Ansuategi (2003)

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Environmental and Resource Economics 26: 305328, 2003.

2003 Kluwer Academic Publishers. Printed in the Netherlands.305

Economic Growth and Transboundary Pollution inEurope: An Empirical Analysis

ALBERTO ANSUATEGIEkonomi Analisiaren Oinarriak I Saila, Ekonomi Zientzien Fakultatea, Lehendakari Agirre

Etorbidea, 83, 48015 Bilbao (Spain)

(E-mail: [email protected])

Accepted 13 February 2003

Abstract. The existing empirical evidence suggests that environmental Kuznets curves only exist for

pollutants with semi-local and medium term impacts. Ansuategi and Perrings (2000) have considered

the behavioral basis for the correlation observed between different spatial incidence of environmentaldegradation and the relation between economic growth and environmental quality. They show that

self-interested planners following a Nash-type strategy tend to address environmental effects sequen-

tially: addressing those with the most immediate costs first, and those whose costs are displaced in

space later. This paper tests such behavioral basis in the context of sulphur dioxide emissions in

Europe.

Key words: economic growth, Kuznets, panel data, sulphur oxides, transboundary externalities

Jel classification: O11, Q25, Q28, R15

1. Introduction

An idea that has attracted much interest recently is the notion that economic devel-opment can be of assistance to the environment rather than being a threat to it. Theintuition behind this hypothesis is that development will initially increase pressureon the environment but, as environmental quality becomes more scarce and to the

extent that this scarcity is signaled in the price of polluting activity, substitutionwill take place in both the demand and supply sides of economic activity.

A number of studies in the 1990s have evaluated this hypothesis empiricallyby estimating a reduced form relationship between indicators of environmentaldegradation and income. The pioneering work was the study of the potential

environmental impacts of the North American Free Trade Agreement (NAFTA)carried out by Grossman and Krueger (1991). They studied the relationship

between per capita income and concentrations of a series of air pollutants (sulphurdioxide, dark matter and suspended particulates) in a cross section of countries

for different years. They concluded that economic growth tends to alleviate airpollution problems once a countrys per capita income reaches $4,000$5,000 (US$1985).

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306 ALBERTO ANSUATEGI

Subsequently, a relation of this sort has been found between per capita incomeand a series of indicators of environmental degradation such as sulphur dioxide,suspended particulates, dark matter, nitrogen oxides, carbon monoxide, toxic

intensity of production, hazardous waste, energy use, traffic volumes, hydrocar-bons, and automotive lead emissions. All these findings have come to be known asEnvironmental Kuznets Curves (EKCs) after Kuznets (1955) study of the relationbetween per capita income and income inequality. Ansuategi et al. (1998) and Stern

(1999) thoroughly review the EKC literature.But the evidence does not all run in the same direction. Some environmental

problems such as lack of access to safe water and urban sanitation and concen-tration of lead in rivers and heavy particles in cities decline as income increases.

Other indicators of environmental stress worsen as income increases. This is thecase, for example, with municipal waste and carbon dioxide emissions.

Overall, the existing empirical evidence suggests that EKCs only exist forpollutants with semi-local and medium-term impacts. Access to safe water and

provision of urban sanitation are local and urgent human needs and any threatto these tends to be addressed immediately. However, the effects on health,productivity and welfare of some air pollutants such as sulphur dioxide have lessimmediate effects and so are addressed only as a second order of business. Finally,

the impacts of other pollution problems such as global warming harm people whoare geographically and temporally far from the pollution site and accordingly theytend to be ignored by the polluters.

The importance of the spatial (and temporal) incidence of pollution in order todetermine the shape of the relationship between economic growth and environ-

mental quality has been clearly stated in several critical reviews of the EKCliterature (Arrow et al. 1995; Barbier 1997; Ansuategi et al. 1998). Accord-

ingly, some of the most recent EKC studies have started to consider internationalenvironmental spillovers as crucial parts of their analysis. Cole et al. (1997), for

example, find that a log-linear function for emissions of CFCs and halons in1986 is increasing across countries, whereas the post-Montreal Protocol estimate(emissions in 1990) shows an EKC-type relationship. They therefore conclude thattransboundary pollutants either increase monotonically with income or else have

turning points at high per capita income unless they have been subjected to a multi-lateral policy initiative. From a theoretical point of view, Ansuategi and Perrings(2000) incorporate transboundary externalities into Forsters (1973) simple modelof growth and pollution and show that de-linking environmental degradation from

economic growth critically depends on the rate of externalisation of pollutionacross political borders.

But the EKC literature is still lacking a more thorough empirical assessment ofthe importance of the spatial dispersion of pollutants in determining the pollution-

growth relationship. From a different but related line of work, Murdoch et al.(1997) have derived an econometric specification for the demand of emissionsreductions that adjusts for the spatial dispersion of the pollutants. They conclude

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ECONOMIC GROWTH AND TRANSBOUNDARY POLLUTION IN EUROPE 307

that their model performs reasonably well for sulphur cutbacks in Europe. Theanalysis in this paper tries to bridge the gap between the approach taken byMurdoch et al. (1997) and the existing empirical studies of the relationship between

economic growth and sulphur emissions/concentrations. The analysis in Murdochet al. (1997) has focused on the link between the spatial dispersion of pollu-tion and abatement strategies in European countries, but has not given a stepforward and showed the implications of such strategic behavior in shaping the

emissions-income relationship. The analysis in this paper takes such a step.The paper is structured as follows. Section 2 identifies and motivates the

approach adopted to test the relation between income and sulphur emissions.Section 3 introduces a general model of transboundary pollution and economic

growth. Section 4 develops and interpretates the reduced form equation derivedfrom the theoretical model. The applicability of the theoretical model to Europeansulphur emissions is tested in section 5. Finally, some conclusions and policyimplications are drawn.

