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Figure 3 — The average proportion of servers for on-premise and cloud computing separated by large, medium and small organisational size.

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Research my group did in the 1980s involved looking carefully at the effect of introducing computers in various parts of textile industry. The heart of the research, the PhD thesis of Rolf Bergrath, was an examination of the energy conservation potential of electronics used for air conditioning spinning mills. As it turned out, the energy conservation potential was huge. Figure 1: Industrial activities require the energy, time and information inputs in various proportions [1].

The relationship between the ICT Sector Guidance and these generic product standards is shown in Figure 1. Figure 1: Relationship of the [CT Sector Guidance to generic product standards

outcome of the reference study (2007 values) and this study (2020 values).

Figure 2, The carbon footprint development of the main ICT subsectors 2007-2020. the impact of connectivity of the 16 billion E&M devices were included in the E&M results.

Based on an analysis of historic data for number of devices for example International Telecommunication Union (ITU) statistics of PCs in use and telephone lines[13], and earlier LCA studies performed by Ericsson and TeliaSonera, we found that the ICT sector's total carbon footprint per average user, including also impact from shared resources such as data centers and network equipment, has decreased from about 300 kg CO ze/year in 1995 to about 100 kg in 2007 and is estimated to decrease further to about 80 kg in 2020, see Figure 3.

The main reason for the decreased footprint per average ICT user, shown in Figure 3, is that more and more users use mobile devices and mobile access technologies which have lower footprints per user than fixed PCs and always on-devices like modems/routers. The reason for this is the higher energy efficiency of battery-operated mobile user devices. Fixed networks, on the other hand, have a lower impact per GByte (GB) as shown in Figure 4. Figure 4. The carbon footprint per GB sent in access networks.

Table 1. Cross reference table relating the system boundaries for ICT to the OECD ICT sector definition

Table 2. Cross reference table relating the system boundaries for E&M to the OECD ICT sector definition

Figure 5. Additional impact (red) from the networked society scenario shown together with a business as usual scenario (yellow). Note that the paper media part of E&M sector is not included in this figure.

Our approach was to calculate the annual emissions of on-premise computing and compare it to the energy required to operate the equivalent services in a cloud computing scenario (Figure 1) for a specific county assuming an adoption rate of 80%. This ‘high’ penetration rate was selected to highlight a ‘best case scenario’ [8], one that is likely to take some years to achieve. A sensitivity analysis of this rate was included to highlight the impact of a lower adoption rate. The current (baseline) adoption of cloud computing was also factored into the calculations so as to exclude impact already created by early adopters.

Table 1 — Country summary results of running the mode! against an 80% cloud adoption rate. Figure 2 — The net emission reductions ranked high to low.

fable 2 — The average proportion of emission reductions anc the assumed use of cloud services of all countries by organisation size. The number of on-premise servers removed and cloud servers added for an 80% cloud penetration rate is presented in Table 3. On average moving to cloud reduced server numbers by 92%. The majority of servers removed are from small and medium sized organisations; however, most servers added for cloud services are for large organisations (Figure 3). This highlights the higher adoption of cloud computing and the higher proportion of employed populations in small organisations (Table 2).

fable 3 — The number of servers removed for on-premise and servers added for cloud computing

Figure 4 — A sensitivity analysis of the market penetration rate (0%-100%) against the potential total emissions reductions. 25%, 50% and 75% rates are highlighted. Results in this paper used an 80% penetration rate. A sensitivity analysis was completed on the market penetration rate (Figure 4). This was run against the total emissions reduction and the analysis revealed that for a 100% market penetration rate a total of 7.3 million tonnes of CO,e could be abated. However market penetration and emission reductions were found not be linear. Therefore the 80% rate set in this research accounted for 62% of the total possible reductions by moving to cloud computing. The largest rate of change of 46% occurred between the 75%-100% market rate and the lowest rate of 3% occurred between 0%-25%. The large increase in reductions at 50% penetration comes from the first inclusions of the small organisations (for example, we assume that 60% of small organisations already use cloud computing for email).

Figure 5 — A collated +20% sensitivity analysis of the on- premise to cloud ratio for each service against potential emission reductions.

Table 1: Typical contributions of different components to the total energy consumption a) without optional free cooling [3], and b) with optional free cooling. 2.2 Different Approaches for Improving the Energy Efficiency

Figure 1: Conventional cooling with chilled water

Table 2: Savings through optimization of conventional cooling, as a function of the server inlet temperature 0, out- rhe savings are relative to an inlet temperature 0, ,y; of 15°C.

