UNPACKING INTERNATIONAL POWER AND THE ECOLOGICAL FOOTPRINTS OF NATIONS: A QUANTITATIVE CROSS-NATIONAL STUDY ANDREW K. JORGENSON* Washington State University

ABSTRACT: Although unsustainable natural resource consumption has recently garnered significant attention in macrosociology, empirical studies neglect to analyze the environmental impacts of different forms of international power dynamics. This study dissects international power into its various economic, military, and export dependence characteristics, and analyzes their independent effects on per-capita consumption of natural resources, measured as ecological footprints. Findings of the quantitative cross-national analyses indicate that economic power in the form of capital intensity, military technological power, and overall export dependence are the structural driving forces of per-capita resource consumption. The effects of military technological power and export dependence on percapita footprints are primarily direct, whereas the effect of capital intensity is both direct and indirect, partly mediated by its effects on levels of secondary education and domestic income inequality, both of which impact levels of per-capita consumption. The results advance our collective understanding of the complexities of international power, domestic conditions, and uneven environmental outcomes and illustrate the necessity for taking a more nuanced approach to analyses of anthropogenic degradation of the global ecological system.

Most countries consume natural resources at levels higher than the global ecological system can continue to support. Paradoxically, countries with lower levels of consumption experience greater rates of domestic environmental degradation, including deforestation, organic water pollution, and growing carbon dioxide emissions (e.g., Clapp 2002; Jorgenson 2003, 2004b, 2004c, forthcoming; Jorgenson and Burns 2004; Princen 2002). A burgeoning area of macrosociological literature addresses the structural causes of unsustainable levels of natural resource consumption. These studies pay considerable attention to the economic, productive, and demographic driving forces of environmental degradation and the use of international political–economic power in maintaining and reproducing uneven * Direct all correspondence to: Andrew K. Jorgenson, Department of Sociology, Washington State University, P.O. Box 644020, Pullman, WA 99164-4020; e-mail: [email protected]. Sociological Perspectives, Vol. 48, Issue 3, pp. 383–402, ISSN 0731-1214, electronic ISSN 1533-8673. © 2005 by Pacific Sociological Association. All rights reserved. Please direct all requests for permission to photocopy or reproduce article content through the University of California Press’s Rights and Permissions website, at http://www.ucpress.edu/journals/rights.htm.

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ecological exchanges among countries in the contemporary world economy. However, international power is generally treated in economic terms or empirically addressed through the analysis of comprehensive indicators that combine multiple forms of power in one measure. It is clear that international power is multidimensional and that more powerful countries consume relatively higher total and per-capita levels of natural resources. What is unclear, however, is how and to what extent varying forms of international power independently affect levels of natural resource consumption. In this study, the multidimensionality of international power is unpacked into economic, military, and export dependence characteristics. Drawing from environmental sociology, international political–economy, military sociology, the export dependence literature, and recent cross-national studies of ecological footprints, the extent to which the per-capita ecological footprints of nations are a function of different types of international power and dependence are tested. Relevant controls addressed in previous research, including urban population dynamics, domestic income inequality, and human capital, are modeled. First, a series of direct effect models using ordinary least squares regression are tested. Second, a recursive indirect effects model in which economic power and export dependence are exogenous variables while military technological power, urban population, domestic income inequality, and human capital are endogenous variables is constructed and tested. The direct, indirect, and total effects of the predictor variables, paying particular attention to the impacts of the modeled forms of international power, are then examined. Findings of this study indicate that (1) capital intensity, (2) military technological power, and (3) export dependence are structural driving forces of per-capita footprints. The impacts of military technological power and export dependence are primarily direct, whereas the effect of capital intensity is both direct and indirect, mediated by its effects on military power, urban population dynamics, domestic income inequality, and secondary education. When modeled as intervening factors, secondary education and domestic inequality prove to be significant predictors of ecological footprints. CROSS-NATIONAL STUDIES OF NATURAL RESOURCE CONSUMPTION A growing body of empirical work in the social sciences addresses the structural factors that explain variation in cross-national levels of total and per-capita consumption of natural resources. This comprehensive approach to material consumption and its overall environmental impacts focuses on the ecological footprints of nations (see, e.g., Jorgenson 2003, 2004c; Jorgenson and Burns 2004; Jorgenson and Rice 2005; Jorgenson, Rice, and Crowe 2005, forthcoming; York, Rosa, and Dietz 2003). The ecological footprint quantifies the amount of biologically productive land required to support the consumption of natural resources and assimilation of carbon dioxide waste products of a given population (Chambers, Simmons, and Wackernagel 2002). It is a composite measure of societal impacts on global natural resources and allows comparisons of environmental demand relative to available domestic and global “natural capital.”1

