Scenario Analysis

ion) or treat ICT as a monolithic sector (high level of abstraction). The studies reviewed all cover ICT impacts on the industry, transport, and building sectors and agree on the relevance of most ICT applications concerning GHG emissions, but some ICT applications such as smart city planning are only treated by one study quantitatively. Other ICT applications such as precision farming are not treated by any of the studies, although they are likely to be relevant. The main reason is presumably the studies’ major focus on energy efficiency. The two conceptual frameworks used, growth/structural change/technology effect and first-/second-/third-order effects, have some overlap and some differences. While the first conceptual framework treats ICT as a driver of economic growth, the studies of the second type either treat growth as an external variable or indirectly as a third-order effect (additional growth induced by ICT). Second-order effects include both ICT-fostered dematerialization (by substitution effects) and ICT-enhanced process efficiency (by optimization effects). First-order effects are not explicitly addressed in the first conceptual framework but can be accounted for in principle. We did not find any discussion of the implications of choosing a certain metric for the ICT impact on GHG emissions. Even if a common metric were agreed upon, the different scopes would still make it difficult to compare results. We found that the five older references are rich and diverse inmethodology, whereas the five more recent studies take the simpler approach of quantifying GHG reduction potentials at the level of second-order effects, ignoring firstand/or third-order effects. In view of the various sources of uncertainty in this field of study, it seems indispensable to disaggregate the uncertainty as far as possible (by breaking it down to specific ICT application fields) and to make a clear difference between future uncertainty and data uncertainty. Only Erdmann and colleagues (2004) make a systematic attempt to treat data uncertainty quantitatively. As contributors to this study, however, we have to concede that data uncertainty is merged with future uncertainty in the presentation of the results. Therefore, we will reinterpret the results of this study in the next section and demonstrate the benefits of separating scenarios from data assumptions at the level of ICT applications. Scenario Analysis In this section we present a new assessment of the future impact of ICTs on GHG emissions based on the study by Erdmann and colleagues (2004), which was commissioned by the Institute for Prospective Technological Studies (IPTS) of the European Commission. This study (in the following referred to as the IPTS study) included a system dynamics model of the causal relationships between relevant ICT applications and a set of given environmental indicators. The subsequent scenario analysis did not involve any change to the model or the input data. Instead, now in a new way, the two types of uncertainty were separated in the calculation at the level of ICT applications: • data uncertainty indicating research demand; • future uncertainty indicating a need for political action. In doing this analysis, we want to show that a systematic treatment of uncertainty can improve the practical utility of simulation results in that it makes a contribution to setting research agendas and policy agendas. We will first summarize the integrated assessment methodology of the IPTS study and then present the new scenario analysis. Integrated Assessment Methodology The methodology of the IPTS study has been described by Erdmann and colleagues (2004) and contains amodeling focus byHilty and colleagues (2004, 2006). Here we recapitulate the methodological elements necessary to understand the subsequent analysis. Goal Definition and Scope The aim of the study was to explore qualitatively and to assess quantitatively the way that ICT can influence a set of given environmental indicators within the time horizon of 2020. Erdmann and Hilty , Macroeconomic Impacts of ICT on GHG Emissions 831 RESEARCH AND ANALYS I S The geographical coverage of the study was the EuropeanUnion at the time the study was put out to tender (EU 15). For each result, the generalizability to an enlarged European Union has been discussed qualitatively (Hilty et al. 2004). Impacts caused outside the European Union (such as emissions from ICT production in Asia) were not covered by the study. The basic approach was to model the effect of ICT application domains on the sectors energy, transport, production, and waste, and then to aggregate the effects to estimate the overall ICT impacts. Table 2 lists the application domains considered and examples of established and newer applications. An ICT impact was defined as a net change—caused by applying ICT—of one of the following given indicators: total freight transport volume (tonne-kilometers per year, tkm/a), total passenger transport volume (passenger kilometers per year, pkm/a), the share of private cars in the 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 on the last indicator. The strength of an integrated assessment approach is that it is comprehensive with regard to sectors and can address the dynamic changes implied by the long-term diffusion of a technology. In practice, an integrated assessment of ICT impacts is so comprehensive that it cannot consider as much technological detail as an LCA study of ICT products or services does. Our study therefore relied on abstractions of ICT devices (“client-type device,” “server-type device,” and “network-type device”; Hilty et al. 2004) and their application in generically defined domains to cope with technical progress. It was assumed that if more specific ICT devices and applications had been included, they would have become obsolete during the simulation period. Scenario Building There are many drivers other than ICT that may change the environmental indicators (such as energy prices or governmental regulation) and drivers that influence the diffusion of ICT applications. To reduce the number of possibilities spanned by these drivers (or external factors), three plausible scenarios were created and validated