Prediction of Carbon Emissions Trading Price in Fujian Province: Based on BP Neural Network Model
To achieve carbon peak and carbon neutrality targets, it has become a common choice for all countries to introduce the carbon emissions trading market to foster low carbon sustainable development. The construction of national carbon emissions trading market in China is still in its initial stage. Ho...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-07-01
|
| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2022.939602/full |
| _version_ | 1811325718336897024 |
|---|---|
| author | Yi Du Keren Chen Simin Chen Kai Yin |
| author_facet | Yi Du Keren Chen Simin Chen Kai Yin |
| author_sort | Yi Du |
| collection | DOAJ |
| description | To achieve carbon peak and carbon neutrality targets, it has become a common choice for all countries to introduce the carbon emissions trading market to foster low carbon sustainable development. The construction of national carbon emissions trading market in China is still in its initial stage. However, the carbon market in Fujian province has already accumulated certain experience, and its unique energy mix of “higher share of the clean energy and low share of fossil fuels consumption” can provide guidance to China’s future development. Therefore, an accurate forecast of the carbon price in Fujian province not only provides conducive suggestions for the further optimization of the carbon market in Fujian province, but also offers a significant reference for the development of China’s carbon trading market. By adopting the effective daily data from 2017.01 to 2022.02, this paper predicts the carbon emissions trading price in Fujian province based on the BP neural network model and analyzes the mechanism of different influencing factors on carbon price from six dimensions. The results show that the BP neural network model works well in predicting carbon price in Fujian province and in the impact mechanism analysis. This paper also puts forward corresponding policy recommendations, which provide theoretical support for the sound development of the carbon market in Fujian province. |
| first_indexed | 2024-04-13T14:37:45Z |
| format | Article |
| id | doaj.art-4d30329fd24746b5af34a8c0718bef99 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-04-13T14:37:45Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-4d30329fd24746b5af34a8c0718bef992022-12-22T02:42:59ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2022-07-011010.3389/fenrg.2022.939602939602Prediction of Carbon Emissions Trading Price in Fujian Province: Based on BP Neural Network ModelYi Du0Keren Chen1Simin Chen2Kai Yin3Economic and Technological Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou, ChinaEconomic and Technological Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou, ChinaEconomic and Technological Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou, ChinaChina Center for Energy Economics Research, School of Economics, Xiamen University, Xiamen, ChinaTo achieve carbon peak and carbon neutrality targets, it has become a common choice for all countries to introduce the carbon emissions trading market to foster low carbon sustainable development. The construction of national carbon emissions trading market in China is still in its initial stage. However, the carbon market in Fujian province has already accumulated certain experience, and its unique energy mix of “higher share of the clean energy and low share of fossil fuels consumption” can provide guidance to China’s future development. Therefore, an accurate forecast of the carbon price in Fujian province not only provides conducive suggestions for the further optimization of the carbon market in Fujian province, but also offers a significant reference for the development of China’s carbon trading market. By adopting the effective daily data from 2017.01 to 2022.02, this paper predicts the carbon emissions trading price in Fujian province based on the BP neural network model and analyzes the mechanism of different influencing factors on carbon price from six dimensions. The results show that the BP neural network model works well in predicting carbon price in Fujian province and in the impact mechanism analysis. This paper also puts forward corresponding policy recommendations, which provide theoretical support for the sound development of the carbon market in Fujian province.https://www.frontiersin.org/articles/10.3389/fenrg.2022.939602/fullcarbon marketcarbon emissions trading pricemechanism analysisBP neural network modelprice forecast |
| spellingShingle | Yi Du Keren Chen Simin Chen Kai Yin Prediction of Carbon Emissions Trading Price in Fujian Province: Based on BP Neural Network Model Frontiers in Energy Research carbon market carbon emissions trading price mechanism analysis BP neural network model price forecast |
| title | Prediction of Carbon Emissions Trading Price in Fujian Province: Based on BP Neural Network Model |
| title_full | Prediction of Carbon Emissions Trading Price in Fujian Province: Based on BP Neural Network Model |
| title_fullStr | Prediction of Carbon Emissions Trading Price in Fujian Province: Based on BP Neural Network Model |
| title_full_unstemmed | Prediction of Carbon Emissions Trading Price in Fujian Province: Based on BP Neural Network Model |
| title_short | Prediction of Carbon Emissions Trading Price in Fujian Province: Based on BP Neural Network Model |
| title_sort | prediction of carbon emissions trading price in fujian province based on bp neural network model |
| topic | carbon market carbon emissions trading price mechanism analysis BP neural network model price forecast |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2022.939602/full |
| work_keys_str_mv | AT yidu predictionofcarbonemissionstradingpriceinfujianprovincebasedonbpneuralnetworkmodel AT kerenchen predictionofcarbonemissionstradingpriceinfujianprovincebasedonbpneuralnetworkmodel AT siminchen predictionofcarbonemissionstradingpriceinfujianprovincebasedonbpneuralnetworkmodel AT kaiyin predictionofcarbonemissionstradingpriceinfujianprovincebasedonbpneuralnetworkmodel |