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Environ. Res. Lett. 13 (2018) 063003 https://doi.org/10.1088/1748-9326/aabff4

TOPICAL REVIEW

Negative emissions—Part 3: Innovation and upscaling

Gregory F Nemet1,8, Max W Callaghan2, Felix Creutzig2,3,SabineFuss

2, Jens Hartmann5,J

erˆ

ome

Hilaire2,6, William F Lamb2,JanCMinx

2,4, Sophia Rogers1and Pete Smith7

1La Follette School of Public Affairs, University of Wisconsin–Madison, 1225 Observatory Drive, Madison, WI 53706, United States of

America

2Mercator Research Institute on Global Commons and Climate Change, Torgauer Straße 12–15, EUREF Campus #19, 10829 Berlin,

Germany

3Technische Universit¨

at Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany

4School of Earth and Environment, University of Leeds, Leeds LS2 9JT, United Kingdom

5Institute for Geology, Center for Earth System Research and Sustainability (CEN), Universit¨

at Hamburg, Bundesstraße 55, 20146

Hamburg, Germany

6Potsdam Institute for Climate Impact Research, D-14473 Potsdam, Germany

7Institute of Biological and Environmental Sciences School of Biological Sciences, University of Aberdeen, 23 St Machar Drive, Aberdeen,

AB24 3UU, Scotland, United Kingdom

8Author to whom any correspondence should be addressed.

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E-mail: nemet@wisc.edu

Keywords: negative emissions, Paris agreement, carbon removal, geo-engineering

Abstract

We assess the literature on innovation and upscaling for negative emissions technologies (NETs)

using a systematic and reproducible literature coding procedure. To structure our review, we employ

the framework of sequential stages in the innovation process, with which we code each NETs article in

innovation space. We find that while there is a growing body of innovation literature on NETs, 59%

of the articles are focused on the earliest stages of the innovation process, ‘research and development’

(R&D). The subsequent stages of innovation are also represented in the literature, but at much lower

levels of activity than R&D. Distinguishing between innovation stages that are related to the supply of

the technology (R&D, demonstrations, scale up) and demand for the technology (demand pull, niche

markets, public acceptance), we find an overwhelming emphasis (83%) on the supply side. BECCS

articles have an above average share of demand-side articles while direct air carbon capture and

storage has a very low share. Innovation in NETs has much to learn from successfully diffused

technologies; appealing to heterogeneous users, managing policy risk, as well as understanding and

addressing public concerns are all crucial yet not well represented in the extant literature. Results from

integrated assessment models show that while NETs play a key role in the second half of the 21st

century for 1.5 ◦Cand2◦C scenarios, the major period of new NETs deployment is between 2030 and

2050. Given that the broader innovation literature consistently finds long time periods involved in

scaling up and deploying novel technologies, there is an urgency to developing NETs that is largely

unappreciated. This challenge is exacerbated by the thousands to millions of actors that potentially

need to adopt these technologies for them to achieve planetary scale. This urgency is reflected neither

in the Paris Agreement nor in most of the literature we review here. If NETs are to be deployed at the

levels required to meet 1.5 ◦Cand2◦C targets, then important post-R&D issues will need to be

addressed in the literature, including incentives for early deployment, niche markets, scale-up,

demand, and—particularly if deployment is to be hastened—public acceptance.

1. Introduction

Meeting even moderately ambitious goals to address

climate change could require removing substantial

amounts of greenhouse gases from the atmosphere

at a rate much faster than existing natural removal

processes (Sanderson et al 2016). Several methods of

anthropogenic removal have been proposed, which fall

under the rubric of negative emissions technologies

(NETs). The notion of ‘technology’here is broad, a

Environ. Res. Lett. 13 (2018) 063003 GFNemetet al

means to an end (Arthur 2007), encompassing not

only devices or hardware but also soft innovations,

such as management practices and behavior. NETs

thus include industrial processes, such as bioenergy

with carbon capture and sequestration (BECCS), and

direct air carbon capture and storage (DACCS), which

is sometimes referred to simply as ‘direct air capture.’

NETs also include ecosystem management approaches

(Field and Mach 2017,Griscom2017) such as: soil

carbon sequestration (SCS), biochar, afforestation and

reforestation (AR), blue carbon (BC), enhanced weath-

ering (EW), and ocean fertilization (OF). Methods

to remove greenhouse gases other than CO2include

chemical decomposition of methane and laser removal

of CFCs; they are typically known as greenhouse gas

removal technologies (GGRs) and are not covered in

this review, but are reviewed elsewhere (Boucher and

Folberth 2010, Stolaroff et al 2012,Minget al 2017).

We provide a taxonomy for the NETs approaches

reviewed here in Minx et al (2018).

This review is part of a series of three reviews

papers on NETs. The first presents scientometric

trends and provides an overall summary (Minx et al

2018). The second includes an assessment of costs

and potentials of NETs, as well as a summary of the

level of NETs included in climate stabilization sce-

narios such as 1.5 ◦Cand2

◦C(Fusset al 2018).

In this paper, we review the extent to which the

NETs literature includes topics related to innovation

and upscaling.

1.1. Demand, supply, and costs of NETs

An up-to-date assessment of the potential rate (in

Gt CO2yr−1) at which NETs could remove CO2from

the atmosphere shows that all of these methods—with

the exception of soils— have a high-end potential to

remove multiple, and in cases tens of, Gt CO2yr−1,

while soils could remove on the order of single-digit

Gt CO2yr−1, albeit all with wide ranges of uncer-

tainty (Fuss et al 2018). The heterogeneity of NETs,

especially with respect to their limitations, geograph-

ical accessibility, and side effects, strongly imply the

need to think in terms of portfolios of NETs to manage

risk and maximize removal efficacy. The main insight

from thesmall set of studies that do consider morethan

one NET (Chen and Tavoni 2013,Florianet al 2014,

Marcucci et al 2017), is that when deployed jointly,

the total negative emission potential from NETs is

increased while the individual deployment of NETs is

reduced, suggesting that portfolios provide an avenue

to mitigate adverse impacts. An important insight

is that even though integrated assessment model

(IAM) results typically have a large role for NETs

to play in the second half of the 21st-century for

meeting the climate goals of the Paris Agreement,

there is still urgency in developing NETs now due

to the expected lengthy time periods required to

deploy them at the scale of gigatonnes-per-year of

removal.

Fuss et al (2018) also reviews recent cost estimates

of various NETs. These estimates vary considerably

both within and among NETs technology categories.

For example, we see ranges at the low end of sin-

gle digit dollars per ton of removal (e.g. AR and

OF) with high end estimates in the several 100s of

dollars per ton (BECCS, DACCS, and EW). Like

mitigation technologies, only considerable effort will

render NETs technically as well as economically fea-

sible. Importantly, the costs of NETs vary not only

quantitatively but also qualitatively; whereas the costs

of DACCS include capital equipment purchases and

energy input costs, the costs of SCS relate to the perma-

nence of the carbon in soil, the effects on agricultural

yields, and the adoption behavior of farmers. Deploy-

ing NETs at a meaningful scale will require them to be

affordable, including all costs, and socially acceptable,

in a broad sense.

1.2. Innovation in NETs

The speed at which NETs can be scaled up so that they

are commercially available at affordable costs, deliver

climate benefits and non-climate co-benefits, with rea-

sonably tolerable adverse impacts, will determine their

utility for addressing climate change. Innovation in

NETs and in supporting environments will be cen-

tral to this scale up process, and thus crucial to their

outcomes on the climate and on society. We employ a

broad definition of ‘innovation’in this review spanning

the full range of the process, from scientific discov-

ery to issues associated with widespread adoption. We

separatetheinnovationprocessintocategories thatcor-

respond to a succession of stages. Using a dichotomy

prevalent in the innovation literature (Nemet 2009,Di

Stefano et al 2012), we find it useful to group these

stages into two categories: (1) those related to the

supply of innovation in a technology, e.g. including

scientific research, and (2) those related to the demand

for innovation in that technology, e.g. public accep-

tance. Supply side activities involving improving the

costs and performance of technology while demand

side activities involve the markets in which NETs com-

pete, who wants them, how they use them, and the

extent to which the broader public accepts them.

The past two decades have seen a steady increase in

publications on NETs. While this body of work repre-

sents a small portion of the broader climate literature,

it is growing faster–particularly more recently (Minx

et al 2017b). In a companion piece to this review,

Minx et al (2018) conduct a scientometric assess-

ment of the extant literature on NETs and find (i)

steady growth in the literature across different tech-

nologies with notable exceptions of ocean acidification

and enhanced weathering; (ii) a development of dis-

tinct scientific discourses for all major technologies

that in turn broadly cluster into land-based and ocean-

based approaches as well as those involving geological

storage; and (iii) the lack of a distinct cluster with

studies of NETs portfolios.

Environ. Res. Lett. 13 (2018) 063003 GFNemetet al

1.3. This review: literature on innovation and scale

up in NETs

As rapid and sustained emissions reductions continue

to be forestalled, societies face an increasing depen-

dence on NETs to achieve ambitious climate goals. The

study highlights that while a risk management perspec-

tive requires limiting the growing importance of NETs

in climate policy by ratcheting up short-term ambi-

tions, these efforts will need to be accompanied by a

focus on innovation and scale-up in order to realize

required levels of carbon removal in the 21st century for

meeting the international climate goals. This motivates

our review and frames the following research question:

what does the literature on NETs say about innovation

and how to achieve up-scaling?

This review starts with the body of literature identi-

fied via the scientometric analysis conducted in (Minx

et al 2018). We identified 2134 articles that fit a def-

inition of NETs, were published in the peer-reviewed

literature, and are cataloged by the Web of Science

and Scopus between 1970 and mid-2017. We assess

how many of these articles address the process of

innovation, the stages associated with: creating new

technical knowledge, transforming that knowledge into

commercial products, diffusing them widely in soci-

ety, and dealing with societal issues resulting from

their use. We refer to this set of innovation processes

as ‘innovation and upscaling.’We choose the focus

on increasing scale because of the inherently large

scale at which NETs need to be deployed in order to

have a material impact on the Earth’sclimate.Even

in portfolio approaches, in which multiple NETs are

deployed, several gigatonnes of removal for each indi-

vidual technology are required. We thus categorize

each article as covering topics that relate to one or

more categories of the innovation process. We pro-

vide a descriptive analysis of the trends in publications

acrossbothNETstechnologycategoriesandinnovation

stages. We use the articles we identified to summarize

some of the key insights that have emerged so far on

innovation in NETs. An overview of the entire search

selection procedure is provided in Minx et al (2018).

We first look (section 2) at general insights from

the innovation literature and the framework it pro-

vides for evaluating NETs. In section 3, we develop

scientometric estimates of the trends and foci of activ-

ity in academic publishing on NETs. In section 4,we

substantively review the key articles from the literature

on innovation in NETs. We provide summary insights

and conclusions from this review in section 5.

2. Insights from the innovation literature

2.1. Definitions of innovation

Innovation is central to many aspects of address-

ing climate change. Innovation includes performance

improvements in mitigation technologies, such as in

the efficiency of end use devices like electric motors; it

alsoencompasseseffortsatadaptation,suchasdrought

resistant crops; and it can also involve new business

models, such as providing access to capital for low-

carbon technologies in credit-constrained economies.

The promise of innovation is that it can make efforts

to address climate change more effective and more

affordable (Popp 2010).

Depending on the disciplinary venue, innovation is

defined in different ways: the general notion of innova-

tion is sometimes referred to as ‘technological change,’

originally defined as ‘new combinations of productive

means’(Schumpeter 1934). More specifically in the

context of climate change, a useful definition of tech-

nological change is: ‘a process typically involving stages

of invention, innovation, and diffusion, whereby users

can produce more or better outputs from the same

amount of input.’(Nemet 2013). While this definition

carries with it the framework specific to the discipline of

Economics from which it originates, it remains apro-

pos for NETs. One can think of innovation in NETs

as generating better outputs, such as more carbon

removed, fewer adverse side effects, and more soci-

etal acceptance. Similarly, one can think of innovation

as reducing the amount of capital, labor, land, water,

or energy required as inputs. More succinctly, inno-

vation can be reduced to performance improvements

and cost reductions (Funk 2015), where performance

can encompass a broad set of characteristics, not lim-

ited to efficiency, but extending to aspects such as

public acceptance (Fri and Savitz 2014).

