Search Results (10,146)

The western desert of Iraq is one of the most water-stressed environments in the Middle East. Characterized by unpredictable rainfall and long periods of evaporation. Several small earth dams have been constructed in this region, which are the only available surface water source. This makes the reliability of these structures extremely important. Four small dams, Al-Rutbah, Al-Ubiliah, Horan 2, and Horan 3, are evaluated concurrently across five dry seasons (2016, 2018–2021), a design chosen to separate each dam’s own structural characteristics from the shared regional climate. Surface area was extracted from Landsat 8 imagery for 2019 and 2020 and from the JRC Global Surface Water dataset for 2016, 2018, and 2021, then converted to storage volume and used to build a seasonal water balance around evaporation and infiltration losses; a volumetric adequacy index and a composite drought vulnerability index were derived from this balance. Al-Rutbah’s mean adequacy reached the marginal band (0.488), with a mean vulnerability score at the high-risk threshold (0.501). Al-Ubiliah, Horan 2, and Horan 3 remained unreliable by the adequacy index (0.294, 0.154, and 0.256, respectively) and high-risk by the vulnerability index. Al-Rutbah’s greater design capacity provided a partial buffer against the deficits observed at the smaller dams. Full article

Antibiotics and antibiotic resistance genes (ARGs) are widespread in aquatic ecosystems affected by climate variability, urbanization, and intensifying aquaculture. Yet parent-antibiotic concentrations and resistance-related genetic endpoints do not always vary in parallel across seasons, sites, or environmental compartments. This discrepancy matters for aquatic animals as ecological receptors, potential vectors, and targets for antimicrobial resistance monitoring and management. To examine whether transport, retention, and biological processes may contribute to these mismatches, we conducted a structured narrative review. We screened studies retrieved from Web of Science Core Collection, Scopus, and PubMed for paired measurements in aquatic systems. Of 98 field studies, 25 provided sufficient paired data for matched chemical–genetic assessment. Among these, 16 provided Moderate evidence and 9 provided Suggestive evidence for coupled, mismatched, or compartment-dependent responses. No study met the stricter criteria for field-supported legacy persistence. Seasonal reversals, spatial divergence, and differences among water, particles, and sediments suggest that water-column antibiotic concentrations alone are incomplete proxies for ARG abundance. Such measurements alone cannot assess ARG mobility or host context. Potential contributors include sediment and biofilm retention, extracellular DNA, hydrodynamic redistribution, horizontal gene transfer, transformation products, and co-selective stressors, although their relative roles remain unresolved. These findings support monitoring that combines chemical and genetic endpoints with information on sources, hydrology, compartments, and hosts to inform aquatic-animal risk assessment and management. Full article

Eco-anxiety, commonly known as distress relating to perceived climate and ecological crises, is increasingly reported among young people, particularly in high-income and highly climate-exposed contexts such as Australia and other OECD countries. While empirical research on eco-anxiety has expanded rapidly, the underlying conceptual and theoretical frameworks remain fragmented, with limited consensus on definitions, mechanisms, and normative implications. This systematic conceptual review synthesises and critically evaluates the theoretical and conceptual models used to study eco-anxiety and related constructs among young adults in Australia and across OECD nations. By conducting a systematic search of multidisciplinary databases, the paper explores English-language studies published up to March 2026 that explicitly applied, developed, or implied conceptual frameworks of eco-anxiety or climate change anxiety in young adult samples. A broad array of models are grouped into a typology of seven families: (1) clinical and psychopathology-oriented models, (2) environmental attitudes and concern models, (3) risk perception and appraisal models, (4) existential and phenomenological frameworks, (5) socio-ecological and political-economy models, (6) coping and resilience frameworks, and (7) integrative and emergent eco-anxiety-specific frameworks. Across these families, eco-anxiety is variously conceptualised as a maladaptive symptom, a rational moral emotion, a driver of climate engagement, or a sociopolitical affect rooted in structural injustice. Major gaps include conflicting definitions, limited attention to developmental specificity in young adulthood, sparse cross-cultural and Indigenous perspectives in the Australian context, and insufficient theorisation of digital media, colonial histories, and policy in shaping eco-anxiety. The paper proposes a novel conceptual typology that locates eco-anxiety at the intersection of emotional, cognitive, relational, and sociopolitical processes. A future research trajectory underpinned by pragmatic theoretical bases that prioritise pluralistic, context-sensitive models and mixed-methods designs is suggested. Implications for measurement, intervention, and climate and mental health policy are discussed, with particular attention to young adults in Australia and OECD settings. Full article

