Climate impacts of energy technologies depend on
emissions timing
Morgan R. Edwards ∗, Jessika E. Trancik∗†
Published 25 April, 2014 in Nature Climate Change
Abstract
Energy technologies emit greenhouse gases with differing radiative efficiencies and atmospheric lifetimes [1–3]. Standard practice for evaluating technologies, which uses
the global warming potential (GWP) to compare the integrated radiative forcing of
emitted gases over a fixed time horizon [4], does not acknowledge the importance of a
changing background climate relative to climate change mitigation targets [5, 6]. Here
we demonstrate that the GWP misvalues the impact of CH4 -emitting technologies as
mid-century approaches, and we propose a new class of metrics to evaluate technologies
based on their time of use. The instantaneous climate impact (ICI) compares gases
in an expected radiative forcing stabilization year, and the cumulative climate impact
(CCI) compares their integrated radiative forcing up to a stabilization year. Using
these dynamic metrics, we quantify the climate impacts of technologies and show that
high-CH4 -emitting energy sources become less advantageous over time. The impact
of natural gas for transportation, with CH4 leakage, exceeds that of gasoline within
1-2 decades for a commonly-cited 3 W/m2 stabilization target. The impact of algae
biodiesel overtakes that of corn ethanol within 2-3 decades, where algae co-products are
used to produce biogas and corn co-products are used for animal feed. The proposed
metrics capture the changing importance of CH4 emissions as a climate threshold is
approached, thereby addressing a major shortcoming of the GWP for technology evaluation [7, 8].
Comparing the climate impacts of energy technologies is challenging because they emit
differing types and quantities of greenhouse gases, most notably CH4 and CO2 , and these
gases have dissimilar properties (Fig. 1a,b). Present approaches to technology evaluation
use an equivalency metric to convert emissions to their CO2 -equivalent value [1–3, 9]. The
most common metric is the global warming potential (GWP(τ )), which takes the ratio of
the time-integrated radiative forcing of pulse non-CO2 and CO2 emissions over a fixed time
horizon (τ ), typically 100 years. The GWP(100) was initially intended as a placeholder [10],
in large part because of its sensitivity to the arbitrarily selected time horizon [7] (Fig. 1c,d),
but it remains the standard metric for technology evaluation.
∗
†
Engineering Systems Division (ESD), Massachusetts Institute of Technology (MIT)
Email: trancik@mit.edu
1
Various alternative metrics have been proposed for the purposes of emissions trading [11–
13] and demand sector emissions evaluations [8, 14]. These metrics are formulated to make
either instantaneous [15, 16] or time-integrated comparisons of gases [1, 9], based on their
relative contributions to radiative forcing [1, 12], temperature change [15, 16], or economic
impacts [17, 18]. Alternatively, some have argued that direct comparisons of gases are not
feasible, and have called for a multi-basket emissions policy [5, 6], where similar gases are
grouped into baskets and trading between baskets is prohibited.
Technologies emit multiple gases during their life cycles, however, and therefore an equivalency metric is required to compare their climate impacts on a single scale. For technology
evaluation, equivalency metrics must be forward-looking and robust to inherent uncertainties
about the future climate scenario, to inform the advanced commitment needed to develop
new technologies and infrastructure. Determining an appropriate metric for this application is becoming increasingly urgent as we consider major public and private investments in
technologies with significant CH4 emissions, including natural gas [1, 19–22].
Although many equivalency metrics have been proposed, previous research has not emphasized testing their performance against intended climate goals to determine a principled
treatment of time in metric formulations. Here we propose a new class of dynamic metrics
that, unlike the static GWP(τ ), are designed to avoid an overshoot of an intended radiative
forcing stabilization level. We develop a method to test the performance of these and other
metrics against this climate change mitigation goal. The new metrics differ from other dynamic metrics [16–18] in that they do not require detailed information about the emissions
scenario for achieving the mitigation goal. Climate targets are commonly formulated around
a stabilization level [23], which can be reached by a number of emissions scenarios. The
proposed metrics are designed to evaluate technologies in this context.
2
b!b!
100!
100!
10!10!
800!
800!
CH4!
CH
CH
CH4!
4 4
CO
CO
CO2!
CO2!
2! 2!
!
!
!
!
!
Gasoline
Gasoline!!
400!
400!
1! 1!
200!
200!
0.1!
0.1!
0.01!
0.01!
0! 0! 20!20! 40!40! 60!
60! 80!
80! 100!
100!
Time
Since
Emission
(years)!
Time
Since
Emission
(years)!
Gasoline! !
Gasoline
Baseline!
Baseline!
200!
200!
100!
100!
Gasoline!
Gasoline!
Diesel!
Diesel!
CNG!
CNG!
Corn E85!
Corn E85!
Cane E85!
Cane E85!
Soy BD!
Soy BD!
Algae BD!
Algae BD!
Electricity!
Electricity!
0! 0!
300!
300!
200!
200!
Gasoline! !
Gasoline
Baseline! N2O!
Baseline!
N2O!
NN2O!
2O!
! !
CH4!4!
CH
CH4!
CH4!
! !
100!
100!
0!0!
CO2!2!
CO
CO2!
CO2!
Electricity!
Electricity!
300!
300!
0!0!
0!0! 20!
20! 40!
40! 60!
60! 80!
80! 100!
100!
Time
TimeSince
SinceEmission
Emission(years)!
(years)!
d!d!400!
400!
400!
400!
Impact (g CO2-eq/km)!
Impact (g CO2-eq/km)!
c! c!
Impact (g CO2-eq/km)!
Impact (g CO2-eq/km)!
CNG
CNG!
!
600!
600!
Gasoline!
Gasoline!
Diesel!
Diesel!
CNG!
CNG!
Corn E85!
Corn E85!
Cane E85!
Cane E85!
Soy BD!
Soy BD!
Algae
Algae BD!
BD!
RF (W/m2 x 10-18)!
RF (W/m2 x 10-18)!
1000!
RF (W/m2 x 10-18/km)!
RF (W/m2 x 10-18/km)!
a!1000!
a!
Figure 1: Comparisons of greenhouse gases and technologies depend on the evaluation horizon. a, CH4 has 102 times the radiative forcing per gram of CO2 but decays
more quickly, with the gases having equal radiative forcing (RF) 67 years after emission [4].
b, As a result, the impact of using technologies decays over time at different rates, as the
comparison of gasoline and CNG illustrates. c,d, These dynamics explain why the impacts of
technologies, notably algae biodiesel with a biogas co-product, change when evaluated over
a 100-year versus (c) versus a 20-year (d) time horizon. (BD, biodiesel; CNG, compressed
natural gas.)
