Highlights in Business, Economics and Management
Highlights in Business, Economics and Management is an open access, peer-reviewed publication series published by Darcy & Roy Press. It aims to publish conference proceedings dealing with all areas of business, economics and management, etc.
Design of Financial Risk Assessment System for Listed Companies: A Perspective Based on Deep Learning
Financial risk assessment is very important for the development of enterprises. It can detect potential risks in advance, help enterprises to rationally plan funds, optimize resource allocation, reduce losses, and ensure stable operation. Aiming at the problems of traditional financial risk assessment methods relying on subjective experience and single data dimension, this study develops a deep learning-driven framework for evaluating financial risks in publicly traded firms. Drawing upon the historical financial data of A-share listed companies, this study constructs a risk assessment system through a neural network model based on deep learning, effectively captures the temporal correlation of financial indicators, and introduces interdisciplinary analysis methods to reduce the subjective bias in traditional assessment. The experimental results demonstrate that the MAE of the model is about 0.3, and the evaluation accuracy is 97%. The accuracy and stability of the risk classification show promising advantages in terms of the traditional methods. The research results provide a new technical path and theoretical support for data-driven enterprise risk quantitative management.
Listed companies; financial risks; deep learning.
Copyright (c) 2025 Highlights in Business, Economics and Management https://creativecommons.org/licenses/by-nc/4.0
Research on the Implementation Path and Effect of Precision Marketing in Logistics Enterprises
This study explores the implementation path and effect of precision marketing in logistics enterprises, taking JD.com as a typical case. Against the background of fierce competition in the logistics industry, precision marketing has become a core strategy, but there are gaps in its application. Through case analysis, it is found that JD.com has achieved remarkable results by integrating multi-dimensional data to build a 360° customer view, adopting segmentation strategies, designing precision marketing strategies and implementing customer relationship management. It has not only improved operational efficiency such as increasing the revenue share of high-value users and optimizing the marketing input-output ratio but also enhanced customer experience like improving member NPS and logistics fulfillment satisfaction. The study enriches the theoretical connotation of precision marketing in the logistics field and provides practical references for logistics enterprises to enhance competitiveness.
Logistics precision marketing;Customer profile construction;Marketing strategy design;;Efficiency-experience balance;JD Logistics Case.
Research on Willingness to Pay for Cascade Utilization of New Energy Vehicle Battery Products under the “Double Carbon” Goal
With the growing global concern about climate change, China, as the world's largest carbon emitter, faces the great challenge of achieving the carbon peak and carbon neutral goals. This paper explores the factors affecting consumers' willingness to pay for secondary utilization of battery products and, based on the Theory of Planned Behavior (TPB), adds three variables, namely incentive policy, knowledge and perceived risk, and applies interpretative structural modeling (ISM) to analyze how these factors affect consumers' willingness to pay. It was found that knowledge and incentive policy are deep-level factors that affect willingness to pay, while consumers' perceived risk, perceived behavioral control, and subjective norms are mid-level influences that directly affect attitude and willingness to pay. Attitude, as a superficial factor, is a direct determinant of consumers' willingness to pay. Through ISM analysis, the study reveals the interactions and the dynamic circular system between these factors, emphasizing the importance of policy and knowledge dissemination in promoting consumer acceptance of new products, as well as the importance of reducing perceived risk and increasing self-efficacy in forming positive attitudes. Finally, the theoretical and practical implications of the findings are discussed and relevant policy recommendations are made.
“Double Carbon” Goal; New Energy; Secondary Utilization; Willingness to pay.
An Empirical Study on the Impact of Charitable Donations on Audit Fees
As the fulfillment of corporate social responsibility is increasingly emphasized, auditors are paying more attention to the social responsibility information of the audited entities, such as charitable donations. Therefore, this paper, after elaborating on the basic concepts of charitable donations and audit fees and reviewing relevant theories and research, deeply analyzes the influence path of charitable donations on audit fees and puts forward relevant hypotheses. This paper takes A-share non-financial listed companies from 2015 to 2024 as samples to study the relationship between charitable donations and audit fees as well as the moderating role of property rights nature. The results show that corporate charitable donations are positively correlated with audit fees; the larger the amount of corporate charitable donations, the higher the audit fees. The property rights nature of enterprises plays a positive moderating role in the impact of corporate charitable donations on audit fees.
Charitable donation; audit fee; nature of property rights.
