Energy Economics 31 (2009) S131–S143

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Energy Economics

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Deterministic and stochastic analysis of alternative climate targets under differentiatedcooperation regimes

Richard Loulou a,b,⁎, Maryse Labriet b, Amit Kanudia c

a McGill University, 7320 de Roquancourt, Montreal, Quebec Canada H3R 3C9b KANLO Consultants, Lyon, Francec KanORS Consulting, Delhi, India

⁎ Corresponding author.E-mail addresses: richardloulou@gmail.com (R. Loulo

(M. Labriet), amit.kanudia@gmail.com (A. Kanudia).

0140-9883/$ – see front matter © 2009 Elsevier B.V. Aldoi:10.1016/j.eneco.2009.06.012

a b s t r a c t

a r t i c l e i n f o

Article history:Received 31 March 2009Received in revised form 12 June 2009Accepted 12 June 2009Available online 21 June 2009

Keywords:Climate policiesImperfect cooperationEnergy modelPartial equilibriumStochastic programming

This article analyzes the feasibility of attaining a variety of climate targets during the 21st century, underalternative cooperation regimes by groups of countries. Five climate targets of increasing severity areanalyzed, following the EMF-22 experiment. Each target is attempted under two cooperation regimes, a FirstBest scenario where all countries fully cooperate from 2012 on, and a Second Best scenario where the Worldis partitioned into three groups, and each group of countries enters the cooperation at a different date, andimplement emission abatement actions in a progressive manner, once in the coalition. The resulting tencombinations are simulated via the ETSAP-TIAM technology based, integrated assessment model. In additionto the 10 separate case analyses, the article proposes a probabilistic treatment of three targets under the FirstBest scenario, and shows that the three forcing targets may in fact be interpreted as a single target on globaltemperature change, while assuming that the climate sensitivity Cs is uncertain. It is shown that such aninterpretation is possible only if the probability distribution of Cs is carefully chosen.The analysis of the results shows that the lowest forcing level is unattainable unless immediate coordinatedaction is undertaken by all countries, and even so only at a high global cost. The middle and the high forcinglevels are feasible at affordable global costs, even under the Second Best scenario. Another originalcontribution of this article is to explain why certain combinations of technological choices are made by themodel, and in particular why the climate target clearly supersedes the usually accepted objective ofimproving energy efficiency. The analysis shows that under some climate targets, it is not optimal to improveenergy efficiency, but rather to take advantage of certain technologies that help to reach the climateobjective, but that happen to be less energy efficient than even the technologies in the reference scenario.This is particularly observable in the power generation sector and in some end-use sectors. Finally, the articlediscusses the pros and cons of the stochastic programming treatment of forcing targets, and compares it withthe separate simulations of the various deterministic cases.

© 2009 Elsevier B.V. All rights reserved.

1. Introduction

This research is inscribed in the EMF-22 collective work on climatepolicies in the transitional period. The EMF-22 plan is to model andanalyze somebroadclasses of global climatepolicies, taking into accountthe large uncertainties that exist both on the knowledge of climatephenomena, and concerning the conditions under which the globalcommunitymight agree onways to insure against potentially disastrousclimate changes in the 21st century. As one can readily observe from thelatest IPCC reports (IPCC, 2007) and from numerous other researcharticles on the subject, uncertainties about the future global climateabound. Some of these uncertainties are linked to the inherentrandomness of climate processes — perhaps simply reflecting our lack

u), maryse.labriet@gmail.com

l rights reserved.

of precise understanding of such phenomena. Others are due to theuncertain socio-economic, political, and technological developments,that will in fine determine the anthropogenic emissions of greenhousegases (and other causes of the greenhouse effect) by different countries.

In this research,webynomeans claim to tackle evena small portionofthe many unknowns influencing future climate changes. However, bytaking into account some key uncertainties on the two sides of the issuementioned above, we hope to obtain at least a coherent view of themorecomplex realworld issue. This article is based on scenarios definedwithintheTransitionPoliciesWorkGroupof EMF-22, describedelsewhere in thisissue, but contains some extensions beyond these, especially concerningthe treatment of climate uncertainty. The general perspective of the EMF-22 experiment is to assume some transitional agreements betweencountries with respect to their participation in the reduction of globalGHG emissions, while aiming at specific climate targets in the longer run.

In order to address the uncertainty on the climatic and environ-mental processes, and following thework plan of the EMF-22 Transition

Table 1The ten cases.

Target 2.6 W/m2 2.6 W/m2 3.7 W/m2 3.7 W/m2 4.5 W/m2

Scenario Max Overshoot Max Overshoot Max

First Best 1B-2p6max 1B-2p6over 1B-3p7max 1B-3p7over 1B-4p5maxSecond Best INFEASIBLE 2B-2p6over 2B-3p7max 2B-3p7over 2B-4p5max

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Policies Workgroup, we set alternative climate targets, each character-ized by a specific value for atmospheric radiative forcing of the Kyotogases only (abbreviatedRF). The three values are: 2.6, 3.7, and4.5 W/m2,which translate into respective values of the GHG atmosphericconcentrations equal to 450, 550, and 650 ppmv for Kyoto gases,expressed in CO2-equivalent. The work plan distinguishes two cases fortargets 2.6 and 3.7, either that the target cannot be exceeded at any timeduring the 21st century (so-called ‘max’ case) or is to be satisfied in year2100 only (the so-called ‘overshoot’ case, abbreviated OS).

Regarding the participation of various countries to emission reduc-tions, the realworld uncertainties aremultiple, but the bottom line is theability and willingness of individual nations to reduce their emissions ofsuch substances into the atmosphere in a quantified manner. The workplan treats this vast issue in a simplified manner by partitioning theWorld into three country groups, and assuming that all countries withineach group agree on adopting the same timing and pace for theirmitigation actions. The precise conditions are outlined in detail in thecapstone article of this special issue, and are summarized as follows. Foreach climate target, two contrasted scenarios are considered: the FirstBest scenario is an efficient one, assuming full cooperation of allcountries toward reaching the target efficiently, starting in 2012. TheSecondBest scenario assumes thateachgroupof countries hasadifferentstarting date for commencing its GHG mitigation actions, and further-more that each group except group 1 will proceed in a progressivemanner, starting at a low level of emission reductions, and reaching fullcooperation mode 20 years after their start date. Emission trading by agroup starts at the datewhen that group starts full cooperation. Group 1(essentially OECD countries) starts reductions in 2012, group 2 (Brazil,India, China, Russia, the so-called BRIC's) starts in 2030 and reaches fullcooperation (and trading) with group 1 in 2050, and group 3 (the rest ofthe World) starts reducing in 2050 and reaches full speed (and startstrading) in 2070. More details are given in Section 2.2.

Table 1 exhibits the 10 combinations of scenarios and targets thatwere attempted in the EMF-22 exercise. As it turns out, the SecondBest scenario with target 2.6 W/m2 and no overshoot (i.e. max) istechnically infeasible for our model (forcing exceeds the 2.6 limit inthe early decades of the 21st century). The other runs are feasible,although as we shall see, those with the 2.6 W/m2 target are verycostly. In addition to these policy scenarios, the Reference scenario(with no climate target) is also run, in order to provide a basis forcalculating the cost of each policy scenario.

One contribution of this article is that it provides an alternativeinterpretation of the multiple climate targets: in addition to simulatingeach target separately (as in EMF-22 work plan), we also argue(Section 4) that the three ‘overshoot’ RF targets may be viewed asexpressing the same target on global temperature increase in 2100(namely 2.45 °C) above pre-industrial time, while postulating threedifferent values of the climate sensitivityCs (namely, Cs=2,Cs=2.9, andCs=5). This new interpretation leads quite naturally to a probabilistictreatment of these three climate targets by stochastic programming, andto the computation of a single optimal hedging strategy. In the presence ofsubstantial and skewed uncertainty on the pathway to a giventemperature change, a hedging strategy is appropriate, as it provides astrategy that is robust during the period of uncertainty.

Section 2 describes the methodology used to simulate the variousscenarios and combinations, including a brief description of the TIAMmodel, and a description of the simulation of the 10 deterministiccases. Section 3 is devoted to the presentation and analysis of key

results from the 10 separate deterministic cases. Section 4 presents themotivation and justification of the probabilistic interpretation oftargets, presents some results obtained from the alternative treatmentofmultiple targets via stochastic programming, and stresses the addedvalue of such an approach. Section 5 concludes the article and outlinespossible extensions of the present research. The electronic Appendixincludes additional description of the TIAM model, and manyadditional results, some of which were used in writing the commentsin the article itself, but could not be explicitly shown due to space limit.

2. Methodology

2.1. The ETSAP-TIMES integrated assessment model (TIAM)

The scenarios are run with the ETSAP-TIAM model, which webriefly describe now. The TIMES integrated assessment model (TIAM)is a global partial equilibriummodel based on the TIMESparadigm, anddevelopedunder the sponsorship of ETSAPover the period2004–2007.The TIMES model generator was also developed through ETSAP from1997 to 2003. A complete description of the TIMES equations appearsin www.etsap.org/documentation. The TIAM incarnation of TIMES isdescribed in Loulou (2007) and in Loulou and Labriet (2007). A muchshorter description appears in the electronic Appendix to this article.

TIAM is amulti-regional energymodelwith 15 regions that exchangeenergy and emission rights. It covers a 96 year horizon extending from2005 to 2100, divided into periods of user-chosen, perhaps unequallengths. In this research, we have defined 9 time periods that coincidewith important entry dates of the country groups into the climatecoalition. However, results are presented for years 2010, 2020, 2030,2040, 2050, 2060, 2070, 2080, 2090, and 2100, using interpolation. Themodel is driven by a set of 42 demands for energy services in all sectors:agriculture, residential, commercial, industry, and transportation.Demands for energy services are user specified only for the Referencescenario, and have each a user-defined own-price elasticity. Therefore,each demand varies endogenously in alternate scenarios, in response toendogenous price changes. The model thus computes a partialequilibrium on world-wide energy and emissions markets that max-imizes the discounted present value of global surplus. The supplydemand equilibrium is computed via linear programming.

The investment costs are all first annualized (using hurdle rates thatare sector dependent). These annualized investment costs are thenadded to annual costs (fixedandvariable), to form the total annual costs.The stream of annual costs is then discounted to year 2005 using thegeneral discount rate of 5% (the interest rate). All costs are expressed inUSD2005. The hurdle rates range from 6% to 9% per year for large utilitiesand heavy industries, to more than 25% per year for investments in theresidential, commercial, and private transportation sectors. The hurdlerates were obtained from the work done in the European Unionintegrated project NEEDS (Cosmi et al., 2006, and references therein).

A salvage value term is subtracted from the cost objective in orderto account for the residual value of technologies still extant after theend of the horizon, and thus attenuate the end-of-horizon bias.

The TIMES model generator contains more than 30 types ofstandard constraints, ranging from flow balance equations to capacitytransfer equations, to bounds on the utilization rate of technologies, totechnical equations that simulate the utilization regime of someelectricity generation plants, etc. In addition, there are a number of adhoc user-defined constraints that express specific conditions. Forexample, inmany cases, the penetration of new technologies or of newfuels is partially controlled by upper bounds that are progressivelyrelaxed as time goes on.

Themodel contains explicit descriptions of more than one thousandtechnologies and one hundred commodities in each region, logicallyinterrelated in a Reference Energy System. Each technology has its owntechnical and economic parameters. Such technological detail allowsprecise tracking of capital turnover, and provides a detailed description

Table 2Global surplus losses relative to REF (NPV in $T, discounted at 5%/yr).

Target 2.6 W/m2 2.6 W/m2 3.7 W/m2 3.7 W/m2 4.5 W/m2

Scenario Max Overshoot Max Overshoot Max

First Best 31.2 20.4 2.8 2.5 0.14Second Best INFEASIBLE 43.1 4.0 3.4 0.28

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of technological competition. The model's scope covers extraction,processing, conversion, trading, and end-uses of all energy forms.Primary resources are disaggregated by type (e.g. proven vs. futurenatural gas reserves, connected vs. not, frontier gas, CBM, associated gas,etc). Each type of non renewable resource is described in each region bymeans of a step-wise supply curve (3 steps) for the cumulative amountsin the ground, technical annual extraction limits, fixed and variablecosts, thus constituting a many stepped supply curve for each primaryenergy form (coal, oil, and gas). All renewable energy formshave annualpotentials in each region, also with multiple steps.

ETSAP-TIAM explicitly models emissions of CO2, N2O and CH4 fromall anthropic sources (energy, industry, land, agriculture, and waste), atthe technology level. In addition, there are measures to destroy N2O,others to burn methane (by flaring or use in electric power plants), andstill others to capture CO2 and store in underground or undersea in avariety of reservoirs. Capture and geological storage of CO2 is available atelectric power plants, at oil wells, at plants that produce synthetic fuels,and at hydrogenplants. In each case, the capture is not complete (from9to 11% of the CO2 escapes to the atmosphere). Regarding N2O and CH4,only about 30–35%of the emissions can be destroyedor burned, the rest,mainly from the agricultural sector, have no abatement measures.

