diff --git a/Easy_IMPLAN.r b/Easy_IMPLAN.r
new file mode 100644
index 0000000..d1d749f
--- /dev/null
+++ b/Easy_IMPLAN.r
@@ -0,0 +1,62 @@
+library(tidyverse)
+#R script to quickly find IMPLAN inputs or adjustment factors
+GET_INPUTS_GAS <- function(YEAR){
+  FILES <- list.files("./Raw_Output/Detailed_Economic_Indicators/Prelim-Run")
+  FILE <- paste0("./Raw_Output/Detailed_Economic_Indicators/Prelim-Run/",FILES[grep(paste0("-",YEAR),FILES)])
+  #Dollar value added to the RNG in addition to the main production
+  RNG_ADD_YEARS <- c(0,380102,3097001,5943721,11224037,18050255,18050255)
+ RNG_ADD <-  RNG_ADD_YEARS[YEAR-2023]
+  DF <- read_csv(FILE) %>% filter(ImpactType=="Direct",EventName=="Gas Production (Campbell)")
+  DF <- DF %>% mutate(IntermediateInputs=Output-EmployeeCompensation-ProprietorIncome-OtherPropertyIncome-TaxesOnProductionAndImports)%>% select(WageAndSalaryEmployment,EmployeeCompensation,Output,ProprietorEmployment,OtherPropertyIncome,Employment,IntermediateInputs)%>% mutate(Output=Output+RNG_ADD)
+  return(DF)
+}
+#Find the input values for the beet factories
+GET_INPUTS_BEET <- function(YEAR,OP_COST=75){
+  MAX_WASH_EMP <- 8
+  MAX_GOSHEN_EMP <-15
+  GOSHEN_WAGE <- 53007.10
+  WASH_WAGE <- 71017.25
+  
+  WASH_PROD <- c(0,0,0,35000,37500,50000,50000)
+  GOSHEN_PROD <- c(0,0,0,52500,112500,150000,150000)
+  
+  GOSHEN_OP_COST <- OP_COST*GOSHEN_PROD
+  WASH_OP_COST <- OP_COST*WASH_PROD
+  
+  GOSHEN_TAX_CRED <- GOSHEN_PROD*0.62*90
+  WASH_TAX_CRED <- WASH_PROD*0.62*90
+ 
+  WASH_EMP <- MAX_WASH_EMP*(WASH_PROD)/max(WASH_PROD)
+  GOSHEN_EMP <- MAX_GOSHEN_EMP*(GOSHEN_PROD)/max(GOSHEN_PROD)
+  TOPI <- GOSHEN_EMP*0
+  OTH_PROP <- GOSHEN_EMP*0
+
+  GOSHEN_COMP <- GOSHEN_WAGE*GOSHEN_EMP
+  WASH_COMP <- WASH_WAGE*WASH_EMP
+ 
+  WASH_RETURN <- WASH_TAX_CRED-WASH_COMP-WASH_OP_COST
+  GOSHEN_RETURN <- GOSHEN_TAX_CRED-GOSHEN_COMP-GOSHEN_OP_COST
+  RES <- rbind(as.numeric(cbind(WASH_EMP,WASH_COMP,WASH_RETURN,TOPI,OTH_PROP,WASH_OP_COST)[YEAR-2023,]),as.numeric(cbind(GOSHEN_EMP,GOSHEN_COMP,GOSHEN_RETURN,TOPI,OTH_PROP,GOSHEN_OP_COST)[YEAR-2023,])) %>% as_tibble
+  colnames(RES) <- c("Wage_Emp","Compensation","Proprietor_Income","TOPI","OTHER_PROP","Inter_Inputs")
+  RES$County <- c("Washakie","Goshen")
+  RES <- RES %>% select(County,everything())
+
+  return(RES)
+}
+
+4415*c(0.19,0.06)
+
+#Pull the truck and rail transportation induced, to remove it from IMPLAN results. Those costs are explicitly modeled.
