World Journal of Economics and Business Research
World Journal of Economics and Business Research (WJEBR) is an international peer-reviewed, open access journal.
THE BCPE FRAMEWORK AND THE CONSTRUCTION OF AN AUTONAMOUS KNOWLEDGE SYSTEM FOR CHINA’S ECONOMIC DEVELOPMENT
This article proposes a dynamic “Basic–Constraint–Purpose–Effect” (BCPE) framework to explain China’s paradox of “stability through transformation” over seven decades. Moving beyond theories that treat the state as a passive market corrector, BCPE operationalizes CCP’s leadership as institutionalized political integrative capacity—manifested in strategic planning, organizational penetration, nationwide coordination, and self-correction. This capacity sustains a dual-loop learning system: an experience-elevation loop that scales local innovations into national policy, and a risk-correction loop that recalibrates goals and institutions in response to systemic failures. Development purposes are not exogenously given but endogenously shaped through the CCP’s evolving diagnosis of the principal societal contradiction, blending normative values (e.g., equity, sustainability) with instrumental concerns. Four middle-range propositions follow: (1) political integrative capacity enhances collective action in large societies; (2) constraints, when cognitively reframed, drive innovation; (3) a strategic state dynamically optimizes multiple, often competing goals; and (4) effective markets and promising government co-evolve through complementary institution-building. Rooted in China’s governance practice yet articulated with generalizable mechanisms, BCPE draws on indigenous epistemic traditions—particularly practical rationality and holistic thinking—while offering testable, boundary-specified claims. It thus advances a path toward an autonomous, globally engaged political economy, providing analytically rigorous tools for understanding adaptive governance in complex developing contexts.
Copyright (c) 2026 Wei Xin https://creativecommons.org/licenses/by/4.0
CONSTRUCTION OF JUNEAU’S TOURISM SOCIAL COST-ECONOMIC BENEFIT MODEL AND KEY FACTOR ANALYSIS BASED ON AHP AND MULTIVARIABLE ADJUSTMENT
In 2023, Juneau, Alaska, received a record 1.6 million cruise visitors, bringing significant revenue but severe overcrowding. To address this, this study develops a sustainable tourism model prioritizing economic benefit maximization. It uses linear fitting/trigonometric functions for visitor numbers, logarithmic/linear functions for tourism revenue, differential equations for environmental pressure, and the Analytic Hierarchy Process (AHP) for a constrained social cost model. Analysis shows Juneau’s total economic benefit peaks at ~1,026,453 visitors, with an income-expenditure plan and feedback mechanism proposed. A multivariable adjustment approach further quantifies impacts of variables (e.g., visitor spending, tax rates) and identifies key factors (e.g., visitor numbers, glacier melting rate) to support tourism decision-making.
Copyright (c) 2026 Min Zeng; LvJie Fan; XinYu Zheng https://creativecommons.org/licenses/by/4.0
EXECUTIVES’ ENVIRONMENTAL BACKGROUNDS AND GREEN INNOVATION BUBBLES: EVIDENCE FROM CHINESE LISTED FIRMS
The study examines the impact of executives’ environmental backgrounds on corporate green innovation bubbles, analyzing a sample of Chinese listed firms from 2010 to 2022. The findings indicate that appointing executives with environmental expertise significantly mitigates green innovation bubbles, a phenomenon characterized by the decoupling between the quantity and quality of green innovation. Mechanism analyses reveal that such executives operate through two key channels: by enhancing the green cognition of the management team and by improving government–enterprise communication. Furthermore, cross-sectional tests show that the restraining effect is more pronounced under specific conditions—particularly in firms facing higher performance pressure, those with higher resource allocation efficiency, or those receiving fewer government green R&D subsidies. Overall, the results suggest that executives with environmental backgrounds help firms shift their focus from short-term innovation metrics toward substantive green innovation. These insights offer meaningful implications for corporate governance and for policy design aimed at fostering high-quality green transitions.
Copyright (c) 2026 Nan Yang; WenXi Sun https://creativecommons.org/licenses/by/4.0
NEW-QUALITY PRODUCTIVE FORCES AND AGRITOURISM INTEGRATION: INSIGHTS FROM GREENFOOD FARM
Taking Yantai Greenfood Farm as a case study, this research explores the integration mechanisms between new-quality productive forces and the agritourism industry in areas such as green and low-carbon development, digitalization, mechanization, and intelligentization. It focuses on analyzing practical applications of new-quality productive forces, including how intelligent breeding technologies enhance agricultural production efficiency, how ecological circulation models support low-carbon development, and the value creation pathways of integrated agritourism business formats. It identifies key challenges in industrial practice, such as ambiguous definitions of new-quality productivity technology applications and insufficient integration depth between agritourism formats and technologies, while proposing optimization directions and improvement suggestions. The technology application models and industrial integration pathways presented in this article can provide practical references for the high-quality development of the agritourism industry, thereby contributing to rural revitalization.
