战略性承诺实现脱碳：大型企业、共同所有权与政府的作用(Strategic Commitments to Decarbonize： The Role of Large Firms, Common Ownership, and Governments)

研究问题：本研究探讨在政府受限的政策环境和双重外部性（环境损害与绿色技术溢出）情形下，大型企业及其共同行业所有权如何通过战略性碳中和承诺推动低碳转型以及与政府碳税、绿色创新补贴政策之间的互动机制。  
主要结论：研究发现，即使企业仅出于利润动机，在政策约束下，大型企业和由共同行业投资者构成的联盟通过超额投资绿色创新起到协调作用，不仅能激励其他企业加速低碳转型，还能降低政府未来设置高碳税的需求，尽管碳税仍是实现最佳社会福利的核心政策工具。  
研究贡献：本文通过构建一个综合环境与技术外部性的理论模型并辅以实证分析，揭示了企业战略承诺、共同行业所有权与政府低碳政策之间的互补关系，为制定更为高效的低碳转型政策提供了新的理论视角与实证支持。

We study how government policies and corporate commitments to decarbonize interact under two externalities： environmental damages and green innovation spillovers. Unconstrained carbon taxes and innovation subsidies could achieve first-best outcomes, but when government policies face constraints, commitments by large firms and institutional investors can serve as profit-driven coordination devices that spur green innovation and technology adoption, and thereby reduce overall transition costs. Firm commitments also enhance government policy credibility by lowering the need for high future carbon taxes. Our empirical evidence confirms that firm size and green common ownership drive Net Zero commitments and decarbonization investments.

论文原文：Viral V. Acharya,Robert F. Engle III,Olivier Wang,，Strategic Commitments to Decarbonize： The Role of Large Firms, Common Ownership, and Governments，DOI---- 10.3386/w33335

空间环境经济学(Spatial Environmental Economics)

研究问题：本研究探讨如何在区域和空间层面上，通过分析人口密度、产业构成、交通网络和自然地理等因素的相互作用，理解环境污染和气候变化对经济活动及交通基础设施投资的影响，并探索优化环境政策设计的路径。  
主要结论：研究表明，空间异质性显著影响污染排放与环境损害，且合理的政策调控与技术投入能够实现区域环境改善与经济发展的双重效应。  
研究贡献：本文通过整合空间经济学与环境经济学的理论与高分辨率实证数据，构建了评估区域环境政策与交通基础设施相互作用的新框架，为制定精细化、区域适应性的政策提供了理论依据和实证支持。

How do environmental goods and policies shape spatial patterns of economic activity? How will climate change modify these impacts over the coming decades? How do agglomeration, commuting, and other spatial forces and policies affect environmental quality? We distill theoretical and empirical research linking urban, regional, and spatial economics to the environment. We present stylized facts on spatial environmental economics, describe insights from canonical environmental models and spatial models, and discuss the building blocks for papers and the research frontier in enviro-spatial economics. Most enviro-spatial research remains bifurcated into either primarily environmental or spatial papers. Research is only beginning to realize potential insights from more closely combining spatial and environmental approaches.

论文原文：Clare A. Balboni,Joseph S. Shapiro,，Spatial Environmental Economics，DOI---- 10.3386/w33377

单边脱碳是否能自我补偿？(Does Unilateral Decarbonization Pay For Itself?)

研究问题： 本研究探讨了在衡量全球温度损害下，单方面（不依赖国际合作）大规模去碳化是否在经济上能够自我支付成本。
主要结论： 研究发现，对于美国和欧盟这类大经济体，考虑全球温度损害时，单方面去碳化达到约84%–86%的水平是成本效益合理的，而基于局部温度损害的评估则显示其经济效应大大减弱。
研究贡献： 本文通过整合全球温度冲击下的经济损害估算与边际减排成本曲线，构建了国内碳成本模型，为评估单边去碳化政策经济性提供了新的理论框架和实证依据。

This paper shows that unilateral decarbonization pays for itself in large economies. We estimate economic damages from global temperature shocks and combine them with a climate-economy model to construct Domestic Costs of Carbon： $226 per ton for the United States and $216 per ton for the European Union. When compared to marginal abatement costs, these values imply over 80% unilateral decarbonization for both economies, an order of magnitude larger than under conventional damages estimated based on local temperature.

