Mechanisms of support of “green” projects financing: experience of countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objective: to assess the effectiveness of the mechanisms supporting “green” projects’ funding in developed countries and in Russia.Methods: comparative analysis, regression analysis.Results: the article substantiates the necessity of mainstreaming the environmental protection issues under modern conditionsof the world economy development. It is emphasized that, despite the advantages of the development of “green” economyfor society as a whole, the market highlights a variety of hindering factors. In this context, it is increasingly important tostudy the experience of countries in implementing projects on “green” economy formation.We analyze the experience of Great Britain in creating special institutions to support “green” investment, raising funds mainly through the use of credit and warranty programs. The UK also demonstrates the experience of applying environmental taxes and a wide range of environmental financial products. Analysis of the experience of South Korea showed the country's strategy for “green” growth and the functioning of a framework law providing financial support to “green” companies and private investment in this area. The experience of Canada province of Ontario shows that in the field of “green” economy such support mechanisms are applied as “green” bonds, preferential tariff programs, etc. Germany also demonstrates progress in addressing environmental problems by imposing requirements for the population in this area, as well as the creation of preferential programs of financing “green” projects.The analysis showed that, in contrast to the studied countries, in Russia there is no comprehensive mechanism of state support for environmental projects. The existing mechanisms are associated with the implementation of state programs in the sphere of high-tech industries.Basing on regression analysis, we estimated the influence of state support measures for “green” funding on the volume of environmental investment from the private sector in the studied countries. The analysis has demonstrated the effectiveness of measures to support environmental projects in all countries except Russia.Scientific novelty: the article systematizes the experience of countries in the use of support mechanisms of funding “green”projects and assesses their effectiveness for attracting private capital into “green” projects.Practical significance: the studied mechanisms to support funding for "green" projects that are applied in foreign countries can be used by the authorities to increase the amount of “green” investment from the private sector in Russia; the presented model will help to assess the effectiveness of budget spending in this area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it