Transportation Research Part E-Logistics and Transportation Review最新文献_第7页

Enhancing ship collision risk assessment by integrating virtual ship-based shared nearest neighbor clustering and game-theoretic modeling 基于共享近邻聚类和博弈论建模的虚拟船舶碰撞风险评估方法

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2026-01-13 DOI: 10.1016/j.tre.2026.104675

Congcong Zhao , Zhuoyi Li , Tsz Leung Yip , Bing Wu

With the rapid growth in global shipping activities, the assessment of vessel collision risk has become a critical concern for maritime safety management. This study develops a comprehensive framework for identifying high-risk areas in congested waterways by integrating the Shared Nearest Neighbor Density-Based Spatial Clustering of Applications with Noise (SNN-DBSCAN) algorithm and Stackelberg game-theoretic model. The proposed framework first applies SNN-DBSCAN to enable robust waterway regionalization and vessel clustering under highly heterogeneous traffic densities, enhancing the accuracy and efficiency of collision risk assessment. To prevent critical crossing interactions from being fragmented by purely spatial clustering, we introduce a virtual-ship representation based on a risk-invariance principle, ensuring that high-risk encounters are preserved in the interaction set. Furthermore, a leader-follower game is employed to characterize strategic vessel responses by jointly considering safety, efficiency, and decision uncertainty to predict the next actions of the target vessel. The proposed framework is validated using empirical data from the busy waters of Hong Kong under daytime, nighttime, heavy precipitation, and strong winds. The results reveal pronounced scenario-dependent changes in vessel collision risk. Reduced nighttime visibility shifts hotspots and elevates risk, daytime port activities create new high-risk zones, and severe weather drives vessels to typhoon shelters where higher density and poorer maneuverability increase danger. The proposed approach captures these shifts and yields an interpretable, actionable tool for collision risk assessment and maritime traffic management, supporting future maritime safety management.

随着全球航运活动的快速增长，船舶碰撞风险评估已成为海上安全管理的一个重要问题。本研究通过整合基于共享最近邻密度的噪声应用空间聚类（SNN-DBSCAN）算法和Stackelberg博弈论模型，开发了一个综合框架，用于识别拥挤水道中的高风险区域。该框架首先应用SNN-DBSCAN实现了高度异构交通密度下稳健的航道区划和船舶聚类，提高了碰撞风险评估的准确性和效率。为了防止关键交叉交互被纯粹的空间聚类分割，我们引入了基于风险不变性原则的虚拟船表示，确保在交互集中保留高风险相遇。此外，通过联合考虑安全性、效率和决策不确定性来预测目标船舶的下一步行动，采用领导者-追随者博弈来表征船舶的战略反应。利用香港繁忙水域在白天、夜间、强降水和强风下的经验数据验证了所提出的框架。研究结果显示，船舶碰撞风险随场景变化而显著变化。夜间能见度降低会转移热点并增加风险，白天港口活动会产生新的高风险区域，恶劣天气会迫使船只前往密度更高、机动性更差的台风避难所，从而增加危险。拟议的方法抓住了这些变化，并为碰撞风险评估和海上交通管理提供了一种可解释、可操作的工具，为未来的海上安全管理提供支持。

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引用次数: 0

With whom to ally? Alliance strategy for EV battery supplier considering echelon utilization and disassembly recycling 与谁结盟？考虑梯次利用和拆解回收的电动汽车电池供应商联盟策略

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2026-01-12 DOI: 10.1016/j.tre.2025.104656

Zhangzhen Fang , Yuhan Guo , Gaoxiang Lou , Zhixuan Lai , Haicheng Ma , Li Zhou

The rapid expansion of the electric vehicle (EV) industry has heightened the need for sustainable and efficient closed-loop supply chains (CLSC) that can simultaneously improve economic returns and mitigate environmental impacts. To address this challenge, this study develops a game-theoretic model from the perspective of the power battery supplier and examines four inter-firm alliance modes: Non-alliance (N), supplier-manufacturer alliance (SM), supplier-recycler alliance (SR), and comprehensive alliance (SMR). The results reveal that (1) in the forward supply chain, suppliers under the SM and SMR modes consistently achieve higher battery capacity and EV sales. In the reverse supply chain, suppliers in alliance modes (SM, SR, SMR) are able to pay lower recycling prices while securing higher recycling quantities. (2) When recycling competition is weak, alliance with the manufacturer improves economic performance, whereas that with the recycler enhances environmental outcomes; however, the two benefits cannot be achieved simultaneously. By contrast, under intense recycling competition, forming a comprehensive alliance allows suppliers to improve both environmental and economic performance. (3) When extending the analysis to include suppliers’ investment in echelon utilization technology innovation, increased recycling competition intensity leads to a decline in the supplier’s echelon utilization performance, thereby amplifying the advantage of the comprehensive alliance.

