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Seasonal temperature variations and energy demand

A panel cointegration analysis for climate change impact assessment

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Abstract

This paper presents an empirical study of the relationship between residential energy demand and temperature. Unlike previous studies in this field, the data sample has a global coverage and special emphasis is given to the heterogeneous response of different regions and to the contrasting effects on energy demand for cooling and heating purposes. To account for this we distinguish between different regions, seasons, and energy sources. Short- and long-run temperature demand elasticities are estimated. These features make the model results especially valuable in the analysis of climate change impacts as they provide an empirical basis for the study of the impact of climate change on energy demand. To illustrate the potential of the results as a basis for the study of climate change impacts, the estimates are used in a simple exercise that projects changes in energy demand due to temperatures increase in 2085.

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Notes

  1. We exclude the analysis of coal because of data limitations regarding coal prices.

  2. The choice of the indicator used for temperature change is discussed in the following section.

  3. Note that the distinction between colder and warmer climates can only partially account for the geographic variability between and within regions. In fact, variables other than average temperatures may be relevant, such as the presence of mountains, coastal areas, lakes, precipitations or monsoons.

  4. Degree days are defined in relation to the difference between the observed temperature and a threshold value, which can vary across regions. When the average daily temperature is above a certain threshold, the day is classified as a cooling degree day. It is a heating degree day when the average daily temperature is below the threshold and therefore, when it is cold. The threshold is calculated based on the heating and cooling needs.

  5. The present work deals with a panel of countries that belong to different hemispheres. In this context simply using seasonal averages for all countries would have created a bias in the different behavior between northern- and southern-hemisphere countries. Consequently, seasonal temperatures were calculated as the average temperature in the months related to a certain season. For example, winter temperature in France is the average between the temperatures of December, January and February, whereas in Australia it is the average between the temperatures of June, July, and August.

  6. Recent econometric literature on panel data have compared the validity of homogeneous versus heterogeneous estimators, to obtain energy demand elasticities with respect to price and income. Whereas some authors favor the pool estimator despite the rejection of the poolability assumption (Baltagi and Griffin 1997), Pesaran and Smith (1995) favor an estimator based on the individual time series.

  7. Not all countries are included as the tree only displays countries with a certain degree of difference in annual temperatures. Nevertheless, the cluster analysis is applied to all countries in the sample used for the estimation and they are all assigned to a certain group.

  8. To this end, we considered baseline consumption levels at 2000. Increases in energy consumption have been obtained on the basis of available population (source:GGI Scenario Database, http://www.iiasa.ac.at/Research/Models/index.html, referring to IPCC SRES scenario B2) and income per capita growth scenarios (source: own elaboration from World Bank data). Changes in energy demand due to higher income levels were obtained using income demand elasticities estimated in this study.

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Acknowledgements

The authors would like to thank Francesco Bosello, Claudio Agostinelli and Andrea Bigano for their guidance, and Carlo Carraro and Ian Sue Wing for useful comments. Andrea Bigano, Francesco Bosello and Giuseppe Marano are also gratefully acknowledged for providing the initial dataset used for the analysis.

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Authors and Affiliations

  1. Fondazione Eni Enrico Mattei, Isola di San Giorgio Maggiore, 30124, Venice, Italy

    Enrica De Cian & Elisa Lanzi

  2. Economics Department, Ca’ Foscari University, Cannaregio 873, Venice, Italy

    Roberto Roson

  3. IEFE Bocconi University, Milan, Italy

    Roberto Roson

Authors
  1. Enrica De Cian
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  2. Elisa Lanzi
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  3. Roberto Roson
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Corresponding author

Correspondence to Elisa Lanzi.

Appendices

Appendix A: Data statistics summary

Table 9 Summary statistics for the three temperature groups
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Table 10 Summary statistics for the three temperature groups
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Appendix B: Additional regressions

Table 11 Error correction model results shorter time period 1984–2001—Coefficients (t-statistic)
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Table 12 Error correction model results for electricity including all variables—Coefficients (t-statistic)
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De Cian, E., Lanzi, E. & Roson, R. Seasonal temperature variations and energy demand. Climatic Change 116, 805–825 (2013). https://doi.org/10.1007/s10584-012-0514-5

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  • DOI: https://doi.org/10.1007/s10584-012-0514-5

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