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

== 16.3 A Systemic View of Technological Innovation Processes ==

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The innovation process, which consists of a set of sequential phases ( [[#16.2.1|Section 16.2.1]] ), is often simplified to a linear process. Yet, it is now well understood that it is also characterised by numerous kinds of interactions and feedbacks between the domains of knowledge generation, knowledge translation and application, and knowledge use ( [[#Kline--1986|Kline and Rosenberg 1986]] ). Furthermore, it is not just invention that leads to technological change; the cumulative contribution of incremental innovations over time can be very significant ( [[#Kline--1986|Kline and Rosenberg 1986]] ). Innovations can come, not just from formal research and development (R&amp;D) but also sources such as production engineers and the shop floor ( [[#Kline--1986|Kline and Rosenberg 1986]] ; [[#Freeman--1995|Freeman 1995]] ).

This section reviews the literature focusing on innovation as a systemic process. This now predominant view enriches the understanding of innovation as presented in [[#16.2|Section 16.2]] ; it conceptualises innovation as the result of actions by, and interactions among, a large set of actors, whose activities are shaped by, and shape, the context in which they operate and the user group with which they are engaging. This section aligns with the discussion of socio-technical transitions ( [[IPCC:Wg3:Chapter:Chapter-1#1.7.3|Section 1.7.3]] , [[IPCC:Wg3:Chapter:Chapter-5|Chapter 5]] Supplementary Material, and Cross-Chapter Box 12 in this chapter).

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=== 16.3.1 Frameworks for Analysing Technological Innovation Processes ===

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The resulting overarching framework that is commonly used in the innovation scholarship and in policy analyses is termed an ‘innovation system’, where the key constituents of the systems are actors, their interactions, and the institutional landscape, including formal rules, such as laws, and informal restraints, such as culture and codes of conduct, that govern the behaviour of the actors ( [[#North--1991|North 1991]] ).

One application of this framework, ''national innovation systems (NIS)'' , highlight the importance of national and regional relationships for determining the technological and industrial capabilities and development of a country ( [[#Lundvall--1992|Lundvall 1992]] ; [[#Nelson--1993|Nelson 1993]] ; [[#Freeman--1995|Freeman 1995]] ). [[#Nelson--1993|Nelson (1993)]] and [[#Freeman--1995|Freeman (1995)]] highlight the role of institutions that determine the innovative performance of national firms as a way to understand differences across countries, while [[#Lundvall--1992|Lundvall (1992)]] focuses on the ‘elements and relationships which interact in the production, diffusion and use of new, and economically useful, knowledge’ – that is, notions of interactive learning, in which user-producer relationships are particularly important ( [[#Lundvall--1988|Lundvall 1988]] ). Building on this, various other applications of the ‘innovation system’ framework have emerged in the literature.

''Technological innovation systems (TIS),'' with a technology or a set of technologies (more narrowly or broadly defined in different cases) as the unit of analysis, focus on explaining what accelerates or hinders their development and diffusion. [[#Carlsson--1991|Carlsson and Stankiewicz (1991)]] define a technological system as ‘a dynamic network of agents interacting in a specific economic/industrial area under a particular institutional infrastructure and involved in the generation, diffusion, and utilisation of technology’. More recent work takes a ‘functional approach’ to TIS ( [[#Hekkert--2007|Hekkert et al. 2007]] ; [[#Bergek--2008|Bergek et al. 2008]] ), which was later expanded with explanations of how some of the sectoral, geographical and political dimensions intersect with technology innovation systems ( [[#Bergek--2015|Bergek et al. 2015]] ; [[#Quitzow--2015|Quitzow 2015]] ).

''Sectoral innovation systems (SIS)'' are based on the understanding that the constellation of relevant actors and institutions will vary across industrial sectors, with each sector operating under a different technological regime and under different competitive or market conditions. A sectoral innovation, thus, can be defined as ‘that system (group) of firms active in developing and making a sector’s products and in generating and utilising a sector’s technologies’ ( [[#Breschi--1997|Breschi and Malerba 1997]] ).

''Regional innovation systems (RIS) and global innovation systems (GIS)'' , recognise that the many innovation processes have a spatial dimension, where the development of system resources such as knowledge, market access, financial investment, and technology legitimacy may well draw on actors, networks, and institutions within a region ( [[#Cooke--1997|Cooke et al. 1997]] ). In other cases, the distribution of many innovation processes are highly internationalised and therefore outside specific territorial boundaries ( [[#Binz--2017|Binz and Truffer 2017]] ). Importantly, [[#Binz--2017|Binz and Truffer (2017)]] note that the GIS framework ‘differentiates between an industry’s dominant innovation mode... and the economic system of valuation in which markets for the innovation are constructed’.

The relevance of ''mission-oriented innovation systems (MIS),'' comes into focus with the move towards mission-oriented programmes as part of the increasing innovation policy efforts to address societal challenges. Accordingly, an MIS is seen as consisting of ‘networks of agents and sets of institutions that contribute to the development and diffusion of innovative solutions with the aim to define, pursue and complete a societal mission’ ( [[#Hekkert--2020|Hekkert et al. 2020]] ).

Notably the innovation systems approach has been used in a number of climate-relevant areas such as agriculture ( [[#Echeverría--1998|Echeverría 1998]] ; [[#Horton--2003|Horton and Mackay 2003]] ; [[#Brooks--2011|Brooks and Loevinsohn 2011]] ; [[#Klerkx--2012|Klerkx et al. 2012]] ), energy ( [[#Sagar--2002|Sagar and Holdren 2002]] ; [[#OECD--2006|OECD 2006]] ; [[#Gallagher--2012|Gallagher et al. 2012]] ; [[#Wieczorek--2013|Wieczorek et al. 2013]] ; [[#Darmani--2014|Darmani et al. 2014]] ; [[#Mignon--2016|Mignon and Bergek 2016]] ), industry ( [[#Koasidis--2020b|Koasidis et al. 2020b]] ) and transport ( [[#Koasidis--2020a|Koasidis et al. 2020a]] ), and sustainable development ( [[#Anadon--2016b|Anadon et al. 2016b]] ; [[#Clark--2016|Clark et al. 2016]] ; [[#Bryden--2017|Bryden and Gezelius 2017]] ; [[#Nikas--2020|Nikas et al. 2020]] ).