Copyright (c) 2026 YuQing Qiu https://creativecommons.org/licenses/by/4.0
NEW PRODUCTIVITY ENABLES HIGH QUALITY DIGITAL AGRICULTURE IN JILIN PROVINCE DEVELOPMENT LOGIC AND IMPLEMENTATION COUNTERMEASURES
As a pioneering model driving the transformation and upgrading of traditional agriculture, digital agriculture has long been prioritized by government authorities. The new quality productive forces, characterized by technological innovation, high efficiency, and superior quality, provide robust support for the high-quality development of digital agriculture. Under the impetus of these advanced productive forces, digital agriculture will fully embrace the development concepts of innovation, openness, coordination, green practices, and sharing, thereby comprehensively empowering the high-quality development of digital agriculture in Jilin Province. However, various challenges remain in the province that urgently require resolution. To achieve sustainable development in the new era, Jilin must focus on cultivating and advancing high-quality digital agriculture as its core objective, establishing distinctive local strategies for digital agricultural advancement.
Copyright (c) 2025 AiLing Zhu; Tao Ding; XinXin Liu https://creativecommons.org/licenses/by/4.0
THE IMPACT OF DIGITAL INCLUSIVE FINANCE ON URBAN INNOVATION AND ENTREPRENEURSHIP
Innovation and entrepreneurship are of great significance to enhance the overall national strength and promoting regional development. Promoting innovation and entrepreneurship is an inevitable requirement for China to enhance the internal driving force and high-quality economic development. Based on the panel data of 279 prefecture-level cities in China from 2011 to 2019, this paper constructs a fixed-effect model to verify the relationship between digital inclusive finance and urban innovation and entrepreneurship and its influence mechanism. The study found that digital inclusive finance can significantly affect the improvement of the level of urban innovation and entrepreneurship, and the conclusion is still valid after changing the measure of urban innovation and entrepreneurship and deleting the robustness test of the provincial capital cities. Secondly, through the construction of the intermediary effect model, it is found that digital inclusive finance promotes the level of urban innovation and entrepreneurship by improving the development level and marketization degree of the Internet.
Copyright (c) 2025 KaiWen Li; YanLin Ji; Min Liu; SuiYi Liao https://creativecommons.org/licenses/by/4.0
DYNAMIC DEPLOYMENT OF FINANCIAL PERSONNEL BASED ON THE HOLT-WINTERS ALGORITHM-PROPHET-LSTM PREDICTIVE MODEL
In the context of refined enterprise management, the dynamic optimal allocation of human resources has become the key to improving operational efficiency and reducing labor costs. Financial reimbursement, as a high-frequency and important business process, exhibits significant fluctuation in its workload due to influences from business cycles, project milestones, and other factors. A fixed number of finance personnel can easily lead to backlogs in expense review during peak periods, resulting in employee dissatisfaction, while creating idle human resources during off-peak periods. To resolve this contradiction, this paper aims to establish a dynamic adjustment mechanism for finance personnel based on predictive modeling. This study utilizes historical reimbursement claim volume time series data from enterprises, integrating the Prophet-LSTM predictive model based on the Holt-Winters algorithm to accurately predict future claim volumes for specific cycles (e.g., monthly, quarterly). Through this model, enterprise managers can proactively anticipate future workloads and dynamically adjust staffing levels for financial reimbursement positions accordingly, enabling flexible allocation of financial personnel. Empirical research demonstrates that this predictive model exhibits high accuracy, with an MSE of only 152.47, an RMSE as low as 12.35, an MAE of 7.89, a MAPE of just 6.58%, and an R2 as high as 0.948. The dynamic adjustment strategy based on this model effectively smooths workloads, significantly shortens reimbursement processing cycles, and enhances the overall utilization efficiency of corporate human resources. This study provides a feasible theoretical framework and practical pathway for enterprises to achieve data-driven human resource management in finance and other volatile business domains.
Copyright (c) 2026 HuiJun Liang; ZiTian Sheng; ZhiJie Luo https://creativecommons.org/licenses/by/4.0
THE IMPACT MECHANISM OF SMES’ PERSONALIZED TAX SERVICE DEMAND UNDER THE BACKGROUND OF DIGITAL ECONOMY
In the context of the digital economy, tax services are shifting from a management-oriented approach to a service-oriented one, and the demand for personalized tax services from small and medium-sized enterprises (SMEs) is increasingly prominent. This paper takes SMEs in City A as the research object, constructs a structural equation model based on questionnaire survey data, and empirically analyzes the influence mechanisms of tax-handling channel preference, tax policy awareness and information acquisition, and tax service satisfaction on the demand for personalized tax services. The results show that tax-handling channel preference, tax service satisfaction, and tax policy awareness all have significant positive effects on the demand for personalized tax services among SMEs, with the effect of tax-handling channel preference being the most prominent. The research conclusions provide empirical evidence for optimizing the supply of tax services to SMEs and improving tax administration efficiency in the context of the digital economy.
