IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v104y2021ics0140988321004904.html
   My bibliography  Save this article

Industrial SO2 technical efficiency, reduction potential and technology heterogeneities of China's prefecture-level cities: A multi-hierarchy meta-frontier parametric approach

Author

Listed:
  • Yang, Jun
  • Cheng, Jixin
  • Zou, Ran
  • Geng, Zhifei

Abstract

Excessive emission of industrial SO2 has seriously restricted the sustainable development in China. Therefore, in this paper, considering the heterogeneities of resources endowments, and regional development levels, a multi-hierarchy meta-frontier parametric approach was proposed to evaluate the industrial SO2 technical efficiency (STE) of China's cities from the year of 2003 to 2016, which was further divided into structural, technical and management efficiency. Moreover, the statistical noises in linear programming parameters were taken into account by following the bootstrap approach. Furthermore, the “resource curse” and “regional development imbalance” in China were discussed, and the industrial SO2 reduction potential was estimated according to the sources of inefficiency. The conclusions are drawn as follows: (1) The STE values in most of the cities of China have greatly improved and the cities with efficiency improved more than twice were mainly located in central and western China. Meanwhile, the average STE in the mainland China showed an upward trend, from 0.43 in 2003 to 0.81 in 2016. (2) The average STE and structural efficiency in non-resource cities were greater than those of resource-based cities, which reflected significant production technology heterogeneity between both types of cities. (3) Irrespective of both types of cities, the industrial production technology showed distinct spatial gradient characteristics. Meanwhile, due to the relatively high management efficiency, the industrial input of resources in eastern cities could be more rationally allocated. (4) By optimizing industrial structure, narrowing the technical gaps, and promoting market-oriented reforms and strengthening environmental regulations, China's industry SO2 emissions could have been reduced by about 1800 kt. And the specific SO2 emissions reduction strategies and pathways for the non-resource and resource-based cities were proposed according to the causes of inefficiency.

