Analyzing the Causal Relationship Between Bitcoin Trade and Environmental Quality in the United States
Bibliographic record
Abstract
In the literature, the environmental impact of financial development and innovations has been shown to vary across regions. There has, however, been limited focus on specific financial innovations. This chapter explores the causal relationship between Bitcoin trade and CO 2 emissions in the United States. Findings reveal unidirectional causality from both total energy CO 2 emissions and commercial sector CO 2 emissions to Bitcoin volume at the 5% significance level. Additionally, reverse causality from Bitcoin volume to total and commercial sector energy consumption is observed at the 10% significance level. However, no causality is found between Bitcoin price and environmental variables. Robustness checks using time-varying Granger causality tests show that causality from CO 2 emissions to Bitcoin volume occurs at several points across time. Reverse causality from Bitcoin volume affecting environmental variables also occurs intermittently, while causality between Bitcoin price and environmental variables is weak. Given the limited environmental impact on Bitcoin, it can serve as a safe haven for real investments with environmental implications. However, since Bitcoin trade volume affects the environment, a framework should be developed to utilize cryptocurrencies for sustainable investments on the blockchain.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.008 | 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".