Analysis ofcarbon emission influencing factors and carbon peak forecasting in Xinjiang regional power system
LU Sirui
LU Hao
SU Qinglong
LI Yaohui
Abstract：As an important energy base in China,it is crucial for China's low-carbon and green development to carry out research on carbon emission prediction and peaking time,and actively explore carbon peaking paths.The relationship between the number of iterative convergence and grid topology is constructed using an iterative carbon emission flow algorithm based on the node proximity characteristics of the power system.Based on the actual operation data of the Xinjiang power system,the whole-process perspective,combined with the LMDI decomposition method and the STIRPAT model,is used to systematically analyze the driving mechanism and regional heterogeneity characteristics of the carbon emission growth,and to predict the carbon emissions and the time of carbon peaking in Xinjiang.It is found that the effect of gross regional product and the intensity of industrial electricity consumption are the main drivers of carbon emission growth,with a joint contribution of 126.47％,while the effect of industrial structure optimization is the biggest inhibiting factor,in which every 1％decrease in the proportion of secondary industry can reduce carbon emissions by 0.87 billion tons/year.Energy transition and structural adjustment have significant potential to reduce carbon emissions,and active industrial upgrading reduces peak carbon emissions compared to the baseline development scenario.Xinjiang needs to build a differentiated emission reduction path of"short-term elimination of inefficient coal power,medium-term promotion of green-hydrogen coupled power generation,and long-term optimization of the cross-provincial carbon market".The results of this study not only provide a basis for the formulation of green and low-carbon development policies in Xinjiang,but also provide a reference model for the design of transition paths in other energy-intensive provinces in China.
Keywords：carbon peakingLMDI modelSTIRPAT modelscenario analysisinfluencing factors
Publication Date：2025-06-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：13( 82-94 )