Impact of Economic Sanctions on Environmental Performance of the Target State

Introduction

In a globalized world in which the action of one state often affects parts of or even the whole international community and in which states barely use military force against each other anymore, economic sanctions have become an increasingly prominent and effective tool of statecraft in international politics. They are considered a nonviolent, more humane alternative to military interventions as they aim at changing the target nation’s policies by inflicting economic damage (Neuenkirch & Neumeier, 2016).

That is why international sanctions and their impact constitute a broadly discussed research topic. Studies have focused on the factors that determine the use of sanctions as a policy tool (e.g. Cox & Drury, 2006), whether and under what circumstances they achieve their intended goals (e.g. Ang & Peksen, 2007; Dashti-Gibson, Davis, & Radcliff, 1997; Escribà-Folch, 2010; Pape, 1998; Regan & Aydin, 2006), and the effectiveness of sanctions (Hufbauer, Schott, & Elliott, 2008).

After the human suffering in Iraq following the UN sanctions in the 1990s scholars started to devote more attention to possible counterproductive consequences of foreign economic coercion (Hultman & Peksen, 2017). In this regards, studies investigated the impact of economic sanctions on economic aspects. Research demonstrated that they have a damaging effect on income inequality (Afesorgbor & Mahadevan, 2016), poverty (Alnasrawi, 2001; Neuenkirch & Neumeier, 2016; Sen, Al-Faisal, & AlSaleh, 2012), and GDP growth (Neuenkirch & Neumeier, 2015). Extensive research has also focused on sanction’s effects on sociopolitical matters. They can negatively affect the target’s regime stability (Marinov, 2005; Oechslin, 2014), women’s access to economic and social status (Drury & Peksen, 2014), democracy levels (Peksen & Drury, 2010), human rights (Peksen, 2009), and public health (Allen & Lektzian, 2013; Schneider & Shevchuk, 2019), and might increase repression (Adam & Tsarsitalidou, 2019; Wood, 2008) and terrorism (Choi & Luo, 2013).

This paper contributes to the literature by empirically examine for the first time the impact of economic sanctions on environmental performance of the target state. As Lim and Duit (2018) point out, states set up comprehensive administrative structures and implemented policies in order to protect the environment since the late 1960s. With climate change becoming one of the most severe threats to the world’s economic, social and political system, the international community has developed ambitious targets outlined in the United Nations 2015 Sustainable Development Goals and the Paris Climate Agreement (Rüttinger, Smith, Tänzler, & Vivekananda, 2015; Wendling, Emerson, Esty, Levy, & de Sherbinin, 2018). To meet these goals, countries must integrate environmental performance metrics across a range of pollution control and natural resources policies (Wendling et al., 2018). This paper investigates if economic sanctions affect the investment and engagement of countries in the protection of the environment.

To the best of my knowledge, there is no literature assessing the impact of economic sanctions on the target states’ economic performance. This paper intends to fill this research gap by empirically assessing the effect of economic coercion on several output variables indicating the environmental performance of the target state during the period 1989-2015. I assume that the sanctioned governments try to compensate economic damage caused by the sanctions through cuts in expenditures for environmental protection.

Econometrically, I employ a case-control study in which the average treatment effects for the treatment and for the control groups are calculated at first using panel models and then substituted to get the overall average treatment effect. This research design accounts for inter-linked problems of endogeneity and selection bias.

Neumayer & Neuenkrich 2016: Our results indicate that US sanctions are adversely affecting the

poor as we observe a 3.8 percentage point (pp) larger poverty gap in

sanctioned countries compared to the control group that is as similar

as possible in terms of observable pretreatment characteristics. In addition,

we showthat the impact of sanctions on poverty (i) increaseswith

the severity of sanctions, (ii) is larger for multilateral sanctions than for

unilateral sanctions imposed by only the United States, and (iii) is longlasting

as the poverty gap increases over the first 21 years of a sanction

regime. Finally, we provide some evidence that a slump in exports and

imports as well as a decrease in foreign aid are transmission channels

through which US sanctions adversely affect the target’s level of

poverty.