Figure 4: Conventional cooling in function of server inlet temperature. Blue: Energy usage of IT, CRAH and chillers. Red: Free cooling hours in one year.

Figure 5 shows the absolute contribution of IT, CRAH and cooling equipment to the combined energy consumption within a year. When the server inlet temperature is increased, the energy consumed by the cooling equipment (chiller, free cooler) is decreased. The energy consumption of the IT equipment is constant in the simulation. The CRAHs consume a relatively low amount of energy, since we assume a constant server inlet temperature 40, of 10°C in the simulation, which allows the CRAHs to run on a low operating point.

Table 3: Savings of DFC relative to optimized conventional cooling, as a function of threshold temperature 9op,,pFC,max

Figure 6 shows the sum of the energy consumption of IT, CRAH and chiller and the amount of direct free cooling hours within a year. Compared to Figure 4, we notice a more significant reduction of the energy consumption.

Figure 7: Contribution of different components to the energy usage as a function of the threshold outdoor air temperature for DFC Oop,a,prc,maw USing direct free cooling Figure 7 shows the contribution of different components. The IT equipment energy consumption is again considered constant.

Table 4: Energy savings after reduction of IT equipment power consumption while using conventional cooling and without cooling optimization. Savings are relative to a Py, of 400 kw.

Figure 8 gives the results of a simulation with P;; at 100%, 90%, 80%, 70% and 60%. Table 4 shows that the combined energy usage of IT, CRAH and chillers is decreased in an almost linear fashion when the IT energy usage is decreased.

Figure 1: Main categories for parameters influencing consumption for smart meters.

Figure 2: Onion model that explains the layerwise modeling process used to calculate system-wide own consumption of metering infrastructure.

Figure 5: Test circuit using current transducers - for power measurements at the meter in real use cases, when there is a certain load, caused by the end-user. Figure 4: Test circuit using shunts - for power measurements at the meter when it is operated without load.

Figure 3: Network model showing the di erences between the categories A, B, C, and D for the Technical Scenarios used in the SMc project. The main distinction between di erent data collection technologies is derived from the priorities a power utility sets for making decisions about its investments, and it is not based on their contribution to the total energy consumption.

Figure 6: Anonymized comparison of sensitivity for load of different manufacturer-specific smart meter products, normalized to state without load.

Figure 7: Input power curve of a_ 3-phase GPRS-connected smart meter - extract of a data transfer event (whole data log lasted for 24 hrs).

Figure 7 shows that the readout of data over the tested GPRS system (as described for TSC B) showed an input power increase during a timespan of about five minutes. The readout process over the tested PLC system lasted for 6 hrs, as shown in figure 8. Figure 8: Input power curve of a _ 3-phase PLC-connected smart meter - during 24 hrs.

Figure 9: Comparison of system-wide occurring annual energy consumptions to operate smart metering systems which were analyzed in the SMc project.

Figure 10: Comparison of annual _ energy consumptions according to different assumed Roll Out Scenarios for Austria.

Figure 11: Comparison of annual _ energy) consumptions according to different assume Roll Out Scenarios for Switzerland.

Table 1: Available e-waste country assessment according to the Empa methodology.

Figure 1: Typical recycling processes applied in the informal sector of developing countries (open burning of cables, leaching of gold from PWB, desoldering of PWB)

Table 2: SWOT analysis of the e-waste recycling chain in formal vs. informal scenarios efficiency of the informal sector and the development of altemative business models to allow the sector to transform themselves towards sustainable operations. The key results can be grouped into the three main stages of the recycling chain (collection, pre-processing and end-processing) and concluded with a description of the “Best-of-2-Worlds” philosophy. 25%. In contrast, today’s state-of-the-art integrated smelters, as used in most formalized recycling systems, achieve recovery efficiencies as high as 95% [51].

This study covers the informal recycling of electronic waste from the ICT sector (Figure 1). To be more precise, informal recycling actually includes refurbishing, dismantling, and recycling. Hence, it covers the service chain, from the point of arrival at one of the many informal recycling sites in Pakistan, until it is either refurbished and sold as second-hand electronics; dismantled, recycled and sold as raw material; sent to formal recycling; or finally disposed of. Figure 1. Flow chart illustrating informal recycling of electronic waste from the ICT sector in Pakistan, as included in this study.

Table 1 Subcategories identified in informal recycling

Table 2. Social impact assessment of informal recycling in Pakistan

Figure 1: Theory of Acceptance and Use of Mobile Phone Return Programs Based on [34]

Table 1. Overview of International Mobile Phone Return Programs - Partners: They partnered with non-profit institutions well known for their reliability and trustworthiness and non- scandalous history phones was received: 1) The Austrian “O3 Wundertiite” [24 and 2) campaigns by the German Telekom Company [29][30].

