Abstract
Small commercial and industrial (CIS) electricity demand is an important category of electric energy consumption. Historically, it has received substantially less research attention than residential usage, potentially due to data constraints. This study seeks to partially fill that gap in the energy economics literature by employing a fairly unique data set for the El Paso, Texas, USA metropolitan economy that includes private capital stock estimates from 1978 through2017. The empirical model is specified using a recently developed analytical framework based on duality theory and a derived input demand function. Parameter estimation is completed using an Autoregressive-Distributed Lag model (ARDL) and an Error Correction Model (ECM). In the long-run, CIS customers in El Paso respond only to own-price and the quantity of capital stock per capita. In the short-run, CIS customers adjust their electricity usage in response to changes in all variables except for the price of electricity. The most unexpected result from this analysis is a short-run income elasticity of -0.32, indicating that CIS electricity usage decreases with economic expansion in El Paso, Texas.Keywords: Electricity usage, Metropolitan economic growth, Small commercial and industrial customers, Capital stocks, Duality theory, Derived input demandJEL Classifications: Q41; R11; M21DOI: https://doi.org/10.32479/ijeep.8233
Highlights
This study analyzes the usage of electricity as an input in production by small commercial and industrial (CIS) customers in El Paso, Texas from 1978 to 2017
These results indicate that the data are appropriate for analysis within an autoregressive-distributed lag model (ARDL) framework
Degree of freedom constraints impose a maximum of four lags of the dependent variable and three lags of each explanatory variable in the ARDL equation specification
Summary
This study analyzes the usage of electricity as an input in production by small commercial and industrial (CIS) customers in El Paso, Texas from 1978 to 2017. Parameter estimation techniques employed include an autoregressive-distributed lag model (ARDL) and an error correction model (ECM). It is helpful for regional utilities and regulatory agencies to understand how changes in economic conditions affect small industrial and commercial electricity consumption. The average number of small industrial and commercial (CIS) customers in 2017 was 41,978. Allen and Fullerton: Metropolitan Evidence Regarding Small Commercial and Industrial Electricity Consumption during this period was approximately 2411 MWH of the total 7844 MWH (megawatt-hours) retail sales in 2017 (EPEC, 2017a). The study concludes with result implications and suggestions for future research
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