论文原文：Adrien Bilal,Diego R. Känzig,，Does Unilateral Decarbonization Pay For Itself?，DOI---- 10.3386/w33364

太阳能补贴的价值评估(Valuing Solar Subsidies)

研究问题：本研究探讨了不同财富群体在住宅太阳能板采用决策中如何通过隐含贴现率差异影响长期收益评价及政策激励（如净计量和前期补贴模式）的有效性和分配效应。
主要结论：经实证发现，高财富家庭的隐含贴现率约为10%，而低财富家庭约为15.3%，导致现有NEM政策上对高财富家庭更有利，通过转换为前期补贴政策可在保持太阳能市场规模的同时改善分配公平性与提升政策成本效益。
研究贡献：本研究利用丰富的微观数据和动态离散选择模型，创新性地识别并量化了住宅太阳能采用中的财富异质性效应，为太阳能激励政策设计及持久耐用消费品的长期投资行为提供了严谨的实证依据和政策启示。

Individuals trade present for future consumption across a range of economic behaviors, and this tradeoff may differ across socioeconomic groups. To assess these tradeoffs, we estimate a dynamic model of residential solar adoption and system sizing in California using household-level data on solar irradiance, electricity consumption, and electricity rates that offer plausibly exogenous variation in the future benefits from adopting relative to upfront costs. We find implicit discount rates of 15.3%, 13.8%, and 10.0% for low-, medium-, and high-wealth households. Counterfactual simulations demonstrate opportunities to reduce the regressivity of solar adoption, increase policy cost-effectiveness, and improve welfare for low-wealth households.

论文原文：Bryan Bollinger,Kenneth Gillingham,A. Justin Kirkpatrick,，Valuing Solar Subsidies，DOI---- 10.3386/w33368

实证区分混合状态下跨界与国内空气污染对健康的影响(Empirically Distinguishing Health Impacts of Transboundary and Domestic Air Pollution in Mixture)

研究问题：本研究探讨了来自不同来源（包括国内与跨境）的空气中颗粒物是否在化学、物理特性及健康损害效应上存在显著差异，以及如何精确区分和量化各来源颗粒物对呼吸健康的影响。  
主要结论：研究结果显示，相同剂量的颗粒物因其来源不同而导致的健康成本存在显著差异，其中跨境来源（如来自中国和北朝鲜）的颗粒物单位健康损害远高于国内颗粒物，并在混合效应下产生非线性健康响应。  
研究贡献：本研究首次借助大气传输模型与高分辨率健康数据相结合的方法，实现了对不同来源颗粒物的独立估计，不仅为评估跨境污染责任提供了新的实证依据，同时也为制定更精确的环境及公共健康政策提供了创新的量化工具。

Particulate matter (PM) is a major, clinically important air pollutant. A large portion of emitted PM crosses borders, damaging health outside of its originating jurisdiction, but due in part to technical obstacles these pollutant flows remain unregulated. Proposed attribution approaches assume that units of PM originating in different jurisdictions cause the same harm, despite a widespread understanding that differing chemical and physical features of PM could generate distinct health effects. We use an atmospheric model to decompose the origins of PM individuals are exposed to at each location in South Korea, the nexus of one of the world's most contentious transboundary air pollution disputes, every day during 2005–2016. We then link these data to universal healthcare records in an econometric analysis that simultaneously measures and accounts for harms from seven types of PM, each from a distinct origin. We discover that the health harm of a unit of transboundary PM is approximately 5× (North Korea) and 2.6× (China) greater than a unit of PM originating within South Korea, and that health responses to PM from natural sources differs from those to anthropogenic sources. Because harms differ by origin, we compute that transboundary sources contribute only 43% of anthropogenic PM exposure in South Korea but generate over 70% of its associated respiratory health costs. Our results suggest that PM should be treated as a mixture of distinct pollutants, each with a unique measurable impact on human health.

