Cèlia Estruch-Garcia
Cèlia Estruch Garcia
PhD Researcher, Department of Government
London School of Economics C.Estruch-Garcia@lse.ac.uk
Welcome! I am an MRes/Doctoral Researcher at the Department of Government at the London School of Economics and Political Science (LSE). I am affiliated with the LSE Data Science Institute and the Grantham Research Institute on Climate Change and the Environment. My research focuses on the distributive and political impacts of sustainable urban policies.
Before starting my PhD, I worked as a Research Assistant at Barcelona’s Institute of Economics (IEB) and the Open University of Catalonia (UOC).
I hold a Master’s degree in Political Science and Political Economy from LSE, a Bachelor’s in Philosophy, Politics and Economics from Pompeu Fabra University, and training in Data Analysis for Social Sciences from the University of Barcelona.
Research
Published Work
The Limits of Alarm: How Climate Scenarios Fail to Increase Willingness to Act and Pay for Climate Change Policies
with Toni Rodon, and Marc Guinjoan. Political Studies Review. Publisher’s version
Working papers
The electoral effects of banning cars from the streets: Evidence from Barcelona’s Superblocks
with Albert Solé-Ollé, Filippo Tassinari, and Elisabet Viladecans-Marsal.
R&R, American Journal of Political Science
Awards: Best Paper Award at the 11th Environmental Politics and Governance Conference
Media: VoxTalks Economics, Weekendavisen
Do climate events drive support for climate change policies?
with Toni Rodon, Marc Guinjoan, and Roger Sanjaume. Under Review. Abstract
In progress
Do climate events drive support for climate change policies?
with Max Bradley, and Pau Grau-Vilalta. Draft available upon request.
Low Emission Zones and Electoral Accountability in Spain
Pedagogical Resources
MY474/MY557 Applied Machine Learning for Social Science Notes (LSE, Master’s Level, 2025/2026)
- Resources Developed: Exam Preparation Notes: Theory and code-checking notes covering the full course: bias-variance tradeoff, logistic regression, regularisation, model evaluation, active learning, and more.