Path set

rm(list=ls())
# path      <- file.path("C:/Users/Richard.Curtin/OneDrive - bord iascaigh mhara/Documents/Dropbox Files/2014/BIM ECON/EU_Meetings/STECF EWG CelticSea 2021_2118/STECF-EWG-21-18/ToR3/StaticAnalysis")
# path_data <- file.path("C:/Users/Richard.Curtin/OneDrive - bord iascaigh mhara/Documents/Dropbox Files/2014/BIM ECON/EU_Meetings/STECF EWG CelticSea 2021_2118/STECF-EWG-21-18/ToR1/NWW-MS-dataset/ToR1-2-ThresholdsAnalysis")
user_path <- "C:/STECF_Celtic_Sea_EWG-21-18/Software/"
user_path_data <- "C:/STECF_Celtic_Sea_EWG-21-18/Software/"
path <- paste0(user_path, file.path("STECF-EWG-21-18/ToR3/StaticAnalysis"))
path_data <- paste0(user_path_data, file.path("STECF-EWG-21-18/ToR1/NWW-MS-dataset/ToR1-2-ThresholdsAnalysis"))

tables_path <- "//galwayfs03/FishData/STECF/Meetings/STECF_technical_measures_2021/static_Richard/tables_ToR3_CatchReductionAnalysis"

Read data

#import retention coefficients from BIM
coeffs           <-read.csv2(file.path(path, "20200206_coeffs_reduc_esp.csv"))
colnames(coeffs) <- c("species", "Coefficient", "Source", "Source.1")
knitr::kable(coeffs[,c("species", "Coefficient", "Source")])
species Coefficient Source
BLL 0.88 BIM 2019
COD 0.71 BIM 2019
HAD 0.79 BIM 2019
HKE 0.93 BIM 2019
JOD 0.74 BIM 2019
LEM 0.30 BIM 2019
LEZ 0.17 BIM 2019
LIN 0.60 BIM 2019
MEG 0.17 BIM 2019
MNZ 0.32 BIM 2019
PLE 0.32 BIM 2019
POL 0.22 BIM 2019
RAJ 0.22 BIM 2019
RJx 0.22 BIM 2019
SOL 0.30 BIM 2019
SYC 0.12 BIM 2019
SYT 0.12 BIM 2019
TUR 0.88 BIM 2019
WHG 0.72 BIM 2019
WIT 0.30 BIM 2019
Others-Bulk Catch 0.51 BIM 2019
RJC 0.22 BIM 2019
RJE 0.22 BIM 2019
RJF 0.22 BIM 2019
RJH 0.22 BIM 2019
RJI 0.22 BIM 2019
RJM 0.22 BIM 2019
RJN 0.22 BIM 2019
RJO 0.22 BIM 2019
RJR 0.22 BIM 2019
RJU 0.22 BIM 2019
load(file.path(path_data, "NWW_data_set_w_thresholds.RData"))

data <- subset(data, year %in% c(2017, 2018, 2019)) # subset for 2017 to 2019

data$Coefficient <- factor(data$fao_cod)
levels(data$Coefficient) <-  coeffs[match(levels(data$Coefficient), coeffs$species), "Coefficient"]
data$Coefficient <- as.numeric(as.character(data$Coefficient))
coeffs$Coefficient <- as.numeric(as.character(coeffs$Coefficient))
data$Coefficient[is.na(data$Coefficient)] <- coeffs[coeffs$species=="Others-Bulk Catch",]$Coefficient # mean between "others and bulk"
head(data[,c("MS_cod",  "vessel_id", "fao_cod", "Coefficient")])
##   MS_cod  vessel_id fao_cod Coefficient
## 1    FRA  FRA_v8770     LIO        0.51
## 2    FRA FRA_v18133     MEG        0.17
## 3    FRA FRA_v51660     WIT        0.30
## 4    FRA FRA_v20708     HAD        0.79
## 5    FRA  FRA_v9240     MAC        0.51
## 6    FRA FRA_v18133     LEM        0.30
trips_in_cspz  <- unique(data[data$in_CSPZ==1,]$trip_id)
length(trips_in_cspz)
## [1] 29323
trips_out_cspz <- unique(data[data$in_CSPZ==0,]$trip_id)
length(trips_out_cspz)  # outside trips AND CSPZ overlapping trips
## [1] 287150
data$trip_w_overlap <- 0
data[(data$trip_id %in% trips_in_cspz) & (data$trip_id %in% trips_out_cspz), "trip_w_overlap"] <- 1
length(unique(data[data$trip_w_overlap==1, "trip_id"]))  # nb overlapping trips
## [1] 6130
trips_out_cspz <- trips_out_cspz[!trips_out_cspz %in% trips_in_cspz]
length(trips_out_cspz)  # entirely outside trips
## [1] 281020
# calculate reduced catches and values
data$catch_volume_kg_red <- data$catch_volume_kg * data$Coefficient
data$catch_value_euro_red <- data$catch_value_euro * data$Coefficient

