Dr. Kristin Limbach | 0721 608-44677 | kristin.limbach@kit.edu

Background
Renewable energy generation offers opportunities to improve local resilience and
support net zero goals. However, renewables projects have encountered resistance
both within local communities and with the wider public due to privatization of the
gains from large-scale installations such as solar parks that cause landscape
impairment. This study develops a decision experiment to examine preferences for
alternative system design integrated within current land-use practices. Across land-use
scenarios, the experiment investigates preferences for system attributes selected to
capture trade-offs between prosumption concepts, decarbonization, climate
adaptation, and resource management. The choice experiment is designed to quantify
respondent’s willingness to accept integrated PV systems and to estimate the relative
importance of co-benefits such as decarbonization of local energy use, community
implication and sustainability.
Objectives of the thesis
The goal of this thesis is to implement a Discrete Choice Experiment (DCE) that will
empirically examine preferences for PV deployment in rural areas. Specifically, in an
online experiment we will establish the stated preference ranking among different
options for PV systems. The acceptability of a particular system can vary across
different user groups depending on socio-economic characteristics and lifestyles. The
study will benefit from existing foundations as the experimental design and
accompanying survey are already developed, the experimental setup is mostly
programmed. In a first step, the efficient choice design for the experiment will have to
be calculated and the programming finalized. Second, a first pilot study will be run in
the experimental lab. Third, once the insights from the pilot study are incorporated,
the full experiment will be conducted online. Finally, the empirical data will be
analyzed and results interpreted.

Requirements
▪ Interest in energy and social science topics (especially integrated PV and user
behavior) and empirical research (e.g. decision making, experiments)
▪ Willingness to work independently
▪ Ideally existing skills in programming and/or statistical data analysis (e.g. R, Python)