Fossil-fueled development (SSP5): An energy and resource intensive scenario for the 21st century

Supplementary Online Material (SOM)

Elmar Krieglera,*, Nico Bauera, Alexander Poppa, Florian Humpenödera, Marian Leimbacha, Jessica Streflera, Lavinia Baumstarka, Benjamin Bodirskya,h, Jérôme Hilairea,g, David Kleina, Ioanna Mouratiadoua, Isabelle Weindla, Christoph Bertrama, Jan-Philipp Dietricha, Gunnar Luderera, Michaja Pehla, Robert Pietzckera, Franziska Pionteka, Hermann Lotze-Campena,b, Anne Biewalda, Markus Bonscha,, Anastasis Giannousakisa, Ulrich Kreidenweisa, Christoph Müllera, Susanne Rolinskia, Anselm Schultesa, Jana Schwanitza,1, Miodrag Stevanovica, Katherine Calvinc, Johannes Emmerlingd, Shinichiro Fujimorie, Ottmar Edenhofera,f,g

a Potsdam Institute for Climate Impact Research, Telegraphenberg A 31, 14473 Potsdam, Germany b Humboldt-Universität Berlin, Department of Agricultural Economics, Berlin, GermanycPacific Northwest National Laboratory's Joint Global Change Research Institute, College Park, MD, United States dFondazione Eni Enrico Mattei, Milan and Euro-Mediterranen Center on Climate Change, Italy eNational Institute for Environmental Studies, Tsukuba, Japan fTechnische Universität Berlin, Berlin, GermanygMercator Research Institute on Global Commons and Climate Change, Berlin, Germany hCommonwealth Scientific and Industrial Research Organization, St. Lucia, QLD, Australia

1 Present address: Sogn og Fjordane University College, Norway

* Corresponding author: E-mail: kriegler@pik-potsdam.de, Tel.: (+49) (0)331 288-2616 Fax: (+49) (0)331 288-2570

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S1. Supplementary Figures

Figure S1.1: Regional final energy demand per capita (in GJ/capita) in REMIND as a function of GDP (PPP) per capita for SSP1, SSP2 and SSP5. Shown are the baseline cases and two mitigation cases aiming at an intermediate forcing target of 4.5 W/m2 (4.5) and a stringent target of 2.6 W/m2 (2.6) in 2100. Energy demand decreases in the mitigation cases relative to the baseline due to increasing energy prices.

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Figure S1.2: Regional food demand per capita (kcal/cap/day) in MAgPIE including crop and livestock products as a function of GDP (PPP) per capita (Thousand US$2005/cap) for SSP1, SSP2 and SSP5. Note the logarithmic scale on the horizontal axis. See section S3.2.1 for more details regarding food demand in MAgPIE. Food demand did not vary between baseline and mitigation cases.

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Figure S1.3: Final energy demand by carrier in the SSP5 baseline scenario (top row) and the 4.5 W/m2 (SSP5-4.5) and 2.6 W/m2 (SSP5-2.6) mitigation cases (bottom row). The REMIND-MAgPIE (R-M) results are compared with the SSP5 scenarios from AIM/CGE (A), GCAM (G), and WITCH-GLOBIOM (W-G) for the years 2050 and 2100. The grey bands show the 5 th to 95th percentile range of final energy use in baseline and mitigation scenarios (580-650 ppm CO2e scenarios compared to SSP5- 4.5 and 430-480 ppm CO2e to SSP5-2.6) collected in the scenario database of the 5th Assessment Report (AR5) of Working Group III of the IPCC (IPCC, 2014). Historic data is from IEA (2012).

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Figure S1.4: Primary energy supply by source (in direct equivalent units) in the SSP5 baseline scenario (top row) and the 4.5 W/m2 (SSP5-4.5) and 2.6 W/m2 (SSP5-2.6) mitigation cases (bottom row). The REMIND-MAgPIE (R-M) results are compared with the SSP5 scenarios from AIM/CGE (A), GCAM (G), and WITCH-GLOBIOM (W-G) for the years 2050 and 2100. The grey bands show the 5 th to 95th percentile range of primary energy in baseline and mitigation scenarios in the AR5 scenario database (IPCC, 2014; see caption of Fig. S1.3 for details). Historic data is from IEA (2012).

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Figure S1.5: Electricity generation by source in the SSP5 baseline scenario (top row) and the 4.5 W/m2 (4.5) and 2.6 W/m2 (2.6) mitigation cases (bottom row). The REMIND-MAgPIE (R-M) results are compared with the SSP5 scenarios from AIM/CGE (A), GCAM (G), and WITCH-GLOBIOM (W-G) for the years 2050 and 2100. The grey bands show the 5th to 95th percentile range of electricity generation in baseline and mitigation scenarios in the AR5 scenario database (IPCC, 2014; see caption of Fig. S1.3 for details). Historic data is from IEA (2012).

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Figure S1.6: Liquids supply in the SSP5 baseline scenario and the 4.5 W/m2 (SSP5-4.5) and 2.6 W/m2

(SSP5-2.6) mitigation cases. The REMIND-MAgPIE results are compared with SSP1 and SSP2 for the years 2050 and 2100. The dots in the bar plots indicate liquids supply projections across IAMs and the white diamonds represent the SSP marker scenarios. The grey bands show the 5th to 95th

percentile range of liquids supply in baseline and mitigation scenarios in the AR5 scenario database (IPCC, 2014; see caption of Fig. S1.3 for details).

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Figure S1.7: Gases supply in the SSP5 baseline scenario and the 4.5 W/m2 (4.5) and 2.6 W/m2 (2.6) mitigation cases. The REMIND-MAgPIE results for SSP5 are compared with SSP1 and SSP2 for the years 2050 and 2100. The dots in the bar plots indicate gases supply projections across IAMs and the white diamonds represent the SSP marker scenarios. The category “Other” describes leaked natural gas captured from landfills etc. and re-used. The grey bands show the 5 th to 95th percentile range of gases supply in baseline and mitigation scenarios in the AR5 scenario database (IPCC, 2014; see caption of Fig. S1.3 for details).

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Figure S1.8: Primary energy trade between world regions in SSP5 for the baseline scenario (left column) and an intermediate (4.5 W/m2; middle column) and stringent mitigation scenario (2.6 W/m2; right column) as calculated by REMIND. Shown is trade of coal (top row), oil (upper middle row), gas (lower middle row) and biomass (bottom row) over the 21st century.

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Figure S1.9: Time-series of global cereal and bioenergy crop yields in SSP1, SSP2 and SSP5 for the baseline case (Baseline) and the 4.5 W/m2 (45) and 2.6 W/m2 (26) mitigation cases as calculated by MAgPIE.

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Figure S1.10: Maps of land-use change between 2005 and 2100 for SSP5-Baseline (left) and SSP5-26 (right) as calculated by MAgPIE.

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Figure S1.11: Global land-use change by land type relative to 2010 in the SSP5-Baseline scenario (top row) and the 4.5 W/m2 (SSP5-45) and 2.6 W/m2 (SSP5-26) mitigation cases (bottom row). The REMIND-MAgPIE results are compared with SSP5 Baseline and policy scenarios from other models (AIM/CGE and GCAM4) for the years 2050 and 2100. For the SSP5-Baseline scenario (top row) we include an additional comparison with the RCP 8.5 scenario (Riahi et al., 2011). Note that the RCP8.5 scenario differentiates between intensive and extensive grasslands, whereas we show the sum of both categories as Pasture. Since intensive and extensive grasslands in the RCP8.5 scenario develop in different directions (intensive grasslands increase at the cost of extensive grasslands), positive and negative land-use changes in our representation of the RCP8.5 scenario (single Pasture category) do not balance out.

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Figure S1.12: Time-series of agricultural trade for food/feed crops and livestock in SSP5-Baseline, SSP5-45 and SSP5-26 as calculated by MAgPIE. Agricultural trade is calculated as the difference between regional production and regional demand. Thus, positive values reflect exports, while negative values reflect imports.

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Figure S1.13: Greenhouse gas (GHG) emissions by source in the SSP5 baseline scenario (top row) and the 4.5 W/m2 (SSP5-4.5) and 2.6 W/m2 (SSP5-2.6) mitigation cases (bottom row). The REMIND-MAgPIE (R-M) results are compared with the SSP5 scenarios from AIM/CGE (A), GCAM (G), and WITCH-GLOBIOM (W-G) for the years 2050 and 2100. Black horizontal bars show net greenhouse gas emissions after substraction of sinks. The grey bands show the 5th to 95th percentile range of GHG emissions in baseline and mitigation scenarios in the AR5 scenario database (IPCC, 2014; see caption of Fig. S1.3 for details).

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Figure S1.14: Kyoto gas emissions (CO2, CH4, N2O and F-gases) by region in the SSP5 baseline scenario and the 4.5 W/m2 (SSP5-4.5) and 2.6 W/m2 (SSP5-2.6) mitigation cases. The REMIND-MAgPIE results are compared with SSP1 and SSP2 for the years 2050 and 2100. CO 2-equivalent emissions were calculated using AR4 global warming potentials. Bunker fuels were allocated to individual regions according to IEA statistics.

