Di
Zhang
ab,
Mai
Bui
ab,
Mathilde
Fajardy
ab,
Piera
Patrizio
c,
Florian
Kraxner
c and
Niall Mac
Dowell
*ab
aCentre for Environmental Policy, Imperial College London, Exhibition Road, London, SW7 1NA, UK. E-mail: niall@imperial.ac.uk; Tel: +44 (0)20 7594 9298
bCentre for Process Systems Engineering, Imperial College London, Exhibition Road, London, SW7 2AZ, UK
cInternational Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A-2361 Laxenburg, Austria
First published on 23rd September 2019
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 MtCO2 per year, though for cost reasons, it may be preferable to import some biomass.
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
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
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.
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.
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.
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%:
![]() | (2) |
Total non-fuel operating costs, TNFOC, is the sum of total pellet production cost, total BECCS operation cost and total CHP operation cost:
![]() | (3) |
The total raw material supply cost (TRMC) is:
![]() | (4) |
Total imported pellets cost (TIPC) is:
![]() | (5) |
The total transport costs, TTC, is the sum of total material transportation cost between regions and total local raw material transportation cost:
![]() | (6) |
TAEt = TCEt + TEEt + TTEt | (7) |
Total captured emissions:
![]() | (8) |
Total embodied emissions:
![]() | (9) |
Total transportation emissions, TTE, is the sum of total material transportation emissions and total local raw material transportation emissions.
![]() | (10) |
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:
TAEt ≤ targett ∀t | (11) |
BWminigt ≤ Pigt ≤ BWigt ∀i ∈ RW, g ∈ G,t | (12) |
BAminigtAigt ≤ Pigt ≤ BAmaxigtAigt ∀i ∈ RA, g ∈ G,t | (13) |
For each region, the area available for cultivating virgin biomass is constrained by local availability:
![]() | (14) |
The total area for planting virgin biomass should be less than the planned total biomass land availabilities of each time period:
![]() | (15) |
![]() | (16) |
In each time period, t, the number of available pellet plants in each region g is ONpelletgt, which is the sum of those plants available from the previous period, ONpelletg,t−1, and the number of plants built in that region and period, Npelletgt, less the number of plants decommissioned in that period, NFpelletgt:
ONpelletgt = ONpelletg,t−1 + Npelletgt − NFpelletgt ∀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:
Npelletgt = NFpelletg,t+θ ∀g ∈ G,t | (18) |
![]() | (19) |
PBECCSele,gt ≤ CABECCSONBECCSgt ∀g ∈ G,t | (20) |
PCHPele,gt ≤ CACHPONCHPgt ∀g ∈ G,t | (21) |
Pele,gt = PBECCSele,gt + PCHPele,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:
ONBECCSgt = ONBECCSg,t−1 + NBECCSgt ∀g ∈ G,t | (23) |
ONCHPgt = ONCHPg,t−1 + NCHPgt ∀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:
![]() | (25) |
![]() | (26) |
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:
HeatCHPgt = κPCHPele,gt ∀g ∈ G,t | (27) |
![]() | (28) |
Pi′gt = γiDigt ∀i ∈ FI, g ∈ G,t | (29) |
![]() | (30) |
The x-axis position on the segment is determined with the constraints below:
xst,ln ≥ XlowlnYlnt,ln ∀t,ln | (31) |
![]() | (32) |
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).
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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. |
![]() | ||
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.
![]() | ||
Fig. 5 (Top) Example of the pellet price calculation for miscanthus. (Bottom) Pellet conversion rates84,86 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 biomass85–87 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. |
(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 MWe 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 Dowell16,98,99 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.
![]() | ||
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).
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 |
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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 |
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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 |
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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 |
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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 |
The average system cost can be expressed as the cost per net tCO2 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%.
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.
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).
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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 tCO2−1).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 tCO2−1 and £50 tCO2−1, 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 − ![]() | (33) |
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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).
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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.
Impact factor | Scenario |
---|---|
BECCS power plant efficiency | High: ∼38%HHV ultra-supercritical BECCS plant using advanced solvent & heat recovery89,90 |
Low: ∼26%HHV subcritical BECCS plant using conventional MEA89,90 | |
