Developing a decision-making tool for sustainable climate action while harmonizing economic, GHG, and ecosystem service indicators in local initiatives: A case study in Québec, Canada for buffer strip implementation in the agricultural sector
Bibliographic record
Abstract
In the global pursuit of climate change mitigation and biodiversity preservation, effective leadership from public decision-makers is paramount. While national strategies outline overarching plans, successful execution hinges mainly on local public authority initiatives toward the adoption of sustainable practices by local stakeholders in their activities. In rural areas, the agricultural sector is of a primary interest to contribute both to the climate and ecological emergency by adopting innovative agri-environmental practices such as extended riparian strip at the edge of crops field. Our research illustrates the core decision-making challenges that lie in arbitrating between the public cost of a buffer strip project lead by a municipality in collaboration with local farmers and the anticipated benefits, both environmental (riparian restauration, greenhouse gas emissions (GHG) reductions and others ecosystem services) and economics (harvested biomass revenues, carbon market). To operationalize a buffer strip, we first review the various assessment approaches of the expected benefits from similar conservation project with GHG protocols and ecosystem services valuation. Then, we introduce a case study of such municipal-famers projects and emphasize the dependence of the project to government financial support. We delve into the parameters of our assessment model of this case study, including time horizon, costs, revenues, and the presence of a public grant. Preliminary numerical results, such as breakeven analysis and the determination of the ecosystem service value, are presented, underscoring the complexity of decision-making in sustainable initiatives at the intersection of economic, environmental, and socio-ecological considerations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".