Table 2. Measures Influencing the Return Program Acceptance Factors; in bold the seemingly most inductive factors Table 2. Measures Influencing the Return Program Acceptance Factors; in bold the seemingly most inductive factors

(hence the name “Smart Grid”) and organized by category columns. The game supports levels so that a large number of actions can be presented in a sequence of smaller grids. Each grid contains four different types of actions: activities, commitments, events, and excursions.

Figure 4: Power Meter widget A fundamental requirement for enabling more active partici- pation by consumers in the smart grid is feedback regarding their energy usage. One of the most simple mechanisms provided by Makahiki for this purpose is the Power Meter widget, illustrated in Figure 4.

The Daily Energy Goal Game widget provides a way for players to earn points by reducing their current energy con- sumption from a baseline. This baseline can be calculated using historical data or dynamically throughout the compe- tition. Both the baseline data and the current consumption is typically provided by API calls from Makahiki to an un- derlying WattDepot server. Figure 5 illustrates this widget.

The Raffle Game widget provides a way to incentivize par- ticipation from all individuals, even those who are not in the running for a top prize. For every 25 points a player earns, they receive one virtual raffle ticket. Players can dy- namically allocate their tickets to any raffle prizes they are interested in at any time, up to the end of the raffle. Figure 6 shows an example of the Raffle Game.

Figure 7: Game analytic widgets: User Stats and Energy Goal Status

To provide a concrete example of the numbers involved: one UK household we looked at with a 3.3kWh peak system generated 3215 kWh in 2011 and in return received £1543 in FIT payments for generation and export. Yet how much kWh of the generated kWh was exported and how much was consumed is unknown to the household.

Table 1. Technology Probe Design

Table 2 — Participating households.

None of the participants really understood how actual energy production is influenced by the peak capacity of their installation and environmental aspects. Even knowing the peak generation capacity is fraught with problems: the peak capacity of a PV installation only applies under cloudless conditions, around noon for an installation under perfect conditions: south facing roof, 30 degree roof pitch, no shading. As we discovered few installations satisfy all five of these condition. Figure 3 shows one of the participant’s homes with a less than ideal PV installation due to partial shading by trees, orientation and roof pitch. Figure 3: PV panels (2.7 kWh peak) on Participant's roof facing south-east, shaded by trees, at 42 degree pitch

In general the approach consists in creating a community of interest that will represent the field of application and provide the relevant expertise, analyse the state of the art and develop scenarios forming a vision of the future. In the next step a roadmap that will show the way for the implementation is created. A strategy that explains different aspects of the development and implementation (e.g. barriers and drivers) accompanies the roadmap (see Figure 1).

Figure 3 shows the share of all outcomes showing e.g. if a tool or methodology was developed or both. The number of projects is larger than the total number of analysed projects as a project could cover a tool and a standard at the same time. Mostly methodologies and tools were found, rarely frameworks and standards.

Figure 4: Application Areas Covered The distribution of projects by area of application can be seen in Figure 4. Projects can be represented in more than one category. Only a small share of projects dealt with the topic of neighbourhoods. One of the reasons for this is that the neighbourhood topic is a complex one. Transport is underrepresented, too. Buildings cover a large fraction of projects with a maximum in residential buildings.

Figure 2: Taxonomy Matrix: Application Fields and ICT Groups

The distribution of projects by type of technology can be seen in Figure 5. Again, projects can be in more than one category. It shows that the topic of energy management is the most covered one whereas design, planning and realisation are not so common.

Figure 6: Geographical Distribution of Projects (based on Mundig} [8]) located in the EU with most being in Spain and France, few in the US and Asia, one in Africa and none of them in Australia.

2.2 Energy Efficient Software and Recommendations Table 1 Comparison of the Approaches on measuring energy consumption of ICT

Figure 3 Architecture of the measurement setup [10] The SUT is the computer hosting the application program, whose induced energy consumption will be evaluated. It is defined by the combination of hardware components that make up the computer system, the operating system, the runtime environment, and the application program.

middleware is partly also used by another application. The node map that we constructed for the application is shown in Figure 2.

2: OpenMalaria grid computing project processes during a period of three days. Energy consumption remains steady. Only when downloading new work blocks, the CPU utilization drops and hence energyconsumption also.

1: Measurement within the datacenter. Everything is measured, however what goes on inside the server remains a black box.