Carbon-based nanomaterials have emerged as promising tools for addressing major challenges in sustainable agriculture, including declining soil fertility, climate change, resource inefficiency, and increasing environmental pollution. Among these materials, carbon nanotubes (CNTs) and carbon quantum dots (CQDs) have attracted considerable attention because of their unique physicochemical properties and diverse interactions with plants and soil systems. This review provides a comprehensive comparative analysis of CNTs and CQDs, emphasizing how their structural characteristics govern environmental fate, plant uptake, physiological responses, and stress mitigation mechanisms. CNTs primarily function as one-dimensional nanostructures that improve soil properties, facilitate nutrient delivery, and immobilize environmental contaminants, whereas CQDs, owing to their ultrasmall size, excellent water dispersibility, and intrinsic fluorescence, actively regulate plant metabolism, photosynthesis, nutrient acquisition, and antioxidant defense. Their distinct transport pathways, rhizosphere interactions, and subcellular localization are critically evaluated alongside recent advances in synthesis, surface functionalization, and physicochemical modification. The review further summarizes current evidence regarding their roles in enhancing tolerance to heavy metal toxicity, salinity, and drought stress through modulation of reactive oxygen species scavenging, osmotic regulation, ion homeostasis, and stress-responsive signaling pathways. Potential phytotoxicity, environmental persistence, ecological risks, and green synthesis strategies are also discussed to provide a balanced assessment of their agricultural applications. Emerging opportunities for synergistic CNT–CQD composite systems are discussed, together with major knowledge gaps and future research priorities, including machine learning-assisted nanomaterial design, multi-omics characterization, long-term field validation, and life cycle-based risk assessment. This review establishes a conditional, context-dependent structure–behavior–function conceptual framework that provides theoretical guidance for the rational design and safe implementation of carbon nanomaterials in next-generation sustainable agriculture. Full article

Green buildings not only operate as energy-efficient structures but are also enriched with various sensors and network-supported environments. These are capable of sustaining high indoor environmental quality. This paper presents a simplified deterministic simulation framework and an extended simulation scenario for assessing how IoT-monitored green buildings may reduce indoor environmental risk for occupants with chronic health vulnerabilities. The model is motivated by previous work on green residences, housing conditions, and their relationship to physical and psychological health, where indoor air quality, ventilation, thermal comfort, humidity, noise, lighting, dust, chemical exposure, and smoke are treated as relevant housing-related factors. A baseline simulation with a virtual duration of 100 days was conducted using 100 code-generated participants distributed across two building categories: a bad climate house (BCH) and a green monitored house (GMH). Three chronic-condition profiles were taken into consideration: asthma, chronic obstructive pulmonary disease, and anxiety/stress. This prototype version of the developed Octave-compatible code allows the baseline behavior of the proposed risk model to be examined directly. The simulation protocol included nine indoor environmental indicators and a normalized risk score ranging from 0 to 100, with values classified as low, moderate, or high risk. An extended simulation was then introduced to examine a broader scenario. This extension added a normal house (NH) as an intermediate building category and expanded the health-condition set from three to five assigned profiles by including allergies and depression/low well-being. Controlled building-profile and health-profile deviations were also included, while allergies and depression/low well-being were parameterized with building-specific coefficients. This allowed the model to examine not only the separation between the two extreme cases but also the intermediate role of a partially controlled building scenario. The results showed a clear difference in risk response between the building categories. In the basic scenario, the BCH produced high final risk scores, whereas the GMH achieved low final risk scores. At the building level, the final mean risk was approximately 100.00 for the BCH and 9.37 for the GMH. In the extended simulation, the aggregated final mean risk was 99.08 for the BCH, 44.95 for the NH, and 9.02 for the GMH. These results show a clear building-level risk stratification, i.e., high risk for the adverse building case, moderate risk for the intermediate building case, and low risk for the green monitored building case. These findings should be interpreted as deterministic and extended simulation results under predefined normalized environmental profiles, not as direct clinical or field validation. The proposed code strategy is not intended as a clinical diagnostic tool, but as an oriented modeling approach for future IoT-based green building assessment, digital twin integration, and risk-aware environment management. Full article

