National authorities collect information on displacement and evacuations linked to disasters, both in collecting data at the national, sub-national and local level, and also in coordination with other actors such as international organizations. Examples of national displacement databases include Brazil’s Integrated Disaster Information System, the Philippines Disaster Response Operations Monitoring and Information Center (DROMIC) database, Uruguay’s MIRA database, and Sri Lanka’s Desinventar database; analyses of these can be found in IOM, 2024. While national data is crucial for national and sub-national information and response, methodologies and displacement definitions between national authorities are not standardized, so these databases have limited comparability on an international level. Local-level disaster displacement data are available from (international and national) humanitarian agencies (NGOs, UN agencies) engaged in relief operations, which collect data in order to respond to the needs of affected populations. Planned relocation of communities in the context of environmental and climate change is increasingly implemented by governments. For a summary of relocation programmes, see Ionesco, Mokhnacheva and Gemenne, 2016; Benton, 2017 and Georgetown University, UNHCR and IOM, 2017).

International and national humanitarian agencies (NGOs, UN agencies) engaged in relief operations collect data in order to respond to the needs of affected populations, providing local-level disaster displacement data. IOM’s Displacement Tracking Matrix (DTM) is a system used to track and monitor disaster displacement and population mobility. Data, which can be disaggregated by gender and sex, are regularly captured, processed and disseminated to provide a better understanding of the movements and evolving needs of displaced populations and migrants, whether in situ or en route, before, during and in the aftermath of disasters. The data are presented in the DTM Data Portal. A study on how current DTM practices collect data on human mobility in the context of environmental degradation, climate change and disasters also provides recommendations on how to improve current tools and practices. Recommendations include improving the focus of some DTM questions and increasing the amount of options available for respondents to provide more granular data on the mobility , environment and climate nexus (IOM, 2023).

Administrative data sources, such as the numbers of humanitarian visas (such as in the US, Brazil, Ecuador or Mexico) or residence permits granted (for instance, by Argentina) linked to disasters, can provide information on cross-border displacement and movements in the context of environmental events more generally.

Innovative data sources include mobile phone-based sources such as call detail records (CDRs). Big data generated by mobile phone users before and after disasters, such as the 2010 earthquake in Haiti (Bengtsson et al., 2011) and several typhoons in the Philippines and Bangladesh (Lu et al., 2016), can indicate where displaced persons moved to and help deliver prompt and targeted humanitarian assistance or to understand internal movements (Laczko and Rango, 2014; GMG, 2017). Meta’s Data for Good collects mobility data from its users and compiles datasets such as the Facebook Movement During Crises datasets for example around the volcanic explosion in St Vincent and the Grenadines in 2021. Mobile phone-based data can be a means to collect complementary quantitative data on movements at small-scale and on seasonal patterns linked to adaptation to environmental change and disasters that are difficult to account for in traditional household survey tools (Lu et al., 2016). Other projects aim at using big data sources, such as satellite images or social media data, to identify early the environmental stressors that could lead to displacement (see for instance Isaacman et al., 2017).

Several research projects have and are collecting new data on the links between the environment and human mobility, but few with a comparative approach. There are three notable exceptions. First, the Migration, Environment and Climate Change: Evidence for Policy (MECLEP) project, implemented by IOM and six research partners in 2014-2017, and funded by the EU, conducted a comparative quantitative and qualitative study of six countries (Dominican Republic, Haiti, Kenya, Papua New Guinea, Mauritius and Viet Nam). The methodology developed for the project could easily be applied to other countries.

Second, the Pacific Climate Change Migration and Human Security (PCCCMHS) programme by in partnership with the United Nations Economic and Social Commission for Asia and the Pacific (ESCAP), International Labour Organization (ILO), Office of the United Nations High Commissioner for Human Rights (OHCHR), Pacific Islands Forum Secretariat (PIFS), Platform on Disaster Displacement (PDD) between 2019 and 2021 sought to protect and empower communities adversely affected by climate change and disasters in the Pacific region, focusing specifically on climate change and disaster-related migration, displacement, and planned relocation. 

Third, the HABITABLE project is a EU-funded project (2020-2024) aiming at significantly advancing our understanding of the current interlinkages between climate impacts and migration and displacement patterns, and to better anticipate their future evolution. Led by the Hugo Observatory, the project brings together 21 partners from 18 countries and focuses on four regions: West Africa, East Africa, Southern Africa and South-East Asia.  

The Internal Displacement Monitoring Centre (IDMC) has compiled data on internal displacement in the context of disasters since 2008 globally through its online Global Internal Displacement Database (GIDD). The estimates are based on information by national authorities, UN agencies including IOM, the International Federation of the Red Cross (IFRC) and the UN Office for the Coordination of Humanitarian Affairs (OCHA), non-governmental organizations and in particular media reports. Figures are published in the annual Global Report on Displacement (GRID), which also covers internal displacement due to conflict and violence. IDMC is developing methodologies to map and assess future disaster displacement risks and is starting to gather data on cross-border displacement.

The HELIX project (High-End Climate Impacts and Extremes) provided research on climate impacts and adaptation in relation to varying global warming scenarios (2, 4 and 6 degrees Celsius), using predictive analytics. Human migration was included in the impact studies. The recent Groundswell: Preparing for internal climate migration report (Rigaud et al., 2018) developed a model for future population distribution in 2050 in three regions (sub-Saharan Africa, South Asia and Latin America) if no action is taken.

The CLIMIG database of studies on environmental migration, both of qualitative and quantitative nature, was developed by the University of Neuchatel (Switzerland).

