Decomposing the Drivers of CO₂ Emissions from Terminal Energy Consumption in Sichuan, China Based on LMDI Method

Quantifying the drivers of CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions from energy consumption is essential for effective carbon reduction strategies. As a strategically significant region in southwestern China, decomposing the drivers of CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions in Sichuan Province is of paramount importance. Currently, there is a limited analysis of the drivers of CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions from terminal energy consumption in Sichuan Province. Therefore, this research uses the LMDI method to analyze the carbon emission drivers across several key industries in Sichuan Province from 2010 to 2022, It classifies these drivers into three major categories based on data completeness and industry characteristics. The results show the main driver of CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions in farming, forestry, animal husbandry and fishery, construction and transport is the economic effect, the energy intensity effect and the energy structure effect can effectively inhibit CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions in these industries. The production scale effect in iron and steel industry, paper industry and non-ferrous metal industry contributes the most to CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions, while enhancing energy efficiency can help reduce emissions The per capita disposable income effect plays a strong role in promoting CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions from residential consumption, And then there're urbanization rate and population effect. Both the effect of residential energy intensity and residents' willingness to consume can inhibit CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions.