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This study assesses the environmental potential of crop residues and the techno-economic potential of biojet fuel (BJF) production in Brazil. Different production routes are evaluated from two types of biomass residues (sugarcane straw and eucalyptus harvest residue), and four different technological pathways (alcohol to jet, Fischer–Tropsch, hydrothermal liquefaction and pyrolysis). The environmental potential of biomass residues is determined utilizing spatio-temporal projections of land-use change in Brazil and by explicitly modeling the erosion risk and the soil organic carbon (SOC) balance spatially. The assessment of the techno-economic potential of BJF production from the environmental potential of sugarcane straw (SCS) and eucalyptus harvest residues (EHRs) considers the BJF total costs, which result from a summation of biomass residue recovery costs, BJF conversion costs, and BJF transportation costs. These BJF total costs are compared with the range of fossil jet fuel prices at Brazilian airports to quantify the techno-economic potential. The environmental potential of biomass residues varies from 70 Mt in 2015 to 102 Mt in 2030, with SCS being highly constrained by SOC, whereas EHRs are more constrained by the high erosion risk. These quantities can generate a techno-economic BJF potential ranging from 0.45 EJ in 2015 (46 US$/GJ – 65 US$/GJ) to 0.67 EJ in 2030 (19 US$/GJ – 65 US$/GJ). In 2030, several BJF production routes can be competitive with fossil jet fuel prices. The northeast and southeast regions have the highest potential, especially in 2030.

Land-use change (LUC)-related greenhouse gas (GHG) emissions determine largely whether bioenergy is a suitable option for climate change mitigation. This study assesses how LUC emissions influence demand for bioenergy to mitigate GHG emissions, and how this affects the energy mix, using Brazil as a case study. A methodological framework is applied linking bioenergy supply curves, with associated costs and spatially explicit LUC emissions, to a bottom-up energy system model. Furthermore, the influence of four key determining parameters is assessed: agricultural productivity, time horizon, natural succession (NS), and the use of dynamic emission factors (EFs). Demand for new bioenergy plantations range from 0.5 to 6.7 EJ in 2050, and is avoided when its EF reaches above 15 kg CO2/GJbiomass. Dynamic EFs result in earlier and larger use of bioenergy. Static EFs attenuate all emissions evenly over time, resulting in relative high emissions around 2050 when the carbon budget is most stringent. This in contrast to dynamic EFs, having early high peaks because of clearance of natural vegetation, but relatively small long-term emissions when the carbon budget is most stringent. Exclusion of NS, in combination with spared agricultural land, results in a demand of 6.7 EJ, because of its low carbon penalty. Assuming that land is spared due to continuous yield increase (which is the reason to include NS as and EF component), bypasses the fact that yield improvements (that make those lands available) take place because of demand for bioenergy. When low-carbon biomass is in limited availability, increasing electrification is observed, leading to electric capacity increase of 62% (mainly wind and solar energy), and a 12% energy system costs increase. Inclusion of spatiotemporal explicit supply potential and LUC emissions leads to improved bioenergy deployment pathways that come closer to the real situation as the dynamic nature of LUC emissions is included.

Sustainable intensification (SI) of agriculture is a promising strategy for boosting the capacity of the agricultural sector to meet the growing demands for food and non-food products and services in a sustainable manner. Assessing and quantifying the options for SI remains a challenge due to its multiple dimensions and potential associated trade-offs. We contribute to overcoming this challenge by proposing an approach for the ex-ante evaluation of SI options and trade-offs to facilitate decision making in relation to SI. This approach is based on the utilization of a newly developed SI metrics framework (SIMeF) combined with agricultural systems modelling. We present SIMeF and its operationalization approach with modelling and evaluate the approach’s feasibility by assessing to what extent the SIMeF metrics can be quantified by representative agricultural systems models. SIMeF is based on the integration of academic and policy indicator frameworks, expert opinions, as well as the Sustainable Development Goals. Structured along seven SI domains and consisting of 37 themes, 142 sub-themes and 1128 metrics, it offers a holistic, generic, and policy-relevant dashboard for selecting the SI metrics to be quantified for the assessment of SI options in diverse contexts. The use of SIMeF with agricultural systems modelling allows the ex-ante assessment of SI options with respect to their productivity, resource use efficiency, environmental sustainability and, to a large extent, economic sustainability. However, we identify limitations to the use of modelling to represent several SI aspects related to social sustainability, certain ecological functions, the multi-functionality of agriculture, the management of losses and waste, and security and resilience. We suggest advancements in agricultural systems models and greater interdisciplinary and transdisciplinary integration to improve the ability to quantify SI metrics and to assess trade-offs across the various dimensions of SI.

