Global climate and environmental issues pose severe challenges to the sustainable development of human society. As major contributors to environmental pollution and carbon emissions, the quality of enterprises' environmental data has gained significant attention in academic and industrial circles. This study analyzes information from Chinese A-share companies spanning 2012 to 2023 to investigate the pathways through which artificial intelligence (AI) technology influences corporate environmental information disclosure (EID). The results indicate that AI significantly enhances the quality of corporate EID by optimising internal control levels and strengthening external supervision mechanisms. These conclusions have been validated through robustness and endogeneity tests. The heterogeneity analysis further reveals that the promoting effect of AI is more significant in large corporates, corporates in central cities, mature corporates, corporates audited by the Big Four international accounting firms, high-tech corporates, and heavily polluting industries. The study innovatively constructs a dual-path theoretical framework of ‘internal management optimisation–external supervision strengthening’ and integrates macro urban AI indicators with micro enterprise data, contributing new empirical support for the digital transformation and green governance of developing countries. Based on these findings, policymakers should promote the innovative application of AI technology in corporate environmental governance, improving internal control norms, optimising the external supervision system, and implementing a classified guidance strategy for different enterprise attributes, so as to help enterprises achieve low-carbon transformation and sustainable development.

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.

Some cities in China are facing challenges due to population loss while also attempting to address energy conservation and emissions reduction. Although urban shrinkage can relieve pressure from energy consumption demands, such as water, electricity and gas, does it improve urban energy efficiency? This study attempts to answer this question. Based on Point of Interest (POI) big data and Global Human Settlement Layer (GHSL) raster data, this study identifies urban shrinkage from the coupling perspective of administrative and economic boundaries. It also examines the impact of urban shrinkage on energy efficiency. The results suggest that Chinese counties’ overall energy efficiency is experiencing a four-stage “decline-rise-decline-rise” trend, and the urban shrinkage of Chinese counties exists in three major areas: the Northeast, the Southwest, and the Centre. Compared to non-shrinking cities, urban shrinkage has a significant negative impact on improving energy efficiency. This impact exhibits significant heterogeneity. Specifically, compared with mature resource cities and cities in Western China, regenerative cities, non-resource cities and cities in Central China have less impact on energy efficiency. In addition, urban shrinkage may impede energy efficiency improvement by hindering industrial structure transformation and upgrading, energy-saving technology innovation, and financial development. Clarifying the relationship between urban shrinkage and energy efficiency is helpful for shrinking cities to change their development strategies, which is critical for sustainable development.

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 progressive financialization of oil, in tandem with the advancement of economic globalization, has led to a sharp increase in oil prices. The growing volatility in the global economic and financial landscape has had some impact on the green bond market. Emerging markets, such as China, are particularly interesting due to their rapid evolution. This paper empirically analyzes the dynamic impact of oil market price uncertainty on China's Green Bond (GB) using the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroscedasticity (DCC-GARCH) model. The empirical findings indicate that the uncertainty of oil has a remarkable time-varying influence on China's green bonds. Specifically, when oil prices rise, the yields on green bonds decrease. Dynamic correlation analysis reveals that oil market uncertainty exhibits a negative correlation with green bonds, with a more pronounced impact during the COVID-19 pandemic. Furthermore, an impulse response analysis shows that long-term interactions between oil prices and green bonds gradually stabilize, and short-term fluctuations are frequent and complex due to market factors. These fluctuations were more pronounced during the COVID-19 pandemic, consistent with the above conclusions. Oil market uncertainty increases risk levels in the overall financial market, which may affect investors' perceptions of green bonds. Drawing on the research outcomes, this study presents targeted policy recommendations aimed at promoting the stable and sustainable development of China's GB market. These measures are designed to bolster the nation's transition toward a green economy and align with its long-term sustainability goals.