Search Results (1,826)

Climate risk is the negative effect of climate change on several aspects of the environment, business, and society. There are two categories of climate risks: physical risks include direct impacts due to extreme events and chronic changes due to climate modifications that have become commonplace; the transition risk arises from the economic and regulatory adjustments required to shift toward reducing greenhouse gas emissions and the transition to renewable energy. The problem, in financial terms, is the correct assessment and quantification of transition risk, as it is not univocal in the literature. This research aims to provide a literature review on transition risk that permits filling this gap and identifying the proxies used for its representation and evaluation. Moreover, the analysis considers the critical aspect of the connection between transition and credit risk, as firms exposed to high transition risks may face challenges in maintaining creditworthiness. Results highlight the most commonly used proxies, including carbon pricing, CO₂ or GHG emissions, and metrics from various databases. However, the findings emphasize the importance of integrating these indicators with broader factors, such as a company’s negative environmental impacts (e.g., waste production and water usage) and delays in technological adaptation from a forward-looking perspective. Full article

Currently, one of the major threats to cities is the escalating risk of flooding, which is attributed to the alteration of climate and hastened urbanization. The purpose of this study was to introduce the Strahler ordering method for simplifying drainage networks and to avoid randomness in developing flooding models. A coupled hydrodynamic model that combines SWMM and LISFLOOD-FP was developed to simulate urban inundation. Results showed that the coupled model had satisfactory applicability for waterlogging simulation. The Strahler ordering method could construct clear topological relations of the drainage network and showed good performance in drainage network simplification. Higher-density drainage networks could increase peak discharge and total volume of discharge, while decreasing the maximum water depth and the total inundation area. Taking “5.29” rainstorm events as an example, compared to Level 3, the relative rates of change in the total flow and peak flow of Level 2 and Level 1 networks are −33.18% and −23.29%. The total inundation area was decreased from 14.14 ha to 1.43 ha when the level of drainage network hierarchy was increased from Level 1 to Level 3. This study highlights the importance of re-assessment of current and future urban drainage networks for coping with the changes in urban floods induced by local and large-scale changes. Full article

This study aims to assess the factors influencing homeowner behaviour regarding climate-adaptive renovations. This study extends the Theory of Planned Behaviour (TPB) by integrating additional factors such as inherent homeowner qualities (IHQs) and building attributes (BAs) to better capture climate-adaptive renovation decisions. Different configurations for the impacts of these additional factors were tested, and their correlation to homeowner attitudes (ATs) and homeowner intentions (INTs) was studied. The results indicate that attitudes related to beliefs about climate change impacts are the strongest predictors of climate-adaptive behaviour. It was also found that IHQ was a strong determinant of homeowner attitudes and had a strong indirect impact on homeowner intentions to perform climate-adaptive renovations. Given the significant role of cognitive attitudes in shaping climate-adaptive behaviours, policy interventions should focus on fostering more climate-conscious attitudes. Targeted public campaigns can highlight localised climate risks and the benefits of adaptive renovations. Sharing narratives from regions affected by severe climate events, potentially in the form of targeted workshop sessions, could make climate risks more tangible, especially for those without direct exposure, fostering greater public engagement and adaptive actions. Full article

