{"title":"碳中和:综述","authors":"Bin He, Xin Yuan, Shusheng Qian, Bi Li","doi":"10.1115/1.4062545","DOIUrl":null,"url":null,"abstract":"\n The introduction of the idea of “carbon neutrality” gives the development of low carbon and decarbonization a defined path. Climate change is a significant worldwide concern. To offer a theoretical foundation for the implementation of carbon reduction, this research first analyzes the idea of carbon footprinting, accounting techniques, and supporting technologies. The next section examines carbon emission reduction technologies in terms of lowering emissions and raising carbon sequestration. Digital intelligence technologies like the Internet of Things, big data, and artificial intelligence will be crucial throughout the process of reducing carbon emissions. The implementation pathways for increasing carbon sequestration primarily include ecological and technological carbon sequestration. Nevertheless, proving carbon neutrality requires measuring and monitoring greenhouse gas emissions from several industries, which makes it a challenging undertaking. Intending to increase the effectiveness of carbon footprint measurement, this study created a web-based program for computing and analyzing the whole life-cycle carbon footprint of items. The practical applications and difficulties of digital technologies, such as blockchain, the Internet of Things, and artificial intelligence in achieving a transition to carbon neutrality are also reviewed, and additional encouraging research ideas and recommendations are made to support the development of carbon neutrality.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"74 1","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Carbon Neutrality: A Review\",\"authors\":\"Bin He, Xin Yuan, Shusheng Qian, Bi Li\",\"doi\":\"10.1115/1.4062545\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n The introduction of the idea of “carbon neutrality” gives the development of low carbon and decarbonization a defined path. Climate change is a significant worldwide concern. To offer a theoretical foundation for the implementation of carbon reduction, this research first analyzes the idea of carbon footprinting, accounting techniques, and supporting technologies. The next section examines carbon emission reduction technologies in terms of lowering emissions and raising carbon sequestration. Digital intelligence technologies like the Internet of Things, big data, and artificial intelligence will be crucial throughout the process of reducing carbon emissions. The implementation pathways for increasing carbon sequestration primarily include ecological and technological carbon sequestration. Nevertheless, proving carbon neutrality requires measuring and monitoring greenhouse gas emissions from several industries, which makes it a challenging undertaking. Intending to increase the effectiveness of carbon footprint measurement, this study created a web-based program for computing and analyzing the whole life-cycle carbon footprint of items. The practical applications and difficulties of digital technologies, such as blockchain, the Internet of Things, and artificial intelligence in achieving a transition to carbon neutrality are also reviewed, and additional encouraging research ideas and recommendations are made to support the development of carbon neutrality.\",\"PeriodicalId\":54856,\"journal\":{\"name\":\"Journal of Computing and Information Science in Engineering\",\"volume\":\"74 1\",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2023-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computing and Information Science in Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1115/1.4062545\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computing and Information Science in Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1115/1.4062545","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
The introduction of the idea of “carbon neutrality” gives the development of low carbon and decarbonization a defined path. Climate change is a significant worldwide concern. To offer a theoretical foundation for the implementation of carbon reduction, this research first analyzes the idea of carbon footprinting, accounting techniques, and supporting technologies. The next section examines carbon emission reduction technologies in terms of lowering emissions and raising carbon sequestration. Digital intelligence technologies like the Internet of Things, big data, and artificial intelligence will be crucial throughout the process of reducing carbon emissions. The implementation pathways for increasing carbon sequestration primarily include ecological and technological carbon sequestration. Nevertheless, proving carbon neutrality requires measuring and monitoring greenhouse gas emissions from several industries, which makes it a challenging undertaking. Intending to increase the effectiveness of carbon footprint measurement, this study created a web-based program for computing and analyzing the whole life-cycle carbon footprint of items. The practical applications and difficulties of digital technologies, such as blockchain, the Internet of Things, and artificial intelligence in achieving a transition to carbon neutrality are also reviewed, and additional encouraging research ideas and recommendations are made to support the development of carbon neutrality.
期刊介绍:
The ASME Journal of Computing and Information Science in Engineering (JCISE) publishes articles related to Algorithms, Computational Methods, Computing Infrastructure, Computer-Interpretable Representations, Human-Computer Interfaces, Information Science, and/or System Architectures that aim to improve some aspect of product and system lifecycle (e.g., design, manufacturing, operation, maintenance, disposal, recycling etc.). Applications considered in JCISE manuscripts should be relevant to the mechanical engineering discipline. Papers can be focused on fundamental research leading to new methods, or adaptation of existing methods for new applications.
Scope: Advanced Computing Infrastructure; Artificial Intelligence; Big Data and Analytics; Collaborative Design; Computer Aided Design; Computer Aided Engineering; Computer Aided Manufacturing; Computational Foundations for Additive Manufacturing; Computational Foundations for Engineering Optimization; Computational Geometry; Computational Metrology; Computational Synthesis; Conceptual Design; Cybermanufacturing; Cyber Physical Security for Factories; Cyber Physical System Design and Operation; Data-Driven Engineering Applications; Engineering Informatics; Geometric Reasoning; GPU Computing for Design and Manufacturing; Human Computer Interfaces/Interactions; Industrial Internet of Things; Knowledge Engineering; Information Management; Inverse Methods for Engineering Applications; Machine Learning for Engineering Applications; Manufacturing Planning; Manufacturing Automation; Model-based Systems Engineering; Multiphysics Modeling and Simulation; Multiscale Modeling and Simulation; Multidisciplinary Optimization; Physics-Based Simulations; Process Modeling for Engineering Applications; Qualification, Verification and Validation of Computational Models; Symbolic Computing for Engineering Applications; Tolerance Modeling; Topology and Shape Optimization; Virtual and Augmented Reality Environments; Virtual Prototyping