I E F J, Vol. 7, No. 1, January-June (2012) : 169-207

* Department of Economics, Tunghai University, Taichung, Taiwan, E-mail: ycwang@thu.edu.tw

AN EMPIRICAL SURVEY OF TESTINGTHE ENVIRONMENTAL KUZNETS CURVE HYPOTHESIS

Yi-Chia Wang*

Abstract: The environmental Kuznets curve (EKC) is a long-run inverted-U-shaped relationshipbetween a country’s environmental degradation and material living standard. As thorough aspossible, this paper collects most of the pre-2007 representative studies that tested the EKChypothesis, summarizes their similarities and categorizes their differences. With a complete reviewof the EKC literature, this paper concludes whether the EKC hypothesis can be supported dependscritically on data sources, sample countries, length of time period, the adoption of a marketexchange rate or purchasing power parity to adjust GDP, regression functional forms and variousestimation techniques.

Keywords:Environmental Kuznets Curve; EKC; Survey

J.E.L. Codes: Q50; Q53

1. INTRODUCTION

Analysisof the environment–income relationship can betraced back to the late 1960s. Atthat time the Club of Romesuggested in its report of ‘Limits to Growth’ that the depletionofraw materials, energy and (non-renewable) natural resources hadshared a similar upwardtrend with economic development over severaldecades (Meadows et al. (1972)). Thisstarted serious concern withregard to environmental protection. After people gainedsatisfactionfrom a better material life, their willingness to pay for a cleanerenvironmentand eagerness to reduce the intensity of energy use roseaccordingly. Especially indeveloped economies, pollution control,energy renewal and afforestation have becomemajor items ofexpenditure in both public and private sectors in recent decades.Wildavsky(1988)remarked on this growing consciousness andattempted to protect the environmentby concluding that ‘richer issafer and cleaner’. However, convincing empirical evidencetosupport the causality and the relationship between the evolution ofpollution and growingincome was scant at the time when the aboveauthors proposed their observationalarguments.

Due to the constraint of data availability and quality, empiricalanalysis of therelationship between environmental degradation and acountry’s wealth did not commenceuntil the beginning of the 1990s.2 A pioneering working paper written by Grossman andKrueger (1991) (later published in 1995) investigatedthe relationship between GDP percapita and some air pollutantsusing data from the archive of Global EnvironmentalMonitoring System (GEMS) for 42 low- and high-income countries from 1977 to1984.

170 Yi-Chia Wang

Their selected air pollutants include ambient concentrationsof sulfur dioxides (SO2),smoke/dark matters, and suspendedparticulate matters (SPM). Their random effectestimation revealedthat both ambient concentrations of SO2 and smoke/darkmattersfollowed an N-shaped trajectory against GDP per capita. That is, theconcentrationlevels of these two pollutants accumulate before GDPper capita reaches the vicinity of5,000 US$ and then declines asthe economy keeps developing until second turning points(around15,000 US$ for SO2 and 12,000 US$ for smoke/dark matters,respectively) aresurpassed. These two pollutants eventually growwithout bound as long as people continueto become wealthy.

Instead of using the measure of ambient concentrations of airpollutants,Panayotou(1993) chose airborne SO2 emissionsper capita on a national basis and partly echoed theresults from Grossman and Krueger (1991). He concluded that the relationshipbetweenSO2 emissions and income per capita reveals abell-shaped pattern, which is similar to therelationship betweenincome inequality and income per capita proposed in the mid-1950sbyKuznets (1955). Therefore, Panayotou (1993) labeled thisbell-shaped pollution–incomerelationship as the ‘environmentalKuznets curve’ (EKC), which has become a commonly-cited term in theenvironmental literature.

Most of the EKC researchers employ a panel of countries with avariety of incomelevels over a number of time points. They are infavor of estimating the following reduced-form regression:

2 3, 0 1 , 2 , 2 , , , ,i t i t i t i t i t i t i tEP Y Y Y Z= β + β + β + β + β + η + γ + (1)

where EPi,t and Yi,t respectively represent a certainindex of environmental pressure (airpollution, for example) and thelevel of income per capita in country i at time t.3 β denotes arow vector ofcoefficients of other non-income explanatory variables, zi,t,

4 and theregression leftoverincludes country-specific effects (ηi), time-specificfactors (γt) and apure white noise (i,t). Different combinations of the estimated β1, β2, and β3 can lead todistinct shapes of environmentalpressure–income relationship. Figure1demonstratesseveral distinguished forms of this relationship forthe following cases:

(1) β1 and β2 = β3 = 0 suggest that environmental pressure tends to monotonicallyincrease with economic development. Thus, the shape of their relationship yields astraight line with a positive slope β1.

(2) β1 < 0 and β2 = β3 = 0 suggest thatthere exists a decreasing trend of environmentalpressure as income per capitagrows. That is, a straight line with a negative slope β1

can bedrawn for environmental pressure against income percapita.

(3) β1 > 0, β2 < 0 and β3 = 0 (where |β1| > |β2| > 0) suggest aninverted-U quadraticrelationship between EP and Y which represents the EKC pattern. The peak of this

quadratic curveis at the turning point where1

2

.2

Yβ= −β

An Empirical Survey of Testing the Environmental Kuznets Curve Hypothesis 171

(4) β1 < 0, β2 > 0 and β3 = 0 (where |β1| > |β2| > 0) suggest a U-shaped curve of the

relationship between EP and Y witha turning point1

2

.2

Yβ= −β

(5) β1 > 0, β2 < 0 and β3 > 0 (where |β1| > |β2| > |β3| > 0) suggest a cubic-polynomial N-shaped relationship in whichenvironmental pressure tends to decline after theeconomyreaches a certain level of income per capita, but climbsupwards afteranother higher level of income per capita is achieved. The two turning points are

22 2 1 3 5

3

3.

3

−β β − β ββ

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points2

2 2 1 3 6

3

3.

3

−β β − β ββ

(7) β1 = β2 = β3 = 0 suggestsno correlation between EP and Y.

When estimating the reduced-form regression (1),pooled OLS and panel estimationare the commonly used econometrictechniques. Panayotou (1993), for example, appliedpooledcross-section OLS estimation using three air pollutants (emissionsper capita) asdependent variables: sulfur dioxide (SO2), suspended particles and nitrogen oxides (NOX).Hisfindings revealed that the above pollutants support the EKChypothesis with thethresholds of income per capita equaling 3,000,4,500 and 5,500 US$ (market exchangerate adjusted), respectively.However, these results may not be reliable because hisregressionsfailed to correct possible econometric problems of omitted-variablebias andheteroskedasticity in residuals.

Pooled OLS estimation was nearly abandoned after the late 1990s andgraduallyreplaced by panel estimation techniques to avoid theabove-mentioned econometricproblems. Stern and Common (2001), forexample, used the data of sulfur emissions fromA.S.L. and Associates’ yearly report for 73 countries and concluded that fixedeffectestimation for the EKC regression was preferred to randomeffect on the basis of theHausman test (Hausman (1978)). Withthe elimination of country-specific factors byapplying fixed effectestimation, their results seem to provide some support to theexistenceof an inverted-U pattern between (logarithmic) per capitasulfur emissions and(logarithmic) income per capita in the selectedfull sample and the two subsamples (OECDand non-OECD countries). Based on the significant coefficients estimated, thisconcavefunction has a turning point for the non-OECD subsample equaling 908,178 in real1990 US$ per capita (purchasing power parityadjusted), which is far beyond the samplerange of per capita incomelevels in any existing economy. This means that we can have

172 Yi-Chia Wang

littleconfidence in predictions about a turning point for these countries.By contrast, theresult in the OECD group suggests that thesedeveloped countries will be on the decliningpath after achieving 9,239 US$ per capita income level, while this result from their OECDsubsample was claimed to be generated by a spurious regression.

