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Social Science Research 36 (2007) 834–853 www.elsevier.com/locate/ssresearch

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The political-economic causes of change in the ecological footprints of nations, 1991–2001: A quantitative investigation Andrew K. Jorgenson a,¤, Thomas J. Burns b

Department of Sociology, Washington State University, P.O. Box 644020, Pullman, WA 99164-4020, USA b Department of Sociology, University of Oklahoma, USA

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Available online 10 July 2006

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Abstract

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This study tests a series of hypotheses concerning the political-economic causes of change in per capita consumption-based environmental impacts. To test the hypotheses, panel regression analyses are conducted to assess the eVects of level of economic development, export intensity, domestic economy structure, and other factors on growth in per capita ecological footprints of nations, 1991–2001. Analyses conWrm multiple hypotheses: more-developed nations and those with a greater intensity in the services sector experience higher increases in per capita footprints, while manufacturing intensity and export intensity are inversely related to growth in consumption-based impacts. The Wndings support key tenets of treadmill of production theory, uneven ecological exchange theory, export dependence theory, and world-systems theory. © 2006 Elsevier Inc. All rights reserved.

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1. Introduction

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Keywords: Ecological footprints; Political-economy; Sustainable development; Globalization; Environmental degradation; Panel regression

A growing body of research in political-economic sociology and environmental sociology investigates the environmental impacts of resource consumption. Often, these analyses focus on the per capita ecological footprints of nations, which trace the average amount of *

Corresponding author. Fax: +1 509 335 6419. E-mail address: [email protected] (A.K. Jorgenson).

0049-089X/$ - see front matter © 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.ssresearch.2006.06.003

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2. Background 2.1. The ecological footprint

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resources a person in a given country consumes and a portion of the waste they generate.1 The ecological footprint approach provides a comprehensive unit of measurement that allows for comparisons of various types of consumption-based impacts (Wackernagel et al., 2000). The footprint is also an eVective tool for communicating human dependence on life-support ecosystems. It can be applied to a variety of issues to help identify the complementarities between natural capital, economic development, and other social structural factors (Deutsch et al., 2000). Findings from sociological studies suggest that per capita footprints are largely a function of a country’s level of economic development, export dependence, urbanization, and other social factors (e.g., Jorgenson, 2003, 2005; Jorgenson and Rice, 2005; Jorgenson et al., 2005; see also York et al., 2003). Due to the unavailability of adequate data, this body of prior research on per capita footprints is all cross-sectional by design. However, it is quite possible that the more salient factors contributing to change in per capita footprints might vary from the causes found to be most relevant in cross-sectional analyses (e.g., Davis et al., 2005). Fortunately, adequate panel data are now available that allow for investigations of these types of human/environment relationships. Through a series of panel regression analyses of 138 countries, this study begins to address such questions. Building on prior research, we test hypotheses concerning the eVects of economic development, export intensity, domestic economy structure, the environmental commitment of nation-states, urbanization, and other factors on change in consumption-based environmental impacts 1991–2001, measured as per capita footprints of nations. Prior to the analyses, we provide an overview of Wndings from recent cross-sectional studies of per capita footprints, and summarize the theoretical justiWcations for including certain predictors in the tested models.

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The footprint approach was primarily developed by Mathis Wackernagel and William Rees (e.g., 1996). The ecological footprint quantiWes the amount of biologically productive land required to support the consumption of renewable natural resources and assimilation of carbon dioxide waste products of a given population2 (e.g., Wackernagel et al., 2002). National footprints are measures of societal demand upon domestic as well as global natural resources. They allow for comparisons of a nation’s environmental demand relative to available domestic and global natural capital.3 In particular, the ecological footprint The majority of sociological research on per capita footprints has focused on international political-economic factors. However, sociological research on the total footprints of nations underscores the importance of structural human-ecological factors when examining the overall scale of environmental outcomes (Rosa et al., 2004; York et al., 2003). 2 See Jorgenson (2003) and York et al. (2003) for more thorough discussions of the methodology involved in the calculation of national footprints and recent debates concerning the overall utility of the footprint method for social scientiWc research. 3 The concept of natural capital is an extension of the economic notion of capital. It is usually deWned as the stock of natural assets, such as water and forest resources, producing a Xow of services and resources for human societies. The term is often criticized as being anthrocentric by political-ecologists and other environmental social scientists [see Wackernagel and Rees (1996) for a more ecologically sensitive deWnition and its relationship to ecological footprints].

