GREEN INVESTMENTS AND ITS INFLUENCE ON GREEN GROWTH IN RA: INTEGRATING ECONOMIC GROWTH WITH SUSTAINABILITY
Bibliographic record
Abstract
The main objective of this research is to explore the macroeconomic implications of green investment in the transformation to a green economy, while defining main sectoral priorities for investment allocation and underlining the short- and long-term macroeconomic effects of the “green” investment on the basis of which we can build possible scenarios for green transformation in the Republic of Armenia. For this purpose, we have examined the features of the new taxonomy of investments proposed in the “LowGrow SFC” model which was developed based on the Canadian economy. We propose to classify green investments in Armenia: “productive” or “non-productive”, “additional” and “non-additional”. Within the framework of the above-mentioned logic of presenting investments according to their macroeconomic impacts, it has been highlighted the main directions of the RA economy that can contribute to the growth of the green economy. In the it is suggested two possible Scenarios for Armenia’s green transformation, each of which takes into account different levels of investment, policy actions, and technological deployment. Which scenario is most effective for Armenia depends on the ability of the Armenian economy to attract green investments.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 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".