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Investigating the BECCS resource nexus: delivering sustainable negative emissions

Mathilde Fajardyab, Solene Chiquierab and Niall Mac Dowell*ab
aCentre for Environmental Policy, Imperial College London, Exhibition Road, London, SW7 1NA, UK
bCentre for Process Systems Engineering, Imperial College London, Exhibition Road, London, SW7 2AZ, UK. E-mail: niall@imperial.ac.uk; Tel: +44 (0)20 7594 9298

Received 11th June 2018 , Accepted 20th August 2018

First published on 16th October 2018


Bioenergy with carbon capture and storage (BECCS), and other negative emissions technologies (NETs), are integral to all scenarios consistent with meeting global climate ambitions. BECCS's ability to promptly remove CO2 from the atmosphere in a resource efficient manner, whilst being a net energy generator to the global economy, remains controversial. Given the large range of potential outcomes, it is crucial to understand how, if at all, this technology can be deployed in a way which minimises its impact on natural resources and ecosystems, while maximising both carbon removal and power generation. In this study, we present a series of thought experiments, using the Modelling and Optimisation of Negative Emissions Technologies (MONET) framework, to provide insight into the combinations of biomass feedstock, origin, land type, and transport route, to meet a given CO2 removal target. The optimal structure of an international BECCS supply chain was found to vary both quantitatively and qualitatively as the focus shifted from conserving water, land or biomass, to maximising energy generated, with the water use in particular increasing threefold in the land and biomass use minimisation scenario, as compared to the water minimisation scenario. In meeting regional targets, imported biomass was consistently chosen over indigenous biomass in the land and water minimisation scenarios, confirming the dominance of factors such as yield, electricity grid carbon intensity, and precipitation, over transport distance. A pareto-front analysis was performed and, in addition to highlighting the strong trade-offs between BECCS resource efficiency objectives, indicated the potential for tipping points. An analysis of the sensitivity to the availability of marginal land and agricultural residues showed that (1) the availability of agricultural residues had a great impact on BECCS land, and that (2) water use and land use change, two critical sustainability indicators for BECCS, were negatively correlated. Finally, we showed that maximising energy production increased water use and land use fivefold, and land use change by two orders of magnitude. It is therefore likely that an exclusive focus on energy generation and CO2 removal can result in negative consequences for the broader environment. In spite of these strong trade-offs however, it was found that BECCS could meet its electricity production objective without compromising estimated safe land use boundaries. Provided that the right choices are made along BECCS value chain, BECCS can be deployed in a way that both satisfies its resource efficiency and technical performance objectives.



Broader context

While the European Academies Science Advisory Council (EASAC) reaffirmed the importance of NETs for climate mitigation in their latest report, none of the six technologies investigated, from biological methods such as afforestation and ocean fertilisation, to technical methods such as BECCS and Direct Air Capture, emerge as a panacea for achieving carbon dioxide removal at the gigatone scale. With a potentially positive CO2 balance, and negative impacts on ecosystems and biodiversity, BECCS performance, in particular, remains a controversial topic. However, with CCS demonstration projects under way, and existing biomass supply chains and facilities, BECCS presents two key advantages. Firstly, from a technology stand point, BECCS is relatively easily deployable and scalable. Secondly, BECCS uniquely provides two services to society: carbon dioxide removal and energy production. Therefore, understanding (a) how to deploy BECCS in a truly sustainable way, and (b) the trade-offs between BECCS key performance indicators (KPIs) in the context of BECCS optimal value chains, is therefore vital to unlocking BECCS deployment at the gigatone scale.

1 Introduction

1.1 BECCS potential for climate mitigation is uncertain

With a remaining carbon budget of 800 GtCO2, and total global emissions approaching 40 GtCO2 per year, the need for net CO2 removal from the atmosphere in order to maintain a 2 to 1.5 C trajectory for 2100 is unequivocal. As no negative emissions technology (NETs) has been found to be an obvious and unique winner, which, how, and how much of these technologies should be deployed to guarantee efficient, sustainable and permanent CO2 removal remains a fundamental research challenge.1,2 Combining two existing technologies – bioenergy and carbon capture and storage (CCS), and presenting the co-benefit of producing energy whilst removing CO2 from the atmosphere, BECCS has received particular focus. In particular, the veracity of claims that BECCS has the potential to simultaneously produce power, and remove CO2 from the atmosphere in material quantities and in a relevant time frame, whilst having limited effects on ecosystems and biodiversity, is the subject of current study.3–8 Concerns surrounding excessive freshwater use, land use, biochemical flows, land use change, and impact on biodiversity have been raised. In Smith et al.,8 additional water volumes as high as 720 km3 as compared to a business as usual scenario, and land area between 380 and 700 Mha were required to remove 12 GtCO2 per year, highlighting BECCS as one of the most resource intensive NETs. In Boysen et al.,6 it is argued that even assuming substantial emissions reduction, BECCS scale of deployment would have considerable economic and environmental impacts, using over 1.1 Gha of the most productive land, or eliminating over 50% of natural forests, in addition to using over 100 Mt per year of nitrogen fertiliser. In a recent study by Heck et al.,5 the authors studied different BECCS pathways including biomass to hydrogen (B2H2) and biomass to liquid fuels (B2L), with different feedstocks, and argued that, were BECCS to be deployed in strict respect of the planetary boundaries (PBs) as defined in Steffen et al.,9 actual CO2 removal would be of the order of 0.2 GtCO2 per year, hence two orders of magnitude below what would theoretically be required by 2100.10,11 Allowing BECCS to trespass in the PBs uncertainty zone however, could enable the removal of up to 22 GtCO2 per year. In previous contributions,3,4 using the Modelling and Optimisation of Negative Emissions Technologies (MONET) framework, we quantified the extent to which BECCS resource mobilisation may be region and biomass specific, putting forth the need for case specific BECCS value chain design. Careful design and optimisation of BECCS value chains therefore appears vital to unlock the potential large-scale deployment of this technology.

1.2 BECCS value chain design is a multi-criteria optimisation problem

Cost-based optimisation is a common approach in the field of supply chain design. In a study by Tagomori et al.,12 the authors investigated BECCS potential in Brazil by determining the cost-optimal CO2 transport network, with CO2 captured from biogenic sources. Akgul et al.13 studied the optimisation of BECCS at the process scale, by determining the BECCS optimal technological pathway for power generation. Through a pareto-front analysis, trade-offs between the cost and carbon intensity of the system were examined. Other studies have looked at spatially-explicit cost-optimal BECCS deployment pathways in South Korea,14 France15 and the US.16,17 However, owing to the range of potential environmental impacts associated with BECCS, as well as services provided – power generation and carbon dioxide removal, BECCS key performance indicators (KPIs) are necessarily highly diverse. BECCS value chain optimisation is therefore inherently multi-objective, and by focusing either on cost, or on the trade-offs between economic and environmental performance, one could easily cloud the complex interactions existing between BECCS environmental impacts. In their work, Heck et al.5 presented a global land and biomass optimal allocation model for BECCS via B2H2 and B2L, in which the weighted sum of BECCS environmental impacts – freshwater use, forest loss, biosphere integrity and biochemical flows – resulting from achieving a fixed biomass harvest objective, was minimised. The results highlighted trade-offs between bioenergy production and negative emissions potential, as well as freshwater use and forest loss. However, the difficulty with preference-based optimisation is that the optimisation results obtained are highly dependent on the values attributed to the weights, thus on the relative importance of each objective, which can be highly region specific. Furthermore, whilst the model carefully considered planetary boundaries and regional biomass production potential, BECCS downstream logistics, such as biomass processing and transport to potential CO2 storage, were not included. This contribution thus addresses this gap via the development of a BECCS value chain optimisation model which explicitly accounts for biomass processing, transport and use in the vicinity of CO2 sinks, and investigates the trade-offs between BECCS KPIs through pareto-analysis.

1.3 Deploying BECCS within planetary boundaries: the case of marginal lands and agricultural residues

In order to be sustainable, BECCS needs to be deployed within all planetary boundaries. To avoid potential land use change18,19 and competition with other land uses, there have been many attempts to evaluate the amount of marginal, yet suitable, land for bioenergy production. The main caveat comes from the difficulty in defining the nature of marginal land (MAL). Edrisi et al.20 differentiates wastelands for biomass cultivation by two views: the suitability/quality of the land, and the socio-economic value of the land. In this context, marginal land is considered to be at the intersection of under-utilised lands and neglected unused land. The definition of marginal land can also vary in time. A farmer might choose to use a parcel of marginal land one year, and leave it unused the next, depending on the profitability of this land in this specific year.

This diversity in definition results in a variety of marginal land evaluation. In 2011, Cai et al.21 provided an extensive mapping of marginal land, by quantifying the mixed crops, natural vegetation land, cropland, schrubland, savanna and grassland with marginal productivity. This work resulted in the spatial determination of marginal land availability with a spatial resolution of 30 arc second geographic. Total world marginal land availability was quantified between 320 and 1107 Mha, with between 108 and 256 Mha in South America, 18 and 151 Mha in India, 33 and 111 Mha in Europe, 52–152 Mha in China, and 66–314 Mha in Africa. This evaluation was later on downscaled by Fritz et al.22 to 56 to 1035 Mha, with adjustments made to land cover and human impact assumptions. Several studies were also performed at the regional level. In Brazil, Lossau et al.23 evaluated the spatial distribution of marginal land in Brazil by calculating the residual land from cropland, pastures, forest, build up, barren, water bodies, and the protected Amazon biome area. The residual area was then overlayed with the FAO/IIASA land suitability modelling framework24 to assess its suitability. A total of 37.8 Mha was found to be available and unprotected, with approximately 20% of this land was considered very suitable for biofuel production. It is worth noting however that the suitability modelling framework was used for conventional oil and grain crops production, and perennial grasses such as Miscanthus and Switchgrass could potentially be more resilient. In China, marginal land including saline land, steep hillside and idle land was evaluated at 35–75 Mha,25 while another study pointed to 44 Mha exploitable for energy plants.26 A more detailed study on miscanthus production in China evaluated at only 17 Mha the potential Miscanthus production area in China, with yields as low as 2 t per ha in bare areas.27 In Europe, a study by Strapasson et al.28 based on FAO land cover and land use data quantified the land available in the EU for bioenergy production to 20 Mha. In India, a study by Edrisi et al.20 evaluated the potential of MAL for bioenergy production to 39 Mha, providing suitable soil amendments and agro-technologies are used to improve the fertility/productivity of the various wasteland considered. Table 1 summarises these findings, highlighting the great range in marginal land availability assessments in the literature. Using agricultural residues could represent an alternative to using marginal land, while still avoiding land use change. However, mismanagement or over-utilisation of agricultural residues could led to various negative impacts among increased water evapotranspiration, soil depletion, productivity loss, erosion.29,30 The use of agricultural residues in an attempt to reduce BECCS's impact on land use, water use and land use change, therefore needs to be carefully monitored.

Table 1 Literature review on marginal land availability
Region Year MAL (Mha) Sources
a Mixed crop and natural vegetation land with marginal productivity.b Mixed crops and natural vegetation land, cropland, scrubland, savanna and grassland with marginal productivity, discounting the total pasture land.c Total protected MAL suitable or very suitable for conventional oil and grain crops.d Total unprotected MAL.e Total MAL including saline, steep and idle land.f Total MAL.g Fraction of the MAL which is suitable.h Total MAL for Miscanthus.i Total MAL for bioenergy based on FAO land use/land cover data.j Relatively high quality land for perennial crops.k 0.2 for miscanthus, 0.4 for SRC willow from agricultural land quality and yield map.l Total available arable and grassland for bioenergy in 2030.m Cai et al. MAL values downscaled after land cover and human impacts corrections.
South America 2011 108a–256b 21
Brazil 2015 10c–38d 23
China 2009 35–75e 25
China 2011 44f 26
China 2011 52a–152b 21
China 2016 8g–21h 27
UK 2009 1.4j 31
England and Wales 2010 0.6k 32
UK 2015 3.4l 33
Europe 2011 33a–111b 21
EU28 2016 20i 28
India 2011 18a–151b 21
India 2016 39g–47e 20
USA 2011 43a–123b 21
 
World 2011 320a–1107b 21
World 2013 56m–1035m 22


1.4 Achieving negative emissions via BECCS: the example of the UK

As part of its transition to a low-carbon economy, the UK has committed to be carbon neutral by 2050. Forecasts anticipate that in achieving this target, 50 Mt per year of carbon dioxide could be sustainably removed from the atmosphere, in order to offset remaining emissions from various sectors of the industry.34 Furthermore, at the time of writing, the Committee for Climate Change (CCC) has been instructed to investigate the implications of meeting the Paris targets on UK carbon budgets, signalling a potential increase in ambition.35 Were NETs to be delivered via BECCS, building sustainable biomass supply chains, as well as deploying an efficient CCS network, will be crucial in reaching this target. In 2015, the total EU pellet consumption reached 20 Mt of biomass pellets, with 6.2 Mt of imports, coming at 90% through the North America-EU trading route. In the UK, Drax power plant alone used 6.5 MtCO2 of pellets in 2016 for its three biomass-dedicated 660 MW units. Though the majority of Drax feedstock originates from sawmill and forestry residues,36 an increasing biomass demand in the UK, for both bioenergy and negative emissions purposes, will inevitably result in the diversification of the biomass feedstock, likely combining both domestic and imported agricultural residues and dedicated energy crops. On the CCS front, sizable volumes of CO2 storage have been identified in both offshore and onshore aquifers.37 Given the UK's 2050 carbon removal target and identified available CO2 storage in the North Sea, the design of optimal BECCS value chains for UK-based CO2 removal from the atmosphere is the central case study investigated in this contribution. However, the framework is applicable to any region with identified CO2 storage and CO2 removal targets, and we further extend this work to present a series of thought experiments describing optimal supply chains to meet US and China-specific carbon removal targets, in southern US and eastern China, respectively.

1.5 Contribution of this study

This study presents a region-specific optimal allocation of resources – biomass feedstock, land, water, energy – to meet region specific carbon dioxide removal target via BECCS. The MONET framework was used to determine the optimal combination of feedstock type, region, land type, and transport route to a given region to remove CO2 with a fleet of 500 MW UK, US and China-based pulverised combustion power plants, in conjunction with CO2 capture and storage. Section 2 presents the model and assumptions used for this analysis, detailing the amendments and additions made to the MONET framework since its first implementation.4 Section 3 presents the different optimal BECCS value chains to minimise either the total water use, land use and biomass use. Section 3.2 investigates the trade-offs between these different environmental indicators, while Section 4 investigates the sensitivity of these indicators to the availabilities of marginal land and crop residues. Finally Section 5 further investigates the relationship between the two services provided by BECCS – carbon dioxide removal and energy production – by highlighting the trade-offs between BECCS environmental performance indicators and energy production service.

2 Methodology

In order to sustainably contribute to climate change mitigation, negative emissions technologies must (1) deliver the service(s) for which they were deployed, i.e., CO2 removal and, in the case of BECCS, energy production, (2) at a low resource cost, and (3) with limited indirect impact on the markets and ecosystems. We summarise these three criteria by the NETs trilemma, illustrated in Fig. 1. The NETs key performance indicators (KPIs) include net CO2 removal, tNCO2, and net electricity production, tNE, to evaluate technical performance, water use, tWU, land use, tLU, and biomass use, tBU, to evaluate resource efficiency, and agricultural residue use, tRU, and land use change, tLUC, to evaluate BECCS economic-environmental impacts. To clarify, no cost analysis was included in the MONET framework, which means that the total system cost is not one of the objective functions explored in this study. This is left for future work.
image file: c8ee01676c-f1.tif
Fig. 1 Schematic of the NETs trilemma. NETs key performance indicators are reassembled in three categories: technical performance – net CO2 removal and electricity production, resource efficiency – water, land and biomass use (equivalent to CO2 efficiency), and economic-environmental impacts – land use change and agricultural residues use (with potential impact on soil productivity and erosion).

