Mathilde
Fajardy
ab 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
First published on 6th April 2017
Negative emissions technologies (NETs) in general and bioenergy with CO2 capture and storage (BECCS) in particular are commonly regarded as vital yet controversial to meeting our climate goals. In this contribution we present a whole-systems analysis of the BECCS value chain associated with cultivation, harvesting, transport and conversion in dedicated biomass power stations in conjunction with CCS, of a range of biomass resources – both dedicated energy crops (miscanthus, switchgrass, short rotation coppice willow), and agricultural residues (wheat straw). We explicitly consider the implications of sourcing the biomass from different regions, climates and land types. The water, carbon and energy footprints of each value chain were calculated, and their impact on the overall system water, carbon and power efficiencies was evaluated. An extensive literature review was performed and a statistical analysis of the available data is presented. In order to describe the dynamic greenhouse gas balance of such a system, a yearly accounting of the emissions was performed over the lifetime of a BECCS facility, and the carbon “breakeven time” and lifetime net CO2 removal from the atmosphere were determined. The effects of direct and indirect land use change were included, and were found to be a key determinant of the viability of a BECCS project. Overall we conclude that, depending on the conditions of its deployment, BECCS could lead to both carbon positive and negative results. The total quantity of CO2 removed from the atmosphere over the project lifetime and the carbon breakeven time were observed to be highly case specific. This has profound implications for the policy frameworks required to incentivise and regulate the widespread deployment of BECCS technology. The results of a sensitivity analysis on the model combined with the investigation of alternate supply chain scenarios elucidated key levers to improve the sustainability of BECCS: (1) measuring and limiting the impacts of direct and indirect land use change, (2) using carbon neutral power and organic fertilizer, (3) minimising biomass transport, and prioritising sea over road transport, (4) maximising the use of carbon negative fuels, and (5) exploiting alternative biomass processing options, e.g., natural drying or torrefaction. A key conclusion is that, regardless of the biomass and region studied, the sustainability of BECCS relies heavily on intelligent management of the supply chain.
Broader contextNegative emissions technologies, in general, and bioenergy with carbon capture and storage (BECCS), in particular, are fundamental to achieving the 1.5 °C goal as articulated by the 2015 Paris COP agreement. However, as a technology BECCS remains dogged by controversy arising from the competition for arable land and fresh water, in addition to questions concerning its ability to actually remove CO2 from the atmosphere. In this contribution, we present a whole-systems assessment of BECCS, explicitly accounting for the cultivation, harvesting, transport and conversion of biomass and the subsequent sequestration of the arising CO2. Owing to CO2 emissions associated with the initial land use change and these subsequent emissions, BECCS projects incur an initial and ongoing carbon debt. Thus, the viability of BECCS as a negative emissions technology option depends entirely on the choices made throughout the supply chain. Moreover, owing to the uncertainty primarily associated with land use change, “one size fits all” regulation may be particularly challenging. In particular, the carbon breakeven time and the lifetime net CO2 removal from the atmosphere tend to be case-specific. Key policy challenges will likely include (a) how carbon negative should the BECCS project be in order to warrant support and (b) how should a BECCS plant which has not yet started to remove CO2 from the atmosphere be supported? |
However, despite its potential advantages, BECCS is not without controversy. Land competition for food production,18 as well as CO2 emissions associated with biomass cultivation, harvesting and processing19 cast doubt on the general ability of a BECCS facility to actually result in a net removal of CO2 from the atmosphere. It is recognised20 that a detailed assessment of the water, land and carbon intensity of the biomass supply chain and conversion technology is vital to quantitatively addressing the uncertainty in this area, identifying key points for improvement and thus facilitating the large scale deployment of BECCS.
This efficiency penalty is further increased by the use of a potentially lower quality fuel – biomass – in complement or supplement to coal. While the average higher heating value (HHV) of bituminous coal is approximately 27 MJ tMW−1 at 11% moisture and 64% carbon content,26 raw biomass with a higher moisture content – up to 50% with woody biomass27 – and a lower carbon content – around 48% dry mass – tends to exhibit an HHV around 18–20 MJ tDM−1.28–33 In addition to the efficiency loss at the boiler, the physical properties of biomass will increase the costs associated with fuel storage, handling and size reduction. In the case of high moisture biomass, drying will automatically represent a substantial energy cost. Furthermore, as shown in Williams et al.,34 biomass grindability is significantly lower than that of coal, resulting in a grinding energy cost up to four to five times higher on a mass basis. On an HHV basis, grinding energy cost can be up to seven times higher for biomass relative to coal, with an energy requirement of 20 kW h MW hHHV−1 in the case of wood pellets, against 3 kW h MW hHHV−1 in the case of coal. In addition to a reduction in efficiency associated with increased fuel processing costs, another concern is associated with transitioning from dedicated coal to dedicated biomass combustion and the resulting impact on the boiler technology. Experimental studies35 and recent reports from Drax power station, which converted two 660 MW units to dedicated biomass in 2014,36 demonstrate that large scale biomass firing is feasible with no major change, provided a modern boiler technology is used. Utility-scale biomass-fired power plants are inherently more costly than their coal-fired counterparts, primarily as a result of the more costly fuel handling and storage infrastructure. In addition to the capital cost, the ongoing operating cost associated with providing a nitrogen-rich atmosphere for the biomass is also an important consideration. Moreover, an improved understanding of the impact of ash formation and slagging behaviour on boiler efficiency and maintenance37,38 costs is also necessary.
However, Mac Dowell and Fajardy showed that BECCS facilities that are less efficient at converting biomass to electricity could remove more CO2 at a lower cost than their more efficient counterparts.39 This paradoxical observation has important implications for the way in which CDR technologies will be integrated with the broader energy system.
This dependence on time also raises the question of dynamic accounting of carbon emissions. Some studies indicate that on a transient basis, the “carbon debt” initiated by land conversion to biomass production cannot offset CO2 savings from displacing coal, or only over a period of time that is greater than the power plant lifetime.109,110 This is referred to as “carbon payback time”, or carbon breakeven time, and can be understood as the amount of time required for a system to reach carbon neutrality. In those studies however, biofuels pay back this initial carbon debt by offsetting fossil fuel related emissions, and carbon breakeven time thus has to be calculated relatively to a given fossil fuel. In the BECCS case, because it physically captures carbon from the atmosphere, we define it as the time required for a BECCS power plant to pay back this initial debt by biomass combustion and carbon storage.
The aforementioned uncertainties result in BECCS' net CO2 removal which varies greatly on both a spatial and temporal basis. As a result, input data uncertainty needs to be captured in BECCS CO2 balance for the assessment to be meaningful.
The purpose of this contribution is to present a spatially and temporally explicit, whole-systems assessment of the BECCS supply chain, accounting for the cultivation, harvesting, processing, transport, and conversion of biomass and the subsequent sequestration of the arising CO2. We evaluate each distinct BECCS system on the basis of energy, carbon, water, and land-use intensity, using the net removal of CO2 from the atmosphere as a determining key performance indicator. The rest of the paper is structured as follows. Section 2 provides an overview of the approach adopted in this analysis, in particular presenting a detailed analysis of the uncertainty associated with the published data required to characterise the biomass supply chain. Section 3 quantifies and qualifies the different contributions to the water, energy and land intensities of the integrated BECCS system, and the resulting impact on the overall system thermo- and hydrodynamic efficiencies, in addition to the carbon intensity and carbon efficiency. Finally, Section 4 focuses on the dynamic accounting of GHG emissions over the system lifetime, on its sensitivities, and concludes with some perspectives on the implications for the potential of BECCS to result in the net removal of CO2 from the atmosphere.
The evaluation of the thermo- and hydrodynamic efficiencies and carbon intensity of a 500 MW BECCS power plant was carried out in the conversion model. Different cooling technologies (once-through, wet cooling tower), CO2 capture rates (60–90%) and co-firing proportions (0–100%) were considered in the model. Biomass physical properties and supply chain footprints previously evaluated were included in the model to evaluate how BECCS performed with each feedstock. BECCS efficiency to convert biologically stored CO2 into geologically stored CO2 was also determined. Finally, in order to capture BECCS CO2 removal time efficiency, this model included an evaluation of the cumulative emissions and carbon breakeven time of a hectare of land used in such a system over 50 years. The different interactions between these sub-models are outlined in Fig. 1.
Fig. 1 Biomass water, carbon, energy and land footprints were calculated using energy and carbon data, region specific data, biomass data and land data. The power plant calculations were carried out in IECM.26 BECCS hydro, energy and carbon efficiencies were calculated by combining the supply chain and the power plant model. The temporal carbon efficiency of BECCS was evaluated with a dynamic GHG balance over 50 years. |
• Energy data: all indirect energy data – chemicals, seeds and fuels embodied energy – and direct energy data – fuel LHV, energy for irrigation, transport fuel efficiency and drying system characteristics.
• Carbon data: all indirect emission data – carbon footprint of chemicals, seeds and fuels – and direct emission data – fuel and transport emission factors, N2O model emission factors, gas global warming potential and land conversion factors.
• Crop data: the physical properties of each biomass type – composition, Cp, HHV – as well as farming data – yields over lifetime and in different regions, moisture content, lifetime and harvest rotation cycle, crop coefficients and calendar, chemical input rates, fuel efficiencies and processing energy requirements of in-field operations.
• Climate data: precipitations, altitude, longitude, sunshine hours, wind speed, average low and high temperatures and relative humidity for each region.
• Land type for direct and indirect land use change evaluation.
Some input parameters have a great impact on all aspects of the supply chain. Yield for example is a key parameter. As all supply chain inputs during the growth stage are on a per hectare basis (land use effects, chemicals input, in-field operations), biomass productivity (or yield) in dry tons per hectare will directly impact the extent of these contributions per dry ton or GJ of biomass delivered at the power plant. Another example is biomass moisture content. It directly impacts not only drying energy requirements, but also all of the processing and transport stages whose energy requirements depend on the incoming wet biomass mass flow, and are finally converted into MJ per dry ton.
