DarkStar (DARKSTAR) sustainability report

DarkStar is present on the following networks: Binance Smart Chain, Klaytn.

The Binance Smart Chain (BSC) network utilizes a hybrid consensus mechanism known as Proof of Staked Authority (PoSA). This innovative approach integrates key elements from both Delegated Proof of Stake (DPoS) and Proof of Authority (PoA) to achieve a balance of high transaction speeds, cost-efficiency, and network security, while striving to maintain a reasonable level of decentralization. The core participants in the PoSA mechanism include Validators, referred to as "Cabinet Members," Delegators, and Candidates.

Validators play a critical role, being responsible for creating new blocks, verifying transactions, and upholding the overall security of the network. To qualify as a validator, an entity must stake a substantial quantity of BNB, which serves as collateral to ensure honest conduct. These validators are selected through a dynamic process that considers both the amount of BNB they have staked and the votes they receive from token holders. At any given time, there are 21 active validators, whose rotation aims to enhance decentralization and security. Delegators are token holders who opt not to operate a validator node themselves but can contribute to network security by delegating their BNB tokens to chosen validators. This delegation bolsters a validator's total stake, increasing their likelihood of being selected for block production. In return, delegators receive a share of the rewards earned by their chosen validators, fostering broader participation in network governance and security. Candidates represent potential validators who have met the minimum BNB staking requirements and are awaiting election into the active validator set through community voting. Their presence ensures a continuous pool of ready-to-serve nodes, contributing to the network's resilience and decentralization.

During the consensus process, validators are chosen based on their accumulated BNB stake and delegator votes. The higher these metrics, the greater the chance of selection for validating transactions and producing new blocks. Once selected, these validators take turns in a PoA-like fashion to produce blocks rapidly and efficiently, validating transactions, adding them to blocks, and broadcasting them across the network. BSC boasts fast block times, typically around 3 seconds, leading to quick transaction finality. This rapid finality is a direct benefit of the efficient PoSA mechanism, which allows validators to reach consensus swiftly. To further ensure network integrity, validators face economic incentives such as slashing, where a portion of their staked BNB can be forfeited if they engage in malicious activities. This mechanism aligns validators' interests with the network's well-being, complementing the rewards they receive for their honest participation.

Klaytn utilizes a sophisticated consensus mechanism known as a modified Istanbul Byzantine Fault Tolerance (IBFT) algorithm, which operates as a variant of the Proof of Authority (PoA) model. This design is engineered to deliver high transactional performance and ensure immediate transaction finality, meaning that once a block is validated, it is irreversibly settled. This approach significantly enhances the user experience by guaranteeing rapid and secure transaction processing. The core of Klaytn's consensus architecture is structured around several key components. The modified IBFT algorithm is crucial for its ability to provide immediate transaction finality, a feature that distinguishes it from many other blockchain networks by offering instant settlement guarantees. Governance of the Klaytn network is entrusted to the Klaytn Governance Council, a collective body comprising global organizations. This council is pivotal in selecting and overseeing the Consensus Nodes (CNs) that maintain the network's integrity. This council-driven governance model strikes a balance between decentralization and operational efficiency, promoting transparent decision-making. For any block to achieve finality and be added to the blockchain, it must secure signatures from over two-thirds of the council members, a stringent requirement that ensures robust consensus and heightened network security. Furthermore, Klaytn employs a distinctive three-tiered node architecture to optimize its operations. Consensus Nodes (CNs) are the primary validators, responsible for the critical tasks of producing new blocks and validating transactions, thus forming the backbone of the network's security and stability. Supporting the CNs are Proxy Nodes (PNs), which serve as vital intermediaries, facilitating the relay of data between the Consensus Nodes and the wider network. This distributed data relay mechanism aids in managing network traffic and improving overall accessibility. The final tier consists of Endpoint Nodes (ENs), which act as the direct interface for end-users, enabling them to initiate transactions, execute smart contracts, and access the Klaytn network seamlessly. This layered architecture supports Klaytn's objective of combining high performance with a secure and stable operational environment.

DarkStar is present on the following networks: Binance Smart Chain, Klaytn.

The Binance Smart Chain (BSC) network employs a robust system of incentive mechanisms and applicable fees, primarily built around its Proof of Staked Authority (PoSA) consensus, designed to secure the network, encourage participation, and maintain operational efficiency. This system ensures that validators, delegators, and other participants are economically motivated to act in the network's best interest.

