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Practicing the ESG investment philosophy is not only a proactive response to China's "dual carbon" strategic goals,but also a crucial avenue for mitigating "green swan" risks,enhancing market reputation,and ensuring the sustainability of investment returns.Existing ESG portfolio models fail to fully integrate market information to cope with market uncertainty,and they struggle to achieve rational allocation and precise management of portfolio risk.This paper moves beyond the common integration approaches in ESG portfolio strategies and constructs a green bi-level risk budgeting model based on ESG integration.The upper level estimates the risk budgeting parameters,while the lower level determines the portfolio weights,thereby achieving a comprehensive optimization of expected returns,portfolio risk,and ESG benefits.Using the constituent stocks of the CSI 300 Index from 2019 to 2023 as the sample,we conduct statistical tests on the effectiveness of the model and algorithm.The empirical results show that the bi-level risk budgeting model outperforms traditional methods in both dynamic and static financial performance.This study enriches the research on risk-driven ESG asset allocation and portfolio construction,provides theoretical and algorithmic support for green investment,and offers a reference for promoting the ESG concept to better serve the low-carbon economic transition.
Data elements are the core driver of the digital economy,and compliant and efficient trading markets are vital to high-quality economic development.However,the data trading market currently suffers from low circulation efficiency,incomplete regulation,and weak compliance awareness,hindering value realization.This paper constructs a tripartite stochastic evolutionary game model involving the government,data trading platforms,and data buyers to explore governance pathways from a multi-agent perspective.Using stochastic differential equations and the Milstein method,this paper analyzes system stability and simulates dynamic strategic interactions.Results show that moderate but stringent government regulation balances regulatory effectiveness with market vitality and fosters platform collaboration;platform regulation is more effective than direct government intervention in standardizing buyer behavior;and curbing illicit buyer gains while stabilizing the market environment is key to rational decision-making and market stability.This paper therefore proposes an indirect collaborative governance model,where the government regulates platforms while platforms govern transactions,thereby fostering the healthy,efficient,and steadily growing development of the data trading market.
Against the backdrop of green development,corporate ESG information disclosure serves not only as a vital pathway to improve the modern environmental governance system and pursue high-quality green development,but also as a core criterion for evaluating enterprises' sustainable development capabilities.However,Chinese enterprises generally suffer from prominent drawbacks in current ESG disclosure practices,including low disclosure willingness,selective reporting,and self-serving bias.In this context,how to effectively improve the quality of corporate ESG information disclosure has become an urgent and practical issue to be addressed.Green investors,as a new hybrid practice subject of green finance and environmental goals,provide new ideas for answering the above questions.Based on a sample of Chinese A-share listed companies from2009 to 2023,this study examines the impact of green investor entry on the quality of corporate ESG information disclosure.It is found that green investors significantly improve the quality of corporate ESG information disclosure.The mechanism test finds that green investors mainly play a role by standardizing the internal governance of enterprises and enhancing the attention of external media;government regulation and public supervision can strengthen the impact of green investors,and the effect has a trend of "increasing marginal effect" with government regulation as the single threshold,while public supervision has no threshold effect.Further analysis reveals that this effect occurs in enterprises with a high proportion of green investors' holdings and those that make voluntary disclosures.In addition,the improvement of ESG disclosure quality caused by green investors has a positive impact on corporate green innovation,total factor productivity and debt financing cost,and also drives the improvement of regional environmental quality.Based on the above findings,this study proposes three policy implications:enterprises should deeply integrate the introduction of green investors with their ESG development strategies;as the core subject of policy regulation and institutional supply,governments shall foster a sound policy environment for ESG information disclosure through institutional innovation and regulatory optimization;the cultivation of green investor groups and the guidance of corporate ESG disclosure behaviors cannot be separated from effective market incentive mechanisms.The research results provide important theoretical references and practical guidance for enterprises to promote sustainable development from the perspective of green investment under the background of high-quality development.
