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Measuring energy and environmental performance for regions in China by using DEA-based Malmquist indices

Author

Listed:
  • Jie Wu

    (University of Science and Technology of China)

  • Qingyuan Zhu

    (University of Science and Technology of China)

  • Pengzhen Yin

    (University of Science and Technology of China)

  • Malin Song

    (Anhui University of Finance and Economics)

Abstract

China’s rapid economic development has intensified the country’s many problems in the areas of energy shortage and environmental pollution. However, little research has been done which pays close attention to the evaluation of energy and environmental performance even though such evaluation is considered a crucial method in the fight to save energy, protect the environment, and mitigate global climate change. In this study, we utilize improved data envelopment analysis (DEA) models to evaluate the regional total-factor energy and environmental efficiency of China during the 11th 5-year plan period (2006–2010). The total-factor energy and environmental efficiency is considered using a joint production framework of both non-energy inputs and energy inputs, as well as desirable outputs and undesirable outputs. In addition, the DEA-based Malmquist index is applied to evaluate the dynamic productivity change considering the undesirable outputs and energy inputs. An empirical study is done on 30 of mainland China’s provincial-level regions, showing that most of them have low energy and environmental efficiency. On average, eastern China had the highest energy and environmental efficiency, followed by central China, with the efficiency of western China being the worst. Considering the Malmquist index, most regions’ productivity improved each year of 2006–2010. In addition, most regions had a declining trend in technical efficiency even though most regions had an increasing trend in technical progress.

Suggested Citation

  • Jie Wu & Qingyuan Zhu & Pengzhen Yin & Malin Song, 2017. "Measuring energy and environmental performance for regions in China by using DEA-based Malmquist indices," Operational Research, Springer, vol. 17(3), pages 715-735, October.
    • Handle: RePEc:spr:operea:v:17:y:2017:i:3:d:10.1007_s12351-015-0203-z
    DOI: 10.1007/s12351-015-0203-z
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