Price responsiveness of residential demand for natural gas in the United States

Raymond LI, Chi Keung WOO, Asher TISHLER, Jay ZARNIKAU

Research output: Contribution to journalArticlespeer-review

Abstract

While price responsiveness of residential demand for natural gas has important implications on resource planning and energy modelling, its estimates from prior studies are very diverse. Applying panel data analysis and five parametric specifications to monthly data for the lower 48 states in 1990–2019, we estimate own-price elasticities of residential demand for natural gas in the United States (US). Using results from cross-section dependence (CD) test, panel unit root tests, panel time-series estimators, and rolling-window analysis, we document: (1) the statistically significant (p-value ≤ 0.05) static own-price elasticity estimates are −0.271 to −0.486, short-run −0.238 to −0.555 and long-run −0.323 to −0.796; (2) these estimates vary by elasticity type, sample period, parametric specification, treatment of CD and assumption of partial adjustment; (3) erroneously ignoring the highly significant (p-value < 0.01) CD shrinks the size of these estimates that vary seasonally, regionally, and nonlinearly over time; and (4) residential natural gas shortage costs decline with the size of own-price elasticity estimates. These findings suggest that achieving deep decarbonization may require strategies that do not rely solely on prices, such as energy efficiency standards and demand-side-management programs. Demand response programs may prove useful for managing natural gas shortages. Copyright © 2022 by the authors.
Original languageEnglish
Article number4231
JournalEnergies
Volume15
Issue number12
DOIs
Publication statusPublished - Jun 2022

Citation

Li, R., Woo, C.-K., Tishler, A., & Zarnikau, J. (2022). Price responsiveness of residential demand for natural gas in the United States. Energies, 15(12). Retrieved from https://doi.org/10.3390/en15124231

Keywords

  • Residential natural gas demand
  • Price elasticity
  • Cross-section dependence
  • Panel data analysis

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