Large language models (LLMs) have powerful natural language processing capabilities. In this paper, we systematically review the recent literature in this field and highlight the new research opportunities that LLMs bring to text analysis in economics and finance. First, we introduce GPT and BERT, the two most representative LLMs, as well as a number of LLMs developed specifically for economic and financial applications. Additionally, we also elaborate on the fundamental principles behind applying LLMs for text data analysis. Second, we summarize the applications of LLMs in economic and financial text analysis from two perspectives. On the one hand, we highlight the significant advantages of LLMs in traditional text analysis scenarios, such as calculating text similarity, extracting text vectors for prediction, text data identification and classification, building domain-specific dictionaries, topic modeling and analysis, and text sentiment analysis. On the other hand, LLMs have strong human alignment capabilities, thus opening up entirely new application scenarios, i.e., acting as economic agents that simulate humans in generating beliefs or expectations about texts and making economic decisions. Finally, we summarize the limitations and existing research gaps that LLMs face in pioneering new paradigms of economic and financial text analysis research, and discuss potential new research topics that may arise from these issues.

In order to explore how green technology innovation and the development of the digital economy can jointly promote green economic growth, this paper constructs a general equilibrium model that includes green technology innovation and digital transition. Combining with the real-world data at the city level in China, from the two aspects of economic growth and carbon emissions, it analyzes the impact and the mechanism of action of the digital economy collaborating with green technology innovation on green economic growth. It found that: 1) Green technology innovation has a "U-shaped" impact on economic growth and carbon emissions. That is, after exceeding a specific threshold of technological innovation level, with the continuous increase in the level of green technology innovation, the economic growth rate will continuously increase, and carbon emissions will continue to decrease. Moreover, the development of digital economy will strengthen the impact of green technology innovation, resulting in a steeper "U-shaped" relationship. 2) The development of economic digitalization has both mediating and moderating effects. Green technology innovation has a positive "U-shaped" impact on the development of the digital economy. That is, an increase in green technology innovation can promote the development of digital economy. In turn, the development of digital economy further moderates the impact of green technology innovation on economic growth and carbon emission reduction, strengthening the positive effect of green technology innovation on green economic growth. 3) The digital economy's enhancement of the impact of green technology innovation on green total factor productivity is the primary mechanism by which the digital economy, in collaboration with green technology innovation, drives green economic growth. 4) Policies to promote the development of economic digitalization need to be accompanied by higher carbon taxes. Although there are short-term economic costs, there are advantages in terms of long-term economic growth and environmental quality. This research combines the study of the green transition of economic development with that of digital transition, providing crucial theoretical support for the coordinated advancement of the green and digital transition of the economy to ensure stable economic growth.

This paper introduces digital financial capability into the intertemporal decision model, constructs a theoretical analysis framework to explore the impact mechanism of digital financial capability on household wealth accumulation, and conducts an empirical test based on the data of China Household Finance Survey (CHFS). The research shows that digital financial capability can significantly promote household wealth accumulation in China, particularly for rural households and those with low education and low wealth levels. Mechanism analysis shows that increasing financial investment returns and promoting social interaction are two channels through which digital financial capability can improve household wealth accumulation. Further analysis shows that there are structural differences in the impact of digital financial capability on household wealth accumulation, which can improve the allocation of productive assets and financial assets, and reduce the holding of housing assets and other non-financial assets. The above research conclusions provide a new perspective to explain the accumulation of household wealth in China, and also provide a reference for the formulation of relevant policies to promote common prosperity.

In the process of low-carbon transition, enterprises require substantial financial support for related investments. Therefore, the effectiveness of carbon pricing policies depends on a well-functioning financial market. However, in reality, financial markets face various frictions that hinder the flow of capital, leading to inefficient allocation of resources. These frictions may affect corporate investment behavior, thereby weakening the implementation effects of carbon pricing policies. This paper, focusing on the issue of financing constraints, constructs an environmental-dynamic stochastic general equilibrium (E-DSGE) model incorporating a financing collateral constraint mechanism to analyze the impact of financing constraints on the effectiveness of carbon pricing policies and explores corresponding policy responses. The results show that: 1) From the perspective of environmental benefits, financing constraints weaken the "emission reduction effect" of carbon pricing policies, suppress corporate low-carbon investments, and reduce corporate emission intensity; 2) From the perspective of economic costs, financing constraints amplify the cost impact of carbon pricing on enterprises, restrict output growth, and increase the overall economic cost of the low-carbon transition; 3) Introducing carbon asset-backed loans as a complementary measure to carbon pricing policies can effectively mitigate the negative impact of financing constraints on carbon pricing policies; 4) Numerical simulation shows that financing constraints increase the proportion of carbon pricing-related costs in enterprises' total production costs from an average of 15.31% to 19.47% annually, while reducing the annual average scale of low-carbon investments by approximately 37%. Furthermore, providing more carbon asset-backed loans to high-emission enterprises can significantly enhance policy benefits. The conclusions of this paper are of great significance for improving mechanisms for green and low-carbon development and establishing a systematic climate policy framework.

