Making sense of global sensitivity analyses

Wainwright, H. M.; Finsterle, Stefan; Jung, Yoojin; Zhou, Quanlin; Birkhölzer, Jens

doi:10.1016/j.cageo.2013.06.006

Cited by 178 publications

(119 citation statements)

References 13 publications

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“…It is such parameter interdependence and nonlinearity effects that would render a local sensitivity analysis almost useless, specifically if being concerned about conditions that occur, with a small probability. A detailed discussion of how to interpret variability in the elementary effect can be found in Morris (1991) and Wainwright et al (2013).…”

Section: Report On Thmc Modeling Of the Near Field Evolution Of A Genmentioning

confidence: 99%

Report on THMC Modeling of the Near Field Evolution of a Generic Clay Repository: Model Validation and Demonstration

Liu¹

2013

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Section: Report On Thmc Modeling Of the Near Field Evolution Of A Genmentioning

confidence: 99%

Report on THMC Modeling of the Near Field Evolution of a Generic Clay Repository: Model Validation and Demonstration

Liu¹

2013

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“…In most cases, local or one-at-a-time sensitivity analyses are conducted, which, however, do not capture the interactions between input parameters (Saltelli et al, 2010). This has led to a couple of studies using global sensitivity techniques, which vary several uncertain input parameters at a time to explore the interaction effects, in some cases through probabilistic and Monte Carlo methods (Wainwright et al, 2014). Recent studies in energy systems research include van Vuuren et al (2008), Anthoff & Tol (2013), Anderson et al (2014), Branger et al (2015), Pye et al (2015) and Usher (2016) …”

Section: Annexmentioning

confidence: 99%

Impact of technology uncertainty on future low-carbon pathways in the UK

Fais

Keppo

Zeyringer

et al. 2016

Energy Strategy Reviews

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Energy and climate policy-making requires strong quantitative scientific evidence to devise robust and consistent long-term decarbonisation strategies. Energy system modelling can provide crucial insights into the inherent uncertainty in such strategies, which needs to be understood when designing appropriate policy measures.This study contributes to the growing research area of uncertainty analysis in energy system models. We combine consistent and realistic narratives on several technology dimensions with a global sensitivity analysis in a national, bottom-up, optimizing energy system model. This produces structured insights into the impact of low-carbon technology and resource availability on the long-term development of the UK energy system under ambitious decarbonisation pathways. We explore a variety of result metrics to present policy-relevant results in a useful and concise manner. The results provide valuable information on the variability of fuel and technology use across the uncertainty space (e.g. a strong variation in natural gas demand). We demonstrate the complementarities and substitutability of technologies (e.g. the dependency of hydrogen technologies on the availability of CCS). We highlight critical low-carbon options and hedging strategies (e.g. the early decarbonisation of the electricity sector or the stronger use of renewable sources as a hedging against failure in other technologies) and demonstrate timing and path dependencies (e.g. the importance of early decarbonisation action in the presence of multiple technology uncertainty). The results also show how the availability of a given technology can have wider impacts elsewhere in the energy system, thus complicating the management of a long-term energy transition.

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“…Since the mean of EE represents the average effect of each parameter over the parameter space, the mean EE can be regarded as a global sensitivity measure (Wainwright et al, 2014). The mean and the standard deviation of EEs denoted as µ and σ, respectively, are calculated as …”

Section: Elementary Effectmentioning

confidence: 99%