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A Shapley Decomposition of Carbon EmissionsWithout Residuals
ARTICLE in ENERGY POLICY · FEBRUARY 2002
Impact Factor: 2.58 · DOI: 10.1016/S0301-4215(01)00131-8
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FACULTEIT ECONOMIEEN BEDRIJFSKUNDE
HOVENIERSBERG 24B-9000 GENT
Tel. : 32 - (0)9 – 264.34.61Fax. : 32 - (0)9 – 264.35.92
WORKING PAPER
A Shapley Decomposition of Carbon Emissions
without Residuals
Johan Albrecht a, Delphine François a and Koen Schoors b1
December 2001
2001/123
a Ghent University, Faculty of Economics and Business Administration (CEEM), Hoveniersberg 24,
9000 Ghent, Belgium (E-mail : johan.albrecht@rug.ac.be ; delphine.francois@rug.ac.be ) b Ghent University, Faculty of Economics and Business Administration (CERISE), Hoveniersberg 24,9000 Ghent, Belgium (E-mail : koen.schoors@rug.ac.be)
1 We wish to thank Dirk Van de gaer for very useful comments on this paper that will mainly bear fruitin the next paper on this topic.
D/2001/7012/24
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Abstract
Conventional decomposition techniques for historical evolutions of carbon emissions present path
dependent factor weights of selected variables next to significant residuals. Especially for analyses over
long periods with many variables, high residuals make it almost impossible to derive reliableconclusions. As an alternative, we present the Shapley decomposition technique for carbon emissions
over the period 1960-1996. This technique makes it possible to present a correct and symmetric
decomposition without residuals. The starting point of our analysis was an extended Kaya Identity with
nine components.
In a study of four countries, the Shapley decomposition showed that the carbon intensity of energy use
and the decarbonization of economic growth – variables that are targeted with current climate policy
measures - have more effect on total emissions than generally suggested in conventional decomposition
exercises. Another interesting conclusion from our analysis was that the effect of population growth on
emissions can be for some countries more important than the decarbonization efforts.
Keywords : carbon dioxide emissions ; (Shapley)decomposition ; Kaya Identity
1. Introduction
In many fields of social sciences, decomposition techniques are used to help
disentangle the impact of various contributing factors. An analysis of the driving
factors of energy-related carbon emission patterns can provide useful information for further policy studies on national strategies and the use of flexible instruments in
climate policy. Specifically, a decomposition of total CO2 emissions over a number of
contributing factors sheds light on some crucial parameters – like the ongoing
decarbonization of energy services or the rate of autonomous energy efficiency
improvements- that are used in scenarios to calculate the possible cost of climate
policy scenarios for developed countries.
While sophisticated forecast technologies are available for the latter type of exercises,
traditional decomposition analyses with a limited number of factors still yield
important residuals, even over short periods of time. Another problem is that the value
of the contribution assigned to any given factor depends on the order in which the
factors appear in the elimination sequence. Factors that are not treated symmetrically
lead to an important ‘path dependence’ problem (Shorrocks, 1999). This strongly
reduces the relevance of decomposition exercises for studies over longer periods.
Next to these imperfect decomposition methods, the literature has recently come up
with a number of methods that yield a perfect decomposition. We add to this literature
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by proposing another perfect and symmetric decomposition method, based on the
Shapley value.
After an introduction to the Kaya Identity, we first work with four contributing factors
or components (carbon/energy, energy/GDP, GDP/population and population) for the
period 1960-1996. We then proceed with a more complex variant on the Kaya-
identity. Here the interpretation of the results becomes difficult, as there is no way to
allocate the residual. Hence a perfect decomposition is required. For the same data set,
we present the result of a traditional decomposition and the results of the Shapley
decomposition. Our calculations are based on data for Belgium, France, Germany and
the United Kingdom. In a last step, we decompose the first two factors over three
economic sectors (industry, transport and other sectors) and discuss the main findings
from this detailed Shapley decomposition.
2. The Kaya Identity and a decomposition for four countries
The aim of a decompostion analysis is to reveal the importance of distinct
components or factors that drive historical data. The relative weight of each factor in
the observed change can be relevant information for policy measures. In the field of
climate policy, reliable information on the ongoing decarbonisation of industrialised
economies and on the sensitivity of energy intensity to energy price shocks is
necessary input for policymakers. This information is necessary to evaluate various
strategies to achieve the of the reduction targets of the six Kyoto Protocol greenhouse
gases, with or without international flexibility instruments.
There are two broad categories of decomposition techniques: input-output techniques
and disaggregation techniques. Both techniques have different data requirements but
the latter are more suitable for international comparisons, which explains their
widespread use. For methodological details we refer to Liaskas e.a. (2000) and Park
(1992). We concentrate on disaggregation or decomposition techniques. We first
present a simple mathematical expression of total emissions by means of the Kaya
Identity (Kaya, 1990). This equation provides a useful tool to decompose total
national carbon emissions (C):
C = (C/E)*(E/GDP)*(GDP/P)*P (1)
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The formula links energy-related carbon emissions (C) to energy (E), the level of
economic activity (GDP = gross domestic product) and population (P). C/E denotes
the carbon intensity of energy use, E/GDP is the energy intensity of economic activity
and GDP/P is the per capita income. At any moment in time, the level of energy-
related carbon emissions – next to emissions that result from changes in land-use - can
be seen as the product of the four Kaya Identity components. For small to moderate
changes in the Kaya components between any two years, the sum of the percent
changes in each of the variables closely approximates the percent change in carbon
emissions between those two years:
d(lnC)/dt = d(ln C/E)/dt + d(ln E/GDP)/dt + d(ln GDP/P)/dt + d(ln P)/dt (2)
The historical trends in the Kaya Identity components provide a reference point for
evaluating current and future climate policy projections of carbon emissions as well as
the key economic, demographic and energy intensity factors leading to those
emissions. With the availability of detailed data, the impact of for instance the
replacement of coal in electricity generation by natural gas or nuclear power can be
compared to the impact of economic growth on energy-related emissions. The Kaya
Identity can reveal interesting differences between emission patterns of developed and
developing countries. For an analysis based on the Kaya Identity of the implications
of emission trading under the Kyoto Protocol for the U.S. economy, we refer to
Dougher (1999).
2.1 Kaya in the International Energy Outlook 2001
A global view is given in Table 1 with the Kaya Identity components for three world
regions. A historical analysis for the period 1970-1999 is complemented with the
reference case projections for 1999-2020 from the International Energy Outlook 2001
(EIA, 2001). Positive annual average growth rates of carbon emissions between 1970
and 1999 are found for developed as well as for developing countries. For all
countries, economic growth and population growth outpaced declines in energy
intensity and carbon intensity of energy use. The average annual decline of carbonemissions of 5.4% in the 1990s in Eastern Europe and the Former Soviet Union
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presents a special case. This decline is the result of a severe drop in economic output
per capita (-4% per year). The IEO2001 reference case projections illustrate that
reductions of carbon emissions require accelerated declines in energy intensity and/or
carbon intensity. Such changes may in turn require significant changes in the existing
energy infrastructure. It remains questionable whether these necessary changes can be
realised in one or two decades. Energy use in the transport sector will continue to
depend on oil since there are currently few economical alternatives. And if such an
alternative would soon arrive (e.g. ethanol, bio-methanol, hydrogen, fuel cell
technology,…), reshaping the current fuel delivery infrastructure would take a long
time.