Suggested Citation

  • Yang, Jun & Cheng, Jixin & Zou, Ran & Geng, Zhifei, 2021. "Industrial SO2 technical efficiency, reduction potential and technology heterogeneities of China's prefecture-level cities: A multi-hierarchy meta-frontier parametric approach," Energy Economics, Elsevier, vol. 104(C).
  • Handle: RePEc:eee:eneeco:v:104:y:2021:i:c:s0140988321004904
    DOI: 10.1016/j.eneco.2021.105626
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988321004904
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.eneco.2021.105626?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Feng, Chao & Huang, Jian-Bai & Wang, Miao, 2018. "Analysis of green total-factor productivity in China's regional metal industry: A meta-frontier approach," Resources Policy, Elsevier, vol. 58(C), pages 219-229.
    2. 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.
    3. Dariush Khezrimotlagh & Yao Chen, 2018. "Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 217-234, Springer.
    4. Rolf Färe & Carlos Martins-Filho & Michael Vardanyan, 2010. "On functional form representation of multi-output production technologies," Journal of Productivity Analysis, Springer, vol. 33(2), pages 81-96, April.
    5. Zhou, X. & Fan, L.W. & Zhou, P., 2015. "Marginal CO2 abatement costs: Findings from alternative shadow price estimates for Shanghai industrial sectors," Energy Policy, Elsevier, vol. 77(C), pages 109-117.
    6. Barbara Casu & Alessandra Ferrari & Tianshu Zhao, 2013. "Regulatory Reform and Productivity Change in Indian Banking," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 1066-1077, July.
    7. Shao, Shuai & Tian, Zhihua & Fan, Meiting, 2018. "Do the rich have stronger willingness to pay for environmental protection? New evidence from a survey in China," World Development, Elsevier, vol. 105(C), pages 83-94.
    8. Sueyoshi, Toshiyuki & Yuan, Yan, 2016. "Marginal Rate of Transformation and Rate of Substitution measured by DEA environmental assessment: Comparison among European and North American nations," Energy Economics, Elsevier, vol. 56(C), pages 270-287.
    9. Rolf Färe & Shawna Grosskopf & Carl A. Pasurka & William L. Weber, 2012. "Substitutability among undesirable outputs," Applied Economics, Taylor & Francis Journals, vol. 44(1), pages 39-47, January.
    10. Wu, Jianxin & Ma, Chunbo & Tang, Kai, 2019. "The static and dynamic heterogeneity and determinants of marginal abatement cost of CO2 emissions in Chinese cities," Energy, Elsevier, vol. 178(C), pages 685-694.
    11. Wei, Xiao & Zhang, Ning, 2020. "The shadow prices of CO2 and SO2 for Chinese Coal-fired Power Plants: A partial frontier approach," Energy Economics, Elsevier, vol. 85(C).
    12. 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.
    13. Yang, Zhenbing & Fan, Meiting & Shao, Shuai & Yang, Lili, 2017. "Does carbon intensity constraint policy improve industrial green production performance in China? A quasi-DID analysis," Energy Economics, Elsevier, vol. 68(C), pages 271-282.
    14. Lin, Boqiang & Du, Kerui, 2013. "Technology gap and China's regional energy efficiency: A parametric metafrontier approach," Energy Economics, Elsevier, vol. 40(C), pages 529-536.
    15. Fare, Rolf, et al, 1989. "Multilateral Productivity Comparisons When Some Outputs Are Undesirable: A Nonparametric Approach," The Review of Economics and Statistics, MIT Press, vol. 71(1), pages 90-98, February.
    16. Ke Wang & Linan Che & Chunbo Ma & Yi-Ming Wei, 2017. "The Shadow Price of CO2 Emissions in China's Iron and Steel Industry," CEEP-BIT Working Papers 105, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    17. Wang, Zhaohua & Feng, Chao, 2015. "A performance evaluation of the energy, environmental, and economic efficiency and productivity in China: An application of global data envelopment analysis," Applied Energy, Elsevier, vol. 147(C), pages 617-626.
    18. Zhou, P. & Ang, B.W. & Poh, K.L., 2008. "Measuring environmental performance under different environmental DEA technologies," Energy Economics, Elsevier, vol. 30(1), pages 1-14, January.
    19. Zeng, Shihong & Jiang, Xue & Su, Bin & Nan, Xin, 2018. "China's SO2 shadow prices and environmental technical efficiency at the province level," International Review of Economics & Finance, Elsevier, vol. 57(C), pages 86-102.
    20. 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.
    21. Park, Hojeong & Lim, Jaekyu, 2009. "Valuation of marginal CO2 abatement options for electric power plants in Korea," Energy Policy, Elsevier, vol. 37(5), pages 1834-1841, May.
    22. Wang, Zhaohua & Feng, Chao & Zhang, Bin, 2014. "An empirical analysis of China's energy efficiency from both static and dynamic perspectives," Energy, Elsevier, vol. 74(C), pages 322-330.
    23. 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.
