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

== 16.2 Elements, Drivers and Modelling of Technology Innovation ==

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Models of the innovation process, its drivers and incentives provide a tool for technology assessment, constructing projections of technological change and identifying which macro conditions facilitate development of low-carbon technologies. The distinction between stages of the innovation process allows for assessment of technology readiness ( [[#16.2.1|Section 16.2.1]] ). Qualitative and quantitative analysis of the main elements underpinning innovation – research and development (R&amp;D), learning by doing, and spillovers – allows for an explanation of past and projected future technological changes ( [[#16.2.2|Section 16.2.2]] ). In addition, general purpose technologies can play a role in climate change mitigation.

In the context of mitigation pathways, the feasibility of any emission reduction targets depends on the ability to promote innovation in low- and zero-carbon technologies, as opposed to any other technology. For this reason, [[#16.2.3|Section 16.2.3]] reviews the literature of the levers influencing the ''direction'' of technological change in favour of low- and zero-carbon technologies. Moreover, representation of drivers in mathematical and statistical models from [[#16.2.2|Section 16.2.2]] allows integration of its analysis with economic and climate effects within integrated assessment models (IAMs), hence permitting more precise modelling of decarbonisation pathways ( [[#16.2.4|Section 16.2.4]] ).

In addition to technological innovation, other innovation approaches are relevant in the context of climate mitigation and more broadly sustainable development ( [[#16.6|Section 16.6]] ). Frugal innovations, that is, ‘good enough‘ innovations that fulfil the needs of non-affluent consumers mostly in developing countries ( [[#Hossain--2018|Hossain 2018]] ), are characterised by low costs, concentration on core functionalities, and optimised performance level ( [[#Weyrauch--2016|Weyrauch and Herstatt 2016]] ) and are hence often associated with (ecological and social) sustainability ( [[#Albert--2019|Albert 2019]] ). Grassroots innovations are products, services and processes developed to address specific local challenges and opportunities, and which can generate novel, bottom-up solutions responding to local situations, interests and values. ( [[#Pellicer-Sifres--2018|Pellicer-Sifres et al. 2018]] ; [[#Dana--2021|Dana et al. 2021]] ).

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<span id="stages-of-the-innovation-process"></span>
=== 16.2.1 Stages of the Innovation Process ===

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The innovation cycle is commonly thought of as having three distinct innovation phases on the path between basic research and commercial application: Research and development (R&amp;D); demonstration; and deployment and diffusion ( [[#IPCC--2007|IPCC 2007]] ). Each of these phases differs with respect to the kind of activity carried out, the type of actors involved and their roles, financing needs, and the associated risks and uncertainties. All phases involve a process of trial and error, and failure is common; the share of innovation that successfully reaches the deployment phase is small. The path occurring between basic research and commercialisation is not linear ( [[#16.3|Section 16.3]] ); it often requires a long time and is characterised by significant bottlenecks and roadblocks. Furthermore, technologies may regress in the innovation cycle, rather than move forward ( [[#Skea--2019|Skea et al. 2019]] ). Successfully passing from each stage to the next one in the innovation cycle requires overcoming ‘valleys of deaths’ ( [[#Auerswald--2003|Auerswald and Branscomb 2003]] ; [[#UNFCCC--2017|UNFCCC 2017]] ), most notably the demonstration phase ( [[#Frank--1996|Frank et al. 1996]] ; [[#Weyant--2011|Weyant 2011]] ; [[#Nemet--2018|Nemet et al. 2018]] ). Over time, new and improved technologies are discovered; this often makes the dominant technology obsolete, but this is not discussed in this report.

Table 16.2 summarises the different innovation stages and main funding actors, and maps phases into the technology readiness levels (TRLs) discussed in [[#16.2.1.4|Section 16.2.1.4]] .

'''Table 16.2 | Stages of the innovation process (Section 16''' '''.''' '''2.1) mapped onto technology readiness levels ( [[#16.2.1.4|Section 16.2.1.4]] ).''' Source: adapted from [[#Auerswald--2003|Auerswald and Branscomb (2003)]] , [[#TEC--2017|TEC (2017)]] , [[#IEA--2020a|IEA (2020a)]] .

{| class="wikitable"
|-
! '''Stage'''

! '''Main funding actors'''

! '''Phases'''

! '''Related technology readiness levels (TRLs)'''

|-
| rowspan="5"| Research and development

| rowspan="5"| Governments

Firms

| Basic research

| 1 – Initial idea (basic principles defined)

|-
| rowspan="4"| Applied research and technology development

| 2 – Application formulated (technology concept and application of solution formulated)

|-
| 3 – Concept needs validation (solutions need to be prototyped and applied)

|-
| 4 – Early prototype (prototype proven in test conditions)

|-
| 5 – Full prototype at scale (components proven in conditions to be deployed)

|-
| rowspan="4"| Demonstration

| rowspan="4"| Governments

Firms

Venture Capital

Angel investors

| rowspan="4"| Experimental pilot project or full-scale testing

| 6 – Full prototype at scale (prototype proven at scale in conditions to be deployed)

|-
| 7 – Pre-commercial demonstration (solutions working in expected conditions)

|-
| 8 – First-of-a-kind commercial (commercial demonstration, full-scale deployment in final form)

|-
| rowspan="3"| 9 – Commercial operation in early environment (solution is commercial available, needs evolutionary improvement to stay competitive)

10 – Integration needed at scale (solution is commercial and competitive but needs further integration efforts)

11 – Proof of stability reached (predictable growth)

|-
| rowspan="2"| Deployment and diffusion

| Firms

Private equity

Commercial banks

Mutual funds

| Commercialisation and scale-up

( ''business'' )

|-
| International organisations and financial institutions

Non-governmental organisations (NGOs)

| Transfer

|}

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<span id="research-and-development"></span>
==== 16.2.1.1 Research and Development ====

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This phase of the innovation process focuses on generating knowledge or solving particular problems by creating a combination of artefacts to perform a particular function, or to achieve a specific goal. R&amp;D activities comprise basic research, applied research and technology development. Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundations of phenomena and observable facts, without any particular application or use in view. Applied research is original investigation undertaken in order to acquire new knowledge, primarily directed towards a specific, practical aim or objective ( [[#OECD--2015a|OECD 2015a]] ). Importantly, R&amp;D activities can be incremental – that is, focused on addressing a specific need by marginally improving an existing technology – or radical, representing a paradigm shift, promoted by new opportunities arising with the accumulation of new knowledge ( [[#Mendonça--2018|Mendonça et al. 2018]] ). Technology development, often leading to prototyping, consists of generating a working model of the technology that is usable in the real world, proving the usability and customer desirability of the technology, and giving an idea of its design, features and function ( [[#OECD--2015a|OECD 2015a]] ). These early stages of technological innovation are referred to as the ‘formative phase’, during which the conditions are shaped for a technology to emerge and become established in the market ( [[#Wilson--2013|Wilson and Grubler 2013]] ) and the constitutive elements of the innovation system emerging around a particular technology are set up ( [[#Bento--2016|Bento and Wilson 2016]] ; [[#Bento--2018|Bento et al. 2018]] ) ( [[#16.3|Section 16.3]] ).

The outcomes of R&amp;D are uncertain: the amount of knowledge that will result from any given research project or investment is unknown ''ex ante'' ( [[#Rosenberg--1998|Rosenberg 1998]] ). This risk to funders ( [[#Goldstein--2020|Goldstein and Kearney 2020]] ) translates into underinvestment in R&amp;D due to low appropriability ( [[#Weyant--2011|Weyant 2011]] ; [[#Sagar--2014|Sagar and Majumdar 2014]] ). In the case of climate mitigation technologies, low innovation incentives for the private sector also result from a negative environmental externality ( [[#Jaffe--2005|Jaffe et al. 2005]] ). Furthermore, in the absence of stringent climate policies and targets, incumbent fossil-based energy technologies are characterised by lower financing risk, are heavily subsidised ( [[#Davis--2014|Davis 2014]] ; [[#Kotchen--2021|Kotchen 2021]] ), and depreciate slowly ( [[#Arrow--1962a|Arrow 1962a]] ; [[#Nanda--2016|Nanda et al. 2016]] ; [[#Semieniuk--2021|Semieniuk et al. 2021]] ) ( [[#16.2.3|Section 16.2.3]] ). In this context, public research funding plays a key role in supporting high-risk R&amp;D, both in developed and developing economies: it can provide patient and steady funding not tied to short-term investment returns ( [[#Kammen--2007|Kammen and Nemet 2007]] ; [[#Anadon--2014|Anadon et al. 2014]] ; [[#Mazzucato--2015a|Mazzucato 2015a]] ; [[#Chan--2016|Chan and Diaz Anadon 2016]] ; [[#Anadón--2017|Anadón et al. 2017]] ; [[#Howell--2017|Howell 2017]] ; [[#Zhang--2019|Zhang et al. 2019]] ) ( [[#16.4|Section 16.4]] ). Public policies also play a role in increasing private incentives in energy research and development funding ( [[#Nemet--2013|Nemet 2013]] ). R&amp;D statistics are an important indicator of innovation and are collected following the rules of the ''Frascati Manual'' ( [[#OECD--2015a|OECD 2015a]] ) ( [[#16.3.3|Section 16.3.3]] , Box 16.3 and Table 16.7).

