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- ItemThe role of methane in future climate strategies: mitigation potentials and climate impacts(Dordrecht [u.a.] : Springer Science + Business Media B.V, 2019) Harmsen, Mathijs; Mathijs, Detlef P.; Bodirsky, Benjamin Leon; Chateau, Jean; Durand-Lasserve, Olivier; Drouet, Laurent; Fricko, Oliver; Fujimori, Shinichiro; Gernaat, David E.H.J.; Hanaoka, Tatsuya; Hilaire, Jérôme; Keramidas, Kimon; Luderer, Gunnar; Moura, Maria Cecilia P.; Sano, Fuminori; Smith, Steven J.; Wada, KenichiThis study examines model-specific assumptions and projections of methane (CH4) emissions in deep mitigation scenarios generated by integrated assessment models (IAMs). For this, scenarios of nine models are compared in terms of sectoral and regional CH4 emission reduction strategies, as well as resulting climate impacts. The models’ projected reduction potentials are compared to sector and technology-specific reduction potentials found in literature. Significant cost-effective and non-climate policy related reductions are projected in the reference case (10–36% compared to a “frozen emission factor” scenario in 2100). Still, compared to 2010, CH4 emissions are expected to rise steadily by 9–72% (up to 412 to 654 Mt CH4/year). Ambitious CO2 reduction measures could by themselves lead to a reduction of CH4 emissions due to a reduction of fossil fuels (22–48% compared to the reference case in 2100). However, direct CH4 mitigation is crucial and more effective in bringing down CH4 (50–74% compared to the reference case). Given the limited reduction potential, agriculture CH4 emissions are projected to constitute an increasingly larger share of total anthropogenic CH4 emissions in mitigation scenarios. Enteric fermentation in ruminants is in that respect by far the largest mitigation bottleneck later in the century with a projected 40–78% of total remaining CH4 emissions in 2100 in a strong (2 °C) climate policy case. © 2019, The Author(s).
- ItemLooking under the hood: A comparison of techno-economic assumptions across national and global integrated assessment models(Amsterdam [u.a.] : Elsevier Science, 2018) Krey, Volker; Guo, Fei; Kolp, Peter; Zhou, Wenji; Schaeffer, Roberto; Awasthy, Aayushi; Bertram, Christoph; de Boer, Harmen-Sytze; Fragkos, Panagiotis; Fujimori, Shinichiro; He, Chenmin; Iyer, Gokul; Keramidas, Kimon; Köberle, Alexandre C.; Oshiro, Ken; Reis, Lara Aleluia; Shoai-Tehrani, Bianka; Vishwanathan, Saritha; Capros, Pantelis; Drouet, Laurent; Edmonds, James E.; Garg, Amit; Gernaat, David E.H.J.; Jiang, Kejun; Kannavou, Maria; Kitous, Alban; Kriegler, Elmar; Luderer, Gunnar; Mathur, Ritu; Muratori, Matteo; Sano, Fuminori; van Vuuren, Detlef P.Integrated assessment models are extensively used in the analysis of climate change mitigation and are informing national decision makers as well as contribute to international scientific assessments. This paper conducts a comprehensive review of techno-economic assumptions in the electricity sector among fifteen different global and national integrated assessment models. Particular focus is given to six major economies in the world: Brazil, China, the EU, India, Japan and the US. The comparison reveals that techno-economic characteristics are quite different across integrated assessment models, both for the base year and future years. It is, however, important to recognize that techno-economic assessments from the literature exhibit an equally large range of parameters as the integrated assessment models reviewed. Beyond numerical differences, the representation of technologies also differs among models, which needs to be taken into account when comparing numerical parameters. While desirable, it seems difficult to fully harmonize techno-economic parameters across a broader range of models due to structural differences in the representation of technology. Therefore, making techno-economic parameters available in the future, together with of the technology representation as well as the exact definitions of the parameters should become the standard approach as it allows an open discussion of appropriate assumptions. © 2019 The Authors