Table 1– Average annual percentage change in CO2 emissions and the Kaya Identity
Components by region, 1970-2020
History Reference case projection
Parameter 1970-1980 1980-1990 1990-1999 1999-2010 2010-2020
Industrialized WorldC/E -0.5% -0.7% -0.5% 0.0% 0.1%
E/GDP -1.1% -2.0% -0.7% -1.3% -1.3%GDP/P 2.4% 2.2% 1.6% 2.2% 2.0%P 0.9% 0.7% 0.6% 0.5% 0.4%
C emissions 1.7% 0.2% 1.0% 1.4% 1.1%
Developing WorldC/E -0.8% -0.2% -0.7% -0.1% -0.1%
E/GDP -0.4% 0.9% -1.0% -1.4% -1.4%GDP/P 3.5% 1.7% 3.1% 3.7% 4.2%P 2.2% 2.1% 1.7% 1.7% 0.8%
C emissions 4.6% 4.5% 3.1% 3.9% 3.5%
Eastern Europe and the Former Soviet UnionC/E -0.8% -0.3% -1.0% -0.2% -0.3%
E/GDP 1.4% 0.6% -0.5% -2.4% -2.6%GDP/P 2.4% 0.6% -4.0% 4.1% 4.5%P 0.9% 0.7% 0.0% 0.0% 0.0%
C emissions 3.9% 1.6% -5.4% 1.4% 1.5%
Source : Energy Information Administration (2001). International Energy Outlook 2001, p.162
Furthermore, we need to consider actual political decisions that will later have
important consequences for energy infrastructures and energy-related emissions.
Several European countries already committed to a complete phase-out of domestic
nuclear power generation. This decision will slow down the decline in carbon
intensity as a result of the increased use of fossil fuels.
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2.2 An analysis for four countries
Table 2 – Carbon emissions (percentage changes per period)
1960-1996 1960-1973 1974-1986 1987-1996
Carbon emissions
Belgium +86% +85.1% -12.8% +22.3%
France +102% +109.7% -12.6% +15.2%
Germany +96% +123.3% +1.6% -9.7%
United Kingdom +9.7% +13.8% -6.3% +6.5%
Carbon/Energy
Belgium -24.3% -12.4% -10.8% -1.8%
France -26.1% -10.4% -14.7% -1.7%
Germany -21.6% -10.4% -5.5% -6.4%
United Kingdom -23.1% -12.7% -5.1% -5.9%
Energy/GDP
Belgium -15.9% +13.1% -19.4% +3.2%
France -12.1% +17.9% -18.9% -1.1%
Germany -8.4% +43.6% -15.9% -20.4%
United Kingdom -37.3% -12.6% -21.4% -3.7%
GDP/Population
Belgium +162% +75.1% +19.9% +17.7%
France +145% +74% +19.2% +13.4%
Germany +143% +59.9% +30% +14.9%
United Kingdom +103% +39.1% +24.2% +14%Population
Belgium +11.4% +6.8% +1.2% +2.6%
France +27.8% +14.1% +5.9% +4.6%
Germany +12.7% +8.6% -1.6% +5.4%
United Kingdom +12.2% +7.3% +1.1% +3.1%
In Table 2, we present for four European countries data for the period 1960-1996.
Instead of working with average annual percentage changes as in Table 1, we still usethe Kaya Identity but subdivide the total period in three subperiods: the early years
before the oil crisis (1960-1973), the oil crisis years (1974-1986) and recent history or
the period after the oil crisis (1987-1996). Information on the used sources and
calculations that were necessary to compile Table 2 are given in the appendix.
Carbon or CO2 emissions did increase in the four countries over the period 1960-1996
but the differences are remarkable. Total emissions increased strongly in Belgium,
Germany and France, but only modestly the United Kingdom (UK). It is important to
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note that the different situation in the UK seems to be the result of what happened in
the early years of the analysis. From 1960 to 1973, emissions in the UK did grow by
only 13.8% while emissions in Germany and France more than doubled (+123% resp.
+ 109%). Emissions in Belgium did increase by 85.1% from 1960 to 1973. These
divergent post-war evolutions depend on strategic (and even environmental) fuel
choices for electricity production and on the strong and uneven development of
energy-intensive industries like iron and steel production, chemical manufacturing
and mining. London experienced a sequence of ‘killer smogs’ in the late 1940s and
early 1950s, leading to the Clean Air Act of 1956 that imposed much lower sulfur
dioxide concentrations (Elsom, 1997). New technologies were installed and coal
gradually was replaced by gas and oil. The result was a decline of carbon and sulfur
dioxide emissions.
A good example of the different evolution in energy-intensive industries is the metals
industry in the UK that did grow by only 0.06% per year during the period 1954-
1973. For that period the average annual growth rate of ‘all manufacturing’ was
0.88% with the highest growth rates found for instruments (+1.25%), electrical
engineering (+1.39%) and vehicles (+1.38%). For the period 1973-1986, the metals
industry faced an average annual growth of -0.73% while the average for UK
manufacturing was –0.47% (Oulton and O’Mahony, 1994). For countries like
Belgium, the strong growth of the iron and steel industry was one of the driving
economic forces in the post-war era.
During the oil crisis years, emissions decreased in all countries with the exception of
Germany (+1.6% over the period 1974-1986). After 1986, the reverse happened:
German emissions decreased with 9.7% as a result of the closing down of parts of the
former Eastern German economy in the early and mid-1990s, while emissions in other
countries did increase. The growth of emissions in the UK is again more modest than
in Belgium and in France.
With the Kyoto Protocol of December 1997, the year 1990 became the base year for
the necessary reductions of emissions. Especially Germany will benefit from this
choice since its emissions increased strongly in the period 1960-1990, followed by asharp reduction as a result of the German unification.
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The impact of the oil crisis on the energy intensity of production (energy/GDP)
provides an indication of the potential of prices instruments (like energy or CO2 taxes)
to reduce energy use in developed economies. Table 2 shows that during the period
1974-1986, the energy intensity in the four countries has been reduced by 15.9 to
21.4%. The oil crisis did clearly lead to a similar reaction in the four countries but it is
interesting to note that from 1960 to 1974, the energy intensity in the UK decreased
by 12.6% while in the other countries a completely different evolution did take place.
