Abstract

With the deepening advancement of the “dual carbon” goals and the digital intelligence process, enhancing green innovation performance has become a critical manifestation of high-quality and sustainable development for manufacturing enterprises. However, existing research focusing on the marginal effects of single factors struggles to reveal the complex causal mechanisms underlying such improvement. Based on the TOE (Technology–Organization–Environment) framework, this study takes Chinese A-share manufacturing companies listed on the Shanghai and Shenzhen stock exchanges from 2018 to 2024 as the sample and employs fuzzy-set Qualitative Comparative Analysis (fsQCA) and Necessary Condition Analysis (NCA) to explore the diverse driving paths through which digital intelligence empowers enterprises to achieve high green innovation performance. Six antecedent conditions are considered: technology application, technology R&D, digitally savvy executives, equity incentives, market competition, and environmental regulation. The findings reveal: (1) No single condition is necessary for achieving high or low green innovation performance; improvement requires the synergistic interplay of multiple factors. (2) Six configurational paths lead to high green innovation performance, which can be summarized into three driving models: technology-dominated, executive-led, and digital-intelligence fusion. (3) After further distinguishing between substantive and strategic green innovation performance, two shared paths and one unique path are identified. These findings provide a theoretical basis and practical reference for manufacturing enterprises to select differentiated pathways for improving green innovation performance.

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