ranking national environmental environmental quality plays

CHAPTER 2.1

Ranking National EnvironmentalRegulation and Performance: A Leading Indicator of FutureCompetitiveness?DANIEL C. ESTY, Yale University

MICHAEL E. PORTER, Harvard Business School and Director,

Institute for Strategy and Competitiveness

Environmental performance, encompassing the control ofpollution and stewardship of natural resources, is of grow-ing concern in both advanced and developing economies.Environmental quality plays a major role in quality of life,with a direct impact on the health and safety of a nation’scitizens as well as its attractiveness as a place to live. It isbecoming increasingly clear, however, that environmentalperformance has a further, more indirect, effect on livingstandards through its impact on a nation’s capacity to sus-tain economic growth.The ability to grow requiresresources and places strains on the environment that candrive up costs, especially in the longer run. But a growingbody of research suggests that economic competitivenessand environmental performance are compatible, if notmutually reinforcing. Low pollution and efficient energyuse are a sign of the highly productive use of resources.Policies that stimulate improvements in environmentalquality, then, may actually foster improvements in compet-itiveness that underpin a rising standard of living in thelong run.

Despite growing concern for the environment acrossalmost all countries, and protestors from Seattle to Genoademanding more emphasis on pollution control and natu-ral resource management, environmental policymakingremains more an art than a science. Statistical analyses ofthe determinants of environmental performance acrossnations have been rare—indeed, almost nonexistent.Research in the environmental realm has traditionallyrelied heavily on anecdotal evidence and case studies.There are precious little systematic data on which to baseenvironmental judgments at both the public policy andcorporate levels.This may explain why the environmentalfield remains mired in deep controversies over the bestpath forward, with debate often dominated by emotionalclaims and heated rhetoric.We believe that more sophisti-cated use of environmental indicators and statistical toolsto develop objective ways to gauge progress offer a con-structive way out of the current stasis.

This chapter builds on our previous effort to investi-gate statistically the causes of environmental performanceand to use the findings to rank countries in terms of envi-ronmental outcomes and environmental policies.i In par-ticular, we seek to explain differences in national environ-mental outcomes—as measured by levels of air pollution(particulates and SO2) and energy use—based on nationalpolicy choices in environmental regulation as well as inbroader economic, political, and legal structures.We alsoexplore empirically the question of whether strong envi-ronmental performance must come at the expense ofcompetitiveness and economic development, as traditional

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economic theory has suggested (Jaffee et al 1995). Morebroadly, we also aim to put environmental decision-making on a firmer analytic footing and to encourage further efforts to generate better data and improve statistical methods.

Although hampered by imperfect data, a lack of time-series data that would permit more definitive tests of causality, and the need to utilize relatively crude methods, we find substantial evidence that environmentalperformance varies systematically with both the quality of a country’s environmental regulatory regime and itsbroader economic and legal context.We utilize our modelto create a framework for measuring the quality of nation-al environmental regulation and to rank countries on both the quality of regulation and on environmental performance (see Table 8).We find a significant correlationbetween income and environmental performance,suggesting that alleviating

poverty should be seen as a priority for environmentalpolicymakers. However, dramatic differences in environ-mental performance occur among countries at similareconomic levels.This finding implies that environmentalimprovement is not merely a function of economic devel-opment but benefits from conscious policy choices. Ouranalysis suggests that a country’s broader economic, legal,and other institutional underpinnings are also important indetermining environmental performance. On the tradeoffbetween green and competitive, we find no evidence thatimproving environmental quality compromises economicprogress. In fact, strong environmental performanceappears to be positively correlated with competitiveness.

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Figure 1: Determinants of environmental performance

Stringency of Standards • Air regulation• Water regulation• Toxic waste regulation• Chemical regulation

Regulatory Structure • Options for compliance• Confusing and changing• Early or late• Compliance hurts or helps

competitiveness• Regulation adversarial or

cooperative

Subsidies • Government subsidies

Information • Tracking of ESI variables• Availability of Sustainable

development information• Number of sectoral EIA

guidelines• Number of environmental

strategies & action plans

Regulatory Enforcement • Enforcement• International agreements

Environmental Institutions • IUCN organizations per

million population• Membership in intergov-

ernmental environmentalorganizations

• Prevalence of ISO 14000

• Urban Particulate Concentration• Energy Usage per GDP unit• Urban SO2 Concentration

• GDP Per Capita• Growth Index• Current Competitiveness Index

Administrative Infrastructure• Civil liberties• Public sector competence• Government fairness in treatment of

private sector• Property rights• Independence of judiciary• Irregular payments• Sound legal framework• Regulatory burden• Level of administrative corruption• Honoring of Policies through government

Scientific & Technical Infrastructure • Scientists and engineers per capita• Country technology position• Institutions• Licensing of foreign technologies• Company R&D spending• Willingness to absorb new technology• Importance of innovation to revenue• Government purchase decision for

technology products

Environmental performance

Environmental regulatory regime

Economic competitiveness Economic and legal context

Modeling environmental performance and its causesWe employ three measures of environmental performance(environmental “output”) that are available with broadcountry coverage: the level of urban particulates, urbanSO2 concentrations, and energy usage per unit of GDP.ii

These measures constitute the dependent variables for theanalysis.

Building on theoretical work in the economic, legal,regulatory, and environmental domains, we then assembledata on policy variables that potentially determine envi-ronmental outcomes.iii The framework for the analysis isshown in Figure 1. Environmental performance is hypoth-esized to result from two broad sets of independent vari-ables. One set, which we term the environmental regulatoryregime, is comprised of measures of various aspects of acountry’s environmental regulatory system including stan-dards, implementation and enforcement mechanisms, andassociated institutions.These variables capture regulatoryelements that directly affect pollution control and naturalresource management.

The second set of independent variables, which weterm economic and legal context, are indicators of a country’smore general administrative, scientific, and technical insti-tutions and capabilities.These include measures of theextent of the rule of law, protection of property rights, andtechnological strength.The hypothesis is that a nation’senvironmental regulatory regime will be more effective inproducing the desired outcomes if the economic and legalcontext is sound. Hence context indirectly (but perhapsimportantly) determines environmental performance.

The dotted arrows in Figure 1 represent the finalstage of the analysis, in which we examine the connectionbetween environmental performance and economic suc-cess.We explore, in particular, the relationship betweenour environmental quality measures and GDP per capita,as well as the relationship between an index measuring theoverall environmental regulatory regime (the environmen-tal regulatory regime index (ERRI)) and GDP per capita.We also examine the relationship between the ERRI andthe Current Competitiveness Index reported in Chapter1.2.These relationships shed light on the longstandingdebate over the extent of the tradeoff between environ-mental progress and economic success—a question of particular interest in the developing world.

Environmental OutcomesEnvironmental output data are notoriously spotty, unreli-able, and uneven, as are data on the characteristics ofnational regulatory regimes. Hence, establishing a sufficientdatabase for a broad empirical analysis is no small under-taking.The performance measures used in this study aredrawn from data assembled for the World EconomicForum’s Environmental Sustainability Index (ESI)Project.iv

Three measures of environmental performanceemerge as reliable enough and available in a large enoughnumber of countries to utilize in our analysis.The first isurban particulate concentration, derived from World Bankand World Health Organization (WHO) data sources.Thismeasure provides the mean total suspended particulateconcentrations in the air (airborne dust) normalized by acountry’s urban population.A higher concentration indi-cates more pollution and thus worse air quality.

The second performance measure is mean SO2 con-centration normalized by urban population.This measureis also drawn from World Bank and WHO data.Again,higher figures represent worse air pollution.

The third environmental performance measure gauges energy efficiency. Using US Department of Energydata, we measure total energy consumption per unit of acountry’s GDP. Higher figures represent more energy consumed per unit of economic output and thus greaterenergy inefficiency. In comparing this measure acrosscountries, we need to account for the fact that Russia and the countries of the former Soviet bloc operated fordecades under an energy regime with prices set wellbelow market prices.This history has left a legacy of energy inefficiency in these countries that is only slowlybeing corrected.We therefore include a dummy variablein our model to control for this history, which proves tobe highly significant statistically.

Table 1 provides absolute rankings by country foreach of the three environmental performance measures.Urban particulate data are available for just 42 of the 75countries covered by the Global Competitiveness Report2001–2002 (GCR).The United States and the UnitedKingdom track particulates, but on a more refined basisthan the rest of the world; thus their particulate rankingsare not comparable, so they are therefore excluded fromthe urban particulate analysis.v Sweden and Norway are atthe top of the particulate ranking, with China andHonduras at the bottom.

