Added New Mexico, Updated figures

Analysis.r

@@ -2,21 +2,34 @@ library(tidyverse)
 library(scales)
 library(RcppRoll)
 RES <- readRDS("Results/Storage_Values_by_Facility_and_Variable_Discounts.Rds")
-RES 
 RES_REDUCED_FEE <- RES
 RES_INCREASED_FEE <- RES
 
-CV <- 1.3074*(10607030-1060703)
+CV1 <- 1.3074*(10607030-1060703) #Data from Texas Report, converted from 2018 to Dec 2025 dollars
+CV2 <- 1.2874*(6984013-1117442) #Data from New Mexico Report, Converted from 2019 to  Dec 2025
+
 LENGTH <- length(RES)
 SUPPLY_RESULTS <- list()
+SUPPLY_RESULTS_WITH_SHIPPING <- list() #This shows the amount availble to ship today, the SUPPLY_RESULTS shows the demand to have a CIFS available sometime in the future (present value brought forward with a discount). This SUPPLY_RESULTS_WITH_SHIPPING  shows only SNF that has a current Net Present Cost high enough to exceed the shipping costs.
+
+
+SHIPPING_COST <- 1.2874*26000 #Inflation adjusted from New Mexico Report
 for(i in 1:LENGTH){
-	RES[[i]]$Revenue <-CV*as.numeric(RES[[i]]$Revenue)
+	RES[[i]] <- RES[[i]] %>% filter(!(Facility %in% c("Palo Verde","Vogtle"))) #The shipping cost is higher than the marginal value per SNF at these locations in the lat period of study 2083.
+	RES[[i]]$Revenue <-CV2*as.numeric(RES[[i]]$Revenue)
 	RES[[i]]$Year<-as.numeric(RES[[i]]$Year)
 	DISCOUNT <- as.numeric(gsub("Per_","",names(RES)[i]))/100
-	SUPPLY_RESULTS[[i]] <- RES[[i]]  %>% left_join(read_csv("Data/Raw_Data/Curie_Spent_Fuel_Site_Totals.csv")) %>%  mutate(Marginal_Value=Revenue/Total_Tons)  %>% select(Year,Marginal_Value,Total_Tons) %>% group_by(Year)  %>% arrange(Year,desc(Marginal_Value))  %>% mutate(Q=cumsum(Total_Tons),Discount=factor(percent(DISCOUNT,1),levels=c("3%","5%","7%","10%")))
+	SUPPLY_RESULTS[[i]] <- RES[[i]]  %>% left_join(read_csv("Data/Raw_Data/Curie_Spent_Fuel_Site_Totals.csv")) %>%  mutate(Marginal_Value=Revenue/Total_Tons) %>% select(Year,Marginal_Value,Total_Tons) %>% group_by(Year)  %>% arrange(Year,desc(Marginal_Value))  %>% mutate(Q=cumsum(Total_Tons),Discount=factor(percent(DISCOUNT,1),levels=c("3%","5%","7%","10%")))
+
+	SUPPLY_RESULTS_WITH_SHIPPING[[i]] <- RES[[i]]  %>% left_join(read_csv("Data/Raw_Data/Curie_Spent_Fuel_Site_Totals.csv")) %>%  mutate(Marginal_Value=Revenue/Total_Tons) %>% filter(Marginal_Value>=SHIPPING_COST)  %>% select(Year,Marginal_Value,Total_Tons) %>% group_by(Year)  %>% arrange(Year,desc(Marginal_Value))  %>% mutate(Q=cumsum(Total_Tons),Discount=factor(percent(DISCOUNT,1),levels=c("3%","5%","7%","10%")))
 }
 names(SUPPLY_RESULTS) <- names(RES)
-KEY_DATA <- do.call(rbind,SUPPLY_RESULTS[c("Per_3","Per_5","Per_7","Per_10")])
+names(SUPPLY_RESULTS_WITH_SHIPPING) <- names(RES)
+
+KEY_DATA <- do.call(rbind,SUPPLY_RESULTS[c("Per_3","Per_5","Per_7","Per_10")]) %>% ungroup
+SUPPLY_RESULTS_WITH_SHIPPING
+KEY_DATA_WITH_SHIPPING <- do.call(rbind,SUPPLY_RESULTS_WITH_SHIPPING[c("Per_3","Per_5","Per_7","Per_10")]) %>% ungroup
+
 KEY_YEARS <- c(1986,2026,2066)
 #Data for reduced and increased fee at 5% and 2026
 FEE_DATA <- rbind(KEY_DATA %>% filter(Year %in% 2026 ,Q>=500,Discount=="5%") %>% mutate(Fee="Current Fee"),KEY_DATA %>% filter(Year %in% 2026 ,Q>=500,Discount=="5%") %>% mutate(Marginal_Value=Marginal_Value/2,Fee="Reduced Fee"),KEY_DATA %>% filter(Year %in% 2026 ,Q>=500,Discount=="5%") %>% mutate(Marginal_Value=2*Marginal_Value,Fee="Increased Fee"))
@@ -26,11 +39,14 @@ FEE_DATA$Fee <- factor(FEE_DATA$Fee,levels=c("Reduced Fee","Current Fee","Increa
 DEMAND_CURVE_YEARS <- ggplot(KEY_DATA %>% filter(Year %in% KEY_YEARS ,Q>=500,Discount=="5%") ,aes(x=Q/1000,y=Marginal_Value/1000,group=Year,color=Year))+geom_step(linewidth=1,arrow = arrow(length = unit(0.25, "cm")))+theme_bw()+scale_color_binned(high= "#132B43", low= "#56B1F7",breaks = KEY_YEARS)+scale_y_continuous(breaks=seq(0,2000,by=50))+scale_x_continuous(breaks=seq(0,150,by=5))+ylab("Price ($1000 per ton)")+xlab("Quantity (Thousand Tons)")+theme(text = element_text(size = 16),legend.position = "top")+  guides(color = guide_legend(reverse = FALSE))
 DEMAND_CURVE_YEARS 
 