The representation of land-use CO2 emissions is quite simplified:firstthere is a fixed, exogenous trajectory of CO2 emissions from land use,derived from the CCSP estimates (see Prinn et al., 2008), which shows aquasi linear decline from 3.3 GtCO2 in 2010 to 0.1 GtCO2 in 2100. Then,there are biomass potentials in each region, to produce energy crops orwood. These potentials arefixed, and themodel chooses to use part or allof each potential, at each model run. The degree to which the biomasspotentials are (or are not) used does not affect CO2 emissions, since thebiomass is emission neutral, whether harvested or not. However, if inaddition, the harvested biomass is used in plants with capture andgeological storage of CO2, then thenet effect is thatof negative emissions,whichwe then chose to attribute to land use (perhaps a little arbitrarily).

Emissions of some Kyoto gases (CFCs, HFCs, SF6) are not explicitlymodeled, but a special forcing term is added as described in the nextparagraph. Emissions of chemically active gases such as NOx, CO, andVOCs are not modeled either. Their influence on the life cycles of GHGgases is implicitly accounted for in the concentration equations for thethree main GHG's (IPCC AR4, 2007, vol.1, ch. 2), but only through thecalibration phase of the equations (Nordhaus and Boyer, 1999).

The climate module per se is directly inspired by the Nordhaus andBoyer (1999) model. It consists of three sets of equations, shown in theAppendix. The first set accepts as inputs the emissions of three green-house gases, CO2, CH4, and N2O, from all sources (energy, industrial,land-use), net of geological capture and storage or destruction, andcalculate the atmospheric concentrations of the three gases usingrecursive dynamic equations.

The second set of equations accepts as inputs the atmosphericconcentrations, calculates the atmospheric radiative forcings of these

Table 3Regional surplus losses relative to REF (NPV in B$, discounted at 5%/yr).

1BEST-2p6- 1BEST-2p6- 1BEST-3p7- 1BEST-3p7- 1

MAX OS MAX OS M

Group 1 10,547 4924 889 833 8Group 2 15,724 12,436 1390 1328 7Group 3 4917 3,289 494 369 −

three gases, and adds them up. Then, a fourth (exogenous) forcing isadded to represent the impact of Kyoto GHGs that are not explicitlymodeled inTIAM (i.e. CFCs, HFCs, SF6), so as to account for the totality ofthe Kyoto gases when setting the forcing target. The third group ofequations accepts as input the total radiative forcing, and recursivelycalculates the yearly change in mean global temperature in two layers(atmosphere upper ocean, and deep ocean). Heat transfer equationsbetween these layers are represented in the equations. The transitionfrom forcing to temperature change involves a crucial parameter, theclimate sensitivity Cs, which is much uncertain. In Section 4, we indicatehow this uncertainty is taken into account in this research.

Themodel of the ETSAP-TIAM climatemodule has been compared tomore sophisticated climate models and found quite accurate within therange of emissions usually considered (Nordhaus and Boyer, 1999).

2.2. Modeling the ten EMF-22 cases with ETSAP-TIAM

The Reference scenario is run without any climate target or futureclimate policies, but includes those climate actions that either havebeen already implemented, or have been adopted. One example is theKyoto reductions agreed upon by most Annex I countries, another oneis the European Union decision to reach a 20% emission reduction in2020, with respect to 1990 level.

The five First Best scenario cases consist in setting constraints onRadiative Forcing from Kyoto gases (noted RF), either in 2100(overshoot cases) or at each period from 2010 to 2100 (max cases).In addition, we have imposed that the First Best solution must agreewith the Reference scenario solution from 2005 to 2009, in order toprevent the model from ‘optimizing the past’.

The Second Best scenario is more complex to simulate in a modelsuch as ETSAP-TIAM. The first condition of scenario 2 is that countrygroup 2 (respectively group 3) is absent from the climate coalitionuntil 2030 (resp. 2050). This is done in ETSAP-TIAM, by selectively‘freezing’ the emissions at the levels obtained in the Referencescenario until those dates. However, since the climate scenarios havean impact even on regions not in the coalition (for instance via thechange in imports and exports of energy), we also conducted a checkon the total forcing and verified that the targets were not exceeded.The precise procedure is described in the electronic Appendix.

The second condition of scenario 2 specifies that when groups 2and 3 enter the coalition (in 2030 and 2050 respectively), they do soby initially limiting their GHG reductions to those whose cost per tonis less that the cost per ton of group 1 that prevailed in 2012. Later,each group increases its threshold GHG price, reaching the fullcoalition price 20 years after the entry date. This condition is moredifficult to simulate with TIAM because the model does not have adirect, explicit handle on prices. Therefore, an iterative, trial-and-errorapproach is used, also described in the Appendix.

3. Key findings from the deterministic scenarios

3.1. Costs and prices

3.1.1. Global costThe cost concept in ETSAP-TIAM is expressed as the loss of total

surplus. Comparing the surplus losses (relative to REF) resulting from

BEST-4p5- 2BEST-2p6- 2BEST-3p7- 2BEST-3p7- 2BEST-4p5-

AX OS MAX OS MAX

3 5775 −2240 −2576 −35055 23,629 −810 −1,278 −316418 13,720 7098 7207 6947

Fig. 1. Evolutions of annual surplus losses expressed as % of GDP (these are not GDPlosses). Fig. 3. GHG prices for the three groups, one scenario.

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various policies is a convenient and compact way of globallycomparing the relative severities of the scenarios-targets combina-tions. Table 2 shows the net present value in 2005 of the stream ofglobal surplus losses, whereas Fig. 3 shows the 9 global surplus losses,expressed as percentages of global GDP at each period. The tableclearly shows the vast discrepancy in cost between the 2.6 target andthe others. It also shows that the 4.5 target can be considered as mildin terms of global costs. Finally, the NPV's show that there aresignificant differences in costs between the First Best and the SecondBest cases with same target. In contrast, the cost differences betweenMAX and OS cases with the 3.7 target are quite small. The moredetailed annual costs of Fig. 3 show that the 2.6 W/m2 target isexceedingly costly to attain, even in the early years of the century. Asexpected, the cases using the 4.5 W/m2 target (the 2 bottom lines inFig. 3) are achieved at relatively low global cost (less than 1% of GDPthroughout the century), and the costs of the four cases with the 3.7target (the four solid lines) stay below 1% of GDP until 2050, and thenunder 2% of GDP for most of the second half of the century.

