Search Results (1,231)

The relationship between corporate digitalization and green management practices has received increasing scholarly attention, but existing empirical findings remain inconsistent. To clarify this relationship, this research conducts a meta-analysis based on 94 effect sizes from 82 empirical studies, adopting a multivariable research framework to integrate existing findings and explore the factors that contribute to the generation of heterogeneity. The findings indicate that corporate digitalization facilitates green management practices, a conclusion robust across three key dimensions: environmental performance, green innovation, and green supply chain management. Furthermore, the findings show that digitalization exerts a stronger positive effect in non-manufacturing firms, non-heavy-polluting firms, and high-tech firms, while measurement approaches emerge as a critical factor influencing empirical outcomes. These findings provide integrated evidence on the digitalization–green management relationship, clarify its key boundary conditions, and offer practical implications for firms seeking to advance low-carbon transformation through digital technologies. Full article

Morocco has developed one of the most comprehensive sustainability governance architectures among middle-income emerging economies, yet the relationship between its formal regulatory ambition and on-the-ground implementation effectiveness remains poorly understood. This narrative literature review provides an integrated, critically analytical account of Morocco’s sustainability governance system, organised around three interlocking dimensions: (i) a progressively strengthened legislative corpus anchored by the 2011 Constitution and Framework Law 99-12; (ii) a portfolio of national sustainability strategies aligning domestic policy with Paris Agreement commitments, Nationally Determined Contributions (NDCs), and the United Nations Sustainable Development Goals (SDGs); and (iii) corporate sustainability practices driven by regulatory obligations, international supply chain pressures, and ESG disclosure norms. Drawing on 124 sources, comprising 62 peer-reviewed articles, 38 legislative texts, and 24 institutional reports, and applying institutional isomorphism theory as an integrating analytical lens, the review advances three theoretical propositions concerning the conditions under which formal governance architectures translate into effective sustainability outcomes. It further proposes a validated conceptual framework and develops a comparative positioning of Morocco against peer economies (Tunisia, Egypt, South Africa, and Turkey). Critical implementation gaps are identified in enforcement capacity, SME integration, sustainability data infrastructure, and green finance, contributing a balanced and evidence-grounded assessment of Morocco’s sustainability transition. These findings offer actionable insights for policymakers, regulators, and business leaders operating in the Moroccan and broader African context. Full article

A short-term performance test of blended biodiesel (FAME), green diesel (HVO), and diesel was experimentally assessed in a 100 kW Cummins 6BTAA5.9-G12 diesel engine under multiple load conditions. The objective was to determine the technical feasibility, operational trade-offs, and optimal blend formulations for renewable energy deployment in diesel power plants. All tested blends operated stably without engine modification, confirming the “drop-in capability” of FAME–HVO mixtures for existing diesel engines. Specific fuel consumption (SFC) increased notably at high loads, with penalties up to 15.15% for B30D20 and B35D15 relative to neat diesel, although overall efficiency improved with load. Among the ternary fuels, B30D10 and B30D20 provided the most balanced compromise between combustion reactivity and flow properties. Exhaust gas temperatures rose with load for all fuels, with FAME-rich blends exhibiting higher temperatures than neat diesel, while coolant-side analysis showed D100 and D50 as thermally favorable and B50–B100 imposing the highest cooling demand. The results emphasize the need for injection system recalibration on an energy basis for HVO-rich fuels, and for strengthened filtration and maintenance practices for FAME-rich blends to avoid filter clogging and injection instability. Considering performance, operability, and system stability up to 100 kW, B30D10 and B35D15 are identified as optimal compromise blends. The study highlights the necessity of future work on long-term durability, fuel system compatibility, supply chain robustness, and techno-economic viability to safely scale green diesel use in Indonesian stationary power generation. 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

As the automotive industry undergoes a green transformation, digital upgrading, and increasingly intensive supply chain collaboration, the supply chain finance credit risks faced by small and medium-sized enterprises (SMEs) in the sector exhibit characteristics such as multi-source interaction, nonlinear transmission, and class imbalance. This study uses 210 SMEs in China’s A-share automotive sector from 2020 to 2024 and constructs a credit risk evaluation system covering 56 indicators across the macro environment, financing enterprises, supply chain characteristics, and core enterprise credit support. Methodologically, DE-LightGBM is employed for feature selection to reduce redundancy and noise, while TabPFGen is introduced to generate synthetic risk-class samples. Business logic constraints and a Nearest Neighbor Distance Ratio filtering mechanism are further applied to improve the plausibility and fidelity of generated samples. Empirical results show that the TabPFN model achieves superior predictive performance after feature selection and data augmentation, and the Wilcoxon signed-rank test confirms the effectiveness and stability of sample augmentation. In addition, the ablation experiment demonstrates that green-related features provide significant incremental predictive value for supply chain finance credit risk identification. The proposed framework provides a useful reference for SME credit assessment, risk early warning, and green financial resource allocation in the automotive industry. Full article

