Editing IPCC:AR6/WGIII/Chapter-12 (section)

==== 12.3.1.1 Direct Air Carbon Capture and Storage (DACCS) ====

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Direct air capture (DAC) is a chemical process to capture ambient CO 2 from the atmosphere. Captured CO 2 can be stored underground (direct air carbon capture and storage, DACCS) or utilised in products (direct air carbon capture and utilisation, DACCU). DACCS shares with conventional CCS the transport and storage components but is distinct in its capture part. Because CO 2 is a well-mixed GHG, DACCS can be sited relatively flexibly, though its locational flexibility is constrained by the availability of low-carbon energy and storage sites. Capturing the CO 2 involves three basic steps: (i) contacting the air, (ii) capturing on a liquid or solid sorbent or a liquid solvent, and (iii) regeneration of the solvent or the sorbent (with heat, moisture and/or pressure). After capture, the CO 2 stream can be stored underground or utilised. The duration of storage is an important consideration; geological reservoirs or mineralisation result in removal for more than 1000 years. The duration of the removal through DACCU ( [[#Breyer--2019|Breyer et al. 2019]] ) varies with the lifetime of respective products ( [[#Wilcox--2017|Wilcox et al. 2017]] ; [[#Bui--2018|Bui et al. 2018]] ; [[#Fuss--2018|Fuss et al. 2018]] ; [[#Gunnarsson--2018|Gunnarsson et al. 2018]] ; Royal Society and Royal Academy of Engineering 2018; [[#Creutzig--2019|Creutzig et al. 2019]] ), ranging from weeks to months for synthetic fuels to centuries or more for building materials (e.g., concrete cured using mineral carbonation) ( [[#Hepburn--2019|Hepburn et al. 2019]] ). The efficiency and environmental impacts of DACCS and DACCU options depend on the carbon intensity of the energy input (electricity and heat) and other lifecycle assessment (LCA) considerations (Zimmerman 2018; [[#Jacobson--2019|Jacobson 2019]] ). See Chapters 6 and 11 for further details regarding carbon capture and utilisation. Another key consideration is the net carbon CO 2 removal of DACCS over its lifecycle ( [[#Madhu--2021|Madhu et al. 2021]] ). [[#Deutz--2021|Deutz and Bardow (2021)]] and [[#Terlouw--2021|Terlouw et al. (2021)]] demonstrated that the life-cycle net emissions of DACCS systems can be negative, even for existing supply chains and some current energy mixes. They found that the GHG intensity of energy sources is a key factor.

DAC options can be differentiated by the specific chemical processes used to capture ambient CO 2 from the air and recover it from the sorbent ( [[#Fasihi--2019|Fasihi et al. 2019]] ). The main categories are (i) liquid solvents with high-temperature regeneration, (ii) solid sorbents with low-temperature regeneration and (iii) regenerating by moisturising of solid sorbents. Other approaches such as electro-swing ( [[#Voskian--2019|Voskian and Hatton 2019]] ) have been proposed but are less developed. Compared to other CDR methods, the primary barrier to upscaling DAC is its high cost and large energy requirement ( ''high confidence'' ) ( [[#Nemet--2018|Nemet et al. 2018]] ), which can be reduced through innovation. It has therefore attracted entrepreneurs and private investments ( [[#IEA--2020b|IEA 2020b]] ).

'''Status:''' There are some demonstration projects by start-up companies and academic researchers, who are developing various types of DAC, including aqueous potassium solvent with calcium carbonation and solid sorbents with heat regeneration ( [[#NASEM--2019|NASEM 2019]] ). These projects are supported mostly by private investments and grants or sometimes serve utilisation niche markets (e.g., CO 2 for beverages, greenhouses, enhanced oil recovery). As of 2021, there are more than ten plants worldwide, with a scale of ktCO 2 yr –1 or smaller ( [[#Larsen--2019|Larsen et al. 2019]] ; [[#NASEM--2019|NASEM 2019]] ; [[#IEA--2020b|IEA 2020b]] ). Because of the fundamental difference in the CO 2 concentration at the capture stage, DACCS does not benefit directly from research, development and demonstration (RD&amp;D) of conventional CCS. Public RD&amp;D programmes dedicated to DAC have therefore been proposed ( [[#Larsen--2019|Larsen et al. 2019]] ; [[#NASEM--2019|NASEM 2019]] ). Possible research topics include development of new liquid solvents, novel solid sorbents, and novel equipment or system designs, and the need for third-party evaluation of techno-economic aspects has also been emphasised ( [[#NASEM--2019|NASEM 2019]] ). However, since basic research does not appear to be a primary barrier, both [[#NASEM--2019|NASEM (2019)]] and [[#Larsen--2019|Larsen et al. (2019)]] argue for a stronger focus on demonstration in the US context. Though the US and UK governments have begun funding DACCS research ( [[#IEA--2020b|IEA 2020b]] ), the scale of R&amp;D activities is limited.

