Elman neural network optimized by firefly algorithm for forecasting China's carbon dioxide emissions
With the development of China's economy, more and more energy consumption has led to serious environmental problems. Faced with the enormous pressure of large amounts of carbon dioxide ( ${\rm CO}_2 $) emissions, China is now actively implementing the development strategy of low-carbon and emis...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2019-11-01
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| Series: | Systems Science & Control Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/21642583.2019.1620655 |
| _version_ | 1830511651735994368 |
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| author | Yuansheng Huang Hongwei Wang Hui Liu Shijian Liu |
| author_facet | Yuansheng Huang Hongwei Wang Hui Liu Shijian Liu |
| author_sort | Yuansheng Huang |
| collection | DOAJ |
| description | With the development of China's economy, more and more energy consumption has led to serious environmental problems. Faced with the enormous pressure of large amounts of carbon dioxide ( ${\rm CO}_2 $) emissions, China is now actively implementing the development strategy of low-carbon and emission reduction. Through the analysis of the influencing factors of ${\rm CO}_2 $ emissions in China, five key influencing factors are selected: urbanization level, gross domestic product (GDP) of secondary industry, thermal power generation, real GDP per capital and energy consumption per unit of GDP. This paper applies the Elman neural network optimized by the Firefly Algorithm (FA) to forecast the ${\rm CO}_2 $ emissions in China. And the results show that the performance of the FA–Elman is better than the Elman neural network and Back Propagation Neural Network (BPNN), verifying the effectiveness of the FA–Elman model for the ${\rm CO}_2 $ emissions prediction. Finally, we make some suggestions for low-carbon and emission reduction in China by analysing key influencing factors and forecasting ${\rm CO}_2 $ emissions using the FA–Elman model from 2017 to 2020. |
| first_indexed | 2024-12-22T02:14:22Z |
| format | Article |
| id | doaj.art-30b5ab4f9df14f75980df87dfe25f6e8 |
| institution | Directory Open Access Journal |
| issn | 2164-2583 |
| language | English |
| last_indexed | 2024-12-22T02:14:22Z |
| publishDate | 2019-11-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Systems Science & Control Engineering |
| spelling | doaj.art-30b5ab4f9df14f75980df87dfe25f6e82022-12-21T18:42:18ZengTaylor & Francis GroupSystems Science & Control Engineering2164-25832019-11-017281510.1080/21642583.2019.16206551620655Elman neural network optimized by firefly algorithm for forecasting China's carbon dioxide emissionsYuansheng Huang0Hongwei Wang1Hui Liu2Shijian Liu3North China Electric Power UniversityNorth China Electric Power UniversityNorth China Electric Power UniversityNorth China Electric Power UniversityWith the development of China's economy, more and more energy consumption has led to serious environmental problems. Faced with the enormous pressure of large amounts of carbon dioxide ( ${\rm CO}_2 $) emissions, China is now actively implementing the development strategy of low-carbon and emission reduction. Through the analysis of the influencing factors of ${\rm CO}_2 $ emissions in China, five key influencing factors are selected: urbanization level, gross domestic product (GDP) of secondary industry, thermal power generation, real GDP per capital and energy consumption per unit of GDP. This paper applies the Elman neural network optimized by the Firefly Algorithm (FA) to forecast the ${\rm CO}_2 $ emissions in China. And the results show that the performance of the FA–Elman is better than the Elman neural network and Back Propagation Neural Network (BPNN), verifying the effectiveness of the FA–Elman model for the ${\rm CO}_2 $ emissions prediction. Finally, we make some suggestions for low-carbon and emission reduction in China by analysing key influencing factors and forecasting ${\rm CO}_2 $ emissions using the FA–Elman model from 2017 to 2020.http://dx.doi.org/10.1080/21642583.2019.1620655Carbon dioxide emissionsforecasting modelElman neural networkfirefly algorithm |
| spellingShingle | Yuansheng Huang Hongwei Wang Hui Liu Shijian Liu Elman neural network optimized by firefly algorithm for forecasting China's carbon dioxide emissions Systems Science & Control Engineering Carbon dioxide emissions forecasting model Elman neural network firefly algorithm |
| title | Elman neural network optimized by firefly algorithm for forecasting China's carbon dioxide emissions |
| title_full | Elman neural network optimized by firefly algorithm for forecasting China's carbon dioxide emissions |
| title_fullStr | Elman neural network optimized by firefly algorithm for forecasting China's carbon dioxide emissions |
| title_full_unstemmed | Elman neural network optimized by firefly algorithm for forecasting China's carbon dioxide emissions |
| title_short | Elman neural network optimized by firefly algorithm for forecasting China's carbon dioxide emissions |
| title_sort | elman neural network optimized by firefly algorithm for forecasting china s carbon dioxide emissions |
| topic | Carbon dioxide emissions forecasting model Elman neural network firefly algorithm |
| url | http://dx.doi.org/10.1080/21642583.2019.1620655 |
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