2. Designing a Meaningful EKC

EKCs have been typically estimated regressing some measure of environmentaldegradation against income and a set of exogenous factors. This is also the

approach taken in this paper. However, the main difference of the analysis proposedhere with respect to previous studies centers on the variables included in boththe left hand side (LHS) and right hand side (RHS) of the equation representingthe reduced form relationship between environmental degradation and economic

development.

2.1. LHS OF THE EKC EQUATION

This paper focuses on environmental degradation caused by sulphur dioxide emis-

sions. The negative impact of sulphur dioxide in terms of ambient air qualityis positively linked to the quantity of the pollutant per unit of area or volume.This explains why many EKC studies have chosen ambient concentrations as thedependent variable in their regressions (Grossman and Krueger 1991, 1995; Shafik

1994; Panayotou 1997; Kaufmann et al. 1998; Torras and Boyce 1998). However,there is an important shortcoming as regards the use of ambient concentrations.Concentrations measure local impact of emission activities without regard to the

origin of such activities. In the case of transboundary pollution problems such assulphur dioxide emissions this means that ambient concentrations do not providea clear link with local economic activity. It could well be the case that a countrywith few local pollution sources showed high ambient concentration due to trans-boundary spillins from neighbouring countries. Thus, if we want to establish a

causal relation between environmental degradation and local economic activity,

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ambient concentrations do not constitute the most appropriate indicator of locallycaused environmental impact.

The alternative is to use emission estimates, as they are directly linked to local

economic activity. Panayotou (1993), Selden and Song (1994), de Bruyn (1997),Carson et al. (1997), Cole et al. (1997), Stern et al. (1998), Perman and Stern(1999), Stern (1999) and Perrings and Ansuategi (2000) use per capita emissions asdependent variables in their regressions. However, there are two important caveats

in this approach. First, per capita emissions are not good proxies for environmentalimpact as they do not provide information about the size of the area into whichthey are released. We could have very densely populated areas where per capitaemissions were quite low but aggregated emissions were quite high relative to the

area into which they were released.This leads us to consider that emission density(emissions per area) constitutes a better indicator of environmental degradation.Second, we have to bear in mind that emission density measures the impact of localpolluting activities without regard to the location of such impacts. This will require,

as we will suggest immediately, some adjustments in the RHS of the equation.

2.2. RHS OF THE EKC EQUATION

The main variable in the RHS of the equation is an indicator of economic devel-opment. In line with previous studies, the indicator of economic development usedin this paper is real income.1 However, real income can be presented in several

ways. Most studies have chosen per capita income as a single indicator of devel-opment, but Panayotou (1997) and Kaufmann et al. (1998) include income density(income per area) as an additional variable in the set of independent variables. InPanayotous paper income density is considered to represent the scale of economic

activity, whereas per capita income is interpreted as a representation of the equi-librium level of abatement. Kaufmann et al. motivate the inclusion of these twovariables in a slightly different manner. In their study income density representsthe supply side factors that drive reductions in SO2 concentrations whereas per

capita income represents the demand side factors.The analysis in this paper also considers the inclusion of both specifications

(per capita and per area) for the income variable. The motivation is as follows.In the case of sulphur dioxide, emissions are generated by both consumption and

production activities and a single variable is not able to represent adequately thecontribution of both types of activities. Per capita income represents consumerspurchasing power and therefore proxies consumption patterns. If we accompany

per capita income with population density in the RHS of the equation, then bothscale and composition of consumption activities are captured. Income densityrepresents the income generating capacity of a unit of land and consequently maybe considered as a proxy for capital accumulation. By including income density inthe set of explanatory variables, the model captures the effect of production patterns

on emissions.

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It is also necessary to account for the potential transfer of pollutants acrossborders. Previous EKC-type studies of the relation between economic growthand environmental degradation generated by sulphur oxides emissions have not

succeeded in fully integrating the transboundary nature of this type of pollution. 2

Some studies have included location-specific characteristics (topography andclimate) as explanatory variables for urban concentrations of sulphur dioxides.This implies that they were aware of the importance of topography and meteorolo-

gical conditions in determining whether pollutants accumulate in locally restrictedzones or are shifted to different areas. What they have not taken into account isthe interaction between physical and socio-economic aspects. Including a separatevariable for location-specific characteristics that does not interact with income

reveals the implicit assumption that decision-makers do not change their behaviorwhen dealing with environmental impacts occurring at different locations. But ifdecision-makers act strategically, we would expect that the income elasticity ofabatement expenditures will differ between countries whose capacity to externalise

transboundary pollution varies. This is the theoretical result obtained in Ansuategiand Perrings (2000). This result will be modeled here through dummy variablesrepresenting the degree of externalisation of pollution across borders and crossingthem with the development indicators.

In the next section a generalized version of the model of transboundary pollutionand economic growth used in Ansuategi and Perrings (2000) is presented whosemain aim is to provide some theoretical support to the specification of the EKCequation motivated in this section.