'''Costs:''' As the process captures dilute CO 2 (~0.04%) from the ambient air, it is less efficient and more costly than conventional carbon capture applied to power plants and industrial installations (with a CO 2 concentration of ~10%) ( ''high confidence'' ). The cost of a liquid solvent system is dominated by the energy cost (because of the much higher energy demand for CO 2 regeneration, which reduces the efficiency) while capital costs account for a significant share of the cost of solid sorbent systems ( [[#Fasihi--2019|Fasihi et al. 2019]] ). The range of the DAC cost estimates found in the literature is wide (USD60–1000 tCO 2 –1 ) ( [[#Fuss--2018|Fuss et al. 2018]] ) partly because different studies assume different use cases, differing phases (first plant vs ''n'' th plant) ( [[#Lackner--2012|Lackner et al. 2012]] ), different configurations, and disparate system boundaries. Estimates of industrial origin are often on the lower side ( [[#Ishimoto--2017|Ishimoto et al. 2017]] ). [[#Fuss--2018|Fuss et al. (2018)]] suggest a cost range of USD600–1000 tCO 2 –1 for first-of-a-kind plants, and USD100–300 tCO 2 –1 as experience accumulates. An expert elicitation study found a similar cost level for 2050 with a median of around USD200 tCO 2 –1 ( [[#Shayegh--2021|Shayegh et al. 2021]] ) ( ''medium evidence'' , ''medium agreement'' ). [[#NASEM--2019|NASEM (2019)]] systematically evaluated the costs of different designs and found a range of 84–386 USD2015 tCO 2 –1 for the designs currently considered by active technology developers. This cost range excludes the site-specific costs of transportation or storage.

'''Potentials:''' There is no specific study on the potential of DACCS but the literature has assumed that the technical potential is virtually unlimited provided that high energy requirements could be met ( ''medium evidence'' , ''high agreement'' ) ( [[#Marcucci--2017|Marcucci et al. 2017]] ; [[#Fuss--2018|Fuss et al. 2018]] ; [[#Lawrence--2018|Lawrence et al. 2018]] ) since DACCS encounters fewer non-cost constraints than any other CDR method. Focusing only on the Maghreb region, [[#Breyer--2020|Breyer et al. (2020)]] reported an optimistic potential 150 GtCO 2 at less than USD61 tCO 2 –1 for 2050. [[#Fuss--2018|Fuss et al. (2018)]] suggest a potential of 0.5–5 GtCO 2 yr –1 by 2050 because of environmental side effects and limits to underground storage. In addition to the ultimate potentials, [[#Realmonte--2019|Realmonte et al. (2019)]] noted the rate of scale-up as a strong constraint on deployment. [[#Meckling--2021|Meckling and Biber (2021)]] discuss a policy roadmap to address the political economy for upscaling. More systematic analysis on potentials is necessary; first and foremost on national and regional levels, including the requirements for low-carbon heat and power, water and material demand, availability of geological storage and the need for land in case of low-density energy sources such as solar or wind power.

