Gunz (GUN) sustainability report

Gunz is present on the following networks: Avalanche, Solana.

The Avalanche blockchain network implements a sophisticated Proof-of-Stake (PoS) mechanism known as Avalanche Consensus, distinguishing itself from many other PoS protocols by incorporating a novel, subsampling-based approach rather than a traditional Byzantine Fault Tolerant (BFT) consensus. This unique consensus process is built upon three integrated protocols: Snowball, Snowflake, and Avalanche, all working in concert to achieve high throughput, rapid finality, and robust security. The process begins with the Snowball protocol, where each validator randomly samples a small, fixed-size group of other validators. Through repeated polling of these sampled validators, a preference for a particular transaction is established. Validators maintain confidence counters for each transaction, incrementing them as sampled validators express support for their chosen transaction. A transaction is deemed accepted once its confidence counter surpasses a predefined threshold. Building upon Snowball, the Snowflake protocol refines the process by introducing a binary decision system, compelling validators to choose between two conflicting transactions. Binary confidence counters track the preferred binary choice, and once a specific confidence level is attained, the decision becomes final and irreversible. The overarching Avalanche protocol organizes transactions using a Directed Acyclic Graph (DAG) structure. This DAG architecture is crucial for facilitating parallel transaction processing, which significantly enhances the network's overall throughput and efficiency. Transactions are added to the DAG based on their intrinsic dependencies, ensuring a consistent and logical order across the network. Ultimately, validators reach consensus on both the structure and content of this DAG through the iterative application of the Snowball and Snowflake protocols. The Avalanche X-Chain, a component of the broader Avalanche network, also utilizes this Avalanche consensus protocol, emphasizing repeated subsampling of validators to achieve agreement on transactions. Furthermore, networks like Flare integrate the Avalanche Consensus with a Federated Byzantine Agreement (FBA) model to further bolster scalability, security, and decentralization, leveraging a gossip protocol for rapid node communication and transaction confirmation.

The Solana blockchain architecture operates through a hybrid consensus model that integrates Proof of History (PoH) with Proof of Stake (PoS). This combination is designed to optimize transaction throughput and reduce network latency while maintaining a high degree of security. Proof of History functions as a decentralized clock, using a Verifiable Delay Function (VDF) to create a permanent, timestamped record of events. This cryptographic sequence allows the network to agree on the chronological order of transactions without requiring nodes to communicate extensively, thereby solving traditional synchronization bottlenecks found in other distributed ledgers. Parallel to PoH, the Proof of Stake component manages the selection of validators and the finalization of the ledger state. Validators are chosen to act as leaders for specific blocks based on the total quantity of the native network assets they have staked. Users who do not run their own hardware can participate in network security by delegating their assets to existing validators, sharing in the rewards generated by successful block production. The consensus process begins when transactions are broadcast and collected for validation. A designated leader then generates a PoH sequence to order these transactions within a block. Subsequently, other validators in the network verify the integrity of the PoH hashes and the validity of the transactions. Once a sufficient number of signatures are collected, the block is finalized and appended to the blockchain. This dual approach ensures that the network remains resilient against attacks; validators must provide collateral through staking, and any malicious activity, such as producing invalid blocks or double-signing, can result in the loss of staked assets through a process known as slashing. This economic deterrent ensures that participants remain aligned with the network's health and operational standards.

Gunz is present on the following networks: Avalanche, Solana.

