Search Results (896)

Sustainable aviation fuel is among the most practical near-term routes for aviation decarbonization because it can be used in existing aircraft, engines, and airport fuel systems with limited infrastructure changes while minimizing disruption to the aviation fuel supply chain. This review examines SAF applications in aerospace propulsion systems, focusing on production pathways, aero-engine compatibility, property prediction, and fuel heat sink potential. It compares hydroprocessed esters and fatty acids (HEFA), Fischer–Tropsch (FT), alcohol-to-jet (ATJ), synthesized iso-paraffins (SIP), and power-to-liquid (PtL) fuels in terms of feedstock type, process complexity, product composition, and blending constraints. It also assesses how molecular composition governs density, cold-flow behavior, thermal stability, coking propensity, seal compatibility, and emissions. Recent advances in molecular dynamics, machine learning, spectroscopic analysis, and uncertainty quantification show a shift from empirical estimation toward composition-based prediction, prescreening, and fuel design. For high-thermal-load propulsion systems, SAF is further evaluated as a fuel heat sink in active regenerative cooling. Current evidence points to advantages in thermal stability and low coking tendency, but important gaps remain in transcritical and supercritical heat transfer, pyrolytic heat absorption, wall-material effects, coke deposition, and heat sink capacity modeling across wide operating ranges. Full article

Ukraine’s energy strategy under martial law necessitates decentralized local energy systems to counter electricity shortages and systemic infrastructure failures. The study develops and validates an optimization model for designing the architecture of decentralized “energy islands” based on circular bioenergy models for agricultural waste use. Empirical verification was conducted using data from the Vinnytsia region in Ukraine. The model accounts for a multi-level structure that separates micro/small generation (0.1–2.0 MW) from medium generation (1–20 MW) based on the logistical radius for raw material collection. The model incorporated the Value of Lost Load (VLL), enabling the monetization of avoided socio-economic losses from energy shortages. In addition, the coefficient of energy island sustainability (I_sred) was introduced to quantitatively assess the effectiveness of investments in terms of replacing external resources. The modeling revealed the nonlinear nature of the total cost function, enabling us to determine an optimal energy-autonomy range of 40% to 50% for communities. At this threshold, the total construction and logistics costs are minimized. The potential socio-economic losses from blackouts are effectively mitigated, as confirmed by the calculated sustainability coefficient (I_sred), which ranges from 0.78 to 0.94 across the studied communities. The resource potential assessment confirms that the region’s total potential is approaching 30 million tons of oil equivalent, driven by solid biofuels, agricultural residues, and energy crops (miscanthus, switchgrass). The classification of biomass supply chains shows that exceeding the transportation radius by more than 70 km at the meso level, or deviating from the optimal logistics lever by 20%, reduces the profitability of projects below the critical limit of 15%, which justifies strict localization within raw-material clusters. This enables local communities to eliminate natural gas consumption, reduce energy supply operating costs by 15%, and ensure the autonomous and stable operation of critical infrastructure facilities during prolonged disruptions to the national power grid. Full article

Against the backdrop of the dual convergence of the digital economy and sustainable development strategies, digital innovation has emerged as a pivotal driver for reshaping firms’ competitive advantages and fulfilling social responsibilities. In this study, the analysis draws on a sample of Chinese listed firms from 2014 to 2023. ESG performance is measured with Hua Zheng ESG scores, digital innovation is captured via digital patent identification, and a dual-mediation framework is employed to examine internal organizational and external supply chain mechanisms, along with heterogeneity analysis. The findings indicate that digital innovation exerts a significant positive effect on ESG performance and reveal a unique dual mediating mechanism: within the internal boundary of the firm, digital innovation releases available organizational slack significantly through internal resource orchestration, and this abundant slack provides a necessary resource buffer for ESG investment; within the external network of the firm, digital innovation reduces supplier concentration significantly, thereby enhancing bargaining and supply chain discourse power, which in turn improves corporate ESG performance. Heterogeneity analysis further demonstrates that the promoting effect of digital innovation on ESG performance is more pronounced in subsamples characterized by CEO duality, low technological intensity, and high levels of marketization. By drawing on the resource-based view and resource dependence theory respectively to explain internal and external mediating pathways, this study advances understanding of the complementary mechanisms through which digital innovation enhances ESG performance. It demonstrates how digital innovation comprehensively reshapes firm capabilities, providing valuable insights for formulating strategic ESG initiatives in the digital era. Full article

