Search Results (3,641)

Floods pose a growing threat globally, causing tragic loss of life, billions in economic damage annually, and disproportionately affecting socio-economically vulnerable populations. This paper aims to improve flood-risk communication for community leaders by exploring the application of artificial intelligence. We categorize U.S. flood information sources, review communication modalities and channels, synthesize the literature on community leaders’ roles in risk communication, and analyze existing technological tools. Our analysis reveals three key challenges: the fragmentation of flood information, information overload that impedes decision-making, and the absence of a unified communication platform to address these issues. We find that AI techniques can organize data and significantly enhance communication effectiveness, particularly when delivered through infographics and social media channels. Based on these findings, we propose FLAI (Flood Language AI), an AI-driven flood communication platform that unifies fragmented flood data sources. FLAI employs knowledge graphs to structure fragmented data sources and utilizes a retrieval-augmented generation (RAG) framework to enable large language models (LLMs) to produce contextualized narratives, including infographics, maps, and cost–benefit analyses. Beyond flood management, FLAI’s framework demonstrates how AI can transform public service data management and institutional AI readiness. By centralizing and organizing information, FLAI can significantly reduce the cognitive burden on community leaders, helping them communicate timely, actionable insights to save lives and build flood resilience. Full article

In comparative analyses, specific features of the Spanish welfare and housing systems have often been emphasized. The case of Barcelona illustrates the extent to which these features are the result of a long-standing historical trajectory and the decisive impact of the challenges and policy responses adopted during Franco’s lengthy, dark, and gloomy regime. This period marked a significant shift, not only due to the persistent shortage of social rental housing, but also because of the early consolidation of a homeownership culture and its dominance in working-class suburban areas—a legacy that is completely different from that of the welfare states of Western Europe. Through a review of the literature and the analysis of primary sources, ongoing research on Barcelona seeks to clarify the factors and processes that led to this transformation, as well as its evolution during the democratic period, within an international context of economic liberalization and the dismantling of the welfare state. Full article

Resource-exhausted cities have long played a key role in national energy development. Urban renewal projects, such as the renovation of old underground commercial spaces, can improve urban vitality and promote sustainable development. However, in resource-based cities, traditional industries dominate, while new industries such as modern commerce develop slowly. This results in low economic dynamism and weak motivation for urban development. To address this issue, we propose a systematic method for analyzing construction risks during the decision-making stage of renovation projects. The method includes three steps: risk value assessment, risk factor identification, and risk weight calculation. First, unlike previous studies that only used SWOT for risk factor analysis, we also applied it for project value assessment. Then, using the Work Breakdown Structure–Risk Breakdown Structure framework method (WBS-RBS), we identified specific risk sources by analyzing key construction technologies throughout the entire lifecycle of the renovation project. Finally, to enhance expert consensus, we proposed an improved Delphi–Analytic Hierarchy Process method (Delphi–AHP) to calculate risk indicator weights for different construction phases. The risk analysis covered all lifecycle stages of the renovation and upgrading project. The results show that in the Fushun city renovation case study, the established framework—consisting of five first-level indicators and twenty s-level indicators—enables analysis of renovation projects. Among these, management factors and human factors were identified as the most critical, with weights of 0.3608 and 0.2017, respectively. The proposed method provides a structured approach to evaluating renovation risks, taking into account the specific characteristics of construction work. This can serve as a useful reference for ensuring safe and efficient implementation of underground commercial space renovation projects in resource-exhausted cities. Full article

Virginia’s coastal location and abundant marine resources make its seafood industry a vital contributor to the state’s economy, supporting both local communities and tourism. This study applied input–output models and updates the economic contributions of the Virginia seafood industry using 2023 data, building on models developed for 2019 that capture both direct effects and broader economic ripple effects. In 2023, the industry generated USD 1.27 billion in total economic output and supported over 6500 jobs—including watermen, aquaculture farmers, processors, and distributors—resulting in USD 238.3 million in labor income. Contributions to state GDP totaled USD 976.7 million, and tax revenues exceeded USD 390.4 million. The study also evaluates the economic role of Virginia seafood products sold in retail stores and restaurants, based on secondary data sources. In 2023, these sectors generated USD 458 million in economic output, supported more than 3600 jobs, produced USD 136.7 million in labor income, and USD 280.8 million in value-added. Combined tax contributions surpassed USD 74 million. Importantly, the analysis results for the Virginia seafood products from retail and restaurant should not be summed to the seafood industry totals to avoid double-counting, as seafood products move as output from one sector as an input to another. These results provide evidence-based insights to guide decision-making, inform stakeholders, and support continued investment in Virginia’s seafood supply chain and related economic activities. Full article

