Supporting Online Materials
Can Radiative Forcing Be Limited to 2.6Wm -2 Without Negative
Emissions From Bioenergy AND CO 2 Capture and Storage? In
Climatic Change
Authors:
James Edmonds,1,2 Patrick Luckow,3 Katherine Calvin2, Marshall Wise2, Jim Dooley2, Page
Kyle2, Sonny Kim2, Pralit Patel2, and Leon Clarke2
Limitations and Future Research Directions
Integrated assessment models, like all models, are simplified representations of real world phenomena.
But, they are not the real world itself. Simplifications are intended to allow a clearer focus on
phenomena of interest, these simplifications are also limiting. The potential virtue of integrated
assessment models grows out of the fact that they couple a diverse set of phenomena to shed light on
the implications of interactions among the major elements of the human and biogeophysical Earth
system. While many interesting results flow from the numerical experiments that we report here, it is
important to bear in mind that, like any model results, they depend on numerous explicit and implicit
assumptions ranging from model structure and parameterization to driving assumptions such as
technology performance, future economic activity levels, future population, and the future policy
environment. Several of these areas stand out as potentially fruitful areas for future research.
We have undertaken this analysis under the assumption that the climate remains unchanged. This is
clearly an unrealistic assumption. The question that needs to be tested by future research is the degree
to which this assumption might affect the results reported here. By experimental design we are
exploring a world in which RF is limited. That in turn will limit the degree of climate change feedback,
particularly when coupled with feedbacks from changing CO2 concentrations. But, fully coupled
experiments, including albedo and other direct land-use-land-cover effects, need to be undertaken.
Assumptions about economic activity and demographics determine the scale of human activities and
hence energy and land-use systems. We explore only one set of underlying assumptions. Other
socioeconomic pathways are clearly possible and experiments to test the robustness of results in the
face of these assumptions are also needed.
Similarly, this analysis is predicated on one set of future technology performance assumptions. Other
possible technology development paths are also possible. This analysis explores the relative
contribution of some technologies but clearly not all technologies. Potentially one of the more
interesting technology assumptions left unexplored was the rate of improvement in crop yields. Land1
Corresponding author, email: jae@pnnl.gov; telephone: +1 301.314.6749; fax: +1 301.314.6719.
Joint Global Change Research Institute (JGCRI) of the Pacific Northwest National Laboratory, 5825 University Research Court, Suite 3500,
College Park, MD, 20740.
3
Synapse Energy Economics, Inc., 485 Massachusetts Avenue, Suite 2, Cambridge, MA 02139
2
1
use patterns and land-use change emissions are affected by the rate of change of crop yields and
exploring the effect of varying crop-yield improvement assumptions is a potentially fruitful direction for
future research. Similarly, given the importance of transportation technology in a world with deep
emissions reduction, would these results be different in a world with advanced hydrogen or electric
vehicle technologies available?
The specific set of policy assumptions that we employ are another potentially rich vein of future
research. We have assumed that institutions exist that reward negative emissions at the same carbon
price as emissions are penalized—everywhere in the system including land-use systems as well as
energy and industrial systems. We assumed that terrestrial carbon emissions could be priced at the
same rate as fossil fuel and industrial emissions. Implications of imperfect policy institutions need to be
considered. Given the importance of land-use change emissions in limiting RF to low levels, research to
explore alternative land-use policies is clearly a high near-term priority. One interesting question that
needs investigation is, would BECCS increase or decrease the cost of emissions mitigation in a world that
continued to value terrestrial carbon emissions at a price of zero? What if the REDD were the world’s
terrestrial emissions mitigation policy? What if bioenergy were treated as if it were a fossil fuel rather
than a renewable energy form?
Implementation of the Delayed Accession Policy Scenarios
SOM Table 1: Assumed pattern of regional accession to emissions mitigation
Regions Joining Mitigation Regime
Western Europe, Eastern Europe, Japan
Australia/NZ, Canada, China, Korea, USA
India, L. America, Other South & East Asia
Africa, FSU, Middle East
Year when Region
First Takes on
Emissions Mitigation
Obligation
2020
2030
2050
2070
2
Year when Region Ends
Phase-in Period and
Faces Common Global
Emissions Price
NA
2040
2070
2095
A price is applied to carbon emissions as soon as a region begins emissions mitigation activities. It is
assumed that each region phases in the carbon price over a period of years after which the path follows
the reference international price Table 14.
