Environmental Performance Index in comparative perspective

Dalson Britto Figueiredo Filho

(dalsonbritto@yahoo.com.br)

Marcelo de Almeida Medeiros

(mam14@uol.com.br)

Simone Marques

(simonebarbio@gmail.com)

Andrea Steiner

(ecodea@gmail.com)

Abstract

This paper analyzes Environmental Protection Index in comparative perspective,

giving special attention to Latin America countries. The focus regards the level of

association between EPI and it’s to two objectives: (1) environmental health and (2)

ecosystem vitality. Methodologically, the research design adopts a nested analysis

approach, combining multivariate statistics with case study. In addition, we replicate

data from the Yale Center for Environmental Law and Policy to estimate a new

measure of environmental performance that accounts for variables correlation levels.

The preliminary results suggest that: (1) besides Europe (62.89), Latin America

shows the highest EPI level (55.37); (2) within Latin America, Brazil has third

maximum intensity of EPI (60.90), following Colombia (62.33) and Costa Rica

(69.03) and (3) particular matter indicator is not highly correlated with

environmental health objective and therefore should not be included to estimate EPI

in the first place.

Keywords: Latin America; environmental performance index; nested analysis.

Paper delivered at 7º Latin American Political Science Congress, organized by Latin

American Association Political Science (ALACIP), Bogotá (Colombia)m September 25 and

27 of 2013.

Draft version. Please, do not quote without authors’ permission.

This paper was prepared as a case study under the International Environment Policies and its Effects for Coastal

Marine Ecosystems in Brazil project.

INTRODUCTION

How do Environmental Performance Index (EPI) varies across world regions? This paper

examines distribution EPI in comparative perspective, giving special attention to Latin America

countries. The focus regards the correlation levels between EPI and it’s to two objectives: (1)

environmental health and (2) ecosystem vitality. On methodological grounds, the research design

uses both descriptive and multivariate statistics. In addition, we replicate data from the Yale

Center for Environmental Law and Policy to estimate a new measure of environmental

performance that accounts for variables correlation levels.

Biodiversity from terrestrial, marine, coastal, and inland water ecosystems provides the

basis for ecosystems and the services they provide that underpin human well being. However,

biodiversity and ecosystem services are declining at an unprecedented rate, and in order to

address this challenge, adequate local, national and international policies need to be

implemented. To do so, decision makers need scientifically credible information that takes into

account the complex relationships between biodiversity, ecosystem services, and people

(HULME ET AL., 2011; TURNHOUT ET AL., 2012). Recently, there has been a growing

awareness in society about the potential detrimental effects of the continued economic growth

for the public health and welfare of current and future generations. This made the environmental

performance of countries an established topic at the political agenda with politicians recognizing

the pressing need for effective environmental policies (ROGGE, 2012). To provide policy

makers and other interested parties (e.g. environmental scientists, non-governmental

organizations, the general public, etc.) with a quantitative basis for evaluating the environmental

policies of countries, several institutions collaborated in the design of a composite indicator, the

Environmental Performance Index (EPI), to benchmark the environmental performances of

countries’ policies.

First in 2000, the Yale Center for Environmental Law and Policy (YCELP) and the

Center for Earth Information Science Information Network (CIESIN) at Columbia University

responded to this need for sustainability metrics with the Environmental Sustainability Index

(ESI). The objective of the ESI was to provide science-based quantitative metrics as an aid to

achieving long-term sustainable development goals to support the Millennium Development

Goals (MDGs) and helped governments around the world incorporate sustainability into

mainstream policy goals. Secondarily in 2006 an Environmental Performance Index (EPI)

appeared to set of environmental issues for which governments can be held accountable. The EPI

indicators are based on available data in core policy categories and seek to promote action

through metrics that allow political leaders to see the strengths and weaknesses of their

nation’s performance compared to peer countries. The main environmental objectives

proposed by EPI are: reducing environmental stresses on human health and promoting ecosystem

vitality and sound natural resource management (EMERSON ET AL., 2012).

