Amid escalating global economic uncertainty, a comprehensive analysis of the effect of artificial intelligence (AI) development on urban economic resilience (UER) is crucial for promoting sustainable global economic development. This study utilizes panel data from 284 Chinese cities from 2010 to 2022 to empirically test the influence of urban AI on UER and its role mechanism by using the fixed-effects, mediating-effects, and moderating-effects models. The study reveals that AI significantly enhances UER, with an improvement of 7.44%. Harnessing AI for UER remains valid even after conducting the robustness and endogeneity tests. Mechanism analysis discovered that AI enhances UER by increasing urban innovation ability. Industrial structure and wage structure positively moderate the effect of AI on UER. Heterogeneity analysis demonstrates that the improvement effect of AI level on UER is more evident in large (7.49% increase), southern (5.11% increase), non-resource-based (10.84% increase), and high-economic cities (11.17% increase). This paper discusses the path selection from an AI perspective to enhance UER, which provides a useful reference for cities seeking to navigate the new wave of technological revolution.

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.

The present study explores the impact of energy natural resource productivity and environmental tax on environmental sustainability in six major CO2-emitting economies: the Euro Area, China, South Korea, Japan, the United Kingdom and the United States, from 1997 to 2019. This analysis aims to reveal novel findings and implications for different energy natural resource productivity types and environmental regulations. We employed data regarding leading national and regional CO2 emitters from 1997 to 2020 to conduct an empirical analysis using the panel non-linear auto-regressive distributed lag (NARDL) and panel quantile ARDL (QARDL) methods. The results show that energy natural resource productivity and environmental tax are crucial components in reducing CO2 emissions by controlling for innovation technology and renewable energy consumption. The main findings demonstrate that the impact is stronger in the presence of increased energy natural resource productivity and vice versa. These findings have novel implications for sustainable development and carbon neutrality.