The Rise and Fall of the Environmental Kuznets Curvehome.cerge-ei.cz/richmanova/UPCES/Stern - The Rise... · The Rise and Fall of the Environmental Kuznets Curve DAVID I. STERN *

World Development Vol. 32, No. 8, pp. 1419–1439, 2004� 2004 Elsevier Ltd. All rights reserved

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lddev.2004.03.004
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The Rise and Fall of the Environmental

Kuznets Curve

DAVID I. STERN *

Rensselaer Polytechnic Institute, Troy, NY, USA
Available online
Summary. — This paper presents a critical history of the environmental Kuznets curve (EKC). TheEKC proposes that indicators of environmental degradation first rise, and then fall with increasingincome per capita. Recent evidence shows however, that developing countries are addressingenvironmental issues, sometimes adopting developed country standards with a short time lag andsometimes performing better than some wealthy countries, and that the EKC results have a veryflimsy statistical foundation. A new generation of decomposition and efficient frontier models canhelp disentangle the true relations between development and the environment and may lead to thedemise of the classic EKC.� 2004 Elsevier Ltd. All rights reserved.

Key words — environmental Kuznets curve, pollution, economic development, econometrics,

review, global

*I thank Cutler Cleveland, Quentin Grafton and two

anonymous referees, for comments on drafts of this

manuscript. Participants at seminars at the University of

Wollongong, Clark University, University of York,

University of Cambridge, Rensselaer Polytechnic Insti-

tute, and the Australian National University and at

conferences organized by the Association of American

Geographers in Los Angeles and the International

Society for Ecological Economics in Sousse, Tunisia

aided in developing my arguments. Additionally, I am

grateful to my collaborators Mick Common, Roger

Perman, and Kali Sanyal for their contributions dis-

cussed in this paper. Final revision accepted: 23 March

2004.

1. INTRODUCTION

The environmental Kuznets curve (EKC) is ahypothesized relationship between variousindicators of environmental degradation andincome per capita. In the early stages of eco-nomic growth degradation and pollutionincrease, but beyond some level of income percapita, which will vary for different indicators,the trend reverses, so that at high income levelseconomic growth leads to environmentalimprovement. This implies that the environ-mental impact indicator is an inverted U-shapedfunction of income per capita. Typically, thelogarithm of the indicator is modeled as aquadratic function of the logarithm of income.An example of an estimated EKC is shown inFigure 1. The EKC is named for Kuznets (1955)who hypothesized that income inequality firstrises and then falls as economic developmentproceeds.The EKC concept emerged in the early 1990s

with Grossman and Krueger’s (1991) path-breaking study of the potential impacts ofNAFTA and the concept’s popularizationthrough the 1992 World Bank DevelopmentReport (IBRD, 1992). If the EKC hypothesiswere true, then rather than being a threat to theenvironment, as claimed by the environmental

141

movement and associated scientists in the past(e.g., Meadows, Meadows, Randers, & Beh-rens, 1972), economic growth would be themeans to eventual environmental improvement.This change in thinking was already underwayin the emerging idea of sustainable economicdevelopment promulgated by the World Com-mission on Environment and Development(1987) in Our Common Future. The possibilityof achieving sustainability without a significantdeviation from business as usual was an obvi-ously enticing prospect for many––letting

9

0

50

100

150

200

250

300

0 5000 10000 15000 20000 25000 30000

$ GNP per Capita

Kg

SO

2 p

er C

apit

a

Figure 1. Environmental Kuznets curve for sulfur emissions. Source: Panayotou (1993) and Stern, Common, andBarbier (1996).

WORLD DEVELOPMENT1420

humankind ‘‘have our cake and eat it’’ (Rees,1990, p. 435).The EKC is an essentially empirical phe-

nomenon, but most of the EKC literature iseconometrically weak. In particular, little or noattention has been paid to the statistical prop-erties of the data used––such as serial depen-dence or stochastic trends in time-series 1––andlittle consideration has been paid to issues ofmodel adequacy such as the possibility ofomitted variables bias. 2 Most studies assumethat, if the regression coefficients are nominallyindividually or jointly significant and have theexpected signs, then an EKC relation exists.However, one of the main purposes of doingeconometrics is to test which apparent rela-tionships, or ‘‘stylized facts,’’ are valid andwhich are spurious correlations.When we do take diagnostic statistics and

specification tests into account and use appro-priate techniques, we find that the EKC doesnot exist (Perman & Stern, 2003). Instead, weget a more realistic view of the effect of eco-nomic growth and technological changes onenvironmental quality. It seems that emissionsof most pollutants and flows of waste aremonotonically rising with income, though the‘‘income elasticity’’ is less than one and is not asimple function of income alone. Income-inde-pendent, time-related effects reduce environ-mental impacts in countries at all levels ofincome. The new (post-Brundtland) conven-tional wisdom that developing countries are‘‘too poor to be green’’ (Martinez-Alier, 1995)

is, itself, lacking in wisdom. In rapidly growingmiddle-income countries, however the scaleeffect, which increases pollution and otherdegradation, overwhelms the time effect. Inwealthy countries, growth is slower, and pol-lution reduction efforts can overcome the scaleeffect. This is the origin of the apparent EKCeffect. The econometric results are supported byrecent evidence that, in fact, pollution problemsare being addressed and remedied in developingeconomies (e.g., Dasgupta, Laplante, Wang, &Wheeler, 2002).This paper follows the development of the

EKC concept in approximately chronologicalorder. I do not attempt to review or cite all ofthe rapidly growing number of studies. Thenext two sections of the paper review in moredetail the theory behind the EKC and theeconometric methods used in EKC studies. Thefollowing sections review some EKC analysesand their critique. Sections 6 and 7 discuss themore important recent developments that havechanged the picture that we have of the EKC.The final sections discuss alternative approa-ches––decomposition of emissions and efficientfrontiers––and summarize the findings.

2. THEORETICAL BACKGROUND

The EKC concept emerged in the early 1990swith Grossman and Krueger’s (1991) path-breaking study of the potential impacts ofNAFTA and Shafik and Bandyopadhyay’s

THE ENVIRONMENTAL KUZNETS CURVE 1421

(1992) background study for the 1992 WorldDevelopment Report. The EKC theme waspopularized by the World Bank’s WorldDevelopment Report 1992 (IBRD, 1992), whichargued that: ‘‘The view that greater economicactivity inevitably hurts the environment isbased on static assumptions about technology,tastes and environmental investments’’ (p. 38)and that ‘‘As incomes rise, the demand forimprovements in environmental quality willincrease, as will the resources available forinvestment’’ (p. 39). Others have expoundedthis position even more forcefully with Beck-erman (1992) claiming that ‘‘there is clear evi-dence that, although economic growth usuallyleads to environmental degradation in the earlystages of the process, in the end the best––andprobably the only––way to attain a decentenvironment in most countries is to becomerich.’’ (p. 491). In his highly publicized andcontroversial book, The Skeptical Environmen-talist, Lomborg (2001) relies heavily on the1992 World Development Report (Cole, 2003a)to argue the same point, while many environ-mental economists take the EKC as a stylizedfact that needs to be explained by theory. Allthis is despite the fact that the EKC has neverbeen shown to apply to all pollutants or envi-ronmental impacts and recent evidence, 3 dis-cussed in this paper, challenges the notion ofthe EKC in general. The remainder of thissection discusses the economic factors thatdrive changes in environmental impacts andmay be responsible for rising or decliningenvironmental degradation over the course ofeconomic development.If there were no change in the structure or

technology of the economy, pure growth in thescale of the economy would result in growth inpollution and other environmental impacts.This is called the scale effect. The traditionalview that economic development and environ-mental quality are conflicting goals reflects thescale effect alone. Proponents of the EKChypothesis argue that

at higher levels of development, structural change to-wards information-intensive industries and services,coupled with increased environmental awareness,enforcement of environmental regulations, bettertechnology and higher environmental expenditures,result in leveling off and gradual decline of environ-mental degradation (Panayotou, 1993, p. 1).

Thus there are both proximate causes of theEKC relationship––scale, changes in economic

structure or product mix, changes in techno-logy, and changes in input mix, as well asunderlying causes such as environmental regu-lation, awareness, and education, which canonly have an effect via the proximate variables.Let us look in more detail at the proximatevariables:

(a) Scale of production implies expandingproduction at given factor-input ratios, out-put mix, and state of technology. It is nor-mally assumed that a 1% increase in scaleresults in a 1% increase in emissions. Thisis because if there is no change in the in-put–output ratio or in technique there hasto be a proportional increase in aggregateinputs. However, there could, in theory, bescale economies or diseconomies of pollu-tion (Andreoni & Levinson, 2001). Somepollution control techniques may not bepractical at a small scale of productionand vice versa or may operate more or lesseffectively at different levels of output.(b) Different industries have different pollu-tion intensities. Typically, over the course ofeconomic development the output mixchanges. In the earlier phases of develop-ment there is a shift away from agriculturetoward heavy industry which increasesemissions, while in the later stages of devel-opment there is a shift from the more re-source intensive extractive and heavyindustrial sectors toward services and light-er manufacturing, which supposedly havelower emissions per unit of output. 4

(c) Changes in input mix involve the substitu-tion of less environmentally damaging inputsfor more damaging inputs and vice versa.Examples include: substituting natural gasfor coal and substituting low sulfur coal inplace of high sulfur coal. As scale, outputmix, and technology are held constant, thisis equivalent to moving along the isoquantsof a neoclassical production function.(d) Improvements in the state of technologyinvolve changes in both:• Productivity in terms of using less, ceteris

paribus of the polluting inputs per unit ofoutput. A general increase in total factorproductivity will result in lower emis-sions per unit of output even though thisis not necessarily an intended conse-quence.

