RESEARCH AND ANALYSIS Scenario Analysis -...

R E S E A R C H A N D A N A LYS I S

Scenario AnalysisExploring the Macroeconomic Impacts ofInformation and CommunicationTechnologies on Greenhouse Gas Emissions

Lorenz Erdmann and Lorenz M. Hilty

Keywords:

impact assessmentindustrial ecologyinformation and communication

technology (ICT)rebound effectsystem dynamicsuncertainty

Supplementary material is availableon the JIE Web site

Address correspondence to:Lorenz ErdmannAssociate Research ScientistInstitute for Futures Studies and

Technology AssessmentSchopenhauerstr. 26 Berlin [email protected]

c© 2010 by Yale UniversityDOI: 10.1111/j.1530-9290.2010.00277.x

Volume 14, Number 5

Summary

During the past decade, several macroeconomic studies onthe potentials of information and communication technology(ICT) to reduce greenhouse gas (GHG) emissions have beenpublished. The mitigation potentials identified in them vary toa high degree, mainly because they are not consistently definedand diverse methodologies are applied. The characteristics ofICT—exceptional dynamics of innovation and diffusion, socialembedment and cross-sector application, diverse and com-plex impact patterns—are a challenge for macroeconomicstudies that quantify ICT impacts on GHG emissions.

This article first reviews principal macroeconomic studieson ICT and GHG emissions. In the second part, we recon-sider our own study on this topic and present an in-depthscenario analysis of the future impacts of ICT applications onGHG emissions. We conclude that forthcoming macroeco-nomic studies could strengthen the state of the art in en-vironmental ICT impact modeling (1) by accounting for thedynamics of new ICT applications and their first-, second-,and third-order effects on a global scale, (2) by reflecting theerror margins resulting from data uncertainty in the final re-sults, and (3) by using scenario techniques to explore futureuncertainty and its impacts on the results.

824 Journal of Industrial Ecology www.wileyonlinelibrary.com/journal/jie


Introduction

Applications of information and communi-cation technology (ICT) are expected to re-duce transport volumes, as well as energy andmaterial demand. Such effects of ICT appli-cations are closely coupled to mass flows intothe environment, such as emissions into air,water, and soil. In particular, the potential ofICT to reduce greenhouse gas (GHG) emis-sions is increasingly acknowledged as relevantin intergovernmental policy documents (e.g.,Organization for Economic Cooperation andDevelopment 2009). The extent, however, towhich ICTs might help governments to achieveGHG emission reduction targets in the mid-to long term is discussed controversially amongexperts.

ICT components, products, and servicesare constantly being invented and reinvented(Rejeski 2003), thus providing the basis for anincreasing diversity of new applications. If inno-vation is followed by rapid diffusion, macroeco-nomic impacts of ICT applications may materi-alize fast. ICT applications pervade, transform,and link all economic sectors. In improving theefficiency of production and consumption pro-cesses and in enabling new activities and so-cietal relations, ICT is an important driver fornew lifestyles, structural change (i.e., the sharecomprised by new industries in the economy in-creases in relation to old industries), and eco-nomic growth.

This particular set of characteristics of ICT isa challenge to deal with in macroeconomic stud-ies on ICT effects on GHG emissions. There aretwo basic ways to quantify these effects: (1) in-corporate ICTs in general macroeconomic mod-els explicitly or (2) build specific macroeconomicICT impact assessment models.

Studies of the first type commonly conceptu-alize ICT as a driver of technological efficiency,economic growth, and structural change (Rommet al. 1999; Kuhndt et al. 2003). This article fo-cuses on studies of the second type. They are oftenbuilt upon a distinction among first-, second-, andthird-order effects of ICT on the environment(Fichter 2003; Kohler and Erdmann 2004; Hilty2008):

1. First-order effects: The impacts of the life-cycle of ICT hardware, for example, theenergy consumed by the ICT hardware ofan intelligent transport system (ITS).

2. Second-order effects: The impacts of theservices provided by ICT applications, forexample, the energy saved in transport byusing an ITS.

3. Third-order effects (including rebound ef-fects): The emerging effects of ICT in theeconomic system, for example, increaseddemand for transport as a long-term impli-cation of efficiency gains induced by ITSapplications.

The dynamic impacts of ICT originate fromthe feedback of third-order effects to first- andsecond-order effects. If an increase in efficiency(second-order effect) is expected to reduce theenvironmental impact of an activity, but addi-tional demand (third-order effect) compensatesfor the reduction, this is a typical rebound ef-fect (Hilty 2008). ICT has the potential to en-hance the efficiency of production and consump-tion processes with regard to cost, time, energy,material, and other factors in the entire economicsystem significantly. Because one of the core prin-ciples of industrial ecology is to view systemsin their entirety and full complexity (Allenby1999), it seems indispensable to account for third-order effects—in particular rebound effects—inany macroeconomic assessment of ICT impactson GHG emissions.