During the 15th Five-Year Plan period,China's high-quality economic development has entered a critical phase,with digital technology innovation emerging as a key driver of structural transformation and the release of development momentum.To assess the spatial network linkages of highquality economic development in China from multiple frequency-domain perspectives,this study combines a spatio-temporal entropy-weighted TOPSIS approach with a Bayesian TVP-VAR-BK connectedness model.It also combines the patent database of the National Intellectual Property Administration to examine the impact effect and mechanism of digital technology innovation on the spatial network correlations.The results show that:(1) Compared to the short term,spatial network interconnectivity in the long term and across the entire cycle is relatively similar,and the spatial spillover effects of high-quality economic development among economic entities exhibit a certain degree of lag.(2) China's regions can be divided into three major functional blocks:the eastern region serves as the "primary radiating" block;the central region plays a "bridging" and intermediary role in regional linkages;and the western region acts as the "primary beneficiary" block.(3) Digital technological innovation significantly strengthens the spatial linkages of high-quality economic development across regions;its effects manifest over a longer time horizon,with stronger impacts observed in central regions and areas not reliant on natural resources.Mechanism analysis reveals that digital technological innovation enhances spatial network connectivity by promoting the interregional mobility of production factors.Meanwhile,the advancement of new digital infrastructure further strengthens the positive role of such innovation in improving network connectivity.Therefore,a sound policy system for differentiated regional collaboration should be established.Efforts should be made to enhance the cross-regional flow of production factors and the coordinated construction of digital infrastructure,and to improve the long-term incentive mechanism for regional innovation collaboration.Continuous efforts should be made to enhance the spatial linkage effect of high-quality economic development.
In the era of the digital economy,data and intelligence,as twin engines driving development,are profoundly reshaping the landscape and pathways of environmental governance.The dual pilot policies of the National Big Data Comprehensive Pilot Zones and the Artificial Intelligence Innovation and Development Pilot Zones are adopted as quasi-natural experiments.Based on panel data covering 269 prefecture-level cities in China from 2008 to 2023,a dual machine learning model is constructed to identify the causal effects of digital and intelligent policies on urban pollution reduction and carbon control,as well as the underlying action mechanisms.Empirical results show that digital and intelligent policies exert a significant synergistic promotion effect on urban pollution reduction and carbon control performance.This conclusion remains valid after a series of robustness tests including Bacon decomposition and parameter resetting.Mechanism analysis indicates that these policies generate sustained synergistic benefits for urban pollution reduction and carbon control through three core channels:optimizing economic structure,fostering a digital and intelligent environment,and advancing ecological and environmental protection.Furthermore,compared with individual policies,the cumulative effects of dual digital and intelligent policies present a stronger enabling effect.Heterogeneity analysis reveals that the positive enabling effect of digital and intelligent policies on pollution reduction and carbon control is more pronounced in cities with higher digital transformation levels,inland cities,and non-resource-based cities.Accordingly,the scope of pilot digital and intelligent policies should be expanded to amplify their synergistic effects.Priority should be given to leveraging the dual pilot policies to drive industrial low-carbon transformation,develop digital and intelligent industries and entrepreneurial environments,and promote ecological development.Tailored measures adapted to local conditions should be implemented to strengthen the synergy between inland and digital transformation within the policy framework,so as to facilitate the coordinated improvement of urban pollution reduction and carbon control.