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Table 1: Absolute environmental performance by country

Urban Particulate Concentration* (Per City Population)

Rank Country Annual Mean

1 Sweden 9.02 Norway 10.33 France 14.24 Iceland 24.05 New Zealand 27.36 Switzerland 30.77 Canada 31.38 Netherlands 40.09 Australia 43.210 Germany 43.311 Japan 43.612 Austria 45.713 Finland 49.914 Argentina 50.015 Portugal 50.416 Venezuela 53.017 Czech Republic 58.418 Denmark 61.019 Hungary 63.720 Slovak Republic 64.521 Spain 72.722 Romania 82.023 Korea 83.824 Italy 86.925 Malaysia 91.626 Latvia 100.027 Russia 100.028 Brazil 106.229 Lithuania 114.330 Colombia 120.031 Ecuador 125.732 Greece 178.033 Bulgaria 199.234 Philippines 200.035 Thailand 223.036 Costa Rica 244.537 Indonesia 271.038 Guatemala 272.339 India 277.540 Mexico 279.041 China 310.842 Honduras 320.0

* Not all data were available for all countries.

Urban SO2 Concentration* (Per City Population)

Rank Country Annual Mean

1 Argentina 1.022 Lithuania 2.103 New Zealand 3.494 Finland 4.385 Iceland 5.006 Sweden 5.237 Latvia 5.368 Norway 5.479 Denmark 7.0010 Portugal 9.2211 Netherlands 10.0012 Romania 10.0013 Spain 11.0014 Thailand 11.0015 Switzerland 11.3416 Germany 12.8017 Canada 12.8718 Australia 13.1719 Austria 13.2120 France 13.8921 United States 15.4322 Italy 15.5523 Ireland 18.8924 Singapore 20.0025 Malaysia 20.4926 Belgium 21.0227 Ecuador 21.5228 United Kingdom 21.9629 South Africa 22.3730 Slovak Republic 22.6631 Japan 24.3332 Czech Republic 27.3433 India 27.5534 Chile 29.0035 Philippines 33.0036 Venezuela 33.0037 Greece 34.0038 Hungary 37.3339 Costa Rica 38.8440 Korea 52.4141 Bulgaria 52.4542 Poland 54.7243 Egypt 69.0044 Mexico 74.0045 Brazil 75.7846 China 97.0747 Russia 97.55


Energy Usage (Per Mil. $ GDP)

Rank Country Bil. BTU

1 Denmark 4.842 Switzerland 5.193 Japan 6.554 Italy 6.665 Ireland 6.856 Austria 7.097 Germany 7.288 France 7.399 Finland 8.3710 United Kingdom 8.5911 Spain 8.7312 Honduras 8.9713 Mauritius 9.1114 Sweden 9.1415 Israel 9.9616 Peru 10.8117 Netherlands 11.0118 Slovenia 11.2619 Australia 11.4620 Guatemala 11.5221 Portugal 11.7722 Belgium 11.8323 Norway 12.1724 Argentina 12.2225 Uruguay 12.8626 Greece 12.9527 Bangladesh 13.1528 United States 13.4129 Sri Lanka 13.7030 El Salvador 13.7531 Brazil 14.0132 Iceland 14.4933 New Zealand 15.0934 Paraguay 15.3235 Estonia 16.0936 Costa Rica 16.1337 Chile 16.6338 Canada 17.5439 Mexico 17.7240 Korea 17.9141 Bolivia 18.4142 Dominican Republic 18.6843 Panama 18.7044 Thailand 19.2945 Philippines 19.7446 Singapore 20.4147 Zimbabwe 22.3448 Malaysia 22.8849 Indonesia 22.9650 Nigeria 23.6651 Colombia 23.9852 Latvia 25.0153 Ecuador 27.5754 India 28.1355 Egypt 31.0356 Hungary 32.2957 Jordan 34.5258 Jamaica 35.5859 Nicaragua 36.4660 South Africa 37.9261 China 39.1062 Venezuela 44.1163 Poland 45.0564 Lithuania 54.9265 Czech Republic 56.2266 Romania 58.3967 Bulgaria 60.7168 Slovak Republic 63.9569 Vietnam 64.5770 Russia 74.1971 Ukraine 96.53

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Argentina

Australia

Austria

Brazil

Bulgaria

Chile

China

Costa Rica

Czech Republic

Denmark

Egypt

Finland

Germany

Greece

India

Italy

Japan

Korea

Latvia

Malaysia

Mexico

NetherlandsNorway

Philippines

Russia

CanadaEcuador

France

Hungary

IcelandLithuania New Zealand

PortugalRomania

Slovak Republic

Spain

Sweden

SwitzerlandThailand

BelgiumIreland

Poland

SingaporeSouth Africa United Kingdom

United States

Venezuela

y = -16.515Ln(x) + 182.61R2 = 0.2473

0

20

40

60

80

100

120

0 5,000 10,000 15,000 20,000 25,000 30,000 35,000

2000 GDP per capita

Urb

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(Per

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Figure 3: Relationship between urban SO2 concentration and GDP per capita

y = –16.515Ln(x) + 182.61R 2 = 0.2473

VenezuelaArgentina

AustraliaAustria

Brazil

Bulgaria

China

Costa Rica

Czech Republic

DenmarkFinland

Germany

Greece

Guatemala

Honduras

IndiaIndonesia

Italy

Japan

Korea

LatviaMalaysia

Mexico

Netherlands Norway

Philippines

Russia

Canada

ColombiaEcuador

France

Hungary

Iceland

Lithuania

New Zealand

Portugal

Romania

Slovak RepublicSpain

Sweden

Switzerland

Thailand y = -92.822Ln(x) + 977.24R2 = 0.6283

0

50

100

150

200

250

300

350

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Urb

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Figure 2: Relationship between urban particulate concentration and GDP per capita

y = –92.822Ln(x) + 977.24R 2 = 0.6283

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Table 2: Energy usage relative to expected given GDP per capita, listed by income groups

Nigeria

Bangladesh

Vietnam

Nicaragua

India

Bolivia

Honduras

Zimbabwe IndonesiEcuador

Sri Lanka

Egypt

Jamaica

Guatemala

China

Philippines

Jordan

ParaguayEl Salvador

Peru

Venezuela

Colombia

Dominican Republic

Panama

ThailandLatvia

Brazil

Uruguay

Mexico

Malaysia

EstoniaChile

South Africa

Costa RicaMauritius

ArgentinaGreece

PortugalSlovenia

Korea

Spain

Israel

New Zealand

Singapore

United Kingdom

Italy

Sweden

France

Finland

Germany

IrelandNetherlands

Australia

JapanAustria

Belgium

Denmark

Canada

Switzerland

IcelandNorway

United States

Ukraine

Bulgaria RomaniaLithuania

Russia

Poland

Slovak Republic

Hungary

Czech Republic

0

20

40

60

80

100

120

0 5000 10000 15000 20000 25000 30000 35000 40000

2000 GDP Per Capita

Ener

gy U

sage

(Per

GD

P U

nit) Y=84.76+41.57*dum

7.36*Ln(x)R2=0.71

Note: Former Soviet Block

Figure 4: Relationship between energy usage and GDP per capita (log model)

y = 84.76 + 41.57*dum –7.36*Ln(x)R 2 = 0.71

Low-Income Countries (≤ $6,500)

Rank Country Residual

1 Honduras –18.292 Bangladesh –17.483 Guatemala –12.604 Peru –11.575 Nigeria –11.286 Sri Lanka –10.967 El Salvador –9.138 Bolivia –9.049 Paraguay –7.6910 Zimbabwe –4.2711 Philippines –4.0512 Romania –3.5313 Indonesia –2.8414 Bulgaria –2.2615 Dominican Republic –2.0916 Panama –1.8217 Thailand –0.8818 India 0.6719 Ecuador 1.9020 Colombia 3.1721 Egypt 6.5522 Nicaragua 8.9823 Jordan 10.9624 Jamaica 11.2125 China 15.3026 Venezuela 22.9827 Ukraine 30.6628 Vietnam 35.66

Middle-Income Countries ($6,500–$23,000)


1 Hungary –24.702 Poland –14.293 Mauritius –8.224 Lithuania –6.245 Brazil –5.196 Uruguay –4.967 Spain –3.448 Argentina –3.219 Israel –2.0610 Slovenia –1.7411 Estonia –1.5112 Costa Rica –1.4213 Portugal –1.3414 Chile –0.9615 Greece –0.4116 Mexico –0.0917 Czech Republic 0.0218 New Zealand 3.2419 Korea 4.9820 Malaysia 5.0821 Latvia 5.2522 Slovak Republic 6.1423 Singapore 9.5824 Russia 14.2025 South Africa 20.33

