Tackling Climate Change with Machine Learning

3 code implementations • 10 Jun 2019 • David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mukkavilli, Konrad P. Kording, Carla Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer Chayes, Yoshua Bengio

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help.

BIG-bench Machine Learning Management

Using machine learning to understand causal relationships between urban form and travel CO2 emissions across continents

1 code implementation • 31 Aug 2023 • Felix Wagner, Florian Nachtigall, Lukas Franken, Nikola Milojevic-Dupont, Rafael H. M. Pereira, Nicolas Koch, Jakob Runge, Marta Gonzalez, Felix Creutzig

Here, we address all three gaps via causal graph discovery and explainable machine learning to detect urban form effects on intra-city car travel, based on mobility data of six cities across three continents.

Causal Discovery Specificity

The built environment and induced transport CO2 emissions: A double machine learning approach to account for residential self-selection

no code implementations • 7 Dec 2023 • Florian Nachtigall, Felix Wagner, Peter Berrill, Felix Creutzig

Understanding why travel behavior differs between residents of urban centers and suburbs is key to sustainable urban planning.