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

== 16.4 Innovation Policies and Institutions ==

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Building on the frameworks for identifying market failures ( [[#16.2|Section 16.2]] ) and systemic failures ( [[#16.3|Section 16.3]] ) in the innovation system for climate-related technologies, [[#16.4|Section 16.4]] proceeds as follows. First, it considers some of the policy instruments introduced in [[IPCC:Wg3:Chapter:Chapter-13|Chapter 13]] that are particularly relevant for the pace and direction of innovation in technologies for climate change mitigation and adaptation. Second, it explains why governments put in place policies to promote innovation in climate-related technologies. Third, it takes stock of the overall empirical and theoretical evidence regarding the relationship between policy instruments with a direct and an indirect impact on innovation outcomes (including intellectual property regimes) and also other outcomes (competitiveness and distributional outcomes). Fourth, it assesses the evidence on the impact of trade-related policies and of sub-national policies aiming to develop cleantech industrial clusters.

This section focuses on innovation policies and institutions which are implemented at the national level. Whenever relevant, this section highlights examples of policies or initiatives that delve more deeply into the main high-level sectors: power, transport, industry, buildings, and agriculture, forestry and other land-use (AFOLU). Whenever possible, this section also discusses issues in policy selection, design, and implementation that have been identified as more relevant in developing countries and emerging economies.

Overall, this section shows that national and subnational policies and institutions are one of the main factors determining the redirection and acceleration of technological innovation and low-emission technological change ( [[#Anadon--2016b|Anadon et al. 2016b]] ; [[#Rogge--2016|Rogge and Reichardt 2016]] ; [[#Åhman--2017|Åhman et al. 2017]] ; [[#Anadón--2017|Anadón et al. 2017]] ; [[#Roberts--2018|Roberts et al. 2018]] ) ( ''robust evidence'' , ''high agreement'' ) ''.'' Both technology push (e.g., scientific training, research and development (R&amp;D)) and demand pull (e.g., economic and fiscal support and regulatory policy instruments), as well as instruments promoting knowledge flows and especially research-firm technology transfer, can be part of the mix ( ''robust evidence'' , ''medium agreement'' ) (Sections 16.2 and 16.3).

Public R&amp;D investments in energy and climate-related technologies have a positive impact on innovation outcomes ( ''medium evidence'' , ''high agreement'' ). The evidence on procurement is generally positive, but limited. The economic policy instruments that can be classified as market pull instruments when it comes to the competitiveness outcome (at least in the short term) is more mixed. The review of the literature in this section shows that market pull policy instruments had positive but also some negative impacts on outcomes in some instances on some aspects of competitiveness and distributional outcomes ( ''medium evidence'' , ''medium agreement'' ) ( [[#Peñasco--2021|Peñasco et al. 2021]] ). For several of them – such as carbon taxes or feed-in tariffs – the evidence of a positive impact on innovation is more consistent than the others. Evidence suggests that complementary policies or improved policy design can mitigate such short-term negative distributional impacts.

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<span id="overview-of-policy-instruments-for-climate-technology-innovation"></span>
=== 16.4.1 Overview of Policy Instruments for Climate Technology Innovation ===

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Government policies can influence changes in technologies, as well as changes to the systems they support ( [[#Somanathan--2014|Somanathan et al. 2014]] ) ( [[IPCC:Wg3:Chapter:Chapter-13|Chapter 13]] and Sections 16.2 and 16.3).

Technology-push policy instruments stimulate innovation by increasing the supply of new knowledge through funding and performing research; increasing the supply of trained scientists and engineers which contribute to knowledge-generation and provide technological opportunities, which private firms can decide to commercialise ( [[#Mowery--1979|Mowery and Rosenberg 1979]] ; [[#Anadon--2009|Anadon and Holdren 2009]] ; [[#Nemet--2009b|Nemet 2009b]] ; [[#Mazzucato--2013|Mazzucato 2013]] ).

Governments can also stimulate technological change through demand-pull (or market-pull) instruments which support market creation or expansion and technology transfer, and thus promote learning by doing, economies of scale, and automation ( [[#16.2|Section 16.2]] ). Demand-pull policy instruments include regulation, carbon prices, subsidies that reduce the cost of adoption, public procurement, and intellectual property regulation. Typically, technology push is especially important for early-stage technologies, characterised by higher uncertainty and lower appropriability ( [[#16.2|Section 16.2]] ); demand-pull instruments become more relevant in the later stages of the innovation process ( [[#Mowery--1979|Mowery and Rosenberg 1979]] ; [[#Anadon--2009|Anadon and Holdren 2009]] ; [[#Nemet--2009b|Nemet 2009b]] ) ( [[#16.2|Section 16.2]] ).

The second column of Table 16.8 summarises the set of policies shaping broader climate outcomes over the past few decades in many countries outlined in Chapter 13, [[IPCC:Wg3:Chapter:Chapter-13#13.6|Section 13.6]] , which groups them into economic and financial, regulatory, and soft instruments. Other policies, such as monetary, banking and trade policies, for instance, can also shape innovation, but most government action to shape energy has not focused on them. As Table 16.8 shows, this section discusses the set of policy instruments on innovation outcomes, or a subset of the ‘Transformative Potential’ criterion presented in Chapter 13, and thus complements the more general discussion presented there. Table 16.8 specifically prioritises the impact of the subset of policy instruments on innovation outcomes for which evidence is available. This focus is complemented by a discussion of the impact of the same policy instruments on competitiveness (a subcomponent of the economic effectiveness evaluation criterion) and on distributional outcomes. Many of the policy instrument types listed in Table 16.8 have been implemented or proposed to address the different types of market or systemic failures or bottlenecks described in Sections 16.2 and 16.3 ( [[#OECD--2011a|OECD 2011a]] ).

'''Table 16.8 | Overview of policy instrument types covered in [[IPCC:Wg3:Chapter:Chapter-13|Chapter 13]] and their correspondence to the subset of policy instrument types reviewed in [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-16 Chapter 16] with a focus on innov''' '''ation outcomes.'''

{| class="wikitable"
|-
! '''High-level categorisation'''

! '''Lower-level policy instrument type in Chapter 13'''

! '''Policy instrument types reviewed in [[#16.4|Section 16.4]] (for definitions see Peñasco''' '''et al.''' '''2021)'''

|-
| rowspan="10"| '''Economic or financial policy instrument types'''

| Research and development (R&amp;D) investments

| R&amp;D investments (including demonstration) (Box 16.3)

|-
| rowspan="3"| Subsidies for mitigation

| Feed-in tariffs or premia (set administratively)

|-
| Energy auctions

|-
| Other public financing options (public investment banks, loans, loan guarantees)

|-
| Emissions trading schemes

| Emissions trading scheme

|-
| Carbon taxes

| Taxes/tax relief (including carbon taxes, energy taxes and congestion taxes)

|-
| Government provision

| Government provision (focus on innovation procurement)

|-
| Removing fossil fuel subsidies

| ''Not covered''

|-
| Border carbon adjustments

| ''Not covered''

|-
| Offsets

| ''Not covered''

|-
| rowspan="7"| '''Regulatory policy instrument types'''

| rowspan="6"| Performance standards (including with tradeable credits)

| Renewable obligations with tradeable green certificates

|-
| Efficiency obligations with tradeable white certificates

|-
| Clean energy or renewable portfolio standards (electricity)

|-
| Building codes (building efficiency codes)

|-
| Fuel efficiency standards

|-
| Appliance efficiency standards

|-
| Technology standards

| ''Not covered''

|-
| rowspan="3"| '''Soft policy instruments'''

| Divestment and disclosure

| ''Not covered''

|-
| rowspan="2"| Voluntary agreements (public voluntary programmes and negotiated agreements)

| Voluntary agreements

|-
| Energy labels

|}

[[#16.3|Section 16.3]] characterised technological innovation as a systemic, non-linear and dynamic process. Figure 16.1 below presents a stylised (and necessarily incomplete) view connecting the innovation process stages presented in [[#16.2|Section 16.2]] , some of the key mechanisms in technology innovation systems, and some of the decarbonisation policy instruments that have been assessed in terms of their impact on technological innovation outcomes in [[#16.4.4|Section 16.4.4]] . As noted in the caption and discussed in [[#16.4.4|Section 16.4.4]] , regulatory policy instruments also shape the early stages of technology development.

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[[File:58d4a938317017deb24c44d75542f4b5 IPCC_AR6_WGIII_Figure_16_1.png]]

'''Figure 16.1 | Technology innovation process and the (illustrative) and role of different public policy instruments (on the right-hand side).''' Source: adapted from [[#IEA--2020a|IEA (2020a)]] . Note that, as shown in [[#16.4.4|Section 16.4.4]] , demand-pull instruments in the regulatory instrument category, for instance, can also shape the early stages of the innovation process. Their position on the latter stages is highlighted in this figure because typically these instruments have been introduced in latter stages of the development of the technology.

