The Demonstration and Capture of the Value of an Ecosystem Service: A Quasi‐Experimental Hedonic Property Analysis
Bibliographic record
Abstract
Water management can generate valuable ecosystem services but can be costly to implement. We examine this issue using irrigation water storage infrastructure which has the potential to provide desirable services to residential properties affected by the condition of the storage structure. We examine a particular prairie setting where concerns regarding fluctuations in water levels of an irrigation storage lake led to an agreement between the irrigation agency and the owners of properties around the lake to stabilize water levels. Using quasi‐experimental hedonic property approaches with two different control groups we estimate the subsequent impact of this agreement on shoreline property values using a time series of sales data. The methods utilized in this article represent an effective approach to produce plausible estimates of some of the economic values captured by the infrastructure generating ecosystem services. We find that property values increased as a result of the agreement and that the additional property tax revenues arising from these values can be used to some extent to offset the annual service fees paid to the irrigation agency to provide the stabilized lake levels. This article illustrates the potential for irrigation infrastructure management to provide increases in ecosystem service values beyond irrigation, and also that these values can be captured to pay for the costs of providing these increased values.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".