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Figure S1.15: Radiative forcing by source in the SSP5 baseline scenario and the 4.5 W/m2 (SSP5-4.5) and 2.6 W/m2 (SSP5-2.6) mitigation cases. REMIND-MAgPIE results are compared with SSP1 and SSP2 for the years 2050 and 2100. The dots in the bar plots indicate radiative forcing projections across IAMs and the white diamonds represent the SSP marker scenarios.

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S2. REMIND-MAgPIE model description

S.2.1 REMIND (REgional Model of INvestment and Development)A detailed description of the REMIND model is available from Luderer et al. (2015) and further documentation including model equations can be found at https://www.pik-potsdam.de/research/sustainable-solutions/models/remind. In the following, we summarize the key features of REMIND relevant for the SSP scenarios presented in this paper.

Figure S2.1: General structure of the REMIND model.

REMIND is a global energy-economy-climate model spanning the years 2005–2100 (Bauer et al. 2008; Leimbach et al. 2010; Luderer et al. 2013). Figure S2.1 provides a schematic overview of its general structure. The macro-economic core of REMIND is a Ramsey-type optimal growth model in which inter-temporal welfare is maximized. REMIND divides the world into 11 regions: five individual countries (China, India, Japan, United States of America, and Russia) and six aggregated regions formed by the remaining countries (European Union, Latin America, sub-Saharan Africa without South Africa, Middle East / North Africa / Central Asia, other Asia, Rest of the World) (see Figure S2.2). For the SSP1, SSP2 and SSP5 scenarios, the model computes a Pareto-optimal market equilibrium in line with the approach proposed by Negishi (1972). We thus implicitly assume a cooperative world in which global welfare is maximized subject to the assumption that each region’s current account is balanced over the time frame of the analysis. The model explicitly represents trade in final goods, primary energy carriers, and in the case of climate policy, emissions allowances. Macro-economic production factors are capital, labor, and final energy. REMIND uses economic

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output for investments in the macro-economic capital stock as well as consumption, trade, and energy system expenditures.

Figure S2.2: Regional definitions used in the REMIND model.

The macro-economic core and the energy system module are hard-linked via the final energy demand and costs incurred by the energy system (Bauer et al. 2008). Economic activity results in demand for final energy such as transport energy, electricity, and non-electric energy for stationary end uses. In REMIND, the economy’s demand for final energy is determined by a system of nested production functions with constant elasticity of substitution (Arrow et al. 1961). The CES production system was calibrated to reproduce the harmonized SSP-specific GDP pathways (Dellink et al., this issue) and final energy demand storylines (O’Neill et al., this issue; Bauer et al. this issue).

REMIND features a representation of the energy conversion system with significant process detail. It accounts for endowments of exhaustible primary energy resources (coal, oil, gas and uranium) as well as renewable energy potentials (biomass, hydro power, wind power, solar energy, geothermal energy). The assumptions on fossil fuel availability are discussed in detail by Bauer et al. (2016) and are varied across SSPs (see Section S3.1.6 and Figure S3.5). Renewable resource potentials were compiled from a number of studies, cf. Luderer et al. (2015) for details. In the coupled REMIND-MAgPIE framework, bioenergy use emerges endogenously from the iterative equilibration of demand and supply between the two modeling subsystems (see Section S2.4).

REMIND represents capacity stocks and vintage structure of more than 50 technologies for the conversion of primary energy into secondary energy carriers as well as for the distribution of secondary energy carriers to end use sectors. The default techno-economic assumptions used in REMIND for these technologies are summarized in Luderer et al. (2015). We applied SSP-specific adjustments for some of these parameters to ensure consistency with the storyline, as documented in Section S3.1. Notably, REMIND accounts for the possibility of combining fossil fuel and bioenergy use with CCS not only for electricity, but also liquids, gases and hydrogen (Bauer 2005; Klein et al. 2014b). Further, the model has a detailed representation of wind, solar photovoltaics and concentrating solar power technologies. To account for challenges related to the variability of power supply from these sources, REMIND represents integration cost markups that depend on the share of wind and solar in electricity supply and account for additional costs for storage, long-range transmission grid and curtailment (Pietzcker et al. 2014). Learning-by-doing effects are explicitly represented via learning curves for wind and solar technologies as well as electric vehicles.

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REMIND does not have any hard limits on the expansion rate of new technologies. In order to mimic real-world inertias in technology up-scaling, a cost penalty (“adjustment costs”) is applied that scales with the square of the relative change in capacity investments. This yields technology diffusion rates that are broadly in line with historical patterns (Wilson et al. 2013). The retirement of fossil capacities before the end of their technological life-times is possible, but limited to a rate of 4% per year.

The representation of emissions from fossil fuel combustion and industry in REMIND is discussed together with the modeling of land use emissions in MAgPIE in Section S2.3.

S2.2. MAgPIE (Model of Agricultural Production and its Impact on the Environment)A detailed description of the MAgPIE model including its equations is available at https://redmine.pik-potsdam.de/projects/magpie/wiki/. In the following, we summarize the key features of MAgPIE relevant for the SSP scenarios presented in this paper.

Figure S2.3: MAgPIE economic world regions

MAgPIE (Lotze-Campen et al. 2008; Popp et al. 2010; Popp et al. 2012; Bodirsky et al. 2012; Humpenöder et al. 2014; Popp et al. 2014; Humpenöder et al. 2015) is an economic land-use optimization model that integrates several spatial scales (see Figure S2.4 for an overview of the model structure). The objective function of MAgPIE is the fulfilment of agricultural demand for ten world regions (Figure S2.3, Table S2.2) at minimum global costs. To account for trade barriers, the regions have to produce a certain share of their demand domestically (Schmitz et al. 2012). Major cost types in MAgPIE are factor requirement costs (capital, labor, fertilizer), land conversion costs, transportation costs to the closest market, investment costs for yield-increasing technological change (TC) and costs for GHG emission rights. For meeting the demand, the model endogenously decides, based on cost-effectiveness, about the level of intensification (yield-increasing technological change), extensification (land-use change) and production relocation (intra-regionally and inter-regionally through international trade) (Lotze-Campen et al. 2010; Popp et al. 2011; Schmitz et al. 2012; Dietrich et al. 2014). The model is solved in a recursive dynamic mode with a time step length of five years on a timescale from 1995 to 2100. Biophysical information on initial land-cover, crop yields, carbon densities and water availability is spatially explicit (0.5 degree longitude/latitude). Due to computational constraints, spatially explicit data is aggregated to 200 simulation units for the

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optimization process. For each of the ten world regions in MAgPIE, the clustering algorithm combines grid cells to simulation units based on the similarity of data (Dietrich et al. 2013).

Figure S2.4: Schematic overview of the MAgPIE model.

Land types in MAgPIE comprise cropland, pasture, forest, other land (e.g. non-forest natural vegetation, abandoned agricultural land, deserts) and urban land (static over time) (Krause et al. 2013). In 2005, the global land area (12907 Mha in total) consists of 1471 Mha cropland, 3129 Mha pasture, 4208 Mha forest and 4135 Mha other land (including urban) (maps in Figure S2.5).

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Figure S2.5: Spatially explicit land use / land cover in MAgPIE in 2005. For each grid cell, the cell shares of the four land types add up to 1.

The cropland covers cultivation of 16 food/feed crop types (e.g. temperate and tropical cereals, maize, rice, oilseeds, roots), both rainfed and irrigated systems, and two 2 nd generation bioenergy crop types (grassy and woody). Biophysical yields for these crop types as well as carbon densities of natural vegetation and water availability for irrigation are derived by a dynamic global vegetation, hydrology and crop growth model, the Lund-Potsdam-Jena model for managed Land (LPJmL) (Bondeau et al. 2007; Müller and Robertson 2014). LPJmL simulations of crop yields assume that all crops are grown in all grid cells to assess the possible crop productivity also in areas currently not used for the cultivation of that crop to inform possible shifts in cropping areas. In seven individual LPJmL runs, crop yields are derived for seven different intensity levels. LPJmL represents potential crop yields, while MAgPIE aims to represent actual crop yields. Thus, cropping intensities are selected to match observed yields from FAO (2015) at country level under the initial land-use pattern in MAgPIE. In a second calibration step, regional crop yields are adjusted to maximize agreement between MAgPIE and FAO cropland in the starting year for each of the 10 world regions (see FigureS2.6 for tropical cereal yields in LPJmL and MAgPIE).

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Figure S2.6: Tropical cereal yields in rainfed and irrigated production systems as derived by LPJmL, and how these yields are adjusted before entering MAgPIE.

Bioenergy yields are additionally calibrated using information about observed land-use intensity (Dietrich et al. 2012). It is assumed that LPJmL bioenergy yields represent yields achieved under highest currently observed land use intensity, which is observed in EUR. Accordingly, LPJmL bioenergy yields for all other regions than EUR are reduced proportional to the land use intensity in the given region (see Figure S2.7 for bioenergy grasses). Due to endogenous investments in yield-increasing technological change in the MAgPIE model, MAgPIE yields can exceed LPJmL yields in future time steps (Dietrich et al. 2014).

