downscaling socioeconomic and emissions scenarios for global environmental change research: a review

Focus Article

Downscaling socioeconomicand emissions scenarios for globalenvironmental change research:a reviewDetlef P. van Vuuren,1∗ Steve J. Smith2 and Keywan Riahi3

Global change research encompasses various scales from global to local. Impactsanalysis in particular often requires spatial downscaling, whereby socioeconomicand emission variables specified at relatively large spatial scales are translatedto values at a country or grid level. In this article, the methods used for spatialdownscaling are reviewed, classified, and current applications discussed. It isshown that in recent years, improved methods for downscaling have beendeveloped. 2010 John Wiley & Sons, Ltd. WIREs Clim Change 2010 1 393–404

In studying global change, spatial scales play animportant role. Some phenomena can be studied

at the global scale, such as the contribution ofwell-mixed greenhouse gases to global warming.Other phenomena, however, need to be analyzedat a much finer scale as local circumstances play amore important role, such as land-use change, climatechange impacts and adaptation. In studying long-termchanges in the global environment, analysts oftenwork at the level of a relatively small number of largeregions. This level of aggregation is used as a com-promise between having sufficient detail to capturethe most salient differences between regions at themacro level and avoiding the additional complexityof exploring spatial details, including the challenge ofcommunicating results and assumptions at a very fineresolution. Examples of such global scenario studiesinclude the Special Report on Emission Scenarios,1

the Global Environment Outlook (GEO),2 and theMillennium Ecosystem Assessment (MA).3 Thesestudies are all developed using models that typicallydistinguish between 10 and 20 world regions.

Studies looking into finer-scale phenomenaneed to account for more detailed information (e.g.,

∗Correspondence to: [email protected] Assessment Agency, PO Box 303,3720 AH, Bilthoven, Netherlands2Pacific Northwest National Laboratories, Joint Global ChangeResearch Institute, College Park, MD 20740, USA3International Institute for Applied System Analysis, Schlossplatz 1,Laxenburg, Austria

DOI: 10.1002/wcc.50

geographic characteristics, land-use patterns, or thelocation of cities). However, as the global context stillplays an important role, there is often a need to trans-late information from global scenario studies to finergeographic scales. This process is referred to as spatialdownscaling: i.e., a process where information at alarge spatial scale is translated to smaller scales whilemaintaining consistency with the original dataset.Analogous methodologies can also be used to enhancethe temporal resolution of global study, e.g., providingseasonal emissions information, which is importantfor climate/chemistry modeling. Temporal aspects,however, will not be discussed further herein. Severalmethods have been developed to downscale infor-mation from the coarse scale of current (and likelyfuture) global integrated scenarios to the detailed levelof individual countries or grid cells. Such downscalingmethods have been developed for socioeconomicparameters, land use, climate variables, and air pollu-tion emissions. The above definition of downscalingincludes a wide range of methods, and for the purposeof the overview presented in this study we havedeliberately used an inclusive definition. We includemethods ranging from simple algorithmic scaling(arithmetic procedures for disaggregating macro-leveldata to the micro level) to the use of process modelsat the microscale drawing on macro-level data (seesection on General Methods Used for Downscalingof Global Environmental Change Scenarios).

An important research area where downscalingis often conducted is the study of climate changeimpacts. The sensitivity of climate impacts to

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Focus Article wires.wiley.com/climatechange

socioeconomic conditions has been highlighted byseveral studies, finding that the latter often have astronger influence on impacts than the experiencedclimate change itself.4,5 In this context, Pitcher6 iden-tifies further development of downscaling methodsas an important area of research. Issues include,for instance, improving the internal consistencyand plausibility of the downscaling approach. Thisarticle is meant to provide an overview of the mostrelevant existing research and issues. We focus on thedownscaling techniques that exist for socioeconomic(population, income) data, and data on emissionsand land use, and we briefly review some of thedownscaling methods that are used or have been usedin the past. We also briefly discuss the available datathat is required for downscaling.

GENERAL METHODS USED FORDOWNSCALING OF GLOBALENVIRONMENTAL CHANGESCENARIOS

Different types of downscalingAs mentioned in the introductory text, the term‘downscaling’ is used for a wide range of differentapproaches in which information on a coarser scaleis transformed and made available on a finer scale.Before discussing these approaches, it is useful to notethat based on the available literature on downscaling,some important distinguishing features of the variousmethods can be identified.

• Coverage: information can be downscaled to oneparticular entity which only encompasses a partof the original dataset, for example, a particularcountry,7 or to a set of units that, taken together,encompass the total domain.

• Scale and resolution: A second important factoris the scale and resolution, as downscaling canrefer to anything from global regions to countriesto city or grid level.

• Type of information: A third factor is the natureof the information that is downscaled. Although,in the case of the IPCC-SRES (IntergovernmentalPanel on Climate Change/Special Report onEmissions Scenarios) scenarios, this informationcan comprise socioeconomic data as well asclimate data (e.g., Ref 8,9), we limit this overviewto socioeconomic data. It should be noted thatdownscaling is not only restricted to scenariodata for possible future conditions, but can alsoinvolve data on the current situation or historical

periods (see section on Downscaling DifferentTypes of Information).

• Purpose: An important question is whether thedownscaling is employed to enhance the under-standing of local phenomena and their dynamicsof change, or whether downscaling is only usedas an intermediate step (in order to add detail),or pragmatic approach to permit comparabilityacross different aggregated entities. The latter, forinstance, is done in constructing the quantitativeMillennium Ecosystem Assessment scenarios,10

where regional information is downscaled tothe country level only to facilitate the couplingof simulation models that use slightly differentregional definitions.