With the intensification of global climate change, climate risk has become a systemic external shock faced by enterprises, profoundly affecting their strategic choices and transformation behaviors. Achieving the United Nations Sustainable Development Goals (SDGs)—in particular, SDG 7 (affordable and clean energy), SDG 9 (industry, innovation, and infrastructure), SDG 11 (sustainable cities and communities), and SDG 13 (climate action)—requires enterprises, as microeconomic agents, to undergo fundamental low-carbon transformation. Against the backdrop of continued advancement of the “dual carbon” targets and government green governance, whether there is a link between climate risk and enterprises’ low-carbon transformation, and through which pathways this occurs, have become important questions in urgent need of answers. This study uses listed companies on China’s A-share market from 2011 to 2023 as research samples to empirically examine the relationship between climate risk and enterprises’ low-carbon transformation and its possible mechanisms. The study finds a significant positive association between climate risk and low-carbon transformation at the enterprise level, manifested as a relative decline in carbon emission intensity. Mechanism analysis shows that mitigating information asymmetry and promoting digital transformation may serve as important channels for this positive association; government green attention may strengthen the aforementioned relationship. Heterogeneity analysis indicates that the positive association is more pronounced among samples in eastern China, heavily polluting industries, and non-state-owned enterprises. These findings broaden the research perspective on the microeconomic consequences of climate risk and the driving factors of low-carbon transformation in enterprises. They also offer practical insights for governments to improve climate governance systems and guide enterprises in seizing transformation opportunities. At the same time, they provide practical implications for enterprises to link climate response with the broader sustainable development agenda, and offer policy-makers and corporate managers feasible pathways for turning climate risk awareness into measurable progress in SDG 13 (climate action) and SDG 9 (industry, innovation, and infrastructure). Full article

Contemporary global tourism is undergoing a structural transformation toward immersive, experiential “diving” modalities, establishing the countryside as a critical axis for constructive and regenerative travel. Despite this shift, academic literature remains bottlenecked by informational fragmentation, routinely prioritizing consumer demand over supply-side operator agency. To resolve this empirical deficit, this study utilizes a qualiquantitative, multi-sited, sequential mixed-methods research design across Europe and Oceania, focusing on agritourism as a significant sub-sector. The empirical findings reveal that while global agritourism is unified by a profound biographical territorial embedding, its operational architecture splits into two polarized configurations. The European Institutionalized Mature Model is highly market-driven, demand-responsive, and insulated by top-down public policy frameworks, yet it is severely constrained by generational succession grids and an aging demographic. In contrast, the Oceanian Decentralized Identity Frontier Model represents a youth-centric, female-dominated structure born of necessity within Small Island Developing States, driven by intrinsic cultural pride but exposed to a complete policy vacuum and severe climate variability. The research diagnoses an advanced sub-specialization training shortfall in Europe regarding environmental and social agriculture, and a foundational corporate capacity deficit in Oceania concerning financial risk hedging, compliance, and direct-to-consumer digital infrastructure. Full article

