Climate Change Adaptation Planning Research Articles

ICT promotes smallholder farmers’ perceived self-efficacy and adaptive action to climate change: Empirical research on China's economically developed rural areas

Understanding the impact of technological advancements, such as ICT, on the climate change adaptation actions of smallholder farmers is crucial for comprehending their adaptive strategies. This study utilizes data from a survey of 2230 smallholder farmer households in the developed rural region of the Yangtze River Delta in China to examine the factors influencing their perceived self-efficacy and actions to adapt to climate change. Using binary logit regression and OLS models, we identify the role that determinants of ICT use play in shaping smallholders' perceived self-efficacy and adaptive action. Our findings corroborate that perceived self-efficacy is a robust, positive predictor of adaptive action. The data indicate that the sole presence of adaptation leaders predominantly enhances perceived self-efficacy. In contrast, adaptive investments at the village level are primarily associated with an increase in adaptive actions. However, peer effects may diminish smallholder perceived self-efficacy and adaptive action. In addition, our study indicates that while ICT has not currently supplanted traditional social networks in influencing smallholder climate change adaptation perceived self-efficacy and adaptive action, we cannot dismiss the potential substitution effect. We also clarify why the peer effects of traditional networks have starkly contrasting impacts in developed and less-developed rural regions in China. Overall, our findings underscore the importance of incorporating objective influencing factors of smallholder adaptation actions and their effects on subjective perceived self-efficacy into future climate change adaptation plans and policies to foster adaptation actions.

Climate Change Adaptation Planning for a Rail Transit Line through Multicriteria Decision Analysis

Climate Change Adaptation Planning for a Rail Transit Line through Multicriteria Decision Analysis

Projection and identification of vulnerable areas due to heavy snowfall using machine learning and K-means clustering with RCP scenarios

Heavy snowfall is a natural disaster that causes extensive damage in South Korea. Therefore, predicting heavy snowfall occurrence, identifying vulnerable areas, and establishing response plans to reduce risk are crucial. In this study, to project heavy snowfall, meteorological and geographic data from the past 30 years were collected, and four machine learning algorithms were trained and compared: multiple linear regression, support vector regression, random forest regressor (RFR), and extreme gradient boosting. We observed that the RFR model (R2 = 0.64) demonstrated the most optimal performance in projecting snowfall compared to other models. Representative concentration pathway (RCP) scenario data was input into the RFR model to generate projection data up to 2100. Projection results of more than 48.2 cm based on heavy snowfall events in the past 20 years were observed 17 times in RCP2.6, 19 times in RCP4.5, 16 times in RCP6.0, and 17 times in RCP8.5. The annual GIS-based projected snowfall images for the RCP8.5 scenario were classified into five distinct groups using K-means clustering. These groups were then further divided based on the vulnerability of regions, including Gangwon-do, Jeollabuk-do, and northern Gyeonggi-do. Our study can aid decision-making on policies related to heavy snowfall disaster prevention standards, snow removal plans, budgeting, and the establishment of mid- to long-term climate change adaptation plans for government, public institutions and private organizations.

A multi-hazard framework for coastal vulnerability assessment and climate-change adaptation planning

When compounded by climate change, ecosystem degradation puts coastal communities at increased vulnerability to hazards. However, there have been few quantitative assessments of coastal vulnerability to multiple hazards that synthesize climate scenarios and ecological and demographic characteristics to inform adaptive strategies. This study presents such a framework for assessing coastal vulnerability to sea level rise, storm waves and winds under present and future climate scenarios. As a case study, we applied it to Macao, one of the most densely populated and highly urbanized coastal area in the world. We found 19% of the coastline hosting 349,000 people – distributed primarily along the Macao Peninsula coast – are highly exposed to coastal hazards at present. Almost half of Macao's total coastline with about 121,000 people are protected by natural habitats. The proportion of highly-exposed coastline was estimated to double, and that of highly-threatened population was estimated to increase by 26.9–44.1%, by 2100. Our findings provide insights into the spatiotemporal heterogeneity of coastal vulnerability to multiple hazards and highlight the role played by habitats in shielding coastal communities. The results can inform prioritizing coastal areas for conservative and adaptive strategies, thereby minimizing deleterious development pressures and enhancing the resilience of coastal communities.

