Based on China's 30 provincial panel data from 2006 to 2018, this paper uses the spatial Durbin model to empirically study the influence path and transmission mechanism of financial development on regional technological innovation, and introduce digital finance environment, marketization degree, and government environmental management as an adjustment variable to verify its regulating effect on financial development and regional technological innovation. The study found that the overall promotion of digital finance to local technological innovation is not significant. Besides, the characteristics of ownership discrimination and weak risk appetite in the bank's medium and long-term credit market have led to its failure to promote regional technological innovation. In contrast, the stock market and bond market in direct financing channels have enhanced local innovation capabilities. When the external environmental system is used as the adjustment variable, the results show that an excellent digital finance development, marketization degree, and government environmental management can effectively and positively regulate the effect of financial development and technological innovation.

This study analyzes how the presence and the composition of nomination committees could influence innovation. Specifically, we focus on the committee size, the frequency of meetings, and the presence of independent and female members. Innovation is measured by (1) the firm’s ability to produce innovations such as new or improved products/processes and the number of patents, and (2) innovation expenditures (R&D spending and the number of scientists and experts per R&D teams). This study is drawn on firms listed on the SBF120 index, between 2002 and 2016. It provides the following results: First, the presence of nomination committees is negatively associated with the number of patent applications and the number of scientists and engineers in R&D teams. Furthermore, most of the nomination committee’s attributes (such as the presence of independent and women members) have no significant association with innovation proxies. We show, also, that large nomination committees are prone to increase innovation income (such as the R&D expenditures and the number of scientists and experts per R&D teams) as well as the number of filed patents. Turning to the committee functioning provides evidence that the frequency of meetings could reduce asymmetric information regarding innovation projects and lead to an increase of R&D expenses. This article brings practical insights to board structures issues. This study contributes to the current debate on how boards should be organized, specifically; the nomination committees. It shows that boards should pay attention to the nomination committee features as they could undermine innovation projects, specifically in innovation-sensitive industries.

Improving energy conservation and emission reduction (ECER) efficiency is a virtuous cycle of economic development and environmental protection, promoting countries around the world towards sustainable development. As a strategic technology leading a new round of technological revolution and industrial transformation, the large-scale application of artificial intelligence (AI) is driving the transformation of manufacturing production methods, which is increasingly essential for improving the effectiveness of environmental governance. This study aims to analyze the impact of AI technology on ECER in the manufacturing industry, as well as the specific impact paths and heterogeneity. We contribute to previous literature by measuring ECER of Chinese manufacturing sector using the EBM model. The mediation effect model is used to analyze the impact mechanism between AI technology and ECER. The results indicate that AI promotes the ECER efficiency in the manufacturing sector. The positive effects are attributed to the development of energy consumption structure and technological innovation. The impact of AI on ECER exhibits an evident heterogeneous effect across industries with different pollution intensity, R&D intensity and labor intensity, and ownership dominant industry. Additionally, higher levels of environmental regulation lead to an increase in the positive effects of robot promotion on ECER. The research conclusions provide important reference for understanding the relationship between AI technology and ECER, and contribute a new way to promote environmental governance and carbon neutrality.

When human civilization is thriving to the rapid economic and social development, the deteriorating ecological environment has also pressured the society to put environmental protection issues on the agenda future development. Therefore, green innovation is not merely a requirement for a corporate’s long-term development but the basics of sustainable development of human society. As an objective indicator to measure a company's commitment to social responsibility, corporate environmental responsibility has an extremely important impact on a corporate's business philosophy and innovation strategy. However, the existing literature rarely studies between these two variables. This paper collects the data of listed companies from 2010 to 2019 and empirically tests the impact of corporate environmental responsibility on corporate green innovation. The study finds that corporate environmental responsibility can promote green innovation by reducing financing constraints, improving corporate governance and increasing government subsidies and R&D investment, and the result is still significant under a series of robustness checks. The placebo test suggests that the Jiangsu enterprise responsibility construction pilot has facilitated the development of enterprises' green innovation to a great extent. The conclusions enrich existing literature on corporate environmental responsibility and corporate green innovation, providing implications for government policy orientation, social atmosphere guidance and corporate strategic decision-making process.

