International Journal of Global Economics and Management

Abstract: With the rapid depletion of the Earth’s natural resources, green development approaches such as low-carbon development and energy transition are becoming major directions for economic development across the world. In recent years, Morocco has introduced a series of incentive measures to support renewable energy, injecting strong momentum into the transformation of its energy structure. However, current technical barriers, talent structure issues, and energy cost pressures continue to constrain the country’s energy transition process. Based on a literature analysis method, this paper systematically examines policy documents and publicly available data to investigate the major technological risks and governance mechanisms involved in Morocco’s renewable energy transition. Read More

Abstract: As a vital component of China’s socialist market economy, the sound development of the private economy cannot be separated from appropriate government intervention and institutional safeguards under economic law. Centered on economic law, this paper explores the dialectical relationship between the private economy and government intervention, and analyzes the legal boundary of government intervention as well as adjustment approaches under economic law. The study finds that economic law provides institutional guarantees for the private economy and regulates government intervention by establishing the principle of equal market access and improving macro-control mechanisms. At present, government intervention suffers from both excessive regulation and insufficient oversight. It is necessary to realize the organic integration of private economic vitality and government regulatory efficiency by improving the legal system, clarifying intervention boundaries and strengthening judicial remedies. Read More

Abstract: Fresh-cut flowers are highly perishable products whose quality deteriorates rapidly over time, posing significant challenges to supply chain management. This paper investigates pricing and coordination strategies in a two-echelon fresh-cut flower supply chain consisting of one supplier and one e-commerce platform that operates a two-period selling model combining presale and spot sales. We develop Stackelberg game models that explicitly incorporate consumer freshness preference and freshness decay dynamics, and derive optimal pricing and freshness-keeping decisions under both decentralized and centralized decision-making structures. Our analysis reveals that the double marginalization effect becomes more pronounced as consumers become more sensitive to freshness, resulting in efficiency losses of 20%–30% under decentralized decision-making. We further demonstrate that increasing the presale proportion incentivizes greater freshness-keeping investment throughout the supply chain, as preselling reduces demand uncertainty and associated waste. To address the efficiency loss, we design a combined "revenue sharing and cost sharing" (RS-CS) contract and prove that perfect coordination is achieved when the cost-sharing ratio equals the revenue-sharing ratio and the wholesale price satisfies a specific compensation condition. Numerical experiments validate the model results and confirm the robustness of the coordination mechanism. This study provides both theoretical insights and practical guidance for fresh-cut flower supply chain management. Read More

Abstract: Nowadays, the long-standing belief that product prices are uniform for everyone almost no longer exists. With the big data explosion and the growth of artificial intelligence (AI), personalized pricing has entered an in-depth stage where businesses leverage online activity data, browsing records, and real-time demographics to formulate tailored pricing. This paper examines the multi-faceted impacts of personalized pricing across corporate and governmental sectors in 2026. While personalized pricing drives short-term corporate profits and expands market access for price-sensitive groups through a data-driven "Robin Hood" effect, these gains face rapid erosion in the long term. High data infrastructure overhead, algorithmic fragmentation, and customer poaching shrink corporate margins, while perceived unfairness severely damages brand loyalty. In the governance landscape, governments can leverage personalized pricing to automate social welfare and optimize infrastructure demand. However, risks such as public trust erosion, algorithmic bias, and systemic discrimination persist. We conclude that strict algorithmic audits are necessary to protect consumer privacy and market equity. Read More

Abstract: With the rapid development of e-commerce, logistics distribution speed has become a key competitive factor for e-commerce enterprises. This paper addresses the location decision problem of regional logistics distribution centers for a large e-commerce platform in East China. Four candidate cities—Nanjing, Hangzhou, Suzhou, and Wuxi—are evaluated using a combined AHP-Entropy Weight-TOPSIS model. The Analytic Hierarchy Process (AHP) determines subjective weights reflecting expert experience, while the Entropy Weight Method (EWM) calculates objective weights based on data information content. A multiplicative synthesis method integrates both weights into comprehensive weights. TOPSIS is then applied to rank alternatives by their relative closeness to the positive ideal solution. Six evaluation indicators are constructed: land cost, labor cost, transportation convenience, market radiation capacity, policy preference, and infrastructure completeness. The results show that Nanjing ranks first with a closeness coefficient of 0.7918, followed by Hangzhou (0.6301), Suzhou (0.3056), and Wuxi (0.2125). Sensitivity analysis and comparative analysis with single-weighting methods confirm the robustness and superiority of the combined weighting approach. This study provides a scientific decision-making framework for logistics distribution center location selection. Read More

