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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Digital finance, as one of the “five major areas” of finance, plays a crucial role in improving the efficiency of financial services, promoting inclusive finance, and empowering high-quality economic development. Based on relevant literature from core English and Chinese-language databases spanning 2014-2025, this paper constructs a research framework for digital finance using bibliometrics and qualitative analysis, analyzes the shortcomings of existing research, and proposes potential future research directions. The results show that: First, Chinese-language literature focuses on policy guidance and local practices, while English-language literature focuses on sustainable development and global comparisons. Second, the research framework for digital finance can be systematically summarized through a logical thread of “measurement indicators, influencing factors, economic and social effects, innovative practices”. Third, future research can focus on refining measurement indicators, accurately identifying influencing factors, comprehensively addressing economic and social effects, and advancing the research on innovative practices. This paper broadens the perspective for further research on digital finance and provides insights for promoting its high-quality development in practice.

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.

This paper provides a classification framework to integrate multi-source information, including available platform firm data, survey data, and industry reports, to estimate the size of platform employment. We first define and conceptualize platform employment, which is then classified into cloud-based and location-based platform employment. We then estimate the total size and distribution of platform employment in 2023, and correct for duplicates arising from workers engaged on multiple platforms. We also estimate full-time and part-time workers separately. The results show that the total number of platform workers in China has reached 247 million in 2023, including approximately 118 million part-time and 129 million full-time workers, the latter accounts for 14.9% of China’s working-age population. Notably, cloud-based platforms employ significantly more workers than location-based platforms, with cloud-based roles predominantly part-time whereas location-based positions are primarily full-time. The paper provides a consistent framework for incorporating updated information from various sources to provide timely estimates of China’s platform workforce.

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.

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.

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.

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.

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.

Educational equity is a fundamental guarantee to accelerate the modernization of education and build a strong education country. How to alleviate and eliminate the negative impact of the intense education competition caused by superior and limited educational resources is an issue with little research in current theory and practice. This paper applies data from the China Family Panel Studies (CFPS) in 2018 and 2020 to investigate the spatial spillover effects and threshold social interactions of education expenditures among the Chinese households by the spatial autoregressive model and the social threshold model. We first provide a model specification test for the social threshold regression and show by simulation that the test statistic performs well in finite samples. The empirical study shows that within the same province (county), there is a significant spatial positive correlation in household education expenditure. For every 1% increase in other similar household education expenditure in the same province (county/district), household education expenditures increase by 0.32%(0.152%), showing that there is a spatial spillover effect among family education expenditures. The estimation results based on the social threshold regression show that the endogenous interaction effect of family education expenditure is significantly positive among families with high children’s education expectation, families with low income, and families with high information asymmetry, who are more likely to fall into educational entanglement. This study is the first to empirically test the education spatial competition, which essentially reflects issues such as educational equity and competition for educational resources in China. The study provides practical guidance and management basis for educational policy making and educational resource planning.

New quality productive forces is an important engine for high-quality economic development and a crucial force in promoting the great rejuvenation of the Chinese nation in the new era. This article constructs a corresponding indicator evaluation system based on the connotation of the theory of new quality productive forces, and uses the entropy method to calculate the level of new quality productive forces in 236 cities in China from 2010to 2020, by constructing a theoretical model and conducting empirical research, the impact of new quality productive forces on economic growth is explored. The results indicate that new quality productive forces significantly promotes economic growth, and the three dimensions of new quality productive forces have heterogeneous effects on promoting economic growth. Mechanism analysis shows that environmental regulation intensity plays a regulatory role in promoting economic growth through new quality productive forces. In further analysis, by constructing a spatial lag model to test the spatial spillover effect of new quality productive forces, it was found that new quality productive forces can not only promote the macro economy of local cities, but also promote the economic growth of surrounding cities.

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 continuous decline of China’s manufacturing share has garnered significant attention in recent years, but existing research has yet to reach a consensus on the causes of China’s deindustrialization. Given the accelerating integration of manufacturing and services, the impact of the servitization of industrial linkages on the manufacturing share cannot be overlooked. This paper examines this impact from both theoretical and empirical perspectives. Theoretically, based on the production network model, we demonstrate that the servitization of industrial linkages decreases the manufacturing share. Empirically, using a structural decomposition model and time-series input-output data from 1981 to 2018, we decompose the annual changes in China’s manufacturing value-added share into the contributions from industrial linkages and five other drivers. The findings reveal that from1981 to 2006, industrial linkages had a positive impact on the manufacturing value-added share. However, due to servitization, this impact turned negative between 2006 and 2018, making industrial linkages the most significant driver of deindustrialization in China. This discovery complements traditional theoretical explanations of deindustrialization from the perspective of industrial linkages. The paper also explores China’s industrial linkage servitization from both time-varying and sectoral perspectives. The role of industrial linkage servitization in China’s deindustrialization should be viewed dialectically. On the one hand, the servitization of industrial linkages is a theoretically reasonable trend in light of the information technology revolution and intensified global competition. On the other hand, China’s industrial linkage servitization is primarily reflected in the rising proportion of wholesale and retail sectors in the manufacturing input structure, as well as finance and real estate in the services input structure. Therefore, strong promotion of deep and two-way integration of manufacturing and services remains essential to prevent the economy from shifting from a real to a fictitious basis.

