Will economic growth and fossil fuel scarcity help or hinder climate stabilization? Overview of the RoSE multi-model study

Electronic Supplementary Material 1 –

Extended Analysis, Tables and Figures

Elmar Kriegler1,*, Ioanna Mouratiadou1, Gunnar Luderer1, Nico Bauer1, Robert J. Brecha1,2, Katherine Calvin3, Enrica DeCian4, Jae Edmonds3, Jiang Kejun5, Massimo Tavoni4, Ottmar Edenhofer1,6,7

1Potsdam Institute for Climate Impact Research, Potsdam, Germany

2University of Dayton, Dayton, USA

3Pacific Northwest National Laboratory, Joint Global Change Research Institute at the University of Maryland, College Park, Maryland, United States

4Fondazione Eni Enrico Matteiand Euro-Mediterranean Center on Climate Change, Milan, Italy

5Energy Research Institute, National Development and Reform Commission, Beijing, China

6Technische Universität Berlin, Berlin, Germany

7Mercator Research Institute on Global Commons and Climate Change, Berlin, Germany

* Corresponding author:

S1. Population and GDP as emissions drivers in the baselines

S2. Final energy intensity as a function of economic growth

Figure S2: Final energy intensity over GDP per capita (in MER terms) for different assumptions about economic growth (see legend for scenario color codes). Results are given for the baseline scenario. Model trajectories are shown in dashed lines and assigned to individual models by the use of their first letter as marker for the points in 2010, 2050 and 2100. Trajectories run from top left to bottom right over time, i.e. from high energy intensity and low GDP per capita to low energy intensity and high GDP per capita. Dotted isolinesindicate final energy demand levels of 20 to 140 GJ per capita and year (in steps of 40 GJ).

Extended analysis: It can be seen that increases in GDP per capita levels overcompensate energy intensity improvements in almost all cases. The exception is IPAC in the slow growth cases, because IPAC assumes strong energy intensity improving technological progress particularly in the second half of the century. A constant income elasticity of final energy demand, ɛ, would show as a straight line with slope ɛ-1 in the loglog plot format chosen above. Models show some deviations from a straight line, but in the large majority of cases project an income elasticity of 0 < ɛ < 1 throughout the century.

S3. Energy and carbon intensities in the baselines

Figure S3:Carbon vs. energy intensity for all models and various assumptions about economic growth and population (Panel a) and fossil fuel availability (Panel b; indicated by marker colors). Model trajectories are shown in dashed lines and assigned to individual models by use of their first letter as marker for the starting point in 2010 and endpoint in 2100. Trajectories run from right to left, i.e. from high to low final energy intensity over time. Dotted curves in the carbon vs. energy intensity graph show isolines of 100 to 800 gCO2 emissions per unit GDP (in steps of 100 gCO2). All results are given for the baseline scenarios.

S4. Cumulative fossil fuel use and fossil fuel prices

Cumulative Extraction of Fossil Fuels (2010-2100) [ZJ]
Model / Policy Case / Coal / Oil / Gas / Total
GCAM / Baseline / 26-38 / 12-21 / 18-23 / 59-72
550 ppm / 12-16 / 12-17 / 17-19 / 42-46
450 ppm / 8-11 / 10-13 / 14-15 / 34-37
IPAC / Baseline / 20-30 / 15-16 / 11-13 / 46-58
550 ppm / 7-9 / 11 / 10-11 / 29-31
450 ppm / 4 / 8 / 7-8 / 19-21
REMIND / Baseline / 19-41 / 14-31 / 19-35 / 54-84
550 ppm / 3-11 / 14-22 / 17-22 / 41-44
450 ppm / 2-5 / 12-16 / 12-14 / 28-30
WITCH / Baseline / 18-23 / 22-36 / 21-23 / 61-79
550 ppm / 8-9 / 18-20 / 10-12 / 38
450 ppm / 7 / 10-11 / 8-9 / 26

Table S1:Ranges of cumulative fossil fuel use across different assumptions about fossil fuel availability as estimated by the four global models in the RoSE study for the baseline, 550 ppm and 450 ppm CO2e scenarios.