The values for these parameters are reported over a wide range in the literature. Miscanthus in Europe for example has been widely investigated, and productivity as low as 4 tDM ha−175 and as high as 60 tDM ha−177 have been reported. To capture this uncertainty in the model, a database gathering the 150 input parameters, and their value according to different sources, was developed. 50 input parameters were selected on the basis of their impact on the model, number of sources and scatter. Normalized data series length and scatter of these parameters are presented in Fig. 2.
As can be observed in Fig. 2, it is important to acknowledge the level of variability in some of the input data, e.g. the embodied energy and carbon footprint of chemicals, land conversion emission factors, and yield values. This uncertainty associated with key input parameters can clearly lead to a wide range of model outcomes. In order to evaluate the uncertainty in these outcomes, the model was evaluated with the lower bound and upper bound of the selected parameters. If the data series contained more than five sources, and in order to avoid unlikely data (very extreme yield value for example), the lower bounds and upper bounds chosen were the 95% confidence interval bounds. To use the example of miscanthus productivity in Europe, the range 15.6–22.1 was used instead of the 4–60 found in the literature. For parameters with less than five sources, minimum and maximum values were taken as the uncertainty bounds. Tables 17–19 in Appendix C gather the average value and range of uncertainty of each input parameter of the model.
From this perspective, water consumption during wheat growth was not included in the evaluation of the water intensity of a wheat straw-based BECCS facility. However, wheat water footprint is presented in this analysis to compare the water footprint of energy dedicated crops with that of conventional crops such as wheat.
Country/region | City | Climate |
---|---|---|
Brazil | Sao-Paulo | Subtropical summer rainfalls |
China | Zhengzhou | Subcontinental temperate |
Europe (Netherlands) | Eindhoven | Oceanic temperate |
India | Amritsar | Semi-arid |
The USA | Orlando (Florida) | Subtropical summer rainfalls |
Precipitation was then collected from the FAO-clim database,123 based on weather stations located in the aforementioned cities. Data were retrieved for a period of ten years. In the FAO guidelines to compute crop evapotranspiration,47 water run-off has to be considered in the determination of the available water from rainfall. The FAO developed tool CROPWAT 8.0124 enables the calculation of effective precipitation in a given region, based on empirical linear correlations established on a specific soil type. CROPWAT default soil composition was used since soil type was not considered as an input parameter in the analysis. A 10 year average of monthly precipitation data was implemented in CROPWAT 8.0 to get the monthly effective precipitation in mm ha−1. The green water was then determined for a given crop in m3 tDM−1 of biomass using the crop annual dry mass yield in tDM ha−1.
(1) |
(2) |
(3) |
(4) |
Given that the grey and blue water footprints represent the marginal amount of water required in addition to the green water (supplied by rainfall), these water footprints are presented in Fig. 3. Water requirements per hectare of energy dedicated crops were found to be higher on average than that of wheat: 834 mm ha−1 for miscanthus against 532 mm ha−1 for wheat. This is consistent with the BECCS water footprint analysis presented by Smith et al.,42 and can be explained in the difference in crop calendars and coefficients. However, as pointed out by Smith et al., this tendency changes when water footprints are expressed on a mass basis, due to the higher yield of energy dedicated crops as compared to that of conventional crops. Because of their low fertilizer input (impacts the grey water footprint) and relatively high yield, herbaceous biomass like miscanthus and switchgrass are thus found to be more sustainable water wise than conventional crops.
Among the energy dedicated crops, the water footprint of SRC willow was found to be slightly higher than that of switchgrass and miscanthus, which is mainly due to willow lower yield per hectare. However, the feasibility of irrigating and fertilizing SRC willow by waste water has been investigated in Sweden,125,126 which could considerably lower both grey and blue water footprints.
Energy dedicated crops are also characterized by a wider uncertainty range, relative to conventional crops. The difference lies in the greater uncertainty in input data (fertilizer use and yield) for the latter crops, derived from local experiments or simulations, as opposed to wheat data extracted from national or regional scale database on actual fertilizer use127 and yield.128 Switchgrass crop coefficients for example were not available in the literature, and, as suggested by Yimam et al.,129 coefficients of Sudan grasses,47 the closest species in terms of growth, were used.
The evaluation of the water footprint of agriculture products has been carried out in different studies,49 and with a growing interest in biomass production, studies now also focus on modeling or measuring water footprints of conventional and less-conventional biomass crops.51,90,129 Studies however can differ in input data (climate conditions, yield, fertilizer use, fertilizer leaching), boundaries (crop theoretical requirement, green and blue water footprints, grey footprint), and complexity of the model (water stress correction, soil water capacity and type). Keeping in mind these differences, Table 2 compares model results with those in the literature. Overall results were found to be consistent with the sample of values selected from the literature.
Crop, regionsource | Boundaries | Literature data | Model |
---|---|---|---|
a Model lower bound–upper bound (average) results for Europe, Netherlands. b Model lower bound–upper bound (average) results for USA, Florida. c Model average result for Europe, Netherlands. d Model average result for USA, Florida. | |||
Miscanthus, Netherlands51 | CWR (m3 tDM−1) | 334 | 175–248 (205)a |
Miscanthus, Florida90 | GnWF + BWF (m3 tMW−1) | 330–495 (413) | 289–373 (321)b |
Switchgrass, Oklahoma129 | CWR (mm year−1) | 521–786 (654) | 414c–693d |
Willow69 | CWR (mm year−1) | 100–1790 (698) | 628c–1034d |
Wheat50 | GnWF (m3 tMW−1) | 1277 | 728c–1712d |
Wheat50 | BWF (m3 tMW−1) | 342 | 255c–530d |
Wheat50 | GyWF (m3 tMW−1) | 207 | 62c—262d |
(5) |
In a second instance, it is interesting to see how the water intensity of a coal + CCS system changes as coal is substituted by biomass, and CCS capture rate increases. Fig. 4 shows the system water intensity evolution as a function of co-firing proportion and capture rate.
It is observed in Fig. 4 that co-firing and capture rate increases the water intensity, as such a system is likely to be less efficient, hence to burn more fuel per MW h of power generated, and therefore need more cooling water.
Given that the overall aim of this study is to evaluate BECCS performance across the entire supply chain, BECCS water intensity can also be calculated including the biomass water footprint WFWM on a wet biomass basis:
(6) |
It can be observed that the water footprint of the biomass supply chain far outweighs that of the actual power plant. At 90% co-firing and 100% biomass firing, the water intensity can be up to 150 times higher when including biomass production water cost in the case of willow. This highlights the importance of performing a whole-systems assessment of any given BECCS option in order to ensure its sustainability, and of including water footprint in this assessment.
• Farming indirect energy use which includes the embodied energy in different input (chemicals, seeds, fuel, power, fuel) used for crop establishment and maintenance,
• Farming direct energy (energy density) use which includes the use of fuel for product transportation and infield operations,
• Indirect (embodied energy) and direct energy use (energy density) of fuel or power input in biomass processing (size reduction, grinding, drying in rotary dryer, pelleting), transport and further pellet grinding at the power plant.
In order to compare the different contributions, each energy input was calculated per dry ton of pellet delivered to the power plant. This distinction between the embodied energy GJ tDM−1 harvested and delivered is important since it accounts for dry and wet mass loss along the supply chain. The methodology of embodied energy evaluation is presented in Fig. 23 in Appendix B, with equations detailed in Appendix A. Similarly to the water footprint, comparing the embodied energy results with literature results is only possible when the boundaries and assumptions (yield, moisture, transportation distance, etc.) of the models are known. For a proper comparison, the model was evaluated under the closest conditions to those of the source. The analysis presented in Table 4 indicates that our results are in good agreement with the literature ones.
Crop, regionsource | Units | Results | Model |
---|---|---|---|
a Model lower bound–upper bound (average) results for Europe, production only no irrigation. b Model lower bound–upper bound (average) results for Europe, production only no irrigation, 4k transport. c Model lower bound–upper bound (average) results for Europe, production (bale), 100k transport, chopping and milling for combustion. d Model lower bound–upper bound (average) results for Europe, production (bale) no irrigation, 100k transport, drying and pelleting. e Model lower bound–upper bound (average) results for the US, production (bale or chopped). f Model lower bound–upper bound (average) results for the US, production (bale) no irrigation. g Model lower bound–upper bound (average) results for Europe, production and 50k transport. h Model lower bound–upper bound (Europe–US) results, production, drying (50%), pelleting and transport. i Drying 45% to 15%. j Model lower bound–upper bound (average) results for the US, collection (bale), 90k transport grinding, combustion. k Model lower bound–upper bound (average) results for Europe, collection (bale), 40k transport grinding, combustion. | |||
Miscanthus, average67 | GJ tDM−1 | 0.52 | 0.31–1.02 (0.57)a |
Miscanthus, Poland69 | GJ tDM−1 | 0.77 | 0.32–1.03 (0.58)b |
Miscanthus, Germany76 | GJ tDM−1 | 1.25 | 0.86–2.00 (1.29)c |
Miscanthus, Ireland65 | GJ GJHHV−1 | 0.28 | 0.08–0.14 (0.10)d |
Switchgrass, US87 | GJ tDM−1 | 1.5–1.9 | 0.7–1.8 (1.1)e |
Switchgrass, US79 | GJ tDM−1 | 0.2 | 0.4–1.4 (0.8)f |
Switchgrass, US67 | GJ tDM−1 | 1.1 | 0.4–1.4 (0.8)f |
Willow, Sweden131 | GJ tDM−1 | 0.86 | 1.5–2.6 (1.8)g |
Willow, average67 | GJ tDM−1 | 1.5 | 0.5–1.5 (0.9)a |
Willow, Belgium132 | GJ (ha year)−1 | 3–16 | 6–12 (9)a |
Wood pellets, Australia and Russia94 | GJ tDM−1 | 1.13–7.5 | 3.7–8.0 (5.2)h |
Willow, Poland69 | GJ tDM−1 | 0.16 | 0.58–1.67 (1.01)b |
Wood pellets57 | MJ tH2O | 4455 | 4563i |
Wheat straw, New Zealand133 | GJ GJe−1 | 0.24 | 0.16–0.30 (0.22)j |
Wheat straw, UK97 | GJ GJe−1 | 0.44 | 0.16–0.29 (0.22)k |
Fig. 6 shows the different energy contributions along the chain in GJ tDM−1 pellets, with the overall uncertainty range.
