{"title":"人工智能减少中国 5G 网络的碳排放","authors":"","doi":"10.1038/s41893-023-01208-3","DOIUrl":null,"url":null,"abstract":"A data-driven framework has been developed to assess the carbon emissions of mobile networks in China, revealing that the launch of 5G networks leads to a decline in carbon efficiency. A deep reinforcement learning algorithm, DeepEnergy, is proposed to increase the carbon efficiency of mobile networks and reduce carbon emissions.","PeriodicalId":19056,"journal":{"name":"Nature Sustainability","volume":"6 12","pages":"1522-1523"},"PeriodicalIF":25.7000,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence for reducing the carbon emissions of 5G networks in China\",\"authors\":\"\",\"doi\":\"10.1038/s41893-023-01208-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A data-driven framework has been developed to assess the carbon emissions of mobile networks in China, revealing that the launch of 5G networks leads to a decline in carbon efficiency. A deep reinforcement learning algorithm, DeepEnergy, is proposed to increase the carbon efficiency of mobile networks and reduce carbon emissions.\",\"PeriodicalId\":19056,\"journal\":{\"name\":\"Nature Sustainability\",\"volume\":\"6 12\",\"pages\":\"1522-1523\"},\"PeriodicalIF\":25.7000,\"publicationDate\":\"2023-08-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Sustainability\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.nature.com/articles/s41893-023-01208-3\",\"RegionNum\":1,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Sustainability","FirstCategoryId":"93","ListUrlMain":"https://www.nature.com/articles/s41893-023-01208-3","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Artificial intelligence for reducing the carbon emissions of 5G networks in China
A data-driven framework has been developed to assess the carbon emissions of mobile networks in China, revealing that the launch of 5G networks leads to a decline in carbon efficiency. A deep reinforcement learning algorithm, DeepEnergy, is proposed to increase the carbon efficiency of mobile networks and reduce carbon emissions.
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Nature Sustainability aims to facilitate cross-disciplinary dialogues and bring together research fields that contribute to understanding how we organize our lives in a finite world and the impacts of our actions.
Nature Sustainability will not only publish fundamental research but also significant investigations into policies and solutions for ensuring human well-being now and in the future.Its ultimate goal is to address the greatest challenges of our time.
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