ArticlePDF Available

Biophysical and economic limits to negative CO2 emissions

  • Mercator Research Institute on Global Commons and Climate Change and Humboldt University of Berlin

Abstract and Figures

To have a >50% chance of limiting warming below 2 °C, most recent scenarios from integrated assessment models (IAMs) require large-scale deployment of negative emissions technologies (NETs). These are technologies that result in the net removal of greenhouse gases from the atmosphere. We quantify potential global impacts of the different NETs on various factors (such as land, greenhouse gas emissions, water, albedo, nutrients and energy) to determine the biophysical limits to, and economic costs of, their widespread application. Resource implications vary between technologies and need to be satisfactorily addressed if NETs are to have a significant role in achieving climate goals.
Content may be subject to copyright.
Despite two decades of eort to curb emissions of CO2 and
other greenhouse gases (GHGs), emissions grew faster dur-
ing the 2000s than in the 1990s1, and by 2010 had reached
~50GtCO2equivalent (CO2eq)yr−1 (refs2,3). e continuing rise
in emissions is a growing challenge for meeting the international
goal of limiting warming to less than 2°C relative to the pre-indus-
trial era, particularly without stringent climate policies to decrease
emissions in the near future2–4. As negative emissions technologies
(NETs) seem ever more necessary3,5–10, society needs to be informed
of the potential risks and opportunities aorded by all mitigation
Biophysical and economic limits to negative
CO2 emissions
Pete Smith1*, Steven J. Davis2, Felix Creutzig3,4, Sabine Fuss3, Jan Minx3,5,6, Benoit Gabrielle7, 8,
Etsushi Kato9, Robert B. Jackson10, Annette Cowie11, Elmar Kriegler5, Detlef P. van Vuuren12,13,
Joeri Rogelj14,15, Philippe Ciais16, Jennifer Milne17, Josep G. Canadell18, David McCollum15,
Glen Peters19, Robbie Andrew19, Volker Krey15, Gyami Shrestha20, Pierre Friedlingstein21,
Thomas Gasser16,22, Arnulf Grübler15, Wolfgang K. Heidug23, Matthias Jonas15, Chris D. Jones24,
Florian Kraxner15, Emma Littleton25, Jason Lowe24, José Roberto Moreira26, Nebojsa Nakicenovic15,
Michael Obersteiner15, Anand Patwardhan27, Mathis Rogner15, Ed Rubin28, Ayyoob Sharifi29,
Asbjørn Torvanger19, Yoshiki Yamagata30, Jae Edmonds31 and Cho Yongsung32
To have a >50% chance of limiting warming below 2°C, most recent scenarios from integrated assessment models (IAMs)
require large-scale deployment of negative emissions technologies (NETs). These are technologies that result in the net
removal of greenhouse gases from the atmosphere. We quantify potential global impacts of the dierent NETs on various fac-
tors (such as land, greenhouse gas emissions, water, albedo, nutrients and energy) to determine the biophysical limits to, and
economic costs of, their widespread application. Resource implications vary between technologies and need to be satisfactorily
addressed if NETs are to have a significant role in achieving climate goals.
options, to be able to decide which pathways are most desirable for
dealing with climate change.
ere are distinct classes of NETs, such as: (1) bioenergy with
carbon capture and storage (BECCS)11,12; (2) direct air capture of
CO2 from ambient air by engineered chemical reactions (DAC)13,14;
(3) enhanced weathering of minerals (EW)15, where natural weath-
ering to remove CO2 from the atmosphere is accelerated and
the products stored in soils, or buried in land or deep ocean16–19;
(4) aorestation and reforestation (AR) to x atmospheric carbon
in biomass and soils20–22; (5) manipulation of carbon uptake by the
1Institute of Biological & Environmental Sciences, University of Aberdeen, 23St Machar Drive, Aberdeen, AB24 3UU, UK. 2University of California, Irvine,
Department of Earth System Science, Irvine, California 92697-3100, USA. 3Mercator Research Institute on Global Commons and Climate Change,
Torgauer Street 12-15, 10829 Berlin, Germany. 4Technical University Berlin, Straβe des 17, Junis 135, 10623 Berlin, Germany. 5Potsdam Institute for Climate
Impact Research (PIK), PO Box60 12 03, 14412 Potsdam, Germany. 6Hertie School of Governance, Friedrichstrasse 180, 10117 Berlin, Germany. 7AgroParisTech,
UMR1402 ECOSYS, F-78850 Thiverval-Grignon, France. 8National de la Recherche Agronomique (INRA), Environment and Arable Crops Research Unit,
UMR1402 ECOSYS, F-78850 Thiverval-Grignon, France. 9The Institute of Applied Energy (IAE), Minato 105-0003, Tokyo, Japan. 10Department of Earth System
Science, Woods Institute for the Environment and Precourt Institute for Energy, Stanford University, Stanford, California 94305, USA. 11NSW Department
of Primary Industries, University of New England, Armidale NSW 2351, Australia. 12Copernicus Institute for Sustainable Development, Department of
Environmental Sciences, Utrecht University, Utrecht, 3584CS, The Netherlands. 13PBL Netherlands Environmental Assessment Agency, PO Box 303 3720,
AH Bilthoven, The Netherlands. 14Swiss Federal Institute of Technology (ETH Zürich), Universitätstrasse 16, Zürich 8092, Switzerland. 15International Institute
for Applied Systems Analysis (IIASA), Schlossplatz 1, Laxenburg A-2361, Austria. 16Laboratoire des Sciences du Climat et de l’Environnement (LSCE),
Institut Pierre-Simon Laplace (IPSL), CEA-CNRS-UVSQ, CEA l’Orme des Merisiers, 91191Gif-sur-Yvette Cedex, France. 17Stanford University 473Via Ortega,
Stanford, California, 94305-2205, USA. 18Global Carbon Project, CSIRO Oceans and Atmosphere Research, GPO Box3023, Canberra, Australian Capital
Territory 2601, Australia. 19Center for International Climate and Environmental Research-Oslo (CICERO), Gaustadalléen 21, Oslo 0349, Norway. 20US Carbon
Cycle Science Program, US Global Change Research Program, Washington, DC20006, USA. 21University of Exeter, North Park Road, Exeter EX4 4QF, UK.
22Centre International de Recherche sur l’Environnement et le Développement (CIRED), CNRS-PontsParisTech-EHESS-AgroParisTech-CIRAD, Campus
du Jardin Tropical, 45 bis avenue de la Belle Gabrielle, 94736 Nogent-sur-Marne Cedex, France. 23King Abdullah Petroleum Studies and Research Center,
POBox88550, Riyadh 11672, Saudi Arabia. 24Met Oce Hadley Centre, FitzRoy Road, Exeter, Devon EX1 3PB, UK. 25University of East Anglia, Norwich
Research Park, Norwich NR4 7TJ, UK. 26Institute of Energy and Environment, University of Sao Paulo, Av. Prof. Luciano Gualberto, 1.289 – Cidade, Universitaria,
São Paulo 05508-010, Brazil. 27University of Maryland, 2101Van Munching Hall, School of Public Policy, College Park, MD 20742, USA. 28Carnegie Mellon
University, Baker Hall 128A, Pittsburgh, Pennsylvania 15213, USA. 29Global Carbon Project — Tsukuba International Oce, c/o NIES, 16-2 Onogawa, Tsukuba,
Ibaraki 305-8506, Japan. 30National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba 305-8506, Ibaraki, Japan. 31Pacific Northwest National
Laboratory Joint Global Change Research Institute, 5825 University Research Court, Suite 3500, College Park, Maryland 20740, USA. 32Korea University, 5-ga,
Anam-dong, Seongbuk-gu, Seoul 136-701, Korea. *e-mail:
© 2015 Macmillan Publishers Limited. All rights reserved
ocean, either biologically (that is, by fertilizing nutrient-limited
areas23,24) or chemically (that is, by enhancing alkalinity25); (6) altered
agricultural practices, such as increased carbon storage in soils26–28;
and (7) converting biomass to recalcitrant biochar, for use as a soil
amendment29. In this Review, we focus on BECCS, DAC, EW and
AR, because there are large uncertainties with ocean-based strate-
gies (for example, ocean iron fertilization30), and other land-based
approaches (for example, soil carbon and biochar storage) have been
evaluated elsewhere31–33. Figure1 depicts the main ows of carbon
among atmospheric, land, ocean and geological reservoirs for fossil
fuel combustion (Fig.1a), bioenergy (Fig.1b), carbon capture and
storage (CCS; Fig.1c) and the altered carbon ows entailed by each
NET (Fig.1d–h) when carbon is removed from the atmosphere.
Coupled energy–land-use analyses of NETs using IAMs have
so far focused primarily on BECCS7,34,35 and AR36–39 strategies, and
suggest that they may have considerable cost-competitive potential.
Although other NET options have also been studied13,19,40, they are
not yet represented in most IAMs. e majority of IAMs allow bio-
mass-based NETs in the production of electricity and heat in power
stations as well as hydrogen generation, and sometimes for generating
other transport fuels or bioplastics. e key distinguishing feature of
NETs is their ability to remove CO2 from the atmosphere. Depending
on the development of overall emissions, this may lead to: (1) a global
net removal of CO2 from the atmosphere by osetting emissions that
were released either in the past or in the near future41; or (2) osetting
ongoing emissions from dicult-to-mitigate sources of CO2, such as
the transportation sector42,43, as well as non-CO2 GHGs.
e Fih Assessment Report (AR5) by the Intergovernmental
Panel on Climate Change (IPCC) database includes 116 scenar-
ios that are consistent with a >66% probability of limiting warm-
ing below 2 °C (that is, with atmospheric concentration levels of
430–480 ppm CO2eq in 2100)41. Of these, 101 (87%) apply global
NETs in the second half of this century, as do many scenarios that
allow CO2 concentrations to grow between 480 and 720ppm CO2eq
by 2100 (501/653 apply BECCS; with 235/653 (36%) delivering net
negative emissions globally41; see also Fig.2).
