Pub Date : 2026-01-02DOI: 10.1016/j.clscn.2025.100295
Mats Janné, Niki Matinrad
Mass logistics centers (MLCs) are lately facing increasing recognition for their potential to improve transport efficiency in soil and rock material flows and to support circular mass systems, resulting in reduced CO2 emissions. Despite this, decisions about where to locate MLCs are often made without any systematic analysis and instead are done ad hoc or, at times, based on opinions of experts. This study addresses that gap by proposing an optimization model to determine the optimal location for an MLC within a mass logistics system, where all materials are routed through one or several MLCs. We test the model using experimental data generated based on a real development project in Sweden. Furthermore, we perform a sensitivity analysis on several parameters to investigate their effect on the performance of the proposed model. The results show that a close relationship between transport efficiency, circularity and upscaling rate, transport capacity, and need for space at MLCs exists – the higher the circularity rate, the better the transport efficiency with reduced CO2 emissions. They also indicate the necessity of finding the right balance between these factors in a given system rather than solely focusing on transport efficiency, to ensure the applicability of the solutions in the real-life system. This study, in a broad way, contributes to understanding how MLCs can reduce the environmental impact of mass transport and supports more strategic planning in infrastructure development.
{"title":"Optimal location of mass logistics centers: improving transport efficiency for circular material flows","authors":"Mats Janné, Niki Matinrad","doi":"10.1016/j.clscn.2025.100295","DOIUrl":"10.1016/j.clscn.2025.100295","url":null,"abstract":"<div><div>Mass logistics centers (MLCs) are lately facing increasing recognition for their potential to improve transport efficiency in soil and rock material flows and to support circular mass systems, resulting in reduced CO<sub>2</sub> emissions. Despite this, decisions about where to locate MLCs are often made without any systematic analysis and instead are done ad hoc or, at times, based on opinions of experts. This study addresses that gap by proposing an optimization model to determine the optimal location for an MLC within a mass logistics system, where all materials are routed through one or several MLCs. We test the model using experimental data generated based on a real development project in Sweden. Furthermore, we perform a sensitivity analysis on several parameters to investigate their effect on the performance of the proposed model. The results show that a close relationship between transport efficiency, circularity and upscaling rate, transport capacity, and need for space at MLCs exists – the higher the circularity rate, the better the transport efficiency with reduced CO<sub>2</sub> emissions. They also indicate the necessity of finding the right balance between these factors in a given system rather than solely focusing on transport efficiency, to ensure the applicability of the solutions in the real-life system. This study, in a broad way, contributes to understanding how MLCs can reduce the environmental impact of mass transport and supports more strategic planning in infrastructure development.</div></div>","PeriodicalId":100253,"journal":{"name":"Cleaner Logistics and Supply Chain","volume":"18 ","pages":"Article 100295"},"PeriodicalIF":6.8,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926498","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 : 2025-12-27DOI: 10.1016/j.clscn.2025.100294
Wei Zhao , Hao Chen , JunKang Zhang , Aldis Bulis
This study examines the evolving relationship between global value chains and carbon emissions from 2000 to 2014 from a historical economic perspective, using a framework that integrates distinct economic phases and major global events. Based on panel data from 52 countries, the analysis identifies heterogeneous effects across three periods. Global value chain participation reduced carbon intensity during the Technology Diffusion phase (2001–2007) through efficiency improvements, increased carbon intensity during the Crisis Disruption phase (2008–2009) amid economic shocks, and exhibited a weakened impact during the Policy Realignment phase (2010–2014) under changing regulatory conditions. The results further reveal a U-shaped relationship, whereby initial global value chain integration lowers carbon intensity, while excessive participation leads to higher emissions. Additional analyses highlight differences between developed and developing economies, as well as evidence of regional convergence in Asia. Drawing on international trade, climate, and economic datasets, the study applies advanced econometric techniques to ensure robust estimation. The findings support differentiated policy implications, including development-adjusted carbon pricing, national green technology requirements, and enhanced corporate emissions transparency, offering integrated insights into balancing trade integration with climate objectives during the study period.