Copyright (c) 2026 Qian Zhang https://creativecommons.org/licenses/by/4.0
AN EMPIRICAL INVESTIGATION INTO THE STRATEGIC IMPACT OF RECIPROCAL TRADE COUNTERMEASURES ON U.S. ECONOMIC RESILIENCE AND MANUFACTURING RE-INDUSTRIALIZATION
The 2025 U.S. “reciprocal tariff” policy has intensified global trade frictions, triggering retaliatory countermeasures that reshape supply chains and industrial dynamics. Whether such protectionist strategies can sustainably revive domestic manufacturing or instead undermine macroeconomic stability remains an open empirical question. This study investigates the nonlinear effects of reciprocal trade countermeasures on U.S. manufacturing performance and economic resilience using a 13-dimensional monthly panel dataset. We construct composite manufacturing and macroeconomic indices via the Entropy Weight Method (EWM), examine policy–economy linkages using Grey Relational Analysis (GRA) and Pearson correlations, and model dynamic impacts through a heterogeneous machine learning framework combining XGBoost and Support Vector Regression (SVR). Empirical results show that retaliatory trade measures significantly disrupt intermediate input supplies and raise domestic production costs, offsetting short-term gains from tariff protection. High-precision forecasts further indicate that international retaliation largely neutralizes the intended reindustrialization effects. The findings suggest that sustainable manufacturing revitalization requires structural investment and innovation-driven policies rather than reliance on tariff barriers alone.
Copyright (c) 2026 XinYi Chen; TianTian Liu https://creativecommons.org/licenses/by/4.0
A HOUSING PRICE PREDICTION MODEL BASED ON BACKPROPAGATION NEURAL NETWORK
The real estate industry serves as a crucial pillar of the national economy, playing an indispensable role in both national and local economic development. Analyzing and forecasting housing price trends can provide more reliable decision-making references for homebuyers, real estate agents, and market analysts. This study selects 150,000 data samples and addresses the complex non-linear characteristics of housing prices influenced by multiple factors by proposing a housing price prediction model based on a Backpropagation (BP) neural network. The model effectively simulates and predicts housing prices in the test set, achieving successful non-linear fitting,such as R is 0.8376, MAPE is 466873.78%%. This research not only offers a more reliable decision-making tool for homebuyers, real estate intermediaries, and market analysts but also provides a practical modeling approach for non-linear housing price prediction problems, thereby contributing positively to the rational development of the real estate market.
Copyright (c) 2025 ShiYu Jiao; Dong Wang https://creativecommons.org/licenses/by/4.0
DEVELOPMENT COUNTERMEASURES FOR THE CONSTRUCTION OF ZHENGZHOU NATIONAL LOGISTICS HUB
Under the new development paradigm, addressing the critical issue of the "Construction of Zhengzhou National Logistics Hub," this paper deeply analyzes the current status of logistics development in Zhengzhou, discusses the strategic significance of constructing the Zhengzhou National Logistics Hub, clarifies the strategic direction, and proposes implementation strategies for its construction.The construction of the Zhengzhou National Logistics Hub is an important part of the logistics network system construction. It is conducive to promoting logistics network construction, reducing total social logistics costs, improving logistics service levels and quality, optimizing the economic spatial layout and economic system construction of Zhengzhou and Henan Province, and promoting the rapid and healthy development of Zhengzhou's logistics industry and national economy.
Copyright (c) 2025 YuQing Fan; BoYe Zhang; YuXin Luo https://creativecommons.org/licenses/by/4.0
THE DETECTION OF HOUSEHOLD ECONOMIC ANOMALIES BASED ON THE CFPS2022 DATABASE: AN EXAMPLE OF INCOME-CONSUMPTION DEVIATION PATTERN ANALYSIS
In recent years, China's household debt has witnessed explosive growth, increasing financial vulnerability and posing a potential threat to economic stability. Based on the China Family Tracking Survey 2022 database, this study innovatively synthesizes three algorithms, namely the statistically based 3-criteria, Isolation Forest, and Local Outlier Factor (LOF), to detect anomalies with higher accuracy, and further applies a variety of data analysis techniques, such as cluster analysis, association rule mining, and Random Forest algorithm, to conduct a systematic and in-depth study of household economic anomalies. We also utilize various data analysis techniques such as cluster analysis, association rule mining, and random forest algorithm to conduct a systematic and in-depth study of household economic anomalies. Consequently, the study proposes policy recommendations such as strengthening financial education, providing precise support, and establishing a risk warning mechanism. These measures are expected to assist high-risk households, foster healthy economic development, and contribute to national economic stability.
Copyright (c) 2025 Jia Yu; JiaCong Jiang; BoRui Zhao https://creativecommons.org/licenses/by/4.0
IMPACT OF DIGITAL ECONOMY DEVELOPMENT ON POLLUTION REDUCTION AND CARBON REDUCTION
Exploring the impact and spatiotemporal characteristics of the digital economy on pollution and carbon reduction is of great significance in the context of the "dual carbon" goal and The digital economy is advancing at a fast pace. Currently, China is facing the dual task of reducing pollution and carbon emissions, and the "technological dividend" of the digital economy provides a new path for achieving environmental governance goals. However, there are still shortcomings in the systematic analysis of the spatial spillover effects, regional adaptation differences, and dynamic mechanisms of pollution and carbon reduction in the digital economy, which urgently require in-depth research from a multidimensional perspective. This study comprehensively utilized entropy weight method, benchmark regression model, mediation effect model, spatial-Durbin-model (SDM), spatiotemporal-geographically-weighted-regression (GTWR), and graph attention network (GAT) to construct a research framework covering impact effect assessment, mechanism identification, and spatiotemporal dynamic analysis. Firstly, This study utilized the entropy weight method for quantifying the development degree of the digital economy, and conducted an analysis on the spatiotemporal features of both pollutant emission intensity and carbon intensity of emissions, the fundamental correlation between the digital economy and environmental performance is revealed; Secondly, through the application of a fixed effects model, the digital economy exerts a direct influence on achieving the twin goals of reducing pollution and carbon emissions, and identifying indirect paths such as industrial structure upgrading, technological progress, and green finance through a mediation effect model; Finally, using spatial econometric models and GAT models, analyze the spatial dependence, regional heterogeneity, and the digital economy’s emission reduction effects: Spatiotemporal dynamic evolution characteristics.