The Ratchet Effect in Performance Target Setting: A Literature Review
Performance target setting is one of the core issues in the design of incentive contract, while the ratchet effect is among the most important and extensively discussed areas in goal setting research. This paper reviews relevant studies from three perspectives: the existence of target ratcheting, the causes and impacts of target ratcheting, and the influencing factors of target ratcheting. It focuses on evaluating the influencing factors that mitigate the impact of the ratchet effect in target setting. By organizing and summarizing existing research findings, this paper aims to assist researchers in advancing studies related to performance targets and identifying potential research gaps in target ratcheting.
Performance Target; Ratchet Effect; Target Ratcheting; Literature Review.
A Configurational Study on Enhancing Sustainable Development Capabilities of "Little Giant" Enterprises under the Dual Transformation Challenge
Confronting the challenges and opportunities of dual digital-green transformation, how specialized, refined, unique, and innovative ("Little Giants") enterprises can achieve steady improvements in sustainable development capabilities has become pivotal for driving high-quality economic growth. Grounded in the "Resource-Capability-Environment" framework and leveraging fuzzy-set Qualitative Comparative Analysis (fsQCA), this study examines the configurational effects of antecedent variables—including digital technology application, smart manufacturing, greening practices, digital absorptive capacity, organizational adaptability, government subsidies, and market competition—on sustainable development capabilities across a sample of 332 listed "Little Giants" in China. Key findings reveal: (1) Four distinct pathways to high sustainable development capability: Technology Resources-Policy Symbiosis (Y1), Ambidextrous Technology Resources-Capability Alignment (Y2, Y3), Technology Resource Dominance (Y4), and Ambidextrous Technology Resources-Policy Catalysis (Y5); (2) Low-capability configurations exhibit technology isolation and policy drift, highlighting configuration traps of "prioritizing hardware over software" and "emphasizing external over internal factors"; (3) Digital technology application serves as a foundational condition for most high-capability configurations, while the interaction between policy and market competition demonstrates context dependency. By deconstructing the asymmetric relationships among resources, capabilities, and environmental factors, this study offers practical pathways for enterprises to achieve sustainable leaps under dual transformation pressures.
Specialized, Refined, Unique, and Innovative (
Path and Performance of Digital Transformation of Specialized, Refined, Distinctive and Innovative Enterprises
The world is currently experiencing a rapid development of the digital economy. Digital transformation has become a key focus of long-term strategic deployment for enterprises, in order to adapt to the demands of the digital age, enhance operational efficiency, strengthen core competitiveness, and achieve sustainable development goals. In the rapidly changing market environment, digital transformation is not only an inevitable choice for Specialized, refined, distinctive and innovative enterprises (SRDI enterprises), but also as a key strategy for long-term sustainability. Therefore, how to effectively promote digital transformation and achieve optimal performance has become a central issue in the strategic planning of SRDI enterprises, attracting widespread attention at both the national and social levels. This study adopts the case analysis method to deeply explore the digital transformation path and performance of Jinpan Smart Technology, a representative SRDI enterprise, and concludes that digital transformation significantly has a significant positive effect on the performance of SRDI enterprises. Not only does this study enrich academic research on the digital transformation of SRDI enterprises, but it also provides theoretical foundations and reference value for the digital transformation practice of SRDI enterprises through the case of Jinpan Smart Technology.
Digital Transformation; SRDI Enterprises; Path; Enterprise Performance.
Research on Goodwill Measurement Models Based on Real Options Theory
High-premium mergers and acquisitions often result in high goodwill and associated impairment risks, which can significantly impact a company's equity and the quality of its accounting information, exacerbate fluctuations in operating performance, and mislead investors' decision-making. Therefore, accurately measuring goodwill and its impairment amounts is of particular importance to both companies and the market. Addressing the shortcomings of the current goodwill initial measurement methods used in practice, this study proposes to incorporate real options theory to improve the cut difference method and establish a modified coefficient assessment system based on corporate capabilities and industry characteristics to accurately evaluate a company's goodwill and impairment amounts. This approach aims to provide dual theoretical support and practical guidance for merger and acquisition decisions and goodwill management in the electronics manufacturing industry.
Real options; corporate capabilities; goodwill measurement.
