A mathematical modeling framework to optimize the climate change mitigation potential of the forest sector in British Columbia, Canada
Bibliographic record
Abstract
Forests play a crucial role in the global carbon cycle, sequestering atmospheric carbon dioxide and storing it within various ecosystem pools and harvested wood products (HWPs). HWPs can further reduce greenhouse gas (GHG) emissions by substituting carbon-intensive materials and energy sources. This thesis develops an integrated mathematical modeling framework designed to simulate and optimize the climate change mitigation potential of the forest sector in British Columbia (BC), Canada. This framework consists of the Strategic Forest Management Model (SFMM), the Forest Carbon Budget Model (FCBM), and the Wood Product Carbon Model (WPCM), incorporating key carbon indicators such as the total carbon stock of the forest and the net carbon emissions from the forest. The study uses the Prince George Timber Supply Area (TSA) as the case study area. Scenarios ranging from a no-harvest approach to a maximize-harvest approach are simulated to explore the impact of management activities on forest carbon dynamics. In addition, the method used to connect SFMM and FCBM is rigorously tested to ensure the credibility and reliability of the modeling results, demonstrating the framework’s robustness in accurately predicting forest carbon dynamics under various management scenarios. This study also evaluates trade-offs between timber supply and forest carbon management. By employing the epsilon-constraint method, the research generates Pareto front curves that elucidate the quantitative trade-offs between maximizing wood supply versus maximizing total forest carbon stock or minimizing net forest carbon emissions. It reveals the inverse relationship between maximizing wood supply and forest carbon benefits. Extensive sensitivity analyses further examine how variations in the climate impacts of HWPs influence the model’s optimal forest carbon management solution, highlighting the importance of product longevity and substitution effects in carbon forestry decision-making. The research findings underscore important insights for policymakers and forest managers aiming to balance economic and climate objectives during the forest management planning process, ultimately contributing to BC’s goals of reducing GHG emissions and enhancing the forest sector’s role in climate change mitigation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".