We begin by examining the GWP(100) for later comparison with the new metrics proposed. Although it has been shown that the GWP(100) leads to an imperfect equivalency
when used to replace CO2 with CH4 [5], and can lead to economically inefficient decisions
in scenarios with exogenously-constrained climate outcomes [24], its performance has not
been tested against intended climate goals. We find that using the GWP(100) leads to a
significant overshoot of radiative forcing stabilization targets. For example, on the basis of
the GWP(100), compressed natural gas (CNG) appears to have an advantage over gasoline
per kilometer traveled (Fig. 1c). However, in a hypothetical scenario in which CNG is used
to meet US passenger vehicle energy demand, radiative forcing is significantly higher than
GWP(100)-based projections suggest (Fig. 2a). In a scenario in which global transportation
demand is supplied by an energy source with the CH4 intensity of CNG, a stabilization
target of 3 W/m2 is exceeded by almost 5% (Fig. 2b), and by 12% if CH4 emissions are
maintained at their current percentage of global CO2 -equivalent emissions. These errors
become increasingly concerning over time as climate thresholds are approached.
The metrics proposed here aim to address this concern by appropriately evaluating technologies with significant CH4 emissions. The objective is to limit the risk of a significant
and sustained overshoot of an intended radiative forcing stabilization level.
3
b!
8 !
!
60!
6!
RF (W/m2)!
RF (W/m2 x 10-2)!
a! 80!
!
40!
4!
!
2!
20!
0!
2010!
b!
!
60!
6!
!
40!
4!
!
!
2050!
3 W/m2 !
Gasoline
3! Projected!
Gasoline
Projected!
Threshold!
CNG
Projected!
CNG Projected!
RF (W/m2)!
RF (W/m2 x 10-2)!
a! 80!
8 !
2030!
Year!
!
3!
2!
2010!
Gasoline
Actual!
Gasoline
Actual!
CNG
Actual!
CNG Actual!
2030!
Year!
!
2050!
Intended
IntendedTrajectory!
Trajectory!
Actual
Trajectory!
Actual Trajectory!
2.5!
Figure 2: GWP(100) underestimates the radiative forcing contribution of CH4 emitting technologies.
a, The actual radiative forcing (RF) impacts of satisfying constant
0!
2!
US
passenger
vehicle
energy
with gasoline
2010!
2030!
2050! 2010! demand
2030!
2050! versus CNG differ from GWP(100)-based
Year!
Year)! for CNG due to its higher CH4 intensity. b, A hypoprojections,
with a greater discrepancy
situation
is shown (based
onTrajectory!
scenario C in Fig. 3a), in which global transportation
NG Actual!
CNG
Projected!
CNG Actual! thetical
CNG Projected!
Intended
emissions
are
replaced
by
emissions
from
a technology with the CH4 intensity of CNG, using
Gasoline
Actual!Actual!
Gasoline
Projected!
Gasoline
Gasoline
Projected!
Actual Trajectory!
the GWP(100) to determine CO2 equivalence (Supplementary Section 1.2). This results in
a significant deviation from the intended radiative forcing scenario.
20!
2!
The first metric, the CCI, is based on the time-integrated radiative forcing from the
emission time (t0 ) to an intended stabilization time (tS ) and the second metric, the ICI, on
the radiative CNG
forcing
at time tS :
CNG Actual!
Projected!
Rt
Gasoline Actual!
Gasoline Projected!
AM t0S fM (t00 , t0 )dt00
0
for all t0 ≤ tS
(1)
CCI (t , tS ) =
R tS
00
0
00
AK t0 fK (t , t )dt
AM fM (tS , t0 )
ICI (t , tS ) =
AK fK (tS , t0 )
0
for all t0 ≤ tS
(2)
where A is the radiative efficiency, f (t, t0 ) is the removal function (see equation (5) in Methods), and subscripts K and M refer to CO2 and CH4 , respectively. For emission times t0 ≤ tS ,
the radiative forcing is evaluated over time t00 (from t0 to tS ) for the CCI and at time tS for
the ICI. For emission times t0 > tS , both metrics are defined as the instantaneous radiative
forcing ratio of the two gases. The CCI places a greater value on CH4 emitted prior to tS ,
after which time the two metrics are equivalent.
These dynamic metrics, in contrast to the static GWP(τ ), explicitly account for climate
stabilization goals in their formulation (Supplementary Section 2.1). As the emission time
(t0 ) nears the intended stabilization time (tS ), the evaluation time horizon decreases and
the CO2 -equivalent value of CH4 increases, in order to limit the overshoot of the intended
stabilization level. (Any overshoot arises from CH4 rather than CO2 emissions, since CO2
emissions are evaluated directly, not through an equivalency metric (Supplementary Section
3).) Unlike previously-proposed dynamic metrics (Supplementary Section 2.2), including
those comparing the cost effectiveness of emissions reductions along a mitigation scenario [17]
4
!
!
!
Metric
(g CO2)!
CH4)! CO2-eq/g CH4!
RF (W/m2)! Global
RF (W/m
2-eq/g Grams
Global RF (W/m2)!
Global RF (W/m2)!
RF (W/m2)!
RF (W/m2)!
and those based on temperature [15,16,18], the CCI and ICI are formulated so that the only
input they require on the climate scenario is the intended radiative forcing stabilization level.
A set of possible stabilization years is determined based on this level, giving a range of metric
values for each emission year.
The approach developed to compute the metrics and test their performance is illustrated
with an example below. The approach involves developing reference scenarios for emissions
and radiative forcing stabilization (equations (3)-(6) in Methods and Supplementary Section
1.1), calculating metric values for the reference scenario range (equations (1) and (2) and
Supplementary Section 2.1), and testing metrics against the reference scenarios (equations
(7) and (8) in Methods, and Supplementary Section 1.2 and 3).
To calculate the emission-time-dependent CCI and ICI values, a range of possible scenarios is determined for a commonly cited 3 W/m2 radiative forcing threshold (Fig. 3a), which
a!
b!
in equilibrium is associated with a 2◦ C temperature
change. These scenarios define a range
S-A! 100!
ICI S-A!
Scenario
A!
3! 3 W/m2 !
of years when radiative forcing must stabilize to avoid exceeding 3 W/m2!Scenario
. This roughly
A!
S-B!
2.8! Threshold!
Scenario
B!
80!
CCI
15-year range defines the possible
values of tS for calculating the CCI and! ICI.S-A!
Figure 3b
Critical!
2.6!
60!
S-C!
Scenario A!
! of Scenario
ICI S-E!
shows the CCI
and ICI for this Time
range
tS values.C!
2.4!
!
40!
S-D!
Scenario
D!
Scenario
CCI
S-E! E!
2.2!
20!
S-E! b! E!
Scenario
a!
Scenario E!
0!
2!
A!
ICI-A!
ICI Scenario
Scenario
A!
3!
100!
!
!
2010!
2025!
2040!
2055!
2010! 2025! 2040! 2055!
A!
B!
