Assessing the impact of afforestation as a natural climate solution in the Canadian boreal
Bibliographic record
Abstract
Abstract Natural climate solutions (NCSs) are conservation, restoration, and improved land management actions that have potential to provide climate mitigation across different land cover types. NCS related to forests offer a significant portion of cost-effective NCS mitigation required to limit warming to below 2 °C. Afforestation—planting trees in areas where forests can occur but does not currently exist has been proposed as a viable NCS. Here, we examine how long-term, medium resolution satellite datasets and physiological growth models can be used to inform potential carbon accumulation from forest afforestation. We leverage free and open Landsat-derived datasets to examine potential increases in aboveground biomass (AGB) and tons of CO2 equivalent (CO2e) that afforestation may provide by 2050 in the Canadian boreal. We utilized contemporary Landsat-scale definitions of land cover, forest age, and species datasets to identify opportunities for new forest growth in areas previously unforested across study sites. These datasets, along with terrain and climate, were used as inputs for the 3-PG physiological growth model, which converts solar radiation into net primary productivity on a monthly time-step, and was parameterized for key natural species to simulate forest growth and carbon accumulation under three different future climate scenarios. We compared these new fine-scale, climate-adapted estimates with previous findings. The amount of CO2e sequestered per hectare per year increased with increasing CO2 emissions (4.0%–12.4% more carbon). Using a reference area, the fertilized simulation sequestered 24.38 Tg CO2e yr−1 in 2050 compared to 24.9 Tg CO2e yr−1 proposed in other research under the warmest scenario. The use of physiological models linked to satellite data to support NCS calculations, particularly for unforested areas, is a new application. The results highlight the potential for 3-PG to be used to estimate AGB and provide valuable information for the performance of NCS under a changing climate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".