Factors Favoring the Adoption of Green Finance by Financial Institutions In Dakar: An Exploratory Study
Bibliographic record
Abstract
As with any organization, financial institutions, particularly in Dakar, play a crucial role in low-income and emerging economies by mobilizing capital and investing in climate change mitigation. Drawing on neo-institutional theory, this exploratory study aims to understand and identify the factors that may influence the adoption of green finance by banks in Dakar. To achieve this, interviews were conducted with five (5) bank CEOs and three (3) solar energy stakeholders (bank clients), and the data were analyzed using thematic categorical content analysis. The findings, based on the interviewees’ responses, reveal that top management support, proactive institutional strategies, capacity building, incentive policies, market share development, allocation of funds to green projects, and the digitization of banking services are likely to influence the adoption of green finance by banks in Dakar. Furthermore, the direct involvement of monetary and regulatory authorities, through the development of specific regulations that encourage financial institutions to adopt green finance, is essential.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".