Environmental Kuznets CurveDAVID I. STERN
Rensselaer Polytechnic InstituteTroy, New York, United States

1. Introduction
2. Theoretical Background3. Econometric Framework
4. Results of EKC Studies
5. Theoretical Critique of the EKC
6. Econometric Critique of the EKC
7. Other Evidence
8. Decomposing Emissions
9. Conclusions

Glossary
cointegration Classical regression analysis assumes thatthe error term in a regression is normally andindependently distributed. An extreme violation of thiscondition is when the error term is a random walk. Sucha regression is termed a spurious regression and theresults are not reliable. It usually indicates that arequired variable with random walk behavior has been
omitted from the model or that the variables in themodel follow random walks and are not related to eachother.
consistent estimation May show bias and inefficiency,
but both are reduced toward zero as the sample size
increases.
developed economies Countries with high income levels;
typically have an income per capita greater than U.S.
$10,000, life expectancy greater than 70 years, andliteracy rates greater than 95%.
developing economies Countries with low- and middleincome levels; equivalent to the term ‘‘Third World.’’
econometrics The specialized branch of mathematicalstatistics applied to the analysis of economic data.
economic growth An increase in economy-wide economic
production usually measured by an increase in grossdomestic product; also, the process of the economy
growing over time.
environmental degradation Any human-caused deterioration in environmental quality.
externality When a production or consumption activity
has unintended damaging effects, such as pollution, on

Encyclopedia of Energy, Volume 2. r 2004 Elsevier Inc. All rights reserved.

other firms or individuals and no compensation is paidby the producer or consumer to the affected parties(negative externality); when activities have beneficial
effects for others, such as freely available research
results, and the recipients do not pay the generator of
the effect (positive externality).
heteroskedasticity When the variance of the error term in aregression model varies systematically across observations or cases.
income elasticity The percentage increase in some variable
when income increases by 1%.
income per capita Usually computed as the gross national
product of a country for one year divided by the total
population.
integrated variable A random walk is an integrated
variable—the current value is equal to the previous
period’s value plus a random shock. This means that theeffect of these shocks on the future does not decline overtime. Rather, the current value of the variable is an
integration of all past shocks.
omitted variables bias Occurs when variables that should
be in a regression model are omitted and they arecorrelated with those variables that are included.
panel data A data set that includes observations of anumber of individuals, countries, firms, and the likeover a number of time periods such as months or years.
simultaneity bias When there is mutual feedback between
two variables and ordinary regression analysis cannot
provide consistent estimates of model parameters.
sustainable development If the current average well-beingof people could be maintained into the indefinite future.
technological change The invention and introduction of
new methods of production as well as new products.

The environmental Kuznets curve is a hypothesized
relationship among various indicators of environmental degradation and income per capita. During
the early stages of economic growth, degradationand pollution increase, but beyond some level ofincome per capita (which will vary for different
indicators) the trend reverses, so that at high income
levels economic growth leads to environmental
improvement. This implies that the environmental

517

518

Environmental Kuznets Curve

impact indicator is an inverted U-shaped function ofincome per capita. Typically, the logarithm of theindicator is modeled as a quadratic function of thelogarithm of income.

1. INTRODUCTION
An example of an estimated environmental Kuznets
curve (EKC) is shown in Fig. 1. The EKC is named forSimon Kuznets, who hypothesized that income inequality first rises and then falls as economic development proceeds. Emissions of various pollutants,
such as carbon dioxide, sulfur, and nitrogen oxides,
are tightly coupled to the use of energy. Hence, the
EKC is a model of the relationship among energy use,
economic growth, and the environment.
The EKC is an essentially empirical phenomenon,
but most of the EKC literature is statistically weak. It
is very easy to do bad econometrics, and the historyof the EKC exemplifies what can go wrong. The EKC
idea rose to prominence because few paid sufficientattention to the relevant diagnostic statistics. Little orno attention has been paid to the statistical properties of the data used such as serial dependence andrandom walk trends in time series, and few tests ofmodel adequacy have been carried out or presented.
However, one of the main purposes of doing
econometrics is to test which apparent relationships
are valid and which are spurious correlations.
When we do take such statistics into account anduse appropriate techniques, we find that the EKC does
not exist. Instead, we get a more realistic view of theeffect of economic growth and technological changes
on environmental quality. It seems that most indica-

