15.04.2024

Cambridge Bitcoin Electricity Consumption Index updated to reflect hardware distribution and hashrate increases

Researchers behind the well-known Cambridge Bitcoin Electricity Consumption Index (CBECI) have officially revised its methodology to enhance the accuracy and reliability of the Index’s estimates for the first time since its inception in 2019.

The CBECI was launched in July 2019 in an effort to provide reliable data-driven insights to questions about Bitcoin mining’s energy-intensive nature and associated environmental impact.

Speaking exclusively to Cointelegraph ahead of announcement of the revision, head researcher Alexander Neumueller unpacked the Index’s role in providing a relatively accurate estimate of the Bitcoin (BTC) network’s electricity consumption and contextualizing the data in a way that is digestible for the layman on the street.

Key takeaways from the revised methodology included a focus on recent developments in Bitcoin mining hardware and hash rate and whether the CBECI was accurately reflecting the changing landscape.The researchers honed in on questions around what had driven substantial increases in hash rate in recent years as newer mining equipment eclipsed older models in computing power.

Neumueller and his fellow researchers noted that the scarcity of hardware-related data posed a significant challenge as it limited the CBECI’s ability to accurately assess the types of hardware that miners use as well as their ubiquity.

This led the researchers to previously create a methodology that simulates a daily hardware distribution based on performance and power usage data of real hardware. Neumeuller notes that the backbone of the previous CBECI methodology assumed that every profitable hardware model released less than five years ago equally fuelled the total network hashrate.

This in turn led to a “disproportionally large number” of older mining hardware compared to newer models in the methodology’s assumed hardware distribution during exceptionally profitable mining periods.

The researchers subsequently discovered that more recently released equipment appeared to be underrepresented while equipment nearing the end of its life cycle was overrepresented. This prompted the change in the CBECI methodology.

Neumeller then explained how his team began comparing hashrate increases with United States import data reflecting recent Bitcoin mining hardware deliveries. This was combined with an examination of publicly available sales data from mining hardware manufacturer Canaan.

CBECI looked at U.S. import records on Bitcoin mining equipment (left) and estimated computing power derived from import data (right). Researchers used the hash rate (in TH/s) and gross weight stated by the manufacturer and applied an equally weighted mix of the following models from Canaan’s Avalon A1246, Avalon A1266, Avalon A1346 and Avalon A1366.

The analysis, which considered a number of in-depth factors, was used to test the hypothesis that increases in network hash rate can be attributed to more recently released mining hardware.

“This hypothesis was based on U.S. import data, and we sought additional evidence to validate it. If Canaan’s sales data is representative of the industry, it corroborates this claim.”

Neumueller highlighted a divide in opinion, with critics suggesting that Bitcoin “jeopardizes environmental advancements and could exacerbate climate change,” while supporters argue that the mining industry could combat climate change and provide other societal benefits.

“However, the intricate nature of the industry and the lack of information are often under-recognised, making room for cherry-picked data points and biased perspectives.”

The CBECI includes a wide range of rich data points and visualizations, including the index’s Bitcoin network power demand, a mining map reflecting the geographic distribution of Bitcoin’s mining hash rate as well as a greenhouse gas emissions index.

The CBECI and greenhouse gas emissions indexes provide three different estimates for both sectors, providing a hypothetical range for these specific metrics.

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