The impact of blockchain technology on the environment
Bibliographic record
Abstract
This paper focuses on the environmental impact of blockchain technology, particularly on electricity consumption for equipment operation and cooling. During its operation, the device energy is converted into heat, which must be efficiently dispersed. Additionally, the paper examines the rate of mining equipment replacement and the subsequent e-waste concerns. The impact of blockchain technology on the environment is a complex and debated topic. Only the following two aspects are discussed in this paper: 1) Energy Consumption: (a) Positive Impact: Blockchain technology, especially in the context of cryptocurrencies like Bitcoin, has been criticized for its high energy consumption due to the consensus mechanism called Proof of Work (PoW). However, some blockchain networks use alternative consensus mechanisms like Proof of Stake (PoS), which is more energy-efficient, and b) Negative Impact: PoW-based blockchains, such as Bitcoin, require significant computational power, leading to high energy consumption. The environmental impact is a concern, especially if the electricity used comes from non-renewable sources. 2) Mining and E-Waste: (a) Positive Impact: Blockchain technology can help in tracking the supply chain and provenance of minerals, which could reduce the use of conflict minerals and promote ethical mining practices. (b)Negative Impact: The mining of cryptocurrencies involves specialized hardware that becomes obsolete quickly, contributing to electronic waste (e-waste). This can have negative environmental consequences if not properly managed and recycled. The central topic of this paper is electric energy consumption and as a consequence CO2 emission footprint. Because of the fast growth of data centers and mining centers, consumption of electric energy has grown exponentially in the past decade. Together with the consumption of electric energy, CO2 emission grows dramatically.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".