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

== 2.5 Technological Change is Key to Reducing Emissions ==

<div id="h1-8-siblings" class="h1-siblings"></div>

Technological change for climate change mitigation involves improvement in and adoption of technologies, primarily those associated with energy production and use. Technological change has had a mitigating effect on emissions over the long term and is central to efforts to achieving climate goals ( ''high confidence'' ). Progress since AR5 shows that multiple low-carbon technologies are improving and falling in cost ( ''high confidence'' ); technology adoption is reaching substantial shares, and small-scale technologies are particularly promising on both ( ''medium confidence'' ). Faster adoption and continued technological progress can play a crucial role in accelerating the energy transition. However, the historical pace of technological change is still insufficient to catalyse a complete and timely transition to a low-carbon energy system: technological change needs to accelerate ( ''high confidence'' ). This section assesses the role of technological change in driving emissions reductions and the factors that drive technological change, with an emphasis on the speed of transitions. Incentives and support for technological change affect technology outcomes ( [[#Sivaram--2018|Sivaram et al. 2018]] ; [[#Wilson--2020a|Wilson et al. 2020a]] ). Work since AR5 has focused on evaluating the effectiveness of policies: those that accelerate technological change by enhancing knowledge (technology push) and those that increase market opportunities for successful technologies (demand pull) ( [[#Nemet--2013|Nemet 2013]] ); as well as the importance of tailoring support to country contexts ( [[#Barido--2020|Barido et al. 2020]] ; [[#Rosenbloom--2020|Rosenbloom et al. 2020]] ), including the limits of carbon-pricing policies to date (Lilliestam et al. 2020). [[#2.8|Section 2.8]] and [[IPCC:Wg3:Chapter:Chapter-13|Chapter 13]] describe how these polices affect emissions; [[IPCC:Wg3:Chapter:Chapter-14|Chapter 14]] and Cross-Chapter Box 12 in [[IPCC:Wg3:Chapter:Chapter-16|Chapter 16]] discuss transition dynamics; and [[IPCC:Wg3:Chapter:Chapter-16|Chapter 16]] provides a more detailed assessment of the evolution and mitigation impacts of technology development, innovation, and transfer.

<div id="2.5.1" class="h2-container"></div>

<span id="technological-change-has-reduced-emissions"></span>
=== 2.5.1 Technological Change Has Reduced Emissions ===

<div id="h2-13-siblings" class="h2-siblings"></div>

Technological change that facilitates efficient energy utilisation from production to its final conversion into end-use services is a critical driver of carbon emissions reductions ( ''high confidence'' ). Technological change can facilitate stringent mitigation, but it can also reduce these effects by changing consumer behaviour, such as through rebound effects ( [[#2.6|Section 2.6]] and Chapter 16). AR6 includes an entire chapter on innovation, technology development, and transfer (Chapter 16). A focus gained in this section is the extent to which aligned, credible, and durable policies can accelerate technological change factors to put emissions reductions on a trajectory compatible with reaching United Nations Framework Convention on Climate Change (UNFCCC) goals.

Technological change has facilitated the provision of more diverse and efficient energy services (heating, cooling, lighting, and mobility) while generating fewer emissions per unit of service. As seen in [[#2.4|Section 2.4]] , in Kaya identity terms ( [[#Lima--2016|Lima et al. 2016]] ) (see ‘Kaya identity’ in Glossary): population and economic growth are factors that have increased emissions, while technological change has reduced emissions (Peters et al. 2017). These Kaya statistics show that, while technological change can facilitate the transition to a low-carbon economy, it needs to proceed at a much faster pace than historical trends (Peters et al. 2017).

