Summary for Policymakers

Summary for Policymakers

SPM

SPM

SPM

Summaryfor Policymakers
Drafting Authors:
Myles Allen (UK), Mustafa Babiker (Sudan), Yang Chen (China), Heleen de Coninck(Netherlands/EU), Sarah Connors (UK), Renée van Diemen (Netherlands), Opha Pauline
Dube (Botswana), Kristie L. Ebi (USA), Francois Engelbrecht (South Africa), Marion Ferrat(UK/France), James Ford (UK/Canada), Piers Forster (UK), Sabine Fuss (Germany), Tania
Guillén Bolaños (Germany/Nicaragua), Jordan Harold (UK), Ove Hoegh-Guldberg (Australia),
Jean-Charles Hourcade (France), Daniel Huppmann (Austria), Daniela Jacob (Germany),
Kejun Jiang (China), Tom Gabriel Johansen (Norway), Mikiko Kainuma (Japan), Kiane deKleijne (Netherlands/EU), Elmar Kriegler (Germany), Debora Ley (Guatemala/Mexico),
Diana Liverman (USA), Natalie Mahowald (USA), Valérie Masson-Delmotte (France),
J. B. Robin Matthews (UK), Richard Millar (UK), Katja Mintenbeck (Germany), Angela Morelli(Norway/Italy), Wilfran Moufouma-Okia (France/Congo), Luis Mundaca (Sweden/Chile),
Maike Nicolai (Germany), Chukwumerije Okereke (UK/Nigeria), Minal Pathak (India), Antony
Payne (UK), Roz Pidcock (UK), Anna Pirani (Italy), Elvira Poloczanska (UK/Australia), HansOtto Pörtner (Germany), Aromar Revi (India), Keywan Riahi (Austria), Debra C. Roberts(South Africa), Joeri Rogelj (Austria/Belgium), Joyashree Roy (India), Sonia I. Seneviratne(Switzerland), Priyadarshi R. Shukla (India), James Skea (UK), Raphael Slade (UK), Drew
Shindell (USA), Chandni Singh (India), William Solecki (USA), Linda Steg (Netherlands),
Michael Taylor (Jamaica), Petra Tschakert (Australia/Austria), Henri Waisman (France),
Rachel Warren (UK), Panmao Zhai (China), Kirsten Zickfeld (Canada).

This Summary for Policymakers should be cited as:
IPCC, 2018: Summary for Policymakers. In: Global Warming of 1.5°C. An IPCC Special Report on the impactsof global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways,
in the context of strengthening the global response to the threat of climate change, sustainable development,
and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla,
A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis,
E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In Press.

3

Summary for Policymakers

Introduction
This Report responds to the invitation for IPCC ‘... to provide a Special Report in 2018 on the impacts of global warming of 1.5°C
above pre-industrial levels and related global greenhouse gas emission pathways’ contained in the Decision of the 21st Conferenceof Parties of the United Nations Framework Convention on Climate Change to adopt the Paris Agreement.1

SPM

The IPCC accepted the invitation in April 2016, deciding to prepare this Special Report on the impacts of global warming of
1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global
response to the threat of climate change, sustainable development, and efforts to eradicate poverty.
This Summary for Policymakers (SPM) presents the key findings of the Special Report, based on the assessment of the available
scientific, technical and socio-economic literature2 relevant to global warming of 1.5°C and for the comparison between global
warming of 1.5°C and 2°C above pre-industrial levels. The level of confidence associated with each key finding is reported using
the IPCC calibrated language.3 The underlying scientific basis of each key finding is indicated by references provided to chapter
elements. In the SPM, knowledge gaps are identified associated with the underlying chapters of the Report.

A.

Understanding Global Warming of 1.5°C

A.1

Human activities are estimated to have caused approximately 1.0°C of global warming5 above
pre-industrial levels, with a likely range of 0.8°C to 1.2°C. Global warming is likely to reach 1.5°C
between 2030 and 2052 if it continues to increase at the current rate. (high confidence) (Figure
SPM.1) {1.2}

A.1.1

Reflecting the long-term warming trend since pre-industrial times, observed global mean surface temperature (GMST) for
the decade 2006–2015 was 0.87°C (likely between 0.75°C and 0.99°C)6 higher than the average over the 1850–1900
period (very high confidence). Estimated anthropogenic global warming matches the level of observed warming to within±20% (likely range). Estimated anthropogenic global warming is currently increasing at 0.2°C (likely between 0.1°C and
0.3°C) per decade due to past and ongoing emissions (high confidence). {1.2.1, Table 1.1, 1.2.4}

A.1.2

Warming greater than the global annual average is being experienced in many land regions and seasons, including two to
three times higher in the Arctic. Warming is generally higher over land than over the ocean. (high confidence) {1.2.1, 1.2.2,
Figure 1.1, Figure 1.3, 3.3.1, 3.3.2}

A.1.3

Trends in intensity and frequency of some climate and weather extremes have been detected over time spans during which
about 0.5°C of global warming occurred (medium confidence). This assessment is based on several lines of evidence,
including attribution studies for changes in extremes since 1950. {3.3.1, 3.3.2, 3.3.3}

4

1 Decision 1/CP.21, paragraph 21.
2 The assessment covers literature accepted for publication by 15 May 2018.
3 Each finding is grounded in an evaluation of underlying evidence and agreement. A level of confidence is expressed using five qualifiers: very low, low, medium, high and very high, andtypeset in italics, for example, medium confidence. The following terms have been used to indicate the assessed likelihood of an outcome or a result: virtually certain 99–100%
probability, very likely 90–100%, likely 66–100%, about as likely as not 33–66%, unlikely 0–33%, very unlikely 0–10%, exceptionally unlikely 0–1%. Additional terms (extremely likely
95–100%, more likely than not >50–100%, more unlikely than likely 0–<50%, extremely unlikely 0–5%) may also be used when appropriate. Assessed likelihood is typeset in italics,
for example, very likely. This is consistent with AR5.
4 See also Box SPM.1: Core Concepts Central to this Special Report.
5 Present level of global warming is defined as the average of a 30-year period centred on 2017 assuming the recent rate of warming continues.
6 This range spans the four available peer-reviewed estimates of the observed GMST change and also accounts for additional uncertainty due to possible short-term natural variability.
{1.2.1, Table 1.1}

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Summary for Policymakers

A.2

Warming from anthropogenic emissions from the pre-industrial period to the present will persist for
centuries to millennia and will continue to cause further long-term changes in the climate system,
such as sea level rise, with associated impacts (high confidence), but these emissions alone areunlikely to cause global warming of 1.5°C (medium confidence). (Figure SPM.1) {1.2, 3.3, Figure 1.5}

A.2.1

Anthropogenic emissions (including greenhouse gases, aerosols and their precursors) up to the present are unlikely tocause further warming of more than 0.5°C over the next two to three decades (high confidence) or on a century time scale(medium confidence). {1.2.4, Figure 1.5}

A.2.2

Reaching and sustaining net zero global anthropogenic CO2 emissions and declining net non-CO2 radiative forcing would
halt anthropogenic global warming on multi-decadal time scales (high confidence). The maximum temperature reached isthen determined by cumulative net global anthropogenic CO2 emissions up to the time of net zero CO2 emissions (highconfidence) and the level of non-CO2 radiative forcing in the decades prior to the time that maximum temperatures arereached (medium confidence). On longer time scales, sustained net negative global anthropogenic CO2 emissions and/or further reductions in non-CO2 radiative forcing may still be required to prevent further warming due to Earth system
feedbacks and to reverse ocean acidification (medium confidence) and will be required to minimize sea level rise (highconfidence). {Cross-Chapter Box 2 in Chapter 1, 1.2.3, 1.2.4, Figure 1.4, 2.2.1, 2.2.2, 3.4.4.8, 3.4.5.1, 3.6.3.2}

A.3

Climate-related risks for natural and human systems are higher for global warming of 1.5°C thanat present, but lower than at 2°C (high confidence). These risks depend on the magnitude and rateof warming, geographic location, levels of development and vulnerability, and on the choices andimplementation of adaptation and mitigation options (high confidence). (Figure SPM.2) {1.3, 3.3,
3.4, 5.6}

A.3.1

Impacts on natural and human systems from global warming have already been observed (high confidence). Many land andocean ecosystems and some of the services they provide have already changed due to global warming (high confidence).
(Figure SPM.2) {1.4, 3.4, 3.5}

A.3.2

Future climate-related risks depend on the rate, peak and duration of warming. In the aggregate, they are larger if global
warming exceeds 1.5°C before returning to that level by 2100 than if global warming gradually stabilizes at 1.5°C, especially
if the peak temperature is high (e.g., about 2°C) (high confidence). Some impacts may be long-lasting or irreversible, suchas the loss of some ecosystems (high confidence). {3.2, 3.4.4, 3.6.3, Cross-Chapter Box 8 in Chapter 3}

A.3.3

Adaptation and mitigation are already occurring (high confidence). Future climate-related risks would be reduced by the
upscaling and acceleration of far-reaching, multilevel and cross-sectoral climate mitigation and by both incremental andtransformational adaptation (high confidence). {1.2, 1.3, Table 3.5, 4.2.2, Cross-Chapter Box 9 in Chapter 4, Box 4.2, Box
4.3, Box 4.6, 4.3.1, 4.3.2, 4.3.3, 4.3.4, 4.3.5, 4.4.1, 4.4.4, 4.4.5, 4.5.3}

SPM

5

Summary for Policymakers

Cumulative emissions of CO2 and future non-CO2 radiative forcing determine
the probability of limiting warming to 1.5°Ca) Observed global temperature change and modeled
responses to stylized anthropogenic emission and forcing pathways

SPM

Global warming relative to 1850-1900 (°C)
2.0

1.5

Observed monthly global
mean surface temperature

1.0

Estimated anthropogenic
warming to date andlikely range
Likely range of modeled responses to stylized pathways
Global CO2 emissions reach net zero in 2055 while net
non-CO2 radiative forcing is reduced after 2030 (grey in b, c & d)

0.5

Faster CO2 reductions (blue in b & c) result in a higher
probability of limiting warming to 1.5°C

2017

No reduction of net non-CO2 radiative forcing (purple in d)results in a lower probability of limiting warming to 1.5°C
0
1960

1980

2000

2020

b) Stylized net global CO2 emission pathways
Billion tonnes CO2 per year (GtCO2/yr)

2040

2060

c) Cumulative net CO2 emissions
Billion tonnes CO2 (GtCO2)

2080

2100

d) Non-CO2 radiative forcing pathways
Watts per square metre (W/m2)

60

CO2 emissions
decline from 2020
to reach net zero in
2055 or 2040

50
40

3 000

3

2 000

2

30
20

Cumulative CO2
emissions in pathways
reaching net zero in
2055 and 2040

1 000

10
0

0
1980

2020

2060

2100

Faster immediate CO2 emission reductions
limit cumulative CO2 emissions shown inpanel (c).

Non-CO2 radiative forcing
reduced after 2030 or
not reduced after 2030

1

0
1980

2020

2060

2100

1980

2020

2060

2100

Maximum temperature rise is determined by cumulative net CO2 emissions and net non-CO2
radiative forcing due to methane, nitrous oxide, aerosols and other anthropogenic forcing agents.

Figure SPM.1 | Panel a: Observed monthly global mean surface temperature (GMST, grey line up to 2017, from the HadCRUT4, GISTEMP, Cowtan–Way, andNOAA datasets) change and estimated anthropogenic global warming (solid orange line up to 2017, with orange shading indicating assessed likely range). Orange
dashed arrow and horizontal orange error bar show respectively the central estimate and likely range of the time at which 1.5°C is reached if the current rateof warming continues. The grey plume on the right of panel a shows the likely range of warming responses, computed with a simple climate model, to a stylized
pathway (hypothetical future) in which net CO2 emissions (grey line in panels b and c) decline in a straight line from 2020 to reach net zero in 2055 and net nonCO2 radiative forcing (grey line in panel d) increases to 2030 and then declines. The blue plume in panel a) shows the response to faster CO2 emissions reductions(blue line in panel b), reaching net zero in 2040, reducing cumulative CO2 emissions (panel c). The purple plume shows the response to net CO2 emissions declining
to zero in 2055, with net non-CO2 forcing remaining constant after 2030. The vertical error bars on right of panel a) show the likely ranges (thin lines) and central
terciles (33rd – 66th percentiles, thick lines) of the estimated distribution of warming in 2100 under these three stylized pathways. Vertical dotted error bars inpanels b, c and d show the likely range of historical annual and cumulative global net CO2 emissions in 2017 (data from the Global Carbon Project) and of net
non-CO2 radiative forcing in 2011 from AR5, respectively. Vertical axes in panels c and d are scaled to represent approximately equal effects on GMST. {1.2.1, 1.2.3,
1.2.4, 2.3, Figure 1.2 and Chapter 1 Supplementary Material, Cross-Chapter Box 2 in Chapter 1}

6

Summary for Policymakers

B.

Projected Climate Change, Potential Impacts and Associated Risks

B.1

Climate models project robust7 differences in regional climate characteristics between present-day
and global warming of 1.5°C,8 and between 1.5°C and 2°C.8 These differences include increases
in: mean temperature in most land and ocean regions (high confidence), hot extremes in mostinhabited regions (high confidence), heavy precipitation in several regions (medium confidence),
and the probability of drought and precipitation deficits in some regions (medium confidence).
{3.3}

B.1.1

Evidence from attributed changes in some climate and weather extremes for a global warming of about 0.5°C supports
the assessment that an additional 0.5°C of warming compared to present is associated with further detectable changes inthese extremes (medium confidence). Several regional changes in climate are assessed to occur with global warming up
to 1.5°C compared to pre-industrial levels, including warming of extreme temperatures in many regions (high confidence),
increases in frequency, intensity, and/or amount of heavy precipitation in several regions (high confidence), and an increase
in intensity or frequency of droughts in some regions (medium confidence). {3.2, 3.3.1, 3.3.2, 3.3.3, 3.3.4, Table 3.2}

B.1.2

Temperature extremes on land are projected to warm more than GMST (high confidence): extreme hot days in mid-latitudes
warm by up to about 3°C at global warming of 1.5°C and about 4°C at 2°C, and extreme cold nights in high latitudes warm
by up to about 4.5°C at 1.5°C and about 6°C at 2°C (high confidence). The number of hot days is projected to increase inmost land regions, with highest increases in the tropics (high confidence). {3.3.1, 3.3.2, Cross-Chapter Box 8 in Chapter 3}

B.1.3

Risks from droughts and precipitation deficits are projected to be higher at 2°C compared to 1.5°C of global warming insome regions (medium confidence). Risks from heavy precipitation events are projected to be higher at 2°C compared to
1.5°C of global warming in several northern hemisphere high-latitude and/or high-elevation regions, eastern Asia andeastern North America (medium confidence). Heavy precipitation associated with tropical cyclones is projected to behigher at 2°C compared to 1.5°C global warming (medium confidence). There is generally low confidence in projectedchanges in heavy precipitation at 2°C compared to 1.5°C in other regions. Heavy precipitation when aggregated at global
scale is projected to be higher at 2°C than at 1.5°C of global warming (medium confidence). As a consequence of heavy
precipitation, the fraction of the global land area affected by flood hazards is projected to be larger at 2°C compared to
1.5°C of global warming (medium confidence). {3.3.1, 3.3.3, 3.3.4, 3.3.5, 3.3.6}

B.2

By 2100, global mean sea level rise is projected to be around 0.1 metre lower with global warmingof 1.5°C compared to 2°C (medium confidence). Sea level will continue to rise well beyond 2100
(high confidence), and the magnitude and rate of this rise depend on future emission pathways.
A slower rate of sea level rise enables greater opportunities for adaptation in the human andecological systems of small islands, low-lying coastal areas and deltas (medium confidence).
{3.3, 3.4, 3.6}

B.2.1

Model-based projections of global mean sea level rise (relative to 1986–2005) suggest an indicative range of 0.26 to 0.77
m by 2100 for 1.5°C of global warming, 0.1 m (0.04–0.16 m) less than for a global warming of 2°C (medium confidence).
A reduction of 0.1 m in global sea level rise implies that up to 10 million fewer people would be exposed to related risks,
based on population in the year 2010 and assuming no adaptation (medium confidence). {3.4.4, 3.4.5, 4.3.2}

B.2.2

Sea level rise will continue beyond 2100 even if global warming is limited to 1.5°C in the 21st century (high confidence).
Marine ice sheet instability in Antarctica and/or irreversible loss of the Greenland ice sheet could result in multi-metre risein sea level over hundreds to thousands of years. These instabilities could be triggered at around 1.5°C to 2°C of global
warming (medium confidence). (Figure SPM.2) {3.3.9, 3.4.5, 3.5.2, 3.6.3, Box 3.3}

SPM

7 Robust is here used to mean that at least two thirds of climate models show the same sign of changes at the grid point scale, and that differences in large regions are statisticallysignificant.
8 Projected changes in impacts between different levels of global warming are determined with respect to changes in global mean surface air temperature.

7

Summary for Policymakers

B.2.3

Increasing warming amplifies the exposure of small islands, low-lying coastal areas and deltas to the risks associated withsea level rise for many human and ecological systems, including increased saltwater intrusion, flooding and damage toinfrastructure (high confidence). Risks associated with sea level rise are higher at 2°C compared to 1.5°C. The slower rateof sea level rise at global warming of 1.5°C reduces these risks, enabling greater opportunities for adaptation including
managing and restoring natural coastal ecosystems and infrastructure reinforcement (medium confidence). (Figure SPM.2){3.4.5, Box 3.5}

B.3

On land, impacts on biodiversity and ecosystems, including species loss and extinction, areprojected to be lower at 1.5°C of global warming compared to 2°C. Limiting global warming to
1.5°C compared to 2°C is projected to lower the impacts on terrestrial, freshwater and coastal
ecosystems and to retain more of their services to humans (high confidence). (Figure SPM.2){3.4, 3.5, Box 3.4, Box 4.2, Cross-Chapter Box 8 in Chapter 3}

B.3.1

Of 105,000 species studied,9 6% of insects, 8% of plants and 4% of vertebrates are projected to lose over half of theirclimatically determined geographic range for global warming of 1.5°C, compared with 18% of insects, 16% of plants and
8% of vertebrates for global warming of 2°C (medium confidence). Impacts associated with other biodiversity-relatedrisks such as forest fires and the spread of invasive species are lower at 1.5°C compared to 2°C of global warming (highconfidence). {3.4.3, 3.5.2}

B.3.2

Approximately 4% (interquartile range 2–7%) of the global terrestrial land area is projected to undergo a transformationof ecosystems from one type to another at 1°C of global warming, compared with 13% (interquartile range 8–20%) at 2°C(medium confidence). This indicates that the area at risk is projected to be approximately 50% lower at 1.5°C compared to
2°C (medium confidence). {3.4.3.1, 3.4.3.5}

B.3.3

High-latitude tundra and boreal forests are particularly at risk of climate change-induced degradation and loss, with woody
shrubs already encroaching into the tundra (high confidence) and this will proceed with further warming. Limiting global
warming to 1.5°C rather than 2°C is projected to prevent the thawing over centuries of a permafrost area in the range of
1.5 to 2.5 million km2 (medium confidence). {3.3.2, 3.4.3, 3.5.5}

B.4

Limiting global warming to 1.5°C compared to 2°C is projected to reduce increases in ocean
temperature as well as associated increases in ocean acidity and decreases in ocean oxygen levels(high confidence). Consequently, limiting global warming to 1.5°C is projected to reduce risks
to marine biodiversity, fisheries, and ecosystems, and their functions and services to humans,
as illustrated by recent changes to Arctic sea ice and warm-water coral reef ecosystems (highconfidence). {3.3, 3.4, 3.5, Box 3.4, Box 3.5}

B.4.1

There is high confidence that the probability of a sea ice-free Arctic Ocean during summer is substantially lower at global
warming of 1.5°C when compared to 2°C. With 1.5°C of global warming, one sea ice-free Arctic summer is projected per
century. This likelihood is increased to at least one per decade with 2°C global warming. Effects of a temperature overshootare reversible for Arctic sea ice cover on decadal time scales (high confidence). {3.3.8, 3.4.4.7}

B.4.2

Global warming of 1.5°C is projected to shift the ranges of many marine species to higher latitudes as well as increase theamount of damage to many ecosystems. It is also expected to drive the loss of coastal resources and reduce the productivity offisheries and aquaculture (especially at low latitudes). The risks of climate-induced impacts are projected to be higher at 2°C
than those at global warming of 1.5°C (high confidence). Coral reefs, for example, are projected to decline by a further 70–90%
at 1.5°C (high confidence) with larger losses (>99%) at 2°C (very high confidence). The risk of irreversible loss of many marine
and coastal ecosystems increases with global warming, especially at 2°C or more (high confidence). {3.4.4, Box 3.4}

SPM

9 Consistent with earlier studies, illustrative numbers were adopted from one recent meta-study.

8Summary for Policymakers

B.4.3

The level of ocean acidification due to increasing CO2 concentrations associated with global warming of 1.5°C is projected toamplify the adverse effects of warming, and even further at 2°C, impacting the growth, development, calcification, survival,
and thus abundance of a broad range of species, for example, from algae to fish (high confidence). {3.3.10, 3.4.4}

B.4.4

Impacts of climate change in the ocean are increasing risks to fisheries and aquaculture via impacts on the physiology,
survivorship, habitat, reproduction, disease incidence, and risk of invasive species (medium confidence) but are projected tobe less at 1.5°C of global warming than at 2°C. One global fishery model, for example, projected a decrease in global annual
catch for marine fisheries of about 1.5 million tonnes for 1.5°C of global warming compared to a loss of more than 3 million
tonnes for 2°C of global warming (medium confidence). {3.4.4, Box 3.4}

B.5

Climate-related risks to health, livelihoods, food security, water supply, human security, andeconomic growth are projected to increase with global warming of 1.5°C and increase further with
2°C. (Figure SPM.2) {3.4, 3.5, 5.2, Box 3.2, Box 3.3, Box 3.5, Box 3.6, Cross-Chapter Box 6 in Chapter
3, Cross-Chapter Box 9 in Chapter 4, Cross-Chapter Box 12 in Chapter 5, 5.2}

B.5.1

Populations at disproportionately higher risk of adverse consequences with global warming of 1.5°C and beyond include
disadvantaged and vulnerable populations, some indigenous peoples, and local communities dependent on agricultural or
coastal livelihoods (high confidence). Regions at disproportionately higher risk include Arctic ecosystems, dryland regions,
small island developing states, and Least Developed Countries (high confidence). Poverty and disadvantage are expected
to increase in some populations as global warming increases; limiting global warming to 1.5°C, compared with 2°C, could
reduce the number of people both exposed to climate-related risks and susceptible to poverty by up to several hundred
million by 2050 (medium confidence). {3.4.10, 3.4.11, Box 3.5, Cross-Chapter Box 6 in Chapter 3, Cross-Chapter Box 9 in
Chapter 4, Cross-Chapter Box 12 in Chapter 5, 4.2.2.2, 5.2.1, 5.2.2, 5.2.3, 5.6.3}

B.5.2

Any increase in global warming is projected to affect human health, with primarily negative consequences (high confidence).
Lower risks are projected at 1.5°C than at 2°C for heat-related morbidity and mortality (very high confidence) and forozone-related mortality if emissions needed for ozone formation remain high (high confidence). Urban heat islands often
amplify the impacts of heatwaves in cities (high confidence). Risks from some vector-borne diseases, such as malaria anddengue fever, are projected to increase with warming from 1.5°C to 2°C, including potential shifts in their geographic range(high confidence). {3.4.7, 3.4.8, 3.5.5.8}

B.5.3

Limiting warming to 1.5°C compared with 2°C is projected to result in smaller net reductions in yields of maize, rice, wheat,and potentially other cereal crops, particularly in sub-Saharan Africa, Southeast Asia, and Central and South America, andin the CO2-dependent nutritional quality of rice and wheat (high confidence). Reductions in projected food availability arelarger at 2°C than at 1.5°C of global warming in the Sahel, southern Africa, the Mediterranean, central Europe, and the
Amazon (medium confidence). Livestock are projected to be adversely affected with rising temperatures, depending on theextent of changes in feed quality, spread of diseases, and water resource availability (high confidence). {3.4.6, 3.5.4, 3.5.5,
Box 3.1, Cross-Chapter Box 6 in Chapter 3, Cross-Chapter Box 9 in Chapter 4}

B.5.4

Depending on future socio-economic conditions, limiting global warming to 1.5°C compared to 2°C may reduce theproportion of the world population exposed to a climate change-induced increase in water stress by up to 50%, although
there is considerable variability between regions (medium confidence). Many small island developing states could
experience lower water stress as a result of projected changes in aridity when global warming is limited to 1.5°C, as
compared to 2°C (medium confidence). {3.3.5, 3.4.2, 3.4.8, 3.5.5, Box 3.2, Box 3.5, Cross-Chapter Box 9 in Chapter 4}

B.5.5

Risks to global aggregated economic growth due to climate change impacts are projected to be lower at 1.5°C than at
2°C by the end of this century10 (medium confidence). This excludes the costs of mitigation, adaptation investments andthe benefits of adaptation. Countries in the tropics and Southern Hemisphere subtropics are projected to experience thelargest impacts on economic growth due to climate change should global warming increase from 1.5°C to 2°C (medium
confidence). {3.5.2, 3.5.3}

SPM

10 Here, impacts on economic growth refer to changes in gross domestic product (GDP). Many impacts, such as loss of human lives, cultural heritage and ecosystem services, are difficult
to value and monetize.

9Summary for Policymakers

B.5.6

Exposure to multiple and compound climate-related risks increases between 1.5°C and 2°C of global warming, with greater
proportions of people both so exposed and susceptible to poverty in Africa and Asia (high confidence). For global warmingfrom 1.5°C to 2°C, risks across energy, food, and water sectors could overlap spatially and temporally, creating new andexacerbating current hazards, exposures, and vulnerabilities that could affect increasing numbers of people and regions(medium confidence). {Box 3.5, 3.3.1, 3.4.5.3, 3.4.5.6, 3.4.11, 3.5.4.9}

B.5.7

There are multiple lines of evidence that since AR5 the assessed levels of risk increased for four of the five Reasons forConcern (RFCs) for global warming to 2°C (high confidence). The risk transitions by degrees of global warming are now:
from high to very high risk between 1.5°C and 2°C for RFC1 (Unique and threatened systems) (high confidence); frommoderate to high risk between 1°C and 1.5°C for RFC2 (Extreme weather events) (medium confidence); from moderate tohigh risk between 1.5°C and 2°C for RFC3 (Distribution of impacts) (high confidence); from moderate to high risk between
1.5°C and 2.5°C for RFC4 (Global aggregate impacts) (medium confidence); and from moderate to high risk between 1°C
and 2.5°C for RFC5 (Large-scale singular events) (medium confidence). (Figure SPM.2) {3.4.13; 3.5, 3.5.2}

B.6

Most adaptation needs will be lower for global warming of 1.5°C compared to 2°C (high confidence).
There are a wide range of adaptation options that can reduce the risks of climate change (highconfidence). There are limits to adaptation and adaptive capacity for some human and natural
systems at global warming of 1.5°C, with associated losses (medium confidence). The number andavailability of adaptation options vary by sector (medium confidence). {Table 3.5, 4.3, 4.5, CrossChapter Box 9 in Chapter 4, Cross-Chapter Box 12 in Chapter 5}

B.6.1

A wide range of adaptation options are available to reduce the risks to natural and managed ecosystems (e.g., ecosystembased adaptation, ecosystem restoration and avoided degradation and deforestation, biodiversity management,sustainable aquaculture, and local knowledge and indigenous knowledge), the risks of sea level rise (e.g., coastal defenceand hardening), and the risks to health, livelihoods, food, water, and economic growth, especially in rural landscapes(e.g., efficient irrigation, social safety nets, disaster risk management, risk spreading and sharing, and communitybased adaptation) and urban areas (e.g., green infrastructure, sustainable land use and planning, and sustainable water
management) (medium confidence). {4.3.1, 4.3.2, 4.3.3, 4.3.5, 4.5.3, 4.5.4, 5.3.2, Box 4.2, Box 4.3, Box 4.6, Cross-Chapter
Box 9 in Chapter 4}.

B.6.2

Adaptation is expected to be more challenging for ecosystems, food and health systems at 2°C of global warming than for
1.5°C (medium confidence). Some vulnerable regions, including small islands and Least Developed Countries, are projected
to experience high multiple interrelated climate risks even at global warming of 1.5°C (high confidence). {3.3.1, 3.4.5,
Box 3.5, Table 3.5, Cross-Chapter Box 9 in Chapter 4, 5.6, Cross-Chapter Box 12 in Chapter 5, Box 5.3}

B.6.3

Limits to adaptive capacity exist at 1.5°C of global warming, become more pronounced at higher levels of warming andvary by sector, with site-specific implications for vulnerable regions, ecosystems and human health (medium confidence).
{Cross-Chapter Box 12 in Chapter 5, Box 3.5, Table 3.5}

SPM

10

Summary for Policymakers

How the level of global warming affects impacts and/or risks associated with
the Reasons for Concern (RFCs) and selected natural, managed and humansystems
Five Reasons For Concern (RFCs) illustrate the impacts and risks ofdifferent levels of global warming for people, economies and ecosystems
across sectors and regions.

SPM

Purple indicates very highrisks of severe impacts/risks
and the presence ofsignificant irreversibility or
the persistence ofclimate-related hazards,
combined with limited
ability to adapt due to thenature of the hazard or
impacts/risks.
Red indicates severe andwidespread impacts/risks.
Yellow indicates that
impacts/risks are detectable
and attributable to climatechange with at least medium
confidence.
White indicates that no
impacts are detectable andattributable to climatechange.

Global mean surface temperature change
relative to pre-industrial levels (0C)

Impacts and risks associated with the Reasons for Concern (RFCs)
Very high
M

2.0
H

H
1.5

H

M

M

M

1.0

M

Moderate

2006-2015

M-H

H

H

High

Undetectable0

RFC1
Unique andthreatenedsystems

RFC2
Extremeweather
events

RFC3Distributionof impacts

RFC4
Globalaggregate
impacts

RFC5
Large scale
singulareventsLevel of additional
impact/risk due
to climate change

Global mean surface temperature change
relative to pre-industrial levels (0C)

Impacts and risks for selected natural, managed and human systems
H
H
1.5
1.0

M

M
M

H

M
VH
VH
H

0

M

H

2.0

M

M

H
M

H

Warm-water Mangroves Small-scalecoralslow-latitude
fisheries

H

H

ArcticregionTerrestrial
ecosystems

H

2006-2015

H
H

M

Coastalflooding

Fluvialflooding

CropyieldsTourismHeat-related
morbidity
and mortality

Confidence level for transition: L=Low, M=Medium, H=High and VH=Very highFigure SPM.2 | Five integrative reasons for concern (RFCs) provide a framework for summarizing key impacts and risks across sectors and regions, and wereintroduced in the IPCC Third Assessment Report. RFCs illustrate the implications of global warming for people, economies and ecosystems. Impacts and/or risks
for each RFC are based on assessment of the new literature that has appeared. As in AR5, this literature was used to make expert judgments to assess the levelsof global warming at which levels of impact and/or risk are undetectable, moderate, high or very high. The selection of impacts and risks to natural, managed andhuman systems in the lower panel is illustrative and is not intended to be fully comprehensive. {3.4, 3.5, 3.5.2.1, 3.5.2.2, 3.5.2.3, 3.5.2.4, 3.5.2.5, 5.4.1, 5.5.3,
5.6.1, Box 3.4}
RFC1 Unique and threatened systems: ecological and human systems that have restricted geographic ranges constrained by climate-related conditions andhave high endemism or other distinctive properties. Examples include coral reefs, the Arctic and its indigenous people, mountain glaciers and biodiversity hotspots.
RFC2 Extreme weather events: risks/impacts to human health, livelihoods, assets and ecosystems from extreme weather events such as heat waves, heavy rain,drought and associated wildfires, and coastal flooding.
RFC3 Distribution of impacts: risks/impacts that disproportionately affect particular groups due to uneven distribution of physical climate change hazards,
exposure or vulnerability.
RFC4 Global aggregate impacts: global monetary damage, global-scale degradation and loss of ecosystems and biodiversity.
RFC5
singularevents:relatively
large,
abruptand (GDP).
sometimes
irreversible
systemsareheritagecausedandby ecosystem
global warming.
Examples10
Here,Large-scale
impacts on economic
growthrefer toarechangesin grossdomestic
product
Many impacts,
suchchanges
as loss of inhuman
lives,that
cultural
services, aredifficultto valueand monetize.of the Greenland and Antarctic ice sheets.
includedisintegration

11

Summary for Policymakers

C.

Emission Pathways and System Transitions Consistent with 1.5°C
Global Warming

C.1

In model pathways with no or limited overshoot of 1.5°C, global net anthropogenic CO2 emissions
decline by about 45% from 2010 levels by 2030 (40–60% interquartile range), reaching net zeroaround 2050 (2045–2055 interquartile range). For limiting global warming to below 2°C11 CO2emissions are projected to decline by about 25% by 2030 in most pathways (10–30% interquartile
range) and reach net zero around 2070 (2065–2080 interquartile range). Non-CO2 emissions inpathways that limit global warming to 1.5°C show deep reductions that are similar to those inpathways limiting warming to 2°C. (high confidence) (Figure SPM.3a) {2.1, 2.3, Table 2.4}

C.1.1

CO2 emissions reductions that limit global warming to 1.5°C with no or limited overshoot can involve different portfolios of
mitigation measures, striking different balances between lowering energy and resource intensity, rate of decarbonization,and the reliance on carbon dioxide removal. Different portfolios face different implementation challenges and potential
synergies and trade-offs with sustainable development. (high confidence) (Figure SPM.3b) {2.3.2, 2.3.4, 2.4, 2.5.3}

C.1.2

Modelled pathways that limit global warming to 1.5°C with no or limited overshoot involve deep reductions in emissionsof methane and black carbon (35% or more of both by 2050 relative to 2010). These pathways also reduce most of thecooling aerosols, which partially offsets mitigation effects for two to three decades. Non-CO2 emissions12 can be reduced
as a result of broad mitigation measures in the energy sector. In addition, targeted non-CO2 mitigation measures can
reduce nitrous oxide and methane from agriculture, methane from the waste sector, some sources of black carbon, andhydrofluorocarbons. High bioenergy demand can increase emissions of nitrous oxide in some 1.5°C pathways, highlighting
the importance of appropriate management approaches. Improved air quality resulting from projected reductions in many
non-CO2 emissions provide direct and immediate population health benefits in all 1.5°C model pathways. (high confidence)(Figure SPM.3a) {2.2.1, 2.3.3, 2.4.4, 2.5.3, 4.3.6, 5.4.2}

C.1.3

Limiting global warming requires limiting the total cumulative global anthropogenic emissions of CO2 since the preindustrial period, that is, staying within a total carbon budget (high confidence).13 By the end of 2017, anthropogenic CO2
emissions since the pre-industrial period are estimated to have reduced the total carbon budget for 1.5°C by approximately
2200 ± 320 GtCO2 (medium confidence). The associated remaining budget is being depleted by current emissions of
42 ± 3 GtCO2 per year (high confidence). The choice of the measure of global temperature affects the estimated remaining
carbon budget. Using global mean surface air temperature, as in AR5, gives an estimate of the remaining carbon budget of
580 GtCO2 for a 50% probability of limiting warming to 1.5°C, and 420 GtCO2 for a 66% probability (medium confidence).14
Alternatively, using GMST gives estimates of 770 and 570 GtCO2, for 50% and 66% probabilities,15 respectively (medium
confidence). Uncertainties in the size of these estimated remaining carbon budgets are substantial and depend on several
factors. Uncertainties in the climate response to CO2 and non-CO2 emissions contribute ±400 GtCO2 and the level of historicwarming contributes ±250 GtCO2 (medium confidence). Potential additional carbon release from future permafrost thawing
and methane release from wetlands would reduce budgets by up to 100 GtCO2 over the course of this century and more
thereafter (medium confidence). In addition, the level of non-CO2 mitigation in the future could alter the remaining carbon
budget by 250 GtCO2 in either direction (medium confidence). {1.2.4, 2.2.2, 2.6.1, Table 2.2, Chapter 2 Supplementary
Material}

C.1.4

Solar radiation modification (SRM) measures are not included in any of the available assessed pathways. Although some
SRM measures may be theoretically effective in reducing an overshoot, they face large uncertainties and knowledge gaps

SPM

11 References to pathways limiting global warming to 2°C are based on a 66% probability of staying below 2°C.
12 Non-CO2 emissions included in this Report are all anthropogenic emissions other than CO2 that result in radiative forcing. These include short-lived climate forcers, such as methane,some fluorinated gases, ozone precursors, aerosols or aerosol precursors, such as black carbon and sulphur dioxide, respectively, as well as long-lived greenhouse gases, such as nitrous
oxide or some fluorinated gases. The radiative forcing associated with non-CO2 emissions and changes in surface albedo is referred to as non-CO2 radiative forcing. {2.2.1}
13 There is a clear scientific basis for a total carbon budget consistent with limiting global warming to 1.5°C. However, neither this total carbon budget nor the fraction of this budget
taken up by past emissions were assessed in this Report.
14 Irrespective of the measure of global temperature used, updated understanding and further advances in methods have led to an increase in the estimated remaining carbon budget ofabout 300 GtCO2 compared to AR5. (medium confidence) {2.2.2}
15 These estimates use observed GMST to 2006–2015 and estimate future temperature changes using near surface air temperatures.

12Summary for Policymakers

as well as substantial risks and institutional and social constraints to deployment related to governance, ethics, and impacts
on sustainable development. They also do not mitigate ocean acidification. (medium confidence) {4.3.8, Cross-Chapter
Box 10 in Chapter 4}
SPM

Global emissions pathway characteristics
General characteristics of the evolution of anthropogenic net emissions of CO2, and total emissions ofmethane, black carbon, and nitrous oxide in model pathways that limit global warming to 1.5°C with no or
limited overshoot. Net emissions are defined as anthropogenic emissions reduced by anthropogenic
removals. Reductions in net emissions can be achieved through different portfolios of mitigation measuresillustrated in Figure SPM.3b.
Non-CO2 emissions relative to 2010

Global total net CO2 emissions

Emissions of non-CO2 forcers are also reduced
or limited in pathways limiting global warming
to 1.5°C with no or limited overshoot, butthey do not reach zero globally.

Billion tonnes of CO2/yr
50

Methane emissions
In pathways limiting global warming to 1.5°C
with no or limited overshoot as well as inpathways with a higher overshoot, CO2 emissions
are reduced to net zero globally around 2050.

40

30

1

0
2020
20

2040

2060

2080

2100

2060

2080

2100

2060

2080

2100

Black carbon emissions
1

10

Four illustrative model pathways
0

0
P1
P2
-10
P3
-20

2020

2040

Nitrous oxide emissions
1

P4
0

2010

2020

2030

Timing of net zero CO2
Line widths depict the 5-95th
percentile and the 25-75th
percentile of scenarios

2040

2050

2060

2070

2080

2090

2100

2020

2040

Pathways limiting global warming to 1.5°C with no or limited overshootPathways with higher overshootPathways limiting global warming below 2°C(Not shown above)

Figure SPM.3a | Global emissions pathway characteristics. The main panel shows global net anthropogenic CO2 emissions in pathways limiting global warming
to 1.5°C with no or limited (less than 0.1°C) overshoot and pathways with higher overshoot. The shaded area shows the full range for pathways analysed in thisReport. The panels on the right show non-CO2 emissions ranges for three compounds with large historical forcing and a substantial portion of emissions comingfrom sources distinct from those central to CO2 mitigation. Shaded areas in these panels show the 5–95% (light shading) and interquartile (dark shading) rangesof pathways limiting global warming to 1.5°C with no or limited overshoot. Box and whiskers at the bottom of the figure show the timing of pathways reaching
global net zero CO2 emission levels, and a comparison with pathways limiting global warming to 2°C with at least 66% probability. Four illustrative model pathways
are highlighted in the main panel and are labelled P1, P2, P3 and P4, corresponding to the LED, S1, S2, and S5 pathways assessed in Chapter 2. Descriptions andcharacteristics of these pathways are available in Figure SPM.3b. {2.1, 2.2, 2.3, Figure 2.5, Figure 2.10, Figure 2.11}

13

Summary for Policymakers

Characteristics of four illustrative model pathways
Different mitigation strategies can achieve the net emissions reductions that would be required to follow apathway that limits global warming to 1.5°C with no or limited overshoot. All pathways use Carbon Dioxide
Removal (CDR), but the amount varies across pathways, as do the relative contributions of Bioenergy with
Carbon Capture and Storage (BECCS) and removals in the Agriculture, Forestry and Other Land Use (AFOLU)
sector. This has implications for emissions and several other pathway characteristics.

SPM

Breakdown of contributions to global net CO2 emissions in four illustrative model pathways
Fossil fuel and industry

AFOLU

BECCS

Billion tonnes CO₂ per year (GtCO2/yr)

Billion tonnes CO₂ per year (GtCO2/yr)

Billion tonnes CO₂ per year (GtCO2/yr)

Billion tonnes CO₂ per year (GtCO2/yr)

40

40

40

40

P1

P2

20

20

20

20

0

0

0

0

-20

-20
2020

2060

2100

2060

2100

P4

-20

-20
2020

2020

2060

2020

2100

2060

2100

P1: A scenario in which social,

P2: A scenario with a broad focus on

P3: A middle-of-the-road scenario in

P4: A resource- and energy-intensive

business and technological innovations
result in lower energy demand up to
2050 while living standards rise,especially in the global South. A
downsized energy system enables
rapid decarbonization of energy supply.
Afforestation is the only CDR option
considered; neither fossil fuels with CCS
nor BECCS are used.

sustainability including energy
intensity, human development,
economic convergence andinternational cooperation, as well as
shifts towards sustainable and healthy
consumption patterns, low-carbon
technology innovation, andwell-managed land systems withlimited societal acceptability for BECCS.

which societal as well as technologicaldevelopment follows historicalpatterns. Emissions reductions aremainly achieved by changing the way inwhich energy and products areproduced, and to a lesser degree by
reductions in demand.

scenario in which economic growth andglobalization lead to widespread
adoption of greenhouse-gas-intensive
lifestyles, including high demand fortransportation fuels and livestock
products. Emissions reductions aremainly achieved through technologicalmeans, making strong use of CDR
through the deployment of BECCS.

Interquartile range

Global indicators

P1

P2

P3

P4

Pathway classification

No or limited overshootNo or limited overshootNo or limited overshootHigher overshoot

-58

-47

-41

4

(-58,-40)

-93

-95

-91

-97

(-107,-94)

-50

-49

-35

-2

(-51,-39)

-82

-89

-78

-80

(-93,-81)

-15

-5

17

39

(-12,7)

-32

2

21

44

(-11,22)

60

58

48

25

(47,65)

77

81

63

70

(69,86)

-78

-61

-75

-59

(-78, -59)(-95, -74)

CO2 emission change in 2030 (% rel to 2010)
in 2050 (% rel to 2010)
Kyoto-GHG emissions* in 2030 (% rel to 2010)
in 2050 (% rel to 2010)
Final energy demand** in 2030 (% rel to 2010)
in 2050 (% rel to 2010)
Renewable share in electricity in 2030 (%)
in 2050 (%)
Primary energy from coal in 2030 (% rel to 2010)

No or limited overshootin 2050 (% rel to 2010)

-97

-77

-73

-97

from oil in 2030 (% rel to 2010)

-37

-13

-3

86

(-34,3)in 2050 (% rel to 2010)

-87

-50

-81

-32

(-78,-31)

from gas in 2030 (% rel to 2010)

-25

-20

33

37

(-26,21)in 2050 (% rel to 2010)

-74

-53

21

-48

(-56,6)from nuclear in 2030 (% rel to 2010)

59

83

98

106

(44,102)

in 2050 (% rel to 2010)

150

98

501

468

(91,190)

-11

0

36

-1

(29,80)

-16

49

121

418

(123,261)

430

470

315

110

(245,436)

833

1327

878

1137

(576,1299)

0

348

687

1218

(550,1017)

0

151

414

1191

(364,662)

Land area of bioenergy crops in 2050 (million km2)

0.2

0.9

2.8

7.2

(1.5,3.2)

Agricultural CH4 emissions in 2030 (% rel to 2010)

-24

-48

1

14

(-30,-11)

-33

-69

-23

2

(-47,-24)

5

-26

15

3

(-21,3)

6

-26

0

39

(-26,1)from biomass in 2030 (% rel to 2010)
in 2050 (% rel to 2010)
from non-biomass renewables in 2030 (% rel to 2010)
in 2050 (% rel to 2010)
Cumulative CCS until 2100 (GtCO2)
of which BECCS (GtCO2)

in 2050 (% rel to 2010)
Agricultural N2O emissions in 2030 (% rel to 2010)
in 2050 (% rel to 2010)

NOTE: Indicators have been selected to show global trends identified by the Chapter 2 assessment.
National and sectoral characteristics can differ substantially from the global trends shown above.

