Effective Adaptation Strategies Research Articles

Climate Dynamics over Kerala, India: Insight from a Century-long Temperature and Rainfall Data Analysis

Historical climate data analysis is of great significance in climate change adaptation and mitigation planning at global as well as regional levels. This article attempted to study the long-term trends of temperature and rainfall across the districts in Kerala, India. CRU monthly time series data of rainfall and temperature data spanning from 1901-2022 were used for the analysis. Mann-Kendall test and Sen’s Slop estimator were applied to detect the presence and magnitude of the trend, and Pettitt’s homogeneity test was used to find the climate change point in temperature time series data. The analysis found a significant and positive temperature trend across all districts in Kerala, with temperature increases ranging from 0.0086°C/year to 0.0102°C/year. In terms of rainfall trends, June and January experienced a significant decrease, while July and September saw a significant increase over the years. The year 1976 was identified as the point of climate change. It was observed that there was an increase in the southwest and Northeast monsoons in the post-period, with higher variability in the latter. Winter rainfall notably decreased during the post-period. The variability in climate parameters identified in this study could impact crop cycles and agricultural productivity, requiring further investigation at a micro-level for effective adaptation and mitigation strategies for the state.

Enhancing Learning Outcomes and Student Engagement: Integrating E-Learning Innovations into Problem-Based Higher Education

With the rapid development of e-learning in higher education, this study explores the application of the problem-based learning (PBL) learning model in the e-learning ecosystem. This article explores the concept of participatory engagement in the e-learning ecosystem and integrates it with PBL theory through the use of ICT to support interaction and collaboration. The research method used is R&D learning models in the Heavy Equipment Technology course at Universitas Negeri Padang. The research findings should be interpreted cautiously due to limitations such as differences in contexts across various courses and factors related to technology implementation in the learning environment. The study results show that it can improve learning outcomes and students’ critical thinking skills. Specifically, 70% have implemented Bloom’s cognitive taxonomy Level C4 and above, aided by a self-reflection participation process that enables students to reflect on content, learning processes, and understanding through internal dialogue, generating new ideas and solutions. This is evidenced by the results of the Path Coefficients test with P-Value EE → LO 0.050 and PBL → LO 0.046, as well as Specific Indirect Effect EE → SPR → LO 0.003 and PBL → SPE → LO 0.047 where the value is ≤ 0.05. The implications include guidance for designing effective e-learning ecosystems and adaptive learning strategies in higher education. Future research is recommended to further explore the impact of this integration and develop more effective PBL models in diverse courses and other educational institutions.

Identification of maladaptive behavioural patterns in response to extreme weather events

Human behaviour has gained recognition as a critical factor in addressing climate change and its impacts. With extreme weather events posing risks to vulnerable communities, understanding cognitive processes driving behaviours becomes essential for effective risk communication. This study focuses on the 2018 “Vaia” storm, which brought unprecedented precipitation and wind velocity to the mountainous regions of North-eastern Italy. Drawing upon the Protection Motivation Theory (PMT) framework, we employ probabilistic models to identify distinct groups with similar behavioural profiles. By administering a web-based survey to 1500 residents affected by the event, we find that threat appraisal is more influential in shaping protective behaviours than coping appraisal. Our findings indicate that by enhancing coping appraisals and discouraging non-protective measures, we can actively mitigate maladaptive responses and promote the adoption of effective adaptation strategies.