Analyzing the two-phase flow behavior in bearing lubrication is crucial for understanding friction and wear mechanisms, optimizing lubrication design, and improving bearing operational efficiency and reliability. However, the complexity of oil–air two-phase flow in high-speed bearings poses significant research challenges. Currently, there is a lack of comparative studies employing different simulation strategies to address this issue, leaving a gap in evidence-based guidance for selecting appropriate simulation approaches in practical applications. This study begins with a comparative analysis between experimental and simulation results to validate the reliability of the adopted simulation approach. Subsequently, a comparative evaluation of different simulation methods is conducted to provide a scientific basis for relevant decision-making. Evaluated from three dimensions—adaptability to rotational speed conditions, research focuses (oil distribution and power loss), and computational economy—the findings reveal that FVM excels at medium-to-high speeds, accurately predicting continuous oil film distribution and power loss, while MPS, leveraging its meshless Lagrangian characteristics, demonstrates superior capability in describing physical phenomena under extreme conditions, albeit with higher computational costs. Economically, FVM, supported by mature software ecosystems and parallel computing optimization, is more suitable for industrial design applications, whereas MPS, being more reliant on high-performance hardware, is better suited for academic research and customized scenarios. The study further proposes that future research could adopt an FVM-MPS coupled approach to balance efficiency and precision, offering a new paradigm for multi-scale lubrication analysis in bearings. Full article

Currently, Poland is witnessing a dynamic development of the offshore wind energy sector, which will be a key component of the national energy mix. While many international studies have addressed wind energy deployment, there is a lack of research that compares the energy and economic performance of both onshore and offshore wind farms under Polish climatic and spatial conditions, especially in relation to turbine spacing optimization. This study addresses that gap by performing a computer-based simulation analysis of three onshore spacing variants (3D, 4D, 5D) and four offshore variants (5D, 6D, 7D, 9D), located in central Poland (Stęszew, Okonek, Gostyń) and the Baltic Sea, respectively. The efficiency of wind farms was assessed in both energy and economic terms, using WAsP Bundle software and standard profitability evaluation metrics (NPV, MNPV, IRR). The results show that the highest NPV and MNPV values among onshore configurations were obtained for the 3D spacing variant, where the energy yield leads to nearly double the annual revenue compared to the 5D variant. IRR values indicate project profitability, averaging 14.5% for onshore and 11.9% for offshore wind farms. Offshore turbines demonstrated higher capacity factors (36–53%) compared to onshore (28–39%), with 4–7 times higher annual energy output. The study provides new insight into wind farm layout optimization under Polish conditions and supports spatial planning and investment decision making in line with national energy policy goals. Full article

Achieving the construction sector’s dual carbon objectives necessitates scaling green energy adoption in new residential buildings. The current literature critically overlooks four unresolved problems: oversimplified penalty mechanisms, ignoring escalating regulatory costs; static subsidies misaligned with market maturity evolution; systematic exclusion of innovation feedback from energy suppliers; and underexplored behavioral evolution of building owners. This study establishes a government–suppliers–owners evolutionary game framework with dynamically calibrated policies, simulated using MATLAB multi-scenario analysis. Novel findings demonstrate: (1) A dual-threshold penalty effect where excessive fines diminish policy returns due to regulatory costs, requiring dynamic calibration distinct from fixed-penalty approaches; (2) Market-maturity-phased subsidies increasing owner adoption probability by 30% through staged progression; (3) Energy suppliers’ cost-reducing innovations as pivotal feedback drivers resolving coordination failures, overlooked in prior tripartite models; (4) Owners’ adoption motivation shifts from short-term economic incentives to environmentally driven decisions under policy guidance. The framework resolves these gaps through integrated dynamic mechanisms, providing policymakers with evidence-based regulatory thresholds, energy suppliers with cost-reduction targets, and academia with replicable modeling tools. Full article

