Pub Date : 2025-02-01DOI: 10.1080/15568318.2025.2459614
Shuping Wu , Zan Yang , Shuyang Yao
This study employs a difference-in-difference (DID) regression to estimate the impact of high-speed rail (HSR) on city-level household carbon dioxide emissions across various consumption categories. The DID analysis is based on a sample of 179 Chinese cities during 2010-2018, and reveals a positive association between HSR and household carbon dioxide emissions. The findings suggest that cities with HSR emit more carbon dioxide due to increased daily consumption, and this effect grows over time. The mechanism analysis shows that the development of HSR stimulates household income growth, leading to increased consumption-based carbon dioxide in cities with HSR. Despite being considered a green transportation mode with a low carbon footprint, this research highlights potential environmental burdens associated with HSR, emphasizing the need for sustainable HSR development and environmental management policies.
{"title":"Impacts of high-speed rail on household carbon dioxide emissions: Evidence from China","authors":"Shuping Wu , Zan Yang , Shuyang Yao","doi":"10.1080/15568318.2025.2459614","DOIUrl":"10.1080/15568318.2025.2459614","url":null,"abstract":"<div><div>This study employs a difference-in-difference (DID) regression to estimate the impact of high-speed rail (HSR) on city-level household carbon dioxide emissions across various consumption categories. The DID analysis is based on a sample of 179 Chinese cities during 2010-2018, and reveals a positive association between HSR and household carbon dioxide emissions. The findings suggest that cities with HSR emit more carbon dioxide due to increased daily consumption, and this effect grows over time. The mechanism analysis shows that the development of HSR stimulates household income growth, leading to increased consumption-based carbon dioxide in cities with HSR. Despite being considered a green transportation mode with a low carbon footprint, this research highlights potential environmental burdens associated with HSR, emphasizing the need for sustainable HSR development and environmental management policies.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 2","pages":"Pages 149-164"},"PeriodicalIF":3.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488047","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 : 2025-02-01DOI: 10.1080/15568318.2025.2460637
Steven R. Gehrke , Manoj Kumar Allam , Armando E. Martinez , Ty M. Holliday , Brendan J. Russo , Edward J. Smaglik
The further motivation of bicycling as a utilitarian travel alternative has been identified as a viable solution to address societal concerns regarding physical inactivity, climate change, and transportation inequities. Yet, a profound increase in bicycling activity for many cities remains elusive to policymakers, practitioners, and researchers largely because of the inability to attract new bicyclists via safer bicycling infrastructure provision. To better understand current bicycling barriers to its future adoption, this study describes the advancement of the Cyclist Routing Algorithm for Network Connectivity (CRANC) and its application as an accessibility-oriented transportation planning tool in eight Arizona metropolitan regions. CRANC, an innovative bicyclist routing platform sensitive to bike network conditions and the varying traffic safety concerns of cyclist types (interested but concerned, enthused and confident, strong and fearless), is designed to support utilitarian bicycling promotion by identifying its latent demand. In this application, local and regional discrepancies in bicycling accessibility to jobs, schools, and grocery stores are identified and visualized by integrating the concepts of cyclist types and bicycle level of traffic stress into a new bicycling accessibility metric. Study findings show significant differences in place-based bicycling accessibility across key sociodemographic and economic indicators for the interested but concerned cyclist type, who prefers dedicated bike facilities, slower vehicle speeds, and lower traffic volumes. A recognition of these variations is important for promoting equitable bicycling access to subsistence and maintenance activities for those individuals who do not presently use this sustainable mode but would if barriers to access were removed.
