together due to the use of common classifications. The strength of the model is that it covers all sectors of the economy and operates at a very
disaggregated level. Moreover, it covers the entire energy production and consump- tion cycle, and is able to distinguish between direct and indirect uses of energy.
In the present study we focus on total CO emissions associated with Danish
2
household consumption, distinguishing between direct and indirect emissions. The direct emissions
are emissions associated with the consumption of energy commodi- ties in the households, i.e. electricity, district heating, gas and other liquids. The
indirect emissions are emissions associated with the production of all other com-
modities for households, i.e. emissions that takes place in the industry producing furniture, food, clothes, services etc. used in households.
Fig. 1 illustrates the basic model. Note that also deri
¨
ed emissions , i.e. emissions
Ž .
associated with direct and indirect production of inputs are included.
Fig. 1. Direct and indirect CO emission.
2
Total CO emissions are defined as
2
Ž . E s E q E
1
h p
J. Munksgaard, K.A. Pedersen r Energy Economics 22 2000 423]440 426
where E
is total CO emissions from households;
2
E is direct CO emissions from households; and
h 2
E is indirect CO emissions from households.
p 2
The decomposition analysis is carried out in two steps. First, direct CO
2
emissions from household energy use are analysed using a simple energy emission model. Second, indirect CO emissions are analysed using an extended input]out-
2
put model that also incorporates energy and emission matrices. 2.1. Decomposition of direct CO emissions
2
Ž . Model 2 below estimates direct CO emissions from household energy use as a
2
product of total energy consumption and the composition of energy types in the household and energy supply sectors.
Ž . E s Q M F
2
h h
h
where: E
denotes a scalar of total direct CO emissions from households;
h 2
Q is a 1 = 5 vector including the absolute level of five categories of household
h
energy consumption, i.e. electricity, gas, oil, gasoline and other heating Ž
. primarily district heating, coke and coal ;
M is a 5 = 30 matrix of fuel mix in the household sector, i.e. demand for 30
h
energy types per unit of total energy demand for five energy consumption categories; and
Ž F
is a 30 = 1 vector of CO emissions per unit for 30 energy types cf.
2
. Appendix A . The emission factors are constant for 27 of the 30 types of
energy as they solely depend on the carbon content of the fuel. For three Ž
. types, however electricity, district heating and gas from gasworks the CO
2
emission factor depends on fuel mix in the energy supply sector, and consequently changes over time.
Ž . According to model 2 , direct CO emissions depend on changes in the factors
2
Q , M and F. The decomposition analysis is carried out by changing the factors
h h
one-by-one in order to quantify the contribution of each factor to total change in emissions. The contribution of each factor, e.g. Q , is estimated as the change in
h
Ž .
Ž .
the factor DQ multiplied by the other factors M and F . The other factors
h h
may figure at base-level or at current-year level, however. Thus all changes may be weighted using either base-year values for the other two factors or current-year
values. Both approaches cause considerable bias, however. This can be overcome, Ž
. using structural decomposition analysis as introduced by Fujimagari 1989 and
Ž .
Betts 1989 in which the effect of a change in each factor is determined using an
J. Munksgaard, K.A. Pedersen r Energy Economics 22 2000 423]440 427
average of the two approaches.
2
Thus, the total change in emissions from time t y
1 until time t is Ž .
Ž .
Ž . E t y E t y
1 s DQ q D M q D F 3
h h
h h
where: DQ
is the effect of changes in household energy consumption level;
h
D M is the effect of changes in the composition of energy types in household
h
energy consumption; and D F
is the effect of changing emission factors, i.e the effect of fuel mix changes in energy production.
Each element in the decomposition formula has the same general form. For reasons of brevity, only one effect } the effect of fuel mix changes in households
Ž .
D M } is used as an example:
h
Ž . w
Ž . Ž
.x Ž . D M s 1r2 Q t y 1 M t y M t y 1 F t
h h
h h
Ž . w Ž .
Ž .x Ž
. Ž
. q
1r2 Q t M t y M t y
1 F t y 1 3a
h h
h
2.2. Decomposition of indirect CO emissions
2
Ž . Model 4 below estimates the indirect CO emissions from household consump-
2
tion by using the extended input]output model as introduced by Leontief and Ford Ž
. 1972 .
y 1
N
Ž . Ž
. Ž .
E s F M aR I y A
C c c 4
p p
p
where: a
denotes element by element multiplication E
denotes a scalar of total indirect CO emissions in production sectors as
p 2
a consequence of production of goods for household consumption; F
is a 30 = 1 vector of CO emissions per unit of consumption of each of
2
the 30 energy types; M
is a 30 = 117 matrix of fuel mix in the production sectors, i.e. demand
p
for 30 energy types per unit of total demand for energy for all produc- tion sectors;
R is a 1 = 117 vector of energy intensities, i.e. total energy consumption
p
per unit of production in all 117 sectors;
2
The choice of decomposition method is a question of choosing how to handle the interaction effect i.e. that part of total change for which no single factor is responsible. There is no unambiguous and
correct way to handle the interaction effect, and several approaches have been applied during the last decade. Some of these apply different indices, of which the Divisia based index approach is the most
Ž .
Ž .
Ž .
commonly used, see, e.g. Boyd et al. 1987 , Li et al. 1990 and Liu et al. 1992 .
J. Munksgaard, K.A. Pedersen r Energy Economics 22 2000 423]440 428
Ž .
y 1
I y A is the 117 = 117 Leontief inverse matrix;
C is a 117 = 66 matrix of the composition of consumption commodity
aggregates, i.e. 66 private consumption commodity aggregates appor- tioned by production sectors;
c is 66 = 1 vector of aggregate commodity mix in private consumption, i.e.
demand for 66 commodities per unit of total consumption; and c
N
denotes a scalar of total private consumption. Ž .
According to model 4 , indirect CO emissions change as a consequence of
2
Ž .
y 1
N
changes in seven factors: F, M , R , I y A , C, c, and c . Whereas C, c, and
p p
c
N
are factors of consumer behaviour, i.e. demand for consumption commodities, Ž
.
y 1
F , M , R , I y A
are factors of behaviour of the firm, i.e. demand for inputs in
p p
the energy supply sector and other production sectors. The total change in emissions from time t y 1 until time t is
y 1
N
Ž . Ž
. Ž
. E t y E t y
1 s D F q D M q D R q D I y A q
DC q Dc q Dc
p p
p p
Ž . 5
where: Ž
D F is the emission factor effect or effect of fuel mix changes in energy
. production ;
D M is the effect of fuel mix changes in the production sectors;
p
D R is the effect of changes in energy intensities;
p
Ž .
y 1
D I y A is the input mix effect;
DC is the effect of changes in mix within aggregated commodities;
Dc is the effect of changes in mix between aggregated commodities in
private consumption; and Dc
N
is the effect of change in total consumption level. Again, each element in the decomposition formula has the same general form.
Ž .
Using the effect of changes in energy intensity D R as an example, this element
p
is
y 1
Ž .
Ž .
Ž . Ž
. Ž
. Ž . Ž .
D R s 1r2 F t y 1 M t y 1 a R t y R t y 1 I y A
t C t
Ž .
p p
p p
y 1
N
Ž . Ž .
Ž . Ž .
Ž . Ž
. Ž
. c t c
t q 1r2 F t
M t a R t y R
t y 1
I y A
Ž .
p p
p
Ž .
Ž .
N
Ž .
Ž .
C t y 1 c t y 1 c
t y 1
5a where a denotes element by element multiplication.
3. Data