Extended analysis: Oil and gas use are predominantly supply driven, with lowest use observed in the LO Fos scenario (12 ZJ over 2011-2100 for oil and 18 ZJ for gas in GCAM, excluding IPAC) and highest use in the HI Fos scenario for oil (36 ZJ in WITCH) and the LO Oil scenario for gas (where gas and coal are as plentiful as in HIFos, but oil limited to LO Fos; 35 ZJ in REMIND). IPAC constitutes an exception as it projects a demand driven constraint on future cumulative oil use due to large technological progress in the transport sector leading to a partial replacement of oil-based transportation fuels even in the baseline.

In contrast, the use of coal as the lowest grade fossil fuel can be strongly affected by the availability of oil and gas, with only slightly higher use in the HI Fos (21-29 ZJ) than in the LO Fos scenario (18-26 ZJ; Fig. S4c). Significantly higher coal use is observed in a situation with plentiful coal but limited oil and gas resources (HI Coal scenario: up to 41 ZJ in REMIND). IPAC and WITCH are exceptions as they do not project coal use to vary significantly between the HI Fos and HI Coal scenarios. For WITCH, this is mostly due to a limited representation of coal use in non-electric sectors. For IPAC, this can be traced back to the favorable economics of coal vs. gas use even in the HI Fos case (see Fig. 4b,c).

Concerning the dependence of fossil fuel prices on cumulative extraction, model differences are sufficiently constrained for oil, where the low (LO Fos), medium (DEF) and high availability (HI Fos) of oil resources dominates the increase in global oil prices as a function of cumulative oil extraction (Fig. S4a). For gas (Fig. S4b) and coal (Fig. S4b), this is only true for the cumulative extraction-price relationships in GCAM and REMIND. WITCH shows consistently lower prices, lower price variations, and lower variations of coal and gas extraction, which can be explained by the limited uses of these fuels outside the power sector in the model version used in the RoSE study. IPAC shows consistently higher price increases, particularly for gas, and as a result significantly lower gas use than the other models. It apparently assumed lower gas supply in the economically more accessible resource grades.

S5. Emissions, climate forcing and global mean warming

S6. Carbon and energy intensity reductions in climate policy cases

Figure S6: Carbon intensity vs energy intensity reductions relative to the baseline in the 550 ppm (Panel a) and 450 ppm CO2e (Panel b) climate policy cases for all models, economic growth and fossil resource assumptions. Model trajectories are shown as dotted lines with markers at the end point in 2100, starting in the upper right corner at 2005. Markers indicate model (by letter) and scenario (by color; see legend). Isolines show hypothetical 0%, 20%, …, 100%, 120% emissions reductions relative to baseline assuming that GDP stays roughly constant.

S7. Primary energy supply

Climate policy induces a clear transformation away from coal and towards non-fossil fuel sources. In the climate policy cases, the structure of transformation remains largely unchanged by economic growth (below) and fossil resource variations (next page).

Figure S7a: Sensitivity of primary energy projections to socio-economic assumptions. Shown is world primary energy supply by source in 2050 (left column) and 2100 (right column) in the baseline (top row) and the 550 ppm (middle row) and 450 ppm CO2e (bottom row) policy cases. Each panel shows the variation of electricity projections across models and different assumptions about global economic growth and population.

Figure S7b: Sensitivity of primary energy projections to assumptions about fossil fuel availability. Shown is world primary energy supply by source in 2050 (left column) and 2100 (right column) in the baseline (top row) and the 550 ppm (middle row) and 450 ppm CO2e (bottom row) policy cases. Each panel shows the variation of electricity mix projections across models and different assumptions about fossil fuel availability.

S8. Electricity generation

The electricity mix is characterized by strong structural transformations with distinct model patterns, in both baseline and policy cases. In the climate policy cases, the effect of economic growth (below) and fossil resource variations (next page) are small.

