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How much energy does it take to power popular Ethereum-backed CryptoArt platforms? And what emissions are associated with this energy use?

These questions do not have clear answers for two reasons:

  1. The overall energy usage and emissions of Ethereum are hard to estimate. I am working on this in a separate repo: kylemcdonald/ethereum-energy
  2. The portion for which a specific user, platform, or transaction might be considered "responsible" is more of a philosophical question than a technical one. Like many complex systems, there is an indirect relationship between the service and the emissions. I am working on different approaches in this notebook: Per-Transaction Models

This table represents one method for computing emissions, as of March 5, 2022. The methodology is described below.

NameFeesTransactionskgCO2
Art Blocks12,006244,59421,531,626
Async22427,403332,657
Foundation8,602661,07414,568,164
KnownOrigin50764,326904,455
Makersplace1,840144,1633,010,383
Nifty Gateway1,621151,9502,385,675
OpenSea314,51520,012,086551,268,013
Rarible20,9301,802,97127,706,539
SuperRare2,215320,6973,172,169
Zora53221,660721,254

Updates:

Preparation

First, sign up for an API key at Etherscan. Create env.json and add the API key. It should look like:

{
    "etherscan-api-key": "<etherscan-api-key>"
}

Install dependencies:

pip install -r requirements.txt

Note: this project requires Python 3.

contracts_footprint.py

This will pull all the transactions from Etherscan, sum the gas and transaction counts, and do a basic emissions estimate. Results are saved in the /output directory as JSON or TSV. Run the script with, for example: python contracts_footprint.py --verbose --tsv data/contracts.json data/nifty-gateway-contracts.json.

This may take longer the first time, while your local cache is updated. When updating after a week, it can take 5 minutes or more to download all new transactions. The entire cache can be multiple gigabytes.

This script has a few unique additional flags:

contracts_history.py

This will pull all the transactions from Etherscan, sum the transaction fees and gas used, and group by day and platform. Results are saved in the /output directory as CSV files. Run the script with, for example: python contracts_history.py --verbose data/contracts.json data/nifty-gateway-contracts.json

The most recent results are cached in the gh_pages branch.

Additional flags

Both scripts have these shared additional flags:

Helper scripts

Methodology

The footprint of a platform is the sum of the footprints for all artwork on the platform. Most platforms use a few Ethereum contracts and addresses to handle all artworks. For each contract, we download all the transactions associated with that address from Etherscan. Then for each day, we take the sum of all fees paid on all those transactions divided by the total fees paid across the whole network for that day. This ratio is multiplied by the daily Ethereum emissions estimate to get the total emissions for that address. Finally, the total emissions for a platform are equal to the emissions for all addresses across all days.

Sources

Contracts are sourced from a combination of personal research, DappRadar, and Etherscan tags.

When possible, we have confirmed contract coverage directly with the marketplaces. Confirmed contracts include:

How to add more platforms

To modify this code so that it works with more platforms, add every possible contract and wallet for each platform to the data/contracts.json file, using the format:

'<Platform Name>/<Contract Name>': '<0xAddress>'

Then submit a pull request back to this repository. Thanks in advance!

Contracts and Addresses

Contracts and addresses used by each platform can be found in data/contracts.json and are also listed here using python print_contracts.py to generate Markdown. Nifty Gateway contracts are listed separately in data/nifty-gateway-contracts.json.

Art Blocks

Async

Foundation

KnownOrigin

Makersplace

Nifty Gateway

OpenSea

Rarible

SuperRare

Zora