Unlocking the potential of BECCS with indigenous sources of biomass at a national scale

Abstract

Bioenergy with carbon capture and storage (BECCS) could play a large role in meeting the 1.5 °C targets, but faces well-documented controversy in terms of land-use concerns, competition with food production, and cost. This study presents a bottom-up assessment of the scale at which BECCS plants – biomass pulverised combustion plants (“BECCS” in this study) and bioenergy combustion in combined heat and power plants (BE-CHP-CCS) – can be sustainably deployed to meet national carbon dioxide removal (CDR) targets, considering the use of both primary and secondary (waste-derived) biomass. This paper also presents a comprehensive, harmonised data set, which enables others to build upon this work. Land availability for biomass cultivation, processing, and conversion is quantified based on a land-use analysis, avoiding all competition with land used for food production, human habitation, and other protected areas. We find that secondary biomass sources provide a valuable supplement to primary biomass, augmenting indigenous biomass supplies. In initial phases of deployment, we observe that infrastructure is initially clustered near cities, and other sources of low cost, secondary biomass, but as CDR targets are increased and indigenous secondary biomass supplies are exhausted, infrastructure begins to move closer to potential biomass planting areas with higher yield. In minimising the cost of CDR on a cost per net tonne CO₂ removed basis, we find that the availability of secondary biomass, land availability, and yield are key factors that drive the cost of CDR. Importantly biomass conversion efficiency of a BECCS plant has an inverse effect on CDR costs, with less efficient plants resulting in lower costs compared to their more efficient counterparts. By consuming secondary biomass in BECCS and BE-CHP-CCS plants, the UK is able to be self-sufficient in biomass supply by utilising available indigenous biomass to remove up to 50 Mt_CO2 per year, though for cost reasons, it may be preferable to import some biomass.

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1 Introduction

1.1 BECCS in carbon reduction

With relatively few exceptions,^1–5 carbon dioxide removal (CDR) is required in all scenarios consistent with a 1.5 °C target.⁶ Whilst there exists a panoply of potential CDR technologies, including afforestation and reforestation, soil carbon sequestration, biochar, enhanced weathering, direct air capture of CO₂ with storage (DACCS) and ocean fertilisation,^7–11 bioenergy with carbon capture and storage (BECCS) tends to occupy a prominent role in many scenarios presented by the integrated assessment modelling (IAM) community.^12,13 BECCS however faces myriad technical, economic, and social challenges, which have been discussed extensively in the literature.^14–17 There are technical concerns around power generation efficiency, as the combination of biomass combustion and CO₂ capture and storage is inefficient relative to incumbent unabated fossil fuel-based thermal power plants. Technical complexities associated with the large-scale processing and conversion of biomass is also another challenge. However, it is the broader set of questions surrounding the sustainability of BECCS deployed at the scale envisioned that tend to cause the most concern.^4,18–21

Whilst there have been a range of options, such as combustion in a circulating fluidised bed (CFB) plant and waste heat recovery,^22–25 proposed to address the technical issues surrounding the reduced power generation efficiency of BECCS, there has been relatively little work done focusing on identifying approaches to address the sustainability concerns of BECCS. This is distinct from the relatively large body of work aimed at elucidating and quantifying those sustainability concerns. As has been recently suggested for the production of second generation biofuels,²⁶ secondary sources of biomass, such as municipal solid wastes (MSW) and agricultural residues provide a means to address some of these concerns.²⁷

1.2 Biomass availability and supply

There exists a relatively wide range of biomass sources which can, potentially, provide a sustainable biomass supply.²⁸ These include urban and industrial wastes, agricultural and forest residues (broadly categorised as secondary sources of biomass), and ligno-cellulosic and herbaceous bioenergy crops (classified as primary sources of biomass).^29,30 Regarded as a promising energy source through Energy-from-Waste (EfW) or Waste-to-Energy (WtE), 1.3 billion tonnes of solid waste is currently generated globally from the urban population (approximately 3 billion), potentially increasing to 4 billion tonnes by 2100.^31–33 Owing to their potential to compete with other land uses, particularly food production and their likely significant requirement for fertilisation and irrigation, primary biomass raises sustainability concerns.^17,34

Moreover, it is far from certain that exploitation of indigenous biomass supplies represents either the most economically viable, or sustainable option. As the global bio-economy grows, an international trade in biomass could play a pivotal role in delivering sustainable and affordable carbon dioxide removal services.³⁵ Importantly, this activity would build upon an already established international trade in biomass.^36,37 For example, in 2014, the UK demand for fuel wood was 4.9 Mt, with only 354 kt being met by indigenous supplies.^38,39 Furthermore, the Drax power plant (Selby, UK), the world's largest consumer of biomass for power generation, imports approximately 80% of its supply from North America.^40–42

1.3 BECCS supply chain modelling

Optimal design of the biomass supply chain network can enhance the economic, environmental and social performance of the supply chain. It has received increasing attention since 1997 and supply chain optimisation models have been developed for various decision levels and research perspectives.^43–51 Deploying BECCS at scale requires the integration of three elements: (i) a biomass supply chain (production, processing and transport), (ii) energy generation, and (iii) CCS infrastructure (capture, transport and CO₂ storage).⁵²

Assessments of the global and regional potential of BECCS power generation can help determine cost-effective pathways to meet ambitious climate objectives up to 2050⁵³ and 2100.⁵⁴ These studies demonstrate that BECCS deployment will be highly distributed in developing countries (e.g., Africa, Central/South America, Former Soviet Union, Middle East, Mexico, developing Asian countries), whereas fast developing countries (China and India) will focus on CCS deployment in fossil fuel-fired power plants.⁵³ Industrialised countries (e.g., EU, Australia, US, Japan) will likely develop CCS mainly on biomass-fired power plants.⁵⁴ The availability of BECCS reduced the cost of meeting ambitious targets for climate change mitigation,⁵⁴ emphasising the important role of BECCS in future decarbonisation pathways.

Meanwhile, spatially-explicit BECCS models implemented on various national scale cases imply that BECCS can enable very stringent GHG targets to be satisfied.^55–57 The biophysical global forestry (G4m) model and BeWhere model were used to estimate biomass availability, optimise the supply chain of biomass, as well as scale and locate greenfield combined heat and power (CHP) plants in South Korea.⁵⁸ Optimal locations and scales of power plants are determined under ‘replacing existing sites’ conditions in the quantitative models presented by Akgul et al.⁵⁹ and Shiraki et al.⁶⁰ However, these top-down studies have not considered the dynamic strategic design of new dedicated BECCS facilities, which consumes both waste and virgin biomass, while avoiding the use of agriculture land, based on future land management assessment as suggested by Powell and Lenton.⁶¹

To the best of our knowledge, this is the first study that presents a bottom-up assessment of a spatial–temporal BECCS design, capable of meeting nation-scale negative emission targets, integrating biogeophysical constraints and a wide range of biomass feedstocks, with the economically optimal design. This study sets out to investigate the cost-optimal evolution of such a supply chain as it grows to meet increasing carbon dioxide removal targets, and, in so doing, quantifies and qualifies the materiality of waste (e.g., MSW, waste wood), and secondary biomass (e.g., forest/crop residues) in meeting these targets. This paper provides additional value by assembling and presenting a comprehensive, harmonised data set to support this kind of analysis by others. The paper is structured as follows: Section 2 provides an overview of the proposed model, the mathematical structure and bottom-up analysis approach. Section 3 implements the model, using the UK as a case study to gain insights. Finally, some conclusions are drawn in Section 4.

2 Bottom-up analysis of BECCS potential

2.1 Model overview

A spatially and temporally-explicit, multi-period optimisation model is proposed to optimise the whole Biomass Utilisation Supply Chain (BUSCh model). Raw biomass material (from farms or waste collection sites) is transported to the pellet production plants to be converted into pellets. These pellets are then transported to the biomass combustion plants to generate electricity/heat, where the generated CO₂ is captured using a post-combustion capture technology, and permanently stored in geological formations. Two types of BECCS plants are considered in this study: biomass pulverised combustion plants (referred to as “BECCS” plants in this study) and bioenergy combustion in combined heat and power plants (BE-CHP-CCS). The model also allows pellets to be imported from abroad when the biomass demand cannot be met by indigenous biomass. Integration of the CO₂ transport and storage infrastructure was considered initially, however, it had negligible influence on BECCS deployment and network design. Thus, CO₂ transport and storage infrastructure was omitted from this work.

Fig. 1 illustrates the modelling structure in this work, where given information is listed as “inputs” (left boxes) and the results are the “outputs” (boxes on the right). The outputs are obtained by minimising the total cost of the whole system subject to the CO₂ removal target of each time period, from 2030 to 2050 in 10 year time steps.

Fig. 1 Biomass Utilisation Supply Chain (BUSCh) model, where the boxes on the left show the input data and the boxes on the right list the outputs. All costs and emissions encompass the whole supply chain starting from the raw material until the final electricity/heat generation in the BECCS/BE-CHP-CCS power plants with 90% CO₂ capture.

The BUSCh model is formulated as an mixed integer linear programming (MILP) model. This section describes the objective function and the various constraints to which the model is subject. Biophysical constraints are also integrated into this economic biomass production optimisation model. The notations of model sets, parameters and variables of the optimisation model are listed in Table 3 of the Appendix. The superscripts indicate technology and the subscripts indicate index.

2.2 Objective function

The objective of the proposed model is the minimisation of the total supply chain costs brought to today's value over the entire planning horizon. The total cost, TC, of a BECCS system is comprised of the total capital cost, TCC, total non-fuel operating costs, TNFOC, total raw material costs, TRMC, total imported pellets costs, TIPC, and the total transport costs, TTC. Thus, the objective function and the cost terms are as follows, where both TCC and TNFOC include the respective cost from pellet plant, BECCS power plant and BE-CHP-CCS plant:

TC = TCC + TNFOC + TRMC + TIPC + TTC (1)

Total capital cost, TCC, is the sum of total BECCS plant capital cost, total BE-CHP-CCS plant capital cost and total pellet plant capital cost, where discount rate (r) is 10%:

Total non-fuel operating costs, TNFOC, is the sum of total pellet production cost, total BECCS operation cost and total CHP operation cost:

The total raw material supply cost (TRMC) is:

Total imported pellets cost (TIPC) is:

The total transport costs, TTC, is the sum of total material transportation cost between regions and total local raw material transportation cost:

2.3 Total annual emissions

The environmental objective of the proposed model considers the calculation of the total annual emissions (TAE) of CO₂, explicitly accounting for carbon leakage throughout the biomass supply chain. The TAE, of a BECCS system is comprised of total capture emissions, TCE, total embodied emissions (i.e., emissions from the biomass feedstock cultivation/collection), TEE, and total transportation emissions, TTE. Thus, this objective function and the emission calculation from each sector are given by:

TAE_t = TCE_t + TEE_t + TTE_t (7)

Total captured emissions:

Total embodied emissions:

Total transportation emissions, TTE, is the sum of total material transportation emissions and total local raw material transportation emissions.

In each time period, there is a target for net atmospheric CO₂ removal (i.e., negative emissions⁶²). The evolution of the BECCS system is then driven by a CO₂ removal target using the following constraint:

TAE_t ≤ target_t ∀t (11)

2.4 Waste biomass production constraints

The local waste biomass supply rate is limited by the minimum and maximum production rate for each region of each time period, while the maximum production is assumed to increase with population:

BW^min_igt ≤ P_igt ≤ BW_igt ∀i ∈ RW, g ∈ G,t (12)

2.5 Virgin biomass production constraints

The local virgin material supply rate is also limited by the minimum and maximum local yield multiplied with the available land area for farming in each region:

BA^min_igtA_igt ≤ P_igt ≤ BA^max_igtA_igt ∀i ∈ RA, g ∈ G,t (13)

For each region, the area available for cultivating virgin biomass is constrained by local availability:

The total area for planting virgin biomass should be less than the planned total biomass land availabilities of each time period:

2.6 Pellet plant production capacity constraints

The total pellet production rate in each region is limited by their unit production capacity multiplied with the number of pellet plants being operated:

In each time period, t, the number of available pellet plants in each region g is ON^pellet_gt, which is the sum of those plants available from the previous period, ON^pellet_g,t−1, and the number of plants built in that region and period, N^pellet_gt, less the number of plants decommissioned in that period, NF^pellet_gt:

ON^pellet_gt = ON^pellet_g,t−1 + N^pellet_gt − NF^pellet_gt ∀g ∈ G,t (17)

Each pellet plant is assigned a lifetime θ, and therefore any plant deployed in year t will be decommissioned in the year t + θ. This is given by:

N^pellet_gt = NF^pellet_g,t+θ ∀g ∈ G,t (18)

2.7 Pellet importation constraints

The amount of biomass imported to a given port is limited by the capacity of that port:

2.8 Power plant generation capacity constraints

The electricity generated in region g is limited by generation capacities multiplied with the number of power plants, i.e., BECCS or BE-CHP-CCS, and the total electricity generated comes from both types of plants:

P^BECCS_ele,gt ≤ CA^BECCSON^BECCS_gt ∀g ∈ G,t (20)

P^CHP_ele,gt ≤ CA^CHPON^CHP_gt ∀g ∈ G,t (21)

P_ele,gt = P^BECCS_ele,gt + P^CHP_ele,gt ∀g ∈ G,t (22)

The number of operating BECCS or BE-CHP-CCS power plants are based on the previously built plants plus the newly built plants in each time period:

ON^BECCS_gt = ON^BECCS_g,t−1 + N^BECCS_gt ∀g ∈ G,t (23)

ON^CHP_gt = ON^CHP_g,t−1 + N^CHP_gt ∀g ∈ G,t (24)

The electricity generation rate at a power plant (BECCS or BE-CHP-CCS) located in region g, is related to the fuel mix burn rate, the energy density of the fuel mix and the generation efficiency:

The heat generation rate at a CHP power plant located in region g, is related to the electricity generation rate and the heat to power ratio κ:

Heat^CHP_gt = κP^CHP_ele,gt ∀g ∈ G,t (27)

2.9 Material balance constraints

The material balance for each material i and region g states that the production of a material in region g plus the incoming flows of that material to that region must be equal to the demand in that region plus the outgoing flows from that region.