论文原文：Jaecheol Lee,Andrew J. Wilson,Solomon M. Hsiang,，Empirically Distinguishing Health Impacts of Transboundary and Domestic Air Pollution in Mixture，DOI---- 10.3386/w33379

降低成本：全球电动汽车电池产业中的边做边学与政府政策(Drive Down the Cost： Learning by Doing and Government Policies in the Global EV Battery Industry)

研究问题：本文探讨了全球电动汽车电池产业中“边际成本学习‐效应”（learning-by-doing）如何与消费者补贴及国内内容要求等政府政策相互作用，从而影响电池成本下降、市场竞争及电动汽车普及问题。  
主要结论：研究发现，在2014年至2020年期间，电池生产的学习效应约贡献了35.5%的成本下降，学习率估计为7.5%，并且该效应显著放大了消费者补贴对电动汽车销量和社会福利的正面影响，同时中国的白名单政策虽促使国内供应商增强竞争力，但可能导致其他国家福利受损。  
研究贡献：本研究通过构建结构化模型并利用全球详实数据，首次系统量化了全球电动汽车电池生产中学习效应与政府政策互动的机制，为制定和评估相关产业政策提供了理论与实证依据。

Electric vehicle (EV) battery costs have declined by more than 90% over the past decade. This study investigates the role of learning-by-doing (LBD) in driving this reduction and its interaction with two major government policies – consumer EV subsidies and local content requirements. Leveraging rich data on EV models and battery suppliers, we develop and estimate a structural model of the global EV industry that incorporates heterogeneous consumer choices and strategic pricing behaviors of EV producers and battery suppliers. The model allows us to recover battery costs for each EV model and quantify the extent of LBD in battery production. The learning rate is estimated to be 7.5% during our sample period after controlling for industry technological progress, economies of scale, input costs, and EV assembly experience. LBD magnifies the effectiveness of consumer EV subsidies and drives cross-country spillovers from these subsidies. Upstream battery suppliers capture only a minor share of LBD’s economic benefits, and consumer EV subsidies correct for the under-provision of learning and improve social welfare. China’s local content requirement helps domestic suppliers gain a competitive advantage at the cost of consumers and foreign suppliers but would have harmed domestic welfare if delayed by five years.

论文原文：Panle Jia Barwick,Hyuk-Soo Kwon,Shanjun Li,Nahim B. Zahur,，Drive Down the Cost： Learning by Doing and Government Policies in the Global EV Battery Industry，DOI---- 10.3386/w33378

充电不确定性：实时充电数据与电动汽车采纳(Charging Uncertainty： Real-Time Charging Data and Electric Vehicle Adoption)

研究问题：本文探讨高速公路沿线直流快充桩实时数据可用性不足及其对消费者充电信心、电动汽车采用和相关充电设施部署的影响问题。
主要结论：研究发现，仅实现实时数据普及对电动汽车普及的推动作用有限，但若伴随充电器实际运行时间和消费者信心的提升，则可显著提高新车电动汽车销量、扩大车队规模并有效减少碳排放。
研究贡献：本文通过整合实地数据、问卷调查及改进的电动汽车市场模型，首次量化了实时充电数据在缓解续航焦虑和促进电动汽车转型中的作用，为相关公共政策制定提供了理论与实证支持。

Charging infrastructure is critical to electric vehicle (EV) adoption, but for chargers to be most useful, EV drivers need to know in real time where they are and whether they are working and available. We investigate the availability of real-time data from DC fast chargers on six major US Interstates and model the impacts of expanding access to real-time data to all DC fast chargers near highways. On average, between March and August 2024, 32.9% of DC fast charging stations adjacent to those six Interstates provided their real-time status on PlugShare, a major charge-finding app, with gaps of up to 1,308 miles without real-time data. Further, we survey potential car buyers and EV owners and find low credibility of currently-available real-time data. We incorporate this data into a two-sided model of consumer vehicle choice and charging station build-out adapted from Cole et al. (2023). If universal real-time data is accompanied by improved charger uptime and driver confidence in the accuracy of the real-time data, we predict that the EV share of new vehicle sales would grow by 8.0 percentage points in 2030, expanding the EV fleet by 13.2%, and reducing 2030 carbon emissions by 22.5 mmt, versus baseline projections for 2030.

