Methodology for Developing the MA Scenarios

28 Pages·2006·1.54 MB·English

Chapter 6
Methodology for Developing the MA Scenarios
Coordinating Lead Authors: Joseph Alcamo, Detlef van Vuuren, Claudia Ringler
Lead Authors: Jacqueline Alder, Elena Bennett, David Lodge, Toshihiko Masui, Tsuneyuki Morita,* Mark
Rosegrant, Osvaldo Sala, Kerstin Schulze, Monika Zurek
Contributing Authors: Bas Eickhout, Michael Maerker, Kasper Kok
Review Editors: Antonio Alonso Concheiro, Yuzuru Matsuoka, Allen Hammond
MainMessages . ............................................ 147
6.1 Introduction ........................................... 147
6.2 BackgroundtotheMAScenarios ........................... 147
6.3 OverviewofProcedureforDevelopingtheMAScenarios ......... 148
6.3.1 OrganizationalSteps
6.3.2 ScenarioStorylineDevelopmentandQuantification
6.3.3 Synthesis,Review,andDissemination
6.3.4 LinkagesbetweenDifferentSpatialandTemporalScales
6.4 BuildingtheQualitativeScenarios:DevelopingStorylines ......... 151
6.5 BuildingtheQuantitativeScenarios:TheGlobalModelingExercise . . 152
6.5.1 OrganizationoftheGlobalModelingExercise
6.5.2 SpecifyingaConsistentSetofModelInputs
6.5.3 Soft-linkingtheModels
6.5.4 ModelingChangesinBiodiversity
6.5.5 CommentsontheModelingApproach
6.6 DiscussionofUncertaintyandScenarios ..................... 155
6.6.1 UsingtheScenarioApproachtoExploreUncertainty
6.6.2 CommunicatingUncertaintiesoftheScenarios
6.6.3 SensitivityAnalysis
6.7 Summary ............................................. 156
APPENDIXES:DESCRIPTIONSOFMODELS
6.1 TheIMAGE2.2Model .................................... 157
6.2 TheIMPACTModel .. .................................... 158
6.3 TheWaterGAPModel .................................... 160
6.4 TheAsia-PacificIntegratedModel ........................... 161
*Deceased.
145
.................11411$ $CH6 10-27-0508:42:01 PS PAGE145
146 EcosystemsandHumanWell-being:Scenarios
6.5 EcopathwithEcosim .................................... 163
6.6 TerrestrialBiodiversityModel . ............................. 166
6.7 FreshwaterBiodiversityModel ............................. 169
6.8 MultiscaleScenarioDevelopmentintheSub-globalAssessments ... 170
REFERENCES .............................................. 171
BOXES Appendix6.8 MultiscaleDesignoftheSub-globalAssessmentsin
SouthernAfricaandPortugal
6.1 MAProcedureforDevelopingScenarios
6.2 AComparisonofGlobalandSub-globalScenario TABLES
Development 6.1 GlobalModelingOutput
6.3 ModelsUsedinMAGlobalModelingExercise Appendix6.1 OverviewofMajorUncertaintiesintheIMAGE
Appendix6.1 TheThreeEcopathwithEcosimModelsUsed Model
Appendix6.2 LevelofConfidenceinDifferentTypesofScenario
FIGURES CalculationsfromIMAGE
Appendix6.3 OverviewofMajorUncertaintiesintheIMPACT
6.1 StatusofInformation,fromLowtoHigh,versusDegreeof
Model
Controllability
Appendix6.4 LevelofConfidenceinDifferentTypesofScenario
6.2 OverallMethodologyofMAScenarioDevelopment
CalculationsfromIMPACT
6.3 ReportingRegionsfortheGlobalModelingResultsofthe Appendix6.5 OverviewofMajorUncertaintiesinWaterGAP
MA* Model
6.4 LinkagesbetweenModels Appendix6.6 LevelofConfidenceinDifferentTypesofScenario
CalculationsfromWaterGAP
6.5 ScaleforAssessingStateofKnowledgeandStatement
Appendix6.7 OverviewofMajorUncertaintiesinAIMModel
Confidence
Appendix6.8 LevelofConfidenceinDifferentTypesofScenario
6.6 SchemeforDescribing‘‘StateofKnowledge’’orUncertainty CalculationsfromAIM
ofStatementsfromModelsorTheories Appendix6.9 OverviewofMajorUncertaintiesinEcopathwith
Appendix6.1 StructureofIMAGE2.2Model Ecosim(EwE)Model
Appendix6.2 StructureofIMPACTModel Appendix6.10 LevelofConfidenceinDifferentTypesofScenario
Appendix6.3 StructureofWaterGAPModel CalculationsfromEcopathwithEcosim
Appendix6.4 StructureofAIM/WaterModel Appendix6.11 OverviewofMajorUncertaintiesintheTerrestrial
Appendix6.5 StructureofAIM/EcosystemModel BiodiversityModel
Appendix6.6 StructureofEcopathwithEcosim(EwE)Model Appendix6.12 OverviewofMajorUncertaintiesintheFreshwater
Appendix6.7 StructureofEcopathModel BiodiversityModel
*ThisappearsinAppendixAattheendofthisvolume.