A theory of economic sanctions’ detrimental effect on environmental performance

The main actor in my theoretical framework is the government of the targeted state. In regards to the environmental performance of a state, it is the government that manages natural resources, regulates emissions into air, stimulates or represses environmentally beneficial behavior among citizens and firms, and choses to cooperate or defect from international environmental treaties (Duit, 2005). Although global, regional, and local scales of governance have become more engaged in responding to environmental problems in the last three decades, the nation-state is still the central actor as successes in tackling environmental problems will be determined in large parts by national commitment, policies, institutions, and capacity (Fiorino, 2011).

By now, most contemporary states accept some form of responsibility for addressing environmental problems while varying largely in ambition and capabilities (Duit, 2016). Applying Hood and Margetts (2007) Tools of Government approach to environmental politics, Duit (2016) identifies four main resources the state has for addressing problems related to market externalizations of environmental costs: regulation, redistribution, administration, and knowledge production. The environmental performance of a country depends mainly on the government’s interest to use these four means and the amount of capital it invests to tackle environmental problems.

Both, the government’s interest and actual investment depend on the economic situation of the state and especially its economic growth (Fiorino, 2011). Only with sufficient financial capacities, the government will make extensive use of the above mentioned resources. Finding itself in an unfavorable economic situation, a government will not risk losing tax revenues from important industrial firms by forcing them to comply with highly ambitious environmental regulation such as new pollution limit values or product standards or by introducing excessive taxes on environmentally harmful activities or products. Furthermore, a government will most likely freeze or even reduce its spending for the environmental public administration apparatus and for the production of environmental knowledge through scientific studies or environmental monitoring programs. In this regard, research has found that “increasing wealth allows a state to accumulate the ‘environmental protection capital’ necessary for acting to protect the environment” (Fiorino, 2011, p. 372).

Because a state’s economic situation considerably determines its environmental performance, economic sanctions might have a significant impact on it as well. Research on the effects of economic coercion on the economy of the target state has demonstrated that such coercion measures cause severe economic damage. According to the sanctions literature, the sender state even wants the cost of sanctions against a target country to result in maximum economic deterioration in order to coerce the target state to alter its policies in favor of the sender states (Afesorgbor & Mahadevan, 2016; Schelling, 1967). Neuenkirch and Neumeier (2015) showed that both unilateral and multilateral economic sanctions lead to a significant decline in GDP per capita. Moreover, Hufbauer et al. (2008) found that such sanction cause a slump in exports and imports, and a concentration of international capital flows, that is, withdrawal of foreign direct investment, foreign aid, and financial grants, as well as high inflation (see also Evenett, 2002). Furthermore, Peksen and Son (2015) demonstrated that economic coercion instigates currency crises by weakening the economy and creating political risks conducive to speculative attacks by currency traders. Because targeted countries are often characterized to be politically and economically fragile, the shortage of supplies and commodities might even provoke an collapse of the target’s entire economy (Neuenkirch & Neumeier, 2016).

According to the above developed argument that environmental performance decreases as a state experiences economic hardship, I expect economic sanctions to have a negative impact on the environmental performance of targeted states.

Moreover, I assume that economic sanctions not only directly reduce the resources a government has to invest in the protection of the environment, but also have an indirect negative effect on the government’s interest of spending their financial capacities on policy areas that don’t immediately reduce the likelihood of staying in power. For the development of this argument, I assume governments of sanctioned states to be rational and self-interested actors who want to maximize their probability of remaining in power. Economic damage constitutes a severe risk for this goal for the following three reasons:

Firstly, economic sanctions might weaken the government’s economic capacity as revenues from trade and financial flows decline and domestic economic actors fail to pay enough taxes because of the economic dislocation (Hultman & Peksen, 2017). This deprives the government of the resources necessary to provide basic public goods and services to the population and the industry. As a consequence, the infrastructure deteriorates, welfare state services decline, and shortages in the supply of water and electricity occur (Neuenkirch & Neumeier, 2016). The caused grievances that the population has to endure and the disadvantages for economic actors boost high discontent with the government and increase the support for opposition groups and the likelihood of regime change.