The Avalanche blockchain network employs a comprehensive system of incentive mechanisms and fees designed to ensure its security, integrity, and efficiency, primarily through its Avalanche Consensus mechanism. Validators, who are critical to the network's operation, are required to stake a certain amount of AVAX tokens. The quantity of staked tokens directly influences their likelihood of being chosen to propose or validate new blocks. In return for their active participation, validators receive rewards, which are calculated proportionally to the amount of AVAX they have staked, as well as their consistent uptime and overall performance in validating transactions. To further decentralize participation, validators can also accept delegations from other token holders. These delegators subsequently share in the earned rewards, thus incentivizing smaller token holders to contribute indirectly to the network's security. The economic incentives for validators extend beyond staking rewards to include block rewards, which are distributed from the inflationary issuance of new AVAX tokens for proposing and validating blocks. Additionally, validators earn a portion of the transaction fees paid by users across the network, covering simple transactions, complex smart contract interactions, and the creation of new assets. Crucially, Avalanche's penalty system differs from some other Proof-of-Stake systems by not employing 'slashing,' which involves the confiscation of staked tokens for misbehavior. Instead, the network relies on the economic disincentive of lost future rewards. Validators who fail to maintain consistent uptime or engage in malicious activities will simply miss out on potential earnings, providing a strong incentive for honest and reliable behavior. The network also imposes clear uptime requirements, where poor performance directly impacts a validator's ability to earn rewards. Fees on the Avalanche blockchain are structured to be dynamic, adjusting based on current network demand and the computational complexity of transactions. This ensures that fees remain equitable and reflect the actual network usage. A significant portion of these transaction fees is 'burned,' meaning they are permanently removed from circulation. This deflationary mechanism helps to offset the inflationary effects of block rewards and aims to enhance the long-term value of AVAX tokens. Fees for deploying and interacting with smart contracts are determined by the required computational resources, promoting efficient resource utilization. Similarly, fees are imposed for creating new assets on the network, a measure designed to deter spam and ensure that network resources are utilized by serious projects. On the Avalanche X-Chain, validator incentives are realized indirectly through the network's overall AVAX issuance, while its transaction fees are fixed and burned to combat spam and progressively reduce the total supply of AVAX.

Incentives within the Solana blockchain network are structured to ensure high performance and decentralized security. The primary participants are validators and delegators, both of whom receive financial compensation for their roles in maintaining the ledger. Validators are rewarded for successfully producing and verifying blocks. These rewards are distributed in the network's native asset and are determined by the validator's overall stake and historical performance. Furthermore, validators receive a portion of the transaction fees associated with the data processed in their blocks, which encourages them to maximize efficiency and maintain uptime. Token holders who prefer not to operate complex infrastructure can delegate their stake to professional validators. This delegation model facilitates a more inclusive security environment, as delegators earn a percentage of the rewards proportional to their contribution, thereby decentralizing the control of the network. Security is further enforced through economic penalties. The network employs a slashing mechanism where a portion of a validator's staked assets is confiscated if they engage in dishonest behavior or fail to meet network requirements, such as remaining offline for extended periods. This introduces an opportunity cost for all participants, ensuring they remain committed to honest operations. Regarding the cost of using the network, the fee structure is designed to be highly competitive and predictable. Users pay transaction fees to compensate for the computational power and bandwidth consumed by nodes. These fees are notably low, facilitating high-volume usage. In addition to transaction costs, the network implements rent fees for data storage. This unique mechanism charges for the persistence of data on the blockchain, discouraging the inefficient use of state storage and prompting developers to prune unnecessary data. Finally, smart contract execution fees are calculated based on the specific resource intensity of the code, ensuring that participants pay a fair rate for the network resources they utilize.

Gunz is present on the following networks: Avalanche, Solana.

The methodology for assessing the Avalanche network's energy consumption is founded on a 'bottom-up' approach, where individual nodes are identified as the primary contributors to the network's overall energy footprint. This comprehensive calculation aggregates energy usage across various interconnected components of the network. The assumptions underpinning these calculations are derived from extensive empirical findings, utilizing a combination of publicly available information sites, sophisticated open-source crawlers, and proprietary in-house developed crawlers. A key aspect of this methodology involves estimating the hardware deployed within the network. This estimation is primarily driven by the technical specifications and operational requirements for running the client software, which dictates the type and performance of necessary hardware devices. The energy consumption profiles of these identified hardware devices are meticulously measured in certified test laboratories to ensure accuracy. To ensure a broad and precise scope, the Functionally Fungible Group Digital Token Identifier (FFG DTI) is leveraged, whenever available, to pinpoint all relevant implementations of the crypto-asset under consideration. These mappings are regularly updated based on current data provided by the Digital Token Identifier Foundation. The data regarding specific hardware usage and the total number of network participants is based on empirically verified assumptions, consistently updated with best-effort validation. A foundational assumption in this model is that network participants generally behave in an economically rational manner. Furthermore, adhering to a precautionary principle, any uncertainties or doubts during the estimation process lead to conservative assumptions, specifically by making higher estimates for potential adverse environmental impacts. When determining the energy consumption attributable to a specific token within the Avalanche ecosystem, the energy consumption of the entire Avalanche network (including subnets like Avalanche X-Chain) is calculated first. Subsequently, a fraction of this total network energy is allocated to the token, proportional to its activity and footprint within the network. This detailed, multi-layered approach aims to provide a robust and conservative estimate of the energy consumption associated with the Avalanche blockchain.

