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

Author

Listed:
  • Frédéric Branger

    (AgroParisTech ENGREF et CIRED)

  • Louis-Gaëtan Giraudet

    (CIRED)

  • Céline Guivarch

    (CIRED)

  • Philippe Quirion

    (CNRS et CIRED)

Abstract

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.

Suggested Citation

  • 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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    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. Glotin, David & Bourgeois, Cyril & Giraudet, Louis-Gaëtan & Quirion, Philippe, 2019. "Prediction is difficult, even when it's about the past: A hindcast experiment using Res-IRF, an integrated energy-economy model," Energy Economics, Elsevier, vol. 84(S1).
    3. Chakraborty, Souvik & Chowdhury, Rajib, 2017. "A hybrid approach for global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 50-57.
    4. Bourgeois, Cyril & Giraudet, Louis-Gaëtan & Quirion, Philippe, 2021. "Lump-sum vs. energy-efficiency subsidy recycling of carbon tax revenue in the residential sector: A French assessment," Ecological Economics, Elsevier, vol. 184(C).
    5. Giraudet, Louis-Gaëtan & Bourgeois, Cyril & Quirion, Philippe, 2021. "Policies for low-carbon and affordable home heating: A French outlook," Energy Policy, Elsevier, vol. 151(C).
    6. Fleckinger, Pierre & Glachant, Matthieu & Tamokoué Kamga, Paul-Hervé, 2019. "Energy Performance Certificates and investments in building energy efficiency: A theoretical analysis," Energy Economics, Elsevier, vol. 84(S1).
    7. Su, Ziyi & Li, Xiaofeng, 2022. "Extraction of key parameters and simplification of sub-system energy models using sensitivity analysis in subway stations," Energy, Elsevier, vol. 261(PA).
    8. 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.
    9. 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.
    10. Louis-Gaëtan Giraudet & Cyril Bourgeois & Philippe Quirion, 2020. "Efficacité économique et effets distributifs de long-terme des politiques de rénovation énergétique des logements," Post-Print hal-03100351, HAL.
    11. 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.
    12. Nunes, Gustavo & Giglio, Thalita, 2022. "Effects of climate change in the thermal and energy performance of low-income housing in Brazil—assessing design variable sensitivity over the 21st century," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    13. Jåstad, Eirik Ogner & Trotter, Ian M. & Bolkesjø, Torjus Folsland, 2022. "Long term power prices and renewable energy market values in Norway – A probabilistic approach," Energy Economics, Elsevier, vol. 112(C).
    14. 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.
    15. Stéphane Poncin, 2018. "Energy policy tools in Luxembourg - Assessing their impact on households’ space heating energy consumption and CO2 emissions by means of the LuxHEI model," DEM Discussion Paper Series 18-23, Department of Economics at the University of Luxembourg.

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    More about this item

    Keywords

    Sensitivity analysis; Monte Carlo; Morris method; Energy efficiency; Building sector;
    All these keywords.

    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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