International Journal of Global Economics and Management
International Journal of Global Economics and Management (IJGEM), a peer-reviewed open access journal published in English-language, provides an international platform for the publication and dissemination of original work that contributes to the understanding of the main and related disciplines of global economics and management either empirical or theoretical. The journal covers the whole spectrum of economics, business, management, which includes, engineering and technology management, finance and accounting, economic behavior and organization, investment and management, sustainability management, theoretical and applied business, economics, finance and management sciences, business and economic development, risk management, public policy and administration, and more.
China-ASEAN Regional Currency Swap and Financial Stability
Against the backdrop of deepening economic globalization and regional integration, the Asian financial crisis and global financial turbulence have highlighted the need to build a regional financial safety net. As the core economic blocs of East Asia, China and ASEAN's currency swap cooperation has become a key institutional arrangement for responding to external shocks and maintaining regional financial stability. This article focuses on the current state of the China-ASEAN currency swap mechanism, its role in regional financial stability, and the existing challenges, exploring how it can enhance regional financial resilience through institutional design and practical innovation. This study aims to provide theoretical reference for improving China-ASEAN financial cooperation and practical insights for emerging market countries to build an independent and controllable regional financial system. It also has theoretical value for promoting the diversification of the global financial governance system and enhancing the financial voice of developing countries.
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A Literature Review of Moral Licensing Theory
This paper reviews moral licensing theory by examining its origin, development, core concepts, theoretical models, moderating variables, applications, and future trends. The theory originated in early research on moral psychology. Early studies focused on social discrimination, and later research extended the theory to multiple domains. Its core idea is that prior moral behavior may create a psychological tendency to relax moral constraints in subsequent behavior. This review also discusses the theory's explanatory models, forms of licensing, extended research fields, and future directions, with the aim of deepening understanding of the decision-making mechanisms underlying human moral behavior.
Empowerment or Disempowerment: The Employment-Stabilizing Effect of Digital Transformation in Private Enterprises
Employment is the foundation of people’s livelihood and a crucial driver of economic growth. Against the backdrop of intensified economic downturn and deep integration of digital technologies, private enterprises, as the "main force" supporting over 80% of urban employment, face a pivotal challenge: whether their digital transformation can solidify the "employment reservoir." Based on data from China’s A-share listed companies between 2013 and 2023, this study empirically examines the impact of digital transformation on employment scale in private enterprises and its underlying mechanisms. The findings reveal that digital transformation expands employment scale in private enterprises, with this effect being more pronounced in firms with higher education levels, mid-to-high-skilled labor, and those located in larger cities. Mechanism analysis demonstrates that digital transformation drives employment expansion by broadening business scale and enhancing productivity. Specifically, digital technologies extend market boundaries and business complexity, leading to increased demand for various types of labor. Simultaneously, efficiency gains from technological applications also structurally facilitate the reallocation of labor resources, thereby driving overall employment growth. The research conclusions provide empirical evidence for comprehensively assessing the actual impact of digital transformation on employment markets in China’s private enterprises. They also offer policy insights into balancing employment stability and labor structure optimization while advancing the development of the digital economy.
How Does Digital Transformation Shape New Competitive Advantages for Enterprises in the Export Market?
In this era of deep integration between globalisation and digitalisation, the digital wave is sweeping across the globe at an unprecedented pace. The synergistic deepening of digital industrialisation, industrial digitalisation, digital governance and data valorisation is profoundly reshaping the division of labour within global value chains. Against this backdrop, this study utilises observational data on Chinese listed companies on the Shanghai and Shenzhen A-share markets from 2012 to 2023 to construct a three-dimensional mediating transmission pathway framework, systematically investigating the impact effects and transmission pathways of digital transformation on corporate export competitiveness. Empirical findings indicate that digital transformation can significantly enhance firms’ export competitiveness, and that it does so through the interplay of three dimensions: resource allocation, production operations and governance levels; simultaneously, this promotional effect exhibits marked heterogeneity in terms of property rights, production factors and geographical location.