Figure S2.7: Grassy bioenergy yields in rainfed and irrigated production systems as derived by LPJmL, and how these yields are adjusted before entering MAgPIE.

The supply of animal food commodities is divided into five livestock production activities (ruminant meat, pig meat, poultry meat, eggs and milk). The quantity of livestock production in combination with regional and livestock-specific feed baskets determines the demand for feed. Endogenous

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technical change in MAgPIE not only increases crop yields but also grassland yields. In contrast, livestock productivity is driven by exogenous projections (Figure S3.7 in section 3.2), and determines feed energy requirements and feed basket composition, leading to a higher share of concentrate with higher yields.

An additional LPJmL simulation assumes that all grid cells are covered with natural vegetation, which involves a spin-up period of 1000 years to bring vegetation patterns and carbon pools into equilibrium. Results from this simulation of natural vegetation are used to provide data on biophysical carbon densities and water availability (maps in Figure S2.8 and Figure S2.9) to the MAgPIE model. The actual cell-specific carbon density in MAgPIE depends on the land allocation within each cell (calculated as area weighted mean). Cropland and pasture carbon densities are estimated based on LPJmL and data from (IPCC 2006, Table 5.5, 6.2). For forest and other land, the LPJmL information is used without modification for all carbon pools.

MAgPIE calculates carbon stocks at the cell level as the product of land-type specific area and carbon density. If, for instance, forest is converted to cropland within the same simulation unit, the carbon stock of this unit decreases according to the difference in carbon density of forest and cropland. In case agricultural land is abandoned (other land pool), ecological succession leads to regrowth of natural vegetation carbon stocks along sigmoid growth curves (Humpenöder et al. 2014). Regrowth of carbon stocks in MAgPIE is constrained by the LPJmL natural vegetation carbon density. If the vegetation carbon density of re-growing vegetation passes a threshold of 20 tC/ha, the respective area is shifted to the forest land pool (based on Hurtt et al 2006, 2011). In climate policy scenario, mitigation of CO2 emissions from land-use change is incentivized by an exogenous CO2 price, which is applied on emission from the conversion of forests and other natural land. The CO2 price is multiplied with CO2 emissions to calculate CO2 emission costs, which enter the cost-minimizing objective function of MAgPIE.

Figure S2.8: Spatially explicit carbon density (tC ha-1) based on LPJmL for four land types and the three carbon pools vegetation, litter and soil (vegc, litc, soilc).

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Figure S2.9: Available surface water for irrigation derived from LPJmL.

S2.3 Modeling of emissions and climate response

S.2.3.1 Emissions from the energy and industry sectors in REMINDREMIND calculates emissions from long-lived GHGs (CO2, CH4, N2O), short-lived pollutants (CO, NOx, VOC, SO2, BC, OC) by source (Strefler et al. 2014a; Strefler et al. 2014b). REMIND accounts for these emissions with different levels of detail depending on the types and sources of emissions (see TableS2.1: Overview of the treatment of GHG emissions in the REMIND-MAgPIE system. 2005 GHGemissions data from EDGAR4.2). CO2 emissions from fossil fuel combustion, CH4 emissions from fossil fuel extraction and residential energy use and N2O emissions from energy supply are based on sources. For each fuel, region and technology, REMIND applies specific emissions factors, which are calibrated to match base year GHG inventories (EDGAR 2011). End-of-pipe mitigation options for CH4

and N2O from energy and waste handling are represented via marginal abatement cost curves from Lucas et al. (2007). Emission factors for air pollutants have recently been updated based on (Klimont et al. in prep. a,b), and adjusted in line with the SSP air pollution storylines (Rao et al., this issue), see section S3.1.8.

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Emissions sector Representation Percentage of 2005 GHG emissions (CO2e)

CO2 fuel combustion By source (REMIND) 54.4 %CO2 from industrial processes Econometric estimate, coupled with capital

investments per capita4.0 %

CO2 LUC By source (MAgPIE) 14.4 %CH4 fossil fuel extraction Baseline by source (REMIND), marginal

abatement cost curves based on Lucas et al. (2007)

19.9 %

CH4 residential energy use By source (REMIND)CH4 land use (agriculture) Baseline by source (MAgPIE), marginal

abatement cost curves based on Lucas et al. (2007)

CH4 land use change (open burning)

By source (MAgPIE)

CH4 and N2O from waste handling

Baseline by source (REMIND), marginal abatement cost curves based on Lucas et al. (2007)

5.7 %

N2O transport and industry Marginal abatement cost curves, baseline exogenous (Lucas et al., 2007)

N2O energy supply By source (REMIND)N2O land use (agriculture) Baseline by source (MAgPIE), marginal

abatement cost curves based on Lucas et al. (2007)

N2O land use change (open burning)

By source (MAgPIE)

CFCs, PFC, SF6 Exogenous based on Van Vuuren (under review)

1.6 %

Table S2.1: Overview of the treatment of GHG emissions in the REMIND-MAgPIE system. 2005 GHG emissions data from EDGAR4.2

Emissions of other GHGs (e.g. F-gases, Montreal gases) are exogenous and taken from the SSP/RCP scenario data set of the IMAGE model (Van Vuuren et al. under review). Regarding F-Gases, HFC emissions from refrigeration is the most important source, with other contributions coming from HFCs from foam blowing and production of HCFC-22, PFCs from aluminium production and semiconductor manufacturing, and SF6 from production, use, and disposal of electrical equipment and magnesium production. Emission projections until 2020 are based on EPA (2013) and afterwards related to population and GDP development, with the exception of aluminium and magnesium production and semiconductor manufactoring that follow exogenous rules. Reduction potentials are mainly based on consumption reductions with MACC data based on Öko-institut (2011) and Lucas et al. (2007).

S2.3.2 Emissions from the land use sector in MAgPIEAgricultural nitrous oxide (N2O) emissions from agricultural soils and manure management are estimated in MAgPIE with the emission factors of the IPCC guidelines for national greenhouse gas inventories (IPCC 2006). Fertilizer and manure application, recycling of crop residues, soil organic matter loss and other emission-relevant activities are estimated endogenously based on a nitrogen mass-balance module (Bodirsky et al. 2012; Bodirsky et al. 2014). A major scenario parameter for the

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estimation of nitrogen flows is the soil nitrogen uptake efficiency (see Figure S3.8 in section 3.2). Methane (CH4) emissions from rice, enteric fermentation and manure management are estimated based on the methodology of IPCC (1996). In mitigation scenarios, N2O and CH4 emissions can be reduced through sector and region-specific reduction factors at additional costs, using the marginal abatement cost curves of Lucas et al (2007).

Aerosol emissions (NOx and NH3) are estimated on the basis of emission factors from the IPCC guidelines for national greenhouse gas inventories (IPCC 2006), emission factors and inventories from van der Werf (2010), and endogenous activity data from the MAgPIE model (e.g. change in pasture or forest land). Aerosol emissions are grouped into agricultural emissions (manure management, soil fertilization, pasture range and paddock), forest burning (standing forest, deforestation and peat), agricultural waste burning (crop residues), and savanna burning.

S2.3.3 Modelling of the climate responseThe REMIND-MAgPIE system uses the MAGICC climate model (Meinshausen et al. 2011) in its updated version 6.3 to translate emissions into changes in atmospheric composition, radiative forcing and temperature increase. An iterative adjustment of the carbon price signal ensures meeting the radiative forcing limits in the mitigation scenarios (see Section S4).

S2.4 Coupling of REMIND and MAgPIE The coupling approach is designed to derive scenarios with equilibrated bioenergy and emissions markets. In equilibrium, bio-energy demand patterns computed by REMIND are fulfilled in MAgPIE at the same bioenergy and emissions prices that the demand patterns were based on. Second, the emissions in REMIND emerging from pre-defined climate policy assumptions account for the GHG emissions from the land-use sector derived in MAgPIE under the emissions pricing and bioenergy use mandated by the same climate policy. The simultaneous equilibrium of bioenergy and emissions markets is established by an iteration of REMIND and MAgPIE simulations in which REMIND provides emissions prices and bioenergy demand to MAgPIE and receives land use emissions and bioenergy prices from MAgPIE in return. The coupling approach with this iterative process at its core is explained elsewhere (Bauer et al. 2014).

The resource potential of bioenergy in REMIND is represented by regional bioenergy supply price curves (see S3.1.5) that are updated (scaled) in the iteration process according to the price response of MAgPIE.

Figure S2.10: Sketch of the REMIND and MAgPIE coupling procedure

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In the coupling process, REMIND and MAgPIE regions are mapped one-to-one, as listed in the table below. The regions that are mapped have similar macro-economic characteristics, albeit slight differences in underlying regional definitions remain. In particular, there is no separate Japan region in the MAgPIE model, and therefore no bioenergy supply curve for JPN could be deduced from MAgPIE. This was translated into the simplifying assumption in REMIND of no purpose grown bioenergy production in Japan, in line with the fact that Japan has very limited land resources for bioenergy production.