Pollution reduction and carbon reduction
Copyright (c) 2025 WeiJie Lin https://creativecommons.org/licenses/by/4.0
THE IMPACT OF TWO-WAY FDI INTERACTIVE DEVELOPMENT ON CHINA’S DIGITAL ECONOMY
Distinct from the existing literature that predominantly focuses on the economic impact of either FDI or OFDI in isolation, this study examines the synergistic effects and coordinated development of two-way FDI on the digital economy across Chinese provincial regions. The findings reveal that the coordination of two-way FDI exerts a positive influence on the digital economy. This impact mechanism exhibits a distinct threshold effect, moderated by two key threshold variables: the level of local trade openness and the characteristics of the industrial structure. Notably, the facilitative effect of two-way FDI coordination is more pronounced within the Yangtze River Economic Belt, whereas in regions outside the Belt, it still contributes to the growth of the digital economy but with statistically weaker significance. This research not only offers policy implications for China's foreign trade and digital economic development but also elucidates the underlying mechanisms and threshold conditions involved. It provides valuable insights for regional economic and digital advancement in China, while also serving as an important supplement to the literature on two-way FDI.
Outward Foreign Direct Investment (OFDI)
Copyright (c) 2025 JingYi Zhao https://creativecommons.org/licenses/by/4.0
GREEN FINANCE POLICY AND TECHNOLOGICAL INNOVATION: EVIDENCE FROM 35 CHINESE AUTOMOTIVE FIRMS (2013–2023)
This study examines how China’s green finance development relates to corporate technological innovation in the automotive industry. Using firm–year observations from 2013–2023 for leading Chinese automotive firms, technological innovation is measured through patent applications and patent grants, expressed as logarithmic outcomes. Green finance is proxied by a national annual index, reflecting the macro policy-finance environment faced by firms each year. To address missing values in firm-level covariates, the analysis employs multiple imputation (m = 5) and reports pooled estimates. Across baseline models with standard controls, green finance is associated with lower patent applications, while the relationship with patent grants is weaker and generally statistically insignificant. Policy-timing analysis around the 2016 green finance policy shift provides limited evidence of differential effects, and additional robustness checks including alternative cutoffs (lead/placebo timing) and pre/post subsample regressions support the overall pattern. Lag models indicate that the negative association is more pronounced for patent applications with one- to two-year delays, whereas patent grants remain comparatively insensitive. Collectively, the findings suggest that macro green finance expansion does not translate mechanically into short-run increases in firm innovation outputs, and that firm fundamentals such as R&D, subsidies, and profitability remain central correlates of innovation performance. The results provide practical implications for policy design and corporate strategy in green transition contexts.
Copyright (c) 2026 Jean Jacques Yves Leslie Bottine Junior https://creativecommons.org/licenses/by/4.0
TECHNOLOGY SELECTION OF ENTERPRISE JAVA WEB FRAMEWORKS BASED ON EVOLUTIONARY PATH ANALYSIS
In response to the rapid iteration of enterprise-level Java Web frameworks and the lack of systematic theoretical guidance for selection decisions, this study proposes an innovative selection methodology based on the analysis of technical evolution paths. The main innovations are as follows: First, it reconstructs the three-dimensional "scenario-driver-paradigm" analysis model for framework evolution, systematically revealing the internal logic and technological inflection points in the transition from monolithic to cloud-native architectures, going beyond traditional version-based reviews. Second, it proposes a quantitative selection framework based on the dual-track "steady-state-agile-state" adaptation, which for the first time comprehensively models core dimensions such as development efficiency, runtime performance (startup time, memory usage), and cloud-native compatibility, clarifying the Pareto boundaries of mainstream frameworks such as Spring Boot and Quarkus under different constraints. Third, it constructs a hybrid architecture decision-making model based on technical debt and transformation cost assessment, providing enterprises with path planning and risk assessment tools for the smooth evolution from legacy systems to cloud-native architectures. This study validates the effectiveness of the method through empirical case comparisons, offering systematic theoretical foundations and practical guidance for enterprises to make forward-looking, low-risk architectural decisions in complex technological ecosystems.
