Forecasting China's agricultural carbon emissions: A comparative study based on deep learning models
文献类型：期刊论文
| 作者 | Xie, Tiantian1,2; Huang, Zetao3; Tan, Tao3,4; Chen, Yong3,4,5 |
| 刊名 | ECOLOGICAL INFORMATICS
 |
| 出版日期 | 2024-09-01 |
| 卷号 | 82页码:13 |
| 关键词 | Forecast Agricultural carbon emissions Deep learning Long short-term memory neural network optimization |
| ISSN号 | 1574-9541 |
| DOI | 10.1016/j.ecoinf.2024.102661 |
| 通讯作者 | Tan, Tao(tantao@scau.edu.cn) |
| 英文摘要 | Given the critical urgency to combat the escalating climate crisis and the continuous rise in agricultural carbon emissions (ACE) in China, accurately forecasting their future trends is crucial. This research employs the emission factor method to assess ACE throughout mainland China from 1993 to 2021. To refine our forecasting approach, both statistical and neural network methodologies were utilized to pinpoint key factors influencing ACE. We crafted forecasting models incorporating both deep learning techniques and traditional methods. Notably, the Tree-structured Parzen Estimator Bayesian Optimization (TPEBO) algorithm was applied to optimize Long ShortTerm Memory (LSTM) neural networks, culminating in the creation of a superior integrated TPEBO-LSTM model that demonstrated strong performance across various datasets. The forecasting outcomes suggest that ACE in 24 provinces are expected to reach their zenith before 2030, primarily driven by farm operations, as well as livestock and poultry manure management. The result provides a significant forecasting tool for assessing agricultural carbon emissions in different regions, offering insights crucial for targeted mitigation strategies. |
| 资助项目 | National Key Research and Development Program of China[2023YFC3905802] ; China Scholarship Council[201708070092] |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001251851700001 |
| 出版者 | ELSEVIER |
| 资助机构 | National Key Research and Development Program of China ; China Scholarship Council |
| 源URL | [http://ir.giec.ac.cn/handle/344007/42168]  |
| 专题 | 中国科学院广州能源研究所 |
| 通讯作者 | Tan, Tao |
| 作者单位 | 1.South China Agr Univ, Inst New Rural Dev, Guangzhou 510642, Peoples R China 2.Univ Paris Cite, Ctr Rech Liens Sociaux CERLIS, F-75005 Paris, France 3.South China Agr Univ, Inst Biomass Engn, Guangzhou 510642, Peoples R China 4.Nanjing Tech Univ, Sch Mech & Power Engn, Nanjing 211816, Peoples R China 5.Chinese Acad Sci, Guangzhou Inst Energy Convers, Guangzhou 510640, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xie, Tiantian,Huang, Zetao,Tan, Tao,et al. Forecasting China's agricultural carbon emissions: A comparative study based on deep learning models[J]. ECOLOGICAL INFORMATICS,2024,82:13. |
| APA | Xie, Tiantian,Huang, Zetao,Tan, Tao,&Chen, Yong.(2024).Forecasting China's agricultural carbon emissions: A comparative study based on deep learning models.ECOLOGICAL INFORMATICS,82,13. |
| MLA | Xie, Tiantian,et al."Forecasting China's agricultural carbon emissions: A comparative study based on deep learning models".ECOLOGICAL INFORMATICS 82(2024):13. |
入库方式： OAI收割
来源：广州能源研究所
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