Multiple challenges exist in accelerating the past rate of technological change. First, an array of physical assets in the energy system are long-lived and thus involve substantial committed carbon ( [[#2.7|Section 2.7]] ) ( [[#Knapp--1999|Knapp 1999]] ; [[#Cui--2019|Cui et al. 2019]] ). A process of ‘exnovation’, accelerating the phase-out of incumbent technology through intentional policy (such as by pricing carbon), provides a means to address long lifetimes ( [[#Davidson--2019|Davidson 2019]] ; [[#Rosenbloom--2020|Rosenbloom and Rinscheid 2020]] ). Second, countries may not have the capacity to absorb the flows of ideas and research results from international knowledge spillovers due to weak infrastructure, limited research capacity, lack of credit facilities (Chapter 15, [[IPCC:Wg3:Chapter:Chapter-15#15.5|Section 15.5]] ), and other barriers to technology transfer ( [[#Adenle--2015|Adenle et al. 2015]] ). In a developing country context, processes of innovation and diffusion need to include competence-building systems ( [[#Lema--2015|Lema et al. 2015]] ; [[#Perrot--2018|Perrot and Sanni 2018]] ; [[#Stender--2020|Stender et al. 2020]] ). Third, public policy is central to stimulating technological change to reduce emissions; policy depends on creating credible expectations of future market opportunities ( [[#Alkemade--2012|Alkemade and Suurs 2012]] ), but the historical evidence shows that, despite recent progress, policies related to energy and climate over the long term have been inconsistent ( [[#Taylor--2012|Taylor 2012]] ; [[#Nemet--2013|Nemet et al. 2013]] ; [[#Koch--2016|Koch et al. 2016]] ). Bolstering the credibility and durability of policies related to low-carbon technology are crucial to accelerating technological change and inducing the private sector investment required ( [[#Helm--2003|Helm et al. 2003]] ; Habermacher et al. 2020).

<div id="2.5.2" class="h2-container"></div>

<span id="a-low-carbon-energy-transition-needs-to-occur-faster-than-previous-transitions"></span>
=== 2.5.2 A Low-carbon Energy Transition Needs to Occur Faster Than Previous Transitions ===

<div id="h2-14-siblings" class="h2-siblings"></div>

An illuminating debate on the possibility of faster transitions has emerged since AR5 – with diverging assumptions about future technological change at the core of the discourse ( [[#Bazilian--2020|Bazilian et al. 2020]] ; [[#Lu--2020|Lu and Nemet 2020]] ). Table 2.5 summarises these arguments.

'''Table''' '''2.5 | Summary of reasons to expect a fast energy transition/slow transition.'''

{| class="wikitable"
|-
|
| Fast transition

| Slow transition

|-
| '''Evidentiary basis'''

| Technology and country cases over 50 years

| Historical global system over 200 years

|-
| '''Systems'''

| Complementary technologies enable integration

| Difficult integration with existing infrastructure

|-
| '''Economics'''

| Falling costs of nascent technology

| Mature incumbent technologies

Upfront costs and capital constraints

|-
| '''Technology'''

| Digitalisation and global supply chains

More abundant innovation

Granular technology

| Long lifetimes of capital stock

Difficult to decarbonise sectors

|-
| '''Actors'''

| Proactive efforts for transition

Bottom-up public concern

Mobilised low-carbon interest groups

| Risk-averse adopters

Attributes do not appeal to consumers

Rent-seeking by powerful incumbents

|-
| '''Governance'''

| Leaders catalyse faster change

| Collective action problems

|}

<div id="2.5.2.1" class="h3-container"></div>

<span id="energy-transitions-can-occur-faster-than-in-the-past"></span>
==== 2.5.2.1 Energy Transitions Can Occur Faster Than in the Past ====

