The Unreasonable Ecological Cost of #CryptoArt (Part 2)
Update — Dec 2021
There is now more research on the emissions of
1. The Ethereum network as a whole,
2. NFT platforms on the Ethereum network, and
3. Per-Transaction footprint / responsibility allocation.
conducted by Kyle McDonald.
The above resources currently contain the most up-to-date figures.
Kyle has used a completely different approach (bottom-up) compared to the method my calculations are based on (top-down, as used by digiconomist). Kyle also uses a Fee-based accounting model as opposed to the Gas-based accounting model that I use (note that Gas and Fee are highly correlated, and at times NFT-related tx fees are disproportionately higher than the Gas that they use, see links #2 and #3 above). Despite these differences in methodology, the carbon footprint of an NFT comes out very similar with both approaches. Using Kyle’s calculations yields an average carbon footprint for a single-edition NFT averaged across all NFT platforms on Ethereum (rounded to closest 100Kg) as:
100 KgCO2 to mint (1–2 hour flight, e.g. London-Frankfurt)
200+ KgCO2 for a sale with a few bids (3 hour flight, e.g. London-Rome)
500+ KgCO2 for more bids and more sales (5+ hour flight, NYC-LA)
Note that these figures are very similar to those calculated in the article below. This is because, (until very recently) digiconomist’s top-down estimates (which is what my calculations are based on) were very close to Kyle’s bottom-up estimates. It’s worth noting that very recently, digiconomist’s estimates have increased significantly higher than Kyle’s estimates. This is most likely due to the differences in the bottom-up and top-down approaches to handling events such as the rapid increases in the value of ETH leading to miners seeking to use older (and less efficient) GPUs, and the banning of mining in China causing miners to relocate to other countries (such as mostly coal-powered Kazakhstan). In reality, most likely digiconomist is now over-estimating, while Kyle is most likely under-estimating. This means that it’s very probable that the current true figures are considerably higher than those mentioned in this article.
This is Part 2 of a N-part series, accompanying the website http://cryptoart.wtf
Part 0 (26th Feb): A short summary of the main article has been published in the online publication Flash Art under the title “Toward a New Ecology of Crypto Art: A Hybrid Manifesto”. I suggest this as a starting point.
Part 1: I present my motivations, findings, and my conclusions as a result of these findings.
Part 2 (this article): I present the underlying data and methodology that gave rise to these findings, along with more statistics. It’s more aimed at those who wish to dig deeper into the numbers, and/or technically interrogate my claims.
Part 3 (25th Feb): I — with contributions from many others — have put together some information with regards to more sustainable NFTs.
These figures are calculated from blockchain data and existing research. Are they “accurate”? They are as accurate as they can be based on the current data and research, which does have many unknowns. Furthermore, the true figures are continually changing. In that respect these figures are best thought of as representing the true scale (e.g. hundreds of Kg C02, tonnes of CO2, tens of tonnes of CO2 etc. i.e. orders of magnitude). The accuracy also depends on the acceptable error margin. One of the most authoritative critiques of the method used by  comes from Jonathan Koomey , who finds the model too “simplistic”. For an analyst who works with clients where lives are literally at stake if errors are made , this is completely understandable. For the purposes of our discussion however, even if the true carbon footprint of a multi-edition NFT is 50 tonnes CO2 instead of 100 tonnes, it really should not affect this conversation — as it is not going to be in reality, 50 grams, or even 50 Kg. Furthermore, the “Bevand” method — which is recommended by Koomey — is used by  to estimate the energy consumption of Bitcoin, and is currently producing 40% higher estimates than the method used by [1, 23], rendering it unlikely that the Ethereum estimates will be significantly (e.g. an order of magnitude) over-estimating.
There is a common fallacy along the lines of “The same energy will be consumed whether a block is empty or contains your transaction, thus a transaction has no impact on the energy consumption of a block being mined, or on the environment”. This is based on a gross misunderstanding of what a carbon footprint is. This statement is analogous to claiming “when a 500 tonne airplane flies from NYC to LA, it consumes the same amount of fuel whether I ride it or not (because the weight of a person is negligible compared to the weight of the plane). Thus my flying has no impact on the environment”. It is true that one person deciding to fly (or not) does not have an immediate effect on emissions. However, there is a footprint associated with a seat on a plane. Furthermore, the market demand for flying, affects how the aviation industry schedules flights. In fact, if all of a sudden, lots more people wanted to fly, the aviation industry would not be able to all of a sudden schedule more flights overnight. However, the mining industry can respond much quicker to increases in demand, compared to the aviation industry. Miners even usually have older mining equipment which are no longer profitable to mine with (i.e. the electricity costs of running the equipment is greater than the financial rewards). But if demand goes up on the network, transaction fees also go up, and mining becomes more profitable to the extent that miners can respond quite quickly by turning on such older equipment . More detail on calculating the footprint of individual transactions can be found in .
