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The Estimation of Environmental Kuznets Curve in China: Nonparametric Panel Approach

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  • Linna Chen
  • Shiyi Chen

Abstract

In this paper, the panel data of industrial CO $$_2$$ 2 emissions among 31 provincial regions in China from 1985 to 2010 are utilized to analyze the nonlinear relationship between industrial pollution and economic development level based on nonparametric method for testing and verifying the environmental Kuznets hypothesis of carbon dioxide (CKC) in China. The industrial carbon dioxide emissions and GDP per capita are as measures of the industrial pollution and the level of economic development. The nonlinear methods have much more flexibility than linear model because there is no linear hypothesis, which may lead to the problem of model misspecification. Thus, nonlinear methods can obtain more accurate and effective results. Our results show that the nonlinear relationship between the industrial carbon dioxide emissions and the level of economic development has an inverted-U shape, which is the pattern of CKC curves. Moreover, Shanghai, Beijing and Tianjin have crossed the CKC inflection point and are now in developmental stage of environmental pollution reduced since 2005, 2006 and 2008 respectively. However, other areas are still in the earlier stage of environmental pollution, economic growth is accompanied by the tendency towards worsening environmental pollution. Copyright Springer Science+Business Media New York 2015

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  • Linna Chen & Shiyi Chen, 2015. "The Estimation of Environmental Kuznets Curve in China: Nonparametric Panel Approach," Computational Economics, Springer;Society for Computational Economics, vol. 46(3), pages 405-420, October.
  • Handle: RePEc:kap:compec:v:46:y:2015:i:3:p:405-420
    DOI: 10.1007/s10614-015-9486-7
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    3. Zhe Li & Renjin Sun & Manman Qin & Dongou Hu, 2020. "Gasoline to Diesel Consumption Ratio: A New Socioeconomic Indicator of Carbon Dioxide Emissions in China," Sustainability, MDPI, vol. 12(14), pages 1-14, July.
    4. Changfeng Shi & Hang Yuan & Qinghua Pang & Yangyang Zhang, 2020. "Research on the Decoupling of Water Resources Utilization and Agricultural Economic Development in Gansu Province from the Perspective of Water Footprint," IJERPH, MDPI, vol. 17(16), pages 1-16, August.
    5. Liyuan Fu & Qing Wang, 2022. "Spatial and Temporal Distribution and the Driving Factors of Carbon Emissions from Urban Production Energy Consumption," IJERPH, MDPI, vol. 19(19), pages 1-29, September.
    6. Mohd Irfan & Krishnendu Shaw, 2017. "Modeling the effects of energy consumption and urbanization on environmental pollution in South Asian countries: a nonparametric panel approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(1), pages 65-78, January.
    7. D. Jingyuan I. & L. Chong I. & L. Marsiliani & D. Jingyuan I. & L. Chong I. & L. Marsiliani, 2018. "Взаимосвязь между экономическим ростом и окружающей средой в Пекине на основе показателя PM2.5 // The Relationship between Growth and the Environment in Beijing, Using PM2.5 Concentrations," Review of Business and Economics Studies // Review of Business and Economics Studies, Финансовый Университет // Financial University, vol. 6(2), pages 5-18.
    8. Alexandra Soberon & Irene D’Hers, 2020. "The Environmental Kuznets Curve: A Semiparametric Approach with Cross-Sectional Dependence," JRFM, MDPI, vol. 13(11), pages 1-23, November.
    9. Ekpeno L. Effiong & Alex O. Iriabije, 2018. "Let the data speak: semiparametric evidence on the environmental Kuznets curve in Africa," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(2), pages 771-782, March.
    10. Zhao, Jing & Zhao, Ziru & Zhang, Huan, 2021. "The impact of growth, energy and financial development on environmental pollution in China: New evidence from a spatial econometric analysis," Energy Economics, Elsevier, vol. 93(C).
    11. Liangliang Liu & Donghong Ding & Jun He, 2019. "Fiscal Decentralization, Economic Growth, and Haze Pollution Decoupling Effects: A Simple Model and Evidence from China," Computational Economics, Springer;Society for Computational Economics, vol. 54(4), pages 1423-1441, December.

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