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Under carbon cap-and-trade regulation, to control carbon emissions and improve the recycling efficiency of waste electrical and electronic equipment (WEEE) from the traditional offline recycling channel, more enterprises attempt to simultaneously combine the online recycling platform with the digital transformation of the supply chain. Based on a closed-loop supply chain (CLSC) with dual recycling channels and considering recycling advertising invested both in offline and online recycling channels, this paper attempts to explore the optimal recycling advertising mode which can both achieve economic and environmental performance under cap-and-trade regulation. Therefore, game models including a manufacturer and above either recycler are established, which are respectively correspond to four recycling advertising modes: manufacturer cooperates with neither recycler (MO mode), manufacturer only cooperates with the offline retailer (MR mode), manufacturer only cooperates with the online recycling platform (MT mode), and manufacturer cooperates with either recycler (MRT mode). Consequently, this paper solves the optimal decisions of the tripartite and focuses on the analysis of the impact of cooperative (competitive) recycling advertising under cap-and-trade regulation. By comparing the profits and total carbon emissions of the whole CLSC under four recycling advertising modes, the results indicate that: cooperative advertising between the manufacturer and two recyclers can achieve economic and environmental performance for the whole CLSC if the free-riding phenomenon occurs in recycling advertising. Otherwise, influenced by competitive recycling advertising, the above mode turns to the suboptimal one for the profits of two recyclers and the total carbon emissions of the CLSC. Moreover, driven by economic benefits, cooperative advertising will eventually become a consensus, but the total carbon emissions may not always be the least. [ABSTRACT FROM AUTHOR]

This paper explains the evolving role of the local supply chain across different disruption scenarios. A systematic literature review of 91 papers from 33 journals supports definition of the local supply chain and explication of benefits before, during and after COVID-19 disruption. Resilience emerges as the prevalent benefit at each stage, but to varying scales. In the pre-COVID-19 era, where the local supply chain serves as a back-up approach to the more dominant global supply chain, localisation's capacity to absorb change promotes resilience to mitigate low-magnitude disruption. At initial outbreak of COVID-19 disruption the local supply chain was necessitated as an emergency response to the untenability of global operations, demonstrating transformational resilience to survive. As disruption continued, the local supply chain upscaled and adapted to recover. In the post-COVID-19 era, resilience is expected to remain a strategic priority, promoting continued investment in local operations to thrive with embedded resilience. As a result, the COVID-19 pandemic welcomed a point of transition for the local supply chain as capacities and benefits are revaluated and localisation recognised as critical in developing resilient supply networks for the future. This study consolidates this evolution to offer a propositional framework showcasing the local supply chain across different disruption scenarios. This offers long overdue definition and explanation of the local supply chain and its relationship with resilience, addressing an existing lack of academic attention and encouraging alignment of local-versus-global decisions with changing strategic priorities. [ABSTRACT FROM AUTHOR]

One of the challenges for suppliers is to increase their market share due to the limited target market. In other words, in the supply chain, the demand for suppliers' products is limited. Therefore, suppliers produce a determined share of the required amount by producers. However, when it comes to the share in the amount of output among suppliers, the output of suppliers in this indicator is interdependent. In the classical data envelopment analysis (DEA) models, there are no models that assess the suppliers according to the dependence of at least one output on each other. In this paper, a model is presented that can assess sustainable suppliers in the presence of interdependent output among suppliers by using DEA models and their extension. It can also determine the total benchmarks of decision making units (DMUs) in such a way that satisfies the interdependent output constraint. In other words, benchmarks are not determined independently. Ultimately, an approach is presented to determine efficient projections for inefficient DMUs by considering the concept of interdependent output. To represent the applicability of our proposed model, a dataset for two consecutive years including 32 sustainable suppliers with consideration of interdependent output has been implemented using the model presented in this paper. The resulting sustainability has been compared with a classical model. [ABSTRACT FROM AUTHOR]

