Search Results (4,513)

This study aims to address the need to operationalize Industry 5.0 (I5.0) by developing a comprehensive Assessment Framework for the adoption of the Human Centricity, Environmental Sustainability, and Industrial Resilience pillars. While existing models largely focus on technological maturity, they fail to provide measurable tools for evaluating I5.0 adoption. To bridge this gap, the paper proposes an Assessment Framework based on a structured set of Key Performance Indicators (KPIs) developed within the EU-funded PROSPECTS 5.0 project. The methodology combines an extensive literature review, a workshop with relevant stakeholders, a Delphi survey with experts, and empirical refinement conducted through workshops involving 14 companies across multiple sectors and of varying sizes. The results highlight that organizations predominantly measure traditional indicators such as health and safety, energy consumption, and supply chain robustness, while underestimating emerging dimensions such as human empowerment, social inclusion, circularity, and advanced human–machine collaboration. The framework introduces a set of KPIs for each of the I5.0 pillars, supporting structured assessment across different industrial contexts while allowing sector-specific adaptation. The findings reveal a gap between the perceived importance of several sustainability and human-centric metrics and their actual implementation. This framework allows organizations to self-assess their practices, guide strategic decisions, and align technological growth with societal and environmental goals. Full article

Supplier selection in healthcare is a complex multi-criteria decision-making (MCDM) challenge requiring a balance of sustainability, resilience, and operational efficiency. Traditional methods struggle with scalability and subjectivity when applied to large administrative datasets. This study introduces a transparent hybrid Machine Learning–MCDM (ML–MCDM) framework, validated using a U.S. Medicare dataset of 661 suppliers. The framework integrates eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) for criterion prioritization, the Full Consistency Method (FUCOM) for mathematically consistent weighting, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for final ranking. As the dataset lacks direct sustainability metrics, seven indicators were synthetically generated; thus, the results serve as proof-of-concept demonstration of the framework’s architecture. Specifically, XGBoost–SHAP is trained to predict a synthetically constructed Overall Performance Score (OPS), meaning that the resulting feature importance output constitutes an algorithmic consistency check—confirming that the pipeline correctly recovers importance signals deliberately embedded in the training target. For interpretability, suppliers were segmented into five performance profiles via K-Means: Strategic Partners (17.7%), Green Leaders (18.6%), Reliable Emergency Suppliers (18.2%), Balanced Performers (20.4%), and Developing Suppliers (25.1%). Carbon Footprint Score (0.408) and Emergency Response Capability (0.316) achieved the highest feature importance. FUCOM-derived weights prioritized On-Time Delivery Rate (0.272), Carbon Footprint Score (0.222), and Emergency Response Capability (0.220). The top supplier attained a TOPSIS closeness coefficient of 0.800, showing strong discrimination. Sensitivity analysis across four scenarios confirmed ranking robustness, maintaining Spearman correlations ρ ≥ 0.977. This ML–FUCOM–TOPSIS approach provides an auditable, scalable, and policy-relevant decision-support tool, enabling procurement managers to navigate high-dimensional data while ensuring operational continuity and environmental responsibility in healthcare supply chains. Full article

Cyanobacteria are increasingly recognised as photosynthetic chassis for sustainable metabolic engineering because oxygenic photosynthesis generates ATP and NADPH via the photosynthetic electron transport chain, which drive CO₂ fixation through the Calvin–Benson–Bassham cycle into carbon intermediates that can be redirected toward engineered heterologous pathways. Their genetic tractability, CO₂-fixing capacity, ecological adaptability, and relatively simple cellular organisation make them attractive platforms for developing low-carbon biotechnological processes. This review explores recent progress in engineering cyanobacteria for heterologous pathway construction, critically evaluating genetic tools including transformation methods, genome integration strategies, promoter systems, and CRISPR-based editing, with specific emphasis on challenges of direct relevance to phototrophic chassis: host–pathway metabolic compatibility, precursor supply, cofactor balancing between photosynthetic output and heterologous pathway demand, and achieving genetic stability in polyploid cyanobacterial genomes. The review also addresses key limitations with mechanistic context: metabolic burden from multi-gene pathway expression reduces growth rate and selects against producing cells; polyploidy delays complete chromosomal segregation of engineered constructs; slow photoautotrophic growth constrains volumetric productivity; native regulatory networks resist carbon flux redirection; and cultivation constraints—including light attenuation in dense cultures and mismatches between photosynthetic ATP/NADPH supply and heterologous pathway demand—further limit achievable yields. Full article

