Difference between revisions of "Buildings and Cities"

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* '''Macro-scale [[Macro-scale energy demand assessment in cities|energy demand]] and [[Greenhouse Gas Emissions Detection|GHG emissions]] assessment in cities:''' While some electricity system operators release publicly-available data on energy use and the emissions associated with fossil fuel generators, this data is not available in many cases. ML can help map greenhouse gas emissions using remote sensing and/or on-the-ground data.
 
* '''Macro-scale [[Macro-scale energy demand assessment in cities|energy demand]] and [[Greenhouse Gas Emissions Detection|GHG emissions]] assessment in cities:''' While some electricity system operators release publicly-available data on energy use and the emissions associated with fossil fuel generators, this data is not available in many cases. ML can help map greenhouse gas emissions using remote sensing and/or on-the-ground data.
 
* '''[[Identifying building retrofit needs]]:''' For reducing energy use for thermal comfort in buildings, many buildings need to be retrofitted to increase their thermal performance. ML can help pinpoint which buildings and which specific parts of buildings would yield the best performance gains.
 
* '''[[Identifying building retrofit needs]]:''' For reducing energy use for thermal comfort in buildings, many buildings need to be retrofitted to increase their thermal performance. ML can help pinpoint which buildings and which specific parts of buildings would yield the best performance gains.
* '''[[Low-carbon urban form from neighborhoods to large agglomerations|Low-carbon urban form, from neighborhoods to large agglomerations]]:''' Urban form, the physical form of cities, has important implications of energy use and GHG emissions, for example sprawled cities can induce mobility demand. ML can help analyze energy use implications of different urban forms, simulate urban development pathways and (re-)designing neighborhoods by finding patterns in urban form data.
+
* '''[[Designing Low-Carbon Urban Form|Designing Low-Carbon Urban Form]]:''' Urban form, the physical form of cities, has important implications of energy use and GHG emissions, for example sprawled cities can induce mobility demand. ML can help analyze energy use implications of different urban forms, simulate urban development pathways and (re-)designing neighborhoods by finding patterns in urban form data.
   
 
=== The future of cities ===
 
=== The future of cities ===

Latest revision as of 07:47, 9 September 2021

This page is about the intersection of urban planning and machine learning in the context of climate change mitigation and adaptation. For an overview of buildings and cities as a whole, please see the Wikipedia page on this topic.
A schematic of selected opportunities to reduce greenhouse emissions from buildings and cities using machine learning. From "Tackling Climate Change with Machine Learning."[1]

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[edit | edit source]

Optimizing buildings[edit | edit source]

  • Forecasting energy loads: The supply and demand of electric and thermal loads must be forecast ahead of time to inform electricity planning and scheduling. ML can help make these forecasts more accurate, improve temporal resolution, and quantify uncertainty.
  • Non-Intrusive Load Monitoring (NILM): A better understanding of the own energy consumption can lead to better energy efficiency by changing one's behavior or exchanging inefficient devices. ML can help to disaggregate a household's smart meter data and attribute energy consumption to individual devices for increased transparency.
  • Controlling HVAC and lighting systems: Current building energy management systems are manually designed by human operators, which leads to energy inefficient operations. ML can help develop predictive models of building energy systems, leading to efficient building operation via advanced model-based optimal control methods.
  • Fault detection and predictive maintenance in building systems: Buildings are embedded with complex engineering systems in dynamic interplay. Component's malfunctions are a constant threat to the building operations economics and comfort and safety of human occupants. ML can help to transfer from reactive maintenance to predictive maintenance and cut the maintenance cost by prolonging the remaining useful life of building engineering systems.
  • Demand response and energy social games: There are opportunities to reduce the GHG emissions due to energy consumption in building by adapting the demand to when the share of renewable energy on the grid is higher and lower. ML provides tools to enable automatic demand response to current supply conditions, it also provides ways for users to optimize their demand based incentives.
  • AI-guided building design and planning: Current building designs are drawn by the joined hands of the architect, mechanical, electrical, and control engineers using various computer-aided design tools. ML can help to navigate and optimize complex design landscapes, often balancing conflicting requirements such as energy efficiency, comfort, and cost.
  • Sector-coupled districts and district heating systems: To achieve decarbonization across the heating, electricity, and mobility sectors, they are increasingly coupled within districts in a joint spatial and organizational context. ML can help by providing surrogate models of thermal processes and quantify uncertainties of loads, supply, and mobility behavior.
  • Surrogate modeling: Building energy simulation (BES) programs are software tools that simulate the complex physics of buildings and are key enabling tools for R&D in the building's domain. However, detailed BES models are notoriously difficult to design, tune and typically have high computational demands. ML, in conjunction with physics, can help to build accurate yet computationally efficient surrogate models for faster simulations.

