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

== 6.7 Low-carbon Energy System Transitions in the Near and MediumTerm ==

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=== 6.7.1 Low-carbon Energy System Transition Pathways ===

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<span id="energy-system-emissions"></span>
==== 6.7.1.1 Energy System Emissions ====

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Without additional efforts to reduce emissions, it is very unlikely that energy system CO 2 emissions will decrease sufficiently to limit warming to well below 2°C ( ''high confidence'' ). Scenarios assuming improvements in technology but no additional climate policies beyond those in place today provide a benchmark for comparison against energy-related CO 2 emissions in mitigation scenarios (Figure 6.26). Emissions in these reference scenarios increase through 2050 but span a broad range ( [[#Riahi--2017|Riahi et al. 2017]] ; [[#Wei--2018|Wei et al. 2018]] ) (Chapter 3, Figure 3.16). The highest emission levels are about four times current emissions; the lowest are modestly below today’s emissions. Emissions in these scenarios increase in most regions, but they diverge significantly across regions ( [[#Bauer--2017|Bauer et al. 2017]] ). Asia and the Middle East and Africa account for the majority of increased emissions across these scenarios (Figure 6.27). While it is unlikely that there will be no new climate policies in the future, these scenarios nonetheless support the conclusion that the energy sector will not be decarbonised without explicit policy actions to reduce emissions.

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[[File:bd644f276f734f3764f0f1c9d8cccabf IPCC_AR6_WGIII_Figure_6_26.png]]

'''Figure 6.26 | Projected energy sector GHG emissions for t''' '''he 1.''' '''5°C scenarios (without and with overshoot), and likely below 2°C scenarios (without and with delayed policy action) during 2020–2050''' (Source: AR6 Scenarios Database). Boxes indicate 25th and 75th percentiles, while whiskers indicate 5th and 95th percentiles. GHG emissions are inclusive of energy sector CO 2 , CH 4 , N 2 O emissions and 80% of global HFC emissions. Number of model-scenario combinations in AR6 Scenarios Database: limit warming to 1.5°C (&gt;50%) with no or limited overshoot: 77; return warming to 1.5°C (&gt;50%) after a high overshoot: 110; limit warming to 2(C (&gt;67%) with action starting in 2020: 164; limit warming to 2°C (&gt;67%) with NDCs until 2030: 97.

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[[File:1169672c2ef7a99fb856291845acd9e4 IPCC_AR6_WGIII_Figure_6_27.png]]

'''Figure 6.27 | Net regional (R6) CO''' 2 '''emissions from energy across scenarios that limit/return''' '''warming to 1.''' '''5°C (&gt;50%) with no or limited/after a high overshoot, and scenarios that limit warming to 2°C (&gt;67%) with action starting in 2020 or with NDCs until 2030, during 2020–2050''' (Source: AR6 Scenarios Database). Boxes indicate 25th and 75th percentiles, while whiskers indicate 5th and 95th percentiles. Most mitigation scenarios are based on a cost-minimising framework that does not consider historical responsibility or other equity approaches.

Warming cannot be limited to well below 2°C without rapid and deep reductions in energy system GHG emissions ( ''high confidence'' ). Energy sector CO 2 emissions fall by 87–97% (interquartile range) by 2050 in scenarios limiting warming to 1.5°C (&gt;50%) with no or limited overshoot and 60–79% in scenarios limiting warming to 2°C (&gt;67%) with action starting in 2020 (Figure 6.26). Energy sector GHG emissions fall by 85–95% (interquartile range) in scenarios limiting warming to 1.5°C (&gt;50%) with no or limited overshoot, and 62–78% in scenarios limiting warming to 2°C (&gt;67%) with action starting in 2020 (Figure 6.26). In 2030, in scenarios limiting warming to 1.5°C (&gt;50%) with no or limited overshoot, net CO 2 and GHG emissions fall by 35–51% and 38–52% respectively. Key characteristics of emissions pathways – the year of peak emissions, the year when net emissions reach zero, and the pace of emissions reductions – vary widely across countries and regions. These differences arise from differences in economic development, demographics, resource endowments, land use, and potential carbon sinks (Schaeffer, et al. 2020; Schreyer, et al. 2020; van Soest, Heleen et al. 2021) (Figure 6.27, Figure 6.28, Box 6.11). If countries do not move quickly to reduce emissions – if reductions are delayed – a more rapid energy transition will subsequently be required to limit warming to 2°C or lower ( [[#Rogelj--2015a|Rogelj et al. 2015a]] , 2018a; [[#IPCC--2018|IPCC 2018]] ).

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[[File:150d9fa6f503c7136a50795faf14c49b IPCC_AR6_WGIII_Figure_6_28.png]]

'''Figure 6.28 | The timing of net-zero emissions for full economy greenhouse gases (GHGs), energy sector CO''' 2 ''', and electricity sector CO''' 2 '''.''' Boxes indicate 25th and 75th percentiles, centre black line is the median, while whiskers indicate 1.5x the inter-quartile range. The vertical dashed lines represent the median point at which emissions in the scenarios have dropped by 95% (pink) and 97.5% (purple), respectively. Dots represent individual scenarios. The fraction indicates the number of scenarios reaching net-zero by 2100 out of the total sample. Source: AR6 Scenario Database.

The timing of net-zero energy system emissions varies substantially across scenarios. In scenarios limiting warming to 1.5°C (&gt;50%) with no or limited overshoot (2°C (&gt;67%)), the energy system reaches net-zero CO 2 emissions (interquartile range) from 2060 onwards (2080–). (Figure 6.28). However, net emissions reach near-zero more quickly. For example, in scenarios limiting warming to 1.5°C (&gt;50%) with no or limited overshoot (2°C (&gt;67%)) net energy system CO 2 emissions drop by 95% between 2056 and 2075 (2073 and 2093). Net full economy GHG emissions reach zero more slowly than net CO 2 emissions. In some scenarios, net energy system CO 2 and total GHG emissions do not reach zero this century, offset by CDR in other sectors.

The timing of emissions reductions will vary across the different parts of the energy sector (Figure 6.28). To decarbonise most cost-effectively, global net CO 2 emissions from electricity generation will likely reach zero before the rest of the energy sector ( ''medium confidence'' ). In scenarios limiting warming to 1.5°C (&gt;50%) with no or limited overshoot (2°C (&gt;67%)), net electricity sector CO 2 emissions (interquartile range) reach zero globally between 2044 and 2055 (2052 and 2078) (Figure 6.28). It is likely to be less costly to reduce net CO 2 emissions close to or below zero in the electricity sector than in other sectors, because there are relatively more low-emissions options in electricity. Sectors such as long-distance transport, air transport, and process heat are anticipated to face greater challenges to decarbonisation than the electricity sector ( [[#Clark--2014|Clark and Herzog 2014]] ; [[#Rogelj--2015b|Rogelj et al. 2015b]] , 2018b; [[#IPCC--2018|IPCC 2018]] ; [[#Luderer--2018|Luderer et al. 2018]] ).

In addition, there are potential options to remove CO 2 from the atmosphere in the electricity sector, notably BECCS, which would allow electricity sector emissions to drop below zero. Without CDR options, electricity sector emissions may not fall all the way to zero. If CDR is accomplished in other sectors and not in electricity, some fossil fuel plants may still lead to positive net electricity sector CO 2 emissions, even in net-zero economies ( [[#Bistline--2021b|Bistline and Blanford 2021b]] ; [[#Williams--2021a|Williams et al. 2021a]] ).

We lack sufficient understanding to pin down precise dates at which energy system CO 2 emissions in individual countries, regions, or sectors will reach net zero. Net-zero timing is based on many factors that are not known today or are bound up in development of key technologies, such as energy storage, bioenergy, or hydrogen. Some countries have low-carbon resource bases that could support deep emissions reductions, while others do not. Timing is also affected by the availability of CDR options, whether these options are in the energy sector or elsewhere, and the discount rate used to assess strategies ( [[#Bednar--2019|Bednar et al. 2019]] ; [[#Emmerling--2019|Emmerling et al. 2019]] ). Moreover, while many scenarios are designed to minimise global mitigation costs, many other frameworks exist for allocating mitigation effort across countries (van den Berg et al. 2019) (Chapter 4).

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<span id="low-carbon-energy-transition-strategies"></span>
==== 6.7.1.2 Low-carbon Energy Transition Strategies ====

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There are multiple technological routes to reduce energy system emissions ( [[#6.6|Section 6.6]] ). Here we discuss three of these: (i) decarbonising primary energy and electricity generation; (ii) switching to electricity, bioenergy, hydrogen, and other fuels produced from low-carbon sources; and (iii) limiting energy use through improvement of efficiency and conservation. CDR is discussed in [[#6.7.1.3|Section 6.7.1.3]] Fossil fuel transitions are discussed in [[#6.7.4|Section 6.7.4]] .

'''Decarbonising primary energy and electricity generation.''' Limiting warming to well below 2°C requires a rapid and dramatic increase in energy produced from low- or zero-carbon sources ( ''high confidence'' ). Low- and zero-carbon technologies produce 74–82% (interquartile range) of primary energy in 2050 in scenarios limiting warming to 1.5°C (&gt;50%) with no or limited overshoot and 55–68% in scenarios limiting warming to 2°C (&gt;67%) (Figure 6.29). The share of low-carbon technologies in global primary energy supply today is below 20% (Chapter 3, [[#6.3|Section 6.3]] , and Figure 6.29). The percentage of low- and zero-carbon energy will depend in part on the evolution of energy demand – the more that energy demand grows, the more energy from low- and zero-carbon sources will be needed, and the higher the percentage of total primary energy these sources will represent.

Low- and zero-carbon sources produce 97–99% of global electricity in 2050 in scenarios limiting warming to 1.5°C (&gt;50%) with no or limited overshoot and 93–97% in scenarios limiting warming to 2°C (&gt;67%) (Figure 6.29) ( ''medium confidence'' ). Decarbonising electricity generation, in tandem with increasing use of electricity (see below), is an essential near-term strategy for limiting warming. The increase in low- and zero-carbon electricity will occur while electricity demand grows substantially. Studies have projected that global electricity demand will roughly double by 2050 and quadruple to quintuple by 2100 irrespective of efforts to reduce emissions ( [[#Bauer--2017|Bauer et al. 2017]] ; [[#Luderer--2017|Luderer et al. 2017]] ; [[#IEA--2019a|IEA 2019a]] ).

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[[File:2ec8c617e648d43aa252ea364b2b481c IPCC_AR6_WGIII_Figure_6_29.png]]

'''Figure 6.29 | Reductions in CO''' 2 '''emissions relative to 2020 levels for scenarios that limit/return warmin''' '''g to 1.''' '''5°C (&gt;50%) with no or limited/after a high, overshoot, and scenarios that limit warming to 2°C (&gt;67%), with action starting in 2020 or NDCs until 2030, during 2030–2050.''' Boxes indicate 25th and 75th percentiles while whiskers indicate 5th and 95th percentiles. Source: AR6 Scenarios Database '''.'''

Renewable energy, especially generation from solar and wind, is likely to have an important role in many low-carbon electricity systems. The contributions of wind and solar electricity will depend on their levelised costs relative to other options, integration costs, system value, and the ability to integrate variable resources into the grid ( [[#6.6|Section 6.6]] ). Electric sector technology mixes will vary by region but will typically include additional resources such as hydropower, nuclear power, fossil generation with CCS, energy storage resources, and geothermal energy, among others. Contributions of different options vary widely across scenarios based on different assumptions about these factors (Figure 6.30).

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[[File:2a92ad8ea229d7a104ddb9f88937f69f IPCC_AR6_WGIII_Figure_6_30.png]]

'''Figure 6.30 | Shares of low-carbon energy (all sources except unabated fossil fuels) and bioenergy (including both traditional and commercial biomass) in total primary energy, and solar+wind, CCS and nuclear in electricity for scenarios that limit/return warming to''' '''1.''' '''5°C (&gt;50%) with no or limited/after a high, overshoot, and scenarios that limit warming to 2°C (&gt;67%), with action starting in 2020 or NDCs until 2030, during 2030–2050''' (Source: AR6 Scenarios Database). Boxes indicate 25th and 75th percentiles while whiskers indicate 5th and 95th percentiles.

Nonetheless, it is likely that wind and solar will dominate low-carbon generation and capacity growth over the next couple of decades due to supporting policies in many countries, and due to their significant roles in early electric sector decarbonisation, alongside reductions in coal generation ( [[#Bistline--2021b|Bistline and Blanford 2021b]] ; [[#Pan--2021|Pan et al. 2021]] ). Clean firm technologies play important roles in providing flexibility and on-demand generation for longer durations, though deployment of these technologies is typically associated with deeper decarbonisation levels (e.g., beyond 70–80% reductions), which are likely to be more important after 2030 in many regions, and with more limited CDR deployment ( [[#Baik--2021|Baik et al. 2021]] ; [[#Bistline--2021a|Bistline and Blanford 2021a]] ; [[#Williams--2021a|Williams et al. 2021a]] ).

