IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v159y2022ics1364032122001083.html
   My bibliography  Save this article

Cool roofs can mitigate cooling energy demand for informal settlement dwellers

Author

Listed:
  • Nutkiewicz, Alex
  • Mastrucci, Alessio
  • Rao, Narasimha D.
  • Jain, Rishee K.

Abstract

Cities are critical to meeting our sustainable energy goals. Informal settlement redevelopment programs represent an opportunity to improve living conditions and curb increasing demand for active cooling. We introduce an energy modeling framework for informal settlements to investigate how building design decisions influence the onset of heat stress and energy-intensive cooling demand. We show that occupants of tropically-located informal settlements are most vulnerable to prolonged heat stress year-round. Up to 98% of annual heat stress exposure can be mitigated by improving the building envelope. We find a universal solution (cool roofs) that reduces up to 91% of annual heat stress exposure. Finally, we show how proposed redevelopment building schemes could worsen thermal conditions of dwellers and further increase urban energy demand. Our results underscore how building design affects human well-being and highlight potential near-term and long-term pathways for reducing energy-intensive cooling demand for 800+ million informal settlement dwellers worldwide.

Suggested Citation

  • Nutkiewicz, Alex & Mastrucci, Alessio & Rao, Narasimha D. & Jain, Rishee K., 2022. "Cool roofs can mitigate cooling energy demand for informal settlement dwellers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    • Handle: RePEc:eee:rensus:v:159:y:2022:i:c:s1364032122001083
    DOI: 10.1016/j.rser.2022.112183
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032122001083
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2022.112183?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yao, Runming & Liu, Jing & Li, Baizhan, 2010. "Occupants' adaptive responses and perception of thermal environment in naturally conditioned university classrooms," Applied Energy, Elsevier, vol. 87(3), pages 1015-1022, March.
    2. Bhattacharyya, Subhes C., 2015. "Influence of India’s transformation on residential energy demand," Applied Energy, Elsevier, vol. 143(C), pages 228-237.
    3. Yıldız, Yusuf & Arsan, Zeynep Durmuş, 2011. "Identification of the building parameters that influence heating and cooling energy loads for apartment buildings in hot-humid climates," Energy, Elsevier, vol. 36(7), pages 4287-4296.
    4. Chen, Xi & Yang, Hongxing & Wang, Yuanhao, 2017. "Parametric study of passive design strategies for high-rise residential buildings in hot and humid climates: miscellaneous impact factors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 442-460.
    5. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    6. Yang, Liu & Yan, Haiyan & Lam, Joseph C., 2014. "Thermal comfort and building energy consumption implications – A review," Applied Energy, Elsevier, vol. 115(C), pages 164-173.
    7. Tian Han & Qiong Huang & Anxiao Zhang & Qi Zhang, 2018. "Simulation-Based Decision Support Tools in the Early Design Stages of a Green Building—A Review," Sustainability, MDPI, vol. 10(10), pages 1-23, October.
    8. Chandel, S.S. & Sharma, Vandna & Marwah, Bhanu M., 2016. "Review of energy efficient features in vernacular architecture for improving indoor thermal comfort conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 459-477.
    9. Chen, Yixing & Hong, Tianzhen & Piette, Mary Ann, 2017. "Automatic generation and simulation of urban building energy models based on city datasets for city-scale building retrofit analysis," Applied Energy, Elsevier, vol. 205(C), pages 323-335.
    10. Ma, Nan & Aviv, Dorit & Guo, Hongshan & Braham, William W., 2021. "Measuring the right factors: A review of variables and models for thermal comfort and indoor air quality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    11. Narasimha D. Rao & Jihoon Min & Alessio Mastrucci, 2019. "Energy requirements for decent living in India, Brazil and South Africa," Nature Energy, Nature, vol. 4(12), pages 1025-1032, December.
    12. Henson, Rosie Mae & Ortigoza, Ana & Martinez-Folgar, Kevin & Baeza, Fernando & Caiaffa, Waleska & Vives Vergara, Alejandra & Diez Roux, Ana V. & Lovasi, Gina, 2020. "Evaluating the health effects of place-based slum upgrading physical environment interventions: A systematic review (2012–2018)," Social Science & Medicine, Elsevier, vol. 261(C).
    13. Ali, Usman & Shamsi, Mohammad Haris & Bohacek, Mark & Purcell, Karl & Hoare, Cathal & Mangina, Eleni & O’Donnell, James, 2020. "A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making," Applied Energy, Elsevier, vol. 279(C).
    14. Indraganti, Madhavi, 2010. "Thermal comfort in naturally ventilated apartments in summer: Findings from a field study in Hyderabad, India," Applied Energy, Elsevier, vol. 87(3), pages 866-883, March.
    15. Fonseca, Jimeno A. & Schlueter, Arno, 2015. "Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts," Applied Energy, Elsevier, vol. 142(C), pages 247-265.
    16. Nutkiewicz, Alex & Jain, Rishee K. & Bardhan, Ronita, 2018. "Energy modeling of urban informal settlement redevelopment: Exploring design parameters for optimal thermal comfort in Dharavi, Mumbai, India," Applied Energy, Elsevier, vol. 231(C), pages 433-445.