As long-distance transportation was considered in some cases (Brazil, China, India and USA to the UK) it is observed that transport accounts for a substantial share of biomass embodied energy, up to 66% in the case of wheat straw imported from India. Road transport, in particular, is what drives the transport cost. With a diesel fuel efficiency of about 1.65 MJ (tons km)−1 in road transport, and a heavy fuel oil (HFO) efficiency of 0.05 MJ (tons km)−1, 1 km of road shipping costs about 30 km of sea shipping. Therefore, transporting national biomass a distance of 400 km by road requires as much energy as shipping biomass over 13000 km. Taking the road distance between the farm to the harbour and the harbour to the power plant into consideration, an equivalent journey in energy cost could be for example transporting feedstock grown within 100 km of the American east coast to Drax power plant in the UK. This shows that global supply chains should be designed so as to minimise their reliance on road transport (ocean and rail being preferred), and that the development of low carbon (or carbon negative via BTL + CCS) transport fuels will be vital in ensuring the sustainability of BECCS. Focusing on road and ocean transport highlights the importance of coastal as opposed to inland regions for bioenergy production. However, this conclusion could be nuanced when exploring the combination of rail and barge transport as an alternative to road transport. This, as well as the impact of biomass storage, is left for future work. Willow stands out as unique in this study in that its embodied energy is primarily driven by drying. This is due to the fact that willow has a high average moisture content at harvest (52%), compared to 23% for miscanthus, 12% for switchgrass and 11% for wheat straw. The moisture target at the drying stage was set to 15% in this analysis, in order to meet the moisture requirement of 10% for the pelleting process, and assuming a 5% moisture loss during grinding. This explains the absence of drying requirement for both switchgrass and wheat straw. In another supply chain scenario (bale or chips), biomass could be dried at a higher moisture content. However transportation energy cost and boiler efficiency penalty in the power plant would automatically be higher, which could potentially offset the energy saved at the drying stage. The embodied energy associated with the chemicals used also constitutes an important share contributing to above 10% of the overall embodied energy. This is due to the fact that willow is characterised by a relatively low yield for a high chemical input rate. Pellet grinding cost represents between 5 and 17% of the total production energy cost. Williams et al. showed that treatment on biomass pellets could reduce this cost by as much as a factor of four with steam-exploded pellets, and up to a factor of 25 with torrefied pellets.34 The grinding cost of torrefied pellets would therefore be over five times lower than that of coal on a mass basis, and four times lower on an energy basis. Torrefaction was not included in this analysis, but the trade-off between torrefaction energy requirement and improved power plant performance with torrefied biomass is clearly an area which warrants further study. The relative magnitude of the uncertainty associated with the total embodied energy is observed to vary significantly between regions. For example, the calculated range for India is much greater than that for Europe. This observation is primarily driven by uncertainty in yield data. Yield data were widely available in Europe, which enabled the use of a 95% confidence interval as the uncertainty range, thus excluding extreme yield values. For India, yield values obtained from experiments in arid or semi-arid climates in other regions were used,73,134,135 but these data were scarce and highly variable.
(7) |
Similarly to the water intensity, it is interesting to see how this efficiency changes as we include biomass production energy use in the overall balance:
(8) |
The effect of including biomass supply chain is quite important, with the greatest efficiency loss (points) between 7.9% in the lower bound scenario, and 10.7% in the upper bound scenario in the case of willow, whose high supply chain embodied energy translates into a poor performance in terms of net chain efficiency. It is worth noting that if one considers BECCS as a climate mitigation technology, assuring that BECCS achieves a net negative CO2 balance at a low water cost is the main objective. In this regard, BECCS power generation performance is not as critical as the system's carbon and water intensities in the evaluation of BECCS potential for climate mitigation. However, the system efficiency and biomass supply chain energy cost will ultimately determine the marginal cost of a BECCS power plant. If we assume that BECCS plants might be expected to operate within a liberalised electricity market, a substantial marginal cost of generation would result in a decreased capacity factor for these plants, in turn limiting the amount of CO2 that would be removed from the atmosphere. This is something which will bear careful examination in the context of understanding how BECCS will operate in practice.
• N2O emissions due to nitrogen-based fertilizer application,
• Negative emissions through biomass combustion and carbon capture and storage (BECCS),
• Direct and indirect land use changes.
As N2O has a global warming potential (GWP) 298 times (100 year-basis) higher than that of CO2, N2O emissions through nitrogen leaching and volatization from fertilizer application were also considered in the analysis. N2O emissions were calculated using the FEAT methodology,67 and the detailed equations are presented in Appendix A.
Biomass carbon footprint results were also compared with existing evaluations from the literature in Table 5, evaluating the model under the same conditions (transport distance, region if possible, process included, etc.). Again, Table 5 shows the great diversity in the results found in the literature, with carbon footprints found between 90 and 600 kgCO2 tDM−1. With an average relative error between the model mean values and the literature results under 30%, and considering the uncertainty range of results, the model results were considered consistent with those found in the literature.
Crop, regionsource | Units | Results | Model |
---|---|---|---|
a Model lower bound–upper bound (average) results for Europe, production (bale), 100k transport, chopping and milling for combustion. b Model lower bound–upper bound (average) results for Europe, production (bale) no irrigation, 100k transport, drying and pelleting. c Model lower bound–upper bound (average) results for the US, production (bale or chopped). d Model lower bound–upper bound (average) results for the US, production only no irrigation. e Model lower bound–upper bound (average) results for US, production only no irrigation, 40k transport. f Model lower bound (av. grassland)–upper bound (av. forest) results for Europe, production, LUC, no irrigation, processing, 25–75k transport. g Model lower bound–upper bound (Europe–US) results, production, drying (50%), pelleting and transport. h Model lower bound–upper bound (average) results for Europe, collection (bale), 40k transport, grinding. | |||
Miscanthus, Germany76 | kgCO2 tDM−1 | 111.8 | 97–237 (152)a |
Miscanthus, Ireland65 | kgCO2 GJHHV−1 | 20.6 | 9.8-20.9 (14.5)b |
Switchgrass, US87 | kgCO2 tDM−1 | 191–204 | 92–274 (162)c |
Switchgrass, US79 | kgCO2 tDM−1 | 144.7–146.7 | 72–246 (139)d |
Switchgrass, US67 | kgCO2 tDM−1 | 172 | 72–246 (139)d |
Switchgrass, US136 | kgCO2 tDM−1 | 195–198 | 95–284 (168)e |
Willow, UK–US99 | kgCO2 GJe−1 | 118–242 | 147–396f |
Willow, average67 | kgCO2 tDM−1 | 89 | 87–253 (153)d |
Wood pellets, Australia and Russia94 | kgCO2 tDM−1 | 143–594 | 166–603 (306)g |
Wheat straw, UK97 | kgCO2 GJe−1 | 66 | 26–53 (38)h |
In order to evaluate biomass carbon negative potential, biomass carbon intensity in tCO2 tDM−1 biomass was also evaluated. For a BECCS power plant operating at a given capture rate, RCCS, biomass carbon intensity has the following expression:
(9) |
Carbon footprints of different feedstock from different regions are presented in Fig. 9.
The addition of N2O emissions, which have a large weight in the overall balance, underlines the double effect of fertilizers, both from their energy and carbon intensive production process, and from the subsequent emissions they cause after their application to the field. Biomass processing also has a higher weight in the carbon balance because of the relatively high electricity carbon intensity values chosen for this model – 470–800 kgCO2-eq MW h−1.137,138 This input is highly dependent on the power source and region, and could be significantly improved in the context of a decarbonised electricity system – 50 kgCO2-eq MW h−1. Drying of high moisture biomass such as willow still constitutes an important contribution. Aside from management practices like natural open-field or storage drying, this cost could be reduced by substituting natural gas with biomass in rotary dryers.94,139 These options are further investigated in Section 4.
To account for direct land use changes, conversion emission factors in tCO2 ha−1 for each land type were taken from the literature.102,103 For indirect land use changes however, conversion factors also depend on the initial use of the land. Land types were thus classified into two categories; managed land, i.e., already allocated to an activity, and unmanaged land. If converting part of a managed land – cropland or grassland – to biomass production, an ILUC conversion factor in tCO2 was attributed. When converting a land unallocated to any activity, ILUC was considered to be zero. ILUC conversion factors in tCO2 ha−1 are not found as such in the literature. Indirect land use changes have been thoroughly investigated in the context of biofuels in which conversion factors are expressed in tCO2 MJ−1, and are a function of the biofuel energy yield MJ (ha year)−1, time horizon (in years), fraction of land displaced (in %ha) and average emission factor resulting from the activity displaced (in tCO2 ha−1). Two different sources were used: an analysis by Plevin et al. based on corn bioethanol in the US,103 and an analysis from Overmars et al. based on bioethanol production in Europe, from three different feedstocks: wheat and sugar beet grown in Europe and sugarcane imported from Brazil and Pakistan.107 As ILUC factors are expressed in kgCO2-eq MJ−1 of biofuels in both analysis, the data provided in the studies were used to derive land ILUC factors in kgCO2-eq per hectare cultivated for bioenergy. These factors can then be used independently from biomass yield and project time horizon. When average net displacement factors (ha displaced/ha of biofuels) and emission factors (tCO2-eq ha−1) were provided,103 the land ILUC factor was obtained by multiplying these two parameters. When the data provided were less straightforward,107 the land ILUC factor was obtained by multiplying the ILUC factor by the time horizon and bioenergy yield. Plevin et al. described two additional analyses on US bioethanol from maize140 and corn,108 and these results were also included in the ILUC coefficient data set to ensure the consistency of the statistical analysis. Fig. 11 provides further details on the methodology and data used.
