Search Results (1,285)

Post-earthquake fires often cause more severe losses than the earthquakes themselves, highlighting the critical role of water distribution networks (WDNs) in mitigating fire risks. This study proposed an improved assessment framework for the post-earthquake firefighting functionality of WDNs. This framework integrates a WDN firefighting simulation model into a cloud model-based assessment method. By combining seismic damage and firefighting scenarios, the simulation model derives sample values of the functional indexes through Monte Carlo simulations. These indexes integrate the spatiotemporal characteristics of the firefighting flow and pressure deficiencies to assess a WDN’s capability to control fire and address fire hazards across three dimensions: average, severe, and prolonged severe deficiencies. The cloud model-based assessment method integrates the sample values of functional indexes with expert opinions, enabling qualitative and quantitative assessments under stochastic–fuzzy conditions. An illustrative study validated the efficacy of this method. The flow- and pressure-based indexes elucidated functionality degradation owing to excessive firefighting flow and the diminished supply capacity of a WDN, respectively. The spatiotemporal characteristics of severe flow and pressure deficiencies demonstrated the capability of firefighting resources to manage concurrent fires while ensuring a sustained water supply to fire sites. This method addressed the limitations of traditional quantitative and qualitative assessment approaches, resulting in more reliable outcomes. Full article

To align with disaster monitoring and sustainable risk assessment, the low-carbon transition of fisheries necessitates comprehensive carbon emission management throughout the supply chain. As China advances supply-side structural reform, transitioning from traditional to low-carbon fisheries is vital for the green development of the industry and its associated sectors. This study employs input–output models and LMDI decomposition to examine the trends and drivers of embodied carbon emissions within China’s fishery production system from 2010 to 2019. By constructing a cross-sectoral full-emission accounting system, the research calculates total direct and indirect emissions, exploring how accounting scopes influence regional responsibility and reduction strategies. Empirical results indicate that while China’s aquatic trade and processing have steadily developed, the sector remains dominated by low-value-added primary products. This structure highlights vast potential for deep processing development amidst shifting global dietary habits. Factor decomposition reveals that economic and technological development are the primary drivers of carbon emissions. Notably, technological progress within fisheries emerges as the most significant factor, playing a pivotal role in both driving and potentially mitigating emissions. Consequently, to effectively lower carbon intensity, the study concludes that restructuring the fishery industry is crucial. Promoting low-carbon development and enhancing the R&D of green technologies are essential strategies to navigate the dual challenges of industrial upgrading and environmental protection. Full article

To identify and evaluate the failure issues in the livestock manure-to-biomethane supply chain, this study employs a Bayesian network approach with three inference analysis methods: diagnostic analysis, sensitivity analysis, and maximum causal chain inference. First, the main hazard categories affecting the failure of the supply chain are identified, establishing risk indicators for feedstock collection, pretreatment, anaerobic digestion, purification and upgrading, transportation, and biomethane end-use. Then, the half-interval method and possibility superiority comparison are used to calculate and rank the severity of related accidents, obtaining the severity ranking of secondary indicators as well as the severity ranking of work items and risk items. Finally, Bayesian forward inference is applied to investigate the failure probability of the supply chain, combined with backward inference to identify the risk factors most likely to cause supply chain failures and trace the formation of failure hazards. The Bayesian sensitivity analysis method is ultimately applied to determine the key hazards affecting supply chain failures and the correlations between accident hazards, followed by validation. The results show that the failure probability of the supply chain through causal inference is approximately 54.76%, indicating relatively high failure risk. The three factors with the highest posterior probabilities are mechanical stirring failure C₃ (88.11%), corrosion-induced ammonia leakage poisoning D6, and equipment explosion caused by excessive pressure due to overheating during dehumidification heating D₉, which are the hazards most likely to cause failures in the supply chain. Improper operations and the toxicity of related chemicals are key hazards leading to supply chain failures, with the correlation between accident hazards presented as a hazard chain by integrating severity and accident probability, and the key risk points in the supply chain are identified. Full article

