Search Results (17,052)

The electrical system is a crucial infrastructure of modern society. It provides the energy needed for society to continue its development. However, this critical infrastructure is increasingly threatened by the extreme weather events driven by the escalating climate crisis, posing significant challenges to sustainable development and energy security. Therefore, it is important to conduct comprehensive risk analyses of the electrical system to prepare for future challenges. This paper presents an electrical risk assessment conducted within the European project ICARIA, aiming to evaluate the effects of global climate change on critical infrastructure resilience. The study improves on the first risk assessment conducted, evaluating the electrical system’s vulnerability to flooding events, such as heavy rains or rising sea levels, in the Metropolitan Area of Barcelona. A key contribution to this research is the integration of direct impact assessments and cascading effect analyses, which identify how localised failures in electrical assets can spread throughout the system, potentially leading to a blackout. The research focuses on modelling various flood projections, using extreme weather scenarios and return periods ranging from 1 to 100 years. These projections are employed to evaluate the risk assessment methodology and quantify potential impacts on the electrical grid, including Expected Annual Damage (EAD) and Energy Not Supplied Cost (ENSC). The results aim to provide policymakers and grid operators with valuable insights, enabling the development of data-driven adaptation strategies and climate-resilient infrastructure planning to mitigate the risks posed by extreme weather events. Full article

Mediterranean water systems face intensifying drought pressure under climate change, yet the long-term macroeconomic consequences of recurrent water restrictions remain largely unquantified at the metropolitan scale. This study estimates the cumulative economic costs of drought-induced water restrictions in the Barcelona Metropolitan Area (AMB) over 2016–2099 using a supply-driven Input–Output (Ghosh) model driven by six hydro-climatic projections. Drought conditions persist in more than half of all simulated months across all climate projections, generating substantial cumulative undiscounted losses of €52–61 billion through repeated restriction episodes rather than isolated extreme events. The present value of total GDP losses ranges between €8.4 and €41.4 billion depending on the discount rate applied (1%, 3% and 5%). Losses concentrate in service sectors due to strong intersectoral propagation effects, despite agriculture exhibiting the highest direct water dependence. The framework provides a transferable approach for assessing long-term climate-driven drought costs in metropolitan urban or regional economies. Full article

Africa’s rainfed agricultural systems are highly exposed to climate change, making shifts in temperature and rainfall a major concern for staple-food crop production. Using a MaxENT ecological niche modelling approach with crop occurrence, elevation, soil and climatic predictors, this study assessed current and future suitability for rainfed maize, millet and sorghum under RCP 4.5 and RCP 8.5. The projections show a notable expansion of 11.1–22.0% in areas suitable for maize cultivation, and a decline of 1.6–7.3% in areas suitable for production of millet and sorghum, indicating likelihood for increased food-security risks in regions dependent on drought-tolerant cereals. These differing shifts highlight the need for targeted adaptation measures, including crop diversification and region-specific planning to help sustain crop production under a changing climate. Full article

Against the background of a warming and humidifying climate on the Qinghai–Tibet Plateau, increasing attention has been paid to the sustainability of water resources and ecosystems in the Qinghai Lake Basin. Investigating the characteristics of precipitation stable isotopes and moisture sources provides critical insights into the driving mechanisms of the regional hydrological cycle. In this study, precipitation samples collected at the Qinghai Lake Wetland Ecosystem National Observation and Research Station from June 2023 to October 2024 were analyzed for hydrogen (δ²H) and oxygen (δ¹⁸O) stable isotopes. The temporal variations of δ²H, δ¹⁸O, and deuterium excess (d-excess) were characterized, and their relationships with air temperature and precipitation amount were examined. In addition, a backward trajectory model was employed to identify the moisture sources of precipitation during the observation period. The results indicate that: (1) precipitation stable isotopes and d-excess exhibit pronounced seasonal variability, characterized by enrichment in summer and depletion in spring and autumn; (2) the Local Meteoric Water Line (LMWL) for the basin is defined as δ²H = 8.15δ¹⁸O + 38.71 (R² = 0.93), with both slope and intercept exceeding those of the Global Meteoric Water Line (GMWL); (3) precipitation isotopes show a discernible temperature effect but are jointly controlled by multiple moisture sources and meteorological factors; and (4) backward trajectory analysis combined with d-excess values reveals that precipitation moisture is primarily derived from westerly transport, while locally recycled moisture and continental air masses also exert significant influences. Overall, these findings reveal the multi-source driving mechanisms of the regional hydrological cycle and provide critical scientific support for understanding hydrological processes in alpine inland basins and their responses to future climate change, thereby contributing to the sustainable management of regional water resources. Full article

