Pub Date : 2025-05-04DOI: 10.1080/15568318.2025.2499037
Ximena Villagrán , William F. Vásquez , Benjamín Leiva
Tourists can support the transition to cleaner transportation by paying higher prices for electric mobility as higher profits may incentivize transport service providers to adopt cleaner vehicles. Using the contingent valuation method, this study investigates whether tourists are willing to pay for electric rickshaw transport services in Panajachel, a renowned tourist destination in Guatemala where rickshaws are the main mode of transportation. Estimating logit models, we found that tourists’ willingness to pay is positively related to income, environmental preferences, and the respondent’s belief that our study will have policy implications (i.e. perceived consequentiality), as well as negatively related to the length of the trip. Our estimates indicate that the median tourist is willing to pay a substantial price increase, i.e. at least 83% of the current price. Our findings provide useful insights to design pricing mechanisms and complementary policies aimed at promoting the adoption of cleaner vehicles in tourist destinations.
{"title":"Tourist preferences for cleaner mobility: A contingent valuation of electric rickshaws in Guatemala","authors":"Ximena Villagrán , William F. Vásquez , Benjamín Leiva","doi":"10.1080/15568318.2025.2499037","DOIUrl":"10.1080/15568318.2025.2499037","url":null,"abstract":"<div><div>Tourists can support the transition to cleaner transportation by paying higher prices for electric mobility as higher profits may incentivize transport service providers to adopt cleaner vehicles. Using the contingent valuation method, this study investigates whether tourists are willing to pay for electric rickshaw transport services in Panajachel, a renowned tourist destination in Guatemala where rickshaws are the main mode of transportation. Estimating logit models, we found that tourists’ willingness to pay is positively related to income, environmental preferences, and the respondent’s belief that our study will have policy implications (i.e. perceived consequentiality), as well as negatively related to the length of the trip. Our estimates indicate that the median tourist is willing to pay a substantial price increase, i.e. at least 83% of the current price. Our findings provide useful insights to design pricing mechanisms and complementary policies aimed at promoting the adoption of cleaner vehicles in tourist destinations.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 5","pages":"Pages 419-430"},"PeriodicalIF":3.1,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-05-04DOI: 10.1080/15568318.2025.2500550
Xiaofeng Liu , Zhenya Ji
In the pursuit of low-carbon transportation, electric vehicles (EVs) are experiencing unprecedented growth, necessitating efficient charging management strategies. Traditional online real-time charging scheduling, often relying on receding horizon policies, overlooks critical challenges posed by the asynchronous nature of EV arrivals and departures. Specifically, these strategies struggle to effectively handle two distinct types of charging sessions: those already connected in real-time with known parameters, and those yet to connect but anticipated within a prediction horizon, characterized by higher uncertainty compromising scheduling efficiency. To fill these gaps, this article proposes Padding-Asynchronous-arrivals, Transition-probabilities-Captured, Heterogenous-horizons-aware (PATCH), a novel framework for EV scheduling. In the first Padding-Asynchronous-arrivals (PA) module, by detecting real-time connected sessions and padding future arrivals, PATCH prepares for optimal charging allocation. In the second Transition-probabilities-Captured (TA) module, the charging scheduling is formulated as a Markov decision process (MDP), leveraging the deterministic arrival time of real-time sessions to accurately model transition probabilities and distinguish uncertainties. The final Heterogenous-horizons-aware (H) module ensures that each session’s charging requirements are met prior to departure, while dynamically adjusting the prediction horizon based on individual session durations. The MDP-based scheduling problem within PATCH is solved using a robust-bonded dynamic programming algorithm, ensuring resilience against various uncertainties while optimizing time costs. Simulations based on real-world EV charging data demonstrate that, under the premise of maintaining a similar computing speed with traditional methods, underscoring its potential to enhance EV charging management.
