The role of biomass in a low-carbon energy system:capturing the complexity of biobased options, land use and carbon balances in an energy system model demonstrated for Brazil

Biomass is considered an important climate change mitigation measure. National energy system models are used to provide insight in the role of biomass to mitigate climate change. However, in those models biomass is modelled too simplistic, especially considering bioenergy-related land use change (LUC) emissions, mainly because they are place- and time dependent. By integrating the place- and time-dependent biomass supply potential in the model, as well as the associated LUC emissions, insight is provided in; i) how much biomass can be used to meet energy demand, ii) the associated LUC emissions, and iii) the energy system costs. The role of CO2 capture and storage in the subsurface in combination with biomass conversion (BECCS) is also analyzed. Brazil is used as a case study.This research shows that biomass can supply between 5-10 EJ of the final energy demand in 2050 (35-60% of the total). Of this, between 0.5-7 EJ is supplied from new bioenergy plantations, with emission factors between 5-15 kg CO2-eq. per GJ of primary biomass. The large bandwidth is caused by:• Intensification of agriculture: with a high intensity, land becomes available for new bio-energy plantations. As a result, little natural land (with a high carbon penalty) is needed;• A CO2 penalty for natural succession on former farmland• The CO2 injection rate of BECCS When there is limited biomass supply with a low CO2 footprint, the costs for the energy system increase significantly because the industry and transport sector must be extensively electrified to achieve CO2 targets

How does the interplay between resource availability, intersectoral competition and reliability affect a low-carbon power generation mix in Brazil for 2050?

Increasing penetration of solar and wind energy can reduce the reliability of power generation systems. This can be mitigated by e.g.; low-carbon dispatchable hydropower and baseload biomass power plants. However, long-term supply potential for those sources is often uncertain, and biomass can also be used for biofuel production. The purpose of this study is to assess the interplay between uncertain supply potential of biomass and hydropower, intersectoral competition and reliability on a low carbon power system for 2050, with Brazil as case study, using a soft-link between an energy model and a power system model. Hydropower acts as a balancing agent for solar and wind energy, even under lower hydropower supply potential. When less biomass is available, low carbon transportation is met more with electric cars instead of ethanol cars, leading to an increase in electric load for charging their batteries. The charging strategy determines whether peak load increases substantially; after commuting, or lowers; in off-peak hours. This shows the importance of using a soft-link between the high temporal resolution power system model to assess the reliability, and a least cost-optimization model to assess the interplay between resource availability and intersectoral competition of low-carbon power systems

BECCS as climate mitigation option in a Brazilian low carbon energy system:Estimating potential and effect of gigatonne scale CO₂ storage

Bioenergy with carbon capture and storage (BECCS) can lead to negative emissions, and is seen as an important option to decarbonize energy systems. Its potential decarbonization contribution depends on low-carbon resource availability, its ability to meet end-use demand and the geological storage potential to safely trap CO2. Here an energy system model is used to assess the BECCS decarbonization potential in Brazil, considering uncertainty in low-carbon biomass resources, and storage potential, injection rates and costs of CO2 storage, assessed in eight scenarios. A spatial explicit analysis is done to make improved estimates on the storage potential, injection rates, and costs for CO2 storage in the Rio Bonito saline aquifer of the Paraná basin. Although there are large differences in storage potential (12–117 Gt CO2) and costs (on average 5–15 $/t CO2), the accumulated volume of CO2 stored between 2010 and 2050 is 2.9 Gt CO2 for all scenarios, with injection rates around 240 Mt CO2 in 2050. This shows that BECCS is a cost-competitive option to decarbonize the Brazilian energy system, even under pessimistic estimates of CO2 storage potential and costs, and low biomass availability. The cheapest sink locations are selected, in the high development scenario. When CCS development is low, injection rates are the limiting factor. Locations are selected with the highest injection rates, even though sometimes more expensive. When CO2 storage is limited, total system costs increase, mainly because decarbonization of the industry and freight transport sector relies on more expensive decarbonization options such as green hydrogen.</p

The impact of land-use change emissions on the potential of bioenergy as climate change mitigation option for a Brazilian low-carbon energy system

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

Exploring the spatiotemporal evolution of bioenergy with carbon capture and storage and decarbonization of oil refineries with a national energy system model of Colombia