Climate change is increasing the frequency of hydrological extremes such as floods, requiring more effective approaches to build community preparedness alongside traditional flood risk management. This study develops and evaluates a serious-game-based flood risk education intervention in Kazakhstan and examines whether post-intervention protective behavioral intention can be predicted from gameplay, hydrological-literacy, and governance-attitude data. A cross-sectional, post-intervention survey of 60 participants from flood-, GLOF-, and drought-exposed regions of Kazakhstan was analyzed using descriptive/inferential statistics and three machine-learning classifiers (logistic regression, random forest, multilayer perceptron) to predict protective behavioral intention and to identify its strongest correlates. The participants showed good knowledge about immediate responses in flood events: 91.7% were able to identify the appropriate evacuation procedures, and the mean hydrological-literacy score was 5.13 out of 7. There was relatively less knowledge about longer-term mitigation measures, especially the cost effectiveness of nature-based flood protection (56.7%). Most participants agreed with the introduction of measures for flood management using nature (58.6%) and mandatory disaster insurance (87.7%), and 81.0% indicated that they planned to take preparedness measures after playing. The predictive models with the highest discrimination were logistic regression and random forest, with an area under the receiver operating characteristic curve (AUC) of 0.81 each, while the multilayer perceptron (MLP) had an AUC of 0.75. Importantly, both machine-learning and conventional statistical analyses revealed that perceived scenario fidelity was the strongest and most consistent predictor of protective behavioral intention. These results suggest that serious-game-based flood risk education provides a useful context for examining hydrological knowledge and generating behavioral data for predictive modeling of preparedness. The framework combines experiential learning, flood-governance assessment, and machine-learning-based behavioral prediction, while the findings indicate an association between perceived scenario realism and preparedness-related behavioral intention. These results provide a basis for further investigation of data-driven disaster risk education in Kazakhstan and other settings facing similar hydrological hazard risks. Full article

The Anti-Atlas mountain range is a region of high geodiversity where geological heritage intersects with fragile socio-economic systems. This study evaluates the Agadir–Ouarzazate transect as a case study of sustainable territorial valorization, combining a standard quantitative geosite assessment framework applied to eleven natural geosites with a novel Geomorphological Scenography Index (GSI) developed to evaluate seven geo-cinematic sites from a broader inventory of eight documented film locations, alongside a survey of eleven vernacular heritage sites—kasbahs, a fortified ksar, city ramparts, and one collective cliff granary (Igoudar). Spatial analysis indicates strong lithological control over the conversion of geodiversity into place-based economic and cultural assets: Protected Designation of Origin Taliouine saffron depends on Siroua volcanic soils and Taznakht carpet production on Precambrian basement pastoralism; earthen Ksour architecture shows close geomorphological adaptation to mountain climates and active geodynamics; and the conversion of geological landscapes into cinematic assets (“geographical surrogacy”) reaches a mean GSI of 3.28/4.00 (SD = 0.48; maximum 3.80, CLA Studios) across the documented inventory. Two roadside geosites reach near-maximum degradation risk (390/400). In line with the UN Sustainable Development Goals and Morocco’s Law No. 33.22 on heritage protection, this study proposes a formalized Geo-Cinematic Route as a data-driven management tool balancing geoconservation with tourism diversification in arid mountain settings. Full article

Extreme climate events increasingly amplify agricultural trade security risks across interconnected stages: agricultural production, commodity conditions, cold-chain storage, logistics distribution, and trade fulfillment. However, existing approaches predominantly focus on isolated stages and fail to model cross-stage risk propagation, enable real-time edge inference, or provide well-calibrated risk warnings. To address these challenges, a multimodal edge-intelligence framework, termed AgriClimate-EdgeNet, is proposed to jointly model climatic conditions, agricultural production, commodity imagery, cold-chain states, logistics trajectories, and trade records. An extreme-climate-aware cross-modal consistency mechanism is developed to capture normal inter-stage correspondence and identify abnormal information conflicts. Depthwise separable temporal convolutions, gated temporal units, lightweight attention, and Teacher–Student distillation are incorporated for efficient edge inference. Furthermore, dynamic modality reliability estimation and dual predictive uncertainty modeling (decoupling epistemic and heteroscedastic aleatoric uncertainties) are integrated to ensure decision trustworthiness under severe sensory noise and missing observations. On a 38,400-window agricultural trade dataset, AgriClimate-EdgeNet achieves an Accuracy of $0.914$, Recall of $0.896$, Macro-F1 of $0.902$, and AUC of $0.949$, while reducing expected calibration error to $0.028$ in routine single-pass Streaming Mode and $0.021$ under multi-sample Deep Audit Mode. In operational edge deployment on NVIDIA Jetson AGX Orin, the model requires $3.96$ M on-device parameters and $1.21$ G FLOPs, achieving an inference latency of $8.6$ ms ($116.3$ samples/s) in Streaming Mode and $38.4$ ms in Deep Audit Mode. These results demonstrate that AgriClimate-EdgeNet provides an accurate, robust, and low-latency solution for full-chain agricultural trade risk sensing under extreme climate shocks. Full article