The increase of energy intensity of the post-war German economy between 1960-
1973 is striking (+43.6%). In the period after the oil crisis years, the energy intensity
of production more or less stabilized in France and even increased in Belgium
(+3.2%). The latter evolution can be ascribed to the strong growth of the basic
chemical industry in Belgium. Over the total period 1960-1996, energy intensity
decreased by 37.3% in the UK while the reduction in the other three countries was
between 8.4% and 15.9%.
Table 2 also shows that pro capita economic growth over the period 1960-1996 was
strongest in Belgium, followed by France and Germany. The difference with the
growth rate for the UK is mainly the result of slower income growth in the UK in the
period before the oil crisis. Before the oil crisis years, pro capita growth was in all
countries higher than during or after the oil crises when Europe faced long periods of
economic recession.
Finally, from 1960 to 1996 the population increased in all countries and especially in
France. The total French population growth is more than double of that in the three
other countries. We will later show that the French population growth is an important
component in the decomposition of total emissions growth. Only since 1987,
Germany faces a higher population growth than France.
The data in Table 2 are used in the analysis in the next sections. There is however an
important innovation in section 4 where we will also include a structural element, this
is the result of changes in the structural composition of the economy.
3. An introduction to the Shapley decomposition
Table 2 provides information on the evolution of Kaya Identity factors for threedifferent periods. Changes in these factors have caused changes in total carbon
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emissions. For small changes, the Kaya- identity is additive in the growth rates of the
contributing factors, and hence no decomposition technique is required. However for
longer periods of time there is a problem of residuals. However, since the Kaya-
formula is of a simple linear form one can assume that the residual is ‘jointly created
and equally ditributed’. This implies that the relative order of magnitude of
contribution of the several factors will not be biased by the residual and that the
results can be correctly interpreted, as we did in the previous section. With more
complex formulas this is, however, not necessarily the case and a more precise
decomposition method is required.
More than 100 decomposition studies in energy and environmental studies are listed
in a survey by Ang and Zhang (Ang and Zhang, 2000). They indicate that the
methods reported prior to 1995 always leave a residual after decomposition. This
residual was sometimes omitted, causing a large estimation error. In other models the
residual was regarded as the interaction effect, which still leaves a new puzzle for the
reader (Sun, 1998). Important residuals constitute the most serious problem with
conventional disaggregative decomposition. Liaskas e.a. (2000) e.g. decompose
industrial CO2 emissions for a number of European countries. They work with two
periods, 1973-1983 and 1983-1993, and the factors in the decomposition are output,
energy intensity, fuel mix and structure. For some countries, the weight of the residual
in the decomposition over only ten years exceeds the weight of three of the other four
components. For the United Kingdom, the Netherlands, Italy, France, Finland, Spain,
Denmark, Belgium and Austria, the weight of the structural component - or the
structural economic effect on emissions - is lower than that of the residual. Obviously,
conclusions from this type of analysis are not always straightforward. For longer
periods and for analyses with more components, the residual becomes even more
problematic. This is illustrated in the next section with data for Belgium, France,
Germany and the United Kingdom.
Some methods proposed after 1995 are perfect, i.e. do not leave a residual term in the
results (Ang and Zhang, 2000). One of these perfect decomposition methods is the
one introduced by Sun (Sun, 1998). In his method, referred to as the refined
Laspeyres index method (Ang and Zhang, 2000), the interactions (residual) aredistributed equally among the main effects based on the ‘jointly created and equally
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distributed’ principle. This underlying assumption is not always appropriate. If the
various main effects are known to be additive, there will be no residual. If we deviate
only marginally from this property of additive factors, it may be appropriate to
assume jointly created and equally distributed residuals. The further we move away
from additivity, however, the more inappropriate this assumption becomes. Sun and
Ang (2000) apply the same principle to the Paasche and Marshall- Edgeworth forms.
Contrary to the Laspeyres index model, which adopts a prospective view, the Paasche
index adopts a retrospective view. The Marshall- Edgeworth index adopts a
compromising view based on the Laspeyres and Paasche indices (Sun and Ang,
2000). The authors prove that when the ‘jointly created and equally distributed’
principle is applied to the Paasche and Marshall-Edgeworth models, the
decomposition results are identical to the Laspeyres form results. But the method still
implies the assumption made by Sun. Another perfect decomposition method
discussed by Ang and Zhang (2000) is the logarithmic mean Divisia method,
proposed by Ang and Choi (1997). They replaced the arithmetic mean weight
function used in the arithmetic mean Divisia index method by a logarithmic mean
weight function. This refinement results in a perfect decomposition, but one has to
take into account the fact that using a logarithmic weight function implies the
assumption of a constant growth rate. Furthermore, this refined Divisia method is
based on the normalization of the weight function, because the sum of the weight
function over all sectors is not unity, but by definition always slightly less than unity
(Ang and Choi, 1997).
We add to this literature by proposing a perfect decomposition of carbon emissions
based on the Shapley value. Indeed, the decomposition problem has formal
similarities with a classical problem in co-operative game theory. Shapley (1953) was
the first to give a formula for the real power of any given voter in a coalition voting
game with transferable utility. This is commonly referred to as the Shapley value. The
Shapley value is the mathematical expression of the real power of a player when all
orders of coalition formation are equiprobable. The Shaply value distributes the real
power among the players, satisfying three axioms, namely symmetry, no inessential
players and additivity. Symmetry means that every player should be treated
symmetrically in the estimation. No inessential players means that players that do notcontribute to the power of any coalition do not receive any power. Additivity means
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that the power derived from every single possible coalition can be added to find the
total real power.
Since 1953 the Shapley value has been used in a number of cost allocation models.
The properties of symmetry and no inessential players are very useful in this context.
A clear and simple explanation of how to use the Shapley value in cost allocation
problems is given in Hamlen, Hamlem and Tschirhart (1977). Okada et al (1982)
proposed to use the Shapley value to allocate costs in water resources development
projects. Kattuman, Bialek and Abi-Samra (1999) proposed to use the Shapley value
to allocate the costs of electricity transmission losses in the network between several
electricity generators. In these Shapley-value based cost allocation models everything
happens as if the cost drivers enter the equation one by one, each of them receiving
their marginal contribution to the total cost. All orders of entering the cost equation
are considered and receive the same weight 1/n! in the computation of the ultimate
allocation of costs.
Shorrocks (1999) points at the formal similarity between the original Shapley value
coalition problem and the general problem of allocating a certain amount of any
output or cost among a set of agents, beneficiaries or cost drivers. Shorrocks builds on
this similarity to construct a general decomposition procedure based on the Shapley
value. Basically, the technique involves estimating the impact of eliminating each
factor in succession, repeating this exercise for all possible elimination sequences (the
symmetry property) and then for each factor averaging its estimated impact over all
the possible elimination sequences (the additivity property).
