Editing IPCC:AR6/WGIII/Chapter-16 (section)

==== 16.2.2.4 Explaining Past and Projecting Future Technology Cost Changes ====

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Researchers and policymakers alike are interested in using observed empirical patterns of learning to project future reductions in costs of technologies. Studies cutting across a wide range of industrial sectors (not just energy) have tried to relate cost reductions to different functional forms, including cost reductions as a function of time (Moore’s law) and cost reductions as a function of production or deployment (Wright’s law, also known as Henderson’s law), finding that those two forms perform better than alternatives combining different factors, with costs as a function of production (Wright’s law) performing marginally better ( [[#Nagy--2013|Nagy et al. 2013]] ). A comparison of expert elicitation and model-based forecasts of the future cost of technologies for the energy transition indicates that model-based forecast medians were closer to the average realised values in 2019 ( [[#Meng--2021|Meng et al. 2021]] ).

Recent studies attempt to separate the influence of learning by doing (which is a basis of Wright’s law) versus other factors in explaining cost reductions, specifically in energy technologies. Some studies explain cost reductions with two factors: cumulative deployment (as proxy for experience); and R&amp;D investment – see the ‘two factor’ learning curve ( [[#Klaassen--2005|Klaassen et al. 2005]] ). However, reliable information on public energy R&amp;D investments for developing countries is not systematically collected. Available data for OECD countries cannot be precisely assigned to specific industrial sectors or sub-technologies ( [[#Verdolini--2018|Verdolini et al. 2018]] ). Some learning-curve studies take into account that historical variation in technology costs could be explained by variation in key materials and fuel costs – for example, steel costs for wind turbines ( [[#Qiu--2012|Qiu and Anadon 2012]] ), silicon costs ( [[#Nemet--2006|Nemet 2006]] ; [[#Kavlak--2018|Kavlak et al. 2018]] ) as well as coal and coal plant construction costs ( [[#McNerney--2011|McNerney et al. 2011]] ). Economies of scale played a significant role in the PV cost reductions since the early 2000s ( [[#Yu--2011|Yu et al. 2011]] ) (Box 16.4), which can also become the case in organic PV technologies ( [[#Gambhir--2016|Gambhir et al. 2016]] ; [[#Kavlak--2018|Kavlak et al. 2018]] ).

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