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<span id="the-drivers-and-politics-of-national-policies-for-climate-change-mitigation-and-adaptation"></span>
=== 16.4.2 The Drivers and Politics of National Policies for Climate Change Mitigation and Adaptation ===

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Governments around the world implement innovation policies in the energy and climate space with the aim of simultaneously advancing environmental, industrial policy (or competitiveness), and security goals ( [[#Anadón--2012|Anadón 2012]] ; [[#Surana--2015|Surana and Anadon 2015]] ; [[#Meckling--2017|Meckling et al. 2017]] ; [[#Matsuo--2019|Matsuo and Schmidt 2019]] ; [[#Peñasco--2021|Peñasco et al. 2021]] ) ( ''medium evidence'' , ''medium agreement'' ). Co-benefits of policies shaping technological innovation in climate-related technologies, including competitiveness, health, and improved distributional impacts can be drivers of climate mitigation policy in the innovation sphere ( [[#Stokes--2017|Stokes and Warshaw 2017]] ; [[#Deng--2018|Deng et al. 2018]] ; [[#Probst--2020|Probst et al. 2020]] ). For instance, this was the case for climate and air pollution policies with local content requirements for different types of renewable energy projects in places including China ( [[#Qiu--2012|Qiu and Anadon 2012]] ; [[#Lewis--2014|Lewis 2014]] ), India ( [[#Behuria--2020|Behuria 2020]] ), South Africa ( [[#Kuntze--2012|Kuntze and Moerenhout 2012]] ), and Canada ( [[#Genest--2014|Genest 2014]] ) ( ''robust evidence'' , ''medi'' ''um agreement'' ).

The emergence of industries and support groups can lead to more sustained support for innovation policies ( [[#Meckling--2015|Meckling et al. 2015]] ; [[#Schmidt--2017|Schmidt and Sewerin 2017]] [[#Stokes--2018|Stokes and Breetz 2018]] ; [[#Meckling--2019|Meckling 2019]] ; [[#Meckling--2019|Meckling and Nahm 2019]] ; [[#Schmid--2020|Schmid et al. 2020]] ). Conversely, policies shaping technology innovation contribute to the creation and evolution of different stakeholder groups ( ''robust evidence'' , ''high agreement'' ). Most of the literature on the role of the politics and interest groups has focused on renewable energy technologies, although there is some work on heating in buildings ( [[#Wesche--2019|Wesche et al. 2019]] ).

As novel technologies become cost-competitive, opposition of incumbents usually grows, as well as the dangers of lock-in that can be posed by the new winner. Addressing this involves adapting policy ( ''robust evidence'' , ''hi'' ''gh agreement'' ).

Three phases of politics in the development of policies to meet climate and industrial objectives can be identified, at the top, the middle and the bottom of the experience curve ( [[#Breetz--2018|Breetz et al. 2018]] ) (see also Figure 16.1, and [[#Geels--2002|Geels 2002]] ). In the first phase of ‘niche market diffusion’, the politics of more sustained support for a technology or set of technologies become possible after a group of economic winners and ‘clean energy constituencies’ are created ( [[#Meckling--2015|Meckling et al. 2015]] ). When technologies grow out of the niche (second phase), they pose a more serious competition to incumbents who may become more vocal opponents of additional support for innovation in the competing technologies ( [[#Geels--2014|Geels 2014]] ; [[#Stokes--2016|Stokes 2016]] ). In a third phase, path-dependence in policymaking and lock-in in institutions need to change to accommodate new infrastructure, the integration of technologies, the emergence of complementary technologies and of new regulatory regimes ( [[#Levin--2012|Levin et al. 2012]] ; [[#Aklin--2013|Aklin and Urpelainen 2013]] ).

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<span id="indicators-to-assess-the-innovation-competitiveness-and-distributional-outcomes-of-policy-instruments"></span>
=== 16.4.3 Indicators to Assess the Innovation, Competitiveness and Distributional Outcomes of Policy Instruments ===

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If policy instruments are created to (at least partly) shape innovation for systemic transitions to a zero-carbon future, they also need to be evaluated on their impact on the whole socio-technical system ( [[#Neij--2006|Neij and Åstrand 2006]] ) and a wide range of goals, including distributional impacts and competitiveness and jobs ( [[#Stern--2007|Stern 2007]] ; [[#Peñasco--2021|Peñasco et al. 2021]] ). Given this and the current policy focus on green recovery and green industrial policy, we assess impacts on competitiveness and equity, although we primarily focus on innovation outcomes. Table 16.9 lists the selected set of indicators used to assess the impact of the policy instrument types covered in the right-hand column in Table 16.8. The table does not include technology diffusion or deployment because these are covered in the technological effectiveness evaluation criterion in Chapter 13. As noted in section 16.2, it is very difficult to measure or fully understand innovation with one or even several indicators. In addition, all indicators have strengths and weaknesses, and may be more relevant in some countries and sectors than in others. The literature assessing the impact of different policy instruments on innovation often covers just one of the various indicators listed in the second column of Table 16.9.

'''Table 16.9 | Outcomes (first row) and indicators (second row) to evaluate the impact of policies shaping innovation to foster carbon neutral economies.''' Sources: innovation outcomes indicators are sourced from Del Rio and Cerdá (2014), [[#Grubb--2021|Grubb et al. (2021)]] and [[#Peñasco--2021|Peñasco et al. (2021)]] ; the indicators under the competitiveness and distributional effects criteria are sourced from [[#Peñasco--2021|Peñasco et al. (2021)]] .

{| class="wikitable"
|-
| '''Policy instrument Outcomes'''

| '''Innovation'''

(Part of [[IPCC:Wg3:Chapter:Chapter-13|Chapter 13]] ‘Transformative potential’ evaluation criterion)

| '''Competitiveness'''

(Part of [[IPCC:Wg3:Chapter:Chapter-13|Chapter 13]] ‘Economic effectiveness’ evaluation criterion)

| '''Distributional impacts'''

(Defined in the same way as in Chapter13)

|-
| '''Examples of indicators used for each outcome in the literature'''

| R&amp;D investments, cost improvements, learning rates, patents, publications, reductions in abatement costs, energy efficiency improvements, other performance characteristics, firms reporting carbon saving innovation

| Industry creation, net job creation, export of renewable energy technology equipment, economic growth (GNP, GDP), productivity, other investments

| Level and incidence of support costs, change in spending on electricity as a percentage of total household spending, participation of different stakeholders, international equity (e.g., tCO 2 -eq per capita), unequal access between large vs. small producers or firms

|}

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<span id="assessment-of-innovation-and-other-impacts-of-innovation-policy-instruments"></span>
=== 16.4.4 Assessment of Innovation and Other Impacts of Innovation Policy Instruments ===

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While it is very difficult to attribute a causal relationship between a particular policy instrument implementation and different innovation indicators, given the complexity of the innovation system ( [[#16.3|Section 16.3]] ), there is a large volume of quantitative and qualitative literature aiming to identify such an impact.

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<span id="assessment-of-the-impact-on-innovation-of-technology-push-policy-instruments-public-rdd-investments-other-rd-incentives-and-public-procurement"></span>
==== 16.4.4.1 Assessment of the Impact on Innovation of Technology Push Policy Instruments: Public RD&amp;D Investments, Other R&amp;D Incentives and Public Procurement ====

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Economic and direct investment policy instrument types are typically associated with a direct focus on technological innovation: research and development (R&amp;D) grants, R&amp;D tax credits, prizes, national laboratories, technology incubators (including support for business development, plans), novel direct funding instruments (e.g., Advanced Research Projects Agency–Energy (ARPA-E)), and innovation procurement.

Public research, development and demonstration (RD&amp;D) investments have been found to have a positive impact on different innovation in energy- and climate-related technologies ( ''robust evidence'' , ''high agreement'' ), but the assessment relies almost entirely on evidence from industrialised countries. Out of 17 publications focusing on this assessment, only three found no relationship between R&amp;D funding and innovation metrics ( [[#Doblinger--2019|Doblinger et al. 2019]] ; [[#Goldstein--2020|Goldstein et al. 2020]] ; [[#Peñasco--2021|Peñasco et al. 2021]] ). Sixteen of them used ''ex post'' quantitative methods, and one relied on theoretical ''ex ante'' assessment; only two of them included some non-industrialised countries, with one being the theoretical analysis. The evidence available does not point to public R&amp;D funding for climate-related technologies crowding out private R&amp;D (an important driver of innovation) but instead crowding it in. Box 16.6 summarises the evidence available of the impact of ARPA-E (a public institution created in the USA in 2009 to allocate public R&amp;D funding in energy) on innovation and competitiveness outcomes. Another institution supporting energy R&amp;D that is the subject of much interest is the institutions of the Fraunhofer Society.

No evidence has been found regarding the specific impact of R&amp;D tax credits on climate mitigation or adaptation technologies, but it is worth noting that, generally speaking, R&amp;D tax credits are found to incentivise innovation in firms, with a greater impact on small and medium firms ( [[#OECD--2020|]] [[#OECD--2020|OECD 2020]] ). This is consistent with the fact that most of the evidence on the positive impact of public R&amp;D support schemes covers small and medium firms ( [[#Howell--2017|Howell 2017]] ; [[#Doblinger--2019|Doblinger et al. 2019]] ; [[#Goldstein--2020|Goldstein et al. 2020]] ). Although there is a high level of agreement in the literature regarding the impact of R&amp;D investments on innovation outcomes in climate-related technologies, it is important to note that this evidence comes from industrialised countries. This does not mean that public R&amp;D investments in energy have been found to have no impact on developing countries innovation or competitiveness outcomes, but rather that we were not able to find such studies focussing on developing countries.