<div id="h3-10-siblings" class="h3-siblings"></div>

Recent studies have identified examples supporting fast energy transitions ( [[#Sovacool--2016|Sovacool 2016]] ; [[#Bond--2019|Bond et al. 2019]] ; [[#Reed--2019|Reed et al. 2019]] ). One describes five rapid national-scale transitions in end-use technologies, including lighting in Sweden, cook-stoves in China, liquefied petroleum gas stoves in Indonesia, ethanol vehicles in Brazil, and air conditioning in the USA ( [[#Sovacool--2016|Sovacool 2016]] ). Adoption of electric vehicles in Norway and in cities in China have also been rapid ( [[#Rietmann--2019|Rietmann and Lieven 2019]] ; [[#Li--2020|Li et al. 2020]] ; [[#Fridstrøm--2021|Fridstrøm 2021]] ). Examples in energy supply, include electrification in Kuwait, natural gas in the Netherlands, nuclear electricity in France and Sweden, combined heat and power in Denmark, renewable energy in Uruguay, and coal retirements in Ontario, Canada ( [[#Qvist--2015|Qvist and Brook 2015]] ). Reasons that these exemplars could be applied more broadly in the future include: growing urgency on climate change, shifting motivation from price response to proactive resource scarcity, and an increase in the likelihood of technological breakthroughs ( ''medium confidence'' ) ( [[#Sovacool--2016|Sovacool 2016]] ; [[#Bazilian--2020|Bazilian et al. 2020]] ). The emergence of smaller unit scale, granular technologies (described below) also creates the potential for faster system change ( [[#Trancik--2006|Trancik 2006]] ; [[#Grubler--2018|Grubler et al. 2018]] ; [[#Wilson--2020a|Wilson et al. 2020a]] ). Energy service prices and government actions that affect demand are critical to the speed and extent of energy transitions ( [[#Kramer--2009|Kramer and Haigh 2009]] ). Reasons scholars consider for expecting a fast transition include: intentional policy and alignment with goals; globalisation which diversifies sources and integrates supply chains; collective action via the Paris Agreement; as well as bottom-up grassroots movements and private sector initiatives ( [[#Kern--2016|Kern and Rogge 2016]] ). Political support for change can also speed transitions ( [[#Burke--2017|Burke and Stephens 2017]] ; [[#Stokes--2018|Stokes and Breetz 2018]] ), as can the credibility of transition-related targets ( [[#Li--2018|Li and Pye 2018]] ; [[#Rogge--2018|Rogge and Dütschke 2018]] ).

The important role of leader countries is often missed when looking only at global aggregates ( [[#Meckling--2018|Meckling and Hughes 2018]] ); leaders accumulate important knowledge, provide scaled market, and set positive examples for followers ( ''medium confidence'' ) ( [[#Schwerhoff--2016|Schwerhoff 2016]] ; [[#Buchholz--2019|Buchholz et al. 2019]] ). In recent years, the conception of where leadership, climate-relevant innovation, and technology transfer originate has shifted to considering more meaningfully direct South-South and South-North forms of technology transfer, flows of capital, drivers for market access, origins of innovation, and other forms of cooperation ( [[#Urban--2018|Urban 2018]] ; [[#Köhler--2019|Köhler et al. 2019]] ). Recent evidence shows that South-South trade is enabling clean technology transfer ( [[#Gosens--2020|Gosens 2020]] ). Leaders can initiate a process of ‘catalytic cooperation’ in which they overcome collective action problems and stimulate rapid change (Hale 2018). Similarly, ‘sensitive intervention points’ – targeted support of social movements, technologies, or policies themselves – can lead to rapid and self-sustaining change ( [[#Farmer--2019|Farmer et al. 2019]] ), such as support for photovoltaics in Germany in the 2000s and student climate activism in Europe in 2019. The focus on leadership, catalysts, and intervention points reflects a systemic view of transitions that emphasises interactions and interdependence ( [[#Geels--2018|Geels 2018]] ; [[#Meckling--2018|Meckling and Hughes 2018]] ). Technological change has been at the core of transitions, but is best understood as part of a system in which social aspects are crucial ( ''medium confidence'' ) ( [[#Cherp--2018|Cherp et al. 2018]] ; [[#Köhler--2019|Köhler et al. 2019]] ; [[#Overland--2020|Overland and Sovacool 2020]] ).

<div id="2.5.2.2" class="h3-container"></div>

<span id="reasons-why-transitions-will-occur-at-historical-rates-of-change"></span>
==== 2.5.2.2 Reasons Why Transitions Will Occur at Historical Rates of Change ====

<div id="h3-11-siblings" class="h3-siblings"></div>

Recent work has also reasserted previous claims that the speed of a low-carbon transition will follow historical patterns ( ''low confidence'' ). Broad transitions involve technological complexity, time-consuming technological development, risk-averse adopters, high upfront costs, and low immediate individual adoption benefits, attributes that are not all present in the examples of rapid change described above ( [[#Grubler--2016|Grubler et al. 2016]] ). Additional factors that slow transitions include: the need for the transition to occur globally, thus requiring nations with unequal economic resources and development circumstances to engage in near-universal participation; slow progress in recent decades; intermittence of renewables, and the time involved in building supporting infrastructure ( [[#Smil--2016|Smil 2016]] ); difficulty in decarbonising transportation and industry ( [[#Rissman--2020|Rissman et al. 2020]] ); and material resource constraints ( [[#Davidsson--2014|Davidsson et al. 2014]] ).

