Climate Change Adaptation Studies Research Articles

Toward a critical theory of social-ecological resilience: Maize and cattle in Southern Province, Zambia.

Climate change threatens the lives and livelihoods of smallholder farmers throughout the global South. In order to address the challenges these farmers face, researchers and practitioners need reasonable theoretical models of how humans and the environment interact within social-ecological systems (SES). Social-ecological resilience theory has proved to be a popular model for understanding human environment relationships within SES; however, the theory lacks a sophisticated understanding of power, relying instead on outdated functionalist sociological approaches. We reconstruct social-ecological resilience theory through a case study of smallholder climate change adaptation in Southern Province, Zambia. Farmers in the region focus on cattle and maize production. Though the changing environment would seem to favor different crops and livestock, institutional (power) dynamics determine whether or not individuals have the capacity (or desire) to adapt. Our critical reconstruction provides researchers and practitioners with an improved social-ecological lens for understanding the causes and consequences of vulnerability and adaptation.

Deep learning modeling framework for multi-resolution streamflow generation

Abstract Generating continuous streamflow information through integrated climate-hydrology modeling at fine spatial scales of the order of a few kilometers is often challenged by computational costs associated with running high-resolution (HR) climate models. To address this challenge, the present study explores deep learning approaches to generate HR streamflow information from that at low resolution (LR), based on runoff generated by climate models. Two sets of daily streamflow simulations spanning 10 years (2011–2020), at LR (50 km) and HR (5 km), for the Ottawa River basin, Canada, are employed. The proposed deep learning model is trained using upscaled features derived from LR streamflow simulation for the 2011–2018 period as input and the corresponding HR streamflow simulation as the target; data for 2019 are used for validation. The model estimates for the year 2020, when compared with unseen HR data for the same year, suggest good performance, with differences in monthly mean values for different accumulation area categories in the −0.7–5% range and correlation coefficients for streamflow values for the same accumulation area categories in the 0.92–0.96 range. The developed framework can be ported to other watersheds for generating similar information, which is required in climate change adaptation studies.

Innovation and digitalization in environmental education: the case study of climate change adaptation and analysis of land surface temperature


 
 
 The purpose of the article is to present the research results on the land surface temperature change in the frame of the Edu- cational and Professional Program «Ecology and Environmental Protection» developed at the Department of Environmental Sciences at National Aviation University, where the importance of the development of education in the noosphere paradigm is emphasized, as the most integral bio-adequate basis for the interaction of society and the environment in the conditions of today’s challenges. The climate’s change and the land surface temperature, as an integral part of it, are highlighted to tackle the environmental challenges in- cluding those connected with the warfare activities in the country. Development of the remote sensing technique within the framework of the Educational and Professional Program provides students with the opportunity to perform the environment changes assessment due to long-term anthropogenic stress, and also acquire knowledge and skills from the new course of «Adaptation to Climate Change», introduced to strengthen the ability to survive and effectively work and study in conditions of global climate change. The land surface temperature assessment is considered on the examples of the highly technologically loaded areas within the central part of the Ukrainian Shield, in particular the mining area and thermal power plant. Land surface temperature is one of the key features that could represent environmental changes caused by anthropogenic influence, for example, urbanization. It makes land surface temperature an appropriate Earth’s attribute that combines remote sensing and environmental sciences in the sense of environmental education. It is clear, that its influence on environment is intuitive for students and it could be represented using geoinformation systems. Land surface estimation is still a challenging task for non-specialists but its ecological significance makes land temperature mapping useful and important in environmental education.
 
 

Effectiveness and resilience of BMPs to watershed climate adaptation considering the uncertainty of hydrological model and GCMs

