Buildings and Cities: Difference between revisions
(standardize headings) |
(add application areas) |
||
| Line 7: | Line 7: | ||
== Machine Learning Application Areas == |
== Machine Learning Application Areas == |
||
=== Optimizing buildings === |
|||
* Modeling building energy |
|||
* Smart buildings |
|||
=== Urban planning === |
|||
* Modeling energy use across buildings |
|||
* Gathering infrastructure data |
|||
=== The future of cities === |
|||
* Data for smart cities |
|||
* Low-emissions infrastructure |
|||
== Background Readings == |
== Background Readings == |
||
''TODO format'' |
|||
=== Relevant IPCC chapters === |
=== Relevant IPCC chapters === |
||
| Line 37: | Line 53: | ||
==Libraries and Tools== |
==Libraries and Tools== |
||
==Data== |
==Data== |
||
''TODO format'' |
|||
=== Building energy use === |
=== Building energy use === |
||
Revision as of 01:48, 2 September 2020
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,[2] a combination of easy-to-implement fixes and state-of-the-art strategies could reduce emissions for existing buildings by up to 90%.[3] It is possible today for buildings to consume almost no energy.[4] Many of these energy efficiency measures actually result in overall cost savings[5] and simultaneously yield other benefits, such as cleaner air for occupants. This potential can be achieved while maintaining the services that buildings provide – and even while extending them to more people, as climate change will necessitate. For example, with the changing climate, more people will need access to air conditioning in regions where deadly heat waves will become common.[6][7]
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.[8] 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.[1] 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.[1]
Machine Learning Application Areas
Optimizing buildings
- Modeling building energy
- Smart buildings
Urban planning
- Modeling energy use across buildings
- Gathering infrastructure data
The future of cities
- Data for smart cities
- Low-emissions infrastructure
Background Readings
TODO format
Relevant IPCC chapters
- Lucon, O. et. al. Buildings (Fifth Assessment Report of the Intergovernmental Panel on Climate Change) (2014)
- Seto, K. et al. Human Settlements, Infrastructure and Spatial Planning (Fifth Assessment Report of the Intergovernmental Panel on Climate Change) (2014)
Academic perspectives
- Bai, X., et al. Six research priorities for cities and climate change. (2018)
- Seto, K C., et al. Sustainability in an urbanizing planet. (2017)
- Seto, K,C., et al. Carbon lock-in: types, causes, and policy implications. (2016)
- Nagendra, H,, et al. The urban south and the predicament of global sustainability. (2018)
- Hittinger, E,, and Jaramillo, P. Internet of Things: Energy boon or bane? (2019)
Perspectives in popular media
Online Courses and Course Materials
Community
Major conferences
Major journals
Major societies and organizations
Libraries and Tools
Data
TODO format
Building energy use
- New York City municipal buildings for buildings over 10,000 square feet, identifying each building’s energy intensity, and available GHG emissions for the calendar years 2010-2014.
- The Commercial Buildings Energy Consumption Survey (CBECS) is a national sample survey that collects information on the stock of U.S. commercial buildings, their energy-related building characteristics, and their energy consumption and expenditures.
City metabolism
The "metabolism" of a city includes the electricity used, waste generated, and GHG emitted.
- Metabolism data for 150 cities
- The China Emission Accounts & Datasets provides energy, emission and socio-economic accounting inventories for China
- First attempts of global databases on cities emissions and relevant ancillary metrics
- The Carbon Disclosure Project provides a global platform for cities to measure and disclose environmental data; a variety of datasets are available
Urban Land Use, Infrastructure Data
- OpenStreetMap is a cooperative alternative to Google Maps where all the data is open access.
- NYU’s Atlas of Urban Expansion contains historical data on 200 cities worldwide.
- Open 3D models are available for a few cities.
- The Urban Atlas of the European Union agency Copernicus includes information on urban land use types.
- The TABULA project combines data on building types across all of Europe.
References
- ↑ 1.0 1.1 1.2 "Tackling Climate Change with Machine Learning". Cite journal requires
|journal=(help) - ↑ 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.
- ↑ Urge-Vorsatz, Diana; Petrichenko, Ksenia; Staniec, Maja; Eom, Jiyong (2013-06). "Energy use in buildings in a long-term perspective". Current Opinion in Environmental Sustainability. 5 (2): 141–151. doi:10.1016/j.cosust.2013.05.004. ISSN 1877-3435. Check date values in:
|date=(help) - ↑ Olsthoorn, Mark; Schleich, Joachim; Faure, Corinne (2019-06). "Exploring the diffusion of low-energy houses: An empirical study in the European Union". Energy Policy. 129: 1382–1393. doi:10.1016/j.enpol.2019.03.043. ISSN 0301-4215. Check date values in:
|date=(help) - ↑ Stephenson, Janet; Barton, Barry; Carrington, Gerry; Gnoth, Daniel; Lawson, Rob; Thorsnes, Paul (2010-10). "Energy cultures: A framework for understanding energy behaviours". Energy Policy. 38 (10): 6120–6129. doi:10.1016/j.enpol.2010.05.069. ISSN 0301-4215. Check date values in:
|date=(help) - ↑ Mora, Camilo; Counsell, Chelsie W.W.; Bielecki, Coral R.; Louis, Leo V (2017-11). "Twenty-Seven Ways a Heat Wave Can Kill You:". Circulation: Cardiovascular Quality and Outcomes. 10 (11). doi:10.1161/circoutcomes.117.004233. ISSN 1941-7713. Check date values in:
|date=(help) - ↑ Mora, Camilo; Dousset, Bénédicte; Caldwell, Iain R.; Powell, Farrah E.; Geronimo, Rollan C.; Bielecki, Coral R.; Counsell, Chelsie W. W.; Dietrich, Bonnie S.; Johnston, Emily T.; Louis, Leo V.; Lucas, Matthew P. (2017-06-19). "Global risk of deadly heat". Nature Climate Change. 7 (7): 501–506. doi:10.1038/nclimate3322. ISSN 1758-678X.
- ↑ Creutzig, Felix; Agoston, Peter; Minx, Jan C.; Canadell, Josep G.; Andrew, Robbie M.; Quéré, Corinne Le; Peters, Glen P.; Sharifi, Ayyoob; Yamagata, Yoshiki; Dhakal, Shobhakar (2016-11-24). "Urban infrastructure choices structure climate solutions". Nature Climate Change. 6 (12): 1054–1056. doi:10.1038/nclimate3169. ISSN 1758-678X.