+DEMAND_CURVE_YEARS_WITH_SHIPPING <- ggplot(KEY_DATA_WITH_SHIPPING %>% filter(Year %in% KEY_YEARS ,Q>=500,Discount=="5%") ,aes(x=Q/1000,y=Marginal_Value/1000,group=Year,color=Year))+geom_step(linewidth=1,arrow = arrow(length = unit(0.25, "cm")))+theme_bw()+scale_color_binned(high= "#132B43", low= "#56B1F7",breaks = KEY_YEARS)+scale_y_continuous(breaks=seq(0,2000,by=50))+scale_x_continuous(breaks=seq(0,150,by=5))+ylab("Price ($1000 per ton)")+xlab("Quantity (Thousand Tons)")+theme(text = element_text(size = 16),legend.position = "top")+  guides(color = guide_legend(reverse = FALSE))
+DEMAND_CURVE_YEARS_WITH_SHIPPING 
+
+
 
 DEMAND_CURVE_YEARS_PLOT_FULL  <-ggplot(KEY_DATA %>% filter(Year %in% KEY_YEARS,Discount=="5%" ) ,aes(x=Q/1000,y=log(Marginal_Value),group=Year,color=Year))+geom_step(linewidth=2,arrow = arrow(length = unit(0.3, "cm")))+theme_bw()+scale_color_binned(high= "#132B43", low= "#56B1F7",breaks = KEY_YEARS)+ guides(color = guide_legend(reverse = TRUE))+scale_y_continuous(breaks=c(seq(4,20,by=0.5)))+scale_x_continuous(breaks=seq(0,150,by=5))+ylab("Price (Log dollar per Ton)")+xlab("Quantity (Thousand Tons)")+theme(text = element_text(size = 16),legend.position = "top")+  guides(color = guide_legend(reverse = FALSE))
 DEMAND_CURVE_YEARS_PLOT_FULL  
 
-
 DEMAND_CURVE_DISCOUNT  <-  ggplot(KEY_DATA %>% filter(Year %in% KEY_YEARS ,Q>=500,Year==2026) ,aes(x=Q/1000,y=Marginal_Value/1000,group=Discount,color=Discount))+geom_step(linewidth=1,arrow = arrow(length = unit(0.1, "cm")))+theme_bw()+scale_y_continuous(breaks=seq(0,2000,by=50))+scale_x_continuous(breaks=seq(0,150,by=5))+ylab("Price ($1000 per ton)")+xlab("Quantity (Thousand Tons)")+theme(text = element_text(size = 16),legend.position = "top")+  guides(color = guide_legend(reverse = FALSE))+scale_color_manual(values = c("tomato1",  "tomato2",  "tomato3","tomato4"))
 DEMAND_CURVE_DISCOUNT 
 
