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Pastureland maintenance is seen as a land-based measure to reduce dependency on feed concentrates and mitigate greenhouse gas (GHG) emissions from livestock production in the EU, while providing other ecosystems services. This paper assesses potential market-mediated impacts, including global Land Use Change (LUC) and GHG emissions, from increased subsidies to pasture-based livestock production in the EU. A tax recycling strategy (TRS) is simulated against a baseline up to 2030 under the shared socioeconomic pathway 2 (SSP2). This implies a budget-neutral increase in the level of pasture subsidies in individual Member States, as land subsidies for other cropping activities decrease. We employ the computable general equilibrium (CGE) model GTAP in its recursive-dynamic version, GTAP-RDEM, extended with the Multi-Regional Input-Output (MRIO) database FABIO to disaggregate agri-food sectors from 21 to 31. This approach allows considering price- and income-dependent feedbacks when assessing long-run changes in the global economy, improving the sectoral resolution relative to GTAP v10. The policy increases pastureland areas and cattle production in almost all EU Member States, whereas cropland and crop production decrease, causing significant changes across EU agri-food markets. Crop prices increase, leading to the reduced output of intensive animal production sectors, mainly pig and poultry. Cropland areas decrease and most EU countries increase imports of grain, oilseeds, and cakes, essentially soybean cake from Brazil and North America. While GHG emissions decrease in those EU countries where pasturelands expand mainly at the cost of croplands, GHG emissions increase in those countries where pastureland expansion comes with forest loss. As a result, net GHG emissions increase in the EU-27 in 2030 (+2.49 Mt CO 2 -eq). Emissions from LUC in major non-EU grain- and oilseed-exporting countries increase, e.g., by 102.52 Mt CO 2 -eq in Brazil and by 129.17 Mt CO 2 -eq in North America. The simulated policy shows that promoting extensive livestock per se does not meet the objectives of the Common Agricultural Policy and the EU Green Deal. The TRS should be complemented with policies to foster crop diversification and promote the use of domestic feed sources (e.g., legumes) to effectively ensure feed self-sufficiency and that extensive cattle production in the EU does not lead to deforestation in carbon-rich countries. [ABSTRACT FROM AUTHOR]

Expanding the production of fuels and fibres based on traditional food crops can put additional pressures on ecosystems and natural resources, with potential spillover effects through induced land use change (iLUC). Computable General Equilibrium (CGE) modelling provides a systematic framework for ex-ante sustainability analysis, capturing the complex interactions between land uses, agri-food markets, and international trade. This study applies an integrated CGE framework that considers loss of natural vegetation to derive quantitative indicators on the sustainability of manufacturing bioplastics from arable crops in five major producing regions (Brazil, China, the European Union, United States and Thailand). The approach consists of increasing bioplastics production at the cost of conventional plastics in each of these regions separately by means of a production subsidy, simulating bioplastic production targets. In order to assess the uncertainty in sustainability metrics, different levels of market penetration are considered, as well as variability in key model parameters. Increasing bioplastics production in Thailand is in general associated with more favourable metrics, although this is related to the relatively small size of the sector, which triggers minor market-mediated effects. When iLUC is included, increased bioplastics production in China is, on average, associated with the largest land footprint (16.93 ha t–1); whereas the highest CO 2 emission intensity is estimated for bioplastics produced in the European Union (10.41 t CO 2 -eq. t–1). Emissions from iLUC outweigh potential greenhouse gas (GHG) savings from fossil fuel substitution, except for Thailand, where increasing bioplastics production from sugarcane and cassava saves on average 2.0 kg CO 2 -eq. t–1. This translates into decades of carbon payback time and high abatement costs even for Thailand, while trade-offs arise among the metrics proposed. Other impacts besides deforestation and GHG emissions should ideally be considered to examine further interactions within the Water-Food-Energy nexus, though this may require combining global with regionalized approaches, with the associated challenges. Image, graphical abstract [ABSTRACT FROM AUTHOR]

• Spatially-explicit CO 2 emissions of Brazilian soy exports using new bottom-up LCA. • Carbon footprint of MATOPIBA states is 2–6 times larger than Brazil average. • Cerrado has the largest footprint (1.00 tCO 2 -eq.t−1soy-eq.) followed by the Amazon. • Carbon footprint of EU (0.77 tCO 2 -eq.t−1soy-eq.) is 13.8% larger than China's. • Total CO 2 emissions are 223.46 Mt in 2010–2015, of which China imports 51%. Reliable estimates of carbon and other environmental footprints of agricultural commodities require capturing a large diversity of conditions along global supply chains. Life Cycle Assessment (LCA) faces limitations when it comes to addressing spatial and temporal variability in production, transportation and manufacturing systems. We present a bottom-up approach for quantifying the greenhouse gas (GHG) emissions embedded in the production and trade of agricultural products with a high spatial resolution, by means of the integration of LCA principles with enhanced physical trade flow analysis. Our approach estimates the carbon footprint (as tonnes of carbon dioxide equivalents per tonne of product) of Brazilian soy exports over the period 2010–2015 based on ~90,000 individual traded flows of beans, oil and protein cake identified from the municipality of origin through international markets. Soy is the most traded agricultural commodity in the world and the main agricultural export crop in Brazil, where it is associated with significant environmental impacts. We detect an extremely large spatial variability in carbon emissions across sourcing areas, countries of import, and sub-stages throughout the supply chain. The largest carbon footprints are associated with municipalities across the MATOPIBA states and Pará, where soy is directly linked to natural vegetation loss. Importing soy from the aforementioned states entailed up to six times greater emissions per unit of product than the Brazilian average (0.69 t t−1). The European Union (EU) had the largest carbon footprint (0.77 t t−1) due to a larger share of emissions from embodied deforestation than for instance in China (0.67 t t−1), the largest soy importer. Total GHG emissions from Brazilian soy exports in 2010–2015 are estimated at 223.46 Mt, of which more than half were imported by China although the EU imported greater emissions from deforestation in absolute terms. Our approach contributes data for enhanced environmental stewardship across supply chains at the local, regional, national and international scales, while informing the debate on global responsibility for the impacts of agricultural production and trade. Image, graphical abstract [ABSTRACT FROM AUTHOR]