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<span id="demonstration"></span>
==== 16.2.1.2 Demonstration ====

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Demonstration is carried out through pilot projects or large-scale testing in the real world. Successfully demonstrating a technology shows its utility and that it is able to achieve its intended purpose and, consequently, that the risk of failure is reduced (i.e., that it has market potential) ( [[#Hellsmark--2016|Hellsmark et al. 2016]] ). Demonstration projects are an important step to promote the deployment of low-carbon energy and industrial technologies in the context of the transition. Government funding often plays a large role in energy technology demonstration projects because scaling up hardware energy technologies is expensive and risky ( [[#Brown--2009|Brown and Hendry 2009]] ; [[#Hellsmark--2016|Hellsmark et al. 2016]] ). Governments’ engagement in low-carbon technology demonstration also signals support for businesses willing to take the investment risk ( [[#Mazzucato--2016|Mazzucato 2016]] ). Venture capital, traditionally not tailored for energy investment, can also play an increasingly important role, thanks to the incentives (e.g., through de-risking) provided by public funding and policies ( [[#Gaddy--2017|Gaddy et al. 2017]] ; [[#IEA--2017a|IEA 2017a]] ).

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==== 16.2.1.3 Deployment and Diffusion ====

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Deployment entails producing a technology at large scale and scaling up its adoption and use across individual firms or households in a given market, and across different markets ( [[#Jaffe--2015|Jaffe 2015]] ). In the context of climate change mitigation and adaptation technologies, the purposeful diffusion to developing countries, is referred to as ‘technology transfer’. Most recently, the term ‘innovation cooperation’ has been proposed to indicate that technologies needs to be co-developed and adapted to local contexts ( [[#Pandey--2021|Pandey et al. 2021]] ). Innovation cooperation is an important component of stringent mitigation strategies as well as international agreements ( [[#16.5|Section 16.5]] ).

Diffusion is often sluggish due to lock-in of dominant technologies ( [[#Liebowitz--1995|Liebowitz and Margolis 1995]] ; [[#Unruh--2000|Unruh 2000]] ; [[#Ivanova--2018|Ivanova et al. 2018]] ), as well as the time needed to diffuse information about the technologies, heterogeneity among adopters, the incentive to wait until costs fall even further, the presence of behavioural and institutional barriers, and the uncertainty surrounding mitigation policies and long-term commitments to climate targets ( [[#Gillingham--2012|Gillingham and Sweeney 2012]] ; [[#Corey--2014|Corey 2014]] ; [[#Jaffe--2015|Jaffe 2015]] ; [[#Haelg--2018|Haelg et al. 2018]] ). In addition, novel technology has been hindered by the actions of powerful incumbents who accrue economic and political advantages over time, as in the case of renewable energy generation ( [[#Unruh--2002|Unruh 2002]] ; [[#Supran--2017|Supran and Oreskes 2017]] ; [[#Hoppmann--2019|Hoppmann et al. 2019]] ).

Technologies have been shown to penetrate the market with a gradual non-linear process in a characteristic logistic (S-shaped) curve ( [[#Grübler--1996|Grübler 1996]] ; [[#Rogers--2003|Rogers 2003]] ). The time needed to reach widespread adoption varies greatly across technologies relevant for adaptation and mitigation ( [[#Gross--2018|Gross et al. 2018]] ); in the case of energy technologies, the time needed for technologies to get from a 10–90% market share of saturation ranges between 5 to over 70 years ( [[#Wilson--2012|Wilson 2012]] ). Investment in commercialisation of low-emission technology is largely provided by private financiers; however, governments play a key role in ensuring incentives through supportive policies, including R&amp;D expenditures providing signals to private investors ( [[#Haelg--2018|Haelg et al. 2018]] ), pricing carbon dioxide emissions, public procurement, technology standards, information diffusion and the regulation for end-lifecycle treatment of products ( [[#Cross--2018|Cross and Murray 2018]] ) ( [[#16.4|Section 16.4]] ).

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<span id="technology-readiness-levels"></span>
==== 16.2.1.4 Technology Readiness Levels ====

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Technology readiness levels (TRLs) are a categorisation that enables consistent, uniform discussions of technical maturity across different types of technology. They were developed by the National Aeronautics and Space Administration (NASA) in the 1970s ( [[#Mankins--1995|Mankins 1995]] , 2009) and originally used to describe the readiness of components forming part of a technological system. Over time, more classifications of TRLs have been introduced, notably the one used by the European Union (EU). Most recently, the International Energy Agency (IEA) extended previous classifications to include the later stages of the innovation process ( [[#IEA--2020b|IEA 2020b]] ) and applied it to compare the market readiness of clean energy technologies and their components ( [[#OECD--2015a|OECD 2015a]] ; [[#IEA--2020b|IEA 2020b]] ). TRLs are currently widely used by engineers, business people, research funders and investors, often to assess the readiness of whole technologies rather than single components. To determine a TRL for a given technology, a technology readiness assessment (TRA) is carried out to examine programme concepts, technology requirements, and demonstrated technology capabilities. In the most recent version of the IEA ( [[#IEA--2020b|IEA 2020b]] ), TRLs range from 1 to 11, with 11 indicating the most mature (Table 16.2).

The purpose of TRLs is to support decision-making. They are applied to avoid the premature application of technologies, which would lead to increased costs and project schedule extensions ( [[#US%20Department%20of%20Energy--2011|US Department of Energy 2011]] ). They are used for risk management, and can also be used to make decisions regarding technology funding, and to support the management of the R&amp;D process within a given organisation or country ( [[#De%20Rose--2017|De Rose et al. 2017]] ).

In practice, the usefulness of TRLs is limited by several factors. These include limited applicability in complex technologies or systems, the fact that they do not define obsolescence, nor account for manufacturability, commercialisation or the readiness of organisations to implement innovations ( [[#European%20Association%20of%20Research%20Technology%20Organisations--2014|European Association of Research Technology Organisations 2014]] ) and do not consider any type of technology-system mismatch or the relevance of the products’ operation environment to the system under consideration ( [[#Mankins--2009|Mankins 2009]] ). Many of these limitations can be eased by using TRLs in combination with other indicators such as system readiness levels and other economic indicators on, for example, investments and returns ( [[#IEA--2020b|IEA 2020b]] ).

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<span id="sources-of-technological-change"></span>
=== 16.2.2 Sources of Technological Change ===

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The speed of technological change could be explained with the key drivers of innovations process: R&amp;D effort; learning by doing; and spillover effects. In addition, new innovations are sometimes enabled by the development of general purpose technologies, such as digitalisation.

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<span id="learning-by-doing-and-research-and-development"></span>
==== 16.2.2.1 Learning by Doing and Research and Development ====

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Learning by doing and R&amp;D efforts are two factors commonly used by the literature to explain past and projected future speed of technological change ( [[#Klaassen--2005|Klaassen et al. 2005]] ; [[#Mayer--2012|Mayer et al. 2012]] ; [[#Bettencourt--2013|Bettencourt et al. 2013]] ). Learning by doing is the interaction of workers with new machines or processes that allows more efficient use ( [[#Arrow--1962b|Arrow 1962b]] ). R&amp;D effort is dedicated to looking for new solutions (e.g., blueprints) that could increase the efficiency of existing production methods or result in entirely new methods, products or services ( [[#16.2.1.1|Section 16.2.1.1]] ).

Learning by doing and R&amp;D are interdependent. [[#Young--1993|Young (1993)]] postulates that learning by doing cannot continue forever without R&amp;D because it is bounded by an upper physical productivity limit of an existing technology. R&amp;D can shift this limit because it allows for replacing the existing technology with a new one. On the other hand, incentives to invest in R&amp;D depend on the future cost of manufacturing, which in turn depends on the scale of learning by doing. The empirical evidence for virtuous circle between cost reduction, market growth and R&amp;D were found in the case of the photovoltaic (PV) market ( [[#Watanabe--2000|Watanabe et al. 2000]] ) (Box 16.4), but could also lead to path dependency and lock-in ( [[#Erickson--2015|Erickson et al. 2015]] ). Sections 16.4.4 and 13.7.3.1 discuss how simultaneous use of technology push and pull policies could amplify the effects of research and learning.

The benefits of R&amp;D and learning by doing are larger at the economy level than at the firm level ( [[#Arrow--1962b|Arrow 1962b]] ; [[#Romer--1990|Romer 1990]] ;). As a result, when left to its own, the market tends to generate less investment than socially optimal. For instance, if the cost of a technology is too high before a large amount of learning by doing has occurred, there is a risk that it will not be adopted by the market, even if it is economically advantageous for the society. Indeed, initially new technologies are often expensive and cannot compete with the incumbent technologies ( [[#Cowan--1990|Cowan 1990]] ). Large numbers of adopters could lower this cost via learning by doing to a level sufficient to beat the incumbent technology ( [[#Gruebler--2012|Gruebler et al. 2012]] ). However, firms could hesitate to be the first adopter and bear the high cost ( [[#Isoard--2001|Isoard and Soria 2001]] ). If this disadvantage overwhelms the advantages of being a first mover [[#footnote-001|1]] and if adopters are not able to coordinate, it will lead to situation of a lock-in ( [[#Gruebler--2012|Gruebler et al. 2012]] ).