Given the acceptance of the EKC relationship between degradingenvironment andupgrading wealth, growth optimists tend to believethat economic development is the mostuseful and surest way to greenour nature (Beckerman (1972), Simon (1981) andWildavsky (1988)). As long as an economy achieves a thresholdliving standard,environmental quality will become a luxury goodwith income elasticity greater than unity,which in turn increasesthe value of environmental amenities (Pezzey (1989), Selden andSong (1994), Baldwin (1995), and Day and Grafton (2003)).7 Thesearguments seeminglysuggest that environmental degradation may be ashort-term phenomenon at the beginningof economic development andwill be mitigated in the long run after income per capitasurpassesthe level where the turning point of the EKC occurs.

However, there exists another pattern with an N-shaped curvaturebetweenenvironmental degradation and income per capita. It suggeststhat the reduction in pollutionis also a short-run achievement. After an economy approaches another wealth level(second turningpoint) higher than the first one, the damage to the environmentwilleventually rise again. As previously noted, this has been proposedby Grossman andKrueger (1991) and then discussed further by, forexample, Grossman and Krueger (1995)and Torras and Boyce (1998). In these papers, city-level ambient concentrations of SO2

anddark matters experience only a short-run reduction as income percapita rises, andreturn to increasing trends in the long run.

Following the earliest EKC work produced byGrossman and Krueger (1991), in thesetwo decades a large number ofresearchers devoted themselves to investigating theexistence of the EKC hypothesis by implementing different measures of environmentaldegradation, choosing various data sets, employing diversepollution-related explanatoryvariables and applying advancedeconometric techniques. As thorough as possible, thispaper collectsmost of the pre-2007 representative EKC studies, summarizestheirsimilarities and categorizes their differences. To the best of myknowledge, this paperis the first attempt to categorize most ofmajor EKC studies according to differentmeasurements ofenvironmentally degradation.

2. AIR POLLUTION

Airborne pollutants are chosen as major proxies to measureenvironmental degradationthroughout the whole of EKC history. Apartfrom the accessibility of data, they areregarded as influentialfactors in deteriorating many aspects of community well-being.Forexample, rising concentration levels of suspended particles and darkmatters can affecthuman health by inducing respiratory andcardiovascular diseases. The issue of greenhousegas-driven climatechange that could be responsible for stronger hurricanes and risingsea

An Empirical Survey of Testing the Environmental Kuznets Curve Hypothesis 173

levels is also blamed for its consequence of economic andinfrastructure loss. Thecontribution to the reduction in airbornepollutants, however, is made mainly by developedcountries withhigher income levels, compared to those developing economieswithrelatively weak progress in abating pollution. Therefore, whetherthe EKC hypothesiscan be considered valid for air pollution ismotivating the interest of both environmentalistsand economists.

There are several air pollutants that have been widely investigatedeconometrically inthe EKC literature. These include the sources ofacid precipitation (sulfur dioxide (SO2)and nitrogen oxides (NOX)), the main components of greenhouse gases (carbonmonoxide(CO) and carbon dioxide (CO2)) and the city cloudingveil of suspended particulate matters(SPM) and dark smoke. Althoughthere are other anthropogenic chemical compounds inthe atmosphere,such as chlorofluorocarbons (CFCs), halons, nitrous oxide (N2O)andmethane (CH4), emitted by human activity, they are hardlydiscussed in EKC studiesprobably because of data availability andquality. Comparison can hardly be made whenpapers are scant. As aresult, the focus of this section is mainly on these air pollutantsthathave been explored in a substantial number of studies.

It is noticeable that the use of emissions or concentrations ofairborne pollutants cangenerate different implications as well asestimation results. Before starting to categorizeand summarize EKCfindings according to various air pollutants, it is worthwhile togain abasic understanding of the differences between ambientconcentrations and emissions.

2.1. Ambient Concentrations versus Emissions

The measures of ambient concentrations and emissions providedifferent information andrepresent different aspects ofenvironmental degradation. Ambient concentrations ofcertainairborne pollutants are the proportion of the mass in a given volumeof air or area ona city basis. Therefore, the common concentrationunits are ppm (part per million,

610massof component

massof solution× ),8 ppb(part per billion,

910massof component

massof solution× ) and

(microgram percubic meter). The measure of anthropogenic emissions, on the otherhand,is based on the amount of pollutants converted from thecontents of different fuels on anational basis. Thus, the units ofemissions are generally in carbon equivalent tonnes andkilograms.From an economist’s perspective, anthropogenic emissions of airpollutants aremore directly related to economic activity such asfossil fuel combustion from powerplants, than ambient concentrationlevels that may be long-lived and generated by naturaldecayprocess.

However, the same quantity of emissions may exacerbate theenvironment moreseriously in a smaller economy than a bigger one (Kaufmann et al. (1998)). For example,one million tons of SO2 emitted by Hong Kong has more significant impact on thelocalenvironment than an equivalent amount emitted by China. Theuse of ambient

174 Yi-Chia Wang

concentrations may avoid this problem of area sizewith respect to environmentaldegradation and is closely related tohuman health as well as environmental quality.

Another drawback of using emissions as the measurement ofenvironmentaldegradation is its inappropriate transformation (Kaufmann et al. (1998)). Emission data isoften calculatedusing a constant conversion rate for various ranks of fuels withdifferentpollutant contents. This problem is especially common indeveloping countries and oftenleads to biased distortion of thetrue quantity of emissions.

The weaknesses of adopting ambient concentrations to measure theenvironmentalquality are also multifold and sometimes moreproblematic than the emission data. First,the level of ambientconcentrations is not dominantly produced by economic activityandcan alter over space and time. Even in the same country, theconcentration levels of airpollutants can vary greatly from ruralareas to urban cities, from winter to summer. Inaddition, theconcentration intensity is also partly determined by natural processandgeographical properties in different areas, as, for example, itis difficult to reduce theconcentration levels with a relativelypersistent decaying process in a ‘stuffy basin’. Theseinherentweaknesses in geography and natural property reveal that ambientlevels are noteasily improved.

Although the level of ambient concentrations is measured directlyfrom reliablemonitoring equipment on a city basis, which increasesthe accuracy compared to emissiondata, it is always affected bynatural disturbances over a very short time. For example, aheavyrainfall can reduce the concentration levels of suspended particlesand dark mattersduring an afternoon. Therefore, temporarilydeclining ambient concentrations of pollutantsdoes not necessarilyimply that the overall environmental burden is mitigated.

In sum, both ambient concentrations and emissions of airbornepollutants have theirmeasuring strengths as well as weaknesses compared in Table 1 pollutants provide city-level information with regard to human health, while national-level emissions representwider environmentalissues, such as climate change and acid deposition. The correlationbetween these two proxies may be weak and no objective judgmentexists in terms of whichone is the better measure of environmentalquality. Cole and Elliott (2003) proposed thatconcentration datatend to be ‘noisier’ than emission data in the sense that differentobservation sites contain varying properties between urban and ruralareas, such astemperatures as well as precipitation rates. Thisrequires a number of dummy variables tocapture site-specificnatures. Thus, emission data are relatively ‘cleaner’ such that theyarepopularly used in recent EKC studies when measuring air-qualitydegradation. Moreover,the main advantage of emissions series is that they have longer and more complete timeseries. Urban concentrations cannot reflect much long-run impacts on the naturalenvironment. This is the main reason that most EKC studies preferemissions data.

2.2. S u l f u r D i o x i d e ( S O 2) Kuznets Curve

Although more than one-half of SO2 emissions are generated bynatural processes (source:the United Nations Environment Programme (UNEP)), such as the explosion of volcanoes,

An Empirical Survey of Testing the Environmental Kuznets Curve Hypothesis 175

decaying organic matterand sea spray, human activities are more and more responsibleforthis noxious gas production. The anthropogenic sources of SO2are mainly fossil fuelcombustion from power plants, heating andcooling demands in various weather patterns,smelting of nonferrousores, automobile exhaust and certain chemical manufactures.

