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Conditional demand analysis as a tool to evaluate energy policy options on the path to grid decarbonization

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Abstract

We implement a conditional demand analysis (CDA) using a large dataset of electricity consumers in a Canadian province with a high market share of electric heating technologies. In doing so we also provide a unifying review of the breadth of interdisciplinary applications of CDA, beginning from the earliest studies up to the present, and test for evidence of unobservable variable bias from random effects panel data estimators. We find that local (i.e. minisplit) heat pumps and thermostat setbacks show the largest electricity savings. Central heat pumps generally do not save heating electricity compared to electric baseboards, and exhibit higher cooling season consumption compared to local heat pumps. We also observe a consistent decline in electricity consumption for newer homes, with the largest effects in the post-2010 period. Our results can inform research to identify promising technologies that support a shift towards large-scale electrification and decarbonization of energy end-uses, on the basis of robust statistical analysis utilizing realized household consumption data.

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  • Maya Papineau & Kareman Yassin & Guy Newsham & Sarah Brice, 2020. "Conditional demand analysis as a tool to evaluate energy policy options on the path to grid decarbonization," Carleton Economic Papers 20-21, Carleton University, Department of Economics.
    • Handle: RePEc:car:carecp:20-21
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