“Global Warming” Metrics in Life Cycle Assessment - American

POLICY ANALYSIS pubs.acs.org/est

Alternative “Global Warming” Metrics in Life Cycle Assessment: A Case Study with Existing Transportation Data Glen P. Peters,*,† Borgar Aamaas,† Marianne T. Lund,† Christian Solli,‡ and Jan S. Fuglestvedt† † ‡

Center for International Climate and Environmental Research
Oslo (CICERO), PB 1129 Blindern, 0318 Oslo, Norway Environmental Systems Analysis (Miljøsystemanalyse, MiSA), Beddingen 14, 7014 Trondheim, Norway

bS Supporting Information ABSTRACT: The Life Cycle Assessment (LCA) impact category “global warming” compares emissions of long-lived greenhouse gases (LLGHGs) using Global Warming Potential (GWP) with a 100-year time-horizon as specified in the Kyoto Protocol. Two weaknesses of this approach are (1) the exclusion of shortlived climate forcers (SLCFs) and biophysical factors despite their established importance, and (2) the use of a particular emission metric (GWP) with a choice of specific time-horizons (20, 100, and 500 years). The GWP and the three time-horizons were based on an illustrative example with value judgments and vague interpretations. Here we illustrate, using LCA data of the transportation sector, the importance of SLCFs relative to LLGHGs, different emission metrics, and different treatments of time. We find that both the inclusion of SLCFs and the choice of emission metric can alter results and thereby change mitigation priorities. The explicit inclusion of time, both for emissions and impacts, can remove value-laden assumptions and provide additional information for impact assessments. We believe that our results show that a debate is needed in the LCA community on the impact category “global warming” covering which emissions to include, the emission metric(s) to use, and the treatment of time.

1. INTRODUCTION The Kyoto Protocol is directed toward the mitigation of the long-lived greenhouse gases (LLGHGs) covering CO2, CH4, N2O, and the fluorinated gases.1 It is well recognized, however, that climate is affected by more than the LLGHGs.2 Both warming and cooling of climate are caused by short-lived climate forcers (SLCFs) such as ozone, black carbon, and sulfate2 and biophysical factors such as land surface change.3 Mitigation studies that focus only on the LLGHGs may lead to different mitigation choices compared to when SLCFs2,4
8 and biophysical factors3,9
11 are included. The Kyoto Protocol additionally compares emissions using a particular metric with a specific treatment of time.1 Many studies show that mitigation choices can differ depending on the climate impact considered and the treatment of time.4,12
18 Our aim in this paper is to demonstrate how the results of a Life Cycle Assessment (LCA) can differ when SLCFs, different emission metrics, and different treatments of time are used. A challenge with multicomponent impact assessment methods is the need to make different emissions comparable.13,17,19 Key challenges are the end-point of the metric (emissions, concentration, radiative forcing, temperature, damages, etc.) and the treatment of time. The standard practice in the LCA impact category “global warming” is to compare emissions using Global Warming Potential (GWP) with a fixed time-horizon independent of when the emissions occur. Within the LCA community, there has been little documented reflection on the use of different end-points20 or on different treatments of time,21 though time is well recognized as r 2011 American Chemical Society

an issue in biofuel discussions.22
24 Several recent non-LCA studies have included SLCFs with a variety of emission metrics, particularly in the transport sector.17,25
30 One study found, for example, that using GWPs international shipping causes a cooling effect for 300 years,25 but in terms of temperature change the cooling persists for less than 40 years.26 Among the LLGHGs, CH4 can be considered as a SLCFs and consequently a change in metric can have a significant effect on its relative weighting with CO2;31 Brazil has suggested the GWP should be replaced in climate policy.32 The growing literature on alternative emission metrics suggests there is a case to reconsider the LCA impact category “global warming”, particularly, what emissions to include, which end-points to use, and how to treat time. LCA typically utilizes the 100-year Global Warming Potential (GWP100)1 to compare the LLGHGs, and SLCFs and biophysical factors are excluded from the impact assessment. Some impact assessment methods apply different time-horizons.33 As stated in the IPCC’s First Assessment Report, the GWP and the three depicted time-horizons (20, 100, and 500 years) are illustrative examples,34 are somewhat arbitrary,34,35 and have vague interpretations.13,34,35 The GWP does not have a clear physical interpretation in terms of climate impact36 and is believed to be inconsistent with the wording Received: February 23, 2011 Accepted: September 2, 2011 Revised: May 31, 2011 Published: September 21, 2011 8633

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Figure 1. Comparison of LLGHGs versus SLCFs for different emission metrics. The CO2-eq in each set of bars are different as they depend on the metric. “Synthetic” represents the mainly halogenated hydrocarbons in the Kyoto and Montreal Protocols.

