Livelihood vulnerability assessment to climate variability and change using fuzzy cognitive mapping approach

Abstract

Existing studies in the context of assessing vulnerability to climate variability and change delineate, rather inadequately, interconnected interactions occurring within the climate-human-environment interaction space. Besides, studies documenting stakeholders' perceptions regarding climate change induced vulnerabilities are limited in terms of providing indicators for decision-making. This paper aims at constructing a livelihood vulnerability index for climate variability and change capturing interconnected interactions based on peoples' perceptions while providing indicators for evidence based decision-making. A semi-quantitative fuzzy cognitive mapping (FCM) approach has been deployed to capture peoples' perceptions of climate induced perturbations and adaptations. This approach helps quantify stakeholders' perspectives while capturing interconnected interactions in order to estimate livelihood vulnerability to climate variability and change of poor agro-pastoralists in the Bhilwara, a district in Western India. Combining the FCM approach with a sustainable livelihood framework warrants an understanding of assets sensitive to climate variability and change along with those serving as adaptive capacities. The findings of this study confirm that financial and natural assets are most susceptible to harm while organisational and financial assets provide resilience against climate variability and change. The results suggest that livelihood vulnerability of agropastoralists lie in the range of being 'vulnerable' to climate variability and change while varying across three seasons summer, winter, and rainfall.

Evaluating poverty alleviation strategies in a developing country

“…We conducted the study with the aid of the FCM-based approach introduced by Kosko in 1986 [58]. The process of data capture in the FCM approach is considered quasi-quantitative because the quantification of concepts and links may be interpreted in relative terms [59] allowing participants to debate the cause-effect relations between the qualitative concepts while generating quantitative data based on their experiences, knowledge, and perceptions of inter-relationships between concepts [60][61][62][63][64][65][66][67][68]. The FCM approach helps us visualise how interconnected factors/ variables/ concepts affect one another while representing selfloop and feedback within complex systems [62,63,69].…”

“…In the case of the 'open-concept design' approach, concepts are determined entirely by participants and are unrestricted [59,60,62,63,[65][66][67]79,80]. While the researcher determines the context of the model by specifying the system being modelled, including the boundaries of the system, participants are allowed to decide what concepts will be included.…”

“…After constructing the FCMs each participant made a presentation, which was video-recorded, explaining their map to the researchers. The researchers, based on causal relationships between the concepts, assigned positive and negative polarities to the weights of the links [59,60,[62][63][64][66][67][68]72].…”

“…The weights of the links, on a scale of 1−10, were normalised in the −1 to +1 range [62,63]. The values were then coded into a square adjacency matrix whenever a connection existed between any two concepts [60,[62][63][64]66].…”

Detailed Sponge City Planning Based on Hierarchical Fuzzy Decision-Making: A Case Study on Yangchen Lake

“…It consists in an inference structure that enables appropriate human reasoning capabilities. It has been widely applied in studies relating to vulnerability assessment [15,17,[21][22][23][24][25] and urban planning [26][27][28][29][30]. The approach sets up a fuzzy inference system (FIS), which consists in user-defined membership functions and decision rules [20].…”