A number of functions can be used to understand and characterise the performance of technological innovation systems ( [[#Hekkert--2007|Hekkert et al. 2007]] ; [[#Bergek--2008|Bergek et al. 2008]] ). The most common functions are listed in Table 16.5.

'''Table 16.5 | Functions that the literature identified as key for well-performing technological innovation systems.''' Source: based on [[#Hekkert--2007|Hekkert et al. (2007)]] and [[#Bergek--2008|Bergek et al. (2008)]] .

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

! '''Description'''

|-
| Entrepreneurial activities and experimentation

| Entrepreneurial activities and experimentation for translating new knowledge and/or market opportunities into real-world application

|-
| Knowledge development

| Knowledge development includes both learning by searching and learning by doing

|-
| Knowledge diffusion

| Knowledge diffusion through networks, both among members of a community (e.g., scientific researchers) and across communities (e.g., universities, business, policy, and users)

|-
| Guidance of search

| Guidance of search directs the investments in innovation in consonance with signals from the market, firms or government

|-
| Market formation

| Market formation through customers or government policy is necessary to allow new technologies to compete with incumbent technologies

|-
| Resource mobilisation

| Resource mobilisation pertains to the basic inputs – human and financial capital – to the innovation process

|-
| Creation of legitimacy/counteract resistance to change

| Creation of legitimacy or counteracting resistance to change, through activities that allow a new technology to become accepted by users, often despite opposition by incumbent interests

|-
| Development of external economies

| Development of external economies, or the degree to which other interests benefit from the new technology

|}

Evidence from empirical case studies indicates that all the above functions are important and that they interact with one another ( [[#Hekkert--2009|Hekkert and Negro 2009]] ). The approach therefore serves as both a rationale for and a guide to innovation policy ( [[#Bergek--2010|Bergek et al. 2010]] ).

A much-used, complementary systemic framework is the Multi-Level Perspective (MLP) ( [[#Geels--2002|Geels 2002]] ), which focuses mainly on the diffusion of technologies in relation to incumbent technologies in their sector and the overall economy. A key point of MLP is that new technologies need to establish themselves in a stable ‘socio-technical regime’ and are therefore generally at a disadvantage, not just because of their low technological maturity, but also because of an unwelcoming system. The MLP highlights that the uptake of technologies in society is an evolutionary process, which can be best understood as a combination of ‘variation, selection and retention’ as well as ‘unfolding and reconfiguration’ ( [[#Geels--2002|Geels 2002]] ). Thus, new technologies in their early stages need to be selected and supported at the micro-level by niche markets, possibly through a directed process that has been termed ‘strategic niche management’ ( [[#Kemp--1998|Kemp et al. 1998]] ). As, at the landscape level, pressures on incumbent regimes mount, and those regimes destabilise, the niche technologies get a chance to get established in a new socio-technical regime. This allows these technologies to grow and stabilise, shaping a changed or sometimes radically renewed socio-technical regime. The MLP takes a systematic and comprehensive view about how to nurture and shape technological transitions by understanding them as evolutionary, multidirectional and cumulative socio-technical processes playing out at multiple levels over time, with a concomitant expansion in the scale and scope of the transition ( [[#Elzen--2004|Elzen et al. 2004]] ; [[#Geels--2005|Geels 2005]] ). There have been numerous studies that draw on the MLP to understand different aspects of climate technology innovation and diffusion ( [[#van%20Bree--2010|van Bree et al. 2010]] ; [[#Geels--2012|Geels 2012]] ; [[#Geels--2017|Geels et al. 2017]] ).

Systemic analyses of innovation have predominantly focused on industrialised countries There have been some efforts to use the innovation systems lens for the developing country context ( [[#Jacobsson--2006|Jacobsson and Bergek 2006]] ; [[#Altenburg--2009|Altenburg 2009]] ; [[#Lundvall--2009|Lundvall et al. 2009]] ; [[#Tigabu--2015|Tigabu et al. 2015]] ; [[#Tigabu--2018|Tigabu 2018]] ; [[#Choi--2019|Choi and Zo 2019]] ) and specific suggestions on ways for developing countries to strengthening their innovation systems (e.g., by universities taking on a ‘developmental’ role ( [[#Arocena--2015|Arocena et al. 2015]] ), or industry associations acting as intermediaries to build institutional capacities ( [[#Watkins--2015|Watkins et al. 2015]] ; [[#Khan--2020|Khan et al. 2020]] ), including specifically for addressing climate challenges ( [[#Sagar--2009|Sagar et al. 2009]] ; [[#Ockwell--2016|Ockwell and Byrne 2016]] ). But the conditions in developing countries are quite different, leading to suggestions that different theoretical conceptualisations of the innovation systems approach may be needed for these countries ( [[#Arocena--2020|Arocena and Sutz 2020]] ), although a system perspective would still be appropriate ( [[#Boodoo--2018|Boodoo et al. 2018]] ).

