School of Economics and Management, North China Electric Power University, No. 2 Beinong Road, Changping District, Beijing, 102206, China.
Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, No. 2 Beinong Road, Changping, Beijing, 102206, China.
Environ Sci Pollut Res Int. 2023 Nov;30(53):113364-113381. doi: 10.1007/s11356-023-30327-9. Epub 2023 Oct 17.
Carbon emissions from the electricity industry (CEEI) account for a large proportion of China's total carbon emissions, and it is important to study the spatial correlation between CEEI and the influencing factors to promote cross-regional synergistic emission reduction and low-carbon development of the power system. In this paper, the quasi-input-output (QIO) model is applied to assess the transfer of carbon emissions generated by electricity trading based on the consideration of electricity carbon transfer, and the exploratory spatial data analysis (ESDA) method is applied to analyze the spatial correlation effect of carbon emissions from China's electric power sector from 2001 to 2020, analyzes its distribution pattern in both spatial and temporal dimensions, and applies the improved logarithmic mean Divisia index (LMDI) two-stage decomposition model to decompose the changes in CEEI into 11 influencing factors from the perspective of the whole industrial chain of power production, transmission, trade, and consumption. The research results show that (1) the spatial distribution of CEEI has obvious unevenness and aggregation characteristics, with high-high aggregation areas and hot spot aggregation areas generally concentrated in the North China Power Grid and the East China Power Grid, but the aggregation trend is gradually decreasing, while low-low aggregation areas and cold spot aggregation areas are concentrated in the Northwest China Power Grid and the Central China Power Grid, but the area is very limited. (2) The direction of carbon emission diffusion in China's electricity industry is gradually transitioning from southwest-northeast to northwest-southeast, and the east-west diffusion trend is stronger than the north-south diffusion trend and carbon emissions are gradually shifting to the northwest grid. (3) The total amount of electricity production is the most influential factor in the change of CEEI, driving the cumulative growth of CEEI by 4495.34 Mt, followed by GDP per capita and electricity consumption intensity. Coal consumption for power generation, the share of thermal power, and net electricity exports were the main factors inhibiting the increase in carbon emissions from the power sector, with cumulative contributions of -797.74 Mt, -619.99 Mt, and -47.76 Mt, respectively.
电力行业碳排放（CEEI）在中国总碳排放量中占很大比例，研究CEEI 与影响因素之间的空间相关性对于促进跨区域协同减排和电力系统低碳发展非常重要。本文应用准投入产出（QIO）模型，在考虑电力碳转移的基础上，评估电力交易产生的碳排放转移，应用探索性空间数据分析（ESDA）方法分析 2001 年至 2020 年中国电力部门碳排放的空间相关效应，分析其在时空维度上的分布模式，并应用改进的对数平均迪氏指数（LMDI）两阶段分解模型，从电力生产、传输、交易和消费全产业链的角度，将 CEEI 的变化分解为 11 个影响因素。研究结果表明：（1）CEEI 的空间分布具有明显的非均衡性和集聚特征，高-高集聚区和热点集聚区一般集中在华北电网和华东电网，但集聚趋势逐渐减弱，而低-低集聚区和冷点集聚区集中在西北电网和华中电网，但面积非常有限。（2）中国电力行业的碳排放扩散方向正逐渐由西南-东北向西北-东南转变，东西扩散趋势强于南北扩散趋势，碳排放逐渐向西北电网转移。（3）发电量总量是 CEEI 变化的最主要影响因素，累计推动 CEEI 增长 4495.34Mt，其次是人均 GDP 和电力消费强度。用于发电的煤炭消耗、火力发电的份额和净电力出口是抑制电力部门碳排放增长的主要因素，累计贡献分别为-797.74Mt、-619.99Mt 和-47.76Mt。
