We propose a sufficient statistics approach to measuring Ricardian comparative advantage in a quantitative trade model featuring cross-country differences in productivity, factor prices, market size, as well as monopolistic competition, endogenous markups, and firm heterogeneity. The model’s micro-foundations do not necessarily imply that the relevant data for the proposed sufficient statistics must include micro information, but its micro-structure is needed to understand how only macro information can be used instead. Applying the approach to Chinese microdata and cross-country macrodata, we show that imperfect competition with endogenous markups and firm heterogeneity have far-reaching implications for correctly measuring Ricardian comparative advantage.

The role of Market-incentive environmental regulation (MIER) within the framework of environmental governance is patently evident. While extant literature lauds the advantageous outcomes attributed to the environmental protection tax (EPT) which as a representative of MIER, our empirical inquiry presents a contrasting narrative. By employing the sophisticated Difference-in-Difference-in-Difference (DDD) methodology and utilizing data from A-share listed firms in Shanghai and Shenzhen from 2015-2022, our investigation reveals a significant decrease in firms’ total factor productivity (TFP) following the implementation of EPT. Our core assertion is fortified through the discernment of two plausible mechanisms, namely, the production downsizing effect and the production capital crowding-out effect. Building upon this revelation, we delve into the nuanced pathways through which firms can strategically mitigate the impacts of EPT, encompassing the enhancement of human capital, amplification of research and development (R&D) investments, and fortification of overall firm resilience. Heterogeneity analysis discloses a notably heightened impact of EPT on TFP of state-owned enterprises (SOEs), larger enterprises and enterprises located in eastern regions. Ultimately, an approximately cost-benefit analysis conclusively demonstrates that the benefits derived from EPT far surpass the costs incurred by the concomitant industrial output reduction, which further illustrates the rationale for the implementation of EPT.

We examine whether large language models (LLMs) can extract contextualized representation of Chinese news articles and predict stock returns. The LLMs we examine include BERT, RoBERTa, FinBERT, Baichuan, ChatGLM and their ensemble model. We find that tones and return forecasts extracted by LLMs from news significantly predict future returns. The equal- and value-weighted long minus short portfolios yield annualized returns of 90% and 69% on average for the ensemble model. Given that these news articles are public information, the predictive power lasts about two days. More interestingly, the signals extracted by LLMs contain information about firm fundamentals, and can predict the aggressiveness of future trades. The predictive power is noticeably stronger for firms with less efficient information environment, such as firms with lower market cap, shorting volume, institutional and state ownership. These results suggest that LLMs are helpful in capturing under-processed information in public news, for firms with less efficient information environment, and thus contribute to overall market efficiency.

本文研究了元宇宙和工业元宇宙的基本概念及其主要相关技术的特点，指出工业元宇宙是工业乃至产业数字化、智能化发展的全新阶段。介绍了广州市各区从战略高度大力支持元宇宙赋能传统产业，各区“元宇宙”产业发展各有侧重；广州具有发展元宇宙产业的硬件和软件等优势，拥有多个元宇宙场景应用的优势产业，数实融合为广州元宇宙产业发展创造了良好基础。阐述了工业元宇宙是广州数字经济与实体经济融合发展的新时空，是大湾区发展新质生产力的助推器，是新型工业化发展的重要推动力量。针对广州不同区域在推进工业元宇宙中的不足或不均衡现象，提出了完善顶层设计、强化统筹协调、构建全面场景示范、构建协同集聚生态、构建技术攻关体系、推进区域创新要素整合共享、构建区域制造业创新协同机制等建议。

本文系统梳理国内外关于人工智能(AI)与公司治理的相关文献，聚焦AI在公司治理中替代人类决策引发的实践难题与理论争议，从法、理、情三大维度剖析核心治理困境：在“法”的维度，面临法律主体缺位、算法黑箱导致的责任界定模糊及跨境法律适配冲突；在“理”的维度，存在技术路径锁定、战略弹性不足及行业特性适配偏差等治理策略问题；在“情”的维度，凸显算法偏见引发的公平性争议及自主决策与人类控制的伦理冲突(如隐私侵犯、情感缺失)。基于上述困境，本文针对性提出破局路径：法理层面构建“AI守住合规底线”的制度体系，战略层面确立“人主导下的AI辅助”实施路径，伦理层面明确“人引领AI”的价值准则，为AI与公司治理的深度融合提供兼具理论与实践意义的研究框架。

