@article{Shu:2026:10.1016/j.tre.2026.104712,
author = {Shu, S and Yang, X and Ming, Z and Na, X and Stettler, MEJ and Lee, DH and Hu, S},
doi = {10.1016/j.tre.2026.104712},
journal = {Transportation Research Part E Logistics and Transportation Review},
title = {A carbon reduction incentive model for crowdsourced urban freight: Facilitating freight pooling and electric truck adoption},
url = {http://dx.doi.org/10.1016/j.tre.2026.104712},
volume = {209},
year = {2026}
}

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TY  - JOUR
AB  - Urban freight transport faces significant decarbonization pressure, yet existing strategies such as freight pooling and electric truck adoption often struggle with limited uptake due to operational complexities, costs, and infrastructure challenges. Critically, current research lacks an integrated, operational incentive framework specifically designed for multi-stakeholder participation in urban crowdsourced logistics, where task-level operational decisions across multiple stakeholders play a central role in system-level carbon reduction. This study introduces a Carbon Reduction Incentive Model (CRIM) that addresses this gap. The CRIM incentivizes individual shippers and independent carriers within a crowdsourced logistics system by assigning task-level rewards for freight pooling and electric truck usage. Rewards are quantified by tonne-kilometer savings relative to conventional individual diesel deliveries, further adjusted by a time-based factor to encourage off-peak operations. The CRIM is embedded within an enhanced pick-up and delivery model that explicitly accounts for stakeholder cost components, vehicle heterogeneity, charging requirements, and time-sensitive feasibility (PDPTW-HEC). To optimize the system’s complex trade-off between costs and carbon emissions, a customized heuristic algorithm is developed. Scenario-based case studies using real-world data and international carbon accounting standards validate the proposed incentive model’s performance. Results demonstrate that CRIM can achieve 9.5–38.1% higher electric truck adoption and an 8.4–28.7% reduction in total carbon emissions. This framework offers a practical and scalable approach for designing and evaluating task-level carbon reduction incentives in urban freight operations.
AU  - Shu,S
AU  - Yang,X
AU  - Ming,Z
AU  - Na,X
AU  - Stettler,MEJ
AU  - Lee,DH
AU  - Hu,S
DO  - 10.1016/j.tre.2026.104712
PY  - 2026///
SN  - 1366-5545
TI  - A carbon reduction incentive model for crowdsourced urban freight: Facilitating freight pooling and electric truck adoption
T2  - Transportation Research Part E Logistics and Transportation Review
UR  - http://dx.doi.org/10.1016/j.tre.2026.104712
VL  - 209
ER  - 

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