Abstract
AbstractAnalysis 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.
Publisher
Springer Science and Business Media LLC
Reference53 articles.
1. IPCC. Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC, 2022).
2. Kriegler, E. et al. Will economic growth and fossil fuel scarcity help or hinder climate stabilization? Clim. Change 136, 7–22 (2016).
3. van Vuuren, D. P., de Vries, B., Beusen, A. & Heuberger, P. S. C. Conditional probabilistic estimates of 21st century greenhouse gas emissions based on the storylines of the IPCC-SRES scenarios. Global Environ. Change 18, 635–654 (2008).
4. van Vuuren, D. P. et al. The costs of achieving climate targets and the sources of uncertainty. Nat. Clim. Change 10, 329–334 (2020).
5. Sobol, I. M. Sensitivity estimates for nonlinear mathematical models. Math. Model. Comput. Exp. 4, 407–414 (1993).