SOCIETIES CONSUMING NATURE: A PANEL STUDY OF THE
ECOLOGICAL FOOTPRINTS OF NATIONS, 1960-2003*
Andrew K. Jorgenson
Department of Sociology and Anthropology
North Carolina State University
Brett Clark
Department of Sociology and Anthropology
North Carolina State University
Running Head: Consuming Nature: A Panel Study
Word Count: 15,896 (including endnotes and references)
Working Draft
* Direct all correspondence to Andrew K. Jorgenson, Department of Sociology and
Anthropology, North Carolina State University, Campus Box 8107, Raleigh, NC 276958107; Phone: (919) 515-9023; FAX: (919) 515-2610; email: akjorgen@sa.ncsu.edu.
SOCIETIES CONSUMING NATURE: A PANEL STUDY OF THE
ECOLOGICAL FOOTPRINTS OF NATIONS, 1960-2003
ABSTRACT
The human dimensions of global environmental change are among the most pressing
issues facing the world today. While increasing attention is paid to such issues, most
sociological research on society / nature relationships is narrowly framed, limited in
temporal and unitary scope, and lacking methodological rigor. To help resolve these
shortcomings, the authors conduct fixed effects and random effects panel regression
analyses of the per capita ecological footprints of 69 nations from 1960 to 2003 to
evaluate multiple theoretical traditions within environmental sociology and other related
orientations. Findings indicate that the consumption-based environmental impacts of
human activities 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 condition the environmental harms of nations.
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 comparative investigations on such topics should be more central to the
discipline.
1
CONSUMING NATURE: ENVIRONMENTAL SOCIOLOGY AND A PANEL
STUDY OF THE ECOLOGICAL FOOTPRINTS OF NATIONS, 1960-2003
INTRODUCTION
There is scientific consensus that humans fundamentally alter the global
environment and that the scale of recent environmental transformations is unprecedented.
In the last fifty years, two-thirds of the world’s ecosystems have been overexploited and /
or polluted (Millennium Ecosystem Assessment 2005). 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). Furthermore, no area of the world’s ocean is unaffected by human influence,
undermining the services and biodiversity of its ecosystems (Halpern et al. 2008).
Although physical and natural scientists underscore that human impacts on the natural
world are escalating and radically changing natural processes and cycles (Vitousek et al.
1997), a deeper understanding of the anthropogenic drivers of environmental change and
the social influences on ecological crisis and sustainability is still needed. Additionally,
the dynamic interaction between nature and society raises pressing concerns regarding
our conception of ecological relationships.
Environmental sociology places society, in all of its grandeur, within the bounds
of natural influences (e.g., Buttel 1987, 2000; Catton and Dunlap 1978; Dunlap and
Catton 1979; Foster 1999; Goldman and Schurman 2000; York, Rosa, and Dietz 2003).
The environment is a social issue, and the environment’s role in shaping—both in terms
of constraining and facilitating societies—is recognized. At the same time, attention is
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paid to how societies transform the biophysical world—whether it is through the
organization of the economic system, demographic trends, political mobilization, or other
structural factors (e.g., Bunker 1984; Downey 2006; Ehrhardt-Martinez 1998; Jorgenson
2006b; Redclift and Sage 1998; Roberts and Parks 2007; Schofer and Hironaka 2005).
Thus, environmental sociology attempts to address the economic, political, and social
dimensions of our ecological relationships. It investigates environmental issues at both
micro and macro levels, from individual consumer behaviors to the international
movement of toxics, from specific forms of environmental degradation to the health risks
associated with exposure to dangerous chemicals.
Like other areas of the discipline, environmental sociology has undergone
numerous reconfigurations, as various theoretical traditions continue to grapple with
ecological concerns and the relationship between society and nature. Different
orientations privilege certain factors and structural relationships—such as economic
growth, technological development, position in the global economy, degree of
modernization, or population dynamics—over others, in part due to the questions being
asked, the state of the field, and the limitations of data. Here we undertake cross-national
analyses that engage many of the most prominent theoretical traditions within
environmental sociology (treadmill of production, metabolic rift, world-systems, and
ecological modernization) that focus on the environmental consequences of development,
affluence, and the structure of the world economy, as well as more recently emerging
theoretical perspectives (treadmill of destruction, domestic economy structure,
ecologically unequal exchange, urban political-economy, and structural human ecology)
that address other sorts of human / environment relationships. We assess a broad range
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of theoretically derived variables in regard to their consumption-based environmental
impacts, measured as the per capita ecological footprints of nations. While our analyses
draw upon previous macrosociological research on the environment, it makes significant
advances employing panel data to better conceptualize historical patterns and
relationships; considering and synthesizing a broader range of theoretical traditions; and
giving serious attention to how both social and ecological conditions influence each other
and to how they play out over time in comparative perspective. As a result, we are able
to assess the explanatory power of different sociological theories and to identify
significant and persistent factors that condition and influence the varying consumptionbased environmental burdens of nations.
We begin with discussions about the most prominent environmental sociology
theories and other relevant perspectives, and explicate particular relationships that we
investigate in the ensuing panel analyses. Next, we describe the substantive
characteristics of the ecological footprint while also highlighting its utility and
importance as an empirical measure. In this discussion we also examine the crossnational temporal patterns of per capita footprints relative to globally sustainable levels of
resource use. 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 panel analyses, which identify the variables and
theoretical positions that help illuminate the relationships and conditions that are most
influential in shaping the consumption-based environmental impacts of nations. We
conclude by highlighting the key findings of the study and we discuss their theoretical
significance.
4
SOCIOLOGICAL APPROACHES TO SOCIETY / NATURE RELATIONSHIPS
A variety of theoretical perspectives have developed—sometimes as an outgrowth
of classical social theory—within environmental sociology and other related areas of
inquiry. The perspectives of interest in this study are ecological modernization (e.g., Mol
1995), the treadmill of production (Schnaiberg 1980; Schnaiberg and Gould 1994), the
metabolic rift (Clark and York 2005; Foster 1999; Burkett 1999), world-systems and the
environment (e.g., Chase-Dunn 1998; Jorgenson and Kick 2006; Roberts and Grimes
2002), treadmill of destruction (Hooks and Smith 2004), and ecologically unequal
exchange (e.g., Bunker 1984; Jorgenson 2006a; Rice 2007a). We also consider different
theoretical orientations concerning the environmental impacts of the structure of domestic
economies (e.g., OECD 1998) as well as urban conditions (e.g., Dickens 2002; Molotch
1976) and structural human ecology (e.g., Dietz, Rosa, and York 2007). These various
approaches are not necessarily exclusive, given that at times arguments compliment each
other and researchers have contributed to more than one tradition. We argue that most
environmental concerns and questions suggest the need for synthesis between specific
theoretical orientations. Nevertheless, brief synopses of these positions will illuminate
how they conceptualize human / environment relationships in the modern world.
Through engaging these theories, we aim to identify the dominant structural conditions
and relationships that are proposed to impact the environment, which we assess in
subsequent panel analyses. We begin with discussions of the perspectives that address
the environmental consequences of economic development and related conditions,
followed by summaries of the other orientations and their arguments of most relevance
for the current study.
5
Economy and Environment: Multiple Perspectives
Ecological Modernization and the Environmental Kuznets Curve
One of the dominant theoretical orientations within contemporary environmental
sociology is ecological modernization (Mol 1995). This theory asserts that the ongoing
development of the market, technology, and industrialism will lead society away from
actions that cause environmental degradation and to a more sustainable interaction between
humans and the environment. It is argued that the power of the market can bring about
ecological reform.