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.

Financialization of physical enterprises will lead to the weakening of their main business, reduced motivation for innovation, and even leading to the hollowing out of industries and turbulence in the financial market, which is one of the most important problems that require addressing. In this digital era, the public, as the important supervisor of enterprise operation and the ultimate purchaser of enterprise products or services, may have a governance role in the excessive financialization of physical enterprises. This paper empirically examines the impact of public attention on the financialization of physical enterprises using the data of A-share listed companies in Shanghai and Shenzhen from 2011 to 2022. We note that public attention inhibits financialization of physical enterprises through supervisory pressure and business expansion, and this inhibitory effect is more pronounced in firms that are non-state-owned and have high proportions of institutional shareholding, with better financial information quality, and low financing constraints.

Based on a nested model of IRIOT and WIOT, and combining Kronecker product to construct a spatiotemporal weight matrix, this paper empirically explores the mechanism of dual value chain embedding on“first rich driving later rich” using dynamic spatial panels. The regression results for the segmented manufacturing industry in various provinces of China show that: 1) The embedding of global value chains has widened economic disparities, while the embedding of domestic value chains can leverage industrial linkages to realize vertical governance, effectively weakening the further expansion of economic disparities. 2) The embedding of dual value chains is based on the active “spatial catch-up effect” and passive “spatial spillover effect” to promote the “first rich driving the second rich” of China’s manufacturing industry, with the former becoming stronger and the latter becoming weaker. At the same time, the inhibitory effect of domestic value chain embedding on economic disparities is gradually increasing under the catch-up effect, which can significantly offset the amplification effect of global value chain embedding on economic disparities. 3) The spatial effect decomposition results for the embedding of dual value chains indicate that the indirect effects generated by “third-party associations” are greater than the direct effects generated by “direct associations”. Under the spatial overflow mechanism, the indirect effect exceeds 65%, and even under the more spontaneous spatial catch-up mechanism, the indirect effect is as high as 55%. In view of this, deepening the construction of the domestic value chain system, strengthening inter provincial industrial linkages, and making full use of the dynamic mechanism of spillover and catch-up have important policy implications for promoting the Chinese path to modernization process of “first rich driving later rich, and ultimately achieving common prosperity”.

Interval-valued data is widely present in the economic and financial fields. Compared to point data, it provides richer information but also faces challenges such as model uncertainty and potential structural changes. To address this issue, this paper proposes, for the first time, a time-varying model averaging (IMA) method based on interval-valued data. This method dynamically selects time-varying weights by minimizing a locally quadratic loss function based on the $D_K$ distance, effectively capturing potential structural changes in interval-valued data. Moreover, the IMA method introduces a penalty term in the weight selection criterion, effectively preventing overfitting and enhancing its robustness and generalization ability. We demonstrate that the IMA method can achieve asymptotic optimality in selecting time-varying weights when all candidate models are misspecified. In the empirical study, we apply the IMA method to forecast the monthly price intervals of COMEX gold futures. The results show that the IMA method significantly outperforms existing model selection and averaging methods in predicting interval endpoints, midpoints, and ranges, especially in long-term forecasting. By dynamically adjusting weights and integrating information from multiple models, the proposed method mitigates the prediction bias that may arise from single-model selection, offering valuable insights for analyzing and forecasting complex and dynamic economic and financial systems.

This paper systematically studies the impact and mechanism of digital government development on household consumption based on cross-border panel data from 118 economies worldwide from 2012 to 2022. Research has found that the improvement of the level of digital government construction significantly promotes the growth of household consumption. This conclusion still holds true after a series of robustness tests such as instrumental variable method, sub sample adjustment, and increasing control variables. Mechanism analysis shows that digital government mainly unleashes residents’ consumption potential through three paths: optimizing the business environment, promoting non-agricultural employment, and advancing inclusive finance development; Heterogeneity analysis reveals that the promotion effect of digital government construction on residents’ consumption is more significant in economies with high social system stability and strong macro governance capabilities, as well as in developed economies. However, in low-income economies, its effect is constrained by insufficient social stability and weak macro governance capabilities. It is necessary to enhance social resilience and improve the governance system in order to fully unleash the effectiveness of digital governance. The research conclusion of this article provides theoretical support and empirical evidence for promoting the construction of digital government in China and promoting household consumption.