High-Income Countries (≥ $23,000)


1 Denmark –4.782 Italy –4.083 Switzerland –4.064 Japan –3.445 Ireland –3.316 France –3.127 Germany –2.968 Austria –2.759 United Kingdom –2.1810 Finland –1.8911 Sweden –1.4212 Netherlands 0.9613 Australia 1.4614 Belgium 2.1615 Norway 3.1716 Iceland 5.4117 United States 5.4318 Canada 8.10

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Table 3: Urban particulate concentration relative to expected given GDP per capita, listed by income groups



1 Venezuela –121.872 Ecuador –106.273 Romania –83.064 Colombia –50.935 Philippines –8.406 Bulgaria 20.927 India 22.788 Indonesia 37.359 Guatemala 59.8010 Thailand 60.2611 Honduras 67.8512 China 102.36



1 Latvia –57.592 Argentina –52.993 Slovak Republic –48.694 Brazil –44.205 Malaysia –41.316 Lithuania –41.177 Russia –40.598 Hungary –39.109 Czech Republic –34.5710 New Zealand –30.6111 Portugal –23.3112 Spain 10.9313 Korea 12.4114 Greece 101.1915 Costa Rica 114.7916 Mexico 146.02



1 Sweden –32.502 France –26.773 Norway –11.654 Iceland 1.055 Canada 3.806 Netherlands 4.937 Switzerland 5.628 Germany 5.759 Australia 8.7310 Japan 9.2811 Finland 12.1312 Austria 13.2013 Denmark 31.2914 Italy 43.13

Table 4: Urban SO2 concentration relative to expected given GDP per capita, listed by income groups



1 Ecuador –28.492 Romania –28.103 Thailand –26.694 India –26.495 Philippines –12.816 Venezuela –6.847 Bulgaria 11.998 Egypt 21.649 China 51.25




1 Lithuania –34.292 Latvia –31.413 Argentina –26.044 New Zealand –15.545 Portugal –12.636 Malaysia –11.887 South Africa –9.528 Spain –8.729 Slovak Republic –6.2110 Chile –2.8911 Czech Republic 2.0712 Singapore 3.2613 Costa Rica 7.0314 Hungary 10.3015 Greece 11.6016 Poland 22.4317 Korea 30.9818 Brazil 40.2919 Mexico 41.6120 Russia 63.80



1 Finland –11.072 Sweden –10.893 Iceland –7.814 Norway –7.165 Denmark –7.026 Netherlands –4.977 Germany –2.608 France –2.129 Switzerland –1.8510 Australia –1.7011 Austria –1.3112 Italy –0.9713 Canada –0.7514 Ireland 3.6715 United States 5.0916 United Kingdom 5.3717 Belgium 6.9118 Japan 9.49

The SO2 rankings cover 47 countries.Argentina andLithuania rank at the top on this measure. China andRussia face the most severe SO2 problems.

Energy usage data are available for 72 countries.Denmark and Switzerland rank highest in energy efficien-cy. Russia and the Ukraine emerge as the most energyinefficient countries.

Figures 2, 3, and 4 plot the relationship between eachmeasure of environmental performance and GDP percapita. One pattern that is immediately discernable acrossall three measures is that richer countries achieve betterresults than poorer ones.The improvement of environ-mental performance as income rises is most pronouncedwith regard to urban particulates and energy efficiency,and least strong for SO2 emissions.Among lower-incomecountries, the variance on all three measures is particularlyhigh relative to more prosperous countries.This suggeststhat environmental performance can be substantiallyimproved in many low-income countries independent ofthe gains that come with economic development.

The regression relationship between environmentalperformance and GDP per capita provides an interestingperspective on how each country performs relative to itswealth. Countries above the regression line in Figures 2, 3,and 4 exhibit weaker environmental results on the partic-ular performance measure than would be expected giventheir level of GDP; those countries below the regressionline demonstrate better performance.These results areshown in Tables 2, 3, and 4.

With regard to particulate levels, Italy, Greece,Mexico, Costa Rica, China, and Denmark are notable lag-gards relative to income. Sweden, Norway,Argentina,Latvia, Ecuador, and Venezuela show relatively strong per-formance. In terms of SO2 performance, Russia, Brazil,Mexico, Korea, China, Egypt, Japan, and Belgium lag rela-tive to income.The United States is also a weak per-former. Iceland, Finland, Sweden,Argentina, Latvia,Lithuania,Thailand, Romania, and Ecuador show relativelystrong results.

In energy efficiency, Denmark, Switzerland, Japan,Italy, Hungary, Poland, Honduras, and Bangladesh, amongothers, appear to be more energy efficient than would beexpected given their level of income.The United States,Canada, Singapore, Russia, South Africa,Venezuela, theUkraine, and Vietnam emerge as relatively poor perform-ers relative to income.As can be seen in Figure 4, thedummy variable for former Soviet bloc countries is highlysignificant, suggesting that the countries that faced artifi-cially low energy prices suffered a common fate of hugeinefficiency.

Taken together, these findings are consistent withestablished theory that suggests that pollution controlimproves with economic development (WorldCommission on Environment and Development 1987).Our data do not, however, reveal an inverted U-shapedenvironmental “Kuznets curve.”A number of other studieshave found such a pattern, characterized by rising emis-sions in the early stages of development and improvingenvironmental performance after middle-income levelshave been reached (Grossman and Krueger 1995;Harbaugh et al. 2000). Our results may be explained bythe fact that our sample of countries contains relativelyfew countries in the “early industrialization” stage ofdevelopment in which emissions and energy usage wouldbe low and rising, especially for the air pollution measures.

The relationship between environmental performanceand level of development supports several preliminary butimportant policy conclusions. First, the evidence thatpoorer countries uniformly perform less well on all threeenvironmental quality measures supports an emphasis onalleviating poverty as a core policy goal from the perspec-tive of environmental progress.

Second, the wide variations in environmental per-formance among countries at a similar level of economicdevelopment suggest that income or development stageaffects, but does not alone determine, environmental out-comes. Some rich countries seem to have learned how toadvance environmental quality ahead of their economicprogress; others have not. Similarly, some developingcountries appear to have achieved far better environmentalquality relative to their level of development, while othercountries seem to be sacrificing environmental goals inthe pursuit of economic growth.We explore whether thisapproach is effective later in this chapter.

Third, it is notable that environmental performancegains with income emerge most quickly for the mostlocalized problem (particulates), least rapidly with regardto environmental impact (energy usage) that generates theharms (CO2 emissions from fossil fuel burning) mostwidely spread over space and time.vi Intermediate resultsoccur for the variable (SO2) that arises on an intermediatespatial and temporal scale.This pattern comports with thetheoretical prediction that the geographic and temporalspread of an environmental issue represents critical policyvariables.Where harms have a trans-boundary or inter-temporal dimension, they constitute “super externalities,”which raise special collective action problems and oftenprove especially difficult to address (Dua and Esty 1997).

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Determinants of environmental performanceData on the environmental regulatory regime and thebroader economic and legal context are drawn from boththe ESI project and the Global Competitiveness Report2001–2002 annual Survey of business and governmentleaders.vii We categorize the determinants of environmen-tal performance in two broad groups: measures related to acountry’s environmental regulatory regime, and measuresof its economic and legal context.The full list of variablesalong with their definitions and sources can be found inAppendix A.

For the purposes of analysis, we divide the environ-mental regulatory regime variables into a number of cate-gories representing different aspects of a country’s regula-tory approach:

• stringency of environmental pollution standards• sophistication of regulatory structure• quality of the environmental information available • extent of subsidization of natural resources • strictness of enforcement• quality of environmental institutions

The stringency of standards category includes measures of the perceived rigor of a nation’s air pollution, waterpollution, toxic waste, and chemical regulations.Thisinformation is drawn from the GCR Survey. We expect a negative relationship between each of the measures ofregulatory stringency and our dependent variables, sincemore rigorous standards should lead to lower levels ofurban particulates, lower SO2 concentrations, and lowerenergy usage per unit of GDP.

The regulatory structure category measures thedegree to which a nation’s environmental regulations areflexible, clear, consistent, progressive, structured to helpcompetitiveness, and designed to promote cooperative ver-sus adversarial business-government relations. In each case,we anticipate a negative relationship between variables andour measures of environmental performance because amore refined and sophisticated regulatory structure isexpected to produce less pollution and energy usage. Inthis category, we have introduced two new variables thisyear drawn from the GCR Survey: (1) a measure ofwhether regulatory structure helps or hurts competitive-ness; and (2) a variable capturing the degree of coopera-tion versus adversarial behavior characteristic of the regu-latory approach.

