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基于注意力机制组合模型的全国碳价分析及预测
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Abstract:
碳排放权交易价格是碳交易市场的核心要素，为了帮助企业、投资者和政府优化碳市场参与行为，需要对碳排放权交易价格进行合理有效的预测。本文利用皮尔逊相关系数法(PCC)提取碳价关键影响因素，然后运用由Atrous Spatial Pyramid Pooling (ASPP)、以Sophia为优化器的LSTM模型和集成学习XGBoost模型组合而成，以及添加了基于注意力机制Efficient Multi-Scale Attention (EMA)的组合模型，对全国碳市场交易价格进行预测，并与单一模型对比，通过模型预测值的MSE值、RMSE值、MAE值和R2值对比预测精度，检验组合模型的有效性。对比结果表明：注意力机制组合模型的预测精度最高，是一种有效的且精度高的碳价预测模型。
Carbon emission rights trading price is the core element of carbon trading market, in order to help enterprises, investors and governments optimize the carbon market participation behavior, it is necessary to make reasonable and effective prediction of carbon emission rights trading price. In this paper, the Pearson Correlation Coefficient (PCC) method is used to extract the key influencing factors of carbon price, and then a combination of Atrous Spatial Pyramid Pooling (ASPP), LSTM model with Sophia as the optimizer, and Integrated Learning XGBoost model is applied, as well as the addition of an attention-based mechanism Efficient Multi-Scale Attention (EMA) combined model to predict the trading price in the national carbon market, and compared with a single model to test the effectiveness of the combined model by comparing the prediction accuracy with the MSE, RMSE, MAE and R2 values of the model prediction values. The comparison results show that the combined model of attention mechanism has the highest prediction accuracy and is an effective and highly accurate carbon price prediction model.
| [1] | Bredin, D. and Muckley, C. (2011) An Emerging Equilibrium in the EU Emissions Trading Scheme. Energy Economics, 33, 353-362. https://doi.org/10.1016/j.eneco.2010.06.009 |
| [2] | Aatola, P., Ollikainen, M. and Toppinen, A. (2013) Price Determination in the EU ETS Market: Theory and Econometric Analysis with Market Fundamentals. Energy Economics, 36, 380-395. https://doi.org/10.1016/j.eneco.2012.09.009 |
| [3] | 张玲, 曹峰. 环境规制对碳交易价格波动的影响分析[J]. 价格月刊, 2023(1): 1-7. |
| [4] | 姜瑜, 吴哲宇. 我国碳排放权交易价格影响因素回归分析[J]. 环境与可持续发展, 2021, 46(1): 77-83. |
| [5] | 王小燕, 周思敏, 徐晓莉, 等. 基于图结构自适应Lasso的碳排放权价格影响因素分析[J]. 统计与信息论坛, 2022, 37(4): 73-83. |
| [6] | 吴慧娟, 张智光. 城市碳价的时空特征及其形成机理的理论模型——基于8个地区碳交易试点的价格数据[J]. 现代城市研究, 2021, 36(1): 19-24. |
| [7] | Zhang, J. and Xu, Y. (2020) Research on the Price Fluctuation and Risk Formation Mechanism of Carbon Emission Rights in China Based on a GARCH Model. Sustainability, 12, Article 4249. https://doi.org/10.3390/su12104249 |
| [8] | Jiang, L. and Wu, P. (2016) International Carbon Market Price Forecasting Research Based on ARIMA-RF Model. Proceedings of the 2016 International Conference on Strategic Management, Sichuan, November 2015, 1089-1092. |
| [9] | 王娜. 基于Boosting-ARMA的碳价预测[J]. 统计与信息论坛, 2017, 32(3): 28-34. |
| [10] | Zhu, B., Han, D., Wang, P., Wu, Z., Zhang, T. and Wei, Y. (2017) Forecasting Carbon Price Using Empirical Mode Decomposition and Evolutionary Least Squares Support Vector Regression. Applied Energy, 191, 521-530. https://doi.org/10.1016/j.apenergy.2017.01.076 |
| [11] | 金林, 马忠芸, 王红红. 基于灰色BP神经网络碳排放交易价格预测[J]. 河北环境工程学院学报, 2020, 30(1): 27-32, 41. |
| [12] | Li, H., Huang, X., Zhou, D., Cao, A., Su, M., Wang, Y., et al. (2022) Forecasting Carbon Price in China: A Multimodel Comparison. International Journal of Environmental Research and Public Health, 19, Article 6217. https://doi.org/10.3390/ijerph19106217 |
