Pub Date : 2026-01-01DOI: 10.1016/j.samod.2026.100051
Amitrajeet A. Batabyal , Hamid Beladi
Water quality in the Ganges River in Kanpur, India, is the result of two opposite factors. The positive factor arises from the regulations that compel tanneries to treat their wastewater before this water is discharged into the Ganges. The negative factor stems from cheating, bribery, and non-compliance by some tanneries which leads to the dumping of insufficiently treated wastewater into the Ganges. We shed light on two goals by analyzing a stochastic model of Ganges water quality that is the outcome of the above two factors. Our first goal is to study the probabilistic evolution of water quality in the Ganges and to then compute the likelihood that water quality will improve to an exogenously specified level denoted by Q. Our second goal is to ascertain the expected amount of time it will take for water quality to get to this level Q and to then discuss related issues.
{"title":"Tanneries, pollution, and water quality in the Ganges in Kanpur, India: a stochastic analysis","authors":"Amitrajeet A. Batabyal , Hamid Beladi","doi":"10.1016/j.samod.2026.100051","DOIUrl":"10.1016/j.samod.2026.100051","url":null,"abstract":"<div><div>Water quality in the Ganges River in Kanpur, India, is the result of two opposite factors. The positive factor arises from the regulations that compel tanneries to treat their wastewater before this water is discharged into the Ganges. The negative factor stems from cheating, bribery, and non-compliance by some tanneries which leads to the dumping of insufficiently treated wastewater into the Ganges. We shed light on two goals by analyzing a stochastic model of Ganges water quality that is the outcome of the above two factors. Our first goal is to study the probabilistic evolution of water quality in the Ganges and to then compute the likelihood that water quality will improve to an exogenously specified level denoted by <em>Q</em>. Our second goal is to ascertain the expected amount of time it will take for water quality to get to this level <em>Q</em> and to then discuss related issues.</div></div>","PeriodicalId":101193,"journal":{"name":"Sustainability Analytics and Modeling","volume":"6 ","pages":"Article 100051"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.samod.2025.100050
Sangung Park , Satish V. Ukkusuri
Regional recovery interdependence is crucial for mitigating the impacts of frequent natural disasters during the post-disaster recovery period. However, this interdependence has been understudied due to the complexity involved in identifying the impacts of inter-regional recovery. Causal discovery techniques allow researchers to infer regional interdependence from time-series datasets. This study utilizes daily points-of-interest data from the 2017 three consecutive hurricanes in the U.S. to examine regional interdependence using structural agnostic modeling, a causal discovery method, without adjusting hyperparameters of the model. The results revealed four patterns related to county and types of relationships: (1) direct recovery impacts, (2) recovery interdependence from urban to suburban and rural counties, (3) exceptions of recovery interdependence due to hurricane trajectories, and (4) the distance as an insignificant factor. These findings demonstrate the transferability of this technique in the 2017 three hurricanes in the U.S. and encourage researchers to develop inter-regional post-disaster recovery simulations and counterfactual scenarios to enhance post-disaster recovery.
