Climate Agreements Based on Responsibility for Global Warming Periodic Updating Policy Choices and...

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Global Environmental Change 16 (2006) 182–194

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Climate agreements based on responsibility for global warming:Periodic updating, policy choices, and regional costs

Nathan Rive�, Asbjørn Torvanger, Jan S. Fuglestvedt

Center for International Climate and Environmental Research—Oslo (CICERO), P.O. Box 1129, Blindern, N-0318 Oslo, Norway

Received 15 April 2005; received in revised form 16 December 2005; accepted 10 January 2006

Abstract

It has been suggested that calculations of historical responsibility for global warming should be used to distribute mitigation

requirements in future climate agreements. For a medium-term mitigation scenario, we calculate regional mitigation costs resulting from

global allocation schemes based on the Brazilian Proposal that solely incorporate historical responsibility as a burden sharing criterion.

We find that they are likely to violate ability-to-pay principles. In spite of less stringent abatement requirements, developing country

regions experience cost burdens (as a percentage of GDP) in the same range as those of developed countries. We also assess the policy

options available for calculating historical responsibility. The periodic updating of responsibility calculations over time, concerns over

the robustness and availability of emissions data, and the question of whether past emissions were knowingly harmful, may lead to policy

choices that increase the relative historical responsibility attributed to developing countries. This, in turn, would increase their mitigation

cost burden.

r 2006 Elsevier Ltd. All rights reserved.

Keywords: Regional historical emissions; Contribution to global climate change; Brazilian Proposal; Dynamic CGE modelling; Regional mitigation costs

1. Introduction

In climate policy, there exist numerous criteria whichmay be used to distribute emissions reduction requirementsto participating countries. The Brazilian Proposal(UNFCCC, 1997a), presented prior to COP 3 in Kyoto,recommends allocating future emissions reduction require-ments among Annex I participants based on their historicalcontribution to climate change. The scheme could con-tribute favorably to the UNFCCC principle of ‘‘commonbut differentiated responsibilities’’ between Annex I andnon-Annex I Parties (UNFCCC, 1992) and thus receivedsignificant attention in the recent literature (Rosa andRibeiro, 2001; den Elzen and Schaeffer, 2002; den Elzenet al., 2005a, b; Hohne and Blok, 2005; Trudinger andEnting, 2005).

The larger part of this attention has gone towards thekey scientific and policy-related options in the calculation

e front matter r 2006 Elsevier Ltd. All rights reserved.

oenvcha.2006.01.002

ing author. Tel.: +4722 85 87 50; fax: +4722 85 87 51.

ess: [email protected] (N. Rive).

of regional historical responsibility (HR). The choice ofclimate change indicator, the emissions time period, theyear in which climate change is evaluated, the greenhousegas (GHG) inclusion, land-use and land-use change(LULUCF) emissions inclusion, attribution method,choice of climate model and the representation of climateprocesses—all influence the responsibility attributed toeach region. The calculations can be particularly sensitiveto a number of these options. If implemented in a burdensharing scheme, these policy choices would impact the costburdens faced by each country, and alternative policychoices would generate relative ‘‘winners’’ and ‘‘losers’’. Ifan HR-based criterion is to be implemented in a futureburden sharing schemes, some level of negotiation wouldbe expected in order to agree upon these scientific andpolicy-related choices.The objective of this paper is to assess the relevant

factors that may influence the choice of policy-relatedoptions in the calculations of HR, and highlight theimplications for burden sharing in a future climateagreement. The factors we consider are fairness principles,operationalization issues related to data, and the periodic

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ARTICLE IN PRESSN. Rive et al. / Global Environmental Change 16 (2006) 182–194 183

updating of climate agreements and associated HR-basedallocations.

The most notable contributions of this paper to theliterature are the use of (i) a global mitigation scenario thatsatisfies a long-term climate goal to 2100 and beyond, (ii)dynamically updated HR calculations to allocate emissionreductions over time, and (iii) a computable generalequilibrium (CGE) model to assess the regional mitigationcost impacts of alternative policy-related options.

In the next section, we review the policy and scientificchoices related to HR calculations. In Section 3, we detailfactors that may influence these policy choices. In Section4, we allocate emission reductions across eight regions onthe basis of alternative HR calculations for a medium-termmitigation scenario. In Section 5, we discuss the results ofthese allocations, and compare the cost burdens faced byindividual regions. In Section 6, we undertake a sensitivityanalysis, and conclude with a summary of the mainfindings and implications for future burden sharingschemes in Section 7.

2. Brazilian Proposal policy-related options

Scientific and policy-related choices and their impact onthe calculation of HR have been the main focus of theliterature on the Brazilian Proposal. We briefly review thesechoices.

HR is calculated for each country or region using anassumed period for attributed emissions—delineated by achosen start and end years. Only emissions that occurredbetween these years will be used to assess each country’sresponsibility. Emissions prior to or after this interval willnot be attributed, but will be a part of the backgroundemissions that contribute to the total climatic change. Theevaluation year is the year in which the attributedemissions’ effect on the chosen climate indicator ismeasured. The evaluation year must obviously not precedethe end of the attribution period.

Ideally, we would seek to attribute responsibility formonetary damages arising climate change. However, giventhe uncertainty of future impacts, and the difficulty inmonetizing these impacts, we use climate indicators as aproxy. In principle, any indicator on the climate changecausal chain (i.e. cumulative emissions, radiative forcing,temperature, and sea level rise) can be used as an indicatorfor attributing HR. Global mean temperature was sug-gested in the original Brazilian Proposal (UNFCCC,1997a), and is most typically considered in the HRliterature.

