Search Results (2,772)

While terrestrial carbon storage is vital for achieving global carbon neutrality, its spatiotemporal evolution in ecologically fragile regions—such as the Pisha sandstone area—is complicated by intense erosion and complex environmental drivers. Widely known as the Pisha sandstone area, often referred to as the “Earth’s ecological cancer” due to its unique geological instability (“hard as rock when dry, soft as mud when wet”), this area is a critical but vulnerable carbon sink in the Yellow River Basin. This study aims to clarify these dynamics and identify their non-linear driving mechanisms by integrating a coupled PLUS–InVEST model with an XGBoost–SHAP framework to simulate land-use cover change and quantify carbon sequestration potential from 1990 to 2040. Our results reveal: (1) a robust path dependence in land use, where grassland remained the dominant landscape matrix (>75%), which partly explains the stable regional carbon-stock structure and the moderate FoM value of the PLUS validation; (2) carbon storage followed a fluctuating but overall increasing trajectory, projected to reach a peak of 3.19 × 10⁵ tC by 2040 under the Ecological Conservation Scenario (ECS), which significantly outperforms the economic-driven and natural growth modes; (3) hot spot analysis showed that statistically notable low-carbon cold spots were concentrated mainly along valley corridors, marginal transition zones, and locally disturbed patches, whereas high-carbon hot spots were spatially limited; and, (4) crucially, XGBoost–SHAP results should be interpreted as model-based associations rather than direct causal proof; the whole-region model and the regional models jointly suggest that topography, water availability, socioeconomic pressure, and erosion-related factors contribute differently across bare, loess-covered, and sand-covered Pisha sandstone units. These findings support differentiated land-use and restoration strategies rather than uniform regional management. The findings suggest that future management in the Pisha sandstone area should transition from general restoration toward targeted and differentiated regulation to improve regional ecosystem services. Full article

Background: Heat exposure is a leading climate-related health threat, yet whether the heat–disease burden relationship is moderated by national adaptive capacity remains poorly quantified at the global level. We examined associations between heat exposure and cause-specific disability-adjusted life year (DALY) burden across climate vulnerability strata and projected future burden to 2050 under IPCC AR6 warming scenarios. Methods: We constructed a country–year panel spanning 187 countries and 34 years (1990–2023) by merging ERA5 reanalysis temperature data; GBD 2023 DALY rates for cardiovascular diseases (CVD), chronic kidney disease (CKD), and chronic respiratory diseases (CRD); ND-GAIN adaptive-capacity scores; and WHO GHO health system indicators. Countries were stratified into adaptive-capacity tertiles (Low: n = 63; Medium: n = 62; High: n = 62). We used two-way fixed-effects panel regression with country-clustered standard errors, a formal Chow test of slope equality, lagged exposure models, and a benefit-of-adaptation counterfactual. Future DALY burden was projected to 2030, 2045, and 2050 using country-specific ERA5 warming trends scaled to IPCC AR6 SSP scenario multipliers. Findings: The heat–CVD dose–response was 26 times larger in Low versus High adaptive-capacity countries (β = −346.2 vs. −13.1 DALY years per 100,000 per °C). The Chow test confirmed statistically significant slope heterogeneity across tertiles for all three outcomes (CVD: F = 22.0, p < 0.0001; CKD: F = 14.9, p < 0.0001; CRD: F = 9.4, p < 0.0001). CKD burden rose 47·8% globally between 1990 and 2023, with the strongest within-country heat–CKD association in Medium adaptive-capacity countries (β = −61.5, p < 0.0001). These findings were robust to lagged exposure specifications. Under SSP5-8.5 by 2050, Low adaptive-capacity countries face a projected CVD DALY rate change 23 times larger than High adaptive-capacity countries (−16.2% vs. −0.7%). Upgrading Low adaptive-capacity countries to High tertile standards would avert 15.6% of projected CVD DALY burden under SSP5-8.5 by 2050. Conclusions: Adaptive capacity substantially moderates the health consequences of heat exposure. The quantified benefit of adaptation investment—expressed as averted DALY burden—provides a direct metric for health-system strengthening and climate adaptation financing, particularly in low-income settings facing the steepest projected burden increases. These results position adaptive capacity as a critical social determinant of planetary health, linking Earth-system boundary transgression to inequitably distributed human disease burden across the global community. Full article