Climate change significantly impacts catchment hydrologic and water quality processes. Best management practices (BMPs) can serve as adaptation strategies to negate the impact of climate change on sediment and total nitrogen (TN) loads. One major controversial issue in climate change adaptation studies is the highly uncertain nature of such changes. Previous studies have rarely focused on the combined impact of the uncertainty in climate change and watershed model parameters, which could be the main sources of uncertainty in climate change adaptation research. In this study, the effectiveness and resilience (defined as continued effectiveness under a changing climate) of BMPs in reducing sediment and TN loads were explored under future climate change in the Shanmei Reservoir watershed (SMW) of Southeast China. Climate change projections provided by 10 general circulation models (GCMs) under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios from 2031 to 2060 were applied in the Annualized Agricultural Nonpoint Source (AnnAGNPS) model to evaluate the effectiveness and resilience of 4 BMPs (riparian buffers (RB), no-tillage (NT), fertilization reduction (FR), and parallel terraces (PT)). The ensemble average of 10 GCMs and 10 behavior parameter groups were adopted to reduce the uncertainty resulting from the hydrological model parameters and GCMs. The results indicated that the average annual temperature and precipitation in the SMW will increase in the future. On a seasonal scale, the average temperature during all seasons will increase, and precipitation will decrease in summer and autumn but increase in spring and winter. The annual sediment and TN loads will decrease, but the loads in spring and winter will increase. BMPs could be effective as climate adaptation strategies for reducing sediment and TN loads under future climate conditions, with PT as the most effective option. Structural BMPs were more effective in reducing sediment and TN loads in spring and winter, whereas nonstructural BMPs were more effective in reducing loads in summer and autumn. BMPs were more resilient when future watershed runoff changes were slight or climate sensitivity was reduced, with higher BMP resilience in spring and winter than in summer and autumn. This study aimed to provide systematic references for watersheds through the implementation of BMPs for mitigating the effects of climate change and extending the boundaries of the AnnAGNPS model application.

Health impact studies of climate change adaptation and mitigation measures – A scoping review

Climate change affects both mental and physical health. Besides limiting the extent and consequences of climate change, mitigation and adaptation measures can have additional and potentially unintended health impacts. This scoping review outlines how health effects of climate mitigation and adaptation measures have been studied in the scientific literature. We conducted a systematic literature search in the databases PubMed, Scopus and Web of Science without time restriction. All peer-reviewed articles reporting quantified health impacts linked to specific climate change adaptation and mitigation measures were included. Overall, the 89 included articles considered only a narrow range of health determinants and health outcomes. Adaptation- and mitigation-related articles most frequently investigated the environmental health determinants air temperature and air pollution, respectively. Non-communicable diseases were predominantly studied while other relevant health outcome categories, such as mental health, food- and nutrition-related issues, and communicable diseases were rarely reported. The scarcity of studies focusing on the social health determinants and providing stratified health impacts among vulnerable population groups in assessments points to an inadequate consideration of health equity aspects. Increased efforts to quantify health impacts more comprehensively and to identify underlying vulnerability factors among specific population groups seem needed. This information could provide policymakers with more accurate evidence to address health equity aspects, limit adverse health impacts and promote health co-benefits of climate change adaptation and mitigation measures.

Advancing participatory backcasting for climate change adaptation planning using 10 cases from 3 continents

In the face of climate change, a major challenge is to inform and guide long-term climate change adaptation planning under deep uncertainty, while aiming at transformative change. Normative futures studies approaches, such as participatory backcasting, visioning and transition management, are increasingly applied, but their potential for climate change adaptation research and practice remains undervalued. This paper aims to advance the potential of backcasting in climate adaptation, by comparing various climate change adaptation studies that have used backcasting or visioning approaches. A framework has been further developed and applied to evaluate 10 cases in Africa, Europe and North America, using four dimensions: (i) inputs andsettings; (ii) process and methods (iii) results, and (iv), impact. Our evaluation provides key insights into the use and further development of backcasting for climate adaptation. Key elements to add are advanced system modeling, robust elements, pathway switching and hybrid pathways, enhancing participation of marginal groups, and contributing to impact by facilitating the utilization of results and knowledge in practice and decision making.

A Comprehensive Review of Different Types of Green Infrastructure to Mitigate Urban Heat Islands: Progress, Functions, and Benefits