Against the backdrop of the dual-carbon goals and the Digital China initiative, the urgent need for enterprises to pursue green innovation and transformation is evident. Supply chain digitalization serves as a critical enabler for enterprises to achieve a low-carbon industrial transformation and high-quality development through the effective coordination of data resources across the entire chain. This study examines A-share listed companies from 2012 to 2023, leveraging the 2018 Supply Chain Innovation and Application Pilot Policy to construct a quasi-natural experiment. Employing a difference-in-differences approach with multiple mediation effects, it investigates the impact of supply chain digitalization on corporate green innovation and its transmission mechanisms. Findings reveal that supply chain digitalization significantly enhances corporate green innovation levels, with this effect being more pronounced in substantive innovation, western regions, and firms with high customer concentration. Mechanism tests reveal that supply chain digitalization promotes green innovation not only through independent pathways of enhancing information transparency and improving ESG performance but also via a chained mediation effect: “supply chain digitalization → information transparency → ESG performance → green innovation”. This study enriches theoretical research on the relationship between supply chain digitalization and green innovation from the dual perspectives of information and governance, offering insights for government initiatives to advance data sharing, implement differentiated policies, and establish green governance systems. 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

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

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

Green Supply Chain Management (GSCM) and sustainable economic development are two areas that have been studied extensively by scholars. However, there continues to exist a lack of cohesion or integration across academic fields regarding how GSCM can act as a catalyst for economic sustainability. This systematic literature review attempts to create a cohesive body of knowledge by exploring the drivers, barriers, and outcome measures associated with GSCM specifically within the context of creating sustainable economic growth in the long term. A structured literature review approach was used; this included conducting an extensive search of all relevant articles using multiple databases, followed by a thorough review and thematic analysis based upon the dimensions outlined above. The results indicate that GSCM is primarily influenced by the pressure of regulatory requirements and expectations of stakeholders. Financial constraints and technology gaps remain significant obstacles to the effective implementation of GSCM. Additionally, our analyses indicate that GSCM will enhance both environmental and economic performance when it is practiced with circular economy strategies and digital technologies such as AI and big data. The review shows that small- to medium-sized enterprises and firms in emerging economies face different practicalities than other types of organizations in terms of implementing GSCM strategically. However, SMEs and firms in emerging economies may benefit proportionally more than others from adopting GSCM strategically. Industry-specific case studies show that the success of GSCM practices varies widely depending on the sector; therefore, consideration of context is required. Additionally, the various theoretical frameworks discussed throughout the literature have developed from linear models towards more dynamic system-based models, indicating a developing discipline. In conclusion, we find that GSCM does not solely serve as an operational tool; rather, it acts as a strategic enabler of sustainable economic development, provided that it is implemented appropriately relative to organizational and regional context. Full article

Background: Although green supply chain management (GSCM) has been widely examined, prior studies have often treated it as a homogeneous construct and have paid limited attention to how different types of GSCM practices operate in logistics firms. This study addresses this gap by distinguishing between technical GSCM practices and behavioral GSCM practices and examining how both dimensions influence organizational performance through green innovation culture (GIC). Methods: Drawing on data from a cross-sectional survey of 426 logistics practitioners involved in supply chain, operations, and sustainability-related functions in Guangzhou, China, the study tested the proposed model using structural equation modeling (SEM). Results: Both technical and behavioral GSCM practices positively influence GIC, with behavioral practices having a stronger effect. GIC significantly improves organizational performance. Technical and behavioral GSCM practices also directly enhance organizational performance, indicating partial mediation. The indirect effect of behavioral GSCM practices through GIC is stronger, suggesting that behavioral governance is especially important for developing an innovation-oriented green culture. Conclusions: The study advances GSCM and green innovation literature and suggests logistics firms can achieve more sustainable performance improvements by combining technological upgrading with leadership support, employee involvement, stakeholder collaboration, and an innovation-oriented green culture. Full article