电动汽车（EV）行业的快速扩张，提高了对可持续、高效的闭环供应链（CLSC）的需求，这种供应链可以同时提高经济回报和减轻环境影响。为了解决这一挑战，本文从动力电池供应商的角度建立了博弈论模型，并考察了四种企业间联盟模式：非联盟(N)、供应商-制造商联盟（SM）、供应商-回收商联盟（SR）和综合联盟（SMR）。结果表明：(1)在正向供应链中，SM模式和SMR模式下的供应商始终实现更高的电池容量和电动汽车销量。在逆向供应链中，联盟模式（SM、SR、SMR）的供应商能够支付较低的回收价格，同时获得较高的回收数量。(2)当回收竞争较弱时，与制造商联盟提高经济绩效，与回收商联盟提高环境绩效；然而，这两个好处不能同时实现。相比之下，在激烈的回收竞争中，形成一个全面的联盟可以使供应商提高环境和经济绩效。(3)将分析扩展到供应商在梯队利用技术创新方面的投入，回收竞争强度的增加导致供应商梯队利用绩效的下降，从而放大了综合联盟的优势。

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引用次数: 0

Algorithmic pricing in supply chains: implications for product quality, pricing, and profits 供应链中的算法定价：对产品质量、定价和利润的影响

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2026-01-12 DOI: 10.1016/j.tre.2026.104679

Kui Song , Jing Chen , Hui Yang , Bintong Chen , Honghu Huang

The rapid adoption of algorithmic pricing by retailers, enabled by big data analytics, is reshaping decisions in supply chains and affecting consumer surplus. We develop a game-theoretic model to examine how a retailer’s operation under an algorithmic-pricing regime, compared with a uniform-pricing regime, influences the manufacturer’s product quality and wholesale pricing decisions, as well as profits and consumer surplus. We uncover three key findings. First, algorithmic pricing affects product quality through two opposing effects: the demand segmentation effect, which encourages quality improvement by better matching products to heterogeneous consumers, and the profit compression effect, which discourages quality investment when the consumer distribution is highly skewed. Second, algorithmic pricing generates asymmetric profit impacts for supply chain members. While the retailer benefits more directly from pricing precision, both firms can benefit, particularly under a balanced mix of consumer types, through increased market coverage and reduced channel conflict. Third, when algorithmic reliability is high and consumer heterogeneity is moderate, algorithmic pricing can improve consumer surplus by aligning prices with willingness-to-pay and incentivizing higher quality. As reliability improves and the consumer distribution becomes more balanced, the system can achieve a tripartite win–win that benefits the manufacturer, the retailer, and consumers. These findings highlight the dual, condition-dependent role of algorithmic pricing as both a coordination tool and a quality-enhancement mechanism in supply chains. They also offer managerial implications for the strategic deployment of algorithmic pricing tools and inform policy debates on regulating algorithm-driven markets.

在大数据分析的推动下，零售商迅速采用算法定价，正在重塑供应链决策，并影响消费者剩余。我们开发了一个博弈论模型来研究零售商在算法定价制度下的运作，与统一定价制度相比，如何影响制造商的产品质量和批发定价决策，以及利润和消费者剩余。我们发现了三个关键发现。首先，算法定价通过两种相反的效应影响产品质量：一是需求细分效应，通过更好地将产品与异质消费者匹配来鼓励质量提高；二是利润压缩效应，当消费者分布高度倾斜时，利润压缩效应阻碍质量投资。其次，算法定价对供应链成员产生不对称的利润影响。虽然零售商更直接地受益于定价的准确性，但两家公司都可以受益，特别是在消费者类型的平衡组合下，通过增加市场覆盖和减少渠道冲突。第三，当算法可靠性高且消费者异质性适中时，算法定价可以通过调整价格与支付意愿和激励更高质量来提高消费者剩余。随着可靠性的提高和消费者分布的更加平衡，该系统可以实现制造商、零售商和消费者三方共赢。这些发现突出了算法定价作为供应链中协调工具和质量提升机制的双重、条件依赖的作用。它们还为算法定价工具的战略部署提供了管理意义，并为规范算法驱动市场的政策辩论提供了信息。

