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Energies 2021, 14, 4254 6 of 22 Performance Index (EPI) through linear regression, to approximate the dependency rela- tionship between our dependent variable Y (which in our case will be the most sustainable country according to its score in the readjusted EPI); m independent variables Xi (which in our case are the six first variables as listed in Table 1) with m ∈ Z +, and a random term ε [50,51]. Y=β0 +β1X1 +β2X2 +... +βnXn +ε (1) Since the parameters β0, β1 . . . are unknown constants, these must be estimated using the sample data we collected [51,52]. For this purpose, starting from the linear regression model and applying it to the data contained in Table A1 of the appendix, we obtained the descriptive statistics contained in Table A2 of the appendix where our statistical analysis sample (N) consists of 133 countries for which complete data are available, despite the fact that 144 countries appear in the initial dataset. The difference is due to the lack of data on the human capital index variable in several countries. After the application of linear regression to refit the EPI with our new variables, we obtained the model summary and the estimation of the β parameters of the model in Tables 2 and 3. Table 2. Model summary. Model R R-Squared Adjusted R-Squared 1 0.887 0.787 0.777 Source: Own elaboration based on data from Table A2 and IBM SPSS Statistics 27. Standard Error of the Estimate Durbin–Watson 2.074 7.5552 As predictors, we used: (Constant); HDI; ban mining/operation with cryptocurrencies; electricity production from renewable sources, excluding hydroelectric (% of total); average temperature per country in degrees; R&D&I; research and development expenditure (% of GDP); electricity prices for households, September 2020 (kWh, USD); and being our dependent variable, the most sustainable country. After applying the above model, we managed to estimate the β0, β1 . . . parameters using the sample data we collected and processed with SPSS [51]; and they are listed in Table 3. If we therefore take the data obtained from the previous table on the unstandardized β coefficients, we obtain that our new adjusted sustainability index (adjusted EPI) is represented by the following equation: ASi = 13.769 + (28.63 × Pe) + (0.142 × Er) + (−0.294 × T) + (2.515 × N) + (4.164 × Id) + (51.519 × Dh). (2) Table 3. Estimation of the parameters β of the model. Dependent variable: most sustainable country. 1 0.142 0.076 −0.294 0.098 2.515 1.180 0.099 1.860 −0.154 −2.987 0.090 2.131 0.252 4.583 −0.489 0.179 2.366 38.064 −0.099 4.851 5.962 64.975 Model (Constant) Electricity prices (kWh, USD) Electricity production from renewable sources, (% of total) Average temperature per country in degrees Ban on cryptocurrency mining/operation R&D expenditure on research and development (% of GDP) HDI Unstandardized Standardized Coefficients Coefficients β Deviation β Error 13.769 4.743 28.643 11.362 0.145 T 2.903 2.521 Sig. 0.004 0.013 0.045 0.003 0.035 0.000 95.0% Confidence Interval for β Lower Upper Limit Limit 4.383 23.156 6.159 51.128 0.009 0.292 4.164 0.908 51.519 6.799 0.000 0.465 7.577 Source: Authors’ elaboration based on data from Table A2 and IBM SPSS Statistics 27. 4. Results As we can see in Table 2, our R-squared coefficient yields a result of 0.787. The result obtained shows that our new EPI is explained by 78.7% of our new independent variables that we introduced. [52–54]. Moreover, in the same table we can observe the result yieldedPDF Image | Cryptocurrency Mining from an Economic and Environmental
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