Wildfires represent an increasing threat to ecosystems and communities, driven by climate change, fuel dynamics, and human activities. In Ambato, Ecuador, a city in the Andean highlands, these risks are exacerbated by prolonged droughts, vegetation dryness, and urban expansion into fire-prone areas within the Wildland–Urban Interface (WUI). This study integrates climatic, ecological, and socio-economic data from 2017 to 2023 to assess wildfire risks, employing advanced geospatial tools, thematic mapping, and machine learning models, including Multinomial Logistic Regression (MLR), Random Forest, and XGBoost. By segmenting the study area into 1 km² grid cells, microscale risk variations were captured, enabling classification into five categories: ‘Very Low’, ‘Low’, ‘Moderate’, ‘High’, and ‘Very High’. Results indicate that temperature anomalies, reduced fuel moisture, and anthropogenic factors such as waste burning and unregulated land-use changes significantly increase fire susceptibility. Predictive models achieved accuracies of 76.04% (MLR), 77.6% (Random Forest), and 76.5% (XGBoost), effectively identifying high-risk zones. The highest-risk areas were found in Izamba, Pasa, and San Fernando, where over 884.9 ha were burned between 2017 and 2023. The year 2020 recorded the most severe wildfire season (1500 ha burned), coinciding with extended droughts and COVID-19 lockdowns. Findings emphasize the urgent need for enhanced land-use regulations, improved firefighting infrastructure, and community-driven prevention strategies. This research provides a replicable framework for wildfire risk assessment, applicable to other Andean regions and beyond. By integrating data-driven methodologies with policy recommendations, this study contributes to evidence-based wildfire mitigation and resilience planning in climate-sensitive environments. Full article

Climate change is leading to an increase in the frequency and intensity of flooding, making it necessary to consider future changes in flood risk management. In regions where ground-based observations are significantly restricted, the implementation of conventional risk assessment methodologies is always challenging. This study proposes an integrated remote sensing and machine learning approach for flood risk assessment in data-scarce regions. We extracted the historical inundation frequency using Sentinel-1 SAR and Landsat imagery from 2001 to 2023 and predicted flood susceptibility and inundation frequency using XGBoost, Random Forest (RF), and LightGBM models. The risk assessment framework systematically integrates hazard components (flood susceptibility and inundation frequency) with vulnerability factors (population, GDP, and land use) in two SSP-RCP scenarios. The results indicate that in the SSP2-RCP4.5 and SSP5-RCP8.5 scenarios, combined high- and very-high-flood-risk areas in the Ili River Basin in China (IRBC) are projected to reach 29.1% and 29.7% of the basin by 2050, respectively. In the short term, the contribution of inundation frequency to risk is predominant, while vulnerability factors, particularly population, contribute increasingly in the long term. This study demonstrates that integrating open geospatial data with machine learning enables actionable flood risk assessment, quantitatively supporting climate-resilient planning. Full article

The Herrera township (86.0 km²), located in La Chorrera, is Panama’s leading pineapple production area. Ensuring sustainable agricultural management in this region is crucial for long-term productivity, resource conservation, and environmental protection. This study evaluates soil and irrigation water quality to provide insights into improved management practices. Soil samples were analyzed for pH, EC, OM, SM, CEC, texture, and content of Al, Ca, Cu, Fe, K, Mg, Mn, N, P, Si, Sr, and Zn. Water samples, including surface water and groundwater, were assessed for Ca, Fe, K, Mg, Mn, Na, N, HCO₃, SO₄, PO₄, NO₃-N, and salinity. Soil quality was evaluated using the Igeo, and geospatial techniques were applied to map the soil parameter distribution. The water quality analysis confirmed its suitability for irrigation, though groundwater in the central area requires caution due to elevated Na levels and a moderate risk of salinization. Soil maps indicate adequate levels of essential nutrients but highlight the need for N amendments. This study is the first comprehensive assessment of an agricultural township in Panama, providing critical data for decision-making and the adoption of sustainable agricultural practices that enhance resource management and mitigate climate change impacts. Full article