Secondly, governments of sanctioned countries often take part in an inter- or intra-state conflict. Economic damage not only reduces the government’s resources to meet its basic functions, but also limits the state’s assets devoted to war-waging efforts such as spending on military expenditures (Hultman & Peksen, 2017). This constitutes a great risk for the government’s target to stay in power as weakened military capacity allows armed internal and external opponents to benefit from the state’s vulnerability by challenging their monopoly on the use of force or even overthrowing the government.

Thirdly, economic sanctions may increase the likelihood of defection of members of the support coalition which is especially risky for authoritarian regimes. Escribà-Folch (2012) argues economic hardship caused by sanctions may harm the economic interests of some coalition members. As doubts regarding the regime’s stability and future benefits may occur, sanctions might also reduce the expected long-run benefits of political support for the regime. In order not to lose their support, the government has to compensate the elite coalition members and its supporters such as those in police, military, and civil services, to maintain their loyalty and support and to make them desist from backing a potential challenger (Hultman & Peksen, 2017).

Confronted with the economic damage and the accompanying risk of the loss of its power, the government of a targeted state must make difficult decisions concerning the allocation of its reduced resources. I argue that in order to countervail the above described risks caused by economic damage, the government cuts expenditures for policy areas that it does not consider as direct risks to its goal to stay in power. Previous literature on the impact of economic sanctions on public health suggests that governments shift the burden of the economic hardship from the elites to the weakest parts of the population through cuts in social welfare and public goods provision (Allen & Lektzian, 2013; Alnasrawi, 2001; Schneider & Shevchuk, 2019). This demonstrates that governments try to compensate lacks in financial capacities by spending less for something they do not consider as that much important to remain in power, in this case the well-being of the weakest parts of the population.

Applying the same reasoning to environmental policies, I argue that governments try to plug financial holes precipitated by economic sanctions by cutting expenditures for environmental protection because they don’t suggest the policy area of environmental protection to reduce the likelihood of staying in power.

Based on the above discussion, I put the following hypothesis to an empirical test:

H1: Economic sanctions have a negative effect on the environmental performance of targeted states.

Research Design

The main dependent variable is environmental performance. I ask whether the presence of economic sanctions against a target state in a given year decrease this state’s environmental performance.

To substantiate the theoretical claims outlined above, I rely on the dyadic EUSANCT dataset which contain 325 sanction threats and imposed sanctions by the European Union, the United Nations, the United States, or the coalition of these senders (Weber & Schneider, 2019). I include in the test 199 countries and the entire post-Cold War era (1989-2015) that, arguably, ended with two key events of 2016, the Brexit vote and the election of Donald (Weber & Schneider, 2019).  The data is broken down into country-year observations for each calendar year. This makes for a total of 5.077 observations in the full data-set. Below, I offer a detailed account for the methodological approach as well as the operationalization of the independent and outcome variables.

Two kinds of data are essential for conducting a test of the main hypothesis: data on the independent variable, economic sanctions, and data on the dependent variable environmental performance. 

Independent Variables

The explanatory (treatment) variable in this study is a binary economic sanctions indicator obtaining a value of 1 if there is an economic sanction in a given country-year and 0 otherwise. An economic sanction is considered to be in place if a targeted financial sanction, a trade sanction, an economic embargo, or a combination of these three has been imposed. I include binary variables for all three types in my models. Thereby, the particular effects of these are captured as well as the varying costs they occasion. Following the approach of Schneider and Shevchuk (2019), I furthermore consider the three main senders of sanctions (i.e. the EU, US and UN) and their combinations for each country year if the case involves more than one sender. Finally, I control for the effects of the time a particular sanction has been in place by controlling for their duration since inception.