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=== 16.3.2 Identifying Systemic Failures to Innovation in Climate-related Technologies ===

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Traditional perspectives on innovation policy were mostly science-driven, and focused on strengthening invention and its translation into application in a narrow sense. Also, a second main traditional perspective on innovation policy was focused on correcting for ‘market failures’ ( [[#Weber--2017|Weber and Truffer 2017]] ) ( [[#16.2|Section 16.2]] ). The more recent understanding of, and shift of focus to, the systemic nature on the innovation and diffusion of technologies has implications for innovation policy, since innovation outcomes depend not just on inputs such as R&amp;D, but much more on the functioning of the overall innovation system (see Sections 16.3.1 and 16.4). Policies can therefore be directed at innovation systems components and processes that need the greatest attention or support. This may include, for example, strengthening the capabilities of weak actors and improving interactions between actors ( [[#Jacobsson--2017|Jacobsson et al. 2017]] ; [[#Weber--2017|Weber and Truffer 2017]] ). At the same time, a systemic perspective also brings into sharp relief the notion of ‘system failures’ ( [[#Weber--2017|Weber and Truffer 2017]] ).

Systemic failures include: infrastructural failures; hard (e.g., laws, regulation) and soft (e.g., culture, social norms) institutional failures; interaction failures (strong and weak network failures); capability failures relating to firms and other actors; lock-in; and directional, reflexivity, and coordination failures ( [[#Klein%20Woolthuis--2005|Klein Woolthuis et al. 2005]] ; [[#Chaminade--2010|Chaminade and Esquist 2010]] ; [[#Negro--2012|Negro et al. 2012]] ; [[#Weber--2012|Weber and Rohracher 2012]] ; [[#Wieczorek--2012|Wieczorek and Hekkert 2012]] ). Most of the literature that unpacks such failures and explores ways to overcome them is on energy-related innovation policy. For example, Table 16.6 summarises a meta-study ( [[#Negro--2012|Negro et al. 2012]] ) that examined cases of renewable energy technologies trying to disrupt incumbents across a range of countries to understand the roles, and relative importance, of the ‘systemic problems’ highlighted in [[#16.3.1|Section 16.3.1]] .

Depending on the sector, specific technology characteristics, and national and regional context, the relevance of these systemic problems varies ( [[#Trianni--2013|Trianni et al. 2013]] ; [[#Bauer--2017|Bauer et al. 2017]] ; [[#Wesseling--2017|Wesseling and Van der Vooren 2017]] ; [[#Koasidis--2020a|Koasidis et al. 2020a]] , b), suggesting that the innovation policy mix has to be tailor-made to respond to the diversity of systemic failures ( [[#Rogge--2017|Rogge et al. 2017]] ). An illustration of how such systemic failures have been addressed is given in Box 16.2, which shows how the Indian government designed its standards and labelling programme for energy-efficient air conditioners and refrigerators. The success of this programme resulted from the careful attention to bring on board and coordinate the relevant actors and resources, the design of the standards, and ensuring effective administration and enforcement of the standards ( [[#Malhotra--2021|Malhotra et al. 2021]] ).

'''Table 16.6 | Examination of systemic problems preventing renewable energy technologies from reaching their potential, including number of case studies in which the particular ‘systemic problem’ was identified.''' Source: [[#Negro--2012|Negro et al. (2012)]] .

{| class="wikitable"
|-
! '''Systemic problems'''

! '''Empirical sub-categories'''

! '''No. of cases'''

|-
| Hard institutions

| – ‘Stop and go policy’: lack of continuity and long-term regulations; inconsistent policy and existing laws and regulations

– ‘Attention shift’: policymakers only support technologies if they contribute to the solving of a current problem

– ‘Misalignment’ between policies on sector level such as agriculture, waste, and on governmental levels, i.e., EU, national, regional level, etc.

– ‘Valley of Death’: lack of subsidies, feed-in tariffs, tax exemption, laws, emission regulations, venture capital to move technology from experimental phase towards commercialisation phase

| 51

|-
| Market structures

| – Large-scale criteria

– Incremental/near-to-market innovation

– Incumbent’s dominance

| 30

|-
| Soft institutions

| – Lack of legitimacy

– Different actors opposing change

| 28

|-
| Capabilities/capacities

| – Lack of technological knowledge of policymakers and engineers

– Lack of ability of entrepreneurs to pack together, to formulate clear message, to lobby to the government

– Lack of users to formulate demand

– Lack of skilled staff

| 19

|-
| Knowledge infrastructure

| – Wrong focus or not specific courses at universities knowledge institutes

– Gap/misalignment between knowledge produced at universities and what is needed in practice

| 16

|-
| Too weak interactions

| – Individualistic entrepreneurs

– No networks, no platforms

– Lack of knowledge diffusion between actors

– Lack of attention for learning by doing

| 13

|-
| Too strong interactions

| – Strong dependence on government action or dominant partners (incumbents)

– Networks allows no access to new entrants

| 8

|-
| Physical infrastructure

| – No access to existing electricity or gas grid for renewable energy technologies

– No decentralised, small-scale grid

– No refill infrastructure for biofuels, hydrogen, biogas

| 2

|}

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=== Box 16.2 | Standards and Labelling for Energy Efficient Refrigerators and Air Conditioners in India ===

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Energy efficiency is often characterised as a ‘low-hanging fruit’ for reducing energy use. However, systemic failures such as lack of access to capital, hidden costs of implementation, and imperfect information can result in low investments into adoption and innovation in energy efficiency measures ( [[#Sorrell--2004|Sorrell et al. 2004]] ). To address such barriers, India’s governmental Bureau of Energy Efficiency (BEE) introduced the Standards and Labelling (S&amp;L) programme to promote innovation in energy efficient appliances in 2006 ( [[#Sundaramoorthy--2017|Sundaramoorthy and Walia 2017]] ). While context-dependent, the programme’s design, policies and scale-up contain lessons for addressing systemic failures elsewhere too.

'''Programme design and addressing of early sy''' '''stemic barriers'''

To design the S&amp;L programme, BEE drew on the international experiences and technical expertise of the Collaborative Labelling and Appliance Standards Program (CLASP) – a non-profit organisation that provides technical and policy support to governments in implementing S&amp;L programmes. For example, since there was no data on the efficiency of appliances in the Indian market, CLASP assisted with early data collection efforts, resulting in a focus on refrigerators and air conditioners ( [[#McNeil--2008|McNeil et al. 2008]] ).