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Wackernagel et al. (2000) provide national-level ecological footprints (both total and per capita) for the majority of nations in the world. Their footprints consist of six subcomponents: (1) the area of cropland required to produce the crops consumed, (2) the area of grazing land required to produce the animal products, (3) the area of forest required to produce the wood and paper, (4) the area of sea required to produce the marine fish and seafood, (5) the area of land required to accommodate housing and infrastructure, and (6) the area of forest that would be required to absorb the carbon dioxide emissions resulting from the unit’s energy consumption. These data are measured in area units where one footprint equals one hectare.2 The footprint method captures indirect effects of consumption that are difficult to measure, and this approach does not require knowing the specific use of each consumed resource.3 Utilizing a world-systems perspective, Jorgenson (2003, 2004c) analyzes the structural causes of per-capita ecological footprints and finds that a country’s level of percapita consumption is largely a function of its relative position in the international stratification system, level of urbanization, domestic income inequality, and human capital. In these studies, a multidimensional indicator of world-economy position is used that combines economic, military, and export dependence characteristics in one measure (Kentor 2000). Natural resources are consumed at higher levels in core urban regions through modern industrial processes of commodity production and corresponding domestic articulated consumer markets and to maintain the built environment (Jorgenson 2003). Slowed rates of economic development coupled with high income inequality in overurbanized4 regions of less-developed countries limit domestic levels of resource consumption. This is further exacerbated by a common shift to exportoriented development. Moreover, many less-developed countries are principal exporters of spatially fixed raw materials (Bunker 1984, 2003; Jorgenson 2004c; Timberlake and Kentor 1983). The overall effect of domestic income inequality on per-capita footprints is negative (Jorgenson 2003). However, in a more nuanced analysis, Jorgenson (2004c) finds that the effect of domestic income inequality is positive in the core and negative in all other zones of the world-economy. This positive effect in the core is partly a function of heightened conspicuous consumption by all socioeconomic groups in the United States (Manning 2000). Noncore countries with higher levels of domestic inequality tend to possess characteristics of disarticulated extractiveoriented economies5 (Bunker 1985). These regions possess relatively lower percapita consumption levels because on average (1) the majority of the population has substantially lower income levels and (2) the domestic market focuses on the export of raw materials and commodities produced by means of dependent industrialization6 (Beer and Boswell 2002; Evans 1979; Jorgenson 2004c). Some evidence suggests that human capital, such as literacy rates, positively affects levels of natural resource consumption (Jorgenson 2003). Higher levels of literacy correspond with higher incomes, which increase the opportunities for greater material consumption. Promotion of consumerist ideologies is often geared toward more literate segments of the population through printed forms of mass media (Clapp 2002; Sklair 2001).

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Other social scientists model and test the effects of population, affluence, and other factors on total national-level ecological footprints (York, Rosa, and Dietz 2003). The argument set forth is derived from ecological science, whereby total consumption is primarily a function of population size and growth, but the impact of population is higher in more developed countries. In the analyses, the researchers apply a measure of world-economy position that quantifies “official development assistance and official assistance a nation gives or receives” (York, Rosa, and Dietz 2003:289). However, this indicator excludes other economic, trade, and military dimensions associated with relative position in the international stratification system (Chase-Dunn 1998; Jorgenson 2003; Kentor 2000). Overall, findings of these studies provide robust evidence that variations in levels of natural resource consumption are largely a function of a country’s relative position in the international stratification system (Jorgenson 2003, 2004c) and level of economic affluence (York, Rosa, and Dietz 2003). Consistent with the theorization of Chase-Dunn (1998), Kentor (2000), and other macrosociologists (e.g., Tilly 1990, 1994), Jorgenson (2003, 2004c) treats international power as multidimensional and models an indicator of world-economy position consisting of economic, military, and trade characteristics. The application of an index that quantifies these interrelated characteristics in one measure allows for the theoretical complexities of international stratification to be empirically accounted for in more parsimonious models. However, Jorgenson’s (2003) study does not analyze the extent to which forms of economic power, military power, and export dependence independently affect levels of natural resource consumption. The treatment of York, Rosa, and Dietz (2003) of relative position consists of a measure of aid assistance, but other characteristics of international power besides economic development are not included in those analyses. Thus, a more empirically nuanced approach is warranted to further our collective understanding of the structural relationships between international stratification and uneven levels of natural resource consumption. This approach involves the unpacking of world-economy position into its most relevant components and analyses of their individual effects on the ecological footprints of nations. INTERNATIONAL POWER CHARACTERISTICS AND MATERIAL CONSUMPTION Position in the world-economy is often defined as a multidimensional function of relative economic power, military coercive power, and level of export dependence (e.g., Chase-Dunn 1998; Chase-Dunn and Jorgenson 2003a, 2003b; ChaseDunn et al. 2005; Kentor 2004; Tilly 1994). Numerous studies attempt to quantify the relative positions of countries in the modern interstate system (e.g., Arrighi and Drangel 1986; Kentor 2000; Kick 1987; Nemeth and Smith 1985; Snyder and Kick 1979; Terlouw 1992). Kentor (2000) most effectively captures these interrelated characteristics in one continuous measure. Kentor’s index of relative position is used in other cross-national studies of natural resource consumption and other environmental outcomes (e.g., Jorgenson 2003, 2004b, 2004c; Jorgenson and Burns 2004).