In order to position BECCS within this performance trilemma, we designed the MONET framework which comprises (1) a BECCS value chain model which calculates the water use, land use, net CO2 removed, CO2 breakeven time, net electricity produced and net CO2 efficiency of different BECCS value chains, and (2) a BECCS value chain optimisation model which determines the optimal combination of BECCS value chain configurations to meet a given CO2 removal target.

2.1 MONET value chain modelling framework

The value chain model specifically accounts for biomass cultivating, harvesting, pelleting, transport to a given region and conversion in a pulverised combustion plant combined with post-combustion CO2 capture and subsequent storage in the vicinity of the power plant. The conversion technology considered is a 500 MW dedicated pulverised biomass thermal power plant, combined with post-combustion amine-based carbon capture. In a previous contribution, we evaluated the power generation efficiency of the facility at 26%HHV, including the CCS energy penalty.4

The value chain configurations are characterised by distinct:

• Biomass feedstock, b: miscanthus, switchgrass and short rotation coppice willow as archetypal dedicated energy crops, and wheat straw as an archetypal agricultural residue,

• Sub-region, sr, from which the biomass is imported: Brazil, China, EU, India and the USA are considered as potential regions of import, and discretised at the state/province level, resulting in 170 potential cells for biomass farming. Each cell is defined by its area and the position of its centroid.

• Land type, l, on which the biomass is grown: cropland, grassland, forest and marginal land. The different land scenarios are included to account for direct (LUC) and indirect (ILUC) land use change, i.e., the direct and indirect CO2 emissions associated with the conversion of a certain land type to bioenergy production. Different types of land are associated with distinct LUC and ILUC, and the resulting emissions are highly dependent on the biomass type, economic use of the land, region, timeframe considered, etc. As a simplification in this study, LUC and ILUC values, within a range of uncertainty, are attributed to the different land types, regardless of the region and biomass type. It was therefore considered that no LUC/ILUC was attributed to marginal land, medium LUC and high ILUC were attributed to cropland and grassland, as using these managed lands means an activity must be re-allocated elsewhere, and high LUC and no ILUC were attributed to forests. Converting a low vegetation land such as a marginal land, to a managed bioenergy crop with deep rooted perennial grasses, could result in negative land use change, i.e. net soil CO2 sequestration.38,39 While these effects could improve BECCS CO2 balance, we adopted the conservative approach of not considering them, given the uncertainty around their amplitude and permanence.

• Port, p, which is used for shipping the biomass from its region of origin to the region of conversion and sequestration. Each sub-region sr has access to a port p as long as there is a road access to this port.

A schematic of the current bio-geo-physical map of the MONET model is presented in Fig. 2, including the ports and biomass collection points.


image file: c8ee01676c-f2.tif
Fig. 2 Representation of the sub-regions sr (or cells) and ports p considered for BECCS value chain modelling in MONET. Each cell is defined by its area and the position of its centroid, which were calculated using ArcGIS 10.5.40 The map also displays the location of the weather stations, indicated by the blue dot in each cell, and obtained from the software CLIMWAT 2.0,41 from which the climate data of each sub-region was collected. As an example in this figure, biomass can be shipped to the UK (black arrows), southern USA (purple arrows) and eastern China (blue arrows) for conversion and CO2 sequestration.

2.2 Spatial discretisation and transport distance

Building on our previous work,3,4 the level of spatial discretisation was increased from the macro-region level – Brazil, China, EU, India, USA – level to the province/state level – Brazilian, Indian and US states, Chinese provinces, EU countries. A consequence of this discretisation in a change in the computation of the road distance for biomass pellet transport. Sub-regions are polygons represented geographically by the latitude Y(sr) and longitude X(sr) of their centroid. Similarly, ports are represented by their latitude YP(p) and longitude XP(p). Three options are considered for biomass transport from a sub-region, sr, where biomass is produced, to a sub-region, srend, where biomass is converted into energy and CO2 is stored: (1) road transport by heavy duty vehicles (HDV) if there is a road access between sr and srend, (2) a combination of road and sea transport by container ship, (3) and short distance transport (50k) by HDV if sr and srend are the same regions. For simplicity, rail and barge are not considered in this analysis. The optimal transport route – option (1), (2) or (3), and optimal ports p and pend in option (2) – is determined by the optimisation program. The road distance considered in the model is therefore the euclidian distance between sr and srend in (1), and the summation of the euclidian distance between sr and p and between srend and pend in (2), corrected by a region-specific tortuosity factor t(sr):
 
Droad(sr,p) = t(sr) × Rearth × arcos(sin[thin space (1/6-em)]YP(p)) × sin[thin space (1/6-em)]Y(sr) + cos[thin space (1/6-em)]YP(p) × cos[thin space (1/6-em)]Y(sr) × cos(X(sr) − YP(p)) (1)
 
tDroad(sr,srend,p,pend) = Droad(sr,p) + Droad(srend,pend) (2)
or
 
image file: c8ee01676c-t1.tif(3)

2.3 Key outputs of the modelling framework

In order to solve the optimisation model, the following outputs are obtained with the value chain modelling framework, for each sub-region sr, biomass b, port p, and land type l:

• WUCO2(sr,b,l,p) is the water required to remove 1 ton of CO2 from the atmosphere, in m3 per tCO2. The MONET tool calculates the water intensity of BECCS by adding three terms: the blue, the green and the grey water. In our model, the green water is considered to be the crop water demand which is met by precipitation, whereas the blue water is the additional amount of fresh water required to grow the biomass, and in the power plant. The grey water is the amount of polluted water resulting from the fertiliser use at the field level.4 In order to only account for the marginal amount of water required for BECCS, WUCO2(sr,b,l,p) only includes the blue and grey water contributions. In the case of biomass residues such as wheat straw, the blue water associated with straw production is allocated to the production of wheat, and therefore considered to be zero.

• PPLUCO2(sr,b,l,p) is the amount of land used by BECCS facilities to remove 1 ton of CO2, in ha per tCO2.

• FLUCO2(sr,b,l,p) is the amount of land harvested for biomass at the field level per ton of CO2 removed, in ha per tCO2. In the case of biomass residues such as wheat straw, the land footprint associated with straw production is allocated to the production of wheat, and therefore considered to be zero for straw.

• NECO2(sr,b,l,p) is the amount of net electricity produced in GJ per ton of CO2 removed, accounting for the energy cost of BECCS value chain. The approach used to calculate this metric has been presented in detail previously,3 and is not repeated here.

• BUCO2(sr,b,l,p) is the amount of biomass used to remove 1 ton of CO2 from the atmosphere, in tDM per tCO2.

• BioC(b) is the biomass carbon content in %DM.

• CNCO2(sr,b,l,p) is the cumulative net amount of CO2 stored by a BECCS configuration, over its lifetime, per hectare of land, in tCO2 per ha.

• CNE(sr,b,l,p) is the cumulative net amount of electricity produced by a BECCS configuration, over its lifetime, per hectare of land, in MJ per ha.

• BETCO2 is the CO2 breakeven time of the BECCS configuration, i.e., the time required for the system to be carbon negative.

• BETE is the electricity breakeven time of the BECCS configuration, i.e., the time required for the system to be energy positive.

2.4 Supply chain optimisation framework

The purpose of this work is to determine the optimal BECCS value chain required to remove 50 MtCO2 per year in a given region, by allocating the amount of CO2 removed annually per configuration CO2rem(sr,b,l,p) while minimising or maximising different objective functions [f1,f2,f3,f4]:

• Minimisation of total water use tWU:

 
image file: c8ee01676c-t2.tif(4)

• Minimisation of total land use tLU which accounts for the harvested land in region sr, and the land used by the BECCS facilities:

 
f2 = tLU = tFLU + tPPLU (5)

Similarly to fresh water use, the cultivated land associated with the production of wheat straw is allocated to wheat production. Wheat straw land footprint at the field level is therefore not accounted for in the summation:

 
image file: c8ee01676c-t3.tif(6)
 
image file: c8ee01676c-t4.tif(7)

• Maximisation of the total CO2 efficiency tηCO2, i.e., the ratio of the amount of CO2 permanently removed tNCO2 to the amount of CO2 stored in the biomass, tBioCO2. The latter is directly related to the amount of biomass used:

 
image file: c8ee01676c-t5.tif(8)
with
 
BioCO2(sr,b,l,p) = BUCO2(sr,b,l,p) × BioC(b) × CtoCO2 (9)
 
image file: c8ee01676c-t6.tif(10)
 
image file: c8ee01676c-t7.tif(11)

As the total amount of CO2 removed is fixed, maximising tηCO2 is equivalent to minimising tBioCO2. As tηCO2 is an nonlinear variable, tBioCO2 is thus minimised to ensure the linearity of the model. It is worth noting that minimising the total amount of CO2 stored in the biomass is equivalent to minimising the total amount of biomass used.

• Maximisation of the net electricity produced tNE by the BECCS value chain:

 
image file: c8ee01676c-t8.tif(12)

The preference-based procedure of using a weighted sum of the different objectives as a unique objective function was not used here for two reasons. First, the inherent diversity of the different objectives – land use, water use, CO2 efficiency and net energy produced – make them complex to convert into one single objective. Secondly we estimated that preference of one objective over the others will be highly region specific, and choosing these factors arbitrarily could therefore give irrelevant results as to BECCS optimal value chain. Therefore, in the first instance, we chose to treat each of these objectives separately, and leave the multi-criteria, multi-stakeholder problem for future work. Thus, we have formulated four distinct scenarios which allow us to perform a series of thought experiments across the BECCS value chain. These four optimisation scenarios are subject to the following constraints:

• The configurations considered must be carbon negative within a relevant time-frame, i.e., the BETCO2(sr,b,l,p) must be smaller than the project lifetime, considered to be 50 years in this analysis:

 
BETCO2(sr,b,l,p) ≤ 50 (13)

This constraint is equivalent to CNCO2(sr,b,l,p) being positive:

 
CNCO2(sr,b,l,p) ≥ 0 (14)

• The amount of net CO2 removed annually by the configuration, CO2rem(sr,b,l,p) must be positive:

 
CO2rem(sr,b,l,p) ≥ 0 (15)

• In a first instance, we also constrained the configurations to be energy positive:

 
BETE(sr,b,l,p) ≤ 50 (16)
or
 
CNE(sr,b,l,p) ≥ 0 (17)

As electricity production is not the primary service delivered by BECCS, this constraint may be relaxed if the optimisation problem cannot solve.

• The total amount of CO2 removed must be equal to the set CO2 removal target:

 
tNCO2(sr,b,l,p) = CO2 target (18)
An equality constraint was chosen over an inequality constraint in eqn (18), as an inequality constraint would lead to BECCS being deployed over the CO2 removal target in the energy maximisation scenario.

• The amount of land harvested in each region for dedicated energy crops is limited by the availability LA(sr,l) of land type l in sub-region sr:

 
image file: c8ee01676c-t9.tif(19)

• Though no land footprint is attributed to wheat straw production, the amount of harvested land for wheat is each region is limited the wheat area availability WA(sr) in sub-region sr:

 
image file: c8ee01676c-t10.tif(20)

To evaluate the extent of land use change under each optimisation scenario, the variable tLUC is calculated as the summation of all land types other than marginal land – i.e., cropland, grassland and forest – used for the production of dedicated energy crops:

 
image file: c8ee01676c-t11.tif(21)

2.5 Pareto-front analysis

The ε-constraint method was used to quantitatively evaluate the trade-offs between the four objective functions. For each combination of objective functions, fj and fk, the following optimisation problem was solved:
 
min fj (22)
 
s.t. fkεmk ∀kj (23)
with ε the upper bound vector [ε1k,ε2k,…,εmk] linearly distributed between the lower and upper bounds of fk, m being the number of points chosen for the purpose of this analysis.

2.6 Data curation

The model input data at the macro-region level has been reported in detail in previous contributions.3,4 However, biomass yield, climate data, and carbon intensity of electricity were desegregated at the state/province level. Ports and centroid locations, as well as distances between ports were also added. Furthermore, data related to land availability constraints, such as land cover (forest, grassland and cropland), marginal land availability and harvested wheat area were added to the model. The software ArcGIS40 was used to process datasets obtained from the literature, and, aggregate the different values at the sub-region level. Fig. 3 illustrates three potential BECCS value chains for CO2 storage in the UK, with key regional input data such as biomass yield, CO2 intensity of the electricity, marginal land area and wheat harvested area. In a UK case study, though using domestic pellets would minimise the pellets' transport distance to the BECCS facility, factors such as the regional electricity carbon footprint, which significantly affects the carbon intensity of biomass pelleting activities, precipitation, which impacts biomass water footprint, biomass yield, which has multiple impacts on the value chain, or marginal land and wheat straw availability, which constraints the amount of BECCS that can be deployed without causing land use change, can be determining in the design of BECCS optimal value chain.
image file: c8ee01676c-f3.tif
Fig. 3 Illustration of three potential BECCS value chain to a UK-based BECCS facility: using domestic pellets, which minimises transport distance, or importing pellets from Louisiana (USA) or Maranhao (Brazil). Factors such as carbon intensity of electricity, precipitation, biomass yield, marginal land area and wheat harvested area are paramount when determining BECCS optimal value chain.
2.6.1 Sub-regional representation. Polygon shapefiles of the administrative boundaries of each macro-region were obtained from the ArcGIS databases.40 All shapefiles were projected in the WGS-1984 coordinate system, before being merged into one world shapefile containing 170 cells. The latitude and longitude of the centroid of each cell, as well as the cell area, were calculated using ArcGIS, and used as inputs in the model. Fig. 2 shows the world map with the positions of the sub-regions centroid.
2.6.2 Spatial disaggregation of the input data. • Climate data: the location and data of climate stations were obtained from the software CLIMWAT,42 and were attributed to each sub-region. Fig. 2 provides the location of these stations. Climate data recorded by the weather stations, such as monthly precipitation, average low and high temperature, relative humidity, sunshine hours, wind speed, monthly precipitation, as well as the location and altitude of the stations, were then read in the software CROPWAT.41

• Yield data: yield data for different regions of the world were collected for each dedicated energy crop from the literature. When available, yield datasets with high regional discretisation were used.27,43 When the yield data of a sub-region sr was unknown, the yield of the sub-region with the closest climate conditions, according to the Koppen Climate Classification44 was used. Wheat grain yield was obtained at the country level from the FAO.41 Low, median and high yields of each biomass type are provided in Tables 5–7 in Appendix A.

• Land cover: in order to determine the total cropland, grassland and forest area available in each sub-region sr, the MODIS global land cover with a spatial resolution of 15 arc second geographic was used.45 Tables 2–4 in Appendix A provide the land cover per cell adapted from the MODIS database. Forest area was calculated summing Evergreen/Deciduous Needleleaf/Broadleaf forests with the mixed forest categories. Grassland and cropland land cover were directly obtained from the land cover categories.

• Marginal land area: in order to use a consistent dataset for all regions, the marginal land dataset from the Cai et al.21 study was used in this work. As a conservative approach, and to be consistent with other literature sources, only the lower bounds values (S1) from this study were considered. Similarly to land cover and wheat harvested area, the 30 arc second resolution raster file was processed to obtain the marginal land area in each sub-region, sr. Data is supplied in Tables 2–4 in Appendix A.

• Wheat harvested area: in order to constrain the amount of wheat straw available per region, the map of the world wheat harvested area with a spatial resolution of 5 minute geographic, obtained from the SPAM model,46 was processed in ArcGIS40 (Tables 2–4 in Appendix A). It is worth noting that the harvested area per cell can be greater than the cell size, in the case of multiple harvests per year.