• Emissions specific changes in process re-sult in lower emissions per unit of input.These innovations are specifically in-tended to reduce emissions. 5


This framework applies most directly toemissions of pollutants. For concentrations ofpollutants, decentralization of economic activitywith development is also important (Stern et al.,1996). Deforestation is also a flow of envi-ronmental degradation. Improved technologywould imply more replanting, selective cutting,wood recovery etc. that reduces deforestationper unit wood produced. Stock pollutants orimpacts need a different, dynamic framework.Though any actual change in the level of

pollution must be a result of change in one ofthe proximate variables, those variables may bedriven by changes in underlying variables thatalso vary over the course of economic devel-opment. A number of papers have developedtheoretical models of how preferences andtechnology might interact to result in differenttime paths of environmental quality. The vari-ous studies make different simplifying assump-tions about the economy. Most of these studiescan generate an inverted U-shape curve ofpollution intensity but there is no inevitabilityabout this. The result depends on the assump-tions made and the values of particularparameters. Lopez (1994) and Selden and Song(1995) assume infinitely lived agents, exogenoustechnological change and that pollution isgenerated by production and not by consump-tion. John and Pecchenino (1994), John, Pec-chenino, Schimmelpfennig, and Schreft (1995),and McConnell (1997) develop models basedon overlapping generations where pollution isgenerated by consumption rather than by pro-duction activities. In addition, Stokey (1998)allows endogenous technical change and Lieb(2001) generalizes Stokey’s (1998) model,arguing that satiation in consumption is neededto generate the EKC. Finally, Ansuategi andPerrings (2000) incorporate transboundaryexternalities. Magnani (2001) discusses howindividual preferences are converted into publicpolicy. Andreoni and Levinson (2001) arguethat none of these special assumptions isneeded and economies of scale in abatement aresufficient to generate the EKC. Most studiesmodel the emission of pollutants. Lopez (1994)and Bulte and van Soest (2001), among others,develop models for the depletion of naturalresources such as forests or agricultural landfertility. It seems easy to develop models thatgenerate EKCs under appropriate assumptions.None of these theoretical models has beentested empirically. Furthermore, if, in fact, theEKC for emissions is monotonic, as morerecent evidence suggests, the ability of a model

to produce an inverted U-shaped curve is not aparticularly desirable property.

3. ECONOMETRIC FRAMEWORK

The earliest EKCs were simple quadraticfunctions of the levels of income. But, eco-nomic activity inevitably implies the use ofresources and, by the laws of thermodynamics,use of resources inevitably implies the produc-tion of waste. Regressions that allow levels ofindicators to become zero or negative areinappropriate except in the case of deforesta-tion where afforestation can occur. A logarith-mic dependent variable will impose thisrestriction. Some studies, including the originalGrossman and Krueger (1991) paper, used acubic EKC in levels and found an N-shapeEKC. This might just be a polynomialapproximation to a logarithmic curve. Thestandard EKC regression model is, therefore:

lnðE=PÞit ¼ ai þ ct þ b1 lnðGDP=PÞitþ b2ðlnðGDP=PÞÞ2it þ eit; ð1Þ

where E is emissions, P is population, and lnindicates natural logarithms. The first twoterms on the RHS are intercept parameterswhich vary across countries or regions i andyears t. The assumption is that, though the levelof emissions per capita may differ over coun-tries at any particular income level, the incomeelasticity is the same in all countries at a givenincome level. The time specific interceptsaccount for time-varying omitted variables andstochastic shocks that are common to allcountries. The ‘‘turning point’’ income, whereemissions or concentrations are at a maximum,is given by:

s ¼ expð�b1=ð2b2ÞÞ: ð2ÞUsually the model is estimated with panel

data. Most studies attempt to estimate both thefixed and random-effects models. The fixed-effects model treats the ai and ct as regressionparameters. The random-effects model treatsthe ai and ct as components of the randomdisturbance. If the effects ai and ct and theexplanatory variables are correlated, then therandom-effects model cannot be estimatedconsistently (Hsiao, 1986). Only the fixed-effects model can be estimated consistently. AHausman (1978) test can be used to test forinconsistency in the random-effects estimate bycomparing the fixed-effects and random-effects


slope parameters. A significant difference indi-cates that the random-effects model is estimatedinconsistently, due to correlation between theexplanatory variables and the error compo-nents. Assuming that there are no other statis-tical problems, the fixed-effects model can beestimated consistently, but the estimatedparameters are conditional on the country andtime effects in the selected sample of data(Hsiao, 1986). Therefore, they cannot be usedto extrapolate to other samples of data. Thismeans that an EKC estimated with fixed-effectsusing only developed country data might saylittle about the future behavior of developingcountries. Many studies compute the Hausmanstatistic and, finding that the random-effectsmodel cannot be consistently estimated, esti-mate the fixed-effects model. But few havepondered the deeper implications of the failureof this orthogonality test.GDP may be an integrated variable. If the

EKC regressions do not cointegrate, then theestimates will be spurious. Until recently, veryfew studies have reported any diagnostic sta-tistics for integration of the variables or coin-tegration of the regressions. Therefore, it isunclear what we can infer from the majority ofEKC studies. Testing for integration and coin-tegration in panel data is a rapidly developingfield. Perman and Stern (2003) employ some ofthese tests and find that sulfur emissions andGDP per capita may be integrated variables.The unit root hypothesis could be rejected forsulfur (but not GDP) using the Im, Pesaran, andShin (2003) (IPS) test when the alternative wastrend stationarity. But alternative hypothesesand tests result in acceptance of the unit roothypothesis. Heil and Selden (1999) find the sameresult for carbon dioxide emissions and GDPusing the IPS test. But they prefer results thatallow for a structural break 1974, in whichallows them to strongly reject the unit roothypothesis for both GDP and carbon. Coondooand Dinda (2002) yield similar results to Per-man and Stern (2003) for carbon dioxide emis-sions. de Bruyn (2000) and Day and Grafton(2003) carry out time-series unit root tests forthe Netherlands, the United Kingdom, theUnited States, West Germany, and Canada fora variety of pollutants with very similar results.

4. RESULTS OF EKC STUDIES

Many basic EKC models relating environ-mental impacts to income without additional

explanatory variables have been estimated. Butthe key features differentiating the models fordifferent pollutants, data etc. can be displayedby reviewing a few of the early studies andexamining a single impact in more detail. Ireview the contributions of Grossman andKrueger (1991), Shafik (1994), and Selden andSong (1994) and then look in more detail atstudies for sulfur pollution and emissions.Finally, I briefly discuss studies that estimate anEKC for energy use.Many EKC studies have also been published

that include additional explanatory additionalexplanatory variables, intended to modelunderlying or proximate factors, such as‘‘political freedom’’ (e.g., Torras & Boyce, 1998)or output structure (e.g., Panayotou, 1997),or trade (e.g., Suri & Chapman, 1998). Stern(1998) reviews several of these. In general, theincluded variables turn out to be significant attraditional significance levels. Testing differentvariables individually is however subject to theproblem of potential omitted variables bias.Further, these studies do not report cointegra-tion or other statistics that might tell us ifomitted variables bias is likely to be a problemor not. Therefore, it is not clear what we caninfer from this body of work. Given theseproblems, I do not review these studies sys-tematically here.To some (e.g., Lopez, 1994) the early EKC

studies indicated that local pollutants weremore likely to display an inverted U-shaperelation with income, while global impacts suchas carbon dioxide did not. This picture fitsenvironmental economics theory––local impactsare internalized within a single economy orregion and are likely to give rise to environ-mental policies to correct the externalities onpollutees before such policies are applied toglobally externalized problems. But as we willsee, the picture is not quite so clear cut even inthe early studies. Furthermore, the more recentevidence on sulfur and carbon dioxide emis-sions shows there may be no strong distinctionbetween the effect of income per capita on localand global pollutants. Stern et al. (1996)determined that higher turning points werefound for regressions that used purchasingpower parity (PPP) adjusted income comparedto those that used market exchange rates andfor studies using emissions of pollutants rela-tive to studies using ambient concentrations inurban areas. In the initial stages of economicdevelopment urban and industrial developmenttends to become more concentrated in a smaller


number of cities which also have rising centralpopulation densities. Many developing coun-tries have a ‘‘primate city’’ that dominates acountry’s urban hierarchy and contains muchof its modern industry––Bangkok is one of thebest such examples. In the later stages of eco-nomic development, urban and industrialdevelopment tends to decentralize. Moreover,the high population densities of less-developedcities are gradually reduced by suburbaniza-tion. So, it is possible for peak ambient pollu-tion concentrations to fall as income rises evenif total national emissions are rising.The first empirical EKC study was the

NBER working paper by Grossman and Krue-ger (1991) 6 that estimated EKCs for SO2, darkmatter (fine smoke), and suspended particles(SPM) using the GEMS dataset as part ofa study of the potential environmental impactsof NAFTA. The GEMS dataset is a panelof ambient measurements from a number oflocations in cities around the world. Eachregression involved a cubic function in levels(not logarithms) of PPP per capita GDP andvarious site-related variables, a time trend, anda trade intensity variable. The turning pointsfor SO2 and dark matter were at around$4,000–5,000 while the concentration of sus-pended particles appeared to decline even atlow income levels. At income levels over$10,000–15,000, Grossman and Krueger’s esti-mates show increasing levels of all three pollu-tants.The results of Shafik and Bandyopadhyay’s

(1992) 7 study were used in the 1992 WorldDevelopment Report (IBRD, 1992) and were,therefore, particularly influential. They esti-mated EKCs for 10 different indicators usingthree different functional forms: log-linear, log-quadratic and, in the most general case, a log-arithmic cubic polynomial in PPP GDP percapita as well as a time-trend and site-relatedvariables. In each case, the dependent variablewas untransformed. They found that lack ofclean water and lack of urban sanitation declineuniformly with increasing income, and overtime. Both measures of deforestation werefound to be insignificantly related to the incometerms. River quality tended to worsen withincreasing income. The two air pollutants,however, conform to the EKC hypothesis. Theturning points for both pollutants were atincome levels of between $3,000 and $4,000.Finally, both municipal waste and carbonemissions per capita increased unambiguouslywith rising income.