Study on the Impact of Digital Transformation in Manufacturing Enterprises on Audit Fees
Digital transformation enhances operational efficiency, reshapes traditional business models and processes, and redefines how enterprises generate and transmit information. This transformation not only impacts corporate performance but also presents risks and challenges for certified public accountants. This study examines the impact of digital transformation levels on audit fees within the manufacturing sector, using a sample of 1,017 listed companies from 2015 to 2022. It analyzes the underlying mechanisms through risk-oriented auditing theory, information asymmetry theory, and cost-benefit theory. Additionally, it introduces variables of managerial competence and corporate property rights to examine their moderating effects on the relationship between digital transformation and audit fees. The study concludes that: (1) digital transformation exhibits a significant positive correlation with audit fees; (2) managerial competence exerts a negative moderating effect on this relationship; (3) property rights structure exerts a negative moderating effect on this relationship. Based on these findings and the characteristics of the manufacturing sector, the paper proposes practical recommendations for governments, enterprises, and accounting firms.
Corporate Digital Transformation, Audit Fees, Managerial Competence.
The Role of Free Trade Agreements in Reducing Trade Costs：A Panel Data Analysis
Since the second half of the 19th century, three waves of globalization have accelerated the process of trade liberalization, leading to a significant reduction in global trade costs. Meanwhile, the number of Free Trade Agreements (FTAs) signed worldwide has increased rapidly, and the proportion of "WTO-X" provisions in newly signed agreements has continued to rise. The correlation between these two phenomena has attracted widespread attention. This paper aims to explore the role of free trade agreements in reducing trade costs. It measures trade costs based on the gravity model framework and constructs a panel data model for analysis. The model takes bilateral trade costs (including total trade costs, agricultural trade costs, and manufacturing trade costs) as the explained variables, with the actual number of FTAs and core depth as the core explanatory variables, while incorporating relevant control variables. The research results show that the actual number and core depth of FTAs have a significant negative impact on total trade costs and manufacturing trade costs, but a significant positive impact on agricultural trade costs. Regarding control variables, the difference in per capita GDP and geographical distance are positively correlated with trade costs, while common currency and WTO membership are negatively correlated with trade costs. Endogeneity and robustness tests verify the reliability of the above results. This study provides a reference for countries to formulate trade policies and promote the development of free trade agreements to reduce trade costs.
Free Trade Agreement; Trade Cost; Panel Data; Gravity Model.
Research on performance evaluation of J enterprise under ESG concept
This study examines Company J through the lens of sustainability theory, stakeholder theory, and triple bottom line theory, analyzing its current development status while evaluating the necessity and feasibility of implementing an ESG performance assessment framework. The ESG evaluation system is constructed across four dimensions: financial, environmental, social, and governance (ESG), with 18 criterion layers derived into 50 representative third-level indicators. The research employs Analytic Hierarchy Process (AHP) for indicator weighting and topsis method to calculate Company J's comprehensive ESG performance scores from 2020 to 2022. Through systematic evaluation, key factors influencing ESG performance scores and existing challenges are identified. Finally, targeted policy recommendations are proposed based on these findings to help companies improve ESG performance and drive sustainable development.
ESG, Performance Evaluation, Analytic Hierarchy Process(AHP), Topsis Method.
The Impact of Macroeconomic Indicators and Economic Fluctuations on Private Equity Investment
As a crucial component of capital markets, private equity investment is significantly influenced by macroeconomic indicators and demonstrates unique performance during economic fluctuations. The field extensively studies how macroeconomic factors such as inflation rates, interest rates, and exchange rates affect private equity investment strategies, returns, and risk management, as well as how private equity investors rapidly adjust their strategies to address market uncertainties triggered by economic crises and global public health events. Current research hotspots include the integration of ESG investment principles, the impact of macroeconomic fluctuations on private equity investment, the influence of financial regulatory system reforms on the industry, the role of local governments in regional innovation systems, empirical research on macroeconomic factors, predictions of structural market changes, changes in investor composition, optimization of exit strategies, and analysis of fundraising and investment scale stability. These research directions not only cover multiple aspects of private equity investment but also reflect how the private equity industry adapts to and captures new investment opportunities in an ever-changing macroeconomic environment. Future research could explore private equity investment performance in emerging markets, industry characteristics under macroeconomic fluctuations, and how to enhance risk resistance through innovative investment strategies. These directions will become the focus of future research, providing more forward-looking guidance for industry development.
private equity investment, Financial regulatory system reform
Framing Effects on Consumer Decision-Making in Online Retail
This study investigates how different framing strategies, such as time, limited scarcity messaging, discount framing, and product recommendations, influence consumers’ product perceptions, evaluations, and purchase decisions in online retail contexts. Based on a systematic review of academic literature, the impact of these framing effects on consumer preferences, risk tolerance, and final decision-making was analyzed. Additionally, moderating factors including consumer attitudes toward promotions, perceived logistical capabilities, brand image, and payment methods were assessed, providing theoretical support for effective e-commerce marketing strategies.
