Mechanisms for supporting "Green Finance" in the world practice and in Kazakhstan
Bibliographic record
Abstract
Object: To study the necessity of strengthen the role of "Green" financing in the economy of Kazakhstan, the most successful examples of financing and implementation of tools to support "Green" projects in more developed countries. Accordingly, the subject of the study is the financing of the "Green" economy in the world practice and in Kazakhstan.Methods: Abstract-logical, system analysis, comparative analysis.Findings: As a result of the study, development state of "Green" financing in Kazakhstan is assessed and the experiences of advanced countries are identified. Thus, in the course of analysis more advanced countries were identified, such as China, Korea, the United Kingdom, Canada and others, which have made some progress in the release of " Green tools implementation of electricity production from renewable sources, recycling of household waste and reduction of biodegradable landfills and formation of targeted environmental investment funds, etc. Obtained results indicate that Kazakhstan has not sufficiently addressed the aspects of economy related to sources of "Green" financing that contribute preservation of environmental quality in conditions of financial resources shortage and bringing it in line with the principles of sustainable development of the country. Also, the issue of "Green economy" is not sufficiently activated, which covers such categories as "Green" economy, "Green" credit, "Green" thinking, etc.The reached conclusions outlined in the study framework are in general nature, we simply set the task-to find out the current situation on this issue and continue to rethink the modern concepts of scientific approaches in this area.Conclusions: The development of Green finance in Kazakhstan and government support the Green incentives should be aimed at ensuring the sustainable development of the Green economy through:- creating an effective mechanism for implementing "Green" finance;- formation of management system for development of "Green" finance and its consolidation in legislative and regulatory acts.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".