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 processing16 |
Low: alternative supply chain, organic fertiliser, in-field operations with biodiesel, drying with biomass, carbon neutral electricity for processing16 | |
Crop yields | High: 7% increase each decade111 |
Low: yield changes as reported by Hastings et al.67 | |
MSW availability | High, central77 or low (±20%) |
Land availability | High, central or low (±20%) |
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 tCO2−1. 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 tCO2−1) 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 tCO2−1) 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 tCO2−1. 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.
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.
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) |
BAmin/maxigt | Minimum/maximum virgin biomass crop yield of raw material i in region g in time period t (t h−1 ha−1) |
BWmin/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 (%) |
CApellet | Pellet production capacity (t h−1) |
CApower | Power generation capacity (MW) |
CAport | 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) |
ICBECCSt | Investment cost of a BECCS power production plant in time period t (£) |
ICCHPt | Investment cost of a CHP power production plant in time period t (£) |
ICpellet | 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 (km2) |
θ | Life time of pellet plant |
κ | Heat to power ratio of CHP power plant |
LAt | Total land availability in time period t (km2) |
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 |
UGCBECCS | Unit BECCS operation cost in power plant (£ MW h−1) |
UGCCHP | 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 (km2) |
D igt | Demand for raw material i in region g in time period t (t h−1) |
HeatCHPgt | 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) |
ONBECCSgt | Number of operating BECCS power plant in region g in time period t (t h−1) |
ONCHPgt | Number of operating CHP power plant in region g in time period t (t h−1) |
ONpelletgt | 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) |
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) | — |
Embodied emissions16 (tCO2 per t DM pellet) | Energy59 density (MJ t−1) | BECCS electrical efficiencya (%)89 | BECCS electrical efficiencyb (%)89 | BE-CHP-CCS electrical efficiencye (%)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![]() |
36.3 | 28.3 | 36.0 | 17.55 |
MSW pellets | 0 | 12![]() |
30.8 | 21.5 | 36.0 | 18.14 |
Miscanthus pellets | 0.272c/0.049d | 18![]() |
36.8 | 29.6 | 36.0 | 17.86 |
Poplar pellets | 0.257c/0.075d | 18![]() |
36.1 | 28.5 | 36.0 | 26.7 |
Crop residue pellets | 0.224 | 18![]() |
36.1 | 28.5 | 36.0 | 18.14 |
Forest residue pellets | 0.268c/0.049d | 15![]() |
36.8 | 29.6 | 36.0 | 18.14 |
Imported pellets (without transportation) | 0.224 | 18![]() |
36.1 | 28.5 | 36.0 | — |
Unit capacity | CAPEXa | CAPEXb | 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 plantc | 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 |
X | Y | Unloading capacity (Mt per year)90 | |
---|---|---|---|
Tyne | 375![]() |
735![]() |
2 |
Hull | 475![]() |
535![]() |
1 |
Immingham | 525![]() |
435![]() |
3 |
Liverpool | 325![]() |
435![]() |
3 |
Region | X | Y | Miscanthus67 (t per ha per year) | Poplar67 (t per ha per year) | Waste wood75,76 (t per ha per year) | MSW75,77 (t per ha per year) | Crop residue78 (t per ha per year) | Forest residue75,77 (t per ha per year) | Biomass land78 (ha) |
---|---|---|---|---|---|---|---|---|---|
1 | 325![]() |
1![]() ![]() |
8.25 | 9.07 | 0.00 | 0.02 | 0.00 | 0.06 | 56![]() |
2 | 375![]() |
1![]() ![]() |
8.25 | 9.07 | 0.00 | 0.03 | 0.00 | 0.06 | 28![]() |
3 | 225![]() |
985![]() |
8.25 | 9.07 | 0.00 | 0.00 | 0.00 | 0.06 | 2400 |
4 | 275![]() |
985![]() |
8.25 | 9.07 | 0.00 | 0.00 | 0.00 | 0.06 | 17![]() |
5 | 325![]() |
985![]() |
8.25 | 9.07 | 0.01 | 0.05 | 0.00 | 0.06 | 15![]() |
6 | 375![]() |
985![]() |
8.25 | 9.07 | 0.00 | 0.00 | 0.00 | 0.06 | 67![]() |
7 | 125![]() |
935![]() |
8.25 | 9.07 | 0.00 | 0.01 | 0.00 | 0.06 | 26![]() |
8 | 175![]() |
935![]() |
8.25 | 9.07 | 0.00 | 0.03 | 0.00 | 0.06 | 2900 |
9 | 225![]() |
935![]() |
8.25 | 9.07 | 0.00 | 0.00 | 0.06 | 0.06 | 49![]() |
10 | 275![]() |
935![]() |
8.25 | 9.07 | 0.00 | 0.01 | 0.00 | 0.06 | 69![]() |
11 | 325![]() |
935![]() |
8.25 | 9.07 | 0.00 | 0.01 | 0.00 | 0.06 | 18![]() |
12 | 75![]() |
885![]() |
8.25 | 9.07 | 0.00 | 0.00 | 0.00 | 0.06 | 23![]() |
13 | 125![]() |
885![]() |
8.25 | 9.07 | 0.00 | 0.00 | 0.00 | 0.06 | 12![]() |
14 | 175![]() |
885![]() |
8.25 | 9.07 | 0.00 | 0.01 | 0.00 | 0.06 | 41![]() |
15 | 225![]() |
885![]() |
8.25 | 9.07 | 0.00 | 0.01 | 0.00 | 0.06 | 48![]() |
16 | 275![]() |
885![]() |
8.25 | 9.07 | 0.02 | 0.14 | 0.00 | 0.06 | 30![]() |
17 | 325![]() |
885![]() |
8.25 | 9.07 | 0.02 | 0.15 | 0.24 | 0.06 | 28![]() |
18 | 375![]() |
885![]() |
8.25 | 9.07 | 0.01 | 0.10 | 0.34 | 0.06 | 7200 |
19 | 75![]() |
835![]() |
8.25 | 9.07 | 0.00 | 0.00 | 1.16 | 0.06 | 13![]() |
20 | 125![]() |
835![]() |
8.25 | 9.07 | 0.01 | 0.06 | 0.00 | 0.06 | 24![]() |
21 | 175![]() |
835![]() |
8.25 | 9.07 | 0.00 | 0.01 | 0.00 | 0.06 | 68![]() |
22 | 225![]() |
835![]() |
8.25 | 9.07 | 0.00 | 0.00 | 0.00 | 0.06 | 74![]() |
23 | 275![]() |
835![]() |
8.25 | 9.07 | 0.02 | 0.19 | 0.24 | 0.06 | 41![]() |
24 | 325![]() |
835![]() |
8.25 | 9.07 | 0.00 | 0.04 | 0.34 | 0.06 | 58![]() |
25 | 375![]() |