Urban green spaces are increasingly threatened by rapid urban expansion, making their continuous monitoring and prediction essential for sustainable urban management. This study investigates the spatiotemporal dynamics of urban garden landscapes in Shiraz, Iran, by integrating multi-temporal Landsat imagery, GIS analysis, and CA–Markov modeling. Landsat data from 2003, 2013, and 2023 were processed to derive the Normalized Difference Vegetation Index (NDVI), which was classified into four vegetation-density categories to quantify land-cover transitions. A CA–Markov framework implemented in IDRISI TerrSet (Version 20.0) was then employed to simulate spatial dynamics and predict vegetation changes for 2033. Results reveal a significant expansion of non-vegetated areas from 711.93 ha in 2003 to 976.66 ha in 2023, accompanied by a decline in dense vegetation from 403.68 ha to 382.64 ha. Model projections indicate a further reduction in dense vegetation to 239.35 ha by 2033, suggesting ongoing fragmentation of urban green infrastructure driven by development pressures. By combining time-series remote sensing, GIS-based spatial analysis, and predictive modeling, this study provides an integrative framework for detecting, interpreting, and forecasting urban land-cover change. The findings offer evidence-based insights to support sustainable urban planning, green infrastructure protection, and climate-resilient city management in rapidly growing urban environments. Full article

The study analyzes flood susceptibility in the Banat Hydrographic Area (Romania) using an integrated GIS framework based on MCDA–AHP multicriteria analysis and the multitemporal evaluation of static and dynamic factors for two scenarios (2005 and 2023). The results highlight differences between the two scenarios, mainly driven by variations in precipitation: although the moderate class remains dominant (~56% of the area), the share of high and very high susceptibility classes is lower in 2023 (~6%) compared to 2005 (~17%), accompanied by an expansion of the low susceptibility class (~26% to ~37%). Validation using flood extent data from April 2005 shows that approximately 99% of the affected area falls within the moderate, high, and very high susceptibility classes (χ² = 9475, p < 0.001). The multitemporal analysis indicates high stability (75% of the territory), while 25.35% exhibits transitions toward lower susceptibility classes. Dynamic factors show differentiated roles: precipitation exerts a dominant regional control (95.44% of the area), while LULC changes contribute locally. The differences between scenarios should be interpreted as a model response to climatic variability rather than as structural changes in intrinsic susceptibility. The approach provides a reproducible framework for susceptibility assessment and supports spatial planning and risk management. Full article

Low-carbon energy transition (LET) has become an important global development strategy. However, in the contemporary industrial era, carbon emissions are intricately intertwined with economic growth based on the extensive use of fossil energy. To this end, the key to a more acceptable push for LET is to achieve carbon emissions decoupling (CED). The rapidly developing digital economy (DE) introduces novel possibilities for it. Using a Finite Mixture Model, this study aims to analyze how DE heterogeneously impacts CED across 66 countries from 2011 to 2022. As of 2022, 41% of countries attained strong decoupling status, 33% reached weak decoupling status. In terms of the effect of DE on CED, both chance and challenge are shown. DE exhibits dual effects: it enhances CED in high-education countries but hinders it in countries with rapid population growth. Government efficiency and gender equality amplify DE’s chance role, while natural gas or clean energy reliance weakens it. DE indirectly promotes CED via low-carbon behavior while raising risks through easier credit access. Meanwhile, the heterogeneity of institutional and economic characteristics in countries may influence the effect of DE on CED. These findings offer a theoretical foundation to reconcile economic sustainability with climate mitigation in digital transitions, providing actionable insights for policymakers to leverage DE’s potential in achieving SDG 13. Full article

Tropical cyclones (TCs) and the Madden–Julian Oscillation (MJO) are two of the most impactful weather systems in the tropics. For example, it is not uncommon for a strong TC to kill hundreds of people and cause tens of billions of dollars in damage. The MJO modulates not only TCs but also monsoons around the world, which contribute essential rainfall for agriculture that supports billions of people, but which also can cause deadly floods. Because of the close coupling between the MJO and TCs, as well as the several week predictability of the MJO, models that can accurately simulate both kinds of weather systems have the potential to be useful for both mid-range weather forecasting and studies of impacts of climate change. This paper describes the further development of one such model, the Lagrangian Atmospheric Model (LAM), which simulates atmospheric motions by predicting motions of individual air parcels, and which has been shown to accurately simulate the MJO in previous studies. In this study, a new parameterization of cloud albedo is included in the LAM, and the model is tuned to improve simulations of TC distributions while still maintaining a robust and realistic MJO. Objective metrics of the model basic state, MJO quality, and TC distributions are used to optimize parameter selections for the cloud albedo parameterization and convective mixing. After tuning the LAM using dozens of 3-year simulations, we conduct two longer simulations forced with observed sea surface temperatures to verify that the new version of LAM has a substantially improved representation of TCs while still maintaining a realistic MJO. Full article

