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Rethinking S-curves for policy-driven energy technologies

  • Avi Jakhmola*
  • , Jessica Jewell
  • , Vadim Vinichenko
  • , Aleh Cherp*
  • *Corresponding author for this work
  • Chalmers University of Technology
  • University of Bergen
  • International Institute for Applied Systems Analysis, Laxenburg
  • Lund University

Research output: Contribution to journalArticlepeer-review

Abstract (may include machine translation)

Debates about the future of wind and solar power are often framed around a false binary: either these technologies will continue accelerating or they are already losing momentum. Both views assume that renewables follow an S-curve with near-exponential expansion followed by slowdown. Using national deployment data, we show that renewables’ growth departs from ideal S-curves. Instead, it proceeds through a formative phase of erratic growth, followed by takeoff and brief acceleration that ends at low levels of market penetration (≈3% of electricity generation). Renewables then enter a prolonged steady growth phase punctuated by pulses of acceleration and deceleration, with an overall cruising speed that is slower than the first growth peak (≈0.7 percentage points [p.p.] year−1; interquartile range [IQR]: 0.4–1.5 for wind and 0.3–1.9 for solar). We develop diagnostic tools and metrics for these phases. Hindcasting shows that these outperform year-on-year trends and fitted S-curve parameters. Peak growth in front-runner countries provides a reliable upper bound for global expansion.

Original languageEnglish
Article number102526
Number of pages15
JournalJoule
DOIs
StatePublished - 22 Jun 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy
  2. SDG 13 - Climate Action
    SDG 13 Climate Action

Keywords

  • backtesting
  • climate change mitigation
  • energy transitions
  • growth models
  • renewable energy
  • S-curves
  • solar PV
  • technology diffusion
  • time series forecasting
  • wind energy

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