A Participatory Approach to Evaluating Strategies for Forest Carbon Mitigation in British Columbia
Bibliographic record
Abstract
To be successful, actions for mitigating climate change in the forest and forest sector will not only need to be informed by the best available science, but will also require strong public and/or political acceptability. This paper presents the results of a novel analytical-deliberative engagement process that brings together stakeholders and Indigenous Peoples in participatory workshops in the interior and coastal regions of British Columbia (BC) to evaluate a set of potential forest carbon mitigation alternatives. In particular, this study examines what objectives are prioritized by stakeholders and Indigenous Peoples when discussing forest carbon mitigation in BC’s forests, as well as the perceived effectiveness of, and levels of support for, six forest-based carbon mitigation strategies. We start by describing the methodological framework involving two series of workshops. We then describe the results from the first round of workshops where participants identified 11 objectives that can be classified into four categories: biophysical, economic, social, and procedural. Afterwards, we discuss the second series of workshops, which allowed participants to evaluate six climate change mitigation strategies against the objectives previously identified, and highlight geographical differences, if any, between BC’s coastal and interior regions. Our results effectively illustrate the potential and efficacy of our novel methodology in informing a variety of stakeholders in different regions, and generating consistent results with a surprising degree of consensus on both key objectives and preference for mitigation alternatives. We conclude with policy recommendations on how to consider various management objectives during the design and implementation of forest carbon mitigation strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".