of the UNFCCC.37,38 Its application in climate policy could be seen as an “inadvertent consensus” of the scientific community.35 The continued use of the GWP100 in LCA probably reflects its perceived policy acceptance, as many scientific studies highlight weaknesses of the GWP concept.12,13,17,19,35,36,39
42 Even though the selection of a metric necessarily requires value judgments,13,19 it is arguably desirable to minimize the number of value judgments and to make the metric sufficiently consistent with policy goals. The treatment of time is a key issue in the evaluation of mitigation options and consequently the development of emission metrics. Two aspects of time are important: (1) the selection of the time-horizon over which to compare climate impacts, and (2) the time at which emissions and climate impacts occur. First, the timehorizon can be used as the time over which to compare climate impacts usually by converting the emissions to the “equivalent” emission of a reference gas, usually CO2. In the particular case of the GWP, different time-horizons additionally represent different climate impacts;34 short time-horizons relate to rates of climate change, while long time-horizons may relate to sea level change.34 Second, and distinct from the time-horizon, is when the emissions occur and when the climate impacts are evaluated;4 for example, the emissions occur from 1990 to 2010 and the climate impacts are evaluated in 2050 and 2100. The timing of emissions and climate impacts are particularly important for prioritizing mitigation options.12,14
16,18 Consequently, mitigation studies generally consider emission scenarios and impacts as a function of time. This use of time is uncommon in LCA, but LCA studies of biofuels have shown that the explicit inclusion of time makes biofuels less favorable to gasoline.21
24 Given the importance of time for impact assessment and the relatively rudimentary treatment of time for “global warming” in LCA, there is arguably scope to reassess the treatment of time in LCA. Our aim in this paper is to invigorate debate in the LCA community on the impact category “global warming” by illustrating

with the transport sector the importance of SLCFs, alternative emission metrics, and different treatments of time. We begin by giving a brief overview of the most common emission metrics, before presenting and discussing LCA results of different transport modes for “global warming” using a variety of emission metrics with different treatments of time.

2. EMISSION METRICS The field of emission metrics has existed since the early 1990s with several reviews available.13,17,19,35,36 The purpose of an emission metric is to compare the climate impact of different emissions.13 A simple comparison by weight (e.g., kilograms) does not consider that the components have substantially different radiative efficiencies and atmospheric lifetimes.2,13 A general formulation of an emission metric can be expressed as2,43 Z ∞ AMi ¼ ½ðIðΔCrþi ðtÞÞ
IðΔCr ðtÞÞÞgðtÞdt ð1Þ 0

where I(ΔCi(t)) is a function describing the “impact” of a change in climate, ΔC, at time t, with a discount function, g(t), and compared to a reference system, r, on which the perturbation occurs, i. The discount function need not be an exponential discounting, but may be used to represent a fixed time-horizon using a step-function (such as in most integrated metrics) or instantaneous evaluation using a Dirac delta function (such as in most end-point metrics). Metrics can be compared for different perturbations, AMi and AMj, or an emission index can be derived by normalizing with a reference component (e.g., Mi = AMi/AMj). 2.1. Absolute Metrics. 2.1.1. Radiative Forcing (RF). Radiative forcing is often used to compare the radiative imbalance of the Earth between two fixed times.2 It is also possible to consider the radiative forcing, RFi, for a time evolving emission, Ei(t). In this case, RFi must consider the atmospheric residence time of the different emissions 8634

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Figure 2. Radiative forcing for emissions occurring over the life-cycle. “Synthetic” represents the mainly halogenated hydrocarbons in the Kyoto and Montreal Protocols.

and this can be expressed with a convolution Z t RFi ðtÞ ¼ ai Ei ðsÞR r, i ðt
sÞds

ð2Þ

0

where ai is the radiative efficiency (radiative forcing per unit mass) and Ri is the Impulse Response Function for a unit pulse of component i into the reference atmosphere Rr, i ðtÞ ¼

K

∑ bk expðt=τkÞ

k¼1

ð3Þ

and represents the fraction of atmospheric component i that remains in the atmosphere after time t relative to the reference system, r.44
46 Most components have a single decay time (K = 1), though CO2 decays over multiple time-scales and has a component remaining in the atmosphere indefinitely.2 The lifetimes and radiative efficiencies used in the IPCC assessment reports are based on a reference system with constant current atmospheric conditions,2 though other choices exist.45 In the context of eq 1, the impact I is the radiative forcing at the top of the atmosphere (RFi) and the metric is evaluated at the end-point which removes the integral. 2.1.2. Integrated Radiative Forcing (iRF). The iRF is the timeintegrated RFi Z t iRFi ðtÞ ¼ RFi ðsÞds ð4Þ 0

With reference to eq 1, the impact I is radiative forcing and the discount function g is a step function ( 1 t e TH gðtÞ ¼ ð5Þ 0 t > TH where TH is the time-horizon. The iRF is rarely used as an absolute emission metric and is usually normalized.