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引用次数: 0

A risk-averse two-stage stochastic programming model for vessel schedule recovery in liner shipping service 班轮运输船舶进度恢复的风险规避两阶段随机规划模型

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2026-01-10 DOI: 10.1016/j.tre.2025.104655

Shuaiqi Zhao , Hualong Yang , Yadong Wang , Zaili Yang

Significant delays caused by disruption events, coupled with regular uncertainties, pose challenges to risk avoidance and vessel schedule recovery problem (RA-VSRP) in liner container shipping services. To address this, we propose a new optimization framework that incorporates a hybrid risk aversion measure with three recovery strategies, including sailing speed adjustment, port skipping, and transshipment. The framework systematically combines ex-ante decision-making and in-progress decision-making. The former helps shorten vessel schedule recovery time and costs by quickly responding to disruption events, while the latter improves the flexibility of selecting vessel schedule recovery strategies. By adopting a scenario-based approach to jointly capture regular uncertainties and disruption events, RA-VSRP is formulated as a chance-constrained two-stage stochastic programming model, where conditional value-at-risk (CVaR) is used as the risk measure. An exact Benders decomposition-based branch-and-cut algorithm is employed to efficiently solve the computationally challenging model. We develop two algorithmic variants based on alternative representations of CVaR. Extensive numerical experiments demonstrate the applicability of the model and the computational efficiency of the algorithm. The results show that the proposed framework can provide reliable vessel schedule recovery solutions through sailing speed adjustments, port skipping, and transshipment. The findings provide managerial insights for shipping companies regarding schedule recovery, risk aversion, and cost control.

在集装箱班轮运输服务中，由于中断事件造成的重大延误，加上常规的不确定性，对风险规避和船舶进度恢复问题（RA-VSRP）提出了挑战。为了解决这个问题，我们提出了一个新的优化框架，该框架结合了混合风险规避措施和三种恢复策略，包括航行速度调整、港口跳过和转运。该框架系统地将事前决策与事前决策相结合。前者通过快速响应中断事件，有助于缩短船舶调度恢复时间和成本，而后者提高了选择船舶调度恢复策略的灵活性。通过采用基于场景的方法来联合捕获常规不确定性和中断事件，RA-VSRP被制定为机会约束的两阶段随机规划模型，其中条件风险值（CVaR）被用作风险度量。采用基于精确Benders分解的分支切断算法有效地解决了计算困难的模型。我们基于CVaR的替代表示开发了两个算法变体。大量的数值实验证明了该模型的适用性和算法的计算效率。结果表明，该框架可以通过航速调整、跳港和转运提供可靠的船舶调度恢复方案。研究结果为航运公司提供了关于进度恢复、风险规避和成本控制的管理见解。

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引用次数: 0

Who should invest in EV charging infrastructure? Policy design under ZEV mandates 谁应该投资电动汽车充电基础设施？ZEV授权下的政策设计

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2026-01-10 DOI: 10.1016/j.tre.2025.104642

Ting Chen , Kannan Govindan , Wenting Yang , Qiuwei Wang , Lu Liu

Governments worldwide have implemented multiple incentive measures to promote electric vehicle (EV) adoption, including consumer subsidies, infrastructure investment subsidies, and Zero Emission Vehicle (ZEV) mandates. Motivated by these policy instruments, various stakeholders (including automakers and EV component suppliers) are actively investing in charging infrastructure. Which subsidy policy is most cost-effective, and which entity’s infrastructure investment yields optimal outcomes? This study constructs a Stackelberg game model comprising government, component supplier, and competing fuel/electric vehicle manufacturers to explore optimal policy structures under three investment modes (i.e., supplier-led infrastructure investment, EV manufacturer-led investment, and traditional automaker-led investment). We systematically evaluate the performance in minimizing government expenditure while achieving adoption targets for each investment mode. More importantly, we extend our analysis to include ZEV mandates and their impact on subsidy effectiveness and government expenditure. Our results provide several interesting and counterintuitive insights. First, we show that supplier-led infrastructure investment (i.e., Mode S) consistently achieves the lowest policy expenditure and highest social welfare. Second, when automakers invest (Modes E and F), consumer subsidies alone are never optimal, contradicting widespread policy practice. Mode S consistently achieves the lowest expenditure and highest social welfare, yet Modes E and F generate superior economic benefits for firms—revealing a fundamental trade-off between public cost-efficiency and private profitability. Third, although ZEV mandates are intended to substitute for costly subsidies and reduce fiscal burden, our analysis reveals they may paradoxically increase government expenditure and discourage EV production when requirements become overly stringent.