CCI Scenario
Scenario80!
B! Time of Emission (years)! CCI-A!
!
Time (years)!
A!
C!
ICI-E!
ICI Scenario
Scenario
C!
c!
d! 60!
!
!
E!
40!
Ref.!
D!
CO2
Only!
Ref.!
CO2
Only!
CCI-E!
CCI Scenario
Scenario D! 3 W/m2 !
tS!
!
3 W/m2 !
! !
20!
E!
3!
3! Threshold!
E!
Threshold!
GWP!
GWP!
GWP!
GWP
Scenario E!
GWP
!
!
0!
2!
Metric!
Metric!
2010! 2025! 2040! 2055! ICI!
2010! 2025! 2040! 2055! ICI!
ICI Metric!
ICI Metric!
!
2.5!
2.5!
!
Year
of
Emission!
Year!
CCI!
CCI
CCI!
CCI
!
c!2!
d!
Metric!
!Metric!
2!
!
Ref.!
Ref.!
CO2 Only!
2010! 2025! 2040! 2055! ! ! CO2 Only!
2010! 2025! 2040! 2055! !
3!
3!
GWP!
GWP!
GWP
GWP Metric!
Time (years)!
Time (years)!
!
!
Frame!
!
!
!
Metric!
!
ICI!
ICI Metric!
!
CCI!
CCI Metric!
2!
2010!
!
!
2025! 2040!
Year!
2!
2010! 2025! 2040! 2055!
Year!
2055!
ICI!
ICI Metric!
CCI!
CCI Metric!
!
!
Figure 3: Metric development. a, A range of scenarios, consistent with a 3 W/m2 stabilization level, is used to calculate equivalency metrics. They range from a delay scenario
followed by rapid emissions reductions (scenario A, tS =2039), to a gradual emissions reduction scenario (scenario E, tS =2054). b, We compare the valuation of CH4 under the ICI,
CCI, and GWP(100), using A and E to represent the range of scenarios consistent with the
3 W/m2 threshold. c,d, The radiative forcing resulting from using these metrics to allocate
5% of CO2 -equivalent emissions to CH4 is shown, using scenario A (c) and scenario E (d).
We simulate emissions decisions using the ICI, CCI, and GWP(100) to test the metrics’
performance. Figure 3c,d shows the results under two scenarios that span the set of feasible
5
scenarios for a 3 W/m2 stabilization level. Radiative forcing remains below 3 W/m2 for all
scenarios using the CCI but exceeds this value using the GWP(100). Using the ICI under
scenario E, a gradual emissions reduction scenario with a later tS , radiative forcing temporarily exceeds 3 W/m2 before tS because of a lower impact value placed on CH4 emissions early
on (Supplementary Section 3). Radiative forcing remains below this level when applying the
ICI under scenario A, a delay scenario followed by rapid emissions reductions. Both the ICI
and CCI avoid the sustained threshold overshoot that results from applying the GWP(100),
with the ICI resulting in a temporary overshoot for some scenarios and the CCI preventing
any overshoot.
Using the GWP(100) and the CCI/ICI with tS defined by scenario C (Fig. 3a), we then
compare the climate impacts of several prominent energy technologies (where technology
refers to a fuel and conversion technology) that emit both CH4 and CO2 . Pairwise comparisons are performed for gasoline and CNG, corn ethanol and algae biodiesel (with corn
co-products used for animal feed and algae co-products used to produce biogas), and coal and
natural gas electricity (Fig. 4 and Supplementary Fig. S9 for other comparisons). We also
generate low-CH4 emissions scenarios for the three high-CH4 emitters (CNG, algae biodiesel,
and natural gas electricity) and compare the results to our baseline scenario (Supplementary
Sections 4 and 5). Results for the low-CH4 scenarios are indicated by curly brackets. Notably, for the two pairs of transportation technologies, the rank ordering of climate impact
depends on the emissions time.
Initially the impact of CNG is lower than that of gasoline, based on the ICI, but then
exceeds it in 6-21 {18-28} years, or 11 {24} years for scenario C. The more conservative
CCI, in contrast, indicates higher impact of CNG for all years {after 9-21 years}. Algae
biodiesel shows a significantly lower impact than corn ethanol initially, but then surpasses
it in 23-38 years when applying the ICI and 21-35 years when applying the CCI, owing
to CH4 emissions in the production of biogas from algae co-products. The impact of algae
biodiesel remains lower than corn ethanol for all years, however, under the low-CH4 emissions
scenario, highlighting the importance of co-product processing techniques in determining the
climate impacts of biofuels. Natural gas electricity starts at 53% {52%} the climate impact
of electricity from coal (using the ICI), but then rises to 82% {72%} the impact of coal over
time.
We then simulate the radiative forcing resulting from several hypothetical scenarios where
all US passenger vehicle energy demand is met with a single technology. These simulations
approximate how technologies are assessed through the lens of each metric. Figure 5a compares the results to the sector radiative forcing budget, again determined using scenario C
(from Fig. 3a). Figure 5b shows the corresponding vehicle kilometers traveled. Assessing
technologies with the GWP(100) results in a large radiative forcing budget overshoot, due
to relatively high energy consumption, which endures past tS (Supplementary Section 3).
Applying the ICI results in a significantly lower and more temporary overshoot that is reduced before tS , concurrent with a decrease in energy consumption. Application of the more
conservative CCI avoids a threshold overshoot and results in the lowest energy consumption.
