World experience of use of the mechanism of fiscal regulation in forestry
Bibliographic record
Abstract
In the article state and problems of fiscal regulation in forestry Ukraine are covered. Directions forestry reform in Ukraine are determined. They should be based on the experience of countries with developed market economies. For example, such as Poland, the USA, Canada, Turkey, France, Czech Republic, Finland, Sweden, Germany, Italy, Austria. Since these countries have a positive experience of economic regulation of the management and use of forest ecosystems. Forest policy instruments of foreign countries are analyzed. Basic factors that affect the functioning of the fiscal regulation mechanism are selected: the level of forest cover of country, ownership (influencing on forest management), elements of taxation and fiscal policy features (fines, incentives, credits, subsidies). Proposals for reforming forestry of Ukraine on the basis of world experience (Poland, Germany, USA, Canada and other countries) are designed and conclusions about necessity of improving the key components of the mechanism of fiscal regulation in the studied area are grounded. Improving the existing mechanism of fiscal regulation through diversification the list of payments for the use of forest resources is proposed. The necessity a combination of the most effective mechanisms of budget and tax regulation and stimulation of scientific and technological progress is emphasized.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".