Figure S8a: Sensitivity of electricity use projections to socio-economic assumptions. Shown is world electricity generation by source in 2050 (left column) and 2100 (right column) in the baseline (top row) and the 550 ppm (middle row) and 450 ppm CO2e (bottom row) policy cases. Each panel shows the variation of electricity projections across models and different assumptions about global economic growth and population.

Figure S8b: Sensitivity of electricity use projections to assumptions about fossil fuel availability. Shown is world electricity generation by source in 2050 (left column) and 2100 (right column) in the baseline (top row) and the 550 ppm (middle row) and 450 ppm CO2e (bottom row) policy cases. Each panel shows the variation of electricity mix projections across models and different assumptions about fossil fuel availability.

S9. Final energy demand

The electricity mix is affected by model differences and economic growth assumptions (below). It is largely unaffected by fossil resource variations (next page).

Figure S9a: Sensitivity of final energy projections to socio-economic assumptions. Shown is world final energy use by energy type in 2050 (left column) and 2100 (right column) in the baseline (top row) and the 550 ppm (middle row) and 450 ppm CO2e (bottom row) policy cases. Each panel shows the variation of electricity projections across models and different assumptions about global economic growth and population.

Figure S9b: Sensitivity of final energy projections to assumptions about fossil fuel availability. Shown is world final energy use by type in 2050 (left column) and 2100 (right column) in the baseline (top row) and the 550 ppm (middle row) and 450 ppm CO2e (bottom row) policy cases. Each panel shows the variation of electricity mix projections across models and different assumptions about fossil fuel availability.

S10. Greenhouse gas emissions

Figure S10a: Sensitivity of emissions projections to socio-economic assumptions. Shown are global Kyoto Gas emissions split into CO2 emissions from fossil fuel combustion and industry and the remaining emissions (in CO2equivalent) in 2050 (left column) and 2100 (right column) in the baseline (top row) and the 550 ppm (middle row) and 450 ppm CO2e (bottom row) policy cases. Each panel shows the variation of emissions projections across models and different assumptions about global economic growth and population.

Figure S10b: Sensitivity of emissions projections to assumptions about fossil fuel availability. Shown are global Kyoto Gas emissions split into CO2 emissions from fossil fuel combustion and industry and the remaining emissions (in CO2equivalent) in 2050 (left column) and 2100 (right column) in the baseline (top row) and the 550 ppm (middle row) and 450 ppm CO2e (bottom row) policy cases. Each panel shows the variation of emissions projections across models and different assumptions about fossil fuel availability.

S11. Carbon prices and mitigation costs

Carbon prices and mitigation costs for the 550 ppm CO2e target are signficiantly lower than for the 450 ppm CO2e target (cmp. Fig. 4 in the main paper).

Figure S11:: (a+b) Net present value mitigation costs (discounted at 5% per year) over the period 2010-2100 (consumption losses in percentage net present consumption for ReMIND and WITCH, area under MAC in percentage net present output for GCAM) and (c+d) carbon prices in 2050 for the 550 ppm CO2e target. IPAC did not report carbon prices and mitigation costs.

Mitigation cost estimates vary with time and their net present value over the 21st century is sensitive to the choice of discount rate. Fig. S12 shows that the mitigation cost patterns discussed in the main paper are robust against such choices.

Figure S12:Sensitivity of mitigation cost estimates to the choice of metric. Shown are net present value mitigation costs over the period 2010-2100 discounted at 5% (Panel a+b; as in Figure 4 in the main text) and discounted at 1% (Panel c+d) as well as actual mitigation costs in the year 2050 (Panel e+f) for the 450 ppm CO2e target. Mitigation costs are calculated as consumption losses in percentage of consumption for ReMIND and WITCH, and area under MAC in percentage of output for GCAM. The variation of mitigation costs across economic growth (left panels a, c, e) and fossil resource assumptions (right panels b, d, f) are shown separately.