2.2. Upscaling

An important consideration in all of these definitions

of innovation is the speed at which innovation occurs.

For climate change and NETs in particular, the rate

of innovation is essential (Bromley 2016). Because we

are ultimately concerned with NETs removing giga-

tonnes of CO2per year, the notion of ‘up-scaling’

provides a useful focus within the innovation pro-

cess. For example, the most recent review of CO2

removal in 1.5 ◦C IAM scenarios found a median

rate of 15 Gt CO2yr−1 by 2100, with a range of 3–29

(Rogelj et al 2018). The most specific meaning of

upscaling is the increase in unit size (e.g. a power plant)

to take advantage of scale economies, i.e. that costs rise

at less than the rise in output (Wilson 2012). The term

is also used in a more general sense when discussing

planetary interventions in the climate system. In that

case, the focus is not on scaling up a unit, but on scaling

up a technological system.

In the case of NETs, this process might involve

up-scaling to thousands of CCS plants (Herzog 2011,

Nemet et al 2015, Peters et al 2017), millions of

farms (Lal 2004, Woolf et al 2017), or teragrams of

iron added to the ocean (Boyd and Bressac 2016,

Hauck et al 2016). One way to consider the mag-

nitudes of scale up required for NETs is to look at

the deployments estimated in IAMs under various

temperature targets. In figure 1we display estimates of

Environ. Res. Lett. 13 (2018) 063003 GFNemetet al

200

400

600

800

1.5°C

Likely 2°C

Medium 2°C

Likely 3°C

29 69 148 44

815 18 15

# models

# scenarios

38 101 200 119

# pathways

BECCS upscaling between 2030 and 2050 [Mt CO2/yr]

Figure 1. Annual deployment of additional BECCS removal capacity 2030–2050 under various temperature targets, scenarios, and

IAMs. The upscaling metric is computed by taking the difference of gross negative emissions in 2050 and 2030 and dividing it by

20 years. Boxplots provide information about the range between the minimum and maximum values (vertical black solid line), the

range between the 15th and 85th percentiles (blue-shaded rectangle), the median (horizontal black thick line) and the mean (white

point). Basic descriptive statistics are provided below the plot for each climate target: the number of model runs (observations), the

number of model versions and the number of scenarios.

the new BECCS9required annually on average between

2030 and 2050 under various scenarios and IAMs that

are consistent with three temperature targets (1.5 ◦C,

2◦C, 3 ◦C), and the likelihoods of meeting them. Note

that ‘likely’is a 66% chance of avoiding temperature

overshoot over the 21st century and ‘medium’is a

50% chance. The 1.5 scenarios feature a different like-

lihood which corresponds to a 50% chance of keeping

warming below 1.5 ◦C in 2100 (see Box in Fuss et al

(2018) for more explanation). Additional information

on the costs of BECCS, its geographical distribution

and its role in the mitigation portfolio is available

in the supporting information (SI) (section A.4).

Annual deployment of BECCS increases with more

ambitious temperature targets but spans a consider-

able range within each target, more than an order

of magnitude. It is striking that for even the least

stringent targets (likely 3 ◦C), the median deployment

rate involves adding 150 Mt CO2yr−1 of new removal

capacity each year between 2030 and 2050. This is,

because NETs are deployed both because they are bio-

physically required and because they are economically

attractive once carbonprices are sufficiently high (Minx

et al 2017b,Fusset al 2018). To put these numbers

in perspective, the first large scale BECCS project, in

Decatur, IL USA, will remove about 1 Mt CO2yr−1

onceinfulloperation. Worldwide,nootheroperational

projects exceed 0.3 Mt CO2yr−1. Only one project in

planning exceeds 1 Mt CO2yr−1. So, these scenarios

involve bringing online hundreds of new plants of

Decatur-scale each year between 2030 and 2050. To

9BECCS is the only technology for which gross negative emis-

sion data are available (for afforestation, only net land-use emission

changes are reported).

further put these results in context, scaling up 1Mt

of a specific NET in 2020 to 1Gt in 2050, average

deployment growth rates of 26% must be sustained

for 30 years. Such a scale of growth had been observed

for other technologies before, in particular solar PV

(Creutzig et al 2017), but is nonetheless extremely

challenging.

To see what factors deployment rates are sensitive

to, besides target stringency, figure 2shows deployment

rates across four different assumptions. The lowest

deployment, and lowest uncertainty, occurs under the

assumption that bioenergy is constrained (‘limited

bioenergy’)orunavailable(‘no CCS/BECCS’), e.g.

due to high social opposition to land use impact

such as food prices. We note that the NET being

modeled here is BECCS so it is among the most

sensitive to land use constraints. At about the same

central tendency, but with much lower confidence,

the ‘low energy intensity’scenario includes some very

low BECCS deployment outcomes, as well as some

moderate ones. The two highest scenarios are ‘full

portfolio’and a scenario in which global mitigation is

delayed until 2030. The latter involves some very high

deployment possibilities for BECCS and none below

150 Mt CO2yr−1 of new capacity annually.

The historical evidence consistently finds that inno-

vations can take decades to be scaled up (Wilson

2012) and widely adopted (Grubler et al 2016), even

if examples of rapid transitions exist (Sovacool 2016).

Given the severe constraints imposed by the global car-

bon budget (Rogelj et al 2016,vanSoestet al 2017),

the speed at which NETs can be scaled up to make

a favorable planetary scale impact is a paramount

issue (Fuss et al 2016). But the innovation literature

makes clear that there are risks involved even if this

Environ. Res. Lett. 13 (2018) 063003 GFNemetet al

Full portfolio

Limited bioenergy &

No CCS/BECCS

Low energy intensity

Delayed action until 2030

12 5 13 12

6756

# models

# scenarios

24 10 24 24

# pathways

BECCS upscaling between 2030 and 2050 [Mt CO2/yr]

600

400

200

Figure 2. Annual deployment of additional BECCS removal capacity 2030–2050 under various assumptions, scenarios, and IAMs.

The upscaling metric is computed by taking the difference of gross negative emissions in 2050 and 2030 and dividing it by 20 years.

Boxplots provide information about the range between the minimum and maximum values (vertical black solid line), the range

between the 15th and 85th percentiles (blue-shaded rectangle), the median (horizontal black thick line) and the mean (white point).

Basic descriptive statistics are provided below the plot for each climate target: the number of model runs (observations), the number

of model versions and the number of scenarios.

R&D demon-

strations scale up niche

markets

demand

pull

public

Supply factors Demand factors

Example feedbacks

Figure 3. Stages of innovation. Arrows indicate flows of knowledge.

scale up successfully achieves its required rate (Buck

2016). Sustained demand for the technology’s benefits

as well as public acceptance of its risks and side effects

will also condition its overall effectiveness.

2.3. Stages of innovation

A basic framework we take from the literature on

innovation is that it can be described as occurring

in a progression of stages (figure 3). Sources useful

for describing and delineating these stages include:

Grubler (1998), Weyant (2011), Gallagher et al (2012),

Nemet (2013), Fri and Savitz (2014), Grubler and Wil-

son (2014) and more recently Anadon et al (2016).

The stages used in these articles use varied termi-

nology and levels of specificity. The use of successive

stages is often critiqued as a simplistic or linear model

(Godin 2005) that abstracts from important features,

such as networks of actors or innovation systems (Geels

2004,Hekkertet al 2007). Some of these critiques

address a strawman version; for example, a consis-

tent insight from work using the notion of stages is

that knowledge flowing between stages does not always

flow in one direction, rather feedbacks from later stages

to earlier ones are important; see dashed arrows in

figure 3for examples. Still, the notion that innova-

tion includes a progression from early stages to later

ones, that there is an essential sequence to them,

remains relevant and useful (Balconi et al 2010). The

literature is also consistent in describing that the mech-

anisms at work, capital required, level of risk, and

actors involved are distinct across stages. With this

review’s goal of assessing the innovation-related lit-

erature, we adopt innovation stages as a framework

for assessing the locus of publication output in NETs.

Work from the innovations literature makes the

case that this innovation lifecycle sits in a context. Net-

works of actors influence the process (Hekkert et al

2007,Bergeket al 2008).Theemergenceofanew

technology to replace others, a technological transition,

involves institutions, financing mechanisms and niche

formation (Rotmans et al 2001, Geels 2002, Jacobsson

and Jacobsson 2014). This perspective is particularly

important for NETs in that, for example, land-based

NETs have the largest potential in the institutionally

Environ. Res. Lett. 13 (2018) 063003 GFNemetet al

weakest regions of the world (Fuss et al 2018). While

we acknowledge the importance of these contextual

factors in accurately describing the innovation process,

for the purposes of this review we adopt a stylized

version of this context by focusing on the distinc-

tion between supply and demand drivers affecting the

direction and speed of the process. ‘Technology-push’

driversreducethecosts ofinnovation, e.g.throughedu-

cation and research. ‘Demand-pull’drivers increase the

pay-offs to innovation, e.g. by increasing the demand

for new technologies in the market place (Nemet 2009).

In figure 3we simply refer to supply and demand

side factors. We also focus on knowledge (repre-

sented by arrows), the most fundamental part of the

innovation process (Lundvall 1998).

For NETs, as with any technology, the ultimate

measure of success for a particular technology is adop-

tion. Adoption of a technology is a function of its

relative advantage—in terms of cost, efficiency, qual-

ity, environmental impact, etc.—and its alignment

with consumer preferences (Rogers 2003,Fouquet

2010). Adoption is far from certain. Many, if not

most innovations, make it through only a few stages

before being abandoned (Scherer et al 2000,Thomke

2003). An inherent aspect of the process is the lack

of ex ante knowledge about which innovations are

likely to be successful (Fleming 2001). The stakes

of this uncertainty are heightened by the robust

research finding of highly skewed payoffs to inno-

vations, implying that there will be many losers,

only a few winners, and large returns for the latter

(Scherer and Harhoff 2000).

We thus employ the stylized framework of the

innovation process depicted in figure 3to provide a

taxonomy of six stages to characterize the literature on

NETs.

2.3.1. Research and development

R&D involves the discovery and assimilation of new

scientific and technical knowledge (Holdren and Bald-

win 2001). The research part of R&D includes studies

of thermodynamics and computer modeling of NETs

systems. The development part of R&D involves exper-

iments and prototypes to improve the technology.

It can also involve studies of the future impacts

of a technology at scale. In our study, all of the

papers we have collected from the Web of Science

could be considered R&D under this definition. To

enhance clarity, we classify papers with a narrower

definition; papers are classified as R&Diftheydo

not get classified in one of the other innovation

categories.

Major efforts to increase energy R&D were agreed

upon during COP21 (Mission Innovation 2015). Yet,

the NETs technologies differ in their technological

maturity and the extent to which R&Dfundingis

the most critical factor in enhancing knowledge about

them. Public R&D is particularly important as there

are many open questions, needs for improvement in

knowledge, for which firms may not have sufficient

incentives (Jones and Williams 1998,Cohenet al2002).

Butitisalsolimitedinthatthereisonlysomuch

that can be accomplished without market feedback.

One important insight is that R&D is effective when

it is maintained even as technologies progress closer

to commercial use, e.g. because new problems develop

in later stages that require new knowledge (Hendry

and Harborne 2011). The notion of ‘formative phases,’

(Wilson 2012,BentoandWilson2016), in which the

optimal designs and configurations undergo experi-

mentation, are particularly important and several NETs

appear to be at this stage at present.

2.3.2. Demonstrations

As they emerge from R&D, technologies need to

prove that their performance is adequate and that they

can function reliably in non-laboratory environments.

