Decision frameworks and the investment in RD

Erin Baker, Olaitan Olaleye, Lara Aleluia Reis
2015 Energy Policy  
In this paper we provide an overview of decision frameworks aimed at crafting an energy technology Research & Development portfolio, based on the results of three large expert elicitation studies and a large scale energy-economic model. We introduce importance sampling as a technique for integrating elicitation data and large IAMs into decision making under uncertainty models. We show that it is important to include both parts of this equation -the prospects for technological advancement and
more » ... interactions of the technologies in and with the economy. We find that investment in energy technology R&D is important even in the absence of climate policy. We illustrate the value of considering dynamic two-stage sequential decision models under uncertainty for identifying alternatives with option value. Finally, we consider two frameworks that incorporate ambiguity aversion. We suggest that these results may be best used to guide future research aimed at improving the set of elicitation data. 2 Energy technology Ambiguity Aversion JEL Classification: Q42 Baker et al. ( 2014) presented the results of the effort to collect, standardize, and aggregate the results from the expert elicitation surveys, highlighting the diversity of results that stem from differences across experts and studies. In the present paper we analyze the impact of this diversity, as well as the impact of the decision framework, on optimal decisions about R&D investment allocations. While an understanding of the distribution of data about technology inputs is very important, it is not easy to anticipate a priori how data distributions translate into economic results and finally into optimal decisions. In some cases, a wide range of probability distributions may nevertheless lead to a single robust decision (Baker & Solak, 2011); while in other cases probability distributions that look very similar may lead to divergent decisions. It is important to note that the optimal decision under uncertainty is not necessarily some average of the optimal decisions under certainty, nor is it necessarily near the optimal decision under a central case ((Baker, 2009); (Dow & Werlang, 1992) ). For example, Santen and Diaz Anadon (2014) show that the investment path in solar R&D is qualitatively different under uncertainty, with a high initial investment followed by a very low investment in the deterministic case, and a medium initial investment followed by high investments in the stochastic case. Some past work has shown optimal technology R&D portfolios to be surprisingly robust to assumptions about climate damages, about the opportunity cost of R&D, and about the underlying policy environment (i.e. a Stern-type stringent policy vs a Nordhaus-type mild policy) (Baker & Solak, 2014) . Other work has shown that the type of policy (e.g., whether CO2 emissions are limited at all, through a cap and trade program, or through a clean energy standard for electricity) affects the optimal R&D investment portfolio . Different questions require different decision support frameworks. In a world in which a stabilization number SES
doi:10.1016/j.enpol.2015.01.027 fatcat:etjdadoqafcxnbtpmtb2bydrei