This paper proposes the “Chapter One Paradox in Macroeconomics”, and organizing and revealing the explicit and implicit theoretical principles of economic statistical within the gross domestic product (GDP)accounting by conducting a systematic deconstruction of eleven embedded measurement paradoxes. The study seeks to dispel two prevalent misconceptions regarding GDP accounting: First, “political arithmetic” is not simplistic merely due to its computational nature, when in reality, achieving “additivity” and “comparability” with socioeconomic significance proves extraordinarily challenging; second, the international statistical standards are often based on the provisional epistemological frameworks of the discipline, and “operation with inherent flaws” is unavoidable, thus requiring thorough examination. Economics statistics is a fundamental discipline that requires continuous in-depth study, otherwise, our judgments on “national significant issues” may be prone to bias, and the development of economic theory itself could face disruptive risks.

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.

Finance is the lifeblood of the national economy and plays a pivotal role in China’s modernization. This paper conducts an in-depth analysis of the 2015 SSE 50ETF options market and reveals the profound impact of institutional investors’ options trading behavior on stock market pricing efficiency during periods of significant financial risk. The findings show that, first, institutional investors’ options trading significantly amplified the deviation of stock prices from fundamentals before the market crash. Although this effect weakened after the crash, it persisted. Second, due to heterogeneous beliefs and short-selling constraints, put and call options exhibited markedly asymmetric effects on the stock market, delaying the absorption of negative information into prices. Third, the analysis of dynamic feedback and momentum strategies demonstrates the existence of positive feedback effects in the put options market. However, informed investors, through “riding the bubble” behavior, absorbed the excess returns from momentum trading in advance. This paper uncovers the complex mechanisms by which institutional investors influence stock price formation through the options market during financial turmoil, offering a new analytical perspective for understanding the interaction between China’s derivatives and stock markets.

This paper utilizes macroeconomic indicators from 18 Belt and Road Initiative (BRI) countries from 2012 to 2023 to construct a quantile factor-augmented vector autoregression (QFAVAR) model. This model captures the median and tail quantiles of various macroeconomic indicators to analyze the impact of shocks from China and other global factors on the macroeconomic variables of BRI countries. The findings reveal that: 1) A decline in China’s economic policy uncertainty and the volatility of the RMB exchange rate helps reduce uncertainty in prices and output in BRI countries; 2) following the COVID-19 pandemic, the inclusion of China’s macroeconomic variables enhances the ability to predict the short-term left-tail risks of interest rates, CPI, industrial output, and exchange rates in BRI countries; 3) after joining the BRI, most participating countries experience improved predictability of both left-tail and right-tail risks, leading to reduced uncertainty. This study provides valuable insights for a deeper understanding of the macroeconomic effects of the BRI and contributes to fostering higher-level cooperation between China and BRI countries.

In this paper, we integrate the selective state space model (SSSM) with the long short term memory (LSTM) neural network to construct a selective long short term memory (SLSTM) neural network model. We propose a novel dynamic covariance matrix prediction method and apply it to improve the hierarchical risk parity (HRP) asset allocation strategy. Firstly, we propose an SLSTM model with long-term memory function and dynamic adjustment mechanism and design a composite loss function for dynamic covariance matrix prediction. Secondly, we apply the prediction results to the HRP to optimize financial asset allocation strategies. Finally, we select data from mainstream fund products in the financial market and conduct empirical and sensitivity analyses to verify the effectiveness and rationality of the model. The results show that: 1) The SLSTM model significantly improves the effectiveness of dynamically predicted covariance matrix compared to the LSTM model. 2) The composite loss function enhances the ability of model to learn the key attributes of the covariance matrix, thereby effectively improving the accuracy of covariance matrix prediction. 3) Compared with other asset allocation strategies, the HRP asset allocation strategy based on covariance matrix prediction by using the SLSTM model can improve risk-adjusted returns.

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.

Stock price jumps serve as a key indicator of macroeconomic shifts and are often viewed as a barometer of national economic conditions. The release of macroeconomic policies can significantly impact stock markets through various channels such as asset liquidity, market expectations, and investor confidence. As a result, understanding how policy announcements influence stock market jumps has become a central topic in financial volatility research, attracting the attention of policymakers, financial regulators, investors, and academics alike. This paper develops a policy model that incorporates both positive incentives and negative suppressive effects to capture the influence of policy announcements on the intensity of market jumps. Empirical results show that policy announcements have a more persistent impact on positive jumps, and the policy model outperforms traditional self-exciting point process models (e.g., Hawkes models) that do not consider policy factors, which demonstrates the effectiveness of incorporating policy information in analyzing market jumps. Furthermore, the study finds that under lower jump thresholds, the policy model exhibits enhanced fitting accuracy and stability by more effectively integrating policy-related information to capture market dynamics. Using high-frequency data, this paper evaluates the effects of policy releases from the perspectives of parameter estimation and jump prediction, offering practical insights and policy recommendations based on the findings.

