In the era of the digital economy (DE), the traditional economic growth paradigm is no longer applicable. To explore whether the DE can improve the urban ecological problems left behind by rough economic growth, this study examines the effects of land resource mismatch (LRM) on urban ecological resilience (UER) and evaluates the mitigating influence of the DE. This analysis utilizes data from 280 prefectural-level cities in China over the period from 2007 to 2021 and reveals that LRM significantly undermines UER, with this conclusion remaining robust across a series of tests. Additionally, the detrimental impact of LRM on UER is more pronounced in megacities, cities with high levels of economic development, and those with a lower degree of advanced industrial structure. In further analysis, this study finds that the digital economy can optimize the allocation of land resources, thereby enhancing urban ecological resilience, which has the effect of “treating the root causes”. In addition, digital government and digital infrastructure, as key elements of the digital economy, also mitigate the negative impacts of land resource misallocation on urban ecological resilience, having the effect of “treating the symptoms”. Finally, this study proposes policy suggestions such as optimizing ecological layout, deepening land reform, and promoting digital government and infrastructure construction to provide a theoretical basis and practical guidance for local governments to enhance UER and help build a new model of greener, more resilient, and sustainable urban development.

In the era of the digital economy (DE), technology factors and data factors, like a two-wheel drive, have not only redefined the mode of production but also innovatively reshaped production relations. To examine how the DE can ensure food security (FS) in China, this study explores the negative impacts of land resource mismatch (LRM) on FS, the mechanisms of the impacts, and the critical role played by the DE in mitigating its negative impacts, based on China’s provincial-level panel data from 2011 to 2022. This study finds that, first, LRM leads to a reduction in food production, which, in turn, threatens FS, and this conclusion remains robust after a series of robustness tests. Second, the heterogeneity analysis finds that LRM has a greater negative impact on FS in regions with high urbanization levels, regions with a short tenure of officials, and regions that are not major food-producing regions. Finally, in a further analysis, the specific channels and solution paths of the negative impact of LRM on FS are explored in depth. LRM negatively affects the material base and production capacity of food production, including reducing the supply of land, labor, and capital factors for food production; the DE reduces the negative impact of LRM on FS through the use of digital technology and open government data.

Purpose: While the supply chain shared auditor is emerging as a novel governance mechanism for enhancing transparency, its role in facilitating the adoption of artificial intelligence (AI) remains underexplored. This study investigates how the presence of a supply chain shared auditor promotes corporate AI adoption. Design/methodology/approach: Utilizing panel data from Chinese listed supply chain companies, this study employs a Tobit model with year and industry fixed effects to analyze the relationship between the supply chain shared auditor and the level of corporate AI adoption. The moderating effect of the CEO’s IT background is further tested through interaction terms. Findings: The existence of a supply chain shared auditor significantly promotes corporate AI adoption. This positive effect is strengthened when the firm’s CEO has an IT background. Mechanism testing indicates that, on the one hand, supply chain shared auditors exert a “governance effect”, mitigating managerial myopia and thereby promoting corporate artificial intelligence adoption. On the other hand, supply chain shared auditors exert an “information effect”, reducing levels of information asymmetry and thereby promoting corporate artificial intelligence adoption. Originality/value: This paper applies signaling theory to the study of artificial intelligence adoption within enterprises by supply chain shared auditors, thereby expanding the application scenarios of signaling theory. Furthermore, this research bridges the fields of auditing studies and corporate governance research, thereby specifying how and when this governance mechanism most effectively translates into technological advancement.