Empirical approach

Our methodology is based on DCSW24, the relation to which is summarized in Fig. 6. DCSW24 derived econometric estimates of per-capita demand for three different final energy carriers associated with heating and cooling in four economic sectors for temperate and tropical countries as a function of per-capita gross domestic product (GDP) and exposure to hot (>27.5 °C) and cold (<12.5 °C) days. The methodology applied in DCSW24 is detailed in this section. This study combines their elasticities, βY (income) and βT (temperature), with a large set of future changes in income and temperatures. In the first step, we combine the income elasticities with population and GDP projections from the Shared Socioeconomic Pathways (SSPs) to construct five scenarios of baseline global energy demand in 2050 without climate-change impacts. In the second step, we impose climatic shocks derived from 21 Earth System Models and 2 emission scenarios (RCPs) on top of the baseline scenarios by combining temperature elasticities with the 2050 present difference in spatial population and hot and cold days from the current mean climate.

Fig. 6 Brief overview of the methodology of this study and its relation to De Cian and Sue Wing (2018). De Cian and Sue Wing (2018) estimated elasticities of energy use with changes in temperature (β T ) and GDP per capita (β Y ). This study combines these elasticities with GDP and population projections for the SSPs to establish baseline projection and with temperature projections from 21 climate models to analyze climate change impacts Full size image

We use long-run income elasticities and temperature semi-elasticities estimated by DCSW24 to explore uncertainty in projected energy demand impacts across a range of climate models and socioeconomic scenarios. DCSW24 uses unbalanced panel data of energy demand, income per capita, prices, and weather covariates for 204 countries over the period 1970–2014 (see detailed references in DCSW24) to estimate income elasticities and temperature semi-elasticities of sectoral energy demand across two regions (temperate, tropical), three energy carriers (electricity, natural gas, and petroleum products), and four sectors (residential, commercial, industry, and agriculture). Differently from the customary approach used in the climate-economy literature12, DCSW24 models, the relationship between energy demand, weather, income, and prices as a dynamic adjustment process. The adjustment in energy demand following a shock is not immediate, because of capital fixity and adjustments in expectations. At each point in time, changes in energy demand are a function of weather, income, and price shocks, as well as of the adjustment process induced by shocks that took place in the previous period (year). Adjustments in energy use to price, income, and weather shocks occur over time, in line with studies finding evidence of persistency12,54. DCSW estimates the model as a dynamic panel using an Error-Correction Model (ECM), which yields short- and long-run elasticities. Over the long run, adjustments on the extensive margin (e.g., purchase of air conditioners, improvements in energy efficiency) also affect the use of energy, and this additional effect is being captured by the long-run elasticities, which we use here in this paper. The ECM can also be represented as an Autoregressive-Distributed-Lag model, and it also allows obtaining statistical inference that is robust to nonstationary data. Long-run estimates can be seen as a weighted average of elasticities estimated in the first difference or using a static model (Hendry, 199555, see also Table 2).

Table 2 summarizes the estimated elasticities from the error-correction model (ECM) specification as reported in DCSW24 and compares them with elasticities obtained from the static and first-difference specifications. The semi-elasticities to temperature bins reported in Table 2 suggest that temperature change has an influence on energy demand in 16 out of 24 energy carrier, sector, region combinations. Temperature semi-elasticities indicate that energy carrier demands tend to increase with hot days with a magnitude that varies among energy carriers and sectors. Responses to hot and cold days are asymmetric, and depending on whether an energy carrier is mostly used for heating or cooling, response in one or both directions can be significant. Several semi-elasticities to cold days are negative, suggesting that extreme cold weather could reduce energy demand, especially in production processes in industry and agriculture. The negative estimates can be due to reduced energy consumption for cooling or irrigation during the shoulder seasons of spring and fall (e.g., reduction in electricity use in commercial activities, industry, and agriculture), or to fuel switching (e.g., from petroleum products to electricity in the commercial sector in the tropics). An increase in cold days can temporarily induce production activities to reduce their electricity demand and/or temporarily shift to cheaper sources such as natural gas. Moreover, commercial and industrial consumers may also have back-up energy generation.

In this paper, we use the historical evidence on energy use over a period of about 30 years summarized in Table 2 as an analog of how we might use energy in the future over the next 30 years to generate a set of counterfactual scenarios aimed at exploring climate and socioeconomic uncertainty. Because the elasticities for cold and hot days are estimated individually, future impacts in response to changes in both hot and cold days can be reported individually or combined.

Baseline projections

To establish mid-century baseline energy demand in the absence of climate change, we applied income elasticities from DCSW24 to the increases in countries’ GDP per capita from 2010 to 2050 corresponding to each Shared Socioeconomic Pathway (SSP) scenario, generating energy carrier x sector consumption growth factors that were used to scale each sector’s per-capita energy demand from its 2010 level. The resulting country-wide average values of per-capita demand for the three energy carriers by four sectors were then combined with gridded maps of future population under the SSPs to yield projected levels of energy demand across the globe on a 0.25° grid. Due to the lack of information for spatial distribution of energy demands, the energy demand in each grid cell is simply the national average energy demand per capita multiplied by the population in the grid cell.

