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Erschienen in: Clean Technologies and Environmental Policy 7/2019

08.06.2019 | Original Paper

The evaluation of energy–environmental efficiency of China’s industrial sector: based on Super-SBM model

verfasst von: Fang Chen, Tao Zhao, Juan Wang

Erschienen in: Clean Technologies and Environmental Policy | Ausgabe 7/2019

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Abstract

With the process of urbanization and industrialization, a growing attention has been paid to the energy–environmental efficiency in China’s industrial sector. Such researches mainly focused on calculating the efficiency and exploring its driving factors at sectoral, provincial and regional perspectives. In this paper, we proposed to evaluate the energy–environmental efficiency of China’ industrial sector and its driving factors at a dynamic change perspective. Combining super-slack-based measure (Super-SBM) model and Malmquist index, this paper calculated the energy–environmental efficiency of China’s 30 provinces (municipalities and autonomous regions) from 2012 to 2016 and captured its dynamic change. Then we aggregated China’s 30 provinces into three groups based on their relevant dynamic change values, namely high-growth, mid-growth and low-growth groups. Finally, we verified the impact of investment in pollution control on industrial energy–environmental efficiency in different groups. The results showed that: Beijing, Inner Mongolia, Guangdong, Hunan, Tianjin and Shaanxi performed efficiently during the whole study period. Most provinces have improved their energy–environmental efficiency from 2012 to 2016. The decomposition results indicated that the technology change was responsible for the growth of energy–environmental efficiency. Economic development level positively and significantly influenced the energy–environmental efficiency of the industrial sector as a whole and three groups, while pollution control investment had a significantly negative effect on energy–environmental efficiency of high-growth and low-growth groups. This study concluded that all the provinces should pay attention to technology progress and sustainable pollution control investment for improving the energy–environmental efficiency in the long term. Additionally, differentiated strategies to improve energy–environmental efficiency for different provinces and groups should be implemented.

Graphic abstract

https://static-content.springer.com/image/art%3A10.1007%2Fs10098-019-01713-0/MediaObjects/10098_2019_1713_Figa_HTML.png

