Role of public and private investments for green economic recovery in the post-COVID-19
Bibliographic record
Abstract
This study evaluates the outlook of government expenditure through public and private financing for the green economic revitalization after COVID-19 in Canada. The various econometric estimations are used to measure the impact of government expenditure on green economic recovery. The implementation of public investment is explicitly associated with private funding. The results suggest that the government policy incentives and non-government financing influence fossil fuel energy sources proportions on non-government investment, which is additional than the feed-in tariffs. According to fixed effects results, the distribution of fossil fuel energy sources is an essential obstacle in solar energy investment. In contrast, the presence of varied types of renewable energy encourages non-government climate investment. Throughout the study period after the breakout of the pandemic phase, neither fossil fuel energy sources nor economic policy is marginally efficient. The different macroeconomic programs in green economic recovery might be ideal for attaining the needed impact. The critical policy conclusion of the results of this research is that an influential role of the public and private investment may be part of an optimal firm innovation plan for green economic recovery in the post-COVID-19 period.
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.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".