14

P3* Kyoto-gas emissions are based on IPCC Second Assessment Report GWP-100** Changes in energy demand are associated with improvements in energy
efficiency and behaviour change

Summary for Policymakers

Figure SPM.3b | Characteristics of four illustrative model pathways in relation to global warming of 1.5°C introduced in Figure SPM.3a. These pathways wereselected to show a range of potential mitigation approaches and vary widely in their projected energy and land use, as well as their assumptions about futuresocio-economic developments, including economic and population growth, equity and sustainability. A breakdown of the global net anthropogenic CO2 emissions
into the contributions in terms of CO2 emissions from fossil fuel and industry; agriculture, forestry and other land use (AFOLU); and bioenergy with carbon captureand storage (BECCS) is shown. AFOLU estimates reported here are not necessarily comparable with countries’ estimates. Further characteristics for each of thesepathways are listed below each pathway. These pathways illustrate relative global differences in mitigation strategies, but do not represent central estimates,
national strategies, and do not indicate requirements. For comparison, the right-most column shows the interquartile ranges across pathways with no or limited
overshoot of 1.5°C. Pathways P1, P2, P3 and P4 correspond to the LED, S1, S2 and S5 pathways assessed in Chapter 2 (Figure SPM.3a). {2.2.1, 2.3.1, 2.3.2,
2.3.3, 2.3.4, 2.4.1, 2.4.2, 2.4.4, 2.5.3, Figure 2.5, Figure 2.6, Figure 2.9, Figure 2.10, Figure 2.11, Figure 2.14, Figure 2.15, Figure 2.16, Figure 2.17, Figure 2.24,
Figure 2.25, Table 2.4, Table 2.6, Table 2.7, Table 2.9, Table 4.1}

C.2

Pathways limiting global warming to 1.5°C with no or limited overshoot would require rapid
and far-reaching transitions in energy, land, urban and infrastructure (including transport andbuildings), and industrial systems (high confidence). These systems transitions are unprecedented
in terms of scale, but not necessarily in terms of speed, and imply deep emissions reductions in all
sectors, a wide portfolio of mitigation options and a significant upscaling of investments in those
options (medium confidence). {2.3, 2.4, 2.5, 4.2, 4.3, 4.4, 4.5}

C.2.1

Pathways that limit global warming to 1.5°C with no or limited overshoot show system changes that are more rapid andpronounced over the next two decades than in 2°C pathways (high confidence). The rates of system changes associatedwith limiting global warming to 1.5°C with no or limited overshoot have occurred in the past within specific sectors,
technologies and spatial contexts, but there is no documented historic precedent for their scale (medium confidence).
{2.3.3, 2.3.4, 2.4, 2.5, 4.2.1, 4.2.2, Cross-Chapter Box 11 in Chapter 4}

C.2.2

In energy systems, modelled global pathways (considered in the literature) limiting global warming to 1.5°C with no or
limited overshoot (for more details see Figure SPM.3b) generally meet energy service demand with lower energy use,
including through enhanced energy efficiency, and show faster electrification of energy end use compared to 2°C (highconfidence). In 1.5°C pathways with no or limited overshoot, low-emission energy sources are projected to have a higher
share, compared with 2°C pathways, particularly before 2050 (high confidence). In 1.5°C pathways with no or limited
overshoot, renewables are projected to supply 70–85% (interquartile range) of electricity in 2050 (high confidence). In
electricity generation, shares of nuclear and fossil fuels with carbon dioxide capture and storage (CCS) are modelled toincrease in most 1.5°C pathways with no or limited overshoot. In modelled 1.5°C pathways with limited or no overshoot,the use of CCS would allow the electricity generation share of gas to be approximately 8% (3–11% interquartile range)of global electricity in 2050, while the use of coal shows a steep reduction in all pathways and would be reduced to close
to 0% (0–2% interquartile range) of electricity (high confidence). While acknowledging the challenges, and differences
between the options and national circumstances, political, economic, social and technical feasibility of solar energy, windenergy and electricity storage technologies have substantially improved over the past few years (high confidence). These
improvements signal a potential system transition in electricity generation. (Figure SPM.3b) {2.4.1, 2.4.2, Figure 2.1, Table
2.6, Table 2.7, Cross-Chapter Box 6 in Chapter 3, 4.2.1, 4.3.1, 4.3.3, 4.5.2}

C.2.3

CO2 emissions from industry in pathways limiting global warming to 1.5°C with no or limited overshoot are projected to
be about 65–90% (interquartile range) lower in 2050 relative to 2010, as compared to 50–80% for global warming of
2°C (medium confidence). Such reductions can be achieved through combinations of new and existing technologies andpractices, including electrification, hydrogen, sustainable bio-based feedstocks, product substitution, and carbon capture,utilization and storage (CCUS). These options are technically proven at various scales but their large-scale deployment
may be limited by economic, financial, human capacity and institutional constraints in specific contexts, and specificcharacteristics of large-scale industrial installations. In industry, emissions reductions by energy and process efficiency
by themselves are insufficient for limiting warming to 1.5°C with no or limited overshoot (high confidence). {2.4.3, 4.2.1,
Table 4.1, Table 4.3, 4.3.3, 4.3.4, 4.5.2}

C.2.4

The urban and infrastructure system transition consistent with limiting global warming to 1.5°C with no or limited overshootwould imply, for example, changes in land and urban planning practices, as well as deeper emissions reductions in transportand buildings compared to pathways that limit global warming below 2°C (medium confidence). Technical measures

SPM

15

Summary for Policymakers

and practices enabling deep emissions reductions include various energy efficiency options. In pathways limiting global
warming to 1.5°C with no or limited overshoot, the electricity share of energy demand in buildings would be about 55–75%
in 2050 compared to 50–70% in 2050 for 2°C global warming (medium confidence). In the transport sector, the share of
low-emission final energy would rise from less than 5% in 2020 to about 35–65% in 2050 compared to 25–45% for 2°Cof global warming (medium confidence). Economic, institutional and socio-cultural barriers may inhibit these urban andinfrastructure system transitions, depending on national, regional and local circumstances, capabilities and the availabilityof capital (high confidence). {2.3.4, 2.4.3, 4.2.1, Table 4.1, 4.3.3, 4.5.2}

SPM

C.2.5

Transitions in global and regional land use are found in all pathways limiting global warming to 1.5°C with no or limited
overshoot, but their scale depends on the pursued mitigation portfolio. Model pathways that limit global warming to 1.5°C
with no or limited overshoot project a 4 million km2 reduction to a 2.5 million km2 increase of non-pasture agricultural land
for food and feed crops and a 0.5–11 million km2 reduction of pasture land, to be converted into a 0–6 million km2 increaseof agricultural land for energy crops and a 2 million km2 reduction to 9.5 million km2 increase in forests by 2050 relative
to 2010 (medium confidence).16 Land-use transitions of similar magnitude can be observed in modelled 2°C pathways(medium confidence). Such large transitions pose profound challenges for sustainable management of the various demands
on land for human settlements, food, livestock feed, fibre, bioenergy, carbon storage, biodiversity and other ecosystemservices (high confidence). Mitigation options limiting the demand for land include sustainable intensification of land-use
practices, ecosystem restoration and changes towards less resource-intensive diets (high confidence). The implementationof land-based mitigation options would require overcoming socio-economic, institutional, technological, financing andenvironmental barriers that differ across regions (high confidence). {2.4.4, Figure 2.24, 4.3.2, 4.3.7, 4.5.2, Cross-Chapter
Box 7 in Chapter 3}

C.2.6

Additional annual average energy-related investments for the period 2016 to 2050 in pathways limiting warming to
1.5°C compared to pathways without new climate policies beyond those in place today are estimated to be around 830
billion USD2010 (range of 150 billion to 1700 billion USD2010 across six models17). This compares to total annual averageenergy supply investments in 1.5°C pathways of 1460 to 3510 billion USD2010 and total annual average energy demand
investments of 640 to 910 billion USD2010 for the period 2016 to 2050. Total energy-related investments increase by
about 12% (range of 3% to 24%) in 1.5°C pathways relative to 2°C pathways. Annual investments in low-carbon energy
technologies and energy efficiency are upscaled by roughly a factor of six (range of factor of 4 to 10) by 2050 compared to2015 (medium confidence). {2.5.2, Box 4.8, Figure 2.27}

C.2.7

Modelled pathways limiting global warming to 1.5°C with no or limited overshoot project a wide range of global average
discounted marginal abatement costs over the 21st century. They are roughly 3-4 times higher than in pathways limiting
global warming to below 2°C (high confidence). The economic literature distinguishes marginal abatement costs from total
mitigation costs in the economy. The literature on total mitigation costs of 1.5°C mitigation pathways is limited and was
not assessed in this Report. Knowledge gaps remain in the integrated assessment of the economy-wide costs and benefitsof mitigation in line with pathways limiting warming to 1.5°C. {2.5.2; 2.6; Figure 2.26}

16 The projected land-use changes presented are not deployed to their upper limits simultaneously in a single pathway.
17 Including two pathways limiting warming to 1.5°C with no or limited overshoot and four pathways with higher overshoot.

16

Summary for Policymakers

C.3

All pathways that limit global warming to 1.5°C with limited or no overshoot project the use ofcarbon dioxide removal (CDR) on the order of 100–1000 GtCO2 over the 21st century. CDR would
be used to compensate for residual emissions and, in most cases, achieve net negative emissions
to return global warming to 1.5°C following a peak (high confidence). CDR deployment of several
hundreds of GtCO2 is subject to multiple feasibility and sustainability constraints (high confidence).
Significant near-term emissions reductions and measures to lower energy and land demand canlimit CDR deployment to a few hundred GtCO2 without reliance on bioenergy with carbon captureand storage (BECCS) (high confidence). {2.3, 2.4, 3.6.2, 4.3, 5.4}

C.3.1

Existing and potential CDR measures include afforestation and reforestation, land restoration and soil carbon sequestration,
BECCS, direct air carbon capture and storage (DACCS), enhanced weathering and ocean alkalinization. These differ widelyin terms of maturity, potentials, costs, risks, co-benefits and trade-offs (high confidence). To date, only a few publishedpathways include CDR measures other than afforestation and BECCS. {2.3.4, 3.6.2, 4.3.2, 4.3.7}

C.3.2

In pathways limiting global warming to 1.5°C with limited or no overshoot, BECCS deployment is projected to range from
0–1, 0–8, and 0–16 GtCO2 yr−1 in 2030, 2050, and 2100, respectively, while agriculture, forestry and land-use (AFOLU)
related CDR measures are projected to remove 0–5, 1–11, and 1–5 GtCO2 yr−1 in these years (medium confidence). The
upper end of these deployment ranges by mid-century exceeds the BECCS potential of up to 5 GtCO2 yr−1 and afforestation
potential of up to 3.6 GtCO2 yr−1 assessed based on recent literature (medium confidence). Some pathways avoid BECCS
deployment completely through demand-side measures and greater reliance on AFOLU-related CDR measures (medium
confidence). The use of bioenergy can be as high or even higher when BECCS is excluded compared to when it is included
due to its potential for replacing fossil fuels across sectors (high confidence). (Figure SPM.3b) {2.3.3, 2.3.4, 2.4.2, 3.6.2,
4.3.1, 4.2.3, 4.3.2, 4.3.7, 4.4.3, Table 2.4}

C.3.3

Pathways that overshoot 1.5°C of global warming rely on CDR exceeding residual CO2 emissions later in the century toreturn to below 1.5°C by 2100, with larger overshoots requiring greater amounts of CDR (Figure SPM.3b) (high confidence).
Limitations on the speed, scale, and societal acceptability of CDR deployment hence determine the ability to return global
warming to below 1.5°C following an overshoot. Carbon cycle and climate system understanding is still limited about theeffectiveness of net negative emissions to reduce temperatures after they peak (high confidence). {2.2, 2.3.4, 2.3.5, 2.6,
4.3.7, 4.5.2, Table 4.11}

C.3.4

Most current and potential CDR measures could have significant impacts on land, energy, water or nutrients if deployedat large scale (high confidence). Afforestation and bioenergy may compete with other land uses and may have significant
impacts on agricultural and food systems, biodiversity, and other ecosystem functions and services (high confidence).
Effective governance is needed to limit such trade-offs and ensure permanence of carbon removal in terrestrial, geologicaland ocean reservoirs (high confidence). Feasibility and sustainability of CDR use could be enhanced by a portfolio of options
deployed at substantial, but lesser scales, rather than a single option at very large scale (high confidence). (Figure SPM.3b){2.3.4, 2.4.4, 2.5.3, 2.6, 3.6.2, 4.3.2, 4.3.7, 4.5.2, 5.4.1, 5.4.2; Cross-Chapter Boxes 7 and 8 in Chapter 3, Table 4.11, Table
5.3, Figure 5.3}

C.3.5

Some AFOLU-related CDR measures such as restoration of natural ecosystems and soil carbon sequestration could provide
co-benefits such as improved biodiversity, soil quality, and local food security. If deployed at large scale, they would
require governance systems enabling sustainable land management to conserve and protect land carbon stocks and otherecosystem functions and services (medium confidence). (Figure SPM.4) {2.3.3, 2.3.4, 2.4.2, 2.4.4, 3.6.2, 5.4.1, Cross-Chapter
Boxes 3 in Chapter 1 and 7 in Chapter 3, 4.3.2, 4.3.7, 4.4.1, 4.5.2, Table 2.4}

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17

Summary for Policymakers

D.

Strengthening the Global Response in the Context of Sustainable
Development and Efforts to Eradicate Poverty

D.1

Estimates of the global emissions outcome of current nationally stated mitigation ambitions as
submitted under the Paris Agreement would lead to global greenhouse gas emissions18 in 2030
of 52–58 GtCO2eq yr−1 (medium confidence). Pathways reflecting these ambitions would not limit
global warming to 1.5°C, even if supplemented by very challenging increases in the scale andambition of emissions reductions after 2030 (high confidence). Avoiding overshoot and relianceon future large-scale deployment of carbon dioxide removal (CDR) can only be achieved if global
CO2 emissions start to decline well before 2030 (high confidence). {1.2, 2.3, 3.3, 3.4, 4.2, 4.4, CrossChapter Box 11 in Chapter 4}

D.1.1

Pathways that limit global warming to 1.5°C with no or limited overshoot show clear emission reductions by 2030 (highconfidence). All but one show a decline in global greenhouse gas emissions to below 35 GtCO2eq yr−1 in 2030, and half ofavailable pathways fall within the 25–30 GtCO2eq yr−1 range (interquartile range), a 40–50% reduction from 2010 levels(high confidence). Pathways reflecting current nationally stated mitigation ambition until 2030 are broadly consistent
with cost-effective pathways that result in a global warming of about 3°C by 2100, with warming continuing afterwards(medium confidence). {2.3.3, 2.3.5, Cross-Chapter Box 11 in Chapter 4, 5.5.3.2}

D.1.2

Overshoot trajectories result in higher impacts and associated challenges compared to pathways that limit global warming
to 1.5°C with no or limited overshoot (high confidence). Reversing warming after an overshoot of 0.2°C or larger during
this century would require upscaling and deployment of CDR at rates and volumes that might not be achievable given
considerable implementation challenges (medium confidence). {1.3.3, 2.3.4, 2.3.5, 2.5.1, 3.3, 4.3.7, Cross-Chapter Box 8 in
Chapter 3, Cross-Chapter Box 11 in Chapter 4}

D.1.3

The lower the emissions in 2030, the lower the challenge in limiting global warming to 1.5°C after 2030 with no or limited
overshoot (high confidence). The challenges from delayed actions to reduce greenhouse gas emissions include the risk of
cost escalation, lock-in in carbon-emitting infrastructure, stranded assets, and reduced flexibility in future response optionsin the medium to long term (high confidence). These may increase uneven distributional impacts between countries atdifferent stages of development (medium confidence). {2.3.5, 4.4.5, 5.4.2}

D.2

The avoided climate change impacts on sustainable development, eradication of poverty and reducing
inequalities would be greater if global warming were limited to 1.5°C rather than 2°C, if mitigation
and adaptation synergies are maximized while trade-offs are minimized (high confidence). {1.1, 1.4,
2.5, 3.3, 3.4, 5.2, Table 5.1}

D.2.1

Climate change impacts and responses are closely linked to sustainable development which balances social well-being,
economic prosperity and environmental protection. The United Nations Sustainable Development Goals (SDGs), adopted in2015, provide an established framework for assessing the links between global warming of 1.5°C or 2°C and development
goals that include poverty eradication, reducing inequalities, and climate action. (high confidence) {Cross-Chapter Box 4 in
Chapter 1, 1.4, 5.1}

D.2.2

The consideration of ethics and equity can help address the uneven distribution of adverse impacts associated with
1.5°C and higher levels of global warming, as well as those from mitigation and adaptation, particularly for poor anddisadvantaged populations, in all societies (high confidence). {1.1.1, 1.1.2, 1.4.3, 2.5.3, 3.4.10, 5.1, 5.2, 5.3. 5.4, CrossChapter Box 4 in Chapter 1, Cross-Chapter Boxes 6 and 8 in Chapter 3, and Cross-Chapter Box 12 in Chapter 5}

D.2.3

Mitigation and adaptation consistent with limiting global warming to 1.5°C are underpinned by enabling conditions, assessedin this Report across the geophysical, environmental-ecological, technological, economic, socio-cultural and institutional

SPM

18 GHG emissions have been aggregated with 100-year GWP values as introduced in the IPCC Second Assessment Report.

18

Summary for Policymakers

dimensions of feasibility. Strengthened multilevel governance, institutional capacity, policy instruments, technologicalinnovation and transfer and mobilization of finance, and changes in human behaviour and lifestyles are enabling conditions
that enhance the feasibility of mitigation and adaptation options for 1.5°C-consistent systems transitions. (high confidence){1.4, Cross-Chapter Box 3 in Chapter 1, 2.5.1, 4.4, 4.5, 5.6}
SPM

D.3

Adaptation options specific to national contexts, if carefully selected together with enabling
conditions, will have benefits for sustainable development and poverty reduction with global
warming of 1.5°C, although trade-offs are possible (high confidence). {1.4, 4.3, 4.5}

D.3.1

Adaptation options that reduce the vulnerability of human and natural systems have many synergies with sustainable
development, if well managed, such as ensuring food and water security, reducing disaster risks, improving healthconditions, maintaining ecosystem services and reducing poverty and inequality (high confidence). Increasing investmentin physical and social infrastructure is a key enabling condition to enhance the resilience and the adaptive capacities
of societies. These benefits can occur in most regions with adaptation to 1.5°C of global warming (high confidence).
{1.4.3, 4.2.2, 4.3.1, 4.3.2, 4.3.3, 4.3.5, 4.4.1, 4.4.3, 4.5.3, 5.3.1, 5.3.2}

D.3.2

Adaptation to 1.5°C global warming can also result in trade-offs or maladaptations with adverse impacts for sustainable
development. For example, if poorly designed or implemented, adaptation projects in a range of sectors can increase
greenhouse gas emissions and water use, increase gender and social inequality, undermine health conditions, and encroachon natural ecosystems (high confidence). These trade-offs can be reduced by adaptations that include attention to poverty
and sustainable development (high confidence). {4.3.2, 4.3.3, 4.5.4, 5.3.2; Cross-Chapter Boxes 6 and 7 in Chapter 3}

D.3.3

A mix of adaptation and mitigation options to limit global warming to 1.5°C, implemented in a participatory and integrated
manner, can enable rapid, systemic transitions in urban and rural areas (high confidence). These are most effective whenaligned with economic and sustainable development, and when local and regional governments and decision makers aresupported by national governments (medium confidence). {4.3.2, 4.3.3, 4.4.1, 4.4.2}

D.3.4

Adaptation options that also mitigate emissions can provide synergies and cost savings in most sectors and systemtransitions, such as when land management reduces emissions and disaster risk, or when low-carbon buildings are also
designed for efficient cooling. Trade-offs between mitigation and adaptation, when limiting global warming to 1.5°C,
such as when bioenergy crops, reforestation or afforestation encroach on land needed for agricultural adaptation, canundermine food security, livelihoods, ecosystem functions and services and other aspects of sustainable development. (highconfidence) {3.4.3, 4.3.2, 4.3.4, 4.4.1, 4.5.2, 4.5.3, 4.5.4}

D.4

Mitigation options consistent with 1.5°C pathways are associated with multiple synergies and tradeoffs across the Sustainable Development Goals (SDGs). While the total number of possible synergies
exceeds the number of trade-offs, their net effect will depend on the pace and magnitude of changes,
the composition of the mitigation portfolio and the management of the transition. (high confidence)(Figure SPM.4) {2.5, 4.5, 5.4}

D.4.1

1.5°C pathways have robust synergies particularly for the SDGs 3 (health), 7 (clean energy), 11 (cities and communities), 12(responsible consumption and production) and 14 (oceans) (very high confidence). Some 1.5°C pathways show potential
trade-offs with mitigation for SDGs 1 (poverty), 2 (hunger), 6 (water) and 7 (energy access), if not managed carefully (highconfidence). (Figure SPM.4) {5.4.2; Figure 5.4, Cross-Chapter Boxes 7 and 8 in Chapter 3}

D.4.2

1.5°C pathways that include low energy demand (e.g., see P1 in Figure SPM.3a and SPM.3b), low material consumption,and low GHG-intensive food consumption have the most pronounced synergies and the lowest number of trade-offs withrespect to sustainable development and the SDGs (high confidence). Such pathways would reduce dependence on CDR. In
modelled pathways, sustainable development, eradicating poverty and reducing inequality can support limiting warming to
1.5°C (high confidence). (Figure SPM.3b, Figure SPM.4) {2.4.3, 2.5.1, 2.5.3, Figure 2.4, Figure 2.28, 5.4.1, 5.4.2, Figure 5.4}

19

Summary for Policymakers

Indicative linkages between mitigation options and sustainable
development using SDGs (The linkages do not show costs and benefits)
Mitigation options deployed in each sector can be associated with potential positive effects (synergies) or
negative effects (trade-offs) with the Sustainable Development Goals (SDGs). The degree to which thispotential is realized will depend on the selected portfolio of mitigation options, mitigation policy design,and local circumstances and context. Particularly in the energy-demand sector, the potential for synergies islarger than for trade-offs. The bars group individually assessed options by level of confidence and take into
account the relative strength of the assessed mitigation-SDG connections.

SPM

Shades show level of confidence

Length shows strength of connection
The overall size of the coloured bars depict the relative
potential for synergies and trade-offs between the sectoral
mitigation options and the SDGs.

The shades depict the level of confidence of theassessed potential for Trade-offs/Synergies.

Very HighNO
POVERTY

SDG1
No Poverty
SDG2
Zero Hunger
SDG 3
Good Healthand Well-being
SDG 4
Quality
Education
SDG 5
GenderEquality
SDG 6
Clean Waterand Sanitation
SDG 7
Affordable andClean Energy

GOOD HEALTH
AND WELL-BEINGQUALITYEDUCATIONGENDEREQUALITY

CLEAN WATER
AND SANITATIONAFFORDABLE AND
CLEAN ENERGY

SDG 8Decent Workand Economic
Growth

DECENT WORK AND
ECONOMIC GROWTH

SDG 9Industry,
Innovation andInfrastructure

INDUSTRY, INNOVATION
AND INFRASTRUCTURE

SDG 10
ReducedInequalities
SDG 11
Sustainable
Cities andCommunities
SDG 12
Responsible
Consumption
and Production
SDG 14
Life Below
Water
SDG 15
Life on Land
SDG 16
Peace, Justiceand Strong
Institutions
SDG 17
Partnerships for
the Goals

20

ZERO
HUNGER

REDUCEDINEQUALITIESSUSTAINABLE CITIESAND COMMUNITIESRESPONSIBLE
CONSUMPTION
AND PRODUCTIONLIFE
BELOW WATER

LIFE
ON LANDPEACE, JUSTICEAND STRONG
INSTITUTIONSPARTNERSHIPSFOR THE GOALS

Low

Energy Supply

Energy DemandLand

Trade-offs

Trade-offs

Trade-offs

Synergies

Synergies

Synergies

Summary for Policymakers

Figure SPM.4 | Potential synergies and trade-offs between the sectoral portfolio of climate change mitigation options and the Sustainable Development Goals(SDGs). The SDGs serve as an analytical framework for the assessment of the different sustainable development dimensions, which extend beyond the time frameof the 2030 SDG targets. The assessment is based on literature on mitigation options that are considered relevant for 1.5°C. The assessed strength of the SDG
interactions is based on the qualitative and quantitative assessment of individual mitigation options listed in Table 5.2. For each mitigation option, the strength of
the SDG-connection as well as the associated confidence of the underlying literature (shades of green and red) was assessed. The strength of positive connections(synergies) and negative connections (trade-offs) across all individual options within a sector (see Table 5.2) are aggregated into sectoral potentials for the whole
mitigation portfolio. The (white) areas outside the bars, which indicate no interactions, have low confidence due to the uncertainty and limited number of studies
exploring indirect effects. The strength of the connection considers only the effect of mitigation and does not include benefits of avoided impacts. SDG 13 (climate
action) is not listed because mitigation is being considered in terms of interactions with SDGs and not vice versa. The bars denote the strength of the connection,and do not consider the strength of the impact on the SDGs. The energy demand sector comprises behavioural responses, fuel switching and efficiency options in
the transport, industry and building sector as well as carbon capture options in the industry sector. Options assessed in the energy supply sector comprise biomass
and non-biomass renewables, nuclear, carbon capture and storage (CCS) with bioenergy, and CCS with fossil fuels. Options in the land sector comprise agriculturaland forest options, sustainable diets and reduced food waste, soil sequestration, livestock and manure management, reduced deforestation, afforestation andreforestation, and responsible sourcing. In addition to this figure, options in the ocean sector are discussed in the underlying report. {5.4, Table 5.2, Figure 5.2}

SPM

Information about the net impacts of mitigation on sustainable development in 1.5°C pathways is available only for a limited number of SDGs and mitigation
options. Only a limited number of studies have assessed the benefits of avoided climate change impacts of 1.5°C pathways for the SDGs, and the co-effects
of adaptation for mitigation and the SDGs. The assessment of the indicative mitigation potentials in Figure SPM.4 is a step further from AR5 towards a morecomprehensive and integrated assessment in the future.

D.4.3

1.5°C and 2°C modelled pathways often rely on the deployment of large-scale land-related measures like afforestation
and bioenergy supply, which, if poorly managed, can compete with food production and hence raise food security concerns(high confidence). The impacts of carbon dioxide removal (CDR) options on SDGs depend on the type of options and thescale of deployment (high confidence). If poorly implemented, CDR options such as BECCS and AFOLU options would leadto trade-offs. Context-relevant design and implementation requires considering people’s needs, biodiversity, and othersustainable development dimensions (very high confidence). (Figure SPM.4) {5.4.1.3, Cross-Chapter Box 7 in Chapter 3}

D.4.4

Mitigation consistent with 1.5°C pathways creates risks for sustainable development in regions with high dependency on
fossil fuels for revenue and employment generation (high confidence). Policies that promote diversification of the economy
and the energy sector can address the associated challenges (high confidence). {5.4.1.2, Box 5.2}

D.4.5

Redistributive policies across sectors and populations that shield the poor and vulnerable can resolve trade-offs for a rangeof SDGs, particularly hunger, poverty and energy access. Investment needs for such complementary policies are only a small
fraction of the overall mitigation investments in 1.5°C pathways. (high confidence) {2.4.3, 5.4.2, Figure 5.5}

D.5

Limiting the risks from global warming of 1.5°C in the context of sustainable development andpoverty eradication implies system transitions that can be enabled by an increase of adaptationand mitigation investments, policy instruments, the acceleration of technological innovation andbehaviour changes (high confidence). {2.3, 2.4, 2.5, 3.2, 4.2, 4.4, 4.5, 5.2, 5.5, 5.6}

D.5.1

Directing finance towards investment in infrastructure for mitigation and adaptation could provide additional resources.
This could involve the mobilization of private funds by institutional investors, asset managers and development orinvestment banks, as well as the provision of public funds. Government policies that lower the risk of low-emission andadaptation investments can facilitate the mobilization of private funds and enhance the effectiveness of other public
policies. Studies indicate a number of challenges, including access to finance and mobilization of funds. (high confidence){2.5.1, 2.5.2, 4.4.5}

D.5.2

Adaptation finance consistent with global warming of 1.5°C is difficult to quantify and compare with 2°C. Knowledge
gaps include insufficient data to calculate specific climate resilience-enhancing investments from the provision of currently
underinvested basic infrastructure. Estimates of the costs of adaptation might be lower at global warming of 1.5°C than for
2°C. Adaptation needs have typically been supported by public sector sources such as national and subnational government
budgets, and in developing countries together with support from development assistance, multilateral development banks,and United Nations Framework Convention on Climate Change channels (medium confidence). More recently there is a21

Summary for Policymakers

growing understanding of the scale and increase in non-governmental organizations and private funding in some regions(medium confidence). Barriers include the scale of adaptation financing, limited capacity and access to adaptation finance(medium confidence). {4.4.5, 4.6}
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22

D.5.3

Global model pathways limiting global warming to 1.5°C are projected to involve the annual average investment needsin the energy system of around 2.4 trillion USD2010 between 2016 and 2035, representing about 2.5% of the world GDP(medium confidence). {4.4.5, Box 4.8}

D.5.4

Policy tools can help mobilize incremental resources, including through shifting global investments and savings andthrough market and non-market based instruments as well as accompanying measures to secure the equity of thetransition, acknowledging the challenges related with implementation, including those of energy costs, depreciation ofassets and impacts on international competition, and utilizing the opportunities to maximize co-benefits (high confidence).
{1.3.3, 2.3.4, 2.3.5, 2.5.1, 2.5.2, Cross-Chapter Box 8 in Chapter 3, Cross-Chapter Box 11 in Chapter 4, 4.4.5, 5.5.2}

D.5.5

The systems transitions consistent with adapting to and limiting global warming to 1.5°C include the widespread adoptionof new and possibly disruptive technologies and practices and enhanced climate-driven innovation. These imply enhanced
technological innovation capabilities, including in industry and finance. Both national innovation policies and international
cooperation can contribute to the development, commercialization and widespread adoption of mitigation and adaptation
technologies. Innovation policies may be more effective when they combine public support for research and developmentwith policy mixes that provide incentives for technology diffusion. (high confidence) {4.4.4, 4.4.5}.

D.5.6

Education, information, and community approaches, including those that are informed by indigenous knowledge and local
knowledge, can accelerate the wide-scale behaviour changes consistent with adapting to and limiting global warming to
1.5°C. These approaches are more effective when combined with other policies and tailored to the motivations, capabilities
and resources of specific actors and contexts (high confidence). Public acceptability can enable or inhibit the implementationof policies and measures to limit global warming to 1.5°C and to adapt to the consequences. Public acceptability depends
on the individual’s evaluation of expected policy consequences, the perceived fairness of the distribution of these
consequences, and perceived fairness of decision procedures (high confidence). {1.1, 1.5, 4.3.5, 4.4.1, 4.4.3, Box 4.3, 5.5.3,
5.6.5}

D.6

Sustainable development supports, and often enables, the fundamental societal and systems
transitions and transformations that help limit global warming to 1.5°C. Such changes facilitate thepursuit of climate-resilient development pathways that achieve ambitious mitigation and adaptationin conjunction with poverty eradication and efforts to reduce inequalities (high confidence). {Box 1.1,
1.4.3, Figure 5.1, 5.5.3, Box 5.3}

D.6.1

Social justice and equity are core aspects of climate-resilient development pathways that aim to limit global warming to
1.5°C as they address challenges and inevitable trade-offs, widen opportunities, and ensure that options, visions, and values
are deliberated, between and within countries and communities, without making the poor and disadvantaged worse off(high confidence). {5.5.2, 5.5.3, Box 5.3, Figure 5.1, Figure 5.6, Cross-Chapter Boxes 12 and 13 in Chapter 5}

D.6.2

The potential for climate-resilient development pathways differs between and within regions and nations, due to different
development contexts and systemic vulnerabilities (very high confidence). Efforts along such pathways to date have been
limited (medium confidence) and enhanced efforts would involve strengthened and timely action from all countries andnon-state actors (high confidence). {5.5.1, 5.5.3, Figure 5.1}

D.6.3

Pathways that are consistent with sustainable development show fewer mitigation and adaptation challenges and areassociated with lower mitigation costs. The large majority of modelling studies could not construct pathways characterized
by lack of international cooperation, inequality and poverty that were able to limit global warming to 1.5°C. (highconfidence) {2.3.1, 2.5.1, 2.5.3, 5.5.2}

Summary for Policymakers

D.7

Strengthening the capacities for climate action of national and sub-national authorities, civil society,the private sector, indigenous peoples and local communities can support the implementation of
ambitious actions implied by limiting global warming to 1.5°C (high confidence). International
cooperation can provide an enabling environment for this to be achieved in all countries and for all
people, in the context of sustainable development. International cooperation is a critical enabler for
developing countries and vulnerable regions (high confidence). {1.4, 2.3, 2.5, 4.2, 4.4, 4.5, 5.3, 5.4, 5.5,
5.6, 5, Box 4.1, Box 4.2, Box 4.7, Box 5.3, Cross-Chapter Box 9 in Chapter 4, Cross-Chapter Box 13 in
Chapter 5}

D.7.1

Partnerships involving non-state public and private actors, institutional investors, the banking system, civil society andscientific institutions would facilitate actions and responses consistent with limiting global warming to 1.5°C (very highconfidence). {1.4, 4.4.1, 4.2.2, 4.4.3, 4.4.5, 4.5.3, 5.4.1, 5.6.2, Box 5.3}.

D.7.2

Cooperation on strengthened accountable multilevel governance that includes non-state actors such as industry, civilsociety and scientific institutions, coordinated sectoral and cross-sectoral policies at various governance levels, gendersensitive policies, finance including innovative financing, and cooperation on technology development and transfer canensure participation, transparency, capacity building and learning among different players (high confidence). {2.5.1, 2.5.2,
4.2.2, 4.4.1, 4.4.2, 4.4.3, 4.4.4, 4.4.5, 4.5.3, Cross-Chapter Box 9 in Chapter 4, 5.3.1, 5.5.3, Cross-Chapter Box 13 in Chapter
5, 5.6.1, 5.6.3}

D.7.3

International cooperation is a critical enabler for developing countries and vulnerable regions to strengthen their action for
the implementation of 1.5°C-consistent climate responses, including through enhancing access to finance and technology
and enhancing domestic capacities, taking into account national and local circumstances and needs (high confidence).
{2.3.1, 2.5.1, 4.4.1, 4.4.2, 4.4.4, 4.4.5, 5.4.1 5.5.3, 5.6.1, Box 4.1, Box 4.2, Box 4.7}.

D.7.4

Collective efforts at all levels, in ways that reflect different circumstances and capabilities, in the pursuit of limiting global
warming to 1.5°C, taking into account equity as well as effectiveness, can facilitate strengthening the global response to
climate change, achieving sustainable development and eradicating poverty (high confidence). {1.4.2, 2.3.1, 2.5.1, 2.5.2,
2.5.3, 4.2.2, 4.4.1, 4.4.2, 4.4.3, 4.4.4, 4.4.5, 4.5.3, 5.3.1, 5.4.1, 5.5.3, 5.6.1, 5.6.2, 5.6.3}

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23

Summary for Policymakers

Box SPM.1: Core Concepts Central to this Special Report
Global mean surface temperature (GMST): Estimated global average of near-surface air temperatures over land andsea ice, and sea surface temperatures over ice-free ocean regions, with changes normally expressed as departures from avalue over a specified reference period. When estimating changes in GMST, near-surface air temperature over both landand oceans are also used.19 {1.2.1.1}

SPM

Pre-industrial: The multi-century period prior to the onset of large-scale industrial activity around 1750. The reference
period 1850–1900 is used to approximate pre-industrial GMST. {1.2.1.2}
Global warming: The estimated increase in GMST averaged over a 30-year period, or the 30-year period centred on aparticular year or decade, expressed relative to pre-industrial levels unless otherwise specified. For 30-year periods thatspan past and future years, the current multi-decadal warming trend is assumed to continue. {1.2.1}
Net zero CO2 emissions: Net zero carbon dioxide (CO2) emissions are achieved when anthropogenic CO2 emissions arebalanced globally by anthropogenic CO2 removals over a specified period.
Carbon dioxide removal (CDR): Anthropogenic activities removing CO2 from the atmosphere and durably storing it ingeological, terrestrial, or ocean reservoirs, or in products. It includes existing and potential anthropogenic enhancement of
biological or geochemical sinks and direct air capture and storage, but excludes natural CO2 uptake not directly caused byhuman activities.
Total carbon budget: Estimated cumulative net global anthropogenic CO2 emissions from the pre-industrial period
to the time that anthropogenic CO2 emissions reach net zero that would result, at some probability, in limiting global
warming to a given level, accounting for the impact of other anthropogenic emissions. {2.2.2}
Remaining carbon budget: Estimated cumulative net global anthropogenic CO2 emissions from a given start date to thetime that anthropogenic CO2 emissions reach net zero that would result, at some probability, in limiting global warming
to a given level, accounting for the impact of other anthropogenic emissions. {2.2.2}
Temperature overshoot: The temporary exceedance of a specified level of global warming.
Emission pathways: In this Summary for Policymakers, the modelled trajectories of global anthropogenic emissions overthe 21st century are termed emission pathways. Emission pathways are classified by their temperature trajectory overthe 21st century: pathways giving at least 50% probability based on current knowledge of limiting global warming to
below 1.5°C are classified as ‘no overshoot’; those limiting warming to below 1.6°C and returning to 1.5°C by 2100 areclassified as ‘1.5°C limited-overshoot’; while those exceeding 1.6°C but still returning to 1.5°C by 2100 are classified as‘higher-overshoot’.
Impacts: Effects of climate change on human and natural systems. Impacts can have beneficial or adverse outcomesfor livelihoods, health and well-being, ecosystems and species, services, infrastructure, and economic, social and cultural
assets.
Risk: The potential for adverse consequences from a climate-related hazard for human and natural systems, resulting
from the interactions between the hazard and the vulnerability and exposure of the affected system. Risk integrates
the likelihood of exposure to a hazard and the magnitude of its impact. Risk also can describe the potential for adverse
consequences of adaptation or mitigation responses to climate change.
Climate-resilient development pathways (CRDPs): Trajectories that strengthen sustainable development at multiple
scales and efforts to eradicate poverty through equitable societal and systems transitions and transformations while
reducing the threat of climate change through ambitious mitigation, adaptation and climate resilience.

19 Past IPCC reports, reflecting the literature, have used a variety of approximately equivalent metrics of GMST change.

24Chapters

1

Framing and Context
Coordinating Lead Authors:
Myles R. Allen (UK), Opha Pauline Dube (Botswana), William Solecki (USA)

Lead Authors:
Fernando Aragón-Durand (Mexico), Wolfgang Cramer (France/Germany), Stephen Humphreys (UK/
Ireland), Mikiko Kainuma (Japan), Jatin Kala (Australia), Natalie Mahowald (USA), Yacob Mulugetta(UK/Ethiopia), Rosa Perez (Philippines), Morgan Wairiu (Solomon Islands), Kirsten Zickfeld (Canada/
Germany)

Contributing Authors:
Purnamita Dasgupta (India), Haile Eakin (USA), Bronwyn Hayward (New Zealand), Diana Liverman(USA), Richard Millar (UK), Graciela Raga (Mexico/Argentina), Aurélien Ribes (France), Mark Richardson(USA/UK), Maisa Rojas (Chile), Roland Séférian (France), Sonia I. Seneviratne (Switzerland), Christopher
Smith (UK), Will Steffen (Australia), Peter Thorne (Ireland/UK)

Chapter Scientist:
Richard Millar (UK)

Review Editors:
Ismail Elgizouli Idris (Sudan), Andreas Fischlin (Switzerland), Xuejie Gao (China)

This chapter should be cited as:
Allen, M.R., O.P. Dube, W. Solecki, F. Aragón-Durand, W. Cramer, S. Humphreys, M. Kainuma, J. Kala, N. Mahowald,
Y. Mulugetta, R. Perez, M. Wairiu, and K. Zickfeld, 2018: Framing and Context. In: Global Warming of 1.5°C. An IPCC
Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhousegas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable
development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla,
A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy,
T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In Press.

49Chapter 1

Framing and Context

Table of Contents
Executive Summary......................................................................51

1

1.1
		

Assessing the Knowledge Basefor a 1.5°C Warmer World.............................................53

Box 1.1: The Anthropocene: Strengthening the
Global Response to 1.5°C Global Warming.............................54

1.5
		
		
		

Assessment Frameworks and Emerging
Methodologies that Integrate Climate
Change Mitigation and Adaptation
with Sustainable Development...................................75

1.5.1
			

Knowledge Sources and Evidence
Used in the Report........................................................75

1.5.2

Assessment Frameworks and Methodologies................76

1.1.1

Equity and a 1.5°C Warmer World.................................54

1.1.2

Eradication of Poverty...................................................55

1.6

Confidence, Uncertainty and Risk...............................77

1.1.3
			

Sustainable Development and a 1.5°C
Warmer World...............................................................55

1.7

Storyline of the Report..................................................77

1.2
		
		

Understanding 1.5°C: Reference Levels,
Probability, Transience, Overshoot,and Stabilization..............................................................56

1.2.1
			

Working Definitions of 1.5°C and 2°C
Warming Relative to Pre-Industrial Levels.....................56

1.2.2

Global versus Regional and Seasonal Warming.............59

1.2.3
			

Definition of 1.5°C Pathways: Probability,
Transience, Stabilization and Overshoot........................59

Cross-Chapter Box 1: Scenarios and Pathways.......................62
1.2.4

Geophysical Warming Commitment..............................64

Cross-Chapter Box 2: Measuring Progress to Net Zero
Emissions Combining Long-Lived and Short-Lived
Climate Forcers...........................................................................66

1.3

Impacts at 1.5°C and Beyond......................................68

1.3.1

Definitions.....................................................................68

1.3.2

Drivers of Impacts.........................................................69

1.3.3

Uncertainty and Non-Linearity of Impacts.....................69

1.4

Strengthening the Global Response..........................70

1.4.1

Classifying Response Options........................................70

1.4.2

Governance, Implementation and Policies.....................71

Cross-Chapter Box 3: Framing Feasibility:
Key Concepts and Conditions for Limiting
Global Temperature Increases to 1.5°C...................................71
1.4.3
			
			
			

Transformation, Transformation Pathways,
and Transition: Evaluating Trade-Offs andSynergies Between Mitigation, Adaptationand Sustainable Development Goals.............................73

Cross-Chapter Box 4: Sustainable Development
and the Sustainable Development Goals................................73

50

Frequently Asked Questions
FAQ 1.1: Why are we Talking about 1.5°C?......................... 79
FAQ 1.2: How Close are we to 1.5°C?.................................. 81

References ......................................................................................83

Framing and Context

Executive Summary
This chapter frames the context, knowledge-base and assessment
approaches used to understand the impacts of 1.5°C global warming
above pre-industrial levels and related global greenhouse gas
emission pathways, building on the IPCC Fifth Assessment Report(AR5), in the context of strengthening the global response to thethreat of climate change, sustainable development and efforts toeradicate poverty.
Human-induced warming reached approximately 1°C (likely
between 0.8°C and 1.2°C) above pre-industrial levels in 2017,
increasing at 0.2°C (likely between 0.1°C and 0.3°C) per
decade (high confidence). Global warming is defined in this reportas an increase in combined surface air and sea surface temperatures
averaged over the globe and over a 30-year period. Unless otherwise
specified, warming is expressed relative to the period 1850–1900,
used as an approximation of pre-industrial temperatures in AR5.
For periods shorter than 30 years, warming refers to the estimatedaverage temperature over the 30 years centred on that shorter
period, accounting for the impact of any temperature fluctuations
or trend within those 30 years. Accordingly, warming from preindustrial levels to the decade 2006–2015 is assessed to be 0.87°C(likely between 0.75°C and 0.99°C). Since 2000, the estimated levelof human-induced warming has been equal to the level of observed
warming with a likely range of ±20% accounting for uncertainty due
to contributions from solar and volcanic activity over the historicalperiod (high confidence). {1.2.1}
Warming greater than the global average has already been
experienced in many regions and seasons, with higher averagewarming over land than over the ocean (high confidence). Mostland regions are experiencing greater warming than the global average,
while most ocean regions are warming at a slower rate. Dependingon the temperature dataset considered, 20–40% of the global humanpopulation live in regions that, by the decade 2006–2015, had alreadyexperienced warming of more than 1.5°C above pre-industrial in atleast one season (medium confidence). {1.2.1, 1.2.2}
Past emissions alone are unlikely to raise global-meantemperature to 1.5°C above pre-industrial levels (medium
confidence), but past emissions do commit to other changes,
such as further sea level rise (high confidence). If allanthropogenic emissions (including aerosol-related) were reduced
to zero immediately, any further warming beyond the 1°C alreadyexperienced would likely be less than 0.5°C over the next two to
three decades (high confidence), and likely less than 0.5°C on a
century time scale (medium confidence), due to the opposing effects
of different climate processes and drivers. A warming greater than
1.5°C is therefore not geophysically unavoidable: whether it will
occur depends on future rates of emission reductions. {1.2.3, 1.2.4}
1.5°C emission pathways are defined as those that, given
current knowledge of the climate response, provide a onein-two to two-in-three chance of warming either remaining
below 1.5°C or returning to 1.5°C by around 2100 following

Chapter 1

an overshoot. Overshoot pathways are characterized by the peakmagnitude of the overshoot, which may have implications for
impacts. All 1.5°C pathways involve limiting cumulative emissionsof long-lived greenhouse gases, including carbon dioxide and nitrous
oxide, and substantial reductions in other climate forcers (highconfidence). Limiting cumulative emissions requires either reducing
net global emissions of long-lived greenhouse gases to zero before
the cumulative limit is reached, or net negative global emissions(anthropogenic removals) after the limit is exceeded. {1.2.3, 1.2.4,
Cross-Chapter Boxes 1 and 2}
This report assesses projected impacts at a global averagewarming of 1.5°C and higher levels of warming. Global warmingof 1.5°C is associated with global average surface temperaturesfluctuating naturally on either side of 1.5°C, together with warming
substantially greater than 1.5°C in many regions and seasons (highconfidence), all of which must be considered in the assessment of
impacts. Impacts at 1.5°C of warming also depend on the emission
pathway to 1.5°C. Very different impacts result from pathways
that remain below 1.5°C versus pathways that return to 1.5°C
after a substantial overshoot, and when temperatures stabilize at
1.5°C versus a transient warming past 1.5°C (medium confidence).
{1.2.3, 1.3}
Ethical considerations, and the principle of equity in particular,
are central to this report, recognizing that many of the impactsof warming up to and beyond 1.5°C, and some potential
impacts of mitigation actions required to limit warming to
1.5°C, fall disproportionately on the poor and vulnerable (highconfidence). Equity has procedural and distributive dimensions andrequires fairness in burden sharing both between generations andbetween and within nations. In framing the objective of holding theincrease in the global average temperature rise to well below 2°C
above pre-industrial levels, and to pursue efforts to limit warming to
1.5°C, the Paris Agreement associates the principle of equity with thebroader goals of poverty eradication and sustainable development,
recognising that effective responses to climate change require a
global collective effort that may be guided by the 2015 United
Nations Sustainable Development Goals. {1.1.1}
Climate adaptation refers to the actions taken to manage
impacts of climate change by reducing vulnerability andexposure to its harmful effects and exploiting any potential
benefits. Adaptation takes place at international, national andlocal levels. Subnational jurisdictions and entities, including urban
and rural municipalities, are key to developing and reinforcing
measures for reducing weather- and climate-related risks. Adaptation
implementation faces several barriers including lack of up-to-date andlocally relevant information, lack of finance and technology, social
values and attitudes, and institutional constraints (high confidence).
Adaptation is more likely to contribute to sustainable development
when policies align with mitigation and poverty eradication goals(medium confidence). {1.1, 1.4}
Ambitious mitigation actions are indispensable to limit
warming to 1.5°C while achieving sustainable development
and poverty eradication (high confidence). Ill-designed responses,
51

1

Chapter 1

1

however, could pose challenges especially – but not exclusively – forcountries and regions contending with poverty and those requiring
significant transformation of their energy systems. This report focuseson ‘climate-resilient development pathways’, which aim to meet thegoals of sustainable development, including climate adaptation andmitigation, poverty eradication and reducing inequalities. But any
feasible pathway that remains within 1.5°C involves synergies andtrade-offs (high confidence). Significant uncertainty remains as towhich pathways are more consistent with the principle of equity.
{1.1.1, 1.4}
Multiple forms of knowledge, including scientific evidence,narrative scenarios and prospective pathways, inform theunderstanding of 1.5°C. This report is informed by traditional
evidence of the physical climate system and associated impacts andvulnerabilities of climate change, together with knowledge drawn
from the perceptions of risk and the experiences of climate impacts
and governance systems. Scenarios and pathways are used to
explore conditions enabling goal-oriented futures while recognizing
the significance of ethical considerations, the principle of equity, andthe societal transformation needed. {1.2.3, 1.5.2}
There is no single answer to the question of whether it
is feasible to limit warming to 1.5°C and adapt to theconsequences. Feasibility is considered in this report as thecapacity of a system as a whole to achieve a specific outcome. The
global transformation that would be needed to limit warming to
1.5°C requires enabling conditions that reflect the links, synergies
and trade-offs between mitigation, adaptation and sustainable
development. These enabling conditions are assessed across many
dimensions of feasibility – geophysical, environmental-ecological,
technological, economic, socio-cultural and institutional – that
may be considered through the unifying lens of the Anthropocene,acknowledging profound, differential but increasingly geologicallysignificant human influences on the Earth system as a whole. Thisframing also emphasises the global interconnectivity of past, present
and future human–environment relations, highlighting the need andopportunities for integrated responses to achieve the goals of theParis Agreement. {1.1, Cross-Chapter Box 1}

52

Framing and Context

Framing and Context

1.1

Assessing the Knowledge Basefor a 1.5°C Warmer World

Human influence on climate has been the dominant cause of observed
warming since the mid-20th century, while global average surface
temperature warmed by 0.85°C between 1880 and 2012, as reported
in the IPCC Fifth Assessment Report, or AR5 (IPCC, 2013b). Manyregions of the world have already greater regional-scale warming,with 20–40% of the global population (depending on the temperaturedataset used) having experienced over 1.5°C of warming in at least
one season (Figure 1.1; Chapter 3 Section 3.3.2.1). Temperature riseto date has already resulted in profound alterations to human andnatural systems, including increases in droughts, floods, and someother types of extreme weather; sea level rise; and biodiversity loss –these changes are causing unprecedented risks to vulnerable persons
and populations (IPCC, 2012a, 2014a; Mysiak et al., 2016; Chapter
3 Sections 3.4.5–3.4.13). The most affected people live in low andmiddle income countries, some of which have experienced a decline
in food security, which in turn is partly linked to rising migration andpoverty (IPCC, 2012a). Small islands, megacities, coastal regions, andhigh mountain ranges are likewise among the most affected (Albert
et al., 2017). Worldwide, numerous ecosystems are at risk of severe
impacts, particularly warm-water tropical reefs and Arctic ecosystems(IPCC, 2014a).
This report assesses current knowledge of the environmental, technical,
economic, financial, socio-cultural, and institutional dimensions of a
1.5°C warmer world (meaning, unless otherwise specified, a worldin which warming has been limited to 1.5°C relative to pre-industrial
levels). Differences in vulnerability and exposure arise from numerous

Chapter 1

non-climatic factors (IPCC, 2014a). Global economic growth has beenaccompanied by increased life expectancy and income in much of
the world; however, in addition to environmental degradation andpollution, many regions remain characterised by significant poverty
and severe inequality in income distribution and access to resources,
amplifying vulnerability to climate change (Dryzek, 2016; Pattberg
and Zelli, 2016; Bäckstrand et al., 2017; Lövbrand et al., 2017). World
population continues to rise, notably in hazard-prone small andmedium-sized cities in low- and moderate-income countries (Birkmann
et al., 2016). The spread of fossil-fuel-based material consumption andchanging lifestyles is a major driver of global resource use, and themain contributor to rising greenhouse gas (GHG) emissions (Fleurbaey
et al., 2014).
The overarching context of this report is this: human influence has
become a principal agent of change on the planet, shifting the world
out of the relatively stable Holocene period into a new geologicalera, often termed the Anthropocene (Box 1.1). Responding to climatechange in the Anthropocene will require approaches that integrate
multiple levels of interconnectivity across the global community.
This chapter is composed of seven sections linked to the remaining
four chapters of the report. This introductory Section 1.1 situates thebasic elements of the assessment within the context of sustainable
development; considerations of ethics, equity and human rights; and the
problem of poverty. Section 1.2 focuses on understanding 1.5°C, global
versus regional warming, 1.5°C pathways, and associated emissions.
Section 1.3 frames the impacts at 1.5°C and beyond on natural andhuman systems. The section on strengthening the global response (1.4)
frames responses, governance and implementation, and trade-offs
and synergies between mitigation, adaptation, and the Sustainable

Figure 1.1 | Human experience of present-day warming. Different shades of pink to purple indicated by the inset histogram show estimated warming for the season
that has warmed the most at a given location between the periods 1850–1900 and 2006–2015, during which global average temperatures rose by 0.91°C in this dataset(Cowtan and Way, 2014) and 0.87°C in the multi-dataset average (Table 1.1 and Figure 1.3). The density of dots indicates the population (in 2010) in any 1° × 1° grid box.
The underlay shows national Sustainable Development Goal (SDG) Global Index Scores indicating performance across the 17 SDGs. Hatching indicates missing SDG index data
(e.g., Greenland). The histogram shows the population (in 2010) living in regions experiencing different levels of warming (at 0.25°C increments). See Supplementary Material
1.SM for further details.

