Tokenomics in the blockchain context revolves around the "economy" that is based on the ecosystem's tokens. The paper proposes a conceptual goal model for tokenomics. This was accomplished by completing a narrative review of existing literature obtained from academic databases. The findings of the review result in a five-step tokenomics goal model: determining the number of tokens to release, determining whether tokens are allocated pre- or post-listing, determining whether the token release is public or private, ensuring that the tokens have inherent value, and finally establishing a suitable price management mechanism.
We live in the digital era, where most information is stored digitally in data centers or the cloud, often operated by centralized entities. Traditionally, developers and users default to these centralized storage solutions, which manage their own infrastructure and support vertically integrated services to capture all the value. However, decentralized storage solutions, such as those pioneered by BitTorrent and later the InterPlanetary File System (IPFS), offer potential solutions to the limitations of centralized storage. This paper proposes a system to enhance decentralized storage solutions by assigning monetary value to data, thus creating new incentives for network participants. We explore various factors influencing data value in decentralized platforms and propose systems for handling unquantifiable metrics such as content and moderation value. Specifically, we introduce a Moderation Voting System with Prediction Markets and discuss how these systems combined could benefit Storage Providers, voters, predictors, and end-users. The paper also discusses the risks and the unpredictability of human behavior, which the solution addresses by tapping into game theory. As this paper focuses mostly on the value proposition of the data, while most of the components are treated as a black box subject to further research and design, we also provide a plan for future research and the methodology for evaluation and comparison against other existing methods.
The European Union's endeavor to achieve climate neutrality by 2050 encompasses the adoption of the Carbon Removal Certification Framework to promote sustainable agricultural practices and sequester carbon through carbon farming. This process entails the conversion of agricultural land into carbon sinks, which presents economic and administrative hurdles, especially impacting small-scale farmers. The proposed incorporation of blockchain technology is aimed at improving the transparency and effectiveness of carbon credit transactions within this framework. The EU framework also establishes certification criteria to guarantee high-quality carbon removals, involving key stakeholders such as certification bodies and a central offset registry to uphold the integrity and credibility of the carbon market. The utilization of blockchain technology and Smart Contracts enables the monitoring of carbon credits and simplifies transactions by removing intermediaries, thus lowering expenses and increasing direct funding for carbon reduction initiatives. This holistic approach not only supports the climate objectives of the EU but also enhances market efficiency and fosters stakeholder confidence in carbon trading.
Considering the importance of cryptography in blockchain networks, the usage of pseudo-random number generators for the secret key and nonce generation is, at least, arguable. Randomness should always be generated from non-deterministic sources, to ensure the random elements are not retrievable from third parties. In this work, a true random number generator based on quantum computation (Q-TRNG) is proposed. This Q-TRNG has allowed to create a quantum secret key generator (Q-SKG), generating keys and nonces whose entropies and serial correlation coefficients showed better values compared to the ones obtained from other widely used blockchain wallets. To the knowledge of the authors, this is the first Q-SKG that has been built for blockchain applications, enhancing the security of secret keys and transaction signatures.
The surge in production and transportation industries stands as the pillar to most daily transactions, yet it also serves as a significant contributor to climate change and global warming. The implementation of blockchain technology has helped the transition of supply chains toward sustainability through successful model applications in logistics and environmental impact innovations. However, those integrations are far from being optimal since they still encounter hindrances in scalability and data privacy, which delays transactions' performance and increases their cost. In this regard, directed acyclic graphs (DAGs) offer parallel, faster, and cost-effective transactions' processing. Unlike traditional blockchain's linear structure, they enable a non-sequential node representation of operations that allows their effective initiating. This paper demonstrates the efficiency of the MetaDAG model integrating blockDAG in the metaverse for carbon footprint reduction in the Moroccan air transportation industry. Based on this, transactions' data will be processed effectively and privately within the interrelated IPFS framework using Agent-Based Modeling (ABM) for the network representation, which will enhance nodes' NFT integration in the metaverse and extension to urban ecosystems for smart cities. Moreover, the framework is tested using OECD dataset displaying the reduction of carbon footprint and increase of carbon credits after the model implementation.