Although these different features lead to differentrequirements with respect to the downscaling method,we suggest that, in general, downscaling methodolo-gies should satisfy three criteria:

1. Some form of consistency with existing localscale data (e.g., with the historical period).

2. Consistency with the original source (thescenario data at the much coarser scale).

3. Transparency and internal consistency, in termsof a well-defined methodology.

As indicated below, the relative importance ofeach of these three criteria depends on the use of thefinal data product. Additional criteria as describedby Grubler et al.,11 for downscaling methodologiesinclude the ability to be scenario specific, the need forinternal logic, and the ability to describe appropriatestructural changes over time. As downscaling methodsbecome more complex the number of adjustableparameters tends to increase. The narrative charac-teristics of scenarios, such as general level of incomeconvergence or attention to environmental protection,can then be used to select values for such parameters.It is also important to note that the results fromdownscaling techniques, such as population densitiesor emission rates, should not violate reasonable phys-ical constraints. Finally, more complex downscalingmethods attempt to incorporate plausible structuralchanges, such as internal migration and urbanization.Another important aspect that most downscalingapproaches have in common is the increasinguncertainty with moving from more aggregatedinformation to disaggregated and spatially detaileddata. For example, while there is reliable informationavailable for most countries with respect to average

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WIREs Climate Change Downscaling socio-economic and emission scenarios

national income, uncertainty increases substantiallyfor income distribution within a population andthe spatial distribution of economic activity. Mostdownscaling approaches therefore provide a represen-tation that is consistent with available geographicallyexplicit data and the scenario rather than providingan exact quantification for individual grid cells.Keeping this quality of data in mind is particularlyimportant for potential users at the local scale.

In our analysis of existing literature, severaldownscaling methods have been identified which canbe seen along a continuum going from the very simplerule-based methods to the more complex modeling ofspatial dynamics of change:

1. algorithmic downscaling,

2. methods of intermediate complexity,

3. fully elaborated model at lower level ofaggregation, and

4. fully coupled models at national or grid scale.

Below, we briefly discuss the different downscal-ing methods. These divisions are somewhat arbitraryand often applications use different approaches atthe same time. For instance, regional informationis first downscaled to the country level, while anadditional methodology is used to produce infor-mation at an even finer spatial scale. The choice ofthe downscaling technique depends on the purpose.For example, global modeling using a relativelycoarse computational grid is less likely to benefit froma more elaborate downscaling method than regionalmodeling using a finer spatial grid. Furthermore,as the complexity of a method increases, so do thedata requirements. Although a number of complexspatial downscaling methods exist, even in countriessuch as the United States the required data may belacking.12 Data limitations will be even more severefor developing regions, where data that does exist isoften highly uncertain.

For integrated analysis there is an advantagein using downscaled scenarios that include anintegrated set of information for a wide range ofparameters (gross domestic product (GDP) income,energy, emissions). We are currently aware ofonly a limited number of such sets, includingthe downscaled scenarios of the IMAGE integratedassessment model,13 the downscaled scenario ofthe MESSAGE model,11 the most recent set ofIPCC ‘reference concentration pathways’ (RCP) thatcontain downscaled information from four differentmodels and scenarios,a and the work on downscalingsocioeconomic parameters consistent with climateinformation for Europe by Abildtrup et al.14

An important factor complicating downscalingis the importance of local processes. Very specificexamples are national and local policies that are usu-ally not taken into account in global assessments—butmay play an important role in understanding nationalor grid-level trends. Examples include the role ofnational policies on energy, land use and spatialplanning.

Algorithmic DownscalingOne set of downscaling techniques is based on clearlydefined algorithms. Such algorithms range from thevery simple to quite complex (discussed in the secondcategory below). Simple algorithms are often usedbecause of a lack of more refined information andthe need for transparency. In terms of the criteriaintroduced above, such methods are generally easy todescribe and thus transparent, the results are generallyhighly consistent with the original (coarser scale) datasource, but use relatively little information on thelocal scale and are limited in their ability to representstructural changes. Three types of simple algorithmsthat are often used are as follows:

1. Proportional downscaling: This method ofdownscaling assumes that all elements within theunit have the same growth rates as the larger unit(e.g., as applied by Refs 15–17). This method isapplicable in cases where changes are relativelysmall, the differences between the units on thefiner scale are relatively small, or when there isno information available to distinguish betweenthem. This approach is scenario independent.

2. Convergence downscaling: An alternative toproportional downscaling is to assume somelevel of convergence to an average regionalvalue, which tends to assure that the local out-come is dependent on the pathway of the largerunit. For some types of data (e.g., income),there are good reasons to assume that someform of convergence within larger globalregions is likely to occur (see Ref 18 for adiscussion of the method). This assumptionapplies especially to cases where large differ-ences between units within a region exist andproportional downscaling results in unlikelyoutcomes. The rate of convergence can beinfluenced by choosing a different convergenceyear. The scenario’s convergence characteristicsat the macro-regional level can be used to guidethe strength of the convergence assumptionsfor the downscaling (providing internal plau-sibility and consistency across different spatialscales).



3. Scenario-based downscaling: This method usesthe relative positions of subunits within thelarger unit of existing scenarios as the basis fordownscaling. For instance, the relative share ofcountries within the Western European regionin a detailed European energy scenario canbe used to downscale the trend from coarserWestern European scenarios to individualcountry level.19 Another example forms thedownscaling of global mean temperature to thegrid levels using existing scenarios from complexclimate models, as often applied by IntegratedAssessment Models for calculating potentialimpacts.20 This method has the advantage thatit is able to capture future dynamics of differentunits as given by the more detailed scenarios.