Climate-driven changes in oceanographic conditions are increasingly affecting the distribution of marine species, particularly vulnerable elasmobranchs. The undulate ray, Raja undulata, is a Near Threatened batoid species distributed throughout the northeastern Atlantic Ocean and parts of the Mediterranean Sea, yet its potential response to future climate change remains poorly understood. This study assessed current and future habitat suitability using species distribution modelling approaches and CMIP6 climate projections under the SSP245 scenario. Species occurrence records were compiled from biodiversity databases and published sources, and environmental predictors were selected following multicollinearity screening. Among twelve evaluated modelling algorithms, MaxEnt showed the highest predictive performance (AUC = 0.99; TSS = 0.95) and was selected for subsequent analyses. Current habitat suitability was concentrated along the Iberian Peninsula, the Bay of Biscay, the English Channel, and parts of the western Mediterranean Sea. Future projections indicated substantial habitat contraction, with habitat loss (57.3%) greatly exceeding habitat gain (2.2%), resulting in a southward redistribution of suitable habitats. Minimum phytoplankton concentration, sea surface temperature, and silicate concentration were identified as the most influential environmental predictors. Areas predicted to remain suitable under both current and future conditions may represent important climate refugia for the species. Overall, the results indicate that R. undulata is highly vulnerable to future environmental change and highlight the need to incorporate climate-driven habitat shifts into conservation planning, fisheries management, and long-term monitoring strategies. Full article

Projecting the spatiotemporal dynamics of the potential distribution of dominant species under climate change is essential for desertification control and vegetation restoration in drylands. Here, we modeled the current (1970–2000) and future (2080–2100) suitable habitats of Caragana korshinskii Kom, an ecologically important shrub species in northwestern China, by constructing an ensemble of eight species distribution models on the Biomod2 platform using three CMIP6 Shared Socioeconomic Pathways (SSP126, SSP370, SSP585) and 40 environmental variables representing climate, soil, topography and drought conditions. Key environmental drivers were identified through variable importance ranking and response curves, while area changes, spatial patterns, and centroid shifts in suitable habitats were quantified. The ensemble model demonstrated good to excellent predictive performance (mean AUC > 0.9, mean TSS > 0.5). Soil base saturation (t-bs) and soil moisture contributed the most (>38%), highlighting the dominant role of edaphic factors. The current total suitable habitat of C. korshinskii is approximately 182.2 × 10⁴ km², with all future scenarios projecting a consistent decline. Under SSP585, habitat loss reached 9.8% with contraction (30.5 × 10⁴ km²) far exceeding expansion (12.6 × 10⁴ km²). The distribution centroid shifted markedly eastward with a minor southward fluctuation, establishing the Ordos–Bayannur region as a stable core habitat. Overall, our findings suggest that the distribution of C. korshinskii is strongly constrained by edaphic and moisture conditions, and future contraction of marginal habitats may compromise ecosystem services. Full article

Sitka spruce (Picea sitchensis (Bong.) Carr.) is the dominant plantation conifer in Atlantic Europe, yet the genomic basis of provenance-level climate adaptation remains poorly resolved. We applied gradient forest analysis to 31,049 genome-wide SNPs from 1106 individual trees representing 79 native-range provenances in the IUFRO collection, using three environmental predictors retained after collinearity screening: Longitude, Temperature Seasonality, and Annual Precipitation. Longitude was the dominant driver of genomic turnover (mean R² = 0.01168), followed by Temperature Seasonality (0.00672) and Annual Precipitation (0.00228), reflecting the long-distance coastal gradient of the native range. A redundancy analysis conditioned on ancestry principal components confirmed a significant multivariate genotype–environment association (F = 8.679, p = 0.001). Genomic offset was negatively correlated with all three provenance-level performance traits measured at the IUFRO common garden after 50 years of growth: height, stem diameter and stem quality, providing empirical validation of the genomic-climatic framework. Projecting the fitted model onto European planting sites using an 8-GCM CMIP6 ensemble showed mean offset increasing from 0.0021 (SSP2-4.5, 2041–2060) to 0.0041 (SSP5-8.5, 2061–2080), with the most climate-exposed cells under the high-emission late-century scenario approaching the upper tail of the source population offset distribution. The European planting region showed a higher projected offset than the North American source range under all scenarios. This supports the hypothesis that provenances from Oregon to southern British Columbia are most suited for planting in regions under future Atlantic European conditions. Full article