Results from two recent modelling exercises10,35,44 show that
median BECCS deployment of around 3.3GtCyr−1 (Supplementary
Table 3) is observed for scenarios consistent with the <2 °C tar-
get (430–480 ppm CO2eq); we assess other NETs for deploy-
ment levels that give the same negative emissions in 2100 (see
Supplementary Methods).
A key question is whether these rates of deployment of NETs can
be achieved and sustained. Most of the NETs require the use of land
and water, some use fertilizer, and may also impact albedo. All NETs
are expected to have considerable costs8,10. Earlier studies have exam-
ined a number of constraints to NETs7,37–39,45–50, but have not assessed
a range of dierent NET types together, or considered the range of
impacts included here. We perform a ‘bottom-up’ implied resource
use analysis rather than a ‘top-down’ potential ecacy analysis, using
the best available data from the most recent literature. e evidence
base for the values used varies greatly between NETs, with some
(for example, BECCS) having been the subject of a large body of
research, whereas others (for example, EW) have received less atten-
tion. e data sources and a qualitative assessment of the condence
and uncertainty in the ranges we derive are described in detail in the
Supplementary Methods. We estimate the impacts of each NET per
unit of negative emission, that is, pertC equivalent (Ceq), then assess
the global resource implications, focussing on the limits to large-scale
NET deployment and how these dier between NETs.
Impacts of NETs per unit of negative emissions
NETs vary dramatically in terms of their requirements for land, GHG
emissions removed or emitted, water and nutrient use, energy pro-
duced or demanded, biophysical climate impacts (represented by
surface albedo) and cost, depending on both their character and on
the scale of their deployment. Figure3 highlights the dierences in
these requirements expressed pert Ceq removed from the atmos-
phere. Geological storage capacity has recently been evaluated as a
potential limit to implementation for CCS (and hence BECCS)51,52,
so is not considered further here. Indirect eects of NETs through
the reduced use of other technologies in pursuit of a given goal —
for example, potentially fewer nuclear reactors, wind farms and solar
arrays — are not considered here. e values we have used are esti-
mated from analyses presented in the latest peer-reviewed literature
(see Supplementary Methods).
Land area and GHG emissions. e area (and type) of land
required per unit of Ceq removed from the atmosphere, also termed
the land use intensity, is particularly important for land-based NETs
(Fig.3a). e land use intensity of BECCS is quite high, with values
ranging from ~1–1.7hat−1Ceqyr−1 where forest residues are used as
the BE feedstock, ~0.6hat−1Ceqyr−1 for agricultural residues, and
0.1–0.4hat−1Ceqyr−1 when purpose-grown energy crops are used.
Supplementary Table2 shows the carbon and GHG emissions and
removals associated with a range of energy crops and forest types, and
the net negative emissions delivered (see Supplementary Methods).
EW and DAC have minimal land requirements, with land use
aFossil fuel energy
dBioenergy + CCS (BECCS)
cCarbon capture and storage (CCS)
Land Ocean
Fossil fuel
Land Ocean
Land Ocean Fossil fuel
fEnhanced weathering
eDirect air capture (DAC)
Land Ocean Capture
Land Ocean Reaction
with minerals
agricultural practices
Land Ocean
hOcean fertilization/alkalinization
Land Ocean
Land Ocean
Figure 1 | Schematic representation of carbon flows among atmospheric,
land, ocean and geological reservoirs. a, Climate change results from
the addition of geological carbon to the atmosphere through combustion
or other processing of fossil fuels for energy. Carbon is indicated in red.
b, Bioenergy seeks to avoid the net addition of carbon to the atmosphere
by instead using biomass energy at a rate that matches the uptake of
carbon by re-growing bioenergy feedstocks. c, Carbon capture and storage
(CCS) technologies intervene to capture most of the potential carbon
emissions from fossil fuels, and return them to a geological (or possibly
ocean) reservoir. dh, NETs remove carbon from the atmosphere, either
through biological uptake (g,h), uptake by biological or industrial processes
with CCS (d,e) or enhanced weathering of minerals (f). Any atmospheric
perturbation will lead to the redistribution of carbon between the other
reservoirs (but these homeostatic processes are not shown). Note that
there are significant dierences in the materials and energy requirements
for each process to remove (or avoid adding) a unit mass of carbon from
(or to) the atmosphere.
© 2015 Macmillan Publishers Limited. All rights reserved
intensities of <0.01hat−1Ceqyr−1 (ref.18) and <0.001hat−1Ceqyr−1
(ref.14), respectively (Fig.3a).
Water us e . is is highly variable between dierent BE feedstocks
(including forest feedstocks) and is generally considered to be
higher for short-rotation coppice and C4 grasses than for annual
crops and grassland (on an area basis)53, although when corrected
for biomass productivity, the ranges are closer and overlap consider-
ably54 (Fig. 3b). In calculating water implications of BECCS, water
use for CCS is added to the BE water use (Supplementary Methods).
Where deployed, irrigation also has a dominant impact on water use.
Estimates of water required pertCeq removed by DAC and EW are
an order of magnitude or more lower than for BECCS (Fig.3b). For
EW of olivine, one molecule of water is required for each molecule
of CO2 removed, so each tCeq would require 1.5m3 water (Fig.3b).
Energy input/output. is varies considerably between dierent
NETs. BECCS has a positive net energy balance, with energy pro-
duction ranges of 3–40GJt−1Ceq for energy crops55 (Fig.3e). DAC
and EW, on the other hand, require considerable energy input to
deliver C removal; the minimum theoretical energy input require-
ment for the chemical reactions of DAC14 is 1.8GJt−1Ceq removed
at atmospheric concentrations of CO2, and for EW of olivine is 0.28–
0.75GJt−1Ceq (Fig.3e). When also including other energy inputs for
mining, processing, transport, injection and so on, the energy inputs
for DAC and EW are much greater, perhaps as much as 45GJt−1Ceq
and 46GJt−1 Ceq, respectively14,56 (Fig.3e). e GHG implication
of this additional energy use depends on the GHG intensity of the
energy supply, which is likely to change over the rest of this century.
Energy requirement is less important if low-carbon energy is used
(for example, using large areas of solar photovoltaic panels to power
DAC plants45), but may still have additional impacts.
Nutrients. ese are depleted when biomass is removed from a eld
or ecosystem for use as a BE feedstock. is is therefore an issue for
BECCS and for AR when biomass is removed from the site, but not
for DAC or EW. Perennial energy crops typically contain around
10kgNt−1Ceq (and 0.8kgPt−1Ceq in the case of Miscanthus57),
trees around 4–5kgPt−1Ceq, and annual energy crops (such as bre
sorghum) around 20kgPt−1Ceq. Nutrient removal therefore diers
several-fold among biomass sources (Fig. 3c), so large-scale transi-
tion to using land for biomass production could deplete nutrients,
but this will depend on the vegetation (or other land use) that is
replaced. Additional nutrient requirements (that is, fertilization) are
dicult to estimate on a net basis, as fertilizer may also have been
used (with varying intensity) on the land before the switch to energy
crops58. Nutrient depletion further translates into agricultural inputs
and upstream GHG emissions and energy consumption.
Albedo. In addition to biogeochemical climate impacts (for exam-
ple, uptake of atmospheric carbon), changes in land use aect cli-
mate by altering the physical characteristics of the Earth’s surface,
such as increased evapotranspiration59 and increased cloud cover in
the tropics60. Important among these physical changes is albedo (here
we focus on surface albedo), which is the reectance of solar energy
by the Earth’s surface. e albedo of lighter-coloured and less-dense
vegetation (for example, food crops and grasses) is much greater than
that of trees53,61. e situation is further complicated in areas where
shorter vegetation may be persistently covered by highly reective
snow in winter, while tall coniferous trees remain exposed and there-
fore much less reective61. is snow-mediated eect is large enough
to mean that AR in northerly latitudes may have a neutral or net
warming eect (larger than the carbon sink provided by the vegeta-
tion)62–65. Figure3d shows the change in albedo under dierent NETs
(focussing on the replacement of cropland or grassland with energy
crops) or under AR, both with and without the eect of snow.
Costs. e economic costs of deploying and operating NETs will
vary according to the specic technologies involved, the scale of
deployment and observed learning, the amount and value of co-
products, site-specic factors and the scale and cost of building and
maintaining any supporting infrastructure (the costs of capturing
and storing a tCeq are from studies using approximate 2005 to 2015
US$ values). In the case of BECCS and DAC, costs can be anticipated
to occur across three stages: (1) capture, (2) transport and (3) storage
(including monitoring and verication). Recent estimates of the total
costs of DAC technologies40,66 are $1,600–2,080 per tCeq, of which
roughly two-thirds are capital costs and one-third operating costs
(Fig.3f). Although there are very wide ranges for costs of BECCS
technologies67, the mean price estimated across 6 IAMs for 210046
was $132 per t Ceq (Fig. 3f); costs of bioenergy without CCS are
lower54,55. AR costs are estimated to be $65–108 per tCeq for 2100,
with a mean of $87 per tCeq. Estimated costs of EW are taken from
Renforth56: $88–2,120 per tCeq, with a mean of around $1,104 per
tCeq; these estimates are uncertain and the relative balance between
capital and operating costs has not yet been thoroughly examined.