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Economic Power Relative economic power generally takes the forms of capital intensity and production/economy size. The former generally refers to the ability of a country to be more competitive in the global marketplace (Kentor 2000). Countries with higher capital intensity contain articulated consumer markets that consume greater levels of natural resources (Jorgenson 2003, 2004c; Jorgenson, Rice, and Crowe 2005; York, Rosa, and Dietz 2003). Productive size, or size of the domestic economy, refers to the ability of a state to resolve domestic economic crises by the expansion of consumer demand and increased commodity production, both of which increase natural resource consumption (Chase-Dunn 1998; Schnaiberg and Gould 1994). Environmental degradation is largely driven by the growth and intensification of market economies. To maintain profits, producers must constantly expand production, which requires additional ecological material inputs (O’Connor 1998; Schnaiberg and Gould 1994). Schnaiberg (1980) and Schnaiberg and Gould (1994) characterize these processes as the heart of the “treadmill of production.” Producers are usually headquartered in developed countries and outsource production and resource extraction to export-dependent countries. The expansion of production and consumption usually takes the form of global commodity chains in which resources are consumed and wastes generated at every link (Gereffi and Korzeniewicz 1994; Jorgenson forthcoming; Jorgenson and Kick 2003; Princen, Maniates, and Conca 2002). Produced commodities are usually transported to and consumed by countries with high economic intensity. Most of the profits derived from these goods further increase the economic development of market economies that house the headquarters of producers (Bornschier and Chase-Dunn 1985; Chase-Dunn 1975; Kentor and Boswell 2003). Military Coercive Power Military coercive power is often conceptualized in two general forms. First, a country’s ability to assert its will both directly and indirectly in the world economy is largely reflected by its total military budget and expenditures (Kentor 2000; Kick et al. 1998; Tilly 1990, 1994). Military institutions in the contemporary world economy consume vast amounts of natural resources and nonrenewable energy for the construction, maintenance, and operation of their infrastructure and hardware (Dycus 1996; Hooks and Smith 2005; Hveem 1979; Klare 2001; Seager 1993). For example, social scientists posit that the Pentagon is the largest consumer of nonrenewable energy resources in the United States and quite possibly the entire world (Hynes 1999; Santana 2002; see also Hooks and Smith 2004). The military apparatuses of the more dominant countries globalize their lines of operation for defensive and geopolitical strategic purposes (Hveem 1979). This form of military globalization taps the natural resources of the global ecological system while emitting waste into the biosphere (Roberts, Grimes, and Manale 2003). Level of military technology characterizes the second form of military power. High levels of military spending per soldier reflect a technologically based mili-

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tary, whereas a lower level generally reflects a reliance on large standing armies (Kentor 2004; Kick et al. 1998). The former characterizes the ability to project military power globally; the latter reflects regional power. Continual arms research, development, and production in military defense, which lead to greater military technological strength, greatly increase national footprints (Hveem 1979; Jorgenson 2003). Capital accumulation and economic intensity lead to social and economic surplus that states divert to war making. This often creates conditions in which technological developments in military hardware become the “normal state of affairs” (Van Creveld 1989:218–19; Hooks and Smith 2005). Thus, one would expect that a nation’s capital intensity will positively affect its level of military technological power, which, in turn, will further impact levels of natural resource consumption (Kentor 2004; Tilly 1990). Hooks and Smith (2004, 2005) characterize the expansionary dynamics and profound environmental impacts associated with militarism and military technological developments as the “treadmill of destruction.” Overall, this treadmill is set in motion by arms races and geopolitical competition. In the contemporary world, the growing environmentally damaging capabilities of militarism are partly a function of technological developments with weaponry that require less military size for their potential use and effectiveness. In the last few decades, relative sizes of military personnel decreased in many of the most powerful countries. The social space occupied by these militaries shrinks, but their relative power and global reach increase with further developments in weapons technology, all of which directly and indirectly impact regional environments and the global ecological system (Hooks and Smith 2005). Besides consuming large amounts of resources, stronger and more technologically advanced militaries utilize their global military reach to directly and symbolically reproduce uneven ecological exchanges between countries (e.g., Lough 1999; Seager 1993). These asymmetrical exchanges increase levels of natural resource consumption in developed countries at the ecological expense of less-developed nations (Jorgenson 2004a). Military-oriented resource consumption also includes strategic stockpiling of fuels and other materials as well as research and development programs. Consumption is further increased through the material requirements of industries that produce and supply marginal equipment to the armed forces and their support economy. The production of these forms of marginal equipment and the stockpiling of fuels help to maintain the overall military infrastructure (Hveem 1979; Lough 1999). Export Dependence The general political–economic argument concerning export dependence asserts that less-developed countries with high levels of exports relative to the size of their domestic economy are more vulnerable to global market forces. This allows the developed nations with whom they trade to obtain favorable asymmetrical terms of exchange (Galtung 1971; Hirschman [1945] 1971; Kentor 2000; Kentor and Boswell 2003). Dependence combines the exports of spatially fixed raw