+GET_ADJ <- function(YEAR){
+  FILES <- list.files("./Raw_Output/Detailed_Economic_Indicators/Prelim-Run/")
+  FILE <- paste0("./Raw_Output/Detailed_Economic_Indicators/Prelim-Run/",FILES[grep(paste0("-",YEAR),FILES)])
+  DF <- read_csv(FILE) %>% filter(ImpactType=="Direct",IndustryCode %in% c(397,399),TagName=="beet purchase")
+   
+  DF$County <-  gsub(" County, WY \\(2023\\)","",DF$DestinationRegion )
+ DF <- DF %>% select(County, Industry=IndustryDescription,Output) %>% mutate(Output=-Output)
+  return(DF)
+}
+
+GET_INPUTS_GAS(2028)
+GET_INPUTS_BEET(2029,10)
+GET_ADJ(2027)
\ No newline at end of file
diff --git a/Model_Timeline.ods b/Model_Timeline.ods
index 90f0a4e..5b84d67 100644
Binary files a/Model_Timeline.ods and b/Model_Timeline.ods differ
diff --git a/R_IMPLAN_Model.R b/R_IMPLAN_Model.R
new file mode 100644
index 0000000..8c92eec
--- /dev/null
+++ b/R_IMPLAN_Model.R
@@ -0,0 +1,142 @@
+#To Do
+  #Adjust to remove transportation cost
+  #Adjust to remove taxes on intermediate SBS production
+
+
+##Inputs
+COST_TO_BUILD_SKID <- 300000
+SKID_OP_COST <- 30000
+SKID_CONSTRUCTION_COST_RATIO <- 0.33
+SKID_EQUIP_COST_RATIO <- 1-0.33
+FACILITY_OP_EX <- 75 #$ per SBS produced
+SBS_PURCHASE_PRICE <- 225 #$ per SBS produced (or molasses)
+BEET_PURCHASE_PRICE <- 100 #$ per for the input price of beets that produce 1 ton of SBS
+FACILITY_OP_COST <- 75 #$ per SBS produced
+MTU_CARBON_PER_TON_BEET <- 0.62 #MTU carbon per ton of SBS injected
+CARBON_TAX_CREDIT_PRICE <- 90 # $ per MTU of carbon
+VCM_PRICE <- 70 # $ per MTU of carbon sold on the market
+RNG_BONUS <- 15 #$/MMBTU
+NG_PRICE <- 3 # $/MMBTU
+MAX_WASH_EMP <- 8 #Total number of employees at the Washakie facility
+MAX_GOSHEN_EMP <-15 #Total number of employees at the Goshen facility
+RAIL_COST <- 0.054162 #Cost in ton miles of train freight. (inflation adjusted from https://www.bts.gov/content/average-freight-revenue-ton-mile)
+TRUCK_COST <- 0.29028 #Cost in ton miles of truck freight. (inflation adjusted from https://www.bts.gov/content/average-freight-revenue-ton-mile)
+ WASHAKIE_TO_CAMPBELL <- 160 #Miles from Washakie SBS facility (and Wyoming Sugar) to Campbell skids
+ GOSHEN_TO_CAMPBELL <- 205 #Miles from Goshen SBS facility to Campbell skids
+ WESTON_TO_CAMPBELL <- 60 #Distance from the train depot for out of state beets in Upton, to the Campbell skids
+ OUT_STATE_TO_WESTON <- 400  #Avg distance from out of state beet suppliers and the Upton train depot.
+#IMPLAN Inputs
+GOSHEN_WAGE <- 53007.10 #Average wage at the Goshen facility (taken from regional IMPLAN data)
+WASH_WAGE <- 71017.25 #Average wage at the Washakie facility (taken from regional IMPLAN data)
+PER_BEET_SPENDING <- 0.6832346 #The percentage of expenses associated with buying beets when operating a beet processing facility.
+
+#Time Series Inputs
+  Year <- 2024:2030
+  #Gas production non-renewable and renewable natural gas
+  GAS_PROD <- 1015*c(0,143500,124650,108277,94325,87253,87253)
+  RNG_PROD <- 1015*c(0,24436,199100,382110,721571,1160415,1160415)
+  #Capital investment in beet processing facilities
+  BEET_CAP_WASHAKIE <- c(0,0,2500000,2500000,0,0,0)
+  BEET_CAP_GOSHEN <- c(0,0,0,7500000,7500000,0,0)
+  #Beet purchases
+  TOTAL_SBS <- c(3000,10000,100000,175000,200000,240000,240000) #Total tons of SBS purchased or produced
+  WYOMING_SUGAR_PURCHASES <-  c(3000,3000,43000,10000,10000,10000,10000) #Amount of SBS (or molasses) purchased from Wyoming Sugar LLC
+  PRODUCED_SGS <- c(0,0,0,87500,150000,200000,200000) #Amount of SBS produced by all CCF owned processing facilities
+  #Skids
+  CONSTRUCTED_SKIDS <- c(1,4,10,10,5,0,0) #Number of skids built in a given year
+  
+######################Natural Gas
+GAS_VALUE <- (RNG_PROD+GAS_PROD)*NG_PRICE
+RNG_ADD_VALUE <- RNG_PROD*RNG_BONUS
+GAS_TOTAL_VALUE <- GAS_VALUE+RNG_ADD_VALUE
+#########################Beet Facility
+#Find the percentage of total capital expenses for use in production allocation.
