Pub Date : 2025-12-28DOI: 10.1016/j.crm.2025.100784
Mark Read , Hennie Smit , Ivan Henrico , Babalwa Mtshawu , Lesley Welman
Climate change poses growing challenges to military institutions, particularly academies where training, education, and infrastructure are co-located. This study assesses how climate change may affect the United States Military Academy (USMA) at West Point and the South African Military Academy (SAMA) in Saldanha, focusing on implications for training activities, academic programmes, and infrastructure resilience. USMA, located in New York’s Hudson Highlands, is experiencing a warming Humid Continental climate with rising temperatures, stronger tropical cyclones, increased precipitation, and heightened flood risk. SAMA, situated on South Africa’s West Coast, faces a hotter and drier Mediterranean climate, with projected declines in rainfall and sustained water scarcity. Using descriptive climatic data and institutional information, the study compares the climate-related pressures likely to affect each academy over coming decades. The analysis shows that although the academies face distinct climatic trajectories, both will need to adapt training protocols, strengthen infrastructure, and integrate climate resilience into defence education. The findings underscore the importance of coordinated planning and proactive adaptation measures to sustain mission readiness and ensure that future military leaders are adequately prepared for climate-related operational and strategic challenges.
{"title":"Assessing climate change impacts on military academies: a comparative analysis of the United States Military Academy and the South African Military Academy","authors":"Mark Read , Hennie Smit , Ivan Henrico , Babalwa Mtshawu , Lesley Welman","doi":"10.1016/j.crm.2025.100784","DOIUrl":"10.1016/j.crm.2025.100784","url":null,"abstract":"<div><div>Climate change poses growing challenges to military institutions, particularly academies where training, education, and infrastructure are co-located. This study assesses how climate change may affect the United States Military Academy (USMA) at West Point and the South African Military Academy (SAMA) in Saldanha, focusing on implications for training activities, academic programmes, and infrastructure resilience. USMA, located in New York’s Hudson Highlands, is experiencing a warming Humid Continental climate with rising temperatures, stronger tropical cyclones, increased precipitation, and heightened flood risk. SAMA, situated on South Africa’s West Coast, faces a hotter and drier Mediterranean climate, with projected declines in rainfall and sustained water scarcity. Using descriptive climatic data and institutional information, the study compares the climate-related pressures likely to affect each academy over coming decades. The analysis shows that although the academies face distinct climatic trajectories, both will need to adapt training protocols, strengthen infrastructure, and integrate climate resilience into defence education. The findings underscore the importance of coordinated planning and proactive adaptation measures to sustain mission readiness and ensure that future military leaders are adequately prepared for climate-related operational and strategic challenges.</div></div>","PeriodicalId":54226,"journal":{"name":"Climate Risk Management","volume":"51 ","pages":"Article 100784"},"PeriodicalIF":5.0,"publicationDate":"2025-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-28DOI: 10.1016/j.crm.2025.100786
Carolina Pereira Marghidan , Osvaldo Inlamea , Granelio Tamele , Paulo Notiço , Pedro Inguana , Américo José , Eduardo Samo Gudo , Erin Coughlan de Perez , Justine Blanford , Maarten van Aalst , Tatiana Marrufo
Intro: Extreme heat is increasing across Mozambique, yet evidence on how heat is perceived, experienced, and how it impacts communities and key sectors remains limited. Methods: This exploratory study examines heat-health risk knowledge and perceptions, occupational and healthcare challenges, and adaptation strategies in Maputo City and Matola Municipality, the country’s largest urban area. Using a purposive sampling approach, we conducted 95 structured surveys between January and April 2023 (56 community members (C); 39 health professionals (H)), combining closed- and open-ended questions. These perspectives offer insight into local heat risks from key actors positioned to recognize and respond to heat risks, providing essential initial evidence to inform heat preparedness and policy in Mozambique. Data were analysed using descriptive statistics, non-parametric group comparison tests and ordinal logistic regression, and inductive thematic analysis for open-ended responses. Results: Nearly all participants (98%) perceived that extreme heat had increased in recent years and viewed it as a severe public health threat, with most rating the risk at the maximum level (10/10). Most respondents perceived themselves as “very much” vulnerable to heat (C: 55%, H: 39%), primarily due to health impacts and inadequate housing and work conditions contributing to high exposure. Heat was reported to affect healthcare delivery through increased patient load, equipment failures, and difficulties in storing medicines, as well as reducing labour productivity due to physical and mental fatigue. Although 94% reported receiving heat warnings, participants emphasized that warnings do not consistently reach vulnerable groups and called for more community-based dissemination. Conclusion: Extreme heat is already affecting daily life and healthcare services in urban Mozambique. Building resilience will require low-cost, equitable adaptation measures, strengthened health system preparedness, and coordinated institutional responses as heatwaves intensify. In data-scarce settings, frontline community and health-system perspectives are particularly valuable to understand local heat-health risks.
