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Analysis of CO 2 Emission Performance and Abatement Potential for Municipal Industrial Sectors in Jiangsu, China

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  • Jie Zhang

    (School of Business, Hohai University, West Focheng Road 8, Nanjing 211100, China
    Collaborative Innovation Center for Coastal Development and Preservation, Xikang Road 1, Nanjing 210098, China)

  • Zhencheng Xing

    (School of Business, Hohai University, West Focheng Road 8, Nanjing 211100, China
    Collaborative Innovation Center for Coastal Development and Preservation, Xikang Road 1, Nanjing 210098, China)

  • Jigan Wang

    (School of Business, Hohai University, West Focheng Road 8, Nanjing 211100, China)

Abstract

As the main source of CO 2 emissions in China, the industrial sector has faced pressure for reducing emissions. To achieve the target of 50% reduction of industrial carbon intensity by 2020 based on the 2005 level, it is urgent to formulate specific CO 2 emission mitigation strategies in the provincial industrial sector. In order to provide decision-making support for the development and implementation of mitigation policy, our undesirable slack based measure (SBM) model is firstly applied to evaluate the industrial CO 2 emission efficiency under total-factor frame (TFICEE) in 13 prefecture-level cities of Jiangsu Province, the largest CO 2 emitter in China. Then, we analyze space-time distribution and distributional evolution tendency of TFICEE by using the GIS visualization method and kernel density estimation, respectively. Finally, we utilize the industrial abatement model to estimate the CO 2 abatement potential of Jiangsu’s industrial sector. The empirical results show that there exists a significant spatial inequality of TFICEE across various regions in Jiangsu, but the regional disparity has been narrowing during our study period. Additionally, average annual industrial CO 2 emission reductions in Jiangsu Province can attain 15,654.00 (ten thousand tons), accounting for 28.2% of its average annual actual emissions, which can be achieved by improving production technology, adjusting industrial structure and raising the level of industry concentration.