'''Risks and impacts:''' DACCS requires a considerable amount of energy ( ''high confidence'' ), depending on the type of technology, water, and make-up sorbents, while its land footprint is small compared to other CDR methods ( [[#Smith--2016|Smith et al. 2016]] ). Yet, depending on the source of energy for DACCS (e.g., renewables vs nuclear), DACCS could require a significant land footprint ( [[#NASEM--2019|NASEM 2019]] ; [[#Sekera--2020|Sekera and Lichtenberger 2020]] ). The theoretical minimum energy requirement for separating CO 2 gas from the air is about 0.5 GJ tCO 2 –1 ( [[#Socolow--2011|Socolow et al. 2011]] ). [[#Fasihi--2019|Fasihi et al. (2019)]] reviewed the published estimates of energy requirements and found that for the current technologies, the total energy requirement is about 4–10 GJ tCO 2 –1 , with heat accounting for about 80% and electricity about 20% ( [[#McQueen--2021|McQueen et al. 2021]] ). At a 10 GtCO 2 yr –1 sequestration scale, this would translate into 40–100 exajoules (EJ) yr –1 of energy consumption (32–80 EJ yr –1 for heat and 8–20 EJ yr –1 electricity), which can be contrasted with the current primary energy supply of about 600 EJ yr –1 and electricity generation of about 100 EJ yr –1 . For the solid sorbent technology, low-temperature heat could be sourced from heat pumps powered by low-carbon sources such as renewables ( [[#Breyer--2020|Breyer et al. 2020]] ), waste heat ( [[#Beuttler--2019|Beuttler et al. 2019]] ), and nuclear energy ( [[#Sandalow--2018|Sandalow et al. 2018]] ). Unless sourced from a clean source, this amount of energy could cause environmental damage ( [[#Jacobson--2019|Jacobson 2019]] ). Because DACCS is an open system, water lost from evaporation must be replenished. Water loss varies, depending on technology (including adjustable factors such as the concentration of the liquid solvent) as well as environmental conditions (e.g., temperate vs tropical climates). For a liquid solvent system, it can be 0–50 tH 2 O tCO 2 –1 ( [[#Fasihi--2019|Fasihi et al. 2019]] ). A water loss rate of about 1–10 tH 2 O tCO 2 –1 ( [[#Socolow--2011|Socolow et al. 2011]] ) would translate into about 10–100 GtH 2 O (10–100 km 3 ) to capture 10 GtCO 2 from the atmosphere. Some solid sorbent technologies actually produce water as a by-product, for example 0.8–2 tH 2 O tCO 2 –1 for a solid-sorbent technology with heat regeneration ( [[#Beuttler--2019|Beuttler et al. 2019]] ; [[#Fasihi--2019|Fasihi et al. 2019]] ). Large-scale deployment of DACCS would also require a significant quantity of materials, and energy to produce them ( [[#Chatterjee--2020|Chatterjee and Huang 2020]] ). Hydroxide solutions are currently being produced as a by-product of chlorine but replacement (make-up) requirement of such materials at scale exceeds the current market supply ( [[#Realmonte--2019|Realmonte et al. 2019]] ). The land requirements for DAC units are not large enough to be of concern ( [[#Madhu--2021|Madhu et al. 2021]] ). Furthermore, these can be placed on unproductive lands, in contrast to biological CDR. Nevertheless, to ensure that CO 2 -depleted air does not enter the air contactor of an adjacent DAC system, there must be enough space between DAC units, similar to wind power turbines. Considering this, [[#Socolow--2011|Socolow et al. (2011)]] estimated a land footprint of 1.5 km 2 MtCO 2 –1 . In contrast, large energy requirements can lead to significant footprints if low-density energy sources (e.g., solar PV) are used ( [[#Smith--2016|Smith et al. 2016]] ). For the issues associated with CO 2 utilisation and storage, see Chapter 6.

'''Co-benefits:''' While [[#Wohland--2018|Wohland et al. (2018)]] proposed solid sorbent-based DAC plants as a Power-to-X technology that could use excess renewable power (at times of low or even negative prices), such operation would add additional costs. Installations would need to be designed for intermittent operations (i.e., at low load factors) which would negatively affect capital and operation costs ( [[#Daggash--2018|Daggash et al. 2018]] ; [[#Sandalow--2018|Sandalow et al. 2018]] ) as a high time-resolution model suggests a high utilisation rate ( [[#Breyer--2020|Breyer et al. 2020]] ). Solid sorbent DAC designs can potentially remove more water from the ambient air than needed for regeneration, thereby delivering surplus water that would contribute to SDG 6 (clean water and sanitation) in arid regions ( [[#Sandalow--2018|Sandalow et al. 2018]] ; [[#Fasihi--2019|Fasihi et al. 2019]] ).

'''Trade-offs and spillover effects:''' Liquid solvent DACCS systems need substantial amounts of water ( [[#Fasihi--2019|Fasihi et al. 2019]] ), although much less than BECCS systems ( [[#Smith--2016|Smith et al. 2016]] ), which could negatively affect SDG 6 (clean water and sanitation). Although the high energy demand of DACCS could affect SDG 7 (affordable and clean energy) negatively through potential competition or positively through learning effects ( [[#Beuttler--2019|Beuttler et al. 2019]] ), its impact has not been thoroughly assessed yet.