3. A Model of Transboundary Pollution and Economic Growth

The model to be presented here is a generalized version of the model used inAnsuategi and Perrings (2000). It assumes n economically closed but environ-mentally open countries indexed i = 1,...,n; each country being represented bya social planner whose interests are those of the countrys citizens. Normalizingthe number of citizens at time 0 as unity, population size at time t is

Ni(t) = eni t (1)

where ni is an exogenous and constant rate of population growth.Each country produces a single homogeneous output (Yi) using capital (Ki) and

labor (Li) as inputs:

Yi = (Ki, Li) (2)

where labor force is supposed to be a constant proportion of population (Li =liNi). exhibits constants returns to scale in K and L, and each input exhibitspositive and diminishing marginal product. Output can be used either for consump-tion (Ci), for pollution abatement (Ai) or capital accumulation (Ii). For simplicity,

no depreciation is assumed.

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Table I. % sulphur emissions by source (1990)

Mobile Stationary

Power Fuel combustion Industrial Misc.

Stations Industrial Others Process

Austria 7.7 17.8 35.5 22.4 16.6

Belgium 5.3 23.4 53 11.7 5.3 1.3

Denmark 10.4 65 12.5 7.7 4.4

Finland 2.2 23.5 25.3 10 38

France 12 26.1 21.6 15.1 15 10.2

W.Germany 6.3 33.6 35.2 15.3 9.6

Hungary 1.6 43 26.5 2.2 25 1.7

Ireland 2.7 55 22 19.3 1

Italy 9 45.6 34 4.9 6.2 0.3

Netherlands 14.7 22.1 40.7 3.8 18.7

Norway 22.2 1.8 13.9 6.5 55.6Poland 1.7 49 15.6 25.4 8.3

Portugal 5.9 61.2 18.9 1.4 12.6

Spain 3.9 66.3 21.7 4.4 1.8 1.9

Sweden 27.2 11.8 16.2 11.8 33

Switzerland 7.9 1.6 42.9 23.8 19 0.5

UK 3 72.5 18.4 5.5 0.6

Source: OECD (1995).

Emissions (Ei) are defined as by-products of both production and consumption

activities. Table I shows the proportion of sulphur oxides emissions by source for17 European countries in 1990. Emission sources are classified in two categories:mobile versus stationary sources. Most stationary sources are devoted to productionactivities (electricity generation and industrial activities), whereas mobile sources

are strongly linked with consumption activities (domestic fuel consumption andtransportation).3

Two types of abatement expenditures are thus assumed. The first type (A1i)represents abatement activity in the production sector. This category of abatement

comprises the incremental production costs imposed by activities such as coalcleaning, burning of low sulphur coals, flue desulphurization and dry scrubbing.4

The second type (A2i) represents expenditures directed to reduce the negative

by-product of consumption activities. This category of abatement comprises theincremental consumption costs imposed by, inter alia, improved insulation ofhouses, substitutions towards cleaner fuels and more energy-efficient domesticappliances, regulations such as emission standards on vehicles, road pricing, depre-ciation or regulatory rules that force an increase in the rate of turnover of vehicle

fleet.

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Thus, total emissions in each territorial unit can be calculated adding upproduction-generated emissions (E1i) and consumption-generated emissions(E2i). Assuming that (1) production and consumption activities are evenly distri-

buted across the area of the country and (2) that there are no area-related externaleconomies/diseconomies with regard to sulphur emissions,5 total emissions can berepresented as

Ei = Si

E1i

Ki

Si,A1i

Si

+E2i

ci,

Ni

Si,A2i

Si

(3)

where ci represents per capita consumption and Si stands for area of country i.

Thus, emission density can be represented as

Ei

Si=

E1i

Ki

Si,A1i

Si

+ E2i

ci,

Ni

Si,A2i

Si

(4)

Sulphur dioxide shows a mean atmospheric residence time of two to three days.This residence time is enough for sulphur dioxide to be transported by wind overhundreds or several thousands of kilometers. Considering the transboundary natureof sulphur dioxide emissions, pollution in country i is thus represented by the flow

Pi =

1

nj=i

Tij

EiSi+

nj=i

Tji

SiEj +

Bi

Si(5)

where Bi represents the background depositions in country i and where Tij repre-sents the proportion of emissions generated in country i and deposited in countryj . It is assumed that spillins from country j spread evenly in country i.6

Each citizens welfare at any time in country i is measured by a quasi-concave

utility function with per capita consumption and local pollution as arguments:

Ui(ci(t),Pi(t)) (6)

The objective of the social planner in country i is to maximize the discountedflow of aggregated utility over time:

Wi(t) =

t

ei (st)Ni(s)Ui(ci(s),Pi(s))ds (7)

where i represents the rate of time preference of country i.The public good nature of air pollution abatement implies that abatement

expenditure by one individual improves environmental quality for all individualssharing the same environment. Thus, individuals will have an incentive to freeride on the efforts of those who control emissions. Modern economies rely ongovernments or central planners for the provision of public goods that would notbe efficiently provided by individual actions. The control of sulphur emissions in

Europe may be considered as one case where a local government provides the

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locally efficient level of abatement, overcoming free-riding incentives withinlocal boundaries. But local governments are not expected to internalise trans-boundary spillovers. In the case of transboundary pollutants such as sulphur

emissions, international spillovers exist that prevent an internationally efficientprovision of abatement and, in the absence of supranational governments withenforcing power, we may expect that local planners will act strategically withregard to national abatement effort. Thus, the interaction between the n sovereign

social planners is formulated as a differential game where planner i believes thatcountry j = i will follow a given time path (Ij(t), Cj(t), A1j(t), A2j (t)) regardlessof what (Ii(t), Ci(t), A1i(t), A2i(t)) might be. This yields an Open-Loop NashEquilibrium, where one country takes the other countries policies as given. Planner

is problem is then to chooseIi(t)

