Editing IPCC:AR6/WGIII/Chapter-6 (section)

== 6.4 Mitigation Options ==

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=== 6.4.1 Elements of Characterisation ===

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This section characterises energy system mitigation options and discusses which factors enable and inhibit their implementation. We touch on a broad range of factors that may enable and inhibit the implementation of mitigation options by considering six dimensions that affect their feasibility (Table 6.1 and Annex II.11). The assessment aims to identify which mitigation options can be readily implemented and which face barriers that would need to be overcome before they can be deployed at scale.

'''Table 6.1 | Dimensions and indicators to assess the barriers and enablers of implementing mitigation options in low-carbon energy systems.'''

{| class="wikitable"
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| Metric

| Indicators

|-
| Geophysical: Are the required resources available?

| – Physical potential: physical constraints to implementation

– Geophysical resources (including geological storage capacity): availability of resources needed for implementation

– Land use: claims on land where an option would be implemented

|-
| Environmental-ecological: What are the wider environmental and ecological impacts of the option?

| – Air pollution: increase or decrease in air pollutants, such as NH 4 , CH 4 and fine dust

– Toxic waste, ecotoxicity and eutrophication

– Water quantity and quality: changes in the amount of water available for other uses

– Biodiversity: changes in conserved primary forest or grassland that affect biodiversity, and management to conserve and maintain land carbon stocks

|-
| Technological: Can the required technology be upscaled soon?

| – Simplicity: is the option technically simple to operate, maintain and integrate?

– Technology scalability: can the option be scaled up technically?

– Maturity and technology readiness: research and development (R&amp;D) and time needed to implement the option

|-
| Economic: What economic conditions can support or inhibit the implementation of the option?

| – Costs in 2030 and in the long term: investment costs, costs in USD tCO 2 -eq –1

– Employment effects and economic growth: decrease or increase in jobs and economic welfare

|-
| Socio-cultural: What social conditions could support or inhibit acceptance, adoption, and use of the option before 2030?

| – Public acceptance: the extent to which the public supports the option and will change their behaviour accordingly

– Effects on health and well-being

– Distributional effects: equity and justice across groups, regions, and generations, including energy, water, and food security and poverty

|-
| Institutional: What institutional conditions could support or inhibit the implementation of the option?

| – Political acceptance: the extent to which politicians support the option

– Institutional capacity and governance, cross-sectoral coordination: capability of institutions to implement and handle the option

– Legal and administrative capacity

|}

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=== 6.4.2 Energy Sources and Energy Conversion ===

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==== 6.4.2.1 Solar Energy ====

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Solar photovoltaic (PV) is increasingly competitive with other forms of electricity generation, and is the low-cost option in many applications ( ''high confidence'' ). Costs have declined by 62% since 2015 ( ''high confidence'' ) and are anticipated to decline by an additional 16% by 2030 if current trends continue ( ''low confidence, medium evidence'' ). Key areas for continued improvement are grid integration and non-module costs for rooftop systems ( ''high confidence'' ). Most deployment is now utility-scale ( ''high confidence'' ). Global future potential is not limited by solar irradiation, but by grid integration needed to address its variability, as well as access to finance, particularly in developing countries ( ''high confidence'' ).

The global technical potential of direct solar energy far exceeds that of any other renewable energy resource and is well beyond the total amount of energy needed to support ambitious mitigation over the current century ( ''high confidence'' ). Estimates of global solar resources have not changed since the IPCC’s Fifth Assessment Report (AR5) ( [[#Lewis--2007|Lewis 2007]] ; [[#Besharat--2013|Besharat et al. 2013]] ) even as precision and near-term forecasting have improved ( [[#Diagne--2013|Diagne et al. 2013]] ; [[#Abreu--2018|Abreu et al. 2018]] ). Approximately 120,000 TW of sunlight reaches the Earth’s surface continuously, almost 10,000 times average world energy consumption; factoring in competition for land use leaves a technical potential of about 300 PWh yr –1 (1080 EJ yr –1 ) for solar PV, roughly double current consumption ( [[#Dupont--2020|Dupont et al. 2020]] ). The technical potential for concentrating solar power (CSP) is estimated to be 45–82 PWh yr –1 (162–295 EJ yr –1 ) ( [[#Dupont--2020|Dupont et al. 2020]] ). Areas with the highest solar irradiation are: western South America; northern, eastern and southwestern Africa; and the Middle East and Australia (Figure 6.7) ( [[#Prăvălie--2019|Prăvălie et al. 2019]] ).

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[[File:f7827e161a3e29fa2c66349a019751aa IPCC_AR6_WGIII_Figure_6_7.png]]

'''Figure 6.7 |Distribution of the daily mean global horizontal irradiation (GHI, kWh m''' –2 '''day''' –1 ''').''' Source: Global Solar Atlas ( [[#ESMAP--2019|ESMAP 2019]] ).

In many parts of the world, the cost of electricity from PV is below the cost of electricity generated from fossil fuels; in some, it is below the operating costs of electricity generated from fossil fuels ( ''high confidence'' ). The weighted average cost of PV in 2019 was USD68 MWh –1 , near the bottom of the range of fossil fuel prices ( [[#IRENA--2019b|IRENA 2019b]] ). The cost of electricity from PV has fallen by 89% since 2000 and 69% since AR5, at a rate of –16% per year. The 5:95 percentile range for PV in 2019 was USD52–190 MWh –1 ( [[#IRENA--2021b|IRENA 2021b]] ). Differences in solar insolation, financing costs, equipment acquisition, installation labour, and other sources of price dispersion explain this range ( [[#Nemet--2016|Nemet et al. 2016]] ; [[#Vartiainen--2020|Vartiainen et al. 2020]] ) and scale. For example, in India, rooftop installations cost 41% more than utility-scale installations, and commercial-scale costs are 39% higher than utility-scale. Significant differences in regional cost persist ( [[#Kazhamiaka--2017|Kazhamiaka et al. 2017]] ; [[#Vartiainen--2020|Vartiainen et al. 2020]] ), with particularly low prices in China, India, and parts of Europe. Globally, the range of global PV costs is quite similar to the range of coal and natural gas prices.

PV costs (Figure 6.8) have fallen for various reasons: lower silicon costs, automation, lower margins, automation, higher efficiency, and a variety of incremental improvements ( [[#Fu--2018|Fu et al. 2018]] ; [[#Green--2019|Green 2019]] ) (Chapter 16). Increasingly, the costs of PV electricity are concentrated in the installation and related ‘soft costs’ (marketing, permitting) associated with the technology rather than in the modules themselves, which now account for only 30% of installed costs of rooftop systems ( [[#O’Shaughnessy--2019|O’Shaughnessy et al. 2019]] ; [[#IRENA--2021b|IRENA 2021b]] ). Financing costs are a significant barrier in developing countries ( [[#Ondraczek--2015|Ondraczek et al. 2015]] ) and growth there depends on access to low-cost finance ( [[#Creutzig--2017|Creutzig et al. 2017]] ).

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[[File:8ce80f0ff3ac41fcc2dfd6d76e3ddd42 IPCC_AR6_WGIII_Figure_6_8.png]]

'''Figure 6.8 | Levelised costs of electricity (LCOE) of solar energy technologies 2000–2020.''' Range of fossil fuel LCOE indicated as dashed lines USD50–177 MWh –1 . Linear fit lines were applied to data for AR4–AR5 and post-AR5 (2012). Yellow dots are capacity-weighted global averages for utility-scale installations. The blue area shows the range between the 5th and 95th percentile in each year. Data: [[#IRENA--2021b|IRENA (2021b)]] .

CSP costs have also fallen, albeit at about half the rate of PV: –9% yr –1 since AR5. The lowest prices for CSP are now competitive with more expensive fossil fuels, although the average CSP cost is above the range for fossil-based power generation. Other data sources put recent CSP costs at USD120 MWh –1 , in the middle of the fossil range (Lilliestam et al. 2020). Continuing the pace of change since AR5 will make CSP competitive with fossil fuels in sunny locations, although it will be difficult for CSP to compete with PV and even hybrid PV-battery systems. CSP electricity can be more valuable, however, because CSP systems can store heat longer than PV battery systems.

The share of total costs of PV-intensive electricity systems attributed to integration costs has been increasing but can be reduced by enhancing grid flexibility ( ''high confidence'' ) (Sections 6.4.3 and 6.6, and Box 6.8). The total costs of PV include grid integration, which varies tremendously depending on PV’s share of electricity, other supply sources like wind, availability of storage, transmission capacity, and demand flexibility (Heptonstall and Gross 2020). Transmission costs can add USD1–10 MWh –1 or 3–33% to the cost of utility-scale PV ( [[#Gorman--2019|Gorman et al. 2019]] ). Distributed (rooftop) PV involves a broader set of grid integration costs – including grid reinforcement, voltage balancing and control, and impacts on other generations – and has a larger range of integration costs from USD2–25 MWh –1 , which is –3% to +37% ( [[#Hirth--2015|Hirth et al. 2015]] ; [[#Wu--2015|Wu et al. 2015]] ; [[#Gorman--2019|Gorman et al. 2019]] ). Other meta-analyses put the range at USD1–7 MWh –1 in the USA (Luckow et al. 2015.; [[#Wiser--2017|Wiser et al. 2017]] ), while a comprehensive study put the range at USD12–18 MWh –1 for up to 35% renewables and USD25–46 MWh –1 above 35% renewables (Heptonstall and Gross 2020). Increased system flexibility can reduce integration costs of solar energy ( [[#Wu--2015|Wu et al. 2015]] ) including storage, demand response, sector-coupling ( [[#Brown--2018|Brown et al. 2018]] ; [[#Bogdanov--2019|Bogdanov et al. 2019]] ), and increase complementarity between wind and solar ( [[#Heide--2010|Heide et al. 2010]] ) (Sections 6.4.3 and 6.4.4).

Since solar PV panels have very low operating costs, they can, at high penetrations and in the absence of adequate incentives to shift demand, depress prices in wholesale electricity markets, making it difficult to recoup investment, and potentially reducing incentives for new installations ( [[#Hirth--2013|Hirth 2013]] ; [[#Millstein--2021|Millstein et al. 2021]] ). Continued cost reductions help address this issue of value deflation, but only partially. Comprehensive solutions depend on adding transmission and storage ( [[#Das--2020|Das et al. 2020]] ) and, more fundamentally, adjustments to electricity market design ( [[#Roques--2017|Roques and Finon 2017]] ; [[#Bistline--2019|Bistline and Young 2019]] ).

The most important ways to minimise PV’s impact on the environment lie in recycling materials at end of life and making smart land-use decisions ( ''medium confidence'' ). A comprehensive assessment of PV’s environmental impacts requires lifecycle analysis (LCA) of resource depletion, land-use, ecotoxicity, eutrophication, acidification, ozone, and particulates, among other things ( [[#Mahmud--2018|Mahmud et al. 2018]] ). LCA studies show that solar PVs produce far less CO 2 per unit of electricity than fossil generation, but PV CO 2 emissions vary due to the carbon intensity of manufacturing energy and offset electricity (Grant and Hicks 2020). Concerns about systemic impacts, such as reducing the Earth’s albedo by covering surfaces with dark panels, have shown to be trivial compared to the mitigation benefits ( [[#Nemet--2009|Nemet 2009]] ) (Box 6.7). Even though GHG LCA estimates span a considerable range of 9–250 gCO 2 kWh –1 ( [[#de%20Wild-Scholten--2013|de Wild-Scholten 2013]] ; [[#Kommalapati--2017|Kommalapati et al. 2017]] ), recent studies that reflect higher efficiencies and manufacturing improvements find lower lifecycle emissions, including a range of 18–60 gCO 2 kWh –1 ( [[#Wetzel--2015|Wetzel and Borchers 2015]] ) and central estimates of 80 gCO 2 kWh –1 ( [[#Hou--2016|Hou et al. 2016]] ), 50 gCO 2 kWh –1 ( [[#Nugent--2014|Nugent and Sovacool 2014]] ), and 20 gCO 2 kWh –1 ( [[#Louwen--2016|Louwen et al. 2016]] ). These recent values are an order of magnitude lower than coal, and natural gas and further decarbonisation of the energy system will make them lower still. Thin films and organics produce half the lifecycle emissions of silicon wafer PV, mainly because they use less material ( [[#Lizin--2013|Lizin et al. 2013]] ; [[#Hou--2016|Hou et al. 2016]] ). Novel materials promise even lower environmental impacts, especially with improvements to their performance ratios and reliability ( [[#Gong--2015|Gong et al. 2015]] ; [[#Muteri--2020|Muteri et al. 2020]] ). Higher efficiencies, longer lifetimes, sunny locations, less carbon-intensive manufacturing inputs, and shifting to thin films could reduce future lifecycle impacts.

Another environmental concern with large PV power plants is the conversion of land to collect solar energy ( [[#Hernandez--2015|Hernandez et al. 2015]] ). Approximately 2 hectares of land are needed for 1 MW of solar electricity capacity ( [[#Perpiña%20Castillo--2016|Perpiña Castillo et al. 2016]] ; [[#Kabir--2018|Kabir et al. 2018]] ); at 20% efficiency, a square of PV panels of 550 km by 550 km, comprising 0.2% of Earth’s land area, could meet global energy demand. Land conversion can have local impacts, especially near cities and where land used for solar competes with alternative uses, such as agriculture. Large installations can also adversely impact biodiversity ( [[#Hernandez--2014|Hernandez et al. 2014]] ), especially where the above-ground vegetation is cleared and soils are typically graded. Landscape fragmentation creates barriers to the movement of species. However, a variety of means have emerged to mitigate land use issues. Substitution among renewables can reduce land conversion ( [[#Tröndle--2020|Tröndle 2020]] ). Solar can be integrated with other uses through ‘agrivoltaics’ (the use of land for both agriculture and solar production) ( [[#Dupraz--2011|Dupraz et al. 2011]] ) by, for example, using shade-tolerant crops (Dinesh and Pearce 2016). Combining solar and agriculture can also create income diversification, reduced drought stress, higher solar output due to radiative cooling, and other benefits ( [[#Elamri--2018|Elamri et al. 2018]] ; [[#Hassanpour%20Adeh--2018|Hassanpour Adeh et al. 2018]] ; [[#Barron-Gafford--2019|Barron-Gafford et al. 2019]] ). PV installations floating on water also avoid land-use conflicts ( [[#Sahu--2016|Sahu et al. 2016]] ; [[#Lee--2020|Lee et al. 2020]] ), as does dual-use infrastructure, such as landfills ( [[#Jäger-Waldau--2020|Jäger-Waldau 2020]] ) and reservoirs where evaporation can also be reduced ( [[#Farfan--2018|Farfan and Breyer 2018]] ).

Material demand for PV will likely increase substantially to limit warming to well below 2°C, but PV materials are widely available, have possible substitutes, and can be recycled ( ''medium confidence'' ) (Box 6.4). The primary materials for PV are silicon, copper, glass, aluminium, and silver – the costliest being silicon, and glass being the most essential by mass, at 70%. None of these materials is considered to be either critical or potentially scarce ( [[#IEA--2020e|IEA 2020e]] ). Thin-film cells, such as amorphous silicon, cadmium telluride and copper indium gallium diselenide (CIGS), use far less material (though they use more glass), but account for less than 10% of the global solar market. Other thin-films, such as those based on perovskites, organic solar cells, or earth-abundant, non-toxic materials such as kesterites, either on their own, or layered on silicon, could further reduce material use per energy produced (Box 6.4).

After a typical lifetime of 30 years of use, PV modules can be recycled to prevent environmental contamination from the toxic materials within them, reusing valuable materials and avoiding waste accumulation. Recycling allows the reuse of nearly all – 83% in one study – of the components of PV modules, other than plastics ( [[#Ardente--2019|Ardente et al. 2019]] ) and would add less than 1% to lifecycle GHG emissions ( [[#Latunussa--2016|Latunussa et al. 2016]] ). Glass accounts for 70% of the mass of a solar cell and is relatively easy to recycle. Recycling technology is advancing, but the scale and share of recycling is still small (Li et al. 2020d). By 2050, however, end-of-life PV could total 80 MT and comprise 10% of global electronic waste ( [[#Stolz--2017|Stolz and Frischknecht 2017]] ), although most of it is glass. IEA runs a programme to enable PV recycling by sharing best practices to minimise recycling lifecycle impacts. Ensuring that a substantial amount of panels are recycled at end of life will likely require policy incentives, as the market value of the recovered materials, aside from aluminium and copper, is likely to be too low to justify recycling on its own ( [[#Deng--2019|Deng et al. 2019]] ). A near-term priority is maximising the recovery of silver, silicon, and aluminium, the most valuable PV material components ( [[#Heath--2020|Heath et al. 2020]] ).

Many alternative PV materials are improving in efficiency and stability, providing longer-term pathways for continued PV costs reductions and better performance ( ''high confidence'' ). While solar PV based on semi-conductors constructed from wafers of silicon still captures 90% of the market, new designs and materials have the potential to reduce costs further, increase efficiency, reduce resource use, and open new applications. The most significant technological advance within silicon PV in the past 10 years has been the widespread adoption of the passivated emitter and rear cell (PERC) design ( [[#Green--2015|Green 2015]] ), which now accounts for the majority of production. This advance boosts efficiency over traditional aluminium backing by increasing reflectivity within the cell and reducing electron-hole recombination ( [[#Blakers--2019|Blakers 2019]] ). Bifacial modules increase efficiency by using reflected light from the ground or roof on the backside of modules ( [[#Guerrero-Lemus--2016|Guerrero-Lemus et al. 2016]] ). Integrating PV into buildings can reduce overall costs and improve building energy performance ( [[#Shukla--2016|Shukla et al. 2016]] ). Concentrating PV uses lenses or mirrors that collect and concentrate light onto high efficiency PV cells ( [[#Li--2020a|Li et al. 2020a]] ). Beyond crystalline silicon, thin films of amorphous silicon, cadmium telluride, and copper indium gallium selenide (among others) have the potential for much lower costs while their efficiencies have increased ( [[#Green--2019|Green et al. 2019]] ). Perovskites, inexpensive and easy to produce crystalline structures, have increased in efficiency by a factor of six in the past decade; the biggest challenge is light-induced degradation as well as finding lead-free efficient compounds, or establishing lead recycling at the end of the lifecycle of the device ( [[#Petrus--2017|Petrus et al. 2017]] ; [[#Chang--2018|Chang et al. 2018]] ; [[#Wang--2019b|Wang et al. 2019b]] ; [[#Zhu--2020|Zhu et al. 2020]] ). Organic solar cells are made of carbon-based semiconductors like the ones found in the displays made from organic light emitting diodes (OLEDs) and can be processed in thin films on large areas with scalable and fast coating processes on plastic substrates. The main challenges are raising the efficiency and improving their lifetime ( [[#Ma--2020|Ma et al. 2020]] ; [[#Riede--2021|Riede et al. 2021]] ). Quantum dots, spherical semi-conductor nanocrystals, can be tuned to absorb specific wavelengths of sunlight, giving them the potential for high efficiency with very little material use ( [[#Kramer--2015|Kramer et al. 2015]] ). A common challenge for all emerging solar cell technologies is developing the corresponding production equipment. Hybrids of silicon with layers of quantum dots and perovskites have the potential to take advantage of the benefits of all three, although those designs require that these new technologies have stability and scale that match those of silicon ( [[#Chang--2017|Chang et al. 2017]] ; [[#Palmstrom--2019|Palmstrom et al. 2019]] ). This broad array of alternatives to making PV from crystalline silicon offer realistic potential for lower costs, reduced material use, and higher efficiencies in future years ( [[#Victoria--2021|Victoria et al. 2021]] ).

Besides PV, alternative solar technologies exist, including CSP, which can provide special services in high-temperature heat and diurnal storage, even if it is more costly than PV and its potential for deployment is limited. CSP uses reflective surfaces, such as parabolic mirrors, to focus sunlight on a receiver to heat a working fluid, which is subsequently transformed into electricity ( [[#Islam--2018|Islam et al. 2018]] ). Solar heating and cooling are also well established technologies, and solar energy can be utilised directly for domestic or commercial applications such as drying, heating, cooling, and cooking ( [[#Ge--2018|Ge et al. 2018]] ). Solar chimneys, (still purely conceptual), heat air using large transparent greenhouse-like structures and channel the warm air to turbines in tall chimneys ( [[#Kasaeian--2017|Kasaeian et al. 2017]] ). Solar energy can also be used to produce solar fuels, for example, hydrogen or synthetic gas (syngas) (Montoya et al. 2016; [[#Nocera--2017|Nocera 2017]] ; [[#Detz--2018|Detz et al. 2018]] ). In addition, research proceeds on space-based solar PV, which takes advantage of high insolation and a continuous solar resource ( [[#Kelzenberg--2018|Kelzenberg et al. 2018]] ), but faces the formidable obstacle of developing safe, efficient, and inexpensive microwave or laser transmission to the Earth’s surface ( [[#Yang--2016|Yang et al. 2016]] ). CSP is the most widely adopted of these alternative solar technologies.

Like PV, CSP facilities can deliver large amounts of power (up to 200 MW per unit) and maintain substantial thermal storage, which is valuable for load balancing over the diurnal cycle ( [[#McPherson--2020|McPherson et al. 2020]] ). However, unlike PV, CSP can only use direct sunlight, constraining its cost-effectiveness to North Africa, the Middle East, Southern Africa, Australia, the Western USA, parts of South America (Peru, Chile), and the Western part of China ( [[#Deng--2015|Deng et al. 2015]] ; [[#Dupont--2020|Dupont et al. 2020]] ). Parabolic troughs, central towers and parabolic dishes are the three leading solar thermal technologies ( [[#Wang--2017d|Wang et al. 2017d]] ). Parabolic troughs represented approximately 70% of new capacity in 2018 with the balance made up by central tower plants ( [[#Islam--2018|Islam et al. 2018]] ). Especially promising research directions are on tower-based designs that can achieve high temperatures, useful for industrial heat and energy storage ( [[#Mehos--2017|Mehos et al. 2017]] ), and direct steam generation designs ( [[#Islam--2018|Islam et al. 2018]] ). Costs of CSP have fallen by nearly half since AR5 (Figure 6.8) albeit at a slower rate than PV. Since AR5, almost all new CSP plants have storage (Figure 6.9) ( [[#Thonig--2020|Thonig 2020]] ).

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[[File:ba7ab45bc21367593d56e42e6ed4510d IPCC_AR6_WGIII_Figure_6_9.png]]

'''Figure 6.9 | CSP plants by storage capacity in hours (vertical), year of installation (horizontal), and size of plant in MW (circle size).''' Since AR5, almost all new CSP plants have storage ( [[#Thonig--2020|Thonig 2020]] ). Source: with permission from https://csp.guru/metadata.html .

Solar energy elicits favourable public responses in most countries ( ''high confidence'' ) ( [[#Mcgowan--2005|Mcgowan and Sauter 2005]] ; [[#Ma--2015|Ma et al. 2015]] ; [[#Hanger--2016|Hanger et al. 2016]] ; [[#Bessette--2018|Bessette and Arvai 2018]] ; [[#Jobin--2018|Jobin and Siegrist 2018]] ; [[#Roddis--2019|Roddis et al. 2019]] ; [[#Hazboun--2020|Hazboun and Boudet 2020]] ) ''.'' Solar energy is perceived as clean and environmentally friendly with few downsides ( [[#Faiers--2006|Faiers and Neame 2006]] ; [[#Whitmarsh--2011b|Whitmarsh et al. 2011b]] ). Key motivations for homeowners to adopt PV systems are expected financial gains, environmental benefits, the desire to become more self-sufficient, and peer expectations ( [[#Korcaj--2015|Korcaj et al. 2015]] ; [[#Vasseur--2015|Vasseur and Kemp 2015]] ; [[#Palm--2017|Palm 2017]] ). Hence, the observability of PV systems can facilitate adoption ( [[#Boudet--2019|Boudet 2019]] ). The main barriers to the adoption of solar PV by households are its high upfront costs, aesthetics, landlord-tenant incentives, and concerns about performance and reliability ( [[#Faiers--2006|Faiers and Neame 2006]] ; [[#Whitmarsh--2011b|Whitmarsh et al. 2011b]] ; [[#Vasseur--2015|Vasseur and Kemp 2015]] ).

