Supplementary Material to
“Fossil resource and energy security dynamics
in conventional and carbon-constrained worlds”
in Climatic Change
David L. McCollum1*, Nico Bauer2, Katherine Calvin3, Alban Kitous4^, Keywan Riahi1,5
1
International Institute for Applied Systems Analysis, Laxenburg 2361, Austria.
Potsdam Institute for Climate Impact Research, Potsdam 14412, Germany.
3
Joint Global Change Research Institute, College Park, MD 20740, USA.
4
Institute for Prospective Technological Studies, European Commission Joint Research Centre, Sevilla 41092,
Spain.
5
Graz University of Technology, Graz 8010, Austria.
2
*
e-mail: mccollum@iiasa.ac.at
^The views expressed are purely those of the author and may not in any circumstances be regarded as stating
an official position of the European Commission.
Table of Contents
1.
Additional information on the fossil fuel supply curves used by a subset of the models in this study ......... 2
2.
Additional information for the fossil resource consumption and price dynamics analysis ........................... 4
3.
Additional information for the CO2 storage requirements analysis .............................................................. 8
4.
Additional explanations and clarifications for the energy security analysis ................................................ 10
References ............................................................................................................................................................ 15
1
1. Additional information on the fossil fuel supply curves used by a subset of the models
in this study
As part of the Energy Modeling Forum 27 (EMF27) exercise, we collected information on producers’
cost curves for many of the models participating in this study. These curves provide information on
fossil fuel availability and the cost of extraction as used by the models. Supplementary Figure 1
shows these curves for coal, natural gas, and crude oil. For presentation purposes, we have
truncated these curves around the levels of energy consumed over the course of the twenty-first
century; to be sure, many models include significantly more fuels at higher costs. The curves shown
are base-year costs in 2005$ per GJ. Some of the models, however, assume that costs change over
time. GCAM and IMAGE, for instance, account for declining costs over time as a result of
technological progress in mining/extraction; in GCAM this decline is exogenous, while in IMAGE it
comes in the form of a learning curve. MESSAGE, on the other hand, assumes that costs will change
in the future due to changes in both the cost of labor (which could result in cost increases) and
technological progress. MERGE assumes implicit technical change in terms of the size of the
ultimately recoverable resource (which scales the long-run cost curve), as it includes expanded
categories from the U.S. Geological Survey (USGS) assessment. It is important to note that modelrealized prices of fossil fuel resources may differ from producer cost, due to the inclusion of other
factors (e.g., transportation cost, taxes, subsidies, scarcity rent, monopoly rent, etc.). The difference
between producer cost and price varies across models and can be significant.
2
(A) COAL
16
30
IMAGE
MESSAGE
GCAM
MERGE
ReMIND
WITCH
GRAPE
DNE21+
BET
12
10
8
IMAGE
MESSAGE
GCAM
MERGE
ReMIND
WITCH
GRAPE
DNE21+
BET
25
20
$/GJ
14
$/GJ
(B) NATURAL GAS
6
15
10
4
5
2
0
0
0
20
40
60
80
100
0
ZJ
10
20
30
40
50
ZJ
(C) CRUDE OIL
50
IMAGE
MESSAGE
GCAM
MERGE
ReMIND
WITCH
GRAPE
DNE21+
BET
45
40
35
$/GJ
30
25
20
15
10
5
0
0
10
20
30
40
50
ZJ
Supplementary Figure 1. Global fossil fuel supply curves (coal, natural gas, crude oil) for selected EMF27
models.
3
2. Additional information for the fossil resource consumption and price dynamics analysis
The challenge of climate change mitigation crucially depends on the future consumption of fossil
fuels, namely coal, oil, and natural gas. First, the use of these resources over the course of the
twenty-first century absent any climate policy is important because it determines the CO2 emissions
baseline from which reductions must be achieved. A second important factor relates to the flexibility
of the energy system to reduce the consumption of freely-emitting fossil fuels, as this influences the
effort required to deviate from the baseline. Thirdly, the future price levels of fossil fuels are a
crucial factor because they will influence the opportunity costs of not using fossil fuels, as well as the
deployment of non-fossil technologies, if there are concerted efforts to abate CO2 emissions. Finally,
climate policies will lead to a restructuring of fossil fuel markets, in terms of expenditures on these
resources as well as on carbon.
Supplementary Figure 2 shows the cumulative consumption of coal, natural gas and oil over the 21st
century for various scenarios and models of the EMF27 exercise. The figure supports the results
discussed in the main text regarding the consumption of all fossil energy carriers.