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materials, monoagricultural goods, and commodities produced through dependent industrialization (e.g., Bunker 1984, 1985; Burns, Kentor, and Jorgenson 2003; Chase-Dunn 1998; Grimes and Kentor 2003; Jorgenson 2004b). These goods are generally exported to higher-consuming countries that utilize their international power to maintain relationships of uneven ecological exchange (Hornborg 2001; Jorgenson 2004a; Rothman 1998). Thus, countries with higher levels of export dependence consume lower levels of natural resources. Moreover, a sizable proportion of resources consumed domestically in export-dependent countries is consumed through export-oriented commodity production in environmentally inefficient facilities. These facilities are controlled largely by foreign capital in partnership with domestic elites (e.g., Grimes and Kentor 2003; Smith 1996). Proponents of comparative advantage theory and other neoliberal perspectives (e.g., Magee 1980; see also McMichael 2004) might argue that the apparent negative relationship between export dependence (or what they identify as “trade specialization”) and the ecological footprints of nations illustrates the overall “benefits” of trade for less-developed countries. However, cross-national studies provide evidence that nations with lower footprints experience higher domestic levels of particular forms of environmental degradation (e.g., Jorgenson 2003; Jorgenson and Burns 2004; see also Lofdahl 2002). A large proportion of less-developed countries exhibit footprints below the bio-capacity per capita7 (Wackernagel et al. 2000). Their relatively low levels of globally sustainable consumption and high levels of domestic environmental degradation are characteristics of underdevelopment stemming from asymmetrical exchanges between developed and lessdeveloped countries (e.g., Chase-Dunn 1998; Hornborg 2001; Smith 1994). In addition to negatively affecting the ecological footprints of nations, export dependence retards rates of economic development and increases domestic income inequality as well as levels of infant mortality, deforestation, and organic water pollution intensity (Burns, Kentor, and Jorgenson 2003; Kentor and Boswell 2003; Kick et al. 1996; Rubinson and Holtzman 1981). Dependence on agricultural exports negatively affects the domestic availability of food, which impacts infant and child mortality rates8 (e.g., Jorgenson 2004b; see also Jenkins and Scanlan 2001). Other studies indicate that domestic income inequality negatively affects the per-capita footprints of countries (Jorgenson 2003, 2004c). Hence, the overall negative effect of export dependence on per capita footprints is tied to the underdevelopmental and ecological consequences of international trade, particularly for less-developed countries (Emmanuel 1972; Galtung 1971). METHODS A series of models derived from other studies and the preceding theorization were tested. Specifically, the extent to which particular characteristics associated with relative position in the international stratification system directly and indirectly affect levels of per-capita natural resource consumption, measured as ecological footprints, was tested. The analyses include three additional independent variables shown by previous studies to impact the per-capita footprints of nations (i.e., urban population, domestic income inequality, human capital). Using list-