53

1Chapter 1

Development Goals (SDGs) under transformation, transformation
pathways, and transition. Section 1.5 provides assessment frameworks
and emerging methodologies that integrate climate change mitigation

Framing and Context

and adaptation with sustainable development. Section 1.6 defines
approaches used to communicate confidence, uncertainty and risk,
while 1.7 presents the storyline of the whole report.

Box 1.1 | The Anthropocene: Strengthening the Global Response to 1.5°C Global Warming

1

Introduction
The concept of the Anthropocene can be linked to the aspiration of the Paris Agreement. The abundant empirical evidence of theunprecedented rate and global scale of impact of human influence on the Earth System (Steffen et al., 2016; Waters et al., 2016) has
led many scientists to call for an acknowledgement that the Earth has entered a new geological epoch: the Anthropocene (Crutzen
and Stoermer, 2000; Crutzen, 2002; Gradstein et al., 2012). Although rates of change in the Anthropocene are necessarily assessedover much shorter periods than those used to calculate long-term baseline rates of change, and therefore present challenges for directcomparison, they are nevertheless striking. The rise in global CO2 concentration since 2000 is about 20 ppm per decade, which is up to
10 times faster than any sustained rise in CO2 during the past 800,000 years (Lüthi et al., 2008; Bereiter et al., 2015). AR5 found thatthe last geological epoch with similar atmospheric CO2 concentration was the Pliocene, 3.3 to 3.0 Ma (Masson-Delmotte et al., 2013).
Since 1970 the global average temperature has been rising at a rate of 1.7°C per century, compared to a long-term decline over the
past 7,000 years at a baseline rate of 0.01°C per century (NOAA, 2016; Marcott et al., 2013). These global-level rates of human-drivenchange far exceed the rates of change driven by geophysical or biosphere forces that have altered the Earth System trajectory in the past(e.g., Summerhayes, 2015; Foster et al., 2017); even abrupt geophysical events do not approach current rates of human-driven change.
The Geological Dimension of the Anthropocene and 1.5°C Global Warming
The process of formalising the Anthropocene is on-going (Zalasiewicz et al., 2017), but a strong majority of the Anthropocene Working
Group (AWG) established by the Subcommission on Quaternary Stratigraphy of the International Commission on Stratigraphy haveagreed that: (i) the Anthropocene has a geological merit; (ii) it should follow the Holocene as a formal epoch in the Geological Time
Scale; and, (iii) its onset should be defined as the mid-20th century. Potential markers in the stratigraphic record include an array ofnovel manufactured materials of human origin, and “these combined signals render the Anthropocene stratigraphically distinct fromthe Holocene and earlier epochs” (Waters et al., 2016). The Holocene period, which itself was formally adopted in 1885 by geologicalscience community, began 11,700 years ago with a more stable warm climate providing for emergence of human civilisation andgrowing human-nature interactions that have expanded to give rise to the Anthropocene (Waters et al., 2016).
The Anthropocene and the Challenge of a 1.5° C Warmer World
The Anthropocene can be employed as a “boundary concept” (Brondizio et al., 2016) that frames critical insights into understanding thedrivers, dynamics and specific challenges in responding to the ambition of keeping global temperature well below 2°C while pursuingefforts towards and adapting to a 1.5°C warmer world. The United Nations Framework Convention on Climate Change (UNFCCC) and
its Paris Agreement recognize the ability of humans to influence geophysical planetary processes (Chapter 2, Cross-Chapter Box 1 in thischapter). The Anthropocene offers a structured understanding of the culmination of past and present human–environmental relations
and provides an opportunity to better visualize the future to minimize pitfalls (Pattberg and Zelli, 2016; Delanty and Mota, 2017), while
acknowledging the differentiated responsibility and opportunity to limit global warming and invest in prospects for climate-resilient
sustainable development (Harrington, 2016) (Chapter 5). The Anthropocene also provides an opportunity to raise questions regarding
the regional differences, social inequities, and uneven capacities and drivers of global social–environmental changes, which in turninform the search for solutions as explored in Chapter 4 of this report (Biermann et al., 2016). It links uneven influences of humanactions on planetary functions to an uneven distribution of impacts (assessed in Chapter 3) as well as the responsibility and response
capacity to, for example, limit global warming to no more than a 1.5°C rise above pre-industrial levels. Efforts to curtail greenhouse gas
emissions without incorporating the intrinsic interconnectivity and disparities associated with the Anthropocene world may themselves
negatively affect the development ambitions of some regions more than others and negate sustainable development efforts (seeChapter 2 and Chapter 5).

1.1.1

Equity and a 1.5°C Warmer World

The AR5 suggested that equity, sustainable development, andpoverty eradication are best understood as mutually supportiveand co-achievable within the context of climate action and areunderpinned by various other international hard and soft law
instruments (Denton et al., 2014; Fleurbaey et al., 2014; Klein et al.,54

2014; Olsson et al., 2014; Porter et al., 2014; Stavins et al., 2014).
The aim of the Paris Agreement under the UNFCCC to ‘pursue
efforts to limit’ the rise in global temperatures to 1.5°C above preindustrial levels raises ethical concerns that have long been central
to climate debates (Fleurbaey et al., 2014; Kolstad et al., 2014).
The Paris Agreement makes particular reference to the principleof equity, within the context of broader international goals ofFraming and Context

sustainable development and poverty eradication. Equity is a longstanding principle within international law and climate change law
in particular (Shelton, 2008; Bodansky et al., 2017).
The AR5 describes equity as having three dimensions: intergenerational(fairness between generations), international (fairness between
states), and national (fairness between individuals) (Fleurbaey et al.,2014). The principle is generally agreed to involve both procedural
justice (i.e., participation in decision making) and distributive justice(i.e., how the costs and benefits of climate actions are distributed)(Kolstad et al., 2014; Savaresi, 2016; Reckien et al., 2017). Concerns
regarding equity have frequently been central to debates aroundmitigation, adaptation and climate governance (Caney, 2005;
Schroeder et al., 2012; Ajibade, 2016; Reckien et al., 2017; Shue,2018). Hence, equity provides a framework for understanding theasymmetries between the distributions of benefits and costs relevant
to climate action (Schleussner et al., 2016; Aaheim et al., 2017).
Four key framing asymmetries associated with the conditions of a
1.5°C warmer world have been noted (Okereke, 2010; Harlan et al.,2015; Ajibade, 2016; Savaresi, 2016; Reckien et al., 2017) and arereflected in the report’s assessment. The first concerns differential
contributions to the problem: the observation that the benefits fromindustrialization have been unevenly distributed and those who
benefited most historically also have contributed most to the current
climate problem and so bear greater responsibility (Shue, 2013;
McKinnon, 2015; Otto et al., 2017; Skeie et al., 2017). The second
asymmetry concerns differential impact: the worst impacts tend to
fall on those least responsible for the problem, within states, between
states, and between generations (Fleurbaey et al., 2014; Shue, 2014;
Ionesco et al., 2016). The third is the asymmetry in capacity to shapesolutions and response strategies, such that the worst-affected states,groups, and individuals are not always well represented (Robinson
and Shine, 2018). Fourth, there is an asymmetry in future response
capacity: some states, groups, and places are at risk of being left
behind as the world progresses to a low-carbon economy (Fleurbaey
et al., 2014; Shue, 2014; Humphreys, 2017).
A sizeable and growing literature exists on how best tooperationalize climate equity considerations, drawing on otherconcepts mentioned in the Paris Agreement, notably its explicit
reference to human rights (OHCHR, 2009; Caney, 2010; Adger etal., 2014; Fleurbaey et al., 2014; IBA, 2014; Knox, 2015; Duyck
et al., 2018; Robinson and Shine, 2018). Human rights compriseinternationally agreed norms that align with the Paris ambitions ofpoverty eradication, sustainable development, and the reduction ofvulnerability (Caney, 2010; Fleurbaey et al., 2014; OHCHR, 2015).
In addition to defining substantive rights (such as to life, health,and shelter) and procedural rights (such as to information andparticipation), human rights instruments prioritise the rights ofmarginalized groups, children, vulnerable and indigenous persons,and those discriminated against on grounds such as gender, race,
age or disability (OHCHR, 2017). Several international humanrights obligations are relevant to the implementation of climate
actions and consonant with UNFCCC undertakings in the areasof mitigation, adaptation, finance, and technology transfer (Knox,
2015; OHCHR, 2015; Humphreys, 2017).

Chapter 1

Much of this literature is still new and evolving (Holz et al., 2017;
Dooley et al., 2018; Klinsky and Winkler, 2018), permitting thepresent report to examine some broader equity concerns raised
both by possible failure to limit warming to 1.5°C and by the rangeof ambitious mitigation efforts that may be undertaken to achievethat limit. Any comparison between 1.5°C and higher levels ofwarming implies risk assessments and value judgements and cannot
straightforwardly be reduced to a cost-benefit analysis (Kolstad etal., 2014). However, different levels of warming can nevertheless be
understood in terms of their different implications for equity – thatis, in the comparative distribution of benefits and burdens for specificstates, persons, or generations, and in terms of their likely impacts
on sustainable development and poverty (see especially Sections
2.3.4.2, 2.5, 3.4.5–3.4.13, 3.6, 5.4.1, 5.4.2, 5.6 and Cross-Chapter
boxes 6 in Chapter 3 and 12 in Chapter 5).

1.1.2

Eradication of Poverty

This report assesses the role of poverty and its eradication in thecontext of strengthening the global response to the threat ofclimate change and sustainable development. A wide range ofdefinitions for poverty exist. The AR5 discussed ‘poverty’ in termsof its multidimensionality, referring to ‘material circumstances’(e.g., needs, patterns of deprivation, or limited resources), as wellas to economic conditions (e.g., standard of living, inequality, or
economic position), and/or social relationships (e.g., social class,dependency, lack of basic security, exclusion, or lack of entitlement;
Olsson et al., 2014). The UNDP now uses a Multidimensional Poverty
Index and estimates that about 1.5 billion people globally live in
multidimensional poverty, especially in rural areas of South Asia and
Sub-Saharan Africa, with an additional billion at risk of falling intopoverty (UNDP, 2016).
A large and rapidly growing body of knowledge explores theconnections between climate change and poverty. Climatic
variability and climate change are widely recognized as factors that
may exacerbate poverty, particularly in countries and regions where
poverty levels are high (Leichenko and Silva, 2014). The AR5 notedthat climate change-driven impacts often act as a threat multiplierin that the impacts of climate change compound other drivers ofpoverty (Olsson et al., 2014). Many vulnerable and poor people are
dependent on activities such as agriculture that are highly susceptible
to temperature increases and variability in precipitation patterns(Shiferaw et al., 2014; Miyan, 2015). Even modest changes in rainfall
and temperature patterns can push marginalized people into poverty
as they lack the means to recover from associated impacts. Extreme
events, such as floods, droughts, and heat waves, especially when
they occur in series, can significantly erode poor people’s assets andfurther undermine their livelihoods in terms of labour productivity,
housing, infrastructure and social networks (Olsson et al., 2014).

1.1.3

Sustainable Development and a 1.5°C
Warmer World

AR5 (IPCC, 2014c) noted with high confidence that ‘equity is an
integral dimension of sustainable development’ and that ‘mitigation
and adaptation measures can strongly affect broader sustainable55

1Chapter 1

1development and equity objectives’ (Fleurbaey et al., 2014). Limiting
global warming to 1.5°C would require substantial societal andtechnological transformations, dependent in turn on global andregional sustainable development pathways. A range of pathways,
both sustainable and not, are explored in this report, including
implementation strategies to understand the enabling conditions andchallenges required for such a transformation. These pathways andconnected strategies are framed within the context of sustainable
development, and in particular the United Nations 2030 Agenda for
Sustainable Development (UN, 2015b) and Cross-Chapter Box 4 on
SDGs (in this chapter). The feasibility of staying within 1.5°C depends
upon a range of enabling conditions with geophysical, environmental–
ecological, technological, economic, socio-cultural, and institutional
dimensions. Limiting warming to 1.5°C also involves identifying
technology and policy levers to accelerate the pace of transformation(see Chapter 4). Some pathways are more consistent than others withthe requirements for sustainable development (see Chapter 5). Overall,
the three-pronged emphasis on sustainable development, resilience,and transformation provides Chapter 5 an opportunity to assess
the conditions of simultaneously reducing societal vulnerabilities,
addressing entrenched inequalities, and breaking the circle of poverty.
The feasibility of any global commitment to a 1.5°C pathway depends,
in part, on the cumulative influence of the nationally determined
contributions (NDCs), committing nation states to specific GHG
emission reductions. The current NDCs, extending only to 2030, do
not limit warming to 1.5°C. Depending on mitigation decisions after
2030, they cumulatively track toward a warming of 3°-4°C above
pre-industrial temperatures by 2100, with the potential for further
warming thereafter (Rogelj et al., 2016a; UNFCCC, 2016). The analysis
of pathways in this report reveals opportunities for greater decouplingof economic growth from GHG emissions. Progress towards limiting
warming to 1.5°C requires a significant acceleration of this trend. AR5
concluded that climate change constrains possible development paths,
that synergies and trade-offs exist between climate responses andsocio-economic contexts, and that opportunities for effective climate
responses overlap with opportunities for sustainable development,
noting that many existing societal patterns of consumption are
intrinsically unsustainable (Fleurbaey et al., 2014).

1.2

Understanding 1.5°C: Reference
Levels, Probability, Transience,
Overshoot, and Stabilization

1.2.1

Working Definitions of 1.5°C and 2°C
Warming Relative to Pre-Industrial Levels

Framing and Context

on continental scales (Deser et al., 2012) and primarily affects thehistorical period, particularly that prior to the early 20th century whendata is sparse and of less certain quality. Most practical mitigation
and adaptation decisions do not depend on quantifying historicalwarming to this level of precision, but a consistent working definitionis necessary to ensure consistency across chapters and figures. We
adopt definitions that are as consistent as possible with key findingsof AR5 with respect to historical warming.
This report defines ‘warming’, unless otherwise qualified, as anincrease in multi-decade global mean surface temperature (GMST)
above pre-industrial levels. Specifically, warming at a given point
in time is defined as the global average of combined land surface
air and sea surface temperatures for a 30-year period centred onthat time, expressed relative to the reference period 1850–1900(adopted for consistency with Box SPM.1 Figure 1 of IPCC (2014a))‘as an approximation of pre-industrial levels’, excluding the impact ofnatural climate fluctuations within that 30-year period and assuming
any secular trend continues throughout that period, extrapolating
into the future if necessary. There are multiple ways of accounting
for natural fluctuations and trends (e.g., Foster and Rahmstorf, 2011;
Haustein et al., 2017; Medhaug et al., 2017; Folland et al., 2018;
Visser et al., 2018), but all give similar results. A major volcanic
eruption might temporarily reduce observed global temperatures,
but would not reduce warming as defined here (Bethke et al., 2017).
Likewise, given that the level of warming is currently increasing at
0.3°C–0.7°C per 30 years (likely range quoted in Kirtman et al., 2013
and supported by Folland et al., 2018), the level of warming in 2017
was 0.15°C–0.35°C higher than average warming over the 30-year
period 1988–2017.
In summary, this report adopts a working definition of ‘1.5°C relative
to pre-industrial levels’ that corresponds to global average combined
land surface air and sea surface temperatures either 1.5°C warmer
than the average of the 51-year period 1850–1900, 0.87°C warmer
than the 20-year period 1986–2005, or 0.63°C warmer than thedecade 2006–2015. These offsets are based on all available publishedglobal datasets, combined and updated, which show that 1986–
2005 was 0.63°C warmer than 1850–1900 (with a 5–95% rangeof 0.57°C–0.69°C based on observational uncertainties alone), and2006–2015 was 0.87°C warmer than 1850–1900 (with a likely rangeof 0.75°C–0.99°C, also accounting for the possible impact of naturalfluctuations). Where possible, estimates of impacts and mitigation
pathways are evaluated relative to these more recent periods. Notethat the 5–95% intervals often quoted in square brackets in AR5
correspond to very likely ranges, while likely ranges correspond to
17–83%, or the central two-thirds, of the distribution of uncertainty.

1.2.1.1 Definition of global average temperature
What is meant by ‘the increase in global average temperature… above
pre-industrial levels’ referred to in the Paris Agreement depends onthe choice of pre-industrial reference period, whether 1.5°C refers tototal warming or the human-induced component of that warming,
and which variables and geographical coverage are used to define
global average temperature change. The cumulative impact of thesedefinitional ambiguities (e.g., Hawkins et al., 2017; Pfleiderer et al.,2018) is comparable to natural multi-decadal temperature variability
56

The IPCC has traditionally defined changes in observed GMST as aweighted average of near-surface air temperature (SAT) changes
over land and sea surface temperature (SST) changes over the oceans(Morice et al., 2012; Hartmann et al., 2013), while modelling studies
have typically used a simple global average SAT. For ambitious
mitigation goals, and under conditions of rapid warming or declining
sea ice (Berger et al., 2017), the difference can be significant. Cowtan

Framing and Context

et al. (2015) and Richardson et al. (2016) show that the use ofblended SAT/SST data and incomplete coverage together can give
approximately 0.2°C less warming from the 19th century to thepresent relative to the use of complete global-average SAT (Stocker
et al., 2013, Figure TFE8.1 and Figure 1.2). However, Richardson et al.
(2018) show that this is primarily an issue for the interpretation of
the historical record to date, with less absolute impact on projectionsof future changes, or estimated emissions budgets, under ambitious
mitigation scenarios.
The three GMST reconstructions used in AR5 differ in their treatmentof missing data. GISTEMP (Hansen et al., 2010) uses interpolation
to infer trends in poorly observed regions like the Arctic (althougheven this product is spatially incomplete in the early record), while
NOAAGlobalTemp (Vose et al., 2012) and HadCRUT (Morice et al.,2012) are progressively closer to a simple average of available
observations. Since the AR5, considerable effort has been devoted
to more sophisticated statistical modelling to account for the impact

Chapter 1of incomplete observation coverage (Rohde et al., 2013; Cowtan andWay, 2014; Jones, 2016). The main impact of statistical infilling is toincrease estimated warming to date by about 0.1°C (Richardson etal., 2018 and Table 1.1).
We adopt a working definition of warming over the historical period
based on an average of the four available global datasets that aresupported by peer-reviewed publications: the three datasets used in theAR5, updated (Karl et al., 2015), together with the Cowtan-Way infilled
dataset (Cowtan and Way, 2014). A further two datasets, Berkeley
Earth (Rohde et al., 2013) and that of the Japan Meteorological Agency(JMA), are provided in Table 1.1. This working definition provides an
updated estimate of 0.86°C for the warming over the period 1880–
2012 based on a linear trend. This quantity was quoted as 0.85°C in
the AR5. Hence the inclusion of the Cowtan-Way dataset does not
introduce any inconsistency with the AR5, whereas redefining GMST
to represent global SAT could increase this figure by up to 20% (Table
1.1, blue lines in Figure 1.2 and Richardson et al., 2016).

Figure 1.2 | Evolution of global mean surface temperature (GMST) over the period of instrumental observations. Grey shaded line shows monthly mean GMST
in the HadCRUT4, NOAAGlobalTemp, GISTEMP and Cowtan-Way datasets, expressed as departures from 1850–1900, with varying grey line thickness indicating inter-dataset
range. All observational datasets shown represent GMST as a weighted average of near surface air temperature over land and sea surface temperature over oceans. Humaninduced (yellow) and total (human- and naturally-forced, orange) contributions to these GMST changes are shown calculated following Otto et al. (2015) and Haustein et al.
(2017). Fractional uncertainty in the level of human-induced warming in 2017 is set equal to ±20% based on multiple lines of evidence. Thin blue lines show the modelled
global mean surface air temperature (dashed) and blended surface air and sea surface temperature accounting for observational coverage (solid) from the CMIP5 historicalensemble average extended with RCP8.5 forcing (Cowtan et al., 2015; Richardson et al., 2018). The pink shading indicates a range for temperature fluctuations over the
Holocene (Marcott et al., 2013). Light green plume shows the AR5 prediction for average GMST over 2016–2035 (Kirtman et al., 2013). See Supplementary Material 1.SM forfurther details.

1.2.1.2 Choice of reference period
Any choice of reference period used to approximate ‘preindustrial’ conditions is a compromise between data coverage
and representativeness of typical pre-industrial solar and volcanic
forcing conditions. This report adopts the 51-year reference period,
1850–1900 inclusive, assessed as an approximation of pre-industrial
levels in AR5 (Box TS.5, Figure 1 of Field et al., 2014). The years
1880–1900 are subject to strong but uncertain volcanic forcing, but

in the HadCRUT4 dataset, average temperatures over 1850–1879,
prior to the largest eruptions, are less than 0.01°C from the averagefor 1850–1900. Temperatures rose by 0.0°C–0.2°C from 1720–
1800 to 1850–1900 (Hawkins et al., 2017), but the anthropogenic
contribution to this warming is uncertain (Abram et al., 2016; Schurer
et al., 2017). The 18th century represents a relatively cool period in
the context of temperatures since the mid-Holocene (Marcott et al.,2013; Lüning and Vahrenholt, 2017; Marsicek et al., 2018), which isindicated by the pink shaded region in Figure 1.2.
57

1Chapter 1

1

Framing and Context

Projections of responses to emission scenarios, and associatedimpacts, may use a more recent reference period, offset by historicalobservations, to avoid conflating uncertainty in past and futurechanges (e.g., Hawkins et al., 2017; Millar et al., 2017b; Simmons
et al., 2017). Two recent reference periods are used in this report:
1986–2005 and 2006–2015. In the latter case, when using a single
decade to represent a 30-year average centred on that decade, it
is important to consider the potential impact of internal climate
variability. The years 2008–2013 were characterised by persistentcool conditions in the Eastern Pacific (Kosaka and Xie, 2013; Medhaug
et al., 2017), related to both the El Niño-Southern Oscillation (ENSO)
and, potentially, multi-decadal Pacific variability (e.g., England et al.,2014), but these were partially compensated for by El Niño conditionsin 2006 and 2015. Likewise, volcanic activity depressed temperatures
in 1986–2005, partly offset by the very strong El Niño event in 1998.
Figure 1.2 indicates that natural variability (internally generated andexternally driven) had little net impact on average temperatures
over 2006–2015, in that the average temperature of the decadeis similar to the estimated externally driven warming. When solar,
volcanic and ENSO-related variability is taken into account following
the procedure of Foster and Rahmstorf (2011), there is no indicationof average temperatures in either 1986–2005 or 2006–2015 being
substantially biased by short-term variability (see Supplementary
Material 1.SM.2). The temperature difference between these two
reference periods (0.21°C–0.27°C over 15 years across available
datasets) is also consistent with the AR5 assessment of the current
warming rate of 0.3°C–0.7°C over 30 years (Kirtman et al., 2013).
On the definition of warming used here, warming to the decade
2006–2015 comprises an estimate of the 30-year average centredon this decade, or 1996–2025, assuming the current trend continues
and that any volcanic eruptions that might occur over the final sevenyears are corrected for. Given this element of extrapolation, we use
the AR5 near-term projection to provide a conservative uncertainty
range. Combining the uncertainty in observed warming to 1986–
2005 (±0.06°C) with the likely range in the current warming trend as

Table 1.1 | Observed increase in global average surface temperature in various datasets.
Numbers in square brackets correspond to 5–95% uncertainty ranges from individual datasets, encompassing known sources of observational uncertainty only.

1850–1900
to (1)
2006–2015

1850–1900
to (2)
1986–2005

1986–2005
to (3)
2006–2015

1850–1900
to (4)
1981–2010

1850–1900
to (5)
1998–2017

Trend (6)1880–2012

Trend (6)1880–2015

0.84[0.79–0.89]

0.60[0.57–0.66]

0.22[0.21–0.23]

0.62[0.58–0.67]

0.83[0.78–0.88]

0.83[0.77–0.90]

0.88[0.83–0.95]

NOAAGlobalTemp
(7)

0.86

0.62

0.22

0.63

0.85

0.85

0.91

GISTEMP (7)

0.89

0.65

0.23

0.66

0.88

0.89

0.94

Cowtan-Way

0.91[0.85–0.99]

0.65[0.60–0.72]

0.26[0.25–0.27]

0.65[0.60–0.72]

0.88[0.82–0.96]

0.88[0.79–0.98]

0.93[0.85–1.03]

Average (8)

0.87

0.63

0.23

0.64

0.86

0.86

0.92

Berkeley (9)

0.98

0.73

0.25

0.73

0.97

0.97

1.02

JMA (9)

0.82

0.59

0.17

0.60

0.81

0.82

0.87

ERA-Interim

N/A

N/A

0.26

N/A

N/A

N/A

N/A

Diagnostic/ dataset
HadCRUT4.6

JRA-55

N/A

N/A

0.23

N/A

N/A

N/A

N/A

CMIP5 global
SAT (10)0.99[0.65–1.37]

0.62[0.38–0.94]

0.38[0.24–0.62]

0.62[0.34–0.93]

0.89[0.62–1.29]

0.81[0.58–1.31]

0.86[0.63–1.39]

CMIP5 SAT/SST
blend-masked

0.86[0.54–1.18]

0.50[0.31–0.79]

0.34[0.19–0.54]

0.48[0.26–0.79]

0.75[0.52–1.11]

0.68[0.45–1.08]

0.74[0.51–1.14]

Notes:
1) Most recent reference period used in this report.
2) Most recent reference period used in AR5.
3) Difference between recent reference periods.
4) Current WMO standard reference periods.
5) Most recent 20-year period.
6) Linear trends estimated by a straight-line fit, expressed in degrees yr−1 multiplied by 133 or 135 years respectively, with uncertainty ranges incorporating observational uncertainty only.
7) To estimate changes in the NOAAGlobalTemp and GISTEMP datasets relative to the 1850–1900 reference period, warming is computed relative to 1850–1900 using the HadCRUT4.6
		 dataset and scaled by the ratio of the linear trend 1880–2015 in the NOAAGlobalTemp or GISTEMP dataset with the corresponding linear trend computed from HadCRUT4.
8) Average of diagnostics derived – see (7) – from four peer-reviewed global datasets, HadCRUT4.6, NOAA, GISTEMP & Cowtan-Way. Note that differences between averages may not		 coincide with average differences because of rounding.
9) No peer-reviewed publication available for these global combined land–sea datasets.
10) CMIP5 changes estimated relative to 1861–80 plus 0.02°C for the offset in HadCRUT4.6 from 1850–1900. CMIP5 values are the mean of the RCP8.5 ensemble, with 5–95% ensemble		 range. They are included to illustrate the difference between a complete global surface air temperature record (SAT) and a blended surface air and sea surface temperature (SST) record
		 accounting for incomplete coverage (masked), following Richardson et al. (2016). Note that 1986–2005 temperatures in CMIP5 appear to have been depressed more than observed temperaturesby the eruption of Mount Pinatubo.

58

Framing and Context

assessed by AR5 (±0.2°C/30 years), assuming these are uncorrelated,
and using observed warming relative to 1850–1900 to provide thecentral estimate (no evidence of bias from short-term variability),
gives an assessed warming to the decade 2006–2015 of 0.87°C with
a ±0.12°C likely range. This estimate has the advantage of traceability
to the AR5, but more formal methods of quantifying externally driven
warming (e.g., Bindoff et al., 2013; Jones et al., 2016; Haustein etal., 2017; Ribes et al., 2017), which typically give smaller ranges of
uncertainty, may be adopted in the future.

1.2.1.3 Total versus human-induced warming andwarming rates
Total warming refers to the actual temperature change, irrespectiveof cause, while human-induced warming refers to the componentof that warming that is attributable to human activities. Mitigation
studies focus on human-induced warming (that is not subject tointernal climate variability), while studies of climate change impacts
typically refer to total warming (often with the impact of internalvariability minimised through the use of multi-decade averages).
In the absence of strong natural forcing due to changes in solar or
volcanic activity, the difference between total and human-induced
warming is small: assessing empirical studies quantifying solar andvolcanic contributions to GMST from 1890 to 2010, AR5 (Figure 10.6of Bindoff et al., 2013) found their net impact on warming over thefull period to be less than plus or minus 0.1°C. Figure 1.2 shows thatthe level of human-induced warming has been indistinguishable fromtotal observed warming since 2000, including over the decade 2006–
2015. Bindoff et al. (2013) assessed the magnitude of human-induced
warming over the period 1951–2010 to be 0.7°C (likely between
0.6°C and 0.8°C), which is slightly greater than the 0.65°C observed
warming over this period (Figures 10.4 and 10.5) with a likely rangeof ±14%. The key surface temperature attribution studies underlying
this finding (Gillett et al., 2013; Jones et al., 2013; Ribes and Terray,2013) used temperatures since the 19th century to constrain humaninduced warming, and so their results are equally applicable to theattribution of causes of warming over longer periods. Jones et al.
(2016) show (Figure 10) human-induced warming trends over theperiod 1905–2005 to be indistinguishable from the corresponding
total observed warming trend accounting for natural variability using
spatio-temporal detection patterns from 12 out of 15 CMIP5 models
and from the multi-model average. Figures from Ribes and Terray(2013), show the anthropogenic contribution to the observed linear
warming trend 1880–2012 in the HadCRUT4 dataset (0.83°C in Table
1.1) to be 0.86°C using a multi-model average global diagnostic, witha 5–95% confidence interval of 0.72°C–1.00°C (see figure 1.SM.6).
In all cases, since 2000 the estimated combined contribution of solar
and volcanic activity to warming relative to 1850–1900 is found to beless than ±0.1°C (Gillett et al., 2013), while anthropogenic warming
is indistinguishable from, and if anything slightly greater than, thetotal observed warming, with 5–95% confidence intervals typicallyaround ±20%.
Haustein et al. (2017) give a 5–95% confidence interval forhuman-induced warming in 2017 of 0.87°C–1.22°C, with a best
estimate of 1.02°C, based on the HadCRUT4 dataset accounting

Chapter 1

for observational and forcing uncertainty and internal variability.
Applying their method to the average of the four datasets shown in
Figure 1.2 gives an average level of human-induced warming in 2017of 1.04°C. They also estimate a human-induced warming trend overthe past 20 years of 0.17°C (0.13°C–0.33°C) per decade, consistent
with estimates of the total observed trend of Foster and Rahmstorf(2011) (0.17° ± 0.03°C per decade, uncertainty in linear trend only),
Folland et al. (2018) and Kirtman et al. (2013) (0.3°C–0.7°C over 30
years, or 0.1°C–0.23°C per decade, likely range), and a best-estimate
warming rate over the past five years of 0.215°C/decade (Leach et al.,2018). Drawing on these multiple lines of evidence, human-induced
warming is assessed to have reached 1.0°C in 2017, having increased
by 0.13°C from the mid-point of 2006–2015, with a likely rangeof 0.8°C to 1.2°C (reduced from 5–95% to account for additional
forcing and model uncertainty), increasing at 0.2°C per decade (witha likely range of 0.1°C to 0.3°C per decade: estimates of humaninduced warming given to 0.1°C precision only).
Since warming is here defined in terms of a 30-year average, corrected
for short-term natural fluctuations, when warming is considered to be
at 1.5°C, global temperatures would fluctuate equally on either sideof 1.5°C in the absence of a large cooling volcanic eruption (Bethke etal., 2017). Figure 1.2 indicates there is a substantial chance of GMST ina single month fluctuating over 1.5°C between now and 2020 (or, by2030, for a longer period: Henley and King, 2017), but this would notconstitute temperatures ‘reaching 1.5°C’ on our working definition.
Rogelj et al. (2017) show limiting the probability of annual GMST
exceeding 1.5°C to less than one-year-in-20 would require limiting
warming, on the definition used here, to 1.31°C or lower.

1.2.2

Global versus Regional and Seasonal Warming

Warming is not observed or expected to be spatially or seasonallyuniform (Collins et al., 2013). A 1.5°C increase in GMST will be
associated with warming substantially greater than 1.5°C in many
land regions, and less than 1.5°C in most ocean regions. This is
illustrated by Figure 1.3, which shows an estimate of the observedchange in annual and seasonal average temperatures between
the 1850–1900 pre-industrial reference period and the decade
2006–2015 in the Cowtan-Way dataset. These regional changes areassociated with an observed GMST increase of 0.91°C in the dataset
shown here, or 0.87°C in the four-dataset average (Table 1.1). Thisobserved pattern reflects an on-going transient warming: features
such as enhanced warming over land may be less pronounced, but stillpresent, in equilibrium (Collins et al., 2013). This figure illustrates themagnitude of spatial and seasonal differences, with many locations,particularly in Northern Hemisphere mid-latitude winter (December–
February), already experiencing regional warming more than double
the global average. Individual seasons may be substantially warmer,
or cooler, than these expected changes in the long-term average.

1.2.3

Definition of 1.5°C Pathways: Probability,
Transience, Stabilization and OvershootPathways considered in this report, consistent with available literatureon 1.5°C, primarily focus on the time scale up to 2100, recognisingthat the evolution of GMST after 2100 is also important. Two broad
59

1Chapter 1

Framing and Context

1

Figure 1.3 | Spatial and seasonal pattern of present-day warming: Regional warming for the 2006–2015 decade relative to 1850–1900 for the annual mean (top),
the average of December, January, and February (bottom left) and for June, July, and August (bottom right). Warming is evaluated by regressing regional changes in the Cowtan
and Way (2014) dataset onto the total (combined human and natural) externally forced warming (yellow line in Figure 1.2). See Supplementary Material 1.SM for further details
and versions using alternative datasets. The definition of regions (green boxes and labels in top panel) is adopted from the AR5 (Christensen et al., 2013).

categories of 1.5°C pathways can be used to characterise mitigation
options and impacts: pathways in which warming (defined as 30-year
averaged GMST relative to pre-industrial levels, see Section 1.2.1)
remains below 1.5°C throughout the 21st century, and pathways
in which warming temporarily exceeds (‘overshoots’) 1.5°C andreturns to 1.5°C either before or soon after 2100. Pathways in which
warming exceeds 1.5°C before 2100, but might return to that level insome future century, are not considered 1.5°C pathways.
Because of uncertainty in the climate response, a ‘prospective’
mitigation pathway (see Cross-Chapter Box 1 in this chapter), in whichemissions are prescribed, can only provide a level of probability ofwarming remaining below a temperature threshold. This probability
cannot be quantified precisely since estimates depend on the method
used (Rogelj et al., 2016b; Millar et al., 2017b; Goodwin et al., 2018;
Tokarska and Gillett, 2018). This report defines a ‘1.5°C pathway’
as a pathway of emissions and associated possible temperature
responses in which the majority of approaches using presently
available information assign a probability of approximately one-intwo to two-in-three to warming remaining below 1.5°C or, in the caseof an overshoot pathway, to warming returning to 1.5°C by around2100 or earlier. Recognizing the very different potential impacts andrisks associated with high-overshoot pathways, this report singles
60

out 1.5°C pathways with no or limited (<0.1°C) overshoot in many
instances and pursues efforts to ensure that when the term ‘1.5°C
pathway’ is used, the associated overshoot is made explicit where
relevant. In Chapter 2, the classification of pathways is based on one
modelling approach to avoid ambiguity, but probabilities of exceeding
1.5°C are checked against other approaches to verify that they lie
within this approximate range. All these absolute probabilities are
imprecise, depend on the information used to constrain them, andhence are expected to evolve in the future. Imprecise probabilities
can nevertheless be useful for decision-making, provided theimprecision is acknowledged (Hall et al., 2007; Kriegler et al., 2009;
Simpson et al., 2016). Relative and rank probabilities can be assessedmuch more consistently: approaches may differ on the absoluteprobability assigned to individual outcomes, but typically agree on
which outcomes are more probable.
Importantly, 1.5°C pathways allow a substantial (up to one-in-two)chance of warming still exceeding 1.5°C. An ‘adaptive’ mitigation
pathway in which emissions are continuously adjusted to achievea specific temperature outcome (e.g., Millar et al., 2017b) reduces
uncertainty in the temperature outcome while increasing uncertainty
in the emissions required to achieve it. It has been argued (Otto etal., 2015; Xu and Ramanathan, 2017) that achieving very ambitious

Framing and Context

temperature goals will require such an adaptive approach to
mitigation, but very few studies have been performed taking thisapproach (e.g., Jarvis et al., 2012).
Figure 1.4 illustrates categories of (a) 1.5°C pathways and associated(b) annual and (c) cumulative emissions of CO2. It also shows (d)an example of a ‘time-integrated impact’ that continues to increase
even after GMST has stabilised, such as sea level rise. This schematic
assumes for the purposes of illustration that the fractional contributionof non-CO2 climate forcers to total anthropogenic forcing (which iscurrently increasing, Myhre et al., 2017) is approximately constantfrom now on. Consequently, total human-induced warming is
proportional to cumulative CO2 emissions (solid line in c), and GMST
stabilises when emissions reach zero. This is only the case in the mostambitious scenarios for non-CO2 mitigation (Leach et al., 2018). A
simple way of accounting for varying non-CO2 forcing in Figure 1.4
would be to note that every 1 W m−2 increase in non-CO2 forcing
between now and the decade or two immediately prior to the timeof peak warming reduces cumulative CO2 emissions consistent withthe same peak warming by approximately 1100 GtCO2, with a rangeof 900-1500 GtCO2 (using values from AR5: Myhre et al., 2013; Allenet al., 2018; Jenkins et al., 2018; Cross-Chapter Box 2 in this chapter).

Chapter 1

time. Hence every year’s delay before initiating emission reductions
decreases by approximately two years the remaining time available
to reach zero emissions on a pathway still remaining below 1.5°C(Allen and Stocker, 2013; Leach et al., 2018).

1.2.3.2 Pathways temporarily exceeding 1.5°C
With the pathways in this category, also referred to as overshootpathways, GMST rises above 1.5°C relative to pre-industrial before
peaking and returning to 1.5°C around or before 2100 (Figure 1.4,
blue lines), subsequently either stabilising or continuing to fall. This
allows initially slower or delayed emission reductions, but lowering
GMST requires net negative global CO2 emissions (net anthropogenic
removal of CO2; Figure 1.4b). Cooling, or reduced warming, throughsustained reductions of net non-CO2 climate forcing (Cross-Chapter
Box 2 in this chapter) is also required, but their role is limited because
emissions of most non-CO2 forcers cannot be reduced to below zero.
Hence the feasibility and availability of large-scale CO2 removal
limits the possible rate and magnitude of temperature decline. In
this report, overshoot pathways are referred to as 1.5°C pathways,
but qualified by the amount of the temperature overshoot, which
can have a substantial impact on irreversible climate change impacts(Mathesius et al., 2015; Tokarska and Zickfeld, 2015).

1.2.3.1 Pathways remaining below 1.5°C
In this category of 1.5°C pathways, human-induced warming either
rises monotonically to stabilise at 1.5°C (Figure 1.4, brown lines)
or peaks at or below 1.5°C and then declines (yellow lines). Figure
1.4b demonstrates that pathways remaining below 1.5°C require net
annual CO2 emissions to peak and decline to near zero or below,
depending on the long-term adjustment of the carbon cycle andnon-CO2 emissions (Bowerman et al., 2013; Wigley, 2018). Reducing
emissions to zero corresponds to stabilizing cumulative CO2 emissions(Figure 1.4c, solid lines) and falling concentrations of CO2 in theatmosphere (panel c dashed lines) (Matthews and Caldeira, 2008;
Solomon et al., 2009), which is required to stabilize GMST if non-CO2
climate forcings are constant and positive. Stabilizing atmosphericgreenhouse gas concentrations would result in continued warming(see Section 1.2.4).
If emission reductions do not begin until temperatures are close to
the proposed limit, pathways remaining below 1.5°C necessarily
involve much faster rates of net CO2 emission reductions (Figure 1.4,
green lines), combined with rapid reductions in non-CO2 forcing andthese pathways also reach 1.5°C earlier. Note that the emissions
associated with these schematic temperature pathways may notcorrespond to feasible emission scenarios, but they do illustrate thefact that the timing of net zero emissions does not in itself determine
peak warming: what matters is total cumulative emissions up to that

1.2.3.3 Impacts at 1.5°C warming associated with different
pathways: transience versus stabilisation
Figure 1.4 also illustrates time scales associated with different
impacts. While many impacts scale with the change in GMST itself,some (such as those associated with ocean acidification) scale withthe change in atmospheric CO2 concentration, indicated by thefraction of cumulative CO2 emissions remaining in the atmosphere(dotted lines in Figure 1.4c). Others may depend on the rate ofchange of GMST, while ‘time-integrated impacts’, such as sea levelrise, shown in Figure 1.4d continue to increase even after GMST has
stabilised.
Hence impacts that occur when GMST reaches 1.5°C could be very
different depending on the pathway to 1.5°C. CO2 concentrations will
be higher as GMST rises past 1.5°C (transient warming) than when
GMST has stabilized at 1.5°C, while sea level and, potentially, global
mean precipitation (Pendergrass et al., 2015) would both be lower(see Figure 1.4). These differences could lead to very different impacts
on agriculture, on some forms of extreme weather (e.g., Baker et al.,2018), and on marine and terrestrial ecosystems (e.g., Mitchell et al.,2017 and Boxes 3.1 and 3.2). Sea level would be higher still if GMST
returns to 1.5°C after an overshoot (Figure 1.4 d), with potentiallysignificantly different impacts in vulnerable regions. Temperature
overshoot could also cause irreversible impacts (see Chapter 3).

