The long-term development of the manufacturing industry relies on sustainable tax management, which plays a key role in optimizing production costs. While artificial intelligence models have been applied to tax-related predictions, research on their application for predicting tax management levels is quite limited, with no studies focused on the manufacturing industry in China. To enhance digital innovation in corporate management, this study applies interpretable artificial intelligence models to predict the tax management level, which helps decision-makers maintain it within a sustainable range. The ratio of total tax expense to total profits (ETR) is used to represent the tax management level, which is predicted using decision trees, random forests, linear regression, support vector regression, and artificial neural networks with eight input features. Comparisons among the developed models indicate that the random forest model exhibits the best performance in terms of prediction accuracy and generalization capability. Additionally, the Shapley additive explanations (SHAP) technique is integrated with the developed model to enhance the interpretability of its predictions. The SHAP results reveal the importance of the input features and also highlight the dominance of certain features. The results show that the ETR from the previous year holds the greatest importance, being more than twice as significant as the second most important factor, whereas the effect of board size is negligible. Moreover, benefiting from the local interpretations using SHAP values, this approach aids managers in making rational tax management decisions.

This paper examines whether executives’ environmental expertise shapes firms’ engagement in open innovation. Open innovation requires long-term, uncertain, and relationship-specific investments, yet such investments are often underprovided due to manage rial short-termism. Based on stewardship theory, we argue that environmental expertise embeds a long-horizon cognitive orientation in executives and alleviates managerial myopia. Based on the upper echelons theory, we posit that executives with environmental expertise could significantly influence corporate strategies, specifically enhancing corporate open innovation activities. Using a panel of 40,133 firm–year observations from Chinese listed firms over 2002–2022, we find that firms led by environmentally expertised executives exhibit significantly greater open innovation, measured by co-patenting activities. To address endogeneity concerns, we implement a stacked difference-in-differences design and propensity score matching. Our results remain robust in a battery of robustness tests. Consistent with the managerial myopia alleviation argument, we show that the effect of executives with green expertise on open innovation is stronger in settings where short-term pressures are more pronounced, including firms with weaker governance and higher capital market pressure. This study highlights and provides micro-level evidence on how environmental human capital enables firms to adopt sustainability-oriented business models through open innovation. Overall, our findings contribute to the literature on sustainable entrepreneurship and open innovation and offer practical implications for firms and policymakers seeking to support long-term, innovation-driven sustainability transitions.