Gold and silver, due to their unique financial properties, have become preferred choices for investment and asset preservation. Accurately quantifying and predicting their price fluctuations is crucial for investors' risk management decisions. This paper introduces a rich set of feature variables and employs a forward rolling algorithm to forecast the realized volatility (RV) of gold and silver futures in Shanghai. We compare the performance of various machine learning models under different loss functions and evaluation methods. The results indicate that the gradient boosting decision tree (GBDT) models demonstrate superior performance in forecasting the futures market for precious metals. Furthermore, this study integrates the XGBoost model with interpretability tools to analyze the dynamic contributions of feature variables to the predicted values in the precious metals futures market. It also assesses the heterogeneous impact of significant variables on predictive performance. Our findings reveal the critical role of market sentiment variables, as well as the relative contributions of macroeconomic variables and volatility decomposition variables under different market conditions. The research provides clear evidence for the selection of factors and models in forecasting precious metal futures market volatility, offering credible investment and management recommendations for investors and regulators in this market.

The open sharing of data resources is key to unlocking the value of data elements. Assessing the impact of government data openness on corporate sustainable development is of significant importance for promoting high-quality economic and social development. This paper uses the openness of government data as a quasi-natural experiment, taking Chinese listed companies from 2009 to 2022 as the research sample, and explores the impact of government data openness on corporate economic performance and environmental performance through the difference-in-differences model. The study demonstrates that government data openness has brought dual benefits to corporate economic and environmental performance, that is, government data openness has promoted corporate sustainable development. The reason is that government data openness can promote corporate technological innovation and improve the efficiency of corporate operation and management. Further research finds that the role of government data openness in promoting economic performance is more significant in state-owned enterprises, regions with a better business environment, and areas with better digital infrastructure conditions, while the enhancement of environmental performance is more fully demonstrated in state-owned enterprises, non-heavy polluting enterprises, and regions with higher environmental regulation intensity. This study reveals the role of government data openness in improving corporate economic and environmental performance, providing important empirical insights for promoting sustainable economic and social development and enhancing the scientific formulation of data openness policies in the context of "Dual Carbon" goals.

This paper pioneers a novel approach to economic and public policy analysis by leveraging multiple large language models (LLMs) as heterogeneous artificial economic agents. We first evaluate five LLMs’economic decision-making capabilities in solving two-period consumption allocation problems under two distinct scenarios: With explicit utility functions and based on intuitive reasoning. While previous research has often simulated heterogeneity by solely varying prompts, our approach harnesses the inherent variations in analytical capabilities across different LLMs to model agents with diverse cognitive traits. Building on these findings, we construct a multi-LLM-agent-based (MLAB) framework by mapping these LLMs to specific educational groups and corresponding income brackets. Using interest income taxation as a case study, we demonstrate how the MLAB framework can simulate policy impacts across heterogeneous agents, offering a promising new direction for economic and public policy analysis by leveraging LLMs’ human-like reasoning capabilities and computational power.

The accuracy of stock return prediction has a critical impact on investment decisions. The advent of deep learning models has markedly improved the accuracy of return forecasts. However, stock market sequences are often observed with anomalies that can distort key statistical measures, obscure the true trends of the data, and diminish the predictive capabilities of deep learning models. In extreme cases, these anomalies can result in erroneous investment decisions. Based on the presence of anomalies and the learning dynamics of gradient descent algorithms, this paper introduces a novel loss function, the threshold distance weighted loss (TDW), which is designed to mitigate the susceptibility of the model to outliers by assigning variable weights to data samples. The TDW loss function has been tested through simulation studies and empirical analysis. These evaluations have confirmed the improved predictive accuracy and robustness of the method, highlighting its potential to deliver consistent positive returns to investment portfolios and to bolster informed financial investment decisions.

Improving residents' happiness is the fundamental purpose of development and an important measure of development effectiveness. Leveraging big data and large language model technologies represented by ChatGPT, this study explores the possibility of accurately measuring residents' subjective happiness on a national scale. We propose a method for constructing a residents' subjective happiness index based on Weibo data and develop a large language model for sentiment analysis, SentiGLM, built on ChatGLM3, to enhance the accuracy and effectiveness of sentiment classification in Weibo texts. The SentiGLM model significantly improves the performance of sentiment analysis tasks on Weibo texts through low-rank adaptation fine-tuning on a multi-task instruction dataset. Based on approximately 60 million Weibo text data points, this study calculates the subjective happiness index at regional and national levels in China for the first time across four temporal granularities: yearly, monthly, weekly, and daily. The study finds that SentiGLM significantly outperforms traditional machine learning models (such as BERT, LSTM, and SnowNLP) in sentiment analysis of Weibo texts. Moreover, compared to traditional survey methods, the measurement approach based on large language models demonstrates superior cost-effectiveness, timeliness, and robustness, while also providing finer granularity in both temporal and spatial dimensions.

