Demographic trends and energy consumption in European Union Nations, 1960–2025

Social

Social Science Research 36 (2007) 855–872

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Demographic trends and energy consumption in European Union Nations, 1960–2025 Richard York

*

Department of Sociology, University of Oregon, Eugene, OR 97403-1291, USA Available online 9 August 2006

Abstract We analyze data for fourteen foundational European Union Nations covering the period 1960–2000 to estimate the effects of demographic and economic factors on energy consumption. We find that population size and age structure have clear effects on energy consumption. Economic development and urbanization also contribute substantially to changes in energy consumption. We use the resultant model to project energy consumption for the year 2025 based on demographic and economic projections to assess the implications of various demographic scenarios. The projections suggest that the expected decline of population growth in Europe will help curtail expansion in energy consumption. Ó 2006 Elsevier Inc. All rights reserved. Keywords: Environmental demography; Environmental Kuznets curve; Energy consumption; Population decline; STIRPAT

1. Introduction At the dawn of the twenty-first century, some nations, particularly those in Western Europe, face a demographic future that is unprecedented. A combination of below replacement fertility rates and growing life expectancy in these nations will, if maintained over this century, lead to a population that is both shrinking in number and

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0049-089X/$ - see front matter Ó 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.ssresearch.2006.06.007

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aging. This trend will surely leave few aspects of society unaffected. Some scholars characterize this trend as a crisis, arguing that low fertility rates will have dire consequences for national economies (Lutz et al., 2003). Other scholars are more cautious, arguing that the challenges presented by these demographic processes are real but manageable and do not necessarily constitute a crisis (Morgan, 2003). From a different perspective, some environmental scientists have ventured to argue that a decline in Europe’s population may in fact be good news, since it will help ease pressure on the earth’s beleaguered ecosystems and natural resources (Ehrlich and Ehrlich, 2004). In particular, managing the growing global demand for energy and addressing the environmental consequences of energy production stand out as key challenges of the twenty-first century. It has now become widely accepted in the scientific community that the combustion of fossil fuels, such as oil and coal, and the greenhouse gas emissions stemming from this are changing the global climate, which may have dire consequences for societies (IPCC, 2001). Furthermore, the potential for replacing fossil fuel with other sources of energy is limited, and non-fossil fuel energy is not without detrimental environmental consequences (Smil, 2003). Still further, after decades of speculation and anticipation, it is becomingly increasingly accepted by energy scientists that global peak oil production will likely come within the next two to three decades, with some analysts estimating that the peak will be reached within this decade (Campbell, 1997; Deffeyes, 2001; Hatfield, 1997; Kerr, 1998; Smil, 2003). Following the peak, oil production will begin a long decline as oil reserves are depleted. The inevitable decline in the availability of oil will almost surely lead to a dramatic escalation in energy prices and may lead to a global economic crisis, since oil is one of the most important sources of energy in the global economy. Additionally, even without impending oil shortages, now that the Kyoto Protocol has come into force, there is rising pressure on nations participating in the treaty (which includes all foundational European Union Nations examined here) to curtail their fossil fuel consumption so as to reduce carbon dioxide emissions and, hopefully, stem rapid climate change. The environmental consequences of energy production, the implementation of the Kyoto Protocol, and the impending decline in global oil production should refocus our attention on the factors that drive energy consumption. Demographic conditions stand out as key potential drivers of energy consumption. To help further our understanding of the potential consequences of demographic trends in Western Europe and to help further the development of the emerging field of environmental demography, we analyze the effects of demographic and economic factors on energy consumption in fourteen foundational members of the European Union (EU) during the period 1960–2000 and estimate the effects that projected demographic changes may have on energy consumption in the future. We focus our analysis on these nations because they have served as the focus of previous research (Lutz et al., 2003), they are a coherent group due to their geographic proximity to one another and shared foundational membership in the EU, and they are reasonably comparable due to their economic, political, and cultural similarity. They also serve as a potential model for the demographic situation many other nations may face in the future if their fertility rates continue to decline and life expectancies continue to grow. We focus on energy consumption due to the direct demands it places on natural resources, because it serves as a reasonable indicator

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of the overall pressure societies place on the global ecosystem, and because it has served as a focus of previous research in environmental demography (Holdren, 1991; Mazur, 1994).1 There is an extensive empirical literature on the social factors that contribute to a variety of environmental problems, including the consumption of natural resources, and some of this literature has taken demography into consideration. This research has examined energy consumption (Holdren, 1991; Mazur, 1994), but a much more substantial part has focused on the factors driving national carbon dioxide emissions (Cole and Neumayer, 2004; Dietz and Rosa, 1997; Rosa et al., 2004; Shi, 2003; York et al., 2003a,b), which are closely related to energy consumption. This research has generally found that population size and economic development are key driving forces of carbon dioxide emissions (as well as other impacts on the environment). In the few instances in which they have been examined (Cole and Neumayer, 2004; York et al., 2003a,b,c), the age structure of the population and level of urbanization also appear to play important roles. Other factors are typically found to have only a minor effect in cross-national research. We, therefore, focus our analysis on assessing the effects of demographic and economic factors on energy consumption.2 We undertake our analysis in five steps. First, we provide a review of research in environmental demography and modernization. Second, we explain the data and methods we use for assessing the historical influence of demographic and other factors on energy consumption and the procedures we use for making projections. Third, we present and interpret the results of our historical analysis. Fourth, we present the projections made based on the model derived from the historical analysis and explain their implications. Finally, we discuss how our results contribute to furthering our understanding of the human factors that influence natural resource consumption. 2. Environmental demography Several Scholars in the natural sciences have made efforts to understand the relationship between population and the environment. Paul Ehrlich and his colleagues are perhaps best-known among them (Ehrlich, 1968; Ehrlich and Ehrlich, 1990, 2004; Ehrlich and

