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Composition and sensitivity of residential energy consumption

Published online by Cambridge University Press:  28 June 2022

Raul Jimenez Mori Open the ORCID record for Raul Jimenez Mori [Opens in a new window] ,
Ariel Yepez-Garcia  and
Demian Macedo
Raul Jimenez Mori*
Affiliation:
IDB Invest, Bogota, Colombia
Ariel Yepez-Garcia
Affiliation:
Inter-American Development Bank (IDB), Washington, DC, USA
Demian Macedo
Affiliation:
Department of Business Economics, Universitat de les Illes Balears (UIB), Palma de Mallorca, Spain
*
*Corresponding author. E-mail: [email protected]
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Abstract

We examine how the composition of residential energy consumption and its sensitivity with respect to income changes. The paper characterizes the energy transition, analyzing the behavior of income elasticity of energy demand along the economic development stages by fuel types. The results indicate a nonlinear relationship between income and domestic energy consumption that can be explained by two factors. First, along the income distribution, consumption of modern fuels increases, replacing traditional and transitional fuels until modern fuels drive all of the growth in domestic energy demand. Second, at the highest income levels, income elasticity starts to decrease, leading to concavity in energy consumption. That is, the income elasticity of residential energy demand follows an inverse U-shape along the world income distribution. This finding suggests that at high income levels, residential energy consumption shows satiation and net energy-saving effects.

Keywords

economic developmentincome elasticity of energy demandresidential energy use

JEL classification

O13: Agriculture • Natural Resources • Energy • Environment • Other Primary Products Q43: Energy and the Macroeconomy
Type
Research Article
Information
Environment and Development Economics , Volume 27 , Issue 6 , December 2022 , pp. 533 - 555
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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References