To calculate the energy consumption of the Solana blockchain network, a "bottom-up" methodology is utilized, placing the network nodes at the center of the analysis. This approach relies on identifying the number of active participants and the specific hardware requirements necessary to run the network's client software. Data collection involves a variety of sources, including open-source web crawlers, internal monitoring tools developed by the legal entities, and public information websites. By analyzing these data points, researchers can estimate the hardware profiles of the various nodes operating globally. To ensure accuracy, the energy consumption of typical hardware devices is measured within certified laboratory environments, providing a baseline for the power usage of each node. Furthermore, the methodology incorporates data from the Digital Token Identifier Foundation to map all implementations of the assets within the network's scope. When specific hardware data is not directly observable, assumptions are made based on the principle of economic rationality, assuming participants optimize their setups for cost-efficiency while meeting software specifications. In instances of uncertainty, a precautionary principle is applied, favoring conservative estimates that likely overstate the environmental impact rather than underestimating it. This ensures that the reported energy footprint represents a credible upper bound of actual consumption. The total network consumption is determined by aggregating the energy needs of all identified nodes, accounting for both the computational requirements of processing transactions and the energy consumed by hardware in an idle or supportive state. This rigorous framework allows for a comprehensive assessment of the network’s total power requirements over a defined reporting period, providing a transparent view of the operational costs associated with maintaining the distributed ledger's infrastructure.

Gunz is present on the following networks: Avalanche, Solana.

The methodology for determining the key energy sources and the proportion of renewable energy utilized by the Avalanche blockchain network relies on a multi-pronged approach that integrates geographical data with energy mix statistics. To ascertain the percentage of renewable energy consumption, the initial step involves accurately identifying the geographical locations of the network's nodes. This crucial data is gathered through a combination of public information sites, advanced open-source crawlers, and proprietary in-house crawlers developed specifically for this purpose. In instances where comprehensive geographical distribution information for the nodes is not readily available, the methodology pivots to utilizing 'reference networks.' These reference networks are carefully selected for their comparability to Avalanche in terms of their incentivization structures and underlying consensus mechanisms, ensuring that the estimated renewable energy mix remains relevant and reflective of similar blockchain operations. Once the geographical data for the nodes (either directly identified or inferred from reference networks) is compiled, this geo-information is meticulously merged with comprehensive public data sets on electricity generation. A primary source for this integration is the data provided by Our World in Data, which offers detailed insights into the global energy landscape. The energy intensity of the network is then calculated as the marginal energy cost incurred for processing one additional transaction. This granular measurement provides a precise understanding of the energy overhead per unit of network activity. The specific datasets and sources referenced for this methodology include: Ember (2025) and the Energy Institute - Statistical Review of World Energy (2024), both of which undergo significant processing by Our World in Data. The dataset titled “Share of electricity generated by renewables – Ember and Energy Institute” is a key input, comprising original data from Ember’s “Yearly Electricity Data Europe” and “Yearly Electricity Data,” alongside the Energy Institute’s “Statistical Review of World Energy.” This information is publicly accessible at Share of electricity generated by renewables – Ember and Energy Institute.

