Artificial intelligence (AI) is increasingly transforming firms’ innovation processes and creating new opportunities for sustainable development; however, empirical evidence on how firm-level AI capability influences eco-innovation under different digital environments remains limited. Drawing on the Natural Resource-Based View and Dynamic Capability Theory, this study examines the effect of AI capability on firm-level eco-innovation and investigates whether national digital economy development strengthens this relationship. The analysis uses an unbalanced panel of 5564 listed firms from 15 Asian economies over the period 2017–2024. Firm fixed-effects models are employed as the primary estimation approach, while moderation analysis is complemented by System GMM, propensity score matching, and Heckman two-step estimation to address potential endogeneity and sample-selection concerns. AI capability is measured using a disclosure-based index derived from textual analysis of firms’ annual and sustainability reports. The results indicate that AI capability is positively associated with eco-innovation and that this relationship is significantly stronger in countries with more advanced digital economies. These findings suggest that supportive digital ecosystems enhance firms’ ability to convert AI capability into sustainable innovation outcomes. The study contributes to the literature by demonstrating that national digital economy development serves as an important contextual condition shaping the effectiveness of firm-level AI capability in promoting eco-innovation.

Weak institutional environments may encourage aggressive corporate tax practices, but firms’ responses can vary depending on the strength of their internal governance. This study examines the relationship between perceived corruption risk and corporate tax avoidance and investigates whether firm-level environmental, social, and governance (ESG) performance moderates this relationship. Using an unbalanced panel of 921 publicly listed firms, comprising 7628 firm-year observations across 11 Middle East and North Africa (MENA) economies from 2013 to 2024, the study employs fixed-effects panel regressions alongside several robustness checks. The findings indicate that greater perceived corruption risk is associated with higher corporate tax avoidance, while stronger ESG performance significantly weakens this association. Subsample analyses also reveal positive CORRISK × ESG interaction coefficients in both higher- and lower-corruption economies, with a larger estimated coefficient in the higher-corruption group. The main findings remain broadly consistent when using an alternative measure of tax avoidance, two-step System GMM, additional firm- and country-level controls, and alternative fixed-effects specifications. However, the country-level corruption measure reflects the broader institutional environment rather than firms’ direct exposure to corruption, which should be considered when interpreting the results. Overall, the study highlights the potential role of firm-level ESG in mitigating the relationship between perceived corruption risk and aggressive corporate tax behavior, emphasizing the importance of considering both institutional conditions and corporate governance practices in MENA economies.