Outcome variables

Different outcome variables are considered in order to assess the impact of sanctions on environmental performance. As Fiorino (2011) stated correctly, “there is no one set of national environmental indicators that is comparable to the standard set of measures used to measure economic performance” such as Gross Domestic Product (GDP) growth, unemployment rates, trade balances and so on. Instead, research on environmental performance has used dozens of different indicators and measures. One distinction among the studies is whether they focus on policy outcomes, for example the adoption of policies adopted or the creation of institutions, or environmental outcomes, such as air pollutant emissions ranging from nitrogen oxides (NO_x) to carbon dioxide (CO₂).

The most direct operationalization for testing the impact of economic sanctions on environmental performance would be to assess the immediate effect of sanction on environmental policy change. Motivations for reforming public policies have been a major research interest of comparative policy analysis also in regards to the field of environmental protection for decades (Tosun & Schnepf, 2018). As a recent example, Lim and Duit (2018) test how distributive commitments in the context of the existing welfare states affect governments’ environmental policy commitments on the basis of a dynamic panel data analysis of environmental policy outputs in 25 OECD member states during the period 1975–2005. Similarly to this illustration, studies using environmental policy output data have limited their data collection to highly industrialized countries or specific geographical regions such as the EU and therefore to a relatively small portion of all nation-states (see e.g. Duit, 2016; Holzinger, Knill, & Arts, 2008; Jahn, 1998; Knill, Tosun, & Heichel, 2008; Tosun, 2013). As many of the sanctioned target states are not highly industrialized or Western countries, specific data on their environmental policy output has not yet been gathered. The same problem of data unavailability emerges with prominently used proxy variables, such as annual government spending data on the protection of the environment or the availability of environmental information provided by the state.

Given this difficulty of data collection on policy change, this study makes use of environmental outcomes as the dependent variable. Many previous studies have relied on data measuring levels of air or water pollution in a country (Fiorino, 2011). Prominent studied air pollutants range from nitrogen oxides (NO_x), over greenhouse emissions to carbon dioxide (CO₂) (see e.g. Bernauer & Koubi, 2009; Esty & Porter, 2005; Neumayer, 2003; Perkins & Neumayer, 2008; Roller, 2005; Wälti, 2004). These environmental outcome data can be used as a proxy for measuring environmental policy change.

Still, the interpretation of test results with outcome data has to be taken with a pinch of salt. The relationship between a policy decision, in this case for example the increase of emission limit values, and its effects is affected by a considerable number of additional factors, such as the degree of implementation by government agencies, the population, or the industry (Knill, Schulze, & Tosun, 2011, 2012; Tosun & Schnepf, 2018). Therefore, one has to examine whether additional variables may confound the measurement. Moreover, data is often collected by international organizations and national agencies. Data from the latter have to be treated with due care, especially when relying on data provided by non-democratic regimes (Tosun & Schnepf, 2018). A final problem with environmental impact data relates to time-lags between governmental action and potential policy effects. As Knill et al. (2011) point out, “it is hardly impossible to exactly determine how much time has to pass until, for instance, new emission standards result in lower or higher levels of pollution. Without specifically investigating policy change, researchers can only assume that changes in environmental performance have primarily been the result of policy adjustments.

However, the measurement of environmental outcome data has two advantages: first, the availability of the data; second, outcome data are often accessible in form of metric variables so that even small differences between the countries and changes over time become apparent (Tosun & Schnepf, 2018). Given this study’s methodological approach of testing panel data, relying on outcome data for measuring the dependent variable constitutes the best possible choice.

