New energy economics papers, updated automatically each week
The site includes a GitHub-native weekly monitor that collects newly published papers, filters them to energy economics, and publishes a static report.
About Me
Hi, I'm Peer. I am a quantitative economist and doctoral candidate at the CAU Kiel Institute. I use large-scale climate and energy data to study how climate change reshapes residential energy demand.
I hold an MSc in Quantitative Economics and work primarily with R, Bayesian econometrics, panel data, and applied forecasting. Depending on the project, I also use Python, SQL, and GIS workflows.
I am especially interested in climate-sensitive energy demand, heterogeneous temperature responses, and evidence that can inform a resilient energy transition. I am open to collaborations with researchers, policy analysts, and data scientists.
Expertise & Interests
- Econometrics & Modeling: Bayesian methods, time-series, panel data
- Data Science & Programming: R, Python, SQL, GIS
- Energy & Environmental Economics: climate impacts on energy demand
- Big Data: georeferenced climate and demographic datasets
- Research & Policy: scientific writing, publishing, and knowledge sharing
- Global Collaboration: engagement with international research networks
Recent Research
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Temperature Sensitivity of Residential Energy Demand on the Global Scale
A Bayesian partial pooling analysis of residential electricity and gas demand across countries, with a focus on nonlinear temperature response, heterogeneous effects, and globally comparable evidence.
Available here: https://doi.org/10.1016/j.eneco.2026.109338.