What is green computing?
The term green computing first appeared in the early 1990s but there was no attempt to formalise a definition until considerably later. In fact, even in 2005 a paper entitled Green Computing was published in which it was used to differentiate a sandboxed working environment from one that was open to the Internet. Concerns over the ecological footprint of computing only started to be recognised in a formal way around 2007. It was in that year that San Murugesan published an executive report for the Cutter Consortium which defined green IT as
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the study and practice of designing, manufacturing, and using computers, servers, monitors, printers, storage devices, and networking and communications systems efficiently and effectively with no or minimal impact on the environment
In these notes, the terms green IT and green computing are used interchangeably.
The definition above is stated in very general terms that may be interpreted differently in different contexts. In addition, technology changes rapidly over time so that what was a priority in 2007 may no longer be so at the time of writing in 2023. The adoption as a buzzword by business managers and marketing executives further complicates the picture raising the question of whether green computing is simply greenwashing.
What is greenwashing?
This piece by the BBC explores the meaning of greenwashing and how a lack of agreed definitions for terms like green, ethical and sustainable lead to deceptive behaviour by companies and other organisations. This in turn leads to scepticism and apathy among consumers as the value of these terms is steadily undermined.
Looking at the detail of the claims made by tech companies reveals why it is often difficult to judge their accuracy. According to the Forbes Global 2000, the world's largest tech company by revenue is Amazon whose operations include warehousing and logistics as well as data centre operation, software development and device manufacturing. In such a complex context, making the distinction between computing and other aspects of the company's operations is virtually impossible.
The second largest company by revenue is Apple who claims to have been carbon neutral since 2020 and that its products will be carbon neutral by 2030. This begs the question of how this distinction can be made when the company and its products are so very tightly connected.
The lengthy environmental statements offered by these companies show that the devil is in the detail and that simple definitions of sustainability or green computing may be difficult to apply across the board. To be genuinely meaningful, a finer breakdown is needed.
Dimensions of green computing
A close examination of Murugesan's definition allows us to start identifying the significant dimensions of the concept. The table below extracts elements of the definition and provides examples of corresponding activities and their impacts.
| Element | Activities | Impacts |
|---|---|---|
| Design | Extraction and refinement of selected materials | Habitat loss, waste disposal |
| Lifecycle planning | Disposal of old devices | |
| Efficiency of operation | Excess energy demand | |
| Manufacture | Energy use during manufacturing | CO2 emissions |
| Transportation of goods and materials | CO2 emissions | |
| Waste disposal | Pollution | |
| Use | Energy used by hardware devices and components | CO2 emissions |
| Costs related to data communications | CO2 emissions | |
| Operation of centralised services | CO2 emissions |
Exercise: Themes in the academic literature
One way to make sense of a term like green computing is to see how it is used in the academic literature. A systematic literature review is a recognised research method where the answer to a research question comes from the literature itself. There are several reliable and extensive repositories of academic material available including Web of Science, Scopus, Google Scholar and Dimensions. The exercise below is based on a corpus of 1643 papers from the Web of Science that were selected using the search criteria
1 | |
Some manual filtering was performed to identify and remove papers where the match was coincidental (for example, where the abstract contained the string "... green. It ...").
In the exercise, you will apply a topic modelling technique called latent Dirichlet allocation (LDA) to discover the main topics in the corpus.
To do the exercise, you will need a username that is typically given to you as part of a class. You will also be asked for a password - the first time you log in, the password you use will be saved for subsequent visits. Please make sure you remember what it is.