# Raised fishing line in regulation only for Bottom trawlers. Appearently TBB is not included
data$bottom_trawl <- grepl("OTB|PTB|OTT",data$gear_cod)

Run scenarios

For all the countries together, and for each country independently. Also save all the tables as .csv files.

data_all_countries <- data

runs <- c("all_countries", unique(as.character(data_all_countries$MS_cod)))

for (i in runs) {
  
  if (i == "all_countries") {
    data <- data_all_countries
  } else {
    print(paste0("Subsetting for ", i))
    
    data <- subset(data_all_countries, MS_cod == i)
  }
  
  print(paste0("Running scenarios for ", i))
  
  #Local hypothesis (simu 1) = "perfect knowledge"
  simu1_nbves   <- length(unique(data[data$bottom_trawl == T & data$in_CSPZ==1 & data$trip_sup_0.2_HAD==1, "vessel_id"])) 
  simu1_nbtrip  <- length(unique(data[data$bottom_trawl == T & data$in_CSPZ==1 & data$trip_sup_0.2_HAD==1, "trip_id"])) 
  simu1_alltons <- sum(data[data$bottom_trawl == T & data$in_CSPZ==1 & data$trip_sup_0.2_HAD==1, "catch_volume_kg"])/1000 
  simu1_codtons <- sum(data[data$bottom_trawl == T & data$fao_cod=="COD" & data$in_CSPZ==1 & data$trip_sup_0.2_HAD==1, "catch_volume_kg"])/1000 
  simu1_hadtons <- sum(data[data$bottom_trawl == T & data$fao_cod=="HAD" & data$in_CSPZ==1 & data$trip_sup_0.2_HAD==1, "catch_volume_kg"])/1000 
  simu1_whgtons <- sum(data[data$bottom_trawl == T & data$fao_cod=="WHG" & data$in_CSPZ==1 & data$trip_sup_0.2_HAD==1, "catch_volume_kg"])/1000  
  simu1_value   <- sum(data[data$bottom_trawl == T & data$in_CSPZ==1 & data$trip_sup_0.2_HAD==1, "catch_value_euro"], na.rm=TRUE)/1000
  simu1_alltonsred <- sum(data[data$bottom_trawl == T & data$in_CSPZ==1 & data$trip_sup_0.2_HAD==1, "catch_volume_kg_red"])/1000 
  simu1_alltondif <- (simu1_alltonsred-simu1_alltons)/simu1_alltons * 100
  simu1_valuered   <- sum(data[data$bottom_trawl == T & data$in_CSPZ==1 & data$trip_sup_0.2_HAD==1, "catch_value_euro_red"], na.rm=TRUE)/1000
  simu1_valuereddif <- (simu1_valuered-simu1_value)/simu1_value * 100
  simu1_codred <- sum(data[data$bottom_trawl == T & data$fao_cod=="COD" & data$in_CSPZ==1 & data$trip_sup_0.2_HAD==1, "catch_volume_kg_red"])/1000 
  simu1_coddif <- (simu1_codred-simu1_codtons)/simu1_codtons * 100
  simu1_costcod <- (simu1_value-simu1_valuered)/(simu1_codtons-simu1_codred)
  simu1 <- cbind.data.frame("Scenario"="Perfect knowledge (ie trips inside, >20%HAD)",
                            "Nb Vessels"=simu1_nbves, "Nb Trips"=simu1_nbtrip,
                            "ALL tons"=simu1_alltons, "COD tons"=simu1_codtons, "HAD tons"=simu1_hadtons, "WHG tons"=simu1_whgtons,
                            "Value (000 euros?)"=simu1_value,
                            "ALL tons red."=simu1_alltonsred, "ALL tons %diff"=simu1_alltondif,
                            "Value red."=simu1_valuered, "Value %diff"=simu1_valuereddif,
                            "COD tons red."=simu1_codred, "COD tons %diff"=simu1_coddif,
                            "Cost per cod ton saved (000 euros?)"=simu1_costcod)
  