Breakthrough innovation serves as a central engine for overcoming bottlenecks in key core technologies and for supporting national technological self-reliance and self-strengthening.As an important avenue for the efficient acquisition of external innovative resources,technology acquisitions are intended to facilitate firms' technological upgrading;however,their actual effects on breakthrough innovation remain contested.Using mergers and acquisitions data and patent data of A-share listed firms in Shanghai and Shenzhen from 2008 to 2024,a breakthrough innovation measurement framework is developed with the aid of large language models,and the impact of technology acquisitions on firms' breakthrough innovation and the underlying mechanisms are empirically examined.The results indicate that technology acquisitions significantly inhibit firms' breakthrough innovation,and the findings remain robust across multiple robustness checks.Mechanism analyses suggest that although technology acquisitions generate positive technological synergy,negative effects dominate overall.Specifically,market-share expansion strengthens managers' market-leader mentality and induces organizational innovation inertia;meanwhile,high acquisition costs intensify short-term performance pressure,triggering organizational rigidity and attention misallocation,ultimately yielding a negative net effect on breakthrough innovation.Heterogeneity analyses further show that moderate technological and product differences between acquirers and targets can alleviate the inhibitory effect through resource complementarity,whereas digital technology acquisitions exacerbate the inhibition due to greater technological compatibility barriers and integration complexity.A novel method for measuring breakthrough innovation is proposed,the theoretical framework on the innovation effects of technology acquisitions is refined,and implications are provided for firms' optimization of technology acquisition decisions and for governments' improvement of innovation-incentive policies.
New agriculture formats,formed through the deep integration of digital technologies and traditional agriculture,plays an increasingly important role in promoting the transformation,upgrading of traditional agriculture,and ensuring sustainable agricultural development.Using the business types and operational scopes of firms within the industry as the classification criteria,we evaluate the clustering levels of new agriculture formats enterprises across China's counties from 2000 to 2024.Examining the spatial distribution patterns and clustering dynamics of these enterprises.Meanwhile by applying cluster analysis and quantile regression models,we further investigate the critical role of factor endowment structures in shaping the agglomeration of new agriculture formats,as well as the heterogeneity of such effects across different levels of agglomeration.The results show that:(1)During the observation period,the agglomeration level of new agriculture formats across counties continuously increased and gradually exhibited a multi-polarized distribution pattern.Regionally,these industries tended to concentrate in capital-intensive areas such as the central-eastern regions,the Yangtze River Delta,and the Pearl River Delta,whereas their clustering in land-intensive regions was relatively weak.(2)Further analysis demonstrates that factor endowment structures are key determinants of agglomeration,with relatively abundant capital being the most decisive factor,followed by labor,and finally land.(3)The effects of factor structures on agglomeration exhibit clear heterogeneity across different quantiles,with the importance of capital rising further as agglomeration levels increase.Therefore,local governments should formulate development strategies for new agriculture formats in line with their regional factor endowment characteristics to avoid resource misallocation resulting from blind transformation.At the same time,by cultivating high-level regional technology diffusion hubs,governments can promote coordinated development of new agriculture formats and foster a complementary and synergistic new agglomeration pattern.
Based on an in-depth analysis of the evolutionary characteristics of financial market risks,low-frequency trend variables with high-frequency trading information and a class of mixed-frequency varying-coefficient quantile models are integrated for risk prediction.The newly proposed mixed-frequency varying-coefficient quantile model has the following features:First,the new model is not only an extension of existing parametric mixed-frequency data processing methods in the direction of semi-parametrics,but also an expansion of ordinary varying-coefficient quantile models in the analysis of mixed-frequency data;The mixed-frequency varying-coefficient quantile model designs coefficients as functions of low-frequency trend variables,which not only leverages the advantages of varying-coefficient models in handling mixedfrequency data and overcomes potential model misspecification issues of existing parametric mixedfrequency data processing methods,but also provides a way to utilize mixed-frequency data information within the framework of ordinary varying-coefficient quantile models.Second,the new model can fully utilize mixed-frequency data information and reasonably characterize the dynamic characteristics of risk evolution rules,thus achieving better prediction performance;Comparisons of prediction results across multiple models show that the mixed-frequency varying-coefficient quantile model can predict risks more accurately,and this advantage is more prominent in the prediction of medium-and long-term risks.Third,the estimators of coefficients in the new model have asymptotic variances with a unique structure;Compared with traditional varying-coefficient quantile models,the locally linear estimators of coefficients in the mixed-frequency varying-coefficient quantile model have an additional term in their asymptotic variances,which is caused by the mixed-frequency phenomenon existing in the data and is elaborated in detail through theoretical analysis and simulation tests in the paper.The proposal of the new model not only serves as a valuable supplement to existing mixed-frequency data processing methods and varyingcoefficient models,but also provides technical support for making full use of mixed-frequency data information to forecast medium and long-term risks,thereby effectively preventing and resolving risks.
