Defining a Standard Carbon Cost Model for Electronic Software Distribution. * Corresponding author: d.williams@student.reading.ac.
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1 Proceedings of Conference: TSBE EngD Conference, TSBE Centre, University of Reading, Whiteknights Campus, RG6 6AF, 6 th July Defining a Standard Carbon Cost Model for Electronic Software Distribution D. R. Williams 1*, D. Strange 2, Y. Tang 3 1 Technologies for Sustainable Environments, University of Reading, UK 2 Microsoft UK, Reading, UK 3 Informatics Research Centre, University of Reading, UK * Corresponding author: d.williams@student.reading.ac.uk This paper focuses upon defining a method and set of parameters in order to successfully calculate the environmental impact of Electronic Software Distribution (ESD) when compared to its physical alternative. Little focus is given to the environmental impact of this service due to its complexity. This model has successfully identified parameters that can act as requirements for the calculation of ESD impacts allowing comparable results to be calculated across many scenarios. In a review of recent model methodologies and results on the impact of electronic distribution, this paper surmises that a focus upon the data centres that serve the hosting and fulfilment of an ESD service is of prime importance. Using three real world scenarios and using the Life Cycle Analysis (LCA) methodology, methods of calculating the carbon emissions have been developed. The development of this model uses the PAS 2050 standard as guidance for model sections. This study includes the carbon embedded within a server and its creation and transport, which is a departure from the PAS 2050 methodology. Keywords: Environment, Green IT, Software, Carbon, Service. ABSTRACT This paper focuses upon defining a method and set of parameters in order to successfully calculate the environmental impact of Electronic Software Distribution (ESD) when compared to its physical alternative. Little focus is given to the environmental impact of this service due to its complexity. This model has successfully identified parameters that can act as requirements for the calculation of ESD impacts allowing comparable results to be calculated across many scenarios. In a review of recent model methodologies and results on the impact of electronic distribution, this paper surmises that a focus upon the data centres that serve the hosting and fulfilment of an ESD service is of prime importance. Using three real world scenarios and using the Life Cycle Analysis (LCA) methodology, methods of 11
2 calculating the carbon emissions have been developed. The development of this model uses the PAS 2050 standard as guidance for model sections. This study includes the carbon embedded within a server and its creation and transport, which is a departure from the PAS 2050 methodology. Keywords: Environment, Green IT, Software, Carbon, Service. 1. INTRODUCTION Quantifying environmental impacts that result from changes in lifestyles, paradigms and human activities is essential to aid businesses and individuals plan and understand what difference their changes will make. This paper focuses upon developing a method to calculate the environmental impact of Electronic Software Distribution (ESD). ESD is a service by which a digital program is transmitted over a computer network, such as the World Wide Web, to a consumer. Measuring the impact of ESD over its physical alternative is often difficult and complex as Abukhader and Jönson (2003) submit in a review of previous studies conducted up to Past studies have indicated that electronic distribution could benefit the environment if the correct hardware and software infrastructures are setup. All previous studies concluded that the variance in model results is too large, and that it is difficult to measure the exact impact; Weber et al. (2009), Seetharam et al. (2010) Moberg et al. (2008) and Toffel and Horvath (2004) have all completed recent similar studies in different e-commerce areas. WSP (2007) & WSP & Accenture (2009) have completed models directly applicable to ESD. These models found large benefits of using ESD over physical distribution (~90% savings) and will be used as foundations for this investigations model. The Carbon Trust (2010) created a Life-cycle assessment (LCA) model that is used to calculate carbon footprints of various products. This tool has no option to calculate ESD, however the models equations, principles, databases and design are the result of continuous improvement and ratification with academic consultants. This model will therefore be used as a guide. The lack of academic attention in this area means that commercial solutions have been created which do not undergo the rigour of academic review and improvement and can be subject to scenario specific conditions and assumed parameters. Modelling any process is difficult as Gard and Keoleain (2003) demonstrated in a LCA of a digital and traditional library that highlighted how sensitive modelling parameters are, and how they can wildly influence results. This investigation aims to create a model foundation that can be used in many scenarios that use ESD and will aim to identify key model parameters to focus upon when calculating impacts of ESD compared to physical distribution. The key objectives of the overall investigation are to create and assess a methodology to measure the carbon emissions for three online service scenarios and to identify key focus areas for an ESD model. The first of a user purchasing and 2