Bjørner, T and Jensen, H (2002) Interfuel substitution within industrial companies: an analysis based on panel data at company level. The Energy Journal 23, 27–50.Google Scholar
BP (British Petroleum) (2016) BP Energy Outlook 2016 Edition. Outlook to 2035.Google Scholar
Burke, PJ (2010) Income, resources, and electricity mix. Energy Economics 32, 616–626.CrossRefGoogle Scholar
Burke, PJ (2013) The national-level energy ladder and its carbon implications. Environment and Development Economics 18, 484–503.CrossRefGoogle Scholar
Burke, PJ and Csereklyei, Z (2016) Understanding the energy-GDP elasticity: a sectoral approach. Energy Economics 58, 199–210.CrossRefGoogle Scholar
Csereklyei, Z, Rubio-Varas, MDM and Stern, DI (2016) Energy and economic growth: the stylized facts. The Energy Journal 37, 223–255.CrossRefGoogle Scholar
Csereklyei, Z and Stern, DI (2015) Global energy use: decoupling or convergence? Energy Economics 51, 633–641.CrossRefGoogle Scholar
Csereklyei, Z, Thurner, PW, Langer, J and Küchenhoff, H (2017) Energy paths in the European Union: a model-based clustering approach. Energy Economics 65, 442–457.CrossRefGoogle Scholar
Dahl, C and Sterner, T (1991) Analyzing gasoline demand elasticities: a survey. Energy Economics 13, 203–210.CrossRefGoogle Scholar
Davis, M (1998) Rural household energy consumption. Energy Policy 26, 207–217.Google Scholar
Fouquet, R (2014) Long-run demand for energy services: income and price elasticities over two hundred years. Review of Environmental Economics and Policy 8, 186–207.CrossRefGoogle Scholar
Galiani, S and Gonzalez-Rozada, M (2002) Inference and estimation in small sample dynamic panel data models. Business School Working Papers 02/2002. Centro de Investigación en Finanzas, Universidad Torcuato Di Tella.Google Scholar
Galli, R (1998) The relationship between energy intensity and income levels: forecasting long term energy demand in Asian emerging countries. The Energy Journal 19, 85–105.CrossRefGoogle Scholar
Hainmueller, J and Hazlett, C (2014) Kernel regularized least squares: reducing misspecification bias with a flexible and interpretable machine learning approach. Political Analysis 22, 143–168.CrossRefGoogle Scholar
Hanna, R and Oliva, P (2015) Moving up the energy ladder: the effect of an increase in economic well-being on the fuel consumption choices of the poor in India. American Economic Review: Papers & Proceedings 105, 242–246.CrossRefGoogle Scholar
Heltberg, R (2004) Fuel switching: evidence from eight developing countries. Energy Economics 26, 869–887.CrossRefGoogle Scholar
Hiemstra-van der Horst, G and Hovorka, AJ (2008) Reassessing the “energy ladder”: household energy use in Maun, Botswana. Energy Policy 36, 3333–3344.CrossRefGoogle Scholar
Hosier, R and Dowd, J (1987) Household fuel choice in Zimbabwe. Resources and Energy 9, 347–361.CrossRefGoogle Scholar
IEA (2016) Energy efficiency market report 2016. OECD/IEA. Paris: IEA.Google Scholar
IEA (International Energy Agency) (2007) World Energy Outlook 2007: China and India Insights. OECD/IEA. Paris: IEA.Google Scholar
Jimenez, R (2016) Rural electricity access penalty in Latin America: income and location. Policy Brief IDB-PB-253. Inter-American Development Bank.CrossRefGoogle Scholar
Judson, R and Owen, A (1999) Estimating dynamic panel data models: a guide for macroeconomists. Economics Letters 65, 9–15.CrossRefGoogle Scholar
Judson, R, Schmalensee, R and Stoker, T (1999) Economic development and the structure of the demand for commercial energy. Energy Journal 20, 29–57.CrossRefGoogle Scholar
Kowsari, R and Zerriffi, H (2011) Three-dimensional energy profile: a conceptual framework for assessing household energy use. Energy Policy 39, 7505–7517.CrossRefGoogle Scholar
Leach, G (1992) The energy transition. Energy Policy 20, 116–123.CrossRefGoogle Scholar
Masera, OR, Saatkamp, BD and Kammen, DM (2000) From linear fuel switching to multiple cooking strategies: a critique and alternative to the energy ladder model. World Development 28, 2083–2103.CrossRefGoogle Scholar
Medlock, KB and Soligo, R (2001) Economic development and end-use energy demand. Energy Journal 22, 77–105.Google Scholar
Nguyen-Van, P (2010) Energy consumption and income: a semiparametric panel data analysis. Energy Economics 32, 557–563.CrossRefGoogle Scholar
Nordhaus, WD (1996) Do real output and real wage measures capture reality? The history of lighting suggests not. In Breshnahan, TF and Gordon, R (eds), The Economic of New Goods. Chicago, IL: Chicago University Press, pp. 27–70.Google Scholar
Pachauri, S and Jiang, L (2008) The household energy transition in India and China. Energy Policy 36, 4022–4035.CrossRefGoogle Scholar
Pedroni, P (1999) Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics 61, 653–670.CrossRefGoogle Scholar
Pesaran, MH (2006) Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica 74, 967–1012.CrossRefGoogle Scholar
Pesaran, MH and Smith, R (1995) Estimating long-run relationships from dynamic heterogeneous panels. Journal of Econometrics 68, 79–113.CrossRefGoogle Scholar
Sathaye, J and Tyler, S (1991) Transitions in household energy use in urban China, India, the Philippines, Thailand, and Hong Kong. Annual Review of Energy and the Environment 16, 295–335.CrossRefGoogle Scholar
Sovacool, BK (2011) Conceptualizing urban household energy use: climbing the energy services ladder. Energy Policy 39, 1659–1668.CrossRefGoogle Scholar
Sovacool, BK (2016) How long will it take? Conceptualizing the temporal dynamics of energy transitions. Energy Research & Social Science 13, 202–215.CrossRefGoogle Scholar
Steinbuks, J (2012) Interfuel substitution and energy use in the U.K. Manufacturing sector. Energy Journal 33, 1–30.CrossRefGoogle Scholar
Stern, DI (2011) The role of energy in economic growth. Annals of the New York Academy of Sciences 1219, 26–51.CrossRefGoogle ScholarPubMed
Stern, DI (2012) Interfuel substitution: a meta-analysis. Journal of Economic Surveys 26, 307–331.CrossRefGoogle Scholar
Tahvonen, O and Salo, S (2001) Economic growth and transitions between renewable and nonrenewable energy resources. European Economic Review 45, 1379–1398.CrossRefGoogle Scholar
van Benthem, AA (2015) Energy leapfrogging. Journal of the Association of Environmental and Resource Economists 2, 93–132.CrossRefGoogle Scholar
van Benthem, A and Romani, M (2009) Fueling growth: what drives energy demand in developing countries? Energy Journal 30, 91–114.CrossRefGoogle Scholar
Westerlund, J (2007) Testing for error correction in panel data. Oxford Bulletin of Economics and Statistics 69, 709–748.CrossRefGoogle Scholar
Wolfram, C, Shelef, O and Gertler, P (2012) How will energy demand develop in the developing world? Journal of Economic Perspectives 26, 119–137.CrossRefGoogle Scholar
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