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Carbon congestion effects in China's industry: Evidence from provincial and sectoral levels

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  • Zhang, Yue-Jun
  • Liu, Jing-Yue
  • Su, Bin

Abstract

Investigating carbon congestion effect can help identify congestion in production, which is of great significance for the rational use of resources and the effective promotion of carbon emissions reduction. Under this circumstance, this study uses the dual model of radial DEA to explore both undesirable/desirable congestion, returns to damage and damages to return during 2005–2015 from both provincial and sectoral levels. Combined with window analysis, the technical efficiency and emissions reduction potential of China's industrial sectors are also discussed. The empirical results show that: (1) China's industrial carbon congestion is obvious and the congestion effect witnesses a trend of regional agglomeration and evident regional and sectoral heterogeneity. In particular, undesirable congestion mainly occurs in the eastern region, and desirable congestion mainly occurs in the western region, followed by the eastern region; both undesirable and desirable congestions mainly occur in some sectors in the Manufacturing and Power-Gas-Water industries. (2) If all sectors produce on the production frontier, the average annual potential carbon emissions reduction would reach 722.82 million tons, with the higher potential in western region and Shanxi province of central region, as well as Manufacturing and six high-energy-consuming sectors. (3) To achieve the “win-win” of industrial development and carbon emissions reduction, China's western region should focus on green technology innovation, while the eastern region and Power-Gas-Water industry should focus on both input resources optimisation and green technology innovation.

