Investments to reverse biodiversity loss are economically beneficial
Bibliographic record
Abstract
Reversing biodiversity loss by 2020 is the objective of the 193 countries that are party to the global Convention on Biological Diversity (CBD). In this context, the Aichi Biodiversity Targets 2020 were agreed upon by the CBD in Nagoya, Japan in 2010 and this was followed by asking a high-level panel to make an assessment of the financial resources needed to achieve these targets globally. First, we review the literature on the costs and benefits of meeting the Aichi Targets. Second, we provide a summary of the main conclusions of the CBD High-Level Panel (HLP) 1 and 2 on the Global Assessment of the Resources for Implementing the Strategic Plan for Biodiversity 2011–2020. A key conclusion of the HLP is that the monetary and non-monetary benefits of biodiversity conservation and sustainable use to be achieved by implementing the Aichi Targets would significantly outweigh the amount of investments required.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".