Deliberating climate change mitigation options and policies in British Columbia’s forests
Bibliographic record
Abstract
Global efforts are urgently needed to mitigate climate change, which involves changing business-as-usual activities to reduce greenhouse gases emissions and increase removals of carbon from the atmosphere. Because of their role as carbon sinks, forests offer climate mitigation potential when and if they are managed effectively. The role of forest management in mitigating climate change is a central concern for the Canadian province of British Columbia (BC). The province faces a decision context where publicly owned forests occupy 60% of the land base and the role of forests in mitigating climate change is scientifically established and politically acknowledged. At the same time, a policy gap exists and little is known in terms of public opinion on forest carbon mitigation options and policy. Drawing upon the idea that any future forest carbon management activities and policies will require strong public and political support and acceptability, I seek to identify what are BC’s public and stakeholders’ perceptions on advantages and issues associated with existing and prospective mitigation options in the forests. First, I analyze the extent of policy change brought by the Forest Carbon Offset Protocol, which until 2016 was one of the only significant forest carbon policy options in BC. I conclude that while policy changes occurred, notably by allowing for offset projects, the extent of these changes is limited by numerous barriers. Second, I explore these barriers and provide recommendations for the future uptake of forest carbon offset. Third, I describe the result of an online survey of BC’s general public that indicates overall support for eight different forest mitigation options, with greater preference for rehabilitation and conservation-focused strategies. Fourth I report on the result of an extensive analytic-deliberative engagement process with stakeholders and indigenous people across BC where participants indicated support for six mitigation strategies, with high consensus between regions and sectoral groups. Fifth, I evaluate the analytic-deliberative process and provide recommendations for the design of similar methodologies. The dissertation concludes that, notwithstanding apparent differences in preferences and priorities, an opportunity exists to move forward with a set of comprehensive, multi-faceted forest carbon mitigation actions and policies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.004 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".