This article is aimed at assessing energy–economy models with a focus on their ability to capture the dynamic structural changes of economic systems and the related energy supply chains. A narrative literature review approach was employed, synthesizing relevant peer-reviewed research. The search yielded 229 publications spanning from 2015 to 2024. After applying screening criteria based on methodological transparency, quantitative modelling, and explicit energy–economy integration, 120 articles were retained, from which 23 representative modelling frameworks were selected. The review identifies five key dimensions shaping the realism and applicability of integrated models: geographical and temporal scope, technological detail, modelling approach, and the degree of micro- and macroeconomic realism. Results show a growing adoption of multi-scale modelling and a gradual shift toward hybrid structures combining technological and macroeconomic components. However, significant gaps remain: only 26% of the models move beyond equilibrium assumptions; 17% incorporate behavioural or heterogeneous agents; and almost half rely on exogenous technological change. Moreover, the representation of policy instruments—particularly performance standards, sectoral benchmarks, and public investment mechanisms—remains incomplete across most frameworks. Overall, this analysis highlights the need for more transparent coupling strategies, enhanced behavioural realism, and improved representation of financial and transition risks. These findings inform the methodological development of next-generation models and indicate priority areas for future research aimed at improving the robustness of policy-relevant transition assessments. Full article

With the acceleration of digital transformation, open-source software has become a fundamental component of modern smart grids and other critical infrastructures. However, the complex dependency structures of open-source ecosystems and the continuous emergence of vulnerabilities pose substantial challenges to software supply chain security. In power information networks and cyber–physical control systems, vulnerabilities in open-source components integrated into Supervisory Control and Data Acquisition (SCADA), Energy Management System (EMS), and Distribution Management System (DMS) platforms and distributed energy controllers may propagate along the supply chain, threatening system security and operational stability. In such application scenarios, large language models (LLMs) often suffer from limited semantic accuracy when handling domain-specific security terminology, as well as deployment inefficiencies that hinder their practical adoption in critical infrastructure environments. To address these issues, this paper proposes KD-SecBERT, a domain-specific semantic bidirectional encoder optimized through multi-level knowledge distillation for open-source software supply chain security in smart grid applications. The proposed framework constructs a hierarchical multi-teacher ensemble that integrates general language understanding, cybersecurity-domain knowledge, and code semantic analysis, together with a lightweight student architecture based on depthwise separable convolutions and multi-head self-attention. In addition, a dynamic, multi-dimensional distillation strategy is introduced to jointly perform layer-wise representation alignment, ensemble knowledge fusion, and task-oriented optimization under a progressive curriculum learning scheme. Extensive experiments conducted on a multi-source dataset comprising National Vulnerability Database (NVD) and Common Vulnerabilities and Exposures (CVE) entries, security-related GitHub code, and Open Web Application Security Project (OWASP) test cases show that KD-SecBERT achieves an accuracy of 91.3%, a recall of 90.6%, and an F1-score of 89.2% on vulnerability classification tasks, indicating strong robustness in recognizing both common and low-frequency security semantics. These results demonstrate that KD-SecBERT provides an effective and practical solution for semantic analysis and software supply chain risk assessment in smart grids and other critical-infrastructure environments. Full article

In third-generation centralized heating systems, qualitative regulation of the heat transfer medium parameters is mainly performed at heat sources, while quantitative regulation is implemented at central and individual heating points, with buildings remaining passive heat consumers. Unlike fourth-generation systems, such systems generally do not employ renewable energy sources, thermal energy storage, or low-temperature operating regimes. Third-generation centralized heating systems operate based on design high-temperature schedules and centralized control, without considering the actual thermal loads of consumers. Under conditions of physical deterioration of heating networks, hydraulic imbalance, and operational constraints, the actual parameters of the heat transfer medium supplied to buildings often deviate from design values, resulting in deviations of thermal conditions at the level of end consumers and disruptions of thermal comfort. This study proposes the concept of an intelligent active individual heating point (IAIHP), designed to provide adaptive qualitative–quantitative regulation of heat transfer medium parameters at the level of individual buildings. Unlike approaches focused on demand-side management, the use of thermal energy storage, or the integration of renewable energy sources, the proposed solution is based on the application of a local thermal energy source. The IAIHP compensates for deviations in heat transfer medium parameters and acts as a local thermal energy source within the building heat supply system (BHSS). Control of the IAIHP operation is performed by a developed automation system that provides combined qualitative and quantitative regulation of the heat transfer medium supplied to the BHSS. The study assesses the potential scale of IAIHP implementation in third-generation centralized heating systems, develops a methodology for selecting the capacity of a local heat source, and presents the operating algorithm of the automatic control system of the IAIHP. At present, the reconstruction of an individual heating point of a kindergarten connected via a dependent scheme is being carried out based on the developed project documentation. Modeling and calculations show that the application of the IAIHP makes it possible to ensure indoor thermal comfort by reducing the risk of temperature deviations, which are otherwise typically compensated for by electric heaters. The proposed concept provides a methodological basis for a gradual transition from third-generation to fourth-generation centralized heating systems, while equipping the IAIHP with an intelligent control system opens opportunities for improving the energy efficiency of urban heating networks. The proposed integrated solution and the developed automatic control algorithms exhibit scientific novelty and practical relevance for Russia and other countries operating third-generation centralized heating systems, including Northern and Eastern European states, where large-scale infrastructure modernization and the implementation of fourth-generation technologies are technically or economically constrained. Full article

