Long-term Effects of Temperature Variations on Economic Growth: A Machine Learning Approach

TL;DR

This paper uses machine learning to analyze how long-term temperature variations influence global economic growth, highlighting significant climate-economic relationships and emphasizing the importance of climate considerations in economic policymaking.

Contribution

It introduces a data-driven machine learning approach to uncover complex climate-economic relationships using global temperature and economic data.

Findings

Significant relationship between temperature and GDP growth

Climate variations substantially impact economic performance

Machine learning effectively uncovers climate-economy links

Abstract

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.