Methods of Intermediate ComplexityMore complex algorithms also are used for down-scaling (still specifically developed as downscalingtools). Statistical downscaling of climate informa-tion, e.g., combines information from global climatemodels with historical spatial-temporal climate statis-tics to produce higher resolution climate scenarios.21

Similarly, sets of algorithms also exist for socioeco-nomic data, such as the use of simple (Rostow type)growth models for downscaling economic growth tonational level, and gravity-based rules to downscalepopulation data to grid-cell level.11 Another exampleis the use of cellular automata models for popula-tion dynamics considering urban/rural split at spatialscale,22 or the use of ‘preference maps’ (based on aset of rules, such as distance to existing land use orwater) in land-use downscaling.23,24 As with the sim-pler methods described above, the global results arenot influenced by the downscaling, although informa-tion gained through downscaling can potentially beused to improve global modeling results. An advantageof these methods are the increased internal plausibil-ity of the downscaling results compared to the moresimple methods, the possibility of structural changesat the local scale, and the explicit consideration ofscenario storylines, including the possibility to derivevariation of spatial results consistent with differentglobal characteristics (the latter also can be achievedwith the ‘convergence downscaling’ method describedabove). While these methods can arguably producemore realistic results, they are often less transparent.

Fully Elaborated Model at Lower Levelof AggregationModeling on a finer scale, conditional upon resultsand/or assumptions at the coarser scale, is used asa rather refined way to downscale scenarios. The

analogous method for downscaling climate variablesis regional climate modeling, where a fully developedregional climate model is driven by boundaryconditions derived from a global climate model.9

Conditional modeling can only be applied if sufficientinformation on the downscaled indicators and theirrelationships to other parameters is available onthe finer scale. Scenario storylines provide a helpfulelement for inferring consistent assumptions on a finerscale, as researchers can interpret the consequences ofthose storylines for their particular scale. Examplesof this method include the work of Bollen25 foreconomic scenarios and Verburg et al.,26 for land-useinformation on Europe. For downscaling globaldata into a fully comprehensive set of country data(worldwide) or a global grid, the use of conditionalmodeling is much more limited, as such models arescarce or even nonexistent. A more general disadvan-tage of using conditional modeling as a downscalingmethod (referring to the use of simple algorithms)is that model-based methods are more complex andtherefore in general less transparent. The method,however, is very suitable for taking available (process)information into account, on a local scale. Withinthis category, methods may concentrate mainly onthe overall characteristics of the global scenario, incombination with detailed modeling at the local level,such as the methods used to develop urbanizationscenarios for the United States,12 to produce socioeco-nomic scenarios for California,27 and population andeconomic scenarios for the Millennium EcosystemAssessment.28 In these studies, a matrix of scenarioparameters was constructed such that scenarios wereproduced from existing models with general charac-teristics that match the storylines of the IPCC-SRESscenarios (Special Report on Emission Scenarios) butusing detailed regional data that was consistent withinformation already used in decision-making pro-cesses in these regions. The resulting trajectories, e.g.,population scenarios from the U.S. EnvironmentalProtection Agency (EPA),12 would not exactly matchthe original scenarios (SRES scenarios in this case),but have the same overall characteristics (see Table 1).

Fully Coupled Models at National or Grid ScaleDownscaling techniques are also used in models thatinteractively couple different scales. Here, informationat more aggregated levels is downscaled to a lowerlevel of aggregation where it is used dynamicallyas part of the modeling process. The processes atthe low level will then influence outcomes on thehigher aggregation levels. The techniques used in thedownscaling steps are those that fall into the categoriesdiscussed previously, or combinations thereof. An



TABLE 1 Demographic Scenario Characteristics Selected to Matchthe Overall Assumptions of the SRES Scenarios as Implemented by theU.S. Environmental Protection Agency12

Demographic Model

Net InternationalStoryline Fertility Domestic Migration Migration

A1 Low High High

B1 Low Low High

A2 High High Medium

B2 Medium Low Medium

Baseline Medium Medium Medium

example of interactive coupling of different scalesis the IMAGE model, where regional land-useinformation is scaled to the grid (using a set of well-defined rules), and subsequently the grid allocationinfluences processes that operate at the larger scale.24

Feedbacks occur via the carbon cycle and greenhousegas concentration levels, and the competition for land.Such methods are more complex by nature, and, ingeneral, not as transparent (the links between large-scale changes and local effects are complex and notalways easy to describe).

Downscaling different types of informationDownscaling techniques can be applied to verydifferent types of information. Below, we discusssome techniques that have been applied to data on (1)population and economy, (2) energy and emissions,and (3) land use.

An element to note is that downscaling is notonly relevant for scenario data for the future but isoften also applied to historical periods. Geograph-ically explicit databases on historical population,income, emissions, and land use (e.g., Refs 29–33)are based on similar downscaling techniques to thosediscussed in this article. The use of downscaling tech-niques for historical periods in some cases may allowvalidation of downscaling methods.

DOWNSCALING DATA ONPOPULATION AND ECONOMY

Available data sourcesThere are several sources of data on population, whichcan be used for downscaling. At the national level,the United Nations’ population data is often used.At the grid level, an important source is the GPWdataset (Gridded Population of the World) availablein 2.5 arc-minutes resolution.34 Dobson et al.35 havealso developed a global population dataset at 30

arc-seconds resolution (LandScan 2005). A third effortto make gridded population datasets was made underthe auspices of the United Nations EnvironmentalProgramme (UNEP),36 although data resulting fromthis effort is only available in preliminary form.