The construction sector contributes substantially to global greenhouse gas emissions, making material substitutions a key strategy for advancing sustainability transitions. Mass timber has emerged as a low-carbon alternative to mineral-based construction materials, offering biogenic carbon storage and compatibility with prefabricated and industrialized building systems. This study aims to systematically synthesize the economic, cost, and market evidence on mass timber construction by reviewing 143 peer-reviewed publications, with the objective of clarifying what is empirically known and where uncertainties remain. The reviewed literature reveals three core findings. First, economic outcomes are mixed: while several studies report regional value creation, supply-chain upgrading, and alignment with circular-economy principles, others highlight persistent constraints such as limited manufacturing capacity and uneven policy support. Second, construction cost findings vary substantially, ranging from cost parity or modest savings relative to conventional systems to premiums of approximately 10–15%, shaped by regional pricing, labor availability, transportation distance, regulatory conditions, and supply-chain maturity. Third, market-oriented studies consistently identify slow diffusion, limited practitioner experience, and risk-averse investment environments as key barriers to adoption. Overall, the review shows that economic performance is not yet consistently established and underscores the need for more standardized, context-sensitive, and methodologically consistent evaluation frameworks to support informed decision-making and the sustainable scaling of mass timber construction. Full article

As a cornerstone of global technological advancement, the semiconductor industry depends critically on supply chain stability, which directly influences the global economy and technological innovation. To address uncertainty in semiconductor supply chains, this study develops a Stackelberg game model incorporating Nash bargaining fairness concern to examine pricing strategies, ESG effort decisions, and their implications for supply chain stability under different fairness concern scenarios. A cost-sharing contract-based coordination mechanism is proposed, and numerical simulations verify the effects of fairness concern and ESG effort on stability, as well as the coordinating role of the cost-sharing contract under market uncertainty. The results show the following: (1) Manufacturer fairness concern boosts its profit and ESG effort, but excessive price hikes erode retailer profit and undermine stability. (2) Retailer fairness concern prompts the manufacturer to rebalance profit allocation via lower wholesale prices and reduced ESG effort, weakening supply chain competitiveness. (3) Cost-sharing contracts effectively mitigate the adverse effects of fairness concern and enhance semiconductor supply chain stability. This study provides a verifiable framework for semiconductor firms to improve cooperative stability and sustainable development. Full article

With the rapid expansion of agricultural e-commerce in China, inefficient cloud warehouse consolidation and high environmental costs have hindered the sustainability of supply chains. To address the challenges of low vehicle loading rates and high carbon emissions, this study proposes an optimization model for collection path planning that integrates sales forecasting and three-dimensional loading constraints. First, STL decomposition is employed to identify seasonal sales patterns, and a hybrid SARIMA and ARIMA-BPNN model is constructed to achieve precise forecasting of future orders to provide data support for dynamic demand. Second, a single-objective path planning model is formulated to minimize the fixed vehicle costs, fuel consumption, and carbon emissions while maximizing the load utilization rates. To solve this complex problem, a two-stage solution framework, consisting of path planning and three-dimensional loading verification, was designed. This framework integrates an improved genetic–hill-climbing hybrid algorithm with a constructive heuristic to handle real-time spatial constraints and achieve the efficient optimization of distribution paths. Finally, a case study on the HLYX agricultural cloud warehouse in Harbin, China, demonstrated that the proposed approach significantly enhances space utilization and reduces transportation and carbon emission costs. This study provides a sustainable development path for the cost reduction, economic efficiency improvement, and carbon emission reduction of smart agricultural logistics. Full article