Picture 4: Workload production server: 112 watt

Picture 6: Workload per process on the 'openmalaria' server. The 'openmalaria' process takes the bulk of the usage.

process on the pilot server. This server is idle. The usage by the software is almost to be neglectec

Picture 9: Usages OpenMalaria (Dell Poweredge 1810) from 3 to 5 May 2012

Picture 1: Usages HOLSIC 001 server Fujitsu (from 10 to 13 April 2012)

behavioural change in 8,331 households to be recruited from 13,643 in eight selected towns in the Pilbara and Kimberley regions of the Northwest of Western Australia. Another 15% savings was targeted from retrofits to houses that provided free product upgrades including new shower roses, toilet cisterns and tap aerators. Water Corporation was the Principal in this program with methodological design, project management, community forums, training of coaches, call and mail centre operations including coaching, meter reading, website development, statistical analysis of data and evaluation by subcontractors. The targets of 15% were overly ambitious in the absence of sound information at the time of scoping. Unknown at the time was the very small number of owner-occupied households. The vast majority of tenancies were employer-owned housing (large mining companies) with free water-use allowances provided as part of the salary package thereby dis-incentivising water conservation practices and participation in the program. Despite the target of engaging 8,331 households the Water Corporation databases only held 4,413 accounts with most of these lacking in customer contact details. 1,428 of these households were eventually registered on the program. This represented only 17% of the desired registration target. Therefore the project team needed to source other databases to increase the potential number of registrations. After several months, when contact details could be augmented from other sources and a total of 4,997 households were ultimately registered, with some withdrawals along the way also, a reduced amount of time was available for engagement of the new recruits with CBSM techniques. The rounds of coaching that occurred as part of the Project Plan are shown in Figure 2, with not all households receiving all calls because of their late recruitment in the program. Nevertheless, final evaluation showed that as an overall average, participating households achieved a 6.9% savings from behavioural change and 9.9% overall if the results were annualised.

Figure 3: The Greensense Process The Greensense product, GreensenseView, is provided as a hosted software service (Figure 3). It monitors electricity, water and gas use, displaying the results on an interactive ‘dashboard’ on your website or kiosk-displays within homes or buildings. Greensense brings the data to life for building users, enabling people to see — in real time — how their actions are impacting on the sustainability performance on the spaces they live and work in. The Greensense product works by providing timely and sustained feedback and evidence of performance improvements. One of the unique features of the Greensense product is its ability to aggregate information across multiple facilities to enable real-time benchmarking, comparative performance tracking, and competitions between home or office occupants.

Figure 4: A Unidata logger and transmitter is connected to this V100 Elster water meter via a T-probe All nine (9) loggers were successfully installed by the end of March, 2012 (Figures 4 & 5). These were only temporary installations for the duration of the trial.

The dashboard (Figure 6) underwent many edits during the establishment period. All wording on the dashboard was revised to ensure it provided softer feedback, for example the wording in right-hand side module was modified from “Worst” and “Best” to “Highest” and “Lowest”. The Water Corporation has supplied a series of tips and the Water Corporation website address to be utilized in the rotating tips box. Figure 6: The Smart Meter Challenge Dashboard Focus groups were conducted during the planning phase of the Perth Home Smart program. From this research appropriate wording and graphics for feedback letters were developed to optimize participant experience. The main feedback graphic was a quintile pyramid, as shown in Figure 7. A quintile is a statistical value of a data set that represents 20% of a given population. Participant households were assigned their quintile according to their water use. The water use quintiles were re-calculated each round by ranking the control households according to their water use (highest to lowest) and allocating 20% of the control households to each water use quintile. This meant that the water

3. RESULTS Figure 7: Quintile module

This report shows the average 24-hour consumption profile over each day of tre week for Water (L)' for ye period 1- atddani deri a. aoa cna. aneaior Figure 8: Heat-map report

Figure 9. Average daily water use (L/p/day) of Greensense VIP households

According to the mentioned trend in waterworks computerization at the Systems Research Institute an integrated IT system for complex water network management has been developed, whose structure is shown in Fig. 1.

Figure 3. Module ‘Kriging applications’ of the IT system.

In the first example the program importing the data from GIS is Mosuw-H that calculates then the water pressures and flows for all elements of the water net. The calculation is mostly done for the static state of the water net work, i.e. for only 1 set of data conceming the water use in the end user nodes of the water net. This data set is won from GIS via the CIS system. The subsequent step of operation is the export of data resulted from Mosuw-H to program Kripow that draws the distribution maps for pressures and flows calculated using the kriging approximation (Studzinski, 2011) (Fig. 6 and Fig. 7). Figure 6. Hydraulic calculation of the water net by Mosuw-H.

the Branch Data Base of GIS to the program performing the hydraulic calculation (Fig. 5). Figure 5. Export of the water net graph from GIS to the hydraulic model (M osuw-H).