GHGs/forcing agents that are included in calculation ofHR will affect the results, as the mix of GHGs emittedvaries significantly across regions. The range of optionsincludes using (i) fossil fuel CO2 emissions only, (ii) fossilfuel and LULUCF CO2 emissions only, (iii) man-madeCO2, N2O, CH4 emissions only, (iv) man-made Kyoto gasemissions, or (v) a wider range of man-made gases

including the Kyoto gases and precursors such as O3 andSO2.The attribution method defines the methodology by

which regional HR is calculated for the chosen start andend years, evaluation year, and climate indicator. There areseveral mathematical methods for attribution of responsi-bilities, which include the marginal, residual, and time-

sliced methods. An in-depth discussion of these is made byTrudinger and Enting (2005).The impact of each of these policy-related options on the

resulting HR calculation is varied (see den Elzen et al.,2005a). The choice of start and end years will decidewhether the large backlog of developed country emissionswill be included, and have a significant impact on theirattributed HR. Extending the evaluation year far into thefuture will reduce the responsibility attributed to develop-ing countries, owing to their higher proportions of short-lived gases. Widening the types of emissions included in thecalculation will also increase their attribution by takinginto account LULUCF emissions. The choice of climateindicator has some impact, but it is less pronounced thanthe other choices, and the impact is dependent on the gasinclusion, and regional gas mix. Finally, the choice ofattribution method is seen to have only a modest impact onthe HR calculation.

3. Factors influencing policy-related options

It is impossible to know what form an HR-based burdensharing scheme could take in a future climate agreement, ifit were employed at all. As discussed above, alternativepolicy-related options for HR calculation may generaterelative advantages and disadvantages for regions partici-pating in a climate regime. As such, a level of negotiationwould be required to agree upon a final set of policyoptions, which is likely to be influenced by a number offactors.Firstly, the chosen policy-related options will depend on

how the HR calculations are to be implemented in thefuture regime. This, in turn, depends on the regime’sbroader design. This includes the goals of the agreement,the modes of participation, the participating countries, andburden sharing (Philibert et al., 2003; Schmalensee, 1996;Philibert and Pershing, 2001; Torvanger and Ringius, 2002;Aldy et al., 2003; Torvanger et al., 2004a). Secondly, thepolicy-related choices may simply be adjusted to obtaindesired HR and burden sharing outcomes. For example,the choice of a specific evaluation year may be largelyarbitrary, and simply adjusted so that the resulting HRcalculation is acceptable to the negotiating parties.Given that these two factors are specific to the

characteristics of the future climate regime, we focusinstead on a third set of ‘‘input-side’’ concerns. They aretermed input-side as they define the bounds in which thealternative HR policy options are considered and nego-tiated. In doing so, these factors will undoubtedly influencethe design of a future HR scheme. We identify three such

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1Canada, USA, Latin America, Africa, OECD Europe, Eastern Europe,

Former Soviet Union, Middle East, India, China, Southeast Asia,

Oceania, Japan.2See Section 4 for details on the DEEP model.

N. Rive et al. / Global Environmental Change 16 (2006) 182–194184

input-side factors: fairness principles, operationalizationissues, and the periodic updating of climate agreements andassociated HR allocations. They are described as follows:

All burden sharing schemes embody a fairness principle

to justify its use. For example, an HR scheme embodies thepolluter pays principle—by allocating mitigation require-ments to those responsible for warming. An alternativescheme, a per capita convergence scheme (Agarwal andNarain, 1991; see also Contraction and Convergence inMeyer, 2000), embodies the principle of egalitarianemission rights by progressing towards per capita emissionallowances for all people over time. In the context of thisstudy, however, we also consider the fairness principlesembodied in the alternative policy-related options within anHR scheme. The choice of these options will be influencedby different interpretations and preferences for fairness.

Operationalization issues may also be a contributingfactor to what policy options are chosen. Specific policyoption alternatives may be difficult to implement due totechnical, data, or logistical constraints in the climateregime. As a result, these options could become ‘unavail-able’ when an HR-based scheme is negotiated in a futureregime.

Finally, the dynamics of a long-term climate strategymay also play a role, through periodic updating of HRcalculations. Future mitigation agreements will likely benegotiated on a short-term basis, as with the KyotoProtocol. This would allow for the incorporation of newparticipants into the climate regime, as well as updatedknowledge of the climate system. A key element of HR-based climate agreements could be the periodic updating ofHR calculations to account for recent GHG emissions inthe period between agreements.

In the remainder of this section, we discuss the influenceof these three factors on each policy-related option for HRcalculation. In this paper, our focus is on the impact ofchoice of start year, end year, evaluation year, and climateindicator.

3.1. Start year

The start year delineates the start of emissions attributedto each region. A start year of 1890 is typically chosen, totake into account the backlog of emissions that contributedto current warming. However, the choice of a given startyear could be thought of as embodying fairness principles.