Fig. 11 Methodology for the derivation of ILUC conversion factors in kgCO2-eq ha−1. Values derived from Plevin et al., Searchinger et al., and Hertel et al. were obtained for bioethanol production in the US,103,108,140 whereas the results from Overmars et al. are the lower and upper bounds obtained for bioethanol production in Europe, considering three different feedstocks: wheat and sugar beet grown in Europe and sugarcane imported from Brazil and Pakistan. |
In this analysis net displacement and emission factors were considered the same for all managed land types (grassland or cropland), biomass types and regions. This is a first estimation to give indications as to the potential impact of including land use change. Applying this analysis to a certain case study in order to provide precise insight for a given region would require the use of region specific coefficients.
For each land type, the initial carbon debt, DC, including both direct and indirect land use changes, was then known. In this analysis, the time horizon chosen was 50 years, as this was deemed to be sufficiently long to give a fair evaluation of every option. The carbon debt was expressed in kgCO2 tDM−1 when dividing by the overall amount of dry biomass delivered by a hectare of land over 50 years:
(10) |
(11) |
As illustrated in Fig. 13, carbon intensity decreases as coal gets displaced by biomass and carbon capture rate decreases. To the power plant carbon intensity are added biomass supply chain CO2 emissions:
(12) |
As can be observed from Fig. 14 and 15, whilst the majority of scenarios result in the net removal of CO2 from the atmosphere, no two scenarios achieve an equivalent amount of net CO2 removal. Importantly, it can be observed that some scenarios, such as those relying upon willow in Fig. 18, appear to be net carbon positive, resulting in the net emission of up to 1200 kgCO2 MW h−1. For comparison, an unabated coal-fired power plant might emit between 700 and 1000 kgCO2 MW h−1. Thus, if the wrong choices are made throughout the supply chain, BECCS could indeed be substantially more carbon intense than an unabated coal-fired power plant. This is one of the core results of this study. At 90% co-firing, a willow-based BECCS system is, in the mean scenario, carbon negative within the 20–100% co-firing range with no land use changes, carbon negative within the 30–100% co-firing range with LUC, and always carbon positive with ILUC. In practical terms, this narrows the feasible range of power plant operability – capture rate and co-firing proportion – for the system to be carbon negative, and ultimately decreases its flexibility. However, it is important to notice that BECCS systems including ILUC can still be carbon negative in the average scenario, within the 30–100% co-firing range for a miscanthus-based system, and 40–100% for a switchgrass-based system, at 90% capture rate. Limiting the effects of indirect land use changes as well as assuring high yield and low fuel, power and chemical input during biomass production will be capital for the BECCS overall balance to be negative.
As can be observed in Fig. 16, BECCS carbon efficiency reduces from 62% (marginal land) to 46% (grassland) when adding LUC and ILUC, with the latter accounting for over 26% of the carbon leakage. Upon adding land use changes, the facility is thus no longer carbon negative. This emphasizes the fact that though efforts throughout BECCS supply chain must be made to reduce further carbon leakages (chemicals, transport, carbon capture), a better understanding and control of land use changes will be necessary to maximise BECCS carbon efficiency.
We have demonstrated the importance of including the biomass supply chain in the evaluation of the thermo- and hydrodynamic efficiencies, and carbon intensity of the overall system. Chemicals, road transport, drying and grinding were identified as important leakages in BECCS efficiencies, though negligible when compared to land use change effects. In the next section, a dynamic accounting of the GHG emissions is performed to evaluate BECCS efficiency at removing CO2 from a time perspective.
Miscanthus imported from Brazil breaks even after 3, 7 and 26 years, if grown on a cropland, central grassland and forest, respectively. When grown on a Brazilian forest, miscanthus is the only crop reaching carbon break even time before year 50. When including the effects of indirect land use changes for cropland and grassland in Fig. 17, carbon breakeven time increases by seven years for cropland and grassland.
(1) Case A: miscanthus from Brazil on marginal land (no LUC and ILUC),
(2) Case B: miscanthus from Brazil on central grassland (LUC and ILUC),
(3) Case C: willow from Europe on marginal land (no LUC and ILUC),
(4) Case D: willow from Europe on central grassland (LUC and ILUC).
Fig. 18 represents the 729 outcomes when evaluating the model with the low, average and high values of biomass moisture content, carbon content, electricity carbon footprint, LUC conversion factor and ILUC conversion factors in those four case studies.
We can note from this analysis the diversity of possible outcomes, associated with a BECCS facility, as a function of the decisions made along the supply chain. In the case of miscanthus, which showed the lowest carbon footprint in the steady-state analysis, all scenarios are carbon negative over 50 years on a marginal land, with a carbon negative potential ranging from 0.7 to 1.6 ktCO2 ha−1 captured over 50 years. When including land use effects, the number of scenarios leading to a capture potential greater than 0.5 kt ha−1 was reduced to 648 out of 729, but no scenario led to a dynamic carbon positive balance. For the willow case study that showed higher carbon footprints on a steady-state basis, the dynamic carbon balance was always negative on a marginal land, with a carbon capture potential ranging from 190 to 390 t ha−1 over 50 years. On a central grassland however, the number of carbon negative scenarios dropped to 223. In terms of carbon breakeven time, without land use effects, the time required for BECCS to be negative varied on average between 1 year for miscanthus and 3 years for willow. This became on average 15 times longer for miscanthus and willow when including land use changes. These results are presented in Table 6.
Outcomes | Units | A | B | C | D |
---|---|---|---|---|---|
Positive scenarios | 0 | 0 | 0 | 460 | |
Negative scenarios | 729 | 729 | 729 | 269 | |
CO2 removed | Mean (t ha−1) | −1124 | −805 | −288 | 31 |
Min (t ha−1) | −1600 | −1429 | −392 | −222 | |
Max (t ha−1) | −718 | −248 | −186 | 285 | |
Breakeven time | Mean (years) | 1 | 15 | 3 | 46 |
Min (years) | 1 | 6 | 3 | 22 | |
Max (years) | 2 | 32 | 3 | >50 |
As can be observed from both figures, ILUC is the primary determining factor, followed by LUC, yield, electricity footprint, biomass carbon content and biomass moisture content. Compared to these parameters, the fuel emission factor and carbon footprints as well as chemicals rates and carbon footprints were found to have a limited impact on these results. The same analysis was performed on a willow-based system. The results are presented in Fig. 24 in Appendix D.
It is evident that LUC and ILUC conversion factors should be carefully evaluated on a case by case basis, but so should be biomass yield, composition and electricity footprint. In practice, a crop yield is a complex function of a range of parameters, including climate, biomass properties, soil type, nutrients and water availability.141
These results also indicate ways of improving BECCS sustainability. In order to evaluate the potential for improvements in BECCS, the following alternate scenarios were investigated:
(1) Organic chemicals (no carbon footprint),
(2) Biodiesel for in-field activities (100%) and road transport (blend 20% with conventional diesel B20),
(3) Bioethanol with CCS for in-field activities (blend 25% with conventional gasoline E25) and road transport (blend 25% with conventional gasoline E25). We assume that bioethanol carbon footprint is −100 gCO2 MJ−1 of fuel used.111
(4) Carbon neutral electricity,
(5) Drying with biomass rather than natural gas.
Ranges of uncertainty of chemical footprint and fuel emission factors were relatively small compared to other parameters, which could explain their limited impact observed in the sensitivity analysis. For this reason, those parameters were included in the alternate scenarios. Table 7 summarizes the ranges of impact on BECCS cumulative capture potential over 50 years in tCO2 year−1 for each alternative scenario, as compared to the base case.
Case study | Base case | Sc1 | Sc2 | Sc3 | Sc4 | Sc5 |
---|---|---|---|---|---|---|
A | −1124 | −1158 | −1192–(−1119) | −1246 | −1364 | −1158 |
B | −805 | −839 | −873–(−800) | −927 | −1044 | −824 |
C | −288 | −316 | −313–(−306) | −345 | −348 | −343 |
D | 31 | 3 | 6–13 | −26 | −29 | 132 |
Due to the uncertainty around biodiesel carbon footprint, and the proportion limit to 20% in volume for biodiesel/diesel blend in road transport, bio-diesel impact was limited and even positive in some cases. With bioethanol + CCS, in-field operations become carbon sinks rather than carbon sources, thus decreasing the overall CO2 emissions of the value chain. The use of bioethanol + CCS in transport further decreases the overall carbon footprint of biomass, though carbon negative road transport could not be reached due to the limitation on bioethanol proportion in engines. Going towards low carbon transport or even carbon negative transport to improve BECCS sustainability will only be possible with dedicated bioethanol engines. However, increasing the use of organic fertilizers for farming and carbon neutral electricity for biomass processing could bring substantial sustainability improvements to the BECCS value chain. Similarly to biodiesel, drying with biomass was found to have both a positive and a negative impact. When land use changes are included in the case of willow, biomass carbon footprint is found to be higher than that of natural gas, hence the negative effect (decrease) on the capture potential, rather than a positive effect (increase). Most importantly, a carbon positive case study, such as willow on central grassland, could turn carbon negative by switching to carbon neutral electricity for biomass processing and carbon negative bioethanol for farming and transport. This demonstrates the importance of intelligent management – organic chemicals, carbon neutral electricity, low carbon or carbon negative fuels for transport and drying – to ensure bioenergy sustainability. This supports the assertion raised in Dale et al. that bioenergy can be sustainable when carefully managed.142
Furthermore, BECCS would need to be deployed at the scale of 1.7 to 2.4 TW. For reference, at the time of writing, the total installed coal-fired thermal power capacity is 1.8 TW. Conversion of both coal and natural gas-fired power plants to dedicated biomass and biogas might well be necessary to meet these deployment targets. Furthermore, assuming a base load operation of the BECCS unit (85% load factor), this deployed capacity would represent an annual power generation of between 9700 and 14600 TW h, or between 44% and 68% of the global power demand in 2012.144 This result is highly dependent on the BECCS annual load factor, and while a BECCS unit should run at full capacity to remove a maximum amount of CO2 from the atmosphere, this value could potentially be dictated by the system short run marginal cost relative to that of the other power sources within the electricity market. Policies rewarding CO2 removal from the atmosphere will be crucial in increasing BECCS competitiveness relative to other technologies, and in turn will maximise the BECCS load factor. However, the BECCS supply chain energy requirement must be subtracted from the annual power generation. Within the uncertainty range, BECCS net energy balance could be both positive and negative, which needs to be considered when talking about BECCS energy supply potential.
