kuznets curve ekc

E C O L O G I C A L E C O N O M I C S 6 8 ( 2 0 0 9 ) 1 1 4 9 – 1 1 5 9

ava i l ab l e a t www.sc i enced i r ec t . com

www.e l sev i e r. com/ loca te / eco l econ

ANALYSIS

Taking the “U” out of KuznetsA comprehensive analysis of the EKC andenvironmental degradationJill L. Caviglia-Harrisa,⁎, Dustin Chambersa, James R. Kahnb

a Salisbury University, 1101 Camden Ave., Salisbury, MD 21804, United Statesb Washington and Lee University, Science AG-15, Lexington, VA 24450, United States

A R T I C L E D A T A

⁎ Corresponding author.E-mail address: jlcaviglia-harris@salisbury.

0921-8009/$ – see front matter © 2008 Elsevidoi:10.1016/j.ecolecon.2008.08.006

A B S T R A C T

Article history:Received 13 August 2007Received in revised form5August2008Accepted 5 August 2008Available online 12 September 2008

Unlike most Environmental Kuznets Curve (EKC) studies which focus on narrow measuresof pollution as proxies for environmental quality, we test the validity of the EKC using theEcological Footprint (EF), amore comprehensivemeasure of environmental degradation.Wefind no empirical evidence of an EKC relationship between the EF and economicdevelopment, and only limited support for such a relationship among the components ofthe EF. In addition, we discover that energy is largely responsible for the lack of an EKCrelationship, and that energy consumption levels would have to be cut by over 50% in orderfor a statistically significant EKC relationship to emerge from the data. Overall, these resultssuggest that growth alone will not lead to sustainable development.

© 2008 Elsevier B.V. All rights reserved.

Keywords:Environmental Kuznets CurveEcological FootprintDevelopmentGrowthSustainabilityEKC

JEL classification:Q0; Q01

1. Introduction

If the Environmental Kuznets Curve (EKC) is valid for all typesof environmental degradation, then sufficient economicdevelopment alone will solve environmental problems inboth developed and underdeveloped nations. Not surprisingly,this simple yet powerful implication has played an importantrole in the ongoing debate regarding appropriate economicgrowth and environmental policies (Ranjan and Shortle, 2007).Unfortunately, most of the empirical investigations of the EKChave focused on the narrow relationship between pollutionoutput (as an inversely proportional proxy for environmental

edu (J.L. Caviglia-Harris).

er B.V. All rights reserved

quality) and economic growth. These particular pollutants areonly a small part of environmental concerns at the globallevel. Consequently, the analysis performed in this paper teststhe validity of the EKC using a much more comprehensivemeasure of environmental degradation, the Ecological Foot-print (EF).

Research on the validity, application, and measurement ofthe Environmental Kuznets Curve (EKC) has been prolific(Azomahou et al., 2006). Adapted from Kuznets' (1955) originalstudy on the influences of economic development on incomeinequality, the EKC reflects the relationship between environ-mental quality and per capita income. The EKC asserts that

.

1150 E C O L O G I C A L E C O N O M I C S 6 8 ( 2 0 0 9 ) 1 1 4 9 – 1 1 5 9

environmental quality first declines (traditionally measuredby an increase in pollution) in response to economic develop-ment, and improves (i.e. pollution levels decline) only after percapita income surpasses a critical threshold. This combinationof falling then rising environmental quality (as measured bypollution output) during the course of economic growth andresulting development results in an inverted “U” shaped curve.

Research on the EKC began with the analysis of panel dataon 42 countries to identify an EKC effect for differentmeasurements of air quality (Grossman and Krueger, 1993).In the same genre, Selden and Song (1994) found support foran EKC for SO2, while Grossman and Krueger (1995) andShafikand Banyopadhyay (1992) found water pollution to declinemonotonically with income per capita while carbon emissionsrise with income per capita. Since these initial studies, manyhave followed, focusing specifically on air pollution (i.e. Listand Gallet, 1999; Heerink et al., 2001; Cole, 2003; Khanna, 2002;Bruvoll et al., 2003; Deacon andNorman, 2006;Merlevede et al.,2006; water pollution (Torras and Boyce, 1998; Paudel et al.,2005), deforestation (i.e. Culas, 2007; Rodriguez-Meza et al.,2003; Heerink et al., 2001; Barbier, 2001), hazardous waste andtoxins (i.e. Gawande et al., 2001; Rupasingha et al., 2004),carbon dioxide (CO2) (Azomahou et al., 2006) among others(see Cavlovic et al., 2000; Dasgupta et al., 2002; Copeland andTaylor, 2004 for reviews). One result of this expansiveliterature is that no simple, predictable relationship betweenan aggregatemeasure of environmental quality and per capitaincome has been identified; instead the EKC has been found tohold only for a subset of environmental measures (Stern, 1998;Plassmann and Khanna, 2006).

Several shortcomings along with inconsistencies in theo-retical modeling have lead to strong criticisms of the EKC(Müller-Fürstenberger and Wagnerb, 2007; Perman and Stern,2003). Critics have challenged both the findings (especiallythose based on cross-sectional data) and policy implications ofthese studies (Dasgupta et al., 2002); pointing out that theresults are often sensitive to the nations (or states) chosen, thepollutant measurement (emissions versus ambient concen-trations), trade effects, functional form, and methodologicalchoice (Harbaugh et al., 2002; also see Cavlovic et al., 2000).And, since much of the analysis on the EKC is derived fromreduced-form models, a variety of (sometimes conflicting),theoretical explanations can apply. For example, severalstudies have proposed the “new toxins” scenario may existin which the traditional pollutants exhibit an inverted U-shape in relation to increases in income; however thepollutants that replace these do not, leading to an overallincrease in environmental degradation (Stern, 2004). Inaddition, an important conclusion that can be drawn from asummary of the literature is that greenhouse gasses, inparticular CO2, exhibit an increasing—and even “U” (notinverted) shaped—relationship with growth (Galeotti et al.,2006; Azomahou et al., 2006).