In the next section, major ICT impact assess-ment studies are reviewed, taking these particularcharacteristics of ICT into account. We will thenpresent a new interpretation of our own study,consisting of a scenario analysis for ICT applica-tions based on the system dynamics model devel-oped in that study. In the final section we willdraw conclusions for future macroeconomic ICTimpact assessment studies.

Review of Existing Studies

In a recent review of research on the envi-ronmental impacts of e-business and ICT, Yi andThomas (2007) identified a diverse body of lit-erature ranging from micro-level to macro-level

Erdmann and Hilty , Macroeconomic Impacts of ICT on GHG Emissions 825


impact assessments, from sector studies to cross-sector studies, from qualitative to quantitativeassessments. The authors of these studies weremainly motivated by the environmental impli-cations of the rise of the Internet and the “neweconomy.” The references dating back to 1998belong to a period that we classify as the “firstgreen ICT wave.”

The publication of the Yi and Thomas (2007)review almost coincided with the issuance of theFourth Assessment Report of the Intergovern-mental Panel on Climate Change in February2007 (IPCC 2007). Since then we have observeda “second green ICT wave,” in which a growingbody of literature focusing on the potentials ofICTs to reduce GHG emissions has been pub-lished. Almost all of these studies cover new ap-plications of ICT.

The present review looks at the mostimportant macroeconomic cross-sector studiesthat have been published in the past decade(table 1).1 We exclude studies that did not breaktheir macro-level assessments down to ICT appli-cations (namely, Laitner and Ehrhardt-Martinez2008; Madlener 2008). However, we make threeexceptions for older studies that stimulated thescientific debate substantially (Romm et al. 1999;Laitner et al. 2001; Kuhndt et al. 2003).

Study Type

The macroeconomic studies reviewed differ intheir basic approaches (see table 1). Some stud-ies are based on a “bottom-up” approach. Theyanalyze ICT applications at the micro-level (e.g.,efficiency gains in specific processes) and aggre-gate the impacts to the meso- and macro-level.Other studies follow a “top-down” approach byanalyzing macroeconomic indicators in order toisolate an ICT impact. Two studies take a “hy-brid” approach. They are anchored in macroeco-nomic analyses, but attribute the ICT sector im-pacts to certain ICT applications (Laitner et al.2001; Matsumoto et al. 2005). Seven studies weanalyzed include prospective applications of ICT,whereas three studies did not disaggregate to thelevel of ICT applications.

Erdmann and colleagues (2004) tailoreda system dynamics model. Laitner and col-leagues (2001) implemented their calculations

in the U.S. National Energy Modeling System.Matsumoto and colleagues (2005) used acomputable general equilibrium model to recal-culate adjusted input/output tables. Two stud-ies applied statistical methods (Romm et al.1999; Kuhndt et al. 2003). All other quantita-tive macroeconomic studies we reviewed calcu-late their results—to our knowledge—with basicarithmetic.

The studies differ in other key characteristics,namely, (1) goal, scope, and system boundaries,(2) data and methodologies, and (3) results.

Goal, Scope, and System Boundaries

The goal of all the studies reviewed is to ana-lyze the macroeconomic impact of ICT on GHGemissions and/or on energy consumption. Mostof the studies put particular emphasis on ICTapplications that produce environmental gains.The geographic scope ranges from national toglobal. The two global studies (Buttazoni 2008;Webb 2008) also provide a geographic resolutionof world economic regions. The time horizon ofthe studies with disaggregated ICT applicationsspans 10 to 25 years.

There is a core of ICT applications that arecovered by almost all studies, whereas some ap-plications are considered by one or a few studiesonly.

ICT in the production sector is broadly ad-dressed by the topics process automation andsmart motors. Several studies address ICT ap-plications in the energy sector by consideringsmart grids, smart metering, and smart appliances,which comprise a basis for an optimized elec-tricity supply and demand management. Webb(2008) also examines the impact of ICT on en-ergy conversion efficiency explicitly.

Smart buildings are covered by most ofthe studies. Applications range from ICT-controlled heating, ventilation, and air condi-tioning (HVAC) systems (e.g., Labouze et al.2008) to presence-based power management ofelectronic devices (Mallon et al. 2007). OnlyButtazoni (2008) quantifies the impact of smartcity planning on transport.

Intelligent transport systems comprise ICT-supported infrastructure and in-vehicle naviga-tion systems, and are included in most of the

826 Journal of Industrial Ecology


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studies. Few studies consider personal travelassistants for public transport explicitly (Mallonet al. 2007). Buttazoni (2008) and Webb (2008)also examine ICT-enhanced fuel efficiency ofvehicles.