The Impact of the Belt and Road Initiative on ASEAN's Investment Environment
The Belt and Road Initiative (BRI), launched in 2013, has become a defining framework for China-ASEAN economic cooperation, reshaping regional investment patterns through large-scale infrastructure development, trade facilitation, and financial integration. This paper examines the multidimensional impact of BRI on ASEAN’s investment environment across four mechanisms: infrastructure connectivity, trade and investment facilitation, industrial park agglomeration, and financial support. Drawing on official trade and investment data from 2013 to 2025, the study employs a combination of descriptive statistical analysis and in-depth case studies of three landmark projects—the China-Laos Railway, the Jakarta-Bandung High-Speed Railway, and the Thai-Chinese Rayong Industrial Park—to identify the specific channels through which BRI influences host-country investment attractiveness. The findings reveal that BRI infrastructure projects generate substantial investment multiplier effects, reducing logistics costs by 30%-40% along key corridors and catalyzing follow-on industrial investment. Industrial parks established under the BRI framework create agglomeration economies that lower entry barriers for foreign investors, while complementary financial mechanisms, including the Asian Infrastructure Investment Bank (AIIB) and expanded RMB settlement networks, address long-standing financing gaps in the region. The comparative case analysis further demonstrates that BRI’s impact follows differentiated pathways depending on host countries’ development levels: transport connectivity as a catalyst for least-developed economies, technology demonstration effects for emerging industrial nations, and industrial park deepening for institutionally mature markets. The paper concludes with policy recommendations for enhancing the quality and sustainability of BRI-driven investment in ASEAN.
Global Cross-border Logistics Risk Research: A Systematic Review and Future Research Agenda
With the continuous deepening of global economic integration and the rapid growth of cross-border trade volume, international cross-border logistics systems have become increasingly complex and vulnerable to external uncertain shocks. Multiple risk factors including regional geopolitical fluctuations, international freight market instability, trade policy adjustments and global public health emergencies have significantly increased the operational risks of cross-border logistics activities. Clarifying the developmental evolution, research hotspots and existing deficiencies of global cross-border logistics risk research is crucial for optimizing international logistics layout and enhancing global supply chain stability. Based on the standard PRISMA systematic review framework, this study collects 218 high-quality academic papers focusing on global cross-border logistics risks from Web of Science and Scopus databases covering the period from 2015 to 2025. Through bibliometric statistical analysis and in-depth thematic content sorting, this paper systematically analyzes the annual publication trends, keyword hotspot distribution and core research dimensions in this field. Statistical results show that the overall publication volume of cross-border logistics risk research has increased by 187% in the past decade. The research focus has gradually shifted from traditional single economic cost risk assessment to multi-dimensional comprehensive research covering geopolitical shock, digital risk prevention and supply chain resilience enhancement. This paper systematically summarizes four core research categories of cross-border logistics risks, analyzes existing research limitations such as insufficient discussion on emerging market logistics risks and lack of dynamic early warning mechanism research, and puts forward targeted future research directions. The research outcomes can provide solid theoretical support for global logistics risk management and operational decision-making of multinational trading enterprises.
The Value and Realization Paths of Customer Participation in Mass Customization
This paper examines the value and realization paths of customer participation in mass customization. From the perspective of production and operations management, it analyzes how customer participation balances individualized product demand and scale requirements through modular design, digital tools, and flexible production systems. The study shows that customer participation should be understood as 'value co-creation under constraints.' Customers participate in part of the production process within a standardized modular framework predefined by the firm. This participation helps firms reduce the cost of demand mismatch, activate momentum for product iteration, and build differentiated competitive advantages. Its realization depends on the coordination of modular production, digital technology, and differentiated supply chains. It also faces challenges such as conflict between customers' willingness and ability to participate and insufficient efficiency in enterprise value conversion. The paper ultimately emphasizes the need to optimize production system design and supply chain coordination in order to improve the efficiency of value conversion.
Research on Cold Chain Logistics Distribution Path Optimization of Fresh E-commerce Considering Carbon Cost and Time Window
Driven by the booming development of digital retail, fresh food e-commerce has expanded rapidly in recent years, while its supporting cold chain logistics system still faces prominent operational problems, including unreasonable route planning, high comprehensive distribution costs, excessive product spoilage, and substantial carbon emissions. To address the multi-constraint and multi-objective optimization characteristics of fresh food last-mile distribution, this study constructs a comprehensive cold chain distribution path optimization model that integrates transportation cost, perishable loss cost, hybrid time window penalty cost, and carbon emission cost. On this basis, an improved adaptive genetic algorithm (IAGA) is proposed to solve the established model, which dynamically adjusts crossover and mutation probabilities during iterations and effectively overcomes the premature convergence and local optimal defects of the traditional genetic algorithm (TGA). Numerical simulation and comparative experiments based on real-world fresh e-commerce distribution scenarios are conducted. The results demonstrate that the proposed model and algorithm can significantly reduce total distribution costs, cut carbon emissions, and mitigate time penalty losses, thereby improving the overall operational efficiency and low-carbon sustainability of cold chain distribution systems. This research provides a reliable theoretical reference and practical operational strategy for refined and green path scheduling management of fresh cold chain logistics enterprises.
