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CO2 Emission Reduction Costs in the Residential Sector: Behavioral Parameters in a Bottom-Up Simulation Model

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  • Mark Jaccard
  • Alison Bailie
  • John Nyboer

Abstract

Cost estimates for reducing energy-related CO2 emissions vary with modeling assumptions and methods. Much debate has centered on the tendency for top-down models to suggest high costs and for bottom-up models to suggest low costs. This study incorporates behavioral parameters, derived from end-use equipment acquisition surveys, in a bottom-up simulation model ofthe residential sector in order to probe the basis for differing cost estimates and to test various policy suggestions. Simulating the effect of carbon taxes on a business as usual forecast, the results suggest that a CO2 tax will lead to significant net costs of adjustment if the factors leading to higher private discount rates reflect in part real costs and risks. The results also suggest that it may be in society's interest to pursue fuel switching policies with equal or greater vigour than energy efficiency improvements for the goal of reducing CO2emissions in the residential sector. As further research helps to distinguish the significance of these perceived costs and risks, and to refine projections of technology costs, the inputs to the model can be adjusted in order to refine the estimates for policy makers of CO2 reduction costs and of appropriate strategies for achieving reduction goals.

Suggested Citation

  • Mark Jaccard & Alison Bailie & John Nyboer, 1996. "CO2 Emission Reduction Costs in the Residential Sector: Behavioral Parameters in a Bottom-Up Simulation Model," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 107-134.
  • Handle: RePEc:aen:journl:1996v17-04-a05
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    Citations

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    Cited by:

    1. Charlier, Dorothée & Risch, Anna, 2012. "Evaluation of the impact of environmental public policy measures on energy consumption and greenhouse gas emissions in the French residential sector," Energy Policy, Elsevier, vol. 46(C), pages 170-184.
    2. Chris Bataille, Mark Jaccard, John Nyboer and Nic Rivers, 2006. "Towards General Equilibrium in a Technology-Rich Model with Empirically Estimated Behavioral Parameters," The Energy Journal, International Association for Energy Economics, vol. 0(Special I), pages 93-112.
    3. Jaccard, Mark & Bataille, Chris, 2000. "Estimating future elasticities of substitution for the rebound debate," Energy Policy, Elsevier, vol. 28(6-7), pages 451-455, June.
    4. Shigeru Matsumoto, 2015. "Electric Appliance Ownership and Usage: Application of Conditional Demand Analysis to Japanese Household Data," Proceedings of International Academic Conferences 3105452, International Institute of Social and Economic Sciences.
    5. Nic Rivers & Mark Jaccard, 2005. "Combining Top-Down and Bottom-Up Approaches to Energy-Economy Modeling Using Discrete Choice Methods," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 83-106.
    6. Murphy, Rose & Jaccard, Mark, 2011. "Energy efficiency and the cost of GHG abatement: A comparison of bottom-up and hybrid models for the US," Energy Policy, Elsevier, vol. 39(11), pages 7146-7155.
    7. Matsumoto, Shigeru, 2016. "How do household characteristics affect appliance usage? Application of conditional demand analysis to Japanese household data," Energy Policy, Elsevier, vol. 94(C), pages 214-223.
    8. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    9. Jaccard, Mark & Murphy, Rose & Rivers, Nic, 2004. "Energy-environment policy modeling of endogenous technological change with personal vehicles: combining top-down and bottom-up methods," Ecological Economics, Elsevier, vol. 51(1-2), pages 31-46, November.
    10. Rivers, Nic & Jaccard, Mark, 2006. "Useful models for simulating policies to induce technological change," Energy Policy, Elsevier, vol. 34(15), pages 2038-2047, October.
    11. Yanghui Guo & Andante Hadi Pandyaswargo & Koki Matsumoto & Hiroshi Onoda, 2023. "Development and Verification of a Regional Residential Electricity Consumption Estimation Method," Energies, MDPI, vol. 16(23), pages 1-18, November.
    12. Marcin Zygmunt & Dariusz Gawin, 2021. "Application of Artificial Neural Networks in the Urban Building Energy Modelling of Polish Residential Building Stock," Energies, MDPI, vol. 14(24), pages 1-15, December.
    13. Mark K. Jaccard & John Nyboer & Crhis Bataille & Bryn Sadownik, 2003. "Modeling the Cost of Climate Policy: Distinguishing Between Alternative Cost Definitions and Long-Run Cost Dynamics," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 49-73.
    14. Martinsen, Thomas, 2011. "Introducing technology learning for energy technologies in a national CGE model through soft links to global and national energy models," Energy Policy, Elsevier, vol. 39(6), pages 3327-3336, June.
    15. Subramanyam, Veena & Kumar, Amit & Talaei, Alireza & Mondal, Md. Alam Hossain, 2017. "Energy efficiency improvement opportunities and associated greenhouse gas abatement costs for the residential sector," Energy, Elsevier, vol. 118(C), pages 795-807.
    16. Jaccard, Mark & Loulou, Richard & Kanudia, Amit & Nyboer, John & Bailie, Alison & Labriet, Maryse, 2003. "Methodological contrasts in costing greenhouse gas abatement policies: Optimization and simulation modeling of micro-economic effects in Canada," European Journal of Operational Research, Elsevier, vol. 145(1), pages 148-164, February.

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    JEL classification:

    • F0 - International Economics - - General

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