Editing IPCC:AR6/SR15/Chapter-4 (section)

== 4.2 Pathways Compatible with 1.5°C: Starting Points for Strengthening Implementation ==

<span id="implications-for-implementation-of-1.5c-consistent-pathways"></span>
=== 4.2.1 Implications for Implementation of 1.5°C-Consistent Pathways ===

<div id="section-4-2-1-block-1"></div>

The 1.5°C-consistent pathways assessed in Chapter 2 form the basis for the feasibility assessment in section 4.5. A wide range of 1.5°C-consistent pathways from integrated assessment modelling (IAM), supplemented by other literature, are assessed in Chapter 2 (Sections 2.1, 2.3, 2.4, and 2.5). The most common feature shared by these pathways is their requirement for faster and more radical changes compared to 2°C and higher warming pathways.

A variety of 1.5°C-consistent technological options and policy targets is identified in the assessed modelling literature (Sections 2.3, 2.4, 2.5). These technology and policy options include energy demand reduction, greater penetration of low-emission and carbon-free technologies as well as electrification of transport and industry, and reduction of land-use change. Both the detailed integrated modelling pathway literature and a number of broader sectoral and bottom-up studies provide examples of how these sectoral technological and policy characteristics can be broken down sectorally for 1.5°C-consistent pathways (see Table 4.1).

Both the integrated pathway literature and the sectoral studies agree on the need for rapid transitions in the production and use of energy across various sectors, to be consistent with limiting global warming to 1.5°C. The pace of these transitions is particularly significant for the supply mix and electrification (Table 4.1). Individual, sectoral studies may show higher rates of change compared to IAMs (Figueres et al., 2017; Rockström et al., 2017; WBCSD, 2017; Kuramochi et al., 2018) <sup>[[#fn:r22|22]]</sup> . These trends and transformation patterns create opportunities and challenges for both mitigation and adaptation (Sections 4.2.1.1 and 4.2.1.2) and have significant implications for the assessment of feasibility and enablers, including governance, institutions, and policy instruments addressed in Sections 4.3 and 4.4.

<div id="section-4-2-1-block-2"></div>

<span id="table-4.1"></span>
<!-- START TABLE -->
'''Table 4.1'''

Sectoral indicators of the pace of transformation in 1.5°C-consistent pathways, based on selected integrated pathways assessed in Chapter 2 (from the scenario database) and several other studies reviewed in Chapter 2 that assess mitigation transitions consistent with limiting warming to 1.5°C. Values for ‘1.5°C-no or -low-OS’’ indicate the median and the interquartile ranges for 1.5°C scenarios. If a number in square brackets is indicated, this is the number of scenarios for this indicator. S1, S2, S5 and LED represent the four illustrative pathway archetypes selected for this assessment (see Chapter 2, Section 2.1 and Supplementary Material 4.SM.1 for detailed description).

<!-- TABLE -->
{| class="wikitable"
|-
! rowspan="2" colspan="2"| Pathways
! rowspan="2"| Number<br />
of scenarios
! colspan="2"| Energy
! Buildings
! colspan="2"| Transport
! Industry
|-
! Share of renewables in primary energy [%]
! Share of renewables in electricity [%]
! Change in energy demand for buildings (2010 baseline) [%]
! Share of low-carbon fuels (electricity, hydrogen and biofuel) in transport [%]
! Share of electricity in transport [%]
! Industrial emissions reductions<br />
(2010 baseline) [%]
|-
! rowspan="6"| IAM<br />
Pathways<br />
2030
| 1.5C-no or low-OS
| 50
| 29 (37; 26)
| 54 (65; 47)
| 0 (7; −7) [42]
| 12 (18; 9) [29]
| 5 (7; 3) [49]
| 42 (55; 34) [42]
|-
| 1.5C-high-OS
| 35
| 24 (27; 20)
| 43 (54; 37)
| −17 (−12; −20) [29]
| 7 (8; 6) [23]
| 3 (5; 3)
| 18 (28; −13) [29]
|-
| S1
|
| 29
| 58
| −8
|
| 4
| 49
|-
| S2
|
| 29

| 48
| −14
| 5
| 4
| 19
|-
| S5
|
| 14

| 25
|
| 3
| 1
|
|-
| LED
|
| 37

| 60
| 30
|
| 21
| 42
|-
! rowspan="3"| Other Studies 2030
| Löffler et al. (2017)
|
| 46