{"title":"Cycling accessibility to employment, schools, and grocery stores in Arizona metropolitan regions","authors":"Steven R. Gehrke , Manoj Kumar Allam , Armando E. Martinez , Ty M. Holliday , Brendan J. Russo , Edward J. Smaglik","doi":"10.1080/15568318.2025.2460637","DOIUrl":"10.1080/15568318.2025.2460637","url":null,"abstract":"<div><div>The further motivation of bicycling as a utilitarian travel alternative has been identified as a viable solution to address societal concerns regarding physical inactivity, climate change, and transportation inequities. Yet, a profound increase in bicycling activity for many cities remains elusive to policymakers, practitioners, and researchers largely because of the inability to attract new bicyclists <em>via</em> safer bicycling infrastructure provision. To better understand current bicycling barriers to its future adoption, this study describes the advancement of the Cyclist Routing Algorithm for Network Connectivity (CRANC) and its application as an accessibility-oriented transportation planning tool in eight Arizona metropolitan regions. CRANC, an innovative bicyclist routing platform sensitive to bike network conditions and the varying traffic safety concerns of cyclist types (interested but concerned, enthused and confident, strong and fearless), is designed to support utilitarian bicycling promotion by identifying its latent demand. In this application, local and regional discrepancies in bicycling accessibility to jobs, schools, and grocery stores are identified and visualized by integrating the concepts of cyclist types and bicycle level of traffic stress into a new bicycling accessibility metric. Study findings show significant differences in place-based bicycling accessibility across key sociodemographic and economic indicators for the interested but concerned cyclist type, who prefers dedicated bike facilities, slower vehicle speeds, and lower traffic volumes. A recognition of these variations is important for promoting equitable bicycling access to subsistence and maintenance activities for those individuals who do not presently use this sustainable mode but would if barriers to access were removed.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 2","pages":"Pages 180-193"},"PeriodicalIF":3.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488048","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 : 2025-02-01DOI: 10.1080/15568318.2025.2459616
Nicola Amati , Luis M. Castellanos Molina , Alessandro Mancarella , Omar Marello , Mario Silvagni
This paper describes a methodology to develop simple energy consumption models of road vehicles exploiting transient experimental datasets obtained from a vehicle/powertrain four-dyno testbed available at the Center for Automotive Research and Sustainable mobility (CARS@POLITO) of Politecnico di Torino. These models, based on a locally weighted linear regression method, can serve as a simpler alternative to more conventional methods based, for example, on engine maps obtained by steady-state characterization at engine testbeds, and combined with powertrain subsystem models. The present methodology was applied to a conventional diesel-powered vehicle. Three different modeling approaches are proposed: vehicle-based (VB), engine-based (EB) and engine-based modified (EB*). The VB approach is the simplest, being able to estimate the vehicle fuel consumption by only using, as inputs, wheel torque and speed, while the EB and EB* approaches enhance modeling accuracy by using engine speed and torque, as inputs, along with transmission-related parameters and/or by considering the moments of inertia of the powertrain rotating parts. The manuscript describes, in full, the process used to develop these models, providing significant guidance for researchers who may want to replicate the procedure with their own experimental data. These energy consumption models can be useful tools for the development and assessment of eco-driving or ADAS functions or for energy consumption comparison between different vehicles that were not tested on the same driving cycle. They can also support the estimation of the total energy consumption of vehicles along different traffic conditions or routes, based on a limited number of experiments and low computational effort.
{"title":"Methodology for developing models to estimate vehicle instantaneous energy consumption based on hub-type dyno test data","authors":"Nicola Amati , Luis M. Castellanos Molina , Alessandro Mancarella , Omar Marello , Mario Silvagni","doi":"10.1080/15568318.2025.2459616","DOIUrl":"10.1080/15568318.2025.2459616","url":null,"abstract":"<div><div>This paper describes a methodology to develop simple energy consumption models of road vehicles exploiting transient experimental datasets obtained from a vehicle/powertrain four-dyno testbed available at the Center for Automotive Research and Sustainable mobility (CARS@POLITO) of Politecnico di Torino. These models, based on a locally weighted linear regression method, can serve as a simpler alternative to more conventional methods based, for example, on engine maps obtained by steady-state characterization at engine testbeds, and combined with powertrain subsystem models. The present methodology was applied to a conventional diesel-powered vehicle. Three different modeling approaches are proposed: vehicle-based (VB), engine-based (EB) and engine-based modified (EB*). The VB approach is the simplest, being able to estimate the vehicle fuel consumption by only using, as inputs, wheel torque and speed, while the EB and EB* approaches enhance modeling accuracy by using engine speed and torque, as inputs, along with transmission-related parameters and/or by considering the moments of inertia of the powertrain rotating parts. The manuscript describes, in full, the process used to develop these models, providing significant guidance for researchers who may want to replicate the procedure with their own experimental data. These energy consumption models can be useful tools for the development and assessment of eco-driving or ADAS functions or for energy consumption comparison between different vehicles that were not tested on the same driving cycle. They can also support the estimation of the total energy consumption of vehicles along different traffic conditions or routes, based on a limited number of experiments and low computational effort.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 2","pages":"Pages 165-179"},"PeriodicalIF":3.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488050","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 : 2025-01-02DOI: 10.1080/15568318.2024.2443827
Xinyi Wu , Yan Zhou , David Gohlke , Jarod Kelly
The electrification of light-duty vehicles (LDVs) is essential for decarbonizing the transportation sector in the United States. Both federal and state governments have begun promoting and incentivizing the adoption of plug-in electric vehicle (PEV) (battery electric vehicles (BEV) and plug-in hybrid electric vehicles (PHEV)) to reduce LDV greenhouse gas (GHG) emissions greatly. However, there remains a critical need for a robust methodology to accurately quantify the distributed emissions impacts of PEV adoption at a granular regional level. Additionally, the role of electricity traded across electrical grids in regional GHG mitigation efforts often goes unrecognized. This study addresses these gaps by developing a top-down approach for assessing county-level emissions benefits arising from PEV adoption while accounting for upstream emissions due to electricity flow across regions. Our findings underscore the significant influence of regional variations in future PEV adoption rates and vehicle usage patterns on emissions reduction potential. Nevertheless, these benefits can be tempered by local emission intensities associated with electricity generation. Importantly, our study reaffirms the necessity of considering electricity flow dynamics across grids in estimating local GHG mitigation outcomes.
