全球物流治理与发展的多因素分析与层次聚类

Delimiro Visbal-Cadavid , Enrique Delahoz-Domínguez , Adel Mendoza-Mendoza
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引用次数: 0

摘要

本研究通过多因素分析(MFA)和分层聚类,将物流绩效指数(LPI)、全球治理指标(WGI)和人类发展指数(HDI)整合在一起,建立了一个全面的全球发展视角。通过根据这些指标对国家进行分类,分析揭示了物流绩效、治理质量和社会经济发展方面的不同概况,为应对全球发展挑战提供了至关重要的见解。出现了三个主要集群,分别代表社会经济脆弱性国家、治理适度的新兴经济体和拥有先进基础设施的高度发达国家。关键结果表明,集群1国家在治理和基础设施方面需要大量支持,而集群2国家则受益于制度和后勤投资。第三组示范了治理和社会经济标准基准,提供了可持续发展模式。MFA和分层聚类已被证明在对具有复杂数据的国家进行分类方面是有效的,这使决策者能够定制发展战略。该研究强调需要进行持续的研究,以捕捉国家概况的变化,并评估随着时间的推移干预措施的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A multiple factor analysis and hierarchical clustering of global logistics governance and development
This study integrates the Logistics Performance Index (LPI), Worldwide Governance Indicators (WGI), and Human Development Index (HDI) through Multiple Factor Analysis (MFA) and hierarchical clustering to create a comprehensive perspective on global development. By clustering countries based on these indicators, the analysis reveals distinct profiles highlighting variations in logistics performance, governance quality, and socio-economic development, yielding insights essential for addressing global development challenges. Three primary clusters emerged, representing countries with socio-economic vulnerabilities, emerging economies with moderate governance, and highly developed nations with advanced infrastructure. Key results demonstrate that Cluster 1 countries require substantial support in governance and infrastructure, while Cluster 2 nations benefit from institutional and logistical investment. Cluster 3 exemplifies governance and socio-economic standards benchmarks, offering sustainable development models. MFA and hierarchical clustering have proven effective in categorising countries with complex data, allowing policymakers to tailor development strategies. The study underscores the need for ongoing research to capture shifts in country profiles and assess intervention impacts over time.
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CiteScore
3.90
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