Early EKC studies used data of SO2 concentrations from the Global EnvironmentalMonitoring System (GEMS) initiated by the United Nations, based on the city stationslocated in both developedand developing economies spanning around 20 years. Grossmanand Krueger (1991) (and their later publication in 1995), Selden and Song (1994) andPanayotou (1997) are representativestudies using this data archive. Later, sulfur emissiondatagradually replaced concentration data in the EKC estimation becauseof their betterproperties (as discussed in the previous section)and longer time span. A.S.L. andassociates’ yearly data, forexample, measure global sulfur emissions from burning hardcoal, brown coal, and petroleum for the period 1850–1990 and this dataset was laterexpanded by Stern (2005) to 2000 (and keptupdated on his website). His extension wasbased on available sulfuremission data in publication from around 70 countries andtheextrapolation using both the econometric emission frontier model andthe EKC methodfor missing records. This long time-series data setwas employed, for example, by Stern andCommon (2001) and Bertinelli and Strobl (2005) for testing the validity of SO2EKChypothesis.

A summary of studies which estimate the relationship between ambientconcentrationsand income per capita is shown in Table 2. As can be seen, most authors prefer to usefixedeffect and random effect estimation to avoid generaleconometric problems from pooledOLS estimation. However, even usingthe same estimation technique, different authors can

Table 1Comparison of Ambient Concentrations and Emissions as theMeasure of

Environmental Degradation

Ambient Concentrations Emissions

Measurement Quantity in a given volume of Conversion from burning fuels toair or area physical units

Strengths (1) Directly related to human health (1) Directly related to economicand environmental quality activity and wider environmental

problem(2) Avoid the problem of area size (2) No natural or site-specific

with respect to environmental degradationdisturbance

(3) Directly measured by reliable (3) Data has relatively longer timemonitoring equipment span

Weaknesses (1) Partly determined by city’s (1) Emissions may be moregeographical and natural exacerbated in smaller economiesproperties

(2) Vary from time to time, space (2) Indirect and inappropriateto space transformation from fuels with

(3) Influenced by site-specific effects different pollutant content

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An Empirical Survey of Testing the Environmental Kuznets Curve Hypothesis 177

produce results with huge differences. For example, Shafik (1994) and Antweiler et al.(2001) both applied fixed effect estimation, but used a different sample and non-incomeexplanatory variables. Shafik (1994)found the inverted U-shaped relationship betweenSO2 concentrations and income per capita with an achievable turning point of 3,670 US$.In contrast, Antweiler et al. (2001) showed a U-shaped relationship between the twovariables with an extremely high turning point, 109,000US$, which implies that SO2

concentrations are monotonicallydecreasing before an economy’s wealth level reaches thisturningpoint. Experienced EKC authors such as Grossman and Krueger mayadopt thesedifferent results by saying that the relationshipbetween SO2 concentrations and income percapita exhibits an N-shaped pattern with two sequential turning points, as demonstratedintheir paper in 1991. However, the problem of serial correlatederrors in Grossman andKrueger (1991) makes the significance of estimated coefficients unreliable. Afteremploying lagged income percapita in their later publication in 1995, the previous N-shaped pattern was rejected and no significant relationship between SO2 concentrationsand contemporaneous income per capita can befound.

Contrary to the fragile results from studies that analyze SO2concentrations, the EKCrelationship between SO2 emissions andincome per capita can seemingly be supportedamong variousestimation techniques categorized in Table 3. Nevertheless, even though theEKC phenomenon seems to be valid in SO2 emissions, the estimated threshold of GDP percapita canrange from 3,000 (1985 US$) (Panayotou (1993)) to 101,166 (1990 US$) (Sternand Common (2001), full sample). From theresults reported by Stern and Common (2001),different turning points can be attributed to sample selection. Their results shows the EKCturning point in developing countries (non-OECD sub sample) is roughly 100 times higherthan that in developed nations (OECD sub sample). In addition, employing differentexplanatory variablescan also lead to other monotonic or even insignificant patterns of SO2

emission–income relationship (Gangadharan and Valenzuela (2001) and Bertinelli andStrobl (2005), for example), Table 3 provides comparable examples for a number ofstudies.

On average, given the existence of an SO2 EKC phenomenon, theestimated turningpoints in sulfur concentrations are generally lower than those in sulfur emissions, assummarized in Tables 2 and 3. This echoes Selden and Song’s (1994) propositionsuggesting that the estimated turning points for city-level concentrations tend to be atlower income levels than those for national emissions since the reduction (or dispersion) ofpollution concentrations within a city is reasonably easy and less costly to achieve and isoften an early environmental target.

2.3. Nitrogen Oxides (NOX) Kuznets Curve

NOX is considered to be a major source of acid rain. NOX includes nitric oxide (NO) andnitrogen dioxide (NO2), both of which are primarily generated from nitrogenoxidizing inthe air and the burning process of nitride. Whenburning nitride, NO, a colorless and odor-less gas that isa major proportion of NOX emissions, can be transformed into NO2 by photo

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r ra

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15,4

12 (

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)19

29–1

994

STR

Mod

el¥

† Ple

ase

refe

r to

the

abbr

evia

tion

of

auth

ors

and

asso

ciat

ed d

ata

sour

ce in

Tab

le 4

.‡ T

he c

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logy

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nsiti

on R

egre

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sing

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-lin

ear

leas

t squ

ares

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PP: P

urch

asin

g Po

wer

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ity a

djus

ted;

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R: M

arke

t Exc

hang

e R

atea

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ted.

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rdin

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st S

quar

es; 2

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age

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st S

quar

es.

An Empirical Survey of Testing the Environmental Kuznets Curve Hypothesis 179

Table 4List of Sulfur EKC Authors and Associated Pollution Data Sources

Abbreviation References Source of SO2 data

ACT01 Antweiler et al. (2001) United Nations Environment ProgrammeAX06 Aslanidis and Xepapadeas (2006) Environmental Protection Agency, The United StatesBS05 Bertinelli and Strobl (2005) A.S.L. and Associates’ yearly reportCE03 Cole and Elliott (2003) United Nations Environment ProgrammeCJM97 Carson et al. (1997) Environmental Protection Agency, The United StatesCRB97 Cole et al. (1997) Environmental Data Compendium 1993, Paris: OECDDCP00 Dinda et al. (2000) World Development Report, World BankDG03 Day and Grafton (2003) Pollution Data Branch, Environment CanadaGK91 Grossman and Krueger (1991) Global Environmental Monitoring SystemGK95 Grossman and Krueger (1995) Global Environmental Monitoring SystemGV01 Gangadharan and Valenzuela (2001) World Development IndicatorsKDGP98 Kaufmann et al. (1998) United Nations Statistical YearbookLG99 List and Gallet (1999) Environmental Protection Agency, The United StatesMLS03 Millimet et al. (2003) Environmental Protection Agency, The United StatesP93 Panayotou (1993) Global Emissions on a National BasisP97 Panayotou (1997) Global Environmental Monitoring SystemPS03 Perman and Stern (2003) A.S.L. and Associates’ yearly reportS94 Shafik (1994) Monitoring and Assessment Research Centre, LondonSB92 Shafik and Bandyopadhyay (1992) World Development Report, World BankSC01 Stern and Common (2001) A.S.L. and Associates’ yearly reportSS94 Selden and Song (1994) Global Environmental Monitoring SystemTB98 Torras and Boyce (1998) Global Environmental Monitoring System

chemical reactions. NO2, a red-brown gasthat stimulates smell and the respiratory system,can easily dissolve in water and then decompose to nitrous and nitric acid by further photochemical reactions with vapor after absorbing sunlight. It can also oxidize to nitrate andlead to rain acidification, eroding land fertility and gradually deteriorating economicinfrastructure.

NOX concentration data can be accessed in several countries, but due to variousestimation standards and equipment, pooled country estimation may not be reliable.Therefore, NOX emission data are employed in a majority of NOX EKC studies. Asummary of these studies is providedin Table 5. In the studies that support the EKCrelationship between NOX emissions and GDP per capita, the estimated turning points areroughly around 15,000 US$ withsmall variation. Panayotou (1993), however, is anobvious deviation with the estimated bell-shaped turning point equaling 5,500 US$. Thisdifference may result from the use of a market exchange rate and the pooled OLSestimation technique. de Bruyn and Heintz (1999) and Stern and Common (2001)remarked that the adoption of a market exchange rate could result in adownward biasedturning point because of the fact that it cannot adequately reflect the true ‘wealth of

180 Yi-Chia WangT

able

5O

verv

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Stu

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hors

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mpl

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atio

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l dum

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ase

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asso

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ce in

Tab

le 6

.‡ T

he c

hoic

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met

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logy

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ore

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st S

quar

es.