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includes the area of cropland required to produce the crops consumed, the area of grazing land required to produce the animal products, the area of forest required to produce the wood and paper, the area of sea required to produce the marine Wsh and seafood, the area of land required to accommodate housing and infrastructure, and the area of forest required to absorb the carbon dioxide emissions resulting from energy consumption (Wackernagel et al., 2002). A nation’s footprint is calculated by adding imports to, and subtracting exports from, domestic production. In mathematical terms: consumption D [production + imports] ¡ exports. This balance is calculated in over sixty categories including both primary resources [e.g., raw timber, wheat, or milk] and manufactured products that are derived from them [e.g., paper, plywood, cereal, and cheese]. Each category is screened for double counting to increase the consistency and robustness of the measures. To avoid exaggerations in measurement, secondary ecological functions that are accommodated on the same space as primary functions are not added to the footprints. These data are measured in area units where one footprint equals one hectare, and one hectare is the equivalent of approximately 2.47 acres. Social scientists and policymakers can compare the per capita footprints of nations to the per capita ecological capacity that exists on the planet. For example, in 1996 per capita footprints ranged from .35 hectares to more than 16 hectares, and the majority of per capita footprints were higher than the earth’s bio-capacity per capita [2.1 hectares],4 illustrating the severity of resource overuse (Wackernagel et al., 2000). With the development and availability of these data, sociologists have conducted numerous studies of the causes of variation in per capita footprints of nations (Jorgenson, 2003, 2004, 2005; Jorgenson and Burns, 2004; Jorgenson and Rice, 2005; Jorgenson et al., 2005) as well as the total footprints of nations (Rosa et al., 2004; York et al., 2003). This study focuses on change in per capita footprints. Without doubt, the relative size and growth of populations are key human drivers of the scale of national footprints and other forms of environmental degradation (e.g., National Research Council, 1999). However, many social scientists argue that the absolute size of populations has a relatively proportional eVect on the total footprints of nations and other environmental outcomes, and per capita diVerences do indeed exist that cannot be explained by this societal characteristic (e.g., Jorgenson, 2006a; Roberts, 2001; Rosa et al., 2004; York et al., 2003). While investigating change in the total footprints of nations is itself a critically important line of social scientiWc inquiry, it is well beyond the scope of the current analyses. 2.2. Economic development

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Cross-sectional analyses consistently show that the per capita footprints of nations are largely a function of a country’s level of economic development, usually measured as per capita Gross Domestic Product (e.g., Jorgenson, 2005; Jorgenson and Rice, 2005). Moreover, studies of both total and per capita footprints of nations fail to Wnd support for the environmental Kuznets curve hypothesis (Jorgenson, 2004; York et al., 2003), meaning that the relationship between development and consumption-based environmental impacts

4

Wackernagel et al. (2000) calculate national bio-capacity per capita by dividing all the biologically productive land and sea on earth by the total world population. The result is an average of 2.1 hectares per person.

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is not curvilinear in the form of an inverted U-shape distribution as commonly argued by other social scientists5 (e.g., Grossman and Krueger, 1995; Ehrhardt-Martinez et al., 2002). Rather, these analyses support the arguments of world-systems theory (e.g., Chase-Dunn, 1998; Roberts and Grimes, 2002) and treadmill of production theory6 (Schnaiberg and Gould, 1994). Higher levels of economic development generally aVord a country the ability to be more competitive in the global marketplace (Chase-Dunn, 1998; Kentor, 2000). More-developed countries contain market economies that consume greater levels of natural resources, and environmental degradation is largely driven by the growth and intensiWcation of market economies (Jorgenson, 2005). To maintain proWts, producers must constantly expand production, which requires additional material inputs. Schnaiberg and Gould (1994) characterize these processes as the heart of the “treadmill of production.” Producers are usually headquartered in developed countries, and outsource increasing levels of production and resource extraction to less-developed, export-dependent countries (Jorgenson and Kick, 2006). The expansion of production and consumption usually takes the form of global commodity chains in which resources are used and waste generated at every link (Princen, 2002). Produced commodities are usually transported to and consumed by populations in more-developed countries. The majority of proWts derived from these goods further increase the economic development of market economies that house the headquarters of producers (e.g., Chase-Dunn, 1975; Kentor and Boswell, 2003). The asymmetrical structure of these “global assembly lines” and their concomitant environmental impacts are partly a function of the historical legacies of colonial and neo-colonial trade patterns (Emmanuel, 1972; Hornborg, 2001). Thus, according to world-systems theory and treadmill of production theory, developed countries are more likely than less-developed countries to exhibit increases in their per capita consumption-based environmental impacts. 2.3. Export dependence and uneven ecological exchange

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Export dependence theory focuses on the negative consequences of uneven trade relationships, particularly for less-developed countries. The theory asserts that high levels of export dependence make an exporting country more vulnerable to world-economic market forces, and allow the developed nations with whom they exchange to obtain favorable terms of trade (e.g., Galtung, 1971; Hirschman, 1980; Rubinson and Holtzman, 1981). This form of dependence tends to have a number of consequences for the domestic ecology (Jorgenson and Kick, 2006). Ecological consequences include the depletion of raw materials and pollution concentrations resulting from practices such as monocropping in the agricultural sector and the concentrated production of commodities with unhealthy byproducts (e.g., Bunker, 1984; Burns et al., 2003a). Raw materials, agricultural goods, and produced commodities are generally exported to higher-consuming countries that utilize their international power to maintain rela5

These researchers argue that environmental impacts increase in the early stages of development, reach a turning point, and then decline as societies further develop. 6 A recent special issue of the Journal of World-Systems Research (2003, volume IX, number 2) deals explicitly with world-systems approaches to studying human caused environmental degradation. Similarly, a recent 2 part series of Organization & Environment (part 1: 2004, volume XVII, number 3; part 2: 2005, volume XVIII, number 1) deals explicitly with current research and theoretical debates in the treadmill of production tradition.

