Pub Date : 2025-12-02DOI: 10.1080/15568318.2025.2544181
Tanguy Baiwir , Sabine Limbourg , Mario Cools
Amidst the rise of e-commerce, understanding the interplay between consumer behaviors and environmental considerations has become pivotal. This study examines how environmental awareness impacts e-commerce consumers’ preferences for sustainable versus express delivery options. To contribute to the literature in this field, we investigate the behaviors of a sample of 299 e-commerce consumers, particularly in light of growing environmental concerns. Leveraging the New Ecological Paradigm Scale (NEPS), we compute an ecological score, offering a comprehensive insight into its influence on varied consumer decisions. Principal Component Analysis of the NEPS items reveals that the first four components account for nearly 50% of the variance, highlighting significant dimensions of environmental perspectives. Additionally, Cronbach’s alpha analysis indicates that the NEPS scale is reliable and has good internal consistency, justifying the use of a summated scale to reflect overall ecological positioning. We then contrast two primary delivery types: sustainable and express. The key metrics under scrutiny include the willingness to wait and the willingness to pay for sustainable delivery and willingness to pay for express delivery. Our findings affirm that NEPS affects positively willingness to pay and willingness to wait for sustainable delivery and negatively WTP for express delivery.
{"title":"The role of the new ecological paradigm scale on the willingness to pay and willingness to wait for e-commerce deliveries","authors":"Tanguy Baiwir , Sabine Limbourg , Mario Cools","doi":"10.1080/15568318.2025.2544181","DOIUrl":"10.1080/15568318.2025.2544181","url":null,"abstract":"<div><div>Amidst the rise of e-commerce, understanding the interplay between consumer behaviors and environmental considerations has become pivotal. This study examines how environmental awareness impacts e-commerce consumers’ preferences for sustainable versus express delivery options. To contribute to the literature in this field, we investigate the behaviors of a sample of 299 e-commerce consumers, particularly in light of growing environmental concerns. Leveraging the New Ecological Paradigm Scale (NEPS), we compute an ecological score, offering a comprehensive insight into its influence on varied consumer decisions. Principal Component Analysis of the NEPS items reveals that the first four components account for nearly 50% of the variance, highlighting significant dimensions of environmental perspectives. Additionally, Cronbach’s alpha analysis indicates that the NEPS scale is reliable and has good internal consistency, justifying the use of a summated scale to reflect overall ecological positioning. We then contrast two primary delivery types: sustainable and express. The key metrics under scrutiny include the willingness to wait and the willingness to pay for sustainable delivery and willingness to pay for express delivery. Our findings affirm that NEPS affects positively willingness to pay and willingness to wait for sustainable delivery and negatively WTP for express delivery.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 12","pages":"Pages 1091-1104"},"PeriodicalIF":3.9,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645875","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 : 2025-12-02DOI: 10.1080/15568318.2025.2553903
B. Mohan , Sudhakaran M. , Srikiruthika S.
With the increasing popularity of electric vehicles (EVs) worldwide, the need for efficient and accessible charging infrastructure has become increasingly critical. The rapid expansion of EV adoption and the importance of strategically locating charging stations (CS) to support this transition. Existing research reveals challenges, such as suboptimal placement resulting in uneven distribution, inadequate coverage, and increased energy loss due to inefficient network configurations. This research addresses the challenge of identifying optimal places and dimensions for EVCS and Distributed Generation (DG) to enhance accessibility and minimize power loss in electrical distribution networks. To overcome the conventional optimization method challenges, the adaptive luminescence moth optimization (ALMO) is utilized to identify optimal CS and DG locations with the best sizes of EVCS and DG concerning the network reconfiguration. The optimal place and dimension chosen for the placement of the EVCS and DG should show minimum voltage deviation and maximum voltage stability. To find the losses and voltage profile fast computing with less memory Backward Forward Sweep (BFS) Load Flow Analysis is considered. The provided approach aims to maximize coverage, minimize power loss and voltage deviation, and improve overall network efficiency. By considering various factors, such as voltage stability, voltage deviation, power loss, and cost analysis ALMO model ensures robust and effective placement and capacity decisions. The simulation results with the analysis based on IEEE33 and IEEE69 bus systems demonstrate the efficiency of the proposed model outperforming the other existing techniques.