The detailed results show that the reductions in coal use (from the Base FullTech reference scenario)
are the most necessary, in order to achieve the two different climate change stabilization targets.
This effect is already significant for the 550 ppm CO2-eq target (550 FullTech). The additional
reductions (from 550 ppm) to achieve the 450 ppm target (450 FullTech) are not quite as great.
Moreover, the use of coal in combination with CCS is reduced for all models.
In addition, oil and gas consumption must be reduced to achieve the climate targets. The effect on
total gas use is very mixed, but a majority of models show more significant use of gas with CCS in the
550 case than in the 450 case. Similarly, oil use across the different scenarios is very mixed.
Reductions are clearly larger in the case of high baseline consumption. The more optimistic
assumptions regarding energy intensity development (Base LowEI scenario) are generally not
sufficient to reduce the use of coal, oil, and gas enough so that the climate targets are met. For some
models, cumulative oil and gas consumption in the 550 scenario is even higher than in the no-policy
scenario of some other models. This result does not hold for coal, which shows a much more robust
reduction. It is also worth noting that all models reduce the use of fossil fuels in the lower energy
intensity scenario – i.e., there is no excessive rebound effect for a single fossil fuel in any of the
models.
4
(B) Gas with CCS
Supplementary Figure 2. Cumulative consumption of fossil energy carriers (2010-2100) across different
scenarios and models. For gas and coal, the quantity of consumption in combination with CCS is also shown.
For oil, the quantity with CCS is negligible in all models and therefore not shown.
Regarding global results (see Figure 1 of main text), in the 550 ppm CO2-eq climate stabilization
scenario, the highest cumulative fossil resource use is reported by GRAPE (55 ZJ), which consumes a
large share of its fossils in combination with CCS. GCAM and IMACLIM similarly consume large
quantities of fossil fuels (45 ZJ) in this climate policy scenario, whereas MERGE (34 ZJ) and EC-IAM
5
(22 ZJ) are on the low end. Hence, the range of cumulative fossil fuel consumption across models is
less in the 550 FullTech scenario (34-45 ZJ) than in the Base FullTech (55-80 ZJ), when excluding the
lowest and highest values in the former set; otherwise, the variations are similar to the baseline
case. The cumulative amount of fossil fuel with CCS ranges between 2 ZJ (EC-IAM) and 28 ZJ (GRAPE)
in the 550 FullTech scenario. When the stabilization target is tightened to the 450 CO2-eq level (450
FullTech1), the lowest total fossil fuel use is 20 ZJ (MERGE). In contrast, the highest value is roughly
twice that level (GCAM with 39 ZJ). For all models, the use of fossils with CCS is generally lower in
the 450 scenario than in the 550 case. However, there is significant variation across models in the
amount of fossil fuels that can be consumed while still achieving this more stringent climate
stabilization target.
What is perhaps most interesting about the reductions in fossil resource use across models in the
climate policy scenarios (450 FullTech and 550 FullTech) is that they are somewhat independent of
fossil use in the baseline (Base FullTech). For instance, the four models with the highest fossil
consumption in the baseline (BET, MERGE, ReMIND and WITCH) simultaneously show relatively
lower fossil fuel consumption in the climate policy scenarios (see Figure 1 in the main text). In
particular, these models make less use of fossil CCS. Hence, the models that follow a fossil fuelintensive pathway in the absence of emission restrictions also exhibit high flexibility in reducing
fossil consumption when the need arises. Alternatively, certain models that exhibit lower fossil use
in the baseline (GCAM, POLES and IMAGE) show smaller reductions in the stabilization scenarios
and, thus, relatively high fossil fuel use in the latter. These trends can be attributed to differences in
the flexibility of models to reduce energy demand and to substitute other fuels in the energy supply,
which itself has much to do with model structure . (GCAM, POLES, and IMAGE are simulation
models, for example.) Some models rely more on energy demand reductions to achieve climate
policy targets than others, while others are more flexible in transforming the energy supply mix. In
all models and for all scenarios, fossil fuels will be consumed up to the point where other
alternatives, like renewables, become cost competitive. However, models differ in their ability to
anticipate future energy prices, something that heavily influences energy consumption in the near
term. For example, MERGE, ReMIND, and WITCH are all examples of inter-temporally optimizing
models; thus, they make decisions about energy use today in anticipation of future carbon and
energy prices. GCAM, in contrast, has limited foresight; it therefore assumes actors make decisions
without full knowledge of future energy and carbon prices. The difference between these two
modeling paradigms is one reason that fossil energy consumption in GCAM (and models like it) is
higher, particularly in the near term.