E-commerce is dealt with in all studies.Business-to-business (B2B) e-commerce includesICT in supply chain management and logisticsand business-to-consumer (B2C) e-commercecovers, for example, teleshopping. Virtual mobil-ity (telework, mobile work, virtual meetings, andalso teleshopping) and dematerialization effectsof ICT (by virtualization or ICT-based services)are treated by all studies.

Some studies distinguish between the growth,structural change, and technology effects of ICT,and other studies build upon the classificationinto first-, second-, and third-order effects. Afew partially account for both conceptual frame-works. A complete coverage of the growth,structural change, and technology effect is pro-vided by Kuhndt and colleagues (2003), whileMatsumoto and colleagues (2005) do not accountfor the growth effect. Laitner and colleagues(2001) account for rebound effects, but the reportdoes not disclose a value for economic growth.First-, second-, and third-order effects are cov-ered quantitatively by Erdmann and colleagues(2004) only; all other studies focus on second-order impacts, some also considering first ordereffects.2

Data and Methodology

Data sources, assumptions, and methodologiesof the studies vary broadly. The bottom-up stud-ies typically combine data for ICT uptake withestimated data for ICT impact. Uncertainty isdealt with in many ways. We distinguish be-tween data uncertainty (incomplete data aboutfacts that could be known in principle) and fu-ture uncertainty (developments that are not de-termined and can be influenced in principle).

Both Labouze and colleagues (2008) and But-tazoni (2008) do quantitative data uncertaintyanalysis at selected points only, but not for allICT applications and impacts under study sys-tematically. Erdmann and colleagues (2004) con-ceptualize data uncertainty as differences amongexpert estimates that could not be resolved by

discussion, propagating the error margins quanti-tatively to the final results.

The consideration of future uncertainty rangesfrom parameter estimations at the desk to com-plex participatory scenario building exercises.In general, scenarios are used to define a mea-sure for the future macroeconomic impact ofICT against a baseline. The diverse definitionsused for ICT impact measures, baselines, dif-ferences in geographic focus, and time hori-zons as well as the different methodologies usedall impede a direct comparison of numericalresults.

Results

Six of the studies reviewed postulate unam-biguous overall reductions of GHG emissions dueto ICT. Four studies show an ambiguous picture.In the study of Erdmann and colleagues (2004),the ICT impact can range from a slight increaseto a strong reduction, depending on data and fu-ture uncertainties. In both the Matsumoto andcolleagues (2005) and Labouze and colleagues(2008) studies, the negative first-order effectsoutweigh the positive second-order effects un-der certain conditions. Kuhndt and colleagues(2003) show that the impact of ICT on GHGemissions can be positive or negative for a coun-try, depending on the relative sizes of the three ef-fects considered (technology, structural change,and growth effect) and the contribution of pri-vate households.

All studies that disaggregate ICT effects toICT applications postulate net GHG-reducingimpacts for each single application, with the ex-ception of the study by Erdmann and colleagues(2004), which will be discussed in detail in thesection “Scenario Analysis.”

Discussion

The pervasive character of ICT requires inclu-sion of all economic sectors in macroeconomicassessments of ICT impacts. The inevitable com-plexity of such an assessment is aggravated bythe exceptional dynamics of ICT innovation anddiffusion. To cope with this problem, mid- tolong-term studies often introduce broad ICT ap-plication domains such as ITS (medium level of



abstraction) or treat ICT as a monolithic sector(high level of abstraction).

The studies reviewed all cover ICT impactson the industry, transport, and building sectorsand agree on the relevance of most ICT applica-tions concerning GHG emissions, but some ICTapplications such as smart city planning are onlytreated by one study quantitatively. Other ICTapplications such as precision farming are nottreated by any of the studies, although they arelikely to be relevant. The main reason is presum-ably the studies’ major focus on energy efficiency.

The two conceptual frameworks used,growth/structural change/technology effect andfirst-/second-/third-order effects, have some over-lap and some differences. While the first con-ceptual framework treats ICT as a driver ofeconomic growth, the studies of the second typeeither treat growth as an external variable or in-directly as a third-order effect (additional growthinduced by ICT). Second-order effects includeboth ICT-fostered dematerialization (by substitu-tion effects) and ICT-enhanced process efficiency(by optimization effects). First-order effects arenot explicitly addressed in the first conceptualframework but can be accounted for in principle.

We did not find any discussion of the implica-tions of choosing a certain metric for the ICT im-pact on GHG emissions. Even if a common met-ric were agreed upon, the different scopes wouldstill make it difficult to compare results.

