Carbon capture and storage: combining economic analysis with expert elicitations to inform climate policy

Baker, Erin; Chon, Haewon; Keisler, Jeffrey M.

doi:10.1007/s10584-009-9634-y

Cited by 84 publications

(59 citation statements)

References 33 publications

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“…In the absence of data that could lend greater credibility to one expert 7 In contrast to the more general setting of our paper, these uncertain parameters were exogenous to the choice of policy.…”

Section: Model Descriptionmentioning

confidence: 92%

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Setting Environmental Policy When Experts Disagree

Athanassoglou¹,

Bosetti

2014

Environ Resource Econ

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How can a decision-maker assess the potential of environmental policies when a group of experts provides divergent estimates on their effectiveness? To address this question, we propose and analyze a variant of the well-studied α-maxmin model in decision theory. In our framework, and consistent to the paper's empirical focus on renewable-energy R&D investment, experts' subjective probability distributions are allowed to be action-dependent. In addition, the decision maker constrains the sets of priors to be considered via a parsimonious measure of their distance to a benchmark "average" distribution that grants equal weight to all experts. While our model is formally rooted in the decision-theoretic framework of Olszewski [37], it may also be viewed as a structured form of sensitivity analysis. We apply our framework to original data from a recent expert elicitation survey on solar energy. The analysis suggests that more aggressive investment in solar energy R&D is likely to yield significant dividends even, or rather especially, after taking expert ambiguity into account.

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Section: Model Descriptionmentioning

confidence: 92%

“…There were two stylized "experts" in the analysis, reflecting different views on the model's damage function parameters. 7 The first expert held that damages will be relatively moderate (in line with…”

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confidence: 99%

Setting Environmental Policy When Experts Disagree

Athanassoglou¹,

Bosetti

2014

Environ Resource Econ

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“…In the UMass studies (Baker & Keisler, 2011;Baker, et al, 2009b;Baker, et al, 2009a;Baker, et al, 2008;Clarke & Baker, 2011) experts reviewed a primer on expert elicitation discussing possible biases. As the experts gave their probabilities (or after completing the survey in the case of mail surveys), the analysts used a series of probes aimed at debiasing, including asking about disconfirming evidence, asking backcasting type questions, and reminding the experts of overconfidence, especially when probabilities were very near 0 or 1.…”

Section: Elicitation Methodologymentioning

confidence: 99%

“…This workshop concluded that (1) the large and growing elicitation data sources need to be integrated with each other and with other relevant data on technology supply, and (2) that the integrated data needs to be communicated in ways that are useful to a variety of users, including both government decision makers and researchers who require expert technology supply information for their research. (Clarke & Baker, 2011) This paper outlines the results of three major expert elicitation efforts carried out independently by researchers at UMass Amherst (Baker & Keisler, 2011;Baker, et al, 2009b;Baker, et al, 2009a;Baker, et al, 2008), Harvard (Anadon, et al, 2011;Anadon, et al, 2012;Anadón, et al, 2014a;Chan, et al, 2011), and FEEM (Bosetti, et al, 2012;Catenacci, et al, 2013;Fiorese, et al, 2013). Each of the three groups covered many of the most promising future clean energy technologies [IPCC 5 th AR, WG III, mitigation2014.org]: liquid biofuels, electricity from biomass, carbon capture (CCS), nuclear power, and solar photovoltaic (PV) power.…”

Section: To Inform Policy Decisions Properly It Is Important For Uncmentioning

confidence: 99%

Future costs of key low-carbon energy technologies: Harmonization and aggregation of energy technology expert elicitation data

et al. 2015

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Abstract.In this paper we standardize, compare, and aggregate results from thirteen surveys of technology experts, performed over a period of five years using a range of different methodologies, but all aiming at eliciting expert judgment on the future cost of five key energy technologies and how future costs might be influenced by public R&D investments. To enable researchers and policy makers to use the wealth of collective knowledge obtained through these expert elicitations we develop and present a set of assumptions to harmonize them. We also aggregate expert estimates within each study and across studies to facilitate the comparison. The analysis showed that, as expected, technology costs are expected to go down by 2030 with increasing levels of R&D investments, but that there is not a high level of agreement between individual experts or between studies regarding the technology areas that would benefit the most from R&D investments. This indicates that further study of prospective cost data may be useful to further inform R&D investments. We also found that the contributions of additional studies to the variance of costs in one technology area differed by technology area, suggesting that (barring new information about the downsides of particular forms of elicitations) there may be value in not only including a diverse and relatively large group of experts, but also in using different methods to collect estimates.

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“…Expert elicitation has been recently applied to investigate the uncertain effects of RD&D investments on the prospect of various energy technologies: carbon capture and storage (CCS; Chan et al, 2011;Baker et al, 2009a), hybrid electric vehicles Baker et al, 2010), solar…”

Section: Figure 2: Technology Paths That Have Been Assessed In the Inmentioning

confidence: 99%

The power of biomass: Experts disclose the potential for success of bioenergy technologies

et al. 2014

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This paper focuses on technologies which use thermochemical or biochemical processes to convert biomass into electricity.We present the results from an expert elicitation exercise involving sixteen leading experts coming from different EU Member States. Aim of the elicitation was to assess the potential cost reduction of RD&D (Research, Development and Demonstration) efforts and to identify barriers to the diffusion of these technologies. The research sheds light on the future potential of bioenergy technologies both in OECD (Organisation for Economic Co-operation and Development) and non-OECD countries. The results we present are an important input both for the integrated assessment modeling community and for policy makers who draft public RD&D strategies.

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Carbon capture and storage: combining economic analysis with expert elicitations to inform climate policy

Cited by 84 publications

References 33 publications

Setting Environmental Policy When Experts Disagree

Setting Environmental Policy When Experts Disagree

Future costs of key low-carbon energy technologies: Harmonization and aggregation of energy technology expert elicitation data

The power of biomass: Experts disclose the potential for success of bioenergy technologies

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