Long-term Effects of Temperature Variations on Economic Growth: A
  Machine Learning Approach
Eugene Kharitonov Oksana Zakharchuk Lin Mei
This study investigates the long-term effects of temperature variations on
economic growth using a data-driven approach. Leveraging machine learning
techniques, we analyze global land surface temperature data from Berkeley Earth
and economic indicators, including GDP and population data, from the World
Bank. Our analysis reveals a significant relationship between average
temperature and GDP growth, suggesting that climate variations can
substantially impact economic performance. This research underscores the
importance of incorporating climate factors into economic planning and
policymaking, and it demonstrates the utility of machine learning in uncovering
complex relationships in climate-economy studies.