Work

Spatiotemporal analysis of CO₂ emissions from heavy-duty trucks in belt and road port transportation using GPS trajectory data

Year: 2025

Type: article

Abstract: Amid global efforts toward carbon neutrality, the emissions from heavy-duty trucks (HDTs) within port transportation systems have become an increasing focus of research and policy discourse. This study constructs a comprehensive framework based on GPS trajectory data from overloaded container trucks, encompassing trajectory matching, carbon emission estimation, and spatial clustering analysis to systematically identify the spatiotemporal evolution characteristics of carbon dioxide (CO₂) emissions within the port transportation network. First, a Hidden Markov Model (HMM) is employed for high-precision trajectory matching, accurately reconstructing vehicle travel paths. Subsequently, a carbon emission estimation model that accounts for vehicle mass uncertainty is developed to quantify emission intensity across different time periods and road segments. Finally, Kernel Density Estimation (KDE) and the Getis-Ord G i * clustering method are utilized to identify emission hotspots and their dynamic changes. The results indicate that carbon emissions in the port collector-distributor roads exhibit a temporal pattern that precedes peak commute times on urban roads while spatially forming a “linear high-density belt” along major corridors, contrasting with the “point aggregation” characteristic of urban roads. Additionally, it is found that low-speed operating conditions, rather than high traffic volume, are key contributors to increased carbon emission intensity. This research provides theoretical support and quantitative evidence for carbon reduction in “Belt and Road” port transportation, offering decision-making references for constructing a low-carbon port logistics system.

Source: Resources Conservation and Recycling

DOI: 10.1016/j.resconrec.2025.108540

Authors: Jingwei Guo, Haofei Guo, Zhen-Song Chen, Zhitao Fan, Wen-Long Shang

Institutions: Shanghai Maritime University, Wuhan University, City University of Macau, Imperial College London

Language: English

Topic: Vehicle emissions and performance

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Climate action, Sustainable cities and communities

Open Access status: hybrid

Estimated APC paid: $5,270