2.10 Pellet production constraints

The amount of raw material i consumed at a pellet production plant located in region g, D_igt, is related to the total pellet production rate at that plant, P_i′gt, by the conversion factor, γ_i:

P_i′gt = γ_iD_igt ∀i ∈ FI, g ∈ G,t (29)

2.11 Piecewise linear formulation of the learning curve model

This study considers the cost reduction from the technology learning for BECCS and BE-CHP-CCS. The learning curve theory can be formulated as an exponential correlation between the unit cost and the cumulative capacity installed. Following Heuberger et al.,⁶³ the exponential cost learning curve is linearised with a piecewise linear approach. The non-linear term IC^power_tN^power_gt in the objective function can be linearised by converting the unit cost curve to a cumulative cost curve. Here y_t represents the cumulative capacity cost of power plants in year t, only one segment Yln_t,ln can be selected:

The x-axis position on the segment is determined with the constraints below:

xs_t,ln ≥ Xlow_lnYln_t,ln ∀t,ln (31)

where the coordinates Xlow_ln, Xup_ln, Ylow_ln and Yup_ln are the coordinates on the cumulative capacity versus cumulative cost curve from the linear segments ln, and slope_ln = (Yup_ln − Ylow_ln)/(Xup_ln − Xlow_ln).

3 Case study insights: UK BECCS potential

As a case study, we employ the Biomass Utilisation Supply Chain (BUSCh) model to elucidate optimal deployment pathways for BECCS and BE-CHP-CCS in the UK. The procedure is as follows; first we quantify the extent and location of land available for biomass cultivation, explicitly accounting for biogeophysical constraints. We then evaluate the optimal evolution of the system design for the period 2020 to 2050, as the national negative emissions target is increased. Importantly, this approach could be similarly employed to study any region. Finally, we perform a sensitivity analysis to identify key bio-techno-economic parameters which influence the environmental and economic performance of the system.

3.1 UK biomass resource availability

In this work, Great Britain is discretised into 140 regions, 50 by 50 km each. Six types of raw biomass material are considered: miscanthus, poplar, municipal solid waste (MSW), waste wood (Grade A and B),^38,39 forest residue and crop residue.

Conditions in the UK are generally favourable for bioenergy crops (i.e., virgin biomass) such as miscanthus and poplar, given the presence of sufficient rain and sunshine over the year and no prolonged periods of frost over most of the country. Frost mainly affects the growth of current miscanthus hybrids, whereas poplar is not frost sensitive in UK climates.

Miscanthus is sensitive to undergrowth during establishment, which can significantly reduce the yield for the first rotation,⁶⁴ therefore weed management is needed. When modelled using the MISCANFOR⁶⁵ process-based model under the Special Report on Emissions Scenarios (SRES) A2 scenario Miscanthus X giganteus† can lead to a net energy yield of 34.13 PJ per year in the UK. With the development of a drought and frost tolerant hybrid, this yield could increase up to 81.41 PJ per year. The highest possible miscanthus yields (25–30 dry tons per ha per year) are possible in the central South and Southwest of the UK, which are also the warmest areas of the UK.⁶⁶

Poplar short rotation forestry is very suitable for UK climate and soil conditions. High yields (20–25 dry tons per ha per year) are possible in areas all over Britain, mostly limited by soil factors.⁶⁶ On average, a net energy yield of 30.6 PJ per year is possible. Poplar is considered to be suitable everywhere in the UK unless other reasons prevent the land conversion to bioenergy plots, e.g., peatland or land contamination. This will not change under climate change scenarios until 2050.⁶⁷ The dry matter (DM) yields of miscanthus and poplar are taken from the work of Hastings et al.⁶⁷ This yield database is based on soil and meteorological data, which accounts for the current and future changes in climate across Great Britain. The virgin biomass DM yields are shown in Fig. 2(a) and (b).

Fig. 2 UK virgin biomass availability in terms of dry matter (DM) yield for: (a) miscanthus, (b) poplar, (c) forest residue, and (d) crop residue. The dry matter yields of miscanthus and poplar are obtained based on the soil and meteorological data in 10 zones,⁶⁷ whereas the yields of forest residue and crop reside are collected from the NNFCC work⁷⁰ and MAPSPAM database,⁷³ respectively.

Based on the analysis from Hamelin et al.,⁶⁸ straw residues (3200 PJ per year) and crop residues (3800 PJ per year) are the main biomass contributors in the EU-27. Forest residues include the logging residues from harvesting wood and the remaining stumps. Great Britain has in total 3.17 Mha of woodland,⁶⁹ with the production of forest residues estimated to be over 1.3 Mton annually by 2036.⁷⁰ The forest residue DM yields (data from the National Non-Food Crops Centre, NNFCC)⁷⁰ are illustrated in Fig. 2(c), where Scotland provides up to 49% of the total forest residue for the UK.

The availability of agricultural crop residue varies with cultivated area, types of crops, yields resulting from different climate conditions, soil conditions and farming practices.⁷¹ Sustainable removal rates of crop residue is considered, varying between 30% and 60%, which allow for an adequate amount of remaining residues to meet the environmental and harvesting constraints, i.e., maintaining organic matter and soil organic carbon.⁷² The UK crop residue availabilities are collected from the MAPSPAM database,⁷³ the values for barley, rapeseed and wheat are summed, whilst the collection rate is assumed to be 35%. This provides the crop residue dry matter (DM) yields shown in Fig. 2(d), which shows that the South-east of England has more crop residue.

The MSW and waste wood availabilities tend to be a function of population density. Thus, waste biomass availability is assumed to be based on the UK population density (Centre for Ecology & Hydrology⁷⁴) and population projections (Office for National Statistics⁷⁵). The Waste & Resources Action Programme report that 3.3 Mt of waste wood was generated in 2010,⁷⁶ which includes the waste wood from construction, demolition, wood manufacturing processes and pellets and wooden packing. Using the municipal waste generated in the UK from Eurostat (approximately 500 kg per person per year)⁷⁷ and waste wood availability, the distribution of waste biomass availability is plotted based on population density (Fig. 3). Populated city areas, such as London and Leeds, have high waste biomass availabilities of up to 18.2 t ha⁻¹.

Fig. 3 Distribution maps showing: (a) UK population density,^74,75 and dry matter yield (i.e., availability) of (b) waste wood,⁷⁶ and (c) MSW.⁷⁷ The (b) MSW and (c) waste wood availabilities are a function of the UK population density (a). Populated city areas have higher waste biomass availabilities of up to 18.2 t per ha per year.

3.2 Land availability

Site location and construction of the power plants and pellet plants are limited by land constraints. Map (a) of Fig. 4 shows the classification of land into three categories based on land cover type.⁷⁸ Red corresponds to land that is not suitable for the construction of plants, which includes water, swamps, suburban areas, national parks and conservation areas. The amber colour indicates land types possible for construction, i.e., heather grassland and mountain habitats. The green colour represents land deemed suitable for siting of power plants and pelleting facilities.

Fig. 4 (a) Land availability for the construction of power plants and pellet plants. (b) Land availability for virgin biomass farms. Maps (a) and (b) are based on land cover type, e.g., swamps are not suitable for construction nor biomass farms. To avoid food production competition, agricultural land for farming and livestock grazing was considered unavailable. The product of biomass land availability map (b), biomass resource yield (Fig. 2 and 3) and the corresponding energy density results in the total bioenergy potential map (c). The residential heat demand map (d) is generated based on the EUROSTAT natural gas household consumption for the year 2015,⁸¹ which is spatially disaggregated based on UK population.

Land availability considerations also apply to biomass cultivation. In this study, land currently used for agriculture is excluded to avoid competition with food production. Land suitable for biomass planting is indicated by the green colour in map (b) of Fig. 4, which is used as the biomass land availability for each cell. The total land available for biomass plantation is 8.4 Mha, which is mostly located in the west of the UK. This biomass land availability is combined with DM yield data (Fig. 2 and 3) to determine the total annual bioenergy potential shown in map (c) of Fig. 4, where the maximum bioenergy potential is 57 MW h ha⁻¹. The permanent grassland used for livestock grazing is about 6.1 Mha,⁷⁹ which is projected to increase in the near future. Once the land for livestock grazing is deducted, the maximum land available to grow biomass crops is 2.3 Mha.

A separate study performed by the UK Energy Technologies Institute (ETI) estimated a maximum of 1.22 Mha of biomass land availability by 2050.⁸⁰ Although the exclusion of agricultural land was not considered, the ETI value is in good agreement with a study by Smith et al.,⁴ which did exclude agricultural land. Here, we evaluate the impact of the ETI assumptions, by proportionally reducing total land availability, and systematically increase from 0.62 Mha to 1.22 Mha.

3.3 Biomass pellet prices and availabilities

The calculation of the average price for each type of biomass pellet considers the different costs incurred along the biomass supply chain (e.g., processing, transport). As an example, Fig. 5 presents the miscanthus pellet price on arrival at the power plants. The final miscanthus pellet price (£119 t⁻¹) includes:

Fig. 5 (Top) Example of the pellet price calculation for miscanthus. (Bottom) Pellet conversion rates^84,86 of different raw feedstock materials. Pellet price calculation considers costs incurred along the supply chain, starting from harvesting of the raw material, then transport to the pelleting plants for pellet production and transport to the power plants as biomass pellets. Pellet conversion rates are applied for the cost calculation, which accounts for moisture removal and material loss during the pellet production process.

(1) The raw material cost of harvesting (£49 t⁻¹).⁸²

(2) Processing cost and personnel cost⁸³ at the pellet plant.

(3) Annualised capital expenses (CAPEX) of the pellet plant.⁵⁹

(4) Conversion rate, considers moisture removal & material loss.

(5) Calculated average transportation costs in the UK.

The pellet conversion rates of the different raw materials are presented in four Sankey diagrams (Fig. 5), e.g., the miscanthus pellet conversion rate is 83.7%.⁸⁴Fig. 6 summarises the calculated pellet prices for forest residue, crop residue, waste biomass^85–87 and poplar pellets.⁸² The calculated energy availability of each type of biomass results from multiplying the biomass energy density by the biomass availability. The error bar shows the range between the two types of virgin biomass crops considered here, miscanthus (higher) and poplar (lower). The availability of imported pine pellets⁸⁸ from abroad is assumed to be unconstrained, and is imported when necessary. Tables 4 and 5 (Appendix) present the data used for pellet cost calculations.

Fig. 6 Biomass pellet price and energy availability. Pellet prices have been calculated using the method in Fig. 5. The energy availability is the product of the biomass availability and corresponding energy density. The error bar shows the energy availability range of indigenous virgin biomass, where miscanthus is higher and poplar is lower. The availability of imported pellets from the US and EU is assumed to be unlimited.

3.4 Technical and economic assumptions for BECCS facility

This study considers two types of bioenergy combustion facilities:

(1) “BECCS” plant: biomass-fired pulverised power plant that generates electricity.

(2) BE-CHP-CCS plant: bioenergy combined heat and power plant, which generates electricity and heat.

There are two types of BECCS power plants: (i) high efficiency ultra-supercritical power plant with advanced solvent post-combustion capture and heat recovery, and (ii) subcritical power plant with post-combustion capture using conventional MEA solvent. The design capacity of the BECCS power plant is 500 MW with 90% CO₂ capture rate. The CAPEX of these systems has been derived based on the capital cost of a coal-fired power plant with CCS (from the Integrated Environmental Control Model⁸⁹) and the capital investment associated with converting the Drax coal-fired power plant into a dedicated biomass-fired plant.⁹⁰ The electricity generation efficiency of BECCS depends on the biomass pellet type and composition (e.g., moisture content).

