Search Results (10,007)

Urban vegetation is widely regarded as a key strategy for mitigating urban heat, but its cooling performance is not constant and varies across climatic zones and urban structures. Most existing evidence derives from single-city or single-climate studies, leaving the differences in vegetation cooling with respect to building density and background climate insufficiently quantified globally. This study examined vegetation cooling efficiency (CE), defined as the absolute slope of the relationship between NDVI and normalized land surface temperature (LST), across 20 major cities spanning tropical, arid, temperate, and continental climate zones during 2000–2020. We combined city-level regression, built-up fraction stratification, and interpretable machine learning to quantify vegetation CE and its variation across climate and urban density gradients. CE varied roughly eightfold across cities (≈0.07–0.60), with the strongest responses in arid and continental cities such as Dubai and Almaty. Increasing built-up fraction systematically weakened the NDVI–LST relationship, turning near-neutral or slightly positive in the most compact temperate cores (80%–100% built-up). The machine learning model reproduced these patterns (out-of-sample R² = 0.757), identifying NDVI and evapotranspiration as dominant drivers. These findings indicate that vegetation cooling is strongly context-dependent, underscoring the need for climate-specific and morphology-based perspectives on urban greening rather than generalized evaluations. Full article

Highway construction projects face interacting risks that affect time, cost, regulatory compliance, and delivery resilience, all of which are closely linked to sustainable infrastructure development. This study develops a hybrid decision-analytical and machine learning framework for sustainability-oriented risk assessment in highway construction under limited-data conditions. The framework combines (i) the Analytic Hierarchy Process (AHP) and tabular Generative Adversarial Networks (GANs) to structure and stress-test expert judgement, and (ii) Probability-Impact (P-I) scoring with a Bayesian Networks (BNs) to model dependencies and derive posterior weights for probability of occurrence, impact on time, and impact on cost across four headline risk factors: weather-related risks, lack of labour, design-related risks, and permitting/regulatory risks. AHP provides transparent and auditable priorities with consistency checks, while GAN-generated synthetic tables support diagnostics for central tendency (P50) and tail behaviour (P90) under data scarcity. The calibrated P-I scores parameterise BN conditional probability tables, enabling the updating of BN scores; and factor-level decomposition of expected contributions. The framework produces model-ready posterior weights that support early planning, contingency allocation, mitigation prioritization, scenario analysis, and subsequent simulation and optimization studies. In sustainability terms, the proposed approach helps project teams improve climate resilience, strengthen regulatory and environmental preparedness, and reduce inefficient use of time, cost, and project resources in data-constrained settings. The results show that permitting/regulatory risks have the highest contribution to probability of occurrence and time impact, while weather-related risks exert the greatest cost impact. The framework therefore offers a practical tool for supporting more resilient, transparent, and sustainable highway project delivery when large historical datasets or questionnaire surveys are unavailable. Full article

Marine Protected Areas (MPAs) are key tools for mitigating the impacts of human activities on marine biodiversity and addressing climate change. Consequently, nations worldwide have committed to international targets to expand MPA coverage, leading to a rapid increase in protected areas and generating significant challenges for financing and effective management, particularly in developing countries. Under this scenario, multiple stakeholders, including local communities, academia, governments, and national and international organizations, are joining efforts to reduce financial gaps and strengthen MPA governance and management. In this study, we present the case of the Juan Fernández Archipelago in Chile, where multiple organizations collaborated to develop a socially robust and locally grounded governance system for a network of MPAs through a comprehensive community engagement process conducted on Robinson Crusoe Island between 2022 and 2024. As a result, a Functional Community Organization was established to co-manage the MPAs with the Chilean government, and three MPA management plans encompassing ~580,000 km² were approved. Among them, the management plan of the Multiple-Use MPA “Mar de Juan Fernández” was the first approved under the new Chilean Biodiversity and Protected Areas Service (Law 21,600), setting a national precedent for co-management. Our findings show that effective MPA governance depends not only on institutional design but also on the extent to which governance arrangements are socially embedded and locally legitimate. In this context, community-grounded and context-sensitive engagement processes facilitated high levels of participation, strengthened representation, and supported the co-production of knowledge, providing a strong foundation for the long-term implementation of conservation objectives. Full article

