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[[File:Buildings cities tccml schematic.png|thumb|A schematic of selected opportunities to reduce greenhouse emissions from buildings and cities using machine learning. From "Tackling Climate Change with Machine Learning."<ref>{{Cite journal|last=|first=|date=|title=Tackling Climate Change with Machine Learning|url=|journal=|volume=|pages=|via=}}</ref>]]
Buildings offer some of the lowest-hanging fruit when it comes to reducing GHG emissions. While the energy consumed in buildings is responsible for a quarter of global energy-related emissions,<ref>IPCC. ''Global warming of 1.5°C. An IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty'' [V. Masson-Delmotte, P. Zhai, H. O. Portner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, Y. Chen, S. Connors, ¨ M. Gomis, E. Lonnoy, J. B. R. Matthews, W. Moufouma-Okia, C. Pean, R. Pidcock, N. Reay, M. Tignor, T. ´ Waterfield, X. Zhou (eds.)]. 2018.</ref> a combination of easy-to-implement fixes and state-of-the-art
Two major challenges are heterogeneity and inertia. Buildings vary according to age, construction, usage, and ownership, so optimal strategies vary widely depending on the context. For instance, buildings with access to cheap, low-carbon electricity may have less need for expensive features such as intelligent light bulbs. Buildings also have very long lifespans; thus, it is necessary both to create new, energy-efficient buildings, and to retrofit old buildings to be as efficient as possible.<ref>{{Cite journal|last=Creutzig|first=Felix|last2=Agoston|first2=Peter|last3=Minx|first3=Jan C.|last4=Canadell|first4=Josep G.|last5=Andrew|first5=Robbie M.|last6=Quéré|first6=Corinne Le|last7=Peters|first7=Glen P.|last8=Sharifi|first8=Ayyoob|last9=Yamagata|first9=Yoshiki|last10=Dhakal|first10=Shobhakar|date=2016-11-24|title=Urban infrastructure choices structure climate solutions|url=http://dx.doi.org/10.1038/nclimate3169|journal=Nature Climate Change|volume=6|issue=12|pages=1054–1056|doi=10.1038/nclimate3169|issn=1758-678X}}</ref> Urban planning and public policy can play a major role in reducing emissions by providing infrastructure, financial incentives, or energy standards for buildings.
Machine learning provides critical tools for supporting both building managers and policy makers in their efforts to reduce GHG emissions. At the level of building management, ML can help select strategies that are tailored to individual buildings, and can also contribute to implementing those strategies via smart control systems. At the level of urban planning, ML can be used to gather and make sense of data to inform policy makers. In addition, ML can help cities as a whole to transition to low-carbon futures.
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