Framing effects; consumer decision-making; online retail; scarcity messaging; discount framing.
Application of machine learning model in stock prediction
Economic stability and investor decision-making are significantly impacted by the stock market, which plays a crucial role in financial markets worldwide. This study focuses on applying machine learning models, particularly Random Forest, Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM), to predict Tesla stock prices. Various models' performance in stock prediction is evaluated using R-squared (R2), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The results show that Random Forest and LSTM achieve the highest forecast accuracy, outperforming other models. These findings suggest that machine learning models are effective tools for stock prediction, providing valuable insights for investors. The superior performance of Random Forest and LSTM may be attributed to their ability to capture complex patterns in the data, especially in volatile markets. To further enhance the precision and reliability of stock predictions, future research should explore multi-source data integration and advanced model optimization techniques. Additionally, incorporating market sentiment and macroeconomic indicators could improve prediction robustness and offer deeper insights into market trends.
learning model, random forest, LSTM prediction
Research on the Optimization Strategy of HAVI Group’s Operation Model
With the development of market globalization and digitization, supply chain management become more and more important for the success of a company. HAVI Group, McDonald's main logistics supplier in China, focuses on cold chain logistics and temperature-controlled delivery services. With the development of digital technology, the traditional logistics operation model faces the opportunity of transformation and upgrading. This paper aims to explore how to use digital technology to optimize the operation model of HAVI Group to improve efficiency and reduce costs. Through case studies and data analysis, this paper identifies the key optimization points in HAVI Group's operation model and proposes a series of innovative optimization strategies. The results show that by use big data models for logistics path planning, inventory management, and customer service improvements, HAVI Group can significantly improve its operational efficiency and customer satisfaction. This study provides valuable optimization strategies and suggestions for HAVI Group and the entire logistics industry.
Supply Chain Management, Big Data Model, Logistics path planning
The Research Status and Prospect of the Relationship between ESG Performance and Audit
In recent times, ESG has become an important aspect and indicator for evaluating the sustainable development, governance performance and social values of enterprises and organizations. In the field of audit, ESG has gained increasing importance, becoming an indispensable reference and influential factor. By thoroughly reviewing the relevant literature on the relationship between ESG and audit published on CNKI and WOS until September 2024, this paper aims to analyze the current research status, summarize the mutual effect and mechanism between these two domains. This study primarily focuses on two lines: firstly, it explores the impact of ESG performance on audit pricing, audit quality, and audit outcomes. Secondly, it studies how audit behavior and processes influence ESG performance. The findings suggest that favorable ESG performance generally leads to reduced audit pricing, enhanced audit quality, and increased likelihood of issuing positive audit reports. However, it is important to note that such conclusions are based on general discussions as some studies have reported contradictory results. Therefore, further exploration and discussion are needed for in-depth study in this area. Moreover, audits predominantly play a constructive role in promoting ESG performance. Based on these insights, this paper categorizes the mechanisms while discussing future research prospects with an aim to provide valuable references.
audit relationship, ESG performance, Audit pricing, audit behavior
Asset Impairment Prediction in the Age of Big Data and Machine Learning
In the context of a globalized economy, accurately predicting asset impairment is critical for maintaining the financial health of enterprises and guiding investment decisions. This paper provides a comprehensive review of the internal and external factors influencing asset impairment and explores how these factors collectively impact asset valuation. It examines asset impairment prediction methods from two perspectives: traditional financial analysis and emerging machine learning techniques. While conventional approaches rely on cash flow forecasting and discounting, machine learning leverages historical data to identify patterns and trends in asset impairment. Machine learning provides distinct advantages compared to traditional methods, such as its capacity to handle large datasets, capture non-linear relationships, and adapt to market fluctuations. These capabilities notably enhance the accuracy and reliability of asset impairment predictions, enabling enterprises to make better-informed investment and operational decisions in increasingly complex market circumstances. Furthermore, these advancements provide valuable insights for accounting standard setters and policymakers. With ongoing advancements in big data and computing power, machine learning holds immense potential in asset impairment forecasting. Its application is expected to enhance risk management for enterprises, paving the way for more effective decision-making and sustainable growth.
