Factors Affecting Renewable Energy Technology Adoption Policies of 50 States and District of Columbia in the United States

doi:10.21203/rs.3.rs-1127912/v1

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Factors Affecting Renewable Energy Technology Adoption Policies of 50 States and District of Columbia in the United States

https://doi.org/10.21203/rs.3.rs-1127912/v1

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Renewable energy policies and adoption of new renewable energy technologies in different states of the United states are not uniform. The factors affecting the trends in adopting electrical energy generation using sustainable resources: wind, solar, hydro, geothermal and biomass as well as registered all electric vehicles in 50 states and district of Columbia in the United States was analyzed using multivariate linear regression analysis. Eight contributing factors: percentages of high school graduates; college graduates; total population; land area; percentage of water covered area; average annual income and registered democrats in the state were selected as predictor variables. Among eight factors considered, percentage of registered democrats was found as the most influencing factor (p = 0.00543) determining the renewable electrical energy adoption. The transition to all electric cars strongly co-related to higher average annual income of the population (p = 0.00141). In conclusion, an aggressive education campaign by environmentalist and pro-sustainability groups may help in de-coupling the energy and environment policies in the United States from political ideologies in order to achieve a more sustainable future.

sustainable energy

wind energy

solar energy

electric vehicles

political affiliation

multivariate regression analysis

Energy policy in the United States engages federal, state, county and city level governmental actions related to the generation, distribution and consumption of different classical or non-renewable and renewable resources based energy. At all levels these energy policies are enacted and enforced through legislation and regulation. Fossil fuels are central to the traditional non-renewable sector and the renewable energy includes solar, wind and hydroelectric power as major sources. Given the multiple policymakers at all levels of government in the United States, energy policy is complicated and interconnected. Unlike in many parts of the world the state governments of the United States have a major role in exercising their local energy policies at the state level (Mazmanian et al. 2020). Generally, the state energy policy has several stakeholders; including citizens, state-level interest groups, agencies, corporations or businesses and elected state officials. In addition, several factors can affect the viability of energy policies, such as the accessibility of energy resources, the cost of extracting certain forms of energy, weather conditions, geography, consumer demand as well as the potential environmental impacts.

In recent years a number of researchers around the world have studied the nexus between renewable energy economic policy and the environmental sustainability of countries; however, there is only a handful of studies on United States (Saint Akadiri et al. 2020), (Aslan et al. 2018), (Mayer 2018), (Koondhar et al. 2018). According to a recent study by Ahmed and co workers the transition to renewable energy in US is impossible without massive public renewable energy research and development budgets along with sustained federal as well as state policies (Ahmed et al. 2021). These efforts in the United States showed significant fluctuations over the preceding three decades and economic policy uncertainty was also highly volatile. Furthermore, they have emphasized the need for stable environmental policies in achieving long-term environmental benefits (Ahmed et al. 2021). In another study on the impact on natural resources, population growth and environmental quality in the US, Khan et al. have encouraged policymakers in the USA to establish policies that control the excessive use of natural resources, promote sustainable lifestyles, develop energy-efficient carbon pricing, and fix the ecological budget to secure a sustainable future for the country (Khan et al. 2021). Their investigation indicates long-run associations among the variables and reveal an inverse relation of natural resources and renewable energy consumption. Furthermore, a positive relationship of non-renewable energy consumption, population growth, and bio capacity with the ecological footprint and CO₂ emissions was also noted during this work (Khan et al. 2021). There are few limited studies on adoption of specific renewable energy technologies, focusing on a state or a region of the US (Pitt et al. 2018), (Pol and Ford 2020). For instance, a 2019 study analyzed the solar photovoltic technology adoption in Pacific Northwest region of the US (Cho et al. 2019). This study found a co-relation between solar adoption trends and higher education levels in the populations. Furthermore, researchers could identify certain areas in the state of Oregon ideal for energy companies to promote, educate and incentivize home solar program, in order to bolster the adoption (Cho et al. 2019).

However, as far as we are aware there are no reports on sustainable energy technology adoption trends in separate states in the United States. In an attempt to fill this void and as a continuation of our efforts in the development (Amarasekara 2013) and evaluation of sustainable energy technologies in US we have studied recent energy technology adoption trends in 50 states and district of Columbia in the United States and their co-relation to a selected set of eight contributing factors. The variables: percentage of high school graduates (HS); college graduates (CD); total population (TP); area (TA); water covered area (PW); annual income (AI); and registered democrats (RD) were selected as predictor variables for this study.

