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Impact of population, age structure, and urbanization on carbon emissions/energy consumption: evidence from macro-level, cross-country analyses

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Abstract

This review summarizes the evidence from cross-country, macro-level studies on the way demographic factors and processes—specifically, population, age structure, household size, urbanization, and population density—influence carbon emissions and energy consumption. Analyses employing time-variant data have produced great variance in population elasticity estimations—sometimes significantly greater than one, sometimes significantly less than one; whereas, cross-sectional analyses typically have estimated population elasticities near one. Studies that have considered age structure typically have used standard World Bank definitions and mostly have found those variables to be insignificant. However, when researchers have considered levels of disaggregation that approximate life-cycle behavior like family size, they have uncovered relationships that are complex and nonlinear. Average household size has a negative relationship with road energy use and aggregate carbon emissions. Urbanization appears positively associated with energy consumption and carbon emissions. Higher population density is associated with lower levels of energy consumption and emissions.

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Notes

  1. Several papers presented more than one regression; when authors articulated a favored regression, data was drawn from it. Also, when stationarity was not explicitly addressed, regressions performed in first differences were deemed to be more robust to stationarity, and thus, preferred. Lastly, an attempt was made to choose results that were most compatible with the results from the other studies listed in Table 1 (e.g., regressions that controlled for population and GDP per capita).

  2. Menz and Kuhling (2011) suggest that the STIRPAT framework is more popular among sociologists; whereas, the EKC framework is more popular among economists—naturally, such discipline distinctions would affect the choice of additional (T and Z) variables.

  3. They do so, albeit, often after subtracting out country effects.

  4. Interestingly, this phenomenon of a population elasticity of unity for cross-sectional analyses is true even for studies considering different dependent variables (e.g., fuelwood consumption by Knight and Rosa 2012) or different units/scales of analysis (e.g., US county-level data in Roberts 2011; international city-level data in Liddle 2013b).

  5. Fang et al. (2012) first differenced their data and included a LDV; thus, it is difficult to compare their results with those of other studies. Perhaps, if one applies the one minus the LDV coefficient transformation (i.e., divides by 1–0.92 for their all countries panel), their results would be comparable to other first differenced, short-run models.

  6. See Liddle (2012) for details of the models, estimators, and regression diagnostics.

  7. That first group of studies includes papers employing GMM or instrumental variables (e.g., Martinez-Zarzoso and Maruotti 2011; Fang et al. 2012). Such techniques include lags of the independent variables to indirectly mitigate endogeneity; however, they do not formally test for the presence of a mutually causal relationship as the second group of studies does.

  8. Most of the mutual causality studies to date have been focused on single countries, and thus, are beyond the scope of this review.

  9. Cross-national data on average household size is difficult to collect; however, there are a few other studies that have analyzed this variable (e.g., Knight and Rosa 2012), but since those studies considered dependent variables other than energy consumption or carbon emissions, they are beyond the scope of this review.

  10. See reviews by Payne (2010a, b).

  11. Some of these estimators were developed/coded by Markus Eberhardt, who also maintains a very helpful website: https://sites.google.com/site/medevecon/home.

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Acknowledgments

Suggestions/comments from the participants at the IUSSP Seminar on Population Dynamics and the Human Dimensions of Climate Change, Canberra, Australia, November 27–29, 2012 and three anonymous reviewers helped to improve the final version.

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    Brantley Liddle

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Liddle, B. Impact of population, age structure, and urbanization on carbon emissions/energy consumption: evidence from macro-level, cross-country analyses. Popul Environ 35, 286–304 (2014). https://doi.org/10.1007/s11111-013-0198-4

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