Implications of NFT as a sustainable fintech innovation for sustainable development and entrepreneurship
Bibliographic record
Abstract
This study explores the global patterns of non-fungible token (NFT) equity funding, focusing on NFTs’ role as a sustainable financial technology (fintech) in promoting the United Nations’ sustainable development goals (SDGs) as well as entrepreneurship and in balancing business growth with social impact. Utilizing descriptive tools and the Wilcoxon rank-sum (Mann–Whitney) test, we analyze global and regional data from 2015 to 2021 to examine NFT funding flows. The results show that funding flows from 2015 to 2021 exhibit notable differences and that funding flows in the United States (US) and Europe differ significantly from those in Latin America and the rest of the world (regions other than the US, Asia, Europe, Latin America, and Canada). Furthermore, using a narrative literature review, we determine that NFT-funded projects support achieving the SDGs (including decent work and economic growth; climate action; peace, justice, and strong institutions; quality education; partnerships for the goals; and industry, innovation, and infrastructure), fighting against hunger and poverty, promoting human well-being, facilitating financial inclusion, and reducing gender gaps, thereby ensuring business growth aligning with social benefits. However, technological barriers, negative environmental impacts, insufficient regulations, unequal benefit distribution, and social distrust may obstruct NFT innovation.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".