@article{DiluisoWalkManychetal.2021, author = {Diluiso, Francesca and Walk, Paula and Manych, Niccolo and Cerutti, Nicola and Chipiga, Vladislav and Workman, Annabelle and Ayas, Ceren and Cui, Ryna Yiyun and Cui, Diyang and Song, Kaihui and Banisch, Lucy A. and Moretti, Nikolaj and Callaghan, Max W. and Clarke, Leon and Creutzig, Felix and Hilaire, Jerome and Jotzo, Frank and Kalkuhl, Matthias and Lamb, William F. and L{\"o}schel, Andreas and M{\"u}ller-Hansen, Finn and Nemet, Gregory F. and Oei, Pao-Yu and Sovacool, Benjamin K. and Steckel, Jan Christoph and Thomas, Sebastian and Wiseman, John and Minx, Jan C.}, title = {Coal transitions - part 1}, series = {Environmental research letters}, volume = {16}, journal = {Environmental research letters}, number = {11}, publisher = {Institute of Physics Publishing (IOP)}, address = {Bristol}, issn = {1748-9326}, doi = {10.1088/1748-9326/ac1b58}, pages = {40}, year = {2021}, abstract = {A rapid coal phase-out is needed to meet the goals of the Paris Agreement, but is hindered by serious challenges ranging from vested interests to the risks of social disruption. To understand how to organize a global coal phase-out, it is crucial to go beyond cost-effective climate mitigation scenarios and learn from the experience of previous coal transitions. Despite the relevance of the topic, evidence remains fragmented throughout different research fields, and not easily accessible. To address this gap, this paper provides a systematic map and comprehensive review of the literature on historical coal transitions. We use computer-assisted systematic mapping and review methods to chart and evaluate the available evidence on historical declines in coal production and consumption. We extracted a dataset of 278 case studies from 194 publications, covering coal transitions in 44 countries and ranging from the end of the 19th century until 2021. We find a relatively recent and rapidly expanding body of literature reflecting the growing importance of an early coal phase-out in scientific and political debates. Previous evidence has primarily focused on the United Kingdom, the United States, and Germany, while other countries that experienced large coal declines, like those in Eastern Europe, are strongly underrepresented. An increasing number of studies, mostly published in the last 5 years, has been focusing on China. Most of the countries successfully reducing coal dependency have undergone both demand-side and supply-side transitions. This supports the use of policy approaches targeting both demand and supply to achieve a complete coal phase-out. From a political economy perspective, our dataset highlights that most transitions are driven by rising production costs for coal, falling prices for alternative energies, or local environmental concerns, especially regarding air pollution. The main challenges for coal-dependent regions are structural change transformations, in particular for industry and labor. Rising unemployment is the most largely documented outcome in the sample. Policymakers at multiple levels are instrumental in facilitating coal transitions. They rely mainly on regulatory instruments to foster the transitions and compensation schemes or investment plans to deal with their transformative processes. Even though many models suggest that coal phase-outs are among the low-hanging fruits on the way to climate neutrality and meeting the international climate goals, our case studies analysis highlights the intricate political economy at work that needs to be addressed through well-designed and just policies.}, 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} }