Enterprise green innovation (EGI) has become an essential measure for manufacturing enterprises to achieve sustainable development, and the application of artificial intelligence (AI) may become a new driving solution. This study empirically analyzes the impact and internal transmission mechanism of AI on EGI of Chinese manufacturing listed enterprises from 2010 to 2022. Research has found that (1) AI significantly impacts EGI, and this basic conclusion has passed various endogeneity and robustness tests. (2) The mechanism test results indicate that enterprise technological capability, innovation investment, and executives’ environmental awareness significantly mediate between AI and EGI. (3) Heterogeneity analysis shows that the significant positive impact of AI on EGI is only established in enterprises with overseas backgrounds, large-scale, highly competitive regional markets, and low-carbon pilot cities. The above conclusions have contributed essentially to the literature on EGI and AI.

In an era marked by volatility, uncertainty, complexity, and ambiguity, constructing resilient regional innovation ecosystems is identified as a critical strategic imperative for achieving high-quality development and advancing sustainable development goals. Drawing on the Technology–Organization–Environment (TOE) integrative framework, this study examines six antecedent conditions of ecosystem resilience from the perspective of digital transformation: digital infrastructure, digital innovation capacity, digital human capital, digital government governance, digital attention, and digital finance. A sample of 48 prefecture-level cities from the Beijing–Tianjin–Hebei, Yangtze River Delta, and Pearl River Delta urban agglomerations in China between 2018 and 2022 is selected. Through the application of dynamic Qualitative Comparative Analysis (QCA), the study explores the multiple configurations across temporal and spatial dimensions through which technological, organizational, and environmental factors contribute to enhancing regional innovation ecosystem resilience. The results indicate that ecosystem resilience is jointly driven by multiple interacting factors, and no single condition is found to be necessary. Four distinct causal pathways are identified as sufficient to enhance resilience: (1) a triadic synergy of technology, organization, and environment; (2) a technology-driven, talent-supported configuration; (3) a technology-driven, government-supported configuration; and (4) a dual technology–environment-driven model. While none of the configurations exhibit consistent temporal effects, some are influenced by unobserved factors in specific years. Moreover, cities do not converge on a single dominant configuration when achieving high levels of ecosystem resilience.

Purpose: Implementing a green strategy to enhance the competitiveness of enterprises is a hot topic in current research. Although most enterprises have formed a green strategy orientation (GSO), it has not been transformed into green competitiveness (GC). Prior studies have not thoroughly studied the effect and mechanism of GSO on GC. To fill this research gap, based on optimal distinctiveness theory, this paper discusses the mediating role of two kinds of green innovation (GI) in the GSO–GC relationship and the moderating role of big data capability (BDC). Design/methodology/approach: This study adopts the quantitative research methods of multiple linear regression, Bootstrap and structural equation modeling (SEM). Data were collected through a questionnaire and a random sampling method was used to survey middle and senior managers and professionals in manufacturing enterprises. About 400 questionnaires were distributed, and 342 valid questionnaires were collected. Findings: The conclusions show that GSO significantly positively affects GI and GC. Still, it turns out that only strategic green innovation (SGI) mediates the GSO–GC relationship. BDC can positively moderate the mediation effect of SGI between GSO and GC, thus supporting the moderated mediation model. Research limitations/implications: This study used a survey questionnaire from Chinese manufacturing enterprises to collect data, but the sample size was limited. Furthermore, the mediating mechanism by which GSO affects GC requires further exploration. This study directly establishes the GSO–GC relationship based on the optimal distinctiveness theory, making an essential contribution to the literature on GSO and GC. At the same time, this paper uses GI as a bridge to connect the relationship between GSO and GC, enriching the literature on GI. In addition, we consider BDC to be a moderator, expanding the boundaries of the GSO–GC relationship. Practical implications: This study provides new knowledge and insights for manufacturing enterprises to construct and implement green strategies to achieve GC. More importantly, managers should attach great importance to the critical role of SGI and BDC. Originality/value: This study understands the importance of GSO, SGI and BDC to GC in theory and practice.