Validators on BSC, often referred to as "Cabinet Members," are critical to the network's operation. They are incentivized through staking rewards, which include a combination of transaction fees and newly generated block rewards. To become a validator, a significant amount of BNB must be staked. Their selection for block production is determined by the total BNB staked, encompassing both their own stake and delegated tokens, as well as the votes received from delegators. This competitive selection process motivates validators to attract delegators and maintain high performance. Delegators, in turn, are crucial for supporting network decentralization and security. By delegating their BNB to validators, they increase the validators' total stake, enhancing their chances of selection. In exchange, delegators receive a share of the rewards earned by their chosen validators, fostering active community involvement. The system also includes a pool of Candidates, nodes that have staked BNB and are ready to become active validators, ensuring a robust and resilient network of potential participants. Economic security is further reinforced through slashing mechanisms, where validators found engaging in malicious behavior or failing to perform their duties face penalties, including the forfeiture of a portion of their staked BNB. The opportunity cost of locking up BNB also provides a strong economic incentive for all participants to act honestly.

BSC is known for its low transaction fees, which are paid in BNB. These fees are vital for network maintenance and compensate validators for processing transactions. The fee structure is dynamic, adjusting based on network congestion and transaction complexity, though it is designed to remain significantly lower than on some other major blockchain networks, such as the Ethereum mainnet. In addition to transaction fees, validators receive block rewards, further incentivizing their role in maintaining and processing network activity. BSC also supports cross-chain compatibility, enabling asset transfers between Binance Chain and Binance Smart Chain, which incur minimal fees to facilitate a seamless user experience. Furthermore, interacting with and deploying smart contracts on BSC involves fees based on the computational resources required. These smart contract fees are also paid in BNB and are structured to be cost-effective, encouraging developers to build and innovate on the BSC platform.

Klaytn's operational framework incorporates a comprehensive incentive structure designed to maintain network security, promote sustainability, and foster community development. This mechanism primarily involves the distribution of block rewards and transaction fees to Consensus Nodes (CNs) and several dedicated network funds. Consensus Nodes, which are central to the network's validation process, receive fixed block rewards in KLAY tokens for their efforts in validating and producing blocks. This predictable income stream provides a strong incentive for CNs to remain actively engaged and committed to securing the network. In addition to these fixed rewards, CNs also receive a share of the transaction fees, which users pay in KLAY tokens. These fees are aggregated by the network and then distributed among the CNs, offering further economic support for their crucial role in upholding network security and stability. Beyond the direct compensation for CNs, Klaytn's block reward distribution mechanism is meticulously structured to allocate resources across various stakeholders and initiatives. A specific portion, 10% of each block reward, is directed to the Consensus Node that successfully proposed the block, thereby encouraging continuous and proactive participation. Furthermore, 40% of the block reward is allocated as a staking award to all members of the Klaytn Governance Council who actively stake KLAY, reinforcing network security by rewarding their commitment to the network. To support broader ecosystem growth, 30% of each block reward is channeled into the Klaytn Community Fund (KCF), which is dedicated to facilitating community development, enabling the creation of decentralized applications (dApps), and fostering overall expansion of the ecosystem. The remaining 20% of the block reward is allocated to the Klaytn Foundation Fund (KFF), which provides essential resources for the network's long-term sustainability and future developmental endeavors. Regarding applicable fees, all transaction fees on the Klaytn network are denominated in KLAY tokens. These fees are dynamically calculated based on the gas usage and gas price associated with each transaction. The revenue generated from these fees plays a critical role in supporting the ongoing maintenance of the network, compensating the validators for their services, and contributing to the overall economic viability and sustainability of the Klaytn blockchain.

DarkStar is present on the following networks: Binance Smart Chain, Klaytn.

The methodology for calculating the energy consumption of the Binance Smart Chain (BSC) network, which then serves as a basis for attributing a fraction of energy to tokens operating on it, primarily utilizes a "bottom-up" approach. This method focuses on the individual components of the network to aggregate a comprehensive energy profile. The central factor in this calculation is identified as the network nodes themselves.

Assumptions regarding the hardware used within the BSC network are derived from extensive empirical findings. These findings are gathered through a combination of public information sites, sophisticated open-source crawlers, and proprietary in-house developed crawlers. The primary determinants for estimating the specific hardware deployed are the technical requirements necessary to operate the client software of the network. To ensure accuracy, the energy consumption of these identified hardware devices is rigorously measured in certified test laboratories. This precise measurement allows for a detailed understanding of the power demands of the operational infrastructure.