Firstly, the choice of a start year reflects an identificationof who was emitting the GHGs, and thus who shouldundertake mitigation action in the climate agreement. Thegeopolitical map has changed significantly and roughly sixgenerations have passed since 1890 (the typical choice forstart year). Countries such as Russia and Germany werenot in their current form prior to 1990. More critically,however, one could easily question whether the currentgeneration should be held responsible for actions of theirancestors. The organization of a society’s economic andenergy structure is largely inherited, and it has been shown

to be difficult to break out of the fossil energy ‘‘lock-in’’(Unruh, 2000). Thus, by choosing a start year far into thepast, we are applying a view of intergenerational fairnesswherein populations can be considered collectively culp-able across both space and time.Secondly, a chosen start year also reflects a judgment

that climate change was known about in the past, and thataction could have been taken to avoid it. However,consensus on the causes and dangers of climate changehas not spanned back many decades—certainly not to1890. The roots of climate change theory are in the late19th century, when Arrhenius (1896) postulated thatincreases in CO2 concentrations in the atmosphere couldlead to a rise in global mean temperatures. Revelle andSuess (1957) showed that anthropogenic CO2 was unlikelyto be fully absorbed by the ocean, and would likely causewarming of the planet. In 1990, the IntergovernmentalPanel on Climate Change (IPCC) First Assessment Report(IPCC, 1990) concluded that man-made activities wouldlead to a warming in the next century of 0.3 1C per decade,yet mitigation obligations were only introduced by theKyoto Protocol (UNFCCC, 1997b). In light of thistimeline, it is difficult to decide a start year that wouldappropriately reflect the start of a nation’s fault incontributing towards warming.Beyond these fairness issues, the choice of start year is

also affected by operationalization issues. HR calculationsare heavily data-dependent. Regional and country-levelresponsibility calculations require region- and country-specific historical emissions scenarios. This gives rise to twospecific data-related concerns.The first of these is data availability. The resolution of

the responsibility calculations (how country-specific theycan be) is ultimately limited by the resolution featured inthe emission data. The EDGAR-HYDE 1.4 database (VanAardenne et al., 2001; adjusted to Olivier and Berdowski,2001), which provides our historical emissions profiles, hasa disaggregation of 13 world regions,1 and we are able toaggregate the EDGAR-HYDE regions to match the eightregions in our economic model, DEEP.2 However, giventhe opportunity to implement further regions in the DEEPmodel, we would have been limited to the 13 regiondisaggregation found in the EDGAR-HYDE database. Ofcourse, the EDGAR-HYDE is not the only historicalemissions database available, but is the most comprehen-sive in terms of gas and region inclusion. And similarregional resolution limitations feature in the alternativeGHG databases: The CDIAC database—which includesonly fossil fuel (Marland et al., 2003) and land-use(Houghton, 1999) CO2 emissions—has comprehensive datafor the period 1890 to present for only 10 regions. Country-specific data is only available for fossil fuel emissions, with


varying historical time horizons. The IVIG database(de Campos et al., 2005) has country-level data, but onlyfor land-use change emissions. The key point, then, is thatour choice of database for HR calculation may restrict thenumber of regions, the choice of start year, and the numberof gases included in the attribution.

The second data concern relates to robustness. It has beenshown that the calculation of HR is sensitive to the choice ofhistorical emissions data (den Elzen et al., 2005a). While wedo not examine this particular sensitivity in this study, ithighlights the importance of an agreed-upon database forhistorical emissions, and an agreed methodology forreporting annual emissions. The methodology for reportingis provided by IPCC guidelines (IPCC, 1996). However, theannual reporting of inventories is only mandatory for AnnexI countries. Annex I emissions inventories according to theIPCC guidelines for the six Kyoto gases are available from1990 onwards. Data for non-Annex I countries (i.e.developing countries) for this period is not complete, ascountries have reported at different times. Thus, while it ispossible in future that a method for calculating historicalemissions could be agreed upon internationally, using onlythe current (incomplete) IPCC inventories for HR calcula-tion would limit the start year to 1990.

In Section 4, we calculate the impact on HR of threealternative start years: 1890, the standard year consideredin the literature; 1945, marking the end of World War II;and 1990, roughly marking the end of the fall ofCommunism, the Rio Summit, and the start of IPCCGHG inventories.

3.2. End year

Like the start year, the end year also serves as adelineator of attributed emissions. However, there arearguably fewer fairness values embodied in the choice ofthis year. If climate action is to be taken in the near future,it is difficult to argue against using the most recent end yearpossible. The data issues surrounding the choice start yeardo not occur with the choice of end year provided an endyear is chosen for which data is available.

However, there are issues related to the dynamics offuture climate agreements. It is likely that future post-Kyoto climate agreements will be updated at regularintervals. Thus, if HR remains a burden sharing criterion,the question arises as to whether the HR calculation shouldremain static to the end year used in the initial climateagreement, or be updated to account for the developmentin emissions in the period between negotiations.

In Section 4, we compare the results of HR-basedabatement allocations using a static end year with thoseusing a dynamically updated end year. For the static endyear, we choose 2000. This is largely arbitrary, as any singleyear would suffice for this comparison. In the dynamiccases, we use 10-year updating (an assumption of the timegap between climate negotiations), and 5-year updating(the maximum resolution of our CGE model).