{"title":"Real-time charging scheduling of aggregated electric vehicles: A novel PATCH framework addressing asynchronous plug-in dynamics","authors":"Xiaofeng Liu , Zhenya Ji","doi":"10.1080/15568318.2025.2500550","DOIUrl":"10.1080/15568318.2025.2500550","url":null,"abstract":"<div><div>In the pursuit of low-carbon transportation, electric vehicles (EVs) are experiencing unprecedented growth, necessitating efficient charging management strategies. Traditional online real-time charging scheduling, often relying on receding horizon policies, overlooks critical challenges posed by the asynchronous nature of EV arrivals and departures. Specifically, these strategies struggle to effectively handle two distinct types of charging sessions: those already connected in real-time with known parameters, and those yet to connect but anticipated within a prediction horizon, characterized by higher uncertainty compromising scheduling efficiency. To fill these gaps, this article proposes Padding-Asynchronous-arrivals, Transition-probabilities-Captured, Heterogenous-horizons-aware (PATCH), a novel framework for EV scheduling. In the first Padding-Asynchronous-arrivals (PA) module, by detecting real-time connected sessions and padding future arrivals, PATCH prepares for optimal charging allocation. In the second Transition-probabilities-Captured (TA) module, the charging scheduling is formulated as a Markov decision process (MDP), leveraging the deterministic arrival time of real-time sessions to accurately model transition probabilities and distinguish uncertainties. The final Heterogenous-horizons-aware (H) module ensures that each session’s charging requirements are met prior to departure, while dynamically adjusting the prediction horizon based on individual session durations. The MDP-based scheduling problem within PATCH is solved using a robust-bonded dynamic programming algorithm, ensuring resilience against various uncertainties while optimizing time costs. Simulations based on real-world EV charging data demonstrate that, under the premise of maintaining a similar computing speed with traditional methods, underscoring its potential to enhance EV charging management.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 5","pages":"Pages 446-461"},"PeriodicalIF":3.1,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-05-04DOI: 10.1080/15568318.2025.2501264
Zhaojie Wang , Feifeng Zheng , Ming Liu
We investigate an electric bus overnight charging scheduling problem considering the uncertainty of bus charge-discharge efficiency. In line with scheduling practice, the vehicle-to-grid technology is considered in this problem. Given a set of appointed electric buses, our objective is to explore charging strategies that minimize the weighted sum of the total charging cost for the charging station, power grid load fluctuations, together with the negative value of rewards obtained from using V2G technology. Inspired by Abdelwahed et al., both discrete-time-optimization formulation and discrete-event-optimization formulation are established to model this problem under uncertain environments. We then employ K-means enhanced sample average approximation approach and K-means enhanced conditional value at risk approach to solve this problem. Numerical experiments are conducted to demonstrate the effectiveness of our approaches.
{"title":"Overnight charging scheduling of electric buses considering V2G technology and uncertain battery charge-discharge efficiency","authors":"Zhaojie Wang , Feifeng Zheng , Ming Liu","doi":"10.1080/15568318.2025.2501264","DOIUrl":"10.1080/15568318.2025.2501264","url":null,"abstract":"<div><div>We investigate an electric bus overnight charging scheduling problem considering the uncertainty of bus charge-discharge efficiency. In line with scheduling practice, the vehicle-to-grid technology is considered in this problem. Given a set of appointed electric buses, our objective is to explore charging strategies that minimize the weighted sum of the total charging cost for the charging station, power grid load fluctuations, together with the negative value of rewards obtained from using V2G technology. Inspired by Abdelwahed et al., both discrete-time-optimization formulation and discrete-event-optimization formulation are established to model this problem under uncertain environments. We then employ K-means enhanced sample average approximation approach and K-means enhanced conditional value at risk approach to solve this problem. Numerical experiments are conducted to demonstrate the effectiveness of our approaches.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 5","pages":"Pages 462-479"},"PeriodicalIF":3.1,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-05-04DOI: 10.1080/15568318.2025.2496894
George Okyere Dokyi , Kwame Kwakwa Osei , John Tookey , Funmilayo Ebun Rotimi
This study explores the economic criterion indicators for sustainable road and highway infrastructure development in Ghana through a phenomenological research approach. Using SWOT analysis and semi-structured interviews with 14 key stakeholders, including government officials, academics, and private sector professionals, the research examines four critical economic indicators: material costs, lifecycle costs, construction time, and maintenance and operation costs Through content analysis using ATLAS.ti 9 software, the study evaluates internal strengths and weaknesses, as well as external opportunities and threats affecting each indicator’s implementation. The findings reveal multifaceted challenges including insufficient expertise in lifecycle cost analysis, inadequate maintenance funding, weak regulatory enforcement, and political interference. The study identifies strategic action plans for each indicator, including local material sourcing, improved procurement practices, capacity building initiatives, enhanced stakeholder collaboration, and technological integration. These recommendations are supported by specific deliverables and expert remarks that provide practical implementation guidance. This research contributes to sustainable infrastructure development theory and practice by providing a comprehensive framework for economic sustainability in Ghana’s road and highway sector. While the study’s geographical focus may limit generalisability, it offers valuable insights for policymakers and practitioners in developing countries facing similar challenges. The findings suggest that the successful implementation of economic sustainability indicators requires a coordinated approach combining technical expertise, policy reform, and stakeholder engagement, while considering local contextual factors.