Perhaps the greatest limitation of earlier EKC studies istheir singular focus on one (or a small group of) pollutants astheir measure of environmental quality. While the implica-tions of single pollutants on health and the environment areimportant issues to address, the impact of individual deci-sions on the entire suite of pollutants along with potentiallyirreversible damage to ecosystems is of equal or greater

importance since the substitution possibilities between dif-ferent pollutants could negate any positive impacts on theenvironment noted for a single source. Notable exceptions tothese studies on single pollutants include Rupasingha et al.(2004), Jha and Murthy (2003), and Boutaud et al. (2006).

Recently, greater effort has been made to constructcomprehensive measures of environmental quality. Forexample, Jha and Murthy (2003) estimate global environmen-tal degradation with an environmental degradation index(EDI) incorporating six environmental indicators: annual percapita fresh water withdrawal, annual fresh water withdrawalas a percentage of water resources, per capita paper con-sumption, per capita CO2 emissions, share of world CO2

emissions, and the average annual rate of deforestation.While broader than a single pollutant, the EDI is limited as ameasurement of overall environmental quality by availabledata. Strong arguments could be made for the inclusion of adifferent or more inclusive set of environmental indicators.Finally, Boutaud et al. (2006) exam the relationship betweenthe Ecological Footprint (EF) and Human Development Index(HDI) and growth. While Boutaud et al. (2006) includeaggregate indices to test for an EKC, the authors rely oncross-sectional data for a single year and graphical represen-tation of the data, resulting in analysis that is not conducive tohypothesis testing. This paper builds on this more inclusiveapproach with the development of a theoretical frameworkincorporating environmental capital into the carrying capacityof a nation and an empirical model utilizing a time series of40 years of data on GDP and an aggregate measurement ofenvironmental damage called the Ecological Footprint. Morespecifically, the goal of the analysis is to determine whetheran EKC can be identified for this cumulative measurement ofenvironmental degradation.

The remainder of the paper is organized as follows: Section2 discusses the Ecological Footprint; Section 3 derives neces-sary conditions if both strong sustainability and balancedeconomic growth are to be achieved; Section 4 describes thedata used in the panel regressions; Section 5 describes thevarious EKC panel models and their estimation results; andSection 6 concludes.

2. The Ecological Footprint

The Ecological Footprint (EF) was introduced by Rees (1992)and further developed in Wackernagel and Rees (1996) todetermine how the environmental damage associated withhuman consumption compares to the biosphere regenerativecapacity. The EF estimates the amount of natural capital(measured in biologically productive area) needed to supportthe resource demand and waste absorption requirements of apopulation and is expressed in global hectares or hectares ofglobally standardized bioproductivity (Wackernagel et al.,2004a,b). Specifically, the EF “measures the human demandon nature by assessing howmuch biologically productive landand sea area is necessary to maintain a given consumptionpattern” (Wiedmann et al., 2006). In the basic calculation of theEF, consumption (categorized by food, services, transporta-tion, consumer goods, and housing) is divided by thepredetermined yield (biological productivity) by land type

1 Francheschi and Kahn (2003) separate natural and environ-mental resources and link sustainability to the continued abilityof environmental resources to provide ecological services, forwhich human capital and human-made capital are not goodsubstitutes.

1151E C O L O G I C A L E C O N O M I C S 6 8 ( 2 0 0 9 ) 1 1 4 9 – 1 1 5 9

including cropland, pasture, forest, built-up land, fisheries, and“energy” land. The ability of these areas to supply ecologicalgoods and services (i.e. the predetermined yield) depends onthe biophysical characteristics of the land (such as soil type,slope, and climate) in addition to socio-economic choices (suchas management decisions and technological inputs).

The EF requires that strong sustainability is maintained, asit assesses physical utilization of environmental resources (i.e.renewable factors of production and ecological services).However, the measurement is not all inclusive as it neglectsatmospheric ozone levels, and does not account for pollutantsthat are difficult to convert to land or water ecosystemequivalents, such as methane and sulfur (Rees, 2000).

The measurement, use, and interpretation of the EF havebeen extensively debated in the literature. A major strength ofthe EF is that it condenses a large array of environmental datainto a single measure, which can be easily compared to aregion's corresponding carrying capacity (Costanza, 2000).This is a relatively simple concept to understand and thereforecan be used to explain issues of sustainability to the generalpublic, and as a result of its rising notoriety has beenincreasingly applied within the literature. For example, themeasurement has been used to evaluate resource use acrossnations (White, 2007), methods have been developed toimprove its robustness across comparative means (Lenzenet al., 2007; Wiedmann and Lenzen, 2007), and to evaluate theimpact of tourism and trade agreements (Hong et al., 2007;Patterson et al., 2007).