The changes in the global energy system also have impacts on fossil fuel prices. This is shown most
clearly in Figure 2 of the main text. Prices are seen to vary quite widely across models, even for the
same level of cumulative fossil consumption, and in general prices vary more than consumption. In
the baseline, models with high initial prices tend to show high price increases, and the other way
around. On the other hand, the price response due to climate policies is ambiguous across models.
Some models show no effect, others estimate lower prices under stringent climate mitigation. Still
others show higher prices. The latter behavior is particularly interesting, as for no single model is the
cumulative consumption of oil or gas resources in the climate mitigation scenario higher than in the
baseline case.
1
Due to omissions in reporting, the GRAPE model is not included in the analysis of this scenario.
6
The marked differences in price developments across models depend to a large extent on model
structure and assumptions. For example, some models, such as POLES2, calibrate initial prices to
historical data, while others, like ReMIND, determine it endogenously. Second, inter-temporal
optimization models like ReMIND – compared to recursive-dynamic models like GCAM and IMAGE –
inherently include a price mark-up over extraction costs through anticipation of future supply
scarcity and/or increasing production costs. For the same level of cumulative resource consumption,
this mark-up ends up being higher in the baseline than in the climate mitigation scenarios. Third, the
inclusion of short-term supply constraints and energy trade costs has an effect on prices in some
models. Finally, as mentioned above, supply curve assumptions vary widely across models, and this
has an important effect on resource prices at any point in time.
Supplementary Table 1 summarizes differences in fossil fuel price impacts across models for the year
2050. The ratio of the relative change of annual fossil use and the global average price can be
interpreted as a market elasticity of fossil fuel output with respect to the price due to climate
policies. A ratio of between 0 and 1 means that the percentage reduction of prices exceeds the
percentage reduction of the quantity. If the ratio is negative, the price and quantity reactions move
in opposite direction. The table illustrates the diversity of model results. In fact, there is no clear
tendency. For each fossil energy carrier, the results cover the whole range of possible values. Also,
only a few models show results for all three fossil fuels that are in sound relation to each other.
More analysis is required to shed light on the reasons for these outcomes, and more fundamental
research is needed to improve the representation of fossil fuel markets in models. The latter will
hopefully help to narrow the range of model outcomes and thus reduce the uncertainty
communicated to policy makers.
Supplementary Table 1. Percentage changes of coal, oil and gas global annual use and global average prices in
the year 2050 and the ratio of the changes. The changes refer to the differences between the 450 FullTech
climate stabilization scenario and the Base FullTech scenario in the year 2050. Note that global average prices
are used. Regional reallocation of coal and gas could imply increasing average prices because of regional price
differences.
Oil
Q
GCAM
IMACLIM
IMAGE
MERGE
MESSAGE
POLES
REMIND
TIAM-WORLD
WITCH
P
Gas
Q/P
Q
P
Coal
Q/P
Q
P
Q/P
-2%
2%
-1.15
-7%
-3%
2.50
-40%
-14%
-27%
-80%
0.34
-11%
-61%
0.19
-46%
-84%
0.55
-30%
-7%
4.52
-19%
-6%
3.24
-38%
-4%
10.29
-15%
-44%
0.34
4%
18%
0.20
-60%
0%
NAN
-25%
-20%
1.28
-8%
11%
-0.71
-53%
-11%
4.96
-21%
-54%
0.39
-22%
-55%
0.40
-52%
-10%
5.32
-7%
-28%
0.25
-23%
-31%
0.75
-80%
-69%
1.17
-16%
-3%
5.12
-16%
28%
-0.57
-62%
8%
-7.76
-24%
-31%
0.79
-33%
-4%
8.32
-55%
-10%
5.57
2
2.85
GCAM also calibrates initial prices. However, the model uses an average price over the past several decades,
which results in a lower starting price than observed in POLES.
7
3. Additional information for the CO2 storage requirements analysis
Carbon dioxide is a waste product of fossil fuel consumption; and in the context of carbon dioxide
capture and storage (CCS), the potential for disposing of this waste is of critical importance. One can
even think of the world’s geological storage capacity for CO2 as a kind of “resource” whose utilization
depends on the amount of fossil fuel consumed over the next decades, or perhaps centuries. The
availability and geographic distribution of such reservoirs will become increasingly important in a
carbon-constrained world, and concerns could eventually surface regarding the potential of this
resource to permanently sequester the huge flows of CO2 that may be required going forward.