Mitigating nitrous oxide (N₂O) emissions from cropland soils is a pressing challenge for climate change mitigation. This study evaluated rockwool-based fertigation (RF) in reducing N₂O emissions from tea plantations. A 17-month field experiment was conducted comparing RF with conventional surface fertilization (CK), measuring tea plant biomass, new tea shoots yield, new tea shoots quality indices, soil N₂O fluxes, physicochemical properties, and nitrogen (N)-cycling functional genes across different soil layers. Results showed that RF treatment significantly increased the aboveground pruning biomass of tea plants, suggesting that RF promotes tea plant growth. The RF treatment showed lower N₂O fluxes and cumulative N₂O emissions within 90 days post-fertilization across the tea-growing season compared with CK, demonstrating that RF effectively mitigates N₂O emissions from tea plantation soils. Random forest analysis further revealed that the RF-induced vertical redistribution of nutrients and N-cycling functional genes was the primary driver of N₂O mitigation. Our findings demonstrate that RF is an effective dual-benefit strategy that simultaneously enhances tea plant productivity and mitigates N₂O emissions by reshaping soil biogeochemical processes and their spatial distribution. Full article

Mediterranean agricultural systems are highly vulnerable to increased climatic variability, which threatens soil water availability and the functionality of the soil carbon (C) cycle. Soil management practices strongly influence water dynamics and C-substrate quality, thus potentially affecting the temperature sensitivity of soil respiration. We evaluated the combined effects of tillage (traditional tillage, TT; reduced tillage, RT), fertilization (mineral, MF; addition of biosolid compost, BC), and rainfall inputs (ambient conditions, C; reduction of 30% rainfall inputs, EX) on soil water content (SWC) and storage (SWS), and in situ soil respiration (Resp) dynamics over three agricultural seasons in a Mediterranean legume–wheat rotation, using a factorial field experiment. We also evaluated how the sensitivity of soil respiration to temperature could be affected by tillage and fertilization types in a complementary laboratory experiment under controlled moisture and temperature conditions. RT was effective in improving SWS and mitigating surface desiccation, although this advantage was attenuated in wet years due to homogenization of moisture along the soil profile. Soil Resp was primarily controlled by SWC. BC stimulated soil respiration mainly during the first crop season, with a residual non-significant trend in the third season. This effect appeared constrained under dry periods, although no significant fertilization × rainfall exclusion interaction was detected. The diurnal cycle of Resp showed a clear decoupling from diurnal soil temperature. Crucially, the intrinsic thermal sensitivity of respiration (Q₁₀) remained stable across all tillage and fertilization treatments, suggesting that field variability is driven by water dynamics and crop phenology and not by microbial responses to changes in substrate availability. Our results confirmed the hierarchical role of climate on C-cycling processes. Full article

Wildfires are a growing threat in Mediterranean regions where climate variability and land-use practices increase vulnerability to fire risk. Developing effective prediction models is essential for robust wildfire management, particularly in such data-scarce environments. Focusing on data-scarce Mediterranean environments, with reference to environmental conditions observed in Morocco, this review presents prediction models across three methodological categories: spatial risk mapping, temporal forecasting, and fire spread simulation, alongside the satellite data products that support their deployment. Each category is assessed in terms of predictive performance, data requirements, and adaptability to low-resource environments. XGBoost showed strong applicability in data-scarce Mediterranean contexts, while ARIMA was validated for forecasting fire-relevant time series under limited data resources. Freely accessible MODIS-derived products represent a significant asset to the region. Based on this synthesis, a hybrid XGBoost-ARIMA framework incorporating MODIS-derived inputs and SHAP-based interpretability is proposed as a promising candidate architecture to be validated after further investigation. The findings aim to support researchers, land managers, and policymakers in strengthening local wildfire prevention and mitigation efforts by aligning model capabilities with regional data and environmental constraints. Full article

