A standard framework for assessing the costs and benefits of restoration: introducing The Economics of Ecosystem Restoration
Bibliographic record
Abstract
While the policy momentum behind ecosystem restoration has never been stronger, restoration finance remains insufficient. A crucial information gap to unlock finance is the lack of robust and consistent data on the costs and benefits of restoration. This is due in part to the wide variety of contexts, interventions, and objectives of restoration projects, and to the absence of well‐defined standards and protocols for cost and benefit data collection. To fill this gap, we developed a standard framework to assess the costs and benefits of restoration projects and specific restoration interventions. The associated template for data collection, which was tested for usability during a piloting phase, is the first output of The Economics of Ecosystem Restoration (TEER), a multi‐partner initiative under the aegis of the UN Decade on Ecosystem Restoration. It is the first attempt ever to improve the robustness and comparability of data on the economics of ecosystem restoration collected from the field at a global scale. Widespread adoption of this framework and associated template by a wide range of organizations implementing or financing restoration would allow for standardized data to be fed into a jointly owned database of restoration costs and benefits and serve as a basis for the further investigation of the economics of ecosystem restoration, including cost‐benefits analysis. Better information on costs and benefits will help to inform accurate budgeting access to finance for restoration projects, and make them more likely to achieve their set goals and desired quantitative outcomes (e.g. area restored).
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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 | 0.053 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".