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wise deletion, ordinary least squares (OLS) regression and path modeling were employed to analyze direct and total effects of the modeled predictors (Kaplan 2000; McClendon 1994). Sample sizes among the tested OLS models vary slightly from 72 to 86 countries, and the path analysis includes a sample of 72 countries.9 Given the relatively high levels of collinearity between some of the independent variables, two forms of each model in the OLS regression analyses were analyzed. One includes independent variables in their “regular” or logarithmic form. The other includes “residualized” forms of particular measures.10 Modeling and analyzing the effects of residuals allow for rather conservative analyses that minimize multicollinearity. Below, this is evidenced by greatly reduced variance inflation factor scores from the first to the second series of each tested model. Table 1 provides descriptive statistics and correlations for all variables included in the analyses. Dependent Variable The dependent variable of natural resource consumption is measured as the ecological footprint per capita for 1996. This variable is used by social scientists in many recent studies (e.g., Jorgenson 2003, 2004c; Jorgenson and Burns 2004; York, Rosa, and Dietz 2003). These data are taken from Wackernagel et al. (2000). Independent Variables International Power Variables Gross domestic product per capita (ln [i.e. natural log]), 1990 is included as a measure of capital intensity, which quantifies the relative ability of a country to be more competitive in the global marketplace (e.g., Burns, Kentor, and Jorgenson 2003; Kentor 2000). These data are taken from World Bank (2000). Total gross domestic product (ln), 1990 quantifies the relative size of a country’s productive infrastructure, which represents a significant dimension of power that a country is able to exert in the global economy (e.g., Chase-Dunn 1998; Kentor 2000; Kentor and Boswell 2003). These data, obtained from World Bank (2000), are analyzed in their regular and residualized forms. Exports of goods and services as percentage of total GDP, 1990 controls for the extent of a country’s integration into the world economy and, more importantly, a country’s relative level of export dependence (e.g., Galtung 1971; Hirschman [1945] 1971; Kentor and Boswell 2003). These data are taken from World Bank (2000). Military expenditures per soldier (ln), 1990 is included as a measure of military technological strength (Jorgenson 2004a; Kentor 2004). Both the regular and residualized forms of this measure were analyzed. This variable is calculated by dividing total military expenditures by total military personnel. Total military personnel estimates are gathered from the 2000 World Development Indicators, and total military expenditures are obtained from the Stockholm International Peace Research Institute (SIPRI) (2000). Total military expenditures (ln), 1990 quantifies an additional form of military power: a country’s ability to assert its will both directly and indirectly in the global economy by use or threat of military force (Chase-Dunn 1998; Kentor 2000, 2004). This

Note: (res.)  residuals

1. Ecological footprint per capita, 1996 2. Gross domestic product per capita (ln), 1990 3. Total gross domestic product (ln), 1990 4. Total gross domestic product (ln), 1990 (res.) 5. Exports/gross domestic product (ln), 1990 6. Expenditures per soldier (ln), 1990 7. Expenditures per soldier (ln), 1990 (res.) 8. Total military expenditures (ln), 1990 9. Total military expenditures (ln), 1990 (res.) 10. Urban population, 1990 11. Urban population, 1990 (res.) 12. Domestic income inequality 13. Secondary school enrollment, 1990 14. Secondary school enrollment, 1990 (res.)

S.D.

.759

50.683 23.841

.656

3

4

.801

.042

.711

.000

.843

.135 .683

.000 .051 .021 .541 .041

.394

.658

.932

.098 .597 .156

.228

6

.538

.068

5

.168 .092

.648

.413 .092 .512

.000

.755

2

.018

.210

.167

7

.568

.362

8

.077

9

10

11

.719 .109

57.026 31.920 .000 16.192

.000

.860

.049

.568

.019 .047

.102 .060 .003

.345

.629

.049

.051 .004

.564 .023

12

13

.028 .048 .368 .510

.695 .032 .516

.000 14.209 .048 .000 .034 .059 .147 .008 .031 .032 .230 .598 39.904 10.397 .491 .432 .391 .154 .071 .380 .075 .442 .098 .280 .212

.009

.855

.688

20.202 2.375 .000

.162

.723

.825

9.437 1.408 .000

.243

.066

.000 1.432 .690

.676

9.130 2.366

3.316

.865

1

8.098 1.079

2.964 2.463

Mean

TABLE 1 Descriptive Statistics and Correlations for all Variables Included in the Analyses

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variable is analyzed in its regular and residualized forms. These data are taken from SIPRI (2000). Additional Control Variables Urban population, 1990 controls for the percentage of a country’s population residing in urban areas. These data are obtained from World Bank (2000). Both the regular and residualized forms of this measure were analyzed. Domestic income inequality, measured as Gini coefficients, controls for the distribution of income within countries. The Gini coefficient score is on a scale of 0 (perfect equality) to 100 (perfect inequality). The years of measurement for Gini coefficients are in the early 1990s. Other studies show that cross-national variation in domestic income inequality remains relatively stable over short periods of time (e.g., Bergesen and Bata 2002). These data are from the World Bank (2001). Secondary school enrollment, 1990 is an indicator of human capital and defined as the ratio of total secondary school enrollment, regardless of age, to the population of the age group corresponding to this level of education. This variable is analyzed in its regular and residualized forms. These data are obtained from World Bank (2000). RESULTS AND DISCUSSION Table 2 presents findings for the OLS regression analyses that consist of four tested models. Each model includes analyses of regular and residualized predictor variables. Model 1 is treated as a baseline. Urban population is added as a control variable in model 2, domestic income inequality is introduced in model 3, and secondary school enrollment is added in model 4. Consistent with previous studies, gross domestic product per capita proves to be a robust predictor of per capita ecological footprints. The most noteworthy finding is that the factor of military expenditures per soldier positively affects percapita levels of natural resource consumption, net of other forms of international power and domestic conditions. The magnitude of the effect lessens in the residual series of analyses, but increases in overall statistical significance. Thus, countries with greater military technological power, part and parcel of the treadmill of destruction, have pernicious environmental impacts independent of capital intensity and the treadmill of production. An additional new and significant finding is the negative effect of export dependence on domestic levels of natural resource consumption, which partly illustrates processes of unequal ecological exchange. The effects of total gross domestic product (GDP) and total military expenditures on per-capita footprints prove to be nonsignificant. Turning to the additional control variables, domestic income inequality has a negative effect on per-capita footprints in model 3 but becomes nonsignificant with the addition of secondary school enrollment in model 4. Secondary school enrollment has a positive effect in model 4, and the effect of urban population is nonsignificant in all models that include this control. It warrants noting that the

