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At the Nexus of Blockchain Technology, the Circular Economy, and Product Deletion
Open accessAbstract
The circular economy (CE) is an emergent concept to rethink and redesign how our economy works. The concept recognizes effective and efficient economic functioning at multiple scales—governments and individuals, globally and locally; for businesses, large and small. CE represents a systemic shift that builds long-term resilience at multiple levels (macro, meso and micro); generating new business and economic opportunities while providing environmental and societal benefits. Blockchain, an emergent and critical technology, is introduced to the circular economy environment as a potential enabler for many circular economic principles. Blockchain technology supported information systems can improve circular economy performance at multiple levels. Product deletion, a neglected but critical effort in product management and product portfolio management, is utilized as an illustrative business scenario as to blockchain’s application in a circular economy research context. Product deletion, unlike product proliferation, has received minimal attention from both academics and practitioners. Product deletion decisions need to be evaluated and analyzed in the circular economy context. CE helps address risk aversion issues in product deletions such as inventory, waste and information management. This paper is the first to conceptualize the relationships amongst blockchain technology, product deletion and the circular economy. Many nuances of relationships are introduced in this study. Future evaluation and critical reflections are also presented with a need for a rigorous and robust research agenda to evaluate the multiple and complex relationships and interplay amongst technology, policy, commerce and the natural environment.
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“Likewise, data collection, analysis, and storage processes in AI development require much computational power, which consumes enormous amounts of energy (Cowls et al. 2021;Kouhizadeh et al. 2019).”
From Artificial intelligence in support of the circular economy: ethical considerations and a path forward (2024) · cites this paper as Kouhizadeh et al. 2019
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Metadata
- Authors
- Kouhizadeh, M., Sarkis, J., & Zhu, Q.
- Affiliations
- Worcester Polytechnic Institute (US)
- University of Alabama in Huntsville (US)
- Corresponding author
- Kouhizadeh, Mahtab
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2019-04-25
- Volume
- 9
- Issue
- 8
- Pages
- 1712
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W2940855133
- Cited by
- 219
- References
- 70
- Citations by year
- 2026: 21 · 2025: 20 · 2024: 35 · 2023: 46 · 2022: 51 · 2021: 28 · 2020: 16 · 2019: 2
- FWCI
- 37.31
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Työväentutkimus Vuosikirja (submittedVersion)
- Funders
- APICS Research, Innovation, & Strategy Committee (227940)
- Topics
- Social Sciences › Business, Management and Accounting › Strategy and Management › Sustainable Supply Chain Management
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Social Sciences › Business, Management and Accounting › Marketing › Environmental Sustainability in Business
- Keywords
- blockchain, circular economy, product portfolio management, product management, inventory management, waste management, information management, risk aversion
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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