| 摘要: |
| 文章基于中国沿海港群的区域视角，运用考虑非期望产出的超效率网络 SBM 模型，对2010—2022 年中国 11 个沿海区域港群总效率和分阶段效率（包括生产阶段和腹地服务阶段）进行评价，在此基础上，运用地理探测器和时空地理加权回归（GTWR）模型进一步分析区域港群效率的影响因素。研究表明：（1）中国沿海区域港群总效率及分阶段效率均保持稳步增长，其腹地服务效率对总效率的贡献大于生产效率；（2）中国沿海区域港群效率总体差异较大，北部—南部差异最突出，东部—南部次之，北部—东部最小；（3）就影响因素而言，公路密度、第三产业比重、R&D 专利授权数量、人均 GDP 影响较大，外商投资总额与城镇人口比重影响较小。 |
| 关键词: 区域港群效率 超效率网络 SBM 模型 地理探测器 时空地理加权回归 |
| DOI：10.20016/j.cnki.hykfygl.2026.02.012 |
| 投稿时间：2025-09-03修订日期：2025-12-23 |
| 基金项目:国家自然科学基金项目“海陆统筹视角下‘港—产—城’系统多尺度耦合效应及空间联动机制研究”（42506239）． |
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| Study on the Detection of Influencing Factors and Driving Mechanism of the Efficiency of China’s Coastal Regional Port Groups |
| MA Qifei,XU Dianming,YANG Mi |
| School of Economics and Management，Zhejiang Ocean University;Zhejiang Institute of Marine Civilization and Economic Research，Zhejiang Ocean University |
| Abstract: |
| From a regional perspective of China’s coastal port groups，this study employs a super-efficiency network SBM（Slack-Based Measure）model that accounts for undesirable outputs to evaluate the overall efficiency and stage-specific efficiencies（including the production stage and the hinterland service stage） of 11 coastal regional port groups in China during the period 2010—2022．On this basis，the Geodetector and Geographically and Temporally Weighted Regression（GTWR）model are applied to further analyze the influencing factors of the efficiency of these regional port groups. The results show that：（1）Both the overall efficiency and stage-specific efficiencies of China’s coastal regional port groups have maintained a steady growth trend，among which the contribution of their hinterland service efficiency to the overall efficiency is far greater than that of the production efficiency；（2）There exists a relatively large overall difference in the efficiency of China’s coastal regional port groups. Spatially，the efficiency difference between the northern and southern port groups is the most prominent，followed by that between the eastern and southern ones，while the difference between the northern and eastern ones is the smallest；（3）In terms of influencing factors，road density，the proportion of the tertiary industry，the number of authorized R&D patents，and per capita GDP exert relatively strong impacts，whereas the total foreign investment and the proportion of the urban population have relatively weak influences. |
| Key words: Efficiency of regional port group Super-efficiency network SBM model Geodetector Geographically and temporally weighted regression |