Si, ci(t),

A1i(t)

Si, A2i(t)

Si

to maximize the integral of

its discounted flow of social net benefits, as specified by

max

0

ei tNi(t)U

i(ci(t),P

i(t))dt (8)

s.t.dKi(t)

Si

dt=

Ki(t)

Si,Li(t)

Si

Ni

Sici(t)

A1i(t)

SiA2i(t)

Si

withA1i

Si 0 and

A2i

Si 0

From the first order conditions of the optimization problem we know that themarginal utility of pollution control in both production and consumption activitiesmust (if possible) equal the marginal cost of pollution control in terms of foregoneconsumption:

NiSiUiPi

1ni=j

Tij E1i A1iSi

= NiSiUiPi

1ni=j

Tij E2i A2iSi

(9)

=Ui

ci+Ni

Si

Ui

Pi

1

ni=j

Tij

E2i

CiSi

Thus, for a given set of preferences and technology, it is possible to define both

types of abatement expenditure as functions of the level of local population, localpolluting activity, the total rate of externalisation of local emissions and the amountof local depositions originated in other countries:7

A1iSi= 1i

NiSi, KiSi, ci,

1ni=j

Tij ,

nj=i

TjiSiEj + Bi

Si

(10)

A2i

Si= 2i

NiSi,Ki

Si, ci,

1

ni=j

Tij

,

nj=i

Tji

SiEj +

Bi

Si

(11)

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Substituting (10) and (11) into (3) we obtain that

Ei

Si= E1i

Ki

Si,1i

Ni

Si,Ki

Si, ci,

1

n

i=j

Tij

,

n

j=i

Tji

SiEjBi

Si

+ (12)

E2i

NiSi, ci,2i

NiSi,Ki

Si, ci,

1

ni=j

Tij

,

nj=i

Tji

SiEj +

Bi

Si

To simplify notation, we can rewrite equation (12) as:

Ei

Si= f

NiSi,Ki

Si, ci,

1

ni=j

Tij

,

nj=i

Tji

SiEj +

Bi

Si

(13)

Equation (13) is a representation of the optimal level of emission density chosen

by a social planner following Nash-type strategic behavior in respect of trans-boundary emissions. Obtaining a detailed analytical description of such an equation

would require a complex calibration exercise of the underlying structural modelthat is far beyond the scope of this paper.8 Instead we will proceed in two steps.First we will review some of the arguments used in the EKC literature to explainthe relationship between growth and the environmental quality in order to generate

a hypothesis as to the direction in which the variables in the LHS of equation (13)affect emission density. Next we will test the hypothesized functional form forequation (13) using data on European sulphur emissions.

4. Emissions, Population, Technology, Affluence and Transboundary

Spillovers

The so-called impact equation put forward by Ehrlich and Ehrlich (1981) singlesout three factors that impact on the environment:

I= P A T (14)

where I, P, A and T stand for impact, population, affluence and technologyrespectively.

Equation (13) may be thought of an alternative form of as the impact equa-tion. The main difference is that in equation (13) the LHS represents potentialimpact and not actual impact on the local environment. The inclusion of trans-

boundary spillovers in the RHS is therefore motivated by this difference. In whatfollows we will discuss the specific polynomial expansion that can be expected forequation (13).

There is no clear a priori expectation as to the direction in which popula-tion density affects emission density. On the one hand, more people per square

kilometre means higher demand in cooking, heating and other energy intensive

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domestic uses that would lead to an increase in emissions per square kilometre. Onthe other hand, there are economies of scale in populations energy demand as wellas increased concern about abating emissions at every level of income in densely

populated areas. Thus, the relationship between emission density and populationdensity could either be linear or quadratic, depending on the relative strength ofthese two effects as population density rises.

The conventional reduced-form relationship between pollution and per capita

income typically yields a positive sign for low levels of per capita income anda reversal in sign for higher levels. Early explanations of the EKC hypothesisfocused on the conjecture that environmental quality is a luxury good, that is, thedemand for improved environmental quality increases more than proportionately

with income. This demand-side argument for the EKC hypothesis predicts aquadratic relationship between environmental quality and per capita income with apositive linear term and negative quadratic term.

Yet there is also a supply-side argument for the EKC hypothesis. The struc-

tural economic change that usually accompanies economic growth may play avery important role in de-linking environmental degradation and economic growth.But we cannot expect per capita income to constitute a good proxy of structuraleconomic change. As suggested in section 2, income density (income generating

capacity of a unit of land) constitutes a better proxy of production patterns ortechnology than per capita income. Given the fact that higher capital intensitiesof economic production tend to be more polluting at first stages of developmentand cleaner afterwards, it is expected that there will be an inverted-U shapedrelationship between emission density and income density.

Finally, it is necessary to provide some insights about the expected role thatthe rate of externalisation of local emissions and the spillin density coming

from abroad plays in our impact equation. Our argument is that the processby which society develops stronger preference towards environmental quality as

per capita income rises will be slower the lower the response of environmentalquality to changes in local economic activities and the lower the backgroundlevel of environmental degradation. In other words, agents act strategically andonly care about actual and local impact of their economic activity. Thus, both

1n

j=i Tij

and

nj=i

Tji

SiEj +

BiSi

will be expected to affect directly the

relationship between affluence and emissions, leading to a flatter inverted-U thelower the rate of externalisation of local emissions and the higher the back-ground level of environmental degradation. But we could also expect the rela-tionship of technology and emissions to be indirectly affected. Note that structural

change of economic activity could be accelerated by policy interventions that aredemand-driven. Thus, a similar flattening can be expected for the inverted U rela-

tionship between income density and emission density with

1n

j=i Tij

andn

j=i

Tji

SiEj +

BiSi

.