| 79
|
|-
| IEA (2017c) (ETP)
|
| 31

| 47
| 2
| 14
| 5
| 22
|-
| IEA (2017g) (WEM)
|
| 27

| 50
| –6
| 17
| 6
| 15
|-
! rowspan="6"| IAM<br />
Pathways<br />
2050
| 1.5C-no or low-OS
| 50
| 60 (67; 52)
| 77 (86; 69)
| −17 (3; −36) [42]
| 55 (66; 35) [29]
| 23 (29; 17) [49]
| 79 (91; 67) [42]
|-
| 1.5C-high-OS
| 35
| 62 (68; 47)
| 82 (88; 64)
| −37 (−13; −51) [29]
| 38 (44; 27) [23]
| 18 (23; 14)
| 68 (81; 54) [29]
|-
| S1
|
| 58
| 81
| −21
|
| 34
| 74
|-
| S2
|
| 53

| 63
| −25
| 26
| 23
| 73
|-
| S5
|
| 67

| 70
|
| 53
| 10
|
|-
| LED
|
| 73

| 77
| 45
|
| 59
| 91
|-
! rowspan="3"| Other Studies 2050
| Löffler et al. (2017)
|
| 100

| 100
|
|-
| IEA (2017c) (ETP)
|
| 58

| 74
| 5
| 55
| 30
| 57
|-
| IEA (2017g) (WEM)
|
| 47

| 69
| −5
| 58
| 32
| 55
|}
<!-- END TABLE -->

<div id="section-4-2-1-1"></div>

<span id="challenges-and-opportunities-for-mitigation-along-the-reviewed-pathways"></span>
==== 4.2.1.1 Challenges and Opportunities for Mitigation Along the Reviewed Pathways ====

<div id="section-4-2-1-1-block-1"></div>

'''Greater scale, speed and change in investment patterns'''

There is agreement in the literature reviewed by Chapter 2 that staying below 1.5°C would entail significantly greater transformation in terms of energy systems, lifestyles and investments patterns compared to 2°C-consistent pathways. Yet there is  ''limited evidence''  and  ''low agreement''  regarding the magnitudes and costs of the investments (Sections 2.5.1, 2.5.2 and 4.4.5). Based on the IAM literature reviewed in Chapter 2, climate policies in line with limiting warming to 1.5°C would require a marked upscaling of supply-side energy system investments between now and mid-century, reaching levels of between 1.6–3.8 trillion USD yr <sup>−1</sup>  globally with an average of about 3.5 trillion USD yr <sup>−1</sup>  over 2016–2050 (see Figure 2.27). This can be compared to an average of about 3.0 trillion USD yr <sup>−1</sup>  over the same period for 2°C-consistent pathways (also in Figure 2.27).

Not only the level of investment but also the type and speed of sectoral transformation would be impacted by the transitions associated with 1.5°C-consistent pathways. IAM literature projects that investments in low-emission energy would overtake fossil fuel investments globally by 2025 in 1.5°C-consistent pathways (Chapter 2, Section 2.5.2). The projected low-emission investments in electricity generation allocations over the period 2016–2050 are: solar (0.09–1.0 trillion USD yr <sup>−1</sup> ), wind (0.1–0.35 trillion USD yr <sup>−1</sup> ), nuclear (0.1–0.25 trillion USD yr <sup>−1</sup> ), and transmission, distribution, and storage (0.3–1.3 trillion USD yr <sup>−1</sup> ). In contrast, investments in fossil fuel extraction and unabated fossil electricity generation along a 1.5°C-consistent pathway are projected to drop by 0.3–0.85 trillion USD yr <sup>−1</sup>  over the period 2016–2050, with investments in unabated coal generation projected to halt by 2030 in most 1.5°C-consistent pathways (Chapter 2, Section 2.5.2). Estimates of investments in other infrastructure are currently unavailable, but they could be considerably larger in volume than solely those in the energy sector (Section 4.4.5).

'''Greater policy design and decision-making implications'''

The 1.5°C-consistent pathways raise multiple challenges for effective policy design and responses to address the scale, speed, and pace of mitigation technology, finance and capacity building needs. These policies and responses would also need to deal with their distributional implications while addressing adaptation to residual climate impacts (see Chapter 5). The available literature indicates that 1.5°C-consistent pathways would require robust, stringent and urgent transformative policy interventions targeting the decarbonization of energy supply, electrification, fuel switching, energy efficiency, land-use change, and lifestyles (Chapter 2, Section 2.5, 4.4.2, 4.4.3). Examples of effective approaches to integrate mitigation with adaptation in the context of sustainable development and to deal with distributional implications proposed in the literature include the utilization of dynamic adaptive policy pathways (Haasnoot et al., 2013; Mathy et al., 2016) <sup>[[#fn:r29|29]]</sup> and transdisciplinary knowledge systems (Bendito and Barrios, 2016) <sup>[[#fn:r30|30]]</sup> .

