Life cycle assessment of Bitcoin mining in the top ten miner countries
Bibliographic record
Abstract
An unprecedented emergence has occurred for the cryptocurrencies among enterprises, customers, and investors as a result of the growing number of internet connections worldwide. The most popular cryptocurrency is Bitcoin representing the rise of digital payment systems. Though, harsh criticism has been also created for cryptocurrencies about their environmental sustainability and power consumption, decelerating the acceptance of bitcoin by consumer as a means of payment. The ecological impact or footprint of a process is determined mainly through life-cycle-assessment (LCA) quantifying all material flows’ inputs and outputs for a process or product and their effect on the environment. This study provides LCA-based framework to show the environmental impacts of Bitcoin mining from top ten miner countries (China, USA, Kazakhstan, Russia, Iran, Malaysia, Canada, Germany, Ireland, Norway). The results show that with the share of 53.3% of the world’s mining, China has the most negative environmental impact specially in marine ecotoxicity with 26.8 kg 1,4-DCB and human health with 0.0043 DALY but with the equal mining ratio Germany and Kazakhstan have the most negative environmental impacts.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".