Electricity generation and CO2 emissions in China using index decomposition and decoupling approach

Over the past three decades, energy and economic growth have been joined by enormous environmental pollution and growing global concerns. It is necessary to check the factors' effects impacting CO2 emissions and decouple CO2 from economic growth for the biggest emitter China. This study uses the logarithmic mean Divisia index extended by introducing electricity substitution factors (i.e., activity, population, electricity intensity, electricity overall, generation structure, energy efficiency, and fuel emission factor effects) and is then combined with Tapio's decoupling method to analyze the CO2 emission drivers, states and sectorial emissions for the years 1991–2020. The findings show that: (1) population and activity effects are the main driving factors in increasing CO2 emissions by adding trade and electricity generation structure effects. (2) Decoupling states presented the two decoupling states through electricity CO2 emissions and economic growth effects, showing that expansive negative decoupling is dominant. This shows that both factors show an increasing return to scale. (3) Individual factors and sectorial decoupling indexes show long-run variations and relationships between them, which means that industrial structure adjustment will help mitigate CO2 emissions and sustain economic development. Finally, based on empirical findings, the results suggest more ambitious targets for emerging low-carbon technologies that could help the rapid decarbonization of China's electricity sector. •Electricity generation and CO2 emissions in China are analyzed from 1991 to 2020.•LMDI is applied using seven major factors and joined with the Tapio's method.•Population and activity effects are the main driving factors in increasing.•Two decoupling states, showing expansive negative decoupling are leading.•Factor and sectorial decoupling indexes show long-run changes and relationships.