Kilograms SO2 per capita

300
250
200
150
100
50
0
0

5000

10000 15000

20000 25000

30000

GNP per capita ($)

FIGURE 1 Environmental Kuznets curve for sulfur emissions.
Data from Panayotou (1993) and Stern et al. (1996).

tors of environmental degradation are monotonicallyrising in income, although the ‘‘income elasticity’’ isless than 1.0 and is not a simple function of income
alone. Time-related effects, intended to model technological change common to all countries, reduce
environmental impacts in countries at all levels of
income. However, in rapidly growing middle-income
countries, the scale effect, which increases pollution
and other degradation, overwhelms the time effect. In
wealthy countries, growth is slower and pollution
reduction efforts can overcome the scale effect. This is
the origin of the apparent EKC effect.
The econometric results are supported by recent
evidence that, in fact, pollution problems are being
addressed and remedied in developing economies.
This article follows the development of the EKC
concept in approximately chronological order. Sections II and III review in more detail the theory
behind the EKC and the econometric methods used
in EKC studies. Sections IV to VI review some EKC
analyses and their critiques. Section VII discusses therecent evidence from Dasgupta and colleagues andothers that has changed the way in which we viewthe EKC. The final two sections discuss an alternative
approach, decomposition of emissions, and summarize the findings.

2. THEORETICAL BACKGROUND
The EKC concept emerged during the early 1990s
with Grossman and Krueger’s pathbreaking study ofthe potential impacts of the North American Free
Trade Agreement (NAFTA) and Shafik and Bandyopadhyay’s background study for the World Development Report 1992. However, the idea that economic
growth is necessary for environmental quality to be
maintained or improved is an essential part of thesustainable development argument promulgated by
the World Commission on Environment and Development in Our Common Future.
The EKC theme was popularized by the International Bank for Reconstruction and Development’s(IBRD) World Development Report 1992, whichargued that ‘‘the view that greater economic activity
inevitably hurts the environment is based on static
assumptions about technology, tastes, and environmental investments’’ and that ‘‘as incomes rise, thedemand for improvements in environmental quality
will increase, as will the resources available forinvestment.’’ Others have expounded this positioneven more forcefully, with Beckerman claiming that‘‘there is clear evidence that, although economic

Environmental Kuznets Curve

growth usually leads to environmental degradationin the early stages of the process, in the end thebest—and probably the only—way to attain a decent
environment in most countries is to become rich.’’However, the EKC has never been shown to apply toall pollutants or environmental impacts, and recent
evidence challenges the notion of the EKC in general.
The remainder of this section discusses the economic
factors that drive changes in environmental impacts
and that may be responsible for rising or declining
environmental degradation over the course of economic development.
If there were no change in the structure or
technology of the economy, pure growth in the scaleof the economy would result in a proportional
growth in pollution and other environmental impacts. This is called the scale effect. The traditionalview that economic development and environmental
quality are conflicting goals reflects the scale effectalone. Panayotou, a proponent of the EKC hypothesis, argued,
At higher levels of development, structural change towards
information-intensive industries and services, coupled withincreased environmental awareness, enforcement of environmental regulations, better technology, and higher environmental expenditures, result in leveling off and gradualdecline of environmental degradation.

Therefore, at one level, the EKC is explained by
the following ‘‘proximate factors’’:

519

quality. The various studies make different simplifying assumptions about the economy. Most of thesestudies can generate an inverted U-shape curve ofpollution intensity, but there is no inevitability about
this. The result depends on the assumptions madeand the value of particular parameters. It seems fairly
easy to develop models that generate EKCs underappropriate assumptions, but none of these theoretical models has been empirically tested. Furthermore, if in fact the EKC for emissions is monotonic
as recent evidence suggests, the ability of a model toproduce an inverted U-shaped curve is not necessarily a desirable property.

