Climate Monetary Policy Design and Modelling
Bibliographic record
Abstract
The significance of green monetary policy design in the face of current climatic issues has been investigated.The study considers the perspectives of other researchers in previous studies while focusing on the Nigeria situation and monetary climate policies in the country.The study's goal is to put current monetary policy instruments to the test and check their alignment with climate fluctuations and policies.The analysis spans the years 1990 to 2020, utilizing data from the World Development Indicators and the Central Bank of Nigeria's statistics archives.Various analytical studies are performed, and the monetary policy tools used are individually assessed to validate their efficiency in reducing climate change.The study applies ordinary least squares methodologies, indicating that the Central Bank's money supply and monetary policy rate processes match with climate change monetary policy adaptation.As a result, the country's money supply and interest rate are environmentally beneficial.Nonetheless, the analysis concludes that inflation and exchange rates are unimportant throughout the time period under consideration.As a response, the research recommends that the government, through its financial institutions, completely implement monetary policy changes in favor of climate change in the country.The research also proposes that the authorities aggressively seek green financing of ecofriendly technology using green bonds, which are currently on the market.The budgeting system is critical for monitoring the green fund's administration and effective application to green initiatives.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".