Economic values for ecosystem services: A global synthesis and way forward
Bibliographic record
Abstract
This paper presents a global synthesis of economic values for ecosystem services provided by 15 terrestrial and marine biomes. Information from over 1,300 studies, yielding over 9,400 value estimates in monetary units, has been collected and organised in the Ecosystem Services Valuation Database (ESVD). This is a substantial expansion of data since the de Groot et al. (2012) description of the ESVD and provides an important juncture to explore developments in the use of valuation methods and the contexts in which valuations are conducted. In this paper we provide summary values for 23 ecosystem services from 15 biomes to represent the magnitude, variation and gaps in economic values. To enable the comparison and synthesis of values, estimates in the ESVD are standardised to a common set of units (Int$/ha/year at 2020 price levels). This data provides a basis for value transfers to inform decision-making in current policy contexts but requires due consideration and adjustment for context specific determinants of value. Although the coverage of the ESVD is global, the geographic distribution of data is not even. There is a particularly high representation of European ecosystems and relatively little information for Russia, Central Asia and North Africa. Therefore, the data are not globally representative of biophysical and socio-economic contexts. The distribution of data across ecosystem services is also far from even, with some services very well represented (e.g. recreation, wild fish and wild animals, ecosystem and species appreciation, air filtration and global climate regulation) and others with almost no value estimates (e.g. disease control, water baseflow maintenance, rainfall pattern regulation). In the past decade, there has been a notable increase in demand for information on the economic value of ecosystem services from both public and private institutions to improve the conservation and management of natural capital. The literature is developing to meet this demand but there is a need for targeted and refined valuation research to ensure sufficient certainty, comparability, and representativeness of the data, and to enable transferability and fill knowledge gaps. This paper concludes by identifying avenues for future development to further increase the amount, quality, representativeness and application of data on economic values for ecosystem services.
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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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.028 | 0.046 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".