Ecological modernization theory builds upon a branch of environmental
economics that recognizes that economic growth has generated environmental harms, but
argues that further economic development can correct these problems (Grossman and
Krueger 1995). 1 The environment is seen as a luxury good, subject to public demand
through the workings of an advanced market. In other words, during the early stages of
economic development, environmental impacts escalate, but as the affluence within these
societies rises, the value the public places on the environment—including wilderness,
clean air, and clean water—will increase. The public desire for environmental quality—
in large part expressed as consumer demand for “green” products and services—will,
economists expect, place pressure on the government and businesses to invest in “ecofriendly” technologies and commodities. It is argued that if the market is allowed to
operate without dramatic interference, continuing economic development will lead to a
leveling and eventual decline in the environmental impacts of societies. The proposed
trend, known as the environmental Kuznets curve, is depicted as an inverted U-shaped
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distribution representing the relationship between environmental impacts and economic
development.
Ecological modernization continues along these lines, insisting 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 lead
human society from its past of environmental degradation and exploitation to
environmental sustainability are the institutions of modernity, including the market,
industrialism, and technology (Mol 1995, 1996, 2001, 2002; Mol and Sonnenfeld 2000;
Spaargaren 2000; Spaargaren and Mol 1992). Ecological modernization theorists assert
that environmental degradation is not an inherent characteristic of economic
development. They contend that the forces of modernization, such as technological
advances, will lead to the dematerialization of society and the decoupling of the economy
from energy and material consumption, allowing human society to transcend
environmental problems (Mol 1995, 1997; Leadbeater 2000).
Ecological modernization theorists argue that the advance of modernization will
lead to the emergence of an “ecological rationality,” where environmental concerns are
incorporated into social decision-making, and ecological costs and benefits are weighed
along with economic considerations (Mol 2001). This will lead to a shift away from the
pure economic rationality that prevailed in earlier stages of modernization. What they are
proposing is an ecological-economic rationality that explicitly recognizes the inputs of
the environment into the economy. This green rationality will provide the knowledge of
how to properly extend economic valuation to the environment and any natural services
that it produces—with the help of environmental regulation. Additionally, new
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technologies and further research innovations will develop to resolve environmental
problems and to enhance the environmental sustainability of society, without significant
restructuring of the market (Hajer 1996).
In summary, the ecological modernization of society will lead to the
“ecologization of economy” and the “economization of ecology” (Mol 2000:15). The
former refers to organizational changes in both the production and consumption
processes of society, making them account for prevailing environmental interests. The
latter entails the extension of economic valuation to the environment and any natural
services that it produces. In the analyses below, we consider the premises of ecological
modernization, which asserts that ecological rationality has significantly influenced
economic development and concomitant environmental conditions. More specifically,
we investigate ecological modernization’s assertion that higher levels of economic
development lead to declines in the consumption-based environmental impacts of
nations. We also use appropriate measures to look for the presence of an environmental
Kuznets curve.
Treadmill of Production Theory
The treadmill of production theory runs counter to ecological modernization
theory. Proponents of this position argue that environmental degradation stems from the
incessant expansion of production to maintain profits, and that environmental
sustainability cannot be obtained within a capitalist society (Schnaiberg 1980; Schnaiberg
and Gould 1994). Technological development leads to the expansion of production, so
the total amount of energy and materials used typically increases. Producers attempt to
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externalize environmental costs to increase their profits. Economic growth, premised on
accumulation, is thus likely to increase environmental degradation.
Schnaiberg posits that any society driven by endless economic expansion is mired
in a conflict with nature, given the increasing scale of human demands upon the finite
world. Such a society is running endlessly, faster and faster, expending energy and
resources at an accelerating pace. The treadmill on which society is caught operates in a
particular, historical way. “The basic social force driving the treadmill is the inherent
nature of competition and concentration of capital” (Schnaiberg 1980:230). An increase
in monetary resources allows for monopoly capital to invest in new technologies, which
necessitates the incorporation of more energy to operate industry. The new technologies
advance the displacement of labor, which helps reduce labor costs and allows for an
expansion in the productive capacity of capital.
The relentless commitment to economic growth, despite the various social and
ecological costs, is dictated by the pursuit of profit. Producers try to enlarge the
operations of production continuously. With the support of government, industrial
production is allowed to expand, increasing the demands placed on nature and creating
ever-greater amounts of waste (Schnaiberg 1980; Schnaiberg and Gould 1994;
Schnaiberg, Pellow, and Weinberg 2002). Producers will not freely internalize the
environmental costs of production, because it would reduce their profits. The economic
power of producers influences politics and prevents major reforms that would threaten
their authority. The state helps support the expansion of capital by expanding consumer
credit, which keeps pace with increases in production (Weinberg, Pellow, and Schnaiberg
2000:34).
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Gould, Pellow, and Schnaiberg (2004:297) explain that the treadmill of
production theory is “primarily an economic change theory, but one that has direct
implications for natural resource extraction as well as for the opportunity structure for
workers.” While workers are displaced, they note, by the incorporation of new machines
and technologies in production, social progress is associated with ongoing growth. So
the public, in general, continues to support the expansion of the economy, as it continues
to invest in its productive capacity. At the same time, the degradation of life and nature
continues to worsen. “The ‘treadmill’ component recognizes that the nature of capital
investment leads to higher and higher levels of demand for natural resources…. 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 wastes that are released into the natural world. Treadmill of
production theorists hope that the dislocations and continuous degradation of the world
will culminate in a public interest for education regarding alternatives to the treadmill
society, otherwise, it will be impossible to slow or reverse its path of environmental
deterioration (Schnaiberg 1980:250; Obach 2004).
The treadmill of production theory provides a model of the productive operations
of society as they exist in relation to a finite world. The withdrawals and additions to
nature, in the form of raw materials and pollution, are “biophysical variables” that can be
measured in relation to economic growth. Such analyses highlight the “enduring
conflict” between society and the environment (Schnaiberg and Gould 1994). The
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treadmill of production perspective illuminates issues of the scale and intensity of
production as well as economic growth and exploitation of natural resources. The
implication of this model is that there is an increase in environmental problems as society
moves along on the treadmill. More generally, treadmill of production theory would
propose that economic development will lead to higher consumption-based
environmental demands.
The Metabolic Rift
The metabolic rift perspective combines Marx’s critique of political-economy and
metabolic analysis, in order to conceptualize the interpenetration of nature and society
and to understand the “complex, dynamic interchange between human beings and
nature,” which involves “material exchanges” and “regulatory action” (Foster 1999:381).
In contrast to other environmental sociology perspectives, metabolic research
incorporates an analysis of natural cycles and systems, attempting to address both
quantitative and qualitative dimensions of socio-ecological relationships. It recognizes
that ecosystems embody specific regulatory processes that involve complex relationships
of interchange that aid in their regeneration and continuance. The labor process
constitutes a metabolic relation of exchange between humans and nature that is situated
and organized by the larger economic system. It notes that capitalism is predicated on the
ceaseless accumulation of capital, which produces a specific social metabolic order. The
social metabolism of capitalism is increasingly separated from natural metabolism,
producing metabolic rifts (ruptures) in natural cycles and processes. As a result, this
leads to a violation of the nature-imposed regulative laws of social production that
maintain the conditions of nature, leading to further ecological degradation.
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Marx’s metabolic analysis, as explicated by Foster (1999, 2000), provides an
examination of capitalism as a metabolic order. Metabolic analysis in this context
emerged out of his studies of agriculture and the depletion of soil, where Marx noted that
the soil required specific nutrients—nitrogen, phosphorus, and potassium—to maintain
its ability to produce crops, because as crops grew they took up these nutrients. In earlier
societies, the produce of nature was recycled back to the land as fertilizer after it was
consumed. But “capitalist production collects the population together [due to the
enclosure movement and the concentration of land] in great centres, and causes the urban
population to achieve an ever-growing preponderance” (Marx 1976:637). This created a
division between town and country. Food and fiber were shipped from the countryside to
distant markets. In this, the nutrients of the soil were transferred from the country to the
city where they accumulated as waste and increased pollution, rather than being returned
to the soil. This type of production “disturbs the metabolic interaction between man and
the earth, i.e. it prevents the return to the soil of its constituent elements consumed by
man in the form of food and clothing; hence it hinders the operation of the eternal natural
condition for the lasting fertility of the soil” (Marx 1976:637). As a consequence, a
rupture is created in the nutrient cycle.