835![]() |
8.25 | 9.07 | 0.03 | 0.30 | 2.27 | 0.06 | 96![]() |
26 | 425![]() |
835![]() |
8.25 | 9.07 | 0.06 | 0.53 | 0.00 | 0.06 | 10![]() |
27 | 125![]() |
785![]() |
8.25 | 9.07 | 0.00 | 0.00 | 0.00 | 0.06 | 16![]() |
28 | 175![]() |
785![]() |
8.25 | 9.07 | 0.00 | 0.01 | 0.00 | 0.06 | 105![]() |
29 | 225![]() |
785![]() |
8.25 | 9.07 | 0.00 | 0.03 | 0.00 | 0.06 | 98![]() |
30 | 275![]() |
785![]() |
8.25 | 9.07 | 0.00 | 0.01 | 0.00 | 0.06 | 39![]() |
31 | 325![]() |
785![]() |
8.25 | 9.07 | 0.00 | 0.04 | 0.00 | 0.06 | 46![]() |
32 | 375![]() |
785![]() |
8.25 | 9.07 | 0.02 | 0.19 | 1.44 | 0.06 | 38![]() |
33 | 75![]() |
735![]() |
8.25 | 9.07 | 0.00 | 0.00 | 0.00 | 0.06 | 3700 |
34 | 125![]() |
735![]() |
8.25 | 9.07 | 0.00 | 0.02 | 0.00 | 0.06 | 20![]() |
35 | 175![]() |
735![]() |
8.25 | 9.07 | 0.00 | 0.01 | 0.00 | 0.06 | 86![]() |
36 | 225![]() |
735![]() |
8.25 | 9.07 | 0.01 | 0.08 | 0.00 | 0.06 | 118![]() |
37 | 275![]() |
735![]() |
8.25 | 9.07 | 0.07 | 0.64 | 1.10 | 0.06 | 107![]() |
38 | 325![]() |
735![]() |
8.25 | 9.07 | 0.04 | 0.42 | 1.46 | 0.06 | 81![]() |
39 | 375![]() |
735![]() |
8.25 | 9.07 | 0.00 | 0.03 | 0.00 | 0.06 | 4500 |
40 | 125![]() |
685![]() |
8.25 | 9.07 | 0.00 | 0.00 | 0.00 | 0.06 | 15![]() |
41 | 175![]() |
685![]() |
8.25 | 9.07 | 0.00 | 0.02 | 0.02 | 0.06 | 36![]() |
42 | 225![]() |
685![]() |
8.25 | 9.07 | 0.05 | 0.45 | 0.35 | 0.06 | 91![]() |
43 | 275![]() |
685![]() |
8.25 | 9.07 | 0.44 | 4.11 | 1.10 | 0.06 | 121![]() |
44 | 325![]() |
685![]() |
8.25 | 9.07 | 0.26 | 2.46 | 1.46 | 0.06 | 58![]() |
45 | 375![]() |
685![]() |
8.25 | 9.07 | 0.02 | 0.17 | 0.00 | 0.06 | 22![]() |
46 | 125![]() |
635![]() |
8.25 | 9.07 | 0.00 | 0.01 | 0.00 | 0.06 | 3400 |
47 | 175![]() |
635![]() |
8.25 | 9.07 | 0.00 | 0.02 | 0.06 | 0.06 | 36![]() |
48 | 225![]() |
635![]() |
8.25 | 9.07 | 0.05 | 0.47 | 0.35 | 0.06 | 95![]() |
49 | 275![]() |
635![]() |
8.25 | 9.07 | 0.04 | 0.41 | 0.69 | 0.06 | 148![]() |
50 | 325![]() |
635![]() |
8.25 | 9.07 | 0.01 | 0.08 | 0.96 | 0.06 | 140![]() |
51 | 375![]() |
635![]() |
8.96 | 10.06 | 0.02 | 0.18 | 2.35 | 0.07 | 84![]() |
52 | 425![]() |
635![]() |
8.96 | 10.06 | 0.01 | 0.11 | 0.00 | 0.07 | 42![]() |
53 | 225![]() |
585![]() |
11.37 | 10.41 | 0.01 | 0.06 | 0.25 | 0.02 | 94![]() |
54 | 275![]() |
585![]() |
11.37 | 10.41 | 0.01 | 0.08 | 0.15 | 0.02 | 129![]() |
55 | 325![]() |
585![]() |
11.37 | 10.41 | 0.03 | 0.31 | 0.33 | 0.02 | 136![]() |
56 | 375![]() |
585![]() |
8.96 | 10.06 | 0.02 | 0.18 | 0.00 | 0.07 | 127![]() |
57 | 425![]() |
585![]() |
8.96 | 10.06 | 0.34 | 3.19 | 0.93 | 0.07 | 56![]() |
58 | 225![]() |
535![]() |
11.37 | 10.41 | 0.00 | 0.01 | 0.00 | 0.02 | 25![]() |
59 | 275![]() |
535![]() |
11.37 | 10.41 | 0.00 | 0.01 | 0.00 | 0.02 | 14![]() |
60 | 325![]() |
535![]() |
11.37 | 10.41 | 0.04 | 0.35 | 0.00 | 0.02 | 166![]() |
61 | 375![]() |
535![]() |
9.39 | 8.97 | 0.01 | 0.12 | 0.00 | 0.02 | 133![]() |
62 | 425![]() |
535![]() |
9.39 | 8.97 | 0.12 | 1.10 | 3.19 | 0.02 | 82![]() |
63 | 475![]() |
535![]() |
9.39 | 8.97 | 0.16 | 1.50 | 0.41 | 0.02 | 36![]() |
64 | 325![]() |
485![]() |
9.39 | 8.97 | 0.03 | 0.25 | 0.00 | 0.02 | 77![]() |
65 | 375![]() |
485![]() |
9.39 | 8.97 | 0.06 | 0.52 | 0.00 | 0.02 | 169![]() |
66 | 425![]() |
485![]() |
9.39 | 8.97 | 0.06 | 0.54 | 3.19 | 0.02 | 102![]() |
67 | 475![]() |
485![]() |
8.96 | 10.06 | 0.07 | 0.61 | 0.41 | 0.02 | 35![]() |
68 | 525![]() |
485![]() |
9.39 | 8.97 | 0.04 | 0.35 | 0.00 | 0.02 | 4500 |
69 | 325![]() |
435![]() |
11.37 | 10.41 | 0.11 | 1.03 | 0.00 | 0.02 | 31![]() |
70 | 375![]() |
435![]() |
7.66 | 10.52 | 0.53 | 4.93 | 0.00 | 0.01 | 137![]() |
71 | 425![]() |
435![]() |
7.66 | 10.52 | 0.63 | 5.82 | 1.68 | 0.01 | 93![]() |
72 | 475![]() |
435![]() |
7.66 | 10.52 | 0.19 | 1.79 | 3.88 | 0.01 | 6200 |
73 | 525![]() |
435![]() |
7.66 | 10.52 | 0.17 | 1.57 | 2.66 | 0.01 | 1300 |
74 | 225![]() |
385![]() |
12.47 | 9.94 | 0.01 | 0.07 | 0.00 | 0.06 | 65![]() |
75 | 275![]() |
385![]() |
12.47 | 9.94 | 0.05 | 0.44 | 0.00 | 0.06 | 108![]() |
76 | 325![]() |
385![]() |
9.09 | 11.45 | 0.30 | 2.76 | 0.06 | 0.01 | 81![]() |
77 | 375![]() |
385![]() |
9.09 | 11.45 | 0.58 | 5.36 | 0.76 | 0.01 | 100![]() |
78 | 425![]() |
385![]() |
9.09 | 11.45 | 0.30 | 2.75 | 0.84 | 0.01 | 128![]() |
79 | 475![]() |
385![]() |
7.66 | 10.52 | 0.25 | 2.30 | 2.24 | 0.01 | 7000 |
80 | 525![]() |
385![]() |
7.66 | 10.52 | 0.07 | 0.64 | 3.85 | 0.01 | 3500 |
81 | 575![]() |
385![]() |
7.37 | 11.62 | 0.02 | 0.15 | 0.00 | 0.01 | 300 |
82 | 225![]() |
335![]() |
12.47 | 9.94 | 0.00 | 0.04 | 0.00 | 0.06 | 139![]() |
83 | 275![]() |
335![]() |
12.47 | 9.94 | 0.01 | 0.10 | 0.00 | 0.06 | 190![]() |
84 | 325![]() |
335![]() |
9.09 | 11.45 | 0.04 | 0.35 | 0.06 | 0.01 | 105![]() |
85 | 375![]() |
335![]() |
9.09 | 11.45 | 0.15 | 1.42 | 0.76 | 0.01 | 94![]() |
86 | 425![]() |
335![]() |
9.09 | 11.45 | 0.32 | 2.97 | 0.84 | 0.01 | 38![]() |
87 | 475![]() |
335![]() |
7.66 | 10.52 | 0.38 | 3.55 | 2.24 | 0.01 | 2600 |
88 | 525![]() |
335![]() |
7.37 | 11.62 | 0.10 | 0.91 | 3.85 | 0.01 | 13![]() |
89 | 575![]() |
335![]() |
7.37 | 11.62 | 0.05 | 0.47 | 0.00 | 0.01 | 25![]() |
90 | 625![]() |
335![]() |
7.37 | 11.62 | 0.11 | 1.06 | 3.97 | 0.01 | 700 |
91 | 275![]() |
285![]() |
12.47 | 9.94 | 0.01 | 0.12 | 0.00 | 0.06 | 182![]() |
92 | 325![]() |
285![]() |
9.09 | 11.45 | 0.02 | 0.17 | 0.00 | 0.01 | 85![]() |
93 | 375![]() |
285![]() |
9.09 | 11.45 | 0.12 | 1.14 | 1.90 | 0.01 | 60![]() |