3.3. Indicator

Related to fairness principles, the choice of indicatormay be influenced by country preferences towards indica-tors that are closely connected to the impacts they areconcerned with. For example, a country like the Maldivesmay be particularly concerned with sea level rise, andprefer its use as indicator for HR calculations. Land-lockedSwitzerland may have a preference for temperature rise,given its impact on tourism. However, given that each ofthese climate indicators are precursors to the climateimpacts we are ultimately concerned with, and that theUNFCCC (1992) does not specify which climate para-meters are most important, it is non-obvious which choiceof indicator would be preferred.Related to operationalization, it has been argued that the

chosen indicator should be robust to calculation, wellunderstood, and discount the importance of emissionsfrom a long time ago (Rosa et al., 2004). Global meantemperature satisfies these criteria as an indicator, and itwas also recommended by the original Brazilian Proposal(UNFCCC, 1997a). It is widely used in other HR studies(e.g. den Elzen et al., 2005a). An alternative proposal is theuse of CO2-equivalent cumulative GHG emissions as anindicator, given its ease of calculation, and simplicity ofcommunication to policymakers (den Elzen et al., 2005b;Hohne and Blok, 2005). However, this indicator fails todiscount the emissions from a long time ago and thediffering lifetimes of gases.In Section 4, we compare the use of global mean

temperature change (DT) and cumulative emissionsweighted by 100-year global warming potential (GWP)(Shine et al., 1990) on HR calculations. See den Elzen et al.(2005a) for a wider treatment of alternative indicators.

3.4. Evaluation year, time gap, and background emissions

scenario (for DT indicator only)

The choice of evaluation year reflects the concern ofwhen the climate change impacts become important foridentifying historical responsibility. A decision must bemade whether a gap is to separate the end and evaluationdates, and what size that gap should be. This is similar tothe choice of time horizon in the application of GWPs.In choosing the evaluation year, it could be argued that

we are more certain of climate change occurring today. Thebackground emissions scenario and climate change in thefuture is associated with larger uncertainties, thus we maynot be willing to set an evaluation year far into the future.Fairness concerns could argue that social time preferencesplace more emphasis on the now. On the other hand, itcould be argued that the evaluation year should be placedsome time in the future, at which time the climate systemwill have had time to react, and the climate impacts fromour emissions will be more cumulative and visible.In Section 4, we examine the impacts on HR calcula-

tions from both near-term (2000) and long-term (2100)

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Table 1

Policy-related options for calculating historical responsibility

Start year 1890, 1945, 1990

End year 2000, 10-year dynamic, 5-year dynamic

Indicator GWP-weighted cumulative emissions,

global mean temperature

Evaluation year 2000, 2010, 2050, 2100

Background scenario Case ME, zero, constant

4

6

8

10

12

14

16

CO

2 E

mis

sio

ns

(GtC

/yea

r)

"Case ME" Scenario


evaluation dates. In addition, we examine the sensitivity toalternative end-year-evaluation year gaps (10 years, 50years) for a fixed end year of emissions (2000).

With an evaluation year far into the future, we alsoexamine the sensitivity of HR calculations to the back-ground emissions scenario. This is important, as we do notknow what the future emissions trajectories will look like,and we do not know how this will affect our HRcalculations. In Section 4, we analyze scenarios where thebackground emissions after the end year either (i) are heldat a constant level equal to the end year emissions, (ii) dropto zero, or (iii) follow our assumed global mitigationpathway (Case ME, described below).

A summary of the policy-related options assessed in thisstudy is shown in Table 1. (The default options are inbold.) For all our HR calculations, the emissions includedare CH4, N2O, and CO2 from industrial, agricultural, andhousehold sources.

0

2

2000 2010 2020 2030 2040Year

DEEP BAU (A1B parameters)

Fig. 1. Annual CO2 emissions in our global mitigation scenario (Case

ME) and the DEEP BAU scenario

20

4. Allocating emission reductions

We focus on the medium-term period from 2000 to 2045,during which period we allocate emission reductions acrosseight world regions: China, Former Soviet Union (FSU),Eastern Europe, India, JCANZ region (Japan, Canada,Australia, and New Zealand), Western Europe, USA, andthe Rest of World (ROW).

0

2

4

6

8

10

12

14

16

18

2000 2020 2040 2060 2080 2100

CO

2 E

mis

sio

ns

(GtC

/yea

r)

SRES A1B"Case ME" ScenarioSRES B2

4.1. Emissions past and future

In this paper, the global emission reduction requirements(i.e. deviation from the BAU) which we allocate across theeight regions for the period 2000–2045 are taken from anexisting long-term global mitigation scenario (from 2000 to2145): the Case ME (Medium climate sensitivity, Earlyclimate action) scenario generated in Torvanger et al.(2004b). This scenario satisfies a long-term global warmingtarget of 2.5 1C above pre-industrial levels in 2100 (and alimited rise afterwards) for a climate sensitivity of 3.0 1C(for 2�CO2 concentrations).

3 This long-term scenario alsoprovides the background emissions for responsibilitycalculations when the evaluation year is far into the future.

3When calculating HR in our climate model, the climate sensitivity is set

at 3.0 1C for consistency. However, it is seen that HR calculations are

insensitive to the choice of climate sensitivity (den Elzen et al., 2005a).