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A.K. Jorgenson, T.J. Burns / Social Science Research 36 (2007) 834–853

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tionships of uneven ecological exchange (Hornborg, 2001; Jorgenson and Rice, 2005). For example, Jorgenson (2005) Wnds that export dependence in the form of export intensity [i.e., exports as % of GDP] in inversely related to the size of a nation’s per capita footprint. This empirical Wnding coupled with the political-economic theory of export dependence is similar to a common argument originating in the Weld of ecology known as the “Netherlands fallacy”, which refers to the error in assuming that the overall environmental impacts of the Netherlands’ population [and other aZuent, higher-consuming countries] are contained within its national borders (e.g., Ehrlich and Holdren, 1971; York and Rosa, 2003). Likewise, a number of other environmental social scientists argue that international trade blurs human responsibility from the eVects of resource consumption (Anderrson and Lindroth, 2001; Lofdahl, 2002; Tucker, 2002). It provides a means by which overall environmental consequences become less directly evident to higher-consuming populations (Rothman, 1998). Proponents of comparative advantage theory would argue that the apparent negative relationship between export dependence [or what they identify as “trade specialization”] and the per capita footprints of nations illustrates the overall “beneWts” of trade for less-developed countries (e.g., Magee, 1980; Ricardo, 1891). The theory of comparative advantage was Wrst developed by Ricardo (1891), who argued that countries have diVerent “endowments” of labor, land, and capital. Countries will specialize in the exportation of those products which use intensively the factors of production which they are most endowed. If each country specializes in those goods and services where they have an advantage, then total output, economic welfare, and social welfare can be increased. However, cross-national studies provide evidence that nations with lower per capita footprints experience higher domestic levels of particular forms of environmental degradation and infant mortality rates (Jorgenson, 2003; Jorgenson and Burns, 2004). Moreover, a large proportion of less-developed countries exhibit footprints well below the bio capacity per capita (Wackernagel et al., 2002). Their relatively low levels of globally sustainable consumption, high levels of domestic environmental degradation, and human suVering are all characteristics of underdevelopment stemming from asymmetrical exchanges between developed and less-developed countries (Chase-Dunn, 1998). Overall, the theories of export dependence and uneven ecological exchange would predict that export intensity is inversely related to growth in consumption-based environmental impacts. More speciWcally, these two theories would assert that the eVect of export intensity on growth in per capita footprints is negative.

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2.4. Urbanization

Built environments, such as urban areas, require high levels of natural resources in their construction and maintenance. Jorgenson (2003, 2004) and York et al. (2003) Wnd that nations with greater levels of urbanization [measured as the percentage of total population residing in urban areas] tend to have larger total and per capita footprints. In addition to requiring substantial material inputs for building infrastructure, urban areas generally contain more articulated consumer markets and overall aZuence, both of which increase the scale and intensity of the consumption-based environmental impacts of given populations. However, some cross-national studies show that urbanization reduces domestic levels of deforestation (e.g., Ehrhardt-Martinez et al., 2002). Thus, the social scientiWc literature on

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the relationship between urbanization and environmental outcomes lacks a general consensus. 2.5. Domestic economy structure

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Cross-sectional studies fail to Wnd evidence suggesting that the structure of domestic economies aVects the size of per capita and total footprints of nations (Jorgenson and Rice, 2005; York et al., 2003; see also Salzman, 2000). However, given the theoretical relevance and potential policy implications, analyses of the structure of domestic economies on change in per capita footprints are clearly warranted. Some macroeconomic perspectives suggest that shifting from manufacturing, agriculture, and extractive activities to a more service-based economy oVers a potential solution to reducing the scale and intensity of the environmental impacts of nation-states (e.g., OECD, 1998). Following this line of reasoning, one could posit that countries with more service-based economies will experience relative declines in per capita footprints, while countries with a greater emphasis on manufacturing or agriculture will experience relative increases in consumption-based environmental impacts. In contrast, international political-economic perspectives, such as world-systems theory (Chase-Dunn, 1998; Roberts and Grimes, 2002) and uneven ecological exchange theory (Bunker, 1984; Hornborg, 2001), would argue that serviced-based economies, which tend to be nested in more aZuent societies, utilize their relative position of international political-economic power to consume vast amounts of resources and externalize their environmental costs. In particular, countries with more service-based economies will experience higher levels of growth in resource consumption, partly through importation of natural resources and commodities from export-dependent nations. The most powerful global cities are located in developed countries (Alderson and BeckWeld, 2004). These cities, often considered as “command points”7 and Wnancial centers for global production systems, house the most lucrative “producer services”8 that cater to transnational Wrms headquartered in global cities (Sassen, 2001), many of which focus on global commodity production. Certain strands of world-systems theory (e.g., Roberts and Grimes, 2002) and uneven ecological exchange theory (e.g., Jorgenson and Rice, 2005) would suggest that countries with more manufacturing-based and agriculture-based economies will experience relative declines in their consumption based environmental impacts. These countries are typically less-developed with a greater focus on export-oriented production of agricultural goods and industrial commodities (McMichael, 2004). The activities taking place under these conditions are often transnationally organized (e.g., Jorgenson, 2006b; Robinson, 2004), with the majority of exports sent to and consumed by the populations of developed countries with more service-based domestic economies.

7

In the global cities literature, the term “command points” is used to highlight the centrality of key global cities as containers of the headquarters of many of the world’s largest transnational corporations that exercise a relatively large degree of control over global commodity production and distribution (e.g., Sassen, 2001). 8 “Producer services” are services for Wrms, including Wnancial, legal, advertising, consulting, transport, communications, cleaning services, security, and storage (Sassen, 2000). A common argument in the global cities literature is that these producer services are disproportionately concentrated in the largest and most powerful global cities, which tend to be located in more-developed countries (Alderson and BeckWeld, 2004).