3 downloading a software package from an online store, secondly the user purchasing via an online store but getting sent the package and finally the user purchasing online, downloading the software and getting sent a backup DVD disk. 2. BACKGROUND Many studies on the environmental impact of transmitting bits instead of moving atoms have been completed since the invention of modern computer systems. During the past 10 years many studies on digital downloads have been completed, and the following studies are those that will be analysed as they share commonalties with the ESD scenarios being analysed. The inclusion of different download scenarios is important when assessing overall impact of ESD to physical alternatives. A large result variance was found by Weber et al. (2009) who analysed the impact of delivering music via the internet or purchasing a CD from a retail store using a LCA methodology using many scenarios. The study performed a full life cycle analysis and showed a potential cut in CO 2 emissions of between 40-80% when the download option was used. A limitation of their study was due to the lack of equivalence found between downloading a CD and purchasing a CD. This investigation differs however as software and purchasing software can be safely assumed in this situation as software does not contain album artwork and in many cases the software media is used only once in its lifetime. This limitation would however be assumed when focusing upon gaming software. Different hardware setups and power saving technologies can swing a carbon footprints value by 270%, therefore this study will place a large emphasis on understanding what hardware technologies are being utilised. This issue was demonstrated by Seetharam et al. (2010) where an LCA investigation discovered the differences in shipping a DVD movie to a consumer as opposed to streaming a DVD over the internet. It was found that currently the CO 2 impact of streaming would be 205% higher than standard shipping but with future improvements in hardware, the footprint could be 65% less than shipping. The model assumed many values and parameters throughout but also highlighted the efficiencies that can be gained over electronic distribution when the file size is very large (> 8GB). Many methods of calculating internet transfer usage exist. Moberg et al. (2008) performed a study on changing Sweden s financial invoicing system from paper to digital platforms. This study implemented innovative ways of calculating internet transfer usage and is useful to compare against as its results on internet usage are current and unlike other studies. Moberg et al. (2007) showed how using different technologies to read a newspaper can also dramatically change the environmental impact, highlighting again that the hardware being used is important in the calculation methodology. WSP, a respected US based worldwide environmental consultancy performed two key studies to this investigation. The first was an evaluation of how ESD via the internet is different from a user purchasing a hard copy of Microsoft Office 2007 from a store (WSP, 2007). This study attempted to follow the PAS 2050 LCA 3
4 methodology (see Section 3) and resulted in the download scenario avoiding 93% of emissions. WSP & Accenture (2009) conducted a similar study comparing the benefits of distributing Microsoft software to its Volume Licence customers (large enterprise software packages) via ESD compared to the traditional route of sending software via the post. The reduction in CO 2 emissions was found to be 91% and as previously discovered the distribution section for the digital download scenario contained over 90% of the overall footprint. Table 1 highlights the areas covered and results from the 2007 study. As found in previous studies, for digital download s the distribution (via servers) of the software is the largest area of importance to focus upon. Table 1 - Results from WSP (2007) study comparing ESD and Purchased product scenarios for Microsoft Office Digital Download kgco 2 eq Full Package Product kgco 2 eq Avoided Emissions kgco 2 eq Materials Distribution Process End of Life Total Emissions From the WSP & Accenture studies the largest source of emissions result from the digital distribution process. It is therefore apparent that the most important area to focus upon when analysing ESD is the data centre s power consumption which includes servers and associated cooling, storage and networking equipment. Calculating this section is difficult as the power used by a data centre and its associated modems, routers, hubs, switches and internet backbones is difficult due to the variety and variability of hardware specifications and software used to run them. The detailed makeup of a data centre is often a guarded company secret and thus assumptions on the server hardware and associated networking equipment are often used. Koomey (2008) provides a brief history of data centre power calculation studies completed and Koomey (2007, 2008) performed in depth analysis and derived average power consumption values for the world, world region and by server class (Table 2). This study used publicly available server data from server manufacturers to determine server power usage using basic industry recognised assumptions. The server calculation methodology from this paper can be applied to any data centre setting and thus provides a good foundation to understand current power consumptions. This methodology, however, excluded the power consumed by data centre data storage and networking equipment and related cooling and auxiliaries, which is of prime importance when providing a user with software stored within a server base. Roth (2002) includes a method to calculate the data storage and the US Environmental Protection Agency estimated power used by data storage and networking equipment (EPA, 2007), which can both be used in this study as good foundations. 4