Tn the second example the data from GIS are going to Mosuw-W that makes the hydraulic calculation and uses the results to compute the water age in all pipes of the water net. To do this the data conceming the loads of the end nodes have to be in form of time series and the time step between two neighboring nodes equals usually to 1 hour. Subsequently the data conceming the water age are sent to program Kripow-W that draws the map of water age distribution in the water net (Fig. 8 and Fig. 9).

fully automatically by starting the collaboratuion process between the modules MOSUW and ‘Kriging applications’ of the IT system. Figure 9. Drawing the map of water age distribution in the net by Kripow-W.

The environmental impacts of the users process are ob- tained by combining the data with the selected impact as- sessment method and are integrated in the textile value chain for a calculation of the whole life cycle of the garment. The rest of the processes in the value chain are calculated based on either speci c data of other suppliers or on default data (e.g. from ecoinvent). Figure 1: The information ow in the benchmarker

resents the set of all possible alternative resource sites, al- ternative processes@processing sites, warehouses and nal destinations, and A is the set of the possible transportation alternatives between the sites (V). Models based on simi- lar ideas can be found in [2], [3],[4]. In Figure 2, where a partial supply chain is presented, the factories, clews and sheep would be the set V, while arrows would be the set A. Notice that the nal part of the supply chain is missing in the gure. It is also important to stress that in the gure there is not factory with alternative processes options: in such a case there would be overlapping factories at a same location. Figure 2: A partial supply chain.

Table 1. Motivation constructs in content-based IS

Table 3. Motives & Incentives for contributing in IO-EIS

Table 2. Motives & Incentives for joining IO-EIS Designing for enjoyment has been previously defined as key for IT artifacts. In particular, positive experience should be created by a user interface which promotes pleasure, enjoyment, and fun in order to enable user satisfaction [1]. The system should also be easy to use. Community-building features and designing for sociability is required to fulfill the desire of individuals to participate in communities. E.g. [4] have identified a number of principles that facilitate sociability in social software. Comparability within the community should also be enabled in order to facilitate an advantageous competition [14].

5. INCENTIVES FOR IO-EIS

6.2 Evaluation A first evaluation took place in a workshop including the five case companies. The motives and incentives were discussed as well as the proposed incentive scheme. Furthermore, a _ concept implementation based on a prototypical IO-EIS developed by the case companies was prepared and illustrated by mockups. Overall, the proposed incentive scheme was seen as a good enabler to enhance user contributions and EPI quality. However, a quantitative evaluation is still outstanding. The main statements of the evaluation workshop are summarized below:

The Archetype Sustainable House’ in Canada aims at a modern resource efficient house design with natural and sustainable materials. It is built with wood, cork, bamboo, organic paint and ash based concrete. New insulation methods and _solar/gas powered heating as well as solar powered low energy light sources aim at a minimal energy consumption. Waste water is treated directly at the house and rain water is collected and used for toilet flushing. Throughout the construction phase, waste was sorted and recycled on site or sent to facilities, compostable plates and cups were used for lunch and the entire site was powered by solar power trailers.

foundations, solar panels for lighting, music and computing, water coming from a nearby spring, a compost toilet, and a pond in the garden to collect rain water from the roof.

Figure 1: System architecture

Figure 2: Sensor deployment

Figure 3: Hydraulics measurement system

Figure 5: Environment measurement system Figure 4: Airbox measurement System

Figure 7: Database Schema

Figure 9: Environment system measurement result Figure 8: Hydraulics system measurement result