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2.6. Domestic income inequality

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Some evidence suggests that domestic income inequality is inversely related to the relative size of a nation’s per capita footprint (Jorgenson, 2003). The argument is that nations with higher domestic income inequality, particularly less-developed countries, exhibit relatively lower per capita consumption-based environmental impacts because [1] the majority of the population has substantially lower income levels, and [2] the domestic market focuses on the export of raw materials, agricultural goods, and commodities produced by means of dependent industrialization9 (e.g. Beer and Boswell, 2002). Other studies that include domestic income inequality as a predictor fail to Wnd evidence of its apparent overall negative eVect, particularly those that include measures of export intensity or other forms of trade dependence (Jorgenson, 2005; Jorgenson and Rice, 2005). However, following this line of theorization, one might hypothesize that domestic income inequality will have an inverse relationship with growth in the per capita footprints of nations.

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2.7. The environmental state

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Sociologists working in the “environmental state” and related approaches argue that the institutionalization of environmental policies could lead to more ecologically eYcient forms of production and consumption with lower levels of environmental degradation (e.g., Goldman, 2001; Mol and Buttel, 2002). Studies in these areas often examine the oYcial adoption of environmentally friendly policies (e.g., Frank et al., 2000). However, evidence of increasing policy formation or environmental treaty ratiWcation does not necessarily lead to improved environmental conditions (Buttel, 2000). For example, Fisher and Freudenburg (2004) conclude that environmental institutionalization fails to reduce carbon dioxide emissions in OECD nations. Yet, they do not include an index of environmental treaty ratiWcation, which many argue is a more appropriate measure of state environmentalism (e.g., Dietz and Kalof, 1992; Roberts et al., 2004). Instead, Fisher and Freudenburg’s (2004) measures of environmental institutionalization include national parks and protected areas as percentage of total land area, the annual number of country chapters of international environmental non-governmental organizations [IENGOs], and nation-state contributions to IENGOs as proportion of total GDP. Using the more appropriate measure [i.e., treaty ratiWcation index], York et al. (2003) Wnd that the eVect of international environmental treaty ratiWcation on the total footprints of nations is non-signiWcant. Conversely, Wndings for Jorgenson’s (2006a) study of per capita methane emissions indicate that international environmental treaty ratiWcation does indeed reduce the intensity of emissions of this greenhouse gas (see also Burns et al., 1997; Schofer and Hironaka, 2005). 3. Hypotheses tested in the present study Previous research indicates that the per capita ecological footprints of nations are largely a function a country’s level of economic development, export dependence, urbani9

Dependent industrialization refers to industrial practices resulting from foreign capital dependence in less-developed countries that focus on the production of goods via cheap labor and less-eYcient, “dirty” production for export to developed countries.

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zation, and domestic income inequality. The potential eVects of domestic economy structure and the environmental commitments of nation-states are inconclusive in this body of research. Due to data limitations, these prior studies were cross-sectional by design. However, adequate panel data are now available for investigating change in the consumption-based environmental impacts of nation-states. In particular, estimates of per capita footprints are available for two time points a decade apart. Using these data, the purpose of the current study is to analyze the political-economic causes of change in per capita consumption-based environmental impacts. In the subsequent analyses we test the following hypotheses derived from prior studies and complementary sociological theories:

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H1: Level of economic development is positively related to growth in per capita footprints. H2: Export dependence is inversely related to growth in per capita footprints. H3: Level of urbanization is positively related to growth in per capita footprints. H4: Manufacturing intensity is inversely related to growth in per capita footprints. H5: Services intensity is positively related to growth in per capita footprints. H6: Agriculture intensity is inversely related to growth in per capita footprints. H7: Domestic income inequality is inversely related to growth in per capita footprints. H8: State environmentalism is inversely related to growth in per capita footprints.

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4. Methods and data 4.1. Panel regression with static score models

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To test the above hypotheses, we employ cross-national OLS panel regression analyses with the lagged dependent variable included as an independent variable. This modeling approach is very common in cross-national sociological research (e.g., Chase-Dunn, 1975; Dixon and Boswell, 1996; Jorgenson, 2006b; Kentor, 1998; London, 1987; Shandra et al., 2003; Soysa and Oneal, 1999). Panel regression analysis is used in the examination of change over time, as it controls for prior states of the countries included in the analyses as well as the possibility of reciprocal causality (Finkel, 1995). Panel models that include the lagged dependent variable are also known as “conditional change models” or “static score models” (e.g., Finkel, 1995; Plewis, 1985). In this type of regression model, researchers are able to analyze the eVects of independent variables on change in the dependent variable, while holding the initial level of the dependent variable constant10 (Finkel, 1995). Besides controlling for the inXuence of the prior level of national per capita footprints, including the lagged dependent variable partially controls for omitted variable bias by accounting for the eVects of unmeasured factors (Menard, 2002). Moreover, including the lagged dependent variable takes into account one of the most pervasive phenomena in the analysis of change: the likely negative correlation between initial scores on a variable and subsequent change. Also known as “regression to the mean” (Finkel, 1995), this issue is directly relevant for the current study11. For the sample in the subsequent analyses, 10

See Finkel (1995) and Allison (1990) for comparisons of conditional change models and unconditional change score models, and discussions of when each modeling approach is more appropriate. 11 See Finkel (1995, 8–9) for an explicit discussion of how the inclusion of the lagged dependent variable in a static score or conditional change model partially controls for the regression toward the mean phenomenon.