Selden and Song (1994) estimated EKCs forfour emissions series: SO2, NOx, SPM, and COusing longitudinal data primarily from devel-oped countries. The estimated turning pointswere all very high compared to the two earlierstudies. For the fixed-effects version of theirmodel they were (converted to US$1,990 usingthe US GDP implicit price deflator): SO2,$10,391; NOx, $13,383; SPM, $12,275; and CO,$7,114. This study showed that the turningpoint for emissions was likely to be higher thanthat for ambient concentrations.Table 1 summarizes several studies of sulfur

emissions and concentrations, listed in order ofestimated income turning point. Panayotou(1993) used cross-sectional data, nominal GDP,and the assumption that the emission factor foreach fuel is the same in all countries––this studyhas the lowest estimated turning point of all.With the exception of the Kaufmann et al.(1998) estimate, all turning point estimatesusing concentration data are less than $6,000.Kaufmann et al. (1998) used an unusual speci-fication that includes GDP per area and GDPper area squared variables.Among the emissions based estimates, both

Selden and Song (1994) and Cole et al. (1997)used databases dominated by, or consistingsolely of, emissions from OECD countries.Their estimated turning points are $10,391 and$8,232 respectively. List and Gallet (1999) useddata for the 50 US states from 1929–94. Theirestimated turning point is the second highest inthe table. Income per capita in their sampleranges from $1,162 to $22,462 in 1987 USdollars. This is a greater range of income levelsthan is found in the OECD-based panels forrecent decades. This suggests that includingmore low-income data points in the samplemight yield a higher turning point. Stern andCommon (2001) estimated the turning point atover $100,000. They used an emissions data-base produced for the US Department ofEnergy by ASL (Lefohn, Husar, & Husar,1999) that covers a greater range of incomelevels and includes more data points than anyof the other sulfur EKC studies.We see that the recent studies that used more

representative samples find that there is amonotonic relation between sulfur emissionsand income just as there is between carbondioxide and income. Interestingly, Dijkgraafand Vollebergh (1998) estimate a carbon EKCfor a panel data set of OECD countries find-ing an inverted U-shape EKC in the sampleas a whole (as well as many signs of poor

Table 1. Sulfur EKC studies

Authors Turning point 1990

USD

Emissions or

concentrations

PPP Additional

variables

Data source for

sulfur

Time period Countries/cities

Panayotou (1993) $3,137 Emissions No – Own estimates 1987–88 55 developed and

developing

countries

Shafik (1994) $4,379 Concentrations Yes Time trend, loca-

tional dummies

GEMS 1972–88 47 cities in 31

countries

Torras and Boyce

(1998)

$4,641 Concentrations Yes Income inequal-

ity, literacy,

political and civil

rights, urbaniza-

tion, locational

dummies

GEMS 1977–91 Unknown num-

ber of cities in 42

countries

Grossman and

Krueger (1991)

$4,772–5,965 Concentrations No Locational

dummies, popu-

lation density,

trend

GEMS 1977, ‘82, ‘88 Up to 52 cities in

up to 32 coun-

tries

Panayotou (1997) $5,965 Concentrations No Population

density, policy

variables

GEMS 1982–84 Cities in 30 deve-

loped and deve-

loping countries

Cole, Rayner, and

Bates (1997)

$8,232 Emissions Yes Country dummy,

technology level

OECD 1970–92 11 OECD

countries

Selden and Song

(1994)

$10,391–10,620 Emissions Yes Population

density

WRI––primarily

OECD source

1979–87 22 OECD and 8

developing

countries

Kaufmann,

Davidsdottir,

Garnham, and

Pauly (1998)

$14,730 Concentrations Yes GDP/Area, steel

exports/GDP

UN 1974–89 13 developed and

10 developing

countries

List and Gallet

(1999)

$22,675 Emissions N/A – US EPA 1929–94 US States

Stern and

Common (2001)

$101,166 Emissions Yes Time and coun-

try effects

ASL 1960–90 73 developed and

developing coun-

tries

THEENVIR

ONMENTALKUZNETSCURVE

1425


econometric behavior). The turning point is atonly 54% of maximal GDP in the sample. Astudy by Schmalensee, Stoker, and Judson(1998) also finds a within sample turning pointfor carbon. In this case, a 10-piece spline wasfitted to the data such that the coefficient esti-mates for high-income countries are allowed tovary from those for low-income countries. Allthese studies suggest that the differences inturning points that have been found for differ-ent pollutants may be due, at least partly, to thedifferent samples used. I will discuss theeconometric reasons for this sample-dependentbehavior below.In an attempt to capture all environmental

impacts of whatever type, a number ofresearchers (e.g., Suri & Chapman, 1998; Coleet al., 1997) have estimated EKCs for a proxytotal environmental impact indicator––totalenergy use. In each case, they found that energyuse per capita increases monotonically withincome per capita. This result does not precludethe possibility that energy intensity––energyused per dollar of GDP produced––declineswith rising income or even follows an invertedU-shaped path (e.g., Galli, 1998).The only robust conclusions from the EKC

literature appear to be that concentrations ofpollutants may decline from middle incomelevels, while emissions tend to be monotonic inincome. As we will see below, emissions maydecline simultaneously over time in countries atwidely varying levels of development. Given thepoor statistical properties of most EKC mod-els, it is hard to come to any conclusions aboutthe roles of other additional variables such astrade. Too few quality studies have been doneof other indicators apart from air pollution tocome to any firm conclusions about thoseimpacts either.

5. THEORETICAL CRITIQUE OF THEEKC

A number of critical surveys of the EKCliterature have been published (e.g., Ansuategi,Barbier, & Perrings, 1998; Arrow et al., 1995;Copeland & Taylor (2004); Dasgupta et al.,2002; Ekins, 1997; Pearson, 1994; Stern, 1998;Stern et al., 1996). This section discusses thecriticisms raised against the EKC in the earliersurveys on theoretical (rather than methodo-logical) grounds. The more recent surveys raisesimilar points but have more evidence to mar-shal.

The key criticism of Arrow et al. (1995) andothers was that the EKC model, as presented inthe 1992 World Development Report and else-where, assumes that there is no feedback fromenvironmental damage to economic productionas income is assumed to be an exogenous var-iable. The assumption is that environmentaldamage does not reduce economic activitysufficiently to stop the growth process and thatany irreversibility is not so severe that it reducesthe level of income in the future. In otherwords, there is an assumption that the economyis sustainable. But, if higher levels of economicactivity are not sustainable, attempting to growfast in the early stages of development whenenvironmental degradation is rising may provecounterproductive. 8

It is clear that emissions of many pollutantsper unit of output have declined over time indeveloped countries with increasingly stringentenvironmental regulations and technical inno-vations. But the mix of residuals has shiftedfrom sulfur and nitrogen oxides to carbondioxide and solid waste so that aggregate wasteis still high and per capita waste may not havedeclined. 9 Economic activity is inevitablyenvironmentally disruptive in some way. Sat-isfying the material needs of people requires theuse and disturbance of energy flows and mate-rials stocks. Therefore, an effort to reduce someenvironmental impacts may just aggravateother problems. 10

Both Arrow et al. (1995) and Stern et al.(1996) argued that, if there was an EKC typerelationship, it might be partly or largely aresult of the effects of trade on the distributionof polluting industries. The Hecksher-Ohlintrade theory suggests that, under free trade,developing countries would specialize in theproduction of goods that are intensive in thefactors that they are endowed with in relativeabundance: labor and natural resources. Thedeveloped countries would specialize in humancapital and manufactured capital intensiveactivities. Part of the reduction in environ-mental degradation levels in the developedcountries and increases in environmental deg-radation in middle income countries may reflectthis specialization (Hettige, Lucas, & Wheeler,1992; Lucas, Wheeler, & Hettige, 1992; Suri &Chapman, 1998). Environmental regulation indeveloped countries might further encouragepolluting activities to gravitate toward thedeveloping countries (Lucas et al., 1992). Theseeffects would exaggerate any apparent declinein pollution intensity with rising income along


the EKC. In our finite world the poor countriesof today would be unable to find furthercountries from which to import resource-intensive products as they, themselves, becomewealthy. When the poorer countries applysimilar levels of environmental regulation theywould face the more difficult task of abatingthese activities rather than outsourcing them toother countries (Arrow et al., 1995; Stern et al.,1996). Copeland and Taylor (2004) concludethat, in contrast to earlier work (e.g., Jaffe,Peterson, Portney, & Stavins, 1995), recentresearch shows that increased regulation doestend to result in more decisions to locate in lessregulated locations. On the other hand, there isno clear evidence that trade liberalizationresults in a shift in polluting activities to less-regulated countries.Furthermore, Antweiler, Copeland, and