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In this paper we hypothesize that the data observed in the real world are gener-ated from the type of behavioral relationship described in the model presentedhere and that the relationship provides a good representation of past behavior. The

following section will try to refute it using past evidence of sulphur emissions inEurope.

Some may question the validity of the Nash-type strategy assumption torepresent European countries behavior, since most European countries ratified on

16 March 1983 the so-called Long-Range Transboundary Air Pollution (LRTAP)Convention and on 8 July 1985 the Helsinki Protocol to the LRTAP Convention wasadopted. Recall that the Helsinki Protocol committed ratifiers to reduce sulphuremissions by at least 30%, based on 1980 levels, by 1993. However, Barrett (1994)

demonstrated that treaties like the Helsinki Protocol, where more than a few parti-cipants are involved, are unlikely to achieve much in cooperative gains. In fact,Murdoch et al. (1997) find that free-riding does, indeed, characterize the behaviorof the ratifiers of the Helsinki Protocol.

Unfortunately time series for European sulphur emissions are very limited andthe analysis will be carried out pooling time series across countries. The validityof the empirical analysis using cross-sectional evidence to test a dynamic modelas the one presented above, heavily depends on the assumption of the existence

of a common structure for the countries in the sample. Restricting the analysis toEurope will make it easier to consider homogeneity of preferences and technologyacross countries.

5. Empirical Test

5.1. DATA

To estimate equation (13) we need data on population density, capital stock per unitof land, per capita consumption, proportion of local emissions that remain withinlocal boundaries and density of spillins originated abroad. Per capita consumptionand capital accumulation are proxied by per capita income and income density

respectively.9

We have 21 countries in Europe10 that generate useful annual observations overseven years (1985, 19871992) for the study. Emissions by country and by yearare obtained from EMEP (Sandnes 1993). Emission density (EMISS) is gener-

ated dividing total emissions by total area reported in the Dobrs Report (Stannersand Bourdeau 1995). Real income per capita (INCOME) and population density(DENSITY) are obtained from World Tables (World Bank 1994) and Dobrs

Report (Stanners and Bourdeau 1995) respectively.

11

Income density (INCDEN)results from the product of INCOME and DENSITY. The proportion of a countryssulphur emissions that falls within its own borders (INTERIOR) is calculated fromthe 1985 Budget of Oxidized Sulphur in Sandnes (1993). INTERIOR is convertedinto a dummy variable (D), which equals 1 if the proportion is higher than 65

per cent, and 0 otherwise. The density of depositions spilled in by other coun-

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Table II. Summary statistics: population density

D = 1, Z = 1 D = 0, Z = 0 D = 0, Z = 1 D = 1, Z = 0

Min 90.05 12.82 113.7 14.5

Mean 137.01 110.96 307.31 94.826

Max 246.2 328 406.9 246.4

N 21 46 17 58

Table III. Summary statistics: per capita income

D = 1, Z = 1 D = 0, Z = 0 D = 0, Z = 1 D = 1, Z = 0

Min 1488 2344 2049 937

Mean 12654 13269 11953 11729

Max 29920 29150 17310 28670

N 21 46 17 58

Table IV. Summary statistics: income density

D = 1, Z = 1 D = 0, Z = 0 D = 0, Z = 1 D = 1, Z = 0

Min 181400 197100 234700 84580

Mean 1858900 1541200 4082400 1105900

Max 5002000 5316000 6910000 4877000

N 21 46 17 58

tries (SPILLIN) is also calculated from the 1985 Budget of Oxidized Sulphur inSandnes (1993). SPILLIN is converted into a dummy variable (Z), which equals1 if the spillin density is > 1.5 Tonnes per square kilometre, and 0 otherwise.12

Permutations ofD and Z equal to 0 and 1 will allow us to consider four differentcases: (i) D = 1 and Z = 1, (ii) D = 0 and Z = 0, (iii) D = 0 and Z = 1, and(iv) D = 1 and Z = 0. In what follows, countries belonging to these cases will

be referred as (i) downstream countries, (ii) upstream countries, (iii) countriesalong the stream, and (iv) environmentally sealed countries, respectively. Notethat this classification is intertemporally consistent, that is, countries remain in thesame category over time.

Tables II to IV provide summary statistics for population density, per capita

income and income density.

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5.2. STATISTICAL MODEL

Using the variables defined above and given the a priori expectations about therelationship between variables presented in the previous section, we re-specify

equation (13) as

EMISSi = i +DENSITYi + DENSITY2i + (15)

(0 +Di1 + Zi2)INCDENi +

(0 +Di1 + Zi2)INCDEN2i +

(0 +Di1 + Zi2)INCOMEi +

(0 +Di1 + Zi2)INCOME2i +

YEAR

A time trend variable is also included (YEAR) to capture any time-relatedexogenous shifts in technology or preferences unrelated to income or density

levels.There is an important complication that restricts both the statistical model and

the methodology in the estimation. Note that the results of our model are strongly

dependent on the validity of the assumption that political decisions accuratelyreflect social demand. However, our sample includes countries with very differentpolitical systems and therefore different abilities to reflect social demand. Thiscan be addressed by including a dummy variable to control for this difference.13

However, such a regressor would not vary within the groups, and this means thata LSDV (fixed effects) could not be computed.14 On the other hand, note that(i) excluding this dummy variable from our analysis, the effect of this variable isabsorbed by the individual effects and (ii) the ability of political systems to reflect

social demand in Europe is highly correlated with income. This would mean thatwe cannot assume that the individual effects are uncorrelated with other regressors,which makes random effects inconsistent.

