Report about the newest farming production in the GTEM-C

Report about the newest farming production in the GTEM-C
So you’re able to assess the structural alterations in this new farming exchange community, we install an inventory in accordance with the dating ranging from posting and you can exporting regions as seized within their covariance matrix

The current types of GTEM-C uses the fresh GTAP 9.step 1 database. I disaggregate the world with the 14 independent economic countries coupled from the farming exchange. Countries of large monetary proportions and distinctive line of organization structures is modelled independently in the GTEM-C, additionally the remainder of the community try aggregated for the countries according so you’re able to geographical proximity and you may weather similarity. When you look at the GTEM-C each region provides a real estate agent home. The brand new fourteen regions utilized in this study was: Brazil (BR); Asia (CN); Eastern China (EA); European countries (EU); Asia (IN); Latin The united states (LA); Middle east and you can North Africa (ME); America (NA); Oceania (OC); Russia and you will neighbour regions (RU); South China (SA); South-east Asia (SE); Sub-Saharan Africa (SS) while the United states (US) (Select Second Advice Table A2). A nearby aggregation found in this research welcome us to manage more than 2 hundred simulations (this new combinations of GGCMs, ESMs and you will RCPs), utilizing the high performing calculating facilities within CSIRO in about good few days. An increased disaggregation could have been as well computationally expensive. Here, i concentrate on the trading away from five major plants: grain, grain, coarse grains, and oilseeds one to comprise from the 60% of peoples calories (Zhao mais aussi al., 2017); not, the fresh new databases utilized in GTEM-C is the reason 57 commodities that individuals aggregated to the sixteen sectors (Pick Second Suggestions 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.

Statistical characterisation of one’s trade community

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.

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