5 Table 2 - Average power consumption per server type from 2000 to (Koomey, 2008) Average power used per server (World) Units Volume Mid-Range High-end Average 2000 Watts/Server Watts/Server The Power Usage Effectiveness (PUE) (aka Site Infrastructure Energy Overhead Multiplier (SI-EOM)) is an industry standard measure to determine how energy efficient a data centre is. The PUE is calculated as a ratio by dividing the amount of power entering the data centre complex by the power used to run the servers and computers of the datacentre thus the smaller the PUE the more efficient the data centre. The PUE has become an IT marketing tool for many companies with Microsoft quoting a PUE of 1.25 at their Dublin datacentre (Microsoft, 2009) and Google claiming a monthly PUE of 1.10 for one facility (Google, 2010). The PUE can thus be determined for a data centre and applied to the power consumed by servers and storage equipment to work out the cooling and auxiliary power demands. The PUE has been used in WSP (2007) and WSP & Accenture (2009) as a way to simplify the model, this technique will be used in this model also. Virtualisation refers to the technique whereby more than one server session can be run on one physical server in unison. A server without virtualisation will run at about 40-60% efficiency (Koomey, 2007) thus virtualised systems use a physical server s hardware more efficiently as more is being completed. Measuring is very difficult as virtualisation techniques mean that the amount of virtualised servers vary with demand. Little academic work has been completed in this realm however Abaza et al. (2009) provides a good technique to work out maximum potentials based upon server hardware. The amount of virtualisation is thus another indication of how efficient a server is and thus needs to be factored into the model; this is something not covered by previous studies. 3. METHODOLOGY & DATA To assess the three scenarios and to identify the key parameters, LCA methodology will be utilised. LCA is an established technique to analyse a product from its creation to its end of useful life (Finnveden, 2010). PAS 2050 LCA requirements will be adhered to and the principles of creating a model that can be used in many situations to allow users to compare results will be a key model design feature. PAS 2050 was drawn up by the Carbon Trust and Defra and is an internationally recognised specification for the assessment of the life cycle greenhouse gas emissions of goods and services and specifies basic requirements for an LCA. This requirement specification means that organisations can undertake an LCA and compare it to a competitor or alternative product knowing exactly what to take into account and what to leave out, i.e. the boundaries are pre-set making the overall LCA process simpler and less time consuming. To begin creating the model structure, each scenario must first be understood. Each scenario was detailed by analysing the processes of the online store of a large 5
6 software company. This analysis involved many interviews and worldwide communications to fully understand what occurs during digital and physical distribution as well as the website hosting stage. The resulting process maps were placed into a functional flow chart following the LCA methodology of splitting the sections into five key stages; raw material extraction, manufacturing and production, distribution and transportation, operations and maintenance and disposal and recycling The resultant process maps were then analysed and the principle rules of PAS 2050 were applied to identify the scope of the subsequent LCA. Using models from WSP (2007, 2009) and from the Carbon Trust (2010) as section examples the filtered process maps were split into sensible sections and placed onto a spread sheet. Each section of the overall process was detailed to include its main inputs and outputs. The equations used for each sections input and/or output calculation were a combination of WSP (2007, 2009), Carbon Trust (2010) and Weber (2009); each was analysed and modified when appropriate. For each calculation, an emission factor is used to relate that process to a CO 2 equivalent value. Many emission factors exist, however the Carbon Trust (2010) produces a list of PAS 2050 certified emission factors or lists PAS2050 certified calculation sources and methods. This certified list was used to produce a list of emission factors relevant to the study. PAS 2050 factors are used as they include cradle to grave emissions thus simplifying the overall model as details about processing energy from each stage is not needed. 