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<span id="box-6.11-illustrative-low-carbon-energy-system-transitions"></span>
=== Box 6.11 | Illustrative Low-carbon Energy System Transitions ===

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There are multiple possible strategies to transform the energy system to reach net-zero CO 2 emissions and to limit warming to 2°C (&gt;67%) or lower. All pathways rely on the strategies for net-zero CO 2 energy systems highlighted in [[#6.6.2|Section 6.6.2]] , but they vary in the emphasis that they put on different aspects of these strategies and the pace at which they approach net-zero emissions. The pathway that any country or region might follow will depend on a wide variety of factors ( [[#6.6.4|Section 6.6.4]] ), including, for example, resource endowments, trade and integration with other countries and regions, carbon sequestration potential, public acceptability of various technologies, climate, the nature of domestic industries, the degree of urbanisation, and the relationship with other societal priorities such as energy access, energy security, air pollution, and economic competitiveness. The Illustrative Mitigation Pathways presented in this box demonstrate four distinct strategies for energy system transformations and how each plays out for a different region, aligned with global strategies that would limit warming to 2.0°C (&gt;67%) or to 1.5°C (&gt;50%). Each pathway represents a very different vision of a net-zero energy system. Yet, all these pathways share the common characteristic of a dramatic system-wide transformation over the coming decades.

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[[File:a447ebe15e6bb3829482689cf40250db IPCC_AR6_WGIII_Box_6_11_Figure_1.png]]

'''Box 6.11, Figure 1 | Illustrative Mitigation Pathway 2.0-Neg: Latin America &amp; Caribbean (LAM) in a scenario that limits warming to 2°C (&gt;67%) (LAM net-zero economy 2040–2045, net-zero energy system 2045–2050).''' Supply-side focus with growing dependency on carbon dioxide removal and agriculture, forestry and other land-use (AFOLU), thus achieves net-zero CO 2 relatively early.

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[[File:39e5c3dc51e7db58657bd74842372431 IPCC_AR6_WGIII_Box_6_11_Figure_2.png]]
'''Box 6.11, Figure 2 | Illustrative Mitigation Pathway 1.''' '''5-Renewables: Africa (AF) in a scenario that limts warming to 1.5°C (&gt;50%) (AF net-zero economy, 2055–2060, AF net-zero energy system 2055–2060).''' Rapid expansion of non-biomass renewables, high electrification, and a fossil fuel phase-out.

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[[File:6cbcfefdce0ab46d5cc843d9b0a909b4 IPCC_AR6_WGIII_Box_6_11_Figure_3.png]]

'''Box 6.11, Figure 3 | Illustrative Mitigation Pathway 1.5-Low Demand: Developed Countries (DEV) in a scenario that limits warming to 1.5°C (&gt;50%) (DEV net-zero economy, 2055–2060, net-zero energy system 2075–2080).''' Major reduction of energy demand, high electrification, and gradual fossil fuel phase-out.

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[[File:4b02c94c8dcb84edba0195cf66e605a7 IPCC_AR6_WGIII_Box_6_11_Figure_4.png]]

'''Box 6.11, Figure 4 | Illustrative Mitigation Pathway 1.5-Shifting Pathways:''' Asia and Pacific '''(''' '''APC) in a scenario that limits warming to 1.5°C (&gt;50%) (APC net-zero economy, 2075–2080, net-zero energy system 2090–2095).''' Renewables, high electrification, fossil fuel phase-out and low agriculture, forestry and other land-use (AFOLU) emissions. Reaches net-zero CO 2 relatively late.

'''Box 6.11, Table 1 | Summary of selected Illustrative Mitigation Pathways energy system characteristics in 2050 for the chosen regions.'''

{| class="wikitable"
|-
|
| Energy sector CO 2 Reduction 2020–2050

| colspan="2"| Energy intensity

| colspan="2"| Variable renewable electricity generation

| colspan="2"| Low-carbon electricity capacity additions

| CO 2 removal BECCS, AFOLU, Total

| colspan="2"| GDP per capita

| colspan="3"| Year net-zero CO 2 emissions

|-
|
| %

| colspan="2"| MJ/PPP USD2010

| colspan="2"| EJ yr –1 (%)

| colspan="2"| GW yr –1

| GtCO 2 yr –1

| colspan="2"| PPP USD2010 per person

| rowspan="2"| Full economy

| rowspan="2"| Energy sector

| rowspan="2"| Electricity

|-
|
| Region

| 2050

| 2020

| 2050

| 2020

| 2050

| 2020

| 2050

| 2050

| 2020

| 2050

|-
| IMP-Neg

| LAM

| 124

| 3

| 2.1

| 0.5 (9)

| 7.7 (53)

| 15.4

| 21.5

| 1.1, 0.2, 1.9

| 12,952

| 24,860

| 2040–2045

| 2045–2050

| 2025– 2030

|-
| IMP-Ren

| AF

| 85

| 7.6

| 1.9

| 0.1 (5)

| 18 (84)

| 5

| 217

| 0.1, 0, 0.1

| 2965

| 8521

| 2055–2060

| 2055–2060

| 2025– 2030

|-
| IMP-LD

| DEV

| 92

| 3.1

| 0.9

| 4.6 (13)

| 37 (72)

| 52

| 188

| 0, 0.6, 0.6

| 42,945

| 61,291

| 2055–2060

| 2075–2080

| 2045– 2050

|-
| IMP-SP

| APC

| 76

| 3.8

| 1.1

| 3 (7)

| 91 (79)

| 123

| 603

| 0.1, 0.4, 0.4

| 10,514

| 37,180

| 2075–2080

| 2085–2090

| 2085– 2090

|}

'''Switching to low-carbon energy carriers.''' Switching to energy carriers produced from low-carbon sources will be an important strategy for energy sector decarbonisation. Accelerated electrification of end uses such as light duty transport, space heating, and cooking is a critical near-term mitigation strategy ( [[#Sugiyama--2012|Sugiyama 2012]] ; [[#Zou--2015|Zou et al. 2015]] ; [[#Rockström--2017|Rockström et al. 2017]] ; [[#IEA--2019f|IEA 2019f]] ; [[#Waisman--2019|Waisman et al. 2019]] ; B. [[#Tang--2021|]] [[#Tang--2021|Tang et al. 2021]] ). Electricity supplies 48–58% (interquartile range) of the global final energy demand by 2050 in scenarios limiting warming to 1.5°C (&gt;50%) with no or limited overshoot and 36–47% in scenarios limiting warming to 2°C (&gt;67%) (Figure 6.29). Globally, the current level of electrification is about 20%.

Indirect electrification encompasses the use of electricity to produce hydrogen and synthetic fuels (efuels or power fuels). The extent of indirect electrification of final energy will depend on resource endowments and other regionally specific circumstances. Although indirect electrification is less efficient compared to direct electrification, it allows low-carbon fuels to be imported from regions with abundant low-carbon electricity generation resources (Fasihi and Bogdanov 2016; [[#Lehtveer--2019|Lehtveer et al. 2019]] ; [[#Fasihi--2020|Fasihi and Breyer 2020]] ) (Box 6.10 on regional integration).

While electrifying end uses is a key decarbonisation strategy, some end uses such as long-distance transport (freight, aviation, and shipping) and energy-intensive industries will be harder to electrify. For these sectors, alternative fuels or energy carriers such as biofuels, hydrogen, ammonia or synthetic methane, may be needed ( [[#6.6|Section 6.6]] and Box 6.9). Most scenarios find that hydrogen consumption will grow gradually, becoming more valuable when the energy system has become predominantly low-carbon (Figure 6.31).

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[[File:84e5f2f3c962ca99e4cac0ee9f526fb8 IPCC_AR6_WGIII_Figure_6_31.png]]

'''Figure 6.31 | Shares of electricity and hydrogen in final energy in scenarios that limit/return''' '''warming to 1.''' '''5°C (&gt;50%) with no or limited/after a high, overshoot, and scenarios that limit warming to 2°C (&gt;67%), with action starting in 2020 or NDCs until 2030, during 2030–2050''' (Source: AR6 Scenarios Database). Boxes indicate 25th and 75th percentiles while whiskers indicate 5th and 95th percentiles.

'''Reducing energy demand.''' Energy service demand is expected to continue to increase with growth of the economy, but there is great uncertainty about how much it will increase ( [[#Bauer--2017|Bauer et al. 2017]] ; [[#Riahi--2017|Riahi et al. 2017]] ; [[#Yu--2018|Yu et al. 2018]] ). Given the need to produce low-carbon energy, the scale of energy demand is a critical determinant of the mitigation challenge ( [[#Riahi--2012|Riahi et al. 2012]] ). Higher energy demand calls for more low-carbon energy and increases the challenge; lower energy demand reduces the need for low-carbon sources and therefore can ease a low-carbon transition. Recent studies have shown that tempering the growth of energy demand, while ensuring services and needs are still satisfied, can materially affect the need for technological CDR ( [[#6.7.1.3|Section 6.7.1.3]] ) ( [[#Grubler--2018|Grubler et al. 2018]] ; [[#van%20Vuuren--2018|van Vuuren et al. 2018]] ). Two of the Illustrative Mitigation Pathways (IMP-SP, IMP-LD) feature substantially lower final energy demand across buildings, transport, and industry than most other pathways in the literature. In some cases, energy demand levels are lower in 2050 (and later) than in 2019. These lower demands result in less reliance on bioenergy and a more limited role for CDR (Figure 3.18).

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<span id="technology-options-to-offset-residual-emissions"></span>
==== 6.7.1.3 Technology Options to Offset Residual Emissions ====

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CDR technologies can offset emissions from sectors that are difficult to decarbonise ( [[#6.6|Section 6.6]] ), altering the timeline and character of energy sector transitions. A number of studies suggest that CDR is no longer a choice, but rather a necessity to limit warming to 1.5°C ( [[#Rogelj--2015a|Rogelj et al. 2015a]] ; [[#Detz--2018|Detz et al. 2018]] ; [[#Luderer--2018|Luderer et al. 2018]] ; [[#Strefler--2018|Strefler et al. 2018]] ; [[#van%20Vuuren--2018|van Vuuren et al. 2018]] ). The reliance on CDR varies across scenarios and is tightly linked to future energy demand and the rate of emission reductions in the next two decades: deeper near-term emissions reductions will reduce the need to rely on CDR to constrain cumulative CO 2 emissions. Some studies have argued that only with a transition to lower energy demands will it be possible to largely eliminate the need for engineered CDR options ( [[#Grubler--2018|Grubler et al. 2018]] ; [[#van%20Vuuren--2018|van Vuuren et al. 2018]] ). Overall, the amount of CDR will depend on CO 2 capture costs, lifestyle changes, reduction in non-CO 2 GHGs, and utilisation of zero-emission end-use fuels (Muratori et al. 2017; [[#van%20Vuuren--2018|van Vuuren et al. 2018]] ).

There is substantial uncertainty about the amount of CDR that might ultimately be deployed. In most scenarios that limit warming to 1.5°C, CDR deployment is fairly limited through 2030 at less than 1 GtCO 2 yr –1 . The key projected increase in CDR deployment (BECCS and DAC only) occurs between 2030 and 2050, with annual CDR in 2050 projected at 2.5–7.5 GtCO 2 yr –1 in 2050 (interquartile range) in scenarios limiting warming to 1.5°C (&gt;50%) with limited or no overshoot, and 0.7–1.4 GtCO 2 yr –1 in 2050 in scenarios limiting warming to 2°C (&gt;67%) with action starting in 2020. This characteristic of scenarios largely reflects substantial capacity addition of BECCS power plants. BECCS is also deployed in multiple ways across sectors. For instance, the contribution (interquartile range) of BECCS to electricity is 1–5% in 2050 in scenarios limiting warming to 1.5°C (&gt;50%) with no or limited overshoot, and 0–5% in scenarios that limit warming to 2°C (&gt;67%) with action starting in 2020. The contribution (interquartile range) of BECCS to liquid fuels is 9–21% in 2050 in scenarios limiting warming to 1.5°C (&gt;50%) with no or limited overshoot and 2–11% in scenarios that limit warming to 2°C (&gt;67%) with action starting in 2020. Large-scale deployment of CDR allows flexibility in timing of emissions reduction in hard-to-decarbonise sectors.