Even early adopters will be skeptical of technologies at

this stage. Firms need to reduce the risk of technologies

at this stage by building one or more examples. One

problem that emerges, known as knowledge spillovers,

is that competitor firms, or countries, can observe

these demonstrations and learn from them without

having to make the required investments themselves

(Teece 1986). This free-rider problem creates weak

incentive for companies to fund demonstrations (Hart-

ley and Medlock 2017). Furthermore, even though

incentives for private investment are weak, govern-

ments are often hesitant to fund these investments

(Weyant 2011,Zhouet al 2015), in part due to the

scales of the investment required (Lupion and Her-

zog 2013), a mixed track record of success (Anadon

and Nemet 2014), and to some extent due to percep-

tions that they will be ‘picking winners’(Cohen and

Noll 1991). This problem, known as the technology

‘valley of death’results in an abundance of promising

technologies that never become tested in commer-

cial markets because they fail to attract sufficient

investment to prove their reliability.

Work assessing the ‘valley of death’problem for

analogous technologies provide several insights appli-

cable to at least some of the NETs. One is that

demonstration programs should be designed as a port-

folio of projects so that the program is robust to

failure in a single project (Hart 2017). However the

scale of the investments required can require some

prioritization (Watson 2008). A fundamental goal of

demonstrations is to generate knowledge, i.e. to learn

(Reiner 2015); that is a higher priority than produc-

tion, such as how much CO2is removed. Excess focus

on production in past demonstrations has reduced

much of their social value (Anadon and Nemet 2014).

Similarly, it is possible to learn from technical fail-

ures (Leoncini 2016). A key to learning is making

sure that knowledge generated is codified, main-

tained, and disseminated (Grubler and Nemet 2014),

which is often not the case.

Environ. Res. Lett. 13 (2018) 063003 GFNemetet al

Figure 4. Effect of carbon pricing on demand for NETs (i.e. BECCS) at three time periods in integrated assessment models.

2.3.3. Scale-up

The process of increasing the unit size of technolo-

gies to commercially-viable scales is non-trivial and

can take considerable time (Wilson 2012). This, asso-

ciated with increasing scale, is a repeated theme in the

literature. It is clear that just because we know we even-

tually need large scale it does not imply we are ready to

do so today. See for example the megawatt scale Ger-

man and US wind turbines in the 1970s that proved to

be dead ends (Gipe 1995). Furthermore, the extent of

the need to integrate NETs into existing infrastructures

affects deployment speed and similarly varies across

NETs categories(Geels2002). Forexample, some NETs

will involve access to CO2pipeline systems (BECCS,

DACCS), others will require extensive mining and

transportation infrastructures (EW, OF, BECCS).

NETs technologies span a wide range of sizes and

thus each will involve quite different scale-up pro-

cesses. In some cases, such as BECCS, the scale up

process involves major increases in unit scale (Nykvist

2013).Inothercases,e.g.inDACCS,theremayalso

be unit scale increases, but the main scale-up chal-

lenge could be in mass manufacturing DACCS units

(Lackner et al 2012). A strategy of iterative upscal-

ing (Nemet et al 2016)—a series of demonstrations in

which later projects learn from earlier ones and adjust

their designs at larger scales—has proven successful,

e.g. with Danish wind turbines (Garud and Karnoe

2003) as well as with PV manufacturing (Powell et al

2015). NETs could benefit from a similar orientation

to scale-up involving a process of starting deployment

early, gradually increasing unit and manufactur-

ing size, and iteratively improving in the migration

to larger scales.

2.3.4. Demand pull

Innovation is affected not only by technology push (e.g.

R&D) but also by the markets in which it competes:

demand pull (Nemet 2009). Learning by doing in the

course of meeting demand can improve the cost and

performance of technologies (Lohwasser and Madlener

2013). Because the climatic benefits of NETs are public

goods, these markets will be highly affected by pol-

icy, which are often uncertain (Brunner et al 2012).

As a result, demand for NETs will be heavily condi-

tioned by policies, including carbon pricing. Indeed, in

IAMs, carbon pricing is the mechanism that triggers

BECCS deployment, with the scale (or demand pull)

determined by the price levels required to achieve a

given temperature goal (Fuss et al 2018). Other analy-

ses have shown required investment in BECCS capacity

on the scale of hundreds of billions of dollars annu-

ally (McCollum et al 2013). In figure 4,usingIAM

results from 207 scenarios in 12 models10,weshow

the effect that demand for NETs—in the form of a

carbon pricing associated with various temperature

targets—has on deployment of BECCS in 2030, 2050,

and 2100. On the horizontal axis (log scale) one can

see the distribution of carbon prices associated with

each stabilization target. Carbon prices are decreasing

in the stringency of the temperature target (including

the likelihood of achieving it), but with large overlap-

ping ranges. Deployment of BECCS is shown on the

vertical axis (linear scale). Deployment is also decreas-

ing in the target stringency but increasing in the year.

We note a substantial amount of scatter in the data,

due to among other items, assumptions about mitiga-

tion technologies, climate sensitivity, and heterogeneity

in model structure. One insight from this analysis is

that the effect of demand for NETs, in the form of a

temperature target, affects the urgency of NETs deploy-

ment rather than its end-of-century level. Deployment

of BECCS is quite similar across targets in 2030 and in

2100; it diverges most in 2050—noting again the differ-

ent biophysical and economic rationales NETs use can

have (Fuss et al 2018). We note that the NET modeled

in this case is mostly BECCS, which in some models

includes exogenous constraints on upper bounds of

deployment in 2100 and thus may contribute to the

similarity of deployment levels therein.

More generally, other policy mechanisms such as

subsidies for deployment, technology mandates, and

even intellectual property regimes can affect demand

10 Model versions are counted as individual models (i.e. GCAM 3.0,

GCAM 3.1, IMACLIM 1.1, IMAGE 2.4, MERGE (EMF27), MES-

SAGE (v4), POLES (AMPERE), REMIND (1.4), REMIND (1.5),

TIAM-ECN, WITCH (AMPERE), WITCH (LIMITS)).

Environ. Res. Lett. 13 (2018) 063003 GFNemetet al

for NETs. There may also be co-benefits that NETs

provide that are independent of the value to society

of carbon removal (Hong-Mei et al 2017). For exam-

ple, some ecosystem-oriented NETs provide ecosystem

services such as flood control that create value and

thus additional demand for NETs; AR can provide

co-benefits with respect to local livelihoods and bio-

diversity.

Other insights from the innovation literature

include the importance of expectations (Alkemade

and Suurs 2012). Expected future demand is often

more important than the level of existing demand for

investment. Adoption settings are crucial, for example

appetite for risk in agricultural settings affects demand

for new technology (Chatrchyan et al 2017). Learn-

ing by doing can occur during the adoption process;

improvement continues after the R&D stage (Arrow

1962,Thompson2012). Moreover, learning by doing

often is distinct and complementary to R&D, not a

substitute for it (Newell 2010, Corradini et al 2014).

This implies that early deployment of NETs may need

to be subsidized, e.g. as an infant industry, in order to

achieve scale, improve carbon removal performance,

and reduce costs over time. Finally, if demand depends

on policies this adds additional risk to adopters in that

the policies can change and thus payoffs can change as

well. Policy credibility is an important issue in climate

policy in general (Nemet et al 2017), and will likely

become even more central to the success of NETs.

2.3.5. Niche markets

Niche markets exist when early adopters have a higher

than average willingness to pay for a technology. An

example would be carbon capture utilization and stor-

age (CCUS), where oil field operators might value

CO2above the existing carbon price and thus pay

for CO2for enhanced oil recovery. Even if the cli-

matic benefits of CCUS, or just CCU, are unclear at

best (von der Assen et al 2013), it can enable subse-

quent scale up to more definitively beneficial removal

at much larger levels. Niche markets can be important

to launching risky new technologies (Kivimaa and Kern

2015), especially those whose initial costs are high but

may fall subsequently through learning by doing. They

may provide some temporary insulation from com-

petition, e.g. when the scales of existing competitors

might make them uncompetitive (Kemp et al 1998)

(Raven et al 2016).

Given the issues of policy credibility described

above, niche markets can provide hedges against uncer-

tain policy. For example, the existence of carbon

utilization markets can render investments in NETs

technologies profitable even if future carbon prices

falls to (or remains at) zero (MacDowell et al 2017).

These benefits seem especially important for NETs

whose value ultimately is determined by governments

finding ways to price the value of the removal of

CO2. An important caveat about niche markets is

that they are generally very small compared to scales

relevant for climate stabilization, in which gigatonnes

are what matter. Niche markets are useful as a way to

get started and possibly hedge in the near term. The

urgency of addressing atmospheric carbon means that

some niches, such as using CO2to produce fuels, are

only viable for a limited period. There is also a risk that

servingnichesdiverts innovation towardcharacteristics

that may not be useful for bulk carbon removal. How-

ever, havinganearly marketwithhighwillingnesstopay

and low competition has been essential for other tech-

nologies, and given the uncertain policy environment,

is likely to be essential for NETs as well.

2.3.6. Public acceptance

While often treated as a separate issue, public accep-

tance of new technologies is crucial to their widespread

adoption. In one sense, acceptance matters in terms of

technology adoption and depends on whether adopters

see value in the new technology, how much risk they

are willing to accept in adopting it, and whether any

adverse side-effects are worthwhile in comparison to

the benefits. Potential adopters have heterogeneous

preferences about these and other aspects. For example,

early adopters of photovoltaics demonstrated high will-

ingness to pay for the green or low-carbon status of the

technology (Sundt and Rehdanz 2015). Adopters also

have varying degrees of agency in determining whether

they will adopt the technology or not. In the case of

NETs, many of the ecosystem management technolo-

gies, such as soils, biochar, and forestry, involve aspects

of this form of acceptance (Zinda et al 2017).

A broader form of public acceptance has to do with

individuals and communities who do not make the

adoption decision directly (Krause et al 2014,Bidwell

2016). They may influence the decision via democratic

processes, public protests, or other politically-oriented

means. But they do not have direct agency, in decid-

ing whether or not to adopt. Thus, this broader

notion of public acceptance includes social, cultural,

and political concepts, including power. Issues with

public acceptance can also emerge before widespread

deployment, e.g. in anticipation. For example, con-

sider CCS demonstrations in Germany (Braun 2017).

The need to deploy NETs at the gigatonne scale height-

ens the likelihood that public acceptance issues will

emerge and need to be addressed. Deploying any tech

at scale large enough to meaningfully benefit the cli-

mate implies likely side-effects (Grubler 1998), which

may not be positive. These can lead to public accep-

tance issues (Batel et al 2013). Some NETs, such as

soil carbon sequestration, seem more challenged by

the first set of public acceptance issues, while oth-

ers, such as BECCS and DACCS seem more likely to

encounter the second type.

A third and more abstract issue is whether NETs

ought to be pursued as a mitigation strategy in the first

place. Ethical reasoning suggests that the availability of

NETs presupposes deep, immediate, and costly emis-

sions reductions, pushing this task to later generations

Environ. Res. Lett. 13 (2018) 063003 GFNemetet al

(thus raising moral hazard), and that they raise con-

siderable procedural and distributive justice concerns

(Anderson and Peters 2016,Shue2017). These issues

may significantly influence the public acceptability of

individual NETs, as well as a NETs strategy in general,

although they are rarely discussed in a public context

(Campbell-Arvai et al (2017) is an exception), nor even

in the climate policy realm (Peters and Geden 2017).

The ethics of NETs are not discussed in this article, but

are reviewed in Minx et al (2018).

3. Scientometrics on innovation in NETs

Our data for the scientometrics part of this review

are the set of 2134 articles identified in Minx et al

(2018). Whereas Minx et al (2018) coded papers by

NETstechnology,herewecodeeachofthesearticles

by stages of the innovation process. As with the tech-

nology categories, each article could also be coded into

multiple innovation categories. Ultimately, we coded

each NETs article as belonging to one or more of the

six categories described above: (1) research and devel-

opment, (2) demonstrations, (3) scale-up, (4) demand

pull, (5) niche markets, and (6) public acceptance.

