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Global Energy Consumption in a Warming Climate

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A Correction to this article was published on 06 March 2019

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Abstract

We combine econometric analysis of the response of energy demand to temperature and humidity exposure with future scenarios of climate change and socioeconomic development to quantify the impacts of future climate warming on final energy consumption across the world. Globally, changes in climate circa 2050 have a moderate impact on energy consumption of 7–17%, depending on the degree of warming. Impacts vary in sign and magnitude across regions, fuels, and sectors. Climatically-induced changes in energy use are larger in tropical regions. Almost all continents experience increases in energy demand, driven by the commercial and industrial sectors. In Europe declines in energy use by residences drive an overall reduction in aggregate final energy. Energy use increases in almost all G20 economies located in the tropics, while outside of Europe G20 countries in temperate regions experience both increasing and declining total energy use, depending on the incidence of changes in the frequency of hot and cold days. The effect of climate change is regressive, with the incidence of increased energy demand overwhelmingly falling on low- and middle-income countries, raising the question whether climate change could exacerbate energy poverty.

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Change history

  • 06 March 2019

    The article was published without the provided acknowledgement. By way of this correction, the authors would like readers to know the following (addendum):

Notes

  1. Here, “petroleum” refers to a composite of heterogeneous fuels that includes refinery gas, ethane, LPG, aviation gasoline, motor gasoline, jet fuels, kerosene, gasoline and diesel, fuel oil, naphtha, white spirit, lubricants, bitumen, paraffin waxes, petroleum coke and other oil products. The last category encompasses products which can be obtained by distillation of crude oil but are normally used outside the refining industry, and exclude finished products classified as refinery feedstocks.

  2. We assume that per capita GDP causes energy use, especially at the level of individual sectors Medlock and Soligo (2001). Aggregate per capita GDP can be considered an exogenous driver of the sectoral demand of a specific fuel, and an individual sector’s demand for a specific fuel is unlikely to exert substantial feedbacks on GDP.

  3. The character of r varies by sector: in residential and commercial sectors it is primarily the maintenance of physiologically comfortable indoor temperature and humidity through the use of space conditioning, in agriculture it encompasses the shielding of crops from extreme heat by pumping irrigation water, or from extreme cold by using sprinklers, heaters or foggers, while in industry it is optimization of temperature-sensitive production processes.

  4. The separability assumption allows the adjustment trajectory to be specified independently from the target level of the control variable.

  5. We first substitute (3) into (2) and rearrange the result to obtain the interperiod adjustment in the demands for \(f'\):

    $$\begin{aligned} \Delta W_{f',t}^*&= \xi _{f'}^{0,W} + \xi _{f'}^{E,W} \Delta \mathcal {E}_t + \xi _{f'}^{X,W} \Delta X_t + \Delta \mathbf {W}_{\lnot f',t}^* \varvec{\xi }_{f'}^{W,W} \nonumber \\&\quad +\, \sigma ^W_{f'} \left\{ W_{f',t-1}^* - \zeta _{f'}^{E,W} \mathcal {E}_{t-1} - \zeta _{f'}^{X,W} X_{t-1} - \mathbf {W}_{\lnot f',t-1}^* \varvec{\zeta }_{f'}^{W,W} \right\} + \mu ^W_{f',t}\\ \Delta N_{f',t}^*&= \xi _{f'}^{0,N} + \xi _{f'}^{E,N} \Delta \mathcal {E}_t + \xi _{f'}^{X,N} \Delta X_t + \Delta \mathbf {N}_{\lnot f',t}^* \varvec{\xi }_{f'}^{N,N} \\&\quad +\, \sigma ^N_{f'} \left\{ N_{f',t-1}^* - \zeta _{f'}^{E,N} \mathcal {E}_{t-1} - \zeta _{f'}^{X,N} X_{t-1} - \mathbf {N}_{\lnot f',t-1}^* \varvec{\zeta }_{f'}^{N,N} \right\} + \mu ^N_{f',t} \end{aligned}$$

    whose coefficients \(\varvec{\xi }\) and \(\varvec{\zeta }\) are functions of the parameters \(\rho \), \(\varvec{\Lambda }\), \(\varvec{\vartheta }\), \(\varvec{\varpi }\), and the error terms \(\varvec{\mu }\) are functions of the parameters and the disturbances. We simplify the foregoing expression by using (4) to eliminate the right-hand side quantities of non-focal fuels (\(\lnot f'\)), yielding (5).

