Editing IPCC:AR6/WGIII/Chapter-2 (section)

==== 2.5.3.3 Granular Technologies Improve Faster ====

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The array of evidence of technology learning that has accumulated both before and since AR5 ( [[#Thomassen--2020|Thomassen et al. 2020]] ) has prompted investigations about the factors that enable rapid technology learning. From the wide variety of factors considered, unit size has generated the strongest and most robust results. Smaller unit sizes, sometimes referred to as ‘granularity’, tend to be associated with faster learning rates ( ''medium confidence'' ) ( [[#Sweerts--2020|Sweerts et al. 2020]] ; Wilson et al. 2020). Examples include solar PV, batteries, heat pumps, and to some extent wind power. The explanatory mechanisms for these observations are manifold and well established: more iterations are available with which to make improvements ( [[#Trancik--2006|Trancik 2006]] ); mass production can be more powerful than economies of scale ( [[#Dahlgren--2013|Dahlgren et al. 2013]] ); project management is simpler and less risky (Wilson et al. 2020); the ease of early retirement can enable risk-taking for innovative designs ( [[#Sweerts--2020|Sweerts et al. 2020]] ); and they tend to be less complicated ( [[#Malhotra--2020|Malhotra and Schmidt 2020]] ; Wilson et al. 2020). Small technologies often involve iterative production processes with many opportunities for learning by doing, and have much of the most advanced technology in the production equipment than in the product itself. In contrast, large unit scale technologies – such as full-scale nuclear power, carbon capture and storage (CCS), low-carbon steel making, and negative emissions technologies such as bioenergy with carbon capture and storage (BECCS) – are often primarily built on site and include thousands to millions of parts, such that complexity and system integration issues are paramount ( [[#Nemet--2019|Nemet 2019]] ). Despite the accumulating evidence of the benefits of granularity, these studies are careful to acknowledge the role of other factors in explaining learning. In a study of 41 energy technologies (Figure 2.23), unit size explained 22% of the variation in learning rates ( [[#Sweerts--2020|Sweerts et al. 2020]] ) and a study of 31 low-carbon technologies showed that unit size explained 33% (Wilson et al. 2020). Attributing that amount of variation to a single factor is rare in studies of technological change. The large residual has motivated studies, which find that small-scale technologies provide opportunities for rapid change, but they do not make rapid change inevitable; a supportive context, including supportive policy and complementary technologies, can stimulate more favourable technology outcomes ( ''high confidence'' ).

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'''Figure 2.23''' '''|''' '''Learning rates for 41 energy demand, supply, and storage technologies.''' Source: [[#Sweerts--2020|Sweerts et al. (2020)]] .

There is also evidence that small technologies not only learn but become adopted faster than large technologies ( ''medium confidence'' ) ( [[#Wilson--2020b|Wilson et al. 2020b]] ). Some of the mechanisms related to the adoption rate difference are associated with cost reductions; for example, smaller, less lumpy investments involve lower risk for adopters ( [[#Dahlgren--2013|Dahlgren et al. 2013]] ; [[#Wilson--2020b|Wilson et al. 2020b]] ). The shorter lifetimes of small technologies allow users to take advantage of new performance improvements ( [[#Knapp--1999|Knapp 1999]] ) and access a large set of small adopters ( [[#Finger--2019|Finger et al. 2019]] ). Other mechanisms for faster adoption are distinctly related to markets: modular technologies can address a wide variety of niche markets ( [[#Geels--2018|Geels 2018]] ) with different willingness to pay ( [[#Nemet--2019|Nemet 2019]] ) and strategically find protected niches while technology is maturing ( [[#Coles--2018|Coles et al. 2018]] ).

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