Japanese modern irrigation management is considered a successful model of water governance worldwide. However, debates continue over whether this success is due to natural water abundance or to water management practices. This study evaluates pre-modern water scarcity risk for six irrigation schemes, developed during that period in the Kinu River Basin (1603–1868); a period without large reservoirs, canal systems, or modern regulatory technologies. As the methodology, pre-modern river flows were reconstructed by removing the effects of four modern dams from the present-day river discharge, adjusting the conveyance efficiency, changes in paddy field area, rainfall input, and return flows. Water demand was assessed using Japanese irrigation standards of 5 mm/d (minimum water demand corresponding to evapotranspiration) and 20 mm/d (easy management), and risk was evaluated under both the prior appropriation and Equal Water Distribution rules. Results show that modern flow in the dry season is approximately 25 m³/s, whereas reconstructed natural flow during drought years declines to 10–18 m³/s, and about 15 m³/s after rainfall adjustment. Under the 20 mm/d demand scenario, scarcity occurred in four schemes (2 of 17 years in the third scheme and 7 of 17 years for the sixth scheme), while no scarcity occurred under the minimum-demand scenario (5 mm/d), even during low-flow conditions. This indicates that the available water in these schemes was at a level where drought damage could occur under extensive irrigation management, but could be avoided by intensive irrigation management to supply the minimum necessary water to all paddy fields. Full article

Blockchain technology has attracted considerable attention in the supply chain literature for its potential to enhance operational traceability, transparency, and trust, as well as to advance greening initiatives. Given current supply chain configurations, exploring barriers to implementation is a consequential agenda, and current studies have devoted substantial effort to identifying and offering guidance to address them. Despite recent findings, insights into how blockchain technology adoption can support green supply chain management are missing, particularly in the maritime sector, which receives limited attention. Thus, this work outlines a methodological approach to examine the suitability of maritime routes for addressing barriers to implementing blockchain technology in green supply chain management. Viewing the evaluation as a multi-criteria decision-making (MCDM) problem, the proposed approach performs the following actions on a case study evaluating four maritime lines. Firstly, from the 13 identified barriers in the literature review and expert interviews, nine relevant barriers were determined after one round of a Delphi process. These barriers eventually comprise the set of evaluation criteria. Secondly, to satisfy the assumption of criterion independence in most MCDM methods, this work proposes a novel extended grey relational analysis (GRA) that allows for the measurement of criterion independence based on the concept of grey relational space. Proposed here for the first time, the extended GRA offers a distribution-free overall independence index for each criterion based on pattern similarity. Finally, an integration of SWARA (Stepwise Weight Assessment Ratio Analysis) and ARAS (Additive Ratio Assessment) methods under Z-information is developed to address the evaluation problem involving expert judgments in a highly uncertain decision-making context. Results show that transaction-level uncertainty is the most critical barrier to blockchain adoption, followed by technology risks and higher sustainability costs. Among the four maritime lines, Line 3 is best prepared for a blockchain-enabled green supply chain. The agreement between these results and those of other MCDM methods is shown in the comparative analysis. Also, ranking remains unchanged even when the criteria weights are adjusted. The proposed approach provides a computationally efficient and tractable framework for maritime managers to make informed decisions about blockchain adoption to promote green supply chains. Full article

Emergency supplies policies are a key component of regional risk governance, yet their design coherence has received limited systematic examination. Focusing on the Yangtze River Delta (YRD), this study conducts a design-oriented evaluation of emergency supplies policy design by integrating policy text mining with the Policy Modeling Consistency (PMC) index model. Based on a corpus of 212 emergency supplies–related policy documents, the study first examines the structural features and thematic emphases of the regional policy system and constructs a PMC-based evaluation framework within a mission–structure–mechanism perspective. On this basis, 16 provincial- and municipal-level policies issued between 2019 and 2023 are identified as core, system-defining policy texts and subjected to in-depth PMC evaluation. The results indicate that the evaluated core emergency supplies policies exhibit an overall “good” level of design coherence. Mission-oriented dimensions, including normative orientation and policy objectives, are generally well articulated, whereas mechanism-oriented dimensions—particularly linkage response and allocation arrangements—are specified less consistently. Observed interjurisdictional differences reflect institutional roles and governance traditions rather than variations in administrative capacity. By shifting analytical attention from implementation outcomes to design-stage coherence in core policy texts, this study provides a structured diagnostic approach for assessing emergency supplies policy design and offers insights for strengthening regional coordination and institutional resilience. Full article