{"title":"A SWOT analysis of stakeholder perspectives on the strategic application of economic sustainability indicators in Ghana’s road infrastructure development","authors":"George Okyere Dokyi , Kwame Kwakwa Osei , John Tookey , Funmilayo Ebun Rotimi","doi":"10.1080/15568318.2025.2496894","DOIUrl":"10.1080/15568318.2025.2496894","url":null,"abstract":"<div><div>This study explores the economic criterion indicators for sustainable road and highway infrastructure development in Ghana through a phenomenological research approach. Using SWOT analysis and semi-structured interviews with 14 key stakeholders, including government officials, academics, and private sector professionals, the research examines four critical economic indicators: material costs, lifecycle costs, construction time, and maintenance and operation costs Through content analysis using ATLAS.ti 9 software, the study evaluates internal strengths and weaknesses, as well as external opportunities and threats affecting each indicator’s implementation. The findings reveal multifaceted challenges including insufficient expertise in lifecycle cost analysis, inadequate maintenance funding, weak regulatory enforcement, and political interference. The study identifies strategic action plans for each indicator, including local material sourcing, improved procurement practices, capacity building initiatives, enhanced stakeholder collaboration, and technological integration. These recommendations are supported by specific deliverables and expert remarks that provide practical implementation guidance. This research contributes to sustainable infrastructure development theory and practice by providing a comprehensive framework for economic sustainability in Ghana’s road and highway sector. While the study’s geographical focus may limit generalisability, it offers valuable insights for policymakers and practitioners in developing countries facing similar challenges. The findings suggest that the successful implementation of economic sustainability indicators requires a coordinated approach combining technical expertise, policy reform, and stakeholder engagement, while considering local contextual factors.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 5","pages":"Pages 375-392"},"PeriodicalIF":3.1,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-15DOI: 10.1080/15568318.2025.2493138
Lan Zhu , Ting Wang , Xiao Ma , Lu Yang , Xuming Lou
In recent years, drones have been used to assist delivering small goods as a solution to traffic jams and air pollutions to serve the purpose of effective and sustainable delivery. Thus, a green truck-drone collaborative delivery routing problem with multiple drops is studied in this paper. A multi-objective truck-drone route planning model considering carbon emissions and traveling time is developed. Compared to most of the existing research in which drones can deliver only one node per trip, this study extends the model to a more complicated realistic situation that drones deliver multiple drops per trip. A NSGA-II algorithm is designed to address the above-described problem. Then, cases with clustered, semi-clustered and randomly distributed customers are analyzed to find out the influence of customer distribution characteristics on the environmental performance and time efficiency. As numerical experiment results show, the improved collaborative delivery model significantly reduces carbon emissions and improves delivery efficiency, especially when customers are randomly distributed or semi-clustered. Finally, the optimal traveling speed range is obtained through the speed sensitivity analysis, which provide a sustainable decision-making basis for daily operations.