Since the native model regions of REMIND are not perfect subsets of the SSP regions, small deviations between the definition of the SSP regions and the country groups mapped to these regions by REMIND exist. Concretely, REMIND REF only contains Russia, while the European states of the former Soviet Union (FSU) and South Africa are mapped to OECD as part of ROW, and the Asian FSU states are mapped to MAF as part of MEA.

REMIND Region MAgPIE Region SSP RegionAFR Sub-Saharan Africa

(excl. South Africa)AFR Sub-Saharan Africa

(incl. South Africa)MAF

CHN China CPA Centrally planned Asia including China

ASIA

EUR European Union (28) EUR Europe including Turkey OECDIND India SAS South Asia including India ASIAJPN Japan - - OECDLAM Latin America LAM Latin America LAMMEA Middle East & North Africa

& Asian Former Soviet UnionMEA Middle East & North Africa MAF

OAS Other Asia PAS Pacific (or Southeast) Asia ASIAROW Rest of World, incl. Canada,

Australia, New Zealand, South Africa, Turkey, Non-EU Eastern Europe

PAO Pacific OECD including Japan, Australia, New Zealand

OECD

RUS Russia FSU States of the former Soviet Union (excl. Baltic States)

REF

USA United States of America NAM North America OECDTable S2.2: Overview of the macro-regions used in REMIND and MAgPIE, and their mapping to the SSP regions.

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S3. Implementation of SSP5This section provides a detailed description of the SSP5 implementation in the REMIND-MAgPIE integrated assessment modeling framework and compares it with the SSP1 and SSP2 implementation. The SSP macro-economic and energy assumptions in the REMIND model are discussed in Section 3.1, and the SSP land use assumptions in the MAgPIE model in Section 3.2. The section also includes a brief discussion of the SSP5 implementation in the other three IAMs that provided SSP5 scenarios (Section 3.3), but a more detailed discussion of their SSP implementation can be found in the articles documenting associated marker scenarios (AIM/CGE: Fujimori et al., this issue; GCAM: Calvin et al., this issue) and in Emmerling et al. (2016) (WITCH-GLOBIOM).

A summary of the REMIND-MAgPIE model parameters and assumptions that were varied across SSPs is given in Table 1 in the main text. This section gives quantitative information on those parameter settings which were described only qualitatively in Table 1. While Table 1 links the parameter settings to the broad specification of key dimensions of the narratives in O’Neill et al. (this issue), Sections 3.1 (Table S3.1) and 3.2 (Table S3.5) will link the energy and land use model assumptions, respectively, to the more detailed energy (Bauer et al. , this issue) and land use SSP tables (Popp et al. , this issue) developed for the IAM interpretation of SSPs (Riahi et al., this issue).

S3.1 Implementation of energy-economy aspects of SSP narratives in the REMIND modelTable S3.1 replicates all REMIND parameters, for which Table 1 of the main text provided only a qualitative description. Most of these parameters are energy-related. For those parameters, a direct reference to corresponding specifications in the SSP energy tables is provided (Bauer et al. this issue). The implementation of the SSP population trajectories from Lutz et al. (this issue) and SSP economic growth trajectories including assumptions about income convergence between regions from Dellink et al. (this issue) is discussed in the main text, and will not be repeated here. Working age population determined the labor input for economic production in REMIND, while total population was used to calculate per capita consumption.

Energy tables (Bauer et al., this issue) REMIND implementationSSP element SSP1 SSP2 SSP5 Parameter SSP1 SSP2 SSP5Energy demand (Section S3.1.2)Traditional fuel use Fast phase

outIntermediate phase out

Fast phase out

Traditional Biomass

Phase out until 2035

Phase out until 2050

Phase out until 2035

Energy intensityI of services, industry & buildings

Low Medium Medium Final energy intensity(Fig. S3.2)

Low Medium High

Energy intensity of transportation

Low Medium High Transport liquids (Fig. S3.3)

Low Medium High

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Energy tables (Bauer et al., this issue) REMIND implementationSSP element SSP1 SSP2 SSP5 Parameter SSP1 SSP2 SSP5Energy technologies

Non-biomass Renewables Conversion (Section S3.1.3)Technology development High Medium Medium Technological

learningGlobal Global Global

Social acceptance High Medium Low Renewable technology(Table S3.2)

Favorable outlook

Intermediate outlook

Pessimistic outlook

Challenges to VRE integration

Low Medium High

Commercial Biomass Conversion (Section S3.1.5)

Technology development High Medium Medium Biomass supply

1st generationPhase out after 2020

Constant after 2020

Phase out after 2020

Social acceptance Low Medium MediumBiomass supply 2nd generation(Fig. S3.4)

Endogenous (tax rising up to 125% in 2100)

Endogenous (tax rising up to 100% in 2100)

Endogenous (tax rising up to 125% in 2100)

Bio trade costs 1.5 $/GJ 3.5 $/GJ 1.5 $/GJ

Nuclear Power (Section S3.1.7)

Technology development Medium Medium High

Uranium resources

Limited Limited Supply curve without limit

Social acceptance Low Medium Medium Investment costs 5000 $/kW 3000 $/kW 4000 $/kW

Conventional and unconventional fossil fuel conversion (synfuel / syngas in parenthesis if different; Section S3.1.4)

Technology development

Medium Medium Medium (High)

Investment cost Coal to Liquids (Table S3.3)

Medium Medium Low

Social acceptance Low Medium High

Fossil fuel subsidies

Phase out by 2030

Phase out by 2050

Constant at 2005 level

Liquids Taxes Converge to 10$/GJ in 2050

Converge to 10$/GJ in 2050

Constant at 2005 level

CCS (under climate policy only; Section S3.1.4)

Technology development Medium Medium High Investment cost

(Table S3.3)High Medium Medium

(Low for CTL)

Social acceptance Low Medium Medium CCS capture rate (Table S3.4)

Medium Medium High

Fossil fuel resources (Section S3.1.6)Coal & conventional hydrocarbons Medium Medium Very high Coal (Fig. S3.5) Low Medium High

Non-Conventional hydrocarbons Slow Medium Very high Oil & Gas

(Fig S3.5)Low Medium Very high

Pollutant Emissions (Section S3.1.8)

See Rao et al. (this issue)

Poll. emissions coefficients

Rapid improvement

Gradual improvement

Rapid improvement

Taxes on air pollutants

600 $/tSO2 250 $/tSO2 600 $/tSO2

Table S3.1: Overview on selected SSP elements of energy tables and corresponding implementation in REMIND

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S3.1.1. Capital intensities In following the narratives with regard to the interregional pattern of economic development, we extended the assumption of convergence in SSP1 and SSP5. The associated GDP marker projections (Dellink et al., this issue) replicated by REMIND-MAgPIE already include the assumption of convergence of regional total factor and labor productivities. In addition, REMIND assumes convergence in capital intensities across regions in these two SSPs. Technically, this is implemented by an adjustment of the capital efficiency parameter. Figure S3.1 shows the convergence of the capital intensities (i.e. the ratio between capital stock and GDP) in the SSP5 reference scenario towards a value of four, which is slightly below the current value of the most capital intensive region today (Japan). In the SSP2 scenario most regions show only a slight increase in capital intensity according to the historical trend (cf. Leimbach et al. 2015) but with hardly any convergence.

Figure S3.1: Capital intensity of SSP2 (top) and SSP5 baseline scenario (bottom)

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S3.1.2 Energy IntensityIn addition to GDP and population, the assumption on final energy intensity, i.e. the amount of final energy needed to produce a unit of GDP, is important for determining final energy demand. In theSSP2 reference scenario, a moderate increase of the productivity of final energy (as production factor) is assumed over time, such that the final energy intensity decreases globally by around 1% peryear until 2100 (). In SSP5, we assume a slightly lower rate of productivity improvement due to the material intensive development pattern in SSP5, and in SSP1 a higher rate based on the narrative of resource efficient production patterns. This results in a decrease of the final energy intensity of around 1.7% per year until 2100 in SSP1. Despite the lower productivity improvements per unit of output in SSP5, SSP5 yields lower energy intensity in 2100 than SSP2 due to considerably faster GDP growth. In accordance with the narratives, the high energy demand in SSP5 is linked to a high demand for liquid fuels in the transport sector. We adjusted the productivity parameters for gasoline and diesel to induce a high consumption of transport fuels in the SSP5 reference scenario. This results in an increasing transport energy share for SSP5 as demonstrated in . At the global level, this share increases from 27% today to around 36% at the end of the century, whereas it decreases to around 25% in SSP1. While the developed regions stabilize their high share of transportation in SSP5, for developing regions like Sub-Saharan Africa substantial growth of this share is assumed.