Furthermore, willingness of individual states to adopt modern renewable energy sources wind (WE), solar (SE), hydro (HE), geothermal (GE) and biomass energy (BE) for electrical energy generation and the current trends are focused in this study. The electrical energy generated by nuclear energy is not taken into account in this contemporary renewable energy trend analysis as nuclear fission electricity generation is in rapid decline in the past 30 years (Markard et al. 2020). Furthermore, nuclear energy is not a part of this sustainability revolution and in a number of large economies such as China, India, United Kingdom and Germany power generation from non-hydro renewables has already surpassed the electricity generation from nuclear power plants (IEA 2019). In addition, recent sharp falls in the cost of renewable energy technologies such as solar cells are likely to undermine the economic competitiveness of nuclear energy still further (IRENA 2018).

We have considered the education standards of the population as a key influencing factor in adopting sustainable technologies. A study on educational attainment in the United States shows that percentage of high school graduates increased from 41 to 91% and college graduates increased from 8 to 38% during the period of from 1960 to 2020; however this increase is not uniform throughout the US and there are considerable irregularities among regions and states (Statista 2022). There are several links, suggesting that education level and cognitive ability of the population should be related to social awareness of costs and benefits of renewable energy use and consequent public acceptance (Eshchanov et al. 2021). Research studies have demonstrated less of a preference for smaller, immediate rewards versus larger, delayed rewards in more highly educated and intelligent groups of people (Shamosh and Gray 2008). Similarly, there is evidence for the co-relation between education level of a society and the abilities to play important role in mitigating environmental degradation (Salahodjaev 2016). In the United States total population (TP) of a state and the total land area (TA) are two independent variables; for instance the small state New Jersey with a high population has a population density of approximately as one thousand times the population density of Alaska (States101 2022). The land area of a state is an important factor in adopting renewable energy technologies such as wind (WE) and hydro (HE) as well; therefore we have included total population (TP) and the total area (TA) of states as two other variables. The water covered land area of a state may have a bearing on the ability of a state to generate hydroelectric power, as most of these power stations are built near a large water reservoir; additionally, water covered area percentage is independent from the total land area of the state as some large areas of the western United states are arid land with minimal annual rainfall (Moore et al. 2021). The median annual income (AI) of a population is an important factor in determining the tendency to adopt renewable energy technologies. This factor is generally independent form most of the other factors considered such as: total population (TP), area (TA), water covered area (PW), and registered democrats (RD); however may have an association to the education of the population in the states. Nevertheless, we have considered annual income (AI) as another variable as this can be correlated to the tendency of embracing more expensive and newest renewable energy technologies such as electric vehicles and roof-top solar panels. Another important factor may be the political ideology; unlike in many parts of the developed world, the renewable energy policy in the United States has a greater bearing on the political affiliation of the decision makers. The US withdrawal from Paris agreement in June 2017 under a republican administration and immediate reentry under democratic control is a reflection of this political bias (Marlow et al. 2021), (Ali et al. 2021). This phenomena is common to federal, state and local government in the US; therefore we have included the percentage of registered democrats in a state as one of the factors influencing the renewable energy adoption trends and decisions.

Multivariable linear regression used to predict an outcome variable (y) on the basis of multiple distinct predictor variables (x) was applied in all analysis. This multivariable linear regression model can be expressed by the following Eq. (1) where b values are regression weights or coefficients.

y = b₀ + b₁x₁ + b₂x₂ + b₃x₃ + b₄x₄ + b₄x₅ + …….. + b_nx_n (1)

The open source R programming environment at: www.r-project.org was used in data analysis.

This research analysis is based on the hypothesis that recent renewable energy technology adoption trends in 50 states and district of Columbia in the United States are co-related to eight contributing factors: percentage of high school graduates (HS), college graduates (CD), total population (TP), area (TA), water covered area (PW), annual income (AI) and registered democrats (RD).