• Road tortuosity: the road distance was computed using euclidian distance, corrected by a tortuosity factor. Approximate tortuosity factors were computed for each sub-region sr, by dividing the road distance of the centroid to the nearest port, by the euclidian distance between these two points. Computed tortuosity factors are provided in Tables 5–7 in Appendix A.

2.6.3 Uncertainty and variability of the data. To capture the uncertainty and/or variability of some of the model input data, the MONET framework can be run under different data scenarios:

• Median scenario: the average values of all parameters are used for the calculations,

• Low scenario or “Optimistic” scenario: values which minimise the water, land, CO2 and energy intensities of BECCS value chain are used to perform the calculations. Lower bound values of the land use change emissions, biomass moisture content, carbon and energy intensities of chemicals and input products, fertiliser and chemical input rates, processing energy requirements, electricity carbon footprint, average power generation of the electricity are used. However, upper bound values for biomass yield per region, biomass energy density and biomass carbon content are used.

• High scenario or “Pessimistic” scenario: values which maximise the water, land, CO2 and energy intensities of BECCS value chain are used to perform the calculations. Upper bound values of the land use change emissions, biomass moisture content, carbon and energy intensities of chemicals and input products, fertiliser and chemical input rates, processing energy requirements, electricity carbon footprint, average power generation of the electricity are used. However, lower values for biomass yield per region, energy density and carbon content are used.

To avoid including additional degrees of freedom to the model, no range of uncertainty or variability was implemented for marginal land availability, harvested wheat area, land cover, road tortuosity and climate data. Quantifying the impact of uncertainty in MONET was the focus of a previous contribution.4 To assess the impact of the uncertainty of the model input data, thorough stochastic modelling would need to be performed. We leave this for future work.

2.7 Measuring BECCS impact on agricultural residues and land use change

In order to investigate BECCS economic-environmental impacts, three impact scenarios were considered in the optimisation framework:

• Scenario I: BECCS is only deployed via dedicated energy crops (DEC) grown on marginal land (MAL). Under this scenario, BECCS deployment does not cause land use change, and does not compete with other uses of agricultural residues (AR).

• Scenario II: BECCS is deployed via dedicated energy crops grown on marginal land and agricultural residues from cropland. BECCS economic-environmental impacts are limited to the use of agricultural residues.

• Scenario III: BECCS is deployed via dedicated energy crops from all land types, and agricultural residues from cropland. Under this scenario, BECCS deployment might compete with other markets and cause substantial land use change.

3 BECCS optimal value chain in the water–land–carbon nexus

In a first instance, this section presents different insights from the optimisation of the BECCS value chain required to remove 50 MtCO2 per year in the UK, under three different objective functions – water minimisation, land minimisation, and CO2 efficiency maximisation, considering only DEC on MAL (I). To illustrate that the modelling framework can also be applied to meet other regional targets, this section includes the BECCS optimal supply chains required to meet US and China carbon removal targets, by storing CO2 in southern USA, and in Eastern China, respectively.

3.1 The optimal structure of BECCS value chain

Fig. 4 presents the selected regions, ports, as well as marginal land use density in each cell (fraction of the total land used by BECCS), biomass pellets transport fluxes (arrows) and amount of net CO2 removed per region for each objective function, in the median, optimistic and pessimistic data scenarios, to meet a UK target (black arrows). The coloured arrows illustrate how these optimal value chains may change as the location of the BECCS facility and CO2 storage, and therefore the biomass transport distance, changes for the US (purple arrows) and China (blue arrows). A first conclusion in that the structure of the optimal BECCS value chain changes substantially depending on which metric is prioritised. Under the water minimisation scenario, represented in Fig. 4a, factors such a climate conditions, precipitation and yield play a central role in the water performance of each combination. In spite of substantial road and sea transport distance, regions from western and central Brazil are selected, owing to their combination of low carbon intensity of their electricity and high biomass yield, which highlights the strong trade-offs between transport and other supply chain parameters. As seen in Fig. 4b, when minimising land use, yield and supply chain emissions have a strong impact on the results, and productive coastal regions from Brazil are selected. Similar results are obtained in the CO2 efficiency maximisation scenario (Fig. 4c), though domestic biomass is also selected in the balance to minimise CO2 leakage from transport. When changing the CO2 storage location from the UK to Southern USA or Eastern China, the change in biomass transport distance significantly changes the optimal configuration in the carbon efficiency maximisation scenario, in which biomass transportation represents an important share of the overall CO2 leakages along the chain. However from a water and land minimisation perspective, the optimal regions do not change significantly, which further confirms the low weight of transport distance as compared to other more prevalent factors, when it comes to resource conservation.
image file: c8ee01676c-f4.tif
Fig. 4 BECCS optimal supply chain to minimise global water use (a), land use (b) and CO2 efficiency (c) in the median, optimistic and pessimistic scenario. There are strong trade-offs between the resource efficiency indicators: water increases threefold from the water minimisation to the CO2 maximisation scenario. Overall, biomass from regions with higher yield, lower grid carbon intensity and higher precipitation is chosen over indigenous biomass. Changing the storage location from the UK to Southern USA or Eastern China brings significant changes to the optimal configuration in the CO2 maximisation scenario, where transport plays an important role, but limited changes to the water and land minimisation configurations.

These results were also found highly dependent on the model input data. As regional yield, fertiliser use, and carbon footprint of the electricity change from the median to the optimistic scenario, thereby decreasing regional pellets' water and carbon footprints, other regions such as northern Europe are selected in the water minimisation scenario. In the land minimisation scenario, miscanthus from the US east coast and southern Europe is selected. This is highly dependent on the yield range considered for each region. In the CO2 efficiency maximisation scenario, a balance of Miscanthus from UK and Brazil are also selected in the optimistic and pessimistic scenarios.

The trade-offs between these resources can be assessed by evaluating the total land use, water use, and biomass use under the three optimisation scenarios, which are also represented in Fig. 4. It is observed that water use, and to a smaller extent land use and CO2 efficiency, are highly dependent on which metric is optimised. Water use increases threefold in the CO2 maximisation scenario, as compared to the water minimisation scenario. CO2 efficiency and land use variations are less important: regardless of the optimised metric, land use remains within 1.8–2.8 Mha for all scenarios, and CO2 efficiency, within 48–54%. The trade-off between CO2 efficiency and land use is less extreme: in the pessimistic scenario, the supply chain required to minimise the land use is the same as the one required to maximise the CO2 efficiency.

The structure and the resource efficiency of BECCS optimal value chain are therefore very dependent on the objective function and parameters such as yield, fertiliser rate, climate and grid carbon intensity. Accurate weighing of each metric is required to determine an optimal BECCS value chain which reconciles all three metrics. Understanding which mechanisms might influence such decisions is therefore key to deploy BECCS optimally. Access to accurate data for the key model input parameters is also crucial.

3.2 Trade-offs within the water–carbon–land nexus

Trade-offs between the objective functions were quantitatively evaluated using the ε-constraint method, and pareto-fronts between each objective functions were generated. Fig. 5 shows the pareto curves between f1, f2 and f3 in the median, optimistic and pessimistic scenarios.
image file: c8ee01676c-f5.tif
Fig. 5 Pareto lines between water use, land use, and CO2 efficiency, in the median scenario. There are strong trade-offs between water use and the other two objective functions. However, the trend of the pareto lines for these two relations ((a) and (b)) indicate the presence of tipping points which could reconcile the different objectives. CO2 efficiency decreases marginally when land use increases, showing the proximity of these two optima.

As discussed in Section 3, the pareto lines in Fig. 5 indicate a strong trade-off between water use and the two other objective functions. When minimising water use, as total land use in constrained from 2.05 Mha to 1.85 Mha, water use increases from 5 to 13 Bm3 per year. Moreover, as carbon efficiency is constrained from 48% to just under 54%, water use increases threefold. However, the shape of the pareto lines indicate the presence of tipping points: beyond 1.95 Mha and 50.1% efficiency, the rate of increase in water use significantly increases. In the case of land use against CO2 efficiency, it is difficult to identify a tipping point, as the relationship is linear. However, the variation of the CO2 efficiency is very limited for a given variation of land use, which shows that the optimal points are closed for both optimisation scenarios. This analysis shows the complex interactions between BECCS resource efficiency indicators. Deploying BECCS such that each indicator is maintained close to the tipping point, rather than optimised, could be a preferred option to maximise BECCS overall resource efficiency.

4 Sensitivity analysis

4.1 Limiting supply

As pointed out in the introduction, reliably quantifying the availability of marginal land is complex owing in part to the diversity in definitions, methodologies and datasets used. In 2013, Fritz et al. downgraded Cai et al. lower bound estimates by 69%, bringing the total marginal land availability from 320 Mha to 98 Mha.22 Whilst the availability of agricultural residues can be evaluated with more certainty, determining which fraction of the residues is both recoverable from the field, and used for bioenergy without competing with other uses – for e.g. soil enriching or fodder, is not straightforward. In a recent study on biomass production potential from Brazil,47 the author evaluated the harvestable proportion of agricultural residues to be below 50%, and the proportion of harvested resource available for bioenergy, below 10%. Owing to this complexity, we use this section to study the impact of constraining the availability of marginal land and agricultural residues on the total land use and total land use change in our various scenarios. Fig. 6 shows the evolution of the total land use tLU in the land minimisation scenario, and the total land use change tLUC for the three objective functions, as a function of marginal land and crop residues availability, in the impact scenario III. Fig. 6a shows that the availability of residues plays a first order role in the system's total land use. When it is limited to 20%, total land use increases by several orders of magnitude. This can be explained by the fact that no land use at the field level, nor CO2 emissions from farming, except from the additional fertiliser cost resulting from the removal of the residues from the field, were attributed to agricultural residues, as opposed to dedicated energy crops. As a grey water footprint is attributed to crop residues because of this additional fertiliser use, water use is decreased to a smaller extent. As far as land use change is concerned, when minimising land use and maximising the CO2 efficiency, land use change only occurs when AR availability is limited below 20%. When minimising water use however, land use change can be high even in high marginal land availability. Though land use change results in higher lifecyle CO2 emissions, as long as the water consumption per CO2 removed is still attractive, bioenergy crops planted on former cropland and grassland may still be preferable in the water minimisation scenario. This indicates the presence of trade-off between carbon removal and water use.
image file: c8ee01676c-f6.tif
Fig. 6 Total land use in the land minimisation scenario (a), and land use change in the water minimisation (b), land minimisation (c) and CO2 efficiency maximisation scenarios, in impact scenario III. Residue availability has a first order impact on BECCS total land use. When minimising land use and maximising the CO2 efficiency, land use change only occurs when AR availability is limited below 20%. When minimising water use however, land use change can be high even in high marginal land availability: there is a greater trade-off between carbon removal and water use.

To provide further insight into this emergent behaviour, Fig. 7 illustrates the evolution of BECCS optimal value chain in the water minimisation scenario at 0% residue use availability, and constraining marginal land availability from 100% to 10%. As marginal land availability decreases, it is interesting to see that the same regions from Brazil are used, but both the amount and proportion of grassland and cropland used increase, as well as the land use density. Other regions that had not been selected before, such as China, also start appearing in the results when land availability is drastically constrained, supporting the conclusion that the nearest regions are not necessarily the optimal regions from a water perspective.


image file: c8ee01676c-f7.tif
Fig. 7 Evolution of BECCS optimal value chain in the water minimisation scenario at 0% AR availability, and constraining marginal land availability from 100% to 10%.

This study has provided insight into the important trade-offs between BECCS resource efficiency and economic-environmental impacts. It was shown that using agricultural residues could drastically relieve BECCS pressure on land use. In order to obtain the same result while avoiding competition with other uses of agricultural residues, using high productivity-high carbon content biomass such as algae, could be a promising alternative.48 A second important trade-off exists between water use and land use change: minimising water use for BECCS might result in high land use change, particularly when the availabilities of marginal land and crop residues are constrained. This conclusion builds upon the previous contribution of Heck et al.,5 where this potential compromise was first alluded to. This further confirms that myopic focus on the trade-offs between BECCS environmental and economic performance is, at best, incomplete: there are complex interactions within BECCS environmental trade-offs which must be understood in order to deploy BECCS in a genuinely environmentally and ecologically benign manner.

4.2 Ramping up the carbon removal target

Though the UK projects a 50 MtCO2 per year, it is conceivable that more CO2 might be stored in the UK for two reasons: (1) the UK's own target might increase over the course of the century,35 and (2) as regional storage availability is limited, other regions could be willing to store CO2 in the UK as well. As a thought experiment, we investigate how ramping up the targeted amount of CO2 to be stored in the UK impacts the key performance indicators of BECCS value chain under the three optimisation and impact scenarios. The resulting water use under the water minimisation scenario (a), land use under the land minimisation scenario (b), CO2 efficiency under the CO2 efficiency maximisation scenario (c), and total land use change in all three optimisation scenarios, in the impact scenario III, are presented in Fig. 8.
image file: c8ee01676c-f8.tif
Fig. 8 Minimal water use (a), minimal land use (b), maximal CO2 efficiency (c) under impact scenario I (DEC on MAL), II (DEC on MAL and AR) and III (all land types, all crops) and land use change in impact scenario III under the three optimisation scenario (d).

Two insights can be derived from this thought experiment. First, the median world scale carbon dioxide removal can only be met in the impact scenario III, i.e., when BECCS is deployed on all types of land and using crop residues in addition to dedicated crops. As observed in Fig. 8, by limiting bioenergy sourcing to dedicated energy crops from marginal land, up to 3.25 GtCO2 per year can be removed by deploying BECCS and storing the CO2 in the UK (a). Adding residues (b) – wheat straw in this study, only marginally increases BECCS carbon removal capacity, which reaches 3.5 GtCO2 per year. Naturally, expanding the MONET framework by implementing other regions – Africa, Russia, Indonesia, Australia, in MONET, as well as climate-tailored biomass crops and agricultural residues would increase this carbon removal potential and nuance this statement. For example, in 2016, a total of 770 Mt of corn was produced by the five regions considered in MONET.49 Assuming a grain to corn stover ratio of 1[thin space (1/6-em)]:[thin space (1/6-em)]1,50 a carbon content of 48%, the same carbon efficiency as using local wheat straw pellets (63% CO2 efficiency, no long distance transport), and that all corn stover is available for BECCS, 850 additional MtCO2 could be removed from the atmosphere. This being noted, this thought experiment shows the potentially negative economic-environmental impacts associated with BECCS deployment at the gigatone scale.

Secondly, resource mobilisation and land use change increase exponentially with the CO2 removal target. Removing 12 GtCO2 per year would require at the minimum 5700 Bm3 per year, 930 Mha, and producing at the maximum 35 TJ per year (net) at a maximum CO2 efficiency of 33%, which, for comparison, is in the upper range of BECCS resource use in the literature. Smith et al.8 evaluated a land requirement of 380–700 Mha, and a marginal water requirement, as compared to the water consumption of a counter factual scenario, of 720 Bm3 to meet a 12 GtCO2 per year target. In Heck et al.,5 removing 5 GtC per year with bioenergy, resulting in a net removal of 8 to 10 GtCO2 per year depending on the CO2 efficiency of the process, requires between 1000 and 4000 km3 of blue water use (no grey water considered), depending on the weighting of the objectives in the objective function. This shows that though more CO2 storage might be available in the UK, there is a limit beyond which CO2 storage in the UK is no longer resource nor CO2 efficient, which confirms the need for multi-polar systems when deploying negative emissions at the gigatone scale.