Figure S3.2: Annual growth rate of final energy intensity

Figure S3.3: Share of transport energy consumption in total final energy consumption

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S3.1.3 Non-biomass renewablesAs a world that is focused on fossil-fuel based development, it is assumed that the perspectives of a penetration of non-biomass renewables are rather pessimistic in SSP5. This reflects to certain extent the low social acceptance that is noted in the energy technology tables. We assume in a number of technical and cost parameters a differentiation between SSP5, SSP1 and SSP2 (see Table S3.2, S3.3 and S3.4). In particular, we assume in SSP5 higher floor and investment costs and a lower rate of technological learning with SPV, wind and CSP technologies. In addition, we assume a substantially higher challenge of the integration of renewable energy technologies into the energy system. This is implemented by a doubling of the required storage at a given amount of wind and solar electricity.

Investment costs ($/kW) Floor costs ($/kW) Learning rateWind Solar PV CSP Wind Solar PV CSP Wind Solar PV CSP

SSP1 1400 4900 8600 900 500 1300 10% 20% 10%SSP2 1400 4900 8600 900 500 1300 10% 20% 10%SSP5 1700 6000 10300 1000 700 1900 7% 16% 8%Table S3.2: Techno-economic characteristics of selected renewable energy technologies

REMIND models resource potentials for non-biomass renewables (hydro, solar, wind, and geothermal) using region-specific potentials (Pietzcker et al., 2014, Luderer et al., 2015). The assumptions on these potentials are the same across the different SSPs. For each renewable energy type, we classify the potentials into different grades, specified by capacity factors as presented in Figure S3.4 for SPV and wind. Superior grades have higher capacity factors, which correspond to more full-load hours per year. This implies higher energy production for a given installed capacity. Therefore, the grade structure leads to a gradual expansion of renewable energy deployment over time as a result of optimization.

Figure S3.4: Regionalized resource potentials for solar PV and wind as a function of resource quality expressed in terms of attainable capacity factors (represented by color-coding).

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S3.1.4 Fossil fuel and biomass technologies with and without CCSWe assume lower investment costs for fossil-fuel and biomass technologies combined with CCS in SSP5 and SSP2 compared with SSP1 (see Table S.3.3). This reflects the lower social acceptance of these technologies in SSP1 as implied by the SSP narratives. Also in line with the narratives, the economics of coal-to-liquid (Fischer-Tropsch) technology is more favorable in SSP5 than in SSP1 and SSP2. The use of biomass is attractive in mitigation scenarios, in particular in combination with CCS technologies. To reflect sustainability concerns, we apply an additional constraint which limits the maximum of bioenergy consumption to 30% of baseline primary energy consumption in mitigation scenarios. For the same reason, the use of modern biomass is also subject to a tax as indicated in Table S3.1. The ad valorem tax level is higher in SSP1 and SSP5 than in SSP2 reflecting greater concerns about the environmental impact of bioenergy supply in these two SSPs.

Technologies SSP1 SSP2 SSP5Integrated coal gasification combined cycle power plant (igcc)

1650 1650 1650

IGCC with CO2 capture (igccc) 2665 2050 2250

Pulverized coal power plant (pc) 1400 1400 1400

PC with amine CO2 capture (pcc) 3120 2400 2640

PC with oxyfuel CO2 capture (pco) 2795 2150 2360

Coal to Liquids Refining (Fischer Tropsch) (coalftcre)

1450 1450 1320

COALFTCRE with CO2 capture (coalftcrec) 1976 1520 1380

Coal to hydrogen (coalh2) 1260 1260 1260

Coal to hydrogen with CO2 capture (coalh2c)

1859 1430 1430

Natural gas combined cycle (ngcc) 650 650 650NGCC with CO2 capture (ngccc) 1430 1100 1200

Gas to hydrogen (gash2) 498 498 498

Gas to hydrogen with CO2 capture (gash2c) 718 552 552

Integrated biomass gasification combined cycle (bioigcc)

1860 1860 1860

BIOIGCC with CO2 capture (bioigccc) 3328 2560 2560

Biomass based Fischer-Tropsch recycle (bioftrec)

2500 2500 2500

BIOFTREC with CO2 capture (bioftcrec) 3900 3000 3000

Biomasss to hydrogen (bioh2) 1400 1400 1400

Biomass to hydrogen with CO2 capture (bioh2c)

2210 1700 1700

Compression of CO2 0.13 0.1 0.1

Transportation of CO2 130 100 100

Injection of CO2 98 75 75

Monitoring of CO2 0.13 0.1 0.1Table S3.3: Investment costs in $/kW of selected technologies

CO2 capture rates vary with technology, with the highest capture rates achieved by oxyfuel capture and the lowest by coal- and biomass-to-liquid Fischer- Tropsch technologies combined with CCS (see

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Table S3.4). Capture rates do not vary across SSPs, with the exception of fossil fuel fed power and hydrogen production technologies which have very high capture rates comparable with oxyfuel capture in SSP5. The higher capture rates for fossil fuel power plants lead to slightly higher investment costs for these technologies in SSP5 compared to SSP2. An important difference between SSPs is the higher maximum annual injection rate into geological formations in SSP5 than in SSP1 and SSP2. This reflects the greater propensity to deploy CCS in SSP5 as suggested by the narrative.

Fossil fuel CCS BECCS Storageigccc,pcc,ngccccoalh2c,gash2c pco coalftcrec bioigccc bioftcrec bioh2c injection rate

SSP1/2 90% 99% 70% 80% 48% 90% 0.5%/year

SSP5 99% 99% 70% 80% 48% 90% 0.75%/year

Table S3.4: CCS capture and injection rates (injection rate represents the maximum amount of CO 2 that can annually be sequestered in percent of the overall storage potential). See table S3.3 for an explanation of the acronyms.

S.3.1.5 Biomass supplyThe major source for bioenergy in REMIND is ligno-cellulosic purpose grown biomass from perennial grasses and short-rotation woody crops (2nd generation biomass). Its resource potential is given by regional supply curves that provide the bioenergy price as a function of bioenergy demand (see Figure S3.4). The supply curves were derived from the MAgPIE model (using the methodology as described in (Klein et al. 2014a) for SSP1, SSP2, and SSP5 each combined with four GHG pricing regimes for land-use emissions: without pricing, with GHG prices consistent with forcing targets of 2.6, 3.4, and 4.5 W/m2 for the respective SSP. To account for adjustment policies that aim at avoiding potential adverse side effects of large-scale bioenergy production, particularly food-competition, an ad valorem tax is applied on the supply-curve price (see Table S3.1). Supply curves in Figure S3.4 are displayed without tax.

In addition to ligno-cellulosic biomass, there are small amounts of food-crop based 1 st generation biomass deployed. The supply of 1st generation biomass (based on Lotze-Campen et al. 2014) is identical in SSP1, SSP2 and SSP5 until 2020 (~7 EJ/yr globally in 2020). After 2020, 1st generation biomass supply is constant in SSP2 (trends continue into the future) but phased out and assumed to be replaced by 2nd generation biomass in SSP1 (sustainable development) and SSP5 (rapid adaptation of new technologies).

The use of traditional biomass is phased out in all SSPs before mid century. The phase out is slower in SSP2 (35 years until 2050) than in SSP1 and SSP5 (20 years until 2035) reflecting the differences in narrative of more rapid development in SSP1 and SSP5.

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Figure S3.4: Biomass supply curves for selected regions for 2050 and 2100. Dashed lines indicate prices that were derived without GHG prices in the landuse sector (Ref). Supply curves displayed with solid lines were derived under a GHG price path consistent with RCP26 (RCP26). The ordering of these supply curves between SSPs is strongly affected by the differences of carbon price levels in SSP5-26 (low), SSP2-26 (medium) and SSP5-26 (high).

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S.3.1.6. Fossil fuel resourcesThe large scale deployment of fossil fuels in the SSP5 reference scenario is caused to a large degree by the availability of abundant fossil fuel resources at low costs. Figure S3.5 presents the cumulated fossil fuel supply curves that are applied in REMIND for the different SSP scenarios. These supply curves map cumulative fossil fuel recovery to recovery costs. The underlying assumptions on fossil fuel availability are linked directly to the SSP narratives as discussed in Bauer et al. (2016). SSP5 exhibits the highest availability of all three fossil fuels (coal, oil and gas), while SSP1 foresees the lowest availability of these fuels.

Figure S3.5: Global aggregate cumulative availability curves for coal, oil and gas for the different SSPs. The bars at the top indicate the minimum, median and maximum cumulative extraction in baseline scenarios in the EMF-27 study; the shaded area covers the range of extraction cost functions given in the EMF27 study (McCollum et al. 2014).

S3.1.7 Nuclear power and uranium resourcesThe use of nuclear power in SSP2 and SSP5 is characterized by medium technological improvements and medium social acceptance, whereas in SSP1 the social acceptance is relatively small. For all SSPs REMIND assumes the availability of conventional reactor technologies fueled with enriched uranium, but does not include fuel-reprocessing and MOX fueled power plants or even fast breeder technologies. Also, the use of nuclear energy is restricted to power generation.