Yet, even with good policy design and effective implementation, 1.5°C-consistent pathways would incur higher costs. Projections of the magnitudes of global economic costs associated with 1.5°C-consistent pathways and their sectoral and regional distributions from the currently assessed literature are scant, yet suggestive. For example, IAM simulations assessed in Chapter 2 project (with a probability greater than 50%) that marginal abatement costs, typically represented in IAMs through a carbon price, would increase by about 3–4 times by 2050 under a 1.5°C-consistent pathway compared to a 2°C-consistent pathway (Chapter 2, Section 2.5.2, Figure 2.26). Managing these costs and distributional effects would require an approach that takes account of unintended cross-sector, cross-nation, and cross-policy trade-offs during the transition (Droste et al., 2016; Stiglitz et al., [[IPCC:Sr15:About:Error-protocol:#errata1|Pollitt, 2018;]] 2017; Sands, 2018; Siegmeier et al., 2018) <sup>[[#fn:r31|31]]</sup> .

'''Greater sustainable development implications'''

Few studies address the relations between the Shared Socio-Economic Pathways (SSPs) and the Sustainable Developments Goals (SDGs) (O’Neill et al., 2015; Riahi et al., 2017) <sup>[[#fn:r32|32]]</sup> . Nonetheless, literature on potential synergies and trade-offs between 1.5°C-consistent mitigation pathways and sustainable development dimensions is emerging (Chapter 2, Section 2.5.3, Chapter 5, Section 5.4). Areas of potential trade-offs include reduction in final energy demand in relation to SDG 7 (the universal clean energy access goal) and increase of biomass production in relation to land use, water resources, food production, biodiversity and air quality (Chapter 2, Sections 2.4.3, 2.5.3). Strengthening the institutional and policy responses to deal with these challenges is discussed in Section 4.4 together with the linkage between disruptive changes in the energy sector and structural changes in other infrastructure (transport, building, water and telecommunication) sectors. A more in-depth assessment of the complexity and interfaces between 1.5°C-consistent pathways and sustainable development is presented in Chapter 5.

<div id="section-4-2-1-2"></div>

<span id="implications-for-adaptation-along-the-reviewed-pathways"></span>
==== 4.2.1.2 Implications for Adaptation Along the Reviewed Pathways ====

<div id="section-4-2-1-2-block-1"></div>

Climate variability and uncertainties in the underlying assumptions in Chapter 2’s IAMs as well as in model comparisons complicate discerning the implications for climate impacts, adaptation options and avoided adaptation investments at the global level of 2°C compared to 1.5°C warming (James et al., 2017; Mitchell et al., 2017) <sup>[[#fn:r33|33]]</sup> .

Incremental warming from 1.5°C to 2°C would lead to significant increases in temperature and precipitation extremes in many regions (Chapter 3, Section 3.3.2, 3.3.3). Those projected changes in climate extremes under both warming levels, however, depend on the emissions pathways, as they have different greenhouse gas (GHG)/aerosol forcing ratios. Impacts are sector-, system- and region-specific, as described in Chapter 3. For example, precipitation-related impacts reveal distinct regional differences (Chapter 3, Sections 3.3.3, 3.3.4, 3.3.5, 3.4.2). Similarly, regional reduction in water availability and the lengthening of regional dry spells have negative implications for agricultural yields depending on crop types and world regions (see for example Chapter 3, Sections 3.3.4, 3.4.2, 3.4.6).

Adaptation helps reduce impacts and risks. However, adaptation has limits. Not all systems can adapt, and not all impacts can be reversed (Cross-Chapter Box 12 in Chapter 5). For example, tropical coral reefs are projected to be at risk of severe degradation due to temperature-induced bleaching (Chapter 3, Box 3.4).