Climate change and rapid urbanization increase/amplify urban heat islands (UHIs). Green infrastructure (GI) is an effective and popularly strategy used to moderate UHIs. This paper aims to better understand the progress of different GI types (urban parks, urban forests, street trees, green roofs, green walls) in mitigating UHIs, and what benefits they provide. Firstly, this paper used CiteSpace to analyze 1243 publications on the Web of Science from 1990 to 2021, then analyzed the function/regulation of ecosystem services/benefits and values of GI types in reducing UHIs. The historical review results show that research on all GI types showed rapid growth since 2013, and their GR increased rapidly. The highest-ranking keywords were urban heat island/heat island, climate/climate change/microclimate, and temperature/land surface temperature/air temperature. “Design,” “vegetation,” “quality,” and “reduction” are the top four strongest keyword bursts. The most published countries are the People’s Republic of China, USA, Australia, Germany, and Italy, and the top three institutions are the Chinese Academy of Sciences, Arizona State University, and the National University of Singapore. Landscape and Urban Planning, Building and Environment, Energy and Building, and Urban Forestry and Urban Greening are the most published journals. In urban areas, different GI types as a form of ecosystem hardware provide multiple functions (reduced land surface temperatures, lower building energy usage, improved thermal comfort and enhanced human health, reduced morbidity and mortality, etc.). GI thus provides a regulated ecosystem service to ameliorate UHIs primarily through temperature regulation and shade. At the same time, GI provides benefits and values (ecological, economic, social, and cultural) to humans and urban sustainable development. GI types determine the functions they provide, afford corresponding regulated ecosystem services, and provide benefits and values in a logical/recycle system. Overall, this review highlights the development and importance of GI, as well as the relationship of GI types and functions of regulating the ecosystem service benefits and values to mitigate UHI, and advances the study of climate change adaptation in cities.

Performance evaluation of CORDEX-Africa regional climate models in simulating climate variables over Ethiopian main rift valley: Evidence from Gidabo river basin for impact modeling studies

Measuring the simulation skill of regional climate models (RCMs) is vital in selecting the best performing model that can be used for climate change studies. To that end, the performance of eleven Coordinated Regional Climate Downscaling Experiment (CORDEX) Africa RCMs were evaluated against observed datasets from 1991 to 2005 over Gidabo river basin (GRB), main Ethiopian rift valley. RCMs’ outputs were evaluated using coefficient of variation (CV), percent of bias (PBIAS), Root Means Square Error (RMSE), Pearson's correlation coefficient (r), revised R-squared (RR2), Taylor Skill Score (TSS), Mann-Kendall (MK) trend test and Sen’s slope estimator. The results confirm the difference of RCMs in capturing the annual and seasonal climate variables. In relation to the spatial pattern of the rainfall, RACMO22T (EC-EARTH) strongly reproduced the mean annual rainfall. CCLM4–8 (MPI) and mean ensemble reproduced the annual patterns of the observed rainfall despite the fact with varying rainfall amounts reproduced. The seasonal rainfall pattern was satisfactorily captured by RACMO22T (EC-EARTH), CCLM4–8 (MPI) and REMO2009 (MPI). The agreement between the observed and modeled rainfall is superior in CCLM4–8 (MPI) and RACMO22T (EC-EARTH) at station level. CRCM5 (MPI) satisfactory replicated the patterns of both minimum and maximum temperature. RACMO22T (EC-EARTH) showed best performance in simulating annual and seasonal rainfall trends in GRB. In overall, models that performs better in replicating the observed climatology include RACMO22T (EC-EARTH), CCLM4–8 (MPI), CRCM5 (MPI), CCLM4–8 (CNRM), and REMO2009 (EC-EARTH). The study underscored the use of the mean ensemble of model simulation did not always guarantee better agreement with observation than individual models. Therefore prior to climate impact study, it is advisable to correct the systematic bias and employ the multi-model ensemble of best performing models for climate change impact and adaptation studies in the GRB.

A statistical approach to multisite downscaling of daily extreme temperature series: A case study using data in Bangladesh

Downscaling techniques are required to describe the linkages between Global Climate Model outputs at coarse-grid resolutions to surface hydrologic variables at relevant finer scales for climate change impact and adaptation studies. In particular, several statistical methods have been proposed in many previous studies for downscaling of extreme temperature series for a single local site without taking into account the observed spatial dependence of these series between different locations. The present study proposes therefore an improved statistical approach to downscaling of daily maximum (Tmax) and minimum (Tmin) temperature series located at many different sites concurrently. This new multisite multivariate statistical downscaling (MMSD) method was based on a combination of the modeling of the linkages between local daily temperature extremes and global climate predictors by a multiple linear regression model; and the modeling of its stochastic components by the combined singular value decomposition and multivariate autoregressive (SVD-MAR) model to represent more effectively and more accurately the space-time variabilities of these extreme daily temperature series. Results of an illustrative application using daily extreme temperature data from a network of four weather stations in Bangladesh and two different NCEP/NCAR reanalysis datasets have indicated the effectiveness and accuracy of the proposed approach. In particular, this new approach was found to be able to reproduce accurately the basic statistical properties of the Tmax and Tmin at a single site as well as the spatial variability of temperature extremes between different locations. In addition, it has been demonstrated that the proposed method can produce better results than those given by the widely-used single-site downscaling SDSM procedure, especially in preserving the observed inter-site correlations.

