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

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.