4.7 Article

Spatial effects of economic performance on the carbon intensity of human well-being: The environmental Kuznets curve in Chinese provinces

期刊

JOURNAL OF CLEANER PRODUCTION
卷 233, 期 -, 页码 681-694

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2019.05.396

关键词

Carbon emissions; Human well-being; Environmental Kuznets curve; Spatial effects; Sustainability

资金

  1. National Natural Science Foundation of China [41861025]
  2. Projects of Humanities and Social Sciences of the Ministry of Education of China [18YJA790082]
  3. Youth Program of National Social Science Foundation of China [15CJL011]

向作者/读者索取更多资源

This study analyzes the interdependent relationship between economic development, and environmental and human well-being in China. We evaluate panel data of the carbon intensity of human wellbeing (CIWB) to measure environmental sustainability and human well-being for 30 Chinese provinces from 1995 to 2016. We then employ a spatial model to test the environmental Kuznets curve. The CIWB is an important indicator that incorporates the environmental, social, and economic dimensions of sustainability. The coefficients of GDP, squared GDP, and cubic GDP are -1.043, 0.123, and -0.0045, respectively, indicating that the relationship between CIWB and economic growth is an inverted-N shape. In other words, as GDP increases from low to high, the CIWB first decreases, then increases for a long period until it reaches a turning point, and declines thereafter. GDP per capita at the two turning points are 850 yuan and 110,000 yuan, respectively. In the long run, economic growth is expected to have a positive effect on sustainable development; however, currently, environmental policies are needed to offset unintended environmental harm. Analysis of spatial evolution and multiplier spatial effects show that spatial disparity and clustering exist simultaneously. This suggests that overall planning for the big picture and cooperative development between adjacent provinces could help to handle regional environmental problems. (C) 2019 Elsevier Ltd. All rights reserved.

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