Estimating Settlement carbon stock and density using an inventory approach and quantifying their variation by land use and parcel size
Bibliographic record
Abstract
A lack of very-high resolution land-cover data and in-situ carbon sampling in Settlement areas has limited the quantification of terrestrial carbon in Canadian Settlements and elsewhere. Without those data, it is difficult to quantify Settlement area terrestrial carbon for United Nations Framework Convention on Climate Change reporting within the Land Use, Land Use Change, and Forestry sector. The presented research takes a step toward filling this gap by first classifying Settlement land cover at a very-high resolution (<=80 cm, 93% overall accuracy). Then, with those data, an inventory approach is used to estimate carbon stocks based on local data, standard rulesets from the IPCC, and practice-based assumptions. Guided by FAIR principles, our approach is operationalized and available in a Jupyter Notebook for distribution, use, and extension by others. Results found that the study Settlement comprised 30% tree cover and 18% turfgrass. When analyzed by parcel size, carbon densities varied little for parcels less than 1.6 ha (3.9–4.2 kg C m-2), but then increased with larger parcels up to 6.6 Gg C m-2 for parcels > 8.1 ha. Among different land uses, industrial, commercial, and transportation had the lowest carbon densities (2.4–2.8 kg C m-2), followed by high, medium, and low-density residential (3.6, 4.0, 8.9 kg C m-2, respectively) with low-density residential almost achieving carbon densities in protected and recreation areas (10.2 kg C m-2). Our results suggest that land use is a stronger driver of carbon-storage relative to parcel size, but their combination best represents the variation in carbon-storage in low-density residential land use. Currently, only carbon change in urban trees and deforestation around urban centres is reported in Canada’s National GHG Inventory Report. The present research quantifying carbon densities provides an analysis that could inform carbon change resulting from other land-use conversions and improve deforestation estimates by better defining the final state of a land-use change.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".