{"title":"Route optimization of green truck-drone collaborative delivery with multiple drops considering demand distribution","authors":"Lan Zhu , Ting Wang , Xiao Ma , Lu Yang , Xuming Lou","doi":"10.1080/15568318.2025.2493138","DOIUrl":"10.1080/15568318.2025.2493138","url":null,"abstract":"<div><div>In recent years, drones have been used to assist delivering small goods as a solution to traffic jams and air pollutions to serve the purpose of effective and sustainable delivery. Thus, a green truck-drone collaborative delivery routing problem with multiple drops is studied in this paper. A multi-objective truck-drone route planning model considering carbon emissions and traveling time is developed. Compared to most of the existing research in which drones can deliver only one node per trip, this study extends the model to a more complicated realistic situation that drones deliver multiple drops per trip. A NSGA-II algorithm is designed to address the above-described problem. Then, cases with clustered, semi-clustered and randomly distributed customers are analyzed to find out the influence of customer distribution characteristics on the environmental performance and time efficiency. As numerical experiment results show, the improved collaborative delivery model significantly reduces carbon emissions and improves delivery efficiency, especially when customers are randomly distributed or semi-clustered. Finally, the optimal traveling speed range is obtained through the speed sensitivity analysis, which provide a sustainable decision-making basis for daily operations.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 9","pages":"Pages 854-862"},"PeriodicalIF":3.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-03DOI: 10.1080/15568318.2025.2467665
Choudhury Siddique , Yan Zhou , Amgad Elgowainy , Md Rakibul Alam , Melissa Shurland
This research establishes a baseline for the energy intensity and emissions of freight and passenger rail applications, focusing on diesel locomotives. We quantified the diesel energy intensity for freight rail (in Btu/revenue ton-mile), and both diesel and electric energy intensity for passenger rail (in Btu/passenger-mile) using publicly available data. Emissions of HC, CO, NOX, and PM emissions from diesel locomotives were analyzed based on real world emissions testing of in-use locomotives and compared to EPA standards. Additionally, greenhouse gas (GHG) emissions from freight and passenger rails were compared to those from other transportation modes, including heavy-duty trucks, transit buses, and freight and passenger aircrafts, using a well-to-wheels (WTW) approach. Results show that freight rail powered by diesel engines produces lower WTW emissions when compared to competing freight transportation modes such as trucks and aviation. In contrast, passenger rail exhibits higher overall WTW emissions compared to other passenger transportation modes (e.g. cars, transit buses, aviation), and greater variability primarily due to the fluctuations in passenger load factors.
{"title":"Energy intensity, GHG and pollutant emissions of freight and passenger rail applications in the United States","authors":"Choudhury Siddique , Yan Zhou , Amgad Elgowainy , Md Rakibul Alam , Melissa Shurland","doi":"10.1080/15568318.2025.2467665","DOIUrl":"10.1080/15568318.2025.2467665","url":null,"abstract":"<div><div>This research establishes a baseline for the energy intensity and emissions of freight and passenger rail applications, focusing on diesel locomotives. We quantified the diesel energy intensity for freight rail (in Btu/revenue ton-mile), and both diesel and electric energy intensity for passenger rail (in Btu/passenger-mile) using publicly available data. Emissions of HC, CO, NO<sub>X</sub>, and PM emissions from diesel locomotives were analyzed based on real world emissions testing of in-use locomotives and compared to EPA standards. Additionally, greenhouse gas (GHG) emissions from freight and passenger rails were compared to those from other transportation modes, including heavy-duty trucks, transit buses, and freight and passenger aircrafts, using a well-to-wheels (WTW) approach. Results show that freight rail powered by diesel engines produces lower WTW emissions when compared to competing freight transportation modes such as trucks and aviation. In contrast, passenger rail exhibits higher overall WTW emissions compared to other passenger transportation modes (e.g. cars, transit buses, aviation), and greater variability primarily due to the fluctuations in passenger load factors.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 4","pages":"Pages 297-307"},"PeriodicalIF":3.1,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Battery Electric Vehicles (BEVs) are pivotal in advancing sustainable mobility and addressing climate change globally. An increasing number of regions, including Taiwan, are implementing plans to phase out the sale of Internal Combustion Engine Vehicles (ICEVs). In Taiwan, where the transportation sector contributes 13% of fossil carbon emissions, nearly half attributed to passenger vehicles, electrification is key to achieving net-zero emissions. This study provides a comprehensive life cycle assessment (LCA) and total cost of ownership (TCO) analysis of 182 vehicle models, including ICEVs, Hybrid Electric Vehicles (HEVs), and BEVs. Our findings reveal that BEVs reduce total CO2 emissions by an average of 20% compared to ICEVs, largely due to decreased emissions from electricity generation and reduced fuel consumption. Although BEVs initially present double the purchase costs of ICEVs, their long-term economic competitiveness is enhanced by lower operational and maintenance costs. Nevertheless, the environmental impact of BEVs depends heavily on the decarbonization of the power grid, and the prevalence of expensive imported BEVs challenges their adoption. This study highlights the necessity for specific policies, such as financial incentives, support for domestic BEV production, and accelerated grid decarbonization, to enhance BEV adoption in Taiwan. Such measures, coupled with increased public awareness, are crucial to ensuring Taiwan’s vehicle electrification aligns with its ambitious 2050 net-zero emission goals.
