Against the dual backdrop of iterative AI advancement and deepening green development imperatives, AI-driven industrial intelligence (INT) has emerged as a pivotal force in driving sustainable economic growth. While the existing literature has explored the correlation between INT and green total factor productivity (GTFP), significant gaps remain in the design of multidimensional variables, analysis of environmental regulation (ER), and capture of dynamic effects. From the perspective of ER, this study utilizes provincial panel data from China (2012–2023) to construct an 11-indicator evaluation system for INT development and employs the EBM super-efficiency model to measure GTFP. Furthermore, a two-way fixed effects model combined with a moderated mediation model is established to systematically elucidate the intrinsic linkage mechanism between INT and GTFP. The key findings are as follows: First, INT has a significant positive impact on GTFP. Second, green innovation and spatio-economic synergy are crucial pathways through which INT empowers GTFP. Third, ER exhibits a substitution effect within both the direct and indirect impacts of INT on GTFP, where intensified ER significantly attenuates INT’s positive impacts. Fourth, the enhancement effect of INT on GTFP remains statistically significant with a one-year lag, and the substitution effect of ER persists. This study provides an in-depth analysis of the mechanisms of INT-driven green economic transformation, offering valuable insights for governments to implement differentiated environmental governance strategies tailored to local conditions.

Against the dual background of restructuring global green trade rules and advancing China’s “dual-carbon” strategy, environmental information disclosure has increasingly become a crucial strategic tool for firms seeking to enhance international competitiveness. Existing studies have mainly examined the quantity, content, and quality of environmental information disclosure, while paying insufficient attention to its relative disclosure position, a strategic feature of information presentation that may affect signal salience and attention allocation. Drawing on signaling theory and the attention-allocation perspective, this study examines Chinese A-share listed exporters by matching environmental information disclosure positions observed during 2021–2023 with firms’ export product quality during 2022–2024. The final baseline sample contains 1472 firm-year observations. The findings show that earlier environmental information placement is significantly associated with higher subsequent export product quality. This relationship is mainly concentrated in the difference between Position_1 and Position_3, whereas Position_2 does not differ significantly from Position_1. The main result remains robust to alternative coding of disclosure position, additional controls for environmental disclosure volume, adjustment of the sample period, a panel-preserving placebo test, and an external peer-based instrumental-variable analysis. The three-step mechanism tests provide channel-consistent evidence for two potential transmission channels. More prominent environmental information placement is associated with a higher likelihood of ISO 14001 certification, reflecting stronger externally verifiable green credibility, and with greater realized green innovation output, reflected in granted green patents. Both mechanism variables are positively associated with subsequent export product quality. This study extends the literature on environmental information disclosure by shifting attention from static disclosure characteristics to the strategic prominence of information presentation, enriches the micro-level application of signaling theory in international trade, and provides practical insights for firms and regulators to improve the prominence, credibility, and substantive consistency of environmental disclosure while promoting export-quality upgrading.