Breakdown of the newest agricultural yields inside the GTEM-C

To help you quantify the fresh new structural alterations in new agricultural trading network, we establish a directory based on the matchmaking ranging from uploading and exporting nations as caught within covariance matrix

The modern type of GTEM-C uses the latest GTAP 9.step one databases. I disaggregate the world toward fourteen autonomous financial regions paired of the farming trade. Regions away from large financial proportions and you may distinctive line of organization formations are modelled on their own inside GTEM-C, and rest of the globe is aggregated into the nations according to help you geographical proximity and you will environment resemblance. Within the GTEM-C per region has a realtor household. The fresh new 14 countries found in this research is actually: Brazil (BR); Asia (CN); East China (EA); European countries (EU); India (IN); Latin The usa (LA); Middle east and you may Northern Africa (ME); The united states (NA); Oceania (OC); Russia and you will neighbor nations (RU); Southern Asia (SA); South-east China (SE); Sub-Saharan Africa (SS) in addition to Usa (US) (Select Additional Pointers Desk A2). A nearby aggregation found in this research greeting us to work on over two hundred simulations (the fresh combos regarding GGCMs, ESMs and you may RCPs), utilising the high performing computing business from the CSIRO within a beneficial times. A greater disaggregation would have been too computationally high priced. Here, we focus on the exchange of five significant plants: grain, grain, coarse grains, and you can oilseeds one make-up on 60% of one’s human caloric intake (Zhao mais aussi al., 2017); not, the latest database utilized in GTEM-C accounts for 57 commodities that https://datingranking.net/nl/connexion-overzicht/ people aggregated to the 16 sectors (Look for Supplementary Guidance Dining table 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.

Analytical characterisation of your own trading community

We represent the spectrum of the eigenvalues of this covariance matrix as the elements, s_ij 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.