1 Note that we examine energy consumption as estimated from a trade balance approach—i.e., indigenous production minus exports, plus imports. This measures direct energy consumption, but does not include energy embodied in traded goods. Thus, some nations may lower their energy consumption by importing energy intensive goods (e.g., steel). Data that estimate the amount of energy embodied in goods are not readily available at the aggregate cross-national level for the full time-series examined here, and such estimates as do exist are controversial. To address the issue of energy embodied in traded goods, and thus assign ‘‘responsibility’’ for energy consumption to where goods are consumed rather than produced, York et al. (2003a,c) present analyses of the ‘‘ecological footprint,’’ which is the best (although controversial) measure available that incorporates embodied energy in its estimates. For a household level analysis that takes into account indirect energy consumption, see Reinders et al. (2003). 2 It is important to recognize that analyses at various levels of aggregation provide different insights into energy consumption. Our focus here is on structural factors that influence national-level energy consumption. Other dynamics may be elucidated at other levels of analysis. For example, analyses complimentary to ours have been done at the household-level (Lutzenhiser, 1997; Lutzenhiser and Hackett, 1993; Reinders et al., 2003).

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Holdren, 1971; Holdren and Ehrlich, 1974).3 This work has served to raise awareness about population issues and to lay out the fundamental aspects of the pressure that the human population places on ecosystems and natural resources. Following in this tradition, Cohen (1995) has done much to further the development of scientific environmental demography. His landmark book, How many people can the Earth support?, presents one of the most thoughtful discussions and thorough analyses available of the complexity of assessing the extent to which the human population is limited by the biophysical environment. His general conclusion is that many factors influence the ‘‘carrying capacity’’ of the earth, including the economic, social, cultural, and political conditions in which humans live, so it is not possible to present one unambiguous estimate of the human carrying capacity. Nonetheless, Cohen makes a compelling case that we should not be sanguine about population growth, since there are indeed genuine biophysical restrictions on our species, as there are on all others. Some attempts have also been made in the social sciences to link demography with environmental analyses, particularly by those in the human ecology tradition. Catton and Dunlap, two of the founders of the subdiscipline of environmental sociology, argued that human societies are dependent on the natural environment, and that demography is an important part of the human-environment interaction (Catton, 1980; Catton and Dunlap, 1978; Dunlap and Catton, 1979, 1983). Catton and Dunlap drew on the work of Duncan (1959, 1961), who was one of the first human ecologists to explicitly link social systems to environmental systems.4 Namboodiri (1988), also drawing on Duncan’s work, but apparently unaware of work in environmental sociology, makes a case for the development of ‘‘ecological demography’’ in sociology, based on a marriage of demography and human ecology. His presentation is more concerned with the Durkheimian human ecology that is focused on the structural integration of social systems, rather than the dependence of societies on ecosystems, but it, nonetheless, points to the important connection between demography and the environment. More recently, Pebley (1998) has reviewed the various ways demography can help us understand the social connection to the natural environment, making a compelling case that demographers have an important contribution to make to the environmental sciences. Population size and growth are the demographic factors that have by far received the most attention in debates about the connections between population characteristics and

3 Our focus here is on the effect of societies on natural resource consumption, not on the consequences of resource depletion for societies. We, therefore, do not situate it specifically in the ‘‘Malthusian’’ tradition, which has typically focused on the consequences of population induced resource shortages on human mortality and quality of life. Note that many natural scientists, including Paul Ehrlich, are commonly identified as, and, indeed, have at times identified themselves as, ‘‘neo-Malthusians.’’ This is an unfortunate situation, since the basic assumptions of most so-called neo-Malthusians about human dependence and impact on ecosystems are different from the assumptions of Malthus (Foster, 2002, 137–154) and do not necessarily imply support for callous conservative social policies, which many take to be central to the Malthusian position. Most so-called neoMalthusians probably do not agree with many aspects of the original Malthusian argument—e.g., fertility rates cannot be dramatically reduced and, therefore, escalating mortality rates are necessarily the key check on population growth—and their identification with Malthus is, thus, misguided. 4 Although the name of the field, human ecology, implies a focus on the natural environment, the early human ecology tradition in the United States was linked to the Chicago School and built on Durkheim’s work. This early style of human ecology was more concerned with the structural interconnection among various aspects of societies, not especially with the natural environment.

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the environment. This is appropriate, since there is substantial evidence for their importance. However, other demographic factors have been neglected. One that is of particular interest in the context of our analysis is age structure, since changes in fertility rates and immigration patterns not only influence population size and growth, but also the relative proportion of various age groups in a population. The population of EU nations has been aging for some time now, and is expected to age dramatically over the coming decades. Dramatic growth in the elderly population is expected as life expectancies increase, and, as appears likely, birth rates remain low. There are reasons to expect that populations with a high proportion of elderly members (‘‘older populations’’) consume more energy than those more dominated by the young. For example, older populations tend to have smaller household sizes and more consumers of energy intensive products, such as cars.5 The age structure of the population may also affect the structure of the economy in various ways that have consequences for energy consumption. Thus, it is important to estimate the effects of age structure on energy consumption, an issue that has to date received scant attention. 3. Modernization and the environment The primary concern of the present analysis is with assessing the effects of demographic factors, particularly population size and age structure, on energy consumption. However, it is also important to take into consideration other influences on energy consumption, particularly factors associated with economic development and modernization. One venerable tradition that focuses on the political economy of capitalist societies suggests that economic development invariably leads to the expansion of resource consumption, since economies are dependent on natural resources and the economic elite have sufficient power to prevent social and environmental concerns from forcing business to internalize the environmental costs of production and inhibiting economic expansion and capital accumulation (Foster, 1992; O’Connor, 1994; Schnaiberg, 1980). Consistent with this perspective, a substantial body of empirical work has found that a variety of environmental impacts increase with economic development (Rosa et al., 2004; Shi, 2003; York et al., 2003c). A different research tradition examining the ‘‘environmental Kuznets curve’’ (EKC) hypothesis suggests that some types of environmental problems follow an inverted-U shaped curve relative to economic develop—i.e., environmental problems are hypothesized to be highest in middle income nations and lowest in poor and affluent nations. The logic behind the EKC hypothesis is that environmental quality is a luxury good, affordable only 5