  #Conservative hypothesis = Only concerned vessels, raised line within ZPMC only (simu 2)
  #(identifying VESSELS with at least one fishing trip >20% HAD within ZPMC)
  concerned_vids      <- unique(as.character(data[data$bottom_trawl == T & data$trip_sup_0.2_HAD==1, "vessel_id"])) #59 "concerned" vessels  
  data$concerned_vids <- 0
  data$concerned_vids[data$bottom_trawl == T & data$vessel_id %in% concerned_vids] <- 1
  simu2_nbves   <- length(unique(data[data$bottom_trawl == T & data$in_CSPZ==1 & data$concerned_vids==1, "vessel_id"])) 
  simu2_nbtrip  <- length(unique(data[data$bottom_trawl == T & data$in_CSPZ==1 & data$concerned_vids==1, "trip_id"]))
  simu2_alltons <- sum(data[data$bottom_trawl == T & data$in_CSPZ==1 & data$concerned_vids==1, "catch_volume_kg"])/1000 
  simu2_codtons <- sum(data[data$bottom_trawl == T & data$fao_cod=="COD" & data$in_CSPZ==1 & data$concerned_vids==1, "catch_volume_kg"])/1000 
  simu2_hadtons <- sum(data[data$bottom_trawl == T & data$fao_cod=="HAD" & data$in_CSPZ==1 & data$concerned_vids==1, "catch_volume_kg"])/1000 
  simu2_whgtons <- sum(data[data$bottom_trawl == T & data$fao_cod=="WHG" & data$in_CSPZ==1 & data$concerned_vids==1, "catch_volume_kg"])/1000
  simu2_value   <- sum(data[data$bottom_trawl == T & data$in_CSPZ==1 & data$concerned_vids==1, "catch_value_euro"], na.rm=TRUE)/1000 
  simu2_alltonsred <- sum(data[data$bottom_trawl == T & data$in_CSPZ==1 & data$concerned_vids==1, "catch_volume_kg_red"])/1000 
  simu2_alltondif <- (simu2_alltonsred-simu2_alltons)/simu2_alltons * 100
  simu2_valuered   <- sum(data[data$bottom_trawl == T & data$in_CSPZ==1 & data$concerned_vids==1, "catch_value_euro_red"], na.rm=TRUE)/1000
  simu2_valuereddif <- (simu2_valuered-simu2_value)/simu2_value * 100
  simu2_codred <- sum(data[data$bottom_trawl == T & data$fao_cod=="COD" & data$in_CSPZ==1 & data$concerned_vids==1, "catch_volume_kg_red"])/1000 
  simu2_coddif <- (simu2_codred-simu2_codtons)/simu2_codtons * 100
  simu2_costcod <- (simu2_value-simu2_valuered)/(simu2_codtons-simu2_codred)
  
  simu2 <- cbind.data.frame("Scenario"="Concerned vessels only (ie at least 1 trip inside >20%HAD)",
                            "Nb Vessels"=simu2_nbves, "Nb Trips"=simu2_nbtrip,
                            "ALL tons"=simu2_alltons, "COD tons"=simu2_codtons, "HAD tons"=simu2_hadtons, "WHG tons"=simu2_whgtons,
                            "Value (000 euros?)"=simu2_value,
                            "ALL tons red."=simu2_alltonsred, "ALL tons %diff"=simu2_alltondif,
                            "Value red."=simu2_valuered, "Value %diff"=simu2_valuereddif,
                            "COD tons red."=simu2_codred, "COD tons %diff"=simu2_coddif,
                            "Cost per cod ton saved (000 euros?)"=simu2_costcod)
  
