ArticleProceedings of the National Academy of Sciences of the United States of America2024
Climate migration amplifies demographic change and population aging.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07124949 (Chinese Migrant Population Health Cohort), which is not on this map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Chinese Migrant Population Health Cohort
Who cites it
7 citing papers in PubMed.
- Article
- Staying put amidst the changing climate: lessons from older Nepalis.Innovation in aging · 2025Article
- Integrating social, climate and environmental changes to confront accelerating global aging.BMC public health · 2024Article
- Global projections of heat exposure of older adults.Nature communications · 2024Article
- Migration and sustainable development.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Scientific frontiers on migration and sustainability.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- The importance of teaching climate-health literacy in psychotherapeutic training and continuing education.F1000Research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The warnings of potential climate migration first appeared in the scientific literature in the late 1970s when increased recognition that disintegrating ice sheets could drive people to migrate from coastal cities. Since that time, scientists have modeled potential climate migration without integrating other population processes, potentially obscuring the demographic amplification of this migration. Climate migration could amplify demographic change-enhancing migration to destinations and suppressing migration to origins. Additionally, older populations are the least likely to migrate, and climate migration could accelerate population aging in origin areas. Here, we investigate climate migration under sea-level rise (SLR), a single climatic hazard, and examine both the potential demographic amplification effect and population aging by combining matrix population models, flood hazard models, and a migration model built on 40 y of environmental migration in the United States to project the US population distribution of US counties. We find that the demographic amplification of SLR for all feasible Representative Concentration Pathway-Shared Socioeconomic Pathway (RCP-SSP) scenarios in 2100 ranges between 8.6-28 M [5.7-53 M]-5.3 and 18 times the number of migrants (0.4-10 M). We also project significant aging of coastal areas as youthful populations migrate but older populations remain, accelerating population aging in origin areas. As the percentage of the population lost due to climate migration increases, the median age also increases-up to 10+ y older in some highly impacted coastal counties. Additionally, our population projection approach can be easily adapted to investigate additional or multiple climate hazards.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.