The "dual carbon" targets represent a significant strategic decision for China's low-carbon economic transformation, carrying profound implications for highquality economic development. This paper first establishes an index system capable of scientifically measuring the progress of China's cities towards achieving the "dual carbon" targets, based on both the absolute level of regional carbon reduction and carbon sink enhancement gaps and the relative level after considering regional population size, energy consumption, and economic development. It then analyzes the patio-temporal evolution characteristics of these gaps, providing a quantitative basis for advancing China's "dual carbon" targets. Subsequently, leveraging the quasinatural experiment of carbon emissions trading pilots and panel data spanning 2006–2020 at the prefecture-level city level, this paper delves into the role of market-based policy instruments in achieving carbon neutrality goals. The research findings indicate that the implementation of carbon emissions trading policies contributes to advancing carbon neutrality in pilot regions across four dimensions: Regionally overall, per capita, in terms of energy consumption, and economic development. The mechanism analysis reveals that this policy fosters carbon neutrality through multiple pathways, including carbon emission reduction, carbon sequestration enhancement, and green innovation. Specifically, the policy aids in optimizing energy structures and enhancing energy effciency at the emission end, promotes afforestation and forest conservation at the carbon sequestration end, and exhibits a "Porter Effect" that stimulates quantitative growth in green innovation in pilot regions. Further research demonstrates that a well-functioning carbon market can amplify the emission reduction and carbon sequestration enhancement effects of carbon emissions trading policies. By breaking down market mechanisms into three aspects, carbon price, liquidity, and relative scale, it is found that higher carbon prices and larger relative scales of carbon markets in pilot regions strengthen the facilitating effects of carbon emissions trading policies on their carbon neutrality progress. Market liquidity, however, only reinforces these policy effects in the dimension of economic development. This study provides empirical evidence and policy recommendations for scientifically measuring the progress towards achieving "dual carbon" targets, improving the national carbon market, and facilitating high-quality economic transformation.

The structure of the financial system is constantly changing under the impact of the macro environment, and risk spillover is the key to analyze systemic risk. In order to break through the dimension limitation and model specification of traditional parametric models, this paper proposes a semiparametric method, Dynamic Bayesian-Local Gaussian Correlation Network (DBN-LGCNET) to measure the time-varying nonlinear correlation between the general and tail risks. The model is applied to the data of 65 listed financial institutions in China's A-share market, and the results show that: 1) There are obvious tail risk spillovers in the financial system. 2) Risk spillover in the financial industry display heterogeneity, with the source of general risk propagation mainly in the banking sector and the source of tail risk propagation mainly in the securities sector. 3) Risks propagate dynamically among financial institutions, state-owned banks demonstrate a consistent capacity to absorb risk spillovers, whereas small and medium-sized banks show a lesser ability to cope with extreme events. 4) After an extreme event, the impact of the banking industry in the general correlation network is enhanced and the impact of the securities industry is weakened. Links between financial institutions in the tail correlation network are strengthened, especially insurance institutions.

The China-U.S. trade dispute has been a significant event in the field of international trade in recent years, with profound impacts on bilateral trade relations. This study examines the effects of the 2018 and 2019 tariff increases imposed by the Trump administration on Chinese goods using the synthetic control method from two perspectives. First, we analyze the dynamic impacts of tariff policies on China's total exports to the U.S., identifying changes before and after policy implementation. Second, we investigate the heterogeneous impacts across 21 HS product categories, uncovering variations in sensitivity to tariff policies. Furthermore, based on the historical policy effects, this study predicts the potential impacts of similar tariff policies under Trump's new administration on China-U.S. trade. The results suggest that if similar policies are implemented, China's total exports to the U.S. will experience further declines. In the early stage of the policy, market behaviors such as stockpiling and anticipatory exports may temporarily drive up export values, but the decline in exports will gradually increase thereafter. Products with high price elasticity are likely to face greater negative impacts compared to high-value-added products, but high-tech products will also face significant pressure due to U.S. technological restrictions. This study provides valuable insights into the economic impacts of the China-U.S. trade dispute, evaluates the consequences of tariff policies, and offers guidance for policymaking.

Stock index forecasting is crucial for financial market regulation and investment decision-making. From a fresh perspective of industry risk connectedness, this study proposes a novel multivariate time series graph neural network (MTGNN) model based on risk spillover network. The model uses risk spillover network as the spatial dependency and multiple node features, including sentiment indicator and net risk spillover index as the temporal dependency to predict industry indices. A comprehensive comparison is conducted among the proposed model and five alternatives. Additionally, this study integrates point and interval forecasting results to propose a trading strategy with interval constraints. The study shows that: 1) The MTGNN model based on risk spillover network outperforms traditional machine learning and deep learning methods in forecasting industry stock indices; 2) the investor sentiment indicator and net risk spillover index significantly enhance the prediction performance of the MTGNN model; 3) the interval-constrained trading strategy ensures high returns and greater stability during backtesting. This study offers investors a practical tool for forecasting stock indices and provides decision support for macroprudential regulation.

The fundamental methodologies of machine learning and econometrics are reviewed. We also discuss the challenges of integrating the data-driven and model-based causal approaches and conjecture how it may yield new insights to empirical economic studies.