The combined effects of climate change and socio-economic development have intensified the risk of water supply–demand imbalance in China. To project future trends, this study develops a multi-scenario coupled prediction framework integrating climate, socio-economic, and human activity drivers, combining data-driven and physically based modeling approaches to assess terrestrial water storage imbalance in nine major river basins under six representative SSP–RCP scenarios through the end of the 21st century. Using ISIMIP multi-model runoff outputs along with GDP and population projections, agricultural, industrial, and domestic water demands were estimated. A Water Conflict Index was proposed by integrating the Water Supply–Demand Stress Index and the Standardized Hydrological Runoff Index to identify high-risk basins. Results show that under high-emission scenarios, the WCI in the Yellow River, Hai River, and Northwest Rivers remains high, peaking during 2040–2069, while low-emission scenarios significantly alleviate stress in most basins. Water allocation inequity is mainly driven by insufficient supply in arid northern regions and limited redistribution capacity in resource-rich southern basins. Targeted strategies are recommended for different risk types, including inter-basin water transfer, optimization of water use structure and pricing policies, and the development of resilient management systems, providing scenario-based quantitative support for future water security and policy-making in China. Full article

The rapid advancement of energy collection and storage systems (ECSSs) is fundamentally reshaping global energy markets and accelerating the transition toward low-carbon energy systems. This study provides a comprehensive assessment of the economic benefits and systemic effects of advanced ECSS technologies, including photovoltaic-thermal (PV/T) hybrid systems, advanced batteries, hydrogen-based storage, and thermal energy storage (TES). Through a mixed-methods approach combining techno-economic analysis, macroeconomic modeling, and policy review, we evaluate the cost trajectories, performance indicators, and deployment impacts of these technologies across major economies. The paper also introduces a novel economic-mathematical model to quantify the long-term macroeconomic benefits of large-scale ECSS deployment, including GDP growth, job creation, and import substitution effects. Our results indicate significant cost reductions for ECSS by 2050, with battery storage costs projected to fall below USD 50 per kilowatt-hour (kWh) and green hydrogen production reaching as low as USD 1.2 per kilogram. Large-scale ECSS deployment was found to reduce electricity costs by up to 12%, lower fossil fuel imports by up to 25%, and generate substantial GDP growth and job creation, particularly in regions with supportive policy frameworks. Comparative cross-country analysis highlighted regional differences in economic effects, with the European Union, China, and the United States demonstrating the highest economic gains from ECSS adoption. The study also identified key challenges, including high capital costs, material supply risks, and regulatory barriers, emphasizing the need for integrated policies to accelerate ECSS deployment. These findings provide valuable insights for policymakers, industry stakeholders, and researchers aiming to design effective strategies for enhancing energy security, economic resilience, and environmental sustainability through advanced energy storage technologies. Full article

Aquatic environments have been a critical source of nutrition for millennia, with wild fisheries supplying protein and nutrients to populations worldwide. A notable shift has occurred in recent decades with the expansion of aquaculture, now representing a fast-growing sector in food production. Aquaculture plays a key role in mitigating the depletion of wild fish stocks and addressing issues related to overfishing. Despite its potential benefits, the sustainability of both wild and farmed aquatic food systems is challenged by anthropogenic pollution. Contaminants from agricultural runoff, industrial discharges, and domestic effluents enter freshwater systems and eventually reach marine environments, where they may be transported globally through ocean currents. Maintaining water quality is paramount to food safety, environmental integrity, and long-term food security. In addition to conventional seafood products such as fish and shellfish, foods such as those derived from microalgae are gaining attention in Western markets for their high nutritional value and potential functional properties. These organisms have been consumed in Asia for generations and are now being explored as sustainable foods and ingredients as an alternative source of protein. Contaminants in aquatic food products include residues of agrochemicals, persistent organic pollutants (POPs) such as dioxins, polychlorinated biphenyls (PCBs), and per- and polyfluoroalkyl substances (PFASs), as well as brominated flame retardants and heavy metals. Public and scientific attention has intensified around plastic pollution, particularly microplastics and nanoplastics, which are increasingly detected in aquatic organisms and are the subject of ongoing toxicological and ecological risk assessments. While the presence of these hazards necessitates robust risk assessment and regulatory oversight, it is important to balance these concerns against the health benefits of aquatic foods, which are rich in omega-3 fatty acids, high-quality proteins, vitamins, and trace elements. Furthermore, beyond direct human health implications, the environmental impact of pollutant sources must be addressed through integrated management approaches to ensure the long-term sustainability of aquatic ecosystems and the food systems they support. This review covers regulatory frameworks, risk assessments, and management issues relating to aquatic environments, including the impact of climate change. It aims to serve as a comprehensive resource for researchers, policymakers, food businesses who harvest food from aquatic systems and other stakeholders. Full article