Overall, public procurement has high potential to incentivise innovation in climate technologies, but the evidence is mixed, particularly in developing countries ( ''limited evidence'' , ''medium agreement'' ). Public procurement accounted for 13% of gross domestic product (GDP) in OECD in 2013 and much more in some emerging and developing economies ( [[#Baron--2016|Baron 2016]] ). Its main goal is to acquire products or services to improve public services, infrastructures and facilities and, in some cases, to also incentivise innovation. It is important to implement several steps in the public procurement procedure to improve transparency, minimise waste, fraud and corruption of public fund. These steps range from the assessment of a need, issuance of a tender, to the monitoring of delivery of the good or service. Box 16.5 outlines a public procurement programme that was implemented in The Netherlands in 2005 with a focus on green technologies. In spite of the fact that green procurement policies have been implemented, the literature assessing the innovation impact of public procurement programmes is relatively limited, and suggests either a positive impact or no impact ( [[#Alvarez--2015|Alvarez and Rubio 2015]] ; [[#Baron--2016|Baron 2016]] ; [[#Fernández-Sastre--2019|Fernández-Sastre and Montalvo-Quizhpi 2019]] ; [[#Peñasco--2021|Peñasco et al. 2021]] ). The majority of cases where the impact is positive are analyses of industrialised countries, while no impact emerges in the case of a developing country (Ecuador). More empirical research in both developing and developed countries is needed to understand the impact of public procurement, which has the potential to support the achievement of other societal challenges ( [[#Edler--2007|Edler and Georghiou 2007]] ; [[#Henderson--2011|Henderson and Newell 2011]] ; [[#Baron--2016|Baron 2016]] ; [[#ICLEI--2018|ICLEI 2018]] ).

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<span id="assessment-of-the-impact-on-competitiveness-of-technology-push-policy-instruments-public-rdd-investments-other-rd-incentives-and-public-procurement"></span>
==== 16.4.4.2 Assessment of the Impact on Competitiveness of Technology Push Policy Instruments: Public RD&amp;D Investments, Other R&amp;D Incentives and Public Procurement ====

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Public R&amp;D investments in the energy, renewables, and environment space are generally associated with positive impacts on industrial development or ‘competitiveness outcome’ ( ''robust evidence'' , ''medium agreement'' ). In a number of cases, negligible or negative impacts emerge ( [[#Doblinger--2019|Doblinger et al. 2019]] ; [[#Goldstein--2020|Goldstein et al. 2020]] ; [[#Peñasco--2021|Peñasco et al. 2021]] ). The majority of the 15 analyses rely on ''ex post'' quantitative methods, while only four use ''ex ante'' modelling approaches. Also, in this case, the vast majority of the evidence is from industrialised countries.

There is limited and mixed evidence regarding the (positive or negative) impact of public procurement for low-carbon or climate technologies in developed countries ( ''limited evidence'' , ''low agreement'' ), and none from developing countries. All of the four evaluations identified in the [[#Peñasco--2021|Peñasco et al. (2021)]] review relied on qualitative methods. One found a positive impact, another a negative impact and two others found no impact. All of the studies covered European country experiences.

R&amp;D and procurement policies have a positive impact on distributional outcomes ( ''limited evidence'' , ''high agreement'' ). [[#Peñasco--2021|Peñasco et al. (2021)]] identify three evaluations of the impact of RD&amp;D funding on distributional outcomes (two using quantitative methods and one ''ex ante'' theoretical methods) and one of procurement on distributional outcomes (relying on qualitative analysis).

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<span id="emerging-insights-on-different-public-rd-and-demonstration-funding-schemes"></span>
==== 16.4.4.3 Emerging Insights on Different Public R&amp;D and Demonstration Funding Schemes ====

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The ability of a given R&amp;D policy instrument to impact innovation and competitiveness depends to some extent on policy design features ( ''limited evidence'' , ''high agreement'' ). As discussed in [[#16.4.4.4|Section 16.4.4.4]] , this is not unique to R&amp;D funding. Most of these assessments use a limited number of indicators (e.g., patents and publications and follow-on private financing, firm growth and survival, respectively), and are focused on the energy sector, and on the USA and other industrialised countries. Extrapolating to emerging economies and low-income countries is difficult. There is no evidence on the impact of different ways of allocating public energy R&amp;D investments in the context of developing countries.

Block funding, which tends to be more flexible, can lead to research that is more productive or novel, but there are other factors that can affect the extent to which block funding can lead to more or less novel outcomes ( ''limited evidence'' , ''medium agreement'' ). Research on national research laboratories, which conduct at least 30% of all research in 68 countries around the world ( [[#Anadon--2016a|Anadon et al. 2016a]] ), are a widespread mechanism to carry out public R&amp;D and allocate funds, but assessments of their performance is limited to developed countries. R&amp;D priorities are also guided by institutions, and research focused on general technology innovation policy finds that institutions often do not embody the goals of the poor or marginalised ( [[#Anadon--2016b|Anadon et al. 2016b]] ).

In the case of the US Department of Energy, block funding that can be quickly allocated to novel projects (such as that allocated to National Labs as part of the Laboratory Directed Research and Development funding) has been found to be associated with improved innovation indicators ( [[#Anadon--2016a|Anadon et al. 2016a]] ). Research in Japan on R&amp;D funding in general (not for climate-related technologies) however, indicates that R&amp;D funds allocated competitively result in higher novelty for ‘high status’ (the term used in the paper to refer to senior male researchers), while block funding was associated with research of higher novelty for ‘lower status’ researchers (e.g., junior female researchers) ( [[#Wang--2018|Wang et al. 2018]] ).

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<span id="box-16.6-arpa-e-a-novel-rd-funding-allocation-mechanism-focused-on-an-energy-mission"></span>
=== Box 16.6 | ARPA-E – A Novel R&amp;D Funding Allocation Mechanism Focused on an Energy Mission ===

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One approach for allocating public R&amp;D funds in energy involves relying on active programme managers and having clear technology development missions that focus on high-risk high-reward areas and projects. This approach can be exemplified by a relatively new energy R&amp;D funding agency in the USA, the Advanced Research Projects Agency for Energy (ARPA-E). This agency was created in 2009 and it was modelled on the experience of Defense Advanced Research Projects Agency (DARPA) – a US government agency funding high-risk, high-reward research in defence-related areas ( [[#Bonvillian--2011|Bonvillian and Van Atta 2011]] ; [[#US%20National%20Academies%20of%20Sciences%20Engineering%20and%20Medicine--2017|US National Academies of Sciences Engineering and Medicine 2017]] ; [[#Bonvillian--2018|Bonvillian 2018]] ). DARPA programme managers had a lot of discretion for making decisions about funding projects, but since energy R&amp;D funding is usually more politically vulnerable than defence R&amp;D funding, the ARPA-E model involved programme managers requesting external review as an informational input ( [[#Azoulay--2019|Azoulay et al. 2019]] ).

As for DARPA, ARPA-E programme managers use an active management approach that involves empowering programme manages to make decisions about funding allocation, milestones and goals. ARPA-E managers also differ from other R&amp;D allocation mechanisms in that ARPA-E staff retain some control on the funded projects after the allocation of funds. As argued by [[#Azoulay--2019|Azoulay et al. (2019)]] , even though this relative control over the project can result in a reduction in the flexibility of funded researchers, some ‘exploration’ happens at the programme manager level.

Research on ARPA-E also sheds light on the process of project selection, or how programme managers decide what projects to fund. Programme managers do not just follow the rankings of peer reviewers (sometimes projects with very disparate rankings were funded) and in many cases programme managers reported using information from review comments instead of the rankings ( [[#Goldstein--2020|Goldstein and Kearney 2020]] ). [[#Azoulay--2019|Azoulay et al. (2019)]] suggest that, if expert disagreement is a useful proxy for uncertainty in research, then the use of individual discretion in ARPA-E would result in a portfolio of projects with a higher level of uncertainty, as defined by disagreement among reviewers. Moreover, under the premise that uncertainty is a corollary to novelty, individual discretion is an antidote to novelty bias in peer review.

While innovation is notoriously hard to track and, particularly for emerging technologies, it can take a lot time to assess, early analysis has shown that this mission-orientation and more ‘actively managed’ R&amp;D funding programme may yield greater innovation patenting outcomes than other US energy R&amp;D funding programmes, and a greater or similar rate of academic publications when compared to other public funding agencies in energy in the USA, ranging from the Office of Science, the more applied Office of Energy Efficiency and Renewable Energy, or the small grants office ( [[#US%20National%20Academies%20of%20Sciences%20Engineering%20and%20Medicine--2017|US National Academies of Sciences Engineering and Medicine 2017]] ; [[#Goldstein--2018|Goldstein and Narayanamurti 2018]] ). In addition, research analysing the first cohort of cleantech start-ups has found that start-ups supported by ARPA-E had more innovative outcomes when compared to those that had applied but not received funding, with others that had not received any government support, and with others that had received other types of government R&amp;D support ( [[#Goldstein--2020|Goldstein et al. 2020]] ). Overall, the mission-oriented ARPA-E approach has been successful in the USA when it comes to innovation outcomes. The extent to which it can yield the same outcomes in other geographies with different innovation and financing environments remains unknown. ( ''limited evidence'' , ''high agreement'' ).

Public financing for R&amp;D and research collaboration in the energy sector is important for small firms, at least in industrialised countries, and it does not seem to crowd out private investment in R&amp;D ( ''medium evidence'' , ''high agreement'' ). Small US and UK firms accrue more patents and financing when provided with cash incentives for R&amp;D in the form of grants ( [[#Howell--2017|Howell 2017]] ; [[#Pless--2019|Pless 2019]] ). US cleantech start-ups which partner with government partners for joint technology development or licensing partnerships accrue more patents and follow-on financing ( [[#Doblinger--2019|Doblinger et al. 2019]] ).

Overall, the body of literature on public R&amp;D funding design in energy- and climate-related technologies provides some high-level guidance on how to make the most of these direct RD&amp;D investments in energy technologies in the climate change mitigation space, including: giving researchers and technical experts autonomy and influence over funding decisions; incorporating technology transfer in research organisations; focusing demonstration projects on learning; incentivising international collaboration in energy research; adopting an adaptive learning strategy; and making funding stable and predictable (Narayanamurti et al. 2009; [[#Narayanamurti--2016|Narayanamurti and Odumosu 2016]] ; [[#Chan--2017|Chan et al. 2017]] ) ( ''medium evidence'' , ''h'' ''igh agreement'' ).