    17. von Grabe, Jörn, 2016. "Potential of artificial neural networks to predict thermal sensation votes," Applied Energy, Elsevier, vol. 161(C), pages 412-424.
    18. Chou, S.K. & Lee, Y.K., 1988. "A simplified overall thermal transfer value equation for building envelopes," Energy, Elsevier, vol. 13(8), pages 657-670.
    19. Harkouss, Fatima & Fardoun, Farouk & Biwole, Pascal Henry, 2018. "Passive design optimization of low energy buildings in different climates," Energy, Elsevier, vol. 165(PA), pages 591-613.
    20. Aklin, Michaël & Bayer, Patrick & Harish, S.P. & Urpelainen, Johannes, 2015. "Quantifying slum electrification in India and explaining local variation," Energy, Elsevier, vol. 80(C), pages 203-212.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Excell, Lauren E. & Jain, Rishee K., 2024. "Examining the impact of energy efficiency retrofits and vegetation on energy performance of institutional buildings: An equity-driven analysis," Applied Energy, Elsevier, vol. 357(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nutkiewicz, Alex & Jain, Rishee K. & Bardhan, Ronita, 2018. "Energy modeling of urban informal settlement redevelopment: Exploring design parameters for optimal thermal comfort in Dharavi, Mumbai, India," Applied Energy, Elsevier, vol. 231(C), pages 433-445.
    2. Perwez, Usama & Yamaguchi, Yohei & Ma, Tao & Dai, Yanjun & Shimoda, Yoshiyuki, 2022. "Multi-scale GIS-synthetic hybrid approach for the development of commercial building stock energy model," Applied Energy, Elsevier, vol. 323(C).
    3. Ning, Haoran & Wang, Zhaojun & Ji, Yuchen, 2016. "Thermal history and adaptation: Does a long-term indoor thermal exposure impact human thermal adaptability?," Applied Energy, Elsevier, vol. 183(C), pages 22-30.
    4. Abbasabadi, Narjes & Ashayeri, Mehdi & Azari, Rahman & Stephens, Brent & Heidarinejad, Mohammad, 2019. "An integrated data-driven framework for urban energy use modeling (UEUM)," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    5. Ren, Zhengen & Chen, Dong, 2018. "Modelling study of the impact of thermal comfort criteria on housing energy use in Australia," Applied Energy, Elsevier, vol. 210(C), pages 152-166.
    6. Ali, Usman & Shamsi, Mohammad Haris & Bohacek, Mark & Purcell, Karl & Hoare, Cathal & Mangina, Eleni & O’Donnell, James, 2020. "A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making," Applied Energy, Elsevier, vol. 279(C).
    7. Zhang, Sheng & Lin, Zhang, 2020. "Standard effective temperature based adaptive-rational thermal comfort model," Applied Energy, Elsevier, vol. 264(C).
    8. Naji, Sareh & Aye, Lu & Noguchi, Masa, 2021. "Sensitivity analysis on energy performance, thermal and visual discomfort of a prefabricated house in six climate zones in Australia," Applied Energy, Elsevier, vol. 298(C).
    9. Prataviera, Enrico & Zarrella, Angelo & Morejohn, Joshua & Narayanan, Vinod, 2024. "Exploiting district cooling network and urban building energy modeling for large-scale integrated energy conservation analyses," Applied Energy, Elsevier, vol. 356(C).
    10. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    11. Younghun Choi & Takuro Kobashi & Yoshiki Yamagata & Akito Murayama, 2021. "Assessment of waterfront office redevelopment plan on optimal building energy demand and rooftop photovoltaics for urban decarbonization," Papers 2108.09029, arXiv.org.
    12. Roth, Jonathan & Martin, Amory & Miller, Clayton & Jain, Rishee K., 2020. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods," Applied Energy, Elsevier, vol. 280(C).
    13. Enescu, Diana, 2017. "A review of thermal comfort models and indicators for indoor environments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1353-1379.
    14. Wang, Zhe & Hong, Tianzhen, 2020. "Learning occupants’ indoor comfort temperature through a Bayesian inference approach for office buildings in United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    15. Dongsu Kim & Jongman Lee & Sunglok Do & Pedro J. Mago & Kwang Ho Lee & Heejin Cho, 2022. "Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends," Energies, MDPI, vol. 15(19), pages 1-30, October.
    16. Walsh, Angélica & Cóstola, Daniel & Labaki, Lucila Chebel, 2018. "Performance-based validation of climatic zoning for building energy efficiency applications," Applied Energy, Elsevier, vol. 212(C), pages 416-427.
    17. Rosenfelder, Markus & Wussow, Moritz & Gust, Gunther & Cremades, Roger & Neumann, Dirk, 2021. "Predicting residential electricity consumption using aerial and street view images," Applied Energy, Elsevier, vol. 301(C).
    18. Yun, Geun Young & Steemers, Koen, 2011. "Behavioural, physical and socio-economic factors in household cooling energy consumption," Applied Energy, Elsevier, vol. 88(6), pages 2191-2200, June.
    19. Jozef Švajlenka & Mária Kozlovská, 2021. "Factors Influencing the Sustainability of Wood-Based Constructions’ Use from the Perspective of Users," Sustainability, MDPI, vol. 13(23), pages 1-16, November.
    20. Chen, Yujiao & Tong, Zheming & Wu, Wentao & Samuelson, Holly & Malkawi, Ali & Norford, Leslie, 2019. "Achieving natural ventilation potential in practice: Control schemes and levels of automation," Applied Energy, Elsevier, vol. 235(C), pages 1141-1152.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:rensus:v:159:y:2022:i:c:s1364032122001083. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.