摘要
采用碳排放系数法测算物流企业运输环节移动源燃烧碳排放量,定义消耗电量转换法测算仓储环节固定源燃烧碳排放量;选取资本存量、劳动力、能源消耗作为碳排放效率评价的投入指标,产值作为期望产出,碳排放量作为非期望产出指标;采用Super-SBM模型(Super-efficiency Slack-based Measurement Model)得到技术效率、纯技术效率和规模效率这3个指标值,用以评价物流企业碳排放效率,以顺丰股份有限公司2013-2020年的营运数据为例进行实证分析。结果显示:2013-2016年、2020年的技术效率均大于1,表示资源分配达到最优状态;2017-2019年均小于1,主要是因为规模效率值偏低,2017年起顺丰提升运输业务量,逐渐调整规模效率,2020年的技术效率有了提升,找到了相对合理的平衡点。2013-2020年的纯技术效率值均大于1,近些年顺丰不断寻求技术提升以提高生产效率水平。结果表明,所提物流企业碳排放量测算方法可行,采用的Super-SBM模型能有效评价物流企业碳排放效率。最后提出物流企业需要从创新生产技术、节能减排技术等方面提升碳排放效率的改进措施。
The carbon emission coefficient method is used to measure the carbon emissions of logistics enterprises'transportation and storage.The input and output indicators of carbon emission efficiency are selected,and the Super efficiency Slack-based Measurement Model is adopted to obtain the three index values of technical efficiency,pure technical efficiency,and scale efficiency to evaluate the carbon emission efficiency of logistics enterprises.The empirical analysis is performed using SF Express's operating data from 2013 to 2020.The analysis results show that the technical efficiency from 2013 to 2016 and 2020 are all greater than 1,indicating that resource allocation has reached the optimal state.The technical efficiency values from 2017 to 2019 are all less than 1,mainly because of the low scale efficiency value.SF Express's transportation business volume has soared since 2017,and the scale efficiency has been gradually adjusted to find a relatively reasonable balance point by 2020,and the technical efficiency has been improved in 2020.The pure technical efficiency values from 2013 to 2020 are greater than 1.In recent years,SF has continued to increase its capital investment in technology to seek technological improvements.These results show that the proposed method is feasible,and the super-SBM model can effectively evaluate the carbon emission efficiency of logistics enterprises.Finally,we put forward the improvement measures for logistics enterprises to improve carbon emission efficiency from the aspects of innovative production technology,and energy saving and emission reduction technology.
作者
姜晓红
陈莎
张毅
JIANG Xiao-hong;CHEN Sha;ZHANG Yi(Automotive and Transportation College,Nanjing Forest University,Nanjing 210037,China)
出处
《交通运输系统工程与信息》
EI
CSCD
北大核心
2022年第2期313-321,共9页
Journal of Transportation Systems Engineering and Information Technology
基金
国家自然科学基金青年科学基金(71701099)。
关键词
物流工程
碳排放量
碳排放效率
Super-SBM模型
物流企业
logistics engineering
carbon emissions
carbon emission efficiency
Super-SBM model
logistics enterprises
作者简介
通信作者:姜晓红(1985-),女,江苏常州人,副教授,博士。jiangxh@njfu.edu.cn。