The technical assumptions for the BE-CHP-CCS plants are based on circulating fluidised bed (CFB) technology, which is capable of burning fuels with high moisture and low heating value at the highest efficiency without requiring fuel pre-processing.³³ Thus, the waste biomass pellets (waste wood and MSW), which tends to be low heating value, have been diverted to BE-CHP-CCS plants for combustion. The capacity of the BE-CHP-CCS plant is assumed to be 100 MW_e with 90% CO₂ capture rate.^91,92 According to UK waste management regulations, waste incineration is permitted.^93,94 Although CHP for Energy-from-Waste (EfW) delivers higher total efficiency compared to power plants,⁹⁵ current EfW plants in the UK are predominantly focussed on electricity export rather than generating a mix of electricity and heat, i.e., hot water and steam, unlike other EU countries with high heat demand.⁹⁶

Due to regulatory constraints in the UK, the biomass pellets produced from waste wood and MSW are not used in BECCS plants. The fuels utilised by BECCS plants are the imported biomass pellets and pellets produced from indigenous virgin biomass, forest and crop residues. As heat demand in the UK is low, BE-CHP-CCS plants have only been deployed for use with the waste wood and MSW fuel pellets to limit heat generation. Additionally, heat production from BE-CHP-CCS is constrained by: (i) the UK residential heat demand of each cell, map (d) in Fig. 4,⁸¹ and (ii) the inability to transport heat between cells.

The details about the BECCS and BE-CHP-CCS plants are provide in the Appendix, Tables 5 and 6. Although the power plants have a life time of 30 years, the economic life time of the investment is assumed to be 20 years. A CAPEX reduction of 1.1% per GW deployed is assumed, based on projected learning curves.⁹⁷‡

Supply chain emissions of virgin biomass pellets from marginal land in the UK were calculated using the MONET framework developed by Fajardy and Mac Dowell^16,98,99 and used to specify the embodied carbon emissions of the biomass. Two biomass supply chain scenarios are considered: (i) the high emissions scenario corresponds to “business as usual”; and (ii) the low emissions scenario corresponds to an alternative advanced supply chain (i.e., organic farming, in-field operations with biodiesel, biomass-fuelled drying, carbon neutral electricity for processing). The high emissions scenario is used as the base case, and the low emissions scenario in the sensitivity analysis. For the embodied emissions of imported pellets, we use the reported number from the Drax annual report.⁴¹ Sourcing of the imported biomass pellets is assumed to utilise the same four ports currently used by Drax,⁹⁰ namely: Port of Tyne, Port of Hull, Port of Immingham and Port of Liverpool.

3.5 Modelling scenarios

In line with the recent targets set by the UK's Committee on Climate Change (CCC), we specify a negative emissions target of 47 Mt_CO2 per year by 2050.¹⁰⁰ For this work, deployment is assumed to follow a linear CO₂ emissions reduction pathway from 2020 to 2050 (total emissions of Fig. 7). The newly built BECCS facilities are the high efficiency systems with ∼38%_HHV electrical efficiency²³ and 1.1% CAPEX learning rate.⁹⁷ The newly built BE-CHP-CCS facilities are CFB incinerators with post-combustion capture, which have 36%_HHV electrical efficiency, 29% heat efficiency and 1.1% CAPEX learning rate.^91,92 The total land availability is increased gradually from 0.62 Mha in 2020 to 1.22 Mha in 2050 (ETI scenario).⁸⁰ Based on the options of biomass resources available, five scenarios have been considered:

Fig. 7 Electricity generation breakdown under the CO₂ removal target for the five scenarios in each decade: (1) indigenous virgin biomass & imported pellets, (2) indigenous virgin biomass, forest residue & imported pellets, (3) indigenous virgin biomass, forest residue, crop residue & imported pellets, (4) indigenous virgin biomass, forest residue, crop residue, waste wood & imported pellets, (5) indigenous virgin biomass, forest residue, crop residue, waste wood, MSW & imported pellets. The CO₂ emissions reduction target between 2020–2050 linearly increases to 47 Mt by 2050.^100,101 Scenarios using MSW and waste wood employ BE-CHP-CCS plants, thus generate both electricity and heat.

(1) Indigenous virgin biomass + imports (V + I);

(2) Indigenous virgin biomass + forest residue + imports (V + F + I);

(3) Indigenous virgin biomass + forest residue + crop residue + imports (V + F + C + I);

(4) Indigenous virgin biomass + forest residue + crop residue + waste wood + imports (V + F + C + W + I);

(5) Indigenous virgin biomass + forest residue + crop residue + waste wood + MSW + imports (V + F + C + W + M + I).

3.6 Optimisation results

3.6.1 Total system cost. Table 1 shows the optimal BECCS and BE-CHP-CCS deployment results. By utilising indigenous waste wood and MSW, the CO₂ reduction target can be achieved with fewer BECCS power plants, and instead, opts to install more BE-CHP-CCS plants. However, more pellet plants are required for the production of indigenous pellets, substituting the use of imported pellets. The total annual cost increases with each decade due to the rise in investment and raw material consumption, which occurs in accordance to the increasing CO₂ removal target over the three decades.

Table 1 Optimal BECCS deployment results under five biomass availability scenarios. Each scenario utilises a combination of indigenous virgin biomass (V), forest residue (F), crop residue (C), waste wood (W), MSW (M) and imported pellets (I). With the availability of waste wood and MSW, waste biomass replaces imported pellets, resulting in an increased number of power plants and pellet plants and the reduction of total system cost by 16–36%

Scenario 1: V + I	2030	2040	2050
Newly installed BECCS capacity (MW)	4500	2500	3000
Newly installed pellet plant capacity (t h^−1)	1080	480	1480
Total cost (M£)	3308	6095	7606
Average system cost (£ tCO2^−1)	211	195	162
Scenario 2: V + F + I	2030	2040	2050
Newly installed BECCS capacity (MW)	3500	3000	3500
Newly installed pellet plant capacity (t h^−1)	1260	440	1680
Total cost (M£)	2900	5842	7830
Average system cost (£ tCO2^−1)	185	186	167
Scenario 3: V + F + C + I	2030	2040	2050
Newly installed BECCS capacity (MW)	2500	3000	3500
Newly installed pellet plant capacity (t h^−1)	1640	1400	2060
Total cost (M£)	2389	5069	7358
Average system cost (£ tCO2^−1)	152	162	157
Scenario 4: V + F + C + W + I	2030	2040	2050
Newly installed BECCS capacity (MW)	1000	3000	1500
Newly installed BE-CHP-CCS capacity (MW)	1000	0	0
Newly installed pellet plant capacity (t h^−1)	1600	1680	2220
Total cost (M£)	2265	4922	6919
Average system cost (£ tCO2^−1)	151	157	147
Scenario 5: V + F + C + W + M + I	2030	2040	2050
Newly installed BECCS capacity (MW)	1000	3000	1500
Newly installed BE-CHP-CCS capacity (MW)	1400	100	700
Newly installed pellet plant capacity (t h^−1)	1660	1780	3220
Total cost (M£)	2125	4669	6384
Average system cost (£ tCO2^−1)	136	149	136

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The average system cost can be expressed as the cost per net t_CO2 removed from the atmosphere. Expressing the system cost in this way assumes that cost is completely independent of any revenue support, i.e., cost required to deliver these services on a commercial basis without any monetary support. The objective is to minimise the total system cost over three decades subject to given CO₂ reduction targets. Consequently, the average system cost has a clear decreasing trend (Table 1). The optimisation prioritises the selection of raw materials with lower cost and higher carbon content, whereas embodied emissions and transportation emissions are less significant. The comparison of all scenarios show that utilisation of waste wood and MSW decreases the demand for imported pellets, reducing total system cost by as much as 16–36%.

3.6.2 Meeting a CO₂ removal target with bioenergy. Fig. 7 shows the energy generation breakdown (electricity and heat) for the five scenarios, which all have the same CO₂ emissions reduction target between 2020–2050. Scenario 5 generates the lowest amount of total electricity due to waste biomass pellets having lower energy density, higher carbon content (dry ash free basis) and lower costs. Scenarios 4 and 5 generate heat from the combustion of waste biomass (waste wood or MSW) in the BE-CHP-CCS plants. The utilisation of secondary sources of biomass (waste and residues) eliminates the need for imported pellets in the first two decades. By 2050, the greater CO₂ reduction target resulted in the use of imported biomass pellets, however, this demand could instead be supplemented with indigenous miscanthus (higher pellet price compared to US import). Therefore, the UK could independently supply enough indigenous biomass to reach its CO₂ reduction targets without encroaching on land used for food production (e.g., agricultural crops, livestock grazing).

As the CO₂ emissions reduction target increases after each decade, there is higher demand for biomass pellets. Consequently, utilisation of virgin biomass and MSW pellets also increases. Miscanthus is preferentially selected over poplar due to lower raw materials and pellet processing costs. Fig. 8 illustrates the total annual cost breakdown for scenario 5. A significant share of the total cost is attributed to raw material cost, followed by the annualised capital cost of the power plants and pelleting plants. By the 2050 decade, the imported pellet cost only represents 4% of the total annual cost.

Fig. 8 Cost breakdown between 2020–2050 of scenario 5, which considers the availability of indigenous virgin biomass, forest residue, crop residue, waste wood, MSW and imported pellets. The main costs are raw material cost and annualised power plant capital cost. The availability of indigenous biomass significantly reduces imported pellet cost, which represents only 4% of the total cost for 2050.

3.6.3 Economic potential of BECCS technologies. BECCS and BE-CHP-CCS plants provide two services: (i) supply electricity and heat to the market which generates revenue, and (ii) avoid the costs associated with the consequences of dangerous climate change. This second service could reasonably be valued at the ‘social cost of carbon’. An incentive scheme, which can accrue the value of such services to the service providers, has the potential to leverage investment. The reported internal rate of return (IRR) that ensures project investment is economically attractive is between 10–14%.¹⁰²

For scenario 5, the negative emissions credit (NEC)^102–105 and the avoided emissions price (eqn (33)) was analysed at 10% and 15% internal rate of return (IRR) on investment for the first decade (2020–2030). The calculation of NECs and avoided emission prices (Fig. 9), for a reference basis, uses an electricity price of a gas-fired combined cycle gas turbine (CCGT) (£85 MW h⁻¹) and heat price of a residential heating system using UK natural gas (£36.4 MW h⁻¹), which have emission rates of 350 kg CO₂ MW h⁻¹ and 220 kg CO₂ MW h⁻¹, respectively.^106–109Table 1 shows that 1000 MW BECCS and 1400 MW BE-CHP-CCS were installed in 2030 of scenario 5. Furthermore, BE-CHP-CCS captured 54% of the total CO₂, and also had lower NEC and avoided emissions price compared to BECCS plants (Fig. 9).

Fig. 9 Negative emissions credits (NEC) and avoided emission prices at 10% and 15% internal return rate (IRR) on investment in 2030. To meet the CO₂ removal target of 15.7 Mt for 2020–2030, more than 54% CO₂ is captured from burning secondary biomass resources in BE-CHP-CCS. Although BE-CHP-CCS has lower electrical efficiency compared to BECCS, it produces heat simultaneously, which results in lower NEC and avoided emission prices under both IRR scenarios. The BECCS NECs are in line with the lower bound of the predicted central carbon values from DECC (£77–200 t_CO2⁻¹).¹¹⁰ The avoided emission prices are calculated using the reference prices and emissions of electricity from gas-fired CCGT and UK residential heating from natural gas.^106–109

To achieve an IRR of 15%, BECCS and BE-CHP-CCS would require a NEC of £121 t_CO2⁻¹ and £50 t_CO2⁻¹, respectively. These NEC values are in line with other UK estimates of “carbon value”.§ In comparison with BECCS, the advantages of BE-CHP-CCS plants are that they use lower cost and higher carbon content waste fuel, also the simultaneous generation of heat provides additional revenue. Therefore, although BE-CHP-CCS plants have lower electrical efficiency compared to BECCS, the NEC and avoided emission prices were lower (in both 10% and 15% IRR scenarios). These results suggest that investment in BECCS and BE-CHP-CCS could become economically attractive (e.g., risk-adjusted rate of return of 10–15%) given an appropriate NEC or avoided emissions price is in place. BE-CHP-CCS plants seem superior in comparison with BECCS under these given assumptions. However, BE-CHP-CCS deployment is limited by (i) heat demand of the local region (lack of heat distribution infrastructure for heat transfer between cells), and (ii) the availability of waste biomass (as BE-CHP-CCS is only employed in scenarios using waste biomass).

Cost of CO₂ avoided = (total system cost − electricity cost from CCGT −  heating cost from natural gas)/avoided CO₂ emissions (33)

3.6.4 Evolution of the biomass supply network. Fig. 10 illustrates the evolution of the biomass supply network for scenario 5 (when all biomass feedstocks are available) from 2030 to 2050. As the CO₂ reduction target increases over the three decades, more land is used for biomass planting of miscanthus crops. For the 2030 decade, the main biomass supply consists of crop residue, waste wood, MSW and miscanthus, which are sourced from agricultural and populated areas, e.g., London and Leeds.