Influenced by an economic system premised on the accumulation of capital,
intensive agricultural practices were implemented to increase the yield of food and fiber
for markets in cities. The transfer of nutrients was tied to the accumulation process and
increasingly took place at the national and international level. Nutrients turned to waste
in the cities and were not recycled back to the land. As a result, the riches of the soil
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were squandered and the soil was continually depleted of its necessary nutrients (Foster
1999). 2
While Marx specifically examined the soil crisis in the 1800s, his metabolic
analysis has been employed by sociologists to study contemporary environmental
problems such as global climate change (Clark and York 2005), the oceanic crisis and the
collapse of global fisheries (Clausen and Clark 2005), and persistent agricultural
problems (Mancus 2007). Metabolic rift analysis illuminates social-natural dislocations
associated with economic growth. The town and country division—whether within a
nation or internationally—removes organic matter from its particular ecosystem and
concentrates it in urban settings, both undermining and overwhelming ecosystems.
Metabolic rift analysis suggests that economic growth—predicated on the
accumulation of capital—leads to environmental degradation through the separation of
social and natural systems. It indicates that ecological degradation takes place in multiple
ways: 1) an economic system predicated on constant growth undermines natural cycles
and processes; and 2) the rupture of natural systems leads to the accumulation of waste
and the production of pollution. In the following analyses, we examine several of these
relationships, in regard to changes in rural / urban populations, economic growth, and the
structure of international trade.
World-Systems: The Division of Nations and Nature
Society / nature relationships have become quite prevalent in world-systems
analyses in recent decades (e.g., Goldfrank, Goodman, and Szasz 1999; Hornborg,
McNeill, and Martinez-Alier 2007; Jorgenson and Kick 2006; Moore 2003; Roberts and
Grimes 2002). Wallerstein (1974) and many others (e.g., Chase-Dunn 1998; Chase-Dunn
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and Grimes 1995) argue that all nations are structurally interconnected and form a single
world-system (see also Mahutga 2006; Smith and White 1992; Snyder and Kick 1979).
With the emergence of capitalism in the long sixteenth century, pre-capitalist modes of
production were eliminated as the new economic system expanded around the globe.
Capitalism, as the dominant mode of production, consists of an integrated social system
composed of interacting subsystems held together through conflicting forces and longterm historical processes. This arrangement, with divisions between the core, semiperiphery, and periphery, facilitates the unequal accumulation of capital between spheres
within the global economy. The constant drive for accumulation, the concentration of
military strength in the hands of the dominant capitalist nations, and the division of the
world into an economic-political hierarchy helps maintain the stratified world-system
(Kentor 2000).
Accompanying the division of the world is a division of nature, as the structure of
the world-system facilitates the transfer of raw materials—such as timber, fiber, minerals,
and oil—from the periphery and semi-periphery to the core (e.g., Bunker 1984; Moore
2000). In a related vein, Frey (1994, 1995) demonstrates how core nations transfer
hazardous materials to countries in the periphery, and McKinney, Fulkerson, and Kick
(2008) illustrate how world-systemic processes contribute to increases in threatened bird
species, particularly in non-core regions. Other comparative research indicates that the
scale and intensity of greenhouse gas emissions and other air pollutants are strongly tied
to the structure of the core / periphery hierarchy (e.g., Jorgenson 2006a; Roberts, Grimes,
Manale 2006; York and Rosa 2006). Of more relevance for the current study, recent
analysts posit that a resource consumption / environmental degradation “paradox” exists
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where core nations consume the highest amounts of natural resources compared to noncore 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 (e.g., Jorgenson 2003, 2005; Jorgenson and Burns 2007). The worldsystems perspective, which complements treadmill of production theory, the metabolic
rift approach, and ecologically unequal exchange theory (discussed below), helps capture
the global movement of capital, raw materials, and waste. More specifically, this
perspective would propose that more affluent and powerful nations exhibit higher
consumption-based environmental demands.
Urban Political-Economy and the Environment
Urban sociology has long been concerned with environmental issues. In fact,
urban environmental justice studies began with Frederick Engels’s seminal work The
Condition of the Working Class in England in 1844. Here Engels (1892) examined the
environmental conditions in the manufacturing establishments and slums of the factory
towns of England. The depopulation of the countryside and the expanding industrial
operations were drawing people to the cities. Engels studied the transformations of the
urban landscape, highlighting widespread pollution, laying the grounds for the
development of social epidemiology, and revealing the gross economic inequalities.
Workers were often forced to reside within polluted urban landscapes where the natural
conditions and their health were deteriorating (Cronon 1991; Davis 2002; Engels 1892).
Ongoing work that focuses on urban political-economy and ecology addresses
issues related to cities as centers of growth (population, economic, sprawl) with
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structured environments that generate ecological contradictions (Bookchin 1974; Davis
2002; Dickens 2002; Downey 2005; Molotch 1976; Mumford 1970). 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 great concentrations of population within mega-cities pose severe ecological
concerns, as nature is increasingly urbanized with sprawling cities that consume vast
amounts of water, raw materials, and energy that is appropriated from already stressed
environments in distant places (Dickens 2002; Gonzalez 2005, 2006). Gonzalez (2005)
demonstrates that urban zones are centers of mass consumption, whether it is services or
commodities. Land developers promote urban sprawl, and highway systems impose
transportation infrastructures that require the use of high quantities of resources in the
daily operation of business and people’s lives. 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 these urban political-economy
perspectives as well as prior cross-national investigations (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.
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The Military, Treadmill of Destruction Theory, and the Environment
Recent sociological investigations address how different aspects of the military
influence levels of domestic income inequality (Kick, Davis, and Kentor 2006), economic
development (Levy 1998), and other social outcomes (Jenkins and Scanlan 2001; Kick et
al. 1998). However, with few exceptions (e.g., Jorgenson 2005; Hooks and Smith 2004,
2005; York forthcoming), 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 the potential environmental harms of military
institutions and behaviors (e.g., Davis 2002; Grimes 1999; Lanier-Graham 1993; Thomas
1995), but as York (forthcoming) suggests, they do not represent the full extent of the
environmental impacts of the military.
Even when armed conflicts are not taking place, military institutions and their
activities are likely to consume vast amounts of nonrenewable energy and other resources
for research and development, maintenance, and operation of their overall infrastructure
and hardware (e.g., Dycus 1996; Sidel and Shahi 1997). At the same time, they generate
large amounts of toxic substances and waste (LaDuke 1999; Shulman 1992; Ward 1999).
Furthermore, according to the United Nations’ Center for Disarmament (1982), the
amount of land used by armed forces for bases and other forms of installations as well as
for training exercises has risen steadily over the last century. A network of military bases
encompasses the globe, requiring a vast amount of resources to staff, operate, and
transport equipment and personnel between destinations. Military-oriented resource use
also includes the strategic stockpiling of fuels and other materials, and consumption is
further increased through the material requirements of industries that produce marginal
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equipment for the armed forces and its support economy. Production of these forms of
marginal equipment and stockpiling of fuels helps to expand and maintain the overall
military infrastructure. The populations of armed forces also consume vast amounts of
food, and use large quantities of various organic as well as synthetic materials for
uniforms and more specialized forms of clothing (Jorgenson 2005).
Besides consuming resources, peacetime activities of the military are known to
generate different forms of waste. For example, the armed forces consume large amounts
of fossil fuels, which directly contribute to anthropogenic carbon dioxide emissions and
the emission of other greenhouse gases known to impact global warming and climate
change (Roberts et al. 2006). 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 (Hynes 1999; Santana
2002). The vast network of military bases around the globe only increases the
consumption of fossil fuels, and 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, biological, chemical, and
nuclear arms generate toxic and radioactive substances that are known to contaminate air,
water, and soil (Davis 2002; LaDuke 1999; Renner 1991; Shulman 1992).