An Empirical Survey of Testing the Environmental Kuznets Curve Hypothesis 181

Table 6List of NOX EKC Authors and Associated Pollution Data Sources

Abbreviation References Source of NOX data

AX06 Aslanidis and Xepapadeas (2006) Environmental Protection Agency, The United StatesCE03 Cole and Elliott (2003) United Nations Environment ProgrammeCJM97 Carson et al. (1997) Environmental Protection Agency, The United StatesCRB97 Cole et al. (1997) Environmental Data Compendium 1993, Paris: OECDGV01 Gangadharan and Valenzuela (2001) World Development IndicatorsLG99 List and Gallet (1999) Environmental Protection Agency, The United StatesMLS03 Millimet et al. (2003) Environmental Protection Agency, The United StatesP93 Panayotou (1993) Global Emissions on a National BasisSS94 Selden and Song (1994) Global Environmental Monitoring System

nations’. In addition, theomitted-variable problem is especially serious when using pooledOLS estimation without controlling for time- and country-specific effects, which mayfurther result in the bias of EKC turning points.

Single country EKC estimation for NOX emissions was undertaken mainly in theUnited States using data from the Environmental Protection Agency (EPA) for the periodfrom the late1920s to the present. Employing a sample period of less than 20 years for 50states, Carson et al. (1997), for example, came to the conclusion that NOX emissions aremonotonically decreasing as per capita income rises. However, applying the same pooledOLS estimation technique, List and Gallet (1999) chose alonger time span from 1929 to1994 for 48 states, and estimated an N-shaped NOX emission–income relationship. List andGallet (1999) further rejected the ‘one-size-fits-all’ reduced-form regression, saying thatthe F-test cannot accept thenull hypothesis that the intercept and slope coefficients are thesame across states. However, the above pooled OLS estimations forthe United States seemto be unreliable when using the fixed-effectsmooth transition regression (STR) model toaccount for thestate-specific effects that may lead to biased OLS coefficients (As lanidisand Xepapadeas (2006)). Although the NOXemission–income relationship was found to bemonotonically increasing byAslanidis and Xepapadeas (2006), they estimated a percapitaincome level equaling 15,658 US$ at which the increasing speed of NOX emissions tend toslow down as income percapita continuously rises.

To sum up, the existence of the EKC hypothesis for NOX emissions depends criticallyon different estimation techniques. Earlier EKC studies tend to support the inverted-Urelationship between NOX emissions and income per capita, but thishas been mostlydisproved in recent studies with newly-developed econometric methods as well as otherexplanatory variables employed.

2.4. Particulates, Dark Matters and Smoke Kuznets Curve

In addition to SO2 and NOX, suspended particulate matter (SPM), or aerosol, is consideredas another typical local airpollutant generated from both natural and anthropogenic

182 Yi-Chia Wang

activities. The former includes dust, sea spray, forest fires, and volcanic explosions, whilethe latter mainly consists of incomplete fuel combustion and industrial chemical processes.Most naturally produced particulates are relatively large, causing rather minorimpacts onhuman health but graying the sky and reducing visibility. Conversely, particulatesproduced by human activities, including raised dust from streets, exhaust fume from cars,outdoor burning, construction, and arable land farming, are smaller than 10 microns, andnot only lead to eye and lung damage but also aggravaterespiratory illness (e.g. coughs andasthma). They also result inincreasing mortality rates among children and the elderlybycarrying carcinogenic or poisonous heavy metals into their lungs. Therefore,anthropogenic SPM is a serious city-level environmental problem.

The reduction in anthropogenic SPM emissions can generally beachieved byincreasing polluters’ costs in buying advanced equipment, controlling burning intensityand combustion quality of coals and seeking alternative cleaner fuels. Definitely, tofinance these costs, an economy’s priority is nothing more than rising overall wealth level.

Most of the existing SPM EKC studies are in favor of investigating SPMconcentrations rather than emissions, though their anthropogenic sources are mutuallycorrelated within a city. In the three SPM emission–income EKC studies shown in Table 7,Selden and Song (1994) and Cole et al. (1997) supported the existence of the SPM EKChypothesis using fixed effect and random effect estimation, respectively. It is noticeablethat the seven OECD countries chosen by Cole et al. (1997) have a relatively lower turningpoint (7,300–8,100 US$) compared to the pooled 30 countries selected by Selden and Song(1994) with the estimatedturning point equaling 10,289 US$. In the case of the 50 states inAmerica during the period 1988–1994, Carson et al. (1997) founda monotonicallydecreasing pattern for SPM emissions as per capita income increases, though this mightnot be reliable when consideringthe inherent weaknesses of their pooled OLS technique.

With regard to SPM concentrations, the existence of the SPM EK Chypothesis is alsofragile, as illustrated in the second part of Table 7. Although Grossman and Krueger (1991)found monotonically decreasing SPM concentration levels as income percapita rises, theirlater work in 1995 replaced previous results byan insignificant relationship after enlargingthe number of nationsas well as incorporating lagged income variables in their model.Shafik (1994) also extended her earlier work of SPM EKC regressions from Shafik andBandyopadhyay (1992) using the same sample and methodology but different data sourcesand explanatory variables. An identical inverted U-shaped relationship between SPMconcentrations and income per capita was generated in these two papers with a turningpoint of 3,280 US$. Panayotou (1993) also provided a support to Shafik andBandyopadhyay (1992) and Shafik (1994) with a slightly higher turning point, but it wasmeasured by a market exchange rate rather than adjusted by purchasing power parity.

Income per capita might not be correlated with SPM concentrations when employingtoo many pollution- and income-related explanatory variables. Torras and Boyce (1998),for example, used the concentration levels of heavy particles as the dependent variable

An Empirical Survey of Testing the Environmental Kuznets Curve Hypothesis 183T

able

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184 Yi-Chia WangA

utho

rs†

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ase

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r to

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evia

tion

of

auth

ors

and

asso

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ed d

ata

sour

ce in

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le 8

.‡ T

he c

hoic

e of

met

hodo

logy

is b

ased

on

the

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test

if m

ore

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est

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ion

tech

niqu

e is

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y ca

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ns o

f tu

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ails

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arity

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ER

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An Empirical Survey of Testing the Environmental Kuznets Curve Hypothesis 185

against several income-related explanatory variables listed in Table 7 and found aninsignificant pollution–income relationship. Gangadharan and Valenzuela (2001) echoedTorras and Boyce (1998) by using a similar set of explanatory variables. As theindependent variables adopted by these two studiesmay be highly correlated with incomeper capita (GINI coefficientsand population density, for instance), their insignificantresultsmay be wrongly produced due to multi-collinearity problems.

Compared to SPM, dark matters and smoke are relatively finer andmore hazardous tohuman health in the short term. They result fromsimilar anthropogenic sources to SPM.Although Beckerman (1992) treated SPM and smoke as the same thing, empirical EKCstudies usually employ the data that combines dark matter and smoke together (forexample, Grossman and Krueger (1991, 1995)).

Data for dark matters and smoke in previous studies was usually extracted from theGEMS archive. Recent EKC studies for these two pollutants are relatively rare probablybecause Grossman and Krueger (1995) and Torras and Boyce (1998) have rejected thesignificant relationship between them and economic development. In addition, it isdifficult to have alternative data sources other than GEMS to compare with existingstudies. On the other hand, because of the similar properties to SPM, with relatively well-recorded data sources, EKC researchers are generally in favor of using SPM as themeasurement of local environmental degradation. Table 9 summarizes an overview of theEKC findings for dark matters and smoke.

