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.

To tackle global climate change and fulfill China's dual carbon objectives, the coordinated abatement of pollution and carbon emissions has emerged as a pivotal focus for sustainable urban development. Land use patterns and pollutant emissions are deeply connected within cities, making them a crucial lever for advancing synergistic pollution and carbon reduction. To investigate this relationship, this study constructs a synergy index (Syg) and a territorial land use compactness index (TD) using the Entropy Weighted TOPSIS method based on land raster data from 2012 to 2023, LandScan population raster data, and prefecture-level city panel data. The findings indicate that the compactness of urban land use patterns positively influences the synergistic effects of pollution and carbon emissions reduction. This conclusion remains valid after undergoing benchmark regression analysis, robustness testing, and endogeneity treatment. Regarding the operational mechanism, the compactness of urban land use patterns primarily exerts its effects through two pathways: the aggregation of innovative factors and overall industrial structure upgrading. Heterogeneity analysis further reveals that the synergistic effects of pollution and carbon emission reduction are more pronounced in eastern and western cities, non-transport hub cities, non-resource-based cities, and mature resource cities. Thus, it is recommended that differentiated land policies and green innovation clusters be established, and that a synergistic assessment system for land use, as well as pollution and carbon emission reduction, be developed to help achieve the dual carbon goals.