Background: Circular logistics translates circular economy principles into practical supply chain processes, but its adoption varies across firms because organizations differ in sustainability commitment, circular supply chain motivation, shared circular identity, and digital traceability capability. This study examines how supplier sustainability commitment and circular supply chain motivation influence circular logistics adoption through circular supply chain identity, while also testing the moderating role of digital traceability capability. Methods: Data were collected from 350 supply chain professionals in Saudi Arabia and analyzed using partial least squares structural equation modeling (PLS-SEM). Results: Supplier sustainability commitment and circular supply chain motivation positively influenced both circular logistics adoption and circular supply chain identity. Circular supply chain identity also positively affected circular logistics adoption and partially mediated the effects of both antecedents. Digital traceability capability acted as a selective moderator: it weakened the circular supply chain motivation–identity relationship, did not significantly moderate the supplier sustainability commitment–adoption relationship, but strengthened the circular supply chain identity–adoption relationship. It also moderated the indirect effect of circular supply chain motivation on circular logistics adoption through circular supply chain identity. Conclusions: Circular logistics adoption is driven not only by commitment and motivation, but also by shared circular identity and digitally enabled traceability. Full article

Environmental supply-chain governance increasingly requires firms to trace climate accountability across buyer–supplier relationships. This study examines whether downstream customer climate pressure is associated with suppliers’ green supply-chain management and value-chain carbon accountability among Chinese listed firms. We construct an exposure-weighted customer pressure measure by combining disclosed top-customer relationships with customer climate-accountability signals, and we decompose this measure into disclosure-based and non-disclosure-based components so that symbolic and substantive accountability can be separated. We then link this measure to supplier green supply-chain indicators, value-chain carbon-disclosure components, Scope 3 disclosure, environmental investment, and reported environmental performance indicators, including air emissions, water pollutant discharge, resource consumption, and environmental tax. Using firm-year panel regressions with fixed effects, alternative pressure measures, selection corrections, and extended outcome tests, we find an association between customer climate pressure and supplier value-chain disclosure. The depth of the association is concentrated where customer carbon-disclosure visibility is observed and is not separately identified in the smaller climate-only subsample, while the value-chain interaction association is positive but imprecisely estimated there. The value-chain disclosure associations are robust to a year-stratified randomization-inference placebo test. We do not find evidence that customer pressure is associated with supplier emissions, resource use, environmental investment, or environmental tax in the available matched samples. The pattern is consistent with symbolic compliance in supply-chain carbon accountability: customer disclosure visibility maps into supplier disclosure visibility, while we do not observe parallel movement in substantive environmental outcomes. The central finding is therefore that downstream customer climate pressure is associated with what suppliers disclose rather than with what they emit, shaping supplier disclosure behavior rather than substantive emission reduction. The estimates apply to supplier-year observations with disclosed and mappable listed-customer links, which we treat as the scope condition of the study rather than as an incidental data limitation. Full article

Blue mold caused by Penicillium italicum triggers severe tissue decay and limits postharvest shelf life, representing the primary constraint to the commercial supply chain of Shatangju mandarins (Citrus reticulata cv. Shatangju). In this study, the biocontrol efficacy of an antagonistic yeast, Meyerozyma guilliermondii SR1, against postharvest blue mold in Shatangju mandarins was evaluated. The results showed that SR1 significantly inhibited the in vitro growth of P. italicum, delayed disease progression and restricted pathogen sporulation in inoculated fruits during storage. Furthermore, SR1 rapidly colonized fruit wounds to establish a population advantage and enhanced the antioxidant defense capacity of the host fruits. Meanwhile, SR1 treatment significantly reduced postharvest weight loss, with no significant differences in total soluble solids (TSS) and titratable acidity (TA) compared with the control. In conclusion, M. guilliermondii SR1 showed significant biocontrol efficacy against postharvest blue mold in Shatangju mandarins, which provides an experimental basis for the research and development of green citrus postharvest preservatives. Full article

Green hydrogen is increasingly discussed as an energy carrier that can link electricity, gas, heat, and transport sectors. However, many existing reviews address this topic from separate viewpoints, such as hydrogen production technologies, Artificial Intelligence (AI) applications, or system integration, with less attention to how policy and market conditions affect deployment. This review brings these related aspects together in one structured discussion. The paper first reviews the hydrogen supply chain, including production, storage, transport, and utilization. It then discusses an integrated multi-energy architecture in which hydrogen interacts with electricity, natural gas, heat, and cooling networks. Policy instruments in five major economies, including the European Union, the United States, China, Japan, and India, are compared. The review also summarizes the main barriers to large-scale deployment, including high production costs, limited infrastructure, technological challenges, regulatory uncertainty, and supply-chain constraints. In addition, the current market structure and selected large-scale hydrogen projects planned in the United States are reviewed. The paper also examines the role of artificial intelligence in green hydrogen systems. AI applications are grouped into four main stages of the hydrogen value chain: forecasting renewable energy generation, improving electrolyzer design and operation, optimizing storage and distribution, and supporting system-level techno-economic assessment. Recent Machine Learning (ML) studies are compared based on their methods and their contributions to operation and planning. Overall, this review highlights the role of AI in enabling green hydrogen integration within multi-energy systems. Full article