SOCIETIES CONSUMING NATURE: A PANEL STUDY OF THE ECOLOGICAL FOOTPRINTS OF NATIONS, 1960-2003 *

Andrew K. Jorgenson Department of Sociology University of Utah

and

Brett Clark Department of Sociology and Anthropology North Carolina State University

Under review, please do not cite without permission

*

Direct all correspondence to Andrew K. Jorgenson, Department of Sociology, University of Utah, 380 South 1530 East, Room 301, Salt Lake City, UT 84112; Phone: (801) 5818093; FAX: (801) 585-3784; email: [email protected]. Earlier versions of this paper were presented at the 2008 Duke University Seminar Series on Global Governance and Democracy, and the 2008 Colloquium Series for the Department of Sociology, University of Utah. The authors thank the attendees for helpful comments.

SOCIETIES CONSUMING NATURE: A PANEL STUDY OF THE ECOLOGICAL FOOTPRINTS OF NATIONS, 1960-2003

ABSTRACT Sociology is poised to greatly enhance our collective understanding of the various sustainability challenges facing the world today. To contribute to this endeavor, the authors conduct panel analyses of the per capita ecological footprints of nations to evaluate multiple theoretical traditions within environmental sociology and its sister approaches. Findings indicate that the consumption-based environmental impacts of nations are tied to economic development, urban population, militarization, and the structure of international trade. Ecological conditions in the context of climate and biogeography also prove to partially shape the environmental harms of human activities. Ultimately, this research suggests that political-economic factors, ecological milieu, and structural associations between nations all influence society / nature relationships. Considering the globally unsustainable levels of resource consumption and concomitant increases in pollution for a growing number of nations throughout the world, the authors contend that theoretically inclusive and methodologically rigorous investigations on such topics should be more central to the discipline.

SOCIETIES CONSUMING NATURE: A PANEL STUDY OF THE ECOLOGICAL FOOTPRINTS OF NATIONS, 1960-2003

INTRODUCTION The human dimensions of global environmental change are among the most pressing issues facing the world today. Peter Vitousek (1994:1862), a leading natural scientist, underscores the importance and magnitude of these changes, stating that humans continue to “alter the structure and function of Earth as a system.” Dramatic examples abound— human activities are the primary forces responsible for the observed warming of the earth’s atmosphere and the associated ecological consequences of climate change (Intergovernmental Panel on Climate Change 2007); in the last fifty years human actions have transformed, degraded, and overexploited the world’s natural landscapes (Millennium Ecosystem Assessment 2005); and no area of the world’s ocean is unaffected by human influence, undermining the services and biodiversity of its ecosystems (Halpern et al. 2008). The dynamic interactions between nature and humans raise pressing questions concerning the structure of societies and the anthropogenic drivers of environmental change. We contend that environmental sociology is poised to make significant contributions to our collective understanding of such relationships. Environmental sociology places society, in all of its grandeur, within the bounds of the physical world (e.g., Buttel 1987; Catton and Dunlap 1978; Dunlap and Catton 1979; Foster 1999; Freudenburg 2005; Goldman and Schurman 2000; York, Rosa, and Dietz 2003). The environment is a social issue, a necessary dimension of human life. It constrains and facilitates societies. At the same time, societies transform the biophysical

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world, whether it be through the organization or changing structure of the world economy, demographic processes and distributions, or other social factors (e.g., Bunker 1984; Downey 2006; Ehrhardt-Martinez 1998; Jorgenson 2003; Redclift and Sage 1998; Roberts and Parks 2007; Rudel 2005, 2009). Thus, environmental sociology considers the structural and ecological dimensions of society / nature relationships in their various manifestations. Here we advance this emergent area of the discipline. We conduct fixed effects and random effects panel regression analyses of the consumption-based environmental impacts of 65 nations—measured as per capita ecological footprints—from 1960 to 2003 to assess multiple theoretical traditions within environmental sociology and related approaches. In the analyses we evaluate the assertions of ecological modernization and treadmill of production theories, with particular attention paid to their divergent propositions concerning a relative decoupling of economic development and subsequent environmental demands. Further, we assess key arguments of ecologically unequal exchange theory about the structure of international trade as well as another key variant of world-systems analysis that focuses on the stratified interstate system, the latter of which complements the treadmill of production theory. We also evaluate the environmental impacts of urbanization from an urban political-economy perspective, national militaries in the context of treadmill of destruction theory, and structural human ecology assertions concerning the extent to which ecological conditions partially shape the consumption-based environmental impacts of nations. While our study draws from previous sociological research on the environment, it makes significant advances by (1) considering and synthesizing a broad range of

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theoretical traditions in environmental sociology and its sister disciplines; (2) employing panel data and rigorous methods to investigate temporal patterns and causal relationships; and (3) giving serious attention to how both social and ecological factors influence society / nature associations in comparative perspective. As a result, we identify multiple factors that influence the consumption-based environmental demands of nations, and ultimately, the findings and their theoretical underpinnings greatly enhance our collective understanding of the human dimensions of global environmental change. To begin, we discuss the prominent environmental sociology theories and other relevant perspectives of interest to the current study, and explicate particular relationships that we examine in the subsequent panel analyses. Next, we describe the substantive characteristics of the ecological footprint while highlighting its utility and importance as a comprehensive measure of consumption-based environmental demand. In this discussion we examine the cross-national temporal patterns of per capita footprints relative to globally sustainable levels of resource consumption, and we summarize the vast multidisciplinary literature that employs the measure and engages its conceptual foundations. We then describe the employed panel regression methods, variable definitions and data sources, and countries included in the tested models. Next, we present and discuss the findings for the analyses, which underscore the importance in taking a theoretically inclusive approach when investigating society / nature relationships. We conclude by highlighting the key results of the study, and we discuss their significance for environmental sociology and other areas of the discipline.

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SOCIOLOGICAL APPROACHES TO SOCIETY / NATURE RELATIONSHIPS 1 Economy and Environment: Multiple Perspectives The Environmental Kuznets Curve, Ecological Modernization Theory, and Decoupling Ecological modernization theory builds upon a branch of environmental economics which recognizes that economic development generates environmental harms, but argues that further growth can largely correct these problems (Grossman and Krueger 1995). 2 The environment is seen as a luxury good, subject to public demand through the workings of an advanced market. During the early stages of development, environmental impacts escalate, but as affluence within these societies rises, the value the public places on the environment will increase. Social interest starts to displace self-interest. The public desire for enhancing quality of life—in large part expressed as consumer demand for “green” products and services—will, environmental economists expect, place pressure on governments and businesses to invest in “eco-friendly” technologies and commodities. It is argued that if the market is allowed to operate without dramatic interference, continuing development will lead to a leveling and eventual decline in the use of natural resources and emission of pollutants. The proposed trend, known as the environmental Kuznets curve, is depicted as an inverted U-shaped distribution representing the relationship between environmental impacts and economic development.

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There are other important theoretical perspectives within or of direct relevance for environmental sociology, such as the metabolic rift (e.g., Foster 1999). However, for the current study we focus on perspectives that suggest relationships more amenable for assessment through quantitative comparative analysis. 2 This branch of environmental economists is more favorable to laissez-faire conservatives than ecological modernization theory. The latter was constructed on the optimism that evolved out of the social-democratic / welfare capitalism tradition in Europe, and proposes a rational capitalism and a degree of state regulation dictated by the market.

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Partly drawing from environmental economics, ecological modernization theory within sociology suggests that the only “possible way out of the ecological crisis is by going further into the process of modernization” (Mol 1995:42). The forces of modernization that are believed to move human society from its past of environmental degradation to sustainability are the institutions of modernity, including markets, industrialism, and technology (Mol 1995, 2001, 2002; Mol and Sonnenfeld 2000; Spaargaren and Mol 1992). In developed societies, an “ecological rationality” will emerge, as environmental concerns are better incorporated into decision-making, and ecological costs are weighed along with economic considerations (Mol 2001). Thus, environmental degradation is not seen as an inherent characteristic of continual economic development. The forces of modernization, such as technological advances, will enhance the environmental sustainability of society, as this new rationality advances the “ecologization of economy” and the “economization of ecology” (Mol 2000:15). This entails the dematerialization of society and a relative decoupling of the economy from material consumption (Mol 1995; Leadbeater 2000). The economy becomes progressively eco-efficient in its utilization of resources, and as a result, economic growth becomes less dependent upon and decoupled from nature. Thus, the dematerialization of the economy—through efficiency gains and technological innovations—decreases the demands that nations and their populations place on nature (Reijnders 1998). Through the ecologization of the economy, it is possible that the consumptionbased environmental impacts of nations will gradually decrease. While it is more likely for such a relative decoupling between economic development and environmental harms

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to take place first for the most economically developed nations, as other lesser-developed nations continue to develop, the magnitude of their environmental impacts should also eventually decline as well. In other words, while the economy might continue to harm the environment, ecological modernization theory suggests that through time the relative magnitude of the impact of economic development on the environment will decrease, and it is likely that these changes will happen first in the more economically developed nations (Mol 2001). Treadmill of Production Theory: Decoupling? The treadmill of production theory runs counter to ecological modernization theory. Proponents of this orientation argue that environmental degradation stems from the incessant expansion of production to maintain profits, and that environmental sustainability cannot be obtained within a growth dependent system (Schnaiberg 1980; Schnaiberg and Gould 1994). Technological development leads to the expansion and intensification of production, so the amount of energy and materials used typically increases. Economic growth, premised on accumulation, is thus likely to increase the scale and intensity of resource use and concomitant environmental degradation. Allan Schnaiberg (1980), the founder of treadmill of production theory, posits that any society driven by economic expansion is mired in a conflict with nature, given the increasing demands upon the finite world. Such a society is running endlessly, expending energy and resources at an accelerating pace. The commitment to economic growth, despite the various social and ecological costs, is dictated by the pursuit of profit (Schnaiberg and Gould 1994). “The ‘treadmill’ component recognizes that the nature of capital investment leads to higher and higher levels of demand for natural resources….