| [13] | 朱亮亮, 肖楚鹏, 余梦, 等. 基于PCC-LSTM模型的全国碳排放权交易价格预测研究[J]. 环境保护科学, 2023, 49(5): 55-62, 129. |
| [14] | 呼雪芳. 基于CEEMDAN分解的碳交易价格组合预测模型研究[D]: [硕士学位论文]. 广州: 华南理工大学, 2021. |
| [15] | 高长征, 李东伟, 王秀娜, 等. 利用智能机器学习方法对区域碳排放权交易价格预测研究——基于湖北碳市场数据的分析[J]. 价格理论与实践, 2022(4): 89-93, 205. |
| [16] | Wang, J., Zhu, H., Cheng, F., Zhou, C., Zhang, Y., Xu, H., et al. (2023) A Novel Wind Power Prediction Model Improved with Feature Enhancement and Autoregressive Error Compensation. Journal of Cleaner Production, 420, Article 138386. https://doi.org/10.1016/j.jclepro.2023.138386 |
| [17] | Du, X., Sun, Y., Zhang, X. and Zhang, R. (2023) Research on the Impact of Energy Price on Carbon Emission Intensity of China—An Empirical Study Based on LMDI Decomposition and Econometric Models. Sustainability, 15, Article 8528. https://doi.org/10.3390/su15118528 |
| [18] | Liu, H., Zhao, T., Wang, S. and Li, X. (2023) A Stock Rank Prediction Method Combining Industry Attributes and Price Data of Stocks. Information Processing & Management, 60, Article 103358. https://doi.org/10.1016/j.ipm.2023.103358 |
| [19] | 洪涓, 陈静. 我国碳交易市场价格影响因素分析[J]. 价格理论与实践, 2009(12): 65-66. |
| [20] | Ge, Q. (2025) Enhancing Stock Market Forecasting: A Hybrid Model for Accurate Prediction of S&P 500 and CSI 300 Future Prices. Expert Systems with Applications, 260, Article 125380. https://doi.org/10.1016/j.eswa.2024.125380 |
| [21] | Galati, G. and Ho, C. (2003) Macroeconomic News and the Euro/Dollar Exchange Rate. Economic Notes, 32, 371-398. https://doi.org/10.1111/1468-0300.00118 |
| [22] | Arlinghaus, J. (2015) Impacts of Carbon Prices on Indicators of Competitiveness: A Review of Empirical Findings. OECD Environment Working Papers 87, OECD Publishing. |
| [23] | Sun, C., Ding, D., Fang, X., Zhang, H. and Li, J. (2019) How Do Fossil Energy Prices Affect the Stock Prices of New Energy Companies? Evidence from Divisia Energy Price Index in China’s Market. Energy, 169, 637-645. https://doi.org/10.1016/j.energy.2018.12.032 |
| [24] | Hambling, T., Weinstein, P. and Slaney, D. (2011) A Review of Frameworks for Developing Environmental Health Indicators for Climate Change and Health. International Journal of Environmental Research and Public Health, 8, 2854-2875. https://doi.org/10.3390/ijerph8072854 |
| [25] | Pratama, I., Permanasari, A.E., Ardiyanto, I. and Indrayani, R. (2016) A Review of Missing Values Handling Methods on Time-Series Data. 2016 International Conference on Information Technology Systems and Innovation (ICITSI), Bandung, 24-27 October 2016, 1-6. https://doi.org/10.1109/icitsi.2016.7858189 |
| [26] | Ji, C., Hu, Y. and Tang, B. (2018) Research on Carbon Market Price Mechanism and Influencing Factors: A Literature Review. Natural Hazards, 92, 761-782. https://doi.org/10.1007/s11069-018-3223-1 |
| [27] | Abounaima, M.C., Mazouri, F.Z.E., Lamrini, L., Nfissi, N., Makhfi, N.E. and Ouzarf, M. (2020) The Pearson Correlation Coefficient Applied to Compare Multi-Criteria Methods: Case the Ranking Problematic. 2020 1st International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), Meknes, 16-19 April 2020, 1-6. https://doi.org/10.1109/iraset48871.2020.9092242 |
