Finance contributes to poverty alleviation through economic growth, and the development of green finance is related to the sustainable development of the world economy and environment. Green finance not only helps promote sustainable economic development but also helps reduce poverty. Based on the analysis of related theories about green finance and poverty alleviation, this paper selects 18 indicators from three dimensions of economic development, financial development, and social environmental development and uses the improved entropy method to measure the green finance development index of China’s 25 provinces and municipalities from 2004 to 2017. The results show that the development level of green finance in China’s 25 provinces and municipalities is quite different. On the basis of the above analysis, make an empirical analysis of the impact of the green finance development index on poverty alleviation using multiple regression analysis and static panel and dynamic panel estimation methods. The research results show that there is a significant positive correlation between green finance and poverty alleviation; the higher the level of green finance development, the more conducive the poverty alleviation. So, this paper suggests that poverty can be better alleviated by improving the level of green finance development, financial asset level, and economic development level.

The World Health Organization (WHO) upgraded the status of the coronavirus disease 2019 (COVID-19) outbreak from epidemic to global pandemic on March 11, 2020. Various mathematical and statistical models have been proposed to predict the spread of COVID-2019 [1]. We collated data on daily new confirmed cases of the COVID-19 outbreaks in Japan and South Korea from January 20, 2020 to April 26, 2020. Auto Regressive Integrated Moving Average (ARIMA) model were introduced to analyze two data sets and predict the daily new confirmed cases for the 7-day period from April 27, 2020 to May 3, 2020. Also, the forecasting results and both data sets are provided.

Autoregressive Integrated Moving Average (ARIMA) models have been widely used to forecast and model the development of various infectious diseases including COVID-19 outbreaks; however, such use of ARIMA models does not respect the count nature of the pandemic development data. For example, the daily COVID-19 death count series data for Canada and the United States (USA) are generally skewed with lots of low counts. In addition, there are generally waved patterns with turning points influenced by government major interventions against the spread of COVID-19 during different periods and seasons. In this study, we propose a novel combination of the segmented Poisson model and ARIMA models to handle these features and correlation structures in a two-stage process. The first stage of this process is a generalization of trend analysis of time series data. Our approach is illustrated with forecasting and modeling of daily COVID-19 death count series data for Canada and the USA.

This paper uses the 5‐five‐minute high‐frequency data of energy‐listed companies in China's A‐share market to extract the jump of energy stock prices and build a dynamic stock price jump complex network. Then, we analyze the clustering effect of the complex network. The research shows that the energy stock price jump is an important part of stock price volatility, and the complex network of energy stock jump risk has obvious time‐varying characteristics. However, the infection problem of stock price jump risks needs specific analysis. China's coal industry has an important influence on the development of China's energy industry. According to the clustering analysis results of the network community, the clustering effect of the network community has time‐varying characteristics. After October 2017, the clustering effect of the jumping risk of the coal industry and the new energy industry is obvious. The risk contagion within the new energy industry community is a key point for the development of the new energy industry.

As coronavirus spreads around the world, the study of its effects is of great practical significance. We collated data on daily new cases of the COVID-19 outbreaks in the six Western countries of the Group of Seven and the dates of governments’ interventions. We studied the periods before and after the dates of major governments’ interventions integrally based on a segmented Poisson model. The relevant results are published in the paper of “Predicting turning point, duration and attack rate of COVID - 19 outbreaks in major Western countries” [1]. Our method can be used to update prediction daily as COVID-19 outbreaks evolve. In this article, we illustrate an updated analysis with our method to facilitate reproducibility. Both datasets used and updated are provided.