Development a policy for the production of Bitcoins with renewable energy sources
Bibliographic record
Abstract
Bitcoin, the first decentralized digital currency introduced by an anonymous person or group since 2008, has attracted worldwide attention. A significant number of economists have introduced Bitcoin as a new phenomenon in the 21st century that could reduce global inflation. Given the tens of thousands of digital currencies that have emerged since the advent of Bitcoin and its price growth trend over more than a decade, which are signs of the growth of this business. In addition to being money, Bitcoin has always been considered a tool for investing and storing value, which is why it is called digital gold. One of the most important problems in the production or extraction of Bitcoins is the high-power consumption by miners. If the energy sources of electricity generation are supplied by non-renewable energy sources, in addition to emitting air pollutant gases, it will increase greenhouse gases and consequently contribute to climate change. In this research, based on the idea of the authors, which is that the economic support of Bitcoin is energy, a strategy for producing Bitcoin from renewable energy sources is considered. First, the amount of electrical energy consumption by Bitcoin production is calculated based on statistical data, and then based on the price of electricity in different countries of the world and its global average, the base price of Bitcoin is calculated. In the following, four scenarios are proposed for the production of Bitcoin by electricity supplied from non-renewable energy sources. These scenarios include coal-fired steam power plants, natural gas-fired power plants, natural gas/oil gas-fired power plants, and dual-cycle (steam and gas cycles) natural gas-fired power plants. Based on the amount of electricity required to produce one Bitcoin, the amount of pollutants emitted to produce Bitcoin and its social costs are calculated. These costs should be added to the base cost of Bitcoin production if non-renewable energy sources are used to produce Bitcoin. Then, renewable energy sources for Bitcoin production based on the price of electricity generated by renewable energy sources are examined. Based on the analyses, how to choose the best renewable energy source to produce Bitcoin is presented as a scenario. This article briefly answers two key questions: 1. At what price of Bitcoin is it cost-effective for governments to produce it? 2. What is the best renewable energy source to produce it? These two questions can be useful in creating a roadmap and strategy for economists and governments.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".