The information category attempts to measure thedegree to which a nation has a sufficient data foundationfor policymaking and to support enforcement of environ-mental regulations.There are no direct measures of thequality of the information underlying each country’s envi-ronmental regime, and we rely on four proxy variablesdrawn from the ESI data set: (1) the degree to which a

country collects data in the 65 categories tracked by theESI analysis; (2) the extent of sustainable developmentinformation and the existence of plans to support nationalenvironmental decision-making (as called for in the RioEarth Summit’s Agenda 21 process); (3) the prevalence ofguidelines for sectoral environmental impact assessments;and (4) the breadth of environmental action plans.The lasttwo of these measures are new and reflect the ESI project’songoing effort to get a better grasp of the quality of environmental information across countries.All of theseinformation indicators are relatively crude, but should provide some basis for gauging whether a nation seeks tomake environmental judgments on an analytically rigorousbasis.We expect a negative relationship between theseinformation variables and our environmental performancemeasures.

The subsidies measure is derived from the GCRSurvey data on the extent of a country’s subsidization of energy and other materials.Where price signals are distorted, we expect to see greater inefficiency and higherlevels of pollution.Thus we anticipate a positive relation-ship between the level of subsidies and particulate levels,SO2 concentrations, and energy usage.

The strictness of enforcement measures are drawnfrom the GCR Survey.The first measure gauges howaggressively a nation’s environmental regulations areenforced, and the second provides a gauge of the depth ofa country’s commitment to treaty requirements and otherinternational environmental obligations.We expect a nega-tive relationship between these measures of enforcementrigor and our dependent variables, as those countries thattake environmental regulations (whether domestic orinternational) seriously should experience better pollutioncontrol and energy usage.

The final regulatory regime category, institutionalquality, seeks to measure the degree to which intergovern-mental (international) organizations and nongovernmentalentities (environmental groups, community organizations,business associations, and other elements of civil society)reinforce governmental environmental efforts.The mecha-nisms for such reinforcement are diverse (Esty 1998). Insome cases, these entities directly undertake environmentalactivities and thus substitute for government action.Environmental groups, for instance, may identify harms,highlight issues that demand attention, undertake datagathering and analyses, or throw a spotlight on poor envi-ronmental performers who should be pursued. NGOsmay also strengthen a society’s capacity for pollution con-trol by providing environmental education to the publicor technical assistance to polluters. Of course, such entitiesmay also play counterproductive roles as well, especially ifthey pursue extreme positions and utilize solely adversarialapproaches, unnecessarily increasing cost.

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Our capacity to measure the degree of institutionalquality is limited, and the variables in this category are, ofnecessity, somewhat crude proxies.We use data from theESI database (a new variable) on the number of entities(scaled by population) that participate in the WorldConservation Union (IUCN), an umbrella organization ofenvironmental NGOs and research centers.We also drawon ESI data that provide a measure of the breadth of acountry’s engagement with intergovernmental environ-mental bodies.A third institutional quality variable comesfrom the GCR Survey and gauges the extent to which anation’s companies utilize the ISO 14000 certificationprocess for environmental management.We expect a nega-tive relationship between these measures and our depend-ent variables gauging environmental outcomes.

The second broad group of independent variablestracks potentially significant dimensions of a country’seconomic and legal context.We analyze this broader set ofsocietal variables based on a growing theoretical literature,which suggests that a country’s underlying political, legal,and economic structures may contribute as much to envi-ronmental protection as the details of its regulatory regime(Esty 1997; Sachs 1998; Esty and Porter 2000).

Under the economic and legal context, there are twocategories of variables. First, we analyze what we calladministrative infrastructure. In this category, we assembledata on civil and political liberties drawn from the ESI andmeasures (from the GCR Survey) of public sector compe-tence, degree of governmental favoritism, how vigorouslyprivate property is protected, the independence of thejudiciary, demands for irregular payments as a price fordoing business, the extent of the rule of law, burdensomeregulations, corruption, and the degree to which newgovernments honor the obligations of prior administra-tions. For each of these variables, we would anticipate anegative relationship vis-à-vis our particulates, SO2, andenergy usage measures.The last three datasets representnew information developed in the 2001 GCR Survey.

The second group of variables under legal and eco-nomic context addresses various aspects of a country’stechnical capacity. It is again hard to measure scientific andtechnological sophistication directly, so we rely upon aseries of proxies.These include ESI data on the number ofscientists and engineers (scaled by population) in eachcountry and GCR Survey data that provide a gauge of acountry’s technology position, the strength of its scientificcommunity, the degree to which foreign technology iscommonly licensed, intellectual property protection,research and development spending, willingness to absorbnew technologies, business commitment to innovation,

and governmental commitment to technology develop-ment and innovation.We expect each of these measures oftechnical capacity to be negatively correlated with envi-ronmental impacts, as greater technical strength shouldlead to better environmental performance.The last threevariables in this category are new datasets drawn from the2001 GCR Survey.

As noted above, the independent variables are farfrom perfect measures of the potential determinants ofnational environmental outcomes.These variables are,however, the best ones currently available, and represent, insome cases, a significant improvement over prior efforts tomodel the policy levers and other drivers of environmen-tal performance. Despite their limitations, the data allow usto begin to identify empirically the variables that deter-mine a nation’s success in controlling pollution andimproving energy efficiency.

Statistical methodologyOur analytic approach unfolds in several stages. First, weuse bilateral regressions (Tables 5, 6, and 7) to explorewhether there is a statistically significant relationshipbetween each independent variable and energy usage,urban particulate levels, and SO2 concentrations. Becausemany of the independent variables are collinear and thedegrees of freedom are limited, multiple regression tech-niques cannot be used to examine the joint influence ofall the variables. Instead, as a second stage of analysis, we“roll up” the significant independent variable in each cate-gory into a subindex using common factor analysis.Then,we regress these subindexes against the dependent vari-ables.viii Appendix B reports the percentage of covarianceexplained by the first factor and the first factor coefficientfor each index variable. Finally, the statistically significantcategory subindexes are rolled up into an overall environ-mental regulatory regime index (ERRI) and an overalleconomic and legal context index (ELCI).

In light of the significant association between percapita GDP and environmental performance, we also ana-lyze performance relative to a peer group of countriesdefined by income level.We regress ERRI against GDPper capita (graphed in Figure 5) and calculate the residuals(distance above or below the regression line) for eachcountry (Table 9).This provides a way of analyzing howeach country performs against expectations established byits income level.We also examine the relationship betweenthe ELCI and ERRI, and the relationship between ELCIand GDP per capita.

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ENVIRONMENTAL REGULATORY REGIME INDEX –5.281 0.67 0.000 68

Stringency Subindex –5.632 0.68 0.000 68

Air Regulation –4.044 0.69 0.000 68Water Regulation –3.859 0.68 0.000 68Toxic Waste Regulation –3.576 0.67 0.000 68Chemical Regulation –3.902 0.68 0.000 68Overall Regulation –3.917 0.67 0.000 68

Regulatory Structure Subindex –4.480 0.64 0.002 68

Options for Compliance –4.005 0.60 0.102 68Confusing and Changing –4.982 0.65 0.001 68Early or Late –4.058 0.67 0.000 68Compliance Hurts or Helps Competitiveness –6.094 0.62 0.016 68Regulation Adversarial or Cooperative –6.355 0.63 0.007 68

Information Subindex –2.507 0.61 0.081 68

ESI–Variables %–available –0.271 0.62 0.020 68Sustainable Development Info –1.009 0.58 0.764 41Number of Sectoral EIA Guidelines 0.041 0.59 0.923 68Number of Environmental Strategies

& Action Plans –0.197 0.59 0.815 68

Subsidies Subindex 0.43 0.33 0.00 39

Government Subsidies 7.065 0.66 0.000 68

Regulatory Enforcement Subindex –4.466 0.65 0.001 68

Enforcement –3.890 0.65 0.001 68International Agreements –3.976 0.64 0.002 68

Environmental Institutions Subindex –4.740 0.65 0.001 68

IUCN –1.392 0.60 0.300 68Memberships –0.699 0.65 0.001 67Prevalence of ISO 14000 –3.994 0.63 0.011 68

ECONOMIC AND LEGAL CONTEXT INDEX –4.836 0.65 0.001 68

Administrative Infrastructure Quality Index –5.647 0.68 0.000 68

Civil Liberties –5.190 0.75 0.000 68Public Sector Competence –2.383 0.59 0.333 68Gov't Favor Private Sector Firms –4.200 0.64 0.003 68Property Rights –4.756 0.71 0.000 68Independent Judiciary –3.426 0.66 0.000 68Irregular Payments –4.973 0.68 0.000 68Legal Framework –3.880 0.66 0.000 68Regulatory Burden –5.144 0.63 0.006 68Level of Administrative Corruption –5.695 0.69 0.000 68Honoring of Policies through Gov. Transition –4.558 0.65 0.001 68

Scientific and Research Infrastructure Index –3.788 0.63 0.008 68

Scientists and Engineers –0.003 0.64 0.004 64Technology Position –3.636 0.66 0.000 68Institutions –3.341 0.62 0.018 68Licensing of Foreign Technology –3.692 0.61 0.055 68Company R & D Spending –4.207 0.64 0.002 68Willingness to Absorb New Technology –3.803 0.62 0.033 68Importance of Innovation to Revenue –6.158 0.62 0.020 68Gov't Purchase Decisions for Tech. Products –2.962 0.60 0.160 68

*Refer to Appendix A for definitions of variables.