This paper presents experimental studies of the formation of thermal and humidity conditions in two wooden historic churches in southern Poland. The environmental and cultural changes taking shape are creating the need to modernize existing buildings to sustainable standards. The modernization of historic religious buildings is complicated by restrictions on the intrusion of vertical partitions, which are often covered with valuable wall paintings. The paper focuses on the important aspect of preserving historically valuable buildings in good condition and assessing the threat posed by vapor condensation on the surface of the partitions. The studied buildings differ in terms of their uses and heating systems. Building A is unheated, while building B is equipped with a heating system. The scope of the study includes continuous measurements of the temperature and relative humidity of the indoor air inside and outside the studied churches. The work presents a detailed analysis and comparison of the formation of thermal and humidity conditions inside the churches. A computational model of the buildings was created, and then a computational simulation of the risk of water vapor condensation on the surface of the external walls was carried out. The analysis presents the influence of the external climate on the formation of the thermo-humidity conditions inside the buildings, especially in the unheated church. Also shown is the effect of the temporary heating of the church on ensuring the optimal heat and moisture conditions for historic wooden buildings. The analysis shows that turning on the heating only during the use of the church slightly improves the thermal and humidity conditions compared to the unheated church. Additionally, the analysis shows that the occasional use of the unheated church contributes to significant cooling of the church (even to −8.4 °C in the winter half year). Another conclusion that the computational analysis reveals is that water vapor condensation on the surface of the external walls is impossible. However, the difference between the air temperature in the church and the dew point temperature, specifically in the unheated church, is 1.6 °C. Therefore, at lower outside air temperatures, there may be a risk of water vapor condensation. Full article

Climate change, particularly the increasing frequency of droughts, poses a critical challenge for agriculture. Rising temperatures and water scarcity threaten both agricultural productivity and ecosystem stability, making the identification of effective drought mitigation strategies essential. This study introduces an innovative approach to agricultural drought monitoring in Poland, utilizing remote sensing (RS) satellite data, collected from 2001 to 2020, and the Drought Identification Satellite System (DISS) index at a 1 km × 1 km spatial resolution, in combination with Copernicus High-Resolution Layers (HRL). To assess areas’ capacities to mitigate drought risks, a multi-criteria decision (MCD) analysis of regional environmental conditions was conducted. Focusing on the Mazowieckie Voivodeship, an algorithm was developed to evaluate regional susceptibility to drought. Spatial datasets were used to analyze environmental indicators, producing a map of communal temperature mitigation capacities. Statistical analysis identified drought vulnerability, highlighting areas in need of urgent intervention, such as increased mid-field tree planting. The study revealed that the frequency of droughts in this region during the growing season from 2001 to 2020 exceeded 40%. As a result, 40 LAU 2 administrative units have been affected by multiple negative environmental factors that contribute to drought formation and its long-term persistence. The proposed methodology, integrating diverse satellite data sources and spatial analyses, offers an effective tool for drought monitoring, mitigation planning, and ecosystem protection in a changing climate. This approach provides valuable insights for policymakers and land managers in addressing agricultural drought challenges and enhancing regional resilience to the impacts of climate change. Full article

Invasive species pose a growing threat to global biodiversity, agricultural productivity, and ecosystem health, as climate change worsens their spread. This study focused on modelling the current and projected distribution of the Japanese beetle (Popillia japonica Newman), an invasive pest with potentially devastating impacts on crops and natural vegetation across Europe. Using the MaxEnt species distribution model, we integrated beetle occurrence data with bioclimatic variables, analyzing current and future climate scenarios based on Shared Socio-economic Pathways (SSP1-2.6, SSP2-4.5, SSP5-8.5) for near-term (2021–2040) and mid-term (2041–2060) periods. By reclassifying the model results, we identified European regions with negligible, low, medium, and high exposure to this invasive pest under climate change pathways. The results identified regions in central Europe covering an area of 83,807 km² that are currently at medium to high risk of Japanese beetle infestation. Future projections suggest northward expansion with suitable areas potentially increasing to 120,436 km² in the worst-case scenario, particularly in northern Italy, southern Germany, the Western Balkans, and parts of France. These spatially explicit findings can inform targeted monitoring, early detection, and management strategies to mitigate the economic and ecological threats posed by the Japanese beetle. Integrating species distribution modelling with climate change scenarios is imperative for science-based policies to tackle the growing challenge of biological invasions. This research provides a framework for assessing invasion risks at the European scale and guiding adaptive responses in agricultural and natural systems. Full article

