A business case for climate change adaptation by forest industry in central Canada: Presented at the CIF/IFC 2020 National Conference and 112<sup>th</sup> Annual General Meeting held 15–17 Sept. 2020
Bibliographic record
Abstract
Extreme weather events and increasing climatic uncertainty are already affecting the Canadian forest sector. Climate change projections indicate impacts will likely worsen with increasing risk to forest operations and resources. Despite the calls for adaptation, there is little evidence that adaptation is taking place, whether in terms of planning or practices. Much of the forest industry response to date has been ad hoc and reactive. In contrast, Louisiana-Pacific Canada Ltd. (LP) in Swan Valley, MB decided to proactively address climate impacts and risks. A Climate Vulnerability Assessment (CVA) was completed to review past weather-related disruptions, identify their vulnerabilities to both the current weather extremes and to future climates. Through the help of an independent facilitator, the Canadian Council of Forest Ministers’ guidebook was tailored to meet LP’s context and needs. The CVA team identified a wide range of possible adaptation options and created business cases for short-listed adaption priorities that LP is beginning to pursue and implement. The outcomes from this effort show what is necessary to support an adaptation process that is mainstreamed into company decision-making procedures and can be applied more broadly across the Canadian forest sector. One key innovation was the incorporation of business cases into the assessment. Identifying and quantifying the expected benefits helped support vulnerability implementation in several different ways. Furthermore, at a more systemic level, the experience identifies the importance local knowledge plays in advancing adaptation action and how these local efforts can contribute towards supporting more effective climate adaptation action across the entire forest management system. This work also contributes to laying the groundwork for future policy focus, integrating science, and management into forest management systems.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".