碳交易和COVID-19:用于国际碳价格预测的混合机器学习方法
Xingmin Zhang1, Zhiyong Li1,2, Yiming Zhao1
1School of Finance and Fintech Innovation Center, Southwestern University of Finance and Economics, Chengdu, China.
Annals of operations research
|June 26, 2023
概括
准确的碳价格预测对于平衡经济增长和气候影响至关重要. 这项研究引入了一个新的两阶段框架,确定支持向量回归模型作为主要市场预测碳价格的最佳表现者.
科学领域:
- 环境经济学环境经济学
- 气候政策分析 气候政策分析
- 机器学习应用 机器学习应用
背景情况:
- 准确的碳价格预测对于有效的排放分配和平衡经济发展与气候变化减缓至关重要.
- 国际碳市场,如欧盟排放交易体系 (ETS) 和中国的试点计划,需要可靠的价格预测工具.
- 现有的预测方法可能无法完全捕捉影响碳价格的复杂动态.
研究的目的:
- 开发和评估一种新的两阶段框架,用于预测国际市场上的碳价格.
- 为了比较各种机器学习和深度学习模型在碳价格预测中的性能.
- 确定在欧盟ETS和中国试点计划中预测碳价格的最有效模型.
主要方法:
- 一个两阶段的框架,涉及分解和重新估计技术.
- 单一频谱分析 (SSA) 用于将原始碳价格分解为"趋势"和"周期"的子因素.
- 应用六种机器学习和深度学习模型,包括支持向量回归 (SVR) 和最小平方支持向量回归 (LSSVR),用于价格预测.
主要成果:
- 拟议的两阶段框架有效预测跨国际市场的碳价格.
- 支持向量回归 (SSA-SVR) 和最小方位支持向量回归 (SSA-LSSVR) 在预测欧盟排放交易体系和中国市场的碳价格方面表现出色.
- 在这个预测任务中,更简单的机器学习模型往往优于更复杂的算法.
- 即使考虑到COVID-19的影响,宏观经济变量和能源价格,该框架仍然有效.
结论:
- 开发的两阶段分解和重新估计框架为碳价格预测提供了一个强大的方法.
- SSA-SVR和SSA-LSSVR是预测主要国际市场碳价格的高效模型.
- 该研究强调,高级复杂性并不一定等同于碳市场中优异的预测性能.
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