Since 2016,China's transition toward high-quality economic development and its pursuit of mutually beneficial international cooperation have heightened the demand for precise economic and social governance.Policymakers and scholars alike are increasingly calling for macro-statistical data that are more accurate,detailed,and aligned with international standards.However,statistical accuracy continues to be affected by industry classification practices formed during earlier periods of rapid growth,when mixed operational structures were common.A key source of inaccuracy lies in the misclassification of auxiliary service activities within enterprises.In China,auxiliary units are typically classified according to the primary industry of their parent enterprise.This practice,however,diverges from the 2008 System of National Accounts(SNA 2008),which stipulates that auxiliary units that are geographically separate or maintain independent accounts should be recorded as distinct statistical entities,even without independent legal personality.This study focuses on how China's "Business Tax to Value-Added Tax Reform"(B2V Reform)addresses this statistical discrepancy.The reform reduced the tax burden on smaller auxiliary units by shifting them from the business tax regime to the value-added tax system,distinct from their parent enterprises.This change creates a strong economic incentive for firms to legally separate their auxiliary service departments.As a result,the reform promotes more accurate industry classification,especially for diversified enterprises in sectors such as manufacturing and mining,whose core activities involve tangible goods production.Given these economic-statistical linkages,the B2V Reform offers a valuable opportunity to evaluate the accuracy of industry classification data and to inform adjustments to historical statistical records.Using provincial panel data from 2003 to 2021,this study applies a three-dimensional time-based analytical framework to empirically assess the impact of the B2V Reform on the accuracy of industry classification data and to examine variations across sectors.Exploratory adjustments are also made to relevant historical data.These findings indicate that the reform significantly improved data accuracy in pilot industries during its initial phase,while its effect on other industries in later stages was more limited.Heterogeneity analysis reveals that the reform had the strongest impact on the "information transmission,software,and information technology services" industry and the "leasing and business services" industry.After historical data adjustment,employment figures in these two corrected sectors-based on urban nonprivate units-exceed the originally reported values.This outcome confirms that the B2V Reform helps mitigate the underestimation of historical data in related service industries.Overall,this study validates the role of the B2V Reform in improving the accuracy of industry classification data.The findings are expected to help reduce statistical discrepancies in China's official practice and to provide a useful reference for the National Bureau of Statistics in revising economic statistical data.
The development of breakthrough,disruptive,and original high-quality innovation represents an essential pathway for Chinese enterprises to overcome technological bottlenecks and achieve high-quality development.Based on an innovation breakthrough index,this study measures the level of high-quality innovation among A-share listed firms in the Shanghai and Shenzhen stock exchanges from 2008 to 2019.Drawing on resource dependence theory,it further examines the impact of patient capital ownership on firms' high-quality innovation.These findings indicate that patient capital ownership significantly promotes high-quality innovation.Mechanism analyses show that this effect is primarily realized through the mitigation of managerial short-termism,the alleviation of financing constraints,and the upgrading of the human capital structure.Heterogeneity analyses further reveal that the promoting effect of patient capital ownership is more pronounced in firms operating in highly competitive industries,in regions with stronger intellectual property protection,among small-sized enterprises,and in samples involving corporate venture capital.These findings uncover the specific effects and underlying mechanisms through which patient capital ownership supports the development of high-quality innovation,and provide valuable insights for fostering and strengthening patient capital,as well as for building a capital support system conducive to high-quality innovation in China.Firms should proactively explore effective approaches to attract patient capital into their ownership structures and cultivate governance environments that enable it to play a full role.Meanwhile,the government should continue to improve the institutional framework for long-term capital investment,strengthen policy guidance and institutional support,reduce the uncertainty and institutional costs associated with long-term investment,and foster an external environment conducive to the value-creation function of patient capital.