In SSP1 and SSP2, cumulative uranium extraction is capped at 23 Mt of uranium globally on top of a cumulative extraction cost function rising up to 260US$ per tU. The estimated 23 MtU comprise identified conventional resources of 6 MtU (NEA 2010; WEC 2010) plus the assumption of additional 17 MtU undiscovered and unconventional resources that become available in the future. The uranium constraint is replaced by a continuously increasing supply curve beyond 23 MtU in SSP5, reflecting stronger overall technological progress in extractive industries in SSP5. The investment costs for nuclear power plants are lowest in SSP2 with 3000US$/kW. The investment costs for nuclear power plants are higher in SSP5 (4000$US/kW) because of stronger environmental and safety regulations. They are highest in SSP1 (5000$US/kW) reflecting even tighter regulations due to low

36

public acceptance of nuclear power. Detailed background and sensitivity analysis with respect to nuclear power in the REMIND model can be found in Bauer et al. (2012).

S.3.1.8. Air pollutant emissionsWith regard to air pollutants, we take account of SO2, SOx, NOx, BC, OC, CO, VOC and NH3 (from land use) emissions, which are calculated by source. Emissions in 2005 are calibrated to EDGAR (2011). BC and OC emissions in 2005 are calibrated to the GAINS model (Cofala et al., 2009; Klimont et al. in prep.; Amann et al. 2011), except for emissions from international shipping, aviation and waste, which are exogenous and taken from Fujino et al. (2006). Land use emissions are calculated based on emission factors from van der Werf et al. (2010).

Emission factors of air pollutants are reduced over time according to air pollution policies based on Rao et al (this issue). In SSP1 and SSP5, air pollution policies in high-income countries are increasingly strict and well-enforced, with substantial technology RDD&D. Low-income countries improve their policy implementation in the near-term, followed by relatively rapid technology and policy transfer and “catch-up” with high-income regions. In SSP2, current near-term policies are enforced in high-income countries, with gradual strengthening of goals over time and gradual technology RDD&D. Low-income countries do not fully implement near-term policies, but gradually improve over the century.

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S3.2 Implementation of land use aspects of SSP narratives in the MAgPIE modelTable S3.5 shows the SSP land-use narratives as described in Popp et al (2015) and their implementation in MAgPIE. The parameter names in the MAgPIE column correspond to the land-use related parameters in Table 1. REMIND and MAgPIE use the same population, GDP and GDP convergence projections.

Land-use tables (Popp et, this issue) MAgPIE implementationSSP element SSP1 SSP2 SSP5 Parameter SSP1 SSP2 SSP5 Referenc

es

Land use change regulation

Strong regulations to

avoid environmental

tradeoffs

Medium regulations; slow decline in the rate of deforestation

Medium regulations; slow decline

inthe rate of

deforestation

Forest protection rate(Fig. S3.10)

high medium medium(FAO FRA

2010, Popp et al 2014)

Land productivity growth

High improvement

s in agricultural

productivity; rapid diffusion

of best practices

Medium pace of

technological change

Highly managed, resource-intensive;

rapid increase in

productivity

Crop productivity(Fig. S1.6)

endogenous

endogenous

endogenous

(Dietrich et al 2014)

Livestock productivity (Fig. S3.8)

medium/

highmedium high (Bodirsky et

al 2015)

Environ-mental Impact of food consumption

Low growth in food

consumption, low-meat

diets

Material-intensive

consumption, medium

meat consumption

Material-intensive

consumption, meat-rich

diets

Calories per capita(Fig. S3.7)

low medium high

Livestock share(Fig. S3.7)

low medium high

Nutrient efficiency(Fig. S3.9)

high medium low Bodirsky et al (2014)

International trade Moderate Moderate High

Agricultural trade(Fig. S3.11)

global regional global (Schmitz et al 2012)Globalization

Connected markets, regional

production

Semi-open globalized economy

Strongly globalized

Table S3.5: Overview on SSP land-use narratives (Popp et al. 2015) and corresponding implementation in MAgPIE.

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S3.2.1 Per capita food demand

Figure S3.6: Time-series of regional per capita food demand in SSP1, SSP2 and SSP5: Estimated using the methodology described in Bodirsky et al (2015) based on SSP population and GDP projections. SSP1 and SSP2 use a different demand system than SSP5, leading to higher demand for the latter. Additionally, it was assumed that per-capita calories in SSP1 fall to a maximum of 3000 kcal per capita per day in 2100, and that the share of livestock products in rich countries does not fall below 15% in SSP2 and SSP5.

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S3.2.2 Livestock productivity

Figure S3.7: Time-series of regional livestock productivity in SSP1, SSP2 and SSP5. Future scenarios of livestock productivity are derived based on informed guesses, taking into account past productivity improvements, GDP projections, cultural particularities, and the general SSP story line. Livestock productivity scenarios determine feed energy requirements and feed basket composition.

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S3.2.3 Nutrient efficiency

Figure S3.8: Time-series of regional soil nitrogen uptake efficiency in SSP1, SSP2 and SSP5, based on Bodirsky et al (2014). The soil nitrogen uptake efficiency reflects the fertilization technology of farmers. Thus, the soil nitrogen uptake efficiency determines, together with the fertilization requirements for the production of crops, the application of inorganic nitrogen fertilizer. First, all available organic nitrogen fertilizer is used to fulfill the fertilization requirements. Subsequently, the remainder needs to be balanced out by the application of inorganic nitrogen fertilizer. Accordingly, the soil nitrogen uptake efficiency influences nitrogen pollution and N2O emissions from soils. As the challenges for climate change mitigation increase from SSP1 to SSP2 to SSP5, the soil nitrogen uptake efficiency is high in SSP1 (low challenges), medium in SSP2 (medium challenges) and low in SSP5 (high challenges).

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S3.2.4 Forest protection rate

Figure S3.9: Left: Shares of protected forest for each region (in terms of regional forest area) are based on the global forest resources assessment report (FAO 2010). Right: Assumed increase of regional forest protection shares in SSP1, SSP2 and SSP5 with respect to 2010. Forest protection takes out land from the pool of available land for agricultural expansion. Thus, forest protection increases competition for land in MAgPIE.

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S3.2.5 Agricultural trade

Figure S3.10: Trade scenarios based on Schmitz et al. There are two trade pools in the MAgPIE model, one with trade according to historic trade patterns, another one with free trade according to comparative advantages. In SSP1 and SSP5 barriers for international trade decline by 1% per year (Policy scenario in Schmitz et al. 2012), reflecting globalized agricultural markets in these scenarios. In SSP2, trade barriers decline only by 0.5% per year, reflecting rather regionalized trade of agricultural goods

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S3.3 Implementation of SSP5 in other IAMsIn addition to the SSP5 marker scenarios by REMIND-MAgPIE, SSP5 scenarios were derived by AIM/CGE (http://www.nies.go.jp/social/dp/pdf/2012-01.pdf), GCAM (http://wiki.umd.edu/gcam), and WITCH-GLOBIOM (http://www.witchmodel.org). Here we include includes a brief discussion of the SSP5 implementation in these three integrated assessment models. A more detailed discussion of their SSP implementation can be found in the articles documenting associated marker scenarios (AIM/CGE: Fujimori et al., this issue; GCAM: Calvin et al., this issue) and in Emmerling et al. (2016) (WITCH-GLOBIOM).

In GCAM, SSP5 was implemented by changing a variety of assumptions about technology, behavior, and policy (see the SOM of Calvin et al., this issue, for detailed, quantitative information). In the fossil fuel sector, GCAM assumes rapid technological change, reducing the cost of extracting fossil fuels. Capital costs for all electric power plants (fossil, nuclear, and renewable) decline, resulting in little change in the relative cost across technologies. In the land use sector, GCAM assumes rapid agricultural productivity growth. Changes in diet are strongly linked to growth in income, resulting in nearly a doubling of per capita livestock consumption. Strong air pollution policy results in a decline in pollutant emissions despite increases in energy consumption. In the mitigation scenarios, GCAM assumes the carbon price applies to land-use change emissions, resulting in strong increases in forest cover.

Table S3.6 documents the SSP5 implementation in AIM/CGE, and is an excerpt of the overview of SSP assumptions in AIM/CGE provided in the Supplementary Online Material to Fujimori et al. (this issue) (Table A.1).

Table S3.6: List of SSP5 assumptions and corresponding parameter adjustments in AIM/CGE

Element SSP5 Corresponding parameters and adjustments

Total and labor population

Based on KC and Lutz (This issue)　

Population and labor force.

Income growthBased on Dellink et al. (This issue)

The total factor productivity is adjusted to hit the targeted GDP.

Renewable energy cost decrease speed Low

Intermediate input and factor productivity parameters of renewable energy sectors are decreased. The rate of change is calculated based on the base-year price and the 2050 power generation cost target. The 2050 target is dependent on IEA (2012) and Med is this reference value. High and low are calculated as 1.25 and 0.75 times this value, respectively.