Urban planning[edit | edit source]

  • Mapping built-up and energy infrastructure: Decarbonizing the building and urban transportation sectors requires accurate mapping of the existing infrastructure but there are large data gaps. ML can help generate such data from remote sensing and existing maps.
  • Macro-scale energy demand and GHG emissions assessment in cities: While some electricity system operators release publicly-available data on energy use and the emissions associated with fossil fuel generators, this data is not available in many cases. ML can help map greenhouse gas emissions using remote sensing and/or on-the-ground data.
  • Identifying building retrofit needs: For reducing energy use for thermal comfort in buildings, many buildings need to be retrofitted to increase their thermal performance. ML can help pinpoint which buildings and which specific parts of buildings would yield the best performance gains.
  • Designing Low-Carbon Urban Form: Urban form, the physical form of cities, has important implications of energy use and GHG emissions, for example sprawled cities can induce mobility demand. ML can help analyze energy use implications of different urban forms, simulate urban development pathways and (re-)designing neighborhoods by finding patterns in urban form data.

The future of cities[edit | edit source]

  • Efficient sensing: The proliferation of sensors poses the question of how to minimize the energy use related to capturing, sending and storing the data. ML can help recognize what is the most information, possibly on the edge, make sensing more efficient.
  • Causal inference of policy interventions: The effect of policy interventions are often uncertain, and it is important to evaluate them to evaluate their effectiveness. Causal inference methods in ML can help observe the effects of policies from observational data.
  • Assessing urban climate: Cities have an influence on their local climate -- which they tend to make hotter -- which has important implications for climate change mitigation and adaptation. ML can help investigate climatic processes in cities at high-resolution and how they related to the built infrastructure.
  • Enabling nature-based solutions in cities: Nature-based solutions, for example planting trees, can provide multiple benefit including sequestrating carbon and providing cooling. ML can help assess what is the current vegetation in cities and pinpoint opportunities for planting trees.
  • Predictive maintenance of public infrastructure: Public infrastructure, for example street lighting, can include a large amount of individual components that are difficult to monitor. ML can help predict which components are more likely to be dysfunctional to ease maintenance operations.

Urban transportation[edit | edit source]

Main article: Transportation

Urban transportation is of high relevance to mitigating climate change in cities, as mobility within cities represents a large share of the total final energy use in the transportation sector (40% in 2010[9]). Transportation topics are treated as a separate section of the wiki, where areas of particular relevance include:

Background Readings[edit | edit source]

Relevant IPCC chapters[edit | edit source]

  • Chapter 9: "Buildings" in the IPCC Fifth Assessment Report (2014)[10]: An overview of issues related to the mitigation of greenhouse gas emissions (GHG) from the buildings sector.
  • Chapter 12: "Human Settlements, Infrastructure and Spatial Planning" in the IPCC Fifth Assessment Report (2014)[11]: An overview of issues related to the mitigation of greenhouse gas emissions (GHG) from urban areas.

