Cryptocurrencies’ hashrate and electricity consumption: evidence from mining activities
Bibliographic record
Abstract
Purpose Given the growing importance of cryptocurrencies and the technique called “SegWit” that allows to compile more transactions in a mined block, the electricity consumed per block might potentially decrease. The purpose of this study is to consider that the difficulty to mine a block might be a better indicator of the Bitcoin\Ether’s electricity consumption. Design/methodology/approach This study applies the vector error correction model to investigate data related to primary energy consumption and electricity production, supply and consumption for Bitcoin and Ether hashrates from 2016M1 to 2021M5. Findings The hashrate (difficulty of solving the cryptographic problem related to the validation of a transaction) is found to have a positive cointegration with energy and electricity consumption. Despite the launch of the Segregation Witness (SegWit) mechanism allowing blocks to handle a higher number of transactions per block, this Bitcoin and Ether growing need in electricity has significantly been increasing since October 2019. Originality/value The major contribution of this study is to investigate a more relevant indicator, namely, hashrate (computational difficulty to solve cryptographic enigma associated with cryptocurrencies-related transaction). The approach of this study can be justified by the fact that there exists a technical solution consisting in increasing the number of transactions per blocks so that less electricity might be required to validate a transaction.
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.
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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".