CDR will influence the potential fossil-related stranded assets (Box 6.13). Availability of low-cost CDR can help reduce premature retirement for some fossil fuel infrastructure. CDR can allow countries to reach net-zero emissions without phasing out all fossil fuels. Specific infrastructure could also be extended if it is used to burn biomass or other non-emitting sources. For example, existing coal-fired power plants, particularly those with CCS, could be co-fired with biomass ( [[#Woolf--2016|Woolf et al. 2016]] ; [[#Lu--2019|Lu et al. 2019]] ; [[#Pradhan--2021|Pradhan et al. 2021]] ). In many scenarios, energy sector CDR is deployed to such an extent that energy sector CO 2 emissions become negative in the second half of the century (Chapter 3).

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<span id="box-6.12-taking-stock-of-the-energy-system-transition"></span>

=== Box 6.12 | Taking Stock of the Energy System Transition ===

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The Global Stocktake is a regularly occurring process under the UN Framework Convention on Climate Change (UNFCCC) in which efforts will be made to understand progress on, among other things, global mitigation. Collective progress of countries towards the Paris Agreement goal will be assessed and its outcome will inform Parties in updating and enhancing their Nationally Determined Contributions (NDCs). This box explores potential indicators to understand energy system mitigation progress.

CO 2 emissions from fuel combustion are the bottom line on energy system progress. Beyond CO 2 emissions, primary energy demand by energy sources, final energy consumption by sectors, and total electricity demand provide a first order assessment of energy system transitions. The year at which CO 2 emissions peak is also important. The Kaya Identity can be used to decompose energy system CO 2 emissions into carbon intensity of the energy system (CO 2 emissions from fossil-fuel combustion and industry divided by energy use), energy intensity (energy use divided by economic output), and economic output. The impacts of energy and climate policy are reflected in the changes of carbon intensity and energy intensity. Carbon intensity captures decarbonisation of energy supply systems, for example, through fuel switching from fossil fuels to non-fossil fuels, upscaling of low-carbon energy sources, and deploying carbon dioxide removal technologies. The carbon intensity of electricity is specifically important, given the role of the electricity sector in near-term mitigation. Economy-wide energy intensity represents efforts of demand-side energy, such as energy conservation, increase of energy performance of technologies, structural change of economy, and development of efficient urban infrastructure.

Beyond these aggregate indicators, a second order assessment would capture more details, such as the electrification rate, share of renewables, nuclear, CCS or other low-carbon technologies in electricity generation, land area used for energy production, and the number of EVs or PHEVs. Consumption of coal, oil and gas captures the underlying factors of CO 2 emissions. The emphasis of these indicators could differ across countries in the context of national specific circumstances. Technology- or project-based statistics are also useful to check the progress of the low-carbon transition, for example, the number of CCS facilities.

A critical challenge in the assessment of energy sector progress is how to measure societal, institutional, and political progress. These factors are difficult to quantify, yet they are fundamental determinants of the ability to reduce emissions. Public opinion, special interest politics, implications of mitigation for employment, energy subsidies, and energy policies are all critical indicators of progress. In addition, while much of the literature focuses on national-level action, mitigation is increasingly being led by cities, states, provinces, businesses, and other sub-national or non-national actors. Understanding the progress of these actors will be critical to assess energy system mitigation progress. New research is needed to better assess these ‘societal’ indicators and the role of non-national actors.

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<span id="investments-in-technology-and-infrastructure"></span>

=== 6.7.2 Investments in Technology and Infrastructure ===

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Total global energy investment was roughly USD1940 billion yr –1 in 2019 ( [[#IEA--2021f|IEA 2021f]] ). This total can be broken down into the following main categories: fossil-related energy supply, including oil, gas, and coal extraction and fossil electricity generation (USD990 billion yr –1 ); renewable electricity, primarily solar and wind (USD340 billion yr –1 ); nuclear energy (USD40 billion yr –1 ); electricity networks (USD270 billion yr –1 ); and end-use energy efficiency (USD270 billion yr –1 ).

Energy investment needs are projected to rise, according to investment-focused scenario studies found in the literature ( [[#McCollum--2018a|McCollum et al. 2018a]] ; [[#Zhou--2019|Zhou et al. 2019]] ; [[#Bertram--2021|Bertram et al. 2021]] ). While these increases are projected to occur in emissions-intensive pathways as well as low-carbon pathways, they are projected to be largest in low-carbon pathways. Average annual global energy investments over the 2016–2050 period range (across six models) from USD2100 to 4100 billion yr –1 in pathways limiting warming to 2°C (&gt;67%) and from USD2400 to 4700 billion yr –1 in pathways limiting warming to 1.5°C (&gt;50%) with no or limited overshoot (McCollum et al. 2018). Whatever the scenario, a significant and growing share of investments between now and 2050 will be channelled toward infrastructure build-out in emerging economies, particularly in Asia ( [[#Zhou--2019|Zhou et al. 2019]] ).

More widespread electrification of buildings, transport, and industry means particularly substantial investment in the electricity system. According to C1–C3 pathways in the IPCC’s ''Sixth Assessment Report'' (AR6 Scenarios Database), such investments could be at the following average annual levels (inter-quartile range, USD2015) over the 2023–2052 timeframe: USD1670 to 3070 billion yr –1 (C1), USD1600 to 2780 billion yr –1 (C2), and USD1330 to 2680 billion yr –1 (C3) (see also [[IPCC:Wg3:Chapter:Chapter-3#3.6.1.3|Section 3.6.1.3]] ).

Beyond these sector-wide numbers, a key feature of stringent mitigation pathways is a pronounced reallocation of investment flows across sub-sectors, namely from unabated fossil fuels (extraction, conversion, and electricity generation) and toward renewables, nuclear power, CCS, electricity networks and storage, and end-use energy efficiency ( [[#McCollum--2018a|McCollum et al. 2018a]] ; [[#Bertram--2021|Bertram et al. 2021]] ; [[#IEA--2021f|IEA 2021f]] ) (Figure 6.32). Investments in solar, wind, and electricity transmission, distribution, and storage increase the most in mitigation scenarios. Up to 2050, the bulk of these investments are made in OECD and Asian countries (Figure 6.33). While fossil fuel extraction investments exhibit a marked downscaling across all regions, compared to reference scenarios, the declines are especially strong in the Middle East, Reforming Economies of Eastern Europe and the Former Soviet Union (REF), and OECD.

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[[File:e3b06bab5171ddf6903d843fac230d0c IPCC_AR6_WGIII_Figure_6_32.png]]

'''Figure 6.32 | Global average annual investments from 2023 to 2052 (undiscounted, in USD billion yr''' –1) '''for electricity supply sub-sectors and for extraction of fossil fuels in scenarios that limit warming to 2°C (&gt;67%) or lower (C1-C3)''' (Source: AR6 Scenarios Database and Chapter 3). Historical investments are also shown for comparison (Source: IEA 2021; approximations are made for hydro and geothermal based on available data; solar and wind values are for 2020). T&amp;D: transmission and distribution of electricity. Bars show median values across models-scenarios, and whiskers the interquartile ranges. See Chapters 3 and 15 for additional information on investments and finance.

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[[File:a8adcb84437bb5c5575af20cb7c84093 IPCC_AR6_WGIII_Figure_6_33.png]]

'''Figure 6.33 | Regional average annual investments from 2023 to 2052 (undiscounted, in USD billion yr''' –1) '''for four of the largest sub-sectors of the energy system in scenarios that limit warming to 2°C (&gt;67%) or lower (C1–C3)''' (Source: AR6 Scenarios Database and Chapter 3). Historical investments are also shown for comparison (Source: IEA, 2016). T&amp;D: transmission and distribution of electricity. Extr.: extraction of fossil fuels. Bars show median values across models-scenarios, and whiskers the inter-quartile ranges. See Chapters 3 and 15 for additional information on investments and finance.

Investments into end-use energy efficiency are projected to also be substantial in mitigation pathways, potentially upwards of several hundred USD billion yr –1 on average to 2050, compared to USD270 billion yr –1 in 2019 ( [[#McCollum--2018a|McCollum et al. 2018a]] ; [[#IEA--2021f|IEA 2021f]] ). However, the literature is inconsistent in how demand-side investments are calculated, as boundary conditions are less clear than for energy supply investments. Taking a broader definition can result in estimates that are an order-of-magnitude higher, meaning as large or larger than supply-side investments ( [[#Grubler--2012|Grubler et al. 2012]] ; [[#IEA--2021f|IEA 2021f]] ).

Increasing low-carbon investment primarily requires shifting existing capital investment through regulation and incentives as well as removing existing investment barriers (McCollum et al. 2018; Hafner et al. 2020; Ameli, N. et al. 2021). While there is a considerable amount of capital in the world, it is not always available to those wishing to invest in certain projects. Total annual global investment in fixed capital was USD22.4 trillion in 2021, over an order-of-magnitude larger than energy sector investment (World Bank 2021).

Future investment patterns will vary by region, as they do now, due to differences in risk profiles, resource endowments and economic and governance structures (Fizaine et al. 2016; [[#Zhou--2019|Zhou et al. 2019]] ; Ameli, N. et al. 2021). In rapidly growing countries, investments to support a low-carbon energy system transition will be integrated with those needed to meet rapidly increasing energy demands, irrespective of whether efforts are made to reduce emissions. In less rapidly growing countries (Sun et al. 2019), investments will focus on transitioning current energy systems to low-carbon configurations. Most current energy investments are concentrated in high- and upper-middle-income countries ( [[#IEA--2021f|IEA 2021f]] ), but this will change as investment needs continue to grow in today’s lower-middle- and low-income countries ( [[#McCollum--2018a|McCollum et al. 2018a]] ; [[#Zhou--2019|Zhou et al. 2019]] ; [[#Bertram--2021|Bertram et al. 2021]] ; [[#IEA--2021f|IEA 2021f]] ).

<div id="6.7.3" class="h2-container"></div>

<span id="dependence"></span>
=== 6.7.3 Dependence ===

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Path dependence refers to resistance to change due to favourable socio-economic conditions with existing systems; decisions made in the past unduly shape future trajectories. Carbon lock-in is a specific type of path dependence ( [[#Seto--2016|Seto et al. 2016]] ). Given that energy system mitigation will require a major course change from recent history, lock-in is an important issue for emission reductions in the energy sector. While lock-in is typically expressed in terms of physical infrastructure that would need to be retired early to reach mitigation goals, it involves a much broader set of issues that go beyond physical systems and into societal and institutional systems (Table 6.11).

'''Table 6.11 | Lock-in types and typical mechanisms.''' Source: Kotilainen et al. 2020), Reproduced under Creative Commons 4.0 International Licence.

{| class="wikitable"
|-
! Type

! Primary lock-in mechanisms

! References

|-
| Technological (and infrastructural)

| – Economies of scale

– Economies of scope

– Learning effects

– Network externalities

– Technological interrelatedness

| – Arthur (1994); Hughes (1994); Klitkou et al (2015)

– David (1985); Panzar and Willig (1981)

– Arthur (1994)

– David (1985); Katz and Shapiro (1986)

– Arrow (1962); Arthur (1994); David (1985); Van den Bergh and Oosterhuis (2008)

|-
| Institutional

| – Collective action

– Complexity and opacity of politics

– Differentiation of power and institutions

– High density of institutions

– Institutional learning effects

– Vested interests

| – Seto et al (2016)

– Foxon (2002); Pierson (2000)

– Foxon (2002)

– Pierson (2000)

– Foxon (2002); Boschma (2005)

– Boschma (2005)

|-
| Behavioural

| – Habituation

– Cognitive switching costs

– Increasing informational returns

| – David (1985); Barnes et al. (2004); Zauberman (2003); Murray and Haubl (2007)

– Zauberman (2003); Murray and Haubl (2007); Van den Bergh and Oosterhuis (2008)

|}

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<span id="societal-and-institutional-inertia"></span>
==== 6.7.3.1 Societal and Institutional Inertia ====

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A combination of factors – user, business, cultural, regulatory, and transnational – will hinder low-carbon energy transitions. Strong path dependencies, even in early formative stages, can have lasting impacts on energy systems, producing inertia that cuts across technological, economic, institutional and political dimensions ( ''high confidence'' ) ( [[#Rickards--2014|Rickards et al. 2014]] ; [[#Vadén--2019|Vadén et al. 2019]] ) (Chapter 5).

Energy systems exemplify the ways in which massive volumes of labour, capital, and effort become sunk into particular institutional configurations ( [[#Bridge--2013|Bridge et al. 2013]] , 2018). Several embedded factors affect large-scale transformation of these systems and make technological diffusion a complex process:

• '''User environments''' affect purchase activities and can involve the integration of new technologies into user practices and the development of new preferences, routines, habits and evenvalues ( [[#Kanger--2019|Kanger et al. 2019]] ).