It can be observed from Table 8 that the model shows very good agreement with the literature for switchgrass.83 However, though the model land requirement for miscanthus is in good agreement with the literature,42 water requirement and energy supply are found to be very different. The difference in water requirement can be explained by the different model assumptions. In Smith et al., the contribution of bioenergy production to the BECCS water footprint is evaluated at 80 m3 tC−1 year−1, and the power plant + CCS contribution around 450 m3 tC−1 year−1. This study assumes an evapotranspiration value for bioenergy between 1176 and 1822 m3 tC−1 year−1.42 For reference, this value compares well with our model for which miscanthus evapotranspiration is 1240 m3 tC−1 and the total water footprint (evapotranspiration + grey water) 1635 m3 tC−1. The value of 80 m3 tC−1 is then obtained by subtracting the evapotranspiration of a reference grassland (the counterfactual) from bioenergy evapotranspiration.42 However, if considered in the water balance, the counterfactual – leaving the land as is – would also need to be considered in the carbon balance, which would mean accounting for the CO2 which would have been captured if the land had been left as is. As this was considered out of scope in this analysis, total evapotranspiration + grey water was selected as the biomass feedstock water footprint. Furthermore the model evaluates the power plant + CCS water contribution at 14 m3 tC−1. It is assumed that the choice of a once-through cooling system – whose water consumption factor can be 30 times higher than that of a cooling tower system – as opposed to a recirculating cooling tower is the explanation behind this difference. For reference, the model would result in a power plant water requirement of 425 m3 tC−1 with a once-through system.
Metrics | Switchgrass US | Smith et al. (2013) | Miscanthus Brazil | Smith et al. (2016) |
---|---|---|---|---|
Nb plants | 3413–4852 (3887) | 3323–5758 (4104) | ||
Biomass (GtDM year−1) | 9.2–13.1 (11.1) | 9.0–15.5 (10.5) | ||
Water (Tm3 year−1) | 7.8–15.7 (10.4) | 5.3–24.4 (14.5) | 3.6–9.7 (5.5) | 0.072 |
Land (M ha) | 1245–2392 (1630) | 726–3270 (1910) | 363–943 (538) | 380–700 |
Energy (EJ year−1) | −22.4 to 1.0 (−15.1) | −15.3 to 37.0 (−0.01) | −170 | |
Nitrogen (Mt year−1) | 61–210 (112) | 21–92 (42) | ||
Phosphate (Mt year−1) | 0–161 (55) | 8–98 (34) | ||
Sources | 83 | 42 |
As for BECCS energy supply, BECCS was found to yield 170 EJ year−1 at this deployment. However, this value does not include BECCS energy requirement which needs to be subtracted from BECCS energy supply.
The same analysis was performed on grassland instead of marginal land, to account for land use changes. Fig. 20 shows the amount of land, water, and BECCS capacity in order to meet the 3.3 GtC annual removal target, with (central grassland) and without (marginal land) land use change. Upon including land use changes (e.g., considering grassland instead of marginal land), resource mobilisation increases up to four times in the upper bound scenario.
Metrics | BECCS | Afforestation | DACS |
---|---|---|---|
Nb plants | 3323–5758 | 3320 | |
Water (Tm3 year−1) | 3.6–15.7 | 5.3–11.6 | |
Land (M ha) | 363–2392 | 1110–2480 | 0.04–3.3 |
Energy (EJ year−1) | −22.4 to 37.0 | 81–274 | |
Nitrogen (Mt year−1) | 21–210 | 0.3–2.4 | |
Phosphate (Mt year−1) | 0–161 | 0.7–2.5 | |
Sources | 83 | 42 and 145–148 |
Given a CO2 annual removal target, afforestation resulted in an overall similar land and water use than that of BECCS, though its land requirement corresponded to the upper range of BECCS results. Within the uncertainty range, BECCS net energy balance could be both positive and negative, but even when positive its energy intensity was found to be lower than that of DACS. DACS land requirement was several orders of magnitude lower than that of BECCS. However, the carbon footprint of the electricity used in the process, which would have had an impact on DACS carbon efficiency, was not considered in the analysis. Using photovoltaic power could limit the carbon efficiency drop, but would on the other hand substantially increase DACS land requirement. These trade-offs will have to be considered in detail when comparing BECCS and DACS suitability for climate mitigation.
In terms of time horizon, depending on the conditions and due to biomass initial carbon cost to the ecosystem, BECCS does not necessarily start being a net carbon sink from year 1. If we consider that BECCS provides a service to the market – removing CO2 out of the atmosphere, therefore avoiding future costs associated with climate change adaptation, it is reasonable to suggest that this service could be remunerated. However, it is also reasonable to suggest that this remuneration does not start until the facility is actually removing CO2 from the atmosphere. Given that this breakeven time could be several years, this could well serve to complicate the delivery of BECCS projects, as the incentive for investing in BECCS might be otherwise insufficient.
Finally, in the case of a global deployment of an unintegrated BECCS value chain, this carbon crediting scheme would also need to acknowledge the diversity of stakeholders – biomass production, power generation, CO2 transport and storage, and possibly countries, involved in the BECCS value chain.
Supply chain results were implemented in the context of a 500 MW BECCS facility to evaluate the impact of the biomass supply chain on the overall plant carbon intensity, water intensity and energy efficiency. The results showed a substantial impact on water and carbon intensities at high co-firing proportions, and to a smaller extent on efficiency. Including direct and indirect land use changes had a great impact on the power plant carbon intensity, narrowing the range of operability for the power plant to be carbon negative.
A dynamic carbon balance was carried out over a 50 year period to evaluate a BECCS power plant carbon negative potential over its lifetime in different biomass–region–land type scenarios. Depending on the conditions of the simulation, carbon breakeven time could vary from 1 (marginal cropland) to 35 years (central grassland) for miscanthus from Brazil, and from 6 to over 50 years for willow from Europe. Within the uncertainty bounds considered for the input data, the key factors impacting the results were identified to be the land conversion factors, electricity carbon footprint, biomass yield and moisture content. The investigation of alternative scenarios such as using carbon neutral electricity, organic chemicals and bioethanol + CCS gave indications as to BECCS potential margin of improvements. However, BECCS overall results were driven by land use conversion factors, which indicate the need for thorough evaluations of these effects. Bypassing this issue with biomass growth on marginal land could be a potential solution. Yet, marginal land availability and uncertain biomass productivity response might not make them long term candidates for BECCS large-scale deployment.
Given the variable outcomes of BECCS sustainability analysis, BECCS large scale deployment was found to have very different implications in terms of resource mobilisation. Based on the analysis on switchgrass and miscanthus, within a scenario excluding direct and indirect land use changes, removing 3.3 GtC year−1 with BECCS could annually require between 360 and 2400M ha of marginal lands, 3600 and 15700B m3 of water, 30 to 360 Gt of nutrients, and 1.7 to 2.9 TW of installed BECCS capacity. As a means of comparison, the upper bounds of these values correspond respectively to over three times the world total harvested land for cereal production, twice the world annual water use for agriculture (including evapotranspiration), 20 times the US annual nutrient use, and 1.6 times the world total coal-fired power plant capacity. This underlines the challenges associated with the large scale deployment of BECCS, especially concerning water and nutrient consumption.
Overall it was shown that over a plant lifetime and upon choosing the right conditions, BECCS can be a reliable option for the sustainable and permanent removal of CO2 from the atmosphere, even when including supply chain, direct and indirect land use change effects. The high variability in BECCS CO2 removal time and space efficiencies in the model outcomes underpinned the need for case-to-case analysis when it comes to BECCS sustainability assessment, especially for the determination of land use change factors. Policy implications of this conclusion are that regulating and attributing value to these systems will have to integrate this regional specificity.
We have identified five key principles which could improve the sustainability of BECCS. The combination of a sensitivity analysis on the model combined with the investigation of alternate supply chain scenarios elucidated the following five key levers: (1) measuring and limiting the impacts of direct and indirect land use changes, (2) using carbon neutral power and organic fertilizers, (3) prioritizing sea and rail over road transport, (4) increasing the use of carbon negative fuels, and (5) exploiting alternative biomass processing options, e.g., natural drying or torrefaction. This indicates that regardless of the biomass and region studied, BECCS sustainability heavily relies on intelligent management.
