The sustainable development of the mining industry is essential for economic growth. However, this practice necessitates environmental protection and social sustainability. This study uses the super-efficient epsilon-based measure (EBM) model to measure mining energy efficiency (MEE) based on panel data for 30 provinces from 2007 to 2021 in China. We empirically examined the effects of technological innovation (TEC) on MEE and the mediating and threshold effects of industrial structure upgrading (IS) between the two through the fixed, mediating, and threshold effects models. The study findings show that TEC is conducive to enhancing MEE and that this role is relatively robust regarding the mechanism of action. TEC enhances the MEE industry through the IS. We observed the impact of TEC on MEE in the threshold effect of IS—as the level of IS rises, the role of TEC on MEE shows an increasing marginal effect. Therefore, the government should encourage the construction and innovation of technology to optimise the industrial structure and layout and improve energy efficiency in the regional mining industry. This study is a useful supplement to the study of MEE and provides new perspectives and methods for understanding and improving MEE. Meanwhile, the study results provide an important reference for the government to formulate long-term planning and policies for mining development, which is of great significance for optimising the structure of mining resources and improving MEE in the region.

Under increasing external uncertainty, promoting sustainable transformation and building data-driven supply chain resilience (SCR) supported by frontier technologies has become an important issue. This study uses Chinese A-share listed companies from 2005 to 2024 as the sample. From the perspective of frontier technologies, it employs text analysis and machine learning to identify large-scale unstructured annual report texts and quantify firm circular economy (CE) practices. SCR is measured using an entropy-weighted indicator system, and the relationship between CE practices and SCR is examined using panel regression models. The results show that CE practices significantly improve SCR, and the findings remain robust across multiple robustness and endogeneity tests. Mechanism analysis indicates that CE practices enhance SCR through three channels: resource effects, structural effects, and collaboration effects. Heterogeneity analysis reveals that the positive effect is more pronounced in non-state-owned firms, firms in non-heavy-polluting industries, high factor-intensive firms, and firms with active environmental information disclosure, as well as in regions with higher economic development and stronger governmental attention to green development, and in the period following the signing of the Paris Agreement in 2015. Further analysis shows that CE practices significantly improve the resistance dimension of SCR, while their effect on recovery is insignificant. Economic consequence analysis indicates that SCR enhancement driven by CE practices helps reduce operational risk and increase firm value. This study provides new empirical evidence on how CE practices strengthen SCR, and offers theoretical and practical implications for enabling sustainable supply chain development through frontier technologies and data-driven analytics.