{"title":"Identifying county-level regional interdependence in post-disaster recovery across three U.S. hurricanes in 2017","authors":"Sangung Park , Satish V. Ukkusuri","doi":"10.1016/j.samod.2025.100050","DOIUrl":"10.1016/j.samod.2025.100050","url":null,"abstract":"<div><div>Regional recovery interdependence is crucial for mitigating the impacts of frequent natural disasters during the post-disaster recovery period. However, this interdependence has been understudied due to the complexity involved in identifying the impacts of inter-regional recovery. Causal discovery techniques allow researchers to infer regional interdependence from time-series datasets. This study utilizes daily points-of-interest data from the 2017 three consecutive hurricanes in the U.S. to examine regional interdependence using structural agnostic modeling, a causal discovery method, without adjusting hyperparameters of the model. The results revealed four patterns related to county and types of relationships: (1) direct recovery impacts, (2) recovery interdependence from urban to suburban and rural counties, (3) exceptions of recovery interdependence due to hurricane trajectories, and (4) the distance as an insignificant factor. These findings demonstrate the transferability of this technique in the 2017 three hurricanes in the U.S. and encourage researchers to develop inter-regional post-disaster recovery simulations and counterfactual scenarios to enhance post-disaster recovery.</div></div>","PeriodicalId":101193,"journal":{"name":"Sustainability Analytics and Modeling","volume":"6 ","pages":"Article 100050"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study examined the influence of socio-demographic and political factors on the public's acceptance of Autonomous Passenger Vehicle (APV) technology for large trucks in the United States. Using data from a nationally representative survey conducted by the Pew Research Center, we examined how socio-demographic attributes, including gender, age, race, education level, and political affiliations, influence public opinions toward the adoption of APV technology in the trucking industry. The study employed Bayesian Networks for this purpose. The results revealed that men, younger individuals (18-49 years old), Whites, individuals with higher education levels, and Democrats exhibit greater support for APV technology in large trucks. Conversely, women, older individuals (65+ years old), non-Whites, individuals with lower education levels, and Republicans show more caution or opposition. Key mediating factors include familiarity with APV technology, willingness to ride an APV, and perception of the widespread use of APVs. These findings underscore the need for targeted public education and outreach to address concerns and increase acceptance of APV technology for large trucks among diverse demographic groups. Moreover, they provide actionable insights for policymakers seeking to promote the adoption of autonomous trucks (ATs).
{"title":"How do socio-demographic and political factors affect public acceptance of autonomous passenger vehicle technology for large trucks?","authors":"Panick Kalambay , Norris Novat , Emmanuel Kidando , Boniphace Kutela , Angela Kitali","doi":"10.1016/j.samod.2025.100049","DOIUrl":"10.1016/j.samod.2025.100049","url":null,"abstract":"<div><div>This study examined the influence of socio-demographic and political factors on the public's acceptance of Autonomous Passenger Vehicle (APV) technology for large trucks in the United States. Using data from a nationally representative survey conducted by the Pew Research Center, we examined how socio-demographic attributes, including gender, age, race, education level, and political affiliations, influence public opinions toward the adoption of APV technology in the trucking industry. The study employed Bayesian Networks for this purpose. The results revealed that men, younger individuals (18-49 years old), Whites, individuals with higher education levels, and Democrats exhibit greater support for APV technology in large trucks. Conversely, women, older individuals (65+ years old), non-Whites, individuals with lower education levels, and Republicans show more caution or opposition. Key mediating factors include familiarity with APV technology, willingness to ride an APV, and perception of the widespread use of APVs. These findings underscore the need for targeted public education and outreach to address concerns and increase acceptance of APV technology for large trucks among diverse demographic groups. Moreover, they provide actionable insights for policymakers seeking to promote the adoption of autonomous trucks (ATs).</div></div>","PeriodicalId":101193,"journal":{"name":"Sustainability Analytics and Modeling","volume":"6 ","pages":"Article 100049"},"PeriodicalIF":0.0,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145802059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.samod.2025.100038
Thomas Stringer , Sandy Mae Gaspay , Varsolo Sunio , Manuel Burelo
This study evaluates the environmental and economic impacts of transitioning to electric minibuses (e-jeepneys) in the informal transportation sector of the Philippines. Using a comprehensive model that simulates different e-jeepney uptake scenarios and electricity grid configurations, we analyze carbon emissions and energy costs. Data from the 2023 energy grid and vehicle emission standards are utilized to assess various grid scenarios, including net-zero and natural gas options. Our findings reveal that a 10% increase in e-jeepney uptake results in significant reductions in both carbon emissions (36 gCO2/km) and energy costs (0.70 PHP/km) when paired with a decarbonized grid. The study underscores the synergistic benefits of combining e-jeepney adoption with grid decarbonization and suggests that policymakers should pursue integrated strategies to maximize environmental and economic gains.