'''•''' '''Business environments''' can shape the development of industries, business models, supply and distribution chains, instrument constituencies and repair facilities ( [[#Béland--2016|Béland and Howlett 2016]] ).

'''•''' '''Culture''' can encompass the articulation of positive discourses, narratives, and visions that enhance cultural legitimacy and societal acceptance of new technologies. Regulatory embedding can capture the variety of policies that shape production, markets and use of new technologies.

• '''Transnational community''' can reflect a shared understanding in a community of global experts related to new technologies that transcends the borders of a single place, often a country.

While low-carbon innovation involves systemic change ( [[#Geels--2018|Geels et al. 2018]] ), these are typically less popular than energy supply innovations among policymakers and the wider public. Managing low-carbon transitions is therefore not only a techno-managerial challenge (based on targets, policies, and expert knowledge), but also a broader political project that involves the building of support coalitions that include businesses and civil society ( ''moderate evidence'' , ''high agreement'' ).

Low-carbon transitions involve cultural changes extending beyond purely technical developments to include changes in consumer practices, business models, and organisational arrangements. The development and adoption of low-carbon innovations will therefore require sustained and effective policies to create appropriate incentives and support. The implementation of such policies entails political struggles because actors have different understandings and interests, giving rise to disagreements and conflicts.

Such innovation also involves pervasive uncertainty around technical potential, cost, consumer demand, and social acceptance. Such uncertainty carries governance challenges. Policy approaches facing deep uncertainty must protect against and/or prepare for unforeseeable developments, whether it is through resistance (planning for the worst possible case or future situation), resilience (making sure you can recover quickly), or adaptation (changes to policy under changing conditions). Such uncertainty can be hedged in part by learning by firms, consumers, and policymakers. Social interactions and network building (e.g., supply and distribution chains, intermediary actors) and the articulation of positive visions, such as in long-term, low-emission development strategies, all play a crucial role. This uncertainty extends to the impacts of low-carbon innovations on energy demand and other variables, where unanticipated and unintended outcomes are the norm. For instance, rapid investments in public transport networks could restrict car ownership from becoming common in developing countries ( [[#Du--2017|Du and Lin 2017]] ).

<div id="6.7.3.2" class="h3-container"></div>

<span id="physical-energy-system-lock-in"></span>
==== 6.7.3.2 Physical Energy System Lock-In ====

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Current investments in fossil infrastructure have committed 500–700 GtCO 2 of emissions, creating significant risks for limiting warming to 1.5°C (Callaghan 2020) ( ''high confidence'' ). These current investments combined with emissions from proposed fossil infrastructure exceed the emissions required to limit warming to 1.5°C ( ''medium confidence'' ). Existing coal- and gas-fired electricity generation accounts for 200–300 GtCO 2 of committed emissions. Emissions from coal generation are larger than for gas plants ( [[#Smith--2019|Smith et al. 2019]] ; [[#Tong--2019|Tong et al. 2019]] ). The lifetime of coal-fired power plants is 25–50 years, creating long-lasting risks to climate goals ( [[#Erickson--2015|Erickson and Tempest 2015]] ). Gas-fired power plants are younger on average than coal-fired power plants. Industry sector lock-in amounts for more than 100 GtCO 2 , while buildings and transport sector together contribute another 50–100 GtCO 2 ( [[#Erickson--2015|Erickson and Tempest 2015]] ).

Lock-in is also relevant to fossil resources. Both coal and gas exploration continue, and new permits are being issued, which may cause economic ( [[#Erickson--2018|Erickson et al. 2018]] ) as well as non-economic issues ( [[#Boettcher--2019|Boettcher et al. 2019]] ).

The nature of lock-in varies across the energy system. For example, lock-in in urban and transport sectors is different from the electricity sector. Broadly, urban environments involve infrastructural, institutional, and behavioural lock-in ( [[#Ürge-Vorsatz--2018|Ürge-Vorsatz et al. 2018]] ). Addressing lock-in in these sectors requires action by multiple stakeholders and is unlikely with just technological evolution (Table 6.11).

Committed carbon emissions are unevenly distributed. The disproportionate high share of committed emissions in emerging economies is the result of rapid growth in recent years, which has led to a comparably young fossil infrastructure with substantial remaining life ( [[#Shearer--2017|Shearer et al. 2017]] ). Mature industrialised countries tend to have older infrastructures, part of which will be up for retirement in the near future ( [[#Tong--2019|Tong et al. 2019]] ). Coal-fired power plants currently planned or under construction are associated with 150–300 GtCO 2 , of which about 75% and about 10% are located in Asia and the OECD respectively ( [[#Edenhofer--2018|Edenhofer et al. 2018]] ; [[#Pfeiffer--2018|Pfeiffer et al. 2018]] ). If implemented, these new fleets will further shorten all coal plants’ lifetimes by another 10 years for meeting climate goals ( [[#Cui--2019|Cui et al. 2019]] ).

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[[File:20ae4946e03d27364072737297f50f7d IPCC_AR6_WGIII_Figure_6_34.png]]

'''Figure 6.34 | Annual emissions from existing, proposed, and future energy system infrastructure.''' Source: with permission from [[#Tong--2019|Tong et al. 2019]] .

Despite the imperative to reduce use of fossil fuels and the multiple health and other benefits from closing coal-based infrastructure ( [[#Portugal-Pereira--2018|Portugal-Pereira et al. 2018]] ; [[#Liu--2019a|Liu et al. 2019a]] ; Karlsson et al. 2020; [[#Rauner--2020|Rauner et al. 2020]] ; [[#Cui--2021|Cui et al. 2021]] ), coal power plants have continued to be commissioned globally ( [[#Jewell--2019|Jewell et al. 2019]] ; [[#Jakob--2020|Jakob et al. 2020]] ), most notably in Asian countries. Gas power plants also continue to be built. In many regions, new fossil electricity generation exceeds needed capacity ( [[#Shearer--2017|Shearer et al. 2017]] ).

Existing policies and the NDCs are insufficient to prevent an increase in fossil infrastructure and associated carbon lock-in ( ''high confidence'' ) ( [[#Bertram--2015|Bertram et al. 2015]] ; [[#Johnson--2015|Johnson et al. 2015]] ). Current investment decisions are critical because there is limited room within the carbon budget required to limit warming to well below 2°C ( [[#Kalkuhl--2019|Kalkuhl et al. 2019]] ; [[#Rosenbloom--2019|Rosenbloom 2019]] ). Delays in mitigation will increase carbon lock-in and could result in large-scale stranded assets if stringency is subsequently increased to limit warming (Box 6.11). Near-term implementation of stringent GHG mitigation policies are likely to be most effective in reducing carbon lock-in ( [[#Haelg--2018|Haelg et al. 2018]] ). Near-term mitigation policies will also need to consider different energy transition strategies as a result of different resources and carbon budgets between countries ( [[#Lucas--2016|Lucas 2016]] ; [[#Bos--2018|Bos and Gupta 2018]] ).

Near-term policy choices are particularly consequential for fast-growing economies. For example, Malik et al. (2020) found that 133 to 227 GW of coal capacity would be stranded after 2030 if India were to delay ambitious mitigation through 2030 and then pursue an ambitious, post-2030 climate strategy. [[#Cui--2021|Cui et al. (2021)]] identified 18% of old, small, inefficient coal plants for rapid near-term retirement in China to help achieve air quality, health, water, and other societal goals and a feasible coal phase-out under climate goals. Comparable magnitudes of stranded assets may also be created in Latin America when adding all announced, authorised, and procured power plants up to 2060 ( [[#González-Mahecha--2019|González-Mahecha et al. 2019]] ). Options to reduce carbon lock-in include reducing fossil fuels subsidies (Box 6.3), building CCS-ready facilities, or ensuring that facilities are appropriately designed for fuel switching ( [[#Budinis--2018|Budinis et al. 2018]] ). Substantial lock-in may necessitate considerable deployment of CDR to compensate for high cumulative emissions.

Past and present energy sector investments have created technological, institutional, and behavioural path dependencies aligned towards coal, oil, and natural gas ( ''high confidence'' ). In several emerging economies, large projects are planned that address poverty reduction and economic development. Coal infrastructure may be the default choice for these investments without policies to invest in low-carbon infrastructure instead ( [[#Joshua--2020|Joshua and Alola 2020]] ; [[#Steckel--2020|Steckel et al. 2020]] ). Path dependencies frequently have sustainability implications beyond carbon emissions. (Box 6.2 and [[#6.7.7|Section 6.7.7]] ). There are several SDG co-benefits associated with decarbonisation of energy systems ( [[#6.7.7|Section 6.7.7]] ) (Sörgel et al. 2021). For example, coal mining communities frequently experience significant health and economic burdens from resource extraction.

<div id="box-6.13" class="h2-container box-container"></div>

<span id="box-6.13-stranded-assets"></span>
=== Box 6.13 | Stranded Assets ===

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Limiting warming to 2°C (&gt;67%) or lower will result in stranded assets ( ''high confidence'' ). Stranded assets can be broadly defined as assets that ‘suffer from unanticipated or premature write-offs, downward revaluations or [conversion] to liabilities’. Stranded assets may create risks for financial market stability and macro-economic stability (Battiston et al. 2017; [[#Mercure--2018|Mercure et al. 2018]] ; Sen and von Schickfus 2020), and they will result in a rapid loss of wealth for the owners of affected assets ( [[#Vogt-Schilb--2017|Vogt-Schilb and Hallegatte 2017]] ; [[#Ploeg--2020|Ploeg and Rezai 2020]] ).

There are two types of stranded assets: fossil-fuel resources that cannot be burned; and premature retirement of fossil infrastructure (e.g., power plants). About 30% of oil, 50% of gas, and 80% of coal reserves will remain unburnable if warming is limited to 2°C ( [[#Meinshausen--2009|Meinshausen et al. 2009]] ; [[#Leaton--2011|Leaton 2011]] ; Leaton Ranger 2013; [[#McGlade--2015|McGlade and Ekins 2015]] ; [[#Bauer--2016|Bauer et al. 2016]] ; [[#IRENA--2017b|IRENA 2017b]] ; [[#Pye--2020|Pye et al. 2020]] ) ( ''high confidence'' ). Significantly more reserves are expected to remain unburned if warming is limited to 1.5°C. Countries with large oil, gas, and coal reserves are most at risk ( [[#Caldecott--2017|Caldecott et al. 2017]] ; [[#Ansari--2020|Ansari and Holz 2020]] ).

About 200 GW of fossil fuel electricity generation per year will likely need to be retired prematurely after 2030 to limit warming to 2°C, even if countries achieve their Nationally Determined Contributions (NDCs) ( ''medium confidence'' ) ( [[#Iyer--2015|Iyer et al. 2015]] ; [[#Johnson--2015|Johnson et al. 2015]] ; [[#Fofrich--2020|Fofrich et al. 2020]] ). Limiting warming to 1.5°C will require significantly more rapid premature retirement of electricity generation capacity ( [[#Binsted--2020|Binsted et al. 2020]] ). Coal- and gas-fired power plants will likely need to retire about 25 years earlier than in the past to limit warming to 2°C, and 30 years earlier to limit warming to 1.5°C ( [[#Cui--2019|Cui et al. 2019]] ; [[#Fofrich--2020|Fofrich et al. 2020]] ). Coal-fired power plants are at significantly greater risk of stranding compared with gas-fired and oil-fired plants ( [[#Iyer--2015|Iyer et al. 2015]] ; [[#Johnson--2015|Johnson et al. 2015]] ; [[#Fofrich--2020|Fofrich et al. 2020]] ). The risks of stranded power plants are greatest in countries with newer fossil infrastructure.

If warming is limited to 2°C, the discounted economic impacts of stranded assets, including unburned fossil reserves, could be as high as USD1–4 trillion from 2015 through 2050 (USD10–20 trillion in undiscounted terms) ( ''medium confidence'' ) ( [[#IRENA--2017c|IRENA, 2017c]] ; [[#Mercure--2018|Mercure et al. 2018]] ). About 40% of these impacts correspond to unburned fossil reserves ( [[#IRENA--2017b|IRENA 2017b]] ). If warming is limited to 1.5°C, the economic impacts of stranded assets are expected to be significantly higher ( [[#Binsted--2020|Binsted et al. 2020]] ).