94 | 425![]() |
285![]() |
9.09 | 11.45 | 0.74 | 6.87 | 3.13 | 0.01 | 59![]() |
95 | 475![]() |
285![]() |
7.37 | 11.62 | 0.22 | 2.02 | 3.26 | 0.01 | 13![]() |
96 | 525![]() |
285![]() |
7.37 | 11.62 | 0.13 | 1.20 | 3.59 | 0.01 | 32![]() |
97 | 575![]() |
285![]() |
7.37 | 11.62 | 0.12 | 1.08 | 2.10 | 0.01 | 24![]() |
98 | 625![]() |
285![]() |
7.37 | 11.62 | 0.06 | 0.54 | 3.97 | 0.01 | 1200 |
99 | 175![]() |
235![]() |
12.47 | 9.94 | 0.00 | 0.03 | 0.05 | 0.06 | 65![]() |
100 | 225![]() |
235![]() |
12.47 | 9.94 | 0.04 | 0.33 | 0.00 | 0.06 | 210![]() |
101 | 275![]() |
235![]() |
12.47 | 9.94 | 0.05 | 0.50 | 0.00 | 0.06 | 207![]() |
102 | 325![]() |
235![]() |
13.1 | 10.61 | 0.07 | 0.64 | 0.00 | 0.02 | 135![]() |
103 | 375![]() |
235![]() |
9.41 | 11.51 | 0.13 | 1.19 | 3.37 | 0.02 | 44![]() |
104 | 425![]() |
235![]() |
9.41 | 11.51 | 0.10 | 0.91 | 3.11 | 0.02 | 62![]() |
105 | 475![]() |
235![]() |
7.37 | 11.62 | 0.20 | 1.89 | 3.64 | 0.01 | 81![]() |
106 | 525![]() |
235![]() |
7.37 | 11.62 | 0.36 | 3.32 | 3.80 | 0.01 | 35![]() |
107 | 575![]() |
235![]() |
7.37 | 11.62 | 0.17 | 1.56 | 3.88 | 0.01 | 22![]() |
108 | 625![]() |
235![]() |
7.37 | 11.62 | 0.14 | 1.33 | 3.25 | 0.01 | 11![]() |
109 | 175![]() |
185![]() |
12.47 | 9.94 | 0.00 | 0.01 | 0.00 | 0.06 | 5300 |
110 | 225![]() |
185![]() |
12.47 | 9.94 | 0.10 | 0.95 | 0.00 | 0.06 | 10![]() |
111 | 275![]() |
185![]() |
12.47 | 9.94 | 0.30 | 2.75 | 0.16 | 0.06 | 52![]() |
112 | 325![]() |
185![]() |
13.1 | 10.61 | 0.29 | 2.73 | 0.00 | 0.02 | 89![]() |
113 | 375![]() |
185![]() |
9.41 | 11.51 | 0.12 | 1.08 | 2.73 | 0.02 | 93![]() |
114 | 425![]() |
185![]() |
9.41 | 11.51 | 0.32 | 2.98 | 2.76 | 0.02 | 68![]() |
115 | 475![]() |
185![]() |
7.37 | 11.62 | 1.96 | 18.16 | 3.38 | 0.01 | 68![]() |
116 | 525![]() |
185![]() |
7.37 | 11.62 | 0.79 | 7.36 | 2.12 | 0.01 | 63![]() |
117 | 575![]() |
185![]() |
7.37 | 11.62 | 0.13 | 1.16 | 2.91 | 0.01 | 55![]() |
118 | 625![]() |
185![]() |
7.37 | 11.62 | 0.00 | 0.00 | 2.34 | 0.01 | 11![]() |
119 | 225![]() |
135![]() |
13.1 | 10.61 | 0.01 | 0.06 | 0.00 | 0.02 | 45![]() |
120 | 275![]() |
135![]() |
13.1 | 10.61 | 0.04 | 0.39 | 0.16 | 0.02 | 175![]() |
121 | 325![]() |
135![]() |
13.1 | 10.61 | 0.08 | 0.77 | 0.00 | 0.02 | 112![]() |
122 | 375![]() |
135![]() |
9.41 | 11.51 | 0.09 | 0.80 | 2.73 | 0.02 | 126![]() |
123 | 425![]() |
135![]() |
9.41 | 11.51 | 0.10 | 0.96 | 2.76 | 0.02 | 70![]() |
124 | 475![]() |
135![]() |
9.41 | 11.51 | 0.29 | 2.73 | 3.38 | 0.02 | 55![]() |
125 | 525![]() |
135![]() |
9.41 | 11.51 | 0.28 | 2.64 | 2.12 | 0.02 | 138![]() |
126 | 575![]() |
135![]() |
9.41 | 11.51 | 0.16 | 1.52 | 2.91 | 0.02 | 120![]() |
127 | 625![]() |
135![]() |
9.41 | 11.51 | 0.07 | 0.62 | 2.34 | 0.02 | 10![]() |
128 | 175![]() |
85![]() |
13.1 | 10.61 | 0.01 | 0.09 | 0.00 | 0.02 | 10![]() |
129 | 225![]() |
85![]() |
13.1 | 10.61 | 0.05 | 0.45 | 0.00 | 0.02 | 113![]() |
130 | 275![]() |
85![]() |
13.1 | 10.61 | 0.13 | 1.25 | 1.01 | 0.02 | 97![]() |
131 | 325![]() |
85![]() |
13.1 | 10.61 | 0.09 | 0.79 | 1.48 | 0.02 | 19![]() |
132 | 375![]() |
85![]() |
9.41 | 11.51 | 0.04 | 0.33 | 2.53 | 0.02 | 39![]() |
133 | 425![]() |
85![]() |
9.41 | 11.51 | 0.13 | 1.16 | 1.28 | 0.02 | 19![]() |
134 | 475![]() |
85![]() |
9.41 | 11.51 | 0.06 | 0.59 | 1.23 | 0.02 | 8900 |
135 | 525![]() |
85![]() |
9.41 | 11.51 | 0.01 | 0.07 | 1.04 | 0.02 | 0 |
136 | 575![]() |
85![]() |
9.41 | 11.51 | 0.01 | 0.12 | 0.80 | 0.02 | 0 |
137 | 125![]() |
35![]() |
13.1 | 10.61 | 0.00 | 0.02 | 0.00 | 0.02 | 7000 |
138 | 175![]() |
35![]() |
13.1 | 10.61 | 0.06 | 0.54 | 0.56 | 0.02 | 20![]() |
139 | 225![]() |
35![]() |
13.1 | 10.61 | 0.00 | 0.02 | 0.00 | 0.02 | 1200 |
140 | 275![]() |
35![]() |
13.1 | 10.61 | 0.01 | 0.06 | 0.00 | 0.02 | 5200 |
Region | X | Y | Miscanthus67 (t per ha per year) | Poplar67 (t per ha per year) | Waste wood75,76 (t per ha per year) | MSW75,77 (t per ha per year) | Crop residue78 (t per ha per year) | Forest residue75,77 (t per ha per year) | Biomass land78 (ha) |
---|---|---|---|---|---|---|---|---|---|
1.00 | 325![]() |
1035![]() |
8.38 | 8.93 | 0.00 | 0.02 | 0.00 | 0.06 | 56![]() |
2.00 | 375![]() |
1035![]() |
8.38 | 8.93 | 0.00 | 0.03 | 0.00 | 0.06 | 28![]() |
3.00 | 225![]() |
985![]() |
8.38 | 8.93 | 0.00 | 0.00 | 0.00 | 0.06 | 2400.00 |
4.00 | 275![]() |
985![]() |
8.38 | 8.93 | 0.00 | 0.00 | 0.00 | 0.06 | 17![]() |
5.00 | 325![]() |
985![]() |
8.38 | 8.93 | 0.01 | 0.06 | 0.00 | 0.06 | 15![]() |
6.00 | 375![]() |
985![]() |
8.38 | 8.93 | 0.00 | 0.00 | 0.00 | 0.06 | 67![]() |
7.00 | 125![]() |
935![]() |
8.38 | 8.93 | 0.00 | 0.01 | 0.00 | 0.06 | 26![]() |
8.00 | 175![]() |
935![]() |
8.38 | 8.93 | 0.00 | 0.03 | 0.00 | 0.06 | 2900.00 |
9.00 | 225![]() |
935![]() |
8.38 | 8.93 | 0.00 | 0.00 | 0.06 | 0.06 | 49![]() |
10.00 | 275![]() |
935![]() |
8.38 | 8.93 | 0.00 | 0.01 | 0.00 | 0.06 | 69![]() |
11.00 | 325![]() |
935![]() |
8.38 | 8.93 | 0.00 | 0.01 | 0.00 | 0.06 | 18![]() |
12.00 | 75![]() |
885![]() |
8.38 | 8.93 | 0.00 | 0.00 | 0.00 | 0.06 | 23![]() |
13.00 | 125![]() |
885![]() |
8.38 | 8.93 | 0.00 | 0.00 | 0.00 | 0.06 | 12![]() |
14.00 | 175![]() |
885![]() |
8.38 | 8.93 | 0.00 | 0.01 | 0.00 | 0.06 | 41![]() |
15.00 | 225![]() |
885![]() |
8.38 | 8.93 | 0.00 | 0.01 | 0.00 | 0.06 | 48![]() |
16.00 | 275![]() |
885![]() |
8.38 | 8.93 | 0.02 | 0.15 | 0.00 | 0.06 | 30![]() |
17.00 | 325![]() |
885![]() |
8.38 | 8.93 | 0.02 | 0.15 | 0.24 | 0.06 | 28![]() |
