Search Results (1,204)

This systematic review examines how digital technologies enable circular economy (CE) transitions across sectors and value chains. Analysing 266 peer-reviewed publications (2016–2025), we develop a comprehensive taxonomy of digital enablers—including IoT, AI, blockchain, cloud computing, additive manufacturing, and digital platforms—and map their applications to circular strategies such as reuse, remanufacturing, and recycling. Our findings reveal that data-driven technologies dominate CE implementation, with 89% of studies involving data collection, storage, analysis, or sharing functions. IoT emerges as the foundational technology for real-time tracking and monitoring, while AI and big data analytics optimise circular processes and predict maintenance needs. Blockchain ensures traceability and trust in circular supply chains, and cloud computing provides scalable infrastructure for collaboration. Manufacturing (41%) and construction (15.5%) are the most studied sectors, with strong European research leadership reflecting policy drivers such as Digital Product Passports. We identify three impact types: enabling (process optimisation), disruptive (business model innovation), and facilitating (ecosystem collaboration). Key barriers include technical complexity, organisational resistance, high implementation costs, and regulatory gaps. The review concludes with recommendations for integrated, multi-stakeholder approaches to realise a digitally enabled circular economy. Full article

Life cycle greenhouse gas (GHG) accounting is increasingly required to substantiate the climate value of wind power beyond “zero-emission” operation, especially under China’s dual-carbon targets. Robust estimation of life cycle GHG emission intensity and the identification of actionable mitigation levers are therefore important for credible transition planning. In this study, a process-based life cycle assessment (LCA) was conducted for a representative 100 MW onshore wind farm in Gaoyou, Jiangsu Province, China, following ISO 14040/14044. To enhance engineering relevance, the construction and installation phase was modeled in a refined manner by decomposing it into road, wind-turbine, booster-station, and transmission-line engineering and further into unit processes. The results show that the overall life cycle GHG emission intensity of the studied wind farm is 24.6 g CO₂-eq/kWh. Scenario analysis further indicates that reducing curtailment and improving end-of-life recycling are effective pathways to lower emission intensity, while the net advantage of hybrid versus steel towers depends on recycling performance when end-of-life credits are included. The study also summarizes practical implications for low-carbon equipment/material procurement and green supply-chain governance, low-carbon construction and logistics, coordinated “source–grid–load–storage” planning to curb curtailment, and more standardized and comparable life cycle carbon accounting for wind projects in China. Full article

Construction and demolition waste (CDW) represents a critical environmental challenge in the building sector, with global generation exceeding 3.57 billion tonnes annually. The circular economy (CE) framework offers a transformative pathway through selective deconstruction and material recovery, yet implementation faces significant barriers including information asymmetry, supply chain fragmentation, and regulatory uncertainty. This study conducts a systematic literature review using the Context–Mechanism–Outcome (CMO) framework to analyze how computational methods, specifically Digital Twins (DT), Building Information Modeling (BIM), Internet of Things (IoT), blockchain, artificial intelligence, and robotics, act as enablers for resilience in CDW management. Following PRISMA 2020 guidelines and realist synthesis principles, we analyzed 42 high-quality empirical studies from Web of Science and Scopus (2015–2025). Our analysis identifies seven primary mechanisms: traceability (M1), simulation (M2), classification (M3), tracking (M4), collaboration (M5), analytics (M6) and robotics (M7). These mechanisms interact with four critical contexts (information asymmetry, supply chain fragmentation, economic uncertainty, operational risks) to generate outcomes at two levels: resilience capabilities (visibility, monitoring, collaboration, flexibility, anticipation) and performance indicators (recovery rates, cost reduction, CO₂ emissions mitigation, occupational safety). Key findings from the CMO analysis reveal that blockchain-enabled traceability increases material recovery rates by 15–25%, DT simulation reduces deconstruction costs by 20–30%, and computer vision automation improves sorting accuracy to 85–95%. The study contributes middle-range theories explaining how digital technologies enable circular transitions under specific contextual conditions, offering actionable strategic implications for researchers, project managers, technology developers, and policymakers committed to advancing computational economics in sustainable construction. Full article

