The Environmental Footprint of Bitcoin Mining Across the Globe: Call for Urgent Action
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
Abstract Based on a multi‐attribute assessment of the environmental impacts and challenges associated with global Bitcoin (BTC) mining activities around the globe, we call for urgent action by the scientific, policy, and advocacy communities. The worldwide BTC mining network consumed 173.42 TWh of electricity during the 2020–2021 period, bigger than the electricity consumption of most nations. The mining process emitted over 85.89 Mt of CO 2 eq in the same timeframe, equivalent to the emission caused by burning 84 billion pounds of coal or running 190 natural gas‐fired power plants. The environmental footprint of BTC mining is not limited to greenhouse gas emissions. In 2020–2021, the global water footprint of BTC mining was about 1.65 km 3 , more than the domestic water use of 300 million people in rural Sub‐Saharan Africa. The land footprint of the global BTC mining network during this period was more than 1,870 square kilometers, 1.4 times the area of Los Angeles. These striking numbers highlight the heavy reliance of the BTC network on fossil fuels and natural resource‐intensive energy sources, resulting in major but unmonitored and unregulated environmental footprints. To mitigate the environmental costs of BTC mining, immediate policy interventions, technological advancements, and scientific research are crucial. Proposed measures include enhanced transparency, economic and regulatory tools, developing energy‐efficient alternative coins, and the adoption of greener blockchain validation protocols.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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