An analytical link of disaggregated green energy sources in achieving carbon neutrality in China: A policy based novel wavelet local multiple correlation analysis

Abstract

As China, the world's largest emerging economy, pursues its objective of achieving net-zero emissions by 2060, the role of reliable and green energy sources, such as biomass, geothermal, and hydro, in facilitating the country's decarbonization efforts assumes utmost significance. In this study, we employ the wavelet local multiple correlation (WLMC) methodology to explore the dynamic interactions between these variables in a multivariate setting, spanning the period of 1990Q1 to 2020Q4. The WLCM approach offers improved signal resolution in the time-frequency domain, allowing for more accurate analysis of non-stationary signals and provides enhanced capability to capture and represent localized features and patterns in data. This analysis uncovers long-run asymmetric dynamic inter-correlations, which describe the mechanisms underlying carbon emissions escalation. Our findings reveal that biomass and hydro energy consumption negatively impact the environment, leading to emissions mitigation, while geothermal energy contributes positively throughout the research period. Notably, we establish that hydro energy consumption is the primary driving factor of carbon neutrality, with a more significant impact than any other determinant, facilitating a substantial reduction in carbon emissions and enhancing environmental quality. Our study has critical policy implications for meeting the Sustainable Development Goals (SDGs) and provides valuable insights for environmentalists in China, emphasizing the need to prioritize hydro energy consumption to achieve carbon neutrality.