For completeness, we are also showing the regional losses of surplusin Table 3, and the losses by individual countries in the Appendix. Thesesurplus losses are based on the assumption that “who acts” is also “whopays”, i.e. that there are nopermits traded. Our discussionof the regionallosses is very succinct, because it is based on a rather artificial assump-tion regarding initial emission rights endowments. A more in-depthdiscussion would require us to define precise — and acceptable,assumptions on the allocation of emission rights to each region, a veryinteresting subject in its own right, butone thatwoulddeserve adetailedanalysis that is not possible within the limits of this article. Our ownsimplistic assumption amounts to auctioning all emission rights, withthe result that noemission trading takes place, and sucha schemeseems

Fig. 2. GHG prices for group 1.

unrealistic in an international situation. Be that as it may, we may stillnotice that group 1 costs are very low, and often negative, due to the factthat a good portion of the reductions occur in other regions, but also tothe fact that group 1's cost for importing fossil fuels is much reduced inthe second best scenario. Group 2 costs on the contrary are very large,since that group bears the brunt of emissions reductions. Finally group 3costs are also large, but for a different reason: in Policy scenarios,consumption (and production) of oil and gas are much reduced, thuspenalizing this group 3, which includes large oil and gas exporters.

Finally, we observe from Fig. 1 that the global cost trajectory ofeach First Best case is not much different from that of thecorresponding second best case, (except for the 2.6 target withovershoot). We shall return to this observation in Subsection 3.2.

3.1.2. GHG pricesGHG price (expressed in dollars per ton of CO2-equivalent), is

another useful indicator of the ease or difficulty of a scenario. In Fig. 2,the GHG prices of the five targets without overshoot, are shown ateach period for group 1, confirming that the 2.6 W/m2 target is indeedhard to satisfy, whereas the 4.5 W/m2 one is quite mild. The secondbest cases exhibit a price trajectory that is initially higher than that ofthe corresponding First Best case, due to the delayed participation ofgroups 2 and 3 which forces group 1 to implement actions that arequite costlier. Later on, the reverse is observed, and the Second Bestprice is lower than the First Best price, because groups 2 and 3 have bynow more potential for cheap actions, since they have not yetexpended them, due to their late entry. The observed downturn of theGHG price around the end of the century indicates that the difficulty ofsatisfying the targets without overshoot lies principally in the mid-century (earlier, the forcing has not yet reached levels close to thetarget and the economic growth has not yet made emissions largeenough, while later the difficulty is less, because more and cheaperGHG abatement options are available). This behavior will be invokedagain in Section 3.2, and is frequently observed for targets that aresevere or very severe, but not for easy targets such as the 4.5 W/m2one. Fig. 2 also shows that the absence of groups 2 and 3 from thecoalition until 2030, considerably increases the price of GHG for group1, compared to the First Best scenario. This continues to be the caseeven after 2030, and until the full participation of all countries in 2070.After that date, abatement options aremore abundant, and allowa fastand large decrease of the price. In the second best scenario, GHGprices are region specific. Fig. 3 shows GHG prices for the 3 groups, inthe 2B-3p7max case, with prices lower in groups 2 and 3 than in group1, until full participation of the late entrants.

3.1.3. Reductions in economic activityIn TIAM, the first order impact of energy decisions on economic

activity is directly measured by the changes in the levels of the 42

Table 4Demand reductions in 2050 and 2100, relative to the Reference case demands (physical units).

Global 2050 2100

Commercial NRG intensive industries Other industries Road transport Commercial NRG intensive industries Other industries Road transport

1B-2p6max 2.7% 10.8% 3.1% 8.3% 2.4% 8.3% 2.1% 7.0%1B-2p6over 2.0% 7.4% 1.7% 5.9% 2.8% 10.6% 4.1% 9.6%1B-3p7max 0.6% 1.9% 0.4% 1.6% 1.0% 4.4% 1.4% 3.9%1B-3p7over 0.6% 1.7% 0.1% 1.4% 2.1% 7.5% 1.9% 6.8%1B-4p5max −0.1% 0.0% 0.1% 0.1% 0.8% 3.7% 1.2% 3.3%2B-2p6over 2.2% 10.0% 2.1% 6.2% 3.5% 12.6% 11.8% 11.7%2B-3p7max 0.7% 2.9% 0.7% 1.6% 0.7% 3.7% 1.3% 3.2%2B-3p7over 0.7% 2.2% 0.3% 1.3% 2.3% 8.2% 2.2% 7.4%2B-4p5max 0.1% 0.3% 0.1% 0.3% 0.8% 3.5% 1.1% 3.2%

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output demand categories, which are reacting to climate policies.Assessing total economic impacts requires a general equilibriumapproach. However, it has been shownwith some confidence that thefirst order impact as estimated by partial equilibriummodels capturesthe vast majority of total impacts on demands (Scheper and Kram,1994; Loulou and Kanudia, 2000).

In order to summarize these impacts in a compact way, we havegrouped the 50 demands into four homogeneous sets: Table 4 showsthe percentage reductions of demands, grouped in four main sectors(energy intensive industries, other industries, buildings, and roadtransport) in 2050 and in 2100, relative to the Reference scenariovalues for these same years, for the five MAX cases only. The mostsevere case is 1B-2p6max, as expected, which provokes in 2050reductions in activity several times larger than the next most severecase, 2B-3p7max. The demand reductions are generally larger in 2100than in 2050 (except for the 2.6max target). This may be explained by

Fig. 4. GHG emissions and Kyoto forcings for the 10 cases. The extension of the forcingtrajectories beyond 2100 assumes emissions that decline linearly to 0 from 2100 to2400.

the fact that most technological options have been exhausted, forcingthe model to resort to demand reductions in order to satisfy theforcing constraint (in all fairness, this may also indicate a lack of newtechnological options in the model's database toward the end of thecentury).

The sector most impacted is the Industrial sector. This is in somepart due to the lack of radical options for reducing emissions in thatsector, at least in the model's database. The other sectors are lessaffected, but the demand reductions remain significant in all sectors.

3.2. Emissions and concentrations

Fig. 4 shows net GHG emissions and the Kyoto forcing trajectories.The GHG emissions include CO2+N2O+CH4 converted to CO2-equivalents using 100-year GWP's. The emissions results confirm theearlier observation that the 2.6 W/m2 target is very difficult to satisfy,requiring very drastic emission reductions very early. In 2050, the FirstBest scenario calls for a global reduction of 65% of 2010 emissions inorder to satisfy the 2.6 W/m2 targets. And when groups 2 and 3 areentering late (the 2-Best scenario), the 2050 reduction is 50% of the

Fig. 5. Emissions by type in two cases.

Fig. 6. Three aspects of energy consumption.

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2010 level, but now the brunt of the early reduction effort is borne byOECD countries. All other targets accept some initial emissionincreases with respect to 2010. They are initially close to the REFtrajectory, and show progressive reductions after 2030.