{"title":"Heat-health risk knowledge, perceptions, adaptation, and challenges in Mozambique: insights from community members and health professionals","authors":"Carolina Pereira Marghidan , Osvaldo Inlamea , Granelio Tamele , Paulo Notiço , Pedro Inguana , Américo José , Eduardo Samo Gudo , Erin Coughlan de Perez , Justine Blanford , Maarten van Aalst , Tatiana Marrufo","doi":"10.1016/j.crm.2025.100786","DOIUrl":"10.1016/j.crm.2025.100786","url":null,"abstract":"<div><div><strong>Intro</strong>: Extreme heat is increasing across Mozambique, yet evidence on how heat is perceived, experienced, and how it impacts communities and key sectors remains limited. <strong>Methods</strong>: This exploratory study examines heat-health risk knowledge and perceptions, occupational and healthcare challenges, and adaptation strategies in Maputo City and Matola Municipality, the country’s largest urban area. Using a purposive sampling approach, we conducted 95 structured surveys between January and April 2023 (56 community members (C); 39 health professionals (H)), combining closed- and open-ended questions. These perspectives offer insight into local heat risks from key actors positioned to recognize and respond to heat risks, providing essential initial evidence to inform heat preparedness and policy in Mozambique<em>.</em> Data were analysed using descriptive statistics, non-parametric group comparison tests and ordinal logistic regression, and inductive thematic analysis for open-ended responses. <strong>Results</strong>: Nearly all participants (98%) perceived that extreme heat had increased in recent years and viewed it as a severe public health threat, with most rating the risk at the maximum level (10/10). Most respondents perceived themselves as “very much” vulnerable to heat (C: 55%, H: 39%), primarily due to health impacts and inadequate housing and work conditions contributing to high exposure. Heat was reported to affect healthcare delivery through increased patient load, equipment failures, and difficulties in storing medicines, as well as reducing labour productivity due to physical and mental fatigue. Although 94% reported receiving heat warnings, participants emphasized that warnings do not consistently reach vulnerable groups and called for more community-based dissemination. <strong>Conclusion</strong>: Extreme heat is already affecting daily life and healthcare services in urban Mozambique. Building resilience will require low-cost, equitable adaptation measures, strengthened health system preparedness, and coordinated institutional responses as heatwaves intensify. In data-scarce settings, frontline community and health-system perspectives are particularly valuable to understand local heat-health risks.</div></div>","PeriodicalId":54226,"journal":{"name":"Climate Risk Management","volume":"51 ","pages":"Article 100786"},"PeriodicalIF":5.0,"publicationDate":"2025-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-22DOI: 10.1016/j.crm.2025.100787
Feiting Gao, Li Peng, Damin Zhou, Shuai Liang
Climate-related disasters such as flash floods pose severe threats to rural communities, making effective adaptation strategies essential for reducing risks and strengthening resilience. Disaster insurance, as an important climate policy instrument, can mitigate economic losses. However, a gap remains between their willingness to buy and actual purchase of disaster insurance. In this study, a research framework integrating partial least squares structural equation modeling (PLS-SEM), probit regression, and mediation analysis was employed to explore the factors influencing farmers’ disaster insurance purchase behavior (DIPB) in the Longmen Mountain region of southwest China, which is under frequent threat of climate-induced hazards. A household survey of 536 farmers was conducted, and the probit regression analysis revealed that their DIPB was strongly linked to their willingness to purchase insurance as well as their insurance awareness. Other factors that influenced their purchase behavior included their age, health status, education level, and labor out-migration. The PLS-SEM results indicated that farmers’ disaster preparedness expectations (DPE) were shaped by village capacity building, trust in government, and risk perception. In addition, participation in community-based disaster management (PCDM) was found to play a mediating role between expectations and behavior. These findings highlight the need for climate policy frameworks that integrate individual decision-making, community-based adaptation, and institutional trust to promote disaster insurance uptake. Policy recommendations are offered for expanding insurance coverage, enhancing rural resilience, and embedding disaster risk transfer into broader climate adaptation strategies.