National scale data on income are availablefrom several sources, such as the World Bank’s WorldDevelopment Indicators.37 Given the absence of sys-tematic, subnational level GDP statistics, many studiesin the past have used spatial population distribution asa scaling factor to derive spatial income distributions.More recently, economic information on grid-cell levelhas become available from the G-Econ database.38

This dataset contains estimates of income data at1 × 1 degree worldwide, developed through the useof information of national statistical agencies on,e.g., income/employment per industry, subnationalurban and rural population or employment, alongwith sectoral data on agricultural and nonagriculturalincomes. A comparatively simple method by Gruebleret al. (2007) draws on the United Nations Devel-opment Programme (UNDP) national-level incomedistribution statistics39 as a proxy for urban/ruralincome differences, which are then used to derivespatial income maps. A still largely under-exploredsource of metadata for constructing spatial economicinformation is detailed national household surveysthat exist for developed countries, and for developingcountries such as India.40

For geographically explicit downscaling,satellite-observed intensity of urban night lights canbe used. The use of this data was proposed as aninitial means for downscaling fossil-fuel emissions.41

Recently, Raupach et al.42 showed that night-lightdata, after some correction for saturation, correlateswell with economic activity, fossil-fuel use, andrelated emissions.

Methods that have been Applied

PopulationA commonly used method in population downscalinguses existing country-scale scenarios. This methodhas been used, for instance, by Gaffin et al.,43

using United Nations’ population scenarios up to2050. For the situation after 2050, however, thisstudy used a proportional method to extend thescenarios, resulting in unrealistic growth patterns.Newer methods have avoided this problem by usingscenarios with a longer time horizon. For translatingfrom country to grid level, several methods have usedproportional downscaling (e.g., Refs 13,43,44), andthe same method is also used to determine projectionsfor individual cities.16 According to Cohen,45 rural



to urban migration often is of less importance inurbanization processes than natural growth of urbanpopulations and reclassification of land surroundingurban areas. However, other researchers have usedmore refined methods, such as simple rules for pref-erential growth in coastal areas,46 extrapolation ofrecent trends at the local area level,47 and algorithmsleading to preferential growth in urban areas.11,48

The method of Grubler et al.11 provided a consistentdescription of grid-based population growth andurbanization scenarios. They used an assumption onlogistic growth of the urban population, combinedwith a gravity approach to calculate population ona grid scale. Earlier, Bengtson et al.44 also provided aset of simple rules to describe urbanization.

An example of a more detailed study is the workon the SERGoM model (Spatially Explicit RegionalGrowth Model49) to generate spatially explicithousing scenarios for the United States. The under-lying historical data for this model is generated byreconstructing historical census data on housing unitsfrom 1940 to 2000 (accounting for natural protectedareas, water features, and assuming a preference forproximity to roadways). This indicates that even theuse of historical data can involve the application ofdownscaling techniques. The model calculates spa-tially explicit growth rates for 16 housing types andhas been shown to reproduce historical patterns well.

IncomeDownscaling methods for GDP have also evolved.Older methods have applied the proportionalalgorithm43 without accounting for country-specific differences in initial conditions and growth

expectations. A proportional algorithm, however,provides unsatisfactory results in cases where thereare very large differences between countries within aregion. For instance, if high-income countries withinthe region, such as South Korea and Singapore, aregiven the average Asian growth rates, this leads toextremely high income levels in the future. Newmethods assume various degrees of convergenceacross countries, depending on the scenario; atechnique that avoids implausibly high growth forrich countries in developing regions.11,13 The methodused by van Vuuren et al. is based on the simpleconvergence algorithm. The method of Grubler et al.defines country-specific income growth equations.While the latter lead to more realistic growth patterns,the methodology is also more complex. Figure 1compares the results from proportional downscalingto those from the more refined techniques. GDPscenarios have also been downscaled to subnationallevel, either by assuming constant shares of GDP ineach grid cell13,43 or through algorithms that differen-tiate income across urban and rural areas.11 The newscenarios developed for IPCC (RCPs) largely use themethods of Grubler et al.11 and van Vuuren et al.13

for downscaling, for both population and income.In addition to these methods for global down-

scaling, several authors have worked at downscalingat finer scales. In most cases, the original storylinewas used to provide an idea of the potential growthin the region of study—after which simple or moreadvanced techniques (modeling) were used to makelocal economic scenarios.7,14 The choice betweenusing this country- and region-specific downscalingand the global downscaled datasets, depends on

0 20000 40000 60000 80000 100000

Brunei Darussalam

MyanMar

Cambodia

Lao People's Democratic Republic

Malaysia

Philippines

Singapore

Viet Nam

Thailand

Total region

Income ($/capita)

Proportional 2050IMAGE 2050MESSAGE 2050

225000

FIGURE 1 | Income projection for 2050 on the basis of the IIASA A2 scenario, the method of van Vuuren et al. 2007 (IMAGE),13 and Grubler et al.2006 (MESSAGE),11 using base-year data according to the separate modeling system (IMAGE and MESSAGE).



the focus of the study. More specific downscalingmethods can bring in local knowledge missing inthe global sets. However, this comes at the cost ofsubstantial effort and potentially less consistency withthe global dataset.

ENERGY AND EMISSIONS

Available data sourcesThere are several sources that can be used as basis forenergy and emission downscaling. Detailed energydata per country and sector is available from theInternational Energy Agency,50 per country and fuelfrom the U.S. Energy Information Agency,51 and perfuel and for most countries from British Petroleum(BP).52 Detailed global spatial data is generally notavailable for all sectors. Data for large, concentratedsources, such as electric power plants, some typesof manufacturing plants, and oil and gas productionfacilities, is either available from industry groupsor can be complied from trade publications. Thelargest current effort to incorporate a diverse setof spatial data into an emission database is theEDGAR (Emission Database for Global AtmosphericResearch) emissions inventory, recently releasedas version 4.53 Table 2 shows a list of spatiallyexplicit data sources complied for the recent EDGARv4 database. In addition to these sources, globaldatabases of road networks are also available.