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For ecosystems, each level of resource extraction becomes commodified into new profits and new investments, which leads to still more rapid increases in demand for ecosystem elements” (Gould, Pellow, and Schnaiberg 2004:297). Nature is used to fuel industry and to produce commodities for the market, and the production process generates an increasing volume of waste that is released into the natural world (Gould et al. 2008). The treadmill of production perspective recognizes that technological innovation often involves improving the efficiency of operations, where fewer inputs are used to produce a specified amount of output. Such changes enhance profits. But whether efficiency leads to an overall reduction in resource demands—a decoupling of the economy and environment or the dematerialization of society—is strongly questioned. According to the Jevons Paradox (Jevons 2001), it is possible for an economy to become more efficient in its resource use, while at the same time expanding its consumption of resources (see also Clark and Foster 2001). 3 The treadmill of production perspective suggests that this situation manifests itself because gains made in improving efficiency are outstripped by increases in the scale and intensification of production. In the subsequent panel analyses, we consider the premises of both treadmill of production and ecological modernization theories. The former predicts a positive association between consumption-based environmental demand and economic development. Regarding temporal changes, ecological modernization theory posits that through time a relative decoupling between economic development and environmental harms is possible and more likely to take place first in the more economically developed

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The Jevons Paradox is named after the British economist William Stanley Jevons, who observed in the nineteenth century that as the efficiency of coal use by industry improved, total coal consumption increased. He noted that improvements in efficiency made coal more cost effective per unit of production, making coal-dependent production more attractive (Jevons 2001).

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nations. Treadmill of production theory rejects the assertion that through time the magnitude of the impact of economic development on the environment will decrease. In the analyses we also evaluate the proposition in environmental economics regarding the presence of an environmental Kuznets curve. World-Systems Analysis, Ecologically Unequal Exchange, and the Environment World-Systems Analysis Society / nature relationships have become quite prevalent in world-systems analysis in recent decades (e.g., Frey 1995; Goldfrank, Goodman, and Szasz 1999; Hornborg, McNeill, and Martinez-Alier 2007). Wallerstein (1974) and others (e.g., Chase-Dunn and Grimes 1995) argue that all nations are structurally interconnected and form a single world-system (see also Mahutga 2006; Snyder and Kick 1979). Capitalism, as the dominant mode of production, consists of an integrated social system composed of interacting subsystems held together through conflicting forces and historical processes. This arrangement facilitates the unequal accumulation of capital between spheres within the global economy. Of particular relevance for the current study, Jorgenson, Austin, and Dick (2009) suggest that a resource consumption / environmental degradation “paradox” exists where the economically developed core nations consume the highest amounts of natural resources compared to lesser-developed non-core nations, yet they tend to have the relatively lowest levels of particular forms of environmental degradation within their borders (e.g., deforestation). It is argued that these inverse relationships are largely structured and maintained by the stratified interstate system, where the relative position of nations is coterminous with their levels of economic development. Thus, consistent with general arguments of treadmill of production theory, world-systems analysis posits

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that the consumption-based environmental demands of nations are positively associated with level of economic development. Ecologically Unequal Exchange The theory of ecologically unequal exchange has deep roots in world-systems analysis, the classical unequal exchange and dependency traditions in comparative sociology (e.g., Emmanuelle 1972; Frank 1967), structural globalization approaches (Chase-Dunn, Kawano, and Brewer 2000), and especially Stephen Bunker’s (1984) work on natural resource extraction and underdevelopment in the Amazon. Bunker (1984), largely from a world-systems perspective, forcefully argued that macrosociology had failed to address how and the extent to which the extraction and export of natural resources from lessdeveloped, peripheral countries (1) involve a vertical flow of value embodied in energy and matter to more-developed countries, and (2) could greatly influence the environmental and structural contexts in which subsequent development efforts unfold. Emerging from these complementary perspectives, ecologically unequal exchange theory asserts that through the “vertical flow of exports” from less-developed countries, more-developed countries partially externalize their consumption-based environmental costs, which in turn increase forms of environmental degradation in the former while suppressing levels of resource consumption within their borders, often well below globally sustainable thresholds (e.g., Hornborg 1998; Jorgenson 2006; Roberts and Parks 2007; Srinivasan et al. 2007). In general, the populations of more-developed countries are positioned advantageously in the world economy, and thus more likely to secure and maintain favorable terms of trade allowing for greater access to the natural resources and sink capacity of bioproductive areas within less-developed countries. More-developed

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countries are able to over-utilize global “environmental space,” which encompasses the stocks of natural resources and waste assimilation properties of ecological systems supporting human social organization (Rice 2007). The misappropriation of environmental space suppresses resource consumption opportunities for many lessdeveloped countries, which also impacts the well-being of their domestic populations (Jorgenson et al. 2009). Thus, ecologically unequal exchange theory emphasizes how structural relationships between nations partly shape their uneven environmental demands. In the ensuing analyses we employ appropriate measures to assess the above propositions of the theory. Urban Political Economy and the Environment Urban political economy analyzes how cities are centers of growth with structured environments that often generate ecological contradictions (Davis 2002; Downey 2005). Molotch (1976:318) argues that cities are growth machines, generally captured by interests focused on expanding profit, which “almost always brings with it the obvious problems of increased air and water pollution, traffic congestion, and overtaxing of natural amenities. This dysfunction becomes increasingly important and visible as increased consumer income fulfills people’s other needs and as the natural cleansing capacities of the environment are progressively overcome with deleterious material.” The concentrations of populations within mega-cities pose ecological concerns, as nature is increasingly urbanized with sprawling cities that often consume massive amounts of water, raw materials, and energy that is appropriated from already stressed environments in distant places (Dickens 2004). Gonzalez (2005) demonstrates that urban zones are centers of mass consumption, whether it is services or commodities. Land

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developers promote urban sprawl, and highway systems impose transportation infrastructures that require the use of large quantities of resources in the daily operation of business and people’s lives (Kay 1998). One end result of this type of built environment is the burning of fossil fuels, which contributes to global warming and climate change. Consistent with the arguments of urban political economy as well as prior crossnational investigations in comparative sociology (e.g., Ehrhardt-Martinez 1998; Jorgenson 2003; York et al. 2003), we consider it crucial to consider the environmental impacts of urban populations, net of the effects of other factors. Thus, in the current study we assess the relationship between the per capita ecological footprints of nations and their relative levels of urbanization. The Military, Treadmill of Destruction Theory, and the Environment With very few exceptions (e.g., Hooks and Smith 2004, 2005; York 2008), theorization and research on the environmental impacts of militarism are non-existent in sociology. The effects of warfare on the environment reveal how important it is to consider military institutions and their behaviors (e.g., Davis 2002; Lanier-Graham 1993; Thomas 1995). However, even when armed conflicts are not taking place, military institutions and their activities are likely to consume sizable quantities of nonrenewable energy and other materials for research and development, maintenance, and operation of their overall infrastructure (Dycus 1996; Sidel and Shahi 1997). At the same time, they generate large amounts of toxic substances and waste (LaDuke 1999; Shulman 1992). According to the United Nations’ Center for Disarmament (1982), the amount of land used by armed forces for bases, other forms of installations, and training exercises

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has risen steadily over the last century. Military-oriented resource use also includes the strategic stockpiling of fuels and other materials, and consumption is further increased through by the industries that produce marginal equipment for the armed forces and its support economy. Further, the populations of armed forces consume immense amounts of food, and use large quantities of various organic and synthetic materials for uniforms and specialized forms of clothing. Renner (1991) estimates that petroleum products for land vehicles, aircrafts, sea vessels, and other forms of machinery account for approximately 75 percent of all energy use by the armed forces worldwide, and some analysts posit that the Pentagon is the largest consumer of nonrenewable energy resources, particularly fossil fuels, in the United States and quite possibly the entire world (e.g., Hynes 1999). Superior combat performance of equipment is likely of greater priority than energy efficiency for military institutions. What is more, the production, testing, maintenance, and disposal of conventional, chemical, and nuclear arms generate toxic and radioactive substances that are known to contaminate air, water, and soil (Davis 2002; Renner 1991; Shulman 1992). Recently, Hooks and Smith (2004, 2005) formulated a theoretical orientation for assessing the potential environmental impacts of militaries. 4 They characterize the expansionary dynamics and environmental impacts associated with militarism as the “treadmill of destruction.” Partly drawing from longstanding perspectives in political sociology (e.g., Mann 1988; Tilly 1990), Hooks and Smith (2005) argue that primarily for geopolitical reasons, states—not classes or firms—declare and wage wars. While military institutions and activities are indeed connected to economic interests and the 4

While Hooks and Smith apply their treadmill of destruction theory to the U.S. military and domestic conditions, we situate the orientation in an international perspective.