Recently, Hooks and Smith (2004, 2005) formulated a theoretical orientation for
assessing the potential environmental impacts of militaries. 3 They characterize the
expansionary dynamics and profound environmental impacts associated with militarism
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as the “treadmill of destruction.” Drawing partly from long-standing 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 treadmill of production (Kalecki 1972; 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. The latter is
clearly articulated by a U.S. military base commander 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. However, military development
under these conditions does not necessarily involve increases in armed forces’ personnel
(Hooks and Smith 2005). 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 less soldiers for their
possible use. While the scale of their material infrastructure may increase, become more
spatially dispersed, or at least remain relatively constant, the overall social space (i.e.,
population) occupied by more technologically advanced militaries often shrinks (e.g.,
Kick et al. 2006). In a similar vein, political-economic sociologists have emphasized that
nations with relatively larger and more technologically advanced militaries utilize their
global military reach to gain disproportionate access to natural resources (Chase-Dunn
1998; Kentor 2000; Magdoff 1969, 1978; Podobnik 2006). While prior cross-national
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research investigates the environmental impacts of either military personnel size (York
forthcoming) or military expenditures (Jorgenson 2005), we consider it crucial to assess
the consumption-based environmental impacts of both.
Domestic Economy Structure
Neoclassical economic perspectives suggest that the structure of domestic
economies is likely to influence their consumption-based environmental demand (e.g.,
OECD 1998). A common assertion derived from this general framework is that a relative
shift towards a more service-based economy offers a potential solution to reducing the
scale and intensity of the environmental impacts of nation-states (Leadbeater 2000).
Following this line of reasoning, one could posit that countries with more service-based
economies will have lower consumption- and production-based environmental impacts.
Moreover, countries with a larger manufacturing base will likely consume greater
amounts of resources and emit higher levels of waste, such as greenhouse gas emissions,
ground-level air pollutants, and industrial water pollutants.
In contrast to neoclassical arguments, critical political-economic perspectives,
such as world-systems and structural globalization approaches to the environment (e.g.,
Jorgenson and Kick 2006; Roberts and Grimes 2002), posit that many serviced-based
economies are nested in more affluent societies, and more affluent societies utilize their
relative positions of international power and influence to gain greater access to and
ultimately consume vast amounts of resources—while social and environmental costs are
imposed on other societies and nature. Moreover, often the most efficient nations are, in
fact, the biggest consumers of natural resources, and economic growth and expansion
typically outstrip gains made in efficiency (Foster 2002; Jorgenson 2008; York and Rosa
20
2003; York et al. 2003). These approaches further suggest that the perceived or real
environmental benefits of having a more service-based economy might be outstripped by
the overall environmental harms associated with economic development or affluence,
since in the contemporary world economy the latter often go hand-in-hand with higher
levels of service-sector activities (Jorgenson and Burns 2007; York 2006). For example,
the most powerful global cities are located in more-developed countries (Alderson and
Beckfield 2004). These cities, often considered as “command points” 4 and financial
centers for global production systems, house the most lucrative “producer services” 5 that
cater to transnational firms headquartered in global cities (Sassen 2001), many of which
organize and largely control global economic activities. To the extent that countries turn
to manufacturing, critical political-economic perspectives generally agree with the
neoclassical economic assertion that economies with a greater intensity of secondarysector activities will likely consume greater amounts of resources and emit higher levels
of waste (e.g., Jorgenson 2007; York and Rosa 2006). Thus it is important to consider
the environmental impacts of the extent to which nations’ economies are manufacturingbased and service-based.
Ecologically Unequal Exchange
Environmental sociology has experienced a recent surge in theorization and
research concerning how the structure of international trade contributes to ecological and
environmental outcomes (e.g., Hornborg et al. 2007; Jorgenson and Kick 2006).
Considering the recent upswings in structural economic globalization (e.g., Chase-Dunn,
Kuwano, and Brewer 2000), the growing attention paid to the environmental impacts of
trade is perhaps not too surprising. One of the primary perspectives in this emergent area
21
of literature is the theory of ecologically unequal exchange, which has much of its roots
in the classical unequal exchange and trade dependence traditions in political-economic
sociology (e.g., Amin 1974; Emmanuelle 1972; Frank 1967; Galtung 1971) as well as
Stephen Bunker’s (1984) seminal work on natural resource extraction and
underdevelopment in the Amazon. Bunker forcefully argued that macrosociology had
failed to address how and the extent to which the extraction and export of natural
resources from less-developed, 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 (Bunker 1984; see also Bunker and Ciccantell 2005; Hornborg 1998, 2001,
2006; Moore 2003; Rice 2007a).
Drawing from these complementary perspectives, the contemporary theory of
ecologically unequal exchange asserts that through the “vertical flow of exports” from
less-developed countries, more-developed countries partially externalize their
consumption-based environmental costs to less-developed countries, which in turn
increase forms of environmental degradation in the latter while suppressing levels of
resource consumption within their borders, often well below globally sustainable
thresholds (e.g., Jorgenson 2006b; Rice 2007b; Roberts and Parks 2007; Srinivasan et al.
2007). Similarly, a global metabolic rift perspective suggests relationships of
ecologically unequal exchange, given the organization of the global economy and the
ruptures in global ecosystems (Clark and York 2005). In general, the populations of
more-developed countries are positioned advantageously in the contemporary world
economy, and thus more likely to secure and maintain favorable terms of trade allowing
22
for greater access to the natural resources and sink capacity of bioproductive areas within
less-developed countries. This greater access facilitates the externalization of
environmental costs of resource extraction and consumption to less-developed countries.
Furthermore, these structural processes help create conditions where more-developed
countries are able to over-utilize global “environmental space” (as well as the global
commons, such as the atmosphere and oceans), which encompasses the stocks of natural
resources and waste assimilation properties of ecological systems supporting human
social organization (Rice 2007a). 6 The misappropriation of environmental space
suppresses resource consumption opportunities for many less-developed countries, which
also impacts the health and well-being of their domestic populations (Jorgenson
forthcoming). To assess key aspects of the theory of ecologically unequal exchange, in
the subsequent panel analyses we employ a weighted measure that quantifies the relative
extent to which the exports of a given country are sent to more-developed countries. We
also use a theoretically relevant interaction variable to compare the effects of ecologically
unequal exchange for less-developed countries and developed countries.
Structural Human Ecology
Human ecologists argue that while the capacity of social institutions, technology,
and culture, separate humans from other species, this unique capacity is to some extent
bounded by the limits imposed by ecological conditions (York et al. 2003:283; see also
Dunlap and Catton 1979; Freese 1997a, 1997b). However, sociological research focusing
on the environmental impacts of political-economic conditions and processes (e.g., Burns
et al. 1994; Jorgenson et al. 2007; Schofer and Hironaka 2005; Rice 2007a; Shandra
2007) typically fails to consider the ecological context in which social factors drive
23
environmental outcomes (Catton 1980; 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 influencing and conditioning how socialstructural factors affect the natural environment (e.g., Dietz and Rosa 1994; Dietz et al.
2007; Rudel 2005). Biogeography in this context deals with the extent to which resource
density and availability influence resource demand and consumption levels. Regarding
the second ecological factor (i.e., climate), conventional wisdom suggests that more
resources are consumed to sustain societies in colder climates. Thus, one would assume
that resource consumption increases the further a nation is from the equator (Diamond
1997; York and Rosa 2006). Considering the potential for invalid inferences, coupled
with the robust findings concerning these sorts of ecological factors in comparative
human ecological research (e.g., York et. al 2003), in the following analyses we include
controls for both climate and biogeography.
THE ECOLOGICAL FOOTPRINTS OF NATIONS
The ecological footprint is perhaps the most comprehensive measure currently
available for assessing global environmental sustainability issues. Thus, to evaluate the
various theoretical perspectives discussed in preceding sections, we employ the per capita
ecological footprint as the dependent variable in subsequent panel analyses.
Conceptually, and as an empirical indicator, the ecological footprint was primarily
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 waste products of a given population.