Fig. 10 Scenario 5 (all biomass feedstocks available): evolution of the biomass supply network between 2020 to 2050. The square cell colours indicate the total biomass supply in TW h per year. The pie charts show the relative proportions of feedstocks supplied in each cell. The use of crop residue, forest residue, waste wood and MSW are prioritised because of their low cost and high CO₂ emissions. Over the three decades, the share of biomass supply from miscanthus feedstock increases as the CO₂ removal targets increase due to the gradually rise of biomass land availability from 0.62 to 1.22 Mha by 2050 (proposed in the ETI report).⁸⁰

As demand and utilisation of waste biomass grows, MSW and waste wood is eventually sourced from the south-east region of Scotland (encompassing Edinburgh). Over the three decades, the total land area used for cultivating miscanthus increases, but does not exceed what is estimated to be sustainably available. By the last decade (2050), the main indigenous biomass supply for the UK is crop residue and miscanthus. The indigenous miscanthus is mainly produced in the southern regions of the UK, which have high crop yield and land availabilities. The evolution of the supply chain network with power plant locations is shown in Fig. 11, where the grey lines show the transport network of raw material and pellets. Pellet plants are located in regions all over the UK (thus, not shown in figure for simplicity).

Fig. 11 Scenario 5 (all biomass feedstocks available): the locations of BE-CHP-CCS and BECCS power plants as the supply chain network evolves across the decades from 2020–2050. More BE-CHP-CCS plants and BECCS power plants are built over the three decades as the CO₂ removal targets increase (shown in Fig. 7). The UK has the potential to be bioenergy self-sufficient between 2030–2050, if all indigenous biomass is available to be deployed (i.e., residues, waste and virgin feedstocks).

As the CO₂ emissions reduction target linearly increases up to 47 Mt by 2050 (Fig. 7), the evolution of BECCS deployment shown in Fig. 10 and 11 (scenario 5) is as follows:

2020–2030: The fuels predominantly utilised (decreasing in contribution) include miscanthus, crop residue, waste wood, MSW and forest residues. As the supply of indigenous biomass is sufficient, there is no need to import biomass. BECCS plants are deployed in the agricultural regions of south/south-west UK (source of miscanthus and crop residues). Due to the large share of waste biomass (waste wood and MSW), more BE-CHP-CCS capacity is built compared to BECCS.

2030–2040: additional BECCS power plants are deployed in the south, where miscanthus is cultivated, and there is still no need to import pellets from abroad. One BECCS power plant is built near Edinburgh, which also utilises a miscanthus supply.

2040–2050: the demand for miscanthus, crop residue and MSW increases further, resulting in a significant cost increase. An additional 700 MW of BE-CHP-CCS plants is built in urban areas, and 1500 MW of BECCS power plant capacity is built in the south of the UK. Very low amounts of biomass pellets (0.83 Mt per year) are imported and unloaded in the port of Hull to be used in neighbouring BECCS facilities, thus resulting in costs associated with logistics.

These results demonstrate that the availability of biomass feedstock is the main driver of both BECCS and BE-CHP-CCS location selection. BE-CHP-CCS deployment tends to begin near highly populated regions of the UK, where waste wood and MSW are more readily available. Deployment of BECCS tends to expand to agricultural regions in the south-east of the UK, which provides access to crop residues, and in the south/south-west regions where virgin biomass is grown. As the demand for virgin biomass increases, the network will then expand further to the south.

3.7 Sensitivity analysis of key impact factors

As can be intuited from the forgoing text, the cost of atmospheric CO₂ removal is a complex, and non-linear function of a number of technical, economic, and biogeophysical parameters. Whilst there has been substantial prior work on developing understanding of individual elements of the value chain, to date, this has not been studied in an integrated fashion. Contributing to addressing this knowledge gap is the purpose of this section. Here, we decompose the cost of delivering negative emissions and avoided emissions via BECCS and BE-CHP-CCS into the following contributions: BECCS power plant efficiency, embodied emissions, land productivity, MSW availability, land availability and CAPEX learning rate. These impact factors are described in Table 2.

Table 2 Impact factors for the total system cost calculation in different scenarios, used in the sensitivity analysis

Impact factor	Scenario
BECCS power plant efficiency	High: ∼38%HHV ultra-supercritical BECCS plant using advanced solvent & heat recovery^89,90
	Low: ∼26%HHV subcritical BECCS plant using conventional MEA^89,90
CAPEX learning rate	High: 5.5%, central: 3.3%, low: 1.1%^97
Embodied emissions	Business as usual supply chain: Fossil-based fertiliser, in-field operations with diesel, drying with natural gas, medium carbon intensity electricity for biomass processing^16
	Low: alternative supply chain, organic fertiliser, in-field operations with biodiesel, drying with biomass, carbon neutral electricity for processing^16
Crop yields	High: 7% increase each decade^111
	Low: yield changes as reported by Hastings et al.^67
MSW availability	High, central^77 or low (±20%)
Land availability	High, central or low (±20%)

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The sensitivity analysis also considers two scenarios of total land availabilities: (i) step increase from 0.62 Mha in 2020 to 1.22 Mha in 2050 (proposed by ETI);⁸⁰ and (ii) constant 2.3 Mha biomass land availability, which considers the exclusion of agricultural land as described in Section 3.2. In addition to the yields presented in Hastings et al.,⁶⁷ the total factor productivity (TFP) of the UK¹¹¹ is used to predict the “high” virgin biomass crop yields, which results in a yield increase of ∼7% each decade (shown in Table 2). A total of 432 scenarios were evaluated for the sensitivity analysis, which considered different combinations of these impact factors.

The results of this sensitivity analysis are illustrated in Fig. 12 and 13. A first key observation is that the uncertainty around both negative emissions price and avoided emissions price increases with time, despite the potential for cost reduction associated with technology learning. The increase of the negative emissions target over the decades, drives the necessary increase in required quantity of biomass, thus driving the system costs curve upwards. As the first decade (2030) does not use imported pellets, there is a narrow range for negative emissions price of between £120–160 t_CO2⁻¹. Importantly, however, it can be observed that the lower bound of the cost range remains approximately constant across the three decades. This is primarily driven by a high yield of biomass coupled with largest land availability (2.3 Mha) and, paradoxically, inefficient BECCS facilities. As has been discussed previously,¹⁰² power plant CAPEX tends to increase in line with power generation efficiency. Thus, the reduced capital cost partially offsets the requirement for additional biomass to meet the increased target, and the combination of increased land and yield reduces the requirement of more expensive imported biomass.

Fig. 12 Sensitivity analysis results of negative emissions prices, excluding revenues from selling power and heat. A total of 432 scenarios were evaluated to analyse the effect of the factors in Table 2. The scenarios with lowest negative emissions price (∼£120 t_CO2⁻¹) correspond to low power plant efficiency, high CAPEX learning rate, high embodied emissions, high biomass crop yield, high MSW availability and high biomass land availability. Of the six impact factors, power plant efficiency, virgin biomass crop yield and land availability have the greatest influence on the negative emissions price.

Fig. 13 Sensitivity analysis results of avoided emissions price, based on the electricity price of a gas-fired CCGT (£85 MW h^₋₁) and heat price for a heating system using UK natural gas (£36.4 MWh⁻¹) with emission rates of 350 kg CO₂ MW h⁻¹ and 220 kg CO₂ MW h⁻¹, respectively.^106–109 A total of 432 scenarios were evaluated to analyse the effect of the factors in Table 2. The scenarios with lowest avoided emissions price (∼£3 t_CO2⁻¹) correspond to high power plant efficiency, high CAPEX learning rate, high embodied emissions, high biomass crop yield, high MSW availability and high biomass land availability. Since revenue from selling energy to the market is considered, high BECCS power plant efficiency is preferred.

However, the calculation of avoided emissions price shows that high efficiency BECCS plants is more favourable, as this considers revenues from the sale of electricity and/or heat. Using the prices and emissions for electricity from gas-fired CCGT and residential heating from UK natural gas, the avoided emissions price is between £3–42 t_CO2⁻¹. This study demonstrates that exploitation of indigenous biomass supplies is potentially an economically viable and sustainable option, facilitating CO₂ removal in order to meet a national scale negative emissions target. The case study shows how the UK would meet its negative emissions target via BECCS and BE-CHP-CCS alone using the available supply of indigenous biomass, which includes, forest residue, crop residue, waste wood, MSW and virgin biomass.

4 Conclusions

This work has proposed a general BECCS deployment framework, which analyses the available indigenous biomass supply and land cover for the expansion of new BECCS/BE-CHP-CCS plants, pellet plants and cultivation of virgin biomass. A key consideration of this study was quantifying the extent to which BECCS can be deployed, whilst avoiding competition for land otherwise in use for food production. The Biomass Utilisation Supply Chain (BUSCh) model presented in this work determines the optimal planning of land use at a national scale, illustrating the evolution of the biomass supply chain network over time. The performance of BECCS power plants and BE-CHP-CCS plants are analysed, where the available raw feedstock materials include waste wood, MSW, forest residue, crop residue, miscanthus, poplar and imported pellets.

The optimisation results of the UK case study showed that secondary sources of biomass (i.e., forest residue, crop residue, MSW and waste wood) played an important role in reducing biomass supply chain carbon emissions, thereby increasing BECCS negative emissions potential. The use of waste-derived biomass provides >16–36% cost saving compared to scenarios using virgin biomass (indigenous/imported) alone, and has the potential to completely replace the demand of imported pellets by 2050. Power plants locations tend to be near cities for the first decade where waste wood and MSW are available. Gradually, more BECCS power plants are built to achieve the increasing CO₂ reduction targets over the three decades, with construction expanding to southern UK where virgin biomass yield and biomass land availabilities are higher.

During the initial phase of deployment, when the CO₂ removal targets are relatively modest, population centres tend to draw the deployment of BE-CHP-CCS infrastructure due to the high supply of least cost waste-derived biomass (e.g., MSW and waste wood). As CDR targets become more ambitious, and the demand of crop residue and virgin biomass rises, BECCS infrastructure is drawn towards agricultural areas (southern UK) to enable the utilisation of available indigenous biomass. Miscanthus is preferred over poplar due to the lower costs of raw material and pellet processing. Miscanthus farming area is selected and optimised based on proximity to power plants and its crop yield. With step increase land availability proposed by ETI, the indigenous biomass supply has the potential to be self-sufficient to meet the 47 Mt CO₂ removal target in the UK.

The optimal BECCS deployment depends on various factors, we have analysed the impacts from six factors in the sensitivity analysis: power plant efficiency, biomass embodied emissions, MSW availability, land availability, BECCS power plant CAPEX learning rate and virgin biomass crop yield. The integration of CO₂ transport and storage had negligible impact to the BECCS network integration, therefore is not considered here. The increase of land availability reduced negative emissions price and avoided emissions price. Of the six factors analysed, BECCS power plant efficiency, biomass crop yield and land availability had the greatest impact on negative emissions price. Thus, priority should be given to research that focuses on improving BECCS power efficiency and biomass crop yield, which in turn, could provide significant reductions in total system cost.

Appendix

Nomenclature and supplementary data associated with this contribution are provided in the tables of the Appendix.