In the current global landscape, food loss and waste (FLW) persist as a significant challenge, accounting for one-third of global food production and directly impacting 700 million individuals. Beyond its social ramifications, FLW incurs substantial economic costs and raises environmental and ethical concerns. To address this issue, our paper presents a comprehensive systematic overview within operations management, offering a framework that delineates diverse supply chain types, defines FLW, examines its multifaceted impacts, and outlines reduction strategies. Emphasizing a holistic perspective, we utilize various case study approaches to shed light on the global implications of FLW, aiming to contribute to ongoing research endeavors. In exploring the depth of FLW, we build upon two foundational premises: the actions taken by rational actors towards FLW reduction hinge on the balance between benefits and costs, guiding policy formulation. Effective policies necessitate a thorough understanding of synergies, potential unintended consequences and trade-offs. The absence of comprehensive information on FLW leads to both excessive and insufficient rates of FLW. Justifying public intervention lies in its potential to enhance food security, efficiency, and environmental sustainability, albeit quantifying these effects poses challenges. Our overview paper identifies key challenges in FLW policies, encompassing measurement, quantification, interdependencies within the and trade-offs, and value chain in food policy objectives. Despite these challenges, the paper guides the enhancement of loss measurement for targeted interventions, emphasizing investments in policies that foster a conducive business environment and government-led initiatives in awareness and incentive modification. The study also sheds light on crucial gaps in knowledge, including data deficiencies and variability in estimates, calling for standardized data collection methodologies and targeted research initiatives. By addressing these gaps and emphasizing collaborative efforts, this research contributes to a nuanced understanding of FLW, advocating for improved strategies to tackle this multifaceted issue. • Comprehensive Ops Framework: Presents systematic framework for FLW reduction in supply chains. • Multifaceted FLW Impacts: Explores economic, environmental, and social impacts. • Urgent Data Gaps: Emphasizes need for current data on FLW, especially in BRICS nations. • Culture-Specific Solutions: Highlights importance of tailored innovations for waste reduction. • Climate Nexus and Renewable Energy Integration: Explores FLW, climate change, and renewable energy synergy for sustainable, low-carbon paths. [ABSTRACT FROM AUTHOR]

With the increasing environmental awareness of citizens, sustainable fashion has become a hot topic in industry, academia, and society. The importance of product eco-design (ED) in ensuring the sustainability of organizations and supply chains cannot be overstated. This paper aims to establish an eco-design performance evaluation system and identify the most effective management mechanism. To achieve this goal, a linked two-stage network Data Envelopment Analysis (DEA) model is developed. This model has a well-defined internal structure and potential causal relationships between variables; and the two stages are Internal and Supplier Management (ISM) and Openness and Collaboration (OC). We analyze stage and overall efficiencies using proactive, reactive, and simultaneous approaches. Additionally, we explore the convergence characteristics of the industry's ED efficiency from 2015 to 2018. The results show that the overall ED efficiency of fashion enterprises is significant when considering the interrelatedness of subsystems. However, there is disharmony between the two stages. As the proactive approach, ISM is the optimal strategy to maximize ED efficiency. On the other hand, OC is critical to eco-design performance. This paper contributes to the existing literature by developing an innovative DEA analytical framework for evaluating eco-design performance. It also identifies the most eco-efficient way to achieve better environmental performances, which allows decision-makers to better allocate their design efforts and improve both short- and long-term corporate sustainability. [ABSTRACT FROM AUTHOR]

A supply chain's ability to avoid an unexpected disruption or quickly return to a normal situation defines supply chain resilience. Prior works have emphasized the integration of supply chain resilience and sustainable development goals (sustainable supply chain resilience), which is the focus of this paper. This paper proposes a four-level supply chain consisting of suppliers, intermodal hubs, manufacturers, and distributors along with a heterogeneous fleet of vehicles. Two types of manufacturers are included: large and small. The small manufacturers are moveable. To contribute to sustainable logistics problems, the objectives are cost, carbon emissions, and job opportunities when the appropriate supply chain members and flow of materials are optimized. To contribute to supply chain resilience, this paper presents a new robust approach and a new multiobjective algorithm to capture uncertain parameters (e.g., supply cost and employee availability). A numerical example based on empirical data shows the applicability of the proposed model. • This paper proposes sustainable supply chain resilience. • A heterogeneous fleet of vehicles and manufacturers are considered. • A new robust approach is introduced. • A new multiobjective algorithm is proposed. • Datasets along with algorithms codes are available for journal readers. [ABSTRACT FROM AUTHOR]