The two options are: (1) include the dummy variable and specify our equation as

a random effects model; or (2) exclude the dummy variable and specify our equa-tion as a fixed effects model.15 Having tried both options, we consider option (2) asthe more appropriate one since in option (1) LIMDEP renders negative estimatorsof2 (which can be interpreted as a signal of inappropriateness of the model).

16

5.3. EMPIRICAL RESULTS

Examining the sensitivity of the estimates to alternative polynomial specificationsof equation (15), the quadratic specification for population density, per capitaincome (in logs) and income density (in logs) yields the best fit. Table V reportsthe results.17

Figures 1 to 3 depict the relationships between emission density and the

explanatory variables that result from the regression reported in Tables V and VI.

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Figure 1. Emission density (logs) vs population density.

Figure 2. Emission density (logs) vs income density (logs).

Table VII reports the elasticities of emission density with respect to income density(EK ), the elasticities of emission density with respect to per capita income (EC )

and the overall income elasticities as obtained from differentiating the estimatingequation with respect to all income-dependent terms and evaluated at mean valuesfor land area and population (EY). Note that elasticities of emission density withrespect to both income density and per capita income have very high magnitudeand opposing sign, which is not surprising given the high correlation of income

density and per capita income.

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Figure 3. Emission density (logs) vs pc income (logs).

Figure 4. Income elasticities of emission density.

Figure 4 represents graphically overall income elasticities of emission density.In the four sub-samples of countries income elasticity of emission density ispositive for low income values but reverses sign as income rises. This means thatthere is a turning point (point at which income elasticity of emission density takeson a value of 0) for each sub-sample. Table VIII presents these turning points both

in total and per capita income levels.

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Table V. Estimation results for SO2 emission density (logs)

Fixed Effects

VARIABLE COEFFICIENT T-RATIO

DENSITY 0.25995 6.974

DENSITY2 0.29103E-03 6.892

LOGINCOME 20.800 7.333

LOGINCOME2 0.26923 6.448

DxLOGINCOME

DxLOGINCOME2 0.26748 3.788

LOGINCDEN 16.063 6.113

LOGINCDEN2

DxLOGINCDEN

DxLOGINCDEN2 0.18016 3.933

ZxLOGINCOME 8.1506 3.097

ZxLOGINCOME2 0.43764 3.207

ZxLOGINCDEN 5.0409 3.075

ZxLOGINCDEN2 0.16834 3.166

YEAR

R2 0.9908

N 142

Table VI. Specification, multicollinearity and heteroscedasticity tests

Fixed Effects

H0 Source Statistic Value Critical value

No Misspecification Ramsey (69) F 3.14 3.92

Multicollinearity Belsley et al. (80) CN 0.0007 10

Homoscedasticity Breusch and Pagan (80) LM 4.47 19.67

5.4. DISCUSSION

These results can be related to results reported in the literature. They give rise to anumber of conjectures.

An increase in population density increases emission density, although it doesso at a diminishing rate (see Figure 1). More people per square kilometreindicates higher demand in energy-intensive domestic uses and, therefore,

more emissions per square kilometre. This scale effect is particularly

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Table VII. Elasticity of emission density with regard to income related variables

EK

Income density D = 1, Z = 1 D = 1, Z = 0 D = 0, Z = 1 D = 0, Z = 0

min = 84580 21.3721 20.1510 17.2277 16.0630

mean = 1714600 21.4432 21.2352 16.2710 16.0630

max = 6910000 21.4762 21.7375 15.8017 16.0630

EC

Per capita income D = 1, Z = 1 D = 1, Z = 0 D = 0, Z = 1 D = 0, Z = 0

min = 937 22.9373 20.7956 19.2768 17.1155

mean = 12392 20.6682 20.7670 15.6263 15.7251

max = 29920 19.8936 20.7639 14.3802 15.2505

EY

Income D = 1, Z = 1 D = 1, Z = 0 D = 0, Z = 1 D = 0, Z = 0

min = 54530 105 2.6853 1.0394 3.3689 1.722

mean = 23336 107 0.70461 0.3273 0.67696 0.30065

max = 12130 108 2.192 0.92697 2.4522 1.1882

Table VIII. Turning points (income)

D = 1, Z = 1 D = 1, Z = 0 D = 0, Z = 1 D = 0, Z = 0

income 10689 107 94914 106 12447 107 13352 107

mean population 5288 4696 6158 6606

strong for population density levels close to the minimum of our sample(12.82 inhabitants per square kilometre). For higher population density levels

economies of scale in populations energy demand and increased concernabout abating emissions at every level of development in densely populatedareas compensate part of the scale effect.18

Emission density monotonically decreases with income density (see Figure 2).The reason may be the change in the type and distribution of industry andservices in these economies. The pattern of emission changes when economiesmove from heavy to light industry or from industry to services19 Emissiondensity should decrease with the expansion of the service sector and the relo-

cation of smokestack industries. Several studies have explored the existence

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of pollution havens (Lucas et al. 1992; Birdsall and Wheeler 1992; Low andYeats 1992; Copeland and Taylor 1994). There are no conclusive results, butmost of these studies consider the migration of industries with higher toxic

intensities as a plausible hypothesis.