  #Reference hypothesis = only concerned vessels, raised line during whole trips that cross ZPMC (simu 3)
  simu3_nbves   <- length(unique(data[data$bottom_trawl == T & (data$in_CSPZ==1 | data$trip_w_overlap==1) & data$concerned_vids==1 , "vessel_id"])) 
  simu3_nbtrip  <- length(unique(data[data$bottom_trawl == T & (data$in_CSPZ==1 | data$trip_w_overlap==1) & data$concerned_vids==1 , "trip_id"]))
  simu3_alltons <- sum(data[data$bottom_trawl == T & (data$in_CSPZ==1 | data$trip_w_overlap==1) & data$concerned_vids==1, "catch_volume_kg"])/1000 
  simu3_codtons <- sum(data[data$bottom_trawl == T & data$fao_cod=="COD" & (data$in_CSPZ==1 | data$trip_w_overlap==1) & data$concerned_vids==1, "catch_volume_kg"])/1000 
  simu3_hadtons <- sum(data[data$bottom_trawl == T & data$fao_cod=="HAD" & (data$in_CSPZ==1 | data$trip_w_overlap==1) & data$concerned_vids==1, "catch_volume_kg"])/1000 
  simu3_whgtons <- sum(data[data$bottom_trawl == T & data$fao_cod=="WHG" & (data$in_CSPZ==1 | data$trip_w_overlap==1) & data$concerned_vids==1, "catch_volume_kg"])/1000
  simu3_value   <- sum(data[data$bottom_trawl == T & (data$in_CSPZ==1 | data$trip_w_overlap==1) & data$concerned_vids==1, "catch_value_euro"], na.rm=TRUE)/1000
  simu3_alltonsred <- sum(data[data$bottom_trawl == T & (data$in_CSPZ==1 | data$trip_w_overlap==1) & data$concerned_vids==1, "catch_volume_kg_red"])/1000 
  simu3_alltondif <- (simu3_alltonsred-simu3_alltons)/simu3_alltons * 100
  simu3_valuered   <- sum(data[data$bottom_trawl == T & (data$in_CSPZ==1 | data$trip_w_overlap==1) & data$concerned_vids==1, "catch_value_euro_red"], na.rm=TRUE)/1000
  simu3_valuereddif <- (simu3_valuered-simu3_value)/simu3_value * 100
  simu3_codred <- sum(data[data$bottom_trawl == T & data$fao_cod=="COD" & (data$in_CSPZ==1 | data$trip_w_overlap==1) & data$concerned_vids==1, "catch_volume_kg_red"])/1000 
  simu3_coddif <- (simu3_codred-simu3_codtons)/simu3_codtons * 100
  simu3_costcod <- (simu3_value-simu3_valuered)/(simu3_codtons-simu3_codred)
  simu3 <- cbind.data.frame("Scenario"="Concerned vessels only, + all crossing trips",
                            "Nb Vessels"=simu3_nbves, "Nb Trips"=simu3_nbtrip,
                            "ALL tons"=simu3_alltons, "COD tons"=simu3_codtons, "HAD tons"=simu3_hadtons, "WHG tons"=simu3_whgtons,
                            "Value (000 euros?)"=simu3_value,
                            "ALL tons red."=simu3_alltonsred, "ALL tons %diff"=simu3_alltondif,
                            "Value red."=simu3_valuered, "Value %diff"=simu3_valuereddif,
                            "COD tons red."=simu3_codred, "COD tons %diff"=simu3_coddif,
                            "Cost per cod ton saved (000 euros?)"=simu3_costcod)
  