61

1

Chapter 1

Framing and Context

1

Figure 1.4 | Different 1.5°C pathways1: Schematic illustration of the relationship between (a) global mean surface temperature (GMST) change; (b) annual rates of CO2
emissions, assuming constant fractional contribution of non-CO2 forcing to total human-induced warming; (c) total cumulative CO2 emissions (solid lines) and the fraction
thereof remaining in the atmosphere (dashed lines; these also indicates changes in atmospheric CO2 concentrations); and (d) a time-integrated impact, such as sea level rise,that continues to increase even after GMST has stabilized. Colours indicate different 1.5°C pathways. Brown: GMST remaining below and stabilizing at 1.5°C in 2100; Green: adelayed start but faster emission reductions pathway with GMST remaining below and reaching 1.5°C earlier; Blue: a pathway temporarily exceeding 1.5°C, with temperatures
reduced to 1.5°C by net negative CO2 emissions after temperatures peak; and Yellow: a pathway peaking at 1.5°C and subsequently declining. Temperatures are anchoredto 1°C above pre-industrial in 2017; emissions–temperature relationships are computed using a simple climate model (Myhre et al., 2013; Millar et al., 2017a; Jenkins et al.,2018) with a lower value of the Transient Climate Response (TCR) than used in the quantitative pathway assessments in Chapter 2 to illustrate qualitative differences between
pathways: this figure is not intended to provide quantitative information. The time-integrated impact is illustrated by the semi-empirical sea level rise model of Kopp et al. (2016).

Cross-Chapter Box 1 | Scenarios and Pathways
Contributing Authors:
Mikiko Kainuma (Japan), Kristie L. Ebi (USA), Sabine Fuss (Germany), Elmar Kriegler (Germany), Keywan Riahi (Austria), Joeri Rogelj(Austria/Belgium), Petra Tschakert (Australia/Austria), Rachel Warren (UK)Climate change scenarios have been used in IPCC assessments since the First Assessment Report (Leggett et al., 1992). The SRES
scenarios (named after the IPCC Special Report on Emissions Scenarios published in 2000; IPCC, 2000), consist of four scenarios that
do not take into account any future measures to limit greenhouse gas (GHG) emissions. Subsequently, many policy scenarios have beendeveloped based upon them (Morita et al., 2001). The SRES scenarios are superseded by a set of scenarios based on the Representative
Concentration Pathways (RCPs) and Shared Socio-Economic Pathways (SSPs) (Riahi et al., 2017). The RCPs comprise a set of four GHG
concentration trajectories that jointly span a large range of plausible human-caused climate forcing ranging from 2.6 W m−2 (RCP2.6)
to 8.5 W m−2 (RCP8.5) by the end of the 21st century (van Vuuren et al., 2011). They were used to develop climate projections in the
Coupled Model Intercomparison Project Phase 5 (CMIP5; Taylor et al., 2012) and were assessed in the IPCC Fifth Assessment Report(AR5). Based on the CMIP5 ensemble, RCP2.6, provides a better than two-in-three chance of staying below 2°C and a median warmingof 1.6°C relative to 1850–1900 in 2100 (Collins et al., 2013).
The SSPs were developed to complement the RCPs with varying socio-economic challenges to adaptation and mitigation. SSP-based
scenarios were developed for a range of climate forcing levels, including the end-of-century forcing levels of the RCPs (Riahi et al., 2017)and a level below RCP2.6 to explore pathways limiting warming to 1.5°C above pre-industrial levels (Rogelj et al., 2018). The SSP-based
1.5°C pathways are assessed in Chapter 2 of this report. These scenarios offer an integrated perspective on socio-economic, energysystem (Bauer et al., 2017), land use (Popp et al., 2017), air pollution (Rao et al., 2017) and, GHG emissions developments (Riahi et al.,162

An animated version of Figure 1.4 will be embedded in the web-based version of this Special ReportFraming and Context

Chapter 1

Cross-Chapter Box 1 (continued)2017). Because of their harmonised assumptions, scenarios developed with the SSPs facilitate the integrated analysis of future climate
impacts, vulnerabilities, adaptation and mitigation.
Scenarios and Pathways in this Report
This report focuses on pathways that could limit the increase of global mean surface temperature (GMST) to 1.5°C above pre-industrial
levels and pathways that align with the goals of sustainable development and poverty eradication. The pace and scale of mitigation
and adaptation are assessed in the context of historical evidence to determine where unprecedented change is required (see Chapter4). Other scenarios are also assessed, primarily as benchmarks for comparison of mitigation, impacts, and/or adaptation requirements.
These include baseline scenarios that assume no climate policy; scenarios that assume some kind of continuation of current climate
policy trends and plans, many of which are used to assess the implications of the nationally determined contributions (NDCs); andscenarios holding warming below 2°C above pre-industrial levels. This report assesses the spectrum from global mitigation scenarios
to local adaptation choices – complemented by a bottom-up assessment of individual mitigation and adaptation options, and theirimplementation (policies, finance, institutions, and governance, see Chapter 4). Regional, national, and local scenarios, as well as
decision-making processes involving values and difficult trade-offs are important for understanding the challenges of limiting GMST
increase to 1.5°C and are thus indispensable when assessing implementation.

1

Different climate policies result in different temperature pathways, which result in different levels of climate risks and actual climate
impacts with associated long-term implications. Temperature pathways are classified into continued warming pathways (in the cases ofbaseline and reference scenarios), pathways that keep the temperature increase below a specific limit (like 1.5°C or 2°C), and pathways
that temporarily exceed and later fall to a specific limit (overshoot pathways). In the case of a temperature overshoot, net negative CO2
emissions are required to remove excess CO2 from the atmosphere (Section 1.2.3).
In a ‘prospective’ mitigation pathway, emissions (or sometimes concentrations) are prescribed, giving a range of GMST outcomes
because of uncertainty in the climate response. Prospective pathways are considered ‘1.5°C pathways’ in this report if, based on current
knowledge, the majority of available approaches assign an approximate probability of one-in-two to two-in-three to temperatures
either remaining below 1.5°C or returning to 1.5°C either before or around 2100. Most pathways assessed in Chapter 2 are prospective
pathways, and therefore even ‘1.5°C pathways’ are also associated with risks of warming higher than 1.5°C, noting that many risks
increase non-linearly with increasing GMST. In contrast, the ‘risks of warming of 1.5°C’ assessed in Chapter 3 refer to risks in aworld in which GMST is either passing through (transient) or stabilized at 1.5°C, without considering probabilities of different GMST
levels (unless otherwise qualified). To stay below any desired temperature limit, mitigation measures and strategies would need tobe adjusted as knowledge of the climate response is updated (Millar et al., 2017b; Emori et al., 2018). Such pathways can be called‘adaptive’ mitigation pathways. Given there is always a possibility of a greater-than-expected climate response (Xu and Ramanathan,2017), adaptive mitigation pathways are important to minimise climate risks, but need also to consider the risks and feasibility (seeCross-Chapter Box 3 in this chapter) of faster-than-expected emission reductions. Chapter 5 includes assessments of two related topics:aligning mitigation and adaptation pathways with sustainable development pathways, and transformative visions for the future thatwould support avoiding negative impacts on the poorest and most disadvantaged populations and vulnerable sectors.
Definitions of Scenarios and Pathways
Climate scenarios and pathways are terms that are sometimes used interchangeably, with a wide range of overlapping definitions(Rosenbloom, 2017).
A ‘scenario’ is an internally consistent, plausible, and integrated description of a possible future of the human–environment system,
including a narrative with qualitative trends and quantitative projections (IPCC, 2000). Climate change scenarios provide a frameworkfor developing and integrating projections of emissions, climate change, and climate impacts, including an assessment of their inherent
uncertainties. The long-term and multi-faceted nature of climate change requires climate scenarios to describe how socio-economic
trends in the 21st century could influence future energy and land use, resulting emissions and the evolution of human vulnerability andexposure. Such driving forces include population, GDP, technological innovation, governance and lifestyles. Climate change scenarios
are used for analysing and contrasting climate policy choices.
The notion of a ‘pathway’ can have multiple meanings in the climate literature. It is often used to describe the temporal evolutionof a set of scenario features, such as GHG emissions and socio-economic development. As such, it can describe individual scenario
components or sometimes be used interchangeably with the word ‘scenario’. For example, the RCPs describe GHG concentration
trajectories (van Vuuren et al., 2011) and the SSPs are a set of narratives of societal futures augmented by quantitative projectionsof socio-economic determinants such as population, GDP and urbanization (Kriegler et al., 2012; O’Neill et al., 2014). Socio-economic

63

Chapter 1

Framing and Context

Cross-Chapter Box 1 (continued)

driving forces consistent with any of the SSPs can be combined with a set of climate policy assumptions (Kriegler et al., 2014) thattogether would lead to emissions and concentration outcomes consistent with the RCPs (Riahi et al., 2017). This is at the core of the
scenario framework for climate change research that aims to facilitate creating scenarios integrating emissions and development
pathways dimensions (Ebi et al., 2014; van Vuuren et al., 2014).

1

In other parts of the literature, ‘pathway’ implies a solution-oriented trajectory describing a pathway from today’s world to achieving a
set of future goals. Sustainable Development Pathways describe national and global pathways where climate policy becomes part ofa larger sustainability transformation (Shukla and Chaturvedi, 2013; Fleurbaey et al., 2014; van Vuuren et al., 2015). The AR5 presented
climate-resilient pathways as sustainable development pathways that combine the goals of adaptation and mitigation (Denton etal., 2014), more broadly defined as iterative processes for managing change within complex systems in order to reduce disruptions
and enhance opportunities associated with climate change (IPCC, 2014a). The AR5 also introduced the notion of climate-resilient
development pathways, with a more explicit focus on dynamic livelihoods, multi-dimensional poverty, structural inequalities, andequity among poor and non-poor people (Olsson et al., 2014). Adaptation pathways are understood as a series of adaptation choices
involving trade-offs between short-term and long-term goals and values (Reisinger et al., 2014). They are decision-making processes
sequenced over time with the purpose of deliberating and identifying socially salient solutions in specific places (Barnett et al., 2014;
Wise et al., 2014; Fazey et al., 2016). There is a range of possible pathways for transformational change, often negotiated throughiterative and inclusive processes (Harris et al., 2017; Fazey et al., 2018; Tàbara et al., 2018).

1.2.4

Geophysical Warming Commitment

It is frequently asked whether limiting warming to 1.5°C is ‘feasible’(Cross-Chapter Box 3 in this chapter). There are many dimensions tothis question, including the warming ‘commitment’ from past emissionsof greenhouse gases and aerosol precursors. Quantifying commitment
from past emissions is complicated by the very different behaviour ofdifferent climate forcers affected by human activity: emissions of longlived greenhouse gases such as CO2 and nitrous oxide (N2O) have avery persistent impact on radiative forcing (Myhre et al., 2013), lastingfrom over a century (in the case of N2O) to hundreds of thousandsof years (for CO2). The radiative forcing impact of short-lived climate
forcers (SLCFs) such as methane (CH4) and aerosols, in contrast,
persists for at most about a decade (in the case of methane) down to
only a few days. These different behaviours must be taken into accountin assessing the implications of any approach to calculating aggregate
emissions (Cross-Chapter Box 2 in this chapter).
Geophysical warming commitment is defined as the unavoidable
future warming resulting from physical Earth system inertia. Different
variants are discussed in the literature, including (i) the ‘constantcomposition commitment’ (CCC), defined by Meehl et al. (2007) as
the further warming that would result if atmospheric concentrationsof GHGs and other climate forcers were stabilised at the current level;and (ii) and the ‘zero emissions commitment’ (ZEC), defined as thefurther warming that would still occur if all future anthropogenic
emissions of greenhouse gases and aerosol precursors were
eliminated instantaneously (Meehl et al., 2007; Collins et al., 2013).
The CCC is primarily associated with thermal inertia of the ocean(Hansen et al., 2005), and has led to the misconception that
substantial future warming is inevitable (Matthews and Solomon,2013). The CCC takes into account the warming from past emissions,but also includes warming from future emissions (declining but still
non-zero) that are required to maintain a constant atmospheric64

composition. It is therefore not relevant to the warming commitment
from past emissions alone.
The ZEC, although based on equally idealised assumptions, allows
for a clear separation of the response to past emissions from theeffects of future emissions. The magnitude and sign of the ZEC
depend on the mix of GHGs and aerosols considered. For CO2, whichtakes hundreds of thousands of years to be fully removed from theatmosphere by natural processes following its emission (Eby et al.,2009; Ciais et al., 2013), the multi-century warming commitment
from emissions to date in addition to warming already observed
is estimated to range from slightly negative (i.e., a slight cooling
relative to present-day) to slightly positive (Matthews and Caldeira,2008; Lowe et al., 2009; Gillett et al., 2011; Collins et al., 2013).
Some studies estimate a larger ZEC from CO2, but for cumulativeemissions much higher than those up to present day (Frölicher et al.,2014; Ehlert and Zickfeld, 2017). The ZEC from past CO2 emissions
is small because the continued warming effect from ocean thermal
inertia is approximately balanced by declining radiative forcing due
to CO2 uptake by the ocean (Solomon et al., 2009; Goodwin et al.,2015; Williams et al., 2017). Thus, although present-day CO2-induced
warming is irreversible on millennial time scales (without humanintervention such as active carbon dioxide removal or solar radiation
modification; Section 1.4.1), past CO2 emissions do not commit to
substantial further warming (Matthews and Solomon, 2013).
Sustained net zero anthropogenic emissions of CO2 and declining net
anthropogenic non-CO2 radiative forcing over a multi-decade period
would halt anthropogenic global warming over that period, although
it would not halt sea level rise or many other aspects of climate system
adjustment. The rate of decline of non-CO2 radiative forcing must be
sufficient to compensate for the ongoing adjustment of the climate
system to this forcing (assuming it remains positive) due to ocean
thermal inertia. It therefore depends on deep ocean response timescales, which are uncertain but of order centuries, corresponding toFraming and Context

decline rates of non-CO2 radiative forcing of less than 1% per year. In
the longer term, Earth system feedbacks such as the release of carbon
from melting permafrost may require net negative CO2 emissions tomaintain stable temperatures (Lowe and Bernie, 2018).
For warming SLCFs, meaning those associated with positive radiative
forcing such as methane, the ZEC is negative. Eliminating emissionsof these substances results in an immediate cooling relative to thepresent (Figure 1.5, magenta lines) (Frölicher and Joos, 2010; Matthews
and Zickfeld, 2012; Mauritsen and Pincus, 2017). Cooling SLCFs (those
associated with negative radiative forcing) such as sulphate aerosols
create a positive ZEC, as elimination of these forcers results in rapid
increase in radiative forcing and warming (Figure 1.5, green lines)(Matthews and Zickfeld, 2012; Mauritsen and Pincus, 2017; Samset
et al., 2018). Estimates of the warming commitment from eliminating
aerosol emissions are affected by large uncertainties in net aerosol
radiative forcing (Myhre et al., 2013, 2017) and the impact of other

Chapter 1

measures affecting aerosol loading (e.g., Fernández et al., 2017).
If present-day emissions of all GHGs (short- and long-lived) andaerosols (including sulphate, nitrate and carbonaceous aerosols) areeliminated (Figure 1.5, yellow lines) GMST rises over the following
decade, driven by the removal of negative aerosol radiative forcing.
This initial warming is followed by a gradual cooling driven by the
decline in radiative forcing of short-lived greenhouse gases (Matthews
and Zickfeld, 2012; Collins et al., 2013). Peak warming following
elimination of all emissions was assessed at a few tenths of a degree in
AR5, and century-scale warming was assessed to change only slightly
relative to the time emissions are reduced to zero (Collins et al., 2013).
New evidence since AR5 suggests a larger methane forcing (Etminan
et al., 2016) but no revision in the range of aerosol forcing (although
this remains an active field of research, e.g., Myhre et al., 2017). This
revised methane forcing estimate results in a smaller peak warming
and a faster temperature decline than assessed in AR5 (Figure 1.5,
yellow line).

Figure 1.5 | Warming commitment from past emissions of greenhouse gases and aerosols: Radiative forcing (top) and global mean surface temperature change(bottom) for scenarios with different combinations of greenhouse gas and aerosol precursor emissions reduced to zero in 2020. Variables were calculated using a simple
climate–carbon cycle model (Millar et al., 2017a) with a simple representation of atmospheric chemistry (Smith et al., 2018). The bars on the right-hand side indicate the medianwarming in 2100 and 5–95% uncertainty ranges (also indicated by the plume around the yellow line) taking into account one estimate of uncertainty in climate response,
effective radiative forcing and carbon cycle sensitivity, and constraining simple model parameters with response ranges from AR5 combined with historical climate observations(Smith et al., 2018). Temperatures continue to increase slightly after elimination of CO2 emissions (blue line) in response to constant non-CO2 forcing. The dashed blue line
extrapolates one estimate of the current rate of warming, while dotted blue lines show a case where CO2 emissions are reduced linearly to zero assuming constant non-CO2
forcing after 2020. Under these highly idealized assumptions, the time to stabilize temperatures at 1.5°C is approximately double the time remaining to reach 1.5°C at thecurrent warming rate.

651Chapter 1

Expert judgement based on the available evidence (including model
simulations, radiative forcing and climate sensitivity) suggests that if
all anthropogenic emissions were reduced to zero immediately, any
further warming beyond the 1°C already experienced would likely beless than 0.5°C over the next two to three decades, and also likely
less than 0.5°C on a century time scale.

1

Since most sources of emissions cannot, in reality, be brought tozero instantaneously due to techno-economic inertia, the current
rate of emissions also constitutes a conditional commitment tofuture emissions and consequent warming depending on achievablerates of emission reductions. The current level and rate of humaninduced warming determines both the time left before a temperaturethreshold is exceeded if warming continues (dashed blue line
in Figure 1.5) and the time over which the warming rate must be
reduced to avoid exceeding that threshold (approximately indicatedby the dotted blue line in Figure 1.5). Leach et al. (2018) use a central
estimate of human-induced warming of 1.02°C in 2017, increasing
at 0.215°C per decade (Haustein et al., 2017), to argue that it willtake 13–32 years (one-standard-error range) to reach 1.5°C if thecurrent warming rate continues, allowing 25–64 years to stabilise
temperatures at 1.5°C if the warming rate is reduced at a constantFraming and Context

rate of deceleration starting immediately. Applying a similar approachto the multi-dataset average GMST used in this report gives anassessed likely range for the date at which warming reaches 1.5°C
of 2030 to 2052. The lower bound on this range, 2030, is supported
by multiple lines of evidence, including the AR5 assessment for the
likely range of warming (0.3°C–0.7°C) for the period 2016–2035
relative to 1986–2005. The upper bound, 2052, is supported by fewerlines of evidence, so we have used the upper bound of the 5–95%
confidence interval given by the Leach et al. (2018) method applied tothe multi-dataset average GMST, expressed as the upper limit of thelikely range, to reflect the reliance on a single approach. Results are
sensitive both to the confidence level chosen and the number of years
used to estimate the current rate of anthropogenic warming (5 years
used here, to capture the recent acceleration due to rising non-CO2
forcing). Since the rate of human-induced warming is proportional
to the rate of CO2 emissions (Matthews et al., 2009; Zickfeld et al.,2009) plus a term approximately proportional to the rate of increasein non-CO2 radiative forcing (Gregory and Forster, 2008; Allen et al.,2018; Cross-Chapter Box 2 in this chapter), these time scales also
provide an indication of minimum emission reduction rates required
if a warming greater than 1.5°C is to be avoided (see Figure 1.5,
Supplementary Material 1.SM.6 and FAQ 1.2).

Cross-Chapter Box 2 | Measuring Progress to Net Zero Emissions Combining Long-Lived and ShortLived Climate Forcers
Contributing Authors:
Piers Forster (UK), Myles R. Allen (UK), Elmar Kriegler (Germany), Joeri Rogelj (Austria/Belgium), Seth Schultz (USA), Drew Shindell(USA), Kirsten Zickfeld (Canada/Germany)
Emissions of many different climate forcers will affect the rate and magnitude of climate change over the next few decades (Myhre et al.,2013). Since these decades will determine when 1.5°C is reached or whether a warming greater than 1.5°C is avoided, understanding
the aggregate impact of different forcing agents is particularly important in the context of 1.5°C pathways. Paragraph 17 of Decision 1of the 21st Conference of the Parties on the adoption of the Paris Agreement specifically states that this report is to identify aggregate
greenhouse gas emission levels compatible with holding the increase in global average temperatures to 1.5°C above pre-industrial
levels (see Chapter 2). This request highlights the need to consider the implications of different methods of aggregating emissions ofdifferent gases, both for future temperatures and for other aspects of the climate system (Levasseur et al., 2016; Ocko et al., 2017).
To date, reporting of GHG emissions under the UNFCCC has used Global Warming Potentials (GWPs) evaluated over a 100-year time
horizon (GWP100) to combine multiple climate forcers. IPCC Working Group 3 reports have also used GWP100 to represent multi-gas
pathways (Clarke et al., 2014). For reasons of comparability and consistency with current practice, Chapter 2 in this Special Reportcontinues to use this aggregation method. Numerous other methods of combining different climate forcers have been proposed, suchas the Global Temperature-change Potential (GTP; Shine et al., 2005) and the Global Damage Potential (Tol et al., 2012; Deuber et al.,2013).
Climate forcers fall into two broad categories in terms of their impact on global temperature (Smith et al., 2012): long-lived GHGs, suchas CO2 and nitrous oxide (N2O), whose warming impact depends primarily on the total cumulative amount emitted over the past century
or the entire industrial epoch; and short-lived climate forcers (SLCFs), such as methane and black carbon, whose warming impact
depends primarily on current and recent annual emission rates (Reisinger et al., 2012; Myhre et al., 2013; Smith et al., 2013; Strefler etal., 2014). These different dependencies affect the emissions reductions required of individual forcers to limit warming to 1.5°C or any
other level.
Natural processes that remove CO2 permanently from the climate system are so slow that reducing the rate of CO2-induced warming
to zero requires net zero global anthropogenic CO2 emissions (Archer and Brovkin, 2008; Matthews and Caldeira, 2008; Solomon et al.,66

Framing and Context

Chapter 1

Cross-Chapter Box 2 (continued)2009), meaning almost all remaining anthropogenic CO2 emissions must be compensated for by an equal rate of anthropogenic carbon
dioxide removal (CDR). Cumulative CO2 emissions are therefore an accurate indicator of CO2-induced warming, except in periods ofhigh negative CO2 emissions (Zickfeld et al., 2016), and potentially in century-long periods of near-stable temperatures (Bowerman etal., 2011; Wigley, 2018). In contrast, sustained constant emissions of a SLCF such as methane, would (after a few decades) be consistent
with constant methane concentrations and hence very little additional methane-induced warming (Allen et al., 2018; Fuglestvedt et al.,2018). Both GWP and GTP would equate sustained SLCF emissions with sustained constant CO2 emissions, which would continue toaccumulate in the climate system, warming global temperatures indefinitely. Hence nominally ‘equivalent’ emissions of CO2 and SLCFs,
if equated conventionally using GWP or GTP, have very different temperature impacts, and these differences are particularly evident
under ambitious mitigation characterizing 1.5°C pathways.

1

Since the AR5, a revised usage of GWP has been proposed (Lauder et al., 2013; Allen et al., 2016), denoted GWP* (Allen et al.,2018), that addresses this issue by equating a permanently sustained change in the emission rate of an SLCF or SLCF-precursor (intonnes-per-year), or other non-CO2 forcing (in watts per square metre), with a one-off pulse emission (in tonnes) of a fixed amountof CO2. Specifically, GWP* equates a 1 tonne-per-year increase in emission rate of an SLCF with a pulse emission of GWPH x H tonnesof CO2, where GWPH is the conventional GWP of that SLCF evaluated over time GWPH for SLCFs decreases with increasing time H,
GWPH x H for SLCFs is less dependent on the choice of time horizon. Similarly, a permanent 1 W m−2 increase in radiative forcing has
a similar temperature impact as the cumulative emission of H/AGWPH tonnes of CO2, where AGWPH is the Absolute Global Warming
Potential of CO2 (Shine et al., 2005; Myhre et al., 2013; Allen et al., 2018). This indicates approximately how future changes in nonCO2 radiative forcing affect cumulative CO2 emissions consistent with any given level of peak warming.
When combined using GWP*, cumulative aggregate GHG emissions are closely proportional to total GHG-induced warming, while
the annual rate of GHG-induced warming is proportional to the annual rate of aggregate GHG emissions (see Cross-Chapter Box 2,
Figure 1). This is not the case when emissions are aggregated using GWP or GTP, with discrepancies particularly pronounced when
SLCF emissions are falling. Persistent net zero CO2-equivalent emissions containing a residual positive forcing contribution from
SLCFs and aggregated using GWP100 or GTP would result in a steady decline of GMST. Net zero global emissions aggregated using
GWP* (which corresponds to zero net emissions of CO2 and other long-lived GHGs like nitrous oxide, combined with near-constant
SLCF forcing – see Figure 1.5) results in approximately stable GMST (Allen et al., 2018; Fuglestvedt et al., 2018 and Cross-Chapter
Box 2, Figure 1, below).
Whatever method is used to relate emissions of different greenhouse gases, scenarios achieving stable GMST well below 2°C
require both near-zero net emissions of long-lived greenhouse gases and deep reductions in warming SLCFs (Chapter 2), in part tocompensate for the reductions in cooling SLCFs that are expected to accompany reductions in CO2 emissions (Rogelj et al., 2016b;
Hienola et al., 2018). Understanding the implications of different methods of combining emissions of different climate forcers is,however, helpful in tracking progress towards temperature stabilisation and ‘balance between anthropogenic emissions by sources
and removals by sinks of greenhouse gases’ as stated in Article 4 of the Paris Agreement. Fuglestvedt et al. (2018) and Tanaka andO’Neill (2018) show that when, and even whether, aggregate GHG emissions need to reach net zero before 2100 to limit warming
to 1.5°C depends on the scenario, aggregation method and mix of long-lived and short-lived climate forcers.
The comparison of the impacts of different climate forcers can also consider more than their effects on GMST (Johansson, 2012; Tolet al., 2012; Deuber et al., 2013; Myhre et al., 2013; Cherubini and Tanaka, 2016). Climate impacts arise from both magnitude andrate of climate change, and from other variables such as precipitation (Shine et al., 2015). Even if GMST is stabilised, sea level rise
and associated impacts will continue to increase (Sterner et al., 2014), while impacts that depend on CO2 concentrations such as
ocean acidification may begin to reverse. From an economic perspective, comparison of different climate forcers ideally reflects theratio of marginal economic damages if used to determine the exchange ratio of different GHGs under multi-gas regulation (Tol etal., 2012; Deuber et al., 2013; Kolstad et al., 2014).
Emission reductions can interact with other dimensions of sustainable development (see Chapter 5). In particular, early action onsome SLCFs (including actions that may warm the climate, such as reducing sulphur dioxide emissions) may have considerable
societal co-benefits, such as reduced air pollution and improved public health with associated economic benefits (OECD, 2016;
Shindell et al., 2016). Valuation of broadly defined social costs attempts to account for many of these additional non-climate factors
along with climate-related impacts (Shindell, 2015; Sarofim et al., 2017; Shindell et al., 2017). See Chapter 4, Section 4.3.6, for a
discussions of mitigation options, noting that mitigation priorities for different climate forcers depend on multiple economic andsocial criteria that vary between sectors, regions and countries.

67

Chapter 1

Framing and Context

Cross-Chapter Box 2 (continued)

1

Cross-Chapter Box 2, Figure 1 | Implications of different approaches to calculating aggregate greenhouse gas emissions on a pathway to net
zero. (a) Aggregate emissions of well-mixed greenhouse gases (WMGHGs) under the RCP2.6 mitigation scenario expressed as CO2-equivalent using GWP100 (blue);
GTP100 (green) and GWP* (yellow). Aggregate WMGHG emissions appear to fall more rapidly if calculated using GWP* than using either GWP or GTP, primarily
because GWP* equates a falling methane emission rate with negative CO2 emissions, as only active CO2 removal would have the same impact on radiative forcing
and GMST as a reduction in methane emission rate. (b) Cumulative emissions of WMGHGs combined as in panel (a) (blue, green and yellow lines & left hand axis)
and warming response to combined emissions (black dotted line and right hand axis, Millar et al. (2017a). The temperature response under ambitious mitigation isclosely correlated with cumulative WMGHG emissions aggregated using GWP*, but with neither emission rate nor cumulative emissions if aggregated using GWPor GTP.

1.3

Impacts at 1.5°C and Beyond

1.3.1

Definitions

Consistent with the AR5 (IPCC, 2014a), ‘impact’ in this report refers
to the effects of climate change on human and natural systems.
Impacts may include the effects of changing hazards, such as thefrequency and intensity of heat waves. ‘Risk’ refers to potential
negative impacts of climate change where something of value is atstake, recognizing the diversity of values. Risks depend on hazards,
exposure, vulnerability (including sensitivity and capacity to respond)
and likelihood. Climate change risks can be managed through efforts
to mitigate climate change forcers, adaptation of impacted systems,
and remedial measures (Section 1.4.1).
In the context of this report, regional impacts of global warming at
1.5°C and 2°C are assessed in Chapter 3. The ‘warming experience at
1.5°C’ is that of regional climate change (temperature, rainfall, andother changes) at the time when global average temperatures, as
defined in Section 1.2.1, reach 1.5°C above pre-industrial (the same
principle applies to impacts at any other global mean temperature).
Over the decade 2006–2015, many regions have experienced higher
than average levels of warming and some are already now 1.5°C or
more warmer with respect to the pre-industrial period (Figure 1.3).
68

At a global warming of 1.5°C, some seasons will be substantiallywarmer than 1.5°C above pre-industrial (Seneviratne et al., 2016).
Therefore, most regional impacts of a global mean warming of 1.5°C
will be different from those of a regional warming by 1.5°C.
The impacts of 1.5°C global warming will vary in both space andtime (Ebi et al., 2016). For many regions, an increase in global
mean temperature by 1.5°C or 2°C implies substantial increases
in the occurrence and/or intensity of some extreme events (Fischer
and Knutti, 2015; Karmalkar and Bradley, 2017; King et al., 2017;
Chevuturi et al., 2018), resulting in different impacts (see Chapter
3). By comparing impacts at 1.5°C versus those at 2°C, this reportdiscusses the ‘avoided impacts’ by maintaining global temperature
increase at or below 1.5°C as compared to 2°C, noting that these
also depend on the pathway taken to 1.5°C (see Section 1.2.3 andCross-Chapter Box 8 in Chapter 3 on 1.5°C warmer worlds). Many
impacts take time to observe, and because of the warming trend,
impacts over the past 20 years were associated with a level of humaninduced warming that was, on average, 0.1°C–0.23°C colder thanits present level, based on the AR5 estimate of the warming trend
over this period (Section 1.2.1 and Kirtman et al., 2013). Attribution
studies (e.g., van Oldenborgh et al., 2017) can address this bias, but
informal estimates of ‘recent impact experience’ in a rapidly warming
world necessarily understate the temperature-related impacts of thecurrent level of warming.

Framing and Context

1.3.2

Drivers of Impacts

Impacts of climate change are due to multiple environmental drivers
besides rising temperatures, such as rising atmospheric CO2, shifting
rainfall patterns (Lee et al., 2018), rising sea levels, increasing ocean
acidification, and extreme events, such as floods, droughts, and heatwaves (IPCC, 2014a). Changes in rainfall affect the hydrological cycle
and water availability (Schewe et al., 2014; Döll et al., 2018; Saeed
et al., 2018). Several impacts depend on atmospheric composition,
increasing atmospheric carbon dioxide levels leading to changes inplant productivity (Forkel et al., 2016), but also to ocean acidification(Hoegh-Guldberg et al., 2007). Other impacts are driven by changes
in ocean heat content such as the destabilization of coastal ice sheets
and sea level rise (Bindoff et al., 2007; Chen et al., 2017), whereas
impacts due to heat waves depend directly on ambient air or ocean
temperature (Matthews et al., 2017). Impacts can be direct, such ascoral bleaching due to ocean warming, and indirect, such as reduced
tourism due to coral bleaching. Indirect impacts can also arise frommitigation efforts such as changed agricultural management (Section
3.6.2) or remedial measures such as solar radiation modification(Section 4.3.8, Cross-Chapter Box 10 in Chapter 4).
Impacts may also be triggered by combinations of factors, including‘impact cascades’ (Cramer et al., 2014) through secondary
consequences of changed systems. Changes in agricultural water
availability caused by upstream changes in glacier volume are atypical example. Recent studies also identify compound events(e.g., droughts and heat waves), that is, when impacts are inducedby the combination of several climate events (AghaKouchak et al.,2014; Leonard et al., 2014; Martius et al., 2016; Zscheischler andSeneviratne, 2017).
There are now techniques to attribute impacts formally toanthropogenic global warming and associated rainfall changes(Rosenzweig et al., 2008; Cramer et al., 2014; Hansen et al., 2016),
taking into account other drivers such as land-use change (Oliver and
Morecroft, 2014) and pollution (e.g., tropospheric ozone; Sitch et al.,2007). There are multiple lines of evidence that climate change has
observable and often severely negative effects on people, especiallywhere climate-sensitive biophysical conditions and socio-economic
and political constraints on adaptive capacities combine to createhigh vulnerabilities (IPCC, 2012a, 2014a; World Bank, 2013). The
character and severity of impacts depend not only on the hazards(e.g., changed climate averages and extremes) but also on thevulnerability (including sensitivities and adaptive capacities) ofdifferent communities and their exposure to climate threats. These
impacts also affect a range of natural and human systems, suchas terrestrial, coastal and marine ecosystems and their services;
agricultural production; infrastructure; the built environment; humanhealth; and other socio-economic systems (Rosenzweig et al., 2017).
Sensitivity to changing drivers varies markedly across systems
and regions. Impacts of climate change on natural and managed
ecosystems can imply loss or increase in growth, biomass or diversity
at the level of species populations, interspecific relationships such aspollination, landscapes or entire biomes. Impacts occur in addition
to the natural variation in growth, ecosystem dynamics, disturbance,

Chapter 1

succession and other processes, rendering attribution of impacts
at lower levels of warming difficult in certain situations. The same
magnitude of warming can be lethal during one phase of the lifeof an organism and irrelevant during another. Many ecosystems(notably forests, coral reefs and others) undergo long-term
successional processes characterised by varying levels of resilience
to environmental change over time. Organisms and ecosystems may
adapt to environmental change to a certain degree, through changes
in physiology, ecosystem structure, species composition or evolution.
Large-scale shifts in ecosystems may cause important feedbacks,
in terms of changing water and carbon fluxes through impacted
ecosystems – these can amplify or dampen atmospheric change atregional to continental scale. Of particular concern is the response of
most of the world’s forests and seagrass ecosystems, which play key
roles as carbon sinks (Settele et al., 2014; Marbà et al., 2015).
Some ambitious efforts to constrain atmospheric greenhouse gas
concentrations may themselves impact ecosystems. In particular,changes in land use, potentially required for massively enhanced
production of biofuels (either as simple replacement of fossil fuels, oras part of bioenergy with carbon capture and storage, BECCS) impact
all other land ecosystems through competition for land (e.g., Creutzig,2016) (see Cross-Chapter Box 7 in Chapter 3, Section 3.6.2.1).
Human adaptive capacity to a 1.5°C warmer world varies markedly
for individual sectors and across sectors such as water supply, publichealth, infrastructure, ecosystems and food supply. For example, density and risk exposure, infrastructure vulnerability and resilience, governance, and institutional capacity all drive different impacts acrossa range of human settlement types (Dasgupta et al., 2014; Revi et al.,2014; Rosenzweig et al., 2018). Additionally, the adaptive capacity ofcommunities and human settlements in both rural and urban areas,
especially in highly populated regions, raises equity, social justice andsustainable development issues. Vulnerabilities due to gender, age,level of education and culture act as compounding factors (AroraJonsson, 2011; Cardona et al., 2012; Resurrección, 2013; Olsson etal., 2014; Vincent et al., 2014).

1.3.3

Uncertainty and Non-Linearity of Impacts

Uncertainties in projections of future climate change and impacts
come from a variety of different sources, including the assumptions
made regarding future emission pathways (Moss et al., 2010), theinherent limitations and assumptions of the climate models used forthe projections, including limitations in simulating regional climate
variability (James et al., 2017), downscaling and bias-correction
methods (Ekström et al., 2015), the assumption of a linear scalingof impacts with GMST used in many studies (Lewis et al., 2017; Kinget al., 2018b), and in impact models (e.g., Asseng et al., 2013). The
evolution of climate change also affects uncertainty with respect
to impacts. For example, the impacts of overshooting 1.5°C andstabilization at a later stage compared to stabilization at 1.5°C
without overshoot may differ in magnitude (Schleussner et al., 2016).
AR5 (IPCC, 2013b) and World Bank (2013) underscored the nonlinearity of risks and impacts as temperature rises from 2°C to 4°C ofwarming, particularly in relation to water availability, heat extremes,
691Chapter 1

1

bleaching of coral reefs, and more. Recent studies (Schleussner et al.,2016; James et al., 2017; Barcikowska et al., 2018; King et al., 2018a)
assess the impacts of 1.5°C versus 2°C warming, with the samemessage of non-linearity. The resilience of ecosystems, meaning
their ability either to resist change or to recover after a disturbance,
may change, and often decline, in a non-linear way. An example
are reef ecosystems, with some studies suggesting that reefs willchange, rather than disappear entirely, and with particular species
showing greater tolerance to coral bleaching than others (Pörtner
et al., 2014). A key issue is therefore whether ecosystems such ascoral reefs survive an overshoot scenario, and to what extent theywould be able to recover after stabilization at 1.5°C or higher levelsof warming (see Box 3.4).

1.4

Strengthening the Global Response

This section frames the implementation options, enabling conditions(discussed further in Cross-Chapter Box 3 on feasibility in thischapter), capacities and types of knowledge and their availability(Blicharska et al., 2017) that can allow institutions, communities
and societies to respond to the 1.5°C challenge in the context ofsustainable development and the Sustainable Development Goals(SDGs). It also addresses other relevant international agreements
such as the Sendai Framework for Disaster Risk Reduction. Equity andethics are recognised as issues of importance in reducing vulnerability
and eradicating poverty.
The connection between the enabling conditions for limiting global
warming to 1.5°C and the ambitions of the SDGs are complex acrossscale and multi-faceted (Chapter 5). Climate mitigation–adaptation
linkages, including synergies and trade-offs, are important when
considering opportunities and threats for sustainable development.
The IPCC AR5 acknowledged that ‘adaptation and mitigation
have the potential to both contribute to and impede sustainable
development, and sustainable development strategies and choices
have the potential to both contribute to and impede climate change
responses’ (Denton et al., 2014). Climate mitigation and adaptation
measures and actions can reflect and enforce specific patternsof development and governance that differ amongst the world’s
regions (Gouldson et al., 2015; Termeer et al., 2017). The role oflimited adaptation and mitigation capacity, limits to adaptation andmitigation, and conditions of mal-adaptation and mal-mitigation areassessed in this report (Chapters 4 and 5).

1.4.1

includes changes that could reduce emissions in the short-term but
could lock in technology choices or practices that include significant
trade-offs for effectiveness of future adaptation and other forms of
mitigation (Chapters 2 and 4).
Carbon dioxide removal (CDR) or ‘negative emissions’ activities
are considered in this report as distinct from the above mitigation
activities. While most mitigation activities focus on reducing theamount of carbon dioxide or other greenhouse gases emitted,
CDR aims to reduce concentrations already in the atmosphere.
Technologies for CDR are mostly in their infancy despite theirimportance to ambitious climate change mitigation pathways (Minxet al., 2017). Although some CDR activities such as reforestationand ecosystem restoration are well understood, the feasibility ofmassive-scale deployment of many CDR technologies remains an
open question (IPCC, 2014b; Leung et al., 2014) (Chapters 2 and 4).
Technologies for the active removal of other greenhouse gases, suchas methane, are even less developed, and are briefly discussed in
Chapter 4.
Climate change adaptation refers to the actions taken to manage
the impacts of climate change (IPCC, 2014a). The aim is to reduce
vulnerability and exposure to the harmful effects of climate change(e.g., sea level rise, more intense extreme weather events or foodinsecurity). It also includes exploring the potential beneficial
opportunities associated with climate change (for example, longer
growing seasons or increased yields in some regions). Different
adaptation pathways can be undertaken. Adaptation can beincremental, or transformational, meaning fundamental attributesof the system are changed (Chapter 3 and 4). There can be limits
to ecosystem-based adaptation or the ability of humans to adapt(Chapter 4). If there is no possibility for adaptive actions that canbe applied to avoid an intolerable risk, these are referred to as
hard adaptation limits, while soft adaptation limits are identified
when there are currently no options to avoid intolerable risks, butthey are theoretically possible (Chapter 3 and 4). While climatechange is a global issue, impacts are experienced locally. Cities andmunicipalities are at the frontline of adaptation (Rosenzweig et al.,2018), focusing on reducing and managing disaster risks due toextreme and slow-onset weather and climate events, installing floodand drought early warning systems, and improving water storage
and use (Chapters 3 and 4 and Cross-Chapter Box 12 in Chapter 5).
Agricultural and rural areas, including often highly vulnerable remote
and indigenous communities, also need to address climate-relatedrisks by strengthening and making more resilient agricultural andother natural resource extraction systems.

Classifying Response Options

Key broad categories of responses to the climate change problem areframed here. Mitigation refers to efforts to reduce or prevent theemission of greenhouse gases, or to enhance the absorption of gases
already emitted, thus limiting the magnitude of future warming(IPCC, 2014b). Mitigation requires the use of new technologies,
clean energy sources, reduced deforestation, improved sustainableagricultural methods, and changes in individual and collective
behaviour. Many of these may provide substantial co-benefits for air
quality, biodiversity and sustainable development. Mal-mitigation
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Framing and Context

Remedial measures are distinct from mitigation or adaptation, as
the aim is to temporarily reduce or offset warming (IPCC, 2012b).
One such measure is solar radiation modification (SRM), also referred
to as solar radiation management in the literature, which involves
deliberate changes to the albedo of the Earth system, with the net
effect of increasing the amount of solar radiation reflected from the
Earth to reduce the peak temperature from climate change (The Royal
Society, 2009; Smith and Rasch, 2013; Schäfer et al., 2015). It should
be noted that while some radiation modification measures, such as
cirrus cloud thinning (Kristjánsson et al., 2016), aim at enhancing

Framing and Context

outgoing long-wave radiation, SRM is used in this report to refer toall direct interventions on the planetary radiation budget. This report
does not use the term ‘geo-engineering’ because of inconsistencies
in the literature, which uses this term to cover SRM, CDR or both,whereas this report explicitly differentiates between CDR and SRM.
Large-scale SRM could potentially be used to supplement mitigationin overshoot scenarios to keep the global mean temperature below
1.5°C and temporarily reduce the severity of near-term impacts (e.g.,
MacMartin et al., 2018). The impacts of SRM (both biophysical andsocietal), costs, technical feasibility, governance and ethical issues
associated need to be carefully considered (Schäfer et al., 2015;
Section 4.3.8 and Cross-Chapter Box 10 in Chapter 4).

1.4.2

Governance, Implementation and Policies

A challenge in creating the enabling conditions of a 1.5°C warmer
world is the governance capacity of institutions to develop, implement
and evaluate the changes needed within diverse and highlyinterlinked global social-ecological systems (Busby, 2016) (Chapter
4). Policy arenas, governance structures and robust institutions are
key enabling conditions for transformative climate action (Chapter4). It is through governance that justice, ethics and equity within
the adaptation–mitigation–sustainable development nexus can beaddressed (von Stechow et al., 2016) (Chapter 5).
Governance capacity includes a wide range of activities and efforts
needed by different actors to develop coordinated climate mitigation
and adaptation strategies in the context of sustainable development,
taking into account equity, justice and poverty eradication. Significant
governance challenges include the ability to incorporate multiple
stakeholder perspectives in the decision-making process to reachmeaningful and equitable decisions, interactions and coordination

Chapter 1

between different levels of government, and the capacity to raisefinancing and support for both technological and human resource
development. For example, Lövbrand et al. (2017), argue that thevoluntary pledges submitted by states and non-state actors to meetthe conditions of the Paris Agreement will need to be more firmly
coordinated, evaluated and upscaled.
Barriers for transitioning from climate change mitigation andadaptation planning to practical policy implementation include
finance, information, technology, public attitudes, social values
and practices (Whitmarsh et al., 2011; Corner and Clarke, 2017),
and human resource constraints. Institutional capacity to deploy
available knowledge and resources is also needed (Mimura et al.,2014). Incorporating strong linkages across sectors, devolution of
power and resources to sub-national and local governments withthe support of national government, and facilitating partnerships
among public, civic, private sectors and higher education institutions(Leal Filho et al., 2018) can help in the implementation of identified
response options (Chapter 4). Implementation challenges of 1.5°C
pathways are larger than for those that are consistent with limiting
warming to well below 2°C, particularly concerning scale and speedof the transition and the distributional impacts on ecosystems andsocio-economic actors. Uncertainties in climate change at different
scales and capacities to respond combined with the complexities ofcoupled social and ecological systems point to a need for diverse andadaptive implementation options within and among different regions
involving different actors. The large regional diversity between highly
carbon-invested economies and emerging economies are importantconsiderations for sustainable development and equity in pursuingefforts to limit warming to 1.5°C. Key sectors, including energy, foodsystems, health, and water supply, also are critical to understanding
these connections.