We found that the five older references arerich and diverse in methodology, whereas the fivemore recent studies take the simpler approachof quantifying GHG reduction potentials at thelevel of second-order effects, ignoring first- and/orthird-order effects.

In view of the various sources of uncertaintyin this field of study, it seems indispensable todisaggregate the uncertainty as far as possible(by breaking it down to specific ICT applica-tion fields) and to make a clear difference be-tween future uncertainty and data uncertainty.Only Erdmann and colleagues (2004) make a sys-tematic attempt to treat data uncertainty quan-titatively. As contributors to this study, how-ever, we have to concede that data uncertaintyis merged with future uncertainty in the presen-tation of the results. Therefore, we will reinter-pret the results of this study in the next section

and demonstrate the benefits of separating sce-narios from data assumptions at the level of ICTapplications.

Scenario Analysis

In this section we present a new assessmentof the future impact of ICTs on GHG emissionsbased on the study by Erdmann and colleagues(2004), which was commissioned by the Institutefor Prospective Technological Studies (IPTS) ofthe European Commission. This study (in thefollowing referred to as the IPTS study) includeda system dynamics model of the causal relation-ships between relevant ICT applications and aset of given environmental indicators.

The subsequent scenario analysis did not in-volve any change to the model or the input data.Instead, now in a new way, the two types of un-certainty were separated in the calculation at thelevel of ICT applications:

• data uncertainty indicating research de-mand;

• future uncertainty indicating a need for po-litical action.

In doing this analysis, we want to show that asystematic treatment of uncertainty can improvethe practical utility of simulation results in that itmakes a contribution to setting research agendasand policy agendas.

We will first summarize the integrated assess-ment methodology of the IPTS study and thenpresent the new scenario analysis.

Integrated Assessment Methodology

The methodology of the IPTS study has beendescribed by Erdmann and colleagues (2004) andcontains a modeling focus by Hilty and colleagues(2004, 2006). Here we recapitulate the method-ological elements necessary to understand thesubsequent analysis.

Goal Definition and ScopeThe aim of the study was to explore qualita-

tively and to assess quantitatively the way thatICT can influence a set of given environmentalindicators within the time horizon of 2020.



The geographical coverage of the study was theEuropean Union at the time the study was put outto tender (EU 15). For each result, the generaliz-ability to an enlarged European Union has beendiscussed qualitatively (Hilty et al. 2004). Im-pacts caused outside the European Union (suchas emissions from ICT production in Asia) werenot covered by the study.

The basic approach was to model the effect ofICT application domains on the sectors energy,transport, production, and waste, and then to ag-gregate the effects to estimate the overall ICT im-pacts. Table 2 lists the application domains con-sidered and examples of established and newerapplications. An ICT impact was defined as a netchange—caused by applying ICT—of one of thefollowing given indicators: total freight transportvolume (tonne-kilometers per year, tkm/a), totalpassenger transport volume (passenger kilome-ters per year, pkm/a), the share of private cars inthe modal split of passenger transport (pkm-%),total energy consumption (terajoules per year,TJ/a), the share of renewables in electricity supply(TJ-%), total municipal solid waste not recycled(tonnes per year, t/a), and total GHG emissions(CO2-equivalents in metric tons per year, t/a).For the purpose of this article, we will focus onthe last indicator.

The strength of an integrated assessment ap-proach is that it is comprehensive with regardto sectors and can address the dynamic changesimplied by the long-term diffusion of a technol-ogy. In practice, an integrated assessment of ICTimpacts is so comprehensive that it cannot con-sider as much technological detail as an LCAstudy of ICT products or services does. Our studytherefore relied on abstractions of ICT devices(“client-type device,” “server-type device,” and“network-type device”; Hilty et al. 2004) andtheir application in generically defined domainsto cope with technical progress. It was assumedthat if more specific ICT devices and applicationshad been included, they would have become ob-solete during the simulation period.

Scenario BuildingThere are many drivers other than ICT that

may change the environmental indicators (suchas energy prices or governmental regulation) anddrivers that influence the diffusion of ICT ap-

plications. To reduce the number of possibili-ties spanned by these drivers (or external fac-tors), three plausible scenarios were created andvalidated in an expert workshop (Goodman andAlakeson 2003). These scenarios differed in thefactors the experts considered both most influen-tial with regard to the system under study andmost uncertain at the same time:

• Scenario A, called “Technocracy,” assumedstrong economic growth and employmentdriven by large companies in the servicesector and is enabled by low regulation.

• Scenario B, called “Government first,” as-sumed strong environmental regulation,resulting in only moderate growth, noprogress in employment, but good condi-tions for small and medium-sized enter-prises.

• Scenario C, called “Stakeholder democ-racy,” was characterized by steady economicgrowth, leading to an increase in the num-ber of households, desk workers, and totallabor force.

