The concept of ESG has become a global consensus, and the fulfillment of corporate ESG responsibilities is a key link in achieving China’s“dual carbon goals”and sustainable development of the whole society. Based on this, the 313 A-share listed companies selected by the MCSI ESG rating were used as the test group samples, and the double difference propensity score matching model (PSM-DID) was used to match the control group samples, and the ESG rating pairing was discussed from the perspective of theoretical mechanism and empirical analysis. The impact mechanism and path of orporate valuations, this study found: ① Whether it is a rating event or a rating score, being selected for ESG ratings can effectively increase the company’s valuation. ② There is a positive correlation between ESG rating adjustments and listed companies’ valuation adjustments, and rating upgrades will promote the increase in valuation, while the downgrade of the rating will lead to the decrease in valuation. However, this correlation has a diminishing effect over time, indicating that the capital market has a process of receiving, digesting, and positive feedback on ESG disclosure information. ③ ESG rating is based on institutional shareholding ratio, stock volatility, annual transaction volume, and financing cost four channels to affect corporate valuation, indicating that ESG has value discovery, risk suppression, information transmission, and financing constraints mitigation. ④ ESG ratings have heterogeneous effects on valuation. Compared with highcarbon companies, industrial companies, and agricultural companies, the valuations of low-carbon companies and service companies are more sensitive to ESG ratings. ⑤ Apply methods such as instrumental variable method, sample reorganization, and replacement of variables for endogeneity and robustness testing, and not change the research conclusions. The research results of this paper have important reference value for establishing the Chinese version of the ESG mandatory information disclosure system, unifying and improving ESG evaluation and rating standards, and promoting the high-quality development and value maximization of listed companies.

Good fiscal rule constraint is an important content of establishing standardized fiscal governance system. This paper uses the European Commission Fiscal Rule Dataset (ECFRD) to empirically analyze the effect of implementing fiscal rules on procyclical fiscal policies, and analyzes the cyclical balance characteristics of the second generation fiscal rules from the perspective of economic cycle fluctuations. The main findings are as follows: First, the implementation of fiscal rules can significantly reduce the procyclical characteristics of fiscal policies and slow down the procyclical fiscal risks; Second, from the perspective of different fiscal rules, expenditure rules and debt rules can effectively control procyclical fiscal risks, while revenue rules and budget balance rules have no significant impact. Third, in the second generation of fiscal rules, the influence coefficient of expenditure rule on fiscal policy procyclicality has been significantly increased, while the revenue rules has no obvious change. However, the budget balance rules and debt rules change to non-significant effect, and the time delay of implementation effect may be an important reason for this phenomenon. Fourth, fiscal rules with the characteristics of supervision mechanism and sanctions against violations can enhance the restraint effect on procyclicality, and improving the openness and flexibility strength of fiscal rules can promote the procyclicality of national fiscal to a certain extent. In conclusion, exploring the formal financial rules system will help to establish the modern financial governance system, promote the sustainable development of finance, and further guarantee the stability of the anti-cyclical financial policy.

Gasoline is the lifeblood of the national economy. The forecasting of gasoline prices is difficult because of frequent price fluctuations, its complex nature, diverse influencing factors, and low accuracy of prediction results. Previous studies mainly focus on forecasting gasoline prices in a single region by single time series analysis which ignores the daily price co-movement of different series from multiple regions. Because price co-movement may contain useful information for price forecasting, this paper proposes the Lasso-CNN ensemble model that combines statistical models and deep neural networks to forecast gasoline prices. In this model, the Least Absolute Shrinkage and Selection Operator (Lasso) screens and chooses the correlated time series to enhance the performance of forecasting and avoid overfitting, while Convolutional Neural Network (CNN) takes the selected multiple series as its input and then forecasts the gasoline prices in a certain region. Forecasting results of gasoline prices at the national level and regional levels by using the new method demonstrate that the new approach provides more accurate results for the predictions of gasoline prices than those results generated by alternative methods. Thus, the relevant series can enhance the performance of forecasting and help to gain better results.