{"title":"Global value chains and carbon intensity across economic phases","authors":"Wei Zhao , Hao Chen , JunKang Zhang , Aldis Bulis","doi":"10.1016/j.clscn.2025.100294","DOIUrl":"10.1016/j.clscn.2025.100294","url":null,"abstract":"<div><div>This study examines the evolving relationship between global value chains and carbon emissions from 2000 to 2014 from a historical economic perspective, using a framework that integrates distinct economic phases and major global events. Based on panel data from 52 countries, the analysis identifies heterogeneous effects across three periods. Global value chain participation reduced carbon intensity during the Technology Diffusion phase (2001–2007) through efficiency improvements, increased carbon intensity during the Crisis Disruption phase (2008–2009) amid economic shocks, and exhibited a weakened impact during the Policy Realignment phase (2010–2014) under changing regulatory conditions. The results further reveal a U-shaped relationship, whereby initial global value chain integration lowers carbon intensity, while excessive participation leads to higher emissions. Additional analyses highlight differences between developed and developing economies, as well as evidence of regional convergence in Asia. Drawing on international trade, climate, and economic datasets, the study applies advanced econometric techniques to ensure robust estimation. The findings support differentiated policy implications, including development-adjusted carbon pricing, national green technology requirements, and enhanced corporate emissions transparency, offering integrated insights into balancing trade integration with climate objectives during the study period.</div></div>","PeriodicalId":100253,"journal":{"name":"Cleaner Logistics and Supply Chain","volume":"18 ","pages":"Article 100294"},"PeriodicalIF":6.8,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926562","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 : 2025-12-17DOI: 10.1016/j.clscn.2025.100292
Jyoti Dhingra Darbari , Shiwani Sharma , Mark Christhian Barrueta Pinto , Richard Daniel Cunyas Romero , P.C. Jha
Reverse Logistics (RL) plays a pivotal role in the Indian electronics industry as it enables products to be recovered, refurbished, recycled, and disposed of in an eco-friendly manner thus gradually bringing the sector to circular economy (CE) practices. In order to manage these intricate and resource-demanding RL processes effectively, manufacturers increasingly collaborate with third-party reverse logistics providers (3PRLPs). On the contrary, such partnerships are naturally subjected to numerous risks due to the inefficiencies in operations, the differences in behaviours, and the strategic uncertainties. Despite previous research having highlighted various risk factors in RL, there remains a distinct and unfilled gap when it comes to the systematic identification and interlinking of these risks with the corresponding mitigation barriers and the potential consequences of collaboration failure, especially in the Indian electronic industry. In order to fill this gap, the current study utilizes a fuzzy bow-tie (BT) model to identify, scrutinize, and map the key risks associated with 3PRLP collaboration to preventive and mitigative barriers along with the cascading consequences of failure. The novelty of this study is in the thorough and detailed mapping of the cause-barrier-consequence relationships which provides a complete and profound understanding of risk propagation in reverse supply chain (RSC) partnerships. The fuzzy BT model is particularly suitable for this purpose as it manages the uncertainty and subjectivity that come with expert-based evaluations while enabling quantitative estimation of risk probabilities. The findings of the study highlight the pressing necessity for proactive and organized risk governance mechanisms to ensure continuous and resilient partnerships in RL. The analysis shows that the absence of timely risk management measures causes 6% probability of failure during collaboration with 3PRLPs. Moreover, if this failure takes place, there is a strong possibility of minor operational hindrances, and lack of responsiveness from RSC actors; a moderate possibility of productivity and revenue loss, and loss of customers and reputation; and a low possibility of major RL disruptions or total financial burden on the manufacturer. Hence, decision-support framework developed in the study can be an effective tool for SC practitioners for strengthening the risk resilience of RL collaboration with 3PRLPs and attainment of sustainability and CE goals.
