The state of the energy transition in Appalachia mirrors the diverging economic trends within the region over the last three decades. Most Appalachian states exceed or are within five percentage points of the US benchmark percentage of energy generated from renewables or nuclear. At the same time, Ohio, Kentucky, Mississippi, and West Virginia remain far behind. Though the Bipartisan Infrastructure Law and Inflation Reduction Act primarily allocate federal funds to transportation infrastructure and broadband internet in Appalachian states, nearly $8 billion in funding has gone towards developing clean energy, buildings, and manufacturing. Much of this $8 billion is earmarked for innovative technology such as batteries, with the remainder aiding grid modernization and resilience. Since the passage of these two bills, numerous companies have announced clean energy investments in Appalachia, with most investments focusing on batteries and electric vehicles.
Defining Appalachia
Appalachia is a region in the United States comprising 423 counties across 13 states. Appalachia spans from southern New York to northern Mississippi, with 26.3 million residents. Appalachia contains parts of Alabama, Georgia, Kentucky, Maryland, Mississippi, New York, North Carolina, Ohio, Pennsylvania, South Carolina, Tennessee, Virginia, and all of West Virginia. Over the last two decades, the economic fortunes of Appalachian communities diverged. Certain parts of the Carolinas, Tennessee, and Virginia saw economic and population growth. At the same time, other areas in Kentucky, West Virginia, Ohio, and Pennsylvania fell further behind on socioeconomic indicators such as population growth, poverty, per capita income, and high school graduation rates. The continued decline of both manufacturing and the coal mining industry, particularly in Central Appalachia, accelerated these trends.
The Appalachian Regional Commission notes that perhaps the most critical factor for economically prosperous regions of Appalachia has been “a diversified economy.” Figure 1 below is a map (created by the Appalachian Regional Commission) displaying the 423 Appalachian counties and their economic prosperity.
Figure 1: Appalachian Counties and Levels of Economic Distress
Figure 2 below is an interactive heatmap displaying the annual percentage of total electricity generation in each Appalachian state from renewable or nuclear resources, compared against the same statistic for the entire United States. South Carolina, Tennessee, New York, Maryland, and North Carolina exceed the US average. Each state also derived significant energy from nuclear power in 2022, ranging from 21% of electricity output in New York to 55% in South Carolina. The eight other Appalachian states’ production from renewable and nuclear resources fell below the US average, with Ohio, Mississippi, Kentucky, and West Virginia all producing less than 16% of energy from renewable or nuclear resources.
Figure 2: Percentage of Generation from Renewable and Nuclear Energy by State (1990 to 2022)
Source: US Energy Information Administration, “Net Generation by State by Type of Producer by Energy Source”, updated October 26, 2023, available at: https://www.eia.gov/electricity/data/state/.
The Impact of the Inflation Reduction Act (IRA) and Bipartisan Infrastructure Law on Appalachia’s Energy Transition
The United States Department of Energy describes the Inflation Reduction Act (IRA) and Bipartisan Infrastructure Law (BIL) as an effort to revitalize “the U.S. energy system by investing in American energy supply chains, clean energy job creation, emissions reduction, and consumer energy savings.” Though Congress passed these two bills separately, the Biden administration released a combined dataset outlining the disbursement of funds from each bill (with certain disbursements funded from both the IRA and BIL). Figure 3 below categorizes the combined funds dedicated to Appalachian states from the IRA and BIL by category. The two largest categories focus on transportation (including infrastructure construction or renovation) and expanding broadband internet access, and a third category of nearly $8 billion focuses on clean energy, buildings, and manufacturing.
Figure 3: Bipartisan Infrastructure Law (BIL) and Inflation Reduction Act (IRA) Investments in Appalachian States, by Category
Figure 4 digs deeper into the subcategories associated with the Clean Energy, Buildings, and Manufacturing category identified above. About two-thirds of this category’s $8 billion in funding supports Clean Energy and Power programs; the remainder goes towards Clean Buildings and Homes, with about 1.6% left over for Clean Manufacturing/Industry.
Figure 4: BIL and IRA Clean Energy, Buildings, and Manufacturing Investments in Appalachian States, by Subcategory
Table 1 below shows the 15 most heavily funded programs within the Clean Energy and Power subcategory by total federal disbursements. Several large programs, including “Battery Materials Processing Grants,” “Advanced Energy Manufacturing and Recycling Grants,” and “Carbon Capture Large-Scale Pilot Projects,” seem to focus on technological innovation. Others focus on grid resilience and modernization. Table 1 also shows the number of unique Appalachian states receiving funding through each program.
