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Measuring the Environmental Efficiency and Technology Gap of PM 2.5 in China’s Ten City Groups: An Empirical Analysis Using the EBM Meta-Frontier Model

Author

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  • Shixiong Cheng

    (School of Business, Hubei University, Wuhan 430062, China
    Institute for Open Economy Research Centre, Hubei University, Wuhan 430062, China)

  • Jiahui Xie

    (School of Business, Hubei University, Wuhan 430062, China
    Institute for Open Economy Research Centre, Hubei University, Wuhan 430062, China)

  • De Xiao

    (School of Business, Hubei University, Wuhan 430062, China
    Institute for Open Economy Research Centre, Hubei University, Wuhan 430062, China)

  • Yun Zhang

    (School of Finance, Shanghai Lixin University of Accounting and Finance, Shanghi 201620, China)

Abstract

Since air pollution is an important factor hindering China’s economic development, China has passed a series of bills to control air pollution. However, we still lack an understanding of the status of environmental efficiency in regard to air pollution, especially PM 2.5 (diameter of fine particulate matter less than 2.5 μm) pollution. Using panel data on ten major Chinese city groups from 2004 to 2016, we first estimate the environmental efficiency of PM 2.5 by epsilon-based measure (EBM) meta-frontier model. The results show that there are large differences in PM 2.5 environmental efficiency between cities and city groups. The cities with the highest environmental efficiency are the most economically developed cities and the city group with the highest environmental efficiency is mainly the eastern city group. Then, we use the meta-frontier Malmquist EBM model to measure the meta-frontier Malmquist total factor productivity index (MMPI) in each city group. The results indicate that, overall, China’s environmental total factor productivity declined by 3.68% and 3.49% when considering or not the influence of outside sources, respectively. Finally, we decompose the MMPI into four indexes, namely, the efficiency change (EC) index, the best practice gap change (BPC) index, the pure technological catch-up (PTCU) index, and the frontier catch-up (FCU) index. We find that the trend of the MMPI is consistent with those of the BPC and PTCU indexes, which indicates that the innovation effect of the BPC and PTCU indexes are the main driving forces for productivity growth. The EC and FCU effect are the main forces hindering productivity growth.

Suggested Citation

  • Shixiong Cheng & Jiahui Xie & De Xiao & Yun Zhang, 2019. "Measuring the Environmental Efficiency and Technology Gap of PM 2.5 in China’s Ten City Groups: An Empirical Analysis Using the EBM Meta-Frontier Model," IJERPH, MDPI, vol. 16(4), pages 1-22, February.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:4:p:675-:d:208928
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