China's environmental policy intensity for 1978-2019.

Area of Science:

• Environmental Policy Analysis
• Computational Social Science
• Chinese Environmental Studies

Background:

• Existing environmental policy research often relies on proxy variables like pollutant emissions or policy counts, which are subject to assumptions and endogeneity.
• Direct measurement of environmental policy intensity has been a significant gap in the field.
• This limitation impacts the accuracy of policy effect evaluations and variable selection in environmental policy research.

Purpose of the Study:

• To develop a robust method for measuring environmental policy intensity in China.
• To create a comprehensive dataset of Chinese environmental policies and their quantified intensity.
• To provide a valuable resource for understanding China's environmental policy system and evaluating its effectiveness.

Main Methods:

• A machine learning algorithm was employed to evaluate policy intensity.
• A comprehensive dataset of Chinese government policies from 1978 to 2019 was collected.
• Policies were categorized into three types based on their attributes.

Main Results:

• The study presents the first comprehensive collection and quantified intensity of Chinese environmental policies from 1978-2019.
• A machine learning approach was successfully applied to measure policy intensity, overcoming limitations of traditional proxy variables.
• Policies were systematically classified, offering deeper insights into China's environmental policy landscape.

Conclusions:

• The developed dataset and methodology provide a more accurate and reliable measure of environmental policy intensity.
• This resource facilitates a better understanding of China's environmental policy evolution and its impacts.
• The findings support further research in environmental policy evaluation and the recommendation of improvement strategies.