FinTech and CO<sub>2</sub> emission: evidence from (top 7) mobile money economies in Africa
Bibliographic record
Abstract
Purpose The impact of FinTech in sub-Saharan Africa has primarily been limited to financial inclusion. Contrarily, this study aims to deviate from this norm to estimate how FinTech affects carbon emissions in the subregion. This provides policy recommendations for FinTech regulators, service providers and practitioners to consider optimal products and services that reduce carbon emissions. Design/methodology/approach A balanced panel data set from 2009 to 2020 is used and estimated with the fully modified ordinary least squares estimator after checking for cross-sectional dependence, unit root, stationarity and cointegration. Findings Results from the estimation suggest a negatively significant relationship between financial technology and carbon emissions in these countries. However, domestic credit to the private sector revealed a statistically insignificant relationship with carbon emissions for the same period. Further, foreign direct investment reduces carbon emissions but gross domestic product and trade openness increase carbon emissions in these countries. Originality/value The impact of FinTech in sub-Saharan Africa has primarily been limited to financial inclusion. Contrarily, this study deviates from this norm and estimates how FinTech affects carbon emissions in the subregion.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".