We exclude variables such as household size and number of cars from the analysis for two reasons, one substantive and one practical. Substantively, we are engaging in a macro-structural analysis and are, therefore, trying to assess the consequences of major structural forces, rather than the particular forces that have a more proximate influence on energy consumption. For example, it is indisputable that cars are responsible for consuming large quantities of energy. Thus, cars are a highly proximate cause of energy consumption. Since, we are attempting to assess the social forces that lead to energy intensive activities, including car use and many other factors, car use is conceptually part of the dependent variable (energy consumption). On the practical front, data are not available on many factors of potential interest. For example, data on household size and number of cars for the nations under examination for the full extent of time assessed here are not available to our knowledge. Data on cars are only available for recent years and household size data are even scarcer. For a cross-sectional analysis of the factors influencing car use, see York (2003). For an assessment of the ecological consequences of changes in household size, see Liu et al. (2003).

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to the affluent. Therefore, poor societies are expected to seek economic development to improve their material quality of life, but this development necessitates increasing withdrawals from and additions to the natural environment, thus generating environmental problems. However, once societies become sufficiently affluent, they may invest in environmentally benign technologies and production systems which can lead to an amelioration of environmental problems. An EKC has typically only been found for local environmental problems, such as water pollution, but not for global problems such as greenhouse gas emissions and resource consumption. Dinda (2004) provides a review of the EKC literature and York and Rosa (2003) provide a critique of its theoretical and empirical foundations. As with economic development, the effect of urbanization on the environment is debated. Ehrhardt-Martinez (1998) argues that environmental impacts may follow an EKC relative to urbanization, rather than economic development per se, since urbanization brings with it key aspects of modernity that may lead to improvements in efficiency and resource management. Contrarily, Foster (1999), drawing on the work of Marx, suggests that urbanization is a key factor leading to a ‘‘metabolic rift,’’ a disruption of the interchange between society and nature. Empirical work has produced mixed results regarding the effect of urbanization on the environment (e.g., Ehrhardt-Martinez, 1998; York et al., 2003b,c), suggesting that the relationship between these two factors is complex and dependent upon both the context of urbanization and the type of environmental impacts examined. We assess the effect of urbanization on energy consumption in the modernized, affluent nations that characterize the European Union context to help shed further light on the influence of urbanization on the environment. 4. Data and methods For our historical analysis, we use a cross-sectional time-series Prais–Winsten regression model with panel-corrected standard errors (PCSE) (Beck and Katz, 1995), allowing for disturbances that are heteroskedastic and contemporaneously correlated across panels, correcting for first-order autocorrelation. We use PCSE because the feasible generalized least-squares (FGLS) estimator developed by Parks (1967) that is commonly used to analyze panel data often produces standard errors that can lead to extreme overconfidence, as Beck and Katz (1995) demonstrate. We treat the AR(1) process as common to all panels, because this approach uses more data to estimate the AR(1) function and because of the lack of theoretical justification for treating serial correlation as panel-specific (Beck and Katz, 1995, 638). We use an elasticity model (commonly used in economics) that has been specifically adapted for analyses of environmental impacts as the basis for our statistical estimation. The model is called STIRPAT, for STochastic Impacts by Regression on Population, Affluence, and Technology, and was originally developed by Dietz and Rosa (1994, 1997; Rosa and Dietz, 1998), and has been widely applied to analyses of a variety of environmental impacts (Cole and Neumayer, 2004; Cramer, 1996, 1998; Rosa et al., 2004; Shandra et al., 2004; Shi, 2003; York et al., 2003a,b,c). Elasticity models, like STIRPAT, often are used for models where the dependent variable is conceptualized as a multiplicative function of the independent variables. A long line of research on the driving forces of environmental degradation is based on a conceptualization of environmental impacts as arising from a multiplicative combination of population, affluence, and technology