Copyright (c) 2026 ZhengLin Wang https://creativecommons.org/licenses/by/4.0
COST OPTIMIZATION PATHWAYS DRIVEN BY DIGITAL FINANCE: EMPIRICAL EVIDENCE FROM DONGGUAN’ S MANUFACTURING SECTOR
As an essential component of the digital economy, digital finance is gradually becoming a key driver of cost reduction, efficiency enhancement, and high-quality development for enterprises. This paper analyzes 4,054 panel data points from 588 listed manufacturing firms in Dongguan spanning 2015 to 2024. Using financial and innovation R&D data from the CSMAR database, and employing SPSS for multiple linear regression analysis, the study empirically investigates the mechanisms and pathways through which digital finance influences enterprise cost optimization. The results reveal: (1) digital finance and soft technology investments significantly increase total asset turnover, validating their role in enhancing resource allocation efficiency; (2) R&D investment exhibits dual effects—while the R&D expenditure ratio positively affects firm efficiency, excessive allocations to R&D personnel and direct inputs impose short-term cost burdens; (3) new productive forces exhibit a phased negative impact, suggesting that digital infrastructure has not yet been fully converted into cost advantages; (4) financial structure and profitability form the core support for cost optimization. The study provides empirical evidence for digital transformation in the manufacturing industry and offers policy recommendations for both enterprises and government bodies.
Copyright (c) 2025 Jing Wang; Ping Peng https://creativecommons.org/licenses/by/4.0
PARADIGM SHIFT IN CHINESE ENTREPRENEURSHIP UNDER THE GUIDANCE OF THE 15TH FIVE-YEAR PLAN: FROM MARKET-DRIVEN TO MISSION-DRIVEN
With the introduction of the 15th Five-Year Plan (FYP), China has set forth new expectations for entrepreneurship, signaling a shift beyond a purely market-driven orientation towards a more profound, mission-driven approach. Historically, market-driven entrepreneurship has played an indispensable role in promoting economic development and enhancing innovative capacity. However, in the new era, the limitations of this model have become increasingly apparent, necessitating a paradigm shift from market-driven to mission-driven entrepreneurship.The 15th FYP emphasizes that entrepreneurs should not only pursue economic returns but also undertake multiple missions, including advancing technological innovation, achieving green development, and safeguarding national security. This transformation represents a deepening of the understanding of the entrepreneur's role and an enrichment of the connotation of entrepreneursh ip. Therefore, this study aims to explore the new connotation, characteristics, and formation mechanisms of mission-driven entrepreneurship, analyzing its practical pathways within the context of the 15th FYP. The research objective is to construct an analytical framework to explain the dimensions and driving factors of mission-driven entrepreneurship. Through case studies, it will reveal how this spirit is embodied in actual business operations. The theoretical contribution of this research lies in enriching existing theories of entrepreneurship, particularly their application in the Chinese context, and providing theoretical support for understanding new trends in entrepreneurial spirit.Statistics show that entrepreneurship is crucial for enhancing a nation's innovative capacity. For instance, research indicates that mission-driven entrepreneurs are more inclined to invest in long-term R&D projects, which significantly drives technological innovation and industrial upgrading. Furthermore, amidst intensified global economic integration and competition, the transformation of entrepreneurship is of great significance for enhancing national competitiveness.Regarding core propositions, this study will explore the following key issues: first, the new requirements for entrepreneurship and their substance as proposed by the 15th FYP; second, the necessity of the paradigm shift from market-driven to mission-driven; and third, how to construct an analytical framework for mission-driven entrepreneurship and the theoretical and practical implications of this framework. Through an in-depth analysis of these issues, this study will provide a systematic understanding of this entrepreneurial transformation and offer policy recommendations.
Copyright (c) 2025 LiXuan Wang; TingJia Fu; BaoYing Yan; ZiLong Li https://creativecommons.org/licenses/by/4.0
CHALLENGES AND FUTURE TRENDS OF MACHINE LEARNING IN DIGITAL FINANCE: AN ANALYSIS OF INTERPRETABILITY, REGULATION, AND DATA GOVERNANCE
Machine learning (ML) technologies are transforming digital finance through applications in credit assessment, fraud detection, and algorithmic trading. However, their deployment faces three critical challenges: model interpretability, robust data governance, and complex regulatory compliance. This paper analyzes these challenges through a systematic examination of recent literature and regulatory developments. We find that the "black-box" nature of complex models conflicts with transparency requirements mandated by financial regulations such as the EU AI Act and GDPR. Data quality issues, including class imbalance and inconsistency, coupled with privacy concerns, further constrain model reliability. Privacy-preserving approaches, particularly federated learning, offer promising solutions but require wider adoption. We identify that current model governance frameworks lack standardization across institutions and jurisdictions. Our analysis suggests that addressing these challenges requires coordinated efforts across three dimensions: advancing explainable AI (XAI) techniques, establishing unified model governance standards, and implementing privacy-preserving technologies. This study contributes to the understanding of socio-technical barriers in financial ML adoption and provides guidance for practitioners and policymakers.
