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Population and Energy Consumption/Carbon Emissions: What We Know, What We Should Focus on Next

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International Handbook of Population and Environment

Part of the book series: International Handbooks of Population ((IHOP,volume 10))

Abstract

This paper surveys the literature on the demographic determinants of energy use and carbon emissions. The literature demonstrates that demographic patterns are important drivers of environmental outcomes. In particular, age structure, household composition, household size and population density all correlate strongly with energy use and, thus, carbon emissions. In addition, life-cycle and cohort effects appear to be important (e.g., millennials tend to consume less fuel from driving; younger homeowners are more likely to invest in technologies like rooftop solar than older homeowners are). In addition to reviewing existing work, we also make recommendations for future research. Our recommendations include empirical best practices, suggestions for new variables like education, and a greater focus on the supply of energy. The existing literature is already quite extensive. Thus, we also stress the need for future work to integrate existing findings with broader models to help inform policy decisions and improve prediction analyses. Finally, we stress the possibility of a feedback from energy use and carbon emissions to demographic outcomes.

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Notes

  1. 1.

    Based on existing studies, we also argue that population density is a better predictor of energy use than urbanization.

  2. 2.

    We do suggest that education has been an overlooked demographic variable in the existing literature.

  3. 3.

    Text from this section has been reprinted by permission from Springer Population and Environment Age-structure, urbanization, and climate change in developed countries: Revisiting STIRPAT for disaggregated population and consumption-related environmental impacts, B. Liddle and S. Lung 2010 as well as from Springer Population and Environment Impact of population, age structure, and urbanization on greenhouse gas emissions/energy consumption: Evidence from macro-level, cross-country analyses, B. Liddle, 2014.

  4. 4.

    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.

  5. 5.

    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 disaggregations by employment status.

  6. 6.

    Some text from this section has been reprinted by permission from Springer Population and Environment Impact of population, age structure, and urbanization on greenhouse gas emissions/energy consumption: Evidence from macro-level, cross-country analyses, B. Liddle, 2014.

  7. 7.

    Ewing and Cervero (2001) argued that urban form—i.e., the co-location of housing, employment, and services—is more important in reducing transport demand than density. Yet measures of urban form are hard to collect; hence, density is the default measure of many studies (Rickwood et al. 2008).

  8. 8.

    Studies employing city-level data, e.g., Kenworthy and Laube (1999); Marcotullio et al. (2012), are among the few examples where information from non-OECD countries has been considered.

  9. 9.

    Indeed, Liddle (2013) determined that the national-level share of people living in urban areas was negatively correlated with the density of large cities located in those respective countries.

  10. 10.

    Such examples include: “Why Car Companies Can’t Win Young Adults,” Fortune 2013; “NOwnership, No Problem: Why Millennials Value Experience Over Owning Things,” Forbes 2015; “The Many Reasons Millennials are Shunning Cars,” Washington Post 2014; “Social Media Trumps Driving Among Today’s Teens,” Forbes 2012.

  11. 11.

    Some text from this section has been reprinted by permission from Springer Population and Environment Impact of population, age structure, and urbanization on greenhouse gas emissions/energy consumption: Evidence from macro-level, cross-country analyses, B. Liddle, 2014.

  12. 12.

    See Liddle (2015) for details of the models, estimators, and regression diagnostics.

  13. 13.

    Integrated assessment models (IAMs) study the relationship between the economy and the climate.

  14. 14.

    It is important to note that the papers mentioned here are explicitly interested in voluntary changes in fertility and their impact on demographic structure and population size. See Das Gupta (2013) for a related discussion.

  15. 15.

    The UN projections employed in existing work are not meant to correspond to different policy scenarios, although existing work uses those projections as proxies for differing levels of policy intervention.

  16. 16.

    This issue is discussed in Liddle (2014).

  17. 17.

    For example, a recent study suggested that education is the true driver of `demographic dividends,’ an important concept in the study of economic growth and sustainability (Lutz et al., 2019).

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Authors and Affiliations

  1. Energy Studies Institute, National University Singapore, Singapore, Singapore

    Brantley Liddle

  2. Bethesda, MD, USA

    Brantley Liddle

  3. Department of Economics, Williams College, Williamstown, MA, USA

    Gregory Casey

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  1. Brantley Liddle
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  1. Institute of Behavioral Science, University of Colorado Boulder, Boulder, CO, USA

    Lori M. Hunter

  2. Department of Geography, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Clark Gray

  3. Campus Condorcet, National Institute for Demographic Studies, Aubervilliers Cedex, France

    Jacques Véron

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Liddle, B., Casey, G. (2022). Population and Energy Consumption/Carbon Emissions: What We Know, What We Should Focus on Next. In: Hunter, L.M., Gray, C., Véron, J. (eds) International Handbook of Population and Environment. International Handbooks of Population, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-76433-3_19

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