Search Results (4,928)

In our earlier methodology paper, we introduced a hierarchical framework combining graph compression, Diffusion Convolutional Recurrent Neural Networks (DCRNNs), and Multi-Agent Reinforcement Learning (MARL) to approximate Bellman’s optimality principle for real-time energy system control, validated using Palm Springs, California weather data. This extension paper addresses three critical advances. First, we provide a rigorous theoretical proof demonstrating that the DCRNN’s Markovian reduction of strongly path-dependent dynamics yields a bounded approximation error, with the error decaying exponentially in the mixing time of the underlying graph diffusion process. Second, we extend the framework to diverse weather regimes—from stable Mediterranean climates (San Diego) to highly variable marine west coast (Seattle), monsoon (Mumbai), and typhoon-prone regions (Hong Kong)—quantifying how weather-induced path dependence affects the required hidden state dimension and forecasting accuracy. We present comprehensive Leave-One-Out Cross-Validation (LOOCV) results across six cities, demonstrating consistent generalization with R² drops of less than 0.1% under out-of-sample testing. A controlled baseline comparison under matched training protocols shows that the graph-free GRU achieves comparable or higher R² on the one-step prediction task, which we attribute to the near-cumulative structure of the target and the small evaluation graph. We frame the DCRNN’s contribution around its theoretical guarantee and its potential advantage on larger graphs and longer horizons. We also characterize conditions under which the model expects to fail, specifically when weather stochasticity violates the geometric mixing assumption or when the effective temporal correlation length exceeds the GRU’s memory capacity. Third, we outline physics-informed enhancements that are proposed as future development: CFD-integrated loss functions, differentiable Model Predictive Control (MPC) heads, and a modular design enabling alternative turbine configurations. We also propose a standardized rooftop solar thermal deployment architecture with 200 m × 100 m, 100 m × 100 m, and 100 m × 50 m modules designed for data center footprints with pre-allocated HVAC space. We conclude with a stage-gated validation roadmap progressing from unit tests to hardware-in-the-loop simulation to full-scale FEED-site deployment. The completed contributions of this paper are the theorem, its empirical assumption verification, the multi-climate LOOCV study, the matched-protocol baseline comparison, and the sensor-failure robustness analysis. The remaining components are described as proposed extensions. Full article

Common bean (Phaseolus vulgaris L.) is among the world’s most widely consumed grain legumes and a foundational species for organic and low-input agriculture, yet its agroecological value extends far beyond nitrogen fixation and nutritional provisioning. This review reframes P. vulgaris as a breeding target defined by ecosystem function rather than by yield alone, synthesizing evidence on functional biology, biological nitrogen fixation, rhizosphere microbiome assembly and soil ecosystem engineering to identify the trait complexes that ecosystem-oriented breeding programs must select for. We examine the constraints limiting expression of these traits under organic management, including weed competition, biotic and abiotic stress, nutrient limitation, and the scarcity of cultivars and certified seed adapted to low-input environments, and evaluate the agroecological management practices, resource-use efficiency metrics and emerging technologies—spanning precision agriculture, phenomics, molecular breeding, genome editing and microbiome engineering—that can accelerate their genetic improvement. Central to this synthesis is a paradigm shift in breeding objectives: common bean is conceptualized as a holobiont whose performance emerges from coordinated plant–microbiome interaction, requiring breeding programs to select explicitly for nitrogen-fixation efficiency, root system architecture, rhizosphere recruitment capacity, nutrient-use efficiency and multi-stress resilience as heritable, ecosystem-oriented traits. We conclude by outlining priority breeding research directions integrating genomics, phenomics, envirotyping and rhizosphere ecology, positioning common bean breeding as a strategic lever for the transition toward regenerative, climate-smart organic agriculture. Full article