Copyright (c) 2025 RongHua Li; ShuRui Xiao https://creativecommons.org/licenses/by/4.0
FROM DUAL-CARBON MANAGEMENT TO GREEN INNOVATION: THE SYNERGISTIC PATHWAYS OF CARBON ACCOUNTING AND CARBON FOOTPRINT IN TECHNOLOGY ENTERPRISES
Under the national dual-carbon strategy, technology enterprises face increasing pressure to reduce carbon emissions resulting from intensive energy use, complex supply chains, and long product life cycles. Identifying effective pathways for achieving green innovation under carbon constraints has become an essential issue for sustainable corporate development. This study establishes a “dual-carbon management” theoretical framework that integrates organizational-level carbon accounting with product-level carbon footprint assessment. Drawing on existing literature, the study examines the roles of carbon accounting in carbon governance, strategic planning, and internal incentive mechanisms, as well as the contributions of carbon footprint analysis to life-cycle assessment, material optimization, and product Eco-design. It further explores the synergistic mechanisms between the two, including data complementary, governance alignment, strategic coordination, and value-chain integration. Based on these insights, the study proposes key principles for constructing green innovation paths, emphasizing data integrity, systemic thinking, innovation-oriented governance, life-cycle perspective, value-chain collaboration, and dynamic adjustment. A representative case of a technology enterprise is analyzed to illustrate how dual-carbon management supports the development of low-carbon technologies, process improvements, and green product innovation. The results show that carbon accounting provides a foundation for organizational carbon governance, while carbon footprint offers detailed life-cycle insights. The integration of these two mechanisms forms a multi-level system that enhances strategic decision-making, operational transformation, and supply-chain-wide collaboration. This study contributes theoretical insights and practical guidance for technology enterprises seeking green innovation pathways under carbon constraints.
Copyright (c) 2025 Na Lei; XinZhi Wang https://creativecommons.org/licenses/by/4.0
BILATERAL DIPLOMATIC RELATIONS AND CROSS-BORDER M&AS: EVIDENCE FROM CHINA
Cross-border mergers and acquisitions (M&A) are one of the most important ways for countries to participate in the global economy. China's bilateral diplomatic relations with other countries are flawed and the data are mainly from the Chinese media, which may lead to omissions in diplomatic events, lack of objectivity and insufficient information, which may lead to miscalculations. Therefore, it is necessary to select a measure that reflects the high level of recognition between the two countries in international affairs or bilateral affairs. Using unbalanced panel data (including cross-border M&A data and United Nations General Assembly (UNGA) voting data) for 181 countries (or regions) from 1992-2017, the paper empirically examines the impact of bilateral diplomatic relations on China's cross-border M&A completion rate and its mechanism of action, by building an estimation model, mediation effect models, and conducting a robustness test. The mediating effect in the mechanism test finds that bilateral diplomatic relations affect the completion rate of cross-border M&As between Chinese and foreign firms through both formal (i.e., the signing of BITs and the establishment of bilateral partnerships) and informal mechanisms (i.e., exchanges among senior bilateral leaders and the establishment of Confucius Institutes). For formal institutions, bilateral diplomatic relations can increase the completion rate of cross-border M&As from and to China by facilitating the signing of BITs and the establishment of bilateral partnerships. For informal institutions, bilateral diplomatic relations increase the completion rate of cross-border M&As from and to China by facilitating exchanges between high-level leaders of the two countries and the establishment of Confucius Institutes. The results of the study show that bilateral diplomatic relations can help improve formal and informal systems to facilitate successful cross-border M&As, and the Chinese Government can draw on the successful experience of political diplomacy to create more opportunities for bilateral political, economic and cultural exchanges. In addition, both China and other countries can actively explore bilateral and multilateral cooperation mechanisms and promote the establishment or formulation of new cooperation rules and agreements.
Copyright (c) 2025 YueChun Xu; ChenXi Liu; YuYing Wei; Min Liu https://creativecommons.org/licenses/by/4.0
ANALYSIS OF THE FLOWER APPRECIATION ROUTE AND ECONOMIC PROMOTION STRATEGY FOR THE "RAIN SCENE" DURING THE QINGMING FESTIVAL
Qingming Festival is an important traditional festival in China that combines cultural and economic value. The unpredictable weather in spring, especially the heavy rain, affects travel and flower viewing experiences. This article explores how to optimize Qingming flower viewing tourism under such weather conditions to improve economic benefits. This article proposes a quantitative analysis method based on the "rain shower" weather, which combines precipitation probability and precipitation amount to construct "rain shower" label data, and uses GRU recurrent neural network for weather classification prediction. In order to improve the accuracy of predictions, the SMOTE oversampling method was introduced, and the final model achieved an accuracy of 87.45% on the test set, providing a scientific basis for predicting the probability of rainfall during the Qingming Festival in 2026. Then, this article used a combination of deep modeling (GDD) and random forest regression to establish a flowering prediction model. The model simulates the flowering period based on temperature changes and integrates monitoring data from various regions to accurately predict the flowering periods of rapeseed flowers, cherry blossoms, and peonies. In the Qingming Festival of 2026, rapeseed flowers are the best viewing period in Wuyuan and Wuhan, peonies are in Luoyang and Xi'an, and cherry blossoms are approaching the end period, providing a basis for tourism planning. Finally, this article establishes a multi-objective path planning model aimed at optimizing the flower viewing experience. The model considers flowering period, weather, scenic spot rating, and transportation to construct a rating function. Under the three-day travel restriction, the best route was selected as "Luoyang Xi'an Turpan", which is suitable for the flowering period, comfortable in weather, and diverse in tourism. Adjustments can be made according to the actual situation.
