4.7 Article

A quantile regression analysis of China's provincial CO2 emissions: Where does the difference lie?

Journal

ENERGY POLICY
Volume 98, Issue -, Pages 328-342

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.enpol.2016.09.003

Keywords

Carbon dioxide emissions; Quantile regression model; Panel data

Funding

  1. Xiamen University - Newcastle University Joint Strategic Partnership Fund
  2. Grant for Collaborative Innovation Center for Energy Economics and Energy Policy [1260-Z0210011]
  3. Xiamen University Flourish Plan Special Funding [1260-Y07200]
  4. National Social Science Foundation of China [15BTJ022]
  5. Jiangxi Soft Science Foundation of Jiangxi Province [20151BBA10037, 20161BBA10042]
  6. National Natural Science Foundation of China [G030602, 71463020, 61263014, 61563018]

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China is already the largest carbon dioxide emitter in the world. This paper adopts provincial panel data from 1990 to 2014 and employs quantile regression model to investigate the influencing factors of China's CO2 emissions. The results show that economic growth plays a dominant role in the growth of CO2 emissions due to massive fixed-asset investment and export trade. The influences of energy intensity on the lower 10th and upper 90th quantile provinces are stronger than those in the 25th-50th quantile provinces because of big differences in R&D expenditure and human resources distribution. The impact of urbanization increases continuously from the lower lath quantile provinces to the 10th-25th, 25th-50th, 50th-75th, 75th-90th and upper 90th quantile provinces, owing to the differences in R&D personnel, real estate development and motor-vehicle ownership. The effect of industrialization on the upper 90th quantile provinces is greater than those on other quantile provinces on account of the differences in the industrial scale and the development of the building industry. Thus, the heterogeneity effects of influencing factors on different quantile provinces should be taken into consideration when discussing the mitigation of CO2 emissions in China. (C) 2016 Elsevier Ltd. All rights reserved.

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