Natural Capital Accounting: A Guide for Action
Bibliographic record
Abstract
How can Ireland better account for nature? On request from Government and drawing on in-depth engagement with key stakeholders, this Council report provides advice on natural capital accounting and presents a guide for action. The report highlights the potential of natural capital accounting as a key part of what is required in order to value, recognise and bring considerations of nature more effectively into policy decision-making in Ireland. \n \nNatural capital accounting is an information framework and an approach to integrating environmental data into the system of national accounts for economic activity. The Central Statistics Office (CSO) and Eurostat are already using the United Nations (UN) standardised approach to natural capital accounting. \n \nThe report outlines the centrality of natural capital accounting to protecting Ireland?s natural capital and biodiversity. It describes how this accounting framework can systematically bring nature?s hidden risk and value into view. It provides examples of how nature is valued and accounted for in other countries, including Australia, Canada, Mexico, the Netherlands, the United Kingdom (UK). \n \nThe Council recommends three areas of action that will help develop natural capital accounting and embed it into the wider policy-making system, supporting the increased policy momentum on nature. The Council considers that natural capital accounting is an important part of the solution to working more closely with nature.
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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 | 0.023 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.056 | 0.084 |
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".