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(for a review of this research, see Dietz and Rosa, 1994; York et al., 2003a,c), and is, therefore, appropriate for our analysis here. Elasticity models are estimated by converting all variables in the analysis into logarithmic form and utilizing an additive regression model (note that addition in logarithmic form is the equivalent of multiplication in original units). The coefficients of an elasticity model are particularly easy to interpret. The coefficient for each continuous independent variable is the estimated percentage change in the dependent variable associated with a 1% increase in the independent variable, controlling for other factors in the model. The interpretation of dummy variables and polynomials is somewhat more complicated (York et al., 2003a), and we discuss interpretation of these below in the results section. Since we are utilizing an elasticity (STIRPAT) model, all continuous variables discussed below are converted into natural logarithmic form for the analysis. We estimate the model lnðy it Þ ¼ b1 lnðxit1 Þ þ b2 lnðxit2 Þ þ    þ bk lnðxitk Þ þ ui þ wt þ eit ; where the subscripts i and t represent each nation (unit) and time period respectively, yit is the dependent variable (energy consumption) in original units for each nation at each point in time, xitk represents each independent variables for each nation at each point in time in original units, bk represents the (elasticity) coefficient for each independent variable, ui is the nation-specific disturbance term that is constant over time (i.e., the nationspecific y-intercept), wt is the time-specific disturbance term that is constant across nations, and eit is the disturbance term unique to each nation at each point in time. In a model based simply on pooling the data, the error (residual) term is the sum of ui, wt, and eit and a single y-intercept is estimated for all nations. Here, we use dummy variables to control for ui and wt, as is standard practice for panel data models (Greene, 2000; Hsiao, 2003; Wooldridge, 1999),6 leaving eit as the stochastic element in the model. This approach controls for potential unobserved heterogeneity that is temporally invariant (during the period of observation) within nations (e.g., geographic factors, natural resource availability, basic economic structure)—due to the dummy coding of each nation (ui)—and that is cross-sectionally invariant within periods (‘‘period effects’’)—due to the dummy coding of each period (wt). Therefore, the model is robust against omitted control variables, thereby more closely approximating experimental conditions. We use annual data for fourteen members of the EU (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Portugal, Spain, Sweden, and United Kingdom) for the period 1960–2000, which yields 14 panels and 41 time periods. However, West and East Germany have been grouped together to allow for analysis as one unit before and after reunification, as is done by the World Bank (2003). Appropriate data for Germany do not begin until 1971. All other nations have data from 1960 to 2000. The total number of observations is, therefore, 563. Our intention was to include all foundational members of the EU—these fourteen plus Luxembourg—but in regression

6

If the model is estimated without the unit-specific intercepts, it is referred to as a ‘‘random-effects’’ model. This model has more statistical power than the ‘‘fixed-effects’’ one (used here), which includes the unit (nation) dummies. However, as Greene (2000; 576) notes, ‘‘There is no justification for treating the individual effects as uncorrelated with the other regressors, as is assumed in the random effects model. The random effects treatment, therefore, may suffer from the inconsistency due to omitted variables.’’ Greene (2000; 567) notes that the fixedeffects approach is particularly appropriate for inter-country comparisons, such as is done here.

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Table 1 Summary statistics for pooled data (n = 563)

Energy Population Pop. 65+ Urbanization GDP per capita

Mean

SD

Minimum

Maximum

77,338 24,100,000 13.05 70.77 17,481

90,810 23,700,000 2.37 16.89 7709

2528 2,818,000 7.20 22.06 2,718

369,590 82,200,000 18.17 97.34 38,482

diagnostics, noted below, we found that Luxembourg had unduly high influence on the results. The exclusion of Luxembourg is also justifiable on substantive grounds, since it has a more specialized economy than the other fourteen foundational members of the EU. All data for the historical analysis are from World Bank (2003). The dependent variable is commercial energy use—i.e., apparent consumption at the national level, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport (World Bank, 2003)—measured in thousands of metric tons of oil equivalent. The key demographic variables are population size, percentage of the population over 64 years of age,7 and the percentage of the population living in urban areas. We also include a quadratic version of (the logarithm of) urbanization8 to assess whether the relationship between urbanization and energy consumption in non-monotonic, as postulated by the urbanization-EKC hypothesis (Ehrhardt-Martinez, 1998). The economic variable is GDP per capita, measured in constant 1995 US dollars. We also include the quadratic of (the logarithm of) GDP per capita in the model to allow for a non-monotonic relationship between GDP per capita and energy consumption as suggested by the EKC hypothesis. Table 1 presents summary statistics for all of the variables in original units and Table 2 presents a correlation matrix for all variables in logarithmic form. For the purposes of making the projections for the year 2025, we use the model derived from the historical analysis. We assume the nation-specific effects estimated in the model remain the same in the period 2000–2025 as they were from 1960–2000. We assume that, controlling for the demographic and economic factors in the model, the period effect makes

7 The percentage of the population age 15–64, the ‘‘nondependent’’ population, has been used elsewhere (e.g., York et al., 2003c). Estimation of the models presented here substituting various indicators of age structure suggests that the percentage of the population 65 and older provides the best fit. This is also the appropriate form for the conceptualization used here, since, we are interested in the effect of aging on energy consumption. 8 The quadratic terms for both urbanization and GDP per capita were calculated using centered versions of each variable—i.e., by subtracting the pooled cross-national mean of the variable in log form (the mean of the log of GDP per capita equals 9.654 and the mean of the log of urbanization equals 4.223) from each observation and squaring the result. This transformation reduces problems with collinearity between the log-linear version of each variable and its quadratic, allowing for a proper test of whether the relationship between each independent variable (urbanization and GDP per capita) and energy use is non-linear in log form. This centering procedure is recommended by Neter et al. (1990, 315–316) for models including polynomials. Using the non-centered version of the variables instead of the centered version to generate the quadratic inflates the standard error of the loglinear term in the model, but does not affect the model in any other way—i.e., the estimated shape of the relationship between each independent variable and energy use is not affected by the version of the quadratic that is used. See York et al. (2003a) for an explanation of this procedure and interpretation of results.