This scenario is shown in Fig. 1 (medium-term perspective)and Fig. 2 (long-term perspective) below.We use both the CICERO simple climate model (SCM)

(Fuglestvedt and Berntsen, 1999) and the Dynamic analysisof Economics and Environmental Policy (DEEP) CGEmodel (Kallbekken, 2004; used in Kallbekken and Wests-kog, 2005) to calculate the regional HR, allocate emissionsreduction requirements, and calculate mitigation costburdens. The DEEP model uses economic growth andtechnological improvement parameters from the IPCC(2001) Special Report on Emissions Scenarios (SRES) A1Bscenario, and thus generates a business as usual scenariowith economic and emissions growth equal to that of the

Year

Fig. 2. Annual CO2 emissions in our global mitigation scenario (Case

ME) and SRES A1B and B2 scenarios. Source: IPCC (2001a).


A1B scenario. (See the Annex for further information onthe models.)

The historical emissions data for each region is takenfrom the EDGAR-HYDE 1.4 database (Van Aardenneet al., 2001, adjusted to Olivier and Berdowski, 2001) forthe period 1890–1995. We join the EDGAR-HYDEdatabase emissions in 1995 to the DEEP emissions in2000 through linear interpolation.

It should be noted that the choice of our eight regionswas largely determined by regional specifications in theEDGAR-HYDE database, and the finite computationalcapabilities of the DEEP model solver. The EDGAR-HYDE database features 13 regions, of which onlyCanada, USA, Japan, China, and India are uniquecountries. The DEEP model is only able to include eightaggregated regions when it is run for the period 2000–2045.

4.2. GHG mitigation

The DEEP model includes emissions and abatement forindustrial, agricultural, and domestic CO2, N2O, and CH4.As such, they are assumed to be the sole source ofmitigation in the abatement scenario, and the onlyemissions included in the HR calculation. Emissions fromthe remaining KP gases (SF6 and the F-gases) are assumedto follow the A1B business as usual scenario. This isacceptable because they are excluded from the HRcalculation, and we find in Section 5 that backgroundemissions have no influence on the calculation. LULUCFemissions are similarly excluded. These exclusions aredictated by DEEP, which is unable to model the emissionsor abatement of SF6, the F-gases, and emissions fromLULUCF sources. It should be noted that by excludingLULUCF sources, our HR calculations are somewhatbiased towards higher developed country HR (compared toif LULUCF sources were included). The exclusion of SF6

and the F-gases has only a small impact (see den Elzen etal., 2005a for the impact of gas mix on HR calculations).Overall, we find the exclusions acceptable given thatindustrial, agricultural, and domestic sources make upthe bulk of national GHG emissions, and the accounting ofemissions from LULUCF sources is highly uncertain.

Table 2

Policy-related HR paramenters in alternative experimental runs

Run Start Year End Year Evaluatio

1 1890 10-year dynamic 2100



4 1890 2000 2100


6 1890 2000 2000

7 1890 2000 2010

8 1890 2000 2050

9 1890 2000 —

10 1890 2000 2100

11 1890 2000 2100

4.3. Attribution methodology

When calculating regional HR with global meantemperature change (DT) as indicator, we employ thenormalized marginal method as described by Trudinger andEnting (2005) and den Elzen et al. (2005a). In short, eachregion’s marginal contribution to warming is simulated inthe climate model. By calculating this warming for eachregion in turn, we are able to calculate the share of totalglobal warming resulting from each region’s emissions.

4.4. Allocation methodologies

In this paper, the global mitigation burden (the requireddeviation from the BAU emissions level, shown in Fig. 1) isallocated among participating countries for each commit-ment period. Table 2 lists the alternative HR-basedallocation schemes examined in this study.For clarity, we introduce the definition of the allocation

year in HR-based allocation schemes: the year in which theGHG abatement (allocated by an HR calculation) takesplace. The use of this term ensures we differentiate theallocation year from the start, end, and evaluation years.We employ an allocation-based methodology (as defined

in Rose et al. (1998)) to apply our HR calculations. Aparticipant will be allocated a share of the globalmitigation requirement (in terms of tons of GHG) equalto its calculated HR. For example, for a given allocationyear, if the USA is calculated to have historicallycontributed to 20% of warming, it will be assigned 20%of global mitigation burden (in terms of CO2 equivalentemission reductions) in the allocation year. Such a simplemethodology is the simplest interpretation of the BrazilianProposal principles; we keep separate alternative fairnesscriteria such as ability-to-pay or grandfathering. It iscertainly feasible to merge an HR-based burden sharingscheme with other fairness criteria, such as ability-to-paythresholds (e.g. den Elzen et al., 2005b). However, wechoose not to do so, in order to assess the effects of using aburden sharing scheme that solely uses HR criteria.With regards to participation, we assume all countries,

both developed and developing, will be participating in

n Year Indicator Background

Temperature Case ME

Temperature Case ME

Temperature Case ME

Temperature Case ME

Temperature Case ME

Temperature Case ME

Temperature Case ME

Temperature Case ME

Cumulative Emissions Case ME

Temperature Zero

Temperature Constant

ARTICLE IN PRESSN. Rive et al. / Global Environmental Change 16 (2006) 182–194188

mitigation in our climate regime. The role of developingcountries in future climate agreements remains underdiscussion; however, it is highly likely that participationin future regimes will be expanded beyond the Annex-Igrouping with some form of obligations. We assume thatall countries will undertake quantitative emission reduc-tions, differentiated only by the HR calculations. Further-more, we allow full emission permit trading for allparticipants. The trading system is based on a GWP100

exchange rate between the three gases. In our experiment,HR will be calculated from the initial emissions permitendowment granted to each country under the climateregime, rather than the number of post-trading permitsheld. This ensures that the calculations account for allmitigation paid for by each country, rather than thegeographical location of that mitigation (as it may bebought from other countries to minimize the abatementcosts).