{"title":"A systematic risk assessment approach to develop a fuzzy bow-tie model for third-party collaboration supporting circular economy: Application to electronic industry","authors":"Jyoti Dhingra Darbari , Shiwani Sharma , Mark Christhian Barrueta Pinto , Richard Daniel Cunyas Romero , P.C. Jha","doi":"10.1016/j.clscn.2025.100292","DOIUrl":"10.1016/j.clscn.2025.100292","url":null,"abstract":"<div><div>Reverse Logistics (RL) plays a pivotal role in the Indian electronics industry as it enables products to be recovered, refurbished, recycled, and disposed of in an eco-friendly manner thus gradually bringing the sector to circular economy (CE) practices. In order to manage these intricate and resource-demanding RL processes effectively, manufacturers increasingly collaborate with third-party reverse logistics providers (3PRLPs). On the contrary, such partnerships are naturally subjected to numerous risks due to the inefficiencies in operations, the differences in behaviours, and the strategic uncertainties. Despite previous research having highlighted various risk factors in RL, there remains a distinct and unfilled gap when it comes to the systematic identification and interlinking of these risks with the corresponding mitigation barriers and the potential consequences of collaboration failure, especially in the Indian electronic industry. In order to fill this gap, the current study utilizes a fuzzy bow-tie (BT) model to identify, scrutinize, and map the key risks associated with 3PRLP collaboration to preventive and mitigative barriers along with the cascading consequences of failure. The novelty of this study is in the thorough and detailed mapping of the cause-barrier-consequence relationships which provides a complete and profound understanding of risk propagation in reverse supply chain (RSC) partnerships. The fuzzy BT model is particularly suitable for this purpose as it manages the uncertainty and subjectivity that come with expert-based evaluations while enabling quantitative estimation of risk probabilities. The findings of the study highlight the pressing necessity for proactive and organized risk governance mechanisms to ensure continuous and resilient partnerships in RL. The analysis shows that the absence of timely risk management measures causes 6% probability of failure during collaboration with 3PRLPs. Moreover, if this failure takes place, there is a strong possibility of minor operational hindrances, and lack of responsiveness from RSC actors; a moderate possibility of productivity and revenue loss, and loss of customers and reputation; and a low possibility of major RL disruptions or total financial burden on the manufacturer. Hence, decision-support framework developed in the study can be an effective tool for SC practitioners for strengthening the risk resilience of RL collaboration with 3PRLPs and attainment of sustainability and CE goals.</div></div>","PeriodicalId":100253,"journal":{"name":"Cleaner Logistics and Supply Chain","volume":"18 ","pages":"Article 100292"},"PeriodicalIF":6.8,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926563","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 : 2025-12-14DOI: 10.1016/j.clscn.2025.100291
Abdelsalam Adam Hamid , Akram Shaabani , Nur Hazwani Karim , Mohamed Battour
The future of sustainable business practices lies in the transition towards integrating circular supply chains (CSC) and circular economy (CE). While this shift promises significant benefits, it also requires organizational transformation and a commitment to continuous learning and adaptation. To ensure a successful transition, it is crucial to understand the key factors that drive green value creation. This research intends to identify and prioritize the most important metrics for developing a green value chain that supports the CE and CSC. To address this challenge, the study introduces an innovative approach of the integration between hierarchical structure and fuzzy logic into the Best-Worst Method. A hierarchy fuzzy best-worst method (HFBWM) is proposed to more accurately capture expert judgements and rank most important criteria in developing green value creation. The applicability and validity of the proposed approach are demonstrated to five different circulars: supplier, product, packaging, logistics, and consumer. The findings reveal that in our model, packaging and supplier as part of early stage of the value chain are substantially crucial for establishing the foundation of green value chain. This study supports the integration of CE and CSC driven by important factors of the green value chain creation through reuse-oriented design, environmentally raw materials, and circular technologies.