Landslides are among the most hazardous natural phenomena affecting Greater Abidjan, causing significant economic and social damage. Strategic planning supported by geographic information systems (GIS) can help mitigate potential losses and enhance disaster resilience. This study evaluates landslide susceptibility using logistic regression and frequency ratio models. The analysis is based on a dataset comprising 54 mapped landslide scarps collected from June 2015 to July 2023, along with 16 thematic predictor variables, including altitude, slope, aspect, profile curvature, plan curvature, drainage area, distance to the drainage network, normalized difference vegetation index (NDVI), and an urban-related layer. A high-resolution (5-m) digital elevation model (DEM), derived from multiple data sources, supports the spatial analysis. The landslide inventory was randomly divided into two subsets: 80% for model calibration and 20% for validation. After optimization and statistical testing, the selected thematic layers were integrated to produce a susceptibility map. The results indicate that 6.3% (0.7 km²) of the study area is classified as very highly susceptible. The proportion of the sample (61.2%) in this class had a frequency ratio estimated to be 20.2. Among the predictive indicators, altitude, slope, SE, S, NW, and NDVI were found to have a positive impact on landslide occurrence. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), demonstrating strong predictive capability. These findings can support informed land-use planning and risk reduction strategies in urban areas. Furthermore, the prediction model should be communicated to and understood by local authorities to facilitate disaster management. The cost function was adopted as a novel approach to delineate hazardous zones. Considering the landslide inventory period, the increasing hazard due to climate change, and the intensification of human activities, a reasoned choice of sample size was made. This informed decision enabled the production of an updated prediction map. Optimal thresholds were then derived to classify areas into high- and low-susceptibility categories. The prediction map will be useful to planners in helping them make decisions and implement protective measures. Full article

In today’s dynamic and highly uncertain environment, organizations, particularly in the aviation sector, face increasing challenges that demand resilient, flexible, and data-driven change management decisions. Responding to the growing need for structured approaches to managing complex uncertainties—geopolitical tensions, economic volatility, social shifts, rapid technological advancements, environmental pressures and regulatory changes—this research proposes a theoretical change management model for aviation service providers, such as airports. Integrating three analytical approaches, the model offers a robust, multi-method approach for supporting sustainable transformation under uncertainty. Normative analysis using Bayesian decision theory identifies influential external environmental factors, capturing probabilistic relationships, and revealing causal links under uncertainty. Prescriptive planning through scenario theory explores alternative future pathways and helps to identify possible predictions, offer descriptive evaluation employing fuzzy comprehensive evaluation, and assess decision quality under vagueness and complexity. The proposed four-stage model—observation, analysis, evaluation, and response—offers a methodology for continuous external environment monitoring, scenario development, and data-driven, proactive change management decision-making, including the impact assessment of change and development. The proposed model contributes to the theoretical advancement of the change management research area under uncertainty and offers practical guidance for aviation organizations (airports) facing a volatile external environment. This framework strengthens aviation organizations’ ability to anticipate, evaluate, and adapt to multifaceted external changes, supporting operational flexibility and adaptability and contributing to the sustainable development of aviation services. Supporting aviation organizations with tools to proactively manage systemic uncertainty, this research directly supports the integration of sustainability principles, such as resilience and adaptability, for long-term value creation through change management decision-making. Full article