Mining is not primarily powered by renewables. One report produced by a crypto-investment agency  claims that (Bitcoin) mining is 75% powered by renewables. However, other independent (and arguably more thorough, as they include more regions and the changing seasons and miner migration) studies put the figure much lower at 29%–39% . The data that I use to calculate emissions, includes usage of low-carbon energy sources weighted by hashrate of regions, with a global weighted average of ~40% lower-carbon energy production (0.58 KgCO2/kWh) — inline with this research. This is based on data from [7, 22], and the limitations of the accuracy of this approach is detailed in those links. (On a side note, using renewables is not a solution if it displaces others from using that renewable source ).
Mining is not primarily powered by surplus hydro-electric power in China. Emphasis mine: “There is a notion that electricity surplus in some APAC areas, such as the province of Sichuan in China, gives hashers who relocate their operations there during the rainy season [May-Oct/Nov] a competitive advantage in minimising their running costs. However, survey data demonstrates that this seasonal advantage appears to be offset by less affordable electricity prices throughout the rest of the year when hashers migrate back to other provinces, such as Xinjiang or Inner Mongolia in China. […] Coal-based mining is principally adopted in regions such as the Chinese provinces of Xinjiang and Inner Mongolia, and in Kazakhstan, whereas hydroelectric energy is mainly generated in South-Western regions of China (Sichuan and Yunnan). China’s oversupply of hydroelectric energy during the rainy season has often been used as evidence in claims that a vast majority of mining is powered by environment-friendly power sources. While it is true that the Chinese government’s strategy to ensure energy self-sufficiency has led to the development of massive hydropower capacity, the same strategy has driven public investments in the construction of large-scale coal mines. Like hydroelectric power plants, these coal power plants often generate surpluses. It should not come as a surprise then that a significant share of hashers in the region equally report using both hydropower and coal energy to power their operations”. p24, p27 .
So far I’ve chosen to focus most of my analysis (and the website http://cryptoart.wtf) on SuperRare — which is just one of many CryptoArt NFT platforms, and by no means the worst offender when it comes to carbon footprint. E.g. SuperRare offers ‘one-off’ NFTs. In Part 1, I also share some figures from NiftyGateway (which I calculate in the same way, measuring Ethereum Gas). NiftyGateway doesn’t track bids and sales on-chain. However, they do allow editions of 100s, which incur footprints orders of magnitude higher than those mentioned below.
On SuperRare I’ve analyzed:
- 17812 NFTs
- 79977 transactions relating to these NFTs
- 633 CryptoArtists
(I don’t know the total number of NFTs, transactions or artists on SuperRare, but I believe this is very close to almost all of them).
Background: how is the footprint calculated?
Different Ethereum transactions require different amounts of Ethereum Gas (not to be confused by ‘real world gas’), based on the complexity of the actions carried out by the smart-contract as a result of the transaction . Blocks also have a Gas limit (currently 12.5 million). This limits the amount of computation that can be required by all of the transactions in a block, effectively also limiting the number of transactions in a block. “In general, the more complex the transactions are, the fewer you can fit within one block” . Thus the amount of Gas required by a transaction is representative of how much of a block it it is taking up. E.g. a simple transaction (~21K Gas) will take up 21000/~12500000 => ~0.17% of a block. Whereas an operation such as ‘minting’ an NFT (~260K Gas) will take up 260000/~12500000 => ~2% of a block. Since the energy required and footprint of mining a block is independent of its contents and number of transactions, the Gas required by a transaction is representative of the portion of a block’s footprint it will incur.
This is explained in more detail in  and a discussion can be found here.
(Kyle McDonald has been doing more research on “Per-Transaction footprint responsibility models”, see Update 27.04.2021 at the top of this page).
Footprint per unit of Gas
From the total amount of Ethereum Gas and energy consumed by the Ethereum network over a period [1, 2, 3], we can calculate the energy footprint per unit of Gas. Then we can calculate the footprint of a particular transaction³, from the amount of Gas that it used. Likewise for each NFT, by adding up the footprints of each transaction. Carbon footprint is calculated in a similar manner, looking at emissions averaged across regions where mining is most common ¹.
