Cambridge University releases new tool to track Bitcoin's global computing power, with China accounting for 65% of the computing power

Cambridge University releases new tool to track Bitcoin's global computing power, with China accounting for 65% of the computing power

According to BlockBeats, a team from the University of Cambridge has released a new Bitcoin data tool that shows the distribution changes of global computing power in the past six months. The chart was produced by the Cambridge Centre for Alternative Finance (CCAF). This Bitcoin mining map will show the monthly computing power share of each country for the first time.

Globally, the map shows that China accounts for 65% of the world's total computing power, while the United States and Russia each account for 7%. Although China is still far ahead, its share of computing power has fallen by about 10% since September last year.

CCAF’s mining map also shows that Xinjiang is the province with the highest computing power in China, but this contradicts the data of crypto asset management company CoinShares. In its semi-annual report, the latter ranked China’s Sichuan Province first (Note: This should have a lot to do with the flood season. May to October every year is the flood season in Sichuan). The contradiction in the data may also be because CCAF’s data began in September 2019, when the flood season in Sichuan was about to end, when the hydropower station’s power generation capacity was lower and the power generation was not that large. If this is the case, CCAF’s map can at least confirm that some Chinese miners may indeed move mining machines from Sichuan to Xinjiang after the flood season.

According to LIDONG, the hashrate data comes from APIs connected to three mining pools (BTC.com, Poolin and ViaBTC), and it took a year to develop. However, the data in this chart may have a large deviation in the hashrate share statistics of North America and Europe. The Cambridge team currently hopes to obtain data from more mining pools to create a more comprehensive picture.

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