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This paper investigates the multi-dimensional risk spillover effects of carbon markets with energy, non-energy commodity and stock markets in China before and after the COVID-19 outbreak, through the DY network with GARCHSK-VaR method. The empirical results denote that the total bidirectional risk spillovers of carbon markets become larger after the COVID-19 outbreak than before the COVID-19 outbreak. Interestingly, in both periods, energy is the largest risk contributor to carbon markets, while carbon markets become the net risk contributors to stock markets under COVID-19. Furthermore, compared with Guangdong pilot, the impacts of Hubei pilot on other markets are stronger in both periods. Finally, the dynamic risk spillover effects have obvious differences between before and after the COVID-19 outbreak. [ABSTRACT FROM AUTHOR]

It is essential to predict carbon prices precisely in order to reduce CO2 emissions and mitigate global warming. As a solution to the limitations of a single machine learning model that has insufficient forecasting capability in the carbon price prediction problem, a carbon price prediction model (GWO–XGBOOST–CEEMDAN) based on the combination of grey wolf optimizer (GWO), extreme gradient boosting (XGBOOST), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is put forward in this paper. First, a random forest (RF) method is employed to screen the primary carbon price indicators and determine the main influencing factors. Second, the GWO–XGBOOST model is established, and the GWO algorithm is utilized to optimize the XGBOOST model parameters. Finally, the residual series of the GWO–XGBOOST model are decomposed and corrected using the CEEMDAN method to produce the GWO–XGBOOST–CEEMDAN model. Three carbon emission trading markets, Guangdong, Hubei, and Fujian, were experimentally predicted to verify the model's validity. Based on the experimental results, it has been demonstrated that the proposed hybrid model has enhanced prediction precision compared to the comparison model, providing an effective experimental method for the prediction of future carbon prices. [ABSTRACT FROM AUTHOR]

Accurate carbon price prediction is a crucial task for the carbon trading market. Previous studies have ignored the impact of online data and are limited to point predictions, which brings challenges to the accurate forecasting of carbon prices. To address those issues, this paper proposes an interval-valued carbon price forecasting method based on web search data and social media sentiment. First, we collect web search data and social media sentiment to improve prediction performance by synthesizing multiple types of data information. Second, we employ principal component analysis (PCA) to preprocess high-dimensional web search data, and utilize BosonNLP for quantifying social media information, thereby enhancing the predictability of the dataset. Subsequently, a variational mode decomposition (VMD) is applied to the carbon price and online data, followed by utilizing particle swarm optimization support vector regression (PSO-SVR) to predict each sub-modes and summing them up to obtain the ultimate forecasting outcome. Finally, using carbon prices in Guangdong and Hubei provinces as case studies, the experimental results demonstrate that web search data and social media sentiment significantly enhance the predictive accuracy of interval-valued carbon prices. Furthermore, the proposed VMD-PSO-SVR outperforms other comparative models in the accuracy and reliability of interval-valued forecasting. [ABSTRACT FROM AUTHOR]

This paper constructs a novel stress measurement system of carbon market from the perspective of trading, emission reduction, and external shocks and simulates the stress indices of national and pilot carbon markets of China with the methods of functional data analysis and criteria importance through intercriteria correlation. It concludes that the overall carbon market stress is in the shape of "W" and still at a high level, with frequent fluctuations and an upward trend. In addition, the stress of Hubei, Beijing, and Shanghai carbon market fluctuates and rises, while the stress of Guangdong carbon market decreases. Moreover, carbon market stress mainly comes from trading and emission reduction. Furthermore, stress fluctuation of Guangdong and Beijing carbon market is more prone to "big waves," indicating that the two markets are sensitive to big events. Finally, the pilot carbon markets are divided into stress-driven and stress-release market and the type of which keeps change in different period. [ABSTRACT FROM AUTHOR]

Effective prediction of carbon prices matters a great deal for risk management in the carbon financial market. This article designs a blended approach incorporating secondary decomposition and nonlinear error-correction technology to predict the regional carbon price in China. Firstly, the variational mode decomposition (VMD) method is used to decompose the carbon price, and then, the time-varying filter-based empirical mode decomposition (TVFEMD) is introduced to decompose the residual term generated by VMD, and the multiple kernel-based extreme learning machine (MKELM) optimized by the sparrow search algorithm (SSA) is innovatively built to forecast the carbon subsequences. Finally, in order to mine the hidden information contained in the forecasted error, the nonlinear error-correction method based on the SSA-MKELM model is introduced to correct the initial prediction of carbon price. The empirical results show that the proposed model improves the prediction accuracy of carbon prices, with RMSE, MAE, MAPE, and DS up to 0.1363, 0.1160, 0.0015, and 0.9231 in Guangdong, respectively. In the case of the Hubei market, the model also performs best. This research innovatively expands the prediction theory and method of China's regional carbon price. [ABSTRACT FROM AUTHOR]