5. Results and discussion

This section discusses the results of our HR-basedallocation scenarios listed in Table 2. Run 1 serves as thereference case for comparison with the alternatives,although it should not be seen as the preferred set ofpolicy-related parameters.

The results of the HR calculations for Run 1 are shownin Fig. 3 for the mitigation period 2020–2045.

It is a step function because it assumes that the HR isonly updated (with a new end year) every 10 years, tosimulate the regular re-negotiation of climate commit-ments. Thus, an HR calculation made with an end year of2019 is used for allocations from 2020 through to 2029. TheHR is then re-calculated for the 2030s with an end year of2029, and so forth. It is seen that for the first years of

0%

5%

10%

15%

20%

25%

30%

2020 2025 2030 2035 2040 2045Allocation Year

Sh

are

of G

lob

al W

arm

ing

ROWUSAChina

W Europe

FSUJCANZIndia

E Europe

Fig. 3. Run 1 historical responsibility results for period 2020–2045, in

terms of share of global contribution to warming.

allocation, the US and ROW respectively have the highestshares of HR. However, beyond 2030, the calculationsbegin to take into account the strong emissions growthamong the developing countries. A key result is thesignificant increases shown by HR attributed to China,India, and ROW over the mitigation period. The ROWregion, which is made up of most of Asia, Africa, andSouth America, sees its share of global HR rise from 20%in 2020 to 27% in 2040. Similarly, China overtakesWestern Europe in terms of share of global HR with arise from 13% in 2020 to 16% in 2040.The results from Fig. 3 are iteratively used to allocate

emission reductions across the eight regions (Fig. 4), withthe resulting emissions then used to calculate HR for thenext periodic update. All reduction requirements areincreasing over time, as a result of the growing globalabatement requirement (see Fig. 1). However, it is seen thatdeveloping countries are allocated a significantly lowerpercentage reduction from their BAU compared todeveloped countries, owing to their smaller shares ofresponsibility for warming (Fig. 4).

5.1. Mitigation costs

We use the DEEP model to calculate the annual totalabatement cost for each country, as a percentage of theirannual GDP. The total abatement cost is calculated bymultiplying the average abatement costs (the opportunitycost of reducing emissions) by the regional emissionreductions. Fig. 5 shows the cost results from Run 1, withthe bars denoting the range of burden associated with allthe alternative HR runs (see Table 2).With the exception of Eastern Europe and the FSU, the

mitigation burden across all regions ranges from roughly0.02% (ROW in Run 10) to 0.13% (India in Run 6) of

0%

5%

10%

15%

20%

25%

2020 2025 2030 2035 2040 2045Allocation Year

W Europe

E Europe

USA

JCANZ

FSU

India

ROW

China

Red

uct

ion

fro

m r

egio

nal

BA

U

Fig. 4. Run 1 allocated annual emission reductions (% reduction from

regional BAU emissions).

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W Europe

USA

ROW

China

E Europe

JCANZ

India

FSU

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

Tota

l An

nu

al A

bat

emen

t Co

st in

204

5 (%

of

GD

P)

Fig. 5. Total annual abatement cost in 2045 by country (% of GDP) for

Run 1. Bars denote range of burden allocated to each region under all

alternative Runs (see Table 2).

N. Rive et al. / Global Environmental Change 16 (2006) 182–194 189

regional GDP.4 What is remarkable is that the cost burdenof developing countries under our HR-based allocationscheme is in a range similar to that of developed countries,even though their abatement requirements (in terms ofpercentage reduction from BAU emissions) are lessstringent. This is driven by the relatively higher carbonintensity of GDP seen in developing countries (see AnnexFig. A1). A similar result is seen by Zhang (2000), whofinds GNP loss in China under less restrictive mitigationscenarios to be comparable to GNP loss in developedregions under stringent mitigation requirements.

It should be noted that measuring abatement costs interms of percentage GDP may be an insufficient means ofmeasuring burden. In particular, it may be a poorrepresentation of the mitigation burden faced by develop-ing countries relative to their developed country counter-parts, as it fails to take into account equity issues. Amitigation burden of 0.5% of GDP will be more severe todeveloping countries with lower per capita GDP (and thushigher marginal utility of consumption) than richerdeveloped countries. This issue could be amelioratedthrough the use of a GDP per capita weighting systemon calculated mitigation costs. However, the simplicity ofpercentage GDP cost reporting is sufficient for ourconclusions.

In the previous section, it was pointed out thatalternative policy-related parameters in attributing HRcan benefit either developing or developed countries. Yet

4Eastern Europe and FSU are anomalous because of their high carbon

intensity of GDP from industrial GHGs (see Fig. A1 in the Annex), and

their proportionally high HR from Soviet-era GHG emissions.

our results in Fig. 5 show that the mitigation costs remainremarkably similar for both developed and developingregions, regardless of the alternative policy-related choicesthat are used to calculate HR. This suggests that pure HR-based allocation schemes considered in this study (whichdo not employ alternative fairness criteria) may violateability-to-pay (or other equity) principles. This result isonly strengthened when considering equity issues: thecalculated mitigation cost burden to developing countrieswill become significantly greater if an equity weighting isapplied. Consequently, it is unlikely that an HR-basedburden sharing scheme would be incorporated into afuture climate agreement in the format considered in thispaper.Next, we briefly highlight the impact of the alternative

policy-related options for calculating HR on the regionalmitigation cost burdens.