Table 3 Indices, sets, parameters, decision variables of the optimisation model

Notation	Description
Indices
g,g′	Square cells (regions)
l	Transport mode (train, truck, ship)
i,i′	Material (e.g., waste wood, MSW, miscanthus, imported pellet)
t	Time period
Sets
G	Set of all domestic regions
F	Set of fuels (waste wood pellet, MSW pellet, miscanthus pellet, poplar pellet)
FP	Set of final products (bioelectricity)
FI	Set of fuels that can be produced from raw material i (waste wood pellet from waste wood, MSW pellet from MSW, miscanthus pellet from miscanthus, poplar pellet from poplar)
FIP	Set of fuels that can be produced from primary resources i (miscanthus pellet from miscanthus, poplar pellet from poplar)
FIW	Set of fuels that can be produced from secondary resources i (waste wood pellet from waste wood, MSW pellet from MSW)
H	Set of regions where ports are located
I	Set of all materials (I = R⋃F⋃FP)
L	Set of transportation mode (truck, rail, ship)
R	Set of raw material types (waste wood, MSW, miscanthus, poplar)
RA	Set of virgin biomass types (miscanthus, poplar e.g.)
RW	Set of waste biomass types (waste wood, MSW e.g.)
Total	Set of total regions
Parameters
ADDgg′l	Actual delivery distance between regions g and g′ via mode l (km)
ALDg	Average local delivery distance in region g (km)
α	Annual operating hours (h per year)
BA^min/maxigt	Minimum/maximum virgin biomass crop yield of raw material i in region g in time period t (t h^−1 ha^−1)
BW^min/maxigt	Minimum/maximum waste biomass availability of raw material i in region g in time period t (t h^−1)
β	Extent of carbon capture and storage rate (%)
CA^pellet	Pellet production capacity (t h^−1)
CA^power	Power generation capacity (MW)
CA^port	Port unloading capacity (t h^−1)
DMR	Dry matter rate in pellets (%)
E ^embodied i	Embodied emissions of raw material i (t CO2 t^−1)
E ^transport l	Transportation emissions from mode l (t CO2 km t^−1)
γ	Conversion factor of raw material i to its pellet (t pellet per t raw material)
IC^BECCSt	Investment cost of a BECCS power production plant in time period t (£)
IC^CHPt	Investment cost of a CHP power production plant in time period t (£)
IC^pellet	Investment cost of a pellet production plant (£)
IMPCig*	Unit import cost for importing pellet from foreign supplier g* (£ t^−1)
LACg	Land availability in region g (km^2)
θ	Life time of pellet plant
κ	Heat to power ratio of CHP power plant
LAt	Total land availability in time period t (km^2)
η ^BECCS i	Electricity generation efficiency from BECCS plant with fuel i
η ^CHP i	Electricity generation efficiency from CHP plant with fuel i
ρ i	Energy density of fuel type i
ϕ i	CO2 stored in pellet i
UGC^BECCS	Unit BECCS operation cost in power plant (£ MW h^−1)
UGC^CHP	Unit CHP operation cost in power plant (£ per t captured CO2)
UPCi	Unit pellet production cost from raw material i (£ per t pellet)
USCig	Unit supply cost of raw material i in region g (£ t^−1)
UTCil	Unit transport cost of product i via mode l (£ t km^−1)
UTC*	Unit transport cost for local raw material transfer (£ t km^−1)
Targett	Emission reduction target for time period t (t per year)
Variables
A igt	Area of biomass farm for virgin biomass material i in region g in time period t (km^2)
D igt	Demand for raw material i in region g in time period t (t h^−1)
Heat^CHPgt	Heat output from CHP power plant in region g in time period t (MW)
N ^BECCS gt	Number of newly built BECCS power plant in region g in time period t (t h^−1)
N ^CHP gt	Number of newly built CHP power plant in region g in time period t (t h^−1)
N ^pellet gt	Number of newly built pellet plant in region g in time period t (t h^−1)
ON^BECCSgt	Number of operating BECCS power plant in region g in time period t (t h^−1)
ON^CHPgt	Number of operating CHP power plant in region g in time period t (t h^−1)
ON^pelletgt	Number of operating pellet plant in region g in time period t (t h^−1)
P igt	Production rate of material i in region g in time period t (t h^−1) (i ≠ elec)
P elec,g,t	Total electricity generation rate in region g in time period t (MW)
P ^BECCSelec,g,t	Electricity generation rate from BECCS power plant in region g in time period t (MW)
P ^CHPelec,g,t	Electricity generation rate from CHP power plant in region g in time period t (MW)
Q igg′lt	Flow rate of material i via mode l from region g to region g′ in time period t (t h^−1)
TC	Total system cost (£)
TAE	Total annual emissions resulting from a bioelectricity supply chain network (t CO2 per year)
UTC*	Unit transport cost for local raw material transfer (£ t km^−1)
Targett	Emission reduction target for time period t (t per year)

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Table 4 Unit supply cost and pellet conversion rate

	Unit supply cost (£ t^−1)	Pellet conversion rate
Waste wood	20 (ref. 85)	85.3% (ref. 84)
MSW	10 (ref. 59)	28.6% (ref. 86)
Miscanthus	49 (ref. 82)	83.7% (ref. 84)
Poplar	44 (ref. 82)	54.4% (ref. 84)
Crop residue	60 (ref. 87)	83.7% (ref. 84)
Forest residue	20 (ref. 85)	83.7% (ref. 84)
Imported pellets (without transportation)	120 (ref. 88)	—

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Table 5 Pellet data: embodied emissions, energy density, electricity generation efficiencies and pellet production cost

	Embodied emissions^16 (tCO2 per t DM pellet)	Energy^59 density (MJ t^−1)	BECCS electrical efficiency^a (%)^89	BECCS electrical efficiency^b (%)^89	BE-CHP-CCS electrical efficiency^e (%)^91,92	Pellet OPEX cost (£ t^−1)^83
a Ultra-supercritical power plant with post-combustion capture using advanced solvent and heat recovery. b Subcritical power plant with post-combustion capture using conventional MEA. c Conventional drying process. d Drying with biomass, biofuel, carbon neutral power and organic chemicals. e Circulating fluidised bed BE-CHP-CCS with 0.81 heat to power ratio.
Waste wood pellets	0	18 700	36.3	28.3	36.0	17.55
MSW pellets	0	12 500	30.8	21.5	36.0	18.14
Miscanthus pellets	0.272^c/0.049^d	18 410	36.8	29.6	36.0	17.86
Poplar pellets	0.257^c/0.075^d	18 500	36.1	28.5	36.0	26.7
Crop residue pellets	0.224	18 700	36.1	28.5	36.0	18.14
Forest residue pellets	0.268^c/0.049^d	15 120	36.8	29.6	36.0	18.14
Imported pellets (without transportation)	0.224	18 700	36.1	28.5	36.0	—

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Table 6 Pellet plant and power plant data: unit capacity, CAPEX and OPEX. The discount rate (r) is 10%

	Unit capacity	CAPEX^a	CAPEX^b	OPEX	Life time
a Ultra-supercritical power plant with post-combustion capture using advanced solvent and heat recovery. b Subcritical power plant with post-combustion capture using conventional MEA. c Circulating fluidised bed BE-CHP-CCS with 0.81 heat to power ratio.
Pellet plant	20 (t h^−1)^83	12 (M£ per unit)^83	12 (M£ per unit)^83	See Table 5 last column	20 (years)^59
BECCS plant	500 (MWe)^59,89	2721 (£ kWe^−1)^89,90	1980 (£ kWe^−1)^89,90	10 (£ MWe h^−1)^59	30 (years)^59
BE-CHP-CCS plant^c	100 (MWe)^91,92	2437 (£ kWe^−1)^91,92	2437 (£ kWe^−1)^89,90	1.27 (£ per tCO2 captured)^91,92	30 (years)^91,92

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Table 7 Total biomass availability and CO₂ emission target of each decade

	ETI total biomass land availability (Mha)^80	CO2 emission target (Mt)^100
2020–2030	0.62	−15.67
2030–2040	0.92	−31.33
2040–2050	1.22	−47

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Table 8 Unit transportation cost and transportation CO₂ emissions

	Unit transportation cost (£ t km^−1)^59	Transportation emissions (kg CO2 t km^−1)^59
Truck	0.47	0.062
Rail	0.17	0.022
Ship	0.06	0.005

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Table 9 Port data: British National Grid coordination and unloading capacity

	X	Y	Unloading capacity (Mt per year)^90
Tyne	375 000	735 996.7	2
Hull	475 000	535 996.7	1
Immingham	525 000	435 996.7	3
Liverpool	325 000	435 996.7	3

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Table 10 Geophysical data: British National Grid coordination, miscanthus and poplar yields, waste wood, MSW, crop residue and waste residue availabilities and biomass land availability for 2020–2030

