物流业绿色全要素生产率影响因素的时空异质性研究——基于中国八大经济区视角
Research on the Spatiotemporal Heterogeneity of Factors Affecting Green Total Factor Productivity in the Logistics Industry—A Perspective from China’s Eight Major Economic Zones
摘要: 文章使用2007~2021年中国30省份的数据,运用Super-SBM模型测算了八大经济区的物流业绿色全要素生产率;运用时空地理加权回归模型研究物流业绿色全要素生产率影响因素的时空异质性。结果表明:全国物流业绿色全要素生产率的整体水平逐渐提升,沿海地区的物流业绿色全要素生产率较高,而黄河中游、长江中游和西南地区的物流业绿色全要素生产率偏低;物流业绿色全要素生产率影响因素的作用机制具有显著的时空非平稳特征,不同时空节点下,各要素的作用方向及时变趋势均不同;城市化进展和交通网络密度对大部分地区的物流业绿色全要素生产率都有正向作用,其中交通网络密度对西南、西北等地形较为复杂的区域有负向作用,而能源消费强度对绝大部分地区都存在负向影响。
Abstract: The article uses data from 30 provinces in China from 2007 to 2021 and uses the Super SBM model to calculate the green total factor productivity of the logistics industry in eight major economic regions; Using a spatiotemporal geographically weighted regression model to study the spatiotemporal heterogeneity of factors affecting green total factor productivity in the logistics industry. The results show that the overall level of green total factor productivity in the national logistics industry is gradually improving. The green total factor productivity of the logistics industry in coastal areas is relatively high, while the green total factor productivity of the logistics industry in the middle reaches of the Yellow River, the middle reaches of the Yangtze River, and the southwest region is relatively low; the mechanism of the influencing factors of green total factor productivity in the logistics industry has significant spatiotemporal non-stationary characteristics, and the direction and trend of each factor’s action are different at different spatiotemporal nodes; the progress of urbanization and the density of transportation networks have a positive effect on the green total factor productivity of the logistics industry in most regions. Among them, the density of transportation networks has a negative effect on more complex terrain areas such as the southwest and northwest, while the intensity of energy consumption has a negative impact on the vast majority of regions.
文章引用:朱明萱. 物流业绿色全要素生产率影响因素的时空异质性研究——基于中国八大经济区视角[J]. 运筹与模糊学, 2024, 14(2): 1146-1155. https://doi.org/10.12677/orf.2024.142213

参考文献

[1] 陈星星. 非期望产出下我国能源消耗产出效率差异研究[J]. 中国管理科学, 2019, 27(8): 191-198.
[2] 刘钻扩, 辛丽. “一带一路”建设对沿线中国重点省域绿色全要素生产率的影响[J]. 中国人口·资源与环境, 2018, 28(12): 87-97.
[3] Zhang, Q., Gu, B., Zhang, H., et al. (2023) Emission Reduction Mode of China’s Provincial Transportation Sector: Based on “Energy ” Carbon Efficiency Evaluation. Energy Policy, 177, Article 113556. [Google Scholar] [CrossRef
[4] 刘鸿燕, 姚倩文. 绿色全要素生产率的测度与应用: 一个文献综述[J]. 产业组织评论, 2020, 14(3): 174-195.
[5] 李健, 刘恋. 物流业全要素能源效率时空演变及减排潜力研究[J]. 环境科学与技术, 2019, 42(11): 222-231.
[6] Yang, J., Tang, L. and Mi, Z. (2019) Carbon Emissions Performance in Logistics at the City Level. Journal of Cleaner Production, 231, 1258-1266. [Google Scholar] [CrossRef
[7] 李铭泓, 黄羿, 朱伟俊, 张发根, 常向阳. 中国交通运输业碳排放全要素生产率研究——基于Global Malmquist-Luenberger指数[J]. 科技管理研究, 2021, 41(9): 203-211.
[8] 杨恺钧, 毛博伟, 胡菡. 长江经济带物流业全要素能源效率——基于包含碳排放的SBM与GML指数模型[J]. 北京理工大学学报(社会科学版), 2016, 18(6): 54-62.
[9] Gan, W., Yao, W. and Huang, S. (2022) Evaluation of Green Logistics Efficiency in Jiangxi Province Based on Three-Stage DEA from the Perspective of High-Quality Development. Sustainability, 14, Article 797. [Google Scholar] [CrossRef
[10] 张瑞, 胡彦勇, 郄晓彤. 中国物流业能源生态效率与其影响因素的动态响应研究[J]. 经济问题, 2021(8): 9-17.
[11] Bai, D., Dong, Q., Khan, S., et al. (2022) Spatio-Temporal Heterogeneity of Logistics CO2 Emissions and Their Influencing Factors in China: An Analysis Based on Spatial Error Model and Geographically and Temporally Weighted Regression Model. Environmental Technology and Innovation, 28, Article 102791. [Google Scholar] [CrossRef
[12] Huang, B., Wu, B. and Barry, M. (2010) Geographically and Temporally Weighted Regression for Modeling Spatio-Temporal Variation in House Prices. International Journal of Geographical Information Science, 24, 383-401. [Google Scholar] [CrossRef
[13] Wang, X., Fan, F. and Liu, C. (2022) Regional Differences and Driving Factors Analysis of Carbon Emissions from Power Sector in China. Ecological Indicators, 142, Article 109297. [Google Scholar] [CrossRef
[14] Chen, J., Lian, X. and Su, H. (2021) Analysis of China’s Carbon Emission Driving Factors Based on the Perspective of Eight Major Economic Regions. Environmental Science and Pollution Research, 28, 8181-8204. [Google Scholar] [CrossRef] [PubMed]
[15] Liu, J., Li, S. and Ji, Q. (2021) Regional Differences and Driving Factors Analysis of Carbon Emission Intensity from Transport Sector in China. Energy, 224, Article 120178. [Google Scholar] [CrossRef
[16] 杨文涛, 黄慧坤, 魏东升, 赵斌, 彭焕华. 大气污染联合治理分区视角下的中国PM2.5关联关系时空变异特征分析[J]. 环境科学, 2020, 41(5): 2066-2074.
[17] 李恩康, 陆玉麒, 陈娱. 中国外贸货物出口的地理格局演化及影响因素分析——基于货物出口距离和GTWR模型[J]. 地理研究, 2019, 38(11): 2624-2638.
[18] 张立国, 李东, 龚爱清. 中国物流业全要素能源效率动态变动及区域差异分析[J]. 资源科学, 2015, 37(4): 754-763.
[19] 刘华军, 郭立祥, 乔列成, 石印. 中国物流业效率的时空格局及动态演进[J]. 数量经济技术经济研究, 2021(5): 57-74.
[20] Liu, H., Yang, R., Wu, J. and Chu, J. (2021) Total-Factor Energy Efficiency Change of the Road Transportation Industry in China: A Stochastic Frontier Approach. Energy, 219, Article 119612. [Google Scholar] [CrossRef