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From Residential Energy Demand to Fuel Poverty: Income-induced Non-linearities in the Reactions of Households to Energy Price Fluctuations

Dorothee Charlier and Sondes Kahouli

Year: 2019
Volume: Volume 40
Number: Number 2
DOI: 10.5547/01956574.40.2.dcha
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Abstract:
The residential energy demand is growing steadily and the trend is expected to continue in the near future. At the same time, under the impulse of economic crises and environmental and energy policies, many households have experienced reductions in real income and higher energy prices. In the residential sector, the number of fuel-poor households is thus expected to rise. A better understanding of the determinants of residential energy demand, in particular of the role of income and the sensitivity of households to changes in energy prices, is crucial in the context of recurrent debates on energy efficiency and fuel poverty. We propose a panel threshold regression (PTR) model to empirically test the sensitivity of French households to energy price fluctuations - as measured by the elasticity of residential heating energy prices - and to analyze the overlap between their income and fuel poverty profiles. The PTR model allows to test for the non-linear effect of income on the reactions of households to fluctuations in energy prices. Thus, it can identify specific regimes differing by their level of estimated price elasticities. Each regime represents an elasticity-homogeneous group of households. The number of these regimes is determined based on an endogenously PTR-fixed income threshold. Thereafter, we analyze the composition of the regimes (i.e. groups) to locate the dominant proportion of fuel-poor households and analyse their monetary poverty characteristics. Results show that, depending on the income level, we can identify two groups of households that react differently to residential energy price fluctuations and that fuel-poor households belong mostly to the group of households with the highest elasticity. By extension, results also show that income poverty does not necessarily mean fuel poverty. In terms of public policy, we suggest focusing on income heterogeneity by considering different groups of households separately when defining energy efficiency measures. We also suggest paying particular attention to targeting fuel-poor households by examining the overlap between fuel and income poverty.



Residential and Industrial Energy Efficiency Improvements: A Dynamic General Equilibrium Analysis of the Rebound Effect

Sondès Kahouli and Xavier Pautrel

Year: 2023
Volume: Volume 44
Number: Number 3
DOI: 10.5547/01956574.44.2.skah
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Abstract:
The aim of this paper is to investigate bi-directional spillovers into residential and industrial sectors induced by energy efficiency improvement (EEI) in both the short- and long-term, and the impact of nesting structure as well as the size of elasticities of substitution of production and utility functions on the magnitude and the transitional dynamic of rebound effect. Developing a dynamic general equilibrium model, we demonstrate that residential EEIs spillover into the industrial sector through the labor supply channel and industrial EEIs spillover into the residential sector through the conventional income channel. Numerical simulations calibrated on the U.S. suggest that not taking into account these spillover effects could lead to misestimating the rebound effect notably of residential sector EEIs. We also demonstrate how the size and the duration of the rebound effect depend on the elasticities of substitution’s values. Numerical simulations suggest that alternative sets of value for the elasticities of substitution may give different sizable patterns of rebound effects in both the short- and long-term. In policy terms, our results support the idea that energy efficiency policies should be implemented simultaneously with rebound effect offsetting policies by considering short- and long-term economy feedbacks. As a consequence, they require considering debates about what type of policy pathways are more effective in mitigating the rebound effect.





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