Table 1: 15 Largest Programs by Disbursement Amounts and Unique Appalachian States Receiving Funds
Program Title
Total Funding ($ millions)
Unique States Receiving Program Funding
Battery Materials Processing Grants
$1,250.46
6
Weatherization Assistance Program
$1,198.85
13
National Laboratory Infrastructure - Office of Science
$783.34
4
Program Upgrading Our Electric Grid and Ensuring Reliability and Resiliency
$437.12
6
Preventing Outages and Enhancing the Resilience of the Electric Grid / Hazard Hardening
$275.29
13
Advanced Energy Manufacturing and Recycling Grants
$170.00
3
Carbon Capture Large-Scale Pilot Projects
$160.00
2
State Energy Program
$122.30
13
Carbon Storage Validation and Testing
$116.71
3
Smart Grid Investment Matching Grant Program
$114.87
3
Energy Efficiency and Conservation Block Grant Program
$110.17
13
Rural Energy for America Program (REAP)
$100.45
13
Enhanced Use of Defense Production Act of 1950
$76.82
3
Grants for Energy Efficiency and Renewable Energy Improvements at Public School Facilities
Major Clean Energy and Clean Manufacturing Investments following the Inflation Reduction Act
The Department of Energy maintains a dataset outlining company-specific investments in clean technology development and manufacturing. Though these investments may not all directly relate to the BIL and IRA funding shown in the figures above, they indicate that Appalachia is benefiting from renewed investment in clean technology manufacturing. Furthermore, several investment clusters have popped up, particularly near Greenville, South Carolina, which boasts over $1.2 billion in investment related to battery manufacturing since the passage of the IRA.
Figure 5: Top 5 Innovative Technology Investments, by Appalachian State ($ millions)
Source: Department of Energy, “Building America’s Clean Energy Future” dataset, available at: https://www.energy.gov/invest
Concluding Thoughts
The energy transition offers Appalachian communities an opportunity to attract innovative manufacturing companies and diversify local economies. Government aid through the BIL and IRA appears to be helping, signified by the many new investments shown in Figure 5. However, policymakers should monitor whether (and how) public and private investment reaches those regions of Appalachia most impacted by decades of manufacturing and mining job losses.
Appendix A: Alternate Variations of Graphs
Percentage of Electricity Output from Renewable Sources and Nuclear, by State (1990 - 2022)
Source Code
---title: "Sustainable Finance Final Project"format: htmlcode-tools: true---# The State of the Energy Transition in AppalachiaAuthor: Galen Erickson### Executive SummaryThe state of the energy transition in Appalachia mirrors the diverging economic trends within the region over the last three decades. Most Appalachian states exceed or are within five percentage points of the US benchmark percentage of energy generated from renewables or nuclear. At the same time, Ohio, Kentucky, Mississippi, and West Virginia remain far behind. Though the Bipartisan Infrastructure Law and Inflation Reduction Act primarily allocate federal funds to transportation infrastructure and broadband internet in Appalachian states, nearly \$8 billion in funding has gone towards developing clean energy, buildings, and manufacturing. Much of this \$8 billion is earmarked for innovative technology such as batteries, with the remainder aiding grid modernization and resilience. Since the passage of these two bills, numerous companies have announced clean energy investments in Appalachia, with most investments focusing on batteries and electric vehicles.