{"title":"Circular supply chain and the circular economy: key criteria for green value creation","authors":"Abdelsalam Adam Hamid , Akram Shaabani , Nur Hazwani Karim , Mohamed Battour","doi":"10.1016/j.clscn.2025.100291","DOIUrl":"10.1016/j.clscn.2025.100291","url":null,"abstract":"<div><div>The future of sustainable business practices lies in the transition towards integrating circular supply chains (CSC) and circular economy (CE). While this shift promises significant benefits, it also requires organizational transformation and a commitment to continuous learning and adaptation. To ensure a successful transition, it is crucial to understand the key factors that drive green value creation. This research intends to identify and prioritize the most important metrics for developing a green value chain that supports the CE and CSC. To address this challenge, the study introduces an innovative approach of the integration between hierarchical structure and fuzzy logic into the Best-Worst Method. A hierarchy fuzzy best-worst method (HFBWM) is proposed to more accurately capture expert judgements and rank most important criteria in developing green value creation. The applicability and validity of the proposed approach are demonstrated to five different circulars: supplier, product, packaging, logistics, and consumer. The findings reveal that in our model, packaging and supplier as part of early stage of the value chain are substantially crucial for establishing the foundation of green value chain. This study supports the integration of CE and CSC driven by important factors of the green value chain creation through reuse-oriented design, environmentally raw materials, and circular technologies.</div></div>","PeriodicalId":100253,"journal":{"name":"Cleaner Logistics and Supply Chain","volume":"18 ","pages":"Article 100291"},"PeriodicalIF":6.8,"publicationDate":"2025-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791809","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 : 2025-12-14DOI: 10.1016/j.clscn.2025.100290
Jan Felix Niemeyer , Robin Maier , Allan N. Zhang , Mark Mennenga , Shanshan Yang , Zhiquan Yeo , Christoph Herrmann
Global production networks have become increasingly complex and interdependent due to decades of intensified globalization. While these developments have driven efficiency gains and competitive advantages, they have also led to significant environmental impacts. In response, regulations such as the Corporate Sustainability Due Diligence Directive are compelling companies to improve sustainability across their value chains. At the same time, recent local and global disruptions have underscored the critical importance of resilience. As a result, organizations must systematically evaluate and improve the sustainability and resilience of both their own operations and the broader production networks. However, research shows that lessons from past crises are often not fully internalized, and companies continue to struggle with fragmented domain-specific knowledge. Moreover, data alone does not yield actionable business insights. It only generates value when effectively combined with analytics and expert interpretation. Many of the proposed solutions for improving sustainability and resilience are not entirely new, but they require more integrated and systematic application to be effective. Against this background, we propose a decision support tool grounded on an iterative, pattern-based methodology. It combines simulation-based analysis using AnyLogistix with a knowledge-based database to enhance resilience and sustainability in production networks. The pattern-based approach enables the systematic identification, capture, and reuse of proven strategies. The methodology is successfully validated through a real-world use case in the electronics industry and shows that both resilience and sustainability can be effectively enhanced through the proposed approach.
由于几十年来全球化的加剧，全球生产网络变得越来越复杂和相互依存。虽然这些发展推动了效率的提高和竞争优势，但它们也导致了重大的环境影响。作为回应，《企业可持续发展尽职调查指令》（Corporate Sustainability Due Diligence Directive）等法规正迫使企业提高整个价值链的可持续性。与此同时，最近的地方和全球破坏凸显了复原力的至关重要性。因此，组织必须系统地评估和改进其自身业务和更广泛的生产网络的可持续性和弹性。然而，研究表明，从过去的危机中吸取的教训往往没有完全内化，公司仍然在与支离破碎的特定领域知识作斗争。此外，数据本身并不能产生可操作的业务见解。它只有在与分析和专家解释有效结合时才能产生价值。为提高可持续性和复原力而提出的许多解决方案并非全新的，但它们需要更加综合和系统的应用才能有效。在此背景下，我们提出了一种基于迭代的、基于模式的方法的决策支持工具。它将使用AnyLogistix的仿真分析与基于知识的数据库相结合，以增强生产网络的弹性和可持续性。基于模式的方法支持系统地识别、捕获和重用经过验证的策略。该方法通过电子行业的实际用例成功验证，并表明通过提出的方法可以有效地增强弹性和可持续性。
{"title":"Pattern-based decision-support-tool to enhance resilience and sustainability in production networks: A framework proposal and application","authors":"Jan Felix Niemeyer , Robin Maier , Allan N. Zhang , Mark Mennenga , Shanshan Yang , Zhiquan Yeo , Christoph Herrmann","doi":"10.1016/j.clscn.2025.100290","DOIUrl":"10.1016/j.clscn.2025.100290","url":null,"abstract":"<div><div>Global production networks have become increasingly complex and interdependent due to decades of intensified globalization. While these developments have driven efficiency gains and competitive advantages, they have also led to significant environmental impacts. In response, regulations such as the Corporate Sustainability Due Diligence Directive are compelling companies to improve sustainability across their value chains. At the same time, recent local and global disruptions have underscored the critical importance of resilience. As a result, organizations must systematically evaluate and improve the sustainability and resilience of both their own operations and the broader production networks. However, research shows that lessons from past crises are often not fully internalized, and companies continue to struggle with fragmented domain-specific knowledge. Moreover, data alone does not yield actionable business insights. It only generates value when effectively combined with analytics and expert interpretation. Many of the proposed solutions for improving sustainability and resilience are not entirely new, but they require more integrated and systematic application to be effective. Against this background, we propose a decision support tool grounded on an iterative, pattern-based methodology. It combines simulation-based analysis using AnyLogistix with a knowledge-based database to enhance resilience and sustainability in production networks. The pattern-based approach enables the systematic identification, capture, and reuse of proven strategies. The methodology is successfully validated through a real-world use case in the electronics industry and shows that both resilience and sustainability can be effectively enhanced through the proposed approach.</div></div>","PeriodicalId":100253,"journal":{"name":"Cleaner Logistics and Supply Chain","volume":"18 ","pages":"Article 100290"},"PeriodicalIF":6.8,"publicationDate":"2025-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977459","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}