3. ECONOMETRIC FRAMEWORKThe earliest EKCs were simple quadratic functions of
the levels of income. However, economic activity
inevitably implies the use of resources, and by thelaws of thermodynamics, use of resources inevitably
implies the production of waste. Regressions thatallow levels of indicators to become zero or negative
are inappropriate except in the case of deforestation
where afforestation can occur. This restriction can beapplied by using a logarithmic dependent variable.
The standard EKC regression model islnðE=PÞit ¼ ai þ gt þ b1 lnðGDP=PÞit
þ b2 ½lnðGDP=PÞ2it þ eit ;ð1Þ

1. Scale of production implies expanding production, with the mix of products produced, the mix ofproduction inputs used, and the state of technology
all held constant.
2. Different industries have different pollution
intensities, and the output mix typically changes
over the course of economic development.
3. Changes in input mix involve the substitutionof less environmentally damaging inputs to production for more damaging inputs and vice versa.
4. Improvements in the state of technology involve changes in both (a) production efficiency interms of using less, ceteris paribus, of the polluting
inputs per unit of output and (b) emissions-specificchanges in process that result in less pollutant being
emitted per unit of input.

where E is emissions, P is population, GDP is grossdomestic product, e is a random error term, and ln
indicates natural logarithms. The first two terms on
the right-hand side are intercept parameters that varyacross countries or regions i and years t. The
assumption is that although the level of emissions
per capita may differ over countries at any particularincome level, the income elasticity is the same in all
countries at a given income level. The time-specificintercepts are intended to account for time-varying
omitted variables and stochastic shocks that arecommon to all countries.
The ‘‘turning point’’ level of income, whereemissions or concentrations are at a maximum, canbe found using the following formula:

These proximate variables may, in turn, be driven
by changes in underlying variables such as environmental regulation, awareness, and education. A
number of articles have developed theoretical models
about how preferences and technology might interact
to result in different time paths of environmental

The model is usually estimated with panel data.
Most studies attempt to estimate both the fixed
effects and random effects models. The fixed effects
model treats the ai and gt as regression parameters,
whereas the random effects model treats them ascomponents of the random disturbance. If the effects

t ¼ exp½b1 =ð2b2 Þ:

ð2Þ

520

Environmental Kuznets Curveai and gt and the explanatory variables are correlated, the random effects model cannot be estimatedconsistently. Only the fixed effects model can be
estimated consistently. A Hausman test can be used
to test for inconsistency in the random effects
estimate by comparing the fixed effects and random
effects slope parameters. A significant differenceindicates that the random effects model is estimatedinconsistently due to correlation between the explanatory variables and the error components.
Assuming that there are no other statistical problems, the fixed effects model can be estimatedconsistently, but the estimated parameters areconditional on the country and time effects in theselected sample of data. Therefore, they cannot be
used to extrapolate to other samples of data. Thismeans that an EKC estimated with fixed effects
using only developed country data might say little
about the future behavior of developing countries.
Many studies compute the Hausman statistic and,finding that the random effects model cannot be
estimated consistently, estimate the fixed effects
model. But few have pondered the deeper implications of the failure of this test.
Tests for integrated variables designed for use withpanel data find that sulfur and carbon emissions and
GDP per capita are integrated variables. This meansthat we can rely only on regression results thatexhibit the cointegration property. If EKC regressions
do not cointegrate, the estimates will be spurious.
Very few studies have reported any diagnostic
statistics for integration of the variables or cointegration of the regressions, and so it is unclear what
we can infer from the majority of EKC studies.