{"title":"Costs and carbon: Evaluating the tradeoffs in Taiwan’s shift toward electric vehicles","authors":"Jing-Siou Tseng , Yuan-Hsi Chien , Ruo-Wen Chen , I-Yun Lisa Hsieh","doi":"10.1080/15568318.2025.2490488","DOIUrl":"10.1080/15568318.2025.2490488","url":null,"abstract":"<div><div>Battery Electric Vehicles (BEVs) are pivotal in advancing sustainable mobility and addressing climate change globally. An increasing number of regions, including Taiwan, are implementing plans to phase out the sale of Internal Combustion Engine Vehicles (ICEVs). In Taiwan, where the transportation sector contributes 13% of fossil carbon emissions, nearly half attributed to passenger vehicles, electrification is key to achieving net-zero emissions. This study provides a comprehensive life cycle assessment (LCA) and total cost of ownership (TCO) analysis of 182 vehicle models, including ICEVs, Hybrid Electric Vehicles (HEVs), and BEVs. Our findings reveal that BEVs reduce total CO<sub>2</sub> emissions by an average of 20% compared to ICEVs, largely due to decreased emissions from electricity generation and reduced fuel consumption. Although BEVs initially present double the purchase costs of ICEVs, their long-term economic competitiveness is enhanced by lower operational and maintenance costs. Nevertheless, the environmental impact of BEVs depends heavily on the decarbonization of the power grid, and the prevalence of expensive imported BEVs challenges their adoption. This study highlights the necessity for specific policies, such as financial incentives, support for domestic BEV production, and accelerated grid decarbonization, to enhance BEV adoption in Taiwan. Such measures, coupled with increased public awareness, are crucial to ensuring Taiwan’s vehicle electrification aligns with its ambitious 2050 net-zero emission goals.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 4","pages":"Pages 363-374"},"PeriodicalIF":3.1,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-03DOI: 10.1080/15568318.2025.2479616
Shin Ying Ng , Yan Cheng , Taku Fujiyama
Many freight trains are still diesel-powered, and their CO2 and other emissions cannot be ignored. This research proposed a framework to evaluate freight trains’ stops during their journeys and the associated emissions from idling and reacceleration. It includes (1) number of stops, (2) stopping time (duration) and (3) stopping percentage (a train’s total stopping time divided by its journey time). The framework was applied to integrated datasets of Great Britain from January to June 2022 to evaluate freight train operations and estimate emissions. We found that 83% of the observed 105,714 freight trains were diesel-powered. Each diesel freight train made an average of 1.34 stops during its journey, with an average total stopping time of 17.2 minutes and a stopping percentage of 6.89%. It was estimated that in that six-month period 53.12 ktonnes of CO2, 0.35 ktonnes of NOx and 6.58 tonnes of PM were emitted from diesel freight trains. Domestic Intermodal (containers) accounted for 60% of the emissions, followed by Construction (23%). Many emission hotspots were along lines with high-frequency passenger services. The developed framework could be used to evaluate railway timetables and operation management at a national level.
{"title":"Investigating dwelling of freight trains with a view to the reduction of carbon and pollutant emissions","authors":"Shin Ying Ng , Yan Cheng , Taku Fujiyama","doi":"10.1080/15568318.2025.2479616","DOIUrl":"10.1080/15568318.2025.2479616","url":null,"abstract":"<div><div>Many freight trains are still diesel-powered, and their CO<sub>2</sub> and other emissions cannot be ignored. This research proposed a framework to evaluate freight trains’ stops during their journeys and the associated emissions from idling and reacceleration. It includes (1) number of stops, (2) stopping time (duration) and (3) stopping percentage (a train’s total stopping time divided by its journey time). The framework was applied to integrated datasets of Great Britain from January to June 2022 to evaluate freight train operations and estimate emissions. We found that 83% of the observed 105,714 freight trains were diesel-powered. Each diesel freight train made an average of 1.34 stops during its journey, with an average total stopping time of 17.2 minutes and a stopping percentage of 6.89%. It was estimated that in that six-month period 53.12 ktonnes of CO<sub>2</sub>, 0.35 ktonnes of NO<sub>x</sub> and 6.58 tonnes of PM were emitted from diesel freight trains. Domestic Intermodal (containers) accounted for 60% of the emissions, followed by Construction (23%). Many emission hotspots were along lines with high-frequency passenger services. The developed framework could be used to evaluate railway timetables and operation management at a national level.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 4","pages":"Pages 322-343"},"PeriodicalIF":3.1,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-03DOI: 10.1080/15568318.2025.2479622
