The current sort of GTEM-C spends the GTAP 9.step one database. We disaggregate the world towards 14 independent monetary places paired by agricultural trade. Regions from high monetary proportions and you may distinct institutional structures is actually modelled alone inside the GTEM-C, in addition to other countries in the community is actually aggregated into nations according to help you geographical proximity and you will climate similarity. In GTEM-C per area possess an agent house. The fresh new fourteen regions found in this study is actually: Brazil (BR); China (CN); East Asia (EA); Europe (EU); India (IN); Latin The usa (LA); Middle eastern countries and North Africa (ME); United states (NA); Oceania (OC); Russia and you will neighbor places (RU); South Asia (SA); South-east Asia (SE); Sub-Saharan Africa (SS) while the Us (US) (Select Additional Pointers Desk A2). The local aggregation found in this study greeting us to focus on over two hundred simulations (the new combos out-of GGCMs, ESMs and you will RCPs), making use of the high performing measuring place at the CSIRO within a week. An increased disaggregation could have been too computationally costly. Here, i concentrate on the exchange from five significant plants: wheat, rice, rough grain, and oilseeds one make-up regarding the 60% of the person calorie consumption (Zhao ainsi que al., 2017); yet not, the database found in GTEM-C makes up 57 products that people aggregated into the sixteen groups (Look for Supplementary 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 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 exchange circle
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. beetalk indir 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.