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Research

Selected research areas

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Green Technology Adoption

Our research investigates the drivers of green technology adoption across the energy value chain—from the diffusion of clean generation assets to the deployment of fossil-free end-use solutions. We focus on key technologies such as solar photovoltaics, battery storage systems, electric mobility, and electric space and water heating.

We examine how adoption decisions are shaped by factors including profitability, household income, political ideology, and government intervention programs. By analyzing these determinants, we provide insights that help evaluate policy scenarios and generate actionable recommendations for firms and policymakers aiming to accelerate the clean energy transition.

Our ongoing projects include:

  • Evaluating the effect of far-right ideology on the diffusion of green technologies
  • Assessing how simple and complex profitability information shape the solar adoption choices of residential households
  • Exploring the potential of e-bike adoption for reducing single-occupancy car trips 
  • Estimating the effect of bicycle infrastructure and regulation on the diffusion of micro-mobility solutions

Through this work, we seek to advance the socio-technical understanding of adoption behaviors and inform evidence-based strategies that drive widespread adoption of green technologies and contribute to a more sustainable future.

Publications

  • Reining, S., Wussow, M., Zanocco, C., & Neumann, D. (2025). Roof renewal disparities widen the equity gap in residential wildfire protection. Nature Communications (Link to article)
  • Rosenfelder, M., Wussow, M., Gust, G., Cremades, R., & Neumann, D. (2021). Predicting residential electricity consumption using aerial and street view images. Applied Energy (Link to article)
  • Rüde, L., Wussow, M., Heleno, M., Gust, G., & Neumann, D. (2024). Estimating electrical distribution network length and capital investment needs from real-world topologies and land cover data. Energy Policy (Link to article)
  • Wussow, M., Zanocco, C., Wang, Z., Prabha, R., Flora, J., Neumann, D., Majumdar, A., & Rajagopal, R. (2024). Exploring the potential of non-residential solar to tackle energy injustice. Nature Energy (Link to article)
  • [Technical Report] Rajagopal, R., Majumdar, A., Wang, Z., Zanocco, C., Prabha, R., Wussow, M., … & Tan, C. W. (2024). Machine-Learning-Based Mapping and Modeling of Solar Energy with Ultra-High Spatiotemporal Granularity (No. DE-EE0009359). Stanford Univ., CA (United States). (Link to article)