Global resource implications of NETs deployment
We use global deployment of BECCS in the recent assessments
featured in Supplementary Table 3 to derive the corresponding
resource implications (Table1), and focus on the scenario giving a
2100 atmospheric CO2 concentration in the range of 430– 480ppm
(consistent with a 2°C target). We compare DAC resource impli-
cations at the same level of negative emission as BECCS (that is,
3.3GtCeqyr–1 in 2100; Table1). For other NETs, which are not able
to meet the same level of emissions removal, we use values compiled
from an analysis of the recent literature to give mean and maxi-
mum implementation levels (see Supplementary Methods). Mean
values for carbon removals from AR are estimated to be around
1.1GtCeqyr–1 by 2100, with a maximum value of 3.3GtCeqyr–1 for
very large-scale deployment6,7,68 (Table1). e potential of carbon
1980 2000 2020 2040 2060 2080 2100
Net emissions (Gt C yr−1)
>1,000 ppm CO2eq
430–480 ppm
480–530 ppm
650–1000 ppm
530–650 ppm
Historical emissions
2015 estimate
Figure 2 | Scenarios including NETs for each of the scenario categories,
corresponding to the ranges and median values shown in Supplementary
Table3. Scenarios with no technology constraints (that is, including NETs)
from the AMPERE10,44 and LIMITS35 modelling comparison exercises are
shown in colours, with all other scenarios from the IPCC AR5 database
shown in grey. See the caption of Supplementary Table3 for an explanation
of the representation of gross positive and gross negative emissions.
Net land use change fluxes are included (note, the 1997 fluctuation is
attributable to Indonesian peat fires). Sources: CDIAC94, IPCC AR5 scenario
database ( and the
Global Carbon Project.
© 2015 Macmillan Publishers Limited. All rights reserved
Crop residues
Dedicated crops
Ded. crops (marginal)
DAC (e.g. amines)
EW (olivine)
Crop residues
Dedicated crops
Ded. crops (marginal)
Crop residues
Dedicated crops
Ded. crop (marginal)
Boreal (Summer)
DAC (e.g. amines)
EW (olivine)
Crop residues
Dedicated crops
Ded. crops (marginal)
Energy requiredEnergy produced
DAC (e.g. amines)
EW (olivine)
Boreal (snow)
Crop residues
Dedicated crops
Ded. crop (marginal)
DAC (e.g. amines)
EW (olivine)
Change in albedo
Water required (thousands of m3 per t Ceq)
Nitrogen concentration in feedstock/biomass (kg N per t Ceq)
Energy Cost
Cost of negative emissions (US$ per t Ceq)
Net energy (GJ per t Ceq)
Area required (hectares per yr per t Ceq)
Figure 3 | The dierent requirements and impacts of NETs. af, Negative emissions technologies have dierent land (a), water (b) and nutrient (c)
requirements, dierent geophysical impacts on climate (for example, albedo; d), generate or require dierent amounts of energy (e), and entail dierent
capital and operating costs (f). For example, carbon dioxide removal (CDR) technologies such as DAC and EW of silicate rock tend to require much less
land and water than strategies that depend on photosynthesis to reduce atmospheric carbon (a,b), but the CDR technologies demand substantial energy
and economic investment per unit of negative emissions (e,f). Among BECCS options, forest feedstocks tend to require less nitrogen than purpose-grown
crops (c), but present greater risk of unwanted changes in albedo (d), and generate less energy (e). AR has been omitted from b,e,f to avoid confusion with
forest BECCS (where the CCS component is included). See Supplementary Methods and Table 1 for data sources.
© 2015 Macmillan Publishers Limited. All rights reserved
removal by EW (including adding carbonate and olivine to both
oceans and soils) has been estimated to be as great as 1GtCeqyr–1
by 2100, but with mean annual removal an order of magnitude less68
at 0.2GtCeqyr–1. Combined with the bottom-up, per-t-Ceq impact
ranges (Supplementary Methods), we then assess the resource
implications, and the extent to which available resources may limit
the deployment of NETs globally.
Land area. DAC has a small direct land footprint (Fig.3a) and can
be deployed on unproductive land that supplies few ecosystem ser-
vices14, although the land footprint could be considerable if solar
pholtovoltaic panels or wind turbines were used to provide the
energy required45. EW has a larger land footprint if the minerals are
applied to the land surface (as opposed to the oceans, or if weather-
ing reactions occur in industrial autoclaves), although crushed oli-
vine or carbonates could be spread on agricultural and forest land
to allow the weathering to take place, with the added benets of
raising the pH of acidic soils to make them more productive15. us,
EW technologies may not always compete for land with other uses,
despite the large areas involved (for example, the estimated poten-
tial of 1GtCeqyr−1 removed might require 10Mha)15.
Assuming per-area carbon in biomass available for capture as
a feedstock for BECCS of widely applicable, high-productivity
dedicated energy crops (willow and poplar short rotation cop-
pice (SRC) and Miscanthus; 4.7–8.6tCeqha−1yr−1; Supplementary
Table2), BECCS delivering 3.3GtCeq yr−1 of negative emissions
would require a land area of approximately 380–700Mha in 2100
(Table 2), with a wider possible range that is determined by pro-
ductivity (Supplementary Table 2). is emissions removal is
equivalent to 21% of total current human appropriated net pri-
mary productivity (NPP) (15.6GtCyr−1 in 2000), or 4% of total
global potential NPP69. Areas for AR that are calculated assuming
a mean carbon uptake over the growth period of 3.4tCeqha−1yr−1
(Supplementary Methods; Fig.3a) give a land area corresponding to
1.1and 3.3GtCyr−1 removed in 2100 of ~320 and ~970Mha, respec-
tively, similar to other estimates50. Estimates of land use by BECCS
and AR are consistent with the values presented in previous stud-
ies47 for three IAMs (Global Change Assessment Model (GCAM),
Integrated Model to Assess the Global Environment (IMAGE)
and Regional Model of Investments and Development/Model
of Agricultural Production and its Impact on the Environment
(ReMIND/MAgPIE)), although other studies suggest larger areas39.
Without global forest protection, increased bioenergy deployment
would increase GHG emissions from land-use change70.
Total agricultural land area in 2000 was ~4,960Mha, with an area
of arable and permanent crops of ~1,520Mha71, so area for BECCS
(380–700Mha) represents 7–25% of agricultural land, and 25–46%
of arable plus permanent crop area. AR (at 1.1–3.3 Gt Ceq yr−1
negative emissions; 320–970Mha, respectively) represents 6–20%
of total agricultural land, and 21–64% of arable plus permanent
crop area. is range of land demands are 2–4 times larger than
land identied as abandoned or marginal72. us, the use of BECCS
and AR on large areas of productive land is expected to impact
the amount of land available for food or other bioenergy produc-
tion12,37,73–75, as well as the delivery of other ecosystem services12,32,76,
which may prove to be a limit to the implementation of BECCS77
and AR. One uncertainty is the future rate of increase of food crop
yields37,78 and whether this will meet future food demand79, thereby
potentially freeing more cropland for BECCS or AR, even if at a
higher price37.
Water. Increasing global water stress is attributable to rising water
demands and reduced supplies, both of which can be exacerbated
in some locations by climate change80. In particular, the evaporative
demand of plants increases with temperature as vapour pressure
decit increases. Evaporative loss can be 20–30mol H2O per mol
CO2 absorbed by an amine DAC unit14,81, giving a water use esti-
mate of ~92 (mean; 73–110) m3t−1Ceq (Fig.3b). Implementation
at levels of 3.3GtCeq yr−1 in 2100 (Table 1) would therefore be
expected to use ~300 km3 yr−1 of water assuming current amine
technology, which is 4% of the total current evapotranspiration
used for crop cultivation82. Sodium hydroxide for DAC, however,
uses 3.7m3t−1 Ceq (Fig. 3b)81, so equivalent levels of implemen-
tation using sodium hydroxide in place of amines would result in
water use of ~10km3yr−1. For EW, with a water use of 1.5m3t−1Ceq
(Fig. 3), deployment to remove 0.2 (mean) or 1 (maximum)
GtC eqyr −1 would involve water use of 0.3and 1.5km3, respectively.
Water use for forests is estimated to be 1,765 (1,176–
2,353) m3t−1Ceqyr−1, which includes both interception and tran-
spiration (Fig.3b). However, because trees replace other vegetation
during AR, the total net impact must be calculated by subtracting
the water use of the previous land cover. Assuming a water use sim-
ilar to short vegetation of 1,450 (900–2,000) m3t−1Ceqyr−1 before
AR (Fig.3b), the additional water use from AR is estimated to be
around 315m3t−1Ceqyr−1, which is 1% of the total evapotranspi-
ration from current forests82. For AR delivering capture of 1.1 or
3.3GtCyr−1 (Table1), additional water use is thus estimated to be
~370 or 1,040km3yr−1, respectively.
Similar calculations can be made for BECCS. For
unirrigated bioenergy, evaporative loss is estimated to be
1,530 (1,176–1,822)m3t−1C eqyr−1, which is 80m3t−1C eqyr−1 more
than for average short vegetation (Fig. 3b). us, deployment of
Table 1 | Global impacts of NETs for the average needed global C removals per year in 2100 in 2°C-consistent scenarios
(430–480ppm scenario category; Supplementary Table 3).