TABLE 2 Standardized Coefficients for Analyses of the Structural Causes of Ecological Footprints Per Capita, 1996 1

2

3

4

Independent Variables

Regular Residuals Regular Residuals Regular Residuals Regular Residuals

Gross domestic product per capita (ln), 1990

.752*** [1.768] (.295) 5.977

.907*** [2.131] (.129) 1.145

.719*** [1.689] (.353) 8.461

.907*** [2.133] (.130) 1.146

.730*** [1.686] (.402) 11.053

.859*** [1.984] (.145) 1.441

.649*** [1.491] (.428) 12.724

.893*** [2.052] (.147) 1.508

Total gross domestic product (ln), 1990

.187 [.225] (.201) 10.638

.062 [.118] (.120) 1.520

.175 [.210] (.205) 10.990

.061 [.116] (.121) 1.524

.260 [.310] (.233) 13.939

.083 [.164] (.126) 1.469

.270 [.322] (.231) 13.715

.096 [.190] (.125) 1.463

Exports/gross domestic product (ln), 1990

.144* [.660] (.301) 1.627

.144* [.660] (.301) 1.627

.143* [.659] (.302) 1.627

.143* [.659] (.302) 1.627

.116 [.538] (.306) 1.576

.116 [.538] (.306) 1.576

.132* [.612] (.305) 1.589

.132* [.612] (.305) 1.589

Military expenditures per soldier (ln), 1990

.275** [.519] (.185) 3.654

.150** [.519] (.185) 1.084

.276** [.521] (.186) 3.655

.150** [.521] (.186) 1.085

.270* [.505] (.200) 4.162

.140** [.505] (.200) 1.129

Total military expenditures (ln), 1990

.094 [.106] (1.71) 8.564

.033 [.106] (.171) 1.063

.083 [.093] (.174) 8.835

.029 [.093] (.174) 1.096

.127 [.145] (.206) 11.829

.039 [.145] (.206) 1.125

.114 [.131] (.204) 11.557

.036 [.131] (.204) 1.117

.038 [.004] (.010) 3.200

.022 [.004] (.010) 1.039

.010 [.001] (.012) 4.149

.005 [.001] (.012) 1.161

.043 [.004] (.012) 4.419

.023 [.005] (.012) 1.233

.126* [.031] (.015) 1.388

.126* [.031] (.015) 1.388

.060 [.015] (.017) 1.687

.060 [.015] (.017) 1.687

.231* [.019] (.009) 4.764

.118* [.019] (.009) 1.237

Urban population 1990 Domestic income inequality Secondary school enrollment, 1990

.127** [.459] (.201) 1.132

13.905 12.107 13.401 12.118 12.196 10.013 10.891 10.960 (1.972) (1.164) (2.329) (1.171) (3.184) (1.552) (3.253) (1.609)

Constant Sample size Adjusted

.245* [.459] (.201) 4.207

R2

86

86 .776

86 .776

86 .774

74 .774

74 .799

72 .799

72 .807

.807

Notes: Unstandardized coefficients appear in brackets. Standard errors are in parentheses. Variance inflation factors appear in italics. * p  .05; ** p  .01; *** p  .001. p value for Exports/gross domestic product (ln), 1990 for model 3 is .083.