CLIMATE CHANGE POLICIES AND OLDER ADULTS: AN ANALYSIS OF STATES’ CLIMATE ADAPTATION PLANS

Abstract As climate change drives more frequent and intense weather events, older adults face disproportionate impacts, including having the highest mortality rates from storms, wildfires, flooding, and heat waves. State governments are critical in deploying local resources to help address climate change impacts. This policy study analyzes states’ climate adaptation plans to assess the methods through which they address the impact of climate change on older adults. This study uses content analysis to analyze available climate change adaptation plans for all U.S. states for strategies designed to increase resilience of older adults to impacts of climate change. Nineteen states have climate adaptation plans, of which 18 describe older adults as a population group with specific health impacts and risks factors. Four categories of adaptation strategies for older adults include communications, transportation, housing and emergency services. State plans vary in terms of the risk factors and adaptation strategies included. To varying degrees, states’ climate change adaptation planning address health, social and economic risks specific to older adults, as well as strategies for mitigating those risks. As global warming continues, collaborations between public and private sectors and across regions will be needed to prevent negative outcomes such as forced relocation and other social and economic disruptions as well as disparate morbidity and mortality.

지자체 기후위기 적응대책의 현황 및 특성 분석

지자체 기후위기 적응대책의 현황 및 특성 분석

A Critical Feminist Perspective on Climate Change Adaptation Plans: Mapping Municipal Recognition, Dialog, and Budgeting

Abstract Feminists’ scholarship and critique of gender climate injustice have exposed just how scarce the practical efforts to correct it are. The challenge of generating incentives designed to encourage urban planning that accounts for expected intersectional vulnerabilities during climate disasters reflects a gap in knowledge: how does professionals’ awareness of intensified vulnerabilities inform climate adaptation plans (CAPs)? We propose an intersectional critical feminist perspective evaluating recognition, dialog, and budgeting that decodes the social process by which professionals’ knowledge of intersectional vulnerabilities is lost before informing CAPs. Based on an empirical investigation of the increasing gender awareness among administrators who accumulate knowledge about women’s vulnerabilities, our analysis contributes an explanation of the marginalization of gender mainstreaming toolkits in urban CAPs. We show that even in municipalities characterized by increasing levels of recognition, relevant knowledge rarely informs the dialog, and planning appropriate responses for intersectional vulnerabilities is left unbudgeted.

Observations of social and environmental change on Kendall Island (Ukiivik), a traditional whaling camp in the Inuvialuit Settlement Region

As climate change intensifies, Inuvialuit in Canada's Western Arctic are facing a rapidly changing environment and associated impacts on human health, safety, and food security. Learning to cope with these changes requires context-based and current information that can inform subsistence activities and environmental management, and no one is better positioned to acquire this information than Inuvialuit themselves. This paper presents findings from in-depth interviews conducted in 2012 with six knowledge holders and seasonal residents of Kendall Island ( Ukiivik in Uummarmiutun), a traditional whaling camp situated along the Beaufort Sea coast bordering the Okeevik Tarium Niryutait Marine Protected Area. A transdisciplinary and Inuvialuit-led effort, this research documents observations of change at this culturally important site and explores how residents are adapting to changing conditions. Interview transcripts were analyzed using iterative rounds of qualitative coding in NVivo software. Findings reveal pervasive social and environmental change on Kendall Island and in adjacent harvesting areas and highlight how changing conditions are affecting residents’ lives. This study identifies benchmarks upon which to compare and evaluate subsequent changes at this site and documents Inuvialuit knowledge and perspectives that can inform local-scale environmental monitoring, management, and climate change adaptation planning.