4. INITIAL RESULTS Figures 1 & 2 describe the sections of each scenario that were taken into account within the model. Figure 1 describes the processes that all scenarios share; when the user uses their computer and logs onto an online store to purchase software. The transfer of information over the internet for this process is assumed to be comparatively small therefore it is excluded however the time that the user spends using their computer is accounted for. The data centre that hosts the website and fulfils the download is accounted for in terms of both energy and components. It is contentious to include the embedded energy for servers in this section as it may contravene PAS 2050 rules. This was included as the data sent to and from a user cannot reach a user s computer in any other way and servers, although highly variable, are standard enough in design to assume components and construction methods. Following PAS 2050 guidelines to not include embedded energy, the user s actual computer system is not accounted for in the model. This is because unlike servers a website could be accessed in a number of different ways, PC, MAC, Phone, Laptop etc. The Digital Distribution route for a user to download from the online store is also included in Figure 1. Commonly online stores will use a different server and data centre to fulfil downloads and thus in this scenario an additional data centre is accounted for using the same calculation method as digital purchasing. This process includes a calculation of internet transfer as the software sent is assumed to be of a size that could impact on carbon emissions. 6
7 Figure 2 highlights physical distribution for a software item. This section comprises a standard LCA and thus this section takes into account the software packaging and raw materials, associated transport, final distribution and end of life calculation. The results of this section follow the results of the WSP (2007, 2009) study closely. A large amount of detail is required for this section. Using the process maps an initial model was calculated. From an initial set of results ~42% of the overall emissions for digital distribution only come from the server fulfilment stage (power usage). The following model parameters were identified as important to calculate this section (and thus the server hosting section also); Server Plate Ratings and Observed Server Power for each server being used Virtualisation Ratio per server (i.e. The server may host 10 virtual servers) The amount of virtual server sessions per measured process (i.e. The online store may use 5 of the 10 virtual servers) Percentage of the process being used by the measured activity (i.e. The download activity may be using on a certain percentage of the server session) The time of the activity being measured The size overall data being transmitted The Network Equipment Ratio for the data centre in question Number of times the measured process is performed (i.e. The process may be interrupted and restarted) Data centre PUE For digital distribution, internet transfer accounted for only ~1% of total emissions and the user download and user purchasing together accounted for ~5% of the total. Materials and associated transport and end of life for the servers accounted for ~10% of total emissions. For the physical distribution model, ~65% of total emissions come from the material stage and 15% coming from the online stores server hosting stage. Distribution and End of Life sections accounted for ~8% and 9% respectively. For this section important parameters to focus upon are the materials section which needs to include a detailed inventory of the packing type and related items such as booklets and the amount of DVD s sent. Like digital distribution, understanding the parameters of the hosting server is also important for this section. 7
8 Online Store Digital Purchasing & Digital Distribution (Scenario 1,2 & 3) Raw Materials Manufacture Distribution End of Life Postage Download with backup Download Raw Material Sourcing, extraction & processing Transportation Server & IT Equipment Materials Manufacture User Download Transportation User Purchasing* Internet Transportation Hosting Server Fullfilement Server Data Storage and Networking Equipment Data Centre Cooling and Auxiliaries Data Centre IT Equipment Materials * This section falls under the Operational section of the LCA Figure 1 The use of the online hosting server is common to all scenarios and involves the user spending time online whilst purchasing the software. When the user selects the download only or download with backup scenario, the use of the fulfilment server and the internet transfer to the user are taken into account. 8