The 2012 EPI was grounded in two core objectives of environmental policy:

Environmental Health, which measures environmental stresses to human health, and Ecosystem

Vitality, which measures ecosystem health and natural resource management. The EPI evaluated

countries on 22 performance indicators spanning ten policy categories that reflect facets of both

environmental public health and ecosystem vitality, including: Environmental Health; Water

(effects on human health); Air Pollution (effects on human health); Air Pollution (ecosystem

effects); Water Resources (ecosystem effects); Biodiversity and Habitat; Forests; Fisheries;

Agriculture; and Climate Change and Energy (EMERSON ET AL., 2012).

The remainder of the paper is divided as follows: the next section describes the main

features of the research design. After, we present the main results. Final section summarizes our

main conclusions.

METHODOLOGY

This research design use both descriptive and multivariate statistics to analyze data from

the Yale Center for Environmental Law and Policy. Table 1 summarizes the main features of our

research design.

Table 1 – Research design features

Focus variable Environmental Performance Index

Group comparison Across world regions and within Latin America countries

Statistical techniques Descriptive statistics, multiple comparisons, Pearson correlation and principal component

analysis (PCA)

All graphs and tables were based on data from Environmental Performance Index Reports

available at http://epi.yale.edu/. The focus variable is the Environmental Performance Index and

the group comparison is based a comparative perspective across world regions having Latin

America as a reference category. In addition, we compare EPI variation within Latin America

countries. In addition, we employ a principal component model (PCA) to estimate a new

measure of EPI that accounts for variables correlation levels.

RESULTS

Descriptive statistics show that there is EPI information available for 132 countries. Iraq

(25.32) displays the lower level of environmental protection while Switzerland shows the higher

(76.69). The mean is 53.06 with a standard deviation of 9.83, suggesting low sample variability.

Table 2 and Figure 2 summarize this information.

Table 2 – EPI descriptive statistics

N min max mean standard

deviation

132 25.32 76.69 53.06 9.83

Figure 2 – EPI distribution

Graphical analysis suggests that EPI follows a approximately normal distribution1. Figure

3 summarizes EPI distribution across world regions.

1 One-Sample Kolmogorov-Smirnov Test (0,509) also suggest that the EPI follows a normal distribution (p-value =

0,958), skewness (-.182) and kurtosis (-.280).

Figure 3 – EPI per world region (95% confidence interval)

Comparatively, Europe clearly displays the higher mean (62.89). Although Americas and

Asia & Pacific show similar levels of EPI, Americas is more homogenous. Middle East & North

Africa (44.29) has the lowest level of environmental protection.

Since our focus is on Latin America, we disaggregated Americas category. Figure 4

displays EPI 95% interval confidence by adopting a more disaggregated group comparison.

Figure 4 – EPI per world region (95% confidence interval) (Latin America included)

The dotted line represents the general mean (53.06). By including Latin America as

reference category, we observe that is has the second higher level of EPI (55.37). In addition,

America’s confidence interval became more spread, suggesting higher variability within the

group. Table 2 summarizes multiple group comparisons.

Table 3 – Multiple comparisons

Region

(I) (J) Dif (I-J) Standard error p-value

CI 95%

inferior Superior

Latin America Americas 1.271 2.810 .999 -11.564 14.106

Asia & Pacific 1.334 2.247 .997 -5.686 8.354

Eastern Europe & Central Asia 9.557 2.760 .028 .713 18.402

Europe -7.515 1.810 .003 -13.097 -1.934

Middle East & North Africa 11.077 2.157 .000 4.300 17.854

Sub-Saharan Africa 6.807 1.810 .009 1.180 12.435

Test: Games-Howell2

Latin America is statistically different from Eastern Europe & Central Asia (Dif = 9.557;

p-value = .028), Europe (Dif = -7.515; p-value = .003), Middle East & North Africa (Dif =

11.077; p-value = .000) and Sub-Saharan Africa (Dif = 6.807; p-value = .009). There were no

significant differences between neither Latin America and Americas (Dif = 1.271; p-value =

.999) nor Latin America and Asia & Pacific (Dif = 1.334; p-value = .997).