2.1.3. Global Temperature Change (ΔT). The Global Temperature Change, ΔT, is an end-point metric and includes a climate model at the expense of increased uncertainty,16,31,42 Z t ΔTi ðtÞ ¼ R Fi ðsÞRT ðt
sÞds ð6Þ 0

where RT is the Impulse Response Function to an instantaneous unit pulse of radiative forcing [c.f., 47]. As for RF, ΔT is an endpoint concept removing the integral in eq 1. As a way of analogy, RT mimics a physical discount function: RT makes the metric “forget” the forcing at early times as energy moves into the deep ocean, while in effect RT = 1 for the iRF and hence iRF “remembers” the forcing at early times. 2.2. Normalized Metrics. It is common to use a normalized metric that converts a component i into the equivalent climate impact of a reference gas, usually CO2, Mi ðTHÞ ¼

AMi ðTHÞ AMCO2 ðTHÞ

ð7Þ

where AM represents iRF or ΔT and leads to the Global Warming Potential (GWP) or Global Temperature change Potential (GTP). The normalized metrics, Mi, are evaluated for a given timehorizon, TH, representing the time over which to compare the climate impact of the two pulses.34 Multiplying a normalized metric with the emission of component i converts Ei into the equivalent emissions of CO2 (Mi  Ei) that ideally would lead to the same climate impact (CO2-equivalent emissions). The GWP is by far the most common metric and is used in the Kyoto Protocol,1 however there have been many critiques of the GWP concept.12,13,17,31,35,36,40 The GTP is the next most discussed emission metric.16,17,30,31,39,47
49 2.3. Other Metrics. Many other metrics exist, though we will not elaborate on them here.19 We have considered metrics which 8635

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effect chain (ΔCf RF f ΔT). Integrated Global Temperature Change (iΔT) could be considered to be a physical metric closer to damages, but we find that it is numerically similar to the GWP in normalized form (see Supporting Information), and hence, we only present results for the GWP. In addition to the common pulse-based metrics, it is also possible to have metrics based on sustained emissions.31

3. TREATMENT OF TIME There are two main treatments of time in relation to metrics. Absolute metrics compare the climate impact of different emissions over time, whereas normalized metrics compare the climate impact relative to a reference gas. 3.1. Time in an Absolute Metric. The metrics RF, iRF, ΔT, and iΔT can be compared for emission profiles as a function of time allowing a comparison of the climate impact in absolute quantities (RF in W/m2, iRF in W/m2yr, ΔT in K, and iΔT in Kyr). This approach is used, for example, to compare historic emissions or emission scenarios in terms of a given climate impact.4 For emission scenarios, it is necessary to know the start and end time of the emissions and the evaluation time (TE) to quantify the climate impact;4 for example, emissions starting in 2010 and finishing in 2050, with the climate impact evaluated in 2100. This use of time is rare in LCA, but common in attribution and mitigation studies. 3.2. Time in a Normalized Metric. The GWP, GTP, and iGTP are emission indices that convert the emission of a given component Ei into the CO2-equivalent emission for the given time-horizon TH (CO2-eq = Mi  Ei). The need for a timehorizon is partly due to the long-term properties of CO2.36 After an initial release into the atmosphere, about 20% of the CO2 remains permanently in the atmosphere while other components decay to zero.50 Thus, as time progresses CO2 will always dominate over other species requiring a value-based cutoff (time-horizon) and this was one of the key challenges developing emission metrics for climate.36 Together with the time-horizon, a normalized metric allows an emission of one component to be compared with that of the reference gas. The existence of the GWP may be the reason that the Kyoto Protocol is a multigas treaty.35 It is less clear, however, that normalized metrics are as relevant in applications like LCA; emissions scenarios, for example, use absolute metrics. The TH is usually fixed, but it has been suggested that the TH could be a function of time giving more weight to SLCFs as a target year is approached.12,16,31.34 There is no reason why the same TH should be used for different metrics as different metrics measure different climate impacts which may operate on different time scales [c.f., 17]. 4. DATA AND METHODS We focus on four transportation modes from the Ecoinvent LCA database:51 (1) regular diesel car based on the 2010 European fleet average (Ecoinvent: “Transport, passenger car, diesel, fleet average 2010/RER U”), (2) regular bus (Ecoinvent: “Transport, regular bus/CH U”), (3) long-distance train with Swiss electricity mix (Ecoinvent: “Transport, long-distance train, SBB mix/CH U”), and (4) passenger aircraft traveling in Europe (Ecoinvent: “Transport, aircraft, passenger, Europe/RER U”). We chose these four transport modes to highlight different mixes of LLGHGs and SLCFs in the life-cycle. The diesel car emits more SLCFs in operation compared to a gasoline car; the bus has

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a higher efficiency per person kilometer; the train has virtually no operation emissions due to the Swiss electricity mix; the aircraft has significant SLCFs during operation. The emissions were taken directly from the EcoInvent database, with the exception of black carbon (BC) and organic carbon (OC) which required further processing (see Supporting Information). We consider different methods of distributing emissions along the life-cycle. First, we assume all emissions occur in year one as a pulse (equivalent to emissions over time with a fixed time-horizon) as is conventional in LCA. Second, we assume the emissions occur according to the life-cycle of the technologies (i.e., construction in year one, operation and maintenance distributed over the life of the transport vehicle, and disposal in the final year). The operation period for cars and buses is taken as 10 years, planes 30 years, and trains 40 years. We calculate the GWP and GTP values analytically using radiative efficiencies and lifetimes from previous studies.2,17 The ΔT values for emissions scenarios are calculated using convolutions between the emissions and analytical metric expressions (see the Supporting Information).

