Decision strategies for policy decisions under uncertainties: The case of mitigation measures addressing methane emissions from ruminants

Hadorn, Gertrude Hirsch; Brun, Georg; Soliva, Carla R.; Stenke, Andrea; Peter, Thomas

doi:10.1016/j.envsci.2015.05.011

Cited by 9 publications

(5 citation statements)

References 27 publications

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“…Thus, many important distinctions, for example concerning the nature of uncertainty (aleatory versus epistemic uncertainty) or the source of uncertainty in modeling (parametric versus structural uncertainty) are shared among disciplines and sub‐disciplines. There are also existing frameworks designed specifically for certain applications, such as for metrology (JCGM, 2008a) or for model‐based decision support (Hirsch Hadorn, Brun, Soliva, Stenke, & Peter, 2015; Smith & Stern, 2011; Walker et al, 2003). However, there is no framework that systematically discusses uncertainties that cannot be expressed by the means of probability density functions, while still considering the specific sources of uncertainties relevant to climate datasets.…”

Section: Existing Uncertainty Concepts and Frameworkmentioning

confidence: 99%

Understanding and assessing uncertainty of observational climate datasets for model evaluation using ensembles

Zumwald

Knüsel

Baumberger

et al. 2020

WIREs Climate Change

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In climate science, observational gridded climate datasets that are based on in situ measurements serve as evidence for scientific claims and they are used to both calibrate and evaluate models. However, datasets only represent selected aspects of the real world, so when they are used for a specific purpose they can be a source of uncertainty. Here, we present a framework for understanding this uncertainty of observational datasets which distinguishes three general sources of uncertainty: (1) uncertainty that arises during the generation of the dataset; (2) uncertainty due to biased samples; and (3) uncertainty that arises due to the choice of abstract properties, such as resolution and metric. Based on this framework, we identify four different types of dataset ensembles—parametric, structural, resampling, and property ensembles—as tools to understand and assess uncertainties arising from the use of datasets for a specific purpose. We advocate for a more systematic generation of dataset ensembles by using these sorts of tools. Finally, we discuss the use of dataset ensembles in climate model evaluation. We argue that a more systematic understanding and assessment of dataset uncertainty is needed to allow for a more reliable uncertainty assessment in the context of model evaluation. The more systematic use of such a framework would be beneficial for both scientific reasoning and scientific policy advice based on climate datasets. This article is categorized under: Paleoclimates and Current Trends > Modern Climate Change

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Section: Existing Uncertainty Concepts and Frameworkmentioning

confidence: 99%

Understanding and assessing uncertainty of observational climate datasets for model evaluation using ensembles

Zumwald

Knüsel

Baumberger

et al. 2020

WIREs Climate Change

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the UNFCCC report, the values that followed from focusing on nutritive technologies while ignoring other sorts of measures were made explicit. This appraisal presumed, however, that production levels should not be affected because of the common regulations in EU countries and in view of natural and technological agricultural conditions in Europe (Hirsch Hadorn et al 2015). Reflection on the ultimate values may thus have resulted in other methods that could have been considered to be more favorable.…”

Section: Cost-benefit Analysismentioning

confidence: 99%

Which Methods Are Useful to Justify Public Policies? An Analysis of Cost–Benefit Analysis, Multi-Criteria Decision Analysis, and Non-Aggregate Indicator Systems

Hadorn

2021

J Gen Philos Sci

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Science-based methods for assessing the practical rationality of a proposed public policy typically represent assumed future outcomes of policies and values attributed to these outcomes in an idealized, that is, intentionally distorted way and abstracted from aspects that are deemed irrelevant. Different types of methods do so in different ways. As a consequence, they instantiate the properties that result from abstraction and idealization such as conceptual simplicity versus complexity, or comprehensiveness versus selectivity of the values under consideration to different degrees. I hold that none of these methods is best in general. Instead, I opt for the valuation method that is useful for the policy issue in question both in terms of its relevance and in terms of its practicability. Relevance requires that the method can represent and account for what is at stake in the policy issue. Practicability refers to aspects such as easy versus difficult handling of the method. To argue for the claim, I evaluate three types of valuation methods: (1) cost–benefit analysis that rests on unidimensional measurement and ranking, (2) multi-criteria decision analysis that applies multi-dimensional measurement but unidimensional ranking, and (3) non-aggregate indicator systems that operate with multi-dimensional measurement and sometimes also multi-dimensional ranking. Second-order justification indicating whether and how the valuation method chosen is capable of accounting for the substantive value considerations that constitute the real-world policy issue in question renders the conditions on which the results of a proposed policy evaluation rest transparent.

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“…Thus, many important distinctions, for example concerning the nature of uncertainty (aleatory versus epistemic uncertainty) or the source of uncertainty in modeling (parametric versus structural uncertainty) are shared among disciplines and sub-disciplines. There are also existing frameworks designed specifically for certain applications, such as for metrology (JCGM, 2008a) or for model-based decision support (Hirsch Hadorn, Brun, Soliva, Stenke, & Peter, 2015;Smith & Stern, 2011;Walker et al, 2003). However, there is no framework that systematically discusses uncertainties that cannot be expressed by the means of probability density functions, while still considering the specific sources of uncertainties relevant to climate datasets.…”

Section: Existing Uncertainty Concepts and Frameworkmentioning

confidence: 99%

Understanding and assessing uncertainty of observational climate datasets for model evaluation using ensembles

2020

WIREs Climate Change

View full text Add to dashboard Cite

show abstract

Decision strategies for policy decisions under uncertainties: The case of mitigation measures addressing methane emissions from ruminants

Cited by 9 publications

References 27 publications

Understanding and assessing uncertainty of observational climate datasets for model evaluation using ensembles

Understanding and assessing uncertainty of observational climate datasets for model evaluation using ensembles

Which Methods Are Useful to Justify Public Policies? An Analysis of Cost–Benefit Analysis, Multi-Criteria Decision Analysis, and Non-Aggregate Indicator Systems

Understanding and assessing uncertainty of observational climate datasets for model evaluation using ensembles

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