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==== 6.4.2.2 Wind Energy ====

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Wind power is increasingly competitive with other forms of electricity generation and is the low-cost option in many applications ( ''high confidence'' ). Costs have declined by 18% and 40% on land and offshore since 2015 ( ''high confidence'' ), and further reductions can be expected by 2030 ( ''medium confidence'' ). Critical areas for continued improvement are technology advancements and economies of scale ( ''high confidence'' ). Global future potential is primarily limited by onshore land availability in wind power-rich areas, lack of supporting infrastructure, grid integration, and access to finance (especially in developing countries) ( ''high confidence'' ).

Energy from wind is abundant, and the estimated technical potentials surpass the total amount of energy needed to limit warming to well below 2°C ( ''high confidence'' ). Recent global estimates of potentially exploitable wind energy resource are in the range of 557–717 PWh yr –1 (2005–2580 EJ yr –1 ) ( [[#Eurek--2017|Eurek et al. 2017]] ; [[#Bosch--2017|Bosch et al. 2017]] , 2018; [[#McKenna--2022|McKenna et al. 2022]] ), or 20–30 times the 2017 global electricity demand. Studies have suggested that ‘bottom-up’ approaches may overestimate technical potentials ( [[#Miller--2015|Miller et al. 2015]] ; [[#Kleidon--2020|Kleidon and Miller 2020]] ). But even in the most conservative ‘top-down’ approaches, the technical wind potential surpasses the amount needed to limit warming to well below 2°C ( [[#Bosch--2017|Bosch et al. 2017]] ; [[#Eurek--2017|Eurek et al. 2017]] ; [[#Volker--2017|Volker et al. 2017]] ). The projected climate change mitigation from wind energy by 2100 ranges from 0.3°C–0.8°C depending on the precise socio-economic pathway and wind energy expansion scenario followed ( [[#Barthelmie--2021|Barthelmie and Pryor 2021]] ). Wind resources are unevenly distributed over the globe and by time of the year ( [[#Petersen--2012|Petersen and Troen 2012]] ), but potential hotspots exist on every continent (Figure 6.10) as expressed by the wind power density (a quantitative measure of wind energy available at any location). Technical potentials for onshore wind power vary considerably, often because of inconsistent assessments of suitability factors ( [[#McKenna--2020|McKenna et al. 2020]] ). The potential for offshore wind power is larger than for onshore because offshore wind is stronger and less variable ( [[#Bosch--2018|Bosch et al. 2018]] ). Offshore wind is more expensive, however, because of higher costs for construction, maintenance, and transmission. Wind power varies at a range of time scales, from annual to sub-seconds; the effects of local short-term variability can be offset by power plant control, flexible grid integration, and storage ( [[#Barra--2021|Barra et al. 2021]] ) ( [[#6.4.3|Section 6.4.3]] ). In some regions, interannual variations in wind energy resources could be important for optimal power system design ( [[#Wohland--2019a|Wohland et al. 2019a]] ; [[#Coker--2020|Coker et al. 2020]] ).

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[[File:c03fe1e68939f3f24ccef6fd14c4e238 IPCC_AR6_WGIII_Figure_6_10.png]]

'''Figure 6.10 | Mean wind power density [W m''' –2 '''] at 100 m above ground level over land and within 100 km of the coastline.''' Source: Global Wind Atlas, available at: https://globalwindatlas.info/ .

Wind power cost reductions (Figure 6.11) are driven mainly by larger capacity turbines, larger rotor diameters and taller hub heights – larger swept areas increase the energy captured and the capacity factors for a given wind speed; taller towers provide access to higher wind speeds ( [[#Beiter--2021|Beiter et al. 2021]] ). All major onshore wind markets have experienced rapid growth in both rotor diameter (from 81.2 m in 2010 to 120 m in 2020) ( [[#IRENA--2021b|IRENA 2021b]] ), and average power ratings (from 1.9 MW in 2010 to 3 MW in 2020). The generation capacity of offshore wind turbines grew by a factor of 3.7 in less than two decades, from 1.6 MW in 2000 to 6 MW in 2020 ( [[#Wiser--2021|Wiser et al. 2021]] ). Floating foundations could revolutionise offshore wind power by tapping into the abundant wind potential in deeper waters. This technology is particularly important for regions where coastal waters are too deep for fixed-bottom wind turbines. Floating wind farms potentially offer economic and environmental benefits compared with fixed-bottom designs due to less-invasive activity on the seabed during installation, but the long-term ecological effects are unknown and meteorological conditions further offshore and in deeper waters are harsher on wind turbine components ( [[#IRENA--2019c|IRENA 2019c]] ). A radical new class of wind energy converters has also been conceived under the name of airborne wind energy systems that can harvest strong, high-altitude winds (typically between 200–800m), which are inaccessible by traditional wind turbines ( [[#Cherubini--2015|Cherubini et al. 2015]] ). This technology has seen development and testing of small devices ( [[#Watson--2019|Watson et al. 2019]] ).

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[[File:636879f79fab6aaa32ece8519dc633fe IPCC_AR6_WGIII_Figure_6_11.png]]

'''Figure 6.11 | Global weighted average total installed costs, capacity factors, and LCOE for onshore (top) and offshore (bottom) wind power of existing power plants per year (2010–2020).''' The shaded area represents the 5th and 95th percentiles, and the red dashed line represents the fossil fuel cost range. Source: with permission from [[#IRENA--2021a|IRENA (2021a)]] .

Wind capacity factors have increased over the last decade (Figure 6.11). The capacity factor for onshore wind farms increased from 27% in 2010 to 36% in 2020 ( [[#IRENA--2021a|IRENA 2021a]] ). The global average offshore capacity factor has decreased from a peak of 45% in 2017. This has been driven by the increased share of offshore development in China, where projects are often near-shore and use smaller wind turbines than in Europe ( [[#IRENA--2021b|IRENA 2021b]] ). Improvements in capacity factors also come from increased functionality of wind turbines and wind farms. Manufactures can adapt the wind turbine generator to the wind conditions. Turbines for windy sites have smaller generators and smaller specific capacity per rotor area, and therefore operate more efficiently and reach full capacity for a longer time period ( [[#Rohrig--2019|Rohrig et al. 2019]] ).

Electricity from onshore wind is less expensive than electricity generated from fossil fuels in a growing number of markets ( ''high confidence'' ). The global average LCOE onshore declined by 38% from 2010 to 2020 (Figure 6.11), reaching USD0.039 kWh –1 . However, the decrease in cost varies substantially by region. Since 2014, wind costs have declined more rapidly than the majority of experts predicted ( [[#Wiser--2021|Wiser et al. 2021]] ). New modelling projects onshore wind LCOE of USD.037 kWh –1 by 2030 ( [[#Junginger--2020a|Junginger et al. 2020a]] ), and additional reductions of 37–39% have been predicted by 2050 ( [[#Wiser--2021|Wiser et al. 2021]] ). The future cost of offshore wind is more uncertain because other aspects besides increases in capacity factors influence the cost ( [[#Junginger--2020b|Junginger et al. 2020b]] ).

The cost of the turbine (including the towers) makes up the largest component of wind LCOE. Total installed costs for both onshore and offshore wind farms have decreased since 2015 (Figure 6.11), but the total installed costs for onshore wind projects are very site- and market-specific, as reflected in the range of LCOEs. China, India, and the USA have experienced the largest declines in total installed costs. In 2020, typical country-average total installed costs were around USD1150 kW –1 in China and India, and between USD1403–2472 kW –1 elsewhere ( [[#IRENA--2021b|IRENA 2021b]] ). Total installed costs of offshore wind farms declined by 12% between 2010 and 2020. But, because some of the new offshore wind projects have moved to deeper waters and further offshore, there are considerable year-to-year variations in their price ( [[#IRENA--2021b|IRENA 2021b]] ). Projects outside China in recent years have typically been built in deeper waters (10–55 m) and up to 120 km offshore, compared to around 10 m in 2001–2006, when distances rarely exceeded 20 km. With the shift to deeper waters and sites further from ports, the total installed costs of offshore wind farms rose, from an average of around USD2500 kW –1 in 2000 to around USD5127 kW –1 by 2011–2014, before falling to around USD3185 kW –1 in 2020 ( [[#IRENA--2020a|IRENA 2020a]] ). The full cost of wind power includes the transmission and system integration costs (Sections 6.4.3 and 6.4.6). A new technology in development is the co-location of wind and solar PV power farms, also known as hybrid power plants. Co-locating wind, solar PV, and batteries can lead to synergies in electricity generation, infrastructure, and land usage, which may lower the overall plant cost compared to single technology systems ( [[#Lindberg--2021|Lindberg et al. 2021]] ).

Wind power plants pose relatively low environmental impact, but sometimes locally significant ecological effects ( ''high confidence'' ). The environmental impact of wind technologies, including CO 2 emissions, is concentrated in the manufacturing, transport, and building stage and in disposal as the end-of-life of wind turbines is reached (Liu and Barlow 2017; [[#Mishnaevsky--2021|Mishnaevsky 2021]] ). The operation of wind turbines produces no waste or pollutants. The LCA for wind turbines is strongly influenced by the operating lifetime, quality of wind resources, conversion efficiency, and size of the wind turbines ( [[#Kaldellis--2017|Kaldellis and Apostolou 2017]] ; [[#Laurent--2018|Laurent et al. 2018]] ). All wind power technologies repay their carbon footprint in less than a year ( [[#Bonou--2016|Bonou et al. 2016]] ).

Wind farms can cause local ecological impacts, including on animal habitat and movements, biological concerns, bird and bat fatalities from collisions with rotating blades, and health concerns ( [[#Morrison--2004|Morrison and Sinclair 2004]] ). The impacts on animal habitats and collisions can be resolved or reduced by selectively stopping some wind turbines in high-risk locations, often without affecting the productivity of the wind farm ( [[#de%20Lucas--2012|de Lucas et al. 2012]] ). Many countries now require environmental studies of impacts of wind turbines on wildlife prior to project development, and, in some regions, shutdowns are required during active bird migration ( [[#de%20Lucas--2012|de Lucas et al. 2012]] ). Offshore wind farms can also impact migratory birds and other sea species ( [[#Hooper--2017|Hooper et al. 2017]] ). Floating foundations pose lower environmental impacts at build stage ( [[#IRENA--2019c|IRENA 2019c]] ), but their cumulative long-term impacts are unclear ( [[#Goodale--2016|Goodale and Milman 2016]] ). Recent studies find weak associations between wind farm noise and measures of long-term human health ( [[#Poulsen--2018a|Poulsen et al. 2018a]] , b, 2019a, b).

Public support for onshore and particularly offshore wind energy is generally high, although people may oppose specific wind farm projects ( ''high confidence'' ) (e.g., Bell et al. 2005; Batel and Devine-Wright 2015; [[#Rand--2017|Rand and Hoen 2017]] ; [[#Steg--2018|Steg 2018]] ). People generally believe that wind energy is associated with environmental benefits and that it is relatively cheap. Yet, some people believe wind turbines can cause noise and visual aesthetic pollution, threaten places of symbolic value ( [[#Devine-Wright--2020|Devine-Wright and Wiersma 2020]] ; [[#Russell--2020|Russell et al. 2020]] ), and have adverse effects on wildlife ( [[#Bates--2015|Bates and Firestone 2015]] ), which challenges public acceptability ( [[#Rand--2017|Rand and Hoen 2017]] ). Support for local wind projects is higher when people believe fair decision-making procedures have been implemented ( [[#Dietz--2008|Dietz and Stern 2008]] ; [[#Aitken--2010a|Aitken 2010a]] ). Evidence is mixed whether distance from wind turbines or financial compensation increases public acceptability of wind turbines ( [[#Cass--2010|Cass et al. 2010]] ; [[#Rand--2017|Rand and Hoen 2017]] ; [[#Rudolph--2018|Rudolph et al. 2018]] ; [[#Hoen--2019|Hoen et al. 2019]] ). Offshore wind farms projects have higher public support, but can also face resistance ( [[#Bidwell--2017|Bidwell 2017]] ; [[#Rudolph--2018|Rudolph et al. 2018]] ).

Common economic barriers to wind development are high initial cost of capital, long payback periods, and inadequate access to capital. Optimal wind energy expansion is most likely to occur in the presence of a political commitment to establish, maintain, and improve financial support instruments, technological efforts to support a local supply chains, and grid investments integrate VRE electricity ( [[#Diógenes--2020|Diógenes et al. 2020]] ).

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<span id="box-6.4-critical-strategic-minerals-and-a-low-carbon-energy-system-transition"></span>
=== Box 6.4 | Critical Strategic Minerals and a Low-carbon Energy System Transition ===

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The secure supply of many metals and minerals (e.g., cobalt, copper, lithium, and rare earth elements (REEs)) is critical to supporting a low-emissions energy system transition ( [[#Sovacool--2020|]] [[#Sovacool--2020|Sovacool et al. 2020]] ). A low-carbon energy system transition will increase the demand for these minerals to be used in technologies like wind turbines, PV cells, and batteries ( [[#World%20Bank--2020|World Bank 2020]] ). Reliance on these minerals has raised questions about possible constraints to a low-carbon energy system transition, including supply chain disruptions (Chapter 10.6). Concerns have also been raised about mining for these materials, which frequently results in severe environmental impacts ( [[#Sonter--2020|Sonter et al. 2020]] ), and metal production itself is energy-intensive and difficult to decarbonise ( [[#Sovacool--2020|]] [[#Sovacool--2020|Sovacool et al. 2020]] ).

Wind energy depends on two critical REEs – neodymium and dysprosium – used in magnets in high-performance generators ( [[#Pavel--2017|Pavel et al. 2017]] ; [[#Li--2020b|Li et al. 2020b]] ). Silicon-wafer-based solar PV, which accounted for 95% of PV production in 2020, does not use REEs but utilises aluminium, copper, and silver ( [[#IEA--2021a|IEA 2021a]] ). Lithium, nickel, cobalt, and phosphorous are used in batteries. Many critical minerals are used in EVs, including aluminium and copper in manufacturing the necessary EV charging infrastructure, and neodymium in permanent magnet motors.

These strategic minerals are found in a limited number of countries, and concerns have been raised that geopolitical factors could disrupt the supply chain necessary for a low-carbon energy system transition. However, excluding cobalt and lithium, no single country holds more than a third of the world reserves. The known supply of some strategic minerals is still close to 600 years at current levels of demand ( [[#BP--2020|BP 2020]] ), but increased demand would cut more quickly into supplies.

Box 6.4

There are alternatives to the strategic minerals currently used to support a low-carbon transition. Wind turbines can be manufactured without permanent magnets to reduce the need for strategic minerals, but the production costs are higher, and their efficiency is reduced ( [[#Månberger--2018|Månberger and Stenqvist 2018]] ). Alternatives to silicon, such as thin films, could be used to produce PVs. Thin-films use much less material than silicon-based PV, but they contain other potentially critical metals like tellurium, cadmium, and gallium. Alternatives to lithium-ion batteries, such as sodium-ion batteries, are becoming more practical and feasible ( [[#Sovacool--2020|]] [[#Sovacool--2020|Sovacool et al. 2020]] ).

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<span id="hydroelectric-power"></span>
==== 6.4.2.3 Hydroelectric Power ====

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Hydropower is technically mature, proved worldwide as a primary source of renewable electricity, and may be used to balance electricity supply by providing flexibility and storage. The LCOE of hydropower is lower than the cheapest new fossil fuel-fired option. However, the future mitigation potential of hydropower depends on minimising environmental and social impacts during the planning stages, reducing the risks of dam failures, and modernising the ageing hydropower fleet to increase generation capacity and flexibility ( ''high confidence'' ).

Estimates of global gross theoretical available hydropower potential varies from 31–128 PWh yr –1 (112–460 EJ yr –1 ), exceeding total electricity production in 2018 ( [[#Banerjee--2017|Banerjee et al. 2017]] ; [[#BP--2020|BP 2020]] ; [[#IEA--2021d|IEA 2021d]] ). This potential is distributed over 11.8 million locations (Figure 6.12), but many of the locations cannot be developed for (current) technical, economic, or political reasons. The estimated technical potential of hydropower is 8–30 PWh yr –1 (29–108 EJ yr –1 ), and its estimated economic potential is 8–15 PWh yr –1 (29–54 EJ yr –1 ) ( [[#Zhou--2015|Zhou et al. 2015]] ; [[#van%20Vliet--2016c|van Vliet et al. 2016c]] ). Actual hydropower generation in 2019 was 4.2 PWh (15.3 EJ), providing about 16% of global electricity and 43% of global electricity from renewables ( [[#BP--2020|BP 2020]] ; [[#IEA--2020f|IEA 2020f]] ; [[#Killingtveit--2020|Killingtveit 2020]] ). Asia holds the largest hydropower potential (48%), followed by South America (19%) ( [[#Hoes--2017|Hoes et al. 2017]] ).

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[[File:ecb735027f9e0293917943f24c6585a3 IPCC_AR6_WGIII_Figure_6_12.png]]

'''Figure 6.12 | Global map of gross hydropower potential distribution [GWh yr''' –1 '''].''' Source: data from Hoeset al. (2017).

Hydropower is a mature technology with locally adapted solutions ( ''high confidence'' ) ( [[#Zhou--2015|Zhou et al. 2015]] ; [[#Killingtveit--2020|Killingtveit 2020]] ). The peak efficiency of hydroelectric plants is greater than 85%. Hydropower plants without storage or with small storage typically produce a few kWs to 10 MWs (examples of plants producing higher amounts do exist), and are useful for providing electricity at a scale from households to small communities ( [[#El%20Bassam--2013|El Bassam et al. 2013]] ; [[#Towler--2014|Towler 2014]] ). However, hydropower plants without or with small storage may be susceptible to climate variability, especially droughts, when the amount of water may not be sufficient to generate electricity ( [[#Premalatha--2014|Premalatha et al. 2014]] ) ( [[#6.5|Section 6.5]] ).

Hydropower plants with storage may produce 10 GW, reaching over 100 TWh yr –1 (0.36 EJ yr –1 ), but generally require large areas. Pumped storage hydropower stores energy by pumping water to higher reservoirs during low-demand periods ( [[#Killingtveit--2020|Killingtveit 2020]] ). The storage in hydropower systems provides flexibility to compensate for rapid variations in electricity loads and supplies. The regulating characteristics of the storage play an important role in assuring continuity of energy supply from renewable sources ( [[#Yang--2018b|Yang et al. 2018b]] ).

Hydropower is one of the lowest-cost electricity technologies ( [[#Mukheibir--2013|Mukheibir 2013]] ; [[#IRENA--2021b|IRENA 2021b]] ). Its operation and maintenance costs are typically 2–2.5% of the investment costs per kW yr –1 for a lifetime of 40–80 years ( [[#Killingtveit--2020|Killingtveit 2020]] ). Construction costs are site-specific. The total cost for an installed large hydropower project varies from USD10,600–804,500 kW –1 if the site is located far away from transmission lines, roads, and infrastructure. Investment costs increase for small hydropower plants and may be as high as USD100,000 kW –1 or more for the installation of plants of less than 1 MW – 20% to 80% more than for large hydropower plants ( [[#IRENA--2015|IRENA 2015]] ). During the past 100 years, total installed costs and LCOE have risen by a few percent, but the LCOE of hydropower remains lower than the cheapest new fossil fuel-fired option ( [[#IRENA--2019b|IRENA 2019b]] , 2021).

Hydroelectric power plants may pose serious environmental and societal impacts ( ''high confidence'' ) ( [[#McCartney--2009|McCartney 2009]] ). Dams may lead to fragmentation of ecological habitats because they act as barriers for migration of fish and other land and water-borne fauna, sediments, and water flow. These barriers can be mitigated by sediment passes and fish migration aids, and with provision of environmental flows. Below dams, there can be considerable alterations to vegetation, natural river flows, retention of sediments and nutrients, and water quality and temperature. Construction of large reservoirs leads to loss of land, which may result in social and environmental consequences. Minimising societal and environmental impacts requires taking into account local physical, environmental, climatological, social, economic, and political aspects during the planning stage ( [[#Killingtveit--2020|Killingtveit 2020]] ). Moreover, when large areas of land are flooded by dam construction, they generate GHGs ( [[#Prairie--2018|Prairie et al. 2018]] ; [[#Phyoe--2019|Phyoe and Wang 2019]] ; [[#Maavara--2020|Maavara et al. 2020]] ). On the other hand, hydropower provides flexible, competitive low-emission electricity, local economic benefits (e.g., by increasing irrigation and electricity production in developing countries), and ancillary services such as municipal water supply, irrigation and drought management, navigation and recreation, and flood control ( [[#IRENA--2021b|IRENA 2021b]] ). However, the long-term economic benefits to communities affected by reservoirs are a subject of debate ( [[#de%20Faria--2017|de Faria et al. 2017]] ; [[#Catolico--2021|Catolico et al. 2021]] ).

Public support for hydroelectric energy is generally high ( [[#Steg--2018|Steg 2018]] ), and higher than support for coal, gas, and nuclear. Yet, public support for hydro seems to differ for existing and new projects ( ''high confidence'' ). Public support is generally high for small- and medium-scale hydropower in regions where hydropower was historically used ( [[#Gormally--2014|Gormally et al. 2014]] ). Additionally, there is high support for existing large hydropower projects in Switzerland ( [[#Rudolf--2014|Rudolf et al. 2014]] ; [[#Plum--2019|Plum et al. 2019]] ), Canada ( [[#Boyd--2019|Boyd et al. 2019]] ), and Norway ( [[#Karlstrøm--2014|Karlstrøm and Ryghaug 2014]] ), where it is a trusted and common energy source. Public support seems lower for new hydropower projects ( [[#Hazboun--2020|Hazboun and Boudet 2020]] ), and the construction of new large hydropower plants has been met with strong resistance in some areas ( [[#Vince--2010|Vince 2010]] ; [[#Bronfman--2015|Bronfman et al. 2015]] ). People generally perceive hydroelectric energy as clean and a non-contributor to climate change and environmental pollution ( [[#Kaldellis--2013|Kaldellis et al. 2013]] ). For example, in Sweden, people believed that existing hydropower projects have as few negative environmental impacts as solar, and even less than wind ( [[#Ek--2005|Ek 2005]] ). However, in areas where the construction of new large-scale hydroelectric energy is met with resistance, people believe that electricity generation from hydro can cause environmental, social, and personal risks ( [[#Bronfman--2012|Bronfman et al. 2012]] ; [[#Kaldellis--2013|Kaldellis et al. 2013]] ).

The construction time of hydroelectric power plants is longer than many other renewable technologies, and that construction time may be extended by the additional time it takes to fill the reservoir. This extended timeline can create uncertainty in the completion of the project. The uncertainty is due to insecurity in year-to-year variations in precipitation and the water inflows required to fill reservoirs. This is especially critical in the case of trans-boundary hydroelectric power plants, where filling up the reservoirs can have large implications on downstream users in other nations. As a result of social and environmental constraints, only a small fraction of potential economic hydropower projects can be developed, especially in developed countries. Many developing countries have major undeveloped hydropower potential, and there are opportunities to develop hydropower combined with other economic activities such as irrigation ( [[#Lacombe--2014|Lacombe et al. 2014]] ). Competition for hydropower across country borders can lead to conflict, which could be exacerbated if climate alters rainfall and streamflow ( [[#Ito--2016|Ito et al. 2016]] ).

<div id="6.4.2.4" class="h3-container"></div>

<span id="nuclear-energy"></span>
==== 6.4.2.4 Nuclear Energy ====

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Nuclear power can deliver low-carbon energy at scale ( ''high confidence'' ). Doing so will require improvements in managing construction of reactor designs that hold the promise of lower costs and broader use ( ''medium confidence'' ). At the same time, nuclear power continues to be affected by cost overruns, high upfront investment needs, challenges with final disposal of radioactive waste, and varying public acceptance and political support levels ( ''high confidence'' ).

There are sufficient resources for substantially increasing nuclear deployment ( ''medium confidence'' ). Estimates for identified uranium resources have been increasing steadily over the years. Conventional uranium resources have been estimated to be sufficient for over 130 years of supply at current levels of use; 100 years were estimated in 2009 ( [[#Hahn--1983|Hahn 1983]] ; [[#NEA/IAEA--2021|NEA/IAEA 2021]] ). In the case of future uranium resource scarcity, thorium or recycling of spent fuel might be used as alternatives. Interest in these alternatives has waned with better understanding of uranium deposits, their availability, and low prices ( [[#IAEA--2005|IAEA 2005]] ; OECD NEA 2015).