{"title":"Optimal allocation of electric vehicle charging station integrated with distributed generation using adaptive luminescence moth optimization","authors":"B. Mohan , Sudhakaran M. , Srikiruthika S.","doi":"10.1080/15568318.2025.2553903","DOIUrl":"10.1080/15568318.2025.2553903","url":null,"abstract":"<div><div>With the increasing popularity of electric vehicles (EVs) worldwide, the need for efficient and accessible charging infrastructure has become increasingly critical. The rapid expansion of EV adoption and the importance of strategically locating charging stations (CS) to support this transition. Existing research reveals challenges, such as suboptimal placement resulting in uneven distribution, inadequate coverage, and increased energy loss due to inefficient network configurations. This research addresses the challenge of identifying optimal places and dimensions for EVCS and Distributed Generation (DG) to enhance accessibility and minimize power loss in electrical distribution networks. To overcome the conventional optimization method challenges, the adaptive luminescence moth optimization (ALMO) is utilized to identify optimal CS and DG locations with the best sizes of EVCS and DG concerning the network reconfiguration. The optimal place and dimension chosen for the placement of the EVCS and DG should show minimum voltage deviation and maximum voltage stability. To find the losses and voltage profile fast computing with less memory Backward Forward Sweep (BFS) Load Flow Analysis is considered. The provided approach aims to maximize coverage, minimize power loss and voltage deviation, and improve overall network efficiency. By considering various factors, such as voltage stability, voltage deviation, power loss, and cost analysis ALMO model ensures robust and effective placement and capacity decisions. The simulation results with the analysis based on IEEE33 and IEEE69 bus systems demonstrate the efficiency of the proposed model outperforming the other existing techniques.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 12","pages":"Pages 1181-1199"},"PeriodicalIF":3.9,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645877","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 : 2025-12-02DOI: 10.1080/15568318.2025.2546038
Hannaneh Abdollahzadeh Kalantari , Wookjae Yang , Reid Ewing
Urban transportation systems face challenges in providing connectivity, particularly for the first/last-miles of commuters’ journeys. Micromobility services, such as e-scooters, have emerged as potential solutions to bridge this gap and enhance the efficiency and accessibility of public transit. As transit ridership demonstrates declining trends in the US, the integration of micromobility options with existing transit infrastructure presents a promising solution. This study aims to investigate the role of e-scooter services in enhancing first/last-mile connectivity within public transit systems, focusing on factors influencing adoption, barriers to integration, and potential policy interventions by studying Salt Lake County area. Methodologically, intercept surveys were conducted to gather demographic and behavioral insights from both e-scooter users and transit riders. Descriptive statistics, chi-square tests, and thematic qualitative analysis were employed. We found that the promise of e-scooters as conduits for transit connectivity remains largely unmet. Despite bustling transit stations, the number of e-scooter users was extremely low, and walking is the preferred way to connect to transit. Disparities in first/last-mile connectivity patterns between e-scooters and traditional modes of transport among transit riders further highlight the need for targeted interventions. The findings also revealed a strong preference for e-scooters among younger demographics, driven by factors such as convenience and enjoyment. However, challenges related to cost, accessibility, safety, and lack of familiarity hinder widespread adoption. While e-scooter services offer opportunities to enhance transit connectivity, addressing barriers requires efforts from policymakers and authorities.