Besides drawing from international knowledge, the involvement of manufacturers, testing laboratories, and customers was crucial for the functioning of the innovation system.

To involve manufacturers, BEE employed three strategies to set the standards at an ambitious yet acceptable level. First, BEE enlisted the Indian Institute of Technology (IIT) Delhi (a public technical university) to engage with manufacturers and to demonstrate cost-effective designs of energy-efficient appliances. Second, BEE agreed to make the standards voluntary from 2006 to 2010. In return, the manufacturers agreed to mandatory and progressively more stringent standards starting in 2010. Third, BEE established a multistakeholder committee with representation from BEE, the Bureau of Indian Standards, appliance manufacturers, test laboratories, independent experts, and consumer groups ( [[#Jairaj--2016|Jairaj et al. 2016]] ) to ensure that adequately stringent standards are negotiated every two years.

At this time, India had virtually no capacity for independent testing of appliances. Here, too, BEE used multiple approaches towards creating the actors and resources needed for the innovation system to function. First, BEE funded the Central Power Research Institute (CPRI) – a national laboratory for applied research, testing and certification of electrical equipment – to set up refrigerator and AC testing facilities. Second, they invited bids from private laboratories, thus creating a demand for testing facilities. Third, BEE developed testing protocols in partnership with universities. Australian standards for testing frost-free refrigerators were adopted until local standards were developed. Thus, once the testing laboratories, protocols and benchmark prices for testing were in place, the appliance manufacturers could employ their services.

Finally, a customer outreach programme was conducted from 2006 to 2008 to inform customers about energy-efficient appliances, to enable them to interpret the labels correctly, and to understand their purchase decisions and information sources ( [[#Jain--2018|Jain et al. 2018]] ; [[#Joshi--2019|Joshi et al. 2019]] ). BEE initiated a capacity-building programme for retailers to be an information source for customers. A comprehensive document with details of different models and labels was provided to retailers, together with a condensed booklet to be shared with customers.

'''Adapting policies to technologies an''' '''d local context'''

While many of India’s standards and testing protocols were based on international standards, they needed to be adapted to the Indian context. For example, because of higher temperatures in India, the reference outside temperature of 32°C for refrigerators was changed to 36°C.

AC testing protocols also had to be adapted because of the emergence of inverter-based ACs. Existing testing done only at a single temperature did not value inverter-based ACs’ better average performance as compared to fixed-speed ACs over a range of temperatures. Thus, the Indian Seasonal Energy Efficiency Ratio (ISEER) was developed for Indian temperature conditions in 2015 by studying International Organization for Standardization (ISO) standards and through consultations with manufacturers ( [[#Mukherjee--2020|Mukherjee et al. 2020]] ).

These measures had multiple effects on technological change. As a result of stringent standards, India has some of the most efficient refrigerators globally. In the case of ACs, the ISEER accelerated technological change by favouring inverter-based ACs over fixed-speed ACs, driving down their costs and increasing their market shares ( [[#BEE--2020|BEE 2020]] ).

'''Scaling up policies for market''' '''transformation'''

As the S&amp;L programme was expanded, BEE took measures to standardise, codify and automate it. For example, to process a high volume of applications for labels efficiently, an online application portal with objective and transparent certification criteria was created. This gave certainty to the manufacturers, enabling diversity and faster diffusion of energy-efficient appliances. Thus by 2019, the programme expanded to cover thousands of products across 23 appliance types ( [[#BEE--2020|BEE 2020]] ).

Besides issuing labels, the enforcement of standards also needed to be scaled up efficiently. BEE developed protocols for randomly sampling appliances for testing. Manufacturers were given a fixed period to rectify products that did not meet the standards, failing which they would be penalised and the test results would be made public.

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=== Box 16.3 | Investments in Public Energy Research and Development ===

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Public energy R&amp;D investments are a crucial driver of energy technology innovation (Sections 16.2.1.1 and 16.4.1). Box 16.3, Figure 1 shows the time profile of energy-related RD&amp;D budgets in OECD countries as well as some key events which coincided with developments of spending (IEA 2019). Such data on other countries, in particular developing countries, are not available, although recent evidence suggests that expenditures are increasing there (IEA 2020c). The IEA collected partial data from China and India in the context of Mission Innovation, but this is only available starting from 2014 and thus not included in Figure 1.

The figure illustrates two points. First, energy-related RD&amp;D has risen slowly in the last 20 years, and is now reaching levels comparable with the peak of energy RD&amp;D investments following the two oil crises. Second, over time there has been a reorientation of the portfolio of funded energy technologies away from nuclear energy. In 2019, around 80% of all public energy RD&amp;D spending was on low-emission technologies – energy efficiency, carbon dioxide capture, use and storage, renewables, nuclear, hydrogen, energy storage and cross-cutting issues such as smart grids. A more detailed discussion of the time profile of RD&amp;D spending in IEA countries, including as a share of GDP, is available in IEA (2020b).

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[[File:d517f4fada5a995c2679f43744e2e4ef IPCC_AR6_WGIII_Box_16_3_Figure_1.png]]

'''Box 16.3, Figure 1 | Fraction of public energy RD&amp;D spending by technology over time for IEA (largely OECD) countries between 1974 and 2018.''' Sources: RD&amp;D Database (2019), IEA (2019) (extracted on November 11, 2020).

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=== 16.3.3 Indicators for Technological Innovation ===

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Assessing the state of technological innovation helps in understanding the progress of current efforts and policies in meeting stated objectives, and how we might design policies to do better.