For the comprehensive identification of all implementations of an asset within scope, the Functionally Fungible Group Digital Token Identifier (FFG DTI) is employed, where available. The mappings associated with the FFG DTI are regularly updated based on data provided by the Digital Token Identifier Foundation. The information regarding both the hardware in use and the total number of participants active within the network is based on assumptions that undergo best-effort verification using empirical data. Generally, participants are presumed to be largely economically rational in their decision-making. As a precautionary principle, in situations of uncertainty, assumptions tend to err on the conservative side, meaning higher estimates are made for potential adverse impacts. When determining the energy consumption for a specific token that operates on BSC, the initial step involves calculating the energy consumption of the entire Binance Smart Chain network. Following this, a fraction of the total network energy consumption is attributed to the particular crypto-asset, a fraction determined by the asset's specific activity within the network.

The methodology employed for calculating the energy consumption associated with digital assets, including those on the Klaytn network, utilizes a "bottom-up" approach, which identifies the various components contributing to the overall energy footprint. Central to this approach is the recognition that network nodes represent the primary factor driving energy consumption within the blockchain infrastructure. The underlying assumptions for these calculations are derived from extensive empirical findings, gathered through the use of public information sites, as well as both open-source and proprietary crawlers developed in-house. These tools systematically collect data to inform the energy assessment. A critical aspect of this methodology involves estimating the hardware utilized across the network. The main criteria for this estimation are the specific requirements necessary to operate the client software of the network. To ensure accuracy, the energy consumption of these identified hardware devices is rigorously measured in certified test laboratories, providing precise data points for the calculations. When determining the scope of assets for energy consumption calculations, the Functionally Fungible Group Digital Token Identifier (FFG DTI) is leveraged where available. This identifier helps in accurately identifying all implementations of the asset under consideration, with these mappings being regularly updated based on data provided by the Digital Token Identifier Foundation. The information concerning both the hardware deployed and the total number of participants active within the network is based on assumptions that undergo diligent verification against empirical data. It is generally assumed that participants in the network behave in a largely economically rational manner. Adopting a precautionary principle, conservative estimates are applied in situations of uncertainty, meaning higher figures are used when estimating potential adverse impacts to ensure a robust and cautious assessment of energy consumption. For any given crypto-asset, its energy consumption is derived as a fraction of the total energy consumption of the underlying network, such as Klaytn, with this fraction being determined by the asset's specific activity within that network.

DarkStar is present on the following networks: Binance Smart Chain, Klaytn.

To ascertain the proportion of renewable energy utilized by the Binance Smart Chain (BSC) network, a detailed methodology focuses on identifying the geographical distribution of its operational nodes. This process begins with leveraging a variety of data sources, including public information websites, general open-source crawlers, and specialized in-house developed crawlers. These tools collectively help pinpoint the physical locations where the network's nodes are hosted. The precise geographic distribution of these nodes is a crucial piece of information for accurately assessing renewable energy integration.

In instances where comprehensive information regarding the geographic distribution of nodes is unavailable or insufficient, the methodology incorporates a fallback mechanism. This involves using reference networks that exhibit comparable characteristics in terms of their incentivization structures and underlying consensus mechanisms. By analyzing the renewable energy usage patterns of these similar networks, an informed estimate can be made for BSC. Once geographical data for the nodes (either direct or inferred from reference networks) is established, this geo-information is meticulously merged with publicly accessible data from Our World in Data. This external dataset provides crucial insights into the share of electricity generated by renewables globally, drawing from sources like Ember (2025) and the Energy Institute’s Statistical Review of World Energy (2024). The integration of this data allows for a granular understanding of the renewable energy mix at the node locations.

Furthermore, the energy intensity of the network is calculated as the marginal energy cost with respect to one additional transaction. This metric quantifies the energy expenditure incurred for each incremental transaction processed on the network, providing a measure of its operational efficiency from an energy perspective. The consistent use of reputable public data sources and a robust methodology ensures transparency and accuracy in reporting the renewable energy profile of the Binance Smart Chain network.