Where direct data on specific sources is notavailable, proxy data must be used. The most commonproxy data is population distribution, which itselfcan be the product of some sort of spatial estimationmethodology.54 Population data is often split intourban and rural, for use as a proxy variable. Spatialestimates of land cover and land use can be usedas proxy variables for both natural and agriculturalemissions. While the relationship of proxy data toactual emission distribution is not always known,the spatial accuracy of proxy data is improved ifemissions are accurately estimated at country leveland then downscaled. Recently, preliminary EDGARv4 emission data53 have been combined with anumber of other data sources, including those oncountry level, to produce a consistent set of gridded

historical emissions of air pollutants from 1850 to2000. Data on the year 2000 have then been usedas calibration data by four teams on integratedassessment modeling, to produce future scenarios.The result is a consistent set, from historical throughto future emissions, from 1850 to 2100.33a

Estimates of emissions and spatial data at ahigh resolution are available for some regions. Oneexample is the Vulcan high-resolution emissionsinventory developed for the United States,55 whichalso provides an example of high resolution (10 km)downscaling of emissions data. Data, including pointsource data for many large sources, was reprocessedto estimate the spatial distribution of CO2 emissionsat a 10 km resolution. County-level emission datawas downscaled using sector-specific methods.Residential, commercial, and industrial sources areallocated to a census tract level using the proportion,per area, of each type of building in each census tract.Transport emissions are similarly downscaled, usinggeographic information system (GIS) databases onroads, airport locations, and flight-path information.While this level of spatial detail is not availableglobally, similar methods might be applied globallyto develop a new global dataset.

Methods usedThe methods used in downscaling energy and emis-sion data are very similar to those discussed earlierfor income. Originally, most studies applied the pro-portional algorithm for downscaling emission, suchas was done by Hohne and Ulhrich17; these resultsbasically share the same type of criticism as discussedearlier for the downscaling of GDP data (e.g., unreal-istic growth rates). An example of the scenario-basedalgorithm was applied by van Vuuren et al.19 todevelop air pollutant scenarios for 30 Europeancountries. The IPAT equation (Impact = Popula-tion × Affluence × Technology, or here: Emissions =Population × Income × Energy intensity) can alsobe used to downscale emissions (or energy) usingemission intensity (e.g., a convergence algorithm) andmultiplying these downscaled maps for populationand income, following the methods described above.13

Figure 2 shows the outcomes of the downscaling

TABLE 2 Spatially Explicit Data Used in the EDGAR v4 Inventory53

Electric Power Power plants

Transportation Road density

Oil and Gas Operations Gas and oil production; coal mining; gas flaring; oil refineries; tanker loading

Manufacturing Adipic acid; aluminum; ammonia; caprolactam; cement; copper; glyoxal; lead and zinc; lime; magnesium;magnesium die casters; nitric acid; pig iron; sinter; steel; sulpfuric acid



MESSAGE MiniCam2050

MESSAGE RCP8.5 Ene NOx emissions (scaled) MiniCAM RCP4.5 Ene NOx emissions

kg/s-cell kg/s-cell

0.0 0.0 0.0 0.0 0.0 0.0

Equirectangular (regional) projection centered on 12.00°E 3.00°N Equirectangular (regional) projection centered on 12.00°E 3.00°NData min = 0.0, Max = 0.0 Data min = 2.3E-18, Max = 6.8E-06

0.0E+08 1.2E-08 2.4E-08 3.6E-08 4.8E-08 6.0E-08

FIGURE 2 | Comparison between spatial emissions from two alternative IPCC-RCP pathways. The left-hand panel shows gridded emissions fromthe MESSAGE model, while the right-hand panel shows the MiniCAM results, which used the downscaling methodology of van Vuuren et al.13 Toallow for a comparison of spatial differences, emissions from the MESSAGE model were scaled such that total emissions from Africa were equal toemissions from the MiniCAM model. Both models begin with identical base-year 2000 grids. Differences in the detailed spatial distribution ofemissions result from the two methods, although the general location of regions of high emissions are similar.

methods applied by the IIASA and MiniCam teams,as part of the developed new IPCC emission pathways(RCPs). The MiniCam team used the algorithms byvan Vuuren et al. (2007) (including proportionaldownscaling of grid cells within one country), whilethe MESSAGE model uses a more complex methodtaking into account the effect of urbanization andexposure, wherein the spatial distribution of emis-sions within a region changes. Both methods are ableto adequately describe possible emission trends at thegrid level. Differences in the detailed spatial distribu-tion of emissions result from the differences betweenthe two methods, although the general location ofregions of high emissions is similar. Some portion ofthe differences is likely due to the different underlyingreference scenario for socioeconomic development.

LAND USE

Available data sourcesChanges in land use and land cover also constitute animportant element in the scenarios relevant for localstudies. Satellite data, ground-based observations,land-use statistics and historical reconstructions allprovide information on current and historical landuse and land cover. Statistical data on land use, forinstance, is collected by the Food and AgricultureOrganization (FAO) of the United Nations.56 Partlybased on these data, various datasets for historicaland present land use have been constructed (e.g.,Refs 30,31,57,58). Estimates on the spatial distribu-tion of specific crops have also been constructed,59

using a combination of statistics from the United

Nation’s FAO and satellite data. For the present day,most spatially explicit datasets are based on remotesensing and national statistics (e.g., Global LandCover Facility (GLCF) products60 and GLC200061).Uncertainties, however, are still significant (e.g.,Ref 62).

