Brief me
Climate Change, population dynamics and migration: a complex nexus
In the debate on environmental migration, one of the most natural questions is also one of the most contentious: how many people are today displaced because of environmental degradation? and how many will be displaced tomorrow?
The CLICIM project – CLImate Change Induced Migration - aims at the provision of evidence on the nexus between climate change and population dynamics in Africa. It features a strong relevance for current and future migration, development, cooperation and climate adaptation policies.
Climate change induced phenomena, such as changes in climate and temperatures, have both direct and indirect impacts on structural migration factors, thus affecting population dynamics through a complex set of interactions.
In this project, the JRC seeks to disentangle the role of climate factors in affecting population migration and mobility in Africa, by focusing on slow-onset climatic events - such as increasing temperatures (UNFCCC, 2012) and net migration estimates at high spatial resolution. The latter are based on population distribution datasets collected for the last five decades (1975-2015) by the JRC Global Human Settlement Layer and on estimated population projections for the decades to come.
By looking at population distribution projections under different Shared Socio Economic Pathways (SSP) scenarios, the project seeks to quantify and anticipate the future response, in terms of mobility and migration, of population exposed to climate change and to provide evidence on population vulnerability to climate change in a long-term perspective.
Mapping population dynamics at high spatial resolution: an example from the Sahel region
A first case study has been conducted on a selected geographical area crossing the borders of Mauritania, Mali, Guinea and Senegal in the Sahel region. The period 1980-85 was selected given the exceptionally serious drought registered in this time span.
Average temperature variation for the 1980-1985 time period is represented on Fig1 (56 km2 grid-cell level). Net migration for the same time period is shown in Fig2 and calculated as:
- Positive net migration: excess of immigration over emigration
- Negative net migration: excess of emigration over immigration
Reports and data
From the beginning of the project in 2020, CLICIM has already produced three reports. An extensive work has been conducted:
Estimating net migration data at high spatial resolution (Alessandrini et al., 2020a)
This technical report presents new estimates of net migration at high spatial resolution produced by the Joint Research Centre (JRC) – Knowledge Centre on Migration and Demography (KCMD). The development of a new global grid on net migration data is the first step of the CLICIM project. The report uses demographic indirect estimation techniques based on population data from the JRC Global Human Settlement Layer (GHSL) to estimate five-year net migration from 1975 to 2015 at a spatial resolution of about 25 km2. Notably, the recent definition of Degrees of Urbanization proposed by the European Commission and developed by the JRC is applied to distinguish net migration in urban and rural areas. Findings from the new datasets constitute the basis for further analyses on the relation between climate change and migration.
You can visualise, explore and download the global map of net migration estimated in CLICIM on the KCMD Dynamic Data Hub.
Screening and Selecting Climate Change Impact Parameters as Potential Drivers of Migration
In this report, JRC conducted a technical screening and selection exercise of 95 climate/weather parameters and indices to assess their suitability for the CLICIM project. Their temporal and geographical coverage and relevance have been analysed to proceed towards the final selection of 37 meteorological parameters and extreme climate/weather indices and provides references on their use in the context of migration studies.
Population dynamics, climate change and variability in Western Africa: the case of Sahel regions (Alessandrini et al., 2021)
In this report, JRC conducted a first case-study analysis of the potential relationship between population, migration and climate change in the Sahel. The report provides a mapping of the population exposure to climate change in the selected territory over the period 1975–2015. In particular, it identifies the geographical distribution of populations affected by droughts and how their demographic dynamics have changed over time. Second, the report develops a modelling exercise to investigate the climate change–migration nexus. Results from the model confirm a significant association between net migration and drought intensity, which is more accentuated in rural areas. Model findings also document that increasing temperatures and precipitation have contributed to decreasing net migration over the considered period.
Population exposure and migrations linked to climate change in Africa (McMahon et al., 2021)
The report quantifies exposed and vulnerable populations to climate change in Africa based on climate, demographic and socio-economic scenarios and analyses past trends, to detect statically significant associations between net migration and climate. It follows a spatial demographic approach and covers the entire territory of Africa with data and projections at high geographical resolution spanning from 1975 to 2100. These macro analyses are complemented by case studies looking at the relation between climate change displacement and urbanization in Egypt, drought migration in the Sahel regions and the role of conflicts in Sudan and South Sudan. Finally, the report considers individual perspectives using survey data on the perceptions in Africa about climate change and the desire to migrate. The findings of this report support the ongoing integration of EU policymaking on climate change, adaptation and migration. Specifically, it highlights the importance of further mainstreaming an understanding of climate change processes into demographic processes and adaptation actions at a local level.
