FinTech and economic, environmental, and social sustainability: Uncovering financial innovation’s sustainable potential
Bibliographic record
Abstract
Purpose The appearance of Financial Technologies (FinTech) is considered a major breakthrough in the financial services industry. With it comes the promise of increasing economic efficiency and performance, achieving equitable social growth, and reducing the degradation of the environment. The present study empirically measures the impact of FinTech on economic, social, and environmental sustainability. As such it aims to fill the gaps in the literature and settle the debate regarding whether FinTech promotes or hinders economic and social development and if it can mitigate environmental degradation. Design/methodology/approach The study uses econometric modeling to test the relationships between FinTech and economic, social, and environmental sustainability. It relies on annual panel data from 20 OECD countries for the period between 2005 and 2021. Findings Results show that FinTech positively affects sustainable economic development and has a positive social impact. Findings also confirm that FinTech enhances environmental sustainability. Further, the results of the study confirm the resource curse as natural resources rent is shown to decrease economic growth and adversely affect environmental sustainability. Originality/value The study differs from previous works as it is not limited to investigating the impact of FinTech on environmental sustainability but rather considers the three dimensions of sustainable development: economic, social, and environmental. The results of this study offer insights for policymakers and regulators to promote and support the agenda of FinTech with higher levels of conviction and confidence.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| 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".