How we rate native cryptocurrencies - a user guide
Updated: May 12, 2022
The rating of the sustainability of cryptocurrencies requires a multidimensional assessment of the underlying blockchain technology and the processing of a broad range of data points. While we use quantitative models and approaches wherever feasible we use qualitative assessments to ensure that soft factors such as roadmaps, incentives, conflicts of interest are properly accounted for in our rating.
We rate cryptocurrencies according to the three ESG dimensions: environment, social, and governance. For each dimension, we collect large amounts of data that form the basis for our quantitative estimation models as well as for qualitative expert judgment. Typically, quantitative estimates such as the energy consumption per transaction will be model-based, while other assessments like the analysis of potential conflicts of interest require at least some level of qualitative assessment and ultimately expert judgment. Combining our models with our expertise allows us to effectively balance consistency across different cryptocurrencies while addressing the specifics of each blockchain.
The cryptocurrency space is continuously evolving and simultaneously we keep up with the current development of the industry by regularly revising our models, incorporating the latest research, and benchmarking our approach with results and publications from developers and academics. In the same spirit, we periodically update our rating to reflect changes and trends in the data that form the basis of our analysis.
Most metrics for our environmental score are model-based. For proof-of-work blockchains, we use a top-down approach where we estimate the total energy consumption for the entire blockchain and break it down for single transactions. Key data points are hash rates, the hash algorithms, and the energy consumption of the mining hardware typically employed by miners.
For Proof of Stake blockchains (and similar types of consensus algorithms), we go bottom-up and directly estimate the energy consumption per transaction. Key inputs are the number of validator nodes and transactions per node. Other factors, such as the importance of ecology in technical decisions of the network and/or the intention to improve the coin’s footprint are based on more qualitative assessments.
Bitcoin for example
Bitcoin uses the SHA-256 mining algorithm which allows for more energy-efficient mining hardware than most other proof-of-work blockchains. However, the extremely high hashrate (almost 2 million times higher than e.g., Ethereum) results in very bad scores in energy consumption (both on an absolute level as well as broken down to a single transaction). At the same time, there are no plans to revise the ecological unfriendly proof-of-work technology. There are groups and initiatives that aim to improve Bitcoin’s ecological footprint by using more sustainable energy sources for Bitcoin mining. However, as long as energy remains a scarce resource, there is little justification not to revise an inherently energy wasteful technology. Therefore, Bitcoin performs badly also in the qualitative factors that flow in our environmental rating. Overall, the excessive energy consumption of the bitcoin network combined with the reluctance to transform its underlying technology leave Bitcoin with the lowest possible environmental rating of D-.
There are several quantitative as well as qualitative factors that impact our social score. Qualitatively, there are many soft components that impact our rating, including the network’s vision, whether it is for-profit or not, whether it aims to have a positive impact on society, etc. On the other hand, there are quantitatively measurable factors that play an important role in making the technology available to everyone (not just the wealthy) such as low transaction costs, equitable asset distribution, and overall low entry barriers. We apply standard statistical tools to identify structural breaks or outliers in these time series and to derive a representative average.
Ethereum for example
Ethereum has very high transaction fees - only bitcoin has higher ones - with a long term average cost per transaction of USD 5. However, there is a clear structural break since the London update in August 2021 when transaction fees spiked even higher and are now fluctuating around USD 40. These high transaction fees prevent a substantial barrier to entry for large parts of the world’s population. Another quantitative metric is the asset distribution where Ethereum excels with a highly equitable allocation. While there are wallets that hold large amounts of ETH in absolute terms, none of them holds more than 10% of ETH and all large wallets with more than 1% of ETH supply together hold less than 20% of all outstanding ETH coins. A large driver of the social score is, however, on the qualitative side. Here, Ethereum scores above average (and broadly in line with other large smart contract platforms) with its mission to empower technological innovation. The ambition to reduce transaction fees in the future and to increase accessibility via a higher scalability with ETH 2.0 further support a typically positive social vision for Ethereum. However, in combination with the large negative social impact from the exorbitantly high current transactions fees, Ethereum’s social score remains at only a C+.
For the governance score, we rely mostly on qualitative factors. We do look at the distribution of miners or validators quantitatively as it feeds into network stability, alignment of incentives and is key to avoid manipulations of any kind. However, to assess the security and conflicts of interest of a blockchain, we conclude a deep analysis of how the network operates whereby soft factors play the most important role.
For the conflict of interests, it is crucial to understand who has a quasi-controlling influence (whether it is due to a large stake in the asset, copyright of the code, a seat in the council, etc.). Especially for less decentralized networks, it is important that a rather small number of validators or council members do not get too powerful. For network security, we look at past incidents, congestion, and/or structures that make a blockchain more vulnerable to attacks as well as other experts/developers that are raising concerns or red flags.
Cardano for example
Cardano has the largest number of validator nodes of all Proof of Stake blockchains (approximately 3,000 relevant ones - only Avalanche gets any close to this number with approximately 1,100). Additionally, there are clear mechanics preventing validator nodes from becoming too large and/or too powerful and the ease to run one’s own validator node further incentivizes a large number of independent nodes. Through one of the highest degrees of decentralization and a clear non-profit orientation, the Cardano infrastructure and setup mitigate conflicts of interests well, although the Cardano Foundation remains powerful. This decentralized structure also boosts the network’s security is also demonstrated by the fact that there are no known incidents since the go-live of the Cardano mainnet in 2017. Overall, the specific design and infrastructure result in one of the best governance scores for Cardano of A.
Green Crypto Research evaluates cryptocurrencies on a technological level and strives to avoid idealistic valuations. A well-founded valuation always results from the interaction between qualitative and quantitative factors, model-based calculations, and the comparison of different blockchains.
 Native cryptocurrencies refer to coins that have an underlying blockchain to which they are ‘native’ (e.g., Bitcoin, Ethereum, Cardano). Typically, these are the coins that are used to pay gas fees on the underlying blockchain.
 While Proof of Work typically consumes substantially more energy than more advanced consensus-algorithm technologies such as Proof of Stake, there are ways to leverage the Proof of Work approach in a much more environmentally friendly way. See Zilliqa for example.
 Profit orientation is not automatically valued lower as long as it follows a purpose or a vision. The same applies vice versa for non-profit blockchains - here, too, purpose and goals play a significant role in our assessment. Green Crypto Research (GCR) is ideologically neutral and does not favor non-profit networks over profit-focused ones.
 GCR does not per se favor decentralized networks. We observe that more centralized networks tend to have better environment scores due to a more effective network operation. However, they tend to perform less well in the governance rating.
Please note that we regularly review and update our ratings. It is therefore possible that the scores mentioned in the blog post no longer correspond to the current rating. You can find an up-to-date overview of all ratings at any time at greencryptoresearch.com/ratings.