Global C removal
(Gt Ceq yr−1 in
Mean (max.)
land requirement
(Mha in 2100)
Estimated energy
(EJ yr−1 in 2100)
Mean (max.)
water requirement
(km3 yr−1 in 2100)
Nutrient impact
(kt N yr−1 in
Albedo impact
in 2100
Investment needs
(BECCS for electricity/
biofuel; US$ yr−1 in 2050)
BECCS 3.3 380–700 −170 720 Variable Variable 138 billion /123 billion
DAC 3.3 Very low (unless
solar PV is used
for energy)
156 10–300 None None >>BECCS
EW* 0.2 (1.0) 2 (10) 46 0.3 (1.5) None None >BECCS
AR* 1.1 (3.3) 320 (970) Very low 370 (1,040) 2.2 (16.8) Negative, or
reduced GHG
benefit where not
*NETs with lower maximum potential than the BECCS emission requirement of 3.3GtCeq per year in 2100; their mean (and maximum) potential is given along with their impacts (see Supplementary Methods).
Wide ranges exist for most impacts, but for simplicity and to allow comparison between NETs (sign and order of magnitude), mean values are presented. See main text and Supplementary Methods for full details.
PV, photovoltaic.
© 2015 Macmillan Publishers Limited. All rights reserved
BECCS at 3.3GtCeq yr−1 in 2100 would lead to additional water
use of ~260km3yr−1 from the crop production phase. ere is an
opportunity cost of using soil moisture for sequestration and/or
bioenergy production rather than for growing food. Our estimates
for water use are an order of magnitude lower than other recent
estimates for bioenergy crops48 and for AR50, as water use in those
studies were expressed as a total rather than additional water use
due to land use change, and those for bioenergy also considered
irrigation48. Irrigated bioenergy crops were estimated to double
agricultural water withdrawals in the absence of explicit water pro-
tection policies48, which could pose a severe threat to freshwater
ecosystems, as human water withdrawals are dominated by agri-
culture and already lead to ecosystem degradation and biodiversity
loss. Land requirements for bioenergy crops would greatly increase
(by ~40%, mainly from pastures and tropical forests) if irrigated
bioenergy production was excluded, meaning that there will be
a trade-o between water and land requirements if bioenergy is
implemented at large scales48.
For BECCS, additional water is required for CCS, adding
about 450m3t
−1 to the evaporative loss relative to bio-
energy alone14 (Fig.3b), equivalent to an additional water use of
~720km3 yr−1 due to BECCS (the sum of additional evaporative
loss plus CCS water use), for the 3.3GtCeqyr −1 by 2100 level of
implementation (Table1). BECCS would thus require an additional
quantity of water equivalent to ~10% of the current evapotranspira-
tion from all cropland areas worldwide82.
To put these gures in context, total global renewable freshwa-
ter supply on land is 110,300km3yr−1, of which humans appropri-
ate 24,980km3 yr−1 (ref. 83), so the implementation of BECCS at
3.3GtCeqyr−1 of negative emissions by 2100 represents an additional
use of ~3% of the freshwater currently appropriated for human use.
AR implemented at 1.1GtCeqyr−1 by 2100 would represent 1–2%
of human-appropriated freshwater. Expressing additional water use
as a proportion of runo in a region would provide a more accu-
rate picture of the threat to water resources at a given location —
but this is not feasible without a spatially disaggregated analysis.
Nevertheless, with human pressures on freshwater increasing80,84,
water use could act as a signicant limitation to implementation of
high-water-demand NETs such as BECCS.
En e r g y. Bioenergy currently supplies about 10% of primary energy
worldwide55, that is, an estimated 44.5EJyr−1. Of this, 74% comes
from fuel wood, 9% from forest and agricultural residues, 8% from
recovered wood, 6% from industrial organic residues and 3% from
dedicated energy crops55. Most of this biomass, however, cannot
currently be used for BECCS, as the vast majority is used in small-
scale applications; for example, for household cooking and heat-
ing in developing countries55. BECCS delivering 3.3GtCeqyr−1 of
negative emissions would deliver ~170 EJ yr−1 of primary energy
in 210010,35,44 (Table 1). Estimates of future energy potential vary
greatly; there is high consensus that 100EJyr−1 could be attained,
and a medium level of agreement that 100–300 EJ yr−1 could be
attained — but there is only low consensus that primary energy
above 300EJyr−1 could be supplied by bioenergy12,32. Stabilization
scenarios from the IAM literature suggest that bioenergy could sup-
ply from 10to 245EJyr−1 of global primary energy by 205070,87, and
deliver a sizable contribution to primary energy in 210041.
e energy required by AR is very low (for site preparation only)
and is assumed here to be negligible. Other NETs have large energy
demands (Fig.3e). Using our realistic estimate of 46GJ of energy
required pert Ceq removed by EW (Fig.3), the 0.2–1.0GtCyr−1
that might be captured (Supplementary Table2) would entail up to
46EJyr−1 of energy in 2100 (Table1). e energy requirements of
amine DAC14 (Fig.3e) deployed for net removal of ~3.3GtCeqyr−1
would amount to a global energy requirement of 156EJyr−1 if all
energy costs are included (Table1). is is equivalent to 29% of total
global energy use in 2013 (540 EJ yr−1), and a signicant propor-
tion of total energy demand in 2100 (which the IPCC AR5 scenario
database estimates will be~500–1,500EJyr−1), which will be a major
limitation unless low-GHG energy could be used, or the energy
requirements signicantly reduced.
Nutrients. DAC has no impact on soil nutrients, and EW may (in
some cases) provide benecial minerals and pH adjustment that are
dicult to quantify at the aggregate level. Nutrient concentrations
in crop biomass are oen higher than in tree biomass (Fig.3c), but
nutrients are removed from cropland and grazing land in agricul-
tural products, whereas AR on agricultural land is likely to increase
the retention of nutrients within an ecosystem. However, nutrient
limitation could limit productivity, which may limit carbon stor-
age49. Nutrients are also removed when bioenergy feedstocks are
removed from the site on which they are grown, resulting in the
depletion of nutrients relative to land uses where biomass is not
removed, but not necessarily at the same level as agricultural land86.
Bioenergy feedstocks with low nutrient concentrations, such as res-
idue, forest and lignocellulosic biomass, should hence be favoured
over feedstocks with higher nutrient concentrations. Assuming
the nutrient concentrations of forests are 2.0to 5.1 kg N t−1 Ceq
(Fig.3c), and that most nutrients are removed at harvest for energy
and food crops, AR areas of ~320and 970 Mha (consistent with
AR removing 1.1 (mean) and 3.3 (high) GtCeq yr−1 (Table 1))
would increase global nitrogen retention in biomass by 2.2–5.6
and 6.6–16.8ktNyr−1, respectively. Scaling values for implemen-
tation of 1GtCeq yr−1 of negative emissions50, P and N demand
to balance the carbon stored is estimated to be 220–990ktPyr−1
and 100–1,000ktN yr−1 for AR at 1.1–3.3GtCeq yr−1 of negative
emissions — although it must be noted that these values are abso-
lute, and do not account for the P and N in the vegetation replaced
by AR.
Albedo. e eect of DAC and EW on the reectivity of the Earth’s
surface is assumed to be small (excluding possible use of solar pho-
tovoltaic panels to generate energy for DAC45; Fig.3d). However,
Negative emissions (Gt Ceq per year)
Energy (EJ per year)
−200 −100 produced 0 100 required 200
(Mha per yr)
Figure 4 | The impacts and investment requirements of NETs to meet
the 2°C target. A schematic representation of the aggregate impacts of
NETs on land, energy and water, and relative investment needs, for levels
of implementation equivalent to BECCS (3.3GtCyr−1 negative emissions
in 2100) in scenarios consistent with a 2°C target (or mean and maximum
attainable, where that level of negative emissions cannot be reached).
Water requirement is shown as water droplets, with quantities in km3yr1.
All values are for the year 2100 except relative costs, which are for 2050
(see Supplementary Methods).
© 2015 Macmillan Publishers Limited. All rights reserved
the land areas required for BECCS and AR can dramatically aect
albedo (Fig.3d). Because the eect is greatly amplied by the pres-
ence of snow, the exact location (latitude and elevation) of the
BECCS or AR, and the vegetation it replaces, is critical in assessing
the impact on albedo (Fig.3d). Albedo can signicantly reduce62 or
even reverse net radiative forcing from AR at northern latitudes63.
is observation could limit the value of AR for climate mitigation
in northerly regions. For BECCS, the replacement of short veg-
etation with taller vegetation (for example, Miscanthus and SRC),
could have similar eects on albedo, although probably less than
the impact of AR with coniferous forest (Fig. 3d). Because AR is
more likely to occur at high latitudes than production of BECCS
feedstocks, BECCS should not have a deleterious impact on albedo.
At low to mid latitudes, AR could increase radiative forcing by
decreasing albedo; but, without a regional distribution, the scale of
these impacts cannot be assessed.