{"title":"Charging ahead: Prioritizing renewable energy for electric minibuses in the Philippines","authors":"Thomas Stringer , Sandy Mae Gaspay , Varsolo Sunio , Manuel Burelo","doi":"10.1016/j.samod.2025.100038","DOIUrl":"10.1016/j.samod.2025.100038","url":null,"abstract":"<div><div>This study evaluates the environmental and economic impacts of transitioning to electric minibuses (e-jeepneys) in the informal transportation sector of the Philippines. Using a comprehensive model that simulates different e-jeepney uptake scenarios and electricity grid configurations, we analyze carbon emissions and energy costs. Data from the 2023 energy grid and vehicle emission standards are utilized to assess various grid scenarios, including net-zero and natural gas options. Our findings reveal that a 10% increase in e-jeepney uptake results in significant reductions in both carbon emissions (36 gCO2/km) and energy costs (0.70 PHP/km) when paired with a decarbonized grid. The study underscores the synergistic benefits of combining e-jeepney adoption with grid decarbonization and suggests that policymakers should pursue integrated strategies to maximize environmental and economic gains.</div></div>","PeriodicalId":101193,"journal":{"name":"Sustainability Analytics and Modeling","volume":"5 ","pages":"Article 100038"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.samod.2025.100039
Florian Cramer , Christian Fikar
Traditional logistics systems face numerous challenges, such as driver shortages, low load factors, and increasingly high barriers to urban distribution. One concept that can mitigate many of these issues is crowd logistics, i.e., the utilization of unused private transport capacities. Despite the advertised benefits of crowd logistics, such as cost, mileage, and emissions reductions, real-world implementations are rare. Many initiatives have been short-lived, and there is a general lack of integration with traditional logistics service providers. Yet, underlying system behavior and intricate interlinkage of crowd logistics system components remain mostly unexplored. Consequently, this research uses a scoping literature review approach combined with elements from systems thinking to explore the causal dependencies and future research opportunities with respect to crowd logistics system behavior for deliveries. Through the review of scientific literature, causal loop diagrams are developed and analyzed concerning the dynamics and potentially prevalent system archetype structures to facilitate insights into crowd logistics systems. Our work shows that combining a scoping literature review with a systems thinking approach can yield valuable insights into system structures and future research opportunities. We identify critical system interlinkages and dynamics, offering a foundation for future quantitative modeling and decision-making in sustainable operations. Furthermore, the work outlines future research directions, such as novel application areas or further elucidating the effects of control mechanisms.
{"title":"Modeling sustainable crowd logistics delivery networks: A scoping systems thinking review","authors":"Florian Cramer , Christian Fikar","doi":"10.1016/j.samod.2025.100039","DOIUrl":"10.1016/j.samod.2025.100039","url":null,"abstract":"<div><div>Traditional logistics systems face numerous challenges, such as driver shortages, low load factors, and increasingly high barriers to urban distribution. One concept that can mitigate many of these issues is crowd logistics, i.e., the utilization of unused private transport capacities. Despite the advertised benefits of crowd logistics, such as cost, mileage, and emissions reductions, real-world implementations are rare. Many initiatives have been short-lived, and there is a general lack of integration with traditional logistics service providers. Yet, underlying system behavior and intricate interlinkage of crowd logistics system components remain mostly unexplored. Consequently, this research uses a scoping literature review approach combined with elements from systems thinking to explore the causal dependencies and future research opportunities with respect to crowd logistics system behavior for deliveries. Through the review of scientific literature, causal loop diagrams are developed and analyzed concerning the dynamics and potentially prevalent system archetype structures to facilitate insights into crowd logistics systems. Our work shows that combining a scoping literature review with a systems thinking approach can yield valuable insights into system structures and future research opportunities. We identify critical system interlinkages and dynamics, offering a foundation for future quantitative modeling and decision-making in sustainable operations. Furthermore, the work outlines future research directions, such as novel application areas or further elucidating the effects of control mechanisms.</div></div>","PeriodicalId":101193,"journal":{"name":"Sustainability Analytics and Modeling","volume":"5 ","pages":"Article 100039"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.samod.2025.100047
Fabian Brockmann, Mario Guajardo
The electrification of trucks progresses slowly, with extended charging times as a major concern for transportation companies. In the comparison of electric versus diesel trucks, an aspect often neglected is that regulations on driver working hours affect both types of trucks. In particular, mandatory break times offer opportunities for electric trucks to be charged while drivers rest and, therefore, without necessarily implying additional time over the traditional route duration. To this aim, this paper develops a mathematical programming model that allows to synchronize break times of the drivers with charging times of the trucks. We implement this model using data on real-world truck specifications and charging station infrastructure from Northwest Germany. Our results indicate that under average conditions, the current features of batteries and charging stations are sufficient for electric trucks to perform routes at very similar times as combustion engine trucks. We also study how variations in features such as usable battery size or charging rates due to aging or ambient conditions affect route duration. Our results show that in these cases synchronization of charging and break times is crucial to keep the competitiveness of electric trucks with respect to diesel trucks.
