摘要
碳排放权交易政策对绿色金融发展的影响受到学者的广泛关注,以碳排放权交易政策的实施作为准自然实验,通过熵值法得出2010—2021年274个地级市的绿色金融发展指数,利用多期双重差分模型,探究碳排放权交易政策对绿色金融发展的影响效应,拓展了碳交易政策与绿色金融发展的研究。结果显示,碳排放权交易政策对于各地区绿色金融的发展有着明显促进作用,经过一系列稳健性检验,该结果依然成立;政府支持与科技创新水平均发挥了积极的调节作用;异质性分析表明,碳排放权交易政策对绿色金融发展的促进作用在非老工业基地、对外开放水平高、金融发展程度高的地区更加明显,并发现碳排放权交易政策具有时期异质性和队列异质性。研究结果可为强化碳排放权交易政策、减少碳排放和发展健全绿色金融体系提供有价值的参考。
The impact of carbon trading policy on the development of green finance has been widely concerned by scholars.This paper takes the implementation of carbon trading policy as a quasi-natural experiment,the Green finance Development Index of 274 prefecture-level cities from 2010 to 2021 was obtained by entropy method,and the effect of carbon emission trading policy on the development of green finance was studied by using multi-period double difference model,the research on carbon trading policy and the development of green finance has been expanded.The results show that the carbon emissions trading policy has a significant role in promoting the development of green finance in various regions The effective intervention of the government and the promotion of the level of scientific and technological innovation have both played a positive regulatory role;The role of carbon emissions trading policy in promoting the development of green finance is more obvious in regions with a high level of openness to the outside world and a high degree of financial development,it is found that the effects of carbon emission trading policies are heterogeneous in different periods and in different cohorts.The results can provide valuable reference for strengthening carbon emissions trading policy,reducing carbon emissions and developing a sound green financial system.
作者
李陶然
朱智强
LI Taoran;ZHU Zhiqiang(School of Finance,Shandong Technology and Business University,Yantai 264005,China)
出处
《煤炭经济研究》
2025年第7期87-96,共10页
Coal Economic Research
基金
山东省社会科学规划研究一般项目(23CJJJ23)。
关键词
碳排放权交易
绿色金融
双重差分模型
时期异质性
队列异质性
carbon emission trading
green finance
difference-in-difference model
heterogeneous in different periods
heterogeneous in different cohorts