{"title":"Light-duty plug-in electric vehicle adoption: County-level emissions benefits using consumption-based emissions intensities","authors":"Xinyi Wu , Yan Zhou , David Gohlke , Jarod Kelly","doi":"10.1080/15568318.2024.2443827","DOIUrl":"10.1080/15568318.2024.2443827","url":null,"abstract":"<div><div>The electrification of light-duty vehicles (LDVs) is essential for decarbonizing the transportation sector in the United States. Both federal and state governments have begun promoting and incentivizing the adoption of plug-in electric vehicle (PEV) (battery electric vehicles (BEV) and plug-in hybrid electric vehicles (PHEV)) to reduce LDV greenhouse gas (GHG) emissions greatly. However, there remains a critical need for a robust methodology to accurately quantify the distributed emissions impacts of PEV adoption at a granular regional level. Additionally, the role of electricity traded across electrical grids in regional GHG mitigation efforts often goes unrecognized. This study addresses these gaps by developing a top-down approach for assessing county-level emissions benefits arising from PEV adoption while accounting for upstream emissions due to electricity flow across regions. Our findings underscore the significant influence of regional variations in future PEV adoption rates and vehicle usage patterns on emissions reduction potential. Nevertheless, these benefits can be tempered by local emission intensities associated with electricity generation. Importantly, our study reaffirms the necessity of considering electricity flow dynamics across grids in estimating local GHG mitigation outcomes.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 1","pages":"Pages 72-82"},"PeriodicalIF":3.1,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-02DOI: 10.1080/15568318.2024.2443821
Yang Zhao , Peijun Li , Yuan Zhang , Xiaoxia Li , Fan Zhang
The automotive industry is undergoing transformative changes propelled by the progress in technology, considerations for the environment, and the evolving tastes of consumers. This quantitative research endeavors to investigate the impact of data-driven advancements in the automotive sector. The research methodology employed a purposive sampling technique, targeting diverse stakeholders within the Chinese automotive industry. A structured questionnaire served as the primary data collection tool. Through direct interactions and visits, 900 questionnaires were distributed over three days, yielding a robust response of 850 returned surveys. Following the removal of invalid responses, the study culled valid data from 800 participants. The collected data underwent analysis using SPSS statistical software. Findings reveal significant trends in the industry, such as the increasing adoption of electric vehicles, evolving customer preferences for advanced features, and the potential impact of ride-sharing and car-sharing services on individual car ownership. Furthermore, the investigation identifies the crucial role of data analysis, predictive analytics, IoT devices, and big data in shaping various aspects of the automotive sector. The study’s novelty lies in its quantitative approach, providing objective insights into demographic characteristics, industry trends, and participants’ perspectives. The study’s exploration of data-driven design processes and their role in fostering innovation and user-friendly vehicles adds a distinctive layer to understanding the transformative impact of data science on automotive development. Overall, this research contributes valuable knowledge for industry practitioners, policymakers, and scholars interested in the intersection of data science and automotive advancements.
