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

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.

[1]  Emmanuel Ziramba,et al.  The demand for residential electricity in South Africa , 2008 .

[2]  Hua Liao,et al.  Energy poverty and solid fuels use in rural China: Analysis based on national population census , 2014 .

[3]  Andreas Kemmler,et al.  Factors influencing household access to electricity in India , 2007 .

[4]  J. Silk,et al.  Short and long-run elasticities in US residential electricity demand: a co-integration approach , 1997 .

[5]  Nadeem Javaid,et al.  Energy Optimization in Smart Homes Using Customer Preference and Dynamic Pricing , 2016 .

[6]  Tooraj Jamasb,et al.  HOUSEHOLD ENERGY EXPENDITURE AND INCOME GROUPS: EVIDENCE FROM GREAT BRITAIN , 2010 .

[7]  Maximilian Auffhammer,et al.  Powering Up China: Income Distributions and Residential Electricity Consumption , 2014 .

[8]  R. Kaufmann,et al.  Is there a turning point in the relationship between income and energy use and/or carbon emissions? , 2006 .

[9]  K. Bollen,et al.  Fixed and Random Effects in Panel Data Using Structural Equations Models , 2008 .

[10]  M. Auffhammer,et al.  Forecasting The Path of U.S. CO2 Emissions Using State-Level Information , 2010, Review of Economics and Statistics.

[11]  Theologos Dergiades,et al.  Estimating Residential Demand for Electricity in the United States, 1965-2006 , 2008 .

[12]  Richard Schmalensee,et al.  World Carbon Dioxide Emissions: 19502050 , 1998, Review of Economics and Statistics.

[13]  J. Musango Household electricity access and consumption behaviour in an urban environment: The case of Gauteng in South Africa , 2014 .

[14]  Hua Liao,et al.  How does carbon dioxide emission change with the economic development? Statistical experiences from 132 countries , 2013 .

[15]  Anna Alberini,et al.  Residential Consumption of Gas and Electricity in the U.S.: The Role of Prices and Income , 2011 .

[16]  Haoran Zhao,et al.  The Impact of Financial Crisis on Electricity Demand: A Case Study of North China , 2016 .

[17]  Massimo Filippini,et al.  Residential electricity demand in Spain: New empirical evidence using aggregate data , 2013 .