Traditionally, input measures such as research, development and demonstration (RD&amp;D) investments, and output measures such as scientific publication and patents were used to characterise innovation activities ( [[#Freeman--2009|Freeman and Soete 2009]] ). This is partly because of the successes of specialised R&amp;D efforts ( [[#Freeman--1995|Freeman 1995]] ), the predominant linear model of innovation, and because such measures can (relatively) easily be obtained and compared. In the realm of energy-related innovation, RD&amp;D investments remain the single most-used indicator to measure inputs into the innovation process (Box 16.3). Patent counts are a widely used indicator of the outputs of the innovation process, especially because they are detailed enough to provide information on specific adaptation and mitigation technologies. Mitigation and adaptation technologies have their own classification (Y02) with the European Patent Office (EPO) ( [[#Veefkind--2012|Veefkind et al. 2012]] ; [[#Angelucci--2018|Angelucci et al. 2018]] ), which can be complemented with keyword search and manual inspection ( [[#Persoon--2020|Persoon et al. 2020]] ; [[#Surana--2020b|Surana et al. 2020b]] ). However, using energy-related patents as an indicator of innovative activities is complicated by several issues ( [[#de%20Rassenfosse--2013|de Rassenfosse et al. 2013]] ; [[#Haščič--2015|Haščič and Migotto 2015]] ; [[#Jaffe--2017|Jaffe and de Rassenfosse 2017]] ), including the fact that the scope of what are considered climate mitigation inventions is not always clear or straightforward.

Conversely, private energy R&amp;D investments and investments by financing firms cannot be precisely assessed for a number of reasons, including limited reporting and the difficulty of singling out energy-related investments. This inability to precisely quantify private investments in energy R&amp;D leads to a patchy understanding of the energy innovation system, and how private energy R&amp;D investments responds to public energy R&amp;D investments. Overall, evidence shows that some of the industrial sectors that are important for meeting climate goals (electricity, agriculture and forestry, mining, oil and gas, and other energy-intensive industrial sectors) are investing relatively small fractions of sales on R&amp;D ( ''medium evidence'' , ''high agreement'' ) (Jasmab and Pollitt 2005; [[#Jamasb--2008|Jamasb and Pollitt 2008]] ; [[#Sanyal--2009|Sanyal and Cohen 2009]] ; [[#European%20Commission--2015|European Commission 2015]] ; [[#American%20Energy%20Innovation%20Council--2017|American Energy Innovation Council 2017]] ; [[#Gaddy--2017|Gaddy et al. 2017]] ; [[#National%20Science%20Board--2018|National Science Board 2018]] ).

Financing firms also play an important role in the energy innovation process, but data availability is limited. The venture capital (VC) financing model, used to overcome the ‘valley of death’ in the biotech and IT space ( [[#Frank--1996|Frank et al. 1996]] ), has not been as suitable for hardware start-ups in the energy space: for example, the percentage of exit outcomes in cleantech start-ups was almost half of that in medical start-ups, and less than a third of software investments ( [[#Gaddy--2017|Gaddy et al. 2017]] ). The current VC model and other private finance do not sufficiently cover the need to demonstrate energy technologies at scale ( [[#Anadón--2012|Anadón 2012]] ; [[#Mazzucato--2013|Mazzucato 2013]] ; [[#Nemet--2018|Nemet et al. 2018]] ). This greater difficulty in reaching the market compared to other sectors may have contributed to a reduction in private equity and venture capital finance for renewable energy technologies after the boom of the late 2000s ( [[#Frankfurt%20School-UNEP%20Centre/BNEF--2019|Frankfurt School-UNEP Centre/BNEF 2019]] ).

Quantitative indicators such as energy-related RD&amp;D spending are insufficient for the assessment of innovation systems ( [[#David--1995|David and Foray 1995]] ): they only provide a partial view into innovation activities, and one that is potentially misleading ( [[#Freeman--2009|Freeman and Soete 2009]] ). Qualitative indicators measuring the more intangible aspects of the innovation process and system are crucial to fully understand the innovation dynamics in a climate or energy technologies or sectors ( [[#Gallagher--2006|Gallagher et al. 2006]] ), including in relation to adopting an adaptive learning strategy and supporting learning through demonstration projects ( [[#Chan--2017|Chan et al. 2017]] ).

In Table 16.7, both quantitative and qualitative indicators for systemic innovation are outlined, using clean energy innovation as an illustrative example, and drawing on a broad literature base, taking into account both the input-output-outcome classification and its variations ( [[#Freeman--1997|Freeman and Soete 1997]] ; [[#Sagar--2002|Sagar and Holdren 2002]] ; [[#Hu--2018|Hu et al. 2018]] ), combined with the functions of technological innovation systems ( [[#Miremadi--2018|Miremadi et al. 2018]] ), while also being cognisant of the specific role of key actors and institutions ( [[#Gallagher--2012|Gallagher et al. 2012]] ). A specific assessment of innovation may focus on part of such a list of indicators, depending on what aspect of innovation is being studied, whether the analysis takes a more or less systemic perspective, and the specific technology and geography considered. Similarly, innovation policies may be designed to specifically boost only some of these aspects, depending on whether a given country/region is committed to strengthen a given technology or phase.

'''Table 16.7 | Commonly used quantitative innovation metrics, organised by inputs, outputs and outcomes.''' Sources: based on [[#Sagar--2002|Sagar and Holdren (2002)]] ; Gallagher et al. (2006, 2011, 2012); [[#Hekkert--2007|Hekkert et al. (2007)]] ; [[#Gruebler--2012|Gruebler et al. (2012)]] ; [[#Hu--2018|Hu et al. (2018)]] ; [[#Miremadi--2018|Miremadi et al. (2018)]] ; [[#Avelino--2019|Avelino et al. (2019)]] .