{"title":"Break-and-charge: Leveraging EU regulations to enhance electric truck competitiveness","authors":"Fabian Brockmann, Mario Guajardo","doi":"10.1016/j.samod.2025.100047","DOIUrl":"10.1016/j.samod.2025.100047","url":null,"abstract":"<div><div>The electrification of trucks progresses slowly, with extended charging times as a major concern for transportation companies. In the comparison of electric versus diesel trucks, an aspect often neglected is that regulations on driver working hours affect both types of trucks. In particular, mandatory break times offer opportunities for electric trucks to be charged while drivers rest and, therefore, without necessarily implying additional time over the traditional route duration. To this aim, this paper develops a mathematical programming model that allows to synchronize break times of the drivers with charging times of the trucks. We implement this model using data on real-world truck specifications and charging station infrastructure from Northwest Germany. Our results indicate that under average conditions, the current features of batteries and charging stations are sufficient for electric trucks to perform routes at very similar times as combustion engine trucks. We also study how variations in features such as usable battery size or charging rates due to aging or ambient conditions affect route duration. Our results show that in these cases synchronization of charging and break times is crucial to keep the competitiveness of electric trucks with respect to diesel trucks.</div></div>","PeriodicalId":101193,"journal":{"name":"Sustainability Analytics and Modeling","volume":"5 ","pages":"Article 100047"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.samod.2024.100036
Tobias Gebhard, Bernhard J. Sattler, Jonas Gunkel, Marco Marquard, Andrea Tundis
The increasing number of crises, including natural disasters and military conflicts, underscores the importance of resilient critical infrastructures (CIs), especially for urban areas. However, current approaches for CI modeling, monitoring, and resilience assessment are lacking a holistic view of cities as complex, interconnected, and socio-technical systems. This paper explores the application of the Digital Twin (DT) concept as a promising tool to assess and improve the resilience of urban CIs in light of various hazards. DTs are virtual real-time representations of a physical system that can be used to perform real-time analysis, simulate what-if scenarios, and provide decision support, during crises and normal operations. To this end, we identify and discuss key challenges for the development of Urban Digital Twins (UDTs), including data management, technical and social modeling of CIs, integrated CI co-simulations, model validation, and resilience assessment. To address the complex nature of urban areas as systems-of-systems, we present overarching modeling concepts by considering CI interdependencies and socio-technical interactions, resulting in the concept of the Socio-technical Digital Twin (STDT). Beside incorporating agent-based modeling, we discuss the issue of demand synchronization and propose the concepts of model selection and model transfer to facilitate the modeling process for UDTs. Furthermore, a multi-layered modeling framework for interdependent urban CIs is presented, where the proposed concepts are integrated and an overview and discussion of the technical and social modeling of CIs is provided, with a particular focus on the power, water, and transportation domain. Finally, we deal with the quantitative resilience assessment for interconnected CIs and discuss ways of integrating these methodologies in DTs. Our approach frames CIs as socio-technical systems and integrates the human perspective into the modeling process and resilience assessment. The presented modeling framework can be used to simulate various scenarios for analyzing their consequences in advance and measuring resilience in a holistic context. Moreover, the proposed concepts and modeling approaches can support future developments towards UDTs for crisis management.
