Against the backdrop of global green development and China’s sustainable economic transition, many firms exaggerate green-transition disclosures to cater to national strategies and capital market preferences, leading to a severe "Say–Do Gap". Based on signaling theory, this study uses the phased establishment of environmental courts in 208 prefecture-level cities as a quasi-natural experiment, adopting a staggered DID design with 2007–2023 panel data of Chinese A-share listed firms for empirical tests. Results show widespread corporate green pandering, with improved disclosure not translating into actual carbon reduction. Environmental courts effectively curb this behavior, with environmental litigation risk as the core mediating channel. Heterogeneity tests reveal stronger deterrence in regions with weaker regulation/heavier pollution and polluting firms with stronger environmental technology. This study enriches literature from a judicial deterrence perspective and provides implications for substantive corporate green transition.

Understanding the impact of local government debt on economic development has emerged as a focal issue for both academic research and policymakers. This study adopts a financing structure perspective and utilizes panel data from 214 cities and 3,228 A-share listed companies in China (2017–2023) to empirically investigate the impact of local government debt on corporate “shift from virtual to real economy” and its underlying mechanisms. The expansion of local government debt significantly promotes enterprises “shift from virtual to real economy”. The positive impact of local government debt on enterprises” transition from financialization to the real economy is more pronounced among firms in first tier and new first-tier cities, non-state-owned enterprises, and labor-intensive industries. Further analysis indicates that local government debt drives capital reallocation from financial investments to real investments by alleviating corporate financing constraints. This study proposes policy recommendations including optimizing debt fund allocation, further optimizing the financing environment, implementing differentiated regulatory measures. These suggestions provide both a theoretical foundation and practical references for synergistically advancing debt governance and real economy revitalization.

The endurance of empire in China over much of the past two millennia contrasts starkly with the political fragmentation in Europe since the fall of Rome. To explain the persistence of China’s empire, we analyze the role of keju, the imperial civil service examination system. We argue that the systematic implementation of keju from the Song dynasty onward tied local elites to the imperial state, sustaining political integration and reducing secessionist conflicts. Exploiting panel data covering 4,243 grid cells and 22 centuries, we find robust evidence that areas producing more jinshi – the top exam passers – experienced significantly fewer secessionist wars. Our study shows how the examination system created the political incentives for imperial durability.

The relationship between economic decentralization and regional economic growth has attracted wide academic attention. However, existing literature rarely explores the validity and applicability of decentralization theory from the perspective of location endowments. This study takes China’s county-to-city reform - a decentralization policy - as a point of entry to construct a decentralization model incorporating location endowments. Using county-level panel data from 1992 to 2009 and employing a difference-in-differences (DID) approach, the study evaluates the impact of county-to-city reform under the constraint of location endowments on county-level economic development. The findings reveal that the policy effects of local decentralization are constrained by location endowments. Specifically, in regions with poorer location endowments, county-to-city reform significantly inhibits local economic development. Indeed, mechanism analysis further indicates that in such disadvantaged regions, local decentralization under the county-to-city reform generates a “political resource curse” effect, which hinders economic growth through three channels such as distorted fiscal expenditure structures, corruption, and misallocation of land resources. The conclusions of this study provide a valuable perspective for understanding the conditions under which decentralization theory holds, while also offering rigorous empirical evidence for government agencies to design more targeted decentralization policies.

Data-factor marketization (DFM) serves as a critical driver for cultivating manufacturing-enterprise new-quality productive forces (ME-NQPF), fundamentally supporting China's transition toward high-quality economic development. Integrating matched panel data from A-share listed Chinese manufacturing firms (2011–2022) with the staggered establishment of regional data trading platforms as a quasi-natural experiment, this study employs a multi-period difference-in-differences (DID) framework to identify the causal impact of DFM on ME-NQPF. Empirical results demonstrate that DFM significantly enhances ME-NQPF, a finding that remains robust across alternative specifications and endogeneity treatments. Mechanism analysis identifies enterprise digital transformation as a pivotal mediator in this relationship, while competitive intensity is found to positively moderate the productivity gains from data marketization. Heterogeneity analysis further indicates that these effects are most pronounced among non-state-owned enterprises, technology-intensive sectors, and firms situated in China's eastern and central regions. These findings suggest that institutionalizing data-factor markets and accelerating digital integration are effective mechanisms for optimizing resource allocation and sustaining advanced industrial productivity.

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.