Decision‐making factor interactions influencing climate migration: A systems‐based systematic review

Abstract

Global migration and mobility dynamics are expected to shift in the coming decades as a result of climate change. However, the extent to which migration is caused by climate hazards, in contrast or addition to other intervening factors, is a point of debate in literature. In this study, we conducted a systematic literature review to identify and consolidate factors which directly and indirectly influence climate change migration. In our review of the literature, we found a total of 21 economic, environmental, demographic, political, social, and personal intervening decision‐making factors which affect climate migration. Causal interactions between these factors were identified using an axial qualitative coding technique called purposive text analysis. By combining causal links, a semi‐quantitative causal loop diagram was created that represented factor interaction and feedback within the “climate migration system.” Using this model, we highlight influential feedback loops and point to how intervention strategies may cause downstream effects. This research helps to address calls for a better understanding of the complex decision‐making dynamics in climate migration. In particular, results from our causal feedback loops show that intervention strategies targeted toward economic factors such as financial capital and livelihoods, as well as food security, would have the greatest impact in assisting climate‐affected communities. These results help inform climate migration policy and aid planners in the future to better understand the interconnected system of factors that lead to the emergent outcome of climate migration. This article is categorized under: Vulnerability and Adaptation to Climate Change > Learning from Cases and Analogies The Social Status of Climate Change Knowledge > Climate Science and Decision Making

Research in environmentally induced human mobility: an analysis of methodological and theoretical dimensions

“…Despite human mobility being a product of a mix of choices and constraints at an individual level (Naser et al, 2023), the results of the analysis of human mobility perspectives revealed a (Kelman, 2020). In practice, Nabong et al (2023) identify around 20 economic, environmental, demographic, political, social and personal intervening decision-making factors that affect migration-related decisions within the context of climate change.…”

“…De Sherbinin et al (2022) identify a general tendency of environment and climate-induced mobility to have limited engagement with existing theory (de Sherbinin et al , 2022), which might further feed general assumptions on how climate change and environmental degradation impact mobility-related decisions when populations are exposed to risks (Kelman, 2020). In practice, Nabong et al (2023) identify around 20 economic, environmental, demographic, political, social and personal intervening decision-making factors that affect migration-related decisions within the context of climate change.…”

“…Specifically, the empirical studies in the field of environmental and climate migration have been marked by a lack of application of the extensive body of migration theory (de Sherbinin et al , 2022). Even though mobility is one possible response to environmental and climatic stresses and shocks (Cattaneo et al , 2019; McLeman, 2019), it is a complex phenomenon, usually part of a wider multi-causal response (Nabong et al , 2023), as it is influenced by different triggers such as environment, economy, politics, society and demography, as defined by the landmark report Foresight (2011). Economic drivers include imbalances in labor markets and wages, as well as the available income and income volatility.…”

Using network analysis to identify leverage points based on causal loop diagrams leads to false inference

“…In seeking alternative methods, network analysis 35 has gained momentum as an accepted practice to quantitatively identify leverage points based on CLDs 8 , 23 – 30 , 36 – 55 —for example in public health 24 , 38 , 45 , 50 , 52 , 53 , 55 . Here, 22 of the 29 papers that we identified that adopt this practice were published in 2021 (4 papers 23 , 25 , 37 , 50 ), 2022 (8 papers 27 , 28 , 43 – 45 , 47 , 49 , 52 ), and 2023 (10 papers to date 26 , 30 , 36 , 38 , 39 , 41 , 42 , 46 , 53 , 55 ). Network analysis seemingly has the potential to mathematically analyse a CLD, which in this context can be considered a network of factors, to systematically find which factors represent leverage points.…”

“…Currently in the literature relating to the development and analysis of CLDs, the term 'leverage point' tends to be used as referring to a factor within the CLD "in which change is likely to yield significant shifts elsewhere in the system" 23 . The key premise is that the system depicted in the CLD is producing undesirable system-level behaviour, and that there are central drivers in the system that could be influenced in such a way that this undesirable system-level behaviour is sustainably disrupted 8,[24][25][26][27][28][29][30] .…”