Region	X	Y	Miscanthus^67 (t per ha per year)	Poplar^67 (t per ha per year)	Waste wood^75,76 (t per ha per year)	MSW^75,77 (t per ha per year)	Crop residue^78 (t per ha per year)	Forest residue^75,77 (t per ha per year)	Biomass land^78 (ha)
1	325 000	1 035 997	8.25	9.07	0.00	0.02	0.00	0.06	56 600
2	375 000	1 035 997	8.25	9.07	0.00	0.03	0.00	0.06	28 100
3	225 000	985 996.7	8.25	9.07	0.00	0.00	0.00	0.06	2400
4	275 000	985 996.7	8.25	9.07	0.00	0.00	0.00	0.06	17 500
5	325 000	985 996.7	8.25	9.07	0.01	0.05	0.00	0.06	15 000
6	375 000	985 996.7	8.25	9.07	0.00	0.00	0.00	0.06	67 100
7	125 000	935 996.7	8.25	9.07	0.00	0.01	0.00	0.06	26 000
8	175 000	935 996.7	8.25	9.07	0.00	0.03	0.00	0.06	2900
9	225 000	935 996.7	8.25	9.07	0.00	0.00	0.06	0.06	49 000
10	275 000	935 996.7	8.25	9.07	0.00	0.01	0.00	0.06	69 500
11	325 000	935 996.7	8.25	9.07	0.00	0.01	0.00	0.06	18 100
12	75 000	885 996.7	8.25	9.07	0.00	0.00	0.00	0.06	23 300
13	125 000	885 996.7	8.25	9.07	0.00	0.00	0.00	0.06	12 900
14	175 000	885 996.7	8.25	9.07	0.00	0.01	0.00	0.06	41 400
15	225 000	885 996.7	8.25	9.07	0.00	0.01	0.00	0.06	48 500
16	275 000	885 996.7	8.25	9.07	0.02	0.14	0.00	0.06	30 800
17	325 000	885 996.7	8.25	9.07	0.02	0.15	0.24	0.06	28 600
18	375 000	885 996.7	8.25	9.07	0.01	0.10	0.34	0.06	7200
19	75 000	835 996.7	8.25	9.07	0.00	0.00	1.16	0.06	13 000
20	125 000	835 996.7	8.25	9.07	0.01	0.06	0.00	0.06	24 500
21	175 000	835 996.7	8.25	9.07	0.00	0.01	0.00	0.06	68 600
22	225 000	835 996.7	8.25	9.07	0.00	0.00	0.00	0.06	74 600
23	275 000	835 996.7	8.25	9.07	0.02	0.19	0.24	0.06	41 800
24	325 000	835 996.7	8.25	9.07	0.00	0.04	0.34	0.06	58 300
25	375 000	835 996.7	8.25	9.07	0.03	0.30	2.27	0.06	96 600
26	425 000	835 996.7	8.25	9.07	0.06	0.53	0.00	0.06	10 300
27	125 000	785 996.7	8.25	9.07	0.00	0.00	0.00	0.06	16 700
28	175 000	785 996.7	8.25	9.07	0.00	0.01	0.00	0.06	105 000
29	225 000	785 996.7	8.25	9.07	0.00	0.03	0.00	0.06	98 500
30	275 000	785 996.7	8.25	9.07	0.00	0.01	0.00	0.06	39 900
31	325 000	785 996.7	8.25	9.07	0.00	0.04	0.00	0.06	46 900
32	375 000	785 996.7	8.25	9.07	0.02	0.19	1.44	0.06	38 800
33	75 000	735 996.7	8.25	9.07	0.00	0.00	0.00	0.06	3700
34	125 000	735 996.7	8.25	9.07	0.00	0.02	0.00	0.06	20 400
35	175 000	735 996.7	8.25	9.07	0.00	0.01	0.00	0.06	86 800
36	225 000	735 996.7	8.25	9.07	0.01	0.08	0.00	0.06	118 200
37	275 000	735 996.7	8.25	9.07	0.07	0.64	1.10	0.06	107 700
38	325 000	735 996.7	8.25	9.07	0.04	0.42	1.46	0.06	81 000
39	375 000	735 996.7	8.25	9.07	0.00	0.03	0.00	0.06	4500
40	125 000	685 996.7	8.25	9.07	0.00	0.00	0.00	0.06	15 000
41	175 000	685 996.7	8.25	9.07	0.00	0.02	0.02	0.06	36 900
42	225 000	685 996.7	8.25	9.07	0.05	0.45	0.35	0.06	91 800
43	275 000	685 996.7	8.25	9.07	0.44	4.11	1.10	0.06	121 100
44	325 000	685 996.7	8.25	9.07	0.26	2.46	1.46	0.06	58 700
45	375 000	685 996.7	8.25	9.07	0.02	0.17	0.00	0.06	22 800
46	125 000	635 996.7	8.25	9.07	0.00	0.01	0.00	0.06	3400
47	175 000	635 996.7	8.25	9.07	0.00	0.02	0.06	0.06	36 500
48	225 000	635 996.7	8.25	9.07	0.05	0.47	0.35	0.06	95 000
49	275 000	635 996.7	8.25	9.07	0.04	0.41	0.69	0.06	148 200
50	325 000	635 996.7	8.25	9.07	0.01	0.08	0.96	0.06	140 400
51	375 000	635 996.7	8.96	10.06	0.02	0.18	2.35	0.07	84 100
52	425 000	635 996.7	8.96	10.06	0.01	0.11	0.00	0.07	42 300
53	225 000	585 996.7	11.37	10.41	0.01	0.06	0.25	0.02	94 200
54	275 000	585 996.7	11.37	10.41	0.01	0.08	0.15	0.02	129 100
55	325 000	585 996.7	11.37	10.41	0.03	0.31	0.33	0.02	136 900
56	375 000	585 996.7	8.96	10.06	0.02	0.18	0.00	0.07	127 200
57	425 000	585 996.7	8.96	10.06	0.34	3.19	0.93	0.07	56 200
58	225 000	535 996.7	11.37	10.41	0.00	0.01	0.00	0.02	25 900
59	275 000	535 996.7	11.37	10.41	0.00	0.01	0.00	0.02	14 800
60	325 000	535 996.7	11.37	10.41	0.04	0.35	0.00	0.02	166 800
61	375 000	535 996.7	9.39	8.97	0.01	0.12	0.00	0.02	133 300
62	425 000	535 996.7	9.39	8.97	0.12	1.10	3.19	0.02	82 300
63	475 000	535 996.7	9.39	8.97	0.16	1.50	0.41	0.02	36 600
64	325 000	485 996.7	9.39	8.97	0.03	0.25	0.00	0.02	77 800
65	375 000	485 996.7	9.39	8.97	0.06	0.52	0.00	0.02	169 600
66	425 000	485 996.7	9.39	8.97	0.06	0.54	3.19	0.02	102 600
67	475 000	485 996.7	8.96	10.06	0.07	0.61	0.41	0.02	35 600
68	525 000	485 996.7	9.39	8.97	0.04	0.35	0.00	0.02	4500
69	325 000	435 996.7	11.37	10.41	0.11	1.03	0.00	0.02	31 800
70	375 000	435 996.7	7.66	10.52	0.53	4.93	0.00	0.01	137 400
71	425 000	435 996.7	7.66	10.52	0.63	5.82	1.68	0.01	93 700
72	475 000	435 996.7	7.66	10.52	0.19	1.79	3.88	0.01	6200
73	525 000	435 996.7	7.66	10.52	0.17	1.57	2.66	0.01	1300
74	225 000	385 996.7	12.47	9.94	0.01	0.07	0.00	0.06	65 400
75	275 000	385 996.7	12.47	9.94	0.05	0.44	0.00	0.06	108 700
76	325 000	385 996.7	9.09	11.45	0.30	2.76	0.06	0.01	81 800
77	375 000	385 996.7	9.09	11.45	0.58	5.36	0.76	0.01	100 000
78	425 000	385 996.7	9.09	11.45	0.30	2.75	0.84	0.01	128 400
79	475 000	385 996.7	7.66	10.52	0.25	2.30	2.24	0.01	7000
80	525 000	385 996.7	7.66	10.52	0.07	0.64	3.85	0.01	3500
81	575 000	385 996.7	7.37	11.62	0.02	0.15	0.00	0.01	300
82	225 000	335 996.7	12.47	9.94	0.00	0.04	0.00	0.06	139 400
83	275 000	335 996.7	12.47	9.94	0.01	0.10	0.00	0.06	190 000
84	325 000	335 996.7	9.09	11.45	0.04	0.35	0.06	0.01	105 500
85	375 000	335 996.7	9.09	11.45	0.15	1.42	0.76	0.01	94 100
86	425 000	335 996.7	9.09	11.45	0.32	2.97	0.84	0.01	38 000
87	475 000	335 996.7	7.66	10.52	0.38	3.55	2.24	0.01	2600
88	525 000	335 996.7	7.37	11.62	0.10	0.91	3.85	0.01	13 900
89	575 000	335 996.7	7.37	11.62	0.05	0.47	0.00	0.01	25 100
90	625 000	335 996.7	7.37	11.62	0.11	1.06	3.97	0.01	700
91	275 000	285 996.7	12.47	9.94	0.01	0.12	0.00	0.06	182 300
92	325 000	285 996.7	9.09	11.45	0.02	0.17	0.00	0.01	85 400
93	375 000	285 996.7	9.09	11.45	0.12	1.14	1.90	0.01	60 200
94	425 000	285 996.7	9.09	11.45	0.74	6.87	3.13	0.01	59 700
95	475 000	285 996.7	7.37	11.62	0.22	2.02	3.26	0.01	13 000
96	525 000	285 996.7	7.37	11.62	0.13	1.20	3.59	0.01	32 000
97	575 000	285 996.7	7.37	11.62	0.12	1.08	2.10	0.01	24 000
98	625 000	285 996.7	7.37	11.62	0.06	0.54	3.97	0.01	1200
99	175 000	235 996.7	12.47	9.94	0.00	0.03	0.05	0.06	65 500
100	225 000	235 996.7	12.47	9.94	0.04	0.33	0.00	0.06	210 200
101	275 000	235 996.7	12.47	9.94	0.05	0.50	0.00	0.06	207 100
102	325 000	235 996.7	13.1	10.61	0.07	0.64	0.00	0.02	135 600
103	375 000	235 996.7	9.41	11.51	0.13	1.19	3.37	0.02	44 700
104	425 000	235 996.7	9.41	11.51	0.10	0.91	3.11	0.02	62 700
105	475 000	235 996.7	7.37	11.62	0.20	1.89	3.64	0.01	81 600
106	525 000	235 996.7	7.37	11.62	0.36	3.32	3.80	0.01	35 200
107	575 000	235 996.7	7.37	11.62	0.17	1.56	3.88	0.01	22 800
108	625 000	235 996.7	7.37	11.62	0.14	1.33	3.25	0.01	11 600
109	175 000	185 996.7	12.47	9.94	0.00	0.01	0.00	0.06	5300
110	225 000	185 996.7	12.47	9.94	0.10	0.95	0.00	0.06	10 600
111	275 000	185 996.7	12.47	9.94	0.30	2.75	0.16	0.06	52 700
112	325 000	185 996.7	13.1	10.61	0.29	2.73	0.00	0.02	89 800
113	375 000	185 996.7	9.41	11.51	0.12	1.08	2.73	0.02	93 600
114	425 000	185 996.7	9.41	11.51	0.32	2.98	2.76	0.02	68 500
115	475 000	185 996.7	7.37	11.62	1.96	18.16	3.38	0.01	68 200
116	525 000	185 996.7	7.37	11.62	0.79	7.36	2.12	0.01	63 100
117	575 000	185 996.7	7.37	11.62	0.13	1.16	2.91	0.01	55 900
118	625 000	185 996.7	7.37	11.62	0.00	0.00	2.34	0.01	11 400
119	225 000	135 996.7	13.1	10.61	0.01	0.06	0.00	0.02	45 500
120	275 000	135 996.7	13.1	10.61	0.04	0.39	0.16	0.02	175 000
121	325 000	135 996.7	13.1	10.61	0.08	0.77	0.00	0.02	112 400
122	375 000	135 996.7	9.41	11.51	0.09	0.80	2.73	0.02	126 400
123	425 000	135 996.7	9.41	11.51	0.10	0.96	2.76	0.02	70 900
124	475 000	135 996.7	9.41	11.51	0.29	2.73	3.38	0.02	55 400
125	525 000	135 996.7	9.41	11.51	0.28	2.64	2.12	0.02	138 700
126	575 000	135 996.7	9.41	11.51	0.16	1.52	2.91	0.02	120 800
127	625 000	135 996.7	9.41	11.51	0.07	0.62	2.34	0.02	10 600
128	175 000	85 996.75	13.1	10.61	0.01	0.09	0.00	0.02	10 400
129	225 000	85 996.75	13.1	10.61	0.05	0.45	0.00	0.02	113 600
130	275 000	85 996.75	13.1	10.61	0.13	1.25	1.01	0.02	97 400
131	325 000	85 996.75	13.1	10.61	0.09	0.79	1.48	0.02	19 900
132	375 000	85 996.75	9.41	11.51	0.04	0.33	2.53	0.02	39 500
133	425 000	85 996.75	9.41	11.51	0.13	1.16	1.28	0.02	19 300
134	475 000	85 996.75	9.41	11.51	0.06	0.59	1.23	0.02	8900
135	525 000	85 996.75	9.41	11.51	0.01	0.07	1.04	0.02	0
136	575 000	85 996.75	9.41	11.51	0.01	0.12	0.80	0.02	0
137	125 000	35 996.75	13.1	10.61	0.00	0.02	0.00	0.02	7000
138	175 000	35 996.75	13.1	10.61	0.06	0.54	0.56	0.02	20 000
139	225 000	35 996.75	13.1	10.61	0.00	0.02	0.00	0.02	1200
140	275 000	35 996.75	13.1	10.61	0.01	0.06	0.00	0.02	5200

---

Table 11 Geophysical data: British National Grid coordination, miscanthus and poplar yields, waste wood, MSW, crop residue and waste residue availabilities and biomass land availability for 2030–2040