Geological CO₂ storage must operate within pressure and stress limits that preserve caprock, fault, and well integrity while sustaining climate-relevant injection. Existing reviews often treat multiphysics simulation, machine learning, and physics-informed learning separately, which obscures the different evidence required for stability screening, first slip, aseismic deformation, dynamic rupture, monitoring analytics, and containment consequences. This structured critical review integrates direct CO₂ storage observations, laboratory studies, injection analogues, multiphysics numerical methods, data-driven machine learning, and scientific machine learning within a target-specific evidence framework. We compare continuum, discontinuum, interface, and diffuse fracture formulations; one-way, staggered, and monolithic coupling; field and laboratory validation; seismic, deformation, pressure, and fiber optic monitoring; and physics-informed neural networks, neural operators, and reduced-order models. The synthesis herein identifies the strongest evidence base for pressure and deformation modeling and seismic signal processing, for which repeated field or operational applications and task-specific evaluation are available. By contrast, prospective fault slip and seismicity forecasting remain at a limited evidence level because of uncertain in situ stress, fault connectivity, CO₂-conditioned friction, monitoring detection limits, model discrepancy, and scarce cross-site validation. We propose task-appropriate metrics, explicit method maturity criteria, a validation ladder, and a staged, human-supervised digital twin roadmap. Machine learning and physics-informed methods are most credible as bounded complements to verified simulators and monitoring systems, and operational readiness should be judged by uncertainty-calibrated prospective evidence rather than algorithm novelty. Full article

Rift Valley fever (RVF) is a vector-borne zoonotic disease whose occurrence is strongly associated with climatic and environmental conditions, making data-driven approaches potentially valuable for epidemiological surveillance and risk assessment. Using a publicly available historical dataset comprising 180,288 monthly observations from geographically defined administrative units across Kenya between 1981 and 2010, this study investigates machine learning (ML) for the retrospective classification of reported RVF occurrence from contemporaneous climatic, environmental, topographic, and seasonal predictors under an extremely imbalanced classification setting. The dataset provides broad geographic coverage across Kenya over a 30-year historical period; however, because the outcome reflects reported events in historical surveillance records, it is not assumed to constitute a formally population-representative national sample or to capture all underlying RVF transmission. Each observation represents a geographic unit and observation month, and the response indicates whether an RVF event was reported during that corresponding period. Therefore, the present analysis should be interpreted as contemporaneous outbreak classification rather than as a fixed-horizon prospective forecast. Thirteen classifiers representing distinct learning paradigms were systematically evaluated: Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbors, Classification and Regression Tree, Naive Bayes, Support Vector Machine, Weighted Logistic Regression, XGBoost, LightGBM, CatBoost, Balanced Random Forest, EasyEnsemble, and RUSBoost. Model performance was assessed before and after SMOTENC-based rebalancing using overall and class-specific metrics, including accuracy, precision, sensitivity, specificity, F1-score, ROC–AUC, and precision–recall-based measures. Under the retrospective stratified hold-out benchmark, XGBoost, CatBoost, Balanced Random Forest, and LightGBM achieved ROC–AUC values of 0.9176, 0.9175, 0.9114, and 0.9062, respectively. Balanced Random Forest attained the highest outbreak sensitivity (0.8851), although at the cost of very low precision, illustrating that high rare-event detection can generate a substantial false-alert burden in surveillance settings. SMOTENC produced strongly model-dependent effects: it increased outbreak sensitivity for XGBoost, LightGBM, CatBoost, KNN, CART, and RUSBoost, but substantially reduced sensitivity for Balanced Random Forest and EasyEnsemble. SHAP-based interpretability analysis indicated that month, rainfall, and slope were among the most influential predictors and further showed that class rebalancing can alter the distribution of feature contributions. Overall, the findings demonstrate that modeling reported RVF occurrence under severe class imbalance requires joint evaluation of minority-class detection, false-positive behavior, discrimination, and model interpretability rather than overall accuracy alone. The present results establish a retrospective classification benchmark for climate-informed RVF risk assessment, but they should not be interpreted as an autonomous outbreak-warning system. Translation into prospective early-warning prediction will require an explicit forecasting horizon, predictors constructed exclusively from information available before the target period, temporally and geographically independent validation, and decision thresholds evaluated against an operationally acceptable false-alert burden. Full article