3.1. Methodology

We began with a set of six innovation stages used

in the literature as described in section 2.Wethen

established a set of keywords describing each stage as

follows. First, we selected key words and phrases from

the brief description of innovation stages in section 2.1.

To find synonyms and related words, we submitted

this set of words and phrases to Google Scholar and

collected relevant words and phrases from the results.

Second, we selected key words and phrases from

four comprehensive review articles on climate change-

related innovation (Weyant 2011,Anadon2012,

Gallagher et al 2012, Fri and Savitz 2014). We entered

the text of each article into the free phrase and word

counters provided by writewords.org.uk. The tools

return a frequency count for each word and phrase.

The list includes non-substantive words such as ‘the’as

wellasmanysubstantivewordssuchas‘innovation.’For

example, it is clear from this search method that words

beginning with the stem ‘invest’are important because

‘investments’appears in the (Gallagher et al 2012)arti-

cle 91 times, ‘investment’appears 30 times, ‘investors’

appears five times, and ‘investing’and ‘invested’each

appear twice.

Third, we assigned these words and phrases to

the six innovation stage queries and began sampling

to establish shares of articles that are relevant to our

intended innovation stage categories. Because we man-

ually read every abstract we designed our searches to

err on the side of including irrelevant articles to ensure

that we do not exclude relevant ones. However, after

sampling, our query for stage ‘6. Public Acceptance’

returned very few relevant articles. We thus expanded

the search by adding frequently used words from two

more public acceptance articles (Krause et al 2014,Bid-

well 2016). The supporting information (SI)provides

the actual Boolean strings used in our searches. We

applied this search string to abstracts and titles of the

articles identified in Minx et al (2018).

Finally, we manually read the title and abstract

of each article and coded it as relevant or not for

each innovation stage in which it was identified using

the Boolean string above. Two researchers read each

article. We coded an article as relevant to that inno-

vation stage if either researcher coded it as relevant

(81% were coded the same by each researcher). We

performed the manual coding using a coding rubric

that reflects the characterization of each innovation

stage in section 2.1. We include details on the manual

coding rubric in the SI.

3.2. Results on locus of research emphasis

Our main result from this analysis is that the liter-

ature on NETs is best described as still ‘scientific,’

which in our framework we categorize as ‘research

and development.’Thesubsequentstagesoftheinno-

vation process are represented in this literature, but

at much lower levels of activity. One implication of

this main result is that if NETs are to be deployed

at the levels needed to meet 1.5 ◦Cand2

◦Ctar-

gets, then important post-R&D issues will need to be

addressed—for example including early deployment,

niche markets, scale-up, demand, and public accep-

tance. For the NETs literature to contribute to this

process, it will need to vastly increase its insights on

post-R&D topics.

In table 1we show the counts of NETs articles

by technology category and innovation stage. In addi-

tion to the NETs technology categories we include

one for ‘synonyms’, which includes cross-cutting men-

tions of NETs, geo-engineering, carbon removal etc

(note that we do not include solar radiation manage-

ment in this review). These counts are made after all

cleaning and removal of irrelevant articles. The R&D

category is distinct in that the search string captures

a large set of articles, over half of all NETs articles.

Articles can be counted in multiple technology cat-

egories as well as in multiple innovation stages. The

totals for rows and columns (‘total positive codes’)

sum to amounts higher than the total number of arti-

cles (‘total distinct articles’). As in paper 1, we see that,

by far, the largest articles counts are for the ecosys-

tem management technologies: SCS and AR. A second

tier includes DACCS, OF, and BECCS. Biochar and

EW are much smaller.

Pooling across NETs technologies, R&D domi-

nates all innovation categories. The next largest counts

are in scale up, demand pull, and public acceptance.

But even these most well-represented innovation stages

include only a minority of the articles. Table 2shows

that 21% of the articles cover scale-up, 9% demand

pull, and 7% public acceptance. Demonstrations

Environ. Res. Lett. 13 (2018) 063003 GFNemetet al

Table 1. Counts of NETs articles by technology (rows) and innovation stage (columns).

Supply-side categories Demand-side categories

Technology RD Demos Scaleup Demand Niche Public Total Total

pull markets accept positive codes distinct articles

Afforestation/reforestation 149 9 62 24 1 7 252 197

BECCS 61 7 37 31 5 10 151 106

Biochar 48 1 15 4 1 5 74 58

Direct air capture 92 7 30 8 2 5 144 108

Enhanced weathering 13 1 7 1 – 5 27 19

Ocean alkalinisation 5 – 6 2 – 2 15 10

Ocean fertilisation 94 4 21 5 – 15 139 103

Soil carbon sequestration 183 4 37 11 – 22 257 209

NETs - General 52 6 30 20 5 9 122 75

Total positive codes 697 39 245 106 14 80 1181 885

Total distinct articles 679 29 208 99 10 79

Table 2. Share of NETs articles in each stage (%), as a proportion of all articles (R&D).

Supply-side categories Demand-side categories Toal

Technology RD Demos Scaleup Demand pull Niche markets Public accept Demand side

Afforestation/reforestation 59 4 25 10 0 3 13

BECCS 40 5 25 21 3 7 30

Biochar 65 1 20 5 1 7 14

Direct air capture 64 5 21 6 1 3 10

Enhanced weathering 48 4 26 4 – 19 22

Ocean alkalinisation 33 – 40 13 – 13 27

Ocean fertilisation 68 3 15 4 – 11 14

Soil carbon sequestration 71 2 14 4 – 9 13

NETs - General 43 5 25 16 4 7 28

Total positive codes 59 3 21 9 1 7 17

and niche markets, which the innovation literature

describes as crucial, are addressed in only a small num-

ber of articles across all NETs; only 1% of the articles

refer to niche markets and 3% refer to demonstration

programs.

Table 2also reveals the disparity between publi-

cations involving the supply side of NETs and those

involving the demand side. The overwhelming share of

work has been on the supply side. When we pool article

counts across the demand side categories and divide

by the total number of innovation codes assigned,

we get an estimate of this supply side focus. For all

NETs, 17% of the codes are on the demand side,

83% involved supply side. Technologies with an above-

average demand side activity are: BECCS (30%) and

enhanced weathering (22%). There are low counts of

demand side discussions for ocean fertilization (14%),

biochar (14%), soils (13%), and direct air capture

(10%). Correspondingly, a typical way in which these

technologies are discussed is that they are ‘deployed’

rather than ‘adopted.’

Looking attheinnovationcategoriesby technology,

beginning with the supply side: table 2shows very low

shares of articles on demonstrations. The only tech-

nologies with more than 4% on demonstrations are

BECCS and DACCS. We note that these are two tech-

nologies that are most tightly connected to industrial

processes, so the notion of needing to demonstrate

the technology before widely using it is most well

accepted there. Still, these are very low values con-

sidering that the most immediate next step toward

planetary-scale deployment for all of these technolo-

gies is demonstrating reliability, efficacy, affordability,

and safety. We see much higher counts for scale-

up, especially for ocean alkalinisation (40%), BECCS

(25%), EW (26%), and DACCS (21%). We note how-

ever that many of the mentions of scale-up did not

typically discuss a pathway or sequence of steps to

scale up the technology. Rather, they indicated that

scale up was necessary and often left it at that, at

least in the abstracts, which are what we read for

coding.

On the demand side, we see only BECCS with well

above average mentions of mechanisms that would

create incentives to adopt (‘demand pull’). Typically,

this involved modeling studies in which these tech-

nologies became widely deployed once a carbon price

was applied. Another frequent demand-pull mecha-

nism was the REDD(+) program relevant to AR. Very

few articles discussed niche markets. BECCS perhaps

is the notable exception in which a few articles dis-

cussed carbon utilization, e.g. for food or enhanced

oil recovery. Public acceptance was much more rep-

resented than niche markets. BECCS, and enhanced

weathering have above average mentions of public

acceptance. Technologies notable for very low counts

of public acceptance articles include DACCS (3%), AR

(3%), and BECCS (7%). We do not have a way to tell

whether these technologies have inherently fewer pub-

lic concerns or whether these concerns are simply being

Environ. Res. Lett. 13 (2018) 063003 GFNemetet al

Figure 5. Trend in NETs publications by innovation stage and by technology. Each panel includes one innovation stage.

overlooked, perhaps because the technologies are at an

early stage.

3.3. Trends in research emphasis

In figure 5we show the trend in NETs publications by

innovation stage. As in the table above, we see the gen-

eral emphasis on R&D publications and much smaller

counts for the other stages. Note that R&D has a larger

y-axis range than the others. In figure 5we add infor-

mation on the technology categories as stacked colors

for each bar. In figure 8, we show the trend in publi-

cations within each NETs category. Each color in each

stacked bar is an innovation stage.

4. Review of innovation topics in the NETs

literature

Going beyond the scientometrics, we review the main

claims about innovation made in the articles. We

include some discussions of innovation stages that were

not positively coded as described in section 3,which

only reflect coding of titles and abstracts rather than the

full content of the article which this section includes.

4.1. Bioenergy carbon capture and storage (BECCS)

We found 106 articles focusing on innovation in

BECCS,whichputitinthemiddleoftherangeoftech-

nologies we assessed. These articles covered a richer

set of innovation stages than did the other technolo-

gies. BECCS was more balanced between supply and

demand side topics than were the other technologies;

it had the highest portion of counts on the demand

side. One possibility is that this is due to their use

in IAMs, which connect demand, in the form of car-

bon prices, with deployment of BECCS. Compared to

other technologies, it also had a higher share of articles

that covered innovation stages other than R&D. It had

a higher share of articles on demonstration than any

other technology. It also had the highest on scale up,

demand pull, niche markets, and public acceptance.

Although BECCS included a smaller share of pure-

R&D articles, it still included many articles covering

the science on, for example, designing optimal feed-

stocks, increasing yields, capture efficiency, and how

to allow for multi-functional land use. Representative

examples include testing a gasification technology at

the sub-MW scale (van der Meijden et al 2010)and

comparing costs of various proposed plant configura-

tions (Schmidt et al 2010). However, since so few plants

and infrastructure have actually been built, projections

rely on models (Azar et al 2013,Kriegleret al 2013,

van Vuuren et al 2013,Kleinet al 2014,Roseet al

2014) and in exceptional cases on expert elicitations

(Vaughan and Gough 2016). Along with AR, BECCS

is one of only two NETs to be regularly and specifi-

cally represented in IAMs, other than some individual

extensions (Popp et al 2011,Riahiet al 2015).

4.1.1. Demonstrations and scale up

Proving that BECCS plants are reliable and trusted

is a key open issue and an obstacle to widespread

adoption (van Alphen et al 2009). While only seven

articles discuss BECCS demonstration projects, this

is still relatively high compared to other technologies

and considering the very limited BECCS deployment to

date. Globally, less than a dozen small demonstrations

have been built (Kemper 2015) with more ambi-

tious projects having recently come online. One

important development is a full scale (1 Mt CO2yr−1)

Environ. Res. Lett. 13 (2018) 063003 GFNemetet al

BECCS demonstration plant online at Decatur, IL

USA (McCulloch 2016). More effort has involved

small pilot scale plants, at scales of hundreds of kWs

e.g. as described in Diego and Alonso (2016). A

larger example is a study in Inner Mongolia of a 24

MW BECCS plant using desert shrubs and storing

the CO2in algae (Pang et al 2017). A helpful pre-

liminary assessment of these plants shows that their

performance is quite similar considering their diver-

sity (Bhave et al 2017). Kemper (2015) points out that

lack of demonstration creates significant uncertain-

ties about feasibility of large scale deployment. Gough

and Upham (2011) point to feedstock availability,

system integration, and CO2transportation infrastruc-

ture as critical components of the scale-up challenge

for NETs.

4.1.2. Niche markets and demand

Turning to the demand side for BECCS, Bhave et al

(2017) make the point that early demonstration plants

struggle with weak or practically non-existent incen-

tives to generate negative emissions. Where CO2

emissions are priced, these markets are volatile and

uncertain (Iyer et al 2015), while incentives for CO2

capture are even more so. BECCS is unique among

NETs in that it produces energy thus also exposing

them to the vagaries of energy prices (MacDowell and

Fajardy 2017).

