{"title":"Research on the Impact of Carbon Trading Market on Electricity Emission Reduction Based on GM-BP Model","authors":"Y. Hu, Yuanjie Xu, Tiantian Ye","doi":"10.1109/AINIT54228.2021.00100","DOIUrl":null,"url":null,"abstract":"In order to achieve energy conservation and emission reduction goals, China has included \"carbon peak\" and \"carbon neutrality\" in its national strategy. Electricity is the industry with the largest carbon emissions in China, and active efforts to reduce electricity emissions have had a significant positive impact on the achievement of the \"dual carbon\" goal. Carbon emissions trading plays an important role in promoting the large-scale optimization of energy allocation in the power industry across the country. At present, reducing carbon emissions from electricity is still focused on technological upgrading and the promotion of new energy. This article conducts an in-depth study on the counter-control of indicator analysis and forecasting methods starting from the carbon trading market. Use the grey relational model to explore the correlation between the carbon trading market and electricity carbon emission reduction. Combined with the results of the electricity carbon emission prediction model based on the BP (back propagation) neural network, it provides a reference basis and reasonable suggestions for the rapid realization of the \"dual carbon\" goal.","PeriodicalId":326400,"journal":{"name":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"2005 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AINIT54228.2021.00100","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In order to achieve energy conservation and emission reduction goals, China has included "carbon peak" and "carbon neutrality" in its national strategy. Electricity is the industry with the largest carbon emissions in China, and active efforts to reduce electricity emissions have had a significant positive impact on the achievement of the "dual carbon" goal. Carbon emissions trading plays an important role in promoting the large-scale optimization of energy allocation in the power industry across the country. At present, reducing carbon emissions from electricity is still focused on technological upgrading and the promotion of new energy. This article conducts an in-depth study on the counter-control of indicator analysis and forecasting methods starting from the carbon trading market. Use the grey relational model to explore the correlation between the carbon trading market and electricity carbon emission reduction. Combined with the results of the electricity carbon emission prediction model based on the BP (back propagation) neural network, it provides a reference basis and reasonable suggestions for the rapid realization of the "dual carbon" goal.