```{r echo = FALSE, message = FALSE}#load packageslibrary(here)library(readxl)library(dplyr)library(ggplot2)library(plotly)library(cowplot)library(patchwork)library(plotly)library(tidyr)library(scales)library(stringr)library(knitr)library(kableExtra)``````{r echo = FALSE, warning = FALSE, message = FALSE}#load White House investment excel filefile_path <- here("Input/Invest.gov_PublicInvestments_Map_Data_CURRENT-2.xlsx")# Read the 'FundingSummary' tab from the Excel filefunding_summary <- read_excel(file_path, sheet = "FundingSummary")#drop obs where funding is non-numeric, create a funding amount var in millions, create alternative subcategory variablefunding_summary_cleaned <- funding_summary %>% filter(!is.na(`Funding Amount`) & `Funding Amount` != "TBD") %>% mutate(funding_amount_mil = as.numeric(`Funding Amount`) / 1000000) %>% mutate(subcategory_cleaned = if_else( str_detect(Subcategory, "Clean Energy"), "Clean Energy and Power", Subcategory )) %>% mutate(Category = if_else( str_detect(Category, "Clean Energy, Buildings, and Manufacturing"), "Clean EBM", Category ))#load DoE company specific investment fileDoE_file_path <- here("Input/investments_ame_20240322.csv")company_investments <- read.csv(DoE_file_path)#clean DoE investment filecompany_investments_cleaned <- company_investments %>% filter(reported_investment != "Not Specified", # Exclude "Not Specified" reported_investment != "", # Exclude blank values reported_investment != 0) # Exclude zero values#load energy data state_energy_output_path <- here("Input/annual_generation_state.xls")state_energy_data <- read_excel(state_energy_output_path, skip = 1)#clean state energy output data, replace "US-Total" with "US-TOTAL" in the STATE columnstate_energy_data <- state_energy_data %>% mutate(STATE = ifelse(STATE == "US-Total", "US-TOTAL", STATE))```### Defining AppalachiaAppalachia is a region in the United States comprising [423 counties across 13 states](https://www.arc.gov/about-the-appalachian-region/). Appalachia spans from southern New York to northern Mississippi, with 26.3 million residents. Appalachia contains parts of Alabama, Georgia, Kentucky, Maryland, Mississippi, New York, North Carolina, Ohio, Pennsylvania, South Carolina, Tennessee, Virginia, and all of West Virginia. Over the last two decades, the economic fortunes of Appalachian communities diverged. Certain parts of the Carolinas, Tennessee, and Virginia saw economic and population growth. At the same time, other areas in Kentucky, West Virginia, Ohio, and Pennsylvania fell further behind [on socioeconomic indicators such as population growth, poverty, per capita income, and high school graduation rates.](https://www.arc.gov/wp-content/uploads/2023/05/PRB_ARC_Chartbook_ACS_2017-2021_FINAL_2023-06.pdf) The continued decline of both manufacturing and the coal mining industry, particularly in Central Appalachia, accelerated these trends.The Appalachian Regional Commission notes that perhaps the most critical factor for economically prosperous regions of Appalachia has been ["a diversified economy."](https://www.arc.gov/wp-content/uploads/2023/05/PRB_ARC_Chartbook_ACS_2017-2021_FINAL_2023-06.pdf) Figure 1 below is a map (created by the Appalachian Regional Commission) displaying the 423 Appalachian counties and their economic prosperity.#### Figure 1: Appalachian Counties and Levels of Economic Distress###### *Source: Appalachian Regional Commission, "Classifying Economic Distress in Appalachian Counties", available at: <https://www.arc.gov/classifying-economic-distress-in-appalachian-counties/>*```{r echo = FALSE, message = FALSE}#develop applachian states listsappalachian_states <- c("Alabama", "Georgia", "Kentucky", "Maryland", "Mississippi", "North Carolina", "New York", "Ohio", "Pennsylvania", "South Carolina", "Tennessee", "Virginia", "West Virginia")appalachian_states_abbreviations <- c("AL", "GA", "KY", "MD", "MS", "NC", "NY","OH", "PA", "SC", "TN", "VA", "WV", "US-TOTAL")```### Electricity Generation in Appalachian StatesFigure 2 below is an interactive heatmap displaying the annual percentage of total electricity generation in each Appalachian state from renewable or nuclear resources, compared against the same statistic for the entire United States. South Carolina, Tennessee, New York, Maryland, and North Carolina exceed the US average. Each state also derived significant energy from nuclear power in 2022, ranging from 21% of electricity output in New York to 55% in South Carolina. The eight other Appalachian states' production from renewable and nuclear resources fell below the US average, with Ohio, Mississippi, Kentucky, and West Virginia all producing less than 16% of energy from renewable or nuclear resources.