Region	X	Y	Miscanthus^67 (t per ha per year)	Poplar^67 (t per ha per year)	Waste wood^75,76 (t per ha per year)	MSW^75,77 (t per ha per year)	Crop residue^78 (t per ha per year)	Forest residue^75,77 (t per ha per year)	Biomass land^78 (ha)
1.00	325 000.00	1035 996.75	8.38	8.93	0.00	0.02	0.00	0.06	56 600.00
2.00	375 000.00	1035 996.75	8.38	8.93	0.00	0.03	0.00	0.06	28 100.00
3.00	225 000.00	985 996.75	8.38	8.93	0.00	0.00	0.00	0.06	2400.00
4.00	275 000.00	985 996.75	8.38	8.93	0.00	0.00	0.00	0.06	17 500.00
5.00	325 000.00	985 996.75	8.38	8.93	0.01	0.06	0.00	0.06	15 000.00
6.00	375 000.00	985 996.75	8.38	8.93	0.00	0.00	0.00	0.06	67 100.00
7.00	125 000.00	935 996.75	8.38	8.93	0.00	0.01	0.00	0.06	26 000.00
8.00	175 000.00	935 996.75	8.38	8.93	0.00	0.03	0.00	0.06	2900.00
9.00	225 000.00	935 996.75	8.38	8.93	0.00	0.00	0.06	0.06	49 000.00
10.00	275 000.00	935 996.75	8.38	8.93	0.00	0.01	0.00	0.06	69 500.00
11.00	325 000.00	935 996.75	8.38	8.93	0.00	0.01	0.00	0.06	18 100.00
12.00	75 000.00	885 996.75	8.38	8.93	0.00	0.00	0.00	0.06	23 300.00
13.00	125 000.00	885 996.75	8.38	8.93	0.00	0.00	0.00	0.06	12 900.00
14.00	175 000.00	885 996.75	8.38	8.93	0.00	0.01	0.00	0.06	41 400.00
15.00	225 000.00	885 996.75	8.38	8.93	0.00	0.01	0.00	0.06	48 500.00
16.00	275 000.00	885 996.75	8.38	8.93	0.02	0.15	0.00	0.06	30 800.00
17.00	325 000.00	885 996.75	8.38	8.93	0.02	0.15	0.24	0.06	28 600.00
18.00	375 000.00	885 996.75	8.38	8.93	0.01	0.11	0.34	0.06	7200.00
19.00	75 000.00	835 996.75	8.38	8.93	0.00	0.00	1.16	0.06	13 000.00
20.00	125 000.00	835 996.75	8.38	8.93	0.01	0.06	0.00	0.06	24 500.00
21.00	175 000.00	835 996.75	8.38	8.93	0.00	0.01	0.00	0.06	68 600.00
22.00	225 000.00	835 996.75	8.38	8.93	0.00	0.00	0.00	0.06	74 600.00
23.00	275 000.00	835 996.75	8.38	8.93	0.02	0.20	0.24	0.06	41 800.00
24.00	325 000.00	835 996.75	8.38	8.93	0.00	0.04	0.34	0.06	58 300.00
25.00	375 000.00	835 996.75	8.38	8.93	0.03	0.31	2.27	0.06	96 600.00
26.00	425 000.00	835 996.75	8.38	8.93	0.06	0.56	0.00	0.06	10 300.00
27.00	125 000.00	785 996.75	8.38	8.93	0.00	0.00	0.00	0.06	16 700.00
28.00	175 000.00	785 996.75	8.38	8.93	0.00	0.01	0.00	0.06	105 000.00
29.00	225 000.00	785 996.75	8.38	8.93	0.00	0.03	0.00	0.06	98 500.00
30.00	275 000.00	785 996.75	8.38	8.93	0.00	0.01	0.00	0.06	39 900.00
31.00	325 000.00	785 996.75	8.38	8.93	0.00	0.04	0.00	0.06	46 900.00
32.00	375 000.00	785 996.75	8.38	8.93	0.02	0.20	1.44	0.06	38 800.00
33.00	75 000.00	735 996.75	8.38	8.93	0.00	0.00	0.00	0.06	3700.00
34.00	125 000.00	735 996.75	8.38	8.93	0.00	0.02	0.00	0.06	20 400.00
35.00	175 000.00	735 996.75	8.38	8.93	0.00	0.01	0.00	0.06	86 800.00
36.00	225 000.00	735 996.75	8.38	8.93	0.01	0.08	0.00	0.06	118 200.00
37.00	275 000.00	735 996.75	8.38	8.93	0.07	0.67	1.10	0.06	107 700.00
38.00	325 000.00	735 996.75	8.38	8.93	0.05	0.44	1.46	0.06	81 000.00
39.00	375 000.00	735 996.75	8.38	8.93	0.00	0.03	0.00	0.06	4500.00
40.00	125 000.00	685 996.75	8.38	8.93	0.00	0.00	0.00	0.06	15 000.00
41.00	175 000.00	685 996.75	8.38	8.93	0.00	0.02	0.02	0.06	36 900.00
42.00	225 000.00	685 996.75	8.38	8.93	0.05	0.47	0.35	0.06	91 800.00
43.00	275 000.00	685 996.75	8.38	8.93	0.46	4.32	1.10	0.06	121 100.00
44.00	325 000.00	685 996.75	8.38	8.93	0.28	2.58	1.46	0.06	58 700.00
45.00	375 000.00	685 996.75	8.38	8.93	0.02	0.18	0.00	0.06	22 800.00
46.00	125 000.00	635 996.75	8.38	8.93	0.00	0.01	0.00	0.06	3400.00
47.00	175 000.00	635 996.75	8.38	8.93	0.00	0.02	0.06	0.06	36 500.00
48.00	225 000.00	635 996.75	8.38	8.93	0.05	0.49	0.35	0.06	95 000.00
49.00	275 000.00	635 996.75	8.38	8.93	0.05	0.43	0.69	0.06	148 200.00
50.00	325 000.00	635 996.75	8.38	8.93	0.01	0.08	0.96	0.06	140 400.00
51.00	375 000.00	635 996.75	9.10	9.91	0.02	0.19	2.35	0.07	84 100.00
52.00	425 000.00	635 996.75	9.10	9.91	0.01	0.11	0.00	0.07	42 300.00
53.00	225 000.00	585 996.75	11.55	10.25	0.01	0.06	0.25	0.02	94 200.00
54.00	275 000.00	585 996.75	11.55	10.25	0.01	0.08	0.15	0.02	129 100.00
55.00	325 000.00	585 996.75	11.55	10.25	0.04	0.33	0.33	0.02	136 900.00
56.00	375 000.00	585 996.75	9.10	9.91	0.02	0.19	0.00	0.07	127 200.00
57.00	425 000.00	585 996.75	9.10	9.91	0.36	3.35	0.93	0.07	56 200.00
58.00	225 000.00	535 996.75	11.55	10.25	0.00	0.01	0.00	0.02	25 900.00
59.00	275 000.00	535 996.75	11.55	10.25	0.00	0.01	0.00	0.02	14 800.00
60.00	325 000.00	535 996.75	11.55	10.25	0.04	0.37	0.00	0.02	166 800.00
61.00	375 000.00	535 996.75	9.54	8.84	0.01	0.13	0.00	0.02	133 300.00
62.00	425 000.00	535 996.75	9.54	8.84	0.12	1.16	3.19	0.02	82 300.00
63.00	475 000.00	535 996.75	9.54	8.84	0.17	1.58	0.41	0.02	36 600.00
64.00	325 000.00	485 996.75	11.55	10.25	0.03	0.26	0.00	0.02	77 800.00
65.00	375 000.00	485 996.75	9.54	8.84	0.06	0.54	0.00	0.02	169 600.00
66.00	425 000.00	485 996.75	9.54	8.84	0.06	0.57	3.19	0.02	102 600.00
67.00	475 000.00	485 996.75	9.54	8.84	0.07	0.64	0.41	0.02	35 600.00
68.00	525 000.00	485 996.75	9.54	8.84	0.04	0.37	0.00	0.02	4500.00
69.00	325 000.00	435 996.75	11.55	10.25	0.12	1.08	0.00	0.02	31 800.00
70.00	375 000.00	435 996.75	7.78	10.36	0.56	5.18	0.00	0.01	137 400.00
71.00	425 000.00	435 996.75	7.78	10.36	0.66	6.11	1.68	0.01	93 700.00
72.00	475 000.00	435 996.75	7.78	10.36	0.20	1.88	3.88	0.01	6200.00
73.00	525 000.00	435 996.75	7.78	10.36	0.18	1.64	2.66	0.01	1300.00
74.00	225 000.00	385 996.75	12.66	9.79	0.01	0.07	0.00	0.06	65 400.00
75.00	275 000.00	385 996.75	12.66	9.79	0.05	0.46	0.00	0.06	108 700.00
76.00	325 000.00	385 996.75	9.23	11.28	0.31	2.90	0.06	0.01	81 800.00
77.00	375 000.00	385 996.75	9.23	11.28	0.60	5.63	0.76	0.01	100 000.00
78.00	425 000.00	385 996.75	9.23	11.28	0.31	2.88	0.84	0.01	128 400.00
79.00	475 000.00	385 996.75	7.78	10.36	0.26	2.42	2.24	0.01	7000.00
80.00	525 000.00	385 996.75	7.78	10.36	0.07	0.67	3.85	0.01	3500.00
81.00	575 000.00	385 996.75	7.48	11.45	0.02	0.16	0.00	0.01	300.00
82.00	225 000.00	335 996.75	12.66	9.79	0.00	0.04	0.00	0.06	139 400.00
83.00	275 000.00	335 996.75	12.66	9.79	0.01	0.11	0.00	0.06	190 000.00
84.00	325 000.00	335 996.75	9.23	11.28	0.04	0.37	0.06	0.01	105 500.00
85.00	375 000.00	335 996.75	9.23	11.28	0.16	1.49	0.76	0.01	94 100.00
86.00	425 000.00	335 996.75	9.23	11.28	0.33	3.12	0.84	0.01	38 000.00
87.00	475 000.00	335 996.75	7.78	10.36	0.40	3.73	2.24	0.01	2600.00
88.00	525 000.00	335 996.75	7.48	11.45	0.10	0.95	3.85	0.01	13 900.00
89.00	575 000.00	335 996.75	7.48	11.45	0.05	0.49	0.00	0.01	25 100.00
90.00	625 000.00	335 996.75	7.48	11.45	0.12	1.11	3.97	0.01	700.00
91.00	275 000.00	285 996.75	12.66	9.79	0.01	0.13	0.00	0.06	182 300.00
92.00	325 000.00	285 996.75	9.23	11.28	0.02	0.18	0.00	0.01	85 400.00
93.00	375 000.00	285 996.75	9.23	11.28	0.13	1.19	1.90	0.01	60 200.00
94.00	425 000.00	285 996.75	9.23	11.28	0.77	7.22	3.13	0.01	59 700.00
95.00	475 000.00	285 996.75	7.48	11.45	0.23	2.13	3.26	0.01	13 000.00
96.00	525 000.00	285 996.75	7.48	11.45	0.13	1.26	3.59	0.01	32 000.00
97.00	575 000.00	285 996.75	7.48	11.45	0.12	1.13	2.10	0.01	24 000.00
98.00	625 000.00	285 996.75	7.48	11.45	0.06	0.57	3.97	0.01	1200.00
99.00	175 000.00	235 996.75	12.66	9.79	0.00	0.04	0.05	0.06	65 500.00
100.00	225 000.00	235 996.75	12.66	9.79	0.04	0.34	0.00	0.06	210 200.00
101.00	275 000.00	235 996.75	12.66	9.79	0.06	0.52	0.00	0.06	207 100.00
102.00	325 000.00	235 996.75	13.30	10.45	0.07	0.67	0.00	0.02	135 600.00
103.00	375 000.00	235 996.75	9.56	11.34	0.13	1.25	3.37	0.02	44 700.00
104.00	425 000.00	235 996.75	9.56	11.34	0.10	0.96	3.11	0.02	62 700.00
105.00	475 000.00	235 996.75	7.48	11.45	0.21	1.98	3.64	0.01	81 600.00
106.00	525 000.00	235 996.75	7.48	11.45	0.37	3.49	3.80	0.01	35 200.00
107.00	575 000.00	235 996.75	7.48	11.45	0.18	1.64	3.88	0.01	22 800.00
108.00	625 000.00	235 996.75	7.48	11.45	0.15	1.39	3.25	0.01	11 600.00
109.00	175 000.00	185 996.75	12.66	9.79	0.00	0.01	0.00	0.06	5300.00
110.00	225 000.00	185 996.75	12.66	9.79	0.11	0.99	0.00	0.06	10 600.00
111.00	275 000.00	185 996.75	12.66	9.79	0.31	2.89	0.16	0.06	52 700.00
112.00	325 000.00	185 996.75	13.30	10.45	0.31	2.86	0.00	0.02	89 800.00
113.00	375 000.00	185 996.75	9.56	11.34	0.12	1.13	2.73	0.02	93 600.00
114.00	425 000.00	185 996.75	9.56	11.34	0.34	3.13	2.76	0.02	68 500.00
115.00	475 000.00	185 996.75	7.48	11.45	2.05	19.07	3.38	0.01	68 200.00
116.00	525 000.00	185 996.75	7.48	11.45	0.83	7.72	2.12	0.01	63 100.00
117.00	575 000.00	185 996.75	7.48	11.45	0.13	1.22	2.91	0.01	55 900.00
118.00	625 000.00	185 996.75	7.48	11.45	0.00	0.00	2.34	0.01	11 400.00
119.00	225 000.00	135 996.75	13.30	10.45	0.01	0.06	0.00	0.02	45 500.00
120.00	275 000.00	135 996.75	13.30	10.45	0.04	0.41	0.16	0.02	175 000.00
121.00	325 000.00	135 996.75	13.30	10.45	0.09	0.81	0.00	0.02	112 400.00
122.00	375 000.00	135 996.75	9.56	11.34	0.09	0.84	2.73	0.02	126 400.00
123.00	425 000.00	135 996.75	9.56	11.34	0.11	1.01	2.76	0.02	70 900.00
124.00	475 000.00	135 996.75	9.56	11.34	0.31	2.87	3.38	0.02	55 400.00
125.00	525 000.00	135 996.75	9.56	11.34	0.30	2.78	2.12	0.02	138 700.00
126.00	575 000.00	135 996.75	9.56	11.34	0.17	1.59	2.91	0.02	120 800.00
127.00	625 000.00	135 996.75	9.56	11.34	0.07	0.66	2.34	0.02	10 600.00
128.00	175 000.00	85 996.75	13.30	10.45	0.01	0.10	0.00	0.02	10 400.00
129.00	225 000.00	85 996.75	13.30	10.45	0.05	0.47	0.00	0.02	11 3600.00
130.00	275 000.00	85 996.75	13.30	10.45	0.14	1.31	1.01	0.02	97 400.00
131.00	325 000.00	85 996.75	13.30	10.45	0.09	0.83	1.48	0.02	19 900.00
132.00	375 000.00	85 996.75	9.56	11.34	0.04	0.34	2.53	0.02	39 500.00
133.00	425 000.00	85 996.75	9.56	11.34	0.13	1.22	1.28	0.02	19 300.00
134.00	475 000.00	85 996.75	9.56	11.34	0.07	0.62	1.23	0.02	8900.00
135.00	525 000.00	85 996.75	9.56	11.34	0.01	0.07	1.04	0.02	0.00
136.00	575 000.00	85 996.75	9.56	11.34	0.01	0.12	0.80	0.02	0.00
137.00	125 000.00	35 996.75	13.30	10.45	0.00	0.02	0.00	0.02	7000.00
138.00	175 000.00	35 996.75	13.30	10.45	0.06	0.56	0.56	0.02	20 000.00
139.00	225 000.00	35 996.75	13.30	10.45	0.00	0.02	0.00	0.02	1200.00
140.00	275 000.00	35 996.75	13.30	10.45	0.01	0.06	0.00	0.02	5200.00

---

Table 12 Geophysical data: British National Grid coordination, miscanthus and poplar yields, waste wood, MSW, crop residue and waste residue availabilities and biomass land availability for 2040–2050