This paper will focus on the of gravity models, which are important theoretical spatial models. The gravity models are generalization of the natural gravity model in physics but they need to be generalized to have interactions with n regions instead of just two regions. A gravity model in economics concerns flow variables. For estimation of those spatial flow models, we will consider the classical estimation approach.

In recent years, Southeast Asian and South Asian countries have significantly increased their share in the global supply chain, showcasing notable economic resilience and growth potential. During this phase of accelerated economic development, rapid industrialization and urbanization have led to a continuous rise in carbon dioxide (CO₂) emissions, exacerbating the challenges of climate change. In this context, emerging economies in Southeast and South Asia must develop tailored emission reduction targets and pathways to mitigate the adverse impacts of climate change effectively. A comprehensive, detailed, and unified CO₂ emission inventory serves as a critical foundation for assessing emission trends and formulating strategic planning pathways. To this end, this study integrates multi-scale energy, population, and economic data to construct CO₂ emission inventories for 11 countries in Southeast and South Asia from 2010 to 2020. The study highlights the carbon emission heterogeneity across countries at various spatial scales, energy types (including biomass), and disaggregated economic sectors. Furthermore, it provides scientific insights to support developing economies in crafting both short- and long-term energy transition strategies, as well as designing context-specific CO₂ emission reduction policies at the national and regional levels.

As the scope, actors, forms, approaches, trends, and influencing factors of innovation inputs continue to evolve, the research objects, domains, and methodological perspectives of innovation inputs analysis are continuously expanding and becoming more refined. The optimal allocation, efficient management, and strategic decision-making of innovation inputs necessitate a systematic and scientific measurement framework. This study develops a theoretical framework for the innovametrics of innovation inputs, emphasizing their role throughout the innovation process. The framework aims to provide analytical perspectives and methodological tools for addressing key measurement issues related to the level, structure, and influencing factors of innovation inputs. Based on a review of the evolution of research on innovation input measurement, this study categorizes key measurement issues into three dimensions: development, structure, and dynamics. It further proposes the key issues and analytical approaches associated with each dimension. Additionally, considering advancements in innovation input management and practical demands, this study outlines future research directions in innovametrics. The development of this theoretical framework not only advances the theoretical and methodological foundations for optimizing innovation input allocation, management, and decision-making but also provides a systematic framework to guide academia, policymakers, and industry practitioners in understanding and effectively applying relevant measurement theories and methodologies.

It is of great value to understand the mechanism of stock return, but China's stock market is affected by many factors at home and abroad, and there are regular behaviors, which makes it diffcult to carry out research. In this paper, a functional varying coeffcient single index model with subgroup structure is proposed and applied to analyze the influencing factors of stock return. The influencing factor system includes three dimensions: macro, mesoscopic and microscopic. At the same time, this paper uses functional data structure to depict calendar effect, and uses subgroup structure to depict industry effect, making the model as comprehensive as possible and suitable for our national conditions. Finally, it is found that the influence direction of macroeconomic factors on stock return varies throughout the year, and the influence of international crude oil price change rate on stock return has industry heterogeneity. At the same time, profitability, value-creating ability and valuation level have great impact on enterprise evaluation and stock return rate. Furthermore, based on some regularity conditions, this paper establishes the asymptotic theory for the estimators of the index parameters, link function, and coeffcient function in the proposed model.

Since the global financial crisis in 2008, the United States has actively promoted the reshoring of manufacturing, creating development opportunities for domestic manufacturing while profoundly impacting the global industrial structure and layout, particularly posing significant challenges to the development of China's manufacturing industry. Based on the multi-regional input-output data in constant prices from 2009 to 2022 released by the Asian Development Bank, this paper measures the dynamic evolution of the manufacturing international competitiveness of China and the United States under the background of the U.S. manufacturing reshoring policies. It also employs a structural decomposition model to explore the driving factors and their contributions to the changes in the manufacturing international competitiveness with different technological levels at various stages. The study finds that: 1) The U.S. manufacturing reshoring policy has achieved certain effects in dealing with the issue of industrial hollowing, increasing employment, reducing supply chain risks, and improving economic conditions; 2) From 2009 to the present, on a global scale, the international competitiveness of U.S. manufacturing has not seen a significant improvement, while China's manufacturing international competitiveness has continued to strengthen, with the trend of industrial transformation and upgrading continues; 3) In recent years, the competition between China and the U.S. in the fields of medium and high-tech manufacturing has intensified; 4) The decomposition of international competitiveness indicates that the resilience of supply chains, the evolution of traditional trade patterns, and the reconstruction of global value chains are beneficial for enhancing China's manufacturing international competitiveness; whereas domestic drivers that could consolidate and strengthen the comparative advantage of high-tech manufacturing in the U.S. have not been formed. The empirical results of this paper provide implications for how China can further enhance the international competitiveness of its manufacturing industry.