Greek olive groves are subject to multiple thermal, water, biotic, and extreme-weather pressures, yet national-scale maps of their combined historical exposure remain limited. The present study quantified climate risk for olive cultivation across Greece using twenty agroclimatic indicators grouped into four thematic categories. Using hourly ERA5-Land data (1995–2024) and quality-controlled ESWD reports (2014–2024), each indicator recorded how often predefined adverse thresholds were met at the grid-cell level during the reference period. We integrated the resulting layers using a weighted multi-criteria decision analysis, with lethal frost applied as a separate constraint, to produce a composite spatial distribution of climate risk and district-level summaries linked to CORINE Land Cover 2018, olive grove class (2.2.3). Composite risk was spatially heterogeneous: cold and frost recurrence predominated in northern and upland areas, whereas water-related indicators occurred most persistently in southern and island districts, including eastern Crete. Most of the mapped olive grove area fell into intermediate composite classes rather than at the extremes of the score range. Comparison with a recent nationwide olive suitability assessment showed agreement in major western and southern producing districts, but also contrasting patterns where high suitability coincided with elevated recurrence-based risk. The resulting products provide a national historical baseline for climate-risk recurrence in Greek olive groves, offer a spatial basis for regionally targeted adaptation planning, and demonstrate the applicability of an AI-assisted geospatial framework for reproducible national-scale climate-risk assessment of perennial crops. Full article

Released in 2025, a year unprecedented in terms of the call on international and regional bodies for advisory opinions on obligations of states for climate action, the Advisory Opinion of the International Court of Justice (ICJ) on the Obligations of States in Respect of Climate Change 2025 represents a significant development in international climate law. The Opinion clarifies that States’ climate obligations arise not only from climate treaties such as the United Nations Framework Convention on Climate Change (UNFCCC) and the Paris Agreement, but also from customary international law, human rights law, and general principles of international law, including due diligence, prevention of harm, equity, and intergenerational responsibility. As a result, climate action is increasingly viewed as a matter of legal obligation rather than political discretion. This study examines the extent to which South Africa’s Second Nationally Determined Contribution under the Paris Agreement (Second NDC) reflects the legal standards articulated by the ICJ. It argues that the Advisory Opinion strengthens the legal significance of Nationally Determined Contributions (NDCs) by transforming them from instruments of international cooperation into benchmarks for assessing state compliance with broader climate obligations. The study finds that South Africa possesses a relatively comprehensive legal framework capable of translating international climate obligations into domestic duties. Its Second NDC demonstrates a deliberate effort to implement these obligations through measurable mitigation targets, adaptation measures, loss and damage responses, and a commitment to a just transition. Nevertheless, questions remain regarding whether the level of mitigation ambition reflected in the NDC is sufficient to satisfy the due diligence, human rights, and intergenerational equity standards identified by the ICJ, particularly considering South Africa’s continued dependence on coal and the objective of limiting global warming to 1.5 °C. The study concludes that the ICJ Advisory Opinion has strengthened climate accountability by providing legal benchmarks against which domestic climate action may increasingly be assessed, challenged, and enforced. Full article