We perform textual analysis to assess banks’ climate-related disclosures in public reports. Banks have greatly increased the quantity and quality of their climate risk discussions over time. We find that higher disclosure scores are associated with a lower probability that a loan is made to a borrower in the climate policy-relevant sector. This association is stronger for banks headquartered in countries with a mandatory ESG disclosure requirement. In contrast, we find mixed evidence when comparing banks with and without a voluntary climate commitment. Our findings indicate that public climate reports contain valuable information about banks’ lending practices. Full article

Climate change is one of the greatest threats to global biodiversity, with profound implications for the distribution and persistence of tree species that provide essential ecological, economic, and social benefits. This study assessed whether forests in the Sudanian zone of Senegal will continue to provide suitable environmental conditions for fifteen high-value tree species under future climate change scenarios. Species distribution models were developed using presence–absence data collected from 2398 forest inventory plots and five selected bioclimatic variables. Generalized linear mixed models with a binomial distribution were fitted using historical climate data and projected under three Shared Socioeconomic Pathway (SSP) scenarios (SSP2-4.5, SSP3-7.0, and SSP5-8.5) for the periods 2041–2060 and 2081–2100. The projections indicate a consistent increase in mean annual temperature across all climate scenarios, whereas changes in precipitation vary depending on the scenario. Species responses differed markedly, reflecting contrasting climatic tolerances. Nine species (Acacia macrostachya, Bombax costatum, Cordyla pinnata, Combretum micranthum, Detarium microcarpum, Prosopis africana, Pterocarpus erinaceus, Sterculia setigera, and Terminalia avicennioides) are projected to undergo substantial reductions in their suitable distribution areas under SSP2-4.5 and SSP3-7.0, with severe contractions or local disappearance under the most extreme scenario (SSP5-8.5). In contrast, three species are predicted to maintain relatively stable distributions or expand their suitable ranges under future climatic conditions. Spatial projections further suggest an overall west–southwest shift in species distributions. Overall, our findings demonstrate that most high-value tree species in the Sudanian zone are highly vulnerable to future climate change and may face an increased risk of local extinction. These results highlight the urgent need to integrate climate change into forest management planning by prioritizing adaptive conservation strategies, strengthening monitoring programs, reducing harvesting pressure on vulnerable species, and promoting their natural regeneration to enhance the long-term resilience of Sudanian forests. Full article

Climate change demands integrated urban responses combining governance, education, and territorial planning. The Educating Cities (ECs) framework offers a normative vision linking lifelong learning, civic participation, and sustainability, yet it still does not systematically position climate change within this paradigm. This study examines how EC principles can orient integrated climate responses by systematizing climate-oriented actions across three evaluative dimensions—importance, urgency, and perceived viability—to inform an empirically grounded framework for climate action in ECs in Brazil. A mixed-methods design combined an integrative literature and document review with a Fuzzy Delphi Method applied to a cross-sectoral panel of 21 experts, who assessed twenty climate-oriented actions derived from the Charter of Educating Cities. Results reveal strong consensus on the strategic relevance and urgency of nearly all proposed actions, particularly participatory climate risk diagnosis, transversal climate education, policy integration, and inclusive territorial planning. However, only a limited number of measures were considered viable under current institutional and financial conditions, exposing a structural gap between recognized priorities and practical feasibility. These findings position the Educating City as a learning-oriented governance framework that can support climate action, while highlighting the need to strengthen municipal capacity, coordination mechanisms, and long-term financing to close this gap. Full article

