Sustainable forestry and energy production from biomass: Ecological aspects and dynamics of forest ecosystems
Bibliographic record
Abstract
The energy use of forest biomass is an important component of modern bioeconomics, which affects the dynamics of forest cover, the balance of greenhouse gases, and the ecological sustainability of forest ecosystems in regions with active harvesting of wood raw materials. The purpose of the study was to assess the environmental impact of using forest biomass in energy, identifying changes in forest cover, and analysing CO2 emissions compared to fossil energy sources. A combination of theoretical analysis of literature sources and empirical analysis of Sentinel-2 and Landsat satellite data (2015-2024) was used. A comparative analysis of changes in forest cover is performed using Normalised Difference Vegetation Index and normalised burning ratio indices. CO2 emissions were calculated based on Intergovernmental Panel on Climate Change emission factors for biomass, coal, and natural gas. In the regions of active biomass use (Amazon, Southeast Asia), forest cover is reduced by 0.8-1.5% annually, while in countries with developed forest policies (Canada, Finland), forest areas remain stable. CO2 emissions from biomass (112 kg/GJ) are higher than natural gas (56 kg/GJ) but lower than coal emissions (97.5 kg/GJ). Assessment of the relationship between forest ecosystems and climatic factors showed that a reduction in forest cover leads to a loss of water retention capacity (up to 20%) and an increase in soil erosion by 3-4 times. The results of the study confirmed the need to introduce environmentally responsible approaches to Forest Resource Management. The use of close-to-nature forestry methods, the development of satellite monitoring, and the introduction of certification standards can contribute to maintaining ecosystem balance
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".