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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]

Electrification of public transit bus has been rapidly promoted to reduce carbon dioxide (CO 2) emissions in China. It is essential to evaluate the impacts of battery electric buses (BEBs) adoption on real-world CO 2 emissions. This paper conducted a well-to-well (WTW) analysis to estimate the CO 2 emissions of BEBs in Shenzhen. First, we identified the regional inventory from an extensive review of research papers and data. Based on the mobile BEBs data, we explored the impacts of different driving conditions on WTW CO 2 emissions. Moreover, a sensitivity analysis was performed to assess the reliability of the WTW results. The experiments results show that BEBs can reduce WTW CO 2 emission by 21.8% relative to conventional diesel buses (CDBs) in 2018. And the WTW CO 2 emissions factor of BEBs has a negative correlation with stop headway and average speed. Notably, the impacts of CO 2 emissions reduction are more sensitive when the stop headway is over 2 km or the average speed is under 25 km/h. Besides, for the current power structure and charging technology in Guangdong, if the growths of the thermal power share and the charging loss are over 5%, BEB has more CO 2 emissions reduction benefits by improving the charging efficiency. Moreover, if the thermal power share and the charging loss are decreased, BEB can obtain more CO 2 emissions reduction potential by reducing the thermal share. The improvement of these two technologies can achieve close CO 2 emission reduction effects in other situations. Image 1 • The BEBs can reduce WTW CO 2 emission by 21.8% relative to CDBs in 2018. • The impacts of CO2 emissions reduction are more sensitive when the stop headway is over 2 km and the average speed is under 25 km/h. • If the growths of the thermal power share and the charging loss are over 5%, it's better to improve the charging efficiency to reduce CO 2 emissions. [ABSTRACT FROM AUTHOR]