Decision frameworks and the investment in R&amp;D

Baker, Erin; Olaleye, Olaitan; Reis, Lara Aleluia

doi:10.1016/j.enpol.2015.01.027

Cited by 15 publications

(10 citation statements)

References 62 publications

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“…1 1 Importance sampling allows us to sample from a different distribution, and renormalize back to the actual distribution of interest. We use it here as in the portfolio analysis discussed in Baker et al 2014b to limit the number of times we ran the three IAMs. Since we have four alternative distributions of technology costs and performance (one for each of the teams that conducted the elicitations-UMass, FEEM and Harvard-plus the combined distribution) and three to five possible R&D portfolios (as we consider three levels of R&D for each of the five technologies, the number of runs needed to capture the impact of technology uncertainty on model outputs in the IAMs would have been exceedingly large.…”

Section: Sampling Methods To Define Model Runs and Policy Scenariosmentioning

confidence: 99%

“…Aside the modeling insights, this analysis is also an essential step toward the design of optimal energy R&D portfolios as described in (Baker et al 2014b), because it improves our understanding of the extent to which technology assumptions drive results as well as of what other parameters affect differences across models. This paper is structured as follows: the next section provides a general overview of the experimental protocol and the methodology used to assess the sensitivity of the models, and in addition it introduces the integrated assessment models used and the ways in which they have been modified to incorporate the information coming from the expert elicitation surveys.…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity to energy technology costs: A multi-model comparison analysis

et al. 2015

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In the present paper we use the output of multiple expert elicitation surveys on the future cost of key low-carbon technologies and use it as input of three Integrated Assessment models, GCAM, MARKAL_US and WITCH. By means of a large set of simulations we aim to assess the implications of these subjective distributions of technological costs over key model outputs. We are able to detect what sources of technology uncertainty are more influential, how this differs across models, and whether and how results are affected by the time horizon, the metric considered or the stringency of the climate policy. In unconstrained emission scenarios, within the range of future technology performances considered in the present analysis, the cost of nuclear energy is shown to dominate all others in affecting future emissions. Climate-constrained scenarios, stress the relevance, in addition to that of nuclear energy, of biofuels, as they represent the main source of decarbonization of the transportation sector and bioenergy, since the latter can be coupled with Carbon Capture and Storage (CCS) to produce negative emissions.

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Section: Sampling Methods To Define Model Runs and Policy Scenariosmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sensitivity to energy technology costs: A multi-model comparison analysis

et al. 2015

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“…In order to approach the problem of Eq. (1), two elements are necessary (Baker et al, 2015b). The first is the quantification of the stochastic relationship between R&D investments and their effect on future technological performance (i.e.…”

Section: Optimal Randd Portfolio Problemmentioning

confidence: 99%

“…The need to consider problems with multiple technologies, coupled with more complex IAMs, has spurred researchers to find new ways to overcome the resulting "curse of dimensionality." In a recent study, Baker et al (2015b) consider four sets of probabilistic distributions, related to different elicitation teams of experts, conditional to three funding levels per set, and five technologies. Furthermore, the economic interactions of technologies are estimated through a large IAM.…”

Section: Introductionmentioning

confidence: 99%

Optimal Clean Energy R&D Investments Under Uncertainty

2017

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The availability of technology plays a major role in the feasibility and costs of climate policy. Nonetheless, technological change is highly uncertain and capital intensive, requiring risky efforts in research and development of clean energy technologies. In this paper, we introduce a two-track method that makes it possible to maintain the rich set of information produced by climate-economy models while introducing the dimension of uncertainty in innovation efforts, without succumbing to computation complexity. In particular, we solve the problem of an optimal R&D portfolio by employing Approximate Dynamic Programming, through multiple runs of an integrated assessment model (IAM) for the purpose of computing the value function, and expert elicitation data to quantify the relevant uncertainties. We exemplify the methodology with the problem of evaluating optimal near-term innovation investment portfolios in four key clean energy technologies (solar, biofuels, bioelectricity and personal electric vehicle batteries), taking into account the uncertainty surrounding the effectiveness of innovation to improve the performance of these technologies. We employ an IAM (WITCH) which has a fairly rich description of the energy technologies and experts' beliefs on future costs for the abovementioned technologies. Focusing on Europe and its short-term climate policy commitments, we find that batteries in personal transportation dominate the optimal public R&D portfolio. The resulting ranking across technologies is robust to changes in risk-aversion, R&D budget limitation and assumptions on crowding out of other investments. These results suggest an important upscaling of R&D efforts compared to the recent past.

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“…(Baker, Olaleye, and Aleluia Reis 2015) used a set of diagnostics based on (Owen 2015) and found that the samples performed in the acceptable range, with the possible exception of the biofuels and CCS efficiency parameters for the UMass and Combined distribution. See (Baker, Olaleye, and Aleluia Reis 2015) for more details.…”

Section: Iii1 Energy Technology Portfolio Modelmentioning

confidence: 99%

Finding Common Ground When Experts Disagree: Robust Portfolio Decision Analysis

2017

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Within the IEB framework, the Chair of Energy Sustainability promotes research into the production, supply and use of the energy needed to maintain social welfare and development, placing special emphasis on economic, environmental and social aspects. There are three main research areas of interest within the program: energy sustainability, competition and consumers, and energy firms. The energy sustainability research area covers topics as energy efficiency, CO2 capture and storage, R+D in energy, green certificate markets, smart grids and meters, green energy and biofuels. The competition and consumers area is oriented to research on wholesale markets, retail markets, regulation, competition and consumers. The research area on energy firms is devoted to the analysis of business strategies, social and corporative responsibility, and industrial organization. Disseminating research outputs to a broad audience is an important objective of the program, whose results must be relevant both at national and international level.The Chair of Energy Sustainability of the University of Barcelona-IEB is funded by the following enterprises ACS, CEPSA, CLH, Enagas, Endesa, FCC Energia, HC Energia, Gas Natural Fenosa, and Repsol) through FUNSEAM (Foundation for Energy and Environmental Sustainability). introducing an approach we call Robust Portfolio Decision Analysis. We introduce the idea of Belief Dominance as a prescriptive operationalization of a concept that has appeared in the literature under a number of names. We use this concept to derive a set of non-dominated portfolios; and then identify robust individual alternatives from the non-dominated portfolios. The Belief Dominance concept allows us to synthesize multiple conflicting sources of information by uncovering the range of alternatives that are intelligent responses to the range of beliefs. This goes beyond solutions that are optimal for any specific set of beliefs to uncover defensible solutions that may not otherwise be revealed. We illustrate our approach using a problem in the climate change and energy policy context: choosing among clean energy technology R&D portfolios. We demonstrate how the Belief Dominance concept can uncover portfolios that would otherwise remain hidden and identify robust individual investments.

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Decision frameworks and the investment in R&D

Cited by 15 publications

References 62 publications

Sensitivity to energy technology costs: A multi-model comparison analysis

Sensitivity to energy technology costs: A multi-model comparison analysis

Optimal Clean Energy R&D Investments Under Uncertainty

Finding Common Ground When Experts Disagree: Robust Portfolio Decision Analysis

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