<span id="system-transitions-and-rates-of-change"></span>
=== 4.2.2 System Transitions and Rates of Change ===

<div id="section-4-2-2-block-1"></div>

Society-wide transformation involves socio-technical transitions and social-ecological resilience (Gillard et al., 2016) <sup>[[#fn:r34|34]]</sup> . Transitional adaptation pathways would need to respond to low-emission energy and economic systems, and the socio-technical transitions for mitigation involve removing barriers in social and institutional processes that could also benefit adaptation (Pant et al., 2015; Geels et al., 2017; Ickowitz et al., 2017) <sup>[[#fn:r35|35]]</sup> . In this chapter, transformative change is framed in mitigation around socio-technical transitions, and in adaptation around socio-ecological transitions. In both instances, emphasis is placed on the enabling role of institutions (including markets, and formal and informal regulation). 1.5°C-consistent pathways and adaptation needs associated with warming of 1.5°C imply both incremental and rapid, disruptive and transformative changes.

<div id="section-4-2-2-1"></div>

<span id="mitigation-historical-rates-of-change-and-state-of-decoupling"></span>
==== 4.2.2.1 Mitigation: historical rates of change and state of decoupling ====

<div id="section-4-2-2-1-block-1"></div>

Realizing 1.5°C-consistent pathways would require rapid and systemic changes on unprecedented scales (see Chapter 2 and Section 4.2.1). This section examines whether the needed rates of change have historical precedents and are underway.

Some studies conduct a de-facto validation of IAM projections. For CO <sub>2</sub> emission intensity over 1990–2010, this resulted in the IAMs projecting declining emission intensities while actual observations showed an increase. For individual technologies (in particular solar energy), IAM projections have been conservative regarding deployment rates and cost reductions (Creutzig et al., 2017) <sup>[[#fn:r36|36]]</sup> , suggesting that IAMs do not always impute actual rates of technological change resulting from influence of shocks, broader changes and mutually reinforcing factors in society and politics (Geels and Schot, 2007; Daron et al., 2015; Sovacool, 2016; Battiston et al., 2017) <sup>[[#fn:r37|37]]</sup> .

Other studies extrapolate historical trends into the future (Höök et al., 2011; Fouquet, 2016) <sup>[[#fn:r38|38]]</sup> , or contrast the rates of change associated with specific temperature limits in IAMs (such as those in Chapter 2) with historical trends to investigate plausibility of emission pathways and associated temperature limits (Wilson et al., 2013; Gambhir et al., 2017; Napp et al., 2017) <sup>[[#fn:r39|39]]</sup> . When metrics are normalized to gross domestic product (GDP; as opposed to other normalization metrics such as primary energy), low-emission technology deployment rates used by IAMs over the course of the coming century are shown to be broadly consistent with past trends, but rates of change in emission intensity are typically overestimated (Wilson et al., 2013; Loftus et al., 2014; van Sluisveld et al., 2015) <sup>[[#fn:r40|40]]</sup> . This bias is consistent with the findings from the ‘validation’ studies cited above, suggesting that IAMs may under-report the potential for supply-side technological change assumed in 1.5°-consistent pathways, but may be more optimistic about the systemic ability to realize incremental changes in reduction of emission intensity as a consequence of favourable energy efficiency payback times (Wilson et al., 2013) <sup>[[#fn:r41|41]]</sup> . This finding suggests that barriers and enablers other than costs and climate limits play a role in technological change, as also found in the innovation literature (Hekkert et al., 2007; Bergek et al., 2008; Geels et al., 2016b) <sup>[[#fn:r42|42]]</sup> .

One barrier to a greater rate of change in energy systems is that economic growth in the past has been coupled to the use of fossil fuels. Disruptive innovation and socio-technical changes could enable the decoupling of economic growth from a range of environmental drivers, including the consumption of fossil fuels, as represented by 1.5°C-consistent pathways (UNEP, 2014; Newman, 2017) <sup>[[#fn:r43|43]]</sup> . This may be relative decoupling due to rebound effects that see financial savings generated by renewable energy used in the consumption of new products and services (Jackson and Senker, 2011; Gillingham et al., 2013) <sup>[[#fn:r44|44]]</sup> , but in 2015 and 2016 total global GHG emissions have decoupled absolutely from economic growth (IEA, 2017g; Peters et al., 2017) <sup>[[#fn:r45|45]]</sup> . A longer data trend would be needed before stable decoupling can be established. The observed decoupling in 2015 and 2016 was driven by absolute declines in both coal and oil use since the early 2000s in Europe, in the past seven years in the United States and Australia, and more recently in China (Newman, 2017) <sup>[[#fn:r46|46]]</sup> . In 2017, decoupling in China reversed by 2% due to a drought and subsequent replacement of hydropower with coal-fired power (Tollefson, 2017) <sup>[[#fn:r47|47]]</sup> , but this reversal is expected to be temporary (IEA, 2017c) <sup>[[#fn:r48|48]]</sup> . Oil consumption in China is still rising slowly, but absolute decoupling is ongoing in megacities like Beijing (Gao and Newman, 2018) <sup>[[#fn:r49|49]]</sup> (see Box 4.9).