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Table 2 Correlation (Pearson’s r) matrix for all independent variables in logarithmic form for pooled data (n = 563)

Population Pop. 65+ Urban Urban2 GDPpc GDPpc2

Population

Pop. 65+

Urban

Urban2

GDPpc

GDPpc2

1.000 .212* .309* .155* .048 .163*

1.000 .581* .412* .711* .395*

1.000 .703* .709* .598*

1.000 .521* .722*

1.000 .489*

1.000

Note: Urbanization and GDP per capita were centered by subtracting their sample means before generating the quadratic terms in order to reduce collinearity with their log-linear counterparts. * p < .01 (two-tailed test).

energy consumption change at the same annual rate for the period 2000–2025 as it did on average over the period 1975–2000. Since there are no ‘‘standard’’ projections for GDP per capita (as there are for demographic factors, such as those produced by the UNPD), we do two sets of projections based on different procedures. In the most straightforward GDP scenario, we assume that the GDP per capita of each nation grows at the same annual rate during the period 2000–2025 as it did on average from 1975 to 2000, although we reduce the estimated rate to the 75th percentile for nations with growth rates above the 75th percentile and we increase the estimated rate to the 25th percentile for nations with growth rates below the 25th percentile to smooth out anomalous rates that are unlikely to be maintained. In the second GDP scenario, we take into account the potential connection between age structure and GDP. Here, we assume that GDP per ‘‘working age’’ person (people aged 15–64) grows at the same rate between 2000 and 2025 as it did from 1975 to 2000, and then calculate the expected GDP per capita for the projection model based on these values. These growth rates are also bounded by the 25th and 75th percentiles. Thus, in the second GDP scenario the GDP per capita estimate varies across population scenarios. Using the above assumptions to set plausible background conditions for comparing the differing effects of various possible demographic futures, we make projections of energy consumption based on the high, medium, and low projections of demographic factors provided by the UNPD (2004). 9 We use the adjustment for projections based on logarithmic models suggested by Wooldridge (1999, 208), since simply using the exponential of the predicted logarithmic value systematically underestimates the value in original units.

9

Note that the differences across the low, medium, and high scenarios are due entirely to assumptions about fertility rates (UNPD, 2004). Projected rates of immigration and mortality are the same for all three scenarios. Hence, we primarily focus our discussion on the implications of changes in fertility, rather than immigration. Changes in immigration would, of course, have serious demographic implications. We do not assess the potential consequences of different immigration scenarios since reliable projections are not available to our knowledge. Further note that projected population size and age structure vary across the low, medium, and high projections, but that urbanization projections do not (i.e., there is only one scenario for the urbanization projections). Also note that we noticed an anomaly with the UNPD (2004) urbanization data for the Netherlands, where estimates of urbanization for only the Netherlands are substantially lower than in the World Bank (2003) data we used for the analysis. We, therefore, adjusted the UNPD urbanization projection for the Netherlands so that it remained in proportion to the World Bank estimates. This is of little consequence for our analysis, since, as explained below, our primary concern is with comparing differences in energy consumption across the various projections for population size and age structure. The projections of other independent variables are, therefore, only used to provide plausible background conditions for this assessment.

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Table 3 Results from cross-sectional time-series Prais-Winsten regression elasticity models of commercial energy use with panel-corrected standard errors correcting for AR(1) disturbances Model 1 coef. (SE) Population Population 65+ Urbanization (Urbanization)2 GDP per capita (GDP per capita)2 N Total R2 ‘‘Within’’ R2 Rho

**

2.753 (.246) .965 (.093)** .530 (.101)** .697 (.084)** 563 .997 .884 .826

Model 2 coef. (SE) **

2.555 (.261) .835 (.092)** .563 (.191)* .265 (.155) .516 (.079)** .173 (.039)** 563 .997 .897 .817

Model 3 coef. (SE) 2.665 (.242)** .872 (.096)** .294 (.109)* .521 (.079)** .154 (.040)** 563 .997 .893 .824

Note: The models include nation-specific and period-specific intercepts that are not shown. All variables are in natural logarithmic form. For generating the quadratics, urbanization and GDP per capita were centered in log form before squaring to reduce collinearity. * p < .05. ** p < .001.

5. Historical analysis: 1960–2000 The regression results for the historical analysis are presented in Table 3.10 We estimate three models. First, we estimate a log-linear model where the quadratic terms for

10 We have done a variety of tests to assess the robustness of our results and identify potential statistical problems and/or anomalies in the data. None of the regression diagnostics we have run point to serious violations of regression modeling assumptions. A histogram of the regression residuals indicates that their distribution approximates normality, and a plot of residuals against predicted values of the dependent variable indicates that the distribution of residuals reasonably approximates homoskedasticity. Furthermore, plots of the residuals against each independent variable indicate that there are no systematic patterns in the errors, and that the error distributions are approximately constant over the range of each variable. The correlation matrix (Table 2) indicates that there is not substantial collinearity among any pair of independent variables. Consistent with what is suggested by the correlation matrix, tests for multicollinearity within panels (tolerance/VIF) find no substantial problems. To test for influential cases and the general robustness of the model, further steps have been taken. First, we estimated the models excluding each decade (1960–1969, 1970–1979, 1980–1989, 1990–2000) in turn (i.e., estimating the models including each combination of 3 of the 4 decades) to assess whether any time period was particularly influential. The results from each of these models were highly similar to the models including all time periods. Second, the models have been estimated excluding, in turn, each nation (i.e., including each combination of 13 of the 14 nations) to determine if any one nation is driving some of our results. The coefficient estimates are remarkably stable across each of these models, indicating that no single nation is driving the results. Note that we originally included Luxembourg in our analysis (for a total of 15 nations), but this check for influence and robustness revealed that Luxembourg was highly influential. In particular, when Luxembourg was included, the urbanization coefficient switched signs. Due to its undue influence, it was excluded from the analysis presented here. Although, as we have argued above, inclusion of the nation and period dummies is the most appropriate (and conservative) approach, since it more closely approximates experimental conditions than the ‘‘random-effects’’ approach, we have estimated the models presented here, excluding (1) the nations dummies, (2) the periods dummies, and (3) both the nation and period dummies. Although the magnitude of the coefficients varies across these models, they are all of the same direction as from the models presented here and all suggest a substantively similar interpretation.

