Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Int J Environ Res Public Health. 2022 Mar 15;19(6):3471. doi: 10.3390/ijerph19063471.
Promoting technological advancements and energy transitions in electricity generation are crucial for achieving carbon reduction goals. Some studies have examined the effectiveness of these measures by analysing the driving forces of "aggregate carbon intensity" (ACI) change. However, only a few studies have considered the effect of the installed capacity mix and capacity factor. Moreover, such analysis has never been applied at China's provincial level after 2015. To alleviate this gap, our study applied a temporal and multi-regional spatial IDA-LMDI model to analyse the driving factors of ACI changes and disparities among the provinces of China from 2005 to 2019. The model notably includes the effects of the installed capacity mix, thermal capacity factor, and overall capacity factor. The analysis revealed that the decline in China's ACI was diminished after 2015, while an ACI rebound was identified in five provinces. The changes in the ACI from 2015 to 2019 were mainly driven by the effect of the installed capacity mix rather than by the thermal efficiency and thermal capacity factor. The overall capacity factor was the only factor with a negative impact on the ACI change. We also found that its combined effect with the thermal capacity factor on increasing ACI can offset the effect of the installed capacity mix by reducing the ACI in provinces with significant additions of renewable energy installed capacity. The analysis of the influencing factors on the provincial ACI differences revealed that the share of hydropower installed capacity was significant. Moreover, the thermal efficiency and thermal capacity factor both played key roles in the ACI disparities in northeast, northwest, and central China. Overall, this study paves the way for data-driven measures of China's carbon peak and carbon neutrality goals by improving the capacity factor of wind and solar power, leveraging the critical impact of hydropower, and narrowing the differences in the thermal power sector among provinces.
推动发电领域的技术进步和能源转型对于实现碳减排目标至关重要。一些研究通过分析“总碳强度”(ACI)变化的驱动因素来检验这些措施的有效性。然而,只有少数研究考虑了装机容量结构和容量系数的影响。此外,自 2015 年以来,此类分析从未在中国省级层面上进行过。为了弥补这一差距,我们的研究应用了时间和多区域空间 IDA-LMDI 模型,以分析 2005 年至 2019 年期间中国各省 ACI 变化的驱动因素及其差异。该模型特别考虑了装机容量结构、热容量系数和总容量系数的影响。分析结果表明,中国 ACI 的下降在 2015 年后趋缓,同时有五个省份出现了 ACI 反弹。2015 年至 2019 年 ACI 的变化主要受装机容量结构变化的影响,而不是热效率和热容量系数的影响。总容量系数是对 ACI 变化产生负面影响的唯一因素。我们还发现,总容量系数与热容量系数的结合对增加 ACI 的影响可以抵消装机容量结构变化对 ACI 的影响,特别是在可再生能源新增装机容量较大的省份。对省级 ACI 差异影响因素的分析表明,水电装机容量份额具有显著影响。此外,热效率和热容量系数在东北、西北和中部地区的 ACI 差异中都发挥了关键作用。总的来说,本研究通过提高风电和太阳能的容量系数、利用水电的关键影响以及缩小各省火电部门的差异,为实现中国的碳达峰和碳中和目标提供了数据驱动的措施。