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the lagged dependent variable (per capita footprints, 1991) and footprint change (per capita footprints 2001–per capita footprints 1991) are correlated at ¡.358. There are, however, a few additional characteristics of this method that should be noted. First, the lagged dependent variable typically accounts for much or most of the variance in the dependent variable, resulting in an “artiWcially” high R2. Second, the standardized coeYcients [betas] of the other independent variables are generally underestimated. Thus, researchers usually examine the relative sizes of betas rather than the absolute levels, and highlight coeYcients with statistical signiWcance at the .10, .05, and .01 levels. In sum, this approach yields a very conservative test of the eVects of the independent variables on change in the dependent variable (e.g., Bohrnstedt, 1969; Heise, 1970; Hannan, 1979; Kessler and Greenberg, 1981). 4.2. Countries included in the analyses12

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The sample consists of 138 countries13 for which data are available for on all the variables included in the analyses.14 Appendix A lists all countries included in the study. 4.3. Dependent variable

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Ecological Footprint per capita, 2001. These data are gathered from Loh and Wackernagel (2004).

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4.4. Independent variables

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Ecological Footprint per capita, 1991 is the lagged dependent variable. We use the 2001 point estimates and the 1991–2001 percent change scores provided by Loh and Wackernagel (2004) to calculate the 1991 point estimates. Gross Domestic Product per capita [natural log], 1990 measures level of economic development (World Bank, 2000,2001). These data are measured in 1995 US dollars. Consistent with other cross-national studies (e.g., Jorgenson, 2003, 2005, 2006a; Kentor, 1998; Kentor and Boswell, 2003), all variables that are logged [natural log] in the current analyses are done so to correct for excessive skewness (Fox, 1997; Meyers et al., 2006). Exports of Goods and Services as percentage of total GDP [natural log], 1990 quantiWes the extent of a country’s integration into the world-economy (World Bank, 2000,2001). Like other studies, here we treat these data as a measure of export intensity and export dependence. Urban population as percentage of total population [residualized], 1990 measures a country’s relative level of urbanization (World Bank, 2000,2001). To minimize collinearity, we regress these data on per capita GDP and use the residuals as measures of urbanization, independent of economic development. We acknowledge that some globalization theorists (e.g., Robinson, 2004; Sklair, 2001) argue that there has been a relative increase in the power of global corporations and a decrease of power of national governments, and that with these two shifts the nation-state as a unit of analysis is increasingly limited. 13 Using Cook’s distance we determine that Bosnia, China, Estonia, Kuwait, Tajikistan, and the United Arab Emirates are overly inXuential cases and thus exclude them from the analyses [resulting in an N of 138]. 14 Initially, two independent variables have a small frequency of missing values [manufacturing (N D 137) and domestic income inequality (N D 132)]. For ease of interpretation and to allow for the maximum use of available data, we employ mean substitution for the few missing values in these two variables to maintain a constant case base of 138 countries.

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5. Results and discussion

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Manufacturing as percentage of total GDP, 1990 quantiWes the extent to which a domestic economy is manufacturing based (World Bank, 2000,2001). We use these data as an indicator of manufacturing intensity. Services as percentage of total GDP, 1990 measures the extent to which a domestic economy is services based (World Bank, 2000,2001). We use these data as an indicator of services intensity. Agriculture as percentage of total GDP, 1990 quantiWes the extent to which a domestic economy is based on agricultural production (World Bank, 2000,2001). We use these data as an indicator of agriculture intensity. Domestic income inequality, measured as Gini coeYcients, measures the distribution of income within countries (World Bank, 2000,2001). A Gini index score of zero equals perfect equality while an index score of one hundred suggests perfect inequality. The year of measurement for Gini coeYcients vary slightly across countries, but range in the early 1990s. State environmentalism is a weighted index of relative state environmental performance. These data are gathered from Dietz and Kalof (1992). The index is a cumulative measure of ratiWcation of international environmental treaties, 1963–1987. Dietz and Kalof weight these data through a series of principal component analyses of the original measure, which consists of a count of identiWed treaties ratiWed by a given nation during the 24 year period. We acknowledge the temporal limitations of this index. However, there is limited availability of comprehensive state environmentalism measures and the Dietz and Kalof index is commonly used in sociological research focusing on environmental outcomes in the post 1987 period (e.g., Jorgenson, 2006a). Table 1 provides descriptive statistics and bivariate correlations for all variables included in the analyses.

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Table 2 presents the Wndings for the multivariate panel regression analyses. The reported analyses consist of 10 models. Model 1 is treated as a baseline, consisting of only the lagged dependent variable. We introduce per capita GDP in Model 2, and Model 3 introduces export intensity as an independent variable. We introduce level of urbanization in Model 4, and add manufacturing intensity in Model 5. In Model 6, we add services intensity, and introduce agriculture intensity in Model 7. Model 8 includes domestic income inequality as well as all predictors introduced in preceding models, and Model 9 is the most fully saturated of the tested series, which also includes level of state environmentalism. The Wnal model [Model 10] excludes independent variables found to be non-signiWcant predictors in Models 1 through 9. Through the use of appropriate diagnostics (e.g., variance inXation factors, residual plots), we conclude that all OLS regression assumptions are met in the reported analyses. Turning to the tested hypotheses, level of economic development is found to increase per capita consumption-based impacts.15 The positive eVect of economic development on 15

In a series of unreported analyses, we include the quadratic of per capita GDP [centered to minimize collinearity (see Neter et al., 1990:315–316)] to control for a potential environmental Kuznet’s curve. The eVects of per capita GDP and the quadratic for per capita GDP are both positive and statistically signiWcant. These Wndings, particularly the latter, directly contradict the arguments posed by ecological modernization theory and other related perspectives concerning the apparent curvilinear relationship between development and environmental outcomes.

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Table 1 Descriptive statistics and correlations (N D 138) Mean

Kurtosis

Max.

