{"title":"基于神经网络的中国能源经济低碳发展模型实证研究","authors":"Kangli Xiang, Xianan Huang, Li Zhang","doi":"10.1109/IPEC51340.2021.9421243","DOIUrl":null,"url":null,"abstract":"This paper on the low-carbon development model at home and abroad is introduced, on the basis of further defined related concepts of low carbon development, and then on the basis of familiar with the related concepts, introduces the related carbon decomposition model, then the particle swarm optimization (pso) algorithm and BP neural network for the corresponding introduction, on the basis of related theory, multi-dimensional decomposition model of carbon productivity in our empirical study, analysis and comparison in compared with the base in different industries in various provinces the contribution values of different influence factors on the carbon productivity in our country.","PeriodicalId":340882,"journal":{"name":"2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC)","volume":"7 3","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Empirical Study on Low Carbon Development Model of China's Energy Economy Based on Neural Networks\",\"authors\":\"Kangli Xiang, Xianan Huang, Li Zhang\",\"doi\":\"10.1109/IPEC51340.2021.9421243\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper on the low-carbon development model at home and abroad is introduced, on the basis of further defined related concepts of low carbon development, and then on the basis of familiar with the related concepts, introduces the related carbon decomposition model, then the particle swarm optimization (pso) algorithm and BP neural network for the corresponding introduction, on the basis of related theory, multi-dimensional decomposition model of carbon productivity in our empirical study, analysis and comparison in compared with the base in different industries in various provinces the contribution values of different influence factors on the carbon productivity in our country.\",\"PeriodicalId\":340882,\"journal\":{\"name\":\"2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC)\",\"volume\":\"7 3\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-04-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IPEC51340.2021.9421243\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPEC51340.2021.9421243","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Empirical Study on Low Carbon Development Model of China's Energy Economy Based on Neural Networks
This paper on the low-carbon development model at home and abroad is introduced, on the basis of further defined related concepts of low carbon development, and then on the basis of familiar with the related concepts, introduces the related carbon decomposition model, then the particle swarm optimization (pso) algorithm and BP neural network for the corresponding introduction, on the basis of related theory, multi-dimensional decomposition model of carbon productivity in our empirical study, analysis and comparison in compared with the base in different industries in various provinces the contribution values of different influence factors on the carbon productivity in our country.