Early niche markets are identified as sugar and

paper processing facilities (Mollersten et al 2003)as

well as industrial and municipal waste (Sanna et al

2012). One of the more circumspect studies on the

important role of niche markets raises the issue of

whether fossil fuel niches, such as EOR, could lock in

fossil fuels rather than phase them out (Vergragt et al

2011).

For adoption in the longer term, Muratori et al

(2016) use an IAM to assess very widespread deploy-

ment of BECCS and point to their impact on food

prices, although they also indicate that such massive

diffusion assumes many barriers are overcome. Others

point to N2O emissions due to associated intensifica-

tion of agriculture (Popp et al 2011). Fridahl (2017)

uses a survey to show that while IAMs depend heav-

ily on BECCS for long-term decarbonization, BECCS

have featured very rarely in policy debates, raising

serious questions about the near-term incentives for

adoption. BECCS is unique among NETs in that it

produces useful energy in the form of electricity. How-

ever, some studies question whether the price of this

electricity can compete with renewables in a mostly

decarbonized system (MacDowell and Fajardy 2017).

And in IAMs BECCS are adopted due to rising carbon

prices not due to the value their electricity provides.

The ability of BECCS to alter their use of inputs and

capture rates provides a way to make them competi-

tive in changing markets for both carbon and power

(Sanchez and Kammen 2016). Modeling studies also

make the point that scaling up to meaningful levels

could take ‘decades’(Azar et al 2013)oreven‘half

acentury’(Azar et al 2010). Beyond IAM studies, a

comprehensive review of impacts of BECCS on sustain-

able development finds positive economic effects but

negative social and environmental impacts (Robledo-

Abad et al 2017).

4.1.3. Public acceptance

BECCS’share of its articles on public acceptance was

close to the NETs average. It is striking how many

of the articles we reviewed claim to take a compre-

hensive approach to BECCS but neglect to mention

public acceptance issues, a sentiment shared in the lit-

erature (Dowd et al 2015). Fridahl (2017)makesthe

point that people are still very unfamiliar with BECCS

but that public acceptance of the technology will be

crucial. They claim it does have more likelihood of

acceptance than fossil CCS. Some argue that the agri-

cultural links in BECCS will make it more publicly

acceptable than CCS from fossil fuels (Wallquist et al

2012). Still, land use concerns (Searchinger et al 2008,

Wise et al 2009,Plevinet al 2010,Poppet al 2011)and

the essential tradeoff with food production, even with

large uncertainties about the precise impacts (Smith

et al 2013, Stevanovicˇ

ıet al 2016,Boysenet al 2017)

could be difficult to overcome (Robledo-Abad et al

2017). However, the transportation of massive quan-

tities of biomass may make it less acceptable than

CCS. Open issues in accounting of land-use change

emissions render the climate benefits of bioenergy

and BECCS highly uncertain (Creutzig et al 2012,

Plevin et al 2014); this uncertainty in climate bene-

fits translates directly into investment uncertainty into

BECCS as a NET. Regulation of stored CO2and its

leakage is another key public acceptance issue (Boot-

Handford et al 2014). Gough et al (2014)findthat

CO2pipelines are perceived more favorably than gas

pipelines, although safety and risk concerns remain

paramount. Given these concerns, Rodriguez et al

(2017) discuss ways in which mitigation could be

enhanced to reduce the need for BECCS or alterna-

tively to use non-food feedstocks, such as algae (Sharp

et al 2017). Similarly, Boysen et al (2017)arguethat

scale-up will be bounded, due to socially unaccept-

able levels of deforestation and food availability, so

that BECCS potential is limited to a role supporting

other mitigation options. Gough and Upham (2011)

suggest smaller scale BECCS will be more acceptable.

In an intriguing analysis using analogous technolo-

gies Buck (2016) describes some of the challenges to

scale up (such as volatile markets) as well as concerns

that could emerge with BECCS (such as unequal dis-

tribution of benefits) due not just to the extent of

deployment but also from its speed. Indeed many of

the concerns for CCS apply to BECCS (Wallquist et al

2012), as do those for generic bioenergy development,

including public perceptions of facility siting, local air

pollution, and feedstock transportation and handling

(Thornley et al 2009).

Environ. Res. Lett. 13 (2018) 063003 GFNemetet al

4.2. Direct air carbon capture and sequestration

(DACCS)

More than any other NET, direct air capture arti-

cles primarily focus on the technology and supply-side

innovation topics. It has an above average share of arti-

cles on both demonstrations and scale up. It has the

lowest share of articles on demand side topics of any

NET. Indeed, in the companion paper that system-

atically reviewed costs and potentials, very little was

found on the removal potential of DACCS (Fuss et al

2018). Plausibly this reflects the current understanding

that economic costs rather than biophysical boundaries

or concerns will determine future success of DACCS

(Smith et al 2016).

R&D is currently the focus of innovation effort.

This emphasis aligns with a recent National Academies

report that recommended DACCS R&D‘to minimize

energy and materials consumption, identify and quan-

tifyrisks,lowercosts,anddevelopreliablesequestration

and monitoring.’At the center of research activity, var-

ious chemicals for absorbing and adsorbing CO2are

investigated (Choi et al 2011, Goeppert et al 2011,

Kong et al 2016). One area of R&D involves com-

paring the technical characteristics of various methods

with which to capture CO2from ambient air, and

the associated mechanisms to maintain this process,

e.g. the energy used for pumping and compressing

(Lackner 2013), as well as chemical processes for regen-

erating solvents (Goeppert et al 2012,NRC2015,

Sanz-P´

erez et al 2016). Humidity is a concern for

DACCS technologies, and R&D involves addressing

humidity issues in ambient air (Darunte et al 2016).

4.2.1. Demonstrations and scale up

One reason for the focus on pure R&D is that the

technology is arguably at a nascent stage. For exam-

ple, Boot-Handford et al (2014) put DACCS in the

context of power plant CCS and mainly dismiss the

technology as ‘in its infancy’and far more expensive

thatothermitigationoptions.GoingbeyondR&D,they

compare their results to a conventional alternative—

using power plant flue gas—and model the result at

full commercial scale, 1200 MW. However, we do see

some examples of demonstrations. Agee and Orton

(2016) discuss a laboratory-scale air capture method,

which achieves deposition of atmospheric CO2via

refrigeration; they extend by discussing the advan-

tages (avoiding refrigeration needs) and challenges

(metal fatigue) of deploying this scheme in Antarc-

tica. Holmes et al (2013) present a prototype of a

cooling tower design, where air flows orthogonal to

a downward flowing hydroxide solution; they demon-

strate more than 1000 hours of operation, validating the

cross-flow contactor design. Rau et al (2013)describe

experiments with absorption of CO2via electrolyzed

solution but use most of the article to discuss the energy

requirements and costs at scale.

DACCS has an above average a share of articles

on scale-up. The possibilities for mass production of

air capture devices are a very attractive characteristic

(Lackner et al 2012). The costs of DACCS appear to

be a much more prominent topic than in other NETs.

For example, a subset of the comparisons mentioned

above involve cost estimates and financial compar-

isons (Socolow et al 2011,Sinhaet al 2017). Other

work more explicitly focuses on scaling up DACCS

and includes estimates of cost reductions associated

with >10Gt of CO2removal per year using component

cost estimates (Lackner 2009)aswellasbottom-up

learning by doing and scale effects (Nemet and Brandt

2012). Comparisons of other estimates to mitigation

costs also shows the feasibility of DACCS at very large

deployment (Pielke 2009). Stolaroff et al (2008)pro-

vide an especially detailed bottom-up cost model of

a sodium hydroxide spray capture system deployed

at scale. Li et al (2015) consider integrating DACCS

with wind power. Comprehensive information on

costs and potentials can be found in (Fuss et al 2018).

4.2.2. Niche markets

Unlike other NETs, DACCS has received significant

attention from entrepreneurial firms. This activity may

be in part due to its main barrier being direct costs,

rather than side effects nor social concerns. Direct

implementation costs could be significantly reduced

with successful innovation. In addition, there is addi-

tional investment safety in that CO2sequestered from

ambient air can be accurately and precisely accounted

for, in contrast to, for example, BECCS. Another driver

of entrepreneurial activity is the existence of robust

niche markets.

DACCS has been utilized routinely in spacecraft

and submarines to reduce the CO2levels of ambient

air in closed systems. High indoor concentrations of

CO2, as prevalent in bed rooms, and classrooms, have

negative effects on performance, health, and human

health (Kotol et al 2014). DACCS applications focusing

on indoor air have the advantage of tangible bene-

fits for occupants, and can work at higher efficiency

due to up to 10 fold higher concentration compared

to ambient open air (Lee et al 2015), and thus could

hold promise as niche market.

Other startups can develop in niche markets that

are focused on utilizing CO2for applications, such as

greenhouse fertilization, industrial use, or enhanced

oil recovery (Lackner et al 2012,Houet al 2017,

Ishimoto et al 2017). Enhanced oil recovery and

microalgae cultivation are judged the most suitable

niche markets where also dilute CO2is an adequate

feedstock, thus requiring less energy for separation

(Wilcox et al 2017). For example, a business case

for power-to-liquid synfuels has been proposed that

would make use of combined hybrid wind/PV, electrol-

ysis, and hydrogen-to-liquid, involving also scrubbing

CO2from ambient air powered by excess heat from

electrolysis (Fasihi et al 2016). Carbon Engineering

is a company that has published substantial data on

important aspects of its technology (Holmes et al 2013,

Environ. Res. Lett. 13 (2018) 063003 GFNemetet al

Holmes and Corless 2014). In addition, Climeworks

had as its original aim the production of synfuels,

relying on direct air capture. Yet, now, Climeworks

is running the first commercial DACCS plant using

the CO2to fertilize plants in a greenhouse. Climeworks

alsoopenedaDACCSfacilityincooperationwithReyk-

javik Energy in Iceland, making use of waste heat from

a neighboring thermal power plant to adsorb CO2from

the filter, and injecting CO2underground as carbon-

ated water, which then mineralizes in basaltic bedrock.

Its technology is based on amine-based nanocellulose

materials, but specifics have not been disclosed. These

startups can develop in niche markets that are focused

on utilizing CO2for applications, such as greenhouse

fertilization, industrial use, or enhanced oil recovery

(Ishimoto et al 2017).

4.2.3. Demand and public acceptance

Although niche markets exist, our coding found that

DACCS has the lowest share of demand side articles

among the NETs. Those that do consider demand for

DACCS, primarily focus on carbon prices that would

be required to justify the costs of DACCS (Pielke

2009). An integrated assessment modeling study found

a primary impact of DACCS is that it extends the

use of oil under climate policy (Chen and Tavoni

2013). Another IAM study found that the availability of

DACCS cansubstitute for BECCS to achieve 1.5◦Ctar-

gets (Marcucci et al 2017). Another source of demand,

potentially, comes from oil producers concerned

about the value of their reserves under climate policy

(Nemet and Brandt 2012).

Only a few articles grapple with public accep-

tance. Lackner and Brennan (2009) lay out a broad

set of possible public concerns and provide some ini-

tial assessments of their risks to the public. Leakage

of stored CO2is one prominent concern, relevant

to other NETs as well (Vilarrasa and Carrera 2015,

van der Zwaan and Gerlagh 2016). DACCS is general

seen as more benign than CCS, as fossil fuels are not

involved. Cheng et al (2013) even develop a vision of

an acceptable use of DACCS within a ‘green town’to

subsequently also improve public acceptance of CCS.

4.3. Biochar and soil carbon sequestration (SCS)

Soils on their own have the most articles of any

NETs, followed by AR. The count of biochar arti-

cles is below the median. This literature on soils and

biochar as NETs is large and mature, with several arti-

cles from the 1990s, though biochar is a more recent

topic than soil carbon sequestration, which has been

an active field for research for decades. Soils articles

were quite representative of NETs in their distribu-

tion across innovation stages. Soils were relatively low

on scale up and slightly lower than the average on

demand pull, but generally were close to the aver-

ages in other innovation categories. Biochar was low on

demonstrations and scale up compared to the average

NETs. It was also low on all demand side categories.