论文原文：Omar Isaac Asensio,Elaine Buckberg,Cassandra Cole,Luke Heeney,Christopher R. Knittel,James H. Stock,，Charging Uncertainty： Real-Time Charging Data and Electric Vehicle Adoption，DOI---- 10.3386/w33342

物种保护的成本：濒危物种法对土地市场的影响(The Cost of Species Protection： The Land Market Impacts of the Endangered Species Act)

研究问题：本研究探讨了美国《濒危物种法案》在实施过程中，物种上市和关键生境划定对土地市场（包括住宅、空地价格与交易）以及建筑许可活动的经济影响及其时空异质性。
主要结论：总体上，法案对住宅和空地价格的平均影响较小，但在特定物种、区域及争议性较高的样本中可观察到明显的负面价格效应和界外价格的上升，同时在建设许可审批方面也存在一定的延迟效应。
研究贡献：本研究构建了全美范围内前所未有的综合数据库，并采用分阶段、空间差分等准实验方法，系统评估了濒危物种保护政策的经济效应，为环境保护与土地利用政策的评估提供了新的实证证据和方法论创新。

Protecting species’ habitats is the main policy tool employed across the globe to reduce biodiversity losses. These protections are hypothesized to conflict with private landowners’ interests. We study the economic consequences of the most extensive and controversial piece of such environmental legislation in US history—the Endangered Species Act (ESA) of 1973. We assemble the most comprehensive data on species conservation efforts, land transactions, and building permits to date. By comparing parcels with identical histories of protections we show that, on average, the ESA shifts transactions from inside to outside of the protected area and leads to a slight appreciation in residential and vacant land values outside of critical habitats. We also show that the federal regulator determines borders for areas with the most stringent protections to avoid large effects on land values, only where it is explicitly allowed to take economic criteria into account. These average findings mask significant heterogeneity at the species and location level, which we document. Furthermore, we find no evidence of the ESA affecting building activity as measured by construction permits. Overall, even taking into account species-level heterogeneity, the number of possibly negatively affected parcels is extremely small. This suggests that the capitalization of the economic impacts of the ESA through the land market channel are likely minor, despite potential delays to development through permitting, for which we provide suggestive evidence. Our findings do not rule out economically significant impacts in a few highly constrained land markets with ESA protections amplified by local regulatory action.

论文原文：Eyal G. Frank,Maximilian Auffhammer,David McLaughlin,Elisheba Spiller,David L. Sunding,，The Cost of Species Protection： The Land Market Impacts of the Endangered Species Act，DOI---- 10.3386/w33352

人工智能驱动的（金融）学术研究(AI-Powered (Finance) Scholarship)

研究问题：本研究探讨了如何利用大规模语言模型构建一个自动化流程，从海量财务数据中筛选和检验股票收益预测信号，并生成完整学术论文以应对后验假设生成和学术完整性风险的问题。  
主要结论：结果表明，该自动化流程能够高效、系统地生成符合学术写作规范的多版本金融研究论文，但同时也揭示了借助AI批量生成理论解释可能引发数据挖掘滥用和学术评价偏差的风险。  
研究贡献：本研究构建了一个从信号筛选、统计验证到自动论文生成的集成化AI研究生产流水线，为金融文献的快速生成、学术指标操控问题以及未来研究评价标准的制定提供了新方法和理论基础。

This paper describes a process for automatically generating academic finance papers using large language models (LLMs). It demonstrates the process’ efficacy by producing hundreds of complete papers on stock return predictability, a topic particularly well-suited for our illustration. We first mine over 30,000 potential stock return predictor signals from accounting data, and apply the Novy-Marx and Velikov (2024) “Assaying Anomalies” protocol to generate standardized “template reports” for 96 signals that pass the protocol’s rigorous criteria. Each report details a signal’s performance predicting stock returns using a wide array of tests and benchmarks it to more than 200 other known anomalies. Finally, we use state-of-the-art LLMs to generate three distinct complete versions of academic papers for each signal. The different versions include creative names for the signals, contain custom introductions providing different theoretical justifications for the observed predictability patterns, and incorporate citations to existing (and, on occasion, imagined) literature supporting their respective claims. This experiment illustrates AI’s potential for enhancing financial research efficiency, but also serves as a cautionary tale, illustrating how it can be abused to industrialize HARKing (Hypothesizing After Results are Known).