5.2. Start year

Moving from the default start year (1890) to more recentyears (1990) for attributing emissions, we find that theburden on developing countries increases (see Fig. 6). Thisis because more recent start years exclude the long backlogof developed country long-lived GHG emissions. This is inagreement with den Elzen et al. (2005a).

5.3. End year

By dynamically updating the end year forward, we findthat developing country burdens are increased, whiledeveloped country burdens fall (see Fig. 7). This is a resultof the fast-growing emissions likely to be seen indeveloping country economies over the coming decades—featured particularly in the SRES A1B scenario employedin this study. Over time, these emissions contribute to anincreasing share of future warming. As a consequence,developing country HR will rise, and developing countrieswill be continuously allocated a larger piece of a growingglobal emissions reduction burden (in terms of tons ofGHG). This is not a surprising result, but this type ofperiodically updated calculation has not been undertakenin the literature before.

5.4. Evaluation year

Extending the gap between the end year (in this case,2000) and evaluation year is seen to favor developingcountries (Fig. 8). These regions have a higher proportionof methane in their emissions mix, whose atmosphericlifetime is relatively short (12 years). As the gap betweenend year and evaluation year is extended beyond thelifetime of methane, there is a significant drop in thedeveloping country burden. This is in agreement with denElzen et al. (2005a).

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Fig. 6. Regional total annual abatement cost (% of GDP) under

alternative start year parameters: 1890 (Run 1), 1945 (Run 3), 1990

(Run 2).

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1), 5-year dynamic (Run 5).

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alternative evaluation year parameters: 2000 (Run 6), 2010 (Run 7),

2050 (Run 8), 2100 (Run 4).

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alternative climate indicators: GWP-weighted cumulative emissions

(Run 9), 2000 global mean temperature change (Run 6), 2100 global

mean temperature change (Run 4).


5.5. Indicator

Using cumulative emissions as a climate indicator isfound to reasonably replicate the results from comparableruns evaluating DT at either 2000 or 2100 (Fig. 9; end yearis 2000). This is in agreement with results from den Elzenet al. (2005a, b).

5.6. Background emissions

We find negligible sensitivity to the choice of a post-2000background scenario (assuming a static end year of 2000),illustrated in Fig. 10. This means that HR calculations willnot be affected by the uncertain background emissionstrajectories in future. This suggests that, if desired, it is

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alternative post-2000 background emissions: Case ME scenario emissions

(Run 4), zero emissions (Run 10), constant emissions (Run 11).

0%

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2020 2025 2030 2035 2040 2045Allocation Year

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Fig. 11. Historical responsibility calculations for period 2000–2045 in

terms of share of global contribution to temperature change, using Run 1

parameters. Solid line denotes results using SRES A1B growth parameters

in DEEP, dotted line denotes SRES B2 parameters.

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Fig. 12. Regional total annual abatement cost (% of GDP) under Run 1,

using A1B and B2 growth parameters in DEEP.


operationally feasible to employ an evaluation year far intothe future without knowing what future backgroundemissions levels will be.

6. Sensitivity analysis

In this section, we undertake a brief sensitivity analysisusing alternative economic growth and technologicalimprovement parameters in the DEEP model. We areparticularly interested in the sensitivity of the dynamicallyupdated HR calculations to our economic growth andtechnological improvement assumptions. For the aboveruns, the DEEP model was set up using parameters fromthe SRES A1B scenario. The A1B scenario describes ahigh-growth future, with rapid emissions growth in thedeveloping world. As an alternative, we consider theimpact of employing economic and technological para-meters from the SRES B2 scenario (IPCC, 2001), whichdescribes a regionalized and environmentally concernedfuture. In contrast to the A1B scenario, the B2 shows onlymodest developing country emissions growth over thecoming decades (see Fig. 2 for reference).

In this sensitivity analysis, we re-run our Run 1 (seeTable 2), but this time with B2 parameters in the DEEPmodel. Our mitigation scenario uses the same percentage

global emissions reductions (from the DEEP B2 BAU) as isrequired in the original scenarios (Fig. 1). The dynamic HRcalculations are shown in Fig. 11. We find that with B2parameters, the HR attributed to developing countries stillincreases over the period 2020–2045, while the developedcountry HR falls. However, the trends are less pronouncedthan with A1B economic and technological assumptions.The difference is seen in the ROW region results in

particular, owing to the slow emissions growth in the SRESAfrica and Latin America (ALM) region under the B2scenario.With regards to mitigation costs (in terms of percentage

GDP), we see in Fig. 12 that under the B2 case, thedeveloping country burdens remain in the same range asthose of the developed countries. While the cost burden toall countries is generally smaller with B2 parameterscompared to A1B parameters (a result of the differing


economic assumptions), our key observation holds: under asimplistic HR-based burden scheme such as this, develop-ing and developed country mitigation cost burdens are notsufficiently differentiated, thereby likely violating ability-to-pay (or other equity) principles.

7. Conclusion

Results from this study, as well as those in the literature,show that historical responsibility (HR) calculations have astrong sensitivity to the policy-related choices used. Whileit is not possible to predict the choice among these optionsin a future climate agreement, we have discussed the factorsthat may influence the choice of HR policy options. Theinfluencing factors we have identified (fairness principles,operationalization, and data issues) are seen to be usefulevaluation criteria. Using an integrated climate andeconomic model, we have assessed the possible implica-tions of these influencing factors on regional mitigationcost burdens arising from an HR-based allocation scheme.

