## Percentage of renewable electrical energy in the state (PRE)

In the first phase of the study co-relation between percentage of all forms of combined renewable electrical energy produced in the state (PRE) to eight predictor variables were studied using multivariate regression analysis according to the Eq. (2):

outcome: PRE = b0 + b1x1 + b2x2 + b3x3 + b4x4 + b5x5 + b6x6 + b7x7 + b8x8 (2)

x1 = HS, x2 = CD, x3 = PD, x4 = TP, x5 = TA, x6 = PW, x7 = AI, x8 = RD

where the predictor variables are: percentage of high school graduates (HS); percentage of college degree graduates (CD); population density of the state (PD); total population of the state (TP); area of the state (TA); percentage of water covered area of the state (PW); average annual personal income of the state (AI); and registered democrats in the state (RD), and the results are shown in Table 2.

Table 2

Multivariate regression analysis p values for the outcome: percentage of combined renewable electrical energy in the state (PRE)

Predictor variable | p |

High school graduates, % (HS) | 0.02033 |

College degree graduates, % (CD) | 0.72897 |

Population density, per sq. mile (PD) | 0.01964 |

Total population (TP) | 0.71355 |

Area of the state, sq. mile (TA) | 0.05995 |

Water covered area % (PW) | 0.05012 |

Average annual income, $ (AI) | 0.1324 |

Registered democrats, % (RD) | 0.00543 |

The model Eq. (3) can be written as follows:

outcome: PRE = − 3.63 + 3.98 HS + 0.432 CD − 0.0000727 PD − 0.00000000197 TP + 0.000000678 TA − 0.664 PW − 0.00000884 AI + 1.74 RD (3)

Three predictor variables with p < 0.02 are significantly co-related to the renewable electrical energy generation in a state as shown in Table 2. The percentage of registered democrats with p = 0.00543 is the most influencing factor determining the tendency of the state policy makers to implement renewable resources based electrical energy generation. The population density (p = 0.01964) and percentage of high school graduates (p = 0.02033) are also showing significant influences. The populations with higher education levels are likely to adopt renewable resources based energy and sustainable environmental policies and similar inclinations are seen in other parts of the world as well (Karasmanaki and Tsantopoulos 2019). In addition, area of the state (p = 0.05995) and water covered area (p = 0.05012) also shows strong positive and negative co-relations.

Percentage of combined renewable electrical energy in the state (PRE) shows positive coefficients for HS, CD, TA and RD; whereas negative coefficients are found for PD, TP, PW and AI. Therefore the analysis predicts an increase in percentage of combined renewable electrical energy in the state (PRE) for the increase in predictor variables HS, CD, TA and RD. In contrary the model predicts a decrease in percentage of combined renewable electrical energy in the state (PRE) for the increase in predictor variables PD, TP, PW and AI.

Percentages of renewable electrical energy components: wind (WE), solar (SE), hydro (HE), geothermal (GE) and biomass energy (BE)

In the second phase, we have expanded the analysis in previous section; where we have studied the co-relations of percentages of individual components of renewable energy: wind (WE), solar (SE), hydro (HE), geothermal (GE) and biomass (BE) based renewable electrical energy in states to the same eight predictor variables as in previous study. Multivariate regression analysis was carried out using R for the model:

outcome: WE/SE/HE/GE/BE = b0 + b1x1 + b2x2 + b3x3 + b4x4 + b5x5 + b6x6 + b7x7 + b8x8 (4)

x1 = HS, x2 = CD, x3 = PD, x4 = TP, x5 = TA, x6 = PW, x7 = AI, x8 = RD

## Percentages of renewable electrical energy generated using wind (WE)

The results of multivariate regression analysis study on wind energy based renewable electrical energy generation in states are shown in Table 3. The two variables: area of the state and the percentage of registered democrats showed significantly low p values in comparison to other variables as shown in Table 3. As expected states with large land area and suitable geographic terrains can produce more wind energy than smaller states. In addition, political affiliation of the policy makers can also be an important influence in adopting wind energy electricity generation in a state, as evident from the second lowest p value in Table 3.

