Greening the Financial System in USA, Canada and Brazil: A Panel Data Analysis
Bibliographic record
Abstract
Each country designs its own scheme to achieve green financing and, in general, credit is considered to be a fundamental source of greening financial systems. The novelty of this study resides in that we examined green financing initiatives in USA, Canada and Brazil by focusing on major components of the financial systems before, during and after the 2008 world financial crisis. By means of panel data analysis conducted on observations ranging across the period 1970–2018, we investigated variables such as domestic credit from banks, domestic credit from the financial sector, GDP, N2O emissions, CO2 emissions and the value added from agriculture, forest and fishing activities. According to our findings, domestic credit from banks was insufficient to achieve green financing. Namely, in order to increase economic growth while reducing global warming and climate change, the financial sector should assume a bigger role in funding green investments. Moreover, our results showed that domestic credit from the financial sector contributed to green financing, while CO2 emissions remained a challenge in capping global warming at the 1.5 °C level. Our empirical study supports the idea that economic growth together with policies targeting climate change and global warming can contribute to green financing. Over and above that, governments should strive to design sustainable fiscal and monetary policies that promote green financing.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".