摘要
识别我国碳价影响因素并分析其对碳价的预测作用,对助推经济社会发展的全面绿色转型具有重要的理论和现实意义。本文以我国碳排放配额成交量和成交额最大的湖北碳排放权交易市场碳价为样本,对可能影响我国碳价的相关因素进行梳理,从五个维度筛选出九个重要影响因素,同时运用各类经典预测模型和动态模型选择(Dynamic model selection,DMS)及动态模型平均(Dynamic model averaging,DMA)方法对我国碳价进行了预测对比研究,并分析了各类影响因素预测作用的时变特征。结果表明:一方面,在所选五类影响因素中,经济形势、金融市场走势、国际碳价和大气环境对我国碳价的影响较大,且可以提供较好的预测作用;而国际化石能源价格对我国碳价的影响力在逐步下降。另一方面,与传统计量模型相比,DMS可以为我国碳价提供更高的预测精度。上述结论可以为我国政府监管政策的制定和相关企业的碳交易决策提供参考。
The identification of influencing factors for carbon price in China and the analysis of their predictive effects have crucial theoretical and empirical significance to promote the overall green transformation of economic and social development.Since the carbon market in Hubei province has the highest trading volume and transaction among the seven pilot carbon markets in China,we take Hubei’s carbon prices as the sample of empirical research.In this paper,the influencing factors that may affect the carbon price in China are sorted out,and nine important factors are selected from five dimensions.Furthermore,carbon price forecasting comparison is also made between various classical prediction models,dynamic model selection(DMS),and dynamic model averaging(DMA)approaches.The time-varying characteristics of the predictive power of various influencing factors are also analyzed.The empirical results show that,on the one hand,the domestic economic situation,financial market trend,international carbon price,and atmospheric environment quality have greater influences on China’s carbon price and can provide better predictive accuracy;while the influence of international fossil energy prices on China’s carbon price is gradually reducing.On the other hand,the dynamic model selection method can produce the most accurate forecasts of carbon price in China compared to other commonly used forecasting methods.The above findings provide vital scientific basis and references for the formulation of government regulatory policies and the carbon trading decisions of relevant enterprises.
作者
魏宇
张佳豪
陈晓丹
WEI Yu;ZHANG Jia-hao;CHEN Xiao-dan(School of Finance,Yunnan University of Finance and Economics,Kunming 650221,China)
出处
《系统工程》
北大核心
2022年第4期1-16,共16页
Systems Engineering
基金
国家自然科学基金资助项目(71671145,71971191)
云南省高校科技创新团队项目(201914)
云南省科技计划基础研究重点项目(202001AS070018)。
关键词
碳交易价格
预测
影响因素
动态模型选择
动态模型平均
Carbon Trading Orice
Forecasting
Influencing Factors
Dynamic Model Selection
Dynamic Model Averaging
