4.8 Article

Does population have a larger impact on carbon dioxide emissions than income? Evidence from a cross-regional panel analysis in China

Journal

APPLIED ENERGY
Volume 180, Issue -, Pages 800-809

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2016.08.035

Keywords

Demographic change; Income growth; Energy use; CO2 emissions; STIRPAT model; China

Funding

  1. National Natural Science Foundation of China [41130748, 41471143]
  2. Major Program of National Social Foundation of China [15ZDA021]
  3. Certificate of China Postdoctoral Science Foundation Grant [2016M591105]

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As global warming intensifies, the accumulation of carbon dioxide (CO2) and other greenhouse gases have attracted great global attention. However, questions regarding whether, how and to what extent demographic factors and processes affect carbon emissions have not yet been fully explained particularly in China. This study used an improved STIRPAT model to reassess the impact of demographic and income changes on China's energy-related CO2 emissions at the national and regional levels using balanced provincial panel data from the 1990-2012 period. Whereas most previous studies of emission population/income elasticity in China have yielded wide-ranging estimates, this study showed that income rather than demographic change has been the dominant driving force behind China's growing CO2 emissions. Urbanisation has increased energy consumption and emissions, except in western China. Changes in the age structure have had a statistically insignificant effect on energy use, but resulted in increased national emissions particularly in eastern China. Shrinking household size did not reduce energy use and emissions, indicating that improved residential energy efficiency might reduce emissions. Changing the traditional mode of economic growth, reasonable controlling the pace of urbanisation, improving energy efficiency and upgrading industrial structures may yet be necessary to mitigate the environmental impact of human activities in China. (C) 2016 Elsevier Ltd. All rights reserved.

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