This study examines retailers’ information sharing aimed at enhancing product greenness within green supply chains, with consumer participation as a pivotal factor and the overarching goal of advancing the sustainable development of the whole supply chain ecosystem. Each supply chain comprises a green product supplier and a retailer with uncertain demand information. A tripartite evolutionary game model involving manufacturers, retailers, and consumers is constructed to analyze the factors influencing information sharing behavior, which serves as a critical pathway to achieve environmental and economic sustainability in green supply chain operations. The findings highlight two key insights: First, strong consumer willingness to purchase green products may inhibit retailers’ inclination towards information sharing, a counterintuitive outcome that needs to be addressed to align individual stakeholder behaviors with long-term sustainable development goals. Second, lower information sharing costs can motivate retailers to share information with manufacturers; otherwise, manufacturers must adopt technological measures to assist retailers in reducing information sharing-related costs, thereby achieving win–win outcomes across the supply chain and fostering a sustainable and collaborative green supply chain system that balances ecological benefits, economic gains, and social value co-creation. Full article

Environmental awareness is playing an increasingly important role in all segments of the world, with sustainability and recycling being key elements. The aim of the research is to examine the challenges companies face in terms of sustainability when implementing procurement and supply chain management processes related to interior design. The research focused on four main questions: how procurement and supply chain management are reflected in construction processes, what challenges these processes face, and how they can influence the sustainable use of materials in architectural supply chains. The literature review was based on a systematic literature review using the PRISMA screening process and the PEO framework, utilizing the SCOPUS database and processing 70 scientific articles following the selection process. During the research, I also used the Critical Incident Technique (CIT), in which I asked interior designers about their positive and negative experiences with the procurement of sustainable materials and supply chain management processes. The methodology thus provided deeper insight into the decision-making processes of professionals, where sustainability conflicts with economic and operational realities. The qualitative research was supplemented by a questionnaire survey, which aimed to assess sustainability, its prevalence, and professional obstacles. The results of the research show that this topic is a research gap, but the openness of professionals shows a positive trend. Companies face numerous challenges related to new technologies and environmental awareness in order to create or transform well-functioning supply chain management processes. Full article

In order to tackle resilient supplier selection (RSS) of high uncertainty in resilient supply chain management, an effective correlation coefficients-based multicriteria group decision-making (MCGDM) methodology has been constructed. The major contribution of the present study is twofold. Firstly, in view of that extant criteria systems are all in lack of theoretical rationality, this paper establishes a capabilities-based analytical framework for intensive evaluation of supplier resilience by taking processual viewpoints of dynamic capabilities theory and risk management theory. Secondly, to empower the proposed correlation coefficients-based MCGDM methodology, probabilistic dual hesitant fuzzy uncertain unbalanced linguistic set (PDHF_UUBLS) is employed to capture hybrid uncertainties in decision processes of RSS. Then, theoretically compliant correlation coefficients (CCs) for PDHF_UUBLS are developed, including statistics-based CC, information energy-based CC and their weighted versions. Especially, information energy-based CCs overcome limitations of statistics-based CCs in special cases, thus exhibiting general applicability. In addition, a compatibility-based programming model has also been developed to objectively derive an unknown weighting vector for DMUs. Furthermore, illustrative case studies and comparative experiments have been carried out to verify effectiveness and stability of the proposed methodology. Taken together, this paper satisfies the new normal demand of resilience building in supply chain management and presents an effective MCGDM methodology for handling the key problems of RSS. Full article

