The costs of subsidies and externalities of economic activities driving nature decline
Bibliographic record
Abstract
Economic sectors that drive nature decline are heavily subsidized and produce large environmental externalities. Calls are increasing to reform or eliminate subsidies and internalize the environmental costs of these sectors. We compile data on subsidies and externalities across six sectors driving biodiversity loss-agriculture, fossil fuels, forestry, infrastructure, fisheries and aquaculture, and mining. The most updated estimates suggest that subsidies to these sectors total between US$1.7 and US$3.2 trillion annually, while environmental externalities range between US$10.5 and US$22.6 trillion annually. Moreover, data gaps suggest that these figures underestimate the global magnitude of subsidies and externalities. We discuss the need and opportunities of building a baseline to account for the costs of subsidies and externalities of economic activities driving nature decline. A better understanding of the complexity, size, design, and effects of subsidies and externalities of such economic sectors could facilitate and expedite discussions to strengthen multilateral rules for their reform.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".