{"title":"Climate risk adaptation through disaster insurance: Understanding purchase behavior of farmers threatened by flash floods in rural China","authors":"Feiting Gao, Li Peng, Damin Zhou, Shuai Liang","doi":"10.1016/j.crm.2025.100787","DOIUrl":"10.1016/j.crm.2025.100787","url":null,"abstract":"<div><div>Climate-related disasters such as flash floods pose severe threats to rural communities, making effective adaptation strategies essential for reducing risks and strengthening resilience. Disaster insurance, as an important climate policy instrument, can mitigate economic losses. However, a gap remains between their willingness to buy and actual purchase of disaster insurance. In this study, a research framework integrating partial least squares structural equation modeling (PLS-SEM), probit regression, and mediation analysis was employed to explore the factors influencing farmers’ disaster insurance purchase behavior (DIPB) in the Longmen Mountain region of southwest China, which is under frequent threat of climate-induced hazards. A household survey of 536 farmers was conducted, and the probit regression analysis revealed that their DIPB was strongly linked to their willingness to purchase insurance as well as their insurance awareness. Other factors that influenced their purchase behavior included their age, health status, education level, and labor out-migration. The PLS-SEM results indicated that farmers’ disaster preparedness expectations (DPE) were shaped by village capacity building, trust in government, and risk perception. In addition, participation in community-based disaster management (PCDM) was found to play a mediating role between expectations and behavior. These findings highlight the need for climate policy frameworks that integrate individual decision-making, community-based adaptation, and institutional trust to promote disaster insurance uptake. Policy recommendations are offered for expanding insurance coverage, enhancing rural resilience, and embedding disaster risk transfer into broader climate adaptation strategies.</div></div>","PeriodicalId":54226,"journal":{"name":"Climate Risk Management","volume":"51 ","pages":"Article 100787"},"PeriodicalIF":5.0,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-21DOI: 10.1016/j.crm.2025.100785
Nina Pirttioja , Päivi Abernethy , Sami Ahonen , Stefan Fronzek , Tiina Jouppila , Kirsti Jylhä , Niina Kautto , Sanna Luhtala , Taru Palosuo , Karoliina Rimhanen , Reija Ruuhela , Kirsti Saarremaa , Timothy R. Carter
Effective adaptation planning requires the integration of diverse forms of knowledge ‒ from local experiences to scientific understanding of projections of climate and societal change and their potential impacts on livelihoods and natural systems. However, the volume, complexity and uncertainty of information can hinder stakeholders from taking decisive action. In this study we present a bottom-up approach for developing adaptation-stories that combine quantitative estimates with qualitative knowledge and experience for portraying past or “imagining” future climate change impacts and adaptation responses. The approach is based on a participatory process comprising five steps: (1) co-definition of a notable climate change impact affecting a chosen livelihood or other specific context; (2) identification of the specific types of climatic and non-climatic factors responsible for the given notable climate change impact; (3) co-evaluation of adaptation measures for ameliorating or exploiting impacts, (4) characterisation of the causal mechanisms and assumptions that specify how the notable impacts and their adaptation have been experienced in the past and how they may develop in the future; and; (5) co-development of adaptation-stories by researchers and stakeholders. We suggest that well-crafted adaptation-stories may empower local actors by exploring climate change adaptation through the lens of their unique experiences and livelihoods. They can also serve as conversation starters between diverse actors and generally spark thinking about adaptation solutions. We illustrate this through a case relating to the planning of a major renewal of a hospital in Finland, reflecting how different actors have adapted to heat-related challenges.