Global forest ecosystems face growing threats from land use change, climate and weather extremes, and insects and diseases. Managing these threats is difficult due to the time, cost, and human error associated with the quality and quantity of data required for research and assessment. While conventional analytical methods are being improved constantly, they are often slow in providing information needed to respond promptly to unprecedented changes driven by both natural and anthropogenic alterations to forest ecosystems. For this reason, potential applications of artificial intelligence (AI) have attracted increasing attention in the field. Here, we examine the benefits and challenges of using AI in near-term forest health monitoring (surveillance, mostly over small scales) and discuss the need for long-term and larger-scale assessment. Abundant evidence shows that existing AI methods already facilitate the rapid collection, compilation, and synthesis of available data from diverse sources. Furthermore, emerging technologies (e.g., agentic AI) are building these capabilities into autonomous systems. However, every AI tool has advantages and limitations. With constant improvements, integrative AI-driven approaches that simultaneously deal with multiple and cross-scale interacting factors are expected to deliver actionable insights about forest health better than any single AI tool. Consequently, they can enhance decision-making processes, reduce monitoring costs, and help mitigate the impacts of forest health threats. As AI continues to evolve, it is essential to circumscribe its role in forest health monitoring. Most importantly, AI should not define what humans value regarding forest health but instead should be applied to help us evaluate data about our chosen value targets. Full article

Rising global temperatures and recurrent heat waves increasingly threaten vegetable production, as vegetable crops are more thermosensitive than most field crops. Vegetable crops frequently experience severe reductions in pollen viability, fruit set, marketable yield, and quality under heat waves. Numerous reviews have substantially advanced our understanding of heat stress perception, signal transduction networks, transcriptional regulation, and thermotolerance mechanisms, primarily in model species and major field crops. However, comprehensive review articles of field-applied mitigation strategies specifically tailored to vegetable production remain limited. This review provides a critical analysis of the use of genetic resources (cultivars and grafting), field management approaches (optimized planting dates, crop rotation, canopy management, and intercropping), irrigation, nutrient optimization, biostimulants, microbial inoculants, and physical microclimate modification strategies. The research consolidates current applied and mechanistic evidence on heat-stress mitigation in vegetable crops and identifies targeted, actionable priorities for field adoption. Emphasis is placed on the integration of complementary mitigation strategies at the field scale where combined approaches may generate synergistic effects. Key research gaps include limited studies on combined heat–drought/salinity stress, lack of standardized field protocols for biostimulants, and insufficient farm-scale economic evaluations of mitigation strategies. Advancing interdisciplinary, field-validated, and climate-smart management frameworks will be essential to ensure sustainable vegetable productivity and quality stability in accelerating global warming. Full article

Acidification and the application of exogenous tannins are well-established oenological practices designed to ensure wine stability and quality, playing a pivotal role to address the grape compositional imbalances associated with climate change. This study investigates precision enology techniques using a 2023 Sangiovese di Romagna, analyzing the interaction between pH modulation (3.2, 3.6, 3.8) and the addition of commercial grape seed tannins with varying medium degrees of polymerization (TanA: 3.1 mdp vs. TanB: 10.8 mdp). Following alcoholic fermentation, a full factorial design was implemented, including control batches (pH adjustment only). After a 40-day mild thermal treatment (T = 25 ± 1 °C) to simulate aging, results indicate that the high-mdp tannin (TanB) dominated color evolution regardless of pH, whereas the low-mdp tannin (TanA) effect was pH-dependent. Notably, a pH of 3.8 resulted in colloidal instability across all samples. The findings highlight the importance of customized protocols to mitigate climate-related challenges in winemaking. Full article

Achieving carbon-neutral cities requires innovative strategies that integrate technological carbon capture, sustainable urban infrastructure, and proactive public engagement. While microalgae-based systems have shown promise for CO₂ sequestration and resource recovery, their scalability remains constrained by high costs and energy-intensive photobioreactor (PBR) designs. Here, we propose the retrofit of existing urban fountains into high-efficiency microalgae cultivation systems—microalgae-enhanced fountain landscapes—as an integrated solution that bridges ecological function and social outreach. This approach capitalizes on ubiquitous fountain infrastructure to minimize deployment costs, employs advanced fountain-style cultivation technology to enhance biomass productivity, and leverages strategic locations in high-footfall urban zones to actively elevate public carbon literacy and motivate low-carbon behavioral shifts through immersive engagement—a vital step toward city-wide participatory climate action. We critically analyze the feasibility of this system, highlighting its potential for multi-stakeholder value creation across developers, municipalities, and citizens. Furthermore, we synthesize recent advances in suspended microalgae cultivation, building-integrated PBRs, and microalgae-informed landscape design to contextualize the development pathway of fountain-based systems. By uniting technical efficiency with civic education, this work establishes a replicable framework for scalable urban deployment—simultaneously advancing carbon mitigation, public awareness, and circular resource flows in the transition toward climate-resilient cities. Full article

