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Provincial Carbon Emissions Efficiency and Its Influencing Factors in China

Author

Listed:
  • Shi Wang

    (School of Economics and Finance, Xi’an International Studies University, Xi’an 710128, China
    “One Belt One Road” Economic and Trade Cooperation Innovation Team, Xi’an International Studies University, Xi’an 710128, China)

  • Hua Wang

    (School of Foreign Studies, Xi’an Jiaotong University, Xi’an 710049, China)

  • Li Zhang

    (School of Journalism and New Media, Xi’an Jiaotong University, Xi’an 710049, China)

  • Jun Dang

    (School of Economics and Finance, Xi’an International Studies University, Xi’an 710128, China
    “One Belt One Road” Cross-Border Electronic Commerce Research Institute, Xi’an International Studies University, Xi’an 710128, China)

Abstract

We calculated provincial carbon emissions efficiency and related influencing factors in China with the purpose of providing a reference for other developing countries to develop a green economy. Using panel data covering the period from 2004–2016 from 30 provinces in China, we calculated the carbon emission performance (CEP) and the technology gap ratio of carbon emission (TGR) with the data envelopment analysis (DEA) method and the meta-frontier model separately to analyze provincial carbon emissions efficiency in China. No matter which indicator was employed, we found that distinct differences exist in the eastern, the central, and the western regions of China, and the eastern region has the highest carbon emission performance, followed by the central and the western regions. Then, the panel data Tobit regression model was employed to analyze the influencing factors of carbon emissions efficiency, and we found that scale economy, industrial structure, degree of opening up, foreign direct investment (FDI), energy intensity, government interference, ownership structure, and capital-labor ratio have different impacts on the carbon emission efficiency in different regions of China, which indicates different policies should be implemented in different regions.

Suggested Citation

  • Shi Wang & Hua Wang & Li Zhang & Jun Dang, 2019. "Provincial Carbon Emissions Efficiency and Its Influencing Factors in China," Sustainability, MDPI, vol. 11(8), pages 1-21, April.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:8:p:2355-:d:224406
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    References listed on IDEAS