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treadmill of production (Schnaiberg 1980), they are also somewhat independent of them. However, like the treadmill of production, the fundamental logic of the treadmill of destruction undermines environmental protection concerns. As articulated by a U.S. military official during a community hearing in Virginia (Renner 1991:152): “We are in the business of protecting the nation, not the environment.” Geopolitical competition often drives arms races as well as concomitant technological advances and infrastructural development. Especially for developed nations, the environmentally damaging capabilities of militarism are often partly a function of technological developments with weaponry and other machinery that require significant capital investments (Hooks and Smith 2005). In a similar vein, politicaleconomic sociologists emphasize that nations with relatively larger and more technologically advanced militaries utilize their global military reach to gain disproportionate access to natural resources (e.g., Kentor 2000; Podobnik 2006). Thus, to assess the validity of treadmill of destruction theory and related political-economic perspectives in macro-comparative contexts, we employ measures in the analyses below that capture these characteristics of national militaries. Structural Human Ecology Human ecologists assert that while the capacity of social institutions, technology, and culture separate humans from other species, this unique aptitude is to some extent bounded by the limits imposed by ecological conditions (e.g., Catton 1980; Dunlap and Catton 1979; Freese 1997; Hawley 1950). More specifically, structural human ecologists argue that ecological factors, such as biogeography (e.g., arable land) and climate (e.g., latitude), play critical roles in conditioning how social-structural factors affect the natural

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environment (e.g., Dietz and Rosa 1994; Dietz et al. 2007). Biogeography in this context deals with the extent to which resource density and availability influence resource demand and consumption levels. Given that such land is bio-productive, structural human ecology posits that a greater local availability of arable land will contribute to increases in consumption-based environmental impacts. In other words, the per capita ecological footprints of nations should be positively associated with arable land per capita (e.g., York et al. 2003). Regarding the second ecological factor (i.e., climate), conventional wisdom suggests that more resources are consumed to sustain societies in colder climates. In such situations, resources are used for (1) fuel to generate heat, (2) built infrastructure, and (3) food. Thus, one would assume that—all else being equal—resource consumption increases the further a nation is from the equator. However, sociological research focusing on the environmental impacts of political-economic conditions and processes typically fails to consider the ecological context in which social factors drive environmental outcomes. Considering the potential for invalid inferences, coupled with the robust findings for prior human ecological research in the structural tradition, in the current study we include measures for both climate and biogeography.

THE ECOLOGICAL FOOTPRINTS OF NATIONS To evaluate the theoretical perspectives on society / nature relationships discussed in preceding sections, we employ the per capita ecological footprint as the dependent variable in the subsequent panel analyses. The ecological footprint is perhaps the most comprehensive measure currently available for assessing global environmental

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sustainability issues, and its utility for such purposes is recognized by the National Academy of Sciences (see Wackernagel et al. 2002). Examining per capita footprints allows for comparison of demands placed on the environment among the world’s people and highlights the differences in levels of resource consumption across nations. 5 For these as well as other reasons, the ecological footprints of nations are employed as a dependent variable in numerous studies in comparative environmental sociology (e.g., Jorgenson 2003; Jorgenson and Burns 2007; Rice 2007; York et al. 2003, 2009), human ecology (e.g., Dietz et al. 2007; Rosa et al. 2004), international relations (e.g., Ozler and Obach 2009), and industrial ecology (e.g., Frey, Harrison, and Billett 2006). The footprint has also gained significant popularity within the field of economics. An indepth search indicates that 251 articles employing the measurement or engaging its conceptual foundations have been published in the journal Ecological Economics alone between January 1995 and July 2009, and the frequency of these articles have increased each year successively. Further, multiple issues of the journal are devoted explicitly to the ecological footprint, including issues in 2009 (volume 68, issue 7) and 1999 (volume 29, issue 3) on methodological advancements in footprint analysis. The ecological footprint was initially developed by Mathis Wackernagel and William Rees (1996), and quantifies the amount of biologically productive land required to support the consumption of renewable natural resources and assimilation of carbon dioxide emissions of a given population. 6 National footprints are measures of societal

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Prior research reveals that the population elasticity of the total ecological footprints of nations is very close 1.0 (e.g., York et al. 2003), further justifying the use of per capita measures. 6 In 1965, Georg Borgstrom introduced the concept of “ghost acres” to refer to Britain’s dependence on food and raw materials from colonial and neocolonial hinterlands. Ghost acres are analogous to today’s ecological footprint. Wackernagel and Rees (1996) also note that William Catton (1980) served as an inspiration in the development of the footprint measure.

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consumption-based demand upon domestic as well as global natural resources, and they allow for comparisons of a nation’s environmental demand relative to available domestic and global “natural capital.” The latter refers to the stock of natural assets, such as water and forest resources, producing a flow of services and resources for human societies (Wackernagel et al. 1999). The updated national footprint estimates available from the Global Footprint Network (2006) measure the bio-productive area required to support consumption levels of a given population from cropland, grassland and pasture, fishing grounds, and forest. They also include the area required to absorb the carbon dioxide released when fossil fuels are burned, and the amount of area required for built infrastructure (e.g., roads, buildings). Regarding the former, the carbon dioxide portion of the footprint deals explicitly with natural sequestration, which involves the biocapacity required to absorb and store the emissions not sequestered by humans, less the amount absorbed by the oceans. A relatively new addition to the comprehensive measure is the nuclear footprint subcomponent. Due to lack of conclusive data, the nuclear portion of the footprint is assumed to be and thus estimated as the same as the equivalent amount of electricity from fossil fuels. However, this subcomponent accounts for less than 4 percent of the global footprint in the year 2000, and this percent is even lower for earlier years. The ecological footprint is measured and reported in global hectares, and is calculated by adding imports to, and subtracting exports from, domestic production. In mathematical terms, consumption = (production + imports) – exports. This balance is calculated for more than 600 products, including both primary resources and manufactured products that are derived from them. Each product or category is screened

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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. The ecological footprint includes only those aspects of resource consumption and waste assimilation for which the Earth has regenerative capacity and where data exist that allow this demand to be quantified in terms of bio-productive area (Wackernagel et al. 2002). The newly available footprint estimates are reported in constant 2003 global hectares, which allows for valid temporal comparisons within and between countries. The per capita footprints of nations can be compared to the global biocapacity per capita, which is calculated by dividing all the biologically productive land and sea on earth by the total world population, which provides an estimate of the globally sustainable level of consumption per person. This measure of sustainable consumption was also developed by Wackernagel et al. (2000) and is available from the Global Footprint Network. The series of boxplots in Figure 1 illustrate the relative distance between the per capita footprints and the global biocapacity per capita in five-year increments from 1960 to 2000 as well as the year 2003 for the countries included in the current study’s panel analyses. 7

During the forty-three year period, the majority of nations represented in Figure 1 went from having globally sustainable levels of consumption-based environmental demand per person to unsustainable levels. Moreover, the cross-national distribution of consumption levels increased dramatically. Besides illustrating the changing 7

Due to the unavailability of data for independent variables, a small number of countries included in Figure 1 are excluded from the panel regression analyses. These countries are flagged in Appendix A. The dataset for the panel analyses is discussed in greater detail below.