This study proposes an integrated, machine learning-based multi-objective optimization framework to evaluate and optimize the utilization of steel slag in road base layers, simultaneously addressing economic costs and environmental impacts. A comprehensive dataset of 482 scenarios was engineered based on literature-informed parameters, encompassing transport distance, processing energy intensity, initial moisture content, gradation adjustments, and regional electricity emission factors. Four advanced tree-based ensemble regression algorithms—Random Forest Regressor (RFR), Extremely Randomized Trees (ERTs), Gradient Boosted Regressor (GBR), and Extreme Gradient Boosting Regressor (XGBR)—were rigorously evaluated. Among these, GBR demonstrated superior predictive performance (R² > 0.95, RMSE < 7.5), effectively capturing complex nonlinear interactions inherent in slag processing and logistics operations. Feature importance analysis via SHapley Additive exPlanations (SHAP) provided interpretative insights, highlighting transport distance and energy intensity as dominant factors affecting unit cost, while moisture content and grid emission factor predominantly influenced CO₂ emissions. Subsequently, the Gradient Boosted Regressor model was integrated into a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) framework to explore optimal trade-offs between cost and emissions. The resulting Pareto front revealed a diverse solution space, with significant nonlinear trade-offs between economic efficiency and environmental performance, clearly identifying strategic inflection points. To facilitate actionable decision-making, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was applied, identifying an optimal balanced solution characterized by a transport distance of 47 km, energy intensity of 1.21 kWh/ton, moisture content of 6.2%, moderate gradation adjustment, and a grid CO₂ factor of 0.47 kg CO₂/kWh. This scenario offered a substantial reduction (45%) in CO₂ emissions relative to cost-minimized solutions, with a moderate increase (33%) in total cost, presenting a realistic and balanced pathway for sustainable infrastructure practices. Overall, this study introduces a robust, scalable, and interpretable optimization framework, providing valuable methodological advancements for sustainable decision making in infrastructure planning and circular economy initiatives. Full article

The global climate crisis has driven unprecedented agreements among nations on carbon mitigation. With China’s commitment to carbon peaking and carbon neutrality targets, the building sector has emerged as a critical focus for emission reduction, particularly because office buildings account for over 30% of building energy consumption. However, a systematic and regionally adaptive low-carbon technology evaluation framework is lacking. To address this gap, this study develops a multidimensional decision-making system to quantify and rank low-carbon technologies for office buildings in Beijing. The method includes four core components: (1) establishing three archetypal models—low-rise (H ≤ 24 m), mid-rise (24 m < H ≤ 50 m), and high-rise (50 m < H ≤ 100 m) office buildings—based on 99 office buildings in Beijing; (2) classifying 19 key technologies into three clusters—Envelope Structure Optimization, Equipment Efficiency Enhancement, and Renewable Energy Utilization—using bibliometric analysis and policy norm screening; (3) developing a four-dimensional evaluation framework encompassing Carbon Reduction Degree (CRD), Economic Viability Degree (EVD), Technical Applicability Degree (TAD), and Carbon Intensity Degree (CID); and (4) conducting a comprehensive quantitative evaluation using the AHP-entropy-TOPSIS algorithm. The results indicate distinct priority patterns across the building types: low-rise buildings prioritize roof-mounted photovoltaic (PV) systems, LED lighting, and thermal-break aluminum frames with low-E double-glazed laminated glass. Mid- and high-rise buildings emphasize integrated PV-LED-T8 lighting solutions and optimized building envelope structures. Ranking analysis further highlights LED lighting, T8 high-efficiency fluorescent lamps, and rooftop PV systems as the top-recommended technologies for Beijing. Additionally, four policy recommendations are proposed to facilitate the large-scale implementation of the program. This study presents a holistic technical integration strategy that simultaneously enhances the technological performance, economic viability, and carbon reduction outcomes of architectural design and renovation. It also establishes a replicable decision-support framework for decarbonizing office and public buildings in cities, thereby supporting China’s “dual carbon” goals and contributing to global carbon mitigation efforts in the building sector. Full article

The transition to sustainable agriculture is a critical challenge for the U.S. food system. A sustainable food system must support the production of healthy and nutritious food while ensuring economic sustainability for farmers and ranchers. It should also reduce negative environmental impacts on soil, water, biodiversity, and climate, and promote equitable and inclusive access to land, farming resources, and food. This narrative review synthesizes U.S. social science literature to identify the key factors that support or impede the adoption of sustainable agricultural practices in the U.S. Our analysis reveals seven overarching factors that influence producer decision-making: awareness and knowledge, social factors, psychological factors, technologies and tools, economic factors, implementation capacity, and policies and regulations. The review highlights the critical role of social science in navigating complexity and uncertainty. Key priorities emerging from the literature include developing measurable, outcome-based programs; ensuring credible communication through trusted intermediaries; and designing tailored interventions. The findings demonstrate that initiatives will succeed when they emphasize measurable benefits, address uncertainties, and develop programs that capitalize on identified opportunities while overcoming existing barriers. Full article