The scientific literature contains contrasting findings about the climate effects of forest bioenergy, partly due to the wide diversity of bioenergy systems and associated contexts, but also due to differences in assessment methods. The climate effects of bioenergy must be accurately assessed to inform policy‐making, but the complexity of bioenergy systems and associated land, industry and energy systems raises challenges for assessment. We examine misconceptions about climate effects of forest bioenergy and discuss important considerations in assessing these effects and devising measures to incentivize sustainable bioenergy as a component of climate policy. The temporal and spatial system boundary and the reference (counterfactual) scenarios are key methodology choices that strongly influence results. Focussing on carbon balances of individual forest stands and comparing emissions at the point of combustion neglect system‐level interactions that influence the climate effects of forest bioenergy. We highlight the need for a systems approach, in assessing options and developing policy for forest bioenergy that: (1) considers the whole life cycle of bioenergy systems, including effects of the associated forest management and harvesting on landscape carbon balances; (2) identifies how forest bioenergy can best be deployed to support energy system transformation required to achieve climate goals; and (3) incentivizes those forest bioenergy systems that augment the mitigation value of the forest sector as a whole. Emphasis on short‐term emissions reduction targets can lead to decisions that make medium‐ to long‐term climate goals more difficult to achieve. The most important climate change mitigation measure is the transformation of energy, industry and transport systems so that fossil carbon remains underground. Narrow perspectives obscure the significant role that bioenergy can play by displacing fossil fuels now, and supporting energy system transition. Greater transparency and consistency is needed in greenhouse gas reporting and accounting related to bioenergy.

In the past decades, sugarcane production in Brazil has expanded rapidly to meet increasing ethanol demand. The large majority of this expansion occurred in Sao Paulo state. We used an integrated approach considering location-specific biophysical characteristics to determine the environmental impacts of sugarcane expansion and their spatial variation in Sao Paulo state (2004&ndash, 2015). The included environmental impacts are greenhouse gas (GHG) emissions, biodiversity, soil erosion, and water quantity. All impacts were integrated into a single environmental performance index to determine trade-offs between impacts. Our results show a strong spatial variation in environmental impacts and trade-offs between them. The magnitude and direction of these impacts are mostly driven by the type of land use change and by the heterogeneity of the biophysical conditions. Areas where expansion of sugar cane has resulted in mostly negative environmental impacts are located in the center and east of the state (related to the change of shrublands, eucalyptus, and forest), while areas where sugar cane expansion has resulted in positive impacts are located in the center-west and north (related to the change of annual crops). Identifying areas with mainly positive and negative impacts enables the development of strategies to mitigate negative effects and enhance positive ones for future sugarcane expansion.

It is expected that Brazil could play an important role in biojet fuel (BJF) production in the future due to the long experience in biofuel production and the good agro-ecological conditions. However, it is difficult to quantify the techno-economic potential of BJF because of the high spatiotemporal variability of available land, biomass yield, and infrastructure as well as the technological developments in BJF production pathways. The objective of this research is to assess the recent and future techno-economic potential of BJF production in Brazil and to identify location-specific optimal combinations of biomass crops and technological conversion pathways. In total, 13 production routes (supply chains) are assessed through the combination of various biomass crops and BJF technologies. We consider temporal land use data to identify potential land availability for biomass production. With the spatial distribution of the land availability and potential yield of biomass crops, biomass production potential and costs are calculated. The BJF production cost is calculated by taking into account the development in the technological pathways and in plant scales. We estimate the techno-economic potential by determining the minimum BJF total costs and comparing this with the range of fossil jet fuel prices. The techno-economic potential of BJF production ranges from 0 to 6.4 EJ in 2015 and between 1.2 and 7.8 EJ in 2030, depending on the reference fossil jet fuel price, which varies from 19 to 65 US$/GJ across the airports. The techno-economic potential consists of a diverse set of production routes. The Northeast and Southeast region of Brazil present the highest potentials with several viable production routes, whereas the remaining regions only have a few promising production routes. The maximum techno-economic potential of BJF in Brazil could meet almost half of the projected global jet fuel demand toward 2030.