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Sustainability 2016, 8(11), 1124; doi:10.3390/su8111124

Using Machine Learning in Environmental Tax Reform Assessment for Sustainable Development: A Case Study of Hubei Province, China

3,4,* and 5,*
Department of Urban and Economic Geography, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
Lincoln Institute Center for Urban Development and Land Policy, Peking University, Beijing 100871, China
Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Department of Business Administration, College of Management, Beijing Union University, Beijing 100101, China
Department of Geography, Kent State University, Kent, OH 44242, USA
Authors to whom correspondence should be addressed.
Academic Editor: Marc A. Rosen
Received: 26 September 2016 / Revised: 28 October 2016 / Accepted: 29 October 2016 / Published: 1 November 2016
(This article belongs to the Special Issue Sustainable Ecosystems and Society in the Context of Big and New Data)
View Full-Text   |   Download PDF [2387 KB, uploaded 14 November 2016]   |  


During the past 30 year of economic growth, China has also accumulated a huge environmental pollution debt. China’s government attempts to use a variety of means, including tax instruments to control environmental pollution. After nine years of repeated debates, the State Council Legislative Affairs Office released the Environmental Protection Tax Law (Draft) in June 2015. As China’s first environmental tax law, whether this conservative “Environmental Fee to Tax (EFT)” reform could improve the environment has generated controversy. In this paper, we seek insights to this controversial issue using the machine learning approach, a powerful tool for environmental policy assessment. We take Hubei Province, the first pilot area as a case of EFT, and analyze the institutional incentive, behavior transformation and emission intensity reduction performance. Twelve pilot cities located in Hubei Province were selected to estimate the effect of the reform by using synthetic control and a rapid developing machine learning method for policy evaluation. We find that the EFT reform can promote emission intensity reduction. Especially, relative to comparable synthetic cities in the absence of the reform, the average annual emission intensity of Sulfur Dioxide (SO2) in the pilot cities dropped by 0.13 ton/million Yuan with a reduction rate of 10%–32%. Our findings also show that the impact of environmental tax reform varies across cities due to the administrative level and economic development. The results of our study are also supported by enterprise interviews. The EFT improves the overall environmental costs, and encourages enterprises to reduce emissions pollution. These results provide valuable experience and policy implications for the implementation of China’s Environmental Protection Tax Law. View Full-Text
Keywords: environmental fee to tax reform; China; synthetic control method; sulfur dioxide (SO2) emissions; machine learning environmental fee to tax reform; China; synthetic control method; sulfur dioxide (SO2) emissions; machine learning

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zheng, Y.; Zheng, H.; Ye, X. Using Machine Learning in Environmental Tax Reform Assessment for Sustainable Development: A Case Study of Hubei Province, China. Sustainability 2016, 8, 1124.

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