Effective environmental monitoring and sustainable landscape management require spatially explicit approaches that can identify ecologically sensitive areas and support targeted conservation and restoration planning. Although Geographic Information Systems (GIS) and multi-criteria decision-making approaches are widely used for ecological sensitivity assessment, integrated frameworks that simultaneously account for biophysical conditions and anthropogenic pressures remain limited in data-scarce Mediterranean mountain environments. This study develops a GIS–AHP framework to assess ecological sensitivity in the Safita region of western Syria by integrating eleven climatic, topographic, hydrological, biophysical, land-use, demographic, and infrastructure-related criteria. The criteria were standardized, weighted using the Analytic Hierarchy Process (AHP), and integrated through a weighted linear combination to derive a spatially explicit Ecological Sensitivity Index (ESI). Moderate and High sensitivity classes dominated the study area, accounting for 55.3% and 35.5%, respectively, whereas the Extremely High class occupied only 0.29%. Vegetation, Soil, Rainfall, and LULC were the dominant criteria, collectively representing 70.08% of the total AHP weight. Sensitivity analysis demonstrated 100% stability in the criterion ranking across all ±10% weight-perturbation scenarios, while 93.9–97.1% of valid raster cells retained their baseline ESI class in the spatial-output tests. Spatial analysis showed that Safita Center and the northern and northeastern sectors contain extensive areas of elevated sensitivity and therefore warrant particular attention for environmental monitoring and restoration-oriented planning. In this context, the high contribution of vegetation reflects the ecological value and disturbance sensitivity of densely vegetated areas rather than environmental degradation per se. The resulting ESI should therefore be interpreted as an integrated measure of relative ecological sensitivity arising from the interaction of intrinsic environmental susceptibility and anthropogenic pressure, rather than as a direct measure of environmental degradation. The proposed framework provides a transparent and reproducible decision-support approach for prioritizing monitoring, conservation, and restoration actions in data-scarce Mediterranean mountain landscapes. Full article

Limited phosphate rock resources and inefficient phosphorus (P) fertilization challenge sustainable crop production. Nano-hydroxyapatite (nHA) serves as a model for poorly soluble calcium phosphates relevant to recycled P fertilizers. We tested whether phosphate-solubilizing bacteria (PSB) enhance P mobilization from nHA and maize (Zea mays L.) nutrition under moderate-temperature conditions. In vitro assays evaluated six bacterial strains and organic acid (OA)-mediated P mobilization; a climate-chamber pot experiment tested nHA, PSB, and their combinations under low and moderate P supply. Several strains increased supernatant P concentrations in vitro; Pseudomonas rhodesiae showed the strongest response, corresponding to 90% of the P introduced with nHA. OA-mediated P mobilization was greater at pH 3.5 than at pH 5.0, although different OA concentrations were used to reach the respective target pH values. The descriptive differences among OAs may reflect additional OA-specific interactions with Ca, including complexation. In the pot experiment, shoot P uptake did not differ significantly among treatments under low P supply. Under moderate P supply, the treatment with nHA depot fertilization without added medium or bacterial inoculation showed the numerically highest shoot P uptake and dry matter production. No consistent additional PSB-mediated benefit relative to the corresponding sterile-medium controls was demonstrated. Strong in vitro nHA mobilization showed limited transferability to buffered soil systems. Full article

Habitat type and elevational gradient are key drivers shaping biodiversity variation in alpine montane grasslands. To explore the effects of habitat type on butterfly communities and their variation along the elevational gradient in the forest–grassland transition zone at the source of the Bailong River, we conducted transect surveys over three consecutive years (2022–2024). The main results are as follows: (1) The butterfly community showed typical characteristics of alpine grassland assemblages, dominated by Nymphalidae (60.0%), while Lycaenidae accounted for 13.0%, followed by Pieridae (23.7%). Papilionidae and Hesperiidae accounted for very low proportions, at 0.7% and 0.5%, respectively. (2) The ecotone generated pronounced habitat edge effects; the forest-edge shrub habitat harbored the highest species richness. (3) Species-composition similarity of butterfly assemblages among habitats was low. (4) The vertical distribution of butterfly species exhibited a distinct double-valley pattern. (5) Parnassiinae species inhabiting high-elevation alpine meadows face major threats from climate change. Conservation implications: Butterfly conservation within this alpine watershed requires coordinated ecological management that balances socioeconomic development and biodiversity protection. Endemic high-elevation butterfly species should be treated as priority conservation targets for future biodiversity management in the Bailong River source area. Full article