The results show the lowest levels of EPI to Peru (50.29), Mexico (49.11) and Haiti

(41.15). However Costa Rica (69.03), Colombia (62.33) and Brazil (60.90) display the opposite

pattern with the highest levels of EPI (Figure 5). The average of EPI for Latin America (55.37) is

represented by dotted line in Figure 5.

Figure 5 – EPI per country in Latin America

2 In addition to the conventional Tukey and LSD test, we also performed the Games-Howell test as long as the data

did not meet the homogeneity of variances assumption. The substantive conclusions were the same.

The analysis of comparisons of EPI and objectives (EH and EV) between the countries of

Latin America showed that Costa Rica was in first place than Colombia, in the second place, and

Brazil in the third place (Figure 5, Table 3).

Table 4. Comparisons of EPI and policy categories (EH: Environmental Health, EV: Ecosystem

Vitality) between first countries represent the best position and all countries of Latin America

Costa Rica Colombia Brazil Latin America

EPI 69.03 62.33 60.90 55.37

EH 76.19 55.55 62.07 61.06

EV 65.96 65.23 60.40 52.93

The EPI index is based on the aggregation of two multiple composite objectives:

Environmental Health (EH) and Ecosystem Vitality (EV). Table 3 summarizes the Pearson

correlation between EPI index and its original dimensions.

Table 5 – Correlation between EPI and its dimensions (EH) and (EV)

All cases Latin America

EPI

Environmental Health

(.573)

[.000]

n = 132 EPI

Environmental Health

(.546)

[.013]

n = 20

Ecosystem Vitality

(.644)

[.000]

n = 132

Ecosystem Vitality

(.695)

[.001]

n = 20

The results suggest that EV is highly correlated with EPI index compared to EH. For all

countries, EPI and EV correlation is .644 (p-value<.000). For Latin America the level of

association is .695 (p-value<.001). The substantive conclusion is that EPI should be more

influenced by variables regarding EV objective. Figure 6 summarizes EH and EV distribution in

Latin America.

Figure 6 - Environmental Health and Ecosystem Vitality distribution in Latin America

Box-plot

Confidence interval (95%)

After examining the descriptive statistics of EPI index and its original components, the

next step is to estimate in what extent the variables included in each objective contribute to its

formation. Table 4 summarizes the communalities of a principal component model.

Table 6 – Communalities3 of the principal component model of Environmental Health objective

Dimension

Environmental Health

(71.31% of total variance)

(Eigenvalue = 3.57)

Indoor air pollution (.784)

Particulate matter (.152)

Access to drinking water (.869)

Access to sanitation (.892)

Child mortality (.868)

The results of principal component model suggest that particulate matter (.152) indicator

is not highly correlated with the EH and therefore should not be included to compute the EPI.

The extracted component has an eigenvalue of 3.57 and explains 77.31% of all variance of the

original variables4. These results suggest that the EH is fairly represented by one-dimensional

construct.

3 The communality represents the degree of correlation between the extracted component/factor and the original

variables. In other words, it represents how much variance in each original variable is explained by the extracted

factors. Technically, we should exclude variables that displays communalities less than .5. 4 We reached a Kaiser-Meyer-Olkin (KMO) of sample adequacy of 0,854 with an approximate chi-square of 579.08

(p-value<0,000) for the Bartlett´s test of Sphericity (BTS), suggesting that the dataset is suitable for principal

Table 7 – Communalities of the principal component model of Ecosystem Vitality objective

Dimension

Ecosystem Vitality

(30.73% of total variance)

(Eigenvalue = 5.22)

S02 emissions per capita (.836)

S02 emissions per GDP (.850)

Change in water quantity (.792)

Biome protection (.755)

Marine protection (.557)

Critical habitat protection (.672)

Forest loss (.718)

Forest cover change (.673)

Growing stock change (.804)

Coastal shelf fishing pressure (.718)

Fish stock overexploited (.673)

Agricultural subsidies (.780)

Pesticide regulation (.599)

CO2 emissions per capita (.832)

CO2 emissions per GDP (.833)

CO2 emissions per electricity generation (.816)

Renewable electricity (.851)

The results indicate that six components have eigenvalue higher than 1. In addition, the

first component carried only 30.73% of the variance of the original variables. On substantive

grounds, it means that the EV objective cannot be fairly represent by one dimensional construct5.

component analysis. To guarantee more robust results we rerun the principal model by adopting estimates of the

policies categories suggested by the Appendix section of the 2012 Environmental Protection Index Report. We

basically estimate each policy category - Air pollution (effects on human health), Water (effects on human health)

and Environmental Burden of Disease – and then run a new model. KMO was .692 with a 318.94 BTS (p-

value<0,000). The component has a 2.51 eigenvalue and explains 83.68 of all variance of the included variables.