National footprints are measures of societal consumption-based demand upon domestic
24
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 recently 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 (food, animal feed, fibre, and
oil); grassland and pasture (grazing of animals for meat, hides, wool, and milk); fishing
grounds (fish and seafood); and forest (wood, wood fibre, pulp, and fuelwood). 7 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. 8 A
relatively new addition to the comprehensive footprint measure is the nuclear footprint
subcomponent. Due to lack of conclusive and available data, the nuclear energy 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 total 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
25
calculated for more than 600 products, including both primary resources (e.g., raw
timber, wheat, milk) and manufactured products that are derived from them (e.g., paper,
plywood, cereal, cheese). Each product or category is screened for double counting to
increase the consistency and robustness of the measures. To avoid exaggerations in
measurement, secondary ecological functions that are accommodated on the same space
as primary functions are not added to the footprints. The footprint calculations also use
(1) equivalence factors to take into account differences in world average productivity
among different land types (e.g., world average forest versus world average cropland),
and (2) yield factors to take into account national differences in biological productivity
(e.g., tons of wheat per U.S. or Sri Lankan hectare versus world average). The ecological
footprint includes only those aspects of resource consumption and waste production 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). Of particular
relevance for the current study, the newly available time series footprint estimates are
reported in constant 2003 global hectares, which allows for more 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. This indicator 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
26
1960 to 2000 as well as the year 2003 for the countries included in the subsequent panel
analyses.
<Figure 1 about here>
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 to unsustainable levels. Moreover, the cross-national distribution of
consumption levels increased dramatically, with the majority of increases taking place
upwardly and well above sustainable thresholds. Besides illustrating the changing
relationships between the footprints of nations and corresponding points of global
biocapacity, Figure 1 highlights the growing temporal and cross-national variation in the
environmental impacts of human societies throughout the world. We now turn to the
panel regression analyses, where we assess the extent to which the various sociological
theories outlined above successfully help in explaining the differences in the per capita
footprints of nations.
THE ANALYSES
The Dataset
We analyze an unbalanced panel dataset consisting of data for 69 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 tested models. For the tested
models, overall sample sizes range from 236 observations to 543 observations, with mean
observations per model ranging from 4.6 to 8.4 per country. Due to the relatively limited
availability of data for the weighted export flows measures discussed below, the third and
27
final set of reported analyses are slightly reduced to the 1975-2000 period for 58
countries. The minimum number of observations per country in any reported 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 recent availability of panel data for the outcome investigated in the
current analyses, we are able to employ estimation methods that deal with potential
heterogeneity bias (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 sociology (see Halaby 2004),
including environmental macrosociology (e.g., Jorgenson 2007, 2008; Jorgenson, Dick,
and Mahutga 2007; Jorgenson and Kuykendall forthcoming; York 2007, forthcoming).
Both methods “simulate” unmeasured time-invariant factors as case-specific intercepts
(e.g., Nielsen and Alderson 1995). 10 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 case-specific intercepts as a random component of the error term
(Frees 2004; Hsiao 2003). For substantive and methodological reasons, in this study we
use STATA (Version 9) software to estimate ordinary least squares (OLS) FE and
generalized least squares (GLS) RE models with robust standard errors (Hamilton
2006). 11
28
FE models have the advantage of avoiding spurious relationships in panel data
(e.g., Lee, Nielsen, and Alderson 2007), and they provide a stringent assessment of the
relationships between independent and dependent variables, given that the associations
between predictors and outcomes are estimated net of unmeasured between-case effects
(e.g., Beckfield 2007). This modeling approach is quite robust against missing control
variables, closely approximates experimental conditions, and allows for rigorous
hypotheses testing (Hsiao 2003). However, FE models are inappropriate for perfectly or
near-perfectly time-invariant variables, including ecological factors of direct interest for
the current study. While the case-specific intercepts might control for such factors, we
are unable to assess and compare their actual impacts relative to other predictors. Also,
when one or more independent variables have relatively low (or no) variation across time
for a noteworthy proportion of cases, FE models can suffer from extreme
multicollinearity since variables under these conditions will be highly collinear with the
case-specific fixed effects (Wooldridge 2002).
The type of interaction used in the subsequent analyses to assess the possible
impacts of ecologically unequal exchange for less-developed countries relative to
developed countries do indeed involve no temporal variation for a negligible proportion
of cases. Thus, we estimate GLS RE models for the analyses that include such
interactions as well as time-invariant ecological factors. For the analyses that exclude the
latter two types of 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),
29
which controls for the potential unobserved heterogeneity that is cross-sectionally
invariant within years (e.g., Alderson and Nielsen 2002).
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. 12
These data are logged (ln) to minimize skewness. For a more detailed description (than
the above discussion) of the calculations used for the updated estimates, see the 2006
Living Planet Report.
Independent Variables
We describe the independent variables in the order they are introduced in the
analyses. The first eight predictors that we discuss are included in the reported FE
models.
Consistent with 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
and affluence. These data, which are logged (ln) to correct for excessive skewness, are
measured in 2000 constant U.S. dollars and obtained from the World Bank (2007). All
other variables in the study that are logged (ln) are done so for analogous reasons.
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), and divided them by
total population, which we gathered from the World Bank (2007). Arable land refers to
land under temporary crops (double-cropped areas are counted only once), temporary
meadows for mowing or pasture, land under market and kitchen gardens, and land
30
temporarily fallow (World Resources Institute 2005). Structural human ecology (e.g.,
York et al. 2003) 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. 13 These data are gathered from the World
Bank (2007).
To test for an environmental Kuznets curve, 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).
We include two measures of militarization 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 obtain overall numbers of military
personnel and divide them by total population measures that we gathered from the same
source. Military expenditures data include all current and 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.
More specifically, such expenditures include operation and maintenance; procurement;
military research and development; military and civil personnel, including retirement
31
pensions of military personnel and social services for personnel; and military aid (in the
military expenditures of the donor country).
Manufacturing as percent total GDP measures the extent to which a nation’s
economy is manufacturing-based. These data are obtained from the World Bank (2007).
Services as percent total GDP controls for the extent to which the economy within
a given nation is services-based. These data are gathered from the World Bank (2007).
Besides the majority of predictors already described, in the RE models we include
two dummy-coded latitude measures to control for climate conditions: temperate and
tropical. 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.
To investigate the potential impacts of ecologically unequal exchange, in the 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. The weighted index is referred
to as “weighted export flows.” This measure, which was developed by Jorgenson
(2006b) to test hypotheses in a cross-national analyses of deforestation, was also recently
used in panel analyses of industrial organic water pollution in less-developed countries
(Shandra, Shor, and London 2008). Data required for the 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.
32
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. 15 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 58 of the 69 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.
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 (Hamilton 1992) between the
weighted export flows measures and a dummy-coded variable for less-developed
countries. 16 The inclusion of this interaction variable involves a slightly more complex
interpretation of the effects. 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
33
interaction term, labeled as “weighted export flows (ln) X LDCs.” 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 developed countries—
differ significantly. Other recent cross-national investigations employ this type of
interaction for similar reasons (e.g., Jorgenson and Kuykendall forthcoming; Kick et al.
1996; Rice 2007b).
Table 1 provides descriptive statistics and Table 2 presents pairwise bivariate
correlations for all variables included in the reported panel analyses.
<Table 1 about here>
<Table 2 about here>
RESULTS AND DISCUSSION
Table 3 provides the results of the OLS FE analyses. We report findings for 9
models. Model 1 consists of only per capita GDP. Models 2 through 8 include the
predictors in the preceding model as well as one additional independent variable. Model
2 includes per capita GDP as well as arable land per capita. We add urban population in
Model 3, and we add the quadratic for per capita GDP in Model 4. Military personnel is
added to Model 5, while military expenditures is added to Model 6. We add
manufacturing as percent GDP to Model 7, and services as percent GDP to Model 8.
Model 9 consists of predictors found to have statistically significant effects on the
outcome in any preceding model. 17
For all panel regression analyses, we report unstandardized coefficients, which are
flagged for statistical significance. We also report standardized coefficients and robust
standard errors for all predictors. For each model, we report values for r-square within, r-
34
square between, and r-square overall, and we also report overall sample sizes, mean
observations per country, minimum observations per country, and maximum observations
per country. All models presented in Table 3 as well as the RE models in Tables 4 and 5
include unreported period-specific intercepts.
