School of Business, Jinggangshan University, Ji'an, 343009, Jiangxi, China.
School of Traffic Management Engineering, Guangxi Police College, Nanning, 530028, Guangxi, China.
Environ Sci Pollut Res Int. 2023 Jun;30(27):70854-70870. doi: 10.1007/s11356-023-27187-8. Epub 2023 May 9.
Based on panel data from 285 Chinese prefecture-level cities from 2003 to 2020, this paper uses the difference-in-difference (DID) method to investigate the policy effect, mechanism, and heterogeneity of green finance (GF) to reduce environmental pollution. (1) Green finance has significant effect on reducing environmental pollution. The parallel trend test demonstrates that DID test results are valid. (2) Following a battery of robustness tests including instrumental variable, propensity score matching (PSM), variable substitution, and changing time-bandwidth, the conclusions are still valid. (3) Mechanism analysis reveals that green finance can reduce environmental pollution by increasing energy efficiency, adjusting industrial structure, and transforming green consumption. (4) Heterogeneity analysis proves that green finance has a substantial impact on reducing the environmental pollution in eastern and western cities, but not in central China. (5) In the "two-control zone" and "low-carbon pilot cities," the results of applying green finance policies are better, and a policy superposition effect exists. To be able to promote environmental pollution control, and green and sustainable development, this paper provides useful enlightenment for environmental pollution control for China and other similar countries.
基于 2003 年至 2020 年中国 285 个地级市的面板数据,本文采用双重差分(DID)方法,研究绿色金融(GF)减少环境污染的政策效应、机制和异质性。(1)绿色金融对减少环境污染具有显著影响。平行趋势检验表明 DID 检验结果有效。(2)经过一系列稳健性检验,包括工具变量法、倾向得分匹配法(PSM)、变量替换法和改变时间带宽法,结论仍然有效。(3)机制分析表明,绿色金融可以通过提高能源效率、调整产业结构和转变绿色消费来减少环境污染。(4)异质性分析证明,绿色金融对东部和西部城市减少环境污染有显著影响,但对中部地区没有显著影响。(5)在“两控区”和“低碳试点城市”中,应用绿色金融政策的效果更好,存在政策叠加效应。为了能够促进环境污染控制和绿色可持续发展,本文为中国和其他类似国家的环境污染控制提供了有益的启示。