Green finance and its impact on achieving sustainable development
Bibliographic record
Abstract
This study aims to investigate the impact of green finance initiatives on achieving sustainable development goals in Jordan, with a specific focus on evaluating the effectiveness of green finance strategies in promoting environmental sustainability. The research applies the Autoregressive Distributed Lag (ARDL) method and assesses the connection of green finance, taken as the number of banks who increase the loan activity on ecology projects, and sustainable growth, given by the records of carbon releases. Relevant control variables involved in this consideration include income level, population, trade openness, and urbanization in addition to other factors that could otherwise cause a deviation which would generate biased results. The statistical tests show that green finance positively contributes to sustainable development in Jordan, and in the short- and long-term perspectives. Green finance and sustainable development have been a tightly connected two-way causality between them according to Dik and Panchenko's test, which implies that a virtuous cycle exists here. The results give extra weight and brilliant examples of the crucial role that green finance plays in the implementation of the sustainable development goals. It is this role that mainly enables reduction of carbon emissions in the world and mitigation of the negative consequences of climate change. They touch on the main issue of shaping the suitable conditions for green investment options and to create the interest for investing in sustainable development projects. This has become part and parcel of the green finance and sustainable development literature through the manifold of envisaged adjustments to our research design, a wide array of relevant control variables considered, and fully developed elaborated econometrics. It offers a direct response to the research gap by unfolding how becoming green finances takes place. This empowers the sustainable development outcomes in Jordan.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".