In order to deeply analyze the current status and trends of electric vertical take-off and landing(eVTOL) aircraft, and reveal the correlation between technological innovation and market demand, the “Incopat” patent database was used to retrieve patent data related to the field of eVTOL aircraft published globally from 2000 to the present. After noise reduction, cleaning and other processing of the retrieved results, a final sample of 2 794 patent data points was determined for analysis. The patent information in the field of eVTOL aircraft was studied from aspects including application trends, layout in major countries, patent technology distribution and analysis of major applicants. It is found that eVTOL aircraft, as an emerging technology field, are experiencing rapid growth in patent application activities globally. The United States leads in technological innovation and patent layout within this field. China still lags behind the United States in the realm of eVTOL aircraft, but it is currently a major source of innovation and a potential industrial base in the market, developing rapidly and poised to become a new leader. Europe, Japan and South Korea are actively making layouts, reflecting a trend of globalized development in this field. Regions such as Shanghai, Jiangsu and Beijing have already emerged as hubs of innovation, while central and western provinces represent future growth areas. However, there is a lack of involvement from relevant research institutes. It is recommended that relevant entities strengthen deep integration and collaborative innovation globally to promote the development of this emerging technology.

The Loess Plateau is an important ecological zone in China. The changes in vegetation coverage were investigated in the Loess Plateau region from 2001 to2016, as well as the impacts of climatic factors and human activities on it. The result shows that the vegetation coverage in the Loess Plateau has generally exhibited an increasing trend, with a significant extension of the growing season. Overall, the vegetation coverage in the Loess Plateau has a weak or negative correlation with temperature, while it shows a positive correlation with precipitation. Among different land use types, the vegetation coverage of arable land, forest land and grassland has generally increased, whereas the vegetation coverage of water bodies, built-up areas and unused land has decreased overall.

As global environmental issues become more prominent, wind power, a low-pollution renewable energy source, has garnered attention. However, the variability and intermittency of wind resources pose challenges for predicting wind farm output, affecting power system scheduling and operation. To improve the accuracy of wind power forecasting, weather characteristics influencing power output must be fully considered. By modeling and predicting actual wind farm data, the effectiveness of different deep learning models for ultra-short-term forecasting was compared. The results show that a multivariate time prediction method based on a long short-term memory(LSTM) network effectively predicts wind power, achieving higher accuracy and stability than other deep learning models.

With increasingly globalized trade, efficiently, safely, and low-carbon distributing products is a critical challenge in the perishable supply chain network design(PSCND). A mixed-integer linear programming(MILP) model was developed considering perishability uncertainty, limited capacity of facility location, flow allocation and transport mood, aiming to minimize cost, carbon emissions and transportation time, as a case study, optimizing fresh-cut flower processing and pre-cooling centers in Kunming, Yunnan Province, China. The Weibull function was introduced to model the loss of perishable products during transportation. Given the intricate nature of the problem, a hybrid algorithm that integrated the minimum element method with the genetic algorithm was devised. The applicability and validity of our proposed model and algorithm was substantiated through rigorous numerical analysis. It draws out the impact of perishability on establishing processing and pre-cooling centers and modes of transport. Enterprises should decide on the mode of transport and adjust the number and capacity of processing and pre-cooling centers according to the perishability of products.

Under the “dual carbon” strategic goals, green credit has become an important driver for high-quality economic development and the transformation of enterprises. Based on operational data from A-share listed companies in China from 2004 to 2022, the implementation of the 2012 “Green Credit Guidelines” was used as a quasi-natural experiment. Employing double machine learning approach to construct an empirical model, the findings indicate that after the implementation of the Guidelines, the reduction of financing constraints, increased R&D investment, and promotion of joint ownership between banks and enterprises effectively drive continued green innovation in environmental protection enterprises.

As an important part of the regional innovation system, the innovation ability of local universities is crucial to the high-quality development of the regional economy. Based on the panel data of 27 provinces in China from 2010 to 2022, the impact of data elementalization on the innovation ability of local universities was empirically explored. The results show that data elementalization significantly enhances the innovation ability of local universities, and the positive effect of different innovation quantile points is gradually enhanced. Data elementalization indirectly promotes the improvement of the innovation ability of local universities through intelligent innovation drive and industrial structure optimization. In the central and western regions, where the innovation intensity and industrialization level are low, the promotion effect of data elementalization on the innovation ability of local universities is more obvious.

The data of A-share listed manufacturing enterprises from 2012 to 2021 were selected to empirically test the impact and mechanism of digital transformation on green ambidextrous innovation. The results show that digital transformation promotes green ambidextrous innovation of manufacturing enterprises, and its promotion effect on green substantive innovation is greater than that of strategic innovation. The mechanism test shows that the scale and social responsibility of enterprises positively regulate the role of digital transformation in promoting green ambidextrous innovation. The government regulation is positively adjusting strategic innovation. The heterogeneity test shows that digital transformation plays a greater role in promoting the substantive green innovation of light polluting enterprises. It plays a greater role in promoting strategic green innovation of heavily polluting enterprises.

The core factors influencing production levels in manufacturing enterprises under the empowerment of the Industrial Internet and their mechanisms of action was investigated. Using grounded theory and the Analytic Hierarchy Process(AHP), key factors were systematically identified that enhance production levels in manufacturing enterprises empowered by the Industrial Internet. Supported by grounded theory, in-depth interviews and open coding were conducted to initially extract the main influencing factors. Subsequently, AHP was applied to perform a weight analysis on these factors, filtering out the most impactful ones. Finally, using a real case study, the mechanism was summarized by which the Industrial Internet empowers the enhancement of production levels in manufacturing enterprises. The results indicate that factors such as operational proficiency, equipment fault warning capability, and data cleansing efficiency significantly promote production levels in manufacturing enterprises under the empowerment of the Industrial Internet. These factors synergistically construct a systematic improvement mechanism. Based on the analysis results, targeted recommendations are provided, offering practical guidance for manufacturing enterprises to more effectively leverage Industrial Internet platforms to improve production levels.

In order to sort out the current research status, stage hotspots and development trend of rural education digitization in China, an in-depth analysis of the high-quality literature included in the China Knowledge Network database in the past 20 years with the help of CiteSpace software was conducted. It is found that: firstly, the research on digitalization of rural education can be divided into four stages, namely initial exploration, rapid growth, steady development and rapid growth; secondly, the researchers have not yet formed a close research cooperation network; thirdly, there is a lack of a single research methodology and multidisciplinary perspectives; fourthly, the research has gradually shifted from a focus on localised rural development to a balanced urban-rural education. Looking to the future, it is necessary to strengthen the construction of the research team and establish an academic research community, expand research perspectives and promote the diversification of research paradigms, and broaden the scope of research and refine the content of research.

Taking panel data from 39 listed commercial banks in China from 2013 to 2022, and empirical methods were used to study the impact and mechanism of digital finance development on the operational performance of commercial banks. The research results indicate that digital finance can significantly promote the improvement of operational performance of listed commercial banks. Digital finance has had a significant negative impact on the operational performance of commercial banks in the central and western regions. The impact of digital finance on the operational performance of state-owned commercial banks, joint-stock commercial banks and local commercial banks varies. The level of risk-taking plays a fully mediating role in the impact of digital finance on the operational performance of commercial banks.