Suggested Citation

  • Jie Zhang & Zhencheng Xing & Jigan Wang, 2016. "Analysis of CO 2 Emission Performance and Abatement Potential for Municipal Industrial Sectors in Jiangsu, China," Sustainability, MDPI, vol. 8(7), pages 1-15, July.
  • Handle: RePEc:gam:jsusta:v:8:y:2016:i:7:p:697-:d:74354
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    as
    1. Tol, Richard S.J. & Pacala, Stephen W. & Socolow, Robert H., 2009. "Understanding Long-Term Energy Use and Carbon Dioxide Emissions in the USA," Journal of Policy Modeling, Elsevier, vol. 31(3), pages 425-445, May.
    2. Wei Wang & Hualin Xie & Tong Jiang & Daobei Zhang & Xue Xie, 2016. "Measuring the Total-Factor Carbon Emission Performance of Industrial Land Use in China Based on the Global Directional Distance Function and Non-Radial Luenberger Productivity Index," Sustainability, MDPI, vol. 8(4), pages 1-19, April.
    3. Ouyang, Xiaoling & Lin, Boqiang, 2015. "An analysis of the driving forces of energy-related carbon dioxide emissions in China’s industrial sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 838-849.
    4. Davidsdottir, B. & Fisher, M., 2011. "The odd couple: The relationship between state economic performance and carbon emissions economic intensity," Energy Policy, Elsevier, vol. 39(8), pages 4551-4562, August.
    5. Zhang, Ning & Wang, Bing & Liu, Zhu, 2016. "Carbon emissions dynamics, efficiency gains, and technological innovation in China's industrial sectors," Energy, Elsevier, vol. 99(C), pages 10-19.
    6. Choi, Yongrok & Zhang, Ning & Zhou, P., 2012. "Efficiency and abatement costs of energy-related CO2 emissions in China: A slacks-based efficiency measure," Applied Energy, Elsevier, vol. 98(C), pages 198-208.
    7. Yu, Shiwei & Agbemabiese, Lawrence & Zhang, Junjie, 2016. "Estimating the carbon abatement potential of economic sectors in China," Applied Energy, Elsevier, vol. 165(C), pages 107-118.
    8. Wang, Ke & Wei, Yi-Ming & Zhang, Xian, 2013. "Energy and emissions efficiency patterns of Chinese regions: A multi-directional efficiency analysis," Applied Energy, Elsevier, vol. 104(C), pages 105-116.
    9. Zhang, Ning & Wei, Xiao, 2015. "Dynamic total factor carbon emissions performance changes in the Chinese transportation industry," Applied Energy, Elsevier, vol. 146(C), pages 409-420.
    10. Wang, Ke & Wei, Yi-Ming, 2014. "China’s regional industrial energy efficiency and carbon emissions abatement costs," Applied Energy, Elsevier, vol. 130(C), pages 617-631.
    11. Ren, Shenggang & Fu, Xiang & Chen, XiaoHong, 2012. "Regional variation of energy-related industrial CO2 emissions mitigation in China," China Economic Review, Elsevier, vol. 23(4), pages 1134-1145.
    12. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    13. Yu, Shiwei & Zhang, Junjie & Zheng, Shuhong & Sun, Han, 2015. "Provincial carbon intensity abatement potential estimation in China: A PSO–GA-optimized multi-factor environmental learning curve method," Energy Policy, Elsevier, vol. 77(C), pages 46-55.
    14. Zhou, P. & Ang, B.W. & Wang, H., 2012. "Energy and CO2 emission performance in electricity generation: A non-radial directional distance function approach," European Journal of Operational Research, Elsevier, vol. 221(3), pages 625-635.
    15. Kanada, Momoe & Dong, Liang & Fujita, Tsuyoshi & Fujii, Minoru & Inoue, Tsuyoshi & Hirano, Yujiro & Togawa, Takuya & Geng, Yong, 2013. "Regional disparity and cost-effective SO2 pollution control in China: A case study in 5 mega-cities," Energy Policy, Elsevier, vol. 61(C), pages 1322-1331.
    16. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    17. 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.
    18. Lu, Qinli & Yang, Hong & Huang, Xianjin & Chuai, Xiaowei & Wu, Changyan, 2015. "Multi-sectoral decomposition in decoupling industrial growth from carbon emissions in the developed Jiangsu Province, China," Energy, Elsevier, vol. 82(C), pages 414-425.
    19. Fang Zhang & Hong Fang & Junjie Wu & Damian Ward, 2016. "Environmental Efficiency Analysis of Listed Cement Enterprises in China," Sustainability, MDPI, vol. 8(5), pages 1-19, May.
    20. Zhang, Moyi & Huang, Xian-Jin, 2012. "Effects of industrial restructuring on carbon reduction: An analysis of Jiangsu Province, China," Energy, Elsevier, vol. 44(1), pages 515-526.
    21. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    22. Tong Wang & W. Richard Teague & Seong C. Park & Stan Bevers, 2015. "GHG Mitigation Potential of Different Grazing Strategies in the United States Southern Great Plains," Sustainability, MDPI, vol. 7(10), pages 1-22, September.
    23. Zhou, P. & Ang, B.W. & Han, J.Y., 2010. "Total factor carbon emission performance: A Malmquist index analysis," Energy Economics, Elsevier, vol. 32(1), pages 194-201, January.
    24. Guo, Xiao-Dan & Zhu, Lei & Fan, Ying & Xie, Bai-Chen, 2011. "Evaluation of potential reductions in carbon emissions in Chinese provinces based on environmental DEA," Energy Policy, Elsevier, vol. 39(5), pages 2352-2360, May.
    25. Nan Xiang & Feng Xu & Jinghua Sha, 2013. "Simulation Analysis of China’s Energy and Industrial Structure Adjustment Potential to Achieve a Low-carbon Economy by 2020," Sustainability, MDPI, vol. 5(12), pages 1-19, November.
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