Investment needs. e deployment of NETs (specically BECCS)
in IAM scenarios is an outcome of an optimization of costs over
time. e existence of large-scale gross negative emissions even
in less-ambitious stabilization pathways indicates that BECCS is
selected as a cost-eective component of the energy mix, allow-
ing higher residual emissions elsewhere, which would otherwise
be more expensive to abate. Investments in BECCS provide an
additional indicator for assessing the scale and speed of BECCS
deployment over the next several decades. Supplementary Table4
summarizes investment estimates from six global integrated assess-
ment models that assessed 2°C scenarios within the context of the
LIMITS model intercomparison87 for 2030 and 2050: US$36.2and
29.4billionyr−1, respectively, worth of investment is estimated as
optimal by 2030 for scaling up biomass electricity and biofuels pro-
duction technologies worldwide on average. By 2050, these invest-
ment levels grow to US$138.3and 122.6billionyr−1, respectively87.
is represents 5and 4%, respectively, of the projected total global
energy system investments required by 2050 of US$2,932 (inter-
model range: $1,889–4,338)billionyr−1 (ref.87). Investment needs
for DAC, EW and AR are not known, but given the much higher
unit costs (per tCeq) for DAC, and the higher costs of EW and the
lower unit costs of AR described above, the investment needs are
estimated qualitatively (relative to BECCS; Table1).
e aggregate impacts of NETs on land, energy and water, and the
relative investment needs for levels of implementation equivalent to
BECCS in scenarios consistent with a 2°C target (3.3GtCeq yr−1,
or the mean and maximum attainable where that level of negative
emissions cannot be reached) described in this section are summa-
rized schematically in Fig.4.
Biophysical, biogeochemical (that is, nutrients), energy and eco-
nomic resource implications of large-scale implementation of NETs
dier signicantly. For DAC, costs and energy requirements are
currently prohibitive and can be anticipated to slow deployment.
Research and development is needed to reduce costs and energy
requirements. For EW, the land areas required for spreading and/
or burying crushed olivine are large, such that the logistical costs
may represent an important barrier, compounded by the fact that
the plausible potential for carbon removal is lower than for other
NETs. In contrast, AR is relatively inexpensive, but the unintended
impacts on radiative forcing through decreased albedo at high lati-
tudes, and increased evapotranspiration increasing the atmospheric
water vapour content, could limit eectiveness; likewise, increased
water requirements could be an important trade-o, particularly in
dry regions. Competition for land is also a potential issue, as it is
for BECCS50,88,89. BECCS may also be limited by nutrient demand,
or by increased water use, particularly if feedstocks are irrigated
and when the additional water required for CCS is considered.
ese biophysical and economic resource implications may directly
impose limits on the implementation of NETs in the future, but they
may also indirectly constrain NETs by interacting with a number of
societal challenges facing humanity in the coming decades, such as
food, water and energy security, and thereby sustainable develop-
ment. In addition to the biophysical and economic limits to NETs
considered here, social, educational and institutional barriers, such
as public acceptance of and safety concerns about new technologies
and related deployment policies, could limit implementation. e
drivers, risks, and limitations of the supply of NETs, showing activi-
ties thought to increase the potential supply of NETs, as well as the
risks and geophysical and societal limits to the potential of NETs,
are shown in Supplementary Fig.1. Commercialization and deploy-
ment at larger scales will also allow more to be learnt about these
technologies, in order to improve their eciency and reduce cost.
To inform society of the potential risks and opportunities
aorded by all mitigation options available, more research on NETs
is clearly required. Although we have collated the best available data
on NET impacts and have reected changes related to deployment
scale as accurately as possible, it is clear that common modelling
frameworks are required to implement learning, cost, supply and
eciency curves for all NETs. By implementing such curves, future
models will be able to develop portfolios of trajectories of NET
development, allowing least-cost options to be selected, and learn-
ing and eciency improvements to be reected. e inconsistency
in coverage of NETs and their impacts highlights this key knowl-
edge gap; this analysis will help to frame these developments in the
modelling community.
For BECCS, research and development is required to deliver
high-eciency energy conversion and distribution processes for
the lowest-impact CCS, and the cost of infrastructure to transport
CO2 from BECCS production areas to storage locations needs to
be further evaluated. To this end, early deployment of CCS would
enhance understanding of the risks and possible improvements
of the technology. Integrated pilot plants need to be built (storing
~1MtCO2 per year) to examine how combined BECCS functions90;
the capital cost of 5–10 full-size demonstrations of BECCS or CCS
would require the investment of approximately US$5–10billion90.
ere is also a need to develop socio-economic governance systems
for all NETs, to provide incentives to fund this research and devel-
opment, and implementation of infrastructure in the most sustaina-
ble manner, to limit adverse impacts in the transition to low-carbon
energy systems, and to manage the risks associated with CCS (such
as leakage, seismic action and environmental impacts)91. Priorities
include investing in renewable and low-carbon technologies, e-
ciency and the integration of energy systems (to make the most of
waste heat, excess electrons from photovoltaic panels and wind, and
to close the carbon cycle of fossil sources by capturing and reusing
CO2 by catalysis), and the realization of additional environmental
benets. In the meantime, emission reductions must continue to be
the central goal for addressing climate change.
Addressing climate change remains a fundamental challenge for
humanity, but there are risks associated with relying heavily on any
technology that has adverse impacts on other aspects of regional or
planetary sustainability. Although deep and rapid decarbonization
may yet allow us to meet the <2°C climate goal through emissions
reduction alone8, this window of opportunity is rapidly closing8,92
and so there is likely to be some need for NETs in the future41,93. Our
analysis indicates that there are numerous resource implications
associated with the widespread implementation of NETs that vary
between technologies and that need to be satisfactorily addressed
before NETs can play a signicant role in achieving climate change
goals. Although some NETs could oer added environmental
benets (for example, improved soil carbon storage28), a heavy reli-
ance on NETs in the future, if used as a means to allow continued
use of fossil fuels in the present, is extremely risky, as our ability
© 2015 Macmillan Publishers Limited. All rights reserved
to stabilize the climate at <2°C declines as cumulative emissions
increase8,35,92. A failure of NETs to deliver expected mitigation in the
future, due to any combination of biophysical and economic limits
examined here, leaves us with no ‘plan B’45. As this study shows, there
is no NET (or combination of NETs) currently available that could
be implemented to meet the <2°C target without signicant impact
on either land, energy, water, nutrient, albedo or cost, and so ‘planA
must be to immediately and aggressively reduce GHG emissions.
Received 23 July 2015; accepted 21 October 2015;
published online 7 December 2015
1. Le Quéré, C. etal. e global carbon budget 1959–2011. Earth Syst. Sci. Data
5, 165–185 (2013).
2. Peters, G.P. etal. e challenge to keep global warming below 2°C.
Nature Clim. Change 3, 4–6 (2013).
A short article outlining the enormous challenge of meeting a 2°C climate
stabilization target.
3. IPCC Climate Change 2014: Mitigation of Climate Change
(eds Edenhofer, O. etal.) (Cambridge Univ. Press, 2014).
e latest IPCC Assessment Report on the mitigation options that are
available to stabilize the climate.
4. Tavoni, M. etal. Post-2020 climate agreements in the major economies assessed
in the light of global models. Nature Clim. Change 5, 119–126 (2015).
5. Krey, V., Luderer, G., Clarke, L. & Kriegler, E. Getting from here to
there — energy technology transformation pathways in the EMF27 scenarios.
Clim. Change 123, 369–382 (2014).
6. Edmonds, J. etal. Can radiative forcing be limited to 2.6Wm−2 without
negative emissions from bioenergy and CO2 capture and storage? Clim. Change
118, 29–43 (2013).
7. Van Vuuren, D.P. etal. e role of negative CO2 emissions for reaching
2°C — insights from integrated assessment modelling. Clim. Change
118, 15–27 (2013).
8. Rogelj, J., McCollum, D.L., Reisinger, A., Meinshausen, M.
& Riahi, K. Probabilistic cost estimates for climate change mitigation. Nature
493, 79–83 (2013).
9. Clarke, L. etal. in Climate Change 2014: Mitigation of Climate Change
(eds Edenhofer, O. etal.) Ch.6 (IPCC, Cambridge Univ. Press, 2014).
10. Riahi, K. etal. Locked into Copenhagen pledges — implications of short-
term emission targets for the cost and feasibility of long-term climate goals.
Technol. Forecast. Soc. 90, 8–23 (2015).
11. Obersteiner, M. etal. Managing climate risk. Science 294, 786–787 (2001).
12. Creutzig F. etal. Bioenergy and climate change mitigation: an assessment.
Global Change Biol. Bioenergy 7, 916–944 (2015).
13. Keith, D. Why capture CO2 from the atmosphere. Science 325, 1654–1655 (2009).
14. Socolow, R. etal. Direct air capture of CO2 with chemicals: a technology
assessment for the APS Panel on Public Aairs. (American Physical
Society, 2011).
An in-depth assessment of direct air-capture technologies.
15. Schuiling, R.D. & Krijgsman, P. Enhanced weathering: an eective and cheap
tool to sequester CO2. Climatic Change 74, 349–354 (2006).
16. Rau, G.H., Knauss, K.G., Langer, W.H. & Caldeira, K. Reducing energy-
related C O2 emissions using accelerated weathering of limestone. Energy
32, 1471–1477 (2007).
17. Köhler, P., Hartmann, J. & Wolf-Gladrow, D.A. Geoengineering potential of
articially enhanced silicate weathering of olivine. Proc. Natl Acad. Sci. USA
107, 20228–20233 (2010).
18. Hartmann, J. & Kempe, S. What is the maximum potential for CO2 sequestration
by “stimulated” weathering on the global scale? Naturwissenschaen
95, 1159–1164 (2008).
19. Kelemen, P.B. & Matter, J.M. Insitu carbonation of peridotite for CO2 storage.
Proc. Natl Acad. Sci. USA 105, 17295–17300 (2008).
20. Arora, V.K. & Montenegro, A. Small temperature benets provided by realistic
aorestation eorts. Nature Geosci. 4, 514–518, (2011).