How to measure the climate transition risk faced by the high-carbon industry, as well as how to effectively identify and mitigate the systemic risk spillover and contagious effects of the high-carbon industry, is an important issue facing policymakers and the academic community. This paper constructs a high-dimensional time-varying vector autoregression index model is used to measure the spillover effects within and between high-carbon industries. The research finds that the high-carbon industry faces the highest climate transition risk, while the financial industry has the lowest. The greater the climate transition risk, the higher the systemic risk faced by listed companies, and the stranded assets of the high-carbon industry are an important transmission path. Under different policy backgrounds, the risk spillover effects of the high-carbon industry to related industries show differentiated inclinations, and the main risk spillover targets of the high-carbon industry are all associated with their own production or financial networks. The banking industry always performs as a risk absorption role at the center of the risk network and is highly associated with the high-carbon industry. This paper provides a basis for governments and regulatory authorities to understand the impact of transition risk on the high-carbon industry and the industry correlation, and provides certain reference value for resolving the cross-industry transmission of systemic risks in the high-carbon industry.

Exploring the risk spillover effects between energy and metal futures has important practical significance for improving the quality and efficiency of systemic risk supervision and ensuring the smooth operation of commodity futures markets. However, most existing studies are limited to the low-order moment level and fail to fully reveal the cross-market risk transmission mechanism. Given this, this paper integrates the autoregressive conditional density model and the time-varying parameter vector autoregressive extended joint connectedness approach to explore the higher-order moment and cross-moment risk spillover effects between China’s energy and metal futures markets; secondly, the nonparametric causality-in-quantile test is used to study the Granger causality relationship between geopolitical risks and total spillovers. Empirical results show that risk spillovers between energy and metal markets show significant differences under different moments, and the total spillover of high-order moments is lower than the total volatility spillover. Copper dominates the volatility and skewness spillover, while fuel oil dominates the kurtosis spillover. Copper skewness, zinc kurtosis, and fuel oil skewness dominate the three cross-moment spillover effects, respectively. The dynamic total spillover and net spillover indexes show significant time-varying characteristics and have risen sharply after the outbreak of major crises such as the COVID-19 epidemic and the Russia-Ukraine war. Geopolitical risk is a key driver of energy-metal spillovers, and its predictive power on cross-moment total spillovers is significantly higher than conditional volatility and higher-order moment total spillovers.

Climate change has emerged as a new source of instability in the global financial system, making the scientific identification and assessment of its transmission channels to the financial sector a critical issue in the field of climate finance. Currently, climate-related financial risk modeling and practical applications still face numerous obstacles. In this context, this paper reviews several key developments in climate-related financial risk studies, including the characteristics of climate risks in financial markets, the methodologies and practices for assessing climate financial risks, and future research directions. To be specific, this study first elaborates on three crucial features of climate financial risks. Second, it systematically reviews three streams of approaches for climate financial risk assessment developed in recent years, analyzes their applicability and limitations, and examines relevant practices adopted by central banks and financial regulators across different countries. Finally, the paper identifies promising directions for future research to support both theoretical advancement and practical implementation in the field of climate financial risk assessment.

As an innovation and development of Marxist productive forces theory, new quality productive forces have received widespread attention since its proposal. In accordance with the connotation and characteristics of new quality productive forces, this study constructs an indicator system based on TEI@I methodology. With the comprehensive consideration of industrial development status and government’s catalytic role, we collect basic data from four aspects: Industry, workers, infrastructure, and policy basis, and explore relevant policy texts to measure new quality productive forces of 31 provinces in China. The results show that the development level of China’s new quality productive forces is at the early stage, and has a certain space aggregation and uneven development between the east and the west. The construction of new infrastructure and the cultivation of new workers will be the key points to the development of new quality productive forces in the future.

In the context of China's emphasis on "maintaining the bottom line of no systemic financial risks", this paper examines the impact of industry bubbles on systemic risks contribution from an industry perspective. Furthermore, we construct inter-industry risk networks to identify the risk sources under different levels of systemic shocks. This provides effective policy references for deepening the reform of the new development pattern in which the domestic grand cycle plays a leading role. The empirical results indicate that, apart from the promoting effect of asset price bubbles in the Agriculture, Water, Environment, and Utilities Management, and Culture, Sports, and Entertainment industries, the impact of most industry bubbles on the systemic risk contributions of firms within those industries is either insignificant or inhibitory. However, when investor sentiment is high, the systemic risk of companies within the industry increases during a bubble period. In the cumulative effects of the local projection model, the promoting effect of bubbles in most industries shows a sustained upward trend, with the cumulative effect of promotion being heterogeneous. In the quantile risk network, industry connections tighten during extreme shocks compared to normal periods. Additionally, the risk defense capabilities of each industry vary depending on the intensity of systemic shock. Besides, the risk sources in industry bubble networks are predominantly in the Agriculture, Manufacturing, Transportation, Catering, and Leasing industries. This implies that risk shocks propagate outward, revolving around production, distribution, circulation, and consumption as the core, indicating that China's economy is transitioning towards a new development pattern centered on the domestic grand cycle.

This study proposes a new risk perception factor (FRP) and adds it to Liu et al.'s (2019) three-factor model to form a four-factor model (henceforth RPM4 model) for the Chinese stock market. The results show that the FRP's volatility, Sharpe ratio, and maximum drawdown have significant advantages over the factors of popular asset pricing models. Further testing reveals that the RPM4 model contains more information and has a clear advantage in explaining stock portfolio returns, and performs well in the R² comparison tests for cross-sectional regressions. The RPM4 model is a useful complement to the Fama and French (1993) three-factor model, as well as Liu et al.'s (2019) three- and four-factor models.