5. RESULTS The results focus on various aspects of LLGHGs, SLCFs, emission metrics, and the treatment of time in LCA using the four transportation modes. Our aim is to highlight how results change relative to standard LCA and not to analyze the transportation results in a policy context. We use 20 and 100 year time-horizons for GWP and 20 and 50 years for GTP [c.f., 17]. The GWP and GTP represent different climate impacts, and there is no reason that different climate impacts should use the same time-horizon. For the GTP, we use 50 years instead of 100 years based on estimates of when business-as-usual scenarios are likely to exceed the two-degree policy target.52 5.1. Importance of LLGHGs versus SLCFs for Different Emission Metrics. Figure 1 shows the CO2-equivalent emissions

for LLGHGs and SLCFs for the GWP and GTP. A standard LCA uses GWP100 including only the LLGHGs in the Kyoto Protocol (CO2, CH4, N2O, and the fluorinated gases). In the case of car, bus, and train, there is a mix of SLCFs, some of which are cooling and some of which are warming. These occur in construction, operation, and disposal. The case of aircraft is quite different and LCA results vary depending on the metric. Emissions from aircraft operation leads to the formation of contrails and cirrus clouds, which have large but uncertain radiative efficiencies.53 The short lifetime of the contrails and cirrus means the effect is short-lived, as can be seen by the large CO2-eq values for GWP20 and small values for GTP50. Overall for the car, bus, and train, the choice of metric does not significantly change the results of a standard LCA because LLGHGs dominate. However, for the case of aircraft the results are highly metric dependent, as is the case for shipping.26,54 The Supporting Information shows Figure 1 with the emissions allocated to life-cycle stages. 5.2. Treatment of Time in LCA. Generally, LCA assumes the same fixed TH independent of when the emissions occur (i.e., if the emissions occur in year t, the TH is the same as if the emissions occurred in year t + Δt); this is the same as assuming all emissions occur in the first year. When including both LLGHGs and SLCFs, the mix of emissions may be different depending on the life-stage and thus the timing of emissions may become more important for the net climate impact. We discuss two aspects of time that could be used in LCA: first, emissions as a function of 8636

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Figure 3. Values of ΔT for emissions occurring over the life-cycle. “Synthetic” represents the mainly halogenated hydrocarbons in the Kyoto and Montreal Protocols.

time using absolute metrics, and second, normalized metrics that convert emissions into CO2-eq. 5.2.1. Emissions as a Function of Time. Figure 2 shows the radiative forcing (RF) as the emissions occur over the life-cycle. The use of the RF allows a comparison of each component in the same units (W/m2) and gives an indication of when the emissions occur in the life cycle; the construction, operation, and disposal phases are clearly shown. Abrupt changes can be seen for the construction and disposal phases due to emissions of SLCFs with large radiative efficiencies but short atmospheric residence times. In terms of total contributions to the climate impact, the construction and disposal phases are small as the total emissions are relatively small and the SLCFs decay quickly (except for the case of the train which has low emissions in operation, Supporting Information). In the operation phase, the SLCFs give a near constant forcing, as the emissions decay rapidly, but are quickly replaced converging to a constant concentration. The increasing components in the use phase relate to the LLGHGs which accumulate in the atmosphere due to slow decay rates and then persist in the atmosphere after the lifetime of the transport mode. Overall, the use of radiative forcing as a metric allows the different components to be compared in the same unit providing additional insight on the importance of timing and of construction and disposal. Figure 3 shows the ΔT for the transportation modes, where it is seen that the SLCFs are quickly removed from the atmosphere after the emissions cease, leading to a decreased climate impact. In all transportation modes, the total net ΔT converges to the ΔT of CO2 only as the SLCFs are progressively forgotten and the other LLGHGs only have small contributions. For example, in the case of aircraft the SLCFs dominate the net ΔT up until the end of the life of the aircraft, in which case CO2 takes over as the dominant component due to its long atmospheric lifetime. This

is distinct to the case of the iRF (Supporting Information), where the SLCFs are relatively more important as the iRF integration remembers the emissions at earlier times (compare with Figure 1). It is standard in LCA to have a fixed time-horizon which implies that all the emissions occur in year one, Figure 4. Comparing Figure 3 (scenario) and Figure 4 (pulse) shows quite different treatments of time (c.f., 21). The peak temperature (ΔT) is larger and appears earlier in the pulse since all the emissions occur at once, while the scenario distributes the emissions over time. The scenario also shows the different life-cycle phases clearly. The explicit representation of time shows the relationship between the timing of emissions and impacts, and provides more information for the end-user of the results. 5.2.2. CO2-Equivalent Emissions Using the Time-Horizon. The treatment of time in the scenario, Figure 3, is quite different from standard LCA. In Figure 3, the emissions occur as a function of time, but the evaluation is also a function of time (TE, time of evaluation). If TE is chosen as 100 years, for example, then the emissions in year 20 are still evaluated at 100 years, which means the emissions are only resident in the atmosphere for 80 years. If the emissions are to be evaluated after being resident in the atmosphere for the same amount of time (analogous to a fixed time-horizon), then they are treated as a hypothetical pulse emission (Figure 4). It is possible to have a variable time-horizon (analogous to Figure 3), TH = TE
t, which means that the relative impact of different emissions varies over time.12,16,18 The consequence of this is that the SLCFs will have a larger effect on temperature relative to CO2 as the time t approaches TE as TH gets progressively smaller.12,16,18 Figure 5 shows the CO2-eq emissions for the different transport modes using a fixed time-horizon and a variable time-horizon (the normalized metric versions of Figure 3 and Figure 4). We took TE = 50 to highlight the differences as it is closer to the 8637

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Figure 4. Values of ΔT for emissions occurring as a pulse for the entire life-cycle. “Synthetic” represents the mainly halogenated hydrocarbons in the Kyoto and Montreal Protocols.

