Optimal Clean Energy R&amp;D Investments Under Uncertainty

Marangoni, Giacomo; Maere, Gauthier De; Bosetti, Valentina

doi:10.2139/ssrn.2947610

Cited by 2 publications

(3 citation statements)

References 26 publications

(28 reference statements)

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“…Chan and Anadon 41 include continuous public R&D levels for 6 technology areas that affect the cost and performance of 25 individual technologies, but only one-stage DMUU. Six papers in Table 3 41,42,53,57 , others solved for the optimal budget level 8, 17,52 , and others do both [54][55][56]58 . It is important for researchers and users of information to be aware of the crucial design decisions being made when conducting this analysis and to understand what trade-offs are being made and why.…”

Section: Modelling Insights From Dmuu Frameworkmentioning

confidence: 99%

“…Results are organized into groups with common elicitations, IAMs, and decision methods. Of the six groups analysed, which come from 4 major studies 41,53,56,59 , four groups (Groups 1-4) compare a baseline 'no policy' scenario with either a lenient and stringent climate stabilization scenario or with just a stringent climate stabilization scenario (the Fig. 2 caption provides information about the specific studies), Group 5 compares a lenient and a stringent climate stabilization scenario and Group 6 presents the optimal R&D allocation under a lenient climate stabilization policy.…”

Section: Policy Insightsmentioning

confidence: 99%

“…This is likely because vehicles R&D (in particular batteries) get a large share to begin with in constrained budgets; and because the marginal improvement seen in the elicitations above and beyond those levels is smaller than that of other technologies, especially CCS. Among the three studies that include bioenergy 41,42,56 the fraction of R&D devoted to total bioenergy (the combination of biofuels and bioelectricity) stays relatively constant, driven to some extent by assumptions about the availability and cost of biomass.…”

Section: Nature Energymentioning

confidence: 99%

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Integrating uncertainty into public energy research and development decisions

2017

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P redicting the future is extremely difficult 1 , yet it is nonsensical to ignore the knowledge and understanding we can acquire from experts when making decisions. It is well understood that using experts, and using them wisely, is a key component of evidence-based policymaking 2 .In the energy sector, policymakers are faced with a number of near-term decisions, such as balancing technology R&D, performance standards, and subsidies; or designing energy technology R&D portfolios. Designing these policies involves managing the significant uncertainty that exists about how technologies will evolve with and without different policies 3 . In the wake of the December 2015 Paris Agreement, in which countries agreed to put in place national action plans to reduce greenhouse-gas emissions, and in the context of tight government budgets around the world, designing robust and cost-effective energy innovation policies in the face of technological uncertainty has become more pressing 4 . The question of how to design portfolios of R&D investments across a range of energy technologies has received much attention, most recently after the launch of the Mission Innovation and Breakthrough Energy Coalition pledges to increase public energy R&D funding and follow-on private investments in energy technologies. The R&D decision literature typically approaches this problem in two stages: first considering how R&D investments affect future technology costs 5 , then considering how future technology costs affect energy and climate policies (for example, EMF28; ref. 6).One method for informing policy decisions is to perform expert elicitations (a structured process for eliciting subjective probability distributions from subject-matter experts 7 ). Over the past 10 years, a number of researchers have performed expert elicitations to characterize the uncertainty around future cost and performance of various energy technologies, often subject to assumptions about public R&D investments. This information, however, is just one of the pieces of analysis needed to inform the design of optimal energy R&D Integrating uncertainty into public energy research and development decisions Laura Díaz Anadón 1,2 † *, Erin Baker 3 † and Valentina Bosetti 4,5 † Public energy research and development (R&D) is recognized as a key policy tool for transforming the world's energy system in a cost-effective way. However, managing the uncertainty surrounding technological change is a critical challenge for designing robust and cost-effective energy policies. The design of such policies is particularly important if countries are going to both meet the ambitious greenhouse-gas emissions reductions goals set by the Paris Agreement and achieve the required harmonization with the broader set of objectives dictated by the Sustainable Development Goals. The complexity of informing energy technology policy requires, and is producing, a growing collaboration between different academic disciplines and practitioners. Three analytical components have emerged to support the ...

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