Due to biochar becoming an active topic of research

more recently, biochar articles were the least likely to

cover non-R&D stages of any technology; most work

on biochar is still scientific.

Recurring R&D topics in prominent articles

include: the amount of soil carbon retained by agricul-

tural practices (Piccoli et al 2016), the corresponding

amount lost (Sanderman et al 2017), and the impacts of

tillage (Sohi et al 2010). Effects on carbon content and

the overall health of soil from application of biochar is

alargefocusarea(Fanget al 2016,Novaket al 2016,

Pandian et al 2016), including for example N content

(Prommer et al 2014). Temperature for pyrolysis is

an important topic among biochar articles (Sohi et al

2010). Assessing the multiple benefits of biochar pro-

ductions and use, e.g. via sugar cane is another research

direction (Quirk et al 2012).

For R&D, two helpful reviews (Olson 2013,Olson

et al 2014) establish research design choices that would

make these experiments most useful for the next

stage of demonstration. There are developing estimates

of global resource potentials for soil carbon storage

(Mishra et al 2012,Paustianet al 2016,Smith2016)

(Mishra et al 2012) as well as life cycle analysis of

greenhouse gas impacts from various cropping systems

(Cooper et al 2011).

The maturity of work on biochar and soils has led to

a set of strong comprehensive review articles, includ-

ing Lal (2005), Lal et al (2007), Woolf et al (2010),

Paustian et al (2016), and more recently Woolf et al

(2017).

4.3.1. Demonstrations and scale up

A recurring assertion in the soils literature is the

need for large and long term demonstrations (Ringius

2002). Only one such study exists, which the authors

claim is unique (Vochozka et al 2016). Other exper-

iments have been relatively large (Piccoli et al 2016)

and long term (Gutzloe et al 2014, Triberti et al

2016) and therefore approach a scale sufficient for

demonstrations to generate new knowledge. Some use

large scale assessments over long periods to quantify

potentials (Liu et al 2014). Large and long term demon-

strations seem most convincing as models for later

adoption (Six et al 2004, Diacono and Montemurro

2010). Promisingly, unlike other NETs very long term

(i.e. several decades) field experiments are common

for soil carbon management (Hofmockel et al 2007,

Smith et al 2012).

For scale up, some studies use field experiments

to model large scale applications of techniques such

as conservation tillage (Jiang et al 2014,Novaket al

2016). Global potentials have been estimated (Paus-

tian et al 2016,Smith2016). One focus has been the

challenge of moving from dispersed land use decisions

to managed and coordinated ones to enable scale up

(Valujeva et al 2016). For biochar, financing mecha-

nisms are also covered as a means to support scale up

(Whitman and Lehmann 2009).

Environ. Res. Lett. 13 (2018) 063003 GFNemetet al

4.3.2. Demand and public acceptance

Even though the overall share of studies on demand is

quite low for soils and especially biochar, several articles

make the point that creating incentives for farmers to

adopt is central to policy design (Dilling and Failey

2013, Stavi and Lal 2013). Estimating and reducing

the costs of biochar are put forth as a key way to spur

demand for it (Spokas et al 2012, Dickinson et al 2015),

in particular, the costs of pyrolysis (Meyer et al 2011).

A key practical issue is how the accounting would

work (Sanderman and Baldock 2010,Downieet al

2014). One model is a carbon credit scheme in Mon-

tana(Wattset al 2011). A small number of articles focus

on the policy aspects that would affect demand (Smith

et al 2007). Less directly, we often see that demand is

implied in the context of discussions of land and soil

management (Valujeva et al 2016).

Given their centrality as adopters, articles dis-

cussing public acceptance generally focus on farmers

rather than the more general population (Olsson and

Jerneck 2010, Jørgensen and Termansen 2016). Beyond

farmers, a focus is on stakeholders and especially

how the world poor stand to benefit (Stringer et al

2012). One notable study actually surveyed people on

their perceptions of its risks and benefits (Glenk and

Colombo 2011). Quite a few studies make the point

that interdisciplinary research from many disciplines

including social sciences is needed, even if such work

isn’t conducted by themselves (Lal 2008). One way the

considerable transactions costs might be overcome is

through international coordination. For example, the

4p1000 Initiative (www.4p1000.org) creates incentives

for farmers to increase C to improve their soil qual-

ity (with C removal as a co-benefit) with a goal of

increasing soil carbon content by 0.4% per year.

4.4. Ocean fertilization (OF)

The share of ocean fertilization articles on demonstra-

tions and scale up was close to the NETs average. There

were considerably fewer articles on the demand side,

including none on niche markets. Scale up was the

most prominent non-pure R&D topic. One observa-

tion is that the early papers (early 2000s) seem much

morefocusedon scale up(bothoptimisticallyandskep-

tically) than later ones, which tend to be more focused

on R&D.

4.4.1. Demonstrations and scale up

A number of experiments have taken place, some of

which are substantially large enough to be considered

demonstrations. For example, Boyd and Bressac (2016)

describes 12 ‘mesoscale’iron fertilizations conducted

in the GEOTRACES global survey. Others include

an early small experiment, almost a demonstration

(Bakker et al 2001) and an early mesoscale experiment

(Boyd et al 2000). These ‘experiments’in the Southern

Ocean are also close to demonstrations (Smetacek and

Naqvi 2008). Two articles take a distinctly innovation-

oriented perspective, one stressing that we need to learn

aboutittoscaleup(Lampittet al 2008)andanother

describing research design for a set of demonstration

projects of increasing scale (Watson et al 2008).

Even though 15% of the articles discuss scale up,

few explicitly address the process of getting from small

experiments and demonstrations to large scale deploy-

ment. Exceptions include an early article including

some discussion of the progression (Benemann 1992),

as well as upscaling from experiments to global scale

(Aumont and Bopp 2006). Another focuses on side

effects but in doing so simulates growth of of over

time (Oschlies et al 2010). We also see articles on

the costs of scale up (Jones 2014)andalsoafocus

on governance associated with scale up (Rabitz 2016).

A more typical topic is to report what the impacts of

very large deployment would be (Cao and Caldeira

2010, Keller et al 2014, Williamson et al 2012)and

including for example, a model that considers tera-

gramsofironadditions(Haucket al 2016). A cluster

of articles from a decade ago considered implementa-

tion issues, such as the legal framework necessary for

scale up (Freestone and Rayfuse 2008), modeling large

scale deployment (Zeebe and Archer 2005), and the

need to involve businesses, not just scientists (Leinen

2008). It is interesting that these quite practically

oriented articles are a decade or more old.

4.4.2. Demand and public acceptance

Discussion of the demand for ocean fertilization is

notably lacking. We do see articles on how carbon mar-

ketscouldleadtodemandforOF(Rickelset al 2012),

accounting and incentives (Rickels et al 2010) and con-

sideration of CDM applied to OF (Bertram 2010). We

found no articles that we would classify as discussing

niche markets for ocean fertilization.

The share of public acceptance articles is above

the NETs average. Some work is not explicit about

public acceptance, but does try to anticipate issues,

for example recommending waiting until we fig-

ure out downstream effects of OF and unintended

consequences (Cullen and Boyd 2008). Some cover

governance issues (Williamson et al 2012), legal sta-

tus (Bertram 2010), and the Law of the Sea (Freestone

and Rayfuse 2008). Others discuss the social implica-

tions for people making a living from coastal areas

(Mayo-Ramsay 2010). Work acknowledges that the

public debate is intensifying—we don’t know enough

to drop it now (Strong et al 2009). One quite risk averse

perspective claims that uncertainty about future state

after deploying OF makes it unacceptable (Hale and

Dilling 2011). Others also cover the public acceptance

of experiments (Strong et al 2009) and why these tend

to be unpopular (Smetacek and Naqvi 2008).

4.5. Afforestation and reforestation (AR)

Forests were second only to soils in the counts of arti-

cles. AR is a mature ‘technology’(using our broad

definition), it already exists at scale, and the potential

for storing gigatonnes of carbon has been recognized

Environ. Res. Lett. 13 (2018) 063003 GFNemetet al

for decades (Canadell and Raupach 2008,Jurgensen

et al 2014). The share of AR articles is close to

the average in all innovation categories. It is slightly

higher in scale up and demand pull, and quite a bit

lower in public acceptance. R&D articles include many

reporting experiments on the sequestration potential

of forests, at various spatial scales and time peri-

ods. In our initial filtering of articles, we discovered

hundreds of AR studies that focus on site or species-

specific carbon sequestration rates. These studies are

not considered or reviewed here as their specificity

does not allow for a straightforward assessment of

NET potential or scale up possibilities. Nonethe-

less, the AR option should be considered against a

backdrop of substantial empirical research into the

basic characteristics and growth patterns of forests

globally.

4.5.1. Demonstrations and scale up

We identified 62 AR articles that address scale up,

however with widely differing interpretations of the

ultimate scale needed. Articles we coded as demonstra-

tions include: Returning Farmland to Forest Program

in China (Zinda et al 2017), Forest Restoration Exper-

imental Project (Gong et al 2013), and a project in

Guangdong, China (Zhou et al 2008). A different type

of demonstration included looking at the financial out-

come of a CDM project after 6 years (Katircioglu et al

2016).

In contrast to other NETs, in many articles there

is an implicit premise that scale could be achieved

if societies wanted to and thus the research frontier

is about side effects and potential size. For example,

some articles discuss implications of scale up but not

with a focus on how to accomplish this, even from

quiteawhileago(Alpertet al 1992,Shvidenkoet al

1997). Others consider the scale-up process itself more

analytically (Zhang et al 2015)(Caughlinet al 2016),

even from earlier (Canadell and Raupach 2008)and

even very early on in the AR literature (Myers and

Goreau 1991). Some of these articles include experi-

ments that explicitly try to assess the potential for scale

up; a small portion of these are demonstrations. Several

projects are already quite large, including government-

led efforts at reforestation, typically for purposes other

than carbon storage, and projects assessing AR out-

comes at the scale of watersheds (Cunningham et al

2015).

A recurring theme is the identification of barri-

ers to scale up, and overcoming them (Vadas et al

2007), often under the rubric of implementation issues

(Polglase et al 2013). For example we see a focus on

measuring monitoring and contracting (van Kooten

and Johnston 2016). There is an ongoing stream of

research into the costs of AR, either in terms of direct

establishment costs (Summers et al 2015), opportu-

nity costs (Nijnik et al 2013), or land-use switching

under certain carbon price assumptions (Monge et al

2016). Already a decade ago a review study compiled

the preceding 12 years of cost studies (Richards and

Stokes 2004).

4.5.2. Demand and public acceptance

Discussions of the demand for AR typically focus on

carbon markets (Adams and Turner 2012,Carwar-

dine et al 2015, Liu and Wang 2016). Demand for

ecosystem services can also lead to AR (Meyfroidt and

Lambin 2011), especially with supportive policies (Liu

et al 2008). For example reduced salinity is one benefit

(Harper et al 2012). A consequence of these multiple

sources of demand is how to optimize across these,

especially when AR and soils are considered jointly

(Valujeva et al 2016). A more precise conception of

demand arises in more spatially explicit estimates of

willingness to pay for AR (Sagebiel et al 2017).

An important agent in AR studies are farmers.

For example one can see small scale family forests as

a niche (Charnley et al 2010). Looking into farmers’

preferences seems crucial to adoption, yet unusual in

the literature, with exceptions (Lienhoop and Brouwer

2015). We see some emphasis on the role of farmers’

negotiating power in these markets. A big barriers is

transactions costs of lots of small scale transactions

(van Kooten et al 2002).

The share of articles on public acceptance was low

compared to other NETs. As with soil carbon seques-

tration and biochar, discussion is typically focused on

private landholders (Schirmer and Bull 2014,Trevisan

et al 2016), rarely going into realm of the public and

their attitudes, beyond occasional economic incen-

tives. AR directly impacts on the visual features of

a landscape, so it is surprising to see such a lack of

engagement between sequestration studies and the rich

literature on landscape aesthetics and social/cultural

expectations of ‘nature’(Hunziker 1995,Daniel2001).