The environmental impact of milk production, particularly its share of greenhouse gas (GHG) emissions, is a topic under investigation in various parts of the world. This paper presents an overview of current knowledge on the carbon footprint (CF) of milk production at the farm level, with a particular focus on technological, environmental and organisational factors affecting emission levels. The analysis is based on a review of, inter alia, 46 peer-reviewed publications and 11 environmental reports, legal acts and databases concerning the CF in different regions and under various production systems. This study identifies the main sources of emissions, including enteric fermentation, manure management, and the production and use of feed and fertiliser. It also demonstrates the significant variability of the CF values, which range, on average, from 0.78 to 3.20 kg CO₂ eq kg⁻¹ of milk, determined by the farm scale, nutritional strategies, local environmental and economic determinants, and the methodology applied. Moreover, this study stresses that higher production efficiency and integrated farm management could reduce the CF per milk unit, with further intensification having, however, diminishing effects. The application of life cycle assessment (LCA) methods is essential for a reliable assessment and comparison of the CF between systems. Ultimately, an effective CF reduction requires a comprehensive approach that combines improved nutritional practices, efficient use of resources, and implementation of technological innovations adjusted to regional and farm-specific determinants. The solutions presented in this paper may serve as guidelines for practitioners and decision-makers with regard to reducing GHG emissions. Full article

Dark skies, a vital natural and cultural resource, have been increasingly threatened by light pollution due to rapid urbanization, leading to ecological degradation and biodiversity loss. As a key strategy for sustainable regional development, dark sky parks (DSPs) not only preserve nocturnal environments but also enhance livability by balancing urban expansion and ecological conservation. This study develops a novel framework for evaluating DSP suitability, integrating ecological and socio-economic dimensions, including the resource base (e.g., nighttime light levels, meteorological conditions, and air quality) and development conditions (e.g., population density, transportation accessibility, and tourism infrastructure). Using the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) as a case study, we employ Delphi expert consultation, GIS spatial analysis, and multi-criteria decision-making to identify optimal DSP locations and prioritize conservation zones. Our key findings reveal the following: (1) spatial heterogeneity in suitability, with high-potential zones being concentrated in the GBA’s northeastern, central–western, and southern regions; (2) ecosystem advantages of forests, wetlands, and high-elevation areas for minimizing light pollution; (3) coastal and island regions as ideal DSP sites due to the low light interference and high ecotourism potential. By bridging environmental assessments and spatial planning, this study provides a replicable model for DSP site selection, offering policymakers actionable insights to integrate dark sky preservation into sustainable urban–regional development strategies. Our results underscore the importance of DSPs in fostering ecological resilience, nighttime tourism, and regional livability, contributing to the broader discourse on sustainable landscape planning in high-urbanization contexts. Full article

Demand for mental health support has exerted unprecedented pressure on statutory services. Innovative solutions such as Green or Nature-Based Social Prescribing (NBSP) programmes may help address unmet need, improve access to personalised treatment, and support the sustainable delivery of primary services within a prevention model of population health. We piloted an innovative health economics-informed Social Return on Investment (SROI) analysis and forecast of a ‘Making Well’ therapeutic craft and horticulture programme for mental health between October 2021 and March 2022. Quantitative and qualitative outcome data were collected from participants with mild-to-moderate mental health conditions at baseline and nine-weeks follow-up using a range of validated measures, including the Short Warwick–Edinburgh Mental Well-being Scale, ICEpop CAPability measure for Adults (ICECAP-A), General Self-Efficacy Scale (GSES), and a bespoke Client Service Receipt Inventory (CSRI). The acceptability and feasibility of these measures were explored. Results indicate that the Making Well programme generated well-being-related social value in the range of British Pound Sterling (GBP) GBP 3.30 to GBP 4.70 for every GBP 1 invested. Our initial pilot forecast suggests that the programme has the potential to generate GBP 5.40 to GBP 7.70 for every GBP 1 invested as the programme is developed and delivered over a 12-month period. Despite the small sample size and lack of a control group, our results contribute to the evidence-base for the effectiveness and social return on investment of NBSP as a therapeutic intervention for improving health and well-being and provides an example of the use of health economic well-being outcome measures such as ICECAP-A and CSRIs in social value analysis. Combining SROI evaluation and forecast methodologies with validated quantitative outcome measures used in the field of health economics can provide valuable social cost–benefit evidence to decision-makers. Full article