.................11411$ $CH6 10-27-0508:42:03 PS PAGE146
MethodologyforDevelopingtheMAScenarios 147
Main Messages The MA developed scenarios to provide decision-makers
and stakeholders with scientific information on the links
TheMillenniumEcosystemAssessment scenariosbreaknewgroundin between ecosystem change and human well-being. This
globalenvironmentalscenariosbyexplicitlyincorporatingbothecosys- chapterdescribesthemethodologyusedtodevelopthesce-
tem dynamics and feedbacks. The goal of the MA is to provide decision- narios. It first provides background information on scenar-
makersandstakeholderswithscientificinformationonthelinksbetweeneco- iosingeneral,followedbyanoverviewofthemethodology
system changeand human well-being. Scenariosare usedin thiscontext to used in the MA scenario development. The development
explore alternative futures on the basis of coherent and internally consistent of the qualitative storylines and the global modeling exer-
sets of assumptions. The scenarios are novel in that they incorporate feed- cisearethendescribedindetail.Finally,webrieflydescribe
backs between social and ecological systems and consider the connections
how uncertainty and scale issues were handled in the sce-
betweenglobalandlocalsocioecologicalprocesses.
narios. Eight Appendixes provide detailed descriptions of
themodelswereliedon.
TheapproachtoscenariodevelopmentusedintheMAcombinesqualita-
tive storyline development and quantitative modeling. In this way, the
scenarioscapturetheaspectsofecosystemservicesthatarepossibleto 6.2 Background to the MA Scenarios
quantify,butalso thosethataredifficultorevenimpossibletoexpress
in quantitative terms. The MA developed scenarios of ecosystem services Applied(naturalandsocial)scienceshaveusedmanymeth-
and human well-being to 2050, with selected results up to 2100. Scenarios ods for devising an understanding of the future, including
weredevelopedinaniterativeprocessofstorylinedevelopmentandmodeling. predictions,projections,andscenariodevelopment.(Foran
Thestorylinescoveredmanycomplexaspectsofsocietyandecosystemsthat overview of methods, see, e.g., Glenn and Gordon 2005.)
aredifficulttoquantify,whilethemodelshelpedensuretheconsistencyofthe Each approach has its own methodology, levels of uncer-
storylines and provided important numerical information where quantification tainty, and tools for estimating probabilities. It should be
waspossible. notedthatthesetermsareoftennotstrictlyseparatedinthe
literature. The conventional difference, however, is that a
IntheMA,scenarioswerepartlyquantifiedbyusinglinkedglobalmodels predictionisanattempttoproduceamostlikelydescription
toensureintegrationacrossfuturechangesinecosystemservices.Avail- orestimateoftheactualevolutionofavariableorsystemin
ableglobalmodelsdonotallowforacomprehensiveassessmentofthelink- the future. (The term ‘‘forecasting’’ is also often used; it is
agesamongecosystemchange,ecosystemservices,humanwell-being,and used interchangeably with prediction in this chapter.) Pro-
social responses to ecosystem change. To assess ecosystem change for a jectionsdifferfrompredictionsinthattheyinvolveassump-
largersetofservices,severalglobalmodelswerelinkedandrunbasedona
tions concerning, for example, future socioeconomic and
consistentsetofscenariodriverstoensureintegrationacrossfuturechanges
technologicaldevelopmentsthatmaynotberealized.They
inecosystemservices.
arethereforesubjecttosubstantialuncertainty.Scenariosare
neither predictions nor projections and may be based on a
WhileadvanceshavebeenmadebytheMAinscenariodevelopmentto
narrative storyline. Scenarios may be derived from projec-
explorepossiblefuturesofthelinkagesbetweenecosystemchangeand
tions but often include additional information from other
society,stillfurtherprogressispossible.Usingquantitativeandqualitative
sources.
tools,theMAscenarioscoveralargenumberofecologicalservicesanddriv-
Over the years, experience with global assessment proj-
ersofecosystemchange.Inthecourseofdevelopingthesescenarios,wealso
ects in the ecological and environmental realm has shown
identifiedareaswhereanalyticaltoolsarerelativelyweak.Forquantificationof
thatpredictionoverlargetimeperiodsisdifficultifnotim-
ecosystemservicescenarios,weparticularlyneedmodelsthatfurtherdisag-
possible,giventhecomplexityofthesystemsexaminedand
gregate services to local scales, address cultural and supporting ecosystem
services,andconsiderfeedbacksbetweenecosystemchangeandhumande- the large uncertainties associated with them—particularly
velopment. for time horizons beyond 10–20 years. In the case of eco-
systems, heterogeneity, non-linear dynamics, and cross-scale
interactions of ecosystems contribute to system complexity
6.1 Introduction
(Holling 1978; Levin 2000). Furthermore, ecological pre-
The goal of the Millennium Ecosystem Assessment is to dictions are contingent on drivers that may be even more
providedecision-makersandstakeholderswithscientificin- difficult to predict, such as human behavior. As a result,
formation on the links between ecosystem change and people rarely have enough information to produce reliable
human well-being. The MA focuses on ecosystem services predictionsofecosystembehaviororenvironmentalchange
(such as food, water, and biodiversity) and on the conse- (Sarewitzetal.2000;FuntowiczandRavetz1993).Despite
quencesofchangesinecosystemsforhumanwell-beingand these problems with predicting the future, people need to
for other life on Earth. Ecosystem change, on the other takedecisionswithimplicationsforthefuture.Scenariode-
hand, is significantly affected by human decisions, often velopmentoffersoneapproachtodealingwithuncertainty.
over long time horizons (Carpenter 2002). For example, TheMAusestheIPCCdefinitionofscenariosas‘‘plau-
changes in soils or biodiversity of long-lived organisms can sible descriptions of how the future may develop, based on
have legacy effects that last for decades or longer. Thus it acoherentandinternallyconsistentsetofassumptionsabout
is crucially important to consider the future when making key relationships and driving forces’’ (such as rate of tech-
decisions about the current management of ecosystem ser- nologychangesandprices)(IPCC2000b).Assuch,scenar-
vices. ios are used as a systematic method for thinking creatively
.................11411$ $CH6 10-27-0508:42:03 PS PAGE147
148 EcosystemsandHumanWell-being:Scenarios
about complex, uncertain futures for both qualitative and to a nonscientific audience. Quantitative scenarios usually
quantitative aspects. Figure 6.1 presents an overview of the rely on modeling tools incorporating quantified informa-
additionalvaluecreatedbyscenarioanalysiscomparedwith tion to calculate future developments and changes and are
moredeterministicapproachessuchas predictions. presentedintheformofgraphsandtables.