Corporate Financial Health, impairment of assets, impairment of assets
The Application of Economic Mathematics in the Field of Financial Economics
This paper discusses its application in the field of financial economics from the connotation and methods of economic mathematics, focusing on risk management, portfolio optimization, financial derivatives pricing and macroeconomic forecast analysis. By expounding specific application examples in economic mathematics such as probability statistics, optimization theory, and stochastic processes in the above fields, its unique role in providing quantitative analysis tools is revealed. This paper provides institutional support for scientific understanding and guidance of policies and operations of financial and economic activities, aiming to highlight the close connection between economic mathematics and financial and economic practice and its good application prospects.
Economic mathematics; finance and economics; risk management; portfolio.
When Strategic Synergy Fails: A Case Study of Alibaba’s Exit from Sun Art Acquisition
This paper is based on the case of Alibaba's acquisition of Sun Art Retail to explore the deep-seated reasons for its strategic synergy failure. The paper systematically sorts out Alibaba's M&A motivations, integration paths, and obstacles in the execution process by analysing M&A theory and strategic synergy theory. Alibaba has invested heavily in technology empowerment and organisational structure adjustment. However, problems such as management overlap, cultural conflicts, and inconsistent system logic have emerged during its integration process. This has led to the failure to achieve synergy and a continuous decline in business performance. In the end, Alibaba chose to sell all its shares by the end of 2024. This marks a major adjustment in the new retail strategy. The paper believes that synergy in cross-industry mergers and acquisitions needs to be achieved through multi-dimensional means such as structural design, cultural integration, and performance monitoring. This case provides important lessons and strategic suggestions for platform-based companies to promote online and offline integration and cross-border integration in the future.
Alibaba; Sun Art Retail; mergers and acquisitions; integration.
Research on the Impact of Supply Chain Finance Based on Blockchain Technology on Enterprise Innovation Performance
This study aims to explore the impact mechanism of supply chain finance on enterprise innovation performance through the chain mediating effect of financing constraints and green technological innovation. Using data from 2014 to 2023 as the research sample, this paper employs a two-way fixed-effect model and mediating effect test method, combined with the background of blockchain technology empowering supply chain finance, to analyze the direct impact of supply chain finance development level on enterprise innovation performance and its indirect action paths. The research results show that supply chain finance can significantly improve enterprise innovation performance, and achieve a significant positive impact through two paths: alleviating financing constraints and improving the level of green technological innovation. Heterogeneity analysis indicates that the benefits are more significant for non-state-owned enterprises, non-heavy pollution industries, and enterprises in eastern regions. This study provides a theoretical basis and practical reference for optimizing supply chain financial services and promoting the construction of enterprise innovation ecosystems.
Supply Chain Finance, Financing Constraints, Green Technological Innovation, Enterprise Innovation Performance, Blockchain Technology.
Investor–firm interactions and stock price crash risk
With the advancement of technology, social media platforms, represented by online interactive platforms, have improved information dissemination in a faster and more dynamic manner. This study examines the relationship between the quality of interactive Q&A between investors and corporate on online platforms and stock price crash risk, using a sample of A-share listed companies from 2010 to 2023. The quality of interaction is measured by the cosine similarity of the Q&A texts. The empirical results show that high-quality interaction between investors and companies can reduce stock price crash risk. The mitigating effect of high-quality interaction on stock price crash risk is more pronounced in firms with lower information transparency.
Investor–firm interactions; Stock price crash risk.
Research on the Application of Applied Mathematics in Modern Economic Management
With the rapid development of science and technology, economic management problems are becoming more and more complex, and the demand for accurate and efficient analysis tools is becoming more and more urgent. With its unique logical and quantitative analysis capabilities, applied mathematics provides strong support for economic management, whether it is market forecasting, cost control, resource allocation, and decision-making optimization, mathematical methods can play an important role. This paper summarizes the applied mathematics, then discusses the application value of applied mathematics in modern economic management, and points out the specific application of applied mathematics in modern economic management, in order to provide a useful reference for promoting the better development of modern economic management.
Applied mathematics; modern economic management; mathematical theory; mathematical methods.