Percentage of renewable electrical energy generated using wind (WE) shows positive coefficients for PW, AI and RD; whereas negative coefficients are found for HS, CD, PD, TP and TA. Therefore the analysis predicts an increase in percentage of electrical energy generated using wind (WE) for the increase in predictor variables PW, AI and RD. In contrary the model predicts a decrease in percentage of renewable electrical energy generated using wind (WE) for the increase in predictor variables HS, CD, PD, TP and TA

Table 3

Multivariate regression analysis p values for the outcome: percentage of wind based renewable electrical energy in the state (WE)

Predictor variable | p |

High School graduates, % (HS) | 0.248 |

College degree graduates, % (CD) | 0.6761 |

Population density, per sq. mile (PD) | 0.3733 |

Total population (TP) | 0.3881 |

Area of the state, sq. mile (TA) | 0.0643 |

Water covered area % (PW) | 0.2835 |

Average annual income, $ (AI) | 0.3548 |

Registered democrats, % (RD) | 0.1014 |

The model Eq. (5) can be written as follows: |

outcome: WE = 0.707–1.08 HS − 0.29 CD − 0.000015 PD − 0.00000000259 TP + 0.000000371 TA + 0.199 PW + 0.000003 AI + 0.554 RD (5)

## Percentages of renewable electrical energy generated using solar (SE)

The results of multivariate regression analysis study on solar energy based renewable electrical energy generation in states are shown in Table 4. The popular roof-top solar panel in home energy generation appear to be strongly co-related to the population density of the state with p = 0.00000761 with a positive co-relation.

Percentage of renewable electrical energy generated using solar (SE) shows positive coefficients for HS, PD, TP, PW and RD; whereas negative coefficients are found for CD, TA and AI. Therefore the analysis predicts an increase in percentage of electrical energy generated using solar (SE) for the increase in predictor variables HS, PD, TP, PW and RD. In contrary the model predicts a decrease in percentage of renewable electrical energy generated using solar (SE) for the increase in predictor variables CD, TA and AI.

Table 4

Multivariate regression analysis p values for the outcome: percentage of solar based renewable electrical energy in the state (SE)

Predictor variable | p |

High School graduates, % (HS) | 0.312 |

College degree graduates, % (CD) | 0.294 |

Population density, per sq. mile (PD) | 0.00000761 |

Total population (TP) | 0.561 |

Area of the state, sq. mile (TA) | 0.419 |

Water covered area % (PW) | 0.622 |

Average annual income, $ (AI) | 0.781 |

Registered democrats, % (RD) | 0.859 |

The model Eq. (6) can be written as follows:

outcome: SE = − 0.121 + 0.214 HS − 0.167 CD + 0.000194 PD + 0.000000000396 TP − 0.0000000363 TA + 0.0207 PW − 0.000000205 AI + 0.0134 RD (6)

## Percentages of renewable electrical energy generated using hydro (HE)

The regression analysis p values for hydro based renewable electrical energy generation in states are shown in Table 5. The p values are relatively high for all the variables studied and the lowest value of 0.145 is for area of the state. This results is logical as states with large land areas are likely to have rivers and waterways that can be dammed for hydroelectric power generation. Interestingly, the water coverage percent, mostly counting lakes in a state shows a much higher p as these are still water bodies.

Percentage of renewable electrical energy generated using hydro (HE) shows positive coefficients for PD, TA and AI; whereas negative coefficients are found for HS, CD, TP, PW and RD. Therefore the analysis predicts an increase in percentage of electrical energy generated using hydro (HE) for the increase in predictor variables PD, TA and AI. In contrary the model predicts a decrease in percentage of renewable electrical energy generated using hydro (HE) for the increase in predictor variables HS, CD, TP, PW and RD.

Table 5

Multivariate regression analysis p values for the outcome: percentage of hydro based renewable electrical energy in the state (HE)

Predictor variable | p |

High School graduates, % (HS) | 0.363 |

College degree graduates, % (CD) | 0.44 |

Population density, per sq. mile (PD) | 0.585 |

Total population (TP) | 0.951 |

Area of the state, sq. mile (TA) | 0.145 |

Water covered area % (PW) | 0.386 |

Average annual income, $ (AI) | 0.334 |

Registered democrats, % (RD) | 0.95 |

The model Eq. (7) can be written as follows:

outcome: HE = 1.23–1.29 HS − 0.818 CD + 0.0000140 PD − 0.0000000002.76 TP + 0.000000442 TA − 0.244 PW + 0.00000478 AI − 0.0318 RD (7)

## Percentages of renewable electrical energy generated using geothermal (GE)

Geothermal electrical energy generation is rare in US; however, we have counted this new source as it is a rapidly developing trend in the last couple of years (Ball 2021), (Ayling 2021). A distinctly low p value of 0.000413 was found for population density of a state, as shown in Table 6. This may be due to the fact that so far only a few very large states with small populations have tapped this promising sustainable source for electricity generation.