Changes in extreme precipitation indices across Algeria climate zones

AbstractExtreme precipitation events can have a significant impact on the environment, agriculture, economy and safety, making close monitoring of their short‐ and long‐term trends essential for the development of effective mitigation and adaptation strategies. In this study, we analysed 16 in situ observation datasets from four different climate zones in Algeria, spanning from 1969 to 2021. The trend analysis was conducted using the original Mann–Kendall test and seven modified tests to eliminate the effects of short‐term persistence. Our findings reveal a significant increasing trend of extreme precipitation variability for most stations in the Warm Mediterranean climate zone, except for the Consecutive dry days index, which showed a negative trend for the same zone, while stations in the Cold/Warm semi‐arid climate and Cold desert climate (Bwk) zones showed a decreasing trend. Additionally, all index series with significant long‐term trends were affected by a significant shift in their means, which was confirmed by both the Lombard and Pettitt tests. However, when we used the modified MPT and the test eliminating the effects of long‐term persistence, the significance of the shifts and the trend decreased. Our results suggest that while extreme precipitation events have been increasing in some parts of Algeria; the trend may not be statistically significant in the long‐run, indicating the necessity of revisiting and refreshing the findings of previous studies for a more current perspective.

Exploring cumulative probability functions for streamflow drought magnitude: Global scale analysis and parametric vs. non-parametric comparisons

Streamflow droughts pose significant challenges to water resource management and environmental sustainability. The accurate characterization of streamflow drought magnitude is essential for effective mitigation, assessment and adaptation strategies. In this study, we explore the implications of choosing different cumulative probability distribution functions (cdfs) on the determination of streamflow drought magnitude, as assessed by the z-score values of the standardized streamflow index (SSI) and return periods. Our investigation encompasses a comprehensive global-scale analysis, with the aim of identifying the most suitable cdfs for characterizing streamflow droughts. To accomplish this objective, we incorporate global simulated streamflow values from the model WaterGAP 2.2d and observed streamflow values from 280 Global Runoff Data Centre (GRDC) stations for the period 1981 – 2010. We evaluate the performance of 118 parametric cdfs for each of the 12 calendar monthly streamflow values at each model grid cell and GRDC station to determine the best fitting parametric cdf. Moreover, we go beyond the conventional scope of such studies by further assessing the best-fitting cdfs alongside non-parametric cdfs such as the Empirical cumulative distribution function (ecdf) and the cumulative distribution function derived from the Kernel density estimation function (kcdf). Among our findings, we establish a subset of 23 parametric cdfs that exhibit the best fit for the global streamflow values concerning all 12 calendar monthly streamflow values at individual GRDC stations or grid cells within the specified period. Furthermore, we observe that the accuracy of streamflow simulation and the length of the reference period directly influence the selection of the most suitable parametric cdf. Our comparative analysis reaffirms the existing understanding that opting for the best-fitting parametric cdf, rather than a non-parametric alternative, mitigates the risk of overestimating drought magnitudes. However, we emphasize the crucial importance of considering the intended purpose, approach-specific sensitivities, and uncertainties when choosing the most suitable parametric cdf for any calendar monthly streamflow values.

Heat Stress in Indian Mustard (Brassica juncea L.): A Critical Review of Impacts and Adaptation Strategies

Indian mustard, a member of the Brassicaceae family, is a cool-season crop cultivated extensively in various agro-ecological conditions, contributing significantly to vegetable oilseed production. However, being a thermosensitive and C3 plant [1], Indian mustard is highly susceptible to high-temperature stress, particularly during critical developmental stages [2]. High temperatures disrupt physiological, morphological, and biochemical mechanisms essential for plant growth and development. From altered chlorophyll content and osmotic water potential to impaired photosynthesis and reduced seed oil accumulation, heat stress detrimentally affects various aspects of Indian mustard physiology. This comprehensive review examines the impacts of heat stress on Indian mustard, a significant oilseed crop, and explores adaptation strategies to mitigate its adverse effects. Additionally, heat stress during reproductive stages leads to flower abortion, reduced fertility, and poor seed development, significantly impacting yield potential. To cope with heat stress, plant response mechanisms involve intricate signaling pathways, leading to cellular readjustments at transcriptome, epigenome, proteome, and metabolome levels. Breeding approaches, including conventional techniques and advanced molecular tools like CRISPR/Cas-based genome editing, offer promising avenues for developing heat-tolerant cultivars. Integration of multi-omics approaches aids in identifying stress-responsive genes and metabolic pathways crucial for adaptation. Agronomic strategies, such as nutrient management and microbial inoculation, play vital roles in mitigating heat stress effects and enhancing plant resilience. Collaborative efforts and interdisciplinary research are essential to deepen our understanding of heat stress responses and develop sustainable solutions to ensure oilseed crop productivity and food security in the face of climate change. This review underscores the urgency of addressing heat stress challenges in Indian mustard cultivation and highlights the need for concerted efforts to develop effective adaptation strategies for sustainable agriculture.