5 Energy production or resource conservation?

In the final section of this paper, we focus on the technical element of the trilemma, i.e., BECCS net electricity production potential. Fig. 9 presents the total water use (a), land use (b), CO2 efficiency (c), net electricity produced (d) and land use change (e) in the four optimisation scenarios, in the impact scenario I.
image file: c8ee01676c-f9.tif
Fig. 9 Total water use, land use, and CO2 efficiency, in scenario I, and land use change (scenario III) under four objective functions, in the median, pessimistic and optimistic scenarios. Energy maximisation presents much stronger trade-offs with the other metrics: to maximise energy the ratio of energy production per CO2 removed needs to be maximised, which results in a high water use, land use, low carbon efficiency and land use change. Only focusing on energy production and negative emissions is detrimental to BECCS environmental performance.

It is observed that energy maximisation presents much stronger trade-offs with the other metrics: water use and land use increase fivefold, land use change by two orders of magnitude, and CO2 efficiency decreases by 13 efficiency points, when maximising electricity production. This can be explained by the fact that, in order to maximise electricity production at a fixed CO2 removal target, the ratio of energy production per amount of CO2 removed needs to be maximised. This leads to the selection of regions featuring a high net electricity production, and/or a low net CO2 removal, i.e., in the latter, regions that are inefficient at removing CO2 from the atmosphere. This results in a high water use, land use and low carbon efficiency. Another interesting insight from this analysis is that in the pessimistic scenario, the system cannot be net electricity positive.

All of these results highlight the strong trade-offs between net electricity production and CO2 efficiency, which is consistent with other studies in the literature.3–5,51,52 In these studies, it was shown that BECCS services – CO2 removal and electricity production – mutually exclude each other. However, this contribution takes the analysis further by showing that the duality is not only between electricity production and CO2 removal, but for a given CO2 removal target, between electricity production and BECCS land use, water use, CO2 efficiency and land use change. One should therefore consider all of BECCS' KPIs when designing its supply chain, as focusing only on BECCS main services – CO2 removal and electricity production – can ultimately counteract the ostensible positive environmental impact of BECCS.

Taking this point further, one can postulate the existence of a global BECCS supply chain which can satisfy both BECCS' energy production objectives and resource use constraints. Annual negative emissions requirements between 1.8 to 9.9 GtCO2 by 2050 have been predicted by integrated assessment models in order to maintain a 2 °C trajectory with median values around 7 GtCO2 per year.5,53 Projections of primary energy delivered by BECCS in 2050 can be found between 35 and 120 EJ per year depending on the scenario, with an average around 80 EJ per year.11,54,55 By removing 50 MtCO2 per year in 2050, the UK would therefore be contributing to approximately 0.7% of the world CO2 removal target. Assuming that the UK would remove CO2 efficiently from the atmosphere, only 0.7% of the world marginal land available could therefore be mobilised to meet the UK CO2 removal target. Using the 390–1107 Mha from Cai et al.,21 the UK could therefore reasonably use up to 2.2–7.8 Mha of marginal land. By this contribution to global negative emissions, the UK should also theoretically supply 570 PJ per year of primary energy, or generate approximately 170 PJ per year of electricity. The droplets on Fig. 10 represent the total electricity generated tE for different land constraints, under the energy maximisation scenario. Land use is separated in three zones: the “safe” zone in green, where land use is constrained to 2.2 Mha, the uncertainty zone in yellow, where land use is constrained under 7.8 Mha, and the forbidden zone in red. The system's total water use is represented by the colour scale, and the total biomass use, by the size of the droplets. Energy production in the water, land and biomass minimisation are also represented for comparison by the triangles.


image file: c8ee01676c-f10.tif
Fig. 10 BECCS total electricity generated under different land constraints, in the energy maximisation scenario (droplets), and the water, land and CO2 efficiency minimisation and maximisation scenarios (triangles). Water use is represented by the colour scale, and biomass use, by the markers' size. Even when land use is constrained to a “safe” value (green zone), the UK electricity generation objective consistent with a 50 MtCO2 per year target is met.

Fig. 10 shows that, even while maintaining the total land use in a “safe” zone (i.e., inferior to 2.2 Mha), BECCS electricity production objective is met; the markers are outside of the grey area. Though maximising energy production drastically increases water, land and biomass use, it is nevertheless possible to reconcile BECCS energy generation objective with land use constraints. Similarly, the triangles representing the water, land and CO2 efficiency minimisation and maximisation are also located in, or close to, the safe zone. Though there may be strong trade-offs between these resource efficiency indicators, they are all compatible with a safe land use.

6 Relevance to policy makers

In this study, we have identified the complex interactions existing between BECCS technical and environmental performance indicators. Assessing BECCS systems from a marginal cost per ton of CO2 removed would therefore be incomplete. In particular, the strong trade-offs between water use and land use change, as well as between energy production and all of BECCS impacts, showed that one cannot optimise these systems from the perspective of a unique environmental impact. A sustainability standard which does not only regulate biomass maximum carbon intensity, but also BECCS water, land and CO2 efficiencies, as well as land use change, will be required to ensure that BECCS is deployed within sustainable boundaries.

Growing perennial grasses on marginal land, whilst sustainable, might not be practical.56 Actively engaging and incentivising all stakeholders of BECCS value chain, including farmers, will be crucial in unlocking BECCS deployment.

Furthermore, in meeting a regional carbon removal target, the prevalence of imported biomass in the optimisation results highlighted the preeminence of parameters such as yield, CO2 intensity of electricity, and climate data, over transport distance. Regions with good compliance with these parameters were repeatedly selected regardless of the CO2 storage location. One implication of this is that BECCS policy frameworks will need to consider the logistics, and negative emissions accounting of a system where regions meet their carbon removal target with imported biomass. Another implication of this is, were CO2 storage to be available in regions providing sustainable biomass, for example Brazil in this case study, a potentially important share of the global carbon removal target could be achieved by a 100% Brazilian BECCS value chain. How much of the world carbon removal target could be met with this value chain, as well as the potential environmental and economic trade-offs between local and international value chains, are important research questions to tackle. If we take this thought experiment further in the context of meeting a global CO2 removal target, it is conceivable that a region A meets its target using biomass feedstock from a region B to store CO2 in a region C. Integrating the multi-polarity of negative emissions in the design of policy frameworks will likely be a crucial policy challenge for BECCS. Implementing different CO2 storage sites, as well as a CO2 transport and storage value chain model, into MONET, is however required to investigate these challenges further.

7 Conclusions

In this contribution, we have presented a framework which enables the study of the complex relationship between the ability of BECCS to be net energy positive, net carbon negative, and the broader environmental impacts of large-scale deployment of this technology. In the context of determining the extent to which each NET should be deployed for efficient and sustainable CO2 removal, this framework could be applied to other NETs.

By highlighting the trade-offs between BECCS resource efficiency, environmental performance and technical performance, this study shows that the design of BECCS value chain needs to be performed in the prism of all BECCS KPIs. Strong trade-offs with tipping points were identified between water use and the other two resource efficiency indicators in particular. How to build an objective function which reconcile all of BECCS' KPIs, while accounting for how BECCS performance may vary from one region to another, is therefore a key research challenge to be addressed.

Another conclusion is that, factors such as yield, carbon intensity of power, and high precipitation led to the selection of imported biomass over indigenous biomass. The design of policy frameworks considering the carbon accounting implications of using foreign biomass to store CO2 in a given region, thereby meeting this region's carbon removal target, are paramount to facilitate local BECCS deployment. What is already complex at the megatone scale becomes manifold at the gigatone scale: how to regulate systems where biomass is imported from a productive region A, CO2 is stored in region B with abundant storage, to meet the CO2 removal target of a region C, as well as how to allocate credits among these actors, are key research and policy question to be investigated.

The availability of sufficient marginal land and agricultural residues were observed to be of paramount importance to our results. However, it is also recognised that their availability is controversial, at best. To provide insight into the impact of their respective availability, a sensitivity analysis of the optimisation results to the availability of marginal land and agricultural residues was performed. A first insight from this analysis is that agricultural residues exerted a first order impact on BECCS land use; residues being attributed low agricultural carbon and water footprints, total land use decreased by several orders of magnitude when using agricultural residues. Assessing precisely how much agricultural residues could be used for BECCS, without trespassing on other uses, could drastically relieve the pressure of BECCS on land use. A strong trade-off between water use and land use change was also identified: when minimising water use, using non-marginal land from low water footprint regions, and therefore causing land use change emissions, was preferable to using marginal land from higher water footprint regions. Water use and land use change being two critical sustainability indicators for BECCS, one should therefore be careful with potential direct and indirect land use effects when deploying BECCS from a water-saving perspective, and vice versa.

When ramping up the CO2 removal target, it was found that the world median CO2 removal target of 12 GtCO2 per year was only achievable by storing CO2 in the UK when residues and all land types were considered for BECCS. It was also found that water use, land use and CO2 efficiency did not increase linearly with the CO2 removal target, as marginal land from “sustainable” regions get depleted as the CO2 target increases, thus leading to the selection of other types of land or less sustainable regions. This shows that, though the UK has the storage capacity to achieve more CO2 storage than its current target, there will be a clear trade-off between how much and how efficient carbon dioxide removal from the UK will be. This further confirms the need for multi-polar systems when deploying negative emissions at the gigatone scale. Implementing other storage sites in MONET will be required to investigate the optimal structure of the world CO2 network for carbon dioxide removal.

Finally, maximising net energy production led to a drastic increase in the system's water use, land use, and land use change, as well as a decrease in CO2 efficiency. This is explained by the fact that, at a given CO2 removal target, regions which are less efficient at removing CO2 are selected to maximise the amount of energy produced. A key insight from this result is that focusing exclusively on energy production and CO2 removal is detrimental to BECCS resource efficiency and impact on ecosystems.

As a final thought experiment, we considered the proportional share of marginal land available, to what the UK is contributing to the world global carbon removal target by 2050, as a safe land use boundary in the context of UK CO2 removal target. Were BECCS in the UK to be deployed subject to this land use constraint, it was found that BECCS electricity production objectives were still met. What this last analysis shows is that, whilst BECCS KPIs may be negatively correlated, they are, however, not incompatible: providing the right choices are made along BECCS value chain, BECCS can be deployed in a way that meets altogether its carbon removal objective, electricity production objective, and land use constraints.

Conflicts of interest

There are no conflicts of interest to declare.

Appendix

A Additional data

Fig. 11.
image file: c8ee01676c-f11.tif
Fig. 11 Land cover with a resolution of 15 arc second geographic, adapted from the MODIS dataset,45 marginal land area with a resolution of 30 arc second geographic, adapted from Cai et al.,21 and harvested wheat area with a resolution of 5 minute geographic, adapted from MAPSPAM.46
Table 2 Land cover, marginal land and harvested wheat area (Brazil and China)
Sub-region sr Croplanda (ha) Grasslanda (ha) Forestsa (ha) Marginal landb (ha) Harvested wheat areac (ha)
a Obtained using the MODIS dataset.45b Obtained using marginal land dataset from Cai et al.21c Obtained using harvested wheat area from MAPSPAM.46
Acre 4529 8744 14[thin space (1/6-em)]796[thin space (1/6-em)]138 65[thin space (1/6-em)]464 0
Alagoas 198[thin space (1/6-em)]379 104[thin space (1/6-em)]788 21[thin space (1/6-em)]278 538[thin space (1/6-em)]378 0
Amapa 75[thin space (1/6-em)]662 99[thin space (1/6-em)]536 12[thin space (1/6-em)]048[thin space (1/6-em)]270 755[thin space (1/6-em)]909 0
Amazonas 42[thin space (1/6-em)]148 264[thin space (1/6-em)]495 149[thin space (1/6-em)]210[thin space (1/6-em)]755 365[thin space (1/6-em)]120 0
Bahia 2[thin space (1/6-em)]332[thin space (1/6-em)]945 2[thin space (1/6-em)]400[thin space (1/6-em)]444 2[thin space (1/6-em)]888[thin space (1/6-em)]010 7[thin space (1/6-em)]602[thin space (1/6-em)]747 139
Ceara 181[thin space (1/6-em)]331 126[thin space (1/6-em)]727 84[thin space (1/6-em)]689 1[thin space (1/6-em)]999[thin space (1/6-em)]432 0
Distrito Federal 1368 786 2198 229[thin space (1/6-em)]625 2057
Espirito Santo 71[thin space (1/6-em)]259 42[thin space (1/6-em)]667 469[thin space (1/6-em)]747 617[thin space (1/6-em)]785 0
Goias 3[thin space (1/6-em)]256[thin space (1/6-em)]098 425[thin space (1/6-em)]759 188[thin space (1/6-em)]220 5[thin space (1/6-em)]852[thin space (1/6-em)]712 12[thin space (1/6-em)]694
Maranhao 528[thin space (1/6-em)]157 156[thin space (1/6-em)]577 4[thin space (1/6-em)]809[thin space (1/6-em)]429 4[thin space (1/6-em)]763[thin space (1/6-em)]187 0
Mato Grosso 6[thin space (1/6-em)]351[thin space (1/6-em)]939 3[thin space (1/6-em)]312[thin space (1/6-em)]967 34[thin space (1/6-em)]375[thin space (1/6-em)]031 6[thin space (1/6-em)]428[thin space (1/6-em)]726 786
Mato Grosso do Sul 1[thin space (1/6-em)]656[thin space (1/6-em)]405 4[thin space (1/6-em)]894[thin space (1/6-em)]778 1[thin space (1/6-em)]779[thin space (1/6-em)]656 7[thin space (1/6-em)]629[thin space (1/6-em)]190 72[thin space (1/6-em)]331
Minas Gerais 2[thin space (1/6-em)]313[thin space (1/6-em)]868 617[thin space (1/6-em)]202 2[thin space (1/6-em)]107[thin space (1/6-em)]767 9[thin space (1/6-em)]237[thin space (1/6-em)]942 16[thin space (1/6-em)]247
Para 1.6[thin space (1/6-em)]315[thin space (1/6-em)]938 296[thin space (1/6-em)]578 95[thin space (1/6-em)]045[thin space (1/6-em)]602 3[thin space (1/6-em)]428[thin space (1/6-em)]322 0
Paraiba 132[thin space (1/6-em)]971 277[thin space (1/6-em)]816 14[thin space (1/6-em)]484 639[thin space (1/6-em)]569 0
Parana 3[thin space (1/6-em)]755[thin space (1/6-em)]553 1[thin space (1/6-em)]494[thin space (1/6-em)]937 4[thin space (1/6-em)]854[thin space (1/6-em)]533 5[thin space (1/6-em)]706[thin space (1/6-em)]602 1[thin space (1/6-em)]143[thin space (1/6-em)]865
Pernambuco 220[thin space (1/6-em)]129 421[thin space (1/6-em)]072 41[thin space (1/6-em)]975 990[thin space (1/6-em)]765 0
Piaui 286[thin space (1/6-em)]497 128[thin space (1/6-em)]473 168[thin space (1/6-em)]498 3[thin space (1/6-em)]462[thin space (1/6-em)]185 0
Rio de Janeiro 93[thin space (1/6-em)]119 45[thin space (1/6-em)]561 791[thin space (1/6-em)]173 397[thin space (1/6-em)]731 0
Rio Grande do Norte 167[thin space (1/6-em)]051 388[thin space (1/6-em)]911 8178 80[thin space (1/6-em)]691 0
Rio Grande do Sul 6[thin space (1/6-em)]038[thin space (1/6-em)]675 7[thin space (1/6-em)]898[thin space (1/6-em)]979 2[thin space (1/6-em)]952[thin space (1/6-em)]537 7[thin space (1/6-em)]876[thin space (1/6-em)]103 851[thin space (1/6-em)]954
Rondonia 84[thin space (1/6-em)]925 351[thin space (1/6-em)]732 14[thin space (1/6-em)]417[thin space (1/6-em)]261 690[thin space (1/6-em)]893 0
Roraima 23[thin space (1/6-em)]040 2[thin space (1/6-em)]278[thin space (1/6-em)]561 17[thin space (1/6-em)]130[thin space (1/6-em)]388 109[thin space (1/6-em)]482 0
Santa Catarina 547[thin space (1/6-em)]862 108[thin space (1/6-em)]752 4[thin space (1/6-em)]919[thin space (1/6-em)]658 1[thin space (1/6-em)]196[thin space (1/6-em)]099 80[thin space (1/6-em)]420
Sao Paulo 3[thin space (1/6-em)]426[thin space (1/6-em)]641 2[thin space (1/6-em)]921[thin space (1/6-em)]162 4[thin space (1/6-em)]024[thin space (1/6-em)]247 6[thin space (1/6-em)]118[thin space (1/6-em)]991 62[thin space (1/6-em)]872
Sergipe 73[thin space (1/6-em)]177 192[thin space (1/6-em)]151 10[thin space (1/6-em)]946 1[thin space (1/6-em)]141[thin space (1/6-em)]001 0
Tocantins 365[thin space (1/6-em)]163 1[thin space (1/6-em)]351[thin space (1/6-em)]570 795[thin space (1/6-em)]593 1[thin space (1/6-em)]369[thin space (1/6-em)]558 0
 
