The Relationship between Residential Electricity Consumption and Income: A Piecewise Linear Model with Panel Data

Abstract

There are many uncertainties and risks in residential electricity consumption associated with economic development. Knowledge of the relationship between residential electricity consumption and its key determinant-income-is important to the sustainable development of the electric power industry. Using panel data from 30 provinces for the 1995-2012 period, this study investigates how residential electricity consumption changes as incomes increase in China. Previous studies typically used linear or quadratic double-logarithmic models imposing ex ante restrictions on the indistinct relationship between residential electricity consumption and income. Contrary to those models, we employed a reduced piecewise linear model that is self-adaptive and highly flexible and circumvents the problem of "prior restrictions". Robust tests of different segment specifications and regression methods are performed to ensure the validity of the research. The results provide strong evidence that the income elasticity was approximately one, and it remained stable throughout the estimation period. The income threshold at which residential electricity consumption automatically remains stable or slows has not been reached. To ensure the sustainable development of the electric power industry, introducing higher energy efficiency standards for electrical appliances and improving income levels are vital. Government should also emphasize electricity conservation in the industrial sector rather than in residential sector.

Efficiency of Energy Taxes and the Validity of the Residential Electricity Environmental Kuznets Curve in the European Union

“…Instead of using the amount of pollutants or the level of GHG emissions as indices for environmental pressures, empirical studies have recently started to use the level of energy or electricity consumption as an indicator of an overall environmental pressure [10][11][12][13][14][15][16]. This paper follows them.…”

“…However, regardless of this direction, a wise use of electricity cannot hinder GDP growth. Recently, several studies have started to consider energy consumption as an indicator of overall environmental pressure and hence conducted research aiming to test the relationship between energy consumption and GDP [10][11][12][13][14] or electricity consumption and GDP [15,16] within the so-called environmental Kuznets curve (EKC). Although some of them have confirmed its existence [10,[13][14][15][16], and therefore that the environmental quality deteriorates at early stages of economic growth and subsequently improves at later stages, their results are inconclusive in general and call for new research.…”

Residential Electricity Consumption Level Impact Factor Analysis Based on Wrapper Feature Selection and Multinomial Logistic Regression

“…The electricity consumption in the residential sector has experienced a substantial increase over the past 40 years [1,2]. The world total electricity consumption reached 20,200 TWh in 2015, increased by 1.6% annually [3].…”

“…p-value test is a common method to judge the significance of factors. It is used to remove the factors with the value of parameter α smaller than 0.05 [1,19]. Similarly, the Australian study conducted by Fan et al [20] put all the factors into a general regression model.…”

“…The prevalent wrapper method in current research is stepwise regression, which incorporates factors one by one and simultaneously removes insignificant or redundant factors to better fit the final linear regression model with higher adjusted R 2 [21,22]. In a word, wrapper methods with higher computation complexity outperform filter methods in the following two aspects: (1) the correlation between candidate feature and other features is considered at the modelling stage; (2) the model is optimized to successfully eliminate irrelevant and redundant features, as well as improve the precision of the model.…”