The environmental cost of cryptocurrency: Analyzing CO2 emissions in the 9 leading mining countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 it