Urban Parks and Native Trees: A Profitable Strategy for Carbon Sequestration and Climate Resilience
Bibliographic record
Abstract
Urban green spaces are increasingly recognized for their potential to mitigate climate change by reducing atmospheric concentrations of greenhouse gases, especially carbon dioxide (CO2). However, enhancing carbon sequestration efficiency in limited urban green areas remains a significant challenge for sustainable urban planning. Trees are among the most cost-effective and efficient natural carbon sinks, surpassing other types of land cover in terms CO2 absorption and storage. The present study aimed to evaluate the carbon sequestration potential of four native tree species, Pongamia pinnata, Azadirachta indica, Melia azedarach, and Dalbergia sissoo, in urban parks across Multan City, Pakistan. A total of 456 trees of selected species within six parks of Multan City were inventoried to estimate the biomass and carbon stock using species-specific allometric equations. Soil organic carbon at two soil depths beneath the canopy of each tree was also estimated using Walkley–Black method. The findings revealed that the highest mean tree biomass (2.16 Mg ha−1), carbon stock (1.04 Mg ha−1) and carbon sequestration (3.80 Mg ha−1) were estimated for Dalbergia sissoo, while Melia azedarach exhibited the lowest (0.12 Mg ha−1, 0.06 Mg ha−1 & 0.23 Mg ha−1, respectively) across all six parks. The soil carbon stocks ranged from 48.86 Mg ha−1 to 61.68 Mg ha−1 across all study sites. These findings emphasize the importance of species selection in urban green planning for carbon sequestration. Strategic planting of effective native trees like Dalbergia sissoo can mitigate climate change and provide urban forest ecosystem services.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".