Suggested Citation

  • Zhang, Yue-Jun & Liu, Jing-Yue & Su, Bin, 2020. "Carbon congestion effects in China's industry: Evidence from provincial and sectoral levels," Energy Economics, Elsevier, vol. 86(C).
  • Handle: RePEc:eee:eneeco:v:86:y:2020:i:c:s0140988319304323
    DOI: 10.1016/j.eneco.2019.104635
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    as
    1. Sueyoshi, Toshiyuki & Wang, Derek, 2014. "Radial and non-radial approaches for environmental assessment by Data Envelopment Analysis: Corporate sustainability and effective investment for technology innovation," Energy Economics, Elsevier, vol. 45(C), pages 537-551.
    2. Sueyoshi, Toshiyuki & Goto, Mika, 2014. "DEA radial measurement for environmental assessment: A comparative study between Japanese chemical and pharmaceutical firms," Applied Energy, Elsevier, vol. 115(C), pages 502-513.
    3. Halkos, George Emm. & Tzeremes, Nickolaos G., 2009. "Exploring the existence of Kuznets curve in countries' environmental efficiency using DEA window analysis," Ecological Economics, Elsevier, vol. 68(7), pages 2168-2176, May.
    4. 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.
    5. Sueyoshi, Toshiyuki & Yuan, Yan, 2016. "Returns to damage under undesirable congestion and damages to return under desirable congestion measured by DEA environmental assessment with multiplier restriction: Economic and energy planning for s," Energy Economics, Elsevier, vol. 56(C), pages 288-309.
    6. Pedro Simões & Rui Marques, 2011. "Performance and congestion analysis of the portuguese hospital services," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 19(1), pages 39-63, March.
    7. Wu, Jie & An, Qingxian & Xiong, Beibei & Chen, Ya, 2013. "Congestion measurement for regional industries in China: A data envelopment analysis approach with undesirable outputs," Energy Policy, Elsevier, vol. 57(C), pages 7-13.
    8. Tone, Kaoru & Sahoo, Biresh K., 2004. "Degree of scale economies and congestion: A unified DEA approach," European Journal of Operational Research, Elsevier, vol. 158(3), pages 755-772, November.
    9. 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.
    10. Jordaan, Sarah M. & Romo-Rabago, Elizabeth & McLeary, Romaine & Reidy, Luke & Nazari, Jamal & Herremans, Irene M., 2017. "The role of energy technology innovation in reducing greenhouse gas emissions: A case study of Canada," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 1397-1409.
    11. Sueyoshi, Toshiyuki & Wang, Derek, 2018. "DEA environmental assessment on US petroleum industry: Non-radial approach with translation invariance in time horizon," Energy Economics, Elsevier, vol. 72(C), pages 276-289.
    12. Ma, Ding & Fei, Rilong & Yu, Yongsheng, 2019. "How government regulation impacts on energy and CO2 emissions performance in China's mining industry," Resources Policy, Elsevier, vol. 62(C), pages 651-663.
    13. Tone, Kaoru & Sahoo, Biresh K., 2005. "Evaluating cost efficiency and returns to scale in the Life Insurance Corporation of India using data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 39(4), pages 261-285, December.
    14. Xu, Bin & Lin, Boqiang, 2015. "How industrialization and urbanization process impacts on CO2 emissions in China: Evidence from nonparametric additive regression models," Energy Economics, Elsevier, vol. 48(C), pages 188-202.
    15. Cooper, W. W. & Deng, Honghui & Huang, Zhimin M. & Li, Susan X., 2002. "A one-model approach to congestion in data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 36(4), pages 231-238, December.
    16. Wei, Quanling & Yan, Hong, 2004. "Congestion and returns to scale in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 153(3), pages 641-660, March.
    17. Brockett, Patrick L. & Cooper, William W. & Wang, Yuying & Shin, Hong-Chul, 1998. "Inefficiency and congestion in Chinese production before and after the 1978 economic reforms," Socio-Economic Planning Sciences, Elsevier, vol. 32(1), pages 1-20, March.
    18. Patrick Brockett & William Cooper & Honghui Deng & Linda Golden & T. Ruefli, 2004. "Using DEA to Identify and Manage Congestion," Journal of Productivity Analysis, Springer, vol. 22(3), pages 207-226, November.
    19. Sueyoshi, Toshiyuki & Goto, Mika, 2012. "Data envelopment analysis for environmental assessment: Comparison between public and private ownership in petroleum industry," European Journal of Operational Research, Elsevier, vol. 216(3), pages 668-678.
    20. Sueyoshi, Toshiyuki & Wang, Derek, 2014. "Sustainability development for supply chain management in U.S. petroleum industry by DEA environmental assessment," Energy Economics, Elsevier, vol. 46(C), pages 360-374.
    21. Xu, Bin & Lin, Boqiang, 2019. "Can expanding natural gas consumption reduce China's CO2 emissions?," Energy Economics, Elsevier, vol. 81(C), pages 393-407.
    22. Su, Bin & Ang, B.W., 2010. "Input-output analysis of CO2 emissions embodied in trade: The effects of spatial aggregation," Ecological Economics, Elsevier, vol. 70(1), pages 10-18, November.
    23. Cooper, William W. & Seiford, Lawrence M. & Zhu, Joe, 2000. "A unified additive model approach for evaluating inefficiency and congestion with associated measures in DEA," Socio-Economic Planning Sciences, Elsevier, vol. 34(1), pages 1-25, March.
