CHAIN adopts an integrated multidisciplinary human-centric approach to develop, synthesize, integrate, and supplement data and models to research the complex relationships among the many factors influencing migration/mobility and its relationship to climate hazards and improve evidence production, public and policy debate, and decision-making.
The increasing frequency and severity of sudden and slow-onset climate-related hazards are bearing visible effects on natural and human systems in low- and middle-income countries (LMICs). The consequences of these hazards threaten to derail national and global efforts to achieve sustainable development goals in LMICs. A consensus among scholars holds that climate hazards are crucially linked to migration. From a scientific research perspective, the research portfolio on droughts and cyclones fails to match the severity of its impact in Africa.
Taking Madagascar, a LMIC characterized by dual exposure to sudden and slow-onset climatic hazards and increasing migration movements, the proposed project, CHAIN, aims to achieve the following intertwined objectives: CHAIN will adopt an integrated multidisciplinary human-centric approach to develop, synthesize, integrate, and supplement data and models to research the complex relationships among the many factors influencing migration/mobility and its relationship to climate hazards, and improve evidence production, public and policy debate, and decision-making.
The increasing frequency and severity of sudden and slow-onset climate-related hazards impacts natural and human systems in low- and middle-income countries (LMICs), threatening to derail national and global efforts to achieve sustainable development goals. Scholars agree that climate hazards are crucially linked to migration, but from a research perspective, four main gaps remain in the extant literature.
1. The research portfolio on droughts and flooding fails to match the severity of its impact in Africa. Case studies are concentrated in specific countries and evidence regarding cyclones and droughts has been overshadowed by analysis of precipitation and temperature anomalies. In addition, assessments of the migration effect from climate hazards are based on historical population or cross-sectional migration statistics, which i) makes it unclear whether variation in population over time stems from migration patterns or other demographic changes, and ii) fails to capture dynamic adaptation within migration trends.
2. Social science theories of migration are often livelihoods-based, focusing on the exposure to a singular hazard event. This ignores the importance of dynamic exposure to a specific type of hazard and the migration implications of a multi-hazard environment. Addressing these concerns requires more holistic and interdisciplinary approaches.
3. Quantitative researchers often oversimplify relationships between migration and climate change by measuring a specific type of migration episode (permanent rather than temporary), or the odds of a singular demographic group to move. There is a need for a more human-centric approach to quantify the relationships underlying migration as adaptive responses by exploring the roles of migration duration, as well as the vulnerability of specific demographic groups (women and youth).
4. Empirical studies focus on migration-climate linkages in a vacuum, ignoring the complexity of adaptation and policy responses in the broader ecological-economic system and their related feedbacks. In addition, current renderings of the climate-migration relationship ignore the possibility of tipping points and thresholds in migration patterns. Modeling such nonlinearities in migratory relationships requires a more systematic modeling approach, such as agent-based modeling.
CHAIN, will address these four gaps by adopting a multidisciplinary human-centric approach to develop, synthesize, and integrate data and models to research the complex system of climate change and migration. Specifically, the project has the following intertwined objectives:
1. Develop a cost-effective procedure to improve our measure of migration in areas with low capacity for data collection and vulnerable to climate change, and over a sufficient spatial and temporal scale required for modeling these processes.
2. Develop and validate cost-effective procedures to measure multiple hazards (e.g. cyclone incidence, flooding, and droughts) using state-of-the-art numerical modeling, remote sensing techniques and satellite data available to the public.
3. Contribute a more human-centric approach to quantifying the relationships underlying migration as adaptive responses by exploring the roles of migration duration in a multi-hazard environment, as well as the vulnerability of specific demographic groups.
4. Investigate anticipated dynamic migration patterns accounting for adaptation and policy responses and their associated feedbacks.
The findings from CHAIN will contribute immensely to the infrastructure of social-eco - logical systems (SES) research, most prominently through the development of remotely sensed, scalable social science models – detecting decision-making and human interaction from space in a way that allows more effective scaling than primary data collection and that acts as an early-warning structure for prioritizing more intensive engagements. That is, in CHAIN, we “socialize the pixel” by developing new tools and expertise, and integrating traditionally disparate research communities in a shared effort to interpret and use remote sensing data for the analysis of fundamental social processes.
The development of an empirically grounded framework will provide a useful starting point for more general research into how the rise in sea levels and climate hazards in LMICs exacerbate socioeconomic and environmental challenges, and subsequently drive displacement and outmigration. The findings of CHAIN should contribute to the design and implementation of effective policy interventions to address climate-induced migration and achieve the targets of United Nations Sustainable Development Goals 1, 2 and 10 in LMICs.