Global Energy Consumption in a Warming Climate


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):


  1. 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. 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. 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. 4.

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

  5. 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. 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. 7.

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

  8. 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. 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. 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. 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. 12.

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

  13. 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. 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. 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. 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. 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. 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 \(4\,^\circ \hbox {C}\).


  1. Amato AD, Ruth M, Kirshen P, Horwitz J (2005) Regional energy demand responses to climate change: methodology and application to the Commonwealth of Massachusetts. Clim Change 71:175e201

    Article  Google Scholar 

  2. Aroonruengsawat A, Auffhammer M (2011) Impacts of climate change on residential electricity consumption: evidence from billing data. In: Libecap GD, Steckel RH (eds) The economics of climate change: adaptations past and present. University of Chicago Press, Chicago

    Google Scholar 

  3. Auffhammer M (2014) Cooling China: the weather dependence of air conditioner adoption. Front Econ China 9:70–84

    Google Scholar 

  4. Auffhammer M, Mansur E (2014) Measuring climatic impacts on energy expenditures: a review of the empirical literature. Working paper, submitted to energy economics

  5. Auffhammer M, Aroonruengsawat A (2011) Simulating the impacts of climate change, prices and population on California’s residential electricity consumption. Clim Change 109:191–210

    Article  Google Scholar 

  6. Auffhammer M, Baylis P, Hausman C (2017) Climate change is projected to have severe impacts on the frequency and intensity of peak electricity demand across the United States. Proc Natl Acad Sci 114:1886–1891

    Article  Google Scholar 

  7. Barreca AI (2012) Climate change, humidity, and mortality in the United States. J Environ Econ Manag 63:19–34

    Article  Google Scholar 

  8. Bazilian M et al (2011) Considering the energy, water and food nexus: towards an integrated modeling approach. Energy Policy 39:7896–7906

    Article  Google Scholar 

  9. Beenstock M, Goldin E, Nabot D (1999) The demand for electricity in Israel. Energy Econ 21:168–183

    Article  Google Scholar 

  10. Berlemann M, Wesselhoft J (2014) Estimating aggregate capital stocks using the perpetual inventory method. A survey of previous implementations and new empirical evidence for 103 countries. Rev Econ 65:1–34

    Article  Google Scholar 

  11. Bigano A, Bosello F, Marano G (2006) Energy demand and temperature: a dynamic panel analysis. Fondazione ENI Enrico Mattei Working Paper No. 112.06

  12. Blackburne EF, Frank MW (2007) Estimation of nonstationary heterogeneous panels. Stata J 7:197–208

    Article  Google Scholar 

  13. Bruckner T et al (2014) Energy systems. In: Edenhofer O et al (eds) Climate change 2014: mitigation of climate change. Contribution of working group iii to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge

    Google Scholar 

  14. Burke M, Hsiang SM, Miguel E (2015) Global non-linear effect of temperature on economic production. Nature 527:235–239

  15. Calvin K, Pachauri S, De Cian E, Mouratiadou I (2013) The effect of African growth on future global energy, emissions, and regional development. Clim Change.

    Google Scholar 

  16. Chontanawat J, Hunt LC, Pierse R (2008) Does energy consumption cause economic growth? Evidence from a systematic study of over 100 countries. J Policy Model 30:209–220

    Article  Google Scholar 

  17. Ciscar J-C, Dowling P (2014) Integrated assessment of climate impacts and adaptation in the energy sector. Energy Econ 46:531–538

    Article  Google Scholar 

  18. Clarke L et al (2014) Assessing transformation pathways. In: Edenhofer O et al (eds) Climate change 2014: mitigation of climate change. Contribution of working group iii to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge

    Google Scholar 

  19. Considine TJ (2000) The impacts of weather variations on energy demand and carbon emissions. Resour Energy Econ 22:295–314

    Article  Google Scholar 

  20. Davis LW, Gertler PJ (2015) Contribution of air conditioning adoption to future energy use under global warming. PNAS 112:19

    Google Scholar 

  21. De Bono A, Mora MG (2014) A global exposure model for disaster risk assessment. Int J Disaster Risk Reduct 10(B):442–451

