| Summary: | This dataset contains food demand and food demand projections by 19 different types in physical tonne for 70 world regions. The scenarios were produced using the intergrated assessment model E3ME. The data for the E3ME food equations is taken from FAO, which captures detailed food supply data and for the purpose it is being used for, at the country level. The data produces forecasts up to 2060 (from 2010) under 2 different scenarios: (i) baseline, based on FAO projections, and (ii) food_tax, a scenario with additional taxes on soy demand in China and Brazil, increasing linearly from 2% in 2025 to 10% by 2030 and held constant thereafter. This proposal aims to develop a framework of analysis and policy engagement to improve the resilience of the Brazilian Food-Water-Energy (FWE) nexus to global environmental and economic change. It will combine established UK expertise and specifically developed, state-of-the-art analytical capacity in socio-economic and environmental modelling to build a robust environmental policy assessment methodology for the Brazilian FWE nexus in the context of global change. The modelling capacity, skills and knowledge will be transferred to relevant actors in Brazil to enable local academics to continue informing and engaging policymakers through a continued sustainability transition during and beyond the end of this project. Brazilian society faces significant uncertainty due to two significant global contextual factors. On one hand, global environmental change, due to global unsustainable resource use and greenhouse gas emissions, is highly likely to change weather patterns, which will affect detrimentally the land cover and biodiversity in Brazil, with severe impacts on agriculture. On the other hand, without appropriate policies in place, the Brazilian economy and environment, relying heavily on exports of natural resources for prosperity, can be vulnerable to global economic change, where changes in demand for commodities could lead to either environmental degradation and large scale ...
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