This systematic review summarises advances in artificial intelligence (AI) and machine learning (ML) for foodborne pathogen detection, covering applications in various technologies (AI-assisted microscopy, spectroscopy, biosensors and sensor-based systems), food supply chains, analytical performance, operational metrics and regulatory developments, addressing gaps in previous reviews limited to individual technologies or lacking regulatory analysis. Following PRISMA 2020 guidelines, Scopus, PubMed, and Web of Science were searched from 1 January 2010 to 25 June 2026 using a validated string. Inclusion criteria were explicit detection of a pathogen, clearly described AI/ML algorithm, study evaluation on food or supply chains, and quantitative validation metrics. Exclusion criteria were chemical-only studies, human-diagnostic studies, or purely theoretical studies. Given heterogeneity in the evidence, qualitative quality indicators were favoured over formal quantitative risk-of-bias tools, in distinction to internal cross-validation versus independent external validation. Key data were extracted using a standardised matrix, and after screening and snowballing, the final corpus consisted of 152 studies. CNN (Convolutional Neural Network)-based microscopy provides >99% accuracy in bacterial identification, SERS (Surface-Enhanced Raman Spectroscopy) and CNN 98.68% for pathogens and 99.85% for resistant strains. ML-driven biosensors show 80–100% prediction accuracy in the presence of environmental noise. Yet, performance drops dramatically on external validation, with models falling from 95% internal to 78–82% on independent test sets. Supply chain applications cover meat, dairy, seafood and produce, but most are still at pilot scale. The main constraints are data heterogeneity, lack of public benchmarks, matrix interference, non-standard validation protocols, and regulatory dissonance. However, the integration of AI with Internet of Things (IoT), blockchain and edge computing improves sensitivity, reduces false results and enables real-time monitoring despite the challenges. AI is a powerful decision-support tool that complements existing food safety controls rather than replacing them. To translate these technologies reliably into routine practice, effective implementation requires rigorous external validation and regulatory harmonisation. Full article

The purpose of this study was to examine the influence of green supply chain management on organizational sustainability, where technology innovation and environmental performance are taken as mediating factors. The focus of this study was on the increasing need for better protection of the environment and the increasing demands of the sustainable development of industries, especially in developing countries, where there are limited empirical data available on sustainability activities. The collected sample comprised 387 managers and technologists from Libyan manufacturing companies using a survey instrument and a stratified random sampling technique. Validity scales were used, and the measurements were done using the five-point Likert scale. The data analysis involved the use of PLS-SEM, where SmartPLS 4.0 was used. It entailed the analysis of the measurement model as well as the structural model, with the bootstrapping being done for 5000 samples. The findings show that GSCM significantly affects the OS. Specifically, it has a positive impact on environmental performance (EP) and technological innovation (TI), which, in turn, enhance the OS. Among the mediating factors, the EP was found to be the strongest predictor and mediator of the OS, followed by TI. The developed model proved to have a high explanatory power, with a coefficient of determination (R²) of 0.660. Thus, the integration of GSCM with innovation and environmental activities can lead to the successful achievement of sustainability objectives among manufacturing enterprises. The study’s limitations are associated with its cross-sectional design and its focus on managers. Future research could employ longitudinal designs and examine other contexts, such as digital transformation, the institutional environment, and the organizational culture. Full article

Against the global transition toward sustainable energy systems and the rapid development of green electricity trading and green certificate systems, traditional regional average electricity emission factors are no longer sufficient to accurately reflect the carbon emission responsibilities associated with enterprises’ actual electricity consumption. This may lead to the double counting of renewable electricity attributes and the overestimation of emission reduction effects. To address this issue, this study focuses on electrical equipment products. Building upon the existing life-cycle accounting framework, a method for constructing provincial residual electricity emission factors coupled with green electricity and green certificate attribute verification mechanisms is proposed. By excluding non-fossil electricity volumes whose environmental attributes have already been claimed through market-based mechanisms, an accounting factor capable of characterizing the actual emission intensity of “residual electricity” is established, together with an accounting model for indirect electricity emissions during the production stage. Furthermore, eleven typical categories of electrical equipment, including transformers, electricity meters, conductors, and power cables, are selected to conduct an empirical cradle-to-gate carbon footprint analysis and evaluate their carbon reduction potential under a 50% green electricity substitution scenario. The results indicate that provincial residual electricity emission factors incorporating renewable electricity attributes can improve the accuracy and transparency of product carbon footprint accounting. This approach provides methodological support for sustainable manufacturing, green procurement, low-carbon supply chain management, and the development of product carbon footprint standards. Full article