As alreadynoted in thepresentation of global costs in subsection3.1.1,the First and Second Best cases are notmuch different, except for the 2.6target. This is also true of the global emission and the forcing trajectoriesshown in Fig. 4. This interesting result suggests that the difficulty ofmeeting the forcing boundmust lie aroundmid-century, even in the 2Bcases. In other words, even when some regions delay participation, theglobal energy system's decisionsdonot really departmuch from those inthe FB scenario (still keeping in mind the important exception of the 2.6target). A more detailed examination of regional results would of coursereveal some significant differences in energy decisions, between 1B and2B cases, even with the milder forcing targets.

Fig. 5a) and b) show the composition of emissions in two casesonly, Reference and 2B-3p7max, the latter being a good illustration ofthe qualitative changes that occur in the Policy cases. Emissionreductions in the energy sector clearly dominate the scene, butreductions of land-use based CO2 emissions play a significant role aswell, even becoming negative after 2040. They comprise the followingactions:

Better management of methane from agriculture (decomposition ofmanure, and of other biomass) which is used to produce electricity andheat. Methane emissions grow by 35% over 90 years in Reference butstay more or less constant over the same time span in the 2B-2p7maxcase, thanks to measures to capture and burn methane from upstreamleakages. Some methane is also used for power generation. N2Odestruction measures are little adopted, except those in the adipic acidindustry.

Increased use of biomass, but not requiring additional tree plantingor additional land for energy crops: in climate scenarios, the modelsimply consumes a larger portion of the existing biomass potentialthan in REF, and since the biomass is mostly used in electricity plantswith geological CCS, this means negative CO2 emissions.

The overall picture in 2060 is that energy based CO2 accounts forabout 80% of emission reductions, CH4 for 12 %, and land-based CO2 for8% of reductions. N2O emissions decrease 10% in 2100 relative to REF,and methane emissions decrease 21% in 2100 relative to REF.

3.3. Main actions in the energy sector: energy efficiency vs. climate efficiency

The detailed sectoral and regional energy results from TIAM cannotbe fully reported in the limits of this article.We content ourselveswithindicating the main trends and the key actions contributing to thesatisfaction of the targets in the 5 selected cases.We especially focus onthose results thatmay appear counter-intuitive at first, but that bring anew perspective on some aspects of energy and climate strategies.

The three panels, Fig. 6a, b, and c, tell an interesting story. Fig. 6ashows the primary energy consumption, using a conventional effi-ciency of 1 for nuclear and renewables, biomass excepted. We observethat as the climate constraint becomesmore severe, total consumptionof primary energy increases (in spite of the conventional accountingassumption). Fig. 6c shows a global increase in electricity productionwhen the climate target is more severe, and Fig. 6b (primary energywith CCS ) indicates that carbon capture and storage increases whenthe target is more severe. These three results must be analyzed simul-taneously: the implementation of CCS occurs mostly in the electricitygeneration sector (plus some in hydrogen production and oil extrac-tion), and the key power plantswith CCS are coal or biomass fired. Coalis chosen because of its lowcost inmany regions, and biomass because,when associated with CCS, these plants in effect produce negativeamounts of CO2, a very useful recourse when CO2 emissions approachor reach zero level. Gas plants plus CCS aremarginally used, and only inregions with abundant gas and/or costlier access to coal and biomass,such as Africa and the Middle-East. Hence, producing more electricity

is a way to capture and store more CO2. Now, to explain the larger totalprimary energy consumption in the severe climate cases, two factorscome into play: the first is that wood and coal fired plants with CCShave lower efficiencies than gas fired or ordinary coal fired plants,explaining the results shown in Fig. 6a. The second factor is preciselythe larger role of electricity in severe climate cases: theway to increaseelectricity use is to adopt electric devices to substitute fossil fuel firedend-use technologies, mostly boilers, furnaces, and some industrialprocesses. But since the overall efficiency of electric devices (i.e. theratio energy service over primary energy used) is in many instancesworse than that of end-use processes that use fuels directly, thisclimate driven choice results in an increase of total primary energy

Fig. 7. Electricity production in four typical cases — EJ/yr.

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consumption. As an example, a typical gas furnace may have anefficiency exceeding 85–90%, whereas an electric furnace's overallefficiency is much lower than that — at best 50%, when electricity isproduced by a coal or biomass fired power plant with CCS, which is adominant way to produce electricity in climate scenarios. This expla-nation does not hold for electric vehicles for instance, but electricvehicles are not massively adopted by the model.

We thus observe a decoupling of the notions of climate efficientstrategy (loosely defined as the best energy choices to reach a climatetarget) and of overall energy efficient strategy, which are therefore notalways synergistic. Note however that this observation does notconcern end-use efficiency, which indeed improves in climatescenarios, as shown by the overall decrease of final energy.

The CCSpotential is prettymuch exhausted in scenarioswith the 3.7target. Therefore, the scenarios with 2.6 target do not implement moreCCS, and resort to additional renewable, as will be seen further down.

Additional details on electricity production, on the production ofother secondary fuels, and on end-use energy are presented andcommented in the next three subsections, further supporting some ofthe assertions made above, and providing new observations.

3.4. A closer look at electricity production and use

3.4.1. Electricity productionSince electricity plays a central role in the model's response to

climate scenarios, we now examine the composition of electricityproduction by various types of plants. Fig. 7 shows the breakdown forfour scenarios, each representing fairly well the four groups of targetsplus REF. The Appendix has the graphs for all cases.

Fig. 7 shows clearly that the increaseof electricity production (relativeto REF) occurs roughly at the same time as CCS and/or Renewablespenetrate heavily the electricity market, confirming the earlier observa-tions and explanations. In 2080, total electricity in the policy cases isbetween 40% and 60% higher than in REF. The dominant new technologydepends on the target: with both the 3.7 and the 2.6 targets, coal+CCSand biomass+CCS penetrate strongly. However,with themore stringent2.6 target, there is not enough storage potential in some regions to satisfythe much larger electricity demand, and thus Renewables (essentiallysolar thermal and wind) come in very strongly. Still with the 2.6 target,gas plants+CCS appear around 2030–2040 in those regions wherebiomass is not abundant, but gas is. For the 4.5 target scenarios (shown inthe Appendix), total electricity is only marginally larger than in REF, andthe coal+CCS technology plays a large role in replacing coal fired plants.

Additional hydro electricity options penetrate in the climatescenarios, adding more than 35% of hydro capacity compared to REFin 2100, consisting of projects up to an investment cost of more than$4000/kW. Nuclear electricity production does increase too, but islimited by the exogenous region dependent bounds imposed on itsspeed of penetration in each region. As mentioned in a previous work(Vaillancourt et al., 2007), the penetration of nuclear plants is difficultto assess in models, due to important but missing data on the full costof nuclear (including waste management) and of the other socio-political dimensions of that technology.