In order to deal with the above mentioned limitations of outcome data, models will be estimated for two comprehensive indicators and three different air pollution measures. The most ambitious effort to measure national environmental performance is the Environmental Policy Index (EPI), a joint initiative of Yale and Columbia Universities. They aim to provide a data-driven assessment of environmental conditions at a national level (Fiorino, 2011). The EPI versions from 2012 and 2016 for the first time incorporated trend data for all of the indicators they measured. The EPI 2012 version covered 22 indicators for 132 countries in the period 2000-2010, whereas the EPI 2016 version ranks 180 countries on 20 performance indicators from (Yale Center for Environmental Law + Policy, Center for International Earth Science Information Network – Columbia University, & World Economic Forum, 2012, 2016). Both indices evaluate the environmental performance in regards to health impacts, air quality, water and sanitation, water resources, agriculture, forests, fisheries, biodiversity and habitat, and climate and energy. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. Because the EPI scores of both versions differed in regards to the underlying methodology, raw data sources, and targets and weighting, they are not comparable with each other.

As both EPI versions only cover a limited time period, I also assess the effect of economic sanctions on three prominently used air pollution measures. These include the carbon dioxideemission (kt), the Nitrous Oxide emissions (kt of CO2 equivalent), and the total greenhouse gas emissions (kt of CO₂ equivalent). For scaling purposes, I have applied logarithmic transformations to these variables. All three variables are collected from the World Development Indicators Databank provided by the World Bank.

Control Variables

Because the environmental performance variables introduced above can be influenced by many confounding variables other than the independent variable, it is of great importance to include control variables that may have a significant effect on the outcome. First, several analyses of the relationship between economic growth and environmental protection have found that many forms of pollution increase in early stages of growth but level off beyond some level of income (Fiorino, 2011). This results in an inverted U-shaped curve (the Environmental Kuznets curve). The underlying reasoning is that demand and supply of environmental policies are expected to increase at higher income levels, which might finally also lead to lower emission (Knill et al., 2012). I control for these factors by including the natural log of GDP per capita (constant 2010 US$) and the growth rate of GDP (annual %) in my models (GDP growth). Furthermore, I suggest that the structural composition of national economies has an impact on states’ environmental performance. Especially the industrial sector is expected to contribute above average to overall (see e.g. Earnhart & Lizal, 2008; Knill et al., 2012). I therefore control for the size of the industrial sector proxied by its contribution to total GDP (Industry).

Another set of controls refers to the effects of international trade (see e.g. Holzinger et al., 2008; Knill et al., 2012). I control for trade as percentage of GDP. Some environmentalists are skeptical of an emphasis on increased trade. They assume that higher levels of trade can exert downward pressures on environmental standards (i.e., the “race to the bottom” effect) and thus promote environmental degradation (Prakash & Potoski, 2006). On the contrary, other studies suggest that an increase in trade may induce convergence toward higher levels of environmental standards due to the positive effects of policy diffusion from high-regulating states to low-regulating ones (Fiorino, 2011; Holzinger et al., 2008; Knill et al., 2012; Vogel, 1995). Following the suggestions of Knill et al. (2012), I also use two more specific controls that capture effects emanating from differential patterns of international trade, namely the share of Manufactures exports and Manufactures imports (% of merchandise exports). They argue that since the production of manufactured goods is known to be particularly pollution intense, the theory of regulatory competition would predict laxer regulation and hence higher emission intensities. Alternatively, Perkins and Neumayer (2008) assert that increased competition from manufacturing imports and exports has positive effects if domestic firms move toward more environmentally efficient product and production technologies. Since similar beneficial technology spillovers can be expected at higher levels of foreign direct investment (FDI) inflows, I also include net FDI inflow (% of GDP) in my models (Knill et al., 2012; Prakash & Potoski, 2006).

Moreover, I control for Population density  (people per sq km of land area) and Urban population (% of total population) in order to rule out confounding effects related to demographics (Knill et al., 2012; Lim & Duit, 2018). All above mentioned controls are derived from the World Bank’s World Development Indicators.

Apart from economic and demographic variables, I finally account for state of democracy proxied by the Electoral democracy index from the V-Dem project. Research supports the view that democratic countries generally are more environmentally protective than less democratic states (Almeida & García-Sánchez, 2017; Fiorino, 2011).

Table X displays descriptive statistics for all variables in our dataset.