Managing supply chain risks is a core pillar of operational and supply chain resilience building in the global industrial chain system, which is essential for the high-quality and sustainable development of manufacturing firms. Against the backdrop of escalating global economic uncertainties and interconnected supply chain vulnerabilities, mitigating the adverse impact of supply chain risks on firms’ overseas market expansion has become a critical research and practical issue in the field of operational and supply chain risk management. Based on the textual analysis of annual reports of listed firms, this study constructs a systematic supply chain risk measurement indicator system through standardized text preprocessing, multi-dimensional feature keyword lexicon construction, context co-occurrence frequency calculation and so on. We further validate the effectiveness of the indicator system by comparing its trend with the global economic uncertainty index, confirming that it can capture firm-specific supply chain risk information effectively. Employing text analysis, this study constructs a systematic supply chain risk measurement indicator system for A-share manufacturing firms and empirically verifies that elevated supply chain risks significantly constrain their overseas market expansion. Three interrelated operational mechanisms, namely surging operating costs, tightened financing constraints, and slumping R&D investments, drive this inhibitory effect. Notably, firms can effectively offset this negative effect by broadening overseas operational scope and intensifying overseas digital and technological innovation. Heterogeneity analyses further reveal that the inhibitory effect is more pronounced for five types of firms: those with lower overseas revenue, located in less market-oriented regions, operating in upstream value chain sectors, with lower current liabilities, and with a lower degree of digital transformation. Full article

Qualification is the evidence-based process through which confidence is established that a component will perform its intended function, in its intended environment, for its intended lifetime, with the required reliability. It is an owner-led activity that defines the type, quantity and quality of data required for codification and for the industrial deployment of components and their structural materials. This paper presents a structured qualification framework and applies it to a fusion machine breeder blanket structure as a representative component. It demonstrates that qualification, rather than material properties alone, dictates the use of fusion structural materials and the deployment of such materials under ASME BPV and AFCEN RCC codes. Current limitations in addressing irradiation synergy, liquid metal corrosion, and joint integrity expose gaps that these codes cannot yet prescribe. Two contrasting structural blanket material case studies: metallic-based ferritic-martensitic steel Eurofer97 and non-metallic-based silicon carbide fibre-reinforced composites (SiC_f/SiC) are used to illustrate the differing evidence requirements for each system type. Industrial scale-up considerations, including alloy specifications, manufacturing readiness, inspection reliability, and supply-chain maturity, are evaluated alongside the need for internationally harmonised datasets and design methodologies. Fusion programmes can use a phased qualification strategy in which early, time-limited operation under controlled conditions builds the evidence needed for codification and scale-up, with the required pre-operation qualification level depending on risk, component criticality and failure consequences, and with the pace of qualification ultimately setting how quickly industry can supply components for commercial fusion. Codification remains essential for commercial deployment because construction codes express codified material behaviour through allowable stresses and permitted fabrication routes, enabling designers to use advanced materials without disclosing proprietary data. In jurisdictions where ASME BPV compliance is mandatory, codification determines whether a material may enter pressure boundary service and must therefore form part of the fusion machine owner’s long-term strategy for deployment. Full article

Green ammonia is increasingly recognised as a sustainability enabler for decarbonising fertiliser production, energy storage, and maritime transport, but offshore wind-to-ammonia pathways remain subject to significant economic and operational uncertainty. This study evaluated the techno-economic and sustainability performance of integrating power-to-ammonia (PtA) with an operating offshore wind farm in Denmark under three supply-chain scenarios (SCs): SC1, a fully offshore PtA with vessel-based ammonia transport; SC2, a fully offshore PtA with pipeline export; and SC3, a hybrid offshore–onshore configuration. An hourly dispatch framework allocated wind electricity between grid export and ammonia production by comparing incremental operating margins, while accounting for minimum-load, ramping, storage, and logistics constraints. Hourly wind generation and DK1 electricity-price data for 2020–2025 are used to construct a deterministic base case and a 30-year block-bootstrap Monte Carlo analysis. Sensitivity analysis is performed by varying electrolyser rated power over 10–200 MW and ammonia selling price over 1400–3200 €/tNH3, with additional breakeven-price estimation and flexibility cases based on reduced minimum-load requirements and faster ramping. A screening-level climate indicator was additionally reported by estimating potential CO₂ emissions avoided if delivered green ammonia displaces conventional natural-gas-based ammonia. Results indicated that SC3 is the most favourable configuration under the adopted assumptions, while overall project viability remained highly sensitive to PtA sizing, ammonia market value, operational flexibility, and the assumed infrastructure cost structure. Full article