Geographic Distribution of Carbon Reservoirs in GCAM Scenarios
SOM Figure 1: Regional Distribution of GCAM CO2 Storage Capacity
Energy System Implications of Technology Availability
The energy system varies dramatically both as a consequence of technology availability and the policy
environment employed to affect RF limits. The global energy system is substantially larger in the
reference scenario than in any of the control scenarios. Limiting RF to 2.6Wm-2 reduces aggregate enduse energy demand, increases reliance on a decarbonized power generation5. The relative roles of
bioenergy and CCS depend importantly on the technology environment in which the 2.6Wm-2 limit is
affected. When bioenergy is assumed to be available for emissions mitigation, bioenergy production
expands dramatically relative to the reference scenario. Deployment under mitigation is more than
double reference scenario deployment6. When both bioenergy and CCS are assumed available,
bioenergy is used extensively in combination with CCS but deployed relatively evenly between use in
power generation and as a liquid fuel feedstock in refining. When CCS was unavailable, bioenergy was
used predominantly as a liquid fuel feedstock.
4
Edmonds et al. (2008) discusses the potential problem of carbon price shock to participants joining a mitigation
coalition already in progress.
5
See Supporting Online Material Figures 2 and 3.
6
See Supporting Online Material Figure 4.
3
Decarbonization of end use energy use leads to expanded use of electricity both in relative and absolute
terms except under the T5 (LowTech) assumptions. Decarbonization of the power sector takes different
forms reflecting both the cost assumptions in GCAM’s technology set and their assumed availability.
When all technologies are assumed available (T1), then there is something of a balanced expansion in
the use of CCS with fossil fuels and bioenergy, expanded use of nuclear power, and expansion of other
renewable energy forms. Except in the T5 (LowTech) scenario, nuclear expands to fill the base-load
power requirements when either CCS or bioenergy is assumed unavailable. In the T5 (LowTech)
scenario decarbonization is affected entirely by non-biomass renewable energy forms.
4
T5 (LowTech) x
Delayed
T5 (LowTech) x
Idealized
T4 (No Bio & No
CCS) x Delayed
T4 (No Bio & No
CCS) x Idealized
T3 (No CCS) x
Delayed
T3 (No CCS) x
Idealized
T2 (No Bio) x
Delayed
T2 (No Bio) x
Idealized
T1 (Ref) x Delayed
T1 (Ref) x Idealized
Reference
SOM Figure 2: End-use Energy by Fuel (Oil, Gas, Coal, Biomass, Electricity, & H2) and Sector
(Residential, Industrial, & Transport)
5
T5 (LowTech) x
Delayed
T5 (LowTech) x
Idealized
T4 (No Bio & No
CCS) x Delayed
T4 (No Bio & No
CCS) x Idealized
T3 (No CCS) x
Delayed
T3 (No CCS) x
Idealized
T2 (No Bio) x
Delayed
T2 (No Bio) x
Idealized
T1 (Ref) x Delayed
T1 (Ref) x Idealized
Reference
SOM Figure 3: Electric Power Generation Technology Mix
6
T3 (No CCS) x Delayed
T3 (No CCS) x Idealized
T1 (Ref) x Delayed
T1 (Ref) x Idealized
Reference
SOM Figure 4: Bioenergy Utilization (EJ per year)
7
The marginal cost of BECCS is set by the carbon price. That is, like all technologies, BECCS is deployed up
to the point at which the marginal cost of mitigating another ton of carbon with BECCS is equal to the
price of carbon. Since the price of bioenergy is driven by the carbon price, the bioenergy price rises in all
of the control scenarios in which bioenergy is available, see SOM Figure 5 below, whereas it is relatively
stable in the reference scenario. Part of the increase in the price of bioenergy is an expansion effect.
There is more land allocated to bioenergy than in the reference scenarios. But a larger part of the story
is the escalation of the land rental rate to reflect the asset value of carbon in terrestrial systems.
SOM Figure 5: Global Bioenergy Prices
$70
2005 USD per GJ
$60
T1 (Ref) x Idealized
T3 (No CCS) x Idealized
$50
T1 (Ref) x Delayed
$40
T3 (No CCS) x Delayed
$30
Reference (NoPolicy)
$20
$10
$0
2000
2020
2040
2060
2080
2100
Terrestrial Responses to Climate Policy
As discussed in the paper, GCAM includes energy, economy and terrestrial system interactions. The
effect of introducing a carbon price dramatically changes land-use in GCAM. One feature of the
scenarios that we examine here is the effect of a carbon price on land use. Consider for example the
T1(Ref)xIdealized scenario. The carbon tax in the first time policy period of the T1(Ref)xIdealized
scenario is $60/tC. The imposition of this price on carbon dramatically changes the economics of land
use and dramatically changes the allocation of land in the model.