While the ease of interpretation adds to the strengths ofthe EF, the assumptions that are made to convert thisencompassing measurement into a single unit have lead tomuch of its criticism. Noted weaknesses include the manysimplifying assumptions required to convert consumptiondata into land area. Specifically, Ayres (2000) faults the energyequivalence assumption used to convert energy flows intoland area, while Van Kooten and Bulte (2000) note severalweaknesses with aggregation, discounting, and sustainability,and take the position that the authors of the EF never present aclear and scientifically rigorous definition of the EF. Similarcriticism focuses on the conversion of energy into land used toabsorb CO2 emissions, as there are several ways to compen-sate for CO2 emissions outside of forest absorption. Finally, theEF, despite its ability to pinpoint areas under environmentalpressure, provides no guidelines for environmental policy(Nijkamp et al., 2004).

Despite these shortcomings, it is important to note that anyaggregate indicator of environmental quality will have bothstrengths and weaknesses (as for example, measures ofaggregate economic output or the price level suffer fromspecific problems). In this paper, we choose the EF as anaggregate measure of environmental quality because itslimitations are well-known, it is a widely referencedmeasure-ment of sustainability (Nijkamp et al., 2004; Haberl et al., 2001),and has been adopted by a growing number of governmentauthorities, agencies, and policy makers as a measure ofecological performance (Wiedmann et al., 2006). An alterna-tive approach would be to devise our own index of environ-mental quality, but in so doing, wewould introduce ameasurethat has not benefited from the normal scrutiny of theproperties and limitations of the indicator.

3. Necessary conditions for sustainability

One cannot analyze ecological degradation over time withoutaddressing the issue of sustainability and building a workingdefinition to incorporate in the analysis. The literature onsustainability has defined two concepts: weak and strongsustainability. Weak sustainability is an economic principlerequiring that productive capacity not decline over time. Onthe other hand, strong sustainability, having evolved from theecological economics perspective, requires that the total stockof natural capital not decline over time (Costanza and Daly,1992; Hediger, 1999). The debate on whether weak or strongsustainability should provide the foundation for public policyreflects differences in opinion regarding the degree to whichnatural capital can be substituted for human and physicalcapital (Cabeza Gutes, 1996). Proponents of strong sustain-ability believe that natural capital is unique and plays animportant role in humanwelfare, and thus cannot be replaced(Barbier, 2005). Furthermore, the concepts of growth anddevelopment are clearly outlined within this literature.Accordingly, time infinite growth cannot be sustainable on afinite planet as it is accomplished through the use of naturalresources. On the other hand, development occurs throughimprovements in efficiency and therefore can lead thesustainable use of resources over time (Costanza and Daly,1992).1

Regardless of one's preferred concept of sustainability, theliterature on sustainable development and growth theoryprovides a number of interesting insights for the long-runpath of the EF. While the sustainability of long-run economicgrowth subject to non-renewable resource constraints hasinterested economists for more than two centuries (see forexample Malthus, 1789), it was not until the energy crises ofthe 1970s that economists rigorously analyzed the affects ofnatural resource scarcity on growth and development withinthe context of dynamic, general equilibrium models (seeSolow, 1974; Stiglitz, 1974 among others). Their findings werestraightforward: so long as the reproducible factor of produc-tion (i.e. physical or man-made capital) is sufficiently sub-stitutable for the non-renewable factor, long-run balancedgrowth (i.e. per capita output growing en infinitum at a constantrate) is possible. Themajor drawback with this research is thatit ignores the impact production has on the state of theenvironment. Addressing this deficiency, Stokey (1998) buildsa model with pollution-generating output and a governmentthat imposes progressively stringent emission regulations(achieved vis-à-vis costly abatement). She finds that even inthis context, sustainable balanced growth is possible provid-ing a sufficiently high rate of return on capital, giving rise to anoutput path of pollution that follows an inverted “U” shapedpattern consistent with the EKC. These results, despite theiradmittedly narrow focus, provide us with the key insight tounderstanding the long-run trajectory of the EF if both strong