Aedes aegypti abundance in urban neighborhoods of Maricopa County, Arizona, is linked to increasing socioeconomic status and tree cover

BackgroundUnderstanding coupled human-environment factors which promote Aedes aegypti abundance is critical to preventing the spread of Zika, chikungunya, yellow fever and dengue viruses. High temperatures and aridity theoretically make arid lands inhospitable for Ae. aegypti mosquitoes, yet their populations are well established in many desert cities.MethodsWe investigated associations between socioeconomic and built environment factors and Ae. aegypti abundance in Maricopa County, Arizona, home to Phoenix metropolitan area. Maricopa County Environmental Services conducts weekly mosquito surveillance with CO2-baited Encephalitis Vector Survey or BG-Sentinel traps at > 850 locations throughout the county. Counts of adult female Ae. aegypti from 2014 to 2017 were joined with US Census data, precipitation and temperature data, and 2015 land cover from high-resolution (1 m) aerial images from the National Agricultural Imagery Program.ResultsFrom 139,729 trap-nights, 107,116 Ae. aegypti females were captured. Counts were significantly positively associated with higher socioeconomic status. This association was partially explained by higher densities of non-native landscaping in wealthier neighborhoods; a 1% increase in the density of tree cover around the trap was associated with a ~ 7% higher count of Ae. aegypti (95% CI: 6–9%).ConclusionsMany models predict that climate change will drive aridification in some heavily populated regions, including those where Ae. aegypti are widespread. City climate change adaptation plans often include green spaces and vegetation cover to increase resilience to extreme heat, but these may unintentionally create hospitable microclimates for Ae. aegypti. This possible outcome should be addressed to reduce the potential for outbreaks of Aedes-borne diseases in desert cities.Graphical

Association between ambient temperature and common allergenic pollen and fungal spores: A 52-year analysis in central England, United Kingdom

Exposure to pollen and fungal spores can trigger asthma/allergic symptoms and affect health. Rising temperatures from climate change have been associated with earlier seasons and increasing intensity for some pollen, with weaker evidence for fungal spores. It is unclear whether climate change has resulted in changes in the exposure-response function between temperature and pollen/fungal spore concentrations over time. This study examined associations between temperature and pollen/fungal spores in different time periods and assessed potential adaptation using the longest pollen/fungal spore dataset in existence (52 years). Daily concentrations of pollen (birch and grass) and fungal spores (Cladosporium, Alternaria, Sporobolomyces and Tilletiopsis) collected between April and October from Derby (1970–2005) and Leicester (2006–2021), UK, were analysed. Cumulative seasonal concentrations (seasonal integral) and start-of-season were calculated and linked to seasonal mean temperatures (Tmeans) using generalized additive models. Daily concentrations were evaluated against daily Tmean with distributed lagged nonlinear models. Models were adjusted for precipitation, relative humidity, long-term trend and location. Seasonal and daily analyses were respectively stratified into two periods (1970–1995, 1997–2021) and five decades. Warmer seasonal Tmeans were associated with higher seasonal integral for birch, Cladosporium and Alternaria, as well as earlier start-of-season for birch, grass and Cladosporium. There were indications of changing associations with temperature in the recent decades. A warmer January was associated with higher seasonal integral for grass in 1997–2021, but not in 1970–1995. In 2000–2021, daily concentrations of birch pollen tended to remain at higher levels, vs. decrease during 1990s, when Tmean was between 13 and 15 °C. Our study suggests higher temperatures experienced in recent decades are associated with higher overall abundance of some pollen/fungal spores, which may increase future disease burdens of allergies. The changing responses of some pollen to higher temperatures over time may indicate adaptation to increasing temperatures and should be considered in climate change mitigation and adaptation planning.

Farm typology and structure of farmer decision making: The case of the production basin of the forest-savannah transition zone, Centre Cameroon Region

Despite having a similar environment, not all of the farmers in a given region have the same history. They don't always share the same traits, and they don't always have the same access to land or diverse natural environment resources. This study aims to describe how agricultural production is organized in the Mbangassina-Ntui-Batchenga-Obala areas to improve food security, where agriculture is the primary economic activity. On 180 farms, mixed data were gathered, and a factorial analysis of the mixed data, along with a hierarchical ascending classification, allowed for the differentiation of 4 groups of farms. Farms in Type 1 (44.44%) have an average size of 2.51 ha, are predominately run by men, primarily employ family labor, and have the highest proportion of farmers. The majority of Type 3 farms (34.44%), which have an average area of 6.62 ha, are run by men and mostly employ family labor. The producers in this group are vulnerable to climate change and lack adaptation plans. Finally, 4 types of farms, which have an average size of 10.9 ha and are operated by both men and women utilizing family labor, account for 5.56% of all farms. All of these farmers are creating adaptation strategies as they recognize the effects of climate change. Through a correct understanding of the reasons that underpin the diversity of farms, this classification serves as a basis for the design of appropriate public policies. Taking into account this diversity of agricultural situations in the northern part of the Centre Cameroon Region is essential for the success of research and rural development operations. In addition, it would allow a more relevant use of typologies of knowledge on farms as a tool for sustainable development and decision support.

