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A good risk warning model is an effective way to guarantee the stable operation of the carbon market and exert its emission reduction function. This paper takes the China's pilot carbon market as the research object and measures the carbon market risk by ARMA-GARCH-VaR. Then, influential factors are selected as non-market characteristics from the perspective of causality and incorporated into the risk warning model, and SMOTE and SMOTEENN are introduced to deal with the unbalanced samples. Results show that (1) The risk fluctuations of Beijing and Shenzhen carbon market are large, and the risk fluctuations of Guangdong carbon market are relatively smooth in the early period and larger in the recent period. (2) Air quality, average daily temperature, CSI 300, power coal, natural gas, European carbon market, exchange rate, and Baidu index are all important factors in the risk volatility of carbon market. (3) The Classification Decision Tree (CDT) has the best performance. CDT has the best performance in Guangdong carbon market, SMOTE-CDT has the best performance in Hubei carbon market, and SMOTEENN-CDT has the best performance in Shanghai carbon market, in which SMOTEENN-CDT has a 76% Recall. The non-equilibrium treatment improves the overall Recall, and the risk warning model after non-equilibrium treatment can avoid missing important risk points. This paper can provide a certain degree of reference for the improvement of carbon trading market and risk management. [Display omitted] • A carbon market warning model is constructed. • The average VaR of the carbon trading markets in Shenzhen and Beijing is larger. • CDT performs better in market risk warning. • The model improves 34.8% in the avoidance of risk compared with other models. [ABSTRACT FROM AUTHOR]

Beijing-Tianjin-Hebei metropolitan area is a significant carbon emission center. The region's early achievement of peak carbon targets is critical to the nation's achievement of peak carbon targets. In this paper, it is proposed to use different orders of grey models to classify into three scenarios. Based on three scenarios, the grey multivariate convolutional model with new information priority accumulation is adopted to predict carbon emissions in the Beijing-Tianjin-Hebei region and select the scenario suitable for local development. The results show that: (1) The Beijing region has already achieved peak carbon, the Tianjin region may not reach its peak carbon target by 2030, and the Hebei region is expected to reach its peak carbon target by 2030. (2) The high rate of carbon emission reduction scenario will greatly improve the air quality of Beijing. The low-speed growth carbon emission scenario is more in line with the future development of Tianjin city. The low-rate carbon reduction scenario is more in line with the synergistic governance of pollution reduction and carbon reduction in Hebei Province. (3) Beijing's population policy in the most recent years has been conducive to improving the local environment. Tianjin's medium-term population policy is more in line with the local area. Hebei's medium-term industrial structure reform is favorable to local development. • Grey correlation analysis is used to analyze the factors affected carbon emissions in a region. • The factors with high correlation are predicted by using grey prediction models of different orders. • The high rate of carbon emission reduction scenario will greatly improve the air quality of Beijing. [ABSTRACT FROM AUTHOR]

Sand and gravel aggregate (SGA) transportation accounts for over 30% of the freight volume in China, which is greater than that of any other cargo; low-carbon development can effectively promote the decarbonization of freight transportation. This study presents a four-step evaluation method to assess CO 2 emission reductions in freight transportation; the proposed method helps determine the relationship between the policy, CO 2 emission reductions, demand, and mode market share. Using Beijing as a case study, this paper estimates the potential of reducing CO 2 emissions from SGA transportation under various scenarios. The scenarios are set based on the avoid, shift, and improve strategies; the results of the Beijing case indicate that the CO 2 emission for SGA transportation is 920.86 kt, which is approximately 1% of the total CO 2 emissions in Beijing. The maximum reductions in CO 2 emissions that can be achieved using the avoid, shift, and improve strategies are 0.92 kt (0.1% of total), 560.52 kt (60.9% of total), and 78.59 kt (8.5% of total), respectively. The shift strategy is confirmed to be the most realistic and cost-effective approach for reducing CO 2 emissions from SGA transportation. In addition, the implementation of subsidy policies is discussed. Marginal abatement costs under the subsidy scenarios are higher than 224 CNY/t, which is higher than the price in the Chinese carbon trading market. • We evaluated CO 2 emission of aggregate transportation in Beijing at the OD level. • CO 2 emission reductions of the avoid, shift, and improve strategies were tested. • Shift strategy performed the best in reducing CO 2 emission. • Avoid and improve strategies can reduce CO 2 emission; but the effects were limited. • Subsidizing rail transport reduced more emission than subsidizing electric trucks. [ABSTRACT FROM AUTHOR]