{"title":"Sustainable transportation through data science: Case studies from the automotive industry","authors":"Yang Zhao , Peijun Li , Yuan Zhang , Xiaoxia Li , Fan Zhang","doi":"10.1080/15568318.2024.2443821","DOIUrl":"10.1080/15568318.2024.2443821","url":null,"abstract":"<div><div>The automotive industry is undergoing transformative changes propelled by the progress in technology, considerations for the environment, and the evolving tastes of consumers. This quantitative research endeavors to investigate the impact of data-driven advancements in the automotive sector. The research methodology employed a purposive sampling technique, targeting diverse stakeholders within the Chinese automotive industry. A structured questionnaire served as the primary data collection tool. Through direct interactions and visits, 900 questionnaires were distributed over three days, yielding a robust response of 850 returned surveys. Following the removal of invalid responses, the study culled valid data from 800 participants. The collected data underwent analysis using SPSS statistical software. Findings reveal significant trends in the industry, such as the increasing adoption of electric vehicles, evolving customer preferences for advanced features, and the potential impact of ride-sharing and car-sharing services on individual car ownership. Furthermore, the investigation identifies the crucial role of data analysis, predictive analytics, IoT devices, and big data in shaping various aspects of the automotive sector. The study’s novelty lies in its quantitative approach, providing objective insights into demographic characteristics, industry trends, and participants’ perspectives. The study’s exploration of data-driven design processes and their role in fostering innovation and user-friendly vehicles adds a distinctive layer to understanding the transformative impact of data science on automotive development. Overall, this research contributes valuable knowledge for industry practitioners, policymakers, and scholars interested in the intersection of data science and automotive advancements.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 1","pages":"Pages 55-71"},"PeriodicalIF":3.1,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453927","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 : 2025-01-02DOI: 10.1080/15568318.2024.2447999
Rachit Soni , Akshay Dvivedi , Pradeep Kumar
GHGs significantly impact climate change, adversely affecting both the environment and human well-being. As energy production and transportation are among the primary contributors to these emissions, many nations have implemented strategies to transition to renewable energy and reduce emissions by 2050-2070. This review focuses on identifying effective policies and pathways to achieve carbon neutrality in the transport supply chain. A bibliometric analysis highlights the growing importance of hydrogen and biomass-generated energy. Key trends include alternative fuels, hydrogen, electric vehicles, solar and wind energy, carbon neutrality, and GHG mitigation. In both the short and long term, integrating green transportation innovations, renewable energy consumption, and sustainable economic growth can substantially lower carbon emissions. Factors such as population growth, urbanization rates, coal consumption, renewable energy adoption, and the increasing use of electric vehicles (EVs) are emerging as critical drivers of environmental sustainability and net-zero emission goals. Policymakers are strongly encouraged to prioritize and implement optimal strategies that capitalize on these opportunities to advance carbon neutrality objectives.
{"title":"Carbon neutrality in transportation: In the context of renewable sources","authors":"Rachit Soni , Akshay Dvivedi , Pradeep Kumar","doi":"10.1080/15568318.2024.2447999","DOIUrl":"10.1080/15568318.2024.2447999","url":null,"abstract":"<div><div>GHGs significantly impact climate change, adversely affecting both the environment and human well-being. As energy production and transportation are among the primary contributors to these emissions, many nations have implemented strategies to transition to renewable energy and reduce emissions by 2050-2070. This review focuses on identifying effective policies and pathways to achieve carbon neutrality in the transport supply chain. A bibliometric analysis highlights the growing importance of hydrogen and biomass-generated energy. Key trends include alternative fuels, hydrogen, electric vehicles, solar and wind energy, carbon neutrality, and GHG mitigation. In both the short and long term, integrating green transportation innovations, renewable energy consumption, and sustainable economic growth can substantially lower carbon emissions. Factors such as population growth, urbanization rates, coal consumption, renewable energy adoption, and the increasing use of electric vehicles (EVs) are emerging as critical drivers of environmental sustainability and net-zero emission goals. Policymakers are strongly encouraged to prioritize and implement optimal strategies that capitalize on these opportunities to advance carbon neutrality objectives.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 1","pages":"Pages 1-15"},"PeriodicalIF":3.1,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453836","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}
In the last decade, the popularity of e-bikes has increased as they have shown potential to relieve congestion and aid the environment. However, with the increase of their popularity, there has been also an increase in their traffic crashes. This study aims to understand factors playing a role in the e-bike crash injury outcomes. The analysis uses 1,351 records of e-bike crashes to estimate random parameters multinomial logit models with heterogeneity in the means and variances of random parameters in four groups. This paper also seeks to provide insights into e-bike crash injury severities across gender (female versus male) and lighting conditions (daytime and nighttime) specific models. Numerous likelihood ratio tests were performed to justify splitting the data. It was found that a variety of factors relating to the weather and road characteristics, crash type, and rider’s demographics play a role in crash outcomes. Particularly interesting are findings relating to the rollover crashes increasing the likelihood of severe outcomes as well as gender specific effects with, for example, male riders have a higher probability of severe injuries during peak traffic hours. The findings can be used to support e-bike safety as well as advocate for a more nuanced and inclusive approach relating to e-bike travel.
