This article views the economic development history of New China as a history of openness and constant adjustment. We examine China’s economic development and early open policies from 1953 to 1978, analyze how the nation navigated between planned and open economic models during the formative period. Through a comprehensive review ofthe key Chinese and Western scholarship, investigates major initiatives including the Soviet-assisted 156 Projects, First Five-Year Plan, The policy of Eight Characters and the later “Four Three Plan” of the Western engagement. The study highlights both achievements and challenges, particularly in industrial growth and technology transfer, while assessing the impact of critical events like the Sino-Soviet split. Statistical analysis and quantitative models demonstrate how these early experiments with economic openness, despite ideological constraints and operational difficulties, established crucial institutional and technological foundations for China’s subsequent reform and modernization. The findings offer valuable insights into China’s unique development path and its implications for emerging economies.

This paper investigates the outstanding economic and scientific-technological achievements of the Soviet Union during the 1945–1953 period and analyzes how these successes influenced New China’s decision to adopt the Soviet development model. Drawing upon a review of the key Chinese studies and the influential Western works, this study synthesizes diverse research methods,including historical institutional analysis, archival research, and econometric modeling, to examine the driving forces behind Soviet economic development, its quantitative achievements, and its evolving international status relative to the United States. Moreover, the paper reviews the development of Sino-Soviet relations during both the Kuomintang era and the early years of the People’s Republic of China, and it provides a comprehensive factor analysis of why New China chose to introduce the “Soviet model.” The paper also discusses recent quantitative research that employs statistical models to assess the impact of Soviet achievements on China’s economic planning. The findings underscore that the rapid reconstruction and modernization of the Soviet economy not only set new benchmarks in industrial output and scientific progress but also provided a persuasive example that shaped China’s early development strategy.

The development of industrialization runs through the history of the People’s Republic of China. In the early years of the founding of the People’s Republic of China, the Soviet Union provided important help for China’s industrialization construction, capital lending, talent training and technology transfer. By analyzing the proportion of China’s industrial output value in the total output value and the output of major industrial products during the period of the Soviet Union, it shows that the assistance of the Soviet Union had a very positive role in promoting the realization of ChinaIs industrialization, and helped China to lay the foundation of industrialization.In addition, some problems in accepting the Soviet Union’s aid to China also deserve our attention when promoting the new industrialization. The research explores both the internal and external factors that led to China’s acceptance of Soviet assistance.The study examines the political, economic, and historical contexts that created a favorable environment for such aid, analyzes the various modes and scales of the assistance implemented, and presents statistical data that underscore the transformative social and economic achievements derived from this partnership.Furthermore, the paper highlights the contributions of quantitative analyses in understanding the aid’s impact, discusses the experiences and lessons learned from the Soviet aid period, and assesses the evolving dynamics of Sino—Soviet relations,particularly the gradual divergence in economic development models. The findings shed light on how Soviet aid laid the groundwork for China’s industrialization while also foreshadowing future ideological and developmental separations.

We study whether commodity risk is efficiently priced in the cross-section of US equities. We first provide novel evidence on firm-level risk exposure to up to 42 individual commodities. We then construct a mispricing measure as the realized return less the commodity-risk-implied return, and find that it strongly predicts next-month return reversal beyond the traditional short-term reversal. This commodity-risk-mispricing reversal is substantially stronger among small, less analyst- or media-covered stocks, is weakly associated with investor sentiment, and is less prevalent among firms that engage in commodity hedging. In particular, underpricing among these hedgers often remains persistent and even intensifies after the 2004 financialization of commodity markets. This implies that despite the overall decline in mispricing, financialization has likely introduced more costs than benefits for corporate risk management.