<div id="section-4-2-2-2"></div>

<span id="transformational-adaptation"></span>
==== 4.2.2.2 Transformational adaptation ====

<div id="section-4-2-2-2-block-1"></div>

In some regions and places, incremental adaptation would not be sufficient to mitigate the impacts of climate change on social-ecological systems (see Chapter 3). Transformational adaptation would then be required (Bahadur and Tanner, 2014; Pant et al., 2015; Gillard, 2016; Gillard et al., 2016; Colloff et al., 2017; Termeer et al., 2017) <sup>[[#fn:r50|50]]</sup> . Transformational adaptation refers to actions aiming at adapting to climate change resulting in significant changes in structure or function that go beyond adjusting existing practices (Dowd et al., 2014; IPCC, 2014a; Few et al., 2017) <sup>[[#fn:r51|51]]</sup> , including approaches that enable new ways of decision-making on adaptation (Colloff et al., 2017) <sup>[[#fn:r52|52]]</sup> . Few studies have assessed the potentially transformative character of adaptation options (Pelling et al., 2015; Rippke et al., 2016; Solecki et al., 2017) <sup>[[#fn:r53|53]]</sup> , especially in the context of warming of 1.5°C.

Transformational adaptation can be adopted at a large scale, can lead to new strategies in a region or resource system, transform places and potentially shift locations (Kates et al., 2012) <sup>[[#fn:r54|54]]</sup> . Some systems might require transformational adaptation at 1.5°C. Implementing adaptation policies in anticipation of 1.5°C would require transformation and flexible planning of adaptation (sometimes called adaptation pathways) (Rothman et al., 2014; Smucker et al., 2015; Holland, 2017; Gajjar et al., 2018) <sup>[[#fn:r55|55]]</sup> , an understanding of the varied stakeholders involved and their motives, and knowledge of less visible aspects of vulnerability based on social, cultural, political, and economic factors (Holland, 2017) <sup>[[#fn:r56|56]]</sup> . Transformational adaptation would seek deep and long-term societal changes that influence sustainable development (Chung Tiam Fook, 2017; Few et al., 2017) <sup>[[#fn:r57|57]]</sup> .

Adaptation requires multidisciplinary approaches integrating scientific, technological and social dimensions. For example, a framework for transformational adaptation and the integration of mitigation and adaptation pathways can transform rural indigenous communities to address risks of climate change and other stressors (Thornton and Comberti, 2017) <sup>[[#fn:r58|58]]</sup> . In villages in rural Nepal, transformational adaptation has taken place, with villagers changing their agricultural and pastoralist livelihood strategies after years of lost crops due to changing rain patterns and degradation of natural resources (Thornton and Comberti, 2017) <sup>[[#fn:r59|59]]</sup> . Instead, they are now opening stores, hotels, and tea shops. In another case, the arrival of an oil pipeline altered traditional Alaskan communities’ livelihoods. With growth of oil production, investments were made for rural development. A later drop in oil production decreased these investments. Alaskan indigenous populations are also dealing with impacts of climate change, such as sea level rise, which is altering their livelihood sources. Transformational adaptation is taking place by changing the energy matrix to renewable energy, in which indigenous people apply their knowledge to achieve environmental, economic, and social benefits (Thornton and Comberti, 2017) <sup>[[#fn:r60|60]]</sup> .

