The Impact of Logistics Industry Clustering on Green Total Factor Productivity: Evidence from China
Year: 2025
Type: article
Abstract: Although logistics underpins the spatial architecture of supply chains, the causal contribution of logistics industry clustering to green total factor productivity (GTFP) remains under-identified relative to aggregate or manufacturing clustering. This study investigates both the local and spatial spillover effects of logistics industry clustering on green total factor productivity, utilizing panel data from 30 Chinese provinces spanning 2010 to 2023. The empirical results demonstrate that logistics industry clustering significantly enhances green total factor productivity within the local province and generates robust positive spillover effects in adjacent regions. Regional heterogeneity analysis reveals that in the eastern provinces, clustering of the logistics industry bolsters green total factor productivity both locally and regionally. In contrast, in the central region, such clustering only benefits neighboring provinces, while in the western region, its impact is not statistically significant for either local or neighboring green total factor productivity. Temporal heterogeneity analysis further indicates that the positive influence of logistics industry clustering on green total factor productivity has become more pronounced since 2018.Additionally, spatial mediation effect analysis uncovers that improvements in local green total factor productivity stem from logistics industry clustering’s capacity to enhance resource allocation efficiency and foster industrial upgrading. Notably, the spatial spillover effect dissipates entirely beyond a distance of 350 km. These findings establish logistics industry clustering as a high-leverage, cross-boundary tool for aligning regional logistics planning with green objectives, delineating the effective radius of collaboration to internalize externalities and providing practical guidance for developing economies.
Source: Sustainability
DOI: 10.3390/su17177978
Authors: Yanmiao Cai, Yuge Zhang, Yuki Gong, Willa Li, Frank Li
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4413990002
