中国能源碳排放因素分解与情景预测

doi:10.12204/j.issn.1000-7229.2021.09.001

摘要/Abstract

摘要：

我国能源活动碳排放占总碳排放85%以上,研究能源活动碳排放的变化规律对于实现碳达峰碳中和目标具有重要意义。首先,采用对数平均迪氏分解法(logarithmic mean Divisia index,LMDI)对1995—2017年我国能源消费碳排放变化的影响因素进行分解,从经济规模、产业结构、能源强度、能源结构、能源价格、人均可支配收入、人口规模这7个方面,模型给出了相关因素对一、二、三产业和居民部门碳排放变化的贡献。结果表明,对于3个产业部门,经济增长是碳排放增长的首要驱动力,而技术进步带来的能源强度下降、产业结构优化和能源消费结构改善呈现负效应,且产业结构优化和能源结构清洁化的作用越来越显著。对于居民部门,人均可支配收入是居民部门碳排放增长的推动力,而能源价格呈现明显的负效应。其次,设计了3种情景,运用可拓展的随机性的环境影响评估模型(stochastic impacts by regression population,affluence and technology,STIRPAT)对2030年我国能源碳排放进行预测,在以实现碳达峰为目标的低碳情景中,我国能源碳排放有望于2025—2029年实现达峰,峰值水平为101亿~110亿t。最后,为实现碳达峰碳中和目标,建议以构建中国能源互联网为基础平台,实施“清洁替代”和“电能替代”,推进能源转型。

关键词: 对数平均迪氏分解法(LMDI), 能源消费强度, 可拓展的随机性的环境影响评估模型(STIRPAT), 碳中和

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

中图分类号:

TM715

王利兵, 张赟. 中国能源碳排放因素分解与情景预测[J]. 电力建设, 2021, 42(9): 1-9.

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.

图/表 14

表1 化石能源碳排放系数

Table 1 Carbon emission factors of fossil energies

图1 中国2000—2017年能源碳排放

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

表2 产业部门的能源碳排放分解结果

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

表3 居民部门的能源碳排放分解结果

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

图2 产业部门每5年能源碳排放分解结果

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

表4 产业部门经济增长对碳排放变动的贡献

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

图3 产业部门能源强度对碳排放变动的贡献

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

表5 产业部门的结构对碳排放变动的贡献

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

表6 能源结构对碳排放变动的贡献

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

表7 居民部门碳排放分解

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

表8 STIRPAT模型变量有关信息

Table 8 Variable information in STIRPAT model

图4 回归拟合结果

Fig.4 Regression fitting results

表9 2030年情景设计与参数设置

Table 9 Scenario design and parameter setting in 2030

图5 不同情景下能源碳排放预测结果

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

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