• Abstract

    The relationship between economic growth and environmental pollution has long been an area of study in economics. Increased global awareness of environmental protection has led to a surge in academic interest in this field. The main purpose of this study is to analyze the scientific output in this field on the basis of bibliometrics, understand the knowledge foundation and scientific collaboration patterns in this area, sort out the research evolution path, and predict future research trends. This paper analyzed 3,127 articles retrieved from the Web of Science repository. With CiteSpace software, network visualization analysis was performed, and knowledge maps were created to identify the most productive authors, institutions, countries, and journals, as well as influential highly cited literature. The most productive authors identified in the field were Bekun.F.Victor; The institution with the highest number of publications is the Chinese Academy of Sciences; China is the country that pays the most attention to the development of this field and obtains research results; The most influential article was "Investigation of environmental Kuznets curve for ecological footprint: The role of energy and financial development" published in Science of The Total Environment. Furthermore, the research results show that scholars' research topics of interest in this field have become more refined over time: Benefits from initial fossil energy consumption to renewable or clean energy; From traditional economic growth to focus on green total factor productivity; The study of environmental indicators ranges from single air pollution to greenhouse gases and ecological footprints. Currently green finance and green innovation are at the research frontiers in this area. Future research trends may focus on the economic and environmental benefits of new energy sources as a research topic.

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Shen, Y., & Latiff, A. R. A. (2025). Visualization of the relationship between economic growth and environmental pollution: A bibliometric analysis. Multidisciplinary Reviews, 8(10), 2025331. https://doi.org/10.31893/multirev.2025331
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