货币政策传导效果具有显著状态依赖特征，其强度和路径随经济周期、金融条件与市场预期动态调整。本文结合叙事符号约束结构向量自回归(SVAR)与平滑迁移局部投影(ST-LP)，基于2006年3月至2025年12月的中国宏观经济月度数据识别外生货币政策冲击，考察不同周期阶段的非对称传导效应，并从利率、信用和预期渠道分析其机制。研究发现：第一，货币政策具有显著经济周期非对称效应，相比扩张期，扩张性货币政策在收缩期对产出和通胀的促进作用更强，但冲击初期存在时滞；第二，利率渠道在两种状态下均有效传导，收缩期宽松政策能推动信用扩张，但信用利差未同步收窄；第三，预期渠道主体分化，企业信心在收缩期明显改善并与产出修复呼应，消费者信心响应较弱。这为完善逆周期宏观调控提供新的经验证据。

Firm characteristics help estimate conditional factor loadings. Larger libraries contain new and overlapping information. We use the cross-sectional distribution of firm characteristics as a ruler to evaluate IPCA loading spaces. With factor counts fixed, expanding from 36 to 153 characteristics can improve in-sample fit while representing momentum and size positions less fully. Reallocating shared exposures changes attribution without changing joint fitted returns. With coefficients fixed, exposure references alter holdings, trading, and subsequent risk at the same characteristic target. Statistical factor models should be evaluated not only by return fit, but also by the exposures they represent and the investment decisions they support.

在全球经济格局深度调整与国际货币体系多极化的背景下，人民币国际化已成为我国参与国际货币体系变革、促进实体经济开放发展的核心战略。然而，既有研究主要从制度和政策层面展开探讨，却相对忽略了一国内部要素禀赋、产业结构与金融结构动态适配的内生逻辑。新结构经济学主张要素禀赋结构内生决定最优产业结构，进而内生出与之相匹配的最优金融结构，这为解读我国人民币国际化路径提出了崭新视角。本文将立足新结构经济学视角，试图以此探讨人民币国际化的阶段性推进路径与政策协同框架，以期丰富理论认知并提供实践参考。

Exploiting China’s 300 billion RMB Special Central Bank Lending Facility for the Clean and Efficient Use of Coal as a quasi-natural experiment across 12,816 firm-year observations from 2018 to 2023, we show that subsidized transition credit increased targeted coal and thermal power firms’ aggregate carbon emissions by 14.18 log points. We establish this backfire as a Financial Jevons Paradox, whereby credit-induced scale expansion overwhelms unit efficiency gains. Although the facility raised total factor productivity and stabilized emission in-tensity, it lowered debt costs, expanded bank loans, and induced production-biased patenting, proving that transition finance requires binding aggregate emission caps.

数字技术大幅降低了信息的生产与传播门槛，但低门槛、高传播的数字舆论监督多大程度上能够发挥企业绿色转型的治理作用仍有待检验。本文以自媒体作为数字舆论监督的典型代表，基于第三方微信公众号发布的32万余篇文章，构建自媒体关注度及其内容特征指标，系统考察了数字舆论监督对企业绿色创新的影响。研究发现，自媒体关注虽能显著提升企业绿色专利申请数量，但未同步提升专利质量，形成了明显的“量质分化”。机制检验表明，自媒体通过提高分析师研报覆盖和投资者互动关注强化了资本市场外部监督强度，但企业主要以更积极的环保表述和更充分的信息披露作出回应，并未转化为环保投资增加和污染排放改善，呈现明显的“言过于行”。上述效应在外部关注压力更强、管理层短期化倾向更明显的企业中更为突出，也主要由聚焦绿色议题、持续跟踪且语调积极的报道所触发。进一步研究发现，自媒体与分析师、传统新闻媒体能够形成互补效应；由数字舆论监督驱动的绿色专利扩张虽能改善第三方环境评价，却受到资本市场的估值折价。本文结论为完善自媒体治理、促进企业的绿色创新转型等提供了重要启示。

This paper examines how major artificial intelligence (AI) breakthroughs affect the market pricing of firms’ pre-existing AI capabilities. Using Chinese listed firms’ 2023–2024 AI patents classified with a large language model, we estimate dynamic stock return and volatility responses to the DeepSeek-R1 release. Stronger pre-event AI capabilities are associated with higher post-release returns and greater short-term volatility, with volatility effects persisting longer. Positive return responses concentrate in algorithms and models, not computing infrastructure, while industry applications show later volatility increases without comparable return gains. These findings highlight technology-specific differences between return and volatility responses to AI breakthroughs.