| [28] | Liu, H., Li, Z., Hall, D., et al. (2023) Sophia: A Scalable Stochastic Second-Order Optimizer for Language Model Pre-training. arXiv:2305.14342. |
| [29] | Hochreiter, S. and Schmidhuber, J. (1997) Long Short-Term Memory. Neural Computation, 9, 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735 |
| [30] | Kingma, D.P. and Ba, J. (2014) Adam: A Method for Stochastic Optimization. arXiv:1412.6980. |
| [31] | Galántai, A. (2000) The Theory of Newton’s Method. Journal of Computational and Applied Mathematics, 124, 25-44. https://doi.org/10.1016/s0377-0427(00)00435-0 |
| [32] | Klinker, F. (2011) Exponential Moving Average versus Moving Exponential Average. Mathematische Semesterberichte, 58, 97-107. https://doi.org/10.1007/s00591-010-0080-8 |
| [33] | Wang, Y., Qin, L., Wang, Q., Chen, Y., Yang, Q., Xing, L., et al. (2023) A Novel Deep Learning Carbon Price Short-Term Prediction Model with Dual-Stage Attention Mechanism. Applied Energy, 347, Article 121380. https://doi.org/10.1016/j.apenergy.2023.121380 |
| [34] | Lin, X., Zhu, X., Feng, M., Han, Y. and Geng, Z. (2021) Economy and Carbon Emissions Optimization of Different Countries or Areas in the World Using an Improved Attention Mechanism Based Long Short Term Memory Neural Network. Science of The Total Environment, 792, Article 148444. https://doi.org/10.1016/j.scitotenv.2021.148444 |
| [35] | Niu, Z., Zhong, G. and Yu, H. (2021) A Review on the Attention Mechanism of Deep Learning. Neurocomputing, 452, 48-62. https://doi.org/10.1016/j.neucom.2021.03.091 |
| [36] | Johnston, W.A. and Dark, V.J. (1986) Selective Attention. Annual Review of Psychology, 37, 43-75. https://doi.org/10.1146/annurev.ps.37.020186.000355 |
| [37] | Woo, S., Park, J., Lee, J. and Kweon, I.S. (2018) CBAM: Convolutional Block Attention Module. In: Ferrari, V., Hebert, M., Sminchisescu, C. and Weiss, Y., Eds., Lecture Notes in Computer Science, Springer International Publishing, 3-19. https://doi.org/10.1007/978-3-030-01234-2_1 |
| [38] | Hou, Q., Zhou, D. and Feng, J. (2021) Coordinate Attention for Efficient Mobile Network Design. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, 20-25 June 2021, 13708-13717. https://doi.org/10.1109/cvpr46437.2021.01350 |
| [39] | Ouyang, D., He, S., Zhang, G., Luo, M., Guo, H., Zhan, J., et al. (2023) Efficient Multi-Scale Attention Module with Cross-Spatial Learning. ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, 4-10 June 2023, 1-5. https://doi.org/10.1109/icassp49357.2023.10096516 |
| [40] | Li, X., Hu, X. and Yang, J. (2019) Spatial Group-Wise Enhance: Improving Semantic Feature Learning in Convolutional Networks. arXiv:1905.09646. |
| [41] | Liu, T., Luo, R., Xu, L., Feng, D., Cao, L., Liu, S., et al. (2022) Spatial Channel Attention for Deep Convolutional Neural Networks. Mathematics, 10, Article 1750. https://doi.org/10.3390/math10101750 |
| [42] | Chen, Y., Kalantidis, Y., Li, J., et al. (2018) A2-Nets: Double Attention Networks. Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montréal, 3-8 December 2018, 350-359. |
| [43] | Chen, L., Papandreou, G., Kokkinos, I., Murphy, K. and Yuille, A.L. (2018) Deeplab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848. https://doi.org/10.1109/tpami.2017.2699184 |
| [44] | He, K., Zhang, X., Ren, S. and Sun, J. (2015) Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 1904-1916. https://doi.org/10.1109/tpami.2015.2389824 |