Without carefully designed public funding for demonstration efforts, often in a cost-shared manner with industry, the experimentation at larger scales needed for more novel technologies needed for climate change mitigation may not take place. ( ''medium evidence'' , ''high agreement'' ). Government funding, specifically for technology demonstration projects, for RD&amp;D in energy technologies, plays a crucial supporting role ( [[#16.2.1|Section 16.2.1]] ). Governments can facilitate knowledge spillovers between firms, between countries, and between technologies ( [[#Cohen--2002|Cohen et al. 2002]] ; [[#Baudry--2019|Baudry and Bonnet 2019]] ) ( [[#16.2|Section 16.2]] ).

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<span id="assessment-of-the-impact-on-innovation-and-on-competitiveness-and-distributional-outcomes-of-market-pull-policy-instruments"></span>
==== 16.4.4.4 Assessment of the Impact on Innovation and on Competitiveness and Distributional Outcomes of Market Pull Policy Instruments ====

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Demand-pull policies such as tradeable green certificates, taxes, or auctions, are essential to support scaling-up efforts ( [[#Remer--2003|Remer and Mattos 2003]] ; [[#Wilson--2012|Wilson 2012]] ; [[#Nahm--2014|Nahm and Steinfeld 2014]] ). Just as for R&amp;D investments, research has indicated that effective demand pull needs to be credible, durable, and aligned with other policies ( [[#Nemet--2017|Nemet et al. 2017]] ) and that the effectiveness of different demand-pull instruments depends on policy design ( [[#del%20Río--2021|del Río and Kiefer 2021]] ). Historical analyses of the relative importance of demand pull and technology push are clear: both are needed to provide robust incentives for investment in innovation. Interactions between them are central as their combination enables innovators to connect a technical opportunity with a market opportunity ( [[#Freeman--1995|Freeman 1995]] ; [[#Jacobsson--2004|Jacobsson et al. 2004]] ; [[#Grubler--2013|Grubler and Wilson 2013]] ). It is important to note that these market pull policies are often put in place primarily to meet security and/or environmental goals, although innovation and competitiveness are sometimes also pursued explicitly.

Overall evidence suggests that the emissions trading schemes, as currently designed, have not significantly contributed to innovation outcomes ( ''medium evidence'' , ''medium/hig'' ''h agreement'' ).

Penasco et al. (2021) review 20 evaluations: eight identified a positive impact (although in at least two cases, the paper indicated that the impact was small or negligible); 11 no impact; and one was associated with a negative impact on innovation indicators. The studies that found no impact and the studies that found some impact covered all three methods (quantitative ''ex post'' , qualitative and theoretical and ''ex ante'' analysis). Another review focused only on empirical studies (mainly quantitative but also qualitative), covered a slightly longer period and identified 19 studies (15 using quantitative methods) ( [[#Lilliestam--2021|Lilliestam et al. 2021]] ). With a narrower set of indicators of innovation, they concluded that there was very little empirical evidence linking innovation with the emissions trading schemes studied to date ( [[#Lilliestam--2021|Lilliestam et al. 2021]] ). This review focused mainly on papers evaluating the earlier stages of the European Emissions Trading Scheme, which featured relatively low carbon dioxide prices, and covered a small set of firms, showing that carbon pricing policy design is an important determinant of innovation outcomes. Combining both reviews, there are a total of 27 individual studies, some of them providing mixed evidence of impact, and 23 of them suggest there was no impact or that (in a couple of cases) it was small. It is important to note that some researchers note that, for particular subsectors and actors, emissions trading schemes have had an impact on patenting trends ( [[#Calel--2016|Calel and Dechezleprêtre 2016]] ). Overall the expectation is that higher prices and coverage would result in higher impacts and that, over time, the impact on innovation would grow.

The impact of carbon taxes on innovation outcomes is more positive than that for emissions trading schemes, but the evidence is more limited ( ''limited evidence'' , ''medium agreement'' ). Assessments of their impact on innovation metrics have been very limited, with only four studies (three quantitative and one ''ex ante'' ). Three of the studies found a positive impact of carbon taxes on innovation outcomes, and one found no impact ( [[#Peñasco--2021|Peñasco et al. 2021]] ).

Depending on the design (including the value and coverage of the tax), carbon taxes can either have positive, negative or null impact on competitiveness and distributional outcomes ( ''medium evidence'' , ''medium agreement'' ). The evidence on the impact of carbon taxes on competitiveness is significant (a total of 27 evaluations) and mixed, with six of them reporting some positive impacts, 10 reporting no impact, and 11 reporting negative impacts (so 59% were not associated with negative impacts). Most of the evaluations reporting negative impacts were theoretical assessments, and only three ''ex post'' quantitative analysis ( [[#Peñasco--2021|Peñasco et al. 2021]] ). Twenty-four evaluations covered distributional impacts of carbon taxes and other environmental taxes, the majority (15) found the existence of some negative distributional impacts, six found positive impacts, and three found no distributional impacts. Differences in the assessment results stem from the design of the taxes ( [[#Peñasco--2021|Peñasco et al. 2021]] ). It is important to note that, once again, the evidence comes from industrialised countries and emerging economies.

Many factors affect the impacts of feed-in tariffs (FITs) on outcomes other than innovation ( ''robust evidence'' , ''high agreement'' ). While FITs have been generally associated with positive innovation outcomes, some of the differences found in the literature may arise from differences in the evaluation method ( [[#Peñasco--2021|Peñasco et al. 2021]] ) or differences in policy design (e.g., the level and the rate of decrease of the tariff) ( [[#Hoppmann--2014|Hoppmann et al. 2014]] ), the policy mixes ( [[#Rogge--2017|Rogge et al. 2017]] ), the technologies targeted and their stage of development ( [[#Huenteler--2016b|Huenteler et al. 2016b]] ), and the geographical and temporal context of where the policy was put in place ( [[#16.3|Section 16.3]] ). Research has also found that, particularly for less mature technologies, a higher technology specificity in the design of FITs is associated with more innovation (Del Río 2012). FITs yield better results if they account for the specificities of the country; or else, the technology and the policy could result in negative distributional and (to a lesser extent) competitiveness impacts. [[#Meckling--2017|Meckling et al. (2017)]] indicate that an ‘enduring challenge’ of technology-specific industrial policy such as some FITs is to avoid locking in suboptimal clean technologies – a challenge which, among other options, could be overcome with targeted niche procurement for next-generation technologies. Other authors have cautioned that the move from renewable FITs to auctions may favour existing PVs (e.g., polysilicon) over more novel solar power technologies ( [[#Sivaram--2018b|Sivaram 2018b]] ) such as thin-film PV, amorphous PV, and perovskites.

Policy design, policy mixes, and domestic capacity and infrastructure are important factors determining the extent to which economic policy instruments in industrialised countries and emerging economies can also lead to positive (or at least not negative) competitiveness outcomes and distributional outcomes ( ''medium evidence'' , ''medium agreement'' ) ( [[#16.3|Section 16.3]] ). Prioritising low-cost energy generation in the design of FIT schemes can result in a lower focus of innovation efforts on more novel technologies and greater barriers to incumbents in less mature technologies ( [[#Hoppmann--2013|Hoppmann et al. 2013]] ). Similarly, case study research from Mexico and South Africa indicates that focusing on low-cost renewable energy generation can only result in a greater reliance on existing foreign value chains and capital, and thus in lower or negative impacts on domestic competitiveness. In other words, some approaches can hinder the development of the local capabilities that could result in greater long-term benefits domestically ( [[#Matsuo--2019|Matsuo and Schmidt 2019]] ). Evidence for developing countries indicates that local and absorptive capacity also play an important role, in particular, on the ability of policies to contribute to competitiveness or industrial policy goals ( [[#Binz--2018|Binz and Anadon 2018]] ). Research comparing China’s and India’s policies and outcomes on wind energy also suggest that policy durability and systemic approaches can affect industrial outcomes ( [[#Surana--2015|Surana and Anadon 2015]] ).

The evidence of the impact of renewable energy auctions on innovation outcomes is very small and provides mixed results ( ''limited evidence'' , ''low agreement'' ). Out of six evaluations, three identify positive impacts, two no impacts, and one negative impacts. All of the evaluations but one were qualitative or theoretical, and the quantitative assessment indicated no impact ( [[#Peñasco--2021|Peñasco et al. 2021]] ). There is more evidence covering emerging economies analysing the impacts of auctions when compared to other policy instrument types. For example, there is work comparing the approaches to renewable energy auctions in South Africa and Denmark ( [[#Toke--2015|Toke 2015]] ) finding a positive impact on the latter stages of innovation (mainly deployment), and broader work on auctions covering OECD countries as well as Brazil, South Africa and China not finding a significant impact on innovation ( [[#Wigand--2016|Wigand et al. 2016]] ). Work comparing renewable energy auctions in different countries in South America generally finds a positive impact on innovation outcomes ( [[#Mastropietro--2014|Mastropietro et al. 2014]] ). The body of evidence on the impact of auctions on competitiveness is also limited (six evaluations) and indicates negative outcomes of renewable auctions of competitiveness ( ''limited evidence'' , ''low agreement'' ). As with other policies, the design of the auctions can affect innovation outcomes ( [[#del%20Río--2021|del Río and Kiefer 2021]] ). Only two studies investigated distributional outcomes, and both were negative.