The aim of this study is to assess the natural value of trees and the multidimensional ecological potential of the dendroflora of Fort V in Warsaw in the context of climate change adaptation. Special attention was paid to evaluating the ecological functions of the tree stand within the historical fortress facility and its theoretical structural capacity to support urban green infrastructure. An integrated methodology combining field inventory with spatial analysis (GIS) was applied. The research included the taxonomic identification of trees, the assessment of threats from invasive species, and the calculation of tree canopy cover areas. The spatial structure and land-cover proportions were analyzed using area-based indicators to assess the theoretical capacity of the site to support urban green infrastructure. The research demonstrates significant species diversity and a high proportion of taxa that promote ecological stability; however, the presence of taxa with invasive potential was also identified. Biologically active areas account for over 94% of the site. The extensive tree canopy cover (the dominant land cover at 61.59%) provides extensive physical shading of the terrain and forms a dense structural barrier, while open areas of low greenery (33.86%) provide favorable spatial conditions for optimizing the recreational and social functions of the fort. The spatial structure and land-cover proportions of Fort V suggest its strong potential to function as an effective node of urban green infrastructure that can support local resilience to climate change. Proper management of dendroflora in post-military areas, which combines heritage conservation with nature-based solutions (NBSs), requires systematic monitoring and the implementation of targeted adaptive measures, including the eradication of invasive species. Full article

Cultural heritage buildings are increasingly exposed to climate-related hazards, yet conventional conservation approaches often give insufficient consideration to climate resilience and adaptive capacity. This study proposes a Heritage Climate Resilience Index (HCRI) for assessing the resilience of cultural heritage buildings under changing climatic conditions. The framework integrates climate hazard exposure, heritage-specific building characteristics, and a dynamic weighting scheme that reflects local hazard conditions. It is applied to two case studies in Bac Giang, Vietnam, and Riga, Latvia, representing distinct climatic and heritage contexts. The assessment demonstrates the framework’s ability to distinguish differences in resilience performance and to identify vulnerable building domains requiring targeted intervention. The results provide a basis for prioritizing adaptation and conservation measures according to site-specific conditions. By combining local climate information with heritage-sensitive assessment criteria, the HCRI provides a practical, transferable tool for climate-informed heritage management. The proposed framework can support stakeholders in prioritizing interventions, allocating resources, and strengthening the long-term resilience of cultural heritage buildings under increasingly frequent and severe climate-related risks. Full article

Wildfires play a key ecological role in Mediterranean ecosystems but increasing fire frequency and severity, exacerbated by climate change, threaten vegetation integrity, biodiversity, and ecosystem services. Understanding early post-fire recovery trajectories is therefore essential for informing adaptation and restoration strategies. This study examined vegetation recovery in a Mediterranean area three years after a large wildfire, comparing natural holm oak stands and two human-mediated reforestation types: broadleaf and non-native coniferous reforestations. Field surveys were conducted in 117 plots 10 × 10 m across burned and unburned stands to assess total vegetation cover and mean height, and cover and mean height of structural species, complemented by an analysis of the differenced Normalized Burn Ratio (dNBR). Among burned plots, no significant differences were detected among natural holm oak stands, broadleaf reforestations, and conifer reforestations in total vegetation cover, vegetation height, or spectral recovery. In contrast, structural species exhibited distinct recovery patterns: broadleaf reforestations closely resembled natural holm oak stands in terms of the structural species involved, whereas conifer reforestations significantly differed. Whereas recovery in natural stands and broadleaf reforestations was driven by species typical of the potential natural vegetation, regeneration in conifer reforestations was dominated by early successional and fire-resistant species such as Cytisus villosus Pourr. and Pteridium aquilinum (L.) Kuhn, and conifer seedlings. Overall, these results highlight that broadleaf reforestations may better support trajectories aligned with natural vegetation dynamics, while conifer reforestations may require targeted management to support post-fire vegetation recovery consistent with natural forest recovery trajectories. Full article

