Purpose: Drawing on knowledge-based theory and from the perspective of dynamic capabilities, this study aims to investigate the impact of digital and intelligent (D&I) transformation on both exploitative and explorative technological innovation, and further examines how knowledge recombinant capabilities, namely, reuse and creation, moderate these effects. Design/methodology/approach: The research model was empirically examined by using a panel data set of Chinese listed firms in Shanghai and Shenzhen Stock Exchanges from 2000 to 2022. To address potential endogeneity concerns and strengthen the validity of the findings, we further conducted a series of robustness checks, including two-stage least squares estimation with the instrumental variable, a firm-level fixed-effect model that controls for unobserved heterogeneity across firms and over time and the panel regression models using robust and clustered standard errors. Findings: The empirical results demonstrate that firm D&I transformation exerts a significantly positive impact on both exploitative and explorative technological innovation, highlighting the dual roles of D&I transformation in reinforcing incremental improvements as well as fostering breakthrough advances. Moreover, the analysis reveals that firms with stronger knowledge recombination capabilities, including both recombinant reuse capability and recombinant creation capability, could gain more technological innovation benefits from D&I transformation. Originality/value: The findings highlight that firms’ benefits from D&I transformation vary, with knowledge recombination capabilities critically shaping how such transformation drives technological innovation, offering insights into how firms can strategically manage and reconfigure knowledge to fully unlock the innovative potential of technological changes.

The rapid evolution of artificial intelligence, big data, and generative AI models has ushered in significant transformations across various sectors, including education. Digital Human Technique, an innovative technology grounded in advanced computer science and artificial intelligence, is reshaping educational paradigms by enabling virtual humans to simulate human behavior, express emotions, and interact with users. This paper explores the application of Digital Human Technique in education reform, focusing on creating immersive, intelligent classroom experiences that foster meaningful interactions between teachers and students. We define Digital Human Technique and delve into its key technical components such as character modeling and rendering, natural language processing, computer vision, and augmented reality technologies. Our methodology involves analyzing the role of educational digital humans created through these technologies, assessing their impact on educational processes, and examining various application scenarios in educational reform. Results indicate that Digital Human Technique significantly enhances the learning experience by enabling personalized teaching, increasing engagement, and fostering emotional connections. Educational digital humans serve as virtual teachers, interactive learning aids, and facilitators of emotional interaction, effectively addressing the challenges of traditional educational methods. They also promote a deeper understanding of complex concepts through simulated environments and interactive digital content.

Implementing environmental protection taxes implies a shift in environmental policy from government enforcement to market incentives, fostering long-term sustainability. Based on institutional theory, this study explores the nonlinear impact of environmental taxes on corporate green innovation and its influencing mechanism, by considering the complex interaction between innovation offsets and environmental costs. Utilizing data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges during 2012 and 2023, the study reveals an inverse U-shaped relationship between environmental taxes and green innovation performance, within which corporate environmental responsibility functions as a mediator. Furthermore, the results also reveal that the relationship between environmental taxes and green innovation is positively moderated by the development level of regional green finance. In addition, the heterogeneity analyses show that the inverse U-shaped relationship is more pronounced among heavily polluting and large-scale firms, and firms in more marketized areas and areas with higher levels of intellectual property protection. The research enriches the literature on the dual-edged effects of environmental taxes anchored in green innovation and unpacks the internal mechanism of the effectiveness of environmental protection tax policy. It also provides practical implications for the design of tiered taxes and green finance policies aimed at achieving sustainable development.

Image super-resolution (SR) enhances the clarity and detail of images produced by consumer electronics, particularly in high-end devices, offering a superior visual experience. In this paper, a SR technique is proposed to enable effective and reliable processing in consumer electronics. Deep networks extract hierarchical structural feature through multiple layers for improving image SR results, however, their robustness may be limited in complex scenes. To address this, a cluster tree network for image SR (CTSRNet) was developed. The proposed tree architecture leverages hierarchical information by branching feature extraction into pathways that process varying, levels of abstraction and complexity, unlike monolithic convolutional neural networks (CNNs), where deep features may suppress shallower ones. Adaptability to diverse scenes is enhanced through conditional parameterized convolutions (Condconv), which learn salient features. By clustering image regions based on their properties, the networks learns inter-region to recover finer details. Experimental results demonstrate that CTSRNet surpasses popular SR methods. With reduced complexity and resource demand, the proposed approach is well-suited for edge computing in consumer electronics. The code is available at https://github.com/xwq325/CTSRNet.