Cèlia Estruch-Garcia

Cèlia Estruch Garcia

Cèlia Estruch Garcia
PhD Researcher, Department of Government
London School of Economics C.Estruch-Garcia@lse.ac.uk

GitHub ORCID Scholar Bluesky X

Welcome! I am a PhD candidate 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 - Pompeu Fabra University.

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 electoral effects of banning cars from the streets: Evidence from Barcelona’s Superblocks
with Albert Solé-Ollé, Filippo Tassinari, and Elisabet Viladecans-Marsal.
Conditionally accepted, American Journal of Political Science

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, 24(2), 325–334. Publisher’s version

Working papers

Do climate events drive support for climate change policies?
with Toni Rodon, Marc Guinjoan, and Roger Sanjaume. Abstract

In progress

Taking out the Trash: Political implications of door-to-door waste management in Catalonia
with Max Bradley, and Pau Grau-Vilalta. Draft available upon request.

Low Emission Zones and Electoral Accountability in Spain

Pedagogical Resources

Teaching Assistant, Introductory R for Political Science, London School of Economics (09/2026 – present)

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.