{"title":"Improving the resilience of socio-technical urban critical infrastructures with digital twins: Challenges, concepts, and modeling","authors":"Tobias Gebhard, Bernhard J. Sattler, Jonas Gunkel, Marco Marquard, Andrea Tundis","doi":"10.1016/j.samod.2024.100036","DOIUrl":"10.1016/j.samod.2024.100036","url":null,"abstract":"<div><div>The increasing number of crises, including natural disasters and military conflicts, underscores the importance of resilient critical infrastructures (CIs), especially for urban areas. However, current approaches for CI modeling, monitoring, and resilience assessment are lacking a holistic view of cities as complex, interconnected, and socio-technical systems. This paper explores the application of the Digital Twin (DT) concept as a promising tool to assess and improve the resilience of urban CIs in light of various hazards. DTs are virtual real-time representations of a physical system that can be used to perform real-time analysis, simulate what-if scenarios, and provide decision support, during crises and normal operations. To this end, we identify and discuss key challenges for the development of Urban Digital Twins (UDTs), including data management, technical and social modeling of CIs, integrated CI co-simulations, model validation, and resilience assessment. To address the complex nature of urban areas as systems-of-systems, we present overarching modeling concepts by considering CI interdependencies and socio-technical interactions, resulting in the concept of the Socio-technical Digital Twin (STDT). Beside incorporating agent-based modeling, we discuss the issue of demand synchronization and propose the concepts of model selection and model transfer to facilitate the modeling process for UDTs. Furthermore, a multi-layered modeling framework for interdependent urban CIs is presented, where the proposed concepts are integrated and an overview and discussion of the technical and social modeling of CIs is provided, with a particular focus on the power, water, and transportation domain. Finally, we deal with the quantitative resilience assessment for interconnected CIs and discuss ways of integrating these methodologies in DTs. Our approach frames CIs as socio-technical systems and integrates the human perspective into the modeling process and resilience assessment. The presented modeling framework can be used to simulate various scenarios for analyzing their consequences in advance and measuring resilience in a holistic context. Moreover, the proposed concepts and modeling approaches can support future developments towards UDTs for crisis management.</div></div>","PeriodicalId":101193,"journal":{"name":"Sustainability Analytics and Modeling","volume":"5 ","pages":"Article 100036"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.samod.2025.100040
Mariana Sousa , Sara Martins , Maria João Santos , Pedro Amorim , Winfried Steiner
Understanding consumer behavior toward perishable products is crucial for optimizing food supply chains, minimizing waste, and reducing lost sales. Such knowledge enables retailers to effectively incentivize the purchase of items nearing the end of their shelf life. Although some retailers apply markdowns to products with low remaining shelf life, limited empirical evidence exists on how these strategies affect consumer valuation and how perceptions evolve as products age. To address this gap, we investigate the effects of freshness, price, product attributes, and markdown labels on consumer purchasing decisions. Using discrete choice models estimated from revealed preference data provided by a major European retailer, we quantify consumers’ willingness to pay (WTP) for each additional day of shelf life across several perishable products, with a focus on the dairy category. Our findings reveal a non-linear relationship between remaining shelf life and WTP, alongside a statistically significant reduction in perceived value for products featuring markdown labels. Based on these findings, we recommend implementing differentiated markdown strategies targeted at specific products, coupled with transparent communication, to mitigate negative perceptions associated with discounted perishables. By improving how labeled products are perceived and strategically optimizing the timing and magnitude of markdowns, retailers can better align their strategies with consumer preferences, reduce waste, and improve profitability.