There are several possible nuclear technology options for the period from 2030 to 2050 ( ''medium confidence'' ). In addition to electricity, nuclear can also be used to produce low-carbon hydrogen and freshwater ( [[#Kavvadias--2014|Kavvadias and Khamis 2014]] ; [[#Kayfeci--2019|Kayfeci et al. 2019]] ).

• '''Large reactors.''' The nuclear industry has entered a new phase of reactor construction, based on evolutionary designs. These reactors achieve improvements over previous designs through small to moderate modifications, including improved redundancy, increased application of passive safety features, and significant improvements to containment design to reduce the risk of a major accident ( [[#MIT--2018|MIT 2018]] ). Examples include European – EPR, Korean – APR1400, USA – AP1000, Chinese – HPR1000 or Russian – VVER-1200.

'''•''' '''Long-term operation (LTO) of the current fleet.''' Continued production from nuclear power will depend in part on life extensions of the existing fleet. By the end of 2020, two-thirds of nuclear power reactors will have been operational for over 30 years. The design lifetime of most of existing reactors is 30–40 years. Engineering assessments have established that reactors can operate safely for longer if key replaceable components (e.g., steam generator, mechanical and electrical equipment, instrumentation and control parts) are changed or refurbished ( [[#IAEA--2018|IAEA 2018]] ). The first lifetime extension considered in most of the countries typically is 10–20 years ( [[#IEA--2020j|IEA 2020j]] ).

• '''Small modular reactors (SMR).''' There are more than 70 SMR designs at different stages of consideration and development, from the conceptual phase to licensing and construction of first-of-a-kind facilities ( [[#IAEA--2020|IAEA 2020]] ). Due to smaller unit sizes, the SMRs are expected to have lower total investment costs, although the cost per unit of generation might be higher than conventional large reactors ( [[#Mignacca--2020|Mignacca and Locatelli 2020]] ). Modularity and off-site pre-production may allow greater efficiency in construction, shorter delivery times, and overall cost optimisation ( [[#IEA--2019c|IEA 2019c]] ). SMR designs aim to offer an increased load-following capability that makes them suitable to operate in smaller systems and in systems with increasing shares of VRE sources. Their market development by the early 2030s will strongly depend on the successful deployment of prototypes during the 2020s.

Nuclear power costs vary substantially across countries ( ''high confidence'' ). First-of-a-kind projects under construction in Northern America and Europe have been marked by delays and costs overruns ( [[#Berthelemy--2015|Berthelemy and Rangel 2015]] ). Construction times have exceeded 13–15 years and cost has surpassed three to four times initial budget estimates ( [[#IEA--2020j|IEA 2020j]] ). In contrast, most of the recent projects in Eastern Asia (with construction starts from 2012) were implemented within five to six years (IAEA 2021). In addition to region-specific factors, future nuclear costs will depend on the ability to benefit from the accumulated experience in controlling the main drivers of cost. These cost drivers fall into four categories: design maturity; project management; regulatory stability and predictability; and multi-unit and series effects ( [[#NEA--2020|NEA 2020]] ). With lessons learned from first-of-a-kind projects, the cost of electricity for new builds are expected to be in the range of USD42–102 MWh –1 depending on the region ( [[#IEA--2020j|IEA 2020j]] ).

Lifetime extensions are significantly cheaper than new builds and cost competitive with other low-carbon technologies. The overnight cost of lifetime extensions is estimated in the range of USD390–630 kWe –1 for Europe and North America, and the LCOE in the range of USD30–36 MWh –1 for extensions of 10–20 years ( [[#IEA--2020j|IEA 2020j]] ).

Cost-cutting opportunities, such as design standardisation and innovations in construction approaches, are expected to make SMRs competitive against large reactors by 2040 ( [[#Rubio--2016|Rubio and Tricot 2016]] ) ( ''medium confidence'' ). As SMRs are under development, there is substantial uncertainty regarding the construction costs. Vendors have estimated first-of-a-kind LCOEs at USD131–190 MWh –1 . Effects of learning for nth-of-a-kind SMR are anticipated to reduce the first-of-a-kind LCOE by 19–32%.

Despite low probabilities, the potential for major nuclear accidents exists, and the radiation exposure impacts could be large and long-lasting ( [[#Steinhauser--2014|Steinhauser et al. 2014]] ). However, new reactor designs with passive and enhanced safety systems reduce the risk of such accidents significantly ( ''high confidence'' ). The (normal) activity of a nuclear reactor results in low volumes of radioactive waste, which requires strictly controlled and regulated disposal. On a global scale, roughly 421 kt of spent nuclear fuel have been produced since 1971 (IEA 2014). Out of this volume, 2–3% is high-level radioactive waste, which presents challenges in terms of radiotoxicity and decay longevity, and ultimately entails permanent disposal.

Nuclear energy is found to be favourable regarding land occupation ( [[#Cheng--2017|Cheng and Hammond 2017]] ; [[#Luderer--2019|Luderer et al. 2019]] ) and ecological impacts ( [[#Brook--2015|Brook and Bradshaw 2015]] ; [[#Gibon--2017|Gibon et al. 2017]] ). Similarly, bulk material requirements per unit of energy produced are low (e.g., aluminum, copper, iron, rare earth metals) ( [[#Vidal--2013|Vidal et al. 2013]] ; [[#Luderer--2019|Luderer et al. 2019]] ). Water-intensive inland nuclear power plants may contribute to localised water stress and competition for water uses. The choice of cooling systems (closed-loop instead of once-through) can significantly moderate withdrawal rates of freshwater ( [[#Meldrum--2013|Meldrum et al. 2013]] ; [[#Fricko--2016|Fricko et al. 2016]] ; [[#Mouratiadou--2016|Mouratiadou et al. 2016]] ; [[#Jin--2019|Jin et al. 2019]] ). Reactors situated on the seashore are not affected by water scarcity issues ( [[#Abousahl--2021|Abousahl et al. 2021]] ). Lifecycle analysis (LCA) studies suggest that the overall impacts on human health (in terms of disability adjusted life years (DALYs)) from the normal operation of nuclear power plants are substantially lower than those caused by fossil fuel technologies and are comparable to renewable energy sources ( [[#Treyer--2014|Treyer et al. 2014]] ; [[#Gibon--2017|Gibon et al. 2017]] ).

Nuclear power continues to suffer from limited public and political support in some countries ( ''high confidence'' ). Public support for nuclear energy is consistently lower than for renewable energy and natural gas, and in many countries as low as support for energy from coal and oil ( [[#Corner--2011|Corner et al. 2011]] ; [[#Pampel--2011|Pampel 2011]] ; [[#Hobman--2013|Hobman and Ashworth 2013]] ). The major nuclear accidents (i.e., Three Mile Island, Chernobyl, and Fukushima) decreased public support ( [[#Poortinga--2013|Poortinga et al. 2013]] ; [[#Bird--2014|Bird et al. 2014]] ). The public remains concerned about the safety risks of nuclear power plants and radioactive materials ( [[#Pampel--2011|Pampel 2011]] ; [[#Bird--2014|Bird et al. 2014]] ; [[#Tsujikawa--2016|Tsujikawa et al. 2016]] ). At the same time, some groups see nuclear energy as a reliable energy source, beneficial for the economy and helpful in climate change mitigation. Public support for nuclear energy is higher when people are concerned about energy security, including concerns about the availability of energy and high energy prices (Groot et al. 2013; [[#Gupta--2019b|Gupta et al. 2019b]] ), and when they expect local benefit ( [[#Wang--2020c|Wang et al. 2020c]] ). Public support also increases when trust in managing bodies is higher ( [[#de%20Groot--2011|de Groot and Steg 2011]] ). Similarly, transparent and participative decision-making processes enhance perceived procedural fairness and public support ( [[#Sjoberg--2004|Sjoberg 2004]] ).

Because of the sheer scale of the investment required (individual projects can exceed USD10 billion in value), nearly 90% of nuclear power plants under construction are run by state-owned or controlled companies, with governments assuming significant part of the risks and costs. For countries that choose nuclear power in their energy portfolio, stable political conditions and support, clear regulatory regimes, and adequate financial framework are crucial for successful and efficient implementation.

Many countries have adopted technology-specific policies for low-carbon energy courses, and these policies influence the competitiveness of nuclear power. For example, feed-in-tariffs and feed-in premiums for renewables widely applied in the EU ( [[#Kitzing--2012|Kitzing et al. 2012]] ) or renewable portfolio standards in the USA ( [[#Barbose--2016|Barbose et al. 2016]] ) impact wholesale electricity price (leading occasionally to low or even negative prices), which affects the revenues of existing nuclear and other plants ( [[#Bruninx--2013|Bruninx et al. 2013]] ; [[#Newbery--2018|Newbery et al. 2018]] ; [[#Lesser--2019|Lesser 2019]] ).

Nuclear power’s long-term viability may hinge on demonstrating to the public and investors that there is a long-term solution to spent nuclear fuel. Evidence from countries steadily progressing towards first final disposals – Finland, Sweden and France – suggests that broad political support, coherent nuclear waste policies, and a well-managed, consensus-based decision-making process are critical for accelerating this process ( [[#Metlay--2016|Metlay 2016]] ). Proliferation concerns surrounding nuclear power are related to fuel cycle (i.e., uranium enrichment and spent fuel processing). These processes are implemented in a very limited number of countries following strict national and internationals norms and rules, such as the International Atomic Energy Agency (IAEA) guidelines, treaties and conventions. Most of the countries that might introduce nuclear power in the future for their climate change mitigation benefits do not envision developing their own full fuel cycle, significantly reducing any risks that might be linked to proliferation ( [[#IAEA--2014|IAEA 2014]] , 2019).

<div id="6.4.2.5" class="h3-container"></div>

<span id="carbon-dioxide-capture-utilisation-and-storage"></span>
==== 6.4.2.5 Carbon Dioxide Capture, Utilisation and Storage ====

<div id="h3-5-siblings" class="h3-siblings"></div>

Since AR5, there have been increased efforts to develop novel platforms that reduce the energy penalty associated with CO 2 capture, develop CO 2 utilisation pathways as a substitute to geologic storage, and establish global policies to support CCS ( ''high confidence'' ). CCS can be used within electricity and other sectors. While it increases the cost of electricity, CCS has the potential to contribute significantly to low-carbon energy system transitions ( [[#IPCC--2018|IPCC 2018]] ).

The theoretical global geologic storage potential is about 10,000 GtCO 2 , with more than 80% of this capacity existing in saline aquifers ( ''medium confidence'' ). Not all the storage capacity is usable because geologic and engineering factors limit the actual storage capacity to an order of magnitude below the theoretical potential, which is still more than the CO 2 storage requirement through 2100 to limit temperature change to 1.5°C ( [[#Martin-Roberts--2021|Martin-Roberts et al. 2021]] ) ( ''high confidence'' ). One of the key limiting factors associated with geologic CO 2 storage is the global distribution of storage capacity (Table 6.2). Most of the available storage capacity exists in saline aquifers. Capacity in oil and gas reservoirs and coalbed methane fields is limited. Storage potential in the USA alone is &gt;1000 GtCO 2 , which is more than 10% of the world total ( [[#NETL--2015|NETL 2015]] ). The Middle East has more than 50% of global enhanced oil recovery potential ( [[#Selosse--2017|Selosse and Ricci 2017]] ). It is likely that oil and gas reservoirs will be developed as geologic sinks before saline aquifers because of existing infrastructure and extensive subsurface data ( [[#Alcalde--2019|Alcalde et al. 2019]] ; [[#Hastings--2020|Hastings and Smith 2020]] ). Notably, not all geologic storage is utilisable. In places with limited geologic storage, international CCS chains are being considered, where sources and sinks of CO 2 are located in two or more countries ( [[#Sharma--2021|Sharma and Xu 2021]] ). For economic long-term storage, the desirable conditions are a depth of 800–3000 m, thickness of greater than 50 m and permeability greater than 500 mD ( [[#Chadwick--2008|Chadwick et al. 2008]] ; [[#Singh--2021|Singh et al. 2021]] ). Even in reservoirs with large storage potential, the rate of injection might be limited by the subsurface pressure of the reservoir ( [[#Baik--2018|Baik et al. 2018]] ). It is estimated that geologic sequestration is reliable with overall leakage rates at &lt;0.001% yr –1 ( [[#Alcalde--2018|Alcalde et al. 2018]] ). In many cases, geological storage resources are not located close to CO 2 sources, increasing costs and reducing viability ( [[#Garg--2017a|Garg et al. 2017a]] ).

'''Table 6.2 | Geologic storage potential across underground formations globally.''' '''These represent order-of-magnitude estimates.''' Data: Selosseand Ricci (2017).

{| class="wikitable"
|-
| '''Reservoir typ''' e

| '''Africa'''

| '''Australia'''

| '''Canada'''

| '''China'''

| '''CSA'''

| '''EEU'''

| '''FSU'''

| '''India'''

| '''MEA'''

| '''Mexico'''

| '''ODA'''

| '''USA'''

| '''WEU'''

|-
| Enhanced oil recovery

| 3

| 0

| 3

| 1

| 8

| 2

| 15

| 0

| 38

| 0

| 1

| 8

| 0

|-
| Depleted oil and gas fields

| 20

| 8

| 19

| 1

| 33

| 2

| 191

| 0

| 252

| 22

| 47

| 32

| 37

|-
| Enhanced coalbed methane recovery

| 8

| 30

| 16

| 16

| 0

| 2

| 26

| 8

| 0

| 0

| 24

| 90

| 12

|-
| Deep saline aquifers

| 1000

| 500

| 667

| 500

| 1000

| 250

| 1000

| 500

| 500

| 250

| 1015

| 1000

| 250

|}

CSA: Central and South America, EEU: Eastern Europe, FSU: Former Soviet Union, MEA: Middle East, ODA: Other Asia (except China and India), WEU: Western Europe.

CO 2 utilisation (CCU) – instead of geologic storage – could present an alternative method of decarbonisation ( ''high confidence'' ). The global CO 2 utilisation potential, however, is currently limited to 1–2 GtCO 2 yr –1 for use of CO 2 as a feedstock ( [[#Hepburn--2019|Hepburn et al. 2019]] ; [[#Kätelhön--2019|Kätelhön et al. 2019]] ) but could increase to 20 GtCO 2 by the mid-century ( ''medium confidence'' ). CCU involves using CO 2 as a feedstock to synthesise products of economic value and as substitute to fossil feedstock. However, several CO 2 utilisation avenues might be limited by energy availability. Depending on the utilisation pathway, the CO 2 may be considered sequestered for centuries (e.g., cement curing, aggregates), decades (plastics), or only a few days or months (e.g., fuels) ( [[#Hepburn--2019|Hepburn et al. 2019]] ). Moreover, when carbon-rich fuel end-products are combusted, CO 2 is emitted back into the atmosphere. Because of the presence of several industrial clusters (regions with high density of industrial infrastructure) globally, a number of regions demonstrate locations where CO 2 utilisation potential could be matched with large point sources of CO 2 ( [[#Wei--2020|Wei et al. 2020]] ).

The technological development for several CO 2 utilisation pathways is still in the laboratory, prototype, and pilot phases, while others have been fully commercialised (such as urea manufacturing). Technology development in some end uses is limited by purity requirements for CO 2 as a feedstock. The efficacy of CCU processes depends on additional technological constraints such as CO 2 purity and pressure requirements. For instance, urea production requires CO 2 pressurised to 122 bar and purified to 99.9%. While most utilisation pathways require purity levels of 95–99%, algae production may be carried out with atmospheric CO 2 ( [[#Voldsund--2016|Voldsund et al. 2016]] ; [[#Ho--2019|Ho et al. 2019]] ).

Existing post-combustion approaches relying on absorption are technologically ready for full-scale deployment ( ''high confidence'' ). More novel approaches using membranes and chemical looping that might reduce the energy penalty associated with absorption are in different stages of development – ranging from laboratory phase to prototype phase ( [[#Abanades--2015|Abanades et al. 2015]] ) ( ''high confidence'' ). There has been significant progress in post-combustion capture technologies that used absorption in solvents such as monoethanolamine (MEA). There are commercial-scale application of solvent-based absorption at two electricity generating facilities – Boundary Dam since 2015 and Petra Nova (temporarily suspended) since 2017, with capacities of 1 and 1.6 MtCO 2 yr –1 respectively ( [[#Mantripragada--2019|Mantripragada et al. 2019]] ; [[#Giannaris--2020|Giannaris et al. 2020]] a). Several second- and third-generation capture technologies are being developed with the aim of not just lowering costs but also enhancing other performance characteristics such as improved ramp-up and lower water consumption. These include processes such as chemical looping, which also has the advantage of being capable of co-firing with biomass with a better efficiency ( [[#Bhave--2017|Bhave et al. 2017]] ; [[#Yang--2019|Yang et al. 2019]] ). Another important technological development is the Allam cycle, which utilises CO 2 as a working fluid and operates based on oxy-combustion capture. Applications using the Allam Cycle can deliver net energy efficiency greater than 50% and nearly 100% CO 2 capture, but they are quite sensitive to oxygen and CO 2 purity needs ( [[#Scaccabarozzi--2016|Scaccabarozzi et al. 2016]] ; [[#Ferrari--2017|Ferrari et al. 2017]] ).

CO 2 capture costs present a key challenge, remaining higher than USD50 tCO 2 –1 for most technologies and regions; novel technologies could help reduce some costs ( ''high confidence'' ). The capital cost of a coal or gas electricity generation facility with CCS is almost double that of one without CCS ( [[#Rubin--2015|Rubin et al. 2015]] ; [[#Zhai--2016|Zhai and Rubin 2016]] ; [[#Bui--2018|Bui et al. 2018]] ). Additionally, the energy penalty increases the fuel requirement for electricity generation by 13–44%, leading to further cost increases (Table 6.3).

'''Table 6.3| Costs and efficiency parameters of CCS in electric power plants.''' Data: [[#Muratori--2017a|Muratori et al. (2017a)]] '''.'''

{| class="wikitable"
|-
|
| Capital cost [USD kW –1 ]

| Efficiency [%]

| CO 2 capture cost [USD tCO 2 –1 ]

| CO 2 avoided cost [USD tCO 2 –1 ]

|-
| Coal (steam plant) + CCS

| 5800

| 28%

| 63

| 88

|-
| Coal (IGCC) + CCS

| 6600

| 32%

| 61

| 106

|-
| Natural gas (CC) + CCS

| 2100

| 42%

| 91

| 33

|-
| Oil (CC) + CCS

| 2600

| 39%

| 105

| 95

|-
| Biomass (steam plant) + CCS

| 7700

| 18%

| 72

| 244

|-
| Biomass (IGCC) + CCS

| 8850

| 25%

| 66

| 242

|}

In addition to reductions in capture costs, other approaches to reduce CCS costs rely on utilising the revenues from co-products such as oil, gas, or methanol, and on clustering of large-point sources to reduce infrastructure costs. The potential for such reductions is limited in several regions due to low sink availability, but it could jump-start initial investments ( ''medium confidence'' ). Injecting CO 2 into hydrocarbon formations for enhanced oil or gas recovery can produce revenues and lower costs ( [[#Edwards--2018|Edwards and Celia 2018]] ). While enhanced oil recovery potential is &lt;5% of the actual CCS needs, they can enable early pilot and demonstration projects ( [[#Núñez-López--2019|Núñez-López and Moskal 2019]] ; [[#Núñez-López--2019|Núñez-López et al. 2019]] ). Substantial portions of CO 2 are effectively stored during enhanced oil recovery ( [[#Menefee--2020|Menefee and Ellis 2020]] ; [[#Sminchak--2020|Sminchak et al. 2020]] ). By clustering together of several CO 2 sources, overall costs may be reduced by USD10 tCO 2 –1 ( [[#Abotalib--2016|Abotalib et al. 2016]] ; [[#Garg--2017a|Garg et al. 2017a]] ), but geographical circumstances determine the prospects of these cost reductions via economies of scale. The major pathways for CO 2 utilisation via methanol, methane, liquid fuel production, and cement curing have costs greater than USD500 tCO 2 –1 ( [[#Hepburn--2019|Hepburn et al. 2019]] ). The success of these pathways therefore depends on the value of such fuels and on the values of other alternatives.

The public is largely unfamiliar with carbon capture, use and storage technologies ( [[#L’Orange%20Seigo--2014|L’Orange Seigo et al. 2014]] ; [[#Tcvetkov--2019|Tcvetkov et al. 2019]] ) ( ''high confidence'' ), and many people may not have formed stable attitudes and risk perceptions regarding these technologies ( [[#Daamen--2006|Daamen et al. 2006]] ; [[#Jones--2015|Jones et al. 2015]] ; [[#Van%20Heek--2017|Van Heek et al. 2017]] ) ( ''medium confidence'' ). In general, low support has been reported for CCS technologies ( [[#Allen--2013|Allen and Chatterton 2013]] ; [[#Demski--2017|Demski et al. 2017]] ). When presented with neutral information on CCS, people favour other mitigation options such as renewable energy and energy efficiency (de Best-Waldhober et al. 2009; [[#Scheer--2013|Scheer et al. 2013]] ; [[#Karlstrøm--2014|Karlstrøm and Ryghaug 2014]] ). Although few totally reject CCS, specific CCS projects have faced strong local resistance, which has contributed to the cancellation of CCS projects ( [[#Terwel--2012|Terwel et al. 2012]] ; [[#L’Orange%20Seigo--2014|L’Orange Seigo et al. 2014]] ). Communities may also consider CCU to be lower-risk and view it more favourably than CCS ( [[#Arning--2019|Arning et al. 2019]] ).

CCS requires considerable increases in some resources and chemicals, most notably water. Power plants with CCS could shut down periodically due to water scarcity. In several cases, water withdrawals for CCS are 25–200% higher than plants without CCS ( [[#Rosa--2020b|Rosa et al. 2020b]] ; [[#Yang--2020|Yang et al. 2020]] ) due to energy penalty and cooling duty. The increase is slightly lower for non-absorption technologies. In regions prone to water scarcity such as the Southwestern USA or Southeast Asia, this may limit deployment and result in power plant shutdowns during the summer months ( [[#Liu--2019b|Liu et al. 2019b]] ; [[#Wang--2019c|Wang et al. 2019c]] ). The water use could be managed by changing heat integration strategies and implementing reuse of wastewater ( [[#Magneschi--2017|Magneschi et al. 2017]] ; [[#Giannaris--2020|Giannaris et al. 2020]] b).

Because CCS always adds cost, policy instruments are required for it to be widely deployed ( ''high confidence'' ). Relevant policy instruments include financial instruments such as emission certification and trading, legally enforced emission restraints, and carbon pricing ( [[#Haszeldine--2016|Haszeldine 2016]] ; [[#Kang--2020|Kang et al. 2020]] ). There are some recent examples of policy instruments specifically focused on promoting CCS. The recent 45Q tax credits in the USA offer nationwide tax credits for CO 2 capture projects above USD35–50 tCO 2 –1 which offset CO 2 capture costs at some efficient plants ( [[#Esposito--2019|Esposito et al. 2019]] ). Similarly, California’s low-carbon fuel standard offers benefits for CO 2 capture at some industrial facilities such as biorefineries and refineries ( [[#Von%20Wald--2020|Von Wald et al. 2020]] ).

<div id="6.4.2.6" class="h3-container"></div>

<span id="bioenergy"></span>
==== 6.4.2.6 Bioenergy ====

<div id="h3-6-siblings" class="h3-siblings"></div>

Bioenergy has the potential to be a high-value and large-scale mitigation option to support many different parts of the energy system. Bioenergy could be particularly valuable for sectors with limited alternatives to fossil fuels (e.g., aviation, heavy industry), production of chemicals and products, and, potentially, in carbon dioxide removal (CDR) via BECCS or biochar. While traditional biomass and first-generation biofuels are widely used today, the technology for large-scale production from advanced processes is not competitive, and growing dedicated bioenergy crops raises a broad set of sustainability concerns. Its long-term role in low-carbon energy systems is therefore uncertain ( ''high confidence'' ). (Note that this section focuses on the key technological developments for deployment of commercial bioenergy.)