Cross-Chapter Box 3 | Framing Feasibility: Key Concepts and Conditions for Limiting Global Temperature
Increases to 1.5°C
Contributing Authors:
William Solecki (USA), Anton Cartwright (South Africa), Wolfgang Cramer (France/Germany), James Ford (UK/Canada), Kejun Jiang(China), Joana Portugal Pereira (UK/Portugal), Joeri Rogelj (Austria/Belgium), Linda Steg (Netherlands), Henri Waisman (France)
This Cross-Chapter Box describes the concept of feasibility in relation to efforts to limit global warming to 1.5°C in the context ofsustainable development and efforts to eradicate poverty and draws from the understanding of feasibility emerging within the IPCC
(IPCC, 2017). Feasibility can be assessed in different ways, and no single answer exists as to the question of whether it is feasible to limit
warming to 1.5°C. This implies that an assessment of feasibility would go beyond a ‘yes’ or a ‘no’. Rather, feasibility provides a frame
to understand the different conditions and potential responses for implementing adaptation and mitigation pathways, and options
compatible with a 1.5°C warmer world. This report assesses the overall feasibility of limiting warming to 1.5°C, and the feasibility ofadaptation and mitigation options compatible with a 1.5°C warmer world, in six dimensions:Geophysical: What global emission pathways could be consistent with conditions of a 1.5°C warmer world? What are the physicalpotentials for adaptation?
Environmental-ecological: What are the ecosystem services and resources, including geological storage capacity and related rateof needed land-use change, available to promote transformations, and to what extent are they compatible with enhanced resilience?
Technological: What technologies are available to support transformation?
Economic: What economic conditions could support transformation?

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Chapter 1

Framing and Context

Cross-Chapter Box 3 (continued)

Socio-cultural: What conditions could support transformations in behaviour and lifestyles? To what extent are the transformationssocially acceptable and consistent with equity?
Institutional: What institutional conditions are in place to support transformations, including multi-level governance, institutional
capacity, and political support?

1

Assessment of feasibility in this report starts by evaluating the unavoidable warming from past emissions (Section 1.2.4) and identifying
mitigation pathways that would lead to a 1.5°C world, which indicates that rapid and deep deviations from current emission pathways
are necessary (Chapter 2). In the case of adaptation, an assessment of feasibility starts from an evaluation of the risks and impacts ofclimate change (Chapter 3). To mitigate and adapt to climate risks, system-wide technical, institutional and socio-economic transitions
would be required, as well as the implementation of a range of specific mitigation and adaptation options. Chapter 4 applies various
indicators categorised in these six dimensions to assess the feasibility of illustrative examples of relevant mitigation and adaptation
options (Section 4.5.1). Such options and pathways have different effects on sustainable development, poverty eradication andadaptation capacity (Chapter 5).
The six feasibility dimensions interact in complex and place-specific ways. Synergies and trade-offs may occur between the feasibility
dimensions, and between specific mitigation and adaptation options (Section 4.5.4). The presence or absence of enabling conditions
would affect the options that comprise feasibility pathways (Section 4.4), and can reduce trade-offs and amplify synergies between
options.
Sustainable development, eradicating poverty and reducing inequalities are not only preconditions for feasible transformations, but
the interplay between climate action (both mitigation and adaptation options) and the development patterns to which they apply may
actually enhance the feasibility of particular options (see Chapter 5).
The connections between the feasibility dimensions can be specified across three types of effects (discussed below). Each of these
dimensions presents challenges and opportunities in realizing conditions consistent with a 1.5°C warmer world.
Systemic effects: Conditions that have embedded within them system-level functions that could include linear and non-linear
connections and feedbacks. For example, the deployment of technology and large installations (e.g., renewable or low carbon energy
mega-projects) depends upon economic conditions (costs, capacity to mobilize investments for R&D), social or cultural conditions(acceptability), and institutional conditions (political support; e.g., Sovacool et al., 2015). Case studies can demonstrate system-levelinteractions and positive or negative feedback effects between the different conditions (Jacobson et al., 2015; Loftus et al., 2015). Thissuggests that each set of conditions and their interactions need to be considered to understand synergies, inequities and unintended
consequences.
Dynamic effects: Conditions that are highly dynamic and vary over time, especially under potential conditions of overshoot or noovershoot. Some dimensions might be more time sensitive or sequential than others (i.e., if conditions are such that it is no longer
geophysically feasible to avoid overshooting 1.5°C, the social and institutional feasibility of avoiding overshoot will be no longer
relevant). Path dependencies, risks of legacy lock-ins related to existing infrastructures, and possibilities of acceleration permitted bycumulative effects (e.g., dramatic cost decreases driven by learning-by-doing) are all key features to be captured. The effects can playout over various time scales and thus require understanding the connections between near-term (meaning within the next several years
to two decades) and long-term implications (meaning over the next several decades) when assessing feasibility conditions.
Spatial effects: Conditions that are spatially variable and scale dependent, according to context-specific factors such as regionalscale environmental resource limits and endowment; economic wealth of local populations; social organisation, cultural beliefs, values
and worldviews; spatial organisation, including conditions of urbanisation; and financial and institutional and governance capacity.
This means that the conditions for achieving the global transformation required for a 1.5°C world will be heterogeneous and vary
according to the specific context. On the other hand, the satisfaction of these conditions may depend upon global-scale drivers, such as
international flows of finance, technologies or capacities. This points to the need for understanding feasibility to capture the interplay
between the conditions at different scales.
With each effect, the interplay between different conditions influences the feasibility of both pathways (Chapter 2) and options (Chapter4), which in turn affect the likelihood of limiting warming to 1.5°C. The complexity of these interplays triggers unavoidable uncertainties,
requiring transformations that remain robust under a range of possible futures that limit warming to 1.5°C.

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1.4.3

Transformation, Transformation Pathways,
and Transition: Evaluating Trade-Offs andSynergies Between Mitigation, Adaptationand Sustainable Development Goals

Embedded in the goal of limiting warming to 1.5°C is theopportunity for intentional societal transformation (see Box 1.1
on the Anthropocene). The form and process of transformation arevaried and multifaceted (Pelling, 2011; O’Brien et al., 2012; O’Brienand Selboe, 2015; Pelling et al., 2015). Fundamental elements of
1.5°C-related transformation include a decoupling of economic
growth from energy demand and CO2 emissions; leap-frogging
development to new and emerging low-carbon, zero-carbon andcarbon-negative technologies; and synergistically linking climate
mitigation and adaptation to global scale trends (e.g., global tradeand urbanization) that will enhance the prospects for effective
climate action, as well as enhanced poverty reduction and greater
equity (Tschakert et al., 2013; Rogelj et al., 2015; Patterson et al.,2017) (Chapters 4 and 5). The connection between transformative
climate action and sustainable development illustrates a complex
coupling of systems that have important spatial and time scale lag
effects and implications for process and procedural equity, includingintergenerational equity and for non-human species (Cross-Chapter
Box 4 in this chapter, Chapter 5). Adaptation and mitigation transition
pathways highlight the importance of cultural norms and values,
sector-specific context, and proximate (i.e., occurrence of an extreme
event) drivers that when acting together enhance the conditions forsocietal transformation (Solecki et al., 2017; Rosenzweig et al., 2018)(Chapters 4 and 5).
Diversity and flexibility in implementation choices exist for adaptation,
mitigation (including carbon dioxide removal, CDR) and remedial
measures (such as solar radiation modification, SRM), and a potential
for trade-offs and synergies between these choices and sustainable
development (IPCC, 2014d; Olsson et al., 2014). The responses

Chapter 1

chosen could act to synergistically enhance mitigation, adaptationand sustainable development, or they may result in trade-offs
which positively impact some aspects and negatively impact others.
Climate change is expected to decrease the likelihood of achieving
the Sustainable Development Goals (SDGs). While some strategies
limiting warming towards 1.5°C are expected to significantly increase
the likelihood of meeting those goals while also providing synergies
for climate adaptation and mitigation (Chapter 5).
Dramatic transformations required to achieve the enabling conditions
for a 1.5°C warmer world could impose trade-offs on dimensionsof development (IPCC, 2014d; Olsson et al., 2014). Some choicesof adaptation methods also could adversely impact development(Olsson et al., 2014). This report recognizes the potential for adverse
impacts and focuses on finding the synergies between limiting
warming, sustainable development, and eradicating poverty, thus
highlighting pathways that do not constrain other goals, such assustainable development and eradicating poverty.
The report is framed to address these multiple goals simultaneously
and assesses the conditions to achieve a cost-effective and sociallyacceptable solution, rather than addressing these goals piecemeal(von Stechow et al., 2016) (Section 4.5.4 and Chapter 5), although
there may be different synergies and trade-offs between a 2°C (vonStechow et al., 2016) and 1.5°C warmer world (Kainuma et al.,2017). Climate-resilient development pathways (see Cross-Chapter
Box 12 in Chapter 5 and Glossary) are trajectories that strengthen
sustainable development, including mitigating and adapting toclimate change and efforts to eradicate poverty while promoting
fair and cross-scalar resilience in a changing climate. They take into
account dynamic livelihoods; the multiple dimensions of poverty,
structural inequalities; and equity between and among poor andnon-poor people (Olsson et al., 2014). Climate-resilient development
pathways can be considered at different scales, including cities, rural
areas, regions or at global level (Denton et al., 2014; Chapter 5).

Cross-Chapter Box 4 | Sustainable Development and the Sustainable Development Goals
Contributing Authors:
Diana Liverman (USA), Mustafa Babiker (Sudan), Purnamita Dasgupta (India), Riyanti Djanlante (Japan/Indonesia), Stephen Humphreys(UK/Ireland), Natalie Mahowald (USA), Yacob Mulugetta (UK/Ethiopia), Virginia Villariño (Argentina), Henri Waisman (France)
Sustainable development is most often defined as ‘development that meets the needs of the present without compromising the abilityof future generations to meet their own needs’ (WCED, 1987) and includes balancing social well-being, economic prosperity andenvironmental protection. The AR5 used this definition and linked it to climate change (Denton et al., 2014). The most significant stepsince AR5 is the adoption of the UN Sustainable Development Goals, and the emergence of literature that links them to climate (vonStechow et al., 2015; Wright et al., 2015; Epstein and Theuer, 2017; Hammill and Price-Kelly, 2017; Kelman, 2017; Lofts et al., 2017;
Maupin, 2017; Gomez-Echeverri, 2018).
In September 2015, the UN endorsed a universal agenda – ‘Transforming our World: the 2030 Agenda for Sustainable Development’– which aims ‘to take the bold and transformative steps which are urgently needed to shift the world onto a sustainable and resilientpath’. Based on a participatory process, the resolution in support of the 2030 agenda adopted 17 non-legally-binding Sustainable
Development Goals (SDGs) and 169 targets to support people, prosperity, peace, partnerships and the planet (Kanie and Biermann,2017).

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Chapter 1

Framing and Context

Cross-Chapter Box 4 (continued)

The SDGs expanded efforts to reduce poverty and other deprivations under the UN Millennium Development Goals (MDGs). There were
improvements under the MDGs between 1990 and 2015, including reducing overall poverty and hunger, reducing infant mortality, andimproving access to drinking water (UN, 2015a). However, greenhouse gas emissions increased by more than 50% from 1990 to 2015,
and 1.6 billion people were still living in multidimensional poverty with persistent inequalities in 2015 (Alkire et al., 2015).

1

The SDGs raise the ambition for eliminating poverty, hunger, inequality and other societal problems while protecting the environment.
They have been criticised: as too many and too complex, needing more realistic targets, overly focused on 2030 at the expense of
longer-term objectives, not embracing all aspects of sustainable development, and even contradicting each other (Horton, 2014; Deathand Gabay, 2015; Biermann et al., 2017; Weber, 2017; Winkler and Satterthwaite, 2017).
Climate change is an integral influence on sustainable development, closely related to the economic, social and environmental
dimensions of the SDGs. The IPCC has woven the concept of sustainable development into recent assessments, showing how climatechange might undermine sustainable development, and the synergies between sustainable development and responses to climatechange (Denton et al., 2014). Climate change is also explicit in the SDGs. SDG13 specifically requires ‘urgent action to address climatechange and its impacts’. The targets include strengthening resilience and adaptive capacity to climate-related hazards and natural
disasters; integrating climate change measures into national policies, strategies and planning; and improving education, awarenessraising and human and institutional capacity.
Targets also include implementing the commitment undertaken by developed-country parties to the UNFCCC to the goal of mobilizing
jointly 100 billion USD annually by 2020 and operationalizing the Green Climate Fund, as well as promoting mechanisms for raising
capacity for effective climate change-related planning and management in least developed countries and Small Island Developing
States, including focusing on women, youth and local and marginalised communities. SDG13 also acknowledges that the UNFCCC is
the primary international, intergovernmental forum for negotiating the global response to climate change.
Climate change is also mentioned in SDGs beyond SDG13, for example in goal targets 1.5, 2.4, 11.B, 12.8.1 related to poverty, hunger,
cities and education respectively. The UNFCCC addresses other SDGs in commitments to ‘control, reduce or prevent anthropogenic
emissions of greenhouse gases […] in all relevant sectors, including the energy, transport, industry, agriculture, forestry and waste
management sectors’ (Art4, 1(c)) and to work towards ‘the conservation and enhancement, as appropriate, of […] biomass, forests andoceans as well as other terrestrial, coastal and marine ecosystems’ (Art4, 1(d)). This corresponds to SDGs that seek clean energy for all(Goal 7), sustainable industry (Goal 9) and cities (Goal 11) and the protection of life on land and below water (14 and 15).
The SDGs and UNFCCC also differ in their time horizons. The SDGs focus primarily on 2030 whereas the Paris Agreement sets out that
‘Parties aim […] to achieve a balance between anthropogenic emissions by sources and removals by sinks of greenhouse gases in thesecond half of this century’.
The IPCC decision to prepare this report on the impacts of 1.5°C and associated emission pathways explicitly asked for the assessment
to be in the context of sustainable development and efforts to eradicate poverty. Chapter 1 frames the interaction between sustainable
development, poverty eradication and ethics and equity. Chapter 2 assesses how risks and synergies of individual mitigation measuresinteract with 1.5°C pathways within the context of the SDGs and how these vary according to the mix of measures in alternative
mitigation portfolios (Section 2.5). Chapter 3 examines the impacts of 1.5°C global warming on natural and human systems withcomparison to 2°C and provides the basis for considering the interactions of climate change with sustainable development in Chapter 5.
Chapter 4 analyses strategies for strengthening the response to climate change, many of which interact with sustainable development.
Chapter 5 takes sustainable development, eradicating poverty and reducing inequalities as its focal point for the analysis of pathways
to 1.5°C and discusses explicitly the linkages between achieving SDGs while eradicating poverty and reducing inequality.

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Chapter 1

Cross-Chapter Box 4 (continued)

1

Cross-Chapter Box 4, Figure 1 | Climate action is number 13 of the UN Sustainable Development Goals.

1.5

Assessment Frameworks and Emerging
Methodologies that Integrate Climate
Change Mitigation and Adaptation
with Sustainable Development

This report employs information and data that are global in scopeand include region-scale analysis. It also includes syntheses ofmunicipal, sub-national, and national case studies. Global levelstatistics including physical and social science data are used, as
well as detailed and illustrative case study material of particularconditions and contexts. The assessment provides the state ofknowledge, including an assessment of confidence and uncertainty.
The main time scale of the assessment is the 21st century and thetime is separated into the near-, medium-, and long-term. Near-term
refers to the coming decade, medium-term to the period 2030–2050,
while long-term refers to 2050–2100. Spatial and temporal contexts
are illustrated throughout, including: assessment tools that include
dynamic projections of emission trajectories and the underlying
energy and land transformation (Chapter 2); methods for assessing
observed impacts and projected risks in natural and managed
ecosystems and at 1.5°C and higher levels of warming in natural andmanaged ecosystems and human systems (Chapter 3); assessmentsof the feasibility of mitigation and adaptation options (Chapter 4);
and linkages of the Shared Socioeconomic Pathways (SSPs) andSustainable Development Goals (SDGs) (Cross-Chapter Boxes 1 and
4 in this chapter, Chapter 2 and Chapter 5).

1.5.1

Knowledge Sources and Evidence
Used in the Report

This report is based on a comprehensive assessment of documented
evidence of the enabling conditions to pursuing efforts to limit the
global average temperature rise to 1.5°C and adapting to this levelof warming in the overarching context of the Anthropocene (Delanty
and Mota, 2017). Two sources of evidence are used: peer-reviewed
scientific literature and ‘grey’ literature in accordance with procedureon the use of literature in IPCC reports (IPCC, 2013a, Annex 2 toAppendix A), with the former being the dominant source. Grey
literature is largely used on key issues not covered in peer-reviewed
literature.
The peer-reviewed literature includes the following sources: 1)
knowledge regarding the physical climate system and human-inducedchanges, associated impacts, vulnerabilities, and adaptation options,established from work based on empirical evidence, simulations,modelling, and scenarios, with emphasis on new information since
the publication of the IPCC AR5 to the cut-off date for this report(15th of May 2018); 2) humanities and social science theory andknowledge from actual human experiences of climate changerisks and vulnerability in the context of social-ecological systems,
development, equity, justice, and governance, and from indigenous
knowledge systems; and 3) mitigation pathways based on climate
projections into the future.

75

Chapter 1

1

The grey literature category extends to empirical observations,interviews, and reports from government, industry, research institutes,
conference proceedings and international or other organisations.
Incorporating knowledge from different sources, settings andinformation channels while building awareness at various levels will
advance decision-making and motivate implementation of contextspecific responses to 1.5°C warming (Somanathan et al., 2014).
The assessment does not assess non-written evidence and does notuse oral evidence, media reports or newspaper publications. With
important exceptions, such as China, published knowledge fromthe most vulnerable parts of the world to climate change is limited(Czerniewicz et al., 2017).

1.5.2

Assessment Frameworks and Methodologies

Climate models and associated simulations
The multiple sources of climate model information used in thisassessment are provided in Chapter 2 (Section 2.2) and Chapter
3 (Section 3.2). Results from global simulations, which have also
been assessed in previous IPCC reports and that are conducted aspart of the World Climate Research Programme (WCRP) Coupled
Models Intercomparison Project (CMIP) are used. The IPCC AR4 andManaging the Risks of Extreme Events and Disasters to Advance
Climate Change Adaptation (SREX) reports were mostly based on
simulations from the CMIP3 experiment, while the AR5 was mostly
based on simulations from the CMIP5 experiment. The simulationsof the CMIP3 and CMIP5 experiments were found to be very
similar (e.g., Knutti and Sedláček, 2012; Mueller and Seneviratne,2014). In addition to the CMIP3 and CMIP5 experiments, resultsfrom coordinated regional climate model experiments (e.g., theCoordinated Regional Climate Downscaling Experiment, CORDEX)
have been assessed and are available for different regions (Giorgi andGutowski, 2015). For instance, assessments based on publications
from an extension of the IMPACT2C project (Vautard et al., 2014;
Jacob and Solman, 2017) are newly available for 1.5°C projections.
Recently, simulations from the ‘Half a degree Additional warming,
Prognosis and Projected Impacts’ (HAPPI) multimodel experiment
have been performed to specifically assess climate changes at 1.5°C
vs 2°C global warming (Mitchell et al., 2016). The HAPPI protocol
consists of coupled land–atmosphere initial condition ensemble
simulations with prescribed sea surface temperatures (SSTs); sea ice,
GHG and aerosol concentrations; and solar and volcanic activity thatcoincide with three forced climate states: present-day (2006–2015)(see Section 1.2.1) and future (2091–2100) either with 1.5°C or 2°C
global warming (prescribed by modified SSTs).
Detection and attribution of change in climate and impacted systems
Formalized scientific methods are available to detect and attribute
impacts of greenhouse gas forcing on observed changes in climate(e.g., Hegerl et al., 2007; Seneviratne et al., 2012; Bindoff et al., 2013)
and impacts of climate change on natural and human systems (e.g.,
Stone et al., 2013; Hansen and Cramer, 2015; Hansen et al., 2016).
The reader is referred to these sources, as well as to the AR5 for morebackground on these methods.

76

Framing and Context

Global climate warming has already reached approximately 1°C(see Section 1.2.1) relative to pre-industrial conditions, and thus
‘climate at 1.5°C global warming’ corresponds to approximately
the addition of only half a degree of warming compared to thepresent day, comparable to the warming that has occurred sincethe 1970s (Bindoff et al., 2013). Methods used in the attribution ofobserved changes associate with this recent warming are therefore
also applicable to assessments of future changes in climate at 1.5°C
warming, especially in cases where no climate model simulations or
analyses are available.
Impacts of 1.5°C global warming can be assessed in part fromregional and global climate changes that have already been detected
and attributed to human influence (e.g., Schleussner et al., 2017) andare components of the climate system that are most responsive tocurrent and projected future forcing. For this reason, when specificprojections are missing for 1.5°C global warming, some of theassessments of climate change provided in Chapter 3 (Section 3.3)
build upon joint assessments of (i) changes that were observed andattributed to human influence up to the present, that is, for 1°C
global warming and (ii) projections for higher levels of warming (e.g.,
2°C, 3°C or 4°C) to assess the changes at 1.5°C. Such assessments
are for transient changes only (see Chapter 3, Section 3.3).
Besides quantitative detection and attribution methods, assessments
can also be based on indigenous and local knowledge (see Chapter 4,
Box 4.3). While climate observations may not be available to assess
impacts from a scientific perspective, local community knowledge
can also indicate actual impacts (Brinkman et al., 2016; Kabir et al.,2016). The challenge is that a community’s perception of loss dueto the impacts of climate change is an area that requires further
research (Tschakert et al., 2017).
Costs and benefits analysis
Cost–benefit analyses are common tools used for decision-making,whereby the costs of impacts are compared to the benefits fromdifferent response actions (IPCC, 2014a, b). However, for thecase of climate change, recognising the complex inter-linkagesof the Anthropocene, cost–benefit analysis tools can be difficult
to use because of disparate impacts versus costs and complex
interconnectivity within the global social-ecological system (seeBox 1.1 and Cross-Chapter Box 5 in Chapter 2). Some costs are
relatively easily quantifiable in monetary terms but not all. Climatechange impacts human lives and livelihoods, culture and values, andwhole ecosystems. It has unpredictable feedback loops and impacts
on other regions (IPCC, 2014a), giving rise to indirect, secondary,tertiary and opportunity costs that are typically extremely difficult toquantify. Monetary quantification is further complicated by the factthat costs and benefits can occur in different regions at very different
times, possibly spanning centuries, while it is extremely difficult if not
impossible to meaningfully estimate discount rates for future costs
and benefits. Thus standard cost–benefit analyses become difficult
to justify (IPCC, 2014a; Dietz et al., 2016) and are not used as an
assessment tool in this report.

Framing and Context

1.6

Confidence, Uncertainty and Risk

This report relies on the IPCC’s uncertainty guidance provided in
Mastrandrea et al. (2011) and sources given therein. Two metrics for
qualifying key findings are used:
Confidence: Five qualifiers are used to express levels of confidence
in key findings, ranging from very low, through low, medium,
high, to very high. The assessment of confidence involves at least
two dimensions, one being the type, quality, amount or internalconsistency of individual lines of evidence, and the second being
the level of agreement between different lines of evidence. Veryhigh confidence findings must either be supported by a high levelof agreement across multiple lines of mutually independent andindividually robust lines of evidence or, if only a single line of evidence
is available, by a very high level of understanding underlying that
evidence. Findings of low or very low confidence are presented only
if they address a topic of major concern.
Likelihood: A calibrated language scale is used to communicate
assessed probabilities of outcomes, ranging from exceptionallyunlikely (<1%), extremely unlikely (<5%), very unlikely (<10%),unlikely (<33%), about as likely as not (33–66%), likely (>66%), very
likely (>90%), extremely likely (>95%) to virtually certain (>99%).
These terms are normally only applied to findings associated withhigh or very high confidence. Frequency of occurrence within a model
ensemble does not correspond to actual assessed probability ofoutcome unless the ensemble is judged to capture and represent thefull range of relevant uncertainties.
Three specific challenges arise in the treatment of uncertainty andrisk in this report. First, the current state of the scientific literature on
1.5°C means that findings based on multiple lines of robust evidence
for which quantitative probabilistic results can be expressed may be
few in number, and those that do exist may not be the most policyrelevant. Hence many key findings are expressed using confidence
qualifiers alone.
Second, many of the most important findings of this report areconditional because they refer to ambitious mitigation scenarios,
potentially involving large-scale technological or societal
transformation. Conditional probabilities often depend strongly on
how conditions are specified, such as whether temperature goals
are met through early emission reductions, reliance on negativeemissions, or through a low climate response. Whether a certain
risk is considered high at 1.5°C may therefore depend strongly on
how 1.5°C is specified, whereas a statement that a certain risk may
be substantially higher at 2°C relative to 1.5°C may be much morerobust.
Third, achieving ambitious mitigation goals will require active,
goal-directed efforts aiming explicitly for specific outcomes andincorporating new information as it becomes available (Otto etal., 2015). This shifts the focus of uncertainty from the climate
outcome itself to the level of mitigation effort that may be required
to achieve it. Probabilistic statements about human decisions are

Chapter 1

always problematic, but in the context of robust decision-making,many near-term policies that are needed to keep open the option oflimiting warming to 1.5°C may be the same, regardless of the actual
probability that the goal will be met (Knutti et al., 2015).

1.7

1

Storyline of the Report

The storyline of this report (Figure 1.6) includes a set of interconnectedcomponents. The report consists of five chapters (plus Supplementary
Material for Chapters 1 through 4), a Technical Summary and a
Summary for Policymakers. It also includes a set of boxes to elucidate
specific or cross-cutting themes, as well as Frequently Asked
Questions for each chapter, a Glossary, and several other Annexes.
At a time of unequivocal and rapid global warming, this report
emerges from the long-term temperature goal of the Paris Agreement– strengthening the global response to the threat of climate change
by pursuing efforts to limit warming to 1.5°C through reducing
emissions to achieve a balance between anthropogenic emissions bysources and removals by sinks of greenhouse gases. The assessment
focuses first, in Chapter 1, on how 1.5°C is defined and understood,
what is the current level of warming to date, and the present
trajectory of change. The framing presented in Chapter 1 provides thebasis through which to understand the enabling conditions of a 1.5°C
warmer world and connections to the SDGs, poverty eradication, andequity and ethics.
In Chapter 2, scenarios of a 1.5°C warmer world and the associatedpathways are assessed. The pathways assessment builds upon
the AR5 with a greater emphasis on sustainable development in
mitigation pathways. All pathways begin now and involve rapid
and unprecedented societal transformation. An important framing
device for this report is the recognition that choices that determine
emissions pathways, whether ambitious mitigation or ‘no policy’
scenarios, do not occur independently of these other changes andare, in fact, highly interdependent.
Projected impacts that emerge in a 1.5°C warmer world and beyond
are dominant narrative threads of the report and are assessed in
Chapter 3. The chapter focuses on observed and attributable global
and regional climate changes and impacts and vulnerabilities. The
projected impacts have diverse and uneven spatial, temporal, human,economic, and ecological system-level manifestations. Central to theassessment is the reporting of impacts at 1.5°C and 2°C, potential
impacts avoided through limiting warming to 1.5°C, and, where
possible, adaptation potential and limits to adaptive capacity.
Response options and associated enabling conditions emerge next, in
Chapter 4. Attention is directed to exploring questions of adaptationand mitigation implementation, integration, and transformation ina highly interdependent world, with consideration of synergies andtrade-offs. Emission pathways, in particular, are broken down into
policy options and instruments. The role of technological choices,institutional capacity and global-scale trends like urbanization andchanges in ecosystems are assessed.
77

Chapter 1

1

Chapter 5 covers linkages between achieving the SDGs and a 1.5°C
warmer world and turns toward identifying opportunities andchallenges of transformation. This is assessed within a transition toclimate-resilient development pathways and connection between theevolution towards 1.5°C, associated impacts, and emission pathways.
Positive and negative effects of adaptation and mitigation response
measures and pathways for a 1.5°C warmer world are examined.

Figure 1.6 | Schematic of report storyline.

78

Framing and Context

Progress along these pathways involves inclusive processes,
institutional integration, adequate finance and technology, andattention to issues of power, values, and inequalities to maximize
the benefits of pursuing climate stabilisation at 1.5°C and the goalsof sustainable development at multiple scales of human and natural
systems from global, regional, national to local and community
levels.

Framing and Context

Chapter 1

Frequently Asked Questions

FAQ 1.1 | Why are we Talking about 1.5°C?
Summary: Climate change represents an urgent and potentially irreversible threat to human societies and theplanet. In recognition of this, the overwhelming majority of countries around the world adopted the Paris Agreement in December 2015, the central aim of which includes pursuing efforts to limit global temperature rise
to 1.5°C. In doing so, these countries, through the United Nations Framework Convention on Climate Change(UNFCCC), also invited the IPCC to provide a Special Report on the impacts of global warming of 1.5°C above preindustrial levels and related global greenhouse gas emissions pathways.

1

At the 21st Conference of the Parties (COP21) in December 2015, 195 nations adopted the Paris Agreement2. The
first instrument of its kind, the landmark agreement includes the aim to strengthen the global response to thethreat of climate change by ‘holding the increase in the global average temperature to well below 2°C above
pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5°C above pre-industrial levels’.
The first UNFCCC document to mention a limit to global warming of 1.5°C was the Cancun Agreement, adopted
at the sixteenth COP (COP16) in 2010. The Cancun Agreement established a process to periodically review the‘adequacy of the long-term global goal (LTGG) in the light of the ultimate objective of the Convention and theoverall progress made towards achieving the LTGG, including a consideration of the implementation of thecommitments under the Convention’. The definition of LTGG in the Cancun Agreement was ‘to hold the increasein global average temperature below 2°C above pre-industrial levels’. The agreement also recognised the needto consider ‘strengthening the long-term global goal on the basis of the best available scientific knowledge…toa global average temperature rise of 1.5°C’.
Beginning in 2013 and ending at the COP21 in Paris in 2015, the first review period of the long-term global goallargely consisted of the Structured Expert Dialogue (SED). This was a fact-finding, face-to-face exchange of views
between invited experts and UNFCCC delegates. The final report of the SED3 concluded that ‘in some regions andvulnerable ecosystems, high risks are projected even for warming above 1.5°C’. The SED report also suggested
that Parties would profit from restating the temperature limit of the long-term global goal as a ‘defence line’or ‘buffer zone’, instead of a ‘guardrail’ up to which all would be safe, adding that this new understanding
would ‘probably also favour emission pathways that will limit warming to a range of temperatures below 2°C’.
Specifically on strengthening the temperature limit of 2°C, the SED’s key message was: ‘While science on the
1.5°C warming limit is less robust, efforts should be made to push the defence line as low as possible’. The
findings of the SED, in turn, fed into the draft decision adopted at COP21.
With the adoption of the Paris Agreement, the UNFCCC invited the IPCC to provide a Special Report in 2018 on‘the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emissions
pathways’. The request was that the report, known as SR1.5, should not only assess what a 1.5°C warmer world
would look like but also the different pathways by which global temperature rise could be limited to 1.5°C. In
2016, the IPCC accepted the invitation, adding that the Special Report would also look at these issues in thecontext of strengthening the global response to the threat of climate change, sustainable development andefforts to eradicate poverty.
The combination of rising exposure to climate change and the fact that there is a limited capacity to adapt to its
impacts amplifies the risks posed by warming of 1.5°C and 2°C. This is particularly true for developing and island
countries in the tropics and other vulnerable countries and areas. The risks posed by global warming of 1.5°C are
greater than for present-day conditions but lower than at 2°C.
(continued on next page)2

Paris Agreement FCCC/CP/2015/10/Add.1 https://unfccc.int/documents/9097

3

Structured Expert Dialogue (SED) final report FCCC/SB/2015/INF.1 https://unfccc.int/documents/8707

79

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Framing and Context

FAQ 1.1 (continued)

1

FAQ 1.1, Figure 1 | Timeline of notable dates in preparing the IPCC Special Report on Global Warming of 1.5°C (blue) embedded within processes and milestonesof the United Nations Framework Convention on Climate Change (UNFCCC; grey), including events that may be relevant for discussion of temperature limits.

80

Framing and Context

Chapter 1

Frequently Asked Questions

FAQ 1.2 | How Close are we to 1.5°C?
Summary: Human-induced warming has already reached about 1°C above pre-industrial levels at the time of writing of this Special Report. By the decade 2006–2015, human activity had warmed the world by 0.87°C (±0.12°C)
compared to pre-industrial times (1850–1900). If the current warming rate continues, the world would reachhuman-induced global warming of 1.5°C around 2040.

1

Under the 2015 Paris Agreement, countries agreed to cut greenhouse gas emissions with a view to ‘holding theincrease in the global average temperature to well below 2°C above pre-industrial levels and pursuing efforts tolimit the temperature increase to 1.5°C above pre-industrial levels’. While the overall intention of strengthening
the global response to climate change is clear, the Paris Agreement does not specify precisely what is meant by‘global average temperature’, or what period in history should be considered ‘pre-industrial’. To answer thequestion of how close are we to 1.5°C of warming, we need to first be clear about how both terms are definedin this Special Report.
The choice of pre-industrial reference period, along with the method used to calculate global averagetemperature, can alter scientists’ estimates of historical warming by a couple of tenths of a degree Celsius. Such
differences become important in the context of a global temperature limit just half a degree above where we arenow. But provided consistent definitions are used, they do not affect our understanding of how human activity
is influencing the climate.
In principle, ‘pre-industrial levels’ could refer to any period of time before the start of the industrial revolution.
But the number of direct temperature measurements decreases as we go back in time. Defining a ‘pre-industrial’reference period is, therefore, a compromise between the reliability of the temperature information and how
representative it is of truly pre-industrial conditions. Some pre-industrial periods are cooler than others forpurely natural reasons. This could be because of spontaneous climate variability or the response of the climate
to natural perturbations, such as volcanic eruptions and variations in the sun’s activity. This IPCC Special Reporton Global Warming of 1.5°C uses the reference period 1850–1900 to represent pre-industrial temperature. Thisis the earliest period with near-global observations and is the reference period used as an approximation of preindustrial temperatures in the IPCC Fifth Assessment Report.
Once scientists have defined ‘pre-industrial’, the next step is to calculate the amount of warming at any given
time relative to that reference period. In this report, warming is defined as the increase in the 30-year global
average of combined air temperature over land and water temperature at the ocean surface. The 30-yeartimespan accounts for the effect of natural variability, which can cause global temperatures to fluctuate fromone year to the next. For example, 2015 and 2016 were both affected by a strong El Niño event, which amplified
the underlying human-caused warming.
In the decade 2006–2015, warming reached 0.87°C (±0.12°C) relative to 1850–1900, predominantly due to humanactivity increasing the amount of greenhouse gases in the atmosphere. Given that global temperature is currently
rising by 0.2°C (±0.1°C) per decade, human-induced warming reached 1°C above pre-industrial levels around2017 and, if this pace of warming continues, would reach 1.5°C around 2040.
While the change in global average temperature tells researchers about how the planet as a whole is changing,looking more closely at specific regions, countries and seasons reveals important details. Since the 1970s, mostland regions have been warming faster than the global average, for example. This means that warming inmany regions has already exceeded 1.5°C above pre-industrial levels. Over a fifth of the global population live
in regions that have already experienced warming in at least one season that is greater than 1.5°C above preindustrial levels.
(continued on next page)81

Chapter 1

Framing and Context

FAQ 1.2 (continued)

FAQ1.2:How close are we to 1.5°C?

Human-induced warming reached approximately 1°C above
pre-industrial levels in 2017

1

2.00

Current
warming rate

Global temperature change
relative to 1850-1900 (°C)

1.75
1.50

2017

1.25
1.00
0.75

Human-induced
warming

Climate uncertainty
for 1.5°C pathway

0.50
Observed
warming

0.25
0.00

1960

1980

2000

2020

2040

2060

2080

2100

FAQ 1.2, Figure 1 | Human-induced warming reached approximately 1°C above pre-industrial levels in 2017. At the present rate, global temperatures would
reach 1.5°C around 2040. Stylized 1.5°C pathway shown here involves emission reductions beginning immediately, and CO2 emissions reaching zero by 2055.

82

Framing and Context

Chapter 1

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Zscheischler, J. and S.I. Seneviratne, 2017: Dependence of drivers affects risks
associated with compound events. Science Advances, 3(6), e1700263,
doi:10.1126/sciadv.1700263.

1

91

2

Mitigation Pathways
Compatible with 1.5°C
in the Context
of Sustainable Development
Coordinating Lead Authors:
Joeri Rogelj (Austria/Belgium), Drew Shindell (USA), Kejun Jiang (China)

Lead Authors:
Solomone Fifita (Fiji), Piers Forster (UK), Veronika Ginzburg (Russia), Collins Handa (Kenya), Haroon
Kheshgi (USA), Shigeki Kobayashi (Japan), Elmar Kriegler (Germany), Luis Mundaca (Sweden/Chile),
Roland Séférian (France), Maria Virginia Vilariño (Argentina)

Contributing Authors:
Katherine Calvin (USA), Joana Correia de Oliveira de Portugal Pereira (UK/Portugal), Oreane
Edelenbosch (Netherlands/Italy), Johannes Emmerling (Italy/Germany), Sabine Fuss (Germany), Thomas
Gasser (Austria/France), Nathan Gillett (Canada), Chenmin He (China), Edgar Hertwich (USA/Austria),
Lena Höglund-Isaksson (Austria/Sweden), Daniel Huppmann (Austria), Gunnar Luderer (Germany), Anil
Markandya (Spain/UK), David L. McCollum (USA/Austria), Malte Meinshausen (Australia/Germany),
Richard Millar (UK), Alexander Popp (Germany), Pallav Purohit (Austria/India), Keywan Riahi (Austria),
Aurélien Ribes (France), Harry Saunders (Canada/USA), Christina Schädel (USA/Switzerland), Chris
Smith (UK), Pete Smith (UK), Evelina Trutnevyte (Switzerland/Lithuania), Yang Xiu (China), Wenji Zhou
(Austria/China), Kirsten Zickfeld (Canada/Germany)

Chapter Scientists:
Daniel Huppmann (Austria), Chris Smith (UK)

Review Editors:
Greg Flato (Canada), Jan Fuglestvedt (Norway), Rachid Mrabet (Morocco), Roberto Schaeffer (Brazil)

This chapter should be cited as:
Rogelj, J., D. Shindell, K. Jiang, S. Fifita, P. Forster, V. Ginzburg, C. Handa, H. Kheshgi, S. Kobayashi, E. Kriegler, L. Mundaca,
R. Séférian, and M.V. Vilariño, 2018: Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable
Development. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above
pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global
response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte,V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors,
J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In Press.

93Chapter 2

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Table of Contents
Executive Summary......................................................................95
2.1
		

2

Introduction to Mitigation Pathways andthe Sustainable Development Context.....................98

2.1.1

Mitigation Pathways Consistent with 1.5°C..................98

2.1.2

The Use of Scenarios....................................................98

2.1.3

New Scenario Information since AR5............................99

2.1.4
			

Utility of Integrated Assessment Models (IAMs)
in the Context of this Report.......................................100

2.2

Geophysical Relationships and Constraints..........101

2.2.1

Geophysical Characteristics of Mitigation Pathways...101

2.2.2

The Remaining 1.5°C Carbon Budget..........................104

2.3

Overview of 1.5°C Mitigation Pathways................108

2.3.1

Range of Assumptions Underlying 1.5°C Pathways.....109

2.3.2

Key Characteristics of 1.5°C Pathways........................112

2.3.3

Emissions Evolution in 1.5°C Pathways.......................115

2.3.4

CDR in 1.5°C Pathways...............................................118

Box 2.1: Bioenergy and BECCS Deployment
in Integrated Assessment Modelling.....................................124
2.3.5

Implications of Near-Term Action in 1.5°C Pathways...126

2.4
Disentangling the Whole-System
		 Transformation...............................................................129
2.4.1

Energy System Transformation....................................129

2.4.2

Energy Supply..............................................................130

2.4.3

Energy End-Use Sectors...............................................136

2.4.4
			

Land-Use Transitions and Changes in
the Agricultural Sector.................................................144

2.5
		

Challenges, Opportunities and Co-Impactsof Transformative Mitigation Pathways.................148

2.5.1

Policy Frameworks and Enabling Conditions...............148

Cross-Chapter Box 5 | Economics of 1.5°C Pathways
and the Social Cost of Carbon................................................150
2.5.2
Economic and Investment Implications of 1.5°C
			 Pathways.....................................................................152
2.5.3
			

94

Sustainable Development Featuresof 1.5°C Pathways.......................................................156

2.6

Knowledge Gaps............................................................157

2.6.1

Geophysical Understanding........................................157

2.6.2

Integrated Assessment Approaches.............................158

2.6.3

Carbon Dioxide Removal (CDR)...................................158

Frequently Asked Questions
FAQ 2.1: What Kind of Pathways Limit Warming
to 1.5°C and are we on Track?............................................159
FAQ 2.2: What do Energy Supply and Demand
have to do with Limiting Warming to 1.5°C?......................161

References ....................................................................................163

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Executive Summary
This chapter assesses mitigation pathways consistent with limiting
warming to 1.5°C above pre-industrial levels. In doing so, it explores
the following key questions: What role do CO2 and non-CO2 emissions
play? {2.2, 2.3, 2.4, 2.6} To what extent do 1.5°C pathways involve
overshooting and returning below 1.5°C during the 21st century? {2.2,2.3} What are the implications for transitions in energy, land use andsustainable development? {2.3, 2.4, 2.5} How do policy frameworks
affect the ability to limit warming to 1.5°C? {2.3, 2.5} What are theassociated knowledge gaps? {2.6}
The assessed pathways describe integrated, quantitative
evolutions of all emissions over the 21st century associatedwith global energy and land use and the world economy. The
assessment is contingent upon available integrated assessment
literature and model assumptions, and is complemented by otherstudies with different scope, for example, those focusing on individual
sectors. In recent years, integrated mitigation studies have improved
the characterizations of mitigation pathways. However, limitations
remain, as climate damages, avoided impacts, or societal co-benefitsof the modelled transformations remain largely unaccounted for, while
concurrent rapid technological changes, behavioural aspects, anduncertainties about input data present continuous challenges. (highconfidence) {2.1.3, 2.3, 2.5.1, 2.6, Technical Annex 2}

The Chances of Limiting Warming to 1.5°C
and the Requirements for Urgent Action
Pathways consistent with 1.5°C of warming above pre-industrial
levels can be identified under a range of assumptions about
economic growth, technology developments and lifestyles.
However, lack of global cooperation, lack of governance of the requiredenergy and land transformation, and increases in resource-intensive
consumption are key impediments to achieving 1.5°C pathways.
Governance challenges have been related to scenarios with highinequality and high population growth in the 1.5°C pathway literature.
{2.3.1, 2.3.2, 2.5}
Under emissions in line with current pledges under the Paris
Agreement (known as Nationally Determined Contributions,or NDCs), global warming is expected to surpass 1.5°C above
pre-industrial levels, even if these pledges are supplemented
with very challenging increases in the scale and ambition of
mitigation after 2030 (high confidence). This increased action
would need to achieve net zero CO2 emissions in less than 15 years.
Even if this is achieved, temperatures would only be expected to remain
below the 1.5°C threshold if the actual geophysical response ends up
being towards the low end of the currently estimated uncertainty range.
Transition challenges as well as identified trade-offs can be reduced if
global emissions peak before 2030 and marked emissions reductions
compared to today are already achieved by 2030. {2.2, 2.3.5, CrossChapter Box 11 in Chapter 4}

1

Chapter 2

Limiting warming to 1.5°C depends on greenhouse gas (GHG)
emissions over the next decades, where lower GHG emissions in
2030 lead to a higher chance of keeping peak warming to 1.5°C(high confidence). Available pathways that aim for no or limited (lessthan 0.1°C) overshoot of 1.5°C keep GHG emissions in 2030 to 25–30
GtCO2e yr−1 in 2030 (interquartile range). This contrasts with medianestimates for current unconditional NDCs of 52–58 GtCO2e yr−1 in
2030. Pathways that aim for limiting warming to 1.5°C by 2100 after
a temporary temperature overshoot rely on large-scale deploymentof carbon dioxide removal (CDR) measures, which are uncertain andentail clear risks. In model pathways with no or limited overshoot of
1.5°C, global net anthropogenic CO2 emissions decline by about 45%
from 2010 levels by 2030 (40–60% interquartile range), reaching net
zero around 2050 (2045–2055 interquartile range). For limiting global
warming to below 2°C with at least 66% probability CO2 emissions
are projected to decline by about 25% by 2030 in most pathways (10–30% interquartile range) and reach net zero around 2070 (2065–2080
interquartile range).1 {2.2, 2.3.3, 2.3.5, 2.5.3, Cross-Chapter Boxes 6 in
Chapter 3 and 9 in Chapter 4, 4.3.7}
Limiting warming to 1.5°C implies reaching net zero CO2emissions globally around 2050 and concurrent deep reductions
in emissions of non-CO2 forcers, particularly methane (highconfidence). Such mitigation pathways are characterized by energydemand reductions, decarbonization of electricity and other fuels,
electrification of energy end use, deep reductions in agriculturalemissions, and some form of CDR with carbon storage on land orsequestration in geological reservoirs. Low energy demand and low
demand for land- and GHG-intensive consumption goods facilitate
limiting warming to as close as possible to 1.5°C. {2.2.2, 2.3.1, 2.3.5,
2.5.1, Cross-Chapter Box 9 in Chapter 4}.
In comparison to a 2°C limit, the transformations required to limit
warming to 1.5°C are qualitatively similar but more pronounced
and rapid over the next decades (high confidence). 1.5°C implies
very ambitious, internationally cooperative policy environments thattransform both supply and demand (high confidence). {2.3, 2.4, 2.5}
Policies reflecting a high price on emissions are necessary
in models to achieve cost-effective 1.5°C pathways (highconfidence). Other things being equal, modelling studies suggest
the global average discounted marginal abatement costs for limiting
warming to 1.5°C being about 3–4 times higher compared to 2°C
over the 21st century, with large variations across models and socioeconomic and policy assumptions. Carbon pricing can be imposed
directly or implicitly by regulatory policies. Policy instruments, like
technology policies or performance standards, can complement explicit
carbon pricing in specific areas. {2.5.1, 2.5.2, 4.4.5}
Limiting warming to 1.5°C requires a marked shift in investment
patterns (medium confidence). Additional annual average energyrelated investments for the period 2016 to 2050 in pathways limiting
warming to 1.5°C compared to pathways without new climate policies
beyond those in place today (i.e., baseline) are estimated to be aroundKyoto-GHG emissions in this statement are aggregated with GWP-100 values of the IPCC Second Assessment Report.