Table 5: Bilateral regressions: energy usage

2001 Dependent Variable:Energy Usage (Per Unit GDP)

(ß) R 2 Sig. df







ESI–Variables %–available –3.86 0.15 0.011 40Sustainable Development Info –58.76 0.18 0.028 25Number of Sectoral EIA Guidelines –0.99 0.00 0.825 40Number of Environmental Strategies

& Action Plans 4.94 0.01 0.525 40

Subsidies Subindex












Table 6: Bilateral regressions: urban particulates

2001 Dependent Variable:Urban Particulates (Per City Pop)

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ESI–Variables %–available 0.207 0.00 0.662 45Sustainable Development Info –21.624 0.25 0.004 29Number of Sectoral EIA Guidelines –0.708 0.01 0.464 45Number of Environmental Strategies

& Action Plans 0.722 0.00 0.732 45

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Table 7: Bilateral regressions: urban SO2 concentration

2001 Dependent Variable:Urban SO2 (Per City Pop)

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Results for individual measures and indexesThe bilateral regression results are shown in Tables 5, 6,and 7.The energy efficiency regressions are shown inTable 5.A large number of the independent variablesshow a statistically significant relationship with energyusage, with the expected negative sign and a reasonabledegree of explained variance.All of the elements of theregulatory stringency category show particular signifi-cance, as do the enforcement variables. Most of the regula-tory structure measures also prove to be highly significant.These categories of variables account for the highestamount of explained variance.The subsidies variable ishighly significant and has the expected positive sign.Thisresult suggests, consistent with economic theory, that mis-priced resources will be inefficiently used, and that subsi-dies represent a major policy error.

The information and institutions measures performless strongly. In the information category, one variable(percentage of ESI variables available) emerges as signifi-cant while the other three measures do not. In the institu-tional category, IUCN membership fails to show signifi-cance while the other two measures of institutional capac-ity are significant.

Among the economic and legal context variables, allbut one (public sector competence) emerge as highly sig-nificant with the expected negative sign.The new vari-ables measuring corruption and whether new govern-ments honor the commitments of prior administrationsprove to be statistically significant. In the scientific andtechnical capacity category, all of the variables except one(government commitment to technology developmentand innovation) show a reasonable degree of significanceand the expected negative sign.

To build the subindexes and indexes, we employ onlythe statistically significant variables.All of the subindexesare highly significant in explaining energy usage, have theexpected negative sign, and account for substantialexplained variance.The ERRI and ELCI register similarlyhigh levels of significance with the expected negative signsand a substantial degree of explained variance.

Although preliminary, the latter results provide someempirical support for the hypothesis that a nation’s under-lying economic and legal structure may be as important toenvironmental success as the specific details of its environ-mental regulatory regime.This conclusion argues for moreattention to “fundamentals”—such as eliminating corrup-tion and building functioning market economies—and to“governance”—such as strengthening the rule of law anddeveloping mechanisms to protect property rights—in set-ting development priorities and in targeting developmentassistance. Interestingly, this is the direction that the recentpolicies of the United Nations Development Programmeare taking.

The ERRI and the ELCI prove to be highly correlat-ed and show similar levels of significance and explainedvariance. Hence it appears that environmental regulationand overall economic and legal context generally improvein parallel.We explored the joint influence of ERRI andELCI on environmental performance. In practice, the highcorrelation between the two indexes (as shown in Figure7) means that their effects on energy usage could not bedistinguished statistically.

Table 6 presents the second set of bilateral regressionsfor urban (air) particulate concentrations.Again, the vastmajority of variables are significant with the expected signand account for a reasonable degree of explained variance.All of the measures of regulatory stringency and structureare highly significant, with the stringency variablesaccounting for the greatest level of explained variance.The subsidies measure is highly significant, has the antici-pated positive sign, and accounts for a reasonable degree of explained variance.

In the information category, two variables emerge assignificant with the expected negative sign, but do notaccount for as high a degree of explained variance. In theinstitutional reinforcement category, the number of IUCNmemberships is again not significant, while the other twovariables (participation in intergovernmental environmen-tal bodies and corporate participation in environmentalmanagement systems) emerge as highly significant.

The regulatory stringency, regulatory structure, infor-mation enforcement, and institutional subindexes allemerge as highly significant with the expected negativesign, as does the cumulative ERRI.Across all of thesesubindexes, however, the degree of explained variance is somewhat lower in the urban particulate regressionsthan in the energy usage ones.Two of the subindexes—information foundations and institutional reinforcement—perform notably less well than the others.This may reflectthe fact that these variables are imperfect proxies or thatinformation and institutions play more mixed roles.

All of the variables in the economic and social con-text regression emerge as significant in the urban particu-lates regulations.All have the expected negative sign, withmany accounting for a substantial degree of explainedvariance.The administrative infrastructure and technicalcapacity subindexes both show very high levels of signifi-cance, the expected negative sign, and a high degree ofexplained variance.The ELCI similarly emerges as highlysignificant. It accounts for almost as much explained vari-ance as the ERRI. However, both the ERRI and theELCI explain a somewhat smaller proportion of variationsin urban particulate concentrations than energy usage.Again, the independent effects of ERRI and ELCI couldnot be distinguished statistically.

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The SO2 regression results are presented in Table 7.Most of the independent variables are once again signifi-cant with the expected negative sign.The degree ofexplained variance is, however, generally much lower for SO2 than for either energy usage or particulate concentrations.This finding may reflect the fact that the benefits of SO2 control (reduced acid rain) accruedownwind—frequently beyond the territorial boundariesof the jurisdiction undertaking regulatory action.Thus,from a cost-benefit perspective, the regulating entity hasless to gain than it has in the control of particulates orwith investments in energy efficiency, both of which provide more localized benefits.

The subsidies measure again shows a high level of sig-nificance and the expected positive sign, but accounts for alower amount of variance than with the other measures ofpollution. In the information category, three of the fourmeasures are not statistically significant.Again, the looserfit may suggest that even a well-informed government thatis serious about environmental protection has less of anincentive to address SO2, given its geographic dispersion,than other more localized issues.

All of the environmental regulatory regime subindexes are significant and have the expected negativesign in the SO2 regressions. Only the regulatory stringencysubindex accounts, however, for a reasonable degree ofexplained variance. ERRI once again proves to be highlysignificant, although the degree of explained variance isnot high.As a general matter, the regression fit for SO2

appears weaker than for particulates or energy usage,perhaps reflecting the more limited regulatory payoffsnoted above.

Among the variables in the economic and legal context grouping, all but one (public sector competence)emerge with high statistical significance and the expectednegative sign in the SO2 regressions. Some of the measuresaccount for a reasonable degree of explained variance (eg, civil liberties, property rights, and irregular payments).In general, the administrative infrastructure variables showgreater significance and higher degrees of explained variance than the technical capacity measures.The administrative infrastructure subindex is highly significantwith a reasonable degree of explained variance.The technical capacity subindex shows a high degree of significance but does not account for an especially large amount of explained variance.The overall ELCI is significant and explains a reasonable amount of the variance in SO2 concentrations.

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Ranking environmental regulatory qualityThe bilateral, subindex, and index regressions establish a statistically significant relationship between the variouspolicy measures and environmental performance.The nextstage in the analysis is to use ERRI to explore the differ-ences across countries in environmental regulatory quality.

Table 8 presents a ranking of countries ranked byabsolute ERRI scores.This index (combining the alwayssignificant regulatory stringency, structure, subsidies,and enforcement subindexes) represents a summary performance measure of the quality of the environmentalregulatory system in a country.Among the top-rankedcountries are Finland, Sweden, and Singapore. Countriesat the bottom include Guatemala, Ecuador, and Paraguay.