Bioenergy is versatile: technology pathways exist to produce multiple energy carriers from biomass – electricity, liquid fuels, gaseous fuels, hydrogen, and solid fuels – as well as other value-added products ( ''high confidence'' ). Different chemical and biological conversion pathways exist to convert diverse biomass feedstocks into multiple final energy carriers (Figure 6.14). Currently, biomass is mostly used to produce heat, or for cooking purposes (traditional biomass), electricity, or first-generation sugar-based biofuels (e.g., ethanol produced via fermentation), as well as biodiesel produced from vegetable oils and animal fats. Electricity generated from biomass contributes about 3% of global generation. Tens of billions of gallons of first-generation biofuels are produced per year. The processing requirements (drying, dewatering, pelletising) of different feedstocks for producing electricity from biomass are energy-intensive, and when utilising current power plants, the efficiency is around 22%, with an increase up to 28% with advanced technologies ( [[#Zhang--2020|Zhang et al. 2020]] ).

Scaling up bioenergy use will require advanced technologies such as gasification, Fischer-Tropsch processing, hydrothermal liquefaction (HTL), and pyrolysis. These pathways could deliver several final energy carriers starting from multiple feedstocks, including forest biomass, dedicated cellulosic feedstocks, crop residues, and wastes (Figure 6.14). While potentially cost-competitive in the future, pyrolysis, Fischer-Tropsch, and HTL are not currently cost-competitive ( [[#IEA--2018c|IEA 2018c]] ; [[#Molino--2018|Molino et al. 2018]] ; [[#Prussi--2019|Prussi et al. 2019]] ), and scaling-up these processes will require robust business strategies and optimised use of co-products ( [[#Lee--2013|Lee and Lavoie 2013]] ). Advanced biofuels production processes are at the pilot or demonstration stage and will require substantial breakthroughs or market changes to become competitive. Moreover, fuels produced from these processes require upgrading to reach ‘drop-in’ conditions – that is, conditions in which they may be used directly consistent with current standards in existing technologies ( [[#van%20Dyk--2019|van Dyk et al. 2019]] ). Additional opportunities exist to co-optimise second-generation biofuels and engines ( [[#Ostadi--2019|Ostadi et al. 2019]] ; [[#Salman--2020|Salman et al. 2020]] ). In addition, gaseous wastes, or high-moisture biomass, such as dairy manure, wastewater sludge and organic municipal solid waste (MSW) could be utilised to produce renewable natural gas. Technologies for producing biogas (e.g., digestion) tend to be less efficient than thermochemical approaches and often produce large amounts of CO 2 , requiring the produced fuels to undergo significant upgrading ( [[#Melara--2020|Melara et al. 2020]] ).

<div id="_idContainer036" class="Basic-Text-Frame"></div>

[[File:c08ff1ffe65209ee327ad1e6315ceae6 IPCC_AR6_WGIII_Figure_6_13.png]]

'''Figure 6.13 | Costs and potential for different CO''' 2 '''utilisation pathways.''' Source: with permission from [[#Hepburn--2019|Hepburn et al. (2019)]] .

<div id="_idContainer036" class="Basic-Text-Frame"></div>

[[File:31b2a571018212806f9a750b7b4413c6 IPCC_AR6_WGIII_Figure_6_14.png]]

'''Figure 6.14 | Range of advanced bioenergy conversion pathways (excluding traditional biomass, direct heat generation, first-generation biofuels, and non-energy products) based on feedstock, targeted end product, and compatibility with carbon dioxide removal (CDR) via carbon capture and storage (CCS) and soil carbon sequestration.''' Source: modified with permission from [[#Baker--2020|Baker et al. (2020)]] .

A major scale-up of bioenergy production will require dedicated production of advanced biofuels. First-generation biofuels produced directly from food crops or animal fats have limited potential and lower yield per land area than advanced biofuels. Wastes and residues (e.g., from agricultural, forestry, animal manure processing) or biomass grown on degraded, surplus, and marginal land can provide opportunities for cost-effective and sustainable bioenergy at significant but limited scale ( [[#Morris--2013|Morris et al. 2013]] ; [[#Saha--2018|Saha and Eckelman 2018]] ; [[#Fajardy--2020|Fajardy and Mac Dowell 2020]] ; [[#Spagnolo--2020|Spagnolo et al. 2020]] ). Assessing the potential for a major scale-up of purpose-grown bioenergy is challenging due to its far-reaching linkages to issues beyond the energy sector, including competition with land for food production and forestry, water use, impacts on ecosystems, and land-use change ( [[#IPCC--2020|IPCC 2020]] ; [[#Roe--2021|Roe et al. 2021]] ) (Chapter 12). These factors, rather than geophysical characteristics, largely define the potential for bioenergy and explain the difference in estimates of potential in the literature. Biomass resources are not always in close proximity to energy demand, necessitating additional infrastructure or means to transport biomass or final bioenergy over larger distances and incur additional energy use ( [[#Baik--2018|Baik et al. 2018]] ; [[#Singh--2021|Singh et al. 2021]] ).

An important feature of bioenergy is that it can be used to remove carbon from the atmosphere by capturing CO 2 in different parts of the conversion process and then permanently storing the CO 2 (BECCS or biochar) ( [[#Smith--2016|Smith et al. 2016]] ; [[#Fuss--2018|Fuss et al. 2018]] ) (Chapters 3 and 7, and [[IPCC:Wg3:Chapter:Chapter-12#12.5|Section 12.5]] ). Some early opportunities for low-cost BECCS are being utilised in the ethanol sector but these are applicable only in the near-term at the scale of ≤100 MtCO 2 yr –1 ( [[#Sanchez--2018|Sanchez et al. 2018]] ). Several technological and institutional barriers exist for large-scale BECCS implementation, including large energy requirements for CCS, limit and cost of biomass supply and geologic sinks for CO 2 in several regions, and cost of CO 2 capture technologies ( ''high confidence'' ). Besides BECCS, biofuels production through pyrolysis and hydrothermal liquefaction creates biochar, which could also be used to store carbon as 80% of the carbon sequestered in biochar will remain in the biochar permanently (Chapter 7). In addition to its ability to sequester carbon, biochar can be used as a soil amendment ( [[#Wang--2014b|Wang et al. 2014b]] ).

First-generation bioenergy is currently competitive in some markets though, on average, its costs are higher than other forms of final energy. Bioenergy from waste and residues from forestry and agriculture is also currently competitive, but the supply is limited ( [[#Aguilar--2020|Aguilar et al. 2020]] ). These costs are context-dependent, and regions having large waste resources are already producing low-cost bioenergy ( [[#Jin--2018|Jin and Sutherland 2018]] ). In the future, technology costs are anticipated to decrease, but bioenergy produced through cellulosic feedstocks may remain more expensive than fossil alternatives. Large-scale deployment of early opportunities, especially in the liquid fuel sector, may reduce the technological costs associated with biomass conversion ( [[#IEA--2020g|IEA 2020g]] ). At the same time, the cost of feedstocks may rise as bioenergy requirements increase, especially in scenarios with large bioenergy deployment (Muratori et al. 2020). The costs of bioenergy production pathways are highly uncertain (Table 6.4).

'''Table 6.4 | The costs of electricity generation, hydrogen production, and second-generation liquid fuels production from biomass in 2020.''' These costs are adapted from [[#Bhave--2017|Bhave et al. (2017)]] , Daioglou et al. (2020), NREL (2020a, 2020b), Witcover and Williams (2020), and Lepage et al. (2021).

{| class="wikitable"
|-
|
| Unit

| Low

| Median

| High

|-
| Bioelectricity with CCS

| USD MWh –1

| 74

| 86

| 160

|-
| Bioelectricity without CCS

| USD MWh –1

| 66

| 84

| 112

|-
| Biohydrogen with CCS a

| USD kg –1

| 1.63

| 2.37

| 2.41

|-
| Biohydrogen without CCS a

| USD kg –1

| 1.59

| 1.79

| 2.37

|-
| Liquid biofuels with CCS

| USD gge –1

| 1.34

| 4.20

| 7.85

|-
| Liquid biofuels without CCS

| USD gge –1

| 1.15

| 4.00

| 7.60

|}

a Using cellulosic feedstocks.

• '''Electricity.''' The costs of baseload electricity production with biomass are higher than corresponding fossil electricity production with and without CCS, and are likely to remain as such without carbon pricing ( [[#Bhave--2017|Bhave et al. 2017]] ). The additional cost associated with CO 2 capture are high for conventional solvent-based technologies. However, upcoming technologies such as chemical looping are well-suited to biomass and could reduce CCS costs.

'''•''' '''Hydrogen.''' The costs of hydrogen production from biomass are somewhat higher than, but comparable, to that produced by natural gas reforming with CCS. Further, the incremental costs for incorporating CCS in this process are less than 5% of the levelised costs in some cases, since the gasification route creates a high-purity stream of CO 2 ( [[#Muratori--2017a|Muratori et al. 2017a]] ; [[#Sunny--2020|Sunny et al. 2020]] ). While these processes have fewer ongoing prototypes/demonstrations, the costs of biomass-based hydrogen (with or without CCS) are substantially cheaper than that produced from electrolysis utilising solar/wind resources ( [[#Kayfeci--2019|Kayfeci et al. 2019]] ; [[#Newborough--2020|Newborough and Cooley 2020]] ), even though electrolysis costs are dropping.

• '''Liquid biofuels.''' First-generation sugar-based biofuels (e.g., ethanol produced via fermentation) or biodiesel produced from vegetable oils and animal fats, are produced in several countries at large scale and costs competitive with fossil fuels. However, supply is limited. The costs for second-generation processes (Fischer-Tropsch and cellulosic ethanol) are higher in most regions ( [[#Li--2019|Li et al. 2019]] ). Technological learning is projected to reduce these costs by half ( [[#IEA--2020g|IEA 2020g]] ).

Large-scale bioenergy production will require more than wastes/residues and cultivation on marginal lands, which may raise conflicts with SDGs relevant to environmental and societal priorities ( [[#Heck--2018|Heck et al. 2018]] ; [[#Gerten--2020|Gerten et al. 2020]] ) (Chapter 12). These include competition with food crops, implications for biodiversity, potential deforestation to support bioenergy crop production, energy security implications from bioenergy trade, point-of-use emissions and associated effects on air quality, and water use and fertiliser use ( [[#Fajardy--2018|Fajardy and Mac Dowell 2018]] ; [[#Fuss--2018|Fuss et al. 2018]] ; [[#Tanzer--2019|Tanzer and Ramírez 2019]] ; [[#Brack--2020|Brack and King 2020]] ). Overall, the environmental impact of bioenergy production at scale remains uncertain and varies by region and application.

Alleviating these issues would require some combination of increasing crop yields, improving conversion efficiencies, and developing advanced biotechnologies for increasing the fuel yield per tonne of feedstock ( [[#Henry--2018|Henry et al. 2018]] ). Policy structures would be necessary to retain biodiversity, manage water use, limit deforestation and land-use change emissions, and ultimately optimally integrate bioenergy with transforming ecosystems. Large-scale international trade of biomass might be required to support a global bioeconomy, raising questions about infrastructure, logistics, financing options, and global standards for bioenergy production and trade (Box 6.10). Additional institutional and economic barriers are associated with accounting of carbon dioxide removal, including BECCS ( [[#Fuss--2014|Fuss et al. 2014]] ; [[#Muratori--2016|Muratori et al. 2016]] ; [[#Fridahl--2018|Fridahl and Lehtveer 2018]] ).

Lifecycle emissions impacts from bioenergy are subject to large uncertainties and could be incompatible with net-zero emissions in some contexts. Due to the potentially large energy conversion requirements and associated GHG emissions (Chapters 7 and 12), bioenergy systems may fail to deliver near-zero emissions depending on operating conditions and regional contexts ( [[#Elshout--2015|Elshout et al. 2015]] ; [[#Daioglou--2017|Daioglou et al. 2017]] ; [[#Staples--2017|Staples et al. 2017]] ; [[#Hanssen--2020|Hanssen et al. 2020]] ; [[#Lade--2020|Lade et al. 2020]] ). As a result, bioenergy carbon neutrality is debated and depends on factors such as the source of biomass, conversion pathways and energy used for production and transport of biomass, and land-use changes, as well as assumed analysis boundary and considered time scale ( [[#Zanchi--2012|Zanchi et al. 2012]] ; [[#Wiloso--2016|Wiloso et al. 2016]] ; [[#Booth--2018|Booth 2018]] ; [[#Fan--2021|Fan et al. 2021]] ). Similarly, the lifecycle emissions of BECCS remain uncertain and will depend on how effectively bioenergy conversion processes are optimised ( [[#Fajardy--2017|Fajardy and Mac Dowell 2017]] ; [[#Tanzer--2019|Tanzer and Ramírez 2019]] ).

Acceptability of bioenergy is relatively low compared to other renewable energy sources like solar and wind ( [[#Poortinga--2013|Poortinga et al. 2013]] ; [[#Ma--2015|Ma et al. 2015]] ; [[#Peterson--2015|Peterson et al. 2015]] ; [[#EPCC--2017|EPCC 2017]] ) and comparable to natural gas ( [[#Scheer--2013|Scheer et al. 2013]] ). People also know relatively little about bioenergy compared to other energy sources ( [[#Whitmarsh--2011a|Whitmarsh et al. 2011a]] ; [[#EPCC--2017|EPCC 2017]] ) and tend be be more ambivalent towards bioenergy compared to other mitigation options ( [[#Allen--2013|Allen and Chatterton 2013]] ). People evaluate biomass from waste products (e.g., food waste) more favourably than grown-for-purpose energy crops, which are more controversial ( [[#Plate--2010|Plate et al. 2010]] ; [[#Demski--2015|Demski et al. 2015]] ). The most pressing concerns for use of woody biomass are air pollution and loss of local forests ( [[#Plate--2010|Plate et al. 2010]] ). Various types of bioenergy additionally raise concerns about landscape impacts ( [[#Whitmarsh--2011a|Whitmarsh et al. 2011a]] ) and biodiversity ( [[#Immerzeel--2014|Immerzeel et al. 2014]] ). Moreover, many people do not see biomass as a renewable energy source, possibly because it involves burning of material.

<div id="box-6.5" class="h2-container box-container"></div>

<span id="box-6.5-methane-mitigation-options-for-coal-oil-and-gas"></span>

=== Box 6.5 | Methane Mitigation Options for Coal, Oil, and Gas ===

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Methane emissions mainly from coal, oil, and gas currently represent in 2019 about 18% of energy supply sector greenhouse gas (GHG) emissions and 90% of global energy supply non-CO 2 emissions in 2019 ( [[#Minx--2021|Minx et al. 2021]] b). While approximately 80% of the lifecycle methane emissions in the coal sector occur during underground mining, oil and gas emissions are spread throughout upstream, midstream, and downstream stages ( [[#Alvarez--2018|Alvarez et al. 2018]] ; [[#IPCC--2019|IPCC 2019]] ). For this reason, methane reductions from coal mining can be accomplished through coal mine methane recovery (where methane and coal are recovered simultaneously) and from the ventilation air, which can cumulatively reduce methane emissions by 50–75% ( [[#Zhou--2016|Zhou et al. 2016]] ; [[#Singh--2018|Singh and Hajra 2018]] ). Governments incentivise such operations through a number of emissions trading and offset programmes ( [[#Haya--2020|Haya et al. 2020]] ). Methane emissions in the oil and gas sector can be reduced by leak detection and repair, relevant across varying time scales (hours to decades) and regional scopes (component/facility level to continental) ( [[#Fox--2019|Fox et al. 2019]] ). Around 50% of the methane emitted from oil and gas infrastructure can be mitigated at net-negative costs; that is, the market price of the recovered methane is higher than the mitigation costs ( [[#IEA--2021e|IEA 2021e]] ). As CO 2 emissions are reduced and fossil fuel consumption decreases, methane emissions associated with these supply chains are anticipated to decline ( [[#6.7|Section 6.7]] ). That said, substantial ‘legacy’ methane emissions – methane leaks after abandonment – will remain, even if a complete fossil fuel phase-out takes place. These legacy emissions are estimated to be less than 1–4% of overall methane emissions across all fossil fuel sources ( [[#Kholod--2020|Kholod et al. 2020]] ; [[#Williams--2021b|Williams et al. 2021b]] ). Even without a complete phase-out, 50–80% of methane emissions from coal, oil and gas could be avoided with currently available technologies at less than USD50 tCO 2 -eq –1 ( [[#Harmsen--2019|Harmsen et al. 2019]] ; [[#Höglund-Isaksson--2020|Höglund-Isaksson et al. 2020]] ). Methane recovery from abandoned coal mines could offset most project costs ( [[#Singh--2018|Singh and Sahu 2018]] ). For abandoned oil and gas wells, low plugging costs could be offset through methane recovery, while high plugging costs would likely require some market or policy support ( [[#Kang--2019|Kang et al. 2019]] ).

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<span id="fossil-energy"></span>
==== 6.4.2.7 Fossil Energy ====

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Fossil fuels could play a role in climate change mitigation if strategically deployed with CCS ( ''high confidence'' ). On the one hand, the primary mechanism for reducing emissions is to eliminate the unabated fossil fuel use. On the other hand, fossil energy combined with CCS provides a means of producing low-carbon energy while still utilising the available base of fossil energy worldwide and limiting stranded assets. While [[#6.4.2.5|Section 6.4.2.5]] discusses the important aspects of CCS with fossil fuels, this section aims to elucidate the feasibility criteria around these fuels itself.

Fossil fuel reserves have continued to rise because of advanced exploration and utilisation techniques ( ''high confidence'' ). A fraction of these available reserves can be used consistent with mitigation goals when paired with CCS opportunities in close geographical proximity ( ''high confidence'' ). Based on continued exploration, the fossil fuel resource base has increased significantly; for example, a 9% increase in gas reserves and 12% in oil reserves was observed in the USA between 2017 and 2018. This increase is a result of advanced exploration techniques, which are often subsidised ( [[#Lazarus--2018|Lazarus and van Asselt 2018]] ; MA et al. 2018). Fossil reserves are distributed unevenly throughout the globe. Coal represents the largest remaining resource (close to 500 ZJ). Conventional oil and gas resources are an order of magnitude smaller (15–20 ZJ each). Technological advances have increased the reserves of unconventional fossil in the last decade. Discovered ultimate recoverable resources of unconventional oil and gas are comparable to conventional oil and gas (Fizaine et al. 2017).

It is unlikely that resource constraints will lead to a phase-out of fossil fuels, and instead, such a phase-out would require policy action. Around 80% of coal, 50% of gas, and 20% of oil reserves are likely to remain unextractable under 2°C constraints ( [[#McGlade--2015|McGlade and Ekins 2015]] ; Pellegrini et al. 2020). Reserves are more likely to be utilised in a low-carbon transition if they can be paired with CCS. Availability of CCS technology not only allows continued use of fossil fuels as a capital resource for countries but also paves the way for CDR through BECCS ( [[#Haszeldine--2016|Haszeldine 2016]] ; [[#Pye--2020|Pye et al. 2020]] ). While the theoretical geologic CO 2 sequestration potential is vast, there are limits on how much resource base could be utilised based on geologic, engineering, and source-sink mapping criteria ( [[#Budinis--2017|Budinis et al. 2017]] ).

Technological changes have continued to drive down fossil fuel extraction costs. Significant decarbonisation potential also exists via diversification of the fossil fuel uses beyond combustion (high evidence). The costs of extracting oil and gas globally have gone down by utilising hydraulic fracturing and directional drilling for resources in unconventional reservoirs ( [[#Wachtmeister--2020|Wachtmeister and Höök 2020]] ). Although the extraction of these resources is still more expensive than those derived from conventional reservoirs, the large availability of unconventional resources has significantly reduced global prices. The emergence of liquefied natural gas (LNG) markets has also provided opportunities to export natural gas significant distances from the place of production ( [[#Avraam--2020|Avraam et al. 2020]] ). The increase in availability of natural gas has been accompanied by an increase in the production of natural gas liquids as a co-product to oil and gas. Over the period from 2014 to 2019, exports of natural gas liquids increased by 160%. Natural gas liquids could potentially be a lower-carbon alternative to liquid fuels and hydrocarbons. On the demand side, natural gas can be used to produce hydrogen using steam methane reforming, which is a technologically mature process (Sections 6.4.4 and 6.4.5). When combined with 90% CO 2 capture, the costs of producing hydrogen are around USD1.5–2 kg(H 2 ) –1 ( [[#Collodi--2017|Collodi et al. 2017]] ; [[#Newborough--2020|Newborough and Cooley 2020]] ), considerably less than hydrogen produced via electrolysis.

Significant potential exists for gasifying deep-seated coal deposits ''in situ'' to produce hydrogen. Doing so reduces fugitive methane emissions from underground coal mining. The integration costs of this process with CCS are less than with natural gas reforming. The extent to which coal gasification could be compatible with low-carbon energy would depend on the rate of CO 2 capture and the ultimate use of the gas ( [[#Verma--2015|Verma and Kumar 2015]] ). Similarly, for ongoing underground mining projects, coal mine methane recovery can be economic for major coal producers such as China and India. Coal mine methane and ventilation air methane recovery can reduce the fugitive methane emissions by 50–75% ( [[#Zhou--2016|Zhou et al. 2016]] ; [[#Singh--2018|Singh and Sahu 2018]] ).

The cost of producing electricity from fossil sources has remained roughly the same with some regional exceptions while the costs of producing transport fuels has gone down significantly ( ''high confidence'' ). The cost of producing electricity from fossil fuels has remained largely static, with the exception of some regional changes, for example, a 40% cost reduction in the USA for natural gas ( [[#Rai--2019|Rai et al. 2019]] ), where the gas wellhead price has declined by almost two-thirds due to large reserves. Similarly, the global price of crude oil has declined from almost USD100 bbl –1 to USD55 bbl –1 in the last five years.

The energy return of investment (EROI) is a useful indicator of full fossil lifecycle costs. Fossil fuels create significantly more energy per unit energy invested – or in other words have much larger EROI – than most cleaner fuels such as biomass or electrolysis-derived hydrogen, where intensive processing reduces EROI ( [[#Hall--2014|Hall et al. 2014]] ). That said, recent years have seen a decrease in fossil EROI, especially as underground coal mining still represents a substantial portion of global production. Exploitation of unconventional gas reservoirs is also energy intensive and has led to a reduction in EROI. The primary energy EROI of fossil fuels has converged at about 30, which represents a 20-point decrease from the 1995 value for coal ( [[#Brockway--2019|Brockway et al. 2019]] ). When processing and refining stages are considered, these EROI values further decrease.

Several countries have large reserves of fossil fuels. Owing to climate constraints, these may become stranded, causing considerable economic impacts ( ''high confidence'' ) (Sections 6.7.3 and 6.7.4, and Box 6.13). While global fossil energy resources are greater than 600 ZJ, more than half of these resources would likely be unburnable, even in the presence of CCS ( [[#McGlade--2015|McGlade and Ekins 2015]] ; [[#Pye--2020|Pye et al. 2020]] ). This would entail a significant capital loss for the countries with large reserves. The total amount of stranded assets in such a case would amount to USD1–4 trillion at present value (Box 6.13).

Apart from CO 2 emissions and air pollutants from fossil fuel combustion, other environmental impacts include fugitive methane leakages and implications to water systems '''.''' While the rate of methane leakage from unconventional gas systems is uncertain, their overall GHG impact is less than coal ( [[#Tanaka--2019|Tanaka et al. 2019]] ; [[#Deetjen--2020|Deetjen and Azevedo 2020]] ). The stated rate of leakage in such systems ranges from 1–8%, and reconciling different estimates requires a combination of top-down and bottom-up approaches ( [[#Zavala-Araiza--2015|Zavala-Araiza et al. 2015]] ; [[#Grubert--2019|Grubert and Brandt 2019]] ). Similarly, for coal mining, fugitive methane emissions have grown, despite some regulations on the degree to which emission controls must be deployed. Recent IPCC inventory guidance also notes considerable CO 2 emissions resulting from spontaneous combustion of the coal surface, and accounting for these emissions will likely increase the overall lifecycle emissions by 1–5% ( [[#IPCC--2019|IPCC 2019]] ; [[#Singh--2019|Singh 2019]] ; [[#Fiehn--2020|Fiehn et al. 2020]] ).

Another key issue consistently noted with unconventional wells (both oil and gas, and coalbed methane) is the large water requirements ( [[#Qin--2018|Qin et al. 2018]] ). The overall water footprint of unconventional reservoirs is higher than conventional reservoirs because of higher lateral length and fracturing requirements ( [[#Scanlon--2017|Scanlon et al. 2017]] ; [[#Kondash--2018|Kondash et al. 2018]] ). Moreover, produced water from such formations is moderately to highly brackish, and treating such waters has large energy consumption ( [[#Bartholomew--2016|Bartholomew and Mauter 2016]] ; [[#Singh--2019|Singh and Colosi 2019]] ).