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relationships between the footprints of nations and corresponding points of global biocapacity per capita, Figure 1 highlights the temporal and cross-national variation in the environmental impacts of human societies throughout the world. While the majority of published works across the social sciences are favorable towards the ecological footprint, notable debates about the methodology for its calculations and use in comparative research have taken place. For example, a decade ago, Van Den Bergh and Verbruggen (1999) questioned the validity of ecological footprints since the weights used to convert resource consumption into required land area were fixed in time and did not vary across forms of land use. Partly in response to this important criticism, the weights employed in calculating the newly available national footprints used in the current panel study are time variant and do account for differences in types of land use, thus allowing for more valid comparisons (Kitzes et al. 2008). More recently, Fiala (2008) argues that ecological footprints underestimate the environmental impacts of intensive production resulting from economic development and subsequent technological advances (especially in agriculture), which he assumes are likely to be more environmentally demanding. Thus, Fiala posits that footprints underestimate the environmental demands of such changes. These assumptions and observations lack a rigorous empirical basis, his critical assessment of the methods used to calculate footprints lacks specificity, and he neglects to engage recent scholarship in multiple disciplines that employ the ecological footprints of nations as a dependent variable. Nonetheless, if we assume Fiala’s claims are partly valid, it would suggest that employing the ecological footprints of nations as a dependent variable to test hypotheses concerning increases in the effects of development or other structural factors would lead

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to more conservative estimations. Such analyses would lessen the likelihood of committing type 1 errors. 8 Considering (1) their inclusive yet conservative nature as a measure of consumption-based environmental demand; (2) the rigorous methods used in their calculations; (3) their wide acceptability across the social sciences; (4) their face validity given the high correlations between them and well-known direct measures of impacts (e.g., anthropogenic carbon dioxide and methane emissions—see Rosa et al. 2004); and (5) their recent availability for a large number of nations in panel data form, we posit that the per capita ecological footprints of nations are a useful tool for hypothesis testing. We now turn to the analyses, where we employ these data as a dependent variable to assess the extent to which the various sociological theories outlined above help in explaining variation in the consumption-based environmental impacts of nations in particular and the human dimensions of global environmental change in general.

THE ANALYSES The Dataset We analyze an unbalanced panel dataset consisting of data for 65 countries from 1960 to 2003. 9 With the exception of 2003, data are point estimates at five-year intervals (i.e., 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000). To maximize the use of available data, we allow sample sizes to vary among the models. Thus, sample sizes range from 156 to 543 observations, with mean observations per model ranging from 4.9

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We thank an anonymous reviewer for suggesting we consider the critiques of Fiala (2008) and Van Den Bergh and Verbruggen (1999). 9 The analyses are restricted to countries where the ecological footprints contain no temporal anomalies in their calculations as identified by a researcher at the Global Footprint Network.

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to 8.4 per country. For substantive reasons discussed below, some models are restricted to only developed countries or less-developed countries, which lead to relatively smaller numbers of observations. The minimum number of observations per country in any model is 2, while the maximum number of observations per country in any model is 10. Appendix A lists all countries included in the study. Fixed Effects and Random Effects Models With the availability of panel data for the study’s outcome, we are able to employ estimation methods that deal with potential heterogeneity bias (Firebaugh 2008; Greene 2000; Wooldridge 2002). Heterogeneity bias in this context refers to the confounding effect of unmeasured time-invariant variables that are omitted from the regression models. Fixed effects (FE) and random effects (RE) models are two approaches designed to correct for the problem of heterogeneity bias, and these two methods have gained popularity in different areas of comparative sociology (see Allison 2009; Halaby 2004). Both methods “simulate” unmeasured time-invariant factors as case-specific intercepts. The FE model treats the case-specific intercepts as fixed effects to be estimated, equivalent to including dummy variables for N—1 cases. The RE model treats casespecific intercepts as a random component of the error term (Frees 2004). For substantive and methodological reasons, in this study we use STATA (Version 9) software to estimate FE and RE models with robust standard errors. 10 FE models have the advantage of avoiding spurious relationships in panel data, and they provide a stringent assessment of the relationships between independent and dependent variables, given that the associations between predictors and outcomes are 10

Elsewhere we include a correction for first-order autocorrelation in all models. The results are substantively identical to the reported findings and available from the authors upon request.

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estimated net of unmeasured between-case effects. This modeling approach is quite robust against missing control variables, closely approximates experimental conditions, and allows for rigorous hypotheses testing (Hsiao 2003). While the majority of reported analyses account for fixed effects, FE models are inappropriate for perfectly or nearperfectly time-invariant variables, including ecological factors of interest for the current study. Fixed effects might control for such factors, but with FE models we are unable to assess and compare their actual impacts relative to other predictors (Wooldridge 2002). Further, the type of interaction used in the subsequent analyses to evaluate the possible impacts of ecologically unequal exchange for less-developed countries relative to developed countries involves a necessary time-invariant control. Thus, we estimate RE models for the analyses that include such interactions as well as time-invariant measures for ecological factors. For the analyses that exclude time-invariant predictors, results of Hausman test statistics (all statistically significant) indicate that FE models are more appropriate than RE models. Moreover, all FE and RE models include unreported period-specific intercepts (i.e., period effects), which controls for the potential unobserved heterogeneity that is cross-sectionally invariant within years. Period-specific intercepts also lessen the likelihood of spurious regression resulting from outcomes and predictors with relatively similar time trends (Wooldridge 2005:366). Dependent Variable The dependent variable for the analyses is the updated estimates for the per capita ecological footprint, which we obtained directly from the Global Footprint Network. 11

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The Global Footprint Network is an international nonprofit organization that works with various partner organizations to coordinate research, develop methodological approaches, and provide resource accounts to help with policy development. Time series datasets of national ecological footprints and biocapacity measures are available from the Global Footprint Network.

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These data are logged (ln) to minimize skewness. All other variables in the study that are logged (ln) are done so for analogous reasons. For a more detailed description (than the above discussion) of the calculations used for the updated per capita ecological footprint estimates, see the 2006 Living Planet Report. Independent Variables We describe the independent variables in the order they are introduced in the analyses. Our choices for the ordering of their introductions in the first set of FE models are based primarily on data availability. Hence, the overall sample sizes for the initial reported models gradually decrease until the most saturated model, and then increase as we remove certain predictors. Subsequent models build on the first set of analyses, and introduce additional predictors for further assessments of theoretical perspectives. Consistent with the majority of prior cross-national research, we include gross domestic product per capita (GDP per capita) as a measure of a nation’s level of economic development. These data, which are logged (ln), are measured in 2000 constant U.S. dollars and obtained from the World Bank (2007). Employing GDP per capita allows us to evaluate general arguments of treadmill of production theory, ecological modernization theory, and world-systems analysis. 12 Arable land per capita (ln) measures in hectares the amount of arable land per person in a given country. To calculate this variable, we obtained estimates of total arable land in hectares from the World Resources Institute (2005), who gathers the

12

Following the suggestion of an anonymous reviewer, in analyses available upon request we instead use GDP per worker, which we construct by dividing total GDP (constant 2000 U.S. dollars) by the total labor force for nations. Both measures are obtained from the World Bank (2007). The effect of GDP per worker is positive, statistically significant in all tested models, and very similar to the effect of GDP per capita. Considering that GDP per worker and GDP per capita are correlated above .95, these similar findings are to be expected.

22

estimates directly from the Food and Agricultural Organization of the United Nations’ FAO Production Yearbooks. We divide the estimates by total population, which we obtained from the World Bank (2007). Arable land refers to land under temporary crops, temporary meadows for mowing or pasture, land under market and kitchen gardens, and land temporarily fallow. Structural human ecology asserts that including this predictor allows us to control for the extent to which domestic resource availability and / or density influences consumption-based resource demand. We include measures of urban population, which quantifies the percent of a country’s population residing in urban areas. These data are gathered from the World Bank (2007). Generally speaking, urban political-economy approaches would posit a positive association between the per capita footprints of nations and their levels of urbanization. As aptly suggested by two anonymous reviewers, a common argument in urban sociology and related fields is urban population density reduces the overall magnitude of environmental impacts, while forms of urban sprawl and “horizontal” urbanization lead to increases in environmental harms (e.g., Davis 2006). We would certainly prefer to assess such important assertions. However, reliable measures of urban population density in panel data form are currently unavailable for cross-national comparisons. To test for the presence of an environmental Kuznets curve as predicted by environmental economics, in the first series of FE models we include the quadratic of GDP per capita (ln). In an effort to minimize collinearity, we center the measures by subtracting the mean of GDP per capita (ln) before squaring them (Neter, Wasserman, and Kutner 1990).

