Web3 and Demurrage Money

George Lovegrove

American Journal of Economics and Sociology2026https://doi.org/10.1111/ajes.70044article
AJG 2ABDC B
Weight
0.50

Abstract

This article explores the application of demurrage money, a concept developed by Silvio Gesell, into Web3. Demurrage money, designed to discourage the hoarding of currency and prevent economic stagnation and concentrations in wealth, offers a potential remedy for the problems of traditional fiat and gold‐backed monetary systems. The article presents an overview of Web3, highlighting its core principles such as being decentralized, permissionless, community governed, and programmable. It critiques the limitations of current Web3 cryptocurrencies, particularly Bitcoin and other networks that have emerged since. By design these networks enable excessive asset storage and face sustainability challenges such as governance centralization and inadequate ecosystem funding. The article proposes that the implementation of a network coin tax, as a form of demurrage, would help to incentivize productive economic activity, decentralize coin ownership, provide reliable funding for node operators and ecosystem development and create opportunities for large‐scale public goods funding. Various monetary supply models are discussed, evaluating their compatibility with demurrage systems. The article concludes that demurrage based economic systems could lead to more resilient, equitable and sustainable Web3 ecosystems that have significant potential for making a global societal impact.

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https://doi.org/https://doi.org/10.1111/ajes.70044

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@article{george2026,
  title        = {{Web3 and Demurrage Money}},
  author       = {George Lovegrove},
  journal      = {American Journal of Economics and Sociology},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1111/ajes.70044},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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