SSP3: AIM implementation of Shared Socioeconomic Pathways

By Shinichiro Fujimori, T. Hasegawa, Toshihiko Masui, Kiyoshi Takahashi, D. Herran, Hancheng Dai, Y. Hijioka, M. Kainuma. 2017

506 citations recorded by the source corpus.

Abstract This study quantifies the Shared Socioeconomic Pathways (SSPs) using AIM/CGE (Asia-Pacific Integrated Assessment/Computable General Equilibrium). SSP3 (regional rivalry) forms the main focus of the study, which is supposed to face high challenges both in mitigation and adaptation. The AIM model has been selected as the model to quantify the SSP3 marker scenario, a representative case illustrating a particular narrative. Multiple parameter assumptions in AIM/CGE were differentiated across the SSPs for quantification. We confirm that SSP3 quantitative scenarios outcomes are consistent with its narrative. Moreover, four key features of SSP3 are observed. First, as SSP3 was originally designed to contain a high level of challenges to mitigation, mitigation costs in SSP3 were relatively high. This results from the combination of high greenhouse gas emissions in the baseline (no climate mitigation policy) scenario and low mitigative capacity. Second, the climate forcing level in 2100 for the baseline scenarios of SSP3 was similar to that of SSP2, whereas CO 2 emissions in SSP3 are higher than those in SSP2. This is mainly due to high aerosol emissions in SSP3. A third feature was the high air pollutant emissions associated with weak implementation of air quality legislation and a high level of coal dependency. Fourth, forest area steadily decreases with a large expansion of cropland and pasture land. These characteristics indicate at least four potential uses for SSP3. First, SSP3 is useful for both IAM and impact, adaptation, vulnerability (IAV) analyses to present the worst-case scenario. Second, by comparing SSP2 and SSP3, IAV analyses can clarify the influences of socioeconomic elements under similar climatic conditions. Third, the high air pollutant emissions would be of interest to atmospheric chemistry climate modelers. Finally, in addition to climate change studies, many other environmental studies could benefit from the meaningful insights available from the large-scale land use change resulting in SSP3.

Subfields: Economics and Econometrics, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/9041045859fd9a4a20c74df77f4d57d848a549d0

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Citations in the corpus

Integrated assessment of biomass supply and demand in climate change mitigation scenarios (2019, background): “This behavior is displayed by other IAMs, which either include liquids with CCS or have more optimistic assumptions on the availability and costs of other renewables (Muratori et al., 2017; Rose et al., 2014; Luderer et al., 2014; Klein et al., 2014; Daioglou et al., forthcoming).”
Global surface air temperatures in CMIP6: historical performance and future changes (2020, unknown): “5, red= SSP3-7.”
A multi-model assessment of food security implications of climate change mitigation (2019, unknown): “47 48 Acknowledgement; 49 SFu, TH, and KT are supported by JSPS KAKENHI Grant Number JP16K18177, JSPS 50 Overseas Research Fellowships, and the Environment Research and Technology 51 Development Fund (2-1702) of the Environmental Restoration and Conservation Agency of 52 Japan.”
Analysing interactions among Sustainable Development Goals with Integrated Assessment Models (2019, methodology): “The models included here are AIM-CGE [33], China TIMES [34], DNE21þ [35], GCAM [36], GEM-E3 [37], IMAGE [38], IPAC [39], PRIMES [37], REMIND-MAgPIE [40], MESSAGEBrazil [41], MESSAGE-GLOBIOM [42], and WITCH [43].”
Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century (2018, background): “…that span potential futures of green or fossil-fueled growth (SSP1 van Vuuren et al., 2017, and SSP5 Kriegler et al., 2017), high inequality between or within countries (SSP3 Fujimori et al., 2017, and SSP4 Calvin et al., 2017), and a “middle-of-the-road” scenario (SSP2 Fricko et al., 2017).”
Fossil-fueled development (SSP5): An energy and resource intensive scenario for the 21st century (2017, unknown): “Afforestation occurs in the SSP5 mitigation scenarios by GCAM and AIM/CGE (only SSP5-26), but is not accessible as mitigation option in the REMIND-MAgPIE implementation of the SSPs.”
Global food self-sufficiency in the 21st century under sustainable intensification of agriculture (2020, background): “…SSP1, diets with low-animal calorie shares prevail; under SSP2 caloric consumption and animal calorie shares converge towards moderate levels; and under SSP3 diets with higher animal shares prevail (Riahi et al 2017, van Vuuren et al 2017, Fricko et al 2017, Fujimori et al 2017, Popp et al 2017).”
Energy, land-use and greenhouse gas emissions trajectories under a green growth paradigm (2017, result): “In several figures, we compare the IMAGE results with the range of outcomes by the implementation of the other IAMs, i.e. AIM (Fujimori et al., 2017), GCAM (Calvin et al., 2017), MESSAGEGLOBIOM (Fricko et al., 2017), REMIND-MagPIE (Kriegler et al., 2017), and WITCH (Emmerling et al., 2016).”
A protocol for an intercomparison of biodiversity and ecosystem services models using harmonized land-use and climate scenarios (2018, background): “0 (high land-use pressure and moderately high level of climate change) (Fujimori et al., 2017), and SSP5 with RCP8.”
Global exposure and vulnerability to multi-sector development and climate change hotspots (2018, background): “Overall, there are approximately factors of ∼30–190x difference of total E&V population between the best case (SSP1/1.5 ◦C) and worst case (SSP3/3 ◦C) scenario combinations: with more severe impacts (i.e. MSR = 6.0) the differences are accentuated (SI figures S16– 21, tables S6–7).”