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This study offers a comparative analysis of sustainable investment practices in three major economies: China, Germany, and the United States. Drawing on empirical data from the Scopus database (2019–2024), it leverages advanced natural language processing (NLP) techniques combined with a multi-algorithm clustering approach to identify and analyze thematic clusters. The results highlight a global convergence toward the adoption of Environmental, Social, and Governance (ESG) criteria and the integration of Sustainable Development Goals (SDGs) into national policies. However, significant contextual divergences persist: the United States faces structural challenges in adopting green technologies, Germany experienced mixed performance of ESG indices during the COVID-19 crisis, and China struggles with inconsistencies in ESG ratings, threatening financial stability. The originality of this research lies in its dual contribution: methodologically, by integrating NLP and co-word analysis to provide a robust and innovative analytical framework; and empirically, by offering practical recommendations for policymakers and investors to harmonize ESG standards and develop sustainable strategies tailored to the economic and cultural specificities of each context. These findings lay the groundwork for aligning sustainable investment practices with global and local development objectives while enhancing the coherence of sustainable finance policies on a global scale.

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Unraveling Differences and Similarities in Sustainable Investment Through Case Studies in China, Germany, and the United States: A Multi-algorithm Clustering Network Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Mohamed Amine Ouhinou,
  • Abdellah Saoualih,
  • Soufiane Lyaqini,
  • Salah Eddine Kartobi,
  • Stoyan Prodanov,
  • Larbi Safaa

摘要

This study offers a comparative analysis of sustainable investment practices in three major economies: China, Germany, and the United States. Drawing on empirical data from the Scopus database (2019–2024), it leverages advanced natural language processing (NLP) techniques combined with a multi-algorithm clustering approach to identify and analyze thematic clusters. The results highlight a global convergence toward the adoption of Environmental, Social, and Governance (ESG) criteria and the integration of Sustainable Development Goals (SDGs) into national policies. However, significant contextual divergences persist: the United States faces structural challenges in adopting green technologies, Germany experienced mixed performance of ESG indices during the COVID-19 crisis, and China struggles with inconsistencies in ESG ratings, threatening financial stability. The originality of this research lies in its dual contribution: methodologically, by integrating NLP and co-word analysis to provide a robust and innovative analytical framework; and empirically, by offering practical recommendations for policymakers and investors to harmonize ESG standards and develop sustainable strategies tailored to the economic and cultural specificities of each context. These findings lay the groundwork for aligning sustainable investment practices with global and local development objectives while enhancing the coherence of sustainable finance policies on a global scale.