Spread in climate policy scenarios unravelled

Mark M. Dekker (), Andries F. Hof, Maarten Berg, Vassilis Daioglou, Rik Heerden, Kaj-Ivar Wijst and Detlef P. Vuuren
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Mark M. Dekker: PBL Netherlands Environmental Assessment Agency
Andries F. Hof: PBL Netherlands Environmental Assessment Agency
Maarten Berg: PBL Netherlands Environmental Assessment Agency
Vassilis Daioglou: PBL Netherlands Environmental Assessment Agency
Rik Heerden: PBL Netherlands Environmental Assessment Agency
Kaj-Ivar Wijst: Utrecht Universiteit
Detlef P. Vuuren: PBL Netherlands Environmental Assessment Agency

Nature, 2023, vol. 624, issue 7991, 309-316

Abstract: Abstract Analysis of climate policy scenarios has become an important tool for identifying mitigation strategies, as shown in the latest Intergovernmental Panel on Climate Change Working Group III report1. The key outcomes of these scenarios differ substantially not only because of model and climate target differences but also because of different assumptions on behavioural, technological and socio-economic developments2–4. A comprehensive attribution of the spread in climate policy scenarios helps policymakers, stakeholders and scientists to cope with large uncertainties in this field. Here we attribute this spread to the underlying drivers using Sobol decomposition5, yielding the importance of each driver for scenario outcomes. As expected, the climate target explains most of the spread in greenhouse gas emissions, total and sectoral fossil fuel use, total renewable energy and total carbon capture and storage in electricity generation. Unexpectedly, model differences drive variation of most other scenario outcomes, for example, in individual renewable and carbon capture and storage technologies, and energy in demand sectors, reflecting intrinsic uncertainties about long-term developments and the range of possible mitigation strategies. Only a few scenario outcomes, such as hydrogen use, are driven by other scenario assumptions, reflecting the need for more scenario differentiation. This attribution analysis distinguishes areas of consensus as well as strong model dependency, providing a crucial step in correctly interpreting scenario results for robust decision-making.

Date: 2023
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DOI: 10.1038/s41586-023-06738-6

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