The United Kingdom is the third-largest peer-to-peer (P2P) lending market in the world, which is surpassed only by the two dominant forces in P2P investing, China and the United States of America. As an innovative financial market in the UK, P2P lending brings not only many opportunities but also many risks, especially the loan default risk. In this context, this paper uses binary logistic regression and survival analysis to evaluate default risk and loan performance in UK P2P lending. The empirical results indicate that credit group, loan purpose for capital needs, sector type, loan amount, interest rate, loan term, and the age of the company all have a significant impact on the probability of loan default. Among them, the interest rate, loan term, and loan purpose for capital needs are the three most important determinants of the probability of loan defaults and survival time of loans.

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It is of great significance to forecast the intraday returns of stock index futures. As the data sampling frequency increases, the functional characteristics of data become more obvious. Based on the functional principal component analysis, the functional principal component score was predicted by BM, OLS, RR, PLS, and other methods, and the dynamic forecasting curve was reconstructed by the predicted value. The traditional forecasting methods mainly focus on “point” prediction, while the functional time series forecasting method can avoid the point forecasting limitation, and realize “line” prediction and dynamic forecasting, which is superior to the traditional analysis method. In this paper, the empirical analysis uses the 5-minute closing price data of the stock index futures contract (IF1812). The results show that the BM prediction method performed the best. In this paper, data are considered as a functional time series analysis object, and the interference caused by overnight information is removed so that it can better explore the intraday volatility law, which is conducive to further understanding of market microstructure.

In recent years, energy efficiency has been considered an extremely cost-effective way to reduce greenhouse gas emissions. China is a country with the world’s largest coal consumption and heavy reliance on thermal power generation. Therefore, the relationship between the coal consumption constraint policy (CCCP) in China and electrical energy efficiency is a topic worthy of study. Based on the panel data of 30 provinces in China during 2005–2016, this paper employs the difference-in-differences (DID) to examine the impact of CCCP on electrical energy efficiency in China. The results indicate that the implementation of the CCCP reduces electrical energy efficiency in the pilot provinces. Based on the mechanism tests, the cost effect outweighs the innovation effect, which is why CCCP decreases electrical energy efficiency. The results of the heterogeneity analysis show that the influence of CCCP is more significant in the provinces with weak law enforcement and small hydropower investment and northern provinces. This study suggests that the Chinese government can promote corporate technological innovation by improving the environmental compensation system and increasing environmental law enforcement to improve electrical energy efficiency. Meanwhile, renewable energy projects should be the focus of future investment. Supplementary Information The online version contains supplementary material available at 10.1007/s12053-022-10023-2.