Evaluating China's Central Environmental Inspection Policy Using Machine Learning and Augmented Synthetic Control Methods

54 Pages Posted: 1 Aug 2023

See all articles by Matthew Cole

Matthew Cole

University of Birmingham - Department of Economics

Robert J. R. Elliott

University of Birmingham

Bowen Liu

University of Birmingham

Tuan Vu

Imperial College London

Zongbo Shi

University of Birmingham

Abstract

To overcome key challenges in environmental policy evaluation we use machine learning based weather normalisation techniques to strip out the effect of weather on air pollution estimates. Combined with Augmented Synthetic Control Methods (ASCM) we provide a causal estimate of the impact of China’s decision to centralise environmental policy enforcement. Focusing on Hebei province we find that the recently introduced Central Environmental Inspection Policy led to a short term reduction in PM2.5 and SO2 immediately after the inspection. However, within 3 months of the inspection team leaving, pollution levels had returned to previous levels. Comparisons with Difference-in-Difference estimations show the importance of both weather normalising and using an ASCM approach, particularly in the absence of parallel pre-trends.

Keywords: Air pollution, Central government environmental inspection program, machine learning, synthetic control, China.

Suggested Citation

Cole, Matthew A. and Elliott, Robert J. R. and Liu, Bowen and Vu, Tuan and Shi, Zongbo, Evaluating China's Central Environmental Inspection Policy Using Machine Learning and Augmented Synthetic Control Methods. Available at SSRN: https://ssrn.com/abstract=4527869 or http://dx.doi.org/10.2139/ssrn.4527869

Matthew A. Cole

University of Birmingham - Department of Economics ( email )

United States

Robert J. R. Elliott

University of Birmingham ( email )

Bowen Liu (Contact Author)

University of Birmingham ( email )

Edgbaston, B15 2TT
United Kingdom

Tuan Vu

Imperial College London ( email )

South Kensington Campus
Exhibition Road
London, SW7 2AZ
United Kingdom

Zongbo Shi

University of Birmingham ( email )

Edgbaston, B15 2TT
United Kingdom

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