It is crucial to explore the impact of digital financial inclusion on tourism for national economic development. This paper utilizes panel data from 256 prefecture-level cities in China between 2011 and 2019 to examine the influence of digital financial inclusion on tourism. The findings demonstrate that digital financial inclusion significantly contributes to the development of the tourism industry. Notably, its coverage breadth, depth of use, and level of digitalization also have positive effects. Mechanism analysis reveals that digital financial inclusion facilitates the growth of tourism by supporting the development of tourism enterprises and enhancing consumer spending. Heterogeneity analysis further reveals regional and urban disparities in the promotion of digital financial inclusion, with the effect being more pronounced in the eastern region and larger cities. In comparison to existing studies, this paper delves into the mechanisms through which digital financial inclusion impacts tourism, as well as investigates regional and city size discrepancies. Consequently, governments should strive to foster the development of digital financial inclusion to attract market players and promote the advancement of residents’ consumption, thereby bolstering tourism development.

The sustainable energy consumption in northeast Asia has a huge impact on regional stability and economic growth, which gives price volatility research in the energy market both theoretical value and practical application. We select China’s fuel oil futures market as a research subject and use recurrence interval analysis to investigate the price volatility pattern in different thresholds. We utilize the stretched exponential function to fit the pattern of the recurrence intervals of price fluctuations and find that the probability density functions of the recurrence intervals in different thresholds do not show the scaling behavior. Then the conditional probability density function and detrended fluctuation analysis prove that there is short-term and long-term correlation. Last, we use a hazard function to introduce the recurrence intervals into the (value at risk) VaR calculation and establish a functional relationship between the mean recurrence interval and the threshold. Following this result, we also shed light on policy discussion for hedgers and government.

The integration of the global economy has led to an increasingly strong connection between the futures and spot markets of commodities. First, based on one‐minute high‐frequency prices, this paper applies the thermal optimal path (TOP) method to examine the lead‐lag relationship between Chinese crude oil futures and spot from March 2018 to December 2021. Second, we apply the Mixed Frequency Data Sampling Regression (MIDAS) model and indicators such as deviation degree to test the degree of prediction of high‐frequency prices in the futures market to the spot market. The experimental results show that the futures markets lead the spot market most of the time, but the lead effect reverses when major events occur; 60‐minute futures high‐frequency prices are the most predictive of daily spot data; crude oil futures’ predictive power declined after the Covid‐19 outbreak and is more predictive when night trading is available. This study has important implications, not only to guide investors but also to provide empirical evidence and valid information for policy makers.

The significant fluctuation of industrial electricity consumption has a high impact on power load, which makes the research on recurrence intervals between extreme events of theoretical and practical significance. The study uses a high-frequency data of heavy and light industries and employs recurrence interval analysis in different thresholds. We find that the reoccurrence interval of volatility can fit with the stretched exponential function and the probability density functions of recurrence intervals in various thresholds shows a scaling behavior. Then, the conditional probability density function and the multifractal detrended fluctuation analysis demonstrate the existence of short-range correlation, long-range correlation, and multifractal properties, respectively. We further construct a hazard function, introduce recurrence intervals into VaR calculation and establish a functional relationship between average recurrence interval and threshold. Following this result, we also shed light on policy discussion for multi-industrial electricity supply management.