The Environmental Migration Portal by IOM, features a searchable research database, initially based on the People on the Move in a Changing Climate: A Bibliography, published by IOM in collaboration with University of Neuchatel. The database also includes migration and environment country assessments published by IOM.

The thematic working group on “Environmental change and migration” of the Global Knowledge Partnership on Migration and Development (KNOMAD) produced an annotated bibliography on Environmental Migration and developed a toolkit on planned relocation with many case study examples (Georgetown University, UNHCR and IOM, 2017).

The first “Atlas on Environmental Migration” was produced by IOM and Sciences Po, Paris (published with Routledge in 2017). The publication brings together, for the first time, existing knowledge on the links between migration and environmental change, presented through comprehensive maps, diagrams and case studies.

The Hugo Observatory at the University of Liège (Belgium) focuses on research on environmental changes and migration. IOM in collaboration with the Hugo Observatory and the City of Paris worked on the Climate Migration in Urban Areas project in 2022 to assist cities in their efforts to better understand how climate and environmental change will affect migration and urbanization trends and to include these considerations in urban planning and key sectoral policies.

The Platform on Disaster Displacement (a follow-up to the Nansen Initiative) provides a global dataset including preliminary findings from over 400 planned relocation cases. Last updated in 2021, it features characteristics including geographic location, spatial pattern, primary hazard and completion status.

IOM GDI’s interactive Climate Mobility Impacts dashboard visualizes where hazard exposure, high population density, and economic vulnerability are projected to coincide in future. These data help identify hotspots sensitive to heatwaves, river floods, drought, wildfire, tropical cyclone and crop failure, and users can map hazards regionally and under two climate warming scenarios and three socio-economic scenarios. These data can also develop effective anticipatory action to support at-risk communities worldwide.  

The CLIMB database contains policy and legal instruments and practices addressing human mobility in the context of the adverse effects of climate change, disasters, and environmental degradation. The tool provides a resource for policymakers as well as researchers, practitioners and other stakeholders working in the area of policy development on human mobility, disasters, climate change, and environmental degradation.  

Academic researchers and specialized agencies are working on improved methodologies for comparative cross-country or cross-region studies, agent-based models and multi-factor simulators designed to predict future trends (such as drought-induced displacement modelling, Ginnetti and Franck, 2014, or IDMC’s Global Displacement Risk Model focused on sudden-onset disasters based on housing destructions), and hotspot identification triangulating environmental and social data, all of which can contribute greatly to improving current evidence and future projections of environmental migration trends so as to better inform policies and action.

Innovative data sources: Big data can provide opportunities that can further be strengthened in trying to estimate the extent of movements in contexts of disasters and degrading environments. These new methods can fill gaps in time series data, indicate where people have moved from and to and enhance the timeliness of this information. In some cases, these new methods could be used to inform life-saving early warnings. At the same time, privacy safeguards and ethical considerations need to be adhered to.

It is challenging to differentiate when the environment is the main factor triggering migration, rather than or in combination with other factors: In most cases, environmental factors are closely linked to socioeconomic, political, demographic, cultural and personal factors that play a role in leading to or preventing mobility (Laczko and Aghazarm, 2009; Foresight, 2011), which makes data collection beyond fast onset disasters leading to evacuations difficult. Information on people moving due to more gradual, so-called slow-onset processes like sea level rise or salinization, is scarce for methodological reasons.

The most comprehensive data available do not capture duration of displacement: Thanks to IDMC’s work, data on internal displacement due to disasters are available for almost all countries. However, differing definitions used by data providers and a lack of reporting by countries remains a challenge, leading to media reports being an important source of events covered in the estimates. IDMC’s estimates reflect new displacements during a calendar year (and since 2019, the stock of IDPs due to disasters as of the end of the year) and does not capture the duration of people’s displacement, their return home or relocation elsewhere, those not sheltered in camps or people caught in long-term displacement, so-called protracted situations, from year to year. Data collection on cross-border movements after disasters is only starting and limited to localized case studies (IDMC, 2018 (a)).  Further research on disaster displacement is being supported as part of the work of the Data and Knowledge Working Group of the State-led Platform on Disaster Displacement.

Underreporting: The quality and the availability of data on displacement vary between countries and from event to event: small-scale events or disasters that occur in isolated and marginalized areas are under-reported and thus not included in the available aggregate estimates (IDMC, 2017: 98; IDMC, 2018 (a)).

Little information on links between conflict and disaster displacement: In cases where conflict is linked to disasters information on movements is lacking, in particular on displacement histories that could inform future predictions (IDMC data for instance are only available since 2008 but since 2017 include drought figures).

Comprehensive datasets on environmental migration or planned relocation are needed: Data on environmental migration and planned relocation have improved in recent years, as an increasing number of studies have been conducted in affected areas. The research databases listed above are important tools providing an overview of the existing available information. However, comparable quantitative, longitudinal, disaggregated and georeferenced data are needed to assess how different forms of mobility can be a beneficial adaptation strategy and what potential risks need to be minimized. The majority of existing surveys focus mainly on the links between migration and the environment as a driver, and are mostly qualitative in nature. More information is needed on the impacts of those movements on adaptation to environmental and climate change.

Few data on trapped populations: Some populations affected by environmental degradation and disasters may not be able to move due to a lack of financial resources, disability, social reasons (such as gender issues) or social networks. They are highly vulnerable populations, but data to inform action and protection are scarce.

Better predictive analytics are needed: When it comes to predicting future trends, the disconnection between the environmental sciences and social sciences communities constitutes an additional challenge, in a context where environmental migration research would greatly benefit from multidisciplinary research and better integration of climate and population data.