The failure of markets to deliver the size of R&amp;D investment and learning by doing that would be socially optimal is one of the justifications of government intervention. Policies to address these market failures can be categorised as technology-push and demand-pull policies. The role of these policies is explained in Table 16.3.

'''Table 16.3 | Categories of policies and interventions accelerating technological changes, the factors promoting them and slowing them down, illustrated''' '''with examples.'''

{| class="wikitable"
|-
!
! What it refers to

! What promotes technological change

! What slows down technological change

! Examples

|-
| Technology push

| Support the creation of new knowledge to make it easier to invest in innovation

| Research and development (R&amp;D), funding and performance of early demonstrations ( [[#Brown--2009|Brown and Hendry 2009]] ; [[#Hellsmark--2016|Hellsmark et al. 2016]] )

| Inadequate supply of trained scientists and engineers ( [[#Popp--2012|Popp and Newell 2012]] ); gap with demand pull ( [[#Grübler--1999b|Grübler et al. 1999b]] )

| Japan’s Project Sunshine, the US Project Independence in the 1970s. Breakthrough Energy Coalition and Mission Innovation, respectively private- and public-sector international collaborations to respectively focus energy innovation and double energy R&amp;D, both initiated concurrently with the Paris Agreement in 2015 ( [[#Sanchez--2017|Sanchez and Sivaram 2017]] )

|-
| Demand pull

| Instruments creating market opportunities

| Enlarging potential markets, increasing adoption of new fuels and mitigation technology

Digital innovations

Social innovation and awareness

| Willingness of consumers to accept new technology

Policy and political volatility can deter investment

| Subsidies for wind power California, the German feed-in tariff for photovoltaic, quotas for electric vehicles in China (F. [[#Wang--2017|]] [[#Wang--2017|Wang et al. 2017]] ) and Norway ( [[#Pereirinha--2018|Pereirinha et al. 2018]] )

Biofuels (Brazil)

Social innovation with wind energy (Denmark, Germany)

|}

[[#16.4|Section 16.4]] discusses individual policy instruments in greater detail.

The size of the learning-by-doing effect is quantified in literature using learning rates, that is estimates of negative correlation between costs and size of deployment of technologies. The results from this literature include estimates for energy technologies ( [[#McDonald--2001|McDonald and Schrattenholzer 2001]] ), electricity generation technologies ( [[#Rubin--2015|Rubin et al. 2015]] ; [[#Samadi--2018|Samadi 2018]] ), for storage (Schmidt 2017), for end-of-pipe control ( [[#Kang--2020|Kang et al. 2020]] ) and for energy demand and energy supply technologies ( [[#Weiss--2010|Weiss et al. 2010]] ). Meta-analyses find that learning rates vary across technologies, within technologies, and over time ( [[#Nemet--2009a|Nemet 2009a]] ; [[#Rubin--2015|Rubin et al. 2015]] ; [[#Wei--2017|Wei et al. 2017]] ). Moreover, different components of one technology have different learning rates ( [[#Elshurafa--2018|Elshurafa et al. 2018]] ). Central tendencies are around 20% cost reduction for each doubling of deployment ( [[#McDonald--2001|McDonald and Schrattenholzer 2001]] ).

Studies of correlation between cumulative deployment of technologies and costs are not sufficiently precise to disentangle the causal effect of increase in deployment from the causal effects of R&amp;D and other factors ( [[#Nemet--2006|Nemet 2006]] ). Numerous subsequent studies attempted to, among others issues, separate the effect of learning by doing and R&amp;D ( [[#Klaassen--2005|Klaassen et al. 2005]] ; [[#Mayer--2012|Mayer et al. 2012]] ; [[#Bettencourt--2013|Bettencourt et al. 2013]] ), economies of scale ( [[#Arce--2014|Arce 2014]] ), and knowledge spillovers ( [[#Nemet--2012|Nemet 2012]] ). Once those other factors are accounted for, some empirical studies find that the role of learning by doing in driving down the costs becomes minor ( [[#Nemet--2006|Nemet 2006]] ; [[#Kavlak--2018|Kavlak et al. 2018]] ). In addition, the relation could reflect reverse causality: increase in deployment could be an effect (and not a cause) of a drop in price ( [[#Nordhaus--2014|Nordhaus 2014]] ; [[#Witajewski-Baltvilks--2015|Witajewski-Baltvilks et al. 2015]] ). Nevertheless, in some applications, learning curves can be a useful proxy and heuristic ( [[#Nagy--2013|Nagy et al. 2013]] ).

The negative relation between costs and experience is a reason to invest in a narrow set of technologies; the uncertainty regarding the parameters of this relation is the reason to invest in wider ranges of technologies ( [[#Fleming--2001|Fleming and Sorenson 2001]] ; [[#Way--2019|Way et al. 2019]] ). Concentrating investment in narrow sets of technologies (specialisation) enables fast accumulation of experience for these technologies and large cost reductions. However, when the potency of technology is uncertain, one does not know which technology is truly optimal in the long run. The narrower the set, the higher the risk that the optimal technology will not be supported, and hence will not benefit from learning by doing. Widening the set of supported technologies would reduce this risk ( [[#Way--2019|Way et al. 2019]] ). Uncertainty is present because noise in historical data hides the true value of learning rates, and due to unanticipated future shocks to technology costs ( [[#Lafond--2018|Lafond et al. 2018]] ). Ignoring uncertainty in integrated assessment models implies that these model results are biased towards supporting a narrow set of technologies, neglecting the benefits of decreasing risk through diversification ( [[#Sawulski--2020|Sawulski and Witajewski-Baltvilks 2020]] ).

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<span id="knowledge-spillovers"></span>
==== 16.2.2.2 Knowledge Spillovers ====

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Knowledge spillovers drive continuous technological change ( [[#Romer--1990|Romer 1990]] ; [[#Rivera-Batiz--1991|Rivera-Batiz and Romer 1991]] ) and are for that reason relevant to climate technologies as well as incumbent, carbon-intensive technologies. Knowledge embedded in innovations by one innovator gives an opportunity for others to create new innovations and increase the knowledge stock even further. The constant growth of knowledge stock through spillovers translates into constant growth of productivity and cost reduction.

By allowing for experimenting with existing knowledge and combining different technologies, knowledge spillovers can result in the emergence of novel technological solutions, which has been referred to as ‘recombinant innovation’ ( [[#Weitzman--1998|Weitzman 1998]] ; [[#Fleming--2001|Fleming and Sorenson 2001]] ; [[#Olsson--2002|Olsson and Frey 2002]] ; [[#Tsur--2007|Tsur and Zemel 2007]] ; [[#Arthur--2009|Arthur 2009]] ). Recombinant innovations speed up technological change by combining different technological solutions, and make things happen that would be impossible with only incremental innovations ( [[#van%20den%20Bergh--2008|van den Bergh 2008]] ; [[#Safarzyńska--2010|Safarzyńska and van den Bergh 2010]] ; [[#Frenken--2012|Frenken et al. 2012]] ). It has been shown that 77% of all patents granted between 1790 and 2010 in the USA are coded by a combination of at least two technology codes ( [[#Youn--2015|Youn et al. 2015]] ). Spillovers related to energy and low-carbon technologies have been documented by a number of empirical studies ( ''high confidence'' ) ( [[#Popp--2002|Popp 2002]] ; [[#Verdolini--2011|Verdolini and Galeotti 2011]] ; [[#Aghion--2016|Aghion et al. 2016]] ; [[#Witajewski-Baltvilks--2017|Witajewski-Baltvilks et al. 2017]] ; [[#Conti--2018|Conti et al. 2018]] ). The presence of spillovers can have both positive and negative impacts on climate change mitigation ( ''hig'' ''h confidence'' ).

The spillover effect associated with innovation in carbon-intensive technologies may lead to lock-in of fossil-fuel technologies. Continuous technological change of carbon-intensive industry raises the bar for clean technologies: a larger drop in clean technologies’ cost is necessary to become competitive ( [[#Acemoglu--2012|Acemoglu et al. 2012]] ; [[#Aghion--2016|Aghion et al. 2016]] ). The implication is that delaying climate policy increases the cost of that policy ( [[#Aghion--2019|Aghion 2019]] ).

On the other hand, the spillover effect associated with innovation in low-emission technologies increases the potency of climate policy ( [[#Aghion--2019|Aghion 2019]] ). For instance, a policy that encourages clean innovation leads to accumulation of knowledge in clean industry which, through spillover effects, encourages further innovation in clean industries. Once the stock of knowledge is sufficiently large, the value of clean industries will be so high that technology firms will invest there, even without policy incentives. Once this point is reached, the policy intervention can be discontinued ( [[#Acemoglu--2012|Acemoglu et al. 2012]] ).