Taylor (2001) and Cole and Elliott (2003) arguethat the capital-intensive activities that areconcentrated in the developed countries aremore polluting and hence developed countrieshave a natural comparative advantage in pol-luting goods in the absence of regulatory dif-ferences. There are no clear answers on theimpact of trade on pollution from the empiricalEKC literature.Stern et al. (1996) argued that early EKC

studies showed that a number of indicators:SO2 emissions, NOx, and deforestation, peak atincome levels around the current world meanper capita income. A cursory glance at the

0.00E+00

2.00E+08

4.00E+08

6.00E+08

8.00E+08

1.00E+09

1.20E+09

1.40E+09

1990 1995 2000 2

To

ns

SO

2

Figure 2. Projected sulfur emissio

available econometric estimates might havelead one to believe that, given likely futurelevels of mean income per capita, environmen-tal degradation should decline from the presentonward. This interpretation is evident in the1992 World Development Report (IBRD, 1992).Income is not however, normally distributedbut very skewed, with much larger numbers ofpeople below mean income per capita thanabove it. Therefore, it is median rather thanmean income that is the relevant variable. Sel-den and Song (1994) and Stern et al. (1996)performed simulations that, assuming that theEKC relationship is valid, showed that globalenvironmental degradation was set to rise for along time to come. Figure 2 presents projectedsulfur emissions using the EKC in Figure 1 andUN and World Bank forecasts of economic andpopulation growth. Despite this and despiterecent estimates that indicate higher or nonex-istent turning points, the impression producedby the early studies in the policy, academic, andbusiness communities seems slow to fade (e.g.,Lomborg, 2001).

6. RECENT DEVELOPMENTS

Significant developments, since my last gen-eral survey of the EKC in 1998, fall into threeclasses: (a) Empirical case study evidence onenvironmental performance and policy indeveloping countries that is discussed in this

005 2010 2015 2020 2025

Year

ns. Source: Stern et al. (1996).


section; (b) improved econometric testing andestimates discussed in the following section;and (c) a new wave in the investigation ofenvironment-development relations using de-composition analysis and efficient frontiermethods, discussed in Section 8.Dasgupta et al. (2002) wrote a critical review

of the EKC literature and other evidence on therelation between environmental quality andeconomic development in the Journal of Eco-nomic Perspectives. Figure 3 illustrates fouralternative viewpoints discussed in the articleregarding the nature of the emissions andincome relation. The conventional EKC needsno further discussion. Two viewpoints arguethat the EKC is monotonic. The ‘‘new toxics’’scenario claims that while some traditionalpollutants might have an inverted U-shapecurve, the new pollutants that are replacingthem do not. These include carcinogenicchemicals, carbon dioxide, etc. As the olderpollutants are cleaned up, new ones emerge, sothat overall environmental impact is notreduced. The ‘‘race to the bottom’’ scenarioposits that emissions were reduced in developedcountries by outsourcing dirty production todeveloping countries. These countries will findit harder to reduce emissions. But the pressureof globalization may also preclude furthertightening of environmental regulation indeveloped countries and may even result in itsloosening in the name of competitiveness.The revised EKC scenario does not reject the

inverted U-shape curve but suggests that it isshifting downward and to the left over time dueto technological change. But this argument isalready present in the 1992 World DevelopmentReport (IBRD, 1992). Dasgupta et al. also

Figure 3. Environmental Kuznets curve: alternative views.(2003

review the theoretical literature and some of theeconometric specification issues. But their maincontribution is presenting evidence that envi-ronmental improvements are possible in devel-oping countries and that peak levels ofenvironmental degradation will be lower thanin countries that developed earlier.According to Dasgupta et al. (2002), regula-

tion of pollution and enforcement increase withincome but the greatest increases happen fromlow to middle income levels and increasedregulation is expected to have diminishingreturns. There is also informal or decentralizedregulation in developing countries––Coasianbargaining. Further, liberalization of develop-ing economies over the last two decades hasencouraged more efficient use of inputs and lesssubsidization of environmentally damagingactivities––globalization is in fact good for theenvironment. The evidence seems to contradictthe ‘‘race to the bottom’’ scenario. Multi-national companies respond to investor andconsumer pressure in their home countries andraise standards in the countries in which theyinvest. Further, better methods of regulatingpollution such as market instruments are hav-ing an impact even in developing countries.Better information on pollution is available,encouraging government to regulate andempowering local communities. Those thatargue that there is no regulatory capacity indeveloping countries seem to be wrong.Much of the Dasgupta et al. evidence is from

China. Other researchers of environmental andeconomic developments in China come tosimilar conclusions. Gallagher (2003) finds thatChina is adopting European Union standardsfor pollution emissions from cars with an

Source: Dasgupta et al. (2002) and Perman and Stern).


approximately 8–10-year lag. Clearly, China’sincome per capita is far more than 10 yearsbehind that of Western Europe. Streets et al.(2001), Zhang (2000), Jiang and McKibbin(2002), and Wang and Wheeler (2003) all reporton substantial reductions of pollution intensi-ties and levels in recent years.

7. ECONOMETRIC CRITIQUEOF THE EKC

Econometric criticisms of the EKC fall intofour main categories: heteroskedasticity, simul-taneity, omitted variables bias, and cointegra-tion issues.Stern et al. (1996) raised the issue of heter-

oskedasticity that may be important in thecontext of regressions of grouped data (seeMaddala, 1977). Schmalensee et al. (1998)found that regression residuals from OLS wereheteroskedastic with smaller residuals associ-ated with countries with higher total GDP andpopulation as predicted by Stern et al. (1996).Stern (2002) estimated a decomposition modelusing feasible GLS. Adjusting for hetero-skedasticity in the estimation significantlyimproved the goodness of fit of globallyaggregated fitted emissions to actual emissions.Cole et al. (1997) and Holtz-Eakin and Sel-

den (1995) used Hausman tests for regressorexogeneity to directly address the simultaneityissue. They found no evidence of simultaneity.In any case, simultaneity bias is less serious inmodels involving integrated variables than in

Table 2. Stern and Comm

Region Model Le

Turning

points

Hausman

test

OECD FE $9,239

RE $9,181 0.3146

(0.8545)

Non-OECD FE $908,178

RE $344,689 14.1904

(0.0008)

World FE $101,166

RE $54,199 10.7873

(0.0045)

All turning points in real 1990 purchasing power parity US

the traditional stationary econometric model(Perman & Stern, 2003). Coondoo and Dinda(2002) test for Granger Causality between CO2

emissions and income in various individualcountries and regions. As the data are differ-enced to ensure stationarity, this test can onlyaddress short-run effects. The overall patternthat emerges is that causality runs from incometo emissions or there is no significant relation-ship in developing countries, while in developedcountries causality runs from emissions toincome. This suggests that simultaneity is notimportant.Stern and Common (2001) use three lines of

evidence to suggest that the EKC is an inade-quate model and that estimates of the EKC inlevels can suffer from significant omitted vari-ables bias: (a) Differences between the para-meters of the random-effects and fixed-effectsmodels, tested using the Hausman test; (b)differences between the estimated coefficients indifferent subsamples, and (c) tests for serialcorrelation. Table 2 presents the key resultsfrom an EKC model estimated with data from74 countries (in the World sample) over 1960–90. For the non-OECD and World samples, theHausman test shows a significant difference inthe parameter estimates for the random-effectsand fixed-effects model. This indicates that theregressors––the level and square of the loga-rithm of income per capita––are correlated withthe country effects and time effects. As theseeffects model the mean effects of omitted vari-ables that vary across countries or across time,this indicates that the regressors are likely

on (2001) key results

vels First differences

Chow F -test q Turning

points

Mean

income

elasticity

0.9109 $55,481 0.67

0.9070

0.8507 $18,039 0.50

0.8574

10.6587

(0.0156)

0.8569 $33,290

4.0256

(0.0399)

0.8624

dollars.


correlated with omitted variables and theregression coefficients are biased. 11 The OECDresults pass this Hausman test but this resultturned out to be very sensitive to the exactsample of countries included in the subsample.As expected, given the Hausman test results,

the parameter estimates are dependent on thesample used, with the non-OECD estimatesshowing a turning point at extremely high-income levels and the OECD estimates a withinsample turning point (Table 2). As mentionedabove, these results exactly parallel those fordeveloped and developing country samples ofcarbon emissions. The Chow F-test testswhether the two subsamples can be pooled, andtherefore that there is a common regressionparameter vector, a hypothesis that is rejected.The parameter q is the first order autore-

gressive coefficient of the regression residuals.This level of serial correlation indicates mis-specification either in terms of omitted vari-ables or missing dynamics.Harbaugh et al. (2002) carry out a sensitivity

analysis of the original Grossman and Krueger(1995) results. They use an updated and largerversion of the ambient pollution data set andtest a number of alternative specifications.Using the new extended dataset with Grossmanand Krueger’s original cubic specificationresults in the coefficients changing sign andpeak and trough levels altering wildly. Alteringthe specification in various ways––addingexplanatory variables, using time dummiesinstead of a time trend, using logs, removingoutliers, and averaging the observations acrossmonitors in each country––changes the shapeof the curve. The final experiment they carryout is to include only countries with GDP percapita above $8,000. In contrast to Stern andCommon (2001), this results in a monotoniccurve. The authors comment:

This may seem counterintuitive. SO2 concentrations inCanada and the United States have declined over timeat ever decreasing rates. . . the regressions. . . include. . .a linear time trend. . . after detrending the data withthe time function, pollution appears to increase as afunction of GDP (p. 548).

