@article{BertramRiahiHilaireetal.2021, author = {Bertram, Christoph and Riahi, Keywan and Hilaire, J{\´e}r{\^o}me and Bosetti, Valentina and Drouet, Laurent and Fricko, Oliver and Malik, Aman and Nogueira, Larissa Pupo and van der Zwaan, Bob and van Ruijven, Bas and van Vuuren, Detlef P. and Weitzel, Matthias and Longa, Francesco Dalla and de Boer, Harmen-Sytze and Emmerling, Johannes and Fosse, Florian and Fragkiadakis, Kostas and Harmsen, Mathijs and Keramidas, Kimon and Kishimoto, Paul Natsuo and Kriegler, Elmar and Krey, Volker and Paroussos, Leonidas and Saygin, Deger and Vrontisi, Zoi and Luderer, Gunnar}, title = {Energy system developments and investments in the decisive decade for the Paris Agreement goals}, series = {Environmental research letters}, volume = {16}, journal = {Environmental research letters}, number = {7}, publisher = {IOP Publishing}, address = {Bristol}, issn = {1748-9326}, doi = {10.1088/1748-9326/ac09ae}, pages = {12}, year = {2021}, abstract = {The Paris Agreement does not only stipulate to limit the global average temperature increase to well below 2 °C, it also calls for 'making finance flows consistent with a pathway towards low greenhouse gas emissions'. Consequently, there is an urgent need to understand the implications of climate targets for energy systems and quantify the associated investment requirements in the coming decade. A meaningful analysis must however consider the near-term mitigation requirements to avoid the overshoot of a temperature goal. It must also include the recently observed fast technological progress in key mitigation options. Here, we use a new and unique scenario ensemble that limit peak warming by construction and that stems from seven up-to-date integrated assessment models. This allows us to study the near-term implications of different limits to peak temperature increase under a consistent and up-to-date set of assumptions. We find that ambitious immediate action allows for limiting median warming outcomes to well below 2 °C in all models. By contrast, current nationally determined contributions for 2030 would add around 0.2 °C of peak warming, leading to an unavoidable transgression of 1.5 °C in all models, and 2 °C in some. In contrast to the incremental changes as foreseen by current plans, ambitious peak warming targets require decisive emission cuts until 2030, with the most substantial contribution to decarbonization coming from the power sector. Therefore, investments into low-carbon power generation need to increase beyond current levels to meet the Paris goals, especially for solar and wind technologies and related system enhancements for electricity transmission, distribution and storage. Estimates on absolute investment levels, up-scaling of other low-carbon power generation technologies and investment shares in less ambitious scenarios vary considerably across models. In scenarios limiting peak warming to below 2 °C, while coal is phased out quickly, oil and gas are still being used significantly until 2030, albeit at lower than current levels. This requires continued investments into existing oil and gas infrastructure, but investments into new fields in such scenarios might not be needed. The results show that credible and effective policy action is essential for ensuring efficient allocation of investments aligned with medium-term climate targets.}, language = {en} } @article{HarmsenKrieglervanVuurenetal.2021, author = {Harmsen, Mathijs and Kriegler, Elmar and van Vuuren, Detlef P. and van der Wijst, Kaj-Ivar and Luderer, Gunnar and Cui, Ryna and Dessens, Olivier and Drouet, Laurent and Emmerling, Johannes and Morris, Jennifer Faye and Fosse, Florian and Fragkiadakis, Dimitris and Fragkiadakis, Kostas and Fragkos, Panagiotis and Fricko, Oliver and Fujimori, Shinichiro and Gernaat, David and Guivarch, C{\´e}line and Iyer, Gokul and Karkatsoulis, Panagiotis and Keppo, Ilkka and Keramidas, Kimon and K{\"o}berle, Alexandre and Kolp, Peter and Krey, Volker and Kr{\"u}ger, Christoph and Leblanc, Florian and Mittal, Shivika and Paltsev, Sergey and Rochedo, Pedro and van Ruijven, Bas J. and Sands, Ronald D. and Sano, Fuminori and Strefler, Jessica and Arroyo, Eveline Vasquez and Wada, Kenichi and Zakeri, Behnam}, title = {Integrated assessment model diagnostics}, series = {Environmental research letters}, volume = {16}, journal = {Environmental research letters}, number = {5}, publisher = {IOP Publishing}, address = {Bristol}, issn = {1748-9326}, doi = {10.1088/1748-9326/abf964}, pages = {13}, year = {2021}, abstract = {Integrated assessment models (IAMs) form a prime tool in informing about climate mitigation strategies. Diagnostic indicators that allow comparison across these models can help describe and explain differences in model projections. This increases transparency and comparability. Earlier, the IAM community has developed an approach to diagnose models (Kriegler (2015 Technol. Forecast. Soc. Change 90 45-61)). Here we build on this, by proposing a selected set of well-defined indicators as a community standard, to systematically and routinely assess IAM behaviour, similar to metrics used for other modeling communities such as climate models. These indicators are the relative abatement index, emission reduction type index, inertia timescale, fossil fuel reduction, transformation index and cost per abatement value. We apply the approach to 17 IAMs, assessing both older as well as their latest versions, as applied in the IPCC 6th Assessment Report. The study shows that the approach can be easily applied and used to indentify key differences between models and model versions. Moreover, we demonstrate that this comparison helps to link model behavior to model characteristics and assumptions. We show that together, the set of six indicators can provide useful indication of the main traits of the model and can roughly indicate the general model behavior. The results also show that there is often a considerable spread across the models. Interestingly, the diagnostic values often change for different model versions, but there does not seem to be a distinct trend.}, language = {en} }