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initial baseline model (Model 1) explains close to 78 percent of the variation in per-capita footprints; the most fully controlled model (Model 4) explains close to 81 percent of variation in the outcome variable. Thus, capital intensity, export dependence, and military technological power are structural driving forces of per-capita natural resource consumption. Causal relationships are complex and not necessarily captured in simple direct effect models (Kaplan 2000; Kentor 2001). Jorgenson (2003) tests a theoretically grounded recursive indirect effects model in which world-economy position is an exogenous variable, and urbanization, domestic inequality, and human capital are endogenous variables that are all partly a function of relative position in the international stratification system. Given the theoretical relevance for such a causal analysis, a recursive indirect effects model in which GDP per capita and exports as a percentage of total GDP are exogenous variables is constructed. Military expenditures per soldier,11 domestic income inequality, urban population, and secondary education are modeled as endogenous variables that partly mediate the effects of economic power and export dependence. Considering the consistent findings reported in Table 2, total GDP and total military expenditures are excluded from this analysis.12 The elimination of nonsignificant predictors in causal models is a common approach in quantitative cross-national research (e.g., Hamilton 1990; Jorgenson and Burns 2004; Kentor 1998, 2000). The effect of urbanization is nonsignificant in the OLS regression analyses presented in Table 2. However, given the robust finding in other studies concerning the impact of urbanization on ecological footprints when theorized and modeled as an intervening variable, urbanization is included as an endogenous factor in the path analysis13 (e.g., Jorgenson 2003, 2004c; see also York, Rosa, and Dietz 2003). Findings are provided in Figure 1. As indicated by Figure 1, a major proportion of variation in per-capita footprints is explained by the causal model (R2  .82). The amount explained is almost identical to the OLS analyses presented in Table 2. Capital intensity (measured as per-capita GDP), military technological power (measured as military expenditures per soldier), and export dependence (measured as exports as percent of total GDP) all directly affect per-capita footprints.14 Moreover, capital intensity indirectly affects per-capita footprints, mediated by its effects on military technological power, domestic inequality, and secondary education, all of which impact levels of per-capita consumption. Consistent with other studies (e.g., Jorgenson 2003), when modeled as intervening variables, the direct effect of secondary education is positive and statistically significant, whereas the effect of domestic income inequality is largely mediated by its effect on secondary education. To further investigate the impacts of the modeled predictors, the total effects of each variable were analyzed. Standardized total effects are provided in Table 3. Overall, capital intensity (GDP per capita) is the strongest predictor of ecological footprints, followed by military technological power (expenditures per soldier), secondary education, domestic income inequality, and export dependence. The total effect of urban population is rather minimal. It warrants noting that the total effect of capital intensity is more than three times as large as the next predictor, military technological power.

Figure 1 Causal Model for Ecological Footprints Per Capita, 1996

TABLE 3 Standardized Total Effects

1. Military expenditures per soldier (ln), 1990 2. Domestic income inequality 3. Urban population, 1990 4. Secondary school enrollment, 1990 5. Ecological footprint per capita, 1996

Exports/ gross domestic product (ln), 1990

Gross domestic product per capita (ln), 1990

.112 .131 .094

.889 .462 .820 .859

.004 .115

.913

1

2

3

.035 .091

.158

.059

.219

.186

.247

.121

.030

4

.219

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CONCLUSION Natural resource consumption and concomitant environmental degradation are among the most pressing global issues confronting us today. The purpose of this study was to unpack international power into its economic, military, and export dependence characteristics and to analyze their independent effects on the percapita ecological footprints of nations. The most noteworthy findings of the reported analyses are the positive effect of military technological power and the overall negative effect of export dependence on per-capita footprints, net of the effects of capital intensity and domestic conditions. These two findings are robust across the OLS regression analyses and the recursive indirect effects model. Results for the additional controls are consistent with previous research, particularly the positive effect of secondary education and the negative effect of domestic income inequality when modeled as intervening factors (Jorgenson 2003, 2004c). Overall, capital intensity, military technological power, and export dependence are primary macrolevel factors impacting per-capita consumption of natural resources. Military technological power contributes to natural resource consumption through continual research and development of warfare. The consumption-based maintenance of the military’s social and material infrastructure also reinforces the global reach of the armed forces. Moreover, military power assists in the reproduction of international unequal ecological exchanges through direct and symbolic geopolitical threats. Like economic and military power, the ways in which export dependence impacts levels of resource consumption are rather complex. Often, export-dependent countries consist of domestic economies largely characterized as a combination of (1) raw material taps for more powerful countries with articulated consumer markets and (2) the export-oriented production of goods for consumption in higherconsuming nations. Moreover, a sizable proportion of resources consumed domestically in export-dependent countries is consumed through export-oriented production, which illustrates the complex ecological impacts of global commodity chains (Gereffi and Korzeniewicz 1994; Princen 2002). The overall negative effect of export dependence should also be placed in the broader context of international inequality and underdevelopment. Many exportdependent countries exhibit ecological footprints well below globally sustainable levels (Wackernagel et al. 2000). Moreover, export-dependent countries generally experience higher levels of domestic income inequality and particular forms of environmental degradation as well as lower levels of human well-being (e.g., Jorgenson and Burns 2004; Rubinson and Holtzman 1981). Domestic income inequality negatively affects the ecological footprints of nations, and the well-being of populations is partly a function of resource consumption (e.g., food, shelter construction and maintenance). Lastly, countries with lower per-capita footprints generally experience higher domestic levels of particular forms of environmental degradation (Jorgenson 2003). Thus, the relationship between international trade and the ecological footprints of nations is not a function of the benefits of comparative advantage (Magee 1980). Rather, the negative effect of export dependence on per-capita footprints is characteristic of asymmetrical international exchanges