  #Whole fleet, raised line within CSPZ only (simu 4)
  simu4_nbves   <- length(unique(data[data$bottom_trawl == T & data$in_CSPZ==1 , "vessel_id"])) 
  simu4_nbtrip  <- length(unique(data[data$bottom_trawl == T & data$in_CSPZ==1 , "trip_id"])) 
  simu4_alltons <- sum(data[data$bottom_trawl == T & data$in_CSPZ==1, "catch_volume_kg"])/1000 
  simu4_codtons <- sum(data[data$bottom_trawl == T & data$fao_cod=="COD" & data$in_CSPZ==1, "catch_volume_kg"])/1000 
  simu4_hadtons <- sum(data[data$bottom_trawl == T & data$fao_cod=="HAD" & data$in_CSPZ==1, "catch_volume_kg"])/1000 
  simu4_whgtons <- sum(data[data$bottom_trawl == T & data$fao_cod=="WHG" & data$in_CSPZ==1, "catch_volume_kg"])/1000
  simu4_value   <- sum(data[data$bottom_trawl == T & data$in_CSPZ==1, "catch_value_euro"], na.rm=TRUE)/1000
  simu4_alltonsred <- sum(data[data$bottom_trawl == T & data$in_CSPZ==1, "catch_volume_kg_red"])/1000 
  simu4_alltondif <- (simu4_alltonsred-simu4_alltons)/simu4_alltons * 100
  simu4_valuered   <- sum(data[data$bottom_trawl == T & data$in_CSPZ==1, "catch_value_euro_red"], na.rm=TRUE)/1000
  simu4_valuereddif <- (simu4_valuered-simu4_value)/simu4_value * 100
  simu4_codred <- sum(data[data$bottom_trawl == T & data$fao_cod=="COD" & data$in_CSPZ==1, "catch_volume_kg_red"])/1000 
  simu4_coddif <- (simu4_codred-simu4_codtons)/simu4_codtons * 100
  simu4_costcod <- (simu4_value-simu4_valuered)/(simu4_codtons-simu4_codred)
  simu4 <- cbind.data.frame("Scenario"="Whole fleet, within CSPZ",
                            "Nb Vessels"=simu4_nbves, "Nb Trips"=simu4_nbtrip,
                            "ALL tons"=simu4_alltons, "COD tons"=simu4_codtons, "HAD tons"=simu4_hadtons, "WHG tons"=simu4_whgtons,
                            "Value (000 euros?)"=simu4_value,
                            "ALL tons red."=simu4_alltonsred, "ALL tons %diff"=simu4_alltondif,
                            "Value red."=simu4_valuered, "Value %diff"=simu4_valuereddif,
                            "COD tons red."=simu4_codred, "COD tons %diff"=simu4_coddif,
                            "Cost per cod ton saved (000 euros?)"=simu4_costcod)
  
  #Whole fleet, raised line during whole trips that cross ZPMC (simu 5)
  simu5_nbves   <- length(unique(data[data$bottom_trawl == T & (data$in_CSPZ==1 | data$trip_w_overlap==1) , "vessel_id"])) 
  simu5_nbtrip  <- length(unique(data[data$bottom_trawl == T & (data$in_CSPZ==1 | data$trip_w_overlap==1), "trip_id"]))
  simu5_alltons <- sum(data[data$bottom_trawl == T & (data$in_CSPZ==1 | data$trip_w_overlap==1), "catch_volume_kg"])/1000 
  simu5_codtons <- sum(data[data$bottom_trawl == T & data$fao_cod=="COD" & (data$in_CSPZ==1 | data$trip_w_overlap==1), "catch_volume_kg"])/1000 
  simu5_hadtons <- sum(data[data$bottom_trawl == T & data$fao_cod=="HAD" & (data$in_CSPZ==1 | data$trip_w_overlap==1), "catch_volume_kg"])/1000 
  simu5_whgtons <- sum(data[data$bottom_trawl == T & data$fao_cod=="WHG" & (data$in_CSPZ==1 | data$trip_w_overlap==1), "catch_volume_kg"])/1000
  simu5_value   <- sum(data[data$bottom_trawl == T & (data$in_CSPZ==1 | data$trip_w_overlap==1), "catch_value_euro"], na.rm=TRUE)/1000
  simu5_alltonsred <- sum(data[data$bottom_trawl == T & (data$in_CSPZ==1 | data$trip_w_overlap==1), "catch_volume_kg_red"])/1000 
  simu5_alltondif <- (simu5_alltonsred-simu5_alltons)/simu5_alltons * 100
  simu5_valuered   <- sum(data[data$bottom_trawl == T & (data$in_CSPZ==1 | data$trip_w_overlap==1), "catch_value_euro_red"], na.rm=TRUE)/1000
  simu5_valuereddif <- (simu5_valuered-simu5_value)/simu5_value * 100
  simu5_codred <- sum(data[data$bottom_trawl == T & data$fao_cod=="COD" & (data$in_CSPZ==1 | data$trip_w_overlap==1), "catch_volume_kg_red"])/1000 
  simu5_coddif <- (simu5_codred-simu5_codtons)/simu5_codtons * 100
  simu5_costcod <- (simu5_value-simu5_valuered)/(simu5_codtons-simu5_codred)
  simu5 <- cbind.data.frame("Scenario"="Whole fleet, + all crossing trips",
                            "Nb Vessels"=simu5_nbves, "Nb Trips"=simu5_nbtrip,
                            "ALL tons"=simu5_alltons, "COD tons"=simu5_codtons, "HAD tons"=simu5_hadtons, "WHG tons"=simu5_whgtons,
                            "Value (000 euros?)"=simu5_value,
                            "ALL tons red."=simu5_alltonsred, "ALL tons %diff"=simu5_alltondif,
                            "Value red."=simu5_valuered, "Value %diff"=simu5_valuereddif,
                            "COD tons red."=simu5_codred, "COD tons %diff"=simu5_coddif,
                            "Cost per cod ton saved (000 euros?)"=simu5_costcod)
  