Figure 5. CO2-eq emissions using GWPs and GTPs for emissions occurring as a function of time using a fixed TH and a variable TH. The TE of 50 years was taken as it is closer to the lifetime of the transportation modes and hence highlights the differences.

lifetime of the transportation modes. In the case of the car and to a lesser extend the bus, the difference between the lifetime of the transport mode and TE is 40 years, which is much longer than the

lifetime of the SLCFs and hence the differences between a fixed and variable TH are small. In the case of the train (disposal phase only) and air transport, the difference between the lifetime of the 8638

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Environmental Science & Technology transport mode and the TE (10 years for a train, and 20 years for a plane) is closer to the lifetime of the SLCFs (but still significantly different). In these cases, the variable TH makes the emissions of the SLCFs relatively more important as t approaches TE (since TH gets smaller). A difference between the results for a fixed and variable TH will occur if (a) SLCFs are important, and (b) the SLCFs are emitted sufficiently close to TE. Given the only modest variations shown here, it is arguable whether the extra complexity of the variable time-horizon is justified.

6. DISCUSSION Our results show that SLCFs are important, particularly for short time-horizons, when the emissions occur close to the evaluation times, or for integrated metrics relative to end-point metrics. The importance of SLCFs and time will also depend on the product under investigation; in our examples, SLCFs are important in all transport modes, but particularly important for aircraft. Thus, it is important to include SLCFs in impact assessments to avoid potentially misleading results.4
8,17,25
27,29 If SLCFs are included in impact assessments of “global warming”, then the choice of metric and treatment of time becomes particularly relevant.13,16,17,19,31,39,43 These results suggest that a more lively discussion of metrics for “global warming” is needed, particularly considering the latest research.17,19,39,55 Our results show that time can be more transparently included in assessments removing value-laden assumptions on the choice of time. Performing calculations depending on the time the emissions occur, “dynamic LCA”,21 provides more information and therefore creates valuable insights to impact assessment. In addition, showing how the climate impact for a given emissions scenario varies over time (RF, iRF, ΔT, iΔT), as opposed to converting to a CO2-eq emissions, may provide a different approach to time as conventionally used in LCA. Particularly if emissions are treated as a function of time, it is perhaps appropriate to compare climate impacts as a function of time as well, as is often done in climate studies.4,26,56,57 We only provide simple discussions and comparisons of emission metrics to highlight key issues in metric choice, but many other issues need to be considered (c.f., 19). Different emission metrics have different uncertainties that generally increase for end-point metrics (compared to integrated metrics) or when the climate impact requires additional modeling.42 Likewise, climate impacts due to SLCFs are usually more uncertain than LLGHGs2 and are usually regionally dependent.58 For long-term climate impacts, CO2 dominates and the impact may be irreversible,50,59 though SLCFs can have persistent climate impacts60 and both LLGHGs and SLCFs may induce feedbacks.55 Cumulative emissions of CO2 may dominate peak warming,52 but SLCFs can be particularly important for rates of change.61 These types of issues need to be considered when deciding which metric(s) to apply in different situations. We see a need for further discussions in the LCA community on the choice of emission metrics and treatment of time in the impact category “global warming”. There is a need to harmonize across impact assessments what components to include, covering LLGHGs, SLCFs, and biophysical factors. There is a need for further discussion on which emission metric(s) to use in different impact assessments, where the goal and scope may define different time-scales. Time is a cross-cutting theme with value laden aspects, but a more transparent representation of time can remove some of the value laden judgments. As highlighted

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before, multidisciplinary research and discussion on emission metrics,35 for example between the LCA and climate communities, can build understanding and strengthen applications in a wide-range of relevant fields.

’ ASSOCIATED CONTENT

bS

Supporting Information. This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

*E-mail: [email protected].

