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Abstract：
The realisation of the low-carbon transition of the energy system in resource-intensive regions, as embodied by Shanxi Province, depends on a thorough understanding of the factors impacting the power sector\'s carbon emissions and an accurate prediction of the peak trend. Because of this, the power industry\'s carbon emissions in Shanxi province are measured in this article from 1995 to 2020 using data from the Intergovernmental Panel on Climate Change (IPCC). To obtain a deeper understanding of the factors impacting carbon emissions in the power sector, factor decomposition is performed using the Logarithmic Mean Divisia Index (LMDI). Second, in order to precisely mine the relationship between variables and carbon emissions, the Sparrow Search Algorithm (SSA) aids in the optimisation of the Long Short-Term Memory (LSTM). In order to implement SSA-LSTM-based carbon peak prediction in the power industry, four development scenarios are finally built up. The findings indicate that: (1) There has been a fluctuating upward trend in Shanxi Province\'s total carbon emissions from the power industry between 1995 and 2020, with a cumulative growth of 372.10 percent. (2) The intensity of power consumption is the main factor restricting the rise of carbon emissions, contributing -65.19%, while the per capita secondary industry contribution factor, contributing 158.79%, is the main driver of the growth in emissions. (3) While the baseline scenario and the rapid development scenario fail to peak by 2030, the low carbon scenario and the green development scenario peak at 243,991,100 tonnes and 258,828,800 tonnes, respectively, in 2025 and 2028. (4) Based on the peak performance and the decomposition results, resource-intensive cities like Shanxi\'s power industry should concentrate on upgrading and strengthening the industrial structure, getting rid of obsolete production capacity, and encouraging the faster development of each factor in order to help the power sector reach peak carbon performance.
摘要：
实现资源密集型地区能源系统低碳转型,正如山西省所体现的那样，取决于对影响电力部门碳排放的因素的透彻了解和对峰值趋势的准确预测。正因为如此,本文利用政府间气候变化专门委员会(IPCC)的数据对山西省1995-2020年电力行业的碳排放进行了测算。为了更深入地了解影响电力部门碳排放的因素，因子分解使用对数平均离差指数(LMDI)进行。第二,为了精确挖掘变量和碳排放之间的关系，麻雀搜索算法(SSA)有助于优化长短期记忆(LSTM)。为了在电力行业实施基于SSA-LSTM的碳峰值预测,最终建立了四个开发场景。研究结果表明:(1)山西省电力工业碳排放总量在1995-2020年间呈波动上升趋势,累计增长372.10%。(2)电力消耗强度是制约碳排放上升的主要因素,贡献-65.19%，而人均第二产业贡献因素，贡献158.79%，是排放量增长的主要驱动力。（3）基准情景和快速发展情景在2030年前未能达到峰值，低碳情景和绿色发展情景的峰值分别为243,99100吨和258,828,800吨，分别,2025年和2028年。(4)根据峰值性能和分解结果，像山西电力工业这样的资源密集型城市应该集中精力升级和加强产业结构，摆脱过时的生产能力，并鼓励每个因素的更快发展，以帮助电力部门达到碳表现的峰值。