There are several differences between theHarbaugh et al. (2002) model and the Stern andCommon (2001) model that may explain thedifferent results obtained for high incomecountries. Harbaugh et al. (2002) use concen-trations data, a linear time trend and a dynamicspecification, while Stern and Common (2001)

use emissions data, individual time dummies,and a static specification. Stern and Common’s(2001) first differences results (Table 2) are verysimilar to the Harbaugh et al.’s (2002) results,which suggests that the dynamic specificationcould be important.Millimet, List, and Stengos (2003) use a dif-

ferent strategy to test the robustness of theparametric EKC––comparing it to semi-para-metric curves estimated using the same datasetfor US states used by List and Gallet (1999).But they claim that parametric models are toopessimistic––finding high turning points––whiletheir alternative semi-parametric models resultin U-shaped curves with lower turning points.In addition, they reject the parametric specifi-cation in favor of the semi-parametric. Butneither parametric nor semi-parametric curvesseem to fit the observed data very well in thefigures presented in the paper. Furthermore,results for individual states are varied, with thenitrogen dioxide curves mostly rising through-out the income range and many of the sulfurdioxide curves falling––the reverse of thenational panel data results. These results could,therefore, be further evidence of the fragility ofthe EKC rather than evidence for a low turningpoint semi-parametric specification.In contrast to Harbaugh et al. (2002), Cole

(2003b) claims that the EKC model is fairlyrobust. But his basic levels sulfur emissionsEKC has a significant Hausman statistic for atest of whether the random and fixed-effectsparameters differ. Adding trade variables to themodel results in an insignificant Hausman sta-tistic and a somewhat higher turning point.Using logarithms increases this turning pointfurther (Stern, 2004). The sulfur series cannotbe tested for unit roots, but other series in thedataset he uses do show unit root behavior andresults using first differences indicate a higherturning point than the levels results. I concludethat the model with trade variables performsbetter but the basic EKC is misspecified andappropriate econometric techniques appear toraise the turning point.Perman and Stern (2003) test Stern and

Common’s (2001) data and models for unitroots and cointegration respectively. Panel unitroot tests indicate that all three series––logsulfur emissions per capita, log GDP capita,and its square––have stochastic trends. Resultsfor cointegration are less clear cut. Around halfthe individual country EKC regressions coin-tegrate, but many of these have parameterswith ‘‘incorrect signs.’’ Some panel cointegra-


tion tests indicate cointegration in all countriesand some accept the noncointegration hypoth-esis. But even when cointegration is found, theform of the EKC relationship varies radicallyacross countries with many countries having U-shaped EKCs. A common cointegrating vectorfor all countries is strongly rejected. Koop andTole (1999) similarly found that random andfixed-effects specifications of a deforestationEKC were strongly rejected in favor of arandom coefficients model with widely varyingcoefficients and insignificant mean coefficients.In the presence of possible noncointegration,

we can estimate a model in first differences. Theestimated turning points indicate a largelymonotonic EKC relationship and are moresimilar across subsamples, though the para-meters are still significantly different (Table 2).The estimated income elasticity is less thanone––there are factors that change with incomewhich offset the scale effect, but they areinsufficiently powerful to overcome fully thescale effect.Figure 4 presents the time effects from the

first difference estimates. The OECD sawdeclining emissions holding income constantover the entire period, though the introductionof the LRTAP agreement in the mid-1980s inEurope resulted in a larger decline. Developing

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

1960 1965 1970 19

Figure 4. Time effects: first differences sulfur

countries saw rising emissions in the 1960s anddeclining emissions since 1973, ceteris paribus.Similarly, Lindmark (2002) uses the Kalmanfilter to extract a technological change trend forcarbon dioxide emissions in Sweden from1870–1997. This trend had a positive growthrate until about 1970 and a negative growthrate since. 12

Day and Grafton (2003) test for cointegra-tion of the EKC relation using Canadian time-series data on a number of pollutants using theEngle-Granger and Johansen methods. Theyfail to reject the noncointegration hypothesis inalmost every case. de Bruyn’s (2000) time-seriesEngle-Granger tests for the Netherlands, theUnited Kingdom, the United States, and WestGermany for SOx, NOx and CO2 finds cointe-gration for the CO2 EKC in the Netherlandsand West Germany, but not in any other case.Using various lines of evidence, the majority

of studies have found the EKC to be a fragilemodel suffering from severe econometric mis-specification. Use of more appropriate methodstends to indicate higher turning points andpossibly a monotonic curve for emissions ofmajor pollutants. A better model may resultfrom including additional variables to representeither proximate or underlying causes of changein emissions. I next turn to a consideration of

75 1980 1985 1990

World

OECD

Non-OECD

EKC. Source: Stern and Common (2001).


a new literature that goes beyond the EKC inthis way.

8. DECOMPOSING EMISSIONS ANDMEASURING ENVIRONMENTAL

EFFICIENCY

Two alternative approaches are emergingthat go beyond the EKC by analyzing theproximate factors driving changes in pollutionemissions described in Section 2: index numberdecompositions of emissions and productionfrontiers estimated using linear programmingor econometrics. The index numbers decom-position approach requires detailed sectoralinformation on fuel use, production, emissionsetc. The average effects of the different factorscan be derived from the results for the indi-vidual countries. No stochasticity is allowedfor. By contrast, the frontier models do notusually require data on fuel use by sector; theycan estimate factors common to all countries aswell as idiosyncratic components for eachcountry, and the econometric versions allow forrandom error. On the other hand, the frontiermodels usually make stronger assumptionsabout production relations. There are alsostudies that are hybrids of the two approaches(e.g., Hettige, Mani, & Wheeler, 2000; Hilton &Levinson, 1998; Judson et al., 1999). 13 Gross-man (1995) and de Bruyn (1997) proposed thefollowing decomposition:

Eit �Xn

j¼1

YitIijtSijt; ð3Þ

where Eit is emissions in country i in year t, Y isGDP, Ij is the emissions intensity of sector j,and Sj is the share of that sector in GDP. Thisdecomposition, therefore, attributes emissionsto what Grossman calls the scale, composition(output mix), and technique effects. The latterincludes the effects of both fuel mix and‘‘technological change’’ and the latter can befurther broken down into general productivityimprovements, where more output is derivedfrom a unit of input, and emissions reducingtechnological change, where less emissions areproduced per unit of input. A problem for theindex number studies is that, usually, fuel usedata are collected on a different sectoral basisthan output. This makes the standard type ofindex number study impossible to implement inmost countries. Hamilton and Turton’s (2002)and Zhang’s (2000) index number decomposi-

tions of CO2 emissions do not however explic-itly include fuel mix or output structure and sodo not require industry level data. Thisdecomposition is given by:

Et �Et

FECt

FECt

TECt

TECt

GDPt

GDPt

PtPt; ð4Þ

where E is emissions, FEC and TEC are fossilfuel and total energy consumption respectively,and P is population. As carbon abatementtechnologies do not yet exist, the first term onthe RHS reflects the impact of shifts in the mixof fossil fuel types. The second term reflectsshifts between fossil and nonfossil fuels, whilethe remaining terms are energy intensity andtwo components of the scale effect.De Bruyn (1997), Viguier (1999), Selden,

Forrest, and Lockhart (1999), and Bruvoll andMedin (2003) all calculate versions of (3) for avariety of European countries and the UnitedStates. A progressively larger number of pol-lutants have been investigated. de Bruyn (1997)only analyzes sulfur, while Bruvoll and Medin(2003) cover 10 pollutants. Output structureeffects are mostly small except for some of theadditional pollutants investigated by Bruvolland Medin (2003) (CH4, N2O, and NH3). Fuelmix effects are varied depending on the pollu-tants and countries investigated––in some casesthey act to increase emissions (for SOx, NOx,and CO2 in the US) and in others to reduceemissions (SOx and CO2 in Norway). In allcases, technique effects were dominant in off-setting the increase in scale. Energy intensity ismost important for carbon and nitrogen but forboth these and sulfur it is insufficient to over-come scale. Therefore, for sulfur, which hasdeclined substantially in these countries, emis-sions specific technological change accounts forthe majority of the decline. For example, deBruyn (1997) found a 55–60% reduction insulfur emissions due to this factor. Similarly,Hamilton and Turton (2002) find that the mainfactor increasing carbon emissions in theOECD during 1982–97 was income per capita(37%) and the second most important waspopulation growth (12%). The main factorreducing emissions was energy intensity. Zhang(2000) finds that the decline in energy intensityin China almost halved the increase in emis-sions that would otherwise have occurred.Other factors had minor effects.All these studies treat energy intensity as an

exogenous variable, whereas actually it is alsopartly determined by shifts between fuels of


