Page 271 - Climate Change and Food Systems

chapter 8: the potential impact of climate change-induced sea level riseon the global rice market and food security in asia
table 2
Sectors in the model
No.
Sectors
No.
Sectors
1 Paddy rice
2 Wheat
3 Other cereal grains
4 Vegetables and fruits
5 Oilseeds
6 Sugar cane and beets
7 Processed rice and flour
8 Animal products
9 Forestry
10
11
12
13
14
15
16
17
Processed meat products
Vegetable oil and fat
Sugar
Other processed food
Beverage and tobacco
Coal, oil and gas
Other manufacture
Other services
To better simulate the SLR impact by
2020, we first update the 2004 GTAP version
7 database to present the 2020 benchmark equilibrium, with gross domestic product (GDP) and population growth forecasts under the IPCC scenario A2 of SRES. The SRES A2 assumes the highest projected population growth and thus is associated with the highest food demand. As illustrated in Figure 1, line E1E2 depicts the baseline growth trajectory of some variable – e.g. supply of a crop – from 2004 to 2020 under the SRES scenario A2.
To construct this baseline, we add region- specific GDP and population growth forecasts
by the International Institute for Applied Systems Analysis (IIASA, 2007a, 2007b) to the GTAP land- use model and gradually update the database
to 2020 – i.e. point E2. The updated database then serves as the benchmark equilibrium for
the second step; that is, to bring the supply-side shocks based on estimates by Dasgupta et al. (2009) of agricultural extent loss to a one-metre SLR for all regions.8
Note that the trade pattern changes driven by population and income growth can be reflected in the GTAP benchmark equilibrium, the base upon which we assess the cross-border impact of SLR- induced land endowment loss in the rice paddies. In reality, trade patterns may also change due to SLR itself and be much larger than the GTAP Armington
with data regarding agricultural extent (Globcover 2.1 dataset6) of 84 coastal developing countries to map out the agricultural extent exposed to
the threat of a one-metre SLR. We aggregate estimates by Dasgupta et al. into 24 developing countries/regions using land area shares as measures consistent with the region aggregation scheme of our GTAP land-use model.
Because the work of Dasgupta et al. (2009) does not assume any particular timing of the global mean SLR in projecting the extent of agricultural loss, we follow the IPCC’s AR4 projections (Bindoff et al., 2007) and assume that adverse impact of a one- metre global mean SLR might occur as early as 2020.7 Figure 1 illustrates our simulation design.
6
7
The Globcover 2.1 dataset was produced by the European Space Agency with a resolution of 300m*300m, available at: http://www.esa.int/due/ionia/globcover. According to Dasgupta et al. (2009), there were three types of agricultural land in this dataset, but only the rainfed/irrigated/ post-flooding cropland area was used in the mapping, which may lead to an underestimation of the impact on agricultural extent.
The IPCC AR5 projects that the likely range of global
SLR by the year 2100 is 28 to 98 cm and the risk of exceeding 98 cm is considered to be 17% (IPCC, 8 2013). The AR5 also warns that, should sectors of
the marine-based Antarctic ice sheets collapse, the sea level could rise by up to 1.2 to 1.5 metres during the twenty-first century. Therefore, our assumed impact of a one-metre SLR can be considered a severe case, but not as an upper limit.
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