Scenarioscanservedifferentpurposes(see,e.g.,Alcamo Both scenario types can be combined to develop inter-
2001; van der Heijden 1997). They can be used in an ex- nally consistent storylines assessed through quantification
plorativemannerorforscientificassessmentinordertoun- and models, which are then disseminated in a narrative
derstand the functioning of an investigated system. For the form.(A‘‘storyline’’isascenarioinwritten form,andusu-
MA, researchers are interested in exploring hypothesized ally takes the form of a story with a very definite message
interactions and linkages between key variables related to or ‘‘line’’ running through it.) This approach was used to
developtheMAscenarios.Thequalitativescenarios(story-
ecosystems and human well-being. Scenario outcomes can
lines)provideanunderstandablewaytocommunicatecom-
then form part of planning and decision-making processes
plex information, have considerable depth, describe
andhelpbridgethegapbetweenthescientificandthepolicy-
comprehensive feedback effects, and incorporate a wide
making communities. The MA scenarios can also be used
range of views about the future. The quantitative scenarios
in an informative or educational way. Depending on the
are used to check the consistency of the qualitative scenar-
process used, scenarios can also challenge the assumptions
ios,toproviderelevantnumericalinformation,andto‘‘en-
that peoplehave aboutthe future andcan illustrate the dif-
rich’’ the qualitative scenarios by showing trends and
ferent views on their outcomes held by participants of the
dynamics not anticipated by the storylines. By ‘‘consis-
scenario-buildingexercise.
tency’’wemeanthatthestorylinesdonotcontainelements
In general, scenarios contain a description of step-wise
thatare contradictoryaccordingtocurrent knowledge.On
changes or a storyline, driving forces, base year, and time
the other hand, the goal of developing consistent scenarios
steps and horizon (Alcamo 2001). They are often classified
should not lead us to omit elements that may look contra-
bythe methodusedto developthem,theirgoalsandobjec-
dictory but in reality are only surprising new connections
tives, or their output. One classification of scenarios distin-
or results. Often, uncovering these unanticipated connec-
guishesbetween‘‘exploratory’’and‘‘anticipatory’’scenarios. tions that challenge current beliefs and assumptions is one
Exploratoryscenariosaredescriptiveandexploretrendsinto of the most powerful results of the scenarios analysis. For
the future. Anticipatory scenarios start with a vision of the example,isascenarioaboutclimateimpactsonlyconsistent
future that could be optimistic, pessimistic, or neutral and when it assumes that climate change will lead to global
workbackwardsintimetodiscernhowthatparticularfuture warming? Indeed, there are ‘‘surprising’’ yet plausible and
might be reached. This type of scenario is sometimes also consistent scenarios that postulate that climate change will
referred to as a ‘‘normative’’ scenario. The MA scenarios leadtocoolingofparts oftheloweratmosphere.
weredevelopedusingmostlyexploratoryapproaches. Together, the qualitativeand quantitative scenarios pro-
Finally, scenarios can consist of qualitative information, vide a powerful combination that compensates for some of
quantitativeinformation,orboth. Chapter2containssome the deficits of either one on its own. The combination of
examples of each of these types of scenarios. Qualitative qualitative with quantitative scenarios has been used in
scenarios,usinganarrativetexttoconveythemainscenario many recent global environmental assessments, such as
IPCC’s Special Report on Emissions Scenarios (IPCC
messages, can be very helpful when presenting information
2000b), UNEP’s Global Environment Outlook (UNEP
2002), the scenarios of the Global Scenario Group (Raskin
et al. 1998), and the World Water Vision scenarios (Cos-
groveandRijsberman2000;Alcamoetal.2000).
The distinction between qualitative and quantitative
scenarios is sometimes blurred, however. Qualitative sce-
narios can be derived by formalized, almost quantitative
methods, while quantitative scenarios can be developed by
soliciting numerical estimates from experts or by using
semi-quantitative techniques such as fuzzy set theory. Sto-
rylines can also be interspersed with numerical data and
therebybeviewedas bothqualitativeandquantitative.
As noted in earlier chapters, the main objective of the
MA scenarios is to explorelinks between future changes in
worldecosystems andtheirservicesandhumanwell-being.
The scenario analysis focuses on the period up to 2050,
withselectedprospectsfor2100.
Figure6.1. StatusofInformation,fromLowtoHigh,versus
DegreeofControllability.PDF(cid:4)probabilitydistributionfunction. 6.3 OverviewofProcedureforDevelopingtheMA
Thefigureshowsthedomainoftraditionaldecisiontoolssuchas Scenarios
utilityoptimizationandthedomainwherescenariosmaybehelpful.