The methodology for determining the proportion of renewable energy utilized by the Klaytn blockchain network is a multi-faceted process that relies on a combination of data collection and analytical techniques. Initially, efforts are focused on identifying the geographical locations of the network's nodes. This crucial data is sourced through various channels, including public information sites, as well as advanced open-source and proprietary crawlers designed to scan the network for relevant details. Should comprehensive information regarding the precise geographic distribution of all nodes be unavailable, the methodology incorporates a fallback mechanism. In such instances, the assessment refers to comparable reference networks whose incentivization structures and consensus mechanisms closely mirror those of Klaytn. This comparative analysis helps to derive reasonable estimates for renewable energy integration. Once the geo-information for the nodes is established, it is systematically integrated with publicly available data from reputable sources, notably Our World in Data. This integration allows for a robust estimation of the renewable energy mix powering the network's operations. The calculation of energy intensity is a key component of this methodology, defined as the marginal energy cost incurred with respect to processing one additional transaction on the network. This metric provides insight into the energy efficiency of the network on a per-transaction basis. The foundational data for estimating the share of electricity generated by renewables, particularly for the merging of geo-information, is extensively processed by Our World in Data, drawing from original datasets such as "Yearly Electricity Data Europe" and "Yearly Electricity Data" from Ember, and the "Statistical Review of World Energy" from the Energy Institute. This comprehensive approach ensures a thorough and well-supported assessment of renewable energy usage within the network. Our World in Data - Share of electricity generated by renewables

DarkStar is present on the following networks: Binance Smart Chain, Klaytn.

The methodology for determining the Greenhouse Gas (GHG) Emissions associated with the Binance Smart Chain (BSC) network, much like the energy consumption assessment, places a strong emphasis on geographically situating its operational nodes. The initial step involves identifying the physical locations of these nodes, which is achieved through a combination of public information sites, open-source crawlers, and specialized in-house developed crawlers. Accurately mapping these locations is fundamental, as regional electricity mixes and their associated carbon footprints vary significantly.

In situations where detailed geographical information for all nodes is not readily available, the methodology incorporates a pragmatic approach. This involves utilizing reference networks that share similar characteristics, specifically in their incentivization structures and consensus mechanisms. By studying these comparable networks, reasonable inferences can be made about the likely geographic distribution and, consequently, the emissions profile of BSC's nodes. Once the geographic data is gathered or estimated, it is then meticulously integrated with publicly available information from Our World in Data. This authoritative dataset provides critical data on the carbon intensity of electricity generation across various regions, compiling information from sources such as Ember (2025) and the Energy Institute’s Statistical Review of World Energy (2024).

This integration allows for the calculation of GHG emissions based on the electricity consumption at specific node locations and the carbon intensity of those regional grids. The intensity of GHG emissions for the network is specifically calculated as the marginal emission with respect to one additional transaction. This metric quantifies the increase in GHG emissions for each incremental transaction processed on the network, offering a direct measure of its environmental impact per unit of activity. The entire process adheres to a principle of transparency, utilizing established external data sources and a consistent approach to ensure the reported GHG emissions are as accurate and comprehensive as possible, always acknowledging that the data from Our World in Data is licensed under CC BY 4.0.

The methodology for assessing the Greenhouse Gas (GHG) emissions attributable to the Klaytn blockchain network is built upon a detailed process that begins with pinpointing the geographical locations of its operational nodes. This foundational data is diligently collected from a variety of sources, including publicly accessible information sites, alongside specialized open-source and internally developed crawlers designed for network analysis. In scenarios where complete geographic distribution data for all nodes cannot be precisely ascertained, the assessment employs a comparative approach. It refers to established reference networks that exhibit similar incentive structures and consensus mechanisms to Klaytn, allowing for an informed estimation of emission factors. The geo-spatial information obtained is then integrated with comprehensive public data sets, prominently featuring information from Our World in Data. This critical step enables the accurate calculation of GHG emissions by correlating node locations with regional carbon intensity metrics of electricity generation. The overall GHG intensity of the network is quantified as the marginal emission generated per additional transaction. This metric offers a granular perspective on the environmental impact of individual network operations. The underlying data for the carbon intensity of electricity generation, which is integral to these calculations, is rigorously processed by Our World in Data. This data draws from authoritative sources such as Ember's "Yearly Electricity Data Europe" and "Yearly Electricity Data," as well as the Energy Institute's "Statistical Review of World Energy." This robust data integration ensures a credible and transparent evaluation of the network's carbon footprint. The methodology aims to provide a clear understanding of the environmental implications of Klaytn's activities by accounting for its energy consumption and associated emissions comprehensively. Our World in Data - Carbon intensity of electricity generation