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

! '''Input indicators'''

! '''Output indicators'''

! '''Outcome indicators'''

! '''Actors'''

! '''Policies'''

! '''Structural and systemic indicators'''

|-
| '''Knowledge development'''

| Higher education investments

Research and development (R&amp;D) investments

Number of researchers

R&amp;D projects over time

| Scientific publications

Highly-cited publications

Patents

New product configurations

| Number of technologies developed (proof-of-concept/prototypes)

Increase in number of researchers

Learning rates

| Governments

Private corporations

Universities

| Research programmes and strategies

Intellectual Property Rights (IPR) policies

International technical norms (e.g., standards)

Higher education policies

| Well-defined processes to define research priorities

Stakeholder involvement in priority-setting

|-
| '''Knowledge diffusion'''

| R&amp;D networks

Number of research agreements

Number of research exchange programmes

Number of scientific conferences

| Citations to literature or patents

Public-private co-publications

Co-patenting

Number of co-developed products

International scientific co-publications

Number of workshops and conferences

| Number of licensed patents

Number of technologies transferred

Knowledge-intensive services exports

Number of patent applications by foreigners

Number of researchers working internationally

| Governments

Private corporations

Scientific societies

Universities

| Development of communication centres

Facilitation of the development of networks

Open-access publication policies

IPR policies

International policy: e.g., treaties, clean development mechanism

| Accessibility to exchange programmes

Strength of linkage among key stakeholders

Participation to framework agreements

ICT access

|-
| '''Guidance of search'''

| Policy action plans and long-term targets

Shared strategies and roadmaps

Articulation of interest from lead customers

Expectations of markets/profits

| Level of media coverage

Scenarios and foresight projects

| Budget allocations

Mission-oriented innovation programmes

| Governments

Interest groups

Media

| Targets set by government for industry

Innovation policies

Credible political support

| Media strength

|-
| '''Resource mobilisation'''

| Access to finance

Graduate in Science, Technology, Engineering, and Mathematics (STEM)

Gross expenditure on R&amp;D/total expenditure

Domestic credit to private sector

Number of researchers in R&amp;D per capita

Public energy R&amp;D expenditure/total expenditure

Expenditure on education

Investment in complementary assets and/or infrastructure (e.g., charging infrastructure for electric vehicles, smart grids)

Venture capital on deals

| Number of green projects/technologies funded

Share of domestic credit granted to low-carbon technology projects

Share of domestic credit granted to projects developing complementary assets/infrastructure

| Employment in knowledge-intensive activities

Employment in relevant industries

Scale of innovative activities

Rate of growth of dedicated investment

Availability of complementary assets and infrastructure

| Governments

Private firms

Private investors (angel, venture capital, private equity)

Banks

| Financial resources support

Development of innovative financing

International agreements (e.g., technology agreements)

Infrastructure support

Project/programme evaluation

Innovation policies

Higher education policies

|
|-
| '''Entrepreneurial activities'''

| Number of new entrants

Percentage of clean energy start-ups/incumbents

Access to finance for cleantech start-ups

| Small and medium-sized enterprises (SMEs) introducing product or process innovation

Market introduction of new technological products

Number of new businesses

Experimental application projects

Creative goods exports

|
| Private firms

Government

Risk-capital providers

Philanthropists

| Ease of starting a business

Risk-capital policies

Start-up support programmes

Incubator programmes

| Start-up support services

|-
| '''Market formation'''

| Public market support

High-tech imports

| Market penetration of new technologies

Increase in installed capacity

Number of niche markets

Number of technologies commercialised

| Environmental performance

Level of environmental impact on society

Renewable energy jobs

Renewable energy production

Trade of energy technology and equipment

High-tech exports

| Private firms

Governments

institutions regulating trade, finance, investment, environment, development, security, and health issues

| Environmental and energy regulation

Fiscal and financial incentives

Cleantech-friendly policy processes

Transparency

Specific tax regimes

| Resource endowments

Attractiveness of renewable energy infrastructure

Coordination across relevant actors (e.g., renewable energy producers, grid operators, and distribution companies)

|-
| '''Creation of legitimacy'''

| Youth and public demonstration

Lobbying activities

Regulatory acceptance and integration

Technology support

| Level of discussion/debate among key stakeholders (public, firms, policymakers, etc.)

Greater recognition of benefits

| Public opinion

Policymaker opinion

Executive opinion on regulation

Environmental standards and certification

| Governments

Stakeholders

Citizens

Philanthropists

| Regulatory quality

Regulatory instruments

Political consistency

| Participatory processes

|}

The systemic approach to innovation and transition dynamics (Cross-Chapter Box 12 in this chapter) has advanced our understanding of the complexity of the innovation process, pointing to the importance of assessing the efficiency and effectiveness in producing, diffusing and exploiting knowledge ( [[#Lundvall--1992|Lundvall 1992]] ), including how the existing stock of knowledge may be recombined and used for new applications ( [[#David--1995|David and Foray 1995]] ). There remains a crucial need for more relevant and comprehensive approaches of assessing innovation ( [[#Freeman--2009|Freeman and Soete 2009]] ; [[#Dziallas--2019|Dziallas and Blind 2019]] ). In the context of climate mitigation, innovation is a means to an end; therefore, there is the need to consider the processes by which the output of innovation (e.g., patents) are translated into real-world outcomes (e.g., deployment of low-carbon technologies) ( [[#Freeman--1997|Freeman and Soete 1997]] ; [[#Sagar--2002|Sagar and Holdren 2002]] ). Currently, there is no available set of quantitative metrics that, collectively, can help get a picture of innovation in a particular energy technology or set of energy technologies. Also we are still lacking an understanding of how to systematically use qualitative indicators to characterise the more intangible aspects of the energy innovation system and to improve front-end innovation decisions ( [[#Dziallas--2019|Dziallas and Blind 2019]] ).