Examples of utilizing ICTs for sustainable living, especially among people living nearby, are already observable in many cities across the world. As an example, peer-to-peer car-sharing service RelayRides (https://relayrides.com) connects people who need a car with vehicle owners in their neighborhoods. This ICT-based service that launched in two cities in 2010 has grown to operate nationwide in the United States, including cities like San Francisco, New york, and Boston. (data accessed in June, 2012) Car owners can make money by renting out their car otherwise sitting idle in parking lots, and renters can save money by paying per use of car, instead of paying all the cost of owning a car. In recent years, similar type of neighborhood car-sharing services has been growing, such as Whipcar (http://www.whipcar.com) in UK, Buzzcar in France (http://www.buzzcar.com), Snappcar in the Netherlands (http://www.snappcar.nl), Tamyca in Germany (http://www.tamyca.de), Getaround (http://www.getaround.com) in the United States, a recent start-up JustSharelt (www.justshareit.com), and campus-based service Wheelz (http:/;www.wheelz.com) operating at Stanford University, UC Berkeley, UCLA. Figure 1. Online platform of car-sharing service RelayRides The idea of neighborhood sharing can be applied to any underutilized assets, ranging from occasional-use household items (e.g. power tools) to space (e.g. garden, parking space). UK-based service Landshare (http://www.landshare.net), which has more than 69,800 members (data accessed in August 2012), helps people who want to grow their own vegetable and fruit but do not have a garden connect with nearby garden owners. Many garden owners who join the service are either too old or too busy to manage their own plots, and by sharing their garden they can reduce the burden of gardening. While no money changes hands in garden sharing, those involved often have an agreement to divide up the fruit and veg produced.' Similar initiatives such as UrbanGardenShare (http://www.urbangardenshare.org), and SharedEarth (http://www.sharedearth.com) also operate the same

Figure 2. Online platform of Timber Wharf Time Bank Not only physical assets, but also intangible assets, such as time and skill, can be shared (or exchanged) between neighbors to meet their needs. A classic example ‘timebanking’ has been revived by the widespread use of the Internet. NeighborFavor (http://www.neighbfav.com) implements a mobile platform to match a person who needs a favor like grocery shopping with neighbors who can do the favor. Similarly, users of TaskRabbit (http://www.taskrabbit.com) can post a task on its online platform to find someone in their neighborhood who would do it for them. While services like NeighborFavor, and TaskRabbit are based on monetary incentives (users get paid for doing a favor for others), timebanking initiatives based on a more traditional sense of ‘mutual help’ are also expanding through digital platforms. These platforms support users to post an offer or request (e.g. www.camdenshares.org.uk, http://twtb.co.uk, www.rgtb.org.uk, http://timebank.org.uk), or provide tools to start a new local timebanking community (e.g. http://timebanks.org). The benefit of timebanking goes beyond making use of the assets and resources existing within a particular community. Timebanking contributes to strengthening local communities by enabling people from different backgrounds, who may not otherwise meet, to form connections and __ friendships. (Timebanking UK, 2011) Van der Wekken (2012) claims that timebanking can play an important role to well-being in society. It can give people more control over their own lives as well as neighborhoods, and empower and engage communities along the principles of equality and reciprocity. Different from running an

Figure 5. Activity stream feature in the main page The front page of the platform is composed of features creating ambient intimacy and providing social affordance, referring to the quality of artifacts in any space physical or virtual, which invite and facilitate social interaction among the participants in that space. (cited in Hanrahan, et al., 2011) Among various features, such as forum topics, and pictures and videos recently uploaded by members, an activity stream is placed in the most visible part of the front page. The activity stream presents an aggregation of recent updates and activities of members within the system, ranging from recent posts, to someone adding another member to his/her friend’s list. Members can also share their activities, and status by directly typing short message into the activity stream. The activity stream can give a sense of vitality of the community, and show new users the range of activities that they can engage in within the platform. (Crumlish & Malone, 2009)

Figure 4. Profile page of a member profile page encourages relationship building by enabling members to send personal messages, add each other as ‘friend’ to personal network on the platform, as well as sharing information about themselves with others. Profiles provide users their identity on the system, and aid in discovery of common interests and articulating relationships. (Hanrahan, et al., 2011)

Figure 7. Another example of forum topics ‘Chi si trova dov. (discover who lives where)’. Names of residents are placed based on house number.

get to know each other (Figure 6), and to find who are living next door. (Figure 7) Some of forum topics (e.g. ‘Let’s get to know each other’) were proposed by the researcher, but gradually more topics were posted by members, such as offers/request of product sharing, suggestion for group purchase of food. Figure 6. One of forum topics ‘Conosciamoci (let’s get to know each other)’

Figure 8. ‘Birthday of today’ feature When there are members whose birthday is coming up, they are introduced on the front page, with a messaging tool that helps easily send a personal message to the person. (Figure 8)

Figure 9. The number of new members in each month (bar), and the total number of members (purple line) The members were joining gradually with some peaks related to face-to-face meetings, and the opening of the platform to the residents of another building in the neighborhood (Figure 9). Most members frequently use the platform after signing up, except 12 non-active members.

Members’ age range from 15 to 68, with the larger group of users between 36 and 46. (Figure 10)

Figure 2. Services applicable for ICT solutions.

