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This paper aims to understand the characteristics and contributions of the secure and trustworthy cyberspace (SaTC) projects funded by the U.S. National Science Foundation (NSF). These research projects were funded during the period of 2015–2023.

The authors applied a data analytics approach to proposal records of 1,025 NSF SaTC projects. These records were downloaded from the NSF proposal database via its search function. The analysis includes bibliometric analysis, shallow natural language processing and manual content analysis.

About 11 NSF divisions or units have sponsored SaTC research. About 214 universities or organizations in 44 states received SaTC funds. The key concepts of these projects include adversarial attacks, cryptography, cloud computing, internet of things, differential privacy, mobile devices and others. These projects were motivated by a lack of understanding or investigation of one or more technologies in the cybersecurity domain or the inefficacy of existing tools or algorithms. The objectives of these proposals included providing new insights and developing new tools, methods, frameworks, training courses and organizing workshops with support for workshop attendees. Among the funded projects, 60.82% proposed providing educational materials that would be beneficial to K-12 students, college students and the public.

The present data range from 2015 to May 2023. New projects awarded after May 2023 were not included.

The findings provide rich and useful information for the funding agency, SaTC researchers and students. The funding agency may want to review their funding focus and fund distributions; SaTC researchers could refer to the topics and the objectives discussed in funded proposals when developing their new projects; and students at all levels could refer to SaTC topics, participating researchers and institutions for their learning.

This study is the first attempt, to the best of the authors’ knowledge, to analyze NSF SaTC projects. The analysis benefits researchers and students to gain an understanding of NSF-funded projects and insights into secure and trustworthy cyberspace areas.

This study aims to investigate the determinants of the capital structure of public–private partnership (PPP) projects in China and the nonlinear relationship between them.

First, this study identifies potential factors that can influence the capital structure of PPP projects based on literature and theoretical analysis. Second, this paper collects data from PPP projects in China and empirically investigates them using multiple linear regression and machine learning methods. Finally, for machine learning model results, this paper adopts the Shapley additive explanations to interpret them.

The results show that project size, contract duration, number of sponsors, urbanization level and regional openness are key factors influencing project capital structure, and there is a nonlinear relationship between all these factors and capital structure.

Theoretically, this study complements the influencing factors of PPP project capital structure and reveals their nonlinear relationship. Practically, the findings of this study can help PPP project participants formulate project capital structure more scientifically.

Practically, the findings of this study can help project managers to recognize the important factors affecting the capital structure of PPP projects and formulate capital structure more scientifically. Moreover the results are conducive to policymakers to predict a reasonable capital structure for PPP projects and better control project risks. These research findings can also help creditors make more accurate loan decisions and promote project success to meet the needs of the general public.

Most existing literature has studied the linear relationship between influencing factors and the capital structure of PPP projects. This study uses machine learning models to explore the nonlinear relationship between influencing factors and the capital structure of PPP projects and explains the working principles.

Low-carbon city policies (LCCP) are crucial environmental regulatory frameworks driving China’s transition toward a low-carbon economy. This study investigated the impact of LCCP on enterprise digital transformation (EDT).

This study employed a staggered difference-in-differences model for Chinese listed companies from 2007 to 2021. It also used a cross-sectional model for further analysis.

We found that the implementation of LCCP can promote EDT. This impact was more pronounced among enterprises with greater media attention in high-energy-consumption industries and well-developed economic areas.

This study has practical implications for the LCCP, as it evaluates the consequences of macro-level LCCP on micro-level corporate economic consequences. It provides an important reference for developing countries to implement LCCP and promote green industry upgrading.

This study broadens the impact of the LCCP, providing valuable insights into substantiating carbon neutrality goals and fostering the influencing factors of EDT.