different qualities and industries with differentenergy intensities. Kander’s (2002) study ofcarbon emissions in Sweden over the two-century period from 1800 to 1998 alsodecomposes changes in energy intensity.Structural change largely explains the increasein energy intensity that occurred over 1870–1913 and contributed to the reduction inenergy intensity during 1913–70. In the latterperiod, rising energy quality may have alsocontributed. But technical change dominatedin that period and was the only importantfactor in reducing energy intensity before 1870and after 1970. Once energy intensity isexplained, carbon per unit energy is explainedby shifts in the fuel mix. In the 19th centurycoal use replaced wood and muscle power,increasing the carbon intensity, coal was thenreplaced by oil, and then by electricity (nuclearand hydro) to some extent. In the last twodecades however, coal and wood have staged acomeback again raising the carbon emitted perunit of energy used.Hilton and Levinson (1998) use a hybrid

approach. Data are available on both the totalconsumption of gasoline and the lead contentof gasoline for a large number of countries forspecific years. Hence, decomposition into scaleand technical change effects is easy in thisspecial case and the two structural effects areabsent. They carry out an EKC type analysisof these variables. Per capita gasoline use risesstrongly with income. In 1992, lead contentper gallon of gasoline was a declining functionof income. There is however a wide scatter indeveloping countries with many low andmiddle-income countries having low lead con-tents. Before 1983, there is no evidence of anEKC type relation in the data.The inference isthat there was a technological innovation thatwas preferentially adopted in high-incomecountries. As Gallagher (2003) suggests, theseinnovations may be adopted with a relativelyshort lag in developing countries. Hettige et al.(2000) use a similar approach to model indus-trial BOD emissions in a range of devel-oping and developed countries. The share ofmanufacturing industry in national incomeand the share of polluting industries withintotal manufacturing represent compositioneffects and actual plant-level end-of-pipeBOD emissions per unit output representtechnique effects. Each component is modeledas a function of income and other variablesand then the components are reassembled topredict emissions at different levels of devel-

opment. Emissions rise up to around $7,000per capita and then are fairly constant athigher income levels. Composition effects worktogether with scale at lower income levels toincrease this form of pollution. At higherincome levels they reduce pollution, but toge-ther with the technique effect, which actsagainst the scale effect at every level ofincome, they only just offset the effects of ris-ing scale.Bruvoll, Fæhn, and Strøm (2003) combine

index number decomposition with a CGEmodel which allows decomposition into theproximate factors of scale, composition, andtechnique, as well as an underlying policy fac-tor which acts through the first three factors.Their study is, however, a policy simulationrather than an empirical analysis. The endoge-nous policy simulation they run over 2000–2030 uses a carbon tax to target a level ofcarbon emissions predicted by a cubic loga-rithmic EKC. The model endogenously com-putes the changes in trade over the forecasthorizon and therefore the changes in emissionsassociated with imports and exports. Theyestimate that emissions leakage would increasethroughout the forecast horizon.Koop (1998) and Zaim and Taskin (2000)

estimate global econometric production fron-tiers for carbon and Stern (2002) for sulfur. Ofthese three studies, however only Stern (2002)actually derives the decomposition into proxi-mate factors. These studies are part of anemerging literature on environmental efficiency.A frontier representing the best-practice tech-nology is constructed using either data envel-opment analysis (e.g., Lansink & Silva, 2003) orestimated econometrically (e.g., Fernandez,Koop, & Steel, 2002; Reinhard, Lovell, &Thijssen, 1999) using data on inputs, outputs,and pollution emissions for a group of coun-tries or firms. 14 This approach allows somecountries to be on the production frontier thatrepresents best practice technology and othercountries to be behind the frontier using atechnology that is less efficient than the bestpractice. Relative movement of the frontier indifferent directions measures emissions specifictechnical change and changes in general totalfactor productivity in the best practice tech-nology. Movement of countries relative to thefrontier reflects the degree to which they adoptthe best practice technology. The impacts ofstructural shifts in inputs and outputs and thescale effect are defined by the estimated pro-duction technology.


Stern (2002) uses the following econometricmodel to decompose sulfur emissions in 64countries during 1973–90:

SitPit

¼ ciYitPit

AtEit

Yit

YJj¼1

yjitYit

� �aj XKk¼1

ekitEit

eit; ð5Þ

where S is sulfur emissions and P populationand the RHS decomposes per capita emissionsinto the following five effects:

YitPit

scale––GDP per capita.

At a common global time effect represent-

ing the effects of emissions specific

technical progress over years t.Eit

Yitenergy intensity––the effect of general

productivity on emissions.y1itYJit

; . . . ;yJitYit

output mix––shares of the output of

different industries y in total GDP Y .eitEit

; . . . ;eKitEit

input mix––shares of different energy

sources e in total energy use E.

Additionally, ci is the relative efficiency ofcountry i compared to best practice, and eit is arandom error term. The contributions of thefive effects to changes in global emissions at theglobal level are given in Table 3. Input andoutput effects contributed little globally,though in individual countries they can haveimportant effects. At the global level the twoforms of technological change reduced theincrease in emissions to half of what it wouldhave been in their absence with emissions spe-cific technological change lowering aggregateemissions by around 20%. The residuals fromthe model show it to be a statistically adequate

Table 3. Contributions to total change in global sulfuremissions

Weighted logarithmic

percent change (%)

Total change

Actual emissions 28.77

Predicted emissions 27.37

Unexplained fraction 1.40

Decomposition

Scale effect 53.78

Emissions related tech-

nical change

)19.86

Energy intensity )10.20Output mix 3.77

Input mix )0.13

representation of the data. A nested test of thismodel and the EKC showed that the incomesquared term in the EKC added no explanatorypower to that provided by the decompositionmodel.In an attempt to determine the effects of

trade on scale, composition, and techniqueeffects, Antweiler et al. (2001) estimate a dif-ferent type of econometric decompositionmodel that derives a reduced form equationfrom a theoretical structural model of thedemand and supply of pollution. The techniqueeffect is however assumed to be induced by theincrease in income due to trade. This model,therefore, takes the EKC hypothesis as a given.The composition effect is expected to differ incapital intensive and labor intensive economies.Trade is likely to increase pollution in theformer and reduce it in the latter. Therefore,the capital/labor ratio is controlled for. Themodel is estimated using the GEMS sulfurdioxide concentration data. Rather than adecomposition of changes in emissions theycompute elasticities with respect to the differentfactors. The ‘‘scale elasticity’’ is estimated toaverage 0.266. The sample mean of the tech-nique elasticity (elasticity of concentrationsw.r.t. GNP per capita) is )1.15. The composi-tion elasticity (elasticity of concentrations w.r.t.capital/labor ratio) is 1.01 and trade intensityhas an elasticity of )0.864. Combining theeffects, trade has a negative impact on emis-sions. Cole and Elliott (2003) extend the anal-ysis to other pollutants and to emissions. Theresults are more mixed.The conclusion from all these studies is that

the main means by which emissions of pollu-tants can be reduced is by time related tech-nique effects and in particular those directedspecifically at emissions reduction, thoughproductivity growth or declining energy inten-sity has a role to play. Though structuralchange and shifts in fuel composition may beimportant in some countries at some times,their average contribution seems less importantquantitatively. Those studies that includedeveloping countries (e.g., Antweiler et al.,2001; Judson et al., 1999; Stern, 2002) find thatthese technological changes are occurring inboth developing and developed countries.Innovations may first be adopted preferentiallyin higher income countries (Hilton & Levinson,1998) but seem to be adopted in developingcountries with relatively short lags (Gallagher,2003). This result is in line with the evidence ofDasgupta et al. (2002) and the EKC-based


estimates of time effects in Stern and Common(2001) and Stern (2002).

9. CONCLUSIONS

The evidence presented in this paper showsthat the statistical analysis on which the envi-ronmental Kuznets curve is based is not robust.There is little evidence for a common invertedU-shaped pathway that countries follow astheir income rises. There may be an inverted U-shaped relation between urban ambient con-centrations of some pollutants and incomethough this should be tested with more rigoroustime-series or panel data methods. It seemsunlikely that the EKC is an adequate model ofemissions or concentrations. I concur withCopeland and Taylor (2004), who state that:‘‘Our review of both the theoretical andempirical work on the EKC leads us to beskeptical about the existence of a simple andpredictable relationship between pollution andper capita income.’’The true form of the emissions–income rela-

tionship is likely a mix of two of the scenariosproposed by Dasgupta et al. (2002) illustratedin Figure 3. The overall shape is that of their‘‘new toxics’’ EKC––a monotonic increase ofemissions in income. But over time this curveshifts down, which is analogous to their‘‘revised EKC’’ scenario. Some evidence showsthat a particular innovation is likely to beadopted preferentially in high-income countries

first with a short lag before it is adopted in themajority of poorer countries. However, emis-sions may be declining simultaneously in low-and high-income countries over time, ceterisparibus, though the particular innovationstypically adopted at any one time could bedifferent in different countries.It seems that structural factors on both the

input and output side do play a role in modi-fying the gross scale effect though they aremostly less influential than time-related effects.The income elasticity of emissions is likely to beless than one––but not negative in wealthycountries as proposed by the EKC hypothesis.In slower growing economies, emissions-

reducing technological change can overcomethe scale effect of rising income per capita onemissions. As a result, substantial reductions insulfur emissions per capita have been observedin many OECD countries in the last fewdecades. In faster growing middle incomeeconomies, the effects of rising income over-whelmed the contribution of technologicalchange in reducing emissions.The research challenge now is to revisit some

of the issues addressed earlier in the EKC lit-erature using the new decomposition andfrontier models and rigorous panel data andtime-series statistics. For example, how can theeffects of trade on emissions be modeled in thecontext of the decomposition and econometricfrontier models? 15 Rigorous answers to suchquestions are central to the debate on global-ization and the environment.