Stronger near-term mitigation will reduce premature retirements of fossil infrastructure, because more rapid mitigation will decrease new builds of fossil infrastructure that might later be stranded ( [[#Johnson--2015|Johnson et al. 2015]] ; [[#Bertram--2018|Bertram et al. 2018]] ) ( ''high confidence'' ). For example, if warming is limited to 2°C, strengthening the NDC pledges beyond their 2015 levels could decrease stranded electricity sector assets by more than 50% ( [[#Iyer--2015|Iyer et al. 2015]] ). By contrast, if countries fail to meet their NDCs and continue to build fossil infrastructure, mitigation will need to be accelerated beyond 2030, resulting up to double the amount of stranded electricity generation capacity ( [[#Iyer--2015|Iyer et al. 2015]] ). This corresponds to a total undiscounted cost of about USD2 trillion from electricity infrastructure alone, from the period 2015 to 2050 (IRENA 2017). CCS (6.4) could potentially help reduce hundreds of gigawatts stranded power plant capacity along with other fossil-based capital ( [[#Clark--2014|Clark and Herzog 2014]] ; [[#Iyer--2017|Iyer et al. 2017]] ; [[#Fan--2018|Fan et al. 2018]] ).

<div id="6.7.4" class="h2-container"></div>

<span id="fossil-fuels-in-a-low-carbon-transition"></span>

=== 6.7.4 Fossil Fuels in a Low-carbon Transition ===

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Global fossil fuel use will need to decline substantially by 2050 to limit warming to 2°C (&gt;67%), and it must decline substantially by 2030 to limit warming to 1.5°C (&gt;50%) with no or limited overshoot ( ''high confidence'' ). Failing to reduce global fossil fuel use below today’s levels by 2030 will make it more challenging to limit warming to below 2°C (&gt;67%). ( ''high confidence'' ). Fossil fuel use declines by 260–330 EJ (52–73% from 2020 levels, interquartile range) through 2050 in scenarios that limit warming to 1.5°C (&gt;50%) with no or limited overshoot, and 124–231 EJ (24–51% reduction compared to 2020 levels) in scenarios that limit warming to 2°C (&gt;67%) with action starting in 2020. This will require a significant reduction in coal, oil and gas investments. Fossil fuels account for about 80% of primary energy today. In scenarios limiting warming to 1.5°C (&gt;50%) with limited or no overshoot, fossil energy provides 59–69% (interquartile range) of primary energy in 2030 and 25–40% primary energy in 2050 (AR6 Scenarios Database). In scenarios limiting warming to 2°C (&gt;67%) with action starting in 2020, fossil energy provides 71–75% (interquartile range) primary energy in 2030 and 41–57% primary energy in 2050 (AR6 Scenarios Database). The timeline for reducing production and usage varies across coal, oil, and gas due to their differing carbon intensities and uses.

Global coal consumption without CCS needs to be largely eliminated by 2040–2050 to limit warming to 1.5°C (&gt;50%), and 2050–2060 to limit warming to 2°C (&gt;67%) ( ''high confidence'' ). New investments in coal-fired electricity without CCS are inconsistent with limiting warming to 2°C (&gt;67%) or lower ( ''high confidence'' ) ( [[#Edenhofer--2018|Edenhofer et al. 2018]] ; [[#Pfeiffer--2018|Pfeiffer et al. 2018]] ; [[#Spencer--2018|Spencer et al. 2018]] ; [[#Cui--2019|Cui et al. 2019]] ). Coal consumption declines 130 EJ yr –1 to 140 EJ yr –1 in 2050 (78–99% compared to 2020 levels, interquartile range) in scenarios limiting warming to 1.5°C (&gt;50%) with no or limited overshoot and 118 EJ yr –1 to 139 EJ yr –1 (65% to 98% compared to 2020 levels) in scenarios limiting warming to 2°C (&gt;67%) with action starting in 2020. Coal consumption without CCS falls by 67% to 82% (interquartile range) in 2030 in scenarios limiting warming to 1.5°C (&gt;50%) with no or limited overshoot. Studies indicate that coal use may decline substantially in the USA and Europe over the coming decade, based on the increasing competitiveness of low-carbon sources and near-term policy actions ( [[#Grubert--2019|Grubert and Brandt 2019]] ; [[#Oei--2020|Oei et al. 2020]] ). In several developing economies, the relative youth of the coal-fired electricity fleet will make a complete phase-out before 2050 difficult ( [[#Garg--2009|Garg and Shukla 2009]] ; [[#Jewell--2016|Jewell et al. 2016]] ). There are considerable differences in projected coal phase-out timelines in major Asian economies. Some studies suggest that coal may continue to be a part of the Chinese energy mix composing around one-third of the total primary energy consumption by 2050, even if emissions are reduced by 50% by 2030 ( [[#He--2020|He et al. 2020]] ). Others indicate that a strategic transition would decrease the risk of stranded assets and enable a near-complete phase-out by 2050 ( [[#Wang--2020a|Wang et al. 2020a]] ; [[#Cui--2021|Cui et al. 2021]] ). This would entail prioritising earlier retirements of plants based on technical (efficiency), economic (profitability, local employment) and environmental considerations (e.g., water scarcity for cooling).

Natural gas may remain part of energy systems through mid-century, both for electricity generation and use in industry and buildings, and particularly in developed economies, even if warming is limited to 2°C (&gt;67%) or lower ( ''medium confidence'' ). The decline in natural gas use from 2020 to 2050 is 38 EJ yr –1 to 78 EJ yr –1 (21–62% decline from 2020 levels, interquartile range) in scenarios limiting warming to 1.5°C (&gt;50%) with no or limited overshoot and –22 EJ yr –1 to 46 EJ yr –1 (–14% to 36% decline from 2020 levels, interquartile range) in scenarios limiting warming to 2°C (&gt;67%) with action starting in 2020. Scenarios indicate that gas use in electricity will likely peak around 2035 and 2050 if warming is limited to 1.5°C (&gt;50%) with limited or no overshoot or to 2°C (&gt;67%) with action starting in 2020, respectively. There is variability in the role gas would play in future scenarios based on national climate commitments and availability of cheap renewables (Malik et al. 2020; [[#Vishwanathan--2020|Vishwanathan and Garg 2020]] ; [[#Vrontisi--2020|Vrontisi et al. 2020]] ). Note that these differences are not only present in the electricity sector but also in other end uses.

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[[File:3aeee484e600be3a552ffbf0ddd74a8d IPCC_AR6_WGIII_Figure_6_35.png]]

'''Figure 6.35 | Global fossil fuel pathways for scenarios that limit/return warm''' '''ing to 1.''' '''5°C (&gt;50%) with no or limited/after a high, overshoot, and scenarios that limit warming to 2°C (&gt;67%), with action starting in 2020 or NDCs until 2030, during 2030–2050.''' Boxes indicate 25th and 75th percentiles while whiskers indicate 5th and 95th percentiles. Results for total consumption are expressed as a percentage relative to 2020 consumption. Results for fossil energy with CCS are expressed in total energy consumption. Oil use with CCS is not shown here as it remains below 5% of total use. Source: AR6 Scenarios Database.

While oil use is anticipated to decline substantially, due to changes in the transport sector, its use will likely continue through the mid-century, even if warming is limited to 2°C (&gt;67%) or lower ( ''medium confidence'' ) ''.'' Oil use declines by 73 EJ yr –1 to 145 EJ yr –1 (30–78% from 2020 levels, interquartile range) in scenarios that limit warming to 1.5°C (&gt;50%) with no or limited overshoot and 26 EJ yr –1 to 86 EJ yr –1 (14–45% from 2020 levels) by 2050 in scenarios that limit warming to 2°C (&gt;67%) with action starting in 2020. While oil use is anticipated to decline immediately in scenarios limiting warming to 1.5°C (&gt;50%), it is likely to continue to be used through 2050. Oil use continues to be a significant source of transport fuels in most scenarios limiting warming to 2°C (Welsby et al. 2021). Oil use may drop to about half of current levels as a transport fuel by 2050 if warming is limited to 2°C, because of the availability of other options (biofuels, green hydrogen) and rapid deployment of EVs ( [[#Feijoo--2020|Feijoo et al. 2020]] ). In the absence of rapid transport electrification, the decline is slower with some studies projecting peak oil use around 2035 ( [[#Delgado--2020|Delgado et al. 2020]] ; [[#Pan--2020|Pan et al. 2020]] ).

There is a lack of consensus about how CCS might alter fossil fuel transitions for limiting warming to 2°C (&gt;67%) or lower. CCS deployment will increase the shares of fossil fuels associated with limiting warming, and it can ease the economic transition to a low-carbon energy system ( [[#Muratori--2016|Muratori et al. 2016]] ; [[#Marcucci--2019|Marcucci et al. 2019]] ). While some studies find a significant role for fossil fuels with CCS by 2050 ( [[#Koelbl--2014|Koelbl et al. 2014]] ; [[#Eom--2015|Eom et al. 2015]] ; [[#Vishwanathan--2020|Vishwanathan and Garg 2020]] ), others find that retirement of unabated coal far outpaces the deployment of coal with CCS ( [[#Budinis--2018|Budinis et al. 2018]] ; [[#Xie--2020|Xie et al. 2020]] ; McJeon et al. 2021) Moreover, several studies also project that, with availability of CO 2 capture technology, BECCS might become significantly more appealing than fossil CCS, even before 2050 (Muratori et al. 2017; [[#Luderer--2018|Luderer et al. 2018]] b).

<div id="6.7.5" class="h2-container"></div>

<span id="policy-and-governance"></span>
=== 6.7.5 Policy and Governance ===

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Policy and governance frameworks are essential for shaping near- and medium-term low-emissions energy system transitions ( ''high confidence'' ). While policy interventions are necessary to achieve low-carbon energy system transitions, appropriate governance frameworks are crucial to ensure policy implementation ( ''high confidence'' ). The policy environment in energy transition pathways relate to climate policy goals, the characteristics of the policy regimes and measures to reach the policy goals including implementation limits and obstacles, and the timing of the climate instrument ( [[#Kriegler--2014b|Kriegler et al. 2014b]] ).

The literature discusses a broad set of policy approaches. Environmental economics focuses mainly on market-based approaches as the least-cost policy to achieve emission reductions ( [[#Kube--2018|Kube et al. 2018]] ). Many countries, however, have implemented policy mixes with a diverse set of complementary policies to achieve energy and climate policy targets. One example is the German Energiewende, which includes substantial support for renewables, an action plan for energy efficiency, and phase-out processes for nuclear- and coal-based power generation next to carbon pricing ( [[#Löschel--2019|Löschel et al. 2019]] ). The halving of CO 2 emissions in UK power generation reflects multiple policies, particularly within the UK’s Climate Change Act 2008 ( [[#Grubb--2018|Grubb and Newbery 2018]] ). More generally, the implementation of the NDCs under the Paris Agreement are all characterised by diverse climate policy mixes.

These policy mixes (or policy packages) are shaped by different factors, including policy goals and objectives (including political, social and technological influences), multiple market, governance or behavioural failures or previous policy choices of earlier policy eras ( [[#Rogge--2017|Rogge 2017]] ). When pursuing multiple policy goals or targeting some type of imperfection,well designed policy mixes can, in principle, reduce mitigation costs ( [[#Corradini--2018|Corradini et al. 2018]] ) or address distributional concerns, especially vulnerable populations. For example, the interaction between carbon pricing and the support for clean energy technologies in the EU clean low-carbon strategy for 2050 can reduce mitigation costs and allow for the early adoption of more stringent climate targets ( [[#Vandyck--2016|Vandyck et al. 2016]] ). Policy efforts to promote adoption of low-carbon technologies are more successful if they focus not only on economic incentives but include behavioural interventions that target relevant cognitive and motivational factors ( [[#Mundaca--2019|Mundaca et al. 2019]] ; [[#Khanna--2021|Khanna et al. 2021]] ) ( [[#6.7.6|Section 6.7.6]] ). Overlapping nudges might not necessarily lead to lower effectiveness ( [[#Brandon--2019|Brandon et al. 2019]] ).

