An economic analysis of afforestation as a carbon sequestration strategy in southwestern Ontario, Canada
Bibliographic record
Abstract
Afforestation, the establishment of trees in areas that have not been forested for at least 50 years, is one possible approach for carbon (C) sequestration to mitigate climate change. This study compares the costs and benefits of afforestation as a carbon sequestration strategy for Eden Mills, a village within Wellington County, Ontario, Canada aiming to achieve C neutrality. We provide net present value analyses for three potential planting schemes under subsidized and unsubsidized financial scenarios that aim to sequester 2012 tonnes of atmospheric carbon dioxide (CO2) using traditional and novel calculations of C sequestration rates. We present the total project costs, the optimal price of C, and the potential for afforestation as a C sequestration tool in southern Ontario. Planting schemes employ mixtures of tree species common to the region. Unsubsidized schemes are projected to cost between $617,976-$1,499,904 (CAD) with the optimal price of CO2 between $6.15-$14.91 per tonne of C sequestered. A deciduous-dominated planting scheme requiring 24 hectares of land resulted in the lowest cost for all scenarios. Our analyses suggest that: 1) fast-growing tree species make afforestation projects more cost-effective, reducing costs by 29-59%; and 2) land management subsidies available to the region reduce costs by approximately 10%. Future cost-benefit analyses for afforestation projects should consider site-specific C sequestration rates and parameter sensitivity analysis when quantifying C absorption.
 
 Keywords: greenhouse gases; carbon sequestration; afforestation; cost-benefit analysis; net present value
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.006 | 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".