The environmental cost of cryptocurrency: Analyzing CO2 emissions in the 9 leading mining countries
Bibliographic record
Abstract
This study examines the environmental impact of cryptocurrency mining, specifically its contribution to CO2 emissions , in nine countries that account for 90% of global mining: the United States, China, Russia, Canada, Germany , Malaysia, Kazakhstan, Ireland, and Iran. Utilizing monthly panel data from 2019 to 2022 across nine countries and applying both pooled and fixed effects econometric techniques, the analysis reveals that ”energy intensity” (the amount of energy used to produce a unit of GDP), as a moderator variable, influences the effect of cryptocurrency mining on CO2 emissions. Specifically, in countries where the annual energy intensity growth rate is greater than − 6 % , cryptocurrency mining tends to result in higher CO 2 emissions. Conversely, in countries with a growth rate of energy intensity below -6%, cryptocurrency mining results in lower CO2 emissions. The findings indicate that all nine countries experience a positive impact on CO2 emissions, albeit to varying degrees. The countries are categorized into three groups based on their performance: underperformers (Russia, the United States, Canada), neutral-effect countries (Iran, Kazakhstan, China), and positive performers (Ireland, Germany, Malaysia). This research underscores the urgent need for sustainable practices in cryptocurrency mining to mitigate its environmental effects.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".