Given the significant relationship between level of development and environmental performance, wewould expect a similar relationship with environmentalregulatory quality.What is most interesting in Table 8,then, is not so much the fact that Finland outranksParaguay on the stringency of environmental regulation,but the reasons why countries with similar incomes perform so differently. For instance, why does Costa Rica(36th place) do better than Panama (42nd place) and Peru(50th place)? Similarly, why do Spain (21st) and Portugal(31st) so dramatically outperform Greece (49th)? Likewise,Chile (25th) distinctly outperforms Argentina (51st), andPoland (29th) comes in way ahead of Russia (57th).Thelast two pairings reveal a general pattern suggesting thatmore aggressively market-oriented economies (Chile andPoland) may outperform those (Argentina and Russia)where a more interventionist economic tradition persists.

To control for income differences and hence the levelof economic development,Table 9 ranks countries by theirresiduals from the regression of ERRI and GDP per capita(plotted in Figure 5).This relative ranking represents ameasure of environmental regulatory quality relative toexpectations established by income level.Among the low-income countries, Jordan and Jamaica come out on top, while Ecuador and Paraguay trail.Among middle-income countries, Singapore, Estonia, and New Zealandrank high. Israel,Argentina, and Greece lag.Among thewealthiest nations, Finland, Sweden, and the Netherlandslead, while Italy, Norway, and Ireland rank low.The United States occupies the bottom rung of the high-income group ladder.

Table 8: Enviornmental regulatory regime index by country, absolute ranking

Rank Country Score

1 Finland 2.3032 Sweden 1.7723 Singapore 1.7714 Netherlands 1.7475 Austria 1.6416 Switzerland 1.6317 Germany 1.5228 France 1.4649 Denmark 1.38410 Iceland 1.35411 New Zealand 1.29912 Canada 1.29713 United Kingdom 1.18514 United States 1.18415 Belgium 1.15916 Australia 1.08317 Japan 1.05718 Norway 1.04519 Ireland 0.54620 Italy 0.49821 Spain 0.43722 Estonia 0.29623 Hungary 0.28324 Slovenia 0.20925 Chile 0.17726 Czech Republic 0.07327 Uruguay 0.05928 Israel 0.02129 Poland 0.00530 Jordan 0.00231 Portugal –0.02832 South Africa –0.02933 Latvia –0.03634 Jamaica –0.03735 Brazil –0.07736 Costa Rica –0.078

Rank Country Score

37 Korea –0.12138 Malaysia –0.12739 Lithuania –0.14640 Slovak Republic –0.17741 Egypt –0.22442 Panama –0.24243 Mauritius –0.29044 China –0.34845 Thailand –0.38946 Colombia –0.41647 Bulgaria –0.58448 Mexico –0.60249 Greece –0.61950 Peru –0.72251 Argentina –0.73252 Zimbabwe –0.73253 Bolivia –0.74354 Indonesia –0.75855 India –0.75956 Vietnam –0.77057 Russia –0.89558 Sri Lanka –0.93659 Philippines –1.01460 Dominican Republic–1.01461 Venezuela –1.07962 Nicaragua –1.16463 El Salvador –1.21564 Romania –1.26865 Ukraine –1.29766 Honduras –1.30067 Nigeria –1.31468 Bangladesh –1.33169 Guatemala –1.53270 Ecuador –1.61671 Paraguay –1.743

Environmental Regulatory Regime Index

As noted earlier, ERRI and ELCI are highly correlat-ed, as shown in Figure 6. Nevertheless, it is evident thatsome countries have an economic and legal context thatoutpaces their environmental regulatory quality, whileothers have advanced environmental regulation faster thancontext. In Israel, India, Ireland, the United States, SouthAfrica, the Philippines, and Nigeria, environmental regula-tion lags overall context, while in Finland,Austria, NewZealand, Panama, and Bolivia, environmental regulatoryquality is ahead of improvements in the broader economicand legal context.The divergence between ERRI andELCI, however, was not statistically associated with differ-ences in environmental outcomes.This may be due to thehigh correlation of ERRI and ELCI in the sample.

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Table 9: Environmental regulatory regime index relative to expected results given GDP per capital, listed by incomegroups



1 Jordan 0.7942 Jamaica 0.7933 Egypt 0.6124 China 0.4555 Panama 0.3556 Vietnam 0.2167 Colombia 0.2048 Bolivia 0.2049 India 0.18810 Zimbabwe 0.18711 Thailand 0.18012 Indonesia 0.13213 Bulgaria 0.07814 Peru 0.00215 Sri Lanka –0.09216 Philippines –0.21117 Nicaragua –0.21718 Nigeria –0.22519 Bangladesh –0.30720 Honduras –0.35921 Dominican Republic –0.39722 Venezuela –0.43623 El Salvador –0.46124 Ukraine –0.47025 Romania –0.68426 Guatemala –0.71427 Ecuador –0.73028 Paraguay –0.981



1 Singapore 0.8062 Estonia 0.6143 New Zealand 0.6124 Latvia 0.4995 Chile 0.4946 Brazil 0.4077 Uruguay 0.4028 Lithuania 0.3749 Poland 0.34310 Hungary 0.30811 South Africa 0.28812 Costa Rica 0.23513 Malaysia 0.21414 Mauritius –0.00315 Czech Republic –0.03116 Slovak Republic –0.03217 Spain –0.17518 Slovenia –0.21119 Mexico –0.25920 Portugal –0.42621 Russia –0.48722 Korea –0.55823 Israel –0.62624 Argentina –0.70525 Greece –0.964



1 Finland 1.1652 Sweden 0.7253 Netherlands 0.5414 France 0.4045 Germany 0.3776 Austria 0.3687 United Kingdom 0.2028 Switzerland 0.1549 Denmark 0.03710 Canada –0.11211 Australia –0.13812 Japan –0.16813 Belgium –0.17314 Iceland –0.18415 Italy –0.49516 Norway –0.52317 Ireland –0.62318 United States –0.792

Parameter ParameterVariable Estimate t Value Prob > |t| Estimate t Value Prob > |t|

Intercept 1.577 3.62 0.001 –2.352 –1.91 0.060

GDP per Capita, 1995 (thousand, ppp) 0.056 1.77 0.082 0.092 2.78 0.007

General Government Spending, 2000 –0.043 1.62 0.111

Gross Fixed Investment (as % of GDP) 0.226 5.17 <0.0001

Environment Regulatory Regime Index Relative to Expected Given GDP per Capita 0.830 1.51 0.135 0.795 1.66 0.102

Model 1 Dependent Variable:Annual Percentage Growth Rate of GDP per Capita, 1995–2000

Model 2 Dependent Variable:Annual Percentage Growth Rate of GDP per Capita, 1995–2000

Table 10: Environmental regulatory stringency and economic growth

The relationship between environmental performanceand competitivenessFinally, we turn to the question of whether environmentalregulatory stringency detracts from or contributes to economic progress. Figure 7 shows that the quality of anation’s environmental regulatory regime is strongly andpositively correlated with its competitiveness as measuredby the Current Competitiveness Index, or CCI (seeChapter 1.2). Many of the nations with top-tier competi-tiveness rankings also have strong environmental perform-ance scores. Finland, for example, ranks at the top of theERRI and at the top of the CCI.The United States standsout as an exception, with a high competitiveness rank anda relatively low environmental regulation score. Figure 5tells a similar story about how high levels of per capitaincome and economic development correlate with highenvironmental regulatory quality.

The correlations revealed in Figures 5 and 7 do not,of course, prove causation. But the finding that a strongenvironmental regulatory regime is not inconsistent withtop-tier economic performance is itself interesting.Indeed, the fact that the top environmental performers donot appear to have suffered economically strongly supportsthe “soft” version of the “Porter hypothesis,” which arguesthat environmental progress can be achieved without sacrificing competitiveness (Porter 1990 and Porter andvan der Linde 1995).Testing the “hard” version of thishypothesis—that countries with forward-leaning environ-mental policies and programs will actually enhance theircompetitiveness—requires time-series data that are not yet available.

Figure 5 highlights the development policy choicethat every nation faces. Countries would like to movefrom the lower left corner of the chart (which representslow levels of environmental performance and low nationalincome) to the upper right quadrant (which representshigh levels of environmental performance and highincome).The question is what path to take. Or, to put itdifferently, must the environment be sacrificed to achieveeconomic progress? Those countries above the regressionline can be seen as having chosen a “clean” developmenttrajectory in which environmental regulatory qualityadvances ahead of economic advancement.Those belowthe line have chosen a relatively “dirty” path to growth,with relatively lax environmental regulation in the hope ofgrowing faster.