In addition, the presence of spillovers implies that a unilateral effort to reduce emissions in one region could reduce emissions in other regions ( ''medium confidence'' ) ( [[#Golombek--2004|Golombek and Hoel 2004]] ; [[#Gerlagh--2014|Gerlagh and Kuik 2014]] ). For instance, in the presence of spillovers, a carbon tax that incentivises clean technological change increases the competitiveness of clean technologies not only locally, but also abroad. The size of this effect depends on the size of the spillovers. If they are sufficiently strong, the reduction of emissions abroad due to clean technological change could be larger than the increase of emissions due to carbon leakage ( [[#Gerlagh--2014|Gerlagh and Kuik 2014]] ). Different types of carbon leakage are discussed in Chapter 13, [[IPCC:Wg3:Chapter:Chapter-13#13.7.1|Section 13.7.1]] , and other consequences of spillovers for the design of policy are discussed in Chapter 13, [[IPCC:Wg3:Chapter:Chapter-13#13.7|Section 13.7]] .3.

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<span id="general-purpose-technologies-and-digitalisation"></span>
==== 16.2.2.3 General-purpose Technologies and Digitalisation ====

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General-purpose technologies (GPTs) provide solutions that could be applied across sectors and industries ( [[#Goldfarb--2011|Goldfarb 2011]] ) by creating technological platforms for a growing number of interrelated innovations. Examples of GPTs relevant to climate change mitigation are hydrogen and fuel cell technology, which may find applications in transport, industry and distributed generation ( [[#Hanley--2018|Hanley et al. 2018]] ), and nanotechnology which played a significant role in advancement of all the different types of renewable energy options ( [[#Hussein--2015|Hussein 2015]] ). Assessing the environmental, social and economic implications of such technologies, including increased emissions through energy use, is challenging ( [[IPCC:Wg3:Chapter:Chapter-5#5.3.4.1|Section 5.3.4.1]] and Cross-Chapter Box 11 in this chapter).

Several GPTs relevant for climate mitigation and adaptation emerged as a result of digitalisation, namely the adoption or increase in the use of information and communication technologies (ICTs) by citizens, organisations, industries or countries, and the associated restructuring of several domains of social life and of the economy around digital technologies and infrastructures ( [[#Brennen--2016|Brennen and Kreiss 2016]] ; [[#IEA--2017b|IEA 2017b]] ). The digital revolution is underpinned by innovation in key technologies, for example, ubiquitous connected consumer devices such as mobile phones ( [[#Grubler--2018|Grubler et al. 2018]] ), rapid expansions of global internet infrastructure and access ( [[#World%20Bank--2014|World Bank 2014]] ), and steep cost reductions and performance improvements in computing devices, sensors, and digital communication technologies ( [[#Verma--2020|Verma et al. 2020]] ). The increasing pace at which the physical and digital worlds are converging increases the relevance of disruptive digitalisation in the context of climate mitigation and sustainability challenges ( [[#European%20Commission--2020|European Commission 2020]] ) (Cross-Chapter Box 11 in this chapter and Chapter 4, [[IPCC:Wg3:Chapter:Chapter-4#4.4.1|Section 4.4.1]] ).

Digital technologies require energy, but increase efficiency, potentially offering technology-specific greenhouse gas (GHG) emission savings; they also have larger system-wide impacts (Kaack et al. 2021). In industrial sectors, robotisation, smart manufacturing (SM), internet of things (IoT), artificial intelligence (AI), and additive manufacturing (AM or 3D printing) have the potential to reduce material demand and promote energy management ( [[IPCC:Wg3:Chapter:Chapter-11#11.3.4.2|Section 11.3.4.2]] ). Smart mobility is changing transport demand and efficiency ( [[IPCC:Wg3:Chapter:Chapter-10#10.2.3|Section 10.2.3]] ). Smart devices in buildings, the deployment of smart grids and the provision of renewable energy increase the role of demand-side management ( [[#Serrenho--2019|Serrenho and Bertoldi 2019]] ) (Sections 9.4 and 9.5), and support the shift away from asset redundancy ( [[IPCC:Wg3:Chapter:Chapter-6#6.4.3|Section 6.4.3]] ). Digital solutions are equally important on the supply side, for example, by accelerating innovation with simulations and deep learning ( [[#Rolnick--2021|Rolnick et al. 2021]] ) or realising flexible and decentralised opportunities through energy-as-a-service concepts and particularly with pay-as-you-go ( [[IPCC:Wg3:Chapter:Chapter-15#15.6.8|Section 15.6.8]] ).

Yet, increased digitalisation could increase energy demand, thus wiping away potential efficiency benefits, unless appropriately governed ( [[#IPCC--2018a|IPCC 2018a]] ). Moreover, digital technologies could negatively impact labour demand and increase inequality (Cross-Chapter Box 11 in this chapter).

<div id="Cross-Chapter Box 11 | Digitalisation: Efficiency Potentials and Governance" class="h2-container"></div>

<span id="cross-chapter-box-11-digitalisation-efficiency-potentials-and-governance-considerations"></span>
=== Cross-Chapter Box 11 | Digitalisation: Efficiency Potentials and Governance Considerations ===

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'''Authors:''' Felix Creutzig (Germany), Elena Verdolini (Italy), Paolo Bertoldi (Italy), Luisa F. Cabeza (Spain), María Josefina Figueroa Meza (Venezuela/Denmark), Kirsten Halsnæs (Denmark), Joni Jupesta (Indonesia/Japan), Şiir Kilkiş (Turkey), Michael König (Germany), Eric Masanet (the United States of America), Nikola Milojevic-Dupont (France), Joyashree Roy (India/Thailand), Ayyoob Sharifi (Iran/Japan)

'''Digital technologies impact positively and negatively on GHG emissions through: their own carbon footprint; technology application for mitigation; and induced larger social change. Digital technologies also raise broader sustainability concerns due to their use of rare materials and associated waste, and their potential negative impact on inequalities and''' '''labour demand.'''

'''Direct impacts emerge because digital technologies consume large amounts of energy, but also have the potential to steeply increase energy efficiency in all end-use sectors through material input savings and increased coordination (''' medium evidence ''',''' medium agreement ''')''' ( [[#Horner--2016|Horner et al. 2016]] ; [[#Huang--2016|Huang et al. 2016]] ; [[#IEA--2017b|IEA 2017b]] ; [[#Jones--2018|Jones 2018]] ). Global energy demand from digital appliances reached 7.14 EJ in 2018 (Chapter 9, Box 9.5), implying higher related carbon emissions. However, a small smartphone offers services previously requiring many different devices ( [[#Grubler--2018|Grubler et al. 2018]] ). Demand for data services is increasing rapidly; quantitative estimates of the growth of associated energy demand range from slow and marginal to rapid and sizeable, depending the efficiency trends of digital technologies ( [[#Avgerinou--2017|Avgerinou et al. 2017]] ; [[#Vranken--2017|Vranken 2017]] ; [[#Stoll--2019|Stoll et al. 2019]] ; [[#Masanet--2020|Masanet et al. 2020]] ) ( [[IPCC:Wg3:Chapter:Chapter-5#5.3.4.1|Section 5.3.4.1]] ). Renewable energy can serve as a low-carbon energy provider for the operation of a data centre, which in turn can provide waste heat for other purposes. Digital technologies can markedly increase the energy efficiency of mobility and residential and public buildings, especially in the context of systems integration ( [[#IEA--2020a|IEA 2020a]] ). Reduction in energy demand and associated GHG emissions from buildings and industry, while maintaining service levels is estimated at 5 to 10%, with larger savings possible. Approaches include building energy management systems (BEMS), home energy management system (HEMS), demand response and smart charging (Cross-Chapter Box 11, Table 1). Data centres can also play a role in energy system management, for example, by increasing renewable energy generation through predictive control ( [[#Dabbagh--2019|Dabbagh et al. 2019]] ), and by helping to drive the market for battery storage and fuel cells ( [[#Riekstin--2014|Riekstin et al. 2014]] ). Temporal and spatial scheduling of electricity demand can provide about 10 GW in demand response in the European electricity system in 2030 ( [[#Wahlroos--2017|Wahlroos et al. 2017]] , 2018; [[#Koronen--2020|Koronen et al. 2020]] ; [[#Laine--2020|Laine et al. 2020]] ).

'''Cross-Chapter Box 11, Table 1 | Selected sector approaches for reducing GHG emissions that are supported by new digital technologies.''' '''Contributions of digitalisation include a) supporting role (+), b) necessary role in mix of tools (++), c) necessary unique contribution (+++), but digitalisation may also increase emissions (−).''' (Chapters 5, 8, 9 and 11).