4. RESULTS OF EKC STUDIESThe key features differentiating the broad variety of
EKC studies can be displayed by reviewing a few ofthe early studies and examining a single indicator(sulfur) in more detail. This section also discusses
EKCs for total energy use, seen by some as a proxy
indicator for all environmental impacts.
The early EKC studies appeared to indicate that
local pollutants, such as smoke and sulfur emissions,were more likely to display an inverted U-shaped
relation with income than were global impacts suchas carbon dioxide. This picture fits environmental
economics theory; local impacts are internalized in asingle economy or region and are likely to give rise toenvironmental policies to correct the externalities on

pollutees before such policies are applied to globally
externalized problems.
Grossman and Krueger produced the first EKC
study as part of a study of the potential environmental
impacts of NAFTA. They estimated EKCs for SO2,
dark matter (fine smoke), and suspended particles(SPM) using the GEMS data set. This data set is apanel of ambient measurements from a number oflocations in cities around the world. Each regression
involved a cubic function in levels (not logarithms) ofpurchasing power parity adjusted (PPP) per capita
GDP and various site-related variables, a time trend,
and a trade intensity variable. The turning points forSO2 and dark matter are at approximately $4000 to$5000, whereas the concentration of suspended
particles appeared to decline even at low income levels.
Shafik and Bandyopadhyay’s study was particularly
influential in that the results were used in the World
Development Report 1992. They estimated EKCs for
10 different indicators using three different functional
forms. Lack of clean water and lack of urban
sanitation were found to decline uniformly withincreasing income and over time. Deforestation
regressions showed no relation between income anddeforestation. River quality tended to worsen withincreasing income. However, local air pollutant concentrations conformed to the EKC hypothesis, withturning points between $3000 and $4000. Finally,both municipal waste and carbon emissions per capita
increased unambiguously with rising income.
Selden and Song estimated EKCs for four emissions series—SO2, NOx, SPM, and CO—using longitudinal data from World Resources. The data wereprimarily from developed countries. The estimatedturning points all were very high compared withthose in the two earlier studies. For the fixed effects
version of their model, the estimated turning points
were as follows (converted to 1990 U.S. dollars using
the U.S. GDP implicit price deflator): SO2, $10,391;
NOx, $13,383; SPM, $12,275; and CO, $7,114.
This study showed that the turning point foremissions was likely to be higher than that forambient concentrations. During the initial stages of
economic development, urban and industrial development tends to become more concentrated in asmaller number of cities that also have rising central
population densities, with the reverse happening
during the later stages of development. So, it ispossible for peak ambient pollution concentrations
to fall as income rises, even if total nationalemissions are rising.
Table I summarizes several studies of sulfur emissions and concentrations, listed in order of estimatedEnvironmental Kuznets Curve

521

TABLE I
Sulfur EKC Studies
Turning point(1990 U.S.
dollars)

Emissions orconcentrations

Purchasing
power parity

Additionalvariables

Data source
for sulfur

Time period

Panayotou (1993)

3,137

EmissionsNo—

Own estimates

1987–1988

55 developedanddeveloping
countries

Shafik andBandyopadhyay(1992)

4,379

Concentrations

Yes

Time trend,
locationaldummies

GEMS

1972–1988

47 cities in 31
countries

Torras and Boyce(1998)

4,641

Concentrations

Yes

IncomeGEMS
inequality,
literacy,
political andcivil rights,
urbanization,
locationaldummies

1977–1991

Unknownnumber ofcities in 42
countries

Grossman andKrueger (1994)

4,772–5,965

Concentrations

No

Locational
GEMS
dummies,population
density, trend

1977, 1982,
1988

Up to 52 cities
in up to 32
countries

Panayotou (1997)

5,965

Concentrations

No

Population
density,policyvariables

GEMS

1982–1984

Cole et al. (1997)

8,232

Emissions

Yes

OECD

1970–1992

Selden and Song(1994)

10,391–10,620

Emissions

Yes

Country
dummy,
technology
level
Population
density

Cities in 30
developed
anddeveloping
countries
11 OECD
countries

Kaufmann et al.
(1997)

14,730Concentrations

Yes

List and Gallet(1999)
Stern andCommon (2001)

22,675Emissions

N/A—

101,166

Emissions

YesTime andcountryeffects

Author(s)income turning points. On the whole, the emissionsbased studies have higher turning points than do theconcentrations-based studies. Among the emissionsbased estimates, both Selden and Song and Cole andcolleagues used databases that are dominated by, or
consist solely of, emissions from Organization for
Economic and Cooperative Development (OECD)
countries. Their estimated turning points were
$10,391 and $8,232, respectively. List and Gallet