Drought is a widespread natural hazard in China that can sequentially trigger meteorological, hydrological, agricultural, and socio-economic drought types, yet traditional drought indices typically focus on a single hydrologic component and cannot capture integrated water deficits across multiple compartments. This study aims to systematically characterize the spatiotemporal evolution of comprehensive drought across China’s ten major river basins and to identify and quantify the main natural and anthropogenic factors associated with drought dynamics. We utilized the Gravity Recovery and Climate Experiment (GRACE) Mascon dataset spanning the entire mission period (April 2002–June 2017), which provides a continuous 15-year observation window suitable for detecting decadal-scale trends and inter-annual variability. Given the documented asynchrony between precipitation and terrestrial water storage changes, a zoned index framework was applied: the Combined Climatologic Deviation Index (CCDI) for arid basins and the Drought Severity Index (DSI) for humid basins. The Theil–Sen estimator and Mann–Kendall test, both non-parametric and robust to outliers, were employed for trend detection, and Pearson correlation analysis was used to evaluate statistical associations between drought indices and potential influencing factors. The results reveal a clear “dry gets drier, wet gets wetter” pattern during 2002–2017: severe drought episodes in humid basins (e.g., the Yangtze) were concentrated in 2002–2006, whereas those in arid basins (e.g., the Haihe) occurred mainly in 2013–2017. Groundwater storage anomaly (GWSA) constituted the primary component of total water storage changes in most basins, with the most rapid depletion rate of −45 mm yr⁻¹ in the northern arid basins. Land use/cover change, especially urban expansion, showed a significant statistical association with drought intensification in arid regions, with its standardized contribution being comparable to that of natural factors such as runoff. This study provides a systematic cross-basin assessment and offers scientific insights for differentiated drought mitigation strategies and water resources management. Full article

Energy security and efficiency governance are among the most critical policy challenges facing emerging economies in the post-Paris Agreement era. While international frameworks such as the IFCMA Climate Policy Database provide unprecedented comparative data on national mitigation instruments, the role of artificial intelligence (AI) in optimizing policy design across the efficiency–security nexus remains underexplored. This study develops an AI-driven analytical framework—integrating K-Means clustering, Principal Component Analysis (PCA), and Random Forest classification—and applies it to the April 2026 edition of the IFCMA Climate Policy Database, encompassing 4627 active policy instruments across 42 countries. We systematically compare the policy instrument portfolios of nine emerging economies with those of thirty-two developed counterparts, with a particular focus on energy efficiency standards, fiscal instruments, and strategic security objectives. The results reveal that emerging economies exhibit structural under-utilization of performance standards and trading schemes, disproportionately high energy security objective ratios relative to their efficiency instrument sophistication, and an over-reliance on tax instruments compared to their counterparts in developed economies. The Random Forest classifier achieves 83.1% cross-validated accuracy in predicting emerging economy status from policy features, with performance standards and efficiency objectives as the strongest discriminators. Three distinct policy regime archetypes are identified: Standard-Dominant Mixed (Cluster A), Tax-and-Label-Dominant (Cluster B), and Trading-Intensive Transition (Cluster C). These findings provide AI-supported, evidence-based policy intelligence for governments seeking to move beyond minimum regulatory compliance and align energy efficiency governance with strategic energy security objectives. Full article