Saudi Vision 2030 and the Saudi Green Initiative (SGI) set national environmental targets; whether university campus operations track these has not yet been tested at system level. This study asks how national policy diffuses into measurable campus performance, using the complete Saudi UI GreenMetric record, including 88 institution-year observations across all 22 participating universities, 2010 to 2025. The quantitative, longitudinal, descriptive design combines secondary archival data, indicator normalisation, global benchmarking, and non-parametric tests, interpreted through neo institutional theory. Participation expanded from one institution to twenty-one, nearly tripling after SGI implementation began. Performance is two-tier; four institutions operate near global top 100 levels; recent entrants populate the lower half. The leading tier exceeds the top 100 mean on water (95.6 per cent) but lags furthest on energy and climate change (76.0 against 86.1). Persistent participants tend to outscore recent entrants (U = 57.0, p = 0.033), although the association between editions and score falls short of significance (rho = 0.40, p = 0.064). This study offers the first system-wide evidence that voluntary benchmarking can shift from legitimacy seeking toward capability building and derives a national reporting framework aligned with SGI targets. All findings are associations, not causal effects. Full article

Background/Objectives: Antibiotic residues in aquatic environments reflect interacting physicochemical, climatic, and microbiological processes. This study characterized selected antibiotics in wastewater and surface water associated with the Danube River near Novi Sad, Serbia, and evaluated explainable stacked machine-learning models for concentration prediction. Methods: Thirty-six samples collected during summer and autumn 2024 were analyzed using SPE-HPLC-MS/MS. Artificial neural network, random forest, support vector machine, XGBoost, stacked linear, and stacked random forest (STACK-RF) models were developed using environmental/physicochemical variables or presumptive resistant bacterial taxa. Models were evaluated by fivefold cross-validation, complementary error metrics, Holm-adjusted Diebold–Mariano tests, XGBoost Gain, and SHAP analysis. Results: All target antibiotics were detected at least once. Azithromycin was most prevalent (75.0%), followed by sulfamethoxazole (58.3%), trimethoprim, and ciprofloxacin (52.8% each), while wastewater generally exhibited broader antibiotic profiles and higher concentrations than surface water. Standalone algorithms showed weak-to-moderate performance, whereas STACK-RF achieved the highest numerical accuracy for all environmental/physicochemical models (R² = 0.745–0.913) and the available microbial-taxa models (R² = 0.819–0.945), with consistently lower prediction errors. However, most pairwise differences were not significant after Holm correction. Influential environmental predictors were compound-specific and included COD, BOD₅, pH, turbidity, electrical conductivity, water temperature, and relative humidity. Leading bacterial predictors included Klebsiella pneumoniae, Escherichia coli, Citrobacter freundii, and Aeromonas veronii. Conclusions: The results provide a proof-of-concept for machine-learning-assisted antibiotic prediction. Explainable STACK-RF modeling captured nonlinear, antibiotic-specific associations among residues, water-quality conditions, and microbial indicators. It may complement targeted chemical monitoring and support hypothesis generation, although larger, externally validated datasets are required before broader application. Full article

Soil erosion in semi-arid mountain–steppe regions is closely associated with climatic variability, vegetation condition and terrain, but their joint spatial patterns remain insufficiently quantified in Kazakhstan. We assessed RUSLE-estimated water-driven soil loss and meteorological drought and vegetation condition in East Kazakhstan during 2001–2024 by integrating RUSLE modelling with SPI-12, VCI and geographically weighted regression (GWR). FAO-classified RUSLE estimates showed that very low erosion (<5 t ha⁻¹ yr⁻¹) generally increases over time, whereas high (20–50 t ha⁻¹ yr⁻¹) and very high (50–100 t ha⁻¹ yr⁻¹) classes remain almost constant at about one third of the area and severe-to-extreme erosion occasionally reaches double-digit shares, indicating modest improvement in regional averages but persistent hotspots on foothills and mountain slopes. SPI-12 and VCI revealed alternating multi-year periods of dry and wet conditions and corresponding changes in vegetation condition. GWR results indicate that slope is the most stable and coherent predictor of soil loss, while local VCI coefficients, although weaker and more variable, tend to be negatively associated with erosion where vegetation cover deteriorates. The integrated RUSLE–SPI–VCI–GWR framework provides a reproducible basis for identifying areas where high modelled erosion susceptibility coincides with topographic vulnerability and adverse hydroclimatic or vegetation conditions, supporting targeted soil-conservation planning, climate-resilient land management, and the long-term protection of soil and water resources in semi-arid mountain–steppe landscapes. Full article