In the context of digital economy, Hebei Province, as an important province in central China, has a huge manufacturing base and potential, and the development of digital economy has driven the transformation and upgrading of manufacturing industry to a certain extent. In order to better play the driving role of digital economy in the transformation and upgrading of manufacturing industry, this paper establishes an intermediary effect model based on three intermediary variables: enterprise resource allocation ability, enterprise cost and enterprise innovation ability, and conducts an empirical study on the panel data of 11 prefecture-level cities in Hebei Province from 2017 to 2022. The mechanism of digital economy driving manufacturing upgrading in Hebei Province was discussed. The results show that: (1) there is a significant positive correlation between the development of digital economy and the transformation and upgrading of manufacturing industry, which indicates that the development of digital economy has a direct driving effect on the transformation and upgrading of manufacturing industry; (2) There is also a positive correlation between the development of digital economy and the resource allocation ability and innovation ability of enterprises, indicating that the development of digital economy can improve the resource allocation ability and innovation ability of enterprises, and is conducive to the stable development of enterprises in the future; (3) Enterprise resource allocation ability and enterprise innovation ability play a significant intermediary role in the relationship between digital economy development and the transformation and upgrading of manufacturing industry, indicating that digital economy can indirectly accelerate the transformation of manufacturing enterprises' R & D achievements by improving enterprises' resource allocation ability and innovation ability, thus improving enterprises' market competitiveness and increasing enterprises' earnings. Drive the transformation and upgrading of the manufacturing industry. Based on this research conclusion, the government should give full play to the role of guidance and support, and introduce relevant policies to help the digital economy drive the transformation and upgrading of manufacturing enterprises. Manufacturing enterprises should seize the tide of the development of the digital economy, use digital technology to improve their resource allocation ability and innovation ability, enhance core competitiveness, and accelerate the transformation and upgrading of enterprises.

The knowledge-capital model explains outward foreign direct investment (FDI) of a country from its relative abundance of its knowledge assets. Early versions of the knowledge-capital theory model these assets as if they were only the results of knowledge investments by private firms. We extend the theory by a formal model of the public-private interaction in knowledge development. This sheds light on the role of the origin country of multinationals. The paper extracts four testable predictions from the model. We use the inter-country variation in national knowledge-creation systems and FDI performance to test the model using a new dataset; it holds knowledge-creation indicators for about 200 countries over the period 2000-2020. Based on a range of non-parametric tests, we can corroborate that the basic tenet of the knowledge-capital model is correct. Moreover, the results show the important role of public knowledge production for outward FDI.

The integration of artificial intelligence (AI) in video game design has transformed traditional workflows, allowing for the generation of text, images, music, videos, and code at unprecedented scales. However, this advancement presents complex challenges for copyright law, traditionally rooted in human originality and authorship. This article examines recent case law that underscores the evolving legal landscape, exploring landmark cases such as Zarya of the Dawn and Andersen v. Stability AI. These cases reveal the tensions between AI-generated outputs and copyright eligibility, especially in the dynamic, multimodal compositions inherent to video games. The review analyzes how various AI tools are employed across the stages of game development—from design documentation to character modeling, soundtrack composition, and cinematic sequences—and the legal uncertainties surrounding each. Emphasis is placed on the role of human input in determining copyright eligibility, proposing that human-AI co-creation models and enhanced metadata standards may offer pathways to reconcile AI-driven innovation with intellectual property protections. As video games exemplify the unique challenges in AI-generated, temporally interactive works, this study calls for a nuanced copyright framework that acknowledges both technological capabilities and the irreplaceable contribution of human creativity. The findings advocate for policy adaptations that align legal protections with the realities of AI-integrated creative processes, ensuring a balanced approach that supports both innovation and creator rights.