Factors Decomposition and Scenario Prediction of Energy-Related CO2 Emissions in China

doi:10.12204/j.issn.1000-7229.2021.09.001

Abstract

Abstract:

Energy-related carbon emissions account for more than 85% of total carbon emissions in China, and the research on changes of energy-related carbon emissions is of great significance for achieving carbon peak and neutrality goals. Firstly, this paper uses the Logarithmic Mean Divisia Index (LMDI) to decompose the impacting factors of China's energy-related CO₂ emissions changes from 1995 to 2017. From the aspects of economic scale, industry structure, energy intensity, energy structure, energy prices, per capita disposable income and population size, the model gives the contribution of related factors to energy-related CO₂ changes in primary, secondary, tertiary and residential sectors. The results show that for the three industry sectors, economic growth is the primary drive of CO₂ emission growth, while declining energy intensity, improved industrial structure and energy consumption structure show negative effects. For the residential sector, per capita disposable income and population size are the driving forces behind the CO₂ emission growth, and energy prices show a significant negative effect. Secondly, in order to predict China's energy-related CO₂ in 2030, three scenarios are designed and the Stochastic Impacts by Regression Population, Affluence and Technology (STIRPAT) model is implemented. In the low carbon scenario with the goal of achieving carbon peaks, China's energy-related CO₂ emissions are expected to peak around 2025-2029 and the level is about 10.1 billion to 11.0 billion tons. Finally, in order to achieve CO₂ emission peak before 2030 and carbon neutrality before 2060, it is recommended to build the China Energy Internet as the basic platform, to implement the clean replacement and electricity replacement, and accelerate the energy transition.

Key words: logarithmic mean Divisia index (LMDI), energy consumption intensity, stochastic impacts by regression population, affluence and technology (STIRPAT) model, carbon neutrality

CLC Number:

TM715

WANG Libing, ZHANG Yun. Factors Decomposition and Scenario Prediction of Energy-Related CO₂ Emissions in China[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(9): 1-9.

Figures/Tables 14

Table 1 Carbon emission factors of fossil energies

Fig.1 China's energy-related CO2 emissions in 2000-2017

Table 2 Decomposition results of energy-related CO2 emissions in industrial sectors 亿t

Table 3 Decomposition results of energy-related CO2 emissions in residential sector 亿t

Fig.2 Decomposition results of industrial sectors' energy-related CO2 emissions every 5 years

Table 4 Contribution of industrial sectors' economic growth to carbon emission changes 亿t

Fig.3 Contribution of industrial sectors' energy intensity to carbon emission changes

Table 5 Contribution of industrial sectors' economic structure to carbon emission changes 亿t

Table 6 Contribution of industrial sectors' energy consumption structure to carbon emission changes 万t

Table 7 Decomposition results of residential sector's energy-related CO2 emissions 亿t

Table 8 Variable information in STIRPAT model

Fig.4 Regression fitting results

Table 9 Scenario design and parameter setting in 2030

Fig.5 Energy-related carbon emission prediction results under different scenarios

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Factors Decomposition and Scenario Prediction of Energy-Related CO₂ Emissions in China

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