WRI: primarily 1979–1987
OECD
source

GDP/Area, steel UN
exports/GDP

Countries/
cities22 OECD and 8
developing
countries

1974–1989

13 developedand 10developing
countries

U.S. EPA

1929–1994

U.S. states

ASL

1960–1990

73 developedanddeveloping
countries

used data for 1929 to 1994 for the 50 U.S. states.
Their estimated turning point is the second highest in
Table I. Income per capita in their sample ranged
from $1,162 to $22,462 (in 1987 U.S. dollars). Thisis a wider range of income levels than was found in
the OECD-based panels for recent decades. Thissuggests that including more low-income data points
in the sample might yield a higher turning point. Stern
and Common estimated the turning point at more

522

Environmental Kuznets Curve

than $100,000. They used an emissions databaseproduced for the U.S. Department of Energy by ASL
that covers a greater range of income levels andincludes more data points than do any of the othersulfur EKC studies.
The recent studies that use more representative
samples of data found that there is a monotonic
relation between sulfur emissions and income, just as
there is between carbon dioxide and income.
Interestingly, an estimate of a carbon EKC for apanel data set of OECD countries found an inverted
U-shaped EKC in the sample as a whole. The turning
point was at only 54% of maximal GDP in thesample. A study using a spline regression implied awithin-sample turning point for carbon for highincome countries. All of these studies suggest that thedifferences in turning points that have been found forvarious pollutants may be due, at least in part, to thedifferent samples used.
In an attempt to capture all environmental
impacts of whatever type, a number of researchers
estimated EKCs for total energy use. In each case,they found that energy use per capita increases
monotonically with income per capita. This result
does not preclude the possibility that energy intensity(i.e., energy used per dollar of GDP produced)
declines with rising income or even follows aninverted U-shaped path.
A number of studies built on the basic EKC modelby introducing additional explanatory variables
intended to model underlying or proximate factors
such as ‘‘political freedom,’’ output structure, andtrade. On the whole, the included variables turned
out to be significant at traditional significance levels.
However, testing different variables individually issubject to the problem of potential omitted variables
bias. Furthermore, these studies did not reportcointegration statistics that might indicate whether
omitted variables bias was likely to be a problem ornot. Therefore, it is not really clear what can beinferred from this body of work.
The only robust conclusions from the EKC
literature appear to be that concentrations ofpollutants may decline from middle-income levels
and that emissions tend to be monotonic in income.
Emissions may decline over time in countries at manydifferent levels of development. Given the likely poor
statistical properties of most EKC models, it is hard
to come to any conclusions about the roles of otheradditional variables such as trade. Too few quality
studies of other indicators apart from air pollution
have been conducted to come to any firm conclusions
about those impacts as well.

5. THEORETICAL CRITIQUE OF
THE EKCA number of critical surveys of the EKC literature
have been published. This section discusses thecriticisms that were made of the EKC on theoretical,
rather than methodological, grounds.
The key criticism of Arrow and colleagues andothers was that the EKC model, as presented in theWorld Development Report 1992 and elsewhere,
assumes that there is no feedback from environmental damage to economic production given that
income is assumed to be an exogenous variable. The
assumption is that environmental damage does not
reduce economic activity sufficiently to stop thegrowth process and that any irreversibility is notsevere enough to reduce the level of income in the
future. In other words, there is an assumption thatthe economy is sustainable. However, if higher levelsof economic activity are not sustainable, attempting
to grow fast during the early stages of development,
when environmental degradation is rising, may prove
to be counterproductive.
It is clear that the levels of many pollutants perunit of output in specific processes have declined indeveloped countries over time with technologicalinnovations and increasingly stringent environmental
regulations. However, the mix of effluent has shiftedfrom sulfur and nitrogen oxides to carbon dioxideand solid waste, so that aggregate waste is still highand per capita waste might not have declined.
Economic activity is inevitably environmentally
disruptive in some way. Satisfying the material needsof people requires the use and disturbance of energy
flows and materials. Therefore, an effort to reducesome environmental impacts might just aggravate
other problems. Estimation of EKCs for total energy
use is an attempt to capture environmental impact
regardless of its nature.
Various critics argued that if there was an EKCtype relationship, it might be partly or largely a resultof the effects of trade on the distribution of polluting
industries. The Hecksher–Ohlin trade theory, thecentral theory of trade in modern economics,
suggests that, under free trade, developing countries
would specialize in the production of goods that are
intensive in the production inputs they are endowedwith in relative abundance: labor and natural
resources. The developed countries would specialize
in human capital and manufactured capital-intensive
activities. Part of the reductions in environmental
degradation levels in developed countries and part ofthe increases in environmental degradation levels inEnvironmental Kuznets Curve