6
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600"
300"
GWP"
8"
200"
600"
200"
GWP"
black"
bd"
CCI"
o"
ICI"
ICI"
10"
grey"
bd"
400"
150
ICI"
CCI"
ICI
o"
CCI"
rd"
400"
ICI""
CCI"
1"
g"
400"
400"
200"
300"
300"
2"
200"
ICI"
4"4"
200"
200"
200"
ICI"
100"
100"
ICI"
ICI""
100
400"
6"
grey"
CCI"
400"
CCI"
od"
grey"dash"
300"
rd"
400"
ICI""
CCI
Gasoline"
CNG"
ICI""
CCI""
rd"
Algae"BD"
300" Corn"E85"
ICI""
Corn"E85"
Algae"BD"
gd"
300"
300"
100"
200"
b"
200"
CCI"
0"
100"
100"
6"
0"
50
0"
100"
CCI"
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Corn"E85"
12"
Algae"BD"
CCI""
ICI""
CCI"
6"
Corn E85
Algae BD
ICI""
200"
grey"dash"
black"
ICI""
Algae"BD"
CCI""
Corn"E85"
gd"
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12"
Corn"E85"
8" 2050"
Algae"BD"
Corn"E85"
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CCI""
200"
1"
gd"
0"2010"
200"
200"
100"
bd"
100"
2010"
2030" 2"
2050" 2010"
2050"
2030"
2030"
2050"
ICI""
2030"
2010"
2030"
0Corn"E85"
o"
0" 2050"
0"2010"
0"
ICI""
0"
2"
Gasoline"
CNG"
Algae"BD"
ICI""
Corn"E85"
Algae"BD"
Gasoline"
CNG"
CCI""
CCI""
100"
Gasoline"
CNG"
800"
2010"
2030"
2050"
2010"
2030"
2050"
CCI""
grey"
4"
800"
800"
2010
2030
2050
r"
2010
2030
2050
bd"
100"
grey"
1"
0"
100"
100"
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2030"
2030"
2050"
0"0"
Corn"E85"
2010"
Algae"BD"
CCI""
0"2010"
2030"
2050"
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2050"
r"
2010"
2030" 2050"
2050" 2010"
2010"
2030"
2050"
od"
0"
GWP"
GWP"
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Corn"E85"
Algae"BD"
CCI""
Corn"E85"
Corn"E85"
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CCI""
500"
10"
1000
4"
0"
4"
2010"
2030"
2050"
2010"
2030"
2050"
2010"
2030"
2050"
grey"dash"
2010"
2030"
2050"
2010"
2030"
10"
g"
2010"
2030"
2050"
2010"
GWP"
2030"
0"
800"
600"
2010"
2030"
2050"
2010"
2030"
2050"
600"
od"
800"
0"
0"
GWP"
grey"dash"
6" 2050" 2010"
od"
c 600"2010"
g"
ICI"
ICI"
black"
0"
2030" 12"
2030"
2050"
GWP"
gd"
400"
800"800
0" 2050"
2010"
2030"12"
2050" 2010"
2030"
2010"
2010"
2030"
2030"
2050"
2010"
2030"
2050"
2050"
ICI"
GWP"
b"
GWP"
2"
800"
800"
ICI"
600"
800"
1"
GWP"
GWP
600"
black"
500"
black"
CCI"
400"
b"
400"
CCI"
400"
500"
grey"
500"
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1"
300"
GWP"
800"
8"
800"
600"600
bd"
GWP"
2"
600"
2"
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8"
ICI"
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o"
r"
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600"
ICI
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GWP"
400"
400"
600"
800"
ICI""
400"
grey"
r"
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4"
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400"
400"
200"
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GWP"
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400
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800"
800"
600"
ICI"
600"
400"
10"
od"
ICI""
ICI"
10"
CCI"
CCI"
ICI""
rd"
400"
CCI"
ICI"
g"
300"
Gasoline" 0"
CNG"
CCI
CCI""
200"
400"
400"
200"
600"
g"
CNG"
400" Gasoline"
CCI""
rd"
grey"dash"
grey"dash"
Gasoline"
CNG"
300"
CCI""
ICI""
300"
100"
ICI"
CCI"
ICI"
6"
CCI"
600"
0"200Coal"
400"
600"
600"
Natural"Gas"
400"
200"
CCI""
0"
Coal"
Natural"Gas"
CCI"
0"
1" Gas
0"
Corn"E85"
black"
0"
Algae"BD"
ICI""
CCI""
6"
ICI""
Natural
gd"
Coal
CCI"
ICI""
200"
0"
b"
200"
200"
400"
gd"
200"
200"
0"
200"
Natural"Gas"
CCI""
2010"
2030" 2" 2050"
2050" 2010"
2010"
2030"
2050"
0"
ICI""
200"
CCI"
ICI""
2010"
2030"
2050"
0 Coal" 2030"
CCI"
1"
2010"
2030"
2050"
2010"
2030"
2050"
1"
400"
200"
ICI""
400"
400"
200"
0"
Coal"
Natural"Gas"
Natural"Gas"
Coal"
CCI""
8"8" 2050"
Coal"2030"
Natural"Gas"
CCI""
Gasoline"
CNG"
2010"
2010"
2030"
2050"
bd"
grey"
ICI""
100"
2030"
CCI""
2010"
2030"
2010"
2030"
2050"
2010"
2050"
2030"
2010
2030
2050
2010
2030
2050
o"
100"
bd"
Coal" 2030" 4" 2050" 2010"
Natural"Gas"
200"
Natural"Gas"
100"
0"0"0"
CCI""
0" Coal"
ICI""
Corn"E85"
Algae"BD"
ICI""
2030"
2050"
500"
Corn"E85"
Algae"BD"
CCI""
200"
0"2010"
Time"of"Use"(years)"
Time"of"Use"(years)"
Corn"E85"
Time"of"Use"(years)"
Time"of"Use"(years)"
0"
Algae"BD"
200"
200"
Coal"
Natural"Gas"
CCI""
500"
0"
Year
of4"Emission
od"
Year
of Emission
500"
grey"dash"
2010"
2030"
2050"
2010"
2030"
2050"
2010"
2030"
2050"
2010"
2030"
2050"
2010"
2030"
2050"
2010"
2030"
2050"
2010"
2030"
2050"
2010"
2030"
2050"
GWP"
rd"
0"
od"
0"
Coal"
Time"of"Use"(years)"
Time"of"Use"(years)"
0"
CCI""
rd"
Coal"
Coal"
Natural"Gas"
Natural"Gas"
GWP"
CCI""
GWP"
2010"
2030" 0" 2050"
2050" 2010"
2010"
2030"
2050"
2010"
2030"
2030"
2050"
400"
2010"
2030"
2050"
2010"
2030"
2050"
GWP"
0"
800"
400"
Time"of"Use"(years)"
Time"of"Use"(years)"
0"
0"
black"
400"
2010"Time"of"Use"(years)"
2030" 6"6" 2050"
2050" 2010"
2010"
Time"of"Use"(years)"
Time"of"Use"(years)"
Time"of"Use"(years)"
2010"
2030"
2030"
2050"
2010"
2030"
2050"
2010"
2030"
ICI"
gd"
black"
Time"of"Use"(years)"
Time"of"Use"(years)"
Time"of"Use"(years)"
Time"of"Use"(years)"
1"
ICI"
gd"
ICI"
2" 2050"
2010"
2030" 2"
2050"
2010"
2030"and2050"
500"2010"
300"
2010"
2030"
2030"
2030"
Figure 4: Technology
a-c,2010"
Gasoline
CNG with ICI"
CH
600"comparisons.