Research on Human Factor Process Improvement of Workshop Operations under Lean Production Mode
In the promotion of lean production, workshop sites are frequently plagued by poor operation processes, prominent operator fatigue, and mismatched human-machine coordination. Taking a machinery processing and assembly workshop as the research object, this paper adopts literature review and on-site tracking investigation. Supported with human factors engineering theories including biomechanics, cognitive load and operation motion analysis, hidden human factor defects in the existing operation processes are identified and diagnosed. On this basis, process optimization schemes are designed from the perspectives of motion simplification, workstation layout adjustment, operation task redistribution and auxiliary equipment deployment. After the implementation of optimization schemes, the risk of musculoskeletal injuries is controlled, invalid motions and operation fatigue are alleviated, and operation efficiency and operating comfort are both improved.
Research on Data-Driven Optimization of Return Merchandise Resale Strategies
Against the backdrop of rapid development in the digital economy, China's e-commerce industry has experienced explosive growth, accompanied by the persistent industry pain point of high return rates. Return rates in fast-moving consumer goods sectors such as apparel and cosmetics are generally high, with some live-streaming e-commerce platforms experiencing return rates exceeding 60%, directly causing industry average annual loss costs to exceed standards and severely constraining the sustainable development of the e-commerce industry. Some e-commerce platforms, in an effort to reduce merchants' return costs, have introduced algorithmic strategies such as "high-refund population shielding," yet these have sparked large-scale algorithmic discrimination controversies due to single metrics and insufficient transparency, both damaging consumers' legitimate rights and undermining market transaction fairness. This paper takes "bidirectional fairness" as its core orientation, focusing on the three-party interest balance of "user-merchant-platform," and constructs a data-driven return merchandise resale optimization system integrating multi-dimensional indicators of "merchandise damage degree-user preference-merchant responsibility ratio." The research employs literature research, empirical analysis, questionnaire surveys, and case analysis methods, based on return records and user behavior data from a major domestic e-commerce platform's apparel category from 2022-2024, to verify the practical effects of the optimization strategies. Results demonstrate that the proposed optimization strategies can effectively reduce algorithmic discrimination complaints in the apparel industry, decrease industry loss costs, and significantly improve the secondary transaction rate of returned merchandise. This research fills the research gap in algorithmic discrimination avoidance in the return merchandise resale field, enriches data-driven reverse logistics management theory, and provides important theoretical support and practical reference for e-commerce platform return management practices, relevant legal and regulatory improvements, and healthy industry development.
A Study on AI Assisting Enterprises' Overseas Layout and International Development in Emerging Economy Markets
With the rapid development of artificial intelligence (AI) in recent years, the form of cross-border business for companies around the world has changed significantly. Systematically explore how artificial intelligence (AI) technology can be used to help multinational enterprises (MNEs), especially those originating from or expanding in emerging economies, optimise their overseas layout and international development strategy in this study. Based on the resource-based view and the Uppsala model for internationalisation, this paper examines how artificial intelligence addresses the problems of foreignness liability and improves cross-border operating efficiency simultaneously. The proposed integration paths are: AI-driven prediction of market demand, algorithmic localisation of the supply chain, and cross-cultural natural language processing for consumer communication. This paper will also point out institutional and infrastructureal deficiencies that limit the application of AI in the development world. AI-Assisted Strategic Planning can therefore optimise the allocation of foreign direct investment and accelerate the speed of market entry. In short, the above research provides all-encompassing theoretical support for using AI as a leading strategic asset to help enterprises obtain a sustainable competitive advantage and expand dynamically in the era of digitalisation.
The Development and Exploration of TikTok's Commercialization Model
This paper investigates the commercialization model of TikTok, tracing its development history, revenue generation mechanisms, and market strategies. Through a module analysis based on the Business Model Canvas, it aims to elucidate its current status, core logic, and future trends, providing theoretical and practical references for the commercialization of short-video platforms. The study reveals that TikTok has established a closed-loop ecosystem of "content–traffic–transaction," with its revenue structure dominated by advertising, rapid growth in e-commerce operations, and live streaming serving as a crucial supplement. Nevertheless, the platform continues to face persistent challenges, including an underdeveloped product governance framework, rising costs of traffic acquisition, and increasing compliance management difficulties.
