Features of residential energy consumption: Evidence from France using an innovative multilevel modelling approach

Recent efforts to reduce residential energy consumption have renewed interest in investigating salient drivers of household energy use. This article contributes to the ongoing literature by developing a model for examining geographic effects on energy use. Using a new, rich, micro-level survey that compiles information about dwelling attributes, occupant characteristics, and behaviors, we suggest a combined bottom-up and top-down statistical approach based on a multilevel regression model (MRM) and an innovative variable selection approach via the Adaptive Elastic Net Regularization technique (AdaEnetR). This approach enables us to extract geographic effects from the total variation in residential energy consumption and simultaneously explain the remaining variation with relevant explanatory variables and their interactions. The current model addresses several interrelated issues posed by the use of econometric methods to examine residential energy demand, including the risk of aggregation/disaggregation bias. Our empirical findings demonstrate the MRM's ability to effectively quantify approximately 0.67% of the geographic effects (aggregate level) and approximately 0.31% of the household and dwelling effects (individual level). Further, the findings show that household attributes are important factors that influence residential energy consumption patterns. (This abstract was borrowed from another version of this item.)

[1]  Fateh Bélaïd.  Untangling the complexity of the direct and indirect determinants of the residential energy consumption in France: Quantitative analysis using a structural equation modeling approach , 2017 .

[2]  Bin Chen,et al.  Factor Analysis of Residential Energy Consumption at the Provincial Level in China , 2014 .

[3]  A. Chiu,et al.  A bibliometric review: Energy consumption and greenhouse gas emissions in the residential sector , 2017 .

[4]  R. Sonderegger.  MOVERS AND STAYERS: THE RESIDENT'S CONTRIBUTION TO VARIATION ACROSS HOUSES IN ENERGY CONSUMPTION FOR SPACE HEATING , 1978 .

[5]  Runa Nesbakken,et al.  Energy Consumption for Space Heating: A Discrete-Continuous Approach , 2001 .

[6]  David Roubaud,et al.  Direct rebound effect of residential gas demand: Empirical evidence from France , 2018 .

[7]  Timothy J. Robinson,et al.  Multilevel Analysis: Techniques and Applications , 2002 .

[8]  Jeffrey A. Dubin,et al.  An Econometric Analysis of Residential Electric Appliance Holdings and Consumption , 1984 .

[9]  Sean Lyons,et al.  Energy use and appliance ownership in Ireland , 2010 .

[10]  B. Dong,et al.  A survey on energy consumption and energy usage behavior of households and residential building in urban China , 2017 .

[11]  J. Quigley,et al.  Residential energy use and conservation: Economics and demographics , 2012 .

[12]  Xuejing Li,et al.  Adaptive Elastic-Net for General Single-Index Regression Models , 2011, NL-MUA.

[13]  Fateh Belaïd,et al.  Exposure and risk to fuel poverty in France: Examining the extent of the fuel precariousness and its salient determinants , 2018 .

[14]  E. Rosa,et al.  Energy and life-style. , 1974, Science.

[15]  Fateh Belaïd,et al.  Understanding the spectrum of domestic energy consumption: Empirical evidence from France , 2016 .

[16]  A. Carle.  Fitting multilevel models in complex survey data with design weights: Recommendations , 2009, BMC medical research methodology.

[17]  Fateh Belaïd,et al.  The determinants of domestic energy consumption in France: Energy modes, habitat, households and life cycles , 2017 .

[18]  W. S. Robinson,et al.  Ecological correlations and the behavior of individuals. , 1950, International journal of epidemiology.

[19]  Muhammad Hisyam Lee,et al.  Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification , 2015, Comput. Biol. Medicine.

[20]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[21]  Runa Nesbakken,et al.  Price sensitivity of residential energy consumption in Norway , 1999 .

[22]  Xavier Labandeira,et al.  A Residential Energy Demand System for Spain , 2005 .

[23]  Qi Li,et al.  An Efficient Elastic Net with Regression Coefficients Method for Variable Selection of Spectrum Data , 2017, PloS one.

[24]  Fateh Belaid,et al.  Understanding the Spectrum of Residential Energy-Saving Behaviours: French Evidence Using Disaggregated Data , 2015 .

[25]  Peter Heindl,et al.  Price and income elasticities of residential energy demand in Germany , 2017 .

[26]  Hao Helen Zhang,et al.  ON THE ADAPTIVE ELASTIC-NET WITH A DIVERGING NUMBER OF PARAMETERS. , 2009, Annals of statistics.

[27]  Zhanming Chen,et al.  Characteristics of residential energy consumption in China: Findings from a household survey , 2014 .

[28]  Geoffrey K.F. Tso,et al.  A multilevel regression approach to understand effects of environment indicators and household features on residential energy consumption , 2014 .

[29]  David J. Fritzsche,et al.  An Analysis of Energy Consumption Patterns by Stage of Family Life Cycle , 1981 .

[30]  H. Jianhua,et al.  A group adaptive elastic-net approach for variable selection in high-dimensional linear regression , 2018 .