There is no explicit literature on the ability of green public banks, and targeted loans, and loan guarantees to lead to upstream innovation investments and activities, although there is evidence on their role in deployment ( [[#Geddes--2018|Geddes et al. 2018]] ). This notwithstanding, the key role of these institutions is in the innovation system ( [[#OECD--2015b|OECD 2015b]] ; [[#Geddes--2018|Geddes et al. 2018]] ) (Sections 16.2.1 and 16.3) and the belief that they can de-risk scale-up and the testing of business models ( [[#Geddes--2018|Geddes et al. 2018]] ; [[#Probst--2021|Probst et al. 2021]] ) (Chapter 17).

There is mixed evidence of the impact of tradeable green certificates (TGCs) on innovation ( ''limited evidence'' , ''low agreement'' ) and competitiveness ( ''limited evidence'' , ''low agreement'' ). Out of the 11 evaluations in [[#Peñasco--2021|Peñasco et al. (2021)]] , six found no impact, two a positive impact, and three a negative impact. All of them used a qualitative research approach. Of the six studies focusing on competitiveness outcomes, three conclude that TGCs have had no impact on competitiveness, while two indicate a negative impact and one a positive impact. Only one of the studies was quantitative, and did not identify an impact on competitiveness.

TGCs are associated with the existence of negative distributional impacts in most applications ( ''medium evidence'' , ''high agreement'' ). Ten out of 12 studies identify the existence of some negative impacts. All but one of these studies (which focused on India) are based on analysis of policies implemented in industrialised countries.

The impact of renewable portfolio standards without tradeable credits on innovation outcomes is negligible or very small ( ''medium evidence'' , ''medium agreement'' ). Out of the nine studies, seven reported no impact on innovation outcomes and two a positive impact ( [[#Peñasco--2021|Peñasco et al. 2021]] ). Most of these papers focused on patenting and private R&amp;D innovation indicators and not cost reductions. Impact on competitiveness is found to be negligible or positive ( ''limited evidence'' , ''medium agreement'' ). Out of eight evaluations, five report a positive impact and three a negligible impact; only two are quantitative studies ( [[#Peñasco--2021|Peñasco et al. 2021]] ). Negative distributional impacts from renewable portfolio standards can emerge in some cases ( ''limited evidence'' , ''low agreement'' ). Out of eight evaluations, four identified positive impacts, and four negative impacts; all of the studies identifying a positive impact were theoretical. There are efforts focused on clean energy portfolio standards which include technologies beyond renewables.

The impact of tradeable white certificates in innovation is largely positive, but the evidence is limited ( ''limited evidence'' , ''medium/high agreement'' ). Out of four evaluations, only one of which was quantitative, three report a positive impact and one reports no impact ( [[#Peñasco--2021|Peñasco et al. 2021]] ). The impact of white certificates on competitiveness is positive ( ''limited evidence'' , ''high agreement'' ) while the impact on distributional outcomes is very mixed ( ''limited evidence'' , ''low agreement'' ). Two theoretical studies report positive competitiveness impacts. Out of 11 evaluations of distributional outcomes, eight rely on theoretical ''ex ante'' approaches. Of the 11 evaluations: seven reported positive impacts (four of them using theoretical methods); three indicated negative impacts (using theoretical methods); and one reported no impact.

There is evidence of the impact of building codes on innovation outcomes ( [[#Peñasco--2021|Peñasco et al. 2021]] ). Only two studies assessed competitiveness impacts (one identified positive impacts and one negligible ones) and three studies identified distributional impacts, all positive.

Overall, the evidence on the impact of the market pull policy instruments covered in [[#16.4.4.4|Section 16.4.4.4]] when it comes to the competitiveness outcome (at least in the short term) is more mixed. For some of them, the evidence of a positive impact on innovation is more consistent than the others (for carbon taxes or FITs, for example). [[#Peñasco--2021|Peñasco et al. (2021)]] found that the disagreements in the evidence regarding the positive, negative or no impact of a policy on competitiveness or distributional outcomes can often be explained by differences in policy design, differences in geographical or temporal context (since the review included evidence from countries from all over the world), or on how policy mixes may have affected the ability of the research design of the underlying papers to separate the impact of the policy under consideration from the others.

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<span id="assessment-of-the-impact-on-innovation-competitiveness-and-distributional-outcomes-of-regulatory-policy-instruments-targeting-efficiency-improvements"></span>
==== 16.4.4.5 Assessment of the Impact on Innovation, Competitiveness and Distributional Outcomes of Regulatory Policy Instruments Targeting Efficiency Improvements ====

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There is medium evidence that the introduction of flexible, performance-based environmental regulation on energy efficiency in general (e.g., efficiency standards) can stimulate innovative responses in firms ( [[#Ambec--2013|Ambec et al. 2013]] ; [[#Popp--2019|Popp 2019]] ) ( ''medium evidence'' , ''high agreement'' ). Evidence comes from both observational studies that examine patenting, R&amp;D or technological responses to regulatory interventions, and from surveys and qualitative case studies in which firms report regulatory compliance as a driving force for the introduction of environmentally-beneficial innovations ( [[#Grubb--2021|Grubb et al. 2021]] ). While the literature examining the impact of environmental regulation on innovation is large, there have been fewer studies on the innovation effects of minimum energy or emissions performance regulations specifically relating to climate mitigation. We discuss in turn two types of efficiency regulations: on vehicles, and on appliances.

The announcement, introduction and tightening of vehicle fleet efficiency or greenhouse gas (GHG) emission standards either at the national or sub-national level positively impacts innovation as measured by patents ( [[#Barbieri--2015|Barbieri 2015]] ) or vehicle characteristics ( [[#Knittel--2011|Knittel 2011]] ; [[#Kiso--2019|Kiso 2019]] ) as summarised in a review by [[#Grubb--2021|Grubb et al. (2021)]] . Detailed studies on the innovation effects of national pollutant (rather than energy) regulations on automotive innovation also indicate that introducing or tightening performance standards has driven technological change ( [[#Lee--2010|Lee et al. 2010]] ). Some studies in the USA that examine periods in which little regulatory change took place have found that the effects of performance standards on fuel economy have been small ( [[#Knittel--2011|Knittel 2011]] ) or not significant relative to the innovation effects of prices ( [[#Crabb--2010|Crabb and Johnson 2010]] ). This is at least in part because ongoing efficiency improvements during this period were offset by increases in other product attributes. For example, a study by [[#Knittel--2011|Knittel (2011)]] observed that size and power increased without a corresponding increase in fuel consumption. It has also been observed that regulatory design may introduce distortions that affect automotive innovation choices: in particular, fuel economy standards based on weight classes have been observed to distort light-weighting strategies for fuel efficiency in both China ( [[#Hao--2016|Hao et al. 2016]] ) and Japan ( [[#Ito--2018|Ito and Sallee 2018]] ).

A number of studies have focused on the impacts of a sub-national technology-forcing policy: the California Zero Emission Vehicle (ZEV) mandate. When it was introduced in 1990, this policy required automotive firms to ensure that 2% of the vehicles they sold in 1998 would be zero-emission. In the years immediately after introduction of the policy, automotive firms reported that it was a significant stimulus to their R&amp;D activity in electric vehicles ( [[#Brown--1995|Brown et al. 1995]] ). Quantitative evidence examining patents and prototypes has indicated that the stringency of the policy was a significant factor in stimulating innovation, though this was, in part, dependent on firm strategy ( [[#Sierzchula--2015|Sierzchula and Nemet 2015]] ). As for the previous instruments, most of the evidence comes from industrialised countries, and additional research on other countries would be beneficial.

Regulation-driven deployment of existing technologies can generate innovation in those technologies through learning by- doing, induced R&amp;D and other mechanisms, although not in all cases ( ''medium evidence'' , ''medium agreement'' ) ( [[#Grubb--2021|Grubb et al. 2021]] ). The introduction or tightening of minimum energy performance standards for appliances (and for buildings, in [[#Noailly--2012|Noailly (2012)]] ) have driven innovation responses, using direct measures of product attributes ( [[#Newell--1999|Newell et al. 1999]] ) and patents ( [[#Noailly--2012|Noailly 2012]] ; [[#Kim--2019|Kim and Brown 2019]] ), though not all studies have found a significant relationship ( [[#Girod--2017|Girod et al. 2017]] ). There is also evidence of a correlation between regulation-driven deployment of energy-efficient products with accelerated learning in those technologies ( [[#Van%20Buskirk--2014|Van Buskirk et al. 2014]] ; [[#Wei--2017|Wei et al. 2017]] ).

In addition to observational studies, evidence on the relationship between innovation and regulation comes from surveys in which respondents are asked whether they have engaged in innovation leading to energy saving or reduced GHG emissions, and what the motivations were for such innovation. Survey evidence has found that expected or current regulation can drive both R&amp;D investment and decisions to adopt or introduce innovations that reduce energy consumption or CO 2 emissions ( [[#Horbach--2012|Horbach et al. 2012]] ; [[#Grubb--2021|Grubb et al. 2021]] ). Survey-based studies, however, tend not to specify the type of regulation.

Minimum energy performance standards and appliance standards have been known to result in negative distributional impacts ( ''limited evidence'' , ''medium/high agreement'' ). Several studies focused on the USA have highlighted that minimum energy performance standards for vehicles tend to be regressive, with poorer households disproportionately affected ( [[#Jacobsen--2013|Jacobsen 2013]] ; [[#Levinson--2019|Levinson 2019]] ), particularly when second-hand vehicles are taken into account ( [[#Davis--2019|Davis and Knittel 2019]] ). Similar arguments, though with less evidence, have been made for appliance standards ( [[#Sutherland--2006|Sutherland 2006]] ).

Overall, the extent to which regulations in energy efficiency result in positive or negative competitiveness impacts in firms is mixed ( ''limited evidence'' , ''high disagreement'' ). A meta-analysis of 107 studies, of which 13 focused on regulations relating to energy consumption or GHG emissions, found that around half showed that regulations resulted in competitiveness impacts, while half did not ( [[#Cohen--2018|Cohen and Tubb 2018]] ). [[#Cohen--2018|Cohen and Tubb (2018)]] also found that studies examining performance-based regulations were less likely to find positive competitiveness impacts than those that examined market-based instruments.

