Can China's low-carbon city pilot policy facilitate carbon neutrality? Evidence from a machine learning approach

Zhenzhen Wang, Feite Zhou and Junhao Zhong

Economic Analysis and Policy, 2024, vol. 84, issue C, 756-773

Abstract: This study examines China's 2060 carbon neutrality goal using the Double/Debiased Machine Learning (DML) model to analyze the causal effects of the low-carbon city pilot (LCCP) policy on urban carbon neutrality in 277 Chinese cities from 2008 to 2017. A series of empirical studies and robustness tests show that the LCCP policy has promoted carbon neutrality in Chinese cities. Through causal mediation analysis, this study identifies potential mechanisms, including increasing green credit, stimulating green innovation, optimizing industrial structures, and strengthening environmental governance. The LCCP policy has a varied impact on cities, depending on their resource endowments, geographical locations, and sizes. Furthermore, this study develops a green development index to assess the causal impact of the LCCP policy on green development, suggesting that it does not hinder the city's economic growth but rather enhances its quality and sustainability. This study provides empirical evidence for regional environmental governance and improvement strategies and offers valuable insights into China's transition from prioritizing rapid economic growth to achieving high-quality development.

Keywords: Low-carbon city pilot policy; Carbon neutrality; Double/Debiased machine learning; Causal effect (search for similar items in EconPapers)
JEL-codes: C14 R11 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecanpo:v:84:y:2024:i:c:p:756-773

DOI: 10.1016/j.eap.2024.09.028

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