Let us consider what this concretely implies for the decomposition. In a simple ceteris
paribus type decomposition one calculates the impact of each variable, leaving other
variables constant. Because of the interactions between several variables, this gives
rise to a residual. The literature has come up with several ways to avoid or allocate
this residual (see higher). One simple method is to calculate the contribution of one
variable, and then add cumulatively more and more variables. The result is a perfect
decomposition without residuals. However, the order in which we include variables
largely determines their calculated contribution because the allocation of the
interaction effects depends on the order of inclusion of the variable. Since the resultsdepend on the order by which variables enter the calculation, this cumulative
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approach is path dependent and hence biased. The underlying problem is that
variables are not treated symmetrically.
The Shapley decomposition iterates the cumulative approach for every possible order
(permutation) of variables. With n variables, we need to make n! calculations, with
each calculation based on another order for including new variables. The Shapley
value implies that taking the average of the n! estimated contributions for every
variable, yields the true contribution of each variable. As a result, the Shapley
decomposition has three major advantages. First of all, the decomposition is perfect,
meaning that the sum of the impacts, allocated to each of the explanatory variables,
equals the observed change in the decomposed variable. One does not need to make
any assumptions or effort to allocate the residual, as the solution is free from
residuals. Secondly, the Shapley decomposition is symmetric (or anonymous): the
factors are treated in an even-handed manner, without making any further theoretical
assumptions. Thirdly, the Shapley decomposition allows for very complex
decompositions that would otherwise be troublesome because of very high residuals
and subsequent interpretation problems.
4. Sectoral and structural effects in the decomposition
Starting from (1) and the data in Table 2, we add sectoral and structural effects to our
analysis over the period 1960-1996. For the UK, the analysis is based on the period
1960-1995. The change in carbon intensity of energy use and the change of energy
intensity of production can be due to changes within sectors (sectors become more or
less intensive in energy and carbon) or changes between sectors (sectors that are
intensive in energy or carbon become more or less important in total production). For
simplicity, we work with three sectors: industry, transport and other sectors. For these
three sectors, changes between 1960 and 1996 in the carbon intensity of energy and in
the energy intensity of the production are calculated. For climate policy
recommendations, this type of information is essential, especially for analyses with
extended time horizons. By including these effects in our analysis, we have nine
components for the decomposition: three sectoral carbon intensity effects (C/Eindustry,
C/Etransport , C/Eother , denoted asi
jC in (3)) three sectoral energy intensity effects (Ei in
(3)), the effect of pro capita GDP ( pcoP in (3)), the population effect (P in (3)) and one
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structural effect. Name α jt the share of a sector j at time t in total production, the final
equation for the change in carbon emissions over n sectors is presented in (3) :
0 pc0
n
1 j
i0 j
i0 j0 j
0 pc0
i0 j
i0 j
n
1 j0 jt
pct
n
1 j
i jt
i jt jt
.
PPEC
PPECPPEC
C
α
α−
α
=
∑
∑∑
=
==(3)
We only calculate one structural effect that captures the net effect on emissions of the
change in the economy’s structure, as mirrored in three α jt. It is possible to calculate
three separate sectoral effects, but this seems not very informative since obviously the
relative decrease in two sectors implies growth in the third sector and vice versa. It is
only the net emission effect of the increasing ‘service-isation’ of the economy that is
of interest for this paper. As a result of the inclusion of sectoral and structural effects,
there are some modest differences in the data, when comparing to the data used in
Table 2. We explain our data in the appendix.
We illustrate the problem of interpreting decomposition residuals first by
decomposing a simplified version of (3) with only four components, namely carbon
intensity (only one iC ), energy intensity (only one E i), GDP and structure of the
economy. These components are used in most decomposition exercises (see Liaskas,
e.a (2000)). Notice that these four factors are not the Kaya Identity factors, since the
Kaya equation does not include a structural effect. We perform the imperfect
decomposition proposed in Liaskas, et al (2000) and the perfect Shapley
decomposition proposed in this paper. Results of the method of Liaskas e.a. are
presented in Table 3, panel (A), while panel (B) shows the results of the Shapley
decomposition. In panel (C) we show results of the Shapley decomposition of the full
formula (3) with nine components.
The information in Table 3 (A) should in principle answer the question ‘how do
carbon emissions change if one component changes and other components are fixed?’
The reliability of the answer when applying a decomposition method with residuals, is
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however very limited since the sum of changes strongly exceeds the real change of
emissions. An ‘overexplanation’ between 44 and 68% is found for the four countries
in our sample. There is no reliable way to attribute these residuals to the four
components and hence interpretation becomes very cumbersome. With nine
components the results of an imperfect decomposition would be even more difficult to
interpret.
Table 3 – Three decompositions of carbon emissions
(A) With residuals (four components)
Components C/E E/GDP GDP Structure Sum Real Residual
Belgium -25.6% -16.3% 192.2% 2.4% 152.6% 84% 68.6%
France -28.4% -10.2% 212.7% 0.2% 174.3% 107.1% 67.2%
Germany -22.6% -14.5% 174% 6.4% 143.2% 98.8% 44.4%
UK -23.9% -36.1% 122.5% -1.9% 60.6% 4.6% 56%
(B) Shapley decomposition without residuals (four components)
Components C/E E/GDP GDP Structure Sum Real Residual
Belgium -42.9% -27.5% 154.1% -0.02% 84% 84% -
France -55% -16.3% 176.4% 2% 107.1% 107.1% -
Germany -39.4% -25.5% 152.4% 11.5% 98.8% 98.8% -
UK -41.1% -71.4% 124.4% -8% 4.6% 4.6% -
(C) Shapley decomposition without residuals (nine components)
C/E E/GDP GDP/P POP Struct. Sum
Industry Transp. Other Industry Transp. Other
BE -19.6% -1.9% -21.4% -31.2% 12.6% -8.9% 144% 10.1% -0.02% 84%
FR -23.1% -3.5% -28.4% -26% 5.9% 3.8% 148% 28.4% 2% 107.1%
GE -17.3% -3.5% -18.6% -12.5% -9.7% -3.3% 140% 12.4% 11.5% 98.8%
UK -12.9% -3.1% -25.1% -20.4% 4.7% -55.7% 111% 13.4% -8% 4.6%
5. Results from the Shapley decomposition
Panels (B) and (C) in Table 3 illustrate the disturbing impact of the residuals that are
found in Table 3. It is shown that the analysis with residuals clearly overestimates the
effect of economic growth on emissions and underestimates the effects of changes in
carbon and energy intensity (C/E and E/GDP), the two important ‘target’ parameters
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in climate policy. Especially the carbon intensity effect on emissions is much more
important in Panels (B) and (C) than suggested in Panel (A) of Table 3. Our exact
decomposition reveals that shifts in fuel mixes influence carbon emissions more than
suggested in the decomposition with residuals. For the four countries, the real weight
of this factor (in Panels (B) and (C)) is almost twice the value suggested in Panel (A).
