Economic value of recreational resources: testing benefit transfer approaches
Bibliographic record
Abstract
There have been various efforts recently to transfer the economic values of recreational resources from one site to another in the United States. In the case of typical non-market goods, the benefit transfer approach has been as useful as the standard nonmarket valuation techniques, such as, contingent valuation method (CVM) and travel cost methods (TCM), in recreation decision making. The most attractive feature of benefit transfer, instead of conducting primary research on non-market valuation, is the use of existing findings and attributes of the primary research. Obviously, benefit transfer could save significant amount of time and resources, if it is practiced in real world policy decisions. However, for the benefit transfer approach to be more useful, it has to demonstrate its scientific basis, theoretical consistency and methodological validity. The main objective of this study is to demonstrate the application of meta-analysis in taking the stock of information, knowledge, and methodological developments in economic valuation of recreational resources and use those results to real world benefit transfer. To this end, the existing CVM and TCM studies of the United States and Canada on outdoor recreation were gathered, and the meta-analyses were performed on the variables obtained from those studies. Since no so called panel effect was found in the datasets, classical liner regression models were estimated. The application of meta-regression models in benefit transfer were further tested and analyzed in terms of out of sample convergent validity confirmation. In this testing procedure the new studies from the United States, not included in the meta-regression models estimated previously, were used. Similar testing and analyses were also performed using international out of sample studies in order to evaluate the application of meta-analysis in international benefit transfer process. The paired t-test revealed that there was no convergence between the means of meta-regression models predicted WTP values and the original out of sample studies WTP values within the United States. However, significant convergence between those values in the international context was revealed. Interestingly, there were significant positive correlations between those values in both national and international cases.
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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 | 0.267 | 0.497 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.028 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 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".