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Forecasting how residential urban form affects the regional carbon savings and costs of retrofitting and decentralized energy supply

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  • Hargreaves, Anthony
  • Cheng, Vicky
  • Deshmukh, Sandip
  • Leach, Matthew
  • Steemers, Koen

Abstract

Low carbon energy supply technologies are increasingly used at the building and community scale and are an important part of the government decarbonisation strategy. However, with their present state of development and costs, many of these decentralised technologies rely on public subsidies to be financially viable. It is questionable whether they are cost effective compared to other ways of reducing carbon emissions, such as decarbonisation of conventional supply and improving the energy efficiency of dwellings. Previous studies have found it difficult to reliably estimate the future potential of decentralised supply because this depends on the available residential space which varies greatly within a city region. To address this problem, we used an integrated modelling framework that converted the residential density forecasts of a regional model into a representation of the building dimensions and land of the future housing stock. This included a method of estimating the variability of the dwellings and residential land. We present the findings of a case study of the wider south east regions of England that forecasted the impacts of energy efficiency and decentralised supply scenarios to year 2031. Our novel and innovative method substantially improves the spatial estimates of energy consumption compared to building energy models that only use standard dwelling typologies. We tested the impact of an alternative spatial planning policy on the future potential of decentralised energy supply and showed how lower density development would be more suitable for ground source heat pumps. Our findings are important because this method would help to improve the evidence base for strategies on achieving carbon budgets by taking into account how future residential space constraints would affect the suitability and uptakes of these technologies.

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  • Hargreaves, Anthony & Cheng, Vicky & Deshmukh, Sandip & Leach, Matthew & Steemers, Koen, 2017. "Forecasting how residential urban form affects the regional carbon savings and costs of retrofitting and decentralized energy supply," Applied Energy, Elsevier, vol. 186(P3), pages 549-561.
  • Handle: RePEc:eee:appene:v:186:y:2017:i:p3:p:549-561
    DOI: 10.1016/j.apenergy.2016.02.095
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    References listed on IDEAS

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    6. Urquizo, Javier & Calderón, Carlos & James, Philip, 2018. "Modelling household spatial energy intensity consumption patterns for building envelopes, heating systems and temperature controls in cities," Applied Energy, Elsevier, vol. 226(C), pages 670-681.
    7. Liu, Xiaoping & Ou, Jinpei & Chen, Yimin & Wang, Shaojian & Li, Xia & Jiao, Limin & Liu, Yaolin, 2019. "Scenario simulation of urban energy-related CO2 emissions by coupling the socioeconomic factors and spatial structures," Applied Energy, Elsevier, vol. 238(C), pages 1163-1178.
    8. Kazas, Georgios & Fabrizio, Enrico & Perino, Marco, 2017. "Energy demand profile generation with detailed time resolution at an urban district scale: A reference building approach and case study," Applied Energy, Elsevier, vol. 193(C), pages 243-262.
    9. Mohajeri, Nahid & Perera, A.T.D. & Coccolo, Silvia & Mosca, Lucas & Le Guen, Morgane & Scartezzini, Jean-Louis, 2019. "Integrating urban form and distributed energy systems: Assessment of sustainable development scenarios for a Swiss village to 2050," Renewable Energy, Elsevier, vol. 143(C), pages 810-826.
    10. Weinand, J.M. & McKenna, R. & Fichtner, W., 2019. "Developing a municipality typology for modelling decentralised energy systems," Utilities Policy, Elsevier, vol. 57(C), pages 75-96.
    11. Mata, Érika & Wanemark, Joel & Nik, Vahid M. & Sasic Kalagasidis, Angela, 2019. "Economic feasibility of building retrofitting mitigation potentials: Climate change uncertainties for Swedish cities," Applied Energy, Elsevier, vol. 242(C), pages 1022-1035.
    12. Zhang, Wenwen & Robinson, Caleb & Guhathakurta, Subhrajit & Garikapati, Venu M. & Dilkina, Bistra & Brown, Marilyn A. & Pendyala, Ram M., 2018. "Estimating residential energy consumption in metropolitan areas: A microsimulation approach," Energy, Elsevier, vol. 155(C), pages 162-173.
    13. Dimitra Tsirigoti & Katerina Tsikaloudaki, 2018. "The Effect of Climate Conditions on the Relation between Energy Efficiency and Urban Form," Energies, MDPI, vol. 11(3), pages 1-29, March.
    14. Olga Porro & Francesc Pardo-Bosch & Núria Agell & Mónica Sánchez, 2020. "Understanding Location Decisions of Energy Multinational Enterprises within the European Smart Cities’ Context: An Integrated AHP and Extended Fuzzy Linguistic TOPSIS Method," Energies, MDPI, vol. 13(10), pages 1-29, May.
    15. Asaee, S. Rasoul & Sharafian, Amir & Herrera, Omar E. & Blomerus, Paul & Mérida, Walter, 2018. "Housing stock in cold-climate countries: Conversion challenges for net zero emission buildings," Applied Energy, Elsevier, vol. 217(C), pages 88-100.
    16. Silva, Mafalda C. & Horta, Isabel M. & Leal, Vítor & Oliveira, Vítor, 2017. "A spatially-explicit methodological framework based on neural networks to assess the effect of urban form on energy demand," Applied Energy, Elsevier, vol. 202(C), pages 386-398.

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