The file is available at http://www.epi.yale.edu/sites/default/files/downloads/Appendix1%2012.20.12.pdf 5 Our first model reached a .558 KMO with a 323.46 BTS (p-value<0,000). Regarding the extracted components, we

observed six of them with eigenvalue higher than 1.

CONCLUSIONS

The preliminary results suggest that: (1) besides Europe (62.89), Latin America shows

the highest EPI level (55.37); (2) within Latin America, Brazil has third maximum intensity of

EPI (60.90), following Colombia (62.33) and Costa Rica (69.03) and (3) particular matter

indicator is not highly correlated with environmental health objective and therefore should not be

included to estimate EPI in the first place. Alternatively, the Model Testing of SNA (Mt-SNA)

used in this study (Steps 2 and 3, see Figure 1) would have reveal important shortcomings in the

initial model and/or the statistical results to answer the main questions. Although the EV

objective explain better the EPI than EH in Latin America, the analysis of indicators of policy

categories separated did not explain totally which of them raise the EPI. According the

methodology of nesting analysis approach when the state of first assessment is initially weak or

refuted by the LNA and/or the quality of the cross-country statistical data is not sufficient to

adequately answer main questions, the SNA could be called on to do more work. In this instance,

the nested analysis approach between the countries of Latin America demands a more wide-

ranging and inductive Model Building SNA (Mb-SNA). Based in this Mb-SNA the outcome of

objectives of policy categories suggest the step 4 to review and test with LNA which indicators

of each objective (EH; EV) could explain the raise of EPI in Latin America countries?

The nested analysis approach applied in this research of EPI in Latin America countries

brought a new vision and possibilities to find the answers through the mix analysis with LNA

and SNA and use the interpretation to build the models until the end of analysis. The EPI

rankings revealed a wide range of environmental sustainability results and showed how many

countries are making progress on at least some of the challenges they face. The results found in

this study showed the EPI scores of Latin America countries presented variation mainly by the

scores of EH objective. The dataset used not reflects the prioritization of EV indicators over

those of EH but rather accomplish a balance between the contributions of these policy objectives

to the overall EPI. As observed by the authors who constructed the EPI scores the change in

weightings simply reflects a much-needed statistical correction to the aggregation method to

produce EPI scores more balanced between the EH and EV and produce in the near future a real

scene of this index across the world countries.

APPENDIX

OBJECTIVES POLICY CATEGORY INDICATOR CODE

Environmental Health

(EH)

Air pollution (effects on human health)

Indoor air pollution INDOOR

Particulate matter PM25

Water (effects on human health)

Access to drink water WATSUP

Access to sanitation ACSAT

Environmental burden of disease Child mortality CHMORT

Ecosystem Vitality

(EV)

Air pollution (effects on ecosystem)

Sulfur dioxide emissions per capita SO2CAP

Sulfur dioxide emissions per GDP SO2GDP

Water (effects on ecosystem) Change in water quantity WATUSE

Biodiversity and habitat

Biome protection PACOV

Marine protection MPAEEZ

Critical habitat protection AZE

Forests

Forest loss FORLOSS

Forest cover change FORCOV

Growing stock change FORGROW

Fisheries

Coastal shelf fishing pressure TCEEZ

Fish stocks overexploited FSOC

Agriculture

Agricultural subsidies AGSUB

Pesticide regulation POPs

Climate change

CO2 emissions per capita CO2CAP

CO2 emissions per GDP CO2GDP

CO2 emissions per electricity generation CO2KWH

Renewable electricity RENEW

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