Chapter 3 Consolidation of global fish database
3.5 Fish market projection to 2050
The data consolidating procedures therefore contribute to filling the gaps in the CAPRI database. The results support that fish, FIML&FIOL markets are better integrated in the CAPRI database after the data consolidation.
Table 3-4 Comparison of reduction ratios computed based on original and consolidated database and from the literature (1000 t; Year 2005)
FAOSTAT Original feed use (1)
RR(A) (3) (1)
CAPRI Consolidated Processing use (2)
RR(B) (3) (2)
FISHSTAT Fishmeal production (3)
RR(C) RR(D)
RSA 464 4.41 9,248 0.22 2,048 0.23 0.22
Chile 911 0.95 3,939 0.22 866 0.23 0.24
Thailand 200 2.37 243 1.95 473 0.23 1.05
China 10,829 0.04 3,974 0.11 455 0.23 0.37
Denmark
(PFIS+DFIS) 140 2.29 644 0.50 320 0.23 0.37
Remarks: RR(A) = Fishmeal production (3) / Original feed use (1); RR(B) = Fishmeal production (3) / Consolidated Processing use (2); RR(C): Reduction ratios used in CAPRI; RR(D): Reduction ratios calculated based on PΓ©ron et al., (2010)
Source: Own compilation
the calibration year with all constraints. The methodology required to obtain preliminary projections for the fish sector is described in this section. Expert supports are of importance in order to generate a baseline which is most predictive of the future. The consolidated global fish database has been extended to 2027 based on the OECD-FAO outlook (FISHERIES). This outlook provides projections only for selected regions1 and aggregated fish quantity. Therefore, disaggregated information for all CAPRI regions and fish groups is not available. Thus, the growth factors are computed based on the aggregated level from the outlook and applied to the mapped CAPRI regions for all species. In the period from 2012 to 2027, the global fish databases will be updated to reflect the multiplication of growth factors and base year quantities.
The current OECD-FAO outlook ends in 2027. Trend estimation after 2027 consists of three stages for the selected regions based on the information provided by OECDStat. The first stage estimates an unconstrained trend curve based on the ex-post database covering the period from 1990 to 2027 at a global level. A simple linear regression (Equation 12) was implemented
1 Argentina (ARG), Australia (AUS), Brazil (BRA), Canada (CAN), Chile (CHL), Columbia (COL), Egypt (EGY), Ethiopia (ETH), NonEU_EU, Europe (EUE), EU28 (EUN), Indonesia (IDN), India (IND), Iran (IRN), Israel (ISR), Japan (JPN), Kazakhstan (KAZ), South Korea (KOR), Latin America (LAMA), Mexico (MEX), Malaysia (MYS), Nigeria (NGA), Pakistan (PAK), the Philippines (PHL), Rest Africa (RestAfr), Rest Asia (RestAsia), Russia (RUS), SUA (Saudi Arabia), Thailand (THA), Turkey (TUR), Ukraine (UKR), United Stated (USA), South Africa (ZAF), China (CHN)
to project the global fish market associated with the single explanatory variable of global population from 2028 to 2050 (Annex Table 8-3 and Table 8-4). The whole period starting from 1990 based on the historical data and predicted values (from 2028) is illustrated in Figure 3-9.
Equation 12
π¦π,π‘= πΌπ+ π½ππ₯π,π‘+ ππ,π‘
y = global quantities of item i; i = AQTOTL, IMPT, EXPT, Demand, EXOG, HCOM, Crush and other use; Ξ±, Ξ² = estimated parameters; x = global population; t = 1990-2027
Figure 3-9 Trend of global fish market from 1990 to 2050 (1000 t) before correction
Source: Own illustration based on Table 8-2
The second step applies a multinomial logit model (Equation 13) to estimate the market shares for selected OECD regions in 2030, 2040 and 2050.
Assigning the coefficients in region r by πΌπ,π and π½π,π, we estimated the following model (Equation 13) for the quantity of each market item i in year t. However, the projection of global processing use displayed in Figure 3-9 is not conclusive when compared to the same method used for processing use. From 2012 to 2027 processing use was essentially stagnant, varying within a narrow margin around the mean (14,107,000 t) plus or minus 10%
(12,648,000 t β 15,566,000 t) as shown in Figure 3-10 (blue line). Using a simple regression (Equation 12) to predict the trend of processing use results in a substantial quantity decline in 2050. The quantity of processing use determines the FIML&FIOL production, which are important elements in fish feed. A strong decline of processing use is therefore technically inconsistent with a strong increase in aquaculture production. The methodology requires modification. Otherwise, the final baseline calibration would need to strongly model the preliminary projections described in this section in order to impose technical consistency and in that case, may run into feasibility problems. In order to obtain a relatively reasonable projection, a hyperbolic function (Equation 13) was applied here exclusively for processing use. The projected results are displayed in Figure 3-10 (green line). The projected human consumption (HCOM) was residually computed given the new processing use (PROC) to ensure a closed global balances system in Figure 3-11.
Figure 3-10 Trend estimations of global processing use from 1990 to 2050 (1000 t)
Remark: PROC (red):, PROC_1970:
Source: Own illustration based on Table 8-2 and Table 8-14
Figure 3-11 Trend of global fish market from 1990 to 2050 (1000 t) after correction
Remark: HCOM: Human consumption, PROC: Processing use, AQTOTL: Total aquaculture production
Source: Own illustration based on Table 8-14
Equation 13
ln ( π π,π
π π,πΆβπππ) = πΌπ,π+ π½π,π( 1 π‘ β 1980)
πππ¦π,π,π‘β = πΌπ,π+ π½π,π( 1 π‘ β 1980)
π π,π: share of item i in region r (except for China), π π,πΆβπππ: share of item i in China;
π¦π,π,π‘β = π π,π/π π,πΆβπππ, r = regions; Ξ±, Ξ² = estimated parameters for each region
The particular specification for the trend component was chosen in order to obtain a stabilized trend over time. The constant term (1980) to subtract in the inner bracket was chosen subjectively because (1) it provides a suitable curvature to reflect the shifts in the OECD-FAO outlook posed with stabilization, thereafter and thus giving a conservative extrapolation to 2050.