21. Canadell, J.G. & Raupach, M.R. Managing forests for climate change
mitigation. Science 320, 1456–1457 (2008).
22. Jackson, R.B. etal. Protecting climate with forests. Environ. Res. Lett.
3, 044006 (2008).
23. Sarmiento, J.L., Gruber, N., Brzezinski, M.A. & Dunne, J.P. High-latitude
controls of thermocline nutrients and low latitude biological productivity.
Nature 427, 56–60 (2004).
24. Joos, F., Sarmiento, J.L. & Siegenthaler, U. Estimates of the eect of Southern
Ocean iron fertilization on atmospheric CO2 concentrations. Nature
349, 772–775 (1991).
25. Kheshgi, H.S. Sequestering atmospheric carbon dioxide by increasing ocean
alkalinity. Energy 20, 915–922 (1995).
26. Smith, P. Soils and climate change. Curr. Opin. Environ. Sust. 4, 539–544 (2012).
27. Powlson, D.S. etal. Limited potential of no-till agriculture for climate change
mitigation. Nature Clim. Change 4, 678–683 (2014).
28. Smith, P. etal. Greenhouse gas mitigation in agriculture. Phil. Trans. R.Soc. B
363, 789–813 (2008).
29. Woolf, D., Amonette, J.E., Street-Perrott. A., Lehmann, J. & Joseph, S.
Sustainable biochar to mitigate global climate change. Nature Commun.
1, 56 (2010).
30. Schiermeier, Q. Convention discourages ocean fertilization. Nature (2007).
31. Smith, P. etal. How much land based greenhouse gas mitigation can be
achieved without compromising food security and environmental goals?
Global Change Biol. 19, 2285–2302 (2013).
32. Smith, P. etal. in Climate Change 2014: Mitigation of Climate Change
(eds Edenhofer, O. etal.) Ch.11 (IPCC, Cambridge Univ. Press, 2014).
33. Smith, P. Soil carbon sequestration and biochar as negative emission
technologies. Global Change Biol. (in the press).
34. Azar, C. etal. e feasibility of low CO2 concentration targets and the role of
bio-energy carbon-capture and storage. Clim. Change 100, 195–202 (2010).
35. Kriegler, E. etal. What does the 2°C target imply for a global climate agreement
in 2020? e LIMITS study on Durban Platform scenarios. Clim. Change Econ.
04, 1340008 (2013).
36. Strengers, B.J., Minnen, J.G. V. & Eickhout, B. e role of carbon
plantations in mitigating climate change: potentials and costs. Clim. Change
88, 343–366 (2008).
37. Wise, M. etal. Implications of limiting CO2 concentrations for land use and
energy. Science 324, 1183–1186 (2009).
38. Reilly, J. etal. Using land to mitigate climate change: hitting the target,
recognizing the trade-os. Environ. Sci. Technol. 46, 5672–5679 (2012).
39. Humpenöder, F. etal. Investigating aorestation and bioenergy CCS as climate
change mitigation strategies. Environ. Res. Lett. 9, 064029 (2014).
40. Chen, C. & Tavoni, M. Direct air capture of CO2 and climate stabilization:
a model based assessment. Clim. Change 118, 59–72 (2013).
41. Fuss, S. etal. Betting on negative emissions. Nature Clim. Change
4, 850–853 (2014).
42. Kriegler E. etal. Is atmospheric carbon dioxide removal a game changer for
climate change mitigation? Clima. Change 118, 45–57 (2013).
43. Sims, R. etal. in Climate Change 2014: Mitigation of Climate Change
(eds Edenhofer, O. etal.) Ch.8 (IPCC, Cambridge Univ. Press, 2014).
44. Kriegler, E. etal. Making or breaking climate targets: e AMPERE study on
staged accession scenarios for climate policy. Technological Forecasting and
Social Change, Part A 90, 24–44 (2015).
A study showing the impact of delay in implementation of mitigation on
climate stabilization over the course of the twenty-rst century.
45. National Academy of Sciences. Climate Intervention: Carbon Dioxide Removal
and Reliable Sequestration (National Academies Press, 2015).
An in-depth report on carbon dioxide removal (equivalent to negative
emissions) technologies.
46. Calvin, K. etal. A multi-model analysis of the regional and sectoral roles of
bioenergy in near-and long-term CO2 emissions reduction. Clim. Change Econ.
4, 1340014 (2013).
47. Popp, A. etal. Land-use transition for bioenergy and climate stabilization:
model comparison of drivers, impacts and interactions with other land use
based mitigation options. Clim. Change 123, 495–509 (2014).
48. Bonsch, M. etal. Trade-os between land and water requirements
for large-scale bioenergy production. Global Change Biol. Bioenergy (2014).
49. Wieder, W.R., Cleveland, C.C., Smith, W.K. & Todd-Brown, K. Future
productivity and carbon storage limited by terrestrial nutrient availability.
Nature Geosci. 8, 441–444 (2015).
50. Smith, L.J. & Torn, M.S. Ecological limits to terrestrial biological carbon
dioxide removal. Clim. Change 118, 89–103 (2013).
A study examining some ecological limits to land-based negative
emission technologies.
51. Scott, V., Haszeldine, R.S., Tett, S.F. B. & Oschlies, A. Fossil fuels in a trillion
tonne world. Nature Clim. Change 5, 419–423 (2015).
52. Benson, S.M. etal. in Global Energy Assessment Toward a Sustainable Future
Ch.13 (Cambridge Univ. Press, 2012).
53. Zhao, K. & Jackson, R.B. Biophysical forcings of land-use changes
from potential forestry activities in North America. Ecol. Monogr.
84, 329–353 (2014).
54. Zhuang, Q., Qin, Z & Chen, M. Biofuel, land and water: maize, switchgrass or
Miscanthus? Environ. Res. Lett. 8, 015020 (2013).
55. IPCC Special Report on Renewable Energy Sources and Climate Change
Mitigation (eds Edenhofer, O. etal.) (Cambridge Univ. Press, 2011).
© 2015 Macmillan Publishers Limited. All rights reserved
56. Renforth, P. e potential of enhanced weathering in the UK.
Int. J.Greenh. Gas Con. 10, 229–243 (2012).
57. Christian, D., Riche, A.B. & Yales, N.E. Growth, yield and mineral content
of Miscanthus × giganteus grown as a biofuel for 14 successive harvests.
Ind. Crop Prod. 28, 320–327 (2008).
58. St Clair, S., Hiller, J. & Smith, P. Estimating the pre-harvest greenhouse gas
costs of energy crop production. Biomass Bioenergy 32, 442–452 (2008).
59. Swann, A.L., Fung, I.Y., Levis, S., Bonan, G.B. & Doney, S.C. Changes in
Arctic vegetation amplify high-latitude warming through the greenhouse
eect. Proc. Natl Acad. Sci. USA 107, 1295–1300 (2010).
60. Ma, D., Notaro, M., Liu, Z.Y., Chen, G.S. & Liu, Y.Q. Simulated impacts of
aorestation in East China monsoon region as modulated by ocean variability.
Clim. Dynam. 41, 2439–2450 (2013).
61. Betts, A.K. & Ball, J.H. Albedo over the boreal forest. J.Geophys. Res.
102, 28901–28910 (1997).
62. Betts R.A. Oset of the potential carbon sink from boreal forestation by
decreases in surface albedo. Nature 408, 187–190 (2007).
63. Betts R.A., Falloon, P. D., Goldewijk, K. K. & Ramankutty, N. Biogeophysical
eects of land use on climate: Model simulations of radiative forcing and large-
scale temperature change. Agr. Forest Meteorol. 142, 216–233 (2007).
64. Schaeer, M. etal. CO2 and albedo climate impacts of extratropical carbon and
biomass plantations. Global Biogeochem. Cy. 20, GB2020 (2006).
65. Jones, A.D. etal. Greenhouse gas policy inuences climate via direct eects of
land-use change. J.Clim. 26, 3657–3670 (2013).
66. Mazzotti, M., Bociocchi, R., Desmond, M.J. & Socolow, R. Direct air capture
of CO2 with chemicals: optimization of a two-loop hydroxide carbonate system
using a countercurrent air-liquid contactor. Clim. Change 118, 119–135 (2013).
67. Klein, D. etal. e value of bioenergy in low stabilization scenarios: an
assessment using REMIND-MAgPIE. Clim. Change 123, 705–718 (2014).
68. Lenton, T.M. in Geoengineering of the Climate System (eds Harrison, R.M.
& Hester, R.E.) (Royal Society of Chemistry, 2014).
69. Haberl, H. etal. Quantifying and mapping the human appropriation of net
primary production in earth’s terrestrial ecosystems. Proc. Natl Acad. Sci. USA
104, 12942–12947 (2007).
70. Schueler, V., Weddige, U., Beringer, T., Gamba, L. & Lamers, P. Global
biomass potentials under sustainability restrictions dened by the European
Renewable Energy Directive 2009/28/EC. Global Change Biol. Bioenergy
5, 652–663 (2013).
71. FAOSTAT (accessed 25 June 2015);
72. Canadell, J.G., Schulze, E.-D. Global potential of biospheric carbon
management for climate mitigation. Nature Commun. 5, 5282 (2014).
73. Smith, P. etal. Competition for land. Phil. Trans. R.Soc. B
365, 2941–2957 (2010).
74. ompson, B. & Cohen, M.J. e impact of climate change and bioenergy on
nutrition (Springer, 2012).
