Buildings and Cities: Difference between revisions

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.<ref name=":0" /> 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.<ref name=":0" />

=== Relevant IPCC chapters ===

* [https://www.theguardian.com/environment/2019/aug/29/the-air-conditioning-trap-how-cold-air-is-heating-the-world The air conditioning trap: how cold air is heating the world, The Guardian ‘Long Read’]

==Community==

===Major societies and organizations===

==Data==

* [https://land.copernicus.eu/local/urban-atlas The Urban Atlas of the European Union agency Copernicus] includes information on urban land use types.

* [http://episcope.eu/building-typology/overview The TABULA project] combines data on building types across all of Europe.

==References==