This study examines the associations among circular economy (CE) awareness, CE maturity, and circular material flows in the construction industry using Partial Least Squares Structural Equation Modeling (PLS-SEM). The analysis is based on cross-sectional self-reported survey data collected from 265 firms within the Sustainable Habitat Cluster of Medellín, Colombia. The proposed model examines relationships among cognitive, organizational, and operational dimensions of circularity across the construction supply chain. Results indicate that CE awareness is positively associated with both CE maturity and circular material flows, suggesting that firms reporting higher levels of CE knowledge, communication, training, and access to information systems also report stronger organizational engagement with circular economy initiatives and better circularity outcomes. In contrast, the association between CE maturity and circular material flows was not statistically significant, and no significant mediation effect was observed. These findings indicate that the CE implementation capabilities captured by the maturity construct were not significantly associated with stronger operational circularity outcomes within the sampled firms. The study provides empirical evidence from an emerging-economy construction cluster and highlights the importance of complementing awareness-building initiatives with organizational and operational mechanisms that facilitate the implementation of circular practices across construction ecosystems. Full article

In light of the growing prominence of environmental issues, the frequent occurrence of unexpected incidents, and the dynamic challenges of a changing market environment, suppliers must possess comprehensive capabilities that encompass both green and sustainable development as well as resilience to risks. Consequently, green supplier selection has emerged as a critical research topic. By integrating virtual and physical systems, digital twin technology enhances supply chain transparency and efficiency—a capability that plays a significant role in advancing sustainable supply chain development. In view of this, this study incorporates risk factors into the green supplier evaluation system, introduces indicators related to digital twin technology, and proposes a stochastic slack-based measure data envelopment analysis method, namely SSBM, for evaluating green suppliers. This approach expands and refines the existing evaluation criteria and the decision-making model. Finally, a numerical case study is conducted to validate the feasibility of the proposed method. This research provides more systematic and scientific decision support for green supplier selection, enriching the theoretical and practical applications in the fields of green supply chain and multi-criteria decision-making. Full article

Background: Given the disruption of the global market, technological factors are impacting food supply chains (FSCs). As a result, traceability is emerging as a potential solution for faster and more secure decision-making within FSCs. Methods: This research aims to define a traceability model for a shrimp food supply chain (FSC) in Ecuador using structural equation modelling which insists not only on the main factors that explain its overall performance but also on the effects of the changes in agents’ behaviours. The research was conducted between March and December 2025. A 41-item questionnaire was used for data collection and was administered to 73 stakeholders. The information was reduced and assembled in five main factors. A structural equation model was applied to explore the impact of agents’ coordination, digital transformations, and customer satisfaction on the traceability of the shrimp FSC. Results: The results show that customer satisfaction is broadly affected by the improvements in chain traceability. Furthermore, the results demonstrate the relevance of coordination, digitalization, and traceability as key factors for strengthening the FSC’s performance. Conclusions: The results could contribute to Sustainable Development Goals 12 and 17 and be applicable to other agri-food chains. Full article

To promote the sustainable development of agriculture and reduce resource waste, this paper investigates sustainable financing strategies for a green fresh agri-food supply chain. We employ a purely theoretical Stackelberg game model and numerical simulations based on hypothetical parameters to develop three financing models for a supply chain consisting of one capital-constrained farmer and one retailer, considering consumers’ dual sensitivity to product freshness and greenness. Analytical and numerical results reveal that: (1) with low financing rates, internal financing effectively alleviates under investment in preservation, leading to higher wholesale/retail prices. In a green-sensitive market, the resulting price premium compensates for cost increases, avoiding the “low quality–low price” trap under external financing. (2) The retailer’s total profit decreases as the internal financing rate rises; higher interest income cannot offset demand loss caused by reduced preservation effort. Thus, a low- or zero-interest strategy maximizes the retailer’s operational profit. (3) As consumer sensitivity to freshness and greenness increases, profit growth under internal financing displays convexity. However, under extremely high freshness sensitivity, external financing yields stronger marginal incentives, suggesting that retailers should adjust profit allocation in the high-end market. The findings provide theoretical guidance for financing mode selection and practical insights for promoting green agricultural sustainable development. Full article