{"title":"Adaptation-stories for imagining futures adjusting to a changing climate","authors":"Nina Pirttioja , Päivi Abernethy , Sami Ahonen , Stefan Fronzek , Tiina Jouppila , Kirsti Jylhä , Niina Kautto , Sanna Luhtala , Taru Palosuo , Karoliina Rimhanen , Reija Ruuhela , Kirsti Saarremaa , Timothy R. Carter","doi":"10.1016/j.crm.2025.100785","DOIUrl":"10.1016/j.crm.2025.100785","url":null,"abstract":"<div><div>Effective adaptation planning requires the integration of diverse forms of knowledge ‒ from local experiences to scientific understanding of projections of climate and societal change and their potential impacts on livelihoods and natural systems. However, the volume, complexity and uncertainty of information can hinder stakeholders from taking decisive action. In this study we present a bottom-up approach for developing adaptation-stories that combine quantitative estimates with qualitative knowledge and experience for portraying past or “imagining” future climate change impacts and adaptation responses. The approach is based on a participatory process comprising five steps: (1) co-definition of a notable climate change impact affecting a chosen livelihood or other specific context; (2) identification of the specific types of climatic and non-climatic factors responsible for the given notable climate change impact; (3) co-evaluation of adaptation measures for ameliorating or exploiting impacts, (4) characterisation of the causal mechanisms and assumptions that specify how the notable impacts and their adaptation have been experienced in the past and how they may develop in the future; and; (5) co-development of adaptation-stories by researchers and stakeholders. We suggest that well-crafted adaptation-stories may empower local actors by exploring climate change adaptation through the lens of their unique experiences and livelihoods. They can also serve as conversation starters between diverse actors and generally spark thinking about adaptation solutions. We illustrate this through a case relating to the planning of a major renewal of a hospital in Finland, reflecting how different actors have adapted to heat-related challenges.</div></div>","PeriodicalId":54226,"journal":{"name":"Climate Risk Management","volume":"51 ","pages":"Article 100785"},"PeriodicalIF":5.0,"publicationDate":"2025-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-19DOI: 10.1016/j.crm.2025.100782
Susana Lincoln , Piyali Chowdhury , Olivia L. Harrod , Sevvandi Jayakody , Karen Vanstaen , Meththika S. Vithanage , John K. Pinnegar
Sri Lanka is highly vulnerable to the impacts of marine climate change due to the low coastal profile, which is densely populated with many rural areas dedicated to fishing and aquaculture. Motivated by this, this study aimed to compile and analyse the available evidence and identify steps to improve climate adaptation by undertaking an assessment of marine climate change risks for Sri Lanka. The stepwise approach consisted of a comprehensive literature review and synthesis of risks, followed by appraisal, validation and scoring by expert stakeholders. Here we present a summary of key findings regarding marine climate variables (temperature, sea-level rise, ocean circulation, salinity, ocean acidification, dissolved oxygen, storminess, precipitation and wind), and risks to marine biodiversity and ecosystem services. The most important biodiversity risks identified include decreasing plankton productivity; threats to sea turtles; changes in fish communities; increasing threats to coral reefs; changes to mangrove and seagrass habitats; shoreline erosion; and increasing risk of bio-invasions. Key risks to ecosystem services include declining fisheries; damage and disruption to critical infrastructure and services; threats to tourism; and loss of protective coastal habitats. We also identified important knowledge gaps and uncertainties involving lack of climate data and evidence of impacts. Finally, we provide recommendations regarding marine monitoring and research, and options to strengthen climate policies and climate adaptation in Sri Lanka.
{"title":"Navigating uncertainty: an assessment of climate change risks to the marine and coastal environment of Sri Lanka","authors":"Susana Lincoln , Piyali Chowdhury , Olivia L. Harrod , Sevvandi Jayakody , Karen Vanstaen , Meththika S. Vithanage , John K. Pinnegar","doi":"10.1016/j.crm.2025.100782","DOIUrl":"10.1016/j.crm.2025.100782","url":null,"abstract":"<div><div>Sri Lanka is highly vulnerable to the impacts of marine climate change due to the low coastal profile, which is densely populated with many rural areas dedicated to fishing and aquaculture. Motivated by this, this study aimed to compile and analyse the available evidence and identify steps to improve climate adaptation by undertaking an assessment of marine climate change risks for Sri Lanka. The stepwise approach consisted of a comprehensive literature review and synthesis of risks, followed by appraisal, validation and scoring by expert stakeholders. Here we present a summary of key findings regarding marine climate variables (temperature, sea-level rise, ocean circulation, salinity, ocean acidification, dissolved oxygen, storminess, precipitation and wind), and risks to marine biodiversity and ecosystem services. The most important biodiversity risks identified include decreasing plankton productivity; threats to sea turtles; changes in fish communities; increasing threats to coral reefs; changes to mangrove and seagrass habitats; shoreline erosion; and increasing risk of bio-invasions. Key risks to ecosystem services include declining fisheries; damage and disruption to critical infrastructure and services; threats to tourism; and loss of protective coastal habitats. We also identified important knowledge gaps and uncertainties involving lack of climate data and evidence of impacts. Finally, we provide recommendations regarding marine monitoring and research, and options to strengthen climate policies and climate adaptation in Sri Lanka.