FinTech and CO<sub>2</sub> emission: evidence from (top 7) mobile money economies in Africa
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it