Dual objective multimodal transportation path optimization based on different carbon tax mechanisms under uncertain demand

By delving into low-carbon transportation research, we can address the imperative for the high-quality development of transportation services, while simultaneously advancing the realization of the dual-carbon objective. This study focuses on optimizing multimodal transport routes under varying carbo...

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Main Authors: Xu Zhang, Hongzhu Chen, Haiyan Zhang, Xumei Yuan
Format: Article
Language:English
Published: Tsinghua University Press 2025-03-01
Series:Journal of Highway and Transportation Research and Development
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Online Access:https://www.sciopen.com/article/10.26599/HTRD.2025.9480051
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author Xu Zhang
Hongzhu Chen
Haiyan Zhang
Xumei Yuan
author_facet Xu Zhang
Hongzhu Chen
Haiyan Zhang
Xumei Yuan
author_sort Xu Zhang
collection DOAJ
description By delving into low-carbon transportation research, we can address the imperative for the high-quality development of transportation services, while simultaneously advancing the realization of the dual-carbon objective. This study focuses on optimizing multimodal transport routes under varying carbon tax frameworks, taking into account the demand uncertainty that arises from unforeseen events such as abrupt restocking or seasonal fluctuations. We formulate a dual-objective 0-1 path optimization model under both a unified carbon tax mechanism and a piecewise progressive carbon tax scheme. The model aims to minimize total cost and carbon emissions in the face of stochastic demand. Utilizing Monte Carlo simulation and the laws of large numbers, we convert the model to maximize the expected value of the uncertain objective. An enhanced non-dominated sorting genetic algorithm is then developed to solve this model, yielding solutions that more effectively meet our objectives. This algorithm is designed to expand the search space, mitigating the “"premature convergence” issue and thereby generating superior individuals and solutions. Finally, we assess the applicability of our model and algorithm to transportation challenges within the context of the dual-carbon initiative through a numerical example. We also explore the influence of different carbon tax mechanisms on total cost and emissions, as well as their applicability and efficacy in the face of demand uncerainty. The findings indicate that companies can achieve emission reductions with minimal cost increases under dual-target cost scenarios, ideal for dual carbon transportation contexts. Moreover, carbon tax rates significantly impact emission control, with segmented progressive taxes proving more effective, especially in high-demand uncertainty. Decision-makers should consider technological capabilities to set optimal tax rates and thresholds, fostering corporate enthusiasm. This research informs policy and decision-making for authorities and firms.
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spelling doaj-art-db2d68a255624ab49c1e2ad047d789c32025-08-20T03:48:14ZengTsinghua University PressJournal of Highway and Transportation Research and Development2095-62152025-03-01191526010.26599/HTRD.2025.9480051Dual objective multimodal transportation path optimization based on different carbon tax mechanisms under uncertain demandXu Zhang0Hongzhu Chen1Haiyan Zhang2Xumei Yuan3School of Economics and Management, Yanshan University, Qinhuangdao, Hebei 066000, ChinaSchool of Economics and Management, Yanshan University, Qinhuangdao, Hebei 066000, ChinaSchool of Economics and Management, Yanshan University, Qinhuangdao, Hebei 066000, ChinaSchool of Economics and Management, Yanshan University, Qinhuangdao, Hebei 066000, ChinaBy delving into low-carbon transportation research, we can address the imperative for the high-quality development of transportation services, while simultaneously advancing the realization of the dual-carbon objective. This study focuses on optimizing multimodal transport routes under varying carbon tax frameworks, taking into account the demand uncertainty that arises from unforeseen events such as abrupt restocking or seasonal fluctuations. We formulate a dual-objective 0-1 path optimization model under both a unified carbon tax mechanism and a piecewise progressive carbon tax scheme. The model aims to minimize total cost and carbon emissions in the face of stochastic demand. Utilizing Monte Carlo simulation and the laws of large numbers, we convert the model to maximize the expected value of the uncertain objective. An enhanced non-dominated sorting genetic algorithm is then developed to solve this model, yielding solutions that more effectively meet our objectives. This algorithm is designed to expand the search space, mitigating the “"premature convergence” issue and thereby generating superior individuals and solutions. Finally, we assess the applicability of our model and algorithm to transportation challenges within the context of the dual-carbon initiative through a numerical example. We also explore the influence of different carbon tax mechanisms on total cost and emissions, as well as their applicability and efficacy in the face of demand uncerainty. The findings indicate that companies can achieve emission reductions with minimal cost increases under dual-target cost scenarios, ideal for dual carbon transportation contexts. Moreover, carbon tax rates significantly impact emission control, with segmented progressive taxes proving more effective, especially in high-demand uncertainty. Decision-makers should consider technological capabilities to set optimal tax rates and thresholds, fostering corporate enthusiasm. This research informs policy and decision-making for authorities and firms.https://www.sciopen.com/article/10.26599/HTRD.2025.9480051transport economicsdual-objective route optimizationimproved non-dominated sorting genetic algorithmmultimodal transportdemand uncertaintycarbon tax mechanism
spellingShingle Xu Zhang
Hongzhu Chen
Haiyan Zhang
Xumei Yuan
Dual objective multimodal transportation path optimization based on different carbon tax mechanisms under uncertain demand
Journal of Highway and Transportation Research and Development
transport economics
dual-objective route optimization
improved non-dominated sorting genetic algorithm
multimodal transport
demand uncertainty
carbon tax mechanism
title Dual objective multimodal transportation path optimization based on different carbon tax mechanisms under uncertain demand
title_full Dual objective multimodal transportation path optimization based on different carbon tax mechanisms under uncertain demand
title_fullStr Dual objective multimodal transportation path optimization based on different carbon tax mechanisms under uncertain demand
title_full_unstemmed Dual objective multimodal transportation path optimization based on different carbon tax mechanisms under uncertain demand
title_short Dual objective multimodal transportation path optimization based on different carbon tax mechanisms under uncertain demand
title_sort dual objective multimodal transportation path optimization based on different carbon tax mechanisms under uncertain demand
topic transport economics
dual-objective route optimization
improved non-dominated sorting genetic algorithm
multimodal transport
demand uncertainty
carbon tax mechanism
url https://www.sciopen.com/article/10.26599/HTRD.2025.9480051
work_keys_str_mv AT xuzhang dualobjectivemultimodaltransportationpathoptimizationbasedondifferentcarbontaxmechanismsunderuncertaindemand
AT hongzhuchen dualobjectivemultimodaltransportationpathoptimizationbasedondifferentcarbontaxmechanismsunderuncertaindemand
AT haiyanzhang dualobjectivemultimodaltransportationpathoptimizationbasedondifferentcarbontaxmechanismsunderuncertaindemand
AT xumeiyuan dualobjectivemultimodaltransportationpathoptimizationbasedondifferentcarbontaxmechanismsunderuncertaindemand