Anhui 9[thin space (1/6-em)]008[thin space (1/6-em)]590 53[thin space (1/6-em)]236 3[thin space (1/6-em)]584[thin space (1/6-em)]114 1[thin space (1/6-em)]240[thin space (1/6-em)]645 1[thin space (1/6-em)]350[thin space (1/6-em)]540
Beijing 492[thin space (1/6-em)]158 235[thin space (1/6-em)]133 573[thin space (1/6-em)]765 163[thin space (1/6-em)]759 37[thin space (1/6-em)]377
Chongqing 1[thin space (1/6-em)]680[thin space (1/6-em)]121 12[thin space (1/6-em)]125 3[thin space (1/6-em)]868[thin space (1/6-em)]173 1[thin space (1/6-em)]380[thin space (1/6-em)]615 297[thin space (1/6-em)]372
Fujian 788[thin space (1/6-em)]547 62[thin space (1/6-em)]106 8[thin space (1/6-em)]141[thin space (1/6-em)]189 476[thin space (1/6-em)]468 5798
Gansu 3[thin space (1/6-em)]626[thin space (1/6-em)]168 16[thin space (1/6-em)]539[thin space (1/6-em)]874 2[thin space (1/6-em)]687[thin space (1/6-em)]901 2[thin space (1/6-em)]306[thin space (1/6-em)]145 649[thin space (1/6-em)]244
Guangdong 2[thin space (1/6-em)]175[thin space (1/6-em)]471 172[thin space (1/6-em)]886 7[thin space (1/6-em)]938[thin space (1/6-em)]579 1[thin space (1/6-em)]371[thin space (1/6-em)]573 4155
Guangxi Zhuang 1[thin space (1/6-em)]232[thin space (1/6-em)]313 55[thin space (1/6-em)]201 9[thin space (1/6-em)]722[thin space (1/6-em)]985 5[thin space (1/6-em)]379[thin space (1/6-em)]165 8483
Guizhou 2[thin space (1/6-em)]704[thin space (1/6-em)]320 126[thin space (1/6-em)]664 7[thin space (1/6-em)]237[thin space (1/6-em)]191 3[thin space (1/6-em)]620[thin space (1/6-em)]343 374[thin space (1/6-em)]575
Hainan 302[thin space (1/6-em)]004 12[thin space (1/6-em)]377 1[thin space (1/6-em)]201[thin space (1/6-em)]111 930[thin space (1/6-em)]152 0
Hebei 9[thin space (1/6-em)]208[thin space (1/6-em)]888 5[thin space (1/6-em)]640[thin space (1/6-em)]878 2[thin space (1/6-em)]278[thin space (1/6-em)]828 3[thin space (1/6-em)]039[thin space (1/6-em)]792 1[thin space (1/6-em)]137[thin space (1/6-em)]209
Heilongjiang 17[thin space (1/6-em)]993[thin space (1/6-em)]228 1[thin space (1/6-em)]568[thin space (1/6-em)]397 16[thin space (1/6-em)]426[thin space (1/6-em)]106 558[thin space (1/6-em)]354 80[thin space (1/6-em)]519
Henan 12[thin space (1/6-em)]928[thin space (1/6-em)]238 155[thin space (1/6-em)]665 1[thin space (1/6-em)]986[thin space (1/6-em)]088 577[thin space (1/6-em)]474 2[thin space (1/6-em)]454[thin space (1/6-em)]033
Hubei 7[thin space (1/6-em)]266[thin space (1/6-em)]175 85[thin space (1/6-em)]837 8[thin space (1/6-em)]066[thin space (1/6-em)]266 1[thin space (1/6-em)]307[thin space (1/6-em)]885 460[thin space (1/6-em)]302
Hunan 3[thin space (1/6-em)]919[thin space (1/6-em)]821 77[thin space (1/6-em)]140 9[thin space (1/6-em)]784[thin space (1/6-em)]477 6[thin space (1/6-em)]191[thin space (1/6-em)]445 53[thin space (1/6-em)]940
Inner Mongolia 9[thin space (1/6-em)]129[thin space (1/6-em)]420 60[thin space (1/6-em)]913[thin space (1/6-em)]567 10[thin space (1/6-em)]450[thin space (1/6-em)]574 4[thin space (1/6-em)]567[thin space (1/6-em)]746 431[thin space (1/6-em)]015
Jiangsu 7[thin space (1/6-em)]828[thin space (1/6-em)]318 172[thin space (1/6-em)]257 426[thin space (1/6-em)]293 76[thin space (1/6-em)]925 1[thin space (1/6-em)]146[thin space (1/6-em)]168
Jiangxi 3[thin space (1/6-em)]711[thin space (1/6-em)]596 97[thin space (1/6-em)]963 8[thin space (1/6-em)]730[thin space (1/6-em)]602 2[thin space (1/6-em)]874[thin space (1/6-em)]002 13[thin space (1/6-em)]663
Jilin 7[thin space (1/6-em)]285[thin space (1/6-em)]991 1[thin space (1/6-em)]828[thin space (1/6-em)]079 7[thin space (1/6-em)]195[thin space (1/6-em)]687 541[thin space (1/6-em)]587 13[thin space (1/6-em)]946
Liaoning 7[thin space (1/6-em)]576[thin space (1/6-em)]514 770[thin space (1/6-em)]886 3[thin space (1/6-em)]422[thin space (1/6-em)]504 1[thin space (1/6-em)]080[thin space (1/6-em)]172 16[thin space (1/6-em)]192
Ningxia Hui 778[thin space (1/6-em)]026 3[thin space (1/6-em)]550[thin space (1/6-em)]427 35[thin space (1/6-em)]323 38[thin space (1/6-em)]509 195[thin space (1/6-em)]390
Qinghai 175[thin space (1/6-em)]418 49[thin space (1/6-em)]673[thin space (1/6-em)]691 122[thin space (1/6-em)]859 9721 87[thin space (1/6-em)]259
Shaanxi 5[thin space (1/6-em)]021[thin space (1/6-em)]710 5[thin space (1/6-em)]487[thin space (1/6-em)]258 8[thin space (1/6-em)]947[thin space (1/6-em)]397 1[thin space (1/6-em)]862[thin space (1/6-em)]026 820[thin space (1/6-em)]401
Shandong 13[thin space (1/6-em)]244[thin space (1/6-em)]852 481[thin space (1/6-em)]401 222[thin space (1/6-em)]284 899[thin space (1/6-em)]162 2[thin space (1/6-em)]179[thin space (1/6-em)]912
Shanghai 426[thin space (1/6-em)]168 21[thin space (1/6-em)]090 11[thin space (1/6-em)]685 924 24[thin space (1/6-em)]371
Shanxi 5[thin space (1/6-em)]694[thin space (1/6-em)]177 6[thin space (1/6-em)]219[thin space (1/6-em)]109 2[thin space (1/6-em)]748[thin space (1/6-em)]386 4[thin space (1/6-em)]463[thin space (1/6-em)]694 347[thin space (1/6-em)]502
Sichuan 7[thin space (1/6-em)]510[thin space (1/6-em)]980 18[thin space (1/6-em)]591[thin space (1/6-em)]401 18[thin space (1/6-em)]967[thin space (1/6-em)]825 1[thin space (1/6-em)]804[thin space (1/6-em)]046 1[thin space (1/6-em)]365[thin space (1/6-em)]270
Tianjin 781[thin space (1/6-em)]202 95[thin space (1/6-em)]431 65[thin space (1/6-em)]487 232[thin space (1/6-em)]087 83[thin space (1/6-em)]118
Xinjiang Uyghur 6[thin space (1/6-em)]343[thin space (1/6-em)]242 38[thin space (1/6-em)]744[thin space (1/6-em)]012 972[thin space (1/6-em)]599 328[thin space (1/6-em)]036 614[thin space (1/6-em)]630
Yunnan 3[thin space (1/6-em)]296[thin space (1/6-em)]249 2[thin space (1/6-em)]467[thin space (1/6-em)]111 23[thin space (1/6-em)]357[thin space (1/6-em)]628 2[thin space (1/6-em)]062[thin space (1/6-em)]503 296[thin space (1/6-em)]344
Zhejiang 2[thin space (1/6-em)]010[thin space (1/6-em)]763 87[thin space (1/6-em)]662 6[thin space (1/6-em)]559[thin space (1/6-em)]833 2[thin space (1/6-em)]627[thin space (1/6-em)]207 52[thin space (1/6-em)]596


Table 3 Land cover, marginal land and harvested wheat area (EU and India)
Sub-region sr Croplanda (ha) Grasslanda (ha) Forestsa (ha) Marginal landb (ha) Harvested wheat areac (ha)
a Obtained using the MODIS dataset.45b Obtained using marginal land dataset from Cai et al.21c Obtained using harvested wheat area from MAPSPAM.46
Austria 1[thin space (1/6-em)]319[thin space (1/6-em)]031 842[thin space (1/6-em)]569 4[thin space (1/6-em)]675[thin space (1/6-em)]105 128[thin space (1/6-em)]706 282[thin space (1/6-em)]813
Belgium 1[thin space (1/6-em)]024[thin space (1/6-em)]230 53[thin space (1/6-em)]644 774[thin space (1/6-em)]849 57[thin space (1/6-em)]725 214[thin space (1/6-em)]079
Bulgaria 5[thin space (1/6-em)]254[thin space (1/6-em)]531 252[thin space (1/6-em)]653 3[thin space (1/6-em)]297[thin space (1/6-em)]318 539[thin space (1/6-em)]360 1[thin space (1/6-em)]033[thin space (1/6-em)]864
Croatia 1[thin space (1/6-em)]354[thin space (1/6-em)]763 282[thin space (1/6-em)]974 2[thin space (1/6-em)]118[thin space (1/6-em)]367 168[thin space (1/6-em)]810 175[thin space (1/6-em)]656
Cyprus 136[thin space (1/6-em)]242 109[thin space (1/6-em)]522 6023 0 3778
Czech Repulic 2[thin space (1/6-em)]520[thin space (1/6-em)]362 32[thin space (1/6-em)]382 2[thin space (1/6-em)]734[thin space (1/6-em)]562 214[thin space (1/6-em)]332 822[thin space (1/6-em)]510
Denmark 2[thin space (1/6-em)]657[thin space (1/6-em)]579 144[thin space (1/6-em)]640 534[thin space (1/6-em)]542 83[thin space (1/6-em)]300 637[thin space (1/6-em)]964
Estonia 247[thin space (1/6-em)]227 136[thin space (1/6-em)]588 2[thin space (1/6-em)]791[thin space (1/6-em)]997 1[thin space (1/6-em)]135[thin space (1/6-em)]160 84[thin space (1/6-em)]237
Finland 241[thin space (1/6-em)]660 413[thin space (1/6-em)]429 16[thin space (1/6-em)]023[thin space (1/6-em)]481 8131 208[thin space (1/6-em)]661
France 25[thin space (1/6-em)]978[thin space (1/6-em)]359 1[thin space (1/6-em)]312[thin space (1/6-em)]489 11[thin space (1/6-em)]362[thin space (1/6-em)]939 965[thin space (1/6-em)]400 5[thin space (1/6-em)]232[thin space (1/6-em)]675
Germany 10[thin space (1/6-em)]909[thin space (1/6-em)]863 439[thin space (1/6-em)]866 11[thin space (1/6-em)]744[thin space (1/6-em)]615 682[thin space (1/6-em)]462 3[thin space (1/6-em)]125[thin space (1/6-em)]000
Greece 4[thin space (1/6-em)]023[thin space (1/6-em)]178 936[thin space (1/6-em)]396 2[thin space (1/6-em)]491[thin space (1/6-em)]456 484[thin space (1/6-em)]050 782[thin space (1/6-em)]626
Hungary 5[thin space (1/6-em)]660[thin space (1/6-em)]820 25[thin space (1/6-em)]761 1[thin space (1/6-em)]218[thin space (1/6-em)]001 17[thin space (1/6-em)]440 1[thin space (1/6-em)]122[thin space (1/6-em)]858
Ireland 260[thin space (1/6-em)]390 5[thin space (1/6-em)]468[thin space (1/6-em)]795 945[thin space (1/6-em)]376 1[thin space (1/6-em)]521[thin space (1/6-em)]890 92[thin space (1/6-em)]307
Italy 12[thin space (1/6-em)]011[thin space (1/6-em)]815 1[thin space (1/6-em)]691[thin space (1/6-em)]114 7[thin space (1/6-em)]546[thin space (1/6-em)]617 504[thin space (1/6-em)]537 1[thin space (1/6-em)]403[thin space (1/6-em)]762
Latvia 659[thin space (1/6-em)]162 87[thin space (1/6-em)]630 3[thin space (1/6-em)]490[thin space (1/6-em)]681 1[thin space (1/6-em)]412[thin space (1/6-em)]723 190[thin space (1/6-em)]236
Lithuania 2[thin space (1/6-em)]067[thin space (1/6-em)]600 45[thin space (1/6-em)]105 1[thin space (1/6-em)]911[thin space (1/6-em)]212 1[thin space (1/6-em)]590[thin space (1/6-em)]564 356[thin space (1/6-em)]901
Luxembourg 53[thin space (1/6-em)]896 2422 98[thin space (1/6-em)]199 1197 13[thin space (1/6-em)]983
Malt 14[thin space (1/6-em)]076 4765 63 0 1271
Netherlands 877[thin space (1/6-em)]514 433[thin space (1/6-em)]984 502[thin space (1/6-em)]616 92[thin space (1/6-em)]373 129[thin space (1/6-em)]071
Poland 11[thin space (1/6-em)]508[thin space (1/6-em)]067 120[thin space (1/6-em)]279 9[thin space (1/6-em)]252[thin space (1/6-em)]908 2[thin space (1/6-em)]379[thin space (1/6-em)]125 2[thin space (1/6-em)]231[thin space (1/6-em)]857
Portugal 2[thin space (1/6-em)]093[thin space (1/6-em)]644 383[thin space (1/6-em)]453 929[thin space (1/6-em)]271 1[thin space (1/6-em)]231[thin space (1/6-em)]336 136[thin space (1/6-em)]834
Romania 11[thin space (1/6-em)]501[thin space (1/6-em)]556 178[thin space (1/6-em)]437 6[thin space (1/6-em)]645[thin space (1/6-em)]953 649[thin space (1/6-em)]723 2[thin space (1/6-em)]227[thin space (1/6-em)]876
Slovakia 1[thin space (1/6-em)]552[thin space (1/6-em)]104 23[thin space (1/6-em)]795 2[thin space (1/6-em)]256[thin space (1/6-em)]763 157[thin space (1/6-em)]578 364[thin space (1/6-em)]508
Slovenia 158[thin space (1/6-em)]684 23[thin space (1/6-em)]056 1[thin space (1/6-em)]344[thin space (1/6-em)]556 33[thin space (1/6-em)]323 30[thin space (1/6-em)]460
Spain 18[thin space (1/6-em)]065[thin space (1/6-em)]148 3[thin space (1/6-em)]742[thin space (1/6-em)]814 6[thin space (1/6-em)]262[thin space (1/6-em)]563 6[thin space (1/6-em)]855[thin space (1/6-em)]073 2[thin space (1/6-em)]122[thin space (1/6-em)]749
Sweden 1[thin space (1/6-em)]004[thin space (1/6-em)]257 1[thin space (1/6-em)]482[thin space (1/6-em)]953 23[thin space (1/6-em)]932[thin space (1/6-em)]037 457[thin space (1/6-em)]029 362[thin space (1/6-em)]669
United Kingdom 6[thin space (1/6-em)]737[thin space (1/6-em)]751 10[thin space (1/6-em)]162[thin space (1/6-em)]945 4[thin space (1/6-em)]223[thin space (1/6-em)]947 1[thin space (1/6-em)]548[thin space (1/6-em)]376 1[thin space (1/6-em)]871[thin space (1/6-em)]551
 