{"title":"E-bike crashes: Who they affect and which circumstances to avoid?","authors":"Yuntong Zhou , Natalia Barbour , Mohamed Abdel-Aty , Xin Gu , Yanyan Chen","doi":"10.1080/15568318.2024.2447993","DOIUrl":"10.1080/15568318.2024.2447993","url":null,"abstract":"<div><div>In the last decade, the popularity of e-bikes has increased as they have shown potential to relieve congestion and aid the environment. However, with the increase of their popularity, there has been also an increase in their traffic crashes. This study aims to understand factors playing a role in the e-bike crash injury outcomes. The analysis uses 1,351 records of e-bike crashes to estimate random parameters multinomial logit models with heterogeneity in the means and variances of random parameters in four groups. This paper also seeks to provide insights into e-bike crash injury severities across gender (female versus male) and lighting conditions (daytime and nighttime) specific models. Numerous likelihood ratio tests were performed to justify splitting the data. It was found that a variety of factors relating to the weather and road characteristics, crash type, and rider’s demographics play a role in crash outcomes. Particularly interesting are findings relating to the rollover crashes increasing the likelihood of severe outcomes as well as gender specific effects with, for example, male riders have a higher probability of severe injuries during peak traffic hours. The findings can be used to support e-bike safety as well as advocate for a more nuanced and inclusive approach relating to e-bike travel.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 1","pages":"Pages 83-101"},"PeriodicalIF":3.1,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453835","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 : 2025-01-02DOI: 10.1080/15568318.2024.2443825
Erma Suryani , M. S. Fadlillah , R. A. Hendrawan , Mudjahidin Mudjahidin , R. J. Pramundito , A. A. Zahra , S. Y. Chou , Anindhita Dewabharata , Z. U. Rizqi , Rafika Rahmawati
This research proposes a comprehensive analysis of the environmental and economic impacts of electric vehicle (EVs) adoption using system dynamics modeling. A system dynamics framework is utilized to integrate various aspects of EVs, economy, environment, and impact of policies on those sectors. Stock and flow diagrams were used to model and predict the impact of government support on electric vehicles based on the existing and future conditions through several proposed strategies. This research mainly contributes to providing causal relationships of variables and parameters influencing the number of EVs and their impact on the economy and environment, modeling and simulation of several sub-systems based on the existing condition, and scenario modeling to predict and improve the number of EV, economic value, and environmentally friendly in the future. This research examines how different policies for electric vehicles (EVs) might affect the numbers of people use them, the pollution caused, and the cost spent. They looked at total emissions, yearly budget, and the number of electric cars and motorcycles. The results show that continuing or increasing government help (scenarios SCN2 & SCN3) for EVs leads to the biggest pollution reduction. Focusing on developing new technologies and industries for EVs (SCN4) shows the biggest short-term pollution reduction. The key takeaway is that long-term support for EVs and technological advancements are essential for success. Finding a balance between the initial costs and the long-term benefits is crucial when designing policies for EVs.