{| class="wikitable"
|-
! '''Sector'''

! '''Approach'''

! '''Quantitative evidence'''

! '''Contribution of digitalisation'''

! '''Systems perspective and broader societal impacts'''

! '''References'''

|-
| '''Residential energy use'''

| Nudges (feedback, information, etc.)

| 2–4% reduction in global household energy use possible

| + In combination with monetary incentives, non-digital information

| New appliances increase consumption

| [[#Zangheri--2019|Zangheri et al. (2019)]] ; [[#Buckley--2020|Buckley (2020)]] ; [[#Nawaz--2020|Nawaz et al. (2020)]] ; [[#Khanna--2021|Khanna et al. (2021)]]

|-
| '''Smart mobility'''

| Shared mobility and digital feedback (ecodriving)

| Reduction for shared cycling and shared pooled mobility; increase for ride hailing/ ride sourcing; reduction for ecodriving

| '''−''' or ++ Apps together with big data and machine learning algorithm key precondition for new shared mobility

| Ride hailing increases GHG emissions, especially due to deadheading

| [[#Zeng--2017|Zeng et al. (2017)]] ; OECD and ITF (2020)

|-
| '''Smart cities'''

| Using digital devices and big data to make urban transport and building use more efficient

| Precise data about roadway use can reduce material intensity and associated GHG emissions by 90%

| ++ Big data analysis necessary for optimisation

| Efficiency gains are often compensated by more driving and other rebound effects; privacy concerns linked with digital devices in homes

| [[#Milojevic-Dupont--2021|Milojevic-Dupont and Creutzig (2021)]] (Chapter 10, Box 10.1)

|-
| '''Agriculture'''

| Precision agriculture through sensors and satellites providing information on soil moisture, temperature, crop growth and livestock feed levels

| Very high potential for variable-rate nitrogen application, moderate potential for variable-rate irrigation

| + ICTs provide information and technologies which enables farmers to increase yields, optimise crop management, reduce fertilisers and pesticides, feed and water; increases efficiency of labour-intensive tasks

| The digital divide is growing fast, especially between modern and subsistence farming;

Privacy and data may erode trust in technologies

| [[#Deichmann--2016|Deichmann et al. (2016)]] ; [[#Chlingaryan--2018|Chlingaryan et al. (2018)]] ; [[#Soto%20Embodas--2019|Soto Embodas et al. (2019)]] ; [[#Townsend--2019|Townsend et al. (2019)]]

|-
| '''Industry'''

| Industrial internet of things (IIoT)

| Process, activity and functional optimisation increases energy and carbon efficiency

| ++ Increased efficiency

++ 1.3 GtCO 2 -eq estimated abatement potential in manufacturing

+ Promote sustainable business models

| Optimisation in value chains can reduce wasted resources

| [[#GeSI--2012|GeSI (2012)]] ; [[#Wang--2016|Wang et al. (2016)]] ; [[#Parida--2019|Parida et al. (2019)]] ; [[#Rolnick--2021|Rolnick et al. (2021)]]

|-
| '''Load management and battery storage optimisation'''

| Big data analysis for optimising demand management and using flexible load of appliances with batteries

| Reduces capacity intended for peak demand, shifts demand to align with intermittent renewable energy availability

| + Accelerated experimentation in material science with artificial intelligence

++ / +++ Forecast and control algorithms for storage and dispatch management

| Facilitate integration of renewable energy sources

Improve utilisation of generation assets

System-wide rebound effects possible

| [[#Akorede--2010|Akorede et al. (2010)]] ; [[#Aghaei--2013|Aghaei and Alizadeh (2013)]] ; [[#de%20Sisternes--2016|de Sisternes et al. (2016)]] ; [[#Voyant--2017|Voyant et al. (2017)]] ; [[#Gür--2018|Gür (2018)]] ; [[#Hirsch--2018|Hirsch et al. (2018)]] ; [[#Sivaram--2018a|Sivaram (2018a)]] ; [[#Vázquez-Canteli--2019|Vázquez-Canteli and Nagy (2019)]] (Chapter 6, [[IPCC:Wg3:Chapter:Chapter-6#6.4|Section 6.4]] )

|}

'''However, system-wide effects may endanger energy and GHG emission savings (''' high evidence ''',''' high agreement ''').''' Economic growth resulting from higher energy and labour productivities can increase energy demand ( [[#Lange--2020|Lange et al. 2020]] ) and associated GHG emissions. Importantly, digitalisation can also benefit carbon-intensive technologies ( [[#Victor--2018|Victor 2018]] ). Impacts on GHG emissions are varied in smart and shared mobility systems, as ride hailing increases GHG emissions due to deadheading, whereas shared pooled mobility and shared cycling reduce GHG emissions, as occupancy levels and/or weight per person km transported improve ( [[IPCC:Wg3:Chapter:Chapter-5#5.3|Section 5.3]] ). Energy and GHG emission impacts from the ubiquitous deployment of smart sensors and service optimisation applications in smart cities are insufficiently assessed in the literature ( [[#Milojevic-Dupont--2021|Milojevic-Dupont and Creutzig 2021]] ). Systemic effects have wider boundaries of analysis, including broader environmental impacts (e.g., demand for rare materials, disposal of digital devices). These need to be integrated holistically within policy design ( [[#Kunkel--2020|Kunkel and Matthess 2020]] ), but they are difficult to quantify and investigate ( [[#Bieser--2018|Bieser and Hilty 2018]] ). Policies and adequate infrastructures and choice architectures can help manage and contain the negative repercussions of systemic effects (Sections 5.4, 5.6 and 9.9).

'''Broader societal impacts of digitalisation can also influence climate mitigation because of induced demand for consumption goods, impacts on firms’ competitiveness, changes the demand for skills and labour, worsening of inequality – including reduced access to services due to the digital divide – and governance aspects (''' low evidence ''',''' medium agreement ''')''' (Sections 4.4, 5.3 and 5.6). Digital technologies expand production possibilities in sectors other than ICTs through robotics, smart manufacturing, and 3D printing, and have major implications on consumption patterns ( [[#Matthess--2020|Matthess and Kunkel 2020]] ). Initial evidence suggests that robots displace routine jobs and certain skills, change the demand for high-skilled and low-skilled workers, and suppress wages ( [[#Acemoglu--2019|Acemoglu and Restrepo 2019]] ). Digitalisation can thus reduce consumers’ liquidity and consumption ( [[#Mian--2020|Mian et al. 2020]] ) and contribute to global inequality, including across the gender dimension, raising fairness concerns ( [[#Kerras--2020|Kerras et al. 2020]] ; [[#Vassilakopoulou--2021|Vassilakopoulou and Hustad 2021]] ). Digital technologies can lead to additional concentration in economic power (e.g., [[#Rikap--2020|Rikap 2020]] ) and lower competition; however, open source digital technologies can counter this tendency (e.g., [[#Rotz--2019|Rotz et al. 2019]] ). Digital technologies play a role in mobilising citizens for climate and sustainability actions ( [[#Segerberg--2017|Segerberg 2017]] ; [[#Westerhoff--2018|Westerhoff et al. 2018]] ).

'''Whether the digital revolution will be an enabler or a barrier for decarbonisation will ultimately depend on the governance of both digital decarbonisation pathways and digitalisation in general (''' medium evidence ''',''' high agreement ''').''' The understanding of the disruptive potential of the wide range of digital technologies is limited due to their ground-breaking nature, which makes it hard to extrapolate from previous history/experience. Municipal and national entities can make use of digital technologies to manage and govern energy use and GHG emissions in their jurisdiction ( [[#Bibri--2019a|Bibri 2019a]] ,b) and break down solution strategies to specific infrastructures, building, and places, relying on remote sensing and mapping data, and contextual machine learning about their use ( [[#Milojevic-Dupont--2021|Milojevic-Dupont and Creutzig 2021]] ). Mobility apps can provide mobility-as-a-service access to cities, ensuring due preference to active and healthy modes ( [[IPCC:Wg3:Chapter:Chapter-9#9.9|Section 9.9]] for the example of the Finnish city of Lahti). Trusted data governance can promote the implementation of local climate solutions, supported by available big data on infrastructures and environmental quality ( [[#Hansen--2017|Hansen and Porter 2017]] ; [[#Hughes--2020|Hughes et al. 2020]] ). Governance decisions, such as taxing data, prohibiting surveillance technologies, or releasing data that enable accountability, can change digitalisation pathways, and thus underlying GHG emission ( [[#Hughes--2020|Hughes et al. 2020]] ).

'''Closing the digital gap in developing countries and rural communities enables an opportunity for leapfrogging (''' medium evidence ''',''' medium agreement ''').''' Communication technologies (such as mobile phones) enable the participation of rural communities, especially in developing countries, and promote technological leapfrogging, for example, decentralised renewable energies and smart farming ( [[#Ugur--2017|Ugur and Mitra 2017]] ; [[#Foster--2020|Foster and Azmeh 2020]] ; [[#Arfanuzzaman--2021|Arfanuzzaman 2021]] ). Digital technologies have sector-specific potentials and barriers, and may benefit certain regions/areas/socio-economic groups more than others. For example, integrated mobility services benefit cities more than rural and peripheral areas ( [[#OECD--2017|OECD 2017]] ).

'''Appropriate mechanisms also need to be designed to govern digitalisation as a megatrend (''' medium evidence ''',''' high agreement ''')''' . Digitalisation is expected to be a fast process, but this transformation takes place against entrenched individual behaviours, existing infrastructure, the legacy of time frames, vested interest and slow institutional processes, and requires trust from consumers, producers and institutions. A core question relates to who controls and manages data created by everyday operations (calls, shopping, weather data, service use, and so on). Regulations that limit or ban the expropriation and exploitation of behavioural data, sourced via smartphones, represent crucial aspects in digitalisation pathways, alongside the possibility to create climate movements and political pressure from the civil society. Governance mechanisms need to be developed to ensure that digital technologies such as AI take over ethical choices ( [[#Craglia--2018|Craglia et al. 2018]] ; [[#Rahwan--2019|Rahwan et al. 2019]] ). Appropriate governance is necessary for digitalisation to effectively work in tandem with established mitigation technologies and choice architectures. Consideration of system-wide effects and overall management is essential to avoid runaway effects. Overall governance of digitalisation remains a challenge, and will have large-scale repercussions on energy demand and GHG emissions.