Bioenergy combined with carbon capture and storage (BECCS) has a high mitigation potential of greenhouse gases in the energy system. However, the feasibility of its deployment depends on co–location of suitable storage basins and biomass resources with low-carbon stocks. Moreover, national transition analyses towards low–carbon energy systems have often given little attention to the mitigation potential of existing oil refineries, which are major components of current energy systems. We parametrized and incorporated these knowledge gaps into an energy system optimization model and used it analyze mitigation pathways towards carbon neutrality of the Colombian energy system by midcentury. Our results show that modern bioenergy could contribute 0.8–0.9 EJ/y (48–51 %) to the final energy consumption by 2050 at a system cost of 29–35 B$/y. BECCS value chains could deliver a mitigation potential of 37–41 % of the cumulative avoided emissions between 2030 and 2050. Low–carbon retrofitting of existing oil refineries could contribute up to 19 % of the total biofuel production and 10 % of the total CO2 capture by 2050. The Andes and Caribbean could be promising regions for BECCS because of their high potential for biomass supply and carbon sinks. In contrast, Orinoquía has a high potential for bioenergy and more uncertainty of CCS, depending on the access to nearby carbon sinks. This framework could be used to harmonize between the visions of the energy and agricultural sectors, national government and the oil sector, and national and regional governments, towards integrated planning for low-carbon development

System analysis of the bio-based economy in Colombia:A bottom-up energy system model and scenario analysis

The transition to a sustainable bio-based economy is perceived as a valid path towards low carbon development for emerging economies that have rich biomass resources. In the case of Colombia, the role of biomass has been tackled through qualitative roadmaps and regional climate policy assessments. However, neither of these approaches have systematically addressed the complexity of the bio-based economy in the wider context of emission mitigation and energy and chemicals supply. In response to this limitation, we extended a bottom-up energy system optimization model by adding a comprehensive database of novel bio-based value chains. We included advanced road and aviation biofuels, (bio)chemicals, bioenergy with carbon capture and storage (BECCS), and integrated biorefinery configurations. A scenario analysis was conducted for the period 2015–2050, which reflected uncertainties in capacity for technological learning, climate policy ambitions, and land availability for energy crops. Our results indicate that biomass can play an important, even if variable, role in supplying 315–760 PJ/y of modern bio-based products. In pursuit of a deep decarbonization trajectory, the largescale mobilization of biomass resources can reduce the cost of the energy system by up to 11 billion $/y, the marginal abatement cost by 62%, and the potential reliance on imports of oil and chemicals in the future. The mitigation potential of BECCS can reach 24–29% of the cumulative avoided emissions between 2015 and 2050. The proposed system analysis framework can provide detailed quantitative information on the role of biomass in low carbon development of emerging economies

BECCS as climate mitigation option in a Brazilian low carbon energy system: Estimating potential and effect of gigatonne scale CO2 storage

Bioenergy with carbon capture and storage (BECCS) can lead to negative emissions, and is seen as an important option to decarbonize energy systems. Its potential decarbonization contribution depends on low-carbon resource availability, its ability to meet end-use demand and the geological storage potential to safely trap CO2. Here an energy system model is used to assess the BECCS decarbonization potential in Brazil, considering uncertainty in low-carbon biomass resources, and storage potential, injection rates and costs of CO2 storage, assessed in eight scenarios. A spatial explicit analysis is done to make improved estimates on the storage potential, injection rates, and costs for CO2 storage in the Rio Bonito saline aquifer of the Paraná basin. Although there are large differences in storage potential (12–117 Gt CO2) and costs (on average 5–15 $/t CO2), the accumulated volume of CO2 stored between 2010 and 2050 is 2.9 Gt CO2 for all scenarios, with injection rates around 240 Mt CO2 in 2050. This shows that BECCS is a cost-competitive option to decarbonize the Brazilian energy system, even under pessimistic estimates of CO2 storage potential and costs, and low biomass availability. The cheapest sink locations are selected, in the high development scenario. When CCS development is low, injection rates are the limiting factor. Locations are selected with the highest injection rates, even though sometimes more expensive. When CO2 storage is limited, total system costs increase, mainly because decarbonization of the industry and freight transport sector relies on more expensive decarbonization options such as green hydrogen

The impact of land-use change emissions on the potential of bioenergy as climate change mitigation option for a Brazilian low-carbon energy system

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

The impact of land-use change emissions on the potential of bioenergy as climate change mitigation option for a Brazilian low-carbon energy system

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