CoChat
Sign Up
Cited by 654Climate Change Policy and EconomicsEnergy, Environment, and Transportation PoliciesEnergy, Environment, Economic Growth

The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.

Amplification of future energy demand growth due to climate change

Open access
van Ruijven, Bas J., De Cian, Enrica, Sue Wing, Ian·2019·Nature Communications
DOI 10.1038/s41467-019-10399-3PMC PMC6591298
Open this paper in CoChat — free accountOpen PDF

Abstract

Future energy demand is likely to increase due to climate change, but the magnitude depends on many interacting sources of uncertainty. We combine econometrically estimated responses of energy use to income, hot and cold days with future projections of spatial population and national income under five socioeconomic scenarios and temperature increases around 2050 for two emission scenarios simulated by 21 Earth System Models (ESMs). Here we show that, across 210 realizations of socioeconomic and climate scenarios, vigorous (moderate) warming increases global climate-exposed energy demand before adaptation around 2050 by 25–58% (11–27%), on top of a factor 1.7–2.8 increase above present-day due to socioeconomic developments. We find broad agreement among ESMs that energy demand rises by more than 25% in the tropics and southern regions of the USA, Europe and China. Socioeconomic scenarios vary widely in the number of people in low-income countries exposed to increases in energy demand.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41467-019-10399-3

Cites

57 of 57 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 Changes in the main drivers of energy demand between 2010 and 2050. The data are smoothed and summarized by latitude: population (a), GDP per capita (b), baseline projections of the total climate-related final energy demand without climate-change impacts as a result of combining our elasticities with GDP and population projections (c) number of days with daily average temperature above 27.5 °C (d), number of days with daily average temperature below 12.5 °C (e) under RCP 8.5. Supplementary Fig. 6 provides a version of this figure for RCP 4.5
Fig. 1 Changes in the main drivers of energy demand between 2010 and 2050. The data are smoothed and summarized by latitude: population (a), GDP per capita (b), baseline projections of the total climate-related final energy demand without climate-change impacts as a result of combining our elasticities with GDP and population projections (c) number of days with daily average temperature above 27.5 °C (d), number of days with daily average temperature below 12.5 °C (e) under RCP 8.5. Supplementary Fig. 6 provides a version of this figure for RCP 4.5CC BY · PubMed Central
Fig. 2 The total energy demand exposed to changes in cold and hot days. The upper half displays the results for RCP 8.5, the bottom half for RCP 4.5. In the detailed panels (a, b, e, f) the blue lines depict present-day energy demand, and the red lines depict SSP5 baseline energy demand for 2050; brown lines indicate energy use under SSP5 after impacts of climate change (mean and all 21 individual ESMs) exposed to changes in hot and cold days. Aggregate panels (c, d, g, h) show the multi-ESM mean for all five SSPs. Impacts from climate change are only shown for changes in hot (b, d, f, h) or cold (a, c, e, g) days; combined impacts are shown in Supplementary Fig. 7
Fig. 2 The total energy demand exposed to changes in cold and hot days. The upper half displays the results for RCP 8.5, the bottom half for RCP 4.5. In the detailed panels (a, b, e, f) the blue lines depict present-day energy demand, and the red lines depict SSP5 baseline energy demand for 2050; brown lines indicate energy use under SSP5 after impacts of climate change (mean and all 21 individual ESMs) exposed to changes in hot and cold days. Aggregate panels (c, d, g, h) show the multi-ESM mean for all five SSPs. Impacts from climate change are only shown for changes in hot (b, d, f, h) or cold (a, c, e, g) days; combined impacts are shown in Supplementary Fig. 7CC BY · PubMed Central
Fig. 3 Global total energy demand amplification from climate change around 2050 under RCP 8.5. Solid bars represent the median of 21 ESM model simulations, error bars represent the interquartile range of change in energy demand across 21 ESM simulations (see Supplementary Fig. 8 for RCP 4.5 results)
Fig. 3 Global total energy demand amplification from climate change around 2050 under RCP 8.5. Solid bars represent the median of 21 ESM model simulations, error bars represent the interquartile range of change in energy demand across 21 ESM simulations (see Supplementary Fig. 8 for RCP 4.5 results)CC BY · PubMed Central
Fig. 4 Agreement across ESMs on changes in final energy demand. Maps depict the number of climate models that agree on decreases and increases in total energy demand across sectors and energy carriers by more than 0% (a, b), 10% (c, d), 25% (e, f), or 50% (g, h) by 2050 under RCP 8.5 and SSP5, as a result of temperature projections and changes in both hot and cold days (see Supplementary Fig. 9 for RCP 4.5 results)
Fig. 4 Agreement across ESMs on changes in final energy demand. Maps depict the number of climate models that agree on decreases and increases in total energy demand across sectors and energy carriers by more than 0% (a, b), 10% (c, d), 25% (e, f), or 50% (g, h) by 2050 under RCP 8.5 and SSP5, as a result of temperature projections and changes in both hot and cold days (see Supplementary Fig. 9 for RCP 4.5 results)CC BY · PubMed Central
Fig. 5 Exposure of people with different adaptation challenges to changes in energy demand. Cumulative distribution of the number of people exposed to percentage change in climate-related final energy demand by country GDP per capita. Panels represent global results (a) and three major developing world regions: Asia (b), South America (c), and Middle East and Africa (d). Lines indicate the multi-model mean and all individual 21 CMIP5 models by 2050 under RCP 8.5. Present-day World Bank definitions for GDP per capita were used to classify countries in income categories, which we used as a proxy for adaptation challenges (GDP per capita <6000 USD/yr for high-adaptation challenge, 6000–15,000 USD/yr for moderate-adaptation challenge, and >15,000 USD/yr for low-adaptation challenge, see Supplementary Note 2 for detailed discussion). Supplementary Fig. 3 presents a version of this figure for RCP 4.5 and Supplementary Figs 1, 2 present the results by sector and fuel
Fig. 5 Exposure of people with different adaptation challenges to changes in energy demand. Cumulative distribution of the number of people exposed to percentage change in climate-related final energy demand by country GDP per capita. Panels represent global results (a) and three major developing world regions: Asia (b), South America (c), and Middle East and Africa (d). Lines indicate the multi-model mean and all individual 21 CMIP5 models by 2050 under RCP 8.5. Present-day World Bank definitions for GDP per capita were used to classify countries in income categories, which we used as a proxy for adaptation challenges (GDP per capita <6000 USD/yr for high-adaptation challenge, 6000–15,000 USD/yr for moderate-adaptation challenge, and >15,000 USD/yr for low-adaptation challenge, see Supplementary Note 2 for detailed discussion). Supplementary Fig. 3 presents a version of this figure for RCP 4.5 and Supplementary Figs 1, 2 present the results by sector and fuelCC BY · PubMed Central
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 (βT) and GDP per capita (βY). 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
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 (βT) and GDP per capita (βY). 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 impactsCC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 57 references