The scenarios were formulated qualitativelyand had to be quantified to be used as parametersettings for the ICT impact model (for detailssee tables S1-1 and S1-2 in the supplementarymaterial on the Journal Web site).

Modelling ICT Impacts Under Given Sce-nariosThe basic approach of the model was to cover

all three types of ICT effects on the environmen-tal indicators in the following way:

• First-order effects: A generic model of theenergy consumption of ICT in the use phaseand of the ICT waste produced was built(key parameters: the growth rate of the en-ergy efficiency of ICT equipment and the[negative] growth rate of the useful life ofICT equipment). The production of ICThardware was neglected because most of theimpacts of production would occur outsidethe given system boundaries.

• Second-order effects: Potentials of ICTapplications (see table 2) for optimiza-tion and/or substitution of products and



Table 2 Generic Description of Information and Communication Technology (ICT) Application Domainsand Relevant Examples of ICT Applications

ICT applicationdomain Generic description

Examples ofestablished ICT

applicationsExamples of

newer ICT applications

Production processmanagement(PPM)

ICT applications increasing theenergy and material efficiencyof production processes

Productionplanning andcontrol systems

Networked multisensorregulators forhigh-temperatureprocesses

Power generationand distributionmanagement(smart grids)

ICT-supported measures toimprove the coordination ofelectricity supply and demand

ICT-based loaddetection

Electricity consumptionfeedback (smartmetering) anddemand-sidemanagement (smartappliances)

Intelligent transportsystems (ITS)

ICT applications assisting driversand managing transportinfrastructure (all modes)

In-vehiclenavigationsystems

Real-time road trafficrouting

Supply chainmanagement(SCM)

ICT applications supporting thecoordination of the processesand the optimization of thephysical mass flows in thesupply chain

Electronic datainterchangealong the supplychain

RFID-based supply chainmanagement

Virtual goods (de-materialization)

ICT applications supporting aproduct-to-service shift andthe digitization of physicalproducts

ICT-enabledservices (e.g.,Web-based carsharing)

E-paper, e-books

Teleshopping Demand-side e-commerce,allowing the purchase ofphysical goods away fromretail

Online orderingand payment

Peer-to-peer merchandiseexchange networks

Telecommuting ICT-supported work away fromthe firm’s premises, whichreplaces commuting travel

Personal computerand Internetaccess at home

Virtual workspace athome

Teleconferencing A mode of telecommunicationwhich replaces business travel

Audioconferencing

High-definition videoconferencing

Mobile work ICT devices making it possibleto work while travelling

PDAs WLAN hotspots in trains

Buildingmanagement

All “soft measures” supported byICT to reduce the energyconsumption of buildings

Intelligent HVACmanagement

Energy consumptionfeedback (smartmetering)

Waste management ICT applications supporting theseparation of waste fractions

Sensor-basedsorting

RFID-based sorting

Note: HVAC = heating, ventilation, and air conditioning; PDA = personal digital assistant; RFID = radio frequencyidentification; WLAN = wireless local area network.Source: Erdmann et al. (2004).

processes were modeled. The speeds atwhich these potentials would be realizedwere determined by parameters dependingon the scenarios.

• Third-order effects (including rebound ef-fects): Realized efficiency potentials changemarket prices or time-use and can thereforechange demand, depending on elasticity



parameters (see table S1-3 in the supple-mentary material on the Journal Web site).

All parts of the model could interact with oneanother; for example, the substitution of virtualfor material goods could decrease freight trans-port demand and increase ICT use. The overallimpact of ICT was calculated by creating a sec-ond variant of each scenario in which all vari-ables concerning ICT were “frozen” during thesimulation run. By comparing the two variantsof each scenario, the overall net effect of furtherICT development and diffusion was calculated.

Figure 1 shows groups of variables that wereused in the model. The details of the model,

which was implemented using the simulation sys-tem PowerSim, have been published in the origi-nal interim report to IPTS (Hilty et al. 2004).

Figure 2 shows the basic causal structure usedby this model to account for the dynamic impactsof ICT on the environmental indicators. Thiscausal-loop diagram is basically an interpretationof the definitions of first-, second-, and third-order ICT effects.