In the context of global climate change, the “dual carbon” goals, and land space planning, this study integrates the Patch-generating Land Use Simulation (PLUS) model, the Carnegie-Ames-Stanford Approach (CASA) model, and a soil respiration model (Heterotrophic Respiration, R_h) to simulate land use change and estimate Net Ecosystem Productivity (NEP) from 2002 to 2023. It projects the carbon sink pattern for 2030 using Hot Spot Analysis. The results show the following: (1) From 2020 to 2030, land use in the Beijing–Tianjin–Hebei region will be characterized by decreases in cropland and grassland and increases in impervious and forest, with cropland-to-impervious conversion dominating. (2) The spatial pattern of NEP exhibits a clear “high in mountainous areas and low in plains” distribution, where forest, grassland, and cropland function as carbon sinks, with forest having the strongest sequestration capacity. The carbon sink core areas cover approximately 59,479 km² and account for about 27.40% of the total area. (3) By 2030, the total carbon sink in the Beijing–Tianjin–Hebei region is projected to range from 31.81 to 32.39 Tg C under different scenarios, with forest contributing nearly 70%. The carbon sink core areas account for approximately 19.12–19.16 Tg C, representing about 60% of the total carbon sink. Full article

Based on previous studies and the ecological characteristics of Portunus trituberculatus, we hypothesized that climate change could substantially reshape its suitable habitat in Zhoushan fishing ground. Under present-day climate conditions (2010–2020), P. trituberculatus exhibits a distinct seasonal distribution pattern in this region. However, its potential spatial response to future climate change, and whether suitable habitat will remain available, remains poorly understood. To address this gap, we combined species occurrence records with environmental variables from the Bio-ORACLE v3.0 database, including benthic temperature, benthic salinity, benthic current velocity, primary productivity, bathymetry, topographic slope, and topographic aspect, to develop a maximum entropy (MaxEnt) model and predict the potential distribution of suitable habitat for P. trituberculatus under present-day conditions and future SSP1-2.6 and SSP2-4.5 scenarios for 2030–2040, 2040–2050, and 2090–2100. Model performance was high across all seasons, with area under the curve values exceeding 0.80. Primary productivity and benthic temperature were the dominant environmental predictors, highlighting the joint influence of trophic conditions and thermal constraints on habitat suitability. Future projections revealed pronounced seasonal reorganization of suitable habitat rather than a uniform range shift. Spring suitable habitat expanded consistently under both scenarios, with the magnitude of expansion increasing toward the end of the century and reaching 46.9% by 2100 under SSP2-4.5, likely because warming relaxed low-temperature limitation during the early seasonal transition. In contrast, suitable habitat in autumn and winter generally contracted. Autumn losses were moderate but persistent, ranging from 5.4% to 16.4%, whereas the strongest declines occurred in winter, particularly under SSP2-4.5, where habitat reductions exceeded 30% after mid-century. These contractions were likely associated with cumulative thermal stress and related environmental changes under continued warming. Summer responses were scenario-dependent, showing weak gains or net declines under SSP1-2.6 but substantial expansion under SSP2-4.5 after mid-century, reaching up to 23.6% by 2050, suggesting that habitat suitability in this season is shaped by interactions among thermal conditions, trophic support, and habitat characteristics. Overall, these findings reveal strong seasonal asymmetry in habitat responses to climate change and provide a scientific basis for seasonally adaptive management of P. trituberculatus resources in Zhoushan fishing ground. Full article

This study systematically examines the impacts of extreme temperatures on the digital economy development index and the underlying mechanisms based on panel data from 281 prefecture-level cities in China from 2012 to 2023. This study explicitly distinguishes the distinctive adaptive capacity of the digital economy in responding to climate risks. Through global and local spatial autocorrelation analysis, the study finds that both extreme temperatures and the digital economy exhibit significant spatial clustering. This study employs the spatial Durbin model (SDM) and effect decomposition and further incorporates the GS2SLS estimator alongside dual instrumental variables constructed from historical geographic characteristics to address endogeneity, thereby identifying the asymmetrical impacts of extreme heat and extreme cold on the digital economy with great rigor. Specifically, extreme heat fosters short-term local digital demand that is subsequently translated into long-term growth in IT human capital and infrastructure, thereby increasing the DEDI. However, its net spatial effect is inhibitory due to energy crowding out. Extreme cold, by contrast, primarily disrupts supply chains and intensifies energy consumption, with its impact largely confined to the local scope. Green technological innovation mitigates the impact of extreme heat on the digital economy through demand substitution, while, under extreme cold, it manifests as the physical protection of infrastructure. Meanwhile, an optimized industrial structure substantially reduces the economy’s dependence on supply chains, amplifying the promotional effect of extreme temperatures on the digital economy and reflecting the transformation capacity of regions under complex environmental conditions. Heterogeneity analysis demonstrates that the effects of extreme temperatures vary significantly across different urban agglomerations, economic zones, geographic regions and city types. This study not only extends the theoretical framework for the economic assessment of climate risks and spatial econometric analysis to the climate sensitivity of the digital economy but also provides empirical evidence for understanding the complex relationship between climate change and digital economy development and offers references for differentiated policies in a coordinated regional digital economy. Full article