As clean energy technologies proliferate globally, demand for critical minerals has surged, sparking widespread concern about the security of the critical minerals supply chain. However, given the traditional scientometric methods lack of consideration of the importance of keywords and thematic analysis means cannot adaptively extract the core theme, this paper extracts the literature related to critical mineral supply chain security (CMSCS) from the Web of Science literature database system during 1995–2022. We first use scientometrics to analyze the publication volume, author collaboration, institutional collaboration, and national collaboration of CMSCS; secondly, we use cosine distance and nearest neighbor classification algorithms to analyze the dynamic development status of CMSCS, and finally, we utilize the TF-IDF algorithm and the Latent Dirichlet Allocation (LDA) text mining method to model the topics in this field. The results show that (1) global research on CMSCS is rapidly developing. China currently has the most literature in the field of CMSCS. The US, China, and the United Kingdom have the most influential research. (2) The authors collaborated to form nine core teams. The core institutions are the Chinese Academy of Sciences (CAS), Massachusetts Institute of Technology (MIT), and Politecnico di Milano. Most collaborative teams or institutions have strong internal collaborative links. (3) The research hotspots of CMSCS are influenced by national policies, technological development, and social consumption demand, "circular economy", "lithium battery", "recycling potential", and "recycling technology" are the directions that need to be focused on in the future. (4) Supply chain risk assessment, resilience enhancement, and recycling are important topics for future research in this field. In addition, future research on CMSCS should focus on assessing the vulnerability, resilience, and robustness of the critical mineral supply chain from the perspective of the "whole chain" to ensure the security of the global critical mineral supply chain. The research in this paper can help researchers and policy makers to quickly understand the research status and trend direction of CMSCS and provide essential references for promoting the sustainable and resilient management of global CMSCS. [ABSTRACT FROM AUTHOR]

Environmental issues drive the manufacturer to strive for green technology innovation. The manufacturer cannot strictly control all the activities in the production process, which may cause yield uncertainty. However, the existing studies that consider uncertainty factors usually assume that yield is deterministic. This paper constructs a green supply chain considering yield uncertainty and corporate social responsibility by a manufacturer and a retailer under different market power structures. We examine that yield uncertainty negatively affects the optimal decision-making strategies under four different power structures, especially in the centralized model. The retailer who adopts corporate social responsibility can alleviate the negative effects of yield uncertainty to a certain extent. Meanwhile, it is interesting that the manufacturer is not maximizing profits in the market dominated by himself and is subject to interference from yield uncertainty and corporate social responsibility. When the yield uncertainty is relatively small, the manufacturer's profit is the largest in the retailer-dominated market structure. However, when the yield uncertainty is significant, the revenue for the manufacturer is the largest in the manufacturer-dominated market. Regardless of how the yield uncertainty changes, the retailer's profit is always the largest in the retailer-led model. To alleviate the manufacturer's green costs, the retailer will share part of their revenue with the manufacturer under the Nash model. The Nash-revenue-sharing contract always benefits the manufacturer but hurts the profits of the retailer with a higher sharing coefficient. Finally, this paper provides prominent implications for managers and governments. [ABSTRACT FROM AUTHOR]

This paper proposes a closed-loop supply chain model to study the impact of product returns and awareness program on e-waste supply chain. The study focuses on the sustainability aspects of e-waste management and the effects such initiatives bring about to the environment at large. Reusing and recycling of collected products in manufacturing will improve the supply of scarce resources and reduce reliance on virgin raw materials. Engaging in CLSC can enhance brand reputation and competitiveness through sustainability while reducing the health risks to informal recycling workers by formalizing practices. The proposed model consists of a manufacturer, retailer, and third-party collector (TPC) under wholesale price and incentive contracts with deterministic demand. From the analysis it is observed that integrating a third-party collector with higher recycling rate boosts the total profit of the decentralized setting with a TPC. The centralized model, with a single decision maker, results in lower retail prices and higher collection rates compared to the decentralized models thereby commanding a higher overall profit. [ABSTRACT FROM AUTHOR]