95

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Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

830 billion USD2010 (range of 150 billion to 1700 billion USD2010
across six models). Total energy-related investments increase by about
12% (range of 3% to 24%) in 1.5°C pathways relative to 2°C pathways.
Average annual investment in low-carbon energy technologies andenergy efficiency are upscaled by roughly a factor of six (range of factorof 4 to 10) by 2050 compared to 2015, overtaking fossil investments
globally by around 2025 (medium confidence). Uncertainties andstrategic mitigation portfolio choices affect the magnitude and focusof required investments. {2.5.2}

Future Emissions in 1.5°C Pathways

2

Mitigation requirements can be quantified using carbon budget
approaches that relate cumulative CO2 emissions to global mean
temperature increase. Robust physical understanding underpins
this relationship, but uncertainties become increasingly relevant as a
specific temperature limit is approached. These uncertainties relate to
the transient climate response to cumulative carbon emissions (TCRE),
non-CO2 emissions, radiative forcing and response, potential additional
Earth system feedbacks (such as permafrost thawing), and historicalemissions and temperature. {2.2.2, 2.6.1}
Cumulative CO2 emissions are kept within a budget by reducing
global annual CO2 emissions to net zero. This assessment
suggests a remaining budget of about 420 GtCO2 for a twothirds chance of limiting warming to 1.5°C, and of about 580
GtCO2 for an even chance (medium confidence). The remaining
carbon budget is defined here as cumulative CO2 emissions from thestart of 2018 until the time of net zero global emissions for global
warming defined as a change in global near-surface air temperatures.
Remaining budgets applicable to 2100 would be approximately
100 GtCO2 lower than this to account for permafrost thawing andpotential methane release from wetlands in the future, and more
thereafter. These estimates come with an additional geophysicaluncertainty of at least ±400 GtCO2, related to non-CO2 response
and TCRE distribution. Uncertainties in the level of historic warming
contribute ±250 GtCO2. In addition, these estimates can vary by±250 GtCO2 depending on non-CO2 mitigation strategies as found inavailable pathways. {2.2.2, 2.6.1}

aerosol) affects carbon budgets and the certainty of pathway
categorizations. Some non-CO2 forcers are emitted alongside CO2,
particularly in the energy and transport sectors, and can be largely
addressed through CO2 mitigation. Others require specific measures,for example, to target agricultural nitrous oxide (N2O) and methane(CH4), some sources of black carbon, or hydrofluorocarbons (highconfidence). In many cases, non-CO2 emissions reductions are similar
in 2°C pathways, indicating reductions near their assumed maximum
potential by integrated assessment models. Emissions of N2O andNH3 increase in some pathways with strongly increased bioenergy
demand. {2.2.2, 2.3.1, 2.4.2, 2.5.3}

The Role of Carbon Dioxide Removal (CDR)
All analysed pathways limiting warming to 1.5°C with noor limited overshoot use CDR to some extent to neutralize
emissions from sources for which no mitigation measures
have been identified and, in most cases, also to achieve
net negative emissions to return global warming to 1.5°C
following a peak (high confidence). The longer the delay in
reducing CO2 emissions towards zero, the larger the likelihoodof exceeding 1.5°C, and the heavier the implied reliance on
net negative emissions after mid-century to return warming to
1.5°C (high confidence). The faster reduction of net CO2 emissions
in 1.5°C compared to 2°C pathways is predominantly achieved bymeasures that result in less CO2 being produced and emitted, andonly to a smaller degree through additional CDR. Limitations on
the speed, scale and societal acceptability of CDR deployment also
limit the conceivable extent of temperature overshoot. Limits to ourunderstanding of how the carbon cycle responds to net negativeemissions increase the uncertainty about the effectiveness of CDR to
decline temperatures after a peak. {2.2, 2.3, 2.6, 4.3.7}
CDR deployed at scale is unproven, and reliance on suchtechnology is a major risk in the ability to limit warming to
1.5°C. CDR is needed less in pathways with particularly strongemphasis on energy efficiency and low demand. The scale andtype of CDR deployment varies widely across 1.5°C pathways,
with different consequences for achieving sustainable
development objectives (high confidence). Some pathways rely
more on bioenergy with carbon capture and storage (BECCS), whileothers rely more on afforestation, which are the two CDR methods
most often included in integrated pathways. Trade-offs with othersustainability objectives occur predominantly through increased land,
energy, water and investment demand. Bioenergy use is substantialin 1.5°C pathways with or without BECCS due to its multiple roles indecarbonizing energy use. {2.3.1, 2.5.3, 2.6.3, 4.3.7}

Staying within a remaining carbon budget of 580 GtCO2 implies
that CO2 emissions reach carbon neutrality in about 30 years,
reduced to 20 years for a 420 GtCO2 remaining carbon budget(high confidence). The ±400 GtCO2 geophysical uncertainty range
surrounding a carbon budget translates into a variation of this timingof carbon neutrality of roughly ±15–20 years. If emissions do not start
declining in the next decade, the point of carbon neutrality would need
to be reached at least two decades earlier to remain within the samecarbon budget. {2.2.2, 2.3.5}

Properties of Energy and Land Transitions in 1.5°C Pathways

Non-CO2 emissions contribute to peak warming and thus
affect the remaining carbon budget. The evolution of
methane and sulphur dioxide emissions strongly influences
the chances of limiting warming to 1.5°C. In the near-term, aweakening of aerosol cooling would add to future warming,but can be tempered by reductions in methane emissions (highconfidence). Uncertainty in radiative forcing estimates (particularly

The share of primary energy from renewables increases while
coal usage decreases across pathways limiting warming to
1.5°C with no or limited overshoot (high confidence). By 2050,
renewables (including bioenergy, hydro, wind, and solar, with directequivalence method) supply a share of 52–67% (interquartile range)of primary energy in 1.5°C pathways with no or limited overshoot;while the share from coal decreases to 1–7% (interquartile range),96

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Developmentwith a large fraction of this coal use combined with carbon captureand storage (CCS). From 2020 to 2050 the primary energy suppliedby oil declines in most pathways (−39 to −77% interquartile range).
Natural gas changes by −13% to −62% (interquartile range), butsome pathways show a marked increase albeit with widespread
deployment of CCS. The overall deployment of CCS varies widely
across 1.5°C pathways with no or limited overshoot, with cumulative
CO2 stored through 2050 ranging from zero up to 300 GtCO2(minimum–maximum range), of which zero up to 140 GtCO2 is storedfrom biomass. Primary energy supplied by bioenergy ranges from40–310 EJ yr−1 in 2050 (minimum-maximum range), and nuclear from
3–66 EJ yr−1 (minimum–maximum range). These ranges reflect both
uncertainties in technological development and strategic mitigation
portfolio choices. {2.4.2}

Chapter 2

Links between 1.5°C Pathways and Sustainable Development
Choices about mitigation portfolios for limiting warming to
1.5°C can positively or negatively impact the achievement ofother societal objectives, such as sustainable development(high confidence). In particular, demand-side and efficiency
measures, and lifestyle choices that limit energy, resource, and
GHG-intensive food demand support sustainable development(medium confidence). Limiting warming to 1.5°C can be achieved
synergistically with poverty alleviation and improved energy security
and can provide large public health benefits through improved air
quality, preventing millions of premature deaths. However, specificmitigation measures, such as bioenergy, may result in trade-offs that
require consideration. {2.5.1, 2.5.2, 2.5.3}

2

1.5°C pathways with no or limited overshoot include a rapid
decline in the carbon intensity of electricity and an increasein electrification of energy end use (high confidence). By 2050,
the carbon intensity of electricity decreases to −92 to +11 gCO2 MJ−1
(minimum–maximum range) from about 140 gCO2 MJ−1 in 2020,and electricity covers 34–71% (minimum–maximum range) of final
energy across 1.5°C pathways with no or limited overshoot fromabout 20% in 2020. By 2050, the share of electricity supplied byrenewables increases to 59–97% (minimum-maximum range) across
1.5°C pathways with no or limited overshoot. Pathways with higher
chances of holding warming to below 1.5°C generally show a faster
decline in the carbon intensity of electricity by 2030 than pathways
that temporarily overshoot 1.5°C. {2.4.1, 2.4.2, 2.4.3}
Transitions in global and regional land use are found in all
pathways limiting global warming to 1.5°C with no or limited
overshoot, but their scale depends on the pursued mitigation
portfolio (high confidence). Pathways that limit global warming to
1.5°C with no or limited overshoot project a 4 million km2 reduction
to a 2.5 million km2 increase of non-pasture agricultural land for foodand feed crops and a 0.5–11 million km2 reduction of pasture land,
to be converted into 0-6 million km2 of agricultural land for energy
crops and a 2 million km2 reduction to 9.5 million km2 increase inforests by 2050 relative to 2010 (medium confidence). Land-use
transitions of similar magnitude can be observed in modelled 2°C
pathways (medium confidence). Such large transitions pose profoundchallenges for sustainable management of the various demands onland for human settlements, food, livestock feed, fibre, bioenergy,
carbon storage, biodiversity and other ecosystem services (highconfidence). {2.3.4, 2.4.4}

Demand-Side Mitigation and Behavioural Changes
Demand-side measures are key elements of 1.5°C pathways.
Lifestyle choices lowering energy demand and the land- and
GHG-intensity of food consumption can further support
achievement of 1.5°C pathways (high confidence). By 2030 and
2050, all end-use sectors (including building, transport, and industry)show marked energy demand reductions in modelled 1.5°C pathways,
comparable and beyond those projected in 2°C pathways. Sectoralmodels support the scale of these reductions. {2.3.4, 2.4.3, 2.5.1}

97Chapter 2

2.1

2

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Introduction to Mitigation Pathways andthe Sustainable Development Context

This chapter assesses the literature on mitigation pathways to limit or
return global mean warming to 1.5°C (relative to the pre-industrial
base period 1850–1900). Key questions addressed are: What types of
mitigation pathways have been developed that could be consistent
with 1.5°C? What changes in emissions, energy and land use do theyentail? What do they imply for climate policy and implementation, andwhat impacts do they have on sustainable development? In terms offeasibility (see Cross-Chapter Box 3 in Chapter 1), this chapter focuseson geophysical dimensions and technological and economic enabling
factors. Social and institutional dimensions as well as additional
aspects of technical feasibility are covered in Chapter 4.
Mitigation pathways are typically designed to reach a predefined
climate target alone. Minimization of mitigation expenditures, but
not climate-related damages or sustainable development impacts,
is often the basis for these pathways to the desired climate target(see Cross-Chapter Box 5 in this chapter for additional discussion).
However, there are interactions between mitigation and multiple othersustainable development goals (see Sections 1.1 and 5.4) that provide
both challenges and opportunities for climate action. Hence there aresubstantial efforts to evaluate the effects of the various mitigation
pathways on sustainable development, focusing in particular on
aspects for which integrated assessment models (IAMs) provide
relevant information (e.g., land-use changes and biodiversity, foodsecurity, and air quality). More broadly, there are efforts to incorporate
climate change mitigation as one of multiple objectives that, in general,reflect societal concerns more completely and could potentially provide
benefits at lower costs than simultaneous single-objective policies(e.g., Clarke et al., 2014). For example, with carefully selected policies,
universal energy access can be achieved while simultaneously reducing
air pollution and mitigating climate change (McCollum et al., 2011;
Riahi et al., 2012; IEA, 2017d). This chapter thus presents both thepathways and an initial discussion of their context within sustainable
development objectives (Section 2.5), with the latter, along with equity
and ethical issues, discussed in more detail in Chapter 5.
As described in Cross-Chapter Box 1 in Chapter 1, scenarios arecomprehensive, plausible, integrated descriptions of possible futures
based on specified, internally consistent underlying assumptions,with pathways often used to describe the clear temporal evolution of
specific scenario aspects or goal-oriented scenarios. We include boththese usages of ‘pathways’ here.

2.1.1

Mitigation Pathways Consistent with 1.5°C

Emissions scenarios need to cover all sectors and regions over the
21st century to be associated with a climate change projection out to
2100. Assumptions regarding future trends in population, consumptionof goods and services (including food), economic growth, behaviour,
technology, policies and institutions are all required to generate2

98

scenarios (Section 2.3.1). These societal choices must then be linked
to the drivers of climate change, including emissions of well-mixed
greenhouse gases and aerosol and ozone precursors as well as landuse and land-cover changes. Deliberate solar radiation modification is
not included in these scenarios (see Cross-Chapter Box 10 in Chapter 4).
Plausible developments need to be anticipated in many facets of the
key sectors of energy and land use. Within energy, these scenarios
consider energy resources like biofuels, energy supply and conversion
technologies, energy consumption, and supply and end-use efficiency.
Within land use, agricultural productivity, food demand, terrestrial
carbon management, and biofuel production are all considered.
Climate policies are also considered, including carbon pricing andtechnology policies such as research and development funding andsubsidies. The scenarios incorporate regional differentiation in sectoraland policy development. The climate changes resulting from such
scenarios are derived using models that typically incorporate physicalunderstanding of the carbon cycle and climate response derived fromcomplex geophysical models evaluated against observations (Sections
2.2 and 2.6).
The temperature response to a given emission pathway (see glossary) is
uncertain and therefore quantified in terms of a probabilistic outcome.
Chapter 1 assesses the climate objectives of the Paris Agreement interms of human-induced warming, thus excluding potential impactsof natural forcing such as volcanic eruptions or solar output changes
or unforced internal variability. Temperature responses in this chapter
are assessed using simple geophysically based models that evaluate
the anthropogenic component of future temperature change and do
not incorporate internal natural variations and are thus fit for purposein the context of this assessment (Section 2.2.1). Hence a scenariothat is consistent with 1.5°C may in fact lead to either a higher orlower temperature change, but within quantified and generally wellunderstood bounds (see also Chapter 1, Section 1.2.3). Consistency
with avoiding a human-induced temperature change limit must
therefore also be defined probabilistically, with likelihood values
selected based on risk-avoidance preferences. Responses beyond
global mean temperature are not typically evaluated in such models
and are assessed in Chapter 3.

2.1.2

The Use of Scenarios

Variations in scenario assumptions and design define to a large
degree which questions can be addressed with a specific scenarioset, for example, the exploration of implications of delayed climate
mitigation action. In this assessment, the following classes of 1.5°Cand 2°C-consistent scenarios are of particular interest to the topics
addressed in this chapter: (i) scenarios with the same climate target
over the 21st century but varying socio-economic assumptions(Sections 2.3 and 2.4), (ii) pairs of scenarios with similar socioeconomic assumptions but with forcing targets aimed at 1.5°C and 2°C(Section 2.3), and (iii) scenarios that follow the Nationally Determined
Contributions or NDCs2 until 2030 with much more stringent mitigation
action thereafter (Section 2.3.5).

Current pledges include those from the United States although they have stated their intention to withdraw in the future.

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Characteristics of these pathways, such as emissions reduction rates,
time of peaking, and low-carbon energy deployment rates, can beassessed as being consistent with 1.5°C. However, they cannot be
assessed as ‘requirements’ for 1.5°C, unless a targeted analysis
is available that specifically asked whether there could be other
1.5°C-consistent pathways without the characteristics in question. AR5
already assessed such targeted analyses, for example, asking which
technologies are important in order to keep open the possibility oflimiting warming to 2°C (Clarke et al., 2014). By now, several suchtargeted analyses are also available for questions related to 1.5°C(Luderer et al., 2013; Rogelj et al., 2013b; Bauer et al., 2018; Strefler
et al., 2018b; van Vuuren et al., 2018). This assessment distinguishes
between ‘consistent’ and the much stronger concept of required
characteristics of 1.5°C pathways wherever possible.
Ultimately, society will adjust the choices it makes as new information
becomes available and technical learning progresses, and theseadjustments can be in either direction. Earlier scenario studies haveshown, however, that deeper emissions reductions in the near term
hedge against the uncertainty of both climate response and futuretechnology availability (Luderer et al., 2013; Rogelj et al., 2013b; Clarke
et al., 2014). Not knowing what adaptations might be put in place inthe future, and due to limited studies, this chapter examines prospective
rather than iteratively adaptive mitigation pathways (Cross-Chapter
Box 1 in Chapter 1). Societal choices illustrated by scenarios may also
influence what futures are envisioned as possible or desirable andhence whether those come into being (Beck and Mahony, 2017).

2.1.3

New Scenario Information since AR5

In this chapter, we extend the AR5 mitigation pathway assessment
based on new scenario literature. Updates in understanding ofclimate sensitivity, transient climate response, radiative forcing, andthe cumulative carbon budget consistent with 1.5°C are discussed in
Sections 2.2.
Mitigation pathways developed with detailed process-based
integrated assessment models (IAMs) covering all sectors and regions
over the 21st century describe an internally consistent and calibrated(to historical trends) way to get from current developments tomeeting long-term climate targets like 1.5°C (Clarke et al., 2014). The
overwhelming majority of available 1.5°C pathways were generated
by such IAMs, and these pathways can be directly linked to climate
outcomes and their consistency with the 1.5°C goal evaluated. The
AR5 similarly relied upon such studies, which were mainly discussed in
Chapter 6 of Working Group III (WGIII) (Clarke et al., 2014).
Since the AR5, several new, integrated multimodel studies haveappeared in the literature that explore specific characteristics ofscenarios more stringent than the lowest scenario category assessedin AR5 that was assessed to limit warming below 2°C with greaterthan 66% likelihood (Rogelj et al., 2015b, 2018; Akimoto et al., 2017;
Marcucci et al., 2017; Su et al., 2017; Bauer et al., 2018; Bertram etal., 2018; Grubler et al., 2018; Holz et al., 2018b; Kriegler et al., 2018a;
Liu et al., 2018; Luderer et al., 2018; Strefler et al., 2018a; van Vuuren
et al., 2018; Vrontisi et al., 2018; Zhang et al., 2018). Those scenarios
explore 1.5°C-consistent pathways from multiple perspectives

Chapter 2(see Supplementary Material 2.SM.1.3), examining sensitivity toassumptions regarding:• socio-economic drivers and developments including energy andfood demand as, for example, characterized by the Shared SocioEconomic Pathways (SSPs; Cross-Chapter Box 1 in Chapter 1)• near-term climate policies describing different levels of strengthening
the NDCs• the use of bioenergy and the availability and desirability of carbon
dioxide removal (CDR) technologies
A large number of these scenarios were collected in a scenario databaseestablished for the assessment of this Special Report (Supplementary
Material 2.SM.1.3). Mitigation pathways were classified by four
factors: consistency with a temperature increase limit (as defined by
Chapter 1), whether they temporarily overshoot that limit, the extent
of this potential overshoot, and the likelihood of falling within thesebounds.
Specifically, they were put into classes that either kept surface
temperature increases below a given threshold throughout the 21st
century or returned to a value below 1.5°C above pre-industrial levels
at some point before 2100 after temporarily exceeding that level earlier– referred to as an overshoot (OS). Both groups were further separated
based on the probability of being below the threshold and the degreeof overshoot, respectively (Table 2.1). Pathways are uniquely classified,with 1.5°C-related classes given higher priority than 2°C classes incases where a pathway would be applicable to either class.
The probability assessment used in the scenario classification is basedon simulations using two reduced-complexity carbon cycle, atmosphericcomposition, and climate models: the ‘Model for the Assessment ofGreenhouse Gas-Induced Climate Change’ (MAGICC) (Meinshausen
et al., 2011a), and the ‘Finite Amplitude Impulse Response’ (FAIRv1.3)
model (Smith et al., 2018). For the purpose of this report, and to facilitatecomparison with AR5, the range of the key carbon cycle and climateparameters for MAGICC and its setup are identical to those used in
AR5 WGIII (Clarke et al., 2014). For each mitigation pathway, MAGICC
and FAIR simulations provide probabilistic estimates of atmosphericconcentrations, radiative forcing and global temperature outcomes until
2100. However, the classification uses MAGICC probabilities directly fortraceability with AR5 and because this model is more established in theliterature. Nevertheless, the overall uncertainty assessment is based on
results from both models, which are considered in the context of thelatest radiative forcing estimates and observed temperatures (Etminan
et al., 2016; Smith et al., 2018) (Section 2.2 and Supplementary Material
2.SM.1.1). The comparison of these lines of evidence shows highagreement in the relative temperature response of pathways, withmedium agreement on the precise absolute magnitude of warming,
introducing a level of imprecision in these attributes. Consideration of
the combined evidence here leads to medium confidence in the overall
geophysical characteristics of the pathways reported here.
In addition to the characteristics of the above-mentioned classes,
four illustrative pathway archetypes have been selected and are usedthroughout this chapter to highlight specific features of and variations
across 1.5°C pathways. These are chosen in particular to illustrate thespectrum of CO2 emissions reduction patterns consistent with 1.5°C,
992

Chapter 2

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Table 2.1 | Classification of pathways that this chapter draws upon, along with the number of available pathways in each class. The definition of each classis based on probabilities derived from the MAGICC model in a setup identical to AR5 WGIII (Clarke et al., 2014), as detailed in Supplementary Material 2.SM.1.4.
Pathway group

1.5°C or
1.5°C-consistent**

22°C or2°C-consistent

Pathway Class

Pathway Selection Criteria and Description

Number of
Scenarios

Below-1.5°C

Pathways limiting peak warming to below 1.5°C during the entire 21st century
with 50–66% likelihood*9

1.5°C-low-OS

Pathways limiting median warming to below 1.5°C in 2100 and with a
50–67% probability of temporarily overshooting that level earlier, generally
implying less than 0.1°C higher peak warming than Below-1.5°C pathways

44

1.5°C-high-OS

Pathways limiting median warming to below 1.5°C in 2100 and with a greaterthan 67% probability of temporarily overshooting that level earlier, generally
implying 0.1–0.4°C higher peak warming than Below-1.5°C pathways

37

Lower-2°C

Pathways limiting peak warming to below 2°C during the entire 21st century
with greater than 66% likelihood

74

Higher-2°C

Pathways assessed to keep peak warming to below 2°C during the entire
21st century with 50–66% likelihood

58

Number of
Scenarios

90132* No pathways were available that achieve a greater than 66% probability of limiting warming below 1.5°C during the entire 21st century based on the MAGICC model projections.
** This chapter uses the term 1.5°C-consistent pathways to refer to pathways with no overshoot, with limited (low) overshoot, and with high overshoot. However, the Summary for Policymakers
		 focusses on pathways with no or limited (low) overshoot.

ranging from very rapid and deep near-term decreases, facilitatedby efficiency and demand-side measures that lead to limited CDR
requirements, to relatively slower but still rapid emissions reductions
that lead to a temperature overshoot and necessitate large CDR
deployment later in the century (Section 2.3).

2.1.4

Utility of Integrated Assessment Models(IAMs) in the Context of this Report

IAMs lie at the basis of the assessment of mitigation pathways in thischapter, as much of the quantitative global scenario literature is derived
with such models. IAMs combine insights from various disciplines in asingle framework, resulting in a dynamic description of the coupledenergy–economy–land-climate system that cover the largest sourcesof anthropogenic greenhouse gas (GHG) emissions from different
sectors. Many of the IAMs that contributed mitigation scenarios to thisassessment include a process-based description of the land system in
addition to the energy system (e.g., Popp et al., 2017), and several havebeen extended to cover air pollutants (Rao et al., 2017) and water use(Hejazi et al., 2014; Fricko et al., 2016; Mouratiadou et al., 2016). Suchintegrated pathways hence allow the exploration of the whole-systemtransformation, as well as the interactions, synergies, and tradeoffs between sectors, and, increasingly, questions beyond climate
mitigation (von Stechow et al., 2015). The models do not, however, fully
account for all constraints that could affect realization of pathways(see Chapter 4).
Section 2.3 assesses the overall characteristics of 1.5°C pathways
based on fully integrated pathways, while Sections 2.4 and 2.5 describe
underlying sectoral transformations, including insights from sectorspecific assessment models and pathways that are not derived from
IAMs. Such models provide detail in their domain of application andmake exogenous assumptions about cross-sectoral or global factors.
They often focus on a specific sector, such as the energy (Bruckner etal., 2014; IEA, 2017a; Jacobson, 2017; OECD/IEA and IRENA, 2017),
buildings (Lucon et al., 2014) or transport (Sims et al., 2014) sector, or100

a specific country or region (Giannakidis et al., 2018). Sector-specificpathways are assessed in relation to integrated pathways because theycannot be directly linked to 1.5°C by themselves if they do not extend
to 2100 or do not include all GHGs or aerosols from all sectors.
AR5 found sectoral 2°C decarbonization strategies from IAMs to beconsistent with sector-specific studies (Clarke et al., 2014). A growing
body of literature on 100%-renewable energy scenarios has emerged(e.g., see Creutzig et al., 2017; Jacobson et al., 2017), which goesbeyond the wide range of IAM projections of renewable energy sharesin 1.5°C and 2°C pathways. While the representation of renewableenergy resource potentials, technology costs and system integration in
IAMs has been updated since AR5, leading to higher renewable energy
deployments in many cases (Luderer et al., 2017; Pietzcker et al., 2017),
none of the IAM projections identify 100% renewable energy solutions
for the global energy system as part of cost-effective mitigation
pathways (Section 2.4.2). Bottom-up studies find higher mitigation
potentials in the industry, buildings, and transport sectors in 2030 thanrealized in selected 2°C pathways from IAMs (UNEP 2017), indicating
the possibility to strengthen sectoral decarbonization strategies until
2030 beyond the integrated 1.5°C pathways assessed in this chapter(Luderer et al., 2018).
Detailed, process-based IAMs are a diverse set of models ranging
from partial equilibrium energy–land models to computable generalequilibrium models of the global economy, from myopic to perfectforesight models, and from models with to models without endogenous
technological change (Supplementary Material 2.SM.1.2). The IAMs
used in this chapter have limited to no coverage of climate impacts.
They typically use GHG pricing mechanisms to induce emissions
reductions and associated changes in energy and land uses consistent
with the imposed climate goal. The scenarios generated by thesemodels are defined by the choice of climate goals and assumptions
about near-term climate policy developments. They are also shapedby assumptions about mitigation potentials and technologies as wellas baseline developments such as, for example, those represented byMitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

different Shared Socio-Economic Pathways (SSPs), especially thosepertaining to energy and food demand (Riahi et al., 2017). See Section
2.3.1 for discussion of these assumptions. Since the AR5, the scenario
literature has greatly expanded the exploration of these dimensions.
This includes low-demand scenarios (Grubler et al., 2018; van Vuuren
et al., 2018), scenarios taking into account a larger set of sustainable
development goals (Bertram et al., 2018), scenarios with restrictedavailability of CDR technologies (Bauer et al., 2018; Grubler et al., 2018;
Holz et al., 2018b; Kriegler et al., 2018a; Strefler et al., 2018b; van Vuuren
et al., 2018), scenarios with near-term action dominated by regulatory
policies (Kriegler et al., 2018a) and scenario variations across the
SSPs (Riahi et al., 2017; Rogelj et al., 2018). IAM results depend upon
multiple underlying assumptions, for example, the extent to which
global markets and economies are assumed to operate frictionlessand policies are cost-optimized, assumptions about technologicalprogress and availability and costs of mitigation and CDR measures,
assumptions about underlying socio-economic developments andfuture energy, food and materials demand, and assumptions about
the geographic and temporal pattern of future regulatory and carbon
pricing policies (see Supplementary Material 2.SM.1.2 for additional
discussion on IAMs and their limitations).

2.2

Geophysical Relationships and Constraints

Emissions pathways can be characterized by various geophysicalcharacteristics, such as radiative forcing (Masui et al., 2011; Riahi etal., 2011; Thomson et al., 2011; van Vuuren et al., 2011b), atmosphericconcentrations (van Vuuren et al., 2007, 2011a; Clarke et al., 2014) or
associated temperature outcomes (Meinshausen et al., 2009; Rogelj
et al., 2011; Luderer et al., 2013). These attributes can be used toderive geophysical relationships for specific pathway classes, such as
cumulative CO2 emissions compatible with a specific level of warming,
also known as ‘carbon budgets’ (Meinshausen et al., 2009; Rogelj et al.,2011; Stocker et al., 2013; Friedlingstein et al., 2014a), the consistentcontributions of non-CO2 GHGs and aerosols to the remaining carbon
budget (Bowerman et al., 2011; Rogelj et al., 2015a, 2016b), or totemperature outcomes (Lamarque et al., 2011; Bowerman et al., 2013;
Rogelj et al., 2014b). This section assesses geophysical relationships forboth CO2 and non-CO2 emissions (see glossary).

2.2.1

Geophysical Characteristics of Mitigation Pathways

This section employs the pathway classification introduced in Section
2.1, with geophysical characteristics derived from simulations with
the MAGICC reduced-complexity carbon cycle and climate model andsupported by simulations with the FAIR reduced-complexity model(Section 2.1). Within a specific category and between models, there
remains a large degree of variance. Most pathways exhibit a temperature
overshoot which has been highlighted in several studies focusing on
stringent mitigation pathways (Huntingford and Lowe, 2007; Wigley
et al., 2007; Nohara et al., 2015; Rogelj et al., 2015d; Zickfeld andHerrington, 2015; Schleussner et al., 2016; Xu and Ramanathan,2017). Only very few of the scenarios collected in the database forthis report hold the average future warming projected by MAGICC
below 1.5°C during the entire 21st century (Table 2.1, Figure 2.1). Most

Chapter 2

1.5°C-consistent pathways available in the database overshoot 1.5°C
around mid-century before peaking and then reducing temperatures
so as to return below that level in 2100. However, because ofnumerous geophysical uncertainties and model dependencies (Section
2.2.1.1, Supplementary Material 2.SM.1.1), absolute temperature
characteristics of the various pathway categories are more difficult todistinguish than relative features (Figure 2.1, Supplementary Material
2.SM.1.1), and actual probabilities of overshoot are imprecise. However,
all lines of evidence available for temperature projections indicate aprobability greater than 50% of overshooting 1.5°C by mid-century inall but the most stringent pathways currently available (Supplementary
Material 2.SM.1.1, 2.SM.1.4).
Most 1.5°C-consistent pathways exhibit a peak in temperature by midcentury whereas 2°C-consistent pathways generally peak after 2050
(Supplementary Material 2.SM.1.4). The peak in median temperaturein the various pathway categories occurs about ten years before
reaching net zero CO2 emissions due to strongly reduced annual
CO2 emissions and deep reductions in CH4 emissions (Section 2.3.3).
The two reduced-complexity climate models used in this assessment
suggest that virtually all available 1.5°C-consistent pathways peak
and then decline global mean temperature, but with varying rates
of temperature decline after the peak (Figure 2.1). The estimateddecadal rates of temperature change by the end of the century aresmaller than the amplitude of the climate variability as assessed in AR5(1 standard deviation of about ±0.1°C), which hence complicates thedetection of a global peak and decline of warming in observations ontime scales of one to two decades (Bindoff et al., 2013). In comparison,many pathways limiting warming to 2°C or higher by 2100 still havenoticeable increasing trends at the end of the century, and thus imply
continued warming.
By 2100, the difference between 1.5°C- and 2°C-consistent pathways
becomes clearer compared to mid-century, not only for the temperature
response (Figure 2.1) but also for atmospheric CO2 concentrations. In
2100, the median CO2 concentration in 1.5°C-consistent pathways is
below 2016 levels (Le Quéré et al., 2018), whereas it remains higher
by about 5–10% compared to 2016 in the 2°C-consistent pathways.

2.2.1.1 Geophysical uncertainties: non-CO2 forcing agents
Impacts of non-CO2 climate forcers on temperature outcomes are
particularly important when evaluating stringent mitigation pathways(Weyant et al., 2006; Shindell et al., 2012; Rogelj et al., 2014b, 2015a;
Samset et al., 2018). However, many uncertainties affect the role ofnon-CO2 climate forcers in stringent mitigation pathways.
A first uncertainty arises from the magnitude of the radiative forcing
attributed to non-CO2 climate forcers. Figure 2.2 illustrates how, for
one representative 1.5°C-consistent pathway (SSP2-1.9) (Fricko et al.,2017; Rogelj et al., 2018), the effective radiative forcings as estimatedby MAGICC and FAIR can differ (see Supplementary Material 2.SM1.1
for further details). This large spread in non-CO2 effective radiative
forcings leads to considerable uncertainty in the predicted temperature
response. This uncertainty ultimately affects the assessed temperature
outcomes for pathway classes used in this chapter (Section 2.1) andalso affects the carbon budget (Section 2.2.2). Figure 2.2 highlights
1012Chapter 2

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

2

Figure 2.1 | Pathways classification overview. (a) Average global mean temperature increase relative to 2010 as projected by FAIR and MAGICC in 2030, 2050 and
2100; (b) response of peak warming to cumulative CO2 emissions until net zero by MAGICC (red) and FAIR (blue); (c) decadal rate of average global mean temperature change
from 2081 to 2100 as a function of the annual CO2 emissions averaged over the same period as given by FAIR (transparent squares) and MAGICC (filled circles). In panel (a),horizontal lines at 0.63°C and 1.13°C are indicative of the 1.5°C and 2°C warming thresholds with the respect to 1850–1900, taking into account the assessed historicalwarming of 0.87°C ±0.12°C between the 1850–1900 and 2006–2015 periods (Chapter 1, Section 1.2.1). In panel (a), vertical lines illustrate both the physical and the scenario
uncertainty as captured by MAGICC and FAIR and show the minimal warming of the 5th percentile of projected warming and the maximal warming of the 95th percentile ofprojected warming per scenario class. Boxes show the interquartile range of mean warming across scenarios, and thus represent scenario uncertainty only.

the important role of methane emissions reduction in this scenario, inagreement with the recent literature focussing on stringent mitigation
pathways (Shindell et al., 2012; Rogelj et al., 2014b, 2015a; Stohl et al.,2015; Collins et al., 2018).
For mitigation pathways that aim at halting and reversing radiative
forcing increase during this century, the aerosol radiative forcing is aconsiderable source of uncertainty (Figure 2.2) (Samset et al., 2018;
Smith et al., 2018). Indeed, reductions in SO2 (and NOx) emissions
largely associated with fossil-fuel burning are expected to reduce thecooling effects of both aerosol radiative interactions and aerosol cloud
102

interactions, leading to warming (Myhre et al., 2013; Samset et al.,2018). A multimodel analysis (Myhre et al., 2017) and a study basedon observational constraints (Malavelle et al., 2017) largely support
the AR5 best estimate and uncertainty range of aerosol forcing.
The partitioning of total aerosol radiative forcing between aerosol
precursor emissions is important (Ghan et al., 2013; Jones et al.,2018; Smith et al., 2018) as this affects the estimate of the mitigation
potential from different sectors that have aerosol precursor emission
sources. The total aerosol effective radiative forcing change in stringent
mitigation pathways is expected to be dominated by the effects fromthe phase-out of SO2, although the magnitude of this aerosol-warmingMitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Chapter 2

effect depends on how much of the present-day aerosol cooling is
attributable to SO2, particularly the cooling associated with aerosol–
cloud interaction (Figure 2.2). Regional differences in the linearity ofaerosol–cloud interactions (Carslaw et al., 2013; Kretzschmar et al.,2017) make it difficult to separate the role of individual precursors.
Precursors that are not fully mitigated will continue to affect the
Earth system. If, for example, the role of nitrate aerosol cooling is at
the strongest end of the assessed IPCC AR5 uncertainty range, futuretemperature increases may be more modest if ammonia emissions
continue to rise (Hauglustaine et al., 2014).
Figure 2.2 shows that there are substantial differences in the evolutionof estimated effective radiative forcing of non-CO2 forcers between
MAGICC and FAIR. These forcing differences result in MAGICC
simulating a larger warming trend in the near term compared to both
the FAIR model and the recent observed trends of 0.2°C per decade
reported in Chapter 1 (Figure 2.1, Supplementary Material 2.SM.1.1,
Chapter 1, Section 1.2.1.3). The aerosol effective forcing is stronger in
MAGICC compared to either FAIR or the AR5 best estimate, though it
is still well within the AR5 uncertainty range (Supplementary Material
2.SM.1.1.1). A recent revision (Etminan et al., 2016) increases themethane forcing by 25%. This revision is used in the FAIR but not in theAR5 setup of MAGICC that is applied here. Other structural differences
exist in how the two models relate emissions to concentrations that
contribute to differences in forcing (see Supplementary Material
2.SM.1.1.1).
Non-CO2 climate forcers exhibit a greater geographical variation inradiative forcings than CO2, which leads to important uncertainties in thetemperature response (Myhre et al., 2013). This uncertainty increases
the relative uncertainty of the temperature pathways associated withlow emission scenarios compared to high emission scenarios (Clarke
et al., 2014). It is also important to note that geographical patternsof temperature change and other climate responses, especially thoserelated to precipitation, depend significantly on the forcing mechanism(Myhre et al., 2013; Shindell et al., 2015; Marvel et al., 2016; Samset etal., 2016) (see also Chapter 3, Section 3.6.2.2).

2.2.1.2 Geophysical uncertainties: climate and Earth system
feedbacks
Climate sensitivity uncertainty impacts future projections as well ascarbon-budget estimates (Schneider et al., 2017). AR5 assessed theequilibrium climate sensitivity (ECS) to be likely in the 1.5°–4.5°C
range, extremely unlikely less than 1°C and very unlikely greaterthan 6°C. The lower bound of this estimate is lower than the rangeof CMIP5 models (Collins et al., 2013). The evidence for the 1.5°C
lower bound on ECS in AR5 was based on analysis of energy-budgetchanges over the historical period. Work since AR5 has suggested
that the climate sensitivity inferred from such changes has been
lower than the 2 × CO2 climate sensitivity for known reasons (Forster,
2016; Gregory and Andrews, 2016; Rugenstein et al., 2016; Armour,2017; Ceppi and Gregory, 2017; Knutti et al., 2017; Proistosescu andHuybers, 2017). Both a revised interpretation of historical estimatesand other lines of evidence based on analysis of climate models withthe best representation of today’s climate (Sherwood et al., 2014;
Zhai et al., 2015; Tan et al., 2016; Brown and Caldeira, 2017; Knutti

2

Figure 2.2 | Changes and uncertainties in effective radiative forcings (ERF)
for one 1.5°C-consistent pathway (SSP2-19) as estimated by MAGICC
and FAIR. The lines are indicative of the total effective radiative forcing from allanthropogenic sources (solid lines) and for non-CO2 agents only (dashed lines), as
represented by MAGICC (red) and FAIR (blue) relative to 2010, respectively. Verticalbars show the mean radiative forcing as predicted by MAGICC and FAIR of relevant
non-CO2 agents for year 2030, 2050 and 2100. The vertical lines give the uncertainty(1 standard deviation) of the ERFs for the represented species.

et al., 2017) suggest that the lower bound of ECS could be revised
upwards, which would decrease the chances of limiting warming
below 1.5°C in assessed pathways. However, such a reassessment has
been challenged (Lewis and Curry, 2018), albeit from a single line ofevidence. Nevertheless, it is premature to make a major revision to thelower bound. The evidence for a possible revision of the upper boundon ECS is less clear, with cases argued from different lines of evidence
for both decreasing (Lewis and Curry, 2015, 2018; Cox et al., 2018)
and increasing (Brown and Caldeira, 2017) the bound presented in theliterature. The tools used in this chapter employ ECS ranges consistent
with the AR5 assessment. The MAGICC ECS distribution has not been
selected to explicitly reflect this but is nevertheless consistent (Rogelj
et al., 2014a). The FAIR model used here to estimate carbon budgets
explicitly constructs log-normal distributions of ECS and transient
climate response based on a multi-parameter fit to the AR5 assessedranges of climate sensitivity and individual historic effective radiative
forcings (Smith et al., 2018) (Supplementary Material 2.SM.1.1.1).
Several feedbacks of the Earth system, involving the carbon cycle, nonCO2 GHGs and/or aerosols, may also impact the future dynamics of thecoupled carbon–climate system’s response to anthropogenic emissions.
These feedbacks are caused by the effects of nutrient limitation (Duce etal., 2008; Mahowald et al., 2017), ozone exposure (de Vries et al., 2017),
fire emissions (Narayan et al., 2007) and changes associated withnatural aerosols (Cadule et al., 2009; Scott et al., 2018). Among these
Earth system feedbacks, the importance of the permafrost feedback’s
influence has been highlighted in recent studies. Combined evidence103Chapter 2

2

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Developmentfrom both models (MacDougall et al., 2015; Burke et al., 2017; Loweand Bernie, 2018) and field studies (like Schädel et al., 2014; Schuur etal., 2015) shows high agreement that permafrost thawing will release
both CO2 and CH4 as the Earth warms, amplifying global warming. Thisthawing could also release N2O (Voigt et al., 2017a, b). Field, laboratory
and modelling studies estimate that the vulnerable fraction inpermafrost is about 5–15% of the permafrost soil carbon (~5300–5600
GtCO2 in Schuur et al., 2015) and that carbon emissions are expected tooccur beyond 2100 because of system inertia and the large proportionof slowly decomposing carbon in permafrost (Schädel et al., 2014).
Published model studies suggest that a large part of the carbon releaseto the atmosphere is in the form of CO2 (Schädel et al., 2016), while theamount of CH4 released by permafrost thawing is estimated to be muchsmaller than that CO2. Cumulative CH4 release by 2100 under RCP2.6
ranges from 0.13 to 0.45 Gt of methane (Burke et al., 2012; Schneider
von Deimling et al., 2012, 2015), with fluxes being the highest in the
middle of the century because of maximum thermokarst lake extent by
mid-century (Schneider von Deimling et al., 2015).
The reduced complexity climate models employed in this assessment
do not take into account permafrost or non-CO2 Earth system
feedbacks, although the MAGICC model has a permafrost module thatcan be enabled. Taking the current climate and Earth system feedbacks
understanding together, there is a possibility that these models
would underestimate the longer-term future temperature response tostringent emission pathways (Section 2.2.2).

2.2.2

The Remaining 1.5°C Carbon Budget

Matthews et al., 2009; Zickfeld et al., 2009; IPCC, 2013a). However,
because the projected changes in non-CO2 climate forcers tend to
amplify future warming, CO2-only carbon budgets overestimate thetotal net cumulative carbon emissions compatible with 1.5°C or 2°C(Friedlingstein et al., 2014a; Rogelj et al., 2016b; Matthews et al., 2017;
Mengis et al., 2018; Tokarska et al., 2018).
Since the AR5, many estimates of the remaining carbon budget for
1.5°C have been published (Friedlingstein et al., 2014a; MacDougall
et al., 2015; Peters, 2016; Rogelj et al., 2016b, 2018; Matthews et al.,2017; Millar et al., 2017; Goodwin et al., 2018b; Kriegler et al., 2018b;
Lowe and Bernie, 2018; Mengis et al., 2018; Millar and Friedlingstein,
2018; Schurer et al., 2018; Séférian et al., 2018; Tokarska and Gillett,2018; Tokarska et al., 2018). These estimates cover a wide range as aresult of differences in the models used, and of methodological choices,
as well as physical uncertainties. Some estimates are exclusively modelbased while others are based on observations or on a combination of
both. Remaining carbon budgets limiting warming below 1.5°C or 2°C
that are derived from Earth system models of intermediate complexity(MacDougall et al., 2015; Goodwin et al., 2018a), IAMs (Luderer et al.,2018; Rogelj et al., 2018), or are based on Earth-system model results(Lowe and Bernie, 2018; Séférian et al., 2018; Tokarska and Gillett,2018) give remaining carbon budgets of the same order of magnitude
as the IPCC AR5 Synthesis Report (SYR) estimates (IPCC, 2014a).
This is unsurprising as similar sets of models were used for the AR5(IPCC, 2013b). The range of variation across models stems mainly fromeither the inclusion or exclusion of specific Earth system feedbacks(MacDougall et al., 2015; Burke et al., 2017; Lowe and Bernie, 2018) ordifferent budget definitions (Rogelj et al., 2018).

2.2.2.1 Carbon budget estimates
Since the AR5, several approaches have been proposed to estimate
carbon budgets compatible with 1.5°C or 2°C. Most of theseapproaches indirectly rely on the approximate linear relationshipbetween peak global mean temperature and cumulative emissionsof carbon (the transient climate response to cumulative emissions ofcarbon, TCRE) (Collins et al., 2013; Friedlingstein et al., 2014a; Rogelj etal., 2016b), whereas others base their estimates on equilibrium climate
sensitivity (Schneider et al., 2017). The AR5 employed two approaches
to determine carbon budgets. Working Group I (WGI) computed
carbon budgets from 2011 onwards for various levels of warming
relative to the 1861–1880 period using RCP8.5 (Meinshausen et al.,2011b; Stocker et al., 2013), whereas WGIII estimated their budgets
from a set of available pathways that were assessed to have a >50%
probability to exceed 1.5°C by mid-century, and return to 1.5°C or
below in 2100 with greater than 66% probability (Clarke et al., 2014).
These differences made AR5 WGI and WGIII carbon budgets difficult tocompare as they are calculated over different time periods, are derived
from a different sets of multi-gas and aerosol emission scenarios,
and use different concepts of carbon budgets (exceedance for WGI,
avoidance for WGIII) (Rogelj et al., 2016b; Matthews et al., 2017).
Carbon budgets can be derived from CO2-only experiments as well
as from multi-gas and aerosol scenarios. Some published estimatesof carbon budgets compatible with 1.5°C or 2°C refer to budgets
for CO2-induced warming only, and hence do not take into accountthe contribution of non-CO2 climate forcers (Allen et al., 2009;104

In contrast to the model-only estimates discussed above and employedin the AR5, this report additionally uses observations to inform its
evaluation of the remaining carbon budget. Table 2.2 shows that theassessed range of remaining carbon budgets consistent with 1.5°C
or 2°C is larger than the AR5 SYR estimate and is part way towards
estimates constrained by recent observations (Millar et al., 2017;
Goodwin et al., 2018a; Tokarska and Gillett, 2018). Figure 2.3 illustrates
that the change since AR5 is, in very large part, due to the applicationof a more recent observed baseline to the historic temperature change
and cumulative emissions; here adopting the baseline period of 2006–
2015 (see Chapter 1, Section 1.2.1). AR5 SYR Figures SPM.10 and 2.3
already illustrated the discrepancy between models and observations,but did not apply this as a correction to the carbon budget because they
were being used to illustrate the overall linear relationship between
warming and cumulative carbon emissions in the CMIP5 models since
1870, and were not specifically designed to quantify residual carbon
budgets relative to the present for ambitious temperature goals. The
AR5 SYR estimate was also dependent on a subset of Earth systemmodels illustrated in Figure 2.3 of this report. Although, as outlined
below and in Table 2.2, considerably uncertainties remain, there is highagreement across various lines of evidence assessed in this report thatthe remaining carbon budget for 1.5°C or 2°C would be larger thanthe estimates at the time of the AR5. However, the overall remaining
budget for 2100 is assessed to be smaller than that derived from therecent observational-informed estimates, as Earth system feedbacks
such as permafrost thawing reduce the budget applicable to centennial
scales (see Section 2.2.2.2).