NOTES

1. The increasing number of exceptions include Cole

(2003b), Coondoo and Dinda (2002), Day and Grafton

(2003), de Bruyn (2000), Stern and Common (2001),

Perman and Stern (2003), Friedl and Getzner (2003),

and Heil and Selden (1999, 2001).

2. Exceptions include Stern and Common (2001) and

Magnani (2001).

3. For example: Dasgupta et al. (2002), Harbaugh,

Levinson, and Wilson (2002), Perman and Stern (2003),

and Koop and Tole (1999).

4. Kander (2002) argues that structural shift in the

economy may largely be an illusion. Due to rising

productivity in manufacturing, manufacturing prices fall

relative to the prices of services and therefore manufac-

turing’s share of GDP declines when measured at

current prices but not when measured at constant prices.

Due to this productivity growth in manufacturing, its

pollution intensity falls over time relative to the pollu-

tion intensity of services.

5. Grossman (1995) calls the combination of 3 and 4

the ‘‘technique effect.’’

6. Later published as Grossman and Krueger (1994).

7. Later published as Shafik (1994).

8. Also see Ezzati, Singer, and Kammen (2001).


9. Solid waste does not necessarily result in ‘‘pollu-

tion,’’ especially as techniques to reduce seepage and

emissions from landfills and increase recycling have

progressed. But landfills still disturb the landscape and

geology and recycling and waste processing require

additional energy and resource use.

10. See the discussion of energy EKCs above.

11. In an alternative test for omitted variables, Mag-

nani (2001) uses the Ramsey test on cross-sectional

EKCs and finds that the null of no omitted variables is

rejected in almost every case.

12. Judson, Schmalensee, and Stoker (1999) estimate

separate EKC relations for energy consumption in each

of a number of energy-consuming sectors for a large

panel data set using spline regression, which allows them

to estimate different time effects in each sector. These

vary substantially energy consumption rises over time,

ceteris paribus, in the household and other sectors but

are flat to declining in industry and construction.

Technical innovations tend to introduce more energy

using appliances to households and energy saving

techniques to industry.

13. Judson et al. (1999) decompose emissions by sector

and then fit EKC models––see the previous endnote.

14. Lansink and Silva (2003), Reinhard et al. (1999),

and Fernandez et al. (2002) all look at agricultural firms

rather than countries.

15. Bruvoll et al. (2003) provide a possible method for

a single country model. But they only investigate

emissions directly associated with imports and exports

and not the effects of trade on the overall pollution

intensity of the economy. Antweiler et al. (2001) and

Cole and Elliott (2003) provide another approach

with different limitations––for example, the technique

effect is assumed to be induced by an EKC like income

effect.

REFERENCES

Andreoni, J., & Levinson, A. (2001). The simpleanalytics of the environmental Kuznets curve. Jour-nal of Public Economics, 80, 269–286.

Ansuategi, A., & Perrings, C. A. (2000). Transboundaryexternalities in the environmental transition hypoth-esis. Environmental and Resource Economics, 17, 353–373.

Ansuategi, A., Barbier, E. B., & Perrings, C. A. (1998).The environmental Kuznets curve. In J. C. J. M. vanden Bergh & M. W. Hofkes (Eds.), Theory andimplementation of economic models for sustainabledevelopment. Dordrecht: Kluwer.

Antweiler, W., Copeland, B. R., & Taylor, M. S. (2001).Is free trade good for the environment. AmericanEconomic Review, 91, 877–908.

Arrow, K., Bolin, B., Costanza, R., Dasgupta, P., Folke,C., Holling, C. S., Jansson, B.-O., Levin, S., M€aler,K.-G., Perrings, C. A., & Pimentel, D. (1995).Economic growth, carrying capacity, and the envi-ronment. Science, 268, 520–521.

Beckerman, W. (1992). Economic growth and theenvironment: Whose growth? Whose environment?World Development, 20, 481–496.

Bruvoll, A., & Medin, H. (2003). Factors behind theenvironmental Kuznets curve: A decomposition ofthe changes in air pollution. Environmental andResource Economics, 24, 27–48.

Bruvoll, A., Fæhn, T., & Strøm, B. (2003). Quantifyingcentral hypotheses on environmental kuznets curvesfor a rich economy: A computable general equilib-rium study. Scottish Journal of Political Economy,50(2), 149–173.

Bulte, E. H., & van Soest, D. P. (2001). Environmentaldegradation in developing countries: Households

and the (reverse) environmental Kuznets Curve.Journal of Development Economics, 65, 225–235.

Cole, M. A. (2003a). Environmental optimists, environ-mental pessimists and the real state of the world––anarticle examining The Skeptical Environmentalist:Measuring the Real State of the World by BjornLomborg. Economic Journal, 113, 362–380.

Cole, M. A. (2003b). Development, trade, and theenvironment: How robust is the environmentalKuznets curve? Environment and Development Eco-nomics, 8, 557–580.

Cole, M. A., Elliott, R. J., & R (2003). Determining thetrade–environment composition effect: The role ofcapital, labor and environmental regulations. Journalof Environmental Economics and Management, 46,363–383.

Cole, M. A., Rayner, A. J., & Bates, J. M. (1997). Theenvironmental Kuznets curve: An empirical analysis.Environment and Development Economics, 2(4), 401–416.

Coondoo, D., & Dinda, S. (2002). Causality betweenincome and emission: A country group-specificeconometric analysis. Ecological Economics, 40,351–367.

Copeland, B. R., & Taylor, M. S. (2004). Trade, growthand the environment. Journal of Economic Litera-ture, 42, 7–71.

Dasgupta, S., Laplante, B., Wang, H., & Wheeler, D.(2002). Confronting the environmental Kuznetscurve. Journal of Economic Perspectives, 16, 147–168.

Day, K. M., & Grafton, R. Q. (2003). Growth and theenvironment in Canada: An empirical analysis.Canadian Journal of Agricultural Economics, 51,197–216.


de Bruyn, S. M. (1997). Explaining the environmentalKuznets curve: Structural change and internationalagreements in reducing sulphur emissions. Environ-ment and Development Economics, 2, 485–503.

de Bruyn, S. M. (2000). Economic growth and theenvironment: An empirical analysis. Dordrecht: Klu-wer Academic Press.

Dijkgraaf, E., & Vollebergh, H. R. J. (1998). Growthand/or (?) environment: Is there a Kuznets curve forcarbon emissions? Paper presented at the 2nd bien-nial meeting of the European Society for EcologicalEconomics, Geneva, March 4–7th.

Ekins, P. (1997). The Kuznets curve for the environmentand economic growth: Examining the evidence.Environment and Planning A, 29, 805–830.

Ezzati, M., Singer, B. H., & Kammen, D. M. (2001).Towards an integrated framework for developmentand environmental policy: The dynamics of environ-mental Kuznets curves. World Development, 29,1421–1434.

Fernandez, C., Koop, G., & Steel, M. F. J. (2002).Multiple-output production with undesirable out-puts: An application to nitrogen surplus in agricul-ture. Journal of the American Statistical Association,97(458), 432–442.

Friedl, B., & Getzner, M. (2003). Determinants of CO2

emissions in a small open economy. EcologicalEconomics, 45, 133–148.

Gallagher, K. S. (2003). Development of cleaner vehicletechnology? Foreign direct investment and technol-ogy transfer from the United States to China, Paperpresented at United States Society for EcologicalEconomics 2nd Biennial Meeting, Saratoga SpringsNY, May.

Galli, R. (1998). The relationship between energyintensity and income levels: Forecasting log-termenergy demand in Asian emerging countries. EnergyJournal, 19(4), 85–105.

Grossman, G. M. (1995). Pollution and growth: Whatdo we know?. In I. Goldin & L. A. Winters (Eds.),The Economics of Sustainable Development (pp. 19–47). Cambridge: Cambridge University Press.

Grossman, G. M., & Krueger, A. B. (1991). Environ-mental impacts of a North American Free TradeAgreement. National Bureau of Economic ResearchWorking Paper 3914, NBER, Cambridge MA.

Grossman, G. M., & Krueger, A. B. (1994). Environ-mental impacts of a North American Free TradeAgreement. In P. Garber (Ed.), The US–Mexico FreeTrade Agreement. Cambridge MA: MIT Press.

Grossman, G. M., & Krueger, A. B. (1995). Economicgrowth and the environment. Quarterly Journal ofEconomics, 110, 353–377.

Hamilton, C., & Turton, H. (2002). Determinants ofemissions growth in OECD countries. Energy Policy,30, 63–71.

Harbaugh, W., Levinson, A., & Wilson, D. M. (2002).Reexamining the empirical evidence for an environ-mental Kuznets curve. Review of Economics andStatistics, 84, 541–551.

Hausman, J. A. (1978). Specification tests in economet-rics. Econometrica, 46, 1251–1271.

Heil, M. T., & Selden, T. M. (1999). Panel stationaritywith structural breaks: Carbon emissions and GDP.Applied Economics Letters, 6, 223–225.

Heil, M. T., & Selden, T. M. (2001). Carbon emissionsand economic development: Future trajectoriesbased on historical experience. Environment andDevelopment Economics, 6, 63–83.