Reducing the environmental impact of logistics warehouses is a critical challenge, particularly during the early design phase when limited data is available to guide decision-making. This study aims to establish carbon footprint targets for logistics warehouses in alignment with climate neutrality objectives. Using Life Cycle Assessment (LCA) methodologies, the environmental impacts of 16 Lidl Company logistics warehouses in France were evaluated. A correlation analysis revealed that warehouse size and cold storage capacity are the strongest predictors of carbon footprint (Pearson coefficients of 0.78 and 0.68, respectively). Based on these relationships, a carbon footprint threshold function was developed using a linear regression model optimized by a Non-dominated Sorting Genetic Algorithm II (NSGA-II), achieving an error margin below 7%. The resulting model quantifies emissions per pallet space according to storage type, ranging from 1.11 TCO2eq/pallet for dry goods (high shelf) to 4.96 TCO2eq/pallet for fresh-produce block storage. These findings demonstrate that achieving carbon neutrality for logistics warehouses requires not only energy-efficient operations but also substantial reductions in embodied emissions through low-carbon materials and optimized design strategies. The predictive carbon footprint threshold function proposed here provides a robust, data-driven tool to guide the design of future industrial buildings aligned with national and international sustainability goals.
{"title":"Investigation of carbon footprint thresholds for designing climate neutral logistics warehouses in France at the early design stage","authors":"Sébastien Visse , Francesca Contrada , Arnaud Lapertot , Andrea Kindinis , Abderrahim Boudenne","doi":"10.1016/j.clscn.2025.100289","DOIUrl":"10.1016/j.clscn.2025.100289","url":null,"abstract":"<div><div>Reducing the environmental impact of logistics warehouses is a critical challenge, particularly during the early design phase when limited data is available to guide decision-making. This study aims to establish carbon footprint targets for logistics warehouses in alignment with climate neutrality objectives. Using Life Cycle Assessment (LCA) methodologies, the environmental impacts of 16 Lidl Company logistics warehouses in France were evaluated. A correlation analysis revealed that warehouse size and cold storage capacity are the strongest predictors of carbon footprint (Pearson coefficients of 0.78 and 0.68, respectively). Based on these relationships, a carbon footprint threshold function was developed using a linear regression model optimized by a Non-dominated Sorting Genetic Algorithm II (NSGA-II), achieving an error margin below 7%. The resulting model quantifies emissions per pallet space according to storage type, ranging from 1.11 TCO<sub>2</sub>eq/pallet for dry goods (high shelf) to 4.96 TCO<sub>2</sub>eq/pallet for fresh-produce block storage. These findings demonstrate that achieving carbon neutrality for logistics warehouses requires not only energy-efficient operations but also substantial reductions in embodied emissions through low-carbon materials and optimized design strategies. The predictive carbon footprint threshold function proposed here provides a robust, data-driven tool to guide the design of future industrial buildings aligned with national and international sustainability goals.</div></div>","PeriodicalId":100253,"journal":{"name":"Cleaner Logistics and Supply Chain","volume":"18 ","pages":"Article 100289"},"PeriodicalIF":6.8,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694763","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 : 2025-12-01DOI: 10.1016/j.clscn.2025.100283
Pranto Chakrabarty , Sanjoy Kumar Paul , Andrea Trianni , Suvash C. Saha
This study presents the development of the long-term Household Hydrogen Supply Chain (HHSC) model, aimed at supporting the decarbonisation of household energy consumption. Structured across three strategic phases: foundation, expansion, and maturation, the model facilitates the systematic phase-out of liquefied petroleum gas (LPG) by 2045 and natural gas (NG) by 2080. Employing demand estimation methodologies grounded in historical data and exponential decay functions, the study forecasts long-term hydrogen adoption trajectories and allocates regional demand to optimise infrastructure placement. A network optimisation model identifies the optimal locations and capacities of national, regional, and local distribution centres (NDCs, RDCs, and LDCs). This staged development ensures operational scalability, geographic equity, and financial viability. A key finding is the substantial increase in profitability from $479 million in 2026 to $88.26 billion by 2090, driven by infrastructure growth and increasing hydrogen demand. Sensitivity analyses indicate that the adoption during the mid years (2040–2060) is particularly vulnerable to cost fluctuations. The model supports net-zero 2050 goals and aligns with several Sustainable Development Goals (SDGs), including SDGs 7, 9, and 13. While the HHSC provides a structured pathway for long-term hydrogen transition, future research should focus on enhancing the resilience of the HHSC by incorporating real-time data integration, assessing vulnerability to supply chain disruptions, and developing risk mitigation strategies to ensure continuity and scalability in hydrogen delivery under uncertain operating conditions.
