The reforestation-TCRE: A metric to quantify the effect of reforestation on global temperature
Bibliographic record
Abstract
With a well-studied potential to remove CO2 from the atmosphere, reforestation is a CO2 removal intervention common to net-zero CO2 pathways, policies, and the voluntary CO2 offset market. However, the relationship between a reforestation-based CO2 removal and temperature change is complicated by the biogeophsyical effects of reforestation on temperature, which have a demonstrated uncertainty across climate models. Furthermore, reforestation is a land-based intervention occurring in specific geographic locations and the relationship between reforestation within a specific locality and global temperature change is not well-defined.Here we address these concerns by asking whether the TCRE framework - the fundamental metric relating anthropogenic CO2 emissions to global temperature change - and its regional variant can be applied to measure the effect of reforestation-based CO2 removal on global temperature. We conduct idealized net-zero CO2 simulations in a climate model of intermediate complexity (the UVic ESCM) to quantify the reforestation-TCRE across large-scales of reforestation. We measure reforestation-based CO2 removals by assessing both the change in above-ground and the change in above and below-ground CO2 in reforested areas as compared to a counter-factual simulation without reforestation. We further isolate the biogeophyical effects of reforestation to constrain the reforestation-TCRE to only the carbon-effects of reforestation. We expect our results to show that the reforestation-TCRE is not equal and opposite to the TCRE, which is accountable to the biogephsical effects of reforestation and asymmetries between the climate effects of a reforestation-based CO2 removal and an anthropogenic CO2 emission. Despite the short-coming, we expect our results to provide a metric for calculating a direct relationship between reforestation-based CO2 removal and global temperature change that is relatable to net-zero frameworks and potentially reproducible across climate models.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".