The Shared Socioeconomic Pathways (SSPs) that we used in this research have been developed by the climate research community as common basis across mitigation and impact research28. The SSPs have diverging narratives that describe how these worlds evolve into high or low challenges to mitigation or adaptation29. SSP1 represents a world with low socioeconomic challenges to both adaptation and mitigation. In SSP2, intermediate progresses have been made, and both adaptation and mitigation challenges remain at a medium level, whereas SSP3 represents a future with high challenges on both dimensions. In SSP4 and SSP5, adaptation or mitigation challenges dominate respectively. Several key variables have been projected forward for each of the SSPs. In this paper, we use SSP projections of future spatial population change30,56 and GDP growth for 183 countries57. A global summary of population and GDP of the Shared Socioeconomic Pathways is shown in Table 3 to indicate the wide variation between SSPs. There is some debate on whether each of the five SSPs can actually be combined with all levels of climate change from the Representative Concentration Pathways (RCPs) (for details, see Figure 8 in Riahi et al.58). This is especially relevant for the highest-emission scenario, RCP8.5, which can only be reached under SSP5. However, the widest diversion between scenarios, both for the SSPs and the RCPs, takes place in the second half of the 21st century. In this paper, we only focus on results for mid-century, a period for which the potential inconsistency between SSPs and RCPs is much less clear. Moreover, we only use GDP and population from the SSPs and whether the projections for economy and population of 2050 are inconsistent with RCP8.5 levels of warming, is still an open question. Finally, in the structure of our impact analysis, the main difference between the SSPs are different distributions of population over the planet (i.e., different countries have higher/lower population growth) and different sectoral composition of energy demand, due to differences in economic growth. These are both relevant uncertainties to explore in the context of this impact study, and both these issues are not the main uncertainties with respect to (in-)consistency between the SSPs and RCP8.5.

Table 3 Key global characteristics of the SSP quantifications for population, GDP, and GDP per capita Full size table

Six Integrated Assessment Modeling (IAM) teams have published energy demand projection for the SSPs58. The methods used by these models are fundamentally different from our econometric approach. IAMs provide simplified representations of human and natural systems and integrate the energy systems in the macroeconomic system. Our econometric elasticities do not capture large structural changes in the economy or changes in energy demand patterns over different stages of development. Supplementary Figs. 4 and 5 show a comparison between the range of IAM quantifications of the SSPs and our econometric method (red dots) for each sector, energy carrier combination for the years 2010 (Supplementary Fig. 4) and 2050 (Supplementary Fig. 5).

Because the SSP quantifications of the IAMs are only publicly available for aggregated sectors and regions, we merged our residential and commercial sectors into a single-building sector. Also, there is no SSP information available on the agriculture sector, so we could not make that comparison. For some sector x energy carrier combinations, we could not estimate statistically significant elasticities, and therefore our method does not project any change from today’s level, with future projections of GDP per capita (for instance, for petroleum products in the industry and building sectors). For the largest (and for the fastest-growing) energy carrier x sector combinations, such as electricity in buildings and industry, our baseline demand projections are in line with IAM projections. For some others, such as natural gas in buildings, our method assumes a tighter relation between energy demand and GDP per capita compared with IAMs. For this comparison, it is also important to note that there are some definitional differences for the base year between the IAMs and our data, as can be seen in Supplementary Fig. 4.

Climate-change impacts

The last methodological step is calculating the effects of mid-century climate change relative to the baseline. Our main data source is the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP)34, which tabulate bias-corrected daily maximum and minimum temperatures on a 0.25° grid over the 2006–2100 period for the RCP4.526 and RCP8.527 scenarios simulated by 21 Earth System models participating in the global Climate Model Intercomparison Project round 5 (CMIP5)33. The NASA-NEX-GDDP are both bias correction as well as spatial downscaling of the CMIP5 data to a consistent spatial grid. The bias correction is based on a statistical approach that compares spatially explicit GCM output for a historical period to actual historic data to compute an offset to shift the local GCM results. The spatial downscaling is a combination of linear interpolation, with preservation of the spatial details of the observational data. A detailed description of the NEX-GDDP data set can be found in ref. 34 and https://nex.nasa.gov/nex/static/media/other/NEX-GDDP_Tech_Note_v1_08June2015.pdf.

For each grid cell, we computed mean daily temperatures from the daily max and min temperature provided by the NEX-GDDP data, and then sum the annual days of exposure to average temperatures <12.5 °C and >27.5 °C. In our 2050 no climate-change baseline scenario, each grid cell’s vector of temperature exposures was assumed to remain at its 2006–2015 average value. To construct ensemble projections of climate-change impact, we computed the difference between each grid cell’s NEX-GDDP projected annual exposure and its baseline exposure over the period 2040–2060 and combined the results with our empirically derived long-run temperature elasticities of energy demand to generate gridded maps of annual changes in energy carrier x sector energy demand, which were then averaged over years to produce the effect of climate circa 2050.

Aggregating the results across energy carriers and sectors yields 21 ESM realizations of future change in total final energy demand for each of two RCP scenarios and five SSP scenarios. The SSP scenarios were developed as discrete storylines with no information as to their relative likelihood of occurrence; accordingly, we treat energy demand under each SSP, as well as its potential to generate a high or low trajectory of radiative forcing, separately. Within each scenario combination, we treat each ESM’s gridded realization of temperature as an independent draw from an unknown conditional probability distribution. The realizations of 2050–2010 changes in temperature exposure, and concomitant effects on energy demand, generated by our ESM ensemble can be weighted in any number of ways59. The simplicity and transparency of the independence assumption make it a useful jumping-off point for characterizing the risk of climate-change impacts.