Digital Currencies, Energy Security, and Environmental Challenges: A G7 Perspective
Bibliographic record
Abstract
This article presents a comprehensive analysis of the impact of cryptocurrencies on the economic and environmental security of the G7 countries, exploring both the potential risks and prospects. The study focuses on the United States, Canada, the United Kingdom, France, Germany, Italy, and Japan, offering a detailed exploration of the increasing adoption of cryptocurrencies in these nations. Despite the benefits such as enhanced financial inclusion and cross-border transaction efficiency, cryptocurrencies pose significant challenges, including their use in illicit activities like money laundering and terrorism financing. The research critically examines the substantial energy consumption associated with certain cryptocurrency mining processes, particularly Proof-of-Work mechanisms, and their consequent environmental impacts, including carbon emissions, electronic waste, and air pollution. It investigates the corresponding energy policies and regulatory responses emerging within the G7 to address these concerns, alongside the development of more energy-efficient alternatives like Proof-of-Stake and the push for renewable energy in mining. The article critically examines these dual aspects, highlighting the measures implemented by regulators and policymakers to mitigate risks. It also delves into the evolving landscape of Central Bank Digital Currencies (CBDCs) and their potential role in enhancing financial system efficiency and security, including considerations for their energy footprint. The study employs a robust methodological framework, combining statistical analysis of market trends, case studies, and policy analysis to provide a balanced view of the current state and future trajectory of cryptocurrencies in the G7 countries. By offering a nuanced understanding of both the opportunities and threats posed by digital currencies, including their energy and environmental dimensions, this article contributes to the ongoing discourse on their integration into global financial systems and their implications for sustainable economic security.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".