College of Computer and Information Science, Fujian Agriculture and Forestry University
Key Laboratory of Smart Agriculture and Forestry (Fujian Agriculture and Forestry University),Fujian Province University
College of Horticulture, Fujian Agriculture and ForestryUniversity

应对全球碳减排的紧迫挑战,可靠的碳达峰路径对中国碳减排的实施具有重要作用。然而,由于碳排放过程受众多因素影响,且相互作用复杂,传统情景分析方法难以有效识别最优减排路径。为此,在分析福建省的能源消费和碳排放数据的基础上,构建了麻雀搜索算法-支持向量回归模型(SparrowSearchAlgorithm-SupportVectorRegression,SSA-SVR)模型,该模型综合考虑了影响碳排放的14个关键因素,并基于SVR模型对福建省1999—2022年的碳排放量进行了预测和验证。随后,采用SSA算法优化了各因素的年度变化率组合,探索满足2030年碳达峰目标的多种可能路径。研究结果表明,模型具有较高的准确性和可靠性,探索出的所有路径均能在2030年实现碳达峰,但碳排放量存在显著差异。SSA-SVR模型能够为福建省工业部门实现碳达峰目标提供科学依据和策略建议。

In response to the urgent global challenge of carbon emission reduction, reliable pathways tocarbon peaking are of significant importance for the implementation of carbon reduction in China. How-ever, the process of carbon dioxide emissions involves numerous influencing factors, and the combina-tions of their change rates are diverse. Traditional scenario analysis methods only enumerate a limitednumber of possible scenarios and struggle to select the optimal combination of change rates. To addressthis, this paper takes Fujian Province as an example, and constructs an SSA-SVR (Sparrow SearchAlgorithm-Support Vector Regression) model based on the analysis of Fujian′s energy consumption andcarbon emission data. The model comprehensively considers 14 key factors affecting carbon emissionsand uses the SVR model to predict and verify Fujian′s carbon emissions from 1999 to 2022, ensuringthe accuracy and reliability of the model. By optimizing the annual change rate combinations of thesefactors through the SSA algorithm, the study seeks to find possible pathways to meet the carbon peaktarget by 2030. The research finds that although there are significant differences in the carbon emissionsof all explored pathways, they can all achieve a carbon peak by 2030. The experimental results alsoshow that the SSA -SVR model can explore a variety of effective carbon peak pathways, providingpolicy makers with a diverse selection of emission reduction pathways and offering a scientific basis andstrategic recommendations for the industrial sector of Fujian Province to achieve the carbon peak target.

福建省科技厅自然科学基金资助项目(2022J01153)

蔡湟,林晓宇,蔡志铃,等.基于麻雀搜索算法的福建省碳达峰路径优化研究[J].能源环境保护,2024,38(3):173-183.

CAI Huang, LIN Xiaoyu, CAI Zhiling, et al. Optimization of carbon peak path in Fujian Province based on spar-row search algorithm[J]. Energy Environmental Protection, 2024, 38(3): 173-183.