Facing the problem of energy shortage and the responsibility of carbon reduction, to achieve sustainable regional economic development, renewable energy must be vigorously developed. Technology can not only boost the development of renewable energy but be a new driving force for the economy. However, the development of the economy, technology, and renewable energy are interrelated, and the coupling and coordination among them lack sufficient evaluation and analysis. Therefore, an evaluation index system is constructed for the economy–technology–renewable energy ternary group. The evaluation model is applied to measure the coupling coordination degree, and a geographically weighted regression model is used to analyze the influence of key factors and their spatial differences. 30 provinces in China are used as examples for the study, and the results show that the coupling coordination degree is low in most regions, except for Guangdong, which has been maintained at an extremely coordinated level, and most provinces are in a fluctuating upward trend during the decade. Additionally, the results of the geographically weighted regression report that the influence of factors has spatial heterogeneity. The study provides a basis for relevant policy formulation in China, and the evaluation and analysis ideas can provide a reference for other countries or regions. [ABSTRACT FROM AUTHOR]

The accurate carbon price prediction is of significance to decrease investment risks, make scientific decisions and improve production efficiency. As matters stand, most studies focusing on carbon price prediction are following the preprocessing models, while the postprocessing models based on the error correction are rarely applied. To enhance the forecasting robustness and provide a relatively comprehensive comparison between the preprocessing and postprocessing model, this research proposes a novel hybrid model KELM-VMD-KELM by combining variational mode decomposition (VMD) and the kernel-based extreme learning machine (KELM), in which the KELM is firstly employed to forecast the daily carbon price series and obtain the initial prediction results, and then the VMD-KELM is utilized to build the predicting models for the residual error series to implement the process of error correction. The particle swarm optimization (PSO) algorithm is made a use to determine the optimal parameters of KELM and VMD. The daily average carbon price from Beijing, Guangdong and Shanghai market are selected to test the validity of the model. The results indicate that there is heterogeneity of the optimal model in different datasets. Both KELM-VMD-KELM and VMD-KELM perform well in the daily carbon price prediction. The postprocessing model can guarantee a high stability in different datasets. [ABSTRACT FROM AUTHOR]

The arrival of the dual-carbon era indicates that it is imperative to reduce industrial carbon emissions with high carbon emissions. The implementation of carbon emission reduction will inevitably have an impact on industrial development. Three different carbon emission scenarios were established. Based on the average annual carbon emission growth rate of the past decade, it is set as a medium carbon scenario. According to the economic development stage and actual social development situation of each region, the corresponding annual average carbon emission growth rate is increased and decreased to set up high carbon and low carbon scenarios. The adjacent accumulation gray multivariate model for industries is established based on different scenarios. The results indicate that the carbon emission scenarios most suitable for industrial development in various regions are different. Heavy industrial provinces and cities such as Beijing, Tianjin and Hebei have the highest industrial output value under high carbon scenarios; Provinces and cities with high levels of industrialization, such as Jiangsu, Shanghai, Guangdong and Chongqing have the highest industrial output value under low-carbon scenarios. The industrial output value of Hebei is expected to grow the fastest under the high carbon scenario, reaching 2.15 trillion yuan by 2030. The industrial output value of Guangdong achieves the greatest improvement under low-carbon scenario. It is expected that the industrial output value of which will reach 6.86 trillion yuan by 2030, ranking first in the country. Therefore, the setting of carbon emission scenarios needs to be tailored to local conditions, increase technological innovation efforts, and further promote industrial development. [ABSTRACT FROM AUTHOR]