    24. Zhang, Yue-Jun & Da, Ya-Bin, 2015. "The decomposition of energy-related carbon emission and its decoupling with economic growth in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1255-1266.
    25. S. A. Montzka & E. J. Dlugokencky & J. H. Butler, 2011. "Non-CO2 greenhouse gases and climate change," Nature, Nature, vol. 476(7358), pages 43-50, August.
    26. Meng, Fanyi & Su, Bin & Thomson, Elspeth & Zhou, Dequn & Zhou, P., 2016. "Measuring China’s regional energy and carbon emission efficiency with DEA models: A survey," Applied Energy, Elsevier, vol. 183(C), pages 1-21.
    27. Zhifu Mi & Jing Meng & Dabo Guan & Yuli Shan & Malin Song & Yi-Ming Wei & Zhu Liu & Klaus Hubacek, 2017. "Chinese CO2 emission flows have reversed since the global financial crisis," Nature Communications, Nature, vol. 8(1), pages 1-10, December.
    28. Yue-Jun Zhang & Jing-Yue Liu, 2019. "Does carbon emissions trading affect the financial performance of high energy-consuming firms in China?," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 95(1), pages 91-111, January.
    29. Yue-Jun Zhang & Jun-Fang Hao, 2017. "Carbon emission quota allocation among China’s industrial sectors based on the equity and efficiency principles," Annals of Operations Research, Springer, vol. 255(1), pages 117-140, August.
    30. Sueyoshi, Toshiyuki & Goto, Mika, 2016. "Undesirable congestion under natural disposability and desirable congestion under managerial disposability in U.S. electric power industry measured by DEA environmental assessment," Energy Economics, Elsevier, vol. 55(C), pages 173-188.
    31. 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.
    32. Su, Bin & Ang, B.W., 2014. "Input–output analysis of CO2 emissions embodied in trade: A multi-region model for China," Applied Energy, Elsevier, vol. 114(C), pages 377-384.
    33. Sueyoshi, Toshiyuki & Goto, Mika, 2012. "Weak and strong disposability vs. natural and managerial disposability in DEA environmental assessment: Comparison between Japanese electric power industry and manufacturing industries," Energy Economics, Elsevier, vol. 34(3), pages 686-699.
    34. Wu, F. & Fan, L.W. & Zhou, P. & Zhou, D.Q., 2012. "Industrial energy efficiency with CO2 emissions in China: A nonparametric analysis," Energy Policy, Elsevier, vol. 49(C), pages 164-172.
    35. Cooper, W. W. & Gu, Bisheng & Li, Shanling, 2001. "Comparisons and evaluations of alternative approaches to the treatment of congestion in DEA," European Journal of Operational Research, Elsevier, vol. 132(1), pages 62-74, July.
    36. Sueyoshi, Toshiyuki & Goto, Mika & Sugiyama, Manabu, 2013. "DEA window analysis for environmental assessment in a dynamic time shift: Performance assessment of U.S. coal-fired power plants," Energy Economics, Elsevier, vol. 40(C), pages 845-857.
    37. Jiao, Jianling & Jiang, Guili & Yang, Ranran, 2018. "Impact of R&D technology spillovers on carbon emissions between China’s regions," Structural Change and Economic Dynamics, Elsevier, vol. 47(C), pages 35-45.
    38. Mi, Zhifu & Zheng, Jiali & Meng, Jing & Zheng, Heran & Li, Xian & Coffman, D'Maris & Woltjer, Johan & Wang, Shouyang & Guan, Dabo, 2019. "Carbon emissions of cities from a consumption-based perspective," Applied Energy, Elsevier, vol. 235(C), pages 509-518.
    39. Limin Du & Aoife Hanley & Chu Wei, 2015. "Marginal Abatement Costs of Carbon Dioxide Emissions in China: A Parametric Analysis," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 61(2), pages 191-216, June.
    40. Sueyoshi, Toshiyuki & Goto, Mika, 2014. "Environmental assessment for corporate sustainability by resource utilization and technology innovation: DEA radial measurement on Japanese industrial sectors," Energy Economics, Elsevier, vol. 46(C), pages 295-307.
    41. Cooper, W. W. & Seiford, L. M. & Zhu, J., 2001. "Slacks and congestion: response to a comment by R. Fare and S. Grosskopf," Socio-Economic Planning Sciences, Elsevier, vol. 35(3), pages 205-215, September.
    42. Fare, Rolf & Grosskopf, Shawna, 2001. "When can slacks be used to identify congestion? An answer to W.W. Cooper, L. Seiford and J. Zhu," Socio-Economic Planning Sciences, Elsevier, vol. 35(3), pages 217-221, September.
    43. Fang, Lei, 2015. "Congestion measurement in nonparametric analysis under the weakly disposable technology," European Journal of Operational Research, Elsevier, vol. 245(1), pages 203-208.
    44. Zhang, Yue-Jun & Chen, Ming-Ying, 2018. "Evaluating the dynamic performance of energy portfolios: Empirical evidence from the DEA directional distance function," European Journal of Operational Research, Elsevier, vol. 269(1), pages 64-78.
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    More about this item

    Keywords

    Carbon congestion effect; Carbon dioxide emissions; Industry; Radial DEA; China;
    All these keywords.

    JEL classification:

    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • O14 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Industrialization; Manufacturing and Service Industries; Choice of Technology
    • P18 - Political Economy and Comparative Economic Systems - - Capitalist Economies - - - Energy; Environment
    • Q00 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - General
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth

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