Cost-Benefit Effects and Breakthrough Strategies of AIGC Digital Transformation for Small and Medium Hardware Manufacturing Enterprises
Against the intelligent manufacturing and new quality productive forces context, generative AI (AIGC) has reconstructed manufacturing R&D, production, quality inspection and supply chain links. Most existing AIGC transformation studies concentrate on well-resourced large listed manufacturers, with scarce quantitative empirical evidence for traditional hardware small and medium enterprises (SMEs) regarding transformation gains and cost pressures. Taking 41 Wenzhou hardware SMEs from 2021 to 2025 as balanced panel samples, this research adopts a two-way fixed-effects DID model to quantify the causal effects of AIGC adoption. Benchmark regressions prove AIGC significantly boosts SME performance: cutting new product R&D cycles by 32.6%, lowering unit manufacturing costs by 18.3% and raising total factor productivity (TFP) by 14%. Such benefits are more pronounced for medium firms with 20-50 million RMB annual revenue and 8-15 years of operation; placebo, variable substitution and parallel trend tests validate result robustness. Cost measurement identifies three key burdens: one-off equipment expenses 11.37% of annual net profit, annual system operation fees (6.99%) and compound talent costs (8.97%). Major transformation obstacles cover industrial AI talent shortages, mismatched universal AIGC models, limited profit margins and inadequate cluster digital ecosystems. Drawing on cost-benefit results, this paper puts forward dual micro-macro solutions: lightweight SaaS AIGC subscriptions for firms, plus tiered subsidies, shared industrial AI platforms and school-enterprise talent training schemes for governments. This research fills the SME AIGC transformation research and supplies actionable empirical references for industrial clusters and local policymakers.
Small and Medium Manufacturing Enterprises
How Financial Technology is Redefining Traditional Banks' Competitiveness
The rapid development of fintech is profoundly transforming the competitive landscape of traditional banking. This study examines how fintech redefines the competitiveness of conventional banks, systematically analyzing its structural impacts across three dimensions: services, products, and channels, proposing pathways for enhancing competitiveness through digital transformation. Research findings reveal that: at the service level, fintech improves efficiency through intelligent customer service and automated processes, replacing traditional offline service models; at the product level, big data and artificial intelligence drive credit approval, risk management, and wealth management toward smarter and more precise operations; at the channel level, the rise of digital platforms and scenario-based financial services has reshaped customer engagement methods, undermining the traffic advantages of physical branches. Building on this, the study conducts an in-depth analysis using China Merchants Bank and CITIC Bank as case studies. China Merchants Bank's "AI First" strategy demonstrates how AI can be fully integrated into banking service systems, risk control frameworks, and operational processes, facilitating a transition from digitalization to intelligentization. CITIC Bank's "Xiaotianyuan" enterprise ecosystem platform exemplifies how banks leverage a "free standard edition & ecosystem integration" model to deeply embed financial services throughout corporate operations, including business management, finance, capital allocation, and taxation, transforming themselves from financial product providers into digital ecosystem infrastructure providers. The research findings indicate that fintech is not a disruptor of traditional banks but rather a catalyst for reshaping their competitiveness; traditional banks should achieve a strategic transformation from "passive response" to "active leadership" through systematic innovation in their service, product, and channel ecosystems. This paper provides theoretical insights and practical pathways for the digital transformation of traditional banks.
Southward Shift of Industrial Chains and the Reshaping of China-ASEAN Manufacturing Division of Labor: An Analysis from the Regional Value Chain Perspective
The relocation of labor-intensive manufacturing from China to ASEAN since 2012 reconfigures the regional division of labor rather than constituting zero-sum industrial transfer. Treating production tasks as the unit of analysis, we integrate global value chain theory, regional value chain analysis, the flying geese model, new economic geography, and the trade-in-tasks literature to identify three reorganization margins—extensive, intensive, and regional integration—shaped by RCEP’s institutional architecture. Drawing on the GVC upgrading literature, we differentiate capability-deepening integration, which builds host-country productive capacities, from capability-thinning assembly operations that entrench dependence on external suppliers. The framework yields implications for China’s industrial upgrading and ASEAN’s industrialization prospects.
