The resource (in)sufficiency of the Caribbean: analyzing socio-metabolic risks (SMR) of water, energy, and food
Bibliographic record
Abstract
Introduction Socio-metabolic risks (SMRs) are systemic risks associated with the availability of critical resources, the integrity of material circulation, and the distribution of their costs and benefits in a socio-ecological system. For resource-stressed systems like small island nations, understanding trade-offs and synergies between critical resources is not only crucial, but urgent. Climate change is already putting small islands at high risk through more frequent and intense extreme weather events, changing precipitation patterns, and threats of inundation with future sea-level rise. Methods This study compares the shifting resource-baseline for 14 Caribbean island nations for the year 2000 and 2017. We analyze water, energy, and food (WEF) and their nexus through the lens of SMRs, using indicators related to their availability, access, consumption, and self-sufficiency. Results Our findings point to the decreasing availability of all three resources within the Caribbean region. Meanwhile, between 2000 and 2017, consumption levels have increased by 20% with respect to water (from 230 to 275 m 3 /cap/yr) and primary energy (from 89 to 110 GJ/cap/yr), and 5% for food (from 2,570 to 2,700 kcal/cap/day). While universal access to these resources increased in the population, food and energy self-sufficiency of the region has declined. Discussion Current patterns of resource-use, combined with maladaptive practices, and climate insensitive development—such as coastal squeeze, centralized energy systems, and trade policies—magnify islands' vulnerability. Disturbances, such as climate-induced extreme events, environmental changes, financial crises, or overexploitation of local resources, could lead to cascading dysfunction and eventual breakdown of the biophysical basis of island systems. This research is a first attempt at operationalizing the concept of SMRs, and offers a deeper understanding of risk-related resource dynamics on small islands, and highlights the urgency for policy response.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".