Well-designed policy mixes can support the pursuit of multiple policy goals, target effectively different types of imperfections and framework conditions and take into account the technological, economical, and societal situation ( ''high confidence'' ). Accounting for the different development stages of new technologies will enhance low-emissions transitions (Graaf and Sovacool 2020). For prototype technologies and technologies in the demonstration phase, research subsidies and demonstration projects are most important. For technologies experiencing early adoption, infrastructure development and strengthening of markets are increasingly important, while retiring or repurposing of existing assets is important for mature technologies ( [[#IEA--2020h|IEA 2020h]] ) Effective policy mixes will address different market frictions and deal with various uncertainties, for example, those pertaining to technological, climate, and socio-economic developments ( [[#Aldy--2020|Aldy 2020]] ), but also with respect to outcomes of individual policies (e.g., [[#Borenstein--2019|Borenstein et al. 2019]] ). Therefore, policy mixes may balance the trade-off between stability and the flexibility to change individual policies ( [[#Gawel--2019|Gawel and Lehmann 2019]] ) and the policy mix over time ( [[#Rayner--2017|Rayner et al. 2017]] ). Some policy instruments may become feasible over time, for example, as technological advancements reduce the transaction costs of comprehensive market-based approaches ( [[#Andoni--2019|Andoni et al. 2019]] ; [[#Di%20Silvestre--2020|Di Silvestre et al. 2020]] ), or as weakened barriers to stringency enable policy sequencing ( [[#Pahle--2018|Pahle et al. 2018]] ). Energy system policy mixes often include sector-specific regulation. Compared to economy-wide approaches, sectoral policies may be able to directly target specific sectors or mitigation options. However, uncoordinated implementation or limited coordination across sectors may lead to efficiency losses (e.g. [[#Rosendahl--2017|Rosendahl et al. 2017]] ). These losses also depend on other policies, such as pre-existing taxes ( [[#Goulder--2016|Goulder et al. 2016]] ; Marten et al. 2018) or research and development policies ( [[#Acemoglu--2016|Acemoglu et al. 2016]] ). Moreover, unilateral policies – those taken by individual countries in the absence of coordination with other countries – could raise carbon leakage risks, while balancing potential issues of (industrial) competitiveness ( [[#Martin--2014|Martin et al. 2014]] ; [[#Rosendahl--2017|Rosendahl et al. 2017]] ). Energy leakage may become more important during low-carbon energy systems. Numerous studies have identified pathways for carbon leakage in electricity markets with incomplete emission markets ( [[#Caron--2015|Caron et al. 2015]] ; [[#Murray--2015|Murray and Maniloff 2015]] ; [[#Thurber--2015|Thurber et al. 2015]] ; [[#Duan--2017|Duan et al. 2017]] ; [[#Fell--2017|Fell and Maniloff 2017]] ; [[#Qian--2018|Qian et al. 2018]] ). Well-designed policy mixes will need to target the whole lifecycle or value chains, for example, through policies on limiting fossil fuel extraction ( [[#Asheim--2019|Asheim et al. 2019]] ), or they will need to include measures to limit carbon leakage (e.g. [[#Cosbey--2019|Cosbey et al. 2019]] ).

Interactions between policy measures including their scope, stringency, and timing, influence the costs of reducing emissions ( [[#Corradini--2018|Corradini et al. 2018]] ). In particular, some policy instruments may lead to lock-in effects ( [[#6.7.3|Section 6.7.3]] ), compete with other regulations (Graaf and Sovacool 2020), or trigger negative policy interactions ( [[#Perino--2015|Perino 2015]] ; [[#Jarke-Neuert--2020|Jarke-Neuert and Perino 2020]] ). Existing policy mixes often reflect different political economy constraints, and sometimes not well coordinated goals. The resulting policy mixes are often economically inefficient. However, comprehensive evaluation of policy mixes requires a broader set of criteria that reflect different considerations, such as broader goals (e.g., SDGs) and the feasibility of policies ( ''high confidence'' ).

Policy mixes might rather emerge piece-by-piece over time out of individual policy interventions rather than be designed as a whole from the outset ( [[#Howlett--2014|Howlett 2014]] ; [[#Rogge--2017|Rogge 2017]] ) and may reflect differences across jurisdictions and sectors ( [[#Howlett--2014|Howlett 2014]] ). For example, taking into account country-specific objectives, failures, and limitations, carbon prices may be only one part of a broader policy mix, and thereby may not be uniform across countries ( [[#Bataille--2020|Bataille 2020]] ). This lack of consistency makes it more difficult to assess economic outcomes since costs of complementary policies are often less visible and are often targeted at high-cost mitigation options ( [[#Borenstein--2019|Borenstein et al. 2019]] ).

Effective assessment of policy mixes requires comprehensive, validated international data, methodologies, and indicators. Existing policy mixes are difficult to evaluate because they target multiple objectives, and the evaluation must consider various criteria ( [[IPCC:Wg3:Chapter:Chapter-13|Chapter 13]] and [[#6.7.7|Section 6.7.7]] ), such as environmental and economic effectiveness, distributional effects, transformative potential, institutional requirements, and feasibility. Economic outcomes depend on policy goals and implementation. Existing studies on policy mixes suggest the benefits of a comprehensive approach ( [[#Rosenow--2017|Rosenow et al. 2017]] ), while also highlighting that an ‘excessive’ number of instruments may reduce overall effectiveness ( [[#Costantini--2017|Costantini et al. 2017]] ). Combining environmental regulation and innovation policies may be of particular importance to tackle both emissions and innovation market failures ( [[#Fabrizi--2018|Fabrizi et al. 2018]] ). The consistency and credibility of policy mixes is positively associated with green innovation ( [[#Rogge--2018|Rogge and Schleich 2018]] ).

Potential future policies are difficult to evaluate due to methodological challenges ( ''high confidence'' ). Recent model-based analyses of future policy mixes based on ‘current policy scenarios’ try to implement existing policies besides explicit or implicit carbon prices (den [[#Elzen--2016|Elzen et al. 2016]] ; [[#Rogelj--2016|Rogelj et al. 2016]] ; [[#van%20Soest--2017|van Soest et al. 2017]] ; [[#Roelfsema--2020|Roelfsema et al. 2020]] ). Many assessments of future low-carbon energy transitions are still based on cost-optimal evaluation frameworks and include only limited analysis of interactions between policy measures. Hence they are often not describing real-world energy transitions properly, but rather differences in implied carbon prices, constraints in technology deployment, and timing of policies ( [[#Trutnevyte--2016|Trutnevyte 2016]] ).

<div id="6.7.6" class="h2-container"></div>

<span id="behaviour-and-societal-integration"></span>
=== 6.7.6 Behaviour and Societal Integration ===

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Members of societies, including individuals, civil society, and businesses, will all need to engage with, and be affected by, low-carbon energy system transitions ( ''high confidence'' ). This raises questions about the extent to which different strategies and policy would effectively promote mitigation behaviours and the factors that increase the social acceptability of mitigation options, policies, and system changes.

<div id="6.7.6.1" class="h3-container"></div>

<span id="strategies-to-encourage-climate-mitigation-actions"></span>
==== 6.7.6.1 Strategies to Encourage Climate Mitigation Actions ====

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Climate policy will be particularly effective if it targets key factors inhibiting, enabling, and motivating mitigation behaviours. As barriers differ across mitigation options, regions, and groups, tailored approaches are more effective ( [[#Grubb--2017|Grubb et al. 2017]] ). When people face important barriers to change (e.g., high costs, legal barriers), policy would be needed make low-carbon actions more attractive, or to make high-carbon actions less attractive. As people generally face multiple barriers for change, combinations of policies would be more effective ( [[#Rosenow--2017|Rosenow et al. 2017]] ).

Financial incentives can motivate mitigation actions ( [[#Santos--2008|Santos 2008]] ; [[#Thøgersen--2009|Thøgersen 2009]] ; [[#Bolderdijk--2011|Bolderdijk et al. 2011]] ; [[#Eliasson--2014|Eliasson 2014]] ; [[#Maki--2016|Maki et al. 2016]] ), particularly when actions are costly ( [[#Mundaca--2007|Mundaca 2007]] ). In many countries, more residential solar PV were installed after the introduction of favourable financial schemes such as feed-in-tariffs, federal income tax credits, and net metering ( [[#Wolske--2018|Wolske and Stern 2018]] ). Similarly, many programs have promoted the installation of lower-carbon household options such as heat pumps, district heating, or solar water heaters across Europe, the Asia-Pacific and Africa (Hu et al. 2012; Sovacool and Martiskainen 2020; [[#Ahmed--2021|Ahmed et al. 2021]] ). Yet, financial incentives may underperform expectations when other factors are overlooked. For example, people may not respond to financial incentives when they do not trust the organisation sponsoring the programme, or when it takes too much effort to receive the incentive ( [[#Mundaca--2007|Mundaca 2007]] ; [[#Stern--2016a|Stern et al. 2016a]] ). Financial incentives are more effective if combined with strategies addressing non-financial barriers.

Communicating financial consequences of behaviour seems less effective than emphasising social rewards ( [[#Handgraaf--2013|Handgraaf et al. 2013]] ) or benefits of actions for people (e.g., public health, comfort) and the environment ( [[#Bolderdijk--2013|Bolderdijk et al. 2013]] ; [[#Asensio--2015|Asensio and Delmas 2015]] , 2016; [[#Schwartz--2015|Schwartz et al. 2015]] ; Ossokina 2020) ''.'' Financial appeals may have limited effects because they reduce people’s focus on environmental consequences, weaken intrinsic motivation to engage in mitigation actions, provide a licence to pollute ( [[#Agrawal--2015|Agrawal et al. 2015]] ; [[#Bolderdijk--2015|Bolderdijk and Steg 2015]] ; [[#Schwartz--2015|Schwartz et al. 2015]] ), and because pursuing small financial gains is perceived not worth the effort ( [[#Bolderdijk--2013|Bolderdijk et al. 2013]] ; [[#Dogan--2014|Dogan et al. 2014]] ).

Providing information on the causes and consequences of climate change or on effective mitigation actions increases people’s knowledge and awareness, but generally does not promote mitigation actions by individuals ( [[#Abrahamse--2005|Abrahamse et al. 2005]] ) or organisations ( [[#Anderson--2004|Anderson and Newell 2004]] ). Fear-inducing representations of climate change may inhibit action when they make people feel helpless (O’Neill and Nicholson-Cole 2009). Energy-related advice and feedback can promote energy savings, load shifting in electricity use and sustainable travel, particularly when framed in terms of losses rather than gains ( [[#Gonzales--1988|Gonzales et al. 1988]] ; [[#Wolak--2011|Wolak 2011]] ; [[#Bradley--2016|Bradley et al. 2016]] ; [[#Bager--2017|Bager and]] [[#Mundaca--2017|Mundaca 2017]] ). Also, credible and targeted information at the point of decision can promote action ( [[#Stern--2016a|Stern et al. 2016a]] ). Information is more effective when delivered by a trusted source, such as peers ( [[#Palm--2017|Palm 2017]] ), advocacy groups ( [[#Schelly--2014|Schelly 2014]] ), and community organisations ( [[#Noll--2014|Noll et al. 2014]] ), and when tailored to actors’ personal situations and core values ( [[#Daamen--2001|Daamen et al. 2001]] ; [[#Abrahamse--2007|Abrahamse et al. 2007]] ; [[#Bolderdijk--2013|Bolderdijk et al. 2013]] ; [[#Boomsma--2014|Boomsma and Steg 2014]] ; [[#Wolsko--2016|Wolsko et al. 2016]] ; van den Broek et al. 2017). This explains why home energy audits promoted energy savings ( [[#Delmas--2013|Delmas et al. 2013]] ; [[#Alberini--2015|Alberini and Towe 2015]] ), and investments in resource efficiency and renewable energy generation ( [[#Kastner--2015|Kastner and Stern 2015]] ).

Energy use feedback can promote energy saving behaviour within households ( [[#Fischer--2008|Fischer 2008]] ; [[#Grønhøj--2011|Grønhøj and Thøgersen 2011]] ; [[#Delmas--2013|Delmas et al. 2013]] ; [[#Karlin--2015|Karlin et al. 2015]] ; [[#Zangheri--2019|Zangheri et al. 2019]] ) and at work ( [[#Young--2015|Young et al. 2015]] ), particularly when provided in real time or immediately after the action so that people learn the impact of different actions ( [[#Abrahamse--2005|Abrahamse et al. 2005]] ; [[#Faruqui--2009|Faruqui et al. 2009]] ; [[#Delmas--2013|Delmas et al. 2013]] ; [[#Yu--2015|Yu et al. 2015]] ; [[#Stern--2016a|Stern et al. 2016a]] ; [[#Tiefenbeck--2016|Tiefenbeck et al. 2016]] ). Energy labels ( [[#Banerjee--2003|Banerjee and Solomon 2003]] ; [[#Stadelmann--2017|Stadelmann 2017]] ), visualisation techniques ( [[#Pahl--2016|Pahl et al. 2016]] ), and ambient persuasive technology ( [[#Midden--2012|Midden and Ham 2012]] ) can encourage energy savings as they immediately make sense and hardly require users’ conscious attention. Feedback can make people aware of their previous mitigation behaviours, which can strengthen their environmental self-identity, and motivate them to engage in other mitigation actions, to act in line with their self-image ( [[#Van%20der%20Werff--2014|Van der Werff et al. 2014]] ).