Temporal and Spatial Analysis of Rainfall and Evapotranspiration in Erbil Plain and the Peripheral Areas

Since analysis of spatial events is region specific and cannot be generalized, local studies are of vital importance to identify the most accurate interpolation methods. Furthermore, the trend analysis of climatic parameters at different time scales is helpful for making a better climate change adaptation and mitigation plan to overcome water scarcity. Accordingly, the current study was proposed to detect trends in rainfall and evapotranspiration at monthly and annual time scales over the Erbil plain and the surrounding area using parametric and non-parametric tests. Moreover, four deterministic and five geostatistical methods were evaluated for searching the best interpolation method to generate a continuous surface for the indicated climatic variables. The results revealed that the majority of data sets are categorized useful class and serially independent. Furthermore, it was found both rainfall and potential evapotranspiration have a mix of upward and downward trends and most of them are insignificant at 5% level of significance. Further, the interpolation analysis indicated that local polynomial interpolation (LPI) method was proven to be best interpolator for generating continuous surfaces for rainfall and ETo over the area under study followed by the empirical Bayesian Kriging method.

Climatic vulnerability, adoption of climate-resilient technologies, and its socioeconomic-institutional-agroecological determinants

Climate change have been identified as the greatest challenge facing global leaders in 21st century. A major obstacle hindering the world from achieving the Sustainable Development Goals (SDGs) is climate change. Climate-resilient agriculture requires the integration of both socioeconomic and agroecological spheres with institutional investment. Numerous studies have advanced our understanding of climate change and its impact on global agriculture, but few have focused on the micro-farming situation. A geographical indicator (GI)-labelled Alphonso mango has developed a high sensitivity to climate change. It is therefore, this study intends to investigate climatic vulnerabilities; the adoption of climate-resilient technologies, and which socioeconomic-institutional-agroecological factors affect the adoption process. We adopted a multistage random sampling method to identify districts, blocks, villages, and mango farmers. A pre-tested interview schedule was employed to obtain field data. A binary logistic regression model was employed in Statistical Package for Social Sciences (SPSS). The results of this study identified extreme climatic vulnerabilities such as temperature, relative humidity, cold waves, fog, and frost that caused damage to the mango plantations around the plains and hills of the west coast India, resulting in decreased production and productivity. Therefore, mango orchardists in coastal agroecosystems have adopted a large number of climate-resilient technologies, albeit at a moderate level. We found that a set of socioeconomic, institutional, and agroecological indicators had a substantial impact on the adoption of climate-resilient technologies (CRTs). This research implied that climate change adaptation plans may be designed and implemented with socioeconomic, institutional, and agroecological indicators in mind at all levels of policy planning such as macro-scale (global), meso (regional), and landscape (local).

PARTICIPATORY APPROACH TO ASSESS COASTAL EROSION MITIGATION AND CLIMATE CHANGE ADAPTATION MEASURES

The economic and social importance of coastal areas combined with their growing erosion problems and climate change impacts allows anticipating an increase in investments in these areas. In the past, strategies to mitigate territorial losses or property damage have been mainly reactive, non-inclusive, non-participatory, and locked-in technical solutions. Moreover, cost-benefit assessments of Coastal and Climate Change Adaptation (CCA) plans are still scarce, underused and poorly understood. So, the goal of this work is to contribute to a complete characterization of coastal erosion mitigation and climate change adaptation strategies and their implementation measures by considering a participatory approach that integrates medium to long-term perspectives, combining, simultaneously, social, environmental, economic and engineering dimensions, to help decision-makers implementing sustainable future-proof CCA strategies.