{"title":"Measuring willingness to pay for freshness in perishable goods: An empirical analysis","authors":"Mariana Sousa , Sara Martins , Maria João Santos , Pedro Amorim , Winfried Steiner","doi":"10.1016/j.samod.2025.100040","DOIUrl":"10.1016/j.samod.2025.100040","url":null,"abstract":"<div><div>Understanding consumer behavior toward perishable products is crucial for optimizing food supply chains, minimizing waste, and reducing lost sales. Such knowledge enables retailers to effectively incentivize the purchase of items nearing the end of their shelf life. Although some retailers apply markdowns to products with low remaining shelf life, limited empirical evidence exists on how these strategies affect consumer valuation and how perceptions evolve as products age. To address this gap, we investigate the effects of freshness, price, product attributes, and markdown labels on consumer purchasing decisions. Using discrete choice models estimated from revealed preference data provided by a major European retailer, we quantify consumers’ willingness to pay (WTP) for each additional day of shelf life across several perishable products, with a focus on the dairy category. Our findings reveal a non-linear relationship between remaining shelf life and WTP, alongside a statistically significant reduction in perceived value for products featuring markdown labels. Based on these findings, we recommend implementing differentiated markdown strategies targeted at specific products, coupled with transparent communication, to mitigate negative perceptions associated with discounted perishables. By improving how labeled products are perceived and strategically optimizing the timing and magnitude of markdowns, retailers can better align their strategies with consumer preferences, reduce waste, and improve profitability.</div></div>","PeriodicalId":101193,"journal":{"name":"Sustainability Analytics and Modeling","volume":"5 ","pages":"Article 100040"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.samod.2025.100045
Shawly Das , Reday Chandra Bhowmik , Trisha Saha , Smarnika Ghosh , Mithun Kumar Biswas , Sonjib Das
This study begins by extending the Marshallian demand framework to examine the long-term and short-term determinants of energy use in Canada. Specifically, it explores the impact of GDP per capita, trade openness, foreign direct investment (FDI), and urbanization on energy use, utilizing annual time-series data from 1990 to 2023. The autoregressive distributed lag (ARDL) bounds-testing approach assesses a cointegrating relationship among the variables. The F-bound cointegration test is applied to verify the long-run association, followed by ARDL model estimation to evaluate both short-run and long-run elasticities. Additionally, a pairwise Granger causality test is conducted to determine the direction of causal interactions between the variables, while various diagnostic tests are performed to validate the model assumptions. In addition to the ARDL long-run findings, three alternative econometric techniques are implemented to ensure their robustness: Dynamic Ordinary Least Squares (DOLS), Canonical Cointegrating Regression (CCR), and Fully Modified Ordinary Least Squares (FMOLS). The results suggest that trade openness and GDP per capita increase energy use, whereas FDI and urbanization decrease it. The ARDL model exhibits significant effects only on GDP and urbanisation, while the FMOLS, DOLS, and CCR models exhibit significant effects on all four variables. The direction of effect stays the same, no matter what method is used, but the coefficients' strengths change. These results highlight the importance of GDP growth and urban expansion in determining Canada's energy needs, as well as the impact of trade openness and FDI. The study employs ARDL, combined with FMOLS, DOLS, and CCR techniques, to provide robust and comparative insights into the impact of economic growth, international trade, foreign investment, and urbanization on long-term energy consumption patterns in Canada.
