| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP16267 |
| DP16267 Machine Learning the Carbon Footprint of Bitcoin Mining | |
| Héctor Calvo Pardo; Jose Olmo; Tullio Mancini | |
| 发表日期 | 2021-06-16 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | Building on an economic model of rational Bitcoin mining, we measure the carbon footprint of Bitcoin mining power consumption using feedforward neural networks. After reviewing the literature on deep learning methods, we find associated carbon footprints of 3.8038, 23.8313 and 19.83472 MtCOe for 2017, 2018 and 2019, which conform with recent estimates, lie within the economic model bounds while delivering much narrower confidence intervals, and yet raise alarming concerns, given recent evidence from climate-weather integrated models. We demonstrate how machine learning methods can contribute to non-for-profit pressing societal issues, like global warming, where data complexity and availability can be overcome. |
| 主题 | Public Economics |
| 关键词 | Machine learning Carbon footprint Cryptocurrencies Nowcasting Feed- forward neural networks Climate change |
| URL | https://cepr.org/publications/dp16267 |
| 来源智库 | Centre for Economic Policy Research (United Kingdom) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/545232 |
| 推荐引用方式 GB/T 7714 | Héctor Calvo Pardo,Jose Olmo,Tullio Mancini. DP16267 Machine Learning the Carbon Footprint of Bitcoin Mining. 2021. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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