'''Role in mitigation pathways:''' There are a few IAM studies that have explicitly incorporated DACCS. Stringent emissions constraints in these studies lead to high carbon prices, allowing DACCS to play an important role in mitigation. [[#Chen--2013|Chen and Tavoni (2013)]] examined the role of DACCS in an IAM (WITCH) and found that incorporating DACCS reduces the overall cost of mitigation and tends to postpone the timing of mitigation. The scale of capture goes up to 37 GtCO 2 yr –1 in 2100. [[#Akimoto--2021|Akimoto et al. (2021)]] introduced DACCS in the IAM DNE21+, and also found the long-term marginal cost of abatement is significantly reduced by DACCS. [[#Marcucci--2017|Marcucci et al. (2017)]] ran MERGE-ETL, an integrated model with endogenous learning, and showed that DACCS allows for a model solution for the 1.5°C target, and that DACCS substitutes for BECCS under stringent targets. In their analysis, DACCS captures up to 38.3 GtCO 2 yr –1 in 2100. [[#Realmonte--2019|Realmonte et al. (2019)]] modelled two types of DACCS (based on liquid and solid sorbents) with two IAMs (TIAM-Grantham and WITCH), and showed that in deep mitigation scenarios, DACCS complements, rather than substitutes, other CDR methods such as BECCS, and that DACCS is effective at containing mitigation costs. At the national scale, [[#Larsen--2019|Larsen et al. (2019)]] utilised the Regional Investment and Operations (RIO) Platform coupled with the Energy PATHWAYS model, and explicitly represented DAC in US energy systems scenarios. They found that in a scenario that reaches net zero emissions by 2045, about 0.6 GtCO 2 or 1.8 GtCO 2 of DACCS would be deployed, depending on the availability of biological carbon sinks and bioenergy. The modelling supporting the European Commission’s initial proposal for net zero GHG emissions by 2050 incorporated DAC, with the captured CO 2 used for both synthetic fuel production (DACCU) and storage (DACCS) ( [[#Capros--2019|Capros et al. 2019]] ). [[#Fuhrman--2021a|Fuhrman et al. (2021a)]] evaluated the role of DACCS across five shared socio-economic pathways with the GCAM modelling framework and identified a substantial role for DACCS in mitigation and a decreased pressure on land and water resources from BECCS, even under the assumption of limited energy efficiency improvement and conservative cost declines of DACCS technologies. The newest iteration of the World Economic Outlook by [[#IEA--2021b|IEA (2021b)]] deploys CDR on a limited scale, and DACCS removes 0.6 GtCO 2 in 2050 for its Net Zero CO 2 Emissions scenario.

Status, costs, potentials, risk and impacts, co-benefits, trade-offs and spillover effects and the role in mitigation pathways of DACCS are summarised in Table 12.6.

'''Table 12.6 | Summary of status, costs, potentials, risk and impacts, co-benefits, trade-offs and spillover effects and the role in mitigation pathways for CDR methods.''' Technology readiness level (TRL) is a measure of maturity of the CDR method. Scores range from 1 (basic principles defined) to 9 (proven in operational environment). Author judgement ranges (assessed by authors in the literature) are shown, with full literature ranges shown in brackets.

{| class="wikitable"
|-
! '''CDR method'''

! '''Status (TRL)'''

! '''Cost (USD tCO''' 2 –1 ''')'''

! '''Mitigation Potential (GtCO''' 2 '''y''' '''r''' –1 ''')'''

! '''Risk and impacts'''

! '''Co-benefits'''

! '''Trade-offs and spillover effects'''

! '''Role in modelled mitigation pathways'''

! '''Section'''

|-
| DACCS

| 6

| 100–300 (84–386)

| 5–40

| Increased energy and water use

| Water produced (solid sorbent DAC designs only)

| Potentially increased emissions from water supply and energy generation

| In a few IAMs; DACCS complements other CDR methods

| 12.3.1.1

|-
| Enhanced weathering

| 3–4

| 50–200 (24–578)

| 2–4 (&lt;1–95)

| Mining impacts; air quality impacts of rock dust when spreading on soil

| Enhanced plant growth, reduced erosion, enhanced soil carbon, reduced soil acidity, enhanced soil water retention

| Potentially increased emissions from water supply and energy generation

| In a few IAMs; EW complements other CDR methods

| 12.3.1.2

|-
| Ocean alkalinity enhancement

| 1–2

| 40–260

| 1–100

| Increased seawater pH and saturation states may impact marine biota. Possible release of nutritive or toxic elements and compounds. Mining impacts

| Limiting ocean acidification

| Potentially increased emissions of CO 2 and dust from mining, transport and deployment operations

| No data

| 12.3.1.3

|-
| Ocean fertilisation

| 1–2

| 50–500

| 1–3

| Nutrient redistribution, restructuring of the ecosystem, enhanced oxygen consumption and acidification in deeper waters, potential for decadal-to-millennial-scale return to the atmosphere of nearly all the extra carbon removed, risks of unintended side effects

| Increased productivity and fisheries, reduced upper ocean acidification

| Subsurface ocean acidification, deoxygenation; altered meridional supply of macro-nutrients as they are utilised in the iron-fertilised region and become unavailable for transport to, and utilisation in, other regions, fundamental alteration of food webs, biodiversity

