The Terra Luna Classic (LUNC) community is currently engaged in a pivotal decision-making process regarding a substantial change in the distribution of burn tax revenue. The proposal, led by Frag from Genuine Labs, seeks to reform the existing structure to provide enhanced support for long-term staking rewards for both validators and the community.
Detailed Overview of the Revised Distribution Plan
Presently, a 0.5% burn tax is imposed, with 80% allocated to token burning and the remaining 20% split between the community pool and validator rewards. The new proposal advocates for an equitable split of 10% each for the Community and Oracle pools, representing a significant shift in the utilization of burn tax revenues to encourage a sustainable reward system for prolonged staking activities.
Frag's comprehensive plan encompasses technical improvements like updates to the ante handler and various system parameters, complemented by extensive testing phases. The anticipated timeline for full implementation stands at 56 hours post-approval, necessitating an estimated expenditure of $3600 in LUNC. This expedited implementation plan is slated for mid-July pending requisite support.
Community and Market Reception
The proposal has amassed nearly unanimous support from validators, boasting an approval rate of 99.97%. Esteemed validators such as Interstellar Lounge and JESUSisLORD have expressed their backing, although some key validators remain undecided. Concurrently, the market has witnessed a decline, with LUNC prices experiencing a 2% decrease amid a general market sell-off. Nonetheless, heightened trading volumes suggest increased activity potentially influenced by market reactions to the proposal.
This crucial vote exemplifies a forward-focused approach to governance and economic strategies within the Terra Luna Classic ecosystem. By realigning the burn tax distribution model, the community is laying the groundwork for improved validator incentives and robust staking frameworks, ensuring network stability amidst market fluctuations.
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