De-materialising and re-materialising: digital technologies and the environment

By Frans Berkhout, J. Hertin. 2004

332 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Information Systems and Management, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/54b38ad41dc7bb8af6745550436432fc6b7517ff

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Anne-Laure Ligozat
2. B. Robinson
3. Alexandra Sasha Luccioni
4. Benjamin Sovacool
5. S. Sorrell

In Information Systems and Management

1. B. Robinson
2. Benjamin Sovacool

In Management Science and Operations Research

1. Judy Brown
2. Giulio Cainelli
3. B. Robinson
4. Alexandra Sasha Luccioni
5. Benjamin Sovacool
6. Bob Frame
7. Sandro Montresor
8. M. Mazzanti

Citations in the corpus

• Environmental Innovations, Local Networks and Internationalization (2012, background): “9 Apart from this and other few recent contributions (Berkhout & Hertin, 2004; Collard et al., 2005; Hilty et al., 2006), how ICT is integrated with EI has been rather ignored.”
• E-waste: an assessment of global production and environmental impacts. (2009, background): “Similarly, fibre optics, which may replace some Cu wires (Berkhout and Hertin, 2004), can contain F, Pb, Y and Zr (Kogo et al., 1995).”
• Developing post-normal technologies for sustainability (2008, background): “…be held to account if information systems make the environmental impacts of individual consumer choices transparent (Spaargaren and van Vliet 2003), Berkhout and Hertin 2004) with websites that enable individuals to calculate personal carbon emissions or assess energy efficiency potentials of…”
• A systematic review of the energy and climate impacts of teleworking (2020, background): “Berkhout and Hertin (2004) draw attention to the potentially small impact of teleworking on energy consumption relative to other driving forces such as population and income growth.”
• Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model (2022, background): “More transparency is needed regarding the environmental impacts of manufacturing computing equipment given the large quantities of chemicals and minerals required (Stephens and Didden, 2013; Crawford, 2021), the significant quantities of ultra-pure water and energy needed to manufacture it (United N”
• Aligning artificial intelligence with climate change mitigation (2022, unknown): “Related literature on assessing the impacts of information and communications technologies (ICT) has often distinguished between the energy-and hardware-related GHG emissions of ICT (‘direct’ impacts) and the emissions impacts of ICT’s applications (‘indirect’ impacts) 5,6,20–22 .”
• Digital transformation of agriculture and rural areas: A socio-cyber-physical system framework to support responsibilisation (2021, background): “Furthermore, and this is perhaps different from SCPS in other settings where this may be a more indirect or remote environmental effect (Berkhout and Hertin, 2004), in an agricultural and rural setting, there may also be a direct impact on the ecological system (Klerkx et al., 2019), as shown by…”