@@ -45,6 +61,7 @@ DEMAND_CURVE_FACET
 
 DEMAND_CURVE_YEARS_PLOT_FULL  
 DEMAND_CURVE_YEARS 
+DEMAND_CURVE_YEARS_WITH_SHIPPING 
 DEMAND_CURVE_DISCOUNT 
 DEMAND_CURVE_FEE 
 DEMAND_CURVE_FACET 
@@ -52,14 +69,28 @@ DEMAND_CURVE_FACET
 
 
 ###
-CIFS <- readRDS("Data/Cleaned_Data/Texas_CIFS_Costs.Rds")
+CIFS <- rbind(readRDS("Data/Cleaned_Data/Texas_CIFS_Costs.Rds"),readRDS("Data/Cleaned_Data/New_Mexico_CIFS_Costs.Rds"))
-CIFS %>% mutate(NPC=Total/(1+0.05)^Year) %>% group_by(Phase) %>% summarize(Cost=sum(NPC)) %>% mutate(Marginal=Cost/ifelse(Phase=="Partial",5000,40000)/1000)
-#Added cost per unit
-M <- (3373397183-1401068722)/(40000-5000)/1000
-#Added cost for intial project
-C <- 1401068722/5000/1000
 
+NPC <-	CIFS %>% select(Year,Location,Phase,Capacity,Cost_Assumption,Total) %>% group_by(Year,Location,Cost_Assumption,Total,Phase,Capacity) %>% summarize(Total=mean(Total)) %>% arrange(Location,Phase,Year) %>% mutate(Cost_3=Total/(1+0.03)^Year,Cost_5=Total/(1+0.05)^Year,Cost_7=Total/(1+0.07)^Year,Cost_10=Total/(1+0.1)^Year) %>% ungroup %>% group_by(Location,Phase,Capacity,Cost_Assumption) %>% summarize('3%'=sum(Cost_3),'5%'=sum(Cost_5),"7%"=sum(Cost_7),"10%"=sum(Cost_10)) %>% pivot_longer(c(-Phase,-Location,-Cost_Assumption,-Capacity),names_to="Discount",values_to="CIFS_Cost")
+NPC <- NPC  %>% filter(Cost_Assumption=="Average") %>% select(-Cost_Assumption)
+NPC
+KEY_DATA
+
+RETURN_DATA <-	KEY_DATA  %>% left_join(NPC) %>% filter(Q<=Capacity) %>% group_by(Year,Discount,Location,Phase,Capacity,CIFS_Cost) %>% summarize(Q=sum(Total_Tons),Revenue=sum(Marginal_Value*Total_Tons),P=Revenue/Q,Profit=sum(Revenue-CIFS_Cost)) %>% ungroup
+
+RETURN_DATA <-	RETURN_DATA  %>% group_by(Discount,Location,Phase,Capacity,CIFS_Cost)  %>% arrange(Discount,Location,Phase,Capacity,CIFS_Cost,Year) %>% mutate(FOC=(lead(Profit)-Profit)-Profit*(parse_number(Discount)/100))
 ####This is a plot of the supply curve in orange UNDER A COMPETITVE MARKET. Add in the monoplosty wating function next (value is gained by waiting another year.
+#Added cost per unit
+#M <- (3373397183-1401068722)/(40000-5000)/1000
+#Added cost for intial project
+#C <- 1401068722/5000/1000
+#EXTENDED_COST <- (M*(100000-5000) - +C)*1000
+#RETURN_DATA 
+
+#RETURN_DATA <- RETURN_DATA %>% mutate(size=ifelse(Phase=="Partial","5,000 MTU Capacity (Phase 1)","40,000 MTU Capacity (Full Build Out)"))
+
+
+FOC_PLOT <- ggplot(RETURN_DATA,aes(x=Year,y=FOC/10^6,group=Discount,color=Discount))+facet_wrap(~Capacity,ncol=2)+scale_x_continuous(breaks=seq(1960,2083,by=10))+geom_point(size=0.5)+geom_step(linewidth=0.75)+ geom_hline(yintercept = 0, color = "black", linetype = "solid", size = 1)+theme(text = element_text(size = 16),legend.position = "top")+ylab("Million Dollars")
+FOC_PLOT 
+
 DEMAND_CURVE_YEARS+geom_segment(aes(x = 0, y = C, xend = 5, yend = C),color="orange")+geom_segment(aes(x = 5, y = C, xend = 5, yend = M),color="orange")+geom_segment(aes(x = 5, y = M, xend = 140, yend = M),color="orange",arrow = arrow(length = unit(0.4, "cm")))
-
-