Global climate change is causing an increase in extreme rainfall events, which impacts the risk of hydrometeorological disasters. To support disaster mitigation and early warning systems, accurate and reliable rainfall predictions are required. Although ensemble forecasting is widely used to model atmospheric uncertainty, raw ensemble results often exhibit insufficient bias and dispersion. Therefore, post-processing techniques are needed to improve the quality of probabilistic predictions. The most commonly used calibration method is Bayesian Model Averaging (BMA). This study conducted a scoping review of peer-reviewed papers on ensemble forecast calibration using BMA, based on the PRISMA-ScR framework. Furthermore, this study presents a comprehensive bibliometric analysis involving co-authorship networks of productive authors and bibliometric maps with clustered terms. A total of 35 relevant articles were identified from 49 screened publications. The bibliometric analysis revealed that “ensemble forecasting” and “Gaussian distribution” are the most dominant terms in the research network, indicating that Gaussian-based approaches remain more widely used in ensemble forecast calibration studies. In contrast, studies explicitly applying Gamma-based approaches are still relatively limited despite their relevance for modeling asymmetric rainfall data. The results obtained in this study highlight the importance of developing and integrating more appropriate probability distributions, such as those within the Extreme Value Theory framework, into BMA models. These findings suggest that the selection of appropriate probabilistic distributions in BMA-based calibration frameworks plays an important role in improving forecast reliability and the representation of uncertainty in rainfall prediction. Furthermore, the development of more suitable probability distributions, including Extreme Value Theory (EVT)-based distributions, has strong potential to enhance probabilistic calibration performance for asymmetric rainfall data. This approach is expected to improve the accuracy and reliability of extreme rainfall predictions. The findings of this study provide an important contribution to the development of early warning systems for hydrometeorological disasters and support the achievement of Sustainable Development Goals (SDGs). Full article

Carbon reduction is an urgent challenge for developing nations that balance socioeconomic development and climate mitigation in global low-carbon control. As a key digital economy means, e-commerce development enables urban low-carbon transition. In this context, drawing on a Chinese panel dataset covering 283 cities during 2006–2022, and taking the National E-commerce Demonstration City Pilot Policy (NEDCP) as a quasi-natural experiment, we use a multi-stage difference-in-differences (DID) strategy to detect how NEDCP affects urban carbon emissions. The results reveal that the NEDCP greatly reduces carbon emissions at an urban scale, which remains robust through a series of robustness tests. Mechanism analysis focuses on three channels, which includes boosting energy efficiency, advancing the digital economy, and promoting green innovation. Heterogeneity tests show that these benefits are more strongly evident in cities with a higher openness, a larger population, better economic conditions, and a stronger innovation capacity. The spatial spillover effect test shows that the NEDCP not only promotes local carbon reduction, but also promotes carbon reduction in neighboring areas. These findings offer theoretical insights for enhancing the NEDCP’s environmental benefits, and a practical guide for differentiated low-carbon development strategies, especially for prioritizing logistics and innovation support and refining green e-commerce standards. Full article

This study presents a gate-to-gate life cycle assessment (LCA) of an industrial sweet cherry sorting and packing facility in Greece, directly addressing environmental sustainability in agri-food supply chains through data-driven impact quantification and improvement pathways in post-harvest operations. The assessment focuses on a gate-to-gate system boundary encompassing all processes inside the cherry sorting and packing facility, while upstream cherry production and downstream waste management are modeled and reported separately to provide system-level context. Core-stage hotspots are then analyzed in detail in the Results section, highlighting the dominant role of electricity use compared with packaging materials. The functional unit is defined as 1 kg of packed, market-ready cherries at the factory gate. Primary data are obtained from high-resolution, batch-level measurements of mass flows, energy use, water consumption, packaging materials and waste streams over a full processing season, structured as virtual sensor outputs. These sensor-informed operational data are combined with secondary life cycle inventory information from established databases to quantify climate change impacts and identify environmental hotspots across materials, energy, water, and waste, thereby delivering a quantified picture of environmental performance in the post-harvest stage. The results show that corrugated cardboard and associated packaging components are among the main contributors within the facility-level, gate-to-gate system, while the Core stage accounts for 28.43% of total GWP100. Upstream cherry production dominates the overall Upstream–Core–Downstream climate footprint with 70.61% of total impacts. Moreover, practical mitigation scenarios are modeled, including packaging optimization, partial substitution of grid electricity with photovoltaic generation, and increased water recirculation. Ιn the combined mitigation scenario, where packaging optimization, low-carbon electricity and improved water management are implemented simultaneously, total GWP100 decreases from 114,207.32 to 92,500.27 kg CO₂-eq (−19.0%) relative to the baseline, providing actionable sustainability improvements for industry stakeholders and supporting Sustainable Development Goals (SDGs) related to climate action and resource efficiency. In addition, the proposed virtual sensor architecture and data workflow support continuous monitoring, eco-efficiency management and near-real-time LCA implementation in post-harvest agri-food systems, enabling operational sustainability. Full article