#### Figure 2: Percentage of Generation from Renewable and Nuclear Energy by State (1990 to 2022)```{r echo = FALSE, message = FALSE, warning = FALSE}# Filter the funding_summary_cleaned and state_energy_data data frames for Appalachian statesfunding_summary_appalachia <- funding_summary_cleaned %>% filter(State %in% appalachian_states) #filter the state energy data down to appalachiaenergy_output_appalachia <- state_energy_data %>% filter(STATE %in% appalachian_states_abbreviations) %>% filter(`ENERGY SOURCE` != "Total") %>% filter(`TYPE OF PRODUCER` == "Total Electric Power Industry") %>% mutate(energy_type = case_when( `ENERGY SOURCE` %in% c("Solar Thermal and Photovoltaic", "Pumped Storage", "Wind", "Hydroelectric Conventional", "Geothermal", "Nuclear") ~ "Renewable_Nuclear", TRUE ~ "Non-renewable" ))#calculate summary of state energy use and summarizeappalachia_output_summary_data <- energy_output_appalachia %>% group_by(YEAR, STATE, energy_type) %>% summarise(`GENERATION (MWh)` = sum(`GENERATION (Megawatthours)`, na.rm = TRUE), .groups = "drop") %>% pivot_wider(names_from = energy_type, values_from = `GENERATION (MWh)`, values_fill = 0) %>% mutate (total_gen = Renewable_Nuclear + `Non-renewable`) %>% mutate(pct_renewable = Renewable_Nuclear / total_gen) %>% mutate(total_gen = total_gen / 1000000) %>% mutate(Renewable_Nuclear = Renewable_Nuclear / 1000000) %>% mutate(`Non-renewable` = `Non-renewable` / 1000000) %>% mutate(`Non-renewable` = round(`Non-renewable`, 1), Renewable_Nuclear = round(Renewable_Nuclear, 1), total_gen = round(total_gen, 1), pct_renewable = round(pct_renewable, 3), pct_renewable_formatted = round(pct_renewable * 100, 1))# Format data to include percentage for 'pct_renewable'appalachia_output_summary_data$pct_renewable_formatted <- scales::percent(appalachia_output_summary_data$pct_renewable)#sort data so that highest pct are on top of the heatmapsorted_states_2022 <- appalachia_output_summary_data %>% filter(YEAR == 2022) %>% arrange(desc(pct_renewable)) %>% pull(STATE)# Ensure all states are presentall_states <- unique(appalachia_output_summary_data$STATE)sorted_states_2022 <- union(sorted_states_2022, all_states)# Reorder the STATE factor in the full dataset based on the sorted 2022 dataappalachia_output_summary_data$STATE <- factor(appalachia_output_summary_data$STATE, levels = sorted_states_2022)# Plotting the heatmap with sorted statesheatmap2 <- plot_ly(data = appalachia_output_summary_data, x = ~YEAR, y = ~STATE, z = ~pct_renewable * 100, type = 'heatmap', colors = 'Blues', hoverinfo = 'text', text = ~paste('Year: ', YEAR, '<br>State: ', STATE, '<br>Percentage: ', round(pct_renewable * 100, 2), '%')) %>% layout( xaxis = list(title = 'Year'), yaxis = list(title = '', autorange = 'reversed'), colorbar = list(showscale = FALSE))# Print the heatmaphide_colorbar(heatmap2)```###### *Source: US Energy Information Administration, "Net Generation by State by Type of Producer by Energy Source", updated October 26, 2023, available at: <https://www.eia.gov/electricity/data/state/>.*### The Impact of the Inflation Reduction Act (IRA) and Bipartisan Infrastructure Law on Appalachia's Energy TransitionThe [United States Department of Energy](https://www.energy.gov/policy/articles/investing-american-energy-significant-impacts-inflation-reduction-act-and) describes the Inflation Reduction Act (IRA) and Bipartisan Infrastructure Law (BIL) as an effort to revitalize "the U.S. energy system by investing in American energy supply chains, clean energy job creation, emissions reduction, and consumer energy savings." Though Congress passed these two bills separately, the Biden administration released a combined dataset outlining the disbursement of funds from each bill (with certain disbursements funded from both the IRA and BIL). Figure 3 below categorizes the combined funds dedicated to Appalachian states from the IRA and BIL by category. The two largest categories focus on transportation (including infrastructure construction or renovation) and expanding broadband internet access, and a third category of nearly \$8 billion focuses on clean energy, buildings, and manufacturing.