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Environmental Performance and Regulation Effect of China’s Atmospheric Pollutant Emissions: Evidence from “Three Regions and Ten Urban Agglomerations”

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Abstract

This paper employs the slack-based measure method and an extended Luenberger productivity indicator to estimate and decompose the atmospheric environmental performance under the constraints of energy and atmospheric pollutant emissions [i.e., the growth of the atmospheric environment total factor productivity (AETFP)] of the “three regions and ten urban agglomerations” (TRTAs) in China. Specifically, undesirable output is considered as both carbon and air pollutant emissions, i.e., CO2, SO2, and NOx emissions. Also, based on the proposed approach, we identify the different paths of the technical change as a crucial driver of the AETFP growth. Furthermore, using the spatial econometric model with a symmetric geographical distance weight matrix and an asymmetric economic geography weight matrix, we investigate the effect of different types of environmental regulation on the AETFP growth to verify the Porter hypothesis in China. The results show that the main drivers of China’s atmospheric environment inefficiency are air pollutant emissions (SO2 and NOx), carbon emissions, and fossil energy use. Spatially, the environment inefficiency presents a decreasing trend from northern China to southern China. The improved performance of SO2 emissions made more contributions to the AETFP growth during China’s 11th “Five-Year Plan” period (2006–2010), while NOx emissions has a marginal positive effect on the AETFP growth is marginal. Despite the differences in the technical change across regions, the technical progress offsets the negative impact of declining technical efficiency on the AETFP growth. Overall, energy-saving and emission-reduction policies and technologies in TRTAs exert a decisive influence on the AETFP growth. In particular, the spatial econometric results indicate that the market-motivated environmental regulation has a positive effect on the AETFP growth and thus conforms to the Porter hypothesis in China but does not cause the “race-to-the-bottom” effect among local governments, while the command-and-control oriented regulation leads to a “race-to-the-bottom” effect and undermines the AETFP growth.

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Notes

  1. In this study, we set \( \left( {g^{x} ,g^{y} ,g^{b} } \right) = \left( {x_{i}^{t} ,y_{i}^{t} ,b_{i}^{t} } \right) \) and arrive at the proportional non-radial directional distance function.

  2. All the results reported in this study are based on the VRS assumption, if not mentioned otherwise.

  3. We attempt to change the weights associated with inputs, desirable output, and undesirable outputs in Eq. (2) to check the robustness of the results. We find that the results and rankings of provincial-level regions do not vary significantly with these alterations. More detailed results are available from the corresponding author upon request.

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Acknowledgements

We acknowledge the financial support from the National Natural Science Foundation of China (Nos. 71403120, 71773075, 71373153, 71690241, 71810107001, 71325006, and 71804071), the Major Research Plan of National Social Science Foundation of China (Nos. 18ZDA051 and 18ZDA052), the National Key Research and Development Program of China (No. 2016YFA0602500), Jiangsu Province (China) Natural Science Foundation (No. BK20151351), Jiangsu Province (China) Fifth “333 Engineering” Research Project (No. BRA2017176), the Project of Philosophy and Social Science Research in Colleges and Universities in Jiangsu Province (China) (No. 2015ZDIXM039), and the Qing Lan Project. We also thank Dr. Jichuan Sheng for his helpful comments for this study.

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Authors and Affiliations

  1. Western China Economic Research Center, Southwestern University of Finance and Economics, Chengdu, 611130, China

    Zhuang Miao

  2. Institute for Urban and Environmental Studies, Chinese Academy of Social Science, Beijing, 100028, China

    Zhuang Miao

  3. Faculty of Economics and Business Administration, Vilnius University, 10222, Vilnius, Lithuania

    Tomas Baležentis

  4. School of Urban and Regional Science, Shanghai University of Finance and Economics, Shanghai, 200433, China

    Zhihua Tian & Shuai Shao

  5. School of Environmental Science and Engineering, Shanghai Jiaotong University, Shanghai, 200240, China

    Yong Geng

  6. School of Business, Nanjing Normal University, Nanjing, 210023, China

    Rui Wu

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Miao, Z., Baležentis, T., Tian, Z. et al. Environmental Performance and Regulation Effect of China’s Atmospheric Pollutant Emissions: Evidence from “Three Regions and Ten Urban Agglomerations”. Environ Resource Econ 74, 211–242 (2019). https://doi.org/10.1007/s10640-018-00315-6

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