Research works printed but not linked: 3

  1. van Benthem, A. A. Energy Leapfrogging. J. Assoc. Environ. Resour. Econ. 2, 93–132 (2015).
  2. Kc, S. & Lutz, W. The human core of the shared socioeconomic pathways: population scenarios by age, sex and level of education for all countries to 2100. Glob. Environ. Change 42, 181–192 (2017). · DOI · Paper page
  3. Dellink, R., Chateau, J., Lanzi, E. & Magné, B. Long-term economic growth projections in the Shared Socioeconomic Pathways. Glob. Environ. Change 42, 200–214 (2017). · DOI · Paper page

Cited by

Sorted by citations

Items 101–200 of 654

Related

See all

Metadata

Authors
van Ruijven, B. J., De Cian, E., & Sue Wing, I.
ORCID
van Ruijven, Bas J.
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2019-06-24
Volume
10
Issue
1
Pages
2762-2762
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-019-10399-3
PMC
PMC6591298
OpenAlex
W2950990727
Cited by
648
References
57
Citations by year
2026: 114 · 2025: 134 · 2024: 112 · 2023: 103 · 2022: 77 · 2021: 66 · 2020: 40 · 2019: 2
FWCI
57.56
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • IIASA PURE (International Institute of Applied Systems Analysis) (acceptedVersion)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • ARCA (Università Ca' Foscari Venezia) (submittedVersion)
Funders
  • National Science Foundation (1240507, 1243095)
  • U.S. Department of Energy (DESC0016162, DESC0005171)
  • National Aeronautics and Space Administration
  • National Center for Atmospheric Research
  • European Commission (HORIZON2020)
  • Office of Science (DESC0005171, DESC0016162)
  • Ames Research Center
  • Biological and Environmental Research (DE-SC0016162, DE-SC0005171)
  • H2020 European Research Council (756194)
Topics
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Climate Change Policy and Economics
  • Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Energy, Environment, and Transportation Policies
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Energy, Environment, Economic Growth
Keywords
energy demand, climate change, temperature rise, socio-economic scenarios, Earth system models, population projections, income growth, climate change adaptation, low-income countries

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review