Gridded value-added of primary, secondary and tertiary industries in China under Shard Socioeconomic Pathways

Gridded distribution of future economy plays an important role in climate change impact assessment. The trend of the output values of different industries is crucial for a variety of planning and design processes. Under the Shared Socioeconomic Pathways (SSPs) global framework, the multidimensional model and Cobb-Douglas production model with localized population and economic parameters are used to develop the annual provincial population and value-added of primary, secondary and tertiary industries in China from 2020 to 2100. The most recently implemented fertility-promoting and industrial planning policies in China are considered in our projections. We build multiple models to evaluate the impact of different types of land use on the value-added of primary, secondary and tertiary industries and then gridded the projected value-added to a 5′ × 5′ resolution, based on recorded county-level economic statistics and gridded land use. The reliability of estimations is verified against 2011–2019 statistical data and multiple published datasets. The high-resolution economic dataset is expected to contribute greatly to national and regional climate change impact, adaptation, and vulnerability studies.

Bias‐adjustment of high‐resolution temperature CORDEX data over the Carpathian region: Expected changes including the number of summer and frost days

AbstractClimate model simulations' outputs are prone to biases compared to observations; furthermore, climate projections can be very different in modelling future temperature characteristics. One possible solution for reducing uncertainty and eliminating possible systematic errors within climate projections is the bias‐adjustment of the raw climate model data. We used the quantile mapping method for bias‐adjustment of a mini ensemble consisting of 8‐member high‐resolution (0.11°) regional climate model simulations provided by the CORDEX community. As the method requires a reliable observational dataset serving as reference data, we used the quality controlled and homogenized observational dataset: CARPATCLIM. Quantile mapping bias‐adjustment technique was applied on the following variables: daily mean, minimum and maximum temperature. We analysed changes in mean temperature characteristics and of climate indices for future periods of 2021–2050 and 2070–2099 with respect to the reference period 1976–2005 for the Carpathian Region. The selected climate indices are based on minimum (frost days, FD) and maximum daily temperature data (summer days, SU). Our bias‐adjusted RCM results suggest a similar degree of mean temperature change as the raw RCMs' data. Bias‐adjusted and raw RCM data project a remarkable annual temperature increase on average under the RCP8.5 scenario (1.4°C and 3.9°C by 2021–2050 and 2070–2099, respectively). The highest temperature increase is likely to occur in summer: it is 4.3°C on average by the end of the 21st century. More pronounced differences were found for the projected changes of the number of summer and frost days based on the bias‐adjusted and the raw RCM data. Our results draw attention to the fact that bias‐adjusted RCM data are crucial for the provision of regional climate change impact and adaptation studies for the Carpathian Region.

Integrating an intrahousehold perspective into climate change adaptation research

Many survey-based studies on climate change adaptation behaviours have methodological inadequacies because of the mismatched scale of analysis between individual-level predictors and household-level adaptation behaviours; thus, addressing intrahousehold dynamics in climate change adaptation research is essential. Synthesising the findings of related literature, we identified seven key intrahousehold factors that are likely to influence household adaptation processes: (1) gendered division of labour, (2) disagreements, (3) conflicts and conflict resolution strategies, (4) decision-making stages, (5) types of decision-making, (6) interpersonal influence, and (7) household life cycle. We also specified three intrahousehold factors that are likely to influence household adaptation outcomes: (1) gendered adaptation behaviours, (2) relational adaptation behaviours, and (3) household life cycle and adaptation behaviours. Acknowledging the potential bias of approaching just one representative per household, we then detailed conceptual frameworks and pragmatic advice for applying a household-based approach in climate change adaptation studies. Future research will benefit from clear articulation of the level of analyses and more coherent and viable methodological solutions that involve consideration of intrahousehold dynamics.