Collaborative innovation can promote scientific productivity and the development of clean technology and thus has a great potential in constraining the ecological footprint. However, current studies on the impact of collaborative innovation on ecological footprint are insufficient, and results remain controversial. To better understand these impacts, this paper took Guangdong-Hong Kong-Macao Greater Bay Area of China as a case, estimated the ecological footprint at the municipal level from 2008 to 2018, measured collaborative innovation both from four dimensions and from a composite approach, then applied threshold regression models to compare the impact of collaborative innovation on the ecological footprint across different economic intervals. The findings showed that: the ecological footprint of the Greater Bay Area displayed an overall upward trend with prominent spatial heterogeneity. The impact of collaborative innovation on the ecological footprint presented a double-threshold effect when examined with different indicators. Among which, the flow of scientific personnel and capital boosted the ecological footprint, which intensified with economic development, while collaboration in technology exerted significant inhibitory effects on ecological footprint, and the influence of inter-city knowledge collaboration was limited. Overall, collaborative innovation inhibited ecological footprint when measured by a composite index. This might inspire policymakers to adopt sustainable strategies depending on the type of collaborative innovation and the economic status of the city to constrain growth of the ecological footprint, thus minimizing the pressures of human activities on the environment and moving towards a more carbon neutral society. [ABSTRACT FROM AUTHOR]

China has established seven pilot carbon markets and has made efforts to establish a national carbon market. This paper constructs an AR-GARCH model and adopts bilaterally modified dummy variables to investigate the impacts of regulatory update events (allowance allocation plans and allowance auctions) on the pilot carbon markets in China. The pilots of Guangdong, Shanghai, and Hubei are chosen as case studies. We find that each pilot reacted differently to the same events due to mechanism design diversity. The allowance allocation plan in the Shanghai and Guangdong pilot markets generated relatively distinct influences on the carbon price. Furthermore, to a certain extent, allowance auctions played a role in carbon price discovery in Guangdong and Hubei. However, such an impact was not remarkable in the Shanghai pilot market. Based on the empirical results, policy recommendations are presented for the establishment of a national carbon market. [ABSTRACT FROM AUTHOR]

In this study, we construct an evaluation index system in terms of three dimensions including internal, external and interface factors. We propose an entropy-based TOPSIS model to measure the maturity of carbon market. The proposed model uses the entropy method to objectively weight the indicators, and the TOPSIS method to measure the maturity of carbon market. Taking China's seven pilot carbon markets from 2013 to 2017 as an example, the empirical results are that the overall maturity of China's pilot carbon markets is relatively low, for the sake of small market size, low market effectiveness, low market activeness and high price volatility. Moreover, due to the difference in the quotas transaction volume, market liquidity and market supporting facilities, there are obvious differences in maturities among China's seven pilot carbon markets. In 2017, the maturity of Hubei market is the highest, reaching 0.6146. The maturity of Guangdong, Shenzhen, Beijing, Shanghai and Tianjin reaches 0.6108、0.5022、0.4351、0.4267 and 0.3888 respectively. The maturity of Chongqing market is the only 0.3633, at the lowest level. What's more, from the perspective of maturity evolution trend, Hubei, Guangdong and Shenzhen fluctuate upward from the market opening to 2017 and steadily rank in the top three. Shanghai and Tianjin show a U-shaped growth trend, declining from 2013 to 2015 and continuing to rise from 2015 to 2017. Beijing shows a fluctuating downward trend from 2013 to 2017, while ranking fourth. Chongqing ranks relatively low, but it shows an escalating trend. Finally, several targeted policy implications are put forward to enhance the maturities of carbon markets. • An evaluation index system of internal, external and interface factors is proposed to examine the maturity of carbon market. • An entropy-based TOPSIS model is proposed to measure the maturity of carbon market. • The empirical results show that the overall maturity of China's pilot carbon markets is relatively low. • There are obvious differences in maturities among China's seven pilot carbon markets. [ABSTRACT FROM AUTHOR]

As the primary source sector of carbon emissions, the power industry's green innovation is an indispensable component of China's low-carbon transformation. This paper took China's Carbon Emission Trading Scheme as a quasi-natural experience and adopted the synthetic control method to evaluate the impact of Carbon Emission Trading Scheme pilot policy on power industry's green innovation based on the provincial industrial level data. Furthermore, this paper examined the impact mechanisms of Carbon Emission Trading Scheme on the power industry's green innovation through policy comparison, moderating and mediating effect analyses. The research findings show that: 1) there is heterogeneity in Carbon Emission Trading Scheme's effectiveness in inducing green innovation in China, the power industry only in Beijing and Guangdong has been significantly promoted, while other pilots have not; 2) the success of Beijing and Guangdong can be attributed to distinctive features of their carbon market and power industry, including the refined quota accounting mechanism, the application of paid quota allocation, the larger enterprise scale and more research and development investment; 3) enterprise scale and research and development investment have positive moderating effect and mediating effect in the promotional effect of the Carbon Emission Trading Scheme on green innovation respectively. Finally, some policy recommendations are put forward to promote the power industry's green innovation and carbon market construction. • Evaluate the impact of China's CETS on the power industry's green innovation in each pilot. • The CETS effects among pilots are heterogeneous, only significant in Beijing and Guangdong. • Rational CETS mechanism design could improve the power industry's green innovation. • Enterprise scale and R&D investment positively moderate and mediate the CETS effect. [ABSTRACT FROM AUTHOR]