Andaman and Nicobar 3114 283 591[thin space (1/6-em)]316 0 0
Andhra Pradesh 13[thin space (1/6-em)]307[thin space (1/6-em)]665 148[thin space (1/6-em)]698 1[thin space (1/6-em)]226[thin space (1/6-em)]242 1[thin space (1/6-em)]822[thin space (1/6-em)]939 9656
Arunachal Prades 45[thin space (1/6-em)]325 498[thin space (1/6-em)]197 6[thin space (1/6-em)]873[thin space (1/6-em)]113 76[thin space (1/6-em)]598 3832
Assam 2[thin space (1/6-em)]186[thin space (1/6-em)]102 182[thin space (1/6-em)]621 1[thin space (1/6-em)]517[thin space (1/6-em)]693 230[thin space (1/6-em)]982 48[thin space (1/6-em)]111
Bihar 7[thin space (1/6-em)]958[thin space (1/6-em)]426 80[thin space (1/6-em)]600 170[thin space (1/6-em)]385 98[thin space (1/6-em)]260 1[thin space (1/6-em)]448[thin space (1/6-em)]068
Chandigarh 1919 31 31 0 606
Chhattisgarh 3[thin space (1/6-em)]574[thin space (1/6-em)]992 26[thin space (1/6-em)]689 1[thin space (1/6-em)]851[thin space (1/6-em)]355 121[thin space (1/6-em)]831 74[thin space (1/6-em)]003
Dadra and Nagar Hav. 6920 79 1195 81 486
Daman and Diu 1541 236 79 0 473
Delhi 59[thin space (1/6-em)]652 975 0 668 19[thin space (1/6-em)]580
Goa 16[thin space (1/6-em)]592 3366 79[thin space (1/6-em)]091 15[thin space (1/6-em)]565 0
Gujarat 11[thin space (1/6-em)]575[thin space (1/6-em)]614 629[thin space (1/6-em)]627 202[thin space (1/6-em)]106 1[thin space (1/6-em)]385[thin space (1/6-em)]494 965[thin space (1/6-em)]926
Haryana 4[thin space (1/6-em)]227[thin space (1/6-em)]329 1148 34[thin space (1/6-em)]914 1615 1[thin space (1/6-em)]166[thin space (1/6-em)]419
Himachal Pradesh 368[thin space (1/6-em)]843 1[thin space (1/6-em)]099[thin space (1/6-em)]908 1[thin space (1/6-em)]621[thin space (1/6-em)]224 75[thin space (1/6-em)]187 369[thin space (1/6-em)]177
Jammu and Kashmir 1[thin space (1/6-em)]371[thin space (1/6-em)]071 3[thin space (1/6-em)]725[thin space (1/6-em)]986 1[thin space (1/6-em)]685[thin space (1/6-em)]578 180[thin space (1/6-em)]611 255[thin space (1/6-em)]180
Jharkhand 3[thin space (1/6-em)]990[thin space (1/6-em)]261 33[thin space (1/6-em)]766 595[thin space (1/6-em)]924 575[thin space (1/6-em)]933 40[thin space (1/6-em)]523
Karnataka 11[thin space (1/6-em)]123[thin space (1/6-em)]324 122[thin space (1/6-em)]450 1[thin space (1/6-em)]207[thin space (1/6-em)]181 1[thin space (1/6-em)]062[thin space (1/6-em)]742 1907
Kerala 322[thin space (1/6-em)]889 3255 1[thin space (1/6-em)]492[thin space (1/6-em)]373 646[thin space (1/6-em)]538 0
Madhya Pradesh 19[thin space (1/6-em)]787[thin space (1/6-em)]998 454[thin space (1/6-em)]460 1[thin space (1/6-em)]167[thin space (1/6-em)]565 4[thin space (1/6-em)]639[thin space (1/6-em)]474 2[thin space (1/6-em)]681[thin space (1/6-em)]304
Maharashtra 20[thin space (1/6-em)]504[thin space (1/6-em)]500 32[thin space (1/6-em)]319 1[thin space (1/6-em)]299[thin space (1/6-em)]262 4[thin space (1/6-em)]310[thin space (1/6-em)]811 752[thin space (1/6-em)]521
Manipur 75[thin space (1/6-em)]316 6951 1[thin space (1/6-em)]838[thin space (1/6-em)]585 6912 4
Meghalaya 52[thin space (1/6-em)]606 15[thin space (1/6-em)]507 948[thin space (1/6-em)]206 113[thin space (1/6-em)]400 2041
Mizoram 1620 220 1[thin space (1/6-em)]894[thin space (1/6-em)]903 449 6
Nagaland 5347 739 1[thin space (1/6-em)]319[thin space (1/6-em)]236 5319 1179
Odisha 4[thin space (1/6-em)]668[thin space (1/6-em)]909 60[thin space (1/6-em)]187 1[thin space (1/6-em)]482[thin space (1/6-em)]198 625[thin space (1/6-em)]011 5571
Puducherry 32[thin space (1/6-em)]020 440 598 3034 0
Punjab 4[thin space (1/6-em)]768[thin space (1/6-em)]287 1069 62[thin space (1/6-em)]908 7026 1[thin space (1/6-em)]575[thin space (1/6-em)]621
Rajasthan 20[thin space (1/6-em)]165[thin space (1/6-em)]145 641[thin space (1/6-em)]437 115[thin space (1/6-em)]671 1[thin space (1/6-em)]237[thin space (1/6-em)]793 2[thin space (1/6-em)]497[thin space (1/6-em)]183
Sikkim 2044 202[thin space (1/6-em)]531 315[thin space (1/6-em)]293 991 4383
Tamil Nadu 4[thin space (1/6-em)]049[thin space (1/6-em)]505 45[thin space (1/6-em)]262 790[thin space (1/6-em)]576 645[thin space (1/6-em)]084 0
Tripura 40[thin space (1/6-em)]607 47 168[thin space (1/6-em)]152 29[thin space (1/6-em)]486 2576
Uttar Pradesh 22[thin space (1/6-em)]126[thin space (1/6-em)]919 120[thin space (1/6-em)]484 411[thin space (1/6-em)]306 106[thin space (1/6-em)]167 5[thin space (1/6-em)]948[thin space (1/6-em)]565
Uttarakhand 543[thin space (1/6-em)]427 906[thin space (1/6-em)]279 2[thin space (1/6-em)]525[thin space (1/6-em)]426 206[thin space (1/6-em)]629 372[thin space (1/6-em)]507
West Bengal 5[thin space (1/6-em)]937[thin space (1/6-em)]802 32[thin space (1/6-em)]272 369[thin space (1/6-em)]975 279[thin space (1/6-em)]391 173[thin space (1/6-em)]474


Table 4 Land cover, marginal land and harvested wheat area densities (US)
Sub-region sr Croplanda (ha) Grasslanda (ha) Forestsa (ha) Marginal landb (ha) Harvested wheat areac (ha)
a Obtained using the MODIS dataset.45b Obtained using marginal land dataset from Cai et al.21c Obtained using harvested wheat area from MAPSPAM.46
Alabama 398[thin space (1/6-em)]268 93[thin space (1/6-em)]544 5[thin space (1/6-em)]593[thin space (1/6-em)]855 1[thin space (1/6-em)]321[thin space (1/6-em)]772 20[thin space (1/6-em)]123
Alaska 53[thin space (1/6-em)]534 22[thin space (1/6-em)]091[thin space (1/6-em)]691 12[thin space (1/6-em)]490[thin space (1/6-em)]700 1465 0
Arizona 426[thin space (1/6-em)]687 4[thin space (1/6-em)]690[thin space (1/6-em)]061 817[thin space (1/6-em)]390 0 39[thin space (1/6-em)]139
Arkansas 3[thin space (1/6-em)]420[thin space (1/6-em)]507 444[thin space (1/6-em)]584 5[thin space (1/6-em)]028[thin space (1/6-em)]882 2[thin space (1/6-em)]385[thin space (1/6-em)]539 143[thin space (1/6-em)]730
California 4[thin space (1/6-em)]272[thin space (1/6-em)]905 5[thin space (1/6-em)]856[thin space (1/6-em)]101 9[thin space (1/6-em)]065[thin space (1/6-em)]097 43[thin space (1/6-em)]884 115[thin space (1/6-em)]813
Colorado 1[thin space (1/6-em)]102[thin space (1/6-em)]173 20[thin space (1/6-em)]027[thin space (1/6-em)]818 3[thin space (1/6-em)]811[thin space (1/6-em)]132 517[thin space (1/6-em)]109 780[thin space (1/6-em)]620
Connecticut 7801 739 985[thin space (1/6-em)]385 10[thin space (1/6-em)]051 1
Delaware 93[thin space (1/6-em)]339 4624 61[thin space (1/6-em)]178 340[thin space (1/6-em)]892 20[thin space (1/6-em)]733
Florida 791[thin space (1/6-em)]771 537[thin space (1/6-em)]435 3[thin space (1/6-em)]669[thin space (1/6-em)]668 834[thin space (1/6-em)]105 3783
Georgia 888[thin space (1/6-em)]444 104[thin space (1/6-em)]033 5[thin space (1/6-em)]173[thin space (1/6-em)]742 1[thin space (1/6-em)]244[thin space (1/6-em)]597 60[thin space (1/6-em)]912
Hawaii 48[thin space (1/6-em)]014 156[thin space (1/6-em)]577 643[thin space (1/6-em)]340 0 0
Idaho 1[thin space (1/6-em)]921[thin space (1/6-em)]733 11[thin space (1/6-em)]494[thin space (1/6-em)]023 7[thin space (1/6-em)]467[thin space (1/6-em)]668 47[thin space (1/6-em)]955 320[thin space (1/6-em)]812
Illinois 10[thin space (1/6-em)]087[thin space (1/6-em)]503 30[thin space (1/6-em)]778 467[thin space (1/6-em)]639 2[thin space (1/6-em)]817[thin space (1/6-em)]237 317[thin space (1/6-em)]882
Indiana 5[thin space (1/6-em)]005[thin space (1/6-em)]968 21[thin space (1/6-em)]341 824[thin space (1/6-em)]892 511[thin space (1/6-em)]783 170[thin space (1/6-em)]056
Iowa 12[thin space (1/6-em)]179[thin space (1/6-em)]716 5756 45[thin space (1/6-em)]639 358[thin space (1/6-em)]433 23[thin space (1/6-em)]168
Kansas 5[thin space (1/6-em)]615[thin space (1/6-em)]008 14[thin space (1/6-em)]634[thin space (1/6-em)]921 15[thin space (1/6-em)]695 1[thin space (1/6-em)]153[thin space (1/6-em)]356 3[thin space (1/6-em)]656[thin space (1/6-em)]919
Kentucky 1[thin space (1/6-em)]874[thin space (1/6-em)]710 27[thin space (1/6-em)]727 3[thin space (1/6-em)]890[thin space (1/6-em)]270 1[thin space (1/6-em)]513[thin space (1/6-em)]903 143[thin space (1/6-em)]754
Louisiana 1[thin space (1/6-em)]953[thin space (1/6-em)]391 74[thin space (1/6-em)]687 3[thin space (1/6-em)]315[thin space (1/6-em)]499 647[thin space (1/6-em)]123 49[thin space (1/6-em)]933
Maine 35[thin space (1/6-em)]747 7014 7[thin space (1/6-em)]676[thin space (1/6-em)]144 98[thin space (1/6-em)]527 0
Maryland 240[thin space (1/6-em)]810 5504 755[thin space (1/6-em)]977 845[thin space (1/6-em)]353 53[thin space (1/6-em)]500
Massachusetts 14[thin space (1/6-em)]909 4985 1[thin space (1/6-em)]598[thin space (1/6-em)]136 99[thin space (1/6-em)]517 0
Michigan 1[thin space (1/6-em)]621[thin space (1/6-em)]019 51[thin space (1/6-em)]128 6[thin space (1/6-em)]890[thin space (1/6-em)]900 350[thin space (1/6-em)]089 247[thin space (1/6-em)]566
Minnesota 10[thin space (1/6-em)]194[thin space (1/6-em)]383 96[thin space (1/6-em)]783 6[thin space (1/6-em)]001[thin space (1/6-em)]748 4[thin space (1/6-em)]858[thin space (1/6-em)]069 688[thin space (1/6-em)]062
Mississippi 1[thin space (1/6-em)]783[thin space (1/6-em)]635 45[thin space (1/6-em)]938 4[thin space (1/6-em)]278[thin space (1/6-em)]708 1[thin space (1/6-em)]733[thin space (1/6-em)]077 38[thin space (1/6-em)]206
Missouri 5[thin space (1/6-em)]507[thin space (1/6-em)]341 811[thin space (1/6-em)]555 3[thin space (1/6-em)]171[thin space (1/6-em)]928 3[thin space (1/6-em)]038[thin space (1/6-em)]770 321[thin space (1/6-em)]011
Montana 1[thin space (1/6-em)]954[thin space (1/6-em)]948 27[thin space (1/6-em)]522[thin space (1/6-em)]033 7[thin space (1/6-em)]857[thin space (1/6-em)]145 1[thin space (1/6-em)]938[thin space (1/6-em)]973 1[thin space (1/6-em)]325[thin space (1/6-em)]102
Nebraska 8[thin space (1/6-em)]139[thin space (1/6-em)]317 11[thin space (1/6-em)]445[thin space (1/6-em)]049 16[thin space (1/6-em)]387 96[thin space (1/6-em)]447 689[thin space (1/6-em)]084
Nevada 187[thin space (1/6-em)]795 15[thin space (1/6-em)]611[thin space (1/6-em)]892 222[thin space (1/6-em)]819 2515 3646
New Hampshire 5536 2579 2[thin space (1/6-em)]235[thin space (1/6-em)]391 6674 0
New Jersey 100[thin space (1/6-em)]511 12[thin space (1/6-em)]157 819[thin space (1/6-em)]781 402[thin space (1/6-em)]441 9632
New Mexico 389[thin space (1/6-em)]964 13[thin space (1/6-em)]555[thin space (1/6-em)]191 1[thin space (1/6-em)]196[thin space (1/6-em)]723 1411 89[thin space (1/6-em)]403
New York 191[thin space (1/6-em)]208 12[thin space (1/6-em)]236 7[thin space (1/6-em)]235[thin space (1/6-em)]303 160[thin space (1/6-em)]895 38[thin space (1/6-em)]571
North Carolina 875[thin space (1/6-em)]989 41[thin space (1/6-em)]818 5[thin space (1/6-em)]136[thin space (1/6-em)]186 2[thin space (1/6-em)]525[thin space (1/6-em)]415 175[thin space (1/6-em)]674
North Dakota 13[thin space (1/6-em)]716[thin space (1/6-em)]895 3[thin space (1/6-em)]926[thin space (1/6-em)]992 38[thin space (1/6-em)]153 2[thin space (1/6-em)]072[thin space (1/6-em)]281 3[thin space (1/6-em)]254[thin space (1/6-em)]269
Ohio 3[thin space (1/6-em)]888[thin space (1/6-em)]697 14[thin space (1/6-em)]626 1[thin space (1/6-em)]848[thin space (1/6-em)]257 184[thin space (1/6-em)]961 357[thin space (1/6-em)]388
Oklahoma 1[thin space (1/6-em)]285[thin space (1/6-em)]391 12[thin space (1/6-em)]604[thin space (1/6-em)]767 696[thin space (1/6-em)]497 606[thin space (1/6-em)]886 1[thin space (1/6-em)]639[thin space (1/6-em)]241
Oregon 1[thin space (1/6-em)]341[thin space (1/6-em)]064 10[thin space (1/6-em)]866[thin space (1/6-em)]425 11[thin space (1/6-em)]766[thin space (1/6-em)]586 22[thin space (1/6-em)]684 299[thin space (1/6-em)]564
Pennsylvania 499[thin space (1/6-em)]219 11[thin space (1/6-em)]135 6[thin space (1/6-em)]103[thin space (1/6-em)]784 547[thin space (1/6-em)]859 58[thin space (1/6-em)]523
Rhode Island 5001 362 196[thin space (1/6-em)]382 16[thin space (1/6-em)]414 0
South Carolina 214[thin space (1/6-em)]200 51[thin space (1/6-em)]380 3[thin space (1/6-em)]274[thin space (1/6-em)]782 428[thin space (1/6-em)]381 60[thin space (1/6-em)]940
South Dakota 8[thin space (1/6-em)]018[thin space (1/6-em)]063 11[thin space (1/6-em)]175[thin space (1/6-em)]820 368[thin space (1/6-em)]812 376[thin space (1/6-em)]552 615[thin space (1/6-em)]808
Tennessee 1[thin space (1/6-em)]394[thin space (1/6-em)]394 35[thin space (1/6-em)]008 4[thin space (1/6-em)]274[thin space (1/6-em)]163 1[thin space (1/6-em)]789[thin space (1/6-em)]359 87[thin space (1/6-em)]527
Texas 5[thin space (1/6-em)]602[thin space (1/6-em)]049 37[thin space (1/6-em)]201[thin space (1/6-em)]942 1[thin space (1/6-em)]626[thin space (1/6-em)]854 2[thin space (1/6-em)]780[thin space (1/6-em)]385 1[thin space (1/6-em)]058[thin space (1/6-em)]287
Utah 498[thin space (1/6-em)]464 11[thin space (1/6-em)]978[thin space (1/6-em)]962 967[thin space (1/6-em)]047 3124 50[thin space (1/6-em)]872
Vermont 12[thin space (1/6-em)]125 2438 2[thin space (1/6-em)]060[thin space (1/6-em)]885 61 4
Virginia 299[thin space (1/6-em)]991 18[thin space (1/6-em)]951 5[thin space (1/6-em)]710[thin space (1/6-em)]124 1[thin space (1/6-em)]118[thin space (1/6-em)]266 63[thin space (1/6-em)]834
Washington 1[thin space (1/6-em)]886[thin space (1/6-em)]521 4[thin space (1/6-em)]691[thin space (1/6-em)]697 9[thin space (1/6-em)]833[thin space (1/6-em)]514 93[thin space (1/6-em)]421 699[thin space (1/6-em)]836
West Virginia 57[thin space (1/6-em)]765 15[thin space (1/6-em)]428 5[thin space (1/6-em)]037[thin space (1/6-em)]956 137[thin space (1/6-em)]764 1983
Wisconsin 2[thin space (1/6-em)]436[thin space (1/6-em)]286 35[thin space (1/6-em)]165 5[thin space (1/6-em)]046[thin space (1/6-em)]464 3[thin space (1/6-em)]277[thin space (1/6-em)]713 84[thin space (1/6-em)]259
Wyoming 217[thin space (1/6-em)]786 22[thin space (1/6-em)]124[thin space (1/6-em)]670 2[thin space (1/6-em)]473[thin space (1/6-em)]527 41[thin space (1/6-em)]702 56[thin space (1/6-em)]313