Similar conclusions are valid for the energy intensity factor.
The Shapley analysis allows some further conclusions. First of all, we notice that the
structural effect is not that important for explaining total emissions growth. For
Germany, the structural effect did lead to an increase of emissions – a growth of
11.5% when holding all the other factors constant - while for the UK emissions
decreased (-8%) as a result of the structural effect. In the analysis with four
components and with residuals, the weight of the structural component for Germany
is lower (+6.4%). For Belgium and France, there is almost no structural effect found
in Table 3 Panel (C). Does the fact that some energy-intensive sectors did become
relatively more or less important without significantly influencing total carbon
emissions suggest that future structural changes will also have a modest impact on
total emissions? Since every economic sector – agriculture, industry and services –
consumes energy, shifts between sectors have a limited impact especially because
energy efficiency is for most service industries not the same priority as it is for
industries like basic chemicals whose profit base depends on it to a large extent.
Population growth had a positive impact on emissions, especially for France. We
notice that for France, the population effect is more important that the separate
sectoral carbon intensity and energy intensity effects. The population effect (+28.4%)
is for France stronger that the effect of the ongoing energy efficiency improvement of
the French economy (-26% + 5.9% + 3.8% = -16.3%). When this population growth
is expected to continue in the next decades, this development will negatively impact
the possibility for France to achieve the same emission reductions as countries with
more stable populations. If this hypothesis would hold, a possible solution would be
to base future European burden-sharing agreements on population data.
The energy needs that follow from the population growth have a stronger impact on
carbon emissions than the effort to reduce the energy intensity of the French
economy. To reverse this evolution, the increase in energy efficiency should not belimited to French industry (-26%) but should become visible in transport and other
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sectors especially housing, hospitals, schools and administrations. These ‘asymmetric
efficiency gains’ seem to be especially valid for France. In Belgium, Germany and the
UK, we find that the efficiency gains of the ‘others’ sector indeed have a negative
impact on emissions when we hold all other factors constant. Especially for the U.K.,
the efficiency gains of the ‘other’ sectors are spectacular (-55.7%) and had a strong
impact on total emissions. The shift from less efficient energy use to more efficient
energy use in households and services is very important. Did this shift already take
place in the other three countries or did they just not recognise this potential yet?
The effect of the average income is, as could be expected, the most important
component in the growth of carbon emissions. As a result of the pro capita income
effect, emissions would ceteris paribus have increased with 111 to 144% (Panel (C)).
This reveals that the impact of economic growth (pro capita income effect plus
population growth effect) is overestimated in traditional decomposition analyses as is
illustrated by the high factor weights in Table 3 (A). Without the effect of economic
growth, carbon emissions would have decreased in the four countries from 1960 to
1996. In contrast to most developing countries, the growth of emissions for the four
countries in Table 3 (B) is lower than the effect of GDP growth. The argument that
the Kyoto Protocol targets can be achieved at a low cost without impacting economic
growth therefore seems to depend on access to low-cost emission reduction
opportunities. These low hanging fruits can probably be found in developing or
transitional countries.
Table 3 (C) shows that for industry, the decrease in energy intensity had an important
impact on total emissions for all countries. This effect is strongest for Belgium where
the increasing energy efficiency of industry could reduce emissions by 31.2% when
other factors are held constant. For Germany, this effect is weakest (-12.5%). There is
still no indication of a trend towards increased energy efficiency in the transport
sector. Cleaner and more fuel-efficient transport equipment cannot compensate the
strong growth of transport activities in most developed countries. Another explanation
can be the declining market share of rail transport in the container market in countries
like Belgium. Holding all other components constant, the evolution of the transport
energy intensity would lead to increasing emissions in Belgium, France and the UK.
Only for Germany, the impact would be negative.
With respect to the carbon intensity of energy use, there are only minor differences between the four countries. The impact of the change in industrial carbon intensity on
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total emissions is between –12.9% (UK) and –23.1% (France). The impact of carbon
intensity changes in transport is very similar: between –1.9% for Belgium and –3.5%
for France and Germany. This is not a surprise since transport infrastructure is very
similar in the four countries. The impact of other carbon intensity changes is more
diverging. For Germany, we find the lowest value with –18.6%. For France this effect
is most important: -28.4%.
6. Growth versus component weight
When we compare Table 2 to Table 3 (B) and (C), some points deserve our further
attention. For the four countries, the differences in the carbon intensity of energy use
during the period 1960-1996 seem to be modest in Table 2. Table 3 (B) shows that
similar evolutions in carbon intensity (from –21.6% for Germany to –26.1% for
France, see Table 2) can have a different impact on total emissions (from –39.4% to –
55%). Precisely this impact provides the most useful information for further policy
development. The difference in carbon intensity between France and Belgium is only
1.8% (see Table 2) while the difference in total weight is 12.1% (see Table 3 (B)).
Holding all other components constant, similar reductions in carbon intensity can lead
to different reductions in carbon emissions because of structural differences between
different economies. We therefore need to be aware of all the interaction effects that
take place inside the economy. The evolution of one single variable can be interesting
but the impact of this single variable on total emissions depends on the evolution of
other variables as well. The more interactions are included in the analysis, the more
reliable the reported effects will be. This explains why we opted for an extended Kaya
equation with nine components. Of course, we do not claim that this extended
equation captures all relevant interactions.The differences in the evolution of the energy intensity of production also correspond
to more explicit differences in the weight of this factor in the decomposition. The
difference between Belgium and France is 3.8% (see Table 2: -15.9% versus –12.1%)
while the difference in the total weight of this factor is 11.2% (-27.5% versus –16.3%
in Table 4). Similarly, compared to the UK, E/GDP is 25.2% lower in France (-37.3%
versus -12.1%). The total weight of this factor is for the UK –71.4% while it is only –
16.3% for France. These findings illustrate again that similar trends for parameters in
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Table 2 can be the result of very different evolutions in similar developed economies
and vice versa.
7. Conclusions
Starting from the Kaya Identity, we presented a Shapley decomposition for carbon
emissions for four European countries. This technique makes it possible to present a
perfect and symmetric decomposition without residuals. Compared to conventional
decomposition techniques - with residuals that amount to more than 50% of carbon
emission growth - this is a promising improvement offering valid and reliable
information on complex questions such as the impact of specific energy-related
evolutions on total emissions growth.
From a limited analysis for four countries, our Shapley decomposition showed that
factors like the carbon intensity of energy use and the decarbonization of economic
growth have more effect on total emissions than suggested in conventional
decomposition exercises. In these exercises, the effect of economic growth on
emissions is overestimated for developed countries since this important variable
captures a significant part of the residuals. But the real weight of this variable is lower
and the weight of the other variables – those that are the essence of climate policy - is
higher. These results seem to lend support to the view that fuel mix changes and the
ongoing decarbonization can in interaction with other effects play an important role in
climate policy. The precise relation however between climate policy and these
variables has not been studied in this paper and is therefore subject to further research.