But the change in the composition of the product is not the only reason

to explain the negative relationship between emission density and incomedensity. The shift into less polluting services and high technology industriesis often complemented by a directed effort to develop less polluting produc-tion techniques. An empirical analysis of OECD data presented by Komen etal. (1997) supports the notion that publicly supported research and develop-

ment aimed at environmental improvements increases with income. De Bruyn(1997) finds that technological innovation is more important than structuralchange in explaining sulphur emission reductions in the Netherlands and WestGermany.

Therefore, it seems that as society develops governments are called upon to

impose more stringent controls to protect the local environment. This leadsto a wider adoption of pollution abatement measures such as coal cleaning,

burning of low sulphur coals, flue desulphurization and dry scrubbing.

However, there is some degree of heterogeneity with regard to the speed

at which emission density decreases as the capital intensity of productionactivities increases (see Table VII):

Downstream countries, that is, countries with more than 65% of localemissions being deposited within national boundaries (D = 1) and witha sulphur deposition density of more than 1.5 Tonnes per square kilo-metre originated by foreign economic activities (Z = 1), show the highestlevel (in absolute value) of elasticity of emission density with respect

to income density. These countries realise that pollution is an internalproblem, which generates strong incentives to speed up both the structuraltransition from industrial activities towards services and the adoption of

abatement measures in the production process.

Environmentally sealed countries, that is, countries with more than 65%

of local emissions being deposited within national boundaries (D = 1)and with sulphur depositions originated abroad amounting to less than 1.5Tonnes per square kilometre (Z = 0), also show high levels of elasticity ofemission density with respect to income density. In fact, even though their

elasticities are lower than elasticities for downstream countries at thelowest end in the income density range, elasticities grow faster with incomedensity for environmentally sealed countries. This result is consistentwith decision makers acting strategically. The stronger the link betweenlocal economic activity and local environmental conditions the weaker thelocal incentives to free-ride other countries abatement efforts.

Countries along the stream, that is, countries with less than 65% of local

emissions being deposited within national boundaries (D = 0) and with a

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sulphur deposition density of more than 1.5 Tonnes per square kilometre,originated from foreign economic activities (Z = 1), find that as incomedensity rises further reductions are more costly and their local effect is

rather small due to the relatively important role played by other coun-tries economic activities in determining local environmental conditions.Thus, free-riding incentives are higher for this set of countries than forthe previous two subsets of countries and this explains why elasticities of

emission density with respect to income density are lower.

Finally, upstream countries, that is countries with less than 65% of localemissions being deposited within national boundaries (D = 0) and with asulphur deposition density of less than 1.5 Tonnes per square kilometre

originating from foreign economic activities (Z = 0), show the lowestlevel of elasticity of emission density with respect to income density. Thesecountries realise that any economic activity, either local or external, doesnot have much impact on local environmental conditions. Thus, there are

few incentives to implement abatement measures. Reductions in emissionsare purely a consequence of the structural change that generally accom-panies growth and are not spurred by the degradation of local environmentalconditions.

Emission density increases as per capita income rises (see Figure 3). Thisresult seems to contradict the general perception that higher per capitaincomes offer the relative luxury of caring about environmental quality.

However, there is an explanation. As mentioned before, environmental qualityhas the properties of a public good and this generates problems of privatesupply and free-riding of pollution abatement effort. In this situation itis the role of governments to implement effective environmental protec-

tion. Governments can choose between direct regulation (command-and-control approach) and indirect regulation (market based instruments). Unlikeproduction activities, consumption activities are not easy to regulate throughcommand-and-control regulatory approaches given the mobile nature of

such activities. But consumers are also very reluctant to accept market-basedinstruments.20 Thus, most controls regarding consumption-related emissionsare predominantly confined to regulation at point of production, whichmeans that governments fail to create the right incentives for consumers.Consequently, it is hardly surprising to find that emissions grow as consumers

purchasing power increases.

A similar insight has been drawn from the study carried out by Murdochet al. (1997) when comparing European nations actions to curb NO

xand

sulphur emissions. They conclude that NOx is more difficult to control due

to the major share of emissions coming from a large number of small mobilepolluters (vehicles).

What is less intuitive is the fact that emission density grows faster in down-

stream countries than in upstream countries (see Table VII). This may have

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an explanation, however. In downstream countries high energy consumptionmay be a way to reduce individual exposure to air pollution. Air conditioningis an example of energy-intensive ways to reduce individual exposure to air

pollution. This would also explain why those who suffer the problem do lessto reduce it (on a collective basis).

Finally, leaving aside partial EKCs subject to variation in individual sets ofincome-related regressors and focusing in the total EKC with (log) income onthe horizontal axis, we find that the inverted U-shaped relationship betweenincome and pollution holds for the four cases, but turning points for environ-

mentally sealed countries and downstream countries are located at higherincome levels than turning points for upstream countries and countriesalong the stream (See Tables VII and VIII and Figure 4). This empiricalresult is in line with the theoretical result presented in Ansuategi and Perrings

(2000): those countries who externalise a greater share of their emissionswill find it more difficult to de-link economic growth from environmental

degradation.

