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.

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.

With the strengthening of China's economic power and the further implementation of China's open-door policy, China's outward investment has increased year by year. Some international comments are only concerned about the former and there are some public opinions about China's outward investment threatening the host country's environmental quality. This paper takes the carbon emissions of 49 countries along the “Belt and Road” and the acceptance of China's outward FDI as samples, studies the impact of China's outward FDI on the carbon emissions of the relevant countries, and comes to the conclusion that: China's outward FDI is a major factor in China's economic development. It is concluded that China's OFDI increases the total carbon emissions of the countries along the Belt and Road while decreasing the carbon intensity, and the impact is carried out through the three major paths of economic scale, technological level and structural effect.

The digital economy has emerged as a crucial driving force for promoting China’s green transformation in the new development phase. The “Broadband China” initiative, a key policy aimed at fostering the digital economy, plays a significant role in enhancing green total factor productivity (GTFP). This paper, based on the "Broadband China" policy and panel data from 283 prefecture-level cities from 2009 to 2022, constructs a theoretical model and employs a multi-period difference-in-differences (DID) method to systematically analyze the impact of digital infrastructure development on urban green total factor productivity (GTFP) and its underlying mechanisms. The findings reveal that the “Broadband China” policy has significantly improved GTFP in pilot cities, with stronger effects observed in economically developed regions and large- to medium-sized cities. Mechanism analysis indicates that technological innovation, industrial structure optimization, and energy conservation and emission reduction are the main pathways through which digital infrastructure promotes the improvement of green total factor productivity (GTFP). The regional heterogeneity analysis reveals that policy effects are more significant in eastern regions and large or medium-sized cities, whereas the effects are relatively weaker in central and western regions and smaller cities. Robustness checks further validate the reliability of the research conclusions. Additionally, this study reveals the critical role of digital infrastructure development in enhancing green total factor productivity and promoting high-quality economic development from both theoretical and empirical perspectives. It provides valuable insights for optimizing digital infrastructure investment strategies and formulating regional development policies.

Based on the results of a simple Agent Based Model (ABM) six common and widely affirmed statements in financial economics are discussed: (1) The better an investor is informed, the higher his performance is expected to be, (2) There is a well-defined state of art how to make sound financial decisions, (3) High standards of public information create private as well as public value, (4) Highly skilled financial analysts usually decide better than their lousy colleagues, (5) The market loses informational efficiency if traders abstain from information, (6) To use Bayes’ updating rule means to make rational financial decisions. Financial markets are complex adaptive systems, in order to understand them, the interaction of autonomous agents has to be brought into the focus. As the ABM methodology allows to do this better than neoclassical methods, this simple technique is adopted and allows to evidence that all these beliefs, even if they may be deeply rooted in the profession, are probably false.

As governments worldwide grapple with the urgent need to mitigate climate change, understanding the nuanced relationship between fiscal targets and environmental innovation is crucial for crafting effective policy frameworks. Green technological advancement stands as the cornerstone of enterprise competitiveness, driving improvements in both economic prosperity and environmental sustainability. Government fiscal policies play a pivotal role in shaping the landscape of green transformation within enterprises, particularly through the establishment of fiscal revenue targets. Drawing upon data from prefecture-level cities and enterprise records, this study employs empirical analysis utilizing a fixed effects model and instrumental variable method. Our findings underscore the significant impact of fiscal revenue target setting on the trajectory of green technological advancement within enterprises. Specifically, the imposition of fiscal revenue growth targets exerts a dampening effect on the propensity for green technological advancement among enterprises. Notably, this disincentive is particularly pronounced among state-owned enterprises, as well as those with lower and moderate returns on capital, and those characterized by labor-intensive operations. Furthermore, our research delves into the underlying mechanisms of this inhibitory effect. We ascertain that the pressure associated with fiscal revenue growth targets hampers corporate green technological advancement by amplifying tax burdens and exacerbating financing constraints. In light of these findings, our study not only contributes to the theoretical understanding of the interplay between government fiscal policies and corporate innovation but also provides empirical evidence to inform strategies aimed at reconciling the imperative of fiscal revenue growth with the imperative of fostering green technological advancement within enterprises. In the broader context of governmental efforts to address climate change, this research underscores the importance of aligning fiscal policies with environmental objectives.

Against the backdrop of deepening globalization, the widespread adoption and application of digital technologies are reshaping global trade patterns and providing new momentum for low-carbon economic cooperation. Based on panel data from 10 ASEAN countries for the period 2010–2020, this study investigates the role of digital trade in the trade-environment nexus. The findings reveal an inverted U-shaped relationship between digital trade and carbon emissions, which is explained through the scale effect, structural effect, and technological effect. Furthermore, the study examines the influence of economic development levels and industrial structures on this relationship, identifying significant heterogeneity. Countries with higher levels of economic development and more advanced industrial structures exhibit a more pronounced inverted U-shaped relationship between digital trade and carbon emissions.

This article analyzes the impact of enterprise information transparency (TRANS) on CSR, while examines intrinsic mechanism, nonlinear relationship as well as the heterogeneous effect, taking the data of Chinese 711 listed enterprises from 2010 to 2020. This article finds: The improvement of TRANS is conducive to the fulfillment of CSR, noticeably there is a nonlinear relationship of inverted "U-shaped". Corporate reputation, internal control and external concerns play a part in mediating the influence of TRANS on CSR. The impact of TRANS varies significantly according to the responsibility orientation of different stakeholders. Multiple heterogeneity analysis reveals there is a more promotional effect in large-scale, non-state-owned and high marketization intensity enterprises. motional effect in large-scale, non-state-owned and high marketization intensity enterprises.

The rapid evolution of information and communication technology (ICT) has resulted in significant transformations across a wide range of social and economic activities. As a result, the ongoing digitalization process is emerging as a critical force in determining progress and modernity. This study looks at the complex relationship between digitalization and economic growth in European Union (EU) countries from 2017 to 2022, using the Digital Economy and Society Index (DESI) issued by the European Commission. Our work employs a sophisticated panel data estimate approach and the fundamental Cobb-Douglas model and presents an empirical perspective on the relationship between digital technology and economic prosperity. Based on the fixed-effect panel estimate, our findings show a significant correlation: A 1% rise in digitalization (as measured by the DESI index) correlates with a 0.3838% impressive rise in GDP growth. This study emphasizes the need to comprehend and utilize digital technologies' revolutionary potential. It presents a solid argument for stakeholders and governments to embrace digital innovation and leverage it to achieve long-term, sustainable economic growth.

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.