Synergy among finance, energy and CO2 emissions in a dynamic setting: Measures to optimize the carbon peaking path /

Abstract

Financial development and energy consumption are expected to exert a significant effect on CO2 emission changes. However, there remains a research gap in optimizing pathways to carbon peaking under financial and energy drivers. This paper constructs a novel model covering finance and energy consumption using the generalized Divisia Index (GDI) method. The GDI allows to quantify the drivers behind the CO2 emission change in China during 2005–2020. Furthermore, it combines a Monte Carlo simulation with scenario analysis to forecast the path of CO2 emissions during 2021–2030. The results demonstrate that energy structure and carbon factor remain the principal drivers for CO2 emission growth in China. Thus, it is still pivotal to optimize energy saving and carbon reduction policies. Financial energy saving has enormous potential for reducing CO2 emissions over time, even though the immediate influence on CO2 emission is not significant. China has a substantial probability of reaching the peaking target in 2026 under the technology breakthrough scenario, whereas in the other two scenarios the target cannot be achieved.