Digital Transformation and Green Innovation Performance in New Energy Enterprises: A Configurational Analysis of Complex Resource Systems Using fsQCA

Green innovation performance (GIP) in new energy enterprises emerges from complex interactions among technological, organizational, and institutional resource subsystems, yet existing research predominantly applies linear, single-factor approaches that fail to capture this systemic complexity. Drawing on the Resource-Based View (RBV) and systems thinking, this study employs fuzzy-set qualitative comparative analysis (fsQCA) on a sample of 54 Chinese A-share listed new energy enterprises—spanning wind power, solar power, hydrogen energy, energy storage, and new energy equipment manufacturing—observed over the 2019–2023 period, to examine the configurational pathways through which these firms achieve high GIP. Green patent grants serve as the outcome measure, and six conditions spanning three resource subsystems are considered: digital transformation and R&D intensity (technological subsystem), firm size and ownership structure (organizational subsystem), and government subsidies and carbon emission performance (institutional subsystem). Three key findings emerge. First, none of the six conditions is individually necessary for high GIP (all consistency scores below 0.90), indicating that high GIP reflects combinations of resources rather than a single driver. Second, the six sufficient configurations identified collapse into two distinct pathway clusters: a “SOE digital-empowerment-driven” cluster, in which digital transformation combines with R&D investment, government subsidies, or organizational scale within state-owned enterprises, and a “resource–capability synergy and substitution” cluster, in which scale resources, R&D investment, and policy support combine with or substitute for digital transformation regardless of ownership. Third, digital transformation appears in five of the six pathways, indicating that it functions as a key—but not universal—enabling element whose effectiveness depends on its alignment with other system components. Beyond confirming that multiple, equally valid resource combinations lead to high GIP, this study’s principal contribution is to embed RBV within an explicit systems framework, showing how technological, organizational, and institutional resources interact as subsystems of a single socio-technical system, and to translate the resulting configurations into differentiated, pathway-specific guidance for enterprises and policymakers navigating the low-carbon energy transition.