Academic perspectives[edit | edit source]

  • Advances Toward a Net-Zero Global Building Sector, Ürge-Vorsatz et al. (2020)[12]: An authoritative review of the existing academic and professional literature towards decarbonizing the building sector globally.
  • Six research priorities for cities and climate change, Bai, X., et al. (2018)[13]: Leading urban sustainability researchers call for long-term, cross-disciplinary studies to reduce carbon emissions and urban risks from global warming.
  • Sustainability in an urbanizing planet, by Seto, K C., et al. (2017)[14]: This introduction to a special issue in PNAS enumerates key common themes, knowledge gaps and research priorities towards sustainability in an urbanizing planet.
  • Carbon lock-in: types, causes, and policy implications, by Seto, K,C., et al. (2016)[15]: This is an authoritative review of carbon lock-ins, the phenomenon of inertia in carbon emissions, for example due to long-lived infrastructure, and which a key issue for climate change mitigation in cities.
  • The urban south and the predicament of global sustainability, by Nagendra, H,, et al.(2018)[16]: This piece highlights the challenges to achieve sustainability in cities from the Global South. The authors call for a renewed research focus, and targeted efforts to correct structural biases in the knowledge production system.
  • Internet of Things: Energy boon or bane?, by Hittinger, E,, and Jaramillo, P. (2019)[17]: This short piece discussed direct and indirect impacts on energy systems of Internet of Things technologies.

Perspectives in popular media[edit | edit source]

  • The air conditioning trap: how cold air is heating the world[18]: This 'long read' from the Guardian introduces the issue of indoors cooling in a warming world, and interlinkages between climate change mitigation and adaptation.

Online Courses and Course Materials[edit | edit source]

  • Management of Urban Infrastructures, by EPFL, on Coursera. Learn how to develop management practices that effectively integrate the processes of urban planning with urban infrastructures planning and management for sustainable and resilient cities. Course available here.
  • Planning for Climate Change in African Cities, by a consortium, on Coursera: Learn the foundations for understanding African cities’ exposure and sensitivity to climate change, and how cities can manage these impacts in the face of growing uncertainty. The course has a focus on adaptation but is also relevant to understand African cities in the context of climate change mitigation. Course available here.
  • Co-Creating Sustainable Cities, by TU Delft and Wageningen University, on edX: Learn how citizens can be co-creators of sustainable cities when they engage in city politics or in the design of the urban environment and its technologies and infrastructure. Course available here.
  • Renewable Energy and Green Building Entrepreneurship, by Duke University, on Coursera: Learn about the tools, trends, and tips from the field of entrepreneurship as a career path for making a difference and generating wealth in the renewable energy and green building sectors. Course available here.
  • Set of courses on Sustainable Buildings Systems, by TU Delft, on edX: Learn about different ways to reduce energy use in buildings without compromising occupant comfort, in a series of courses from a leading Dutch university. Courses available on Energy Demand in Buildings, Efficient HVAC Systems, Energy Supply Systems for Buildings and more on the edX website.

Conferences, Journals, and Professional Organizations[edit | edit source]

Major conferences[edit | edit source]

Building modeling and control

Major journals[edit | edit source]

  • Energy and Buildings: Is an international journal publishing articles with explicit links to energy use in buildings. The aim is to present new research results, and new proven practice aimed at reducing the energy needs of a building and improving indoor environment quality.
  • Applied Energy: Provides a forum for information on innovation, research, development and demonstration in the areas of energy conversion and conservation, the optimal use of energy resources, analysis and optimization of energy processes, mitigation of environmental pollutants, and sustainable energy systems.
  • Building and Environment: Building and Environment is an international journal that publishes original research papers and review articles related to building science, urban physics, and human interaction with the indoor and outdoor built environment.
  • Journal of Building Performance Simulation: Publishes international research on building performance simulation including modelling and simulating thermal processes, energy conversion and weather data.
  • Building Simulation: Publishes original, high quality, peer-reviewed research papers and review articles dealing with modeling and simulation of buildings including their systems.
  • Renewable and Sustainable Energy Reviews: The aim of the journal is to share problems, solutions, novel ideas and technologies to support the transition to a low carbon future and achieve our global emissions targets as established by the United Nations Framework Convention on Climate Change.
  • Energy: Is an international, multi-disciplinary journal in energy engineering and research. The journal aims to be a leading peer-reviewed platform and an authoritative source of information for analyses, reviews and evaluations related to energy.
  • Energies: Is an open access journal of related scientific research, technology development, engineering, and the studies in policy and management.
  • Automation in Construction: The journal publishes refereed material on all aspects pertaining to the use of Information Technologies in Design, Engineering, Construction Technologies, and Maintenance and Management of Constructed Facilities.
  • Indoor Air: An international journal with multidisciplinary content, publishes papers reflecting the broad categories of interest in the field of indoor air quality.
  • Journal of Process Control: This international journal covers the application of control theory, operations research, computer science and engineering principles to the solution of process control problems. The scope of process control problems involves a wide range of applications that includes energy processes, energy storage and conversion, smart grid, and data analytics among others.