Cost_Data_Proc.r

@@ -1,53 +1,12 @@
 library(tidyverse)
-library(fixest)
-ISFSI <- read_csv("./Data/Raw_Data/Cost_Tables/Table_C-9_Number_of_ISFSIs_by_Project_Plan.csv")
-ONE <- ISFSI[1:3] 
-colnames(ONE) <- c("Year","Active","Decom")
-ONE <- ONE %>% mutate(Phase="Partial",Cost_Assumption="Low")
-
-TWO <- ISFSI[,c(1,4:5)] 
-colnames(TWO) <- c("Year","Active","Decom")
-TWO <- TWO%>% mutate(Phase="Full",Cost_Assumption="Low")
-
-THREE <- ISFSI[,c(1,6:7)] 
-colnames(THREE) <- c("Year","Active","Decom")
-THREE <- THREE %>% mutate(Phase="Partial",Cost_Assumption="High")
-
-FOUR <- ISFSI[,c(1,8:9)] 
-colnames(FOUR) <- c("Year","Active","Decom")
-FOUR <- FOUR %>% mutate(Phase="Full",Cost_Assumption="High")
-ISFSI_NUM <- rbind(ONE,TWO,THREE,FOUR)
-SAVE_DIR <- "./Data/Cleaned_Data/"
-dir.create(SAVE_DIR,showWarnings=FALSE,recursive=TRUE)
-saveRDS(ISFSI_NUM,paste0(SAVE_DIR,"ISFSI_Data.Rds"))
-rm(ONE,TWO,THREE,FOUR)
 ######CIFS costs
-CIFS <- rbind( read_csv("./Data/Raw_Data/Cost_Tables/Table_C-3_Undiscounted_Cost_Estimates_Phase_1_Low.csv") %>% mutate(Phase='Partial',Cost_Assumption="Low"),read_csv("./Data/Raw_Data/Cost_Tables/Table_C-4_Undiscounted_Cost_Estimates_Phase_1_High.csv") %>% mutate(Phase='Partial',Cost_Assumption="High"), read_csv("./Data/Raw_Data/Cost_Tables/Table_C-5_Undiscounted_Cost_Estimates_Full_Low.csv") %>% mutate(Phase='Full',Cost_Assumption="Low"), read_csv("./Data/Raw_Data/Cost_Tables/Table_C-6_Undiscounted_Cost_Estimates_Full_High.csv") %>% mutate(Phase='Full',Cost_Assumption="High"))
+CIFS_TEXAS <- rbind( read_csv("./Data/Raw_Data/Cost_Tables/Texas/Table_C-3_Undiscounted_Cost_Estimates_Phase_1_Low.csv") %>% mutate(Phase='Partial',Cost_Assumption="Low"),read_csv("./Data/Raw_Data/Cost_Tables/Texas/Table_C-4_Undiscounted_Cost_Estimates_Phase_1_High.csv") %>% mutate(Phase='Partial',Cost_Assumption="High"), read_csv("./Data/Raw_Data/Cost_Tables/Texas/Table_C-5_Undiscounted_Cost_Estimates_Full_Low.csv") %>% mutate(Phase='Full',Cost_Assumption="Low"), read_csv("./Data/Raw_Data/Cost_Tables/Texas/Table_C-6_Undiscounted_Cost_Estimates_Full_High.csv") %>% mutate(Phase='Full',Cost_Assumption="High")) %>% mutate(Location="Texas",Capacity=ifelse(Phase=='Partial',5000,8*5000)) %>% select(Year,Location,Phase,Capacity,Cost_Assumption,Total,everything())
-saveRDS(CIFS,"Data/Cleaned_Data/Texas_CIFS_Costs.Rds")
 