We do see some critical discussions of the impacts

of ‘carbon farming’(Funk et al 2014), water use

(Jackson et al 2005), nutrient cycling (Smith and

Torn 2013), and the need for stakeholder engagement

(Atela et al 2016). A survey on attitudes toward AR

was a notable exception to the dearth in this area

(Nijnik and Halder 2013), including also a survey by

Schirmer and Bull (2014).

4.6. Enhanced weathering (EW)

After ocean alkalination, EW was the NET with the

lowest number of articles. Within EW, shares were rel-

atively close to NETs averages; above average share of

articles were on scale up and public acceptance.

4.6.1. Demonstrations and scale up

In response to the minimal body of work, Hart-

mann et al (2013) argue that there is a ‘need for

specific experiments’. They propose a cascade of exper-

iments bridging the scales from millimeters to meters

to 100s of meters as a means to scale up. Chemi-

cal engineering studies are particularly well positioned

to consider the issues and opportunities of scale up

Environ. Res. Lett. 13 (2018) 063003 GFNemetet al

(Morales-Florez et al 2011,Hallet al 2014). Com-

puter models are used to estimate potentials (Taylor

et al 2016) which in a very general way simulates

scale up, even if admittedly ‘idealized’and missing

important processes like the biological pump. Stud-

ies also draw on data sets of surface rock types to assess

carbon removal potentials if kinetics are understood

(Moosdorf et al 2014, Strefler et al 2018). The speed

of weathering is a critical issue for scale up. It has

been studied using empirical data on natural systems

(Li et al 2008,Poweret al 2009, Ollivier et al 2010),

in particular the effect of temperature (Li et al 2016),

as well as using laboratory-scale experiments (Renforth

andManning 2011). Inaddition, theappliedrock mate-

rial has fresh surfaces and fines from the production

process, which will lead to enhanced kinetics, but is still

not reliably quantifiable for upscaling. This can be seen

in field studies if comparing kinetics of pyroclastics,

remains from volcanism with fine grains, with other

rocks (Hartmann 2009).

Interactions of minerals with soils and the biol-

ogy is a key research area and important to scale up

(Hartmann et al 2013, Manning and Renforth 2013,

Taylor et al 2017, Beerling et al 2018). An early paper

included a lab scale experiment showing that waste

concreteandalgaecouldbeusedtofixCO

2as calcium

carbonate (Takano and Matsunaga 1995). Use of waste

materials as a source of minerals is included in more

recent work as well (Sanna et al 2012).

As EW also releases geogenic nutrients it will affect

biomass production, and can in addition to inorganic

CO2sequestration be used to enhance biomass pro-

duction (Hartmann et al 2013). However, this part has

not been studied for upscaling so far and would be

important to consider in the simultaneous use of AR

and EW or BECCS and EW over large areas, specifically

in areas with low geogenic nutrient contents in soils and

bedrock.

4.6.2. Demand and public acceptance

We found only a small number of articles that discussed

thedemandsideofEW;oneondemandpullandfive

on public acceptance. As with other NETs, much of

demand will come from carbon pricing; since EW costs

are anticipated to be large they will require a substan-

tial carbon price (Hartmann and Kempe 2008,Taylor

et al 2016). A separate source of demand is the appli-

cation of EW to increase agricultural yields, specifically

in areas with depleted soils (van Straaten 2002,Hart-

mann et al 2013)—however, this is not currently a

widespread practice.

A survey of public perceptions of geo-engineering

technologies found that EW scored in the mid-range

in terms of acceptability; in part because it is con-

sidered ‘indistinct’relative to other GE technologies,

public response was expected to be muted (Wright

et al 2014). Specific public concerns mentioned are

health effects of atmospheric suspension of pulver-

ized rock (Taylor et al 2016) although there is little

analysis to date of the risks of each, which depend also

on the chosen application procedures. An important

advantage of EW in the public domain is that rather

than imposing competition for land it could enhance

productivity (Strefler et al 2018), a major issue for

public acceptance of BECCS and AR.

4.7. Papers that consider innovation topics across

multiple NETs

One way NETs are combined is through comparisons

of their deployment potentials to their risks. For exam-

ple, Field and Mach (2017) argue that the risks are

high even if potentials are large, thus advocating a

focus on research, limited expectations, and a renewed

emphasis on mitigation. Another group compares costs

and potentials (Johnson et al 2017), as well as storage

capacity (Scott et al 2015). Some policy related topics

also involve multiple NETs. For example, Coffman and

Lockley (2017) consider, but ultimately reject as infea-

sible, a carbon removal futures market to account for

the delay in deployment. And in terms of actual pol-

icy, the £8.6 m UK Greenhouse Gas Removal Research

Programme aims to test several approaches, although

at that scale these would be at best pilot and proto-

type experiments rather than demonstrations in the

sense we use it here. Ying and Yuan (2017)provide

a Chinese perspective on designing policy for NETs.

Other papers are less specific on the removal technique

used but raise upscaling issues, such as game theo-

retic incentives (Sandler 2017). In the US, the National

Academies is working on a scale up oriented report on

NETs (National Academies 2017).

5. Conclusions

Our assessment of the extant literature on innova-

tion and scale up in NETs shows a growing literature

across all NETs technologies. The literature on emerg-

ing mitigation technologies emphasizes diversification

to manage risk (Anadon et al 2017); that many NETs

are at early stages in their development makes such an

approach even more appropriate. The heterogeneity of

these technologies, especially in their limitations and

adverse side effects, strongly suggests a portfolio-based

risk management approach to scaling up NETs, rather

than a singular focus.

An important insight from the reviews in (Minx

et al 2018)and(Fusset al 2018), as well as in the IAM

results included in this paper, is that even though IAM

model results typically have a large role for NETs to

play in the second half of the 21st-century, there is

still urgency in developing them. This urgency derives

in part from the generally long time periods required

for the diffusion of technologies to attain widespread

adoption, as described in section 2of this review. That

these technologies need to be removing CO2at the

rate of gigatonnes-per-year (Fuss et al 2018)implies

truly massive deployment, and consequently long

Environ. Res. Lett. 13 (2018) 063003 GFNemetet al

2010 2020 2030 2040 2050

Gross negative emissions

(GtCO2/year)

R&D Demos Scale-up Public

accept.

The scale-up

challenge

Demand

pull

Niche

markets

NETs literature by innovation stage,

1980-2017

Publication count

200

400

600

Demand factors

Supply factors

Negative emissions deployment

in "likely" 2°C scenarios

Feedback

The current focus

of research

Figure 6. The growing need to scale up NETs from IAM results (A. upper) and the R&D-focused literature on NETs (B.lower). We

see a dearth of work on the areas that will be crucial to the widespread deployment of NETs, notably in demonstration projects, niche

markets, demand pull mechanisms, and public acceptance.

diffusion times. Thus, the extent of long term deploy-

ment depends to a great extent on a variety of decisions

in the coming 10–20 years, not just in the second half

of the 21st century (figure 6(a)). The IAM results also

show that delays in mitigation are increasingly locking

us into NETs dependent pathways for achieving the

climate goals. In fact, if current NDCs are good indi-

cations of 2030 emission levels, targeting 2 ◦Cfrom

2030 would require a similar net emissions pathway to

targeting 1.5 ◦C today; that is, a NETs-intensive path-

way. Yet, none of the NDCs contains plans to develop

negative emissions.

The primary insight from the scientometric anal-

ysis is the relative preponderance of articles on the

supply of NETs and the dearth of articles on demand

for NETs (figure 6(b)). The literature on NETs is best

described as still in the R&D phase. The subsequent

stages of the innovation process are represented in

this literature but are much less prevalent. Only one

out of six NETs articles focused on topics related to

the ‘demand’for that technology. BECCS and ocean

alkalization had the highest ratio on the demand

side, about 1/3. Air capture had strikingly low counts

of articles addressing demand for it. The language used

reflects this supply-side focus: NETs are typically dis-

cussed as being ‘deployed’rather than ‘adopted.’Yet

the reality is that for many NETs the array of stake-

holders involved in adoption are manifold. Meeting

median removal potentials for BECCS would involve

bringing on-line hundreds of Decatur-scale CCS

facilities each year; DACCS and others would involve

transporting CO2to thousands of storage locations;

soils and biochar would involve the activities of mil-

lions of farmers. A focus on ‘deploying’NETs ignores

the preferences and attidudes of these actors as well as

the communities, in which they operate. NETs thus

have much to learn from successfully diffused tech-

nologies, for which appealing to heterogeneous users,

managing policy risk, as well as understanding and

addressing public concerns are all crucial elements of

the technology adoption process. Who wants them, for

what reason; who will adopt them; and how will various

publics respond to them are crucial questions, but ones

which the literature has only marginally addressed. It

needs to catch up for it to be relevant.

Taking the IAM results and the innovation scien-

tometrics together in figure 6,weseeanurgencyto

develop NETs and yet the research directions as evi-

denced in our scientometrics do not seem to anticipate

the urgency of the challenge and the need to provide

insights on the upscaling challenges to come. If NETs

aretobedeployedatthelevelsneededtomeet1.5◦Cand

2◦C targets, then important post-R&D issues will need

to be addressed, for example including early deploy-

ment, niche markets, scale-up, demand, and public

acceptance. For the NETs literature to be relevant and

contribute to the opportunities provided by NETs, it

will need to grow its efforts in post-R&D topics.

Environ. Res. Lett. 13 (2018) 063003 GFNemetet al

A. Supporting information

This section provides additional detail on the methods

used to assign NETs articles to innovation categories,

as well as additional descriptions of the results.

A1. Search string applied to web of science

We developed the innovation search queries as

described in the main text. We applied the result-

ing Boolean search strings to the Web of Science. We

applied one string for each innovation stage.

1. Research and development query:

TS = (research or develop∗OR ‘R&D’OR lab∗OR

‘technology push’OR experiment∗)

2. Demonstrations query:

TS = ((demonstrat∗NOT (‘we demonstrate∗’OR

‘study demonstrate∗’OR ‘result∗demonstrate∗’)

OR pilot∗OR ‘non-laboratory’OR ‘Valley of Death’

OR ‘field trial∗‘OR prototype∗))

3. Scale-up query:

TS = (scal∗OR upscal∗OR ‘unit size’OR

commercial∗OR deploy∗OR gigaton∗OR Gt)

4. Demand pull query:

TS = ((demand NOT (‘Ndemand’OR ‘demand for

N’)) OR consumer∗OR learning OR experience OR

diffuse∗OR deploy∗OR REDD OR ‘carbon pric’

OR ‘carbon tax’OR ‘climate policy’OR ‘climate

change policy’OR (climate NEAR regulation) OR

((‘1.5 ◦C’OR ‘1.5 degrees C’) NEAR/3 (warming

OR temperature)))

5. Niche markets query:

TS = (niche OR ‘willingness to pay’OR (utilize∗

NOT (‘we utilize’OR ‘study utilize∗’OR ‘project

utilize∗’OR ‘was utilized’or ‘were utilized’OR ‘is

utilized’OR ‘are utilized’)) OR (utiliz∗AND (early

NEAR/3 market OR early NEAR/3 application or

early NEAR/3 use)))

6. Public acceptance query:

TS = (accept∗OR opinion∗OR attitude∗OR

‘public support" OR oppos∗OR perceive∗OR

perception OR adopt OR demand OR voice OR

consensus OR educat∗OR communicat∗OR peo-

ple OR residents OR individuals OR members

OR customer∗OR public OR popular OR soc∗

OR backyard OR (communit∗NOT ‘bacterial

communit∗" NOT ‘microbial communit∗" NOT

phytoplankton communit∗)ORhomeORpopula-

tion OR officials OR advoca∗OR ethic∗OR moral∗

OR legitima∗OR safe∗OR justif∗OR democra∗

OR (survey AND (respondent∗OR participat∗OR

express OR ∗agree)) OR food)

7. Innovation general query:

This ‘catch-all’search query is intended to include

relevant articles not otherwise identified by our

stage-specific queries.

