A critical review of ecosystem accounting and services frameworks
Bibliographic record
Abstract
Ecological economists currently face an important opportunity to influence national policies and global awareness regarding ecosystems. Ecosystem services (ES) frameworks, including the Intergovernmental Panel on Biodiversity and Ecosystem Services (IPBES) and the System of Environmental Economic Accounting Experimental Ecosystem Accounting (SEEA EEA) will influence how national measurement systems integrate the value of ecosystems and their services into national planning and monitoring progress towards the Sustainable Development Goals (SDGs). Decision-makers are, however, faced with an embarrassment of riches. There is a multitude of ES frameworks, but no formal and integrative evaluation of the entire set exists. We review the IPBES, the SEEA EEA and 14 other ES frameworks using criteria designed to address operational and convergence considerations for national ecosystem accounting. While the frameworks reviewed incorporate many strengths, none fulfils all the criteria of a comprehensive national ecosystem accounting framework. We conclude with suggestions for conceptual, measurement and process developments to broaden the appeal, utility and acceptance of future frameworks. Considering these suggestions could substantially contribute to the development of ecosystem accounting frameworks that foster a constructive dialogue amongst the many disciplines, national contexts and viewpoints involved in understanding, measuring and making decisions about ecosystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".