Negative emissions—Part 3: Innovation and upscaling

R&D involves the discovery and assimilation of new scientific and technical knowledge (Holdren and Baldwin 2001). The research part of R&D includes studies of thermodynamics and computer modeling of NETs systems. The development part of R&D involves experiments 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&D if they do 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&D funding is 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, Cohen et al 2002). But it is also limited in that there is only so much 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, Bento and Wilson 2016), in which the optimal designs and configurations undergo experimentation, are particularly important and several NETs appear to be at this stage at present.

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 (Hartley and Medlock 2017). Furthermore, even though incentives for private investment are weak, governments are often hesitant to fund these investments (Weyant 2011, Zhou et al 2015), in part due to the scales of the investment required (Lupion and Herzog 2013), a mixed track record of success (Anadon and Nemet 2014), and to some extent due to perceptions 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 commercial 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 applicable to at least some of the NETs. One is that demonstration programs should be designed as a portfolio 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 production, such as how much CO₂ is 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 failures (Leoncini 2016). A key to learning is making sure that knowledge generated is codified, maintained, and disseminated (Grubler and Nemet 2014), which is often not the case.

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The process of increasing the unit size of technologies to commercially-viable scales is non-trivial and can take considerable time (Wilson 2012). This, associated with increasing scale, is a repeated theme in the literature. It is clear that just because we know we eventually need large scale it does not imply we are ready to do so today. See for example the megawatt scale German 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 (Geels 2002). For example, some NETs will involve access to CO₂ pipeline 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 processes. In some cases, such as BECCS, the scale up process involves major increases in unit scale (Nykvist 2013). In other cases, e.g. in DACCS, there may also be unit scale increases, but the main scale-up challenge could be in mass manufacturing DACCS units (Lackner et al 2012). A strategy of iterative upscaling (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 manufacturing size, and iteratively improving in the migration to larger scales.

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 policy, which are often uncertain (Brunner et al 2012). As a result, demand for NETs will be heavily conditioned 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 analyses have shown required investment in BECCS capacity on the scale of hundreds of billions of dollars annually (McCollum et al 2013). In figure 4, using IAM results from 207 scenarios in 12 models¹⁰, we show 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 overlapping ranges. Deployment of BECCS is shown on the vertical axis (linear scale). Deployment is also decreasing 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 mitigation 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 deployment 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 different 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 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 example, 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 biodiversity.

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). Learning by doing can occur during the adoption process; improvement continues after the R&D stage (Arrow 1962, Thompson 2012). 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.

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 storage (CCUS), where oil field operators might value CO₂ above the existing carbon price and thus pay for CO₂ for enhanced oil recovery. Even if the climatic benefits of CCUS, or just CCU, are unclear at best (von der Assen et al 2013), it can enable subsequent 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 competition, 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 uncertain 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 CO₂ to produce fuels, are only viable for a limited period. There is also a risk that serving niches diverts innovation toward characteristics that may not be useful for bulk carbon removal. However, having an early market with high willingness to pay and low competition has been essential for other technologies, and given the uncertain policy environment, is likely to be essential for NETs as well.

While often treated as a separate issue, public acceptance 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 willingness 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 technologies, 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 deciding 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, consider CCS demonstrations in Germany (Braun 2017). The need to deploy NETs at the gigatonne scale heightens the likelihood that public acceptance issues will emerge and need to be addressed. Deploying any tech at scale large enough to meaningfully benefit the climate implies likely side-effects (Grubler 1998), which may not be positive. These can lead to public acceptance issues (Batel et al 2013). Some NETs, such as soil carbon sequestration, seem more challenged by the first set of public acceptance issues, while others, 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 emissions reductions, pushing this task to later generations (thus raising moral hazard), and that they raise considerable procedural and distributive justice concerns (Anderson and Peters 2016, Shue 2017). 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).

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 ambitious projects having recently come online. One important development is a full scale (1 Mt CO₂ yr⁻¹) 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 CO₂ in algae (Pang et al 2017). A helpful preliminary assessment of these plants shows that their performance is quite similar considering their diversity (Bhave et al 2017). Kemper (2015) points out that lack of demonstration creates significant uncertainties about feasibility of large scale deployment. Gough and Upham (2011) point to feedstock availability, system integration, and CO₂ transportation infrastructure as critical components of the scale-up challenge for NETs.

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 incentives 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 deployment 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 intensification of agriculture (Popp et al 2011). Fridahl (2017) uses a survey to show that while IAMs depend heavily 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. However, 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 competitive 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) or even 'half a century' (Azar et al 2010). Beyond IAM studies, a comprehensive review of impacts of BECCS on sustainable development finds positive economic effects but negative social and environmental impacts (Robledo-Abad et al 2017).

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 comprehensive approach to BECCS but neglect to mention public acceptance issues, a sentiment shared in the literature (Dowd et al 2015). Fridahl (2017) makes the 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 agricultural 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, Plevin et al 2010, Popp et al 2011) and the essential tradeoff with food production, even with large uncertainties about the precise impacts (Smith et al 2013, Stevanović et al 2016, Boysen et al 2017) could be difficult to overcome (Robledo-Abad et al 2017). However, the transportation of massive quantities 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 benefits translates directly into investment uncertainty into BECCS as a NET. Regulation of stored CO₂ and its leakage is another key public acceptance issue (Boot-Handford et al 2014). Gough et al (2014) find that CO₂ pipelines 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 alternatively to use non-food feedstocks, such as algae (Sharp et al 2017). Similarly, Boysen et al (2017) argue that scale-up will be bounded, due to socially unacceptable 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 technologies 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 distribution 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).

