ArticleCarbon balance and management2025
Temporal-spatial evolution analysis of carbon emission efficiency in the logistics industry of coastal provinces in China based on the super-efficiency SBM model.
Article in Carbon balance and management, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Green frontiers on the "Belt and Road": carbon policy, emission efficiency, and environmental total factor productivity across 41 economies.Carbon balance and management · 2026Article
- How factor digitalization shapes urban carbon TFP: mechanisms, thresholds, and spillovers in Chinese cities.Carbon balance and management · 2026Article
- Research on spatial differentiation, source decomposition and convergence of agricultural carbon emission efficiency in China.Scientific reports · 2026Article
- Distribution dynamics and spatial dependence in healthcare resource allocation efficiency: evidence of path dependence and club formation in China.Frontiers in public health · 2026Article
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3 authors.
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Abstract
backgroundThe logistics industry is a pillar industry of China's national economic development, and coastal provinces, as the core of China's economic development, have highly developed logistics industry. However, the rapid development of the logistics industry in China's coastal provinces is usually accompanied by high carbon emissions. Therefore, improving the carbon emission efficiency of the logistics industry (LCEE) in China's coastal provinces is one of the main contents to achieve "China's dual carbon goals". Existing research indicates that LCEE is closely related to the efficiency levels of neighboring regions, and its temporal and spatial evolution characteristics are also influenced by the change of neighborhood efficiency. However, less attention has been given to the role of geographic proximity in analyzing the temporal and spatial evolution characteristics. Thus, this paper introduces the spatial lag factor into the Markov chain (MC) to obtain the spatial Markov chain (SMC), examining the influence of neighboring provinces' LCEE on the spatial evolution of the local LCEE in China's coastal provinces.
resultsThe results show that: For most years between 2007 and 2022, in China's eleven coastal provinces, the LCEE values were less than one. These low LCEE values indicated that the potential for emission reduction had not been fully tapped, and low-carbon development faced significant challenges. The primary obstacle to improving LCEE during the study period was low technical efficiency, and the development of the technology level was crucial for enhancing LCEE. In 2007-2011 and 2015, the spatial distribution of LCEE exhibited significant spatial clustering features. The primary type of spatial clustering was high-high clustering, which indicated there was an obvious trend of regional coordinated development. The LCEE of neighboring provinces influenced the state transition probabilities of their own states, and spatial spillover effects in these provinces were very evident.
conclusionsThis study conducted an in-depth analysis of the temporal-spatial evolution characteristics of LCEE in China's coastal provinces. There are significant differences in LCEE among these provinces. Each province needs to reduce the carbon dioxide emissions of the logistics industry and improve the LCEE through regional cooperation, technological investment, and targeted policies, so as to promote the sustainable development of the logistics industry in China's coastal provinces.
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