In addressing this choice, we are able to provide acrude test using the available data.We regress a number ofcontrol variables on GDP per capita growth between 1995and 2000, including the initial level of GDP per capita,gross fixed capita formation as a percent of GDP, and gov-ernment spending as a percent of GDP.We then introducea variable, which measures the residual from the regressionof ERRI on GDP per capita (Table 10). Countries withpositive residuals have ERRI scores that are higher thanwould be expected given their income, and vice versa.Theresidual has a positive sign with significance at virtuallythe 90 percent level. Countries that pursue a stringentregulatory regime appear to achieve more rapid growth.Although tentative, this result suggests the possible superi-ority of the “clean” model. However, more years of dataand better controls will be necessary to validate this finding.

ConclusionThe results presented here must be seen as preliminary.The data available suffer from many limitations, narrowingthe feasible statistical approaches. Precise causal linkagesremain unproven. Indeed, a central conclusion of ourresearch is that better environmental data are required atthe global, national, local, and corporate levels if a moresystematic approach to environmental improvement is tobe implemented.As the world community looks towardthe Sustainable Development Summit in Johannesburg in2002, a worldwide commitment to improved environmen-tal data should be adopted as a priority initiative.

With these caveats, however, the relationships that doemerge as statistically significant are striking.The analysisprovides considerable empirical evidence that cross-country differences in environmental performance areassociated with the quality of the environmental regulatory regime in place.We find that the rigor andstructure of environmental regulations have particularimpact, as does emphasis on enforcement.The damagingeffect of subsidies is also clear.Although developing astrong and sophisticated regulatory regime that fully internalizes externalities presents real challenges, endingprice-distorting, inefficiency-creating, and pollution-inducing subsidies is within the policy grasp of everynation. Environmental performance appears to improvewith certain kinds of information and also to improve to the extent that a nation’s environmental regime is rein-forced by an environmentally oriented private sector andbroad-based relationships with international environmentalbodies. Information and institutions have some but lessimpact on environmental performance, based on ouranalysis.This finding may, in part, be due to weaknesses in the available data.

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Our results also suggest that environmental perform-ance requires improvements in a country’s institutionalfoundations. In practice, a nation’s economic and legalcontext and its environmental regulatory regime go handin hand.This association demands further exploration.But the preliminary evidence developed here suggests thatcountries would benefit environmentally from an emphasison developing the rule of law, eliminating corruption, andstrengthening their governance structures.

The strong association between income and environ-mental performance also carries important implications.Among other things, it provides powerful corroborationfor a policy emphasis on poverty alleviation and the promotion of economic growth as a key mechanism forimproving environmental results.

The empirical evidence developed here suggests thatthe anti-globalization arguments of the environmentalprotestors in the streets at every recent major internationalgathering are off the mark. Limiting trade and the engage-ment of developing countries with the rest of the world isa recipe for environmental failure, not environmental suc-cess. Rather, the more fully a country moves to modernizeits economy, institutional structures, and regulatory system,the more quickly its environment performance appears toimprove—along with improvements in per capita income.

The country rankings that emerge from our analysisseem largely to square with observed reality.The variationsin performance highlight the fact that countries varywidely in their environmental outcomes and policy choices, even after controlling for level of income.Thereare clearly better and worse ways to approach pollutioncontrol and natural resource management.The data provided here offer some important clues as to where the search for “best practices” should begin. Moreover,our findings suggest that the environment need not besacrificed on the road to economic progress. Quite to the contrary, the countries that have the most aggressiveenvironmental policy regimes also seem to be the mostcompetitive and economically successful.We also find preliminary evidence that a stringent environmentalregime relative to income may speed up economic growth rather than detract from it.

This study highlights the fact that the environmentaldomain need not rely on guesswork.The results here show that a more analytically rigorous approach to policy-making could pay real dividends. More fundamentally,our analysis strongly supports the notion that the uncer-tainties that plague environmental decision-making can be reduced, and that current levels of policy contentioncould be reduced as well.

Our preliminary efforts to use statistical methods to explain environmental successes and failures seem toconfirm some aspects of the prevailing wisdom. For example, poverty emerges as a source of serious environ-mental degradation and thus deserves ongoing policyattention. Subsidies appear not only to skew prices anddistort trade; they also lead to inefficient production andunnecessary pollution. But some new priorities alsoemerge from this research. Notably, there appear to be significant gains to be had by moving environmental laggards toward the best practices of those jurisdictionswhose performance is top tier.This argues for muchgreater strategic emphasis on information developmentand dissemination. Likewise, the significance of economicand legal context to environmental results argues for a new focus on governance as the foundation for both environmental and economic progress.The results here suggest that there are ways to move beyond the ideological and emotional obstacles that stand in the way of faster environmental progress.

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ReferencesDua, André and Daniel C Esty. 1997. Sustaining the Asia-Pacific Miracle:

Environmental Protection and Economic Integration. Washington DC:Institute for International Economics.

Esty, Daniel C and Michael E Porter. 2000. “Measuring NationalEnvironmental Performance and Its Determinants.” In The GlobalCompetitiveness Report 2000 (Michael E Porter and Jeffrey D Sachset al, eds), New York: Oxford University Press.

Esty, Daniel C 1998. “NGOs at the World Trade Organization: Cooperation,Competition, or Exclusion.” Journal of International Economic LawVol. 1, No. 1 (March).

——— 1997. “Environmental Protection During the Transition to a MarketEconomy.” In Economies in Transition: Asia and Europe (Wing Woo,Stephen Parker, Jeffrey Sachs, eds.), Cambridge: MIT Press.

Grossman, Gene M and Alan B Krueger. 1995. “Economic Growth and theEnvironment.” Quarterly Journal of Economics 110, No. 2 (May):353–375.

Harbaugh, William, Arik Levinson, and David Wilson. 2000. “Re-examiningthe Empirical Evidence for an Environmental Kuznets Curve.” NBERWorking Paper No. 7711 (May).

Jaffee, Adam B, Steven R Peterson, Paul R Portney, and Robert N Stavins.1995. “Environmental Regulation and the Competitiveness of U.S.Manufacturing: What Does the Evidence Tell Us?” Journal ofEconomic Literature 33, No. 1 (March): 132–161.

Porter, Michael E. 1990. The Competitive Advantage of Nations. NewYork: Free Press.

Porter, Michael E and C van der Linde. 1995. “Green and Competitive:Ending the Stalemate,” Harvard Business Review 73, No. 5(September/October): 120–155.

Sachs, Jeffrey. 1998. “Globalization and the Rule of Law,” Yale LawSchool Occasional Papers 2d series, No. 4.

World Commission on Environment and Development. 1987. OurCommon Future. Oxford: Oxford University Press.

World Economic Forum. 2001. Global Leaders for TomorrowEnvironmental Task Force, 2001, Environmental Sustainability Index2001 (Geneva: World Economic Forum) (available atwww.yale.edu/envirocenter/esi).

Notesi This study builds on Daniel C Esty and Michael E Porter, “Measuring

National Environmental Performance and Its Determinants,” inMichael E Porter, Jeffrey Sachs, et al., Global CompetitivenessReport 2000 (New York: Oxford University Press). As is explained inthe pages that follow, the present analysis incorporates new dataand a variety of new variables, but utilizes statistical model similar tothat of the GCR 2000 study.

ii For a further discussion of the data gaps that plague the environmentaldomain, see World Economic Forum Global Leaders for TomorrowEnvironmental Task Force, 2001, Environmental Sustainability Index2001 (Geneva: World Economic Forum) (available atwww.yale.edu/envirocenter/esi).

iii Again, the lack of systematic environmental data gathering in manycountries and the limited information available with regard to a num-ber of key issues constrains our model. Filling these data gaps—both in terms of depth and breadth—should be a policy priority.Better data remains a prerequisite for a more analytically rigorousapproach to environmental decision making.

iv This project, undertaken by the World Economic Forum’s Global Leadersfor Tomorrow Environmental Task Force, with the support of the YaleUniversity Center for Environmental Law and Policy and the Centerfor International Earth Science Information Network (CIESIN) atColumbia University, ranks 122 countries on their “environmentalsustainability” based on performance in 22 categories building on adataset of 65 underlying variables. The Environmental SustainabilityIndex (ESI) report and details on the ESI dataset can be found atwww.yale.edu/envirocenter/esi.

v Both the United States and the United Kingdom track smaller particu-lates than the rest of the world. The United States and the UnitedKingdom emphasis follows the most recent medical evidence, whichsuggests that it is the smaller particles that penetrate deep into thelungs and present a real health threat.

vi Energy usage also has highly localized effects insofar as efficiencydirectly affects competitiveness and some of the harms (particulatesand other local air pollutants) do not spread geographically.

vii The 2001 Survey, undertaken jointly by the World Economic Forum andHarvard University’s Center for International Development andInstitute for Strategy and Competitiveness, builds on questionnaireresponses from more than 4,000 business, government, and non-governmental organization (NGO) leaders in 75 countries.

viii In developing the category subindexes, we use only those variablesthat appear appropriately grouped based on Eigen Value analysis, asshown in the factor analysis results given in Appendix B. Thus, indeveloping the regulatory stringency subindex, we drop the overallregulation measure. The sectoral EIA guidelines measure and theenvironmental strategies and action plans measure drop out of theinformation subindex. The measures of civil liberties, public sectorcompetence, irregular payments, and regulatory burden are alldropped from the administrative infrastructure subindex. The scien-tists/engineers, licensing of foreign technology, and business innova-tion measures fall out of the technical capacity subindex.