Achieving peaceful climate change adaptation through transformative governance

• In this article we ask how urgent climate change adaptation can be realised on the ground without furthering conflicts. • We develop a theoretical framework on transparent and adaptive governance for peaceful climate change adaptation. • We emphasise a learning methodology to cooperatively produce locally relevant and adaptive knowledge. • We highlight the study of climate change adaptation in ‘glocal’ cross-border cases through a bottom-up approach. • Taken together we propose a transformative governance approach vital for peaceful climate change adaptation. Which form of governance is required to bridge tensions that stem from the urgent need of climate change adaptation (CCA) on the one hand, and the imperative of upholding peace and social stability in vulnerable areas on the other? This article proposes transformative governance as a framework and methodology for addressing this question. It recognizes that the increased pace of climate change requires urgent and thorough adjustments to actual or expected climate change effects through a transformation of societies to increase their capacity to build sustainability. Our framework for transformative governance approach responds to this imperative and is based on three components: a theoretical framework for peaceful CCA governance derived from the fields of sustainability governance, political ecology and peacebuilding, second, a ‘glocal’ and bottom-up approach illustrated by two examples of cross-border collaboration, that demonstrate peaceful CCA governance as necessarily glocal, thirdly a learning methodology that implies context-based, goal-oriented, pluralistic and interactive co-production of knowledge. These are argued to be vital conditions for implementing CCA governance that is transformative and supports peaceful societies.

Evaluation of snow extent time series derived from Advanced Very High Resolution Radiometer global area coverage data (1982–2018) in the Hindu Kush Himalayas

Abstract. Long-term monitoring of snow cover is crucial for climatic and hydrological studies. The utility of long-term snow-cover products lies in their ability to record the real states of the earth's surface. Although a long-term, consistent snow product derived from the ESA CCI+ (Climate Change Initiative) AVHRR GAC (Advanced Very High Resolution Radiometer global area coverage) dataset dating back to the 1980s has been generated and released, its accuracy and consistency have not been extensively evaluated. Here, we extensively validate the AVHRR GAC snow-cover extent dataset for the mountainous Hindu Kush Himalayan (HKH) region due to its high importance for climate change impact and adaptation studies. The sensor-to-sensor consistency was first investigated using a snow dataset based on long-term in situ stations (1982–2013). Also, this includes a study on the dependence of AVHRR snow-cover accuracy related to snow depth. Furthermore, in order to increase the spatial coverage of validation and explore the influences of land-cover type, elevation, slope, aspect, and topographical variability in the accuracy of AVHRR snow extent, a comparison with Landsat Thematic Mapper (TM) data was included. Finally, the performance of the AVHRR GAC snow-cover dataset was also compared to the MODIS (MOD10A1 V006) product. Our analysis shows an overall accuracy of 94 % in comparison with in situ station data, which is the same with MOD10A1 V006. Using a ±3 d temporal filter caused a slight decrease in accuracy (from 94 % to 92 %). Validation against Landsat TM data over the area with a wide range of conditions (i.e., elevation, topography, and land cover) indicated overall root mean square errors (RMSEs) of about 13.27 % and 16 % and overall biases of about −5.83 % and −7.13 % for the AVHRR GAC raw and gap-filled snow datasets, respectively. It can be concluded that the here validated AVHRR GAC snow-cover climatology is a highly valuable and powerful dataset to assess environmental changes in the HKH region due to its good quality, unique temporal coverage (1982–2019), and inter-sensor/satellite consistency.

Xylem features detrending methods matter: A case study on earlywood vessels of Fraxinus mandshurica