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GDP per capita and urbanization are excluded (Model 1). Model 1 indicates that all of the independent variables have a significant effect on energy use. Second, we estimate a model including both quadratics (Model 2). In Model 2, the quadratic for urbanization is not significant, indicating that the log-linear specification is appropriate, while the quadratic for GDP per capita is significant, indicating that the relationship between GDP per capita and energy use is non-linear even in logarithmic form. Finally, we estimate a model dropping the non-significant urbanization quadratic (Model 3) to simplify interpretation and provide a parsimonious model for making projections. Note that the R2 is very high—99.7% of the variance in energy consumption is explained by the model. This high R2 value is typical of models including unit and period dummies. The total R2 is, however, somewhat misleading since it includes the amount of variance within and between panels (nations) that is explained by the model. Since the unit (nation) dummies explain the between panel variance, the focus of the analysis is on explaining variance within panels. Therefore, we present the ‘‘within’’ R2, which is the amount of within panel variance explained by the model. Although not as high as the total R2, the within R2 values are still impressive (over .89 in Model 3), indicating that the substantive variables explain the variance of energy use within nations reasonably well. These high R2 values are consistent with the findings of other researchers estimating similar models (Shi, 2003; Rosa et al., 2004), and indicates that at the national level demographic and economic factors provide a reasonably thorough explanation of energy consumption. We focus our interpretation on Model 3 since it contains all significant variables and does not suggest results substantially different from the other models. Population appears to play a major role in driving energy consumption. The coefficient for population indicates that a 1% increase in population size corresponds to a 2.665% increase in energy consumption, a highly elastic relationship, controlling for other factors. The coefficient for the age structure variable, Population 65+, is also positive and significant, indicating that nations with a high proportion of elderly people in their population consume more energy than other nations, controlling for other factors. These two findings point to the complex and subtle ways in which demographic trends affect energy consumption. For example, low fertility rates lead to a reduction in population growth, which inhibits energy consumption, but they also increase the proportion of the population that is elderly, which spurs energy consumption. Immigration, of course, may counter the effects of low fertility, since it contributes to population growth, and immigrants tend to be young. We examine how the combination of these forces plays out in different demographic scenarios below in the Projections section. The GDP per capita coefficient is significant and positive, while its quadratic is significant and negative, indicating that the relationship between GDP per capita and energy consumption is not linear in logarithmic form. Thus, the elasticity varies over the range of GDP per capita and can be calculated for any value of GDP per capita by taking the first partial derivative of the regression equation with respect to the natural logarithm of GDP per capita (York et al., 2003a). There is no EKC, where energy consumption decreases at high levels of affluence, within the range of observations. A turning point is projected at a GDP per capita of approximately $85,000, a value unlikely to be obtained by any nation in the near future and beyond the projected GDP per capita values estimated for the

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Percentage of 1960 Value

105 100 95 90 85 80 75 70 1960

1965

1970

1975

1980

1985

1990

1995

2000

Year Fig. 1. Period effects on energy consumption as a percentage of the 1960 value.

projections (below).11 Thus, over the period examined here increases in GDP per capita consistently led to increases in energy consumption. The quadratic for urbanization was not significant in Model 2, thus it was dropped from the model to simplify interpretation. Model 3 shows that the log-linear term for urbanization is positive and significant, indicating that even when controlling for GDP per capita urbanization contributes to the expansion of energy consumption, counter to the urbanization-EKC hypothesis. This finding in combination with the finding for GDP per capita clearly suggests that further ‘‘development’’ and ‘‘modernization,’’ even in the most modernized nations, will likely contribute to the expansion of energy consumption. The estimated period effects are displayed in Fig. 1. These are derived by taking the antilog of the coefficient for the dummy variable for each year (not presented in Table 3), which is the period specific scalar, and multiplying it by 100 to convert to a percentage (1960 is the omitted category, and, therefore, values are relative to the 1960 value). Controlling for the other factors in the model, the period effects estimated in the regression model (Model 3) remain fairly constant for the early and mid 1960s, but then in the late 1960s and early 1970s the period effect leads to an escalation in energy consumption. However, after the 1973–74 ‘‘energy crisis’’ the general temporal trend, independent of the demographic and economic factors

11

Note that even if the turning point is born out in the future (i.e., nations genuinely do reduce their energy consumption as their level of affluence exceeds this turning point), it does not necessarily indicate that affluent nations are genuinely reducing their demand for natural resources or impact on the environment. Nations my simply reduce energy consumption within their borders because the production of the commodities they consume is shifted to other nations (Brunnermeier and Levinson, 2004; Rothman, 1998; York et al., 2003c). Ehrlich and Holdren (1971) have called the assumption that the forces that drive environmental degradation and the environmental degradation itself are geographically coterminous the ‘‘Netherlands fallacy’’ to draw attention to the fact that some nations depend on natural resources from elsewhere and, therefore, environmental conditions within nations are not necessarily indicative of the environmental impacts of those nations. It should be further noted, then, that here we are examining the factors that drive energy consumption within nations, which does not necessarily take fully into account the demand for energy that national economies, policies, structures, and consumption habits generate.

