This paper examines the intricate relationship between China’s First Five-Year Plan (1953–1957) and the extensive Soviet aid that played a pivotal role in shaping early industrialization and economic policy in New China. Drawing upon both Chinese and Western literature, the study reviews the evolution of planned economy practices and Soviet economic assistance, analyzes the process of plan formulation and the formulation of key industrial targets, and assesses the quantitative and qualitative impact of Soviet aid on the successful implementation of the plan. By employing a multi-perspective literature review, archival research, and statistical data analysis, the paper identifies that Soviet technology, machinery, and planning expertise contributed significantly, estimates suggest that Soviet aid accounted for 30-40% of the capital inputs in key sectors and that this collaboration was instrumental in achieving heavy industry targets before the advent of reform and opening-up policies. Furthermore, the paper explores the subsequent divergence in the economic models of China and the Soviet Union, offering lessons on dependency, technology transfer, and the political dimensions of foreign aid. The findings not only contribute to our understanding of the early stages of China’s economic development but also shed light on the broader dynamics of Cold War-era international economic cooperation.

This study examines the role of artificial intelligence (AI) adoption in the market for corporate control using a sample of Chinese listed firms from 2011 to 2021. We construct a novel firm-level AI Index through textual analysis of annual reports and find that AI adoption significantly enhances both the likelihood of becoming an acquisition target and the valuation premiums commanded in M&A transactions. Specifically, a one-standard-deviation increase in the AI Index is associated with a significant increase in the probability of being acquired and higher deal premiums measured by price-to-earnings multiples. We identify two channels through which AI adoption creates value recognized by the M&A market: an efficiency channel, whereby AI reduces agency costs and improves profitability, and an innovation channel, evidenced by increased high-quality patent output. The persistence of these effects over time further suggests that AI adoption generates substantive improvements in firm fundamentals rather than serving as a transitory informational signal. Importantly, we document significant heterogeneity across ownership structures: the positive effects of AI adoption are substantially weaker for State-Owned Enterprises (SOEs) than for non-SOEs. Our findings contribute to the literature on digital transformation and corporate finance by demonstrating that AI adoption serves as a value-relevant firm attribute that shapes outcomes in the market for corporate control.

High-quality development of water conservancy (HDWC) is of critical value for safeguarding the stability of production systems, livelihoods, and ecosystems. As the digital revolution intersects with China’s “dual carbon” targets, development of water conservancy must transition from traditional engineering approaches to data-driven ecological models. Drawing on institutional logic theory and employing dynamic qualitative comparative analysis across 30 Chinese provinces, this study examines how digital ecology facilitates the HDWC. The findings reveal that none of digital government, digital infrastructure, digital economy, digital capability, or digital society constitutes a necessary condition for the HDWC. Instead, it is the result of the combined effects of multiple institutional logics. Five configurations leading to HDWC are identified and categorized into four types: government-market-driven model, government-market-society-driven model, market-society-driven model, and government-society-driven model. The consistency of the configurations significantly increased during the study period. Furthermore, their distribution showed substantial regional differences. There are two configurations that inhibit the HDWC, namely the government-market-absence type and the market-society-absence type. Digital society emerges as a critical factor. This research uncovers multiple pathways through which digital ecology can empower HDWC, providing valuable insights for optimizing regional digital environments.

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.