While most of the literature addresses the extent to which regulation can induce innovation, a number of case studies highlight that innovation can also influence regulation, as the costs of imposing regulation are reduced and political interests emerge that seek to exploit competitive advantages conferred by successfully developing energy-efficient or low-carbon technologies ( ''medium evidence'' , ''high agreement'' ). Case studies map the causal mechanisms relating regulations and innovation responses in specific firms or industries ( [[#Gann--1998|Gann et al. 1998]] ; [[#Kemp--2005|Kemp 2005]] ; [[#Ruby--2015|Ruby 2015]] ; [[#Wesseling--2015|Wesseling et al. 2015]] ).

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<span id="assessment-of-the-impact-on-innovation-and-on-competitiveness-and-distributional-outcomes-of-soft-instruments"></span>
==== 16.4.4.6 Assessment of the Impact on Innovation and on Competitiveness and Distributional Outcomes of Soft Instruments ====

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The literature specifically focusing on the impacts of labels is very limited and indicates positive outcomes ( ''limited evidence'' , ''high agreement'' ) ''.'' Energy labels may accompany a minimum energy performance standard, and the outcomes of these policies are often combined in literature ( [[#IEA--2015|IEA 2015]] ). But again, given the limited evidence, more research is needed. Although there are many studies on energy efficiency more broadly and for both standards and labels, only eight studies specifically focus on labels. Furthermore, seven of them report positive outcomes and one negative outcomes. Six of the studies used qualitative methods mentioning the impacts of labelling on the development of new products ( [[#Wiel--2006|Wiel et al. 2006]] ). Research specifically comparing voluntary labels with other mechanisms found a significant and positive relationship between labels and the number of energy-efficient inventions ( [[#Girod--2017|Girod et al. 2017]] ). More research is needed, especially in developing countries, that have extensive labelling programmes in place, and also with quantitative methods, to develop evidence on the impacts of labelling on innovation. Box 16.7 discusses an example of a combination of policy instruments in China including labelling, sale bans and financial support.

Voluntary approaches have a largely positive impact on innovation for those that choose to participate ( ''robust evidence'' , ''medium agreement'' ). Research on voluntary approaches focuses on firms adopting voluntary environmental management systems that can be certified based on standards of the widely adopted International Organization for Standardization (ISO 14001 – standard for environmental management) or the European Union’s Eco-Management and Auditing Scheme (EMAS), which is partly mandatory. Out of 16 analyses: 70% report positive innovation outcomes in terms of patents, products or processes; 17% report negligible impacts; and 13% report negative impacts. Positive innovation outcomes have been linked to firms’ internal resource management practices and were found to be strengthened in firms with mature environmental management systems and in the presence of other environmental regulations ( [[#Inoue--2013|Inoue et al. 2013]] ; [[#He--2019|He and Shen 2019]] ; [[#Li--2019a|Li et al. 2019a]] ). Overall, studies are concentrated in a few countries that do not fully capture where environmental management systems have been actually adopted ( [[#Boiral--2018|Boiral et al. 2018]] ). There is a need for research in analyses of such instruments in emerging economies, including China and India, and methodologically in qualitative and longitudinal analyses ( [[#Boiral--2018|Boiral et al. 2018]] ).

The outcomes for performance or endorsement labels have been associated with positive competitiveness outcomes ( ''medium evidence'' , ''medium agreement'' ) ''.'' Out of 19 studies, 89% report positive impact and 11% negligible impact. Although there are several studies analysing competitiveness-related metrics, evidence on most individual metrics is sporadic, except for housing premiums. A large number of studies quantitatively assessing competitiveness find that green labels in buildings are associated with housing price premiums in multiple countries and regions ( [[#Fuerst--2011|Fuerst and McAllister 2011]] ; [[#Kahn--2014|Kahn and Kok 2014]] ; [[#Zhang--2017|Zhang et al. 2017]] ). Of those studies, 32% were qualitative, associating appliance labelling programmes with employment and industry development ( [[#European%20Commission--2018|European Commission 2018]] ). There is a research gap in analyses of developing countries, and also in quantitatively assessing outcomes beyond housing price premiums.

A few studies on the distributional outcomes of voluntary labelling programmes point to positive impacts ( ''limited evidence'' , ''high agreement'' ) ''.'' All four studies that focus on benefits for consumers and tenants report positive impacts ( [[#Devine--2015|Devine and Kok 2015]] ). Although there are benefits for utility companies and other stakeholders, more research is needed to specifically attribute these benefits to voluntary labels rather than energy efficiency programmes in general.

Voluntary agreements are associated with positive competitiveness outcomes ( ''medium evidence'' , ''medium agreement'' ): 14 out of 19 evaluations identified were associated with positive outcomes, while three were associated with negligible outcomes, and two with negative outcomes. Research found an increase in perceived firm financial performance ( [[#de%20Jong--2014|de Jong et al. 2014]] ; [[#Moon--2014|Moon et al. 2014]] ). Studies also show an association with higher exports as more environmentally-conscious trade partners increasingly value environmental certifications ( [[#Bellesi--2005|Bellesi et al. 2005]] ). More research is needed to develop evidence on metrics of competitiveness besides firms’ financial performance, and especially in developing countries.

Voluntary agreements are associated with a positive impact on distributional outcomes ( ''limited evidence'' , ''high agreement'' ). Five studies, mainly using qualitative approaches, report a positive association between a firm adopting an environmental management system and impacts on its supply chains. There is a need for more studies with quantitative assessments and geographical diversity.

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<span id="summary-of-the-size-and-direction-of-the-evidence-of-all-policy-instrument-types-on-innovation-outcomes"></span>
==== 16.4.4.7 Summary of the Size and Direction of the Evidence of All Policy Instrument Types on Innovation Outcomes ====

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Positive impacts have been identified more frequently in some policies than in others. There is also a lot of variation in the density of the literature. Developing countries are severely underrepresented in the decarbonisation policy instrument evaluation literature aiming to understand the impact on innovation. ( ''high evidence, h'' ''igh agreement).''

Figure 16.2 below indicates the extent to which some decarbonisation policy instruments have been more or less investigated in terms of their impact on innovation outcomes (as described in Table 16.9). For example, it indicates the extent to which there has been a greater focus of evaluations of the impact of R&amp;D investments, emissions trading schemes and voluntary approaches on innovation. It also shows a limited amount of evidence on procurement, efficiency obligations with tradeable green certificates (TGCs), building codes and auctions.

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[[File:8237535ad81f439c1f729fef6626ab77 IPCC_AR6_WGIII_Figure_16_2.png]]

'''Figure 16.2 | Number of evaluations available for each policy instrument type covered regarding their impact on innovation and direction of the assessment.''' The vertical axis displays the number of evaluations claiming to isolate the impact of each policy instrument type on innovation outcomes as listed in Table 16.9. The colour indicates whether each evaluation identified a positive impact on the innovation outcome (blue), the existence of a negative impact (in red), and no impact (in grey). It builds on [[#Grubb--2021|Grubb et al. (2021)]] , [[#Lilliestam--2021|Lilliestam et al. (2021)]] and [[#Peñasco--2021|Peñasco et al. (2021)]] , and additional studies identified as part of these reviews. TGC stands for tradeable green certificates. TWC stands for tradeable white certificates.

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<span id="box-16.5-green-public-procurement-in-the-netherlands"></span>

=== Box 16.5 | Green Public Procurement in The Netherlands ===

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In 2005, the Dutch national government acknowledged a move in the House of Representatives to utilise their annual spending power to promote the market for sustainable goods and services, as well as to act as a role model. Hence, a policy for environmentally-friendly procurement was developed and implemented across the national, local and provincial governments. Subsequently, sustainable public procurement has expanded into a multidimensional policy in The Netherlands, accommodating policies on green public procurement, bio-based public procurement, international social criteria, social return on investment, innovation-oriented public procurement and circular economy.

The Green Public Procurement (GPP) policy is targeted at minimising the negative impacts of production and consumption on the nature environment ( [[#Melissen--2012|Melissen and Reinders 2012]] ; [[#Cerutti--2016|Cerutti et al. 2016]] ). It includes a wide range of environmental criteria for different product groups that public organisations frequently procure, such as office equipment, uniforms, road works and catering. There are 45 product groups ( [[#Melissen--2012|Melissen and Reinders, 2012]] ) and six product clusters as part of the government’s purchasing in terms of sustainability ( [[#PIANOo%20Expertisecentrum--2020|PIANOo Expertisecentrum 2020]] ). The six product clusters are: i) automation and telecommunications; ii) energy; iii) ground, road and hydraulic engineering; iv) office facilities and services; v) office buildings; and vi) transport ( [[#PIANOo%20Expertisecentrum--2020|PIANOo Expertisecentrum 2020]] ).The GPP 2020 Tender Implementation Plan spells out the terms and conditions for green public procurement. Some of these are confidential documents and are not shared online. Others are available for download. The tender implementation plan for The Netherlands is available on https://gpp2020.eu/low-carbon-tenders/open-tenders/ . One of the important scenarios is that the public procurers need the details of Life Cycle Analysis (LCA) carried out in a tool called DuboCalc, which calculates the environmental impacts of the materials and methods of an infrastructural projects. GPP 2020 has reported that three million tonnes of CO 2 would be saved in The Netherlands alone if all Dutch public authorities applied the national Sustainable Public Procurement Criteria.