Highly perishable strawberries demand strict post-harvest time management, making accurate short-term yield prediction central to optimizing modern greenhouse production and supply chain scheduling. However, existing models that rely excessively on isolated environmental factors exhibit delayed responsiveness to actual crop physiological dynamics and struggle with integrating multimodal data. To overcome these limitations, we propose a short-term method for predicting greenhouse strawberry yield that integrates visual phenology with meteorological sequences. The proposed method was validated using a multimodal dataset acquired from 150 tracked greenhouse strawberry plants over a 72-day monitoring period (11 December 2025, to 20 February 2026), incorporating continuous microclimate records and an image repository of 784 original images annotated into five distinct phenological classes (flower, green, white, pink, and red). First, using our improved YOLO11-SC model, we effectively resolve challenges of complex illumination and dense foliage occlusion, achieving high-precision automated extraction of five consecutive strawberry phenological stages. Second, by fusing these visual markers with meteorological time series (e.g., temperature, humidity, and light intensity), we construct a multimodal spatiotemporal feature matrix. To accommodate diverse smart agriculture application scenarios, we designed two distinct prediction architectures: on servers with ample computing power, a Bidirectional Temporal Convolutional Network with self-attention (BiTCN-SA) to achieve highly accurate predictions; and for resource-constrained IoT edge nodes, a lightweight machine learning ensemble (Stack-LGR). Experimental results demonstrate that, in predicting the cumulative mature fruit yield within the next harvesting cycle, BiTCN-SA achieves strong performance with a coefficient of determination ($R^2$) of 0.958 and a root mean square error (RMSE) of 3.154. Simultaneously, the edge-deployed Stack-LGR ensemble maintains stable prediction accuracy ($R^2$ = 0.892) while ensuring acceptable inference latency. This study mitigates the latency limitations of single-environment-driven models. It provides a solution for precise crop yield prediction and tiered computational deployment, with good predictive performance, deployment adaptability, and methodological reference value. Full article

Under the “dual carbon” goals (carbon peak and carbon neutrality), the new energy vehicle (NEV) industry has become a strategic focus of great-power competition, and the resilience of its international supply chain is critical to industrial security and development initiatives. As a traditional automobile manufacturing hub in China, Hubei Province faces increasingly prominent global risks in its supply chain during the transition to NEVs; scientifically evaluating and enhancing its international supply chain resilience is therefore of great practical significance. Drawing on supply chain resilience theory, this paper constructs an evaluation index system comprising 18 specific indicators across four dimensions: robustness, redundancy, agility, and innovativeness. To overcome the limitations of a single weighting method, a combined subjective and objective weighting approach that integrates the Analytic Hierarchy Process (AHP) and the entropy weight method was employed to determine indicator weights. Subsequently, the TOPSIS model was applied to measure the supply chain resilience level of Hubei Province from 2018 to 2025, with horizontal comparisons conducted against Shanghai and Guangdong. Finally, an obstacle degree model was introduced to quantitatively diagnose the key factors constraining resilience improvement. The results indicate that the international supply chain resilience of Hubei’s NEV industry has shown a continuous upward trend. By 2025, it ranks in the first tier alongside Guangdong (with closeness coefficients of 0.8180 and 0.8181, respectively), approaching the level of Shanghai. Weaknesses are concentrated primarily in the agility dimension, while upstream resource dependence remains a salient issue within the robustness dimension. “External dependence on key raw materials,” “average recovery time from logistics disruptions,” and “level of supply chain information sharing” are still the top three obstacle factors. Fuzzy-set qualitative comparative analysis (fsQCA) further reveals that low resource autonomy and slow logistics recovery are core conditions leading to low resilience, and the coupling of multiple obstacle factors amplifies the risk transmission effect. Based on this, this study proposes optimization recommendations focusing on foundation strengthening and chain consolidation, digital chain connectivity, and innovation–chain integration, in order to enhance the resilience of the international supply chain for new energy vehicles in Hubei. This research provides a methodological reference for evaluating the supply chain resilience of regionally distinctive industries and offers a quantitative basis for Hubei Province and related enterprises to formulate targeted improvement strategies. Full article

