中国物流业的绿色发展是“双碳”目标的重点关注领域。结合物流行业绿色发展内涵，研究构建了绿色物流发展水平指标体系。基于2014—2019年中国省级尺度交通运输物流仓储行业碳排放清单及统计年鉴数据，采用因子分析法对绿色物流发展指标降维形成8个影响因子，应用地理加权回归模型来解释中国物流行业碳排放强度驱动因素的省级区域差异及其时空演化规律。研究发现，在样本考察期内中国省域尺度物流业碳排放强度出现逐年弱化的聚集现象，并呈现出“西强东弱”空间分异的基本模式，因子重心表现出从东部向中部转移的趋势。其中物流粗放发展因子和物流装备因子对碳排放强度产生最强正向效应，而能源清洁化因子、货运结构调整因子和模式创新因子产生抑制效应。结合分析结果，立足各省资源禀赋及发展战略，提出中国绿色物流发展的政策建议。
The green development of China's logistics industry is a key area for the carbon peaking and carbon neutrality goals. This article combines the connotation of green development in the logistics industry to study and construct a green logistics development level indicator system. Based on the carbon emissions inventory and statistical yearbook data of China's provincial-level transportation, logistics, and warehousing industry from 2014 to 2019, factor analysis was used to reduce the dimensionality of green logistics development indicators to form 8 influencing factors. A geographic weighted regression model was applied to explain the provincial differences and spatiotemporal evolution patterns of the driving factors of carbon emissions intensity in China's logistics industry. The study found that during the sample investigation period, the carbon emission intensity of China's logistics industry at the provincial level showed an aggregation phenomenon of weakening year by year, and showed a basic spatial differentiation pattern of "strong in the west and weak in the east". The factor center of gravity showed a trend of shifting from the east to the central region. The factors of extensive logistics development and logistics equipment have the strongest positive effects on carbon emission intensity, while the factors of energy cleanliness, freight structure adjustment, and innovation have inhibitory effects. Based on the analysis results and the resource endowments and development strategies of each province, policy implications for the development of green logistics in China were proposed.
[1]“碳中和”专题系列研究报告｜中国碳中和重点行业分析（交通运输篇）[EB/OL]. (2021-08-04) [2023-04-08]. http://www.oscpzfo.cn/post/1214.html.
[2] 毛保华, 周琪, 李宁海, 等 .“双碳”目标下中国货物运输体系可持续发展策略[J]. 科技导报, 2022, 40(14): 65-72.
[3] 任豪祥：积极落实碳达峰碳中和目标，加快推进物流行业绿色低碳转型[EB/OL]. (2022-06-15) [2023-04-08]. https://mp.weixin.qq.com/s/ko6rEbhYcK9XqMNtpPbgXA.
[4] 国务院关于加快建立健全绿色低碳循环发展经济体系的指导意见[EB/OL]. (2021-02-22) [2023-03-28]. http://www.gov.cn/zhengce/content/2021-02/22/content_5588274.htm.
[5] 中华人民共和国国民经济和社会发展第十四个五年规划和2035年远景目标纲要[EB/OL]. (2021-03-13)[2023-03-28]. http://www.gov.cn/xinwen/2021-03/13/content_5592681.htm.
[6] 赵洁玉, 刘然, 刘哲, 等. 中国绿色物流的发展现状及建议[J]. 中国经贸导刊(中), 2019(8): 46-47.
[7] 张颖. 我国绿色物流研究的知识结构与研究热点——基于 CiteSpace 的图谱量化研究[J]. 物流科技, 2022, 45(16): 56-59.
[8] 闫述丽.“双碳”目标下绿色物流对流通企业绩效的影响[J]. 商业经济研究, 2022(20): 136-139.
[9] 中国物流与采购联合会 . 物流术语: GB/T 18354—2021[S]. 北京: 中国标准出版社, 2016.
[10] 张晔, 宋国华, 尹航, 等 . 综合交通运输系统碳排放预测的不确定性分析[J]. 交通运输工程与信息学报,2023, 21(1): 64-79.
[11] 刘华军, 田震, 石印. 中国二氧化碳排放的空间差异及其双维内在结构解析: 2000—2019 年[J]. 地理研究, 2023, 42(3): 857-877.
[12] 沈体雁, 于瀚辰, 周麟, 等 . 北京市二手住宅价格影响机制——基于多尺度地理加权回归模型(MGWR)的研究[J]. 经济地理, 2020, 40(3): 75-83.
[13] 李龙, 姚云峰, 秦富仓, 等 . 基于地理加权回归模型的土壤有机碳密度影响因子分析[J]. 科技导报, 2016, 34(2): 247-254.
[14] Mansour S, Al Kindi A, Al-Said A, et al. Sociodemographic determinants of COVID-19 incidence rates in Oman: Geospatial modelling using multiscale geographically weighted regression(MGWR)[J/OL]. Sustainable Cities and Society, 2020, https://doi. org/10.1016/j. scs.2020.102627.
[15] 张立国 . 中国物流业二氧化碳排放变化驱动因素分析[J]. 中国流通经济, 2016, 30(12): 29-39.
[16] 薛薇. SPSS统计分析方法及应用[M]. 第4版. 北京: 电子工业出版社, 2017.
[17] Brunsdon C, Fotheringham A S, Charlton M, et al. Geographically weighted regression modelling spatial non-stationarity[J]. Society, 2010, 47(3): 431-443.
[18] Oshan T M, Li Z, Kang W, et al. MGWR: A python implementation of multiscale geographically weighted regression for investigating process spatial[J]. ISPRS International Journal of Geo-Information, 2019, 8(6): 269.
[19] Wen Q, Qi X L. Evaluation of green logistics efficiency in Northwest China[J]. Sustainability (Switzerland), 2022, 14(11): 1-14.
[20] 交通运输部：“公转铁”“公转水”成效明显 运输结构持续优化[EB/OL]. (2022-10-31)[2023-03-28]. https://m. gmw.cn/baijia/2022-10/31/36126790.html.
[21] 零排放货运行动 . 中国零排放货运年度进展报告[R].北京: 零排放货运行动, 2023.
[22] Pankaj D, Choi T M, Somani S, et al. Blockchain technology in supply chain operations: Applications, challenges and research opportunities[J]. Transportation Research Part E: Logistics and Transportation Review, 2020, doi: 10.1016/j.tre.2020.102067.
[23] 李健, 白子毅, 李柏桐. 双碳背景下京津冀物流业碳排放脱钩及影响因素研究[J]. 城市问题, 2022, 322(5): 69-76.
[24] 侯美玲, 周晓艳, 洪松, 等 . 环境规制下京津冀及周边地区 SO2 污染治理效应与路径[J/OL]. [2023-03-29]. http://kns.cnki.net/kcms/detail/43.1542.N.20230329.1042.002.html.
[25] 冯文波 . 我国运输结构优化调整影响因素与策略研究[J]. 铁道运输与经济, 2019, 41(9): 18-23.
[26] Wang L. Research on logistics carbon emissions under the coupling and coordination scenario of logistics industry and financial industry[J]. PLoS One, 2021, 16: 1-14.
[27] 柯晶琳, 姜维军, 杨兴锐. 中日绿色物流发展差距比较及经验借鉴[J]. 对外经贸实务, 2018(12): 87-91.