The scenario differences were expressed bydifferent settings of the external variables; thesewere thus used to represent future uncertainty (fordetails see table S1-2 in the supplementary mate-rial on the Journal Web site). In order to accountfor data uncertainty, the scenarios were combined

Figure 1 Groups of variables used to represent the scenarios and the three levels of information andcommunications technology (ICT) effects (see Hilty et al. (2004) for a description of all variables).CRT = cathode ray tube; GDP = gross domestic product; LAN = local area network; LCD = liquid crystaldisplay; SMEs = small and medium-sized enterprises.



netenvironmental

impact

energy and material needed to provide one service unit

demand for the service (number of service units consumed)

price per service unit

elasticity of demand with regard to price

efficiency and substitution potentials

provided by ICT

speed of exploiting efficiency and

substitution potentials

external influence determined by scenarios (economic, political and

cultural framework)

ICT development and use

1st order effects

2nd order effects

3rd order effects

Figure 2 Abstract representation of the causal structure used in the system dynamics model of informationand communication technology (ICT) environmental effects by Hilty and colleagues (2004).Dotted arrows = influence from outside the system boundaries; thin solid arrows = first-order effects ofICT; medium solid arrows = second-order effects of ICT; thick arrows = third-order effects of ICT.

with three parameter settings representing best-,mean-, and worst-case data assumptions.3 Thebest-/worst-case variants were calculated with theparameter vector that minimized/maximized to-tal energy consumption, which means total en-ergy consumption was chosen as a lead indicator.For the mean variant, each parameter was set tothe mean of its best- and worst-case values.

Results and Discussion of the ScenarioAnalysis

We will first show the overall ICT impact onGHG emissions that have been calculated anddiscuss the associated data uncertainty and fu-ture uncertainty. Then we will look into indi-vidual ICT application domains and discuss theircontributions to the overall impact with regardto their varying extent and uncertainties.

Overall ICT ImpactsSimulating the three scenarios in three vari-

ants yields the nine value sets of total GHGemissions in 2020 shown in figure 3. All GHGemissions are expressed in percent of the year2000 level. The left bar of each value set showsthe GHG emissions in 2020 that occur if ICTis “frozen” at the 2000 level. The second bar

shows the GHG emissions in 2020 with theprojected ICT development (2000–2020). Thethird bar shows the difference between thesetwo values, representing our metrics of ICTimpact.4

As figure 3 shows, there is only one case (sce-nario B combined with worst-case data assump-tions) in which this value is positive (indicatingthat the net impact of ICT is an increase in GHGemissions). As can be seen, the variation causedby ICT is smaller than both future uncertaintyand data uncertainty.

Future uncertainty is represented by the dif-ferences among the scenarios A, B, and C. Datauncertainty is represented by the best-, mean-and worst-case variants of each scenario. An ex-ample may illustrate the origin of this type ofuncertainty: A parameter called “virtualizationpotential for information products,” expressinghow much of the total physical mass of all infor-mation products (books, newspapers, DVDs, etc.)could potentially be replaced by online services(in EU 15 by 2020), was estimated on a modelingworkshop to range from 30% to 90%. Dozens ofsuch uncertainty ranges combine into the overalldata uncertainty reflected by the difference be-tween the best- and worst-case scenarios (all datapublished by Hilty et al. 2004).



Figure 3 Simulated developments of greenhouse gas (GHG) emissions by 2020 in EU 15 (year2000 = 100%) for the three future scenarios in three data uncertainty variants. ICT = information andcommunication technology.

If we look only at the mean variant, the pos-itive impact of ICT is lowest in scenario B, withroughly a 3% decrease of GHG emissions, andhighest in scenario C, with a 10% decrease. Onepossible interpretation is that this scenario ismore conducive to “decarbonizing” ICT appli-cations than scenario B. However, it should benoted that scenario B already has low GHG emis-sions for reasons other than ICT, as can be seenin figure 3. These reasons include weaker GDPgrowth and higher fossil energy prices in scenarioB. It is therefore possible that the most favorabledevelopment path in terms of GHG reduction isnot the one in which ICT contributes most toGHG reduction.

Impacts by ICT Application DomainsIn order to show in detail how ICTs affect

GHG emissions, we took a look at the disag-gregated effects of ten ICT application domains.These results were produced by “freezing” all butone of the ten ICT application domains for eachdomain. They thus show the net effect of each ap-plication domain on the overall GHG emissions.

“ICT use,” which aggregates the total first-ordereffects of ICT in the use phase, has been addedto figures 4 and 5 for comparison.

To reduce complexity, we selected scenarioC for this analysis because it provided the high-est GHG mitigation potential by ICT. Figure 4shows the results. Except for “ICT use,” thesevalues are net impacts including first-, second-,and third-order effects. The sum of the impactsacross the ten domains does not necessarily addup to the total impact shown in figure 3 becausethe application domains are interacting in themodel.