{"title":"Designing and long-term planning for household hydrogen supply chain in Australia","authors":"Pranto Chakrabarty , Sanjoy Kumar Paul , Andrea Trianni , Suvash C. Saha","doi":"10.1016/j.clscn.2025.100283","DOIUrl":"10.1016/j.clscn.2025.100283","url":null,"abstract":"<div><div>This study presents the development of the long-term Household Hydrogen Supply Chain (HHSC) model, aimed at supporting the decarbonisation of household energy consumption. Structured across three strategic phases: foundation, expansion, and maturation, the model facilitates the systematic phase-out of liquefied petroleum gas (LPG) by 2045 and natural gas (NG) by 2080. Employing demand estimation methodologies grounded in historical data and exponential decay functions, the study forecasts long-term hydrogen adoption trajectories and allocates regional demand to optimise infrastructure placement. A network optimisation model identifies the optimal locations and capacities of national, regional, and local distribution centres (NDCs, RDCs, and LDCs). This staged development ensures operational scalability, geographic equity, and financial viability. A key finding is the substantial increase in profitability from $479 million in 2026 to $88.26 billion by 2090, driven by infrastructure growth and increasing hydrogen demand. Sensitivity analyses indicate that the adoption during the mid years (2040–2060) is particularly vulnerable to cost fluctuations. The model supports net-zero 2050 goals and aligns with several Sustainable Development Goals (SDGs), including SDGs 7, 9, and 13. While the HHSC provides a structured pathway for long-term hydrogen transition, future research should focus on enhancing the resilience of the HHSC by incorporating real-time data integration, assessing vulnerability to supply chain disruptions, and developing risk mitigation strategies to ensure continuity and scalability in hydrogen delivery under uncertain operating conditions.</div></div>","PeriodicalId":100253,"journal":{"name":"Cleaner Logistics and Supply Chain","volume":"17 ","pages":"Article 100283"},"PeriodicalIF":6.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623584","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 : 2025-11-19DOI: 10.1016/j.clscn.2025.100287
Adnane Drissi Elbouzidi , Rosin. Frédéric , Robert Pellerin , Samir Lamouri , Abdessamad Ait El Cadi
Warehouses are increasingly under pressure to reduce their carbon footprint. Yet, traditional carbon accounting approaches remain ill-suited to support real-time or operational decision-making dedicated to lower their environmental impacts. These methods typically rely on aggregated, static data and offer vague emission estimates. To address this issue, this paper introduces a bottom-up carbon accounting framework embedded within a warehouse Digital Twin (DT), enabling real-time, resource-level emissions tracking and scenario analysis. The framework builds upon the Toyota Business Practices (TBP) method to analyze the results of traditional carbon accounting by integrating data streams from Warehouse Management Systems (WMS) and sensor inputs into DT simulation modules to allocate emissions at the level of equipment and processes. A case study conducted in a 3PL warehouse in France demonstrates the model’s ability to match aggregate estimates from conventional carbon accounting (CCA) tools, while delivering substantially higher resolution. Notably, the DT identified overlooked emission hotspots, including employee commuting and the use of packaging materials made from wood and plastic, to support operational “what-if” analysis and evaluate the carbon and cost trade-offs of alternative scenarios. These findings highlight the potential of Warehouse DTs to shift carbon accounting from a static reporting function to an actionable sustainability management tool.