The scientific formulation of procurement and inventory plans is an important topic in the steel industry, when considering both economic and environmental impacts in an uncertain market environment. Robust optimization methods offer an effective solution to this problem. This study has investigated a multi-echelon and multi-period front-end supply chain centered on a steel enterprise under uncertain purchase prices and demand. The goal is to minimize the costs associated with mining, processing, transportation, procurement, and inventory along with the corresponding carbon emission costs. To address this issue, we present a robust rolling horizon model (RRHM) that considers the inventory capacity constraints of steel enterprise and incorporates ore grade requirements. Subsequently, the RRHM was reformulated into a solvable mixed-integer linear programming model, and the relationship between the robust model and a specific deterministic model was established. Finally, the model was validated by generating instances based on real scenarios. The results demonstrate that the robust rolling horizon model proposed in this paper outperforms both the Sample Average Approximation (SAA) strategy and the (s, S) strategy in terms of cost and robustness. In addition, a sensitivity analysis of the key parameters was conducted. The findings reveal that the unit inventory, mining, and carbon emission costs have a limited impact on the total cost, highlighting the robustness of the model. These findings provide valuable insights into the green and sustainable development of the steel industry, enabling the formulation of effective strategies to mitigate the impacts of uncertainties. • Making steel raw material procurement plans amidst demand and price uncertainties. • Robust rolling horizon model is formulated, exploring its relationship with others. • The robust model exhibits certain advantages compared to SAA and (s, S) strategies. • Managerial insights for steel enterprises' procurement strategies are offered. [ABSTRACT FROM AUTHOR]

There has been limited progress in addressing the demand uncertainty inherent over time and the sustainability impact on food logistics. This paper aims to fill this gap, addresses the sustainability of cold food supply chains, and proposes a mixed-integer programming model to solve a multi-period inventory routing problem (IRP) under non-stationary stochastic demand, route-dependent costs, and environmental concerns. The study investigates the replenishment and routing plans to minimise the total expected cost while producing a minimum amount of CO 2 emission. Due to the uncertainty of demand, we apply the static-dynamic strategy and propose a mathematical model under the (R , S) replenishment policy to maintain flexibility in ordering decisions under a pre-determined replenishment schedule. Our numerical experiments suggest that the (R , S) policy reduces inventory costs significantly, since it solves the excess inventory issue caused by the higher buffer stock levels due to the pre-determined order quantities in the (R , Q) policy. However, the resulting CO 2 emission levels and routing costs remain similar in both models. Due to the reduced inventory costs, the (R , S) policy makes a significant improvement on the total cost. Moreover, our numerical experiments show that the difference between the cumulative ending inventory levels for the (R , Q) and (R , S) policies increasingly grows as the time horizon gets longer, and it results in increasingly larger differences in the total cost values for both policies. [ABSTRACT FROM AUTHOR]

As the "Internet+" mode promotes transforming and upgrading the traditional recycling industry, dual-channel recycling has become the focus of commercial activities. Based on such background, this paper constructs the differential game models of the dual-channel closed-loop supply chain (DCCLSC) system in the complete non-cooperation (DN), incomplete cooperation (D), and complete cooperation (C) scenarios by applying game theory. The novelty is that recycling and pricing decision problems are investigated for the DCCLSC system from a dynamic perspective. A cost sharing-transfer payment contract is designed to realize the perfect coordination of the DCCLSC system. Moreover, the extended models with government intervention and inventory management are proposed. Finally, the numerical examples are used to verify the feasibility and validity of the addressed results. The results show that 1) online retail price is higher than offline retail price when μ > μ N ; 2) The retail price in the DN model is always lower than the D model, and their comparative relationship with the C model is closely related to the ratio of recoverability rate; 3) The recoverability rate and the profit of DCCLSC system are highest in the C model; 4) Competition and government intervention can create a win-win situation with improving environmental and economic benefits. • Establish the differential game model for the dual-channel closed-loop supply chain system. • The recycling and pricing strategies are derived from the dynamic perspective. • Design the cost sharing-transfer payment contract to achieve the coordination of dual channel closed-loop supply chain. • Discuss the impact of competition intensity on the recycling and pricing strategies. [ABSTRACT FROM AUTHOR]