This paper makes a number of findings. Firstly, a pureHR-based allocation scheme, as employed in this study,may impose cost burdens on developing countries that,compared to developed country cost burdens, may beunacceptably high. While less stringent emission reductions(in terms of tons of GHG emissions) are allocated todeveloping countries than to developed countries, their costburdens (as a percentage of GDP) are roughly in the samerange. The developing country burden is further increasedwhen taking into account equity issues. This could violateability-to-pay (and other equity) principles. This resultappears to be robust across the range of possible policy-related choices in calculating HR, and future economicgrowth and technological improvement assumptions.

Secondly, the choice of start year is likely to beinfluenced by a number of factors. Concerns over emissionsdata availability and robustness may push for a morerecent start year in place of the typical choice of 1890.Furthermore, while we may wish to include a long backlogof emissions in acknowledgement of their cumulativewarming impact, a problem still exists with regard towhether historical emissions were knowingly causingclimate change. This may also push for a more recent startyear. On the whole, these influencing factors will likelylower the HR attributed to developed countries.

Third, the choice of end year will likely be influenced bythe design and dynamics of the climate agreements. Iffuture climate regimes are negotiated periodically (as withthe Kyoto Protocol), HR may also be updated periodically.This causes developing countries’ attributed HR to riseover time, given their fast-growing greenhouse gas emis-sions. This requires them to take an increasing share of agrowing global abatement burden over time.

Fourth, the evaluation year is likely to be chosen with asufficient gap into the future (from the end year) to accountfor climate inertia. Operationally, we find this to befeasible, as we find no sensitivity in the HR calculations

to the future emissions scenario. Sensitivity to the chosengap depends on whether it is beyond the horizon ofmethane’s lifetime in the atmosphere (assuming, of course,that methane is used in the calculation). A larger gapdecreases the HR of developing countries, which have alarger share of short-lived gases in their emissions profiles.Finally, the use of a cumulative CO2-equivalent emis-

sions indicator generates a similar HR calculation to that ofglobal mean temperature change, with a marginal reduc-tion to developing countries’ responsibility. As pointed outby den Elzen et al. (2005a), cumulative CO2-equivalentemissions may be a preferable indicator to temperaturechange as it allows a more simple calculation, and is easierto explain to policymakers. However, it may be preferableto keep temperature change as an indicator, given its closeassociation with the climate impacts we seek to avoid,understanding by the general public, and flexibility withregard to alternative policy-related choices in the calcula-tions (e.g. evaluation year).In future burden sharing schemes, negotiators will likely

seek to avoid violating ability-to-pay (or other equity)principles when allocating emissions reductions amongcountries. As such, the HR-based scheme as used in thisstudy is likely to be infeasible in political terms. A possiblesolution could be a hybrid burden sharing schemeincorporating at least two of alternative fairness criteria(Rose et al., 1998; Berk and den Elzen, 2001), or aparticipation threshold for those taking on mitigationcommitments based on GDP per capita (den Elzen et al.,2005b). The exploration of more promising combinationsof HR and other allocation schemes is an important issuefor future studies.

Acknowledgments

This work has received funding from the NorwegianResearch Council’s RAMBU program, through the projectA Sustainable Climate Development: Long-term Targets,Global Agreements, and Economic Consequences. Theauthors would like to thank Terje Berntsen, Michel denElzen, Steffen Kallbekken, and Brian O’Neill, and twoanonymous reviewers for their valuable comments.

Appendix A. Model description

The SCM is described in Fuglestvedt and Berntsen(1999) used in, e.g., Fuglestvedt et al. (2000). The modelincorporates a scheme for CO2 from Joos et al. (1996) andan energy-balance climate/up-welling diffusion oceanmodel developed by Schlesinger et al. (1992). The CO2

module uses an ocean mixed-layer pulse response functionthat characterizes the surface to deep ocean mixing incombination with a separate equation describing theair–sea exchange based on the HILDA model (Siegenthalerand Joos, 1992) to account for non-linearities in the carbonchemistry in the ocean.

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Fig. A1. Non-LULUCF carbon intensity of GDP in 2000. Source: GTAP

and GTAP/EPA databases (Dimaranan and McDougall, 2002; Lee, 2002).


The DEEP model is described in Kallbekken (2004) andused in, e.g., Kallbekken and Westskog (2005). The DEEPmodel is a multi-sector, multi-region, multi-gas CGEmodel with a growth benchmark. The model was run forten 5-year periods, from 2000 to 2045. The structure ofproduction and demand in the DEEP model has beenadopted from the GTAP-EG model by Rutherford andPaltsev (2000), and the social accounting matrix (SAM) isversion 5 of the Global Trade Analysis Project (GTAP)database (Dimaranan and McDougall, 2002) (see Fig. A1).We use economic growth rates and technological improve-ment parameters from SRES A1B scenario (IPCC, 2001) togenerate a ‘‘DEEP A1B’’ BAU scenario. There are noexplicit emissions abatement curves in the DEEP model.All abatement is achieved through substitution. Theemissions for all gases are provided by the GTAP/EPAdatabases (Lee, 2002, 2003).

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