9 Online Store Physical Distribution (Scenario 2 Only) Raw Materials Manufacture Distribution End of Life Package Assembly Transport Extraction, processing and transport Transport Distribution Centre (Regional) Manufacture of base Materials DVD & DVD Pressing Distribution Centre (Regional) Transport Labels Transport Material Landfill & Recylcing Packaging (Plastic & Cardboard) Printed Materials Figure 2 -- If the user selects physical distribution (Scenario 2) then segements will be calculated which include the packaging and raw materials for the DVD box and the DVD itself. 5. DISCUSSION From the background literature, previous models and this studies initial models result, it is clear that the most important parameters for all scenarios modelled are those involved with the hosting and fulfilment server sections. This result is not ideal as gaining information on data centre setups is very difficult and thus limits the models usability within the wider community. However, the results of the initial model suggest that when focusing upon digital distribution assumptions about internet transfer can be made as the total impact on the overall footprint are relatively low compared to the data centres use. This is positive as attempting to predict the path way of data across a network (i.e. from the UK to the USA) and 9
10 determining the proportion of the network that the process is using would be a difficult process to complete. From the set of model parameters highlighted as important to calculating server emissions for an activity running on a process, it is obvious that measuring this is going to be a difficult task. Many data centres include process monitors and standard assumptions can be made however, further work needs to be completed in measuring and reporting process usage by servers in order to hone results. This framework for the calculation of ESD sets out an academic solution to a problem only partly attempted by software companies (WSP 2007, 2009). This model has taken best practice and industry solutions to provide an independent analysis of the area s most likely to produce the largest emissions and provides a requirement specification for physical distribution that allows comparisons to be drawn to ESD. Including sections on virtualisation and the actual utilisation of the server for a specific ESD process is a new feature and will allow the implementation of energy saving technologies to improve the carbon impact from a server and thus lessen impacts over time. However, important assumed areas such as PUE values must now be studied and verified if the model is to be utilised and developed. The next step in this investigation will be to use real world data on the model and attempt to test the models outputs against actual emission outputs to gauge how successful the model has been. The model will is also being reviewed by PAS 2050 consultants and will be adjusted appropriately for eventual use in the wider community. 6. REFERENCES Abaza, M. and Allenby, D. (2009). The effect of machine virtualization on the environmental impact of desktop environments. The Online Journal on Electronics and Electrical Engineering, 1(1): Abukhader, S. M. and Jönson, G. (2003). The environmental implications of electronic commerce: A critical review and framework for future investigation. Management of Environmental Quality: An International Journal, 14(4): BSI (2008) PAS 2050:2008 Specification for the assessment of the life cycle greenhouse gas emissions of goods and services. [Online]. Available: Carbon Trust (2010) Carbon Expert Tool. [Online]. Available: EPA (Environmental Protection Agency). (2007). Report to congress on server and data center energy efficiency. public law Technical report, US EPA, ENERGY STAR Program. Finnveden, G. (2010). Life cycle assessment. [Online]. Available: Gard, D. L. and Keoleian, G. A. (2002). Digital versus print: Energy performance in the selection and use of scholarly journals. Journal of Industrial Ecology, 6(2): Google (2010) Data Center Efficiency Measurements. [Online]. Available: Koomey, J. G. (2007). Estimating total power consumption by servers in the u.s. and the world. [Online]. Available: Koomey, J. G. (2008). Worldwide electricity used in data centers. Environmental Research Letters, 3(3):
11 Microsoft (2009) Greening the Dublin data center. [Online]. Available: Moberg, Ã., Johansson, M., Finnveden, G., and Jonsson, A. (2007). Screening environmental life cycle assessment of printed, web based and tablet e-paper newspaper. Technical report, Centre for Sustainable Communications, Sweden. Moberg, Ã., Borggren, C., Finnveden, G., and Tyskeng, S. (2008). Effects of a total change from paper invoicing to electronic invoicing in Sweden. Technical report, KTH Centre for Sustainable Communications, Stockholm. Roth, K. W., Goldstein, F., and Kleinman, J. (2002). Energy consumption by office and telecommunications equipment in commercial buildings volume i: Energy consumption baseline. Technical report, US Department of Energy. Seetharam, A., Somasundaram, M., Towsley, D., Kurose, J., and Shenoy, P. (2010). Shipping to streaming: Is this shift green? In Proc. of First ACM SIGCOMM Workshop on Green Networking. Toffel, M. W. and Horvath, A. (2004). Environmental implications of wireless technologies: news delivery and business meetings. Environmental Science & Technology, 38(11): Weber, C., Koomey, J. G., Matthews, S. (2009). The Energy and Climate Change Impacts of Different Music Delivery Methods. [Online]. Available: WSP (2007). Calculating business value and environmental benefit of digital software distribution. Technical report, WSP. WSP & Accenture (2009) Demonstrating the Benefits of Electronic Software Distribution: A study of greenhouse gas emissions reduction. Technical report, WSP & Accenture. Keywords: Environment, Green IT, Software, Carbon, Service. 11
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