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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]

Digital economy is the indispensable pathway for driving industrial structural upgrading and fostering innovation and entrepreneurship in cities, ultimately facilitating China's economic transformation. Simultaneously, the enhancement of urban total factor productivity (TFP) serves as a crucial means to achieve high-quality economic development in cities. This study examines the specific impact of the digital economy on urban TFP using a panel data model with a sample of 285 Chinese prefecture-level cities from 2011 to 2019. Additionally, it employs a mediation effect model to test the mechanisms through which the digital economy influences urban TFP and utilizes a spatial Durbin model to analyze the spatial spillover effects of the digital economy on urban TFP. The research findings reveal the following key points: (1)The digital economy has an overall significantly positive impact on urban TFP. (2)The digital economy indirectly promotes urban TFP by encouraging the advancement of industrial structure and fostering innovation and entrepreneurship in cities. (3)The influence of the digital economy on urban TFP exhibits spatial spillover effects, where the digital economy in neighboring cities significantly enhances the TFP growth of local cities.The results of this study contribute to elucidating the mechanistic pathways through which the digital economy affects urban TFP, holding significant practical implications for achieving high-quality economic development in urban areas. [ABSTRACT FROM AUTHOR]

According to the proposal of Kyoto protocol, carbon dioxide emission rights are traded as a commodity, and carbon emission trading market emerges as the times require. As the world's largest carbon emitter, China has established eight pilot markets for carbon emission trading. Selecting the closing price of eight carbon trading markets from the establishment to June 23, 2020, this paper analyzes the daily, weekly, and monthly return series data, using the first-order autoregressive process to adjust the daily income series to eliminate the weak trading market effect and then comprehensively uses the traditional variance ratio test and multiple variance ratio test to analyze the weak-form market efficiency of the eight carbon trading markets. The empirical results show that most of the carbon trading markets are non-weak-form market efficiency, and only Tianjin, Shanghai, and Hubei markets are weak-form market efficiency under the daily trading data. However, with the increase of carbon holding period, the weak-form market efficiency continues to strengthen. It shows that liquidity, quantity, and information transparency are important factors that affect the market efficiency. [ABSTRACT FROM AUTHOR]

Under the situation of global low-carbon development, the contradiction among energy consumption, economic growth, and CO2 emissions is increasingly prominent. Considering the possible two-way feedback among the three, based on the panel data of 30 regions in China from 2000 to 2017, this paper establishes a spatial Durbin model including economic growth, energy consumption equation, and CO2 emissions and studies the dynamic relationship and spatial spillover among economic growth, energy consumption, and CO2 emissions effects. The results show that the economic growth can significantly improve carbon dioxide emissions, and China's economic growth level has become a positive driving force for carbon dioxide emissions. However, economic growth will not be significantly affected by the reduction of carbon dioxide emissions. There is a two-way relationship between energy consumption (ENC) and carbon dioxide emissions (CO2). Energy consumption and carbon emissions are interrelated, which has a negative spatial spillover effect on the carbon dioxide emissions of the surrounding provinces and cities. [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]