Table 5 Regional road tortuosity and biomass yield (Brazil and China)
Sub-region sr Road tortuositya t(sr) Miscanthus yieldb Switchgrass yieldc Wheat yieldd Willow yielde
a Own calculations.b Mean, low and high annual dry mass yield of miscanthus in tDM/ha/year. Data was adapted from ref. 27, 32 and 57–63. When yield data for region sr is not available, yield data from regions with the closest climate are used.c Mean, low and high annual dry mass yield of switchgrass in tDM/ha/year. Data was adapted from ref. 43, 58, 60, 61, 63 and 64. When yield data for region sr is not available, yield data from regions with the closest climate are used.d Mean, low and high annual dry mass yield of short rotation coppice willow in tDM/ha/year. Data was adapted from various willow yield datasets in the literature.32,61,64 When yield data for region sr is not available, yield data from regions with the closest climate are used.e Mean, low and high annual dry mass yield of wheat in tDM/ha/year. Dry mass wheat yield data was obtained from the FAO over the period 2010–2014 were used.49 As detailed in a previous contribution,4 to obtain the yield of wheat straw, a grain to straw conversion factor within the range 0.6–2.0 was used.
Acre 1.4 32.3–34.7 (33.5) 14–18 (16) 2.2–2.8 (2.6)
Alagoas 1.3 1–32.3 (14.6) 2–6 (4) 2.2–2.8 (2.6)
Amapa 2.2 32.3–34.7 (33.5) 14–18 (16) 2.2–2.8 (2.6)
Amazonas 2.4 15–41 (26.8) 10–18 (16) 2.2–2.8 (2.6)
Bahia 1.3 32.3–34.7 (33.5) 6–12 (8) 2.2–2.8 (2.6)
Ceara 1.5 32.3–34.7 (33.5) 8–14 (10) 2.2–2.8 (2.6)
Distrito Federal 1.3 32.3–34.7 (33.5) 14–18 (16) 2.2–2.8 (2.6)
Espirito Santo 1.4 32.3–34.7 (33.5) 10–14 (12) 2.2–2.8 (2.6)
Goias 1.4 12–22.8 (17.2) 14–18 (16) 2.2–2.8 (2.6)
Maranhao 1.3 32.3–34.7 (33.5) 14–18 (16) 2.2–2.8 (2.6)
Mato Grosso 1.5 32.3–34.7 (33.5) 14–18 (18) 2.2–2.8 (2.6)
Mato Grosso do Sul 1.1 12–22.8 (17.2) 10–18 (16) 2.2–2.8 (2.6)
Minas Gerais 1.4 12–22.8 (17.2) 6–14 (12) 2.2–2.8 (2.6)
Para 1.6 32.3–34.7 (33.5) 10–18 (18) 2.2–2.8 (2.6)
Paraiba 1.5 32.3–34.7 (33.5) 6–10 (8) 2.2–2.8 (2.6)
Parana 1.5 12–22.8 (17.2) 10–18 (12) 2.2–2.8 (2.6)
Pernambuco 1.1 1–32.3 (14.6) 1–6 (4) 2.2–2.8 (2.6)
Piaui 1.3 32.3–34.7 (33.5) 6–14 (10) 2.2–2.8 (2.6)
Rio de Janeiro 1.5 12–22.8 (17.2) 10–14 (12) 2.2–2.8 (2.6)
Rio Grande do Norte 1.5 12–22.8 (17.2) 6–10 (8) 2.2–2.8 (2.6)
Rio Grande do Sul 1.3 12–22.8 (17.2) 8–14 (10) 2.2–2.8 (2.6)
Rondonia 1.6 32.3–34.7 (33.5) 14–18 (16) 2.2–2.8 (2.6)
Roraima 2.5 32.3–34.7 (33.5) 10–14 (12) 2.2–2.8 (2.6)
Santa Catarina 1.4 12–22.8 (17.2) 10–18 (14) 2.2–2.8 (2.6)
Sao Paulo 1.3 12–22.8 (17.2) 10–18 (16) 2.2–2.8 (2.6)
Sergipe 1.3 32.3–34.7 (33.5) 1–6 (4) 2.2–2.8 (2.6)
Tocantins 1.3 32.3–34.7 (33.5) 14–18 (16) 2.2–2.8 (2.6)
 
Anhui 1.2 27.7–30 (28.9) 14–18 (16) 4.7–5.2 (5.0)
Beijing 1.1 25.4–27.7 (26.6) 2–10 (6) 4.7–5.2 (5.0)
Chongqing 1.4 23.1–27.7 (25.4) 14–18 (16) 4.7–5.2 (5.0)
Fujian 1.4 30–32.3 (31.2) 10–14 (12) 4.7–5.2 (5.0)
Gansu 1.2 0–0 (0) 0–2 (1) 4.7–5.2 (5.0)
Guangdong 1.3 30–32.3 (31.2) 10–18 (14) 4.7–5.2 (5.0)
Guangxi Zhuang 1.3 27.7–32.3 (30) 6–18 (12) 4.7–5.2 (5.0)
Guizhou 1.2 23.1–27.7 (25.4) 6–18 (16) 4.7–5.2 (5.0)
Hainan 1.1 32.3–34.7 (33.5) 2–6 (4) 4.7–5.2 (5.0)
Hebei 1.0 20.8–27.7 (24.3) 2–10 (6) 4.7–5.2 (5.0)
Heilongjiang 1.1 13.9–25.4 (19.6) 6–18 (14) 4.7–5.2 (5.0)
Henan 1.2 23.1–27.7 (25.4) 6–18 (12) 4.7–5.2 (5.0)
Hubei 1.4 25.4–32.3 (28.9) 10–18 (16) 4.7–5.2 (5.0)
Hunan 1.4 27.7–30 (28.9) 10–18 (12) 4.7–5.2 (5.0)
Inner Mongolia 1.4 0–13.9 (6.9) 0–10 (4) 4.7–5.2 (5.0)
Jiangsu 1.2 25.4–30 (27.7) 10–18 (16) 4.7–5.2 (5.0)
Jiangxi 1.3 27.7–30 (28.9) 10–14 (12) 4.7–5.2 (5.0)
Jilin 1.1 18.5–30 (24.3) 8–14 (12) 4.7–5.2 (5.0)
Liaoning 1.1 25.4–34.7 (30) 6–14 (10) 4.7–5.2 (5.0)
Ningxia Hui 1.2 13.9–18.5 (16.2) 2–6 (4) 4.7–5.2 (5.0)
Qinghai 1.3 0–0 (0) 0–2 (1) 4.7–5.2 (5.0)
Shaanxi 1.2 13.9–23.1 (18.5) 2–14 (8) 4.7–5.2 (5.0)
Shandong 1.1 23.1–27.7 (25.4) 6–12 (8) 4.7–5.2 (5.0)
Shanghai 1.5 32.3–34.7 (33.5) 14–18 (16) 4.7–5.2 (5.0)
Shanxi 1.2 13.9–20.8 (17.3) 2–6 (4) 4.7–5.2 (5.0)
Sichuan 1.3 13.9–25.4 (19.6) 0–18 (6) 4.7–5.2 (5.0)
Tianjin 1.0 30–32.3 (31.2) 6–10 (8) 4.7–5.2 (5.0)
Xinjiang Uyghur 1.3 0–0 (0) 0–2 (1) 4.7–5.2 (5.0)
Yunnan 1.3 23.1–32.3 (27.7) 0–18 (14) 4.7–5.2 (5.0)
Zhejiang 1.3 30–32.3 (31.2) 10–14 (12) 4.7–5.2 (5.0)


Table 6 Regional road tortuosity and biomass yield (EU and India)
Sub-region sr Road tortuositya t(sr) Miscanthus yieldb Switchgrass yieldc Wheat yieldd Willow yielde
a Own calculations.b Mean, low and high annual dry mass yield of miscanthus in tDM/ha/year. Data was adapted from ref. 27, 32 and 57–63. When yield data for region sr is not available, yield data from regions with the closest climate are used.c Mean, low and high annual dry mass yield of switchgrass in tDM/ha/year. Data was adapted from ref. 43, 58, 60, 61, 63 and 64. When yield data for region sr is not available, yield data from regions with the closest climate are used.d Mean, low and high annual dry mass yield of short rotation coppice willow in tDM/ha/year. Data was adapted from various willow yield datasets in the literature.32,61,64 When yield data for region sr is not available, yield data from regions with the closest climate are used.e Mean, low and high annual dry mass yield of wheat in tDM/ha/year. Dry mass wheat yield data was obtained from the FAO over the period 2010–2014 were used.49 As detailed in a previous contribution,4 to obtain the yield of wheat straw, a grain to straw conversion factor within the range 0.6–2.0 was used.
Austria 1.6 17.0–22.0 (19.5) 6.0–10.0 (8.0) 4.1–5.9 (4.9) 7.8–11.0 (9.4)
Belgium 1.1 16.0–16.0 (16.0) 10.0–14.0 (12.0) 8.4–8.9 (8.6) 4.0–17.0 (8.9)
Bulgaria 1.9 2.0–30.0 (14.3) 6.0–14.0 (8.0) 3.6–4.2 (3.8) 4.0–13.0 (8.4)
Croatia 1.4 18.0–18.0 (18.0) 6.0–10.0 (8.0) 4.0–5.3 (5.0) 11.0–11.0 (11.0)
Cyprus 1.8 12–27.7 (20.2) 0.0–6.0 (2.0) 2.2–3.1 (2.7) 1–11.0 (6.1)
Czechia 1.3 19.0–19.0 (19.0) 6.0–10.0 (8.0) 4.3–5.7 (5.0) 13.0–13.0 (13.0)
Denmark 2.5 5.0–22.0 (13.3) 4.3–10.0 (7.1) 6.5–7.4 (7.0) 8.0–8.0 (8.0)
Estonia 1.3 2.0–30.0 (14.3) 2.0–6.0 (4.0) 2.7–3.9 (3.3) 5.0–5.0 (5.0)
Finland 1.2 5.0–34.0 (17.1) 0.0–6.0 (2.0) 3.4–3.9 (3.8) 5.0–5.0 (5.0)
France 1.3 15.0–15.0 (15.0) 6.0–14.0 (9.5) 6.2–7.3 (6.8) 4.0–17.0 (8.8)
Germany 1.3 2.0–30.0 (14.4) 6.0–10.0 (8.0) 7.0–8.0 (7.4) 9.0–9.0 (9.0)
Greece 1.3 20.0–44.0 (31.2) 0.0–6.0 (2.0) 2.7–3.2 (2.8) 10.0–10.0 (10.0)
Hungary 1.3 2.0–30.0 (14.3) 10.0–14.0 (12.0) 3.7–4.6 (4.0) 8.0–8.0 (8.0)
Ireland 1.3 5.0–34.0 (14.5) 2.0–6.0 (4.0) 7.2–9.0 (8.4) 4.0–17.0 (8.6)
Italy 1.2 15.0–32.0 (25.7) 6.0–34.0 (13.6) 3.7–4.1 (4.0) 3.0–3.0 (3.0)
Latvia 1.4 2.0–30.0 (14.3) 2.0–6.0 (4.0) 3.0–4.4 (3.8) 5.0–5.0 (5.0)
Lithuania 1.1 2.0–30.0 (14.3) 6.0–10.0 (8.0) 3.3–4.8 (4.1) 9.0–9.0 (9.0)
Luxembourg 1.3 18.0–18.0 (18.0) 10.0–14.0 (12.0) 5.5–6.4 (5.9) 4.0–17.0 (8.8)
Malta 1.1 12.0–27.7 (20.2) 6.0–10.0 (8.0) 4.8–4.8 (4.8) 1.0–11.0 (6.1)
Netherlands 1.2 15.0–15.0 (15.0) 6.0–10.7 (8.3) 7.8–8.9 (8.5) 4.0–17.0 (8.9)
Poland 1.6 15.0–15.0 (15.0) 6.0–10.0 (8.0) 3.9–4.4 (4.2) 8.0–8.0 (8.0)
Portugal 1.2 20.0–20.0 (20.0) 6.0–10.0 (8.0) 1.3–1.8 (1.1) 1.0–1.0 (1.0)
Romania 1.5 16.0–16.0 (16.0) 6.0–10.0 (8.0) 2.7–3.7 (3.0) 8.0–8.0 (8.0)
Slovakia 1.5 16.0–16.0 (16.0) 6.0–10.0 (8.0) 3.3–4.6 (3.8) 7.0–7.0 (7.0)
Slovenia 1.3 16.0–16.0 (16.0) 6.0–10.0 (8.0) 4.4–5.4 (5.0) 10.0–10.0 (10.0)
Spain 1.2 14.0–34.0 (24.0) 2.0–6.0 (4.0) 2.4–3.6 (3.0) 8.0–8.0 (8.0)
Sweden 1.2 2.0–34.0 (16.2) 0.0–6.0 (1.0) 5.4–6.2 (5.8) 4.0–4.0 (4.0)
United Kingdom 1.4 5.0–24.1 (12.8) 2.0–14.6 (8.0) 6.7–7.7 (7.2) 4.0–17.0 (8.8)
 