TS = ((innovat∗OR ‘technological change’OR

‘technicalchange’ORlearn∗ORinvent∗ORknowl-

edge OR appropr∗OR understand∗OR creat∗

OR experience∗OR process∗OR information OR

differen∗)OR(invest∗OR fund∗OR finance∗OR

cost∗OR spend∗OR venture OR expenditure∗OR

econ∗OR produc∗OR price OR cost OR supply

OR efficien∗OR demand OR appl∗OR design)

OR (global OR diffuse∗OR large OR scale OR

manyORquantit∗)OR(public ORprivateOR busi-

ness OR corporate OR institut∗)OR(‘technological

maturity’OR ‘enhance knowledge’OR phase or

stage) OR (effort∗OR future OR role OR level

OR basic OR approach OR department or need∗

or industr∗or structure or resource∗or activit∗

or trust or firm∗or change or business or capital

or agency or incentive∗or benefi∗or spillover∗or

challeng∗or assess∗or approach∗or advanc∗or

early or strategy∗or project or compet∗or uncer-

tainty or trade∗or mechanism or local or value or

transition or potential or outcome or hub or good

or coordinat∗or build∗or tax decision or compan∗

or mission or lab∗or evidence or commercial or

challenge or budget or success∗or pull or adopt∗

or produce∗or own∗or network∗or manage∗or

implement∗orgoal oreffect∗orstage∗orportfolio∗

or enterpris∗or depreciate∗or capacity or better or

best or result∗or actors or ‘private sector’or ‘public

sector’or phase or concept or ‘technology push’or

success))

A2. Coding rubric used to manually code abstracts

The search string above provided a set of articles from

which we manually coded each article into innovation

categories. Two researchers coded each article as rele-

vant to the innovation category for which it had been

selected. The researchers used the following coding

rubric as further guidance, which is meant to pro-

vide more context to the Boolean search strings for

each innovation category. In contrast to a Boolean

these words are meant to convey meaning so that the

researchers would not use these terms strictly, but could

for example use synonyms or related ideas. The terms

below are to be used in addition to the Boolean terms,

not to replace them.

1. R&D: model, laboratory, experiment, investigate,

demonstrate,fieldtrial.Articlesthatarenotassigned

to categories 2–6 are coded as R&D.

2. Demonstrations: pilots, prototypes, larger scale,

long term. Do not include if: laboratory, experi-

ment, or if any of the triggering words in previous

list are mentioned in passing or for future research

rather than a central part of the study.

3. Scale up: economies of scale, global, gigatonnes,

increasing unit size, costs, increasing manufactur-

ing capacity, integrated assessment, expansion. Do

not include if any of the triggering words in previous

Environ. Res. Lett. 13 (2018) 063003 GFNemetet al

Figure 7. Trend in NETs publications by innovation stage.

Table 3. Share of articles in each technology and innovation stages that manual reading classified as ‘relevant’to that technology and

innovation stage.

Supply-side categories Demand-side categories Total

Technology RD Demos Scaleup Demand pull Niche markets Public accept Demand side

Afforestation/reforestation 100 69 78 57 33 8 68

BECCS 100 41 65 55 100 24 63

Biochar 100 11 71 44 100 23 67

Direct air capture 100 29 71 53 67 25 73

Enhanced weathering 100 50 78 100 0 63 82

Ocean alkalinisation 100 60 77 69 100 39 77

Ocean fertilisation 100 0 100 100 0 33 79

Soil carbon sequestration 100 57 62 56 0 58 81

NETs - General 100 31 65 58 0 15 61

list are mentioned in passing or for future research

rather than a central part of the study.

4. Demand pull: markets, carbon tax, policy, prices,

1.5 or 2 degrees, adoption. Do not include if

only deployment, or if any of the triggering words

in previous list are mentioned in passing or for

future research rather than a central part of the

study.

5. Niche markets: willingness to pay, carbon uti-

lization, enhanced oil recovery, co-benefit, early

adopters.

6. Public acceptance: acceptance, public, governance,

ecosystems.

A3. Additional analyses

We include descriptive statistic that show the result of

our manual coding of the articles identified in each cat-

egory. For example, in the cell BECCS/Demos, the 41%

indicates that of the BECCS articles that our Boolean

search identified as ‘demonstrations’,theresearchers

coded 41% of them as relevant using the manual coding

rubric.

Environ. Res. Lett. 13 (2018) 063003 GFNemetet al

Figure 8. Trend in NETs publications by technology and innovation stage. Each panel includes one technology.

In figure 7we show the trend in articles for each

innovation stage. In figure 8we show the trend in

articles for each technology.

A4. Additional scenario data

In this section, we include additional scenario informa-

tion on (i) the costs of and investments in BECCS and

other mitigation technologies, (ii) the role of BECCS

and other technologies in climate change mitigation

and (iii) the geographical distribution of BECCS.

Costs and investments in BECCS and other mitigation

technologies

Technology costs are important factors that drive sce-

nario results. In figure 9we show the total costs of

various power plant technologies that are required

to be deployed annually between 2020 and 2050 to

keep global warming below 2 ◦C. Nuclear costs dom-

inate other technology costs between 2020 and 2030.

Howeverasothermitigationtechnologiesgetdeployed,

this effect diminishes gradually. In 2050, renewables

(i.e. solar and wind) constitute the major part of

global energy system costs (US$200–1200 per year).

In comparison the costs of BECCS are moderate

(range: US$0–850 per year, median: US$200 per year).

Importantly there is a great variability across results.

This can be explained by differences in sce-

nario and model assumptions as well as model

structures.

Another important factor to consider is investment

in technologies. Investments are a share of the total

costs. Likewise we display the investments in various

power plant technologies required annually between

2020 and 2050 to keep global warming below 2 ◦C.

Again,mostinvestmentsgointorenewableenergytech-

nologies (US$250–1500 per year in 2050). Investments

in nuclear and BECCS technologies remain moder-

ate (US$100–300 per year and US$0–400 per year

in 2050, respectively).

Environ. Res. Lett. 13 (2018) 063003 GFNemetet al

●

2020 2030 2040 2050

250

500

750

1000

1250

Total costs [billion US$2005/yr]

Power plant type BECCS

Coal with CCS

Gas with CCS

Oil with CCS

Nuclear

Solar

Wind

Figure 9. Annual total costs of various power plant technologies in a 2 ◦C scenario with immediate action. Boxplots show the statisical

variability across models in the LIMITS-450 scenario. They are provided for each technology in 2020, 2030, 2040 and 2050. For each

boxplot, the middle thick black bar represents the median. The lower and upper bounds of colored rectangles correspond to the first

and third quartiles (the 25th and 75th percentiles). The whiskers extend in both directions to the minimum and maximum values, but

no more than 1.5 time the inter-quantile range. Data beyond the end of the whiskers are defined as outliers and are shown as black

dots. Data sources: LIMITS (Kriegler et al 2013).

●

●●

●

2020 2030 2040 2050

500

1000

1500

Investments [billion US$2005/yr]

Power plant type BECCS

Biomass without CCS

Fossil fuels

Nuclear

Non−bio. renewables

Storage

Trans. and distrib.

Figure 10. Annual investments in various power plant technologies in a2◦C scenario with immediate action. Boxplots show the

statisical variability across models in the LIMITS-450 scenario. They are provided for each technology in 2020, 2030, 2040 and 2050.

For each boxplot, the middle thick black bar represents the median. The lower and upper bounds of colored rectangles correspond to

the first and third quartiles (the 25th and 75th percentiles). The whiskers extend in both directions to the minimum and maximum

values, but no more than 1.5 time the inter-quantile range. Data beyond the end of the whiskers are defined as outliers and are shown

as black dots. ‘Non-bio. renewables’stand for non-biomass renewables whereas ‘Trans. And distrib.’stands for transmission and

distribution. Data sources: LIMITS (Kriegler et al 2013).

Environ. Res. Lett. 13 (2018) 063003 GFNemetet al

●

●●

●

2020 2030 2040 2050

Likely 2.0°C scenario

100

Secondary energy production [EJ/yr]

Technology

Electricity (BECCS)

Electricity (Coal−CCS)

Electricity (Oil−CCS)

Electricity (Gas−CCS)

Electricity (Nuclear)

Electricity (Non−biomass renewables)

Liquids (BECCS)

Liquids (Fossil−CCS)

Hydrogen (BECCS)

Hydrogen (Fossil−CCS)

Figure 11. Annual secondary energy production from various technologies in likely 2 ◦C scenarios with immediate action. Boxplots

are provided for each technology in 2020, 2030, 2040 and 2050. For each boxplot, the middle thick black bar represents the median.

The lower and upper bounds of colored rectangles correspond to the first and third quartiles (the 25th and 75th percentiles). The

whiskers extend in both directions to the minimum and maximum values, but no more than 1.5 time the inter-quantile range. Data

beyond the end of the whiskers are defined as outliers and are shown as black dots.Data sources: AMPERE (Kriegler et al 2015),

LIMITS (Kriegler et al 2013), RoSE (Kriegler et al 2016) and data from Luderer et al (2013)andRogeljet al (2014).

Role of BECCS and other technologies in climate change

mitigation

The role of various mitigation technologies can be

understood by looking at the energy contribution from

individual technologies to the global energy system.

In figure 11, we show the global secondary energy

production split by technologies that is required annu-

ally to keep global warming below 2 ◦C with a 66%

chance. Overall all technologies play a role in mitigating

CO2emissions. For electricity generation, renewables

seem to be the most important technologies (hence

the large investments shown in figure 10). Nuclear

and gas power plants with CCS also play an impor-

tant role. Over this time frame, liquids fuels are mostly

produced with fossil technologies with CCS. BECCS

does not play a large role in energy production. This is

because the role of BECCS power plants is mainly to

remove CO2from the atmosphere and not to generate

electricity.

Geographical distribution of BECCS

Cumulative amounts of negative emissions from

BECCS in various world regions over the period 2010–

2050 are given in table 4for the 2 ◦C climate goal (66%

chance of keeping the increase in global mean temper-

ature below 2 ◦C). These data provide insight about the

geographical distribution of BECCS. Although results

are subject to great variability, they seem to indicate

that Latin America and the USA have a great negative

CO2emission potential. Japan has the lowest potential.

Table 4. Geographical distribution of cumulative sequestered carbon

by BECCS over the period 2010–2100 Units are Gt(CO2). Data

sources: AMPERE (Kriegler et al 2015), LIMITS (Kriegler et al 2013),

RoSE (Kriegler et al 2016) and data from Luderer et al (2013)and

Rogelj et al (2014).

World

region/Country

Minimum Median Maximum Number of

scenarios

Africa 055 147 49

China 0 66 208 128

Europe 044 99 130

Former Soviet

Union

0 46 159 130

India 0 38 149 130

Japan 0 6 22 96

Latin America 0 108 191 115

Middle East 0 15 122 49

Pacific OECD 918

44 12

Southeast Asia 045 63 49

USA 0 70 136 130

Acknowledgments

Gregory Nemet was partially funded by the Carnegie

Corporation of New York. Hartmann was funded

by the German Research Foundation’s priority pro-

gram DFG SPP 1689 on ‘Climate Engineering—Risks,

Challenges and Opportunities?’and specifically the

CEMICS2 project as well as Cluster of Excellence

CLISAP2 (DFG EXEC 177).

Environ. Res. Lett. 13 (2018) 063003 GFNemetet al

ORCID iDs

Gregory F Nemet https://orcid.org/0000-0001-7859-

4580

Sabine Fuss https://orcid.org/0000-0002-8681-

9839

Jens Hartmann https://orcid.org/0000-0003-1878-

9321

William F Lamb https://orcid.org/0000-0003-3273-

7878

Jan C Minx https://orcid.org/0000-0002-2862-0178

Pete Smith https://orcid.org/0000-0002-3784-1124

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