The rapid development of artificial intelligence has profoundly reshaped both the substantive focus and the methodological paradigms of behavioral science. This paper systematically synthesizes three frontier research directions that have emerged from these changes: 1) Investigations of human attitudes and behaviors during interactions with AI, and the mechanisms through which AI affects human decision-making and preferences; 2)behavioral experimental studies examining the behavioral characteristics and preference patterns of large language models; 3) methodological innovations enabled by AI technologies, including the use of AI agents as surrogates for human participants in surveys and experiments, and complex-systems research that builds dynamic interactive systems based on multi-agent frameworks. The paper concludes with a discussion of the challenges confronting, and future directions for, interdisciplinary research at the intersection of AI and behavioral science.
This paper examines how the horizon orientation of capital markets shapes real economic outcomes, focusing on firms’ environmental, social, and governance (ESG) performance in the context of China’s stock market. Building on insights from market microstructure theory, the study proposes a novel measure of stock price horizon orientation to capture the extent to which market prices reflect long-term versus short-term fundamentals. Empirically, we find that as stock prices become more forward-looking, firms exhibit lower ESG performance. Further analysis suggests that long-horizon prices enhance managerial learning by conveying richer information about long-term fundamentals, thereby reducing managers’ incentives to engage in ESG activities as a form of precautionary risk hedging. These findings underscore the dual role of capital markets — as both monitors of corporate behavior and providers of information — and offer new insights into how financial market shape firms’ ESG performance.
Adequate and stable power supply serves as the foundation for enterprise production. It not only safeguards employment in local firms but also attracts potential entrants to create additional job opportunities. This paper develops a firm production model incorporating power supply and investigates the employment-stabilizing effects of power supply by utilizing the China urban power supply index and firm-level tax survey data. The findings reveal that power supply significantly stabilizes employment through two primary mechanisms at the firm level: expanding production scale (output effect) and reducing intermediate input procurement (intermediate input substitution effect). At the regional level, the effect manifests mainly through attracting new firm entry and enhancing local employment capacity. Further analysis demonstrates heterogeneous effects across regions, industries, and firm characteristics, with more pronounced impacts observed in economically less-developed regions, non-energy-intensive industries, electricity-intensive firms, small and medium-sized enterprises, and private firms. These findings provide critical policy insights for employment protection and the realization of high-quality economic development alongside employment stabilization goals.
While agricultural insurance stabilizes agricultural production, it can also generate unintended impacts on the agricultural environment. Taking crop insurance as an example, this study conducts an in-depth analysis of the environmental spillover effects of China’s agricultural insurance policy and its scale heterogeneity, based on farmers’ chemical input usage and adoption of environmentally friendly technologies. Utilizing plot-level survey data and econometric methods, the research finds that agricultural insurance in China reduces chemical inputs and promotes farmers’ adoption of environmentally friendly technologies, indicating a positive environmental impact. Furthermore, agricultural insurance significantly influences the chemical input usage and adoption of environmentally friendly technologies among large-scale farmers, but has a weaker effect on smallholder farmers. This suggests that the current environmental effects of agricultural insurance primarily stem from behavioral changes in production practices among large-scale farmers. On the one hand, the findings demonstrate that establishing coordination mechanisms between agricultural insurance and environmental policies can play a positive role in improving the ecological environment of agriculture and rural areas. On the other hand, the results provide important references for the design of agricultural insurance products and the refinement of related policies.
Major event shocks cause significant fluctuations in macroeconomic variables, leading to a notable decrease in the parameter stability of traditional parameter-driven mixed-frequency dynamic factor models(PD-MFDFM). This severely impacts the stability of the extracted business cycle coincident index, potentially affecting economic situation analysis and macroeconomic policy formulation. To address this issue, this paper proposes a new class of score-driven mixed-frequency dynamic factor models (SD-MFDFM) and provides the corresponding maximum likelihood estimation method, where the distribution can be either normal or Student’s
From the demand-side, risk aversion is the fuse of the spread of crisis, and it is also the important origin of chronic poverty that is difficult to eradicate. In this paper, borrowers’ risk aversion is regarded as a new intervention variable of monetary policy regulation, and explores the impact of monetary policy on effectiveness of credit transmission and impartiality of credit capital distribution through stimulating borrowers’ endogenous motivation to take risks. The results show that borrowers’ risk aversion, which is an enhancer of the credit transmission of monetary policy, magnifies the credit transmission effect of monetary policy at the micro level. In addition, there are differences in the sensitivity of risk aversion of different income groups to monetary policy. The risk aversion of poor householder is greatly affected by monetary policy, because easing monetary policy promotes their endogenous motivation to take risks, but it is difficult to increase bank credit supply and fully satisfied credit demand. The rich households’ risk aversion is relatively less affected by monetary policy, and the credit transmission effect of monetary policy can be effectively amplified under the effect of risk aversion. Although borrowers’ risk aversion on the demand side improves the transmission efficiency of bank credit by monetary policy, it is difficult to solve the fairness problem of credit resource allocation.
