kylemcdonald / ethereum-nft-activity Goto Github PK

ethereum_footprint.py needs to be almost completely replaced:

• 1. In __init__, instead of loading the digiconomist energy consumption and etherscan gasUsed data, load the ethereum-emissions file daily-ktco2.csv which is regularly update on the gh-pages branch. Also, load an instance of the EthereumStats class, which will give you access to the tx_fees dict (mapping dates to total tx_fees).
• 2. Remove kgco2_per_gas() from EthereumFootprint
• 3. Remove get_etherscan_data() from EthereumFootprint
• 3. sum_kgco2() should run a different calculation. First, sum all transactions fees (using tx.get_fees()) based on the date of the transaction (using tx.get_datetime()). Then for each day, divide the total fees from those transactions by the total fees on that day (using tx_fees from the EthereumStats instance). Finally, sum all values, then convert from kilotons to kilograms.

Separately:

• 1. In contracts_footprint.py: add fees to each row and remove gas. Then re-run contracts_footprint.py and update the table in the readme.

Note: any server that regularly computes these numbers is going to need to run ethereum_stats.py on a daily basis to update the total transaction fees. Also, any emissions estimated for the current day should be treated with suspicion, as different data sources may be out of sync with each other.

the files are big enough now to make this worth doing. not so much for speed, because etherscan is the true bottleneck, but for the purpose of maintaining a low memory footprint (and maybe low disk footprint too).

probably can switch this over quickly with sqlite3 if de-duplication is handled in python.

the script has been failing for the last month because it hit memory limits. using a db can fix this.

I think it would be great to see what percentage these NFT's are taking up of the total ethereum network -- it could help create some context.

143,595,457,124 is a very big Gas number but what does that mean? Off hand I think Open Sea is something like 0.5-2% of Ethereum's gas usage for the day depending on the day -- you should show how much of the network it's using

when the stats are computed, the current day is just starting and has few or no transactions. this makes it look like the graph is always heading downwards, which is inaccurate.

Vilusion

Etherscan has a 5 calls per second rate limit for the free tier. Instead of pausing every time we hit a rate limit, we could rate limit the fetch_transactions_in_range function itself.

I've brought this up elsewhere but I'd love your perspective.

Since you're relying on Digiconomist energy estimation maybe you can help me get to the bottom of this:

Digiconomist says they:

1. Calculate total USD Mining revenues
2. Estimate what part is spent on electricity
3. Find out how much miners pay per kWh
4. Convert costs to consumption

If you go to you can see their source of data https://digiconomist.net/ethereum-energy-consumption

However for those four steps:

1. There's no source for the mining revenue for Ethereum
2. There's no source for the how much of that revenue is spent on electricity
3. There's no source for their estimate of "miners are assumed to be paying 10 cents per KWh on average"
4. There's no source for how they get the kWh consumption, at the end of these layers of assumptions with no sources

I think it's really problematic all these calculators like carbon.fyi, cryptoart.wtf and now yours are being built on top of these layers of assumptions and "energy assumed backwards from profit with no sources".

I've seen a handful of other Bitcoin studies that are more "bottom up" than "top down" like https://www.sciencedirect.com/science/article/pii/S2542435119302557 but their estimates are on the low end of Digiconomist and they say at length:

Previous academic studies, such as predictions of future carbon emissions or comparisons of cryptocurrency and metal mining, are based on simplistic estimates of power consumption and lack empirical foundations. Consequently, the estimates produced vary significantly among studies

I don't think you should use Digiconomist as a source of data if they have no sources. If you can find a more rigorous estimate of Ethereum network energy usage I think it would be really a great resource. Digiconomist lacks credibility even though its so highly pointed to

all of this should be replaced https://github.com/kylemcdonald/cryptoart-footprint/blob/main/ethereum_footprint.py#L20-L45 so we don't do the math every time we run.

not for efficiency's sake, but for simplicity.

update: working on new process for this in https://github.com/kylemcdonald/ethereum-energy/