ESG-szempontok a klímakockázat előrejelzésében


  • Helena Naffa Corvinus University of Budapest
  • Fanni Dudás Corvinus University of Budapest
  • Kitti Juhász Corvinus University of Budapest



climate risk, neural networks, sustainability, ESG indicator


Sustainability has been playing an increasingly important role in finance, as well as the measurement of climate risk. In this research, the authors examine the relationship between ESG indicators (Environmental, Social and Governance) and the Global Climate Risk Index (CRI), focusing on the role of environmental factors and countries with different income categories. This analysis uses the World Bank’s countrywide ESG and Germanwatch climate risk databases, which uses linear regression and neural network methodology to predict CRI, which attempts to quantify the consequences of extreme weather events and related socioeconomic data. An essential result of the article is that climate risk is less predictable by environmental indicators, social and governmental factors are more predictive, and countries’ income levels are inversely proportional to climate sensitivity. The results may allow international organizations and institutional investors to pay more attention to ESG indicators in low-income countries with a significant value.


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Author Biographies

Helena Naffa, Corvinus University of Budapest

Senior Lecturer

Fanni Dudás, Corvinus University of Budapest

PhD student

Kitti Juhász, Corvinus University of Budapest

Graduate Student


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How to Cite

Naffa, H., Dudás, F., & Juhász, K. (2021). ESG-szempontok a klímakockázat előrejelzésében. Vezetéstudomány / Budapest Management Review, 52(8-9), 18–33.