Summary of the fresh farming productivity inside the GTEM-C

Summary of the fresh farming productivity inside the GTEM-CTo help you assess the new structural changes in the fresh farming exchange network, i build a collection in line with the relationships anywhere between posting and you can exporting nations since caught within covariance matrix

The current particular GTEM-C uses the newest GTAP nine.step one databases. We disaggregate the nation into the 14 independent monetary regions paired by farming exchange. Countries out-of higher financial size and you can distinctive line of organization structures is modelled independently within the GTEM-C, additionally the remaining portion of the community was aggregated with the nations in respect so you’re able to geographical distance and environment resemblance. In GTEM-C per area have a real estate agent family. The brand new 14 nations used in this research try: Brazil (BR); Asia (CN); Eastern Asia (EA); European countries (EU); Asia (IN); Latin The usa (LA); Middle eastern countries and North Africa (ME); United states (NA); Oceania (OC); Russia and you may neighbour nations (RU); South Asia (SA); South-east China (SE); Sub-Saharan Africa (SS) as well as the Usa (US) (Select Secondary Guidance Desk A2). The regional aggregation included in this study anticipate us to manage over 2 hundred simulations (the fresh combos away from GGCMs, ESMs and you can RCPs), by using the high end calculating facilities at CSIRO within a week. A greater disaggregation would-have-been as well computationally expensive. Here, i focus on the exchange regarding four biggest plants: wheat, rice, coarse cereals, and you may oilseeds you to compose throughout the sixty% of person calorie intake (Zhao ainsi que al., 2017); although not, the fresh database found in GTEM-C makes up about 57 merchandise that individuals aggregated towards 16 groups (Get a hold of Secondary Pointers 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 https://datingranking.net/tr/getiton-inceleme/ to achieve the first Shared Socio-economic Pathway that corresponds to the RCP2.6’s CO2 emissions trajectory.

Mathematical characterisation of the trading 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. 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.