Climatic Change

, Volume 118, Issue 2, pp 381–395 | Cite as

Future capacity growth of energy technologies: are scenarios consistent with historical evidence?

  • C. WilsonEmail author
  • A. Grubler
  • N. Bauer
  • V. Krey
  • K. Riahi


Future scenarios of the energy system under greenhouse gas emission constraints depict dramatic growth in a range of energy technologies. Technological growth dynamics observed historically provide a useful comparator for these future trajectories. We find that historical time series data reveal a consistent relationship between how much a technology’s cumulative installed capacity grows, and how long this growth takes. This relationship between extent (how much) and duration (for how long) is consistent across both energy supply and end-use technologies, and both established and emerging technologies. We then develop and test an approach for using this historical relationship to assess technological trajectories in future scenarios. Our approach for “learning from the past” contributes to the assessment and verification of integrated assessment and energy-economic models used to generate quantitative scenarios. Using data on power generation technologies from two such models, we also find a consistent extent - duration relationship across both technologies and scenarios. This relationship describes future low carbon technological growth in the power sector which appears to be conservative relative to what has been evidenced historically. Specifically, future extents of capacity growth are comparatively low given the lengthy time duration of that growth. We treat this finding with caution due to the low number of data points. Yet it remains counter-intuitive given the extremely rapid growth rates of certain low carbon technologies under stringent emission constraints. We explore possible reasons for the apparent scenario conservatism, and find parametric or structural conservatism in the underlying models to be one possible explanation.


Capacity Growth Duration Relationship Scenario Data Energy System Model Power Generation Technology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Peter Kolp at the International Institute for Applied Systems Analysis (IIASA) provided invaluable technical support. Earlier drafts were much improved by insightful comments from Alex Bowen at the LSE, and from participants at the ETIP Seminar at Harvard University (April 2009) and the International Energy Workshop in Stockholm (June 2010).

Supplementary material

10584_2012_618_MOESM1_ESM.pdf (334 kb)
ESM 1 (PDF 333 kb)
10584_2012_618_MOESM2_ESM.pdf (240 kb)
ESM 2 (PDF 239 kb)


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Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • C. Wilson
    • 1
    Email author
  • A. Grubler
    • 2
    • 4
  • N. Bauer
    • 3
  • V. Krey
    • 2
  • K. Riahi
    • 2
  1. 1.Tyndall Centre for Climate Change ResearchUniversity of East AngliaNorwichUK
  2. 2.International Institute for Applied Systems AnalysisLaxenburgAustria
  3. 3.Potsdam Institute for Climate Impact ResearchPotsdamGermany
  4. 4.School of Forestry and Environmental StudiesYale UniversityNew HavenUSA

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