{"title":"Leveraging digital twins for enhanced sustainable warehouse management","authors":"Adnane Drissi Elbouzidi , Rosin. Frédéric , Robert Pellerin , Samir Lamouri , Abdessamad Ait El Cadi","doi":"10.1016/j.clscn.2025.100287","DOIUrl":"10.1016/j.clscn.2025.100287","url":null,"abstract":"<div><div>Warehouses are increasingly under pressure to reduce their carbon footprint. Yet, traditional carbon accounting approaches remain ill-suited to support real-time or operational decision-making dedicated to lower their environmental impacts. These methods typically rely on aggregated, static data and offer vague emission estimates. To address this issue, this paper introduces a bottom-up carbon accounting framework embedded within a warehouse Digital Twin (DT), enabling real-time, resource-level emissions tracking and scenario analysis. The framework builds upon the Toyota Business Practices (TBP) method to analyze the results of traditional carbon accounting by integrating data streams from Warehouse Management Systems (WMS) and sensor inputs into DT simulation modules to allocate emissions at the level of equipment and processes. A case study conducted in a 3PL warehouse in France demonstrates the model’s ability to match aggregate estimates from conventional carbon accounting (CCA) tools, while delivering substantially higher resolution. Notably, the DT identified overlooked emission hotspots, including employee commuting and the use of packaging materials made from wood and plastic, to support operational “what-if” analysis and evaluate the carbon and cost trade-offs of alternative scenarios. These findings highlight the potential of Warehouse DTs to shift carbon accounting from a static reporting function to an actionable sustainability management tool.</div></div>","PeriodicalId":100253,"journal":{"name":"Cleaner Logistics and Supply Chain","volume":"17 ","pages":"Article 100287"},"PeriodicalIF":6.8,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145578783","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 : 2025-11-14DOI: 10.1016/j.clscn.2025.100285
Konpapha Jantapoon, Krittapha Saenchaiyathon
This study examines the way in which information sharing, technology implementation, and supply chain resilience (SCR) support sustainable supply chain management (SSCM) under volatile, uncertain, complex, and ambiguous (VUCA) conditions in agricultural SMEs. The structural model, which utilizes data from 756 agricultural businesses across five northern Thai provinces, explains 65.7 % of the variance in SCR and 71.3 % of the variance in SSCM. Results show that the implementation of technology improves SCR by 44.1 % and information sharing increases SCR by 42.2 %. Additionally, SCR confirms its essential function in SSCM by strongly promoting sustainability outcomes (β = 0.695, p < 0.001). This link is negatively moderated by VUCA conditions, though, since high VUCA intensity reduces SCR’s sustainability efficacy by 73.6 %. According to these results, although robust resilience (stability during disruptions) and adaptive resilience (responsiveness to changes in the business environment) approaches are essential, they must be supplemented with context-specific dynamic skills during extreme volatility. For agricultural SMEs with limited resources, the study offers practical insights that help managers and policymakers create resilience-driven sustainability strategies suited to unstable conditions.
本研究考察了农业中小企业在不稳定、不确定、复杂和模糊（VUCA）条件下，信息共享、技术实施和供应链弹性（SCR）对可持续供应链管理（SSCM）的支持方式。该结构模型利用了泰国北部5个省份的756家农业企业的数据，解释了SCR中65.7%的方差和SSCM中71.3%的方差。结果表明，技术的实施使SCR提高了44.1%，信息共享使SCR提高了42.2%。此外，SCR通过强烈促进可持续性结果证实了其在SSCM中的基本功能（β = 0.695, p < 0.001）。然而，这种联系受到VUCA条件的负向调节，因为高VUCA强度使SCR的可持续性效率降低了73.6%。根据这些结果，尽管稳健弹性（中断期间的稳定性）和适应性弹性（对业务环境变化的响应）方法是必不可少的，但在极端波动期间，它们必须辅以特定于环境的动态技能。对于资源有限的农业中小企业，该研究提供了实用的见解，帮助管理者和政策制定者制定适应不稳定条件的弹性驱动的可持续发展战略。
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