In the context of digital transformation, artificial intelligence (AI) has emerged as a pivotal driver for enhancing tourism resource allocation efficiency and promoting the high-quality development of the tourism industry. Grounded in the Technology–Organization–Environment (TOE) framework, this study constructs a multidimensional indicator system by integrating heterogeneous data sources, including Baidu search indices, corporate annual reports, and policy documents. Using a balanced panel dataset covering 31 provincial-level regions in China from 2015 to 2023, we empirically examine the mechanisms through which AI penetration affects the efficiency of tourism resource allocation. The super-efficiency SBM-DEA model is employed to measure allocation efficiency, while the spatial Durbin model (SDM) and geographically weighted regression (GWR) are used to identify spatial spillover effects and regional heterogeneity. Furthermore, tourist satisfaction is quantified using a natural language processing (NLP)-based sentiment index derived from online reviews. The results indicate that AI penetration significantly improves tourism resource allocation efficiency, with stronger effects observed in regions with advanced technological infrastructure. Smart tourism pilot policies demonstrate significant spatial spillover effects, positively influencing scenic areas within a 100-kilometer radius. However, diminishing marginal returns are evident, highlighting capacity absorption thresholds and institutional constraints. Based on the empirical findings, the study proposes targeted policy recommendations, including the establishment of provincial tourism data hubs, promotion of AI toolkit systems, enhancement of scenic area evaluation mechanisms, and reinforcement of collaborative governance between government and enterprises. These insights aim to provide both theoretical and practical guidance for the intelligent transformation and coordinated regional development of China’s tourism industry.

This study examines the effect of artificial intelligence (AI) adoption on stock price crash risk using panel data from Chinese A-share listed firms from 2001 to 2022. We find that higher levels of AI application significantly reduce crash risk, primarily by enhancing information transparency, easing financial constraints, and promoting innovation. Notably, AI improves transparency within supply chains by reducing information asymmetry between upstream and downstream firms, thereby enhancing information flow and reducing market frictions. Among AI types, machine learning proves most effective in lowering crash risk due to its data-processing and forecasting capabilities, while natural language processing and computer vision show weaker effects. The impact of AI is particularly pronounced in non-government-regulated industries and high-tech firms. Moreover, its risk-mitigating effect becomes increasingly significant over time. These results are robust to instrumental variable estimation and staggered difference-in-differences (DID) designs. These findings highlight the strategic role of AI in risk management and offer practical implications for firms and policymakers aiming to enhance transparency, financial resilience, and long-term value creation.

We use nearly a decade’s worth of panel data from European Union Chamber of Commerce in China business confidence surveys to analyse the deteriorating outlooks of EU firms in China from 2017 to 2025. All firms in China currently face challenges including slow profit growth and deflation. These circumstances have contributed to a rare drop of foreign direct investment into China over the last two years. However, certain challenges are particularly acute for foreign firms, including those from the EU. According to survey results, business sentiment among EU firms operating in China has never been bleaker. Respondents view their profitability, growth opportunities and competitiveness negatively, while fewer respondents than ever plan to expand their Chinese operations. Moreover, significant shares of respondents report recent increases in political pressure from the Chinese state and media, while nearly a third of respondents say they are siloing their Chinese operations, meaning separating them from other global activities. Disaggregated by size, sector, and years of operation in China, insightful differences emerge between the business strategies of EU firms. We broadly classify these into four categories: doubling-down, hedging, hibernating and ready to exit. EU policymakers should consider how to address the challenges EU firms in China face, such as asset-heavy sectors being ‘stuck’ in China and smaller firms lacking the capacity to operate at a loss in China’s market. The EU might need to facilitate transitions for these companies, helping them to reduce exposure to China and diversify into other emerging markets.

This paper investigates how foreign institutional investors, specifically Qualified Foreign Institutional Investors (QFIIs), influence the investment strategies of Chinese mutual fund management companies (FMCs) in which they hold shares. By analysing panel data from 1,766 mutual funds managed by 44 foreign-invested FMCs in China between 2005 and 2021, we explore whether QFII-invested FMCs (Q-FMCs) learn more from their domestic counterparts (D-FMCs) than other foreign-invested FMCs (NQ-FMCs). Our findings show that Q-FMC-managed mutual funds exhibit portfolio allocations more closely aligned with local DFMCs than those managed by NQ-FMCs. This imitation is particularly pronounced when selecting new stocks, enhancing portfolio performance, but not when rebalancing existing positions. Additionally, Q-FMCs trade more actively than NQ-FMCs. Robustness checks confirm these results across various ownership structures, fund characteristics, market conditions, and regulatory changes. These findings highlight the dual role of QFIIs as both investors and learners in China’s evolving financial landscape, offering insights into how foreign capital integrates into emerging mutual fund markets, informing regulatory policy aimed at fostering cross-border financial development.

This paper examines the role of institutional investors in shaping pricing dynamics within China’s nascent infrastructure Real Estate Investment Trust market. Introduced in 2021, China’s REITs have rapidly gained policy and market attention as a tool for financing large-scale infrastructure projects through equity-based securitisation. Unlike mature REIT markets, China’s infrastructure REITs are characterised by a high concentration of institutional ownership dominated by state-owned financial institutions. Using panel data on first 9 REITs from May 2021 to April 2024, we find that institutional ownership significantly boosts the premium to net asset value. This effect operates primarily through two channels: reduced market liquidity and increased idiosyncratic return volatility, likely reflecting institutions’ trading activity and informational advantages. The findings highlight how institutional capital serves as a confidence signal in China’s emerging REITs ecosystem. The study contributes to the global REITs literature by offering insights from an emerging market context and provides policy recommendations to guide China’s REITs market development toward greater transparency, diversity, and long-term resilience.