2.5. Carbon Kuznets Curve

Carbon pollutants include carbon monoxide (CO) and carbon dioxide (CO2). In addition tothose generated from natural events, suchas forest fires, methane oxidization, and creature

Table 8List of SPM EKC Authors and Associated Pollution Data Sources

Abbreviation References Source of SPM data

CJM97 Carson et al. (1997) Environmental Protection Agency, The United StatesCRB97 Cole et al. (1997) Environmental Data Compendium 1993, Paris: OECDDCP00 Dinda et al. (2000) World Development Report, World BankDG03 Day and Grafton (2003) Pollution Data Branch, Environment CanadaGK91 Grossman and Krueger (1991) Global Environmental Monitoring SystemGK95 Grossman and Krueger (1995) Global Environmental Monitoring SystemGV01 Gangadharan and Valenzuela (2001) World Development IndicatorsP93 Panayotou (1993) Global Environmental Monitoring SystemS94 Shafik (1994) Monitoring and Assessment Research Centre, LondonSB92 Shafik and Bandyopadhyay (1992) World Development Report, World BankSS94 Selden and Song (1994) Global Environmental Monitoring SystemTB98 Torras and Boyce (1998) Global Environmental Monitoring SystemV97 Vincent (1997) Department of Environment, Malaysia

186 Yi-Chia Wang

activities, CO emissions are mainly produced by the incomplete burning process of thepetro chemical fuels for economic purposes. CO is a colorless andodor-less gas lighter thanair but heavier than oxygen. Thus, over-inhalation of this gas may cause low oxygenconcentration in the blood and tissues of a human body, leading to stupor and even death.In addition to the effect on human health, CO also acts as aminor component of greenhouse gases that warm the Earth.

Green house gases are the chemical compounds found in the Earth’s atmosphere thatcan absorb the infrared radiation (heat) reflected from the Earth’s surface. Theaccumulation of these gases can trap the reflection of sunlight and gradually raise globalaverage temperatures. Thus, green house gas emissions, of which CO2 isthe majorcomponent, are regarded as the most serious global air pollutants and difficult to bereduced due to the needs of economic development.

During the past 20 years, about three-quarters of human-made CO2 was emitted byburning fossil fuels (source: the Oak Ridge National Laboratory (ORNL), 2000). Energy-related CO2 emissions, resulting from burning coal, petroleum and natural gas, consist ofmore than 80% of anthropogenic green house gas emissionsin the United States (source:Energy Information Administration, Washington, D. C., 2002), the biggest CO2 emitter inthe world. Around 1.5 billion metric tons of CO2 emissions are emittedannually by theUnited States, which constitutes nearly 50% ofcurrent global annual emissions (3.2 billiontons).

Unlike local pollutants, the reduction in carbon emissions is aninternational or jointtarget in most of the developed countries, rather than a domestic issue. In 1997, 35industrial nations were obliged to cut CO2 emissions by an overall 5.2% below 1990 levelsby 2008–12 in an agreement known as the Kyoto Protocol. Through this international pact,several high-income economies, suchas Germany and France, have successfully eased thegrowth of carbon emissions in recent years. In addition, international assistance forpollution controlling techniques in developing countries also playsa successful role incurbing global carbon emissions.

Whether this achievement requires economies to become rich is also aworth-investigating topic in the EKC literature. In Canada, CO concentrations were found to bedeclining only when income percapita lies between 20,240 and 24,321 US$ (Day andGrafton (2003)). However, this finding might not be robustin the time-series frameworkbecause of its short sample period (24 time points) as well as possible non-stationary OLSresiduals fromtheir reduced-form regression. In addition, it is uncommon to useambientconcentrations in estimating carbon EKC because international data are incomplete,unstandardized, and most importantly meaning less. CO2 is well-mixed and long-livedinthe atmosphere and regional variations are minor. CO emission data, on the contrary, arerelatively accessible in a variety of data sources.

Table10 lists several EKC studies of the CO emission–income relationship.Unsurprisingly, seven OECD economiesselected by Cole et al. (1997) were found to have

An Empirical Survey of Testing the Environmental Kuznets Curve Hypothesis 187T

able

9O

verv

iew

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KC

Stu

dies

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ple

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gy is

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ed o

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188 Yi-Chia WangT

able

10

Ove

rvie

w o

f EK

C S

tudi

es in

the

Rel

atio

nshi

p be

twee

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arbo

nM

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) and

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An Empirical Survey of Testing the Environmental Kuznets Curve Hypothesis 189

Table 11List of CO EKC Authors and Associated Pollution Data Sources

Abbreviation References Source of CO data

SB92 Shafik and Bandyopadhyay (1992) World Development Report, World BankSS94 Selden and Song (1994) Global Environmental Monitoring SystemCJM97 Carson et al. (1997) Environmental Protection Agency, The United StatesCRB97 Cole et al. (1997) Environmental Data Compendium 1993, Paris: OECDDG03 Day and Grafton (2003) Pollution Data Branch, Environment Canada

decreasing levels of CO emissions after a per capita income ranging from 9,900 to 10,100US$ was achieved. In addition, using pooled OL Sestimation, Carson et al. (1997) came tothe conclusion that CO emissions in the United States were monotonically decreasingduring the period 1988–1994. However, the estimated relationship between CO emissionsand income per capita is not consistent among the studies selected in Table 10.

The importance of green house gases, or their representative CO2 emissions, drives theproduction of historical data record for both environmental and economic research. ORNL,a laboratory organized bythe Carbon Dioxide Information Analysis Center (CDIAC), hasrecords for more than 100 years of CO2 emissions for a majority of advanced countries (forthe United Kingdom, ORNL even traces back to 1750), and at least 50 years for manydeveloping nations. ORNL’sestimation of CO2 emissions includes fossil fuels (gas,liquidand solid fuel) consumption, cement production and gas flaring. Other data sources,such as the International Energy Agency (IEA), operated by OECD, also chroniclesseveral decades of CO2 emissions by advanced measure, which is arguably better than thatof ORNL (Galeotti et al. (2006)).

It seems to be a general result that the EKC relationship between CO2 emissions andincome per capita can be produced in thestudies before 2000, but with a wide range ofturning points assummarized in Table 12. This problem may result fromthe differentsources of data and estimation techniques chosen. Using data from the World Bank andORNL, respectively, Shafik and Bandyopadhyay (1992) and Holtz-Eakin and Selden(1995) found two distinct EKC turning points equaling 4,000 and 35,428US$,respectively. Schmalen see et al. (1998) used a similar sample as Holtz-Eakin and Selden(1995) but divided their sample income range by 10 segments in which OLS estimation isindividually applied (spline model). They found that in the 10thsegment where per capitaincome is higher than 10,000 US$, CO2 emissions will be negatively correlated withincome per capita.

In the past five years, most of the early EKC findings between CO2 emissions andincome per capita were rejected and were infavor of a monotonically increasing pattern,under a variety of newly-developed econometric techniques. Bertinelli and Strobl (2005),for example, emphasized that the irsemi-parametric regression places no restriction on thefunctional form, which avoids the general weakness in the traditional reduced-form EKCregression. Their results indicated a monotonically increasing relationship between CO2

190 Yi-Chia WangT

able

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Ove

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nsity

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)

An Empirical Survey of Testing the Environmental Kuznets Curve Hypothesis 191A

utho

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1961

–199

3† P

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able

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ed o

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ay b

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st S

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es.

192 Yi-Chia Wang

emissions and income percapita. Using a non-linear three-parameter Weibull function andthe generalized method of moments (GMM) estimation, Galeotti et al. (2006) found a CO2

EKC pattern in OEC Dcountries with a turning point 16,587 US$, while carbon emissionsare monotonically increasing in selected non-OECD countries.

A rough picture of the CO2 emission–income relationship canalso be drawn byinvestigating the correlation between the grow thrates of carbon emissions and GDP percapita. Tucker (1995), for example, found a positive correlation between the growthratesof CO2 emissions and GDP for 137 economies. On the contrary, four developedcountries, Netherland, the United Kingdom, the United States and West Germany, werefound to have a negative correlation between the two growth rates from 1961 to 1993.

In summary, carbon emissions have drawn great attention from EKC researchers.Recent EKC studies tend to argue that green house gasemissions are global pollutants withthe highest abatement costsamong all sources of environmental degradation. Thus,carbone missions are very likely to grow continuously so long as the economy keepsdeveloping, unless vintage technology of carbon emission control is introduced andcompliance with the international treaty is enforced. In addition, the process of afforestation may soak up a large amount of CO2 emissions, but this effect is notyet included inany EKC studies because of the difficulty in its quantitative measurement.

