ESG serves as a key metric for measuring corporate sustainability, but divergence among rating agencies has led to uncertainty in such an assessment. This investigation identifies ESG rating divergence as a critical catalyst for corporate digital transformation, establishing empirical analysis through a robust positive correlation between the heterogeneity in sustainability assessments and organizational digitalization intensity. Comprehensive robustness examinations and endogeneity controls substantiate the persistent significance of this relationship. Mechanistically, such divergence drives technological adaptation by restructuring the R&D team composition and elevating capital allocation toward innovative initiatives. Contextual heterogeneity manifests through amplified effects in firms with elevated analyst scrutiny and stringent internal governance, whereas pollution-intensive enterprises exhibit significant effect suppression. These findings collectively advance theoretical frameworks concerning ESG evaluation economics and digital transformation drivers, while furnishing actionable implementation blueprints for corporate digitization strategists.

In the pursuit of global industrial sustainable development and carbon neutrality goals, the aviation manufacturing sector serves as a strategic pillar for advancing global economic growth, driving technological innovation and enhancing national competitiveness. Its green innovation has thus become a critical pathway to achieving carbon neutrality targets and spearheading the sustainable transformation of the industrial sector. This study investigates the enabling effect of artificial intelligence (AI) on green innovation within aviation manufacturing enterprises. The findings indicate that AI exerts a promotional impact on green innovation via three primary channels: technological empowerment, labor structure optimization and resource access improvement. Specifically, AI drives the digital transformation of operational processes in aviation manufacturing, rationalizes the human resource framework of the sector, and eases the financing pressures confronted by aviation manufacturing enterprises. A heterogeneity analysis reveals that regional resource endowments, enterprise production attribute characteristics and external market attention can form synergistic interactions with AI technology. What is more prominent is that the positive influence of AI on green innovation is especially distinct in three scenarios: in economically developed urban areas, among enterprises with traditional production attributes, and for enterprises that garner high levels of analyst attention.