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Source Variable Definition Measurement (WEF/ESI)

ENVIRONMENTAL PERFORMANCE

Energy use Energy Usage, 1997 (High = More inefficient) Total energy consumption ESIper unit of country GDP

Urban SO2 Urban SO2 concentration, 1990–96 (High = More particulates) Average normalized ESImean of total SO2 per unit of city population

Urban particulates Urban particulates concentration, 1990–96 (High = More particulates) Average normalized ESImean of total suspended particulates per unit of city population

ENVIRONMENTAL REGULATORY REGIME

Stringency of Standards

Air regulation Stringency of air regulations (High = More stringent) Survey data (scale1–7) WEF

Water regulation Stringency of water regulations (High = More stringent) Survey data (scale1–7) WEF

Toxic waste regulation Stringency of toxic waste regulations (High = More stringent) Survey data (scale1–7) WEF

Chemical regulation Stringency of manufacturing chemical use regulations (High = More stringent) Survey data (scale1–7) WEF

Overall regulation Stringency of overall environmental regulation (High = More stringent) Survey data (scale1–7) WEF

Regulatory Structure

Flexibility Options for achieving compliance in environmental regulations (High = Many options) Survey data (scale1–7) WEF

Stability Environmental regulations in your country are confusing and frequently changing Survey data (scale1–7) WEF(High = Stable)

Early or late Environmental regulations are enacted ahead or much later than other countries Survey data (scale1–7) WEF(High = Ahead)

Compliance hurts or helps Complying with environmental standards hurts/helps competitiveness (High = Helps) Survey data (scale1–7) WEFcompetitiveness

Regulation adversarial or Environmental gains are achieved through adversarial means or government-business Survey data (scale1–7) WEFcooperative cooperation (High = Cooperative)

Information

ESI Variables–% Percentage of ESI variables in publicly available data sets % of total ESI variables ESI(n=64)

Sustainable development info Availability of sustainable development information at the national level ESI

Number of sectoral EIA Numer of sectoral EIA guidelines ESIguidelines

Number of environmental Number of environmental strategies & action plans ESIstrategies & action plans

Subsidies

Government subsidies Government subsidies in your country encourage inefficient use of energy or materials Survey data (scale1–7) WEFor there are no subsidies (High = High subsidies)

Regulatory Enforcement

Enforcement Environmental regulations are not enforced or enforced erratically or are enforced Survey data (scale1–7) WEFconsistently and fairly (High = Consistently and fairly)

International agreements Compliance with international agreements is a high priority in your country's Survey data (scale1–7) WEFgovernment (High = Agree)

Environmental Institutions

IUCN Number of IUCN membership organizations, 1998 (per million population) ESI

Memberships Number of memberships in environmental intergovernmental organizations, 1998 frequency count ESI

Prevalence of ISO 14000 How many Companies utilize environmental management system such as ISO 14000 Survey data (scale1–7) WEF(High = Most)

(cont’d.)

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Appendix A: Description of variables

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Source Variable Definition Measurement (WEF/ESI)

ECONOMIC AND LEGAL CONTEXT

Administrative Infrastructure Quality

Civil and political liberties Index ranging from 1 (Low levels of liberties) to 7 (High levels) ESI

Public sector competence The competence of personnel in the public sector is higher or lower than in the private sector Survey data (scale1–7) WEF(High = Higher than private sector)

Favoritism Public sector officials tend to favor well-connected private firms and individuals (High = Disagree) Survey data (scale1–7) WEF

Property rights Property rights are unclear and unprotected by law or are clearly delineated and protected by Survey data (scale1–7) WEFlaw (High = Clearly delineated and protected)

Independent judiciary The judiciary in your country is independent and not subject to interference by the government Survey data (scale1–7) WEFand/or parties to the dispute (High = True)

Irregular payments Irregular payments connected with import-export permits, business licenses, exchange Survey data (scale1–7) WEFcontrols, tax assessments, etc. (High = Never occur)

Trusted legal framework A trusted legal framework exists in your country for private business to challenge the legality Survey data (scale1–7) WEFof government actions and/or regulations (High = True)

Regulatory burden Administrative regulations in your country are burdensome//not burdensome (High = not) Survey data (scale1–7) WEF

Level of administrative Do other firm' unfair or corrupt activities impose costs on your firm (High = No costs) Survey data (scale1–7) WEFcorruption

Honoring of policies through Do new governments honor the contractual commitments and obligations of previous Survey data (scale1–7) WEFGov’t transition regimes (High = honor)

Administrative Infrastructure Quality

Scientists and engineers Research and development scientists and engineers (per million population) ESI

Technology position Country's position in technology generally lags behind most countries or is a leader Survey data (scale1–7) WEF(High = Leader)

Institutions Scientific research institutions in your country are not internationally reputable Survey data (scale1–7) WEF(High = World class)

Licensing foreign technology Licensing of foreign technology is uncommon or is a common means to acquire new Survey data (scale1–7) WEFtechnology (High = Common)

Intellectual property protection Intellectual property in your country is or is not adequately protected (High = Well protected) Survey data (scale1–7) WEF

Company R & D spending Companies in your country do or do not spend much on R & D relative to international peers Survey data (scale1–7) WEF(High = Spend heavily on R & D)

Willingness to absorb new Companies in your country are not interested//aggressive in absorbing new technology Survey data (scale1–7) WEFtechnology (High = Aggressive)

Importance of innovation In your business, continuous innovation plays a major role in generating revenue not Survey data (scale1–7) WEFto revenue true/true (High = True)

Gov't purchase decisions Government purchase decisions for technology products are based solely on price / on Survey data (scale1–7) WEFfor tech. products technology and encourage innovation (High = On technology)

Appendix A: Description of variables (cont’d.)

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Stringency Subindex 3.97 3.95 99.16

Air Regulation 0.251Water Regulation 0.251Toxic Waste Regulation 0.251Chemical Regulation 0.251Overall Regulation *

Regulatory Structure Subindex 4.05 3.60 81.08

Options for Compliance 0.216Confusing and Changing 0.239Early or Late 0.221Compliance Hurts or Helps Competitiveness 0.224Regulation Adversarial or Cooperative 0.210

Information Subindex 1.15 0.29 57.31

ESI-Variables %-available 0.660Sustainable Development Info 0.660Number of Sectoral EIA Guidelines *Number of Environmental Strategies & Action Plans *

Regulatory Enforcement Subindex 1.93 1.86 96.38

Enforcement 0.509International Agreements 0.509

Environmental Institutions Subindex 1.59 1.19 79.72

IUCN *Memberships 0.560Prevalence of ISO 14000 0.560

Administrative Infrastructure Quality Index 5.35 5.14 89.09

Civil Liberties *Public Sector Competence *Gov't Favor Private Sector Firms 0.172Property Rights 0.178Independent Judiciary 0.179Irregular Payments *Legal Framework 0.180Regulatory Burden *Level of Administrative Corruption 0.176Honoring of Policies through Gov. Transition 0.175

Scientific and Research Infrastructure Index 4.52 4.32 90.38

Scientists and Engineers *Technology Position 0.214Institutions 0.212Licensing of Foreign Technology *Company R & D Spending 0.212Willingness to Absorb New Technology 0.210Importance of Innovation to Revenue *Gov't Purchase Decisions for Tech. Products 0.203

ROLLUP OF SUBINDICES

Environmental Regulatory Regime Index 3.46 3.06 86.56

Stringency Subindex 0.279Regulatory Structure Subindex 0.274Government Subsidies –0.238Regulatory Enforcement Subindex 0.281

Economic and Legal Context Index 1.90 1.79 94.81

Administrative Infrastructure Quality Index 0.513Scientific and Research Infrastructure Index 0.513

Note: * means that variable is not included in the corresponding index

Appendix B: Factor Analysis Results

First Eigen Value

Differencefrom

Second EV

Percent ofVarianceExplained

ScoreCoefficient

FIRST FACTOR

ranking national environmental environmental quality plays

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