Table 1 – Average per capita GDP and footprint by country

Country GDP EF Country GDP EF Country GDP EF

Afghanistan 1713 0.22 Ghana 1071 0.93 Nigeria 1051 1.23Albania 2967 1.26 Greece 10,641 3.19 Norway 20,842 4.69Algeria 4866 1.28 Guatemala 3448 1.03 Pakistan 1735 0.64Angola 1975 0.82 Guinea 2488 1.09 Panama 5484 1.74Argentina 9827 2.81 Guinea–Bissau 623 0.84 Papua New Guinea 3671 1.71Armenia 3444 0.98 Haiti 2040 0.69 Paraguay 4199 1.98Australia 18,419 6.85 Honduras 2142 1.34 Peru 4308 0.98Austria 18,219 3.92 Hungary 9191 3.88 Philippines 2981 0.97Azerbaijan 3060 1.53 India 1606 0.74 Poland 6412 4.26Bangladesh 1544 0.51 Indonesia 2338 0.95 Portugal 10,618 2.94Belarus 9487 3.40 Iran 5378 1.63 Romania 4441 2.92Belgium & Luxembourg 17,411 4.71 Iraq 2278 0.91 Russia 9321 4.48Benin 1125 0.99 Ireland 12,409 3.98 Rwanda 1044 0.87Bolivia 2770 1.23 Israel 14,886 3.81 Saudi Arabia 20,806 3.80Bosnia and Herzegovina 2415 1.69 Italy 15,855 3.17 Senegal 1472 1.42Botswana 4345 1.44 Jamaica 4243 1.65 Serbia and Montenegro 2353 2.38Brazil 5743 1.88 Japan 16,201 3.64 Sierra Leone 1131 0.92Bulgaria 7385 3.07 Jordan 4218 1.46 Slovak Republic 9057 3.03Burkina Faso 778 1.03 Kazakhstan 7197 3.59 Slovenia 16,449 2.85Burundi 829 0.88 Kenya 1245 0.91 Somalia 1033 0.57Cambodia 537 0.74 Korea, Dem. Rep. 1222 1.99 South Africa 7119 2.60Cameroon 2364 0.99 Korea, Rep. of 7082 2.17 Spain 12,825 3.20Canada 18,860 6.78 Kuwait 31,830 6.32 Sri Lanka 2233 0.82Central African Rep. 968 0.94 Kyrgyzstan 3154 1.40 Sudan 1080 1.04Chad 892 1.12 Laos 1136 0.91 Swaziland 6188 1.20Chile 7291 1.66 Latvia 8370 2.99 Sweden 18,696 5.25China 1508 1.19 Lebanon 5083 2.50 Switzerland 23,621 4.56Colombia 4658 1.25 Lesotho 1155 1.00 Syria 1603 1.42Congo, Dem. Rep. 906 0.72 Liberia 1162 0.92 Tajikistan 1697 0.73Congo, Republic of 1842 0.91 Libya 10,335 3.28 Tanzania 586 0.85Costa Rica 6378 1.88 Lithuania 8737 3.88 Thailand 3603 1.06Cote d`Ivoire 2044 1.02 Macedonia 4972 2.35 Togo 1048 0.99Croatia 8241 2.23 Madagascar 1072 0.91 Trinidad &Tobago 10,421 2.40Cuba 5014 1.57 Malawi 662 0.74 Tunisia 4399 1.31Czech Republic 13,064 4.95 Malaysia 5805 1.80 Turkey 4009 1.97Denmark 20,097 5.15 Mali 818 1.01 Turkmenistan 6811 2.85Dominican Republic 4066 1.22 Mauritania 1330 1.23 Uganda 894 1.34Ecuador 3968 1.28 Mauritius 8276 1.24 Ukraine 5449 2.94Egypt 2812 1.15 Mexico 6320 2.04 United Arab Emirates 29,267 7.92El Salvador 3970 0.98 Moldova 2539 1.52 United Kingdom 16,992 4.91Eritrea 606 0.77 Mongolia 1456 3.50 United States 23,351 8.16Estonia 9973 5.07 Morocco 3021 0.89 Uruguay 7804 2.80Ethiopia 661 1.30 Mozambique 1043 0.71 Uzbekistan 3475 1.83Finland 15,785 5.37 Namibia 5067 1.18 Venezuela 7761 2.44France 17,697 4.49 Nepal 1033 0.69 Vietnam 1905 0.69Gabon 13,674 1.34 Netherlands 18,468 3.92 Yemen 998 0.77Gambia, The 878 1.13 New Zealand 16,595 4.60 Zambia 1135 0.85Georgia 3601 1.14 Nicaragua 4921 1.36 Zimbabwe 2985 1.06Germany 19,626 4.90 Niger 1041 1.38

Note: Footprints are measured in standardized global hectares (g ha); GDP in PPP-adjusted (2000) international dollars.


sustainability and balanced growth are be achieved: naturalcapital currently used in production must be replaced (in thelong run) by reproducible factors of production. In order forstrong sustainability to hold, total demands placed on theecosystem or use of natural resources (N) in a given periodcannot exceed the planet's regenerative capacity or the totalstock of natural resources R(t) during the same period:

N tð ÞVR tð Þ ð3:1Þ

Building on this necessary condition, assume that naturalcapital can be partitioned into two components: natural

capital used in environmental services, including necessarylife support and ecological services, Nenv, and the naturalcapital required to produce current output (expressed as theproduct of NY (natural capital per real dollar of output) and Y(t)(GDP)). In other words, at any time period, natural capital canbe divided as follows:

N tð Þ ¼ Nenv tð Þ þ NY tð ÞdY tð Þ ð3:2Þ

Holding natural capital used in non-production activitiesconstant, Nenv(t) =Nenv (which is consistent with strong

Table 2 – Global and income-specific footprint averages

Period World Poor nations Middle income nations Rich nations

EcologicalFootprint

Per CapitaGDP

Population EcologicalFootprint

Per CapitaGDP

EcologicalFootprint

Per CapitaGDP

EcologicalFootprint

Per CapitaGDP

1961–1965 1.70 3414 2.54 1.17 911 1.23 2430 3.84 10,3201966–1970 1.85 4014 2.89 1.11 915 1.22 2739 4.60 12,7011971–1975 2.02 4670 3.48 1.09 1,041 1.23 3153 5.36 14,8951976–1980 2.05 5127 3.83 1.07 1064 1.28 3600 5.51 16,6221981–1985 1.97 5344 4.19 1.04 986 1.25 3773 5.21 17,8101986–1990 2.04 5904 4.60 0.97 982 1.26 3984 5.73 20,3371991–1995 2.13 6340 5.33 0.96 1007 1.29 4184 5.83 21,9441996–2000 2.13 7065 5.83 0.97 1067 1.31 4597 6.14 24,656

Notes1) Figures are period averages.2) Ecological footprints are per capita g ha.3) GDP is expressed in $I 2000.4) Population is in billions.


sustainability), and expressing Eq. (3.2) in per capita termsyields the following identity:

