This special issue, “Internet Finance, Green Finance, and Sustainability”, is focused on the prosperous financial industry and the relationship between finance and sustainability. It especially gathers papers pertaining to the hot topic of internet finance and green finance in this field, as well as the manuscripts exploring the operating mechanism between finance and sustainability, both of which are closely linked to the hot spots and pressing demands of society. Therefore, this special issue is of particular valuable for both academic research and the development of society.

The influential factors of green logistics development have been an important research topic for academics. However, little attention has been devoted to studying the issue of the prioritization and the hierarchical structure of these factors. The purpose of this study is to identify the key factors influencing the development of green logistics, and to explore the prioritization and the hierarchical structure of the influential factors. An indicator system for influential factors of green logistics development is first developed based on the theory of triple bottom line, including sixteen indicators or factors. The comprehensive influences and the prioritization of these sixteen factors are then analyzed using the DEMATEL method and the multi-level hierarchical structure of these factors is constructed by applying the ISM method. The results indicate that government policies, public supports, laws and regulations, green education, green technology innovations, consumer green demands, quantity and quality of green logistics talents are more influential than the other factors. Social factors including laws and regulations and government policies, public supports and green education are the underlying factors affecting the development of green logistics. Economic factors are at the middle levels and environmental factors are at the surface-level of the hierarchical structure. Recommendations are finally given to governmental agencies and business firms. These findings will be beneficial to promoting the sustainable development of green logistics.

The risk measurement of financial credit industry is an important research issue in the field of financial risk assessment. The design of financial credit risk measurement algorithm can help investors avoid greater risks and obtain higher returns, so as to promote the benign development of financial credit industry. Based on the combined deep learning algorithm, this paper studies the risk measurement of financial and credit industry, and proposes a fusion algorithm of deep auto-encoder (DAE) and Long Short-Term Memory (LSTM) network. The algorithm recombines the value of fixed features by using the unsupervised mechanism of DAE, and extracts non fixed features for measurement combined with the memory characteristics of LSTM network. The experimental results show that: compared with single generalized regression neural network and LSTM network, the average accuracy of DAE-LSTM algorithm is improved by about 6.49% and 3.25% respectively, which has a better application effect in credit risk measurement.

The purpose of this study is to explore how to apply blockchain technology to intelligent transportation, create a hierarchical theoretical framework of intelligent transportation, and explore a sustainable application system of intelligent transportation under the blockchain. However, not only this hierarchical theoretical framework must consider unnecessary attributes and the interrelationships between the aspects and the criteria, but also the sustainable application system must be in consideration in multiple stakeholders. Hence, fuzzy set theory is used for screening out the unnecessary attributes, a decision-making trial and evaluation laboratory (DEMATEL) is proposed to manage the complex interrelationships among the aspects and attributes, and interpretive structural modeling (ISM) is used to divide the hierarchy and construct a hierarchical theoretical framework. Finally, the research develops a sustainable GCU application system for intelligent transportation under the blockchain. The results show that (1) solving social problems is the primary link, (2) economic tasks are mainly focused on smart contracts and affected by the social problems, (3) the continuous improvement of environmental issues requires a solution to social problems, and (4) the application system of blockchain in intelligent transportation needs to be built from three levels including the government layer, the company layer, and the user layer. This theoretical hierarchical framework aims to guide intelligent transportation toward the application of blockchain. This study also proposes the engagement of stakeholders for establishing a sustainable application system.

Psychological research shows that as the main component of enterprise decision-making, CEOs are not completely rational, cognitive and psychological biases often influence their decision-making process. CEO narcissism has gradually attracted academic attention. Based on upper echelon theory and subconscious theory, this paper uses advanced artificial intelligence technology to quantify CEO narcissism as a kind of emotional intelligence. Taking A-share listed companies in China from 2010 to 2019 as research objects, this paper empirically tests the impact of CEO narcissism on debt financing and innovation performance. The results show that CEO narcissism has a significant positive impact on firm innovation performance. Debt financing plays a mediating role in the relationship between CEO narcissism and firm innovation performance. CEO narcissism can have a positive impact on firm innovation performance through debt financing. Compared with non-SOEs, SOEs' CEO narcissism has a more significant positive effect on debt financing and enterprise innovation performance. The research in this paper enriches psychology and organizational management and provides a reference for an enterprise's management decisions and for investors' investment decisions.