The Relationship Between LPR, Bank Interest Rate and Real Estate Prices in Shanghai
The real estate market is a crucial pillar of the national economy. As an international financial center, Shanghai boasts a highly active real estate market with distinct price fluctuations, becoming a key area for regulatory policies. Interest rates, as core macroeconomic regulation tools, including traditional bank lending rates and the Loan Prime Rate (LPR) since its 2019 reform, exert significant impacts on real estate prices through the transmission mechanisms of the IS-LM model and credit rationing theory. The 2019 LPR reform has formed a "dual interest rate system", but the combined impacts, effect differences and synergistic effects of the two rates on Shanghai’s real estate prices have not been systematically explored based on classic economic theories. Based on Shanghai’s time-series data from 2001 to 2024, this work constructs an econometric model combined with relevant theories to empirically analyze the impact mechanism of the dual interest rates on Shanghai’s real estate prices. The results indicate that both rates have significant negative impacts on Shanghai’s housing prices and exhibit synergistic regulatory effects; the LPR has a stronger impact on housing prices with an elasticity coefficient of -3.5, higher than that of traditional bank lending rates (-2.8); the transmission lag of LPR is 2–3 quarters, while that of traditional bank lending rates is 3–4 quarters; purchase restriction policies and land supply can significantly weaken the impact of interest rates on real estate prices. This study fills the research gap in the impact of the "dual interest rate system" on real estate prices in megacities after the LPR reform, provides quantitative references for optimizing the interest rate regulation policy of Shanghai’s real estate market, and offers a reference for real estate regulation in similar cities.
Exploration of the Supply Chain in the Fashion Industry Under the Sustainability Concept
The carbon emissions of the fashion industry account for 4–10% of the global total, exceeding the total emissions of international aviation and shipping industries. Greening the fashion supply chain has become an urgent priority. The annual greenhouse gas emissions of China's textile and clothing industry are about 220 million tons, accounting for 2.7% of the total industrial emissions in the country, placing significant emission reduction pressure across all supply chain stages. This paper analyzes path selection and implementation outcomes for fashion supply chain restructuring, drawing on sustainability frameworks, EU policy documents, China's 14th Five-Year Plan, and real-world practices from H&M, ANTA, Patagonia, and other enterprises. As of 2024, data shows that China's production of recycled fibers exceeded 10 million tons, accounting for over 20% of the total fiber processing volume; 90% of textile enterprises have set clear climate action targets, with an average reduction of over 14% in carbon emission intensity across the industry in the past two years. The EU's ESPR Regulation, which will come into effect on July 19, 2026, prohibits the destruction of unsold clothing, forcing the restructuring of the global supply chain system. Against persistent industry headwinds—including fragmented regulatory standards and disproportionate cost burdens on small and medium-sized enterprises (SMEs)—cross-stakeholder collaboration will serve as the core driver of the fashion sector’s transition to a circular economy.
Practical Applications of Digital-Twin-enabled Risk Management in Infrastructure Projects
Infrastructure risk management in China still mainly relies on manual inspections and empirical judgment, which is characterized by incomplete monitoring coverage and insufficient risk forecasting capability, and is no longer able to meet the requirements of refined safety control for modern engineering. The industry is beginning to use digital twins to transform traditional risk control models and optimize the effectiveness of engineering safety management. This paper takes transportation, water conservancy and municipal engineering as the main research domains, and analyzes typical projects including Beixi of Jiulong River, City-wide Underground Pipeline Network of Chongqing, and the Yangtze-to-Han River Water Diversion Project as research samples. This paper explores five core dimensions, including risk identification, assessment, risk response, emergency coordination, and full-lifecycle operation and maintenance. Based on multiple practical engineering projects, this paper summarizes the on-site application patterns of digital twins. According to the publicly available engineering data from the Ministry of Water Resources and the Ministry of Housing and Urban Rural Development, the short-term flood forecasting accuracy for the watershed stands at 90% after the application of this technology, and the timely repair rate of municipal pipeline networks exceeds 98%. This technology reduces missed hazard identification, shortens emergency response time and lowers losses resulting from safety incidents. Practical experience from field applications can provide valuable guidance for similar infrastructure projects.
An Analysis of the Pathways for Financial Institutions to Serve the Real Economy in the Context of the Digital Economy
The rapid development of the digital economy and the digital transformation of the financial industry constitute key pathways to enhance the quality and efficiency of the real economy. This article analyzes the existing problems in financial services for the real economy and studies compliant and feasible digital service paths. This paper draws on publicly released data from the China Academy of Information and Communications Technology, the People's Bank of China, and the State Administration of Financial Regulation, and analyzes the digital transformation practices of domestic commercial banks. Data shows that in 2024, China’s digital economy reached 59.2 trillion yuan, accounting for 43.8% of GDP, and the foundation for digital development is solid. The research proposes five core directions: product innovation, supply chain financial services optimization, inclusive service development, green financial services implementation, and intelligent risk management, indicating that digital services can address the financing challenges of real economy industries and channel credit capital into the real economy. The research results have reference value for financial institutions to optimize services and support the transformation of real economy industries, facilitate finance to fulfill its core mission and advance the integration of the two.