<div id="2.5.3" class="h2-container"></div>

<span id="improvements-in-technologies-enable-faster-adoption"></span>
=== 2.5.3 Improvements in Technologies Enable Faster Adoption ===

<div id="h2-15-siblings" class="h2-siblings"></div>

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'' ).

<div id="2.5.3.1" class="h3-container"></div>

<span id="technological-change-has-produced-dramatic-cost-reductions"></span>
==== 2.5.3.1 Technological Change Has Produced Dramatic Cost Reductions ====

<div id="h3-12-siblings" class="h3-siblings"></div>

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]] ).

<div id="2.5.3.2" class="h3-container"></div>

<span id="technological-change-has-accelerated-since-ar5"></span>
==== 2.5.3.2 Technological Change has Accelerated Since AR5 ====

<div id="h3-13-siblings" class="h3-siblings"></div>

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]] ).

<div id="_idContainer056" class="Basic-Text-Frame"></div>

[[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.

<div id="2.5.3.3" class="h3-container"></div>

<span id="granular-technologies-improve-faster"></span>
==== 2.5.3.3 Granular Technologies Improve Faster ====

<div id="h3-14-siblings" class="h3-siblings"></div>

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'' ).

<div id="_idContainer058" class="Basic-Text-Frame"></div>

[[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]] ).

<div id="2.5.4" class="h2-container"></div>

<span id="rapid-adoption-accelerates-energy-transitions"></span>
=== 2.5.4 Rapid Adoption Accelerates Energy Transitions ===

<div id="h2-16-siblings" class="h2-siblings"></div>

The transition to a more sustainable energy system depends not just on improvement in technologies, but also on their widespread adoption. Work since AR5 has also substantiated the bidirectional causal link between technology improvement and adoption. Cost reductions facilitate adoption, which generates opportunities for further cost reductions through a process of learning by doing ( ''medium confidence'' ). The rate of adoption is thus closely related to the speed at which an energy transition is possible.

Results of integrated assessment models (IAMs) show that scale-up needs are massive for 2°C scenarios. Using logistic growth rates of energy shares as in previous work ( [[#Wilson--2012|Wilson 2012]] ; [[#Cherp--2021|Cherp et al. 2021]] ), most of these technologies include annual adoption growth rates of 20% in the 2020s and 2030s, and are in line with recent adoption of wind and solar. However, it is important to realise that IAMs include faster adoption rates for some mitigation technologies than for others (Peters et al. 2017). Growth rates in IAMs for large-scale CCS – biomass, coal, and gas – are between 15–30% (25th and 75th percentiles) (Figure 2.24). So few plants have been built that there is little historical data to analyse expected growth; with only two full-scale CCS power plants built and a 7% growth rate, if including industrial CCS. In contrast, IAMs indicate that they expect much lower rates of growth in future years for the technologies that have been growing fastest in recent years (wind and solar), without strong evidence for why this should occur.

<div id="_idContainer060" class="Basic-Text-Frame"></div>

[[File:aa274c0efa249285c9725ca1942153f7 IPCC_AR6_WGIII_Figure_2_24.png]]

'''Figure 2.24''' '''|''' '''Growth of key technologies (2020–2040) in Paris-consistent mitigation scenarios compared to historical growth.''' Comparisons of historical growth (grey bars) to growth in 2020–2040 mitigation scenarios (dots). Values on the vertical axis are logistic annual growth rates for share of each technology in electricity supply. Horizontal arrangement of dots within technology categories indicates the count of scenarios at each growth rate. Source: data on scenarios from Chapter 3; historical data from [[#BP--2021|BP (2021)]] .

The overall pattern shows that IAMs expect growth in small-scale renewables to fall to less than half of their recent pace, and large-scale CCS to more than double from the limited deployment assessed ( ''high confidence'' ). The emerging work since AR5 showing the rapid adoption and faster learning in small-scale technologies should prompt a keener focus on what technologies the world can depend on to scale up quickly ( [[#Grubb--2021|Grubb et al. 2021]] ). The scenario results make it quite clear that climate stabilisation depends on rapid adoption of low-carbon technologies throughout the 2020–2040 period.

<div id="2.6" class="h1-container"></div>

<span id="behavioural-choices-and-lifestyles"></span>