Major societies and organizations[edit | edit source]

  • The Global Covenant of Mayors for Climate & Energy: GCoM is the largest global alliance for city climate leadership, built upon the commitment of over 10,000 cities and local governments.
  • The Global Alliance for Buildings and Construction (GlobalABC): With over 130 members, including 29 countries, the GlobalABC is a leading global platform for governments, the private sector, civil society and intergovernmental and international organizations to increase action towards a zero-emission, efficient and resilient buildings and construction sector.
  • National Association of City Transportation Officials (NACTO): NACTO’s mission is to build cities as places for people, with safe, sustainable, accessible, and equitable transportation choices that support a strong economy and vibrant quality of life.
  • https://buildings.lbl.gov/cbs/bpdThe Global GCTC program is a collaborative platform for the development of smart cities and communities, led by National Institute of Standards and Technology (NIST) which enables local governments, nonprofit organizations, academic institutions, technologists, and corporations from all over the world to work onInternet of Things (IoT) and Cyber-Physical Systems (CPS) applications within the city and community environment.
  • International Building Performance Simulation Association (IBPSA): Is a non-profit international society of building performance simulation researchers, developers and practitioners, dedicated to improving the built environment.
  • The American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE): Organization with a mission on advancing the arts and sciences of heating, ventilation, air conditioning, refrigeration and their allied fields.
  • The American Society of Mechanical Engineers (ASME): Serves a wide-ranging engineering community through quality learning, the development of codes and standards, certifications, research, conferences and publications, government relations, and other forms of outreach. Mechanical engineering plays a crucial role in building HVAC systems design, modeling, and control.
  • International Federation of Automatic Control (IFAC): Multinational federation is concerned with automatic control and its representation in the fields of engineering, science and the impact of control technology on society. Building energy systems are one of the prominent applications spaces of the community.

Libraries and Tools[edit | edit source]

🌎 This section is currently a stub. You can help by adding resources, as well as 1-2 sentences of context for each resource.

Data[edit | edit source]

Building energy use[edit | edit source]

  • Building Performance Database: The largest publicly-available collection of measured energy performance data for buildings in the United States, curated by the Berkeley Lab.
  • NYC Municipal Building Energy Benchmarking Results: A database of energy use for buildings over 10,000 square feet, identifying each building’s energy intensity, and available GHG emissions for the calendar years 2010-2014 in New York City.
  • The Commercial Buildings Energy Consumption Survey (CBECS): 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.
  • EU Buildings Database: A database with country-level information on the buildings and their energy performance in the European Union.
  • Datasets from the Department of Energy: A list of datasets including Building Operations Data, Building Stock & Energy Data and Developer Resources.
  • The TABULA project: This project provides typologies of buildings in the EU that are relevant to their energy uses.
  • AlphaBuilding - Synthetic Dataset: A synthetic building operation dataset that includes HVAC, lighting, electric loads, occupant counts, environmental parameters, end-use and whole-building energy consumptions at 10-minute intervals.

City metabolism[edit | edit source]

The "metabolism" of a city includes the electricity used, waste generated, and GHG emitted.