1.825

1.377

1.481

.300

9.500

2.535

1.905

.951

¡.252

.390

8.880

7.403 3.196 .000

1.521 .589 12.667

.371 ¡.675 ¡.013

¡.715 .857 ¡.135

17.839

7.947

.478

.128

47.183 21.241

11.767 15.060

¡.062 .861

40.646

9.460

.217

.097

.934

.584

3.

4.

3.

.867

.743

4.

.218

.246

¡.235

19.500 62.900

7.

.073

6.

.329

.388

.454

.217

.067

7.

.548

.242

.610

.218

.030

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.000

¡.434 ¡.675

.168

¡.204 ¡.157

9. ¡.498 ¡.502 ¡.448 ¡.083

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8.

1.940

9.

.091

8. ¡.605 ¡.451 ¡.769 ¡.492 ¡.176

.585

¡1.410

.366

5. ¡.047 ¡.006

.673

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16.400 73.300 1.060 65.450

¡.644

6.

3.710 41.800

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.830

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2.

5.

4.680 10.720 .960 4.340 ¡31.723 31.814

¡.437 .251

al

2.

1.

10.

Min.

2.339

1. Ecological footprint per capita, 2001 Ecological footprint per capita, 1991 GDP per capita (ln), 1990 Exports / GDP (ln), 1990 Urban population (residualized), 1990 Manufacturing as % GDP, 1990 Services as % GDP, 1990 Agriculture as % GDP, 1990 Domestic income inequality State environmentalism

Skewness

on

Ecological footprint per capita, 2001 Ecological footprint per capita, 1991 GDP per capita (ln), 1990 Exports / GDP (ln), 1990 Urban population (residualized), 1990 Manufacturing as % GDP, 1990 Services as % GDP, 1990 Agriculture as % GDP, 1990 Domestic income inequality State environmentalism

SD

.676

.161 ¡.079

.214

.578

.264 ¡.496

¡.388

Au

change in per capita footprints is robust, which conWrms hypothesis #1. This result does indeed lend support to world-systems theory (e.g., Chase-Dunn, 1998) and treadmill of production theory (Schnaiberg and Gould, 1994). Like prior cross-sectional analyses (e.g., Jorgenson, 2003; Rosa et al., 2004), this Wnding challenges macroeconomic assertions concerning the beneWcial environmental impacts of economic development (e.g., Grossman and Krueger, 1995) and the “decoupling” of environmental degradation from economic activities (e.g., Fisher and Freudenburg, 2004). Results suggest that export dependence is inversely related to growth in per capita footprints. The negative eVect is consistent across all models that include this predictor, which conWrms hypothesis #2. This Wnding is consistent with prior cross-sectional analyses (Jorgenson, 2005; Jorgenson and Rice, 2005). With a focus on export-oriented production and

py co GDP per capita (ln), 1990

Model 2 .417*** [.399] (.051) 2.234 .557*** [.669] (.064) 2.234

Exports / GDP (ln), 1990

Model 4

.411*** [.393] (.050) 2.238 .599*** [.719] (.065) 2.427 ¡.102*** [¡.317] (.116) 1.157

.410*** [.393] (.050) 2.238 .598*** [.717] (.065) 2.428 ¡.099*** [¡.307] (.116) 1.164 ¡.036 [¡.005] (.005) 1.006

Model 5 .422*** [.404] (.049) 2.255 .632*** [.759] (.066) 2.581 ¡.093*** [¡.290] (.114) 1.168 ¡.029 [¡.004] (.005) 1.012 ¡.099*** [¡.023] (.009) 1.281

pe

Urban population (residualized), 1990

Model 3

Manufacturing / GDP, 1990

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.505*** [.483] (.051) 2.663 .441*** [.529] (.085) 4.783 ¡.091*** [¡.283] (.108) 1.169 ¡.037 [¡.005] (.005) 1.016 ¡.061* [¡.014] (.009) 1.368 .184*** [.028] (.007) 2.027

Model 7 .501*** [.480] (.051) 2.718 .457*** [.549] (.094) 5.821 ¡.084** [¡.260] (.118) 1.374 ¡.033 [¡.004] (.005) 1.098 ¡.055* [¡.013] (.009) 1.478 .195*** [.030] (.008) 2.483

Model 8 .484*** [.464] (.053) 2.892 .446*** [.536] (.094) 5.898 ¡.080** [¡.247] (.118) 1.385 ¡.025 [¡.004] (.005) 1.138 ¡.054* [¡.012] (.009) 1.480 .199*** [.031] (.008) 2.497

Model 9

Model 10

.488*** .504*** [.467] [.482] (.055) (.051) 3.137 2.663 .449*** .446*** [.539] [.536] (.096) (.085) 6.058 4.760 ¡.080** ¡.094*** [¡.248] [¡.292] (.119) (.108) 1.390 1.162 ¡.025 [¡.004] (.005) 1.144 ¡.054* ¡.065** [¡.012] [¡.015] (.009) (.009) 1.480 1.358 *** .204 .180*** [.032] [.028] (.009) (.007) 2.916 2.019 (continued on next page)

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Services / GDP, 1990

Model 6

on

.830*** [.796] (.046) 1.000

rs

Model 1

Ecological footprint per capita, 1991

A.K. Jorgenson, T.J. Burns / Social Science Research 36 (2007) 834–853

Independent variables

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Table 2 Dependent variable: ecological footprint per capita, 2001

py co Model 4

.323 (.145) .687 .690

¡3.621 (.392) .826 .829 .139***

¡2.966 (.452) .834 .838 .009***

Adjusted R R2 R2 change

pe

State environmentalism

2

Model 6

Model 7 .033 [.004] (.008) 4.171

Model 8 .037 [.004] (.008) 4.181 ¡.048 [¡.009] (.007) 1.428

Model 9 .039 [.004] (.008) 4.215 ¡.049 [¡.009] (.008) 1.440 ¡.011 [¡.022] (.097) 2.339

