Could ASTAR Become Deflationary with ZK-EVM and DApp Staking V3 Implementations?

Hey everyone,

I’ve been considering the possibility of ASTAR turning into a deflationary token upon the launch of the new features. This involves integrating buyback and burn mechanisms from sequencer revenue, along with the upcoming release of the DApp staking v3 burn feature. To test this, I’ve created an Excel Sheet to evaluate the impact of these burn mechanisms.

The primary objective was to determine the sequencer’s required earnings for ASTAR to achieve a year with zero inflation (sequencer bought and burned tokens = inflation tokens - DApp staking burn - burned tokens from fees yearly - TVL dependant burn (not minting)).
Here are the key assumptions:

  • DApp staking burn: Utilized data from the final tokenomics v2 auditing report - LINK
  • Burned tokens from fees yearly: 20% of the current daily TX fee values from Subscan.
  • Current ASTAR TVL percentage of total supply

In the first experiment, I calculated the needed zkevm sequencer earning for deflation - 32,485.29 ETH. Comparing it to other chains listed in the sheet, it’s approximately 2.5 times more than the largest one currently - Arbitrum. This suggests that it may not be entirely feasible for ASTAR to become deflationary, at least at this stage and level of abstraction.

However, in the second experiment, I used the sequencer earnings of Base L2 (which could be similar to ASTAR ZK-EVM) and attempted to calculate the inflation rate with the previously mentioned assumptions. We can anticipate an inflation reduction of 31.66%, which sets the inflation to - 4.62%. This represents a substantial decrease that could positively impact several ecosystem components.

I’d love to hear your thoughts on this. Feel free to explore the model and share your findings.

Cheers!

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With the update in the Dapp staking mechanism, thanks to the tier system, many projects will have a certain amount of reward. As the manager of the 3rd largest project in DApp staking, to be honest, I think the reward mechanism is a bit too much according to the ASTR price. With the current adjustment, while there will be no decrease in the dollar-based reward amount of the current dapps, in the future, as the value of ASTR increases, this number will be brought within a certain scale and this will make sense.

Secondly, I hope that with the increased use of Astar ZkEvm, the connection of other venture companies in Japan with web3 will increase and become more collective.

Thank you for your research; it has been a truly valuable report. I will share your research in my article tomorrow, citing you as a source.

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That’s a pretty good assessment. Your comparison with the current positions of potential competitors is quite good. I am sure that soon people whose speciality is cryptography inflationist will meet under this article. Your post has been sent to our messenger group.

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Thank you

Make sure to post the research here so I don’t miss it. I also invite you all to test the model out with various new inputs, test your scenarios or add new features. We are more than open to feedback.

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Hi @aleks,

It’s a good initiative to start making projections for the new ASTR tokenomic with all the changes coming to our network with dApp Staking V3 and the launch of Astar zkEVM. The numbers are already promising, but these are assumptions because you’re basing your calculations on several parameters that have yet to be determined:

  • DApp staking burn: the final parameters for Astar are still being researched and will be shared with the community once decided.
  • Sequencer earning: these parameters are still being researched and need to be defined.
  • Tokens burned from annual fees. 80% of tx fees are burned with the new tokenomic and we expect to see tx increase on the native Astar network with the launch of zkEVM as we will attract more users and therefore more stakers into dApp Staking which is on the Astar substrate.

I’d be interested to see the same calculation and research once all the parameters have been decided, shared and implemented, which will give more accurate results.

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In spite of the data presented by G, I find the proposed model very interesting

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In recent weeks, I created content that also includes your research. Thank you:

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