In India, approximately one-third of agricultural produce is wasted every year due to the different issues present in the post-harvest supply chain stages. The supply chain management of perishable products becomes complex and challenging due to the inclusion of its perishability dynamics. Quality is the main factor that governs the buying or discarding of perishable products by consumers. Therefore, the main aim of this research work is to develop an accurate and efficient image processing model for the classification of the product (Tomato) based on its quality for managing the supply chain. There are three novelties in this research work. A four-stage supply chain architecture integrated with the image processing system at mandi, and warehouses is proposed (First). This image processing system is developed in two stages. In stage I, the acquired images of tomato during its life cycle are labelled with the help of machine learning algorithms (Second). This labelled data is used in stage II for the development of a classification model to segregate the product into various grades. For this, an optimized architecture of seven-layer Convolutional Neural Network (CNN) model is developed followed by optimization of its hyperparameters simultaneously using Design of Experiments (DOE) technique (Third). The optimized CNN model achieved maximum accuracy of 88.40% and reported an execution time of 7 min. Further, the results of standard hyperparameter optimization techniques like Grid search, Random search, Bayesian, and Hyperband are compared with the proposed DOE technique on the optimized CNN architecture. The work done in this paper enables the supply chain managers to take accurate and rapid decisions for pricing, procurement, storage, and transportation at various stages of the supply chain leading to Industry 4.0. This will result in reduced post-harvest losses and simultaneously achieve the benefits across social, economic, and environmental dimensions of sustainability leading to better supply chain management. • A supply chain architecture integrated with image processing system is proposed. • Decisions related to pricing, procurement, storage and transportation can be taken. • The quality of product is modelled and labelled using Machine Learning algorithms. • A CNN model is developed for quality-based classification and optimized using DoE. • A comparison of DoE with Grid search, Bayesian and Hyperband is presented. [ABSTRACT FROM AUTHOR]

It is crucial for long-term carbon emission reduction to analyze how the government affects enterprises and customers to reduce carbon emissions and purchase low-carbon products. A tripartite evolutionary game among the government, enterprises, and customers is constructed. In addition, the evolutionary courses of the game among the behaviors of government, enterprises, and customers are examined. On this basis, the influence of internal and external factors on the results is analyzed. MATLAB software is used for numerical simulation analysis to simulate the influence of some parameters on the decision-making of game players. The analysis demonstrates that: the subsidy for enterprises has a more significant influence on the behavior of the government than on those made by enterprises and customers. At this point, enterprises and customers will take more account of the government's decision, which will lead to the strategies of "low-carbon production mode" and "purchasing low-carbon products," respectively. This paper focuses on the impacts of the low-carbon consumption subsidy policy and the "carbon inclusion" system on the evolution of the three parties. When carbon credits are at a reasonable level and low-carbon consumption subsidies are combined with carbon inclusion mechanism, it may be observed that the former is more effective in encouraging customers to purchase low-carbon products. In other words, this is the situation where the two policy implementations are most effective. Simultaneously, it will motivate enterprises to reduce carbon emissions and the government to positively regulate. • This study adopts the research perspective of carbon emission reduction. • A tripartite evolutionary game model is proposed to study stakeholders' strategies. • Government subsidy policies and carbon inclusion mechanism favor game outcomes. • Governmental policies in low-carbon consumption must be strengthened. [ABSTRACT FROM AUTHOR]

Capacity sharing, as a collaborative strategy among manufacturers, aims to alleviate this problem by sharing manufacturing capabilities to meet demand with large fluctuating ranges. This paper explores a novel supply chain network design problem considering capacity sharing with third-party manufacturers. Specifically, the problem involves location choices for owned manufacturing plants and distribution centers and the selection of third-party manufacturers. Third-party manufactured products can only be shipped to distribution centers, whereas products from plants can be shipped directly or through distribution centers to customer zones. In this network, customer demand and the product prices of third-party manufacturers are assumed to be uncertain. To address uncertainties, we formulate a distributionally robust chance-constrained model for the problem. The probability distributions of the uncertainties are characterized using Wasserstein ambiguity sets. The incorporation of chance constraints serves to enhance the level of satisfactory customer demand. To ensure tractability, we reformulate the distributionally robust model as a solvable model employing duality theory. Finally, we conduct numerical experiments based on a real-life manufacturing company to evaluate the effectiveness of the proposed model. The results confirm that the proposed model reduces the cost standard deviation by an average of 21.9% and increases reliability. Our study can present a reliable framework for designing a capacity-sharing supply chain network for manufacturing enterprises that optimizes cost while improving customer satisfaction by over 95%. [ABSTRACT FROM AUTHOR]