Model 10

¡3.393 (.899) .856 .863 .000

¡2.971 (.960) .857 .865 .002

¡3.030 (.998) .856 .865 .000

¡2.970 (.421) .857 .862

rs

Domestic income inequality

Constant

Model 5

al

Model 3

on

Model 2

¡2.987 (.453) .834 .839 .001

¡2.968 (.444) .841 .847 .008***

¡2.992 (.420) .857 .863 .017***

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Notes: N D 138; standardized coeYcients Xagged for statistical signiWcance; unstandardized coeYcients appear in brackets;standard errors are in parentheses; VIFs appear in italics; *p < D .10; **p < D .05; ***p < D .01 (one-tailed tests).

A.K. Jorgenson, T.J. Burns / Social Science Research 36 (2007) 834–853

Model 1

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Table 2 (continued) Independent variables Agriculture / GDP, 1990

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847

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extractive activities, export-dependent countries experience relative decreases in domestic levels of resource consumption. These relative decreases are also partly characteristic of international unequal ecological exchange, particularly the process of higher-consuming nations tempering the levels of resource consumption for populations of export-dependent countries. This is further underscored by the consumption/degradation paradox, which refers to the negative relationship between the size of per capita footprints and diVerent forms of domestic environmental degradation, including deforestation rates, and water pollution intensity (Jorgenson and Burns, 2004; Jorgenson et al., 2006). Moreover, the ecological footprint could be treated as a partial indicator of a population’s material quality of life (see Prescott-Allen, 2001). Thus, the inverse relationship between export intensity and per capita footprint change is not indicative of the environmental beneWts or “comparative advantage” of international trade (e.g., Magee, 1980), but rather asymmetrical exchanges between countries in a stratiWed world-economy (e.g., Lofdahl, 2002; Rubinson and Holtzman, 1981). Urbanization fails to increase per capita consumption-based environmental impacts. This non-signiWcant Wnding, consistent across Models 4–9, diVers from the results of Jorgenson (2003, 2004); Jorgenson and Rice (2005) and York et al. (2003), but corresponds with cross-sectional analyses of per capita footprints that include export controls in addition to urbanization (e.g., Jorgenson, 2005). Thus, we Wnd no support for hypothesis #3. We speculate that this non-signiWcant Wnding could largely be a result of not treating urbanization as a mediating factor, partly a function of economic development, and other political-economic factors (e.g., Jorgenson, 2003; London, 1987). Manufacturing intensity is inversely related to growth in consumption-based impacts, which conWrms hypothesis #4. In particular, the eVect of manufacturing as percentage of GDP on change in per capita footprints is negative and statistically signiWcant across all models that include this predictor. This result supports the arguments of world-systems theory (e.g., Roberts and Grimes, 2002) as well as the theory of uneven ecological exchange (e.g., Hornborg, 2001). However, the support for both theories should be considered in the context of the Wnding for this predictor coupled with the eVect of export intensity. Many less-developed countries are dependent on manufacturing exports to higher-consuming countries (Chase-Dunn, 1998; McMichael, 2004), which reduces domestic consumptionbased environmental impacts. An additional new Wnding involves hypothesis #5: countries with a larger service-based domestic economy exhibit greater increases in their per capita footprints. Like the negative eVect of manufacturing intensity, the positive eVect of service intensity on change in per capita footprints supports tenets of the theory of uneven ecological exchange (e.g., Hornborg, 2001) and world-systems theory (e.g., Chase-Dunn, 1998). Countries with more service-based economies tend to exhibit greater aZuence and relative power in the worldeconomy. Producer services are highly concentrated in more-powerful global cities (Sassen, 2001), and the latter are generally located in developed countries (e.g., Alderson and BeckWeld, 2004). These more-powerful cities are also the command centers of global commodity chains (GereY and Korzeniewicz, 1994), and transnationally produced commodities, raw materials, and agricultural goods are generally exported to and consumed by populations residing in developed countries (Jorgenson, 2003). Unlike manufacturing and services, the eVect of agriculture intensity is non-signiWcant. Hence, hypothesis #6 is not supported. Manufacturing and services prove to be the more germane economic sectors to consider when assessing the structural causes of growth in

848

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per capita consumption-based environmental impacts.16 Similar to agriculture intensity and urbanization, the eVect of domestic income inequality is non-signiWcant. The non-signiWcant eVect fails to conWrm hypothesis #7 and contrasts with cross-sectional analyses of per capita footprints (Jorgenson, 2003, 2004). We speculate that this non-signiWcant eVect can partly be explained by the relationships between level of development and intrainequality, and development and growth in per capita footprints. Middle-developed countries and less-developed countries tend to have relatively higher levels of domestic income inequality [with the United States as a notable exception (e.g., Jorgenson, 2004)], and moredeveloped countries exhibit greater increases in their levels of consumption-based environmental impacts. This suggests that the overall positive eVect of economic development greatly outstrips the impact of domestic income inequality. Indeed, future research should further untangle these complex interrelationships. Results also fail to support the Wnal hypothesis [#8]. In particular, the eVect of state environmentalism is non-signiWcant. This Wnding, coupled with results of other crossnational studies (e.g., Fisher and Freudenburg, 2004; Jorgenson, 2006a; York et al., 2003), illustrate the importance in conducting more nuanced empirical assessments of the environmental commitments of nation states and the extent to which these commitments reduce diVerent forms of environmental degradation. 6. Conclusion