Research has been carried out to determine the prime mover for implementing Green Public Procurement. An online survey was administered among public procurement officers who subscribed to the newsletters of two Dutch associations that provide advice and training to public procurers. This yielded a sample size of more than 200 ( [[#Grandia--2019|Grandia and Voncken 2019]] ). The first association is called Nevi which is the only organisation in The Netherlands that offers certified procurement training programmes. The second association is called PIANOo which is a public procurement expertise centre paid by the Dutch national government to bring together relevant information regarding public procurement and provide public procurers with useful tools through their websites, workshops, meetings and annual conferences. The data from the survey was then analysed using structural equations modelling (SEM) and the results show that ability, motivation and opportunities affect the implementation of GPP. Particularly, opportunity was found to affect GPP, innovation-oriented public procurement and the circular economy, but not the other types of public procurement.

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<span id="box-16.7-china-energy-labelling-policies-combined-with-sale-bans-and-fina-ncial-subsidies"></span>

=== Box 16.7 | China Energy Labelling Policies, Combined with Sale Bans and Financial Subsidies ===

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From 1970 to 2001, China was able to significantly limit energy demand growth through energy-efficiency programmes. Energy use per unit of gross domestic product (GDP) declined by approximately 5% yr –1 during this period. However, between 2002 and 2005, energy demand per unit of GDP increased on average by 3.8% yr –1 . To curb this energy growth, in 2005, the Chinese government announced a mandatory goal of 20% reduction of energy intensity between 2006 and 2010 ( [[#Zhou--2010|Zhou et al. 2010]] ; [[#Lo--2014|Lo 2014]] ).

An energy labelling system was passed in 2004. It requires manufacturers to provide information about the efficiency of their electrical appliances to consumers. From 2004 to 2010, 23 electrical appliances (including refrigerators, air conditioners and flat-screen TVs) being labelled as energy efficient with five different grades – grade 1 being the most energy efficient and grade 5 the least efficient. Any appliances with an efficiency grade higher than 5 cannot be sold in the market.

In addition to providing information to consumers, the National Development and Reform Commission, (which was in charge of designing the policies), and the Ministry of Finance launched in 2009 the ‘energy-saving products and civilian-benefiting project’ ( [[#Zhan--2011|Zhan et al. 2011]] ). It covered air conditioners, refrigerators, flat panel televisions, washing machines, electrical efficient lighting, energy saving and new energy vehicles with the energy grades at 1 or 2. The project also included financial subsidies for the enterprises producing these products. The standard design of these financial subsidies involved the government paying for the price difference of energy-efficient products and general products. The manufacturers that produce the energy-efficient products receive financial subsidies directly from the government (Z. [[#Wang--2017|]] [[#Wang--2017|Wang et al. 2017]] ).

Before 2008, the market share of grade 1 and grade 2 air conditioners was about 5%, and about 70% of all air conditioners were grade 5 (the most inefficient). Driven by the financial subsidies, the selling price of the highly efficient air conditioners became competitive with that of the general air conditioners. Hence, the sales of energy-efficient air conditioners increased substantially, making the market share of grade 1 and 2 air conditioners about 80% in 2010 (Z. [[#Wang--2017|]] [[#Wang--2017|Wang et al. 2017]] ). According to the information from China’s National Institute of Standardization, the energy label system saved more than 1.5 hundred billion kWh power between 2005 and March 2010, equivalent to more than 60 million tonnes of standard coal, 1.4 billion tonnes of carbon dioxide emissions, and 60 tonnes of sulphur dioxide emissions ( [[#Zhan--2011|Zhan et al. 2011]] ), which significantly contributed to energy saving goals of China’s 11th Five-Year Plan.

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=== 16.4.5 Trade Instruments and their Impact on Innovation ===

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There has been long-standing interest on the impact of Foreign Direct Investment (FDI) on domestic capacity, innovation and environmental outcomes. While this section looks at the impact of trade instruments on innovation, it does not cover the much larger body of evidence on the relationship between FDI and economic development and growth.

Overall, research indicates that trade can facilitate the entrance of new technologies, but the impact on innovation is less clear ( ''limited evidence'' , ''low agreement'' ). A recent study indicates that, for countries with high environmental performance, FDI has a negligible impact on environmental performance, while countries with a lower environmental performance may benefit from FDI in terms of their environmental performance ( [[#Li--2019b|Li et al. 2019b]] ). One analysis on China links FDI with improved environmental performance and energy efficiency and also innovation outcomes in general ( [[#Gao--2013|Gao and Zhang 2013]] ). Other work links FDI with increased productivity across firms (not just those engaged in climate-related technologies) through spillovers ( [[#Newman--2015|Newman et al. 2015]] ). In addition, [[#Brandão--2019|Brandão and Ehrl (2019)]] indicate that productivity of the electric power industry is more influenced by the transfer of embodied technology from other industries than by investments of the power industry. Also, they find that countries with high R&amp;D stocks are the main sources of international technology spillovers and the source countries may also benefit from the spillover.

Other emerging work investigates the role of local content requirements on innovation outcomes and suggests that it can lead to increased power costs (negative distributional impacts). The benefits to the domestic innovation system, measured by patents or exports, are unclear if the policies are not part of a holistic and longer-lasting policy framework ( [[#Probst--2020|Probst et al. 2020]] ).

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=== 16.4.6 Intellectual Property Rights, Legal Framework and the Impact on Innovation ===

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Virtually all countries around the world have instituted systems for the protection of creations and inventions, known as intellectual property rights (IPR) systems ( [[#WIPO--2021|WIPO 2021]] ). While several types of intellectual property exist – patents, copyright, design rights, trademarks, and more – this section will focus on patents, as the most relevant property right for technological innovations ( [[#WIPO--2008|WIPO 2008]] ), and hence the most relevant for policy instruments in this context.

Patent systems aim to promote innovation and economic growth, by stimulating both the creation of new knowledge and diffusion of that knowledge ( ''high evidence'' , ''high agreement'' ). National patent systems, as institutions, play a central role in theories on national innovation systems ( ''high evidence'' , ''strong agreement'' ) ''.'' Patent systems are usually instituted to promote innovation and economic growth ( [[#Machlup--1950|Machlup and Penrose 1950]] ; [[#Nelson--1996|Nelson and Mazzoleni 1996]] ; [[#Encaoua--2006|Encaoua et al. 2006]] ). Some countries explicitly refer to this purpose in their law or legislation – for instance, the US Constitution states the purpose of the US IP rights system to ‘promote the progress of science and useful arts’. Patent systems aim to reach their goals by trying to strike a balance between the creation of new knowledge and diffusion of that knowledge ( [[#Scotchmer--1990|Scotchmer and Green 1990]] ; [[#Devlin--2010|Devlin 2010]] ; [[#Anadon--2016b|Anadon et al. 2016b]] ). They promote the creation of new knowledge (e.g., technological inventions) by providing a temporary, exclusive right to the holder of the patent, thus providing incentives to develop such new knowledge and helping parties to justify investments in R&amp;D. They promote the diffusion of this new knowledge via the detailed disclosure of the invention in the patent publication, and by enabling a ‘market for knowledge’ via trading patents and issuing licences ( [[#Arora--2004|Arora et al. 2004]] ). Although IP protections provide incentives to invest in innovation, they can also restrict the use of new knowledge by raising prices or blocking follow-on innovation ( [[#Wallerstein--1993|Wallerstein et al. 1993]] ; [[#Stiglitz--2008|Stiglitz 2008]] ). As institutions, national patent systems feature prominently in models and theories of national innovation systems ( [[#Edquist--1997|Edquist 1997]] ; [[#Klein%20Woolthuis--2005|Klein Woolthuis et al. 2005]] ).

The degree to which patent systems actually promote innovation is subject to debate. Patent protection has been found to have a positive impact on R&amp;D activities in patent-intensive industries, but this effect was found to be conditional on access to finance ( [[#Maskus--2019|Maskus et al. 2019]] ). Patents are believed to be especially important to facilitate innovation in selected areas such as pharmaceuticals, where investments in developments and clinical trials are high, imitation costs are low, and there is often a one-to-one relationship between a patent and a product, referred to as a ‘discrete’ product industry ( [[#Cohen--2000|Cohen et al. 2000]] ). At the same time, an increasing body of theoretical and empirical literature suggests that the proliferation of patents also discourages innovation ( ''medium evidence'' , ''low agreement'' ). Theoretical contributions note that a appropriability regime that is too stringent may greatly limit the diffusion of advanced technological knowledge and eventually block the development of differentiated technological capabilities within an industry, in what is called an ‘appropriability trap’ ( [[#Edquist--1997|Edquist 1997]] ; [[#Klein%20Woolthuis--2005|Klein Woolthuis et al. 2005]] ). There has been a long-standing debate on the impact of patents and other IP rights on innovation and economic development ( [[#Machlup--1958|Machlup 1958]] ; [[#Hall--2019|Hall and Helmers 2019]] ). [[#Jaffe--2004|Jaffe and Lerner (2004)]] and [[#Bessen--2009|Bessen and Meurer (2009)]] highlight how IP rights also hamper innovation in a variety of ways. Other contributions in the literature focus on more specific factors. For example, [[#Shapiro--2001|Shapiro (2001)]] discusses ‘patent thickets’, where overlapping sets of patent rights mean that those seeking to commercialise new technology need to obtain licences from multiple patentees. [[#Heller--1998|Heller and Eisenberg (1998)]] argue that a ‘tragedy of the anticommons’ is likely to emerge when too many parties obtain the right to exclude others from using fragmented and overlapping pieces of knowledge – ultimately leading to no one having the privilege of using the results of biomedical research. [[#Reitzig--2007|Reitzig et al. (2007)]] describe the damaging effects of extreme business strategies employing patents, such as ‘patent trolling’.