These give ¹ ³ :
Energy footprint per unit of Gas = 0.0005454743 kWh
Carbon footprint per unit of Gas = 0.0003182308 KgCO2
Update Mar 03 2021: The figures above are averages across the period Dec 2020 — Jan 2021, for when this study was conducted. Here are annual averages:
2017 (20 May–31 Dec): 0.0008427156 kWh, 0.0004916419 KgCO2
2018 (01 Jan–31 Dec) : 0.0011741214 kWh, 0.0006849847 KgCO2
2019 (01 Jan–31 Dec) : 0.0005121816 kWh, 0.0002988077 KgCO2
2020 (01 Jan–31 Dec) : 0.0003735465 kWh, 0.0002179278 KgCO2
2021 (01 Jan–01 Mar ): 0.0006310654 kWh, 0.0003681648 KgCO2
Footprint per transaction type
The footprint of a single average ETH transaction is estimated to be 35 kWh . However, this is not the energy consumption of a typical transaction relating to a NFT.
Different kinds of NFT transactions have different complexities, and thus different Gas requirements. While the values fluctuate, on SuperRare they are typically in the range:
Calculating footprint of NFTs
For each NFT, I search the Ethereum blockchain for all of the transactions that the NFT has been involved in. Using the footprints per unit of Gas that I mentioned above, I can calculate the footprints relating to the NFT (or artist)³.
I believe this provides a lower-bound for the energy consumption of that NFT being tracked on the Ethereum network.
From what I can tell, SuperRare uses (at least) three smart-contracts, two for managing NFT ownership and transfers (v1 and v2), and the other for bids. They might also be using other contracts for doing other things which I have not come across yet. This would boost the numbers even higher.
Moreover, the contracts dealing with ownership is open-source, so querying it to get ownership and transfer related transactions is relatively trivial. The contract dealing with bids however, is closed-source. And without a so-called ABI (Application Binary Interface), I cannot programmatically query the contract for any bid transactions that involve a specific NFT. So instead I have to resort to more brute-force methods of search. And these are likely to miss transactions that are related to a particular NFT. Given that a transaction cannot use negative gas, any transactions that I do miss, would have pushed the numbers that I calculate even higher.
For this reason, I see the figures that I give as a lower-bound.
NiftyGateway however, is a bit different. They use one smart-contract per ‘collection’, and they don’t log bids or transfers on the blockchain unless the NFT is withdrawn from the platform. Minting does take place on the blockchain, and since it is the most expensive of the transactions, NFTs on NiftyGateway still have a high footprint. Especially when editions are taken into account, because each edition minted is an additional mint transaction (in large batches, the Gas per mint can drop from 260K to 210K, but for editions of 100s, this saving is then overriden by orders of magnitude).
Footprint per average NFT
79,977 transactions on SuperRare used 12,040,321,070 Gas, giving a total of 6,567,686 kWh (6.5 GWh) and 3,831,601 KgCO2 (3831 tonnes CO2).
Across 17812 NFTs, this gives an average of:
4.5 transactions, 369 kWh, 215 KgCO2 per NFT
(More statistics in the following sections)
Footprint per average transaction relating to a NFT
12,040,321,070 Gas across 79,977 transactions gives:
150,547 Gas => 82 kWh / 48 KgCO2 per transaction (on SuperRare).
Note that this is more than double the 35 kWh of an average ETH transaction.
And I would again like to put out a reminder, that these figures relating to energy consumption and CO2 emissions, are only due to the tracking of bids and sales of the NFT on the blockchain. They do not include the production of the works, or even storage of the work online. Just the records of who owns what.
KgCO2 is computed from kWh, which is computed from Gas. So these are linearly correlated. Number of transactions however, is only indirectly correlated. As mentioned above, some transactions (such as minting) are more Gassy compared to others, such as bidding. So while NFTs with more transactions are more likely to have a higher footprint, there can be exceptions (e.g. a NFT with 5 sales and transfers could be higher than a NFT with no sales and transfers but more bids).
Energy consumption per NFT
Carbon emissions per NFT
NFT Age (time since being minted)
SuperRare’s first NFT was minted 33 months ago.
50% of NFTs were minted in the last 9 months
20% of NFTs were minted in the last 3 months
6% of NFTs were minted in the last month.