Oil and coal consistently rank among the least preferred energy sources in many countries ( ''high confidence'' ). The main perceived advantage of fossil energy is the relatively low costs, and emphasising these costs might increase acceptability somewhat ( [[#Pohjolainen--2018|Pohjolainen et al. 2018]] ; [[#Boyd--2019|Boyd et al. 2019]] ; [[#Hazboun--2020|Hazboun and Boudet 2020]] ). Acceptability of fossil fuels is, on average, similar to acceptability of nuclear energy, although evaluations are less polarised. People evaluate natural gas as somewhat more acceptable than other fossil fuels, although they generally oppose hydraulic fracturing ( [[#Clarke--2016|Clarke et al. 2016]] ). Yet, natural gas is evaluated as less acceptable than renewable energy sources, although evaluations of natural gas and biogas are similar ( [[#Liebe--2019|Liebe and Dobers 2019]] ; [[#Plum--2019|Plum et al. 2019]] ). Acceptability of fossil energy tends to be higher in countries and regions that strongly rely on them for their energy production ( [[#Pohjolainen--2018|Pohjolainen et al. 2018]] ; [[#Boyd--2019|Boyd et al. 2019]] ). Combining fossil fuels with CCS can increase their acceptability ( [[#Van%20Rijnsoever--2015|Van Rijnsoever et al. 2015]] ; [[#Bessette--2018|Bessette and Arvai 2018]] ). Some people seem ambivalent about natural gas, as they perceive both benefits (e.g., affordability, less carbon emissions than coal) and disadvantages (e.g., finite resource, contributing to climate change) ( [[#Blumer--2018|Blumer et al. 2018]] ).

Fossil fuel subsidies have been valued in the order of USD0.5–5 trillion annually by various estimates which have the tendency to introduce economic inefficiency within systems ( [[#Jakob--2015|Jakob et al. 2015]] ; [[#Merrill--2015|Merrill et al. 2015]] ) ( ''high confidence'' ). Subsequent reforms have been suggested by different researchers who have estimated reductions in CO 2 emissions may take place if these subsidies are removed ( [[#Mundaca--2017|Mundaca 2017]] ). Such reforms could create the necessary framework for enhanced investments in social welfare – through sanitation, water, clean energy – with differentiating impacts (Edenhofer 2015).

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<span id="geothermal-energy"></span>
==== 6.4.2.8 Geothermal Energy ====

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Geothermal energy is heat stored in the Earth’s subsurface and is a renewable resource that can be sustainably exploited. The geophysical potential of geothermal resources is 1.3 to 13 times the global electricity demand in 2019 ( ''medium confidence'' ). Geothermal energy can be used directly for various thermal applications, including space heating and industrial heat input, or converted to electricit '''y''' depending on the source temperature ( [[#Limberger--2018|Limberger et al. 2018]] ; [[#Moya--2018|Moya et al. 2018]] ; [[#REN21--2019|REN21 2019]] ).

Suitable aquifers underlay 16% of the Earth’s land surface and store an estimated 110,000–1,400,000 PWh (400,000–1,450,000 EJ) that could theoretically be used for direct heat applications. For electricity generation, the technical potential of geothermal energy is estimated to be between 30 PWh yr –1 (108 EJ yr –1 ) (to 3 km depth) and 300 PWh yr –1 (1080 EJ yr –1 ) (to 10 km depth). For direct thermal uses, the technical potential is estimated to range from 2.7–86 PWh yr –1 (9.7–310 EJ yr –1 ) ( [[#IPCC--2011|IPCC 2011]] ). Despite the potential, geothermal direct heat supplies only 0.15% of the annual global final energy consumption. The technical potential for electricity generation, depending on the depth, can meet one third to almost three times the global final consumption – based on International Energy Agency (IEA) database for IPCC. The mismatch between potential and developed geothermal resources is caused by high upfront costs, decentralised geothermal heat production, lack of uniformity among geothermal projects, geological uncertainties, and geotechnical risks ( [[#IRENA--2017a|IRENA 2017a]] ; [[#Limberger--2018|Limberger et al. 2018]] ). A limited number of countries have a long history in geothermal. At least in two countries (Iceland and New Zealand), geothermal accounts for 20–25% of electricity generation ( [[#Pan--2019|Pan et al. 2019]] ; [[#Spittler--2020|Spittler et al. 2020]] ). Furthermore, in Iceland approximately 90% of the households are heated with geothermal energy. In Kenya, as of July 2019, geothermal accounted for 734 MW effective capacity spread over 10 power plants and approximately one third of the total installed capacity (Kahlen 2019).

There are two main types of geothermal resources: convective hydrothermal resources, in which the Earth’s heat is carried by natural hot water or steam to the surface; and hot, dry rock resources, in which heat cannot be extracted using water or steam, and other methods must be developed. There are three basic types of geothermal power plants: (i) dry steam plants use steam directly from a geothermal reservoir to turn generator turbines; (ii) flash steam plants take high-pressure hot water from deep inside the Earth and convert it to steam to drive generator turbines; and (iii) binary cycle power plants transfer the heat from geothermal hot water to another liquid. Many of the power plants in operation today are dry steam plants or flash plants (single, double and triple) harnessing temperatures of more than 180°C.

However, medium temperature fields are increasingly used for electricity generation or combined heat and power. The use of medium temperature fields has been enabled through the development of binary cycle technology, in which a geothermal fluid is used via heat exchangers. Increasing binary generation technologies are now being utilised instead of flash steam power plants. This will result in almost 100% injection and essentially zero GHG emissions, although GHG emissions from geothermal power production are generally small compared to traditional baseload thermal energy power generation facilities ( [[#Fridriksson--2016|Fridriksson et al. 2016]] ).

Additionally, new technologies are being developed like Enhanced Geothermal Systems (EGS), which is in the demonstration stage ( [[#IRENA--2018|IRENA 2018]] ), deep geothermal technology, which may increase the prospects for harnessing the geothermal potential in a large number of countries, or shallow-geothermal energy, which represents a promising supply source for heating and cooling buildings ( [[#Narsilio--2018|Narsilio and Aye 2018]] ). Successful large-scale deployment of shallow geothermal energy will depend not only on site-specific economic performance but also on developing suitable governance frameworks ( [[#Bloemendal--2018|Bloemendal et al. 2018]] ; [[#García-Gil--2020|García-Gil et al. 2020]] ). Technologies for direct uses like district heating, geothermal heat pumps, greenhouses, and other applications, are widely used and considered mature. Given the limited number of plants commissioned, economic indicators (Figure 6.15) vary considerably depending on site characteristics.

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[[File:326f242c673d51a3849ed67058b073a6 IPCC_AR6_WGIII_Figure_6_15.png]]

'''Figure 6.15 | Global weighted averagetotal installed costs, capacity factors and levelised costs of electricity (LCOE) for geothermal power per year (2010–2020).''' The shaded area represents the 5% and 95% percentiles. Source: with permission from [[#IRENA--2021a|IRENA (2021a)]] .

Public awareness and knowledge of geothermal energy is relatively low ( ''high confidence'' ). Geothermal energy is evaluated as less acceptable than other renewable energy sources such as solar and wind, but is preferred over fossil and nuclear energy, and in some studies, over hydroelectric energy ( ''high confidence'' ) ( [[#Pellizzone--2015|Pellizzone et al. 2015]] ; [[#Steel--2015|Steel et al. 2015]] ; [[#Karytsas--2019|Karytsas et al. 2019]] ; [[#Hazboun--2020|Hazboun and Boudet 2020]] ). Some people are concerned about the installation of geothermal facilities close to their homes, similar to solar and wind projects ( [[#Pellizzone--2015|Pellizzone et al. 2015]] ). The main concerns about geothermal energy, particularly for large-scale, high-temperature geothermal power generation plants, involve water usage, water scarcity, and seismic risks of drilling ( [[#Dowd--2011|Dowd et al. 2011]] ). Moreover, noise, smell and damages to the landscape have been reasons for protests against specific projects ( [[#Walker--1995|Walker 1995]] ). However, with the implementation of modern technologies, geothermal presents fewer adverse environmental impacts. At the same time, people perceive geothermal energy as relatively environmentally friendly ( [[#Tampakis--2013|Tampakis et al. 2013]] ).

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<span id="marine-energy"></span>
==== 6.4.2.9 Marine Energy ====

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The ocean is a vast source of energy ( [[#Hoegh-Guldberg--2019|Hoegh-Guldberg et al. 2019]] ). Ocean energy can be extracted from tides, waves, ocean thermal energy conversion (OTEC), currents, and salinity gradients ( [[#Bindoff--2019|Bindoff et al. 2019]] ). Their technical potentials, without considering possible exclusion zones, are explored below. Tidal energy, which uses elevation differences between high and low tides, appears in two forms: potential energy (rise and fall of the tide); and current energy (from tidal currents). The global technically harvestable tidal power from areas close to the coast is estimated as about 1.2 PWh yr –1 (4.3 EJ yr –1 ) ( [[#IRENA--2020b|IRENA 2020b]] ). The potential for tidal current energy is estimated to be larger than that for tidal range or barrage ( [[#Melikoglu--2018|Melikoglu 2018]] ). Ocean wave energy is abundant and predictable and can be extracted directly from surface waves or pressure fluctuations below the surface ( [[#Melikoglu--2018|Melikoglu 2018]] ). Its global theoretical potential is 29.5 PWh yr –1 (106 EJ yr –1 ),which means that wave energy alone could meet all global energy demand ( [[#Mørk--2010|Mørk et al. 2010]] ; [[#IRENA--2020b|IRENA 2020b]] ). The temperature gradients in the ocean can be exploited to produce energy, and its total estimated available resource could be up to 44.0 PWh yr –1 (158 EJ yr –1 ) ( [[#Rajagopalan--2013|Rajagopalan and Nihous 2013]] ). Salinity gradient energy, also known as osmotic power, has a global theoretical potential of over 1.6 PWh yr –1 (6.0 EJ yr –1 ) ( [[#IRENA--2020b|IRENA 2020b]] ). The greatest advantage of most marine energy, excluding wave energy, is that their sources are highly regular and predictable, and energy can be furthermore generated both day and night. An additional use of sea water is to develop lower-cost district cooling systems near the sea ( [[#Hunt--2019|Hunt et al. 2019]] ). The greatest barrier to most marine technology advances is the relatively high upfront costs, uncertainty on environmental regulation and impact, need for investments and insufficient infrastructure ( [[#Kempener--2014a|Kempener and Neumann 2014a]] , b). There are also concerns about technology maturity and performance; thus, not all have the potential to become economically viable ( [[#IRENA--2020b|IRENA 2020b]] ).

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<span id="waste-to-energy"></span>
==== 6.4.2.10 Waste-to-Energy ====

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Waste-to-energy (WTE) is a strategy to recoverenergy from waste in a form of consumable heat, electricity, or fuel ( [[#Zhao--2016|Zhao et al. 2016]] ). Thermal (incineration, gasification, and pyrolysis) and biological (anaerobic digestion and landfill gas to energy) technologies are commonly used ( [[#Ahmad--2020|Ahmad et al. 2020]] ). When WTE technologies are equipped with proper air pollution reduction facilities they can contribute to clean electricity production and reduction of GHG emissions. However, if not properly operated, they can exacerbate air quality issues.

In 2019, there were more than 1,200 WTE incineration facilities worldwide, with estimated capacity of 310 million tonnes per year ( [[#UNECE--2020|UNECE 2020]] ). It is estimated that treatment of a minimum of 261 million tonnes/year of waste could produce 283 TWh (1 EJ) of power and heat by 2022 ( [[#Awasthi--2019|Awasthi et al. 2019]] ). Incineration plants can reduce the mass of waste by 70–80% and the volume of waste by 80–90% ( [[#Haraguchi--2019|Haraguchi et al. 2019]] ). Incineration technology can reduce water and soil pollution ( [[#Gu--2019|Gu et al. 2019]] ). However, if not properly handled, dust, and gases such as SO 2 , HCL, HF, NO 2 , and dioxins in the flue gases can harm the environment ( [[#Mutz--2017|Mutz et al. 2017]] ). Anaerobic digestion technology has a positive environmental impact and the ability to reduce GHG emissions ( [[#Ayodele--2018|Ayodele et al. 2018]] ; [[#Cudjoe--2020|Cudjoe et al. 2020]] ). The by-product of the anaerobic digestion process could be used as a nutrient-rich fertiliser for enhancing soil richness for agricultural purposes ( [[#Wainaina--2020|Wainaina et al. 2020]] ). Due to the potential negative impacts on domestic environment and residents’ health, WTE projects such as incineration encounter substantial opposition from the local communities in which they are located ( [[#Baxter--2016|Baxter et al. 2016]] ; [[#Ren--2016|Ren et al. 2016]] ). Therefore, for WTE to be deployed more widely, policies would need to be tailored with specific guidelines focused on mitigating emissions, which may have an adverse effect on the environment.

Depending on the origin of the waste used, the integration of WTE and carbon capture and storage (CCS) could enable waste to be a net-zero or even net negative emissions energy source ( [[#Kearns--2019|Kearns 2019]] ; [[#Wienchol--2020|Wienchol et al. 2020]] ). For example, in Europe only, the integration of CCS with WTE facilities has the potential to capture about 60 to 70 million tonnes of carbon dioxide annually ( [[#Tota--2021|Tota et al. 2021]] ).

Waste-to-energy is an expensive process compared to other energy sources such as fossil fuels and natural gas ( [[#Mohammadi--2020|Mohammadi and Harjunkoski 2020]] ). However, the environmental and economic benefits make its high financial costs justifiable. In 2019, the global WTE market size was valued at USD31 billion, and it is predicted to experience 7.4% annual growth until 2027 ( [[#UNECE--2020|UNECE 2020]] ).

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<span id="energy-system-integration"></span>

=== 6.4.3 Energy System Integration ===

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Greenhouse gases are emitted across all economic activities. Therefore, cost-effective decarbonisation requires a ‘system of systems’ approach that considers the interaction between different energy sectors and systems. Flexibility technologies and advanced control of integrated energy systems (e.g., considering the interaction between electricity, heating/cooling, gas/hydrogen, transport sectors) could reduce energy infrastructure investments substantially in future low-carbon energy systems ( [[#Strbac--2015b|Strbac et al. 2015b]] ; [[#Jacobson--2019|Jacobson et al. 2019]] ).

The electricity grid will serve as a backbone of future low-carbon energy systems. Integration of large amounts of VRE generation ( [[#Hansen--2019|Hansen et al. 2019]] ), particularly wind and solar generation ( [[#Bistline--2019|Bistline and Young 2019]] ; [[#Perez--2019|Perez et al. 2019]] ), presents economic and technical challenges to electricity system management across different time scales from sub-seconds, hours, days, seasons, to multiple years. Furthermore, electrification of segments of the transport and heat sectors could disproportionately increase peak demand relative to supply ( [[#Bistline--2021|Bistline et al. 2021]] ). Increases in peak demand may require reinforcing network infrastructures and generation in the historical passive system operation paradigm ( [[#Strbac--2020|Strbac et al. 2020]] ).

These challenges to electricity system management can be addressed through system integration and a digitalised control paradigm involving advanced information and communication technologies. Real-time maintenance of supply-demand balance and sufficient flexibility technologies such as electricity storage, flexible demand, and grid forming converters (Strbac et al. 2015a; [[#López%20Prol--2021|López Prol and Schill 2021]] ) would be increasingly valuable for incorporating larger amounts of VRE generation. This flexibility will be particularly important to deal with sudden losses of supply, for example, due to a failure of a large generator or interconnector or a rapid increase in demand ( [[#Teng--2017|Teng et al. 2017]] ; [[#Chamorro--2020|Chamorro et al. 2020]] ).

The transition to a digitalised-based electricity system control paradigm would facilitate radical changes in the security of supply, moving from the traditional approach of redundancy in assets to a smart control paradigm. Advanced control and communication systems can significantly reduce the electricity system investment and operation costs ( [[#Harper--2018|Harper et al. 2018]] ; [[#Münster--2020|Münster et al. 2020]] ).

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<span id="importance-of-cross-sector-coupling-for-cost-effective-energy-system-decarbonisation"></span>
==== 6.4.3.1 Importance of Cross-sector Coupling for Cost-effective Energy System Decarbonisation ====

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Integrated whole-system approaches can reduce the costs of low-carbon energy system transitions ( ''high confidence'' ). A lack of flexibility in the electricity system may limit the cost-effective integration of technologies as part of broader net-zero energy systems. At the same time, the enormous latent flexibility hidden in heating and cooling, hydrogen, transport, gas systems, and other energy systems provides opportunities to take advantage of synergies and to coordinate operations across systems (Martin et al. 2017; [[#Zhang--2018|Zhang et al. 2018]] ; [[#Martinez%20Cesena--2019|Martinez Cesena and Mancarella 2019]] ; [[#Pavičević--2020|Pavičević et al. 2020]] ; [[#Bogdanov--2021|Bogdanov et al. 2021]] ) (Figure 6.16).

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[[File:555573938fc85caac59aa4d52f86200b IPCC_AR6_WGIII_Figure_6_16.png]]

'''Figure 6.16 | Interaction between different energy sectors.''' Source: extracted with permission from [[#Münster--2020|Münster et al. (2020)]] .

Sector coupling can significantly increase system flexibility, driven by the application of advanced technologies (Clegg and Mancarella 2016; [[#Heinen--2016|Heinen et al. 2016]] ; [[#Bogdanov--2019|Bogdanov et al. 2019]] ; [[#Solomon--2019|Solomon et al. 2019]] ; [[#Zhang--2019b|Zhang et al. 2019b]] ; [[#Zhang--2020|Zhang and Fujimori 2020]] ; [[#Zhao--2021|Zhao et al. 2021]] ). For example, district heating infrastructure can generate both heat and power. Cooling systems and electrified heating systems in buildings can provide flexibility through preheating and precooling via thermal energy storage (Z. [[#Li--2016|Li et al. 2016]] ; G. [[#Li--2017|Li et al. 2017]] ). System balancing services can be provided by electric vehicles (EVs) based on vehicle-to-grid concepts and deferred charging through smart control of EV batteries without compromising customers’ requirements for transport ( [[#Aunedi--2020|Aunedi and Strbac 2020]] ).

Hydrogen production processes (power-to-gas and vice versa) and hydrogen storage can support short-term and long-term balancing in the energy systems and enhance resilience (Stephen and Pierluigi 2016; [[#Strbac--2020|Strbac et al. 2020]] ). However, the economic benefits of flexible power-to-gas plants, energy storage, and other flexibility technological and options will depend on the locations of VRE sources, storage sites, gas, hydrogen, and electricity networks ( [[#Jentsch--2014|Jentsch et al. 2014]] ; [[#Heymann--2015|Heymann and Bessa 2015]] ; [[#Ameli--2020|Ameli et al. 2020]] ). Coordinated operation of gas and electricity systems can bring significant benefits in supplying heat demands. For example, hybrid heating can eliminate investment in electricity infrastructure reinforcement by switching to heat pumps in off-peak hours and gas boilers in peak hours ( [[#Fischer--2017|Fischer et al. 2017]] ; [[#Dengiz--2019|Dengiz et al. 2019]] ; [[#Bistline--2021|Bistline et al. 2021]] ). The heat required by direct air carbon capture and storage (DACCS) could be effectively supplied by inherent heat energy in nuclear plants, enhancing overall system efficiency ( [[#Realmonte--2019|Realmonte et al. 2019]] ).

Rather than incremental planning, strategic energy system planning can help minimise long-term mitigation costs ( ''high confidence'' ). With a whole-system perspective, integrated planning can consider both short-term operation and long-term investment decisions, covering infrastructure from local to national and international, while meeting security of supply requirements and incorporating the flexibility provided by different technologies and advanced control strategies ( [[#Zhang--2018|Zhang et al. 2018]] ; [[#O’Malley--2020|O’Malley et al. 2020]] ; [[#Strbac--2020|Strbac et al. 2020]] ). Management of conflicts and synergies between local district and national level energy system objectives, including strategic investment in local hydrogen and heat infrastructure, can drive significant whole-system cost savings ( [[#Zhang--2019b|Zhang et al. 2019b]] ; [[#Fu--2020|Fu et al. 2020]] ). For example, long-term planning of the offshore grid infrastructure to support offshore wind development, including interconnection between different countries and regions, can provide significant savings compared to a short-term incremental approach in which every offshore wind farm is individually connected to the onshore grid ( [[#E3G--2021|E3G 2021]] ).

<div id="6.4.3.2" class="h3-container"></div>

<span id="role-of-flexibility-technologies"></span>
==== 6.4.3.2 Role of Flexibility Technologies ====

<div id="h3-12-siblings" class="h3-siblings"></div>

Flexibility technologies – including energy storage, demand-side response, flexible/dispatchable generation, grid-forming converters, and transmission interconnection – as well as advanced control systems – can facilitate cost-effective and secure low-carbon energy systems ( ''high confidence'' ). Flexibility technologies have already been implemented, but they can be enhanced and deployed more widely. Due to their interdependencies and similarities, there can be both synergies and conflicts for utilising these flexibility options ( [[#Bistline--2021|Bistline et al. 2021]] ). It will therefore be important to coordinate the deployment of the potential flexibility technologies and smart control strategies. Important electricity system flexibility options include the following:

• '''Flexible/dispatchable generation.''' Advances in generation technologies, for example, gas/hydrogen plants and nuclear plants, can enable them to provide flexibility services. These technologies would start more quickly, operate at lower power output, and make faster output changes, enabling more secure and cost-effective integration of VRE generation and end-use electrification. There are already important developments in increasing nuclear plants flexibility (e.g., in France ( [[#Office%20of%20Nuclear%20Energy--2021|Office of Nuclear Energy 2021]] )) and the development of small modular reactors, which could support system balancing ( [[#FTI%20Consulting--2018|FTI Consulting 2018]] ).

'''•''' '''Grid-forming converters (inverters).''' The transition from conventional electricity generation, applying mainly synchronous machines to inverter-dominated renewable generation, creates significant operating challenges. These challenges are mainly associated with reduced synchronous inertia, system stability, and ‘black start’ capability. Grid-forming converters will be a cornerstone for the control of future electricity systems dominated by VRE generation. These converters will address critical stability challenges, including the lack of system inertia, frequency and voltage regulation, and black start services while reducing or eliminating the need to operate conventional generation ( [[#Tayyebi--2019|Tayyebi et al. 2019]] ).

'''•''' '''Interconnection.''' Electricity interconnections between different regions can facilitate more cost-effective renewable electricity deployment. Interconnection can enable large-scale sharing of energy and provide balancing services. Backup energy carriers beyond electricity, such as ammonia, can be shared through gas/ammonia/hydrogen-based interconnections, strengthening temporal coupling of multiple sectors in different regions ( [[#Bhagwat--2017|Bhagwat et al. 2017]] ; [[#Brown--2018|Brown et al. 2018]] ) ( [[#6.4.5|Section 6.4.5]] ).

'''•''' '''Demand-side response''' . Demand-side schemes – including, for example, smart appliances, EVs, and building-based thermal energy storage (Heleno et al. 2014) – can provide flexibility services across multiple time frames and systems. Through differentiation between essential and non-essential needs during emergency conditions, smart control of demands can significantly enhance system resilience ( [[#Chaffey--2016|Chaffey 2016]] ).

• '''Energy storage.''' Energy storage technologies ( [[#6.4.4|Section 6.4.4]] ) can act as both demand and generation sources. They can provide services such as system balancing, various ancillary services, and network management. Long-duration energy storage can significantly enhance the utilisation of renewable energy sources and reduce the need for firm low-carbon generation ( [[#Sepulveda--2021|Sepulveda et al. 2021]] ).

<div id="6.4.3.3" class="h3-container"></div>

<span id="role-of-digitalisation-and-advanced-control-systems"></span>
==== 6.4.3.3 Role of Digitalisation and Advanced Control Systems ====

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A digitalised energy system can significantly reduce energy infrastructure investments while enhancing supply security and resilience ( ''high confidence'' ) ( [[#Andoni--2019|Andoni et al. 2019]] ; [[#Strbac--2020|Strbac et al. 2020]] ). Significant progress has been made in the development of technologies essential for the transition to a digitalised energy control paradigm, although the full implementation is still under development. Electrification and the increased integration of the electricity system with other systems will fundamentally transform the operational and planning paradigm of future energy infrastructure. A fully intelligent and sophisticated coordination of the multiple systems through smart control will support this paradigm shift. This shift will provide significant savings through better utilisation of existing infrastructure locally, regionally, nationally, and internationally. Supply system reliability will be enhanced through advanced control of local infrastructure (Strbac et al. 2015a). Furthermore, this paradigm shift offers the potential to increase energy efficiency through a combination of technologies that gather and analyse data and consequently optimise energy use in real-time.