Table 1. Parameters for defining number of users in the ICT solution scenario including the chronic heart failure (CHF) case as an example Table 2. Examples of drivers and barriers related to the implementation and use of an ICT solution

Table 3. Examples of environmental activity categories

Table 4. Examples of data needed for comparison between cities for the purpose of scaling ICT solutions

The choices we make in this paper can be compared to how the current policy of the City of Stockholm looks like in the area. According to the Stockholm Environment Program 2012-2015 [17], the City uses in principle two target years: 2015 and 2050. Both of those are absolute, thus, they have no reference year. However, the City indirectly relates the 2020-targets to EU-targets and national targets both stated with 1990 as reference year. The object of target is the Municipality of Stockholm and only heating, electricity and transport within the municipality are included. The City does not use any target on energy in their environmental program. Instead, they stick to CO2e/per capita. The aim is set to 3 tons CO2/capita by 2015 aiming at a fossil free Stockholm by 2050. The range of the City’s target is the whole life cycle for fuels and electricity production. However, they do not include energy use for the citizens’ consumption of goods, nor their travel beyond the city borders, but they bring in transport within the city limits by others than the citizens.

Table 2. Analytical framework to explore the potential of ICT solutions to decrease energy use. The matrix shows energy use per household function in Stockholm 2000 [15] combined with the identified ICT opportunities. The latter divided into new opportunities (0), pilots (p) and existing ICT solutions (i) as well as where ICT solutions not is applicable for energy decrease (n.a).

Table 1 ICT energy consumption of nine Dutch HE- institutions [7].

Table 2 Overview of topics in the funding arrangement for innovative green ICT projects at institutions.

Figure 1 Example of the OpenDCME model [4]. already done an ICT-scan and (2) that institutions did not have the time to participate. The latter is quite interesting given the current environment in which institutions are looking for savings in every corner and that (the energy efficiency of) ICT is, on average, hardly exploited. One might think that learning about possible ICT savings might be worth the effort. One of the root causes of this paradox is that ICT departments at institutions are hardly responsible for their own energy bill.

We know from experience that creating and raising a community takes time. Unless there is a true sense of urgency, it takes at least a year for a community to become active and for members to start profiling themselves and making (pro-active) contributions. After about one year and a half we wanted to learn what members themselves thought about the community and where it is going. We asked them a number of questions through a questionnaire (n = 38). In the same period a survey (n = 35) on green ICT was held among higher ICT-managers (directors, CIOs, etcetera) in general. A selection of both sets of questions is mentioned here and reflect well the current mindset in the community and at the higher education institutes in general.

Finally, we asked a number of questions on the ICT-scan. 50% of the community members were familiar with the scan and its results of possible savings averaging 44%. Figure 3 displays the response of all the members to what effect these results had on their institution. Only 10% got to work straight away, and 20% said it had no effect at all. The large ‘No idea’ part is largely explained by the other part of the aforementioned 50% (they were not familiar with the results in the first place).

of a car, an e-bike’ as well as the membership-based access to car sharing, public transport and intercity rail. As these mobility options relate to individuals like personal attributes do, they have to be protected regarding privacy issues as well.

The first LCA study deals with production and disposal of the various (printed and electronic) media. For this case, “classic” cradle-to-gate LCA studies could be applied to the various electronic devices and printing technologies. In Figure 1, these system boundaries are shown for the example of a desktop computer. As a functional unit, this first LCA study is using the complete device in the case of electronic media; i.e. one desktop computer (including keyboard and monitor screen). For the print media, a functional unit of 1’000 kg of printed matter is used. The overall goal of the study is the calculation of the ecological load due to the use of printed and electronic media in the context of a variety of use cases. In a first phase, our objective is the calculation of the environmental impact from the production and disposal of various (printed and electronic) media; as well as the calculation of the impact due to ‘1 unit use’ of the respective medium. These data can then be used to calculate various application cases in the second part of the project. In order to achieve the objectives of the first phase, two distinct LCA studies have been performed, producing the necessary input data for the web-based tool (see chapter 3). The second LCA study has its focus on the (active) use phase. Apart from the electricity consumption due to the use of an electronic medium, this second study defines how the results from the first study (i.e. production, disposal of the devices) are taken

into account per unit of use. Figure 2 shows the various elements of this system for the example of a smartphone. The functional unit for electronic media chosen in this second LCA study is ‘1 hour of (active) use’. In the case of printed matter, the functional unit is ‘1 kg of print products’ (which can be calculated from the format and the square weight of the used paper type). 2.3 Inventory Analysis 1.3.1 Production of electronic media

For paper, the datasets in ecoinvent [17] for newsprint paper, recycling paper, LWC paper, SC paper, woodfree coated as well as woodfree uncoated paper are used.