’ ACKNOWLEDGMENT This research was partly funded by the Norwegian Research Council Project “Transport and Environment
Measures and Policies (TEMPO)”. We thank Rolf Hagman (Institute of Transport Economics, Norway) for useful comments. ’ REFERENCES (1) UNFCCC. The Kyoto Protocol to the United Nations Convention on Climate Change; United Nations: New York, 1997. (2) Forster, P.; Ramaswamy, V.; Artaxo, P.; Berntsen, T.; Betts, R.; Fahey, D. W.; Haywood, J.; Lean, J.; Lowe, D. C.; Myhre, G.; Nganga, J.; Prinn, R.; Raga, G.; Schulz, M.; Dorland, R. V. Changes in Atmospheric Constituents and in Radiative Forcing. In Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change; Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M., Miller, H. L., Eds. Cambridge University Press: Cambridge, U.K. and New York, 2007. (3) Jackson, R. B.; Randerson, J. T.; Canadell, J. G.; Anderson, R. G.; Avissar, R.; Baldocchi, D. B.; Bonan, G. B.; Caldiera, K.; Diffenbaugh, N. S.; Field, C. B.; Hungate, B. A.; Jobbagy, E. G.; Kueppers, L. M.; Nosetto, M. D.; Pataki, D. E. Protecting climate with forests. Environ. Res. Lett. 2008, 3, 044006. (4) den Elzen, M.; Fuglestvedt, J. S.; H€ohne, N.; Trudinger, C.; Lowe, J.; Matthews, B.; Romstad, B.; Pires de Campos, C.; Andronova, N. Analysing a countries’ contribution to climate change: Scientific and policy-related choices. Environ. Sci. Policy 2005, 8, 614–636. (5) Shindell, D.; Lamarque, H.-F.; Unger, N.; Koch, D.; Faluvegi, G.; Bauer, S.; Teich, H. Climate forcing and air quality change due to regional emissions reductions by economic sector. Atmos. Chem. Phys. 2008, 8, 7101–7113. (6) Unger, N.; Shindell, D. T.; Wang, J. S. Climate forcing by the onroad transportation and power generation sectors. Atmos. Environ. 2009, 43, 3077–3085. (7) Unger, N.; Bond, T. C.; Wang, J. S.; Koch, D. M.; Menon, S.; Shindell, D. T.; Bauer, S. Attribution of climate forcing to economic sectors. Proc. Natl. Acad. Sci., U.S.A. 2009, 107, 3382–3387. (8) Unger, N. Short-lived non-CO2 pollutants and climate policy: Fair trade? Environ. Sci. Technol. 2010, 44, 5332–5333. (9) Betts, R. A. Offset of the potential carbon sink from boreal forestation by decreases in surface albedo. Nature 2000, 408, 187–190. (10) Pielke, R.; Marland, G.; Betts, R. A.; Chase, T. N.; Eastman, J. L.; Niles, J. O.; Niyogi, D. D. S.; Running, S. W. The influence of landuse change and landscape dynamics on the climate system: Relevance to climate-change policy beyond the radiative effect of greenhouse gases. Philos. Trans. R. Soc., A 2002, 360, 1705–1719. (11) Bala, G.; Caldeira, K.; Wickett, M.; Phillips, T. J.; Lobell, D. B.; Delire, C.; Mirin, A. Combined climate and carbon-cycle effects of largescale deforestation. Proc. Natl. Acad. Sci., U.S.A. 2007, 104, 6550–6555. 8639