    as
    1. Su, Bin & Ang, B.W. & Li, Yingzhu, 2017. "Input-output and structural decomposition analysis of Singapore's carbon emissions," Energy Policy, Elsevier, vol. 105(C), pages 484-492.
    2. Wang, Juan & Zhang, Kezhong, 2014. "Convergence of carbon dioxide emissions in different sectors in China," Energy, Elsevier, vol. 65(C), pages 605-611.
    3. Xie, Rui & Hu, Guangxiao & Zhang, Youguo & Liu, Yu, 2017. "Provincial transfers of enabled carbon emissions in China: A supply-side perspective," Energy Policy, Elsevier, vol. 107(C), pages 688-697.
    4. Lenzen, Manfred, 1998. "Primary energy and greenhouse gases embodied in Australian final consumption: an input-output analysis," Energy Policy, Elsevier, vol. 26(6), pages 495-506, May.
    5. Yujiro Hayami, 1969. "Sources of Agricultural Productivity Gap Among Selected Countries," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 51(3), pages 564-575.
    6. Ang, B. W., 1999. "Is the energy intensity a less useful indicator than the carbon factor in the study of climate change?," Energy Policy, Elsevier, vol. 27(15), pages 943-946, December.
    7. George E. Battese & D. S. Prasada Rao, 2002. "Technology Gap, Efficiency, and a Stochastic Metafrontier Function," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 1(2), pages 87-93, August.
    8. Wang, H. & Ang, B.W. & Su, Bin, 2017. "A Multi-region Structural Decomposition Analysis of Global CO2 Emission Intensity," Ecological Economics, Elsevier, vol. 142(C), pages 163-176.
    9. Hu, Jin-Li & Wang, Shih-Chuan, 2006. "Total-factor energy efficiency of regions in China," Energy Policy, Elsevier, vol. 34(17), pages 3206-3217, November.
    10. Li, Fangyi & Song, Zhouying & Liu, Weidong, 2014. "China's energy consumption under the global economic crisis: Decomposition and sectoral analysis," Energy Policy, Elsevier, vol. 64(C), pages 193-202.
    11. Kok, Rixt & Benders, Rene M.J. & Moll, Henri C., 2006. "Measuring the environmental load of household consumption using some methods based on input-output energy analysis: A comparison of methods and a discussion of results," Energy Policy, Elsevier, vol. 34(17), pages 2744-2761, November.
    12. Ryoko Morioka & Keisuke Nansai & Koji Tsuda, 2018. "Role of linkage structures in supply chain for managing greenhouse gas emissions," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 7(1), pages 1-21, December.
    13. Luukkanen, Jyrki & Kaivo-oja, Jari, 2002. "ASEAN tigers and sustainability of energy use--decomposition analysis of energy and CO2 efficiency dynamics," Energy Policy, Elsevier, vol. 30(4), pages 281-292, March.
    14. Wu, Libo & Kaneko, Shinji & Matsuoka, Shunji, 2006. "Dynamics of energy-related CO2 emissions in China during 1980 to 2002: The relative importance of energy supply-side and demand-side effects," Energy Policy, Elsevier, vol. 34(18), pages 3549-3572, December.
    15. Stern, David I. & Jotzo, Frank, 2010. "How ambitious are China and India's emissions intensity targets?," Energy Policy, Elsevier, vol. 38(11), pages 6776-6783, November.
    16. Seiford, Lawrence M. & Zhu, Joe, 2002. "Modeling undesirable factors in efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 142(1), pages 16-20, October.
    17. Dong, Feng & Li, Xiaohui & Long, Ruyin & Liu, Xiaoyan, 2013. "Regional carbon emission performance in China according to a stochastic frontier model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 525-530.
    18. Wang, Yiming & Zhang, Pei & Huang, Dake & Cai, Changda, 2014. "Convergence behavior of carbon dioxide emissions in China," Economic Modelling, Elsevier, vol. 43(C), pages 75-80.
    19. Sun, J. W., 2005. "The decrease of CO2 emission intensity is decarbonization at national and global levels," Energy Policy, Elsevier, vol. 33(8), pages 975-978, May.
    20. Lixiao Zhang & Qiuhong Hu & Fan Zhang, 2014. "Input-Output Modeling for Urban Energy Consumption in Beijing: Dynamics and Comparison," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-11, March.
    21. Mingyue Wang & Yu Liu & Yawen Liu & Shunxiang Yang & Lingyu Yang, 2018. "Assessing Multiple Pathways for Achieving China’s National Emissions Reduction Target," Sustainability, MDPI, vol. 10(7), pages 1-16, June.
    22. Førsund, Finn R. & Kittelsen, Sverre A. C., 1998. "Productivity development of Norwegian electricity distribution utilities," Resource and Energy Economics, Elsevier, vol. 20(3), pages 207-224, September.
    23. Christopher O’Donnell & D. Rao & George Battese, 2008. "Metafrontier frameworks for the study of firm-level efficiencies and technology ratios," Empirical Economics, Springer, vol. 34(2), pages 231-255, March.
    24. Hu, Yi & Yin, Zhifeng & Ma, Jian & Du, Wencui & Liu, Danhe & Sun, Luxi, 2017. "Determinants of GHG emissions for a municipal economy: Structural decomposition analysis of Chongqing," Applied Energy, Elsevier, vol. 196(C), pages 162-169.
    25. Shao, Ling & Li, Yuan & Feng, Kuishuang & Meng, Jing & Shan, Yuli & Guan, Dabo, 2018. "Carbon emission imbalances and the structural paths of Chinese regions," Applied Energy, Elsevier, vol. 215(C), pages 396-404.
    26. Ju, Yiyi & Fujikawa, Kiyoshi, 2019. "Modeling the cost transmission mechanism of the emission trading scheme in China," Applied Energy, Elsevier, vol. 236(C), pages 172-182.
    27. Wang, Xi & Cai, Hua & Florig, H. Keith, 2016. "Energy-saving implications from supply chain improvement: An exploratory study on China's consumer goods retail system," Energy Policy, Elsevier, vol. 95(C), pages 411-420.
    28. Herendeen, Robert & Tanaka, Jerry, 1976. "Energy cost of living," Energy, Elsevier, vol. 1(2), pages 165-178.
    29. Fan, Ying & Liu, Lan-Cui & Wu, Gang & Tsai, Hsien-Tang & Wei, Yi-Ming, 2007. "Changes in carbon intensity in China: Empirical findings from 1980-2003," Ecological Economics, Elsevier, vol. 62(3-4), pages 683-691, May.
    30. Zhang, Ming & Mu, Hailin & Ning, Yadong & Song, Yongchen, 2009. "Decomposition of energy-related CO2 emission over 1991-2006 in China," Ecological Economics, Elsevier, vol. 68(7), pages 2122-2128, May.
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