This paper preliminarily explores the potential application of the automated market maker (AMM) mechanism in China’s Central Bank Digital Currency (CBDC) for cross-border transactions. The study focuses on two primary approaches to managing exchange rate volatility risks through AMM: one relies on direct regulation via central bank reserves, while the other achieves reserve-free regulation through market incentives. The research indicates that the reserve-free regulation mechanism, by setting target ranges and providing economic incentives, can effectively guide liquidity concentration and reduce exchange rate volatility risks, while simultaneously decreasing reliance on foreign exchange reserves. However, this mechanism may also exert certain influences on market liquidity distribution and price discovery. Future research could further investigate dynamic market behaviors, policy coordination, and international cooperation to refine the exchange rate regulation mechanisms in CBDC cross-border transactions, manage risks associated with the cross-border use of the digital yuan, and provide theoretical support and practical references for the internationalization of the digital yuan.

The rise of large language models has injected fresh vigor into the development of Robo-advisors in China and promoted the innovation of financial technology. In this context, this paper constructs an AI agent based on the domestic generative language model framework, from sentiment analysis, market prediction, factor indicators and other dimensions, in-depth mining of alternative data and traditional financial data in stock trading signals, and then constructs the Chinese stock market investment and trading strategies. Empirical studies show that AI agent based on the domestic large models have the potential to perform quantitative analysis, and that the investment returns under the combined multiple dimensions will be significantly better than the results of a single dimension. This suggests that by utilizing powerful natural language processing capabilities and data analysis capabilities, the application of domestic large models in Robo-advisors is promising, providing new ideas and methods for the continuous development of quantitative investment. With the evolution of technology, the future AI agent will be able to understand market dynamics and investor needs more deeply, thus providing more targeted support for investment decisions, enhancing overall investment returns and creating more value.

Currently, the non-fungible token (NFT) market is experiencing significant price volatility. This study aims to detect the bubble phenomena in the NFT market and analyze the key features and mechanisms affecting NFT price bubbles. Firstly, the study focuses on the NFT market and three important sub-markets, utilizing the Generalized Supremum Augmented Dickey-Fuller (GSADF) test to identify the occurrence, duration, and dissipation of NFT price bubbles. Secondly, traditional financial asset prices, market sentiment indices, and cryptocurrency prices are incorporated as features to analyze NFT price bubbles using multiple decision tree machine learning models. Finally, the SHapley Additive exPlanation (SHAP) method is employed to visualize the mechanisms influencing NFT price bubbles. Empirical results indicate that there were five instances of bubbles in the NFT market during the observation period, with a significant increase in the duration of bubbles across sub-markets in 2021. Among the three machine learning models, the CatBoost (Categorical Boosting) model demonstrated the best performance in fitting NFT price bubbles. SHAP analysis revealed that gold, the US Dollar Index, and crude oil prices significantly impact bubble formation, whereas the S&P 500 has a relatively weak influence. Additionally, market sentiment indices such as the Chicago Board Options Exchange Volatility Index (VIX) and Google Trend show opposite trends in their influence on bubbles. By incorporating multiple features, this study enhances market participants' understanding of NFT price bubble characteristics and provides datadriven market insights.

The operation of insurance funds, a long-term and patient capital, it is crucial for insurance institutional investors to better play their role as social stabilizers and economic shock absorbers and promote corporate green innovation. Using data from China's A-share listed companies from 2013 to 2021, this paper studies and finds that the shareholding of insurance institutional investors has a significantly positive effect on corporate green innovation. A crucial Mechanism explains this positive impact: Insurance institutional investors can motivate companies to carry out green innovation by alleviating the short-termism of management. The results of the heterogeneity analysis find that more field research by insurance institutions and state-owned and large insurance institutional investors can better promote the green innovation of companies. This paper recommends that regulatory authorities continue to improve the insurance fund operation policy and fully encourage insurance funds to carry out green investment. This study provides a scientific basis for China to further promote the development of a low-carbon economy and corporate growth based on the perspective of insurance institutions' shareholding.

Different from traditional linear models which assume that firms make decisions independently according to their own characteristics, this paper uses a spatial autoregressive model to study the heterogeneous peer effects of firm innovations, under the impact of industry policy. We find that firms supported by the policy will show positive innovation interactions, that is, they will tend to imitate the innovation behavior of their peers in the same region, while the non-supported firms do not have this pattern. The difference between them are significant and robust. The imitation behaviors of supported firms aim to maintain a relative competitive position. As for the non-supported firms, those with large financial constraints prefer the innovation imitation to reduce the information cost of decision-making while firms with small financial constraints are likely to shrinkage the innovation investment if their peers have great innovation output due to competition in product or factor markets. The conclusions of this paper provide a new perspective of innovation interaction for studying the results of policy impact. In addition, by emphasizing the heterogeneity of firm behavior, it shows that different mechanisms will have different explanatory power in different cases, thus providing empirical evidence of heterogeneity effect for the analysis of firm interactions.