dx.doi.org/10.1021/es200627s |Environ. Sci. Technol. 2011, 45, 8633–8641

Environmental Science & Technology (12) Manne, A. S.; Richels, R. G. An alternative approach to establishing trade-offs among greenhouse gases. Nature 2001, 410 (6829), 675–677. (13) Fuglestvedt, J. S.; Berntsen, T. K.; Godal, O.; Sausen, R.; Shine, K. P.; Skodvin, T. Metrics of climate change: Assessing radiative forcing and emission indices. Climatic Change 2003, 58, 267–331. (14) O’Neill, B. C.; Oppenheimer, M. Climate change impacts are sensitive to the concentration stabilization path. Proc. Natl. Acad. Sci., U.S.A. 2004, 101 (47), 16411–16416. (15) Rive, N.; Torvanger, A.; Berntsen, T.; Kallbekken, S. To what extent can a long-term temperature target guide near-term climate change commitments? Climatic Change 2007, 82 (3), 373–391. (16) Shine, K. P.; Berntsen, T.; Fuglestvedt, J. S.; Stuber, N.; Skeie, R. B. Comparing the climate effect of emissions of short and long lived climate agents. Philos. Trans. R. Soc., A 2007, 365, 1903–1914. (17) Fuglestvedt, J. S.; Shine, K. P.; Berntsen, T.; Cook, J.; Lee, D. S.; Stenke, A.; Skeie, R. B.; Velders, G. J. M.; Waitz, I. A. Transport impacts on atmosphere and climate: Metrics. Atmos. Environ. 2010, 44, 4648–4677. (18) Berntsen, T.; Tanaka, K.; Fuglestvedt, J. Does black carbon abatement hamper CO2 abatement? Climatic Change 2010, 103 (3), 627–633. (19) Tanaka, K.; Peters, G. P.; Fuglestvedt, J. S. Multi-component climate policy: Why do emission metrics matter? Carbon Manage. 2010, 1, 191–197. (20) De Schryver, A. M.; Brakkee, K. W.; Goedkoop, M. J.; Huijbregts, M. A. J. Characterization Factors for Global Warming in Life Cycle Assessment Based on Damages to Humans and Ecosystems. Environ. Sci. Technol. 2009, 43 (6), 1689–1695. (21) Levasseur, A.; Lesage, P.; Margni, M.; Deschnes, L.; Samson, R. Considering Time in LCA: Dynamic LCA and Its Application to Global Warming Impact Assessments. Environ. Sci. Technol. 2010, 44, 3169–3174. (22) Kendall, A.; Chang, B.; Sharpe, B. Accounting for TimeDependent Effects in Biofuel Life Cycle Greenhouse Gas Emissions Calculations. Environ. Sci. Technol. 2009, 43, 7142–7147. (23) O’Hare, M.; Plevin, R. J.; Martin, J. I.; Jones, A. D.; Kendall, A.; Hopson, E. Proper accounting for time increases crop-based biofuels’ greenhouse gas deficit versus petroleum. Environ. Res. Lett. 2009, 4, 024001. (24) Cherubini, F.; Peters, G. P.; Berntsen, T.; Strømman, A. H.; Hertwich, E. CO2 emissions from biomass combustion for bioenergy: Atmospheric decay and contribution to global warming. GCB Bioenergy 2011, 3, 413–426. (25) Fuglestvedt, J.; Berntsen, T.; Myhre, G.; Rypdal, K.; Skeie, R. B. Climate forcing from the transport sectors. Proc. Natl. Acad. Sci., U.S.A. 2008, 105 (2), 454–458. (26) Berntsen, T.; Fuglestvedt, J. S. Global temperature responses to current emissions from the transport sectors. Proc. Natl. Acad. Sci., U.S.A. 2008, 105, 19154–19159. (27) Skeie, R. B.; Fuglestvedt, J. S.; Berntsen, T.; Lund, M. T.; Myhre, G.; Rypdal, K. Global temperature change from the transport sectors: Historical development and future scenarios. Atmos. Environ. 2009, 43, 6260–6270. (28) Sausen, R. Transport impacts on atmosphere and climate. Atmos. Environ. 2010, 44, 4646–4647. (29) Borken-Kleefeld, J.; Berntsen, T.; Fuglestvedt, J. Specific Climate Impact of Passenger and Freight Transport. Environ. Sci. Technol. 2010, 44, 5700–5706. (30) Myhre, G.; Shine, K. P.; R€adel, G.; Gauss, M.; Isaksen, I. S. A.; Tang, Q.; Prather, M. J.; Williams, J. E.; van Velthoven, P.; Dessens, O.; Koffi, B.; Szopa, S.; Hoor, P.; Grewe, V.; Borken-Kleefeld, J.; Berntsen, T. K.; Fuglestvedt, J. S. Radiative forcing due to changes in ozone and methane caused by the transport sector. Atmos. Environ. 2011, 45 (2), 387–394. (31) Shine, K. P.; Fuglestvedt, J. S.; Hailemariam, K.; Stuber, N. Alternatives to the global warming potential for comparing climate impacts of emissions of greenhouse gases. Climatic Change 2005, 68, 281–302.