{"title":"Can E-scooters connect first and last-mile of public rail transit? Lessons learned from intercept user survey in Utah","authors":"Hannaneh Abdollahzadeh Kalantari , Wookjae Yang , Reid Ewing","doi":"10.1080/15568318.2025.2546038","DOIUrl":"10.1080/15568318.2025.2546038","url":null,"abstract":"<div><div>Urban transportation systems face challenges in providing connectivity, particularly for the first/last-miles of commuters’ journeys. Micromobility services, such as e-scooters, have emerged as potential solutions to bridge this gap and enhance the efficiency and accessibility of public transit. As transit ridership demonstrates declining trends in the US, the integration of micromobility options with existing transit infrastructure presents a promising solution. This study aims to investigate the role of e-scooter services in enhancing first/last-mile connectivity within public transit systems, focusing on factors influencing adoption, barriers to integration, and potential policy interventions by studying Salt Lake County area. Methodologically, intercept surveys were conducted to gather demographic and behavioral insights from both e-scooter users and transit riders. Descriptive statistics, chi-square tests, and thematic qualitative analysis were employed. We found that the promise of e-scooters as conduits for transit connectivity remains largely unmet. Despite bustling transit stations, the number of e-scooter users was extremely low, and walking is the preferred way to connect to transit. Disparities in first/last-mile connectivity patterns between e-scooters and traditional modes of transport among transit riders further highlight the need for targeted interventions. The findings also revealed a strong preference for e-scooters among younger demographics, driven by factors such as convenience and enjoyment. However, challenges related to cost, accessibility, safety, and lack of familiarity hinder widespread adoption. While e-scooter services offer opportunities to enhance transit connectivity, addressing barriers requires efforts from policymakers and authorities.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 12","pages":"Pages 1121-1144"},"PeriodicalIF":3.9,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645873","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}
Modern data acquisition techniques and the growing concern about climate change have significantly contributed to research on fuel consumption. Thus, approaches for predicting fuel consumption, which is heavily determined by driving behavior, continue to evolve. Driving behavior also defines the characteristics of driving styles, which significantly influence fuel consumption. Previous studies lacked the investigation of the non-linear relationship between driving behavior, driving styles, and fuel consumption. To address this problem, our study employs a deep learning approach, specifically Long Short-Term Memory (LSTM), to investigate the actual non-linear relationship between driving behavior and fuel consumption using real-world driving trajectory data, namely the Vehicle Energy Dataset (VED) from Ann Arbor, Michigan, USA. The time series data consists of driving records from which data on internal combustion engine (ICE) vehicles is filtered to align with the scope of study. The LSTM model, which is also known for capturing complex non-linear patterns and temporal dependencies, is employed to predict fuel consumption. The performance of the proposed model is compared with Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU). The results revealed that the LSTM model outperformed RNN and GRU in predicting fuel consumption. Additionally, a feature importance analysis was conducted using the permutation feature importance score to understand the factors influencing fuel consumption in driving behavior, as reflected in the model’s predictive results. The findings from this study can also inform ICE drivers about the development of personalized strategies to optimize fuel efficiency and contribute to eco-driving.
{"title":"Exploring the non-linear relationship between driving behavior and vehicle fuel consumption using long short-term memory","authors":"Aamir Hussain , Shuyan Chen , Sajan Shaikh , Irfan Ullah , Ghim Ping Ong","doi":"10.1080/15568318.2025.2542293","DOIUrl":"10.1080/15568318.2025.2542293","url":null,"abstract":"<div><div>Modern data acquisition techniques and the growing concern about climate change have significantly contributed to research on fuel consumption. Thus, approaches for predicting fuel consumption, which is heavily determined by driving behavior, continue to evolve. Driving behavior also defines the characteristics of driving styles, which significantly influence fuel consumption. Previous studies lacked the investigation of the non-linear relationship between driving behavior, driving styles, and fuel consumption. To address this problem, our study employs a deep learning approach, specifically Long Short-Term Memory (LSTM), to investigate the actual non-linear relationship between driving behavior and fuel consumption using real-world driving trajectory data, namely the Vehicle Energy Dataset (VED) from Ann Arbor, Michigan, USA. The time series data consists of driving records from which data on internal combustion engine (ICE) vehicles is filtered to align with the scope of study. The LSTM model, which is also known for capturing complex non-linear patterns and temporal dependencies, is employed to predict fuel consumption. The performance of the proposed model is compared with Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU). The results revealed that the LSTM model outperformed RNN and GRU in predicting fuel consumption. Additionally, a feature importance analysis was conducted using the permutation feature importance score to understand the factors influencing fuel consumption in driving behavior, as reflected in the model’s predictive results. The findings from this study can also inform ICE drivers about the development of personalized strategies to optimize fuel efficiency and contribute to eco-driving.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 12","pages":"Pages 1073-1090"},"PeriodicalIF":3.9,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645944","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 : 2025-11-02DOI: 10.1080/15568318.2025.2538685
Tao Li , Xin Lou , Zhuoqian Yang , Jing Zhang , Guoquan Xie , Baoli Gong , Danqi Wang , Kui Wang , Yong Peng
Currently, the raw data exported from the remote on-board diagnostics (OBD) monitoring systems of heavy-duty diesel vehicles exhibit significant issues with missing values in key items such as NOx concentration, posing challenges to effective emission regulation. This study proposes a systematic approach to raw data processing, provides a detailed analysis of NOx data missing patterns, and develops a weighted prediction model based on the AutoRegressive Moving Average with eXogenous variables- Long Short-Term Memory (ARMAX-LSTM) for missing data imputation. The ARMAX-LSTM model combines the capability of LSTM to capture nonlinear patterns with ARMAX’s ability to describe linear data, enhanced by dynamic weighting coefficients to improve prediction accuracy. Using Spearman rank correlation analysis, nine key parameters and the NOx downstream concentration itself were selected as input features for predicting and imputing missing values. Experimental results demonstrate that the proposed model reduces the mean squared error by 26.99% compared to the ARMAX baseline model and by 14.08% compared to the LSTM baseline model for randomly missing data. For naturally missing data segments, the model produced imputed curves with good continuity and stability, meeting engineering application requirements. This study provides technical support for improving OBD data quality and identifying high-emission vehicles, while also highlighting the model’s limitations in handling scenarios with all data items missing over continuous time periods. These findings offer directions for future model optimization.
