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The second step was to compile the performance data of the hardware used in bitcoin mining. In this study, we used data from 43 ASIC, 4 FPGA, 111 CPU (32 AMD and 79 Intel), and 111 GPU (54 ATI and 57 Nvidia) processors. The list of all devices and their release dates are given in the Appendix (Tables A1–A4). The necessary data were collected from two sources. The first source was the data provided by the manufacturers that can be collected from data sheets and white papers of these manufacturers. As a second data source, we used websites such as “userbenchmark” and “passmark” to further collect necessary data and test the reliability of the manufacturers’ data [13,14]. The third step was to review the price of Bitcoin. We used three data sources to find the daily value of the Bitcoin price. It was hard to estimate the price of Bitcoin before the Bitcoin exchange markets were founded. Therefore, we used the famous “Bitcoin pizza day” as our starting point. On 22 May 2010 Laszlo Hanyecz bought two pizzas by paying 10,000 Bitcoins. Each pizza was $25, and hence we assume the price of one Bitcoin to be $0.005 on that date [15]. Mt. Gox was active from 18 July 2010 to 25 February 2014 [16]. We used price data from Mt. Gox for that period. For the data from 25 February onwards, we used Bitstamp [17]. The last step of the data accumulation was the electricity price. To calculate an average electricity price in a given time span, we needed to know the mining locations. According to the estimated Hashrate by Pools (A pool is a place where miners share their sources (computational power) to solve the blocks and then share the reward in proportion to their hashing power) between 1 June and 4 June 2018, 70% of bitcoin mining was done in China. After China, Europe (10%), United States (10%), and rest of the world (10%) follow [18]. We calculated an aggregated Bitcoin electricity price in accordance with the mining locations and the prices in these places [19,20]. The main purpose of the methodology was to make two energy consumption estimations. The first estimation was the Minimum Energy Consumption. The minimum consumption was calculated by assuming that at each difficulty recalculation, the most efficient hardware on the market was used. We know that this is a theoretical minimum since miners will not always buy better performing machines whenever they are available, and they will not simply get rid of their existing hardware. To calculate the upper boundary of the energy consumption, we defined the term Maximum Energy Consumption. Estimation of the maximum consumption did not mean the use of the least efficient devices available in the market. It meant that the miners used the worst performing hardware, but mining was still profitable in terms of electricity prices. In other words, the operational costs due to electricity bills should not exceed the value of the bitcoin. Here, we neglected the capital expenditure and only took energy costs of the miners into account. The challenge was using daily electricity prices in mining locations. Instead, we picked up the mining locations during 1 June and 4 June 2018. After this we calculated an average electricity price for bitcoin mining by taking weighted averages of the mining locations. Table 2 shows the location of bitcoin mining by pools during this time. Pool Blocks BTC.com 179 AntPool 98 SlushPool 79 BTC.TOP 65 ViaBTC 62 F2Pool 59 Unknown 32 BitFury 16 DPOOL 16 BTCC Pool 15 BW.COM 15 Bixin 10 BitClub Network 8 58COIN 6 Percentage (%) 26.76 14.65 11.81 9.72 9.27 8.82 4.78 2.39 2.39 2.24 2.24 1.49 1.2 0.9 Country China 1 China USA 2 China China China NA Georgia China China Iceland China Table 2. Bitcoin pools and locations. China ChinaPDF Image | Energy Consumption of Bitcoin Mining
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