Overview of the new agricultural production for the GTEM-C

So you’re able to assess the structural alterations in new agricultural trading network, we developed a collection according to the relationships ranging from posting and exporting places because the captured inside their covariance matrix

The current style of GTEM-C spends the latest GTAP 9.step one databases. I disaggregate the nation into the fourteen autonomous financial chatavenue mobiel nations coupled by farming exchange. Nations away from highest economic size and type of institutional formations is actually modelled on their own inside the GTEM-C, and remaining portion of the community try aggregated towards the countries according in order to geographic proximity and you will climate similarity. During the GTEM-C each region has actually a representative house. Brand new 14 countries used in this research are: Brazil (BR); China (CN); East China (EA); Europe (EU); India (IN); Latin America (LA); Middle east and you can North Africa (ME); North america (NA); Oceania (OC); Russia and you will neighbor places (RU); South Asia (SA); South east Asia (SE); Sub-Saharan Africa (SS) together with Us (US) (Select Supplementary Information Dining table A2). A nearby aggregation used in this study welcome us to focus on more two hundred simulations (the fresh combos out-of GGCMs, ESMs and you may RCPs), using the high performance calculating establishment during the CSIRO in approximately an excellent week. A heightened disaggregation could have been also computationally expensive. Here, we concentrate on the change of five significant crops: grain, rice, rough grains, and you can oilseeds that form regarding sixty% of human calories (Zhao ainsi que al., 2017); however, the databases utilized in GTEM-C makes up 57 commodities that we aggregated for the 16 groups (See Second Recommendations 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 trade 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.