The Impact of Uncertainty on Firms’ Outward Foreign Direct Investment
This study investigates the impact of policy uncertainty on Chinese firms’ outward foreign direct investment (OFDI) from 2003 to 2023, focusing on the interaction between U.S. trade policy uncertainty (TPU) and China’s economic policy uncertainty (EPU). Using a Poisson pseudo–maximum likelihood (PPML) estimation and incorporating both firm-level and host-country–level controls, the analysis reveals that dual policy uncertainty exerts a significant and robust effect on OFDI probability and scale. These findings contribute to the literature by elucidating the mechanisms through which multiple sources of policy uncertainty jointly shape cross-border investment strategies, offering new insights into risk management and global market reallocation under uncertain environments.
Policy Uncertainty, Outward Foreign Direct Investment (OFDI), Trade Policy Uncertainty (TPU), Economic Policy Uncertainty (EPU).
The Influence of Carbon Emissions Trading Pilot Policies on Supply Chain Risks. -- A Quasi-Natural Experiment Based on A-Share Listed Companies
As a crucial measure for achieving the "dual carbon" goals, carbon emissions trading pilot policies have garnered widespread attention. This study analyzed the impact of carbon emissions trading pilot policies on corporate supply chain risk levels and their underlying mechanisms, using data from A-share listed companies from 2008 to 2021. The findings revealed that carbon emissions trading pilot policies significantly mitigate supply chain risks, and this conclusion remained valid after undergoing parallel trend tests, placebo tests, and PSM-DID analyses. Further analysis indicated that the mitigating effect is more pronounced among state-owned enterprises, large-scale firms, and companies located in eastern China. Mechanism analysis suggested that Carbon Emissions Trading Pilot Policies reduces supply chain risks by promoting technological innovation, enhancing corporate development quality, and improving industrial structures. The conclusions of this study provide valuable insights for firms to leverage carbon policies for supply chain risk management and achieve sustainable development.
Carbon Emissions Trading Pilot Policies, Supply Chain Risk Level, Technological Innovation, High-Quality Corporate Development, Industrial Structure.
A Study of User Loyalty in Mobile China Merchant Bank:A Perspective Based on Technology Perception and User Experience
Taking China Merchants Bank (CMB) as a case study, this research investigates the impact of digital transformation in retail banking on user loyalty. Integrating customer value theory, privacy calculus theory, and the technology acceptance model, this research construct a theoretical framework of "Technology Application Perception → User Experience → User Loyalty". These findings indicate that commercial banks should continuously optimize the design of technological functionalities with user needs as the core focus. They should prioritize delivering multi-dimensional user value through experience, while simultaneously enhancing the digital adaptability of diverse user segments through tiered management to enable personalized service provision. The empirical results of this study can provide targeted suggestions for determining the future direction of digital transformation in commercial banks' retail business, helping them recognize the importance of user-centeredness. By empirically analyzing the impact of users' perception of digital technology and how to indirectly enhance user loyalty through improving user experience, this paper can assist commercial banks' digital platforms in providing more precise and effective services in the future, thereby increasing user stickiness.
China Merchants Bank, Technology Application Perception, Customer Experience, User Loyalty.
The Functions and Effects of New Quality Productive Forces in Promoting Green Development
Based on panel data collected from 30 provinces across China spanning 2012 to 2021, this paper utilizes a two-way fixed effects methodology to thoroughly evaluate the impact of novel quality-oriented productive forces on promoting sustainable and high-level green economic growth.The findings from the analysis reveal that innovative, quality-driven productive forces, primarily by optimizing the allocation of production factors and enhancing green technological innovation, significantly contribute to the progress of the green economy. This positive impact is especially evident in eastern regions, non-resource-based areas, and provinces with more developed green finance, whereas the effects are less pronounced in central and western regions, resource-dependent areas, and regions where green finance is less advanced. These findings highlight the importance of further cultivating new quality productive forces through strengthened technological innovation and human capital development, promoting the improvement of green financial systems to provide sustained support for green industrial transformation, and formulating regionally adaptive strategies to accelerate green transition and achieve coordinated regional development.
New Quality Productive Force; Green Development; Green Technological Innovation.
