My current work sits at the intersection of empirical energy economics, climate impacts, and applied Bayesian modeling.
Temperature Sensitivity of Residential Energy Demand
This project analyzes how residential energy demand responds to temperature and humidity across 127 countries from 1978-2023. Using a Bayesian partial pooling model implemented in brms/Stan, it estimates country-specific demand responses based on a population-weighted exposure index constructed from gridded climate and demographic data.
The goal is to produce a globally comparable assessment of climate-driven energy demand patterns and their heterogeneity.
Forecasting Future Residential Energy Demand under Climate Change
Building on the estimated temperature-humidity response functions, this project generates long-run projections of residential energy demand under multiple SSP climate and population scenarios.
It quantifies how changing climatic conditions and demographic structures may affect future demand and highlights regional differences in adaptation pressures on energy systems.