摘要
东北三省是“双碳”目标下碳减排工作的重点区域。利用STIRPAT(可拓展的随机性环境影响评估)模型对2000—2021年东北三省各省的碳排放影响因素进行分析,并利用情景分析法预测截止到2040年以前、均衡情景下东北三省各省的碳排放趋势。模型分析结果表明:各省只存在导致碳排放量增加的影响因素;能源消费总量、碳排放强度、人均GDP是各省的共同影响因素;各省的影响因素是不同的组合;各省影响因素的促进作用存在省份差异性。预测结果表明:碳排放量同期数值由高到低依次为辽宁省、黑龙江省、吉林省;各省的碳排放量曲线都呈现出倒“U”形并能够看出明显的峰值;黑龙江省和吉林省碳达峰的时间都为2012年,而辽宁省碳达峰的时间为2025年。针对分析和预测结果,提出能源、社会经济、区域协调3个方面的建议。
The Northeast Three Provinces of China are the key region for reducing carbon emissions under the“dual carbon”goal.The STIRPAT(stochastic impacts by regression on population,affluence,and technology)model was used to analyze the influencing factors of carbon emissions of each province from 2000 to 2021,and the scenario analysis method was used to predict the carbon emission trends of each province until 2040.The model analysis results showed that each province only has the influencing factors that lead to an increase in carbon emissions.Total energy consumption,carbon emission intensity,and per capita GDP are common influencing factors for each province.The influencing factors of each province are different combinations.There are differences in the promoting effects of influencing factors among different provinces.The prediction results showed that according to the carbon emissions during the same period,the three provinces are ranked from high to low as Liaoning Province,Heilongjiang Province,and Jilin Province.The carbon emission curves of each province all exhibit an inverted U-shape,where a distinct peak can be observed.The carbon peak time in Heilongjiang Province and Jilin Province are both 2012,while the carbon peak time in Liaoning Province is 2025.In response to the results of analysis and prediction,recommendations were proposed in three aspects:energy,socio-economics,and regional coordination.
作者
刘畅
LIU Chang(China School of Banking and Finance,University of International Business and Economics,Beijing 100029,China)
出处
《科技和产业》
2024年第21期348-358,共11页
Science Technology and Industry
关键词
STIRPAT(可拓展的随机性环境影响评估)模型
碳排放
碳达峰
情景分析法
趋势预测
STIRPAT(stochastic impacts by regression on population,affluence,and technology)model
carbon emission
carbon peaking
scenario analysis
trend prediction
