• Graduate Program
  • Research
  • Browse our Courses
  • Events
    • Events Calendar
    • Events Archive
    • Tinbergen Institute Lectures
    • Summer School
      • Deep Learning
      • Economics of Blockchain and Digital Currencies
      • Foundations of Machine Learning with Applications in Python
      • Marketing Research with Purpose
      • Modern Toolbox for Spatial and Functional Data
      • Sustainable Finance
      • Tuition Fees and Payment
      • Tinbergen Institute Summer School Program
    • Annual Tinbergen Institute Conference archive
  • News
  • Summer School
    • Deep Learning
    • Economics of Blockchain and Digital Currencies
    • Foundations of Machine Learning with Applications in Python
    • Marketing Research with Purpose
    • Modern Toolbox for Spatial and Functional Data
    • Sustainable Finance
  • Alumni

Hinrichsen, P.L., Rehdanz, K. and Tol, RichardS.J. (2026). Temperature sensitivity of residential energy demand on the global scale: A Bayesian Partial Pooling Model Energy Economics, 159:1--36.


  • Journal
    Energy Economics

This paper contributes to the limited literature on the temperature sensitivity of residential energy demand on a global scale. Using a Bayesian Partial Pooling model, we estimate country-specific intercepts and slopes, focusing on non-linear temperature response functions. The results, based on data for up to 126 countries spanning from 1978 to 2023, indicate a higher demand for residential electricity and natural gas at temperatures below -5°C and a higher demand for electricity at temperatures above 30°C. For temperatures above 23°C, the relationship between power demand and temperature steepens. Demand in developed countries is more sensitive to high temperatures than in less developed countries, possibly due to an inability to meet cooling demands in the latter.