    24. Zhang, Ning & Jiang, Xue-Feng, 2019. "The effect of environmental policy on Chinese firm's green productivity and shadow price: A metafrontier input distance function approach," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 129-136.
    25. R. G. Chambers & Y. Chung & R. Färe, 1998. "Profit, Directional Distance Functions, and Nerlovian Efficiency," Journal of Optimization Theory and Applications, Springer, vol. 98(2), pages 351-364, August.
    26. Sun, J.W., 1998. "Accounting for energy use in China, 1980–94," Energy, Elsevier, vol. 23(10), pages 835-849.
    27. Du, Limin & Hanley, Aoife & Zhang, Ning, 2016. "Environmental technical efficiency, technology gap and shadow price of coal-fuelled power plants in China: A parametric meta-frontier analysis," Resource and Energy Economics, Elsevier, vol. 43(C), pages 14-32.
    28. Fare, Rolf & Grosskopf, Shawna & Noh, Dong-Woon & Weber, William, 2005. "Characteristics of a polluting technology: theory and practice," Journal of Econometrics, Elsevier, vol. 126(2), pages 469-492, June.
    29. Zhang, Ning & Wang, Bing, 2015. "A deterministic parametric metafrontier Luenberger indicator for measuring environmentally-sensitive productivity growth: A Korean fossil-fuel power case," Energy Economics, Elsevier, vol. 51(C), pages 88-98.
    30. George Battese & D. Rao & Christopher O'Donnell, 2004. "A Metafrontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies," Journal of Productivity Analysis, Springer, vol. 21(1), pages 91-103, January.
    31. Cheng, Zhonghua & Li, Lianshui & Liu, Jun, 2020. "Natural resource abundance, resource industry dependence and economic green growth in China," Resources Policy, Elsevier, vol. 68(C).
    32. Tang, Kai & Yang, Lin & Zhang, Jianwu, 2016. "Estimating the regional total factor efficiency and pollutants’ marginal abatement costs in China: A parametric approach," Applied Energy, Elsevier, vol. 184(C), pages 230-240.
    33. Haider, Salman & Mishra, Prajna Paramita, 2021. "Does innovative capability enhance the energy efficiency of Indian Iron and Steel firms? A Bayesian stochastic frontier analysis," Energy Economics, Elsevier, vol. 95(C).
    34. Yu, Junqing & Zhou, Kaile & Yang, Shanlin, 2019. "Regional heterogeneity of China's energy efficiency in “new normal”: A meta-frontier Super-SBM analysis," Energy Policy, Elsevier, vol. 134(C).
    35. Zhu, Xuehong & Chen, Ying & Feng, Chao, 2018. "Green total factor productivity of China's mining and quarrying industry: A global data envelopment analysis," Resources Policy, Elsevier, vol. 57(C), pages 1-9.
    36. Feng, Chao & Zhang, Hua & Huang, Jian-Bai, 2017. "The approach to realizing the potential of emissions reduction in China: An implication from data envelopment analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 859-872.
    37. Rolf Färe & Shawna Grosskopf, 2000. "Theory and Application of Directional Distance Functions," Journal of Productivity Analysis, Springer, vol. 13(2), pages 93-103, March.
    38. Feng, Chao & Wang, Miao & Liu, Guan-Chun & Huang, Jian-Bai, 2017. "Sources of economic growth in China from 2000–2013 and its further sustainable growth path: A three-hierarchy meta-frontier data envelopment analysis," Economic Modelling, Elsevier, vol. 64(C), pages 334-348.
    39. Miao, Zhuang & Baležentis, Tomas & Shao, Shuai & Chang, Dongfeng, 2019. "Energy use, industrial soot and vehicle exhaust pollution—China's regional air pollution recognition, performance decomposition and governance," Energy Economics, Elsevier, vol. 83(C), pages 501-514.
    40. Ma, Chunbo & Hailu, Atakelty & You, Chaoying, 2019. "A critical review of distance function based economic research on China’s marginal abatement cost of carbon dioxide emissions," Energy Economics, Elsevier, vol. 84(C).
    41. Fare, Rolf & Grosskopf, Shawna & Norris, Mary, 1997. "Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries: Reply," American Economic Review, American Economic Association, vol. 87(5), pages 1040-1043, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yang, Jun & Zou, Ran & Cheng, Jixin & Geng, Zhifei & Li, Qi, 2023. "Environmental technical efficiency and its dynamic evolution in China's industry: A resource endowment perspective," Resources Policy, Elsevier, vol. 82(C).
    2. Wei, Wei & Hu, Haiqing & Chang, Chun-Ping, 2022. "Why the same degree of economic policy uncertainty can produce different outcomes in energy efficiency? New evidence from China," Structural Change and Economic Dynamics, Elsevier, vol. 60(C), pages 467-481.
    3. Hanhua Shao & Jixin Cheng & Yuansheng Wang & Xiaoming Li, 2022. "Can Digital Finance Promote Comprehensive Carbon Emission Performance? Evidence from Chinese Cities," IJERPH, MDPI, vol. 19(16), pages 1-18, August.