Assessment of NASA POWER for Climate Change Analysis using the De Martonne Climate Index in Northern Peninsular Malaysia

Global warming is threatening human population, one of which is the frequent climate disasters, i.e., floods and drought, which affect the global food security. However, the assessment of regional climatic changes is constrained by the lack of high-quality long-term climate data. Taking the Northern Region of Peninsular Malaysia as the study area, this paper aims to assess the performance of NASA POWER in estimating annual and seasonal variability of the climate variables via De Martonne climate indices from 1985 to 2021. The findings shows that NASA POWER performed satisfactory in estimating both mean temperature and rainfall over the northern Peninsular Malaysia. The NASA POWER project performs the best in Bayan Lepas station with C = 0.70 for annual mean temperature while for annual rainfall is at the Ipoh station with C = 0.50. The annual De Martonne climate index shows that the study area is dominated by wet climate conditions, since the index classify the climate as “very humid” to “extremely humid”. The De Martonne climate indes is calculated from both data source is very similar. Therefore, NASA POWER data is useful in climate studies, while the knowledge of climate variability is beneficial for climate change mitigation and adaptation plans, particularly for agriculture.

Strategies to strengthen a climate-resilient health system: a scoping review

BackgroundClimate change is a major global threat to human health and puts tremendous pressure on health systems. Therefore, a resilient health system is crucial to enhance, maintain, and restore the population’s health. This study aimed to identify interventions and actions to strengthen a climate-resilient health system to deal with the adverse health effects of climate change.MethodThis study was a scoping review. Five databases and Google Scholar search engine were searched using relevant keywords. Initially, 4945 documents were identified, and 105 were included in the review. Content thematic analysis method was applied using MAXQDA 10 software.ResultsOverall, 87 actions were identified for building a climate-resilient health system and were classified into six themes (i.e., governance and leadership; financing; health workforce; essential medical products and technologies; health information systems; and service delivery). The most commonly reported actions were formulating a national health and climate change adaptation plan, developing plans for essential services (electricity, heating, cooling, ventilation, and water supply), assessing the vulnerabilities and capacities of the health system, and enhancing surveillance systems targeting climate-sensitive diseases and their risk sources.ConclusionsA holistic and systemic approach is needed to build a climate-resilient health system owing to its complex adaptive nature. Strong governance and leadership, raising public awareness, strategic resource allocation, climate change mitigation, emergency preparedness, robust health services delivery, and supporting research, are essential to building a climate-resilient health system.

Implementing Agile Data Workflows to Unlock Climate-Resilient Urban Planning

Cities around the world are facing the implications of a changing climate as an increasingly pressing issue. The negative effects of climate change are already being felt today. Therefore, adaptation to these changes is a mission that every city must master. Leading practices worldwide demonstrate various urban efforts on climate change adaptation (CCA) which are already underway. Above all, the integration of climate data, remote sensing, and in situ data is key to a successful and measurable adaptation strategy. Furthermore, these data can act as a timely decision support tool for municipalities to develop an adaptation strategy, decide which actions to prioritize, and gain the necessary buy-in from local policymakers. The implementation of agile data workflows can facilitate the integration of climate data into climate-resilient urban planning. Due to local specificities, (supra)national, regional, and municipal policies and (by) laws, as well as geographic and related climatic differences worldwide, there is no single path to climate-resilient urban planning. Agile data workflows can support interdepartmental collaboration and, therefore, need to be integrated into existing management processes and government structures. Agile management, which has its origins in software development, can be a way to break down traditional management practices, such as static waterfall models and sluggish stage-gate processes, and enable an increased level of flexibility and agility required when urgent. This paper presents the findings of an empirical case study conducted in cooperation with the City of Constance in southern Germany, which is pursuing a transdisciplinary and trans-sectoral co-development approach to make management processes more agile in the context of climate change adaptation. The aim is to present a possible way of integrating climate data into CCA planning by changing the management approach and implementing a toolbox for low-threshold access to climate data. The city administration, in collaboration with the University of Applied Sciences Constance, the Climate Service Center Germany (GERICS), and the University of Stuttgart, developed a co-creative and participatory project, CoKLIMAx, with the objective of integrating climate data into administrative processes in the form of a toolbox. One key element of CoKLIMAx is the involvement of the population, the city administration, and political decision-makers through targeted communication and regular feedback loops among all involved departments and stakeholder groups. Based on the results of a survey of 72 administrative staff members and a literature review on agile management in municipalities and city administrations, recommendations on a workflow and communication structure for cross-departmental strategies for resilient urban planning in the City of Constance were developed.

