ECONOMIC STIMULATION OF ENTREPRENEURSHIP DEVELOPMENT IN THE FIELD OF RENEWABLE ENERGY IN THE WORLD AND IN UKRAINE
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
The article is devoted to the study of areas of economic incentives for entrepreneurship development in the field of renewable energy in the world and Ukraine. Indicators of energy security, economic measurement, and cost of electricity for business are systematized. A graphical interpretation of the cost of electricity in terms of the ratio of the index of economic dimension and energy security in selected countries – some neighbouring countries, partners of Ukraine, and countries with leading economies, which allowed to implement a methodological approach to identify key areas for effective energy development. The expediency of using the experience and adaptation of measures in the energy policy for the development of renewable energy in countries such as Canada, Germany, France, and Turkey are substantiated. It is determined that in these countries the directions of economic stimulation of entrepreneurship in the field of renewable energy are developed, which is reflected in the basic strategies of their energy development. The main mechanisms of financial incentives for renewable and alternative energy in EU member states are presented, in particular, mechanisms with the use of benefits with pricing tools, regulatory mechanisms with quotas, green certificates, tariff auctions. The current mechanisms for stimulating renewable energy in Ukraine are analysed and the dynamics of the levelized cost of electricity and the "green" tariff for electricity from solar and wind power plants from 2009 to 2019 are presented. The main problems in the field of renewable energy regulation in Ukraine are identified. Recommendations for improving the renewable energy market in Ukraine and accelerating the achievement of the Goal 7 of sustainable development in Ukraine are provided.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it