The current variety of GTEM-C spends brand new GTAP 9.step 1 database. We disaggregate the nation on the fourteen independent financial countries coupled from the agricultural exchange. Nations away from high economic proportions and type of organization structures is actually modelled alone in the GTEM-C, together with remaining business was aggregated on the nations in respect so you can geographic proximity and you may climate resemblance. In GTEM-C for every part features an agent household. This new fourteen regions included in this research is actually: Brazil (BR); China (CN); Eastern China (EA); European countries (EU); Asia (IN); Latin The united states (LA); Middle east and Northern Africa (ME); The united states (NA); Oceania (OC); Russia and you will neighbour nations (RU); South China (SA); South east Asia (SE); Sub-Saharan Africa (SS) in addition to Us (US) (Get a hold of Additional Information Table A2). The regional aggregation included in this study greeting me to work on over two hundred simulations (the fresh new combinations off GGCMs, ESMs and RCPs), utilizing the high performing calculating place within CSIRO within an excellent times. A heightened disaggregation would-have-been too computationally costly. Here, i concentrate on the trading out-of five major plants: grain, rice, rough grain, and you can oilseeds you to definitely compose on sixty% of your peoples calories (Zhao mais aussi al., 2017); but not, the fresh new database utilized in GTEM-C accounts for 57 merchandise we aggregated into sixteen sectors (Look for Second Guidance Desk A3).
The RCP8.5 emission scenario was used to calibrate GTEM-C’s business as usual case, as current CO2 emissions are tracking above RCP8.5 levels. A carbon price was endogenously calculated to force the model to match the lower RCP4.5 emissions trajectory. This ensured internal consistency between emissions scenarios and energy production (Cai and Arora, 2015). Climate change affects agricultural productivity, which leads to variations in agricultural outputs. Given the global demand for agricultural commodities, the market adjusts to balance the supply and demand for these commodities. This is achieved within GTEM-C by internal variations in prices of agricultural products, which determine the position and competitiveness of each region’s agricultural sector within the global market, thus shaping the patterns of global agricultural trade.
We use the AgMIP (Rosenzweig et al., 2014; Elliott et al., 2015) dataset to modify agricultural productivities in GTEM-C. The AgMIP database comprises simulations of projected agricultural production based on a combination of GGCM, ESMs and emission scenarios. Here we perturb GTEM-C agricultural production of coarse grains, oilseeds, rice and wheat (the full list of sector modelled in GTEM-C can be seen in Supplementary Information Table A3). The crop yield projections for these four commodities were obtained from seven AgMIP GGCMs accessed in ( EPIC, GEPIC, pDSSAT, LPJml, LPJ-GUESS, IMAGE-LEITAP and PEGASUS. The crop yield projections of the selected commodities are based on five ESMs: HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, GFDL-ESM2M and NorESM1-M (see Table 1 in Villoria et al., 2016). Our scenarios are based on two RCP trajectories, 4.5 and 8.5 and the very optimistic carbon mitigation scenario, RCP2.6 (van Vuuren et al., 2011) was not included in our study for two reasons: first, the AgMIP database contains a https://datingranking.net/nl/matchbox-overzicht/ limited number of simulations for the four analysed commodities for RCP2.6 compare to RCPs 4.5 and 8.5. Second, it would be necessary to include into GTEM-C a negative carbon emissions technology in order to achieve the first Shared Socio-economic Pathway that corresponds to the RCP2.6’s CO2 emissions trajectory.
Mathematical characterisation of the exchange system
We represent the spectrum of the eigenvalues of this covariance matrix as the elements, sij of a diagonal 14 ? 14 matrix, where we have modelled 14 importing and exporting regions in our simulations. It is natural to interpret a rapidly converging spectrum as indicative of a trade network dominated by just a few importers and exporters while a flat spectrum of eigenvalues implies a network with many more equal actors. We capture this difference by the Shannon entropy of the eigenvalue spectrum and define the structural trade index as S. A smaller value of S represents a centralised network structure, where export/import flows are dominated by just few regions; larger values of S indicate a more distributed trading structure, where export/import flows are more uniformly distributed between all regions.