N tð Þpop tð Þ ¼ Nenv þNY tð Þd Y tð Þ

pop tð Þ� �

ð3:3Þ

It is straightforward to demonstrate that a necessarycondition for strong sustainability and balanced economicgrowth is that NY (t)→0 as t approaches infinity. Supposenot, i.e. NY (t)→NYN0 in the limit, then Eq.(3.3) becomesunbounded:

limtYl

N tð Þpop tð Þ

� �¼ Nenv þ NY tð Þd lim

tYl

Y tð Þpop tð Þ

� �Yl ð3:4Þ

Thus, strong sustainability and balanced growth requirethat progressively less natural capital be used per unit ofoutput, a result which clearly echoes Solow (1974), Stiglitz(1974), and Stokey (1998). The only way that the need fornatural capital in the production process could subside, ofcourse, is if it is replaced by some other factor of production.While this finding is explicit in the Solow (1974), Stiglitz (1974),and Stokey, it is only implicit in our simple derivation. If wetake it as given that natural capital usage has been historicallyrising, then the required decline in natural capital (if bothstrong sustainability and balanced growth are to be achieved)gives rise to an inverted “U” shaped relationship between theper capita levels of output and natural capital. The empiricalimplications are clear: the Ecological Footprint (EF) mustfollow an inverted “U” shaped pattern if both strong sustain-ability and sustained economic growth are being simulta-neously achieved.2

2 Note, an empirical Kuznets Curve between EF and per capitaGDP is not a sufficient condition for sustainability (i.e. sustain-ability would require that EF shrink as fast as the populationgrows and that the level of the EF at an given point in time bebelow per capita biocapacity).

4. Data

The data used in this analysis include a panel of the EcologicalFootprint (a proxy for environmental capital) for 146 countriescovering 40 year s (1961 to 2000)3 as defined in our theoreticalmodel, and real per capita GDP (expressed in chain-weighted,PPP-adjusted 2000 international dollars (denoted $I 2000))from the Penn World Tables v 6.2 (see Heston et al., 2006 formore details).4 The list of countries included in the panel,along with sample-average values of real per capita GDP andthe EF are provided in Table 1. The EF is measured instandardized global hectares (g ha) per capita and is con-structed by aggregating seven ecological components.5 Thefirst component, built-up land, measures the land area usedfor buildings and permanent structures and/or eroded anddegraded land. The secondmeasure, cropland, measures landarea used to cultivate crops consumed by the population, inaddition to those fed to poultry and pigs. The third compo-nent, fisheries, includes the ocean area used to generatecurrent marine harvests. The fourth component, pasture,includes the land area used to maintain livestock. The fifthand sixth components include, forest land (managed andunmanaged forest), and land area used to produce timber, fuelwood, charcoal, paper, and pulp. Finally, the seventh compo-nent captures the environmental consequences of consumingfossil fuels, hydroelectricity, and other renewable energysources. Specifically, this measure includes the land arearequired to absorb all CO2 emissions resulting from the directuse of coal, oil, gas, and the indirect use of the consumption ofelectricity, public transportation, manufactured goods orother services, and is divided between 1) CO2—the area used

3 Global Footprint Network (2006). As is common practice inmacroeconomic growth studies, we excluded major oil exportingnations from the analysis as such nations' economies are notdriven by normal production sectors/industries, by rather bycommodity exports.4 Complete data are available upon request from the authors.5 Precise definitions of each of the foregoing can be found in

Haberl et al. (2001).


to sequester carbon dioxide emissions and 2) Nuclear—thearea needed to sequester carbon dioxide emissions fromnuclear power plants if they produced emissions at the samerate (per KWH) as traditional fossil-fuel plants.6

The average global EF was 1.7 g ha per capita in the early1960s, increasing to 2.13 g ha per capita by 2000. During thesame time period, average global GDP per capita grew from$3414 to $7065 ($I 2000), and world population from 2.5 to5.8 billion people (see Table 2). As clearly seen in Table 2, thisincrease in the global EF over roughly the last half century isthe result of middle income and rich nations placing ever-greater demands on the environment. Specifically, the EFincreased from 1.23 to 1.31 g ha per capita in middle incomenations, and ballooned from 3.84 to 6.14 g ha in wealthynations. In contrast, poor nations experienced a decline intheir ecological footprint (albeit small) over the same fortyyear timeframe.

5. Environmental Kuznets Curve model andestimation

Following both the EKC and original Kuznets Curve literature,we first estimate a variety of baseline quadratic EKC modelsusing OLS, and later re-estimate the same models using twostage least squares (2SLS) to correct for any endogeneity. Theresults are surprisingly robust, with only the agriculturalcomponent of the EF exhibiting any signs of an inverted “U”shaped relationship with output.7 Correcting for serial correla-tion in the baseline model's residuals, we next introduce andestimate a dynamic panel version of the EKC model using theArellano and Bond (1991) estimation procedure (henceforthAB). Our AB estimation results support the general finding thatthere is little empirical evidence of an EKC in the ecologicalfootprint or its constituent components. Interestingly, wediscover that when energy is removed from the EF, astatistically significant EKC emerges with a turning point of$652 ($I 2000). To determine how much energy consumptionwould have to be cut in order for the overall EF to be consistentwith sustainability, we conduct a sensitivity exercise wherebythe energy component of the EF is not completely eliminatedfrom the EF time series, but rather is scaled down by aconstant proportion. We find that energy consumption would

