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Global sensitivity analysis of an energy-economy model of the residential building sector


  • Frédéric Branger

    () (AgroParisTech ENGREF et CIRED)

  • Louis-Gaëtan Giraudet

    () (CIRED)

  • Céline Guivarch

    () (CIRED)

  • Philippe Quirion

    () (CNRS et CIRED)


In this paper, we discuss the results of a sensitivity analysis of Res-IRF, an energy-economy model of the demand for space heating in French dwellings. Res-IRF has been developed for the purpose of increasing behavioral detail in the modeling of energy demand. The different drivers of energy demand, namely the extensive margin of energy efficiency investment, the intensive one and building occupants’ behavior are disaggregated and determined endogenously. The model also represents the established barriers to the diffusion of energy efficiency: heterogeneity of consumer preferences, landlord-tenant split incentives and slow diffusion of information. The relevance of these modeling assumptions is assessed through the Morris method of sensitivity analysis, which allows for the exploration of uncertainty over the whole input space. We find that the Res-IRF model is most sensitive to energy prices. It is also found to be quite sensitive to the factors parameterizing the di fferent drivers of energy demand. In contrast, inputs mimicking barriers to energy efficiency have been found to have little influence. These conclusions build confidence in the accuracy of the model and highlight occupants’ behavior as a priority area for future empirical research.

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  • Frédéric Branger & Louis-Gaëtan Giraudet & Céline Guivarch & Philippe Quirion, 2015. "Global sensitivity analysis of an energy-economy model of the residential building sector," Policy Papers 2015.01, FAERE - French Association of Environmental and Resource Economists.
  • Handle: RePEc:fae:ppaper:2015.01

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    References listed on IDEAS

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    Cited by:

    1. Ge, Qiao & Menendez, Monica, 2017. "Extending Morris method for qualitative global sensitivity analysis of models with dependent inputs," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 28-39.
    2. Mingquan Wang & Lingyun Zhang & Xin Su & Yang Lei & Qun Shen & Wei Wei & Maohua Wang, 2019. "Assessing the technology impact for industry carbon density reduction in China based on C3IAM-Tice," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 99(3), pages 1455-1468, December.
    3. David Glotin & Cyril Bourgeois & Louis-Gaëtan Giraudet & Philippe Quirion, 2019. "Prediction is difficult, even when it's about the past: a hindcast experiment using Res-IRF, an integrated energy-economy model," Working Papers 2019.03, FAERE - French Association of Environmental and Resource Economists.
    4. Chakraborty, Souvik & Chowdhury, Rajib, 2017. "A hybrid approach for global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 50-57.
    5. Pizarro-Alonso, Amalia & Ravn, Hans & Münster, Marie, 2019. "Uncertainties towards a fossil-free system with high integration of wind energy in long-term planning," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    6. Price, James & Keppo, Ilkka, 2017. "Modelling to generate alternatives: A technique to explore uncertainty in energy-environment-economy models," Applied Energy, Elsevier, vol. 195(C), pages 356-369.
    7. Sharma, Tarun & Glynn, James & Panos, Evangelos & Deane, Paul & Gargiulo, Maurizio & Rogan, Fionn & Gallachóir, Brian Ó, 2019. "High performance computing for energy system optimization models: Enhancing the energy policy tool kit," Energy Policy, Elsevier, vol. 128(C), pages 66-74.

    More about this item


    Sensitivity analysis; Monte Carlo; Morris method; Energy efficiency; Building sector;

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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