Social influence approaches that communicate what other people do or think can encourage mitigation actions ( [[#Clayton--2015|Clayton et al. 2015]] ), as can social models of desired actions ( [[#Osbaldiston--2012|Osbaldiston and Schott 2012]] ; [[#Abrahamse--2013|Abrahamse and Steg 2013]] ; [[#Sussman--2013|Sussman and Gifford 2013]] ; [[#Wolske--2020|Wolske et al. 2020]] ). Feedback on one’s own energy use relative to others can be effective ( [[#Nolan--2008|Nolan et al. 2008]] ; [[#Allcott--2011|Allcott 2011]] ; [[#Schultz--2015|Schultz et al. 2015]] ), although not always, and effect sizes are small ( [[#Abrahamse--2013|Abrahamse and Steg 2013]] ) compared to other types of feedback ( [[#Karlin--2015|Karlin et al. 2015]] ).

Interventions that capitalise on people’s motivation to be consistent can promote mitigation actions ( [[#Steg--2016|Steg 2016]] ). Examples are commitment strategies where people pledge to act ( [[#Abrahamse--2013|Abrahamse and Steg 2013]] ; [[#Lokhorst--2013|Lokhorst et al. 2013]] ), implementation intentions where they additionally explicate how and when they will perform the relevant action and how they would cope with possible barriers ( [[#Bamberg--2000|Bamberg 2000]] , 2002; [[#Rees--2018|Rees et al. 2018]] ), and hypocrisy-related strategies that make people aware of inconsistencies between their attitudes and behaviour ( [[#Osbaldiston--2012|Osbaldiston and Schott 2012]] ).

Bottom-up approaches can promote mitigation action ( [[#Abrahamse--2013|Abrahamse and Steg 2013]] ). Indeed, community energy initiatives can encourage members’ low-carbon behaviour ( [[#Middlemiss--2011|Middlemiss 2011]] ; [[#Seyfang--2012|Seyfang and Haxeltine 2012]] ; [[#Abrahamse--2013|Abrahamse and Steg 2013]] ; [[#Sloot--2018|Sloot et al. 2018]] ). Organisations can promote mitigation behaviour among their employees and customers by communicating their mission and strategies to mitigate climate change ( [[#Ruepert--2017|Ruepert et al. 2017]] ; van der Werff et al. 2021).

Default options, where a preset choice is implemented if users do not select another option, can promote mitigation actions such as energy savings, green electricity uptake, and meat-free options ( [[#Pichert--2008|Pichert and Katsikopoulos 2008]] ; [[#Bessette--2014|Bessette et al. 2014]] ; [[#Campbell-Arvai--2014|Campbell-Arvai et al. 2014]] ; Kunreuther and Weber 2014; [[#Ölander--2014|Ölander and Thøgersen 2014]] ; [[#Ebeling--2015|Ebeling and Lotz 2015]] ; [[#Liebe--2018|Liebe et al. 2018]] ; [[#Liebe--2021|Liebe et al. 2021]] ).

<div id="6.7.6.2" class="h3-container"></div>

<span id="acceptability-of-policy-mitigation-options-and-system-changes"></span>
==== 6.7.6.2 Acceptability of Policy, Mitigation Options and System Changes ====

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Public acceptability reflects the extent to which the public evaluates climate policy, mitigation options, and system changes (un)favourably, which can shape, enable, or prevent low-carbon energy system transitions. Public acceptability of policy and mitigation options is higher when people expect these have more positive and less negative consequences for self, others, and the environment ( [[#Perlaviciute--2014|Perlaviciute and Steg 2014]] ; [[#Demski--2015|Demski et al. 2015]] ; [[#Drews--2016|Drews and Van den Bergh 2016]] ). Public opposition may result when a culturally valued landscape is affected by renewable energy development ( [[#Warren--2005|Warren et al. 2005]] ; [[#Devine-Wright--2010|Devine-Wright and Howes 2010]] ), particularly when place-based identities are threatened ( [[#Devine-Wright--2009|Devine-Wright 2009]] , 2013; [[#Boudet--2019|Boudet 2019]] ). Acceptability can increase after a policy or change has been implemented and the consequences appear to be more positive than expected ( [[#Schuitema--2010|Schuitema et al. 2010]] ; [[#Eliasson--2014|Eliasson 2014]] ; [[#Weber--2015|Weber 2015]] ; [[#Carattini--2018|Carattini et al. 2018]] ); effective policy trials can thus build public support.

Next, climate policy and low-carbon options are evaluated as more fair and acceptable when costs and benefits are distributed equally, and when nature, the environment and future generations are protected ( [[#Schuitema--2011|Schuitema et al. 2011]] ; [[#Drews--2016|Drews and Van den Bergh 2016]] ). Compensating affected groups for losses due to policy or systems changes enhanced public acceptability in some cases ( [[#Perlaviciute--2014|Perlaviciute and Steg 2014]] ), but people may disagree on which compensation would be worthwhile ( [[#Aitken--2010b|Aitken 2010b]] ; [[#Cass--2010|Cass et al. 2010]] ), on the distribution of compensation ( [[#Devine-Wright--2019|Devine-Wright and Sherry-Brennan 2019]] ; [[#Leer%20Jørgensen--2020|Leer Jørgensen et al. 2020]] ), or feel they are being bribed ( [[#Cass--2010|Cass et al. 2010]] ; [[#Perlaviciute--2014|Perlaviciute and Steg 2014]] ). Pricing policies are more acceptable when revenues are earmarked for environmental purposes ( [[#Steg--2006|Steg et al. 2006]] ; [[#Sælen--2011|Sælen and Kallbekken 2011]] ) or redistributed towards those affected ( [[#Schuitema--2008|Schuitema and Steg 2008]] ).

Climate policy and mitigation options, such as renewable energy projects, are also perceived as more fair and acceptable when the public ( [[#Dietz--2013|Dietz 2013]] ; [[#Bidwell--2014|Bidwell 2014]] ; [[#Bernauer--2016b|Bernauer et al. 2016b]] ) or public society organisations ( [[#Terwel--2010|Terwel et al. 2010]] ; [[#Bernauer--2016b|Bernauer et al. 2016b]] ) could participate in the decision-making ( [[#Arvai--2003|Arvai 2003]] ; [[#Devine-Wright--2005|Devine-Wright 2005]] ; [[#Terwel--2012|Terwel et al. 2012]] ; [[#Walker--2017|Walker and Baxter 2017]] ; [[#Perlaviciute--2020|Perlaviciute and Squintani 2020]] ). People are more motivated to participate in decision-making on local projects than on national or general policy goals ( [[#Perlaviciute--2020|Perlaviciute and Squintani 2020]] ). Public acceptability is also higher when people can influence major rather than only minor decisions, particularly when trust in responsible parties is low ( [[#Liu--2019a|Liu et al. 2019a]] ). Public participation can enhance the quality and legitimacy of decisions by including local knowledge and views that may otherwise be missed ( [[#Dietz--2013|Dietz 2013]] ; [[#Bidwell--2016|Bidwell 2016]] ).

Public support is higher when people trust responsible parties ( [[#Perlaviciute--2014|Perlaviciute and Steg 2014]] ; [[#Drews--2016|Drews and Van den Bergh 2016]] ; [[#Michaels--2016|Michaels and Parag 2016]] ; [[#Jiang--2018|Jiang et al. 2018]] ; [[#Liu--2019a|Liu et al. 2019a]] ). Public support for unilateral climate policy is rather strong and robust ( [[#Bernauer--2016a|Bernauer et al. 2016a]] ), even in the absence of reciprocal commitments by other states ( [[#Bernauer--2015|Bernauer and Gampfer 2015]] ).

Public acceptability of climate policy and low-carbon options differs across individuals. Climate policy and low-carbon options are more acceptable when people strongly value protecting other people and the environment, and support egalitarian worldviews, left-wing or green political ideologies, while acceptability is lower when people strongly endorse self-centred values, and support individualistic worldviews ( [[#Dietz--2007|Dietz et al. 2007]] ; [[#Perlaviciute--2014|Perlaviciute and Steg 2014]] ; [[#Drews--2016|Drews and Van den Bergh 2016]] ). Similarly, public decision-makers support climate policy more when they endorse environmental values ( [[#Nilsson--2016|Nilsson et al. 2016]] ). Climate and energy policy is more acceptable when people are more concerned about climate change ( [[#Hornsey--2016|Hornsey et al. 2016]] ), when they believe their actions would help mitigate climate change, and feel responsible to mitigate climate change ( [[#Steg--2005|Steg 2005]] ; [[#Eriksson--2006|Eriksson et al. 2006]] ; [[#Jakovcevic--2013|Jakovcevic and Steg 2013]] ; [[#Drews--2016|Drews and Van den Bergh 2016]] ; [[#Kim--2017|Kim and Shin 2017]] ; [[#Ünal--2019|Ünal et al. 2019]] ).

<div id="6.7.7" class="h2-container"></div>

<span id="the-costs-and-benefits-of-low-carbon-energy-system-transitions-in-the-context-of-sustainable-development"></span>
=== 6.7.7 The Costs and Benefits of Low-carbon Energy System Transitions in the Context of Sustainable Development ===

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The attractiveness of energy sector mitigation ultimately depends on the way that it provides benefitsand reduces the costs for the many different priorities that societies value ( [[#Yang--2018a|Yang et al. 2018a]] ; [[#Wei--2018|Wei et al. 2018]] , 2020). While costs and benefits of climate mitigation are often considered in the context of pure economic outcomes – for example, GDP effects or changes in value of consumption – costs and benefits should be viewed with a broader lens that accounts for the many ways that the energy system interacts with societal priorities (Karlsson et al. 2020). Climate mitigation is not separate from countries’ broader growth and development strategies, but rather as a key element of those strategies.

Cost reductions in key technologies, particularly in electricity and light-duty transport, have increased the economic attractiveness of near-term low-carbon energy system transitions ( ''high confidence'' ). The near-term, economic outcomes of low-carbon energy system transitions in some sectors and regions may be on par with or superior to those of an emissions-intensive future ( ''high confidence'' ). Even in cases when system costs are higher for low-carbon transitions, these transitions may still be economically favourable when accounting for health impacts and other co-benefits (Gielen et al. 2019). Past assessments have quantified the aggregate economic costs for climate change mitigation using different metrics, for example, carbon prices, GDP losses, investments in energy infrastructure, and energy system costs. Assessments of mitigation costs from integrated assessment and energy system models vary widely. For example, scenarios include carbon prices in 2030 of less than USD20 tCO 2 –1 , but also more than USD400 tCO 2 –1 depending on the region, sector boundary, and methodology (e.g., [[#Bauer--2016|Bauer et al. 2016]] ; [[#Brouwer--2016|Brouwer et al. 2016]] ; [[#Oshiro--2017|Oshiro et al. 2017]] ; [[#Vaillancourt--2017|Vaillancourt et al. 2017]] ; [[#Chen--2019|Chen et al. 2019]] ). Those arise both from different methodologies ( [[#Guivarch--2017|Guivarch and Rogelj 2017]] ) and assumptions about uncertainties in key factors that drive costs ( [[#Meyer--2021|Meyer et al. 2021]] ).

Recent developments, however, raise the prospect that economic outcomes could be substantially superior to prior estimates, particularly if key technologies continue to improve rapidly. In some regions and circumstances, particularly in the electricity sector, near-term mitigation may lead to superior economic outcomes than continuing to invest in and utilise emissions-intensive infrastructure (e.g. [[#Brown--2017|Brown et al. 2017]] ; [[#Kumar--2020|Kumar et al. 2020]] ). Given the importance of electricity decarbonisation in near-term mitigation strategies ( [[#6.7.1|Section 6.7.1]] ), decreasing costs of solar PV, wind power, and batteries to support their integration, have an outsized influence on near-term economic outcomes from mitigation. At the same time, economic outcomes may vary across regions depending, among other things, on the characteristics of the current energy systems, energy resources, and needs for integrating VRE technologies.