-######Onsite costs
+CIFS_TEXAS <- rbind(CIFS_TEXAS,CIFS_TEXAS %>% group_by(Year,Location,Phase,Capacity) %>% summarize(Cost_Assumption="Average",Total=mean(Total),Construction=mean(Construction),Transportation_to_CISF=mean(Transportation_to_CISF),Operations=mean(Operations),Transportation_to_Repository=mean(Transportation_to_Repository),Decommissioning=mean(Decommissioning)) %>% ungroup)
-ONSITE <- rbind( read_csv("./Data/Raw_Data/Cost_Tables/Table_C-10_Undiscounted_No_Action_Phase_1_Low.csv") %>% mutate(Phase='Partial',Cost_Assumption="Low"),read_csv("./Data/Raw_Data/Cost_Tables/Table_C-11_Undiscounted_No_Action_Phase_1_High.csv") %>% mutate(Phase='Partial',Cost_Assumption="High"), read_csv("./Data/Raw_Data/Cost_Tables/Table_C-12_Undiscounted_No_Action_Full_Low.csv") %>% mutate(Phase='Full',Cost_Assumption="Low"), read_csv("./Data/Raw_Data/Cost_Tables/Table_C-13_Undiscounted_No_Action_Full_High.csv") %>% mutate(Phase='Full',Cost_Assumption="High"))
-ISFSI_NUM %>% filter(Year>=30,Phase=='Full') %>% print(n=100)
-#######
-SUMMARY <- ONSITE %>% inner_join(ISFSI_NUM) %>% group_by(Year,Cost_Assumption) %>% summarize(Decom_num=Decom,OP=Operations_Active/ifelse(Active==0,1,Active),Decom=Operations_Decom/ifelse(Decom==0,1,Decom))
-OP <- SUMMARY %>% filter(OP!=0) %>% pull(OP) %>% mean
-TEST <-SUMMARY %>% filter(Cost_Assumption=='High')  %>% unique
-hist(TEST$Decom)
 
-TEST <-SUMMARY   %>% unique
+saveRDS(CIFS_TEXAS,"Data/Cleaned_Data/Texas_CIFS_Costs.Rds") 
-hist(TEST$Decom)
-TEST %>% filter(OP!=0,Year>28) %>% print(n=100)
-SUMMARY <- SUMMARY %>% mutate(CLOSING=ifelse(Year>=31,1,0))
-SUMMARY
-(10*77964491)/36
-(2*125682289)/9
-TEST
-77964491/108647430
-feols(Decom~Decom_num+CLOSING*Year,SUMMARY %>% filter(OP!=0))
-hist(SUMMARY$Decom)
-#####
-M <- (779644907-251364578)/(40000-5000)
-C <- 779644907-M*40000
-(M*150000+C)/10^6
 
+CIFS_NEW_MEXICO <- rbind( read_csv("./Data/Raw_Data/Cost_Tables/New_Mexico/Table_C-3_Undiscounted_Cost_Estimates_Phase_1_Low.csv") %>% mutate(Phase='Partial',Cost_Assumption="Low"),read_csv("./Data/Raw_Data/Cost_Tables/New_Mexico/Table_C-4_Undiscounted_Cost_Estimates_Phase_1_High.csv") %>% mutate(Phase='Partial',Cost_Assumption="High"), read_csv("./Data/Raw_Data/Cost_Tables/New_Mexico/Table_C-5_Undiscounted_Cost_Estimates_Full_Low.csv") %>% mutate(Phase='Full',Cost_Assumption="Low"), read_csv("./Data/Raw_Data/Cost_Tables/New_Mexico/Table_C-6_Undiscounted_Cost_Estimates_Full_High.csv") %>% mutate(Phase='Full',Cost_Assumption="High"))%>% mutate(Location="New Mexico",Capacity=ifelse(Phase=='Partial',8680,8680+5000*19))%>% select(Year,Location,Phase,Capacity,Cost_Assumption,Total,everything())
+CIFS_NEW_MEXICO <- rbind(CIFS_NEW_MEXICO,CIFS_NEW_MEXICO %>% group_by(Year,Location,Phase,Capacity) %>% summarize(Cost_Assumption="Average",Total=mean(Total),Construction=mean(Construction),Transportation_to_CISF=mean(Transportation_to_CISF),Operations=mean(Operations),Transportation_to_Repository=mean(Transportation_to_Repository),Decommissioning=mean(Decommissioning)) %>% ungroup)
+saveRDS(CIFS_NEW_MEXICO,"Data/Cleaned_Data/New_Mexico_CIFS_Costs.Rds")
 

Data/Raw_Data/Cost_Tables/New_Mexico/Table_C-3_Undiscounted_Cost_Estimates_Phase_1_Low.csv

@@ -0,0 +1,42 @@
+Year,Construction,Transportation_to_CISF,Operations,Transportation_to_Repository,Decommissioning,Total
+1,116859908,0,0,0,0,116859908
+2,116859908,0,0,0,0,116859908
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+40,269883561,4709983,0,0,0,274593543
+41,0,0,0,0,24822656,24822656