<div id="16.2.2.4" class="h3-container"></div>

<span id="explaining-past-and-projecting-future-technology-cost-changes"></span>
==== 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]] ).

<div id="16.2.3" class="h2-container"></div>

<span id="directing-technological-change"></span>

=== 16.2.3 Directing Technological Change ===

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Technological change is characterised not only by its speed, but also its direction. The early works that considered the role of technology in economic and productivity growth ( [[#Solow--1957|Solow 1957]] ; [[#Nelson--1966|Nelson and Phelps 1966]] ) assumed that technology can move forward along only one dimension – every improvement led to an increase in efficiency and increased demand for all factors of production. This view, however, ignores the potency of technological change to alter the otherwise fixed relation between economic growth and the use of resources.

Technological change that saves fossil fuels could decouple economic growth and CO 2 emissions ( [[#Acemoglu--2012|Acemoglu et al. 2012]] , 2014; [[#Hémous--2016|Hémous 2016]] ; [[#Greaker--2018|Greaker et al. 2018]] ). Saving of fossils could be obtained with increasing efficiency of producing alternatives to fossils ( [[#Acemoglu--2012|Acemoglu et al. 2012]] , 2014). This is the case of oil consumption by combustion engine cars which could be substituted with electric cars ( [[#Aghion--2016|Aghion et al. 2016]] ). If there is no close substitute for a ‘dirty resource’, then its intensity in production could still be reduced by increasing the efficiency of the dirty resource relative to the efficiency of other inputs ( [[#Hassler--2012|Hassler et al. 2012]] ; [[#André--2014|André and Smulders 2014]] ; [[#Witajewski-Baltvilks--2017|Witajewski-Baltvilks et al. 2017]] ). For instance, energy efficiency improvement leads to a drop in relative demand for energy ( [[#Hassler--2012|Hassler et al. 2012]] ; [[#Witajewski-Baltvilks--2017|Witajewski-Baltvilks et al. 2017]] ).

<div id="16.2.3.1" class="h3-container"></div>

<span id="determinants-of-technological-change-direction-prices-market-size-and-government"></span>
==== 16.2.3.1 Determinants of Technological Change Direction: Prices, Market Size and Government ====

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Firms change their choice of technology upon change in prices: when one input (e.g., energy) becomes relatively expensive, firms pick technologies that allow them to economise on that input, according to price-induced technological change theory ( [[#Reder--1965|Reder and Hicks 1965]] ; [[#Samuelson--1965|Samuelson 1965]] ; [[#Sue%20Wing--2006|Sue Wing 2006]] ). For example, an increase in oil price will lead to a choice of fuel-saving technologies. Such a response of technological change was evident during the oil-price shocks in the 1970s ( [[#Hassler--2012|Hassler et al. 2012]] ). Technological change that is induced by an increase in price of a resource can never lead to an increase in use of that resource. In other words, rebound effects associated with induced technological change can never offset the saving effect of that technological change ( [[#Antosiewicz--2021|Antosiewicz and Witajewski-Baltvilks 2021]] ).

The impact of energy prices on the size of low-carbon technological change is supported by large number of empirical studies ( [[#Popp--2019|Popp 2019]] ; [[#Grubb--2020|Grubb and Wieners 2020]] ). Studies document that higher energy prices are associated with a higher number of low-carbon energy or energy efficiency patents ( [[#Newell--1999|Newell et al. 1999]] ; [[#Popp--2002|Popp 2002]] ; [[#Verdolini--2011|Verdolini and Galeotti 2011]] ; [[#Noailly--2015|Noailly and Smeets 2015]] ; [[#Ley--2016|Ley et al. 2016]] ; [[#Witajewski-Baltvilks--2017|Witajewski-Baltvilks et al. 2017]] ; [[#Lin--2019|Lin and Chen 2019]] ). [[#Sue%20Wing--2008|Sue Wing (2008)]] finds that innovation induced by energy prices had a minor impact on the decline in US energy intensity in the last decades of the 20th century, and that autonomous technological change played a more important role. Several studies explore the impact of a carbon tax on green innovation ( [[#16.4|Section 16.4]] ). However, disentangling the effect of policy tools is complex because the presence of some policies could distort the functioning of other policies ( [[#Böhringer--2010|Böhringer and Rosendahl 2010]] ; [[#Fischer--2017|Fischer et al. 2017]] ) and because the impact of policies could be lagged in time ( [[#Antosiewicz--2021|Antosiewicz and Witajewski-Baltvilks 2021]] ).

The direction of technological change depends also on the market size for dirty technologies relative to the size of other markets ( [[#Acemoglu--2014|Acemoglu et al. 2014]] ). Due to this dependence, climate and trade policy choices in a single region can alter the direction of technological change at the global level ( [[#16.2.3.3|Section 16.2.3.3]] ).

The value of the market for clean technologies is determined not only by current profit, but also by a firm’s expectation of future profits ( [[#Alkemade--2012|Alkemade and Suurs 2012]] ; [[#Greaker--2018|Greaker et al. 2018]] ; [[#Aghion--2019|Aghion 2019]] ). One implication is that bolstering the credibility and durability of policies related to low-carbon technology is crucial to accelerating technological change and inducing the private sector investment required ( [[#Helm--2003|Helm et al. 2003]] ), especially in the rapidly growing economies of Asia and Africa which are on the brink of making major decisions about the type of infrastructure they build as they grow, develop, and industrialise ( [[#Nemet--2017|Nemet et al. 2017]] ).

If governments commit to climate policies, firms expect that the future size of markets for clean technologies will be large and they are eager to redirect research effort towards development of these technologies today. The commitment would also incentivise acquiring skills that could further reduce the costs of those technologies ( [[#Aghion--2019|Aghion 2019]] ). However, historical evidence shows that policies related to energy and climate over the long term have tended to change ( [[#Taylor--2012|Taylor 2012]] ; [[#Nemet--2013|Nemet et al. 2013]] ; [[#Koch--2016|Koch et al. 2016]] ). Still, where enhancing policy durability has proven infeasible, multiple uncorrelated potentially overlapping policies can provide sufficient incentives ( [[#Nemet--2010|Nemet 2010]] ).

<div id="16.2.3.2" class="h3-container"></div>

<span id="determinants-of-direction-of-technological-change-financial-markets"></span>
==== 16.2.3.2 Determinants of Direction of Technological Change: Financial Markets ====

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The challenges of investing in innovation in energy when compared to other important areas, such as ICT and medicine are also reflected in the trends in venture capital funding. Research found that early-stage investments in cleantech companies were more likely to fail and returned less [https://www.sciencedirect.com/topics/social-sciences/capital capital] than comparable investments in software and medical technology ( [[#Gaddy--2017|Gaddy et al. 2017]] ). This led to investors retreating from hardware technologies required for renewable energy generation and storage, and moving to software-based technologies and demand-side solutions ( [[#Bumpus--2017|Bumpus and Comello 2017]] ).

The preference for particular types of investments in renewable energy technologies depends on investors attitude to risk ( [[#Mazzucato--2018|Mazzucato and Semieniuk 2018]] ). Some investors invest in only one technology, others may spread their investments, or invest predominantly in high-risk technologies. The distribution of different types of investors will affect whether finance goes to support deployment of new high-risk technologies, or diffusion of more mature, less-risky technologies characterised by incremental innovations. The role of finance in directing investment is further discussed in Chapter 15, [[IPCC:Wg3:Chapter:Chapter-15#15.6.2|Section 15.6.2]] .

<div id="16.2.3.3" class="h3-container"></div>

<span id="internationalisation-of-green-technological-change"></span>
==== 16.2.3.3 Internationalisation of Green Technological Change ====

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A unilateral effort to reduce emissions (via a combination of climate, industrial and trade policies) in a coalition of regions that are technology leaders will reduce the cost of clean technologies, and induce emissions reduction in the countries outside the coalition ( [[#Golombek--2004|Golombek and Hoel 2004]] ; [[#Di%20Maria--2005|Di Maria and Smulders 2005]] ; [[#Di%20Maria--2008|Di Maria and van der Werf 2008]] ; [[#Hémous--2016|Hémous 2016]] ; [[#van%20den%20Bijgaart--2017|van den Bijgaart 2017]] ). The literature suggests various mechanisms leading to this result. [[#Di%20Maria--2008|Di Maria and van der Werf (2008)]] argue that the effort to reduce emissions in one region reduces global demand for ‘dirty goods’. This will redirect global innovation towards clean technologies, leading to a drop in the cost of clean production in every region.

The model in Hemous (2016) predicts that such a coalition could induce acceleration of clean technological change through a mix of carbon taxation, clean R&amp;D subsidies and trade policies in that region leading to reduction of cost of clean production inside the coalition. Export of goods produced with clean technologies to a region outside the coalition reduces demand for dirty goods in that region. In the model by [[#van%20den%20Bijgaart--2017|van den Bijgaart (2017)]] local advancements of clean technologies by a coalition with strong R&amp;D potential are imitated outside the coalition. Furthermore, advancements of clean technologies will incentivise future clean R&amp;D outside the coalition due to intertemporal knowledge spillovers. In [[#Golombek--2004|Golombek and Hoel (2004)]] an increase in environmental concern in one region increases abatement R&amp;D in that region. Part of this knowledge spills over to other regions, increasing their incentive to increase abatement too, provided that the latter regions did not invest in abatement before.

