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")])
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)
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)
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)
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)
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)
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 |