75. Valentine, J., Clion-Brown, J., Hastings, A., Robson, P, Allison G. &
Smith, P. Food vs. fuel: the use of land for lignocellulosic ‘next generation
energy crops that minimise competition with primary food production.
Global Change Biol. Bioenergy 4, 1–19 (2012).
76. Bustamante, M. etal. Co-benets, trade-os, barriers and policies for
greenhouse gas mitigation in the Agriculture, Forestry and Other Land Use
(AFOLU) sector. Global Change Biol. 20, 3270–3290 (2014).
77. Powell, T.W.R. & Lenton, T.M. Future carbon dioxide removal via
biomass energy constrained by agricultural eciency and dietary trends.
Energy Environ. Sci. 5, 8116–8133 (2012).
78. Smith, P. Delivering food security without increasing pressure on land.
Global Food Sec. 2, 18–23 (2013).
79. Bajželj, B. etal. e importance of food demand management for climate
mitigation. Nature Clim. Change 4, 924–929 (2014).
80. Vörösmarty, C.J. Global water resources: vulnerability from climate change
and population growth. Science 289, 284–288 (2000).
81. Stolaro, J.K., Keith, D.W. & Lowry, G.V. Carbon dioxide capture from
atmospheric air using sodium hydroxide spray. Environ. Sci. Technol.
42, 2728–2735 (2008).
82. Oki, T. & Kanae, S. Global hydrological cycles and world water resources.
Science 313, 1068–1072 (2006).
83. Postel, S.L., Daily, G.C. & Ehrlich, P.R. Human appropriation of renewable
freshwater. Science 271, 785–788 (1996).
84. Rockstrom, J. etal. e unfolding water drama in the Anthropocene: towards
a resilience-based perspective on water for global sustainability. Ecohydrol.
7, 1249–1261 (2014).
85. Creutzig, F. etal. Reconciling top-down and bottom-up modelling on future
bioenergy deployment. Nature Clim. Change 2, 320–327 (2012).
86. Cowie, A.L., Smith, P. & Johnson, D. Does soil carbon loss in biomass
production systems negate the greenhouse benets of bioenergy?
Mitigation Adapt. Strateg. Global Chang. 11, 979–1002 (2006).
87. McCollum, D. etal. Energy investments under climate policy: a comparison of
global models. Clim. Change Econ. 4, 1340010 (2013).
88. Creutzig, F. Economic and ecological views on climate change mitigation
with bioenergy and negative emissions. Global Change Biol. Bioenergy (2015).
89. Kato, E. & Yamagata, Y. BECCS capability of dedicated bioenergy crops under a
future land-use scenario targeting net negative carbon emissions. Earth’s Future
2, 421–439 (2014).
90. Scott, V., Gilllan, S., Markusson, N., Chalmers, H., Haszeldine, R.S. Last
chance for carbon capture and storage. Nature Clim. Change 3, 105–111 (2012).
91. Blackford, J. etal. Detection and impacts of leakage from sub-seaoor deep
geological carbon dioxide storage. Nature Clim. Change 4, 1011–1016 (2014).
92. Luderer, G. etal. Economic mitigation challenges: how further delay closes the
door for achieving climate targets. Environ. Res. Lett. 8, 034033 (2013).
A study showing the urgency of climate mitigation action.
93. Gasser, T., Guivarch, G., Tachiiri, K., Jones, C.D. & Ciais, P. Negative emissions
physically needed to keep global warming below 2°C. Nature Commun.
6, 7958 (2015).
94. Boden, T.A., Marland, G. & Andres, R.J. Global, regional, and national fossil-
fuel CO2 emissions. (Carbon Dioxide Information Analysis Center, 2015).
95. Krey, V. etal. in Climate Change 2014: Mitigation of Climate Change
(eds Edenhofer, O. etal.) Annex Ch.2, 1308–1318 (IPCC, Cambridge Univ.
Press, 2014).
e views expressed herein are those of the authors, and do not represent those of a
particular governmental agency or interagency body. is analysis was initiated at a
Global Carbon Project meeting on NETs in Laxenburg, Austria, in April 2013and
contributes to the MaGNET program (
G.P.P. was supported by the Norwegian Research Council (236296). C.D.J. was
supported by the Joint UK DECC/Defra Met Oce Hadley Centre Climate Programme
(GA01101). J.G.C. acknowledges support from the Australian Climate Change Science
Program. E.Ka. and Y.Y. were supported by the ERTDF (S-10) from the Ministry of the
Environment, Japan.
Author contributions
P.S. led the writing of the paper, with contributions from all authors in the inception of
the study and in writing the dras. P.S. led the analysis with signicant contributions
from S.J.D., F.C., S.F., J.M., B.G., R.B.J., A.C., E.Kr., D.M. and D.V.V. Figures were
conceptualized and produced by S.J.D., J.R., P.C., S.F., P.S., G.P., R.A. and J.M.
Additional information
Supplementary information is available in the online version of the paper. Reprints
and permissions information is available online at
Correspondence and requests for materials should be addressed to P.S.
Competing financial interests
M.O. was given a share in Biorecro, a company that cooperates with BECCS projects
globally, honouring his pioneering work on BECCS. e other authors declare no
competing nancial interests.
© 2015 Macmillan Publishers Limited. All rights reserved
... The impact of climate change on agriculture, a vital industry that supports economies and lives worldwide, is especially concerning. Rising sea levels, one prominent symptom of climate change, have drawn a lot of attention due to their potential to affect agricultural output (Smith et al., 2021). Coastal agricultural lands, infrastructure, and ultimately food security are all threatened by sea level rise (SLR), which is brought on by the thermal expansion of the oceans and the melting of ice caps and glaciers . ...
... One example is bioenergy with carbon capture and storage (BECCS), which is seen as a vital technology in mitigating climate change by decarbonizing energy systems (Fuss et al., 2014;Xu et al., 2022a). BECCS could remove 3.3 Gt Ceq yr − 1 from the atmosphere by 2100, equivalent to one-third of current fossil fuel and industrial emissions, thus achieving the 2 • C target scenario (Harper et al., 2018;Smith et al., 2016). The combination of recycling and sustainable biomass use can even turn a carbon emission industry (e.g., plastics) into a net carbon sink (Stegmann et al., 2022). ...
The non-food bioeconomy is widely recognized as a crucial strategy to address climate change. However, the growing non-food demand for biomass, such as bioenergy and bio-based products, is leading to global land use changes and consequent greenhouse gas emissions. In this study, a global region-and biomass-specific land-use emissions (LUE) inventory and the Food and Agricultural Biomass Input-Output Model (FABIO) were used to quantify the LUE embodied in global non-food biomass supply chains. The results revealed that in 2013, the LUE induced by non-food biomass demand accounted for over one-quarter of the global LUE, with wood as the dominant contributor (47%), followed by live animals (17%) and oil crops (13%). Additionally, over 876 million tons (Mt) of LUE were associated with international biomass trade for non-food use, mainly from tropical countries/regions (e.g., Brazil, Indonesia and Thailand) to large developing ones (Mainland China and India) and major developed ones (the USA, the EU27 and Japan). This underscores the need for consumption-based accounting of non-food biomass uses, including emissions from land use changes. Furthermore, demand-side measures such as enhancing transparency in international biomass supply chains, establishing monitoring and certification systems for certain bio-based commodities to track LUE, and fostering multi-stakeholder collaboration are crucial for effectively reducing LUE.
... Scientists have shown that, by 2100, the global temperature will increase by 2 • C if 3.3 Gt of CO 2 annually has not been removed directly from the atmosphere (Smith et al., 2016;Fawzy et al., 2020). To avoid this, it is not only necessary to limit the emission of greenhouse gases (GHG), but it is also important to reduce the amounts that already exist in the atmosphere (Minx et al., 2017;Nemet et al., 2018). ...
... The weathering has the potential to remove 0.5-4.0 GtCO 2 per annum on a global scale if the managed croplands are treated with basalt dust at the rate of 10-30 tons/hectare (Taylor et al. 2009;Smith et al. 2016). The advantage of using this technique is that no additional land is required as it will be applied to the already managed croplands and can also be deployed with biochar to further increase the soil C-sequestration. ...
Full-text available
Greenhouse gases (GHG) occur naturally in our atmosphere and are essential to the survival of most of the organisms on the planet earth. GHG such as carbon dioxide, methane, nitrous oxide, and ozone play a major role in balancing the radiative budget, by absorbing or emitting some of the infrared rays reflecting from the earth’s surface. But unfortunately, anthropogenic activities like use of fossil fuel, intensive agriculture and livestock farming, use of synthetic fertilizers, deforestation, and industrial processes have drastically interfered in the natural air composition, by releasing excess greenhouse gases into the atmosphere. This has led to the increase in the ability of the atmosphere to absorb more infrared energy. However, long-term studies in different parts of the world also suggest an increase in the concentration of greenhouse gases across the world. This increase has caused one of the most serious threats of the twenty-first century, i.e. climate change and global warming. Increase in average temperature, heat waves, sea level rise, change in precipitation levels, strong hurricanes and storms, and bad air quality are some of the consequences of climate change that are observed as well as anticipated in near future. Directly or indirectly, these will affect all the organisms inhabiting the planet. A shift in the location of most of the organism is expected in search of the most suitable environment. This is further dependent on the type of species, its interaction, and adaptability. In 2015, the Paris Agreement within the framework of United Nations Framework Convention became international law to fight climate change. The major highlight of the agreement is to limit global warming to 2 �C, reducing greenhouse gas emission, burden-sharing (developing nations allowed to use resources), and opting for cleaner energy sources; rich countries should financially support developing nations. Thus, this book begins with a brief background on greenhouse gases sources and sinks and continues with a discussion on different sectors including forest fluxes to human health and modelling techniques to policy measures. The chapters that follow explore in detail the GHG emission budgets, mitigation strategies, technical advancement, and input-output analysis. We hope this book will act as a valuable tool for students with interests in environmental science, ecology, biological science, economics, and agriculture. It will be unique to environmental consultants, researchers and other professionals involved in climate change studies and non-governmental organizations (NGOs).