300"
Time"of"Use"(years)"
Time"of"Use"(years)"
300"
4 leakage (a), corn
grey"
CCI"
GWP"
bd"
grey"
GWP"
CCI"
800"
Time"of"Use"(years)"
Time"of"Use"(years)"
CCI"
Time"of"Use"(years)"
Time"of"Use"(years)"
Time"of"Use"(years)"
200"
400"
bd"
CCI"
GWP"
800" biodiesel4" (BD) with biogas production (b),
ethanol without and 200"
algae
and coal- and natuGWP"
800"
400"
200"
grey"dash"
ICI""
ICI"
4"
od"
grey"dash"
ICI"
ICI""
ICI""
600"
100"
0"
300"
ICI""
od"
ICI"
ral gas-fired electricity
(c)
are
compared
using
the
GWP(100),
ICI,
and
ICI" CCI. The relative
600" Corn"E85" 0"
100"
100"
600"
Algae"BD"
CCI""
200"
CCI"
1"
black"
CCI"
Algae"BD"
CCI""
Corn"E85"
Algae"BD"
CCI""
0" Corn"E85"
400"
200"
1"
Coal"
Natural"Gas"
CCI""
CCI"
impacts change over time.
baseline
CH4 emissions assumptions,
black"
CCI"the impact of CNG
2"
0"
0"0"2010"Under
400"
ICI""
400"
2030" 2" 2050" 2010"
2030"
2050"
ICI""
grey"
100"
200"
2010"
2030"
2050"
2010"
2030"
2050"
2010"
2030"
2050"
2010"
2030"
2050"
overtakes that of gasoline
after
11 years
when
applying
ICI usingICI""
scenario C in Fig. 3a,
2010"
2030"
2050"
ICI""
2050"
2010"
2030" the
grey"
Corn"E85"
Algae"BD"
CCI""
200"
200"
Coal"
Natural"Gas"
CCI""
grey"dash"
0"
Coal"
Time"of"Use"(years)"
Time"of"Use"(years)"
0"the
CCI""
Natural"Gas"
Coal"
Natural"Gas"
and 6-21 years based on
full
range
of
stabilization
scenarios
investigated.
CCI""
0"
GWP"
800"
grey"dash"Algae biodiesel
0"0"
2010"
2030"
2050" 2010"
2010"
2030"
2050"
2010"
2030" 0" 2050"
2030"
2050"
GWP"
GWP"
800"
1"
800"
overtakes corn ethanol600"after
years 2050"
for
C
and the
23-38 years based on
2010" 282030"
2030"
2050"scenario
2010"
2030"
2050"
2010"
2010"
2030"
2050"ICI, andICI"
Time"of"Use"(years)"
Time"of"Use"(years)"
1"
ICI"
ICI"
600"
600"
all scenarios.
Time"of"Use"(years)"
Time"of"Use"(years)"
CCI"
Time"of"Use"(years)"
Time"of"Use"(years)"
GWP"
800"
400"
CCI"
CCI"
400"
400"
ICI""
ICI"
200"
600"
ICI""
ICI""
Natural"Gas"
CCI""
200"
200" Coal"
CCI"
0" Coal"
400"
Natural"Gas"
CCI""
Coal"
Natural"Gas"
CCI""
0"
0"2010"
2030"
2050" 2010"
2030"
2050"
ICI""
200"
2010"
2030"
2050"
2030"
2050"
2010"
2030"
2050" 2010"
2010"
2030"
2050"
Time"of"Use"(years)"
Time"of"Use"(years)"
Coal"
Natural"Gas"
CCI""
0"
Time"of"Use"(years)"
Time"of"Use"(years)"
Time"of"Use"(years)"
Time"of"Use"(years)"
2010"
2030"
2050" 2010"
2030"
2050"
Time"of"Use"(years)"
Time"of"Use"(years)"
7
4!
2!
0!
2010!
8!
Gasoline!
2030!
CNG!
2050!
2010!
2030!
6!
4!
2!
0!
2010!
CNG!
Gasoline!
2030!
Year!
2050!
2010!
2030!
Year!
GWP"
GWP"
o"
Scenario"D"
g"
ICI"
ICI"
GWP"
rd"
Scenario"E"
b"
CCI"
CCI"
r"
GWP"
ICI"
gd"
o"
ICI""
Scenario"A"
ICI""
ICI"
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rd"
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Scenario"B"
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od"
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Scenario"C"
CCI""
r"
b"
r"o"
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r"black"
Scenario"D"
g"
rd"
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r"
ICI"
GWP"
o"
r"
g"
Budget!
GWP"
od"
grey"
Reference"
CO2"Only"
o"
ICI"
g"
GWP"
Scenario"E"
!
ICI"
b"
gd"
"ICI"
g"
CCI"
g"
GWP"
rd"
ICI"
b"
GWP!
GWP"
black"
grey"dash"
CCI"
rd"
ICI"
GWP"Metric"
b"
GWP"
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o"
bd"
!
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b"
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GWP"
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GWP"Metric"
b"
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!
b"
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black"
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ICI!
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ICI"Metric"
!
o"
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CCI"
g"
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ICI"
!
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rd"
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CCI !
b"
CCI"Metric"
ICI""
grey"dash"
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od"
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grey"dash"
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grey"
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grey"
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grey"dash"
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grey"dash"
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GWP"
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ICI""
CCI"
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CCI"
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ICI""
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2)"
2)"
CH4"Value"(g"CO
CH4)"4"Value"(
23eq/g"CH
RadiaIve"Forcing"(W/mRadiaIve"Forcing"(W/m
6!
2)"
2)"
RadiaIve"Forcing"(W/mRadiaIve"Forcing"(W/m
RadiaIve"Forcing"(W/m^2)"
RadiaIve"F
RF (W/m2 x 10-2)!
8!
Impact"(g"CO
Impact"(g"CO
4eq/mile)"
Impact"(g"CO
4eq/mile)"
Impact"(g"CO
Impact"(g"CO
4eq/mile)"
Impact"(g"CO
4eq/mile)"
Impct"(kg"CO
Impact"(g"CO
Impct"(kg"CO
Impct"(kg"CO
Impact"(g"CO
4eq/mile)"
Impact"(g"CO
4eq/mile)"
Impct"(kg"CO
Impact"(g"CO
224eq/mile)"
Impct"(kg"CO
4eq/MWh)"
Impct"(kg"CO
4eq/MWh)"
Impact"(g"CO
Impact"(g"CO
224eq/mile)"
Impact"(g"CO
4eq/mile)"
Impact"(g"CO
2Impact"(g"CO
Impact"(g"CO
Impact"(g"CO
4eq/mile)"
Impact"(g"CO
4eq/mile)"
4eq/mile)"
Impact"(g"CO
24eq/MWh)"
24eq/mile)"
24eq/MWh)"
224eq/MWh)"
Impct"(kg"CO
4eq/MWh)"
Impct"(kg"CO
Impact"(g"CO
Impact"(g"CO
2224eq/MWh)"
24eq/mil
2Impact"(g"CO
2Impact"(g"CO
224eq/mile)"
24eq/mile)"
Impct"(kg"CO
Impct"(kg"CO
Impact"(g"CO
4eq/mile)"
Impact"(g"CO
4eq/mile)"
Impct"(kg"CO
Impct"(kg"CO
Impct"(kg"CO
Impct"(kg"CO
Impact"(g"CO
Impact"(g"CO
Impct"(kg"CO
Impct"(kg"CO
Impact"(g"CO
Impact"(g"CO
24eq/mile)"
24eq/mile)"
22Impact"(g"CO
2Impact"(g"CO
24eq/mile)"
24eq/mile)"
Impact"(g"CO
4eq/mile)"
224eq/MWh)"
24eq/mile)"
24eq/mile)"
24eq/MWh)"
24eq/MWh)"
24eq/MWh)"
24eq/MWh)"
24eq/MWh)"
24eq/MWh)"
224eq/mile)"
24eq/mile)"
24eq/mile)"
24eq/mile)"
224eq/MWh)"
2224eq/MWh)"
24eq/mile)"
24eq/mile
Impct"(kg"CO
Impct"(kg"CO
Impact"(g"CO
4eq/mile)"
Impact"(g"CO
24eq/mile)"
24eq/MWh)"
24eq/MWh)"
24eq/mile)"
b!