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<span id="emerging-policy-perspectives-on-systemic-transformations"></span>
=== 16.3.4 Emerging Policy Perspectives on Systemic Transformations ===

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Because of the multiple market, government, system, and other failures that are associated with the energy system, a range of policy interventions are usually required to enable the development and introduction of new technologies in the market ( [[#Jaffe--2005|Jaffe et al. 2005]] ; [[#Bürer--2009|Bürer and Wüstenhagen 2009]] ; [[#Negro--2012|Negro et al. 2012]] ; [[#Twomey--2012|Twomey 2012]] ; [[#Veugelers--2012|Veugelers 2012]] ; [[#Weber--2012|Weber and Rohracher 2012]] ) used in what is termed as ‘policy mixes’ ( [[#Rogge--2016|Rogge and Reichardt 2016]] ; [[#Edmondson--2019|Edmondson et al. 2019]] , 2020; [[#Rogge--2020|Rogge et al. 2020]] ). Empirical research shows that, in the energy and environment space, when new technologies were developed and introduced in the market, it was usually at least partly as a result of a range of policies that shaped the socio-technical system ( ''robust evidence'' , ''high agreement'' ) ( [[#Bunn--2014|Bunn et al. 2014]] ; [[#Bergek--2015|Bergek et al. 2015]] ; [[#Rogge--2016|Rogge and Reichardt 2016]] ; [[#Nemet--2019|Nemet 2019]] ) ''.'' An example of this systemic and dynamic nature of policies is the 70-year innovation journey of solar photovoltaic (PV), covering multiple countries, which is reviewed in Box 16.4.

There are many definitions of policy mixes from various disciplines ( [[#Rogge--2017|Rogge et al. 2017]] ), including environmental economics ( [[#Lehmann--2012|Lehmann 2012]] ), policy studies ( [[#Kern--2009|Kern and Howlett 2009]] ) and innovation studies. Generally speaking, a policy mix can be characterised by a combination of building blocks, namely elements, processes and characteristics, which can be specified using different dimensions ( [[#Rogge--2016|Rogge and Reichardt 2016]] ). Elements include: (i) the policy strategy with its objectives and principal plans; (ii) the mix of policy instruments; and (iii) instrument design. The content of these elements is the result of policy processes. Both elements and processes can be described by their characteristics in terms of the consistency of the elements, the coherence of the processes, and the credibility and comprehensiveness of the policy mix in different policy, governance, geography and temporal context ( [[#Rogge--2016|Rogge and Reichardt 2016]] ). Other aspects in the evaluation of policy mixes include framework conditions, the type of policy instrument and the lower level of policy granularity, namely design elements or design features ( [[#del%20Río--2014|del Río 2014]] ; [[#del%20Río--2017|del Río and Cerdá 2017]] ). In addition, many have argued for the need to craft policies that affect different actors in the transition, some supporting and some ‘destabilising’ ( [[#Geels--2002|Geels 2002]] ; [[#Kivimaa--2016|Kivimaa and Kern 2016]] ).

Learning from the innovation systems literature, some of the recent policy focus is not only directed on innovation policies that can optimise the innovation system to improve economic competitiveness and growth, but also policies that can induce strategic directionality and guide processes of transformative changes towards desired societal objectives ( [[#Mitcham--2003|Mitcham 2003]] ; [[#Steneck--2006|Steneck 2006]] ). Therefore, the aim is to connect innovation policy with societal challenges and transformative changes through engagement with a variety of actors and ideas and incorporating equity, nowadays often referred to as a ‘just transition’ ( [[#Newell--2013|Newell and Mulvaney 2013]] ; [[#Swilling--2016|Swilling et al. 2016]] ; [[#Heffron--2018|Heffron and McCauley 2018]] ; [[#Jasanoff--2018|Jasanoff 2018]] ) (Chapters 1 and 17). This new policy paradigm is opening up a new discursive space, shaping policy outcomes, and giving rise to the emerging idea of transformative innovation policy ( [[#Fagerberg--2018|Fagerberg 2018]] ; [[#Diercks--2019|Diercks et al. 2019]] ).

Transformative innovation policy has a broader coverage of the innovation process with a much wider participation of actors, activities and modes of innovation. It is often expressed as socio-technical transitions ( [[#Elzen--2004|Elzen et al. 2004]] ; [[#Turnheim--2020|Turnheim and Sovacool 2020]] ) or societal transformations ( [[#Scoones--2015|Scoones 2015]] ; [[#Roberts--2018|Roberts et al. 2018]] ). Transformative innovation policy encompasses different ideas and concepts that aim to address the societal challenges involving a variety of discussions, including social innovation ( [[#Mulgan--2012|Mulgan 2012]] ), complex adaptive systems ( [[#Gunderson--2002|Gunderson and Holling 2002]] ), eco-innovation ( [[#Kemp--2011|Kemp 2011]] ) and a framework for responsible innovation ( [[#Stilgoe--2013|Stilgoe et al. 2013]] ), value-sensitive design ( [[#Friedman--2019|Friedman and Hendry 2019]] ) and social-technical integration ( [[#Fisher--2006|Fisher et al. 2006]] ).

<div id="Box 16.4 | Sources of Cost Reductions in Solar Photovoltaics" class="h2-container"></div>

<span id="box-16.4-sources-of-cost-reductions-in-solar-photovoltaics"></span>
=== Box 16.4 | Sources of Cost Reductions in Solar Photovoltaics ===

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'''No single country persisted in developing solar photovoltaic (PV): five countries each made a distinct contribution, with each leader relinquishing its lead. The free flow of ideas, people, machines, finance, and products across countries explains the success of solar PVs. Barriers to knowledge flow de''' '''lay innovation.'''

Solar PV has attracted interest for decades, and until recently was seen as an intriguing novelty, serving a niche, but widely dismissed as a serious answer to climate change and other social problems associated with energy use. Since the IPCC’s Fifth Assessment Report (AR5), PV has become a substantial global industry – a truly disruptive technology that has generated trade disputes among superpowers, threatened the solvency of large energy companies, and prompted reconsideration of electric utility regulation rooted in the 1930s. More favourably, its continually falling costs and rapid adoption are improving air quality and facilitating climate change mitigation. PV is now so inexpensive that it is important in an expanding set of countries. In 2020, 41 countries, in six continents, had each installed at least 1GW of solar ( [[#IRENA--2020a|IRENA 2020a]] ).

