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

==== 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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