The maturation of ecosystem services: Social and policy research expands, but whither biophysically informed valuation?
Bibliographic record
Abstract
Abstract The concept of ecosystem services (ES) has risen to prominence based on its promise to vastly improve environmental decision‐making and to represent nature's many benefits to people. Yet the field has continued to be plagued by fundamental concerns, leading some to believe that the field of ES must mature or be replaced. In this paper, we quantitatively survey a stratified random sample of more than 1,000 articles addressing ES across three decades of scholarship. Our purpose is to examine the field's attention to common critiques regarding insufficient credible valuations of realistic changes to services; an unjustified preoccupation with monetary valuation; and too little social and policy research (e.g. questions of access to and demand for services). We found that very little of the ES literature includes valuation of biophysical change (2.4%), despite many biophysical studies of services (24%). An initially small but substantially rising number of papers address crucial policy (14%) and social dimensions, including access, demand and the social consequences of change (5.8%). As well, recent years have seen a significant increase in non‐monetary valuation (from 0% to 2.5%). Ecosystem service research has, we summarize, evolved in meaningful ways. But some of its goals remain unmet, despite the promise to improve environmental decisions, in part because of a continued pre‐occupation with numerical valuation often without appropriate biophysical grounding. Here we call for a next generation of research: Integrative biophysical‐social research that characterizes ES change, and is coupled with multi‐metric and qualitative valuation, and context‐appropriate decision‐making. A free Plain Language Summary can be found within the Supporting Information of this article.
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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 | 0.027 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.014 | 0.030 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".