Exploring the Potential of Roadside Plantation for Carbon Sequestration Using Simulation in Southern Quebec, Canada
Bibliographic record
Abstract
Afforestation of urban lands can play an important role in increasing carbon sequestration and mitigating the effects of climate change. This study aimed to evaluate the potential for carbon sequestration and storage in plantations on roadsides in southern Quebec, Canada. We used the Carbon Budget Model of the Canadian Forester Sector 3 (CBM-CFS3) to simulate the carbon sequestration capacity over 100 years of plantations established following afforestation with different species mixtures based on local needs and aspirations. We then compared the carbon sequestration potential of simulated plantations with the carbon storage of natural vegetation of reference roadsides with different histories of land use. Our findings suggested that plantations on roadsides subjected to high anthropogenic pressure, such as road rights-of-way, may provide carbon sequestration benefits relative to baseline conditions (i.e., no plantation). For instance, 15 years after afforestation, the additional carbon sequestration potential of plantations on road rights-of-way varied between 25 and 32 Mg ha−1, depending on the afforestation scenario. However, allowing roadsides classified as abandoned agricultural lands to undergo natural succession could promote higher carbon storage on roadsides than planting, irrespective of species mixtures. Our results indicated that the carbon storage of vegetation resulting from the abandonment of agriculture 35 to 45 years ago showed a range of 260 to 290 Mg ha−1, which exceeded the carbon stocks predicted with afforestation models for 60 to 84 years after planting. Indeed, reference roadsides used for agriculture in the past, but that have otherwise not been subjected to other anthropogenic degradation, appeared to naturally evolve toward forest vegetation with higher carbon stocks than simulated plantations.
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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) | 0.000 | 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".