中国城市电力生产总碳强度的时空演变及影响因素

Spatio-temporal evolution and influencing factors of aggregate carbon intensity of electricity generation in China's cities

电力部门是中国实现碳达峰、碳中和目标的关键部门,剖析城市尺度电力生产总碳强度的时空特征及影响因素,对于细化电力生产减排政策、引导区域协同降碳具有重要意义。本文利用21543座6000 kW及以上电厂的微观数据,结合能源、经济、社会多源统计数据,核算了2003年、2010年和2017年中国城市电力生产总碳强度,并运用探索性空间数据分析、IDALMDI分解、STIRPAT模型等方法,揭示了城市尺度电力生产总碳强度的时空演化规律及影响因素。结果表明:(1) 2003—2017年中国整体电力生产总碳强度加速下降,但内部差异持续扩大;城市尺度空间分异明显,“博台线”东北半壁长期高于西南半壁;空间集聚程度显著提升,东北和华北地区是总碳强度下降的关键点。(2)火电能耗因子是前期电力生产总碳强度下降的主导因素,后期发电结构因子成为重要影响因素,其他电力系统因素影响较小但空间差异明显。(3)各社会经济因素对电力生产总碳强度的影响随时间变化,通过作用于电力需求、电力政策、电力技术、清洁电力发展空间的复杂系统引起电力系统特征变化。(4)电力生产总碳强度与人均GDP在2003年和2010年存在倒“U”型曲线关系,2017年呈线性正相关关系,这是由于新能源电力快速发展突破了适用于火力发电的环境库兹涅茨曲线传统解释框架。未来电力生产部门减排工作应充分考虑空间异质性,并需持续关注新兴电力技术突变对传统理论框架的影响。

The power sector is a critical industry in China's efforts to attain its carbon peaking and carbon neutrality targets.Analyzing the spatio-temporal pattern and influencing factors of the aggregate carbon intensity(ACI)of electricity generation at the city scale is of great significance for refining electricity emission reduction policies and guiding regional collaborative carbon reduction.This study utilizes micro-level data from 21543 power plants with a capacity of 6000 kW or above,in combination with multiple sources of statistical data related to energy,economy,and society,to calculate ACI of electricity generation in China's cities in 2003,2010,and 2017.Exploratory spatial data analysis,IDA-LMDI decomposition,and STIRPAT modeling are employed to reveal the spatio-temporal patterns and influencing factors.The findings show that:(1)From 2003 to 2017,the ACI of China's electricity generation sector exhibited a notable decline,albeit with a trend of increasing internal differences.Significant spatial differentiation was observed at city scale,with the northeast half of the Bole-Taipei Line maintaining higher levels than the southwest half over an extended period.The degree of spatial agglomeration also increased significantly during this period,with Northeast and North China identified as regions of particular concern in the decline of ACI.(2)The thermal efficiency was the dominant factor in the decline of ACI in the early stage,whereas the electricity generation structure became increasingly influential in the later period.Meanwhile,other power system factors exhibited less influence,though significant spatial differences were observed.(3)The impact of diverse socio-economic determinants on ACI fluctuated over time,engendering modifications in the attributes of the power system through their interactions with the intricate network of power demand,policy,technology,and clean energy expansion opportunities.(4)An inverted U-shaped correlation was observed between ACI and per capita GDP in 2003 and 2010,which transformed into a linear positive association in 2017.This shift can be attributed to the swift emergence of renewable electricity that have challenged the traditional interpretive framework of the Environmental Kuznets Curve hypothesis,which was previously applicable only to thermal power generation.In the future,endeavors aimed at reducing emissions in the electricity sector must comprehensively acknowledge the spatial heterogeneity and sustain attention towards the ramifications of abrupt shifts arising from emerging technologies on the conventional theoretical framework.