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Demographic dynamics and per capita environmental impact: using panel regressions and household decompositions to examine population and transport

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  • Brantley Liddle

    (Max Planck Institute for Demographic Research, Rostock, Germany)

Abstract

Demographic variables have tended to be ignored in many environment-development analyses. This paper examines how population changes (in aging, households, and urbanization/density) can help explain changes/differences in personal transport using both macro- and micro- level data. First, panel regressions are performed with IEA-OECD road sector energy use data (spanning 1960-2000) on spatial population measures, average household size, and age structure data. Then US household data is used to determine the extent compositional changes in the nature of households can explain changes in per capita driving. An Environmental Kuznets Curve for per capita road energy use was rejected—the coefficients on the GDP squared terms were insignificant and the implied turning points were well outside the sample range; instead, the relationship between wealth and road energy was found to be monotonic (log-linear). The ideas that more densely populated countries have less personal transport demands, the young drive more, and smaller households mean higher per capita driving were confirmed. The basic result from the household decompositions was that changes in demand were more important than compositional changes, however, during some periods the compositional change component was considerable. A few policy implications can be drawn from these analyses. First, the look at micro data implies that there is much potential for policy to affect transport behavior since the compositional component of change—more difficult for policy to alter—is smaller than the behavioral or demand component. However, the look at the macro data implies that spatial factors, like population density and urbanization—which also can be difficult to alter—are significant in influencing personal transport demand.

Suggested Citation

  • Brantley Liddle, 2003. "Demographic dynamics and per capita environmental impact: using panel regressions and household decompositions to examine population and transport," MPIDR Working Papers WP-2003-029, Max Planck Institute for Demographic Research, Rostock, Germany.
  • Handle: RePEc:dem:wpaper:wp-2003-029
    DOI: 10.4054/MPIDR-WP-2003-029
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    References listed on IDEAS

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    Cited by:

    1. Liddle, Brantley, 2013. "Urban density and climate change: a STIRPAT analysis using city-level data," Journal of Transport Geography, Elsevier, vol. 28(C), pages 22-29.
    2. Salim, Ruhul A. & Shafiei, Sahar, 2014. "Urbanization and renewable and non-renewable energy consumption in OECD countries: An empirical analysis," Economic Modelling, Elsevier, vol. 38(C), pages 581-591.
    3. Liddle, Brantley & Lung, Sidney, 2013. "Might electricity consumption cause urbanization instead? Evidence from heterogeneous panel long-run causality tests," MPRA Paper 52333, University Library of Munich, Germany.
    4. Liddle, Brantley, 2013. "Population, Affluence, and Environmental Impact Across Development: Evidence from Panel Cointegration Modeling," MPRA Paper 52088, University Library of Munich, Germany.
    5. Liddle, Brantley & Lung, Sidney, 2010. "Age-Structure, Urbanization, and Climate Change in Developed Countries: Revisiting STIRPAT for Disaggregated Population and Consumption-Related Environmental Impacts," MPRA Paper 59579, University Library of Munich, Germany.
    6. Sadorsky, Perry, 2013. "Do urbanization and industrialization affect energy intensity in developing countries?," Energy Economics, Elsevier, vol. 37(C), pages 52-59.
    7. Georgina Mace & Emma Terama & Tim Coulson, 2013. "Perspectives on International Trends and Dynamics in Population and Consumption," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 55(4), pages 555-568, August.
    8. Elliott, Robert J.R. & Sun, Puyang & Zhu, Tong, 2017. "The direct and indirect effect of urbanization on energy intensity: A province-level study for China," Energy, Elsevier, vol. 123(C), pages 677-692.
    9. Laureti, Tiziana & Montero, José-María & Fernández-Avilés, Gema, 2014. "A local scale analysis on influencing factors of NOx emissions: Evidence from the Community of Madrid, Spain," Energy Policy, Elsevier, vol. 74(C), pages 557-568.
    10. Fang, Wen Shwo & Miller, Stephen M. & Yeh, Chih-Chuan, 2012. "The effect of ESCOs on energy use," Energy Policy, Elsevier, vol. 51(C), pages 558-568.
    11. Poumanyvong, Phetkeo & Kaneko, Shinji & Dhakal, Shobhakar, 2012. "Impacts of urbanization on national transport and road energy use: Evidence from low, middle and high income countries," Energy Policy, Elsevier, vol. 46(C), pages 268-277.
    12. Zhimin Zhou & Xinyue Ye & Xiangyu Ge, 2017. "The Impacts of Technical Progress on Sulfur Dioxide Kuznets Curve in China: A Spatial Panel Data Approach," Sustainability, MDPI, vol. 9(4), pages 1-27, April.
    13. Liddle, Brantley, 2013. "Urban Transport Pollution: Revisiting the Environmental Kuznets Curve," MPRA Paper 53632, University Library of Munich, Germany.
    14. Xiangyu Ge & Zhimin Zhou & Yanli Zhou & Xinyue Ye & Songlin Liu, 2018. "A Spatial Panel Data Analysis of Economic Growth, Urbanization, and NO x Emissions in China," IJERPH, MDPI, vol. 15(4), pages 1-20, April.
    15. Menz, Tobias & Welsch, Heinz, 2012. "Population aging and carbon emissions in OECD countries: Accounting for life-cycle and cohort effects," Energy Economics, Elsevier, vol. 34(3), pages 842-849.
    16. Liddle, Brantley, 2009. "Long-Run Relationship among Transport Demand, Income, and Gasoline Price for the US," MPRA Paper 52080, University Library of Munich, Germany.
    17. Robert J R Elliott & Puyang Sun & Tong Zhu, 2014. "Urbanization and Energy Intensity: A Province-level Study for China," Discussion Papers 14-05, Department of Economics, University of Birmingham.

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    More about this item

    Keywords

    OECD countries; energy consumption; environmental policy; household size; transport;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

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