{"title":"Enhancing vehicular emissions monitoring: A raw data processing and imputation model for heavy-duty diesel vehicles using remote OBD systems","authors":"Tao Li , Xin Lou , Zhuoqian Yang , Jing Zhang , Guoquan Xie , Baoli Gong , Danqi Wang , Kui Wang , Yong Peng","doi":"10.1080/15568318.2025.2538685","DOIUrl":"10.1080/15568318.2025.2538685","url":null,"abstract":"<div><div>Currently, the raw data exported from the remote on-board diagnostics (OBD) monitoring systems of heavy-duty diesel vehicles exhibit significant issues with missing values in key items such as NOx concentration, posing challenges to effective emission regulation. This study proposes a systematic approach to raw data processing, provides a detailed analysis of NOx data missing patterns, and develops a weighted prediction model based on the AutoRegressive Moving Average with eXogenous variables- Long Short-Term Memory (ARMAX-LSTM) for missing data imputation. The ARMAX-LSTM model combines the capability of LSTM to capture nonlinear patterns with ARMAX’s ability to describe linear data, enhanced by dynamic weighting coefficients to improve prediction accuracy. Using Spearman rank correlation analysis, nine key parameters and the NOx downstream concentration itself were selected as input features for predicting and imputing missing values. Experimental results demonstrate that the proposed model reduces the mean squared error by 26.99% compared to the ARMAX baseline model and by 14.08% compared to the LSTM baseline model for randomly missing data. For naturally missing data segments, the model produced imputed curves with good continuity and stability, meeting engineering application requirements. This study provides technical support for improving OBD data quality and identifying high-emission vehicles, while also highlighting the model’s limitations in handling scenarios with all data items missing over continuous time periods. These findings offer directions for future model optimization.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 11","pages":"Pages 1014-1029"},"PeriodicalIF":3.9,"publicationDate":"2025-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145469205","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 : 2025-11-02DOI: 10.1080/15568318.2025.2545449
Boshuai Qiao , Sigal Kaplan , Jie He
Predicting the future market size of battery electric vehicles (BEVs) and their market share is essential for analyzing transport externalizations and optimizing charging infrastructure deployment. Current smooth-curve models, the system dynamics, and agent-based models for BEV market forecasting are usually static functions or rely on market interactions. Still, they hardly quantify the influencing effects and changes of covariates under dynamic market conditions. Given the above-mentioned, the BEV cumulative sales are forecasted under dynamic market conditions using the artificial neural network and the bidirectional short- and long-term memory models. The samples of five covariates are derived from available data about BEV sales, price changes, fuel-to-electricity ratio, charging piles, driving range, and incentive effects from the priorities of BEV license plates in Jiangsu province. Different evolutionary analyses are set the three future scenarios of the BEV sale market based on the Time-Series Multi-Layer Perceptron model, and the marginal effect of a single covariate was further analyzed. Finally, our results show the advantages of machine-learning methods over smooth-curve models used to generate market predictions, further providing insights on covariates effects for market managers to promote the BEV sale market.