Social acceptance of modern biomass use Med

Two parameters are changed: (1) the biomass power generation logit scale parameter and (2) the transport biofuel logit share parameter. Med is the default number, which is equal across renewable energy sources. High and low are calculated as 1.25 and 0.75 times these values, respectively.

Renewable energy preference Low

The renewable power generation logit scale parameter is changed. Med is the default number. High and low are calculated as 1.25 and 0.75 times this value, respectively.

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Element SSP5 Corresponding parameters and adjustments

Nuclear technological progress speed Med

The intermediate input and factor productivity parameters of the nuclear power sector are changed. The rate of change is calculated based on the base-year price and the 2050 power generation cost target. The 2050 target is dependent on IEA (2012), and Med is the reference value. High and low are calculated as plus or minus a 2% annual change in production costs, respectively.

Social acceptance of nuclear energy Med

The nuclear power generation logit scale parameter is changed. Med is the default number. High and low are calculated as 2.0 and 0.1 times this value, respectively.

Preference for fossil fuel-fired power plants High

The fossil fuel-fired power generation logit scale parameter is changed. Med is the default number. High and low are calculated as 2.0 and 0.5 times this value, respectively.

Autonomous energy efficiency improvement (AEEI) Med

A mark-up parameter for energy input in the CES and LES functions is changed for the industrial sector and the household sector, respectively. High and low are calculated as plus or minus a 1% annual change in the AEEI percentage, respectively. The LES parameters are changed according to income elasticity. High and low are calculated as 1.25 and 0.75 times the default value, respectively. SSP4 differentiates regionally varied AEEI assumptions. SSP4 high-income countries assume the speed of SSP1 while low-income countries assume the speed of SSP3. SSP5 follows the assumption of SSP2, but the energy efficiency improvement of the transport sector is 0.5% higher.

Energy use coal preference High

The logit scale parameter in energy source selection for energy end-use sectors is changed. The coal scale parameter is changed over time. Med is the default number. High and low are calculated as plus or minus an annual 1.5% of the default number, respectively.

Energy use electricity preference or electrification speed

Med

The logit scale parameter in energy source selection for energy end-use sectors is changed. The electricity scale parameter is changed over time. Med is the default number. High is calculated as plus an annual 1.5% of the default number.

Speed of moving away from traditional biomass use

HighA coefficient to determine traditional biomass usage is changed. Med is the default number (2% per year). High and low are calculated as plus or minus an annual 0.5% of the default number, respectively.

Service demand for transport Med

The parameters representing household private car income elasticity and the industrial transport service coefficient are changed. For the former, med = 1.0 and low = 0.75. For the latter, med and low are set at a 0.5% and 1.0% annual improvement, respectively.

Coal mining cost LowThe markup parameter for the coal mining sector production cost is changed. Med is set following Rogner (1997), but the maximum annual increase rate is assumed to 5%. Low means a 1% annual increase.

Oil and gas extraction cost Med

The markup parameters for the oil and gas extraction sectors production cost are changed. Med is set following Rogner (1997) and High has a 1.5 fold-cost. Moreover, the maximum annual increase rate is assumed to be 5%. High means a 7.5% annual increase.

Intermediate input of material decrease rate Low

Intermediate inputs of steel and non-metal and mineral (cement) goods in all production sectors are changed. Med means a 2% annual decrease. High is a 3% decrease and low is a 1% decrease.

CCS (Carbon Capture and Storage) cost Low

Productivity parameters in the CCS service provision sector are changed. Productivity parameters are intermediate inputs and primary factor input coefficients. Med is the default number (Fujimori et al., 2015) and low is calculated as 0.5 times the default number.

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Element SSP5 Corresponding parameters and adjustments

Social acceptance of CCS Med The maximum CCS installation rate is changed. Med is the default value (85%). High and low are 100% and 50%, respectively.

Household preference for manufacturing goods

High

Income elasticity of industrial goods is changed and is reflected in the household consumption LES parameter. Med is the default number. High and low are calculated as 1.5 and 0.75 times the default number, respectively.

Air pollution control level Strong

Legislation determines air pollutant emissions control. The details are explained in (Rao, this issue; Riahi et al., 2015) Strong indicates that the emissions coefficient of air pollutants decreases very quickly and weak means the opposite. he emissions intensity (emission per unit of primary energy) for all gases, three SSPs and climate mitigation cases is shown in Supporting information SI Figure 10.

Non-energy-related emissions reduction measures cost

HighThe non-energy-related emissions reduction parameter is changed. Med is the default value (Fujimori et al., 2015). High and low are plus or minus 25% of the default value, respectively.

Yield growth assumptions High

The coefficient that represents land productivity is changed following Hasegawa et al. (2015b). SSP4 differentiates between high- and low-income countries, which have high and low yield growth, respectively.

Export tax and import tariff rate for agricultural goods

LowThe export tax and import tariff rate for agricultural goods are changed. High assumes an additional 33% of base-year amount, and low assumes no change to base-year level.

Export tax and import tariff rate for energy goods

LowThe export tax and import tariff rate for energy goods are changed. High assumes an additional 33% of base-year amount, and low assumes no change to base-year level.

Livestock-oriented food consumption preference

LowThe income elasticity of livestock goods is changed and is reflected in the household consumption LES parameter. The actual numbers are shown in Hasegawa et al. (2015b).

In WITCH-GLOBIOM, the SSP5 is implemented by following the main socioeconomic drivers, recalibrated energy demand, and various parametric assumptions about technologies, fossil resources, and implied air pollution control policies (see Table 3.7). The strong growth leads to high energy demand projections that are met by fossil fuels and increasingly by nuclear and renewables to satisfy demand. The demand increase is accompanied by increasingly strict air pollution measures over time. Moreover, land-use change due to high calories and lifestock consumption limit the potentials for bioenergy. For a more detailed description of the parametric changes and assumptions see Emmerling et al. (2016).

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Table S3.7: Main SSP5 assumptions in WITCH-GLOBIOM

Element SSP5 Corresponding parameters and adjustments

Population and GDP

Based on KC and Lutz (This issue)　 and Dellink et al. (this issue)

Population and total factor productivity.

Energy Intensity HighBased on income elasticity based EI improvements in SSP2, these improvements are slightly faster by 0.75% per year while still leading to a much higher overall energy demand.

CCS availability Med Low costs for CCS including storage

Nuclear Med Medium costs for Nuclear

Renewables Med Medium costs for Renewables

Transportation demand Med Medium transportation demand and travel intensity.

Transportation costs Low Low fuel efficiency improvement rate and learning rate for batteries.

Fossil fuel resources: Gas and Coal High High fossil fuel supply curves from the ROSE project (http://www.rose-

project.org/)

Fossil fuel resources:Oil Medium Due to a preference for (cheaper) coal, mostly only conventional oil

reserves are available in SSP5.

Land use change High SSP5 adjusted supply curves of biomass from GLOBIOM.

Air pollution policies HighIncreasingly strict, well-enforces policies and substantial technological improvement. Including rapid technology transfer to developing countries.

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S4. Implementation of Shared Climate Policy Assumptions

S4.1 Carbon pricing assumptionsThe shared climate policy assumptions (SPAs) for implementing the SSP-based mitigation scenarios are defined by three different policy phases: a period of regionally fragmented moderate mitigation policies followed by a transition period to the final phase of global cooperative mitigation action. The first period until 2020 aims to reflect existing climate policy pledges. It was implemented in REMIND-MAgPIE by effective carbon pricing as a proxy for the stringency of mitigation policies. The carbon price levels were chosen, inter alia, to roughly achieve regional emissions reductions targets for 2020 as laid down in national policies and the Cancun pledges. As a result, the model regions EUR (=European Union), USA, JPN (= Japan) and ROW (=Rest of World, comprising Eastern European states outside the EU, Turkey, Canada, Australia, New Zealand and South Africa) are assumed to implement more stringent climate policies, reflected by a CO2-price of 15 US$2005/tCO2 for EUR and 12 US$2005/tCO2 for USA, JPN and ROW in 2020. More moderate climate policies are assumed to be implemented by the regions CHN (= China), IND (= India), OAS (= Other Asia, not including the countries of the Middle East) and LAM (= Latin America), captured by a carbon tax of 5 US$2005/tCO 2

in CHN and 3 US$2005/tCO2 in IND, OAS and LAM in 2020. AFR (= Subsahara Africa excluding South Africa) faces only a carbon price of 1 US$2005/t CO2 in 2020, while the fossil fuel exporting regions RUS (= Russia) and MEA (= North Africa and Middle East Asia, in REMIND also including the Central Asian states of the Former Soviet Union) are assumed to not take any climate policy action until 2020.

After 2020, an immediate switch to full regional cooperation with an emergent globally uniform carbon price is assumed in the SPA associated with SSP1 (SPA1). In REMIND-MAgPIE, the globally uniform carbon price path is taken from a scenario with immediate climate policy action in 2015 and then increased to compensate excess emissions due to only moderate action until 2020 so that the forcing target in the year 2100 is achieved. In the SPAs associated with SSP2 (=SPA2) and SSP5 (=SPA5), a smooth transition of regionally fragmented carbon prices to a globally uniform carbon price is assumed between 2020 and 2040. From 2040 on the global carbon price path is derived using the same mechanism as for SPA1.

