The Bahamas at risk: Material stocks, sea‐level rise, and the implications for development
Bibliographic record
Abstract
Abstract Recent research suggests that over 75% of resources extracted globally now go toward creating, maintaining, or operating material stocks (MS) to provide societal services like housing, transport, education, and health. However, the integrity of current and future built environments, and the capacity of the system to continue providing services, are threatened by extreme events and sea‐level rise (SLR). This is especially significant for the most disaster‐prone countries in the world: Small Island Developing States. In the aftermath of disasters, complex rebuilding efforts require substantial material and economic resources, oftentimes incurring massive debt. Understanding the composition and dynamics of MS and environmental threats is essential for current and future sustainable development. Drawing on open‐source OpenStreetMap (OSM) data, we conducted a spatially explicit material stock analysis (MSA) for The Bahamas for 2021, where we included buildings and transport MS, and SLR exposure scenarios. Total MS was estimated at 76 million tonnes (Mt) or 191 tonnes per capita (t/cap) of which transport comprises 43%. These MS are likely to increase by 36 Mt in the future. Simulations show that under 1‐, 2‐, or 3‐m SLR scenarios, around 4, 6, and 9 Mt of current MS will be exposed, with transport MS at greatest risk, with over 80% of total exposure in each scenario. Our findings highlight the critical role that key MS play in sustainability and resilience, contributing to the emphasis on effective development planning and climate change adaptation strategies, and to the exploration of the use of OSM data for studying these objectives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".