| No data

| 12.3.1.3

|-
| Blue carbon management in coastal ecosystems

| 2–3

| Insufficient data, estimates range from ~100 to ~10,000

| &lt;1

| If degraded or lost, coastal blue carbon ecosystems are likely to release most of their carbon back to the atmosphere; potential for sediment contaminants, toxicity, bioaccumulation and biomagnification in organisms; issues related to altering degradability of coastal plants; use of subtidal areas for tidal wetland carbon removal; effect of shoreline modifications on sediment redeposition and natural marsh accretion; abusive use of coastal blue carbon as means to reclaim land for purposes that degrade capacity for carbon removal

| Potential for many non-climatic benefits and can contribute to ecosystem-based adaptation, coastal protection, increased biodiversity, reduced upper ocean acidification; could potentially benefit human nutrition or produce fertiliser for terrestrial agriculture, anti-methanogenic feed additive, or as an industrial or materials feedstock

| If degraded or lost, coastal blue carbon ecosystems are likely to release most of their carbon back to the atmosphere. The full delivery of the benefits at their maximum global capacity will require years to decades to be achieved

| Not incorporated in IAMs, but in some bottom-up studies: small contribution

| 12.3.1.3, 7.4

|-
| BECCS

| 5–6

| 15–400

| 0.5–11

| Competition for land and water resources, to grow biomass feedstock. Biodiversity and carbon stock loss if from unsustainable biomass harvest

| Reduction of air pollutants; fuel security, optimal use of residues, additional income, health benefits and if implemented well can enhance biodiversity, soil health and land carbon

| Competition for land with biodiversity conservation and food production

| Substantial contribution in IAMs and bottom-up sectoral studies

| 7.4

|-
| Afforestation/reforestation

| 8–9

| 0–240

| 0.5–10

| Reversal of carbon removal through wildfire, disease, pests may occur.

Reduced catchment water yield and lower groundwater level if species and biome are inappropriate

| Enhanced employment and local livelihoods, improved biodiversity, improved renewable wood products provision, soil carbon and nutrient cycling. Possibly less pressure on primary forest

| Inappropriate deployment at large scale can lead to competition for land with biodiversity conservation and food production

| Substantial contribution in IAMs and also in bottom-up sectoral studies

| 7.4

|-
| Biochar

| 6–7

| 10–345

| 0.3–6.6

| Particulate and GHG emissions from production; biodiversity and carbon stock loss from unsustainable biomass harvest

| Increased crop yields and reduced non-CO 2 emissions from soil; resilience to drought

| Environmental impacts associated with particulate matter; competition for biomass resource

| In development – not yet in global mitigation pathways simulated by IAMs

| 7.4

|-
| Soil carbon sequestration in croplands and grasslands

| 8–9

| -45–100

| 0.6–9.3

| Risk of increased nitrous oxide emissions due to higher levels of organic nitrogen in the soil; risk of reversal of carbon sequestration

| Improved soil quality, resilience and agricultural productivity

| Attempts to increase carbon sequestration potential at the expense of production. Net addition per hectare is very small; hard to monitor

| In development – not yet in global mitigation pathways simulated by IAMs; in bottom-up studies: with medium contribution

| 7.4

|-
| Peatland and coastal wetland restoration

| 8–9

| Insufficient data

| 0.5–2.1

| Reversal of carbon removal in drought or future disturbance. Risk of increased methane emissions

| Enhanced employment and local livelihoods, increased productivity of fisheries, improved biodiversity, soil carbon and nutrient cycling

| Competition for land for food production on some peatlands used for food production

| Not in IAMs but some bottom-up studies with medium contribution

| 7.4

|-
| Agroforestry

| 8–9

| Insufficient data

| 0.3–9.4

| Risk that some land area lost from food production; requires high skills

| Enhanced employment and local livelihoods, variety of products, improved soil quality, more resilient systems

| Some trade-off with agricultural crop production, but enhanced biodiversity, and resilience of system

| No data from IAMs, but in bottom-up sectoral studies. with medium contribution

| 7.4

|-
| Improved forest management

| 8–9

| Insufficient data

| 0.1–2.1

| If improved management is understood as merely intensification involving increased fertiliser use and introduced species, then it could reduce biodiversity and increase eutrophication

| In case of sustainable forest management, it leads to enhanced employment and local livelihoods, enhanced biodiversity, improved productivity

| If it involves increased fertiliser use and introduced species, it could reduce biodiversity and increase eutrophication and upstream GHG emissions

| No data from IAMs, but in bottom-up sectoral studies with medium contribution

| 7.4

|}

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