About the Migration Scenario Engine

Independent doctoral research on how climate change will reshape international migration over the coming decades — built and maintained by a single researcher, fully self-funded, and released under an open license.

Author

Name: Christian Rogalski, M.Sc.
Role: External Doctoral Researcher · Economist
Institution: Alexandru Ioan Cuza University of Iași (Romania)
Faculty: Faculty of Economics and Business Administration
Department: Department of Statistics and Cybernetics
Contact: rogalski.academic@pm.me
Topics: Climate migration · Machine learning · Econometrics · Scenario modelling

Mission

How will climate change reshape international migration over the coming decades?

This platform is the author's contribution to answering that question — transparently, independently, and with the strongest quantitative methods available.

01 · Research approach

The project combines econometric panel models with machine-learning methods — including ensemble stacking, gradient boosting, and generalised additive models — to identify nonlinear climate thresholds and structural drivers of cross-border mobility. Building on these historical analyses, the author develops scenario-based projections of global bilateral migration under alternative climate and governance pathways through 2100.

02 · Technical focus

Methodological interests centre on machine learning, deep learning and artificial intelligence, as well as on the automation of data pipelines and real-time analytics — with particular attention to uncovering patterns and interdependencies that conventional methods leave hidden.

03 · Why this matters

The author considers climate change — and above all its cascading consequences for societies, economies, and human mobility — to be the defining challenge of this century. The goal is to contribute the strongest possible quantitative foundations so that decision-makers can plan, act, and adapt with clarity.

04 · About this platform

This site is operated entirely at the author's own expense, without external funding, sponsorship, or institutional support. It represents no lobby, no commercial interest, and no institutional agenda. The analyses presented here reflect independent research. Performance may occasionally be limited — the infrastructure is self-financed and maintained by a single person.

05 · Outlook

The platform is planned to be expanded with additional research themes — including crisis-mode monitoring, interactive conflict detection informed by geospatial and social-media data, and the integration of satellite imagery and nightlight data as proxy indicators. A second interactive dashboard on a separate research topic is also in preparation.

How to cite

Rogalski, C. (2026). Migration Scenario Engine: Global Bilateral
Migration Projections under Climate Scenarios, 2020–2100.
CERIFR Research. https://migrationengine.org

Machine-readable: citation.cff · License: CC BY-NC 4.0 (data and projections) · DOI to be assigned upon publication.

Privacy & cookies

No tracking, no third-party cookies, no Google Analytics, no Meta Pixel, no advertising. The dashboard stores exactly one client-side flag in localStorage — mse_welcome_dismissed — which remembers whether the user has dismissed the welcome dialog. Compliant with GDPR and § 25 TTDSG; no consent banner required because no tracking takes place. Server logs: standard HTTP access logs, retained 14 days.