(13) |
(14) |
(15) |
(16) |
(17) |
Processing operations require energy both in the form of fuel (drying) and electrical power (size reduction, drying, grinding, pelleting). Knowing each operation energy requirement EEi in MJ tMW−1 and biomass input Yi,wet, each processing contribution to biomass embodied energy is calculated with the following formula:
(18) |
For drying, a model was designed based on Gebreegziabher et al.112 and Li et al.150 Biomass drying has been covered by many studies,113,151–153 but a precise thermodynamic and kinetic model is hard to obtain due to the lack of data on biomass properties, such as specific heat capacity, diffusivity, equilibrium moisture content, etc. This model uses a thermodynamic approach based on the industrial data provided by Gebreegziabher et al. and Li et al. on wood drying (Fig. 21).
Chopped or chipped biomass is dried in a rotary dryer in contact with hot air. Air is heated through a heat exchanger from room temperature to about 60 °C. Air inlet and outlet relative humidity RH1 and RH3 are known. Air moisture content Yi at any stage of the process is linked with air relative humidity through psychrometric relations, involving the saturated vapor pressure Pwsi and vapor pressure Pwi:
(19) |
Pwi = RHi × Pwsi | (20) |
(21) |
Considering a water mass balance on the pre-heater, air inlet moisture content is equal to air outlet moisture content:
Y2 = Y1 | (22) |
(23) |
Wa(Y3 − Y2) = Ws(X2 − X3) | (24) |
Qh = Wa(Ha2 − Ha1) | (25) |
(26) |
Assuming the heat exchanger uses steam generated by a boiler operating with an efficiency EFFB, the specific boiler heat requirement is calculated:
(27) |
• Short and long distance diesel fueled truck for farm – pellet plant and pellet plant – power plant road transport (EffD),
• Long distance heavy fuel oil (HFO) fueled bulk carriers for pellet plant – power plant sea transport (EffHFO)
The transport stage contribution to biomass embodied energy is thus calculated by the following expression:
(28) |
EFN2O = EFN + EFNH3–N–NOx–N × EFN,volatalized + EFN,leaching × LP | (29) |
(30) |
Fig. 23 Overview of the embodied energy model. Biomass embodied energy was calculated by summing the different energy contributions along the value chain following a life cycle assessment approach. |
Month | Average low T | Average high T | Relative humidity | Wind speed at 2 m | Sunshine hours | Average precipitation |
---|---|---|---|---|---|---|
Units | °C | °C | % | m s−1 | Hours | mm |
January | 19.4 | 27.5 | 84.0 | 3.0 | 5.0 | 224.7 |
February | 19.5 | 28.4 | 84.0 | 3.0 | 4.6 | 149.7 |
March | 19.7 | 28.4 | 83.0 | 2.9 | 4.9 | 129.3 |
April | 18.0 | 26.3 | 83.0 | 2.9 | 5.8 | 44.0 |
May | 15.0 | 23.3 | 83.0 | 2.7 | 5.5 | 38.5 |
June | 14.4 | 23.7 | 82.0 | 2.4 | 5.8 | 24.7 |
July | 13.4 | 23.3 | 81.0 | 2.4 | 6.0 | 47.9 |
August | 14.7 | 25.4 | 79.0 | 2.5 | 6.4 | 25.5 |
September | 14.9 | 25.0 | 80.0 | 3.5 | 4.5 | 48.9 |
October | 16.8 | 26.7 | 82.0 | 3.0 | 3.9 | 110.8 |
November | 17.2 | 25.9 | 82.0 | 3.5 | 4.7 | 112.1 |
December | 18.5 | 27.4 | 83.0 | 3.3 | 5.7 | 164.2 |
Sources | 123 |
Month | Average low T | Average high T | Relative humidity | Wind speed at 2 m | Sunshine hours | Average precipitation |
---|---|---|---|---|---|---|
Units | °C | °C | % | m s−1 | Hours | mm |
January | −4.5 | 5.3 | 75.1 | 4.8 | 2.6 | 15.6 |
February | −1.0 | 8.8 | 79.1 | 3.5 | 2.7 | 15.3 |
March | 3.6 | 15.6 | 80.9 | 3.5 | 3.1 | 22.2 |
April | 9.5 | 23.0 | 81.6 | 3.8 | 2.8 | 32.5 |
May | 12.3 | 27.9 | 83.3 | 4.5 | 2.9 | 59.0 |
June | 18.6 | 32.0 | 84.3 | 5.5 | 2.5 | 100.4 |
July | 21.6 | 30.6 | 79.9 | 8.0 | 2.2 | 197.3 |
August | 20.8 | 30.4 | 79.8 | 7.4 | 2.2 | 173.4 |
September | 14.8 | 26.6 | 77.5 | 6.7 | 2.0 | 86.0 |
October | 8.4 | 22.3 | 73.0 | 6.8 | 2.3 | 43.9 |
November | 1.4 | 16.2 | 72.9 | 6.0 | 2.5 | 24.6 |
December | −2.0 | 8.5 | 73.1 | 5.7 | 2.8 | 13.7 |
Sources | 123 |
Month | Average low T | Average high T | Relative humidity | Wind speed at 2 m | Sunshine hours | Average precipitation |
---|---|---|---|---|---|---|
Units | °C | °C | % | m s−1 | Hours | mm |
January | 0.5 | 6.6 | 89.0 | 4.8 | 1.8 | 65.1 |
February | 0.6 | 7.0 | 88.0 | 3.9 | 2.7 | 69.0 |
March | 1.8 | 10.1 | 81.0 | 4.4 | 3.6 | 58.9 |
April | 5.0 | 16.3 | 75.0 | 3.4 | 5.2 | 45.4 |
May | 8.8 | 19.7 | 73.0 | 3.9 | 6.6 | 60.2 |
June | 11.4 | 22.6 | 74.0 | 3.3 | 5.8 | 45.2 |
July | 13.8 | 24.3 | 76.0 | 3.5 | 6.3 | 98.8 |
August | 12.0 | 21.8 | 78.0 | 3.4 | 6.2 | 63.3 |
September | 9.3 | 19.7 | 80.0 | 3.3 | 4.4 | 56.1 |
October | 7.6 | 16.4 | 85.0 | 3.4 | 3.6 | 57.6 |
November | 3.8 | 10.4 | 88.0 | 4.0 | 2.2 | 64.8 |
December | 1.4 | 6.6 | 90.0 | 4.4 | 1.4 | 69.2 |
Sources | 123 |
Month | Average low T | Average high T | Relative humidity | Wind speed at 2 m | Sunshine hours | Average precipitation |
---|---|---|---|---|---|---|
Units | °C | °C | % | m s−1 | Hours | mm |
January | 2.5 | 18.9 | 74.0 | 1.1 | 9.0 | 35.7 |
February | 6.6 | 22.2 | 70.0 | 1.4 | 9.0 | 38.0 |
March | 10.8 | 27.4 | 64.0 | 1.6 | 11.0 | 19.2 |
April | 16.2 | 35.9 | 47.0 | 2.0 | 12.0 | 13.7 |
May | 21.1 | 40.2 | 38.0 | 1.9 | 13.0 | 24.4 |
June | 23.7 | 39.6 | 48.0 | 1.8 | 13.0 | 110.1 |
July | 24.9 | 35.5 | 72.0 | 1.2 | 11.0 | 135.5 |
August | 24.6 | 34.8 | 77.0 | 0.9 | 10.0 | 119.0 |
September | 22.0 | 34.1 | 70.0 | 1.0 | 11.0 | 54.6 |
October | 16.6 | 32.7 | 67.0 | 0.8 | 11.0 | 10.9 |
November | 10.1 | 27.8 | 73.0 | 0.7 | 10.0 | 5.4 |
December | 4.4 | 21.1 | 76.0 | 0.9 | 9.0 | 9.4 |
Sources | 123 |
Month | Average low T | Average high T | Relative humidity | Wind speed at 2 m | Sunshine hours | Average precipitation |
---|---|---|---|---|---|---|
Units | °C | °C | % | m s−1 | Hours | mm |
January | 10.0 | 24.1 | 71.2 | 3.6 | 7 | 53.8 |
February | 9.7 | 24.2 | 69.7 | 3.7 | 7 | 49.6 |
March | 12.1 | 26.6 | 67.8 | 3.9 | 8 | 68.6 |
April | 14.6 | 29.0 | 66.8 | 4.0 | 10 | 54.3 |
May | 18.3 | 31.6 | 66.8 | 3.7 | 10 | 109.0 |
June | 21.7 | 32.6 | 75.4 | 3.0 | 11 | 234.0 |
July | 23.1 | 33.3 | 76.3 | 2.5 | 10 | 211.3 |
August | 23.4 | 34.0 | 77.8 | 2.8 | 9 | 244.4 |
September | 22.8 | 32.5 | 78.5 | 3.3 | 9 | 152.3 |
October | 19.2 | 29.8 | 75.6 | 3.6 | 8 | 101.4 |
November | 12.4 | 25.5 | 73.8 | 3.5 | 8 | 55.7 |
December | 13.6 | 25.9 | 73.9 | 3.3 | 7 | 74.3 |
Sources | 123 |
Parameter | Unit | Mean | Range | Sources |
---|---|---|---|---|
Nitrogen fertilizer EE | MJ kg−1 | 55.4 | 49.1–61.7 | 67, 76, 81 and 154–162 |
Phosphate fertilizer EE | MJ kg−1 | 10.5 | 6.9–14.1 | 67, 76, 81 and 154–162 |