To investigate the impact of the pilot policy of China's carbon emissions trading system (ETS) on the transition risk of provincial financial institutions and its mechanism is an important research topic with practical significance and theoretical value. In this paper, we constructed transition risk indicators for provincial financial institutions, and analyzed the effects of the ETS pilot policy on the transition risk of financial institutions in the pilot areas and its mechanism by using the staggered double difference method (staggered DID), the two-step method of mediation effect analysis, and the generalized method of moments panel vector autoregression model (GMM-PVAR). The study finds that: firstly, the ETS pilot policy increases the transition risk of financial institutions in the pilot region, and the heterogeneity of environmental regulatory policies on the effects of the pilot policy is more significant than that of geographical factors; secondly, the ETS pilot policy mainly works through the channels of direct costs of green transition, business returns, green technological advances, and green credits, among which there is heterogeneity in the mechanism of green technological advances; lastly, the green technological advances and the green credits of enterprises are related to the direct costs of green transition, and there is heterogeneity in the mechanism of green technological advances. Finally, there is a dynamic lag effect between the green technology progress and the enterprise business income mechanism, which makes the ETS pilot policy have a lagging negative impact trend. This paper is the first to explore the inherent dynamic connection between the ETS pilot policy and the transition risk of provincial financial institutions from the level of quantitative analysis, and puts forward relevant suggestions for balancing the implementation of the policy and the risk management of the transition, so as to provide a reference basis for the steady promotion of the "dual-carbon" process and the safeguarding of financial stability in the low-carbon economy.

Stock price fluctuations are crucial for the healthy and stable development of the stock market. However, the econometrics methods, which have traditionally demonstrated strong performance in addressing nearly all economic issues, exhibit limitations specifically in stock-related analyses — A gap that has only begun to be addressed with the emergence of artificial intelligence methods as cutting-edge applications in this field. Building on this foundation, this study integrates theoretical economic frameworks with empirical methodologies across disciplines to undertake a comparative investigation into the impact of monetary policy on stock prices and its associated asymmetric effects in China. The findings reveal that econometric models (VAR and MS-VAR) yield only partially satisfactory results in analyzing this issue, whereas the artificial intelligence-driven LSTM model demonstrates good explanatory power. Monetary policy exerts asymmetric effects on stock prices: Expansionary policies during bear markets show greater efficacy than contractionary measures during bull markets. Additionally, the transmission effect of rising prices on stock price is stronger, and the drag of economic recession on stock price is more pronounced. This study provides insights for the monetary policy interventions and offers practical guidance for the application of interdisciplinary methodological approaches in addressing complex economic issues.

At the micro level of the impact of digital transformation, there is a lack of research on the impact of digital transformation on management self-interest behavior. This paper constructs a fixed-effects model and analyzes the effect and transmission path of digital transformation on management self-interest based on the data of A-share listed companies. The study shows that the digital transformation of enterprises restrains the self-interested behavior of management by reducing information asymmetry, improving the efficiency of corporate governance, and reducing the flexibility of corporate finance and that the digital transformation of enterprises has heterogeneous effects on the self-interested behavior of management in different regions, levels of competition, and industries. The policy recommendations are to increase the efforts of digital transformation, reduce information asymmetry, improve the efficiency of corporate governance, reduce corporate financial flexibility, and differentially regulate the self-interested behavior of management. This paper responds positively to the spirit of promoting the deep integration of the real economy and the digital economy and takes the self-interested behavior of the management as a breakthrough to provide empirical explanations and decision-making references for the digital transformation of enterprises to promote corporate governance and improve operational efficiency.

Since reform and opening up in 1978, China has achieved a remarkable economic growth miracle. For a socialist market economy with Chinese characteristics, the task of accurately summarizing its developmental experiences and elevating them into a broadly applicable theory of economic growth necessitates the measurement of both publicly owned and non-publicly owned segments of the economy. The Third Plenary Session of the 20th Central Committee of the Communist Party of China has explicitly called for “the calculation of value added in the state-owned economy”. Drawing on the officially published data of the National Bureau of Statistics, this paper measures the value added of the public-ownership economy, primarily composed of state-owned entities, and the value added of the non-public economy, chiefly represented by private enterprises, by examining various sectors of the national economy. In doing so, it provides preliminary supplementary historical data for both publicly owned and non-publicly owned economies dating back to 1978. The results of this study shed light on the shifts in the overall ownership structure of China’s economy, as well as the changes within industry-specific ownership structures since the beginning of reform and opening-up, thus offering a critical foundation for deeper investigations into China’s economic growth miracle.