Long-term energy system planning depends on projections of future wind turbine characteristics, yet existing approaches rely on either costly expert elicitation or deterministic trend extrapolation without formal uncertainty quantification. We present a Bayesian logistic framework that models the temporal evolution of hub height, rotor diameter, and specific power as physically constrained growth and decay processes, producing full posterior predictive distributions via Markov Chain Monte Carlo sampling. The framework is validated across three major onshore wind markets: Austria (534 turbines, 2000–2025), Germany (31,202 turbines, 1988–2026), and the United States (71,457 turbines, 1986–2025); spanning different market structures, regulatory environments, and data availability. Systematic benchmarking against linear, polynomial, and maximum-likelihood alternatives demonstrates superior hindcast performance, particularly for long-range projections where physical saturation constraints become relevant. Prior sensitivity analysis reveals that posteriors are robust for data-rich regions but honestly reflect prior influence for small datasets, identifying where expert knowledge is essential. We extend the framework to climate-aware energy yield estimation by propagating turbine posteriors through synthetic power curves and site-specific wind resource projections under SSP2-4.5 and SSP5-8.5, decomposing the total uncertainty into technology and climate components. When climate uncertainty is measured by scenario spread alone, technology uncertainty dominates. However, accounting for the full inter-model spread across 13 CMIP6 global climate models reveals that climate uncertainty becomes substantial (14–56%) and region-dependent, underscoring that both sources require explicit quantification. The open-source pipeline is designed for direct adoption in energy system planning workflows. Full article

Intelligent cost control in engineering, procurement, and construction (EPC) projects depends on the continuous transmission, updating, warning, correction, and reuse of cost data across multiple project stages. To analyse the resilience of this process, this study constructs an EPC project cost-data transmission network using complex network theory and Monte Carlo simulation. Eighteen core nodes and 27 directed weighted edges are identified according to EPC cost-management logic and expert evaluation. Node importance is analysed using weighted degree centrality, betweenness centrality, and PageRank, while network efficiency is used to evaluate cost-data reachability and transmission-path efficiency. Node failure, edge-weight perturbation, random edge failure, random failure and targeted attack, feedback enhancement, critical-node failure–recovery, and robustness checks are then conducted. The results show that Dynamic cost, Cost deviation warning, and Historical cost database are the three most critical nodes. Their failures reduce network efficiency by 44.54%, 37.43%, and 45.27%, respectively. Random edge failure has a stronger effect on network efficiency than edge-weight perturbation; when the edge failure probability increases from 5% to 20%, the average efficiency loss rate rises from 10.54% to 37.30%. Feedback-link enhancement increases network efficiency from 0.1858 to 0.2009 and produces a larger improvement than forward-link enhancement and random seven-edge enhancement. Robustness checks under alternative network assumptions indicate the relative stability of the critical-node identification results within the proposed network structure. The findings provide a scenario-based network perspective for identifying structurally critical nodes, vulnerable transmission links, and feedback-improvement priorities in EPC cost-data transmission. They also offer a methodological basis for future project-level calibration using BIM/5D BIM records, procurement data, cost-management platform logs, and settlement audit data. Full article