Abstract: Against the backdrop of a profound restructuring of global industrial chains, how to systematically enhance the resilience of these chains has become a central issue. As the leading entities within industrial chains, the ecosystem leadership of lead firms serves as a key driving force in enhancing their resilience. This paper takes Chery Automobile as its case study and employs the grounded theory methodology to conduct an exploratory single-case study, constructing a theoretical framework through open coding, axial coding and selective coding. The research findings are as follows: Firstly, the ecosystem leadership of lead firms comprises five dimensions: the ability to lead technological innovation, the ability to govern the industry and build ecosystems, the ability to empower through digital transformation, the ability to implement globalization strategies, and the ability to foster collaboration between government and enterprises. Secondly, lead firms strengthen the structural, spatial and evolutionary resilience of the industrial chain through four pathways: technological innovation drives breakthroughs in key core technologies; industrial governance and ecosystem building drive collaborative support and standard-setting; digital transformation drives the digital upgrading of the industrial chain; and globalization drives the diversification of supply chain risks. Thirdly, the capacity for government-enterprise collaboration, as an institutional safeguard variable running throughout the entire process, plays a moderating and supportive role through three mechanisms: legitimization, reduction of institutional transaction costs, and scaling of resource allocation. This paper examines the five-dimensional structure of the ecosystem leadership of lead firms and the transmission mechanisms via these four distinct pathways, ultimately achieving a systematic enhancement of industrial chain resilience, thereby establishing a complete causal chain between capability … Read More

Abstract: Against the backdrop of rapid e-commerce growth and increasingly personalized consumer demand, fast-moving consumer goods (FMCG) enterprises face the dual challenges of excess inventory and stockouts, which impose higher requirements on the accuracy of inventory-demand forecasting. Intensifying market competition, frequent promotional campaigns, and rapidly changing consumer preferences have made the forecasting task substantially more complex. This study uses daily sales data for the core products of Company Z, an FMCG enterprise, from 2023 to 2025. A multidimensional indicator system is constructed from product-demand characteristics, and K-means clustering is applied to classify the products. BP neural-network models are then used to forecast daily inventory demand over the following four weeks according to the demand patterns of each product cluster. The results show that, compared with the exponential-smoothing method currently used by the company, the proposed hybrid model achieves a closer fit to actual sales and reduces the root mean square error by an average of 42.3%, thereby demonstrating stronger accuracy and adaptability. The combination of K-means clustering and a BP neural network can effectively capture the nonlinear and volatile characteristics of FMCG demand and provide more accurate support for inventory planning, with considerable practical value and potential for wider application. Read More

Abstract: Small and medium-sized enterprises (SMEs) constitute over 99% of all enterprises in China and serve as the fundamental backbone of the national economy. However, they face persistent challenges in supply chain management characterized by information silos, Cost out of control, rigid processes, and heavy reliance on manual labor—a predicament succinctly described as "no money, no personnel, and no technology." While commercial integrated software products have proliferated in the market, they predominantly offer feature-stacked solutions that require users to self-filter, failing to address the specific pain points of SMEs. This paper proposes a data-driven collaborative optimization framework tailored to the resource-constrained context of SMEs. Drawing on cloud-native architecture, mixed-integer linear programming, heuristic algorithms, and Robotic Process Automation (RPA), the framework addresses four core dimensions: information synergy, cost optimization, process reengineering, and intelligent decision-making. The research identifies that the primary competitive advantage of SMEs lies in their organizational agility—"small boats turn around easily" —which enables rapid adoption of lightweight, SaaS-based solutions without the complex system integration required by large enterprises. By focusing on the "inventory-routing" joint optimization problem and automated quotation systems as core algorithmic breakthroughs, this study demonstrates that significant cost reductions can be achieved through targeted, algorithm-centric interventions rather than comprehensive ERP overhauls. The findings contribute to both the academic literature on SME supply chain management and practical guidance for technology adoption strategies in developing economies. Read More

Abstract: This case study uses the background of China Merchants Bank's digital transformation from 2018 to 2025. It describes the bank's strategic practice of using MAU as the North Star metric and promoting "three shifts" (from customers to users, from bank cards to App, from transaction thinking to customer journey). The case focuses on the dilemma faced by the head of retail finance when MAU growth hit a bottleneck and management disagreed on whether to adjust the North Star metric. The case data comes from CMB's annual reports and public sources. Read More

Abstract: Using the construction of the New Western Land-Sea Corridor as a quasi-natural experiment, this study examines its effects on new-type urbanization with provincial panel data for 2011–2023. A staggered difference-in-differences model, double machine learning, and the synthetic control method are employed. The results show that corridor construction significantly improves the quality of provincial new-type urbanization, and the finding remains robust to parallel-trends tests, placebo tests, and alternative estimators. Mechanism tests indicate that the policy not only has a direct enabling effect but also operates through three channels: improved market access, industrial upgrading, and a narrower urban-rural income gap. The effects are stronger in southwestern provinces and provinces with higher initial urbanization, while the marginal impact is greater where financial development is initially lower. These findings provide evidence for coordinating transport-corridor development with people-oriented urbanization and offer policy implications for improving corridor efficiency, strengthening industrial support, and advancing regional coordination. Read More