世界各国政府已经实施了多种激励措施来促进电动汽车（EV）的采用，包括消费者补贴、基础设施投资补贴和零排放汽车（ZEV）授权。在这些政策工具的推动下，各种利益相关者（包括汽车制造商和电动汽车零部件供应商）都在积极投资充电基础设施。哪种补贴政策最具成本效益，哪个实体的基础设施投资产生最优结果？本文构建了由政府、零部件供应商和燃油/电动汽车竞争厂商组成的Stackelberg博弈模型，探讨了供应商主导基础设施投资、电动汽车制造商主导投资和传统汽车制造商主导投资三种投资模式下的最优政策结构。我们系统地评估了在实现每种投资模式的采用目标的同时最小化政府支出的表现。更重要的是，我们扩展了我们的分析，包括ZEV要求及其对补贴有效性和政府支出的影响。我们的研究结果提供了一些有趣的、违反直觉的见解。首先，我们证明了供应商主导的基础设施投资（即模式S）始终实现最低的政策支出和最高的社会福利。其次，当汽车制造商投资（模式E和模式F）时，仅靠消费者补贴从来都不是最优的，这与广泛的政策实践相矛盾。模式S始终实现最低的支出和最高的社会福利，而模式E和模式F为企业带来了更大的经济效益——揭示了公共成本效率和私人盈利能力之间的基本权衡。第三，尽管ZEV法规旨在取代昂贵的补贴并减轻财政负担，但我们的分析表明，当要求过于严格时，它们可能会矛盾地增加政府支出并阻碍电动汽车生产。

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引用次数: 0

Deep reinforcement learning for the vehicle routing problem with route balancing 基于深度强化学习的车辆路径平衡问题

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2026-01-10 DOI: 10.1016/j.tre.2025.104632

Jianhua Xiao , Detian Kong , Zhiguang Cao , Jingyi Zhao

The Vehicle Routing Problem with Route Balancing (VRPRB) is a multi-objective combinatorial optimization (MOCO) problem that aims to balance workload distribution while minimizing overall travel costs. Unlike traditional Vehicle Routing Problems (VRP), VRPRB introduces fleet size constraints to improve resource utilization and reduce operational costs. Existing deep reinforcement learning (DRL) approaches for VRP rarely address multi-objective optimization and often assume an unlimited fleet size, limiting their practical applicability. To address this issue, we propose an Equity Attention Model (E-AM), a problem-tailored DRL framework designed to generate Pareto-optimal solutions for VRPRB. Our E-AM formulates the problem as a sequential decision-making process, where each decision involves pairing a vehicle with a customer. E-AM is built on an attention-based architecture, incorporating a node encoder, a vehicle context encoder, and a decoder, with a hyper-network technique to efficiently handle multi-objective optimization. Experimental results demonstrate that our approach finds better solutions than current state-of-the-art methods on VRPRB benchmark instances while maintaining higher computational efficiency. By fully leveraging the strengths of deep reinforcement learning, our approach provides a scalable and adaptive alternative to traditional heuristic and exact algorithms, achieving high-quality solutions for complex real-world routing problems. To enhance scalability and training efficiency, we introduce a two-stage reinforcement learning strategy that enables E-AM to solve VRPRB instances with up to 1000 customers. To promote transparency and reproducibility, we have open-sourced our implementation¹.

具有路径平衡的车辆路径问题（VRPRB）是一个多目标组合优化（MOCO）问题，其目标是平衡工作负载分配，同时使总体出行成本最小化。与传统的车辆路径问题（VRP）不同，VRPRB引入了车队规模限制，以提高资源利用率并降低运营成本。现有的深度强化学习（DRL） VRP方法很少解决多目标优化问题，并且通常假设无限的车队规模，限制了它们的实际适用性。为了解决这个问题，我们提出了一个公平注意模型（E-AM），这是一个针对问题定制的DRL框架，旨在为VRPRB生成帕累托最优解决方案。我们的E-AM将问题表述为一个连续的决策过程，其中每个决策都涉及将车辆与客户配对。E-AM建立在基于注意力的架构上，结合节点编码器、车辆上下文编码器和解码器，采用超网络技术有效处理多目标优化。实验结果表明，我们的方法在VRPRB基准实例上找到了比目前最先进的方法更好的解决方案，同时保持了更高的计算效率。通过充分利用深度强化学习的优势，我们的方法为传统的启发式和精确算法提供了可扩展和自适应的替代方案，为复杂的现实世界路由问题提供了高质量的解决方案。为了提高可扩展性和训练效率，我们引入了一种两阶段强化学习策略，使E-AM能够解决多达1000个客户的VRPRB实例。为了提高透明度和可重复性，我们已经开源了我们的实现。