This section describes the process used to develop the MA
(AdaptedfromPetersonetal.2003)
scenarios.Theprocedureconsistsof14stepsorganizedinto
.................11411$ $CH6 10-27-0508:42:05 PS PAGE148
MethodologyforDevelopingtheMAScenarios 149
threephases(seeBox6.1);thedetailsofthestorylinedevel-
opment and the modeling exercise are explained in later
sections. In the first phase, the scenario exercise was orga-
nized and the main questions and focus of the alternative
scenarioswereidentified.Inthesecondphase,thestorylines
were written and the scenarios were quantified using an
iterative procedure. During the third phase, the results of
the scenario analysis were synthesized, and scenarios and
their outcomes were reviewed by the stakeholders of the
MA, revised, anddisseminated.Theseelements arealsoin-
dicatedinFigure6.2.WhileFigure6.2suggeststhatactivi-
ties were completed once processed, in reality earlier
activities wereoftenrevised duringaniterative process.
Two essential activities within the overall scenario de-
velopment framework were the formulation of alternative
scenario storylines and their quantification. These two ele-
ments were designed to be mutually reinforcing. The de-
velopment of scenario storylines facilitates internal
consistency of different assumptions and takes into account
a broad range of elements and feedback effects that are ei-
therdifficultto quantifyorforwhich nomodeling capability
exists,orboth.Basedoninitialstorylines,thequantification
processhelpstoprovideinsightsintothoseprocesseswhere
sufficient knowledge exists to allow modeling, and to take
Figure6.2. OverallMethodologyofMAScenarioDevelopment
intoaccounttheinteractionsamongthevariousdriversand
services. During scenario development, several interactions
wereorganizedbetweenthestorylinedevelopmentandthe
chairpersons and secretariat of the Scenarios Working
modeling exercise in order to increase the consistency of
Group, to lead and coordinate the scenario-building proc-
thetwoapproaches.
ess. In addition, a larger panel, composed mainly of scien-
tificexperts,was assembledtobuildthescenarios.
6.3.1 OrganizationalSteps
Thescenarioguidanceteamconductedaseriesofinter-
The first phase in the MA scenario development consisted views with potential users of the scenarios to obtain their
of establishing a scenario guidance team, composed of the input for developing the goals and focus of the scenarios.
ThiswasespeciallyimportantfortheMAbecausethenum-
berof potentialusersisvery largeanddiverse. These inter-
BOX6.1
views also ensured input from stakeholders and users early
MAProcedureforDevelopingScenarios
on in the study. Understanding the needs and desires of
PhaseI:Organizationalsteps users and their outlook on future development helped the
teamtodevisethemainfocalquestionsofthescenarios.
1. Establishascenarioguidanceteam.
2. Establishascenariopanel. Based on the results of the user interviews and discus-
3. Conductinterviewswithscenarioendusers. sions with the scenario panel, the objectives, focus, leading
4. Determinetheobjectivesandfocusofthescenarios. themes, and hypotheses of the scenarios were derived by
5. Devisethefocalquestionsofthescenarios. the scenario guidance team and panel (and later confirmed
bytheMAAssessmentPanel).FortheMA,themainobjec-
PhaseII:Scenariostorylinedevelopmentandquantification tiveofscenariodevelopmentwastoexplorealternativede-
velopment paths for world ecosystems and their services
6. Constructazero-orderdraftofscenariostorylines.
over the next 50 years and the consequences of these paths
7. Organizemodelinganalysesandbeginquantification.
for human well-being. Based on these results, the scenario
8. Revisezero-orderstorylinesandconstructfirst-orderstorylines.
9. Quantifyscenarioelements. team clarified the focal questions to be addressed by the
10. Revisestorylinesbasedonresultsofquantifications. scenarios (for the rationale behind the choice of questions,
11. Revisemodelinputsfordriversandre-runthemodels. seeChapter5).Themainquestionwas:
Whataretheconsequencesof plausiblechangesin development
PhaseIII:Synthesis,review,anddissemination
paths for ecosystems and their services over the next 50 years
12. Distributedraftscenariosforgeneralreview. and what will be the consequences of those changes for human
13. Developfinalversionofthescenariosbyincorporatinguserfeed-
well-being?
back.
14. Publishanddisseminatethescenarios. Thekeyfocalquestionwasthendefinedthroughaseriesof
morespecificquestions—thatis,whataretheconsequences
.................11411$ $CH6 10-27-0508:42:06 PS PAGE149
150 EcosystemsandHumanWell-being:Scenarios
for ecosystem services and human well-being of strategies In the next step, the modeling team, in consultation
thatemphasize: with the storyline team, developed quantitative driving
• economicandhumandevelopment(e.g.,povertyeradi- forcesthatwereconsideredtobeconsistentwiththestory-
cation, market liberalization) as the primary means of lines. Obviously, there is room for interpretation regarding
management? the consistency of driving forces and the storylines, and
• local and regional safety and protection, giving far less multiple sets of driving forces are possible. The driving
emphasistocross-border andglobalissues? forcesandquantifieddriversforthemodelingexercisecho-
• development and use of technologies, allowing greater senfor theMAscenariosare discussedindetailinChapters
eco-efficiencyandadaptivecontrol? 7, 8, and 9. Based on the model outcomes of the quantita-
• adaptive management and local learning about the con- tive scenarios, the scenario team further elaborated or
sequences of management interventions for ecosystem adapted the storylines. A number of feedback workshops
services? with the MA Board and stakeholder groups were held to
Ecosystem services are defined as ‘‘the conditions and improvethefocusanddetailsofthestorylines.
processes supported by biodiversity through which ecosys- Based on the results of the first round of quantified sce-
tems sustain and fulfill human life, including the provision narios, small adjustments in the specification of drivers and
ofgoods’’(MA2003;seealsoChapter1). Ecosystemproc- linkagesamongmodelsweremade,newmodelcalculations
essesareseldomtradedinmarkets,typicallyhavenomarket were carried out with the modeling framework, and the
price, and therefore usually do not enter in economic storylineswererevised(inotherwords,therewasoneitera-
decision-making or cost-benefit analyses even though they tion between storyline development and quantification).
areessentialforhumanwell-being.TheMAconsideredthe Ideally, a series of iterations between storyline improve-
following interlinkedcategories of ecosystemservices: pro- ment, quantification, and stakeholder feedback sessions
visioning services (food, fresh water, and other biological wouldhavehelpedtobetterharmonizethequantitativeand
products),supportingandregulatingservices(includingsoil qualitative scenarios, but time constraints limited the num-
formation, nutrient cycling, waste treatment, and climate berofiterationsfortheMA.Thequantifiedscenarioresults
regulation),andculturalservices. are describedindetailinChapter9,whilethescenariosto-
rylines canbe foundinChapter8.