Another interesting conclusion from our analysis was that the effect of population
growth on emissions has been for some countries been more important than the
emission effect of decarbonization efforts.
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Appendix
Calculations and Data Sources
PopulationSource: OECD Energy Balances.
GDP global Kaya Identity
Source: OECD National Accounts, I. We used the Gross Domestic Product (Expenditure) data in US $at exchange rates and price levels of 1990.
sectoral decompositionSource: OECD National Accounts, II, Value Added by Kind of Activity approach (for compatibilityreasons with the decomposition of the Energy data).
Sectoral decomposition calculations for each separate country were as follows:
Belgium (MN FB90 Price); France (MN FF80 Price)
Total Industry Sector GDP = Manufacturing + Electricity, Gas and Water + Construction
Total Transport Sector GDP = Transport and StorageTotal Other Sectors GDP = Gross Domestic Product – Total Industry Sector GDP – Total
Transport Sector GDP
Germany (MN DM 91 Price)
German ‘Value Added’ GDP data were only available from 1991. For the years 1960-1990 we used
data from the former Federal Republic of Germany and added 10% as this was estimated to be the GDPshare of the former German Democratic Republic. We assumed that the different sectors were equallyrepresented in both parts of the country, knowing that this would lead to only minor distortions of the
data.
Germany 1991- 1996
Total Industry Sector GDP = Manufacturing + ConstructionTotal Transport Sector GDP = Transport, Storage and Communication
Total Other Sectors GDP = Gross Domestic Product – Total Industry Sector GDP – Total Transport Sector GDP
Germany 1960-1990
Total Industry Sector GDP = Manufacturing + Electricity, Gas and Water + Construction
Total Transport Sector GDP = Transport and Storage
Total Other Sectors GDP = Gross Domestic Product – Total Industry Sector GDP – Total Transport Sector GDP
United Kingdom (MN PS Curr. Price)
Total Industry Sector GDP = Manufacturing + Electricity, Gas and Water + ConstructionTotal Transport Sector GDP = Transport, Storage and Communication
Total Other Sectors GDP = Gross Domestic Product – Total Industry Sector GDP – Total Transport Sector GDP
The baseyear and the used currencies differ from those used in the global Kaya Identity. We thereforemultiplied all sectoral GDP data by correction factors (GDP used in global Kaya formula / ‘Value
Added’ GDP) for each year.
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Energy
Source: OECD Energy Balances.We opted for Total Final Consumption as a basic concept in calculating the Carbon and Energy KayaIdentity Factor. Total Final Consumption is the sum of consumption by the different end-use sectors.
The reason for this choice is the fact that Total Final Consumption data can easily be disaggregated intodifferent sectors, more specifically the industry sector, the transport sector and the other sectors
(Agriculture, Commerce and Publ. Serv., Residential and Non-specified). We did not include Non-Energy Use. A correction factor was included when comparing the sum of the sectoral decompositionswith the global Kaya results.
Carbon
Total Final Consumption data comprise the use of different energy sources, as well as Electricity and
Heat. The decomposition of Electricity and Heat is based on data from Electricity Plants, CHP Plantsand Heat Plants. Emission factors from fossil fuel combustion were found in the ‘Second Netherlands’ National Communication on Climate Change Policies’ and the ‘Revised 1996 IPPC Guidelines for
National Greenhouse Gas Inventories: Reference Manual’.
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97/36 M. VERHUE, E. SCHOKKAERT, E. OMEY, De kloof tussen laag- en hooggeschoolden en de politiekehoudbaarheid van de Belgische werkloosheidsverzekering : een empirische analyse, augustus 1997, 30 p.(gepubliceerd in Economisch en Sociaal Tijdschrift , 1999).
97/37 J. CROMBEZ, R. VANDER VENNET, The performance of conditional betas on the Brussels Stock exchange,September 1997, 21 p. (published in Tijdschrift voor Economie en Management , 2000).
97/38 M. DEBRUYNE, R. FRAMBACH, Effective pricing of new industrial products, September 1997, 23 p. (published inD. Grewal and C. Pechmann (eds.), Marketing theory and applications, vol. 9, Proceedings AMA Winter
Conference 1998).
97/39 J. ALBRECHT, Environmental policy and the inward investment position of US 'dirty' industries, October 1997,20 p. (published in Intereconomics, 1998).
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WORKING PAPER SERIES 3
97/40 A. DEHAENE, H. OOGHE, De disciplinering van het management : een literatuuroverzicht, oktober 1997, 28 p.(published in Economisch en Sociaal Tijdschrift , 2000).
97/41 G. PEERSMAN, The monetary transmission mechanism : empirical evidence for EU-countries, November 1997, 25p.
97/42 S. MANIGART, K. DE WAELE, Choice dividends and contemporaneous earnings announcements in Belgium,November 1997, 25 p. (published in Cahiers Economiques de Bruxelles, 1999).
97/43 H. OOGHE, Financial Management Practices in China, December 1997, 24 p. (published in European BusinessReview , 1998).
98/44 B. CLARYSSE, R. VAN DIERDONCK, Inside the black box of innovation : strategic differences between SMEs,January 1998, 30 p.
98/45 B. CLARYSSE, K. DEBACKERE, P. TEMIN , Innovative productivity of US biopharmaceutical start-ups : insightsfrom industrial organization and strategic management, January 1998, 27 p. (published in International Journal of
Biotechnology , 2000).
98/46 R. VANDER VENNET, Convergence and the growth pattern of OECD bank markets, February 1998, 21 p.(forthcoming as ‘The law of proportionate effect and OECD bank sectors’ in Applied Economics, 2001).
98/47 B. CLARYSSE, U. MULDUR, Regional cohesion in Europe ? The role of EU RTD policy reconsidered, April 1998,28 p. (published in Research Policy , 2000).
98/48 A. DEHAENE, H. OOGHE, Board composition, corporate performance and dividend policy, April 1998, 22 p.
(published as ‘Corporate performance and board structure in Belgian companies’ in Long Range Planning , 2001).
98/49 P. JOOS, K. VANHOOF, H. OOGHE, N. SIERENS , Credit classification : a comparison of logit models anddecision trees, May 1998, 15 p.
98/50 J. ALBRECHT, Environmental regulation, comparative advantage and the Porter hypothesis, May 1998, 35 p.(published in International Journal of Development Planning Literature, 1999)
98/51 S. VANDORPE, I. NICAISE, E. OMEY, ‘Work Sharing Insurance’ : the need for government support, June 1998,20 p.
98/52 G. D. BRUTON, H. J. SAPIENZA, V. FRIED, S. MANIGART , U.S., European and Asian venture capitalists’governance : are theories employed in the examination of U.S. entrepreneurship universally applicable?, June1998, 31 p.
