中国货运结构优化的碳减排效应测度  

Measurement of Carbon Emission Reduction Effect of China's Freight Transportation Structure Optimization

作　　者：诸立超 刘昭然 汪瑞琪[4] 熊强 ZHU Li-chao;LIU Zhao-ran;WANG Rui-qi;XIONG Qiang(School of Business Administration,Zhejiang University of Finance&Economics,Hangzhou 310018,China;Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport,Beijing Jiaotong University,Beijing 100044,China;Institute of Comprehensive Transportation of National Development and Reform Commission,Beijing 100038,China;Guangdong Provincial Transportation Planning Research Center,Guangzhou 510101,China)

机构地区：[1]浙江财经大学,工商管理学院,杭州310018 [2]北京交通大学,综合交通运输大数据行业重点实验室,北京100044 [3]国家发展和改革委员会综合运输研究所,北京100038 [4]广东省交通运输规划研究中心,广州510101

出　　处：《交通运输系统工程与信息》2022年第6期309-315,共7页Journal of Transportation Systems Engineering and Information Technology

基　　金：浙江省哲学社会科学规划课题(21NDQN256YB);浙江省自然科学基金(LQ21E080023);国家自然科学基金(72074141)。

摘　　要：管理部门和学界高度重视货运结构优化问题,因为过高的公路货运量导致货运碳排放居高不下,不利于早日实现“碳达峰、碳中和”目标。除货运结构外,货运碳排放受诸多因素影响,但研究者大多仅关注部分重点因素影响,对于货运结构优化的碳减排效应缺乏准确理解。为解决上述问题,本文利用“自上而下”法测算1999—2019年中国货运碳排放量,并构建综合考虑社会经济变量(如人均GDP)与货运特征变量(如货运分担率)的偏最小二乘回归模型,通过调整不同货运方式使用费用,模拟2030年不同政策刺激情景下货运结构优化的碳减排效应。结果表明:1999—2019年,社会经济变量对货运碳排放增长的年均贡献度为73%,显著高于货运特征变量;公路、铁路和水路货运分担率变化对货运碳排放增长的年均贡献度分别为1.81%、-0.01%和-0.26%;2030年公路货运量全部转为铁路或水路货运量的极端情景,难以实现单位GDP货运碳排放较2005年下降65%的标准;增加高碳货运方式使用费用的碳减排效应比降低低碳货运方式使用费用更显著。Management department and academic community attach great importance to the optimization of freight transportation structure because the excessive volume of road freight transportation leads to high CO_(2) emissions,which is not conducive to early achievement of the"carbon peaking and carbon neutrality".Apart from freight transportation structure,a variety of factors affect CO_(2) emissions in freight transportation.However,researchers mainly focus on the impacts of other key factors,lacking a precise understanding of the impact of freight transportation structure optimization on reducing CO_(2) emissions.In this regard,this paper applies the top-down method to calculate the CO_(2) emissions of freight transportation in China from 1999 to 2019.A partial least square regression(PLSR)model with socio-economic variables(e.g.,per capita GDP)and freight transportation characteristic variables(e.g.,freight transportation structure)is constructed to quantify the contribution of each factor,which is used to simulate CO_(2) emissions reduction in 2030 caused by freight transportation structure optimization under different policy scenarios by adjusting usage fee of different freight modes.The results show that socio-economic variables contributed an average rate of 73%to the increase of CO_(2) emissions in freight transportation in the period of 1999 to 2019,which were significantly higher than freight transportation characteristic variables.The average contribution rate of the changes in the road,rail,and water freight share to the growth of CO_(2) emissions in freight transportation was 1.81%,-0.01%,and-0.26%,respectively.The extreme case of switching all road freight volume to rail or water in 2030 cannot achieve a reduction of 65%in CO_(2) emissions per unit GDP compared to 2005.In addition,the benefits of reducing CO_(2) emissions achieved by increasing the usage fee for high-carbon freight modes are more significant than reducing the usage fee for low-carbon freight modes.

关 键 词：交通运输经济 货运碳排放 影响因素 货运结构 情景模拟 

分 类 号：U9[交通运输工程]