论文原文：Robert Novy-Marx,Mihail Z. Velikov,，AI-Powered (Finance) Scholarship，DOI---- 10.3386/w33363

劳动力市场中的技术颠覆(Technological Disruption in the Labor Market)

研究问题：本研究探讨了历史上通用技术（例如蒸汽和电力）如何引发美国劳动力市场结构剧变，并分析人工智能是否作为一项新型通用技术将引发类似规模和长期性的劳动力市场转型。  
主要结论：研究表明，尽管1990至2017年间美国劳动力市场的变动相对平稳，但后疫情时期表现出显著的技能升级、低薪服务业停滞、STEM职业快速增长以及零售就业大幅下降，这些现象预示着人工智能可能引发更深远和渐进式的市场扰动。  
研究贡献：本文通过构建跨世纪的劳动力市场“波动率”指标以及对比历史数据，为理解通用技术在劳动力市场中催生的结构性变革和预测人工智能的潜在长远影响提供了全新理论框架和定量实证依据。

This paper explores past episodes of technological disruption in the US labor market, with the goal of learning lessons about the likely future impact of artificial intelligence (AI). We measure changes in the structure of the US labor market going back over a century. We find, perhaps surprisingly, that the pace of change has slowed over time. The years spanning 1990 to 2017 were less disruptive than any prior period we measure, going back to 1880. This comparative decline is not because the job market is stable today but rather because past changes were so profound. General-purpose technologies (GPTs) like steam power and electricity dramatically disrupted the twentieth-century labor market, but the changes took place over decades. We argue that AI could be a GPT on the scale of prior disruptive innovations, which means it is likely too early to assess its full impacts. Nonetheless, we present four indications that the pace of labor market change has accelerated recently, possibly due to technological change. First, the labor market is no longer polarizing— employment in low- and middle-paid occupations has declined, while highly paid employment has grown. Second, employment growth has stalled in low-paid service jobs. Third, the share of employment in STEM jobs has increased by more than 50 percent since 2010, fueled by growth in software and computer-related occupations. Fourth, retail sales employment has declined by 25 percent in the last decade, likely because of technological improvements in online retail. The post-pandemic labor market is changing very rapidly, and a key question is whether this faster pace of change will persist into the future.

论文原文：David J. Deming,Christopher Ong,Lawrence H. Summers,，Technological Disruption in the Labor Market，DOI---- 10.3386/w33323

为年轻企业家现代化信贷准入：从FICO评分到现金流(Modernizing Access to Credit for Younger Entrepreneurs： From FICO to Cash Flow)

研究问题：该研究旨在探讨在小企业信贷审查中，利用银行对账单中反映的现金流数据替代或补充传统FICO评分，是否能改善对借款企业违约风险的预测并缓解对年轻创业者的不利影响。  

主要结论：研究证据表明，整合现金流数据的模型较传统FICO模型具有更高的违约预测准确性，尤其能显著改善针对低FICO分数的年轻创业者的信贷审批率，从而降低他们因信用历史不足所面临的不利约束。  

研究贡献：该工作不仅提出了“Tail Analysis for Comparative Outcomes (TACO)”这一新方法以量化模型调整对不同群体的影响，而且为金融科技中采用开放银行数据优化小企业信贷审核提供了实证支持和政策建议。

Younger entrepreneurs are disadvantaged by traditional loan underwriting, which relies heavily on personal credit scores. With data from three fintech companies, we show that incorporating timely information about ability to repay from business checking account statements particularly improves default prediction performance for younger business owners. We develop a novel method to compare model predictions across subgroups—Tail Analysis for Comparative Outcomes (TACO)—which finds that switching from a Baseline (FICO-driven) model to a Cash Flow-enhanced model benefits younger entrepreneurs. We confirm this in causal analysis of approval decisions, showing that access to cash flow-intensive underwriting increases approval rates for younger vs. older entrepreneurs.