Andaman and Nicobar 1.4 32.3–34.7 (33.5) 12.0–18.0 (14.0) 2.8–3.2 (3.1)
Andhra Pradesh 1.1 5.0–34.7 (24.0) 6.0–14.0 (10.0) 2.8–3.2 (3.1)
Arunachal Prades 1.8 0.0–0.0 (0.0) 0.0–6.0 (2.0) 2.8–3.2 (3.1)
Assam 1.9 12.0–22.8 (17.2) 6.0–14.0 (12.0) 2.8–3.2 (3.1)
Bihar 1.4 12.0–22.8 (17.2) 6.0–10.0 (8.0) 2.8–3.2 (3.1)
Chandigarh 1.3 12.0–22.8 (17.2) 10.0–14.0 (12.0) 2.8–3.2 (3.1)
Chhattisgarh 1.4 12.0–22.8 (17.2) 6.0–10.0 (8.0) 2.8–3.2 (3.1)
Dadra and Nagar Hav. 1.4 5.0–34.0 (14.5) 6.0–10.0 (8.0) 2.8–3.2 (3.1)
Daman and Diu 1.2 5.0–34.0 (14.5) 6.0–10.0 (8.0) 2.8–3.2 (3.1)
Delhi 1.2 5.0–34.0 (14.5) 6.0–10.0 (8.0) 2.8–3.2 (3.1)
Goa 1.4 32.3–34.7 (33.5) 6.0–10.0 (8.0) 2.8–3.2 (3.1)
Gujarat 1.4 0.0–34.0 (7.3) 6.0–10.0 (8.0) 2.8–3.2 (3.1)
Haryana 1.3 5.0–34.0 (15.9) 2.0–10.0 (6.0) 2.8–3.2 (3.1)
Himachal Pradesh 1.4 0.0–22.8 (8.6) 2.0–6.0 (4.0) 2.8–3.2 (3.1)
Jammu and Kashmir 1.4 0.0–0.0 (0.0) 0.0–2.0 (1.0) 2.8–3.2 (3.1)
Jharkhand 1.2 12.0–22.8 (17.2) 6.0–10.0 (8.0) 2.8–3.2 (3.1)
Karnataka 1.4 5.0–41.0 (19.5) 6.0–14.0 (9.0) 2.8–3.2 (3.1)
Kerala 1.4 32.3–34.7 (33.5) 10.0–14.0 (12.0) 2.8–3.2 (3.1)
Madhya Pradesh 1.2 5.0–34.0 (15.4) 6.0–14.6 (10.4) 2.8–3.2 (3.1)
Maharashtra 1.7 32.3–34.7 (33.5) 6.0–10.0 (8.0) 2.8–3.2 (3.1)
Manipur 2.4 12.0–22.8 (17.2) 6.0–14.0 (10.0) 2.8–3.2 (3.1)
Meghalaya 2.3 12.0–22.8 (17.2) 6.0–14.0 (10.0) 2.8–3.2 (3.1)
Mizoram 3.3 12.0–22.8 (17.2) 6.0–14.0 (10.0) 2.8–3.2 (3.1)
Nagaland 1.9 12.0–22.8 (17.2) 6.0–14.0 (10.0) 2.8–3.2 (3.1)
Odisha 1.5 32.3–34.7 (33.5) 6.0–10.0 (8.0) 2.8–3.2 (3.1)
Puducherry 1.3 32.3–34.7 (33.5) 6.0–14.0 (10.0) 2.8–3.2 (3.1)
Punjab 1.3 5.0–34.0 (14.5) 2.0–6.0 (4.0) 2.8–3.2 (3.1)
Rajasthan 1.3 0.0–0.0 (0.0) 6.0–10.0 (8.0) 2.8–3.2 (3.1)
Sikkim 1.3 5.0–34.0 (14.5) 10.0–14.0 (12.0) 2.8–3.2 (3.1)
Tamil Nadu 1.1 5.0–34.0 (14.5) 10.0–14.0 (12.0) 2.8–3.2 (3.1)
Tripura 4.0 12.0–22.8 (17.2) 6.0–14.0 (10.0) 2.8–3.2 (3.1)
Uttar Pradesh 1.2 12.0–22.8 (17.2) 6.0–10.0 (8.0) 2.8–3.2 (3.1)
Uttarakhand 1.3 12.0–22.8 (17.2) 2.0–10.0 (6.0) 2.8–3.2 (3.1)
West Bengal 1.4 32.3–34.7 (33.5) 6.0–10.0 (8.0) 2.8–3.2 (3.1)


Table 7 Regional road tortuosity and biomass yield (US)
Sub-region sr Road tortuositya t(sr) Miscanthus yieldb Switchgrass yieldc Wheat yieldd Willow yielde
a Own calculations.b Mean, low and high annual dry mass yield of miscanthus in tDM/ha/year. Data was adapted from ref. 27, 32 and 57–63. When yield data for region sr is not available, yield data from regions with the closest climate are used.c Mean, low and high annual dry mass yield of switchgrass in tDM/ha/year. Data was adapted from ref. 43, 58, 60, 61, 63 and 64. When yield data for region sr is not available, yield data from regions with the closest climate are used.d Mean, low and high annual dry mass yield of short rotation coppice willow in tDM/ha/year. Data was adapted from various willow yield datasets in the literature.32,61,64 When yield data for region sr is not available, yield data from regions with the closest climate are used.e Mean, low and high annual dry mass yield of wheat in tDM/ha/year. Dry mass wheat yield data was obtained from the FAO over the period 2010–2014 were used.49 As detailed in a previous contribution,4 to obtain the yield of wheat straw, a grain to straw conversion factor within the range 0.6–2.0 was used.
Alabama 1.3 27.7–27.7 (27.7) 14.0–18.0 (16.0) 2.9–3.2 (3.1) 5.6–5.6 (5.6)
Alaska 1.3 5.0–5.0 (5.0) 0.0–2.0 (1.0) 2.9–3.2 (3.1) 0.0–0.0 (0.0)
Arizona 1.2 2.4–2.4 (2.4) 2.0–6.0 (4.0) 2.9–3.2 (3.1) 0.0–0.0 (0.0)
Arkansas 1.2 12.0–22.8 (17.2) 10.0–14.0 (12.0) 2.9–3.2 (3.1) 5.9–5.9 (5.9)
California 1.2 28.1–28.1 (28.1) 0.0–6.0 (2.0) 2.9–3.2 (3.1) 0.0–0.0 (0.0)
Colorado 1.2 2.4–2.4 (2.4) 0.0–9.9 (5.8) 2.9–3.2 (3.1) 7.5–7.5 (7.5)
Connecticut 1.2 15.5–15.5 (15.5) 6.0–10.0 (7.7) 2.9–3.2 (3.1) 5.6–11.0 (7.9)
Delaware 1.3 12.0–22.8 (17.2) 9.4–18.0 (14.4) 2.9–3.2 (3.1) 5.6–5.6 (5.6)
Florida 1.2 12.0–22.8 (17.2) 10.0–14.0 (11.9) 2.9–3.2 (3.1) 5.5–5.5 (5.5)
Georgia 1.3 26.0–41.0 (31.9) 11.8–18.0 (14.9) 2.9–3.2 (3.1) 6.2–6.2 (6.2)
Hawaii 1.2 27.7–27.7 (27.7) 0.0–0.0 (0.0) 2.9–3.2 (3.1) 0.0–0.0 (0.0)
Idaho 1.2 2.4–2.4 (2.4) 0.0–6.0 (2.0) 2.9–3.2 (3.1) 0.0–0.0 (0.0)
Illinois 1.3 16.9–22.8 (19.9) 10.0–18.0 (13.4) 2.9–3.2 (3.1) 6.2–6.2 (6.2)
Indiana 1.3 16.9–22.8 (19.9) 10.0–14.0 (12.0) 2.9–3.2 (3.1) 6.2–6.2 (6.2)
Iowa 1.2 22.8–22.8 (22.8) 7.4–18.0 (13.0) 2.9–3.2 (3.1) 5.6–5.6 (5.6)
Kansas 1.1 18.3–18.3 (18.3) 6.0–18.0 (12.1) 2.9–3.2 (3.1) 6.1–6.1 (6.1)
Kentucky 1.2 27.7–27.7 (27.7) 6.0–14.0 (10.2) 2.9–3.2 (3.1) 5.9–5.9 (5.9)
Louisiana 1.1 28.1–28.1 (28.1) 10.0–18.0 (14.0) 2.9–3.2 (3.1) 5.1–5.1 (5.1)
Maine 1.2 15.5–15.5 (15.5) 6.0–10.0 (8.1) 2.9–3.2 (3.1) 5.5–5.5 (5.5)
Maryland 1.4 12.0–27.7 (20.2) 9.9–14.0 (11.5) 2.9–3.2 (3.1) 6.7–6.7 (6.7)
Massachusetts 1.1 15.5–15.5 (15.5) 10.0–14.0 (11.5) 2.9–3.2 (3.1) 6.2–11.0 (8.1)
Michigan 1.3 16.9–22.8 (19.9) 10.0–14.0 (11.7) 2.9–3.2 (3.1) 5.9–5.9 (5.9)
Minnesota 1.2 16.9–16.9 (16.9) 10.0–16.0 (12.8) 2.9–3.2 (3.1) 6.0–11.0 (8.0)
Mississippi 1.2 27.7–28.1 (27.9) 10.0–18.0 (13.5) 2.9–3.2 (3.1) 5.9–5.9 (5.9)
Missouri 1.2 12.0–22.8 (17.2) 10.7–18.0 (14.7) 2.9–3.2 (3.1) 5.6–5.6 (5.6)
Montana 1.2 2.4–2.4 (2.4) 0.0–6.0 (4.0) 2.9–3.2 (3.1) 0.0–0.0 (0.0)
Nebraska 1.1 12.0–12.0 (12.0) 6.0–14.0 (10.0) 2.9–3.2 (3.1) 6.6–6.6 (6.6)
Nevada 1.3 2.4–2.4 (2.4) 0.0–2.0 (1.0) 2.9–3.2 (3.1) 0.0–0.0 (0.0)
New Hampshire 1.3 15.5–15.5 (15.5) 6.0–10.7 (8.7) 2.9–3.2 (3.1) 5.9–11.0 (8.0)
New Jersey 1.2 15.5–15.5 (15.5) 9.9–14.0 (11.5) 2.9–3.2 (3.1) 7.5–7.5 (7.5)
New Mexico 1.1 2.4–2.4 (2.4) 2.0–6.0 (4.0) 2.9–3.2 (3.1) 0.0–0.0 (0.0)
New York 1.0 15.5–16.9 (16.2) 10.0–14.0 (11.5) 2.9–3.2 (3.1) 5.4–11.0 (7.8)
North Carolina 1.2 19.3–27.7 (22.6) 8.7–18.0 (14.2) 2.9–3.2 (3.1) 4.6–4.6 (4.6)
North Dakota 1.1 12.0–12.0 (12.0) 6.0–14.0 (10.4) 2.9–3.2 (3.1) 6.0–6.0 (6.0)
Ohio 1.4 16.9–22.8 (19.9) 10.0–14.0 (11.7) 2.9–3.2 (3.1) 5.3–5.3 (5.3)
Oklahoma 1.1 18.3–18.3 (18.3) 10.0–14.0 (12.0) 2.9–3.2 (3.1) 7.5–7.5 (7.5)
Oregon 1.6 12.0–27.7 (20.2) 0.0–11.1 (4.8) 2.9–3.2 (3.1) 5.8–5.8 (5.8)
Pennsylvania 1.2 15.5–16.9 (16.2) 6.0–14.0 (10.0) 2.9–3.2 (3.1) 7.4–7.4 (7.4)
Rhode Island 1.2 15.5–15.5 (15.5) 10.0–14.0 (11.6) 2.9–3.2 (3.1) 5.4–11.0 (7.8)
South Carolina 1.1 27.7–27.7 (27.7) 10.1–18.0 (14.5) 2.9–3.2 (3.1) 5.3–5.3 (5.3)
South Dakota 1.1 12.0–12.0 (12.0) 6.0–14.0 (10.6) 2.9–3.2 (3.1) 6.0–6.0 (6.0)
Tennessee 1.2 27.7–27.7 (27.7) 10.0–14.0 (11.6) 2.9–3.2 (3.1) 5.0–5.0 (5.0)
Texas 1.2 18.3–18.3 (18.3) 4.0–14.0 (9.2) 2.9–3.2 (3.1) 6.2–6.2 (6.2)
Utah 1.2 2.4–2.4 (2.4) 0.0–2.0 (1.0) 2.9–3.2 (3.1) 1.0–11.0 (6.3)
Vermont 1.3 15.5–15.5 (15.5) 6.0–11.1 (8.8) 2.9–3.2 (3.1) 5.4–11.0 (7.8)
Virginia 1.2 27.7–27.7 (27.7) 14.0–18.0 (16.0) 2.9–3.2 (3.1) 7.3–7.3 (7.3)
Washington 1.2 5.0–34.0 (17.3) 0.0–12.3 (5.1) 2.9–3.2 (3.1) 5.2–5.2 (5.2)
West Virginia 1.4 27.7–27.7 (27.7) 6.0–10.1 (8.5) 2.9–3.2 (3.1) 6.1–6.1 (6.1)
Wisconsin 1.2 12.0–27.7 (20.2) 8.0–16.0 (12.0) 2.9–3.2 (3.1) 5.2–11.0 (5.8)
Wyoming 1.2 5.0–34.0 (17.3) 0.0–6.0 (2.0) 2.9–3.2 (3.1) 5.4–11.0 (6.5)


Acknowledgements

The authors thank Imperial College London for the funding of a President's PhD Scholarship, as well as the Greenhouse Gas Removal (GGR) grant, funded by the Natural Environment Research Council (NERC), under grant NE/P019900/1. The authors also acknowledge the reviewers who provided a very thorough review and insightful comments.

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