> For the complete documentation index, see [llms.txt](https://jack-coin.gitbook.io/jack-coin/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://jack-coin.gitbook.io/jack-coin/whitepaper/deflationary-rewards.md). # Deflationary Rewards ## Tokenomics: Deflationary Rewards At the core of "Jack Coin" lies its unique tokenomic system. With every transaction, 5.5% of the transaction amount is divided among various stakeholders: 1% is burned, reducing the total supply and creating scarcity over time. 1.25% is distributed to all holders, encouraging long-term participation and loyalty. 0.75% is allocated to animal charities, supporting organizations that champion the well-being of animals 0.84% goes towards marketing initiatives, amplifying our reach and fostering community growth. 2.75% is added to the liquidity pool, ensuring seamless trading experiences on DEXs. ### Deflationary Mechanism The burning mechanism in "Jack Coin" aims to reduce the token supply over time, driving value appreciation for holders. As more tokens are burned in each transaction, the scarcity of "Jack Coin" increases, potentially leading to a positive impact on its price trajectory. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://jack-coin.gitbook.io/jack-coin/whitepaper/deflationary-rewards.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.