#### Figure 3: Bipartisan Infrastructure Law (BIL) and Inflation Reduction Act (IRA) Investments in Appalachian States, by Category```{r echo = FALSE, message = FALSE}#creating summary statsfunding_sum_all_app <- funding_summary_appalachia %>% group_by(Category) %>% summarise(Total_Funding_Mil = sum(funding_amount_mil, na.rm = TRUE))# Plot pie chart p <- plot_ly(funding_sum_all_app, labels = ~Category, values = ~Total_Funding_Mil, type = 'pie', #marker = list(colors = ~colors[Category]), textinfo = 'label+percent', insidetextorientation = 'radial') %>% layout(title = '') p <- p %>% layout(showlegend = FALSE)p```###### *Source: WhiteHouse.gov, "Investing in America Dataset", tab "FundingSummary", available at: <https://www.whitehouse.gov/invest/>*Figure 4 digs deeper into the subcategories associated with the Clean Energy, Buildings, and Manufacturing category identified above. About two-thirds of this category's \$8 billion in funding supports Clean Energy and Power programs; the remainder goes towards Clean Buildings and Homes, with about 1.6% left over for Clean Manufacturing/Industry.#### Figure 4: BIL and IRA Clean Energy, Buildings, and Manufacturing Investments in Appalachian States, by Subcategory```{r echo = FALSE, message = FALSE}#creating summary statsclean_energy_funding_app <- funding_summary_appalachia %>% filter(Category == "Clean EBM") %>% group_by(subcategory_cleaned) %>% summarise(Total_Funding_Mil = sum(funding_amount_mil, na.rm = TRUE))#plot pie chart p2 <- plot_ly(clean_energy_funding_app, labels = ~subcategory_cleaned, values = ~Total_Funding_Mil, type = 'pie', #marker = list(colors = ~colors[Category]), textinfo = 'label+percent', insidetextorientation = 'radial') %>% layout(title = '') p2 <- p2 %>% layout(showlegend = FALSE)p2```###### *Source: WhiteHouse.gov, "Investing in America Dataset", tab "FundingSummary", available at: <https://www.whitehouse.gov/invest/>*Table 1 below shows the 15 most heavily funded programs within the Clean Energy and Power subcategory by total federal disbursements. Several large programs, including "Battery Materials Processing Grants," "Advanced Energy Manufacturing and Recycling Grants," and "Carbon Capture Large-Scale Pilot Projects," seem to focus on technological innovation. Others focus on grid resilience and modernization. Table 1 also shows the number of unique Appalachian states receiving funding through each program.#### Table 1: 15 Largest Programs by Disbursement Amounts and Unique Appalachian States Receiving Funds```{r echo = FALSE, message = FALSE}#creating summary stats of the Clean Energy and Power subcategoryfunding_sum_clean_energy_power <- funding_summary_appalachia %>% mutate(program_name_clean = if_else( str_detect(`Program Name`, "REAP"), "Rural Energy for America Program (REAP)", `Program Name` )) %>% filter(subcategory_cleaned == "Clean Energy and Power") %>% group_by(program_name_clean) %>% summarise(Total_Funding_Mil = sum(funding_amount_mil, na.rm = TRUE), Unique_States = n_distinct(State))#develop table for output; keep top 15 programs by $ valuefunding_ordered <- funding_sum_clean_energy_power %>% arrange(desc(Total_Funding_Mil))top_fifteen <- funding_ordered %>% slice(1:15)#create "other" rowothers <- funding_ordered %>% slice(16:n()) %>% summarise( program_name_clean = paste("Other (n=", n(), ")"), # Adding the count of observations in "Other" Total_Funding_Mil = sum(Total_Funding_Mil, na.rm = TRUE) )#append other rowdf_simplified_cleanenergy <- bind_rows(top_fifteen, others)#create total rowtotal_funding <- sum(df_simplified_cleanenergy$Total_Funding_Mil, na.rm = TRUE)total_row <- data.frame( program_name_clean = "Total", Total_Funding_Mil = total_funding)# Combine the original data frame with the total rowdf_with_total <- bind_rows(df_simplified_cleanenergy, total_row)#format for outputdf_with_total$Total_Funding_Mil <- dollar_format(prefix = "$", suffix = "", big.mark = ",", decimals = 1)(df_with_total$Total_Funding_Mil)#relabeldf_with_total <- df_with_total %>% rename( `Program Title` = program_name_clean, `Total Funding ($ millions)` = Total_Funding_Mil, `Unique States Receiving Program Funding` = Unique_States )#output tableknitr::kable(df_with_total, format = "html") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = F)```###### *Source: WhiteHouse.gov, "Investing in America Dataset", tab "FundingSummary", available at: <https://www.whitehouse.gov/invest/>*### Major Clean Energy and Clean Manufacturing Investments following the Inflation Reduction ActThe Department of Energy maintains a dataset outlining company-specific investments in clean technology development and manufacturing. Though these investments may not all directly relate to the BIL and IRA funding shown in the figures above, they indicate that Appalachia is benefiting from renewed investment in clean technology manufacturing. Furthermore, several investment clusters have popped up, particularly near Greenville, South Carolina, which boasts over \$1.2 billion in investment related to battery manufacturing since the passage of the IRA.#### Figure 5: Top 5 Innovative Technology Investments, by Appalachian State (\$ millions)```{r echo = FALSE, message = FALSE, warning = FALSE}# clean DoE investment dataset to remove duplicates and company_investments_cleaned <- unique(company_investments_cleaned)appalachia_investments <- company_investments_cleaned %>% filter(state %in% appalachian_states) top_investments_by_state <- appalachia_investments %>% group_by(state) %>% arrange(desc(reported_investment)) %>% slice_head(n = 5)``````{r echo = FALSE, message = FALSE}# Define a color map for technology types# Adjust the color names and corresponding technologiescolor_map <- setNames(c('red', 'blue', 'green', 'orange', 'purple', 'brown', 'yellow'), c('hydrogen', 'batteries', 'solar', 'wind', 'bioenergy', 'geothermal', 'other'))# Create an empty plot with a geographic layoutmap <- plot_geo(locationmode = 'USA-states') %>% layout(geo = list(scope = 'usa', projection = list(type = 'albers usa'), showland = TRUE, landcolor = 'rgb(217, 217, 217)', subunitwidth = 1, countrywidth = 1, subunitcolor = 'rgb(255,255,255)', countrycolor = 'rgb(255,255,255)', center = list(lon = -82, lat = 38), lonaxis = list(range = c(-94, -74)), lataxis = list(range = c(28, 44))))# Add a trace for each technology typefor(tech in unique(top_investments_by_state$technology)) { tech_data <- top_investments_by_state[top_investments_by_state$technology == tech,] map <- map %>% add_trace(data = tech_data, type = 'scattergeo', mode = 'markers', lat = ~latitude, lon = ~longitude, text = ~paste('City: <b>', city, '</b><br>', 'State: <b>', state, '</b><br>', 'Investment: <b>$', format(reported_investment, big.mark=',', decimals = 1, scientific=FALSE), '</b><br>', 'Company: <b>', company_name, '</b>'), hoverinfo = 'text', # Specify to use only text field for hover information name = tech, marker = list(size = 10, color = color_map[tech], line = list(width = 1, color = 'DarkSlateGrey')))}# Display the mapmap```###### *Source: Department of Energy, "Building America's Clean Energy Future" dataset, available at: <https://www.energy.gov/invest>*### Concluding ThoughtsThe energy transition offers Appalachian communities an opportunity to attract innovative manufacturing companies and diversify local economies. Government aid through the BIL and IRA appears to be helping, signified by the many new investments shown in Figure 5. However, policymakers should monitor whether (and how) public and private investment reaches those regions of Appalachia most impacted by decades of manufacturing and mining job losses. ## Appendix A: Alternate Variations of Graphs#### Percentage of Electricity Output from Renewable Sources and Nuclear, by State (1990 - 2022)```{r echo = FALSE, message = FALSE, warning = FALSE}# Create the plotappalachia_output_plot <- plot_ly(data = appalachia_output_summary_data, x = ~YEAR, y = ~pct_renewable, type = 'scatter', mode = 'lines+markers', color = ~STATE, text = ~paste("State: ", STATE, "<br>", "Year: ", YEAR, "<br>", "Renewable/Nuclear: ", scales::comma(Renewable_Nuclear), " TWh<br>", "Non-renewable: ", scales::comma(`Non-renewable`), " TWh<br>"), hoverinfo = "text+y") %>% layout(xaxis = list(title = "Year"), yaxis = list(title = "Generation from Renewable and Nuclear Energy (%)", tickformat = ",.0%")) # Formatting Y-axis as percentageappalachia_output_plot```