论文原文：Christopher M. Hair,Sabrina T. Howell,Mark J. Johnson,Siena Matsumoto,，Modernizing Access to Credit for Younger Entrepreneurs： From FICO to Cash Flow，DOI---- 10.3386/w33367

CEO与企业匹配及42个国家的生产率(CEO-Firm Matches and Productivity in 42 Countries)

研究问题：本研究探讨了CEO的行为型态与企业需求之间的匹配关系，以及这种匹配（或不匹配）如何影响企业生产率。  
主要结论：研究发现，企业中CEO行为与企业理想需求不匹配会导致显著的生产率损失，其中不匹配的企业生产率可能降低多达20%，而消除此种不匹配可使整体销售提升约9%。  
研究贡献：本研究通过构建简洁的调查工具大规模衡量CEO行为型态，提供了跨国实证证据，揭示了CEO与企业匹配在提升企业生产率和经济增长中的关键作用，为优化CEO选用政策提供了有力的实证参考。

Firms are key to economic development, and CEOs are key to firm productivity. Are firms in countries at varying stages of development led by the right CEOs, and if not, why? We develop a parsimonious measure of CEO time use that allows us to differentiate CEOs into “leaders” and “managers” in a survey of 4,800 manufacturing firms across 42 countries, with income per capita ranging from USD 4,000 to 45,000. We find that poorer countries have fewer leaders and relate this to training opportunities. Even when suitable leaders are available, they often do not lead the firms that would benefit the most, resulting in mismatches that can cause up to a 20% loss in productivity for the mismatched firms. The findings imply that policies that address the causes of mismatch could significantly enhance growth without additional resources.

论文原文：Amanda Dahlstrand,Dávid László,Helena Schweiger,Oriana Bandiera,Andrea Prat,Raffaella Sadun,，CEO-Firm Matches and Productivity in 42 Countries，DOI---- 10.3386/w33324

人工智能资产定价模型(Artificial Intelligence Asset Pricing Models)

研究问题：本文探讨如何将transformer架构嵌入随机贴现因子(SDF)模型中，通过跨资产信息共享与非线性建模来提升资产定价预测的准确性并降低定价误差。
主要结论：实证结果显示，包含跨资产信息共享机制的线性与深度非线性transformer模型均显著超越传统小规模模型，在样本外夏普比率提高、定价误差降低，并且模型性能随着参数复杂度的提升而不断优化。
研究贡献：本研究首次构建了人工智能资产定价模型(AIPM)框架，提供了一个具有解释性的线性portfolio transformer及其深度非线性扩展，推动了机器学习方法在资产定价领域中利用跨资产信息共享和复杂度优势的应用与理论进展。

The core statistical technology in artificial intelligence is the large-scale transformer network. We propose a new asset pricing model that implants a transformer in the stochastic discount factor. This structure leverages conditional pricing information via cross-asset information sharing and nonlinearity. We also develop a linear transformer that serves as a simplified surrogate from which we derive an intuitive decomposition of the transformer's asset pricing mechanisms. We find large reductions in pricing errors from our artificial intelligence pricing model (AIPM) relative to previous machine learning models and dissect the sources of these gains.

论文原文：Bryan T. Kelly,Boris Kuznetsov,Semyon Malamud,Teng Andrea Xu,，Artificial Intelligence Asset Pricing Models，DOI---- 10.3386/w33351

自然损失与气候变化：双重危机的乘数效应(Nature Loss and Climate Change： The Twin-Crises Multiplier)

研究问题：本文探讨了气候变化与自然资源损失之间相互作用下，双危机乘数效应如何影响经济活动和最优环境政策的制定。  
主要结论：研究表明，气候变化与自然资源损失之间的正反馈机制会放大各生产要素对碳排放和产出损害的影响，从而降低资本及生态系统服务的有效产出，并要求更高程度的环境保护投资。  
研究贡献：本文通过构建一个整合气候变化与自然损失相互作用的理论模型，首次量化了双危机乘数效应，为制定更协调的碳税与土地使用管控政策提供了理论依据和政策启示。

We study the economic effects of the interaction of nature loss and climate change in a model that incorporates important aspects of both processes. We capture the distinct ways in which they affect economic activity—with nature constituting a key factor of production and climate change destroying parts of output—but also the ways in which they interact： climate change causes nature loss, and nature provides both a carbon sink and adaptation tools to reduce climate damages. Our analysis of these feedback loops reveals a novel amplification channel—the Twin-Crises Multiplier—that systematically affects optimal climate and nature conservation policies.

论文原文：Stefano Giglio,Theresa Kuchler,Johannes Stroebel,Olivier Wang,，Nature Loss and Climate Change： The Twin-Crises Multiplier，DOI---- 10.3386/w33361