Methods usedLand-use patterns could also be crucial for downscaling other parameters, such as demographicand economic patterns, although such coupling indownscaling has barely been explored. A limitednumber of integrated assessment models explicitlymodel land use and land cover at the grid scale, usingdownscaling techniques. However, most integratedassessment models that include land use operate atthe level of regions and use downscaling techniques aspost-processing steps. Global downscaling techniquesfor land-use patterns tend to be based on somewhatmore refined algorithms with variables that assignland-use patterns to the grid, such as the use ofcropland, on the basis of grid-based information(e.g., near existing areas, near water bodies).24 Earlierapproaches included a proportional approach todownscaling the IPCC-SRES land-cover scenariosapplied to global ecosystem modeling.63 This method,however, leads to inconsistencies, for instance, as landuse is likely to be more intense near human population.The University of New Hampshire recently developeda method for downscaling land-use information onthe basis of regional (or previously downscaledgrid) information. A methodology developed forhistorical reconstruction has been applied to data from



integrated assessment models, to produce a 0.5 × 0.5grid, using various preference rules—distinguishingbetween crop areas, pasture, forestry, and primaryand secondary forests.64 A challenge for most land-use-based downscaling algorithms is the uncertaintyin both satellite observations of land-cover classesand the aggregate statistics of national metadata(e.g., information on multicropping). A probabilisticapproach that tries to account for those uncertaintieshas been adopted by Fischer et al.65 for the estimationof spatially distributed crop production. An attractiveoption in land-use downscaling is the use of specificmodels that are available at a finer level of detail. Ithas been shown that land-use change scenarios devel-oped at a finer scale might be very different fromthose generated by global models.66 Often this reflectsthe constraints of national or regional policy, whichcan rarely be accounted for in global studies. Sev-eral examples are available where finer-scale modelsare used as downscaling tools, using the output fromglobal models (mostly IPCC-SRES) as a boundarycondition.14,67–71 Interestingly, most of these exer-cises find local trends to be extremely important forthe larger-scale results.72

CONCLUSIONDownscaling methodologies will undoubtedly con-tinue to play an important role in global environ-mental studies linking results from global models tofiner scales. Over the past few years, downscalingmethodologies have become more refined, avoidingobvious problems, such as incomes becoming unreal-istically high for some regions. Techniques vary fromrather simple algorithms to actual modeling at a finescale, using the output of global models as boundaryconditions. Selecting a downscaling method involves

a trade-off between transparency, complexity, andeffort. While the use of more complex methods couldresult in more refined results, compared to using sim-pler methodologies, the latter are easier to describeand apply. The best outcome of this trade-off dependson the purpose of the exercise, the intended audience,and available information. For example, studies usinga relatively coarse spatial grid at a global scale areless likely to benefit from a more elaborate down-scaling method than regional modeling using a finespatial grid. Often, available data and the ability toreasonably forecast the location of parameters (e.g., ofpower plants) also plays a role in determining the cor-rect method. For global modeling applications, onlythe general location of emissions on a continent maybe relevant. Other applications, e.g., impact analy-sis, may require a ‘reasonable’ statistical relationshipto population centers. As the requirements for down-scaled data become better defined, different downscal-ing methods should be tested against historical datato determine which methods can best reproduce thecharacteristics required for particular applications.

As the complexities of these methods increase,so do the data requirements and data limitations.This can be a limiting factor, especially in developingcountries. In many cases, historical information isavailable only at aggregate levels, particularly as thetime period considered becomes more distant fromthe present. The methods discussed here can also beapplied to reconstructions of historical data. Whilehistorical data is often used for calibration, particu-larly for the more complex methods, it is used lessoften for validation.

NOTEahttp://www.iiasa.ac.at/web-apps/tnt/RcpDb

ACKNOWLEDGEMENTS

We acknowledge the help of April Volke and Angelica Mendoza Beltran for assistance with the figures. We alsothank G. Page Kyle for useful comments on the draft text.

REFERENCES

1. Nakicenovic N, Alcamo J, Davis G, de Vries B,Fenhann J, et al. Special Report on Emissions Scenarios.Cambridge: Cambridge University Press; 2000.

2. UNEP Global Environment Outlook 3: Past, Presentand Future Perspectives. New York: UNEP; 2002.

3. Reid WV, Mooney HA, Cropper A, CapistranoD, Carpenter SR, et al. MA Millennium Ecosystem

Assessment—Synthesis Report. Millennium EcosystemAssessment; 2005.

4. Arnell N. Climate change and global water resources:SRES emissions and socio-economic scenarios. GlobalEnviron Chang 2004, 14:31–52.

5. Levy PE, Cannell MGR, Friend AD. Modeling theimpact of future changes in climate, CO2 concentration



and land use on natural ecosystems and the terres-trial carbon sink. Global Environ Chang 2004, 14:21–52.

6. Pitcher HM. The future of scenarios: issues in devel-oping new climate change scenarios. Environ Res Lett2009, 4:1–7.

7. Carter TR, Fronzek S, Barlund I. FINSKEN: a frame-work for developing consistent global change scenariosfor Finland in the 21st century. Boreal Environ Res2004, 9:91–107.

8. Mearns L, Nychka D, Meehl J. Annual Report(2003/2005) NCAR Weather and Climate ImpactAssessment Science Initiative. NCAR; 2004.

9. Christensen JH, Hewitson BC, Busuioc A, Chen A,Gao X, et al. In: Solomon S, Qin D, Manning M,Chen Z, Marquis M, et al., eds. Climate Change 2007:The Physical Science Basis. Contribution of WorkingGroup I to the Fourth Assessment Report of the Inter-governmental Panel on Climate Change. Cambridge:Cambridge University Press; 2007.

10. Alcamo JA, van Vuuren DP, Ringler C. Method-ology for developing the MA scenarios. In: Car-penter S, Pingali P eds. Millennium EcosystemAssessment—Scenarios Assessment. Washington DC:Island Press; 2005.

11. Grubler A, O’Neill B, Riahi K, Chirkov V, Goujon A,et al. Regional, national, and spatially explicit scenariosof demographic and economic change based on SRES.Technol Forecast Soc 2007, 74:980–1029.

12. EPA. Land-Use Scenarios: National-Scale Housing-Density Scenarios Consistent with Climate ChangeStorylines. Washington, DC: U.S. Environmental Pro-tection Agency; 2009.

