Investigating environment-related migration processes in Ethiopia – A participatory Bayesian network

Abstract

The influence of environmental change on human migration is complex. Despite major strides in understanding the environment's role in migration processes, uncertainties associated with multi-scale factor interactions and their influence on migration still persist. This study aims to (a) understand how soil degradation and rainfall changes-in combination with socioeconomic factors-in the northern Ethiopian highlands contribute to human decisions to migrate; and (b) identify barriers for adopting local policy measures to reduce migration needs. Therefore, we integrate place-based research with literature findings and the participatory development of a Bayesian network (BN) involving local policy makers and representatives from both villages and NGOs. Our results show two major influential factors that affect environment-related migration: (a) the sufficiency of a household's agricultural production and (b) non-farm activities. According to our participatory BN, we conclude that environmental changes increase migration, either through high migration needs due to low agricultural production and/or through increased non-farm activities, which increases the necessary financial means to enable migration. We conclude that contradicting policy programs greatly hinder local measures to prevent soil degradation, potentially fueling migration, and that bottom-up and locally adapted strategies are particularly promising for sustainable land management in the region.

A future agenda for research on climate change and human mobility

“…They show promise for assessing the strength, directional influence and the interplay of direct and indirect migration drivers using conditional probabilities and as such are promising tools to deal with multicausality and uncertainty of complex processes such as mobility. A participatory study of the Ethiopian highlands used Bayesian Networks to unveil the links between environmental perception and to produce recommendations such as how to implement social and water conservation measures (Groth et al, 2021). Probabilistic event attribution and attribution studies are a fast‐evolving field of climate science (Otto et al, 2016).…”

A Systematic Review of International and Internal Climate-Induced Migration in Africa

“…According to these studies, various environmental forces, including drought, strong winds, and high temperatures, interact to drive migration. Out of the twenty-two reviewed studies, ten studies investigate the role of environmental factors in migration decision-making (see Akinbami, 2021 [44]; Bada et al, 2021; Groth et al, 2021 [36]; Call and Gray, 2020 [43]; Groth et al, 2020 [34]; Tafere, 2018 [38]; Codjoe, 2017 [41]; Sanfo et al, 2017 [32]; Veronis and McLeman, 2014 [37]; Marchiori, Maystadt, Schumacher, 2012 [40]).…”

Clustering Analysis of Integrated Rural Land for Three Industries Using Deep Learning and Artificial Intelligence

“…Notably, the last two residual modules do not employ down-sampling. Within this framework, the Batch Normalization layer is commonly utilized to adjust the output data distribution from the convolution layer, thereby expediting convergence (Groth et al, 2021). Suppose that the input of a batch at a specific neural network layer is represented as X=[x0, x1⋯, xn], where xi denotes a rural land sample, and n signifies the batch size.…”