Xylem anatomy can provide valuable information about tree allometry and ecophysiological performance. It has unique advantages in the study of global climate change and forest adaptation compared to traditional ring-width or density proxy parameters. Developing appropriate detrended sequences of xylem features is necessary to carry out a series of further studies. However, the differences between detrending methods and their effects for xylem features are still unclear, presenting a challenge to the choice of proper detrending methods. In this study, we used the earlywood vessel features of Fraxinus mandshurica as an example and compared the differences in four common detrending methods (Spline, CSS: cubic smoothing spline, NEP: negative exponential curve, and RCS: regional curve standardization) to highlight the importance of the detrending methods in dendroanatomical studies. The results show that detrending methods significantly affect the long-term growth trends and climate signals recorded in xylem anatomical features. Among them, NEP has good performance in terms of ring width and vessel number, which is not suitable for other vessel features because of its deterministic nature. Spline and CSS are relatively conservative methods, in which are not easy to make mistakes, but the problem of overfitting (lack of climate signals) deserves attention. Multiple RCS may be a promising detrending method with quantitative wood anatomy technology development if sufficient samples are available. Our results highlight the importance of selecting detrending methods for xylem feature identification and emphasize the importance of testing various detrending methods rather than arbitrarily choosing a standard method. The detrending method should be selected based on the purpose, such as the balance between climate signals and thorough detrended effects. We recommend applying multiple methods to test the growth trends in xylem features before selecting a detrending method. More interdisciplinary studies involving ecophysiology, wood anatomy, and dendrochronology will help to distil the valuable information in xylem features accurately.

An evaluation of CMIP5 and CMIP6 climate models in simulating summer rainfall in the Southeast Asian monsoon domain

AbstractIn this study, 28 climate models from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) and 32 models from the sixth phase (CMIP6) have been evaluated for their ability to simulate large‐scale atmospheric circulations (using rainfall, wind fields, geopotential height, temperature, and moisture flux convergence) for the summer monsoon in Southeast Asia. Using a multi‐criteria decision making technique, models have been ranked based on 25 metrics which compare their performances with observation data. Results indicate a better representation of annual rainfall cycles as well as spatial pattern by CMIP6 models compared to CMIP5. Though majority of the models from both CMIPs show late onset and early retreat of the rainy season, CMIP6 GCMs simulate the onset, retreat, and the length of the rainy season closer to the observation. Large‐scale circulation patterns evaluated using spatial correlation and root mean square error (RMSE) show improvements in CMIP6 across all metrics, especially for the moisture flux convergence. Performances for large‐scale circulation are generally reflected in rainfall simulation; however, few models showed that better simulations of rainfall do not exclusively depend on their performance for large‐scale variables. Overall, CMIP6 models are found to be superior to CMIP5 models in simulating rainfall and large‐scale circulation, which is likely attributable to CMIP6 model's higher spatial resolutions, increased number of vertical levels, improved atmospheric and land surface parameterization, etc. Finally, subsets of optimal models from CMIP5 and CMIP6 that proved to be better at representing the summer monsoons in the study area are identified. These models are recommended to develop robust future projections that can be used for climate change impact and adaptation studies.

Evaluation of the CORDEX regional climate models (RCMs) for simulating climate extremes in the Asian cities

This study evaluates the ability of 21 Regional Climate Models (RCMs) from the Coordinated Regional Climate Downscaling Experiment (CORDEX) in simulating climate extremes in the fast growing Asian cities which are highly vulnerable to climate change. The three Asian cities have two different climate characteristics, namely Bangkok and its vicinity and Ho Chi Minh City in tropical climate region and Kathmandu in sub-tropical and temperate climate region. The RCMs were evaluated to simulate the six climate indices; Consecutive Dry Days (CDD), Simple Daily Intensity Index (SDII), Number of extremely heavy precipitation days (R50mm), Maximum 1-day precipitation amount (RX1day), Mean of daily maximum temperature (TX mean) and Mean of daily minimum temperature (TN mean). The performance indicators used were correlation coefficient, normalized root mean square deviation, absolute normalized root mean square deviation and average absolute relative deviation. The Entropy method was endorsed to acquire weights of these four indicators and weightage average techniques were used for ranking of 21 RCMs. The result demonstrated that the best model for one climate index is not the same best model for other climate indices. The 3 RCMs; WAS44_SMHI_RCA4_IPSL_CM5A_MR, WAS44_SMHI_RCA4_MIROC5, and WAS44_IITM_REGCM4-4_CSIRO_MK3-6-0 are the best performing RCMs for simulating future climate extremes in Bangkok and its vicinity, Ho Chi Minh city and Kathmandu valley, respectively. Therefore, they are recommended to use for climate change impact and adaptation studies in water resources management in the selected cities.