3.4.2. Electricity useThe Appendix shows global electricity use in three broad sectors:

buildings, transportation, industry, and fuel production. Electricity use inbuildings varies in a modest way across scenarios, showing a slightincrease for severe climate targets. In Industry, the climate target has amore profound impact: from 2050 on, electricity use is little affected bythe 3.7 and 4.5 targets, but multiplied by 3 to 4 for the 2.6 target. Theincrease occurs in the supply of heat and steam (electric boilers) but alsoin the use of electrometallurgy for steel and non ferrous metal pro-duction. In transportation, electric vehicles appear around 2040–2050.Compared toREF, electricityuse ismultipliedby1.5 to2 for the3.7 and4.5targets in2050, rising to3-fold in2100. For the2.6 target, useof electricity

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is three times that of REF in 2050, rising to 5 times in 2100.Howeverhighmay these increases appear, they remain relatively modest as percent-ages of total transportation fuels, as will be discussed further down. Theuse of electricity for fuel production shows a sharp decline relative to itsREF levels. This isdirectly due to thedeclineof oil andgasproduction andrefining (which require electricity). Note that hydrogen production inpolicy scenarios (discussed below) is not from electrolysis.

3.5. Other energy transformations

We make brief comments on three “alternative” fuels: alcohols,hydrogen, and synthetic fossil fuels. Additional detailed results areshown in the Appendix.

3.5.1. AlcoholsThe main results are shown in Fig. 8 for some representative

scenarios. Of all “new” secondary fuels, alcohol is the one showing thelargest increase due to policy scenarios. The figure shows, in the REFscenario, a stabilized production of 21 EJ/yr after 2050 (all from coaland gas sources, with emission of CO2), whereas the levels are muchhigher for the policy scenarios, growing to 40 EJ, 80 EJ, and more than100 EJ, respectively for the 4.5, 3.7, and 2.6 targets, the growthoccurring very soon for the 2.6 target, and mostly after 2050 for theother two targets. Only a small portion of alcohol produced in theclimate scenarios emits full CO2, the majority being produced withCCS (and thus low emissions), from cellulosic biomass, from sugarcrops, or from coal. We use the phrase “low emitting” since there aresmall amounts of CO2 emitted when using CCS. In the 2B-2p6OSscenario, there is a decline of alcohol use from 2070 to 2100, due to thecompeting use of biomass and of storage sinks by the electricity sector.

Fig. 8. Alcohol production with and without emission of CO2 —EJ/yr.

Fig. 9. Hydrogen production (EJ/yr).

3.5.2. Synthetic fuelsOur model includes technologies for producing fuel oil, diesel, and

kerosene from coal, with or without CO2 capture. Note however that(just like in the case of alcohols produced from coal with CCS), the CO2

thus captured is only the CO2 that would be emitted at the productionstage. For this reason, this option is little used in the Policy scenario,whereas it is much used in the REF scenario. Details on the productionof synthetic fuels appear in the Appendix.

3.5.3. HydrogenAs shown in Fig. 9, hydrogen production is stimulated by the Policy

scenarios, especially in the second half of the century. In REF, theproduction is only from coal without CCS, and peaks at 25 EJ (still anon negligible amount).With themilder 4.5 target, there is still a goodamount of production with full CO2 emission, but this is no longer thecase for the scenarios with 3.7 or 2.6 target, where the quasi totality ofthe (much increased) production is either from biomass or from coalwith CCS. Hydrogen thus plays a less prominent role than alcohols inthe policy scenarios, but one that is far from negligible, capturingaround 4% of total world energy from 2050 on.

3.6. End-use sectors

Due to the space limit, our comments will be brief and reserved toresults that show a notable departure from those of the REF scenario.The full graphs are shown in the Appendix.

3.6.1. TransportationThis sector undergoes major changes when climate targets are

imposed. Themain (expected) fuel switch is away from oil based fuels,which are, roughly speaking replaced by alcohols and biodiesel. Thisswitch is most pronounced in scenarios with the 2.6 forcing target. Inaddition, electric (and plug-in hybrid) vehicles increase their market

Fig. 10. Temperature changes resulting from the three targets, assuming three differentCs values. The maximum temperature change is the same in all three cases (2.45 °C).

Fig. 11. Continuous probability distribution of Cs and a three-point discreteapproximation.

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share several fold, but electricity still stays below 10% of the totalenergy of the sector, in all cases (however, given the larger efficiencyof electric motors, this represents about 16–18% of the transportationservice demand). Hydrogen fuel-cell heavy vehicles and hydrogenfueled planes consume together around 12% of the sector's energy inthe most severe climate cases, using hydrogen that is predominantlyproduced from low emitting sources, as already mentioned. Gasmarket share grows to around 12–15% by mid-century, but thendeclines to 0 in 2100, except in REF and in the scenarios with a 4.5target. In all others, gas disappears as a transportation fuel by 2100.Gas is used mainly by light duty vehicles for urban travel. Note thatenergy savings in the transportation sector are relatively insensitive tothe climate scenarios, for two reasons: first, many energy savings areembedded in the vintage vehicles of the TIAM database, and thusalready appear in the REF scenario. The second reason is that alcoholfueled vehicles are not more efficient than oil fueled ones. The onlycase where overall final energy shows a marked decrease is the 2B-2.6OS scenario toward the end of the century, because alcohol's shareis then small, replaced by more electricity and hydrogen.

3.6.2. IndustryThe most remarkable result in this sector is the sharp penetration

of electricity and/or gas in most climate scenarios, and a correspond-ing decline of coal. Coal never totally disappears but its residual shareis reserved for dedicated uses in metallurgy. Electricity penetration ishighest in scenarios with a 2.6 target, reaching up to 70% of the sectorenergy. In less severe climate scenarios, electricity and gas have aboutequal shares. Both these fuels are used massively to produce heat andsteam in efficient furnaces and boilers. In addition, some of themetallic industries are converted to electrometallurgy. Biomass usedoes increase in percentage of the total, but remains largely confinedto the production of heat and steam in the pulp and paper industry. Oilkeeps a fairly steady share, mostly because it is used for petrochem-icals and other non-energy uses. Finally, Industry shows the largestpercentage of final energy savings, but of course this is largely theconsequence of electricity penetration, with its very high efficiency(thus, here too, one should be careful in assessing the meaning andimportance of energy savings).

3.6.3. BuildingsAdditional energy savings are modest, but this is because most

insulation and other energy conserving measures are already adoptedas no-regret actions in the Reference scenario. In policy scenarios, oildeclines progressively to 0, gas maintains a small market share, andelectricity increases somewhat its already largemarket share (more soin scenarios with the 2.6 target). Interestingly, biomass loses marketshare in all policy scenarios relative to REF, probably because it is

needed in electricity production, but also because biomass fired stovesand furnaces are less efficient than other heating devices.

