Feasibility of peak temperature targets in light of institutional constraints

Download information: Please do not request a data download here. Rather, the data is available for download at the ENGAGE Scenario Explorer under this download link: https://data.ece.iiasa.ac.at/engage/#/downloads. In order to download click on Guest login. You will be forwarded to the downloads page. At the bottom of the downloads page you can download the data under the name: ENGAGE Feasibility of peak temperature targets in light of institutional constraints (DOI: 10.5281/zenodo.11562539). The license permits use of the scenario ensemble for scientific research and commercial use, but restricts redistribution of substantial parts of the data. Please refer to the FAQ and legal code for more information. Data set description This data set includes global climate change mitigation scenarios as summarized by Bertram et al., 2024. The scenarios are developed as part of the ENGAGE project, and explore a wide set of dedicated feasibility assumptions along the institutional, technological, geophysical, and socio-cultural dimensions. In terms of mitigation ambition, the study follows the "net-zero budget" approach from Riahi et al. 2021 and contrasts both a below 2°C scenario (defined as 1000 Gt CO2 from 2018 until reaching net-zero CO2 emissions globally), and a scenario exploring the lowest possible peak temperature (aiming for a 550 Gt CO2 net-zero budget).  Author Contributions C.B., E.B. and K.R. designed the study with input by L.D., E.K., G.L., B.v.R., R.S., D.v.V. and Z.V.; E.B. and C.B. prepared the governance input data for the IAMs; C.B., E.B., L.D., B.v.R., L.A.R., L.B.B., H.-S.d.B., V.D., F.F., D.F., O.F., S.F., K.K., V.K., R.M., P.R., R.S., D.S., I.T. and Z.V. produced the IAM scenario results; R.C., G.I. and N.H. provided a review of results and framing; R.D.L. and J.R. provided temperature probabilities as a function of carbon budgets; C.B. performed the data analysis and produced the plots with input by E.B. and L.D.; C.B. wrote the first draft and all authors contributed to writing the paper. Acknowledgments This research received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 821471 (ENGAGE) (C.B., E.B., L.D., G.L., B.v.R., L.A.R., L.B.B., H.-S.d.B., R.C., V.D., F.F., D.F., O.F., S.F., N.H., G.I., K.K., V.K., E.K., R.D.L., R.M., P.R., J.R., R.S., D.S., I.T., D.v.V., Z.V., K.R.). We thank A. Cherp for permission to use Figure 2 , the entire modelling teams for the development of the used IAMs and participants of the IAMC 2023 conference for helpful feedback. S.F. and D.S. are supported by the Environment Research and Technology Development Fund (JPMEERF20241001) of the Environmental Restoration and Conservation Agency of Japan and JST ASPIRE project grant number JPMJAP2331.