Predicting Carbon Prices: A New Approach with Dual Decomposition

Forecasting carbon prices is vital for smart government decisions and stable carbon markets. But with so many factors at play, from economics to politics, predictions often go awry. This is where a new model comes in, blending dual decomposition and error correction. First, the model breaks down carbon price patterns into simpler parts using the sparrow search algorithm. Then, it classifies these parts based on their complexity. For tricky parts, it uses a special type of machine learning called long short-term memory networks, optimized by a whale algorithm.

For simpler parts, it uses extreme learning machines, which are faster but less powerful. To make predictions even better, the model then checks its errors and corrects them. It did all this using real data from Chinese carbon exchanges. Compared to other models, it performed much better, boosting prediction accuracy by at least 19. 89% on average. This makes it a reliable tool for carbon market players and climate policy makers.

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