The determination of energy sources for the Solana blockchain network involves a sophisticated geolocation mapping of the global node infrastructure. By utilizing internal and open-source crawlers, the physical locations of validator nodes are identified. Once the geographic distribution is established, this information is cross-referenced with regional energy data to calculate the percentage of renewable energy utilized by the network. For regions where specific node data is unavailable, researchers utilize reference networks that share similar consensus mechanisms and incentive structures as proxies to estimate the geographic spread of the infrastructure. The primary data source for these regional energy profiles is the Share of electricity generated by renewables dataset provided by Our World in Data, which incorporates research from Ember and the Energy Institute. This dataset provides yearly electricity data that allows for a granular assessment of how much of the network's power is derived from wind, solar, hydro, and other renewable sources. In addition to the total percentage of green energy, the methodology focuses on energy intensity, which is defined as the marginal energy cost required to process a single additional transaction on the network. This figure helps quantify the efficiency of the blockchain's resource usage relative to its utility. By integrating global energy statistics with real-time node distribution data, the network can report a more accurate picture of its sustainability, currently indicating that a significant portion of its operational energy comes from renewable sources, reflecting the broader global transition toward cleaner power grids.

Gunz is present on the following networks: Avalanche, Solana.

The methodology employed to determine the Greenhouse Gas (GHG) emissions associated with the Avalanche blockchain network involves a detailed process of locating network infrastructure and integrating this geographical data with carbon intensity statistics. The initial step is to precisely identify the locations of the network's nodes, a task accomplished through the diligent use of public information sites, sophisticated open-source crawlers, and specialized in-house crawlers. This geographical mapping is fundamental to understanding the specific energy grids from which the nodes draw their power. In situations where direct geographical information on node distribution is insufficient, the methodology relies on 'reference networks.' These are selected based on their structural similarities to Avalanche, particularly concerning their incentivization mechanisms and consensus protocols, ensuring that the estimates are as representative as possible. The collected geo-information, whether direct or inferred, is then carefully integrated with public data regarding the carbon intensity of electricity generation. A significant source for this critical data is Our World in Data, which provides comprehensive global information on electricity generation’s carbon footprint. The GHG intensity of the network is quantified as the marginal emission generated per additional transaction processed. This metric allows for a precise evaluation of the environmental impact as network activity scales. The foundational data and citations for this methodology include: Ember (2025) and the Energy Institute - Statistical Review of World Energy (2024), which have been extensively processed by Our World in Data. The specific dataset used is titled “Carbon intensity of electricity generation – Ember and Energy Institute,” drawing original data from Ember’s “Yearly Electricity Data Europe” and “Yearly Electricity Data,” as well as the Energy Institute’s “Statistical Review of World Energy.” This crucial resource for carbon intensity data is available under a CC BY 4.0 license at Carbon intensity of electricity generation – Ember and Energy Institute.

Quantifying the greenhouse gas (GHG) emissions of the Solana blockchain network requires a methodology focused on carbon intensity and the geographic footprint of its decentralized nodes. Similar to the energy source analysis, the process begins by locating active nodes using a combination of public data and specialized web crawling technology. This geographic information is critical because the carbon footprint of electricity varies significantly between different jurisdictions depending on their local power generation mix. For nodes that cannot be precisely located, the analysis uses data from comparable blockchain networks to ensure the estimation remains as complete as possible. The carbon intensity of the electricity used by these nodes is derived from the Carbon intensity of electricity generation dataset, accessible via Our World in Data. This dataset, which is licensed under CC BY 4.0, provides essential metrics on the amount of CO2 equivalent emitted per kilowatt-hour of electricity produced in various countries. By merging node locations with these carbon intensity values, the network can calculate its Scope 2 emissions, which represent the indirect emissions from the generation of purchased electricity. The methodology also focuses on GHG intensity, measuring the marginal emissions generated by one additional transaction on the blockchain. This allows for a performance-based assessment of the network's environmental impact. The results are typically reported in tonnes of CO2 equivalent (tCO2e), providing a standardized metric that allows for comparison with other industries and financial systems. This data-driven approach ensures that the network’s environmental disclosures are rooted in empirical global energy statistics and verifiable infrastructure data.