In order to fully implement the Party’s tough fight against poverty, conscientiously implement the policy of targeted poverty alleviation, and accelerate the development of the socialist market economy and the national economy, it is particularly important to strengthen digital information management in the audit of special fiscal poverty alleviation funds. With the continuous advancement of science and technology, the informatization of audit work has become an inevitable trend. However, in the past audit work of special financial funds, it was found that there were a series of problems such as imperfect audit system, weakened audit work, poor audit projects, imperfect supporting facilities, inadequate poverty alleviation funds, slow project progress, and failure to timely check the completion of projects. In order to better realize the timeliness and practicability of audit information of special fiscal poverty alleviation funds and solve the loopholes and deficiencies found in the past, based on the original digital information submanagement system, this paper adopts the J2EE system framework, MVC mode, and B/S security detection system to build a more stable, efficient, and secure financial special poverty alleviation fund management system. After a series of tests, under the premise of effective use of poverty alleviation funds, the system data shows that, from 2012 to 2018, the per capita disposable income of residents in poverty-stricken areas in China increased by 12.1% in nominal terms, an increase of 8.9% compared with 2012, and the gap between the national rural averages has narrowed again.

Environmental education plays a significant role in improving environmental knowledge and shaping the eco-friendly lifestyles of young people. Young people’s daily actions and habits will determine the future of the Earth as a planet. The literature regarding youths’ environmental knowledge, climate change awareness, environmental attitude, and their impact on pro-environmental intentions and sustainable household consumption practices is very scarce. Therefore, this study explored the relationship between environmental knowledge, climate change awareness, environmental attitude, and the pro-environmental intentions of university students. The study also assessed the moderating effect of environmental education on pro-environmental behavior and sustainable household consumption practices, providing a comparative analysis of students with and without environmental education, which is unique in the literature. The data were collected from 2137 Chinese university students selected through a purposive and random sampling method through survey questionnaires. Descriptive statistics and partial least squares structural equation modeling (PLS-SEM) were used to analyze the collected data. The findings revealed that environmental knowledge, climate change awareness, and environmental attitudes of the students positively affected their pro-environmental intentions. Moreover, pro-environmental intentions also positively affected the adoption of sustainable practices. The result also showed that the impact of pro-environmental intentions on sustainable consumption practices was greater for those whose education included environmental courses than for those whose education did not. Therefore, it is suggested that environment-related courses be incorporated into the study plans of each discipline as a compulsory subject for promoting green intentions and shaping eco-friendly lifestyles for environmental sustainability.

Since the reform and opening up, China’s economy has developed rapidly, becoming the second largest economy in the world. The state of macroeconomic development has a great influence on the government’s policy introduction and the investment decisions of individual institutions. Therefore, forecasting the macroeconomy is of great significance to the country. This paper aims to study the macroeconomic forecasting model system based on digital information and blockchain technology. This paper proposes an artificial neural network prediction algorithm based on digital information and blockchain technology, and the artificial neural network and particle swarm algorithm are combined to become the hybrid artificial neural network algorithm, and the conception of the establishment of the macroeconomic forecast model is proposed. The experimental results of this paper show that according to the prediction results of artificial neural network, the prediction and actual error in 2010 is 0.10, while the new method proposed in this paper predicts the error of 0.056. In 2016, the prediction error based on artificial neural network was 0.14, while the prediction result of the new method proposed in this paper showed an error of 0.008. In each year’s economic forecast, the neural network model of the new method proposed in this paper has higher prediction accuracy and smaller error. It can be seen that the neural network model based on artificial neural network and PSO algorithm proposed in this paper is beneficial to macroeconomic forecasting.