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Capturing spatial effects, technology interactions, and uncertainty in urban energy and carbon models: Retrofitting newcastle as a case-study

  • Keirstead, James
  • Calderon, Carlos
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    Local authorities often rely upon urban energy and carbon modelling tools to develop mitigation policies and strategies that will deliver reductions in greenhouse gas emissions. In this paper the UK example of Newcastle-upon-Tyne is used to critique current practice, noting that important features of urban energy systems are often omitted by bottom-up tools including interactions between technologies, spatial disaggregation of demand, and the ability to pursue over-arching policy goals like cost minimization. An alternative optimization-based approach is then described and applied to the Newcastle case, at the scale of both the whole city and the South Heaton district, and using Monte Carlo techniques to address policy uncertainty. The results show that this new method can help policy makers draw more robust policy conclusions, sensitive to spatial variations in energy demand and capturing the interactions between developments in the national energy system and local policy options. Further work should focus on improving our understanding of local building stocks and energy demands so as to better assess the potential of new technologies and policies.

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    Article provided by Elsevier in its journal Energy Policy.

    Volume (Year): 46 (2012)
    Issue (Month): C ()
    Pages: 253-267

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    Handle: RePEc:eee:enepol:v:46:y:2012:i:c:p:253-267
    DOI: 10.1016/j.enpol.2012.03.058
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    1. Kikegawa, Yukihiro & Genchi, Yutaka & Kondo, Hiroaki & Hanaki, Keisuke, 2006. "Impacts of city-block-scale countermeasures against urban heat-island phenomena upon a building's energy-consumption for air-conditioning," Applied Energy, Elsevier, vol. 83(6), pages 649-668, June.
    2. Girardin, Luc & Marechal, François & Dubuis, Matthias & Calame-Darbellay, Nicole & Favrat, Daniel, 2010. "EnerGis: A geographical information based system for the evaluation of integrated energy conversion systems in urban areas," Energy, Elsevier, vol. 35(2), pages 830-840.
    3. Pohekar, S. D. & Ramachandran, M., 2004. "Application of multi-criteria decision making to sustainable energy planning--A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 8(4), pages 365-381, August.
    4. Shimoda, Yoshiyuki & Yamaguchi, Yukio & Okamura, Tomo & Taniguchi, Ayako & Yamaguchi, Yohei, 2010. "Prediction of greenhouse gas reduction potential in Japanese residential sector by residential energy end-use model," Applied Energy, Elsevier, vol. 87(6), pages 1944-1952, June.
    5. Dhakal, Shobhakar & Shrestha, Ram M., 2010. "Bridging the research gaps for carbon emissions and their management in cities," Energy Policy, Elsevier, vol. 38(9), pages 4753-4755, September.
    6. Thomas Bruckner & Robbie Morrison & Chris Handley & Murray Patterson, 2003. "High-Resolution Modeling of Energy-Services Supply Systems Using deeco: Overview and Application to Policy Development," Annals of Operations Research, Springer, vol. 121(1), pages 151-180, July.
    7. Keirstead, James & Schulz, Niels B., 2010. "London and beyond: Taking a closer look at urban energy policy," Energy Policy, Elsevier, vol. 38(9), pages 4870-4879, September.
    8. Keirstead, James & Jennings, Mark & Sivakumar, Aruna, 2012. "A review of urban energy system models: Approaches, challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3847-3866.
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