  6. Relative humidity is a better indicator of the demand for cooling to counteract heat stress because it accounts for the attenuation of evaporative cooling through perspiration. Notwithstanding this, we use specific humidity because it is less correlated with temperature.

  7. The number of countries varies between 6 in the gas, commercial, tropical combination and 49 in electricity, agriculture, tropical countries.

  8. Global total final energy consumption in 2010 was 376 EJ. Our smaller total reflects countries excluded because of missing data as well as the exclusion of other energy sources.

  9. As dynamic population maps were not available, we assume identical weights for all years in our sample, \(\overline{w}_{c,i,\text {Current}}\).

  10. As fuel price series for the agriculture and commercial sectors were not available, industrial fuel prices were used. The transportation sector includes the price of gasoline only, Enerdata, Grenoble, France, 2016.

  11. The following subsamples had insufficient data: residential, commercial, transportation and industrial natural gas use and transportation electricity use in the tropics, and commercial petroleum use in temperate regions. We fail to reject null of no cointegration for natural gas (electricity and petroleum) use in tropical (temperate) agriculture, as well as industrial natural gas use in temperate regions, see Table 9 in “Appendix”.

  12. If there are no estimates significant at 10% but there are estimates significant at 15% level, we use them

  13. Relative to other ESMs participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5), CMCC-CM generally exhibits less warming in the tropics and more cold days in the mid-latitudes (Bas van Ruijven, personal communication).

  14. SSP5 evisages a future with conventional economic development, slow population growth, rapid growth in aggregate productivity and international convergence of GDP, and rapid increases in final energy consumption mostly through fossil fuels (ONeill et al. 2014).

  15. The tropics also experience the additional negative demand response of transportation, but the associated baseline electricity use and its elasticity to hot days are both too small to compensate for the increases described in the text.

  16. For a few low-latitude temperate countries that experience substantially more hot days (e.g., South Africa, Cyprus) the cooling effect prevails despite their smaller high-temperature elasticites. Consequently, total commercial energy consumption in different countries at the same latitude can change in opposite directions, depending on the baseline mix of fuels. For example, Namibia’s commercial sector overwhelmingly uses petroleum, whose demand falls with the decline cold days in southern Africa.

  17. Our intensive margin estimate for temperate residential electricity is slightly lower than Davis and Gertler ’s range of 0.016–0.032, while our extensive-margin estimate at the 50th percentile of 2005 capital stock per person lies within the range of their estimates for the response to an additional day \(>27.5^\circ \,\hbox {C}\) for multiple levels of AC penetration—10% (0.010–0.026), 10–50% (0.017–0.029) and \(>50\%\) (0.019–0.042).

  18. e.g., Wenz et al. ’s normalized response shows an additional daily average degree above (below) \(22\,^\circ \hbox {C}\) increasing (decreasing) electric load by a fraction 0.35 (0.06) of the difference between average European loads at 14 and \(4\,^\circ \hbox {C}\).

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Acknowledgements

Funding was provided by European Union’s Seventh Framework Programme (FP7/2007-2013) (Grant no. REA grant agreement No. 298436 (DYNAMIC)). Italian Ministry of Education, University and Research and the Italian Ministry of Environment, Land and Sea (Grant no. GEMINA).

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Authors and Affiliations

  1. Centro Euro-Mediterraneo sui Cambiamenti Climatici and Fondazione Eni Enrico Mattei, Venice, Italy

    Enrica De Cian

  2. Department of Earth and Environment, Boston University, Boston, MA, USA

    Ian Sue Wing

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  1. Enrica De Cian
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Correspondence to Enrica De Cian.

Electronic supplementary material

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Regression summary tables are available in the online Appendix (Excel 76 KB)

Appendix

Appendix

See Tables 7, 8, 9, 10, 11, 12, 13 and 14.

Table 7 Sector definitions
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Table 8 Descriptive statistics of the dataset
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Table 9 Cointegration test
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Table 10 Long-run semi-elasticities of energy demand with respect to temperature exposures and income
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Table 11 Long-run estimated semi-elasticities of energy demand to temperature bins
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Table 12 Static semi-elasticities of energy demand with respect to temperature exposures and income: extensive margin specification
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Table 13 Long-run estimated semi-elasticities of energy demand to temperature in the agricultural sector, country temperature exposures weighted by harvested area
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Table 14 Aggregate energy demand responses (%) to cold and hot days for different warming scenarios
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De Cian, E., Sue Wing, I. Global Energy Consumption in a Warming Climate. Environ Resource Econ 72, 365–410 (2019). https://doi.org/10.1007/s10640-017-0198-4

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