Distance (km x 1012)!
a!
Frame"
800"
10"8" 2.4"
600"
12"
800"
600"
2.2"
800"
400"
12"
A."
600"
3"W/m2"Threshold"
400"
8"6" 3"
12"
2"
10"
600"
200"
2010"
2025"
2040"
2055"
400"
200"
Gasoline" 12" 2.8"
CNG"
10"
Gasoline" 12"
CNG"Time"(years)" CriIcal"
400"
0"
4"
6"
10"
200"
0"
2.6" 2010"
2010"
2030" 12"
2030"
2050"
Time""
8" 2050"
12"
800"
Gasoline"
CNG" 2030"
2010"
2030"12"
2050" 2010"
2050"
200"
Frame"
800"
0"
2"Threshold"
800"
2.4"
10"
Gasoline"
CNG"
3"W/m
800" 8!
500"
600"
2"
4"8"
2030"10"
2010"
2030"
2050"
0"2010"
8" 2050"
500"
800"
600"
10"
3"
6" 2050"
12"
2.2"
800"
600"
10"
400"
2010"
2030"
2010"
2030"
2050"
10"
600"
6!
800"
800"
400"
12"
12"
400"
800"
A."
500"
600"
400"
8"
12"
300"
2"
600"
400"4!
6"
8"
2"
0"
300"
400"
500"
600"
600"
6"
200"
400"
8"
600"
2025"
2040"
2055"
400"
200"
200"
8" 2.5"2010" CNG"
8"4"
1"
10"
200"
400"2! Gasoline" 10"
400"
300"
10"
200"
Gasoline" 6"
CNG"Time"(years)"
400"
400"
0"
100"
400"
Gasoline"
CNG"
10"
200"
12"
Corn E85!
BD!
0" Corn"E85"
100"
CNG" Algae
300"
0"
6"
4"
Algae"BD"
2010"
2030" 12"
2030"
2050"
200"
200"
0"
0! Gasoline"
4" 2050" 2010"
Corn"E85"
Algae"BD"
Gasoline"
CNG"
12"
6"
0" 2010"
200"
2030"
2010"
2030"
2050"
200"
2"
6"
1"
6" 2050"
800"
2010!
2030!8"
2050! 2010"
2010!
2030! 2050"
2050!C."
2050! 200"
Gasoline"
CNG"
0"
200"
2010"
2030"
2050"
2030"
8"
100"
2"Threshold"
Gasoline"
CNG"
Gasoline"
CNG"
2010"
2030"
2050"
2010"
2030"
2050"
3"W/m
800"
2"
0"
8"
500"
Corn"E85"
Algae"BD"
Gasoline"
CNG"
16!
2010"
2030"
2050"
2010"
2030"
2050"
4"8"
0"
0"
100"
500"
10"
600"
2025"
0"0"
3"2010"2010"
2010"
2030"12"
2050"
2030" 2040"
2050" 2055"
4"
2"
500"
Corn"E85"
800"
10"
Algae"BD"
10"
400"
2010"
2030"
2030"
2050"
2" 2050"
2010"
2030"12"
2050" 2010"
2010"
600"
4"
800"
500"
4"0"
2010"
2030"
2050"
4" 2050"
0"12!
400"
2010" 2030"
2030"6"
2050" 2010"
2010" Time"(years)"
2030"
2050"
800"
800"
500"
6"
400"
1"
800"
300"
500"
600"
2010"
2030" 2"6" 2050" 2010"
2030"
2050"
400"
300"
500"
600"
500"
6"
400"
8"
600"
600"8!
300"
2"
500"
200"
200"
0"
8"
8"
400"
600"
10"
800"
0" 2.5"
300"
2"
400"
2"
200"
400"
10"
2"
400"
300"
4"
200"
Gasoline"
CNG" 1"
4!
400"
400"
200"
400"
100"
4"
1"
800"
300"
400"
400"
0" Corn"E85"
600"
200"
100"
Gasoline"
CNG" Algae BD!
300"
Corn E85!
Algae"BD"
0"4"
300"
200"
200"
4"
100"
200"
6"
Algae"BD"
0! Corn"E85"
300"
0"
0"
2010"
2030"
2050"
2010"
2030"
2050" C."
200"
0"
600"
6"
6" 2050! 2010!
200"
Corn"E85"
Algae"BD"
Gasoline"
CNG" 1" 2030!
200"
400"
8"
0"
0" 2010!
200"
Coal"
Natural"Gas"
2050! 100"
2050!
2030!2"
8"
100"
200"
0" 2050"
2010"
2030"
2030"
2050"
2010"
2030"
2050"
Gasoline"
CNG"
2" 2010"
0"
Coal"
Natural"Gas"
Corn"E85"
1"
Algae"BD"
200"
0"
Corn"E85"
100"
Algae"BD"
Gasoline"
CNG"
1"
2010"
2030"
2050"
2010"
2030"
2050"
400"
2"
Year!
Year!