The transition to advanced data-driven control of energy system operations ( [[#Cremer--2019|Cremer et al. 2019]] ; [[#Sun--2019a|Sun et al. 2019a]] ) will require advanced information and communication technologies and infrastructure, including the internet, wireless networks, computers, software, middleware, smart sensors, internet of things components, and dedicated technological developments ( [[#Hossein%20Motlagh--2020|Hossein Motlagh et al. 2020]] ). The transition will raise standardisation and cyber-security issues, given that digitalisation can become a single point of failure for the complete system ( [[#Ustun--2019|Ustun and Hussain 2019]] ; [[#Unsal--2021|Unsal et al. 2021]] ). Implementing peer-to-peer energy trading based on blockchain is expected to be one of the key elements of next-generation electricity systems ( [[#Qiu--2021|Qiu et al. 2021]] ). This trading will enable consumers to drive system operation and future design, increasing overall system efficiency and security of supply while reducing emissions without sacrificing users’ privacy ( [[#Andoni--2019|Andoni et al. 2019]] ; [[#Ahl--2020|Ahl et al. 2020]] ). When deployed with smart contracts, this concept will be suitable for energy systems involving many participants, where a prerequisite is digitalisation (e.g., smart meters, end-use demand control systems) (Juhar and Khaled 2018; [[#Teufel--2019|Teufel et al. 2019]] ).

<div id="6.4.3.4" class="h3-container"></div>

<span id="system-benefits-of-flexibility-technologies-and-advanced-control-systems"></span>
==== 6.4.3.4 System Benefits of Flexibility Technologies and Advanced Control Systems ====

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New sources of flexibility and advanced control systems provide a significant opportunity to reduce low-carbon energy system costs by enhancing operating efficiency and reducing energy infrastructure and low-carbon generation investments, while continuing to meet security requirements ( ''high confidence'' ). In the USA, for example, one study found that flexibility in buildings alone could reduce US CO 2 emissions by 80 Mt yr –1 and save USD18 billion yr –1 in electricity system costs by 2030 ( [[#Satchwell--2021|Satchwell et al. 2021]] ). Key means for creating savings are associated with the following:

• '''Efficient energy system operation.''' Flexibility technologies such as storage, demand-side response, interconnection, and cross-system control will enable more efficient, real-time demand and supply balancing. This balancing has historically been provided by conventional fossil-fuel generation ( [[#Nuytten--2013|Nuytten et al. 2013]] ).

'''•''' '''Savings in investment in low-carbon/renewable generation capacity.''' System flexibility sources can absorb or export surplus electricity, thus reducing or avoiding energy curtailment and reducing the need for firm low-carbon capacity such as nuclear and fossil-fuel plants with CCS ( [[#Newbery--2013|Newbery et al. 2013]] ; [[#Solomon--2019|Solomon et al. 2019]] ). For example, one study found that flexibility technologies and advanced control systems could reduce the need for nuclear power by 14 GW and offshore wind by 20 GW in the UK’s low-carbon transition ( [[#Strbac--2015b|Strbac et al. 2015b]] ).

'''•''' '''Reduced need for backup capacity.''' System flexibility can reduce energy demand peaks, reducing the required generation capacity to maintain the security of supply, producing significant savings in generation investments ( [[#Strbac--2020|Strbac et al. 2020]] ).

• '''Deferral or avoidance of electricity network reinforcement/addition.''' Flexibility technologies supported by advanced control systems can provide significant savings in investment in electricity network reinforcement that might emerge from increased demand, for example, driven by electrification of transport and heat sectors. Historical network planning and operation standards are being revised considering alternative flexibility technologies, which would further support cost-effective integration of decarbonised transport and heat sectors ( [[#Strbac--2020|Strbac et al. 2020]] ).

<div id="6.4.4" class="h2-container"></div>

<span id="energy-storage-for-low-carbon-grids"></span>
=== 6.4.4 Energy Storage for Low-carbon Grids ===

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Energy storage technologies make low-carbon electricity systems more cost-effective, allowing VRE technologies to replace more expensive firm low-carbon generation technologies (Carbon Trust 2016) and reducing investment costs in backup generation, interconnection, transmission, and distribution network upgrades ( ''high confidence'' ). Energy system decarbonisation relies on increased electrification ( [[#6.6.2.3|Section 6.6.2.3]] ). Meeting increasing demands with variable renewable sources presents challenges and could lead to costly infrastructure reinforcements. Energy storage enables electricity from variable renewables to be matched against evolving demands across both time and space, using short-, medium- and long-term storage of excess energy for delivery later or at a different location. In 2017, an estimated 4.67 TWh (0.017 EJ) of electricity storage was in operation globally ( [[#IRENA--2017b|IRENA 2017b]] ). If the integration of renewables is doubled from 2014 levels by 2030, the total capacity of global electricity storage could triple, reaching 11.89–15.27 TWh (0.043–0.055 EJ) ( [[#IRENA--2017b|IRENA 2017b]] ).

Energy storage technologies can provide a range of different grid services (Table 6.5). Energy storage enhances security of supply by providing real-time system regulation services (voltage support, frequency regulation, fast reserve, and short-term reserve). A greater proportion of variable renewable sources reduces system inertia, requiring more urgent responses to changes in system frequency, which rapid response storage technologies can provide (stability requires responses within sub-second time scale for provision of frequency and voltage control services). Energy storage also provides intermittent renewable sources with flexibility, allowing them to contribute a greater proportion of electrical energy and avoiding curtailment (capacity firming). Investment costs in backup generation, interconnection, transmission, and distribution network upgrades can thus be reduced (upgrade deferral), meaning that less low-carbon generation will need to be built while still reducing emissions. In the event of an outage, energy storage reserves can keep critical services running (islanding) and restart the grid (black start). The ability to store and release energy as required provides a range of market opportunities for buying and selling of energy (arbitrage).

'''Table 6.5 | Suitability of low-carbon energy storage technologies, interms of the grid services they can provide, and overall features such as technology maturity: where Low represents an emerging technology; Med represents a maturing technology; and High a fully mature technology.''' The opportunity for the cost of a technology to reduce over the next decade is represented by Low, Med and High and the lifetime of installations by: Long, for projects lasting more than 25 years; Med for those lasting 15–25 years; Short, for those lasting less than 15 years.

{| class="wikitable"
|-
| Suitability factor

| PHS

| CAES

| LAES

| TES

| FES

| LiB

| Scap

| RFB

| PtX

| RHFC

|-
| ''Upgrade deferral''

| ''''''

| ''''''

| ''''''

| ''''''

| ''''''

| ''''''

| ''''''

| ''''''

| ''''''

| ''''''

|-
| ''Energy arbitrage''

| ''''''

| ''''''

| ''''''

| ''''''

|
| ''''''

|
| ''''''

| ''''''

| ''''''

|-
| ''Capacity firming''

| ''''''

| ''''''

| ''''''

| ''''''

| ''''''

| ''''''

|
| ''''''

| ''''''

| ''''''

|-
| ''Seasonal storage''

|
| ''''''

|
| ''''''

| ''''''

|-
| ''Stability''

| ''''''

|
| ''''''

| ''''''

| ''''''

| ''''''

| ''''''

| ''''''

|-
| ''Frequency regulation''

| ''''''

| ''''''

| ''''''

|
| ''''''

| ''''''

| ''''''

| ''''''

| ''''''

| ''''''

|-
| ''Voltage support''

| ''''''

| ''''''

| ''''''

|
| ''''''

| ''''''

| ''''''

| ''''''

| ''''''

| ''''''

|-
| ''Black start''

| ''''''

| ''''''

| ''''''

|
| ''''''

|
| ''''''

| ''''''

| ''''''

|-
| ''Short-term reserve''

| ''''''

| ''''''

| ''''''

|
| ''''''

|
| ''''''

| ''''''

| ''''''

|-
| ''Fast reserve''

| ''''''

| ''''''

| ''''''

|
| ''''''

| ''''''

|
| ''''''

| ''''''

| ''''''

|-
| ''Islanding''

|
| ''''''

| ''''''

| ''''''

|
| ''''''

|
| ''''''

| ''''''

| ''''''

|-
| ''Uninterruptible power supply''

|
| ''''''

| ''''''

| ''''''

| ''''''

|
| ''''''

|-
| Maturity

| High

| High

| Med

| Low

| High

| Med

| Low

| Low

| Low

| Low

|-
| Opportunity to reduce costs

| Low

| Low

| Low

| Med

| Med

| High

| High

| High

| Med

| High

|-
| Lifetime

| Long

| Long

| Long

| Long

| Med

| Short

| Med

| Med

| Med

| Short

|-
| Roundtrip efficiency

| 60–80%

| 30–60%

| 55–90%

| 70–80%

| 90%

| &gt;95%

| &gt;95%

| 80–90%

| 35–60%

| &lt;30%

|}

Note: PHS – Pumped Hydroelectric Storage; CAES – Compressed Air Energy Storage; LAES – Liquid Air Energy Storage; TES – Thermal Energy Storage; FES – Flywheel Energy Storage; LIB – Li-ion Batteries; Scap – Supercapacitors; RFB – Redox Flow Batteries; RHFC – Reversible Hydrogen Fuel Cells; PtX – Power to fuels. Source: PHS – [[#Barbour--2016|Barbour et al. 2016]] , Yang 2016, [[#IRENA--2017b|IRENA 2017b]] ; CAES – [[#Luo--2014|Luo et al. 2014]] , [[#Brandon--2015|Brandon et al. 2015]] , [[#IRENA--2017b|IRENA 2017b]] ; LAES – [[#Luo--2014|Luo et al. 2014]] , Highview 2019; TES – [[#Brandon--2015|Brandon et al. 2015]] , [[#Gallo--2016|Gallo et al. 2016]] , [[#Smallbone--2017|Smallbone et al. 2017]] ; FES – [[#IRENA--2017b|IRENA 2017b]] , [[#Yulong--2017|Yulong et al. 2017]] ; LIB – [[#IRENA--2015|IRENA 2015]] b, [[#Hammond--2015|Hammond and Hazeldine 2015]] , [[#Nykvist--2015|Nykvist and Nilsson 2015]] , Staffell, I. and Rustomji, M. et al. 2016, [[#IRENA--2017b|IRENA 2017b]] , [[#Schmidt--2017|Schmidt et al. 2017]] c, [[#May--2018|May et al. 2018]] ; Scap – [[#Brandon--2015|Brandon et al. 2015]] , [[#Gur--2018|Gur 2018]] ; RFB – [[#IRENA--2017b|IRENA 2017b]] ; RHFC – IEA 2015, [[#Gur--2018|Gur 2018]] .

No single, sufficiently mature energy storage technology can provide all the required grid services – a portfolio of complementary technologies working together can provide the optimum solution ( ''high confidence'' ). Different energy storage technologies can provide these services and support cost-effective energy system decarbonisation (Carbon Trust 2016). To achieve very low-carbon systems, significant volumes of storage will be required (Strbac et al. 2015a; [[#6.4.3.2|Section 6.4.3.2]] ). There are few mature global supply chains for many of the less-developed energy storage technologies. This means that, although costs today may be relatively high, there are significant opportunities for future cost reductions, both through technology innovation and through manufacturing scale. Adding significant amounts of storage will reduce the price variation and, therefore, the profitability of additional and existing storage, increasing investment risk.

Energy storage extends beyond electricity storage and includes technologies that can store energy as heat, cold, and both liquid and gaseous fuels. Energy storage is a conversion technology, enabling energy to be converted from one form to another. This diversification improves the overall resilience of energy systems, with each system being able to cover supply shortfalls in the others. For example, storage can support the electrification of heating or cooling, as well as transport through electric vehicles, powered by batteries or by fuel cells. Storage significantly reduces the need for costly reinforcement of local distribution networks through smart charging schemes and the ability to flow electricity back to the grid (e.g., through vehicle-to-grid). By capturing otherwise wasted energy streams, such as heat or cold, energy storage improves the efficiency of many systems, such as buildings, data centres and industrial processes.

<div id="6.4.4.1" class="h3-container"></div>

<span id="energy-storage-technologies"></span>
==== 6.4.4.1 Energy Storage Technologies ====

<div id="h3-15-siblings" class="h3-siblings"></div>

'''Pumped hydroelectric storage (PHS).''' PHS makes use of gravitational potential energy, using water as the medium. Water is pumped into an elevated reservoir using off-peak electricity and stored for later release when electricity is needed. These closed-loop hydropower plants have been in use for decades and account for 97% of worldwide electricity storage capacity ( [[#IRENA--2017b|IRENA 2017b]] ; [[#IEA--2018b|IEA 2018b]] ). PHS is best suited to balancing daily energy needs at a large scale, and advances in the technology now allow rapid response and power regulation in both generating and pumping mode ( [[#Valavi--2018|Valavi and Nysveen 2018]] ; [[#Dong--2019|Dong et al. 2019]] ; [[#Kougias--2019|Kougias et al. 2019]] ). The construction itself can cause disruption to the local community and environment ( [[#Hayes--2019|Hayes et al. 2019]] ), the initial investment is costly, and extended construction periods delay return on investment ( [[#6.4.2.3|Section 6.4.2.3]] ). In addition, locations for large-scale PHS plants are limited.

Advanced pump-turbines are being developed, allowing both reversible and variable-speed operation, supporting frequency control and grid stability with improved round-trip efficiencies ( [[#Ardizzon--2014|Ardizzon et al. 2014]] ). New possibilities are being explored for small-scale PHS installations and expanding the potential for siting ( [[#Kougias--2019|Kougias et al. 2019]] ). For example, in underwater PHS, the upper reservoir is the sea, and the lower is a hollow deposit at the seabed. Seawater is pumped out of the deposit to store off-peak energy and re-enters through turbines to recharge it ( [[#Kougias--2019|Kougias et al. 2019]] ). Using a similar concept, underground siting in abandoned mines and caverns could be developed reasonably quickly ( [[#IEA--2020h|IEA 2020h]] ). Storage of energy as gravitational potential can also be implemented using materials other than water, such as rocks and sand. Pumped technology is a mature technology ( [[#Rehman--2015|Rehman et al. 2015]] ; [[#Barbour--2016|Barbour et al. 2016]] ) and can be important in supporting the transition to future low-carbon electricity grids ( [[#IHA--2021|IHA 2021]] ).

'''Batteries.''' There are many types of batteries, all having unique features and suitability, but their key feature is their rapid response time. A rechargeable battery cell is charged by using electricity to drive ions from one electrode to another, with the reverse occurring on discharge, producing a usable electric current ( [[#Crabtree--2015|Crabtree et al. 2015]] ). While lead-acid batteries (LABs) have been widely used for automotive and grid applications for decades ( [[#May--2018|May et al. 2018]] ), LIBs are increasingly being used in grid-scale projects ( [[#Crabtree--2015|Crabtree et al. 2015]] ), displacing LABs. The rapid response time of batteries makes them suitable for enhanced frequency regulation and voltage support, enabling the integration of variable renewables into electricity grids ( [[#Strbac--2016|Strbac and Aunedi 2016]] ). Batteries can provide almost all electricity services, except for seasonal storage. LIBs, in particular, can store energy and power in small volumes and with low weight, making them the default choice for EVs ( [[#Placke--2017|Placke et al. 2017]] ). EV batteries are expected to form a distributed storage resource as this market grows, both impacting and supporting the grid ( [[#Staffell--2016|Staffell and Rustomji 2016]] ).

Drawbacks of batteries include relatively short lifespans and the use of hazardous or costly materials in some variants. While LIB costs are decreasing ( [[#Schmidt--2017|Schmidt et al. 2017]] ; [[#Vartiainen--2020|Vartiainen et al. 2020]] ), the risk of thermal runaway, which could ignite a fire ( [[#Gur--2018|Gur 2018]] ; [[#Wang--2019a|Wang et al. 2019a]] ), concerns about long-term resource availability ( [[#Olivetti--2017|Olivetti et al. 2017]] ; [[#Sun--2017|Sun et al. 2017]] ), and concerns about global cradle-to-grave impacts ( [[#Peters--2017|Peters et al. 2017]] ; [[#Kallitsis--2020|Kallitsis et al. 2020]] ) need to be addressed.

The superior characteristics of LIBs will keep them the dominant choice for EV and grid applications in the medium term ( ''high confidence'' ). There are, however, several next-generation battery chemistries ( [[#Placke--2017|Placke et al. 2017]] ), which show promise ( ''high confidence'' ). Cost reductions through economies of scale are a key area for development. Extending the life of the battery can bring down overall costs and mitigate the environmental impacts ( [[#Peters--2017|Peters et al. 2017]] ). Understanding and controlling battery degradation is therefore important. The liquid, air-reactive electrolytes of conventional LIBs are the main source of their safety issues ( [[#Janek--2016|Janek and Zeier 2016]] ; [[#Gur--2018|Gur 2018]] ), so all-solid-state batteries, in which the electrolyte is a solid, stable material, are being developed. They are expected to be safe, be durable, and have higher energy densities ( [[#Janek--2016|Janek and Zeier 2016]] ). New chemistries and concepts are being explored, such as lithium-sulphur batteries to achieve even higher energy densities ( [[#Van%20Noorden--2014|Van Noorden 2014]] ; [[#Blomgren--2017|Blomgren 2017]] ) and sodium chemistries because sodium is more abundant than lithium ( [[#Hwang--2017|Hwang et al. 2017]] ). Cost-effective recycling of batteries will address many sustainability issues and prevent hazardous and wasteful disposal of used batteries ( [[#Harper--2019|Harper et al. 2019]] ). Post-LIB chemistries include metal sulphur, metal-air, metal ion (besides lithium) and all-solid-state batteries.

'''Compressed air energy storage (CAES).''' With CAES, off-peak electricity is used to compress air in a reservoir – either in salt caverns for large-scale or in high-pressure tanks for smaller-scale installations. The air is later released to generate electricity. While conventional CAES has used natural gas to power compression, new low-carbon CAES technologies, such as isothermal or adiabatic CAES, control thermal losses during compression and expansion ( [[#Wang--2017c|Wang et al. 2017c]] ). Fast responses and higher efficiencies occur in small-scale CAES installations, scalable to suit the application as a distributed energy store, offering a flexible, low-maintenance alternative ( [[#Luo--2014|Luo et al. 2014]] ; [[#Venkataramani--2016|Venkataramani et al. 2016]] ).

CAES is a mature technology in use since the 1970s. Although CAES technologies have been developed, there are not many installations at present ( [[#Wang--2017b|Wang et al. 2017b]] ; [[#Blanc--2020|Blanc et al. 2020]] ). While the opportunities for CAES are significant, with a global geological storage potential of about 6.5 PW ( [[#Aghahosseini--2018|Aghahosseini and Breyer 2018]] ), a significant amount of initial investment is required. Higher efficiencies and energy densities can be achieved by exploiting the hydrostatic pressure of deep water to compress air within submersible reservoirs ( [[#Pimm--2014|Pimm et al. 2014]] ). CAES is best suited to bulk diurnal electricity storage for buffering VRE sources and services, which do not need a very rapid response. In contrast to PHS, CAES has far more siting options and poses few environmental impacts.

'''Liquid air energy storage (LAES).''' LAES uses electricity to liquefy air by cooling it to –196°C and storing it in this condensed form (largely liquid nitrogen) in large, insulated tanks. To release electricity, the ‘liquid air’ is evaporated through heating, expanding to drive gas turbines. Low-grade waste heat can be utilised, providing opportunities for integrating with industrial processes to increase system efficiency. There are clear, exploitable synergies with the existing liquid gas infrastructure ( [[#Peters--2016|Peters and Sievert 2016]] ).

LAES provides bulk daily storage of electricity, with the additional advantage of being able to capture waste heat from industrial processes. This technology is in the early commercial stage ( [[#Brandon--2015|Brandon et al. 2015]] ; [[#Regen--2017|Regen 2017]] ). Advances in whole systems integration can be developed to integrate LAES with industrial processes, making use of their waste heat streams. LAES uniquely removes contaminants in the air and could potentially incorporate CO 2 capture ( [[#Taylor--2012|Taylor et al. 2012]] ).

'''Thermal energy storage (TES).''' TES refers to a range of technologies exploiting the ability of materials to absorb and store heat or cold, either within the same phase (sensible TES), through phase changes (latent TES), or through reversible chemical reactions (thermochemical TES). Pumped Thermal Energy Storage (PTES), a hybrid form of TES, is an air-driven electricity storage technology storing both heat and cold in gravel beds, using a reversible heat-pump system to maintain the temperature difference between the two beds and gas compression to generate and transfer heat ( [[#Regen--2017|Regen 2017]] ). TES technologies can store both heat and cold energy for long periods, for example, in underground water reservoirs for balancing between seasons ( [[#Dahash--2019|Dahash et al. 2019]] ; [[#Tian--2019|Tian et al. 2019]] ), storing heat and cold to balance daily and seasonal temperatures in buildings and reducing heat build-up in applications generating excessive waste heat, such as data centres and underground operations.

TES can be much cheaper than batteries and has the unique ability to capture and reuse waste heat and cold, enabling the efficiency of many industrial, buildings, and domestic processes to be greatly improved ( ''high confidence'' ). Integration of TES into energy systems is particularly important, as the global demand for cooling is expected to grow (Elzinga et al. 2014; [[#Peters--2016|Peters and Sievert 2016]] ) ''.'' Sensible TES is well developed and widely used; latent TES is less developed with few applications. Thermochemical TES is the least developed, with no application yet ( [[#Prieto--2016|Prieto et al. 2016]] ; [[#Clark--2020|Clark et al. 2020]] ). The potential for high-density storage of industrial heat for long periods in thermochemical TES ( [[#Brandon--2015|Brandon et al. 2015]] ) is high, with energy densities comparable to that of batteries ( [[#Taylor--2012|Taylor et al. 2012]] ), but material costs are currently prohibitive, ranging from hundreds to thousands of dollars per tonne.

'''Flywheel energy storage (FES).''' Flywheels are charged by accelerating a rotor/flywheel. Energy is stored in the spinning rotor’s inertia which is only decelerated by friction (minimised by magnetic bearings in a vacuum), or by contact with a mechanical, electric motor. They can reach full charge very rapidly, their state of charge can be easily determined ( [[#Amiryar--2017|Amiryar and Pullen 2017]] ), and they operate over a wide range of temperatures. While they are more expensive to install than batteries and supercapacitors, they last a long time and are best suited to stationary grid storage, providing high power for short periods (minutes). Flywheels can be used in vehicles, but not as the primary energy source.

Flywheels are a relatively mature storage technology but not widely used, despite their many advantages over electrochemical storage (Dragoni 2017). Conventional flywheels require costly, high tensile strength materials, but high-energy flywheels, using lightweight rotor materials, are being developed ( [[#Hedlund--2015|Hedlund et al. 2015]] ; [[#Amiryar--2017|Amiryar and Pullen 2017]] ).

'''Supercapacitors – also known as ultracapacitors or double layer capacitors (Scap).''' Supercapacitors consist of a porous separator sandwiched between two electrodes, immersed in a liquid electrolyte ( [[#Gur--2018|Gur 2018]] ). When a voltage is applied across the electrodes, ions in the electrolyte form electric double layers at the electrode surfaces, held by electrostatic forces. This structure forms a capacitor, storing electrical charge ( [[#Brandon--2015|Brandon et al. 2015]] ; [[#Lin--2017|Lin et al. 2017]] ) and can operate from –40°C to 65°C.

Supercapacitors can supply high peaks of power very rapidly for short periods (seconds up to minutes) and are able to fulfil the grid requirements for frequency regulation, but they would need to be hybridised with batteries for automotive applications. Their commercial status is limited by costly materials and additional power electronics required to stabilise their output ( [[#Brandon--2015|Brandon et al. 2015]] ). Progress in this area includes the development of high-energy supercapacitors, LIB-supercapacitor devices ( [[#Gonzalez--2016|Gonzalez et al. 2016]] ), and cheaper materials ( [[#Wang--2017a|Wang et al. 2017a]] ), all providing the potential to improve the economic case for supercapacitors, either by reducing manufacturing costs or extending their service portfolio.