In the era of accelerated globalization, the issue of technology blockade in complex product supply chains has taken centre stage, making the resilience and security of these chains a focal point in the strategic manoeuvres of major nations. This paper presents a tripartite evolutionary game model encompassing main manufacturers, domestic suppliers, and foreign suppliers. The aim is to delve into the internal mechanisms of transnational supply chain enterprises dealing with complex products, utilizing the "double cycle" framework to address technological blockade. The study's findings reveal a progression in the evolutionary system linked to the technological innovation capabilities of domestic suppliers. The system evolves from the independent innovation of domestic suppliers to cooperative innovation within domestic enterprises and ultimately culminates in joint ventures and cooperation involving multinational suppliers. Additionally, a noteworthy technology gap exists between domestic and foreign countries, suggesting that introducing foreign advanced technology initially and mimicking innovation can effectively mitigate research and development risks. Furthermore, the study underscores the role of Chinese government subsidies in incentivizing technological innovation among domestic suppliers, particularly pronounced when facing significant technology gaps. Simultaneously, the foreign government's imposition of penalties on premium sales by foreign suppliers serves as a catalyst for internal innovation within Chinese enterprises. These actions also motivate multinational enterprises to invest and cooperate, fostering the development of an international cycle. This study provides a fresh perspective on overcoming foreign technological blockades. Its positive implications extend to the advancement of technology in China's complex product landscape and the bolstering of supply chain resilience. • Evolutionary Game Theory Research on the Dynamic Impact of Technology Blockade on China's Complex Product Supply Chain. • Improving the level of technological innovation capability helps to break the foreign technological blockade. • The increase in fines imposed by foreign governments helps break the technological blockade through international circulation. • Domestic government subsidies are beneficial for breaking the technological blockade in domestic circulation. • Significant technological gap between domestic and foreign countries should be introduced first, and innovation should be based on imitation. [ABSTRACT FROM AUTHOR]

Scientific literature and regulation acknowledge the positive social and economic impacts of local food productions, especially for Geographical Indication products, but their relationship with environmental sustainability remains still to be better investigated. The current European reform of the Geographical Indications system is adopting environment protection as one of the main objectives, in conformity with the European Green Deal and Farm to Fork strategies. In this framework, the present paper is aimed to show how Geographical Indications food products are related to environmental sustainability by the means of a systematic literature review. Firstly, the relevant publications are analyzed and classified referring to the Sustainable Development Goals (SDGs) framework. Results show a contradictory picture regarding the positive and negative implications of Geographical Indications production for the different components of environmental protection (e.g., SDG 9 or SDG 12). Secondly, the literature analysis identifies four features (namely products specification, producers' environmental consciousness, role of institutions and link with the territory) of Geographical Indications that may mitigate environmental pressures, when present. Finally, after the analysis of the current and emerging European Geographical Indications regulation, policy suggestions are advanced. In particular, to enhance the environmental sustainability of Geographical Indication products, explicit environmental objectives should be introduced in Geographical Indication specification, while consciousness of producers, multidimensional cooperation and link with the territory should be strengthened. [Display omitted] • Environmental impacts of GIs are investigated since controversial in the literature. • The SDGs-based classification of the contributions shows a focus on SDGs 2 and 12. • Four drivers to make GIs effective for environmental sustainability are identified. • New explicit criteria in GI specification should be defined accordingly. • GI mark could incorporate environmental features due to the new EU regulation. [ABSTRACT FROM AUTHOR]

In recent years, heightened attention has been given to the challenges of sustainable development precipitated by environmental pollution in the Textile and Apparel (TA) industry. The burgeoning demand for fast fashion, often reliant on cheap and environmentally unsustainable textiles, exacerbates this environmental degradation. To facilitate a harmonized and sustainable progression of the entire TA industry, this study employs the Stackelberg game model to investigate the prerequisites for establishing Environmental, Social, and Governance (ESG) related cost-sharing contracts and their consequential effects on supply chain coordination. Specifically, the paper compares four distinct supply chain models under two conditions: whether market demand is stochastic or deterministic. Through this comparative analysis, the study finds that the ESG related cost-sharing contracts bring more profit to the textile and apparel supply chain (TASC) in deterministic and stochastic demand, which effectively mitigates the risks associated with unpredictable demand. Numerical results indicate that the ESG cost-sharing contract significantly improves TASC's ESG performance with customers' sustainable awareness increasing. Furthermore, such contracts are instrumental in enhancing the aggregate ESG environmental performance of the Textile and Apparel Supply Chain (TASC), elevating profits for both manufacturers and retailers and alleviating the impact of fluctuating demand on supply chain participants. [ABSTRACT FROM AUTHOR]