Supplementary Figure 3 compares the cumulative (2010-2100) volumes of CO2 stored (regionally and
globally) by the EMF27 models in the 450 FullTech climate stabilization scenario to several estimates
of carbon storage capacity from the literature. These estimates indicate a large uncertainty in
capacity. For instance, the International Energy Agency (2010) has calculated that, theoretically,
global storage capacity could be as high as 16,000 GtCO2; however, when restricting this capacity to
what is actually deemed viable, the estimates are found to be much lower: 3,360 GtCO2 in the “20%
Viable” case and 1,680 GtCO2 in the “10% Viable” case). Meanwhile, a more recent analysis
conducted within the framework of the Global Energy Assessment (Benson et al. 2012) estimates
that global capacity may actually be far higher: between 5,000 GtCO2 and 24,000 GtCO2. (Note that
Supplementary Figure 3 only shows the lower estimates from the IEA and GEA.)
By the end of the century, EMF27 model results point to global CO2 storage requirements of around
1,300 GtCO2 (median across models), with the highest model requiring approximately 2,400 GtCO2.
Thus, under most estimates, enough storage capacity exists to meet demand in the twenty-first
century. However, should carbon capture and storage be needed post-2100 – and indeed this seems
to be the trend in essentially all models, if one further extrapolates the scenario results into the
twenty-second century – then CO2 reservoir constraints could potentially become scarce, depending
on the resource potential considered.3 The situation is less clear-cut at the regional level: whereas
the OECD90 appears to have plenty of storage capacity, the other regions (especially ASIA) could
approach, or even exceed, their capacity limits. To some extent, the global picture is distorted by the
huge surplus of capacity in the OECD90, relative to the projected future size of this region’s energy
system. It is not clear, however, that this surplus could be shared with the other more capacitylimited regions, as this would depend on the transport of CO2 over long distances via pipelines or
ocean-going vessels. In sum, this comparison shows that concerns over the future scarcity of CO2
storage reservoirs are not unwarranted if one takes a fairly pessimistic view of the capacity
estimates currently found in the literature. Yet, even with a bit of optimism, these concerns are
alleviated, at least over the twenty-first century.
3
It is also important to note that two major options for geological storage are depleted oil and gas fields. The
storage capacity of these fields will increase as more oil and gas are consumed.
8
1400
1200
4000
800
GtCO2
GtCO2
1000
5000
Min
Median
Max
IEA, 10% Viable
IEA, 20% Viable
GEA, lower bound
600
3000
2000
400
1000
200
0
0
ASIA
LAM
MAF
OECD90
REF
World
Supplementary Figure 3. Cumulative CO2 storage requirements (2010-2100) across models in the 450 FullTech
scenario compared with storage capacity estimates from the literature. Minimum, maximum, and median
values across all models are shown. (Note: we have excluded the GEA estimates from the regional charts since
different methodologies were used to develop the different regional estimates. As a result, the estimates are
not necessarily comparable across regions.)
9
4. Additional explanations and clarifications for the energy security analysis
Shannon-Wiener diversity index
The Shannon-Wiener diversity index (SWDI), referred to in the main text, has been used in previous
studies to measure different aspects of energy system diversity (Jansen et al. 2004; Kim et al. 2009;
Kruyt et al. 2009; Riahi et al. 2012; Stirling 1994). In this study, we apply the SWDI to (1) the
diversity of the primary energy resource supply mix in individual regions, and (2) the diversity of the
global markets for oil and gas exports. (Note that in the former the direct-equivalent accounting
method is used for nuclear energy and non-biomass renewables.) A number of energy security
indicators can be found in the literature (Kruyt et al. 2009), each with their own strengths and
weaknesses. The reasons we chose to work with the SWDI are multifold. First, it is analytically
straightforward and can be reproduced by others fairly easily. Second, it offers a way to measure
energy system resilience, a key component of security. Third, unlike compound indicators that
consider multiple policy goals, the SWDI does not obscure policy trade-offs. And lastly, it does not
require implicit assumptions about the stability of future political regimes or the implicit weighting
of future risks.
The exact value of the SWDI has little intuitive meaning; rather, the indicator’s true explanatory
power rests on its ability to shed light on relative changes in diversity over time and across
countries/regions/sectors. The higher the diversity indicator, the greater the diversity – and by
extension, the more secure is the particular energy system under study.