18.00 | 375![]() |
885![]() |
8.38 | 8.93 | 0.01 | 0.11 | 0.34 | 0.06 | 7200.00 |
19.00 | 75![]() |
835![]() |
8.38 | 8.93 | 0.00 | 0.00 | 1.16 | 0.06 | 13![]() |
20.00 | 125![]() |
835![]() |
8.38 | 8.93 | 0.01 | 0.06 | 0.00 | 0.06 | 24![]() |
21.00 | 175![]() |
835![]() |
8.38 | 8.93 | 0.00 | 0.01 | 0.00 | 0.06 | 68![]() |
22.00 | 225![]() |
835![]() |
8.38 | 8.93 | 0.00 | 0.00 | 0.00 | 0.06 | 74![]() |
23.00 | 275![]() |
835![]() |
8.38 | 8.93 | 0.02 | 0.20 | 0.24 | 0.06 | 41![]() |
24.00 | 325![]() |
835![]() |
8.38 | 8.93 | 0.00 | 0.04 | 0.34 | 0.06 | 58![]() |
25.00 | 375![]() |
835![]() |
8.38 | 8.93 | 0.03 | 0.31 | 2.27 | 0.06 | 96![]() |
26.00 | 425![]() |
835![]() |
8.38 | 8.93 | 0.06 | 0.56 | 0.00 | 0.06 | 10![]() |
27.00 | 125![]() |
785![]() |
8.38 | 8.93 | 0.00 | 0.00 | 0.00 | 0.06 | 16![]() |
28.00 | 175![]() |
785![]() |
8.38 | 8.93 | 0.00 | 0.01 | 0.00 | 0.06 | 105![]() |
29.00 | 225![]() |
785![]() |
8.38 | 8.93 | 0.00 | 0.03 | 0.00 | 0.06 | 98![]() |
30.00 | 275![]() |
785![]() |
8.38 | 8.93 | 0.00 | 0.01 | 0.00 | 0.06 | 39![]() |
31.00 | 325![]() |
785![]() |
8.38 | 8.93 | 0.00 | 0.04 | 0.00 | 0.06 | 46![]() |
32.00 | 375![]() |
785![]() |
8.38 | 8.93 | 0.02 | 0.20 | 1.44 | 0.06 | 38![]() |
33.00 | 75![]() |
735![]() |
8.38 | 8.93 | 0.00 | 0.00 | 0.00 | 0.06 | 3700.00 |
34.00 | 125![]() |
735![]() |
8.38 | 8.93 | 0.00 | 0.02 | 0.00 | 0.06 | 20![]() |
35.00 | 175![]() |
735![]() |
8.38 | 8.93 | 0.00 | 0.01 | 0.00 | 0.06 | 86![]() |
36.00 | 225![]() |
735![]() |
8.38 | 8.93 | 0.01 | 0.08 | 0.00 | 0.06 | 118![]() |
37.00 | 275![]() |
735![]() |
8.38 | 8.93 | 0.07 | 0.67 | 1.10 | 0.06 | 107![]() |
38.00 | 325![]() |
735![]() |
8.38 | 8.93 | 0.05 | 0.44 | 1.46 | 0.06 | 81![]() |
39.00 | 375![]() |
735![]() |
8.38 | 8.93 | 0.00 | 0.03 | 0.00 | 0.06 | 4500.00 |
40.00 | 125![]() |
685![]() |
8.38 | 8.93 | 0.00 | 0.00 | 0.00 | 0.06 | 15![]() |
41.00 | 175![]() |
685![]() |
8.38 | 8.93 | 0.00 | 0.02 | 0.02 | 0.06 | 36![]() |
42.00 | 225![]() |
685![]() |
8.38 | 8.93 | 0.05 | 0.47 | 0.35 | 0.06 | 91![]() |
43.00 | 275![]() |
685![]() |
8.38 | 8.93 | 0.46 | 4.32 | 1.10 | 0.06 | 121![]() |
44.00 | 325![]() |
685![]() |
8.38 | 8.93 | 0.28 | 2.58 | 1.46 | 0.06 | 58![]() |
45.00 | 375![]() |
685![]() |
8.38 | 8.93 | 0.02 | 0.18 | 0.00 | 0.06 | 22![]() |
46.00 | 125![]() |
635![]() |
8.38 | 8.93 | 0.00 | 0.01 | 0.00 | 0.06 | 3400.00 |
47.00 | 175![]() |
635![]() |
8.38 | 8.93 | 0.00 | 0.02 | 0.06 | 0.06 | 36![]() |
48.00 | 225![]() |
635![]() |
8.38 | 8.93 | 0.05 | 0.49 | 0.35 | 0.06 | 95![]() |
49.00 | 275![]() |
635![]() |
8.38 | 8.93 | 0.05 | 0.43 | 0.69 | 0.06 | 148![]() |
50.00 | 325![]() |
635![]() |
8.38 | 8.93 | 0.01 | 0.08 | 0.96 | 0.06 | 140![]() |
51.00 | 375![]() |
635![]() |
9.10 | 9.91 | 0.02 | 0.19 | 2.35 | 0.07 | 84![]() |
52.00 | 425![]() |
635![]() |
9.10 | 9.91 | 0.01 | 0.11 | 0.00 | 0.07 | 42![]() |
53.00 | 225![]() |
585![]() |
11.55 | 10.25 | 0.01 | 0.06 | 0.25 | 0.02 | 94![]() |
54.00 | 275![]() |
585![]() |
11.55 | 10.25 | 0.01 | 0.08 | 0.15 | 0.02 | 129![]() |
55.00 | 325![]() |
585![]() |
11.55 | 10.25 | 0.04 | 0.33 | 0.33 | 0.02 | 136![]() |
56.00 | 375![]() |
585![]() |
9.10 | 9.91 | 0.02 | 0.19 | 0.00 | 0.07 | 127![]() |
57.00 | 425![]() |
585![]() |
9.10 | 9.91 | 0.36 | 3.35 | 0.93 | 0.07 | 56![]() |
58.00 | 225![]() |
535![]() |
11.55 | 10.25 | 0.00 | 0.01 | 0.00 | 0.02 | 25![]() |
59.00 | 275![]() |
535![]() |
11.55 | 10.25 | 0.00 | 0.01 | 0.00 | 0.02 | 14![]() |
60.00 | 325![]() |
535![]() |
11.55 | 10.25 | 0.04 | 0.37 | 0.00 | 0.02 | 166![]() |
61.00 | 375![]() |
535![]() |
9.54 | 8.84 | 0.01 | 0.13 | 0.00 | 0.02 | 133![]() |
62.00 | 425![]() |
535![]() |
9.54 | 8.84 | 0.12 | 1.16 | 3.19 | 0.02 | 82![]() |
63.00 | 475![]() |
535![]() |
9.54 | 8.84 | 0.17 | 1.58 | 0.41 | 0.02 | 36![]() |
64.00 | 325![]() |
485![]() |
11.55 | 10.25 | 0.03 | 0.26 | 0.00 | 0.02 | 77![]() |
65.00 | 375![]() |
485![]() |
9.54 | 8.84 | 0.06 | 0.54 | 0.00 | 0.02 | 169![]() |
66.00 | 425![]() |
485![]() |
9.54 | 8.84 | 0.06 | 0.57 | 3.19 | 0.02 | 102![]() |
67.00 | 475![]() |
485![]() |
9.54 | 8.84 | 0.07 | 0.64 | 0.41 | 0.02 | 35![]() |
68.00 | 525![]() |
485![]() |
9.54 | 8.84 | 0.04 | 0.37 | 0.00 | 0.02 | 4500.00 |
69.00 | 325![]() |
435![]() |
11.55 | 10.25 | 0.12 | 1.08 | 0.00 | 0.02 | 31![]() |
70.00 | 375![]() |
435![]() |
7.78 | 10.36 | 0.56 | 5.18 | 0.00 | 0.01 | 137![]() |
71.00 | 425![]() |
435![]() |
7.78 | 10.36 | 0.66 | 6.11 | 1.68 | 0.01 | 93![]() |
72.00 | 475![]() |
435![]() |
7.78 | 10.36 | 0.20 | 1.88 | 3.88 | 0.01 | 6200.00 |
73.00 | 525![]() |
435![]() |
7.78 | 10.36 | 0.18 | 1.64 | 2.66 | 0.01 | 1300.00 |
74.00 | 225![]() |
385![]() |
12.66 | 9.79 | 0.01 | 0.07 | 0.00 | 0.06 | 65![]() |
75.00 | 275![]() |
385![]() |
12.66 | 9.79 | 0.05 | 0.46 | 0.00 | 0.06 | 108![]() |
76.00 | 325![]() |
385![]() |
9.23 | 11.28 | 0.31 | 2.90 | 0.06 | 0.01 | 81![]() |
77.00 | 375![]() |
385![]() |
9.23 | 11.28 | 0.60 | 5.63 | 0.76 | 0.01 | 100![]() |
78.00 | 425![]() |
385![]() |
9.23 | 11.28 | 0.31 | 2.88 | 0.84 | 0.01 | 128![]() |
79.00 | 475![]() |
385![]() |
7.78 | 10.36 | 0.26 | 2.42 | 2.24 | 0.01 | 7000.00 |
80.00 | 525![]() |
385![]() |
7.78 | 10.36 | 0.07 | 0.67 | 3.85 | 0.01 | 3500.00 |
81.00 | 575![]() |
385![]() |
7.48 | 11.45 | 0.02 | 0.16 | 0.00 | 0.01 | 300.00 |
82.00 | 225![]() |
335![]() |
12.66 | 9.79 | 0.00 | 0.04 | 0.00 | 0.06 | 139![]() |
83.00 | 275![]() |
335![]() |
12.66 | 9.79 | 0.01 | 0.11 | 0.00 | 0.06 | 190![]() |