Percentage of renewable electrical energy generated using geothermal (GE**)** shows positive coefficients for HS, PD, TP, AI and RD; whereas negative coefficients are found for CD, TA and PW. Therefore the analysis predicts an increase in percentage of electrical energy generated using geothermal (GE**)** for the increase in predictor variables HS, PD, TP, AI and RD. In contrary the model predicts a decrease in percentage of renewable electrical energy generated using geothermal (GE**)** for the increase in predictor variables CD, TA and PW.

Table 6

Multivariate regression analysis p values for the outcome: percentage of geothermal based renewable electrical energy in the state (GE)

Predictor variable | p |

High School graduates, % (HS) | 0.349 |

College degree graduates, % (CD) | 0.157 |

Population density, per sq. mile (PD) | 0.000413 |

Total population (TP) | 0.481 |

Area of the state, sq. mile (TA) | 0.399 |

Water covered area % (PW) | 0.375 |

Average annual income, $ (AI) | 0.483 |

Registered democrats, % (RD) | 0.799 |

The model Eq. (8) can be written as follows:

outcome: GE = − 0.0958 + 0.134 HS − 0.153 CD + 0.00000984 PD + 0.000000000324 TP − 0.0000000256 TA − 0.02.52 PW + 0.000000349 AI + 0.013 RD (8)

## Percentages of renewable electrical energy generated using biomass (BE)

The regression analysis results for biomass based renewable electrical energy generation in states are shown in Table 7. A high co-relation is found with percentage of college degree graduates with p values of 0.039. However, the coefficient for this correlation is negative, indicating that increase in percentage of college graduates can result a decrease in renewable electrical energy generation in the state using biomass (BE).

Percentage of renewable electrical energy generated using biomass (BE**)** shows positive coefficients for HS, PD, TP, AI and RD; whereas negative coefficients are found for CD, TA and PW. Therefore the analysis predicts an increase in percentage of electrical energy generated using biomass (BE**)** for the increase in predictor variables HS, PD, TP, AI and RD. In contrary the model predicts a decrease in percentage of renewable electrical energy generated using biomass (BE**)** for the increase in predictor variables CD, TA and PW.

Table 7

Multivariate regression analysis p values for the outcome: percentage of biomass based renewable electrical energy in the state (BE)

Predictor variable | p |

High School graduates, % (HS) | 0.365 |

College degree graduates, % (CD) | 0.039 |

Population density, per sq. mile (PD) | 0.568 |

Total population (TP) | 0.471 |

Area of the state, sq. mile (TA) | 0.821 |

Water covered area % (PW) | 0.787 |

Average annual income, $ (AI) | 0.172 |

Registered democrats, % (RD) | 0.135 |

The model Eq. (9) can be written as follows: |

outcome: GE = − 0.0958 + 0.134 HS − 0.153 CD + 0.00000984 PD + 0.000000000324 TP − 0.0000000256 TA − 0.0252 PW + 0.0000000349 AI + 0.0130 RD (9)

## Percentage of fully electric vehicles registered in a state (PEV)

In the third phase of the study, co-relation between percentage of fully electric vehicles registered in a state (PEV) and six selected predictor variables were studied using multivariate regression analysis. The results for electric vehicles registered in a state analyzed using six variables: percentage of high school graduates (HS); college graduates (CD); total population (TP); annual income (AI); and registered democrats (RD) are shown in Table 8. The average annual income shows a very small p value of 0.00141, indicating a strong co-relation between the personnel income and purchasing a fully electric vehicle. Currently, on average fully electric battery powered cars are more expensive than gasoline cars, therefore personnel annual income appears to be the most important factor in determining the acceptance of this renewable energy technology.

Percentage of fully electric vehicles registered in a state (PEV) shows positive coefficients for TP, AI and RD; whereas negative coefficients are found for HS, CD and PD. Therefore the analysis predicts an increase in percentage of fully electric vehicles registered in a state (PEV) for the increase in predictor variables TP, AI and RD. In contrary the model predicts a decrease in percentage of fully electric vehicles registered in a state (PEV) for the increase in predictor variables HS, CD and PD.

Table 8

Multivariate regression analysis p values for the outcome: percentage of fully electric vehicles registered in a state (PEV)

Predictor variable | p |

High School graduates, % (HS) | 0.410 |

College degree graduates, % (CD) | 0.251 |

Population density, per sq. mile (PD) | 0.937 |

Total population (TP) | 0.011 |

Average annual income, $ (AI) | 0.00141 |

Registered democrats, % (RD) | 0.077 |

The model Eq. (10) can be written as follows:

outcome: PEV = 0.00148–0.00672 HS − 0.00673 CD − 0.0000000116 PD + 0.0000000000678 TP + 0.0000000857AI + 0.00511 RD (10)