23

To evaluate particular arguments of treadmill of destruction theory, we include two military measures that we gathered from the World Bank (2007): military personnel as percent total population (ln) and military expenditures as percent total GDP (ln). Military personnel are active duty military personnel as well as paramilitary forces if the training, organization, and equipment suggest they may be used to support or replace regular military forces. We obtained overall numbers of military personnel and divided them by total population measures that we gathered from the same source. Military expenditures include all current capital expenditures on the armed forces as percent GDP, including peacekeeping forces; defense ministries and other government agencies engaged in defense projects; paramilitary forces, if these are judged to be trained and equipped for military operations; and military space activities. Such expenditures include operation and maintenance; procurement; military research and development; military and civil personnel; and military aid (in the expenditures of the donor country). In the first set of analyses we also control for manufacturing as percent GDP and services as percent GDP (World Bank 2007). While these predictors are not used here for testing particular theoretical orientations, their inclusion controls for domestic economy structure, which allows for more valid assessments of the complementary and competing perspectives of interest. For the second series of reported FE models we calculate and use slope-dummy interactions (Allison 2009; Hamilton 1992) between GDP per capita and each time point, where 1960 is the reference category. These measures allow us to evaluate the contrasting assertions of ecological modernization theory and treadmill of production theory concerning a relative decoupling of consumption-based environmental demand

24

and economic development over time. The inclusion of these interaction variables necessitates a somewhat more complex interpretation of the effects. The coefficient for GDP per capita is the unit change in the dependent variable in 1960 for each unit increase in GDP per capita for the same year. The overall effect for the other time points (i.e., 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2003) equals the sum of the coefficients for 1960 and the appropriate interaction term. The test of statistical significance for the slope-dummy coefficients determines whether the slope for the particular interaction and the reference category—in this case 1960—differ significantly. Unreported periodspecific intercepts are also contained in the models that include these interactions as well as all other reported models. To control for climate conditions, we introduce dummy-coded latitude measures (temperate and tropical) that are time-invariant in the first set of RE models. Using the same criteria as York et al. (2003), countries where the predominant latitude is less than 30 degrees from the equator are coded as tropical, and countries where the predominant latitude is between 30 and 55 degrees from the equator are coded as temperate. Arctic countries, meaning those where the predominant latitude is greater than 55 degrees from the equator, are the reference category. Structural human ecologists posit that—all else being equal—nations in colder climates are likely to have relatively larger consumptionbased environmental impacts than nations in warmer climates. To investigate the potential impacts of ecologically unequal exchange, in the second set of RE models we include a weighted index (ln) that quantifies the relative extent to which a country’s exports are sent to more-developed countries. 13 The weighted index is referred to as “weighted export flows.” Data required for the 13

This measure was originally developed by Jorgenson (2006).

25

construction of the index include (1) relational measures in the form of exports between sending and receiving countries, and (2) attributional measures of economic development for receiving countries in the form of GDP per capita. The weighted index is calculated as: N

Wi =

Σ

pijaj

j=1

Where: Wi = weighted export flows for country i pij = proportion of country i’s total exports sent to receiving country j aj = GDP per capita of receiving country j The exports data are taken from the International Monetary Fund’s 2003 Direction of Trade Statistics CD ROM database, and reported in current U.S. dollars. 14 These data include export flows for all commodity types. Per capita GDP data are taken from the World Bank (2007) and are in constant 2000 U.S. dollars. Due to data limitations at the time of the study, the weighted export flows measures are available for every five years from 1975 to 2000 for 53 of the 65 countries included in the current analyses. Hence, the RE models that include this predictor are temporally restricted to the same 6 time points across the 25-year period for 53 countries, which are flagged accordingly in Appendix A. In order to assess the theoretically derived notion that the impact of ecologically unequal exchange is more pronounced for less-developed countries than for developed countries, we calculate and use a slope-dummy interaction between the weighted export

14

Due to the calculations involved in creating the weighted index, the use of export flows data reported in current U.S. dollars is not problematic for the panel analyses.

26

flows measures and a dummy-coded variable for less-developed countries. 15 For these analyses, the coefficient for weighted export flows is the unit change in per capita footprints for developed countries (i.e., the reference category) for each unit increase in the former for the same year. The effect of export flows for less-developed countries equals the sum of the coefficients for developed countries and the appropriate interaction term, labeled as “weighted export flows (ln) X LDCs.” Like the decoupling analyses that involve interactions between GDP per capita and time, the test of statistical significance for these slope-dummy coefficients determines whether the slope for the particular interaction and the reference category—in this case developed countries—differ significantly. In the RE models consisting of this interaction, we also include the timeinvariant dummy variable for less-developed countries (Brambor, Clark, and Golder 2006). Table 1 provides descriptive statistics and Table 2 presents pairwise correlations for the majority of variables included in the analyses. 16

RESULTS The findings are presented in Tables 3 through 6. Table 3 reports FE model estimates that assess the linear effects of the time variant predictors as well as the possible curvilinear effect of GDP per capita. Table 4 presents FE model estimates that include the 15

Here, less-developed countries are those in the dataset that fall below the upper quartile of the World Bank’s (2007) income quartile classification of countries. Appendix A highlights the countries in the dataset that are classified as less-developed. 16 For sake of space we exclude the univariate descriptive statistics for the interactions between GDP per capita and time as well as their pairwise correlations with all other predictors.

27

interactions between time and GDP per capita, which allow for evaluating the competing assertions of ecological modernization theory and treadmill of production theory concerning a relative decoupling between economic development and environmental harms. Tables 5 and 6 report RE model estimates, which introduce the time invariant factors employed to test additional propositions of structural human ecology (Table 5) as well as the assertions of ecologically unequal exchange theory (Table 6). The two sets of RE models also include time varying independent variables found to have statistically significant effects in the FE analyses presented in Table 3. As a reminder, all models include unreported period-specific intercepts. We report 8 FE models in Table 3. Model 1 consists of GDP per capita, arable land per capita, and urban population. Models 2 through 6 include the predictors in the preceding model as well as one additional independent variable. The centered quadratic for per capita GDP is added in Model 2, and military personnel is introduced in Model 3. We add military expenditures to Model 4, introduce manufacturing as percent GDP in Model 5, and add services as percent GDP to Model 6. Model 7 consists of predictors found to have statistically significant effects in any preceding model, and Model 8 excludes independent variables with non-significant effects in Model 7. 17 Given the unbalanced structure of the panel dataset, we estimate the models in this order for two

17

Elsewhere, we also include measures derived from world polity theory (Meyer et al. 1997) and environmental state approaches (Fisher and Freudenburg 2004). These measures include counts of environmental international non-governmental organizations (EINGO) present in a given country, whether or not a nation has an environmental ministry, level of state strength, and level of democratization. The effects of all the additional controls are non-significant, and their inclusion does not substantively alter the reported findings. The EINGO measures were originally analyzed by Smith and Wiest (2005). We thank them for sharing the data with us. We thank David John Frank for the environmental ministry data, which he analyzed with coauthors in prior research (Frank, Hironaka, and Schofer 2000). The state strength and democratization measures were obtained from the World Bank (2007).

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key reasons. First, it allows us to maximize the use of available data for the different predictors. Second, the changing overall sample size and number of observations per country across the models allows for evaluating if the results are biased by such characteristics. Findings for the FE analyses in Table 3 yield multiple theoretically relevant relationships. 18 First, the effect of per capita GDP is positive, relatively strong in magnitude, and remains stable across all models. Jumping to the quadratic for level of economic development, the effect of per capita GDP squared is also positive, and the magnitude of its effect increases with the inclusion of additional predictors in Models 3 through 6. The positive effects of both per capita GDP and its quadratic on the per capita footprints of nations support general arguments of treadmill of production theory and variants of world-systems analysis. 19 However, these results run counter to

18

To consider potential regional and geographic effects, in analyses available upon request we include a series of dummy variables for continents. Given the time invariance of the regional measures, we estimate RE models. The inclusion of the regional controls does not substantively alter the reported findings. We thank an anonymous reviewer for suggesting these important sensitivity analyses. 19 As an anonymous reviewer suggested, GDP per capita is often a nonstationarity variable (see also Kennedy 2008), and employing this measure and its quadratic as predictors in time series or panel analyses with relatively large numbers of time points can lead to spurious results due to unit root problems. Indeed, as shown by Wagner (2008), prior time series research in environmental economics that suggests the existence of environmental Kuznets curve distributions is plagued by unit roots and thus spurious results. While our findings suggest that there is no inverted curvilinear relationship between level of development and the consumption-based environmental impacts of nations, and the current study’s dataset consists of a relatively larger number of cases and smaller number of time points, the same estimation problems could apply here as well. Thus, to further assess the validity of the findings concerning the positive effects of both GDP per capita and its quadratic, we estimate a series of first-difference models, which we report in Appendix B (Cameron and Trivedi 2009; Wooldridge 2002). First-difference models are commonly used to correct for nonstationarity (Wooldridge 2005). The consequences of not firstdifferencing when there is a unit root problem are serious (e.g., spurious results), whereas if the data are differenced when there is no unit root, the main consequence is only loss of efficiency because of the moving average error created by the differencing (Kennedy 2008:309).