Using leaf economic spectrum and photosynthetic acclimation to evaluate the potential performance of wintersweet under future climate conditions.

The function of landscape plants on the ecosystem can alleviate environmental issues of urbanization and global change. Global changes due to elevated CO2 affect plant growth and survival, but there is a lack of quantitative methods to evaluate the adaptability of landscape plants to future climate conditions. Leaf traits characterized by leaf economic spectrum (LES) are the universal currency for predicting the impact on plant ecosystem functions. Elevated CO2 usually leads to photosynthetic acclimation (PC), characterised by decreased photosynthetic capacity. Here, we proposed a theoretical and practical framework for the use of LES and PC to project the potential performance of landscape plants under future climatic conditions through principal component analysis, structural equation modelling, photosynthetic restriction analysis and nitrogen allocation analysis. We used wintersweet (an important landscaping species) to test the feasibility of this framework under elevated CO2 and different nitrogen (N) supplies. We found that elevated CO2 decreased the specific leaf area but increased leaf N concentration. The results suggest wintersweet may be characterized by an LES with high leaf construction costs, low photosynthetic return, and robust stress resistance. Elevated CO2 reduced photosynthetic capacity and stomatal conductance but increased photosynthetic rate and leaf area. These positive physio-ecological traits, e.g., larger leaf area (canopy), higher water use efficiency and stress resistance, may lead to improved performance of wintersweet under the predicted future climatic conditions. The results suggest planting more wintersweet in urban landscaping may be an effective adaptive strategy to climate change.

Rising Tide of Ocean Acidification

This comprehensive review explores the escalating environmental crisis of ocean acidification, primarily driven by anthropogenic CO2 emissions. A detailed examination reveals significant impacts on marine life, ecosystems, and socio-economic structures, with variations in effects observed across different regions. The study underscores the alarming rate of change in ocean chemistry, posing a severe threat to marine biodiversity. Calcifying organisms like mollusks and corals are particularly vulnerable, leading to potential cascading effects within aquatic food webs and ecosystem collapses. Socio-economic ramifications are profound, especially for fisheries, tourism, and coastal communities reliant on marine resources. The economic consequences could be substantial, exacerbating global social and economic disparities. Emerging research and forecasting tools enhance our understanding of this phenomenon and aid response strategies. This review advocates a multi-faceted approach to address the crisis; including reducing CO2 emissions through renewable energy and carbon capture technologies, adaptation strategies for marine ecosystems and communities, strengthened policy frameworks, and international collaboration. Speculative considerations highlight the potential for significant global impacts and the urgent need for proactive, coordinated action. This review emphasizes the importance of continued research and monitoring to develop effective mitigation and adaptation strategies, underscoring the critical role of global cooperation and innovation in environmental management. This review aims to serve as a call to action, highlighting the urgency to preserve marine ecosystems and their services to humanity in the face of this growing environmental challenge.

Assessment and spatialization of vulnerability of Benin coast to sea level rise using composite/blended approach