Africa is one of the regions most severely impacted by global climate change. However, there are few accurate methods for assessing climate vulnerability in Africa due to data challenges. This paper, based on 3.2million news event texts from 2015 to 2023, uses a large language model to extract climate-related news texts. A fine-tuned climate-themed large language model is employed to identify the impact of climate shock events on dimensions such as the economy, health, and politics. A climate change vulnerability assessment index system for Africa is constructed, and the spatial-temporal variation characteristics of climate change vulnerability in the Great Lakes region of Africa are calculated. The study also tests the effectiveness of the climate vulnerability index using a mixed-frequency dynamic single-factor model. The results show that the climate vulnerability index significantly improves the predictive accuracy of national conflicts. Climate change vulnerability has become a key driving factor for national and regional conflicts in Africa. The climate vulnerability index measurement method proposed in this paper contributes to enhancing the effectiveness of international engagement in climate cooperation and regional conflict prediction in Africa.
Short video marketing has become a core scenario for e-commerce conversion. While accelerated information dissemination and information overload have reduced consumer patience thresholds, the non-linear mechanisms through which video duration affects sales conversion remain unclear. This study examines the inverted U-shaped relationship between short video duration and product sales from the perspectives of information effectiveness and overload, revealing how this relationship is moderated by product attributes (price and type). Key findings include: 1) Information effectiveness (positive mediation) and information overload (negative mediation)constitute dual mediation pathways that jointly drive the inverted U-shaped relationship; 2) Higher-priced products reduce the optimal duration threshold and strengthen the relationship, whereas experiential products exhibit lower thresholds and weaker relationships compared to search-oriented products. The research establishes a dynamic trade-off framework for digital content marketing, demonstrating that the interplay between information density and cognitive load determines duration threshold effects. These conclusions provide empirical support for optimizing platform algorithms and innovating content production paradigms.
Financial market segmentation between formal and informal markets has long existed and has profound impacts on the development of capital markets and enterprise operations. This paper investigates the impact of financial market segmentation on enterprise capacity utilization using Chinese A-share listed manufacturing companies from 2008 to 2020 as the research object. Empirical tests show that under unchanged conditions, a one standard deviation increase in financial market segmentation will cause enterprise capacity utilization to decrease by about 10.03% standard deviations. The analysis of channels shows that financial market segmentation reduces enterprise capacity utilization by suppressing new investment and R&D innovation. Heterogeneity analysis reveals that for enterprises with high administrative intervention, or non-state-owned nature, financial market segmentation has a more significant inhibitory effect on capacity utilization. Based on the allocation and flow of capital elements, this paper deeply explores the impact of financial market segmentation on enterprise capacity efficiency, providing practical implications for the integration of financial markets into a unified national market and the marketization of capital elements.
Pledging company shares to financial institutions for financing is a common but high-risk financing method among shareholders of listed companies. Against this backdrop, reducing the proportional risk associated with equity pledge financing and constraining the scale of shadow banking are important and practically relevant research topics. This paper uses firm-level data of listed companies from 2012 to 2023 to examine the effectiveness of the policy regulating asset management businesses of financial institutions — The Guiding Opinions on Regulating the Asset Management Business of Financial Institutions (hereinafter referred to as the “New Asset Management Rules”). It analyzes the transmission mechanisms and heterogeneous effects through which the New Asset Management Rules influence the proportional risk of equity pledge activities. The findings show that the New Asset Management Rules can effectively reduce the proportional risk of equity pledge financing. Mediation analysis reveals that the rules significantly suppress such risk by limiting the scale of shadow banking. Further heterogeneity tests indicate that the impact of the New Asset Management Rules on equity pledge risk varies across regions, ownership structures, and the professional backgrounds of senior executives. It is recommended to strengthen the implementation of the New Asset Management Rules, reduce proportional risk of equity pledge financing and the scale of shadow banking, apply differentiated regulatory measures to different types of firms, moderately broaden financing channels, ease financing constraints, and ensure the stable operation of both the real economy and the capital markets.