  # bind all scenarios
  assign(paste0("Scenarios_", i), rbind.data.frame(simu1, simu2, simu3, simu4, simu5))
  assign(paste0("Scenarios_round_", i), cbind(Scenario=get(paste0("Scenarios_", i))$Scenario, round(get(paste0("Scenarios_", i))[,names(get(paste0("Scenarios_", i)))!="Scenario"], 0)))

  # save the Scenarios table
  write.csv(get(paste0("Scenarios_", i)), paste0(tables_path, "/Scenarios_", i, ".csv"), row.names=F)
  
}
## [1] "Running scenarios for all_countries"
## [1] "Subsetting for FRA"
## [1] "Running scenarios for FRA"
## [1] "Subsetting for BEL"
## [1] "Running scenarios for BEL"
## [1] "Subsetting for ESP"
## [1] "Running scenarios for ESP"
## [1] "Subsetting for IRL"
## [1] "Running scenarios for IRL"

Rounded tables

knitr::kable(Scenarios_round_all_countries, caption = "All countries: Scenarios on impacted OTB,PTB,OTT vessels (2017-2019)")
All countries: Scenarios on impacted OTB,PTB,OTT vessels (2017-2019)
Scenario Nb Vessels Nb Trips ALL tons COD tons HAD tons WHG tons Value (000 euros?) ALL tons red. ALL tons %diff Value red. Value %diff COD tons red. COD tons %diff Cost per cod ton saved (000 euros?)
Perfect knowledge (ie trips inside, >20%HAD) 208 1800 9287 550 2972 751 27080 5605 -40 16044 -41 391 -29 69
Concerned vessels only (ie at least 1 trip inside >20%HAD) 208 12584 55686 2440 6156 8488 183441 30201 -46 96682 -47 1732 -29 123
Concerned vessels only, + all crossing trips 208 12585 71798 2637 9959 9251 235462 38654 -46 121917 -48 1872 -29 148
Whole fleet, within CSPZ 301 14595 71678 2647 6475 8737 226650 36661 -49 113880 -50 1879 -29 147
Whole fleet, + all crossing trips 308 14634 103433 2876 10429 9513 316715 51753 -50 153544 -52 2042 -29 196
knitr::kable(Scenarios_round_FRA, caption = "France: Scenarios on impacted OTB,PTB,OTT vessels (2017-2019)")
France: Scenarios on impacted OTB,PTB,OTT vessels (2017-2019)
Scenario Nb Vessels Nb Trips ALL tons COD tons HAD tons WHG tons Value (000 euros?) ALL tons red. ALL tons %diff Value red. Value %diff COD tons red. COD tons %diff Cost per cod ton saved (000 euros?)
Perfect knowledge (ie trips inside, >20%HAD) 80 1038 7381 462 2370 433 22145 4392 -40 13080 -41 328 -29 68
Concerned vessels only (ie at least 1 trip inside >20%HAD) 80 3702 22593 1386 3831 984 71410 11533 -49 36684 -49 984 -29 86
Concerned vessels only, + all crossing trips 80 3703 35952 1528 6197 1693 115562 18193 -49 57157 -51 1085 -29 132
Whole fleet, within CSPZ 107 4208 26342 1489 3992 1000 85995 12924 -51 41839 -51 1057 -29 102