Copyright (c) 2025 JiaLe Zhang; TiLiang Zhang https://creativecommons.org/licenses/by/4.0
INNOVATIVE REFORM PATHS OF BASIC ECONOMICS COURSE FROM THE PERSPECTIVE OF CURRICULUM IDEOLOGICAL AND POLITICAL EDUCATION
As a foundational core course for economics and management majors, Basic Economics covers core theories including supply and demand, market mechanisms, macroeconomic regulation, and economic growth. It undertakes the dual task of imparting economic theoretical knowledge and shaping students’ correct economic values. From the perspective of Curriculum Ideological and Political Education (CIPE), traditional Basic Economics teaching faces three key issues: the separation of theoretical teaching from ideological guidance, insufficient connection with China’s economic practice, and inadequate response to digital economic challenges. This paper proposes a “three-dimensional integration + four-system promotion” reform framework. Based on the course’s “micro-macro-development” theoretical hierarchy, it systematically integrates ideological elements (such as national conditions awareness, social responsibility, and fairness-efficiency concepts), constructs an “AI+BOPPPS” teaching model adapted to economic scenario simulation, establishes a multi-dimensional evaluation system covering theoretical mastery and value shaping, and strengthens the “school-enterprise-expert-technology” collaborative guarantee mechanism. The aim is to build an educational system centered on “theory as the foundation, practice as the link, and values as the core,” cultivating compound talents who understand economic principles, recognize national conditions, and possess a sense of social responsibility.
Curriculum ideological and political education
Copyright (c) 2025 YuHan Xiao https://creativecommons.org/licenses/by/4.0
DESIGN AND APPLICATION OF FINANCIAL FRAUD IDENTIFICATION MODEL UNDER TOPSIS BASED ON ENTROPY WEIGHT METHOD
In recent years, China’s capital market has witnessed increasingly sophisticated and concealed financial fraud schemes among listed companies, posing substantial threats to market integrity and stakeholder protection. Addressing this challenge, this study develops a comprehensive multi-dimensional detection framework grounded in accounting theory, integrating financial indicators, industrial characteristics, regional factors, and corporate governance elements. The research employs an innovative entropy-weighted TOPSIS methodology that effectively balances quantitative precision with theoretical foundations. Through rigorous empirical analysis of 176 documented fraud cases spanning 2000-2022, we demonstrate that the Operational Scale indicator induces “information overload” that compromises model discrimination, while optimized corporate governance factors—particularly Executive Education Level and Board Meeting Frequency—demonstrate enhanced predictive power with a combined weight of 0.658. The proposed model achieves 65.53% classification accuracy, showing particular efficacy in detecting characteristic fraud patterns involving revenue inflation and fictitious transactions. Furthermore, Our findings validate an integrated human-machine framework for financial regulation, balancing methodological rigor with practical adaptability in dynamic market environments.
Copyright (c) 2025 Qiao Xia https://creativecommons.org/licenses/by/4.0
THE CONSTRUCTION PATH OF SMART CAMPUS DRIVEN BY ARTIFICIAL INTELLIGENCE AND BIG DATA
With the rapid development of artificial intelligence—especially Large Language Model (LLM)—and big data technologies, smart campus construction in universities faces new opportunities and challenges. This paper proposes a system architecture based on "data governance + model services + application integration" and conducts a benchmark analysis of its feasibility and effectiveness using representative university cases. The study also outlines a four-stage construction path: top-level design, driven by Data Middle Platforms, empowered by intelligent applications, and safeguarded by institutional mechanisms.In addition, it establishes a supporting framework composed of organizational coordination, institutional regulation, and data security compliance.The findings of this study provide both theoretical guidance and practical reference for system planning, technology selection, and scenario deployment in the construction of smart campuses in higher education.
Copyright (c) 2025 JiaoJiao Sun; Fei Wang; QuanDe Dong; Cui Wang; Yaliu Yang https://creativecommons.org/licenses/by/4.0
A HYBRID GA-PSO AND COUPLING COORDINATION FRAMEWORK FOR MULTI-OBJECTIVE OPTIMIZATION IN SUSTAINABLE TOURISM SYSTEMS
Rapid tourism growth has boosted local economies but also posed environmental and social challenges, highlighting the need for sustainable management. This study presents a hybrid optimization framework that integrates Genetic Algorithm with Particle Swarm Optimization (GA-PSO) with a Coupling Coordination Degree (CCD) mechanism to balance social, economic, and environmental subsystems. The model targets maximization of employment, tourism revenue, and glacier stability under statistical and coordination constraints. Regression analysis and the entropy weight–TOPSIS method were applied to quantify subsystem interactions. Results show that GA-PSO outperforms single GA and PSO, achieving an optimal coordination degree of 0.6254. Employment, tourism revenue (USD 147 million), and environmental sustainability all showed notable improvements. This research extends the use of hybrid evolutionary algorithms in sustainable tourism and provides quantitative support for policymakers seeking to balance economic growth, social well-being, and ecological resilience.
