Machine learning for geographically differentiated climate change mitigation in urban areas. (January 2021)

• Record Type: Journal Article
• Authors: Milojevic-Dupont, Nikola; Creutzig, Felix
• Is Part Of: Sustainable cities and society. Volume 64(2021)
• Publication Date: 2021-01
• Subjects: Machine learning -- Cities -- Climate change mitigation -- Urban governance; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2020.102526
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: We review the ML literature used for climate change mitigation in cities. We find a rapidly growing literature base and discern algorithmic approaches. Existing researches focuses on increasing efficiency. Machine learning can be effectively utilized for low-carbon urban planning. Abstract: Artificial intelligence and machine learning are transforming scientific disciplines, but their full potential for climate change mitigation remains elusive. Here, we conduct a systematic review of applied machine learning studies that are of relevance for climate change mitigation, focusing specifically on the fields of remote sensing, urban transportation, and buildings. The relevant body of literature spans twenty years and is growing exponentially. We show that the emergence of big data and machine learning methods enables climate solution research to overcome generic recommendations and provide policy solutions at urban, street, building and household scale, adapted to specific contexts, but scalable to global mitigation potentials. We suggest a meta-algorithmic architecture and framework for using machine learning to optimize urban planning for accelerating, improving and transforming urban infrastructure provision.