Whole fleet, + all crossing trips 107 4209 45886 1657 6476 1715 155560 21854 -52 71168 -54 1177 -29 176
knitr::kable(Scenarios_round_IRL, caption = "Ireland: Scenarios on impacted OTB,PTB,OTT vessels (2017-2019)")
Ireland: Scenarios on impacted OTB,PTB,OTT vessels (2017-2019)
Scenario Nb Vessels Nb Trips ALL tons COD tons HAD tons WHG tons Value (000 euros?) ALL tons red. ALL tons %diff Value red. Value %diff COD tons red. COD tons %diff Cost per cod ton saved (000 euros?)
Perfect knowledge (ie trips inside, >20%HAD) 128 762 1906 89 602 318 4934 1212 -36 2964 -40 63 -29 77
Concerned vessels only (ie at least 1 trip inside >20%HAD) 128 8882 33093 1054 2325 7504 112030 18669 -44 59998 -46 748 -29 170
Concerned vessels only, + all crossing trips 128 8882 35846 1109 3762 7559 119900 20461 -43 64760 -46 788 -29 171
Whole fleet, within CSPZ 159 9668 35615 1123 2410 7703 124688 19984 -44 66471 -47 798 -29 179
Whole fleet, + all crossing trips 159 9668 38644 1183 3864 7761 133828 21921 -43 71888 -46 840 -29 181
knitr::kable(Scenarios_round_BEL, caption = "Belgium: Scenarios on impacted OTB,PTB,OTT vessels (2017-2019)")
Belgium: Scenarios on impacted OTB,PTB,OTT vessels (2017-2019)
Scenario Nb Vessels Nb Trips ALL tons COD tons HAD tons WHG tons Value (000 euros?) ALL tons red. ALL tons %diff Value red. Value %diff COD tons red. COD tons %diff Cost per cod ton saved (000 euros?)
Perfect knowledge (ie trips inside, >20%HAD) 0 0 0 0 0 0 0 0 NaN 0 NaN 0 NaN NaN
Concerned vessels only (ie at least 1 trip inside >20%HAD) 0 0 0 0 0 0 0 0 NaN 0 NaN 0 NaN NaN
Concerned vessels only, + all crossing trips 0 0 0 0 0 0 0 0 NaN 0 NaN 0 NaN NaN
Whole fleet, within CSPZ 7 89 760 22 32 34 3212 257 -66 1153 -64 16 -29 321
Whole fleet, + all crossing trips 7 89 780 22 32 37 3317 265 -66 1192 -64 16 -29 330
knitr::kable(Scenarios_round_ESP, caption = "Spain: Scenarios on impacted OTB,PTB,OTT vessels (2017-2019)")
Spain: Scenarios on impacted OTB,PTB,OTT vessels (2017-2019)
Scenario Nb Vessels Nb Trips ALL tons COD tons HAD tons WHG tons Value (000 euros?) ALL tons red. ALL tons %diff Value red. Value %diff COD tons red. COD tons %diff Cost per cod ton saved (000 euros?)
Perfect knowledge (ie trips inside, >20%HAD) 0 0 0 0 0 0 0 0 NaN 0 NaN 0 NaN NaN
Concerned vessels only (ie at least 1 trip inside >20%HAD) 0 0 0 0 0 0 0 0 NaN 0 NaN 0 NaN NaN
Concerned vessels only, + all crossing trips 0 0 0 0 0 0 0 0 NaN 0 NaN 0 NaN NaN
Whole fleet, within CSPZ 28 630 8962 12 41 0 12756 3497 -61 4418 -65 9 -29 2363
Whole fleet, + all crossing trips 35 668 18122 14 58 0 24011 7713 -57 9295 -61 10 -29 3617