Opportunities for forest-based biorefining to reverse decline in Canada’s forest products sector
Bibliographic record
Abstract
This thesis explores the impacts of disruptive change drivers on the Canadian forest industry and evaluates the extent to which socio-economic policies can advance or constrain transition to new future directions. Canada’s forest sector has experienced a substantive disruption. Since 2009, average harvest rates are 44 million m3/year lower than was observed in 2000-2004. The drop in forest harvest is related to major market changes driven by widespread uptake of the Internet, which reduced the need for printed newspapers dramatically impacting revenues from classified ads; and declines in single-family home construction and corresponding increases in multi-family, multi-storey buildings which has reduced demand for wood building products. In response, policy initiatives have been launched by Canada’s Federal and Provincial governments. Policies launched between 2000-2015 were examined through four analytical frameworks drawn from social life-cycle assessment (S-LCA) principles: industry sector; industrial process stage; policy mechanism; and, broad policy domain. The examination identified a shift from industry sector focus towards broader policy goals related to climate change with subsequent reversion to a focus on multi-sector biorefining opportunities. A specialized tool to model biorefinery implementation - the I-BIOREF application developed by CANMET Energy - is used. Identified measures are evaluated against three important criteria: required data be available, collection be automated, and the measure be acceptable to key stakeholders. Three measures, including employment, employment income, and tax filings, meet these criteria. Impacts of deploying different biorefinery technologies are examined in three case communities - Prince George BC, Thunder Bay ON, and Corner Brook NL - with significant dependence on the forest sector. Using the I-BIOREF tool, a different biorefinery technology configuration is applied at each site and assessed at different sizes and levels of government support to identify financially feasible configurations. The resulting analysis illustrates the benefits or pitfalls associated with biorefinery deployment, providing insights both into regional benefits and comparative performance across different configurations. The analysis suggests that the three socio-economic indicators examined would provide a valuable addition to the I-BIOREF software and be useful for determining the potential impact of possible public policy and program initiatives to support forest biorefining development.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".