In general, IP protection and enforcement may have different impacts on economic growth in different types of countries ( ''limited evidence'' , ''high agreement'' ). There has been a significant degree of harmonisation and cooperation between national IP systems over time. The most recent milestone is the World Trade Organization (WTO) 1994 Trade-Related Aspects of Intellectual Property Rights (TRIPS) Agreement, entered into by all WTO members, which sets down minimum standards for the regulation by national governments of many forms of IP as applied to nationals of other WTO member nations ( [[#WTO--1994|WTO 1994]] ). Developing countries successfully managed to include some flexibilities into TRIPS, both in terms of timing of legislative reform, and the content of the reforms. In an attempt to understand the effects of the introduction of TRIPS, [[#Falvey--2006|Falvey et al. (2006)]] find that the effect of IP protection on growth is positively and significantly related to growth for low- and high-income countries, but not for middle-income countries. They argue that low-income countries benefit from increased technology flows, but middle-income countries may have offsetting losses from the reduced scope for imitation. Note that [[#Falvey--2006|Falvey et al. (2006)]] do not break down their results in different technological areas, and they do not focus on innovation, but instead on growth. It has been argued that the increasingly globalised IP regime through initiatives such as the TRIPS agreement will diminish prospects for technology transfer and competition in developing countries, particularly for several important technology areas related to meeting sustainable development needs ( [[#Maskus--2017|Maskus and Reichman 2017]] ).

In principle, patent holders are not required to take their protected invention into use, and neither have the obligation to allow (i.e., license) others to use the inventions in question ( ''high evidence'' , ''high agreement'' ). Studies have shown that the way patent holders use their patent differs considerably across industrial sectors: in pharmaceutics, patents are typically used to enable exclusive production of a certain good (and obtain monopoly rents), while in industries such as computers, semiconductors, and communications, patents are often used to strengthen positions in cross-licensing negotiations and to generate licensing income ( [[#Cohen--2000|Cohen et al. 2000]] ; [[#Foray--2004|Foray 2004]] ). There are also companies that predominantly obtain patents for defensive reasons: they seek freedom to design and manufacture, and by owning a patent portfolio themselves, they hope to prevent becoming the target of litigation by other patent holders ( [[#Hall--2001|Hall and Ziedonis 2001]] ). Patents are often used strategically to impede the development and diffusion of competing, alternative products, processes or services, by employing strategies known as ‘blanketing’ and ‘fencing’ ( [[#Grandstrand--2000|Grandstrand 2000]] ), although the research is not specific to the climate space.

There are notable but specific exceptions to the general principle that patent holders are not obliged to license their patent to others. These exceptions include the compulsory licence, fair, reasonable and non-discriminatory (FRAND) policies, and statement on licences of right ( ''high evidence'' , ''high agreement'' ) ''.'' While patent holders are, in principle, free to choose not to license their innovation, there are three important exceptions to this. First, most national patent laws have provisions for compulsory licensing, meaning that a government allows someone else to produce a patented product or process without the consent of the patent holder, or plans to use the patent-protected invention itself ( [[#WTO--2020|WTO 2020]] ). Compulsory licences may be issued in cases of public interest or events of abuse of the patent ( [[#WIPO--2008|WIPO 2008]] ; [[#Biadgleng--2009|Biadgleng 2009]] ). Compulsory licensing is explicitly allowed in the WTO TRIPS agreement, and its use in context of medicine (for instance, to control diseases of public health importance, including HIV, tuberculosis and malaria) is further clarified in the ‘DOHA Declaration’ from 2001 ( [[#Reichman--2009|Reichman 2009]] ; WHO 2020). Second, standard-setting organisations have policies to include patented inventions in their standards only if the patent holder is willing to commit FRAND licensing conditions for those patents ( [[#Contreras--2015|Contreras 2015]] ). While a patent holder can choose not to make such a commitment, by doing so, its patent is no longer a candidate for inclusion in the standard. In the (many) fields where standards are of key importance, it is very unusual for patent holders not to be willing to enter into FRAND commitments ( [[#Bekkers--2017|Bekkers 2017]] ). Third, when a patent holder files at the patent office and opts for the ‘licence of right’ regime, in return for reduced patent fees, they enter into a contractual agreement that obliges them to license the patent to those who request it. While not all national patent systems feature this regime, it is a feature present in the new European Community patent ( [[#EPO--2017|EPO 2017]] ), and may therefore increase in importance.

For a discussion on the impact of intellectual property rights (IPR) on international technology diffusion, see Box 16.9 in [[#16.5|Section 16.5]] .

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=== 16.4.7 Sub-national Innovation Policies and Industrial Clusters ===

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Research examining the impacts of sub-national policies on innovation and competitiveness is sporadic – regional variations have been quantitatively assessed in the USA or China, or with case studies in these and other countries. Research on wind energy in the USA, distributed PV balance of systems in China, and renewable energy technologies in Italy have found that policies that incentivised local demand were associated with inducing innovation, measured with patents ( [[#Corsatea--2016|Corsatea 2016]] ; [[#Fu--2018|Fu et al. 2018]] ; [[#Gao--2019|Gao and Rai 2019]] ). Different policies may have different impacts – for example, in the USA, state-level tax incentives and subsidies induced innovation within the state; but for renewable portfolio standards, policies in other states were associated with innovation because of impact on demand, but own-state policies were not ( [[#Fu--2018|Fu et al. 2018]] ). Research has also noted that the outcomes of policy and regulation on innovation are spatially heterogenous, because of differences in local planning authorities and capabilities ( [[#Corsatea--2016|Corsatea 2016]] ; [[#Song--2019|Song et al. 2019]] ).

Sub-national deployment policies have been associated with different impacts on competitiveness metrics ( ''limited evidence'' , ''medium agreement'' ). Research on green jobs shows positive association between sub-national policies and green jobs or green firms at the metropolitan level as well as the state of provincial level, in both China and the USA ( [[#Yi--2013|Yi 2013]] ; [[#Yi--2015|Yi and Liu 2015]] ; [[#Lee--2017|Lee 2017]] ), while others find no impact of renewable portfolio standards on green job growth in the state ( [[#Bowen--2013|Bowen et al. 2013]] ). Other examples of competitiveness are in the impact of regional green industrial policy in Brazil’s Rio Grande do Sul region in attracting auctioned contracts for wind energy ( [[#Adami--2017|Adami et al. 2017]] ) or in the changes in net positive state revenues associated with removing tax incentives for wind producers in Idaho in the USA ( [[#Black--2014|Black et al. 2014]] ).

Sub-national policies also directly support innovation and competitiveness through green incubators and direct grants or R&amp;D funding for local companies working on clean energy, intending to promote local economic development ( ''limited evidence'' , ''medium agreemen'' t). The literature on the impacts of such policies on innovation and competitiveness is sparse. Some case studies and programme evaluation reports, primarily in the USA, have identified the impacts of sub-national policies on competitiveness — for example, job creation from direct R&amp;D funding in North Carolina ( [[#Hall--2015|Hall and Link 2015]] ), perceptions for local industry development and support for follow-on financing for companies receiving state-funded grants in Colorado ( [[#Surana--2020b|Surana et al. 2020b]] ), and return on investments for the state in research and innovation spending from the New York state’s energy agency ( [[#NYSERDA--2020|NYSERDA 2020]] ). There is a general paucity of metrics on innovation and competitiveness for systematic assessments of such programmes in developed countries, and even more so in India and other developing countries where such programmes have been increasing ( [[#Gonsalves--2019|Gonsalves and Rogerson 2019]] ; [[#Surana--2020a|Surana et al. 2020a]] ).

Although states and local governments increasingly support clean energy deployment as well as directly support innovation, given its link with economic development goals, there is a lack of systematic research on the impacts of these policies at the subnational level. More research – qualitative and quantitative, and in developed and developing countries – is needed to systematically develop evidence on these impacts and to understand the reasons behind regional differences in terms of the type of policy as well as the capabilities in the region.

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=== 16.4.8 System-oriented Policies and Instruments ===

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Although previous sections summarised the research disentangling the role of individual policies in advancing or hindering innovation (as well as impacts on other objectives), other research has tried to characterise the impact of a policy mix on a particular outcome. Although the outcome studied was not innovation, but diffusion (technology effectiveness is in the set of criteria outlined in Chapter 13), it seems relevant to discuss overall findings. Research reviewing renewable energy policies in nine OECD countries concludes that, over time, a broad set of policies characterised by a ‘balance’ metric has been put in place. This research also identifies a significant negative association between the balance of policies in renewable energy and the diffusion of total renewable energy capacity, but no significant effect of the overall intensity (coded as the 46 weighted average of six indicators) on renewable capacity ( [[#Schmidt--2019|Schmidt and Sewerin 2019]] ). This indicates that a neutral conception of balance across all possible policies may not be desirable, and that policy mix intensity by itself does not explain technology diffusion.

A growing body of research aims to understand how different policies interact and how to characterise policy mixes ( [[#del%20Río--2010|del Río 2010]] ; [[#Howlett--2015|Howlett and del Rio 2015]] ; [[#Rogge--2016|Rogge and Reichardt 2016]] ; [[#del%20Río--2017|del Río and Cerdá 2017]] ). The empirical impact on the innovation outcomes is not yet discussed. A more detailed discussion of this literature is located in Chapter 13.

An emerging stream of research in complex systems suggests that relatively small changes in policy near a possible tipping point in climate impacts in areas, including changing strategies related to investments in innovation, could trigger large positive societal feedbacks in the long term ( [[#Farmer--2019|Farmer et al. 2019]] ; [[#Otto--2020|Otto et al. 2020]] ).

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