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上海碳排放权配额价格影响因素——基于VAR模型
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Abstract:
碳市场的有效建立对于我国达成“双碳”目标至关重要，然而碳排放权配额价格影响因素众多。本文采用向量自回归模型(VAR)，以上海碳排放权配额(SHEA)成交均价为研究对象，从能源价格、宏观经济状况、环境因素、公众情绪四个方面分别选取相应指标进行研究。结果表明，能源价格类影响因素对上海碳排放权配额价格的影响均为正向；宏观经济状况方面，沪深300指数对上海碳排放权配额价格表现为先呈现负面影响，后转为正面影响，汇率则相反，首先呈现正面影响，而后转负；环境因素未能通过格兰杰因果检验，即与被解释变量不存在因果关系；公众情绪对上海碳排放权配额价格波动影响较为明显，且时间较长。本文实证结果可以帮助投资者正确认识碳排放权配额价格变化，为政策制定者提供参考，以及为进一步构建反映真实、全面信息的全国碳排放权交易市场提供依据。
It is crucial for China to achieve its “dual carbon” goals through the establishment of an efficient carbon market mechanism. However, there are many factors that influence the price of carbon emission quotas. This study utilizes a Vector Autoregressive (VAR) model to investigate the average transaction price of Shanghai Emission Allowances (SHEA) from four aspects, energy prices, macroeconomic conditions, environmental factors, and public sentiment. The results indicate that energy price-related factors all have a positive impact on the price of SHEA; Concerning macroeconomic conditions, CSI 300 Index initially has a negative impact on the price of SHEA, which later turns positive, while the exchange rate shows the opposite trend, starting with a positive impact and then turning negative; Environmental factors did not show causal relationships with the explained variable as per Granger causality tests; Public sentiment has a significant and long-lasting impact on the fluctuation of SHEA. The empirical results of this study can help investors understand the changes in carbon emission quota prices correctly, provide references for policymakers, and serve as a basis for further establishing a nationwide carbon emission trading market reflecting true and comprehensive information.
| [1] | 易兰, 李朝鹏, 杨历, 等. 中国7大碳交易试点发育度对比研究[J]. 中国人口·资源与环境, 2018, 28(2): 134-140. |
| [2] | 陈星星. 中国碳排放权交易市场: 成效、现实与策略[J]. 东南学术, 2022(4): 167-177. |
| [3] | Lin, Y.L., Jie, J.Z. and Yan. F. (2023) The Driving Factors of China’s Carbon Prices: Evidence from Using ICEEMDAN-HC Method and Quantile Regression. Finance Research Letters, 54, Article ID: 103756. https://doi.org/10.1016/j.frl.2023.103756 |
| [4] | Yang, M., Zhu, S. and Li, W. (2022) Carbon Price Prediction Based on Multi-Factor MEEMD-LSTM Model. Heliyon, 8, e12562. https://doi.org/10.1016/j.heliyon.2022.e12562 |
| [5] | Wei, Q., Bian, Y. and Yang, X. (2020) Influencing Factors of Price Fluctuation in China’s Carbon Market. E3S Web of Conferences, 218, Article No. 01044. https://doi.org/10.1051/e3sconf/202021801044 |
| [6] | Lei, H., Xue, M. and Liu, H. (2022) Probability Distribution Forecasting of Carbon Allowance Prices: A Hybrid Model Considering Multiple Influencing Factors. Energy Economics, 113, Article ID: 106189. https://doi.org/10.1016/j.eneco.2022.106189 |
| [7] | 熊萍萍, 王亚琦. 中国煤炭价格与碳排放权交易价格的传导路径研究[J]. 价格月刊, 2024(2): 11-20. |
| [8] | 蔡彤娟, 林润红, 张旭. 中欧碳排放权交易的市场化比较——基于国家金融学视角[J]. 金融经济学研究, 2023, 38(2): 127-143. |
| [9] | Naik, N., Mohan, R.B. and Jha, A.R. (2020) GARCH-Model Identification Based on Performance of Information Criteria. Procedia Computer Science, 171, 1935-1942. https://doi.org/10.1016/j.procs.2020.04.207 |
| [10] | Wang, J., Zhuang, Z. and Gao, D. (2023) An Enhanced Hybrid Model Based on Multiple Influencing Factors and Divide-Conquer Strategy for Carbon Price Prediction. Omega, 120, Article ID: 102922. https://doi.org/10.1016/j.omega.2023.102922 |
| [11] | 王庆山, 李健. 基于时变参数模型的中国区域碳排放权价格调控机制研究[J]. 中国人口·资源与环境, 2016, 26(1): 31-38. |
| [12] | Zhao, X., Han, M., et al. (2018) Usefulness of Economic and Energy Data at Different Frequencies for Carbon Price Forecasting in the EU ETS. Applied Energy, 216, 132-141. https://doi.org/10.1016/j.apenergy.2018.02.003 |
| [13] | 魏宇, 张佳豪, 陈晓丹. 基于DMS和DMA的我国碳排放权交易价格预测方法——来自湖北碳市场的经验证据[J]. 系统工程, 2022, 40(4): 1-16. |
| [14] | Da, Z., Engelberg, J. and Gao, P. (2011) In Search of Attention. Journal of Finance, 66, 1461-1499. https://doi.org/10.1111/j.1540-6261.2011.01679.x |