84.00 | 325![]() |
335![]() |
9.23 | 11.28 | 0.04 | 0.37 | 0.06 | 0.01 | 105![]() |
85.00 | 375![]() |
335![]() |
9.23 | 11.28 | 0.16 | 1.49 | 0.76 | 0.01 | 94![]() |
86.00 | 425![]() |
335![]() |
9.23 | 11.28 | 0.33 | 3.12 | 0.84 | 0.01 | 38![]() |
87.00 | 475![]() |
335![]() |
7.78 | 10.36 | 0.40 | 3.73 | 2.24 | 0.01 | 2600.00 |
88.00 | 525![]() |
335![]() |
7.48 | 11.45 | 0.10 | 0.95 | 3.85 | 0.01 | 13![]() |
89.00 | 575![]() |
335![]() |
7.48 | 11.45 | 0.05 | 0.49 | 0.00 | 0.01 | 25![]() |
90.00 | 625![]() |
335![]() |
7.48 | 11.45 | 0.12 | 1.11 | 3.97 | 0.01 | 700.00 |
91.00 | 275![]() |
285![]() |
12.66 | 9.79 | 0.01 | 0.13 | 0.00 | 0.06 | 182![]() |
92.00 | 325![]() |
285![]() |
9.23 | 11.28 | 0.02 | 0.18 | 0.00 | 0.01 | 85![]() |
93.00 | 375![]() |
285![]() |
9.23 | 11.28 | 0.13 | 1.19 | 1.90 | 0.01 | 60![]() |
94.00 | 425![]() |
285![]() |
9.23 | 11.28 | 0.77 | 7.22 | 3.13 | 0.01 | 59![]() |
95.00 | 475![]() |
285![]() |
7.48 | 11.45 | 0.23 | 2.13 | 3.26 | 0.01 | 13![]() |
96.00 | 525![]() |
285![]() |
7.48 | 11.45 | 0.13 | 1.26 | 3.59 | 0.01 | 32![]() |
97.00 | 575![]() |
285![]() |
7.48 | 11.45 | 0.12 | 1.13 | 2.10 | 0.01 | 24![]() |
98.00 | 625![]() |
285![]() |
7.48 | 11.45 | 0.06 | 0.57 | 3.97 | 0.01 | 1200.00 |
99.00 | 175![]() |
235![]() |
12.66 | 9.79 | 0.00 | 0.04 | 0.05 | 0.06 | 65![]() |
100.00 | 225![]() |
235![]() |
12.66 | 9.79 | 0.04 | 0.34 | 0.00 | 0.06 | 210![]() |
101.00 | 275![]() |
235![]() |
12.66 | 9.79 | 0.06 | 0.52 | 0.00 | 0.06 | 207![]() |
102.00 | 325![]() |
235![]() |
13.30 | 10.45 | 0.07 | 0.67 | 0.00 | 0.02 | 135![]() |
103.00 | 375![]() |
235![]() |
9.56 | 11.34 | 0.13 | 1.25 | 3.37 | 0.02 | 44![]() |
104.00 | 425![]() |
235![]() |
9.56 | 11.34 | 0.10 | 0.96 | 3.11 | 0.02 | 62![]() |
105.00 | 475![]() |
235![]() |
7.48 | 11.45 | 0.21 | 1.98 | 3.64 | 0.01 | 81![]() |
106.00 | 525![]() |
235![]() |
7.48 | 11.45 | 0.37 | 3.49 | 3.80 | 0.01 | 35![]() |
107.00 | 575![]() |
235![]() |
7.48 | 11.45 | 0.18 | 1.64 | 3.88 | 0.01 | 22![]() |
108.00 | 625![]() |
235![]() |
7.48 | 11.45 | 0.15 | 1.39 | 3.25 | 0.01 | 11![]() |
109.00 | 175![]() |
185![]() |
12.66 | 9.79 | 0.00 | 0.01 | 0.00 | 0.06 | 5300.00 |
110.00 | 225![]() |
185![]() |
12.66 | 9.79 | 0.11 | 0.99 | 0.00 | 0.06 | 10![]() |
111.00 | 275![]() |
185![]() |
12.66 | 9.79 | 0.31 | 2.89 | 0.16 | 0.06 | 52![]() |
112.00 | 325![]() |
185![]() |
13.30 | 10.45 | 0.31 | 2.86 | 0.00 | 0.02 | 89![]() |
113.00 | 375![]() |
185![]() |
9.56 | 11.34 | 0.12 | 1.13 | 2.73 | 0.02 | 93![]() |
114.00 | 425![]() |
185![]() |
9.56 | 11.34 | 0.34 | 3.13 | 2.76 | 0.02 | 68![]() |
115.00 | 475![]() |
185![]() |
7.48 | 11.45 | 2.05 | 19.07 | 3.38 | 0.01 | 68![]() |
116.00 | 525![]() |
185![]() |
7.48 | 11.45 | 0.83 | 7.72 | 2.12 | 0.01 | 63![]() |
117.00 | 575![]() |
185![]() |
7.48 | 11.45 | 0.13 | 1.22 | 2.91 | 0.01 | 55![]() |
118.00 | 625![]() |
185![]() |
7.48 | 11.45 | 0.00 | 0.00 | 2.34 | 0.01 | 11![]() |
119.00 | 225![]() |
135![]() |
13.30 | 10.45 | 0.01 | 0.06 | 0.00 | 0.02 | 45![]() |
120.00 | 275![]() |
135![]() |
13.30 | 10.45 | 0.04 | 0.41 | 0.16 | 0.02 | 175![]() |
121.00 | 325![]() |
135![]() |
13.30 | 10.45 | 0.09 | 0.81 | 0.00 | 0.02 | 112![]() |
122.00 | 375![]() |
135![]() |
9.56 | 11.34 | 0.09 | 0.84 | 2.73 | 0.02 | 126![]() |
123.00 | 425![]() |
135![]() |
9.56 | 11.34 | 0.11 | 1.01 | 2.76 | 0.02 | 70![]() |
124.00 | 475![]() |
135![]() |
9.56 | 11.34 | 0.31 | 2.87 | 3.38 | 0.02 | 55![]() |
125.00 | 525![]() |
135![]() |
9.56 | 11.34 | 0.30 | 2.78 | 2.12 | 0.02 | 138![]() |
126.00 | 575![]() |
135![]() |
9.56 | 11.34 | 0.17 | 1.59 | 2.91 | 0.02 | 120![]() |
127.00 | 625![]() |
135![]() |
9.56 | 11.34 | 0.07 | 0.66 | 2.34 | 0.02 | 10![]() |
128.00 | 175![]() |
85![]() |
13.30 | 10.45 | 0.01 | 0.10 | 0.00 | 0.02 | 10![]() |
129.00 | 225![]() |
85![]() |
13.30 | 10.45 | 0.05 | 0.47 | 0.00 | 0.02 | 11![]() |
130.00 | 275![]() |
85![]() |
13.30 | 10.45 | 0.14 | 1.31 | 1.01 | 0.02 | 97![]() |
131.00 | 325![]() |
85![]() |
13.30 | 10.45 | 0.09 | 0.83 | 1.48 | 0.02 | 19![]() |
132.00 | 375![]() |
85![]() |
9.56 | 11.34 | 0.04 | 0.34 | 2.53 | 0.02 | 39![]() |
133.00 | 425![]() |
85![]() |
9.56 | 11.34 | 0.13 | 1.22 | 1.28 | 0.02 | 19![]() |
134.00 | 475![]() |
85![]() |
9.56 | 11.34 | 0.07 | 0.62 | 1.23 | 0.02 | 8900.00 |
135.00 | 525![]() |
85![]() |
9.56 | 11.34 | 0.01 | 0.07 | 1.04 | 0.02 | 0.00 |
136.00 | 575![]() |
85![]() |
9.56 | 11.34 | 0.01 | 0.12 | 0.80 | 0.02 | 0.00 |
137.00 | 125![]() |
35![]() |
13.30 | 10.45 | 0.00 | 0.02 | 0.00 | 0.02 | 7000.00 |
138.00 | 175![]() |
35![]() |
13.30 | 10.45 | 0.06 | 0.56 | 0.56 | 0.02 | 20![]() |
139.00 | 225![]() |
35![]() |
13.30 | 10.45 | 0.00 | 0.02 | 0.00 | 0.02 | 1200.00 |
140.00 | 275![]() |
35![]() |
13.30 | 10.45 | 0.01 | 0.06 | 0.00 | 0.02 | 5200.00 |
Region | X | Y | Miscanthus67 (t per ha per year) | Poplar67 (t per ha per year) | Waste wood75,76 (t per ha per year) | MSW75,77 (t per ha per year) | Crop residue78 (t per ha per year) | Forest residue75,77 (t per ha per year) | Biomass land78 (ha) |
---|---|---|---|---|---|---|---|---|---|
1 | 325![]() |
1![]() ![]() |
8.51 | 8.80 | 0.00 | 0.02 | 0.00 | 0.06 | 56![]() |
2 | 375![]() |
1![]() ![]() |
8.51 | 8.80 | 0.00 | 0.03 | 0.00 | 0.06 | 28![]() |
3 | 225![]() |
985![]() |
8.51 | 8.80 | 0.00 | 0.00 | 0.00 | 0.06 | 2400 |
4 | 275![]() |
985![]() |
8.51 | 8.80 | 0.00 | 0.00 | 0.00 | 0.06 | 17![]() |
5 | 325![]() |
985![]() |