0"
1"
100"
500"
0"
200"
Corn"E85"
Algae"BD"
2010"
2030" 2" 2050"
2050"
2030" 2040"
2050" 2055"
2010"2010"
2025"
0"2010"
100"
2010"
0"
2030"
2030"
2050"
Corn"E85"
Algae"BD"
100"
2" 2050"
0"
Coal" 2030"
Natural"Gas"
Corn"E85"
2010"
2030" 4"4"
2050" 2010"
2010"
2030"
2050"
4"
Algae"BD"
2010"
2030"
500"
200"
2030"
2050"
2030"
2050"
0"
400"
2010"
2030"6"
2050" 2010"
2010"
2030"
2050"
Corn"E85"
Algae"BD"
Time"(years)"
800"
Time"of"Use"(years)"
Time"of"Use"(years)"
0"2010"
6"
0"
2010"
2030"
2050"
2010"
2030"
2050"
Natural"Gas"
Time"of"Use"(years)"
Time"of"Use"(years)"
800"
0" Coal"
500"
2010"
2030"
2050"
2010"
2030"
2050"
400"
2050"
0"
300"
800"
2010"
2030"
2050"
2010"
2030"
1"
0"
500" 2010"
600"
2030" 0" 2050" 2010"
2030"
2050"
500"
400"
600"
800"
2010" Time"of"Use"(years)"
2030" 2"2"
2030"
2050"
800"
0" 2050" 2010" Time"of"Use"(years)"
300"
1"
200"
2"
600"
400"
800"
4"
400"
1"
400" Time"of"Use"(years)"
4"
1"
800"
Time"of"Use"(years)"
300"
400"
600"
600"
800"
200"
100"
300"
400"
800" Corn"E85"
600"
200"
Algae"BD"
300"
200"
600"
200"
0"0"
100"
400"
0" Coal"
400"
600"
Natural"Gas"
200"
200"
600"
400"
Corn"E85"
Algae"BD"
Coal" 2030" 2"0" 2050" 2010"
Natural"Gas"
200"
0"2010"
100"
1"
2030"
2050"
400"
2"
Coal"
Natural"Gas"
1"
0"
1"
0"
200"
Corn"E85"
Algae"BD"
200"
400"
100"
2010"
2030"
2050" 2010"
2030"
2050"
0"
400"
200"
100"
0"
Coal"
Natural"Gas"
Corn"E85"
Algae"BD"
Coal"
Natural"Gas"
2010"
2030"
2050"
2010"
2030"
2010"
2030"
2050"
2010"
2030"
2050"
200"2010"
Corn"E85"
Algae"BD"
Coal"
Natural"Gas"
2030" 0" 2050"
2050" 2010"
2010"
2030"
2050"
Time"of"Use"(years)"
Time"of"Use"(years)"
0"2010"
0"
200"
2030"
2030"
2050"
Natural"Gas"
800"
0" Coal"
Time"of"Use"(years)"
Time"of"Use"(years)"
0"
200"
Coal"
Natural"Gas"
2030"
2050"
0"2010"
Time"of"Use"(years)"
Time"of"Use"(years)"
2010"
2030"
2050"
2010"
2030"
2050"
2010"
2030"
2050"
0"
1"
Coal" 2030"
Natural"Gas"
2010"
2030"
2050" 2010"
2010"
2030"
2050"
2050"
2030"
2050"
0"2010"
600"
800"
2010"
2030"
2050" 2010"Time"of"Use"(years)"
2030"
2050"
0"
Time"of"Use"(years)"
Time"of"Use"(years)"
1"
2030"
2050" 2010"Time"of"Use"(years)"
2030"
2050"
800"2010"Time"of"Use"(years)"
Time"of"Use"(years)"
2030"
2050" 2010"Time"of"Use"(years)"
2030"
2050"
Time"of"Use"(years)"
400"2010"
600"
800"
Time"of"Use"(years)"
Time"of"Use"(years)"
800"
600"
Time"of"Use"(years)"
Time"of"Use"(years)"
200"
400"
600"
600" Coal"
400"
Natural"Gas"
0"
200"
400"
400"
200"2010"
Coal" 2030"
Natural"Gas"
2050" 2010"
2030"
2050"
Coal"
Natural"Gas"
0"
200"
Time"of"Use"(years)"
Time"of"Use"(years)"
0" Coal"
200"
Natural"Gas"
2010"
2030"
2050" 2010"
2030"
2050"
2050" 2010"Natural"Gas"
2030"
2050"
0"2010"Coal" 2030"
0" Time"of"Use"(years)"
Time"of"Use"(years)"
2010"Time"of"Use"(years)"
2030"
2050" 2010"Time"of"Use"(years)"
2030"
2050"
2010"
2030"
2050" 2010"
2030"
2050"
Time"of"Use"(years)"
Time"of"Use"(years)"
Time"of"Use"(years)"
Time"of"Use"(years)"
40"
20"
0"
100"
2010"
80"
60"
40"
3"
20"
3"W/m2"Threshold"
0"
2010"
2.5"
2025"
20
Time"of"Emission
2" 3"W/m2"Threshold"
2025"
20
3"2010"
Time"(years
2.5"
2"
2010"
Figure 5: Simulations of radiative forcing and vehicle kilometers traveled. a,b, Using metrics to examine hypothetical technology pathways results in differing radiative forcing
trajectories, as compared to the sector budget (a), and differences in vehicle kilometers traveled (b). The sector budget is calculated by allocating a percentage of global emissions,
based on scenario C (Fig. 3a), to the US passenger vehicle sector, based on the present value
(v3%), and simulating the associated radiative forcing (RF) (Supplementary Section 1.2.2).
The metric-based radiative forcing is determined by using a metric to calculate emissions
and associated energy consumption levels. Average present vehicle efficiencies and driving
patterns are assumed (Supplementary Section 4).
These results shed light on technology evaluation debates. One debate focuses on natural
gas [19,20]. Using the GWP(100), the US natural gas mix has a slight climate advantage over
gasoline, although this conclusion depends on assumptions about the source of the gas and
associated emissions, with unconventional natural gas having higher emissions during well
construction but conventional gas generally having higher emissions during production [21].
Using the ICI and CCI, CNG used today ranges from slightly advantageous to slightly
disadvantageous, while CNG used in 2040 has a significantly higher climate impact and
lower climate-goal-compliant consumption than gasoline. These general conclusions also
hold under the low-CH4 emissions scenario (Supplementary Section 5.1).
Another debate focuses on the climate benefits of various biofuels and co-product processing techniques. For the processes studied (Fig. 4b), algae biodiesel appears more favorable
than corn ethanol in 2010. However, using the ICI and CCI we observe that by 2040 the
climate impact of algae biodiesel surpasses that of corn ethanol, owing to CH4 leakage in
biogas production from algae co-products, which partially offsets the energy requirements
of algae production and processing [2]. Although these offsets make biodigestors attractive,
with applications in corn ethanol production [25] and other biofuels, resulting CH4 emissions
may make this process less advantageous over time. An alternative catalytic hydrothermal
gasification process may achieve much lower CH4 emissions (Supplementary Fig. 8). These
results suggest the importance of developing alternative low-CH4 co-product processing techniques [26].
The CCI and ICI identify high-CH4 , low-CO2 technologies as candidates for near-term
but not long-term climate change mitigation. Based on an intended radiative forcing stabilization level, these metrics identify an approximate bridging timeline to transition away
8
2025"
20
Time"of"Emission
2025"
20
Time"(years
from CH4 -emitting, short-term mitigation technologies. The approximate bridging time is
not particularly scenario dependent, owing to the constrained range of emissions scenarios
and stabilization years that correspond to commonly cited stabilization targets. The results
of applying the metrics to CH4 -emitting algae biodiesel and natural gas for electricity and
transportation are particularly notable, highlighting the dependence of their mitigation potential on their time of use. The proposed technology and metric evaluation approach can
also be adapted to new information on the timing, location, and form of climate thresholds [23, 27, 28], and the desired trade-off between the risks of exceeding thresholds and
the benefits of economic activity [29] (Supplementary Section 2.2). While no equivalency
metric is perfect, we find that dynamic metrics that are based on an approximate climate
stabilization target and corresponding time horizon can improve technology evaluation for
policy-making, private investment, and engineering design.