middle-income countries may reflect this specialization. Environmental regulation in developed countries might further encourage polluting activities togravitate toward developing countries.
These effects would exaggerate any apparent
decline in pollution intensity with rising income
along the EKC. In our finite world, the poor
countries of today would be unable to find othercountries from which to import resource-intensive
products as they themselves become wealthy. Whenthe poorer countries apply similar levels of environmental regulation, they would face the more difficult
task of abating these activities rather than outsourcing them to other countries. There are no clear
solutions to the impact of trade on pollution from theempirical EKC literature.
Some early EKC studies showed that a number ofindicators—SO2 emissions, NOx, and deforestation—peak at income levels around the current
world mean per capita income. A cursory glance at
the available econometric estimates might have led
one to believe that, given likely future levels of mean
income per capita, environmental degradation
should decline from this point onward. However,income is not normally distributed but rather very
skewed, with much larger numbers of people below
mean income per capita than above it. Therefore, it ismedian income, rather than mean income, that is therelevant variable. Both Selden and Song and Sternand colleagues performed simulations that, assumingthat the EKC relationship is valid, showed that
global environmental degradation was set to rise fora long time to come. More recent estimates showedthat the turning point is higher; therefore, there
should be no room for confusion on this issue.

6. ECONOMETRIC CRITIQUE OF
THE EKC
Econometric criticisms of the EKC concern fourmain issues: heteroskedasticity, simultaneity, omitted
variables bias, and cointegration issues.
Heteroskedasticity may be important in thecontext of cross-sectional regressions of grouped
data. Data for a country are a sum or mean ofobservations for all of the regions, firms, or households within that country. Smaller residuals may be
associated with countries with higher total GDPs andpopulations. Taking heteroskedasticity into accountin the estimation stage seems to significantly improve
the goodness of fit of globally aggregated fitted
emissions to actual emissions.

523

Simultaneity may arise because increasing energy
use drives increasing income, but increasing income
may also increase the demand for energy in consumption activities. In addition, if environmental degradation is sufficiently severe, it may reduce national
income, whereas the scale effect implies that increased
income causes increased environmental degradation.
A Hausman-type statistical test for regressor exogeneity can be used to directly address the simultaneity
issue. No evidence of simultaneity has been foundusing such a test. In any case, simultaneity bias is lessserious in models involving integrated variables thanin the traditional stationary econometric model. The
Granger causality test can be used to test the directionof causality between two variables. In tests of
causality between CO2 emissions and income invarious individual countries and regions, the overall
pattern that emerges is that causality runs fromincome to emissions or that there is no significant
relationship in developing countries, whereas causality runs from emissions to income in developed
countries. However, in each case, the relationship is
positive, so that there is no EKC-type effect.
Three lines of evidence suggest that the EKC is an
incomplete model and that estimates of the EKC inlevels can suffer from significant omitted variables
bias: (1) differences between the parameters of therandom effects and fixed effects models tested using
the Hausman test, (2) differences between theestimated coefficients in different subsamples, and(3) tests for serial correlation. Hausman-type test
statistics show a significant difference in the parameter estimates for estimated random effects andfixed effects models in the majority of EKC studies.
This indicates that the regressors—the level andsquare of the logarithm of income per capita—are
correlated with the country effects and time effects,
indicating that the regressors are likely correlatedwith omitted variables and that the regression
coefficients are biased. As expected, given the Hausman test results, parameter estimates turn out to be
dependent on the sample used, with the global anddeveloping country estimates of emissions EKCs forsulfur and carbon showing a turning point atextremely high income levels and the developed
country estimates showing a within-sample turning
point. In the cases where serial correlation in theresiduals was tested, a very high level was found,indicating misspecification in terms of either omitted
variables or missing dynamics.
Tests for integrated variables designed for use withpanel data indicate that sulfur emissions per capita,
carbon emissions per capita, and GDP per capita are524