At a price of $60/tC, the asset value of the terrestrial carbon stock, which is approximately 2000 billion
tons, immediately shifts from zero to approximately $120 trillion. Every ton of carbon stored is worth
$60, which is the same price that is charged for each ton emitted. This in turn changes the expected
profitability of every activity undertaken by landowners. Carbon management becomes an important
8
economic consideration. The initial impact changes relative food prices and therefore shifts dietary
composition. The shift away from high-carbon-intensity elements such as cattle, frees up land both
because less pasture is needed, but also because less land is tied up growing crops to feed animals. In
addition, carbon emissions/sequestration effects of land use directly affect profitability. This in turn
affects relative profitability of every land-use, shifting land use toward options that are less carbonintensive. Land owners shift toward holding a larger stock of carbon. Unmanaged forest land holdings
are rewarded and increase dramatically. Carbon prices, which are plotted in Figure 2 in the paper, for
the 10 policy scenarios are given in the Table below:
SOM Table 2: CO2 Prices for Ten Policy Scenarios
2020
2025
2030
2035
2040
2045
2050
2055
2060
2065
2070
2075
2080
2085
2090
2095
Units
T1 (Ref)
x
Idealized
$16
$20
$26
$33
$42
$54
$69
$88
$112
$144
$183
$234
$298
$381
$486
$620
2005$/tC
O2
T2 (No
Bio) x
Idealized
$39
$50
$63
$81
$103
$132
$168
$215
$274
$350
$447
$570
$728
$929
$1,185
$1,512
2005$/tC
O2
T3 (No
CCS) x
Idealized
$28
$36
$46
$59
$76
$96
$123
$157
$200
$256
$326
$417
$532
$678
$866
$1,105
2005$/tC
O2
T4 (No
Bio & No
CCS) x
Idealized
$43
$55
$71
$90
$115
$147
$188
$240
$306
$390
$498
$636
$812
$1,036
$1,322
$1,687
2005$/tC
O2
T5
(LowTec
h) x
Idealized
$57
$72
$93
$118
$151
$192
$246
$313
$400
$510
$651
$831
$1,061
$1,354
$1,728
$2,206
2005$/tC
O2
T1 (Ref)
x
Delayed
$21
$27
$35
$44
$57
$72
$92
$118
$150
$192
$245
$312
$399
$509
$649
$829
2005$/tC
O2
T2 (No
Bio) x
Delayed
$177
$227
$289
$369
$471
$601
$767
$979
$1,249
$1,595
$2,035
$2,597
$3,315
$4,231
$5,400
$6,892
2005$/tC
O2
T3 (No
CCS) x
Delayed
$53
$68
$87
$111
$141
$180
$230
$294
$375
$479
$611
$780
$996
$1,271
$1,622
$2,070
2005$/tC
O2
T4 (No
Bio & No
CCS) x
Delayed
$187
$239
$305
$390
$497
$635
$810
$1,034
$1,320
$1,684
$2,150
$2,744
$3,502
$4,469
$5,704
$7,280
2005$/tC
O2
T5
(LowTec
h) x
Delayed
$254
$324
$413
$527
$673
$859
$1,096
$1,399
$1,785
$2,279
$2,908
$3,712
$4,737
$6,046
$7,716
$9,848
2005$/tC
O2
The monetization of the carbon stocks in the terrestrial biosphere leads to dramatic shifts in land use.
Between 2015 (year in which policy is initiated) and 2020 in the T1(Ref)xIdealized scenario, is
approximately 10Mkm2 (1Bha) change use to forest systems predominantly, but not exclusively in the
tropics. By the end of the century approximately 18Mkm2 (1.8Bha) have changed use relative to 2015
shifting toward unmanaged forest systems. Total land-use change between 2015 and 2095 is
approximately 14% of terrestrial land area tracked by GCAM (128.8 x 10^6 km2).
GCAM uses a dynamic terrestrial carbon cycle model and treats each land use and land-use change
explicitly. For example it uses a forest-growth model which assumes an “S” shaped forest growth curve.
