The Greater Sustainability of Stablecoins Relative to Other Cryptocurrencies
Bibliographic record
Abstract
Cryptocurrencies are decentralized digital financial services that do not physically exist in the world of tangible products and goods, and therefore purportedly offer some positive environmental sustainability features. However, since they are based on blockchain technology, which requires a relatively large input of energy, their climatic impact is not benign. Furthermore, they are very volatile and characterized by low levels of transparency and control, thus creating some negative economic and social sustainability effects. Stablecoins, which are a pegged type of cryptocurrency, exhibit much less volatility and have higher levels of management and interoperability. This raises the following question: are stablecoins more sustainable compared to other cryptocurrencies? To explore this, a sustainability assessment was conducted, comparing cryptocurrencies and stablecoins across environmental, social, and economic dimensions while identifying the key characteristics of sustainability. It was found that stablecoins can mitigate the economic and social risks associated with cryptocurrencies and thus increase their overall sustainability. Moreover, since stablecoins are managed and governed to a greater extent, a key consideration in their development is the selection and implementation of more appropriate mechanisms that can reduce energy use and enhance sustainability. Finally, stablecoins offer more effective—and not just more efficient—solutions, based on value co-creation between several providers and a customer.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".