Energy Efficiency of NFTs on Flow Blockchain
Dapper Labs has highlighted that the energy consumption involved in minting non-fungible tokens (NFTs) on the Flow blockchain is even lower than the energy used during a Google search or an Instagram post. This assertion was confirmed by Deloitte Canada, making it a notable point amidst the ongoing debates regarding NFTs. While some view them as a revolutionary advancement in gaming, others criticize them as mere monetization schemes. According to Dapper Labs, the Flow blockchain’s energy requirement is just 0.18 gigawatt hours (GWh) annually, based on data from 2021. This blockchain was specifically designed to facilitate the creation of NFTs for NBA Top Shot, a platform offering unique collectible videos for basketball enthusiasts. Dapper Labs recognizes that there is widespread concern about the environmental impact of Proof of Work blockchains like Bitcoin and Ethereum, but it argues that Flow’s energy consumption is significantly lower.
Understanding Proof of Stake vs. Proof of Work
Dapper Labs employs a different consensus mechanism for Flow, known as Proof of Stake, which contrasts sharply with the energy-intensive Proof of Work used by other cryptocurrencies. Proof of Work involves solving complex cryptographic puzzles to validate transactions and secure the blockchain, a process that consumes vast amounts of energy. In contrast, blockchain games often utilize Proof of Stake systems like Solana, Avalanche, Hedera, Polygon, and Immutable X, which require far less energy, comparable to that of cloud-based services. Flow’s annual energy consumption remains low at 0.18 GWh, reaffirming its efficiency in comparison to the energy demands of other blockchain networks.
Comparative Analysis of Energy Consumption
When comparing energy usage across various Web3 platforms, publicly available data reveals substantial differences. Bitcoin consumes approximately 178,040 GWh annually, while Ethereum requires around 108,390 GWh. In comparison, Solana’s energy usage is 11.05 GWh, and Polygon’s is 0.79 GWh. Flow, with its minimal consumption of 0.18 GWh, stands out as the most energy-efficient option. Nathan Steeghs, a partner at Deloitte Canada, commented on the firm’s commitment to assessing future energy consumption trends. He noted that their review revealed Flow’s energy use is considerably lower than that of other protocols, attributing this efficiency to its Proof of Stake consensus mechanism, which is less demanding in terms of energy and carbon output.
Flow’s Unique Multi-Role Node Architecture
Flow’s innovative multi-role node architecture further enhances its efficiency by distributing processing tasks among different specialized node types. This design allows the network to maintain its energy efficiency even as user activity scales up significantly, potentially increasing by 100 times or more. Dapper Labs indicates that this results in a diminishing energy footprint per transaction over time. The energy consumption is primarily driven by collection and consensus nodes, which perform less labor-intensive tasks than execution nodes but have a larger number of them, leading to higher overall energy consumption. The network’s seven execution nodes are notably efficient, requiring only seven machines, thus mitigating the baseline energy impact.
Deloitte’s Assessment Methodology
Deloitte Canada’s evaluation of Flow’s blockchain energy consumption for 2021 focused on various node operations, including execution, consensus, collection, and verification. The assessment also considered related Scope 2 emissions, following the Greenhouse Gas (GHG) Protocol. To gauge energy usage and emissions accurately, Deloitte surveyed node operators, capturing data from approximately 90% of the node network. However, the inherent challenges in measuring energy consumption and GHG emissions must be acknowledged, as variations in measurement techniques can yield differing results. Deloitte used detailed operational data from the survey to estimate energy consumption per node type, extrapolating this data to estimate the energy usage for the remaining 10% of nodes, adhering to relevant quantification guidance for GHG emissions reporting.