(2) if nothing were subtracted, then there would be no stabilizing effect left since all yearly indexes are relatively high. The transformed equation can be estimated using OLS, and the estimated parameters are displayed in Table 8-13 in the Annex. The estimation of π π,πΆβπππ is computed as 1/
(β exp(πππ¦π π,π,π‘β ) + 1), and the estimated π π,πis equal to exp(πππ¦π,π,π‘β ) β π π,πΆβπππ. Subsequently the estimated quantity of each market item i in region r is the multiplication of the share of item i in region r and the global quantity of item i. Figure 3-12 to Figure 3-17 shows the trends of the market share for item i in the top ten producing, consuming or trading regions. China is
the biggest aquaculture producer in the world, accounting for about half of global production. The share of aquaculture production in China is excluded in Figure 3-12 to give a better overview of the other producing regions.
Figure 3-9 shows an increasing trend of aquaculture production (AQTOTL), meaning that both increasing and stagnant market shares in Figure 3-12 indicate increasing production over time. Although the market share of the EU decreases markedly over time, its aquaculture is still increasing slightly.
In contrast, as the capture production remains unchanged in Figure 3-9, the quantity changes in regions over time depends on the trend of the share projection. Figure 3-13 shows that the rest of Asia region had the largest capture landings (35% - 40% of global catch) and gains increasing fishing harvest in 2050, while Latin America and the rest of Europe are expected to catch less.
Figure 3-12 Trend of share in aquaculture production from 1990 to 2050 for top ten producing regions (except for China)
Source: Own illustration based on Table 8-5
Figure 3-13 Trend of share in catch production from 1990 to 2050 for top ten producing regions
Source: Own illustration based on Table 8-9
In terms of demand, Figure 3-9 shows a steady growth in seafood consumption, in contrast to the steady decline in processing use. Figure 3-14 shows a decreasing trend in shares in seafood consumption taking place in
0%
2%
4%
6%
8%
10%
1990 2000 2010 2020 2030 2040 2050
RestAsia IND IDN LAMA EGY
EUE NOR CHL EUN THA
0%
5%
10%
15%
20%
25%
30%
35%
40%
1990 2000 2010 2020 2030 2040 2050
RestAsia CHN LAMA RestAfr EUE
IDN EUN IND USA RUS
the EU and Japan. However, the computed seafood consumption quantities in these two regions are still assumed to increase through 2050 due to the growth in global demand. Latin America has obviously the largest FIML and FIOL production according to Figure 3-15, accounting for 30% of global processing use. The quantity of global processing use is expected to decrease from 10,000 tons in 2030 to about 4,000 tons in 2050. Therefore, although the trend of market share increases in China, the processing use quantity is decreasing over time.
Figure 3-14 Trend of share in human consumption from 1990 to 2050 for top ten consuming regions
Source: Own illustration based on Table 8-10 0%
2%
4%
6%
8%
10%
12%
14%
1990 2000 2010 2020 2030 2040 2050
CHN RestAsia EUN IDN IND
RestAfr USA LAMA JPN EUE
Figure 3-15 Trend of share in processing use from 1990 to 2050 for top ten consuming regions
Source: Own illustration based on Table 8-11
Figure 3-16 and Figure 3-17 displays the market shares of top ten importing and exporting countries. According to Figure 3-9, global imports are equal to global exports, and both show an increasing trend. Thailand is the biggest importer as well as biggest exporter in the world, accounting for 15% - 20%
and 10% - 15% of global imports and exports, respectively. Although both Figure 3-16 and Figure 3-17 shows decreasing trends in Thailand, the trading quantities are increasing over time. The second biggest importer is South Korea, and the second biggest exporter is the US. Historical information and estimated results of trend of shares and quantities in production, demand and trade items are shown from Table 8-5 to Table 8-12 in the Annex.
0%
10%
20%
30%
40%
50%
1990 2000 2010 2020 2030 2040 2050
LAMA CHN EUE CHL RestAsia
USA NOR EUN JPN RestAfr
Figure 3-16 Trend of share in imports from 1990 to 2050 for top ten importing regions
Source: Own illustration based on Table 8-7
Figure 3-17 Trend of share in exports from 1990 to 2050 for top ten exporting regions
Source: Own illustration based on Table 8-8
The final set of preliminary projections for the CAPRI baseline for the period from 2030, 2040 and 2050 was calculated based on the estimated global trend and regional market shares for the six market items. The projected data obtained for selected regions have been mapped to CAPRI
0%
5%
10%
15%
20%
25%
30%
1990 2000 2010 2020 2030 2040 2050
EUN USA CHN JPN RestAsia
RestAfr EUE LAMA KOR THA
0%
5%
10%
15%
20%
25%
1990 2000 2010 2020 2030 2040 2050
CHN EUE RestAsia LAMA NOR
EUN RUS RestAfr THA USA
regions for all fish species. The procedure for the growth factor computation that is applied through 2030 to the CAPRI base year is applied for subsequent projection years. As the final piece focuses only on fish market projections in 2030, 2040 and 2050, the gap between 2027 and 2030 is simply neglected. The quantities used for 2030 are from data from 2027 and the growth factors used were applied for 2040 and 2050. The final database covering the period from 1990 to 2027, 2030, 2040 and 2050 is complete for the baseline calibration phase in the CAPRI modelling system.