{"title":"Forecasting the consumption evolution of battery electric vehicles under dynamic market conditions: The case study of Jiangsu Province, China","authors":"Boshuai Qiao , Sigal Kaplan , Jie He","doi":"10.1080/15568318.2025.2545449","DOIUrl":"10.1080/15568318.2025.2545449","url":null,"abstract":"<div><div>Predicting the future market size of battery electric vehicles (BEVs) and their market share is essential for analyzing transport externalizations and optimizing charging infrastructure deployment. Current smooth-curve models, the system dynamics, and agent-based models for BEV market forecasting are usually static functions or rely on market interactions. Still, they hardly quantify the influencing effects and changes of covariates under dynamic market conditions. Given the above-mentioned, the BEV cumulative sales are forecasted under dynamic market conditions using the artificial neural network and the bidirectional short- and long-term memory models. The samples of five covariates are derived from available data about BEV sales, price changes, fuel-to-electricity ratio, charging piles, driving range, and incentive effects from the priorities of BEV license plates in Jiangsu province. Different evolutionary analyses are set the three future scenarios of the BEV sale market based on the Time-Series Multi-Layer Perceptron model, and the marginal effect of a single covariate was further analyzed. Finally, our results show the advantages of machine-learning methods over smooth-curve models used to generate market predictions, further providing insights on covariates effects for market managers to promote the BEV sale market.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 11","pages":"Pages 1030-1057"},"PeriodicalIF":3.9,"publicationDate":"2025-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145469207","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 : 2025-11-02DOI: 10.1080/15568318.2025.2542292
Shivam Sakshi , Sumithra S , Sabari Shankar Ravichandran
India’s electric vehicle (EV) market is growing fast, worth USD 2 billion in 2023 and expected to reach USD 7.09 billion by 2025, with annual sales estimated at 10 million units by 2030. Though the growth indicates a good policy and innovation landscape, the adoption process continues to be troubled by high prices and inadequate charging infrastructure. The Research explores post-adoption consumer behavior within the context of the Indian electric vehicle (EV), examining brand engagement, customer satisfaction, and green self-identity in influencing sustained usage and recommendation. Leveraging applicable behavioral and technology-fit theories, the Research uses Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine data from a stratified sample of Indian EV users. The findings underscore the significance of psychological motivators and user-technology congruence in maintaining consumer loyalty to green mobility. The Research provides actionable recommendations for marketers, policymakers, and sustainability advocates seeking to drive long-term EV uptake and enable the shift toward environmentally sustainable transport.
{"title":"Driving EV adoption in India: Exploring green self-identity, task-technology fit, and brand engagement","authors":"Shivam Sakshi , Sumithra S , Sabari Shankar Ravichandran","doi":"10.1080/15568318.2025.2542292","DOIUrl":"10.1080/15568318.2025.2542292","url":null,"abstract":"<div><div>India’s electric vehicle (EV) market is growing fast, worth USD 2 billion in 2023 and expected to reach USD 7.09 billion by 2025, with annual sales estimated at 10 million units by 2030. Though the growth indicates a good policy and innovation landscape, the adoption process continues to be troubled by high prices and inadequate charging infrastructure. The Research explores post-adoption consumer behavior within the context of the Indian electric vehicle (EV), examining brand engagement, customer satisfaction, and green self-identity in influencing sustained usage and recommendation. Leveraging applicable behavioral and technology-fit theories, the Research uses Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine data from a stratified sample of Indian EV users. The findings underscore the significance of psychological motivators and user-technology congruence in maintaining consumer loyalty to green mobility. The Research provides actionable recommendations for marketers, policymakers, and sustainability advocates seeking to drive long-term EV uptake and enable the shift toward environmentally sustainable transport.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 11","pages":"Pages 1058-1071"},"PeriodicalIF":3.9,"publicationDate":"2025-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145469204","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 : 2025-11-02DOI: 10.1080/15568318.2025.2538680
Seyfettin Artan , Ugur Korkut Pata , Pınar Hayaloglu , Mehmet Ali Cakir , Mursit Recepoglu , Sumeyra Cay Cakir
The need for transportation is increasing with the effect of globalization in the developing economic system. The transportation sector, which is essential for economic development, may pose some threats to a sustainable environment. Although European countries have achieved various successes in reducing carbon emissions, they are struggling to reduce carbon dioxide (CO2) emissions from transportation. To overcome this difficulty, European countries are using various policy instruments such as environmental taxes. In this context, this study investigated the impact of disaggregated environmental taxes (pollution, energy, and transport), structural change, and institutional quality on transport-related carbon emissions (water, air, and road transport) in 24 European countries over the period 2008–2022. To this end, the study uses the novel half-panel jackknife estimation and the bias-corrected method of moments. The results indicate that there is no long-term relationship between environmental taxes and water transportation-related CO2 emissions. In contrast, structural changes and pollution taxes are effective in reducing road and air transport-related CO2 emissions. These results suggest that European countries should focus on reducing CO2 emissions through effective pollution taxes and encourage carbon taxes instead of energy and transport taxes, thereby supporting the European Green Deal’s goal of becoming the first climate-neutral continent.
