Assessing ecological infrastructure investments
Bibliographic record
Abstract
Conventional markets can underprovide ecosystem services. Deliberate creation of a market for ecosystem services [e.g., a payments for ecosystem services (PES) scheme] can close the gap. The new ecosystem service market alters behaviors and quantities of ecosystem service provided and reveals prices for the ecosystems service: a market-clearing equilibrium. Assessing the potential for PES programs, which often act as ecological infrastructure investment mechanisms, requires forecasting the market-clearing equilibrium. Forecasting the equilibrium is complicated, especially at relevant social and ecological scales. It requires greater disciplinary integration than valuing ecosystem services or computing the marginal cost of making a land-use change to produce a service. We conduct an ex ante benefit–cost assessment and forecast market-clearing prices and quantities for ecological infrastructure investment contracts in the Panama Canal Watershed. The Panama Canal Authority could offer contracts to private farmers to change land use to increase dry-season water flow and reduce sedimentation. A feasible voluntary contracting system yields a small program of about 1,840 ha of land conversion in a 279,000-ha watershed and generates a 4.9 benefit–cost ratio. Physical and social constraints limit market supply and scalability. Service delays, caused by lags between the time payments must be made and the time services stemming from ecosystem change are realized, hinder program feasibility. Targeting opportunities raise the benefit–cost ratio but reduce the hectares likely to be converted. We compare and contrast our results with prior state-of-the-art assessments on this system.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".