Measurement and Driving Factors of Carbon Emissions from Coal Consumption in China Based on the Kaya-LMDI Model

Abstract

As the top emitter of carbon dioxide worldwide, China faces a considerable challenge in reducing carbon emissions to combat global warming. Carbon emissions from coal consumption is the primary source of carbon dioxide emissions in China. The decomposition of the driving factors and the quantification of regions and industries needs further research. Thus, this paper decomposed five driving factors affecting carbon emissions from coal consumption in China, namely, carbon emission intensity, energy structure, energy intensity, economic output, and population scale, by constructing a Kaya-Logarithmic Mean Divisia Index (Kaya-LMDI) decomposition model with data on coal consumption in China from 1997 to 2019. It was revealed that the economic output and energy intensity effects are major drivers and inhibitors of carbon emissions from coal consumption in China, respectively. The contribution and impact of these driving factors on carbon emissions from coal consumption were analyzed for different regions and industrial sectors. The results showed that carbon emissions from coal consumption increased by 3211.92 million tons from 1997 to 2019. From a regional perspective, Hebei Province has the most significant impact on carbon emissions from coal consumption due to the effect of economic output. Additionally, the industrial sector had the most pronounced influence on carbon emissions from coal consumption due to the economic output effect. Finally, a series of measures to reduce carbon emissions including controlling the total coal consumption, improving the utilization rate of clean energy, and optimizing the energy structure is proposed based on China’s actual development.

Quantitative Study of Post-Pandemic Electricity Productivity Fluctuations Based on LMDI

“…Therefore, in this paper, to analyze the reasons for changes in electricity productivity, the LMDI method is employed to decompose the electricity productivity. The foundation of the LMDI model is the Kaya Identity, a commonly used international method for examining the sources of carbon dioxide emissions, breaking down the target function of carbon dioxide emissions into factors such as energy use, economic output, and population [7]. Drawing inspiration from the Kaya Identity, this paper extends the concept to decompose the factors influencing electricity productivity into four elements: industry structure, industry energy utilization efficiency, energy structure, and end-use electrification rate.…”

Carbon Emissions Drivers and Reduction Strategies in Jiangsu Province

“…These studies have developed more generalized policy recommendations from a regional perspective and have not taken into account the specificities of the cities in the region [24,25]. And when analyzing the factors affecting carbon emissions, most of the literature considers energy efficiency, industrial structure, and economic growth [26,27].…”

Mechanism and Spatial Spillover Effect of New-Type Urbanization on Urban CO2 Emissions: Evidence from 250 Cities in China

“…Additionally, environmental policies play an important part in CO 2 emissions, such as environmental regulations being a fundamental reason for curbing CO 2 emissions [31,32]. Furthermore, the STIRPAT model, IPAT model, EKC curve, Kaya constant equation, and LMDI model were used to evaluate the impact of CO 2 emissions [24,[33][34][35].…”