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

=== 2.5.3 Improvements in Technologies Enable Faster Adoption ===

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Since AR5, multiple low-carbon technologies have shown dramatic improvement, particularly solar photovoltaic (PV), wind, and batteries ( ''high confidence'' ). The observed pace of these changes and the likelihood of their continuation support the arguments in the previous section that future energy transitions are likely to occur more quickly than in the past ( ''medium confidence'' ).

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==== 2.5.3.1 Technological Change Has Produced Dramatic Cost Reductions ====

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A wide array of technologies shows long-term improvements in performance, efficiency, and cost. Among the most notable are solar PV, wind power, and batteries ( ''high confidence'' ) (Chapters 6 and 16). The dynamics for PVs are the most impressive, having fallen in cost by a factor of 10,000 from the first commercial application on a satellite in 1958 ( [[#Maycock--1975|Maycock and Wakefield 1975]] ) to power purchase agreements signed in 2019 ( [[#IRENA--2020|IRENA 2020]] ). Wind has been on a nearly as steep trajectory ( [[#Wiser--2019|Wiser and Bolinger 2019]] ) as are lithium-ion battery packs for electric vehicles ( [[#Nykvist--2015|Nykvist and Nilsson 2015]] ; [[#Service--2019|Service 2019]] ). The future potential for PV and batteries seems especially promising given that neither industry has yet begun to adopt alternative materials with attractive properties as the cost reductions and performance improvements associated with the current generation of each technology continue ( ''medium confidence'' ) ( [[#Kwade--2018|Kwade et al. 2018]] ). A key challenge is improving access to finance, especially in developing country contexts, where the costs of financing are of crucial importance ( [[#Creutzig--2017|Creutzig et al. 2017]] ; [[#Schmidt--2019|Schmidt 2019]] ).

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==== 2.5.3.2 Technological Change has Accelerated Since AR5 ====

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Figure 2.22 shows changes in the costs of four dynamic energy technologies. One can see rapid changes since AR5, cost data for which ended in 2010. Solar PV is by far the most dynamic technology, and its cost since AR5 has continued on its steep decline at about the same rate of change as before AR5, but now costs are well within the range of fossil fuels ( ''high confidence'' ) (Chapter 6). Very few concentrating solar power (CSP) plants had been built between the 1980s and 2012. Since AR5, 4GW have been built and costs have fallen by half. Onshore wind has continued its pace of cost reductions such that it is well within the range of fossil fuels. Offshore wind has changed the most since AR5. Whereas costs were increasing before AR5, they have decreased by 50% since. None of these technologies shows indications of reaching a limit in their cost reductions. Crucial to their impact will be extending these gains in the electricity and transportation sectors to the industrial sector ( [[#Davis--2018|Davis et al. 2018]] ).

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[[File:e9c50ea82434853d62f95c3309677bc7 IPCC_AR6_WGIII_Figure_2_22.png]]

Figure 2.22 '''| Unit cost reductions and use in some rapidly changing mitigation technologies.''' The '''top panel''' shows global costs per unit of energy (USD per MWh) for some rapidly changing mitigation technologies. Solid blue lines indicate average unit cost in each year. Light blue shaded areas show the range between the 5th and 95th percentiles in each year. Grey shading indicates the range of unit costs for new fossil fuel (coal and gas) power in 2020 (corresponding to USD55–148 per MWh). In 2020, the levelised costs of energy (LCOE) of the four renewable energy technologies could compete with fossil fuels in many places. For batteries, costs shown are for 1 kWh of battery storage capacity; for the others, costs are LCOE, which includes installation, capital, operations, and maintenance costs per MWh of electricity produced. The literature uses LCOE because it allows consistent comparisons of cost trends across a diverse set of energy technologies to be made. However, it does not include the costs of grid integration or climate impacts. Further, LCOE does not take into account other environmental and social externalities that may modify the overall (monetary and non-monetary) costs of technologies and alter their deployment. The '''bottom panel''' shows cumulative global adoption for each technology, in GW of installed capacity for renewable energy and in millions of vehicles for battery-electric vehicles. A vertical dashed line is placed in 2010 to indicate the change since AR5. Shares of electricity produced and share of passenger vehicle fleet are indicated in text for 2020 based on provisional data, i.e., percentage of total electricity production (for PV, onshore wind, offshore wind, CSP) and of total stock of passenger vehicles (for EVs). The electricity production share reflects different capacity factors; for example, for the same amount of installed capacity, wind produces about twice as much electricity as solar PV. {2.5, 6.4} Renewable energy and battery technologies were selected as illustrative examples because they have recently shown rapid changes in costs and adoption, and because consistent data are available. Other mitigation options assessed in the report are not included as they do not meet these criteria.

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==== 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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[[File:e19ef2686179cb1c6e5b42a5589487bc IPCC_AR6_WGIII_Figure_2_23.png]]

'''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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