To promote green agricultural production, the government often provides two types of subsidies: the Green Technology Operation Subsidy (GOS), and the Green Technology Investment Subsidy (GIS). This paper examines the impact of government subsidy policies on a two-tier agricultural supply chain in which the farmer can choose to self-invest in green technologies or outsource them to the service provider. Our results indicate that outsourcing is more conducive to higher green technology inputs than self-directed investment in most cases and can generate higher profits for small-scale farmers; however, as the land scale increases, farmers prefer to invest in green technologies by themselves. In addition, if the farmer decides to self-invest in green technologies, the farmer prefer the GIS (GOS) policy when the land scale is small (large). Further analysis shows that if the farmer decides to outsource green technologies, the farmer's profit under the two subsidy policies is equal when the land scale is small, while the farmer benefits more from the GOS policy when the land scale is large. Finally, from the perspective of promoting agricultural green production, the GIS policy is always better than the GOS policy, regardless of whether the farmer outsource green technologies. • Investigate the production decision of farmers when outsourcing green technologies. • Outsourcing generates higher profits for small-scale farmers than self-investment. • Farmers benefits more from green technology operation subsidy when land scale is large. • Outsourcing is in most cases more effective than self-investment in increasing green technology inputs. • Green technology investment subsidy yields higher levels of agricultural green production. [ABSTRACT FROM AUTHOR]

The textile industry is a significant source of global greenhouse gas emissions; therefore, it is crucial to implement decarbonization measures throughout the supply chain (SC) to reduce carbon emissions and achieve sustainable development. However, there exists a lack of systematic review regarding decarbonization in textile SCs. The objective of this review is to identify themes related to decarbonization in textile SCs, address research gaps, and propose an integrated framework. To do this, we conducted a systematic literature review of 68 identified papers discussing decarbonization in textile SCs. Based on descriptive and thematic findings, we have developed a comprehensive conceptual framework that synthesizes antecedents, decarbonization practices, influential factors, and outcomes within the context of textile SCs. Furthermore, our review highlights current research gaps and recommends future directions for studying decarbonization in textile SCs. By providing the first comprehensive framework for addressing decarbonization across all stages of the textile SC process, this review contributes significantly to the existing literature. [ABSTRACT FROM AUTHOR]

Given increased awareness about modern slavery in supply chain management (SCM), this paper undertakes a systematic literature review (SLR) covering 106 published articles. We first carry out a descriptive analysis, with results showing that modern slavery research is on an upward trajectory but suffers from a lack of primary data and theory application. We then extract themes from the published articles and use them to provide theory elaboration of Gold et al. (2015) model of modern slavery in SCM. Specifically, we introduce business culture and high impact risk factors like pandemics as new institutional factors; highlight external stakeholders like recruitment agents and audit firms as integral to understanding the business context of modern slavery; and add prevention and remediation to detection and response as discrete categories in managing modern slavery risks. We also discuss the gaps in modern slavery research in supply chains. Finally, we propose that reshoring, industry 4.0 and supply chain collaboration represent SCM specialisms that can inform modern slavery research going forward. [ABSTRACT FROM AUTHOR]

This study investigates the international Photovoltaic Supply Chain (PVSC) from the perspective of the recent literature on Strategic Dependencies and Technological Sovereignty. In so doing, the paper fills existing literature gaps by providing: i) a fine-grained long-term analysis of the PVSC, considering all its segments and integrating production and technology dimensions; ii) detailed evidence of the changing hierarchical relationships within the supply chain; iii) an analysis of the drivers of strategic dependencies in the PVSC in different economic areas. Focusing on China, the EU, Japan, South Korea, and the US over the 2007–2021 period, the empirical evidence highlights the existence of strong strategic dependencies in the EU, especially in the mid and downstream segments of the PVSC. A similar, and in some cases even worse, situation is found in the US, although some diversification of the portfolio of suppliers, especially in the downstream, has been emerging in more recent years. These results have relevant implications in terms of policy theory with specific reference to EU and US initiatives in the field of industrial policies for sustainable transition. [ABSTRACT FROM AUTHOR]

