Building a Green Archiving Model: Archival Retention Levels, Information Value Chain and Green Computing

Information and Communication Technologies (ICTs) affect the environment in various ways. Their energy consumption is growing exponentially, with and without the use of ‘green’ energy. Increasing environmental awareness within information science has led to discussions on sustainable development. ‘Green Computing’ has been introduced: the study and practice of environmentally sus- tainable computing. This can be defined as ‘designing, manufacturing, using, and disposing of com- puters, servers, and associated subsystems - such as monitors, printers, storage devices, and net- working and communications systems - efficiently and effectively with minimal or no impact on the en- vironment’. Nevertheless, the data deluge makes it not only necessary to pay attention to the hard- and software dimensions of ICTs but also to the value of the data stored. We explore the possibilities to use information and archival science to reduce the amount of stored data. In reducing this amount of stored data, it’s possible to curb unnecessary power consumption. The objectives of this paper are to develop a model (and test its viablility) to [1] increase awareness in organizations for the environ- mental aspects of data storage, [2] reduce the amount of stored data, and [3] reduce power consump- tion for data storage. This model integrates the theories of Green Computing, Information Value Chain (IVC) and Archival Retention Levels (ARLs). We call this combination ‘Green Archiving’. Our explora- tory research was a combination of desk research, qualitative interviews with information technology and information management experts, a focus group, and two exploratory case studies. This paper is the result of the first stage of a research project that is aimed at developing low power ICTs that will automatically appraise, select, preserve or permanently delete data based on their value. Such an ICT will automatically reduce storage capacity and curb power consumption used for data storage. At the same time, data disposal will reduce overload caused by storing the same data in different for- mats, it will lower costs and it reduces the potential for liability.