6 This is an odd component of the EF given that nuclear plantsproduce no CO2 emissions. The justification commonly given forthis accounting rule is that nuclear energy produces radioactivewaste, and thus nuclear power production is penalized in the CO2

accounting measure.7 Critics of the traditional empirical techniques used in the EKC

(see for exampleMüller-Furstenberger and Wagnerb, 2007) pointout that specifying a quadratic relationship between pollutionand output imposes strong parametric restrictions and impliesthat all nations in the panel possess the same “turning point.”While we acknowledge these criticisms, the use of spline orsemiparametric methods would not eliminate the “commonturning point” problem. Moreover, the temporal dimension ofour dataset is too short to estimate separate, nation-specificquadratic relationships between output and the EF. Thus, weadopt the commonly accepted practice of estimating a singlequadratic relationship.

have to be cut by 50% in order for the EF to display astatistically significant EKC relationship. Further details onthese findings follow below.

5.1. Baseline panel model

To determine the functional form of our empirical model, wedraw from both the EKC and original Kuznets Curve literature(e.g. Ahluwalia, 1976; Barro, 2000; among others). Amajority ofthe papers on the EKC follow the original Kuznets Curveliterature by including log GDP in quadratic form (see Bimonte,2002; Mason and Swanson, 2003; Perman and Stern, 2003;Halkos, 2003; Martinez-Zarzoso and Bengochea-Morancho,2003; Harbaugh et al., 2002 for additional sources); whileothers add cubic functions of log GDP to test for additionalthreshold effects (Cole, 2003; List and Gallet, 1999; Rupasinghaet al., 2004); and still others add additional control factors suchas energy intensity (Agras and Chapman, 1999), populationdensity (Selden and Song, 1994), among others (Cavlovic et al.,2000; Khanna, 2002; Hill and Magnani, 2002). Following thetraditional approach, we include log GDP in quadratic form inthe following fixed-effects panel model:

EFit ¼ b1yit þ b2y2it þ ai þ gt þ eit ð5:1Þ

where EFit is the per capita ecological footprint (g ha) incountry i during period t, yit is the log of real per capita GDP ($I2000), αi is a nation-specific fixed-effect, ηt is a period-specificeffect, and εit is initially assumed to be an i.i.d. stochasticshock.

The unbalanced panel consists of 146 nations spanningeight, 5-year time periods (1961–1965, 1966–1970,…, 1995–2000). Using standard, fixed-effect OLS estimation methods,we estimate Eq. (3.1) using various measures of the EF and itscomponents (i.e. timber, pasture, etc.).8 One critical issue thatmust be addressed is the possible bias resulting fromendogeneity between the EF and GDP. This stems from thefact that GDP is a function of natural capital (and otheraggregate factors of production), and the EF is an imperfectproxy for the use of natural capital. To address this issue, weuse two stage least squares (2SLS) estimation with laggedregressors as instruments, which is acceptable given theassumptions of our regressionmodel (i.e. no lagged dependentvariables and i.i.d. stochastic errors).9

In order to construct a benchmark for later comparison, wefirst estimate the EKC and its subcategories with OLS, with the

8 Alternative estimation techniques could have been employedat this stage, including random effects estimation. Randomeffects estimation may produce more efficient estimates, butcarries the cost of greater likelihood of bias/inconsistency (e.g. ifthe country-specific effects are correlated with output (whichthey almost surely are), random effect estimation is biased andinconsistent). To demonstrate this, we conduct a Hausmanspecification test (under the null that the common random andfixed effects coefficient values are identical) and reject the null atany standard level of significance (the chi-squared distributedtest statistic is 71.5). While not as efficient, fixed effect estimationis unbiased, consistent, and generally more robust than randomeffects.9 In Section 5.2 we will relax this assumption and use a more

appropriate dynamic panel model.

Table 3 – Baseline Environmental Kuznets Curve estimates

Baseline EKC model (5.1) estimates

OLS coefficient estimates

Dependent footprint variable: regressors Total Built Crop CO2 Fish Fuelwood Pasture Timber

log rgdp −4.520⁎⁎⁎ 0.015 0.711⁎⁎⁎ −3.959⁎⁎⁎ −0.023 −0.187⁎⁎ 0.193 −0.273⁎⁎log rgdp squared 0.328⁎⁎⁎ −0.0003 −0.044⁎⁎⁎ 0.280⁎⁎⁎ 0.003 0.012⁎⁎ −0.011 0.021⁎⁎⁎Observations 904 904 904 904 904 904 904 904R2 0.970 0.982 0.932 0.944 0.788 0.923 0.870 0.971EKC No No Yes No No No No NoTurning point ($I 2000) – – 3028 – – – – –

2SLS coefficient estimates

log rgdp −4.190⁎⁎⁎ −0.015 0.875⁎⁎⁎ −3.477⁎⁎⁎ −0.006 −0.214⁎⁎⁎ 0.136 −0.057log rgdp squared 0.302⁎⁎⁎ 0.001 −0.056⁎⁎⁎ 0.247⁎⁎⁎ 0.001 0.014⁎⁎⁎ −0.007 0.006Observations 765 765 765 765 765 765 765 765R2 0.978 0.986 0.948 0.962 0.803 0.940 0.905 0.972EKC No No Yes No No No No NoTurning point ($I 2000) – – 2647 – – – – –