Climate change, particularly sea level rise, poses significant challenges to global coastal regions, necessitating a profound understanding of their vulnerability for effective mitigation and adaptation strategies. This study focuses on enhancing scientific insight into West African coastal vulnerability, aiming to assist policymakers and coastal managers in developing sustainable coastal zone management strategies. The methodology employed combines Geographic Information System (GIS) and remote sensing techniques with the Coastal Vulnerability Index (CVI) formula. Parameters such as slope, topography, distance to shoreline, population density, accessibility, land cover, and soil and vegetation types are assessed for their contribution to physical and socio-economic changes induced by sea level rise. These parameters undergo weighting using the Analytic Hierarchy Process (AHP). The socio-economic vulnerability is quantified using the Community-Based Risk Screening Tool-Adaptation and Livelihoods (CRiSTAL). The assessment of the Benin coast reveals significant vulnerability, with very high vulnerability (36.69–70.4, indicated by red cells) and high vulnerability (30.84–36.69, indicated by orange cells) observed in all coastal municipalities (Grand-Popo, Ouidah, Abomey-Calavi, Cotonou, Sèmè-Kpodji), encompassing 40 % and 21 % of the study area, respectively. Moderate (25.69–30.84) and low (10.87–25.69) vulnerability areas are situated at higher elevations inland, covering 35 % and 4 % of the total study area, respectively. No areas of very low vulnerability have been identified. This research contributes significantly to coastal management decisions, adaptation strategies, and climate change policies in Benin by advancing scientific understanding of coastal vulnerability and facilitating the development of sustainable strategies.

Climate change adaptation policies and institutional arrangement: Agriculture and fishery sector in India

<p>India’s agriculture and fishing sectors confront significant challenges due to climate change because of its distinctive geographical location and predominantly agrarian economy, highlighting the urgent need for institutional frameworks and effective adaptation strategies. This study delves into the current state of institutional structures and policies aimed at climate change adaptation within these key sectors in India. Through an exhaustive analysis of literature, official reports, and policy documents, the research evaluates the policies and interventions implemented to mitigate the impacts of climate change on agriculture and fisheries. Several policies are in abode to facilitate the planning, establishment, and implementation of adaptation programs at the national and regional scales in the country. However, given India’s broad geographical size and varied socio-cultural settings, the adaptation requirements of diverse sectors and susceptible populations are still not sufficiently recognized and addressed. Key areas under scrutiny include crop diversification, water management techniques, technological advancements, and community-based adaptation approaches.<em> </em>Furthermore, the study evaluates the effectiveness of existing institutional arrangements including governmental bodies, academic institutions, and community organizations in fostering climate resilience across different domains. By synthesizing insights from diverse sources, this research aims to offer valuable perspectives on the institutional dynamics and policy landscape shaping climate change adaptation efforts in India’s agriculture and fisheries sectors to build resilience and sustainability.<strong><em> </em></strong>This research paper highlights some of the evolving as well as existing adaptation requirements and suggests how new research, policy, and practice engagements could meet these requirements.<strong></strong></p>

Investigating Vulnerability, Adaptation, and Resilience: A Comprehensive Review within the Context of Climate Change

This review seeks to enhance the understanding of the critical concepts of vulnerability, adaptation, and resilience within the context of global environmental challenges, with a particular focus on climate change. Climate change is characterized by rising global temperatures and an increase in extreme weather events, making the comprehension and addressing of these concepts crucial for effective adaptation strategies. Despite widespread recognition of the interconnectedness of vulnerability, adaptation, and resilience, there remains a gap in a comprehensive understanding of how these concepts interrelate. Through synthesizing existing literature, this review provides a detailed examination of their definitions and the interrelationships among vulnerability, adaptation, resilience, and climate-related disasters. Additionally, it explores the impact of climate change on future disaster risk reduction efforts by analyzing the nexus between climate change adaptation and disaster risk reduction. Key findings highlight the necessity of incorporating social, institutional, economic, and environmental factors into adaptation planning and call for innovative approaches to boost adaptive capacity and resilience. This review not only furthers the discourse in research, policy, and practice in this vital area but also offers strategic insights for developing more resilient and adaptive societies amidst the challenges posed by climate change.

