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Identifying the determinants and spatial nexus of provincial carbon intensity in China: A dynamic spatial panel approach

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  • Zheng, Xinye
  • Yu, Yihua
  • Wang, Jing
  • Deng, Huihui

Abstract

Is emission intensity of carbon dioxide (CO2) spatially correlated? What determines the CO2 intensity at a provincial level? More importantly, what climate and economic policy decisions should the China’s central and local governments make to reduce the CO2 intensity and prevent the environmental pollution given that China has been the largest emitter of CO2? We aim to address these questions in this study by applying a dynamic spatial system-GMM (generalized method of moment) technique. Our analysis suggests that provinces are influenced by their neighbours. In addition, CO2 intensities are relatively higher in the western and middle areas, and that the spatial agglomeration effect of the provincial CO2 intensity is obvious. Our analysis also shows that CO2 intensity is nonlinearly related to GDP (gross domestic product), positively associated with secondary-sector share and FDI (foreign direct investment), and negatively associated with population size. Important policy implications are drawn on reducing carbon intensity.

Suggested Citation

  • Zheng, Xinye & Yu, Yihua & Wang, Jing & Deng, Huihui, 2013. "Identifying the determinants and spatial nexus of provincial carbon intensity in China: A dynamic spatial panel approach," MPRA Paper 56088, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:56088
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    Cited by:

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    2. Jianhuan Huang & Jiejin Xia, 2016. "Regional Competition, Heterogeneous Factors and Pollution Intensity in China: A Spatial Econometric Analysis," Sustainability, MDPI, vol. 8(2), pages 1-26, February.
    3. Xinye Zheng & Feng Song & Yihua Yu & Shunfeng Song, 2015. "In Search of Fiscal Interactions: A Spatial Analysis of Chinese Provincial Infrastructure Spending," Review of Development Economics, Wiley Blackwell, vol. 19(4), pages 860-876, November.
    4. Zheng, Bo & Zhang, Qiang & Borken-Kleefeld, Jens & Huo, Hong & Guan, Dabo & Klimont, Zbigniew & Peters, Glen P. & He, Kebin, 2015. "How will greenhouse gas emissions from motor vehicles be constrained in China around 2030?," Applied Energy, Elsevier, vol. 156(C), pages 230-240.
    5. Wang, Shaojian & Wang, Jieyu & Zhou, Yuquan, 2018. "Estimating the effects of socioeconomic structure on CO2 emissions in China using an econometric analysis framework," Structural Change and Economic Dynamics, Elsevier, vol. 47(C), pages 18-27.
    6. Hezareh, Reza & Shayanmehr, Samira & Darbandi, Elham & Schieffer, Jack, 2017. "Energy Consumption and Environmental Pollution: Evidence from the Spatial Panel Simultaneous-Equations Model of Developing Countries," 2017 Annual Meeting, February 4-7, 2017, Mobile, Alabama 252818, Southern Agricultural Economics Association.
    7. Atwi, Majed & Barberán, Ramón & Mur, Jesús & Angulo, Ana, 2018. "CO2 Kuznets Curve Revisited: From Cross-Sections to Panel Data Models," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 40, pages 169-196.
    8. Cheng, Zhonghua & Li, Lianshui & Liu, Jun, 2018. "Industrial structure, technical progress and carbon intensity in China's provinces," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2935-2946.

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    More about this item

    Keywords

    Carbon intensity; Environmental Kuznets curve; Dynamic spatial panel;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • O53 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Asia including Middle East
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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