POLICY ANALYSIS

(32) Brazil. Second National Communication of Brazil to the United Nations Framework Convention on Climate Change; Ministry of Science and Tecnology, General-Coordination on Global Climate Change: Brasilia, 2010. (33) Goedkoop, M. J.; Heijungs, R.; Huijbregts, M.; de Schryver, A.; Struijs, J.; van Zelm, R. ReCiPe 2008, A life cycle impact assessment method which comprises harmonised category indicators at the midpoint and the endpoint level; Report I: Characterisation; Ministry of Housing, Spatial Planning and Environment (VROM): The Hague, the Netherlands, 2009. (34) IPCC. Climate Change: The IPCC Scientific Assessment; Cambridge University Press: Cambridge, United Kingdom, 1990. (35) Shine, K. P. The global warming potential - The need for an interdisciplinary retrail. Climatic Change 2009, 96, 467–472. (36) O’Neill, B. C. The Jury is Still Out on Global Warming Potentials. Climatic Change 2000, 44 (4), 427–443. (37) Tol, R. S. J.; Berntsen, T. K.; O’Neil, B. C.; Fuglestvedt, J. S.; Shine, K. P.; Balkanski, Y.; Makra, L. Metrics for Aggregating the Climate Effect of Different Emissions: A Unifying Framework; Economic and Social Reseach Institute: Dublin, Ireland, 2008. (38) O’Neill, B. A Unifying Framework for Metrics for Aggregating the Climate Effect of Different Emissions. In IPCC Expert Meeting on the Science of Alternative Metrics, Plattner, G.-K.; Stocker, T. F.; Midgley, P.; Tignor, M., Eds. IPCC Working Group I Technical Support Unit: Bern, Switzerland, 2009. (39) IPCC. IPCC Expert Meeting on the Science of Alternative Metrics; IPCC Working Group I Technical Support Unit: Bern, Switzerland, 2009. (40) Johansson, D.; Persson, U.; Azar, C. The Cost of Using Global Warming Potentials: Analysing the Trade off Between CO2, CH4 and N2O. Climatic Change 2006, 77 (3), 291–309. (41) Manning, M.; Reisinger, A. Broader perspectives for comparing different greenhouse gases. Proc. R. Soc., A 2011, 369, 1891–1905. (42) Reisinger, A.; Meinshausen, M.; Manning, M.; Bodeker, G. Uncertainties of global warming metrics: CO2 and CH4. Geophys. Res. Lett. 2010, 37 (14), L14707. (43) Kandlikar, M. Indices for comparing greenhouse gas emissions: integrating science and economics. Energy Econ. 1996, 18, 265–281. (44) Maier-Reimer, E.; Hasselmann, K. Transport and storage of CO2 in the ocean - an inorganic ocean-circulation carbon cycle model. Climate Dynamics 1987, 2, 63–90. (45) Enting, I. G.; Wigley, T. M. L.; Heimann, M. Future Emissions and Concentrations of Carbon Dioxide: Key Ocean/Atmosphere/Land Analyses; Technical Paper no. 31; CSIRO Division of Atmospheric Research: Oslo, 1994. (46) Enting, I. G. Laplace transform analysis of the carbon cycle. Environ. Model. Softw. 2007, 22 (10), 1488–1497. (47) Boucher, O.; Reddy, M. S. Climate trade-off between black carbon and carbon dioxide emissions. Energy Policy 2008, 36, 193–200. (48) Boucher, O.; Friedlingstein, P.; Collins, B.; Shine, K. P. The indirect global warming potential and global temperature change potential due to methane oxidation. Environ. Res. Lett. 2009, 4, 044007. (49) Collins, W. J.; Sitch, S.; Boucher, O. How vegetation impacts affect climate metrics for ozone precursors. J. Geophys. Res. 2010, 115 (D23), D23308. (50) Solomon, S.; Plattner, G.-K.; Knutti, R.; Friedlingstein, P. Irreversible climate change due to carbon dioxide emissions. Proc. Natl. Acad. Sci., U.S.A. 2009, 106 (6), 1704–1709. (51) Spielmann, M.; Bauer, C.; Dones, R.; Tuchschmid, M. Transport Services; Swiss Centre for Life Cycle Inventories: D€ubendorf, 2007. (52) Meinshausen, M.; Meinshausen, N.; Hare, W.; Raper, S. C. B.; Frieler, K.; Knutti, R.; Frame, D. J.; Allen, M. R. Greenhouse-gas emission targets for limiting global warming to 2 °C. Nature 2009, 458 (7242), 1158–1162. (53) Lee, D. S.; Pitari, G.; Grewe, V.; Gierens, K.; Penner, J. E.; Petzold, A.; Prather, M. J.; Schumann, U.; Bais, A.; Berntsen, T.; Iachetti, D.; Lim, L. L.; Sausen, R. Transport impacts on atmosphere and climate: Aviation. Atmos. Environ. 2010, 44 (37), 4678–4734. 8640

dx.doi.org/10.1021/es200627s |Environ. Sci. Technol. 2011, 45, 8633–8641

Environmental Science & Technology

POLICY ANALYSIS

(54) Eyring, V.; Isaksen, I. S. A.; Berntsen, T.; Collins, W. J.; Corbett, J. J.; Endresen, O.; Grainger, R. G.; Moldanova, J.; Schlager, H.; Stevenson, D. S. Transport impacts on atmosphere and climate: Shipping. Atmos. Environ. 2010, 44 (37), 4735–4771. (55) Gillett, N. P.; Matthews, H. D. Accounting for carbon cycle feedbacks in a comparison of the global warming effects of greenhouse gases. Environ. Res. Lett. 2010, 5 (3), 034011. (56) H€ohne, N.; Blum, H.; Fuglestvedt, J. S.; Skeie, R. B.; Kurosawa, A.; Hu, G.; Lowe, J.; Gohar, L.; Matthews, B.; de Salles, A. C. N.; Ellermann, C. Contributions of individual countries’ emissions to climate change and their uncertainty. Climatic Change 2010, 106, 359–391. (57) Prather, M. J.; Penner, J.; Fuglestvedt, J. S.; Raper, S.; de Campos, C. P.; Jain, A.; van Aardenne, J.; Lal, M.; Wagner, F.; Kurosawa, A.; Skeie, R. B.; Lowe, J.; Stott, P.; H€ohne, N. Tracking uncertainties in the causal chain from human activities to climate. Geophys. Res. Lett. 2009, 36. (58) Berntsen, T.; Fuglestvedt, J. S.; Myhre, G.; Stordal, F.; Berglen, T. F. Abatement of greenhouse gases: Does location matter? Climatic Change 2006, 74, 377–411. (59) Fr€olicher, T.; Joos, F. Reversible and irreversible impacts of greenhouse gas emissions in multi-century projections with the NCAR global coupled carbon cycle-climate model. Climate Dynamics 2010, 35 (7), 1439–1459. (60) Solomon, S.; Daniel, J. S.; Sanford, T. J.; Murphy, D. M.; Plattner, G.-K.; Knutti, R.; Friedlingstein, P. Persistence of climate changes due to a range of greenhouse gases. Proc. Natl. Acad. Sci. 2010, 107 (43), 18354–18359. (61) Penner, J. E.; Prather, M. J.; Isaksen, I. S. A.; Fuglestvedt, J. S.; Klimont, Z.; Stevenson, D. S. Short-lived uncertainty? Nat. Geosci. 2010, 3 (9), 587–588.

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dx.doi.org/10.1021/es200627s |Environ. Sci. Technol. 2011, 45, 8633–8641