Evidence map›Paper›PMID 40465029›Full record

ArticleCarbon balance and management2025

Carbon reduction strategies for logistics based on emission prediction under multi-scenarios in coastal developed region.

Junyu Chen, Yan Zhu, Shengnan Wu, Chuanming Yang, Huimin Wang

Abstract read
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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. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Junyu ChenSchool of Business, Suzhou University of Science and Technology, Suzhou, 215009, Jiangsu Province, China.
Yan ZhuSchool of Business, Suzhou University of Science and Technology, Suzhou, 215009, Jiangsu Province, China.
Shengnan WuSchool of Business, Suzhou University of Science and Technology, Suzhou, 215009, Jiangsu Province, China.
Chuanming YangSchool of Business, Suzhou University of Science and Technology, Suzhou, 215009, Jiangsu Province, China. ycmpaper@163.com.
Huimin WangCollege of Management and Economics, Tianjin University, Tianjin, 300072, China.

Funding

National Natural Science Foundation of China 72404204National Social Science Fund of China 22BJY130Postgraduate Research & Practice Innovation Program of Jiangsu Province KYCX23_3361Social Science Foundation of Jiangsu Province 21GLC015
6 · The paper itself

Abstract

The differences in logistics carbon emission and carbon absorption in different areas lead to potential conflicts in the green development of regional logistics. The Yangtze River Delta (YRD) in China is a critical coastal developed region for economic integration development and opening up, with logistics playing a substantial role in energy consumption and carbon emissions. Therefore, addressing the low-carbon transformation of logistics in the YRD is a matter of great concern. The framework of carbon balance accounting and prediction of logistics consist of 'basic accounting-factor analysis-prediction simulation' is constructed. Then, this study accounts the logistics carbon emissions (LCE) and logistics carbon capacity (LCC) in the four subregions (Shanghai, Jiangsu, Zhejiang and Anhui) from 2010 to 2021. Estimates the influencing factors of LCE through the geographically and Temporally Weighted Regression model (GTWR). Then, constructs the prediction model for the logistics carbon balance statue based on System Dynamics (SD) structure under four single-factor scenarios and two cross-factor scenarios from 2022 to 2030. Results showed that: (1) The logistics carbon deficit in the YRD is prominent. And the four sub-regions show different spatio-temporal evolution characteristics. (2) The influences of economic level and technical level on LCE are particularly obvious and also has spatio-temporal heterogeneity. (3) There is a trade-off between the pursuit of economic development and carbon emission control. S1 and S2 will continue to witness the increase of logistics carbon pollution. Under S3-S4, the effect of LCE reduction is relatively weak. S5 shows a significant carbon reduction effect, S6 could achieve a good balance between economic development and carbon emissions. (4) Promote the reform of transportation from highway to railway, ensure access to affordable and clean energy for logistic, promote the coordinated carbon reduction of regional logistics and synchronous construction of ecological and artificial carbon pool based on the conditions of developed coastal areas could be feasible paths to achieve carbon balance for YRD.

Indexed as

Carbon reductionGTWR modelLogistics carbon capacityLogistics carbon emissionsSD modelYangtze River delta

Identifiers

PMID40465029
PMCPMC12139084

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.