{"title":"Examining the effects of economic development, trade openness, FDI, and urbanization on energy use in Canada: An ARDL analysis","authors":"Shawly Das , Reday Chandra Bhowmik , Trisha Saha , Smarnika Ghosh , Mithun Kumar Biswas , Sonjib Das","doi":"10.1016/j.samod.2025.100045","DOIUrl":"10.1016/j.samod.2025.100045","url":null,"abstract":"<div><div>This study begins by extending the Marshallian demand framework to examine the long-term and short-term determinants of energy use in Canada. Specifically, it explores the impact of GDP per capita, trade openness, foreign direct investment (FDI), and urbanization on energy use, utilizing annual time-series data from 1990 to 2023. The autoregressive distributed lag (ARDL) bounds-testing approach assesses a cointegrating relationship among the variables. The F-bound cointegration test is applied to verify the long-run association, followed by ARDL model estimation to evaluate both short-run and long-run elasticities. Additionally, a pairwise Granger causality test is conducted to determine the direction of causal interactions between the variables, while various diagnostic tests are performed to validate the model assumptions. In addition to the ARDL long-run findings, three alternative econometric techniques are implemented to ensure their robustness: Dynamic Ordinary Least Squares (DOLS), Canonical Cointegrating Regression (CCR), and Fully Modified Ordinary Least Squares (FMOLS). The results suggest that trade openness and GDP per capita increase energy use, whereas FDI and urbanization decrease it. The ARDL model exhibits significant effects only on GDP and urbanisation, while the FMOLS, DOLS, and CCR models exhibit significant effects on all four variables. The direction of effect stays the same, no matter what method is used, but the coefficients' strengths change. These results highlight the importance of GDP growth and urban expansion in determining Canada's energy needs, as well as the impact of trade openness and FDI. The study employs ARDL, combined with FMOLS, DOLS, and CCR techniques, to provide robust and comparative insights into the impact of economic growth, international trade, foreign investment, and urbanization on long-term energy consumption patterns in Canada.</div></div>","PeriodicalId":101193,"journal":{"name":"Sustainability Analytics and Modeling","volume":"5 ","pages":"Article 100045"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145361920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.samod.2025.100046
Johannes Gückel , Teodor Gabriel Crainic , Pirmin Fontaine
Two-tier city logistics systems (2T-CLSs) offer the potential for more efficient and environmentally friendly freight management. Since such systems require substantial infrastructure and multiple Logistics Service Providers (LSPs) operate within a city, cooperation among LSPs presents opportunities for cost and emissions reductions.
However, cooperation requires ensuring every LSP has an incentive to participate and feels fairly treated. To address this, we present a service network design formulation for multi-day tactical planning in a 2T-CLS with cooperating LSPs, incorporating fairness constraints on workload, costs, and service regularity.
Through a numerical study, we quantify the impact of fairness constraints, showing that overly strict constraints harm the coalition. Lower cost increases and greater environmental benefits occur when fairness is enforced over multiple days rather than daily. Further, we observe a 47% reduction in CO emissions through cooperation, providing valuable policy implications for LSPs and municipal authorities pursuing sustainable city logistics.
{"title":"Tactical planning in cooperative two-tier city logistics systems with fairness constraints","authors":"Johannes Gückel , Teodor Gabriel Crainic , Pirmin Fontaine","doi":"10.1016/j.samod.2025.100046","DOIUrl":"10.1016/j.samod.2025.100046","url":null,"abstract":"<div><div>Two-tier city logistics systems (2T-CLSs) offer the potential for more efficient and environmentally friendly freight management. Since such systems require substantial infrastructure and multiple Logistics Service Providers (LSPs) operate within a city, cooperation among LSPs presents opportunities for cost and emissions reductions.</div><div>However, cooperation requires ensuring every LSP has an incentive to participate and feels fairly treated. To address this, we present a service network design formulation for multi-day tactical planning in a 2T-CLS with cooperating LSPs, incorporating fairness constraints on workload, costs, and service regularity.</div><div>Through a numerical study, we quantify the impact of fairness constraints, showing that overly strict constraints harm the coalition. Lower cost increases and greater environmental benefits occur when fairness is enforced over multiple days rather than daily. Further, we observe a 47% reduction in CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions through cooperation, providing valuable policy implications for LSPs and municipal authorities pursuing sustainable city logistics.</div></div>","PeriodicalId":101193,"journal":{"name":"Sustainability Analytics and Modeling","volume":"5 ","pages":"Article 100046"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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