8.51 | 8.80 | 0.01 | 0.06 | 0.00 | 0.06 | 15![]() |
6 | 375![]() |
985![]() |
8.51 | 8.80 | 0.00 | 0.00 | 0.00 | 0.06 | 67![]() |
7 | 125![]() |
935![]() |
8.51 | 8.80 | 0.00 | 0.01 | 0.00 | 0.06 | 26![]() |
8 | 175![]() |
935![]() |
8.51 | 8.80 | 0.00 | 0.03 | 0.00 | 0.06 | 2900 |
9 | 225![]() |
935![]() |
8.51 | 8.80 | 0.00 | 0.00 | 0.06 | 0.06 | 49![]() |
10 | 275![]() |
935![]() |
8.51 | 8.80 | 0.00 | 0.01 | 0.00 | 0.06 | 69![]() |
11 | 325![]() |
935![]() |
8.51 | 8.80 | 0.00 | 0.01 | 0.00 | 0.06 | 18![]() |
12 | 75![]() |
885![]() |
8.51 | 8.80 | 0.00 | 0.00 | 0.00 | 0.06 | 23![]() |
13 | 125![]() |
885![]() |
8.51 | 8.80 | 0.00 | 0.00 | 0.00 | 0.06 | 12![]() |
14 | 175![]() |
885![]() |
8.51 | 8.80 | 0.00 | 0.01 | 0.00 | 0.06 | 41![]() |
15 | 225![]() |
885![]() |
8.51 | 8.80 | 0.00 | 0.01 | 0.00 | 0.06 | 48![]() |
16 | 275![]() |
885![]() |
8.51 | 8.80 | 0.02 | 0.16 | 0.00 | 0.06 | 30![]() |
17 | 325![]() |
885![]() |
8.51 | 8.80 | 0.02 | 0.16 | 0.24 | 0.06 | 28![]() |
18 | 375![]() |
885![]() |
8.51 | 8.80 | 0.01 | 0.11 | 0.34 | 0.06 | 7200 |
19 | 75![]() |
835![]() |
8.51 | 8.80 | 0.00 | 0.00 | 1.16 | 0.06 | 13![]() |
20 | 125![]() |
835![]() |
8.51 | 8.80 | 0.01 | 0.06 | 0.00 | 0.06 | 24![]() |
21 | 175![]() |
835![]() |
8.51 | 8.80 | 0.00 | 0.01 | 0.00 | 0.06 | 68![]() |
22 | 225![]() |
835![]() |
8.51 | 8.80 | 0.00 | 0.00 | 0.00 | 0.06 | 74![]() |
23 | 275![]() |
835![]() |
8.51 | 8.80 | 0.02 | 0.20 | 0.24 | 0.06 | 41![]() |
24 | 325![]() |
835![]() |
8.51 | 8.80 | 0.00 | 0.04 | 0.34 | 0.06 | 58![]() |
25 | 375![]() |
835![]() |
8.51 | 8.80 | 0.03 | 0.33 | 2.27 | 0.06 | 96![]() |
26 | 425![]() |
835![]() |
8.51 | 8.80 | 0.06 | 0.58 | 0.00 | 0.06 | 10![]() |
27 | 125![]() |
785![]() |
8.51 | 8.80 | 0.00 | 0.00 | 0.00 | 0.06 | 16![]() |
28 | 175![]() |
785![]() |
8.51 | 8.80 | 0.00 | 0.01 | 0.00 | 0.06 | 105000 |
29 | 225![]() |
785![]() |
8.51 | 8.80 | 0.00 | 0.03 | 0.00 | 0.06 | 98![]() |
30 | 275![]() |
785![]() |
8.51 | 8.80 | 0.00 | 0.01 | 0.00 | 0.06 | 39![]() |
31 | 325![]() |
785![]() |
8.51 | 8.80 | 0.00 | 0.04 | 0.00 | 0.06 | 46![]() |
32 | 375![]() |
785![]() |
8.51 | 8.80 | 0.02 | 0.21 | 1.44 | 0.06 | 38![]() |
33 | 75![]() |
735![]() |
8.51 | 8.80 | 0.00 | 0.00 | 0.00 | 0.06 | 3700 |
34 | 125![]() |
735![]() |
8.51 | 8.80 | 0.00 | 0.02 | 0.00 | 0.06 | 20![]() |
35 | 175![]() |
735![]() |
8.51 | 8.80 | 0.00 | 0.01 | 0.00 | 0.06 | 86![]() |
36 | 225![]() |
735![]() |
8.51 | 8.80 | 0.01 | 0.09 | 0.00 | 0.06 | 118![]() |
37 | 275![]() |
735![]() |
8.51 | 8.80 | 0.08 | 0.70 | 1.10 | 0.06 | 107![]() |
38 | 325![]() |
735![]() |
8.51 | 8.80 | 0.05 | 0.46 | 1.46 | 0.06 | 81![]() |
39 | 375![]() |
735![]() |
8.51 | 8.80 | 0.00 | 0.03 | 0.00 | 0.06 | 4500 |
40 | 125![]() |
685![]() |
8.51 | 8.80 | 0.00 | 0.01 | 0.00 | 0.06 | 15![]() |
41 | 175![]() |
685![]() |
8.51 | 8.80 | 0.00 | 0.02 | 0.02 | 0.06 | 36![]() |
42 | 225![]() |
685![]() |
8.51 | 8.80 | 0.05 | 0.49 | 0.35 | 0.06 | 91![]() |
43 | 275![]() |
685![]() |
8.51 | 8.80 | 0.48 | 4.52 | 1.10 | 0.06 | 121![]() |
44 | 325![]() |
685![]() |
8.51 | 8.80 | 0.29 | 2.70 | 1.46 | 0.06 | 58![]() |
45 | 375![]() |
685![]() |
8.51 | 8.80 | 0.02 | 0.18 | 0.00 | 0.06 | 22![]() |
46 | 125![]() |
635![]() |
8.51 | 8.80 | 0.00 | 0.01 | 0.00 | 0.06 | 3400 |
47 | 175![]() |
635![]() |
8.51 | 8.80 | 0.00 | 0.02 | 0.06 | 0.06 | 36![]() |
48 | 225![]() |
635![]() |
8.51 | 8.80 | 0.05 | 0.51 | 0.35 | 0.06 | 95![]() |
49 | 275![]() |
635![]() |
8.51 | 8.80 | 0.05 | 0.45 | 0.69 | 0.06 | 148![]() |
50 | 325![]() |
635![]() |
8.51 | 8.80 | 0.01 | 0.08 | 0.96 | 0.06 | 140![]() |
51 | 375![]() |
635![]() |
9.24 | 9.76 | 0.02 | 0.20 | 2.35 | 0.07 | 84![]() |
52 | 425![]() |
635![]() |
9.24 | 9.76 | 0.01 | 0.12 | 0.00 | 0.07 | 42![]() |
53 | 225![]() |
585![]() |
11.72 | 10.10 | 0.01 | 0.07 | 0.25 | 0.02 | 94![]() |
54 | 275![]() |
585![]() |
11.72 | 10.10 | 0.01 | 0.08 | 0.15 | 0.02 | 129![]() |
55 | 325![]() |
585![]() |
11.72 | 10.10 | 0.04 | 0.34 | 0.33 | 0.02 | 136![]() |
56 | 375![]() |
585![]() |
9.24 | 9.76 | 0.02 | 0.19 | 0.00 | 0.07 | 127![]() |
57 | 425![]() |
585![]() |
9.24 | 9.76 | 0.37 | 3.50 | 0.93 | 0.07 | 56![]() |
58 | 225![]() |
535![]() |
11.72 | 10.10 | 0.00 | 0.01 | 0.00 | 0.02 | 25![]() |
59 | 275![]() |
535![]() |
11.72 | 10.10 | 0.00 | 0.01 | 0.00 | 0.02 | 14![]() |
60 | 325![]() |
535![]() |
11.72 | 10.10 | 0.04 | 0.38 | 0.00 | 0.02 | 166![]() |
61 | 375![]() |
535![]() |
9.68 | 8.70 | 0.01 | 0.14 | 0.00 | 0.02 | 133![]() |
62 | 425![]() |
535![]() |
9.68 | 8.70 | 0.13 | 1.21 | 3.19 | 0.02 | 82![]() |
63 | 475![]() |
535![]() |
9.68 | 8.70 | 0.18 | 1.65 | 0.41 | 0.02 | 36![]() |
64 | 325![]() |
485![]() |
11.72 | 10.10 | 0.03 | 0.28 | 0.00 | 0.02 | 77![]() |
65 | 375![]() |
485![]() |
9.68 | 8.70 | 0.06 | 0.57 | 0.00 | 0.02 | 169![]() |
66 | 425![]() |
485![]() |
9.68 | 8.70 | 0.06 | 0.60 | 3.19 | 0.02 | 102![]() |
67 | 475![]() |
485![]() |
9.68 | 8.70 | 0.07 | 0.67 | 0.41 | 0.02 | 35![]() |
68 | 525![]() |
485![]() |
9.68 | 8.70 | 0.04 | 0.39 | 0.00 | 0.02 | 4500 |
69 | 325![]() |
435![]() |
11.72 | 10.10 | 0.12 | 1.13 | 0.00 | 0.02 | 31![]() |
70 | 375![]() |
435![]() |
7.90 | 10.21 | 0.58 | 5.41 | 0.00 | 0.01 | 137![]() |
71 | 425![]() |
435![]() |
7.90 | 10.21 | 0.68 | 6.39 | 1.68 | 0.01 | 93![]() |
72 | 475![]() |
435![]() |
7.90 | 10.21 | 0.21 | 1.96 | 3.88 | 0.01 | 6200 |
73 | 525![]() |
435![]() |
7.90 | 10.21 | 0.18 | 1.72 | 2.66 | 0.01 | 1300 |
74 | 225![]() |
385![]() |