The cost of generating electricity from solar PV is now lower in sunny locations than running existing fossil fuel power plants ( [[#IEA--2020c|IEA 2020c]] ) (Chapter 6). Prices in 2020 were below where even the most optimistic experts expected they would be in 2030.

The costs of solar PV modules have fallen by more than a factor of 10,000 since they were first commercialised in 1957. This four orders of magnitude cost reduction from the first commercial application in 1958 until 2018 can be summarised as the result of distinct contributions by the USA, Japan, Germany, Australia, and China – in that sequence ( [[#Green--2019|Green 2019]] ; [[#Nemet--2019|Nemet 2019]] ). As shown in Box 16.4, Figure 1, PV improved as the result of:

i. scientific contributions in the 1800s and early 1900s, in Europe and the USA, that provided a fundamental understanding of the ways that light interacts with molecular structures, leading to the development of the p-n junction to separate electrons and holes ( [[#Einstein--1905|Einstein 1905]] ; [[#Ohl--1941|Ohl 1941]] );

ii. a breakthrough at a corporate laboratory in the USA in 1954 that made a commercially available PV device available and led to the first substantial orders, by the US Navy in 1957 ( [[#Ohl--1946|Ohl 1946]] ; [[#Gertner--2013|Gertner 2013]] );

iii. a government R&amp;D and public procurement effort in the 1970s in the USA, that enlisted skilled scientists and engineers into the effort and stimulated the first commercial production lines ( [[#Christensen--1985|Christensen 1985]] ; [[#Blieden--1999|Blieden 1999]] ; [[#Laird--2001|Laird 2001]] );

iv. Japanese electronic conglomerates, with experience in semiconductors, serving niche markets in the 1980s and in 1994 launching the world’s first major rooftop subsidy programme, with a declining rebate schedule, and demonstrating there was substantial consumer demand for PV ( [[#Kimura--2006|Kimura and Suzuki 2006]] );

v. Germany passing a feed-in tariff in 2000 that quadrupled the market for PV, catalysing development of PV-specific production equipment that automated and scaled PV manufacturing ( [[#RESA--2001|RESA 2001]] ; [[#Lauber--2016|Lauber and Jacobsson 2016]] );

vi. Chinese entrepreneurs, almost all trained in Australia and using Australian-invented passivated emitter rear cell technology, building supply chains and factories of gigawatt scale in the 2000s. China became the world’s leading installer of PVs from 2013 onward ( [[#Quitzow--2015|Quitzow 2015]] ; [[#Helveston--2019|Helveston and Nahm 2019]] ); and

vii. a cohort of adopters with high willingness to pay, accessing information from neighbours, and installer firms that learnt from their installation experience as well as that of their competitors, to lower soft costs ( [[#Ardani--2015|Ardani and Margolis 2015]] ; [[#Gillingham--2016|Gillingham et al. 2016]] ).

As this evolution makes clear, no individual country persisted in leading the technology, and every world-leading firm lost its lead within a few years ( [[#Green--2019|Green 2019]] ). Solar followed an overlapping but sequential process of technology creation, market creation and cost reduction (comparable to emergence, early adoption, diffusion and stabilisation in Cross-Chapter Box 12 in this chapter). In the technology creation phase, examples of central processes include flows of knowledge from one person to another, between firms, and between countries as well as US and Japanese R&amp;D funding in the 1970s and early 1980s. During market creation, PVs modular scale allowed it to serve a variety of niche markets from satellites in the 1950s to toys in the 1980s, when Germany transformed the industry from niche to mass market with its subsidy programme that began in 2000 and became important for PV in 2004. The dramatic increase in size combined with its 20-year guaranteed contracts reduced risk for investors and created confidence in PV’s long-term growth. Supportive policies also emerged outside Germany, in Spain, Italy, California, and China, which spread the risk, even as national policy support was more volatile. Rapid and deep cost reductions were made possible by: learning by doing in the process of operating, optimising, and combining production equipment; investing and improving each manufacturing line to gradually scale up to massive sizes; and incremental improvements in the PV devices themselves.

Central to PV development has been its modularity, which provided two distinct advantages: access to niche markets, and iterative improvement. Solar has been deployed as a commercial technology across nine orders of magnitude: from a 1W cell in a calculator to a 1GW plant in the Egyptian desert, and almost every scale in between. This modular scale enabled PV to serve a sequence of policy-independent niche markets (such as satellites and telecoms applications), which generally increased in size and decreased in willingness to pay, in line with the technology cost reductions. This modular scale also enabled a large number of iterations, such that in 2020 over three billion solar panels had been produced. Compared to, for instance, approximately 1000 nuclear reactors that were ever constructed, a million times more opportunities for learning by doing were available to solar PV: to make incremental improvements, to introduce new manufacturing equipment, to optimise that equipment, and to learn from failures. More generally, recent work has pointed to the benefits of modularity in the speed of adoption ( [[#Wilson--2020|Wilson et al. 2020]] ) and learning rates ( [[#Sweerts--2020|Sweerts et al. 2020]] ).

While many technologies do not fit into the solar model, some – including micro nuclear reactors and direct air capture – also have modular characteristics that make them suitable for following solar’s path and benefit from solar’s drivers. However, it took solar PV 60 years to become cheap, which is too slow for addressing climate change if a technology is now still at the lab scale. A challenge in learning from the solar model is therefore how to use public policy to speed up innovation over much shorter time frames, for example, 15 or fewer years.

[[File:7baddbde436deb70917365f153762391 IPCC_AR6_WGIII_Box_16_4_Figure_1.png]]

'''Box 16.4, Figure 1 | Milestones in the development of low-cost solar photovoltaics. Source :''' [[#Nemet--2019|Nemet (2019)]] .

<div id="16.4" class="h1-container"></div>

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