Faster alternative to nested loops - r

Question

I have written the below function, which contains a nested loop. In short, it calculates differences in emissions between i (28) pairs alternative technologies for j (48) countries. For a single combination and a single country, it takes 0.32 sec, which should give a total time of 0.32*28*48 = around 7 min. The function actually takes about 50 min, which makes me think there may be some unnecessary computing going on. Is a nested loop the most efficient approach here?
Any help is greatly appreciated!
alt.comb.p <- function(Fmat){
y.empty = matrix(data = 0,ncol = 2,nrow = nrow(FD)-1)
row.names(y.empty) <- paste(FD$V1[2:nrow(FD)],FD$V2[2:nrow(FD)],sep = " ")
country.list = unique(FD$V1)
for (j in 1:length(country.list)){ # for every country
for (i in 1:ncol(alt.comb)){ # for every possible combination
# the final demand of the first item of the combination is calculated
first = alt.comb[,i][1]
first.name = row.names(Eprice.Exio)[first]
loc1 = grep(pattern = first.name,x = row.names(y.empty))
country.first = substr(x = row.names(y.empty)[loc1[j]],start = 0,stop = 2)
y.empty[,1][loc1[j]] <- Eprice.Exio[first.name,country.first]
# the final demand of the second item of the combination is calculated
second = alt.comb[,i][2]
second.name = row.names(Eprice.Exio)[second]
loc2 = grep(pattern = second.name,x = row.names(y.empty))
country.second = substr(x = row.names(y.empty)[loc2[j]],start = 0,stop = 2)
y.empty[,2][loc2[j]] <- Eprice.Exio[second.name,country.second]
# calculates the difference between the total pressures from item 1 and item 2
r.1 = sum(Fmat%*%as.vector(y.empty[,1]))
r.2 = sum(Fmat%*%as.vector(y.empty[,2]))
r.dif = r.1-r.2 # negative means alternative 1 is better
alt.comb[2+j,i] <- r.dif
row.names(alt.comb)[2+j] <- country.first
y.empty = matrix(data = 0,ncol = 2,nrow = nrow(FD)-1)
row.names(y.empty) <- paste(FD$V1[2:nrow(FD)],FD$V2[2:nrow(FD)],sep = " ")
}
}
return(alt.comb)
}
Edit:
A simplified example would be:
Fmat = matrix(data = runif(1:9600), ncol=9600, nrow=9600)
alt.comb.p <- function(Fmat){
y.empty = matrix(data = 0,ncol = 2,nrow = 9600)
country.list = runif(n = 10)
alt.comb = matrix(data=0,ncol=5,nrow=10)
for (j in 1:10){ # for every country
for (i in 1:5){ # for every possible combination
y.empty[50,1] <- runif(1)
y.empty[60,2] <- runif(1)
# calculates the difference between the total pressures from item 1 and item 2
r.1 = sum(Fmat%*%as.vector(y.empty[,1]))
r.2 = sum(Fmat%*%as.vector(y.empty[,2]))
r.dif = r.1-r.2 # negative means alternative 1 is better
alt.comb[j,i] <- r.dif
y.empty = matrix(data = 0,ncol = 2,nrow = 9600)
}
}
return(alt.comb)
}