'''Redox flow batteries (RFB).''' Redox flow batteries use two separate electrolyte solutions, usually liquids, but solid or gaseous forms may also be involved, stored in separate tanks, and pumped over or through electrode stacks during charge and discharge, with an ion-conducting membrane separating the liquids. The larger the tank, the greater the energy storage capacity, whereas more and larger cells in the stack increase the power of the flow battery. This decoupling of energy from power enables RFB installations to be uniquely tailored to suit the requirements of any given application. There are two commercially available types today: vanadium and zinc bromide, and both operate at near ambient temperatures, incurring minimal operational costs.

RFBs respond rapidly and can perform all the same services as LIBs, except for onboard electricity for EVs. Lower cost chemistries are emerging, to enable cost-effective bulk energy storage ( [[#Brandon--2015|Brandon et al. 2015]] ). A new membrane-free design eliminates the need for a separator and also halves the system requirements, as the chemical reactions can coexist in a single electrolyte solution ( [[#Navalpotro--2017|Navalpotro et al. 2017]] ; [[#Arenas--2018|Arenas et al. 2018]] ).

'''Power to fuels (PtX)''' (see also [[#6.4.3.1|Section 6.4.3.1]] ). The process of using electricity to generate a gaseous fuel, such as hydrogen or ammonia, is termed power-to-gas (PtG/P2G) ( [[#IEA--2020h|IEA 2020h]] ). When injected into the existing gas infrastructure ( [[#6.4.5|Section 6.4.5]] ), it has the added benefit of decarbonising gas ( [[#Brandon--2015|Brandon et al. 2015]] ). Electricity can be used to generate hydrogen, which is then converted back into electricity using combined-cycle gas turbines that have been converted to run on hydrogen. For greater compatibility with existing gas systems and appliances, the hydrogen can be combined with captured carbon dioxide to form methane and other synthetic fuels ( [[#Thema--2019|Thema et al. 2019]] ), however, methane has high global warming potential and its supply chain emissions have been found to be significant ( [[#Balcombe--2013|Balcombe et al. 2013]] ).

PtX can provide all required grid services, depending on how it is integrated. However, a significant amount of PtX is required for storage to produce electricity again ( [[#Bogdanov--2019|Bogdanov et al. 2019]] ) due to the low roundtrip efficiency of converting electricity to fuel and back again. However, portable fuels (hydrogen, methane, ammonia, synthetic hydrocarbons) are useful in certain applications, for example, in energy systems lacking the potential for renewables. The high energy density of chemical storage is essential for more demanding applications, such as transporting heavy goods and heating or cooling buildings ( [[#IEA--2020h|IEA 2020h]] ). Research is needed into more efficient and flexible electrolysers which last longer and cost less ( [[#Brandon--2015|Brandon et al. 2015]] ).

'''Hydrogen and reversible hydrogen fuel cells (H/RHFC).''' Hydrogen is a flexible fuel with diverse uses, capable of providing electricity, heat, and long-term energy storage for grids, industry, and transport, and has been widely used industrially for decades ( [[#6.4.5.1|Section 6.4.5.1]] ). Hydrogen can be produced in various ways and stored in significant quantities in geological formations at moderate pressures, often for long periods, providing seasonal storage ( [[#Gabrielli--2020|Gabrielli et al. 2020]] ). A core and emerging implementation of PtX is hydrogen production through electrolysers. Hydrogen is a carbon-free fuel holding three times the energy of an equivalent mass of gasoline but occupying a larger volume. An electrolyser uses excess electricity to split water into hydrogen and oxygen through the process of electrolysis. A fuel cell performs the reverse process of recombining hydrogen and oxygen back into water, converting chemical energy into electricity (Elzinga et al. 2014). Reversible hydrogen fuel cells (RHFCs) can perform both functions in a single device, however, they are still in the pre-commercial stage, due to prohibitive production costs.

Hydrogen can play an important role in reducing emissions and has been shown to be the most cost-effective option in some cases, as it builds on existing systems (Staffell et al. 2018). Fuel cell costs need to be reduced and the harmonies between hydrogen and complementary technologies, such as batteries, for specific applications need to be explored further. Hydrogen can provide long-duration storage to deal with prolonged extreme events, such as very low output of wind generation, to support resilience of future low-carbon energy systems. Research in this technology focuses on improving roundtrip efficiencies, which can be as high as 80% with recycled waste heat and in high-pressure electrolysers, incorporating more efficient compression ( [[#Matos--2019|Matos et al. 2019]] ). Photo-electrolysis uses solar energy to directly generate hydrogen from water ( [[#Amirante--2017|Amirante et al. 2017]] ).

'''Table 6.6 | Technical characteristics of a selected range of battery chemistries, categorised as those which precede LIBs (white background), LIBs (yellow background) and post LIBs (blue background).'''

{| class="wikitable"
|-
| Battery type

| Technology maturity

| Lifespan (cycles)

| Energy density (Wh L –1 )

| Specific energy (Wh kg –1 )

| Price (USD kWh –1 ) in 2017

|-
| Lead acid

| High

| 300–800 e

| 102–106 e

| 38–60 e

| 70–160 e

|-
| Ni MH

| High

| 600–1200 e

| 220–250 e

| 42–110 e

| 210–365 e

|-
| Ni Cd

| High

| 1350 b

| 100 b

| 60 b

| 700

|-
| High-temperature Na batteries

| High

| 1000 e

| 150–280 h

| 80–120 a

| 315–490 h

|-
| LIB state of the art

| High

| 1000–6000 e

| 200–680 c

| 110–250 c

| 176 f

|-
| LIB energy-optimised

| Under development

|
| 600–850 c

| 300–440 c

|
|-
| Classic Li Metal (CLIM)

| Under development

|
| 800–1050 c

| 420–530 c

|
|-
| Metal Sulphur (Li S)

| Near commercialisation

| 100–500 e

| 350–680 c, h

| 360–560 c, h

| 36–130 e

|-
| Metal Sulphur (Na S)

| Under development

| 5000–10,000 h

|
|-
| Metal Air (Li/air)

| Under development

| 20–100 e

|
| 470–900 d

| 70–200 e

|-
| Metal Air (Zn/air)

| Under development

| 150–450 e

|
| 200–410 d

| 70–160 e

|-
| Na ion

| Under development

| 500 g

|
| 600 g

|
|-
| All-solid-state

| Under development

|
| 278–479 c

|
|-
| Redox

| Under development

| &gt;12,000–14,000 j

| 15–25 j

| 10–20 j

| 66 j

|}

Note: With the exception of the All-solid-state batteries, all use liquid electrolytes. Source: a Mahmoudzadeh et al. 2017; b [[#Manzetti--2015|Manzetti and Mariasiu 2015]] ; c [[#Placke--2017|Placke et al. 2017]] ; d [[#Nykvist--2015|Nykvist and Nilsson 2015]] ; e [[#Cano--2018|Cano et al. 2018]] ; f [[#Bloomberg%20Energy%20Finance--2019|Bloomberg Energy Finance, 2019]] ; g You and Manthiram 2017; h [[#Fotouhi--2017|Fotouhi et al. 2017]] ; i [[#IRENA--2017b|IRENA 2017b]] ; j [[#Yang--2020|Yang et al. 2020]] .

<div id="6.4.4.2" class="h3-container"></div>

<span id="societal-dimensions-of-energy-storage"></span>
==== 6.4.4.2 Societal Dimensions of Energy Storage ====

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Public awareness and knowledge about electricity storage technologies, their current state, and their potential role in future energy systems is limited ( [[#Jones--2018|Jones et al. 2018]] ). For instance, people do not perceive energy system flexibility and storage as a significant issue, or assume storage is already taking place. Public perceptions differ across storage technologies. Hydrogen is considered a modern and clean technology, but people also have safety concerns. Moreover, the public is uncertain about hydrogen storage size and the possibility of storing hydrogen in or near residential areas ( [[#Eitan--2021|Eitan and Fischhendler 2021]] ). Battery storage both on the household and community level was perceived as slightly positive in one study in the UK ( [[#Ambrosio-Albala--2020|Ambrosio-Albala et al. 2020]] ). However, financial costs are seen as a main barrier. The potential of EV batteries to function as flexible storage is limited by the current numbers of EV owners and concerns that one’s car battery might not be fully loaded when needed.

<div id="6.4.5" class="h2-container"></div>

<span id="energy-transport-and-transmission"></span>
=== 6.4.5 Energy Transport and Transmission ===

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The linkage between energy supply and distribution, on the one hand, and energy use on the other is facilitated by various mechanisms for transporting energy. As the energy system evolves, the way that energy is transported will also evolve.

<div id="6.4.5.1" class="h3-container"></div>

<span id="hydrogen-low-carbon-energy-fuel"></span>
==== 6.4.5.1 Hydrogen: Low-carbon Energy Fuel ====

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Hydrogen is a promising energy carrier for a decarbonised world (Box 6.9). It can be utilised for electricity, heat, transport, industrial demand, and energy storage ( [[#Abdin--2020|Abdin et al. 2020]] ). In low-carbon energy systems, hydrogen is expected to be utilised in applications that are not as amenable to electrification, such as a fuel for heavy-duty road transport and shipping, or as a chemical feedstock ( [[#Schemme--2017|Schemme et al. 2017]] ; [[#Griffiths--2021|Griffiths et al. 2021]] ). Hydrogen could also provide low-carbon heat for industrial processes or be utilised for direct reduction of iron ore ( [[#Vogl--2018|Vogl et al. 2018]] ). Hydrogen could replace natural gas-based electricity generation ( [[#do%20Sacramento--2013|do Sacramento et al. 2013]] ) in certain regions and support the integration of variable renewables into electricity systems by providing a means of long-term electricity storage. Hydrogen-based carriers, such as ammonia and synthetic hydrocarbons, can likewise be used in energy-intensive industries and the transport sector ( [[#Schemme--2017|Schemme et al. 2017]] ; [[#IRENA--2019b|IRENA 2019b]] ) (e.g., synthetic fuels for aviation). These hydrogen-based energy carriers are easier to store than hydrogen. At present hydrogen has limited applications – mainly being produced onsite for the creation of methanol and ammonia ( [[#IEA--2019c|IEA 2019c]] ), as well as in refineries.

Low- or zero-carbon produced hydrogen is not currently competitive for large-scale applications, but it is likely to have a significant role in future energy systems, due to its wide-range of applications ( ''high confidence'' ). Key challenges for hydrogen are: (i) cost-effective low/zero carbon production; (ii) delivery infrastructure cost; (iii) land area (i.e., ‘footprint’) requirements of hydrogen pipelines, compressor stations, and other infrastructure; (iv) challenges in using existing pipeline infrastructure; (v) maintaining hydrogen purity; (vi) minimising hydrogen leakage; and (vii) the cost and performance of end uses. Furthermore, it is necessary to consider the public perception and social acceptance of hydrogen technologies and their related infrastructure requirements ( [[#Iribarren--2016|Iribarren et al. 2016]] ; [[#Scott--2020|Scott and Powells 2020]] ).

'''Hydrogen production.''' Low- or zero-carbon hydrogen can be produced from multiple sources. While there is no consensus on the hydrogen production spectrum, ‘blue’ hydrogen ( [[#Goldmann--2018|Goldmann and Dinkelacker 2018]] ) generally refers to hydrogen produced from natural gas combined with CCS through processes such as steam methane reforming (SMR) ( [[#Sanusi--2019|Sanusi and Mokheimer 2019]] ) and advanced gas reforming ( [[#Zhou--2020|Zhou et al. 2020]] ). Low-carbon hydrogen could also be produced from coal coupled with CCS ( [[#Hu--2020|Hu et al. 2020]] ) (Table 6.7). Current estimates are that adding CCS to produce hydrogen from SMR will add on average 50% on the capital cost, 10% to fuel, and 100% to operating costs. For coal gasification, CCS will add 5% to the capital and fuel costs and 130% to operating costs ( [[#Staffell--2018|Staffell et al. 2018]] ; [[#IEA--2019d|IEA 2019d]] ). Further, biomass gasification could produce renewable hydrogen, and when joined with CCS could provide negative carbon emissions. ‘Green’ hydrogen ( [[#Jaszczur--2016|Jaszczur et al. 2016]] ) is most often referred to as hydrogen produced from zero-carbon electricity sources such as solar power and wind power ( [[#Schmidt--2017|Schmidt et al. 2017]] ) (Table 6.8). Nuclear power could also provide clean hydrogen, via electrolysis or thermochemical water splitting ( [[#EERE--2020|EERE 2020]] ). Hydrogen can even be produced by pyrolysis of methane (Sánchez-Bastardo et al. 2020) – sometimes called ‘turquoise’ hydrogen, solar thermochemical water splitting, biological hydrogen production (cyanobacteria) ( [[#Velazquez%20Abad--2017|Velazquez Abad and Dodds 2017]] ) – and microbes that use light to make hydrogen (under research) ( [[#EIA--2020|EIA 2020]] ).

'''Table 6.7 | Key performance and cost characteristics of different non-electric hydrogen production technologies, including carbon capture and storage (CCS).'''

{| class="wikitable"
|-
| rowspan="2"| Technology

| colspan="2"| LHV efficiency (%)

| rowspan="2"| Carbon intensity (kgCO 2 (kgH 2 ) –1 )

| colspan="2"| Cost estimates * (USD (kgH 2 ) –1 )

|-
| Current

| Long-term

| Current

| Long-term

|-
| Steam methane reforming (SMR)

| 65 e

| 74 e,f

| 1.0–3.6 e,i

| 1.0–2.7 a,b,c,d,e

| 1.5–2.6 e

|-
| Advanced gas reforming

| –

| 81–84 e,f

| 0.9–2.9 e

| 1.3–2.1 e

| 1.2–3.4 e,f

|-
| Hydrogen from coal gasification

| 54 e

| 54 (5)

| 2.1–5.5 e,i

| 1.8–3.1 a,b,c,d,e

| 2.4–3.3 e

|-
| Hydrogen from biomass gasification

| 53.6 g

| 40–60 e

| Potential to achieve negative emission e,h

| 4.9 e

| 2.9–5.9 e,f

|}

Source: a CSIRO 2021; b IEA 2020; c IRENA 2019; d Hydrogen Council 2020; e CCC 2018; f [[#BEIS--2021|BEIS 2021]] ; g Ishaq et al. 2021; h Al-Mahtani et al. 2021; i IEA 2019.

\* USD per GBP exchange rate: 0.72 (August 2021); LHV: Lower Heating Values; Long-term refers to 2040 and 2050 according to different references.

'''Table 6.8 | Efficiency and cost characteristics of electrolysis technologies for hydrogen production.'''

{| class="wikitable"
|-
| rowspan="2"| Technology

| colspan="2"| LHV efficiency (%)

| colspan="2"| CAPEX (USD kW e –1 )

| colspan="2"| Cost estimates *,† (USD (kgH 2 ) –1 )

|-
| Current

| Long-term b,e,f,h

| Current g

| Long-term g

| Current

| Long-term

|-
| Alkaline Electrolysers

| 58–77 a,b,e,f,h

| 70–82

| 500–1400

| 200–700

| 2.3–6.9 a,b,c,e

| 0.9–3.9 c,e

|-
| Polymer electrolyte membrane (PEM)

| 54–72 a,b,e,f,h

| 67–82

| 1100–1800

| 200–900

| 3.5–9.3 a,d,e,f

| 2.2–7.2 e,f

|-
| Solid oxide electrolyser cell (SOEC)

| 74–81 b,f,h

| 77–92

| 2800–5600

| 500–1000

| 4.2 e

| 2.6–3.6 e

|}

Source: a CSIRO 2021; b IEA 2020; c IRENA 2019; d Hydrogen Council 2020; e CCC 2018; f [[#BEIS--2021|BEIS 2021]] ; g IEA 2019; h [[#Christensen--2020|Christensen 2020]] .

\* USD per GBP exchange rate: 0.72 (August 2021); † The cost of hydrogen production from electrolysers is highly dependent on the technology, source of electricity, and operating hours, and some values provided are based on the assumptions made in the references.

'''Hydrogen energy carriers.''' Hydrogen can be both an energy carrier itself, be converted further into other energy carriers (such as synthetic fuels) and be a means of transporting other sources of energy. For example, hydrogen could be transported in its native gaseous form or liquefied. Hydrogen can also be combined with carbon and transported as a synthetic hydrocarbons ( [[#Gumber--2018|Gumber and Gurumoorthy 2018]] ) ( [[#IRENA--2019d|IRENA 2019d]] ) as well as be transported via liquid organic hydrogen carriers (LOHCs) or ammonia ( [[#IRENA--2019d|IRENA 2019d]] ). For synthetic hydrocarbons such as methane or methanol to be considered zero carbon, the CO 2 used to produce them would need to come from the atmosphere either directly through DACCS or indirectly through BECCS ( [[#IRENA--2019b|IRENA 2019b]] ). LOHCs are organic substances in liquid or semi-solid states, which store hydrogen based on reversible catalytic hydrogenation and de-hydrogenation of carbon double bounds ( [[#Niermann--2019|Niermann et al. 2019]] ; [[#Rao--2020|Rao and Yoon 2020]] ). Hydrogen produced from electrolysis could also be seen as an electricity energy carrier. This is an example of the PtX processes ( [[#6.4.4|Section 6.4.4]] ), entailing the conversion of electricity to other energy carriers for subsequent use.

Ammonia is a promising cost-effective hydrogen carrier ( [[#Creutzig--2019|Creutzig et al. 2019]] ). Onsite generation of hydrogen for the production of ammonia already occurs today, and the ammonia (NH 3 ) could be subsequently ‘cracked’ (with a 15–25% energy loss) to reproduce hydrogen ( [[#Hansgen--2010|Hansgen et al. 2010]] ; [[#Montoya--2015|Montoya et al. 2015]] ; [[#Bell--2016|Bell and Torrente-Murciano 2016]] ). Because the energy density of ammonia is 38% higher than liquid hydrogen ( [[#Osman--2018|Osman and Sgouridis 2018]] ), it is potentially a suitable energy carrier for long-distance transport and storage ( [[#Salmon--2021|Salmon et al. 2021]] ). Moreover, ammonia is more easily condensable (liquefied at 0.8 MPa, 20°C), which provides economically viable hydrogen storage and supply systems. Ammonia production and transport are also established industrial processes (about 180 MMT yr –1 ) ( [[#Valera-Medina--2017|Valera-Medina et al. 2017]] ), and hence ammonia is considered to be a scalable and cost-effective hydrogen-based energy carrier. At present, most ammonia is used in fertilisers (about 80%), followed by many industrial processes, such as the manufacturing of mining explosives and petrochemicals ( [[#Jiao--2018|Jiao and Xu 2018]] ). In contrast to hydrogen, ammonia can be used directly as a fuel without any phase change for internal combustion engines, gas turbines, and industrial furnaces ( [[#Kobayashi--2019|Kobayashi et al. 2019]] ). Ammonia can also be used in low- and high-temperature fuel cells ( [[#Lan--2014|Lan and Tao 2014]] ), whereby both electricity and hydrogen can be produced without any nitrogen oxide (NO x ) emissions. Furthermore, ammonia provides the flexibility to be dehydrogenated for hydrogen-use purposes. Ammonia is considered a carbon-free sustainable fuel for electricity generation, since in a complete combustion, only water and nitrogen are produced ( [[#Valera-Medina--2017|Valera-Medina et al. 2017]] ). Like hydrogen, ammonia could facilitate management of VRE, due to its cost-effective grid-scale energy storage capabilities. In this regard, production of ammonia via hydrogen from low- or zero-carbon generation technologies along with ammonia energy recovery technologies ( [[#Afif--2016|Afif et al. 2016]] ) could play a major role in forming a hydrogen and/or ammonia economy to support decarbonisation. However, there are serious concerns regarding the ability to safely use ammonia for all these purposes, given its toxicity – whereas hydrogen is not considered toxic.

In general, challenges around hydrogen-based energy carriers – including safety issues around flammability, toxicity, storage, and consumption – require new devices and techniques to facilitate their large-scale use. Relatively high capital costs and large electricity requirements are also challenges for technologies that produce hydrogen energy carriers. Yet, these energy carriers could become economically viable through the availability of low-cost electricity generation and excess of renewable energy production ( [[#Daiyan--2020|Daiyan et al. 2020]] ). A key challenge in use of ammonia is related to the significant amount of NO x emissions, which is released from nitrogen and oxygen combustion, and unburned ammonia. Both have substantial air pollution risks, which can result in lung and other injuries, and can reduce visibility ( [[#EPA--2001|EPA 2001]] ). Due to the low flammability of hydrogen energy carriers such as liquefied hydrogen ( [[#Nilsson--2016|Nilsson et al. 2016]] ) and ammonia (Li et al. 2018), a stable combustion ( [[#Lamas--2019|Lamas and Rodriguez 2019]] ; [[#Zengel--2020|Zengel et al. 2020]] ) in the existing gas turbines is not currently feasible. In recent developments, however, the proportion of hydrogen in gas turbines has been successfully increased, and further development of gas turbines may enable them to operate on 100% hydrogen by 2030 ( [[#Pflug--2019|Pflug et al. 2019]] ).

'''Long-distance hydrogen transport.''' Hydrogen can allow regional integration and better utilisation of low- or zero-carbon energy sources (Boxes 6.9 and 6.10). Hydrogen produced from renewables or other low-carbon sources in one location could be transported for use elsewhere ( [[#Philibert--2017|Philibert 2017]] ; [[#Ameli--2020|Ameli et al. 2020]] ). Depending on the distance to the user and specific energy carrier utilised (e.g., gaseous hydrogen or LOHC), various hydrogen transport infrastructures, distribution systems, and storage facilities would be required ( [[#Hansen--2020|Hansen 2020]] ; [[#Schönauer--2021|Schönauer and Glanz 2021]] ) (Figure 6.17).

<div id="_idContainer052" class="Basic-Text-Frame"></div>

[[File:72ef3135851f13d316c17d144338f3e3 IPCC_AR6_WGIII_Figure_6_17.png]]

'''Figure 6.17 | Hydroge''' '''n value chain.''' '''Hydrogen can be produced by various means and input and fuel sources.''' These processes have different emissions implications. Hydrogen can be transported by various means and in various forms, and it can be stored in bulk for longer-term use. It also has multiple potential end uses. CHP: Combined heat and power.

Hydrogen can be liquefied and transported at volume over the ocean without pressurisation. This requires a temperature of –253°C and is therefore energy-intensive and costly ( [[#Niermann--2021|Niermann et al. 2021]] ). Once it reaches its destination, the hydrogen needs to be re-gasified, adding further cost. A demonstration project is under development exporting liquid hydrogen from Australia to Japan ( [[#Yamashita--2019|Yamashita et al. 2019]] ). Hydrogen could also be transported as ammonia by ocean in liquid form. Ammonia is advantageous because it is easier to store than hydrogen ( [[#Zamfirescu--2008|Zamfirescu and Dincer 2008]] ; [[#Soloveichik--2016|Soloveichik 2016]] ; [[#Nam--2018|Nam et al. 2018]] ). Liquid ammonia requires temperatures below –33°C and is therefore more straightforward and less costly to transport than liquefied hydrogen and even liquefied natural gas ( [[#Singh--2018|Singh and Sahu 2018]] ). A project exporting ammonia from Saudi Arabia to Japan is under consideration (Nagashima 2018). LOHCs could also be used to transport hydrogen at ambient temperature and pressure. This advantageous property of LOHCs makes them similar to oil products, meaning they can be transported in existing oil infrastructure including oil tankers and tanks (IEA 2019; [[#Niermann--2019|Niermann et al. 2019]] ). A project is under development to export hydrogen from Brunei to Japan using LOHCs ( [[#Kurosaki--2018|Kurosaki 2018]] ).

'''Intra-regional hydrogen transportation.''' Within a country or region, hydrogen would likely be pressurised and delivered as compressed gas. About three times as much compressed hydrogen by volume is required to supply the same amount of energy as natural gas. Security of supply is therefore more challenging in hydrogen networks than in natural gas networks. Storing hydrogen in pipelines (linepack) would be important to maintaining security of supply ( [[#Ameli--2017|Ameli et al. 2017]] , 2019). Due to the physics of hydrogen, in most cases exiting gas infrastructure would need to be upgraded to transport hydrogen. Transporting hydrogen in medium- or high-pressure networks most often would require reinforcements in compressor stations and pipeline construction routes ( [[#Dohi--2016|Dohi et al. 2016]] ). There are several recent examples of efforts to transport hydrogen by pipeline. For example, in the Iron Mains Replacement Programme in the UK, the existing low-pressure gas distribution pipes are being converted from iron to plastic (Committee on Climate Change 2018). In the Netherlands, an existing low-pressure 12 km natural gas pipeline has been used for transporting hydrogen ( [[#Dohi--2016|Dohi et al. 2016]] ).