    4. Yang Li & An-Chi Liu & Shu-Mei Wang & Yiting Zhan & Jingran Chen & Hsiao-Fen Hsiao, 2022. "A Study of Total-Factor Energy Efficiency for Regional Sustainable Development in China: An Application of Bootstrapped DEA and Clustering Approach," Energies, MDPI, vol. 15(9), pages 1-13, April.
    5. Zhang, Hui & Zhou, Peng & Sun, Xiumei & Ni, Guanqun, 2024. "Disparities in energy efficiency and its determinants in Chinese cities: From the perspective of heterogeneity," Energy, Elsevier, vol. 289(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Ning & Huang, Xuhui & Qi, Chao, 2022. "The effect of environmental regulation on the marginal abatement cost of industrial firms: Evidence from the 11th Five-Year Plan in China," Energy Economics, Elsevier, vol. 112(C).
    2. Yang, Jun & Zou, Ran & Cheng, Jixin & Geng, Zhifei & Li, Qi, 2023. "Environmental technical efficiency and its dynamic evolution in China's industry: A resource endowment perspective," Resources Policy, Elsevier, vol. 82(C).
    3. Feng, Chao & Zhang, Hua & Huang, Jian-Bai, 2017. "The approach to realizing the potential of emissions reduction in China: An implication from data envelopment analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 859-872.
    4. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    5. Nakaishi, Tomoaki, 2021. "Developing effective CO2 and SO2 mitigation strategy based on marginal abatement costs of coal-fired power plants in China," Applied Energy, Elsevier, vol. 294(C).
    6. Zhang, Ning & Huang, Xuhui & Liu, Yunxiao, 2021. "The cost of low-carbon transition for China's coal-fired power plants: A quantile frontier approach," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    7. Dai, Sheng & Zhou, Xun & Kuosmanen, Timo, 2020. "Forward-looking assessment of the GHG abatement cost: Application to China," Energy Economics, Elsevier, vol. 88(C).
    8. Ma, Chunbo & Hailu, Atakelty & You, Chaoying, 2019. "A critical review of distance function based economic research on China’s marginal abatement cost of carbon dioxide emissions," Energy Economics, Elsevier, vol. 84(C).
    9. Du, Limin & Lu, Yunguo & Ma, Chunbo, 2022. "Carbon efficiency and abatement cost of China's coal-fired power plants," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    10. Tang, Kai & Yang, Lin & Zhang, Jianwu, 2016. "Estimating the regional total factor efficiency and pollutants’ marginal abatement costs in China: A parametric approach," Applied Energy, Elsevier, vol. 184(C), pages 230-240.
    11. Jindal, Abhinav & Nilakantan, Rahul & Sinha, Avik, 2024. "CO2 emissions abatement costs and drivers for Indian thermal power industry," Energy Policy, Elsevier, vol. 184(C).
    12. Yongrok Choi & Yunning Ma & Yu Zhao & Hyoungsuk Lee, 2023. "Inequality in Fossil Fuel Power Plants in China: A Perspective of Efficiency and Abatement Cost," Sustainability, MDPI, vol. 15(5), pages 1-15, March.
    13. Rakesh Kumar Jain & Surender Kumar, 2018. "Shadow price of CO2 emissions in Indian thermal power sector," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 20(4), pages 879-902, October.
    14. Zhou, Haibo & Yang, Yi & Chen, Yao & Zhu, Joe, 2018. "Data envelopment analysis application in sustainability: The origins, development and future directions," European Journal of Operational Research, Elsevier, vol. 264(1), pages 1-16.
    15. Du, Limin & Hanley, Aoife & Zhang, Ning, 2016. "Environmental technical efficiency, technology gap and shadow price of coal-fuelled power plants in China: A parametric meta-frontier analysis," Resource and Energy Economics, Elsevier, vol. 43(C), pages 14-32.
    16. Wang, Zhaohua & Song, Yanwu & Shen, Zhiyang, 2022. "Global sustainability of carbon shadow pricing: The distance between observed and optimal abatement costs," Energy Economics, Elsevier, vol. 110(C).
    17. Ke Wang & Linan Che & Chunbo Ma & Yi-Ming Wei, 2017. "The Shadow Price of CO2 Emissions in China's Iron and Steel Industry," CEEP-BIT Working Papers 105, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    18. Zhao, Yu & Zhong, Honglin & Kong, Fanbin & Zhang, Ning, 2023. "Can China achieve carbon neutrality without power shortage? A substitutability perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    19. Wei, Xiao & Zhang, Ning, 2020. "The shadow prices of CO2 and SO2 for Chinese Coal-fired Power Plants: A partial frontier approach," Energy Economics, Elsevier, vol. 85(C).
    20. Kumar, Surender & Jain, Rakesh Kumar, 2019. "Carbon-sensitive meta-productivity growth and technological gap: An empirical analysis of Indian thermal power sector," Energy Economics, Elsevier, vol. 81(C), pages 104-116.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:eneeco:v:104:y:2021:i:c:s0140988321004904. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.