in an expert workshop (Goodman and Alakeson 2003). These scenarios differed in the factors the experts considered both most influential with regard to the system under study and most uncertain at the same time: • ScenarioA, called “Technocracy,” assumed strong economic growth and employment driven by large companies in the service sector and is enabled by low regulation. • Scenario B, called “Government first,” assumed strong environmental regulation, resulting in only moderate growth, no progress in employment, but good conditions for small and medium-sized enterprises. • Scenario C, called “Stakeholder democracy,” was characterized by steady economic growth, leading to an increase in the number of households, desk workers, and total labor force. The scenarios were formulated qualitatively and had to be quantified to be used as parameter settings for the ICT impact model (for details see tables S1-1 and S1-2 in the supplementary material on the Journal Web site). Modelling ICT Impacts Under Given Scenarios The basic approach of the model was to cover all three types of ICT effects on the environmental indicators in the following way: • First-order effects: A generic model of the energy consumption of ICT in the use phase and of the ICT waste produced was built (key parameters: the growth rate of the energy efficiency of ICT equipment and the [negative] growth rate of the useful life of ICT equipment). The production of ICT hardware was neglected because most of the impacts of production would occur outside the given system boundaries. • Second-order effects: Potentials of ICT applications (see table 2) for optimization and/or substitution of products and 832 Journal of Industrial Ecology RESEARCH AND ANALYS I S Table 2 Generic Description of Information and Communication Technology (ICT) Application Domains and Relevant Examples of ICT Applications ICT application domain Generic description Examples of established ICT applications Examples of newer ICT applications Production process management (PPM) ICT applications increasing the energy and material efficiency of production processes Production planning and control systems Networked multisensor regulators for high-temperature processes Power generation and distribution management (smart grids) ICT-supported measures to improve the coordination of electricity supply and demand ICT-based load detection Electricity consumption feedback (smart metering) and demand-side management (smart appliances) Intelligent transport systems (ITS) ICT applications assisting drivers and managing transport infrastructure (all modes) In-vehicle navigation systems Real-time road traffic routing Supply chain management (SCM) ICT applications supporting the coordination of the processes and the optimization of the physical mass flows in the supply chain Electronic data interchange along the supply chain RFID-based supply chain management Virtual goods (dematerialization) ICT applications supporting a product-to-service shift and the digitization of physical products ICT-enabled services (e.g., Web-based car sharing) E-paper, e-books Teleshopping Demand-side e-commerce, allowing the purchase of physical goods away from retail Online ordering and payment Peer-to-peer merchandise exchange networks Telecommuting ICT-supported work away from the firm’s premises, which replaces commuting travel Personal computer and Internet access at home Virtual workspace at home Teleconferencing A mode of telecommunication which replaces business travel Audio conferencing High-definition video conferencing Mobile work ICT devices making it possible to work while travelling PDAs WLAN hotspots in trains Building management All “soft measures” supported by ICT to reduce the energy consumption of buildings Intelligent HVAC management Energy consumption feedback (smart metering) Waste management ICT applications supporting the separation of waste fractions Sensor-based sorting RFID-based sorting Note: HVAC = heating, ventilation, and air conditioning; PDA = personal digital assistant; RFID = radio frequency identification; WLAN = wireless local area network. Source: Erdmann et al. (2004). processes were modeled. The speeds at which these potentials would be realized were determined by parameters depending on the scenarios. • Third-order effects (including rebound effects): Realized efficiency potentials change market prices or time-use and can therefore change demand, depending on elasticity Erdmann and Hilty , Macroeconomic Impacts of ICT on GHG Emissions 833 RESEARCH AND ANALYS I S parameters (see table S1-3 in the supplementary material on the Journal Web site). All parts of the model could interact with one another; for example, the substitution of virtual for material goods could decrease freight transport demand and increase ICT use. The overall impact of ICT was calculated by creating a second variant of each scenario in which all variables concerning ICT were “frozen” during the simulation run. By comparing the two variants of each scenario, the overall net effect of further ICT development and diffusion was calculated. Figure 1 shows groups of variables that were used in the model. The details of the model, which was implemented using the simulation system PowerSim, have been published in the original interim report to IPTS (Hilty et al. 2004). Figure 2 shows the basic causal structure used by this model to account for the dynamic impacts of ICT on the environmental indicators. This causal-loop diagram is basically an interpretation of the definitions of first-, second-, and thirdorder ICT effects. The scenario differences were expressed by different settings of the external variables; these were thus used to represent future uncertainty (for details see table S1-2 in the supplementary material on the Journal Web site). In order to account for data uncertainty, the scenarios were combined Figure 1 Groups of variables used to represent the scenarios and the three levels of information and communications 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 crystal display; SMEs = small and medium-sized enterprises. 834 Journal of Industrial Ecology RESEARCH AND ANALYS I S net environmental 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