6. Concluding Remarks

This paper addresses the relationship between income and sulphur emissions byconsidering the transboundary nature of this type of pollutant. We have hypothe-sized that past behavior in European countries may be represented by a Nash-type

pollution abatement strategy. Estimation of the model yields two different insights.First, the prediction that nations that face more severe environmental pressure willaccompany economic growth with increased abatement in production is confirmed.But, second, there has been no national strategic response to environmental

pressure in respect of consumption.These results are consistent with those of Murdoch et al. (1997), where it is

demonstrated that a theoretical model of emission reductions with national stra-tegic behavior yields reasonable empirical results for sulphur, but less satisfying

results for NOx. Murdoch et al. conclude that the reason why the model performsworse for NOx is the fact that NOx emissions are generated by a large number ofsmall polluters whose uncoordinated actions call into question the unitary decision-maker assumption at the national level. This explanation could also apply to our

results. Consumption activities are more difficult to control by the authoritiesthan production activities. Thus, the results for the relationship between emissiondensity and income density evidence a coordinated action at the national level and

strategic behaviour at the supranational level with regard to regulation of produc-tion activities. In contrast, the results for the relationship between emission densityand per capita income reveal the lack of government control and the presence offree-riding behavior at the infranational level with regard to consumption activities.This greater role for strategic behavior at the local level helps understanding the

positive relationship between sulphur emissions density and per capita income.

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Of course, the conclusions presented here rely on the assumption that emissiondensity and per capita income are good proxies of production and consumptionpatterns respectively. We must also accept that the income-environment relation-

ship is unidirectional: that economic growth affects environmental quality but thereare no feedback-effects from the environment to economic growth.21 But giventhis, there are two implications of this analysis. For those who argue that economicgrowth increases emission levels it shows that de-linking emissions from economic

growth is possible for local pollutants that are easily controlled by local govern-ments. For those who argue that economic growth is the cure of environmentalproblems, it shows that de-linking is less likely to happen when pollution is difficultto control by local authorities and/or it can be easily externalized to other countries.

Acknowledgements

This work was partially supported by Research Project UPV 035.321-HA059/97from the University of the Basque Country and Research Project HU-1998-

133 from the Basque Government. I benefitted from useful suggestions ofC. A. Perrings, G. Halkos, R. Martnez Espieira and an anonymous referee.However, I am responsible for any remaining errors.

Notes

1. Perrings and Ansuategi (2000) have employed conventional EKC methodology using other

development indicators such as the Human Development Index or the Integrated Poverty Index

and they have not found substantial changes in the results.

2. Murdoch et al. (1997) has been the only study where the transboundary nature of sulphur

emissions is taken into account as a determinant of abatement effort.

3. The oil-refining process that yields the fuels used in cars, trucks and aircraft removes most of

the sulphur in the crude oil. Hence, transportation represents a small percentage of the total

anthropogenic emissions of sulphur dioxide.

4. See, Amman and Kornai (1987), Amman (1990) and Halkos (1995) for further details on these

technologies.

5. We are assuming that in each country emission patterns in each square kilometre are a scaled

representation of national emissions.

6. The main reason for this simplifying assumption is that we are going to work with national

averages in the empirical part of the analysis and we want the model to fit the data set. However,

this assumption will be partially relaxed in future development of this research by considering

intranational (NUTS 3 level) decision making units.

7. In order to simplify the analysis, we also consider the natural environment as another foreign

country.

8. Otherwise, it is possible to get some general insights working with simplifying assumptions

regarding preferences and technology as those made in Ansuategi and Perrings (2000).

9. We could simply use measures of both consumption and capital accumulation since those data

are available. However, we choose to proxy them through income data because we want to

produce results that are consistent with conventional EKC studies.

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10. The set of countries comprises Austria, Belgium, Bulgaria, Denmark, Finland, France,W. Germany, Greece, Hungary, Ireland, Italy, Luxembourg, Netherlands, Norway, Poland,

Portugal, Romania, Spain, Sweden, Switzerland and Great Britain.

11. The lack of income data for Romania in years 1985, 1986 and 1987 and West Germany in years

1991 and 1992 forces us to work with an unbalanced panel of 142 observations.12. The choice of thresholds to generate the D and Z dummy variables has been conditioned by the

size of the data set.

13. Murdoch et al. (1997) use a binary variable generated using Gastils (1989) index of civil liberties

and index of political freedom.

14. The dummy variable could be perfectly collinear with the fixed effect, which would prevent

computation of the LSDV estimator.

15. Actually, there does not seem to be any justification to consider the fixed effects as uncorrelated

with the other regressors.

16. Random effects estimators for the model including the dummy variable could be computed using

STATA. However, when asked to implement Hausmans specification test, such a test rejected

the random effects specification.

17. The panel regression results for equation (15) with emission density, per capita income and

income density expressed in logs shows a very high overall fit (adjusted R

2

= 0.9907), althoughfour variables are statistically insignificant. These four variables are the square of income density,

the multiplicative term of the D dummy variable and income density, the multiplicative term of

the D dummy variable and per capita income and the variable representing the time trend. Thus,

we drop these four terms and run the regression again. The overall fit of the model improves

only marginally (adjusted R2 = 0.9908), but remaining variables become more significant after

dropping those four terms.

18. The turning point for population density would be located at a level of 446 inhabitants per square

kilometre. However, our sample does not include observations with a population density level

higher than 407 inhabitants per square kilometre.

19. Note that a unit of land will probably render more income when devoted to services or high-techproduction activities than when devoted to heavy industry.

20. Public perception is such that people think that pollution is the sole responsibility of the

production sector.

21. The possibility of contemporaneous feedback from environmental degradation to economicgrowth has been examined testing the null of exogeneity of the income variables. The test is

similar to the test used by Holtz-Eakin and Selden (1995) and Cole et al. (1997). This test is a

Hausman specification test where lagged income is used as the instrumental variable. Results

indicate that simultaneity is not present. However, the test used has low power to reject the null

of exogeneity.

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