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

=== 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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