13. van Vuuren DP, Lucas P, Hilderink H. Downscalingdrivers of global environmental change. Enabling useof global SRES scenarios at the national and grid levels.Global Environ Chang 2007, 17:114–130.

14. Abildtrup J, Audsley E, Fekete-Farkas M, Giupponi C,Gylling M, et al. Socio-economic scenario developmentfor the assessment of climate change impacts on agri-cultural land use: a pairwise comparison approach.Environ Sci Policy 2006, 9:101–115.

15. Gaffin SR, Rosenzweig C, Xing X, Yetman G. Down-scaling and geo-spatial gridding of socio-economicprojections from the IPCC Special Report on Emis-sions Scenarios (SRES). Global Environ Chang 2004,14:105–123.

16. Nicholls RJ, Hanson S, Herweijer C, Patmore N,Hallegatte S, et al. Ranking Port Cities with High Expo-sure and Vulnerability to Climate Extremes: ExposureEstimates. Paris: OECD; 2008.

17. Hohne N, Ullrich S. Emission allowances under theproposal of the ‘‘South north dialogue—equity in thegreenhouse’’. Cologne, Germany: ECOFYS GmbH;2005.

18. van Vuuren DP, Lucas PL, Hilderink H. Downscal-ing drivers of global environmental change scenar-ios: enabling use of the IPCC-SRES scenarios at thenational and grid level. Global Environ Chang 2007,17:114–130. DOI:10.1016/j.gloenvcha.2006.04.004

19. van Vuuren DP, Cofala J, Eerens HE, Oostenrijk R,Heyes C, et al. Exploring the ancillary benefits of theKyoto Protocol for air pollution in Europe. Energ Policy2006, 34:444–460.

20. Carter TR, Parry ML, Harasawa H, Nishioka S. IPCCTechnical Guidelines for Assessing Impacts of ClimateChange. Geneva: IPCC; 1994.

21. Wilby RL, Charles SP, Zorita E, Timbal B, Whetton P,et al. Guidelines for use of climate scenarios developedfrom statistical downscaling methods. London: King’sCollege; 2004.

22. Solecki WD, Oliveri C. Downscaling climate changescenarios in an urban land use change model. J EnvironManag 2004, 72:105–115.

23. Verburg PH. Conversion of Land Use and its Effects(CLUE) Model. In: Geist H, ed. Our Earth’s ChangingLand: An Encyclopedia of Land-use and Land-coverChange. Westport (Connecticut, USA): GreenwoodPress; 2006.

24. Bouwman L, Kram T, Klein-Goldewijk K. Inte-grated Modelling of Global Environmental Change.An Overview of IMAGE 2.4. Bilthoven: NetherlandsEnvironmental Assessment Agency; 2006.

25. Bollen J. A Trade View on Climate Change Policies.A Multi-region Multi-sector Approach. Amsterdam:University of Amsterdam; 2004.

26. Verburg PH, Eickhout B, Van Meijl H. A multi-scale,multi-model approach for analyzing the future dynam-ics of European land use. Ann Reg Sci 2008, 42:57–77.

27. Sanstad AH, Johnson H, Goldstein N, Franco G. Long-Run Socioeconomic and Demographic Scenarios forCalifornia. California Climate Change Center; 2009.

28. Alcamo JA, van Vuuren DP, Cramer W. Changesin provisioning and regulating ecosystem goods andservices and their drivers across the scenarios. In:Carpenter S, Pingali P, eds. Millennium EcosystemAssessment—Scenarios Assessment. Washington, DC:Island Press; 2006.

29. Klein Goldewijk K, Beusen A, Janssen P. Long termdynamic modeling of global population and built-up area in a spatially explicit way HYDE 3.1. TheHolocene 2010, doi:10.1177/0959683609356587.

30. Klein Goldewijk K, Van Drecht G. HYDE 3: Currentand historical population and land cover. In MNP, ed.Integrated Modelling of Global Environmental Change.An Overview of IMAGE 2.4. Bilthoven, The Nether-lands: Netherlands Environmental Assessment Agency(MNP); 2006.

31. Ramankutty N, Evan AT, Monfreda C, Foley JA. Farm-ing the planet: 1. Geographic distribution of global



agricultural lands in the year 2000. Global BiogeochemCycles 2008, 22.

32. Nordhaus WD. Geography and macroeconomics: Newdata and new findings. Proc Nat Acad Sci 2006,103:3510–3517

33. Lamarque J-F, Granier C, Bond T, Eyring V, Heil V,et al. Gridded emissions in support of IPCC AR5.IGACtivities 2009, 41.

34. CIESIN & CIAT. Gridded Population of the WorldVersion 3 (GPWv3): Population Grids. Palisades, NY,Center for International Earth Science Information Net-work and Centro Internacional de Agricultura Tropical;2005.

35. Dobson JE, Bright EA, Coleman PR, Durfee RC,Worley BA. LandScan: A global population databasefor estimating population at risk. Photogram EngRemote Sensing 2000, 66:849–857.

36. UNEP/GRID UNEP/GRID spatial data clearinghouse.2006, http://na.unep.net/datasets/datalist.php.

37. World Bank World Development Indicators. Washing-ton DC: World Bank; 2006.

38. Nordhaus B. Geography and macroeconomics: Newdata and new findings. Proc Nat Acad Sci U S A 2007,103:3510–3517.

39. UNDP. Human Development Report, 2002. Deepen-ing Democracy in a Fragmented World. New York:Oxford University Press; 2002.

40. NSSOI National Sample Survey Organization of India,2000, http://www.mospi.gov.in/nsso 4aug2008/web/nsso.htm.

41. Doll CNH, Muller J-P, Elvidge CD. Night-time imageryas a tool for global mapping of socioeconomic param-eters and greenhouse gas emissions. Ambio 2000,29:157–162.