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macroeconomic orientations that predict an environmental Kuznets curve. 20 Some evidence is found indicating that more urbanized societies exhibit higher levels of consumption-based environmental demand, lending support for urban political-economy approaches. More specifically, the effect of urban population is positive and statistically significant in four out of eight models. 21 Turning to the military measures, it is not the labor intensity of militaries that influences consumption-based environmental demand per person, but rather, the level of capital investment in the form of military expenditures as percent GDP. 22 The positive effect of military expenditures is statistically significant in 4 out of 5 models. While its relative magnitude is small, the effect of military expenditures is consistent with the assertions of treadmill of destruction theory as well as the arguments of politicaleconomic sociologists concerning the ability of nation-states with more technologically

Nonetheless, results of the first-difference models are substantively identical to those reported in Table 3. The effects of both GDP per capita and its centered quadratic are positive, statistically significant, and very similar to their coefficients resulting from the FE model estimates. We thank an anonymous reviewer for raising this important set of issues. 20 Elsewhere we include either Kentor’s (2000) or Snyder and Kick’s (1979) world-system position measure as a predictor. Given the extreme collinearity between these measures and per capita GDP, the latter is excluded from the models. Further, due to the time-invariant characteristics of both position measures, we estimate RE models. The effects of both measures are positive and their inclusions do not substantively alter the reported findings. 21 To assess if the results concerning urban population are affected by high collinearity with GDP per capita, in sensitivity analyses available upon request we regress the urban population data on GDP per capita and employ the residuals as predictors in analyses of the per capita footprints. This “residualizing” technique is common in comparative international sociology (e.g., Jorgenson and Burns 2007; Kentor and Kick 2008). The effect of the residualized urban population measure is positive and statistically significant in the same models as those reported in the current study. Also, the coefficient for GDP per capita slightly increases in all models where both are employed. Thus, the reported effects of urban population and GDP per capita are not overly influenced by their high collinearity. We thank an anonymous reviewer for raising this important concern. 22 Elsewhere, we employ military expenditures per soldier as a predictor. However, this measure is highly correlated with per capita GDP. Thus, in these additional analyses we exclude per capita GDP. Upon doing so, the effect of military expenditures per soldier is positive and statistically significant, which corresponds with the proposed theorization concerning the environmental impacts of capital intensive and more technologically developed militaries.

30

advanced militaries to gain disproportionate access to the global ecosystem’s resource taps and pollution sinks. Little evidence is found suggesting the importance of domestic economy structure relative to other factors when considering the consumption-based environmental impacts of nations. The effect of services as percent GDP is non-significant, while the effect of manufacturing as percent GDP is positive yet only statistically significant in one out of three models (Model 5). 23 However, while these predictors are employed only as statistical controls for the current study, we would not dismiss their potential importance when investigating other environmental outcomes. The effect of arable land per capita is positive and statistically significant in all but the first model. As posited by structural human ecology, the domestic availability of bio-productive land increases consumption opportunities since such areas provide natural resources and fertile grounds for the growing of agricultural goods and other primary sector consumables. Available arable land also allows for the development of built infrastructure, which contributes to the ecological footprints of nations as well. Besides validating fundamental principles of human ecology, these findings, coupled with the reported effects of economic development, urbanization, and military expenditures, underscore the importance in considering the relevance of ecological factors when focusing on the environmental impacts of social-structural conditions. 24 We now turn to Table 4, which presents the results of the FE models that include the series of slope-dummy interactions between GDP per capita and time. The 23

In unreported analyses we also control for agriculture as percent GDP. The effect of this predictor on the outcome is non-significant, and its inclusion does not alter the reported findings. 24 Elsewhere we also control for percent of population aged between 15 and 64. The effect of this measure on the per capita footprints of nations is non-significant. Prior structural human ecology analyses (e.g. York et al. 2003) include such measures to account for populations’ age structures.

31

interactions are employed to assess the competing propositions of treadmill of production theory and ecological modernization theory concerning a relative decoupling between the economy and environment. We test the same model for three unique datasets: all countries combined (labeled DCs and LDCs), only developed countries (labeled DCs), and only less-developed countries (labeled LDCs). 25 The tested model for all three datasets also includes arable land per capita, urban population, and military expenditures as percent GDP. While the coefficients for the latter three predictors are unreported, their effects for all three samples are consistent with the analyses presented in Table 3. Regardless of whether the analysis is for all countries combined or restricted to either developed countries or less-developed countries, the effect of level of economic development on the per capita ecological footprints of nations increases successively. All employed interactions between time and per capita GDP are statistically significant except for the year 1965 in the analyses restricted to only developed countries and only less-developed countries. We speculate that given the statistically significant effects for all other interactions across the three models, the two non-significant coefficients could be a function of reduced samples relative to the combined dataset. Nevertheless, the findings call into question the validity of ecological modernization theory’s assertions concerning the possibility of a relative decoupling taking place between the consumptionbased environmental impacts of nations and their economic development. The results suggest the contrary: it appears that through time, economic development has become relatively more resource use intensive and thus increasingly environmentally taxing, which support the arguments of treadmill of production theory. Considering the 25

Countries classified as developed are highlighted in Appendix A.

32

comprehensive nature of the dependent variable as well as the analyses’ temporal scope and methodological rigor, these findings are far from inconsequential. Ecological modernization theory emphasizes the potential for a relative decoupling between environmental harms and economic development. The theory suggests that “we are moving beyond the era of a global treadmill of production that only further degrades the environment” (Mol 2001:205). However, the results suggest that the world is nowhere near moving beyond such an economic treadmill and its environmental consequences. The analyses indicate that the opposite trend has taken place in both developed countries and less-developed countries. 26 Table 5 presents the findings for two RE models that focus on the impacts of climate in the context of latitude while also taking into account the time-variant factors found to have statistically significant effects in the first set of FE models reported in Table 3. 27 As a reminder, the climate measures are introduced to assess key assertions of structural human ecology. Model 1 consists of the two time-invariant climate / latitude dummy variables as well as per capita GDP, arable land per capita, urban population, and military expenditures as percent GDP. Model 2 excludes any predictors with non-

26

Readers might conclude that these analyses do not consider potential asymmetrical causality (Lieberson 1985). Indeed, a common assumption in social science research is that relationships between variables are symmetrical in the sense that the return of an independent variable (e.g., GDP per capita) to its value at a previous point in time will lead to the return of the dependent variable (e.g., per capita footprint) to the value it had at that point in time. This can be a serious issue in panel or time series analyses where the predictor of interest and outcome change in only one direction (e.g., GDP per capita and per capita footprints increasing through time). While many countries included in the current study did experience such increases in both, the panel dataset also includes numerous countries that experienced notable declines in both as well (e.g., Angola, Chad, Haiti, Senegal), which strengthen the reliability of the reported findings (see also York 2008). We thank an anonymous reviewer for raising this important concern. 27 Considering the robust positive effect of the centered quadratic for GDP per capita in Table 3, we elect to exclude GDP per capita squared as a predictor from the current set of analyses. However, its inclusion in sensitivity analyses does not substantively alter the reported findings.

33

significant effects in the preceding model, which increases the sample size by 109 observations. As expected, the effects of level of development, arable land per capita, and urban population are all positive and statistically significant in both RE models. However, the effect of military expenditures is non-significant and thus excluded from Model 2. Turning to climate conditions, the effects of both latitude measures are negative, with the negative impact being most pronounced for nations in tropical climates. Simply, and all else being equal, more resources per person are consumed to sustain societies located in colder climates. Table 6 provides findings for the RE analyses that evaluate the assertions of ecologically unequal exchange theory. Results of three models are reported. Model 1 includes the weighted export flows index, which we use to assess the theory. The first model also consists of per capita GDP, arable land per capita, urban population, and the two time-invariant measures for latitude / climate. Model 2 includes all predictors in Model 1 as well as the slope-dummy interaction for weighted export flows and lessdeveloped countries. Model 3 is reduced to both weighted export flows measures and other predictors with statistically significant effects in the preceding models. 28 The latter two models also include the unreported time-invariant dummy variable for lessdeveloped countries, which controls for the possibility of differing intercepts and their potential impacts on the slopes (Brambor et al. 2006). As discussed above, due to data

28

Elsewhere we also include military expenditures as percent GDP as a predictor. The inclusion of military expenditures does not substantively change the reported findings, and like the RE models reported in Table 5, its effect on the per capita footprints of nations is non-significant.

34

availability limitations for the weighted exports flows measures, the models presented in Table 6 are limited to the 1975-2000 period for 53 countries. 29 GDP per capita and arable land per capita both positively affect the ecological footprints of nations, while climate in the context of latitude continues to partially shape the consumption-based environmental harms of nations. However, the effect of urban population is non-significant. We speculate that the latter result is likely a function of the reduced (1) sample sizes, (2) number of countries, and (3) temporal depth for the current models. Turning to the findings of interest for this final set of analyses, the effect of weighted export flows is negative and statistically significant in Model 1. However, as indicated by its standardized coefficient (reported in brackets), the relative magnitude of the effect is quite small. In Models 2 and 3, the effect of the slope-dummy interaction between weighted export flows and less-developed countries is negative, statistically significant, and moderately strong in magnitude. The results, particularly the moderately strong negative effect for the interaction between weighted export flows and lessdeveloped countries, support key assertions of ecologically unequal exchange theory. Due to their structurally advantageous positions in the world economy, the populations of developed countries are able to secure and maintain favorable terms of trade, which allows for greater access to the sink capacity and natural resource stocks within less-

29

It is important to note that the focus here is on the structure of international trade in the context of the vertical flow of exports to more-developed countries, which captures key aspects of ecologically unequal exchange theory. Levels of exports (and imports) along with levels of production for hundreds of commodity types are used in the calculation of the dependent variable. The inclusion of exports as percent GDP in models of per capita footprints would be analogous to (unintentionally) partially including the outcome as a predictor.