Recently, the problem of food waste management has attracted the attention of producers, processors, retailers, and consumers due to economic, environmental, food safety, and sustainability consequences, affecting the entire food supply chain. This article reviews data on food waste of animal origin at different stages along the production and transformation systems, from an environmental, economic, or social perspective. Results show differences between developed and developing countries. While in developed countries, most waste occurs at the end of the food chain, in developing countries, most waste occurs in primary production and transportation. Food waste is very expressive in production and retail, but also in final consumption in households and food services. Mitigating measures include upcycling, i.e., recovering valuable food components for industrial use with economic and environmental benefits, and alternatives for food waste reutilization. The role of the consumer is unquestionable, particularly when shopping for food for the household or when consuming food in restaurants or canteens. Hence, it is crucial to understand the behaviours leading to food waste as a way to reduce it and implement strategies to effectively reduce food waste at various levels. The role of education, regulation, and policies is pivotal in achieving minimal food waste. Full article

The Digital Product Passport (DPP) is considered a critical tool for sustainable supply chains within the scope of the European Green Deal. DPP significantly contributes to improving traceability, transparency, reliability, and circularity in supply chains, enabling a more robust and secure structure. However, despite this significant potential, achieving full integration of DPP is hampered by various organizational, technological, and environmental barriers. This study used the Grey Decision Making Testing and Evaluation Laboratory (Grey DEMATEL) approach, the Technology–Organization–Environment (TOE) framework, and Force Field Theory to identify and categorize these barriers. A total of 27 barriers were identified based on a comprehensive literature review and the opinions of academic and industry experts, and these barriers were categorized into organizational, technological, and environmental categories. The study findings demonstrate that technological barriers, in particular, have a causal effect that strongly triggers both organizational and environmental challenges. The causal analysis conducted reveals the interdependencies among barriers and guides practitioners and policymakers in identifying resistance points to change. Furthermore, the study offers important insights that will help supply chain stakeholders transition from reactive approaches to proactive strategies when managing DPP-related barriers. The insights gained in this regard support the design of collaborative governance mechanisms to create a more resilient, transparent, manageable, secure, and circular supply chain ecosystem. Full article

Sustainable fresh food supply chains depend heavily on effective coordination under uncertain market conditions. Citrus export systems are particularly sensitive to perishability, seasonality, and cold-chain constraints. Under these conditions, reliable price forecasts are important for export planning, logistics efficiency, and sustainable supply-chain management. This study examines the relationship between orange and mandarin export prices in Türkiye using monthly data from 2016 to 2025. Export prices are proxied by real unit values derived from official trade statistics. The analysis applies Augmented Dickey–Fuller tests, Johansen cointegration analysis, and Vector Error Correction Models (VECMs). Forecast performance is evaluated using a rolling-origin framework and compared with ETS, SARIMA, XGBoost, and a seasonal naïve benchmark. The results identify one cointegrating relationship between the two export markets. The estimated long-run coefficient is 0.92, indicating near one-to-one price co-movement. Adjustment toward equilibrium is asymmetric, with orange prices responding faster (ECT = −0.44) than mandarin prices (ECT = −0.21). Forecasting results show that VECM-based models outperform all alternative specifications. The robust VECM achieves the lowest forecast errors (MAPE = 8.3%), compared with 9.8% for XGBoost, 10.6% for SARIMA, 11.5% for ETS, and 14.1% for the seasonal naïve benchmark. Diebold–Mariano tests confirm that these improvements are statistically significant. The findings indicate that orange and mandarin export prices should be analyzed jointly rather than separately. In closely connected citrus supply chains, cointegration-based forecasting models provide more reliable forecasts and a stronger analytical basis for sustainable market coordination. Full article