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Chapter 22

Figure 2.3 | Temperature changes from 1850–1900 versus cumulative CO2 emissions since 1st January 1876. Solid lines with dots reproduce the globally
averaged near-surface air temperature response to cumulative CO2 emissions plus non-CO2 forcers as assessed in Figure SPM10 of WGI AR5, except that points marked withyears relate to a particular year, unlike in WGI AR5 Figure SPM.10, where each point relates to the mean over the previous decade. The AR5 data was derived from 15 Earthsystem models and 5 Earth system models of Intermediate Complexity for the historic observations (black) and RCP8.5 scenario (red), and the red shaded plume shows therange across the models as presented in the AR5. The purple shaded plume and the line are indicative of the temperature response to cumulative CO2 emissions and non-CO2
warming adopted in this report. The non-CO2 warming contribution is averaged from the MAGICC and FAIR models, and the purple shaded range assumes the AR5 WGI TCRE
distribution (Supplementary Material 2.SM.1.1.2). The 2010 observation of surface temperature change (0.97°C based on 2006–2015 mean compared to 1850–1900, Chapter
1, Section 1.2.1) and cumulative carbon dioxide emissions from 1876 to the end of 2010 of 1,930 GtCO2 (Le Quéré et al., 2018) is shown as a filled purple diamond. The value
for 2017 based on the latest cumulative carbon emissions up to the end of 2017 of 2,220 GtCO2 (Version 1.3 accessed 22 May 2018) and a surface temperature anomaly of
1.1°C based on an assumed temperature increase of 0.2°C per decade is shown as a hollow purple diamond. The thin blue line shows annual observations, with CO2 emissions
from Le Quéré et al. (2018) and estimated globally averaged near-surface temperature from scaling the incomplete coverage and blended HadCRUT4 dataset in Chapter 1. The
thin black line shows the CMIP5 multimodel mean estimate with CO2 emissions also from (Le Quéré et al., 2018). The thin black line shows the GMST historic temperature trends
from Chapter 1, which give lower temperature changes up to 2006–2015 of 0.87°C and would lead to a larger remaining carbon budget. The dotted black lines illustrate theremaining carbon budget estimates for 1.5°C given in Table 2.2. Note these remaining budgets exclude possible Earth system feedbacks that could reduce the budget, such as
CO2 and CH4 release from permafrost thawing and tropical wetlands (see Section 2.2.2.2).

2.2.2.2

CO2 and non-CO2 contributions to the remaining
carbon budget

A remaining carbon budget can be estimated from calculating theamount of CO2 emissions consistent (given a certain value of TCRE)
with an allowable additional amount of warming. Here, the allowable
warming is the 1.5°C warming threshold minus the current warming
taken as the 2006–2015 average, with a further amount removed toaccount for the estimated non-CO2 temperature contribution to theremaining warming (Peters, 2016; Rogelj et al., 2016b). This assessment
uses the TCRE range from AR5 WGI (Collins et al., 2013) supported
by estimates of non-CO2 contributions that are based on publishedmethods and integrated pathways (Friedlingstein et al., 2014a; Allen etal., 2016, 2018; Peters, 2016; Smith et al., 2018). Table 2.2 and Figure
2.3 show the assessed remaining carbon budgets and key uncertainties
for a set of additional warming levels relative to the 2006–2015 period(see Supplementary Material 2.SM.1.1.2 for details). With an assessedhistorical warming of 0.87°C ± 0.12°C from 1850–1900 to 2006–2015(Chapter 1, Section 1.2.1), 0.63°C of additional warming would beapproximately consistent with a global mean temperature increaseof 1.5°C relative to pre-industrial levels. For this level of additional
warming, remaining carbon budgets have been estimated (Table 2.2,
Supplementary Material 2.SM.1.1.2).
The remaining carbon budget calculation presented in the Table
2.2 and illustrated in Figure 2.3 does not consider additional Earthsystem feedbacks such as permafrost thawing. These are uncertain
but estimated to reduce the remaining carbon budget by an order ofmagnitude of about 100 GtCO2 and more thereafter. Accounting forsuch feedbacks would make the carbon budget more applicable for2100 temperature targets, but would also increase uncertainty (Table
2.2 and see below). Excluding such feedbacks, the assessed range forthe remaining carbon budget is estimated to be 840, 580, and 420
GtCO2 for the 33rd, 50th and, 67th percentile of TCRE, respectively,
with a median non-CO2 warming contribution and starting from 1
January 2018 onward. Consistent with the approach used in the
IPCC Fifth Assessment Report (IPCC, 2013b), the latter estimatesuse global near-surface air temperatures both over the ocean and105

Chapter 2

2

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

over land to estimate global surface temperature change since preindustrial. The global warming from the pre-industrial period until the
2006–2015 reference period is estimated to amount to 0.97°C withan uncertainty range of about ±0.1°C (see Chapter 1, Section 1.2.1).
Three methodological improvements lead to these estimates of theremaining carbon budget being about 300 GtCO2 larger than thosereported in Table 2.2 of the IPCC AR5 SYR (IPCC, 2014a) (medium
confidence). The AR5 used 15 Earth System Models (ESM) and 5
Earth-system Models of Intermediate Complexity (EMIC) to derive an
estimate of the remaining carbon budget. Their approach hence madeimplicit assumptions about the level of warming to date, the futurecontribution of non-CO2 emissions, and the temperature response
to CO2 (TCRE). In this report, each of these aspects are considered
explicitly. When estimating global warming until the 2006–2015
reference period as a blend of near-surface air temperature over landand sea-ice regions, and sea-surface temperature over open ocean,by averaging the four global mean surface temperature time series
listed in Chapter 1 Section 1.2.1, the global warming would amount
to 0.87°C ±0.1°C. Using the latter estimate of historical warming andprojecting global warming using global near-surface air temperaturesfrom model projections leads to remaining carbon budgets for limiting
global warming to 1.5°C of 1080, 770, and 570 GtCO2 for the 33rd,
50th, and 67th percentile of TCRE, respectively. Note that future
research and ongoing observations over the next years will provide a
better indication as to how the 2006–2015 base period compares withthe long-term trends and might affect the budget estimates. Similarly,
improved understanding in Earth system feedbacks would result in abetter quantification of their impacts on remaining carbon budgets for
1.5°C and 2°C.
After TCRE uncertainty, a major additional source of uncertainty is themagnitude of non-CO2 forcing and its contribution to the temperaturechange between the present day and the time of peak warming.
Integrated emissions pathways can be used to ensure consistency
between CO2 and non-CO2 emissions (Bowerman et al., 2013; Collins
et al., 2013; Clarke et al., 2014; Rogelj et al., 2014b, 2015a; Tokarska etal., 2018). Friedlingstein et al. (2014a) used pathways with limited to
no climate mitigation to find a variation due to non-CO2 contributionsof about ±33% for a 2°C carbon budget. Rogelj et al. (2016b) showedno particular bias in non-CO2 radiative forcing or warming at the timeof exceedance of 2°C or at peak warming between scenarios withincreasing emissions and strongly mitigated scenarios (consistent
with Stocker et al., 2013). However, clear differences of the nonCO2 warming contribution at the time of deriving a 2°C-consistentcarbon budget were reported for the four RCPs. Although the spreadin non-CO2 forcing across scenarios can be smaller in absolute terms
at lower levels of cumulative emissions, it can be larger in relative
terms compared to the remaining carbon budget (Stocker et al., 2013;Friedlingstein et al., 2014a; Rogelj et al., 2016b). Tokarska and Gillett(2018) find no statistically significant differences in 1.5°C-consistentcumulative emissions budgets when calculated for different RCPs fromconsistent sets of CMIP5 simulations.
The mitigation pathways assessed in this report indicate that emissionsof non-CO2 forcers contribute an average additional warming of around
0.15°C relative to 2006–2015 at the time of net zero CO2 emissions,
reducing the remaining carbon budget by roughly 320 GtCO2. This
106

arises from a weakening of aerosol cooling and continued emissionsof non-CO2 GHGs (Sections 2.2.1, 2.3.3). This non-CO2 contribution
at the time of net zero CO2 emissions varies by about ±0.1°C acrossscenarios, resulting in a carbon budget uncertainty of about ±250
GtCO2, and takes into account marked reductions in methane emissions(Section 2.3.3). If these reductions are not achieved, remaining carbon
budgets are further reduced. Uncertainties in the non-CO2 forcing andtemperature response are asymmetric and can influence the remaining
carbon budget by −400 to +200 GtCO2, with the uncertainty in aerosol
radiative forcing being the largest contributing factor (Table 2.2). The
MAGICC and FAIR models in their respective parameter setups andmodel versions used to assess the non-CO2 warming contribution givenoticeable different non-CO2 effective radiative forcing and warming
for the same scenarios while both being within plausible ranges of
future response (Figure 2.2 and Supplementary Material 2.SM.1.1,
2.SM.1.2). For this assessment, it is premature to assess the accuracyof their results, so it is assumed that both are equally representativeof possible futures. Their non-CO2 warming estimates are therefore
averaged for the carbon budget assessment and their differences used
to guide the uncertainty assessment of the role of non-CO2 forcers.
Nevertheless, the findings are robust enough to give high confidence
that the changing emissions of non-CO2 forcers (particularly the
reduction in cooling aerosol precursors) cause additional near-termwarming and reduce the remaining carbon budget compared to theCO2-only budget.
TCRE uncertainty directly impacts carbon budget estimates (Peters,
2016; Matthews et al., 2017; Millar and Friedlingstein, 2018). Basedon multiple lines of evidence, AR5 WGI assessed a likely range for
TCRE of 0.2°–0.7°C per 1000 GtCO2 (Collins et al., 2013). The TCRE
of the CMIP5 Earth system models ranges from 0.23°C to 0.66°C
per 1000 GtCO2 (Gillett et al., 2013). At the same time, studies using
observational constraints find best estimates of TCRE of 0.35°–0.41°C
per 1000 GtCO2 (Matthews et al., 2009; Gillett et al., 2013; Tachiiri etal., 2015; Millar and Friedlingstein, 2018). This assessment continues
to use the assessed AR5 TCRE range under the working assumptionthat TCRE is normally distributed (Stocker et al., 2013). Observationbased estimates have reported log-normal distributions of TCRE (Millar
and Friedlingstein, 2018). Assuming a log-normal instead of normal
distribution of the assessed AR5 TCRE range would result in about a
200 GtCO2 increase for the median budget estimates but only about
half at the 67th percentile, while historical temperature uncertainty
and uncertainty in recent emissions contribute ±150 and ±50 GtCO2
to the uncertainty, respectively (Table 2.2).
Calculating carbon budgets from the TCRE requires the assumptionthat the instantaneous warming in response to cumulative CO2
emissions equals the long-term warming or, equivalently, thatthe residual warming after CO2 emissions cease is negligible. The
magnitude of this residual warming, referred to as the zero-emissioncommitment, ranges from slightly negative (i.e., a slight cooling)
to slightly positive for CO2 emissions up to present-day (Chapter 1,
Section 1.2.4) (Lowe et al., 2009; Frölicher and Joos, 2010; Gillett etal., 2011; Matthews and Zickfeld, 2012). The delayed temperaturechange from a pulse CO2 emission introduces uncertainties in emission
budgets, which have not been quantified in the literature for budgets
consistent with limiting warming to 1.5°C. As a consequence, thisMitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

uncertainty does not affect our carbon budget estimates directly but
it is included as an additional factor in the assessed Earth system
feedback uncertainty (as detailed below) of roughly 100 GtCO2 on
decadal time scales presented in Table 2.2.
Remaining carbon budgets are further influenced by Earth system
feedbacks not accounted for in CMIP5 models, such as the permafrost
carbon feedback (Friedlingstein et al., 2014b; MacDougall et al., 2015;
Burke et al., 2017; Lowe and Bernie, 2018), and their influence on
the TCRE. Lowe and Bernie (2018) used a simple climate sensitivity
scaling approach to estimate that Earth system feedbacks (such as
CO2 released by permafrost thawing or methane released by wetlands)
could reduce carbon budgets for 1.5°C and 2°C by roughly 100
GtCO2 on centennial time scales. Their findings are based on an older
understanding of Earth system feedbacks (Arneth et al., 2010). Thisestimate is broadly supported by more recent analysis of individual
feedbacks. Schädel et al. (2014) suggest an upper bound of 24.4 PgC(90 GtCO2) emitted from carbon release from permafrost over the next
forty years for a RCP4.5 scenario. Burke et al. (2017) use a single model
to estimate permafrost emissions between 0.3 and 0.6 GtCO2 y-1 fromthe point of 1.5°C stabilization, which would reduce the budget byaround 20 GtCO2 by 2100. Comyn-Platt et al. (2018) include carbon
and methane emissions from permafrost and wetlands and suggest the
1.5°C remaining carbon budget is reduced by 116 GtCO2. Additionally,
Mahowald et al. (2017) find there is possibility of 0.5–1.5 GtCO2 y-1
being released from aerosol-biogeochemistry changes if aerosol
emissions cease. In summary, these additional Earth system feedbacks
taken together are assessed to reduce the remaining carbon budget
applicable to 2100 by an order of magnitude of 100 GtCO2, comparedto the budgets based on the assumption of a constant TCRE presented
in Table 2.2 (limited evidence, medium agreement), leading to overall
medium confidence in their assessed impact. After 2100, the impactof additional Earth system feedbacks is expected to further reduce theremaining carbon budget (medium confidence).
The uncertainties presented in Table 2.2 cannot be formally combined,
but current understanding of the assessed geophysical uncertainties
suggests at least a ±50% possible variation for remaining carbon
budgets for 1.5°C-consistent pathways. By the end of 2017,
anthropogenic CO2 emissions since the pre-industrial period are
estimated to have amounted to approximately 2200 ±320 GtCO2(medium confidence) (Le Quéré et al., 2018). When put in the context
of year-2017 CO2 emissions (about 42 GtCO2 yr-1, ±3 GtCO2 yr-1, highconfidence) (Le Quéré et al., 2018), a remaining carbon budget of
580 GtCO2 (420 GtCO2) suggests meeting net zero global CO2 emissions
in about 30 years (20 years) following a linear decline starting from2018 (rounded to the nearest five years), with a variation of ±15–20
years due to the geophysical uncertainties mentioned above (highconfidence).

Chapter 2

ocean thermal and carbon cycle inertia (Herrington and Zickfeld, 2014;
Krasting et al., 2014; Zickfeld et al., 2016). This asymmetrical behaviour
makes carbon budgets path-dependent in the case of a budget and/or
temperature overshoot (MacDougall et al., 2015). Although potentiallylarge for scenarios with large overshoot (MacDougall et al., 2015), thispath-dependence of carbon budgets has not been well quantified for
1.5°C- and 2°C-consistent scenarios and as such remains an important
knowledge gap. This assessment does not explicitly account for pathdependence but takes it into consideration for its overall confidence
assessment.
This assessment finds a larger remaining budget from the 2006–2015
base period than the 1.5°C and 2°C remaining budgets inferred from
AR5 from the start of 2011, which were approximately 1000 GtCO2
for the 2°C (66% of model simulations) and approximately 400 GtCO2
for the 1.5°C budget (66% of model simulations). In contrast, thisassessment finds approximately 1600 GtCO2 for the 2°C (66th TCRE
percentile) and approximately 860 GtCO2 for the 1.5°C budget (66thTCRE percentile) from 2011. However, these budgets are not directly
equivalent as AR5 reported budgets for fractions of CMIP5 simulations
and other lines of evidence, while this report uses the assessed rangeof TCRE and an assessment of the non-CO2 contribution at net zero CO2emissions to provide remaining carbon budget estimates at various
percentiles of TCRE. Furthermore, AR5 did not specify remaining
budgets to carbon neutrality as we do here, but budgets until the time
the temperature limit of interest was reached, assuming negligible zero
emission commitment and taking into account the non-CO2 forcing atthat point in time.
In summary, although robust physical understanding underpins the
carbon budget concept, relative uncertainties become larger as a
specific temperature limit is approached. For the budget, applicableto the mid-century, the main uncertainties relate to the TCRE, non-CO2
emissions, radiative forcing and response. For 2100, uncertain Earthsystem feedbacks such as permafrost thawing would further reduce
the available budget. The remaining budget is also conditional upon
the choice of baseline, which is affected by uncertainties in both
historical emissions, and in deriving the estimate of globally averaged
human-induced warming. As a result, only medium confidence can be
assigned to the assessed remaining budget values for 1.5°C and 2.0°C
and their uncertainty.

The remaining carbon budgets assessed in this section are consistent
with limiting peak warming to the indicated levels of additional
warming. However, if these budgets are exceeded and the use of
CDR (see Sections 2.3 and 2.4) is envisaged to return cumulative
CO2 emissions to within the carbon budget at a later point in time,
additional uncertainties apply because the TCRE is different underincreasing and decreasing atmospheric CO2 concentrations due to107

2Chapter 2

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Table 2.2 |The assessed remaining carbon budget and its uncertainties. Shaded blue horizontal bands illustrate the uncertainty in historical temperature increase
from the 1850–1900 base period until the 2006–2015 period as estimated from global near-surface air temperatures, which impacts the additional warming 		until a specific temperature limit like 1.5°C or 2°C relative to the 1850–1900 period. Shaded grey cells indicate values for when historical temperature increaseis estimated from a blend of near-surface air temperatures over land and sea ice regions and sea-surface temperatures over oceans.

Additional
Warmingsince
2006–2015[°C]*(1)Approximate
Warming
since
1850–1900[°C]*(1)Remaining Carbon Budget(Excluding Additional
Earth System Feedbacks*(5))[GtCO2 from 1.1.2018]*(2)Percentiles of TCRE
*(3)

2

33rd

50th

67th

0.3

290

160

80

0.4

530

350

230

0.5
0.53

~1.5°C

770

530

380

840

580

420

0.6

1010

710

530

0.63

1080

770

570

0.7

1240

900

680

0.78

1440

1040

800

0.8

1480

1080

830

0.9

1720

1260

980

1

1960

1450

1130

1.03

~2°C

2030

1500

1170

1.1

2200

1630

1280

1.13

2270

1690

1320

1.2

2440

1820

1430

Key Uncertainties and Variations*(4)Earth System
Feedbacks*(5)

Non-CO2
scenario
variation*(6)

Non-CO2
forcing andresponse
uncertainty

TCRE
distribution
uncertainty*(7)

Historicaltemperature
uncertainty*(1)

Recentemissions
uncertainty*(8)[GtCO2][GtCO2][GtCO2][GtCO2][GtCO2][GtCO2]±250–400 to +200+100 to +200

±250

±20

Budgets on
the left are
reduced byabout ­–100on centennial
time scales

Notes:*(1) Chapter 1 has assessed historical warming between the 1850–1900 and 2006–2015 periods to be 0.87°C with a ±0.12°C likely (1-standard deviation) range, and global near-surface airtemperature to be 0.97°C. The temperature changes from the 2006–2015 period are expressed in changes of global near-surface air temperature.
*(2) Historical CO2 emissions since the middle of the 1850–1900 historical base period (mid-1875) are estimated at 1940 GtCO2 (1640–2240 GtCO2, one standard deviation range) until end
2010. Since 1 January 2011, an additional 290 GtCO2 (270–310 GtCO2, one sigma range) has been emitted until the end of 2017 (Le Quéré et al., 2018).
*(3) TCRE: transient climate response to cumulative emissions of carbon, assessed by AR5 to fall likely between 0.8–2.5°C/1000 PgC (Collins et al., 2013), considering a normal distribution
consistent with AR5 (Stocker et al., 2013). Values are rounded to the nearest 10 GtCO2.
*(4) Focussing on the impact of various key uncertainties on median budgets for 0.53°C of additional warming.
*(5) Earth system feedbacks include CO2 released by permafrost thawing or methane released by wetlands, see main text.
*(6) Variations due to different scenario assumptions related to the future evolution of non-CO2 emissions.
*(7) The distribution of TCRE is not precisely defined. Here the influence of assuming a lognormal instead of a normal distribution shown.
*(8) Historical emissions uncertainty reflects the uncertainty in historical emissions since 1 January 2011.

2.3

Overview of 1.5°C Mitigation Pathways

Limiting global mean temperature increase at any level requires global
CO2 emissions to become net zero at some point in the future (Zickfeld
et al., 2009; Collins et al., 2013). At the same time, limiting the residual
warming of short-lived non-CO2 emissions can be achieved by reducing
their annual emissions as much as possible (Section 2.2, Cross-Chapter
Box 2 in Chapter 1). This would require large-scale transformations of
the global energy–agriculture–land-economy system, affecting theway in which energy is produced, agricultural systems are organized,
and food, energy and materials are consumed (Clarke et al., 2014). Thissection assesses key properties of pathways consistent with limiting
global mean temperature to 1.5°C relative to pre-industrial levels,
including their underlying assumptions and variations.
108

Since the AR5, an extensive body of literature has appeared on integrated
pathways consistent with 1.5°C (Section 2.1) (Rogelj et al., 2015b, 2018;
Akimoto et al., 2017; Löffler et al., 2017; Marcucci et al., 2017; Su et al.,2017; Bauer et al., 2018; Bertram et al., 2018; Grubler et al., 2018; Holzet al., 2018b; Kriegler et al., 2018a; Liu et al., 2018; Luderer et al., 2018;
Strefler et al., 2018a; van Vuuren et al., 2018; Vrontisi et al., 2018; Zhang
et al., 2018). These pathways have global coverage and represent all
GHG-emitting sectors and their interactions. Such integrated pathways
allow the exploration of the whole-system transformation, and hence
provide the context in which the detailed sectoral transformations
assessed in Section 2.4 of this chapter are taking place.
The overwhelming majority of published integrated pathways havebeen developed by global IAMs that represent key societal systems

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

and their interactions, like the energy system, agriculture and land use,
and the economy (see Section 6.2 in Clarke et al., 2014). Very often
these models also include interactions with a representation of thegeophysical system, for example, by including spatially explicit landmodels or carbon cycle and climate models. The complex features ofthese subsystems are approximated and simplified in these models.
IAMs are briefly introduced in Section 2.1 and important knowledge
gaps identified in Section 2.6. An overview to the use, scope andlimitations of IAMs is provided in Supplementary Material 2.SM.1.2.
The pathway literature is assessed in two ways in this section. First,
various insights on specific questions reported by studies can be assessed
to identify robust or divergent findings. Second, the combined body of
scenarios can be assessed to identify salient features of pathways in linewith a specific climate goal across a wide range of models. The lattercan be achieved by assessing pathways available in the database tothis assessment (Section 2.1, Supplementary Material 2.SM.1.2–4). The
ensemble of scenarios available to this assessment is an ensemble ofopportunity: it is a collection of scenarios from a diverse set of studies
that was not developed with a common set of questions and a statisticalanalysis of outcomes in mind. This means that ranges can be useful toidentify robust and sensitive features across available scenarios andcontributing modelling frameworks, but do not lend themselves to astatistical interpretation.To understand the reasons underlying the ranges,
an assessment of the underlying scenarios and studies is required. To thisend, this section highlights illustrative pathway archetypes that help toclarify the variation in assessed ranges for 1.5°C-consistent pathways.

2.3.1

Range of Assumptions Underlying 1.5°C Pathways

Earlier assessments have highlighted that there is no single pathway toachieve a specific climate objective (e.g., Clarke et al., 2014). Pathways
depend on the underlying development processes, and societal
choices, which affect the drivers of projected future baseline emissions.
Furthermore, societal choices also affect climate change solutions inpathways, like the technologies that are deployed, the scale at which
they are deployed, or whether solutions are globally coordinated.
A key finding is that 1.5°C-consistent pathways could be identified
under a considerable range of assumptions in model studies despite
the tightness of the 1.5°C emissions budget (Figures 2.4, 2.5) (Rogelj
et al., 2018).
The AR5 provided an overview of how differences in model structure
and assumptions can influence the outcome of transformation
pathways (Section 6.2 in Clarke et al., 2014, as well as Table A.II.14
in Krey et al., 2014b) and this was further explored by the modelling
community in recent years with regard to, e.g., socio-economic drivers(Kriegler et al., 2016; Marangoni et al., 2017; Riahi et al., 2017),
technology assumptions (Bosetti et al., 2015; Creutzig et al., 2017;
Pietzcker et al., 2017), and behavioural factors (van Sluisveld et al.,2016; McCollum et al., 2017).

2.3.1.1

Socio-economic drivers and the demand forenergy and land in 1.5°C pathways

There is deep uncertainty about the ways humankind will use energy
and land in the 21st century. These ways are intricately linked to

Chapter 2

future population levels, secular trends in economic growth andincome convergence, behavioural change and technological progress.
These dimensions have been recently explored in the context of
the SSPs (Kriegler et al., 2012; O’Neill et al., 2014), which provide
narratives (O’Neill et al., 2017) and quantifications (Crespo Cuaresma,
2017; Dellink et al., 2017; KC and Lutz, 2017; Leimbach et al., 2017;
Riahi et al., 2017) of different world futures across which scenario
dimensions are varied to explore differential challenges to adaptationand mitigation (Cross-Chapter Box 1 in Chapter 1). This framework
is increasingly adopted by IAMs to systematically explore the impact
of socio-economic assumptions on mitigation pathways (Riahi et al.,2017), including 1.5°C-consistent pathways (Rogelj et al., 2018). The
narratives describe five worlds (SSP1–5) with different socio-economic
predispositions to mitigate and adapt to climate change (Table 2.3). As
a result, population and economic growth projections can vary strongly
across integrated scenarios, including available 1.5°C-consistentpathways (Figure 2.4). For example, based on alternative future
fertility, mortality, migration and educational assumptions, population
projections vary between 8.5 and 10.0 billion people by 2050 and
between 6.9 and 12.6 billion people by 2100 across the SSPs. An
important factor for these differences is future female educational
attainment, with higher attainment leading to lower fertility rates andtherefore decreased population growth up to a level of 1 billion people
by 2050 (Lutz and KC, 2011; Snopkowski et al., 2016; KC and Lutz,2017). Consistent with population development, GDP per capita also
varies strongly in SSP baselines, ranging from about 20 to more than
50 thousand USD2010 per capita in 2050 (in purchasing power parity
values, PPP), in part driven by assumptions on human development,
technological progress and development convergence between andwithin regions (Crespo Cuaresma, 2017; Dellink et al., 2017; Leimbach
et al., 2017). Importantly, none of the GDP projections in the mitigation
pathway literature assessed in this chapter included the feedback ofclimate damages on economic growth (Hsiang et al., 2017).
Baseline projections for energy-related GHG emissions are sensitive toeconomic growth assumptions, while baseline projections for land-use
emissions are more directly affected by population growth (assuming
unchanged land productivity and per capita demand for agricultural
products) (Kriegler et al., 2016). SSP-based modelling studies of
mitigation pathways have identified high challenges to mitigation
for worlds with a focus on domestic issues and regional security
combined with high population growth (SSP3), and for worlds with
rapidly growing resource and fossil-fuel intensive consumption (SSP5)(Riahi et al., 2017). No model could identify a 2°C-consistent pathway
for SSP3, and high mitigation costs were found for SSP5. This picturetranslates to 1.5°C-consistent pathways that have to remain withineven tighter emissions constraints (Rogelj et al., 2018). No model
found a 1.5°C-consistent pathway for SSP3 and some models could not
identify 1.5°C-consistent pathways for SSP5 (2 of 4 models, comparedto 1 of 4 models for 2°C-consistent pathways). The modelling analysis
also found that the effective control of land-use emissions becomeseven more critical in 1.5°C-consistent pathways. Due to high inequality
levels in SSP4, land use can be less well managed. This caused 2 of
3 models to no longer find an SSP4-based 1.5°C-consistent pathway
even though they identified SSP4-based 2°C-consistent pathways at
relatively moderate mitigation costs (Riahi et al., 2017). Rogelj et al.
(2018) further reported that all six participating models identified
109

2

Chapter 2

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Table 2.3 | Key Characteristics of the Five Shared Socio-Economic Pathways (SSPs) (O’Neill et al., 2017).
Socio-Economic
Challenges to
Mitigation

High

Socio-Economic Challenges to Adaptation
Low

MediumHigh

SSP5: Fossil-fuelled development

SSP3: Regional rivalry• low population• very high economic growth per capita• high human development• high technological progress• ample fossil fuel resources• very resource intensive lifestyles• high energy and food demand per capita• economic convergence and global cooperation• high population• low economic growth per capita• low human development• low technological progress• resource-intensive lifestyles• resource-constrained energy and food demand
per capita• focus on regional food and energy security• regionalization and lack of global cooperation

SSP2: Middle of the road2

• medium population• medium and uneven economic growth• medium and uneven human development• medium and uneven technological progress• resource-intensive lifestyles• medium and uneven energy and food demand
per capita• limited global cooperation and economic convergence

MediumLow

SSP1: Sustainable development

SSP4: Inequality• low population• high economic growth per capita• high human development• high technological progress• environmentally oriented technological andbehavioural change• resource-efficient lifestyles• low energy and food demand per capita• economic convergence and global cooperation• Medium to high population• Unequal low to medium economic
growth per capita• Unequal low to medium human development• unequal technological progress: high in globalized
high-tech sectors, slow in domestic sectors• unequal lifestyles and energy /food consumption:
resource intensity depending on income• Globally connected elite, disconnected domestic
work forces

1.5°C-consistent pathways in a sustainability oriented world (SSP1) and
four of six models found 1.5°C-consistent pathways for middle-of-theroad developments (SSP2). These results show that 1.5°C-consistentpathways can be identified under a broad range of assumptions, butthat lack of global cooperation (SSP3), high inequality (SSP4) and/or
high population growth (SSP3) that limit the ability to control land use
emissions, and rapidly growing resource-intensive consumption (SSP5)
are key impediments.
Figure 2.4 compares the range of underlying socio-economic
developments as well as energy and food demand in available
1.5°C-consistent pathways with the full set of published scenariosthat were submitted to this assessment. While 1.5°C-consistentpathways broadly cover the full range of population and economic
growth developments (except for the high population developmentin SSP3-based scenarios), they tend to cluster on the lower end forenergy and food demand. They still encompass, however, a wide range
of developments from decreasing to increasing demand levels relative
to today. For the purpose of this assessment, a set of four illustrative
1.5°C-consistent pathway archetypes were selected to show thevariety of underlying assumptions and characteristics (Figure 2.4). They
comprise three 1.5°C-consistent pathways based on the SSPs (Rogelj
et al., 2018): a sustainability oriented scenario (S1 based on SSP1)
developed with the AIM model (Fujimori, 2017), a fossil-fuel intensive
110

and high energy demand scenario (S5, based on SSP5) developed with
the REMIND-MAgPIE model (Kriegler et al., 2017), and a middle-ofthe-road scenario (S2, based on SSP2) developed with the MESSAGEGLOBIOM model (Fricko et al., 2017). In addition, we include a scenario
with low energy demand (LED) (Grubler et al., 2018), which reflects
recent literature with a stronger focus on demand-side measures(Bertram et al., 2018; Grubler et al., 2018; Liu et al., 2018; van Vuuren
et al., 2018). Pathways LED, S1, S2, and S5 are referred to as P1, P2, P3,
and P4 in the Summary for Policymakers.

2.3.1.2

Mitigation options in 1.5°C pathways

In the context of 1.5°C pathways, the portfolio of mitigation options
available to the model becomes an increasingly important factor. IAMs
include a wide variety of mitigation options, as well as measures that
achieve CDR from the atmosphere (Krey et al., 2014a, b) (see Chapter 4,
Section 4.3 for a broad assessment of available mitigation measures).
For the purpose of this assessment, we elicited technology availability
in models that submitted scenarios to the database as summarizedin Supplementary Material 2.SM.1.2, where a detailed picture of thetechnology variety underlying available 1.5°C-consistent pathways
is provided. Modelling choices on whether a particular mitigation
measure is included are influenced by an assessment of its global
mitigation potential, the availability of data and literature describing

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development(a)Chapter 2

(b)
S1
S2
S5
LED
All scenarios
1.5C pathways

(c)

(d)

2

Figure 2.4 | Range of assumptions about socio-economic drivers and projections for energy and food demand in the pathways available to thisassessment. 1.5°C-consistent pathways are blue, other pathways grey. Trajectories for the illustrative 1.5°C-consistent archetypes used in this Chapter (LED, S1, S2, S5;referred to as P1, P2, P3, and P4 in the Summary for Policymakers.) are highlighted. S1 is a sustainability oriented scenario, S2 is a middle-of-the-road scenario, and S5 is a
fossil-fuel intensive and high energy demand scenario. LED is a scenario with particularly low energy demand. Population assumptions in S2 and LED are identical. Panels show(a) world population, (b) gross world product in purchasing power parity values, (c) final energy demand, and (d) food demand.

its techno-economic characteristics and future prospects, and thecomputational challenge of representing the measure, e.g., in terms ofrequired spatio-temporal and process detail.
This elicitation (Supplementary Material 2.SM.1.2) confirms that
IAMs cover most supply-side mitigation options on the process level,
while many demand-side options are treated as part of underlying
assumptions, which can be varied (Clarke et al., 2014). In recent years,
there has been increasing attention on improving the modellingof integrating variable renewable energy into the power system(Creutzig et al., 2017; Luderer et al., 2017; Pietzcker et al., 2017) andof behavioural change and other factors influencing future demandfor energy and food (van Sluisveld et al., 2016; McCollum et al., 2017;
Weindl et al., 2017), including in the context of 1.5°C-consistentpathways (Grubler et al., 2018; van Vuuren et al., 2018). The literatureon the many diverse CDR options only recently started to develop
strongly (Minx et al., 2017) (see Chapter 4, Section 4.3.7 for a detailed
assessment), and hence these options are only partially included in
IAM analyses. IAMs mostly incorporate afforestation and bioenergy
with carbon capture and storage (BECCS) and only in few cases also
include direct air capture with CCS (DACCS) (Chen and Tavoni, 2013;
Marcucci et al., 2017; Strefler et al., 2018b).
Several studies have either directly or indirectly explored thedependence of 1.5°C-consistent pathways on specific (sets of)
mitigation and CDR technologies (Bauer et al., 2018; Grubler et al.,2018; Holz et al., 2018b; Kriegler et al., 2018a; Liu et al., 2018; Rogelj etal., 2018; Strefler et al., 2018b; van Vuuren et al., 2018). However, there
are a few potentially disruptive technologies that are typically not yet
well covered in IAMs and that have the potential to alter the shape of
mitigation pathways beyond the ranges in the IAM-based literature.
Those are also included in Supplementary Material 2.SM.1.2. The
configuration of carbon-neutral energy systems projected in mitigation
pathways can vary widely, but they all share a substantial relianceon bioenergy under the assumption of effective land-use emissions
control. There are other configurations with less reliance on bioenergythat are not yet comprehensively covered by global mitigation pathway
modelling. One approach is to dramatically reduce and electrify energy
demand for transportation and manufacturing to levels that makeresidual non-electric fuel use negligible or replaceable by limited
amounts of electrolytic hydrogen. Such an approach is presented ina first-of-its kind low-energy-demand scenario (Grubler et al., 2018)
which is part of this assessment. Other approaches rely less on energy
demand reductions, but employ cheap renewable electricity to push
the boundaries of electrification in the industry and transport sectors(Breyer et al., 2017; Jacobson, 2017). In addition, these approaches
deploy renewable-based Power-2-X (read: Power to “x”) technologies
to substitute residual fossil-fuel use (Brynolf et al., 2018). An importantelement of carbon-neutral Power-2-X applications is the combinationof hydrogen generated from renewable electricity and CO2 captured
from the atmosphere (Zeman and Keith, 2008). Alternatively, algae
are considered as a bioenergy source with more limited implications
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Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

for land use and agricultural systems than energy crops (Williams andLaurens, 2010; Walsh et al., 2016; Greene et al., 2017).

2

Furthermore, a range of measures could radically reduce agriculturaland land-use emissions and are not yet well-covered in IAM modelling.
This includes plant-based proteins (Joshi and Kumar, 2015) and cultured
meat (Post, 2012) with the potential to substitute for livestock products
at much lower GHG footprints (Tuomisto and Teixeira de Mattos, 2011).
Large-scale use of synthetic or algae-based proteins for animal feed
could free pasture land for other uses (Madeira et al., 2017; Pikaar et al.,2018). Novel technologies such as methanogen inhibitors and vaccines(Wedlock et al., 2013; Hristov et al., 2015; Herrero et al., 2016; Subharat
et al., 2016) as well as synthetic and biological nitrification inhibitors(Subbarao et al., 2013; Di and Cameron, 2016) could substantiallyreduce future non-CO2 emissions from agriculture if commercialized
successfully. Enhancing carbon sequestration in soils (Paustian et al.,2016; Frank et al., 2017; Zomer et al., 2017) can provide the dual benefitof CDR and improved soil quality. A range of conservation, restorationand land management options can also increase terrestrial carbon
uptake (Griscom et al., 2017). In addition, the literature discusses
CDR measures to permanently sequester atmospheric carbon in rocks(mineralization and enhanced weathering, see Chapter 4, Section
4.3.7) as well as carbon capture and usage in long-lived products likeplastics and carbon fibres (Mazzotti et al., 2005; Hartmann et al., 2013).
Progress in the understanding of the technical viability, economics andsustainability of these ways to achieve and maintain carbon neutralenergy and land use can affect the characteristics, costs and feasibilityof 1.5°C-consistent pathways significantly.

2.3.1.3

Policy assumptions in 1.5°C pathways

Besides assumptions related to socio-economic drivers and mitigation
technology, scenarios are also subject to assumptions about the
mitigation policies that can be put in place. Mitigation policies caneither be applied immediately in scenarios or follow staged or delayed
approaches. Policies can span many sectors (e.g., economy-wide carbon
pricing), or policies can be applicable to specific sectors only (like theenergy sector) with other sectors (e.g., the agricultural or the land-use
sector) treated differently. These variations can have an important
impact on the ability of models to generate scenarios compatible withstringent climate targets like 1.5°C (Luderer et al., 2013; Rogelj et al.,2013b; Bertram et al., 2015b; Kriegler et al., 2018a; Michaelowa et al.,2018). In the scenario ensemble available to this assessment, several
variations of near-term mitigation policy implementation can be found:
immediate and cross-sectoral global cooperation from 2020 onward
towards a global climate objective, a phase-in of globally coordinatedmitigation policy from 2020 to 2040, and a more short-term oriented
and regionally diverse global mitigation policy, following NDCs until
2030 (Kriegler et al., 2018a; Luderer et al., 2018; McCollum et al., 2018;
Rogelj et al., 2018; Strefler et al., 2018b). For example, the abovementioned SSP quantifications assume regionally scattered mitigation
policies until 2020, and vary in global convergence thereafter (Kriegler
et al., 2014a; Riahi et al., 2017). The impact of near-term policy choices
on 1.5°C-consistent pathways is discussed in Section 2.3.5. The
literature has also explored 1.5°C-consistent pathways that build on
a portfolio of policy approaches until 2030, including the combinationof regulatory policies and carbon pricing (Kriegler et al., 2018a),
112

and a variety of ancillary policies to safeguard other sustainable
development goals (Bertram et al., 2018; van Vuuren et al., 2018).
A further discussion of policy implications of 1.5°C-consistent pathways
is provided in Section 2.5.1, while a general discussion of policies andoptions to strengthen action are subject of Chapter 4, Section 4.4.

2.3.2

Key Characteristics of 1.5°C Pathways

1.5°C-consistent pathways are characterized by a rapid phase outof CO2 emissions and deep emissions reductions in other GHGs andclimate forcers (Section 2.2.2 and 2.3.3). This is achieved by broadtransformations in the energy; industry; transport; buildings; andagriculture, forestry and other land-use (AFOLU) sectors (Section 2.4)
(Bauer et al., 2018; Grubler et al., 2018; Holz et al., 2018b; Kriegler
et al., 2018b; Liu et al., 2018; Luderer et al., 2018; Rogelj et al.,2018; van Vuuren et al., 2018; Zhang et al., 2018). Here we assess
1.5°C-consistent pathways with and without overshoot during
the 21st century. One study also explores pathways overshooting
1.5°C for longer than the 21st century (Akimoto et al., 2017), butthese are not considered 1.5°C-consistent pathways in this report(Chapter 1, Section 1.1.3). This subsection summarizes robust andvarying properties of 1.5°C-consistent pathways regarding systemtransformations, emission reductions and overshoot. It aims to provide
an introduction to the detailed assessment of the emissions evolution(Section 2.3.3), CDR deployment (Section 2.3.4), energy (Section 2.4.1,
2.4.2), industry (2.4.3.1), buildings (2.4.3.2), transport (2.4.3.3) andland-use transformations (Section 2.4.4) in 1.5°C-consistent pathways.
Throughout Sections 2.3 and 2.4, pathway properties are highlighted
with four 1.5°C-consistent pathway archetypes (LED, S1, S2, S5; referred
to as P1, P2, P3, and P4 in the Summary for Policymakers) covering awide range of different socio-economic and technology assumptions(Figure 2.5, Section 2.3.1).

2.3.2.1

Variation in system transformations underlying 1.5°C
pathways

Be it for the energy, transport, buildings, industry, or AFOLU sector,
the literature shows that multiple options and choices are available ineach of these sectors to pursue stringent emissions reductions (Section
2.3.1.2, Supplementary Material 2.SM.1.2, Chapter 4, Section 4.3).
Because the overall emissions total under a pathway is limited by ageophysical carbon budget (Section 2.2.2), choices in one sector affectthe efforts that are required from others (Clarke et al., 2014). A robustfeature of 1.5°C-consistent pathways, as highlighted by the set ofpathway archetypes in Figure 2.5, is a virtually full decarbonization of thepower sector around mid-century, a feature shared with 2°C-consistentpathways. The additional emissions reductions in 1.5°C-consistentcompared to 2°C-consistent pathways come predominantly from thetransport and industry sectors (Luderer et al., 2018). Emissions can beapportioned differently across sectors, for example, by focussing on
reducing the overall amount of CO2 produced in the energy end-use
sectors, and using limited contributions of CDR by the AFOLU sector(afforestation and reforestation, S1 and LED pathways in Figure 2.5)
(Grubler et al., 2018; Holz et al., 2018b; van Vuuren et al., 2018), orby being more lenient about the amount of CO2 that continues tobe produced in the above-mentioned end-use sectors (both by 2030
and mid-century) and strongly relying on technological CDR options

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Chapter 22

Figure 2.5 | Evolution and break down of global anthropogenic CO2 emissions until 2100. The top-left panel shows global net CO2 emissions in Below-1.5°C,
1.5°C-low-overshoot (OS), and 1.5°C-high-OS pathways, with the four illustrative 1.5°C-consistent pathway archetypes of this chapter highlighted. Ranges at the bottom of the
top-left panel show the 10th–90th percentile range (thin line) and interquartile range (thick line) of the time that global CO2 emissions reach net zero per pathway class, and forall pathways classes combined. The top-right panel provides a schematic legend explaining all CO2 emissions contributions to global CO2 emissions. The bottom row shows how
various CO2 contributions are deployed and used in the four illustrative pathway archetypes (LED, S1, S2, S5, referred to as P1, P2, P3, and P4 in the Summary for Policymakers)
used in this chapter (see Section 2.3.1.1). Note that the S5 scenario reports the building and industry sector emissions jointly. Green-blue areas hence show emissions from thetransport sector and the joint building and industry demand sector, respectively.

like BECCS (S2 and S5 pathways in Figure 2.5) (Luderer et al., 2018;
Rogelj et al., 2018). Major drivers of these differences are assumptions
about energy and food demand and the stringency of near-term climate
policy (see the difference between early action in the scenarios S1,
LED and more moderate action until 2030 in the scenarios S2, S5).
Furthermore, the carbon budget in each of these pathways depends
also on the non-CO2 mitigation measures implemented in each of them,
particularly for agricultural emissions (Sections 2.2.2, 2.3.3) (Gernaat etal., 2015). Those pathways differ not only in terms of their deploymentof mitigation and CDR measures (Sections 2.3.4 and 2.4), but also interms of the resulting temperature overshoot (Figure 2.1). Furthermore,they have very different implications for the achievement of sustainable
development objectives, as further discussed in Section 2.5.3.

