Digital talent has emerged as a pivotal resource for advancing artificial intelligence (AI) within enterprises, and it constitutes a critical digital asset that firms prioritize in their AI development strategies, aiming to attract and retain such talent. To elucidate the interconnections and mechanisms linking the digital backgrounds of executives with the level of AI utilization in enterprises, this study undertakes an examination of the relationship between these two variables, drawing on a sample of Chinese A-share listed companies spanning from 2012 to 2022. The research findings are as follows: (1) The presence of a substantial number of executives with digital backgrounds within a company significantly enhances the development of AI utilization within the enterprise. (2) The influence of executives’ digital backgrounds on the level of AI utilization in enterprises is modulated through the enhancement of the enterprise’s total factor productivity. (3) Executives’ digital backgrounds elevate the level of AI utilization by bolstering their proficiency in applying digital technology. (4) Heterogeneity analysis, conducted based on the nature of enterprise property rights, geographical regions, and technological proficiency, reveals that the positive effect of executives’ digital backgrounds on AI utilization is more pronounced in private enterprises compared to state-owned enterprises. Regionally, the impact of executives’ digital backgrounds on AI utilization diminishes progressively from the eastern to the central and western regions. Robustness checks, including the substitution of key variables, model alteration, the application of the instrumental variable method, and the Heckman two-stage method, confirm the persistence of these findings. This article contributes to the understanding of the impact and pathways through which executives’ digital backgrounds influence AI utilization in enterprises against the backdrop of high-quality economic development. It enriches the scholarly discourse on executives’ digital backgrounds and AI utilization in enterprises, offering both theoretical support and practical insights for the advancement of AI within enterprises.

Purpose: In the contemporary landscape, where digital transformation and macroeconomic volatility converge, delineating strategies to augment financial flexibility has become a salient concern for both the corporate and academic realms. This article takes manufacturing firms listed on China’s A-share market as the research object and explores how digital transformation enhances financial flexibility through improving supply chain efficiency, considering digital transformation potential both as an enabler of financial flexibility and a formidable challenge.Design/methodology/approach: Utilising an empirical framework that assesses manufacturing firms listed on the Shanghai and Shenzhen A-share markets over the period from 2007 to 2022, we deploy a two-way fixed effects model to dissect the interplay between the magnitude of digital transformation and financial flexibility. Robustness checks, including propensity score matching (PSM), the system generalised method of moments (system GMM) and alternative specifications for key variables, ensure empirical validity.Findings/results: Our analysis yields evidence of a positive correlation between digital transformation and financial flexibility, particularly pronounced through enhancements in supply chain efficiency. Heterogeneity analysis further reveals that the beneficial effects are more markedly felt in non-state-owned enterprises.Practical implications: This study provides empirical evidence to guide corporate managers and policymakers in leveraging digital transformation to fortify financial flexibility, particularly by enhancing supply chain efficiency and addressing challenges faced by non-state-owned enterprises.Originality/value: This research contributes novel insights into the role of digital transformation in enhancing financial flexibility, offering empirical foundations to inform the development of targeted policy initiatives.

E-commerce (EC) promotes the effective connection between enterprises and the market, and provides a platform and application scenario for the value conversion of enterprise ESG information. In order to clarify the linkage characteristics between EC and enterprise ESG performance and the function mechanism, this paper examines the relationship between EC on enterprise ESG performance in Shanghai and Shenzhen from 2010 to 2023. Research found that: (1) EC significantly suppresses corporate greenwashing (GW) behavior. (2) EC suppresses corporate GW behavior by strengthening ESG information disclosure. After parallel trend testing, multi period DID, and placebo testing, the above results remain robust. Further analysis shows that: (1) EC significantly promotes ESG performance. (2) In enterprises with high levels of green innovation, EC has a significant impact on their ESG performance; On the contrary, in companies with low levels of green innovation, EC has no significant impact on their ESG performance. It reveals the influence and ways of EC on the ESG performance of enterprises under the background of high-quality sustainable development of economy, enriches the research on EC and the ESG performance of enterprises, and provides certain theoretical support and practical enlightenment for the sustainable development of enterprises.

