人口因素驱动下的中国碳排放强度预测  

Predicting China's carbon emission driven by population factors

作　　者：李汉东 向梓航 崔雪峰 LI Handong;XIANG Zihang;CUI Xuefeng(School of Systems Science,Beijing Normal University,100875,Beijing,China)

机构地区：[1]北京师范大学系统科学学院,北京100875

出　　处：《北京师范大学学报（自然科学版）》2023年第5期822-829,共8页Journal of Beijing Normal University(Natural Science)

基　　金：中国农业农村部农业设施结构工程重点实验室开放课题资助项目(202003)。

摘　　要：基于组合神经网络(LSTM-IPSO-BP)模型,研究中国碳排放强度的影响因素以及在未来人口数量和结构变动条件下中国碳排放强度的变化趋势.结果表明:1)中国未来人口数量的持续下降将会导致碳排放强度的增加,人口数量下降的速率与碳排放强度正相关;2)城镇化水平提高虽然会降低碳排放强度,但中国未来城镇化进程放缓会增加碳减排的压力;3)在现有各影响因素发展趋势下,特别是人口数量和结构变动的条件下,中国将难以在2030年前实现碳达峰,这也表明未来10年中国政府需要加大碳排放政策的调控力度.Combined neural network model LSTM-IPSO-BP was applied to study factors influencing the intensity and change trend in China's carbon emission driven by future population and structure changes.It is found that continuous decline in China's population in the future will lead to increased carbon emission intensity,the rate of population decline is positively correlated with carbon emission intensity.Although improved urbanization level will reduce carbon emission intensity,the slowdown of China's future urbanization process will increase pressure on carbon emission reduction.Under current policies and development trend of various influencing factors,especially population and structural changes,it is difficult for China to achieve a carbon peak before 2030,therefore the Chinese government needs to strengthen the control of carbon emission policies in the following decade.

关 键 词：碳排放 路径预测 人口负增长 城镇化 神经网络模型 

分 类 号：F062.2[经济管理—政治经济学]