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引用次数: 0

Corrigendum to “Cost allocation in a robust two-stage resource allocation game: fairness and robustness”. [Trans. Res. Part E: Logist. Trans. Rev. 207 (2026) 104633] “稳健的两阶段资源分配博弈中的成本分配：公平与稳健”的勘误表。(反式。答：E部分：医生。反式。Rev. 207 (2026) 104633]

IF 10.6 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2026-01-10 DOI: 10.1016/j.tre.2026.104680

Menghang Wang, Lan Lu, Lindong Liu, Jie Wu

引用次数: 0

An integrated framework of vessel demand shifting and port capacity utilization for congestion mitigation 船舶需求转移和港口能力利用的综合框架缓解拥堵

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2026-01-09 DOI: 10.1016/j.tre.2025.104660

Haowen Lei , Shuai Jia

Port congestion remains a critical hurdle for global maritime logistics, particularly as opportunities for infrastructure expansion become increasingly limited. In this study, we introduce a unified optimization framework that coordinates vessel service demand with port capacity, empowering the mitigation of port congestion. The integrated framework comprises three key components: A demand shifting model that optimizes vessel arrival distributions over time; a descriptive queueing model that characterizes vessel arrival and service processes, enabling a holistic evaluation of congestion levels; and a resource management model that effectively allocates available port resources to vessels by leveraging a data-driven throughput envelope calibrated from port operational data. To address the computational challenges posed by the integration these models, we develop a bi-level iterative solution algorithm that iteratively enhances the coordination between demand-side and supply-side decisions, achieving a satisfactory performance with respect to tractability and scalability. Empirical validation is performed using a large-scale, real-world operational dataset from a major container port in Shanghai. Our algorithm proves highly scalable, solving large-scale instances 20–30 times faster than Gurobi and achieving near-optimal solutions (e.g., optimality gaps as low as 0.91 %). The framework delivers economic value with a substantial reduction of total system costs (ranging from 14.47 %-16.67 %) over a non-integrated baseline. The results highlight the practical value and generalizability of the unified approach for enhancing the efficiency and resilience of port systems.

港口拥堵仍然是全球海运物流的一个关键障碍，尤其是在基础设施扩张的机会越来越有限的情况下。在本研究中，我们引入了一个统一的优化框架，协调船舶服务需求和港口容量，增强港口拥堵的缓解能力。集成框架包括三个关键部分：需求转移模型，优化船舶到达分布；描述船舶到达和服务过程的描述性排队模型，能够全面评估拥堵程度；资源管理模型，通过利用港口运营数据校准的数据驱动吞吐量包络，有效地将可用的港口资源分配给船舶。为了解决这些模型集成带来的计算挑战，我们开发了一种双级迭代求解算法，该算法迭代地增强了需求侧和供给侧决策之间的协调，在可追溯性和可扩展性方面取得了令人满意的性能。使用上海一个主要集装箱港口的大规模真实操作数据集进行实证验证。我们的算法被证明具有高度的可扩展性，解决大规模实例的速度比robi快20-30倍，并获得接近最优的解决方案（例如，最优性差距低至0.91 %）。与非集成基线相比，该框架提供了经济价值，大大降低了总系统成本（范围从14.47 %-16.67 %）。结果突出了统一方法在提高港口系统效率和弹性方面的实用价值和可推广性。

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引用次数: 0

Service network design for electric vehicles with combined battery swapping and recharging 混合换电池充电的电动汽车服务网络设计

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2026-01-09 DOI: 10.1016/j.tre.2026.104668

Xudong Diao , Meng Qiu

Incorporating both battery swapping and recharging strategies within electric vehicle (EV) service networks provides a flexible means of mitigating range limitations. While jointly optimizing these strategies poses significant modeling and computational challenges, it also yields valuable insights into their relative operational performance. We develop an optimization framework that jointly determines service network design, EV routing decisions, and the scheduling of recharging and battery swapping operations, while respecting capacity constraints on both activities. This study establishes a unified mixed-integer programming framework for EV service network design that integrates the two replenishment strategies under system-wide capacity limitations. To handle large-scale instances efficiently, we employ a column generation scheme, in which the pricing subproblem is solved using a bidirectional labeling algorithm supported by tailored dominance rules and problem-specific resource extension functions. In addition, a dedicated heuristic is designed to construct high-quality integer solutions. Computational experiments based on real-world case studies show that when both strategies are available, battery swapping tends to outperform recharging due to its shorter service time, highlighting the scalability and practical relevance of the proposed approach.

