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.

The amount of local government debt has an important impact on the economic and social sustainability of a country. The rapid increase in local government debt in China over the past decade and the associated risks have profound implications for financial and economic sustainable development. While existing research has investigated governmental strategic interactions of tax and spending, little attention has been given to the spatial interaction of local government debt. This study employs Two-Regime Spatial Lag Models to investigate the spatial interaction of the debt among China’s 332 prefectural-level local governments over the period of 2015 to 2019. The findings show significant interaction effects between neighbouring governments, both in the acquisition and utilisation of debt quotas, and the interaction during the acquisition process is higher than that during the utilisation process. In addition, the interaction between neighbouring governments within the same province is more pronounced than that between governments adjacent but located in different provinces. Furthermore, the interaction of special debt is more manifest than that of general debt. These findings pass various robustness tests. Additionally, the mechanism test shows that fiscal competition is one of the driving forces behind the observed interdependence of local governments’ debt strategies.

Amid rising global economic uncertainty and frequent external shocks, strengthening marine economic resilience has become a core priority for coastal nations to stabilize industrial supply chains and achieve sustainable marine development. China’s traditional resource-driven marine economy faces persistent structural bottlenecks, including homogeneous industrial structure, low value addition and weak risk resistance. As a landmark national policy for sustainable marine economic growth, the Marine Economic Innovation and Development Policy (MEIDP) has been piloted in 15 coastal cities across two batches, yet its causal impact on marine economic resilience remains under systematic evaluation. Using panel data of 51 Chinese coastal cities from 2008 to 2023, this study employs a multi-period difference-in-differences approach with supporting analyses to systematically evaluate the MEIDP’s impact on marine economic resilience, as well as its moderating mechanisms and heterogeneous patterns. The key findings are threefold. First, the MEIDP significantly improves coastal cities’ marine economic resilience, and this positive effect remains stable after multiple robustness tests. Second, public health emergencies exert a significant positive moderating effect, where the industrial support capacity and risk-resilience foundations established through policy implementation function more effectively under shock conditions, thereby amplifying the enhancement of resilience. Third, the policy effect shows prominent heterogeneity, being more pronounced in high-tourism cities and the Northern Marine Economic Circle, while statistically insignificant in low-tourism cities and the Southern Marine Economic Circle. This study enriches the theoretical framework of marine economic policy evaluation and provides empirical evidence from a major developing country for global marine governance, confirming that marine policies that promote innovation are an effective path to strengthen economic risk resistance. In light of these findings, we propose targeted policy recommendations to steadily enhance overall marine economic resilience. Coastal regions should deepen marine policies that promote innovation to bolster industrial upgrading and technological empowerment, adopt differentiated schemes aligned with local industrial foundations and resource endowments, promote marine industrial diversification and chain extension to reduce structural vulnerability, and improve public risk response mechanisms to strengthen the counter-cyclical buffering capacity of the marine economy.