Environmental Kuznets Curve

integrated. This means that we can rely only on
regression results that exhibit the cointegration
property. Results for cointegration are less clear-cut
than those for integration in the individual timeseries. For sulfur emissions in 74 countries from 1960
to 1990, approximately half of the individual country
EKC regressions cointegrate, but many of these haveparameters with ‘‘incorrect signs.’’ Some panel
cointegration tests indicate cointegration in all
countries, and some accept the non-cointegration
hypothesis. But even when cointegration is found, theforms of the EKC relationship vary radically acrosscountries, with many countries having U-shaped
EKCs. A common cointegrating vector in all countries is strongly rejected.

7. OTHER EVIDENCE
Dasgupta and colleagues present evidence thatenvironmental improvements are possible in developing countries and that peak levels of environmental
degradation will be lower than those in countries thatdeveloped earlier. They present data that show
declines in various pollutants in developing countries
over time. They show that although regulation ofpollution increases with income, the greatest increases occur from low- to middle-income levels. In
addition, diminishing returns to increased regulation
would be expected, although better enforcement at
higher income levels would also be expected. There isalso informal or decentralized regulation in developing countries. Furthermore, liberalization of developing economies over the past two decades has
encouraged more efficient use of inputs and lesssubsidization of environmentally damaging activities.
Multinational companies respond to investor andconsumer pressure in their home countries and raise
standards in the countries in which they invest.
Furthermore, better methods of regulating pollution,such as market instruments, are having an impact
even in developing countries. Better information on
pollution is available, encouraging government toregulate and empowering local communities. Those
who argue that there is no regulatory capacity indeveloping countries would seem to be wrong.
Much of Dasgupta and colleagues’ evidence isfrom China. Other researchers of environmental andeconomic developments in China came to similar
conclusions. For example, China is adopting European Union standards for pollution emissions fromcars with an approximately 8- to 10-year lag.
Clearly, China’s income per capita is far more than

10 years behind that of Western Europe. Furthermore, China has reduced sulfur emissions and evencarbon emissions during recent years. Ambient air
pollution has been reduced in several major cities,
and with the exception of some encouragement ofroad transport, the government is making a sustained
effort in the direction of environmentally friendly
policies and sustainable development.

8. DECOMPOSING EMISSIONSAs an alternative to the EKC, an increasing numberof studies carry out decompositions of emissions into
the proximate sources of emissions changes describedin section II. The usual approach is to use index
numbers and detailed sectoral information on fueluse, production, emissions, and so on that, unfortunately, is unavailable for most countries. Econometric decomposition models may require lessdetailed data. Decomposition models represent therelations between energy use and the environment
more explicitly than do EKC models.
The conclusion from the studies conducted so far
is that the main means by which emissions ofpollutants can be reduced is by time-related technique effects, particularly those directed specifically atemissions reduction, although productivity growthor declining energy intensity has a role to play,
particularly in the case of carbon emissions where
specific emissions reduction technologies do not yet
exist. Estimates of emissions-specific reductions forsulfur range from approximately 20% globally overa 20-year period to 50 to 60% in West Germany and
The Netherlands during the 1980s alone.
Although the contributions of structural change inthe output mix of the economy and shifts in fuelcomposition may be important in some countries andat some times, their average effect seems lessimportant quantitatively. Those studies that include
developing countries find that technological changes
are occurring in both developing and developed
countries. Innovations may first be adopted preferentially in higher income countries but seem to be
adopted in developing countries with relatively shortlags. This is seen, for example, regarding lead ingasoline, where most developed countries had substantially reduced the average lead content of gasoline by the early 1990s but many poorer countries
also had low lead contents. Lead content was much
more variable at low income levels than at highincome levels.