It can be seen that two application domainsincrease the total emissions according to the IPTSmodel, namely ITS and mobile work. ITS have,among other effects, the effect of saving time inthe transport of passengers or freight. ITS there-fore make transport not only more energy effi-cient and cheaper, but also faster. This creates arebound effect based on assumed elasticities of de-mand and the time-use approach included in themodel. In a similar way, mobile work is assumedto create a rebound effect by enabling better



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Figure 4 The impacts ofinformation and communicationtechnology (ICT) use and ICTapplications on total greenhouse gas(GHG) emissions, 2020, in EU 15 forscenario C (‘Stakeholder democracy’)as a percentage of the GHGemissions 2000. The length of thevertical lines indicates the uncertaintyof the results caused by input datauncertainty. ITS = intelligenttransport system; PPM = productionprocess management; SCM = supplychain management.

utilization of travel time: When some types ofwork and travel can be combined, the cost oftravel time decreases, which leads to an increasein the demand for travel.

With respect to data uncertainty, our anal-ysis reveals three types of ICT application do-mains: first, small impacts with small degrees ofdata uncertainty (smart grids, supply chain man-agement [SCM], teleshopping, telecommuting,teleconferencing, and mobile work); second, sig-nificant impacts with low to medium data uncer-tainty (production process management [PPM],ITS, and building management); third, signifi-cant impacts with high data uncertainty (virtual

goods and ICT use). The key driver for total datauncertainty (as shown in figure 3 for scenario C)is the data uncertainty of the impacts of virtualgoods on GHG emissions (−0.1% to −10.5%).This uncertainty represents a lack of knowledgeabout the effects of virtualizing goods.

In order to take a closer look at the future un-certainty of the application domains, we selectedthe mean data values and calculated the impactsof the application domains for all three scenarios(figure 5).

For most of the domains, scenario B is out-standing as the scenario (1) with the highest ad-verse impacts (ICT use, mobile work) and (2) the

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Figure 5 The impacts ofinformation and communicationstechnology (ICT) use and ICTapplications on total greenhouse gas(GHG) emissions, 2020, in EU 15 formean input values for three scenarios(A, B, and C) as a percentage of theGHG emissions 2000. The length ofthe vertical lines indicates thevariation of the results caused byfuture uncertainty. ITS = intelligenttransport system; PPM = productionprocess management; SCM = supplychain management.



lowest beneficial impacts (PPM, SCM, virtualgoods, building management). One applicationis clearly outstanding in its GHG-reducing ef-fect: building management, which includes intel-ligent HVAC systems and soft measures such ascreating user awareness for energy consumptionin buildings. The impacts as well as their varia-tion among scenarios are small for teleshopping,telecommuting, and teleconferencing. These ap-plications seem to be “robust” in having almostno net impact due to rebound effects that com-pensate for their second-order impact. In gen-eral, the impacts of ICT application domains aresimilar in scenarios A and C, with a significantadvantage of scenario C in the domain of vir-tual goods. The main driver for the outstand-ing character of scenario B are the higher energyprices assumed in this scenario (see the analysis byArnfalk 2004).

Conclusions

This article has reviewed principal macroeco-nomic studies on the impact of ICT on GHGemissions and discussed the challenges encoun-tered in modeling ICT impacts at the macroeco-nomic level. When assessing impacts of ICT, it isnecessary to introduce a baseline: This can be his-torical data, a “business as usual” scenario, or—inthe case of our own study—an “ICT freeze” sce-nario in which the development and diffusion ofICT were frozen at the beginning of the simu-lation period. As our review has shown, most ofthe studies are not comparable due to differentbaselines.

We have presented an in-depth scenario anal-ysis providing a reinterpretation of the results ofour own study. It showed that the impacts of dif-ferent ICT application domains as well as dif-ferent types of impacts can compensate for eachother as a result of the system’s dynamics. ICTapplications that are assumed to mitigate GHGemissions may turn out to have an adverse ef-fect, as shown in the cases of ITS and mobilework. The impact of some applications is sensi-tive to the scenarios chosen and to the data as-sumptions made, whereas other ICT applicationsseem to reduce GHG emissions in a relatively ro-bust manner, such as building management andvirtual goods, although the extent of the reduc-

tion remains uncertain. In the latter case, bothtypes of uncertainty are remarkably high, indi-cating a great need for further research as well assome political responsibility: Whether the dema-terialization potential of ICT will unfold in theeconomic system seems to depend on frameworkconditions—otherwise, in the worst case, it willjust fizzle out.

In contrast to the other studies, we find bothpositive and negative net impacts depending onthe type of ICT application. We attribute thisto the fact that our simulation model treatsfirst-, second-, and third-order effects of ICT inan integrated manner.