Region	X	Y	Miscanthus^67 (t per ha per year)	Poplar^67 (t per ha per year)	Waste wood^75,76 (t per ha per year)	MSW^75,77 (t per ha per year)	Crop residue^78 (t per ha per year)	Forest residue^75,77 (t per ha per year)	Biomass land^78 (ha)
1	325 000	1 035 997	8.51	8.80	0.00	0.02	0.00	0.06	56 600
2	375 000	1 035 997	8.51	8.80	0.00	0.03	0.00	0.06	28 100
3	225 000	985 996.7	8.51	8.80	0.00	0.00	0.00	0.06	2400
4	275 000	985 996.7	8.51	8.80	0.00	0.00	0.00	0.06	17 500
5	325 000	985 996.7	8.51	8.80	0.01	0.06	0.00	0.06	15 000
6	375 000	985 996.7	8.51	8.80	0.00	0.00	0.00	0.06	67 100
7	125 000	935 996.7	8.51	8.80	0.00	0.01	0.00	0.06	26 000
8	175 000	935 996.7	8.51	8.80	0.00	0.03	0.00	0.06	2900
9	225 000	935 996.7	8.51	8.80	0.00	0.00	0.06	0.06	49 000
10	275 000	935 996.7	8.51	8.80	0.00	0.01	0.00	0.06	69 500
11	325 000	935 996.7	8.51	8.80	0.00	0.01	0.00	0.06	18 100
12	75 000	885 996.7	8.51	8.80	0.00	0.00	0.00	0.06	23 300
13	125 000	885 996.7	8.51	8.80	0.00	0.00	0.00	0.06	12 900
14	175 000	885 996.7	8.51	8.80	0.00	0.01	0.00	0.06	41 400
15	225 000	885 996.7	8.51	8.80	0.00	0.01	0.00	0.06	48 500
16	275 000	885 996.7	8.51	8.80	0.02	0.16	0.00	0.06	30 800
17	325 000	885 996.7	8.51	8.80	0.02	0.16	0.24	0.06	28 600
18	375 000	885 996.7	8.51	8.80	0.01	0.11	0.34	0.06	7200
19	75 000	835 996.7	8.51	8.80	0.00	0.00	1.16	0.06	13 000
20	125 000	835 996.7	8.51	8.80	0.01	0.06	0.00	0.06	24 500
21	175 000	835 996.7	8.51	8.80	0.00	0.01	0.00	0.06	68 600
22	225 000	835 996.7	8.51	8.80	0.00	0.00	0.00	0.06	74 600
23	275 000	835 996.7	8.51	8.80	0.02	0.20	0.24	0.06	41 800
24	325 000	835 996.7	8.51	8.80	0.00	0.04	0.34	0.06	58 300
25	375 000	835 996.7	8.51	8.80	0.03	0.33	2.27	0.06	96 600
26	425 000	835 996.7	8.51	8.80	0.06	0.58	0.00	0.06	10 300
27	125 000	785 996.7	8.51	8.80	0.00	0.00	0.00	0.06	16 700
28	175 000	785 996.7	8.51	8.80	0.00	0.01	0.00	0.06	105000
29	225 000	785 996.7	8.51	8.80	0.00	0.03	0.00	0.06	98 500
30	275 000	785 996.7	8.51	8.80	0.00	0.01	0.00	0.06	39 900
31	325 000	785 996.7	8.51	8.80	0.00	0.04	0.00	0.06	46 900
32	375 000	785 996.7	8.51	8.80	0.02	0.21	1.44	0.06	38 800
33	75 000	735 996.7	8.51	8.80	0.00	0.00	0.00	0.06	3700
34	125 000	735 996.7	8.51	8.80	0.00	0.02	0.00	0.06	20 400
35	175 000	735 996.7	8.51	8.80	0.00	0.01	0.00	0.06	86 800
36	225 000	735 996.7	8.51	8.80	0.01	0.09	0.00	0.06	118 200
37	275 000	735 996.7	8.51	8.80	0.08	0.70	1.10	0.06	107 700
38	325 000	735 996.7	8.51	8.80	0.05	0.46	1.46	0.06	81 000
39	375 000	735 996.7	8.51	8.80	0.00	0.03	0.00	0.06	4500
40	125 000	685 996.7	8.51	8.80	0.00	0.01	0.00	0.06	15 000
41	175 000	685 996.7	8.51	8.80	0.00	0.02	0.02	0.06	36 900
42	225 000	685 996.7	8.51	8.80	0.05	0.49	0.35	0.06	91 800
43	275 000	685 996.7	8.51	8.80	0.48	4.52	1.10	0.06	121 100
44	325 000	685 996.7	8.51	8.80	0.29	2.70	1.46	0.06	58 700
45	375 000	685 996.7	8.51	8.80	0.02	0.18	0.00	0.06	22 800
46	125 000	635 996.7	8.51	8.80	0.00	0.01	0.00	0.06	3400
47	175 000	635 996.7	8.51	8.80	0.00	0.02	0.06	0.06	36 500
48	225 000	635 996.7	8.51	8.80	0.05	0.51	0.35	0.06	95 000
49	275 000	635 996.7	8.51	8.80	0.05	0.45	0.69	0.06	148 200
50	325 000	635 996.7	8.51	8.80	0.01	0.08	0.96	0.06	140 400
51	375 000	635 996.7	9.24	9.76	0.02	0.20	2.35	0.07	84 100
52	425 000	635 996.7	9.24	9.76	0.01	0.12	0.00	0.07	42 300
53	225 000	585 996.7	11.72	10.10	0.01	0.07	0.25	0.02	94 200
54	275 000	585 996.7	11.72	10.10	0.01	0.08	0.15	0.02	129 100
55	325 000	585 996.7	11.72	10.10	0.04	0.34	0.33	0.02	136 900
56	375 000	585 996.7	9.24	9.76	0.02	0.19	0.00	0.07	127 200
57	425 000	585 996.7	9.24	9.76	0.37	3.50	0.93	0.07	56 200
58	225 000	535 996.7	11.72	10.10	0.00	0.01	0.00	0.02	25 900
59	275 000	535 996.7	11.72	10.10	0.00	0.01	0.00	0.02	14 800
60	325 000	535 996.7	11.72	10.10	0.04	0.38	0.00	0.02	166 800
61	375 000	535 996.7	9.68	8.70	0.01	0.14	0.00	0.02	133 300
62	425 000	535 996.7	9.68	8.70	0.13	1.21	3.19	0.02	82 300
63	475 000	535 996.7	9.68	8.70	0.18	1.65	0.41	0.02	36 600
64	325 000	485 996.7	11.72	10.10	0.03	0.28	0.00	0.02	77 800
65	375 000	485 996.7	9.68	8.70	0.06	0.57	0.00	0.02	169 600
66	425 000	485 996.7	9.68	8.70	0.06	0.60	3.19	0.02	102 600
67	475 000	485 996.7	9.68	8.70	0.07	0.67	0.41	0.02	35 600
68	525 000	485 996.7	9.68	8.70	0.04	0.39	0.00	0.02	4500
69	325 000	435 996.7	11.72	10.10	0.12	1.13	0.00	0.02	31 800
70	375 000	435 996.7	7.90	10.21	0.58	5.41	0.00	0.01	137 400
71	425 000	435 996.7	7.90	10.21	0.68	6.39	1.68	0.01	93 700
72	475 000	435 996.7	7.90	10.21	0.21	1.96	3.88	0.01	6200
73	525 000	435 996.7	7.90	10.21	0.18	1.72	2.66	0.01	1300
74	225 000	385 996.7	12.86	9.64	0.01	0.07	0.00	0.06	65 400
75	275 000	385 996.7	12.86	9.64	0.05	0.48	0.00	0.06	108 700
76	325 000	385 996.7	9.37	11.11	0.32	3.03	0.06	0.01	81 800
77	375 000	385 996.7	9.37	11.11	0.63	5.89	0.76	0.01	100 000
78	425 000	385 996.7	9.37	11.11	0.32	3.01	0.84	0.01	128 400
79	475 000	385 996.7	7.90	10.21	0.27	2.53	2.24	0.01	7000
80	525 000	385 996.7	7.90	10.21	0.07	0.70	3.85	0.01	3500
81	575 000	385 996.7	7.60	11.27	0.02	0.17	0.00	0.01	300
82	225 000	335 996.7	12.86	9.64	0.00	0.04	0.00	0.06	139 400
83	275 000	335 996.7	12.86	9.64	0.01	0.11	0.00	0.06	190 000
84	325 000	335 996.7	9.37	11.11	0.04	0.39	0.06	0.01	105 500
85	375 000	335 996.7	9.37	11.11	0.17	1.56	0.76	0.01	94 100
86	425 000	335 996.7	9.37	11.11	0.35	3.26	0.84	0.01	38 000
87	475 000	335 996.7	7.90	10.21	0.42	3.90	2.24	0.01	2600
88	525 000	335 996.7	7.60	11.27	0.11	0.99	3.85	0.01	13 900
89	575 000	335 996.7	7.60	11.27	0.06	0.52	0.00	0.01	25 100
90	625 000	335 996.7	7.60	11.27	0.12	1.16	3.97	0.01	700
91	275 000	285 996.7	12.86	9.64	0.01	0.13	0.00	0.06	182 300
92	325 000	285 996.7	9.37	11.11	0.02	0.18	0.00	0.01	85 400
93	375 000	285 996.7	9.37	11.11	0.13	1.25	1.90	0.01	60 200
94	425 000	285 996.7	9.37	11.11	0.81	7.54	3.13	0.01	59 700
95	475 000	285 996.7	7.60	11.27	0.24	2.22	3.26	0.01	13 000
96	525 000	285 996.7	7.60	11.27	0.14	1.31	3.59	0.01	32 000
97	575 000	285 996.7	7.60	11.27	0.13	1.18	2.10	0.01	24 000
98	625 000	285 996.7	7.60	11.27	0.06	0.60	3.97	0.01	1200
99	175 000	235 996.7	12.86	9.64	0.00	0.04	0.05	0.06	65 500
100	225 000	235 996.7	12.86	9.64	0.04	0.36	0.00	0.06	210 200
101	275 000	235 996.7	12.86	9.64	0.06	0.55	0.00	0.06	207100
102	325 000	235 996.7	13.51	10.29	0.07	0.70	0.00	0.02	135600
103	375 000	235 996.7	9.70	11.17	0.14	1.30	3.37	0.02	44 700
104	425 000	235 996.7	9.70	11.17	0.11	1.00	3.11	0.02	62 700
105	475 000	235 996.7	7.60	11.27	0.22	2.07	3.64	0.01	81 600
106	525 000	235 996.7	7.60	11.27	0.39	3.65	3.80	0.01	35 200
107	575 000	235 996.7	7.60	11.27	0.18	1.72	3.88	0.01	22 800
108	625 000	235 996.7	7.60	11.27	0.16	1.46	3.25	0.01	11 600
109	175 000	185 996.7	12.86	9.64	0.00	0.01	0.00	0.06	5300
110	225 000	185 996.7	12.86	9.64	0.11	1.04	0.00	0.06	10 600
111	275 000	185 996.7	12.86	9.64	0.32	3.02	0.16	0.06	52 700
112	325 000	185 996.7	13.51	10.29	0.32	2.99	0.00	0.02	89 800
113	375 000	185 996.7	9.70	11.17	0.13	1.18	2.73	0.02	93 600
114	425 000	185 996.7	9.70	11.17	0.35	3.27	2.76	0.02	68 500
115	475 000	185 996.7	7.60	11.27	2.13	19.93	3.38	0.01	68 200
116	525 000	185 996.7	7.60	11.27	0.86	8.07	2.12	0.01	63 100
117	575 000	185 996.7	7.60	11.27	0.14	1.28	2.91	0.01	55 900
118	625 000	185 996.7	7.60	11.27	0.00	0.00	2.34	0.01	11 400
119	225 000	135 996.7	13.51	10.29	0.01	0.07	0.00	0.02	45 500
120	275 000	135 996.7	13.51	10.29	0.05	0.43	0.16	0.02	175 000
121	325 000	135 996.7	13.51	10.29	0.09	0.85	0.00	0.02	112 400
122	375 000	135 996.7	9.70	11.17	0.09	0.87	2.73	0.02	126 400
123	425 000	135 996.7	9.70	11.17	0.11	1.06	2.76	0.02	70 900
124	475 000	135 996.7	9.70	11.17	0.32	3.00	3.38	0.02	55 400
125	525 000	135 996.7	9.70	11.17	0.31	2.90	2.12	0.02	138 700
126	575 000	135 996.7	9.70	11.17	0.18	1.66	2.91	0.02	120 800
127	625 000	135 996.7	9.70	11.17	0.07	0.68	2.34	0.02	10 600
128	175 000	85 996.75	13.51	10.29	0.01	0.10	0.00	0.02	10 400
129	225 000	85 996.75	13.51	10.29	0.05	0.49	0.00	0.02	113 600
130	275 000	85 996.75	13.51	10.29	0.15	1.37	1.01	0.02	97 400
131	325 000	85 996.75	13.51	10.29	0.09	0.87	1.48	0.02	19 900
132	375 000	85 996.75	9.70	11.17	0.04	0.36	2.53	0.02	39 500
133	425 000	85 996.75	9.70	11.17	0.14	1.28	1.28	0.02	19 300
134	475 000	85 996.75	9.70	11.17	0.07	0.65	1.23	0.02	8900
135	525 000	85 996.75	9.70	11.17	0.01	0.08	1.04	0.02	0
136	575 000	85 996.75	9.70	11.17	0.01	0.13	0.80	0.02	0
137	125 000	35 996.75	13.51	10.29	0.00	0.02	0.00	0.02	7000
138	175 000	35 996.75	13.51	10.29	0.06	0.59	0.56	0.02	20 000
139	225 000	35 996.75	13.51	10.29	0.00	0.02	0.00	0.02	1200
140	275 000	35 996.75	13.51	10.29	0.01	0.06	0.00	0.02	5200

---

Conflicts of interest

There are no conflicts to declare.

Acknowledgements

The authors would like to acknowledge funding from the Research Councils UK (RCUK) under grants EP/M001369/1 (MESMERISE-CCS), EP/M015351/1 (Opening New Fuels for UK Generation), EP/N024567/1 (CCSInSupply), and NE/P019900/1 (GGR Opt). Mathilde Fajardy thanks Imperial College London for funding a PhD scholarship. Acknowledgement and thanks to the International Institute for Applied Systems Analysis (IIASA) for their contribution. The authors thank Dagmar Henner from the University of Aberdeen for input on crop yield potentials.

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Footnotes

† The Special Report on Emissions Scenarios (SRES) was published in 2000. Similar to the Representative Concentration Pathways (RCPs), the scenarios described in the report depict the relationships between the forces driving greenhouse gas and aerosol emissions and their evolution during the 21st century for large regions and globally. SRES A2 family of scenarios describe a very heterogeneous world with continuously increasing global population and regionally oriented economic growth that is more fragmented and slower than the other pathway scenarios.

‡ This cost reduction is embodied mathematically in the form of learning curves designed to project cost trends of future technology. The learning curve theory can be formalised in an exponential correlation between unit cost and the cumulative capacity installed.⁶³ This study considers the current CAPEX values at 1 GW installed capacity, which is subjected to a cost reduction as cumulative capacity increases to 30 GW.

§ The UK Department of Energy and Climate Change (DECC is now the Department for Business, Energy & Industrial Strategy) predicted the lower bound of the predicted central carbon value to be between £77–200 per t_CO2).¹¹⁰

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