3. WATER POLLUTION

Water pollution is typically a local degradation of the environment when an economy isdeveloping. Undoubtedly, access to clean water resources is directly related to humanhealth and reflects acommunity’s living standard. In general, the abatement of waterpollution is relatively easy to achieve because the (opportunity) costs of developing newtechnology and regulation enforcement are lower than most air pollutants.

Levels of fecal coliform, nitrates, dissolved oxygen (DO), biochemical oxygendemand (BOD) and chemical oxygen demand (COD) arecommon indicators representingcity water quality. In fact, they areclosely related to each other. Naturally, there exists aconstantamount of oxygen produced by plants’ photo synthesis as well as water churningin rivers. The sources of oxygen consumption mainly includethe respiration of aquaticanimals, decomposition of bacteria, andvarious chemical reactions. If oxygenconsumption in a stream system exceeds its production, DO levels fall and some sensitiveanimals may move away, weaken, or even die. Overall, the natural processes of oxygenproduction and consumption are balanced in the ecological system without considering theexternal damage resulting from human activities.

So why and how does oxygen consumption increase in rivers? Wastewater fromsewage treatment plants, on-site septic systems, domestic and wild animal manure, andstorm runoff often contain organic materials and fecal bacteria (coliform and fecalstreptococci). These by products are decomposed by micro organisms that exhaust oxygenin the process. The amount of oxygen consumed by the seorganisms for decomposing the

An Empirical Survey of Testing the Environmental Kuznets Curve Hypothesis 193

waste is called biochemical oxygen demand (BOD). Similarly, chemical oxygen demand(COD) is the amount of oxygen consumed by chemical oxidization for converting organicwater constituents to inorganic end products. This chemical process also consumes oxygenin rivers. Other sources of oxygen-exhausting waste include storm water run off from farmland and urban streets, feedlots, and failing septic systems.

Fecal coliforms are a group of bacteria. Although they are notharmful themselves, theycould carry pathogenic viruses and protozoans that can live in human and animal’sdigestive systems. Therefore, their presence in streams reveals that pathogenic microorganisms might also be carried by shellfish so that eating aquatic animals from fecalcoliform contaminated streams might be ahealth risk. In addition, the decomposition offecal coliforms requires the demand for oxygen, which in turn reduces DO levels andharms rivers.

Nitrate contamination is also causing serious damage to the aquatic system. Theevolution of nitrates comes from nitrogen, including ammonia (NH3), nitrates (NO3), and

Table 13List of CO EKC Authors and Associated Pollution Data Sources

Abbreviation References Source of CO2 data

AC99 Agras and Chapman (1999) Oak Ridge National LaboratoryBS05 Bertinelli and Strobl (2005) World Development IndicatorsCE03 Cole and Elliott (2003) Oak Ridge National LaboratoryCJM97 Carson et al. (1997) Environmental Protection Agency, The United StatesCRB97 Cole et al. (1997) Oak Ridge National LaboratoryDG03 Day and Grafton (2003) Pollution Data Branch, Environment CanadaDVO98 de Bruyn et al. (1998) Oak Ridge National LaboratoryGL99 Galeotti and Lanza (1999) International Energy Agency, OECDGL05 Galeotti and Lanza (2005) International Energy Agency, OECDGLP06 Galeotti et al. (2006) Oak Ridge National Laboratory

International Energy Agency, OECDGV01 Gangadharan and Valenzuela (2001) World Development IndicatorsHES95 Holtz-Eakin and Selden (1995) Oak Ridge National LaboratoryL05 Liu (2005) no reportMB04 Martínez-Zarzoso and no report

Bengochea-Morancho (2004)MU97 Moomaw and Unruh (1997) Oak Ridge National LaboratoryRG97 Roberts and Grimes (1997) Oak Ridge National LaboratoryUM98 Unruh and Moomaw (1998) Oak Ridge National LaboratoryS94 Shafik (1994) Oak Ridge National LaboratoryS99 Sun (1999) World Development Report, World BankSB92 Shafik and Bandyopadhyay (1992) World Development Report, World BankSSJ98 Schmalensee et al. (1998) Oak Ridge National LaboratoryT95 Tucker (1995) World Development Indicators

194 Yi-Chia Wang

nitrites (NO2). Anthropogenic sources of nitrates mainly include waste water treatmentplants, run off from fertilized lawns, cropl and and animal manure storage areas, failing on-site septic systems, and industrial discharges that contain corrosion inhibitors. Nitrates areaquatic plants’ essential nutrients, but excess amounts of nitrates, together withphosphorus, can cause significant water problems inthe form of eutrophication, theconsequence of which will lead todramatic growth in aquatic plants (e.g. algae). Over-accumulation ofalgae absorbs the existing amount of oxygen and prevents sunlight fromreaching the deep layers of a river, which in turn expels andeven kills aquatic lives due tothe reduction in oxygen levels and temperatures.

The above-mentioned indicators of water quality are broadly recordedin the GEMS/water data archive, containing observations from up to287 stations in 58 countries from1991 onwards. Thus, it is the most preferred data source in recent water EKC studies.Other records ofwater quality indicators, such as the World Bank’s World DevelopmentReport, monitor the DO and fecal coliforms levels in major streamsfor more than 100countries, and were also used in early water EKC research.

A summary of several major water EKC studies is categorized in Table 14 according todifferent measurements of water quality. As can be seen, DO levels fall continuously asincome percapita rises in the early studies carried out by Shafik and Bandyopadhyay(1992) and Shafik (1994). Although Grossman and Krueger (1995) found that the DOEKC pattern does not significantly exist when taking into account mean temperature andlagged income variables. Torras and Boyce (1998) later proposed that DO levels mightincrease when income per capita reaches the level between 5,085 and 19,865 US$.

Choosing a similar time span, BOD and COD levels, on the other hand,were found tohave no EKC phenomenon by Grossman and Krueger (1995) for 58 countries and Vincent(1997) in Malaysia. However, Hettige et al. (2000) and Cole and Elliott (2003) bothapplied fixed effect estimation and generated a U-shaped relationship between BODemissions and income per capita but with distinct turning points, 5,000 (1990 US$) and39,698 (1987 US$), respectively.

In terms of fecal coliform, two successive studies, Shafik and Bandyopadhyay(1992) and Shafik (1994), used different data sources (World Development Report andCanada Centerfor Inland Water, respectively) but generated a similar N-shapedrelationship between fecal coliform levels and income per capita. However, some laterfindings from Grossman and Krueger (1995) and Torras and Boyce (1998) indicated thatincreasing income per capitahas no significant correlation with the levels of fecalcoliform inrivers. A similar insignificant relationship between nitrates and income wasalso found by Grossman and Krueger (1995) for 58 countries, most of which were intheir developing stage. In the 15OECD countries selected by Cole et al. (1997),however, a30-river sample produced an EKC pattern between nitrates levels and incomeper capita with two turning points, 25,000 US$ from logarithm specification of income,and 15,600 US$ from level specification.

An Empirical Survey of Testing the Environmental Kuznets Curve Hypothesis 195T

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196 Yi-Chia WangA

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An Empirical Survey of Testing the Environmental Kuznets Curve Hypothesis 197

All in all, the EKC relationship between various water pollutants and income per capitais inconsistent. The quality of a river may presumably be affected not only by humanactivities (contaminationand abatement) but also by its characteristics, such as watertemperature and mobility. One can also infer that the selection of sample rivers and periodmay also determine the existence of water EKC hypothesis.

4. TIMBER HARVESTING

Unlike air and water pollution, deforestation does not directly affect human health, butavoiding it, or affore station, brings many benefits to the environment. First, worldwidevegetation is the lung of the Earth that absorbs an estimated 6.1 billion metric tons ofanthropogenic CO2 emissions per annum from the atmosphere in aso-called ‘CarbonCycle’.9 Thus, over-logging of trees worsens the problem of global warming. Second, theforest protects various kinds of end angered species, maintains a food chain among forestinhabitants and keeps ecological systems balanced. Third, treescreate a natural barrieragainst intense storms and rising tides. Last but not least, trees trap water in their roots sopeople canavoid disasters such as landslides. Deforestation therefore causesnothing butlong-run catastrophes to the whole ecosystem.

