Transport Carbon Emission Measurement Models and Spatial Patterns Under the Perspective of Land–Sea Integration–Take Tianjin as an Example

Lina Ke (), Zhiyu Ren, Quanming Wang, Lei Wang, Qingli Jiang, Yao Lu, Yu Zhao and Qin Tan
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Lina Ke: School of Geographical Science, Liaoning Normal University, Dalian 116029, China
Zhiyu Ren: School of Geographical Science, Liaoning Normal University, Dalian 116029, China
Quanming Wang: National Sea Environmental Monitoring Center, Dalian 116023, China
Lei Wang: School of Geographical Science, Liaoning Normal University, Dalian 116029, China
Qingli Jiang: School of Geographical Science, Liaoning Normal University, Dalian 116029, China
Yao Lu: School of Geographical Science, Liaoning Normal University, Dalian 116029, China
Yu Zhao: School of Geographical Science, Liaoning Normal University, Dalian 116029, China
Qin Tan: School of Geographical Science, Liaoning Normal University, Dalian 116029, China

Sustainability, 2025, vol. 17, issue 7, 1-19

Abstract: The goal of “double carbon” puts forward higher requirements for the control of transport carbon emissions, and the exploration of transport carbon emission modelling driven by big data is an important attempt to reduce carbon accurately. Based on the land Vehicle Miles Traveled data (VMT) and the sea Automatic Identification System (AIS) data, this study establishes a refined, high-resolution carbon emission measurement model that incorporates the use of motor vehicles and ships from a bottom-up approach and analyzes the spatial distribution characteristics of land and sea transport carbon emissions in Tianjin using geospatial analysis. The results of the study show that (1) the transportation carbon emissions in Tianjin mainly come from land road traffic, with small passenger cars contributing the most to the emissions; (2) high carbon emission zones are concentrated in economically developed, densely populated, and high road network density areas, such as the urban center Binhai New Area, and the marine functional zone of Tianjin; (3) carbon emission values are generally higher in the segments where ports, airports, and interchanges are connected. The transportation carbon emission measurement model developed in this study provides practical, replicable, and scalable insights for other coastal cities.

Keywords: traffic flow big data; land and sea transport integration; bottom-up approach; carbon emission estimation modelling; spatial analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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