Abstract
We focus on three environmental impacts particularly influenced by population age-structure—carbon emissions from transport and residential energy and electricity consumption—as well as aggregate carbon emissions for a panel of developed countries, and take as our starting point the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework. Among our contributions is to further disaggregate population into three particularly key age groups: 20–34, 35–49, and 50–64, and by doing so demonstrate that population’s environmental impact differs considerably across age groups, with the older age groups (ones typically associated with larger households) actually exerting a negative influence. Furthermore, those age-specific population influences are different (in absolute and relative terms) for the different environmental impacts we analyze. Also, we find that urbanization, in developed countries, best measures access to a country’s power grid, and thus, is positively associated with energy consumption in the residential sector. Finally, we suggest some modeling and methodological improvements to the STIRPAT framework.
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Notes
Perhaps the level of aggregation encountered in the literature is so popular because it is the level of aggregation in the widely used World Bank data set. The UN does publish (with free access) population data in 5-year age groupings, but the data is only available at 5-year intervals (beginning in 1950), and that data set requires considerably more manipulation by the analyst to compile in a form suitable for regressions (http://esa.un.org/unpp).
The first published population–environment study to consider households as the unit of analysis we know of was MacKellar et al. (1995). However, household size can be a difficult variable to collect for an empirical panel analysis; thus, few other macro-level, cross-country studies have followed MacKellar et al.’s lead—two exceptions are Cole and Neumayer (2004) and Liddle (2004).
This number is estimated because the last household size category supplied in the data is “seven or more” members, i.e., the number of households with exactly eight, nine, etc., members is not explicitly known from the data.
The working or retired designation is merely to distinguish between two household types that do not include children. The data set used does not otherwise allow for disaggregation’s by employment status.
This point was made by an anonymous reviewer.
Unit root tests are used to determine stationary, and were originally developed for time-series, but have been expanded to cover panels.
Two or more non-stationary variables are said to be cointegrated if some linear combination of them is stationary. The finding of cointegration among economic variables is interpreted as evidence of a long-run, equilibrium relationship. Like for unit roots, cointegration tests were originally designed for time-series but have been expanded to cover TSCS data sets.
This variation is now available as an option on most statistical programs too.
Those countries are: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Greece, Ireland, Italy, Japan, Netherlands, Portugal, Spain, Sweden, United Kingdom, and United States.
Of course, as a secondary energy source, electricity’s ultimate greenhouse gas impact depends on the extent to which fossil fuels are used to generate it.
Initially, we planned to use the population levels of these age groups, but were deterred due to the multi-colinearity problems such variables created. The size (but not the shares) of population cohorts is highly correlated.
This variable is constructed as follows: industrial energy consumption (from the International Energy Agency—IEA) is divided by industrial output. Industrial output is derived by scaling the IEA’s industrial production index, which is indexed to year-2000, by 2000s GDP from industry—itself calculated by multiplying total GDP (from Penn World Table) by industry’s share of value added (from the World Bank). A few missing observations in the IEA’s industrial production index are filled from a similar index produced by the International Monetary Fund.
The non-fossil fuel sources considered are: geothermal, nuclear, hydro, and solar/wind.
The panels used in the carbon dioxide from transport estimations are missing two observations as described in Table 3.
When including a lagged dependent variable, the Durbin–Watson test for serial correlation is no longer accurate. The recommended test is a Lagrange multiplier (LM) test that involves regressing the residuals from an OLS estimation on the first lag of those residuals as well as all the independent variables (including the lagged dependent variable) used in that OLS estimation. One then performs a LM test on the null hypothesis that the coefficient on the lagged residual term is zero—a rejection of that null is evidence of first-order serial correlation.
In addition, variance inflation factors were calculated, and all were found to be below 3. However, it is nearly impossible for regressions comprised of IPAT variables to avoid completely multicollinearity (mutual association among variables) based on the theories developed to explain how those variables interact. For example, affluence (or GDP per capita) is believed to affect population—through human capital’s influence on birth rates (e.g., Becker et al. 1990) and higher income’s ability to lower death rates; meanwhile, population has been shown to impact affluence—when the size of the working-age population increases faster than the size of the dependent-age population (e.g., Bloom and Williamson 1998); and human capital and technology have been recognized as drivers of economic growth (affluence) since Solow (1956). The above theories suggest that the best way to perform STIRPAT regressions may be to treat the variables as part of a cointegrated system; however, such analysis requires annual observations, and is beyond the scope of this article.
In addition, tests on the redundancy of the fixed effects were strongly rejected for our models, as were Hausman tests on the consistency and efficiency of a random effects alternative to fixed effects.
When economic or demographic variables are non-stationary, they are typically I(1), i.e., if differencing is applied once they become stationary. Orders of integration greater than I(2) are very rare among economic/demographic variables.
As pointed out by an anonymous reviewer, national, aggregate carbon emissions are calculated from national, aggregate energy consumption; thus, for countries with carbon intensive energy sources, aggregate carbon emissions and aggregate energy intensity run the risk of being highly correlated with construction. It can be seen from the correlation matrix (Table 4) that our industrial energy intensity variable does not suffer from this problem.
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Liddle, B., Lung, S. Age-structure, urbanization, and climate change in developed countries: revisiting STIRPAT for disaggregated population and consumption-related environmental impacts. Popul Environ 31, 317–343 (2010). https://doi.org/10.1007/s11111-010-0101-5
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DOI: https://doi.org/10.1007/s11111-010-0101-5