Ecological role of tree and shrub plantations in urban landscapes
Bibliographic record
Abstract
The study aimed to determine the impact of green spaces on the environmental sustainability of cities and to identify the most effective methods of greening. The study analysed the impact of different types of tree and shrub plants on reducing the level of pollutants, including particulate matter (PM2.5 and PM10) and carbon dioxide, as well as their ability to regulate temperature and create comfortable climatic conditions. The results demonstrated that the most effective plant species for urban landscaping were black poplar, which had the highest particulate matter filtration capacity (9.8 g/m2/year PM2.5 and PM10) and high CO2 uptake (24 kg/tree/year), Tatar maple, which provided significant air pollution reduction (7.5 g/m2/year PM2.5 and PM10) and CO2 capture (18 kg/tree/year), common pine, which showed high efficiency in particulate matter reduction (8.7 g/m2/year PM2.5 and PM10) and carbon dioxide uptake (22 kg/tree/year), and Tien Shan spruce, which combined air cleaning ability (7.9 g/m2/year PM2.5 and PM10) with high CO2 uptake (21 kg/tree/year). These plant species demonstrate a high ability to absorb pollutants and reduce air temperature by 3.2-4.5°C in summer. In Kyrgyzstan, the area of green spaces in cities is 12% of the total area, which is significantly lower than in developed countries such as Singapore (47%), Germany (40%, Canada (38%) and Sweden (44%). In Bishkek, the capital of Kyrgyzstan, there are 9 m2 of green spaces per inhabitant, while in Singapore this figure reaches 50 m2 and in Germany 38 m2. Analyses of international experience revealed that developed countries actively applied innovative landscaping methods. Singapore made extensive use of vertical gardens and water-saving technologies, Germany prioritised the regeneration of natural areas and the creation of eco-parks, Canada implemented integrated forest protection programmes, and Sweden introduced adaptive landscaping and sustainable forest planting. These measures contributed to significant improvements in the environmental sustainability of urban environments. The findings of the study emphasise the need for an integrated approach to urban greening in Kyrgyzstan, based on the selection of the most sustainable tree and shrub species, as well as the introduction of modern greening technologies
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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 | 0.001 | 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) | 0.001 | 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".