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controlled for in the model, is toward lower energy consumption. There is also another distinct drop after the 1979 energy crisis. The decline in energy consumption due to the period effects for the period 1975–2000 was on average approximately 1.160% per year. Period effects are difficult to interpret substantively, since they include all factors associated with time that are common across nations and not accounted for by other variables in the model. However, the close association between the initiation of decline in energy consumption (after controlling for the effects of other factors in the model) and the energy crises of the 1970s suggests that the period effects are picking up general improvements in the energy efficiency of national economies. This suggests that nations did make important improvements in the efficiency of use of natural resources, although these improvements were independent of other forces, particularly demographic change, economic development, and urbanization (all of which served to counter the period effects, pushing energy consumption higher). 6. Projections Before we interpret our projections, it is important to note we do not present these as predictions. The assumptions we make regarding trends in period effects and GDP per capita are only made to provide plausible background conditions for assessing the effects of various potential demographic scenarios, and we do not make any claims regarding the likelihood that these assumptions will prove to be correct in the future. Furthermore, there is always the potential that structural conditions may change globally in the future thus invalidating the specification of the model (since it is based on historical data) and undermining its ability to make reliable projections. Finally, we must also recognize that demographic projections are themselves potentially unreliable and prone to change (see Cohen (1995) and Wyman (2003) on this point). We do not wish to reify either the demographic projections of the UNPD or our own projections of energy consumption. It is important to recognize that the future remains open and that projections are not destiny. We, therefore, present these projections to illustrate potential consequences of various demographic trends on energy consumption based on plausible assumptions about historic processes, but not to predict the future or inhibit efforts to change the forces that underlie these trends. Table 4 presents the UNPD (2004) population projections for the combined fourteen EU nations examined here for the year 2025, as well as the actual population in the year 2000 (World Bank, 2003). EU Nations are projected to have quite modest changes in population size and urbanization over the first quarter of the twenty-first century, although Table 4 Observed and projected population (thousands) and commercial energy use (thousands of metric tons of oil equivalent) for fourteen EU Nations combined

2000—Observed 2025—Low pop. 2025—Med. Pop. 2025—High pop.

Population

Energy use GDP scenario 1

Energy use GDP scenario 2

376,037 374,902 (95.2) 393,659 (100.0) 412,144 (104.7)

1,460,284 1,837,994 (91.6) 2,005,992 (100.0) 2,175,559 (108.5)

1,460,284 1,805,397 (92.4) 1,953,477 (100.0) 2,101,007 (107.6)

Note: Values as a percentage of the medium variant projections for population and energy use are presented in [brackets]. Energy use scenario 1 assumes GDP per capita grows at the same rate for each nation from 2000 to 2025 as it did from 1975 to 2000 (bounding growth rate by the 25th and 75th cross-national percentiles). Energy use scenario 2 adjusts GDP per capita growth rate based on changes in age structure (see text).

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their populations are expected to age substantially. Based on medium variant projections, the population of the combined fourteen EU Nations examined here is projected by the UNPD to grow by 4.7% between 2000 and 2025. In this same scenario, the percentage of the population that is 65 and older is projected to increase from 16.3% in 2000 to 22.6% in 2025. Urbanization is projected to increase only modestly, from 79.4% in 2000 to 83.5% in 2025. Table 4 also presents the projected energy consumption for the combined fourteen EU Nations examined here based on various demographic scenarios (low, medium, and high), the two GDP scenarios (Scenario 1 assumes GDP growth is unaffected by age structure and Scenario 2 assumes that GDP growth is affected by age structure, as explain above), and the projected period effects as described in Section 4. Our primary interest here is not in the absolute level of projected energy consumption, due to the inherent problems with predicting the future mentioned above, but rather with the relative differences among the various demographic scenarios, which illustrate how the combination of different trends in population growth and aging may play out over time. For 2025, the low variant population scenario, in which fertility rates are assumed to be particularly low, and therefore aging particularly high, projects a population size 4.8% below the medium variant scenario (see Table 4). The high variant scenario, where fertility rates are assumed to be particularly high, and therefore aging particularly low, projects a population 4.7% above the medium variant scenario (see Table 4). The energy consumption projections differ substantially across the different demographic scenarios, but only modestly across the GDP scenarios. The GDP scenario that takes into account the potential effect of age structure on GDP (GDP Scenario 2) suggests similar differences across the demographic scenarios as does the GDP scenario that does not take into account the effect of age structure on GDP (GDP Scenario 1), although the differences among the demographic scenarios are slightly attenuated in GDP Scenario 2 relative to GDP Scenario 1. The demographic low variant projects energy consumption to be 8.4% below the medium variant in GDP Scenario 1 and 7.6% below in GDP Scenario 2, while the demographic high variant projects energy consumption to be 8.5% above the medium variant in GDP Scenario 1 and 7.6% above in GDP Scenario 2 (see Table 4). It appears clear that which demographic scenario is actualized may have quite substantial implications for energy consumption. It is worth noting that, as mentioned above, the relationship between energy consumption and population size is highly elastic, where, all else being equal, a 1% increase in population size is expected to lead to an increase in energy consumption of over 2% (see Table 3). However, due to the effect the degree of aging of the population has on energy consumption and the relationship between aging and population growth stemming from fertility rates—i.e., populations with relatively high fertility rates will grow faster but age less compared to populations with relatively low fertility rates, all else being equal—in practice population growth will not typically lead to such a dramatic growth in energy consumption as may be expected based on a consideration of the population coefficient alone. Although in the high variant scenario the population size is projected to be higher than in the medium variant scenario, the population is projected to age less since the growth is concentrated in the young segment of the population. Likewise, in the low variant scenario, although the population size is projected to be lower than the medium variant scenario, the population will age more. Thus, the projected changes in age structure are expected to moderately attenuate the effects of the changes in population size.

