alex/Cowboy_Clean_Fuels
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Add scripts to update IMPLAN easily

Browse Source
This commit is contained in:
alex 2025-05-08 17:59:52 -06:00
parent edd4cade07
commit 060335f18c
3 changed files with 204 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

62
Easy_IMPLAN.r Normal file
View File

@ -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)

BIN
Model_Timeline.ods
View File

Binary file not shown.

142
R_IMPLAN_Model.R Normal file
View File

@ -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