城市物流标准化效率的空间差异及与产业结构升级的交互溢出效应
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张宝友(1977—),男,浙江兰溪人,教授,博士,主要从事产业标准化与质量、对外直接投资研究。E-mail: zby_0103@163.com |
收稿日期: 2024-11-20
修回日期: 2025-01-23
网络出版日期: 2026-01-15
基金资助
国家社会科学基金项目(21BJY195)
国家社会科学基金项目(24BGL242)
浙江省哲学社会科学规划课题(23NDJC183YB)
版权
Spatial differences in the efficiency of urban logistics standardization and its interactive spillover effects on industrial structure upgrading
Received date: 2024-11-20
Revised date: 2025-01-23
Online published: 2026-01-15
Supported by
National Social Science Fund Project(21BJY195)
National Social Science Fund Project(24BGL242)
Zhejiang Provincial Philosophy and Social Sciences Planning Project(23NDJC183YB)
Copyright
本文通过评估2006—2022年中国284个城市的物流标准化效率,重点考察了区域差异及其来源与动态分布演化特征,并采用空间联立方程模型对城市的物流标准化效率与物流产业结构升级之间的空间溢出效应进行探究。研究发现:①全国总体及东部、中部与西部3个区域的城市物流标准化效率逐年提升,但区域之间的绝对差距不断扩大;②全国总体差异以年平均增幅5.48%的速度不断扩大,造成总体差异的主要来源是区域间差异与超密度。中部和西部区域内的差异高于东部区域,东部-中部、东部-西部之间的差异大于中部-西部地区。③中国城市物流标准化效率与其产业结构升级之间存在正向交互效应,互促关系中城市产业结构升级处于相对优势地位,且二者在地理空间上均存在显著的空间溢出效应及空间交互影响效应。
张宝友 , 郭鹏丽 , 孟丽君 , 徐茜 . 城市物流标准化效率的空间差异及与产业结构升级的交互溢出效应[J]. 地理科学, 2026 , 46(2) : 451 -464 . DOI: 10.13249/j.cnki.sgs.20240209
This article assesses the logistics standardization efficiency of 284 cities in China from 2006 to 2022, focusing on the regional differences and their sources and dynamic distribution evolution characteristics. It also uses the spatial simultaneous equation model to explore the spatial spillover effect between the logistics standardization efficiency of cities and the upgrading of the logistics industrial structure. The study finds: 1) The overall logistics standardization efficiency of cities across the country and the three major regions (east, central, and west) has been increasing year by year, but the absolute gap between regions has been continuously expanding; 2) The overall regional difference has been expanding at an average annual growth rate of 5.48%, and the main source of the overall difference is the regional differences and over-density. The differences within the central and western regions are higher than those in the eastern region, and the differences between the eastern and central, and the eastern and western regions are greater than those between the central and western regions. 3) There is a positive interactive effect between the logistics standardization efficiency of Chinese cities and the upgrading of the industrial structure. In the mutual promotion relationship, the upgrading of the industrial structure of cities is in a relatively advantageous position, and both have significant spatial spillover effects and spatial interaction effects in geographical space.
表1 城市物流标准化效率评价指标Table 1 Evaluation indicators for urban logistics standardization efficiency |
| 变量类型 | 变量 | 指标含义 |
| 投入指标 | 物流建设用地 | 城市辖区内的物流产业建设用地面积/km2 |
| 标准研发人员 | 城市辖区内的物流标准研发从业人员/万人 | |
| 标准研发经费 | 城市辖区内的物流标准研发投资额/万元 | |
| 产出指标 | 经济效益(期望产出) | 城市辖区内的物流业总产值/万元 |
| 社会效益(期望产出) | 城市辖区内的物流从业人员的平均工资/元 | |
| 环境效益(非期望产出) | 城市辖区内的物流业单位产值能耗/(MJ/元) | |
| 城市辖区内的物流业单位产值污染物排放量/t | ||
| 核心解释变量 | 城市物流标准化效率(ρ*) | 利用超效率的SBM-DEA模型计算 |
| 城市物流产业结构升级(isu) | 城市辖区内物流业的人均增加值 |
表2 2006—2022年中国城市物流标准化效率与产业结构升级全局Moran’s ITable 2 Global Moran’s I for efficiency of urban logistics standardization and industrial structure upgrade in China from 2006 to 2022 |
| 变量 | 检验值 | 2006年 | 2008年 | 2010年 | 2012年 | 2014年 | 2016年 | 2018年 | 2020年 | 2022年 |
| 注:ρ*为城市物流标准化效率,isu为物流产业结构升级,未含港澳台数据。 | ||||||||||
| ρ* | Moran’s I | 0.287 | 0.183 | 0.198 | 0.125 | 0.109 | 0.054 | 0.042 | 0.125 | 0.142 |
| P | 0.002 | 0.001 | 0.002 | 0.002 | 0.032 | 0.121 | 0.165 | 0.012 | 0.004 | |
| isu | Moran’s I | 0.211 | 0.265 | 0.279 | 0.213 | 0.264 | 0.302 | 0.276 | 0.268 | 0.237 |
| P | 0.003 | 0.002 | 0.001 | 0.001 | 0.001 | 0.001 | 0.001 | 0.001 | 0.001 | |
表3 空间联立方程估计结果Table 3 Estimation results of spatial system of equations |
| 变量 | 城市物流标准化效率 | 物流产业结构升级 | |||
| 地理距离空间权重(1) | 地理邻接空间权重(2) | 地理距离空间权重(3) | 地理邻接空间权重(4) | ||
| 注:*、**、***分别表示在10%、5%和1%水平上显著,ρ*为城市物流标准化效率,isu为物流产业结构升级,Gov为政府支持,Env为环境规制,Open为市场开放,inno为创新氛围,tech为信息化水平,Tran为交通设施水平,Buy为居民购买能力。 表格内空白处表示空值,括号内为t值,未含港澳台数据。 | |||||
| W×(lnρ*) | 0.032*** (3.253) | 0.124* (7.567) | –0.016** (–2.345) | –0.014*** (–1.711) | |
| W×(lnisu) | 0.019** (2.272) | 0.078*** (4.217) | –0.009* (–1.151) | –0.008 (–1.279) | |
| lnisu | 3.001* (7.13) | 3.124* (11.24) | |||
| lnEnv | 2.254 (7.423) | 2.002 (–4.367) | |||
| lninno | 1.732** (1.756) | 1.021*** (1.007) | |||
| lninfo | 2.325* (1.113) | 1.237* (1.732) | 1.261* (2.887) | 1.152*** (2.151) | |
| lnOpen | 1.137*** (3.756) | 1.137*** (3.756) | 0.279*** (1.901) | 0.194* (1.103) | |
| lnGov | -1.002** (–2.654) | –1.002** (–2.654) | –0.261** (–2.623) | -0.201* (–2.664) | |
| lnρ* | 0.154* (2.867) | 0.049* (1.712) | |||
| lnTran | 1.221*** (3.114) | 1.119** (2.415) | |||
| lnBuy | 1.225* (4.472) | 1.110*** (1.199) | |||
| 常数项 | –2.867** (–6.856) | –2.314*** (–5.611) | 0.452*** (5.175) | 0.761* (6.011) | |
| R2 | 0.623 | 0.654 | 0.398 | 0.452 | |
| F | 189.987 | 199.457 | 106.754 | 114.856 | |
| 观测值 | |||||
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