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碳交易是实现双碳目标的重要金融工具，准确预测碳价可以帮助政策制定者建立稳定有效的碳定价机制。本文提出基于动态多元网络的预测模型，考虑响应变量网络的动态性、内生性和多元性，具有更广的适用性。利用百度搜索指数、资讯指数、能源价格、经济政策不确定性指数、汇率、环境意识和人均GDP等不同类型的变量，对我国8个碳交易试点的碳价进行预测。实证结果显示，本文构建的DMNP模型具有较好的预测效果，明显优于对比模型，并能够达到降维的目的，为我国碳交易提供了更科学合理的碳价预测新方法及实证基础。
Abstract：Carbon trading is an important financial tool to achieve the targets of carbon peak and carbon neutrality. Accurate prediction of carbon price can help policy makers establish a stable and effective carbon pricing mechanism. In this paper, a prediction model based on dynamic multivariate network is proposed, which considers the dynamics, endogeneity and multivariate of response variable network, and has wider applicability. Using different types of variables such as Baidu search index,information index, energy prices, economic policy uncertainty index, exchange rates, environmental awareness and GDP per capita, we predict the carbon price of China's eight carbon trading pilots. The empirical results show that the DMNP model constructed in this paper has an obviously better prediction effect than the comparison model, and can achieve the purpose of dimension reduction. It provides a more scientific and reasonable new method and empirical basis for carbon price prediction in China's carbon trading.
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(1)因篇幅所限，国内碳价趋势以附图1展示，见《统计研究》网站所列附件。
(2)碳价数据均取自Wind数据库。
(3)数据使用爬虫技术取自百度指数官网：https://index.baidu.com/。
(1)能源价格均取自Wind数据库。
(2)中国经济政策不确定性指数数据取自：https://economicpolicyuncertaintyinchina.weebly.com。英国、美国经济政策不确定性指数取自：http://www.policyuncertainty.com。
(3)汇率均取自Wind数据库。
(1)数据取自中国国家调查数据库：http://cnsda.ruc.edu.cn。
(2)各省份人均GDP均取自Wind数据库。
基本信息:
DOI：10.19343/j.cnki.11-1302/c.2023.01.004
中图分类号:F832.5;X196
引用信息:
[1]王娜.基于动态多元网络的中国碳价预测[J].统计研究,2023,40(01):49-61.DOI:10.19343/j.cnki.11-1302/c.2023.01.004.
2022-12-30
2022-12-30
2022-12-30