Ecosystem and ecosystem services accounts: time for applications: Thematic Working Group 17 of the Ecosystem Services Partnership: Ecosystem Services Accounting and Greening the Economy
Bibliographic record
Abstract
This report outlines the ecosystem accounting applications that have been presented in the Ecosystem Services World Conference in Hannover, Germany in 2019. Eight cases are summarized here; applications of ecosystem accounts in Europe, Canada, Czech Republic, Germany, Uganda, Bulgaria, Andalusia-Spain and Oslo-Norway. Most of these applications are in line with the System of Integrated Environmental and Economic Accounts (SEEA) framework and the outcomes are regarded as pilot attempts concluding in interesting messages that should be accounted for in next development steps. All cases discuss the compilation process of certain type of ecosystem asset or ecosystem services accounts either at regional, national or local level depicting the methodological process as well as the main outcomes. They also report the policy priorities that these accounts attempted to address and the policy implications that may follow given the accounts’ results. Some of the strong highlights emerged from these cases are summarized as follows: countries should initiate the development of accounts using currently available data and then evolve this attempt based on pilot accounts. It is imperative that collaboration between institutes is ensured as ecosystem accounting is a complex process that demands strong joint forces between different experts as well as stakeholders. Demand for ecosystem accounts should be systematically developed if ecosystem accounting is to be institutionalized. Accounts need to demonstrate clear messages and be linked to certain policy needs even in this primary stage to foster policy support.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.073 | 0.037 |
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".