Purpose The study investigates the impact of government intervention policy of capital markets (“National Team”) on firms’ sustainable management, i.e., green mergers and acquisitions (GMAs) in China, aiming to understand how such interventions influence corporate investment activities amidst a growing focus on green transition. Design/methodology/approach The research employs a dynamic analysis of quarterly data from Chinese companies (2014 Q1 to 2022 Q4), utilizing identified strategies, such as double machine learning-DID and multiple panel data regressions to assess the effects of government intervention on GMAs, and examines potential economic channels like liquidity, market stabilization, and informativeness. Findings The study finds that increased government intervention via direct stock purchases significantly boosts both the number and amount of GMAs, with economic significance of 23% and 45%, respectively. It identifies liquidity, market stability, and informativeness efficiency as underlying economic channels for this effect. Practical implications The findings suggest that government interventions can enhance corporate investment in green sectors, guiding firms to align strategies with sustainability goals. This can inform policymakers regarding the effectiveness of direct stock purchases in fostering a green economy, especially for large emerging countries. Social implications By promoting GMAs, government interventions contribute to green innovation and energy transition, ultimately benefiting society through enhanced environmental sustainability and compliance with eco-friendly regulations. Originality/value This research uniquely documents the direct effects of government stock purchases on corporate green financial activities, particularly GMAs, in a Chinese context characterized by tight credit, thereby expanding the understanding of government intervention in emerging markets.

This study employs a Triple-Difference (Triple-DID) model, utilizing balanced panel data at the district level from Beijing, Shanghai, Shenzhen, and Hangzhou between 2018 and 2024, to critically evaluate the effectiveness of the Multi-School Zoning Policy (MSZP) in suppressing the capitalization of educational resources into housing prices and promoting educational equity. The research explicitly accounts for spatial and institutional heterogeneity as well as household strategic behavior.The results indicate that: (1) MSZP significantly reduced the average housing price premium associated with elite school districts by 15.2%, with the strongest effect observed in Beijing and the weakest in Hangzhou; (2) The policy's effectiveness diminishes as the spatial concentration of high-quality educational resources increases, highlighting persistent structural inequalities; (3) In areas characterized by resource monopolization and strong institutional inertia, the policy's suppressive effect on educational capitalization and its gains in educational equity are both constrained.The findings suggest that MSZP alone cannot fully overcome the "spatial lock-in" effect of high-quality educational resources. Achieving lasting equity requires complementary deeper institutional innovations, such as robust cross-district teacher rotation, transparent resource allocation mechanisms, and adaptive zoning algorithms. This research offers quantitative evidence for optimizing policy and institutional tools in the pursuit of comprehensive urban education reform.

This study examines how firm-specific characteristics and monetary tightening influence the composition and dynamics of trade credit received by Chinese listed firms. Using panel data, the analysis distinguishes among three sources of trade credit: related parties, non-related parties, and controlling shareholders. The findings reveal a clear asymmetry in firms’ financing responses to monetary tightening: while trade credit from non-related parties declines, credit from related parties—especially controlling shareholders—increases. This underscores the strategic role of intra-group financing in buffering firms against external financial shocks during periods of constrained liquidity. Moreover, firm-specific factors such as size, profitability, market power, and ownership have differing effects depending on the source of trade credit. These effects are most pronounced when the credit is extended from controlling shareholders, reflecting the influence of intra-group trust and reduced information asymmetries. The results also highlight a substitute relationship between bank credit and trade credit, which weakens when trade credit is sourced from related parties and disappears entirely in the case of controlling shareholders. By shedding light on the distinct mechanisms of intra-group trade credit in China’s underdeveloped financial system, this study contributes to a deeper understanding of corporate financing strategies of Chinese firms.

This study employs an imbalanced panel dataset of 524 Chinese commercial banks from 2009 to 2020 to investigate the influence of Basel III on banks' loan loss provisions. Our findings reveal no significant change in the relationship between loan loss provisions and capital adequacy, although it indicates a heightened impetus for Tier 1 capital management. Furthermore, the study finds that earnings management motivations, particularly related to pre-provision profits, influence banks' loan loss provisions. Basel III's enactment reduces the ability of high-earning banks to manipulate earnings using loan loss provisions. This research provides empirical evidence from China for the global assessment of Basel III's impact on commercial banks.

This research examines the impact of short selling on the financing cost of corporate bonds using panel data from Chinese A-share listed companies spanning the period from 2007 to 2022. The study aims to investigate the potential cross-market information spillover effects within the short selling system. The findings indicate that short selling significantly reduces the financing cost of corporate bonds, with a more pronounced effect observed under greater short selling forces. The robustness of the results is confirmed by controlling for various potential influencing factors and addressing the endogeneity issue through Propensity Score Matched Difference in Differences (PSM-DID) methodology. Moreover, the research reveals that the alleviation of information asymmetry serves as the primary mechanism through which short selling exerts its impact, particularly in regions with well-developed financial markets and favorable legal environments. This study offersa novel perspective of short selling in China and it sheds light on its cross-market spillover effects. By effectively enhancing resource allocation efficiency in capital markets, short selling emerges as a potent tool for mitigating information disparities between bond investors and enterprises.