Figure S4.1 shows the resulting carbon price trajectories until 2050 for the mitigation scenarios in SSP1 (Panel a), SSP2 (Panel b), and SSP5 (Panel c) on the level of native REMIND model regions. It can be seen that the global carbon pricing towards the long term climate forcing target is a function of both the stringency of the target (the more stringent the target, the higher the price) and the SSP-SPA combination. For reaching a given target, lowest carbon pricing is needed in SSP1-SPA1 and highest in SSP5-SPA5. Combining SSP1 or SSP2 characterized by low to medium challenges to mitigation with only a moderate long-term target can lead to global carbon prices that are lower than the near term regional carbon prices in the EUR, USA, JPN and ROW regions, indicating that the near term policies adopted in these regions are more ambitious than justified by the particular target. In such cases, regional carbon prices are relaxed during the transition to a global climate policy regime.

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Figure S4.1 Regional carbon prices in the mitigation scenarios based on SSP1, SSP2 and SSP5 from 2005 to 2050

S4.2 Near term emissions reductions in the SSP mitigation scenariosKyoto gas emissions and carbon intensities are reduced in response to the carbon pricing associated with the SPAs. The carbon pricing is fully applied to the energy and land use sectors in SSP1 and SSP5 as both foresee effective institutions to manage sustainable development including land use. This differs from SSP2, where only agricultural Non- CO2 emissions are priced at the level of GHG pricing in the energy sector, while deforestation is allowed to continue until 2030. CO2 emissions from land use are only priced after 2030 in SSP2.

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Figure S4.2 shows the resulting Kyoto gas emissions reductions relative to 2005 for four major economies, and compares them with their Cancun Pledges for 2020 and INDCs for 2030. The same is shown for carbon intensity in India and China in Figure S4.3. It can be seen that the emissions and carbon intensity response to carbon pricing varies significantly across SSPs reflecting their different challenges to mitigation. Emissions reductions are strongest in SSP1, and weakest in SSP5. As a result, the near term policy pledges are in many cases overachieved in SSP1 and underachieved in SSP5.

Figure S4.2: Relative difference of Kyoto gas emissions in percent of emissions in 2005 for the regions USA, EUR, Russia and Japan from 2005 to 2050 in comparison to the existing Cancun Pledges in 2020 and INDCs in 2025 / 2030 (black points). Pledges that are relative to emissions in 1990 are translated into pledges relative to 2005 based on Kyoto gas emissions from EDGAR for 1990 to 2005.

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Figure S4.3: Relative Reduction of carbon intensity (CO2 emissions/GDP) in percent of carbon intensity in 2005 for China and India from 2005 to 2050 in comparison to the existing Cancun Pledges in 2020 and INDCs in 2030 (black points). Pledges that are relative to emissions in 1990 are translated into pledges relative to 2005 based on Kyoto gas emissions from EDGAR for 1990 to 2005.

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S5. SSP5 narrative The SSP5 narrative is reproduced from O’Neill et al. (this issue). We include here the full version of the narrative as documented in the Supplementary Information of O’Neill et al. (this issue).

SSP5: Fossil-fueled Development – Taking the Highway

Sketch

Driven by the economic success of industrialized and emerging economies, this world places increasing faith in competitive markets, innovation and participatory societies to produce rapid technological progress and development of human capital as the path to sustainable development. Global markets are increasingly integrated, with interventions focused on maintaining competition and removing institutional barriers to the participation of disadvantaged population groups. There are also strong investments in health, education, and institutions to enhance human and social capital. At the same time, the push for economic and social development is coupled with the exploitation of abundant fossil fuel resources and the adoption of resource and energy intensive lifestyles around the world. All these factors lead to rapid growth of the global economy. There is faith in the ability to effectively manage social and ecological systems, including by geo-engineering if necessary. While local environmental impacts are addressed effectively by technological solutions, there is relatively little effort to avoid potential global environmental impacts due to a perceived tradeoff with progress on economic development. Global population peaks and declines in the 21st century. Though fertility declines rapidly in developing countries, fertility levels in high income countries are relatively high (at or above replacement level) due to optimistic economic outlooks. International mobility is increased by gradually opening up labor markets as income disparities decrease. The strong reliance on fossil fuels and the lack of global environmental concern result in potentially high challenges to mitigation. The attainment of human development goals, robust economic growth, and highly engineered infrastructure results in relatively low challenges to adaptation to any potential climate change for all but a few.

Additional Information

Motivating forces: Two major factors enable a break with historical patterns that showed a lack of regional convergence in institutional arrangements and economic growth. First, the economic success of emerging economies and more recently least developed countries gives rise to an emergent global middle class that has been lacking in most regions of the world. The new middle class stabilizes global economic development by promoting robust growth in demand for services and goods especially in cities. The new middle class also fosters the more widespread adoption of world views oriented towards market solutions and participatory societies in many world regions. In particular, developing countries aim to follow the fossil- and resource-intensive development model of the industrialized countries. Second, the digital revolution enables a global discourse of a significant and increasing share of the population for the first time in human history leading to a rapid rise in global institutions and promoting the ability for global coordination.

Policies, institutions and social conditions: On a national and regional level, institutional changes are initiated to foster competitive markets, leading – by and large – to more effective institutions with

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lower levels of corruption, strong rule of law, and the removal of market entry barriers for disadvantaged population groups. Social cohesion, gender equality, and political participation are strengthened in most world regions. As a consequence, social conflicts are gradually decreased, although the more pervasive adoption of participatory and market oriented world views creates significant tension with traditional views during a transition phase.

On the international level, countries pursue a global “development first” agenda and increasingly cooperate on economic, development, and security policies. Regional conflicts are met with assertive international action, and decline with rapid development and decreasing levels of social conflict. Institutions that further market penetration and lower trade barriers are strengthened, leading to accelerated globalization and high levels of international trade. International cooperation on environmental policies is much more limited due to a perceived trade-off between development and environmental goals for global, long-term issues.

Human development: Development policies emphasizing education and health are put in place to accelerate human capital development. These policies, aided by rapid economic development, lead to a strong reduction of extreme poverty and significantly improved access to education, safe drinking water and modern energy in the medium term.

Economy and lifestyles: Economies become increasingly globalized over time with high levels of international trade. The gross world product at the end of the century is very high. Per capita incomes in developing countries increase rapidly, leading to strong convergence of interregional income distributions and a measurable decline of income inequality within regions. At the same time industrialized countries continue their focus on economic growth, driven in part by consumerism and resource-intensive status consumption, including a preference for individual mobility, meat-rich diets, and tourism and recreation. Developing countries rapidly adopt these consumption patterns.

Population and urbanization: Global population peaks and declines in the 21st century, a result of rapid fertility declines in developing countries driven by improving education, health, and economic conditions. In high income countries, fertility is above replacement due to optimistic outlooks for economic conditions. International mobility is increased by gradually opening up labor markets as income disparities decrease. Migration from poorer to wealthier countries buffers the effect of aging populations in industrialized countries. All regions reach high levels of urbanization. Urban planning and land use management play crucial roles, but struggle to keep up with the rapid migration of rural population into cities in the first few decades of the century. While urbanization rates converge over time, urban structure and form develop in different world regions to reflect historic patterns and prevailing local and national policies. This includes dense mega-cities in densely populated countries, and large metropolitan areas with significant urban sprawl in other regions of the world.

Technology: Technological progress is seen as a major driver of development and economic growth. Fostered by widespread technology optimism, investments in technological innovation are very high, with a focus on increasing labor productivity, fossil energy supply, and managing the natural environment. In continuation of the current shale revolution, fossil resource extraction is maximized at low cost, and local externalities of fossil energy production (e.g. health effects) are well controlled by continued technological advancements in the fossil energy sector. Due to the strong reliance on fossil energy, alternative energy sources are not actively pursued.

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Environment and resources: Environmental consciousness exists on the local scale, and is focused on end-of-pipe engineering solutions for local environmental problems that have obvious impacts on well-being, such as air and water pollution particularly in urban settings. On the other hand, individualistic lifestyles give rise to local opposition against engineering solutions that affect local environments (NIMBY). Agro-ecosystems become more and more managed in all world regions, facilitated by productivity improvements and the diffusion of resource-intensive management practices in the agricultural sector. The resulting large increases in agricultural productivity and a peaking and declining world population can support high per capita food consumption and meat-rich diets globally. However, some deforestation continues due to incomplete regulations. In the long run, land and environmental systems are highly managed across the world, and there is a general tendency to decouple human-engineered systems from natural systems as much as possible.

Challenges: The strong reliance on fossil fuels and the lack of global environmental concern result in potentially high challenges to mitigation. The attainment of human development goals, robust economic growth, and highly engineered infrastructures results in relatively low challenges to adaptation for all but a few.

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