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Energies 2021, 14, 4254 7 of 22 by the Durbin–Watson test for our proposed model. The value reached is 2.074 that is very close to the value of 2, which according to the Durbin–Watson test indicates that there is an absence of autocorrelation in the regression model used [55]. Therefore, as can be seen in the Table 2, our regression shows a high R-squared value and the adjusted R-squared result, and also highlights the absence of endogeneity. On the other hand, as shown in Table 4, after performing the ANOVA test the p-value (Sig.) is less than 0.05. Therefore, the hypothesis presented can be accepted, which means that at least one of the parameters is statistically different from “zero”, hence the model is valid as a whole [51,53]. Table 4. ANOVA. Model Regression 1 Residual Total Source: Authors’ elaboration based on data from Table A2 and IBM SPSS Statistics 27. Sum of Squares 26,583.481 7192.229 gl Quadratic Average 6 4430.580 126 57.081 F Sig. 77.619 0.000 33,775.709 132 Where the following were used as new predictors in the adjusted EPI: HDI; mining ban/operation with cryptocurrencies; electricity production from renewable sources, ex- cluding hydroelectric (% of total); average temperature per country in degrees; R&D&I research and development expenditure (% of GDP); and electricity prices for households, September 2020 (kWh, U.S. Dollar). Finally, if we review the p-value (Sig.) in Table 3, they are statistically different from zero (Sig < 0.05), so they can remain in the model [51,52]. Table A3 in the appendix contains the statistics of the residuals. If we apply the equation obtained from the linear regression to the data contained in Table A1 in the appendix in each country, we can obtain a visual ranking of those countries in which cryptocurrency mining would be more sustainable. Considering the price of energy, but also weighting the production of energy from clean or renewable sources of that country, the existing legal restrictions on cryptocurrency mining, the human development index, and the investment in R&D&I made, and last but not least, the average temperature of the country under study. The average temperature is important, since in hot countries energy will be needed for mining cryptocurrencies, but also much more energy will be needed to cool the computer equipment while performing the mathematical calculations necessary for mining. This is shown in Figure 2. Therefore, once we adjusted the new index of the most sustainable countries, in this case considering variables related to cryptocurrency mining, we can establish a composite index. Table 5 shows the 15 most and least sustainable countries for cryptocurrency mining derived from the index we have constructed. Table A4 in the appendix shows the new complete EPI with all the countries in the sample. We therefore see that the most sustainable countries for mining cryptocurrencies are Denmark and Germany, followed by some distance by Sweden, South Korea, and Switzerland. It is worth noting that within the top 10 most sustainable countries for mining cryptocurrencies, eight of them are European (Denmark, Germany, Sweden, Switzerland, Finland, Austria, and the United Kingdom), and the remaining two are Asian countries (South Korea and Japan). In Europe, the trend seems clear: these are advanced countries in clean energy production, where the price of electricity is average and there are no legal obstacles. In addition, these are countries that invest in R&D&I, and human capital. In Asia, the European pattern is repeated, as South Korea and Japan coincide with the above characteristics. In both cases (European and Asian) there is another parallelism: they are all countries in the northern hemisphere, and within the northern hemisphere, most of them are located in the northern part of the northern hemisphere. In addition to their location in the northern hemisphere, all the countries at the top of the ranking share a common characteristic: they have a high or very high GDP and GDP per capita. Additionally, according to severalPDF Image | Cryptocurrency Mining from an Economic and Environmental
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