There are three underlying sources of deforestation: the desire to convert forest andtimber land areas to pasture and cropland forlive stock farming and agriculture; timberharvesting for architecture; and the extraction of underground resources and woodfuels. Ingeneral, population pressure is always emphasized as acrucial factor of the above sourcesof deforestation in a country’sdeveloping stage (Panayotou (1993) andCorpper andGriffiths (1994)). As an economy starts developing, itsforest areas gradually shrink butmay be recovered when people’smarginal utility of environmental amenity exceeds theconsumption of material goods. That is, growing income levels lead to a higher demand fortrees, national parks and the forest to beautify theenvironment, and therefore, it isreasonably in the field of EKC research.

Early forest-related EKC studies are in favor of adopting an annual rate ofdeforestation as a measure of environmental degradation.10 Panayotou (1993) and Shafik(1994), for example, used the records of forest areafrom World Bank’s WorldDevelopment Report for a number of countriesand generated an EKC pattern between therate of deforestation andincome per capita, though Panayotou’s estimated turning pointseemsto be downward biased due to the use of a market exchange rate andpooled OLStechniques (de Bruyn and Heintz (1999) and Stern and Common (2001)). The overview ofthis series of EKC studies is summarized in Table 16. In addition to the forest data from theWorld Bank, the United Nations’ Food and Agriculture Organization (FAO) also recordsthe forest area for many tropical countries located in Africa, Asia and Latin America.Corpper and Griffiths (1994), for example, used this data set and found a deforestationEKC pattern for selected countries in Africa and Latin America with sensible turningpoints, but this pattern is insignificant in Asia due to insufficient sample size andvariationin their fixed effect estimation. Koop and Tole (1999) also workedon the FAO’s

198 Yi-Chia Wang

data set, pooled 76 tropical countries together, butfound an insignificant deforestation–income relationship. This insignificant result was also supported by Gangadharan andValenzuela (2001) using 2SLS estimation. Similarly, using annual rates of affore station(negative rates ofdeforestation), Antle and Heidebrink (1995) also claimed that there existsno significant relationship between growing trees and acountry’s wealth.

Although the problem of deforestation is part of the cost of economic development, itis difficult to take the effect of natural disasters into account. Torrential rains, for example,are commonand regular features in tropical areas and usually causecatastrophic landslidesand floods washing away soils and timbers. Therefore, the measure of deforestation seemsto be a problematic proxy of environmental degradation, especially in tropical regions.

5. PROGRESS OF THE ENVIRONMENTAL KUZNETS CURVE

Early environmental economists embarked on investigating the patterns of variouspollutants against growing income levels by asking three general questions: will theenvironmental pressure resulting from economic development also be reduced byeconomic development itself? If so, at what level of income per capita will any pollutant becurbed? Even when pollution levels are decreasing, will they rise again? At first, theyestimated a reduced-formregression for certain pollutants with levels, second- and third-order polynomial of income per capita to capture the curvature between the variables andgenerate plausible turning points. Later, they incorporate a number of non-incomeregressors on the extended topics of their interests. The inclusion of non-income regressorswas suggested by the fact that, in addition to increasing income percapita, there are otherpossible mechanisms in reducing pollution levels. These pollution-reducing channels inthe EKC literature arelisted as follows:

(1) Trade Effect: by importing pollution-intensive products, or by moving heavyfactories to adjacent countries with less strict environmental regulations, high-income economies can reduce their pollution levels through these channels known

Table 15List of Water EKC Authors and Associated Data Sources

Abbreviation References Source of Water-quality Indicator

CE03 Cole and Elliott (2003) World Development IndicatorsCRB97 Cole et al. (1997) Environmental Data Compendium 1993, Paris: OECDGK95 Grossman and Krueger (1995) Global Environmental Monitoring SystemGV01 Gangadharan and Valenzuela (2001) World Development IndicatorsHMW00 Hettige et al. (2000) Individual Country’s Environmental Protection AgencySB92 Shafik and Bandyopadhyay (1992) World Development Report, World BankS94 Shafik (1994) Canada Center for Inland Water

World Development Report, World BankTB98 Torras and Boyce (1998) Global Environmental Monitoring SystemV97 Vincent (1997) Department of Environment, Malaysia

An Empirical Survey of Testing the Environmental Kuznets Curve Hypothesis 199T

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200 Yi-Chia Wang

Figure1: Different Patterns between Environmental Pressure and Income Per Capita

(1) A monotonically increasing pattern where β1 > 0 and β

2 = β

3 = 0,

(2) A monotonically decreasing pattern where β1 < 0 and β

2 = β

3 = 0,

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2 < 0 and β

3 = 0,

(4) A U-shaped curve represents β1 < 0, β

2 > 0, and β

3 = 0,

(5) An N-shaped pattern reveals β1 > 0, β

2 < 0 and β

3 > 0, and

(6) An inverted N-shaped curve represents β1 < 0, β

2 > 0 and β

3 < 0.

Note: Given the significance of all βi′s, two conditions —|β

1| > |β

2| > |β

3| and β2

2> 3β

1β

3 — must hold to

have distinct turning points.

as ‘displacement hypothesis’. Grossman and Krueger (1991) and Shafik andBandyopadhyay (1992) both concluded that higher physical trade intensity willleadto lower concentrations of air pollutants. Rather than using physical trademeasures such as export and import, Hettige et al. (1992) adopted the Dollar indexas the proxy to the openness of trade and reached similar conclusions in the issueof toxic intensity.

(2) Stringent Environmental Regulation: de Bruyn (1997) and Torras and Boyce(1998) proposed that the environmental policy, fostered by internationalagreements such as the Kyoto Protocol and emissions-trading scheme in theEuropean Union, explains why pollution can be successfully curbed in high-income nations. It is especially noticed that some local pollutants can alsobemitigated by the environmental enforcement of rule of law (Panayotou (1997)and Aslanidis and Xepapadeas (2006)).

An Empirical Survey of Testing the Environmental Kuznets Curve Hypothesis 201

(3) Population Effect: a city with higher population density creates higher demandfor energy and causes unavoidable pollution and deforestation. Therefore, thereduction in population intensity has been found to have significant contributionsto curb SO2 concentrations (Grossman and Krueger (1991)) andairborne emissionsof CO, NOX, SPM and SO2 (Selden and Song (1994)), as well as discouragedeforestation (Corpper and Griffiths (1994)).

(4) Technology Progress: most of the decomposition analyses (discussed later) tendto believe that technological innovation is the most crucial factor that curbspollution. Hilton and Levinson (1998) found that the EKC phenomenon exists inautomotive lead emissions because the technological reductionin lead componentsper gallon of gasoline (pollution intensity) gradually offsets the increase of totalgasoline consumption (pollution activity).Stern (2004)also reached the conclusionthat emissions-related technology progress leads to the reduction in sulfuremissions in his selected OECD countries.

(5) Input Mix and Output Mix Effects: rapid progress in technology may be helpfulto use less pollution-intensive input, or reduce the production of environmentallytaxing output. The former was upheld by Stern (2002) with the conclusion that thereduction of energy intensity (energy consumption) contributes to 10.2%decreases of global sulfur emissions during 1973–90, while the latter was hardlydiscussed with empirical evidence.

(6) Energy Price Effect: the reduction in energy consumption can also be attributedto increasing energy prices. Unruh and Moomaw (1998) observed that the oil crisisin 1973 had triggered the reduction in CO2 emissions per capita in selected oil-dependent high-income countries. Similarly, de Bruyn et al. (1998) indicated thatenergy prices and CO2 emissions are negatively correlated in growth rates in theUnited States during the period 1961–1993.

(7) Income Inequality Effect: the absolute income effect suggests that rising incomeper capita will increase the capability to pay for environmental amenity. However,the relative income effect will reduce this capability since rising income inequalityresulting from increased income per capita may reduce people’s willingness toprotectthe environment (Magnani (2000)). Thus, the improvement of a country’sincome inequality may create median voters’ incentive towards environmentalprotection.

