Empirical analysis of the impact of China’s carbon emissions trading policy using provincial-level data

被引:1
|
作者
Jiang X. [1 ]
Xu W. [1 ]
Du L. [2 ]
机构
[1] School of Business, Anhui University of Technology, Ma’anshan
[2] School of Economics and Management, Nanchang University, Nanchang
基金
中国国家社会科学基金;
关键词
Carbon emissions; Difference-in-difference; Market-oriented mechanism; Mediating effect;
D O I
10.1186/s42162-024-00346-y
中图分类号
学科分类号
摘要
Investigating the impact of carbon emissions trading policy and elucidating the underlying mechanisms are crucial for enhancing policy effectiveness and refining related systems. This study examines the impact of carbon emissions trading policy by constructing a difference-in-difference model utilizing unbalanced panel data from China’s provinces spanning the period from 2005 to 2019. Additionally, a mediating effect model is employed to delve into the underlying mechanisms. The key findings are as follows: Firstly, the implementation of carbon emissions trading policy has a notable inhibitory impact on carbon emissions. Secondly, both the upgrading of industrial structure and the reduction of energy intensity play mediating roles in carbon emissions reduction. However, the development of clean energy industries does not exhibit a significant mediating effect. In conclusion, this study offers policy recommendations aimed at facilitating carbon reduction. These include enhancing the market-based trading mechanism for carbon emissions, optimizing and upgrading industrial structures, fostering innovation in green and low-carbon technologies, and promoting the development and utilization of clean energy. © The Author(s) 2024.
引用
收藏
相关论文
共 50 条
  • [41] Spatial spillover effect of carbon emission trading policy on carbon emission reduction: Empirical data from transport industry in China*
    Li, Sujuan
    Liu, Jiaguo
    Wu, Juanjuan
    Hu, Xiyuan
    JOURNAL OF CLEANER PRODUCTION, 2022, 371
  • [42] The role of digital finance in reducing agricultural carbon emissions: evidence from China’s provincial panel data
    Jianxin Chang
    Environmental Science and Pollution Research, 2022, 29 : 87730 - 87745
  • [43] The role of digital finance in reducing agricultural carbon emissions: evidence from China's provincial panel data
    Chang, Jianxin
    ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2022, 29 (58) : 87730 - 87745
  • [44] The mediating effect of financial development on CO2 emissions: An empirical study based on provincial panel data in China
    Xiong, Feng
    Zhang, Rui
    Mo, Huidong
    SCIENCE OF THE TOTAL ENVIRONMENT, 2023, 896
  • [45] Carbon emissions, logistics volume and GDP in China: empirical analysis based on panel data model
    Guo, Xiaopeng
    Ren, Dongfang
    Shi, Jiaxing
    ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2016, 23 (24) : 24758 - 24767
  • [46] Carbon emissions, logistics volume and GDP in China: empirical analysis based on panel data model
    Xiaopeng Guo
    Dongfang Ren
    Jiaxing Shi
    Environmental Science and Pollution Research, 2016, 23 : 24758 - 24767
  • [47] Decoupling and decomposition analysis of residential building carbon emissions from residential income: Evidence from the provincial level in China
    Huo, Tengfei
    Ma, Yuling
    Yu, Tao
    Cai, Weiguang
    Liu, Bingsheng
    Ren, Hong
    ENVIRONMENTAL IMPACT ASSESSMENT REVIEW, 2021, 86
  • [48] Assessment of the co-benefits of China's carbon trading policy on carbon emissions reduction and air pollution control in multiple sectors
    Xian, Botong
    Wang, Yanan
    Xu, Yalin
    Wang, Juan
    Li, Xiaoyan
    ECONOMIC ANALYSIS AND POLICY, 2024, 81 : 1322 - 1335
  • [49] The Goal of Carbon Peaking, Carbon Emissions, and the Economic Effects of China's Energy Planning Policy: Analysis Using a CGE Model
    Hu, Haisheng
    Dong, Wanhao
    INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2023, 20 (01)
  • [50] Provincial cultivated land use efficiency in China: Empirical analysis based on the SBM-DEA model with carbon emissions considered
    Kuang, Bing
    Lu, Xinhai
    Zhou, Min
    Chen, Danling
    TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2020, 151