Potash fertilizer EE | MJ kg−1 | 7.3 | 4.6–13.6 | 67, 76, 81 and 154–162 |
Lime EE | MJ kg−1 | 1.0 | 0.12–1.71 | 67, 81, 160 and 161 |
Herbicide EE | MJ kg−1 | 292.9 | 243.8–342.0 | 67, 81, 154, 160, 161 and 163 |
Miscanthus rhizome EE | MJ kg−1 | 6 | 4–8 | 76 and 97 |
Switchgrass seed EE | MJ kg−1 | 14.7 | 6–26.1 | 67, 71, 81 and 164 |
Willow planting EE | MJ per cutting | 0.101 | 97 | |
Diesel EE | MJ L−1 | 4.7 | 3.7–6.2 | 67, 96, 97 and 165 |
Biodiesel EE | MJ L−1 | 14.3 | 97 | |
Natural gas EE | MJ kg−1 | 4.7 | 97 | |
Diesel LHV | MJ L−1 | 37.4 | 35.9–39.1 | 76, 97, 116, 144 and 166 |
Biodiesel LHV | MJ L−1 | 34.8 | 33.7–37.3 | 97 and 167 |
Bioethanol LHV | MJ L−1 | 21.2 | 168 | |
Natural gas LHV | MJ kg−1 | 47.0 | 46.9–47.1 | 169 and 170 |
Transport of supplies (diesel) | MJ kg−1 | 0.52 | 0.44–0.64 | 69 and 158 |
Road transport diesel efficiency | L (km tMW)−1 | 0.044 | 0.028–0.06 | 65, 92, 97, 116, 170 and 171 |
Road transport bioethanol E25 efficiency | L ethanol per L diesel | 1.12 | Own calculations | |
Sea transport HFO efficiency | MJ (km tMW)−1 | 0.049 | 0.0302–0.0882 | Adapted from ref. 97 and 172 |
Irrigation | MJ mm−1 | 15.8 | 1.1–26.4 | Adapted from ref. 76, 114 and 139 |
Parameter | Unit | Mean | Range | Sources |
---|---|---|---|---|
Nitrogen fertilizer CF | kgCO2-eq kg−1 | 3.6 | 2.9–4.4 | 67, 76, 88, 158, 161, 173 and 174 |
Phosphate fertilizer CF | kgCO2-eq kg−1 | 1.1 | 0.6–1.6 | 67, 76, 158, 161 and 174 |
Potash fertilizer CF | kgCO2-eq kg−1 | 0.64 | 0.44–0.86 | 67, 76, 158, 161 and 174 |
Lime CF | kgCO2-eq kg−1 | 1.1 | 0.6–1.6 | 67, 158, 159, 161 and 174 |
Herbicide CF | kgCO2-eq kg−1 | 20.3 | 17.2–25 | 67, 158, 161 and 174 |
Miscanthus rhizome CF | kgCO2-eq kg−1 | 0.01 | 67 and 76 | |
Switchgrass seed CF | kgCO2-eq kg−1 | 14.7 | 6–26.1 | 67 and 71 |
Willow planting CF | kgCO2-eq kg−1 | 0.01 | 97 | |
Electricity CF | kgCO2-eq MJ−1 | 4.7 | 3.7–6.2 | 67, 76, 137, 138, 158, 161, 162 and 175 |
Diesel EF | kgCO2-eq L−1 | 3.4 | 3.2–3.5 | 67, 76, 89, 97, 109, 111, 158, 161, 174 and 176 |
Biodiesel EF | kgCO2-eq L−1 | 3.4 | 3.2–3.5 | 89, 97 and 106 |
Bioethanol + CCS EF | kgCO2-eq L−1 | −2.12 | Adapted from ref. 111 | |
Natural gas EF | kgCO2-eq L−1 | 3.4 | 3.2–3.5 | 97 |
Transport of supplies (diesel) EF | kgCO2-eq (km tMW)−1 | 0.05 | 67 and 158 | |
Road transport diesel EF | kgCO2-eq (km tMW)−1 | 0.077 | 0.073–0.08 | 97 and 177 |
Road transport biodiesel 20% (B20) EF | kgCO2-eq (km tMW)−1 | 0.065 | 0.062–0.068 | 169 |
Road transport bioethanol 25% (E25) EF | kgCO2-eq (km tMW)−1 | 0.034 | 0.028–0.040 | Own calculations |
Sea transport HFO efficiency | kgCO2-eq (km tMW)−1 | 0.004 | 0.00247–0.00722 | 172 |
ILUC | kgCO2-eq ha−1 | 183025 | 95700–270350 | Adapted from ref. 103, 107, 108 and 140 |
LUC grassland | kgCO2-eq ha−1 | 136300 | 75000–200000 | 102 and 103 |
LUC cropland | kgCO2-eq ha−1 | 37500 | 102 | |
LUC marginal land | kgCO2-eq ha−1 | 25 | 0–69 | 102 |
LUC forest | kgCO2-eq ha−1 | 573200 | 350000–719500 | 102 and 103 |
LUC wetland | kgCO2-eq ha−1 | 2186500 | 1000000–3452000 | 102 and 103 |
EF for N addition | kgN2O–N kgN−1 | 0.01 | 67 and 178 | |
EF for N volatization | kgN2O–N kgNH3–N+NOx–N−1 | 0.01 | 67 and 178 | |
EF for NH3 − N + NOx − N | kgNH3–N+NOx–N kgN−1 | 0.1 | 67 and 178 | |
EF for N leaching | kgN2O–N kgN−1 | 0.04 | 0.0075–0.075 | 65, 67 and 178 |
Parameter | Unit | Mean | Range | Sources |
---|---|---|---|---|
N2O–N to N2O conversion | 1.57 | 67 and 178 | ||
P to P2O5 conversion | 2.18 | 56 | ||
K to K2O conversion | 1.21 | 56 | ||
C to CO2 conversion | 3.67 | 166 | ||
CCS plant area | ha | 15 | ||
DAC plant area for 10000 tCO2 day−1 | ha | 13 | ||
DAC energy requirement | MW h tCO2−1 | 4.64 | 3.33–6.3 | Adapted from ref. 146 and 147 |
Chopping solid recovery | %DM | 98 | ||
Drying solid recovery | %DM | 98 | ||
Drying target moisture | %DM | 15 | ||
Drying energy requirement | MJ tH2Oevaporated−1 | 3734 | Own calculations | |
Boiler efficiency with natural gas for drying | % | 90 | ||
Boiler efficiency with biomass for drying | % | 75 | ||
Grinding solid recovery | %DM | 98 | ||
Grinding moisture loss | % | 5 | ||
Pelleting solid recovery | %DM | 98 | ||
Pelleting moisture loss | % | 5 | ||
Road transport solid recovery | %DM | 95 | ||
Sea transport solid recovery | %DM | 95 |
Parameter | Unit | Mean | Range | Sources |
---|---|---|---|---|
HHV | MJ kgDM−1 | 19.2 | 17.3–21.2 | 28 and 31 |
Cp | MJ (kgDM K)−1 | 1.3 | 179 | |
C% | %DM | 48.4 | 44.4–52.3 | 28, 31, 33 and 56 |
H% | %DM | 5.5 | 5.0–6.1 | 28, 31, 33 and 56 |
O% | %DM | 39.1 | 34.0–43.3 | 28, 31, 33 and 56 |
N% | %DM | 0.61 | 0.45–0.78 | 28, 31, 33 and 56 |
P% | %DM | 0.09 | 56 | |
K% | %DM | 1.5 | 56 | |
S% | %DM | 0.10 | 0.02–0.31 | 28, 31, 33 and 56 |
Cl% | %DM | 0.32 | 0.01–0.73 | 28, 31, 33 and 56 |
Ash% | %DM | 4.5 | 1.6–7.3 | 28, 31 and 33 |
Moisture content at harvest% | %WM | 10.9 | 5.2–16.0 | 31, 33, 56, 180 and 181 |
Straw/grain ratio | %DM | 1.3 | 0.64–2.03 | 180 |
Brazil harvest grain yield | tDM ha−1 year−1 | 2.49 | 2.08–2.83 | 2009–2013128 |
China harvest grain yield | tDM ha−1 year−1 | 4.87 | 4.74–5.05 | 2009–2013128 |
Europe harvest grain yield | tDM ha−1 year−1 | 8.66 | 7.78–9.29 | 2009–2013128 |
India harvest grain yield | tDM ha−1 year−1 | 3.13 | 2.84–4.02 | 2009–2013128 |
US harvest grain yield | tDM ha−1 year−1 | 3.07 | 2.94–3.17 | 2009–2013128 |
Lifetime | Years | 1 | ||
Rotation of harvests | Years | 1 | ||
Growing cycle length | Days | 180 (Brazil, China, India); 335 (Europe); 120 (US) | 143 | |
Growing cycle starting month | 11 (Brazil); 12 (China); 10 (Europe, India); 5 (US) | 143 | ||
Growing cycle starting day | 15 (Brazil, Europe, India, US); 1 (China) | 143 | ||
Initial stage t1 | 20 (Brazil, China, India); 160 (Europe); 15 (US) | 143 | ||
Development stage t2 | 80 (Brazil, China, India); 235 (Europe); 40 (US) | 143 | ||
Mid-season stage t3 | 150 (Brazil, China, India); 310 (Europe); 90 (US) | 143 | ||
K c,ini | 0.7 | 143 | ||
K c,mid | 1.15 | 143 | ||
K c,end | 0.3 | 143 | ||
Nitrogen rate | kg ha−1 year−1 | 52.3 (Brazil); 185.5 (China); 53.6 (Europe); 139.6 (India); 80.4 (US) | 2010127,128 | |
% N available in straw | %N | 30 | 56 | |
% P available in straw | %P | 100 | 56 | |
% K available in straw | %N | 100 | 56 | |
Collection (bales) | L per ha per harvest | 4.4 | 56 | |
Chopping (diesel) | MJ tMW−1 | 7.4 | 56 | |
Grinding (power) | MJ tMW−1 | 100–323 | 57, 182 and 183 | |