Under China’s rural revitalization and agricultural modernization strategies, digital village construction overcomes resource limits to drive transformation. Using 2013–2022 provincial panel data and a case study of Lin’an, Hangzhou, this study reveals how digital villages boost high-quality agriculture. The empirical results show they significantly enhance agricultural total factor productivity via three paths: IoT-driven precision production, blockchain-enabled green value addition, and e-commerce direct sales demonstrate more pronounced effectiveness in major grain-producing regions and those characterized by balanced production and sales. Simultaneously, this study employs the instrumental variable (TI) approach to address endogeneity from reverse causality and omitted variables. Mechanism testing reveals agricultural technological innovation exerts a significant 77.5% mediating effect. Finally, digital rural construction exhibits a non-linear threshold (0.3082); surpassing it triggers a gradual slowdown in growth with decreasing marginal returns. The Lin’an case validates the empirical results while revealing structural barriers, including industrial chain penetration gaps, data silos, and factor supply constraints, leading to the formulation of targeted optimization strategies. The practical contribution of this study is the proposal of a “data-value-technology” closed loop: public brands like “Tianmu Mountain Treasures” channel premiums into R&D funds, creating a self-sustaining mechanism. The findings indicate that digital villages drive high-quality agriculture primarily through direct effects, powered by full-chain tech coordination, institutional reform, and inclusive factor supply. Finally, this study proposes a coordinated governance framework encompassing “technical synergy, institutional innovation, and factor optimization,” providing theoretical support and strategic references for optimizing the pathways of regional agricultural digital transformation. Full article

Green technological innovation is a core driving force for firms’ low-carbon transformation. However, because critical green technologies and knowledge are often dispersed across upstream and downstream partners within supply chains, firms’ green transformation faces substantial challenges. Previous studies have primarily focused on internal drivers at the firm level while overlooking the empowering role of green collaborative cooperation among supply chain partners. To address this gap, this study introduces empowerment theory to systematically examine how supply chain green strategic alliances enhance firms’ green innovation capability. Using a sample of Chinese A-share listed firms from 2011 to 2023, we construct a firm-level indicator of supply chain green strategic alliances based on textual analysis and machine learning techniques and empirically test its impact on green innovation. The results show that participation in green strategic alliances significantly promotes firms’ green innovation. Mechanism analyses further reveal that this effect operates through the reconstruction of green knowledge, increased environmental investment, and improved green governance. Moreover, the positive effect is more pronounced in regions with stronger intellectual property protection, greater green credit support, and stricter environmental regulation, as well as among firms with closer supply chain relationships. This study identifies supply chain green strategic alliances as a key inter-organizational empowerment mechanism and provides important practical implications for leveraging supply chain collaboration to accelerate sustainable development and firms’ green transformation. Full article

