While 2050 net zero targets often do not explicitly define the level of GGRs, they implicitly rely upon BECCS and other engineered GGR options. The alternative would be closer to an absolute-zero target, where reliance on GGRs is significantly minimized. An example of this can be seen in Figure 1, where emissions are rapidly decreased as ‘business and technological innovations result in lower energy demand’, and GGR is provided by natural systems, such as afforestation. Six countries have now legislated for net zero (including the UK), a further six have proposed net zero legislation (including the EU), Chinese President Xi has pledged carbon neutrality by 2060, and President Biden has pledged to implement net zero legislation.
1.2 The scale of BECCS being relied upon
One of the seminal papers to advocate the advantages of BECCS anticipated removals of 12 GtCO₂/yr in 2100. Most of the integrated assessment models (IAMs) utilized by the IPCC to assess future emissions and carbon budgets heavily rely on NETs. In the 2018 IPCC special report on Global Warming of 1.5°C (SR1.5), 81 of the 90 scenarios relied on NETs. The pathways consistent with limiting global warming to 1.5°C required 0 to 8 GtCO₂/yr of BECCS removals by 2050. As can be seen in Figure 1, BECCS exceeds 20 GtCO₂/yr of removals from 2060 onwards in the extreme IPCC illustrative scenario, equivalent to almost two-thirds of current annual energy sector emissions. However, a more cautious assessment in a systematic review of the literature concluded that the most likely scope for BECCS, accounting for other sustainability aims, was 0.5–5 GtCO₂/year. The ‘middle-of-the-road’ IPCC pathway illustrates around 1.5 GtCO₂/yr of BECCS removals globally by 2050. In this pathway, technological development follows historical patterns. Under an International Energy Agency (IEA) ‘beyond 2°C’ scenario, BECCS deployment reaches 4.9 GtCO₂/yr by 2060, however, under the IEA’s most recent net zero analysis reliance on BECCS has decreased, ‘1.9 Gt CO₂ are removed in 2050 via BECCS and DACCS’.
1.3 BECCS in the integrated assessment models
The IAM models are critical in influencing climate policy, globally and nationally. They underpin the decarbonization pathways published by the IPCC, which national governments look to when setting their national targets and legislation.
IAMs are complex modelling frameworks that span diverse disciplines, such as energy systems, land use, climate and macroeconomic modelling. As with all models, the quality of the output is constrained by the quality of the underlying assumptions. In the case of BECCS, these assumptions include the amount of energy produced for a given input of bioenergy feedstock, the capture rate, and supply chain emissions, to name a few.
Critical to understanding why many of the IAM scenarios indicate such heavy reliance on BECCS is their cost optimizing nature. The models attempt to find the least-cost means of achieving a given temperature limit. As BECCS is anticipated to produce energy and remove atmospheric CO₂ simultaneously, and that both these societal goods have associated costs and benefits to them, it is arguable that there is an inbuilt bias in IAMs towards selecting BECCS. This is concerning as many of the cost assumptions pertaining to decarbonization options within the IAMs are out of date, such as solar PV and other renewables, which have rapidly fallen in cost over the last decade.
A recent analysis of six IAMs by Butnar et al. (2020) provides an excellent resource with which to understand the quality of the BECCS parameters within the IAMs. Many assumptions lack transparency, this is particularly true of the technological elements of BECCS, such as the transport and storage of CO₂, as can be seen in Figure 2. Of particular importance is that all six IAMs Butnar et al. (2020) assessed assume the bioenergy burnt within a BECCS facility is carbon neutral. Or in other words, that the emissions associated with producing the bioenergy is sequestered over the life-time growth of the biomass. However, as is discussed in Chapter 4, supply chain emissions are non-marginal.
Another key observation from Butnar et al. (2020) is that the efficiency of BECCS in converting feedstock embodied energy to useful energy often depends on exogenous inputs to the IAM models. Hence, these inputs are determined outside the model and are not therefore dynamically connected to other model parameters. The efficiency of BECCS is crucial. Taking the example of a BECCS-to-power facility: if the generating efficiency of the facility is low then the power produced for a given input of feedstock will be lower, meaning it is less likely to be selected by the cost optimizing IAMs (energy has an associated cost). However, as discussed in Box 1, the efficiency of BECCS in producing power is causally linked to the capture rate. The more CO₂ that is captured, the more heat (in post-combustion capture) that is required to separate the solvent from the captured CO₂. Hence, IAMs utilizing exogenous efficiencies and capture rates is concerning if these two exogenous inputs are not consistent with each other, given they are causally connected.