{"title":"Revealing the environmental influences of energy, transport, and pollution taxes on different transportation modes","authors":"Seyfettin Artan , Ugur Korkut Pata , Pınar Hayaloglu , Mehmet Ali Cakir , Mursit Recepoglu , Sumeyra Cay Cakir","doi":"10.1080/15568318.2025.2538680","DOIUrl":"10.1080/15568318.2025.2538680","url":null,"abstract":"<div><div>The need for transportation is increasing with the effect of globalization in the developing economic system. The transportation sector, which is essential for economic development, may pose some threats to a sustainable environment. Although European countries have achieved various successes in reducing carbon emissions, they are struggling to reduce carbon dioxide (CO<sub>2</sub>) emissions from transportation. To overcome this difficulty, European countries are using various policy instruments such as environmental taxes. In this context, this study investigated the impact of disaggregated environmental taxes (pollution, energy, and transport), structural change, and institutional quality on transport-related carbon emissions (water, air, and road transport) in 24 European countries over the period 2008–2022. To this end, the study uses the novel half-panel jackknife estimation and the bias-corrected method of moments. The results indicate that there is no long-term relationship between environmental taxes and water transportation-related CO<sub>2</sub> emissions. In contrast, structural changes and pollution taxes are effective in reducing road and air transport-related CO<sub>2</sub> emissions. These results suggest that European countries should focus on reducing CO<sub>2</sub> emissions through effective pollution taxes and encourage carbon taxes instead of energy and transport taxes, thereby supporting the European Green Deal’s goal of becoming the first climate-neutral continent.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 11","pages":"Pages 1005-1013"},"PeriodicalIF":3.9,"publicationDate":"2025-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145469203","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 : 2025-11-02DOI: 10.1080/15568318.2025.2533307
Leonard Arning , Heather Kaths
Electric bicycles are transforming the active mobility landscape, potentially increasing active mode uptake and delivering environmental and health benefits. This study examines electric bicycle mode choice and which modes they replace. It employs a trip-level nested logit mode choice model with six alternatives, including conventional and electric bicycles. The model is estimated using 194,524 trips from the “Mobility in Germany” survey, augmented with data on gradient, spatial typology, public transport departures, and bicycle infrastructure coverage. We validate the model to infer generalizability, derive elasticities, and compute substitution rates. Our results reject nesting electric with conventional bicycles, underscoring their distinct characteristics and minimal shared unobserved attributes. The choice to use an electric bicycle is less affected by the availability of bicycle infrastructure and the length of a trip compared to the decision to use a conventional bicycle. In fact, electric bicycles are closer to cars than to conventional bicycles in terms of distance sensitivity. For both types of bicycle, mode choice is strongly and similarly dependent on gradient, with this effect furthermore depending on age. 43.1% of electric bicycle trips and 63.2% of electric bicycle mileage would have been undertaken using a car if no e-bike had been available, highlighting their substantial potential to reduce transport-related CO2 emissions. These findings support the role of e-bikes in advancing sustainable mobility by displacing car trips and broadening access to active transportation.
