Social discount rates for member countries of the European Union

Evans, David; Sezer, Haluk

doi:10.1108/01443580510574832

Cited by 81 publications

(50 citation statements)

References 18 publications

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“…Stern 2008) see a strong normative component. Others infer values for ρ and η from data (Evans and Sezer 2005). Nordhaus (2008) equates the SDR to the observed real rate of return on capital with a constant value for G(t), and sees ρ and η as "unobserved normative parameters" (p. 60) or "taste variables" (p. 215) which are excluded from uncertainty quantification.…”

Section: Deep and Knightian Uncertaintymentioning

confidence: 99%

Deep and Shallow Uncertainty in Messaging Climate Change

Cooke

2014

SSRN Journal

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Deep and shallow uncertainty are defined and contrasted with regard to messaging the uncertainty about climate change. Deep uncertainty is often traced back to the writings of Frank Knight, where in fact it simply meant subjective probability. Although Knight envisioned a scientifically grounded quantification of subjective uncertainty, deep uncertainty is frequently invoked to disable quantification, with attendant problems in communicating and propagating uncertainty through chains of reasoning. These issues together with science based uncertainty quantification are illustrated with recent applications to ice sheet dynamics. The issues of performance assessment and validation are addressed.

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Section: Deep and Knightian Uncertaintymentioning

confidence: 99%

Deep and Shallow Uncertainty in Messaging Climate Change

Cooke

2014

SSRN Journal

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“…Others infer values for ρ and η from data. (Evans and Sezer 2005). Nordhaus (2008) equates the SDR to the observed real rate of return on capital with a constant value for G(t), and sees ρ and η as "unobserved normative parameters" (p. 60 ) or "taste variables" (p. 215) which are excluded from uncertainty quantification.…”

Section: Operational Meaningmentioning

confidence: 99%

Uncertainty analysis comes to integrated assessment models for climate change…and conversely

Cooke

2012

Climatic Change

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This article traces the development of uncertainty analysis through three generations punctuated by large methodology investments in the nuclear sector. Driven by a very high perceived legitimation burden, these investments aimed at strengthening the scientific basis of uncertainty quantification. The first generation building off the Reactor Safety Study introduced structured expert judgment in uncertainty propagation and distinguished variability and uncertainty. The second generation emerged in modeling the physical processes inside the reactor containment building after breach of the reactor vessel. Operational definitions and expert judgment for uncertainty quantification were elaborated. The third generation developed in modeling the consequences of release of radioactivity and transport through the biosphere. Expert performance assessment, dependence elicitation and probabilistic inversion are among the hallmarks. Third generation methods may be profitably employed in current Integrated Assessment Models (IAMs) of climate change. Possible applications of dependence modeling and probabilistic inversion are sketched. It is unlikely that these methods will be fully adequate for quantitative uncertainty analyses of the impacts of climate change, and a penultimate section looks ahead to fourth generation methods.

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“…Different models such as social rate of time preferences (SRTP), marginal social opportunity cost of capital (SOC), weight average (WA) and shadow price of capital (SPC) have been applied for selecting the social discount rate. Evans and Sezer (2005) estimated discount rates , based on the social rate of time preferences, for countries such as USA, UK, Germany, Japan, France and Australia. Their result ranged between (3-5%) which clearly contradicted with the official discount rate at the time.…”

Section: Introductionmentioning

confidence: 99%