Copyright (c) 2025 DingYi Ma; KeYin Zhu; Lei Zang https://creativecommons.org/licenses/by/4.0
EMPIRICAL ANALYSIS ON THE EFFECT AND MECHANISM OF GREEN FINANCE RELATING TO REDUCTION AND CARBON EMISSION REDUCTION RELYING ON THE EMPIRICAL EVIDENCE FROM CHINESE LISTED COMPANIES
In the process of implementing the "dual-carbon" strategy, research on whether green finance can truly translate into emission reduction effects and its conversion mechanism is crucial for testing green finance policies. The present study takes A-share listed non-financial enterprises in China from 2011 to 2022 as samples, matches enterprise emission data of sulfur dioxide (SO 2 ), nitrogen oxides (NO x ), and soot, and constructs a "firm-year" green finance index built on seven types of tools including green credit and green bonds to systematically examine the impact of green finance on corporate pollution reduction and carbon emission reduction. Baseline regression results show that for each 1-unit rise in the green finance index, the logarithm of total corporate pollutant emissions decreases by an average of 0.366, and this conclusion stays robust after instrumental variable and robustness examinations. Mechanism analysis reveals that green finance exerts its effects through a dual-pathway of alleviating financing constraints and promoting green technological innovation: on one hand, it significantly reduces the SA index, providing low-cost funds for corporate green investments; on the other hand, it significantly increases the quantity of applications submitted for green utility model patents, facilitating process upgrading and end-of-pipe treatment. Heterogeneity tests reveal that the emission reduction effect of green finance stands out more in non-state-owned enterprises (non-SOEs), eastern regions, and the manufacturing industry, presenting a pattern of "non-SOEs outperforming SOEs, eastern regions outperforming central and western regions, and manufacturing outperforming non-manufacturing". This study provides micro-level evidence for constructing a precise and efficient green finance support system, and also offers actionable policy references for accomplishing the "dual-carbon" goals.
Pollution control and carbon emission reduction
Copyright (c) 2025 ZiMo Chen https://creativecommons.org/licenses/by/4.0
MACHINE LEARNING IN DIGITAL FINANCE: APPLICATIONS, METHODS AND CHALLENGES
Machine learning (ML) technology is profoundly reshaping digital financial services and risk management systems. This paper systematically reviews the current applications of ML in four core scenarios: credit scoring, fraud detection, algorithmic trading, and customer segmentation. Literature analysis reveals that deep learning and ensemble methods demonstrate superior performance compared to traditional statistical approaches in financial risk prediction tasks, with supervised learning techniques predominating in fraud detection systems and algorithmic trading becoming increasingly prevalent in capital markets. However, the trade-off between model interpretability and predictive performance remains a critical challenge in regulated financial environments. Furthermore, data quality limitations and regulatory compliance requirements impose substantial constraints on model deployment. This review identifies key research gaps and suggests that future developments must prioritize explainable AI techniques, privacy-preserving methods, and regulatory-compliant frameworks to enable the sustainable adoption of ML in the financial sector.
Copyright (c) 2025 Yun Li; RongHua Li https://creativecommons.org/licenses/by/4.0
SUSTAINABILITY TOURISM OPTIMIZATION BASED ON GENETIC ALGORITHM
This paper aims to develop an optimization model to balance tourist numbers, tourism revenue, and environmental impact, ensuring sustainable tourism development. Using a genetic algorithm, we optimize the model to maximize revenue and tourist flow while minimizing environmental impact and infrastructure pressure. Additionally, we propose a reinvestment plan where optimized tax revenue is allocated to environmental protection and infrastructure, forming a feedback loop that enhances sustainability. We successfully balances tourism revenue, tourist flow, and environmental impact in Juneau using an optimization model. The extra expenditure plan also ensures funding for long-term environmental and infrastructure sustainability, making this model a valuable reference for both Juneau and other destinations facing similar challenges.
Copyright (c) 2025 YuXin Wang; Yang Rong; ShunYu Li https://creativecommons.org/licenses/by/4.0
ANALYSIS OF INTEREST RELATIONSHIPS IN THE INDUSTRY-EDUCATION INTEGRATION COMMUNITY FOR FINANCIAL AND COMMERCIAL PROFESSIONAL GROUPS IN HIGHER VOCATIONAL COLLEGES
The core of the industry-education integration community for financial and commercial professional groups in higher vocational colleges lies in deepening industry-education integration and school-enterprise cooperation to build a technical and skilled talent training system adapted to the modern industrial system. This involves systematically integrating government policy guidance, industry standard specifications, enterprise practical resources, and college educational resources to form a "government-industry-enterprise-college" four-party collaborative education pattern. The government promotes community construction through policy tools such as financial subsidies and tax incentives; industry associations leverage their market information advantages to build supply-demand docking platforms, formulate industry talent capability maps, issue job skill white papers, and establish coupling mechanisms for standard docking. Enterprises provide real job scenarios to promote the transformation of teaching practice, showing differentiated value orientations in the participation process. This collaborative network of multi-dimensional interests not only reflects the value consensus of each subject on the goal of talent training but also implies deep-seated game relationships in resource investment, income distribution, responsibility boundaries, etc.
Financial and commercial professional groups
Industry-education integration community
Government-industry-enterprise-college collaborative education
Copyright (c) 2025 DeJun Zou https://creativecommons.org/licenses/by/4.0