RESEARCH ON THE DIRECT CARBON EMISSION FORECAST OF CHINA'S PROVINCIAL RESIDENTS BASED ON NEURAL NETWORK
Global climate change, which mainly effected by human carbon emissions, would affect the regional economic, natural ecological environment, social development and food security in the near future. It’s particularly important to make accurate predictions of carbon emissions based on current carbon em...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2018-04-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3/2291/2018/isprs-archives-XLII-3-2291-2018.pdf |
| _version_ | 1818162485029502976 |
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| author | T. Zhang B. Zhou S. Zhou W. Yan |
| author_facet | T. Zhang B. Zhou S. Zhou W. Yan |
| author_sort | T. Zhang |
| collection | DOAJ |
| description | Global climate change, which mainly effected by human carbon emissions, would affect the regional economic, natural ecological environment, social development and food security in the near future. It’s particularly important to make accurate predictions of carbon emissions based on current carbon emissions. This paper accounted out the direct consumption of carbon emissions data from 1995 to 2014 about 30 provinces (the data of Tibet, Hong Kong, Macao and Taiwan is missing) and the whole of China. And it selected the optimal models from BP, RBF and Elman neural network for direct carbon emission prediction, what aim was to select the optimal prediction method and explore the possibility of reaching the peak of residents direct carbon emissions of China in 2030. Research shows that: 1) Residents’ direct carbon emissions per capita of all provinces showed an upward trend in 20 years. 2) The accuracy of the prediction results by Elman neural network model is higher than others and more suitable for carbon emission data projections. 3) With the situation of residents’ direct carbon emissions free development, the direct carbon emissions will show a fast to slow upward trend in the next few years and began to flatten after 2020, and the direct carbon emissions of per capita will reach the peak in 2032. This is also confirmed that China is expected to reach its peak in carbon emissions by 2030 in theory. |
| first_indexed | 2024-12-11T16:34:25Z |
| format | Article |
| id | doaj.art-c59488cf54c5418281bbbd0cbaa081e0 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-11T16:34:25Z |
| publishDate | 2018-04-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-c59488cf54c5418281bbbd0cbaa081e02022-12-22T00:58:30ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342018-04-01XLII-32291229610.5194/isprs-archives-XLII-3-2291-2018RESEARCH ON THE DIRECT CARBON EMISSION FORECAST OF CHINA'S PROVINCIAL RESIDENTS BASED ON NEURAL NETWORKT. Zhang0B. Zhou1S. Zhou2W. Yan3College of Environment and Planning, Henan University, Kaifeng 475004, Henan, ChinaCollege of Environment and Planning, Henan University, Kaifeng 475004, Henan, ChinaCollege of Environment and Planning, Henan University, Kaifeng 475004, Henan, ChinaCollege of Environment and Planning, Henan University, Kaifeng 475004, Henan, ChinaGlobal climate change, which mainly effected by human carbon emissions, would affect the regional economic, natural ecological environment, social development and food security in the near future. It’s particularly important to make accurate predictions of carbon emissions based on current carbon emissions. This paper accounted out the direct consumption of carbon emissions data from 1995 to 2014 about 30 provinces (the data of Tibet, Hong Kong, Macao and Taiwan is missing) and the whole of China. And it selected the optimal models from BP, RBF and Elman neural network for direct carbon emission prediction, what aim was to select the optimal prediction method and explore the possibility of reaching the peak of residents direct carbon emissions of China in 2030. Research shows that: 1) Residents’ direct carbon emissions per capita of all provinces showed an upward trend in 20 years. 2) The accuracy of the prediction results by Elman neural network model is higher than others and more suitable for carbon emission data projections. 3) With the situation of residents’ direct carbon emissions free development, the direct carbon emissions will show a fast to slow upward trend in the next few years and began to flatten after 2020, and the direct carbon emissions of per capita will reach the peak in 2032. This is also confirmed that China is expected to reach its peak in carbon emissions by 2030 in theory.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3/2291/2018/isprs-archives-XLII-3-2291-2018.pdf |
| spellingShingle | T. Zhang B. Zhou S. Zhou W. Yan RESEARCH ON THE DIRECT CARBON EMISSION FORECAST OF CHINA'S PROVINCIAL RESIDENTS BASED ON NEURAL NETWORK The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | RESEARCH ON THE DIRECT CARBON EMISSION FORECAST OF CHINA'S PROVINCIAL RESIDENTS BASED ON NEURAL NETWORK |
| title_full | RESEARCH ON THE DIRECT CARBON EMISSION FORECAST OF CHINA'S PROVINCIAL RESIDENTS BASED ON NEURAL NETWORK |
| title_fullStr | RESEARCH ON THE DIRECT CARBON EMISSION FORECAST OF CHINA'S PROVINCIAL RESIDENTS BASED ON NEURAL NETWORK |
| title_full_unstemmed | RESEARCH ON THE DIRECT CARBON EMISSION FORECAST OF CHINA'S PROVINCIAL RESIDENTS BASED ON NEURAL NETWORK |
| title_short | RESEARCH ON THE DIRECT CARBON EMISSION FORECAST OF CHINA'S PROVINCIAL RESIDENTS BASED ON NEURAL NETWORK |
| title_sort | research on the direct carbon emission forecast of china s provincial residents based on neural network |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3/2291/2018/isprs-archives-XLII-3-2291-2018.pdf |
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