<Table 3 about here>
Findings for the FE analyses yield multiple theoretically relevant statistical
relationships. First, the effect of per capita GDP is positive and remains relatively stable
in magnitude across all tested models. Jumping to the quadratic for 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 5 through 8. The positive effects of
both per capita GDP and its quadratic on the per capita footprints of nations provide
strong support for the theoretical traditions that argue that economic growth increases the
degradation of nature, particularly treadmill of production, metabolic rift, and worldsystems perspectives. Additionally, these results run counter to key tenets of ecological
modernization theory and orientations that predict an environmental Kuznets curve. In a
related vein, some evidence is found indicating that more urbanized societies exhibit
higher levels of consumption-based environmental demand, lending support for certain
premises of the metabolic rift perspective and urban political-economy approaches. More
specifically, the effect of urban population is positive and statistically significant in two
out of seven models (Models 7 and 8). However, as articulated by multiple theoretical
perspectives (e.g., Foster 1999; Molotch 1976) and prior research (e.g., Jorgenson 2003;
York et al. 2003), in general, economic development and urbanization are related
processes with shared structural conditions. 18
35
Turning to the military, results suggest that it is not the relative size of the military
personnel that influences consumption-based environmental demand, but rather, the
relative level of capital investment in the form of military expenditures as percent GDP. 19
The positive effect of military expenditures on per capita footprints is statistically
significant in 3 out of 4 models (Models 7 through 9). While its relative magnitude is
small, the positive effect of military expenditures is consistent with Hooks and Smith’s
(2004, 2005) treadmill of destruction theorization as well as the assertions of politicaleconomic sociologists (e.g., Chase-Dunn 1998; Kentor 2000) concerning the enhanced
ability of nation-states with more technologically advanced militaries to gain
disproportionate access to the global ecosystem’s resource taps and waste sinks (e.g.,
Podobnik 2006). What is more, the positive effect of military expenditures, net of
economic development, underscores that while military institutions and their
environmental impacts are connected to economic interests and the treadmill of
production’s ecological consequences (Schnaiberg and Gould 2000), they are also
somewhat independent of them (Jorgenson 2005; York 2007).
Little evidence is found suggesting the importance of domestic economy structure
relative to other factors when considering the consumption-based environmental impacts
of nations. More directly, 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 relevant models (Model 7). While these results lend little if any support
to arguments posed by neoclassical economic perspectives and more critical politicaleconomic perspectives about the potential environmental impacts of manufacturing-based
economies, they also do not provide support for neoclassical economic assertions
36
concerning the likely environmental benefits of more service-based economies. 20 Still,
we would not dismiss their potential importance for more particular sorts of
environmental degradation (e.g., deforestation) or pollution (e.g., industrial water
pollutants, anthropogenic greenhouse gas emissions). Rather, the current findings
indicate that other structural factors are more relevant when considering the overall
consumption-based environmental impacts of societies, at least in the context of the per
capita footprints of nations.
Consistent with structural human ecology (e.g., York et al. 2003), biogeography
in the context of the local availability of arable land influences the environmental demand
posed by the consumption habits of nations. The positive effect of arable land per capita
is statistically significant in six out of eight relevant models (Models 4 through 9), while
the relative magnitude of its effect is well beyond trivial. Besides validating fundamental
principles of human ecology, these findings, coupled with the reported effects of
economic development, urbanization, and military expenditures, underscore the
importance for considering the relevance of ecological conditions when focusing on the
environmental impacts of political-economic factors and other structural conditions.
Furthermore, the FE models account for temporally invariant ecological factors, such as
latitude. In the subsequent RE analyses, however, we are able to directly assess and
compare the relative impacts of latitude to other ecological and structural factors.
Table 4 presents the results of GLS RE analyses that focus on the impacts of the
factors found to have statistically significant effects in the preceding analyses, while also
assessing the effects of latitude. We report the findings for three models. Model 1
consists of per capita GDP, arable land per capita, urban population, the quadratic of
37
GDP per capita, and the two latitude dummy variables: temperate and tropical. Model 2
also includes military expenditures as percent GDP. Model 3 includes only the predictors
found to have statistically significant effects in either Model 1 or Model 2.
<Table 4 about here>
Similar to the FE analyses, the effects of level of development and its quadratic,
arable land availability, urban population, and military expenditures are all positive and
statistically significant in the RE models. Turning to climate, results indicate that nations
closer to the equator do indeed have relatively lower consumption-based environmental
impacts than those located in more temperate and arctic climates. In particular, and all
else being equal, nations located in tropical climates—meaning those less than 30 degrees
from the equator—have relatively lower per capita footprints than nations located further
away from the equator. Simply, more resources per person are consumed to sustain
societies located in colder climates. Given that the effect of the temperate control is
statistically significant in only one of two models (Model 1), and the relative magnitude
of its effect in Model 1 is smaller than the tropical control, it appears that the most
relevant difference is between tropical and non-tropical nations. In addition to social
structural factors, biogeography and climate both play important roles in conditioning the
environmental impacts of human activities and their built environments (e.g., Dietz et al.
2007).
Table 5 provides findings for the RE analyses that evaluate assertions of
ecologically unequal exchange theory. Results of three models are reported. Model 1
includes the weighted export flows index as well as per capita GDP, arable land per
capita, urban population, military expenditures as percent GDP, and the tropical dummy-
38
coded measure for latitude. Model 2 includes all predictors in Model 1 as well as the
slope-dummy interaction for weighted export flows and less-developed countries. Model
3 is reduced to both weighted export flows measures and other predictors with
statistically significant effects in the preceding two models. As discussed above, due to
data availability limitations for the weighted exports flows measures, the models
presented in Table 5 are limited to the 1975-2000 period for 58 countries, thus reducing
the temporal depth and overall sample sizes relative to the analyses presented in the two
preceding tables. 21
<Table 5 about here>
GDP per capita and arable land per capita both continue to positively affect the
consumption-based environmental impacts of nations. 22 However, the effects of urban
population, military expenditures, and tropical climate are all non-significant. We
speculate that these results, which contradict the preceding analyses, are likely a function
of the overall reduced sample sizes, lesser number of countries, and reduced temporal
depth for the current models. Turning to the findings of particular interest for this final
set of analyses, the effect of weighted export flows is non-significant. However, the
slope-dummy interaction between weighted export flows and less-developed countries is
negative and statistically significant in both relevant models. To further assess these
relationships, elsewhere we include the dummy-coded measure for less-developed
countries, which controls for the possibility of differing intercepts (and their potential
impacts on the differing slopes of interest) for this group of countries relative to the
developed countries. Including the dummy variable does not substantively alter the
reported findings concerning the slope-dummy interaction and the main effect for the
39
reference category. We also conduct additional analyses separately for the developed
countries and less-developed countries. Consistent with the results presented in Table 5,
the effect of weighted export flows is non-significant in the analyses of only the
developed countries, but the effect of this predictor is negative and statistically significant
in the analyses of only the less-developed countries.
The results, particularly the reported negative effect for the interaction between
weighted export flows and less-developed countries, support the theory of ecologically
unequal exchange (e.g., Bunker 1984; Jorgenson 2006a; Rice 2007a). Due the their
structurally advantageous positions in the world economy, the populations of moredeveloped 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 lessdeveloped countries, which facilitates the externalization of environmental costs of
resource consumption. Consequently, these structural relationships suppress resource
consumption opportunities for less-developed countries, well below globally sustainable
thresholds in many cases. The results lend support to these theoretical articulations,
while also taking into account the impacts of other structural factors as well as
biogeography. More simply, and taken as a whole, this research indicates that structural
relationships between societies (ecologically unequal exchanges), the structural attributes
of societies (economic development, military expenditures, urbanization), and the
ecological conditions in which societies are located (climate, biogeography) all play
important roles in conditioning their consumption-based environmental impacts.