The Digital Shock Absorber: Internet Access and New Business Creation after COVID 19
The pandemic that began in 2020 disrupted the ordinary work of starting a business, and many observers suggested that wider internet access would help economies keep forming new firms when face-to-face activity became difficult. This study tests that idea with a country-year panel from the World Bank Indicators API, covering 164 economies between 2010 and 2022, with the main models estimated on 1,251 country-years from 131 economies. New business density is the outcome, while internet use and fixed broadband measure digital access alongside development controls. The pooled estimate is positive but not significant, and the association turns positive and significant only under country fixed effects, before fading to nothing once year fixed effects absorb the shocks that all economies faced together. The internet by post-2020 interaction is positive in most specifications yet never significant, and it is sensitive to how the outcome is measured, while broadband shows no clear relationship. The evidence offers only limited and fragile support for the idea that digital access acted as a shock absorber for new business creation, and the design is observational with little independent within-country variation in internet use.
Transmission Mechanism and Macroprudential Policy Framework of Climate Financial Risk: Theoretical Construction and Application Challenges
This article takes climate financial risk prevention and control as the core of research, uses literature review, case analysis and other methods to clarify the transmission mechanism and specific path of climate physical risk and transformation risk, explain the core essence and coordination mechanism of the macro-prudential policy framework of climate financial risk, and analyse the application challenges faced by the existing policy framework in practice in combination with the actual domestic supervision. Based on China's institutional environment, drawing on the experience of international supervision, I put forward targeted improvement suggestions: build an independent, legalised and normalised macro-prudential framework for climate transition risks, pilot the implementation of the internal rating method of climate risks of financial institutions, and build a macro-prudential linkage mechanism for cross-border transformation risks. The research can provide reference for optimising the macro-prudential supervision system of climate financial risks in China, preventing systemic climate financial risks, and helping the coordinated promotion of green transformation and financial stability.
Research on the Construction of Underground Logistics Network
E-commerce industry continues to expand to promote urban express demand explosive growth, the current domestic urban goods distribution is highly dependent on fuel trucks road transport, the model brings traffic congestion, logistics cost escalation, tailpipe noise pollution and other multiple "big city disease". 2024 national highway freight turnover amounted to 76,847.5 billion tons kilometers, the number of goods vehicles exceeded 33.58 million shortboards. In 2024, the national road freight turnover will reach 7684.75 billion tons kilometers, and the number of trucks will exceed 33.58 million, and the short boards of diesel trucks with high emissions and low traffic efficiency will continue to magnify the pressure of urban governance. The urban subway network has the advantages of independent underground right of way, large capacity, all-weather stable operation, and low-carbon power drive, which can divert the pressure of ground freight transportation and build a multi-level underground logistics system. This paper takes Chongqing rail transit network as the research carrier, compares the ground truck transportation mode, and quantitatively demonstrates the economic and efficiency advantages of underground logistics system; adopts K-means clustering algorithm with improved Haversine formula to complete the siting of first and second level logistics nodes, matches the transit nodes of subway stations relying on Floyd algorithm, and solves the shortest transportation path between subway stations by Dijkstra algorithm, and establishes the shortest transportation path with the total transportation distance, and establishes the shortest transportation path with the total transportation distance. The shortest transportation path between subway stations is solved by Dijkstra's algorithm, which establishes a multi-level underground logistics optimization model with the goal of minimizing the total transportation distance. The empirical results show that compared with the traditional truck road distribution, the subway underground logistics network constructed in this paper reduces the comprehensive transportation cost by 32%~41%, shortens the average distribution time of a single batch of goods by 54%, and reduces the carbon emission of a unit of goods by 85%; relying on the existing subway stock resources without the need for large-scale construction of new corridors, which is of great value for popularization. The study can provide a quantitative reference and technical framework for underground logistics planning in mountainous and high-density rail transit cities in China.