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Literatur
  1. BP Amoco (2017) BP statistical review. https://​www.​bp.​com/​zh_​cn/​china/​reports-and-publications/​_​bp_​2017-_​.​html
  2. Färe R, Grosskopf S, Lindgren B, Roos P (1994) Productivity developments in Swedish hospitals: a Malmquist output index approach. In: Charnes A, Cooper WW, Lewin AY, Seiford LM (eds) Data envelopment analysis: theory, methodology, and applications. Springer, Dordrecht, pp 227–235
  3. Gao GL, Zeng XT, An CJ, Yu L (2018) A sustainable industry-environment model for the identification of urban environmental risk to confront air pollution in Beijing, China. Sustainability 10(4):962View Article
  4. Hasanuzzaman, Chandan B, Varnita S (2018) Environmental capability: a Bradley–Terry model-based approach to examine the driving factors for sustainable coal-mining environment. Clean Technol Environ 20:995–1016View Article
  5. He Y, Liao N, Zhou Y (2018) Analysis on provincial industrial energy efficiency and its influencing factors in China based on DEA-RS-FANN. Energy 142:79–89View Article
  6. Huang J, Du D, Hao Y (2017) The driving forces of the change in China’s energy intensity: an empirical research using DEA-Malmquist and spatial panel estimations. Econ Model 65:41–50View Article
  7. IPCC (2007) IPCC fourth assessment report. http://​www.​ipcc.​ch/​publications_​and_​data/​publications_​and_​data_​reports.​htm
  8. Jiang JH (2004) Strategic analysis of improving energy efficiency and economic structure. Res Quant Econ Technol Econ 21(10):16–23 (in Chinese)
  9. Li K, Lin B (2015) Measuring green productivity growth of Chinese industrial sectors during 1998–2011. China Econ Rev 36:279–295View Article
  10. Li H, Shi J (2014) Energy efficiency analysis on Chinese industrial sectors: an improved Super-SBM model with undesirable outputs. J Clean Prod 65(4):97–107View Article
  11. Li M, Wang Q (2014) International environmental efficiency differences and their determinants. Energy 78:411–420View Article
  12. Li YJ, Shi X, Emrouznejad A, Liang L (2017) Environmental performance evaluation of Chinese industrial systems: a network SBM approach. J Oper Res Soc. https://​doi.​org/​10.​1057/​s41274-017-0257-9 View Article
  13. Liu X, Jie X (2017) A Malmquist index-based dynamic industrial green efficiency evaluation in Sichuan province. In: International conference on management science and engineering management. Springer, Cham, pp 1361–1373
  14. Ma X, Liu Y, Wei X, Li Y, Zheng M, Li Y, Cheng C, Wu Y, Liu Z, Yu Y (2017) Measurement and decomposition of energy efficiency of Northeast China—based on super efficiency DEA model and Malmquist index. Environ Sci Pollut Res 24(24):19859–19873View Article
  15. Malmquist S (1953) Index numbers and indifference surfaces. Trabajos de Estadistica 4(2):209–242View Article
  16. Meng FY, Fan LW, Zhou P, Zhou DQ (2013) Measuring environmental performance in China’s industrial sectors with non-radial DEA. Math Comput Model 58:1047–1056View Article
  17. National Bureau of Statistics of China (NBSC) (2013–2017a) Chinese energy statistics yearbook (CESY). China Statistics, Beijing
  18. National Bureau of Statistics of China (NBSC) (2013–2017b). Chinese statistics year book (CSY). China Statistics, Beijing
  19. Pérez K, González-Araya Marcela C, Iriarte A (2017) Energy and GHG emission efficiency in the Chilean manufacturing industry: sectoral and regional analysis by DEA and Malmquist indexes. Energy Econ 66:290–302View Article
  20. Sanz-Díaz MT, Velasco-Morente F, Yñiguez R, Díaz-Calleja E (2017) An analysis of Spain’s global and environmental efficiency from a European union perspective. Energy Policy 104:183–193View Article
  21. Shi GM, Bi J, Wang JN (2010) Chinese regional industrial energy efficiency evaluation based on a DEA model of fixing non-energy inputs. Energy Policy 38(10):6172–6179View Article
  22. Simar L, Wilson PW (2007) Estimation and inference in two- stage, semi-parametric models of production processes. J Econom 136(1):31–64View Article
  23. Sueyoshi T, Goto M (2015) DEA environmental assessment in time horizon: radial approach for Malmquist index measurement on petroleum companies. Energy Econ 51(1):329–345View Article
  24. Sueyoshi T, Yuan Y, Goto M (2017) A literature study for DEA applied to energy and environment. Energy Econ 62:104–124View Article
  25. Tang D, Tang J, Xiao Z, Ma T, Bethel BJ (2017) Environmental regulation efficiency and total factor productivity—effect analysis based on Chinese data from 2003 to 2013. Ecol Indic 73:312–318View Article
  26. Tone K (2001) A slacks-based measure of efficiency in data envelopment analysis. Eur J Oper Res 130:498–509View Article
  27. Tone K (2002) A slacks-based measure of super-efficiency in data envelopment analysis. Eur J Oper Res 143:32–41View Article
  28. Tone K, Sahoo BK (2003) Scale, indivisibilities and production function in data envelopment analysis. Int J Prod Econ 84:165–192View Article
  29. Wang J, Zhao T (2017) Regional energy–environmental performance and investment strategy for China’s non-ferrous metals industry: a non-radial DEA based analysis. J Clean Prod 163(2017):187–201View Article
  30. Wang ZH, Zeng HL, Wei YM, Zhang YX (2012) Regional total factor energy efficiency: an empirical analysis of industrial sector in China. Appl Energy 97:115–123View Article
  31. Wang J, Zhao T, Zhang X (2016) Environmental assessment and investment strategies of provincial industrial sector in China—analysis based on DEA model. EIA Rev 60:156–168
  32. Wu J, Li MJ, Zhu QY, Zhou ZX, Liang L (2019) Energy and environmental efficiency measurement of China’s industrial sectors: a DEA model with non-homogeneous inputs and outputs. Energy Econ 78:468–480View Article
  33. Xu X, Zhao T, Liu N, Kang J (2014) Changes of energy–related GHG emissions in China: an empirical analysis from sectoral perspective. Appl Energy 132(11):298–307View Article
  34. Yu YT, Huang JH, Zhang N (2018) Industrial eco-efficiency, regional disparity, and spatial convergence of China’s regions. J Clean Prod 204:872–887View Article
  35. Zhan GH, Chen GG (2013) Empirical analysis of the impact of China’s technological progress on energy efficiency. Stat Decis Mak 1:150–153 (in Chinese)
  36. Zhao X, Rui Y, Qian M (2014) China’s total factor energy efficiency of provincial industrial sectors. Energy 65:52–61View Article
  37. Zhou P, Ang BW, Poh KL (2008) A survey of data envelopment analysis in energy and environmental studies. Eur J Oper Res 189(1):1–18View Article
Metadaten
Titel
The evaluation of energy–environmental efficiency of China’s industrial sector: based on Super-SBM model
verfasst von
Fang Chen
Tao Zhao
Juan Wang
Publikationsdatum
08.06.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
Clean Technologies and Environmental Policy / Ausgabe 7/2019
Print ISSN: 1618-954X
Elektronische ISSN: 1618-9558
DOI
https://doi.org/10.1007/s10098-019-01713-0

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