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Impact of environmental regulation on green growth in China’s manufacturing industry–based on the Malmquist-Luenberger index and the system GMM model

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Abstract

Green growth in manufacturing is critical to the sustainable development of manufacturing, and environmental regulations can help ensure green growth. The impact of environmental regulations on China’s manufacturing industry sectors is investigated to further green development in manufacturing. Using panel data for manufacturing industry sectors from 2008 to 2015, the Malmquist-Luenberger index model is employed to calculate green growth efficiency and an econometric model is constructed to measure the impact of environmental regulations on green growth. By using the system generalized method of moments (system GMM) model and other panel estimation models to generate regression results, it is found that environmental regulation exhibits a U-shaped nonlinear influence on green growth; as the intensity of environmental regulations increases, there is an initial inhibiting effect followed a positive impact on green growth in the manufacturing industry. Once environmental regulation intensity reaches a certain level, it mainly promotes green growth through technological progress. Further findings include the following: impacts of environmental regulation on green growth are heterogeneous across industries, and effects (e.g. U-shaped impacts) are most significant among high-energy industries, high-pollution industries, and medium-pollution industries.

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Notes

  1. M1–M21 correspond to the following manufacturing industrial sectors (from the China Statistical Yearbook): M1 (Processing of Food from Agricultural Products); M2 (Manufacture of Foods); M3 (Manufacture of Liquor, Beverages and Refined tea); M4 (Manufacture of Tobacco); M5 (Manufacture of Textiles); M6 (Manufacture of Textiles, Apparel and Accessories); M7 (Manufacture of Paper and Paper Products); M8 (Processing of Petroleum, Coking and Processing of Nuclear Fuel); M9 (Manufacture of Raw Chemical Materials and Chemical Products); M10 (Manufacture of Medicines); M11 (Manufacture of Chemical Fibres); M12 (Manufacture of Non-metallic Mineral Products); M13 (Smelting and Pressing of Ferrous Metal); M14 (Smelting and Pressing of Non-ferrous Metal); M15 (Manufacture of Metal Products); M16 (Manufacture of General Purpose Machinery); M17 (Manufacture of Special Purpose Machinery); M18 (Manufacture of Transportation Equipment); M19 (Manufacture of Electrical Machinery and Apparatuses); M20 (Manufacture of Computers and Communications and other Electronic Equipment); M21 (Manufacture of Measuring Instruments and Machinery).

  2. Since the China Environmental Statistics Yearbook does not include three waste discharge data by industry after 2015, to ensure the validity and authenticity of the data panel, we used the 21 manufacturing industry sectors’ data for 2008 to 2015 as our research scope to standardize the statistical calibre of the indicators as much as possible.

  3. In Table 3, the values of AR(1) are greater than 0.05, but less than 0.1. Accordingly, we may reasonably believe that the null hypothesis (i.e. there is no autocorrelation of the error term) is rejected at the significance level 0.10. Consequently, this means that there is a first-order serial correlation in the model. This is in accordance with Mileva (2007), da Silva and Cerqueira (2017) who also obtained AR(1) between 0.05 and 0.1. According to Mileva (2007), the test for AR(2) is more important as it will detect autocorrelation in levels. The results of the AR(2) test are larger than 0.1, and thus there is no second-order serial correlation in the model. In summary, we believe, when AR(1) < 0.1 and AR(2) > 0.1, the requirements of significance level is met and hence we conclude that the first-order sequence is correlated and the second-order sequence is uncorrelated. In addition, we have also noticed that some authors do not exhibit the results of AR(1), and they only require AR(2) to be higher than 0.1 (Berk et al. 2018; Lahouel et al. 2019). We gratefully acknowledge the recommendations given by the anonymous reviewer on this point.

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Funding

This study was funded by the National Natural Science Foundation of China (Grant No. 71973068), Social Science Foundation Major Project of Jiangsu, China (Grant No. 18ZD003), Humanities and Social Sciences Research Planning Foundation of China’s Ministry of Education (Grant No. 19YJA790055), and Social Science Foundation of Jiangsu, China (Grant No. 16EYB012).

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Authors and Affiliations

  1. China Institute of Manufacturing Development & School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing, 210044, China

    Yaru Cao, Jun Liu & Yu Yu

  2. Department of Mathematics & Computer Science, University of North Carolina at Pembroke, Pembroke, NC, 28372, USA

    Guo Wei

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  1. Yaru Cao
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  2. Jun Liu
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  3. Yu Yu
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  4. Guo Wei
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Correspondence to Jun Liu.

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Appendices

Appendix 1

Table 5 Descriptive statistics of the main variable indicators
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Appendix 2

Table 6 Industry classification by energy intensity
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Table 7 Industry classification by pollution intensity
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Cao, Y., Liu, J., Yu, Y. et al. Impact of environmental regulation on green growth in China’s manufacturing industry–based on the Malmquist-Luenberger index and the system GMM model. Environ Sci Pollut Res 27, 41928–41945 (2020). https://doi.org/10.1007/s11356-020-10046-1

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