4. A probabilistic interpretation of multiple forcing targets

4.1. Alternate probabilistic modeling of three of the RF targets

In Section 3, the ten EMF-22 scenarios are treated as independentcases, where each target (combined with either First Best or SecondBest scenarios) leads to a specific model run, independent from otherruns.

It seemed interesting and useful to attempt an alternative inter-pretation of the targets, by examining them simultaneously, as themanifestation of a single temperature target, subject to alternate out-comes of a random variable, the Climate Sensitivity parameter Cs. Toillustrate and test this approach, we selected the First Best scenarioand the three alternate RF targets with overshoot, and we sought thesingle temperature target (if such exists) that is equivalent to them. Byexamining the RF trajectories issued from the 3 separate scenario runs,we discovered that the 2.45 °C target on temperature increase fulfillsthis condition, as long as we assume specific values for the climatesensitivity parameter Cs. We were therefore able to replace the threealternate RF targets by that single temperature target, while acknowl-edging the randomness of Cs. The detailed procedure is as follows:

The first task before us was to calculate the temperature changesresulting from each of the three RF trajectories obtained from thethree separate scenario runs. To do so, we first added to the RFtrajectories, the extra forcing due to non-Kyoto substances, in order toobtain the trajectories of Total Radiative Forcing (noted TRF) over the21st century. The non-Kyoto forcings (Montréal gases, aerosol effects(direct and indirect), dust, water vapor, OH− radical, ozone, solar andvolcanic activity) were estimated from the 2005 inventory of IPCC(2007, vol. 1), and from their likely future evolutions as discussed inpart in IPCC (2007) and in Prinn et al. (2008). These additional forcingterms were assumed to remain the same for all scenarios and allvalues of Cs.

The three TRF trajectories thus obtained were then injected intoTIAM's global temperature change equationsmentioned in Section 2.1,in order to obtain the 3 trajectories for temperature change, whileassuming a different value for Cs in each case. After some trial-and-errorsearch, we found that when choosing the respective values Cs=2 °C,Cs=2.9 °C, Cs=5 °C, the temperature equations yielded the samemaximum temperature change of 2.45 °C, as illustrated in Fig. 10. Inorder to calculate the temperature change beyond themodel's horizon(2100), we made the assumption that GHG emissions decline linearlyto 0 over a 300 year period, i.e. from 2100 to 2400 (we also verified

Fig. 12. Global GHG emissions.

Fig. 13. GHG prices.

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that changing this period to 200 years or to 400 years made very littledifference in the peak temperature change).

We then verified that the three selected Cs values were indeedcompatible with a simplified — but valid, discrete approximation ofthe continuous probability distributions of Cs. Such a distribution isreported in IPCC (2007 vol.1, chapter 2), and a similar one fromAndronova and Schlesinger (2001) is shown in Fig. 2. Our three Csvalues are a valid (albeit crude) discrete approximation of the continuousdistribution of Fig. 11 as long as we choose their respective probabilitiesof occurrence equal to 0.50, 0.35, and 0.15, as also shown in Fig. 11. Notethat, in a distinct Hedging Workgroup of EMF-22, a similar probabilitydistribution for Cs was recommended, with four discrete values ratherthan three (Yohe et al., 2004; Labriet et al., 2009, for details on the EMF-22 Hedging scenarios). We do not claim that our 3-point probabilitydistribution is a very accurate approximation of the continuous distri-bution, but we have indeed statistically verified that our approximationwas a valid one among the set of all 3-point discrete approximations. Notehowever that our simplistic 3-point approximation neglects the pos-sibility of very large Cs values, which may yet occur with non negligibleprobability, as the right tail of the distribution of Fig. 11 shows. Labrietet al. (2009) used stochastic programming in a situation when Cs mayhave values up to 8 °C.

We are now in a position to consider the single Temperaturechange target of 2.45 °C with random Cs, as long as we choose the Csvalues and probabilities mentioned above. The logical step followingthis is to use the stochastic programming (Wets, 1989) version of theTIAM partial equilibrium, where the Forcing limit is now explicitlymodeled as a random variable with the probability distribution ofFig. 11. One more assumption is needed, namely the choice of the dateof resolution of the uncertainty on Cs. We are well aware that the dateof resolution of uncertainty on Cs is subject to intense debate. Whileall climate scientists agree that uncertainty will endure for severaldecades, some argue in favor of a more radical view of the pheno-menon, stating that Cs will remain uncertain for a very long time, andperhaps forever (Weitzman, 2008). In this article, we adhere to themiddle-of-the-road resolution date of 2040, in accordance with therecommendation of the EMF-22 Hedging Workgroup (Yohe et al.,2004; Labriet et al., 2009).

Remark. Even though the above discussion shows that the RF targetsused in the scenario analysis and the temperature target used in thestochastic analysis are compatible, there is no reason to think that thestrategies used by the two approaches will be identical, or even close!In particular, the stochastic approach will by definition produce asingle hedging trajectory up to 2040, while the three separatedeterministic strategies will differ starting in 2010. Some key resultsof the stochastic programming treatment of the three targets arediscussed next, and compared with those of Section 3 obtained fromthe deterministic scenarios.

4.2. Results from the stochastic approach

This subsection briefly discusses some results obtained for thestochastic approach to multiple targets discussed in the previoussubsection. Since the uncertainty on the target is assumed resolved in2040, it follows that the stochastic programming (hedging) strategy isunique from 2005 to 2040 inclusive, and branches out after 2040. Themain benefit of hedging lies precisely in revealing a single set ofhedging actions during the uncertainty period. Therefore, we focusour brief analysis of results on that time span only, and we comparethemwith those of the corresponding deterministic strategies for thesame three targets. What happens beyond 2040 in the stochasticprogramming solution is of course interesting too, but less so than thehedging actions up to 2040, which are the focus of the TransitionalPolicies Work Group of EMF-22. It is fair to say that our discussion ofthe stochastic programming results remains sketchy, our mainpurpose being to provide a set of demonstration results to illustratethe added benefits of the method, but leaving a full discussion and useof the approach for future work. In what follows, we focus on globalresults on emissions, emission prices, and energy.

4.3. A preliminary remark

When a probabilistic interpretation of the three targets is adopted(as explained just above), it is important to realize that once the threeforcing targets have been selected, the choice of their probabilities ofoccurrence is not arbitrary. Rather, it is (approximately) dictated by thefact that the three probabilities of the outcomes must conform to theprobability distribution shown in Fig.11. This conformation is howeverapproximate since the distribution is not precisely known. This obser-vation is in itself interesting, as it indicates that when alternate targetsare selected for separate analysis, some care should be taken to selectthem in a way that represents a fair sample of the possible conse-quences of these targets. Such consequences include temperaturechange, but beyond that, they ultimately aim at evaluating damagescaused by the various emission trajectories. It should also be noted thatsome important feedbacks from climate to the socio-economic systemare ignored in this research. We now turn to a few observations on thehedging strategy, compared to the deterministic scenarios.