98/53 S. MANIGART, K. DE WAELE, M. WRIGHT, K. ROBBIE, P. DESBRIERES, H. SAPIENZA, A. BEEKMAN ,Determinants of required return in venture capital investments : a five country study, June 1998, 36 p. (forthcomingin Journal of Business Venturing , 2001)
98/54 J. BOUCKAERT, H. DEGRYSE, Price competition between an expert and a non-expert, June 1998,29p. (published in International Journal of Industrial Organisation, 2000).
98/55 N. SCHILLEWAERT, F. LANGERAK, T. DUHAMEL, Non probability sampling for WWW surveys : a comparison of methods, June 1998, 12 p. (published in Journal of the Market Research Society , 1999).
98/56 F. HEYLEN. Monetaire Unie en arbeidsmarkt : reflecties over loonvorming en macro-economisch beleid, juni 1998,15 p. (gepubliceerd in M. Eyskens e.a., De euro en de toekomst van het Europese maatschappijmodel , Intersentia,1999).
98/57 G. EVERAERT, F. HEYLEN, Public capital and productivity growth in Belgium, July 1998, 20 p. (published inEconomic Modelling , 2001).
98/58 G. PEERSMAN, F. SMETS, The Taylor rule : a useful monetary policy guide for the ECB ?, September 1998, 28 p.(published in International Finance, 1999).
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WORKING PAPER SERIES 4
98/59 J. ALBRECHT, Environmental consumer subsidies and potential reductions of CO2 emissions, October 1998, 28 p.
98/60 K. SCHOORS, A payment system failure and its consequences for interrepublican trade in the former Soviet Union,December 1998, 31 p.
98/61 M. DE LOOF, Intragroup relations and the determinants of corporate liquid reserves : Belgian evidence, December 1998, 29 p. (published in European Financial Management , 2000).
98/62 P. VAN KENHOVE, W. VAN WATERSCHOOT, K. DE WULF, The impact of task definition on store choice andstore-attribute saliences, December 1998, 16 p. (published in Journal of Retailing , 1999).
99/63 P. GEMMEL, F. BOURGONJON , Divergent perceptions of TQM implementation in hospitals, January 1999, 25 p.(forthcoming in Journal of Management in Medicine, 2000)
99/64 K. SCHOORS, The credit squeeze during Russia's early transition. A bank-based view, January 1999, 26 p.
99/65 G. EVERAERT, Shifts in balanced growth and public capital - an empirical analysis for Belgium, March 1999, 24 p.
99/66 M. DE LOOF, M. JEGERS , Trade Credit, Corporate Groups, and the Financing of Belgian Firms, March 1999, 31 p.(published in Journal of Business Finance and Accounting , 1999).
99/67 M. DE LOOF, I. VERSCHUEREN, Are leases and debt substitutes ? Evidence from Belgian firms, March 1999,11 p. (published in Financial Management , 1999).
99/68 H. OOGHE, A. DEHAENE, De sociale balans in België : voorstel van analysemethode en toepassing op hetboekjaar 1996, April 1999, 28 p. (gepubliceerd in Accountancy en Bedrijfskunde Kwartaalschrift , 1999).
99/69 J. BOUCKAERT, Monopolistic competition with a mail order business, May 1999, 9 p. (published in EconomicsLetters, 2000).
99/70 R. MOENAERT, F. CAELDRIES, A. LIEVENS, E. WOUTERS , Communication flows in international productinnovation teams, June 1999, p. (published in Journal of Product Innovation Management , 2000).
99/71 G. EVERAERT, Infrequent large shocks to unemployment. New evidence on alternative persistence perspectives,July 1999, 28 p.
99/72 L. POZZI, Tax discounting and direct crowding-out in Belgium : implications for fiscal policy, August 1999, 21 p.
99/73 I. VERSCHUEREN, M. DE LOOF, Intragroup debt, intragroup guaranties and the capital structure of Belgian firms, August 1999, 26 p.
99/74 A. BOSMANS, P. VAN KENHOVE, P. VLERICK, H. HENDRICKX, Automatic Activation of the Self in a PersuasionContext , September 1999, 19 p. (forthcoming in Advances in Consumer Research, 2000).
99/75 I. DE BEELDE, S. COOREMAN, H. LEYDENS, Expectations of users of financial information with regard to thetasks carried out by auditors , October 1999, 17 p.
99/76 J. CHRISTIAENS, Converging new public management reforms and diverging accounting practices in Belgian localgovernments, October 1999, 26 p. (forthcoming in Financial Accountability & Management , 2001)
99/77 V. WEETS, Who will be the new auditor ?, October 1999, 22 p.
99/78 M. DEBRUYNE, R. MOENAERT, A. GRIFFIN, S. HART, E.J. HULTINK, H. ROBBEN, The impact of new productlaunch strategies on competitive reaction in industrial markets, November 1999, 25 p.
99/79 H. OOGHE, H. CLAUS, N. SIERENS, J. CAMERLYNCK, International comparison of failure prediction modelsfrom different countries: an empirical analysis, December 1999, 33 p.
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Fax. : 32 - (0)9 – 264.35.92
WORKING PAPER SERIES 5
00/80 K. DE WULF, G. ODEKERKEN-SCHRÖDER, The influence of seller relationship orientation and buyer relationshipproneness on trust, commitment, and behavioral loyalty in a consumer environment, January 2000, 27 p.
00/81 R. VANDER VENNET, Cost and profit efficiency of financial conglomerates and universal banks in Europe.,February 2000, 33 p . (forthcoming in Journal of Money, Credit, and Banking , 2001)
00/82 J. BOUCKAERT, Bargaining in markets with simultaneous and sequential suppliers, April 2000, 23 p. (forthcomingin Journal of Economic Behavior and Organization, 2001)
00/83 N. HOUTHOOFD, A. HEENE, A systems view on what matters to excel, May 2000, 22 p .
00/84 D. VAN DE GAER, E. SCHOKKAERT, M. MARTINEZ, Three meanings of intergenerational mobility, May 2000, 20p. (forthcoming in Economica , 2001)
00/85 G. DHAENE, E. SCHOKKAERT, C. VAN DE VOORDE, Best affine unbiased response decomposition, May 2000,9 p.
00/86 D. BUYENS, A. DE VOS, The added value of the HR-department : empirical study and development of anintegrated framework, June 2000, 37 p .
00/87 K. CAMPO, E. GIJSBRECHTS, P. NISOL, The impact of stock-outs on whether, how much and what to buy, June2000, 50 p .
00/88 K. CAMPO, E. GIJSBRECHTS, P. NISOL, Towards understanding consumer response to stock-outs, June 2000,
40 p. (published in Journal of Retailing , 2000)
00/89 K. DE WULF, G. ODEKERKEN-SCHRÖDER, P. SCHUMACHER, Why it takes two to build succesful buyer-seller relationships July 2000, 31 p.
