中国省级能源消耗CO_(2)排放状况及未来趋势分析  

Analysis of the Current Situation and Future Development of CO_(2) Emissions in China's Provincial Energy Consumption

作　　者：唐小焱 TANG Xiaoyan(School of Big Data Application and Economics,Guizhou University of Finance and Economics,Guiyang,Guizhou 550025)

机构地区：[1]贵州财经大学大数据应用与经济学院,贵州贵阳550025

出　　处：《管理现代化》2024年第1期180-189,共10页Modernization of Management

基　　金：贵州省省级科技计划项目资助(20201Y288);贵州财经大学绿色金融校级科研基金项目(2019DYL03)。

摘　　要：制定“碳减排”措施需要信息支持,为获得必要的辅助信息,首先用动态时间规整聚类方法将中国30个省份按CO_(2)排放的相似性划分为4种类型,再对每种排放类型用LASSO回归模型识别出总CO_(2)排放量的关键排放源,最后用长短期记忆和门控循环单元神经网络模型预测了4种场景下的排放量,并探索“碳达峰”路径。结果显示:类型1和2的关键排放源是交通运输业的柴油、发电供热行业的原煤和非金属矿物产品部门的生产过程(水泥);类型3的关键排放源是发电供热行业的原煤和钢铁行业的焦炭,类型4的关键排放源是交通运输业的汽油、发电供热行业的原煤以及非金属矿物产品部门的生产过程(水泥)。针对当前的“碳达峰”目标,在基准场景下所有类型都不能按时达峰;在达峰任务方面,类型3的省份比其它各类型的省份任务更为艰巨。Developing carbon mitigation measures requires information.In order to obtain necessary auxiliary information,the dynamic time warping cluster method is first used to divide 30 provinces in China into four types based on the similarity of CO_(2) emissions;Then for each emission category,the critical driving emission sources of the total emissions are identified us-ing LASSO regression;Finally,the emissions in four scenarios are predicted using long short-term memory and gated recurrent unit neural network models,and the"carbon peak"path is explored.The results showes that the critical emission sources of Types 1 and 2 are diesel con-sumption in the transportation industry and coal consumption in the power&heat industry;the critical emission sources of Type 3 are coal consumption in the power&heat Industry and coke consumption in the steel industry;The key emission sources for Type 4 are gasoline in the trans-portation industry,raw coal in the power generation and heating industry,and the production process of non-metallic mineral products in the sector.In terms of peak tasks,type 3 provinces have more challenging tasks than other types of provinces.

关 键 词：省级CO_(2)排放 聚类分析 神经网络模型 

分 类 号：X196[环境科学与工程—环境科学] F124[经济管理—世界经济] F222.1