  • Metabolism of Cities Data Hub: The Metabolism of Cities Data Hub serves as a central repository for a wide variety of information pertaining to urban metabolism in cities around the world.
  • The China Emission Accounts & Datasets: This page provides energy, emission and socio-economic accounting inventories for China
  • Nangini et al 2019: A database published in Scientific Data harmonizing global databases on cities emissions and relevant ancillary metrics.
  • The Carbon Disclosure Project: A global platform enabling cities to measure and disclose environmental data; a variety of datasets are available

Urban Land Use, Infrastructure Data[edit | edit source]

References[edit | edit source]

  1. 1.0 1.1 1.2 "Tackling Climate Change with Machine Learning". Cite journal requires |journal= (help)
  2. 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.
  3. 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)
  4. 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)
  5. 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)
  6. 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)
  7. 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.
  8. 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.
  9. IEA (2013). Policy Pathways: A Tale of Renewed Cities. International Energy Agency. Paris. p. 98.
  10. "Lucon O., D. Ürge-Vorsatz, A. Zain Ahmed, H. Akbari, P. Bertoldi, L.F. Cabeza, N. Eyre, A. Gadgil, L.D.D. Harvey, Y. Jiang, E. Liphoto, S. Mirasgedis, S. Murakami, J. Parikh, C. Pyke, and M.V. Vilariño, 2014: Buildings. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA" (PDF). line feed character in |title= at position 127 (help)
  11. "Seto K.C., S. Dhakal, A. Bigio, H. Blanco, G.C. Delgado, D. Dewar, L. Huang, A. Inaba, A. Kansal, S. Lwasa, J.E. McMahon, D.B. Müller, J. Murakami, H. Nagendra, and A. Ramaswami, 2014: Human Settlements, Infrastructure and Spatial Planning. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA" (PDF). line feed character in |title= at position 122 (help)
  12. Ürge-Vorsatz, Diana, et al. "Advances toward a net-zero global building sector." Annual Review of Environment and Resources 45 (2020): 227-269.https://doi.org/10.1146/annurev-environ-012420-045843
  13. Bai, Xuemei; Dawson, Richard J.; Ürge-Vorsatz, Diana; Delgado, Gian C.; Barau, Aliyu Salisu; Dhakal, Shobhakar; Dodman, David; Leonardsen, Lykke; Masson-Delmotte, Valérie; Roberts, Debra C.; Schultz, Seth (2018-03). "Six research priorities for cities and climate change". Nature. 555 (7694): 23–25. doi:10.1038/d41586-018-02409-z. Check date values in: |date= (help)
  14. Seto, Karen C.; Golden, Jay S.; Alberti, Marina; Turner, B. L. (2017-08-22). "Sustainability in an urbanizing planet". Proceedings of the National Academy of Sciences. 114 (34): 8935–8938. doi:10.1073/pnas.1606037114. ISSN 0027-8424. PMID 28784798.
  15. Seto, Karen C.; Davis, Steven J.; Mitchell, Ronald B.; Stokes, Eleanor C.; Unruh, Gregory; Ürge-Vorsatz, Diana (2016-11). "Carbon Lock-In: Types, Causes, and Policy Implications". Annual Review of Environment and Resources. 41 (1): 425–452. doi:10.1146/annurev-environ-110615-085934. ISSN 1543-5938. Check date values in: |date= (help)
  16. Nagendra, Harini; Bai, Xuemei; Brondizio, Eduardo S.; Lwasa, Shuaib (2018-07). "The urban south and the predicament of global sustainability". Nature Sustainability. 1 (7): 341–349. doi:10.1038/s41893-018-0101-5. ISSN 2398-9629. Check date values in: |date= (help)
  17. Hittinger, Eric; Jaramillo, Paulina (2019-04-26). "Internet of Things: Energy boon or bane?". Science. 364 (6438): 326–328. doi:10.1126/science.aau8825. ISSN 0036-8075. PMID 31023909.
  18. "The air conditioning trap: how cold air is heating the world, The Guardian 'Long Read'".