6.3.2 ScenarioStorylineDevelopmentand
Quantification 6.3.3 Synthesis,Review,andDissemination
Following a review and evaluation of current and past sce-
The scenario outcomes were assessed in the context of the
nario efforts, scenario building blocks for driving forces,
focal questions and user needs of the various MA user
ecologicalmanagementdilemmas,branchpoints,andsoon
groups. These results are described in Chapters 11, 12, 13,
were mapped out. Storyline outlines were then developed
and14,basedonananalysis ofbothqualitativeandquanti-
aroundthese buildingblocks. Additionaldetailson thesto-
tative scenario outcomes. Feedback from the assessment
rylinedevelopmentareprovidedlaterin thischapter.
component of the scenario team led to further refinement
While the initial storylines were being developed, a
of the storylines and the provision of additional model de-
team of modelers representing several global models was
tails.
organizedtoquantifythescenarios.Fiveglobalmodelscov-
Thescenarios,consistingofthequalitativestorylinesand
ering global change processes or ecosystem provisioning
quantitative model calculations, were disseminated for re-
services and two global models describing changes in bio-
view by interested user groups. This was accomplished
diversity were chosen. Criteria that were used to select
through presentations, workshops, the MA review process,
these models included global coverage, publications of
and Internet communications. Reviewer comments were
modelstructureand/ormodelapplicationinpeer-reviewed
then incorporated into the scenarios. Both review and dis-
literature, relevance in describing the future of ecosystem
semination are considered important elements for the suc-
services, and ability to be adapted to the storylines of the
cessofthescenarioexercise.
MA. (The Ecopath with Ecosim models used to describe
marineecosystemsandtheirservicetoglobalfisheriesforms 6.3.4 LinkagesbetweenDifferentSpatialand
an exception to the rule of global coverage, as no global
TemporalScales
model for this issue was available.) Although all the models
hadbeendevelopedpreviously,linkagesamongmodelsand In order to deal with the multiscale aspects of the relation-
projections out to 2050 and 2100 did require adjustments ships between ecosystem services and human well-being,
forseveralofthem.Testcalculationswerecarriedoutusing the MA called for alarge number of sub-global assessments
preliminary driving force assumptions. These test calcula- in addition to the global assessment. Several of the sub-
tions were helpful in identifying the potential contribution global assessments also developed scenarios. As these were
ofdifferentmodelstotheanalysisandinclarifyingthepro- oftentargetedatspecificusergroups oraddressedveryspe-
ceduresoflinkingthedifferentmodels. cificquestions,itwasnotalwayspossibletodirectlylinkthe
Afteraseriesofiterations,thezero-orderstorylineswere sub-global assessments to the global assessment. (See also
revised and cross-checked for internal consistency. One Box 6.2.) Nevertheless, to harmonize the global and sub-
measure used to accomplish this was the development of global scenario exercises as much as possible, the following
timelinesandmilestonesforthevariousscenarios. stepswere taken:
.................11411$ $CH6 10-27-0508:42:08 PS PAGE150
MethodologyforDevelopingtheMAScenarios 151
narios were primarily developed to the year 2050, scenario
BOX6.2 resultsofthequantitativescenarioelaborationwerealsore-
AComparisonofGlobalandSub-globalScenario
portedfor2020,2050,and2100.The2020reportprovided
Development
a link between the scenarios and medium-term policy ob-
One of the goals of both the global and sub-global scenarios was to jectives, such as the 2015 Millennium Development Goals.
provideforesightaboutpotentialfuturesforecosystems,includingthe It also linked the global and sub-global scenarios, many of
provisionofecosystemservicesandhumanwell-being. whichextendonlytoapproximately2025.Meanwhile, the
Despitesimilargoals,sub-globalscenariodevelopmentwassome- results for 2100 impart insight into longer-term trends in
whatdifferentfromglobalscenario development.Thekey differences ecosystemservices.Resultsfor2100wereonlyreportedfor
weretheextensiveuseofquantitativemodelsintheglobalscenarios, parameters that are determined by strong inertiawithin the
greater involvement of decision-makers in the sub-global scenarios, naturalsystem,suchasclimatechangeandsea levelrise.
anduseofsub-globalscenariosdirectlyasatoolfordecision-making
While several of the models used within the modeling
(versusabroaderlearning-focusedglobalscenarioset).
exerciseperformtheircalculationsfor10–40globalregions
Becausethegroupofdecision-makersattheglobalscaleismore
or countries, a much lower resolution was chosen for re-
diffuse,involvementofdecision-makersintheglobalscenariodevelop-
porting. Quantitative results (Chapter 9) are mainly pre-
mentwaslessintensethanitwasforthesub-globalscenarios.Repre-
sentedforsixreportingregions:sub-SaharanAfrica,Middle
sentatives from the business community, the public sector, and the
East and North Africa, the Organisation of Economic Co-
internationalconventionswereperiodicallyinformedofprogressinthe
operation and Development, the former Soviet Union,
developmentoftheglobalscenariosandaskedforfeedback.Thesub-
Latin America, and Asia. (See Figure 6.3 in Appendix A.)