Recent transformations of utopia as a form can be followed from modernist totalizing grand narratives that depicted new socio-spatial orderings to its fragmentation, pluralization, and critical turn in the second half of the 20th century. But if we think about utopia as a critical form in our contemporary context, we often encounter it being perceived either as a pejorative term for a concept too outlandish and impossible to even be considered, or as a term used in conjunction with large-scale ideological projects which hold little regard for their socio-spatial context. Refusing to concede that utopia as a critical form has lost its relevance within the architectural discipline, the paper asks how contemporary utopian production could be identified, mapped, and interpreted after the fragmentation of modernist grand narratives. To that aim, the paper develops a three-axis analytical framework which observes contemporary forms of utopian architectural production. Viewing utopia not as a prescriptive image of an ideal future, but as a critical apparatus aimed at projection and inquiry, the framework maps utopian production according to its position between the possible and the impossible, the critical and the affirmative, and the uncovering and the projective. Building on the positions and relationships revealed through the structured three-axis framework, the paper constructs a typology of four ideal-typical protagonists: the Critical Thinker, the Speculative Designer, the Architect, and the Developer, demonstrating that contemporary utopian thought has not disappeared, but has dispersed across different forms of theory, speculative design, practice, and spatial production. Identifying through the four protagonists the potential of utopia not as a representational or prescriptive form, but rather as an operative strategy and a method of inquiry, the paper offers both a conceptual tool for analyzing architecture’s contemporary engagement with utopia as a critical method, and demonstrates how utopian thinking operates as critique, intervention, ideological projection, and a speculative scenario building within our fragmented and individualized contemporary condition. Full article

Cathaya argyrophylla Chun et Kuang is an endangered relict gymnosperm endemic to China. Its habitat has been severely fragmented due to Quaternary glaciations, a condition further exacerbated by modern, fragmented administrative management. We compiled 98 spatially filtered occurrence records across four provinces and developed a combined analysis framework integrating the Biomod2 ensemble model with the Marxan systematic planning algorithm. Our optimal model (TSS = 0.911, AUC = 0.986) identified mean diurnal range and ultraviolet-B seasonality radiation as the dominant ecophysiological drivers of the species’ distribution. Currently, suitable habitats cover 7.10% of the study area, with highly suitable habitats accounting for only 3.08% (21.76 × 10³ km²). Priority conservation areas account for 2.48% (17.55 × 10³ km²) of the total area. A gap analysis revealed that 76.98% (13.51 × 10³ km²) of the optimized priority conservation areas currently lack formal protection under China’s protected area system and the World Database on Protected Areas. Under four future climate scenarios (2030s–2090s), projections indicated overall habitat contraction, with limited spatial expansion observed only under specific scenarios (SSP1-2.6 in the 2030s and 2090s; SSP5-8.5 in the 2030s), and the population centroid was projected to shift southeastward by an average of 42.67 km in Huaihua City. Twenty-one core habitat patches were identified under current climate conditions. As these core habitat patches are concentrated along interprovincial boundaries, specifically the Dalou Mountains and the Yuecheng Ridge, our findings emphasize the need to bridge local administrative barriers. This spatial framework provides actionable guidelines for establishing transboundary protected areas, optimizing in situ conservation networks, and implementing model-based assisted migration. Full article

Amid China’s transition from rapid urbanization to high-quality development, quantifying urban building metabolism is crucial for building resilient resource management systems. However, current research predominantly focuses on eastern cities, largely overlooking non-residential buildings. Here, we apply dynamic material flow analysis (dMFA) to quantify the material stocks of residential and non-residential buildings in two major economic hubs in western China, Xi’an and Chengdu. The stock patterns from 1950 to 2050 and the underlying drivers are further clarified. Model projections suggest that material stocks in both cities will peak around 2040, reaching 2.2 billion tons in Chengdu and 1.08 billion tons in Xi’an, under the intensive scenario. Chengdu reaches stock saturation 2 to 3 years earlier than Xi’an, and the total stocks are approximately twice those of Xi’an. Reinforced concrete and steel structures dominate future building development and increase the accumulation of cement and steel. Sand and gravel still account for the majority of building materials. Demand for new construction materials shows a pronounced double-peak pattern, occurring in 2016 and 2026. Construction waste is projected to rise sharply by mid-century; scenario analysis indicates that an 80% material recovery rate has the potential to largely offset new material demand. Sensitivity analysis identifies building lifetime extension and construction technology improvement as the strategies with the greatest potential for mitigating future waste generation. This study expands the scope of urban building material metabolism research and provides a scientific basis for low-carbon urban planning and construction waste management in China. Full article