Context: Accurate citrus crop mapping is essential for agricultural monitoring, production planning, and supply-chain management, particularly in Brazil, one of the world’s leading orange producers and the leading orange-juice exporter. Satellite image time series from Sentinel-2 provide rich spectral and temporal information for crop identification. However, citrus mapping remains challenging due to fragmented agricultural landscapes, cloud contamination, class imbalance, and spectral overlap with other vegetation classes. Problem: Conventional machine learning models often depend on handcrafted vegetation indices, while attention-based deep learning models may require larger datasets and can become unstable under geographically constrained conditions. Therefore, there is a need for a compact and robust deep learning architecture capable of extracting citrus phenological signatures directly from multispectral time-series data. Methods: This study evaluates a Spatio-Temporal Pixel-Set Encoder Convolutional Neural Network (PSE-CNN) for citrus crop classification in the immediate geographic regions of São João da Boa Vista and Mogi Guaçu, São Paulo, Brazil. MapBiomas Collection 10.1 data from 2019 to 2024 were used to derive reference polygons, and Sentinel-2 imagery was processed into cloud-masked, 15-day temporal composites using ten spectral bands. The proposed PSE-CNN was benchmarked against PSE-TAE, PSE-Transformer, Random Forest, and XGBoost using spatially grouped data partitioning and temporal test years. Results: The proposed PSE-CNN achieved the highest Unified F1-Score of $0.704$ and the lowest coefficient of variation of $3.03\%$, indicating stronger inter-annual stability across test years and random seeds among the evaluated models. It also outperformed classical models that relied on handcrafted vegetation indices and demonstrated greater overall stability than attention-based deep learning alternatives. Conclusions: The results indicate that combining pixel-set encoding with temporal convolution provides a resource-aware and stable framework for retrospective citrus crop mapping from Sentinel-2 satellite image time series. These findings suggest that PSE-CNN can support scalable agricultural monitoring, contributing to sustainable crop inventory systems in regions where labeled data and computational infrastructure are limited. Full article

The rapid expansion of e-commerce has reshaped global consumption systems by transforming production processes, logistics infrastructures, and consumer behaviour. While this transformation has generated significant economic opportunities, it has simultaneously intensified environmental pressures, particularly through last-mile delivery emissions, excessive packaging waste, and high return rates. At the same time, the growing diffusion of corporate sustainability reporting has raised increasing concerns about greenwashing, defined as the misrepresentation of environmental performance through selective disclosure or symbolic communication. This study aims to provide a comprehensive assessment of sustainability practices in e-commerce, focusing on the relationship between environmental performance, transparency, and economic outcomes. Particular attention is devoted to the role of blockchain technology as a potential mechanism for enhancing verifiable transparency in complex supply chains. The research adopts a multiple case study design grounded in the methodological frameworks and integrates qualitative analysis with a semi-quantitative evaluation model. Seven companies operating in different segments of the e-commerce ecosystem are analyzed through an extensive review of secondary data sources, including ESG reports, financial disclosures, NGO assessments, and industry benchmarks. The findings reveal a substantial gap between declared sustainability commitments and actual implementation, with significant heterogeneity across firms. Companies that embed sustainability into their strategic core demonstrate stronger alignment between environmental and economic performance, whereas firms relying primarily on communication-driven approaches exhibit higher implementation gaps. The study contributes to the literature by introducing an analytical framework centered on the concept of the implementation gap and by demonstrating the central role of transparency in determining sustainability effectiveness. It also highlights the potential, yet still largely unrealized, role of blockchain technology in addressing information asymmetry and reducing greenwashing in e-commerce. Full article