Pelleting (power) | MJ tMW−1 | 300–409 | 57 and 183 | |
Pellet grinding (power) | MJ tMW−1 | 345 | 34 |
Parameter | Unit | Mean | Range | Sources |
---|---|---|---|---|
HHV | MJ kgDM−1 | 18.4 | 17.7–19.1 | 28, 31, 32, 76 and 184 |
Cp | MJ (kgDM K)−1 | 1.4 | 179 | |
C% | %DM | 47.8 | 45.1–50.4 | 28 and 31–33 |
H% | %DM | 5.5 | 4.9–6.1 | 28 and 31–33 |
O% | %DM | 42.3 | 40.2–44.3 | 28 and 31–33 |
N% | %DM | 0.56 | 0.21–0.92 | 28 and 31–33 |
S% | %DM | 0.22 | 0.04–0.37 | 28 and 31–33 |
Cl% | %DM | 0.26 | 0.10–0.42 | 28 and 31–33 |
Ash% | %DM | 3.9 | 2.5–5.4 | 28 and 31–33 |
Moisture content at harvest% | %WM | 23 | 15–31 | 31–33, 69, 76 and 185 |
1st harvest yield proportion | % | 21 | 69 | |
2nd harvest yield proportion | % | 64 | 69 | |
Brazil full harvest yield | tDM ha−1 year−1 | 28.9 | 23.2–34.7 | 61, 70 and 73 |
China full harvest yield | tDM ha−1 year−1 | 24.3 | 23.1–25.4 | 73 |
Europe full harvest yield | tDM ha−1 year−1 | 18.8 | 15.6–22.1 | 64, 70, 75–78, 97 and 184–188 |
India full harvest yield | tDM ha−1 year−1 | 15.0 | 12.8–17.3 | 73 |
US full harvest yield | tDM ha−1 year−1 | 28.0 | 21.2–34.4 | 61, 70 and 90 |
Lifetime | Years | 18 | 16–21 | 65, 69 and 97 |
Rotation of harvests | Years | 1 | 69 | |
Growing cycle length | Days | 209 | 203–215 | 62 |
Growing cycle starting month | 7 (N); 9 (S) | 4–9 (N) | 62 | |
Growing cycle starting day | 1 | 62 | ||
Initial stage t1 | 45 | 42–48 | 62 | |
Development stage t2 | 67 | 64–70 | 62 | |
Mid-season stage t3 | 163 | 152–174 | 62 | |
K c,ini | 0.4 | 143 | ||
K c,mid | 0.95 | 143 | ||
K c,end | 0.4 | 143 | ||
Nitrogen rate | kg ha−1 year−1 | 78 | 50–100 | 63, 67, 71, 76 and 185 |
Phosphate rate | kg ha−1 year−1 | 63 | 50–100 | 63, 67, 71, 76 and 186 |
Potash rate | kg ha−1 year−1 | 124 | 60–200 | 63, 67, 71, 76 and 186 |
Lime rate | kg ha−1 year−1 | 643 | 67 and 189 | |
Rhizome rate | kg ha−1 year−1 | 52.6 | 97 | |
Herbicide rate | kg ha−1 year−1 | 0.88 | 0.105–2.81 | 65, 67 and 71 |
Land preparation (diesel) | L ha−1 | 75.1 | 69 | |
Maintenance – harvest (bales) | L per ha per harvest | 51.6 | 69 | |
Chopping (diesel) | MJ tMW−1 | 108 | 76 | |
Grinding (power) | MJ tMW−1 | 124 | 68–182 | 76 |
Pelleting (power) | MJ tMW−1 | 579 | 232–925 | 76 |
Pellet grinding (power) | MJ tMW−1 | 345 | 34 |
Parameter | Unit | Mean | Range | Sources |
---|---|---|---|---|
HHV | MJ kgDM−1 | 18.4 | 17.3–19.4 | 32 and 86 |
Cp | MJ (kgDM K)−1 | 1.3 | 179 | |
C% | %DM | 47.1 | 46.5–47.8 | 30 and 136 |
H% | %DM | 5.9 | 5.4–6.3 | 30,136 |
O% | %DM | 41.4 | 40.3–42.5 | 30 and 136 |
N% | %DM | 0.8 | 0.51–1.1 | 30 and 136 |
S% | %DM | 0.11 | 0.08–0.20 | 30 and 136 |
Cl% | %DM | 0.13 | 0–0.16 | 30 and 136 |
Ash% | %DM | 5.7 | 5.2–6.1 | 30 and 136 |
Moisture content at harvest% | %WM | 12 | 11–14 | 67, 79, 87, 136 and 190 |
1st harvest yield proportion | % | 21 | 11–30 | 87 and 191 |
2nd harvest yield proportion | % | 69 | 67–70 | 87 and 191 |
Brazil full harvest yield | tDM ha−1 | 28.9 | 23.2–34.7 | 67, 84 and 122 |
China full harvest yield | tDM ha−1 year−1 | 13.0 | 10.6–15.3 | 67 and 192 |
Europe full harvest yield | tDM ha−1 year−1 | 11.9 | 7.5–16.3 | 82 and 191 |
India full harvest yield | tDM ha−1 year−1 | 8.7 | 5.2–12.6 | 67, 135 and 192 |
US full harvest yield | tDM ha−1 year−1 | 9.5 | 28.1–10.9 | 67, 79, 87, 122, 135, 192 and 193 |
Lifetime | Years | 12 | 10–15 | 79 and 86 |
Rotation of harvests | Years | 1 | 79 and 86 | |
Growing cycle length | Days | 128 | 90–160 | 86 and 143 |
Growing cycle starting month | 5 (N); 9 (S) | 143 | ||
Growing cycle starting day | 1 | 143 | ||
Initial stage t1 | 10 | 143 | ||
Development stage t2 | 25 | 143 | ||
Mid-season stage t3 | 100 | 143 | ||
K c,ini | 0.5 | 47 | ||
K c,mid | 1.03 | 0.9–1.15 | 47 | |
K c,end | 0.98 | 0.85–1.1 | 47 | |
Nitrogen rate | kg ha−1 year−1 | 68.5 | 49.5–98.8 | 67, 79, 86, 87 and 193 |
Phosphate rate | kg ha−1 year−1 | 34 | 0–67 | 67, 86 and 193 |
Potash rate | kg ha−1 year−1 | 34 | 0–67 | 67, 86 and 193 |
63, 67, 71, 76 and 186 | ||||
Lime rate | kg ha−1 year−1 | 569 | 494–643 | 67, 136 and 189 |
Seed rate | kg ha−1 year−1 | 0.8 | 0.4–1.1 | 67, 71, 81, 87, 97, 164 and 194 |
Herbicide rate | kg ha−1 year−1 | 0.48 | 0.42–0.54 | 67, 87 and 158 |
Land preparation (diesel) | L ha−1 | 15.7 | 87 | |
Maintenance – harvest (bales) | L per ha per harvest | 32.6 | 87 | |
Chopping (diesel) | MJ tMW−1 | 135 | 76 | |
Grinding (power) | MJ tMW−1 | 124 | 72–90 | 92 |
Pelleting (power) | MJ tMW−1 | 46 | 92 | |
Pellet grinding (power) | MJ tMW−1 | 345 | 34 |
Parameter | Unit | Mean | Range | Sources |
---|---|---|---|---|
HHV | MJ kgDM−1 | 19.1 | 18.4–19.8 | 28, 29, 67, 92, 96 and 97 |
Cp | MJ (kgDM K)−1 | 1.5 | 179 | |
C% | %DM | 48.1 | 46–49.2 | 28 and 29 |
H% | %DM | 5.9 | 5.3–6.4 | 28 and 29 |
O% | %DM | 42.3 | 40.0–43.0 | 28 |
N% | %DM | 0.5 | 0.2–0.8 | 28 and 29 |
S% | %DM | 0.05 | 0.02–0.1 | 28 and 29 |
Cl% | %DM | 0.03 | 0.01–0.05 | 28 and 29 |
Ash% | %DM | 2.0 | 1.1–4.0 | 28, 29 and 92 |
Moisture content at harvest% | %WM | 52 | 50–53 | 27, 67 and 69 |
1st harvest yield proportion | % | 72 | 69 | |
2nd harvest yield proportion | % | 100 | 69 | |
Europe full harvest yield | tDM ha−1 year−1 | 9.7 | 8.1–11.2 | 67, 69, 80, 96, 97, 100, 120, 131, 132, 175, 188 and 195–198 |
US full harvest yield | tDM ha−1 year−1 | 7.2 | 4.7–11.0 | 122 and 183 |
Lifetime | Years | 16 | 69 and 96 | |
Rotation of harvests | Years | 3 | 2–4 | 69, 92 and 96 |
Growing cycle length | Days | 150 | 199 | |
Growing cycle starting month | 5 (N); 9 (S) | 199 | ||
Growing cycle starting day | 15 | 199 | ||
Initial stage t1 | 40 | 199 | ||
Development stage t2 | 100 | 199 | ||
Mid-season stage t3 | 130 | 199 | ||
K c,ini | 0.65 | 199 | ||
K c,mid | 1.6 | 199 | ||
K c,end | 0.90.93 | 199 | ||
Nitrogen rate | kg ha−1 year−1 | 80 | 50–100 | 67, 80, 131, 175, 196 and 198 |
Phosphate rate | kg ha−1 year−1 | 15 | 11–30 | 67, 80, 131, 175 and 198 |
Potash rate | kg ha−1 year−1 | 40 | 0–67 | 67, 80, 131, 175 and 198 |
Lime rate | kg ha−1 year−1 | 643 | 67 and 189 | |
Cuttings rate | kg ha−1 year−1 | 608 | 284–875 | 67, 97, 175 and 200 |
Herbicide rate | kg ha−1 year−1 | 0.43 | 0.11–1.08 | 67, 80, 131, 175 and 198 |
Land preparation (diesel) | L ha−1 | 82.8 | 69 | |
Maintenance – harvest (chips) | L per ha per harvest | 164.2 | 69 | |
Grinding-pelleting (power) | MJ tMW−1 | 461 | 94 | |
Pellet grinding (power) | MJ tMW−1 | 367 | 34 |
This journal is © The Royal Society of Chemistry 2017 |