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  1. Technology cost forecasting and the energy transition 1 Dr Rupert Way Affiliated researcher in the Complexity Economics Group at the Institute for New Economic Thinking University of Oxford OECD IFI Climate working group 8 December 2022
  2. 2 Rupert Way Matt Ives Penny Mealy Doyne Farmer
  3. Motivation 3 • All energy-economic models require cost forecasts for all energy technologies • Where do these forecasts come from? • How do they model technological change? • How reliable have their forecasts been? • How do technology cost forecasts affect model output?
  4. Motivation 4 Whydowebelievewhatwebelieveaboutmitigationcosts? Technology cost forecasts • All energy-economic models require cost forecasts for all energy technologies • Where do these forecasts come from? • How do they model technological change? • How reliable have their forecasts been? • How do technology cost forecasts affect model output?
  5. 5 • “Stopping climate change will be very slow or very expensive” • “Lower economic growth, lower energy consumption” • “Higher energy prices, lots of Carbon Capture and Storage” • Why?… Because Integrated Assessment Models (IAMs) say it will cost 1 - 5% of GDP The existing narrative
  6. 6 Howwellhavemajormodels capturedtechnological progress? 10000 1000 100 1980 2000 2020 2040 $(2020)/kW Actual PV cost 2014 IAMs 2018 IAMs (SR15) 2019 IAMs 2022 IAMs (AR6) PV system costs in IAMs 2010-2022 • Systematically overestimated future costs of key green technologies Why? • Floor cost assumptions • Deployment rate limits • Intermittency constraints • Cost-production feedback loop
  7. 7 It’snotjustsolar • Different technologies progress at very different rates • Fossil fuels, nuclear, CCS, biofuels (pipes, pistons, fluids, combustion) • High knowledge technologies (electricity / electronics / computing) progress rapidly • PV, wind, batteries, EVs, electrolyzers, P2X fuels, heat pump heating 100 years of energy technology costs
  8. Ourstrategy 8 • Collect as much tech data as possible • Backtest different cost forecasting models to evaluate their performance • Choose the “best” forecasting model • Apply to the energy system • Empirical laws work well (Moore/Wright) • For progressing techs, we use Wright’s law (aka learning curve / experience curve / endogenous tech change etc.) • For others, we use a simple AR1 model Source: Lafond et al. 2018
  9. Probabilisticlearningcurveforecasts 9 • Increasing experience leads to higher probability of progress along the experience curve • i.e. if we invest in certain techs then we expect innovation • BUT… different techs progress at different rates, so we need to focus on high learning techs Cost ($/MWh) Experience (TWh) Solar PV Cost vs. Experience
  10. Threescenarioswithidenticalusefulenergy 10 • Combine exogenous deployment trajectories to form full scenarios • Generate cost forecasts, sum costs, apply discounting to calculate Net Present Cost of each scenario Fast Transition Slow Transition No Transition 2020 2040 2060 2020 2040 2060 2020 2040 2060
  11. Results:example-solarforecasts 11 Cost ($/MWh) PV costs and experience Cumulative generation (TWh) Year Fast transition No transition
  12. 12 • Faster deployment of key green techs pushes cost distributions down • At 1.4% discount rate, the Expected NPC saving is 12 $TN • Avoided climate damages lead to 30-700 $TN extra savings Conclusion:afasttransitionislikelycheaperatalldiscountrates
  13. Summary 13 • Fossil fuel costs have barely changed over the last century • Key green technology costs have been falling consistently for decades • These trends are likely to continue • We can accelerate these cost reductions by accelerating experience (in specific technologies only) • Major models do not capture these trends well, have systematically overestimated key green tech costs, and hence transition costs • The chance of all key green tech costs failing to fall is very small, yet this is the space of scenarios considered by major models and the IPCC • Our model does capture these trends and shows net savings of many trillions • High FF prices just make the learning dynamic more obvious, increase savings
  14. 14 • We should move beyond the “all of the above” approach, e.g. hydrogen cars, nuclear, CCS have made no progress despite sustained effort • We will need new factories and supply chains, upgraded grid infrastructure, EV charging etc, plus… skills to get all of this equipment installed and maintained • There will be huge opportunities in the transition • We should ensure each sector and industry is prepared to use cheap renewables as soon as they can. This will unlock the largest savings • We must overturn the notion that transition is expensive, it’s not • But also… we can make it even cheaper, by coordinating action and going faster Moresummary
  15. Thank you 15
  16. 16 References Pindyck 1999 - The long-run evolution of energy prices Nagy et al. 2013 - Statistical basis for predicting technological progress Trancik et al. 2015 - Technology improvement and emissions reduction as mutually reinforcing efforts Farmer & Lafond 2016 - How predictable is technological progress? Lafond et al 2018 - How well do experience curves predict technological progress- A method for making distributional forecasts Meng et al. 2021 - Comparing expert elicitation and model-based probabilistic technology cost forecasts for the energy transition Way et al. 2022 - Empirically grounded technology forecasts and the energy transition