Recent technological advancements have enabled the emergence of business organizations fully managed by algorithms, such as decentralized autonomous organizations (DAOs) or through artificial intelligence (AI), as observed in China’s online food delivery sector. These organizations are collectively referred to as algorithm-managed firms (AMFs). Given machines’ capabilities in data collection and analysis, human directors are increasingly being replaced by algorithms or AI in specific sectors. This article contends that algorithms can effectively take over human directors’ managerial, monitoring, and mediating roles. The diminishing role of human directors raises certain concerns of stakeholder protection. Unlike human directors, algorithm directors or managers would not consider stakeholders’ interests unless clearly instructed to do so. However, the algorithm supplier and the AMFs may lack the incentives to fully consider stakeholders because they do not always internalize the social costs. To address the challenges of AMFs, policymakers need to consider different regulation strategies. First, they must choose between command-and-control regulations and target-based regulations. Command-and-control regulations often do not work well because regulators lack enough information or control over complex algorithms. Instead of setting detailed technical rules, policymakers should adopt target-based regulations that let the algorithm balance various interests and regulate its own operations. Second, policymakers should decide between entity-based and algorithm-based regulations. Algorithm-based regulation is more suitable because it prevents companies from passing costs onto society. The state could consider regulating the composition of the board of directors of the algorithm supplier to ensure that they incorporate the concerns of stakeholders’ interests in the development of the algorithm. Additionally, corporate law doctrines that protect creditors and other stakeholders, such as piercing the corporate veil and limiting liability for corporate torts, must be revisited and modified because their foundational assumptions no longer align with the realities of AMFs.

The increasing institutional participation and deepening integration of physical trading and financial operations in commodity markets have elevated the interconnectedness of energy futures and equity markets to prominence in both scholarly discourse and industry analysis. Employing the Nelson-Siegel framework and Fama-French factor model, this study examines the dynamic relationships between energy futures holding cost variations and equity returns across coal and oil sectors. Our analysis yields three principal findings: First, the Fama-French three-factor model exhibits robust explanatory power in China's energy sector equity market, revealing significant statistical relationships between holding cost curve parameters—level, slope, and curvature—and industry excess returns. Second, holding cost variations manifest substantial heterogeneity in their impact on stock returns across coal and oil sectors. Third, carrying cost components demonstrate dominance over shock transmission effects in explaining industry stock return volatility, indicating complex, asymmetric interaction mechanisms between futures and equity markets. Drawing from these empirical results, we advance targeted policy prescriptions addressing futures market architecture and financial stability.

We investigate the stock market reaction in China to Trump’s tariff policy announcement on April 2, 2025. We find that the tariff policy reduced stock prices of Chinese firms except those in the agricultural sector. Large-cap stocks, value stocks, stocks of high profitability firms, and stocks of state-owned enterprises experienced smaller negative impacts. Stocks with higher institutional holdings by mutual funds and Social Security Funds exhibited higher resilience, possibly due to these investors' superior capability in selecting stocks and forecasting trade war risks. In contrast, stocks held by Qualified Foreign Institutional Investors (QFII) did not exhibit such resilience.

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.

The northwest region of China faces many practical challenges, and its digital economy lags behind other areas of China. Digital transformation is a new source of competitive advantage in the digital economy era, which can help northwest enterprises rebuild their competitive advantage in the digital age, accelerate the development of the digital economy in the northwest region, bridge the digital gap between the East and the West, and promote the high-quality development of the national digital economy. In this study, the PLS-SEM method is used to collect data from 172 enterprises across five provinces in northwest China, to deeply analyze the mechanism and path through which digital transformation reshapes enterprise competitive advantage, identify the key sticking point hindering digital transformation in northwest China, and then propose more targeted strategic suggestions. It is found that the resource base of enterprises in northwest China is generally weak, making it difficult to deliver direct competitive advantage; existing enterprise resources can provide basic conditions for digital transformation and resource-orchestration capability; although digital transformation cannot directly create competitive performance, it can indirectly deliver competitive advantage by positively affecting resource-orchestration capability; resource-orchestration capability directly and significantly affects enterprise competitive performance and is the core competency for enterprises to build digital resilience.

We conduct a field experiment in China, o↵ering small business owners a conditional social media advertising subsidy. Beyond boosting business revenue and employment, the inter-vention significantly increases access to fintech credit: treated firms are more likely to open online stores and obtain online loans, while bank credit remains una↵ected. Our findings reveal a “fintech accelerator” mechanism—digital marketing drives sales growth that directly improves firms’ eligibility for fintech lending—demonstrating how targeted digital interven-tions can enhance financial inclusion and reshape credit allocation for small businesses.