Does digital financial inclusion alleviate poverty? This study investigates this question by integrating the Digital Financial Inclusion Index of Peking University with microdata from the China Family Panel Studies (CFPS) to examine how the expansion of digital financial inclusion affects household multidimensional poverty in China. Anchored in Amartya Sen ’ s capability approach and operationalized through the Alkire–Foster (A–F) framework, the study identifies multidimensional poverty across five key dimensions: income, health, education, insurance, and living standards. Probit models are employed to estimate how digital financial inclusion influences both the likelihood and structure of multidimensional poverty, while instrumental variable techniques are used to address potential endogeneity. Beyond the average effects, the study further explores the mechanisms through which digital financial inclusion contributes to poverty alleviation, focusing on three channels—promoting household consumption, increasing financial investment, and enhancing access to credit. The results reveal that digital financial inclusion significantly mitigates multidimensional poverty, particularly by improving income, living standards, and health outcomes, though its effects on education and insurance are limited. These findings underscore the transformative role of digital finance in fostering inclusive growth, suggesting that policies expanding digital financial infrastructure and literacy can amplify its poverty-reducing effects and advance equitable development.

This study investigates how the development of digital finance influences the geographical distribution of corporate supply chains using data from Chinese A-share listed companies from 2010 to 2023. We examine whether digital finance enables firms to overcome traditional geographical constraints and adopt different supply chain distribution strategies. The analysis identifies two primary mechanisms through which digital finance influences supply chain geography: governance effects, which operate through enhanced risk management and information transparency, and financing effects, which function through alleviated capital constraints and trade credit provision. We further explore heterogeneous impacts across four dimensions: regional economic development, regional digital infrastructure, industry market competition, and enterprise lifecycle stages. By examining the geographical distribution of supply chains as an outcome of digital finance development, this study provides novel evidence on the micro-governance implications of digital finance. Our findings contribute to understanding how digital finance fundamentally changes the geographical constraints that have historically shaped supplier selection decisions and enables firms to develop more flexible supply chain configurations.

This paper examines how artificial intelligence (AI) adoption influences the total factor productivity (TFP) of Chinese A-share manufacturing firms from 2010 to 2023. Results show that AI significantly raises TFP, robust across multiple specifications and instrumental variable tests. AI also boosts operational efficiency by accelerating accounts receivable and inventory turnover, revealing a “technology–operation–productivity” pathway. The positive effect is stronger in regions with better digital infrastructure and in firms with stronger governance. The findings provide fresh evidence on AI’s productivity effects and offer policy implications for intelligent transformation and high-quality manufacturing development.

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 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.

This study investigates the impact of RegTech adoption in the M&A regulatory review process on deal performance. Leveraging the staggered implementation of the SOEs Online Supervision System (SOSS) by China’s State-Owned Assets Supervision and Administration Commission (SASAC) across its central and 31 provincial offices from 2018 to 2021, we find that SOSS directly enhances SASAC’s decision-making efficiency and improves its capacity to screen and approve higher-quality M&A deals. More importantly, SOE-led M&A transactions exhibit higher announcement returns as well as improved long-run stock and operating performance following the system’s implementation. The positive impact of SOSS is more pronounced for acquirers with stronger technological infrastructure, in transactions characterized by low transparency and weak governance, and in provinces with more stringent external scrutiny. Overall, by addressing regulator-firm information asymmetry and reinforcing managerial accountability, SOSS improves regulatory effectiveness in overseeing major investment activities among SOEs.

With the digital transformation of the capitalist economy, some contemporary scholars have put forward the Techno-Feudalism narrative of the digital economy. This narrative emphasizes that digital platform enterprises, as emerging market entities in the digital economy, have many practices that are highly similar to those of feudal lords. For example, digital platform enterprises plundering user data is similar to feudal lords plundering land; digital platform enterprises collecting digital rent is similar to feudal lords collecting land rent; digital platform enterprises controlling users and workers is similar to feudal lords controlling slaves. However, this narrative has many theoretical fallacies. Marx's theory of surplus value shows that the above phenomena are essentially still the contemporary form of capital seizing surplus value through technological innovation. The techno-feudalism narrative ignores the internal logic of capital using technological iteration to reconstruct the exploitation mechanism and falls into a superficial misjudgment. In contrast, the Chinese governance practice of digital economy breaks the monopoly of platforms on data elements through the innovation of the separation of three rights of data property rights; promotes fair competition and optimal allocation of resources in the digital economy by strengthening anti-monopoly supervision and promoting the construction of digital infrastructure; proves that the socialist system can break the capital proliferation cycle and achieve "people-centered" development by building a labor rights protection system to promote the creation and sharing of value and transcending the techno-feudalism phenomenon of the digital economy.

We construct a new geospatial measure using high-resolution river vector data from National Geomatics Center of China (NGCC) to study how urban riparian exposure shapes local government debt financing costs. Our base-line results show that cities with higher riparian exposures have significantly lower credit spreads, with a one-standard-deviation increase in riparian exposure reducing credit spreads by approximately 12 basis points. By comparing cities crossed by natural rivers with those intersected by artificial canals, we disentangle the dual role of riparian zones as sources of natural capital benefits (e.g., enhanced transportation capacity) versus climate risks (e.g., flood vulnerability). We find that climate change has amplified the impact of natural disasters, such as floods and droughts, particularly in riparian zones, thus weakening the cost-reducing effect of riparian exposure on bond financing. In contrast, improved water infrastructure and flood-control facilities strengthen the cost-reduction effect. Our findings contribute to the literature on natural capital and government financing, offering valuable implications for public finance and risk management.

The impact of Artificial Intelligence (AI) on firm performance is an emerging issue in both practice and research. However, discussions surrounding the effect of AI on productivity are enshrouded in a paradoxical quandary. This study examines the relationship between AI implementation and total factor productivity (TFP), considering the moderation effects of digital infrastructure quality, business diversification, and demand uncertainty. Using data from 2155 Chinese firms over 2016-2021, our empirical analysis reveals a nuanced pattern: while moderate AI implementation achieves the best TFP, excessive and insufficient implementation yields diminishing returns. The curvature of this inverted U-shaped relationship flattens with higher levels of digital infrastructure quality but steepens when firms undertake diversified businesses and face heightened demand uncertainty. The findings suggest that the impact of AI on TFP is not universally beneficial, and the relationship between AI and TFP varies across different contexts. These findings also provide implications on how firms can strategically implement AI to maximize its value.

Amid growing global interest in state interventions, this paper examines the impact of Chinese government infrastructure investments on improving firm productivity. It centers on a policy aimed at directing regional governments to foster a more conducive market environment for private enterprises. Our analysis reveals that the positive effect of infrastructure investment on firm productivity is increased by 42.5% for private firms in industries that benefitted from improved market entry opportunities and an even more striking 97.9% in provinces where arbitrary fines were curtailed. These findings underscore the complementary roles of state interventions and the development of market mechanisms in boosting firm productivity.