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7. Discussion and conclusion Our analysis points to the important contributions demography can make to our understanding of human-environment interactions. We encourage the development of a scientific environmental demography that aims to understand how population characteristics influence societal interaction with the environment, but does not deny the important role of other social factors. Our analysis helps to further our understanding of how demographic factors contribute to resource consumption, sometimes in fairly subtle ways. We found that the relationship between population size and energy consumption in fourteen European Union Nations over the period 1960–2000 was highly elastic, all else being equal. At first glance, this finding seems particularly surprising, since the general expectation may be that the relationship would be one of unitary elasticity—where energy consumption changes in direct proportion to population. However, this finding is not unintelligible, nor is it inconsistent with findings from several other studies. Shi (2003) found in an analysis of carbon dioxide emissions for 93 nations over the period 1960– 1996 that population had a disproportionately large (highly elastic) effect. Likewise, in a cross-sectional analysis of the carbon dioxide emissions of 111 nations, Dietz and Rosa (1997) found the effect of population exceeded unitary elasticity. Cole and Neumayer (2004), examining sulfur dioxide emissions in 54 nations over the period 1971–1990, also found evidence that population has a highly elastic effect, at least in some nations. DeHart and Soule´ (2000), even found evidence that population’s effect on greenhouse gas emissions is elastic at the local level within the United States. Thus, our findings about the effect of population on energy consumption are consistent with several other empirical analyses and validate Dietz and Rosa’s (1994) argument that the nature of the relationship between population and environmental impacts should be empirically estimated, rather than assumed a priori. What is the substantive meaning of an elastic relationship between population and energy consumption, and why does such a relationship exist? Literally, the finding that the relationship exceeds unitary elasticity indicates that there is a diseconomy of scale as population grows—i.e., societies become more energy intensive, all else being equal, as their populations become large (or less intensive as their populations decline). Note that this does not necessarily mean that each new person consumes more energy than already existing people. This is an aggregate level analysis, and inferences about what happens on a micro-level cannot be directly drawn from these findings. Rather, the finding is about societies as a whole; as they become large, they collectively use more energy. Holdren (1991), in his analysis of energy consumption, has suggested that this may be, in part, due to the effects of population levels on settlement patterns and the types of energy resources nations rely on. Furthermore, he suggests that changes in population may challenge societies’ ability to plan for efficient utilization of energy resources (p. 249). Thus, there are several theoretical reasons to expect high elasticity of energy consumption with respect to population, as found in our analysis and in other studies. Of course, the extent to which this relationship is maintained in the future is an open question. If the elastic relationship is due to the destabilizing effects of population growth, perhaps a different relationship will emerge as populations shrink. The effect of population size must be interpreted in the context of the changes in age structure that accompany shifts in fertility, mortality, and immigration. We found that

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changes in age structure influence energy consumption. In particular, we found that an increase in the proportion of the population that is older than 65 corresponds with an increase in energy consumption, all else being equal. Note that this finding does not necessarily suggest that older people consume more energy than younger people. As we just noted, we are examining aggregate data, not individual level data, so firm conclusions cannot be drawn about what happens on a micro-level. Changes in age structure likely influence the structure of the economy, particularly the composition of production and consumption, as well as the spatial distribution of the population, transportation infrastructure, and social services. Of course, this factor, like other factors in macro-structural analyses, may be only an indicator of more proximate factors. Further analyses will be necessary to identify the specific mechanisms by which age structure influences energy consumption and to determine whether alternative indicators of age structure are more appropriate than the one we use here. Within the range of observations, economic development showed no sign of an inverted U-shaped relationship with energy consumption, counter to the suggestion of environmental Kuznets curve theorists. Rather the findings indicate that for the foreseeable future, economic growth is likely to lead to an increase in energy consumption. We also found urbanization has a positive, monotonic effect on energy consumption, further countering the claims of modernization theorists. We illustrated how demographic trends may play out in the future by projecting energy consumption for the year 2025 based on UNPD population projections. Since low fertility can lead to a decline in population size, which reduces energy consumption, but also can lead to an increase in the proportion of the population that is elderly, which increases energy consumption, it appears that the net effect of these demographic processes may be that energy demand will change greater than proportionately with changes in population size, all else being equal, over the coming decades, although not quite as dramatically as is suggested by the population coefficient alone. Although it has been suggested that low fertility rates and consequent population decline in western European nations may have detrimental consequences for the economies of those societies, the findings of the present study point to one substantial benefit stemming from low fertility: The stabilization of population size will help to curb energy demand, thus reducing the environmental and social consequences of energy production. In light of the growing consensus in the scientific community that the use of fossil fuels is contributing to global climate change, the recent implementation of the Kyoto Protocol, which aims to reduce carbon dioxide emissions in European and other developed nations, and the impending peak in world oil production, trends that serve to limit energy consumption are of no small importance. Our findings suggest that the efforts of some commentators to construct low fertility rates in Western Europe as a crisis are premature and do not fully recognize the various potential consequences of low fertility, some of which may be desirable, particularly from an environmental perspective. Acknowledgments I thank Robert O’Brien and the anonymous SSR reviewers for their helpful comments. This research was supported in part by the University of Oregon Summer Research Award.

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