Measuring the Environmental Efficiency and Technology Gap of PM2.5 in China's Ten City Groups: An Empirical Analysis Using the EBM Meta-Frontier Model
Measuring the Environmental Efficiency and Technology Gap of PM2.5 in China's Ten City Groups: An Empirical Analysis Using the EBM Meta-Frontier Model
复制标题
衡量中国十个城市群 PM2.5 的环境效率和技术差距：使用 EBM 元前沿模型的实证分析
DOI：
10.3390/ijerph16040675
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发表时间：
2019-02-02
影响因子：
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通讯作者：
Zhang, Yun
中科院分区：
文献类型：
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作者：
Cheng, Shixiong;Xie, Jiahui;Zhang, Yun
由于空气污染是阻碍中国经济发展的重要因素，中国通过了一系列法案来控制空气污染。然而，我们对空气污染特别是PM2.5（直径小于2.5 m的细颗粒物）污染的环境效率状况还缺乏了解。利用2004年至2016年中国十大城市群的面板数据，我们首先通过基于epsilon的测度（EBM）元前沿模型估算了PM2.5的环境效率。结果表明，城市及城市群之间PM2.5环境效率存在较大差异。环境效率最高的城市是经济最发达的城市，环境效率最高的城市群主要是东部城市群。然后，我们使用元前沿Malmquist EBM模型来衡量每个城市组的元前沿Malmquist全要素生产率指数（MMPI）。结果表明，总体来看，考虑或不考虑外界因素影响时，我国环境全要素生产率分别下降了3.68%和3.49%。最后，我们将MMPI分解为四个指标，即效率变化（EC）指数、最佳实践差距变化（BPC）指数、纯技术追赶（PTCU）指数和前沿追赶（FCU）指数。我们发现MMPI的走势与BPC和PTCU指数的走势一致，这表明BPC和PTCU指数的创新效应是生产率增长的主要驱动力。 EC和FCU效应是阻碍生产率增长的主要力量。
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 PM2.5 (diameter of fine particulate matter less than 2.5 m) pollution. Using panel data on ten major Chinese city groups from 2004 to 2016, we first estimate the environmental efficiency of PM2.5 by epsilon-based measure (EBM) meta-frontier model. The results show that there are large differences in PM2.5 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.