Number of transactions per NFT
1 in 9 NFTs (11%) have no transactions in addition to being minted
half (48%) have 3 or fewer transactions
3 in 4 (73%) have 5 or fewer transactions
1 in 14 (7%) have 10 or more
0.5% have 20 or more
0.1% have 30 or more
1 NFT has more than 50 transactions
Number of NFTs minted per artist
1 in 17 artists (6%) have minted only 1 NFT
1 in 3 (31%) have 5 or fewer NFTs
half (55%) have 10 or more
1 in 3 (36%) have 20 or more
1 in 6 (16%) have 50 or more
1 in 14 (7%) have 100 or more
1% have 200 or more
2 artists have 300 or more NFTs
Energy consumption per artist
Carbon emissions per artist
Artist Age (time since joining platform)
1 artist joined over 2.5 years ago
1 in 7 (14%) joined over 2 years ago
2 in 3 (65%) joined in the last year
half (45%) joined in the last 6 months
1 in 4 (27%) joined in the last 3 months
1 in 11 (9%) artists joined in the last month
Income and revenue distributions
NFT primary sales
The average sale price is 0.868 ETH ($975)
1 in 3 NFTs (37%) have not sold at all
Half of the NFTs sold for less than $225 (i.e. median)
4 out of 5 NFTs (78%) sold for less than $1000
1.5% sold for more than $10K
1 NFT sold for more than $100K
NFT secondary sales (10% goes to the artist)
90% have not resold (i.e. zero secondary sales royalties)
99% generated less than $5K (less than $500 for the artist)
3 NFTs (0.02%) generated more than $100K (more than $10K for the artist)
Total revenue per NFT (including primary and 10% secondary sales)
Artist primary sales
The average primary sales income is 24 ETH ($30K)
1 in 7 artists (14%) have made no primary sales at all
Half of the artists made less than $9.5K (i.e. median)
1% made more than $250K
1 artist made more than $1M
Artist secondary sales (10% goes to the artist)
Half of the artists (56%) did not receive any secondary sales royalties.
1 in 3 (38%) generated less than $1K (i.e. less than $100 per artist).
3 artists (0.5%) generated more than $250K (i.e. more than $25K per artist)
Total income per artist (including primary and 10% secondary sales)
The top 0.1% of artists earned 8% of total income ($1.5M of $19M)
The top 1% earned 21% of total income ($4M of $19M)
The top 10% earned 57% of total income ($11M of $19M)
The top 20% earned 75% of total income ($14.5M of $19M)
The bottom 40% earned 2% of total income ($390K of 19M)
The bottom 20% earned 0.2% of total income ($40K of 19M)
Though it wasn’t designed to apply at this scale, we can try looking at a metric of income inequality such as The Palma Ratio (PR) — a ratio of the top 10%’s share of income, compared to the bottom 40% (lower PR implies more equally distributed income, higher PR implies more inequality) .
The US’s PR is relatively high within the ‘Western’ world at 1.85. Japan and Denmark are more egalitarian at around 0.9. Countries like South Africa are amongst the worst with a PR of 7.The entire world’s PR — i.e. the income gap between the richest people in the world, compared to the poorest people in the world is 32.
SuperRare has a Palma Ratio of 29.
See Part 1 for conclusions, I won’t repeat them here.
- The KgCO2 calculation has a larger margin of error compared to kWh. First, it inherits the error of kWh. Second, naturally some modes of energy production (e.g. coal, oil, gas) release more greenhouse gases compared to others (e.g. hydro, wind, solar, nuclear) . Perhaps unsurprisingly, there are heated disagreements over whether the energy fueling the massive mining farms around the world are predominantly greener than average, or worse. The current estimate with regards to percentage of total hashing energy coming from renewables is 39% . However, even with renewables, unless the source is an off-grid, or otherwise underutilized plant, one must also take displaced renewables into account . Footnote 5. has more information.
- It’s important to note, that while there are some very useful tools out there to calculate kWh and KgCO2 estimations for ETH wallets , these only look at the transactions that a ETH wallet was directly involved in (e.g. transfer of ETH funds). However, minting, sales and bids on an NFT are not necessarily linked to the authors’ ETH account depending on the platform used, so these tools will miss them. Instead, to calculate the footprint of an NFT, one must look at the smart-contract(s) involved. (Note that there may be more than one smart-contract involved for a single NFT, as it is on SuperRare: one contract for minting and transfers, another contract for bids).
- It might be more accurate to compute the energy consumption per unit of Gas at the time of the transaction, by using historical figures. However I don’t do this, and I use recent figures. The reason for this is because I’m less interested in the footprint of past NFTs, and I’m more interested in the footprint of current NFTs.
- Due to sheer volume, simple, typical, daily, activities also add up. A single email is thought to produce in order of ~1–10g of CO2. But just the CO2 emissions of spam emails were estimated to be around 3 billion KWh per year according to a report in 2009 .
- (moved to main body)