{"title":"Dynamic model to assess the impacts of government support for electric vehicles on the economy and environment sectors in Indonesia","authors":"Erma Suryani , M. S. Fadlillah , R. A. Hendrawan , Mudjahidin Mudjahidin , R. J. Pramundito , A. A. Zahra , S. Y. Chou , Anindhita Dewabharata , Z. U. Rizqi , Rafika Rahmawati","doi":"10.1080/15568318.2024.2443825","DOIUrl":"10.1080/15568318.2024.2443825","url":null,"abstract":"<div><div>This research proposes a comprehensive analysis of the environmental and economic impacts of electric vehicle (EVs) adoption using system dynamics modeling. A system dynamics framework is utilized to integrate various aspects of EVs, economy, environment, and impact of policies on those sectors. Stock and flow diagrams were used to model and predict the impact of government support on electric vehicles based on the existing and future conditions through several proposed strategies. This research mainly contributes to providing causal relationships of variables and parameters influencing the number of EVs and their impact on the economy and environment, modeling and simulation of several sub-systems based on the existing condition, and scenario modeling to predict and improve the number of EV, economic value, and environmentally friendly in the future. This research examines how different policies for electric vehicles (EVs) might affect the numbers of people use them, the pollution caused, and the cost spent. They looked at total emissions, yearly budget, and the number of electric cars and motorcycles. The results show that continuing or increasing government help (scenarios SCN2 & SCN3) for EVs leads to the biggest pollution reduction. Focusing on developing new technologies and industries for EVs (SCN4) shows the biggest short-term pollution reduction. The key takeaway is that long-term support for EVs and technological advancements are essential for success. Finding a balance between the initial costs and the long-term benefits is crucial when designing policies for EVs.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 1","pages":"Pages 35-54"},"PeriodicalIF":3.1,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453837","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 : 2025-01-02DOI: 10.1080/15568318.2024.2443819
Suleyman Kose
This study measured emissions from 76 oil tankers at Eastern Black Sea petroleum terminals to determine their emission factors. Emissions of CO, CO2, NOX, and SO2 were measured during cruise (C), maneuvering (M), and hotelling (H) activities of main engines (ME) and auxiliary engines (AE). Using an activity-based approach, emission factors were calculated from the collected data. Real-time data from 2013 to 2021 were utilized to determine total emissions for each year, while regression analysis forecasted emissions until 2040 under various scenarios. Weighted emission factors for ME were determined as 1.1 ± 0.22 g/kWh for CO, 654 ± 13 g/kWh for CO2, 13.95 ± 2.75 g/kWh for NOX, and 11.45± g/kWh for SO2, and for AE, 1.1 ± 0.21 g/kWh for CO, 706 ± 15 g/kWh for CO2, 15.3 ± 1.4 g/kWh for NOX, and 11.15 ± 2.25 g/kWh. Average load factors were as follows: C (ME): 67%, C (AE): 35%, M (ME): 34%, M (AE): 53%, H (ME): 76%, H (AE): 62%. Total emissions from oil tankers in 2022 were projected to be 235 tons for CO, 151580 tons for CO2, 3018 tons for NOX, and 2251 tons for SO2. Future scenarios indicate these amounts could increase by 3, 5, and 12 times by 2040 under optimistic, normal, and pessimistic scenarios, respectively.
本研究测量了东黑海石油码头76艘油轮的排放量，以确定其排放因子。测量了主、辅发动机巡航(C)、机动(M)和制动(H)活动时CO、CO2、NOX和SO2的排放。采用基于活动的方法，根据收集的数据计算排放因子。利用2013年至2021年的实时数据确定每年的总排放量，而回归分析预测了各种情景下到2040年的排放量。ME的加权排放系数分别为CO 1.1±0.22 g/kWh、CO2 654±13 g/kWh、NOX 13.95±2.75 g/kWh、SO2 11.45±g/kWh； AE的加权排放系数分别为CO 1.1±0.21 g/kWh、CO2 706±15 g/kWh、NOX 15.3±1.4 g/kWh、11.15±2.25 g/kWh。平均载客率为：C (ME): 67%, C (AE): 35%, M (ME): 34%, M (AE): 53%, H (ME): 76%, H (AE): 62%。2022年，油轮的总排放量预计为CO 235吨，CO2 151580吨，NOX 3018吨，SO2 2251吨。未来情景表明，到2040年，在乐观、正常和悲观情景下，这些数量可能分别增加3倍、5倍和12倍。
{"title":"Oil tanker emissions: Measurement, factors, and future scenarios","authors":"Suleyman Kose","doi":"10.1080/15568318.2024.2443819","DOIUrl":"10.1080/15568318.2024.2443819","url":null,"abstract":"<div><div>This study measured emissions from 76 oil tankers at Eastern Black Sea petroleum terminals to determine their emission factors. Emissions of CO, CO<sub>2</sub>, NO<sub>X</sub>, and SO<sub>2</sub> were measured during cruise (C), maneuvering (M), and hotelling (H) activities of main engines (ME) and auxiliary engines (AE). Using an activity-based approach, emission factors were calculated from the collected data. Real-time data from 2013 to 2021 were utilized to determine total emissions for each year, while regression analysis forecasted emissions until 2040 under various scenarios. Weighted emission factors for ME were determined as 1.1 ± 0.22 g/kWh for CO, 654 ± 13 g/kWh for CO<sub>2</sub>, 13.95 ± 2.75 g/kWh for NO<sub>X</sub>, and 11.45± g/kWh for SO<sub>2</sub>, and for AE, 1.1 ± 0.21 g/kWh for CO, 706 ± 15 g/kWh for CO<sub>2</sub>, 15.3 ± 1.4 g/kWh for NO<sub>X</sub>, and 11.15 ± 2.25 g/kWh. Average load factors were as follows: C (ME): 67%, C (AE): 35%, M (ME): 34%, M (AE): 53%, H (ME): 76%, H (AE): 62%. Total emissions from oil tankers in 2022 were projected to be 235 tons for CO, 151580 tons for CO<sub>2</sub>, 3018 tons for NO<sub>X</sub>, and 2251 tons for SO<sub>2</sub>. Future scenarios indicate these amounts could increase by 3, 5, and 12 times by 2040 under optimistic, normal, and pessimistic scenarios, respectively.