摘要
中国碳达峰是全球气候治理的重要议程,中国各省级地区的碳达峰研究对全国碳达峰任务达成和路径安排具有重要影响和现实支撑。基于混合型能源投入产出模型,延伸构建了碳达峰时间预测模型。在经济发展和碳排放强度改善的9种组合情景下,预测了2020—2040年全国30个省级地区(除西藏、台湾、香港和澳门)的碳排放总量,并通过拟合计算与历史期峰值的比较,得到碳达峰时间。在此基础上,利用Probit模型对各地区是否能够在2030年前达到碳峰值做影响因素分析。结果显示:(1)中国各省级地区碳达峰时间差异明显,在空间格局上呈南北条带状聚集。(2)碳排放强度的改善对碳达峰时间影响较大,4%yr-1的改善对2030年前达峰最有利。(3)产业结构、政府干预程度、对外开放程度对能否在2030年前实现碳达峰目标影响显著。
China's carbon emission peak is an important agenda for global climate governance.The research on carbon emission peak in China by province has important influence and practical support for the national carbon mission peak and path arrangement.Based on the Hybrid-units Energy Input-Output model,this paper built a carbon emissions peak prediction model.Under 9 combined scenarios of economic development and carbon emission intensity improvement,the total carbon emissions of 30 provincial-level regions from 2020 to 2040 were predicted(except Tibet,Hong Kong,Macao and Taiwan).Then,this paper compared the peak values of different periods to estimate the carbon emission peak time.On this basis,the Probit model was used to analyze the influencing factors of whether regions can reach carbon emission peak before 2030.The results show that:(1)The carbon peak time varies significantly among provincial regions of China with a north-south strip aggregation in the spatial pattern.(2)The improvement of carbon emission intensity has a greater impact on the emission peak time,and the improvement rate of 4%per year is most favorable for reaching the peak by 2030.(3)Industrial structure,the degree of government intervention,and the degree of openness have a significant impact on whether the carbon peak target can be achieved by 2030.
作者
蒋昀辰
钟苏娟
王逸
黄贤金
JIANG Yun-chen;ZHONG Su-juan;WANG Yi;HUANG Xian-jin(School of Geographic and Oceanographic Sciences,Nanjing University,Nanjing 210023,China;Key Laboratory of Carbon Neutral and Territorial Spatial Optimization,Ministry of Natural Resources,Nanjing 210023,China)
出处
《自然资源学报》
CSSCI
CSCD
北大核心
2022年第5期1289-1302,共14页
Journal of Natural Resources
基金
国家自然科学基金项目(71921003)
江苏省“333”工程科研资助立项项目(BRA2020031)。