Application of Dynamic Weight Mixture Model Based on Dual Sliding Windows in Carbon Price Forecasting

Rujie Liu, Wei He, Hongwei Dong, Tao Han, Yuting Yang, Hongwei Yu and Zhu Li ()
Additional contact information
Rujie Liu: Three Gorges Electric Power Co., Ltd., Wuhan 430021, China
Wei He: Three Gorges Electric Power Co., Ltd., Wuhan 430021, China
Hongwei Dong: Three Gorges Electric Power Co., Ltd., Wuhan 430021, China
Tao Han: Three Gorges Electric Power Co., Ltd., Wuhan 430021, China
Yuting Yang: Three Gorges Electric Power Co., Ltd., Wuhan 430021, China
Hongwei Yu: Institute of Quality Development Strategy, Wuhan University, Wuhan 430072, China
Zhu Li: Electronic Information School, Wuhan University, Wuhan 430072, China

Energies, 2024, vol. 17, issue 15, 1-18

Abstract: As global climate change intensifies, nations around the world are implementing policies aimed at reducing emissions, with carbon-trading mechanisms emerging as a key market-based tool. China has launched carbon-trading markets in several cities, achieving significant trading volumes. Carbon-trading mechanisms encompass cap-and-trade markets and voluntary markets, influenced by various factors, including policy changes, economic conditions, energy prices, and climate fluctuations. The complexity of these factors, coupled with the nonlinear and non-stationary nature of carbon prices, makes forecasting a substantial challenge. This paper proposes a dynamic weight hybrid forecasting model based on a dual sliding window approach, effectively integrating multiple forecasting models such as LSTM, Random Forests, and LASSO. This model facilitates a thorough analysis of the influences of policy, market dynamics, technological advancements, and climatic conditions on carbon pricing. It serves as a potent tool for predicting carbon market price fluctuations and offers valuable decision support to stakeholders in the carbon market, ultimately aiding in the global efforts towards emission reduction and achieving sustainable development goals.

Keywords: carbon trading; carbon price prediction; hybrid prediction model; sustainable development (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/15/3662/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/15/3662/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:15:p:3662-:d:1442624

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().