Hettige, H., Lucas, R. E. B., & Wheeler, D. (1992). Thetoxic intensity of industrial production: Globalpatterns, trends, and trade policy. American Eco-nomic Review, 82(2), 478–481.

Hettige, H., Mani, M., & Wheeler, D. (2000). Industrialpollution in economic development: The environ-mental Kuznets curve revisited. Journal of Develop-ment Economics, 62, 445–476.

Hilton, F. G. H., & Levinson, A. M. (1998). Factoringthe environmental Kuznets curve: Evidence fromautomotive lead emissions. Journal of EnvironmentalEconomics and Management, 35, 126–141.

Holtz-Eakin, D., & Selden, T. M. (1995). Stoking thefires. CO2 emissions and economic growth. Journalof Public Economics, 57, 85–101.

Hsiao, C. (1986). Analysis of Panel Data. Cambridge:Cambridge University Press.

IBRD (1992). World Development Report 1992. Devel-opment and the Environment. New York: OxfordUniversity Press.

Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing forunit roots in heterogeneous panels. Journal ofEconometrics, 115, 53–74.

Jaffe, A. B., Peterson, S. R., Portney, P. R., & Stavins,R. N. (1995). Environmental regulation and thecompetitiveness of US manufacturing: What does theevidence tell us? Journal of Economic Literature, 33,132–163.

Jiang, T., & McKibbin, W. J. (2002). Assessment ofChina’s pollution levy system: An equilibrium pol-lution approach. Environment and Development Eco-nomics, 7, 75–105.

John, A., & Pecchenino, R. (1994). An overlappinggenerations model of growth and the environment.Economic Journal, 104, 1393–1410.

John, A., Pecchenino, R., Schimmelpfennig, D., &Schreft, S. (1995). Short-lived agents and the long-lived environment. Journal of Public Economics, 58,127–141.

Judson, R. A., Schmalensee, R., & Stoker, T. M. (1999).Economic development and the structure of demandfor commercial energy. The Energy Journal, 20(2),29–57.

Kander, A. (2002). Economic growth, energy consump-tion and CO2 emissions in Sweden 1800–2000, LundStudies in Economic History, 19, University ofLund, Lund.

Kaufmann, R. K., Davidsdottir, B., Garnham, S., &Pauly, P. (1998). The determinants of atmosphericSO2 concentrations: Reconsidering the environmen-tal Kuznets curve. Ecological Economics, 25, 209–220.

Koop, G. (1998). Carbon dioxide emissions and eco-nomic growth: A structural approach. Journal ofApplied Statistics, 25, 489–515.


Koop, G., & Tole, L. (1999). Is there an environmentalKuznets curve for deforestation? Journal of Devel-opment Economics, 58, 231–244.

Kuznets, S. (1955). Economic growth and incomeinequality. American Economic Review, 49, 1–28.

Lansink, A. O., & Silva, E. (2003). CO2 and energyefficiency of different heating technologies in theDutch glasshouse industry. Environmental and Re-source Economics, 24(4), 395–407.

Lefohn, A. S., Husar, J. D., & Husar, R. B. (1999).Estimating historical anthropogenic global sulfuremission patterns for the period 1850–1990. Atmo-spheric Environment, 33, 3435–3444.

Lieb, C. M. (2001). The environmental Kuznets curveand satiation: A simple static model. Environmentand Development Economics, 7, 429–448.

Lindmark, M. (2002). An EKC-pattern in historicalperspective: Carbon dioxide emissions, technology,fuel prices and growth in Sweden 1870–1997. Eco-logical Economics, 42(1–2), 333–347.

List, J. A., & Gallet, C. A. (1999). The environmentalKuznets curve: Does one size fit all? EcologicalEconomics, 31, 409–424.

Lomborg, B. (2001). The skeptical environmentalist:Measuring the real state of the world. Cambridge:Cambridge University Press.

Lopez, R. (1994). The environment as a factor ofproduction: The effects of economic growth andtrade liberalization. Journal of Environmental Eco-nomics and Management, 27, 163–184.

Lucas, R. E. B., Wheeler, D., & Hettige, H. (1992).Economic development, environmental regulationand the international migration of toxic industrialpollution: 1960–1988. In P. Low (Ed.), InternationalTrade and the Environment. World Bank DiscussionPaper No. 159, World Bank, Washington, DC.

Maddala, G. S. (1977). Econometrics. Singapore:McGraw Hill.

Magnani, E. (2001). The environmental Kuznets curve:Development path or policy result? EnvironmentalModelling and Software, 16, 157–166.

Martinez-Alier, J. (1995). The environment as a luxurygood or ‘too poor to be green’. Ecological Economics,13, 1–10.

McConnell, K. E. (1997). Income and the demand forenvironmental quality. Environment and DevelopmentEconomics, 2, 383–399.

Meadows, D. H., Meadows, D. L., Randers, J., &Behrens, W. (1972). The Limits to Growth. NewYork: Universe Books.

Millimet, D. L., List, J. A., & Stengos, T. (2003). Theenvironmental Kuznets curve: Real progress ormisspecified models? Review of Economics and Sta-tistics, 85(4), 1038–1047.

Panayotou, T. (1993). Empirical tests and policy analysisof environmental degradation at different stages ofeconomic development. Working Paper WP238, Tech-nology and Employment Programme, InternationalLabour Office, Geneva.

Panayotou, T. (1997). Demystifying the environmentalKuznets curve: Turning a black box into a policytool. Environment and Development Economics, 2,465–484.

Pearson, P. J. G. (1994). Energy, externalities, andenvironmental quality: Will development cure theills it creates. Energy Studies Review, 6, 199–216.

Perman, R., & Stern, D. I. (2003). Evidence from panelunit root and cointegration tests that the environ-mental Kuznets curve does not exist. AustralianJournal of Agricultural and Resource Economics, 47,325–347.

Rees, J. A. (1990). Natural Resources: Allocation,Economics and Policy (2nd edn.). London: Routledgeand Kegan Paul.

Reinhard, S., Lovell, C. A. K., & Thijssen, G. (1999).Econometric estimation of technical and environ-mental efficiency: An application to Dutch dairyfarms. American Journal of Agricultural Economics,81(1), 44–60.

Schmalensee, R., Stoker, T. M., & Judson, R. A. (1998).World carbon dioxide emissions: 1950–2050. Reviewof Economics and Statistics, 80, 15–27.

Selden, T. M., & Song, D. (1994). Environmental qualityand development: Is there a Kuznets curve for airpollution? Journal of Environmental Economics andManagement, 27, 147–162.

Selden, T. M., & Song, D. (1995). Neoclassical growth,the J curve for abatement and the inverted U curvefor pollution. Journal of Environmental Economicsand Management, 29, 162–168.

Selden, T. M., Forrest, A. S., & Lockhart, J. E. (1999).Analyzing reductions in US air pollution emissions:1970 to 1990. Land Economics, 75, 1–21.

Shafik, N. (1994). Economic development and environ-mental quality: An econometric analysis. OxfordEconomic Papers, 46, 757–773.

Shafik, N., & Bandyopadhyay, S. (1992). EconomicGrowth and Environmental Quality: Time Series andCrosscountry Evidence. Background Paper for theWorld Development Report 1992, The World Bank,Washington, DC.

Stern, D. I. (1998). Progress on the environmentalKuznets curve? Environment and Development Eco-nomics, 3, 173–196.

Stern, D. I. (2002). Explaining changes in global sulfuremissions: An econometric decomposition approach.Ecological Economics, 42, 201–220.

Stern, D. I. (2004). Comment on: Cole M. A. (2003)Development, trade, and the environment: howrobust is the environmental Kuznets curve? Environ-ment and Development Economics, 8, 557–580.

Stern, D. I., & Common, M. S. (2001). Is there an envi-ronmental Kuznets curve for sulfur? Journal of Envi-ronmental Economics and Management, 41, 162–178.

Stern, D. I., Common, M. S., & Barbier, E. B. (1996).Economic growth and environmental degradation:The environmental Kuznets curve and sustainabledevelopment. World Development, 24, 1151–1160.

Stokey, N. L. (1998). Are there limits to growth?International Economic Review, 39(1), 1–31.

Streets, D. G., Jiang, K., Hu, X., Sinton, J. E., Zhang,X.-Q., Xu, S., Jacobson, M. Z., & Hansen, J. E.(2001). Recent reductions in China’s greenhouse gasemissions. Science, 294, 1835–1837.

Suri, V., & Chapman, D. (1998). Economic growth,trade and the energy: Implications for the environ-


mental Kuznets curve. Ecological Economics, 25,195–208.

Torras, M., & Boyce, J. K. (1998). Income, inequality,and pollution: A reassessment of the environmentalKuznets curve. Ecological Economics, 25, 147–160.

Viguier, L. (1999). Emissions of SO2, NOx, and CO2 intransition economies: Emission inventories and Div-isia index analysis. Energy Journal, 20(2), 59–87.

Wang, H., & Wheeler, D. (2003). Equilibrium pollutionand economic development in China. Environmentand Development Economics, 8, 451–466.

World Commission on Environment and Development(1987). Our common future. Oxford: Oxford Univer-sity Press.

Zaim, O., & Taskin, F. (2000). Environmental efficiencyin carbon dioxide emissions in the OECD: A non-parametric approach. Journal of Environmental Man-agement, 58, 95–107.

Zhang, Z. (2000). Decoupling China’s carbon emissionsincrease from economic growth: An economic anal-ysis and policy implications. World Development, 28,739–752.

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