Against the backdrop of heightened global economic uncertainty and increasingly frequent risks in agricultural supply chains, enhancing agricultural supply chain resilience has become a critical issue for safeguarding national food security and promoting high-quality agricultural development. As key actors within agricultural supply chains, the impact of financialization—defined as the shift of resources to non-core financial assets—among agricultural listed firms on supply chain resilience warrants systematic examination. Using panel data from 165 Chinese agricultural listed firms (2010–2022), this study empirically investigates the impact of corporate financialization on agricultural supply chain resilience and its underlying mechanisms. An entropy-weighted composite index based on 16 parameters is used to assess agricultural supply chain resilience. It is composed of three dimensions: resistance capability, recovery capacity, and renewal capacity. The results show that: Financialization significantly undermines supply chain resilience, with the most substantial negative effect on recovery capacity, followed by renewal capacity, and the weakest on resistance capacity. Heterogeneity analyses show more pronounced negative effects among non-state-owned enterprises, non-primary sector firms, and capital-intensive enterprises. Financing constraints and capital expenditures partially mediate the negative relationship between financialization and resilience, while profitability persistence exacerbates the crowding-out effect. These findings suggest that policymakers should strike a compromise between reducing excessive financialization and strengthening agricultural supply chains. While prudently guiding agricultural firms’ financial asset allocation, greater emphasis should be placed on developing a diverse and coordinated industrial support system, thereby diverting financial capital away from crowding out core operations and toward effectively serving the real economy, ultimately contributing to national food security and agricultural modernization. Full article

Artificial intelligence (AI) is increasingly regarded as a transformative enabler of sustainable logistics and supply chain management, particularly in the context of global energy transition and decarbonization efforts. This study provides a comprehensive conceptual and exploratory assessment of the multidimensional role of AI, highlighting both its potential to enhance energy efficiency and reduce greenhouse gas emissions, as well as its inherent environmental costs associated with digital infrastructures such as data centers. The findings reveal the dual character of digitalization: while predictive algorithms and digital twin applications facilitate demand forecasting, process optimization, and real-time adaptation to market fluctuations, they simultaneously generate additional energy demand that must be offset through renewable energy integration and intelligent energy balancing. The analysis underscores that the effectiveness of AI deployment cannot be captured solely through economic metrics but requires a holistic evaluation framework that incorporates environmental and social dimensions. Moreover, regional disparities are identified, with advanced economies accelerating AI-driven green transformations under regulatory and societal pressures, while developing economies face constraints linked to infrastructure gaps and investment limitations. The analysis emphasizes that AI-driven predictive models and digital twin applications are not only tools for energy optimization but also mechanisms that enhance systemic resilience by enabling risk anticipation, adaptive resource allocation, and continuity of operations in volatile environment. The contribution of this study lies in situating AI within the digital–green synergy discourse, demonstrating that its role in logistics decarbonization is conditional upon integrated energy–climate strategies, organizational change, and workforce reskilling. By synthesizing emerging evidence, this article provides actionable insights for policymakers, managers, and scholars, and calls for more rigorous empirical research across sectors, regions, and time horizons to verify the long-term sustainability impacts of AI-enabled solutions in supply chains. Full article

Rivers and groundwater supply 88% of Europe’s freshwater and are critical for public health. We examined whether cross-country differences in arsenic, lead, mercury, and nickel concentrations in groundwater and rivers are associated with disease burden. In an ecological cross-sectional study of 24 European countries, nationally aggregated concentrations from the European Environment Agency’s Waterbase Water Quality (2016–2019) were linked to cause-specific disability-adjusted life years (DALYs) from the Global Burden of Disease 2021 for six disease groups. Variables were z-standardized. Associations were assessed using Pearson correlations and linear regression with Benjamini–Hochberg correction. Missing concentrations were addressed via multiple imputation by chained equations using 1980–2025 monitoring records, and models were sequentially adjusted for health system, demographic, and economic indices. In groundwater, lead was positively associated with diabetes and kidney disease DALYs and remained significant after imputation and adjustment (β = 0.60, p = 0.011). In rivers, arsenic was positively associated with all-cause, cardiovascular, and neoplasm DALYs in unadjusted analyses but attenuated after adjustment and/or imputation. No consistent associations were observed for mercury or nickel. These continent-wide, non-causal findings can help prioritize monitoring and risk management and support progress toward Sustainable Development Goal 6. Full article