在电动汽车（EV）服务网络中结合电池交换和充电策略提供了一种灵活的缓解里程限制的方法。虽然联合优化这些策略会带来重大的建模和计算挑战，但它也会对它们的相对操作性能产生有价值的见解。我们开发了一个优化框架，共同确定服务网络设计、电动汽车路线决策以及充电和电池交换操作的调度，同时尊重这两项活动的容量约束。本文建立了一个统一的混合整数规划框架，用于电动汽车服务网络设计，该框架在全系统容量限制下集成了两种补充策略。为了有效地处理大规模实例，我们采用了一种列生成方案，其中定价子问题使用由定制的优势规则和问题特定的资源扩展函数支持的双向标记算法来解决。此外，还设计了一个专门的启发式算法来构造高质量的整数解。基于实际案例研究的计算实验表明，当两种策略都可用时，电池交换往往优于充电，因为其使用时间更短，突出了所提出方法的可扩展性和实际相关性。

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引用次数: 0

Climate shock impacts on supply chains: the case of the truckload spot market 气候冲击对供应链的影响：以卡车现货市场为例

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2026-01-09 DOI: 10.1016/j.tre.2025.104609

Sara Hsu , Andrew Balthrop , Dan Pellathy , Travis Kulpa , Gonzalo Andrew Ferrada , Joshua Fu

Climate shocks increasingly disrupt supply chains, yet research has focused primarily on mitigation strategies (i.e., carbon reduction), leaving adaptation strategies comparatively understudied. We begin to fill this gap by studying how transportation managers within a supply chain respond to climate-related shocks, defined as a month in which a state’s exposure to extreme temperature or precipitation events rises significantly, measured by the custom University of Tennessee Climate Index (UTCI), which combines anomalies in high/low temperature and heavy precipitation with population exposure. Drawing on structured interviews with transportation managers, we uncover beliefs that shippers tend to be less demand-responsive in the short-term to climate-related shocks, often prioritizing the desire to move freight at any reasonable cost. Motor carriers, in contrast, are more sensitive to price. To test these qualitative assessments, we regress monthly state-level truckload spot market data from the contiguous 48 states on the UTCI in reduced-form two-way fixed effects specifications, finding that a one-standard-deviation increase in climate shocks increases freight prices by 1.9%, with minimal effects on freight volume, indicating that market adjustments occur primarily through price rather than quantity. We further estimate IV specifications based on three-stage least squares (3SLS) models to disentangle the net causal effects from the reduced form specification. Consistent with our interviews, we find motor carriers are more sensitive than shippers to climate shocks. The results have important implications, offering shippers, carriers, and brokers with concrete price-change benchmarks they can use to budget transportation spend, design contract–spot portfolios, and plan capacity during climate shocks.

气候冲击日益扰乱供应链，但研究主要集中在缓解战略（即碳减排）上，对适应战略的研究相对不足。我们开始通过研究供应链内的运输管理人员如何应对气候相关冲击来填补这一空白，气候冲击的定义是一个州暴露于极端温度或降水事件显著上升的一个月，由定制的田纳西大学气候指数（UTCI）测量，该指数将高/低温和强降水的异常与人口暴露相结合。通过对运输经理的结构化访谈，我们发现，托运人在短期内对气候相关冲击的需求反应往往较弱，往往优先考虑以任何合理的成本运输货物。相比之下，汽车运输公司对价格更为敏感。为了验证这些定性评估，我们以简化形式的双向固定效应规范对UTCI上连续48个州的月度州一级卡车现货市场数据进行回归，发现气候冲击增加一个标准差会使货运价格上涨1.9%，对货运量的影响最小，这表明市场调整主要通过价格而不是数量发生。我们基于三阶段最小二乘（3SLS）模型进一步估计IV规范，以从简化形式规范中分离出净因果效应。与我们的采访一致，我们发现汽车运输公司比托运人对气候冲击更敏感。研究结果具有重要意义，为托运人、承运人和经纪人提供了具体的价格变化基准，他们可以利用这些基准来预算运输支出，设计合同现货组合，并在气候冲击期间规划运力。

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