global assessments,because theyoften focusedon issues forwhich
These are sometimes aggregated into ‘‘rich’’ or ‘‘wealthy’’
keydecision-makerscould beidentified, hadcloser contactwiththeir
primaryintendedusers.Theultimateresultofhavingdecision-makers countriesand‘‘poorer’’countries.
moreinvolvedinscenariodevelopment wasthatthescenariosthem- The reasons for using this lower resolution include the
selveswerebuiltmoreasadirecttoolforengagingpeopleindecision- amount of information that could be presented within this
findingprocesses.Thus,inmostsub-globalassessments,scenariode- volume and checked for internal consistency. In addition,
velopmentfocusedon futuresoverwhichlocal decision-makershave some models use a global grid of half-degree latitude and
atleastsomedirectcontrol. longitude to calculate changes in environmental and eco-
The global scenarios provide four global storylines from where a logical parameters. The latter are presented in case they are
look down enriches these stories with regional and local details. The relevant. Grid-level results should be interpreted as broad-
sub-global scenarios provide a large number of local stories from brush visualizations of the geographic patterns underlying
wherealookupenrichesthestorieswithregionalandglobal‘‘details.’’ thescenarios,notasspecificpredictionsforsmallregionsor
Amorecompletedescriptionofthesub-globalscenarioscanbefound
evengridcells.
inChapter9oftheMAMultiscaleAssessmentsvolume.
6.4 Building the Qualitative Scenarios:
• Representatives of some sub-global assessments partici- Developing Storylines
patedintheglobalscenarioteamandcontributedtothe
Significant emphasis was placed on storyline development.
scenariodevelopment.
Storylines can be provocative because they challenge the
• Members of the global scenario guidance team partici-
tendencyofpeopletoextrapolatefromthepresentintothe
pated at various occasions in meetings of the sub-global
future.Theycanbeusedtohighlightkey uncertaintiesand
scenario assessments, explaining both the preliminary
surprisesabout thefuture.They can considernonlinearities
globalscenarioresultsandtheprocedurefollowedinde-
andcomplicatedcausallinksmoreeasilythanglobalmodels
velopingtheglobalscenarios.
can. Moreover, they can incorporate important ecological
• Some of the sub-global assessments used the storylines
processes, which so far have not been satisfactorily consid-
oftheglobalassessmentasbackgroundfortheirworkor
ered in existing global models. (See Chapter 3.) Since the
otherwiselinkedtheirscenariostotheglobalassessment.
MA’s goal for scenarios development was to specifically
• After the storylines and the model runs of the global
consider the future of ecosystems and their services, story-
scenarios were finalized, results and findings of the sub-
linedevelopmentwasusedtoincorporateprocessesthatthe
globalassessmentwereusedtoillustratehowthescenarios models could not fully address. Moreover, the qualitative
couldplayoutatthelocalscale.(SeeBoxesinChapter8.) storiesprovidedtheinput variablesfortheglobalmodels.
As well as addressing changes in ecosystems and their The qualitative storylines were developed through a se-
servicesatseveralspatialscales,theMAalsoconsidereddif- ries of discussions among the scenario development panel
ferenttemporalscales.Foramoredetaileddiscussiononthe alternatingwithfeedbackfromMAusergroupsandoutside
general issue of scales in the MA, see the MA conceptual experts.Thestorylinedevelopmentfollowedsixsteps:
framework report (MA 2003). (See also Chapter 7 of this • identification of what the MA user groups wanted to
volume and Chapter 4 in the MA Multiscale Assessments learnfromthescenarios,
volume). • developmentofa setofscenariobuildingblocks,
The question of temporal scale was important for the • determination of a set of basic storylines that reflected
constructionoftheMAscenarios.Althoughtheglobalsce- theMAgoalandrespondedtouser needs,
.................11411$ $CH6 10-27-0508:42:08 PS PAGE151
152 EcosystemsandHumanWell-being:Scenarios
• developmentofrich detailsforthestorylines, alinked set ofglobalmodels.Thepurpose of themodeling
• harmonization of the storylines with modeling results, exercisewasboth totesttheconsistencyofthestorylinesas
and developed in the first round and to elaborate and illustrate
• feedbackfromexpertsandusergroupsanditsincorpora- thescenariosinnumericalform.This‘‘quantificationofthe
tioninto thefinalstorylines. scenarios’’hadfivemainsteps:
Although these steps are presented in order, the process • Assembling several global models to assess possible fu-
inrealitycycledthroughsomeofthestepsmanytimesuntil
ture changes in the world’s ecosystems and their ser-
itwasfeltthatconsensuswas reachedonthestorylines.
vices.ThesemodelsarebrieflydescribedinBox6.3and
Keyquestionsaboutthefutureandmainuncertaintiesof
in the Appendixes. In addition, several models were
MAusergroupswereidentifiedthroughaseriesoffeedback
usedtodescribecertainaspectsofchangesinbiodiversity.
techniques. Approximately 70leaders anddecision-makers
• Specifying a consistent set of model inputs based on the
from around the world and in many different decision-
scenariostorylines.
making positions were interviewed about their hopes and
• Running themodelswiththespecifiedmodelinputs.
fearsfor thefuture.A formalUserNeeds surveydeveloped
• Soft-linking the models by using the output from one
by the MA at the beginning of the assessment was used as
model as input to another (we use the term soft-link as
additional input. This survey was sent to representatives of
the MA user community and contained questions on ex- themodelswerenotrunsimultaneously).