<div id="section-4-2-2-3"></div>

<span id="disruptive-innovation"></span>
==== 4.2.2.3 Disruptive innovation ====

<div id="section-4-2-2-3-block-1"></div>

Demand-driven disruptive innovations that emerge as the product of political and social changes across multiple scales can be transformative (Seba, 2014; Christensen et al., 2015; Green and Newman, 2017a) <sup>[[#fn:r61|61]]</sup> . Such innovations would lead to simultaneous, profound changes in behaviour, economies and societies (Seba, 2014; Christensen et al. 2015), but are difficult to predict in supply-focused economic models (Geels et al., 2016a; Pindyck, 2017) <sup>[[#fn:r62|62]]</sup> . Rapid socio-technical change has been observed in the solar industry (Creutzig et al. (2017) <sup>[[#fn:r63|63]]</sup> . Similar changes to socio-ecological systems can stimulate adaptation and mitigation options that lead to more climate-resilient systems (Adger et al., 2005; Ostrom, 2009; Gillard et al., 2016) <sup>[[#fn:r64|64]]</sup> (see the Alaska and Nepal examples in Section 4.2.2.2). The increase in roof-top solar and energy storage technology as well as the increase in passive housing and net zero-emissions buildings are further examples of such disruptions (Green and Newman, 2017b) <sup>[[#fn:r65|65]]</sup> . Both roof-top solar and energy storage have benefitted from countries’ economic growth strategies and associated price declines in photovoltaic technologies, particularly in China (Shrivastava and Persson, 2018) <sup>[[#fn:r66|66]]</sup> , as well as from new information and communication technologies (Koomey et al., 2013) <sup>[[#fn:r67|67]]</sup> , rising demand for electricity in urban areas, and global concern regarding greenhouse gas emissions (Azeiteiro and Leal Filho, 2017; Lutz and Muttarak, 2017; Wamsler, 2017) <sup>[[#fn:r68|68]]</sup> .

System co-benefits can create the potential for mutually enforcing and demand-driven climate responses (Jordan et al., 2015; Hallegatte and Mach, 2016; Pelling et al., 2018) <sup>[[#fn:r69|69]]</sup> , and for rapid and transformational change (Cole, 2015; Geels et al., 2016b; Hallegatte and Mach, 2016) <sup>[[#fn:r70|70]]</sup> . Examples of co-benefits include gender equality, agricultural productivity (Nyantakyi-Frimpong and Bezner-Kerr, 2015) <sup>[[#fn:r71|71]]</sup> , reduced indoor air pollution (Satterthwaite and Bartlett, 2017) <sup>[[#fn:r72|72]]</sup> , flood buffering (Colenbrander et al., 2017) <sup>[[#fn:r73|73]]</sup> , livelihood support (Shaw et al., 2014; Ürge-Vorsatz et al., 2014) <sup>[[#fn:r74|74]]</sup> , economic growth (GCEC, 2014; Stiglitz et al., 2017) <sup>[[#fn:r75|75]]</sup> , social progress (Steg et al., 2015; Hallegatte and Mach, 2016) <sup>[[#fn:r76|76]]</sup> and social justice (Ziervogel et al., 2017; Patterson et al., 2018) <sup>[[#fn:r77|77]]</sup> .

Innovations that disrupt entire systems may leave firms and utilities with stranded assets, as the transition can happen very quickly (IPCC, 2014b; Kossoy et al., 2015) <sup>[[#fn:r78|78]]</sup> . This may have consequences for fossil fuels that are rendered ‘unburnable’ (McGlade and Ekins, 2015) <sup>[[#fn:r79|79]]</sup> and fossil fuel-fired power and industry assets that would become obsolete (Caldecott, 2017; Farfan and Breyer, 2017) <sup>[[#fn:r80|80]]</sup> . The presence of multiple barriers and enablers operating in a system implies that rapid change, whether the product of many small changes (Termeer et al., 2017) <sup>[[#fn:r81|81]]</sup> or large-scale disruptions, is seldom an insular or discrete process (Sterling et al., 2017) <sup>[[#fn:r82|82]]</sup> . This finding informs the multidimensional nature of feasibility in Cross-Chapter Box 3 in Chapter 1 which is applied in Section 4.5. Climate responses that are aligned with multiple feasibility dimensions and combine adaptation and mitigation interventions with non-climate benefits can accelerate change and reduce risks and costs (Fazey et al., 2018) <sup>[[#fn:r83|83]]</sup> . Also political, social and technological influences on energy transitions, for example, can accelerate them faster than narrow techno-economic analysis suggests is possible (Kern and Rogge, 2016) <sup>[[#fn:r84|84]]</sup> , but could also introduce new constraints and risks (Geels et al., 2016b; Sovacool, 2016; Eyre et al., 2018) <sup>[[#fn:r85|85]]</sup> .

Disruptive innovation and technological change may play a role in mitigation and in adaptation. The next section assesses mitigation and adaptation options in energy, land and ecosystem, urban and infrastructure and industrial systems.

<span id="systemic-changes-for-1.5c-consistent-pathways"></span>