Empirical methodology

The relationship between the five dependent variables for environmental performance and the independent variables is estimated by means of standard panel analysis techniques. In order to most suitable model specification given the available data, tests for poolability, individual or time unobserved effects, correlation between these latter and the regressors (Hausman-type-tests), and serial correlation are conducted. The results of the tests for each dependent variable are provided in Table X.

I employ more consistent two-way fixed effects model instead of random fixed specification because my sample is non-random (there is only a finite number of countries to include and I did not pick them from the broader population) and individual time-invariant effects are likely to be correlated with the explanatory variables (Schneider & Shevchuk, 2019). I include country fixed effects in my analysis because I am interested in the effects particular sets of economic sanctions had on the specific target states, rather than in the effects of sanctions across countries. Robust HAC sandwich standard errors clustered at the country level are used in order to adjust for disturbances arising from cross-sectional heteroscedasticity, contemporaneous correlation, and temporal autocorrelation (Beck & Katz, 1995, 2011). By including time-fixed affects and thereby allowing the intercept to vary over time, the models are able to capture time specific influences such as globalization, trade liberalization, or technological development which are common to all countries (Schneider & Shevchuk, 2019). The first models are estimated using the plm R package (Croissant & Millo, 2008; Millo, 2017).

As Neuenkirch and Neumeier (2016) point out research on the side-effects of sanctions has to take into account the inter-linked problems of selection and endogeneity bias (see also Schneider & Shevchuk, 2019). Potential endogeneity would mean in this case that the reasons for imposing economic sanctions – such as, for instance, interstate or intrastate conflict, human rights violations or political repression – could be associated with the target state’s political and economic situation, which in turn, both are related to economic performance (Neuenkirch & Neumeier, 2016).

To overcome the potential endogeneity which the above explained panel models might not be able to sufficiently account for, I employ a matching approach. The quantity of interest is the treatment effect for the treated (ATT):   = [ (1)| =1]− [ (0)| =1], where (∙) is the potential outcome for the environmental performance variables and is the treatment indicator (1 indicates that the unit is under economic sanctions and 0 otherwise) (see for a similar approach based on entropy balancing Schneider & Shevchuk, 2019). The intuition behind the matching approach is to compare the environmental performance of countries targeted by economic sanction to non-sanctioned countries that are very similar to the targeted ones. The average difference in environmental performance between sanctioned countries and the “closest” non-sanctioned countries must then be due to treatment (Neuenkirch & Neumeier, 2016).

The population of non-sanctioned countries that are similar to the targeted ones is compiled using the potential sanction variable of the EUSANCT dataset. This variable indicates whether the respective country-year has been identified as a potential sanction-dyad meaning that the country could have been sanctioned. The variable is calculated by taking the values of several variables related to regime-type, human rights, political terror, nuclear proliferation, military coups, as well as armed conflicts, and one-sided violence into account (see Weber & Schneider, 2019). The control group thereby contains all countries that never could have been sanctioned at some point between 1989 and 2015 but have not been although. This already assures that the treatment and control units share some similarities in regards to factors that are expected to explain the imposition of sanctions such as conflict intensity, bad governance, nuclear weapons, etc.

First, weights are computed that are assigned to units

not subject to treatment. These weights are chosen to satisfy prespecified

balanced constraints involving sample moments of pretreatment

characteristics by remaining, at the same time, as close as possible

to uniformbaseweights. In our analysis, the balance constraints require

equal covariate means across the treatment and the control group,

which ensures that the control group contains, on average, units not

subject to treatment that are as similar as possible to units that received

treatment. Second, the weights obtained in the first step are used in a

regression analysis with the treatment indicator as an explanatory variable.