12.86 | 9.64 | 0.01 | 0.07 | 0.00 | 0.06 | 65![]() |
75 | 275![]() |
385![]() |
12.86 | 9.64 | 0.05 | 0.48 | 0.00 | 0.06 | 108![]() |
76 | 325![]() |
385![]() |
9.37 | 11.11 | 0.32 | 3.03 | 0.06 | 0.01 | 81![]() |
77 | 375![]() |
385![]() |
9.37 | 11.11 | 0.63 | 5.89 | 0.76 | 0.01 | 100![]() |
78 | 425![]() |
385![]() |
9.37 | 11.11 | 0.32 | 3.01 | 0.84 | 0.01 | 128![]() |
79 | 475![]() |
385![]() |
7.90 | 10.21 | 0.27 | 2.53 | 2.24 | 0.01 | 7000 |
80 | 525![]() |
385![]() |
7.90 | 10.21 | 0.07 | 0.70 | 3.85 | 0.01 | 3500 |
81 | 575![]() |
385![]() |
7.60 | 11.27 | 0.02 | 0.17 | 0.00 | 0.01 | 300 |
82 | 225![]() |
335![]() |
12.86 | 9.64 | 0.00 | 0.04 | 0.00 | 0.06 | 139![]() |
83 | 275![]() |
335![]() |
12.86 | 9.64 | 0.01 | 0.11 | 0.00 | 0.06 | 190![]() |
84 | 325![]() |
335![]() |
9.37 | 11.11 | 0.04 | 0.39 | 0.06 | 0.01 | 105![]() |
85 | 375![]() |
335![]() |
9.37 | 11.11 | 0.17 | 1.56 | 0.76 | 0.01 | 94![]() |
86 | 425![]() |
335![]() |
9.37 | 11.11 | 0.35 | 3.26 | 0.84 | 0.01 | 38![]() |
87 | 475![]() |
335![]() |
7.90 | 10.21 | 0.42 | 3.90 | 2.24 | 0.01 | 2600 |
88 | 525![]() |
335![]() |
7.60 | 11.27 | 0.11 | 0.99 | 3.85 | 0.01 | 13![]() |
89 | 575![]() |
335![]() |
7.60 | 11.27 | 0.06 | 0.52 | 0.00 | 0.01 | 25![]() |
90 | 625![]() |
335![]() |
7.60 | 11.27 | 0.12 | 1.16 | 3.97 | 0.01 | 700 |
91 | 275![]() |
285![]() |
12.86 | 9.64 | 0.01 | 0.13 | 0.00 | 0.06 | 182![]() |
92 | 325![]() |
285![]() |
9.37 | 11.11 | 0.02 | 0.18 | 0.00 | 0.01 | 85![]() |
93 | 375![]() |
285![]() |
9.37 | 11.11 | 0.13 | 1.25 | 1.90 | 0.01 | 60![]() |
94 | 425![]() |
285![]() |
9.37 | 11.11 | 0.81 | 7.54 | 3.13 | 0.01 | 59![]() |
95 | 475![]() |
285![]() |
7.60 | 11.27 | 0.24 | 2.22 | 3.26 | 0.01 | 13![]() |
96 | 525![]() |
285![]() |
7.60 | 11.27 | 0.14 | 1.31 | 3.59 | 0.01 | 32![]() |
97 | 575![]() |
285![]() |
7.60 | 11.27 | 0.13 | 1.18 | 2.10 | 0.01 | 24![]() |
98 | 625![]() |
285![]() |
7.60 | 11.27 | 0.06 | 0.60 | 3.97 | 0.01 | 1200 |
99 | 175![]() |
235![]() |
12.86 | 9.64 | 0.00 | 0.04 | 0.05 | 0.06 | 65![]() |
100 | 225![]() |
235![]() |
12.86 | 9.64 | 0.04 | 0.36 | 0.00 | 0.06 | 210![]() |
101 | 275![]() |
235![]() |
12.86 | 9.64 | 0.06 | 0.55 | 0.00 | 0.06 | 207100 |
102 | 325![]() |
235![]() |
13.51 | 10.29 | 0.07 | 0.70 | 0.00 | 0.02 | 135600 |
103 | 375![]() |
235![]() |
9.70 | 11.17 | 0.14 | 1.30 | 3.37 | 0.02 | 44![]() |
104 | 425![]() |
235![]() |
9.70 | 11.17 | 0.11 | 1.00 | 3.11 | 0.02 | 62![]() |
105 | 475![]() |
235![]() |
7.60 | 11.27 | 0.22 | 2.07 | 3.64 | 0.01 | 81![]() |
106 | 525![]() |
235![]() |
7.60 | 11.27 | 0.39 | 3.65 | 3.80 | 0.01 | 35![]() |
107 | 575![]() |
235![]() |
7.60 | 11.27 | 0.18 | 1.72 | 3.88 | 0.01 | 22![]() |
108 | 625![]() |
235![]() |
7.60 | 11.27 | 0.16 | 1.46 | 3.25 | 0.01 | 11![]() |
109 | 175![]() |
185![]() |
12.86 | 9.64 | 0.00 | 0.01 | 0.00 | 0.06 | 5300 |
110 | 225![]() |
185![]() |
12.86 | 9.64 | 0.11 | 1.04 | 0.00 | 0.06 | 10![]() |
111 | 275![]() |
185![]() |
12.86 | 9.64 | 0.32 | 3.02 | 0.16 | 0.06 | 52![]() |
112 | 325![]() |
185![]() |
13.51 | 10.29 | 0.32 | 2.99 | 0.00 | 0.02 | 89![]() |
113 | 375![]() |
185![]() |
9.70 | 11.17 | 0.13 | 1.18 | 2.73 | 0.02 | 93![]() |
114 | 425![]() |
185![]() |
9.70 | 11.17 | 0.35 | 3.27 | 2.76 | 0.02 | 68![]() |
115 | 475![]() |
185![]() |
7.60 | 11.27 | 2.13 | 19.93 | 3.38 | 0.01 | 68![]() |
116 | 525![]() |
185![]() |
7.60 | 11.27 | 0.86 | 8.07 | 2.12 | 0.01 | 63![]() |
117 | 575![]() |
185![]() |
7.60 | 11.27 | 0.14 | 1.28 | 2.91 | 0.01 | 55![]() |
118 | 625![]() |
185![]() |
7.60 | 11.27 | 0.00 | 0.00 | 2.34 | 0.01 | 11![]() |
119 | 225![]() |
135![]() |
13.51 | 10.29 | 0.01 | 0.07 | 0.00 | 0.02 | 45![]() |
120 | 275![]() |
135![]() |
13.51 | 10.29 | 0.05 | 0.43 | 0.16 | 0.02 | 175![]() |
121 | 325![]() |
135![]() |
13.51 | 10.29 | 0.09 | 0.85 | 0.00 | 0.02 | 112![]() |
122 | 375![]() |
135![]() |
9.70 | 11.17 | 0.09 | 0.87 | 2.73 | 0.02 | 126![]() |
123 | 425![]() |
135![]() |
9.70 | 11.17 | 0.11 | 1.06 | 2.76 | 0.02 | 70![]() |
124 | 475![]() |
135![]() |
9.70 | 11.17 | 0.32 | 3.00 | 3.38 | 0.02 | 55![]() |
125 | 525![]() |
135![]() |
9.70 | 11.17 | 0.31 | 2.90 | 2.12 | 0.02 | 138![]() |
126 | 575![]() |
135![]() |
9.70 | 11.17 | 0.18 | 1.66 | 2.91 | 0.02 | 120![]() |
127 | 625![]() |
135![]() |
9.70 | 11.17 | 0.07 | 0.68 | 2.34 | 0.02 | 10![]() |
128 | 175![]() |
85![]() |
13.51 | 10.29 | 0.01 | 0.10 | 0.00 | 0.02 | 10![]() |
129 | 225![]() |
85![]() |
13.51 | 10.29 | 0.05 | 0.49 | 0.00 | 0.02 | 113![]() |
130 | 275![]() |
85![]() |
13.51 | 10.29 | 0.15 | 1.37 | 1.01 | 0.02 | 97![]() |
131 | 325![]() |
85![]() |
13.51 | 10.29 | 0.09 | 0.87 | 1.48 | 0.02 | 19![]() |
132 | 375![]() |
85![]() |
9.70 | 11.17 | 0.04 | 0.36 | 2.53 | 0.02 | 39![]() |
133 | 425![]() |
85![]() |
9.70 | 11.17 | 0.14 | 1.28 | 1.28 | 0.02 | 19![]() |
134 | 475![]() |
85![]() |
9.70 | 11.17 | 0.07 | 0.65 | 1.23 | 0.02 | 8900 |
135 | 525![]() |
85![]() |
9.70 | 11.17 | 0.01 | 0.08 | 1.04 | 0.02 | 0 |
136 | 575![]() |
85![]() |
9.70 | 11.17 | 0.01 | 0.13 | 0.80 | 0.02 | 0 |
137 | 125![]() |
35![]() |
13.51 | 10.29 | 0.00 | 0.02 | 0.00 | 0.02 | 7000 |
138 | 175![]() |
35![]() |
13.51 | 10.29 | 0.06 | 0.59 | 0.56 | 0.02 | 20![]() |
139 | 225![]() |
35![]() |
13.51 | 10.29 | 0.00 | 0.02 | 0.00 | 0.02 | 1200 |
140 | 275![]() |
35![]() |
13.51 | 10.29 | 0.01 | 0.06 | 0.00 | 0.02 | 5200 |
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 tCO2).110 |
This journal is © The Royal Society of Chemistry 2020 |