Perovskite solar cells (PSCs) have emerged as one of the most promising next-generation photovoltaic (PV) technologies due to their high power conversion efficiencies, low-temperature processing, and potential for low-cost manufacturing. Despite these advantages, several challenges remain that hinder their large-scale commercialization, particularly related to environmental sustainability, long-term stability, and end-of-life management (EoL). Life cycle assessment (LCA) has become an essential tool to evaluate the environmental impacts of emerging PV technologies and to identify critical hotspots across the supply chain. At the same time, concerns regarding material toxicity, particularly lead content, as well as the lack of established recycling pathways, highlight the importance of effective EoL management strategies. This review examines the current state of research on the life cycle environmental performance of perovskite solar modules (PSMs) and evaluates emerging approaches for sustainable EoL management. The study synthesizes the existing literature on manufacturing processes, environmental impact indicators, material recovery, recycling technologies, and circular economy strategies relevant to perovskite PVs. Particular attention is given to innovation-driven approaches that integrate sustainability considerations into technology development and commercialization pathways. By identifying key environmental hotspots, technological challenges, and research gaps, this review provides insights into how sustainable innovation and circular resource management can support the transition of PSMs from laboratory-scale research to economically viable and scalable commercial deployment. Full article

Distributed energy systems are increasingly important for strengthening critical infrastructure resilience under conditions of technological, climatic, geopolitical, and cyber disruption. However, existing research on energy resilience is still dominated by technical approaches focused on reliability, renewable energy integration, microgrid control, and storage optimisation, while the role of distributed energy systems in the full crisis-management cycle remains insufficiently conceptualised. This article addresses this gap by combining a scoping review, lexicographic and semantic analysis using IRaMuTeQ version 0.7 alpha 2, and a conceptual-methodological framework for assessing distributed energy systems as instruments of crisis management. The main contribution of the study is the M_ZK-DES model, which integrates technological-infrastructural, decision-operational, legal-institutional, and socio-organisational dimensions with four crisis-management phases: prevention, preparedness, response, and recovery. The model distinguishes distributed energy systems, distributed energy resources, distributed generation, microgrids, prosumers, energy communities, and energy clusters and links them to measurable resilience indicators. These include SAIDI, SAIFI, energy not supplied, restoration time, share of critical load served, islanding capability, voltage and frequency stability, storage autonomy, procedural readiness, and local coordination capacity. The analysis shows that distributed energy systems may reduce vulnerability to cascading failures, support islanded operation, protect vulnerable consumers, improve emergency power continuity, and strengthen local energy autonomy. The proposed scoring and weighting logic enables future empirical validation, scenario testing, and comparative assessment across regions and crisis types, including extreme weather events, cyberattacks, and supply-chain disruptions. The article contributes to energy resilience and crisis-management studies by offering an integrated and operational framework for evaluating distributed energy systems as practical tools for critical infrastructure protection and continuity of essential public services. Full article

Modern agricultural systems heavily rely on carbon-intensive energy inputs, emphasizing the urgent need to assess and optimize specific crop supply chains from thermodynamic and environmental perspectives. This study presents a comprehensive cumulative assessment of the energy, exergy, and carbon emissions of onion production in Türkiye, utilizing mass, energy, and entropy balance formulations combined with field-survey data from Adıyaman province. The results indicate that the total cumulative energy consumption is 722.28 MJ/ton, with nitrogen fertilizer contributing 61%. The thermodynamic analysis reveals that nitrogen fertilizer, irrigation water, and diesel fuel drive a cumulative exergy consumption of 465.83 MJ/ton, while irrigation water dominates the carbon emission at 20.65 kg CO₂/ton. Based on these streams, integrated sustainability indicators specifically the Cumulative Degree of Perfection (CDP) and the Renewability Index (RI) were calculated under conventional and solar modernization scenarios. A renewable energy scenario incorporating photovoltaic-powered irrigation and electrified machinery substantially enhanced thermodynamic perfection and process renewability, increasing CDP from 3.33 to 6.22 and RI from 0.70 to 0.84. These findings offer actionable insights for scaling local solar-driven modernization to mitigate grid dependency and support global Sustainable Development Goals (SDGs) by reducing fossil-fuel integration. Full article

