Albrechtetal 2001 Shapley Decomposition

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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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Delphine François

Ghent University

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Koen Schoors

Ghent University

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Available from: Koen Schoors

Retrieved on: 03 November 2015

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FACULTEIT ECONOMIEEN BEDRIJFSKUNDE

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WORKING PAPER

A Shapley Decomposition of Carbon Emissions

without Residuals

Johan Albrecht a, Delphine François a and Koen Schoors b1

December 2001

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a Ghent University, Faculty of Economics and Business Administration (CEEM), Hoveniersberg 24,

9000 Ghent, Belgium (E-mail : [email protected] ; [email protected] ) b Ghent University, Faculty of Economics and Business Administration (CERISE), Hoveniersberg 24,9000 Ghent, Belgium (E-mail : [email protected])

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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12

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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14

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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15

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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16

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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17

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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18

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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


United Kingdom (MN PS Curr. Price)

Total Industry Sector GDP = Manufacturing + Electricity, Gas and Water + ConstructionTotal Transport Sector GDP = Transport, Storage and Communication


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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21

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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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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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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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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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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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.

Documents

Albrechtetal 2001 Shapley Decomposition