Evaluating the Cost-Benefit Analysis of Climate Adaptation Strategies on Coastal Communities

Climate change poses significant challenges to coastal communities worldwide, necessitating the development and implementation of effective adaptation strategies. This research undertakes a rigorous evaluation of the cost-benefit analysis (CBA) associated with various climate adaptation measures tailored to coastal settings. Employing a multidisciplinary approach, encompassing economic, environmental, and social dimensions, the study aims to elucidate the intricate interplay between costs and benefits inherent in these strategies. Through meticulous data collection and methodological rigor, key factors influencing the cost-effectiveness of adaptation interventions are identified and analyzed. The findings reveal nuanced insights into the effectiveness and efficiency of different adaptation approaches, shedding light on their relative merits and limitations. Moreover, this research contributes to advancing the theoretical underpinnings of CBA in the context of climate adaptation, offering practical recommendations for policymakers, planners, and stakeholders tasked with enhancing the resilience of coastal communities. By bridging the gap between theory and practice, this study underscores the imperative of informed decision-making and proactive action in safeguarding coastal regions against the impacts of climate change.

Simulation of national-scale groundwater dynamics in geologically complex aquifer systems: an example from Great Britain

ABSTRACT The national-scale British Groundwater Model (BGWM) is implemented to simulate groundwater dynamics and budgets in Great Britain. Notwithstanding the challenges of integrating a very large amount of data, finding a trade-off between computational efficiency and realism, performing automatic calibration, and addressing multiple sources of structural and parameter uncertainty, a quantitative–qualitive evaluation approach showed that the BGWM provides a reasonably accurate digital representation of groundwater systems and processes at a national scale. In this work, the model was applied to understand the variability of budget components across multiple spatial and temporal scales. Comparisons showed regional differences linked to lithological and climatic factors, which in turn can be associated with more or less groundwater resilience to extreme climatic events. There is confidence that the current and future versions of the BGWM can become valuable tools for effective water resources management and adaptation strategies under future climatic and population changes.

Future climate projections for Vietnam: temperature and precipitation changes under SSP2-4.5 and SSP5-8.5 scenarios

The IPCC's 6th Assessment Report in 2023 highlights Vietnam's undeniable vulnerability to climate change, ranking it among the most severely impacted countries. In response, experts developed the CMIP6-VN dataset, offering high-resolution downscaled temperature and precipitation data tailored for Vietnam using the CMIP6 GCMs model. This study aims to analyze climate projections for Vietnam throughout the 21st century, incorporating data from 25 models to provide valuable insights into potential future climate scenarios. The investigation compares projected temperature and precipitation changes for the mid-term (2040÷2059) and long-term (2080÷2099) periods relative to the historical period (1980÷2014) in the six sub-climatic regions of the country. The results show a consistent upward trend in average temperature, projecting an average of 2.30C (SSP2-4.5) and 4.30C (SSP5-8.5) increases by the end of the century. The Northern regions, especially the Northwest, are experiencing the most significant temperature rise, while moving towards lower latitudes, the temperature increase becomes less severe. The Southern regions, on the other hand, are experiencing a relatively lowest temperature increase. Rainfall variability indicates a slight increase across Vietnam by the century’s end, aligned with rising temperatures, though not as pronounced as the temperature changes. This study emphasizes temperature disparities between the Northern and Southern regions, with the Northwest showing the highest increase and the Southern region displaying the least prominent rise. Policymakers and stakeholders can use these research insights to devise effective adaptation and mitigation strategies in addressing climate change challenges in Vietnam. Continued monitoring and further research are essential to enhance the accuracy and reliability of climate projections in the region, enabling Vietnam to better prepare and respond to the inevitable impacts of climate change.

Escaping Environmental Hazards? Human Mobility in Response to Air Pollution and Extreme Cold Events

Understanding the patterns of human mobility and how they relate to different environmental risks is key to developing effective adaptation strategies. To this end, this study applied large-scale mobile phone data to examine the differential mobility responses to and interaction between air pollution and extreme cold events and attempted to identify nonlinearities in the relationship between short-term movements and the two environmental hazards. The results revealed that extreme cold events had a strong influence on the mobility decisions of residents in the city of Chengdu, China. Residents were more likely to react to climate-related hazards in their daily mobility than air pollution, which is often characterized as a slow-onset, small-impact, and frequent concern. The findings from the estimation of an interaction term indicated an antagonistic effect; that is, the combined effect of air pollution and extreme cold events on short-term movements was significantly lower than expected from the sum of their individual effects. Using a piecewise regression approach, this study uncovered nonlinearities in mobility as a response to air pollution and cold events as the number of trips from hazardous places to less hazardous places increased more than proportional to the differentials in air quality and magnitude of cold events.

