Interplay of multiple factors behind decarbonisation of thermal electricity generation: A novel decomposition model

Achieving carbon neutrality is crucial for the global economy to ensure sustainable development. Quantitative approaches are needed to track the emissions and determinants thereof. The Generalized Divisia Index (GDI) has received wide attention to quantify major factors driving CO2 emissions. However, there has been no research on the application of GDI to decompose the relative variables, such as Aggregate Carbon Intensity (ACI). What is more, GDI has not been applied for spatial decomposition. To address these gaps, the present study constructs temporal-spatial GDI models to decompose the dynamics in ACI of thermal electricity generation. The case of China during 2000-2019 is considered. The results suggest that East, Central, and Northwest regions contributed to the mitigation of national ACI, whereas North and Northeast lagged behind. The temporal decomposition results imply that CO2 emissions is the major factor causing ACI increase, whereas energy-mix promotes reduction in ACI. Energy consumption can be decoupled from ACI, but it is cumbersome to decouple CO2 emission and electricity production from ACI. The spatial decomposition results indicate that CO2 emission and energy consumption induce spatial differences to the highest extent.

Automated summary

Achieving carbon neutrality is crucial for sustainable development of the global economy, and the Generalized Divisia Index (GDI) is used to quantify major factors driving CO2 emissions. However, there is a lack of research on applying GDI to decompose relative variables and for spatial decomposition. This study constructs temporal-spatial GDI models to decompose Aggregate Carbon Intensity (ACI) of thermal electricity generation in China during 2000-2019. The results show regional contributions to ACI mitigation and the role of CO2 emissions and energy-mix in ACI dynamics.