CO2 fluxes of vegetation in the Greenbelt of Ontario and increased net ecosystem emissions associated with its removal
Bibliographic record
Abstract
The fluxes of carbon dioxide (CO2) to and from vegetation can be significant on a regional scale. It is therefore important to understand the biogenic fluxes of CO2 in order to quantify local carbon budgets. The Greenbelt of Ontario is a protected region of cropland and natural vegetation surrounding the Greater Toronto and Hamilton Area (GTHA) in Ontario, Canada. Recently, changes were proposed to the Greenbelt, including the removal of 2,995 ha (7,400 acres) of protected land to be replaced with housing. In this study, we estimate the biogenic CO2 fluxes of the entire Greenbelt as well as the areas that were proposed for removal by using a modified version of the Solar-induced fluorescence for Modeling Urban biogenic Fluxes vegetation model. We find that, on average, the entire Greenbelt has a net sequestration of 9.9 ± 6.4 TgCO2 each year, where the uncertainty represents half of the interannual variability plus error from the individual years, for the years 2018–2020. The net amount of CO2 absorbed by the Greenbelt is roughly equivalent to a fifth of the annual human-made emissions reported for the entire GTHA. The areas proposed for removal are found to have a net sequestration of 0.0061–0.031 TgCO2 annually. During construction, these lands will remain barren, and the soil will continue to emit CO2, thus changing the area from a net sink to a net source of CO2. For a 3- to 5-year construction period, this soil efflux would result in net ecosystem emissions of 0.314 ± 0.078 TgCO2, in addition to the net sequestration lost by removing the original vegetation (−0.077 ± 0.035 TgCO2). This results in a net difference in biogenic CO2 fluxes of 0.390 ± 0.083 TgCO2, which is equivalent to the average CO2 emissions of roughly 85,000 gasoline passenger vehicles over the course of a year. In addition to biogenic fluxes, there will be CO2 emissions associated with the construction of the proposed single-family housing developments as well as larger per capita emissions associated with low-density housing compared to creating higher density housing using less land.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".