Extreme rainfall events are significant manifestations of climate change, causing substantial impacts on urban infrastructure and public life. This study takes the extreme rainfall event in Beijing in 2023 as the background and utilizes data from Sina Weibo. Based on large language models and prompt engineering, disaster information is extracted, and a multi-factor coupled disaster multi-sentiment classification model, Bert-BiLSTM, is designed. A disaster analysis framework focusing on three dimensions of theme, location and sentiment is constructed. The results indicate that during the pre-disaster stage, themes are concentrated on warnings and prevention, shifting to specific events and rescue actions during the disaster, and post-disaster, they express gratitude to rescue personnel and highlight social cohesion. In terms of spatial location, the disaster shows significant clustering, predominantly occurring in Mentougou and Fangshan. There is a clear difference in emotional expression between official media and the public; official media primarily focuses on neutral reporting and fact dissemination, while public sentiment is even richer. At the same time, there are also variations in sentiment expressions across different affected regions. This study provides new perspectives and methods for analyzing extreme rainfall events on social media by revealing the evolution of disaster themes, the spatial distribution of disasters, and the temporal and spatial changes in sentiment. These insights can support risk assessment, resource allocation, and public opinion guidance in disaster emergency management, thereby enhancing the precision and effectiveness of disaster response strategies. Full article

Flooding is among the most destructive natural disasters globally, and it inflicts severe damage on both natural environments and human-made structures. The frequency of floods has been increasing due to unplanned urbanization, climate change, and changes in land use. Flood susceptibility maps help identify at-risk areas, supporting informed decisions in disaster preparedness, risk management, and mitigation. This study aims to generate a flood susceptibility map for Davidson County of Tennessee using an integrated geographic information system (GIS) and analytical hierarchical process (AHP). In this study, ten flood causative factors are employed to generate flood-prone zones. AHP, a form of weighted multi-criteria decision analysis, is applied to assess the relative impact weights of these flood causative factors. Subsequently, these factors are integrated into ArcGIS Pro (Version 3.3) to create a flood susceptibility map for the study area using a weighted overlay approach. The resulting flood susceptibility map classified the county into five susceptibility zones: very low (17.48%), low (41.89%), moderate (37.53%), high (2.93%), and very high (0.17%). The FEMA flood hazard map of Davidson County is used to validate the flood susceptibility map created from this approach. Ultimately, this comparison reinforced the accuracy and reliability of the flood susceptibility assessment for the study area using integrated GIS and AHP approach. Full article

Cities are increasingly vulnerable to climate change, prompting many municipalities to adopt Municipal Strategic Plans for the Adaptation to Climate Change (MSPACC). The success of these plans depends on coordinated efforts, particularly from Municipal Civil Protection Services (MCPS), which play a key role in climate adaptation. To assess their involvement, a survey of MCPS coordinators was conducted, yielding 279 responses (90.6% of all Portuguese MCPS). Statistical analysis revealed that 53.4% were familiar with adaptation strategies, with 59.1% identifying MSPACC development and implementation as a key approach. Additionally, 63.4% acknowledged that climate risks were anticipated in their Municipal Civil Protection Emergency Plans (MCPEP). However, findings highlighted a critical misalignment between MSPACC and MCPEP, potentially weakening both adaptation and mitigation efforts. Bridging this gap through integrated planning and collaboration is essential to strengthening municipal resilience and safeguarding communities. Full article

Flooding is one of the most significant natural hazards in Iran, primarily due to the country’s arid and semi-arid climate, irregular rainfall patterns, and substantial changes in watershed conditions. These factors combine to make floods a frequent cause of disasters. In this case study, flood susceptibility patterns in the Marand Plain, located in the East Azerbaijan Province in northwest Iran, were analyzed using five machine learning (ML) algorithms: M5P model tree, Random SubSpace (RSS), Random Forest (RF), Bagging, and Locally Weighted Linear (LWL). The modeling process incorporated twelve meteorological, hydrological, and geographical factors affecting floods at 485 identified flood-prone points. The data were analyzed using a geographic information system, with the dataset divided into 70% for training and 30% for testing to build and validate the models. An information gain ratio and multicollinearity analysis were employed to assess the influence of various factors on flood occurrence, and flood-related variables were classified using quantile classification. The frequency ratio method was used to evaluate the significance of each factor. Model performance was evaluated using statistical measures, including the Receiver Operating Characteristic (ROC) curve. All models demonstrated robust performance, with an area under the ROC curve (AUROC) exceeding 0.90. Among the models, the LWL algorithm delivered the most accurate predictions, followed by RF, M5P, Bagging, and RSS. The LWL-generated flood susceptibility map classified 9.79% of the study area as highly susceptible to flooding, 20.73% as high, 38.51% as moderate, 29.23% as low, and 1.74% as very low. The findings of this research provide valuable insights for government agencies, local authorities, and policymakers in designing strategies to mitigate flood-related risks. This study offers a practical framework for reducing the impact of future floods through informed decision-making and risk management strategies. Full article