However, this chain breaks if the regions that are behind the technological frontier (i.e., technological followers) are not able to absorb the solutions developed by regions at the frontier. New technologies might fail due to deficiencies of political, commercial, industrial, and financial institutions, which we list in Table 16.4. For instance, countries might not benefit fully from international knowledge spillovers due to insufficient domestic R&amp;D investment, since local knowledge is needed to determine the appropriateness of technologies for the local market, adapting them, installing and using effectively ( [[#Gruebler--2012|Gruebler et al. 2012]] ). From the policy perspective, this implies that simple transfer of technologies could be insufficient to guarantee adoption of new technologies ( [[#Gruebler--2012|Gruebler et al. 2012]] ).

'''Table 16.4 | Examples of institutional deficiencies preventing deployment of new technologies in countries behind the technolo''' '''gical frontier.'''

{| class="wikitable"
|-
! Institutions

! Examples of deficiencies

! Literature reference

|-
| Industrial

| Inability to benefit fully from international knowledge spillover due to insufficient domestic R&amp;D investment

| [[#Mancusi--2008|Mancusi (2008)]] ; [[#Unel--2008|Unel (2008)]] ; [[#Gruebler--2012|Gruebler et al. (2012)]]

|-
| Commercial

| Insufficient experience with the organisation and management of large-scale enterprise

| [[#Abramovitz--1986|Abramovitz (1986)]] ; [[#Aghion--2005|Aghion et al. (2005)]]

|-
| Political

| Vested interests and customary relations among firms and between employers and employees

| [[#Olson--1982|Olson (1982)]] ; [[#Abramovitz--1986|Abramovitz (1986)]]

|-
| Financial

| Financial markets incapable of mobilising capital for individual firms at large scale

| [[#Abramovitz--1986|Abramovitz (1986)]] ; [[#Aghion--2005|Aghion et al. (2005)]]

|}

Research relying on patent citations has indicated that Foreign Direct Investment (FDI) is a mechanism for firms to contribute to the recipient country’s innovation output as well as benefit from the recipient country in industrialised countries ( [[#Branstetter--2006|Branstetter 2006]] ) and in developing countries ( [[#Newman--2015|Newman et al. 2015]] ). However, insights specific for energy or climate change mitigation areas are not available, nor is there much information about how other innovation metrics may react to FDI.

Finally, technologies could be not efficient in developing countries, even if they are efficient in countries at the technological frontier. For instance, technologies that are highly capital intensive and labour saving will be efficient only in countries where costs of capital are low and costs of labour are high. Similarly, technologies which require a large number of skilled labour will be more competitive in a country where skilled labour is abundant (and hence cheap) than where it is scarce ( [[#Basu--1998|Basu and Weil 1998]] ; [[#Caselli--2006|Caselli and Coleman 2006]] ).

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==== 16.2.3.4 Market Failures in Directing Technological Change ====

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Market forces alone cannot deliver Pareto optimal (i.e., social) efficiency due to at least two types of externalities: GHG emissions that cause climate damage; and knowledge spillovers that benefit firms other than the inventor. [[#Nordhaus--2011|Nordhaus (2011)]] argues that these two problems would have to be tackled separately: once the favourable intellectual property right regimes (i.e., the laws or rules or regulation on protection and enforcement) are in place, a price on carbon that corrects the emission externality is sufficient to induce optimal level of green technological change. [[#Acemoglu--2012|Acemoglu et al. (2012)]] demonstrates that subsidising clean technologies (and not dirty ones) is also necessary to break the lock-in of dirty technological change. Recommendations for technical changes are often based on climate considerations only and neglect secondary externalities and environmental costs of technology choices (such as loss of biodiversity due to inappropriate scale-up of bioenergy use). The scale of adverse side effects and co-benefits varies considerably between low-carbon technologies in the energy sector ( [[#Luderer--2019|Luderer et al. 2019]] ).

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=== 16.2.4 Representation of the Innovation Process in Modelled Decarbonisation Pathways ===

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A variety of models are used to generate climate mitigation pathways, compatible with 2°C and well below 2°C targets. These include integrated assessment models (IAMs), energy system models, computable general equilibrium models, and agent based models. They range from global (Chapter 3) to national models and include both top-down and bottom-up approaches (Chapter 4). Innovation in energy technologies, which comprises the development and diffusion of low-, zero- and negative-carbon energy options, but also investments to increase energy efficiency, is a key driver of emissions reductions in model-based scenarios.

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<span id="technology-cost-development"></span>
==== 16.2.4.1 Technology Cost Development ====

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Assumptions on energy technology cost developments is one of the factors that determine the speed and magnitude of the deployment in climate-energy-economy models. The modelling is informed by the empirical literature that estimates the rates of cost reduction for energy technologies. A first strand of literature relies on the extrapolation of historical data, assuming that costs decrease either as a power law of cumulative production, exponentially with time ( [[#Nagy--2013|Nagy et al. 2013]] ) or as a function of technical performance metrics ( [[#Koh--2008|Koh and Magee 2008]] ). Another approach relies on expert estimates of how future costs will evolve, including expert elicitations ( [[#Verdolini--2018|Verdolini et al. 2018]] ).

In these models, technology costs may evolve exogenously or endogenously ( [[#Mercure--2016|Mercure et al. 2016]] ; [[#Krey--2019|Krey et al. 2019]] ). In the first case, technology costs are assumed to vary over time at some predefined rate, generally extrapolated from past observed patterns or based on expert estimates. This formulation of cost dynamics generally underestimates future costs ( [[#Meng--2021|Meng et al. 2021]] ) as, among other things, it does not capture any policy-induced carbon-saving technological change or any spillover arising from the accumulation of national and international knowledge (Sections 16.2.2 and 16.2.3) or positive macroeconomic effects of a transition ( [[#Karkatsoulis--2016|Karkatsoulis et al. 2016]] ). The influence of cost and diffusion assumptions may be evaluated through sensitivity analysis. In the second case, costs are a function of a choice variable within the model. For instance, technology costs decrease as a function of either cumulative installed capacity (learning by doing) ( [[#Seebregts--1998|Seebregts et al. 1998]] ; [[#Kypreos--2003|Kypreos and Bahn 2003]] ) or R&amp;D investments or spillovers from other sectors and countries.

One factor in this ‘learning by researching’ is applied to a wide range of energy technologies but also to model improvements in the efficiency of energy use ( [[#Goulder--1999|Goulder and Schneider 1999]] ; [[#Popp--2004|Popp 2004]] ). More complex formulations include two-factor learning processes ( [[#Criqui--2015|Criqui et al. 2015]] ; [[#Emmerling--2016|Emmerling et al. 2016]] ; [[#Paroussos--2020|Paroussos et al. 2020]] ) ( [[#16.2.2.1|Section 16.2.2.1]] ), multifactor learning curves ( [[#Kahouli--2011|Kahouli 2011]] ; [[#Yu--2011|Yu et al. 2011]] ), or other drivers of cost reduction such as economies of scale and markets ( [[#Elia--2021|Elia et al. 2021]] ). The application of two-factor learning curves to model energy technology costs is often constrained by the lack of information on public and/or private energy R&amp;D investments in many fast-developing and developing countries ( [[#Verdolini--2018|Verdolini et al. 2018]] ). The approach used to model energy technology cost reductions varies across technologies, even within the same model, depending on the availability of data and/or the level of maturity. Less mature technologies generally depend highly on learning by research, whereas learning by doing dominates in more mature technologies ( [[#Jamasb--2007|Jamasb 2007]] ).

In addition to learning, knowledge spillover effects are also integrated in climate-energy-economy models to reflect the fact that innovation in a given country depends also on knowledge generated elsewhere ( [[#Emmerling--2016|Emmerling et al. 2016]] ; [[#Fragkiadakis--2020|Fragkiadakis et al. 2020]] ). Models with a more detailed representation of sectors ( [[#Paroussos--2020|Paroussos et al. 2020]] ) can use spillover matrices to include bilateral spillovers and compute learning rates that depend on the human capital stock and the regional and/or sectoral absorption rates ( [[#Fragkiadakis--2020|Fragkiadakis et al. 2020]] ). Accounting for knowledge spillovers in the EU for PV, wind turbines, electric vehicles, biofuels, industry materials, batteries and advanced heating and cooking appliances can lead to the following results in a decarbonisation scenario over the period 2020–2050 as compared to the reference scenario: an increase of 1.0–1.4% in GDP, 2.1–2.3% in investment, and 0.2–0.4% in employment by clean energy technologies ( [[#Paroussos--2017|Paroussos et al. 2017]] ). When comparing two possible EU transition strategies – being a first-mover with strong unilateral emission reduction strategy until 2030 versus postponing action for the period after 2030 – endogenous technical progress in the green technologies sector can alleviate most of the negative effects of pioneering low-carbon transformation associated with loss of competitiveness and carbon leakage ( [[#Karkatsoulis--2016|Karkatsoulis et al. 2016]] ).

