Title |
Future images of data in circular economy for textiles |
ID_Doc |
607 |
Authors |
Luoma, P; Penttinen, E; Tapio, P; Toppinen, A |
Title |
Future images of data in circular economy for textiles |
Year |
2022 |
Published |
|
DOI |
10.1016/j.techfore.2022.121859 |
Abstract |
Rapid expansion of digitalization and in the volume of data available constitutes a major driver toward circular economy. In the textile industry, with its vast quantities of waste and huge environmental impact, transformation toward such circularity is necessary but challenging. To explore how the use of data could support building sustainability-aligned pathways to circular economy of textiles, a study employing a two-round disaggregative Delphi approach (engaging 33 experts in the first round, in May 2021, and 26 in the second, in June 2021) articulated alternative images of the future. The three images, dubbed Transparency, Conflicting Interests, and Sustainable Textiles, imply that the role for data is intertwined with sustainability aspirations. The results highlight that exploiting data in pursuit of circular economy is a collaborative effort involving business value networks that include consumers and regulators. Availability and sharing of accountability-affording, meaningful data on textiles' life cycle and value network function as a key enabler. By working with the images developed, actors can better assess their circular-economy commitments, planned actions, and the consequences of these. Furthermore, the images provide a tool for mutual discussion of the development desired and of related re-sponsibilities and uncertainties. |
Author Keywords |
Circular economy; Data management; Digitalization; Future images; Delphi method; Textile industry |
Index Keywords |
Index Keywords |
Document Type |
Other |
Open Access |
Open Access |
Source |
Social Science Citation Index (SSCI) |
EID |
WOS:000877225300006 |
WoS Category |
Business; Regional & Urban Planning |
Research Area |
Business & Economics; Public Administration |
PDF |
https://doi.org/10.1016/j.techfore.2022.121859
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