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Cited by 302

The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.

Green IS: Concepts and Issues for Information Systems Research

Open access
Dedrick, Jason·2010·Communications of the Association for Information Systems

Abstract

While public awareness of environmental sustainability is growing, there is concern about the economic costs of shifting to a greener economy. In the case of climate change, a critical issue is the relationship of economic output to greenhouse gas emissions, which has been labeled carbon productivity. Increasing carbon productivity means that economic growth can be sustained while emissions are reduced. Information technology has great potential to enhance carbon productivity, as IT is used to increase the energy efficiency of buildings, transportation systems, supply chains and electrical grids. On the other hand, the production and use of computers is a fast-growing component of global energy consumption and greenhouse gas emissions, a fact that must be balanced against the benefits of IT use. Green IS refers to the use of information systems to achieve environmental objectives, while Green IT emphasizes reducing the environmental impacts of IT production and use. This article focuses primarily on Green IS. It reviews existing Green IS research, presents a model of IT investment and carbon productivity, and lays out suggestions for future research.

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Metadata

Authors
Dedrick, J.
Venue
Communications of the Association for Information Systems
Published
2010
Volume
27
ISSN
1529-3181 (ISSN-L)
OpenAlex
W201032658
Cited by
302
References
32
FWCI
13.21
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Green IS: Concepts and Issues for Information Systems Research. https://app.cochat.ai/papers/10.17705/1cais.02711
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Dedrick, J. (2010). Green IS: Concepts and Issues for Information Systems Research. Communications of the Association for Information Systems. https://doi.org/10.17705/1cais.02711

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.