Notes1) ⁎⁎⁎, ⁎⁎, ⁎ refer to 1%, 5%, and 10% levels of significance respectively.2) Each regression included time and cross-section dummies.3) The 2SLS instruments consist of dummies and one-period lags of the log rgdp and log rgdp squared.


results reported in Table 3. Empirical evidence only supportsthe existence of an EF Kuznets Curve for agricultural land (i.e.“Crops”), with a turning points of $3028 ($I 2000). Becauseof potential endogeneity problems between GDP and the EF,Eq.(5.1) is re-estimated using 2SLS.10 The results, also reportedin Table 3, change very little. The only EF series with astatistically significant Kuznets Curve relationship is agricul-tural land (i.e. “Crops”), with a turning point of $2647 ($I 2000).Thus, there is robust and strong empirical evidence that thereexists a Kuznets Curve relationship between crop land and percapita output, but very little evidence in support of a broaderKuznets Curve. A glance at the global production (tones) andharvested area (ha) time series from United Nation's Food andAgriculture Organization (FAO) provides insight into thisfinding (see Fig. 1). Between 1961 and 2000, world agriculturalproduction grew by an astounding 127%, while harvested areagrew by only 20%.

5.2. Dynamic panel model

The baseline model (Eq. (5.1) appears to possess seriallycorrelated residuals, as evidenced by DurbinWatson statisticsfrom the OLS regressions. The Durbin Watson statistics rangein value from a low of 0.35 (for the pasture component) to ahigh of 1.65 (for the CO2 component), all of which lie below thelower 5% critical value of 1.89. Endogeneity notwithstanding,the OLS estimates of Eq. (5.1) are still unbiased and consistentdespite the presence of serial correlation, while the 2SLSestimates are biased (as wemust assume that the error term is

10 One-period lagged values of GDP and GDP squared and thecontemporaneous dummies serve as instruments. The laggedregressors are good instruments because GDP is a highly persistentprocess.

i.i.d. in order to use lagged regressors as instruments). In orderto overcome this problem, we introduce a dynamic panelmodel which explicitly captures the autocorrelation in the EFseries:

EFit ¼ b1EFit�1 þ b2yit�1 þ b3y2it�1 þ ai þ gt þ eit ð5:2Þ

Following the estimation procedure of Arellano and Bond(1991), unbiased and consistent estimates of the coefficients inEq.(5.2) are obtained and reported in Table 4. The results areremarkably similar to both the OLS and 2SLS estimates,finding a statistically significant EKC in neither the overallEF series nor any of its components (save pasture and timber,

Fig. 1 –FAO global production and harvested area time series.

Table 4 – Dynamic Environmental Kuznets Curve estimates

Arellano and Bond coefficient estimates

Dependent footprint variable: lagged regressors Total Built Crop CO2 Fish Fuelwood Pasture Timber

Footprint 0.568⁎⁎⁎ 0.415⁎⁎⁎ 0.755⁎⁎⁎ −0.332⁎⁎⁎ 0.448⁎⁎⁎ 0.769⁎⁎⁎ 0.779⁎⁎⁎ 0.127⁎⁎⁎log GDP −0.452⁎⁎⁎ −0.075⁎⁎⁎ 0.057 −1.577⁎⁎⁎ −0.124⁎⁎⁎ −0.046⁎⁎⁎ 0.075⁎⁎⁎ 0.165⁎⁎⁎log GDP squared 0.029⁎⁎⁎ 0.004⁎⁎⁎ −0.007⁎⁎ 0.108⁎⁎⁎ 0.002 0.004⁎⁎⁎ −0.004⁎⁎⁎ −0.013⁎⁎⁎Observations 628 628 628 628 628 628 628 628EKC No No No No No No Yes YesTurning point ($I 2000) – – – – – – 8153 656

Notes1) ⁎⁎⁎, ⁎⁎, ⁎ refer to 1%, 5%, and 10% levels of significance respectively.2) GDP and GDP squared measured in real terms.3) Each regression includes period dummies.


which have turning points of $8153 and $918 ($I 2000)respectively).11

Returning to the “Total” EF series, it appears that most ofthe growth in that series is due to energy consumption (CO2

accounts for up to 50% of the EF measurement). Interestingly,log GDP and log GDP squared are found to be significant, but inthe opposite direction that the EKC would predict. In otherwords, the EF is found to be increasing at an increasing ratewith growth and development. To illustrate this relationship,Fig. 2 plots the overall “Total” EF series for “rich,” “middleincome,” and “poor” nations.12 Clearly, the overall EFincreased sharply in rich countries (by approximately 60%),and increased more mildly in middle income nations (byapproximately 7%), while poor nations' actually experienced a17% decline in total EF between 1961 and 2000. This contrastssharply with total EF when the energy series are removed, asreported in Fig. 3. In this case, all three income groupsexperienced a near continuous decline in the EF between1961 and 2000. Rich nations total EF (less energy) declined 18%,while middle income and poor nations experienced a 26% and27% decline respectively. The growth in energy consumptiontherefore explains the large increase in the ecological foot-prints in both rich and middle income countries. Conse-quently, the dynamic EKC model (Eq. (5.2)) is re-estimatedusing total EF (less energy) as the dependent variable (Table 5).According to the regression results, there is a EKC relationshipbetween total EF (less energy) and per capita GDP, with aturning point of $652 ($I 2000). As of 2003, 176 of 182 (or 97%) ofnations had per capita GDP levels in excess of this threshold,which is consistent with the overall downward trend over thepast 40 years in this series across all income groups.13