The long-term economic characteristics of low-emissions energy system transitions are not well understood,and they depend on policy design and implementation along with future costs and availability of technologies in key sectors (e.g., process heat, long-distance transport), and the ease of electrification in end-use sectors ( ''high confidence'' ). The long-term aggregate economic outcomes from a low-emissions future are not likely to be substantially worse than in an emissions-intensive future and may prove superior ( [[#Child--2019|Child et al. 2019]] , Farmer et al. 2020; [[#Bogdanov--2021|Bogdanov et al. 2021]] ) ( ''medium confidence'' ). For the whole economy, the interquartile range of estimated mitigation costs is between 140 USD2015 and 340 USD2015 tCO 2 –1 in 2050 in scenarios limiting warming to 2°C (&gt;67%) and between 430 USD2015 and 990 USD2015 tCO 2 –1 in scenarios limiting warming to 1.5°C (&gt;50%) with no or limited overshoot (Chapter 3). For energy sectors in various regions and globally, different scenarios show a wide range of implied carbon prices in 2050 to limit warming to 1.5°C, from below USD50 tCO 2 –1 to more than USD900 tCO 2 –1 ( [[#Brouwer--2016|Brouwer et al. 2016]] ; [[#Rogelj--2018a|Rogelj et al. 2018a]] ). Mitigation costs for scenarios limiting warming to 2°C (&gt;67%) were 3–11% in consumption losses in AR5, but the median in newer studies is about 3% in GDP losses ( [[#Su--2018|Su et al. 2018]] ; [[#Gambhir--2019|Gambhir et al. 2019]] ).

Estimates of long-run mitigation costs are highly uncertain and depend on various factors. Both faster technological developments and international cooperation are consistently found to improve economic outcomes ( [[#Paroussos--2019|Paroussos et al. 2019]] ). Long-term mitigation is likely to be more challenging than near-term mitigation because low-cost opportunities get utilised first and later efforts would require mitigation in more challenging sectors ( [[#6.6|Section 6.6]] ). Advances in low-carbon energy resources and carriers such as next-generation biofuels, hydrogen produced from electrolysis, synthetic fuels, and carbon-neutral ammonia would substantially improve the economics of net-zero energy systems ( ''high confidence'' ). Current estimates of cumulative mitigation costs are comparably high for developing countries, amounting to up to 2–3% of GDP, indicating difficulties for mitigation without adequate support from developed countries ( [[#Dorband--2019|Dorband et al. 2019]] ; [[#Fujimori--2020|Fujimori et al. 2020]] ). In scenarios involving large amounts of stranded assets, the overall costs of low-carbon transitions also include the additional costs of early retirements (Box 6.11).

Focusing only on aggregate economic outcomes neglects distributional impacts, impacts on broader SDGs, and other outcomes of broad societal importance. Strategies to increase energy efficiency and energy conservation are, in most instances, mutually reinforcing with strategies to support sustainable development. Improving efficiency and energy conservation will promote sustainable consumption and production of energy and associated materials (SDG 12) ( ''high confidence'' ). Contrastingly, successful implementation of demand-side options requires sustainable partnerships (SDG 17) between different actors in energy systems, for example, governments, utilities, distributors, and consumers. Many authors have argued that energy efficiency has a large untapped potential in both supply and demand ( [[#Lovins--2018|Lovins 2018]] ; [[#Méjean--2019|Méjean et al. 2019]] ). For example, improved fossil power plant efficiency has been estimated to lower the costs of CCS from USD80–100 tCO 2 –1 for a subcritical plant to &lt;USD40 tCO 2 –1 for a high-efficiency plant ( [[#Hu--2017|Hu and Zhai 2017]] ; [[#Singh--2017|Singh et al. 2017]] ). This could enhance energy access and affordability. Eliminating electricity transmission losses has been estimated to mitigate 500 MtCO 2 per year globally ( [[#Surana--2019|Surana and Jordaan 2019]] ). For several other options, such as methane mitigation from the natural gas sector, the costs of infrastructure refurbishing could be offset with the value of the recovered natural gas ( [[#Kang--2019|Kang et al. 2019]] ).

Efficient end-use technologies are likely to be particularly cost-effective in developing countries where new infrastructure is rapidly getting built and there is an opportunity to create positive path dependencies ( [[#6.7.3|Section 6.7.3]] ). Aside from reducing energy consumption, efficient end-use technologies reduce resource extraction, for example, fossil fuel extraction or mining for materials used in wind turbines or solar PV cells ( [[#Luderer--2019|Luderer et al. 2019]] ). Reduced resource extraction is an important precursor to SDG 12 on sustainable consumption and production of minerals. End-use efficiency strategies also reduce the need for, and therefore SDG trade-offs associated with, CDR towards the end of the century and avoid temperature overshoot ( [[#van%20Vuuren--2018|van Vuuren et al. 2018]] ). But fully leveraging the demand-side efficiency would entail behavioural changes and thus rely on strong partnerships with communities (SDG 17). For instance, approaches that inform households of the economic value of conservation strategies at home could be particularly useful ( [[#Niamir--2018|Niamir et al. 2018]] ). Improved energy efficiency is interlinked with higher economic growth in Africa ( [[#Lin--2020|Lin and Abudu 2020]] ; [[#Ohene-Asare--2020|Ohene-Asare et al. 2020]] ). An important distinction here between SDGs focusing on infrastructural and behavioural interventions is the temporal context. Improving building heat systems or the electricity grid with reduced T&amp;D losses would provide climate mitigation with one-time investments and minor maintenance over decades. On the other hand, behavioural changes would be an ongoing process involving sustained, long-term societal interactions.

Increasing electrification will support and reduce the costs of key elements of human development, such as education, health, and employment ( ''high confidence'' ). Greater access to electricity might offer greater access to irrigation opportunities for agricultural communities ( [[#Peters--2016|Peters and Sievert 2016]] ) which could have the potential for increasing farmer incomes in support of SDG 1. Coordinated electrification policies also improve enrolment for all forms of education ( [[#Kumar--2018|Kumar and Rauniyar 2018]] ; [[#López-González--2020|López-González et al. 2020]] ). Empirical evidence from India suggests that electrification reduced the time for biomass collection, and thus increased the time children have available for schooling (SDGs 4 and 5) ( [[#Khandker--2014|Khandker et al. 2014]] ). Reduced kerosene use in developing countries has improved indoor air quality (SDG 3) ( [[#Barron--2017|Barron and Torero 2017]] ; [[#Lewis--2020|Lewis and Severnini 2020]] ). These positive linkages between climate change mitigation and other goals have improved perceptions of solar PV among the public and policymakers. ‘Goodwill’ towards solar PV is the highest among all the major mitigation options considered in this chapter ( [[#6.4.2|Section 6.4.2]] ).

Past trends have also indicated that, in some Asian countries, electrification has been obtained at lower income levels as compared to developed countries ( [[#Rao--2017|Rao and Pachauri 2017]] ), with corresponding impacts for development goals. For example, a human development index (HDI) greater than 0.7 (Figure 6.36) which signifies high development is now possible at close to 30 GJ yr –1 per person. This was attainable only at the energy consumption of 50 GJ yr –1 per person in preceding decades.

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[[File:0215b425221c7be3a795dbf8e858f93c IPCC_AR6_WGIII_Figure_6_36.png]]

'''Figure 6.36 | The relationship between total per capita energy use, rate of electrification and human development index (HDI).''' Improved efficiency has lowered the energy demand required for meeting a threshold HDI during 2012–2017.

Electrification also improves energy efficiency, with corresponding implications for development goals. For example, the availability of electric cooking may reduce the cooking primary energy requirement considerably compared to traditional stoves ( [[#Yang--2018|Yang and Yang 2018]] ; [[#Batchelor--2019|Batchelor et al. 2019]] ; [[#Khan--2020|Khan and Alam 2020]] ) while also promoting improved indoor air quality (SDG 3). Similarly, PV-powered irrigation and water pumping reduces pumping energy demands, which has the added advantage of promoting SDG 6 on clean water ( [[#Rathore--2018|Rathore et al. 2018]] ; [[#Elkadeem--2019|Elkadeem et al. 2019]] ).

Phasing out fossil fuels in favour of low-carbon sources is likely to have considerable SDG benefits, particularly if trade-offs such as unemployment to fossil fuel workers are minimised ( ''high confidence'' ). A phase-out of coal (Box 6.2) will support SDGs 3, 7 and 14, but it is also anticipated to create large job losses if not properly managed. At the same time, there are large potential employment opportunities that may be created in alternative sectors such as renewables and bioenergy for both skilled and unskilled workers. ‘Sustainable transition’ pathways have indicated a complete fossil phase-out which could entail numerous other co-benefits. For instance, fossil fuels are estimated to generate only 2.65 jobs per million USD as compared to projected 7.49 from renewables ( [[#Garrett-Peltier--2017|Garrett-Peltier 2017]] ). Similar synergies may also emerge for nuclear power in the long term, though the high costs create trade-offs in developing country contexts ( [[#Agyekum--2020|Agyekum et al. 2020]] ; [[#Castor--2020|Castor et al. 2020]] ). While bioenergy production may create jobs, it may also be problematic for SDG 2 on zero hunger by affecting the supplies and prices of food. Phasing out of fossil fuels will also improve air quality (SDG 3) and premature deaths by reducing PM2.5 emissions, ( [[#He--2020|He et al. 2020]] ; [[#Li--2020c|Li et al. 2020c]] ). Energy transitions from fossil fuels to renewables, as well as within fossil fuels (coal to gas switching), are already occurring in some regions, spurred by climate concerns, health concerns, market dynamics, or consumer choice (e.g., in the transport sector).

CDR and CCS can create significant land and water trade-offs ( ''high confidence'' ). For large-scale CDR and CCS deployment to not conflict with development goals requires efforts to reduce implications on water and food systems. The water impacts of carbon capture are large, but these impacts can be strategically managed ( [[#Magneschi--2017|Magneschi et al. 2017]] ; [[#Liu--2019a|Liu et al. 2019a]] ; [[#Realmonte--2019|Realmonte et al. 2019]] ; [[#Giannaris--2020|Giannaris et al. 2020]] c). In addition, high-salinity brines are produced from geologic carbon storage, which may be a synergy or trade-off depending on the energy intensity of the treatment process and the reusability of the treated waters ( [[#Klapperich--2014|Klapperich et al. 2014]] ; [[#Arena--2017|Arena et al. 2017]] ); if the produced brine from geologic formations can be treated via desalination technologies, there is an opportunity to keep the water intensity of electricity as constant ( [[#6.4.2.5|Section 6.4.2.5]] ). Both implications of CCS and CDR are related to SDG 6 on clean water. CDR discussions in the context of energy systems frequently pertains to BECCS which could affect food prices based on land management approaches ( [[#Daioglou--2020a|Daioglou et al. 2020a]] ). Several CDR processes also require considerable infrastructure refurbishment and electrification to reduce upstream CO 2 emissions ( [[#Singh--2021|Singh and Colosi 2021]] ). Large-scale CDR could also open the potential for low-carbon transport and urban energy (by offsetting emissions in these sectors) use that would create synergies with SDG 11 (sustainable cities and communities). Effective siting of CDR infrastructure therefore requires consideration of trade-offs with other priorities. At the same time, several SDG synergies have also been reported to accompany CCS projects, such as with reduced air pollution (SDG 3) ( [[#Mikunda--2021|Mikunda et al. 2021]] ).

Greater energy system integration (Sections 6.4.3 and 6.6.2) would enhance energy-SDG synergies while eliminating trade-offs associated with deploying mitigation options ( ''high confidence'' ). Energy system integration strategies focus on codependence of individual technologies in ways that optimise system performance. Accordingly, they can improve economic outcomes and reduce negative implications for SDGs. For example, VRE electricity options raise intermittency concerns and hydrogen can be expensive due to the costs of electricity. Both are relevant to SDG 7 on affordable and reliable energy access. Routing excess solar generation during daytime for hydrogen production will improve grid stability as lower hydrogen costs ( [[#Tarroja--2015|Tarroja et al. 2015]] ). Due to the varying patterns of solar and wind energy, these two energy sources could be operated in tandem, thus reducing the material needs for their construction and for storage, thus promoting SDG 12 on sustainable production ( [[#Weitemeyer--2015|Weitemeyer et al. 2015]] ; Wang et al. 2019d). For CCS facilities, co-firing of fossil fuels and biomass could enable a more gradual, near-term low-carbon transition ( [[#Lu--2019|Lu et al. 2019]] ). This could enable early retirements (associated with SDG 1) while also providing air pollution reductions (associated with SDG 3).

Overall, the scope for positive interactions between low-carbon energy systems and SDGs is considerably larger than the trade-offs (Figure 6.37) ( [[#McCollum--2018b|McCollum et al. 2018b]] ). Some critical trade-offs include impact to biodiversity due to large-scale mineral mining needed for renewable infrastructure ( [[#Sonter--2020|Sonter et al. 2020]] ).

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[[File:163dff3047bb58740489fdc359109b0f IPCC_AR6_WGIII_Figure_6_37.png]]

'''Figure 6.37 | Nature of the interactions between SDG 7 (Energy) and the non-energy SDGs.''' Source: [[#McCollum--2018c|McCollum et al. 2018c]] , reproduced under Creative Commons 3.0 Licence.

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