This yields an estimate for the ATT, that is, the conditional difference

in means for the outcome variable between the treatment and

control group.3

The collected data contains missing values in some dependent and control variables. Both, panel regression and

Table 1: Model Specification Tests

Outcomes	T1	T2	T3	T4	T5	T6	T7
EPI_12	2.2e-16	2.2e-16	2.2e-16	0.068	2.2e-16	0.01	2.2e-16
EPI_16	1.85e-6	2.2e-16	2.2e-16	0.024	2.2e-16	0.01	2.2e-16
CO2	0.08631	2.2e-16	2.3e-16	2.2e-16	2.2e-16	0.01	2.2e-16
N2O	0.3145	2.2e-16	2.2e-16	2.2e-16	2.2e-16	0.01	2.2e-16
Greenhouse Gas	2.2e-16	2.2e-16	2.3e-16	2.2e-16	2.2e-16	0.01	2.2e-16

Note: T1 =  Hausmann Test; T2 = Lagrange Multiplier Test – two-ways effects (Breusch-Pagan) for unbalanced panels; T3 = Breusch-Pagan LM test for cross-sectional dependence in panels; T4 = Pesaran CD test for cross-sectional dependence in panels; T5 = Breusch-Godfrey/Wooldridge test for serial correlation in panel models; T6 = Augmented Dickey-Fuller Test; T7 = Breusch-Pagan test.

Table1: Descriptive Statistics

Statistic	N	Mean	St. Dev.	Min	Max
Economic Sanction (binary)	4,524	0.160	0.367	0	1
Potential Sanction (binary)	4,524	0.510	0.500	0	1
Financial sanction (binary)	4,524	0.029	0.166	0	1
trade sanction (binary)	4,524	0.014	0.119	0	1
Economic embargo (binary)	4,524	0.027	0.163	0	1
Duration of continous sanctioning	4,524	2.043	6.133	0	66
EPI 12	1,187	49.688	8.938	25.215	77.994
EPI 16	1,356	62.560	14.017	25.650	91.050
CO2	4,131	8.737	2.530	1.993	16.147
N2O	3,820	7.473	2.729	-4.155	13.283
Greenhouse gas	3,551	10.256	2.254	1.589	16.338
GDP pc	4,203	8.021	1.398	4.749	11.886
GDP growth	4,255	3.881	6.894	-64.047	149.973
Population density	4,355	4.052	1.494	0.321	9.845
Urban population	4,390	51.379	23.508	5.342	100.000
Industry	3,874	27.749	13.088	2.073	87.797
Trade	3,832	79.837	43.111	0.021	437.327
Manufactures imports	3,118	65.156	12.950	0.033	96.997
Manufactures exports	3,125	38.379	30.326	0.0005	373.228
FDI inflow	4,034	5.895	38.918	-55.234	1,282.633
Electoral democracy Index	3,931	0.472	0.248	0.016	0.927

Results

Why Economic factors not always explain environmental performance:

Fiorino 2011: The results of the EKC studies should be interpreted carefully. The dependent variables

have focused on a limited number of indicators, largely common air pollutants and

organics and metals in water coming from industrial sources. The inverted-U applies less to

consumption-related indicators or to pollutants, such as carbon dioxide, whose effects are

not immediately apparent and may be shifted to other areas or future generations. The

research applies mostly to countries that have undergone some degree of industrial growth,

and the findings may not apply for those at earlier or later stages. Finally, as the later

studies recognize, the economy-environment relationship is not predetermined; a developing

country is not necessarily destined to repeat the patterns of its predecessors.

Although it seems clear that some forms of pollution tend to decline with growth, one

should be skeptical of there being a ‘‘common, U-shaped pathway that countries follow as

their income rises.’’ (Stern 2004, p. 1435; see also Levinson 2002; Dinda 2004). This

conclusion in itself is significant; the pay-off from this work may lie in suggesting policies

that allow countries to pursue more environmentally sound growth paths (Miah et al. 2011;

Panayotou 1997). Indeed, the principal value of this and the other streams of research is to

define policy and development alternatives that would enable countries to ‘‘tunnel

through’’ the historical pattern observed and to short-circuit much of the environmental

harm that other nations have undergone (Munasinghe 1999).

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