Adaptive Silviculture and Climate Change—A Forced Marriage of the 21st Century?

Climatic changes significantly impact forest ecosystems, inevitably affecting forestry and forest-related industry. Considering that most forests are actively managed, there is a need to define the future risks and set a strategy for forestry and silviculture in a changing world. This review provides insight into the new challenges and opportunities forest management and silviculture face in the coming decades. There is sound recognition of risk factors expected from climate change, yet great uncertainty exists in the predictions of the response of forests to new conditions. Additionally, the stakeholders’ interests in the goods and services offered by forests are changing, and this also needs to be taken into account in future forest management. Undoubtedly, the goal of future forestry and silviculture in the 21st century will be primarily to ensure the continuity and sustainability of the forest. Sustainable use of goods and ecosystem services from forests will be directly related to the continuity and sustainability of the forest in the future. Adaptive forest management aims to promote the adaptive capacity of forests to new conditions resulting from climate change. If adaptation efforts are effective, adaptive forest management should be a kind of risk management. There is no one-fits-all strategy for adaptation to uncertain future conditions. Silviculture in the 21st century is expected to be more conducive to adapting forests to changes. Operational silvicultural activities should focus on ensuring the resilience and adaptation of forests to future environmental conditions. Modern silviculture offers activities that fall within the scope of contemporary close-to-nature silviculture practices. However, some of the currently applied practices will require review and modification to be applicable under new conditions. This review also identifies the need to fill knowledge gaps in order to develop more effective and flexible adaptation strategies to foster sustainable forest development and, thus, sustainable forestry.

When Model Meets New Normals: Test-Time Adaptation for Unsupervised Time-Series Anomaly Detection

Time-series anomaly detection deals with the problem of detecting anomalous timesteps by learning normality from the sequence of observations. However, the concept of normality evolves over time, leading to a "new normal problem", where the distribution of normality can be changed due to the distribution shifts between training and test data. This paper highlights the prevalence of the new normal problem in unsupervised time-series anomaly detection studies. To tackle this issue, we propose a simple yet effective test-time adaptation strategy based on trend estimation and a self-supervised approach to learning new normalities during inference. Extensive experiments on real-world benchmarks demonstrate that incorporating the proposed strategy into the anomaly detector consistently improves the model's performances compared to the existing baselines, leading to robustness to the distribution shifts.

Analysis of the perceptions of flood and effect of adoption of adaptation strategies on income of informal settlements of Mamelodi in South Africa

Extreme weather events are being experienced all over the world because of climate change, posing challenges for individuals and households who rely on agricultural operations as their major source of livelihood. Household-level adaptation is an efficient way of dealing with global climate change. As such, this study aims to examine the perception of informal settlers to flood risk and their adoption of adaptation strategies to flood. This study applied the seemingly unrelated regression (SUR) to identify factors influencing the perception of flooding on community members and probit regression to identify the factors influencing the adoption of adaptation strategies to floods and examined the impact of the adoption of adaptation strategies on income in Eerste Fabriek informal settlement in Mamelodi using two-step quasi-maximum likelihood estimates of fractional response model. Our results show that community members are perceptive of floods and their impact on the environment and their livelihoods, and on average, they believe flood impact is significant. Age, marital status, education, employment status, income, and household size are demographic factors that tend to influence their perception of the impact of flood events. Access to institutional facilities such as health and recreational facilities was also a significant factor in how community members adapt to the impact of floods. Timely healthcare access services are a significant precursor for people to form their perception, which is intended to help them adapt appropriately to situations as health is wealth. Community members' perceptions and adaptive capacity can be improved through policies that foster the adoption of effective adaptation strategies. Community-based adaptation strategies are necessary for involving all stakeholders and necessary for mitigating the effects of flooding.