Green innovation, as the integration of the “green” and “innovation” themes under the concept of high-quality development, is an important pathway to drive China’s economic growth and enhance its international industrial competitiveness. This paper based on panel data from 214 prefecture-level cities from 2007 to 2022, this study first employs a multi-period synthetic difference-in-differences approach to examine the impact of low-carbon city pilots, innovative city pilots, and dual “low-carbon–innovation” policy pilots on green innovation. The study then further compare the policy effects of the dual pilot sequence on green innovation, and analyze in depth the heterogeneous characteristics of the impact of the pilot sequence on green innovation in different city locations, scientific and educational levels, and city levels. Finally, a spatial difference-in-differences model is constructed to explore the spatial spillover effects of the dual policy pilots. The findings reveal that low-carbon city pilots, innovative city pilots, and dual policy pilots all significantly promote green innovation, with the dual policy pilots demonstrating stronger policy effects compared to single pilots, and the effects of both single and dual pilots progressively strengthen over time. Overall, the policy of innovation first and low carbon later exhibits a greater promoting effect on green innovation levels compared to “low carbon first, then innovation”. The impact of the implementation sequence of the dual policies on green innovation varies depending on geographical location, urban level of science and education, and urban level heterogeneity. Specifically, the policy of low carbon first and innovation later fosters green innovation in eastern regions and cities with moderate levels of scientific and educational development, while the policy of innovation first and low carbon later shows larger innovation effects in eastern regions, cities with high levels of scientific and educational development, and key cities. Furthermore, the dual policy pilots demonstrate significant positive spatial spillover effects on neighboring regions and areas with similar levels of economic development. The research conclusions enrich the study of the economic effects of dual-policy pilot programs, while providing significant empirical evidence and policy references for the country to achieve high-quality development and comprehensive transformation and upgrading.
The heterogeneous input-output (IO) table disaggregates industrial sectors along specific dimensions, enabling the depiction of differentiated production technologies within sectors and revealing important insights that would otherwise be obscured by the assumption of homogeneity. However, the construction of heterogeneous IO tables is constrained by the limited detail in available statistical data, necessitating the use of non-survey techniques, such as proportional assumptions, which introduce significant errors. This creates an inherent trade-off between resolution and accuracy, and the uncertainty resulting from this trade-off has not been adequately assessed due to the lack of necessary reference data for accuracy evaluations. This paper distinguishes between two aspects of accuracy in heterogeneous IO analysis: the accuracy of the heterogeneous IO table and the accuracy of the heterogeneous IO model. It explores the impact of errors introduced by non-survey table construction methods on the accuracy of IO model applications. To address the absence of reference data for accuracy assessment, we propose a method that combines Monte Carlo simulation with TRAS techniques to generate a simulated true value matrix that reflects the structural characteristics of heterogeneous IO tables. By comparing the simulated true values with results from non-survey methods, this study evaluates the accuracy of non-survey techniques across various dimensions of IO model applications. Using the 2020 and 2017 Chinese non-competitive IO table distinguished between domestic and foreign investment as an example, 10,000 simulations were conducted for the elements of the intermediate flow matrix under three distribution scenarios. The distances between the simulated true values and non-survey method results were calculated at different levels of model application, including the direct consumption coefficient matrix, Leontief inverse matrix, output multipliers, and export value-added. The simulations reveal that the accuracy of heterogeneous IO models constructed using non-survey methods significantly improves as the integration level of IO operations deepens. Moreover, the sensitivity of model accuracy to the validity of non-survey assumptions is low. Further regression analysis of the accuracy of matrix or vector elements in the model results shows that diagonal elements of the Leontief inverse matrix exhibit higher accuracy, as do elements corresponding to homogeneous consumption relationships. Additionally, there is a positive correlation between the accuracy of elements and their sensitivity to changes in direct consumption coefficient. These findings provide valuable insights for evaluating the accuracy of heterogeneous IO models constructed using non-survey techniques and offer important guidance for more effective use of model results.
