How renewable energy policies cut greenhouse gas emissions: Insights from advanced data analysis techniques.

Journal: Journal of environmental management
Published Date:

Abstract

Renewable Energy Policies (REPs) have gained prominence at recent UNFCCC COPs as pivotal tools for mitigating greenhouse gas (GHG) emissions. In response, many countries have devised Renewable Energy Strategic Plans (RESPs) and set Renewable Energy Targets (RETs), committing substantial investments to renewable energy. However, comprehensive research assessing the impact of RESPs and RETs on emissions reduction is lacking. Using comprehensive and credible data from 80 countries spanning 1971 to 2020, this paper employs the Multi-period Difference-in-Differences method and Double Machine Learning model and examines the effects of RESPs and RETs on GHG emission reductions. It also tests several controversial hypotheses and conducts mediation tests to explore how RESPs and RETs potentially contributes to emission reductions. Our findings refute the hypothesis that RESPs led to reductions in GHG emissions, while confirming that RETs effectively reduce emissions, significant at the 1 % level. Additionally, mediation tests reveal that RETs achieve emission reductions by decreasing fossil fuel consumption, increasing the proportion of renewable energy in the energy mix, and promoting industrial structure upgrading. The total effects of RETs on GHG emission reductions through the three pathways are -0.223, -0.07, and -0.219, respectively, with significance levels of 1 %, 10 %, and 1 %. The discussion section explores the potential of RESPs to reduce fossil energy consumption and promote industrial upgrading, as indicated by the mediation test results; however, these effects are found to be insufficient for significantly reducing GHG emissions. These findings are crucial for policymakers and stakeholders in shaping policy frameworks to meet GHG emissions mitigation targets and realize many UN Sustainable Development Goals.

Authors

  • Xinyue Gao
    School of Mathematics and Statistics, Lanzhou University, Lanzhou, 730000, PR China.
  • Yujie Ge
    College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, PR China.
  • Jiansheng Qu
    School of Mathematics and Statistics, Lanzhou University, Lanzhou, 730000, PR China; College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, PR China; National Science Library (Chengdu), Chinese Academy of Sciences, Chengdu, 610299, PR China. Electronic address: jsqu@lzb.ac.cn.
  • Jinyu Han
    Department of Radiology, General Hospital of Ningxia Medical University, 804 Shengli Road, Yinchuan, 750004, People's Republic of China.
  • Tek Narayan Maraseni
    Institute for Life Sciences and the Environment, University of Southern Queensland, Toowoomba, 4350, Australia.
  • Chunsen Liu
    School of Mathematics and Statistics, Lanzhou University, Lanzhou, 730000, PR China.
  • Kemin Huang
    National Science Library (Chengdu), Chinese Academy of Sciences, Chengdu, 610299, PR China.
  • Li Xu
    College of Acupuncture and Massage, Shandong University of Traditional Chinese Medicine, Jinan, China.
  • Dai Wang
    College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, PR China.
  • Hengji Li
    Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, PR China.