One reason for the focus on pure R&D is that the technology is arguably at a nascent stage. For example, 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 that other mitigation options. Going beyond R&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 CO₂ via refrigeration; they extend by discussing the advantages (avoiding refrigeration needs) and challenges (metal fatigue) of deploying this scheme in Antarctica. Holmes et al (2013) present a prototype of a cooling tower design, where air flows orthogonal to a downward flowing hydroxide solution; they demonstrate more than 1000 hours of operation, validating the cross-flow contactor design. Rau et al (2013) describe experiments with absorption of CO₂ via 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 comparisons (Socolow et al 2011, Sinha et al 2017). Other work more explicitly focuses on scaling up DACCS and includes estimates of cost reductions associated with >10Gt of CO₂ removal per year using component cost estimates (Lackner 2009) as well as bottom-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) provide 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).

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 additional investment safety in that CO₂ sequestered 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 CO₂ levels 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 benefits 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 CO₂ for applications, such as greenhouse fertilization, industrial use, or enhanced oil recovery (Lackner et al 2012, Hou et al 2017, Ishimoto et al 2017). Enhanced oil recovery and microalgae cultivation are judged the most suitable niche markets where also dilute CO₂ is 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, electrolysis, and hydrogen-to-liquid, involving also scrubbing CO₂ from 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, 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 CO₂ to fertilize plants in a greenhouse. Climeworks also opened a DACCS facility in cooperation with Reykjavik Energy in Iceland, making use of waste heat from a neighboring thermal power plant to adsorb CO₂ from the filter, and injecting CO₂ underground as carbonated 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 CO₂ for applications, such as greenhouse fertilization, industrial use, or enhanced oil recovery (Ishimoto et al 2017).

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 can substitute for BECCS to achieve 1.5 °C targets (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 acceptance. Lackner and Brennan (2009) lay out a broad set of possible public concerns and provide some initial assessments of their risks to the public. Leakage of stored CO₂ is 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.

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 experiments 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 demonstrations 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, Novak et al 2016). Global potentials have been estimated (Paustian et al 2016, Smith 2016). 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 mechanisms are also covered as a means to support scale up (Whitman and Lehmann 2009).

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, Downie et al 2014). One model is a carbon credit scheme in Montana (Watts et 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 discussing 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 quality (with C removal as a co-benefit) with a goal of increasing soil carbon content by 0.4% per year.

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 about it to scale up (Lampitt et al 2008) and another 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 deployment. 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) and also a focus 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 teragrams of iron additions (Hauck et al 2016). A cluster of articles from a decade ago considered implementation 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.

Discussion of the demand for ocean fertilization is notably lacking. We do see articles on how carbon markets could lead to demand for OF (Rickels et al 2012), accounting and incentives (Rickels et al 2010) and consideration 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 figure out downstream effects of OF and unintended consequences (Cullen and Boyd 2008). Some cover governance issues (Williamson et al 2012), legal status (Bertram 2010), and the Law of the Sea (Freestone and Rayfuse 2008). Others discuss the social implications 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).

We identified 62 AR articles that address scale up, however with widely differing interpretations of the ultimate scale needed. Articles we coded as demonstrations include: Returning Farmland to Forest Program in China (Zinda et al 2017), Forest Restoration Experimental 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 outcome 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 quite a while ago (Alpert et al 1992, Shvidenko et al 1997). Others consider the scale-up process itself more analytically (Zhang et al 2015) (Caughlin et 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 experiments 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 outcomes at the scale of watersheds (Cunningham et al 2015).

A recurring theme is the identification of barriers 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), opportunity 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).

Discussions of the demand for AR typically focus on carbon markets (Adams and Turner 2012, Carwardine 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 sequestration 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 incentives. 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, Daniel 2001). 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).

In response to the minimal body of work, Hartmann et al (2013) argue that there is a 'need for specific experiments'. They propose a cascade of experiments bridging the scales from millimeters to meters to 100s of meters as a means to scale up. Chemical engineering studies are particularly well positioned to consider the issues and opportunities of scale up (Morales-Florez et al 2011, Hall et al 2014). Computer 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. Studies 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, Power et al 2009, Ollivier et al 2010), in particular the effect of temperature (Li et al 2016), as well as using laboratory-scale experiments (Renforth and Manning 2011). In addition, the applied rock material 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).

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Interactions of minerals with soils and the biology 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 concrete and algae could be used to fix CO₂ as 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 CO₂ sequestration be used to enhance biomass production (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.

We found only a small number of articles that discussed the demand side of EW; one on demand pull and five 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 substantial carbon price (Hartmann and Kempe 2008, Taylor et al 2016). A separate source of demand is the application of EW to increase agricultural yields, specifically in areas with depleted soils (van Straaten 2002, Hartmann 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 considered '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 pulverized 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.

Technology costs are important factors that drive scenario results. In figure 9 we 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 dominate other technology costs between 2020 and 2030. However as other mitigation technologies get deployed, 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 scenario 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, most investments go into renewable energy technologies (US$ 250–1500 per year in 2050). Investments in nuclear and BECCS technologies remain moderate (US$ 100–300 per year and US$ 0–400 per year in 2050, respectively).

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 annually to keep global warming below 2 °C with a 66% chance. Overall all technologies play a role in mitigating CO₂ emissions. 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 important 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 CO₂ from the atmosphere and not to generate electricity.

Cumulative amounts of negative emissions from BECCS in various world regions over the period 2010–2050 are given in table 4 for the 2 °C climate goal (66% chance of keeping the increase in global mean temperature 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 CO₂ emission potential. Japan has the lowest potential.