Data/Raw_Data/Cost_Tables/New_Mexico/Table_C-4_Undiscounted_Cost_Estimates_Phase_1_High.csv

@@ -0,0 +1,43 @@
+Year,Construction,Transportation_to_CISF,Operations,Transportation_to_Repository,Decommissioning,Total
+1,116859908,0,0,0,0,116859908
+2,116859908,0,0,0,0,116859908
+3,269883561,0,27892625,0,0,297776186
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+40,0,0,27892625,269883561,0,297776186
+41,0,0,0,0,24822656,24822656
+

Data/Raw_Data/Cost_Tables/New_Mexico/Table_C-5_Undiscounted_Cost_Estimates_Full_Low.csv

@@ -0,0 +1,43 @@
+
+Year,Construction,Transportation_to_CISF,Operations,Transportation_to_Repository,Decommissioning,Total
+1,116859908,0,0,0,0,116859908
+2,116859908,0,0,0,0,116859908
+3,103399272,269883561,4709983,0,0,377992816
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+9,103399272,155462880,4709983,0,0,263572135
+10,103399272,155462880,4709983,0,0,263572135
+11,103399272,155462880,4709983,0,0,263572135
+12,103399272,155462880,4709983,0,0,263572135
+13,103399272,155462880,4709983,0,0,263572135
+14,103399272,155462880,4709983,0,0,263572135
+15,103399272,155462880,4709983,0,0,263572135
+16,103399272,155462880,4709983,0,0,263572135
+17,103399272,155462880,4709983,0,0,263572135
+18,103399272,155462880,4709983,0,0,263572135
+19,103399272,155462880,4709983,0,0,263572135
+20,103399272,155462880,4709983,0,0,263572135
+21,103399272,155462880,4709983,0,0,263572135
+22,0,155462880,4709983,0,0,160172863
+23,0,0,4709983,167022002,0,171731985
+24,0,0,4709983,167022002,0,171731985
+25,0,0,4709983,167022002,0,171731985
+26,0,0,4709983,167022002,0,171731985
+27,0,0,4709983,167022002,0,171731985
+28,0,0,4709983,167022002,0,171731985
+29,0,0,4709983,167022002,0,171731985
+30,0,0,4709983,167022002,0,171731985
+31,0,0,4709983,167022002,0,171731985
+32,0,0,4709983,167022002,0,171731985
+33,0,0,4709983,167022002,0,171731985
+34,0,0,4709983,167022002,0,171731985
+35,0,0,4709983,167022002,0,171731985
+36,0,0,4709983,167022002,0,171731985
+37,0,0,4709983,167022002,0,171731985
+38,0,0,4709983,167022002,0,171731985
+39,0,0,4709983,167022002,0,171731985
+40,0,0,4709983,167022002,0,171731985
+41,0,0,0,0,496453127,496453127

Data/Raw_Data/Cost_Tables/New_Mexico/Table_C-6_Undiscounted_Cost_Estimates_Full_High.csv

@@ -0,0 +1,42 @@
+Year,Construction,Transportation_to_CISF,Operations,Transportation_to_Repository,Decommissioning,Total
+1,116859908,0,0,0,0,116859908
+2,116859908,0,0,0,0,116859908
+3,103399272,269883561,27892625,0,0,401175458
+4,103399272,155462880,27892625,0,0,286754777
+5,103399272,155462880,27892625,0,0,286754777
+6,103399272,155462880,27892625,0,0,286754777
+7,103399272,155462880,27892625,0,0,286754777
+8,103399272,155462880,27892625,0,0,286754777
+9,103399272,155462880,27892625,0,0,286754777
+10,103399272,155462880,27892625,0,0,286754777
+11,103399272,155462880,27892625,0,0,286754777
+12,103399272,155462880,27892625,0,0,286754777
+13,103399272,155462880,27892625,0,0,286754777
+14,103399272,155462880,27892625,0,0,286754777
+15,103399272,155462880,27892625,0,0,286754777
+16,103399272,155462880,27892625,0,0,286754777
+17,103399272,155462880,27892625,0,0,286754777
+18,103399272,155462880,27892625,0,0,286754777
+19,103399272,155462880,27892625,0,0,286754777
+20,103399272,155462880,27892625,0,0,286754777
+21,103399272,155462880,27892625,0,0,286754777
+22,0,155462880,27892625,0,0,183355505
+23,0,0,27892625,167022002,0,194914627
+24,0,0,27892625,167022002,0,194914627
+25,0,0,27892625,167022002,0,194914627
+26,0,0,27892625,167022002,0,194914627
+27,0,0,27892625,167022002,0,194914627
+28,0,0,27892625,167022002,0,194914627
+29,0,0,27892625,167022002,0,194914627
+30,0,0,27892625,167022002,0,194914627
+31,0,0,27892625,167022002,0,194914627
+32,0,0,27892625,167022002,0,194914627
+33,0,0,27892625,167022002,0,194914627
+34,0,0,27892625,167022002,0,194914627
+35,0,0,27892625,167022002,0,194914627
+36,0,0,27892625,167022002,0,194914627
+37,0,0,27892625,167022002,0,194914627
+38,0,0,27892625,167022002,0,194914627
+39,0,0,27892625,167022002,0,194914627
+40,0,0,27892625,167022002,0,194914627
+41,0,0,0,0,496453127,496453127

