Global inequities between polluters and the polluted: climate change impacts on coral reefs
Bibliographic record
Abstract
For many ecosystem services, it remains uncertain whether the impacts of climate change will be mostly negative or positive and how these changes will be geographically distributed. These unknowns hamper the identification of regional winners and losers, which can influence debate over climate policy. Here, we use coral reefs to explore the spatial variability of climate stress by modelling the ecological impacts of rising sea temperatures and ocean acidification, two important coral stressors associated with increasing greenhouse gas (GHG) emissions. We then combine these results with national per capita emissions to quantify inequities arising from the distribution of cause (CO2 emissions) and effect (stress upon reefs) among coral reef countries. We find pollution and coral stress are spatially decoupled, creating substantial inequity of impacts as a function of emissions. We then consider the implications of such inequity for international climate policy. Targets for GHG reductions are likely to be tied to a country's emissions. Yet within a given level of GHG emissions, our analysis reveals that some countries experience relatively high levels of impact and will likely experience greater financial cost in terms of lost ecosystem productivity and more extensive adaptation measures. We suggest countries so disadvantaged be given access to international adaptation funds proportionate with impacts to their ecosystem. We raise the idea that funds could be more equitably allocated by formally including a metric of equity within a vulnerability framework.
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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 | 0.001 | 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 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".