人工智能（AI）在公司治理中的应用引发其替代董事的合法性争议。本文以公司法合法性框架为视角，梳理AI参与董事职能的起源、发展及制度障碍。研究表明，AI替代面临三重困境：主体资格上，非生物属性与董事资格要求冲突，受法律人格排斥及身份职能不可替代性制约；决策程序上，算法黑箱违背透明度原则，且缺乏伦理决策能力；责任承担上，多方推诿形成归责真空，监督机制适配失效。现阶段AI替代尚不具备完整合法性，但可通过技术创新、制度突破与全球协同构建“有限替代”范式。本文揭示其合法性演进逻辑，为AI治理合法化及人机协同治理提供理论与实践参考。

This paper examines the impact of asset bubbles on innovation and long-run economic growth within a semi-endogenous growth framework, incorporating idiosyncratic productivity shocks and endogenous credit constraints in the R&D sector. It demonstrates that pure bubbles tied to intrinsically useless assets and equity bubbles linked to intermediate goods firms can coexist, relaxing credit constraints and boosting entrepreneurs’ total factor productivity (TFP), which stimulates R&D and enhances growth along the transitional path. However, these bubbles generally do not influence the long-run economic growth rate. The model’s mechanisms and predictions are supported by aggregate and firm-level evidence, showing a positive correlation between equity bubbles and R&D investment, with stronger effects during periods of tightened financial constraints.

本文以“横向垄断”创新概括数智治理的趋势性特征及其全球性挑战，它正在改变产业成本的边际变化，给企业管理带来生态危机，形成边际成本低位固化和无际趋近于零，由此催生纵向垄断和平台垄断的量变，以完成向横向垄断的质变，加剧了以美国为首，以数据资产为新内核的社会财富和权力结构的高度极化，这已然成为全球治理亟待解决的关键问题。本文通过分析“横向垄断”宏观结构的全球性风险和个人数据资产微观结构中左旋与右旋的不同趋势，宽谱系地展现出横向垄断给全球治理带来的挑战与危机，即“共识消解”与“意义不复”，由此提出以“主动型或被动型社会企业”的方式，建构非中心化分布式数智媒介的新生态，还公共数据以社会价值，有效缓解或消除社会的过度极化。

壮大耐心资本实现高质量发展是当前理论界与实践界共同关注的焦点，但鲜有研究探讨由耐心资本形成的共同所有权网络及其绿色治理效应。鉴于此，本文以2016—2023年沪深A股制造业上市公司为样本，探究长期共同机构所有权对企业绿色创新的影响及机制。研究发现，长期共同机构所有权对企业绿色创新具有显著的正向影响，人工智能技术能够增强长期共同机构所有权对企业绿色创新的驱动效应。机制检验表明，长期共同机构投资者可以通过抑制研发操纵与提升内部控制信息质量来促进绿色创新。进一步分析发现，长期共同机构投资者通过抑制绿色创新的“黑暗面”，推动企业全面履行其社会责任，从而促进企业绿色创新的“言行一致”。异质性检验发现，在高科技行业和行业竞争程度较高的行业中，长期机构所有权的绿色治理效应更为明显。本文从长期共同机构所有权视角揭示了AI赋能耐心资本网络在促进绿色创新中的显著作用，拓展了耐心资本作用机制及绿色创新驱动因素方面的研究文献，也为政府监管政策制定与企业绿色创新实践提供了重要参考。

作为资本市场的重要定价因素，股票意见分歧多由分析师预测差异来度量，但该指标具有低覆盖、高时滞、报喜藏忧等问题。为此，本文依照监管机构要求的投资者分类标准，构造保守型、稳健型、平衡型、积极型、激进型五类AI智能体，利用各智能体对股票新闻的评价差别构建AI分歧指标，识别由新闻引发的股票意见分歧。实证分析发现：（1）新闻意见分歧在当月推高股票价格，致使未来4个月股票产生较低的收益率和较高的暴跌概率。（2）在套利成本更高的股票中，意见分歧对股票价格的扰动更为剧烈。（3）意见分歧吸引小单和中单交易的追捧，引致特大单的反向交易。（4）新闻意见分歧导致股票高波动和价格高估，可以部分解释特质性波动率之谜。本研究弥补了意见分歧在当期推高估值的实证缺失，一定程度上解决了AI收益率预测的前视偏差顾虑。