40
CONCLUSION
A “human exemptionalist” paradigm—where social processes are treated as
independent of the biophysical environment—is untenable within sociology (Dunlap and
Catton 1979). While natural scientists emphasize that humans are radically altering the
biophysical processes of Earth, too often environmental sociology is marginalized or seen
as a quaint realm of inquiry. But the threat of global climate change and other pressing
environmental problems (e.g., the collapse of the world’s fisheries, deforestation, water
pollution) compels sociology to direct more attention to studying associations between
society and nature. Even with the importance of such problems being recognized by
scholars across the sciences, environmental sociology still operates from the wings of the
discipline to investigate various sorts of society / nature relationships, with growing focus
on social structures that influence environmental conditions. At the same time, attention
is increasingly paid to how constant as well as changing natural conditions affect the
social world. Here, we illuminated how distinct, yet overlapping, theoretical traditions
(ecological modernization, treadmill of production, metabolic rift, and world-systems)
have developed within environmental sociology. We engaged this rich field, while also
including other emerging perspectives (treadmill of destruction, domestic economy
structure, ecologically unequal exchange, urban political-economy, and structural human
ecology) within it, to explicate the various theoretical arguments in regard to how they
understand society / nature connections. From this, we identified a range of social and
ecological factors that the various perspectives suggest influence the consumption-based
environmental impacts of nations.
41
Findings for the fixed effects and random effects panel regression analyses
highlight how there is no basis to remain agnostic about whether or not social processes
are tied to the biophysical environment, given that a wide range of variables impact the
consumption-based environmental demands of societies, leading to larger per capita
ecological footprints. The results lend strong support to the arguments of most of the
major theoretical orientations within environmental sociology, which indicates that these
positions identify significant human / environment dynamics that deserve attention.
Many environmental sociology perspectives focus on the economic dimensions of
society / nature relationships. We find that per capita GDP and its quadratic both
positively affect consumption-based environmental demands, which are consistent with
the treadmill of production, metabolic rift, and world-systems perspectives. All three
theoretical traditions argue that economic growth increases the degradation of nature.
These results sharply contest key propositions of ecological modernization theory and
perspectives that claim the existence of an environmental Kuznets curve in
macrocomparative context. Perhaps it is not too surprising that economic operations
influence natural conditions, but it is significant to note that expanding economic systems
weigh heavily upon nature, regardless of whether an economy is more service-based or
manufacturing-based. Thus, affluence and economic growth should continue to be issues
of utmost concern when considering the human dimensions of global (and local)
environmental change.
Consistent with arguments of urban political-economy scholars and the metabolic
rift perspective, we find that more urbanized societies have higher levels of consumptionbased environmental impacts. Cities remain centers of growth and demand massive
42
amounts of external resources (often from distant lands) to sustain daily operations. The
structural configurations of urban areas establish patterns of consumption that are often
only reinforced with the continued expansion of modern cities. The dimensions of these
relationships are recognized by the global cities literature, as particular “command
points” embody a large degree of control over commodity production and distribution
within the world economy. In part, we capture these issues, but at the level of nations,
between more- and less-developed countries, in the context of the vertical flow of
exports. Environmental space (whether in the form of natural resources or land) in lessdeveloped countries is assimilated into the economies of the more-developed countries.
Here our results are consistent with the theory of ecologically unequal exchange, as well
as positions taken by both the world-systems and metabolic rift perspectives.
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 issue, especially for core nations, which sometimes employ their military to
secure natural resources. Military expenditures redirect social spending, in part, to
sustain and build armed forces. This institution can have a momentum of its own, as far
as garnering resources and expansion. Here we find a positive relationship between
consumption-based environmental impacts and military expenditures, consistent with the
treadmill of destruction theory and other political-economy approaches, including worldsystems analysis. Ironically it seems that military expenditures may often be employed to
secure valuable resources, but at the same time, this social structure necessitates the vast
consumption of resources, increasing the per capita footprints of nations.
43
Human society is bounded by ecological conditions, which influence the capacity
of social institutions to operate as well as the ability of humans to support themselves.
While accounting for the role of nature on society can take many forms, here we
considered the influence of biogeography and climate. Consistent with the human
ecology perspective, we find these ecological conditions to partially shape the resource
consumption habits of nations. Questions of environmental sustainability are rooted in
concerns for ecological factors, so further consideration of changing natural conditions is
an important issue to incorporate into subsequent research—and no doubt such work will
demand both quantitative and qualitative approaches.
Environmental sociology has made important strides in studying the social
structures and conditions that shape the environmental burdens of nations. From our
analyses, it is clear that various theoretical perspectives successfully identify structural
contributors to environmental degradation—economic development, urbanization,
militarization, and ecologically unequal exchange. While there are differences between
the perspectives, it is important to note commonalities in their positions. Furthermore,
environmental sociology is raising critical questions about the structural organization of
nations and the world economy, not to mention how forms of environmental degradation
are tied to other sorts of global inequalities. Political-economy perspectives reveal how
the dissection of the modern world—which involves the division of both nature and
nations—threatens to undo the conditions on which we depend. Such conclusions are
echoed by the leading climatologist, James Hansen, who warns that if societies continue
to operate “business as usual,” we are ensuring that we will confront further ecological
crises with grave implications for all of humanity. The gravity of this issue is evident in
44
the fact that, as we revealed, the majority of nations now have unsustainable levels of
consumption-based environmental demand, as determined by per capita footprints
relative to the global biocapacity per capita. In closing, as this research suggests,
sociology is poised to make noteworthy and necessary contributions to understanding
society / nature relationships as well as issues surrounding ecological crisis and social
mobilization, all of which are critically important areas of inquiry that should be central
to the discipline as a whole. Besides advancing our collective understanding of such
topics, it is our hope that this study will encourage other comparative sociologists to
consider the complex interrelationships between society and nature in subsequent
research and theory constructions.
45
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58
ENDNOTES
1
This branch of environmental economists is more favorable to laissez-faire
conservatives than ecological modernization. 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.
It proposes a “third way” that avoids a break with the current economic system.
2
The application of artificial fertilizers helps continue production, but does not resolve
the metabolic rift.
3
While Hooks and Smith apply their treadmill of destruction theory to the U.S. military
and domestic conditions, we situate the orientation in comparative perspective.
4
In the global cities literature, the term “command points” is used to highlight the
centrality of key global cities as containers of the headquarters of many of the world’s
largest transnational corporations that exercise a relatively large degree of control over
global commodity production and distribution (e.g., Sassen 2001).
5
“Producer services” are services for firms, including financial, legal, advertising,
consulting, transport, communications, cleaning services, security, and storage. A
common argument in the global cities literature is that these producer services are
disproportionately concentrated in the largest and most powerful global cities, which tend
to be located in more-developed countries (Alderson and Beckfield 2004).
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 in order
to sustain the productive, consumption, and trade operations of that nation. These ghost
acres are analogous to today’s ecological footprint (see Borgstrom 1965).
59
7
Wackernagel and associates have continued to advance the methodology used to
calculate ecological footprints (e.g., Global Footprint Network 2006).
8
It is important to recognize that the capacity of a carbon sink to absorb carbon dioxide
changes. Recently, scientists have indicated that the decline in the capacity of carbon
sinks has contributed to the upswing in carbon accumulation in the atmosphere, at a rate
that is stronger, and sooner, than anticipated (Canadell et al. 2007).
9
The analyses are restricted to countries where the ecological footprints contain no
temporal anomalies in their calculations as identified by Susannah Buchan, a research
associate for the Global Footprint Network.
10
Here, cases refer to the countries included in the current study’s sample.
11
Elsewhere we include a correction for first-order autocorrelation (i.e. AR[1] correction)
in all FE and RE models. The results are substantively identical to those reported in the
current study and available from the authors upon request.
12
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 ecological footprints and biocapacity measures are directly available from the
Global Footprint Network.
13
Since an increase in the percent of a nation’s population residing in urban areas
corresponds with a decrease in the percent of the domestic population residing in rural
areas, we only include measures of urban population in the analyses.