35

developed countries. Consequently, these structural relationships suppress resource consumption opportunities for less-developed countries relative to developed countries, well below globally sustainable thresholds in many cases. 30

DISCUSSION Decades ago, Dunlap and Catton (1979) argued that a “human exemptionalist” paradigm—where social processes are treated as independent of the biophysical environment—is untenable within sociology. Undeniably, the growing threat of global climate change and other pressing environmental problems compels sociology to direct more attention to society / nature relationships. With a focus on the human dimensions of environmental change in comparative perspective, here we attempted to contribute to our collective understanding of such associations. Foremost, we engaged many of the dominant perspectives in environmental sociology and its sister disciplines to examine the structural causes of the consumption-based environmental impacts of nations. We also considered the extent to which ecological conditions partially shape the resource consumption habits of human populations. Level of economic development and its quadratic both positively affect the per capita footprints of nations, which support assertions of treadmill of production theory and world-systems analysis. These results contest environmental economics approaches that claim the existence of an environmental Kuznets curve. Furthermore, we found no

30

While the findings lend support to these theoretical articulations, particularly the apparent consequential differences of such structural relationships for developed countries and lessdeveloped countries, in sensitivity analyses available upon request we restrict the sample to lessdeveloped countries and estimate FE models to assess the effect of weighted export flows on the per capita ecological footprints of nations. The effect is negative and statistically significant, which further validates the assertions of ecologically unequal exchange theory.

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evidence of a relative decoupling of environmental harms and economic development, regardless of whether the panel analysis is (1) restricted to either developed countries or less-developed countries, or (2) for all countries in which appropriate data are currently available. Perhaps it is not too surprising that economic development influences natural conditions, but it is critical to note that development in this context increasingly weighs heavily upon nature. Our temporal analyses of economic development and ecological footprints speak directly to the debates about decoupling, which continue to be central to environmental sociology and the other environmental social sciences. Given the Jevons Paradox, we should not assume that improvements in ecoefficiency equate to environmental sustainability when it corresponds with increases in the scale and intensification of production. However, if human ingenuities had not led to better ecological efficiencies in production systems, we speculate that the noted increases in the environmental impacts of economic development would likely be even more pronounced than they already are. Consistent with arguments of urban political-economy scholars, we found that more urbanized nations have higher levels of consumption-based environmental impacts. Cities remain centers of growth that require massive amounts of natural resources to sustain daily operations. Such areas establish patterns of consumption that are often only reinforced with the continued expansion of modern cities and the human activities taking place within them. While the results suggest a positive association between the per capita footprints of nations and level of urbanization, due to data limitations, this research is unable to speak directly to the potentially differing environmental impacts of unique urban configurations, such as sprawl, density, and the growing mega-slums in many less-

37

developed countries. It is our hope that appropriate measures will become available for such nuanced investigations of urban / environment relationships. In an increasingly interconnected world, the consequences of resource overuse by more affluent societies are more easily externalized at the expense of others in distant places. We considered these issues in the context of the vertical flow of exports between nations. Environmental space in less-developed countries is assimilated into the highly consumptive economies of the more-developed countries, which suppresses resource consumption opportunities for the populations of the former, often well below globally sustainable thresholds. These results are consistent with the contemporary theory of ecologically unequal exchange as well as longstanding arguments of natural resource use and underdevelopment scholars (e.g., Bunker 1984; Bunker and Ciccantell 2005). While the structure of the global economy influences the distribution of money and commodities between nations, control of and access to the world’s resources remains an important and related issue. Thus, nations often employ their militaries to secure natural resources, and military expenditures redirect social spending, in part, to build and sustain armed forces. The analyses reveal a positive association between consumptionbased environmental impacts and military expenditures, which supports the treadmill of destruction theory. There is a longstanding tradition in the environmental social sciences to focus on the environmental consequences of economic development. This is a critically important line of inquiry, but the results of the current study indicate that other institutions—including the military—are imperative to consider as well. Societies are bounded by ecological conditions, which affect the capacity of social institutions to operate as well as the ability of humans to support them. While accounting

38

for the role of nature on society can take many forms, here we considered the effects of biogeography and climate. Consistent with the structural human ecology perspective, we find both ecological factors to partially shape the resource consumption habits of nations. The effects of these ecological characteristics underscore the importance in considering how social structural factors and ecological conditions influence societies’ impacts on the environment. Sociological research on society / nature relationships lacking either set of characteristics is imbalanced, if not incomplete.

CONCLUSION Through panel analyses of the per capita ecological footprints of nations, this research suggests that political-economic factors, ecological milieu, and structural associations between nations all influence the resource consumption habits of societies. Besides highlighting the diversity of anthropogenic drivers of environmental harms and how ecological conditions can partially shape such processes, this study illustrates the importance in taking a theoretically inclusive approach when investigating society / nature relationships. We suggest that other areas of comparative sociology would benefit from such comprehensive approaches as well. Our findings and their theoretical underpinnings speak to a grave situation. James Hansen (2008), a leading climatologist, warns that if societies continue to operate “business as usual,” we are ensuring that we will confront further ecological crises with severe implications for all of humanity. It is our hope that this study will encourage other sociologists to consider the complex interrelationships between society and nature in subsequent analyses and theory construction. As this research demonstrates, sociology is

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poised to make important and necessary contributions to our collective understanding of the human dimensions of global environmental change.

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Appendix A. Countries Included in the Analyses Albania Algeria*# Angola*# Argentina*# Austria+* Belgium+* Bosnia & Herzegovina Brazil*# Bulgaria*# Cameroon*# Canada+* Central African Republic*# Chad*# China*# Colombia*# Cote Divoire*# Croatia Democratic Republic of Congo*# Egypt*# Estonia Finland+* France+* Gambia*# Germany+* Haiti*# Hungary*# India*# Indonesia*# Iran*# Ireland+ Italy+* Japan+* Kenya*#

Kuwait+ Laos*# Latvia Lebanon*# Mali*# Mexico*# Nepal*# Netherlands+* Pakistan*# Panama*# Poland*# Portugal+* Romania*# Rwanda*# Saudi Arabia+ Senegal*# South Africa+ Sudan Sweden+* Switzerland+* Syria*# Tanzania*# Thailand*# Tunisia*# Turkey*# Turkmenistan Uganda*# United Kingdom+* United States of America+* Venezuela*# Vietnam*# Yemen

Notes: all countries listed are included in the analyses reported in Figure 1 and Tables 3-5; + denotes countries classified as developed for the analyses reported in Table 4; starred countries are included in the analyses reported in Table 6; # denotes countries classified as less-developed for the calculation of the slope-dummy interaction variable used in the analyses reported in Table 6; the analyses reported in Appendix B are restricted to the countries flagged with #

Appendix B. Unstandardized Coefficients for the Regression of per capita Ecological Footprints on GDP per capita: First Difference Model Estimates for 2 to 10 Observations on 65 Countries, 1960-2003

GDP per capita (ln)

Model 1

Model 2

Saturated

.246*** [.436] (.031)

.262*** [.464] (.038)

.174*** [.375] (.033)

.035*** [.218] (.010)

.031*** [.226] (.009)

-.003 (.004)

-.006 (.004)

-.002 9.007)

.191 477

.237 477

.196 227

GDP per capita Squared Constant R2 N

Notes: ***p<.001 (two-tailed tests); unstandardized coefficients flagged for statistical significance; standardized coefficients appear in brackets; robust standard errors are in parentheses; Saturated model consists of all predictors in Model 6, Table 3

Figure 1 Distance between the Global Biocapacity Per Capita and the Per Capita Footprints of Nations, 1960-2003

Notes: see Appendix A for the list of countries included in the current figure and subsequent panel analyses; zero for the Y axis represents the global biocapacity per capita for the given year indicated by the X axis; negative values on the Y axis correspond with globally sustainable footprint levels, while positive values correspond with globally unsustainable footprint levels

Table 1. Descriptive Statistics N

Mean

Std. Dev. Skewness

Min.

Max.

Ecological Footprint per capita (ln)

543

1.122

.492

.525

.440

2.480

GDP per capita (ln)

543

7.587

1.675

.109

4.440

10.760

Arable Land per capita (ln)

543

.266

.184

1.491

.000

1.150

Urban Population

543

49.119

23.675

.009

2.400

98.260

GDP per capita squared

543

2.799

2.361

.586

.000

10.030

Military Personnel as % total population (ln)

491

.417

.261

.721

.000

1.260

Military Expenditures as % GDP (ln)

412

1.222

.439

.717

.100

3.140

Manufacturing as % GDP

284

15.249

7.651

.723

2.060

41.180

Services as % GDP

284

49.625

13.333

-.161

15.900

78.530

Temperate

543

.510

.500

-.041

.000

1.000

Tropical

543

.425

.495

.303

.000

1.000

Weighted Export Flows (ln)

285

9.384

.301

-.878

8.080

10.070

Weighted Export Flows (ln) X LDCs

285

6.598

4.281

-.892

.000

10.070

Table 2. Pairwise Correlations

1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

Ecological Footprint per capita (ln)

1.

