Next Article in Journal
Variation in Cropping Intensity in Northern China from 1982 to 2012 Based on GIMMS-NDVI Data
Next Article in Special Issue
The Delimitation of Urban Growth Boundaries Using the CLUE-S Land-Use Change Model: Study on Xinzhuang Town, Changshu City, China
Previous Article in Journal
Thinking about Smart Cities: The Travels of a Policy Idea that Promises a Great Deal, but So Far Has Delivered Modest Results
Previous Article in Special Issue
Exploring Land Use and Land Cover of Geotagged Social-Sensing Images Using Naive Bayes Classifier
Article Menu

Export Article

Open AccessArticle
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

1,2
,
3,4,* and 5,*
1
Department of Urban and Economic Geography, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
2
Lincoln Institute Center for Urban Development and Land Policy, Peking University, Beijing 100871, China
3
Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
4
Department of Business Administration, College of Management, Beijing Union University, Beijing 100101, China
5
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]   |  

Abstract

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
Figures

Figure 1

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sustainability EISSN 2071-1050 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top