An experiment in own-price elasticity estimation for non-residential electricity demand in the U.S.

K. H. Cao; H. S. Qi; R. Li; C. K. Woo; A. Tishler; J. Zarnikau

doi:10.1016/j.jup.2023.101489

An experiment in own-price elasticity estimation for non-residential electricity demand in the U.S.

K. H. Cao, H. S. Qi, R. Li, C. K. Woo, A. Tishler, J. Zarnikau^*

^*Corresponding author for this work

Research output: Contribution to journal › Journal article › peer-review

1 Citation (Scopus)

Abstract

This paper uses an experiment to identify what modeling decisions significantly affect estimates of own-price elasticity for non-residential (commercial and industrial) electricity demand in the United States (U.S.). Based on 174,240 panel data model runs involving 10,944 monthly state-level observations from the Energy Information Administration for 2001–2019, these decisions are parametric specification, estimation method, and treatment of cross-section dependence. As most of the many generated elasticity estimates are between 0.0 and −0.2, price-induced conservation is likely modest, thus justifying continued policy support for energy efficiency standards and demand-side management in the U.S. path to deep decarbonization.

Original language	English
Article number	101489
Number of pages	14
Journal	Utilities Policy
Volume	81
Early online date	13 Jan 2023
DOIs	https://doi.org/10.1016/j.jup.2023.101489
Publication status	Published - Apr 2023

Scopus Subject Areas

Business and International Management
Development
Sociology and Political Science
Management, Monitoring, Policy and Law

User-Defined Keywords

Price elasticity
Estimation experiment
Non-residential electricity demand
U.S

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.jup.2023.101489

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title = "An experiment in own-price elasticity estimation for non-residential electricity demand in the U.S.",

abstract = "This paper uses an experiment to identify what modeling decisions significantly affect estimates of own-price elasticity for non-residential (commercial and industrial) electricity demand in the United States (U.S.). Based on 174,240 panel data model runs involving 10,944 monthly state-level observations from the Energy Information Administration for 2001–2019, these decisions are parametric specification, estimation method, and treatment of cross-section dependence. As most of the many generated elasticity estimates are between 0.0 and −0.2, price-induced conservation is likely modest, thus justifying continued policy support for energy efficiency standards and demand-side management in the U.S. path to deep decarbonization.",

keywords = "Price elasticity, Estimation experiment, Non-residential electricity demand, U.S",

author = "Cao, {K. H.} and Qi, {H. S.} and R. Li and Woo, {C. K.} and A. Tishler and J. Zarnikau",

note = "Funding Information: We thank two reviewers and the Editor-in-Chief for their constructive comments that have greatly improved our paper's exposition. This paper extends C.K. Woo's research, funded by grants R3698 and 04564 from the Education University of Hong Kong. Without implications, all errors are ours. Publisher Copyright: {\textcopyright} 2023 Elsevier Ltd",

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doi = "10.1016/j.jup.2023.101489",

language = "English",

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AU - Cao, K. H.

AU - Qi, H. S.

AU - Li, R.

AU - Woo, C. K.

AU - Tishler, A.

AU - Zarnikau, J.

N1 - Funding Information: We thank two reviewers and the Editor-in-Chief for their constructive comments that have greatly improved our paper's exposition. This paper extends C.K. Woo's research, funded by grants R3698 and 04564 from the Education University of Hong Kong. Without implications, all errors are ours. Publisher Copyright: © 2023 Elsevier Ltd

PY - 2023/4

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N2 - This paper uses an experiment to identify what modeling decisions significantly affect estimates of own-price elasticity for non-residential (commercial and industrial) electricity demand in the United States (U.S.). Based on 174,240 panel data model runs involving 10,944 monthly state-level observations from the Energy Information Administration for 2001–2019, these decisions are parametric specification, estimation method, and treatment of cross-section dependence. As most of the many generated elasticity estimates are between 0.0 and −0.2, price-induced conservation is likely modest, thus justifying continued policy support for energy efficiency standards and demand-side management in the U.S. path to deep decarbonization.

AB - This paper uses an experiment to identify what modeling decisions significantly affect estimates of own-price elasticity for non-residential (commercial and industrial) electricity demand in the United States (U.S.). Based on 174,240 panel data model runs involving 10,944 monthly state-level observations from the Energy Information Administration for 2001–2019, these decisions are parametric specification, estimation method, and treatment of cross-section dependence. As most of the many generated elasticity estimates are between 0.0 and −0.2, price-induced conservation is likely modest, thus justifying continued policy support for energy efficiency standards and demand-side management in the U.S. path to deep decarbonization.

KW - Price elasticity

KW - Estimation experiment

KW - Non-residential electricity demand

KW - U.S

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DO - 10.1016/j.jup.2023.101489

M3 - Journal article

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SN - 0957-1787

VL - 81

JO - Utilities Policy

JF - Utilities Policy

M1 - 101489

ER -

An experiment in own-price elasticity estimation for non-residential electricity demand in the U.S.

Abstract

Scopus Subject Areas

User-Defined Keywords

UN SDGs

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