Digital transformation (DT) reshapes supply chain ecosystems and promotes inter-firm synergistic development. Using a sample of 2417 focal firm–partner dyads of Chinese A-share listed firms from 2013 to 2023, we employ regressions with industry and year fixed effects and mediation analysis to examine how focal firms’ DT affects partners’ internal control (IC) quality. We find that focal firms’ DT enhances partners’ IC quality, robust to various tests (e.g., IV, PSM). Mechanism analysis reveals two distinct pathways: transformation contagion (focal firms’ DT drives partners’ synchronized DT) and management spillover (focal firms’ DT-driven control activities exported to partners). Heterogeneity analysis shows that the positive transmission effect is stronger in geographically distant or low-concentration supply chain relationships, as well as for focal firms with greater market power. This study extends research on IC determinants beyond firm boundaries and shifts DT externality research from operational to governance outcomes, providing a governance-level synergistic pathway to supply chain sustainability. Full article

This study examines how client ESG performance affects supplier total factor productivity (TFP) from a knowledge spillover perspective, using matched client–supplier–year data for Chinese A-share listed firms from 2010 to 2023. The results show that client ESG significantly improves supplier TFP; specifically, a one-unit increase in client ESG is associated with an average increase of approximately 8.3% in supplier TFP. These results remain robust across a series of robustness tests. Mechanism analysis indicates that client ESG enhances supplier productivity through three knowledge spillover channels: technical assistance, management sharing, and innovation induction. Heterogeneity analysis further shows that this positive effect is more pronounced in long-term cooperative relationships, among clients with stronger market power, for state-owned suppliers, and when clients and suppliers have aligned ownership structures. Further analysis shows that the positive effect of client ESG persists for at least three fiscal years and is more pronounced in industries characterized by lower volatility. These findings suggest that policymakers and firms should strengthen supply chain ESG governance to promote knowledge spillovers and improve productivity. Full article

Offshore geothermal energy and the reuse of decommissioned oil and gas platforms are emerging as linked pathways for reducing the carbon intensity of marine energy supply while extending the value of mature offshore assets. This review examines offshore geothermal development from a full-chain perspective that connects resource assessment, platform and wellbore reuse, heat extraction, medium- and low-temperature conversion, multi-energy coupling, techno-economic evaluation and environmental risk management. The paper first clarifies the resource logic of offshore geothermal systems, especially sedimentary-basin resources that spatially overlap with mature petroleum provinces. It then analyzes two principal engineering routes: the reuse of existing offshore platforms as energy hubs and the reutilization of abandoned wells as open-loop or closed-loop heat-extraction systems. The review finds that platform and wellbore reuse can reduce drilling demand, shorten offshore construction cycles and lower life-cycle environmental burdens, but engineering feasibility remains constrained by wellbore integrity, thermal losses, corrosion and scaling, platform life extension, regulatory liability and the limited availability of field-scale demonstration data. Coupling geothermal energy with offshore wind power, hydrogen production, OTEC and desalination can improve system stability and equipment utilization; however, standardized assessment boundaries and comparable cost models are still insufficient. Future research should focus on resource-engineering-economic integrated assessment, standardized reuse packages, long-term offshore reliability databases, corrosion-resistant material systems, auditable TEA/LCA models and risk-based regulatory frameworks. This review provides a technical basis for offshore geothermal pilot projects and for the low-carbon transformation of offshore oil and gas infrastructure. Full article

Global value chains (GVCs) are undergoing significant reconfiguration in a context of slower trade growth, rising geopolitical tensions and repeated supply chain disruptions. This article examines how these pressures have shaped the sourcing geography of Spanish apparel imports between 1999 and 2023. Drawing on a panel of the 25 main supplier countries (625 country-year observations), it analyses the changing structure of sourcing through three restructuring dynamics widely discussed in the recent literature: nearshoring, diversification and friendshoring. The results show that diversification, rather than regionalization, has been the main response to recent disruptions. While Spain’s apparel sourcing has become less concentrated, this shift has not led to a sustained shortening of supply chains or to a clear reduction in dependence on Asia. Geopolitical alignment has limited explanatory power at the aggregate level, although it becomes more relevant among semi-proximity suppliers competing on the basis of speed, flexibility and political reliability. Overall, the findings suggest that post-pandemic restructuring in Spanish apparel is better understood as a selective form of risk management within an existing buyer-driven GVC than as a broad move toward nearshoring. Full article