    Article  Google Scholar 

  22. De Cian E, Lanzi E, Roson R (2013) Seasonal temperature variations and energy demand. A panel cointegration analysis for climate change impact assessment. Clim Change 116:805–825

    Article  Google Scholar 

  23. Deschenes O, Greenstone M (2013) Climate change, mortality, and adaptation: evidence from annual fluctuations in weather in the US. Am Econ J Appl Econ 3:152–185

    Article  Google Scholar 

  24. Engle RF, Granger CWJ, Rice J, Weiss A (1986) Semiparametric estimates of the relation between weather and electricity sales. J Am Stat Assoc 81:310–320

    Article  Google Scholar 

  25. Eskeland GS, Mideksa TK (2010) Electricity demand in a changing climate. Mitig Adapt Strat Glob Change 15:877–897

    Article  Google Scholar 

  26. Fanelli L (2006) Dynamic adjustment cost models with forward-looking behaviour. Econ J 9:23–47

    Google Scholar 

  27. Flato G, Marotzke B, Abiodun P, Braconnot P, Chou SC, Collins W, Cox P, Driouech F, Emori S, Eyring V, Forest C, Gleckler P, Guilyardi E, Jakob C, Kattsov V, Reason C, Rummukainen M (2013) Evaluation of climate models. In: Stocker TF et al (eds) Climate change 2013: the physical science basis. Contribution of working group i to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, New York

    Google Scholar 

  28. Fouquet R (2014) Long-run demand for energy services: income and price elasticities over two hundred years. Rev Environ Econ Policy.

    Google Scholar 

  29. Howell M, Rogner HH (2014) Assessing integrated systems. Nat Clim Change 4:246–247

    Article  Google Scholar 

  30. Hunt LC, Ryan DL (2015) Economic modelling of energy services: rectifying misspecified energy demand functions. Energy Econ 50:273–285

    Article  Google Scholar 

  31. IEA (2013) World energy outlook 2013, p 708

  32. Isaac M, Van Vuuren DP (2009) Modeling global residential sector energy use for heating and air conditioning in the context of climate change. Energy Policy 37:507–521

    Article  Google Scholar 

  33. Jones B, ONeill B (2015) Spatially explicit global population scenarios for the shared socioeconomic pathways. Environ Res Lett (Submitted 28 May)

  34. Kriegler E et al (2012) The need for and use of socioeconomic scenarios for climate change analysis. Glob Environ Change 22:807–822

    Article  Google Scholar 

  35. Labandeiraa X, Labeagac JM, Lpez-Oteroa X (2017) A meta-analysis on the price elasticity of energy demand. Energy Policy 102:549–568

    Article  Google Scholar 

  36. Maddigan RJ, Chern WS, Rizy CG (1982) The irrigation demand for electricity. Am J Agric Econ 64:673–680

    Article  Google Scholar 

  37. Mansur ET, Mendelsohn R, Morrison W (2008) Climate change adaptation: a study of fuel choice and consumption in the US energy sector. J Environ Econ Manag 55:175–193

    Article  Google Scholar 

  38. Masish AMM, Masish R (1996) Energy consumption, real income and temporal causality: results from a multy-country study based on cointegration and error-correction modeling techniques. Energy Econ 18:165–183

    Article  Google Scholar 

  39. McNeil MA, Letschert VE (2008) Future air conditioning energy consumption in developing countries and what can be done about it: the potential of efficiency in the residential sector, Lawrence Berkeley National Lab paper no. LBNL–63203

  40. Medlock KB, Soligo R (2001) Economic development and end-use energy demand. Energy J 22:77–105

    Google Scholar 

  41. ONeill BC, Kriegler E, Riahi K, Ebi KL, Hallegatte S, Carter TR, Mathur R, van Vuuren DP (2014) A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Clim Change 122:387–400

    Article  Google Scholar 

  42. Persyn D, Westerlund J (2008) Error-correction based cointegration tests for panel data. Stat J 8:232–241

    Article  Google Scholar 

  43. Pesaran MH, Smith RP (1995) Estimating long-run relationships from dynamic heterogeneous panels. J Econ 68:79–113

    Article  Google Scholar 

  44. Pesaran MH, Shin Y, Smith RP (1999) Pooled mean group estimation of dynamic heterogeneous panels. J Am Stat Assoc 94:621–634