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.
60
15
For a more in-depth discussion of the weighted index, see Jorgenson (2006b).
16
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 (based on level of
economic development). Appendix A highlights the countries in the dataset that are
classified using the above criteria as less-developed.
17
Elsewhere, we also include measures partially derived from world society theory (e.g.,
Meyer et al. 1997) in additional panel models. These measures include counts of
environmental international non-governmental organizations (EINGO) present in a given
country (both weighted and un-weighted by population size) as well as whether or not a
nation has an environmental ministry. The effects of the EINGO and environmental
ministry measures on per capita footprints are all non-significant, and their inclusion does
not substantively alter the reported findings. The EINGO data were analyzed by Smith
and Wiest (2005) as an outcome, who we thank for sharing these data with us. We thank
David John Frank for the environmental ministry data, which he analyzed as a predictor
with coauthors in prior research (Frank, Hironaka, and Schofer 2000).
18
As indicated by Table 2, GDP per capita and urban population as percent total
population are also highly correlated, which could partially explain why the effect of
urban population is non-significant in many of the reported models.
19
Elsewhere, we employ an additional military predictor: military expenditures per
soldier. However, this measure is very highly correlated with per capita GDP
(approximately .95). 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 prior cross-sectional analyses (Jorgenson 2005) as
61
well as the proposed theorization concerning the potential environmental impacts of a
more capital intensive and thus more technologically developed military. These
additional analyses are available from the authors upon request.
20
In additional unreported analyses we also control for agriculture as percent GDP. The
effect of this additional predictor on the outcome is non-significant, and its inclusion does
not substantively alter the reported findings.
21
It is important to note that the focus here is on the structure of exports in the context of
the vertical flow of exports to more-developed countries, which captures key aspects of
ecologically unequal exchange theory. As discussed in preceding sections, levels of
exports (and imports) along with levels of production for hundreds of commodity types
are used in the calculation of the dependent variable. Thus, the inclusion of exports as
percent GDP in models of per capita footprints would be analogous to controlling for the
per capita footprint in analyses of per capita carbon dioxide emissions, meaning to some
extent the outcome is unintentionally included as a predictor (see Fisher and Freudenburg
2004; York and Rosa 2005).
22
Elsewhere, we also include the quadratic for per capita GDP. The effect of this
predictor is positive and statistically significant, and its inclusion does not alter the
reported findings of interest, particularly the effects of weighted export flows (nonsignificant) and the slope-dummy interaction for weighted export flows and lessdeveloped countries (statistically significant). These additional analyses are available
from the authors upon request.
62
Appendix. 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, Table 3, and Table 4;
starred countries are included in the analyses reported in Table 5;
# denotes countries classified as less-developed for the calculation of
the slope-dummy interaction variable used in the analyses reported in Table 5
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 69 Countries, 1960-2003
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9
GDP per capita (ln)
.200*** .207*** .202*** .205*** .189*** .165*** .131*** .131*** .141***
[.674]
[.699]
[.682]
[.692]
[.637]
[.558]
[.444]
[.443]
[.479]
(.014)
(.015)
(.019)
(.017)
(.021)
(.019)
(.030)
(.032)
(.031)
Arable Land
per capita (ln)
.116
[.043]
(.065)
Urban Population
.169
[.062]
(.099)
.236*
[.087]
(.100)
.272*
[.100]
(.134)
.324*
[.119]
(.142)
.458*
[.168]
(.228)
.459*
[.169]
(.232)
.484*
[.178]
(.234)
.001
[.040]
(.001)
.001
[.052]
(.001)
.002
[.097]
(.002)
.003
[.145]
(.002)
.005*
[.263]
(.002)
.005*
[.262]
(.003)
.005
[.255]
(.003)
GDP per capita
squared
.030*** .035*** .048*** .052*** .052*** .048***
[.145]
[.166]
[.229]
[.249]
[.250]
[.230]
(.003)
(.004)
(.004)
(.014)
(.014)
(.014)
Military Personnel as %
total Population (ln)
.008
[.004]
(.048)
Military Expenditures
as % GDP (ln)
.020
[.011]
(.057)
-.078
[-.042]
(.069)
-.077
[-.042]
(.067)
.036
[.032]
(.022)
.060**
[.053]
(.024)
.060*
[.053]
(.025)
.046*
[.041]
(.023)
.003*
[.056]
(.001)
.003
[.055]
(.002)
.002
[.025]
(.002)
Manufacturing
as % GDP
Services
as % GDP
Constant
2
R Within
.001
[.002]
(.001)
-.393*** -.479*** -.497*** -.634*** -.582*** -.534***
(.109)
(.123)
(.122)
(.102)
(.131)
(.134)
-.529
(.319)
-.531
(.326)
-.573
(.336)
.362
.365
.367
.458
.449
.519
.300
.300
.283
2
.825
.840
.847
.866
.861
.840
.857
.857
.883
2
.828
543
8.4
2
10
.842
543
8.4
2
10
.846
543
8.4
2
10
.880
543
8.4
2
10
.868
491
7.7
2
10
.867
412
6.6
2
10
.860
284
4.9
2
10
.859
284
4.9
2
10
.890
301
5.0
2
10
R Between
R Overall
N
Mean Observations
Minimum Observations
Maximum 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 Selected Independent Variables: Random Effects
Model Estimates for 2 to 10 Observations on 69 Countries, 1960-2003
Model 1
Model 2
Model 3
GDP per capita (ln)
.206***
[.703]
(.017)
.180***
[.606]
(.017)
.181***
[.610]
(.017)
Arable Land
per capita (ln)
.316***
[.118]
(.076)
.383***
[.141]
(.081)
.428***
[.158]
(.075)
.002*
[.093]
(.001)
.003**
[.167]
(.001)
.004***
[.178]
(.001)
.031***
[.149]
(.003)
.045***
[.215]
(.004)
.045***
[.217]
(.004)
.041*
[.036]
(.018)
.039*
[.034]
(.018)
Urban Population
GDP per capita
squared
Military Expenditures
as % GDP (ln)
Temperate
-.189*
[-.192]
(.090)
-.175
[-.178]
(.108)
Tropical
-.296**
[-.298]
(.101)
-.391**
[-.304]
(.115)
-.140**
[-.142]
(.054)
Constant
-.472**
(.150)
-.450**
(.165)
-.634***
(.118)
2
R Within
.457
.525
.524
2
.890
.881
.874
2
.888
543
8.4
2
10
.888
434
6.9
2
10
.886
434
6.9
2
10
R Between
R Overall
N
Mean Observations
Minimum Observations
Maximum 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 5. Unstandardized Coefficients for the Regression of per capita
Ecological Footprints on Selected Independent Variables: Random Effects
Model Estimates for 2 to 6 Observations on 58 Countries, 1975-2000
Model 1
Model 2
Model 3
GDP per capita (ln)
.238***
[.806]
(.021)
.187***
[.636]
(.023)
.206***
[.698]
(.018)
Arable Land
per capita (ln)
.397***
[.136]
(.089)
.426***
[.147]
(.077)
.352***
[.121]
(.074)
Urban Population
.001
[.034]
(.001)
.001
[.068]
(.001)
Military Expenditures
as % GDP (ln)
.024
[.018]
(.023)
.018
[.013]
(.024)
Tropical
-.108
[-.105]
(.062)
-.087
[-.086]
(.057)
Weighted Export
Flows (ln)
-.037
[-.023]
(.027)
-.006
[-.004]
(.032)
-.001
[-.001]
(.026)
-.026**
[-.215]
(.008)
-.031***
[-.259]
(.008)
-.456
(.293)
-.233
(.279)
-.322
(.169)
Weighted Export
Flows (ln) X LDCs
Constant
2
R Within
.301
.312
.305
2
.890
.912
.901
2
.880
236
4.6
2
6
.904
236
4.6
2
6
.902
285
5.4
3
6
R Between
R Overall
N
Mean Observations
Minimum Observations
Maximum 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