Dengue Hemorrhagic Fever (DHF) is a tropical infectious disease transmitted by the Aedes aegypti mosquito and exhibits seasonal patterns with periodic increases in cases throughout the year. The control of vector-borne diseases such as DHF is very important for strengthening public health resilience against climate change, in line with the Sustainable Development Goals (SDGs) for Good Health, Well-being, and Climate Action. Therefore, this study was focused on Bogor city, which experiences high rainfall and continues to face an elevated risk of DHF. The objective was to develop a time series forecasting model to predict DHF outbreaks using Singular Spectrum Analysis (SSA). This is a statistical method for identifying patterns in time series data. Lunar and Solar calendars were adopted to capture seasonal patterns and determine the optimal window length for prediction. The results showed that the Lunar calendar more accurately captured local seasonal variation related to DHF risk. Moreover, the SSA model with one component and a window length of 7 achieved the best performance with a Mean Absolute Percentage Error (MAPE) of 0.0757. The forecast accuracy decreased with longer horizons, but the model provided reliable predictions for short-term periods (approximately 1 month, i.e., up to 4 weeks ahead), which were considered useful for planning DHF mitigation. The results emphasized that the combination of SSA with appropriate calendar systems could improve the accuracy of epidemiological predictions and support vector control policymaking in tropical regions. Full article

Growing concerns about global climate change and its negative consequences for communities have put immense pressure on the building industry, which is one of the primary sources of greenhouse gas emissions. Due to the environmental issues associated with the manufacture of sustainable construction materials, alkali-activated concrete (AAC) has emerged as a competitive alternative to cement. To predict the compressive strength (CS) of AAC, four machine learning (ML) models, namely, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), were employed in this study using 193 data points. The input variables include Precursor “P” (kg/m³), Blast Furnace Slag “BFS ratio”, Sodium hydroxide “Na” (kg/m³), silicate modulus “Ms”, water content “W” (kg/m³), fine aggregate “FA” (kg/m³), coarse aggregate “A” (kg/m³), and curing time “CT” (day), with CS (MPa) as the output variable. The dataset was checked for stationarity and then normalized to decrease data redundancy and increase integrity. Furthermore, three model combinations were developed based on the relationship between the input and target variables. The XGB-M3 model outperformed all other models with a high degree of accuracy, according to the study’s findings. Specifically, the Pearson correlation coefficient (PCC) was 0.9577, and the mean absolute percentage error (MAPE) was 14.95% during the calibration phase. SHAP, an explainable AI approach that provides interpretable insights into complex AI systems by assigning feature importance to model predictions, was employed. Results suggest the higher predictions from the XGB-M3 and RF-M3 models were largely driven by curing time (CT). Full article

Massive-scale, ultra-high-resolution numerical simulations for climate change prediction provide data of exceptional accuracy and reliability. However, this comes at the cost of enormous computational resources, and the underlying processes often remain a “black box”. In contrast to these sophisticated methods, we theoretically analyzed the water vapor feedback effect using a highly simplified model that focuses exclusively on the most critical physical factors governing climate change. Specifically, we formulated a two-layer box model by dividing the entire atmosphere into layers of equal optical thickness. Using this model, we quantitatively verified the extent to which the water vapor feedback effect—a key driver of global warming—can be theoretically reproduced. Full article

This paper assesses the role of nature-based solutions as a way to scale sustainability goals, focusing on the use of carbon credits in voluntary corporate climate commitments. To accomplish this, we adapt the DICE23 model by incorporating a demand function for voluntary corporate carbon abatement and by including the costs of supplying nature-based and non-CO₂ credits to that market. Through scenario analysis, we examine how likely current and proposed new commitments are to meet 1.5 °C and 2 °C climate thresholds by 2030 and 2050 with and without the use of nature-based carbon credits. We find that the inclusion of nature-based credits would increase the probability of meeting a 2 °C threshold by 2030 by lowering costs and significantly increasing overall mitigation. A key result of this paper is that allowing companies to utilize nature-based credits to deliver on near-term mitigation targets can provide the same number of emission reductions as efforts to expand corporate commitments three-fold, but is limited to reductions in the energy sector alone. Overall, incorporating forests and other nature-based credits into corporate commitments could provide immediate and substantial climate benefits while also supporting people and nature impacts today, enabling companies to better achieve multiple social and sustainability goals simultaneously. Full article