Data/Raw_Data/Cost_Tables/New_Mexico/test.r

@@ -0,0 +1,57 @@
+library(tidyverse)
+#########CIFS Costs
+DAT <- read_csv("Table_C-3_Undiscounted_Cost_Estimates_Phase_1_Low.csv")
+rowSums(DAT[,2:6])-DAT$Total
+sum(DAT[,2:6])
+
+
+DAT <- read_csv("Table_C-4_Undiscounted_Cost_Estimates_Phase_1_High.csv")
+rowSums(DAT[,2:6])-DAT$Total
+sum(DAT[,2:6])
+sum(DAT[,7])
+DAT[5,7]-sum(DAT[5,2:6])
+61242023-(40629430+7642396+12970196)
+
+DAT <- read_csv("Table_C-5_Undiscounted_Cost_Estimates_Full_Low.csv")
+rowSums(DAT[,2:6])-DAT$Total
+sum(DAT[,2:6])
+sum(DAT$Total)
+
+
+DAT <- read_csv("Table_C-6_Undiscounted_Cost_Estimates_Full_High.csv")
+rowSums(DAT[,2:6])-DAT$Total
+sum(DAT[,2:6])
+
+################ISFS
+DAT <- read_csv("Table_C-9_Number_of_ISFSIs_by_Project_Plan.csv")
+ONE <- DAT[1:3] 
+colnames(ONE) <- c("Year","Active","Decom")
+ONE <- ONE %>% mutate(Phase="Partial",Cost_Assumption="Low")
+
+TWO <- DAT[,c(1,4:5)] 
+colnames(TWO) <- c("Year","Active","Decom")
+TWO <- TWO%>% mutate(Phase="Full",Cost_Assumption="Low")
+
+THREE <- DAT[,c(1,6:7)] 
+colnames(THREE) <- c("Year","Active","Decom")
+THREE <- THREE %>% mutate(Phase="Partial",Cost_Assumption="High")
+
+FOUR <- DAT[,c(1,8:9)] 
+colnames(FOUR) <- c("Year","Active","Decom")
+FOUR <- FOUR %>% mutate(Phase="Full",Cost_Assumption="High")
+ISFSI_NUM <- rbind(ONE,TWO,THREE,FOUR)
+ISFSI_NUM  %>% filter(Year==40)
+
+####No Action Costs
+DAT <- read_csv("Table_C-10_Undiscounted_No_Action_Phase_1_Low.csv")
+rowSums(DAT[,2:4])-DAT$Total
+sum(DAT$Total)
+#Table 11 is identical to Table 10 so not check needed
+DAT <- read_csv("Table_C-12_Undiscounted_No_Action_Full_Low.csv")
+rowSums(DAT[,2:4])-DAT$Total
+sum(DAT$Total)
+
+DAT <- read_csv("Table_C-13_Undiscounted_No_Action_Full_High.csv")
+rowSums(DAT[,2:4])-DAT$Total
+sum(DAT$Total)
+

Data/Raw_Data/Cost_Tables/Texas/Table_C-6_Undiscounted_Cost_Estimates_Full_High.csv

@@ -1,4 +1,3 @@
-
 Year,Construction,Transportation_to_CISF,Operations,Transportation_to_Repository,Decommissioning,Total
 1,76552618,73711378,12170532,0,0,162434529
 2,65910317,224660997,12170532,0,0,302741846