Ecosystem Service Payments as a Climate Solution: an examination into Successful Aspects of Ecosystem Service Payment Policy Programs
Bibliographic record
Abstract
The purpose of this research was to conduct a national and provincial examination of ecosystem service payment policy programs. Various Canadian programs were examined to gain insights into program successes and/or challenges. A great deal of academic literature looks at ecosystem service payment policy of individual programs, yet few compare multiple programs. This research added to this gap because it compared multiple programs across various jurisdictions. A qualitative methodological approach was used, whereby professionals with expertise on ecosystem service payment programs were interviewed. Programs were assessed for measurable indicators of success, impacts on broader public policy, and recognition of social-power relations. The ecosystem service payment policy programs examined in this research included: the Ontario Conservation Land Tax Incentive Program, the Canadian Ecological Gifts Program, the Manitoba Riparian Tax Credit, and the Ontario Managed Forest Tax Incentive Program. An Ecological Economics approach was applied when examining climate solutions and transitions through carbon sequestration by understanding improved ways of increasing conservation lands through regulatory market-based public policy programs. Overall, the examination of social-power relations in these programs provided an original and thoughtful approach.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".