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Energy demand elasticities and weather worldwide

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Abstract

The purpose of this paper is to empirically estimate a model of aggregate residential and commercial energy demand elasticities, taking into account capital stock and climatic effects. We model a theoretically founded non-linear energy demand system, the generalized almost ideal, for the most important 117 countries in the world, which represent around 95% of the world population and 97% of the primary residential energy consumption, for the period 1978–2012. To this end, we assume a multi-stage utility maximization process, which models energy demand within a comprehensive theoretical framework. This paper offers three new contributions to research. First, we model energy aggregate demand response with a flexible and theoretically plausible simultaneous system. Second, we empirically measure the complete structure of price and expenditure elasticities of energy demand worldwide. Third, we explicitly estimate the impact of climate conditions on energy demand, with a newly constructed measure of weather impact based on geo-located heating and cooling degree-days. Econometric estimation reveals quantitative evidence of different income and price elasticities across countries and highlights the weather and capital stock impact on energy demand, inducing energy efficiency. Electricity tends to be a luxury good in advanced economies. Our results have welfare-improving policy implications, because appropriate policy strategies can help public decision-makers promote production efficiency and consumer welfare.

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Notes

  1. Sato and Dechezleprêtre (2015) use exogenous industrial prices to explain international trade patterns for a panel of 42 countries.

  2. This method was justified by Miljkovic et al. (2016), in their estimate of major fossil fuel demand, to method to tackle the problem of cross-country correlation among quantities and prices.

  3. In particular, Kaufmann et al. (2013) estimate the energy-weather relationship, arguing in favor of using hourly weather measures and computing heating degree-days using hourly values, without fully considering prices and capital stock effects.

  4. The time series relating to the CDD and HDD degree-days were extracted from the CMCC-KAPSARC database (Atalla et al. 2015), which provides annual population weighted degree days values for 147 countries covering from 1948 to 2013. The dataset was developed by actual values and reanalysis of several location appropriate climate parameters—air temperature, relative humidity, solar radiation—on a daily, 6-h frequency for the whole period. The weather data was later population-weighted using extrapolations from both the UNEP/GRID-Sioux Falls and the Columbia University’s Gridded Population of the World datasets for the period considered. Gridded local values were later summed up within national boundaries to obtain country values. Atalla et al. (2015) calculated various thermal comfort indices at varying reference temperatures. A degree-day is defined as the variation between a reference temperature (T ref ) and the average of the period’s temperature (T mean ). If T ref is higher than T mean the absolute value of the difference is counted as HDD while the opposite is accounted for as CDD.

  5. Multi-stage cost functions allow parsimonious parameterization without sacrificing empirical flexibility. Note that we are interested in only one sub-group at the second level: the energy group. Thus, we are not interested in the issue of price dependence and preference homotheticity (Blundell 1988; Edgerton 1997).

  6. Further algebraic development of Eqs. (7)–(12) deriving Allen elasticity of substitution shows that elementary goods could be complements within the same group but substitutes in the overall allocation (Caves and Christensen 1980).

  7. Our choice of aggregating residential and commercial energy demand depends on the price regime and the economic conditions. Commercial demand tends to react in parallel with residential (with minimal lag) as most of the demand for energy is used for heating and cooling or appliances. This is in contrast with the manufacturing sector where energy demand is used for machinery work that is seldom affected by temperature variation.

  8. In this way, the capital stock variable represents a proxy for the relative endowment of energy consuming capital goods of the households worldwide.

  9. Moreover, Chang et al. (2001) report that using their non-linear estimator does not gain efficiency of the coefficients of the stationary variables in the regression; it is not affected by the inclusion of trend regressors; allows for Chi square tests to be performed. This is so because their non-linear estimator is more for non-linear regressions arising from solutions of rational expectations and dynamic stochastic general equilibrium model.

  10. In the test we specified the null hypothesis always with and without a constant, but without a trend. This is in line with the literature which analyses energy quantities of consumption, because the inclusion of a trend leads to mixed results, based on arbitrary choice of broken trends, as highlighted by Smyth (2013). The results with and without a constant are similar, so we report only the test without a constant. In summary, in Table 2, the null for ADF in unit root; the null for EG is no cointegration, the null for KAP and KAPS is unit root.

  11. This is a well-known result due to the integrability conditions, which have to be respected to recover the underlying utility function from the demand functions, demonstrated by Houthakker (1960) and Gorman (1981).

  12. There are only few examples of electricity demand estimation explicitly based on multi-stage utility or separability theory, e.g. Hausman et al. (1979), Dean and Lawson (1992), Petersen (2002). Multistage demand estimation is more popular in agricultural economics, e.g. Hassan (2011).

  13. Precisely, in each stage, excluding the country dummies, there are 16 structural parameters and 12 free parameters, taking into account the theoretical restrictions. There are six committed quantity parameters: f 1o f 1h f 1c f 2o f 2h f 2c ; one a i and one γ i and one ζ i , given adding up ∑h α h  = 1 and ∑h γ h  = ∑h ζ k  = 0; three β ij given symmetry β ij  = β ji , for ∀ j.

  14. As already explained above, detailed data to breakdown energy demand into its main components is available only for a subset of countries. In the following, we have also estimated all the different model specifications at the first stage, only for the subset of countries. We find that all the restrictions remain valid and the model estimation of the preferred model GAI is robust. Detailed results are available upon request.

  15. We estimated the demand systems with both specifications of the P* functions, namely Eq. (13b) at the first stage and (16b) at the second stage, obtaining very similar results. We report estimation values based on the Stone index specifications.

  16. Parameters are almost all significant at 1% and are available upon request. The standard errors are derived from the estimated covariance matrix computed in the non-linear SUR estimation by summing the outer product of the gradient vector for all structural parameters and error covariance parameters over all observations, inverting this matrix and taking the submatrix corresponding to the structural parameters.

    This is a consistent estimate of the information matrix, with good small sample properties.

  17. In other words, we reject the possibility of estimating electricity demand with a single equation approach, because this test implies that electricity is not separable from other energy sources (contrary to Petersen 2002).

  18. We have computed the elasticities, estimating both the exact formulas and the Stone approximations (see Alston et al. 1994) for Eqs. (13b) and (16b), without finding appreciable differences. We report the empirical results for (uncompensated) elasticities using the Stone approximations. We report unconditional elasticities at the second stage. Compensated elasticities are available upon request.

  19. For the impact of capital formation on energy demand, see also Steinbuks and Neuhoff (2014 ).

  20. We are grateful to a referee for this suggestion.

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Authors and Affiliations

  1. KAPSARC, Riyadh, Saudi Arabia

    Tarek Atalla & Carlo Andrea Bollino

  2. University of Perugia, Perugia, Italy

    Simona Bigerna & Carlo Andrea Bollino

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  1. Tarek Atalla
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  2. Simona Bigerna
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  3. Carlo Andrea Bollino
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Correspondence to Tarek Atalla.

Appendix

Appendix

1.1 Detailed estimated elasticities

See Tables 6 and 7.

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Atalla, T., Bigerna, S. & Bollino, C.A. Energy demand elasticities and weather worldwide. Econ Polit 35, 207–237 (2018). https://doi.org/10.1007/s40888-017-0074-2

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