Table 2. Example for linking project-specific data with background data from the database ecoinvent v2.2 (data shown for “smartphone’”) 2.3.2 Production of print media

Table 4. Overview of the energy consumption (in W) of the various use modes for the different electronic media In addition, default use pattern (for the average daily use) as well as the overall life-time of the devices have been established in this first part of the study; the use patterns are summarized in Table 5. These defaults can later be overriden by the user of the tool.

Table 5. Summary of the default use patterns assumed for the different electronic media Additional infrastructure required to access the Internet is taken into account by using the relevant dataset from ecoinvent described in [26].

Figure 3. Web-tool mat - general overview (showing the GWP results of case study 1 described in chapter 4)

part of this figure, the numerous input variables to define the various printed and electronic media are shown; allowing to the user e.g. a definition of two different print products in the same comparison. For the three types of computers (netbook - laptop - desktop), the same variables are used.

Figure 6. Case study 1, advertisement of a discount shop: Impact in terms of Ecosystem Quality, Human Health and Resources according to the ReC iPe method (in % of the highest impact each) for a single advertisement campaign Figure 5. Case study 1, advertisement of a discount shop: Global warming potential (GW P) in kg CO, eq per advertisement campaign and household

The results are shown in Figure 5 and Figure 6 below, representing two different ways of presenting the results (i.e. either the details of each life stage or the total impact only).

In order to support the user in interpreting the results, the tool provides a comparison with the impact of one (or several) cups of capsule coffee. The data for this benchmark are taken from a simplified LCA study about coffee capsules established in Spring 2011 and published as a press release by Empa [34]. In Figure 7, the impact of one cup of coffee has been added to the presentation of the results. As shown in Figure 7, even the way of advertisement with the highest impact is in case of the Global Warming Potential and the Resource consumption (according to ReCiPe endpoints) still lower than the sole impact of one cup of coffee. Comparing only the printed version with the cup of coffee shows that only in case of the factor “Ecosystem Quality” (according to ReCiPe), the impact from the coffee is lower - in all other cases, producing a single cup of coffee has a higher impact than the leaflet.

Figure 8. Case study 2, the paper at hand: Global warming potential (GW P) in kg CO>-Fq for 8 pages

Figure 9. Case study 2, the paper at hand: impact in terms of Ecosystem Quality, Human Health and Resources according to the ReC iPe method (in % of the highest impact each) for 8 pages

Figure 10. Case study 2, the paper at hand: influence of changes in use patterns and the electricity mix in the use phase on the results for reading 8 pages In Figure 10, the results of these variations are shown by the grey bars added on the top of the black bars, representing the results from the Option 1 case (no printout).

Table 1. Description of the system studied

Table 2. Sources of electricity in Swedish and UCTE mix [11] The transportation of electronic office equipment was assumed to be by boat from Shanghai to Europe (Rotterdam) and further to the users in Stockholm by truck (>16 tons). Data on truck and freight ship transportation were taken from the Ecoinvent database [28].

2.3.3 Reading magazine Table 3. Network components and their electricity use and manufacturing greenhouse gas emissions in 2010 for an average user

Figure 1. Climate change impact of (a) the emerging and (b) the mature version of the tablet magazine in the reference scenarios (per reader of a copy) Figure 1 presents the potential climate change impact associated with different activities in the life cycle of emerging and mature versions of the tablet edition of Skona Hem.

Figure 2. Sensitivity analyses for the tablet magazine concerning the electricity mix, total use time of tablet and number of electronic copies. The effect of the changed assumptions is illustrated in relation to the potential climate impacts of the reference scenarios (emerging ref. = 100).

Figure 1. End user view, Android app.

Table 2. Grades received by biometrics for the selected criteria aspects

Table 3. Table of findings - Mean score achieved by the selected mainstream biometrics

Table 4. Significant value for the selected criteria aspects through ANOVA

Explanation: Following is the explaination given for performances of the three mainstream biometrics particularly for the selected criteria aspect - Non Intrusiveness:

Table 6. Table of result

Figure 1. The model components

Figure 3. Different actions for different types of agent Agents are active. According to the conceptual framework below described the agents can perform different methods (Fig. 3). Actions are related to reduce the limited (or critical) resource consumption. Agent awareness level corresponds to the ability of an agent to perform different kind of action.

Figure 2. Types of agent and awareness levels i.e. the less aware type of agent) to the top (evangelist agent, i.e. the more aware type of agent), as described in Figure 2. An intuitive color code, from red to green, is used for visualize the agent typing and its dynamics.