GDP per capita (ln)

2.

.910

Arable Land per capita (ln)

3.

.143

-.006

Urban Population

4.

.805

.862

.017

GDP per capita squared

5.

.257

.119

-.134

-.046

Military Personnel as % total population (ln)

6.

.465

.441

-.011

.442

-.174

Military Expenditures as % GDP (ln)

7.

.200

.118

-.006

.162

-.021

.536

Manufacturing as % GDP

8.

.307

.381

.018

.320

-.133

.089

-.159

Services as % GDP

9.

.547

.633

.085

.564

.003

.101

-.162

.254

Temperate

10. .351

.404

-.081

.351

-.068

.425

.096

.419

.254

Tropical

11. -.553

-.556

-.101

-.473

.002

-.478

-.051

-.464

-.374

-.878

Weighted Export Flows (ln)

12. .080

.200

-.108

.216

.061

-.312

-.160

.103

.280

-.055

-.014

Weighted Export Flows (ln) X LDCs

13. -.834

-.820

-.009

-.558

-.624

-.075

-.310

-.377

-.602

-.367

.567

12.

-.089

Table 3. Unstandardized Coefficients for the Regression of per capita Ecological Footprints on Selected Independent Variables: Fixed Effects Model Estimates for 2 to 10 Observations on 65 Countries, 1960-2003

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 GDP per capita (ln)

.202*** [.682] (.019)

.205*** [.692] (.017)

.189*** [.637] (.021)

.165*** [.558] (.019)

.131*** [.444] (.030)

.131*** [.443] (.032)

.141*** [.479] (.031)

.169*** [.570] (.017)

Arable Land per capita (ln)

.169 [.062] (.099)

.236* [.087] (.100)

.272* [.100] (.134)

.324* [.119] (.142)

.458* [.168] (.228)

.459* [.169] (.232)

.484* [.178] (.234)

.320** [.118] (.107)

Urban Population

.001 [.040] (.001)

.001 [.052] (.001)

.002 [.097] (.002)

.003 [.145] (.002)

.005* [.263] (.002)

.005* [.262] (.003)

.005* [.255] (.003)

.003** [.140] (.001)

.030*** [.145] (.003)

.035*** [.166] (.004)

.048*** [.229] (.004)

.052*** [.249] (.014)

.052*** [.250] (.014)

.048*** [.230] (.014)

.047*** [.228] (.004)

.008 [.004] (.048)

.020 [.011] (.057)

-.078 [-.042] (.069)

-.077 [-.042] (.067)

.036 [.032] (.022)

.060** [.053] (.024)

.060* [.053] (.025)

.046* [.041] (.023)

.032* [.029] (.016)

.003* [.056] (.001)

.003 [.055] (.002)

.002 [.025] (.002)

GDP per capita squared Military Personnel as % total Population (ln) Military Expenditures as % GDP (ln) Manufacturing as % GDP Services as % GDP Constant 2 R Within

.001 [.002] (.001) -.497*** -.634*** -.582*** -.534*** (.122) (.102) (.131) (.134)

-.529 (.319)

-.531 (.326)

-.573 (.336)

-.538*** (.135)

.367

.458

.449

.519

.300

.300

.283

.526

2

.847

.866

.861

.840

.857

.857

.883

.848

2

.846 543 8.4

.880 543 8.4

.868 491 7.7

.867 412 6.6

.860 284 4.9

.859 284 4.9

.890 301 5.0

.879 434 6.9

R Between R Overall N Mean Observations

Notes: all models include unreported period-specific intercepts; *p<.05 **p<.01 ***p<.001 (two-tailed tests); unstandardized coefficients flagged for statistical significance; standardized coefficients appear in brackets; robust standard errors are in parentheses

Table 4. Unstandardized Coefficients for the Regression of per capita Ecological Footprints on GDP per capita Across Time: Fixed Effects Model Estimates for 2 to 10 Observations on 65 Developed and Less-Developed Countries, 18 Developed Countries, and 47 Less-Developed Countries, 1960-2003

GDP per capita (ln)

GDP per capita X 1965

GDP per capita X 1970

GDP per capita X 1975

GDP per capita X 1980

GDP per capita X 1985

GDP per capita X 1990

GDP per capita X 1995

GDP per capita X 2000

GDP per capita X 2003

Constant 2 R Within

DCs and LDCs

DCs

LDCs

.107*** [.360] (.022) .014*** [.053] (.002) .034*** [.138] (.003) .046*** [.194] (.004) .062*** [.272] (.004) .069*** [.306] (.005) .082*** [.373] (.006) .094*** [.437] (.006) .105*** [.494] (.008) .114*** [.539] (.008) .014 (.026)

.126** [.260] (.044) .009 [.077] (.006) .029** [.257] (.011) .036* [.332] (.015) .046* [.441] (.019) .047* [.448] (.023) .059* [.557] (.027) .068* [.652] (.032) .074* [.723] (.036) .080* [.788] (.039) .021 (.020)

.133*** [.464] (.027) .008 [.037] (.005) .020*** [.099] (.006) .025*** [.133] (.007) .037*** [.208] (.009) .040*** [.230] (.010) .044*** [.272] (.012) .053*** [.328] (.013) .059*** [.374] (.016) .066*** [.419] (.017) .011 (.035)

.613

.831

.340

2

.791

.619

.553

2

.812 434 6.9

.679 156 8.7

.584 278 6.2

R Between R Overall N Mean Observations

Notes: DCs are developed countries, LDCs are less-developed countries; all models include unreported period-specific intercepts; *p<.05 **p<.01 ***p<.001 (two-tailed tests); unstandardized coefficients flagged for statistical significance; standardized coefficients appear in brackets; robust standard errors are in parentheses; models also include arable land per capita, urban population, and military expenditures as % GDP

Table 5. Unstandardized Coefficients for the Regression of per capita Ecological Footprints on Selected Independent Variables: Random Effects Model Estimates for 2 to 10 Observations on 65 Countries, 1960-2003

Model 1

Model 2

Temperate

-.201* [-.206] (.099)

-.231* [-.235] (.093)

Tropical

-.261* [-.264] (.108)

-.328** [-.331] (.104)

GDP per capita (ln)

.213*** [.720] (.018)

.210*** [.708] (.019)

Arable Land per capita (ln)

.323*** [.119] (.092)

.241*** [.089] (.075)

Urban Population

.002* [.111] (.001)

.001 [.071] (.001)

Military Expenditures as % GDP (ln)

.010 [.009] (.020)

Constant

-.480 (.170)

-.337 (.158)

2 R Within

.371

.366

2

.863

.866

2

.855 434 6.9

.857 543 8.4

R Between R Overall N Mean Observations

Notes: all models include unreported period-specific intercepts; *p<.05 **p<.01 ***p<.001 (two-tailed tests); unstandardized coefficients flagged for statistical significance; standardized coefficients appear in brackets; robust standard errors are in parentheses

Table 6. Unstandardized Coefficients for the Regression of per capita Ecological Footprints on Selected Independent Variables: Random Effects Model Estimates for 3 to 6 Observations on 53 Countries, 1975-2000

Weighted Export Flows (ln)

Model 1

Model 2

Model 3

-.044* [-.027] (.021)

-.010 [-.005] (.027)

.001 [.001] (.026)

-.027** [-.226] (.008)

-.028*** [-.235] (.008)

Weighted Export Flows (ln) X LDCs GDP per capita (ln)

.217*** [.737] (.018)

.181*** [.612] (.019)

.194*** [.658] (.018)

Arable Land per capita (ln)

.282*** [.097] (.082)

.348*** [.120] (.077)

.345*** [.119] (.074)

.001 [.049] (.001)

.001 [.071] (.001)

Temperate

-.219** [-.217] (.080)

-.092 [-.091] (.079)

Tropical

-.352*** [-.346] (.089)

-.186* [-.182] (.094)

-.110* [-.107] (.054)

Constant

.018 (.225)

-.019 (.222)

-.221 (.187)

Urban Population

2 R Within

.299

.305

.304

2

.899

.914

.909

2

.892 285 5.4

.911 285 5.4

.906 285 5.4

R Between R Overall N Mean Observations

Notes: all models include unreported period-specific intercepts; *p<.05 **p<.01 ***p<.001 (two-tailed tests); unstandardized coefficients flagged for statistical significance; standardized coefficients appear in brackets; robust standard errors are in parentheses; Models 2 and 3 include unreported LDC dummy variable

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