pectedoutcomesoftheMAprocess.Synthesisofthesesur- • Compiling and analyzing model outputs about changes
veys led to the formulation of the key questions listed in futureecosystemservices andimplicationsforhuman
earlier. well-being.Themodelswereusedtoanalyzethefuture
Inthesecondstep,thescenarioteam developedanum- state of indicators for ‘‘provisioning,’’ ‘‘regulating,’’ and
berofscenariobuildingblocks,includingthefactorsdiffer- ‘‘supporting’’ ecosystem services. These indicators are
entiating the scenarios. In addition, the scenario team listed in Table 6.1. The analysis of modeling results is
identified possible driving forces of socioecological systems presentedinChapter9.
into the future, as well as the main uncertainties of these
drivingforcesandtheprospectsforbeingabletosteerthem. 6.5.2 SpecifyingaConsistentSetofModelInputs
Otherscenariobuildingblocksincludeddiscussionsoneco-
logical dilemmas that decision-makers are likely to face in The first version of the storylines of the MA scenarios (and
the near future, possible branching points of scenarios, the in particular, tables containing their main characteristics)
occurrenceofcross-scaleecologicalfeedbackloops,andas- formed the basis of the main model assumptions for the
sumptionsthat decision-makersholdabout thefunctioning quantitative exercise. Over several workshops, the story-
of ecological systems (such as whether ecological systems lines were translated into a consistent set of model assump-
arefragileorresilient). tions that closely corresponded to the ‘‘indirect drivers’’ of
A first set of scenario storylines was developed using a ecosystemservices. Theseincluded:
number of different development paths to distinguish • population development, including total population and
among them. This was done through a combination of age distributionindifferentregions;
writing, presentations, and discussions within the scenario • economic development as represented by assumed growth
team and feedback from other working groups within the
in per capita GDP per region and changes in economic
MA.Oncethesestorylinesweredeveloped,theywerepre-
structure;
sented to a wider group of experts, including the MA • technologydevelopment,coveringmanymodelinputssuch
Board,membersoftheWorldBusinessCouncilforSustain-
astherateofimprovementintheefficiencyofdomestic
ableDevelopment,scenarioexpertsfromotherscenarioex-
wateruseor therateofincreasein cropyields;
ercises, and severaldecision-maker communities. Feedback
• human behavior, covering model parameters such as the
fromthisexerciseledtofurtherrefinementofthestorylines.
willingnessofpeopletoinvesttimeormoneyinenergy
As the results of the quantified scenarios became avail-
conservationor waterconservation;and
able, they were compared with the qualitative storylines.
• institutional factors, such as the existence and strength of
Thisledtofurtherdiscussionsaboutthelogicalpathwaysto
the final sequence of events in the scenarios. These discus- institutions to promote education, international trade,
sions were encouraged by the structure of the scenario andinternationaltechnologytransfer.Thelatterarerep-
team, which included both storyline-writers and members resenteddirectly(tradebarriers,forinstance,andimport
of the modeling teams. As a result of these discussions, sto- tariffs) or indirectly (income elasticity for education) in
rylines and model driving forces were adjusted. These dis- themodels,basedonthestorylines.
cussionsalsoledtonewinterpretationsofthestorylinesinto For each of these factors, trends were developed for
modelparameters. model inputs that corresponded to the qualitative state-
ments of thestorylines. For example, statements inthe sto-
6.5 Building the Quantitative Scenarios: The rylines about ‘‘high’’ or ‘‘low’’ mortality were interpreted
Global Modeling Exercise such that the trend in mortality would be in the upper or
lower 20% of the probabilistic demographic projections.
6.5.1 OrganizationoftheGlobalModelingExercise (See also Chapter 9.) Another example is that the scenario
As noted in previous sections, the storyline development with the highest level of agricultural intensification was as-
wascomplementedbybuildingquantitativescenariosusing sumedtohavethefastestrateofimprovementofcropyield
.................11411$ $CH6 10-27-0508:42:09 PS PAGE152
MethodologyforDevelopingtheMAScenarios 153
Table6.1. GlobalModelingOutput
BOX6.3
ModelsUsedinMAGlobalModelingExercise
ModelUsedto
Themodelsintheglobalmodelingexerciseinclude: Ecosystem Calculate
Service Indicator Indicator
• TheIMPACTmodeloftheInternationalFoodPolicyResearchInsti-
tute in the United States, which computes food supply, demand, Directdriversofecosystemchange
trade,andinternationalfoodpricesforcountriesandregions(Rose- Climatechange temperaturechange,precipitation IMAGE
grantetal.2002). change
• TheWaterGAPmodeloftheUniversityofKasselinGermany,which Changesinland areasperlandcoverandland IMAGE
computes global water use and availability on a watershed scale useandland usetype
cover
(Alcamoetal.2003a,2003b).
