China’s One Region Accounts for 35.76% of Bitcoin Hash Rate
A Bitcoin Mining Map is launched by the Cambridge Centre for Alternative Finance (CCAF) that allows checking the average monthly bitcoin hash rate produced by different countries around the globe.
Based on the geolocation data (IP addresses) provided by Poolin, BTC.com, and ViaBTC mining pools that collectively represent approximately 37% of Bitcoin total hashrate, this is the first geographical breakdown of bitcoin hash power distribution.
Unsurprisingly, China is the dominant force in the Bitcoin hash rate that accounts for 65.08% of the average monthly share of the total hash rate in April.
This share has decreased from 67.26% in March, 72.03% in February, and 72.82% in January.
In China, Xinjiang provided over 35.76% of this hash power followed by Sichuan (9.66%), Nei Mongol (8.07), Yunnan (5.42%), and Beijing (1.73%).
In April, the US came at second place with a share of 7.24%, followed by Russia 6.90%, Kazakhstan (6.17%), Malaysia (4.33%), and Iran (3.82%).
Canada, Germany, Norway, and Venezuela contributed less than 1% of the Bitcoin network’s hash power.
But this third halving that will cut the bitcoin block reward from 12.5 BTC to 6.25 coins will intensify the competition to capture the hash rate of the network and may result in a shift in hash power.
Just four days away, amidst the rising hash rate and difficulty, small and overleveraged miners would be washed out from the market and could see the balance of hash rate power tilt in North America’s favor.
Meanwhile, the number of computers running the bitcoin program is dominated by the US at 19.10% after the geographical location of 21.35% share not available, as per Bitnodes.
The US is followed by Germany(17.39%), France (5.82%), Netherlands (4.25%), Canada (3.01%), the UK (2.59%), Singapore (2.58%), Russia (2.31%), and China (2.02%).
These nodes that validate new transactions and store copies of the network’s shared transaction history have fallen to a level not seen since 2017. This decline could be because running a node is getting harder at a rate that is surpassing technological improvement.