4.4. Emissions and emission price

GHG emissions in Hedging are from 4 to 7% larger than those of the1B-2.6over deterministic scenario from 2010 to 2040 (Fig. 12), con-firming that the severe 2.6 forcing target has a strong influence onemissions in the hedging strategy. However, this relatively minordifference in emissions provokes a 40% decrease in the GHG shadow

Fig. 14. Primary energy.

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prices in hedging relative to 1B-2p6over, at all years until 2040(Fig. 13).

4.5. Energy

What is interesting in the hedging strategy and adds value to theprobabilistic approach, is that it is not obtained by a simple averagingof deterministic strategies. The structure of the optimal hedging stra-tegy is subtler than straight averaging, and thus defies a simplistic

Fig. 15. Primary energy with CCS.

Fig. 16. Electricity production. Fig. 17. Electricity production mix: hedging and deterministic cases.

S142 R. Loulou et al. / Energy Economics 31 (2009) S131–S143

interpretation. This is illustrated by observations on the energydimension of the hedging strategy. While the total primary energy(Fig. 14) is pretty much unaffected, the CCS trajectories show a moreinteresting pattern (Fig. 15): the CCS trajectory for the hedgingstrategy lies between those of the 2.6 and 3.7 deterministic ones, butcloser to the 2.6 trajectory, whereas Fig. 16 shows that total electricityproduced in hedging is quasi identical to that in the 3.7 target, andquite below that of the 2.6 target.

Another example is provided by the examination of the composi-tion of electricity supply, shown in Fig. 17 for the three deterministicand the hedging strategies: the amount of electricity produced withCCS in the hedging strategy is higher than in anydeterministic strategyin year 2040, but not so in preceding periods. Overall, the hedgingstrategy presents a smoother transition from the initial electricity mixto a radically different one in 2040, than the 3.7 or the 2.6 strategies. Itshows a more gradual decline of conventional coal plants and a moregradual increase of CCS technologies. This ‘smoothing’property is oftenobserved in hedging solutions (Labriet et al., 2009), and is easier toimplement in practice. In particular, it tells us that the CCS technologyin electricity plants should be fully commercialized by 2020–25,whereas the 2.6OS (resp. 3.7OS) deterministic strategy recommends2015 (resp. 2035) for that technology.

5. Conclusion

There are a few lessons learned from this research, which wesummarize below.

The first lesson is that the 2.6 W/m2 forcing target (i.e. 450 ppmvconcentration) for Kyoto gases is a very difficult one to reach, even ifovershooting is allowed. In our simulations, staying below 2.6 W/m2is feasible either when all countries decide to act vigorously starting in2012 (the First Best scenarios), or, if they don't, only when over-shooting is allowed. In both cases, the overall cost is very high. For theintermediate target of 3.7 W/m2 (550 ppmv), we conclude that thelate and progressive entry of less developed nations into the climatecoalition increases the overall difficulty (and cost) in a moderate wayonly. The 4.5 W/m2 (650 ppmv) target appears to be benign, requiringlittle effort or cost.

Second, the use of a detailed technologicalmodel to analyze climatescenarios confirms that the two issues of overall energy efficiency andof climate efficiency are distinct ones that may sometimes pull theenergy system in opposite directions: when climate targets are theprimary concern, increasing energyefficiency in the fuel and electricityproduction sectors should no longer be the main guiding principle. Onthe other hand, end-use efficiency is fully coherent with climateefficiency, when correctly interpreted.

The third lesson is that, while emission reductions in the energy-industrial sector play a central role for attaining moderate or severeclimate targets, actions in the land-use sector are also significant, andtheir contribution to the overall abatement strategies is far fromnegligible.

Fourth, the research has confirmed that there is no silver bullet tothe attainment of severe climate targets. A multi-gas, multi-sector,multi fuel approach to GHG emission abatement seems to benecessary to reach tight forcing targets. Abatement of methaneemissions plays a significant role. And, new secondary fuels and directrenewable energy play very important roles: in particular, biomass is akey resource, especially when used to fire power plants equippedwithcarbon capture, but also for alcohol and hydrogen production. Solarthermal and wind power plants, as well as direct solar heating incommercial and residential buildings and farms, penetrate sharply.Hydroelectricity reaches much higher levels in the policy scenariosthat in the reference case. Hydrogen from low emitting sources plays anon negligible (but not major) role starting in 2040–50, and capturinga 4% share of world total energy by the end of the century. It is usedmostly as fuel for planes and for large fuel-cell vehicles (trucks and

buses). Alcohols play an even larger role as a road transportation fuel,dominating that subsector in the second half of the century.

The end-use sectors undergo very significant changes in theirenergy consumption patterns: In the transportation sector, alcoholsplay a dominant role in all policy scenarios, alongside hydrogen andelectric vehicles. In other end-use sectors, energy savings reachanother 5 to 10% above the savings already present in the Referencescenario as ‘no-regret’ decisions. Industry sees a massive penetrationof electricity and gas, whereas commercial and residential buildingsimplement solar based water and space heating on a large scale.

Fifth, the probabilistic interpretation of the multiple climatetargets seems promising. It is our view that analyzing alternativeclimate targets via independent deterministic model runs is not assatisfactory an approach as explicitly acknowledging the uncertaintiesunderlying the choice of alternate targets, and then tackling these viaappropriate tools. In particular, the approach via stochastic program-ming, although simplified, has the merit of producing a single hedgingpolicy when facing major uncertainties, and thus remedying the maindefect of traditional scenario analysis. The hedging strategy was itselfshown not to be a naïve average of deterministic ones, and to present amix of abatement actions that could not easily be found otherwise.The application of stochastic programming to larger sets of uncertain-ties and scenarios would be desirable, albeit computationallycumbersome.

As final caveats, it should be understood that technologicalcharacterization and resource assessments on a regional basis remainuncertain, especially in the long term, so that conclusions drawn fromour analyses are to be takenwith care and subjected to further testing.Furthermore, in spite of some prudent controlling of the speed oftechnology adoption in the model, there may be non modeled factorsthat impede the adoption of some technologies, due for instance tolow social acceptance.

Additional work on the subject should include the examination ofmore complex cooperation schemes between groups of countries, aswell as a more detailed analysis of the regional implications of suchschemes.

Appendix A. Supplementary data

Supplementary data associatedwith this article (theAppendix) canbe found, in the online version, at doi:10.1016/j.eneco.2009.06.012.

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