Leveraging the inauguration of the China-Europe Railway Express (CRE) as a natural experiment, this study employs a multi-period difference-in-differences (DID) model using data from China’s listed manufacturing firms spanning 2011 to 2021. The findings reveal that the CRE has significantly enhanced the green innovation capabilities of manufacturing enterprises, with a notably stronger impact on substantive green innovation than strategic green innovation. These results are further reinforced through rigorous robustness checks, including instrumental variable approaches, propensity score matching combined with difference-in-differences (PSM+DID) regression, and variable substitution. The study identifies two primary mechanisms driving the CRE’s positive impact on corporate green innovation: alleviating financial constraints and facilitating cross-border innovation resource flows. Heterogeneity analysis indicates that the CRE’s benefits are particularly pronounced among non-stateowned enterprises, firms with lower degrees of internationalization, and smaller firms. Regionally, the impact is most substantial in the central regions, followed by the eastern regions, while the western regions show limited effects due to structural challenges. Industry-level analysis reveals a hierarchical impact, with high-tech manufacturing deriving the greatest benefit, followed by medium-tech and traditional manufacturing sectors. This study contributes theoretical insights and offers actionable policy recommendations to maximize the CRE’s potential in fostering green innovation, addressing regional inequalities, and advancing sustainable international trade and logistics.

Against the combined backdrop of intensifying global climate governance, China’s carbon-peaking and carbon-neutrality goals, and the rapid deployment of artificial intelligence (AI) for low-carbon development, this study examines how AI adoption affects the carbon emission intensity of energy firms. Using panel data for listed Chinese energy firms from 2010 to 2023 and fixed-effect models, we find that AI adoption significantly reduces carbon emission intensity; this result remains robust to alternative variable measures, lagged specifications, and other sensitivity tests. Executive digital background strengthens this effect, while the low-carbon city pilot policy provides a complementary institutional incentive that generates additional emission reductions when combined with AI. Mechanism tests indicate that AI lowers carbon emission intensity by increasing capacity utilization, improving supply-chain efficiency, and raising production efficiency. The effect is stronger in eastern and central China, state-owned enterprises, conventional energy firms than in western China, non-state-owned enterprises, and new-energy firms. These findings provide firm-level evidence on how AI facilitates the low-carbon transition of the energy sector and offers actionable implications for policy design and corporate strategy.

The formal establishment of national parks can alter expectations about surrounding development and the conditions governing land allocation, yet there is little firm-level evidence on how long-lived corporate land assets adjust. Using data for nonfinancial, non-real-estate firms listed on China’s Shanghai and Shenzhen A-share markets from 2015 to 2025, we construct land-use-right accounts from annual reports and measure spatial exposure by the distance from each firm’s fixed pre-policy office location to the boundaries of the first five national parks. Exploiting the parks’ staggered pilot launches and unified formal establishment in 2021, we compare changes in land assets across distance groups between the pilot stage and the post-establishment period. Relative to firms whose pre-policy offices were 100–200 km from a park boundary, firms 30–100 km away show significant relative increases in the gross and net book values of land-use rights in 2022–2023, with the gap narrowing markedly in 2024–2025. Placebo formal-establishment dates in 2018 and 2019 show no comparable shifts. Positive changes in reported land additions and purchases are concentrated in 2022, while the book value of buildings and structures declines and operating outcomes change little. The 30–50 km and 50–100 km groups both exhibit sizable increases in land stocks, and alternative distance windows, leave-one-park-out estimates, and spatial placebo tests show systematic spatial variation. The core land-stock estimates remain positive under several alternative inference and spatial specifications, while city × year fixed effects and flexible differential trends yield wider uncertainty. Overall, the evidence documents an early post-establishment adjustment in corporate land accounts that varies systematically with spatial exposure measured by fixed pre-policy office location.