Climate Change Data Portal
| DOI | 10.1038/s41558-022-01377-7 |
| Aligning artificial intelligence with climate change mitigation | |
| Kaack L.H.; Donti P.L.; Strubell E.; Kamiya G.; Creutzig F.; Rolnick D. | |
| 发表日期 | 2022 |
| ISSN | 1758-678X |
| 起始页码 | 518 |
| 结束页码 | 527 |
| 卷号 | 12期号:6 |
| 英文摘要 | There is great interest in how the growth of artificial intelligence and machine learning may affect global GHG emissions. However, such emissions impacts remain uncertain, owing in part to the diverse mechanisms through which they occur, posing difficulties for measurement and forecasting. Here we introduce a systematic framework for describing the effects of machine learning (ML) on GHG emissions, encompassing three categories: computing-related impacts, immediate impacts of applying ML and system-level impacts. Using this framework, we identify priorities for impact assessment and scenario analysis, and suggest policy levers for better understanding and shaping the effects of ML on climate change mitigation. © 2022, Springer Nature Limited. |
| 语种 | 英语 |
| scopus关键词 | climate change; greenhouse gas; machine learning; mitigation; scenario analysis |
| 来源期刊 | Nature Climate Change
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/262136 |
| 作者单位 | Data Science Lab, Hertie School, Berlin, Germany |
| 推荐引用方式 GB/T 7714 | Kaack L.H.； Donti P.L.； Strubell E.； Kamiya G.； Creutzig F.； Rolnick D.. Aligning artificial intelligence with climate change mitigation[J],2022,12(6). |
| APA | Kaack L.H.； Donti P.L.； Strubell E.； Kamiya G.； Creutzig F.； Rolnick D..(2022).Aligning artificial intelligence with climate change mitigation.Nature Climate Change,12(6). |
| MLA | Kaack L.H.； Donti P.L.； Strubell E.； Kamiya G.； Creutzig F.； Rolnick D.."Aligning artificial intelligence with climate change mitigation".Nature Climate Change 12.6(2022). |
| 条目包含的文件 | 条目无相关文件。 | |||||
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