    Article  Google Scholar 

  45. Portmann FT, Siebert S, Doll P (2010) MIRCA2000—Global monthly irrigated and rainfed crop areas around the year 2000: a new high-resolution data set for agricultural and hydrological modeling. Global Biogeochem Cycles 24:GB1011

    Article  Google Scholar 

  46. Riahi K (2017) The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob Environ Change 42:153–168

    Article  Google Scholar 

  47. Rodell M, Houser PR, Jambor U, Gottschalck J, Mitchell K, Meng C-J, Toll D (2004) The global land data assimilation system (GLDAS). Bull Am Meteorol Soc 85:381–394

    Article  Google Scholar 

  48. Ruth M, Lin AC (2006) Regional energy demand and adaptations to climate change: methodology and application to the state of Maryland, USA. Energy Policy 34:2820–2833

    Article  Google Scholar 

  49. Sailor DJ, Pavlova AA (2003) Air conditioning market saturation and long-term response of residential cooling energy demand to climate change. Energy 28:941–951

    Article  Google Scholar 

  50. Scapin S, Apadula F, Brunetti M, Maugeri M (2015) High-resolution temperature fields to evaluate the response of italian electricity demand to meteorological variables: an example of climate service for the energy sector. Theoret Appl Climatol.

    Google Scholar 

  51. Schaeffer R (2012) Energy sector vulnerability to climate change: a review. Energy 38:1–12

    Article  Google Scholar 

  52. Scoccimarro E, Gualdi S, Bellucci A, Sanna A, Fogli P, Manzini E, Vichi M, Oddo P, Navarra A (2011) Effects of tropical cyclones on ocean heat transport in a high resolution coupled general circulation model. J Clim 24:4368–4384

    Article  Google Scholar 

  53. Shah T, Hassan MU, Khattak MZ, Banerjee PS, Singh OP, Rehman SU (2008) Is irrigation water free? A reality check in the Indo-Gangetic basin. World Dev 37:422–434

    Article  Google Scholar 

  54. Stern DI (2000) A multivariate cointegration analysis of the role of energy in the US macroeconomy. Energy Econ 22:267–289

    Article  Google Scholar 

  55. Van Benthem AA (2015) Energy leapfrogging. J Assoc Environ Resour Econ 2:93–132.

    Google Scholar 

  56. Van Vuuren DP et al (2011) The representative concentration pathways: an overview. Clim Change 109:5–31

    Article  Google Scholar 

  57. Van Vuuren D, Kriegler E, ONeill B, Ebi K, Riahi K, Carter T, Edmonds J, Hallegatte S, Kram T, Mathur R, Winkler H (2014) A new scenario framework for climate change research: scenario matrix architecture. Clim Change 122:373–386

    Article  Google Scholar 

  58. Wenz L, Levermann A, Auffhammer M (2017) North south polarization of European electricity consumption under future warming. Proc Natl Acad Sci 114:E7910–E7918

    Article  Google Scholar 

  59. Westerlund J (2007) Testing for error correction in panel data. Oxford Bull Econ Stat 69:709–748

    Article  Google Scholar 

  60. Wilbanks T, Fernandez S, Backus G, Garcia P, Jonietz K, Kirshen P, Savonis M, Solecki B, Toole L (2012) Climate change and infrastructure, urban systems, and vulnerabilities. Technical report to the U.S. Department of Energy in Support of the National Climate Assessment, 119 pp., Oak Ridge National Laboratory. U.S. Department of Energy, Office of Science, Oak Ridge, TN.

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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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Correspondence to Enrica De Cian.

Electronic supplementary material

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



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

Table 7 Sector definitions
Table 8 Descriptive statistics of the dataset
Table 9 Cointegration test
Table 10 Long-run semi-elasticities of energy demand with respect to temperature exposures and income
Table 11 Long-run estimated semi-elasticities of energy demand to temperature bins
Table 12 Static semi-elasticities of energy demand with respect to temperature exposures and income: extensive margin specification
Table 13 Long-run estimated semi-elasticities of energy demand to temperature in the agricultural sector, country temperature exposures weighted by harvested area
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).

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  • Panel data
  • Climate change
  • Adaptation
  • Energy

JEL Classification

  • N5
  • O13
  • Q1
  • Q54