After the EKC history passes the stage in which people adopted miscellaneousexplanatory variables listed above to capture different aspects of pollution-reducingmechanisms, studies at thenext stage started to criticize existing results and some of themalso proposed possible corrections for conventional EKC regressions. In general,econometric problems were mainly criticized at this stage. First, the possibility of jointdetermination of income and environmental degradation will result in inconsistentestimation when suitable instrumental variables are notconsidered.11 That is, notonly can

202 Yi-Chia Wang

income per capita affect the level of pollutants but the irreverse causality may exist as well.Second, without the correction of heteroskedasticity and serial correlation in regressionerrors, the estimated coefficients may be accompanied by small standarderrors anddistorted t-statistics such that the significance of coefficients may be unreliable.

Pooled OLS estimation has also been criticized for its omitted variable problem.Therefore, most EKC researchers are in favor ofusing fixed effect estimation to eliminateomitted variable bias. Holtz-Eakin and Selden (1995), for example, found an EKCrelationship between CO2 emissions and income per capita byusing pooled OLS and fixedeffect estimations, but there were huge differences in the turning points from these twotechniques, which were eight million US$ and 35,428 US$, respectively. Day and Grafton(2003) also revealed that their results of SPM and CO2 EKC regressions in the case studyof Canada failed to perform some key misspecification tests.

The recent generation of models and estimation techniques has been developed toavoid various problems when using conventional reduced-form EKC regression. Galeottiand Lanza (1999),12 forexample, estimated a non-linear Weibull function using maximumlikelihood estimation. Although this function is sometimes used inapplied environmentaland ecological economics to capture thenon-linear curvature between pollution andincome per capita, it isnot practical to include more than one explanatory variableforfurther discussion of other pollution-reducing mechanisms. Similarproblems exist inthe semi-parametric regression withoutrestrictions on the EKC functional form (Taskinand Zaim (2000) and Bertinelli and Strobl (2005), for example).

In order to distinguish different channels in which certainpollutants are growing,decomposition analysis has been used frequently in recent years. The idea ofdecomposition analysis canbe demonstrated as follows:

1 ( 2) ( 1),

1 2 ( 1)t t t t

t tt t t t

EP factor factor N factor NEP factor N

factor factor factor N factor N

− −= × × × × ×−

(2)

where the environmental pressure at time t (EPt) can bedecomposed by the product of $N$factor ratios. In general, most of the decomposition studies use factor (N–1)t and factor Nt

as GDP and total population, respectively. Therefore,( 1)t

t

factor N

factor N

− represents GDP per

capita (scale effect) and factor Nt denotes a population effect. Decompositionanalysischooses two distinct years for dividing the rate of change in EP into the sum of the rates ofchange in N factors. Takinglogarithm transformation and linearizing equation (2), wehave:

( 1)( ),

1t t

t tt t

EP factor NIn EP In In In factor N

factor factor N

−∆ = ∆ + + ∆ + ∆

(3)

An Empirical Survey of Testing the Environmental Kuznets Curve Hypothesis 203

where ∆ denotes the difference of variables at between thetwo time points. The main meritof decomposition analysis is toexplicitly understand the sign and magnitude of thecontribution of every factor ratio to the growth rates of pollutants. Stern (2002), forexample, decomposed total change in global sulfur emissions into (1) the impact of shifts

in the mixed fossilfuel type ,global sulfur emissions

fossil fuel consumpon

(2) the shift between fossil and

non-fossil fuels ,fossil fuel consumption

total fuel consumption

(3) energy intensity ,total fuel consumption

totalGDP

(4) a scale effecttotalGDP

total population

and (5) apopulation effect (total population). His

calculation shows that thegrowth rate of global sulfur emissions is 28.77% from 1973 to1990,among which the contribution of scale effect consists of 53.78%,while the mainsources of the reduction in global sulfur emissionscome from the change of emissions-related technical progress (–19.86%) and the change of energy intensity (–10.2%).

Most decomposition analysts reach similar conclusions that growingincome per capitaleads to positive and significant change in sulfurand carbon emissions,13although thefactors chosen as decomposition components are fairly subjective. In addition, a linearizeddecomposition approach cannot capture a possible non-linear relationship betweenemissions and scale effects from increasing income per capita. This is the main reason whythis approach is not applicable in the EKC literature.

6. CONCLUSIONS

The relationship between environmental protection and economic development has longbeen theoretical and empirical issues. However, the measurement of environmental qualityis further complicated dueto its multi-dimensional nature (Antle and Heidebrink (1995)). Itis admitted that most of the ecological damage is difficult tomeasure and anticipate. Ofparticular importance are irreversible changes, including desertification, loss ofbiodiversity, soilerosion and the depletion of ground water reservoirs. Arrow et al. (1995)provided a short but elaborate discussion of this issue. Their two major conclusions are:

(1) If human activities are to be sustainable, we need to ensurethat the ecologicalsystems on which our economies depend areresilient.

(2) When a country has attained a sufficient high standard of living, people givegreater attention to environmental amenities. However, economic growth is not apanacea for environmental quality.

Even though measuring environmental quality is difficult, somedevelopment-relatedpollutants and deforestation can representvarious aspects of environmental degradation.This paper collectsand discusses numerous EKC studies according to different measuresof

204 Yi-Chia Wang

environmental deterioration. Broadly speaking, the bell-shapedrelationship betweenincome per capita and the proxy toenvironmental pressure can be found in earlier EKCstudies, butrecent researchers tend to question their validity and applicabilityfrom manyperspectives. This paper concludes whether the EKChypothesis can be supported dependscritically on data sources,sample countries, length of time period, the adoption of amarketexchange rate or purchasing power parity to adjust GDP, regressionfunctionalforms and various estimation techniques.

Notes1. To date, the multi-country data of various air pollutants can be found in a joint project of the World

Health Organization (WHO) collaborated with the United Nations Environment Programme (UNEP) inoperating Global Environmental Monitoring System(GEMS) on air and water quality, Toxic ReleaseInventory (TRI) on toxic emissions from the US manufacturers, the long time-series dataof CO2emissions from the Oak Ridge National Laboratory (ORNL), the Environmental Data Compendiumcompiled by the OECD, WorldResources Institute (WRI) from Washington D.C., A.S.L. andAssociates’ yearly report for sulfur emissions from Washington D.C.,the emission of greenhouse gasesfrom the Department for Environment Food and Rural Affairs (DEFRA), various environmentalmeasures fromWorld Development Indicators (WDI) established by World Bank and a number ofenvironmental monitoring institutes in individual country.

2. Logarithm transformation of variables is also commonlyused.

3. To be precise, these are the variables excluding‘contemporaneous’ income per capita.

4. If 22 1 33 0,β − β β < there exists noturning point and environmental pressure tends to ‘increase’ as income

grows with curvature.

5. If 22 1 33 0,β − β β < there exists noturning point and environmental pressure tends to ‘decrease’ as income

grows with curvature.

6. Day and Grafton (2003) suggestedthat such an argument is one of many explanations of theEKCphenomenon in literature. They pointed out that this argument isinadequate, given the fact thatmany low- to middle-income countriesare at the per capita income levels lower than the reportedturningpoints in many EKC studies (collected by Ekins (2000)). Even inCanada, environmentalproblems are still an on-going issue.

7. Mass ofsolution is a homogeneous mixture of two or more substances.

8. National Energy InformationCenter (NEIC): http://www.eia.doe.gov/environment.html.Accessed: 1Apr. 2010.

9. The annual rate of deforestation is defined as, 1 ,

, 1

,i t i t

i t

F F

F−

−

− where F

i,t denotes thetotal forest area in

country i at time t.

10. This is the problem of simultaneity andirreversibility discussed byStern (1998). When thedependentvariable and regressors are endogenously and systematicallydetermined, both of them will becorrelated with regressionresiduals. In this situation, the estimated OLS coefficients are notconsistent.However if variables cointegrate in the long run, due tosuperconsistency, simultaneity issues aremitigated.

11. Also, seeGaleotti and Lanza (2005) andGaleotti et al. (2006).

12. These studies include Zheng (2000), Viguier (1999), Stern (2002), Hamilton and Turton (2002), Bruvolland Medin (2003) and Stern (2004).

An Empirical Survey of Testing the Environmental Kuznets Curve Hypothesis 205

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