Methods
In this section we describe the approach to generating the reference scenarios used to calculate
the range of CCI and ICI values and to test metric performance. We also describe the
technology emissions data used in the research.
Reference Scenarios. The reference scenarios are CO2 emissions, multi-gas concentration scenarios: all emissions in the simulation are composed entirely of CO2 , but previous
emissions of non-CO2 greenhouse gases are also modeled (Supplementary Section 1.1). These
reference scenarios are used to calculate the ICI and CCI and to test all metrics, by allocating
a portion of CO2 -equivalent emissions to non-CO2 gases using the metrics.
Emissions scenarios are constructed [30], where initial emissions e0 change over time
according to
"Z 0
#
t
e(t0 ) = e0 exp
g(t00 )dt00
(3)
0
00
where g(t ) is an evolving, exponential growth rate (t00 is a dummy, integration variable).
Emissions grow at a constant rate g0 , based on present growth rates, until the mitigation
onset time (t1 ), after whichg(t00 ) is reduced by a fixed annual amount until it reaches the
final growth rate gf .
Concentrations ci (t) of each gas i are a function of pre-industrial concentrations ci (t0 ),
historical emissions (t0 < t0 ≤ 0), and new emissions (0 < t0 ≤ t),
Z 0
Z t
0
0
0
ci (t) = ci (t0 ) +
fi (t, t ) ei (t ) dt +
fi (t, t0 ) ei (t0 ) dt0
(4)
t0
0
where fi (t, t0 ) gives the fraction of a gas emitted at t0 remaining at time t [4],
n
X
t − t0
0
aj · exp −
fi (t, t ) = a0 +
τj
j=1
(5)
and ai and τi are constants (see Supplementary Table 2 for CO2 , CH4 , and N2 O values).
(Equation (5) is also used in the CCI and ICI formulations (equations (1) and (2)), where t
9
is replaced by t00 for the CCI, which ranges from t0 to tS , and is replaced by tS for the ICI.)
As n = 1 and a0 = 0 for non-CO2 greenhouse gases, no information about the emissions
timing is needed to calculate concentrations from historical emissions. For CO2 , where
n = 3 and a0 6= 0, the rate of removal must be approximated using historical emissions data
(Supplementary Section 1.1.2).
Radiative efficiency (radiative forcing per unit concentration) values are used to determine
radiative forcing from concentrations [4].
X
RF(t) = RFA (t) +
[Ai [ci (t) − ci (0)] + RFi (ci (0))]
(6)
i
where RFA (t) refers to all radiative forcing not due to the presence of modeled gases i, and
Ai is the radiative efficiency of gas i (Supplementary Section 1.1.3).
A scenario family is a set of pathways RF(t) that stabilize at the same radiative forcing
threshold but approach it at different rates. To generate stabilization scenarios, emissions
are adjusted after the threshold is reached such that radiative forcing equals the threshold
value in all subsequent years. Emissions scenarios within a scenario family are defined based
on their values of t1 , which is varied to the greatest extent possible. Earlier values of t1 result
in gradual emissions reductions, while later values of t1 result in delayed emissions reductions
followed by rapid reductions. The scenario family for 3 W/m2 stabilization defines the range
of stabilization times (tS ) for the analysis presented in the paper.
Metric Testing. The performance of emissions metrics is tested by budgeting a trajectory
e(t0 ) for total CO2 -equivalent emissions, and allocating a fraction q of these emissions to a
non-CO2 greenhouse gas. Consider the case of two gases, CO2 and CH4 . Given a metric
µ(t0 ), the sum of CO2 emissions eK (t0 ) and CO2 -equivalent CH4 emissions µ(t0 )eM (t0 ) must
equal e(t0 ). The radiative forcing scenario can be derived from equation (6),
Z t
0
0
0
RF(t) = AK cK (t0 ) + cKL (t) + (1 − q)
e(t )fK (t, t )dt − cK (0) + RFK (cK (0))
0
Z t
1
0
0
0
dt − cM (0)
+ AM cM (t0 ) + cML (t) + q
e(t )fM (t, t ) ·
µ(t0 )
0
+ RFM (cM (0)) + RFA (t),
(7)
where K and M subscripts refer to CO2 and CH4 respectively, all other contributions to
radiative forcing are encompassed in the term RFA (t), and concentrations are disaggregated
into the three contributions given in equation (4): pre-industrial concentrations, concentrations from historical emissions (abbreviated ciL (t)), and concentrations from new emissions.
The difference between the actual radiative forcing in the mixed gas case (where q 6= 0) and
the budgeted, CO2 emissions case (where q = 0) is
Z t
1
0
0
0
e(t ) AM fM (t, t ) ·
∆RF(t) = q
− AK fK (t, t ) dt0
(8)
0
µ(t )
0
A similar approach is used to test metric performance for technology evaluation, given a
budgeted emissions trajectory e(t0 ) and using historical data to allocate a fraction p of these
emissions to the sector of interest (Supplementary Section 1.2.2).
10
Data. Global emissions, concentration, and radiative forcing data are published by the
International Institute for Applied Systems Analysis. Technology life cycle emissions are
taken from the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation
(GREET) Version 2012, published by Argonne National Laboratory. In GREET, natural gas
CH4 emissions that arise from liquid unloading in conventional production offset increased
leakage during unconventional well completion, with conventional gas having 27% higher
emissions than unconventional gas. Present US breakdowns of conventional and unconventional gas are used [22]. In GREET, corn co-products are used as animal feed while algae
co-products are used to create biogas in a state-of-the-art facility with CH4 leakage rates of
2% [2]. Emissions for electricity generation technologies are taken from a recent study. [1].
Low-CH4 emissions scenarios for natural gas are based on updated EPA estimates of natural
gas system leakage and an alternative catalytic hydrothermal gasification scenario for algae
biodiesel [2] (Supplementary Section 4).
Acknowledgements
The authors thank James McNerney for his contributions to the development of the metric
testing model. We thank Susan Solomon for helpful feedback on this manuscript. This
research was partially financially supported by a grant from the New England University
Transportation Center at MIT under DOT grant No. DTRT07-G-0001.
Author Contributions:
J.E.T. developed the concept and designed the methods for the study, M.R.E. and J.E.T. performed
the analysis, J.E.T. and M.R.E. wrote the paper.
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