The current sorts of GTEM-C uses this new GTAP nine.step one databases. We disaggregate the country on the 14 autonomous financial nations coupled of the agricultural trade. Places of higher economic proportions and type of institutional structures is actually modelled independently in the GTEM-C, additionally the remaining community is https://datingranking.net/nl/get-it-on-overzicht/ actually aggregated towards places in respect so you can geographic proximity and you may weather similarity. In GTEM-C for each region have a realtor home. This new fourteen places used in this study is actually: Brazil (BR); China (CN); Eastern Asia (EA); Europe (EU); Asia (IN); Latin America (LA); Middle east and you will Northern Africa (ME); The united states (NA); Oceania (OC); Russia and neighbor regions (RU); Southern area Asia (SA); South-east China (SE); Sub-Saharan Africa (SS) and also the Usa (US) (Select Supplementary Recommendations Table A2). A nearby aggregation used in this research welcome us to focus on over two hundred simulations (the brand new combinations out of GGCMs, ESMs and you can RCPs), making use of the high performance computing place from the CSIRO in about good month. An increased disaggregation would have been also computationally expensive. Here, we concentrate on the trade off four big harvest: grain, grain, coarse grains, and you can oilseeds one comprise about 60% of one’s people calorie consumption (Zhao ainsi que al., 2017); yet not, the new databases used in GTEM-C makes up about 57 commodities that individuals aggregated on 16 circles (Get a hold of Additional 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 the trade 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.