The social cost of carbon is a tool for assessing the appropriateness of emission reduction measures and climate policy, and is affected by socioeconomic and climatic factors. This study aimed to explore the impact of socioeconomic factors and climate on the social cost of carbon; to this end, this study considered Chinese provinces as the focus of research. This study constructed an integrated framework for carbon emissions considering socioeconomic and climatic factors, which consisted of shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs). Subsequently, social cost of the carbon estimation model was used to calculate the social cost of carbon for China's provinces from 2022 to 2100 under different carbon emission scenarios. The results show that: under most carbon emission scenarios, provinces with a high social cost of carbon are located in the eastern developed region. For instance, Jiangsu and Guangdong had the highest values of 6.31 $/tC. Second, SSPs that are highly dependent on fossil fuels have a high social cost of carbon, which is higher than 60 $/tC in 2022 in China. The social cost of carbon under other SSPs is at a fluctuating value of 40 $/tC. Third, in terms of RCPs, the social cost of carbon for the middle baseline emission scenario (RCP6.0) is considerably lower than that for the high baseline emission scenario (RCP8.0), and the difference between them is 3.7 times that of two medium emission scenarios (RCP6.0 and RCP4.5). Fourth, there is a substantial difference between the dynamic and fixed discount rates in the social cost of carbon in the same scenario. Studying the impact of socioeconomic and climatic factors on the social cost of carbon will help in its regulation and provide a scientific basis for Chinese provinces to optimize climate policies and emission reduction measures. [Display omitted] • Update the social cost of carbon based on a provincial perspective. • Analyze the social cost of carbon from socioeconomic and climatic aspects. • The Chinses eastern developed provinces have the high social cost of carbon. • The fossil-fuel and high emission scenario will increase the social cost of carbon. • Two types of discounts lead to various social cost of carbon in the same scenario. [ABSTRACT FROM AUTHOR]

This paper examines the risk spillover effect between the carbon market and the stock market in China and the role of corporate social responsibility (CSR) on this effect. Employing Beijing, Hubei, and Guangdong carbon markets, we apply time-domain and frequency-domain spillover approaches and find that during the Chinese stock market crisis in 2015, risk spillovers from the stock market to the carbon market were more pronounced. Additionally, CSR firms are more dominant as information transmitters than those non-CSR (NCSR) firms in the carbon market. However, plausibly, due to the infancy of carbon trading, our results show that the level of connectedness between the carbon market and the stock market in China is relatively low. [ABSTRACT FROM AUTHOR]

This paper proposes a new prediction method incorporating a set of measuring models applicable to the financing efficiency of China's carbon market to quantify such market's maturity, trading risk coefficient, and financing income. Our theoretical analysis indicates that the price of carbon emission quota essentially affects the financing efficiency of carbon market. While a robust safeguard measure to obtain a respectable income from carbon market financing is the long-term average quota price exceeding the initial quota price. Empirical findings derived from China's emissions trading scheme pilots reveal that these pilots can be divided into the growth-oriented market (i.e. Guangdong whose financing capacity is always significant), the balance-oriented market (i.e. Shanghai and Hubei whose quota pricing mechanisms and the financing level of carbon markets both maturely develop), and the risk-oriented market (i.e. Beijing whose quota price runs at a high-level with an intense financing income volatility). It is therefore achieved that carbon market's maturity level and quota price volatility are both robustly explicable for different financing effects among these pilots. Our key findings show that expanding the coverage of quota trading parties, stabilizing carbon price, and promoting carbon asset management help to improve the financing efficiency of carbon market. • The financing mechanism is analyzed with clarifying its main influence factors. • A stochastic process is used to build a price simulation equation that meets the operation law of China's carbon market. • An income model is built and thus evaluate market financing effect based on price expression and financing mechanism. • The financing effect of China's 8 pilot markets is measured, and thus 3 kinds of development modes are proposed. [ABSTRACT FROM AUTHOR]