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Consumption-based environmental impacts are among the most pressing issues confronting the world today (National Research Council, 1999). In this study, we tested a series of hypotheses concerning the political-economic causes of change in the per capita ecological footprints of nations. Our Wndings indicate that more-developed countries experienced greater increases than less-developed countries in their per capita consumptionbased environmental impacts from 1991 to 2001. This result strongly supports treadmill of production theory (Schnaiberg and Gould, 1994) and world-systems theory (e.g., ChaseDunn, 1998; Roberts and Grimes, 2002). Export intensity is inversely related to growth in per capita footprints, which underscores the validity of the theory of uneven ecological exchange (e.g., Hornborg, 2001; Jorgenson, 2005; Jorgenson and Rice, 2005) as well as export dependence theory (e.g., Galtung, 1971; Hirschman, 1980). The eVects of urbanization, domestic income inequality, and state environmentalism were found to be non-signiWcant. Countries with more service-based economies [services intensity] experienced increased levels of consumption-based environmental impacts, while manufacturing intensity is inversely related to growth in per capita footprints. The eVect of agriculture intensity was found to be non-signiWcant. These Wndings strongly challenge the wisdom of some macroeconomic perspectives (e.g., OECD, 1998) concerning the apparent overall environmental beneWts for nations to shift towards a more service-based economy. As posited by worldsystems theory (Wallerstein, 2005) and other related perspectives (e.g., Burns et al., 2003b, 2006; Lofdahl, 2002), as nation-states move up in the global political-economic hierarchy, they tend to experience a concomitant strengthening of their bargaining position relative 16

In an unreported analysis, we exclude per capita GDP as a control to further assess the eVects of manufacturing, services, and agriculture on change in the per capita footprints of nations. The eVects of the latter three are very similar to those reported in models that include per capita GDP.

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to weaker countries. These countries tend to move toward a more articulated economy, with a growth in the service sector while their general standard of living increases. Yet, the implications of this for changes in consumption-based impacts are tremendous. As this transition occurs, domestic levels of consumption tend to increase dramatically (e.g., Amsden, 2001; Stiglitz, 2002). Thus, when assessing the eVects of economic activities on the environment and human societies, researchers would do well to consider the stratiWed interconnections of all national economies and the “hidden” eVects of producer services (Sassen, 2001). What is more, the increased outsourcing of consumption-based environmental impacts by moredeveloped countries to less-developed countries highly dependent on manufacturing exports corresponds with the recent upswing in the globalization of production, Wnance, and trade (Chase-Dunn et al., 2000; Chase-Dunn and Jorgenson, 2003). This is further illustrated by the consumption/degradation paradox in which nations with larger per capita footprints experience lower levels of environmental degradation within their borders as well as higher levels of human suVering (Jorgenson, 2003, 2005). While prior research suggests that these social and environmental outcomes are partly caused by similar international political-economic factors, additional empirical investigations in this vein are indeed warranted to better understand these social/environmental relationships. Future footprint research should investigate change in the total consumption-based environmental impacts of nations. Like the per capita footprints analyzed in the present study, adequate cross-national panel data are now available for total footprints. While we Wnd that political-economic factors greatly contribute to change in per capita footprints, prior crosssectional analyses of total footprints identify structural human ecological factors as well as political-economic conditions to be central causal mechanisms. However, consistent with studies of per capita footprints, we should not assume that the most salient predictors in cross-sectional analyses will hold for analyses of change in the total footprints of nations. Once appropriate panel data are available, future footprint research should also analyze change in the subcomponents of the per capita and total “combined” footprints (see Jorgenson et al., 2005; Rosa et al., 2004). The subcomponents include six general areas: cropland, forest, grazing land, Wsheries, energy, and built-up land; the combined footprint for a country consists of the sum of the adjusted calculations of each of the subcomponent areas. Conducting these types of more nuanced analyses would indeed increase our collective understanding of how and to what extent humans degrade the biosphere. Lastly and more generally, we hope that this study will encourage sociologists and other social scientists to make more concerted eVorts to conduct panel analyses of environmental outcomes if and when appropriate data are available.

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Appendix A. Countries Included in the Analyses Afghanistan Albania Algeria Angola Argentina Armenia

Guinea Guinea-Bissau Haiti Honduras Hungary India

Panama Papua New Guinea Paraguay Peru Philippines Poland (continued on next page)

A.K. Jorgenson, T.J. Burns / Social Science Research 36 (2007) 834–853

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Portugal Romania Russia Rwanda Saudi Arabia Senegal Serbia & Montenegro Sierra Leone Slovak Rep. Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Rep. Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Kingdom United States Uruguay Uzbekistan Venezuela Vietnam Zambia Zimbabwe

co al

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Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea Kyrgyz Rep. Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Pakistan

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Appendix A (continued) Australia Austria Azerbaijan Belarus Belgium Belize Benin Bolivia Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Rep. Chad Chile Colombia Congo, Dem. Rep. of Congo, Rep. of Costa Rica Côte d’Ivoire Croatia Czech Republic Denmark Dominican Rep. Ecuador Egypt El Salvador Eritrea Ethiopia Finland France Gabon Gambia Georgia Germany Ghana Greece Guatemala

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