GCAM does NOT assume constant carbon uptake for land-use change. See for example the carbon
density function for above-ground biomass for Tropical Unmanaged Forest in AEZ6 of Latin America
shown below. Mature age there is 40years, with a potential carbon density of 19.7KgC/m2. This is a
9
Chapman-Richards equation, which is a pretty standard function to model forest growth. See for
example, Pienaar, L. V., and K. J. Turnbull. 1973. “The Chapman-Richards Generalization of Von
Bertalanffy’s Growth Model for Basal Area Growth and Yield in Even - Aged Stands”. Forest Science,
Volume 19, Number 1, 1 March 1973, pp. 2-22(21)
SOM Figure 6: Above-ground carbon dynamics in a tropical forest in GCAM land region AEZ6 located
in Latin America
In the GCAM T1(Ref)xIdealized scenario carbon uptake is approximately 4.1PgC/y (15 PgCO2-e/y) in 2025.
Cumulative uptake over the course of the century is ~160PgC (between 2020 and 2095). This is smaller
than the ~300 PgC cumulative sequestration that are implied by a continuation of the linear
assumptions cited in the comment. Between 2015 and 2095 the GCAM T1(Ref)xIdealized scenario
stores ~160 PgC (584 PgCO2-e)on ~18Mkm2.
10
1000 Km2
80,000
40,000
40,000
20,000
20,000
0
0
120,000
120,000
100,000
100,000
80,000
80,000
40,000
40,000
20,000
20,000
0
0
120,000
120,000
100,000
100,000
80,000
80,000
40,000
40,000
20,000
20,000
0
0
120,000
120,000
100,000
100,000
80,000
80,000
40,000
40,000
20,000
20,000
0
0
120,000
120,000
100,000
100,000
80,000
80,000
60,000
40,000
40,000
20,000
20,000
0
0
T5 (LowTech)
x Delayed
11
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
2055
2060
2065
2070
2075
2080
2085
2090
2095
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
2055
2060
2065
2070
2075
2080
2085
2090
2095
1000 Km2
80,000
60,000
T4 (No Bio & No CCS)
x Delayed
60,000
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
2055
2060
2065
2070
2075
2080
2085
2090
2095
1000 Km2
60,000
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
2055
2060
2065
2070
2075
2080
2085
2090
2095
60,000
1000 Km2
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
2055
2060
2065
2070
2075
2080
2085
2090
2095
1000 Km2
T1 (Ref)
x Idealized
60,000
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
2055
2060
2065
2070
2075
2080
2085
2090
2095
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
2055
2060
2065
2070
2075
2080
2085
2090
2095
1000 Km2
T2 (No Bio)
x Idealized
100,000
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
2055
2060
2065
2070
2075
2080
2085
2090
2095
60,000
1000 Km2
0
2005 2015 2025 2035 2045 2055 2065 2075 2085 2095
1000 Km2
desert
grass
pasture (other)
urban
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
2055
2060
2065
2070
2075
2080
2085
2090
2095
20,000
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
2055
2060
2065
2070
2075
2080
2085
2090
2095
crops
forest (unmanaged)
pasture (grazed)
tundra
1000 Km2
80,000
100,000
60,000
T3 (No CCS)
x Delayed
biomass
forest (managed)
otherarable
shrubs
1000 Km2
40,000
T3 (No CCS)
x Idealized
100,000
120,000
60,000
T2 (No Bio)
x Delayed
60,000
T4 (No Bio & No CCS)
x Idealized
120,000
T5 (LowTech)
x Idealized
Reference
120,000
60,000
T1 (Ref)
x Delayed
1000 Km2
SOM Figure 7: Land Use by Major Category, 2020 to 2095, Across Scenarios
SOM Figure 8: CO2 Capture from Fossil Fuels, Cement Manufacture, and Bioenergy, 2020 to 2095
12
T5 (LowTech) x
Delayed
T5 (LowTech) x
Idealized
T4 (No Bio & No
CCS) x Delayed
T4 (No Bio & No
CCS) x Idealized
T3 (No CCS) x
Delayed
T3 (No CCS) x
Idealized
T2 (No Bio) x
Delayed
T2 (No Bio) x
Idealized
T1 (Ref) x Delayed
T1 (Ref) x Idealized
Reference
SOM Figure 9: Primary Energy Consumption by Energy Form (Primary Energy Equivalent, Fixed Values)
13
T5 (LowTech) x
Delayed
T5 (LowTech) x
Idealized
T4 (No Bio & No
CCS) x Delayed
T4 (No Bio & No
CCS) x Idealized
T3 (No CCS) x
Delayed
T3 (No CCS) x
Idealized
T2 (No Bio) x
Delayed
T2 (No Bio) x
Idealized
T1 (Ref) x Delayed
T1 (Ref) x Idealized
Reference
SOM Figure 10: Refined Liquid Fuel Supply by Source
14
15