Review of the fresh new agricultural efficiency in the GTEM-C

Review of the fresh new agricultural efficiency in the GTEM-C
So you’re able to measure the brand new structural changes in the farming change community, we build a directory according to research by the relationship anywhere between posting and you can exporting countries since grabbed within their covariance matrix

The present day style of GTEM-C spends this new GTAP 9.step 1 database. We disaggregate the country into the 14 independent financial nations paired because of the agricultural trade. Countries from highest monetary size and you may distinctive line of institutional structures are modelled independently inside GTEM-C, additionally the other countries in the industry is aggregated towards nations in respect in order to geographic distance and environment similarity. In the GTEM-C for each and every part keeps an agent household. This new fourteen places used in this study was: Brazil (BR); Asia (CN); East China (EA); Europe (EU); India (IN); Latin The usa (LA); Middle east and Northern Africa (ME); United states (NA); Oceania (OC); Russia and you may neighbour nations (RU); Southern area Asia (SA); South east China (SE); Sub-Saharan Africa (SS) while the Us (US) (Come across Supplementary Pointers Table A2). The neighborhood aggregation used in this research greet us to work with more than two hundred simulations (the latest combos from GGCMs, ESMs and RCPs), utilizing the powerful computing facilities from the CSIRO within good day. A heightened disaggregation would-have-been also computationally pricey. Right here, i concentrate on the trade out of five biggest plants: grain, rice, coarse cereals, and you may oilseeds one to compensate from the sixty% of your own human calorie intake (Zhao ainsi que al., 2017); however, the fresh database found in GTEM-C is the reason 57 products we aggregated on 16 groups (Come across Supplementary Pointers 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.

Mathematical characterisation of 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.

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