Unpacking the modeling process for energy policy making

Samuele Lo Piano; Máté János Lőrincz; Arnald Puy; Steve Pye; Andrea Saltelli; Stefán Thor Smith; Jeroen van der Sluijs

doi:10.1111/risa.14248

Unpacking the modeling process for energy policy making

Samuele Lo Piano^*, Máté János Lőrincz, Arnald Puy, Steve Pye, Andrea Saltelli, Stefán Thor Smith, Jeroen van der Sluijs

^*Corresponding author for this work

Geography, Earth and Environmental Sciences

Research output: Contribution to journal › Article › peer-review

Abstract

This article explores how the modeling of energy systems may lead to an undue closure of alternatives by generating an excess of certainty around some of the possible policy options. We retrospectively exemplify the problem with the case of the International Institute for Applied Systems Analysis (IIASA) global modeling in the 1980s. We discuss different methodologies for quality assessment that may help mitigate this issue, which include Numeral Unit Spread Assessment Pedigree (NUSAP), diagnostic diagrams, and sensitivity auditing (SAUD). We illustrate the potential of these reflexive modeling practices in energy policy‐making with three additional cases: (i) the case of the energy system modeling environment (ESME) for the creation of UK energy policy; (ii) the negative emission technologies (NETs) uptake in integrated assessment models (IAMs); and (iii) the ecological footprint indicator. We encourage modelers to adopt these approaches to achieve more robust, defensible, and inclusive modeling activities in the field of energy research.

Original language	English
Journal	Risk Analysis
Early online date	14 Nov 2023
DOIs	https://doi.org/10.1111/risa.14248
Publication status	E-pub ahead of print - 14 Nov 2023

Bibliographical note

Acknowledgments:
This research received funding from UK Research and Innovation grant agreement EP/R035288/1 as part of the Centre for Research into Energy Demand Solutions (CREDS). Andrea Saltelli acknowledges the funding of i4Driving, an EU Horizon Europe R&I project (Grant Agreement ID 101076165). Arnald Puy acknowledges funding by the UK Research and Innovation (UKRI) under the UK government's Horizon Europe funding guarantee [grant number EP/Y02463X/1, project DAWN].

Keywords

postnormal science
non‐neutrality of methods
energy systems modeling
uncertainty
sensitivity auditing
NUSAP

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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PianoS2023UnpackingFinal published version, 525 KBLicence: Creative Commons: Attribution (CC BY)

Cite this

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title = "Unpacking the modeling process for energy policy making",

abstract = "This article explores how the modeling of energy systems may lead to an undue closure of alternatives by generating an excess of certainty around some of the possible policy options. We retrospectively exemplify the problem with the case of the International Institute for Applied Systems Analysis (IIASA) global modeling in the 1980s. We discuss different methodologies for quality assessment that may help mitigate this issue, which include Numeral Unit Spread Assessment Pedigree (NUSAP), diagnostic diagrams, and sensitivity auditing (SAUD). We illustrate the potential of these reflexive modeling practices in energy policy‐making with three additional cases: (i) the case of the energy system modeling environment (ESME) for the creation of UK energy policy; (ii) the negative emission technologies (NETs) uptake in integrated assessment models (IAMs); and (iii) the ecological footprint indicator. We encourage modelers to adopt these approaches to achieve more robust, defensible, and inclusive modeling activities in the field of energy research.",

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note = "Acknowledgments: This research received funding from UK Research and Innovation grant agreement EP/R035288/1 as part of the Centre for Research into Energy Demand Solutions (CREDS). Andrea Saltelli acknowledges the funding of i4Driving, an EU Horizon Europe R&I project (Grant Agreement ID 101076165). Arnald Puy acknowledges funding by the UK Research and Innovation (UKRI) under the UK government's Horizon Europe funding guarantee [grant number EP/Y02463X/1, project DAWN].",

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AU - Saltelli, Andrea

AU - Smith, Stefán Thor

AU - van der Sluijs, Jeroen

N1 - Acknowledgments: This research received funding from UK Research and Innovation grant agreement EP/R035288/1 as part of the Centre for Research into Energy Demand Solutions (CREDS). Andrea Saltelli acknowledges the funding of i4Driving, an EU Horizon Europe R&I project (Grant Agreement ID 101076165). Arnald Puy acknowledges funding by the UK Research and Innovation (UKRI) under the UK government's Horizon Europe funding guarantee [grant number EP/Y02463X/1, project DAWN].

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N2 - This article explores how the modeling of energy systems may lead to an undue closure of alternatives by generating an excess of certainty around some of the possible policy options. We retrospectively exemplify the problem with the case of the International Institute for Applied Systems Analysis (IIASA) global modeling in the 1980s. We discuss different methodologies for quality assessment that may help mitigate this issue, which include Numeral Unit Spread Assessment Pedigree (NUSAP), diagnostic diagrams, and sensitivity auditing (SAUD). We illustrate the potential of these reflexive modeling practices in energy policy‐making with three additional cases: (i) the case of the energy system modeling environment (ESME) for the creation of UK energy policy; (ii) the negative emission technologies (NETs) uptake in integrated assessment models (IAMs); and (iii) the ecological footprint indicator. We encourage modelers to adopt these approaches to achieve more robust, defensible, and inclusive modeling activities in the field of energy research.

AB - This article explores how the modeling of energy systems may lead to an undue closure of alternatives by generating an excess of certainty around some of the possible policy options. We retrospectively exemplify the problem with the case of the International Institute for Applied Systems Analysis (IIASA) global modeling in the 1980s. We discuss different methodologies for quality assessment that may help mitigate this issue, which include Numeral Unit Spread Assessment Pedigree (NUSAP), diagnostic diagrams, and sensitivity auditing (SAUD). We illustrate the potential of these reflexive modeling practices in energy policy‐making with three additional cases: (i) the case of the energy system modeling environment (ESME) for the creation of UK energy policy; (ii) the negative emission technologies (NETs) uptake in integrated assessment models (IAMs); and (iii) the ecological footprint indicator. We encourage modelers to adopt these approaches to achieve more robust, defensible, and inclusive modeling activities in the field of energy research.

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M3 - Article

SN - 0272-4332

JO - Risk Analysis

JF - Risk Analysis

ER -

Unpacking the modeling process for energy policy making

Abstract

Bibliographical note

Keywords

UN SDGs

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