</div></div>","PeriodicalId":54226,"journal":{"name":"Climate Risk Management","volume":"51 ","pages":"Article 100782"},"PeriodicalIF":5.0,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-16DOI: 10.1016/j.crm.2025.100783
Yixuan Liu , Alim Samat , Peijun Du , Jin Chen , Jilili Abuduwaili , Kaiyue Luo , Enzhao Zhu , Dana Shokparova
The escalating impacts of global climate change and extreme weather have intensified flood risks worldwide, including in arid and semi-arid regions traditionally considered low-risk. This study examines the spatiotemporal dynamics of flood events across Kazakhstan from 2000 to 2024 by integrating remote sensing (RS) with machine learning (ML). Using Google Earth Engine (GEE), we address data gaps and cloud interference through spatiotemporal fusion (STARFM), denoising, smoothing, and sample transferring techniques. In addition, this study incorporates the Time-Disaggregated Water Frequency (TWF) method, which enables the identification of water bodies with temporal variability, eliminates permanent water bodies, and distinguishes flood from non-flood conditions in seasonal water bodies, thereby enhancing the accuracy of flood reconstruction and enabling precise delineation of flood inundation areas. Landsat and MODIS imagery are combined to produce high-resolution flood distribution maps, while spectral similarity indicators guide the transfer of samples from the Global Flood Database. A range of spectral, texture, environmental, and socioeconomic features is extracted, with flood classification performed using random forest (RF) and attribution analysis conducted via XGBoost and SHAP. Results highlight a high flood risk in northern, southwestern, and western Kazakhstan, primarily driven by changes in precipitation (PRE), temperature (TEM), soil moisture (SM), and land use. Floods occur most frequently in spring — especially in March and April — due to snowmelt and extreme precipitation. The ML models achieve over 80 % classification accuracy, demonstrating their reliability. This work improves flood monitoring and provides essential insights for climate adaptation and targeted flood risk management in Kazakhstan.
{"title":"High-resolution spatiotemporal analysis and driver attribution of floods in Kazakhstan using SHAP and remote sensing integration","authors":"Yixuan Liu , Alim Samat , Peijun Du , Jin Chen , Jilili Abuduwaili , Kaiyue Luo , Enzhao Zhu , Dana Shokparova","doi":"10.1016/j.crm.2025.100783","DOIUrl":"10.1016/j.crm.2025.100783","url":null,"abstract":"<div><div>The escalating impacts of global climate change and extreme weather have intensified flood risks worldwide, including in arid and semi-arid regions traditionally considered low-risk. This study examines the spatiotemporal dynamics of flood events across Kazakhstan from 2000 to 2024 by integrating remote sensing (RS) with machine learning (ML). Using Google Earth Engine (GEE), we address data gaps and cloud interference through spatiotemporal fusion (STARFM), denoising, smoothing, and sample transferring techniques. In addition, this study incorporates the Time-Disaggregated Water Frequency (TWF) method, which enables the identification of water bodies with temporal variability, eliminates permanent water bodies, and distinguishes flood from non-flood conditions in seasonal water bodies, thereby enhancing the accuracy of flood reconstruction and enabling precise delineation of flood inundation areas. Landsat and MODIS imagery are combined to produce high-resolution flood distribution maps, while spectral similarity indicators guide the transfer of samples from the Global Flood Database. A range of spectral, texture, environmental, and socioeconomic features is extracted, with flood classification performed using random forest (RF) and attribution analysis conducted via XGBoost and SHAP. Results highlight a high flood risk in northern, southwestern, and western Kazakhstan, primarily driven by changes in precipitation (PRE), temperature (TEM), soil moisture (SM), and land use. Floods occur most frequently in spring — especially in March and April — due to snowmelt and extreme precipitation. The ML models achieve over 80 % classification accuracy, demonstrating their reliability. This work improves flood monitoring and provides essential insights for climate adaptation and targeted flood risk management in Kazakhstan.</div></div>","PeriodicalId":54226,"journal":{"name":"Climate Risk Management","volume":"51 ","pages":"Article 100783"},"PeriodicalIF":5.0,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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