젠더 차이를 고려한 기후변화 연구 리뷰

This study reviews literatures on gender perspectives of climate change over 20 years (2000-2019). Such studies have steadily increased since 2008 and surged in 2019 due to multiple international conventions. Among these, literatures about gender sensitivity are more abundant than are those about gender responsiveness (73% vs. 27%). Likewise, climate change adaptation studies were significantly more frequent in comparison to climate change mitigation studies (83% vs. 17%). Geographically, a majority of studies deal with issues in developing countries in Africa and Southeast Asia. The present study classifies literatures into four categories: climate change adaptation-gender sensitivity (AS), climate change adaptation-gender responsiveness (AR), climate change mitigation-gender sensitivity (MS), and climate change mitigation-gender responsiveness (MR). Analysis shows that vulnerability and inequality are the main subjects of AS studies, while gender differences comprise most MS studies. Educational/financial/technological support is the main subject of both AR and MR studies. However, technical studies are scarce in all categories. To summarize these studies, due to the combination of biological vulnerability and social inequality, women suffer from increased workload under climate change. In many developing countries, women are not given the social rights to overcome these challenges. Although efforts to find political and technical solutions to these issues are underway, many are ineffective; women should actively participate in the effort to achieve gender equality. This review concludes that i) Research on climate change and gender is advancing in line with international conventions. ii) Research on this subject is being actively conducted in many developed countries, but not in South Korea. iii) Gender perspectives on climate change will introduce more effective application of scientific findings to society. It is expected that this review paper works as a catalyst for more scientists to pay attention to gender issues related to the climate change.

젠더 차이를 고려한 기후변화 연구 리뷰

This study reviews literatures on gender perspectives of climate change over 20 years (2000-2019). Such studies have steadily increased since 2008 and surged in 2019 due to multiple international conventions. Among these, literatures about gender sensitivity are more abundant than are those about gender responsiveness (73% vs. 27%). Likewise, climate change adaptation studies were significantly more frequent in comparison to climate change mitigation studies (83% vs. 17%). Geographically, a majority of studies deal with issues in developing countries in Africa and Southeast Asia. The present study classifies literatures into four categories: climate change adaptation-gender sensitivity (AS), climate change adaptation-gender responsiveness (AR), climate change mitigation-gender sensitivity (MS), and climate change mitigation-gender responsiveness (MR). Analysis shows that vulnerability and inequality are the main subjects of AS studies, while gender differences comprise most MS studies. Educational/financial/technological support is the main subject of both AR and MR studies. However, technical studies are scarce in all categories. To summarize these studies, due to the combination of biological vulnerability and social inequality, women suffer from increased workload under climate change. In many developing countries, women are not given the social rights to overcome these challenges. Although efforts to find political and technical solutions to these issues are underway, many are ineffective; women should actively participate in the effort to achieve gender equality. This review concludes that i) Research on climate change and gender is advancing in line with international conventions. ii) Research on this subject is being actively conducted in many developed countries, but not in South Korea. iii) Gender perspectives on climate change will introduce more effective application of scientific findings to society. It is expected that this review paper works as a catalyst for more scientists to pay attention to gender issues related to the climate change.

Developing a Conceptual Model for Identifying Determinants of Climate Change Adaptation

The conceptual model seems to be an integral part of academic research. Any conceptual model should be developed based on theoretical reasoning and practical relevance of the topic. However, for most instances, climate change adaptation studies do not ground on such rationale and relevance. A relatively lower R2 value of such studies further indicates a lack of care for developing the conceptual modelling of the study. Thus, the current study attempted to propose a conceptual model for assessing the determinants of farmers’ adaptation to climate change in Bangladesh. A three-stage approach was used in constructing the model. First, relevant literatures were collected and analysed. Second, a list of factors compiled from the first stage (i.e., analysis of literature review) was refined using the Delphi method. Finally, the result of the Delphi method was cross-evaluated by Focus Group Discussion (FGD). Based on this, the present study proposes a multi-dimensional conceptual model that includes personal, economic, institutional and environmental determinants and might influence farmers’ adaptation decision to climate change. This finding is expected to be suitable, in particular, for Bangladesh and other developing countries in general. Most importantly, it is expected to guide future research in assessing the factors influencing farmers’ adaptation strategies to climate change effect and other similar events such as adopting agricultural technologies.