Technology wateruseefficiency,energy IMPACT,IMAGE,
• TheAIMglobalchangeintegratedmodeloftheNationalInstitutefor
adaptationand efficiency,areaandnumbers WaterGAP
EnvironmentStudiesinJapan,whichcomputeslandcoverandother
use growth,cropyieldgrowth,and
indicators of global change worldwide, with an emphasis on Asia changesinlivestockcarcass
(Kainumaetal.2002). weight
• The IMAGE 2.2 global change model of the National Institute of Exogenous fertilizeruse,irrigation,wage IMPACT,IMAGE
PublicHealthandtheEnvironmentintheNetherlands,whichcom- inputs rates
putes climate and global land cover on a grid scale and several Airpollution sulfurandNO emissions AIM,IMAGE
x
otherindicatorsofglobalchange(IMAGE-team2001). emissions
• TheEcopathwithEcosimmodeloftheUniversityofBritishColumbia Provisioningservices
in Canada, which computes dynamic changes in selected marine Food totalmeat,fish,andcrop IMPACT
ecosystemsasafunctionoffishingefforts(Paulyetal.2000). production;consumption;trade,
foodprices
Food potentialfoodproduction,crop IMAGE,AIM
and largest expansion of irrigation development. An over- area,pasturearea,andareafor
viewof modelinputsisprovidedin Chapter9. biofuels
Fish stock Ecopath/Ecosim
6.5.3 Soft-linkingtheModels
Fuelwood biofuelsupply IMAGE,AIM
To achieve greater consistency between the calculations of Freshwater annualrenewablewater WaterGAP,AIM
resources,waterwithdrawalsand
the different models, they were ‘‘soft-linked’’ in the sense
consumption,returnflows
that output files from one model were used as inputs to
other models. (See Figure 6.4.) The time interval of data Regulatingservices
that were exchanged between the models was usually one Climate netcarbonflux IMAGE
year.Thefollowing modellinkageswere included: regulation
• Computations of regional food supply, demand, and Erosion erosionrisk IMAGE
trade from the IMPACT model were aggregated to the
Supportingservices
17 IMAGE world regions and the 12 IMAGE animal
Primary primaryproduction IMAGE,AIM
and crop types. These data were then used as input to
production
theIMAGElandcovermodelthatcomputedonaglobal
Foodsecurity calorieavailability,foodprices, IMPACT
grid the changes in agricultural land that are consistent
shareofmalnourishedpre-school
withtheagriculturalproductioncomputedinIMPACT.
childrenindevelopingcountries
In addition, IMAGE was used to calculate the amount
Watersecurity waterstress WaterGAP,AIM
of grassland needed for the livestock production com-
puted in IMPACT. Two iterations were done between Inputtobiodiversitycalculations
IMPACTandIMAGEtoincreasetheconsistencyofthe Terrestrial landusearea,climatechange, IMAGE
information on agricultural production, availability of biodiversity nitrogenandsulfurdeposition
land, and climate change. (First, preliminary runs were Aquatic riverdischarge WaterGAP
usedasabasisfordiscussionbetweenthetwogroupson biodiversity
the consistency of trends under each of the four scenar- climatechange IMAGE
ios for agricultural production, yields changes, and im-
pacts ontotal land use ineach region; for thefinal runs,
an additional iteration was done, providing information • Changes in irrigated areas computed in IMPACT were
from IMAGE to IMPACT on yield changes resulting allocated to a global grid in the WaterGAP model and
from climate change and use of marginal lands.) The then used to compute regional irrigation water require-
linkage between IMPACT and IMAGE was done for ments. These irrigation water requirements were then
2000,2020,2050,and2100. added to water withdrawals from the domestic and in-
.................11411$ $CH6 10-27-0508:42:10 PS PAGE153
154 EcosystemsandHumanWell-being:Scenarios
Figure6.4. LinkagesbetweenModels
dustrialsectors(changesforthesesectorswerecalculated lingofvariousdata.TheIMPACTmodel,forinstance,uses
byWaterGAP)andcomparedwithlocalwateravailabil- a more detailed regional disaggregation level than the
ity. From this comparison, WaterGAP estimated water IMAGE model. In almost all cases, however, regional scal-
stressona globalgrid. ing was possible by combining regions (upscaling) or by
• Changes in temperature and precipitation were calcu- assuming proportional changes (downscaling). The models
latedinIMAGEbasedontrendsingreenhousegasemis- werenotrecalibratedonthebasisofthenewinputparame-
sions from energy and land use. These climate change ters provided by the other models, but in most cases the
calculations were used in WaterGAP to compute models had been calibrated using comparable international
changesinwateravailabilityandinIMPACTtoestimate databases.Thenewlinkagesthereforedidnotleadtomajor
changesin cropyield. inconsistenciesinassumptionsbetweenthemodels.
• The IMAGE model was used to compute changes in
electricity use and livestock production, the latter ob- 6.5.4 ModelingChangesinBiodiversity
tained from IMPACT, which were used by WaterGAP
Currently,therearenoglobalmodelsthatdescribechanges
to estimate future water requirements in the electricity
in biodiversityon thebasis of globalscenarios. Forthe MA
andlivestocksectors.
• The model for Freshwater Biodiversity used inputs of analysis, several smallermodelsoralgorithms were developed
on the basis of linkages between global change parameters
river discharge from WaterGAP and climate from
and species diversity to describe elements of biodiversity
IMAGE.
• Thecalculationsofchangesinterrestrialbiodiversityre- change. Outcomes from these tools were then used for a
moreelaboratediscussionofthepossibleimpactsofthedif-
lied on calculations of land cover changes, nitrogen de-
ferent scenarios for global biodiversity. The methods used
position, and climate from IMAGE. Calculations were
are discussedindetailinChapter10.
doneonthebasisof annualIMAGEoutput data.
• TheAIMmodelingteamusedthesamedriversinterms
6.5.4.1 TerrestrialBiodiversity
ofpopulation,economicgrowth,andtechnologydevel-
opment, but no linkages were made between the AIM The possiblefuturechangesinterrestrialbiodiversityunder
modelandoutputsofother models. the four MA scenarios were explored on the basis of the
Linkages between models are seldom straightforward, assessmentofchangesinnativehabitatcoverovertime,cli-
andusuallyrequiretheupscaling(aggregation)ordownsca- matechange,andchangesinnitrogendeposition.Areview
.................11411$ $CH6 10-27-0508:42:13 PS PAGE154