The Balance Between Value and Risk: A Study on Engineering Philosophy and Practice Based on the Hong Kong-Zhuhai-Macao Bridge
As human engineering capabilities enter the era of "mega-projects," the value created by engineering activities and the accompanying risks have reached unprecedented scales and complexity. How to maximize engineering value under high-risk environments has become a key issue in major project management. Using the iconic Hong Kong-Zhuhai-Macao Bridge (HZMB) as the research subject, this paper conducts a systematic case study to deeply explore its embodiment within multi-dimensional value systems and complex risk spectra. Focusing on the dynamic decision-making process of "acceptable risk," this paper reveals how engineering decision-makers, guided by strategic, economic, technological, social, and environmental values, utilize technological innovation, meticulous management, and system synergy to reduce various risks from unacceptable levels to the lowest reasonably practicable level of acceptability. This study indicates that risk management for major projects is essentially an art of dynamic balance guided by value rationality. Its successful practice offers significant theoretical insights and practical references for future major project management. This study not only systematically summarizes the innovative practices in risk management during the HZMB project but also reflects on the value realization paths of modern mega-projects from the perspective of engineering philosophy, providing useful references for the decision-making and implementation of similar major infrastructure projects.
Research on Bottleneck Constraints and Countermeasures of Green Logistics Development in China
With the proposal of the “Dual Carbon” goals and the in-depth advancement of ecological civilization construction, green logistics has become an inevitable choice for the high-quality development of China’s logistics industry. This paper adopts the normative analysis method to systematically sort out the policy background and practical significance of green logistics development in China, and deeply analyzes the main bottleneck constraints currently faced by green logistics development, including five major aspects: cost constraints, technical bottlenecks, standard deficiencies, imperfect policies, and weak awareness. Corresponding countermeasures and suggestions are proposed from five dimensions: government guidance, enterprise subjectivity, technological innovation, standard construction, and social co-governance. The research shows that the development of green logistics is a systematic project that requires coordinated efforts from the government, enterprises, industry associations, and consumers to jointly build an efficient, low-carbon, and circular green logistics system.
Challenges and Paths of Hydrogen Energy Transportation Risk Management from a Full Life Cycle Perspective
Under the impetus of the dual carbon strategy, hydrogen energy faces a critical transport bottleneck due to high costs and safety risks, intensified by hydrogen’s extreme flammability, wide explosive limits, and metal embrittlement. This study introduces lifecycle management into hydrogen transport safety, systematically identifying key risks across high-pressure tube trailers, liquid hydrogen tankers, pipelines, and solid-state storage, and analyzing accident mechanisms including fatigue failure, hydrogen embrittlement cracking, vapor flash ignition, and vacuum failure, with emphasis on amplification effects in tunnels and refueling station unloading. The root cause is identified as fragmented management and information silos across production, transport, utilization, and disposal, enabling risk propagation without full-lifecycle accountability. A three-dimensional strategy is proposed: technologically, dynamic risk models using connected vehicle data and active leak defense like acoustic detection and catalytic abatement; managerially, lifelong electronic equipment archives and closed-loop handover information flows; institutionally, green design and recycling standards aligned with dual carbon goals, plus carbon trading to incentivize safety upgrades. Breaking cross-chain barriers and building a safety narrative from inception to decommissioning for every asset is fundamental to achieving intrinsic hydrogen transport safety.
Incorporating Customer Demand Heterogeneity for Adaptive Cold Chain Routing Optimization: An Improved Ant Colony Algorithm
To mitigate the structural trade-offs among heterogeneous timeliness constraints in same-city fresh food distribution, an adaptive vehicle routing optimization model considering customer heterogeneity is formulated to minimize the total operating cost. To efficiently solve this complex problem, an improved ant colony algorithm featuring adaptive pheromone updates and elite ant reinforcement is developed to escape local optima traps. Numerical simulations demonstrate that the proposed algorithm converges rapidly and robustly. Driven by order-scale dynamic penalty weights, the framework successfully guides the fleet to prioritize strategic large supermarkets, substantially compressing time-window penalties with minimal marginal increases in transport expenses. This decision-making framework ultimately enables cold chain enterprises to achieve an optimal balance between transport economics and commercial credit.
Construction and Optimization of Investment Return Model for Logistics Robots in Warehouse Automation Upgrade
With the rapid development of the logistics industry, warehouse automation upgrades have become crucial for improving logistics efficiency and competitiveness. As a key component of warehouse automation, the return on investment (ROI) of logistics robots has attracted considerable attention. This paper aims to construct an ROI model for logistics robots in the context of warehouse automation upgrades, and through optimization analysis of the model, to provide a scientific basis for enterprises in making investment decisions regarding logistics robots. First, it elaborates on the background of warehouse automation upgrades and the current state of logistics robot applications, and analyzes the importance of constructing an ROI model. Next, it introduces the model construction process in detail, including cost analysis, benefit analysis, and the assumptions and simplifications of the model. Then, the model is validated and analyzed using a real case study, and the key factors affecting the ROI are discussed. Finally, based on the results of the model analysis, strategies and recommendations for optimizing the ROI of logistics robots are proposed, offering a reference for warehouse automation upgrades in warehousing enterprises.