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 1","pages":"Pages 16-34"},"PeriodicalIF":3.1,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453926","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 : 2024-12-21DOI: 10.1080/15568318.2024.2443657
Yifan Zhang , Meng Xu
A carbon emissions simulation framework for assessing Mobility-as-a-Service (MaaS) schemes is proposed in this study. The MaaS platform incorporates an added recommendation of combined modes based on traditional travel recommendations. Different MaaS schemes are represented by the proportion of rides taken by ride-hailing services in the combined mode. A carbon emission prediction model integrating the IPCC (Intergovernmental Panel on Climate Change) moving source emission model with a multi-agent system has been developed. This model employs a multi-agent approach to simulate heterogeneous user mode choice behavior and uses the IPCC’s “bottom-up” method to calculate system carbon emissions, considering the impact of congestion. The model is validated through a case study using data from the Beijing MaaS platform. This study examines the effects of two MaaS recommendation schemes on system carbon emissions under varying user numbers and heterogeneity, as well as the impact of green incentive policies. The case study reveals that disregarding user heterogeneity overestimates system carbon emissions. Increasing user numbers raises total system carbon emissions but does not necessarily increase per capita emissions. MaaS schemes with a lower proportion of ride-hailing trips in combined modes more effectively encourage car users to switch to subway and ride-hailing, and help carless users adopt the subway + ride-hailing combination, thus reducing system emissions. Higher rewards in green incentive policies do not necessarily lead to lower carbon emissions. The model provides a detailed approach for carbon emission calculations, which could apply for MaaS carbon reduction assessment, MaaS scheme design, and green incentive design.
{"title":"Promoting low carbon mobility: A case study of Beijing MaaS","authors":"Yifan Zhang , Meng Xu","doi":"10.1080/15568318.2024.2443657","DOIUrl":"10.1080/15568318.2024.2443657","url":null,"abstract":"<div><div>A carbon emissions simulation framework for assessing Mobility-as-a-Service (MaaS) schemes is proposed in this study. The MaaS platform incorporates an added recommendation of combined modes based on traditional travel recommendations. Different MaaS schemes are represented by the proportion of rides taken by ride-hailing services in the combined mode. A carbon emission prediction model integrating the IPCC (Intergovernmental Panel on Climate Change) moving source emission model with a multi-agent system has been developed. This model employs a multi-agent approach to simulate heterogeneous user mode choice behavior and uses the IPCC’s “bottom-up” method to calculate system carbon emissions, considering the impact of congestion. The model is validated through a case study using data from the Beijing MaaS platform. This study examines the effects of two MaaS recommendation schemes on system carbon emissions under varying user numbers and heterogeneity, as well as the impact of green incentive policies. The case study reveals that disregarding user heterogeneity overestimates system carbon emissions. Increasing user numbers raises total system carbon emissions but does not necessarily increase per capita emissions. MaaS schemes with a lower proportion of ride-hailing trips in combined modes more effectively encourage car users to switch to subway and ride-hailing, and help carless users adopt the subway + ride-hailing combination, thus reducing system emissions. Higher rewards in green incentive policies do not necessarily lead to lower carbon emissions. The model provides a detailed approach for carbon emission calculations, which could apply for MaaS carbon reduction assessment, MaaS scheme design, and green incentive design.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 9","pages":"Pages 800-814"},"PeriodicalIF":3.9,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907833","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号