Can green finance pilot policies reduce carbon emissions in chinese cities?
SUN Yongbo
LUO Qingyuan
LIANG Jiayuan
Abstract：Based on the panel data of 286 prefecture-level cities in China from 2011 to 2021,this paper takes the pilot policy of green finance reform and innovation as a quasi-natural experiment and employs the double machine learning model to conduct empirical research on the dynamic effects,pathways,and heterogeneity of the policy in curbing the growth rate of urban carbon emission intensity.The main findings are as follows:① The Green Finance Reform and Innovation Pilot Policy significantly curbs the growth rate of urban carbon emission intensity;this result remains robust after a battery of sensitivity checks,including parallel-trend and placebo tests.② The inhibitory effect operates primarily through three transmission channels:inducing green technological innovation,facilitating industrial structure upgrading,and lowering energy consumption intensity.③ Policy impacts exhibit pronounced heterogeneity across resource endowments,industrial bases,and geographic locations;non-resource-oriented cities,cities outside traditional industrial bases,and those in the central and western regions experience larger gains.Building on these findings,the paper puts forward targeted policy optimization recommendations.
Keywords：green finance reform and innovationgrowth rate of carbon emission intensitydouble machine learningpolicy evaluation
Publication Date：2025-10-28
Online Publishing Date：2025-12-02(First online date of this platform, not the publication date of the document)
Pages：9( 112-120 )