Research on Supply Chain Information Integration and Overall Optimization from the Perspective of Smart Logistics
With the continuous changes of the market environment, the core of enterprise competition has shifted from the competition of individual scale and cost to the overall operational efficiency of supply chains. As a core link connecting production and consumption, traditional logistics models suffer from delayed information feedback, inefficient resource allocation and insufficient cross-entity collaboration, which make them unable to adapt to complex and volatile market demands. New-generation information technologies such as the Internet of Things, big data and artificial intelligence have accelerated the large-scale application of smart logistics, which has become a mainstream development trend in supply chain management. Meanwhile, national policies have continuously guided the in-depth development of modern logistics and smart supply chains. Nevertheless, the practical effects of smart logistics vary widely among enterprises at present. Some enterprises overemphasize hardware and system construction while ignoring the upgrading of organizational collaboration and management mechanisms, failing to fully unlock the empowering value of smart logistics for supply chains. From the perspective of smart logistics, this paper adopts the methods of literature analysis and case analysis. It sorts out relevant theories of smart logistics and supply chain management, analyzes the current operation status of supply chains under smart logistics, explores supply chain optimization paths combined with typical enterprise cases, summarizes existing pain points and puts forward targeted optimization suggestions. The study finds that current supply chains are generally confronted with four major problems: prominent information silos, imperfect collaboration mechanisms, mismatch between technology application and management capabilities, and heavy pressure on comprehensive cost control. Smart logistics can improve supply chain performance through three functional mechanisms: enhancing information transparency, integrating logistics resources and strengthening supply chain collaboration. Accordingly, this paper proposes optimization paths from four dimensions: breaking information barriers, establishing multi-party collaboration mechanisms, promoting the in-depth integration of technology and management, and forming systematic cost control thinking. The research results can provide theoretical references and practical ideas for enterprises to deploy smart logistics and realize overall supply chain optimization, as well as a basis for government departments to formulate relevant logistics industrial policies.
Research on Dynamic Evaluation and Optimization Strategies of Tourists' Immersive Experience Based on Multi-platform UGC Texts in Smart Tourism Scenarios: A Case Study of Chang'an 12th Hour Theme Street
With the deep integration of smart tourism and immersive cultural tourism, user-generated content (UGC) has become the core data source for capturing tourists 'experiential perceptions. Taking the Xi' an Chang 'an Twelve Hours themed street as a case study, this paper collects nearly two years of UGC texts (a total of 13,864 entries) from three major platforms—TikTok, Xiaohongshu, and Ctrip—and employs LDA topic mining, sentiment analysis, and staged analysis to construct a dynamic evaluation framework for tourist immersive experiences. This framework identifies the emotional tendencies and temporal patterns of core experience themes and extracts key factors influencing the experience. The study reveals that the core value of the street's immersive experience lies in the dual dimensions of "cultural perception + technological interaction," with satisfaction levels exhibiting significant fluctuations during holidays and special events. Technological integration must balance "immersion" and "convenience," as excessive reliance on devices may lead to experience discontinuity. Based on these findings, strategies are proposed across three dimensions—smart operation, product optimization, and service enhancement—to provide references for the sustainable development of immersive cultural tourism projects.
Evaluating the Predictive Effectiveness of Digital Credit Scoring Compared with Traditional Credit Assessment
This paper is an in-depth empirical analysis of the digital credit scoring processes versus traditional credit assessment processes in the fast-growing fintech landscape in China. Based on the 6 credit scoring models, which are based on traditional statistical algorithms (logistic regression, linear discriminant analysis, PBOC-based scoring), and sophisticated machine learning algorithms (Random Forest, XGBoost, Deep Neural Networks), we systematically analyses 6 Chinese credit datasets with 95,000 observations of peer-to-peer lending platforms, commercial banks, and digital financial services providers. The findings indicate that digital models based on alternative data of Alipay, online shopping websites, and social networks are significantly better at all metrics of evaluation than the traditional methods XGBoost has an AUC-ROC value of 0.932 over the logistic regression AUC-ROC value of 0.748, which is a 24.6 percent increase in predictive power. The results indicate that credit scoring using machine learning can boost default prediction rates by 42 percent of thin-file borrowers, as well as increase financial inclusion among the heterogeneous urban and rural population of China. This study will offer evidence-based information to Chinese financial institutions, and fintech platforms that are undergoing the transformation of the traditional systems based on the PBOC to holistic digital credit evaluation systems in accordance with the regulatory environment in China.