{"title":"Electric versus diesel: Green supply chain network design with carbon footprint labeling","authors":"Ensieh Ghaedyheidary, Samir Elhedhli","doi":"10.1016/j.trc.2025.105277","DOIUrl":null,"url":null,"abstract":"<div><div>Transportation electrification and carbon footprint labeling are strong indicators of environmental commitment in green supply chains. We study this framework and assess its environmental and financial sustainability. We consider cradle-to-gate operations, from manufacturing to retail, mandate the use of electric trucks whenever their range allows, and impose a cap on product carbon footprints as would be advertised on a carbon label. We optimize the locations of distribution centers, the allocation of demand, and the transportation choices between diesel trucks and electric trucks. We account for the nonlinear relationship between emissions and payload for diesel trucks, focus on two representative functional forms- concave and convex- and propose a mixed-integer nonlinear optimization model to minimize costs and CO2-equivalent emissions. We use Lagrangean relaxation to decompose the model by echelon and isolate the convex and concave nonlinearity in an easy-to-solve subproblem. We then design a Lagrangean heuristic based on the solution of one of the subproblems, which has proven efficient and near-optimal. Based on a case study, we evaluate the impact of the emission function and the carbon label on the supply chain network, as well as the trade-off between the use of diesel trucks and electric trucks. We find that the relationship between emissions and payload for diesel trucks significantly influences the adoption of electric trucks. When concave, as would be the case for steady driving conditions, long hauls, well-maintained infrastructure, and light traffic, conventional diesel trucks continue to be the cost-efficient option, especially when electric truck mileage costs are high and the cap on unit emissions is elevated. In contrast, when diesel emissions are convex, corresponding to challenging driving conditions such as urban delivery, congested road networks, stop-and-go traffic, and degraded road infrastructure, transportation emissions dominate total emissions, diesel truck usage decreases, and electric trucks become a better choice even if the cap on unit emissions is high and diesel trucks are cheaper to operate. Furthermore, extending the range of electric trucks increases their usage under convex emissions but not under concave emissions, especially when the cap on carbon footprint is not tight.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105277"},"PeriodicalIF":7.6000,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part C-Emerging Technologies","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0968090X25002815","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"TRANSPORTATION SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Transportation electrification and carbon footprint labeling are strong indicators of environmental commitment in green supply chains. We study this framework and assess its environmental and financial sustainability. We consider cradle-to-gate operations, from manufacturing to retail, mandate the use of electric trucks whenever their range allows, and impose a cap on product carbon footprints as would be advertised on a carbon label. We optimize the locations of distribution centers, the allocation of demand, and the transportation choices between diesel trucks and electric trucks. We account for the nonlinear relationship between emissions and payload for diesel trucks, focus on two representative functional forms- concave and convex- and propose a mixed-integer nonlinear optimization model to minimize costs and CO2-equivalent emissions. We use Lagrangean relaxation to decompose the model by echelon and isolate the convex and concave nonlinearity in an easy-to-solve subproblem. We then design a Lagrangean heuristic based on the solution of one of the subproblems, which has proven efficient and near-optimal. Based on a case study, we evaluate the impact of the emission function and the carbon label on the supply chain network, as well as the trade-off between the use of diesel trucks and electric trucks. We find that the relationship between emissions and payload for diesel trucks significantly influences the adoption of electric trucks. When concave, as would be the case for steady driving conditions, long hauls, well-maintained infrastructure, and light traffic, conventional diesel trucks continue to be the cost-efficient option, especially when electric truck mileage costs are high and the cap on unit emissions is elevated. In contrast, when diesel emissions are convex, corresponding to challenging driving conditions such as urban delivery, congested road networks, stop-and-go traffic, and degraded road infrastructure, transportation emissions dominate total emissions, diesel truck usage decreases, and electric trucks become a better choice even if the cap on unit emissions is high and diesel trucks are cheaper to operate. Furthermore, extending the range of electric trucks increases their usage under convex emissions but not under concave emissions, especially when the cap on carbon footprint is not tight.
期刊介绍:
Transportation Research: Part C (TR_C) is dedicated to showcasing high-quality, scholarly research that delves into the development, applications, and implications of transportation systems and emerging technologies. Our focus lies not solely on individual technologies, but rather on their broader implications for the planning, design, operation, control, maintenance, and rehabilitation of transportation systems, services, and components. In essence, the intellectual core of the journal revolves around the transportation aspect rather than the technology itself. We actively encourage the integration of quantitative methods from diverse fields such as operations research, control systems, complex networks, computer science, and artificial intelligence. Join us in exploring the intersection of transportation systems and emerging technologies to drive innovation and progress in the field.