High-tech zones serve as crucial platforms for implementing China’s innovation-driven strategy and act as key engines for achieving high-quality economic development. The implementation of the “upgrading for promotion” policy in high-tech zones has profound implications for advancing technological innovation and fostering emerging industries. Based on panel data from Chinese cities from 2003 to 2022, this study conducts an in-depth analysis of the relationship between high-tech zone upgrades and the entry of strategic emerging enterprises. The findings indicate that the policy significantly attracts the entry of strategic emerging enterprises, and this conclusion remains robust after a series of robustness tests. Mechanism analysis reveals that the policy promotes enterprise entry through channels such as digital talent reserves, inclusive digital finance, government technology support, and fiscal and tax incentives. Heterogeneity analysis shows that the policy’s effect is more pronounced in regions with higher levels of artificial intelligence development, stronger official incentives, and greater utilization of foreign capital. Furthermore, the study finds that the policy enhances the total factor productivity (TFP) of strategic emerging enterprises within high-tech zones. These findings provide empirical evidence for promoting the advanced development of high-tech zones and accelerating the cultivation and expansion of emerging industries.

The advent of large language models (LLMs) represents a paradigm shift that has profoundly affected market risk spillover mechanisms. Technological breakthroughs, such as the release of ChatGPT-3.5, trigger changes within the technology industries, but also indirectly impact energy markets by increasing the demand for computational resources. We develop an interval-valued vector autoregressive model and propose an interval-based risk spillover matrix and total spillover index (TSI). Utilizing daily stock price data from 2022 to 2025, we quantify the dynamic impact of LLM development on risk spillovers between the technology and energy industry sectors. Our findings indicate that the release of ChatGPT-3.5 amplified risk spillovers among leading technology companies, such as NVIDIA. As various technology giants announced the construction of new data centers, risk transmission emerged between the energy and technology industries. Moreover, Trump’s election victory exacerbated risk spillovers both within and across these industries. Interestingly, our proposed TSI index exhibits a rapid response to significant events and circumvents the discontinuity issues inherent in traditional Diebold-Yilmaz spillover indices based on point-valued volatility estimates, thereby demonstrating enhanced robustness.

Crude oil price forecasting is critical for global economic stability and the development of the energy industry. However, existing single neural network methods face significant limitations: Increasing the depth and width of the network provides only marginal performance improvements while exacerbating overfitting risks and reducing generalization capabilities. To address these challenges, this paper proposes a method based on model merging with multiple structural neural networks. By constructing shallow MultiLayer Perceptron variant networks with sparse connections and skip connections, and assigning appropriate weights to the predictions of these neural networks, the proposed method significantly enhances prediction performance. Experimental results demonstrate that, for crude oil price time series forecasting, the method achieves superior accuracy compared to deep neural networks, without relying on complex and deep architectures of neural network. Instead, it leverages the merging of multiple structurally simple small-scale neural networks to deliver robust and precise predictions.

Accurate measurement is a prerequisite for understanding the digital economy and conducting follow-up research. This article follows the practices of BEA and OECD and calculates the added value of China’s provincial digital economy. Based on the calculation, we examine how development of the digital economy affects income gap between urban and rural areas. The results show that China’s digital economy is developing rapidly with huge gaps between provinces, and the digital infrastructure is the foundation of digital economy development. The development of the digital economy has a U-shape effect on the income gap between urban and rural areas. Further mechanistic analysis showed that the development of the digital economy promotes the transfer of rural labor and delay the U-shape turning point. It also promotes the development of agricultural modernization, increases the income of farmers and narrows the income gap between urban and rural areas. Moreover, it can narrow the urban-rural income gap in adjacent areas through spatial spillover effects. This paper provides policy recommendations for promoting inclusive development of the digital economy.

China’s mass construction of new road infrastructure, represented by high-speed railways, provides an excellent narrative for the relationship between transport infrastructure and income inequality. Based on the data of Chinese industrial enterprises and the statistical information about prefecture cities from 2000 to 2013, this paper explores the impact of new road of urban high-speed railways on the labor pay gap within enterprises from the perspective of labor market change. Our results show that the new road of urban high-speed railways will accelerate the inter-regional flow of labor factors, cause the adjustment of labor market, and reduce the labor pay gap within enterprises by promoting the integration of labor market and changing the labor and employment structure. The heterogeneity analysis shows this pro-equal effect of new road infrastructure varies among industries, cities and enterprises. Further analysis verifies the existence of spatial spillover effect of the impact of urban high-speed trains on the labor market, indicating that the operation of urban high-speed trains will also reduce the labor market segmentation degree of other cities, which is conducive to the formation of a unified national labor market.

The construction of digital government enhances governance capacity through government information disclosure, digital governance, online services, and external supervision. It empowers enterprises by improving their information access, technological innovation, and cost reduction, thereby promoting ESG performance. This paper examines the impact of digital government on corporate ESG performance using A-share listed companies from 2012 to 2023 and a quasi-natural experiment of “big data management reform” with the DID method. The results show that digital government significantly improves corporate ESG performance. Robustness tests, including controlling for concurrent policies, digital economic levels, and market expectations, confirm the stability of the findings. Heterogeneity analysis reveals that the impact is more pronounced for firms with low digital transformation, high pollution levels, and non-state ownership. Mechanism analysis indicates that digital government affects ESG performance by reducing financing constraints, promoting digital innovation, and lowering transaction and agency costs. This study provides empirical evidence for the positive effects of digital government and offers insights for policy-making.