End-of-life (EOL) objects such as tyres have been categorised as hazardous to the environment due to heavy metals and chemicals. To manage the recovery and remanufacturing operations of these EOL items, an effective network design is necessary. In this paper, we developed a mixed integer linear programming (MILP) model to minimize the total cost of the proposed EOL tyre remanufacturing supply chain network. Since, the problem is Non-deterministic Polynomial time hardness (NP-hard) in nature, we propose a prediction model that evaluates the operational feasibility of vehicle allocation and an evolutionary algorithm-based technique is employed to identify optimal facility locations, material flows among facilities, and demand distribution to individual automobiles for near-optimal solutions. Numerical experiments were conducted on a leading tyre remanufacturing company as a case study, and the results were compared with several evolutionary algorithms found that the Modified Genetic Algorithm (MGA) outperforms other algorithms. We demonstrate how, in each case, an optimal cost-effective location for a factory is found based on the cost and demand of the products. The study findings can assist governments and industry stakeholders in developing successful EOL tyre management strategies. • End-of-life (EOL) tyre remanufacturing supply chain network design was developed. • A Mixed Integer Linear Programming model for EOL tyre remanufacturing unit. • A novel prediction and optimization approach has been proposed. • Evolutionary approaches are studied where Modified Genetic Algorithm outperformed. • Based on a real case, experiments conducted to obtain near-optimal results. [ABSTRACT FROM AUTHOR]

Textile waste presents significant environmental risks for the textile and apparel (T&A) industry; however, most textiles can be recycled, enabling the remanufacturing of energy and fiber for resale in the secondary market. In this regard, the importance of a Government Fund Policy (GFP) has been emphasized to promote recycling and remanufacturing efforts aimed at establishing T&A closed-loop supply chains (CLSCs). Simultaneously, blockchain technology and green-product design have transformed T&A brand marketing dynamics due to their crucial roles in enhancing brand trust and increasing market demand. Nevertheless, there is limited research dedicated to examine the interactive effects of GFP and blockchain technology within T&A supply chains. This paper proposes a stylized CLSC game involving a T&A manufacturer and a retailer. The manufacturer sells new and remanufactured T&A products to the retailer while determining green-product design efforts (i.e., green degree). The retailer determines selling prices but incurs usage costs associated with adopting blockchain technology. Through equilibrium analyses, we find that the simultaneous implementation of GFP and blockchain technology can incentivize T&A manufacturers to enhance the green degree of their products, thereby improving brand trust and market demand. All supply chain agents can benefit from adopting blockchain technology as long as its usage cost is below a threshold level, regardless of whether GFP is implemented or not. Similarly, irrespective of whether blockchain technology is adopted or not, if the ratio between remanufacturing subsidies and environmental taxes exceeds a specific threshold value, GFP will serve as an incentive for sustainability efforts among T&A firms while providing them with greater advantages. Additionally, the simultaneous implementation of GFP and blockchain technology holds significant potential for mitigating environmental pollution, i.e., by carefully selecting appropriate degrees of GFP and blockchain technology parameters, a mutually beneficial outcome can be achieved for T&A firms, consumers, and the environment. These findings have important implications for T&A supply chain managers seeking to optimize CLSCs while integrating blockchain technology. Furthermore, they provide valuable insights into government strategies for implementing GFPs. • The optimal blockchain introduction strategy in a textile and apparel closed-loop supply chain (T&A-CLSC) is investigated. • The interaction of government fund policy and T&A-CLSC firms' blockchain introduction decisions is investigated. • Blockchain and government fund policy could encourage firms to heighten brand goodwill, and expand more potential demands. • An "all-win" situation for T&A-CLSC, consumers, and society can be achieved under certain conditions. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic happened exactly when no one expected it and many people died due to the lack of medical equipment. Although pulse oximeters and thermometers were only one of the virus detection equipment, the lack of that equipment could lead to people not knowing about the disease and then the death of those people. Given that these medical devices are very vital in the era of a pandemic, and much less in the later, it is required to provide the best conditions for their use in a closed-loop healthcare supply chain network in post-pandemic. In this paper, a scenario-based two-stage stochastic programming three-objective model for designing a green closed-loop supply chain network is presented. Cost minimization, reliability maximization, and critical response maximization are the objectives of the proposed model. The third objective function is considered for the critical response based on the choice of transportation method. The greenhouse gas emissions for all supply chain elements are considered uncertain and controlled in several possible scenarios. By using an accelerated Benders decomposition algorithm, the mathematical model was solved in different dimensions and the result was analyzed and evaluated in different scenarios. The results demonstrate that the acceleration of Benders bonds reduces the convergence speed of the algorithm by 41%. Our study provides managerial insights into sustainability and reliability, enhancing healthcare supply chain resilience during and after pandemics. [ABSTRACT FROM AUTHOR]