Rural residential buildings in China’s Central Plains region suffer from poor envelope thermal performance, resulting in severe thermal discomfort and excessive energy consumption for both winter heating and summer cooling. This study presents a systematic three-in-one envelope retrofit strategy (roof, exterior walls, and windows, with supplementary door replacement) tailored to the region’s cold climate zone (GB 50176-2016, Zone IIB; HDD18 = 2309 °C·d, CDD26 = 131 °C·d), which experiences hot summers and cold winters and thus requires both effective winter insulation and summer heat protection, utilizing cost-effective materials suitable for rural construction. A typical brick-concrete rural residence in Anyang was selected as the case study building. Field measurements of indoor thermal conditions were conducted over 72 h in winter to characterize baseline performance. An Ecotect simulation model was developed and calibrated against measured data using actual hourly meteorological observations from the Anyang National Meteorological Station for the monitored period; the CSWD Typical Meteorological Year (TMY) file was used for the annual simulation. To address concerns regarding discontinued software, key annual load results were cross-validated against an independent EnergyPlus v22.2 model using identical geometry and envelope inputs, yielding agreement within 5%. The proposed retrofit scheme retains the existing 240 mm solid brick walls, adds external insulation consisting of 100 mm EPS panels for walls and 50 mm XPS panels installed at ceiling level within the attic (without disturbing the existing asbestos-cement roof sheeting, in accordance with strict asbestos-handling protocols) for roofs, and replaces single-glazed windows with 6 + 12A + 6 insulated hollow glass units. The results demonstrate that the optimized envelope significantly reduces overall heat transfer coefficients: wall U-value decreases from 1.79 to 0.32 W/(m²·K), roof U-value from 2.46 to 0.48 W/(m²·K), and window U-value from 6.40 to 2.40 W/(m²·K). The passive adaptability index (PAI)—the annual proportion of free-running hours within the fixed 18–28 °C screening band specified in ^{Table 4}.3.1 of GB/T 50785-2012—improves from 0.41 to 0.68, and annual heating and cooling energy consumption is reduced by 50.4% (49.6% when the supplementary door replacement is excluded; (123 − 62)/123 = 49.6%). Under this fixed-band screening criterion, indoor operative temperature lies within the band for 68% of annual hours, compared with 41% in the baseline; these figures are fixed-temperature-range screening results rather than a formal adaptive-comfort evaluation (^{Section 3.3} and ^{Section 6.3}). A Glaser method condensation analysis confirms no interstitial condensation risk in the externally insulated EPS wall assembly. This study provides validated, region-specific technical parameters and demonstrates that a coordinated three-component envelope retrofit can achieve over 50% energy savings while substantially improving indoor thermal conditions as measured by the fixed 18–28 °C screening band in rural Central Plains buildings; a formal adaptive thermal comfort evaluation was not conducted and is identified as future work (^{Section 3.3} and ^{Section 6.3}). The material-cost-based simple payback is approximately 5.5 years (CNY 19,500 ÷ CNY 3550/year); including estimated rural labor and scaffolding costs (CNY 4000), the full project payback is approximately 6.6 years (CNY 23,500 ÷ CNY 3550/year). The findings offer practical guidance for large-scale rural building energy retrofitting programs in cold climate zones of China with transitional characteristics. Full article

As the global aquaculture industry moves towards high density and integrated efficiency, the precision and immediacy of water quality management have become key to increasing productivity and reducing risks. Traditional aquaculture relies on manual experience or simple threshold controls, which often suffer from response delays and energy waste. This study proposes an IoT environmental prediction model based on edge computing designed specifically to address complex and variable outdoor aquaculture environments. The system integrates multimodal sensor data such as water level, temperature, and turbidity, and employs a 1D-CNN-LSTM (One-Dimensional Convolutional Neural Network–Long Short-Term Memory) model deployed on ESP32 edge computing nodes to achieve low-latency environmental change prediction. Based on five core control rules (turbidity control and bidirectional regulation of water level and temperature), this study simulates 360 days of operational data in a real-world environment, covering seasonal climate changes and extreme weather events (such as typhoons). Experimental results show that, compared with traditional hysteresis control, the predictive control strategy proposed in this study can provide early warnings of environmental anomalies 15 to 60 min in advance, effectively increasing the proportion of time in which water quality parameters are maintained within safe thresholds to 99.8%. This paper details the system architecture, prediction model design, and empirical benefits of long-term simulation data analysis, providing a solution with both academic depth and practical value for smart aquaculture. Full article