42. Raupach MR, Rayner PJ, Paget M. Regional vari-ations in spatial structure of nightlights, populationdensity and fossil-fuel CO2 emissions. Energ Policy2009, doi:10.1016/j.enpol.2009.08.021 (In press).

43. Gaffin S, Xing X, Yetman G. Downscaling and geo-spatial gridding of socio-economic projections fromthe IPCC special report on emissions scenarios (SRES).Global Environ Chang 2004, 14:105–123.

44. Bengtsson M, Shen Y, Oki T. A SRES-based grid-ded global population dataset for 1990–2100. PopulEnviron 2006, 28:113–131.

45. Cohen B. Urban growth in developing countries: Areview of current trends and a caution regarding exist-ing forecasts. World Dev 2004, 32:23–51.

46. Nicholls RJ. Coastal flooding and wetland loss inthe 21st century: Changes under the SRES climateand socio-economic scenarios. Global Environ Chang2004, 14:69–86.

47. Hachadoorian L, Gaffin SR, Engleman R. Projecting agridded population of the world using ratio methodsof trend extrapolation. In: Cincotta RP, Gorenflo L,

Mageean D, eds. Human Population: The Demographyand Geography of Homo Sapiens and Their Implica-tions for Biological Diversity. Berlin: Springer-Verlag;2007.

48. Reginster I, Rounsevell MDA. Future scenarios ofurban land use in Europe. Environ Plan B 2006,33:619–636.

49. Theobald D. Landscape patterns of exurban growth inthe USA from 1980 to 2020. Ecol Soc 2005, 10:32.

50. IEA. Energy Balances of OECD and Non OECD Coun-tries. Paris: International Energy Agency; 2006.

51. EIA. International Energy Annual 2008. Washington,DC, USA: U.S. Energy Information Administration;2008.

52. BP BP Statistical Review of World Energy; 2009, http://www.bp.com/productlanding.do?categoryId=6929&contentId=7044622.

53. EC/JRC & PBL. (European Commission, JointResearch Centre (JRC), Netherlands EnvironmentalAssessment Agency (PBL); 2009.

54. Klein Goldewijk K. Estimating global land use changeover the past 300 years: the HYDE database. GlobalBiogeochem Cycles 2001, 15:417–433.

55. Gurney KR, Mendoza DL, Zhou Y, Fischer ML, MillerCC, et al. High resolution fossil fuel combustion CO2

emission fluxes for the United States. Environ Sci Tech-nol 2009, 43:5535–5541.

56. FAO FAOSTAT database collections. Food and Agri-culture Organization of the United Nations, Rome,2008. http://faostat.fao.org/.

57. Pongratz J, Reick C, Raddatz T, Claussen M. A recon-struction of global agricultural areas and land coverfor the last millennium. Global Biogeochem Cycles2008, 22, GB3018, doi:10.1029/2007GB003153.

58. Ramankutty N, Foley J. Estimating historical changesin global land cover: Croplands from 1700 to 1992.Global Biogeochem Cycles 1999, 13:997–1028.

59. Monfreda C, Ramankutty N, Foley JA. Farming theplanet: 2. Geographic distribution of crop areas, yields,physiological types, and net primary production inthe year 2000. Global Biogeochem Cycles 2008, 22,GB1022, doi:10.1029/2007GB002947.

60. GLFC. Global Land Cover Facility. 2009. http://glcf.umiacs.umd.edu/index.shtml.

61. GLC. 2000, Global Land Cover 2000 Project,2009. http://bioval.jrc.ec.europa.eu/products/glc2000/glc2000.php.

62. Jung M, Henkel K, Herold M, Churkina G. Exploitingsynergies of global land cover products for carboncycle modeling. Remote Sensing of Environ 2006,101:534–553.

63. Arnell NW, Livermore MJL, Kovats S, Levy PE,Nicholls R, et al. Climate and socio-economic scenariosfor global-scale climate change impacts assessments:



characterising the SRES storylines. Global EnvironChang 2004, 14:3–20.

64. Hurtt G, Chini L, Frolking S, Betts R, Fedema J, et al.Harmonization of global land-use scenarios for theperiod 1500–2100 for IPCC-AR5. iLEAPS Newsletter2009, 7:6–8.

65. Fischer G, Ermolieva T, Ermoliev Y, Van Velthuizen H.Sequential downscaling methods for estimation fromaggregate data. Lecture Notes in Economics and Math-ematical Systems 2006, 581:155–169, 10.1007/3-540-35262-7 9.

66. Busch G. Future European agricultural landscapes:what can we learn from existing quantitative landuse scenario studies?. Agr Ecosyst Environ 2006, 114:121–140.

67. Kankaanpaa S, Carter TR. Construction of Euro-pean Forest Land Use Scenarios for the 21st Century.Helsinki: Finnish Environment Institute; 2004.

68. Ewert F, Rounsevell MDA, Reginster I, Metzger M,Leemans R. Future scenarios of European agricultural

land use. I. Estimating changes in crop productivity.Agr Ecosyst Environ 2005, 107:101–116.

69. Rounsevell MDA, Ewert F, Reginster I, Leemans R,Carter TR. Future scenarios of European agriculturalland use. II. Estimating changes in land use andregional allocation. Agr Ecosyst Environ 2005, 107:117–135.

70. Van Meijl H, Van Rheenen T, Tabeau A, Eickhout B.The impact of different policy environments on agricul-tural land use in Europe. Agr Ecosyst Environ 2006,114:21–38.

71. Engelen G, White R, De Nijs T. Environment explorer:a spatial support system for the integrated assessmentof socioeconomic and environmental policies in theNetherlands. Integrated Assessment 2003, 4:97–105.

72. Schroter D, Cramer W, Leemans R, Prentice IC, AraujoMB, et al. Ecosystem service supply and vulnerabil-ity to global change in Europe. Science 2005, 310:1333–1337.


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