Municipalities face increasing vulnerability to climate-related risks, giving rise to a set of different challenges and problems, ultimately threatening long-term sustainability. Addressing these challenges requires proactive adaptation measures, innovative solutions, and stakeholder engagement to enhance climate resilience at the municipal level. This study adapts the Prospective Rapid Impact Assessment (PRIA) approach for planning critical climate change actions, promoting environmental, social, and economic sustainability. Using a case study in Tauragė, Lithuania, the study explores the PRIA method’s application in municipal planning. Various stakeholders and experts participated in testing the method, emphasising the need for inclusive governance in sustainable urban development. The research identifies key climate challenges and corresponding adaptation actions across three tiers: individual (person), household, and municipal levels, underscoring a multi-scale approach to sustainability. A five-point Likert scale was used to evaluate challenges, with the five most significant ones highlighted for each tier. A comprehensive list of sustainability-driven climate actions was compiled, and experts prioritised the most impactful and feasible strategies. These recommendations were presented to Tauragė’s decision-makers to inform the development or revision of the municipal Action Plan, ensuring that climate resilience, resource efficiency, and social equity are embedded in local policies. Furthermore, the study demonstrates the effectiveness of the PRIA method and the practical utility of the InTo tool in assessing climate actions and identifying priority sustainability measures. By integrating sustainability considerations into municipal strategic planning, the findings underscore the necessity of proactive, forward-thinking approaches to safeguard communities and ecosystems against climate change. The study results confirm that the PRIA method can be successfully utilised as a robust framework for regional and municipal climate change action planning. It facilitates the identification of key issues, the prioritisation of actions, stakeholder engagement, and the integration of interdependencies within the climate change action planning process. This approach ensures that actions are well-founded, specifically tailored to the needs of various community levels, and account for the interdependencies among different climate change mitigation and adaptation options. Full article

Along with the progression of globalized climate change, flooding has become a significant challenge in low-lying plain river network regions, where urban areas face increasing vulnerability to extreme climate events. This study explores climate-adaptive land use strategies by coupling blue–green infrastructure (BGI) with conventional gray infrastructure, forming blue–green–gray infrastructure (BGGI), to enhance flood resilience at localized and regional scales. By integrating nature-based solutions with engineered systems, this approach focuses on flood mitigation, environmental co-benefits, and adaptive land-use planning. Using the Minhang District in Shanghai as a case study, the research employs geospatial information system (GIS) analysis, hydrological modeling, and scenario-based assessments to evaluate the performance of BGGI systems under projected climate scenarios for the years 2030, 2050, and 2100. The results highlight that coupled BGGI systems significantly improve flood storage and retention capacity, mitigate risks, and provide ecological and social benefits. Water surface-to-catchment area ratios were optimized for primary and secondary catchment areas, with specific increases required in high-risk zones to meet future flood scenarios. Ecological zones exhibited greater adaptability, while urban and industrial areas required targeted interventions. Scenario-based modeling for 2030, 2050, and 2100 demonstrated the scalability, feasibility, and cost-effectiveness of BGI in adapting to climate-induced flooding. The findings contribute to the existing literature on urban flood management, offering a framework for climate-adaptive planning and resilience building with broader implications for sustainable urban development. This research supports the formulation of comprehensive flood management strategies that align with global sustainability objectives and urban resilience frameworks. Full article