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Evaluating the Intervention Effect of China’s Emissions Trading Policy: Evidence from Analyzing High-Frequency Dynamic Trading Data via Double Machine Learning

Author

Listed:
  • Peng Xu

    (College of Economics and Management, Northeast Forestry University, Harbin 150040, China)

  • Jingye Li

    (School of Future Technology, Northeast Forestry University, Harbin 150040, China)

  • Yukun Cao

    (College of Economics and Management, Northeast Forestry University, Harbin 150040, China)

Abstract

China launched a nationwide unified Emissions Trading System (ETS) in 2021 and issued the Interim Regulations on the Administration of Carbon Emissions Trading in 2024 to regulate trading activities. To examine the effectiveness of China’s ETS policies, this study collected dynamic high-frequency data from 915 trading days, spanning from 16 July 2021 to 29 April 2025, and constructed a policy evaluation model based on the double machine learning framework. The findings indicate that China’s ETS policies have significantly increased the total trading volume of carbon emission allowances. This result has passed a series of robustness tests. Mechanism tests show that China’s ETS trading policies have significantly increased the transaction price and trading volume of carbon emission allowances. Compared with ETS in other countries and regions, China’s ETS policies are characterized by effectiveness, stability, and incrementalism, which can promote the orderly and efficient operation of the carbon emissions trading market. By refining the time span of the data and introducing cutting-edge causal inference methods, this study summarizes the successful experiences in the supervision and development of China’s carbon emissions trading market, providing precise evidence and feasible insights for global climate action and the development of ETS in developing countries.

Suggested Citation

  • Peng Xu & Jingye Li & Yukun Cao, 2025. "Evaluating the Intervention Effect of China’s Emissions Trading Policy: Evidence from Analyzing High-Frequency Dynamic Trading Data via Double Machine Learning," Sustainability, MDPI, vol. 17(18), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:18:p:8361-:d:1752017
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    References listed on IDEAS

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