Researchers behind the well-known Cambridge Bitcoin Electricity Consumption Index (CBECI<\/a>) have officially revised its methodology to enhance the accuracy and reliability of the Index\u2019s estimates for the first time since its inception in 2019.<\/p>\n The CBECI was launched in July 2019 in an effort to provide reliable data-driven insights to questions about Bitcoin mining\u2019s energy-intensive nature and associated environmental impact.\u00a0<\/p>\n Speaking exclusively to Cointelegraph ahead of announcement of the revision, head researcher Alexander Neumueller unpacked the Index\u2019s role in providing a relatively accurate estimate of the Bitcoin (BTC<\/a>) network\u2019s electricity consumption and contextualizing the data in a way that is digestible for the layman on the street.<\/p>\n 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.\u00a0The researchers honed in on questions about what had driven substantial increases in hash rate in recent years as newer mining equipment eclipsed older models in computing power.<\/p>\n Related:\u00a0Nuclear and gas fastest growing energy sources for Bitcoin mining: Data<\/a><\/em><\/strong><\/p>\n Neumueller and his fellow researchers noted that the scarcity of hardware-related data posed a significant challenge as it limited the CBECI\u2019s ability to accurately assess the types of hardware that miners use as well as their ubiquity. <\/p>\n This led the researchers to 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.<\/p>\n This in turn led to a \u201cdisproportionally large number\u201d of older mining hardware compared to newer models in the methodology\u2019s assumed hardware distribution during exceptionally profitable mining periods.<\/p>\n Related:\u00a0Iris Energy buys 248 Nvidia GPUs worth $10M for generative AI and Bitcoin mining<\/a><\/em><\/strong><\/p>\n 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.<\/p>\n 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.<\/p>\n 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.<\/p>\n \u201cThis 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.\u201d<\/p><\/blockquote>\n Neumueller highlighted a divide in opinion, with critics suggesting that Bitcoin \u201cjeopardizes environmental advancements and could exacerbate climate change,\u201d while supporters argue that the mining industry could combat climate change and provide other societal benefits. <\/p>\n \u201cHowever, 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.\u201d<\/p><\/blockquote>\n The CBECI includes a wide range of rich data points and visualizations, including the index\u2019s Bitcoin network power demand, a mining map reflecting the geographic distribution of Bitcoin\u2019s mining hash rate, and a greenhouse gas emissions index. <\/p>\n The CBECI and greenhouse gas emissions indexes provide three different estimates for both sectors, providing a hypothetical range for these specific metrics. <\/p>\n![](\"https:\/\/s3.cointelegraph.com\/uploads\/2023-08\/0bdf98c4-5da5-4b6a-a314-1f7b8f3cc6b1.png\")
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