Background: Stroke remains a leading cause of mortality, disability, and health-system burden in Korea’s rapidly aging population. We aimed to describe national stroke incidence trends from 2011 to 2023, characterize ecological associations between stroke incidence and health-screening indicators, and generate model-based projections through 2028 to support health-system planning. Methods: This retrospective ecological longitudinal analysis used three publicly available aggregate national data sources: (1) NHIS annual aggregate statistics on crude and age-standardized stroke incidence, stroke case counts, first-onset vs. recurrent stroke, and case-fatality rates (2011–2023); (2) regional standardized health-awareness survey rates for stroke symptoms, myocardial infarction symptoms, blood pressure, and blood glucose (2017–2025); and (3) national cancer-screening outcome tallies for breast and cervical cancer (2010–2024). All analyses used pre-aggregated annual summary data; individual-level NHIS records were not used. Annual trends were modeled with ordinary least-squares linear regression (n = 13 annual observations). Pearson correlations were computed only for overlapping observation windows. Model-based projections are presented with 95% prediction intervals and are explicitly distinguished from observed NHIS values. This study is purely descriptive and ecological; no causal inference is made. Results: Crude stroke incidence increased from 199.2 to 221.1 per 100,000 (2011–2023; slope +2.32/year, R² = 0.83), whereas age-standardized incidence declined from 158.3 to 113.2 per 100,000 (slope −3.41/year, R² = 0.96), a pattern consistent with demographic aging as a contributing factor to growing absolute burden, though formal age-decomposition analysis would be required to confirm this inference. Total cases increased from 99,837 to 113,098; the 30-day case-fatality rate declined from 8.5% to 7.5%. Ecological correlations showed that blood glucose awareness was strongly negatively correlated with age-standardized incidence (r = −0.944, p = 0.001, n = 7), though these are ecological associations and must not be interpreted as individual-level causal relationships. Model-based projections estimate crude incidence near 230.7 (95%PI 219.2–242.2) and age-standardized incidence near 103.2 (95%PI 95.7–110.8) per 100,000 by 2026. Conclusions: Concurrent increase in crude burden and decline in age-standardized incidence reflects demographic aging as the primary driver of Korea’s stroke burden. Projections support integrated cardiovascular prevention, public health education, and age-sensitive service planning. All projections are short-horizon statistical extrapolations intended for policy scenario planning only and must not be interpreted as observed future NHIS outcomes. Full article

Data-driven models can be used to understand basin-wide hydrological processes and generate predictions for future conditions, particularly in cases of scarce data availability related to basin characteristics. Although they have long been applied in hydrological modeling, there is still limited information regarding their ability to produce reliable long-term projections under climate change conditions. This study evaluates the long-term predictive performance of data-driven models by employing a hybrid deep learning architecture combining Wavelet Transform (WT) and Deep Neural Network (DNN). The dataset used in this study was obtained from the Yamula Reservoir Basin, a semi-arid agricultural basin in Türkiye. Monthly streamflow was simulated based on climate projection data from the HadGEM2-ES model under the RCP4.5 and RCP8.5 scenarios. Results showed that the WT–DNN framework was successful in learning the system dynamics and reproducing observed streamflow behavior. The model produced continuous projections for the future period; however, these projections should be interpreted with caution due to the increasing uncertainty associated with long-term climate forcing and the sensitivity of data-driven approaches to shifts in climatic and hydrological regimes. Full article