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in the contemporary world economy (Chase-Dunn 1998; Emmanuel 1972; Jorgenson 2004a; Lofdahl 2002). Indeed, additional research is warranted to further understand the interconnections between these factors. The policy implications for the reported findings are significant. The majority of social scientific research on global environmental degradation focuses primarily on the impacts of population growth and economic power and the ecological inefficiencies of capitalistic commodity production. Results of this study clearly indicate that forms of international power other than capital intensity contribute to unsustainable levels of natural resource consumption. Thus, the complexities of the problem require much more comprehensive solutions involving global military and trade reforms as well as the reduction of production-based environmental impacts, the lowering of affluent conspicuous consumption, and the overall reduction in material forms of international inequality in which all global citizens consume resources at or below the global bio-capacity per capita (Wackernagel et al. 2000). The next step in this research agenda involves direct quantitative analyses of the extent to which more economically and militarily powerful countries “outsource” their ecological costs associated with high levels of resource consumption to less-developed countries dependent on export relations with them. This involves the development of export flows indicators weighted by attributes of receiving countries, particularly their levels of capital intensity and military technological strength. The weighted indicators would quantify the extent to which exports of less-developed countries are sent to nations with greater capital intensity or military technological power. With these data, researchers could more adequately test hypotheses concerning uneven international ecological exchange and the resulting externalization of environmental costs by higher-consuming countries. For example, in cross-national studies of particular forms of environmental degradation (i.e., deforestation), a positive effect of these weighted indices would provide additional support for fundamental macrosociological assertions concerning structural relationships in which more powerful countries impact the environment of less-developed countries through the tapping of their natural resources. Findings of the present study provide a theoretically grounded, empirical basis for this forthcoming series of social scientific inquiry. Acknowledgments: The author would like to thank Christopher Chase-Dunn, Robert Hanneman, Gregory Hooks, Jeffrey Kentor, Gene Rosa, Richard Serpe, and the anonymous reviewers for helpful comments on earlier drafts of this article. NOTES 1. The concept of natural capital is an extension of the economic notion of capital. It is usually defined as the stock of natural assets, such as water and forest resources, producing a flow of resources for human societies. The term is often criticized as being anthrocentric (see Wackernagel and Rees 1996 for a more ecologically sensitive definition and its relationship to ecological footprints). 2. One hectare is the equivalent of approximately 2.47 acres.

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3. See Jorgenson (2003) for a review of the literature devoted to the strengths and weaknesses of the footprint measures. 4. Overurbanization usually refers to an excessive growth of a region’s urban population relative to its economic growth (Kentor 1981; Smith 1996). 5. Disarticulated economies depend on external markets. Articulated economies are able to focus on internal, domestic markets. 6. Dependent industrialization refers to industrial practices resulting from foreign capital dependence in less-developed countries that focus on the production of goods via cheap labor and less-efficient “dirty” production for export to developed countries. 7. Wackernagel et al. (2000) calculate the global bio-capacity per capita by dividing all the biologically productive land and sea on earth by the total world population. The result is an average of 2.1 hectares per person. Out of the 146 countries in the sample by Wackernagel and colleagues, 75 have per capita footprints at or below the bio-capacity per capita. 8. The ecological footprint of a nation includes the amount of resources required to provide for a given population’s food consumption. 9. Using Cook’s distance and residual diagnostics, I determine that the reported analyses do not include any influential cases or those with inflated standardized residuals. 10. This method involves identifying pairings of independent variables that are highly correlated and regressing one on the other. For example, as illustrated by Table 1, percapita GDP (GDP pc) and military expenditures per soldier (MXPS) are correlated at .843. By regressing MXPS on GDP pc, I save the residuals and include them as a measure of MXPS entirely independent of GDP pc. The correlation between MXPS residualized and GDP pc is .000. Given the causal ordering of the tested path model (Figure 1), this approach is only necessary for the OLS analyses. 11. Consistent with the preceding theorization and arguments of other social scientists (e.g., Kentor 2004; Tilly 1990), I treat military technological power as an intervening variable, partly a function of capital intensity. 12. In an unreported path analysis, I include total GDP and total military expenditures. Findings differ very little from those reported in Figure 1 (e.g., R2  .80) and are available from the author upon request. 13. Rather extensive bodies of social scientific literature address how international political– economic factors affect levels and qualitative characteristics of urbanization, domestic income inequality, and human capital (see, e.g., Beer and Boswell 2002; Evans 2002; Jorgenson 2003; Jorgenson and Burns 2004; Kentor 1981; Pelizzon and Casparis 1998; Rubinson and Holtzman 1981; Smith 1996, 2003; Timberlake and Kentor 1983). 14. The direct effect of exports as percentage of total GDP on per-capita footprints is statistically significant at the .10 level (one-tailed test).

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