Environmental Kuznets Curve

9. CONCLUSIONSThe evidence presented in this article shows that thestatistical analysis on which the EKC is based is notrobust. There is little evidence for a common inverted
U-shaped pathway that countries follow as theirincomes rise. There may be an inverted U-shaped
relation between urban ambient concentrations ofsome pollutants and income, although this should betested with more rigorous time series or panel data
methods. It seems unlikely that the EKC is acomplete model of emissions or concentrations.
The true form of the emissions–income relationshipis likely to be monotonic, but the curve shifts downover time. Some evidence shows that a particularinnovation is likely to be adopted preferentially inhigh-income countries first with a short lag before it isadopted in the majority of poorer countries. However,
emissions may be declining simultaneously in lowand high-income countries over time, ceteris paribus,
although the particular innovations typically adopted
at any one time could be different in various
countries.
It seems that structural factors on both the input
and output sides do play a role in modifying the grossscale effect, although on the whole they are lessinfluential than time-related effects. The income
elasticity of emissions is likely to be less than 1.0
but not negative in wealthy countries, as proposed by
the EKC hypothesis.
In slower growing economies, emissions-reducing
technological change can overcome the scale effect ofrising income per capita on emissions. As a result,substantial reductions in sulfur emissions per capita
were observed in many developed countries during
the past few decades. In faster growing middleincome economies, the effects of rising income overwhelmed the contribution of technological change in
reducing emissions.

SEE ALSO THE
FOLLOWING ARTICLESDevelopment and Energy, Overview  Economic
Growth and Energy  Economic Growth, Liberalization, and the Environment  Energy Ladder in
Developing Nations  Environmental Change andEnergy  Sustainable Development: Basic Concepts
and Application to Energy

525

Further Reading
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Holling, C. S., Jansson, B-O., Levin, S., Mäler, K-G., Perrings,
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Beckerman, W. (1992). Economic growth and the environment:
Whose growth? Whose environment? World Dev. 20,
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Cole, M. A., Rayner, A. J., and Bates, J. M. (1997). The
environmental kuznets curve: An empirical analysis. Environ.
Dev. Econ. 2, 401–416.
Dasgupta, S., Laplante, B., Wang, H., and Wheeler, D. (2002).
Confronting the environmental kuznets curve. J. Econ. Perspect. 16, 147–168.
Grossman, G. M., and Krueger, A. B. (1994). Environmental
impacts of a North American Free Trade Agreement. In ‘‘The
U.S.–Mexico Free Trade Agreement’’ (P. Garber, Ed.). MIT
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International Bank for Reconstruction and Development. (1992).
World Development Report 1992: Development and theEnvironment. Oxford University Press, New York.
Kaufmann, R. K., Davidsdottir, B., Garnham, S., and Pauly, P.
(1997). The determinants of atmospheric SO2 concentrations:
Reconsidering the environmental Kuznets curve. Ecol. Econ.
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List, J. A., and Gallet, C. A. (1999). The environmental Kuznets
curve: Does one size fit all? Ecol. Econ. 31, 409–424.
Panayotou, T. (1993). ‘‘Empirical Tests and Policy Analysis ofEnvironmental Degradation at Different Stages of Economic
Development, working paper WP238.’’ International Labor
Office, Geneva, Switzerland.
Panayotou, T. (1997). Demystifying the environmental Kuznets
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Perman, R., and Stern, D. I. (2003). Evidence from panel unit rootand cointegration tests that the environmental Kuznets curve
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Stern, D. I., and Common, M. S. (2001). Is there an environmental
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World Commission on Environment and Development. (1987).
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