5.3. Energy sensitivity analysis

It is clear that energy use dominates themeasure of ecologicalfootprint, subjecting the measure to potential criticism. An

11 The “Crop” series was marginally significant, with a negativeand statistically significant coefficient on log GDP squared, but aninsignificant coefficient on log GDP.12 The “rich” nations consist of those nations in the top third ofthe global income distribution over the entire time span of thepanel. Likewise, the “middle income” and “poor” nations consistof the nations in the middle-third and bottom third of the globalincome distribution over the entire time span of the panel.13 Based on Penn World v 6.2 data , see Heston et al. (2006).

interesting exercise would be to diminish the importance ofenergy in the EF and see if doing so would generate atraditionally shaped EKC. To accomplish this we, construct anew counterfactual dependent variable, denoted EFitλ:

EFkituEF�energyit þ kEFenergyit ; ka 0;1½ � ð5:3Þ

where EFit−energy is the total ecological footprint less the energycomponents, and EFit−energy consists of the energy componentsof the ecological footprint. The scale factor, λ, is varied from0.10 to 0.90, in increments of 0.10, producing nine footprintseries (i.e. EFit0.10, EFit0.20,…, EFit0.90). Employing each of the nineforegoing EF series, model(5.2) is re-estimated using ABmethodology. According to the results, provided in Table 6,energy consumption levels would have to be cut by 50% acrossthe board before a statistically significant EKC emerges fromthe estimation process. In other words, even if the impact ofenergy use was overstated in the EF by 100%, the EF would notconform to the traditional EKC hypothesis. The estimatedturning points are also consistent, ranging from a low of $862to $955 ($I 2000) of real per capita GDP. This is graphicallyrepresented in Fig. 4, which plots the estimated relationship

Fig. 2 –Total footprint by income group. Note: The periods are5 years in length, beginning with period 1 which spans theyears 1961 to 1965, and continuing in this manner ends withperiod 8, which spans the years 1996 to 2000.

Table 6 – Energy sensitivity analysis

Energy sensitivity analysis

Lamda (λ) Arellano and Bond estimationmodel (5.2) coefficients

EKC Turningpoint

log GDP log GDP squared

0.1 0.251⁎⁎⁎ −0.019⁎⁎⁎ Yes 8620.2 0.270⁎⁎⁎ −0.020⁎⁎⁎ Yes 9620.3 0.266⁎⁎⁎ −0.019⁎⁎⁎ Yes 9710.4 0.245⁎⁎⁎ −0.018⁎⁎⁎ Yes 9810.5 0.203⁎⁎⁎ −0.015⁎⁎⁎ Yes 9550.6 0.129 −0.010⁎ No –0.7 0.021 −0.002 No –0.8 −0.115 0.007 No –0.9 −0.262⁎⁎ 0.016⁎⁎ No –

Notes1) ⁎⁎⁎, ⁎⁎, ⁎ refer to 1%, 5%, and 10% levels of significance respectively.2) Lagged dependent variable and period dummyestimates not reported.

Fig. 3 –Total footprint net of energy consumption by incomegroup. Note: The periods are 5 years in length, beginningwith period 1 which spans the years 1961 to 1965, andcontinuing in this manner ends with period 8, which spansthe years 1996 to 2000.


between EF0.10, EF0.50 and EF0.90 and log per capita output usingthe coefficient estimates provided in Table 6.

6. Conclusions

Departing from the current Environmental Kuznets Curveliterature which typically uses pollution as a measure ofenvironmental quality, this paper empirically investigates theEKC using an aggregate index of environmental degradationcalled the Ecological Footprint. We find no evidence of an EKCrelationship between per capita output and the EF or any of itssubcomponents, with the exception of land in agriculture, andto a lesser extent land used in pasture or for timber. The EF isfound to increase at an increasing rate for both rich and poornations on average, with much of the increase attributed toenergy use (i.e. CO2). When the energy components of the EFare removed, the resulting series yields a statistically signifi-

Table 5 – Kuznets Curve Estimates for EF less energycomponents

Dependent variable: total EF lessenergy components

Arellano and Bondcoefficient estimates

Regressors Model 5.2

log GDP 0.207⁎⁎⁎log GDP squared −0.016⁎⁎⁎Observations 628EKC YesTurning point ($I 2000) 652

Notes1) ⁎⁎⁎, ⁎⁎, ⁎ refer to 1%, 5%, and 10% levels of significance respectively.2) GDP and GDP squared measured in real terms.3) Model 5.2 uses one-period lags of the reported regressors.4) Coefficient estimates of the lagged dependent variable not.

cant EKC with a turning point of approximately $652 ($I 2000).More importantly, we find that the energy use componentmust be discounted by a full 50% before a traditional EKC isfound.

The literature has found evidence of an EKC relationshipbetween pollution and economic development, suggestingthat comprehensive environmental policy is not necessary fordeveloping nations, as growth is expected to improve envir-onmental quality over time. Our empirical results show that ifa broadermeasure of the loss of environmental capital is used,there is little evidence of an EKC. Going one step further, wefind that energy consumption is, by and large, the majorculprit behind this result, and that substantial cuts in CO2

emissions are necessary to bring about an EKC relationshipbetween the ecological footprint and economic growth.Together these results suggest that growth alone will notserve as a solution to environmental problems. Instead, the

Fig. 4 –Estimated relationship between the EF and log percapita output.


impacts of energy usage must be included in developmentpolicy if sustainability is to be achieved.

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