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.

Taking the study of national conditions as its perspective and “observing national affairs, studying national conditions, and seeking national policies” as its logical thread, this paper systematically sorts out the origin, constituent elements, and disciplinary nature of statistics. By expounding on the connotations and interrelationships of the three official statistical elements — Data, facts, and policies — Behind “observing national affairs, studying national conditions, and seeking national policies”, it reveals the essential characteristics of statistics as a methodological science that takes data as its research object and uses facts to support policy–making. In light of the complexity of problems faced by human social development and the diversification of data forms in the era of big data, the paper proposes that statistics should pursue a path of interdisciplinary integration. It should redefine relevant concepts, reconstruct the logical framework of statistical analysis, and innovate statistical methods through algorithmic breakthroughs to continuously enhance the ability to analyze and study big data, thereby continuing to play its unique role in national governance, global governance, and the sustainable development of human society.

The current external environment of China is undergoing profound and complex changes. Under the dual pressures of “low-end diversion” and “high-end reshoring,” the over-reliance on imported intermediate products has increasingly become a barrier to the development of China’s manufacturing industry. This study uses industry-level data from the 2016 World Input-Output Database (WIOD) to calculate the import dependency of intermediate products in China’s manufacturing sector as a core indicator. It systematically examines the impact mechanism of robot application on the domestic value-added rate of exported products. Empirical results show that excessive reliance on imported intermediate products has a significant inhibitory effect on the domestic value-added rate of products. However, the implementation of industrial automation, especially the application of robots, can effectively weaken this negative relationship, and this conclusion remains consistent across multiple robustness tests. Mechanism analysis further reveals that automation achieves this through three pathways: First, by standardizing production processes to improve product quality; second, by promoting production technology innovation to generate spillover effects; and third, by optimizing the allocation of production factors to create a labor substitution effect. This study not only expands the research boundaries of the economic effects of robot technology application but also deepens the understanding of the impact mechanisms of industrial intelligence on the quality of participation in the global value chain from a micro perspective. Against the backdrop of the “Made in China 2025” strategy, exploring the impact of intermediate product import dependency on Chinese export enterprises holds significant practical importance. This research provides practical reference value for the transformation and upgrading of China’s manufacturing industry under the current dual pressures.

Under the “dual carbon” strategy, systemic risk prevention and control between China’s carbon market and commodity futures market is one of the important issues to maintain financial stability, but existing studies only focus on the low-order moment risk spillovers, and there is little literature to study the key driving mechanism. In view of this, this paper first constructs a high-order moment risk spillover measurement framework of“carbon-commodity futures” to measure the dynamic time-varying spillover index. On this basis, the SHAP algorithm and cutting-edge machine learning model are further integrated to deeply explore the driving mechanism of each influencing factor on the high-order moment risk spillovers between carbon-commodity futures markets. The findings reveal that risk spillovers between these markets exhibit significant time-varying characteristics, with kurtosis spillovers displaying the highest volatility. Financial market volatility indicators play a pivotal role in driving risk spillovers: Oil market volatility contributes 32% to volatility spillovers, EU carbon market volatility accounts for 37% of skewness spillovers, and oil market volatility explains 28% of kurtosis spillovers. The impact of various factors on risk spillovers demonstrates significant nonlinear features. Notably, economic policy uncertainty exhibits a threshold effect, where exceeding critical values triggers substantial increases in risk spillovers, while climate policy uncertainty shows an inverse nonlinear relationship. Moreover, significant interactions exist among the driving factors, with the most pronounced being the synergistic effects between financial market volatility indicators, which can potentially amplify the cross-market risk spillovers.

Air quality across different regions in China varies due to factors such as population density, geographical location, and climate. To address air pollution, the Chinese government has implemented a series of stringent energy-saving and emission-reduction measures to mitigate environmental damage. To gain a deeper understanding of the impacts of these policies on air quality, this paper proposes a policy evaluation framework based on a class of spatio-temporal model incorporating exogenous variables. By integrating the generalized Yule-Walker estimation method, it innovatively addresses the challenges of estimating exogenous variables within the model. Through theoretical derivations, simulation experiments, and empirical analysis, the reliability and applicability of the proposed model are validated. Notably, in evaluating air quality management policies during the 2014 APEC summit, this study provides more detailed quantitative results, revealing the heterogeneous impacts of policy implementation across different times and regions.