+BEET_TOTAL_CAP_WASHAKIE <- c()
+BEET_TOTAL_CAP_GOSHEN  <- c()
+for(i in 1:length(BEET_CAP_GOSHEN)){
+  BEET_TOTAL_CAP_WASHAKIE[i] <-  sum(BEET_CAP_WASHAKIE[1:i])
+  BEET_TOTAL_CAP_GOSHEN[i] <-  sum(BEET_CAP_GOSHEN[1:i])
+}
+BEET_TOTAL_CAP <- BEET_TOTAL_CAP_WASHAKIE+BEET_TOTAL_CAP_GOSHEN
+PROD_PER_WASHAKIE <- BEET_TOTAL_CAP_WASHAKIE/ifelse(BEET_TOTAL_CAP==0,1,BEET_TOTAL_CAP)
+PROD_PER_GOSHEN <- BEET_TOTAL_CAP_GOSHEN/ifelse(BEET_TOTAL_CAP==0,1,BEET_TOTAL_CAP)
+
+#########################Beet Production
+PROD_GOSHEN <- PRODUCED_SGS*PROD_PER_GOSHEN
+PROD_WASHAKIE <- PRODUCED_SGS*PROD_PER_WASHAKIE
+
+VALUE_SGS_PRODUCED_GOSHEN <- PROD_GOSHEN*SBS_PURCHASE_PRICE
+VALUE_SGS_PRODUCED_WASHAKIE <- PROD_GOSHEN*PROD_WASHAKIE
+
+OP_COST_GOSHEN <- PROD_GOSHEN*(FACILITY_OP_EX+BEET_PURCHASE_PRICE)
+OP_COST_WASHAKIE <- PROD_WASHAKIE*(FACILITY_OP_EX+BEET_PURCHASE_PRICE)
+
+
+
+#Assume employment scales with production
+WASH_EMP <- MAX_WASH_EMP*(PROD_WASHAKIE)/max(PROD_WASHAKIE)
+GOSHEN_EMP <- MAX_GOSHEN_EMP*(PROD_GOSHEN)/max(PROD_GOSHEN)
+#Total Compensation to employees
+GOSHEN_COMP <- GOSHEN_WAGE*GOSHEN_EMP
+WASH_COMP <- WASH_WAGE*WASH_EMP
+#########################SBS Purchases
+SBS_LOCAL_COST <- SBS_PURCHASE_PRICE*WYOMING_SUGAR_PURCHASES
+SBS_NATIONAL_COST <- SBS_PURCHASE_PRICE*(TOTAL_SBS-WYOMING_SUGAR_PURCHASES-PRODUCED_SGS)
+#########################Beet Purchases
+GOSHEN_BEET_PURCHASE <- PROD_GOSHEN*BEET_PURCHASE_PRICE
+WASHAKIE_BEET_PURCHASE <- PROD_WASHAKIE*BEET_PURCHASE_PRICE
+#########################Skids
+SKID_TOTAL_CONST_COST <- COST_TO_BUILD_SKID*CONSTRUCTED_SKIDS
+SKID_EQUIP_COST <- SKID_TOTAL_CONST_COST*SKID_EQUIP_COST_RATIO #Cost of building the skid associated with equipment.
+SKID_BUILD_COST <- SKID_TOTAL_CONST_COST*SKID_CONSTRUCTION_COST_RATIO #Cost of building the skid associated with general building costs.
+  
+NUM_SKIDS <- 0
+ACTIVE_SKIDS <- c()
+for(i in 1:length(CONSTRUCTED_SKIDS)){
+  ACTIVE_SKIDS[i] <- NUM_SKIDS
+  NUM_SKIDS <- NUM_SKIDS+CONSTRUCTED_SKIDS[i]
+}
+SKID_MAINT_COST <- ACTIVE_SKIDS*SKID_OP_COST #Cost to upkeep the active skids
+#########################Transportation Costs
+FROM_GOSHEN_COST <- TRUCK_COST*PROD_GOSHEN*GOSHEN_TO_CAMPBELL #Cost to ship SBS from Goshen processing facility to the final site.
+FROM_WASHAKIE_COST <- TRUCK_COST*(PROD_WASHAKIE+WYOMING_SUGAR_PURCHASES)*WASHAKIE_TO_CAMPBELL #Cost to ship SBS from Wyoming sugar and the Washakie processing facility to the final site.
+FROM_WESTON_COST <- TRUCK_COST*WESTON_TO_CAMPBELL*(TOTAL_SBS-WYOMING_SUGAR_PURCHASES-PRODUCED_SGS) #Cost to ship out of state SGS from the Weston rail depot to the skids.
