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

=== 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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==== 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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==== 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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==== 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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==== 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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