library(tidyverse)
YEAR <- 2028:2036
CONSTRUCTION_CAPEX <- c(1860,1860,3720,3720,3720,3720,1860,1860,0)*10^6
CONSTRUCTION_CAPEX_PER <- CONSTRUCTION_CAPEX/sum(CONSTRUCTION_CAPEX  )
CONSTRUCTION_CAPEX_PER 
1.6/4.8

POWER_GEN_EQUIP <- CONSTRUCTION_CAPEX_PER*3062082645#*0.8 #80% local spending 
GENERAL_BUILDINGS <- CONSTRUCTION_CAPEX_PER*1871548496 
PLANT_EQUIP <-  CONSTRUCTION_CAPEX_PER*5248715967#*0.60 #69% local spending 

cbind(YEAR,cbind(POWER_GEN_EQUIP,GENERAL_BUILDINGS,PLANT_EQUIP)/10^6)
#0.228+0.233+0.467+0.472+0.473+0.382+0.248+0.150
#0.231	+0.237	+0.484	+0.499	+0.515	+0.522	+0.308	+0.300	+0.039	+0.027
#1-(0.257	+0.264	+0.540	+0.559	+0.578	+0.585	+0.324	+0.313	+0.044)/5.2

#Data Center Inputs
PREDEV_2026 <- 1595000+2392500
PREDEV_2027 <- 2*2392500+797500
PREDEV_2026/10^6 
PREDEV_2027/10^6 

UTILITY_2026 <- 10^6
UTILITY_2027 <- 21428571+2*32142857
UTILITY_2028 <- 2*32142857
round(c(UTILITY_2026,UTILITY_2027 ,UTILITY_2028 ) /10^6 ,2)
CONS_2026 <- 150000+450000
CONS_2027 <- 450000+7950000+450000+4287501
CONS_2028 <- 186328937+216309410+468145389+791934505
CONS_2028/10^6 
CONS_2029 <- 468145389+247398869

CONS_TOTAL <- c(CONS_2026,CONS_2027,CONS_2028,CONS_2029)/10^6
#Split based on research
BUILDING <- 0.196
COMPUTERS <- 0.682
OTHER <- 0.061
TOTAL_PER <- BUILDING+COMPUTERS+OTHER
BUILDING <- BUILDING/TOTAL_PER
COMPUTERS <- COMPUTERS/TOTAL_PER
OTHER <- OTHER/TOTAL_PER
BUILDING+COMPUTERS+OTHER

##Million Dollar inputs
CONS_TOTAL*BUILDING
CONS_TOTAL*COMPUTERS
CONS_TOTAL*OTHER

EMP_2027 <-13.1
EMP_2028 <- 39.4
EMP_2029_FORWARD <- 52.5