+OUT_STATE_SHIPPING_COST <- RAIL_COST*OUT_STATE_TO_WESTON*(TOTAL_SBS-WYOMING_SUGAR_PURCHASES-PRODUCED_SGS) #Cost to ship the out of state SGS to the rail depot in Weston (Upton,WY)
+###############Tax Credit and voluntary Carbon Market
+CARBON_ABATEMENT <- TOTAL_SBS*MTU_CARBON_PER_TON_BEET
+#Shift by one year, so credits are acquired after one year of injection
+CARBON_ABATEMENT_SHIFT <- c(0,CARBON_ABATEMENT[-length(CARBON_ABATEMENT)])
+CARBON_TAX <- CARBON_ABATEMENT_SHIFT*CARBON_TAX_CREDIT_PRICE
+VCM_CREDIT <- VCM_PRICE*CARBON_ABATEMENT
+##################Adjustments
+  #Negative commodity output shock to remove the beet spending from the facility (since it is modeled separately)
+  BEET_REMOVAL_COEF_GOSHEN <- -PER_BEET_SPENDING*OP_COST_GOSHEN
+  BEET_REMOVAL_COEF_WASHAKIE <- -PER_BEET_SPENDING*OP_COST_WASHAKIE
+  #Negative transportation adjustments since direct transportation costs are modeled separately.
+  GET_ADJ <- function(YEAR){
+    FILES <- list.files("./Raw_Output/Detailed_Economic_Indicators/Prelim-Run/")
+    FILE <- paste0("./Raw_Output/Detailed_Economic_Indicators/Prelim-Run/",FILES[grep(paste0("-",YEAR),FILES)])
+    DF <- read_csv(FILE) %>% filter(ImpactType=="Direct",IndustryCode %in% c(397,399),TagName=="beet purchase")
+    
+    DF$County <-  gsub(" County, WY \\(2023\\)","",DF$DestinationRegion )
+    DF$Year <- YEAR
+    DF <- DF %>% select(Year, County, Industry=IndustryDescription,Output) %>% mutate(Output=-Output)
+    return(DF)
+  }
+  YEARS_TO_PROC <- Year[c(-1,-7)]
+  for(i in YEARS_TO_PROC){
+    if(!exists("ADJUST_COSTS")){ADJUST_COSTS <- GET_ADJ(i)} else{ADJUST_COSTS <- rbind(ADJUST_COSTS,GET_ADJ(i))}
+  }
+WASHAKIE_TRUCK_ADJ <-  rbind(ADJUST_COSTS %>% filter(County=="Washakie"),c(2024,"Washakie","Truck transportation",0),c(2030,"Weston","Rail transportation",0)) %>% unique %>% arrange(Year) %>% pull(Output)
+WESTON_RAIL_ADJ <-  rbind(ADJUST_COSTS %>% filter(County=="Weston",Industry=="Rail transportation"),c(2024,"Weston","Rail transportation",0),c(2030,"Weston","Rail transportation",0))%>% unique %>% arrange(Year) %>% pull(Output)
+WESTON_TRUCK_ADJ <-    rbind(ADJUST_COSTS %>% filter(County=="Weston",Industry=="Truck transportation"),c(2024,"Weston","Truck transportation",0),c(2030,"Weston","Rail transportation",0))%>% unique %>% arrange(Year) %>% pull(Output)
+
+DATA <- cbind(Year,CARBON_TAX,VCM_CREDIT,GAS_VALUE,RNG_ADD_VALUE,
+      SBS_LOCAL_COST,SBS_NATIONAL_COST,GOSHEN_BEET_PURCHASE,WASHAKIE_BEET_PURCHASE,
+      SKID_EQUIP_COST,SKID_BUILD_COST,SKID_MAINT_COST,
+      BEET_CAP_WASHAKIE,BEET_TOTAL_CAP_GOSHEN,OP_COST_WASHAKIE,OP_COST_GOSHEN,WASH_EMP,GOSHEN_EMP,WASH_COMP,GOSHEN_COMP,PROD_WASHAKIE,PROD_GOSHEN,
+      FROM_GOSHEN_COST,FROM_WASHAKIE_COST,FROM_WESTON_COST,OUT_STATE_SHIPPING_COST,
+      WASHAKIE_TRUCK_ADJ,WESTON_RAIL_ADJ,WESTON_TRUCK_ADJ,BEET_REMOVAL_COEF_GOSHEN,BEET_REMOVAL_COEF_WASHAKIE     ) %>% as_tibble
+
+
+