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==== 16.2.4.2 Technology Deployment and Diffusion ====

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To simulate possible paths of energy technology diffusion for different decarbonisation targets, models rely on assumptions about the cost of a given technology relative to the costs of other technologies, and its ability to supply the energy demand under the relevant energy system and physical constraints. These assumptions include, for example, considerations regarding renewable intermittency, inertia on technology lifetime (for instance, under less stringent temperature scenarios, early retirement of fossil plants does not take place), distribution, capacity and market growth constraints, as well as the presence of policies. These factors change the relative price of technologies. Furthermore, technological diffusion in one country is also influenced by technology advancements in other regions ( [[#Kriegler--2015|Kriegler et al. 2015]] ).

Technology diffusion may also be strongly influenced, either positively or negatively, by a number of non-cost, non-technological barriers or enablers regarding behaviours, society and institutions ( [[#Knobloch--2016|Knobloch and Mercure 2016]] ). These include network or infrastructure externalities, the co-evolution of technology clusters over time (‘path dependence’), the risk-aversion of users, personal preferences and perceptions and lack of adequate institutional framework which may negatively influence the speed of (low-carbon) technological innovation and diffusion, heterogeneous agents with different preferences or expectations, multi-objectives and/or competitiveness advantages and uncertainty around the presence and the level of environmental policies and institutional and administrative barriers ( [[#Marangoni--2014|Marangoni and Tavoni 2014]] ; [[#Baker--2015|Baker et al. 2015]] ; [[#Iyer--2015|Iyer et al. 2015]] ; [[#Napp--2017|Napp et al. 2017]] ; [[#Biresselioglu--2020|Biresselioglu et al. 2020]] ; [[#van%20Sluisveld--2020|van Sluisveld et al. 2020]] ). These types of barriers to technology diffusion are currently not explicitly detailed in most of the climate-energy-economy models. Rather, they are accounted for in models through scenario narratives, such as the ones in the ''Shared Socioeconomic Pathways'' ( [[#Riahi--2017|Riahi et al. 2017]] ), in which assumptions about technology adoption are spanned over a plausible range of values. Complementary methods are increasingly used to explore their importance in future scenarios ( [[#Turnheim--2015|Turnheim et al. 2015]] ; [[#Geels--2016|Geels et al. 2016]] ; [[#Doukas--2018|Doukas et al. 2018]] ; [[#Gambhir--2019|Gambhir et al. 2019]] ; [[#Trutnevyte--2019|Trutnevyte et al. 2019]] ). It takes a very complex modelling framework to include all aspects affecting technology cost reductions and technology diffusion, such as heterogeneous agents ( [[#Lamperti--2020|Lamperti et al. 2020]] ), regional labour costs ( [[#Skelton--2020|Skelton et al. 2020]] ), materials cost and trade and perfect foresight multi-objective optimisation (Aleluia Reis et al. 2021). So far, no model can account for all these interactions simultaneously.

Another key aspect of decarbonisation regards issues of acceptability and social inclusion in decision-making. Participatory processes involving stakeholders can be implemented using several methods to incorporate qualitative elements in model-based scenarios on future change ( [[#van%20Vliet--2010|van Vliet et al. 2010]] ; [[#Nikas--2017|Nikas et al. 2017]] , 2018; [[#Doukas--2020|Doukas and Nikas 2020]] ; [[#van%20der%20Voorn--2020|van der Voorn et al. 2020]] ).

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<span id="implications-for-the-modelling-of-technical-change-in-decarbonisation-pathways"></span>
==== 16.2.4.3 Implications for the Modelling of Technical Change in Decarbonisation Pathways ====

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Although the debate is still ongoing, preliminary conclusions indicate that integrated assessment models tend to underestimate innovation on energy supply but overestimate the contributions by energy efficiency ( [[#IPCC--2018b|IPCC 2018b]] ). Scenarios emerging from cost-optimal climate-energy-economy models are too pessimistic, especially in the case of rapidly changing technologies such as wind and batteries in the past decade. Conversely, they tend to be too optimistic regarding the timing of action, or the availability of a given technology and its speed of diffusion ( [[#Shiraki--2020|Shiraki and Sugiyama 2020]] ). Furthermore, some technological and economic transformations may emerge as technically feasible from IAMs, but are not realistic if taking into account political economy, international politics, human behaviours, and cultural factors ( [[#Bosetti--2021|Bosetti 2021]] ).

There is a range of projected energy technology supply costs included in the IPCC’s Sixth Assessment Report (AR6) Scenario Database (Box 16.1). Variations of costs over time and across scenarios are within ranges comparable to those observed in recent years. Conversely, model results show that limiting warming to 2°C or 1.5°C will require faster diffusion of installed capacity of low-carbon energy options and a rapid phase-out of fossil-based options. This points to the importance of focusing on overcoming real-life barriers to technology deployment.

<div id="Box 16.1 | Comparing Observed Energy Technology Costs and Deployment Rates with Projections from AR6 Global Mo" class="h2-container"></div>

<span id="box-16.1-comparing-observed-energy-technology-costs-and-deployment-rates-with-projections-from-ar6-global-mo-delled-pathways"></span>
=== Box 16.1 | Comparing Observed Energy Technology Costs and Deployment Rates with Projections from AR6 Global Modelled Pathways ===

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Currently observed costs and deployment for electricity supply technologies from a variety of sources are compared with projections from two different sets of scenarios contained in the AR6 Scenario database: (i) scenarios that limit warming to 3°C (&gt;50%) and scenarios that limit warming to 4°C (&gt;50%), and (ii) scenarios that limit warming to 2°C (&gt;67%) or lower (AR6 Scenarios Database). Global aggregate costs are shown for the following technologies: coal with carbon dioxide capture and storage (CCS), gas with CCS, nuclear, solar PV, onshore and offshore wind.

The decrease in forecasted capital costs is not large compared to current capital costs for most technologies, and does not differ much between the two sets of scenarios (Box 16.1, Figure 1a). For offshore wind some of the models are more optimistic than the current reality ( [[#Timilsina--2020|Timilsina 2020]] ). Several sources of current solar PV costs report values that are at the low end of the AR6 Scenario Database. By 2050, the median technology cost forecasts decrease by between 5% for nuclear and 45–52% for solar (Box 16.1, Figure 1c).

Median values of renewables installed capacity increase with respect to 2020 capacity in scenarios that limit warming to 3°C (&gt;50%) and in scenarios that limit warming to 4°C (&gt;50%) (Box 16.1 Figure 1b), where energy and climate policies are implemented in line with NDCs announced prior to COP26. More stringent targets (2°C) are achieved through a higher deployment of renewable technologies: by 2050 solar (wind) capacity is estimated to increase by a factor of 15 (10) (Box 16.1, Figure 1c). This is accompanied by an almost complete phase-out of coal (–87%). The percentage of median changes in installed capacity in scenarios that limit warming to 3°C (&gt;50%) and in scenarios that limit warming to 4°C (&gt;50%)is within comparable ranges of that observed in the last decade. In the case of scenarios that limit warming to 2°C (&gt;67%) or lower, capacity installed is higher for renewable technologies and nuclear, and lower for fossil-based technologies (Box 16.1, Figure 1c).

The higher deployment in scenarios that limit warming to 2°C (&gt;67%) or lower cannot be explained solely as a result of technology cost dynamics. In IAMs, technology deployment is also governed by system constraints that characterise both 3°C (&gt;50%) and 4°C (&gt;50%) scenarios, for example, the flexibility of the energy system, the availability of storage technologies. From a modelling point of view, implementing more stringent climate policies to meet the 2°C limit forces models to find solutions, even if costly, to meet those intermittency and flexibility constraints and temperature target constraints.

[[File:20c1ac75e6f0db982e51b92df5c50095 IPCC_AR6_WGIII_Box_16_1_Figure_1.png]]

'''Box 16.1, Figure 1 | Global technology cost and deployment in two groups of AR6 scenarios: (i) scenarios that limit warming to 3°C (&gt;50%) and scenarios that limit warming to 4°C (&gt;50%) (“Reference and current policies”), and (ii) scenarios that limit warming to 2°C (&gt;67%) or lower (“2°C and 1.5°C”).''' Panel ''(a)'' Current capital costs are sourced from Table 1 ( [[#Timilsina--2020|Timilsina 2020]] ); distribution of capital costs in 2030 and 2050 (AR6 Scenarios Database). Blue symbols represent the mean. ‘Current’ capital costs for coal and gas plants with CCS are not available; Panel ''(b)'' Total installed capacity in 2019 ( [[#IEA--2020c|IEA 2020c]] ; [[#IRENA--2020a|IRENA 2020a]] , b); distribution of total installed capacity in 2030 and 2050 (AR6 Scenario Database). Blue symbols represent the mean; Panel ''(c)'' Percentage of change in capital costs and installed capacity between (2010–2020) and percentage of median change (2020–2030 and 2020–2050) (Median year –Median 2020 )/Median 2020 *100. ‘M’ indicates the number of models, ‘S’ the number of scenarios for which this data is available. ‘Reference and current policies’ are scenarios that limit warming to 3°C (&gt;50%) and scenarios that limit warming to 4°C (&gt;50%) (C6 and C7 AR6 scenario categories). ‘2C and 1.5C’ are scenarios that limit warming to 2°C (&gt;67%) or lower (C1, C2 and C3 AR6 scenario categories). Each model may have submitted data for more than one model version.

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