Meanwhile, there is a growing body of lit-erature on the extent of direct rebound effects(e.g., Sorrell et al. 2009) that could be used toimprove the approach. Furthermore, indirect re-bound effects, which will increase the demandfor other goods and services if less money or timeis spent on one service, could be easily incor-porated into an ICT impact assessment modeltoday as marginal consumption values of expen-diture and their related impacts on GHG emis-sions (Thiesen et al. 2008; Tukker et al. 2006).However, macroeconomic rebound effects of en-ergy efficiency measures leading to a series ofeconomy-wide price and quantity adjustments—with energy-intensive goods and sectors gain-ing at the expense of less energy-intensive ones(Greening et al. 2000)—can hardly be integratedinto an ICT impact assessment study, as they af-fect all industries. Instead, it would be necessaryto incorporate ICT in a general macroeconomicmodel, as done by Laitner and colleagues (2001).

Most of the existing studies on ICT impactson GHG emissions (including the IPTS study)have a limited geographical scope, both regard-ing the applications and the impacts of ICT.This we consider a serious limitation, since trans-boundary effects at any level (first-, second-, orthird-order) are then neglected. For example,energy consumption of mining metals abroadto produce ICT hardware may compensate forsome of the domestic progress in energy effi-ciency. Conversely, materials saved due to ICT-enabled efficiency could reduce the demand forresources outside the system boundaries. Finally,resource efficiency induced by ICT can influ-ence world market prices and produce global



rebound effects. We therefore think that the nextgeneration of ICT impact models should combinethe scope and ambition of the two global studies(Buttazoni 2008; Webb 2008) with a method-ology that is able to address effects on all threelevels.

A process that adjusts the scenarios used insuch assessments periodically to real-world devel-opments and recalculates the implications couldprovide opportunities for mutual learning be-tween researchers and decision makers.

Acknowledgements

The authors wish to thank Thomas Ruddyfor many improvements of this article and threeanonymous reviewers for their in-depth evalua-tion and helpful feedback.

Notes

1. The original Global e-Sustainability Initiativestudy (Webb 2008) was complemented by the U.S.,German, and Portuguese addenda and a subsequentindustry study focusing on mobile telecommuni-cations (McVeigh et al. 2009). The Gibson andcolleagues (2008) study represents a series of ICTindustry-sponsored studies of similar methodologyinvolving the World Wildlife Fund (e.g., Pamlinand Szomolanyi 2007). The Matsumoto and col-leagues (2005) article represents a body of hybridmodeling studies that have been prepared in Japan.We could not analyze some presumably relevantJapanese studies at a meaningful level mainly due tolanguage barriers (e.g., the background of the Reportfrom Study Group on ICT Policy for Addressing GlobalWarming [Ministry of Internal Affairs and Commu-nications 2008]). Because ICTs pervade, transform,and link all economic sectors, any cross-sector def-inition to capture the impacts of ICT is somewhatarbitrary. For example, it can be argued that e-commerce and virtual mobility are cross-sector ICTapplication domains. However, the major studiesfocusing on these domains, we found, do not pro-vide additional insight on the topic of this article.

2. From a life cycle perspective, the studies addressmainly the use phase of ICT equipment. Few stud-ies also account for upstream and/or downstreamprocesses (Kuhndt et al. 2003; Labouze et al. 2008;Webb 2008). Erdmann and colleagues (2004) in-clude electronic waste.

3. We are aware that upper and lower bounds based onexpert judgment do not imply that the true valueis within these ranges. However, if all uncertaintyranges input to the model are selected amply and thesimulation still produces some unambiguous results,these results can be considered more credible thanindividual judgments.

4. These values may differ slightly from those pub-lished previously (Erdmann et al. 2004; Hilty et al.2004) because of a different GHG emission refer-ence year. The former publications took the respec-tive GHG emissions of each ICT “freeze” simula-tion run by the year 2020 as the reference, whereasin this article the ICT impact for all simulation runsis measured against the GHG emissions in the year2000 to allow for cross-comparisons of scenarios.

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About the Authors

Lorenz Erdmann is an Associate ResearchScientist at the Institute for Futures Studiesand Technology Assessment in Berlin, Germany.Lorenz M. Hilty is Head of the Technology and

Society Lab (TSL) at Empa, the Swiss FederalLaboratories for Materials Testing and Research,and is also professor of Informatics and Sustain-ability at the Department of Informatics at theUniversity of Zurich.

Supplementary Material

Additional supplementary material may be found in the online version of this article:

Supplement S1: This supplement fleshes out the three scenarios presented in the article, addingbrief characterizations for each as well as the specific value assumptions used in the final model.In these scenarios, as efficiency potentials become realized, market prices and therefore demandcan change; the extent to which this happens is dependent on elasticity parameters in tableS1–3. The final table, S1–4, presents the disaggregated simulation results for all future scenariosand data uncertainty variants.

Please note: Wiley-Blackwell is not responsible for the content or functionality of any supple-mentary material supplied by the authors. Any queries (other than missing material) should bedirected to the corresponding author for the article.


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