... Particularly, global warming will also accelerate the decomposition of SOC, thus creating a strong positive feedback with climate change (Wang et al. 2022). The soil carbon sink capacity has received extensive attention, and thus increasing SOC storage and reducing GHG emissions from soil are effective ways to address the climate change issues (Smith et al. 2016). The SOC stock exhibits significant spatial heterogeneity but is mainly affected by local-scale land use patterns and has been confirmed to be vulnerable to LUCs (Don et al. 2011). ...
Full-text available
Background Anthropogenic land use changes (LUCs) impart intensifying impacts on soil organic carbon (SOC) turnover, leading to uncertainty concerning SOC mineralization patterns and determining whether soils act as “source” or “sink” in the global carbon budget. Therefore, understanding the SOC mineralization characteristics of different LUC patterns and their potential influencing factors is crucial. An indoor incubation experiment was conducted to study the SOC mineralization patterns and their relevance to soil physicochemical properties, soil enzyme activity, SOC fractions, and bacterial alpha diversity. The soils were collected from two layers of five typical LUC patterns in Yellow Sea Forest Park, including four that were converted from wheat–corn rotation systems [a gingko plantation (G), a metasequoia plantation (M), a gingko–wheat–corn agroforestry system (GW), and a gingko–metasequoia system (GM)] and a traditional wheat–corn system (W). Results LUCs had significant and diverse impacts on the SOC content and SOC fraction contents and on soil enzyme activity. The cumulative SOC mineralization was significantly higher in the M systen than in the W and GW systems at 0–20 cm depth and higher in the G system than in the GW system at 20–40 cm depth after 60-day incubation. The mineralization ratio was highest in the W system and lowest in the GW system. The soil pH and bulk density had a significant negative correlation with the cumulative SOC mineralization, while the soil bacterial Shannon index had a significant positive correlation with cumulative SOC mineralization. Multiple stepwise linear regression analysis showed that the SOC mineralization potential was dominantly explained by the bacterial Shannon index and operational taxonomic units (OTUs). The GW system had lower potentially mineralizable SOC and higher SOC stability. Additionally, the incubation time and cumulative SOC mineralization were well fitted by the first-order kinetic equation. Conclusions LUCs significantly changed SOC mineralization characteristics and the results highlighted the important roles of the bacterial community in soil carbon cycling, which contributes to the fundamental understanding of SOC turnover regulation.
The sustainability of air transport is increasingly studied in relation to climate issues. The objective of this paper is to provide the key elements for assessing whether a given transition scenario for aviation could be considered as sustainable in the context of the Paris Agreement. Addressing this question relies on a broad range of concepts which are reviewed. First, ethical considerations related to effort-sharing mitigation principles and physical considerations on climate impacts of aviation are introduced. Then, the technological levers of action for mitigating CO2 and non-CO2 effects are detailed. Concerning CO2 emissions, low-carbon alternative energy carriers represent the main lever, with a wide range of solutions with varying degrees of maturity and decarbonization potentials. Other significant CO2 levers include improving aircraft architecture efficiency and accelerating fleet renewal. Concerning non-CO2 effects, contrail effect mitigation through operational strategies is one of the most promising lever. Aviation transition scenarios are then reviewed, with a particular focus on scenario simulation and sustainability assessment methodologies. Prospective scenarios are a useful framework for assessing the impacts of technological levers on the achievement of climate objectives. This review leads to the conclusion that technological levers have an important role to play in making aviation sustainable; however, significant uncertainties weigh on their feasibility, particularly for the most ambitious scenarios which rely on strong technological and political trade-off assumptions. The paper ends by raising the question about the meaning of sustainable aviation, which must be based on technological but also, for instance, social, economic and ethical considerations.
Attaining carbon dioxide (CO2) emissions mitigation has become one of the central goals, and it has received wider attention from policymakers. The CO2 as a negative greenhouse gas (GHG) has been increasing in the atmosphere by anthropogenic activities, including the burning of fossil fuels. In view of this issue, negative emissions technologies can play a very useful role in reducing CO2 emissions concentration and mitigating climate change. Therefore, deploying these technologies is imperative. There are multiple available negative emissions technologies that should be assessed on the basis of different principles (criteria). A decision support system is needed to prioritize these technologies and select the most appropriate option. In this regard, a novel hybrid system including the two multi-criteria decision-making techniques under the Fermatean fuzzy environment is proposed. The Fermatean fuzzy set is applied for evaluating uncertainty in the water-energy-carbon nexus and sustainability principles performance levels of these technologies. The prioritization of negative emissions technologies considering the defined principles is assessed through the utilization of the proposed method. The results indicate that the technology of bioenergy with carbon capture and storage is the most suitable alternative for reaching the carbon management goals in Bidboland Persian Gulf gas refinery in southwestern Iran, Behbahan. This work extracts a roadmaping for piloting the oil & gas industry shift towards a circular carbon economy. Therefore, the industries such as the oil and gas industry should boost environmental awareness, ample funds, and incentives for implementing negative emissions technologies to encourage sustainable energy systems, develop low-carbon strategies, mitigate climate change, and promote sustainable economic growth. Concerning the government, corresponding policies and measures should be adopted to emphasize increasing the use of these technologies, especially in high-emission areas.
Full-text available
This paper presents the extension of industry modelling within the REMIND integrated assessment model to industry subsectors, and the projection of future industry subsector activity and energy demand for different baseline scenarios for use with the REMIND model. The industry sector is the largest greenhouse gas-emitting energy demand sector and considered a mitigation bottleneck. At the same time, industry subsectors are heterogeneous and face distinct emission mitigation challenges. By extending the multi-region, general equilibrium integrated assessment model REMIND to an explicit representation of four industry subsectors (cement, chemicals, steel, and other industry production), along with subsector-specific carbon captrure and sequestration (CCS), we are able to investigate industry emission mitigation strategies in the context of the entire energy-economy-climate system, covering mitigation options ranging from reduced demand for industrial goods, over fuel switching and electrification, to endogenous energy efficiency increases and carbon capture. We also present the derivation of both activity and final energy demand trajectories for the industry subsectors for the use with the REMIND model in baseline scenarios, based on short-term the continuation of historic trends and long-term global convergence. The system allows for selective variation of specific subsector activity and final energy demand across scenarios and regions to create consistent scenarios for a wide range of socioeconomic drivers and scenario story lines, like the shared socioeconomic pathways (SSPs).
Despite 20 years of effort to curb emissions, greenhouse gas (GHG) emissions grew faster during the 2000s than in the 1990s, which presents a major challenge for meeting the international goal of limiting warming to <2 °C relative to the preindustrial era. Most recent scenarios from integrated assessment models require large-scale deployment of negative emissions technologies (NETs) to reach the 2 °C target. A recent analysis of NETs, including direct air capture, enhanced weathering, bioenergy with carbon capture and storage and afforestation/deforestation, showed that all NETs have significant limits to implementation, including economic cost, energy requirements, land use, and water use. In this paper, I assess the potential for negative emissions from soil carbon sequestration and biochar addition to land, and also the potential global impacts on land use, water, nutrients, albedo, energy and cost. Results indicate that soil carbon sequestration and biochar have useful negative emission potential (each 0.7 GtCeq. yr−1) and that they potentially have lower impact on land, water use, nutrients, albedo, energy requirement and cost, so have fewer disadvantages than many NETs. Limitations of soil carbon sequestration as a NET centre around issues of sink saturation and reversibility. Biochar could be implemented in combination with bioenergy with carbon capture and storage. Current integrated assessment models do not represent soil carbon sequestration or biochar. Given the negative emission potential of SCS and biochar and their potential advantages compared to other NETs, efforts should be made to include these options within IAMs, so that their potential can be explored further in comparison with other NETs for climate stabilization.
Climate changes will affect food production in a number of ways. Crop yields, aquatic populations and forest productivity will decline, invasive insect and plant species will proliferate and desertification, soil salinization and water stress will increase. Each of these impacts will decrease food and nutrition security, primarily by reducing access to and availability of food, and also by increasing the risk of infectious disease. Although increased biofuel demand has the potential to increase incomes among producers, it can also negatively affect food and nutrition security. Land used for cultivating food crops may be diverted to biofuel production, creating food shortages and raising prices. Accelerations in unregulated or poorly regulated foreign direct investment, deforestation and unsustainable use of chemical fertilizers may also result. Biofuel production may reduce women's control of resources, which may in turn reduce the quality of household diets. Each of these effects increases risk of poor food and nutrition security, either through decreased physical availability of food, decreased purchasing power, or increased risk of disease. The Impact of Climate Change and Bioenergy on Nutrition articulates the links between current environmental issues and food and nutrition security. It provides a unique collection of nutrition statistics, climate change projections, biofuel scenarios and food security information under one cover which will be of interest to policymakers, academia, agronomists, food and nutrition security planners, programme implementers, health workers and all those concerned about the current challenges of climate change, energy production, hunger and malnutrition. © Springer Science+Business Media B.V. 2012. All rights reserved.