Forecasting carbon price: A novel multi-factor spatial-temporal GNN framework integrating Graph WaveNet and self-attention mechanism

Jin-Hui Cao, Chi Xie, Yang Zhou, Gang-Jin Wang and You Zhu

Energy Economics, 2025, vol. 144, issue C

Abstract: Precisely forecasting carbon price helps to make comprehensive plans for promoting green development. However, the carbon price is affected by many factors that are not isolated but influences each other, including energy, international carbon allowance, stock, foreign exchange and metal price. The common multivariate forecasting methods assume that each factor plays an equally important role, but they fail to (i) dynamically distinguish the relative importance of these factors; (ii) timely capture the time-varying interactions among factors; and (iii) selectively aggregate the information from different types of factors. To overcome this obstacle, we present a novel multi-factor spatial-temporal GNN framework integrating Graph WaveNet and self-attention mechanism, which incorporates factor interactions. In our empirical analysis, we take the Hubei emission allowances (HBEA) price as predictive target, and investigate how the carbon price is affected by various factors. We find that, on the one hand, our framework significantly performs better than the baseline models, and the aforementioned interactions obviously change when major events occur; on the other hand, European Union Allowance (EUA), the steel rebar futures and natural gas futures exert considerable influence on HBEA, while the foreign exchange rate and stock index are not crucial factors that explain the variation in the carbon price.

Keywords: Carbon price forecast; Spatial-temporal graph neural network; Factors interaction; Graph WaveNet; Self-attention mechanism (search for similar items in EconPapers)
JEL-codes: G11 G15 G17 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:144:y:2025:i:c:s0140988325001410

DOI: 10.1016/j.eneco.2025.108318

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