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To further enhance the precision of carbon trading price forecasting, this research proposes a combined forecasting model, CEEMDAN–VMD–IPSO–BiLSTM, considering the unsatisfactory high‐frequency sequence decomposition and the reliance on unidirectional neural networks in current carbon price‐prediction models. First of all, the original sequence of carbon prices is decomposed into multiple independent subsequences through the completely ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique. The sample entropy values of each subsequence are calculated to reconstruct them as high‐frequency, low‐frequency, and trend sequences. Second, we employ the variational mode decomposition (VMD) approach to decompose the high‐frequency series. The obtained subsequences, along with the low‐frequency and trend sequences, are separately input into an improved particle swarm optimization (IPSO) optimized bidirectional long short‐term memory neural network (BiLSTM) model for forecasting. Finally, an IPSO–BiLSTM model is used to integrate the forecasting outcomes from the previous step, yielding the ultimate results for predicting carbon prices. The case studies reveal that compared with the benchmark model, this model exhibits superior predictive precision and universality. It offers theoretical support for optimizing carbon market operations and fostering low‐carbon economic growth, holding practical importance. [ABSTRACT FROM AUTHOR]

Global climate change has become the greatest threat to mankind, endangering the ecological security of the earth and the long-term development of human society. Therefore, how to effectively reduce greenhouse gas emissions and curb the trend of global warming has become a common challenge facing all countries in the world. Fossil energy combustion is the fundamental cause of climate change. In this paper, the price return series of domestic energy and carbon markets are taken as the research objects. Firstly, the cross-correlation test is used to verify that there is an obvious cross-correlation between the return series of the energy and carbon markets. Based on this, the multifractal characteristics of energy market and carbon market cross-series are empirically studied. The empirical results show that there is an interactive correlation between energy and carbon markets and this relationship has multifractal characteristics; the interactive correlation between return series has multifractal characteristics. The long-range correlation between small fluctuations and large fluctuations and the fat tail distribution of return series are all reasons for the formation of multifractality. These conclusions will help to understand the non-linear dependence and potential dynamic mechanism between energy and carbon markets in China. [ABSTRACT FROM AUTHOR]

With the development of carbon market, the complex dynamic relationship between electricity and carbon market has become the focus of energy research area. In this paper, we applied a new developed multifractal detrended cross-correlation analysis method to investigate the cross-correlation and multifractality between electricity and carbon markets. We analyze the daily return of electricity and carbon prices over a period of 6 years to do the research. The results show that, firstly, we find that there is a strong negative correlation between domestic carbon price and electricity price and a significant cross-correlation between the return series of electricity and carbon markets. Secondly, through multifractal detrended fluctuation analysis, it is proven that there are obvious multifractal characteristics in the return series of electricity and carbon markets, and the results of traditional linear analysis are unreliable. We also find that, based on multifractal detrended cross-correlation analysis, the law cross-correlation between electricity and carbon markets exists significantly. The long-range correlation of small fluctuations and large fluctuations and the fat tail distribution of return series are the reasons for the formation of multifractality. [ABSTRACT FROM AUTHOR]