Climate projections indicate significant changes in temperature patterns and other meteorological parameters under different climate change scenarios, with temperature receiving special attention due to its influence on thermal conditions and human discomfort. This study examines the relationship between diurnal temperature range (DTR) and thermal discomfort in the five largest cities of Greece during summer. Thermal discomfort is assessed using Thom’s discomfort index (DI), where values ≥ 21 indicate the onset of thermal discomfort, focusing on thermal conditions at the upper (DI_h) and lower (DI_c) boundaries of daily variability. The analysis uses multiple CMIP6 projections for the reference period (1981–2010) and the near future (2031–2060) under the SSP2-4.5 and SSP5-8.5, representing intermediate and high greenhouse gas forcing pathways, respectively. The study aims to investigate associations between DTR and DI-based thermal discomfort. DTR is projected to increase in most cities in the near future relative to the reference period. This reflects a regional specific response that differs from the global tendency reported in the literature for minimum air temperatures (T_min) to increase faster than maximum air temperatures (T_max). Effect size analysis of DTR indicates generally small effects in Thessaloniki, medium to large effects in Larissa depending on the scenario, and large effects in Heraklion, Athens and Patra. Projected differences in DTR are consistent with the asymmetrical response of air temperature, specifically to the higher increase rate in T_max than in T_min in most cities. DI-based thermal discomfort shows a clear contrast between upper (DI_h) and lower (DI_c) boundaries of daily variability, reflected in higher discomfort classes for DI_h and lower classes for DI_c. Higher DTR values are associated with higher DI_h-based thermal discomfort, while the corresponding association between DTR and DI_c is weak or absent. The positive association observed for the DI_h-based conditions is largely governed by the shared contribution of T_max to both DTR and the discomfort index, whereas the absent or weak association for DI_c-based conditions may reflect the weaker association between DTR and T_min as well as the relatively smaller variability of T_min. Full article

In the context of global climate warming, sea surface temperature (SST) rise and sea level (SL) rise are projected to amplify typhoon-related marine dynamic disaster risks. These are idealized sensitivity experiments designed to isolate the individual effects of SST warming and SL rise, not full climate projections. This study investigates Super Typhoon Doksuri (2023) using the WRF-SWAN-ROMS coupled model, with sensitivity experiments designed for SST (+0.8 °C, +2.0 °C, +3.5 °C) and SL rise (+0.4 m, +0.6 m, +0.8 m) scenarios referenced to IPCC AR6 projections. Results indicate that SST rise enhances typhoon intensity by approximately 16% at +3.5 °C, elevates mean wave height by 25.0%, and increases extreme significant wave height by 24.0%, with the extreme wave height sensitivity approximately 2.75 times that of the mean. Storm surge exhibits a nonlinear response, with the extreme surge sensitivity approximately 13.2 times that of the mean. SL rise has relatively minor effects on open sea areas but affects coastal regions notably, expanding the inundation area by approximately 47% under the 0.8 m scenario. The Taiwan Strait channeling effect amplifies wave heights and surges on the right side of the track. Comparative analysis suggests that SST indirectly amplifies disasters by enhancing typhoon intensity, while SL rise directly constrains nearshore dynamics through static water level elevation. These findings offer process-based insights into the contrasting physical mechanisms through which SST rise and SL rise affect coastal hazards in semi-enclosed regions and may inform future ensemble-based climate impact assessments. Full article

Renewable energy technologies have historically been offered market support to facilitate their deployment and aid the transition away from fossil fuels. This work shows the costs of subsidising utility-scale renewable electricity generation in the UK, focusing on wind, solar and tidal stream technologies in the Renewables Obligation (RO) and Contracts for Difference (CfD) schemes. The subsidy of each technology is calculated using published data, including an estimate of committed costs over the full project lifetime, which is not always assessed. For the technologies considered, the RO supported 24.8 GW of installed capacity at a lifetime cost of about £103 bn. To date, CfD have been awarded for 45.3 GW of wind, solar and tidal stream, with total lifetime cost of £40 bn, although this is sensitive to future gas generation costs, with a range of £8–71 bn. The CfD scheme offers better value for money to consumers than the previous RO schemes, and this is true for all technologies assessed. By design, the CfD also helps to insulate billpayers from spikes in the wholesale market caused by high fossil fuel prices, decoupling the costs of electricity from gas. Credible scenarios for future deployment out to 2050 are also presented, along with discussion of potential socioeconomic benefits and the mechanisms to achieve these. Full article