Urban green development has become a crucial approach for balancing ecological conservation and socio-economic development. The digital economy (DE) and new-type urbanization (NTU), as technological and social systems, respectively, are both driving urban green development. In this context, furthering their synergistic effects could substantially improve urban sustainability outcomes. Grounded in sociotechnical systems theory, this study applied pooled and multi-period fuzzy-set qualitative comparative analysis (fsQCA) to analyze urban green development pathways in 79 Yellow River Basin cities (2020–2022). The pooled fsQCA indicates that urban green development is driven by synergistic interaction within the NTU-DE subsystem, especially industrial digitalization–spatial urbanization. The multi-period fsQCA further demonstrates that industrial digitization has always existed as a core condition, which means that it plays a more general role. In addition, the Yellow River Basin exhibits distinct regional variations in urban green development, where the downstream region is dominantly driven by DE and spatial urbanization, the upstream region by industrial digitization, and the midstream region demonstrates diversified pathways. This study enhances understanding of complex system interactions in urban green development and provides policy-relevant insights. For policy implementation, local governments should not only prioritize effective synergies between industrial digitization and spatial urbanization but also develop differentiated strategies for the DE and NTU subsystems based on local conditions.

With the introduction of the dual carbon goals of “carbon peak” and “carbon neutrality”, low-carbon economic development has become the focus of attention for all sectors in China. Improving carbon productivity (CP) is the key to developing a low-carbon economy and is essential to achieving carbon emission reduction and economic growth. The rapid development of the digital economy (DE) provides a new breakthrough for improving carbon productivity, but there are few studies on the digital economy and carbon productivity in the existing literatures. We empirically investigate the impact mechanism of the digital economy on provincial carbon productivity using China’s provincial panel data from 2010 to 2019. This study finds that the digital economy significantly raises provincial carbon productivity; this result remains valid after a series of robustness tests. The digital economy can impact provincial carbon productivity through both energy consumption structure (ECS) and green technology innovation (GTI) and can contribute to the improvement of provincial carbon productivity by optimizing energy consumption structure and improving green technology innovation. In addition, different environmental regulations have different impacts. Command-and-control environmental regulation (CER) weakens the positive impact of the digital economy on provincial carbon productivity, whereas market-based environmental regulation (MER) enhances the positive impact of the digital economy on provincial carbon productivity. This study extends the research on the digital economy in the field of low-carbon development, providing insights and policy suggestions for relevant departments to promote low-carbon development from the perspective of the digital economy.

Using panel data for 30 provinces in mainland China (2013–2022), this research examines how artificial intelligence (AI) affects green innovation resilience (GIR) and the mechanisms through which this occurs. It tests industrial structure advancement and industrial structure rationalization as mediating channels, and evaluates threshold effects associated with public environmental concern and environmental regulation. The results indicate that AI is positively and significantly related to GIR, and the conclusion remains stable under multiple alternative specifications and robustness checks. Further analysis reveals that different dimensions of industrial structure upgrading play distinct roles. AI indirectly strengthens innovation resilience through the quantity and quality dimensions of industrial structure advancement, whereas industrial structure rationalization does not constitute an effective transmission channel, highlighting heterogeneity in technological–structural synergy. Moreover, the threshold effects of public environmental concern and environmental regulation differ markedly. Public environmental concern exhibits a critical threshold that needs to be maintained within a reasonable range, whereas stronger environmental regulation amplifies the technological dividends of AI in a staircase reinforcement pattern. Overall, this study systematically explores the mechanisms and boundary conditions through which AI drives green innovation resilience, providing new theoretical insights and empirical evidence for green transformation in the AI era.

Purpose: This study aims to examine new product development (NPD) performance to identify effective knowledge update strategies and assess the impact of environmental uncertainty on these dynamics. It aims to understand how different knowledge potentials and organizational routines interact to enhance product outcomes. The specific subsidiary context enriches understanding by identifying challenges and opportunities that are not typically visible in broader organizational studies. Design/methodology/approach: A survey of 310 business managers was conducted to measure their knowledge potential and organizational routine updating. The hypotheses were tested using hierarchical regression analysis to determine the optimal combinations of knowledge and practice updates for NPD performance. Findings: Results indicate that both knowledge potential and organizational routine updating significantly enhance subsidiary performance. Specifically, knowledge accumulation paired with routine creation and knowledge difference paired with routine revision optimally boosts product development. Moreover, this study reveals an inverted U-shaped relationship between environmental uncertainty and the effectiveness of these combinations, suggesting a complex interplay that affects NPD performance. Originality/value: This study enhances understanding of NPD performance by integrating resource concordance theory with empirical analysis of knowledge and organizational strategy adaptations. It underscores the moderating role of environmental uncertainty, offering new theoretical insights into enhancing product development performance. Although the focus on subsidiaries limits broader applicability, it provides valuable insights into the nuanced NPD dynamics in these specific entities, suggesting avenues for future research to expand this study’s findings across different organizational types.

Achieving the low-carbon transformation of energy consumption structure is central to carbon-neutral development, yet how artificial intelligence (AI) contributes to sustained structural change remains insufficiently understood. Addressing this gap, this study examines not only whether AI facilitates the green and low-carbon transformation of energy consumption structure in China, but also through which mechanisms and under what conditions this effect is sustained. Using provincial panel data for 2013–2022, we estimate the direct effect of AI and test the mediating role of green innovation resilience and the moderating role of green finance. The results show that AI significantly promotes the low-carbon transformation of energy consumption structure. Green innovation resilience serves as an important transmission mechanism, while green finance strengthens this relationship. These findings deepen understanding of the AI-energy transition nexus and provide policy implications for effectively coordinating digital technology, green innovation, and green finance.