Biodiversity and Economic-Ecological Assessment of Organic and Conventional Agriculture Based on the Lotka-Volterra Model
In this era, the pace of global agricultural intensification is accelerating. Under such circumstances, although traditional agricultural practices have increased crop yields, they have also brought about some major problems, such as the reduction of biodiversity and the deterioration of ecosystems. Now, for agriculture to achieve sustainable development, it is facing a significant challenge. The significant challenge is how to ensure profitability while simultaneously achieving effective ecological protection. To solve this problem, this paper conducts a study, adopts the extended Lotka-Volterra dynamic model, and combines the Shannon Diversity Index to construct a coupled analysis framework. This framework can capture the interactions among different species such as crops, weeds, trees, pests, and birds. At the same time, economic value and ecological value can also be evaluated. Subsequently, this paper simulated and compared the long-term dynamic changes of the traditional agricultural system and the organic agricultural system under different management systems. The results show that organic agriculture has significantly improved the diversity of species in the ecosystem. After approximately 15 years, organic agriculture surpassed traditional agriculture in both economic and ecological benefits. From these results, it can be seen that organic agriculture not only promotes the joint development of economic and ecological achievements, but also provides a better approach for sustainable agricultural management.
Organic Agriculture, Conventional Agriculture, Shannon Diversity Index, GE Matrix.
Generative AI and Digital Governance Transformation: Evidence from China’s Prefecture-level Cities on Adaptive Institutional Dynamics
Generative Artificial Intelligence plays a pivotal role in transforming digital governance systems amid global challenges of high accessibility but low public satisfaction. To systematically investigate its impact mechanisms, this study develops an integrated analytical framework combining governance theory with empirical econometrics, leveraging large-scale prefecture-level data across China. Our approach employs variable substitution tests and mediation analyses to disentangle complex causal pathways, revealing that GAI significantly elevates administrative efficiency through smart service innovation while exhibiting regionally divergent effects moderated by institutional adaptability. Crucially, the research demonstrates that strategic policy-infrastructure coordination can amplify technological benefits and mitigate data sovereignty risks. These findings provide actionable insights for designing adaptive governance paradigms that balance operational efficiency, algorithmic accountability, and equitable service delivery in the intelligent governance era.
Generative Artificial Intelligence, Digital Government, Adaptive Governance.
Prediction of stock market volatility based on public opinion mining and multi-source data fusion: A case study of Guoxuan Hi-Tech
This study aims to enhance the accuracy and robustness of stock market volatility forecasting in the context of rapidly evolving public opinion and heterogeneous data environments. Traditional models often struggle to capture the nonlinear and dynamic interactions between investor sentiment and market behavior, particularly during high-volatility periods. To address these limitations, a unified deep learning framework is developed that incorporates multi-source sentiment signals into the forecasting process. Specifically, a Transformer–LSTM threshold fusion model is constructed to extract semantic features from investor comments and temporal patterns from historical market data. The model introduces a dynamic fusion mechanism based on historical volatility thresholds and integrates a dual-dimensional sentiment indicator system (CAI and CSEI) to reflect behavioral signals from both current and potential investors. Empirical evaluation using Guoxuan Hi-Tech stock data from 2019 to 2024 shows that the proposed model significantly outperforms benchmark methods such as BERT–GRU and LDA–Prophet in predictive accuracy, generalization, and robustness. These findings underscore the effectiveness of integrating deep learning with multi-source sentiment modeling and suggest a scalable framework for broader applications in financial risk forecasting.
Volatility Forecasting, Public Opinion Mining, Data Fusion, Transformer-LSTM.
Optimization Research on Crop Planting Strategies Based on Multi-objective Optimization Model and Game Theory
This paper aims to promote rural revitalization and support the long-term development of national agriculture by optimizing planting strategies, developing organic farming according to local conditions, and rationalizing the use of cultivated land. Based on the data related to cultivated land area in North China from 2017 to 2022, a multi-objective function optimization model is constructed, which comprehensively considers constraints such as land type, crop planting area limitations, crop continuous cropping effects, and legume crop rotation requirements. On this basis, the target-MOTAD model and importance sampling-Monte Carlo simulation are introduced to establish a comprehensive objective function for maximizing revenue and minimizing risk, which is solved by a hybrid genetic algorithm. Furthermore, complementarity coefficients and substitution coefficients are introduced to establish a market response model based on principal-agent game, which is dynamicized and solved by the NSGA-II algorithm, aiming to provide scientific decision support for agricultural producers and improve their revenue.
Planting strategy optimization, Target-MOTAD model, Hybrid genetic algorithm, Principal-agent game, NSGA-II.