𝑆𝑊𝐷𝐼 = − ∑𝑗(𝑝𝑗 ∙ ln 𝑝𝑗 )
where:
- pj: share of primary energy resource j in total primary energy supply (in the case of the primary energy
diversity indicator); or share of oil/gas exports coming from region j out of the total pool of exports at
the global level (in the case of the geographic supply diversity indicator for oil/gas)
The figures below expand upon the energy discussion in the main text of the paper and are thus
referred to in the appropriate parts therein. They illustrate the impacts of stringent energy
efficiency and climate mitigation efforts on (i) natural gas imports, and (ii) the geographic supply
diversity of oil and natural gas exports at the global level.
Natural gas trade
The global market for natural gas trade is at the moment not as large as for oil, though this situation
is poised to change markedly over the next several decades (see Supplementary Figure 4). Compared
to today, nearly all EMF27 models show increasing imports of gas into the OECD90 and especially
ASIA by 2030. In the case of the former, the level of import dependence appears to depend little on
policies for energy efficiency or climate mitigation. In ASIA, on the other hand, while an efficiencyonly focus is likely to yield little impact, stringent climate mitigation efforts could actually worsen the
energy security situation. One explanation for the diverging trends in the two regions is the relatively
lower potential for renewable energy sources in ASIA (IPCC 2011; Luderer and Krey this issue): with
fewer options to replace coal, rapidly growing, yet carbon-constrained, Asian countries may need to
upscale their utilization of natural gas (which is less carbon-intensive, especially when combined
with CCS) in the near to medium term.
10
Supplementary Figure 4. Impacts of stringent energy efficiency and climate mitigation efforts on natural gas
imports for the OECD90 and ASIA regions in 2030. Top panels: gas imports as a share of total regional gas
consumption. Bottom panels: total gas imports in absolute terms. Boxes indicate the 25-75 percentile ranges
across the EMF27 models; green lines within boxes denote medians; red crosses are outliers. Dashed lines
refer to 2010 values. Abbreviations used for labeling: ‘D’ (DNE21+), ‘MS’ (MESSAGE), ‘R’ (ReMIND), ‘IC’
(IMACLIM), ‘IM’ (IMAGE), ‘T’ (TIAM-WORLD), ‘P’ (POLES), ‘B’ (BET), ‘MR’ (MERGE), ‘GC’ (GCAM).
11
Diversity of global exports
Supplementary Figure 5. Impacts of stringent energy efficiency and climate mitigation efforts on the
geographic supply diversity of oil and natural gas exports at the global level (measured by the SWDI; all model
data harmonized to historical 2010 values with future trends preserved). Top panel: concentration of oil
exports. Bottom panel: concentration of gas exports. Boxes indicate the 25-75 percentile ranges across the
EMF27 models; green lines within boxes denote medians; red crosses are outliers. Dashed lines refer to 2010
values. Abbreviations used for labeling: ‘D’ (DNE21+), ‘MS’ (MESSAGE), ‘R’ (ReMIND), ‘IC’ (IMACLIM), ‘IM’
(IMAGE), ‘T’ (TIAM-WORLD), ‘P’ (POLES), ‘W’ (WITCH), ‘B’ (BET), ‘MR’ (MERGE), ‘GC’ (GCAM).
12
Primary energy diversity
There is at least one resilience-related energy security concern that stringent climate mitigation
efforts seem almost sure to alleviate: the current dominance of a limited number of energy carriers
(e.g., coal, oil, gas, uranium) in the primary energy resource mix of certain countries and regions. As
illustrated by the top panels of Supplementary Figure 6, in both the OECD90 and ASIA the
combination of efficiency and renewables, as in the “Climate Mitigation” scenario, leads to much
greater diversity (measured by the SWDI) in the near term. Solely focusing on energy efficiency does
not appear to yield the same benefits. Consistent with financial portfolio theory, the more diverse a
region’s energy supply, the less susceptible it is to risk and unforeseen shocks to the system
(Markowitz 1952).
13
Supplementary Figure 6. Diversity of primary energy supply in the OECD90 and ASIA regions in 2030.
Measured by the SWDI; all model data harmonized to historical 2010 values with future trends preserved.
Boxes indicate the 25-75 percentile ranges across the EMF27 models; green lines within boxes denote
medians; red crosses are outliers. Dashed lines refer to 2010 values. Abbreviations used for labeling: ‘D’
(DNE21+), ‘MS’ (MESSAGE), ‘R’ (ReMIND), ‘A’ (AIM-Enduse), ‘E’ (ENV-Linkages), ‘GR’ (GRAPE), ‘IC’ (IMACLIM),
‘IM’ (IMAGE), ‘T’ (TIAM-WORLD), ‘P’ (POLES), ‘W’ (WITCH), ‘B’ (BET), ‘MR’ (MERGE), ‘GC’ (GCAM).
14
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