Quantifying High-Temperature and Drought Stress Effects on Soybean Growth and Yield in the Western Guanzhong Plain

High-temperature and drought events significantly impact crop growth and development. In the soybean-producing region of the Guanzhong Plain in China, understanding the dynamics of these climatic phenomena is vital for soybean yield preservation. Through a fixed-position observation experiment that analyzed four growth stages, nine agronomic traits, and soybean yield per unit area from 1998 to 2023, this research evaluated the characteristics of high-temperature and drought processes in various growth stages. It also examined the influence of high-temperature processes, drought processes, and their combined effects on agronomic traits and yield. The results indicate the following: (1) High temperature was a constant factor during the soybean growth period, with temperature-related indices markedly surpassing those related to drought. Notably, the occurrence of high-temperature and drought events was more prevalent during the flowering–podding stage than at the podding or grain-filling stages. (2) High temperature profoundly affected soybean yield components, primarily through a decrease in the number of grains per plant during the flowering–podding stage, subsequently impacting the grain weight per plant and yield. In years with extremely high temperatures, the soybean plant height was reduced by 6.1 to 15 cm, the main stem node number decreased by 0.1 to 2.9, the branch number decreased by 0.2 to 0.6, the number of pods per plant decreased by 4.8 to 13.7, the number of grains per pod decreased by 0.1 to 0.3, the number of grains per plant decreased by 13.5 to 32.6, the grain weight per plant decreased by 3.8 to 6.9 g, and the 100-grain weight decreased by 0.1 to 4.5 g. The common impact of high temperature combined with drought processes in different growth stages was reflected in the reduction in the number of branches by 0.1 to 1.4 and the reduction in the number of grains per pod by 0.02 to 13.7. This study underscores the importance of addressing the quantitative effects of climate change and extreme weather on soybean yield, which could help to develop effective adaptation and mitigation strategies.

Developing behavioral models of citizens for adapting to and mitigating climate change: a study on four prefectures in Japan

Individual behaviors of citizens are important for implementing the necessary measures for adapting to climate change. However, studies on the adaptive behaviors of ordinary citizens are limited. Therefore, in this study, we developed behavioral models to understand individual behaviors for adaption to and mitigation of climate change. We compared four behavior groups, namely, G1, individual mitigation; G2, individual adaptive; G3, long-term adaptive; and G4, solar-energy system installation behaviors. Following a questionnaire survey for the four behavioral groups, behavioral models were developed using structural equation modeling, which considered psychological factors along with selected attitudes, perceived effectiveness, norms, benefits, practicality, and intention. Furthermore, we considered the recognition and attribution of local climate change as key factors for adaptation behaviors toward local climate change. The behaviors of citizens in four prefectures in Japan, namely, Nagano, Tokyo, Saitama, and Kanagawa were considered. Among them, Nagano Prefecture, wherein the implementation of adaptation measures was at a low level while that of mitigation measures was at a high level, showed a differing trend. In the behavioral models, the recognition of local climate change directly affected the behavior of citizens in the individual adaptive behavior group. In both the individual adaptive and long-term adaptive behavior groups, the impact of benefits was substantial, with significant differences across local areas. Thus, effective adaptation strategies might involve tangible and familiar examples demonstrating the consequences of behavior, thus enhancing citizens' behavior evaluation perception and fostering its acceptance as a desirable behavior. Tailoring adaptation strategies to specific local contexts might also be necessity. To encourage citizens to adapt to climate change and its impacts, we propose two-way efforts, instead of providing one-way information, by supporting citizens to think about self-help and mutual help effectively. Our study can serve as a reference for future studies focusing on citizen behavior about climate change mitigation and enhance their adaptation for locally implemented strategies.