To bypass gas infrastructure in transporting hydrogen, methane can be transported using the existing gas infrastructure, while hydrogen can be produced close to the demand centres. This approach will only make sense if the methane is produced in a manner that captures carbon from the atmosphere and/or if CCS is used when the methane is used to produce hydrogen.

'''Bulk hydrogen storage''' . Currently, hydrogen is stored in bulk in chemical processes such as metal and chemical hydrides as well as in geologic caverns ( [[#Andersson--2019|Andersson and Grönkvist 2019]] ; [[#Caglayan--2019|Caglayan et al. 2019]] ) (e.g., salt caverns operate in Sweden) ( [[#Elberry--2021|Elberry et al. 2021]] ). There are still many challenges, however, due to salt or hard rock geologies, large size, and minimum pressure requirements of the sites ( [[#IEA--2019c|IEA 2019c]] ). Consequently, alternative carbon-free energy carriers, which store hydrogen, may become more attractive ( [[#Lan--2012|Lan et al. 2012]] ; [[#Kobayashi--2019|Kobayashi et al. 2019]] ).

<div id="6.4.5.2" class="h3-container"></div>

<span id="electricity-transmission"></span>
==== 6.4.5.2 Electricity Transmission ====

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Given the significant geographical variations in the efficiency of renewable resources across different regions and continents, electricity transmission could facilitate cost-effective deployment of renewable generation, enhance resilience and security of supply, and increase operational efficiency ( ''high confidence'' ). The diurnal and seasonal characteristics of different renewable energy sources such as wind, solar, and hydropower can vary significantly by location. Through enhanced electricity transmission infrastructure, more wind turbines can be deployed in areas with high wind potential and more solar panels in areas with larger solar irradiation. Increases in electricity transmission and trade can also enhance operational efficiency and reduce or defer the need for investment in peaking plants, storage, or other load management techniques needed to meet security of supply requirements associated with localised use of VRE sources. Increased interconnectivity of large-scale grids also allows the aggregation of ‘smart grid’ solutions such as flexible heating and cooling devices for flexible demand in industrial, commercial, and domestic sectors ( [[#Hakimi--2020|Hakimi et al. 2020]] ) and EVs ( [[#Muratori--2020|Muratori and Mai 2020]] ; Li et al. 2021). In general, interconnection is more cost-optimal for countries that are geographically close to each other and can benefit from the diversity of their energy mixes and usage ( [[#Schlachtberger--2017|Schlachtberger et al. 2017]] ). Such developments are not without price, however, and among other concerns, raise issues surrounding land use, public acceptance, and resource acquisition for materials necessary for renewable developments ( [[#Capellán-Pérez--2017|Capellán-Pérez et al. 2017]] ; [[#Vakulchuk--2020|Vakulchuk et al. 2020]] ).

A number of studies have demonstrated the cost benefits of interconnected grids in a range of geographical settings, including across the USA ( [[#Bloom--2020|Bloom et al. 2020]] ), Europe ( [[#Newbery--2013|Newbery et al. 2013]] ; Cluet et al. 2020), between Australia and parts of Asia ( [[#Halawa--2018|Halawa et al. 2018]] ), and broader global regions, for example between the Middle East and Europe or North Africa and Europe ( [[#Tsoutsos--2015|Tsoutsos et al. 2015]] ). While there is growing interest in interconnection among different regions or continents, a broad range of geopolitical and socio-techno-economic challenges would need to be overcome to support this level of international cooperation and large-scale network expansion ( [[#Bertsch--2017|Bertsch et al. 2017]] ; [[#Palle--2021|Palle 2021]] ).

'''Status of electricity transmission technology.''' Long-distance electricity transmission technologies are already available. High voltage alternating current (HVAC), high-voltage direct current (HVDC), and ultra HVDC (UHVDC) technologies are well-established and widely used for bulk electricity transmission ( [[#Alassi--2019|Alassi et al. 2019]] ). HVDC is used with underground cables or long-distance overhead lines (typically voltages between 100–800 kV) ( [[#Alassi--2019|Alassi et al. 2019]] ) where HVAC is infeasible or not economic. A project development agreement, worth approximately USD17 billion, was signed in January 2021 that would connect 10 GW of PVs in the north of Australia via a 4500 km 3 GW HVDC cable to Singapore, suggesting that this would be cost effective ( [[#Sun%20Cable--2021|Sun Cable 2021]] ). In September 2019, the Changji-Guquan ±1,100 kV UHVDC transmission project built by State Grid Corporation of China was officially completed and put into operation. The transmission line is able to transmit up to 12 GW over 3341 km ( [[#Pei--2020|Pei et al. 2020]] ). This is the UHVDC transmission project with the highest voltage level, the largest transmission capacity, and the longest transmission distance in the world ( [[#Liu--2015|Liu 2015]] ).

Other technologies that could expand the size of transmission corridors and/or improve the operational characteristics include low-frequency AC transmission (LFAC) (Y. [[#Tang--2021|]] [[#Tang--2021|Tang et al. 2021]] ; [[#Xiang--2021|Xiang et al. 2021]] ) and half-wave AC transmission (HWACT) ( [[#Song--2018|Song et al. 2018]] ; [[#Xu--2019|Xu et al. 2019]] ). LFAC is technically feasible, but the circumstances in which it is the best economic choice compared to HVDC or HVAC still needs to be established ( [[#Xiang--2016|Xiang et al. 2016]] ). HWACT is restricted to very long distances, and it has not been demonstrated in practice, so its feasibility is unproven. There are still a number of technological challenges for long-distance transmission networks such as protection systems for DC or hybrid AC-DC networks ( [[#Chaffey--2016|Chaffey 2016]] ; Franck C. et al. 2017), improvement in cabling technology, and including the use of superconductors and nanocomposites ( [[#Ballarino--2016|Ballarino et al. 2016]] ; [[#Doukas--2019|Doukas 2019]] ), which require advanced solutions.

'''Challenges, barriers, and recommendations.''' The main challenge to inter-regional transmission is the absence of appropriate market designs and regulatory and policy frameworks. In addition, there are commercial barriers for further enhancement of cross-border transmission. The differing impacts of cross-border interconnections on costs and revenues for generation companies in different regions could delay the development of these interconnectors. It is not yet clear how the investment cost of interconnections should be allocated and recovered, although there is growing support for allocating costs in accordance with the benefits delivered to the market participants. Increased cross-border interconnection may also require new business models which provide incentives for investment and efficient operation, manage risks and uncertainties, and facilitate coordinated planning and governance ( [[#Poudineh--2017|Poudineh and Rubino 2017]] ).

Optimising the design and operation of the interconnected transmission system, both onshore and offshore grids, also requires more integrated economic and reliability approaches ( [[#Moreno--2012|Moreno et al. 2012]] ) to ensure the optimal balance between the economics and the provision of system security while maximising the benefits of smart network technologies.

A wide range of factors, including generation profiles, demand profiles circuit losses, reliability characteristics, and maintenance, as well as the uncertainties around them will need to be considered in designing and operating long-distance transmission systems if they are to be widely deployed ( [[#Djapic--2008|Djapic et al. 2008]] ; [[#Du--2009|Du 2009]] ; [[#De%20Sa--2011|De Sa and Al Zubaidy 2011]] ; [[#E3G--2021|E3G 2021]] ). Public support for extending transmission systems will also be crucial, and studies indicate that such support is frequently low ( [[#Vince--2010|Vince 2010]] ; [[#Perlaviciute--2018|Perlaviciute et al. 2018]] ).

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=== 6.4.6 Demand-side Mitigation Options from an Energy Systems Perspective ===

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Demand-side measures are fundamental to an integrated approach to low-carbon energy systems ( ''high confidence'' ). Mitigation options, such as wind parks, CCS, and nuclear power plants, may not be implemented when actors oppose these options. Further, end users, including consumers, governments, businesses and industry, would need to adopt the relevant options, and then use these as intended; user adoption can be a key driver to scale up markets for low-carbon technologies. This section discusses which factors shape the likelihood that end users engage in relevant mitigation actions, focusing on consumers; strategies to promote mitigation actions are discussed in [[#6.7.6.1|Section 6.7.6.1]] .

A wide range of actions of end users would reduce carbon emissions in energy systems ( [[#Abrahamse--2007|Abrahamse et al. 2007]] ; [[#Dietz--2013|Dietz 2013]] ; [[#Hackmann--2014|Hackmann et al. 2014]] ; [[#Creutzig--2018|Creutzig et al. 2018]] ; [[#Grubler--2018|Grubler et al. 2018]] ), including:

• use of low-carbon energy sources and carriers. Actors can produce and use their own renewable energy (e.g., install solar PV, solar water heaters, heat pumps), buy shares in a renewable energy project (e.g., wind shares), or select a renewable energy provider.

'''•''' adoption of technologies that support flexibility in energy use and sector coupling, thereby providing flexibility services by balancing demand and renewable energy supply. This would reduce the need to use fossil fuels to meet demand when renewable energy production is low and put less pressure on deployment of low-emission energy supply systems. Examples are technologies to store energy (e.g., batteries and EVs) or that automatically shift appliances on or off (e.g., fridges, washing machines).

'''•''' adoption of energy-efficient appliances and systems and increase of resource efficiency of end uses so that less energy is required to provide the same service. Examples are insulating buildings, and passive or energy-positive buildings.

'''•''' change behaviour to reduce overall energy demand or to match energy demand to available energy supplies. Examples include adjusting indoor temperature settings, reducing showering time, reducing car use or flying, and operating appliances when renewable energy production is high.

• purchase and use products and services that are associated with low GHG emissions during their production (e.g., reduce dairy and meat consumption) or for transporting products (e.g., local products). Also, end users can engage in behaviour supporting a circular economy, by reducing waste (e.g., of food), sharing products (e.g., cars, equipment), and refurbishing products (e.g., repair rather than buying new products) so that fewer new products are used.

Various factors shape whether such mitigation actions are feasible and considered by end users, including contextual factors, individual abilities, and motivational factors. Mitigation actions can be facilitated and encouraged by targeting relevant barriers and enablers ( [[#6.7.6.1|Section 6.7.6.1]] ).

Contextual factors, such as physical and climate conditions, infrastructure, available technology, regulations, institutions, culture, and financial conditions define the costs and benefits of mitigation options that enable or inhibit their adoption ( ''high confidence'' ). Geographic location and climate factors may make some technologies, such as solar PV or solar water heaters, impractical ( [[#Chang--2009|Chang et al. 2009]] ). Culture can inhibit efficient use of home heating or PV ( [[#Sovacool--2020|Sovacool and Griffiths 2020]] ), low-carbon diets ( [[#Dubois--2019|Dubois et al. 2019]] ), and advanced fuel choices ( [[#Van%20Der%20Kroon--2013|Van Der Kroon et al. 2013]] ). Also, favourable financial conditions promote the uptake of PV ( [[#Wolske--2018|Wolske and Stern 2018]] ), good facilities increase recycling ( [[#Geiger--2019|Geiger et al. 2019]] ), and vegetarian meal sales increase when more vegetarian options are offered.

Mitigation actions are more likely when individuals feel capable to adopt them ( [[#Pisano--2017|Pisano and Lubell 2017]] ; [[#Geiger--2019|Geiger et al. 2019]] ), which may depend on income and knowledge. Low-income groups may lack resources to invest in refurbishments and energy-efficient technology with high upfront costs ( [[#Chang--2009|Chang et al. 2009]] ; [[#Andrews-Speed--2016|Andrews-Speed and Ma 2016]] ; [[#Wolske--2018|Wolske and Stern 2018]] ). Yet, higher-income groups can afford more carbon-intensive lifestyles ( [[#Golley--2012|Golley and Meng 2012]] ; [[#Frederiks--2015|Frederiks et al. 2015]] ; [[#Wiedenhofer--2017|Wiedenhofer et al. 2017]] ; [[#Namazkhan--2019|Namazkhan et al. 2019]] ; Santillán Vera and de la Vega Navarro 2019; [[#Mi--2020|Mi et al. 2020]] ). Knowledge of the causes and consequences of climate change and of ways to reduce GHG emissions is not always accurate, but lack of knowledge is not a main barrier to mitigation actions ( [[#Boudet--2019|Boudet 2019]] ).

Motivation to engage in mitigation action, reflecting individuals’ reasons for actions, depends on general goals that people strive for in their life (i.e., values). People who strongly value protecting the environment and other people are more likely to consider climate impacts and to engage in a wide range of mitigation actions than those who strongly value individual consequences of actions, such as pleasure and money ( [[#Taylor--2014|Taylor et al. 2014]] ; [[#Steg--2016|Steg 2016]] ). Values affect which types of costs and benefits people consider and prioritise when making choices, including individual, affective, social, and environmental costs and benefits ( [[#Gowdy--2008|Gowdy 2008]] ; [[#Steg--2016|Steg 2016]] ).

First, people are more likely to engage in mitigation behaviour (i.e., energy savings, energy efficiency, resource efficiency in buildings, low-carbon energy generation) when they believe such behaviour has more individual benefits than costs ( [[#Harland--1999|Harland et al. 1999]] ; [[#Steg--2009|Steg and Vlek 2009]] ; [[#Kastner--2015|Kastner and Stern 2015]] ; [[#Korcaj--2015|Korcaj et al. 2015]] ; [[#Kardooni--2016|Kardooni et al. 2016]] ; [[#Kastner--2016|Kastner and Matthies 2016]] ; [[#Wolske--2017|Wolske et al. 2017]] ), including financial benefits, convenience, comfort, autonomy, and independence in energy supply ( [[#Wolske--2018|Wolske and Stern 2018]] ). Yet, financial consequences seem less important for decisions to invest in energy-efficiency and renewable energy production than people indicate (Zhao et al. 2012).

Second, people are less likely to engage in mitigation behaviours that are unpleasurable or inconvenient ( [[#Steg--2016|Steg 2016]] ), and more likely to do so when they expect to derive positive feelings from such actions ( [[#Smith--1994|Smith et al. 1994]] ; [[#Pelletier--1998|Pelletier et al. 1998]] ; [[#Steg--2005|Steg 2005]] ; [[#Carrus--2008|Carrus et al. 2008]] ; [[#Brosch--2014|Brosch et al. 2014]] ; [[#Taufik--2016|Taufik et al. 2016]] ). Positive feelings may be elicited when behaviour is pleasurable, but also when it is perceived as meaningful ( [[#Bolderdijk--2013|Bolderdijk et al. 2013]] ; [[#Taufik--2015|Taufik et al. 2015]] ).

Third, social costs and benefits can affect climate action ( [[#Farrow--2017|Farrow et al. 2017]] ), although people do not always recognise this ( [[#Nolan--2008|Nolan et al. 2008]] ; [[#Noppers--2014|Noppers et al. 2014]] ). People engage more in mitigation actions when they think others expect them to do so and when others act as well ( [[#Harland--1999|Harland et al. 1999]] ; [[#Nolan--2008|Nolan et al. 2008]] ; [[#Rai--2016|Rai et al. 2016]] ). Being part of a group that advocates mitigation encourages such actions ( [[#Biddau--2016|Biddau et al. 2016]] ; [[#Fielding--2016|Fielding and Hornsey 2016]] ; [[#Jans--2018|Jans et al. 2018]] ). Talking with peers can reduce uncertainties and confirm benefits about adoption of renewable energy technology ( [[#Palm--2017|Palm 2017]] ), and peers can provide social support ( [[#Wolske--2017|Wolske et al. 2017]] ). People may engage in mitigation actions when they think this would signal something positive about them ( [[#Milinski--2006|Milinski et al. 2006]] ; [[#Griskevicius--2010|Griskevicius et al. 2010]] ; [[#Noppers--2014|Noppers et al. 2014]] ; [[#Kastner--2015|Kastner and Stern 2015]] ). Social influence can also originate from political and business leaders ( [[#Bouman--2019|Bouman and Steg 2019]] ); GHG emissions are lower when legislators have strong environmental records ( [[#Jensen--2011|Jensen and Spoon 2011]] ; [[#Dietz--2015|Dietz et al. 2015]] ).

Fourth, mitigation actions, including saving energy and hot water, limiting meat consumption, and investing in energy efficiency, resource efficiency in buildings, and renewable energy generation are more likely when people care more strongly about others and the environment ( [[#Steg--2015|Steg et al. 2015]] ; Van Der Werff and Steg 2015; [[#Wolske--2017|Wolske et al. 2017]] ). People across the world generally strongly value the environment ( [[#Steg--2016|Steg 2016]] ; [[#Bouman--2019|Bouman and Steg 2019]] ), suggesting that they are motivated to mitigate climate change. The more individuals are aware of the climate impact of their behaviour, the more they think their actions can help reduce such impacts, which strengthens their moral norms to act accordingly, and promotes mitigation actions ( [[#Steg--2010|Steg and de Groot 2010]] ; [[#Jakovcevic--2013|Jakovcevic and Steg 2013]] ; [[#Chen--2015|Chen 2015]] ; [[#Wolske--2017|Wolske et al. 2017]] ).

Initial mitigation actions can encourage engagement in other mitigation actions when people experience that such actions are easy and effective ( [[#Lauren--2016|Lauren et al. 2016]] ), and when initial actions make them realise they are a pro-environmental person, motivating them to engage in more mitigation actions so as to be consistent (van der Werff et al. 2014; [[#Lacasse--2015|Lacasse 2015]] , 2016; Peters et al. 2018). This implies it would be important to create conditions that make it likely that initial mitigation actions motivate further actions.

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=== 6.4.7 Summary of Mitigation Options ===

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Designing feasible, desirable, and cost-effective energy sector mitigation strategies requires comparison between the different mitigation options. One such metric is the cost of delivering one unit of energy, for example, the levelised cost, or USD MWh –1 , of electricity produced from different sources. Levelised costs of electricity (LCOE) are useful because they normalise the costs per unit of service provided. While useful in characterising options in broad strokes, it is important to acknowledge and understand several caveats associated with these metrics, particularly when applied globally. They may be constructed with different discount rates; they require information on energy input costs for options that require energy inputs (e.g., fossil electricity generation, biofuels); they depend on local resource availability, for example, solar insolation for solar power, wind classes for wind power, and rainfall and streamflow for hydropower; and actual implementation costs may include additional elements, for example, the costs of managing electricity grids heavily dependent on VRE electricity sources. These complicating factors vary across regions, some depend strongly on the policy environment in which mitigation options are deployed, and some depend on how technologies are constructed and operated.

The literature provides multiple LCOE estimates for mitigation options today and in the future (see Table 6.9 for electricity generation options). LCOE ranges for low- and zero-carbon electricity technologies overlap with LCOE’s of fossil generation without CCS. For example, LCOEs for utility solar and wind today and in the future overlap with those of new coal and gas without CCS (IEA WEO 2020; Lazard, 2020; [[#NREL--2021|NREL 2021]] ) (Figure 6.18). Some of the overlap stems from differences in assumptions or regional conditions that apply to all technologies (e.g., variations in assumed discount rates), but the overlap also reflects the fact that low- and zero-carbon electricity generation options are, and will be, less expensive than emitting options in many regions. Future cost projections also illustrate that several technologies are anticipated to experience further cost declines over the coming decades, reinforcing the increasingly competitiveness of low- and zero-carbon electricity. For example, IEA’s LCOEs estimates for offshore wind halve between 2020 and 2040 in several regions (IEA WEO 2020).

'''Table 6.9 | Examples ofcost of mitigation for selected electricity options.''' Results represent variations in mitigation options and displaced fossil generation. LCOEs are illustrative, but consistent with recent estimates. Negative values mean that the mitigation option is cheaper than the displaced option, irrespective of emissions benefits. NGCC: natural gas combined cycle.

{| class="wikitable"
|-
|
| colspan="4"| Baseline

|-
|
| New coal

| Existing coal

| New NGCC

| Existing NGCC

|-
|
| Baseline emissions rate (tCO 2 MWh –1 )

| 0.8

| 0.9

| 0.34

| 0.42

|-
|
| LCOEs (USD2020 kWh –1 )

| 0.065

| 0.041

| 0.044

| 0.028

|-
| Utility scale solar PV (poor resource site)

| 0.100

| USD44 tCO 2 -eq –1

| USD66 tCO 2 -eq –1

| USD165 tCO 2 -eq –1

| USD171 tCO 2 -eq –1

|-
| Utility scale solar PV (good resource site)

| 0.035

| –38 USD tCO 2 -eq –1

| –7 USD tCO 2 -eq –1

| –26 USD tCO 2 -eq –1

| USD17 tCO 2 -eq –1

|}

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[[File:fa944fde7460966113d459de4e16ebeb IPCC_AR6_WGIII_Figure_6_18.png]]

'''Figure 6.18 | Range of LCOE (in USD kWh''' –1 ''') from recent studies for different electricity-generating technologies circa 2020 and in the future between 2020–2040.''' LCOEs are primarily taken from recent studies, because the costs of some technologies are changing rapidly. To make the figure more tractable across the studies, we highlight the data from IEA WEO 2020 STEPS scenario in yellow (IEA 2020), the EIA AEO 2021 in light blue (EIA 2021), NREL ATB 2021 in brown, ( [[#NREL--2021|NREL 2021]] ), and IRENA’s 2020 Renewable Power Generation Costs in dark blue (IRENA 2021). All other studies are shown in light grey markers. Marker shapes identify the regions included in the studies. Studies that included several regions are labelled as global. Only sources that provided LCOEs are included. Ranges for studies frequently reflect variations among regional estimates. Studies that are shown as a mid-point and a solid line represent studies that reported either a median or an average, and that had either a confidence interval or a minimum and a maximum reported. Dashed lines with markers at the end represent the range of values reported in studies that had several point estimates for either different regions or used different assumptions. All estimates were converted to USD2020. The publication year was used if no USD year was provided. Some studies included transmissions costs, and some of the CCS studies included storage and sequestration costs, while others did not. Vertical axis is capped at USD2020 0.30 kWh –1 , but some estimates for hydro, geothermal, natural gas and bioelectricity were higher than 0.30. The grey horizontal band denotes the range of fossil fuel electricity LCOEs circa 2020.

A more direct metric of mitigation options is the cost to reduce one tonne of CO 2 or equivalent GHGs, or USD tCO 2 -eq –1 avoided. In addition to the comparison challenges noted above, this metric must account for the costs and emissions of the emitting options that are being displaced by the low-carbon option. Assumptions about the displaced option can lead to very different mitigation cost estimates (Table 6.9). Despite these challenges, these metrics are useful for identifying broad trends and making broad comparisons, even from the global perspective in this assessment. But local information will always be critical to determine which options are most cost-effective in any specific applications.

The feasibility and desirability of mitigation options extends well beyond the market economic costs of installation and operation ( [[#6.4.1|Section 6.4.1]] ). Figure 6.19 summarises the barriers and enablers for implementing different mitigation options in energy systems. The feasibility of different options can be enhanced by removing barriers and/or strengthening enablers of the implementation of the options. The feasibility of options may differ across context (e.g., region), time (e.g., 2030 versus 2050), scale (e.g., small versus large) and the long-term warming goal (e.g., 1.5°C versus 2°C).

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[[File:86c580c1b7671fb436126467a37fcf26 IPCC_AR6_WGIII_Figure_6_19.png]]

'''Figure 6.19 | Summary of the extent to which different factors would enable or inhibit the deployment of mitigation options in energy systems.''' Blue bars indicate the extent to which the indicator enables the implementation of the option (E) and orange bars indicate the extent to which an indicator is a barrier (B) to the deployment of the option, relative to the maximum possible barriers and enablers assessed. An X signifies that the indicator is not applicable or does not affect the feasibility of the option, while a forward slash indicates that there is no or limited evidence whether the indicator affects the feasibility of the option. The shading indicates the level of confidence, with darker shading signifying higher levels of confidence. Appendix II provides an overview of the factors affecting the feasibility of options and how they differ across context (e.g., region), time (e.g., 2030 versus 2050), and scale (e.g., small versus large), and includes a line of sight on which the assessment is based. The assessment method is explained in Annex II.11.

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