Environmental and non-environmental drivers of migration from global drylands




Stafford Smith, M.
Bastin, G.
Chewings, V.


Environmental and non-environmental drivers of migration from global drylands




Government Office for Science UK


Global drylands, particularly in developing countries, have long been recognised as a planetary pressure point where the coincidence of growing populations, declining productivity, political marginalisation and unpredictable drought puts their 2 billion inhabitants at risk. Although trends vary across the world, many of these regions have already experienced increased drought impacts in the late twentieth century, and most future climate projections suggest further impacts as a result of drying in the twenty-first century. It is a common perception that a failure to address these issues in drylands could create a globally significant problem of outmigration in the future, with impacts that reach into countries without drylands.
However, migration, including that which may be environmentally triggered, is a complex and contentious process; diverse drivers act in diverse contexts to create a variety of outcomes that cannot be characterised simply. This project therefore aimed to consolidate concepts of movement out of drylands and to test whether a broad synoptic view on these concepts could provide predictive power at a global scale with subnational resolution in the drylands.
Recent syntheses have highlighted key drivers of drylands functioning, including environmental variability, remoteness, limited livelihood options and vital cross- scale relationships with governance institutions outside drylands. An extensive literature review found many insightful case studies on drylands migration; taken together, these paint a bewildering picture of claim and counterclaim about alternative drivers or modulators of movement. Studies have tended either to claim primacy for one or two individual drivers that are evidently highly context specific and tended to be highlighted because disciplinary bias or to produce unhelpful ‘shopping lists’ of potential influences that are not quantified or prioritised. There have been only a few case studies with a quantitative, predictive approach to movement, and these were all single-country studies.
Building on the insights from within-country studies, therefore, we formalised a conceptual model which embraced sufficient cross-scale issues to have the potential to explain the differences between contexts yet remain abstracted and simple enough to be amenable to quantification. This model emphasised core drivers related to the levels of ecosystem services per person, with both acute and chronic changes in these causing households to seek the services elsewhere. Not all ecosystem services are equally important in drylands; here we focus initially on services related to primary production, whilst acknowledging that water-related services are also important, as are certain non-environmental services such as safety. The model also recognises that a series of factors at local and broader scales affect how critical these ecosystem services are to local livelihoods and what adaptive capacity households may have to stay or move. These encompass levels of national investment in drylands and their outcomes in terms of local human capital (e.g. health and education) and physical capital (e.g. regional infrastructure); these are also affected by the level of attention likely to be paid to drylands by the centres of power at the national scale. The potential role of inequality in modulating the mean likelihood of movement was included. Specifically, the five terms of the model are indicators of local trends in ecosystem services per head, probable national investment in dryland regions, investment outcomes in dryland regions, recent or current environmental conditions, and levels of income inequality.
In practical terms, we then sought to parameterise and test this model. Given the absence of a consolidated view of dryland migration case studies, a modest database was constructed with cases from most parts of the world over 30 years and from diverse conditions, including some counterfactuals where environmental change was known to have occurred yet there was no sign of migration. We then assembled a suite of indicators of the factors suggested to be important in the conceptual model. Although the database is modest, at this synoptic global level of analysis, the model explained a surprisingly large part (78%) of the case study diversity. The analysis also provided some insights into the more ambiguous terms (such as the role played by infrastructure development in both facilitating and discouraging migration). The parameterised model was then applied to all global drylands; the areas predicted to have high environmentally related migration pressure are plausible, but we could find no suitable independent data source to test this formally.
Accepting that the model is plausible, it was then applied to future projections to 2030 and 2060. Only a few of the model variables have plausible future time series, and none of these are available at the level of spatial resolution needed for the drylands. Taking the expected trends qualitatively (which is likely to be the most robust application), southern Africa and western and south central Asia are regions at particular risk of increased migration pressures from the convergence of social changes and potential drying according to the conceptual model formu lation.


Stafford Smith, M., Bastin, G., & Chewings, V. (2011). Environmental and non-environmental drivers of migration from global drylands. London : Government Office for Science UK. URL : http://www.dti.gov.uk/assets/foresight/docs/migration/drivers/11-1174-dr6-environmental-and-non-drivers-migration-from-drylands.pdf