Data Availability

Table 1

Datasets related to this article
Data Source	Available data
https://www.eia.gov/state/seds/; www.eia/electricity/data/state	Electricity generation of states
https://www.census.gov/programs-surveys/popest/technical-documentation/research/evaluation-estimates/2020-evaluation-estimates/2010s-state-total.html	Population data of states
https://www.census.gov/geographies/reference-files/2010/geo/state-area.html	Land and water area of states
https://www.census.gov/search-results.html?searchType=web&cssp=SERP&q=median%20income	Median income of residence in a state
https://data.census.gov/cedsci/table?q=educational%20attainment&g=0100000US,.04000.001_0400000US72&tid=ACSST1Y2019.S1501&tp=true&hidePreview=true	Educational attainment of residence in a state
https://www.archives.gov/electoral-college/2020	Electoral college results by state
https://www.statista.com/statistics/1080003/political-party-identification-state-us/	Identification with political parties by state
https://afdc.energy.gov/data	Bio-Energy and electric vehicles by state

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)

We have analyzed the factors influencing the renewable electrical energy generation and transition to fully electric cars in 50 states and district of Columbia in the United States. This communication reports the possible underlying factors that determines why some states in the US are far behind in adopting sustainable energy technologies, and some are ahead in new electrical energy generation using sources such as solar and wind as well as in use of all electric cars. We have found that political inclination of state's population as the most co-related factor in determining the adoption of sustainable electrical energy generation technologies. In contrary to most other developed countries, sustainability and environment has become highly politicized issues in the United States. This may be due to two reasons: firstly, extensive lobbying by large oil and fossil fuel power companies in US have persuaded the policy makers to dismiss and ignore environmental implications of fossil fuels. This symbiosis is particularly strong in the republican side of the aisle at both federal and state levels. Secondly, the traditional conservative principals of less government control or freedom to make your own decisions and less taxes, commonly identified as republican core values are often misinterpreted for the continuation of the anti-sustainability agenda of large oil and fossil fuel power companies.

In further investigation of co-relation between renewable electrical energy sources and policies; we have analyzed the individual components: wind, solar, hydro, geothermal and biomass energy and their co-relation to eight selected variables as well. The sustainable energy technologies such as wind and hydroelectric power generation that require large land areas showed the expected co-relations to larger states. Solar energy electrical power generation is showing the highest level of energy penetration in last couple of years and is expected to revolutionize the domestic electrical energy supply in US. In our analysis, solar electrical energy adoption strongly co-related to the population density and somewhat co-related to the percentage of college degree graduates in a state, indicating that educated urban populations are in the forefront of solar roof-top adoption.

The transition to fully electric battery powered automobiles is another major indicator in sustainability revolution. Our study shows a very strong co-relation between electric vehicle adoption and the average annual income. This result is logical as the current fully electric cars cost as twice or more than an equivalent gasoline powered car. In addition, total population of the state and percentage of democrats in the state are also co-related to the electric automobile adoption; again showing the strong influence in political ideology on sustainability adoption in US. However, the recent advancements in battery materials, recycling technologies as well as anticipated introduction of a number electric car models by major US automakers in the next 2–5 years may expect to lower the electric car prices to match the gasoline models, boosting the affordability of electric cars. Our study has implications for renewable energy technology adoption policies of state and at federal level in the US. This multivariate regression analysis clearly demonstrate a significant co-relation between the political affiliation and total renewable electrical energy percentage as well as to some of the individual components of sustainable energy transformation. Then there are variables such as land area, water cover and population with limited control; where as political bias is a more flexible variable and can be manipulated. Therefore, an aggressive education campaign by environmentalist and pro-sustainability groups may help in de-coupling the energy and environment policies in the United States from political ideologies in order to achieve a more sustainable future.

Samantha Roberts: data collection; literature review; methodology; analysis using R, Tristan Roland: data collection; analysis using R, Ananda S. Amarasekara: conceptualization; writing, editing.

Authorship contribution statement

Samantha Roberts: data collection; literature review; methodology; analysis using R, Tristan Roland: data collection; analysis using R, Ananda S. Amarasekara: conceptualization; writing, editing.

Competing interests

Authors declare that they have no known competing financial interests or personnel relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

We thank United States National Science Foundation grant: NSF DMS -1950677, REU Site: Mathematical Modeling in the Sciences, for financial support.

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You are reading this latest preprint version

Factors Affecting Renewable Energy Technology Adoption Policies of 50 States and District of Columbia in the United States

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Abstract

Figures

Introduction

Methodology

Data Availability

Results And Discussion

Percentage of renewable electrical energy in the state (PRE)

Percentages of renewable electrical energy generated using wind (WE)

Percentages of renewable electrical energy generated using solar (SE)

Percentages of renewable electrical energy generated using hydro (HE)

Percentages of renewable electrical energy generated using geothermal (GE)

Percentages of renewable electrical energy generated using biomass (BE)

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

Conclusion And Policy Implications

Authorship Contribution Statement

Declarations

References

Status:

Version 1