{"title":"Further, steeper, greener: Implications from an electric bicycle mode choice model","authors":"Leonard Arning , Heather Kaths","doi":"10.1080/15568318.2025.2533307","DOIUrl":"10.1080/15568318.2025.2533307","url":null,"abstract":"<div><div>Electric bicycles are transforming the active mobility landscape, potentially increasing active mode uptake and delivering environmental and health benefits. This study examines electric bicycle mode choice and which modes they replace. It employs a trip-level nested logit mode choice model with six alternatives, including conventional and electric bicycles. The model is estimated using 194,524 trips from the “Mobility in Germany” survey, augmented with data on gradient, spatial typology, public transport departures, and bicycle infrastructure coverage. We validate the model to infer generalizability, derive elasticities, and compute substitution rates. Our results reject nesting electric with conventional bicycles, underscoring their distinct characteristics and minimal shared unobserved attributes. The choice to use an electric bicycle is less affected by the availability of bicycle infrastructure and the length of a trip compared to the decision to use a conventional bicycle. In fact, electric bicycles are closer to cars than to conventional bicycles in terms of distance sensitivity. For both types of bicycle, mode choice is strongly and similarly dependent on gradient, with this effect furthermore depending on age. 43.1% of electric bicycle trips and 63.2% of electric bicycle mileage would have been undertaken using a car if no e-bike had been available, highlighting their substantial potential to reduce transport-related CO<sub>2</sub> emissions. These findings support the role of e-bikes in advancing sustainable mobility by displacing car trips and broadening access to active transportation.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 11","pages":"Pages 979-994"},"PeriodicalIF":3.9,"publicationDate":"2025-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145469206","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}
We surveyed 1,936 participants in Bradford, England, to examine patterns of travel modes for commuting, school travel, and general transportation, and how these patterns differ based on attitudes toward air quality. Participants rated air quality, their level of concern, and the importance of improving it. Logistic regression models estimated odds ratios (ORs) and 95% confidence intervals (CIs) to assess associations between air quality concerns and transportation mode choices. Our findings revealed a significant reliance on unsustainable travel modes—54% of participants reported exclusively using petrol/diesel/van vehicles for commuting, and 75% for traveling around town. In contrast, 50% of participants used sustainable travel modes (public transit, active transportation, or electric vehicles) for school trips. Active travel was more common among White British participants, while South Asian participants were more likely to rely on unsustainable vehicles. Participants concerned about air quality had significantly lower odds of using petrol/diesel/van vehicles for commuting (OR = 0.72, 95% CI: 0.53–1.01), school trips (OR = 0.75, 95% CI: 0.54–1.01), and traveling around town (OR = 0.70, 95% CI: 0.52–0.94) compared to those unconcerned. Additionally, concerned individuals were more likely to use sustainable transportation, with increased odds of choosing active travel modes for commuting (OR = 1.46, 95% CI: 1.04–2.07) and traveling around town (OR = 1.95, 95% CI: 1.46–2.60). These findings suggest that air quality concerns independently influence travel behavior, encouraging the adoption of sustainable transport options. Future research should explore how changing attitudes shape long-term transportation choices and policy interventions aimed at promoting environmentally friendly mobility.
{"title":"Exploring transportation mode choices and air quality concerns: Insights from a diverse urban sample","authors":"Behnam Tajik , Rosemary McEachan , Amy Hough , Cathy Knamiller , Kirsty Crossley , Rumana Hossain , Kate Pickett , Maria Bryant","doi":"10.1080/15568318.2025.2534160","DOIUrl":"10.1080/15568318.2025.2534160","url":null,"abstract":"<div><div>We surveyed 1,936 participants in Bradford, England, to examine patterns of travel modes for commuting, school travel, and general transportation, and how these patterns differ based on attitudes toward air quality. Participants rated air quality, their level of concern, and the importance of improving it. Logistic regression models estimated odds ratios (ORs) and 95% confidence intervals (CIs) to assess associations between air quality concerns and transportation mode choices. Our findings revealed a significant reliance on unsustainable travel modes—54% of participants reported exclusively using petrol/diesel/van vehicles for commuting, and 75% for traveling around town. In contrast, 50% of participants used sustainable travel modes (public transit, active transportation, or electric vehicles) for school trips. Active travel was more common among White British participants, while South Asian participants were more likely to rely on unsustainable vehicles. Participants concerned about air quality had significantly lower odds of using petrol/diesel/van vehicles for commuting (OR = 0.72, 95% CI: 0.53–1.01), school trips (OR = 0.75, 95% CI: 0.54–1.01), and traveling around town (OR = 0.70, 95% CI: 0.52–0.94) compared to those unconcerned. Additionally, concerned individuals were more likely to use sustainable transportation, with increased odds of choosing active travel modes for commuting (OR = 1.46, 95% CI: 1.04–2.07) and traveling around town (OR = 1.95, 95% CI: 1.46–2.60). These findings suggest that air quality concerns independently influence travel behavior, encouraging the adoption of sustainable transport options. Future research should explore how changing attitudes shape long-term transportation choices and policy interventions aimed at promoting environmentally friendly mobility.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 11","pages":"Pages 995-1004"},"PeriodicalIF":3.9,"publicationDate":"2025-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145469208","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}
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