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

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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.

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References

  • BP Amoco (2017) BP statistical review. https://www.bp.com/zh_cn/china/reports-and-publications/_bp_2017-_.html

  • 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

    Google Scholar 

  • 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):962

    Article  Google Scholar 

  • 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 20:995–1016

    Article  CAS  Google Scholar 

  • 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 142:79–89

    Article  Google Scholar 

  • 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 65:41–50

    Article  Google Scholar 

  • IPCC (2007) IPCC fourth assessment report. http://www.ipcc.ch/publications_and_data/publications_and_data_reports.htm

  • Jiang JH (2004) Strategic analysis of improving energy efficiency and economic structure. Res Quant Econ Technol Econ 21(10):16–23 (in Chinese)

    Google Scholar 

  • Li K, Lin B (2015) Measuring green productivity growth of Chinese industrial sectors during 1998–2011. China Econ Rev 36:279–295

    Article  Google Scholar 

  • 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):97–107

    Article  Google Scholar 

  • Li M, Wang Q (2014) International environmental efficiency differences and their determinants. Energy 78:411–420

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

  • 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):19859–19873

    Article  Google Scholar 

  • Malmquist S (1953) Index numbers and indifference surfaces. Trabajos de Estadistica 4(2):209–242

    Article  Google Scholar 

  • Meng FY, Fan LW, Zhou P, Zhou DQ (2013) Measuring environmental performance in China’s industrial sectors with non-radial DEA. Math Comput Model 58:1047–1056

    Article  Google Scholar 

  • National Bureau of Statistics of China (NBSC) (2013–2017a) Chinese energy statistics yearbook (CESY). China Statistics, Beijing

  • National Bureau of Statistics of China (NBSC) (2013–2017b). Chinese statistics year book (CSY). China Statistics, Beijing

  • 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 66:290–302

    Article  Google Scholar 

  • 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 104:183–193

    Article  Google Scholar 

  • 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):6172–6179

    Article  Google Scholar 

  • Simar L, Wilson PW (2007) Estimation and inference in two- stage, semi-parametric models of production processes. J Econom 136(1):31–64

    Article  Google Scholar 

  • Sueyoshi T, Goto M (2015) DEA environmental assessment in time horizon: radial approach for Malmquist index measurement on petroleum companies. Energy Econ 51(1):329–345

    Article  Google Scholar 

  • Sueyoshi T, Yuan Y, Goto M (2017) A literature study for DEA applied to energy and environment. Energy Econ 62:104–124

    Article  Google Scholar 

  • 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 73:312–318

    Article  Google Scholar 

  • Tone K (2001) A slacks-based measure of efficiency in data envelopment analysis. Eur J Oper Res 130:498–509

    Article  Google Scholar 

  • Tone K (2002) A slacks-based measure of super-efficiency in data envelopment analysis. Eur J Oper Res 143:32–41

    Article  Google Scholar 

  • Tone K, Sahoo BK (2003) Scale, indivisibilities and production function in data envelopment analysis. Int J Prod Econ 84:165–192

    Article  Google Scholar 

  • 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):187–201

    Article  Google Scholar 

  • Wang ZH, Zeng HL, Wei YM, Zhang YX (2012) Regional total factor energy efficiency: an empirical analysis of industrial sector in China. Appl Energy 97:115–123

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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 78:468–480

    Article  Google Scholar 

  • 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):298–307

    Article  Google Scholar 

  • Yu YT, Huang JH, Zhang N (2018) Industrial eco-efficiency, regional disparity, and spatial convergence of China’s regions. J Clean Prod 204:872–887

    Article  Google Scholar 

  • 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)

    Google Scholar 

  • Zhao X, Rui Y, Qian M (2014) China’s total factor energy efficiency of provincial industrial sectors. Energy 65:52–61

    Article  Google Scholar 

  • Zhou P, Ang BW, Poh KL (2008) A survey of data envelopment analysis in energy and environmental studies. Eur J Oper Res 189(1):1–18

    Article  Google Scholar 

Download references

Acknowledgements

This study is funded by National Natural Science Foundation of China (71373172), the Major Program of Social Science Foundation of Tianjin Municipal Education Commission (2016JWZD04) and the Independent Innovation Fund Project of Tianjin University (2017XSZ-0055). We also appreciate the anonymous reviewers for their valuable comments on an earlier draft of our paper.

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Correspondence to Fang Chen.

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Table 9 Nomenclature

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Chen, F., Zhao, T. & Wang, J. The evaluation of energy–environmental efficiency of China’s industrial sector: based on Super-SBM model. Clean Techn Environ Policy 21, 1397–1414 (2019). https://doi.org/10.1007/s10098-019-01713-0

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