Climate Change Vulnerability Assessment and Adaptation: A Comparative Analysis of Reports Produced for Local Governments in South Africa and South East Queensland, Australia

Climate change is dominating social, political and economic agendas. Because of the salience of the issue, scientists, researchers and consultants have developed a plethora of climate adaptation strategies. In this article I analyse two such strategies: Adapting South African Cities and Towns: A Local Government Guide to Climate Change Adaptation Planning by Ziervogel and Methner; and Climate Change Vulnerability in South East Queensland: A Spatial and Sectoral Assessment by Choy et al. For this analysis I utilised the PULSE3 analytical framework. My analysis indicated that the two adaptation strategies were predominantly positivist in scientific orientation. Other paradigms and theories are also applicable in explaining the issue of climate change and how to adapt to it.

Drought Severity and Trends in a Mediterranean Oak Forest

Drought is a significant natural hazard with widespread socioeconomic and environmental impacts. This study investigated the long-term drought characteristics in a Mediterranean oak forest ecosystem using the Standardized Precipitation Evapotranspiration Index (SPEI) at various time scales and seasons. The analysis was based on a long-term time series dataset obtained from a meteorological station located at the University Forest of Taxiarchis in Greece. The dataset encompassed a substantial time span of 47 years of continuous monitoring, from 1974 to 2020. To accomplish the goals of the current research, the SPEI was calculated for 3, 6, 12, and 24-month periods, and drought events were identified. The Mann-Kendall (M-K) test was used to analyze the trends in drought severity and evaluate the trends significance. The results showed that shorter time scales (SPEI3 and SPEI6) were more efficient for identifying short-term droughts, while longer time scales (SPEI12 and SPEI24) were better for identifying less frequent but longer-lasting drought episodes. The analysis consistently revealed positive trends across all seasons and time scales, indicating an overall transition towards wetter conditions. Nearly all the data series for SPEI12 and SPEI24 exhibited statistically significant upward trends (wetter conditions) at a 95% confidence level. However, more intense events were detected during the recent decade using the seasonal analysis. Additionally, as the time scale expanded, the magnitude of these trends increased. The findings contributed to a better understanding of drought dynamics in Mediterranean oak forests and provided valuable information for forest management and climate change adaptation planning.

Accelerated vegetative growth measured by gross primary productivity in China from 1980 to 2018

Vegetation response to climate change can be characterized as long-term trends, but with periodic oscillations. Revealing vegetation response trends, with oscillations, is essential for formulating climate change mitigation and adaptation planning, as well as adopting ecological protection and restoration measures. Here, the long-term trend and periodic oscillations in vegetation changes were explored through a measure of gross primary production (GPP), an indicator of vegetation photosynthesis, using data of both high spatial resolution (1 km2) and a long-term of nearly 4 decades, from 1980 to 2018. The GPP data were estimated for China’s terrestrial ecosystem and validated against the eddy covariance-based observations of 70 site-year in China and compared to other GPP data products. The results showed the estimated GPP can explain 76% of spatial–temporal variance with the same data accuracy as other products, but with the advantage of both high spatial resolution and long time periods that other products do not have. From the estimated GPP data, Chinese terrestrial vegetation growth was found to have a periodic oscillation of close to 2.79 years and a long-term increasing trend of 59.8 Mt C per year over 92% of all vegetated land in China from 1980 to 2018. Moreover, it was found that vegetation growth accelerated significantly in the two recent decades over the previous two decades. The growth rate was 83.5 Mt C a−1 from 2000 to 2018, which was 2.31 times faster than the rate from 1980 to 2000. The underlying mechanism of the vegetation responses was analyzed by ridge regression, a method to decrease the effects of multicollinearity. Long-term variability and trends in vegetation could be attributed to a warming minimum air temperature when examined with precipitation, maximum air temperature, and solar shortwave radiation. Though more influences, such as those from freezing and thawing and human activities, should be explored in the future, this study offers insight that warming, with increasing daily minimum temperatures, is likely to be a vital contributor to ecosystem changes, and will be essential knowledge for mitigation of effects and adaptation to future climate.