Foreign direct investment (FDI), particularly high-quality FDI, serves as a critical driver in achieving the Sustainable Development Goals (SDGs). However, understanding how to enhance FDI quality remains a pressing challenge for policymakers and researchers alike. As a core determinant of FDI quality, the business environment necessitates a thorough examination of its underlying mechanisms. Drawing on provincial-level data and firm-level data from listed foreign-invested enterprises in China spanning 2011 to 2023, this study constructs an FDI quality evaluation index system aligned with the goal of sustainable development at the micro-enterprise level, empirically examines the impact of the business environment on FDI quality. Our findings reveal a consistent upward trajectory in China’s FDI quality throughout the sample period, with the business environment exerting a significantly positive influence. Dimensional decomposition reveals that the government-legal environment and openness to foreign investment demonstrate particularly pronounced positive effects. These effects operate primarily through three mechanisms: stimulating entrepreneurship, accelerating digital transformation, and optimizing supply chain configurations. Moreover, these effects are more pronounced among wholly foreign-owned enterprises, firms with superior knowledge absorption capacity, and those facing higher perceived economic policy uncertainty. Extended analysis further demonstrates that enhanced FDI quality makes substantial contributions to sustainable development outcomes. This study extends the research on FDI quality from the macro level to the micro level, broadening the research perspective of related fields. The conclusions not only furnish robust theoretical evidence on how business environments foster high-quality FDI, but also provide actionable policy insights for countries seeking to optimize their institutional frameworks to attract quality foreign investment in alignment with the SDGs. Full article

Modern supply chains are increasingly expected to meet ambitious sustainability targets, yet they often suffer from limited visibility into upstream relationships, environmental risks, and ethical sourcing practices. This paper presents an artificial intelligence (AI)-based approach for supporting sustainability-oriented decision-making in supply chains through knowledge graph completion and link prediction. We construct a multi-relational supply chain knowledge graph that captures heterogeneous entities and relationships, including suppliers, products, certifications, and locations, and apply graph neural networks to infer missing links and sustainability-related attributes. By enabling reasoning over incomplete and sparse data, the proposed approach supports feasibility-oriented decisions, such as identifying alternative supplier relationships and assessing sustainability alignment across multi-tier networks. Building on recent advances in knowledge graph reasoning and heterogeneous graph learning, the framework integrates relational structure with inductive learning to provide interpretable recommendations under uncertainty. The approach is evaluated on two real-world supply chain datasets, demonstrating its applicability in complex, data-sparse settings. The results indicate that graph-based AI can provide a practical foundation for transparent and sustainability-aware supply chain decision support. Full article

The rapid chilling and aging stages following pork slaughter represent a critical window for determining final physicochemical quality and flavor development. To address the destructive nature of conventional meat quality assessment methods and the limitations of rigid spectral probes when applied to irregular biological surfaces, this study developed and validated a wireless monitoring system integrating a flexible visible/near-infrared (VIS/NIR) sensing array with ensemble learning algorithms. The proposed system enables non-destructive, continuous monitoring of pork quality during cold-chain storage. A DeepEnsemble regression model based on a stacking framework was constructed by integrating Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) to predict pH, moisture content, and total amino acid concentration. During a 26 h dynamic aging experiment, the proposed model achieved coefficients of determination (R²) of 0.9019, 0.9687, and 0.9600 for pH, moisture content, and total amino acids, respectively, with prediction performance exceeding that of individual regression models. The wireless transmission module maintained stable data communication under low-temperature and high-humidity conditions (−20 °C and 0–4 °C), with packet loss rates below 0.1%. These results indicate that the proposed system can effectively capture the dynamic evolution of pork quality during aging and provides a practical non-destructive approach for intelligent pork quality evaluation, cold-chain monitoring, and digital management of meat supply chains. Full article

In the digital economy, the effective use of artificial intelligence (AI) is crucial for maintaining supply chain stability (SCS) in sports enterprises (SEs). Leveraging systems theory and supply chain management theory, we construct a dual machine learning model (DML) to empirically assess the impact of AI on the SCS of SE. This analysis is based on panel data from 45 Chinese listed SEs over the period 2012–2023. The results indicate that AI significantly enhances supplier stability but notably reduces customer stability in SE. Talent attraction emerges as the primary mechanism, while logistics efficiency fails to fulfill its anticipated role. The impact of AI on SCS in SE exhibits heterogeneity based on enterprise type and profitability status. Our findings offer valuable insights for harnessing the potential of AI and fostering its deeper integration into the supply chains of SE. Full article