LEGAL REGULATION OF THE APPLICATION OF ECONOMIC MECHANISMS FOR ENSURING THE RATIONAL USE OF NATURAL RESOURCES: THE EXPERIENCE OF FOREIGN COUNTRIES
Bibliographic record
Abstract
The purpose of the study is to determine the most optimal ways to solve the problems of financing the implementation of environmental protection measures formulated based on the positive experience of foreign countries in the field of ensuring the rational use of natural resources. Methodology. The method of comparative legal research was used to analyse foreign experience in the application of economic mechanisms for ensuring the rational use of natural resources, and the methods of modelling, analysis, and synthesis allowed determining the directions of increasing the efficiency of economic incentive use in Ukraine. Research results. Based on the studied experience of legal regulation of the application of economic mechanisms for ensuring the rational use of natural resources in the countries of the European Union, the United States of America, Great Britain, Canada, and other foreign countries, it was concluded on the expediency of its implementation in Ukraine. The author determined the appropriateness of the introduction of tax privileges of economic activity of entities that promote the use of advanced technologies, which contribute to reducing the negative impact on the environment. The expediency of application in Ukraine of an extensive system of taxation of activities associated with the use of natural resources by means of activating the introduction of direct taxes paid directly by natural users is substantiated. The practicability of inclusion of payments for obtaining appropriate special permits (licenses), which are granted not only for the use of natural resources but also for the activities related to environmental pollution, in the system of economic mechanisms for ensuring the rational use of natural resources is substantiated. The practical importance of the study. The expediency of introducing in Ukraine the approach of financial substantiation of national and regional programs in the sphere of natural resources utilization, which are realized at the expense of state and local budgets and successfully applied in foreign countries, is proved. The originality of the research. The results and conclusions of th e research can be used in the process of improving the legal regulation of the application of economic mechanisms for ensuring the rational use of natural resources.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".