Harprinderjot Singh , Hamid Mozafari , Mohammadreza Kavianipour , Mehrnaz Ghamami , Ali Zockaie , Robert Jackson
The rapid growth of the electric vehicles (EVs) market penetration rate and the resulting energy demand will impact the electricity supply-demand balance and stability in the electricity distribution network. These impacts could be mitigated by distributed energy resources (DERs) (i.e. second-life batteries (SLB), new batteries (NB), solar panels, and flywheels). To support the energy demand of EVs at fast-charging stations whilst minimizing the cost of the system, a mixed-integer optimization model is developed considering the spatiotemporal demand (existing demand and EV demand), the details of the electric grid distribution network, spatiotemporal power generation of solar panels, energy storage systems’ (ESSs’) charge/discharge schedule, and the capacity constraints. The case study (major cities in Michigan) shows sensitivity to the seasonal variation in the grid and solar conditions and the DER’s unit cost. Based on the result, providing the maximum area for solar panels leads to the maximum cost savings. Lithium-ion SLBs offer a cost-effective solution for energy storage, efficiently utilizing time-of-use electricity rates and intermittent solar energy. Depending on the existing and fast-charging energy demand, grid upgrades may be necessary at some locations.
{"title":"Enhancing urban sustainability through optimizing Distributed energy resources for electric vehicles’ fast charging","authors":"Harprinderjot Singh , Hamid Mozafari , Mohammadreza Kavianipour , Mehrnaz Ghamami , Ali Zockaie , Robert Jackson","doi":"10.1080/15568318.2025.2479622","DOIUrl":"10.1080/15568318.2025.2479622","url":null,"abstract":"<div><div>The rapid growth of the electric vehicles (EVs) market penetration rate and the resulting energy demand will impact the electricity supply-demand balance and stability in the electricity distribution network. These impacts could be mitigated by distributed energy resources (DERs) (i.e. second-life batteries (SLB), new batteries (NB), solar panels, and flywheels). To support the energy demand of EVs at fast-charging stations whilst minimizing the cost of the system, a mixed-integer optimization model is developed considering the spatiotemporal demand (existing demand and EV demand), the details of the electric grid distribution network, spatiotemporal power generation of solar panels, energy storage systems’ (ESSs’) charge/discharge schedule, and the capacity constraints. The case study (major cities in Michigan) shows sensitivity to the seasonal variation in the grid and solar conditions and the DER’s unit cost. Based on the result, providing the maximum area for solar panels leads to the maximum cost savings. Lithium-ion SLBs offer a cost-effective solution for energy storage, efficiently utilizing time-of-use electricity rates and intermittent solar energy. Depending on the existing and fast-charging energy demand, grid upgrades may be necessary at some locations.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 4","pages":"Pages 344-362"},"PeriodicalIF":3.1,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-03DOI: 10.1080/15568318.2025.2476102
Kelong Chen , Zhenyu Cai , Mengting Peng , Changlin Ao , Xiaola Wu , Libin Shi , Yuntao Lin
In the development of the electric vehicle(EV) industry, grasping consumers’ choice preferences is an important part of current research. However, no research reports have been found on consumers’ vehicle purchase preferences in severe cold regions. For this reason, we conducted a choice experiment(CE) in Heilongjiang province, which is located in northeastern China, with the aim of investigating consumers’ choice preferences for EV in severe cold regions. In this paper, we applied multinomial logit models(MNL), random parameters logit models(RPL), and latent classes models(LCM) to explore the differences in attribute preferences and willingness to pay(WTP) for consumers’ choice of EV in cold environments. The results of this study show that the respondents are more concerned about the purchase price, driving range, charging station density, and charging time of EV. Based on the results of the LCM model, respondents were divided into two categories: price-sensitive type and charging time-sensitive type. We found that low-income women who own a garage are more likely to fall into the first category, focusing more on the purchase price, fuel cost, and carbon emission attributes of the EV. Higher-income men without a garage are more likely to fall into the second category, focusing more on purchase price, charging time,driving range, and the car’s power performance attributes during the selection process. These findings can provide policy recommendations for the government to promote the development of the electric vehicle industry, as well as guidance on technology development for automakers to open up the severe cold market.
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