00/90 J. CROMBEZ, R. VANDER VENNET, Exact factor pricing in a European framework, September 2000, 38 p.
00/91 J. CAMERLYNCK, H. OOGHE, Pre-acquisi tion profile of privately held companies involved in takeovers : anempirical study, October 2000, 34 p.
00/92 K. DENECKER, S. VAN ASSCHE, J. CROMBEZ, R. VANDER VENNET, I. LEMAHIEU, Value-at-risk predictionusing context modeling, November 2000, 24 p. (forthcoming in European Physical Journal B, 2001)
00/93 P. VAN KENHOVE, I. VERMEIR, S. VERNIERS, An empirical investigation of the relationships between ethicalbeliefs, ethical ideology, polit ical preference and need for closure of Dutch-speaking consumers in Belgium,
November 2000, 37 p. (forthcoming in Journal of Business Ethics, 2001)
00/94 P. VAN KENHOVE, K. WIJNEN, K. DE WULF, The influence of topic involvement on mail survey responsebehavior, November 2000, 40 p.
00/95 A. BOSMANS, P. VAN KENHOVE, P. VLERICK, H. HENDRICKX, The effect of mood on self-referencing in apersuasion context, November 2000, 26 p. (forthcoming in Advances in Consumer Research, 2001)
00/96 P. EVERAERT, G. BOËR, W. BRUGGEMAN, The Impact of Target Costing on Cost, Quality and DevelopmentTime of New Products: Conflicting Evidence from Lab Experiments, December 2000, 47 p.
00/97 G. EVERAERT, Balanced growth and public capital: An empirical analysis with I(2)-trends in capital stock data,December 2000, 29 p.
00/98 G. EVERAERT, F. HEYLEN, Public capital and labour market performance in Belgium, December 2000, 45 p.
00/99 G. DHAENE, O. SCAILLET, Reversed Score and Likelihood Ratio Tests, December 2000, 16 p.
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FACULTEIT ECONOMIE EN BEDRIJFSKUNDE HOVENIERSBERG 24 9000 GENT Tel. : 32 - (0)9 – 264.34.61
Fax. : 32 - (0)9 – 264.35.92
WORKING PAPER SERIES 6
01/100 A. DE VOS, D. BUYENS , Managing the psychological contract of graduate recruits: a challenge for humanresource management, January 2001, 35 p.
01/101 J. CHRISTIAENS, Financial Accounting Reform in Flemish Universities: An Empirical Study of the implementation,February 2001, 22 p.
01/102 S. VIAENE, B. BAESENS, D. VAN DEN POEL, G. DEDENE, J. VANTHIENEN, Wrapped Input Selection usingMultilayer Perceptrons for Repeat-Purchase Modeling in Direct Marketing, June 2001, 23 p. (published inInternational Journal of Intelligent Systems in Accounting, Finance & Management , 2001).
01/103 J. ANNAERT, J. VAN DEN BROECK, R. VANDER VENNET , Determinants of Mutual Fund Performance: ABayesian Stochastic Frontier Approach, June 2001, 31 p.
01/104 S. VIAENE, B. BAESENS, T. VAN GESTEL, J.A.K. SUYKENS, D. VAN DEN POEL, J. VANTHIENEN, B. DEMOOR, G. DEDENE, Knowledge Discovery in a Direct Marketing Case using Least Square Support Vector Machines, June 2001, 27 p. (published inInternational Journal of Intelligent Systems, 2001).
01/105 S. VIAENE, B. BAESENS, D. VAN DEN POEL, J. VANTHIENEN, G. DEDENE, Bayesian Neural Network Learningfor Repeat Purchase Modelling in Direct Marketing, June 2001, 33 p. (forthcoming inEuropean Journal of Operational
Research, 2002).
01/106 H.P. HUIZINGA, J.H.M. NELISSEN, R. VANDER VENNET , Efficiency Effects of Bank Mergers and Acquisitions inEurope, June 2001, 33 p.
01/107 H. OOGHE, J. CAMERLYNCK, S. BALCAEN, The Ooghe-Joos-De Vos Failure Prediction Models: a Cross-Industry Validation, July 2001, 42 p.
01/108 D. BUYENS, K. DE WITTE, G. MARTENS, Building a Conceptual Framework on the Exploratory Job Search, July2001, 31 p.
01/109 J. BOUCKAERT, Recente inzichten in de industriële economie op de ontwikkelingen in de telecommunicatie,augustus 2001, 26 p.
01/110 A. VEREECKE, R. VAN DIERDONCK, The Strategic Role of the Plant: Testing Ferdows' Model, August 2001, 31 p.
01/111 S. MANIGART, C. BEUSELINCK, Supply of Venture Capital by European Governments, August 2001, 20 p.
01/112 S. MANIGART, K. BAEYENS, W. VAN HYFTE, The survival of venture capital backed companies, September 2001, 32 p.
01/113 J. CHRISTIAENS, C. VANHEE, Innovations in Governmental Accounting Systems: the Concept of a "Mega GeneralLedger" in Belgian Provinces, September 2001, 20 p.
01/114 M. GEUENS, P. DE PELSMACKER, Validity and reliability of scores on the reduced Emotional Intensity Scale,September 2001, 25 p.
01/115 B. CLARYSSE, N. MORAY, A process study of entrepreneurial team formation: the case of a research based spinoff, October 2001, 29 p.
01/116 F. HEYLEN, L. DOBBELAERE, A. SCHOLLAERT , Inflation, human capital and long-run growth. An empiricalanalysis, October 2001, 17 p.
01/117 S. DOBBELAERE, Insider power and wage determination in Bulgaria. An econometric investigation, October 2001,30 p.
01/118 L. POZZI, The coefficient of relative risk aversion: a Monte Carlo study investigating small sample estimator problems, October 2001, 21 p.
01/119 N. GOBBIN, B. VAN AARLE, Fiscal Adjustments and Their Effects during the Transition to the EMU, October 2001, 28 p.
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FACULTEIT ECONOMIE EN BEDRIJFSKUNDE HOVENIERSBERG 24 9000 GENT Tel. : 32 - (0)9 – 264.34.61
Fax. : 32 - (0)9 – 264.35.92
WORKING PAPER SERIES 7
01/120 A. DE VOS, D. BUYENS, R. SCHALK, Antecedents of the Psychological Contract: The Impact of Work Values andExchange Orientation on Organizational Newcomers’ Psychological Contracts, November 2001, 41 p.
01/121 A. VAN LANDSCHOOT, Sovereign Credit Spreads and the Composition of the Government Budget, November 2001, 29 p.
01/122 K. SCHOORS, The fate of Russia’s former state banks: Chronicle of a restructuring postponed and a crisis foretold ,November 2001, 54 p.