2.3.2.2

Pathways keeping warming below 1.5°C or temporarily
overshooting it

This subsection explores the conditions that would need to be fulfilled
to stay below 1.5°C warming without overshoot. As discussed in Section
2.2.2, to keep warming below 1.5°C with a two-in-three (one-in-two)chance, the cumulative amount of CO2 emissions from 2018 onwards
need to remain below a carbon budget of 420 (580) GtCO2; accounting
for the effects of additional Earth system feedbacks until 2100 reduces
this estimate by 100 GtCO2. Based on the current state of knowledge,

exceeding this remaining carbon budget at some point in time would
give a one-in-three (one-in-two) chance that the 1.5°C limit is overshot(Table 2.2). For comparison, around 290 ± 20 (1 standard deviation
range) GtCO2 have been emitted in the years 2011–2017, with annual
CO2 emissions in 2017 around 42 ± 3 GtCO2 yr−1 (Jackson et al., 2017;
Le Quéré et al., 2018). Committed fossil-fuel emissions from existingfossil-fuel infrastructure as of 2010 have been estimated at around500 ± 200 GtCO2 (with about 200 GtCO2 already emitted through2017) (Davis and Caldeira, 2010). Coal-fired power plants contributethe largest part. Committed emissions from existing coal-fired power
plants built through the end of 2016 are estimated to add up to roughly
200 GtCO2, and a further 100–150 GtCO2 from coal-fired power plants
under construction or planned (González-Eguino et al., 2017; Edenhofer
et al., 2018). However, there has been a marked slowdown of planned
coal-power projects in recent years, and some estimates indicate thatthe committed emissions from coal plants that are under construction
or planned have halved since 2015 (Shearer et al., 2018). Despite these
uncertainties, the committed fossil-fuel emissions are assessed toalready amount to more than two thirds (half) of the remaining carbon
budget.
An important question is to what extent the nationally determined
contributions (NDCs) under the Paris Agreement are aligned with theremaining carbon budget. It was estimated that the NDCs, if successfully
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Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

implemented, imply a total of 400–560 GtCO2 emissions over the
2018–2030 period (considering both conditional and unconditional
NDCs) (Rogelj et al., 2016a). Thus, following an NDC trajectory would
already exhaust 95–130% (70–95%) of the remaining two-in-three(one-in-two) 1.5°C carbon budget (unadjusted for additional Earthsystem feedbacks) by 2030. This would leave no time (0–9 years) tobring down global emissions from NDC levels of around 40 GtCO2 yr−1
in 2030 (Fawcett et al., 2015; Rogelj et al., 2016a) to net zero (further
discussion in Section 2.3.5).

2

Most 1.5°C-consistent pathways show more stringent emissions
reductions by 2030 than implied by the NDCs (Section 2.3.5) The lower
end of those pathways reach down to below 20 GtCO2 yr−1 in 2030
(Section 2.3.3, Table 2.4), less than half of what is implied by the NDCs.
Whether such pathways will be able to limit warming to 1.5°C without
overshoot will depend on whether cumulative net CO2 emissions overthe 21st century can be kept below the remaining carbon budget atany time. Net global CO2 emissions are derived from the gross amountof CO2 that humans annually emit into the atmosphere reduced by theamount of anthropogenic CDR in each year. New research has looked
more closely at the amount and the drivers of gross CO2 emissions
from fossil-fuel combustion and industrial processes (FFI) in deep
mitigation pathways (Luderer et al., 2018), and found that the larger
part of remaining CO2 emissions come from direct fossil-fuel use in
the transport and industry sectors, while residual energy supply sector
emissions (mostly from the power sector) are limited by a rapid approachto net zero CO2 emissions until mid-century. The 1.5°C pathways withno or limited (<0.1°C) overshoot that were reported in the scenario
database project remaining FFI CO2 emissions of 610–1260 GtCO2 overthe period 2018–2100 (5th–95th percentile range; median: 880 GtCO2).
Kriegler et al. (2018b) conducted a sensitivity analysis that explores thefour central options for reducing fossil-fuel emissions: lowering energy
demand, electrifying energy services, decarbonizing the power sector
and decarbonizing non-electric fuel use in energy end-use sectors. By
exploring these options to their extremes, they found a lowest valueof 500 GtCO2 (2018–2100) gross fossil-fuel CO2 emissions for the
hypothetical case of aligning the strongest assumptions for all four
mitigation options. The two lines of evidence and the fact that available
1.5°C pathways cover a wide range of assumptions (Section 2.3.1)
give a robust indication of a lower limit of about 500 GtCO2 remaining
fossil-fuel and industry CO2 emissions in the 21st century.
To compare these numbers with the remaining carbon budget, CO2
emissions from agriculture, forestry and other land use (AFOLU) need
to be taken into account. In many of the 1.5°C-consistent pathways,
AFOLU CO2 emissions reach zero at or before mid-century and thenturn to negative values (Table 2.4). This means human changes to theland lead to atmospheric carbon being stored in plants and soils. This
needs to be distinguished from the natural CO2 uptake by land, which is
not accounted for in the anthropogenic AFOLU CO2 emissions reported
in the pathways. Given the difference in estimating the ‘anthropogenic’sink between countries and the global integrated assessment andcarbon modelling community (Grassi et al., 2017), the AFOLU CO2
estimates included here are not necessarily directly comparable withcountries’ estimates at global level. The cumulated amount of AFOLU
CO2 emissions until the time they reach zero combine with the fossil-fueland industry CO2 emissions to give a total amount of gross emissions
114

of 650–1270 GtCO2 for the period 2018–2100 (5th–95th percentile;
median 950 GtCO2) in 1.5°C pathways with no or limited overshoot.
The lower end of the range is close to what emerges from a scenario
of transformative change that halves CO2 emissions every decadefrom 2020 to 2050 (Rockström et al., 2017). All these estimates areabove the remaining carbon budget for a one-in-two chance of limiting
warming below 1.5°C without overshoot, including the low end of the
hypothetical sensitivity analysis of Kriegler et al. (2018b), who assumes
75 Gt AFOLU CO2 emissions adding to a total of 575 GtCO2 gross CO2
emissions. As almost no cases have been identified that keep gross CO2
emissions within the remaining carbon budget for a one-in-two chanceof limiting warming to 1.5°C, and based on current understanding of
the geophysical response and its uncertainties, the available evidence
indicates that avoiding overshoot of 1.5°C will require some type of
CDR in a broad sense, e.g., via net negative AFOLU CO2 emissions(medium confidence). (Table 2.2).
Net CO2 emissions can fall below gross CO2 emissions, if CDR isbrought into the mix. Studies have looked at mitigation and CDR
in combination to identify strategies for limiting warming to 1.5°C(Sanderson et al., 2016; Ricke et al., 2017). CDR, which may include
net negative AFOLU CO2 emissions, is deployed by all 1.5°C-consistentpathways available to this assessment, but the scale of deployment
and choice of CDR measures varies widely (Section 2.3.4). Furthermore,
no CDR technology has been deployed at scale yet, and all come withconcerns about their potential (Fuss et al., 2018), feasibility (Nemet etal., 2018) and/or sustainability (Smith et al., 2015; Fuss et al., 2018) (seeSections 2.3.4, 4.3.2 and 4.3.7 and Cross-Chapter Box 7 in Chapter 3
for further discussion). CDR can have two very different functions in
1.5°C-consistent pathways. If deployed in the first half of the century,
before net zero CO2 emissions are reached, it neutralizes some of theremaining CO2 emissions year by year and thus slows the accumulationof CO2 in the atmosphere. In this first function it can be used to remain
within the carbon budget and avoid overshoot. If CDR is deployed in thesecond half of the century after carbon neutrality has been established,
it can still be used to neutralize some residual emissions from othersectors, but also to create net negative emissions that actively draw
down the cumulative amount of CO2 emissions to return below a
1.5°C warming level. In the second function, CDR enables temporary
overshoot. The literature points to strong limitations to upscaling
CDR (limiting its first abovementioned function) and to sustainability
constraints (limiting both abovementioned functions) (Fuss et al.,2018; Minx et al., 2018; Nemet et al., 2018). Large uncertainty hence
exists about what amount of CDR could actually be available before
mid-century. Kriegler et al. (2018b) explore a case limiting CDR to
100 GtCO2 until 2050, and the 1.5°C pathways with no or limited
overshoot available in the report’s database project 40–260 GtCO2
CDR until the point of carbon neutrality (5th to 95th percentile; median
110 GtCO2). Because gross CO2 emissions in most cases exceed theremaining carbon budget by several hundred GtCO2 and given the limits
to CDR deployment until 2050, most of the 1.5°C-consistent pathways
available to this assessment are overshoot pathways. However, thescenario database also contains nine non-overshoot pathways thatremain below 1.5°C throughout the 21st century (Table 2.1).

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

2.3.3

Emissions Evolution in 1.5°C Pathways

This section assesses the salient temporal evolutions of climate forcers
over the 21st century. It uses the classification of 1.5°C pathways
presented in Section 2.1, which includes a Below-1.5°C class, as wellas other classes with varying levels of projected overshoot (1.5°C-lowOS and 1.5°C-high-OS). First, aggregate-GHG benchmarks for 2030
are assessed. Subsequent sections assess long-lived climate forcers(LLCF) and short-lived climate forcers (SLCF) separately because they
contribute in different ways to near-term, peak and long-term warming(Section 2.2, Cross-Chapter Box 2 in Chapter 1).
Estimates of aggregated GHG emissions in line with specific policy
choices are often compared to near-term benchmark values frommitigation pathways to explore their consistency with long-termclimate goals (Clarke et al., 2014; UNEP, 2016, 2017; UNFCCC, 2016).
Benchmark emissions or estimates of peak years derived from IAMs
provide guidelines or milestones that are consistent with achieving agiven temperature level. While they do not set mitigation requirements
in a strict sense, exceeding these levels in a given year almost invariably
increases the mitigation challenges afterwards by increasing the rates
of change and increasing the reliance on speculative technologies,
including the possibility that its implementation becomes unachievable(see Cross-Chapter Box 3 in Chapter 1 for a discussion of feasibility
concepts) (Luderer et al., 2013; Rogelj et al., 2013b; Clarke et al., 2014;
Fawcett et al., 2015; Riahi et al., 2015; Kriegler et al., 2018a). These
trade-offs are particularly pronounced in 1.5°C pathways and are
discussed in Section 2.3.5. This section assesses Kyoto-GHG emissions
in 2030 expressed in CO2 equivalent (CO2e) emissions using 100-year
global warming potentials.3
Appropriate benchmark values of aggregated GHG emissions depend
on a variety of factors. First and foremost, they are determined by thedesired likelihood to keep warming below 1.5°C and the extent to which
projected temporary overshoot is to be avoided (Sections 2.2, 2.3.2,
and 2.3.5). For instance, median aggregated 2030 GHG emissions areabout 10 GtCO2e yr−1 lower in 1.5°C-low-OS compared to 1.5°C-highOS pathways, with respective interquartile ranges of 26–31 and 36–49
GtCO2e yr−1 (Table 2.4). These ranges correspond to about 25–30 and
35–48 GtCO2e yr−1 in 2030, respectively, when aggregated with 100year Global Warming Potentials from the IPCC Second Assessment
Report. The limited evidence available for pathways aiming to limit
warming below 1.5°C without overshoot or with limited amounts of
CDR (Grubler et al., 2018; Holz et al., 2018b; van Vuuren et al., 2018)indicates that under these conditions consistent emissions in 2030
would fall at the lower end and below the above mentioned ranges.
Due to the small number of 1.5°C pathways with no overshoot in thereport’s database (Table 2.4) and the potential for a downward bias in
the selection of underlying scenario assumptions, the headline range
for 1.5°C pathways with no or limited overshoot is also assessed to
be of the order of 25–30 GtCO2e yr−1. Ranges for the 1.5°C-low-OS
and Lower-2°C classes only overlap outside their interquartile ranges,

3

Chapter 2highlighting the more accelerated reductions in 1.5°C-consistentcompared to 2°C-consistent pathways.
Appropriate emissions benchmark values also depend on theacceptable or desired portfolio of mitigation measures, representing
clearly identified trade-offs and choices (Sections 2.3.4, 2.4, and 2.5.3)(Luderer et al., 2013; Rogelj et al., 2013a; Clarke et al., 2014; Krey et al.,2014a; Strefler et al., 2018b). For example, lower 2030 GHG emissions
correlate with a lower dependence on the future availability anddesirability of CDR (Strefler et al., 2018b). On the other hand, pathways
that assume or anticipate only limited deployment of CDR during
the 21st century imply lower emissions benchmarks over the coming
decades, which are achieved in models through further reducing
CO2 emissions in the coming decades. The pathway archetypes
used in the chapter illustrate this further (Figure 2.6). Under middleof-the-road assumptions of technological and socioeconomic
development, pathway S2 suggests emission benchmarks of 34, 12
and −8 GtCO2e yr−1 in the years 2030, 2050, and 2100, respectively.
In contrast, a pathway that further limits overshoot and aims at
eliminating the reliance on negative emissions technologies like
BECCS as well as CCS (here labelled as the LED pathway) shows
deeper emissions reductions in 2030 to limit the cumulative amountof CO2 until net zero global CO2 emissions (carbon neutrality). The LED
pathway here suggests emission benchmarks of 25, 9 and 2 GtCO2e yr−1
in the years 2030, 2050, and 2100, respectively. However, a pathwaythat allows and plans for the successful large-scale deployment of
BECCS by and beyond 2050 (S5) shows a shift in the opposite direction.
The variation within and between the abovementioned ranges of2030 GHG benchmarks hence depends strongly on societal choices
and preferences related to the acceptability and availability of certain
technologies.
Overall these variations do not strongly affect estimates of the
1.5°C-consistent timing of global peaking of GHG emissions. Both
Below-1.5°C and 1.5°C-low-OS pathways show minimum–maximum
ranges in 2030 that do not overlap with 2020 ranges, indicating the
global GHG emissions peaked before 2030 in these pathways. Also,2020 and 2030 GHG emissions in 1.5°C-high-OS pathways only
overlap outside their interquartile ranges.
Kyoto-GHG emission reductions are achieved by reductions in CO2
and non-CO2 GHGs. The AR5 identified two primary factors that
influence the depth and timing of reductions in non-CO2 Kyoto-GHG
emissions: (i) the abatement potential and costs of reducing theemissions of these gases and (ii) the strategies that allow making
trade-offs between them (Clarke et al., 2014). Many studies indicate
low-cost, near-term mitigation options in some sectors for non-CO2
gases compared to supply-side measures for CO2 mitigation (Clarke etal., 2014). A large share of this potential is hence already exploited in
mitigation pathways in line with 2°C. At the same time, by mid-century
and beyond, estimates of further reductions of non-CO2 Kyoto-GHGs –in particular CH4 and N2O – are hampered by the absence of mitigation

In this chapter GWP-100 values from the IPCC Fourth Assessement Report are used because emissions of fluorinated gases in the integrated pathways have been reported
in this metric to the database. At a global scale, switching between GWP-100 values of the Second, Fourth or Fifth IPCC Assessment Reports could result in variations inaggregated Kyoto-GHG emissions of about ±5% in 2030 (UNFCCC, 2016).

115

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2

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

options in the current generation of IAMs, which are hence not able
to reduce residual emissions of sources linked to livestock production
and fertilizer use (Clarke et al., 2014; Gernaat et al., 2015) (Sections
2.3.1.2, 2.4.4, Supplementary Material 2.SM.1.2). Therefore, while net
CO2 emissions are projected to be markedly lower in 1.5°C-consistentcompared to 2°C-consistent pathways, this is much less the case formethane (CH4) and nitrous-oxide (N2O) (Figures 2.6–2.7). This results
in reductions of CO2 being projected to take up the largest share ofemissions reductions when moving between 1.5°C-consistent and
2°C-consistent pathways (Rogelj et al., 2015b, 2018; Luderer et al.,2018). If additional non-CO2 mitigation measures are identified andadequately included in IAMs, they are expected to further contribute to
mitigation efforts by lowering the floor of residual non-CO2 emissions.
However, the magnitude of these potential contributions has not beenassessed as part of this report.
As a result of the interplay between residual CO2 and non-CO2 emissions
and CDR, global GHG emissions reach net zero levels at different times
in different 1.5°C-consistent pathways. Interquartile ranges of theyears in which 1.5°C-low-OS and 1.5°C-high-OS reach net zero GHG
emissions range from 2060 to 2080 (Table 2.4). A seesaw characteristic
can be found between near-term emissions reductions and the timingof net zero GHG emissions. This is because pathways with limited
emissions reductions in the next one to two decades require net
negative CO2 emissions later on (see earlier). Most 1.5°C-high-OS
pathways lead to net zero GHG emissions in approximately the third
quarter of this century, because all of them rely on significant amountsof annual net negative CO2 emissions in the second half of the
century to decline temperatures after overshoot (Table 2.4). However,in pathways that aim at limiting overshoot as much as possible ormore slowly decline temperatures after their peak, emissions reachthe point of net zero GHG emissions slightly later or at times never.
Early emissions reductions in this case reduce the requirement for net
negative CO2 emissions. Estimates of 2030 GHG emissions in line withthe current NDCs overlap with the highest quartile of 1.5°C-high-OS
pathways (Cross-Chapter Box 9 in Chapter 4).

2.3.3.1

Emissions of long-lived climate forcers

Climate effects of long-lived climate forcers (LLCFs) are dominated by
CO2, with smaller contributions of N2O and some fluorinated gases(Myhre et al., 2013; Blanco et al., 2014). Overall net CO2 emissions
in pathways are the result of a combination of various anthropogenic
contributions (Figure 2.5) (Clarke et al., 2014): (i) CO2 produced by fossilfuel combustion and industrial processes, (ii) CO2 emissions or removals
from the agriculture, forestry and other land use (AFOLU) sector, (iii)
CO2 capture and sequestration (CCS) from fossil fuels or industrial
activities before it is released to the atmosphere, (iv) CO2 removal bytechnological means, which in current pathways is mainly achievedby BECCS and AFOLU-related CDR, although other options could
be conceivable (see Chapter 4, Section 4.3.7). Pathways apply thesefour contributions in different configurations (Figure 2.5) depending
on societal choices and preferences related to the acceptability andavailability of certain technologies, the timing and stringency of nearterm climate policy, and the ability to limit the demand that drives
baseline emissions (Marangoni et al., 2017; Riahi et al., 2017; Grubler
et al., 2018; Rogelj et al., 2018; van Vuuren et al., 2018), and come with116

very different implication for sustainable development (Section 2.5.3).
All 1.5°C pathways see global CO2 emissions embark on a steadydecline to reach (near) net zero levels around 2050, with 1.5°C-lowOS pathways reaching net zero CO2 emissions around 2045–2055(Table 2.4; Figure 2.5). Near-term differences between the various
pathway classes are apparent, however. For instance, Below-1.5°C and
1.5°C-low-OS pathways show a clear shift towards lower CO2 emissions
in 2030 relative to other 1.5°C and 2°C pathway classes, although in all
1.5°C classes reductions are clear (Figure 2.6). These lower near-termemissions levels are a direct consequence of the former two pathway
classes limiting cumulative CO2 emissions until carbon neutrality inorder to aim for a higher probability of limiting peak warming to 1.5°C(Section 2.2.2 and 2.3.2.2). In some cases, 1.5°C-low-OS pathways
achieve net zero CO2 emissions one or two decades later, contingent on
2030 CO2 emissions in the lower quartile of the literature range, thatis, below about 18 GtCO2 yr−1. Median year-2030 global CO2 emissions
are of the order of 5–10 GtCO2 yr−1 lower in Below-1.5°C comparedto 1.5°C-low-OS pathways, which are in turn lower than 1.5°C-highOS pathways (Table 2.4). Below-1.5°C and 1.5°C-low-OS pathways
combined show a decline in global net anthropogenic CO2 emissionsof about 45% from 2010 levels by 2030 (40–60% interquartile range).
Lower-2°C pathways show CO2 emissions declining by about 25% by2030 in most pathways (10–30% interquartile range). The 1.5°C-highOS pathways show emissions levels that are broadly similar to the
2°C-consistent pathways in 2030.
The development of CO2 emissions in the second half of the century in
1.5°C pathways is characterized by the need to stay or return within
a carbon budget. Figure 2.6 shows net CO2 and N2O emissions fromvarious sources in 2050 and 2100 in 1.5°C pathways in the literature.
Virtually all 1.5°C pathways obtain net negative CO2 emissions at somepoint during the 21st century, but the extent to which net negativeemissions are relied upon varies substantially (Figure 2.6, Table 2.4).
This net withdrawal of CO2 from the atmosphere compensates forresidual long-lived non-CO2 GHG emissions that also accumulate in
the atmosphere (like N2O) or cancels some of the build-up of CO2 due
to earlier emissions to achieve increasingly higher likelihoods thatwarming stays or returns below 1.5°C (see Section 2.3.4 for a discussionof various uses of CDR). Even non-overshoot pathways that aim atachieving temperature stabilization would hence deploy a certain
amount of net negative CO2 emissions to offset any accumulating
long-lived non-CO2 GHGs. The 1.5°C overshoot pathways display
significantly larger amounts of annual net negative CO2 emissions inthe second half of the century. The larger the overshoot the more net
negative CO2 emissions are required to return temperatures to 1.5°C
by the end of the century (Table 2.4, Figure 2.1).
N2O emissions decline to a much lesser extent than CO2 in currently
available 1.5°C pathways (Figure 2.6). Current IAMs have limited
emissions-reduction potentials (Gernaat et al., 2015) (Sections 2.3.1.2,
2.4.4, Supplementary Material 2.SM.1.2), reflecting the difficulty ofeliminating N2O emission from agriculture (Bodirsky et al., 2014).
Moreover, the reliance of some pathways on significant amounts ofbioenergy after mid-century (Section 2.4.2) coupled to a substantial
use of nitrogen fertilizer (Popp et al., 2017) also makes reducing N2Oemissions harder (for example, see pathway S5 in Figure 2.6). AsMitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Chapter 22

Figure 2.6 | Annual global emissions characteristics for 2020, 2030, 2050, 2100. Data are shown for (a) Kyoto-GHG emissions, and (b) global total CO2 emissions,(c) CO2 emissions from the agriculture, forestry and other land use (AFOLU) sector, (d) global N2O emissions, and (e) CO2 emissions from fossil fuel use and industrial processes.
The latter is also split into (f) emissions from the energy supply sector (electricity sector and refineries) and (g) direct emissions from fossil-fuel use in energy demand sectors(industry, buildings, transport) (bottom row). Horizontal black lines show the median, boxes show the interquartile range, and whiskers the minimum–maximum range. Icons
indicate the four pathway archetypes used in this chapter. In case less than seven data points are available in a class, the minimum–maximum range and single data points
are shown. Kyoto-GHG, emissions in the top panel are aggregated with AR4 GWP-100 and contain CO2, CH4, N2O, HFCs, PFCs, and SF6. NF3 is typically not reported by IAMs.
Scenarios with year-2010 Kyoto-GHG emissions outside the range assessed by IPCC AR5 WGIII assessed are excluded (IPCC, 2014b).

117

Chapter 2

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Developmenta result, sizeable residual N2O emissions are currently projected tocontinue throughout the century, and measures to effectively mitigate
them will be of continued relevance for 1.5°C societies. Finally, thereduction of nitrogen use and N2O emissions from agriculture is alreadya present-day concern due to unsustainable levels of nitrogen pollution(Bodirsky et al., 2012). Section 2.4.4 provides a further assessment of
the agricultural non-CO2 emissions reduction potential.

2.3.3.2

2

Emissions of short-lived climate forcers andfluorinated gases

SLCFs include shorter-lived GHGs like CH4 and some fluorinated gases
as well as particles (aerosols), their precursors and ozone precursors.
SLCFs are strongly mitigated in 1.5°C pathways, as is the case for
2°C pathways (Figure 2.7). SLCF emissions ranges of 1.5°C and 2°C
pathway classes strongly overlap, indicating that the main incremental
mitigation contribution between 1.5°C and 2°C pathways comes fromCO2 (Luderer et al., 2018; Rogelj et al., 2018). CO2 and SLCF emissions
reductions are connected in situations where SLCF and CO2 are
co-emitted by the same process, for example, with coal-fired power
plants (Shindell and Faluvegi, 2010) or within the transport sector(Fuglestvedt et al., 2010). Many CO2-targeted mitigation measuresin industry, transport and agriculture (Sections 2.4.3–4) hence also
reduce non-CO2 forcing (Rogelj et al., 2014b; Shindell et al., 2016).
Despite the fact that methane has a strong warming effect (Myhreet al., 2013; Etminan et al., 2016), current 1.5°C-consistent pathways
still project significant emissions of CH4 by 2050, indicating only alimited CH4 mitigation potential in IAM analyses (Gernaat et al., 2015)(Sections 2.3.1.2, 2.4.4, Table 2.SM.2). The AFOLU sector contributes animportant share of the residual CH4 emissions until mid-century, with
its relative share increasing from slightly below 50% in 2010 to around
55–70% in 2030, and 60–80% in 2050 in 1.5°C-consistent pathways(interquartile range across 1.5°C-consistent pathways for projections).
Many of the proposed measures to target CH4 (Shindell et al., 2012;
Stohl et al., 2015) are included in 1.5°C-consistent pathways (Figure2.7), though not all (Sections 2.3.1.2, 2.4.4, Table 2.SM.2). A detailed
assessment of measures to further reduce AFOLU CH4 emissions has
not been conducted.
Overall reductions of SLCFs can have effects of either sign on
temperature depending on the balance between cooling and warming
agents. The reduction in SO2 emissions is the dominant single effect as
it weakens the negative total aerosol forcing. This means that reducing
all SLCF emissions to zero would result in a short-term warming,
although this warming is unlikely to be more than 0.5°C (Section 2.2
and Figure 1.5 (Samset et al., 2018)). Because of this effect, suggestions
have been proposed that target the warming agents only (referred toas short-lived climate pollutants or SLCPs instead of the more general
short-lived climate forcers; e.g., Shindell et al., 2012), though aerosols
are often emitted in varying mixtures of warming and cooling species(Bond et al., 2013). Black carbon (BC) emissions reach similar levels
across 1.5°C-consistent and 2°C-consistent pathways available in theliterature, with interquartile ranges of emissions reductions acrosspathways of 16–34% and 48–58% in 2030 and 2050, respectively,
relative to 2010 (Figure 2.7). Recent studies have identified further
reduction potentials for the near term, with global reductions of about
118

80% being suggested (Stohl et al., 2015; Klimont et al., 2017). Because
the dominant sources of certain aerosol mixtures are emitted during
the combustion of fossil fuels, the rapid phase-out of unabated fossil
fuels to avoid CO2 emissions would also result in removal of theseeither warming or cooling SLCF air-pollutant species. Furthermore,
SLCFs are also reduced by efforts to reduce particulate air pollution.
For example, year-2050 SO2 emissions (precursors of sulphate aerosol)in 1.5°C-consistent pathways are about 75–85% lower than their 2010
levels. Some caveats apply, for example, if residential biomass use
would be encouraged in industrialised countries in stringent mitigation
pathways without appropriate pollution control measures, aerosol
concentrations could also increase (Sand et al., 2015; Stohl et al., 2015).
Emissions of fluorinated gases (IPCC/TEAP, 2005; US EPA, 2013; Velders
et al., 2015; Purohit and Höglund-Isaksson, 2017) in 1.5°C-consistentpathways are reduced by roughly 75–80% relative to 2010 levels(interquartile range across 1.5°C-consistent pathways) in 2050,
with no clear differences between the classes. Although unabatedhydrofluorocarbon (HFC) emissions have been projected to increase(Velders et al., 2015), the Kigali Amendment recently added HFCs tothe basket of gases controlled under the Montreal Protocol (HöglundIsaksson et al., 2017). As part of the larger group of fluorinatedgases, HFCs are also assumed to decline in 1.5°C-consistentpathways. Projected reductions by 2050 of fluorinated gases under
1.5°C-consistent pathways are deeper than published estimates ofwhat a full implementation of the Montreal Protocol including its
Kigali Amendment would achieve (Höglund-Isaksson et al., 2017),
which project roughly a halving of fluorinated gas emissions in 2050
compared to 2010. Assuming the application of technologies thatare currently commercially available and at least to a limited extent
already tested and implemented, potential fluorinated gas emissions
reductions of more than 90% have been estimated (Höglund-Isaksson
et al., 2017).
There is a general agreement across 1.5°C-consistent pathways that
until 2030 forcing from the warming SLCFs is reduced less strongly
than the net cooling forcing from aerosol effects, compared to 2010.
As a result, the net forcing contributions from all SLCFs combined areprojected to increase slightly by about 0.2–0.3 W m−2, compared to2010. Also, by the end of the century, about 0.1–0.3 W m−2 of SLCF
forcing is generally currently projected to remain in 1.5°C-consistentscenarios (Figure 2.8). This is similar to developments in 2°C-consistentpathways (Rose et al., 2014b; Riahi et al., 2017), which show median
forcing contributions from these forcing agents that are generally nomore than 0.1 W m−2 higher. Nevertheless, there can be additional gains
from targeted deeper reductions of CH4 emissions and troposphericozone precursors, with some scenarios projecting less than 0.1 W m−2
forcing from SLCFs by 2100.

2.3.4

CDR in 1.5°C Pathways

Deep mitigation pathways assessed in AR5 showed significant
deployment of CDR, in particular through BECCS (Clarke et al., 2014).
This has led to increased debate about the necessity, feasibility anddesirability of large-scale CDR deployment, sometimes also called‘negative emissions technologies’ in the literature (Fuss et al., 2014;
Anderson and Peters, 2016; Williamson, 2016; van Vuuren et al.,Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Chapter 2

Table 2.4 | Emissions in 2030, 2050 and 2100 in 1.5°C and 2°C scenario classes and absolute annual rates of change between 2010–2030, 2020–2030 and
2030–2050, respectively.
Values show median and interquartile range across available scenarios (25th and 75th percentile given in brackets). If fewer than seven scenarios are available(*), the minimum–maximum range is given instead. Kyoto-GHG emissions are aggregated with GWP-100 values from IPCC AR4. Emissions in 2010 for total
net CO2, CO2 from fossil-fuel use and industry, and AFOLU CO2 are estimated at 38.5, 33.4, and 5 GtCO2 yr−1, respectively (Le Quéré et al., 2018). Percentage
reduction numbers included in headline statement C.1 in the Summary for Policymakers are computed relative to 2010 emissions in each individual pathway, andhence differ slightly from a case where reductions are computed relative to the historical 2010 emissions reported above. A difference is reported in estimating the‘anthropogenic’ sink by countries or the global carbon modelling community (Grassi et al., 2017), and AFOLU CO2 estimates reported here are thus not necessarily
comparable with countries’ estimates. Scenarios with year-2010 Kyoto-GHG emissions outside the range assessed by IPCC AR5 WGIII are excluded (IPCC, 2014b),
as are scenario duplicates that would bias ranges towards a single study.

Annual emissions/sequestration(GtCO2 yr-1)
Name
Total CO2(net)

CO2 fromfossil fuels
and industry(gross)CO2 fromfossil fuels
and industry(net)

CO2 from
AFOLU

Bioenergy
combinedwith carbon
captureand storage(BECCS)

Category

Timing of
Global Zero

#

2030

2050

2100

2010–2030

2020–2030

2030–2050

Year

Below-1.5°C

5*13.4 (15.4, 11.4)–3.0 (1.7, –10.6)–8.0 (–2.6, –14.2)–1.2 (–1.0, –1.3)–2.5 (–1.8, –2.8)–0.8 (–0.7, –1.2)

2044 (2037, 2054)

1.5°C-low-OS

37

20.8 (22.2, 18.0)–0.4 (2.7, –2.0)–10.8 (–8.1, –14.3)–0.8 (–0.7, –1.0)–1.7 (–1.4, –2.3)–1.0 (–0.8, –1.2)

2050 (2047, 2055)

1.5°C with noor limited OS

42

20.3
(22.0, 15.9)–0.5 (2.2, –2.8)–10.2(–7.6, –14.2)–0.9 (–0.7, –1.1)–1.8 (–1.5, –2.3)–1.0 (–0.8, –1.2)

2050(2046, 2055)

1.5°C-high-OS

36

29.1 (36.4, 26.0)

1.0 (6.3, –1.2)–13.8 (–11.1, –16.4)–0.4 (0.0, –0.6)–1.1 (–0.5, –1.5)–1.3 (–1.1, –1.8)

2052 (2049, 2059)

Lower-2°C

54

28.9 (33.7, 24.5)

9.9 (13.1, 6.5)

–5.1 (–2.6, –10.3)–0.4 (–0.2, –0.6)–1.1 (–0.8, –1.6)–0.9 (–0.8, –1.2)

2070 (2063, 2079)

Higher-2°C

5433.5 (35.0, 31.0)

17.9 (19.1, 12.2)–3.3 (0.6, –11.5)–0.2 (–0.0, –0.4)–0.7 (–0.5, –0.9)–0.8 (–0.6, –1.0)

2085(2070, post–2100)

Below-1.5°C

5*18.0 (21.4, 13.8)

10.5 (20.9, 0.3)

8.3 (11.6, 0.1)

–0.7 (–0.6, –1)–1.5 (–0.9, –2.2)–0.4 (0, –0.7)-

1.5°C-low-OS

37

22.1 (24.4, 18.7)

10.3 (14.1, 7.8)

5.6 (8.1, 2.6)

–0.5 (–0.4, –0.6)–1.3 (–0.9, –1.7)–0.6 (–0.5, –0.7)-

1.5°C with noor limited OS

42

21.6
(24.2, 18.0)

10.3 (13.8, 7.7)

6.1 (8.4, 2.6)

–0.5 (–0.4, –0.7)–1.3 (–0.9, –1.8)–0.6 (–0.4, –0.7)-

1.5°C-high-OS

3627.8 (37.1, 25.6)

13.1 (17.0, 11.6)

6.6 (8.8, 2.8)

–0.2 (0.2, –0.3)–0.8 (–0.2, –1.1)–0.7 (–0.6, –1.0)-

Lower-2°C

5427.7 (31.5, 23.5)

15.4 (19.0, 11.1)

7.2 (10.4, 3.7)

–0.2 (–0.0, –0.4)–0.8 (–0.5, –1.2)–0.6 (–0.5, –0.8)-

Higher-2°C

5431.3 (33.4, 28.7)

19.2 (22.6, 17.1)

8.1 (10.9, 5.0)

–0.1 (0.1, –0.2)–0.5 (–0.2, –0.7)–0.6 (–0.5, –0.7)-

Below-1.5°C

5*16.4 (18.2, 13.5)

1.0 (7.0, 0)–2.7 (0, –9.8)–0.8 (–0.7, –1)–1.8 (–1.2, –2.2)–0.6 (–0.5, –0.9)-

1.5°C-low-OS

37

20.6 (22.2, 17.5)

3.2 (5.6, –0.6)–8.5 (–4.1, –11.6)–0.6 (–0.5, –0.7)–1.4 (–1.1, –1.8)–0.8 (–0.7, –1.1)-

1.5°C with noor limited OS

42

20.1
(22.1, 16.8)

3.0 (5.6, 0.0)

–8.3(–3.5, –10.8)–0.6 (–0.5, –0.8)–1.4 (–1.1, –1.9)–0.8 (–0.7, –1.1)-

1.5°C-high-OS

36

26.9 (34.7, 25.3)

4.2 (10.0, 1.2)

–10.7 (–6.9, –13.2)–0.3 (0.1, –0.3)–0.9 (–0.3, –1.2)–1.2 (–0.9, –1.5)-

Lower-2°C

54

28.2 (31.0, 23.1)

11.8 (14.1, 6.2)

–3.1 (–0.7, –6.4)–0.2 (–0.1, –0.4)–0.8 (–0.5, –1.2)–0.8 (–0.7, –1.0)-

Higher-2°C

5431.0 (33.0, 28.7)

17.0 (19.3, 13.1)–2.9 (3.3, –8.0)–0.1 (0.1, –0.2)–0.5 (–0.2, –0.7)–0.7 (–0.5, –1.0)

Below-1.5°C

5*–2.2 (–0.3, –4.8)–4.4 (–1.2, –11.1)–4.4 (–2.6, –5.3)–0.3 (–0.2, –0.4)–0.5 (–0.4, –0.8)–0.1 (0, –0.4)-

1.5°C-low-OS

37–0.1 (0.8, –1.0)–2.3 (–0.6, –4.1)–2.4 (–1.2, –4.2)–0.2 (–0.2, –0.3)–0.4 (–0.3, –0.5)–0.1 (–0.1, –0.2)-

1.5°C with noor limited OS

42–0.1 (0.7, –1.3)–2.6 (–0.6, –4.5)–2.6 (–1.3, –4.2)–0.2 (–0.2, –0.3)–0.4 (–0.3, –0.5)–0.1 (–0.1, –0.2)-

1.5°C-high-OS

36

1.2 (2.7, 0.1)

–2.1 (–0.3, –5.4)–2.4 (–1.5, –5.0)–0.1 (–0.1, –0.3)–0.2 (–0.1, –0.5)–0.2 (–0.0, –0.3)-

Lower-2°C

54

1.4 (2.8, 0.3)

–1.4 (–0.5, –2.7)–2.4 (–1.3, –4.2)–0.2 (–0.1, –0.2)–0.3 (–0.2, –0.4)–0.1 (–0.1, –0.2)-

Higher-2°C

54

1.5 (2.7, 0.8)

–0.0 (1.9, –1.6)–1.3 (0.1, –3.9)–0.2 (–0.1, –0.2)–0.2 (–0.1, –0.4)–0.1 (–0.0, –0.1)-

Below-1.5°C

5*

0.4 (1.1, 0)

3.4 (8.3, 0)

5.7 (13.4, 0)

0 (0.1, 0)

0 (0.1, 0)

0.2 (0.4, 0)-

1.5°C-low-OS

36

0.3 (1.1, 0.0)

4.6 (6.4, 3.8)

12.4 (15.6, 7.6)

0.0 (0.1, 0.0)

0.0 (0.1, 0.0)

0.2 (0.3, 0.2)-

1.5°C with noor limited OS

41

0.4 (1.0, 0.0)

4.5 (6.3, 3.4)

12.4 (15.0, 6.4)

0.0 (0.1, 0.0)

0.0 (0.1, 0.0)

0.2 (0.3, 0.2)-

1.5°C-high-OS

36

0.1 (0.4, 0.0)

6.8 (9.5, 3.7)

14.9 (16.3, 12.1)

0.0 (0.0, 0.0)

0.0 (0.0, 0.0)

0.3 (0.4, 0.2)-

Lower-2°C

54

0.1 (0.3, 0.0)

3.6 (4.6, 1.8)

9.5 (12.1, 6.9)

0.0 (0.0, 0.0)

0.0 (0.0, 0.0)

0.2 (0.2, 0.1)-47

0.1 (0.2, 0.0)

3.0 (4.9, 1.6)

10.8
(15.3, 8.2) [46]0.0 (0.0, 0.0)

0.0 (0.0, 0.0)

0.1 (0.2, 0.1)

5*22.1 (22.8, 20.7)

2.7 (8.1, –3.5)–2.6 (2.7, –10.7)–1.4 (–1.3, –1.5)–2.9 (–2.1, –3.3)–0.9 (–0.7, –1.3)

2066(2044, post–2100)

1.5°C-low-OS

31

27.9 (31.1, 26.0)

7.0 (9.9, 4.5)

–3.8 (–2.1, –7.9)–1.1 (–0.9, –1.2)–2.3 (–1.8, –2.8)–1.1 (–0.9, –1.2)

2068 (2061, 2080)

1.5°C with noor limited OS

36

27.4 (30.9, 24.7)

6.5 (9.6, 4.2)

–3.7 (–1.8, –7.8)–1.1 (–1.0, –1.3)–2.4 (–1.9, –2.9)–1.1 (–0.9, –1.2)

2067 (2061, 2084)

1.5°C-high-OS

32

40.4 (48.9, 36.3)

8.4 (12.3, 6.2)

–8.5 (–5.7,–11.2)–0.5 (–0.0, –0.7)–1.3 (–0.6, –1.8)–1.5 (–1.3, –2.1)

2063 (2058, 2067)

46

39.6 (45.1, 35.7)

18.3 (20.4, 15.2)

2.1 (4.2, –2.4)–0.5 (–0.1, –0.7)–1.5 (–0.9, –2.2)–1.1 (–0.9, –1.2)

post–2100(2090 post–2100)

4245.3 (48.5, 39.3)

25.9 (27.9, 23.3)

5.2 (11.5, –4.8)–0.2 (–0.0, –0.6)–1.0 (–0.6, –1.2)–1.0 (–0.7, –1.2)

post–2100(2085 post–2100)

Higher-2°C
Kyoto
GHG (AR4)[GtCO2e]Absolute Annual Change(GtCO2/yr–1)

Below-1.5°C

Lower-2°C
Higher-2°C

-

119

2

Chapter 2

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

2

Figure 2.7 | Global characteristics of a selection of short-lived non-CO2 emissions until mid-century for five pathway classes used in this chapter. Data
are shown for (a) methane (CH4), (b) fluorinated gases (F-gas), (c) black carbon (BC), and (d) sulphur dioxide (SO2) emissions. Boxes with different colours refer to different
scenario classes. Icons on top the ranges show four illustrative pathway archetypes that apply different mitigation strategies for limiting warming to 1.5°C. Boxes show theinterquartile range, horizontal black lines the median, and whiskers the minimum–maximum range. F-gases are expressed in units of CO2-equivalence computed with 100-year
Global Warming Potentials reported in IPCC AR4.

Figure 2.8 | Estimated aggregated effective radiative forcing of SLCFs for 1.5°C and 2°C pathway classes in 2010, 2020, 2030, 2050, and 2100,
as estimated by the FAIR model (Smith et al., 2018). Aggregated short-lived climate forcer (SLCF) radiative forcing is estimated as the difference between total
anthropogenic radiative forcing and the sum of CO2 and N2O radiative forcing over time, and is expressed relative to 1750. Symbols indicate the four pathways archetypes
used in this chapter. Horizontal black lines indicate the median, boxes the interquartile range, and whiskers the minimum–maximum range per pathway class. Because very few
pathways fall into the Below-1.5°C class, only the minimum–maximum is provided here.

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Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

2017a; Obersteiner et al., 2018). Most CDR technologies remain largely
unproven to date and raise substantial concerns about adverse sideeffects on environmental and social sustainability (Smith et al., 2015;
Dooley and Kartha, 2018). A set of key questions emerge: how strongly
do 1.5°C-consistent pathways rely on CDR deployment and what typesof CDR measures are deployed at which scale? How does this varyacross available 1.5°C-consistent pathways and on which factors does
it depend? How does CDR deployment compare between 1.5°C- and2°C-consistent pathways and how does it compare with the findings
at the time of the AR5? How does CDR deployment in 1.5°C-consistentpathways relate to questions about availability, policy implementationand sustainable development implications that have been raised
about CDR technologies? The first three questions are assessed in thissection with the goal to provide an overview and assessment of CDR
deployment in the 1.5°C pathway literature. The fourth question is only
touched upon here and is addressed in greater depth in Chapter 4,
Section 4.3.7, which assesses the rapidly growing literature on costs,
potentials, availability and sustainability implications of individual
CDR measures (Minx et al., 2017, 2018; Fuss et al., 2018; Nemet
et al., 2018). In addition, Section 2.3.5 assesses the relationshipbetween delayed mitigation action and increased CDR reliance. CDR
deployment is intricately linked to the land-use transformation in
1.5°C-consistent pathways. This transformation is assessed in Section
2.4.4. Bioenergy and BECCS impacts on sustainable land management
are further assessed in Chapter 3, Section 3.6.2 and Cross-Chapter Box
7 in Chapter 3. Ultimately, a comprehensive assessment of the land
implication of land-based CDR measures will be provided in the IPCC
AR6 Special Report on Climate Change and Land (SRCCL).

2.3.4.1

CDR technologies and deployment levels in 1.5°C
pathways

A number of approaches to actively remove carbon-dioxide fromthe atmosphere are increasingly discussed in the literature (Minxet al., 2018) (see also Chapter 4, Section 4.3.7). Approaches underconsideration include the enhancement of terrestrial and coastal
carbon storage in plants and soils such as afforestation andreforestation (Canadell and Raupach, 2008), soil carbon enhancement(Paustian et al., 2016; Frank et al., 2017; Zomer et al., 2017), and otherconservation, restoration, and management options for natural andmanaged land (Griscom et al., 2017) and coastal ecosystems (McLeod
et al., 2011). Biochar sequestration (Woolf et al., 2010; Smith, 2016;
Werner et al., 2018) provides an additional route for terrestrial carbon
storage. Other approaches are concerned with storing atmosphericcarbon dioxide in geological formations. They include the combinationof biomass use for energy production with carbon capture and storage(BECCS) (Obersteiner et al., 2001; Keith and Rhodes, 2002; Goughand Upham, 2011) and direct air capture with storage (DACCS) usingchemical solvents and sorbents (Zeman and Lackner, 2004; Keith etal., 2006; Socolow et al., 2011). Further approaches investigate themineralization of atmospheric carbon dioxide (Mazzotti et al., 2005;
Matter et al., 2016), including enhanced weathering of rocks (Schuilingand Krijgsman, 2006; Hartmann et al., 2013; Strefler et al., 2018a).
A fourth group of approaches is concerned with the sequestrationof carbon dioxide in the oceans, for example by means of ocean
alkalinization (Kheshgi, 1995; Rau, 2011; Ilyina et al., 2013; Lenton etal., 2018). The costs, CDR potential and environmental side effects ofChapter 2

several of these measures are increasingly investigated and comparedin the literature, but large uncertainties remain, in particular concerning
the feasibility and impact of large-scale deployment of CDR measures(The Royal Society, 2009; Smith et al., 2015; Psarras et al., 2017; Fusset al., 2018) (see Chapter 4.3.7). There are also proposals to remove
methane, nitrous oxide and halocarbons via photocatalysis from theatmosphere (Boucher and Folberth, 2010; de Richter et al., 2017), but
a broader assessment of their effectiveness, cost and sustainability
impacts is lacking to date.
Only some of these approaches have so far been considered in IAMs
(see Section 2.3.1.2). The mitigation scenario literature up to AR5
mostly included BECCS and, to a more limited extent, afforestation
and reforestation (Clarke et al., 2014). Since then, some 2°C- and
1.5°C-consistent pathways including additional CDR measures suchas DACCS (Chen and Tavoni, 2013; Marcucci et al., 2017; Lehtilä andKoljonen, 2018; Strefler et al., 2018b) and soil carbon sequestration(Frank et al., 2017) have become available. Other, more speculative
approaches, in particular ocean-based CDR and removal of non-CO2
gases, have not yet been taken up by the literature on mitigation
pathways. See Supplementary Material 2.SM.1.2 for an overview on
the coverage of CDR measures in models which contributed pathways
to this assessment. Chapter 4.3.7 assesses the potential, costs, andsustainability implications of the full range of CDR measures.
Integrated assessment modelling has not yet explored land conservation,
restoration and management options to remove carbon dioxide fromthe atmosphere in sufficient depth, despite land management having a
potentially considerable impact on the terrestrial carbon stock (Erb etal., 2018). Moreover, associated CDR measures have low technologicalrequirements, and come with potential environmental and social
co-benefits (Griscom et al., 2017). Despite the evolving capabilities of
IAMs in accounting for a wider range of CDR measures, 1.5°C-consistentpathways assessed here continue to predominantly rely on BECCS andafforestation/reforestation (see Supplementary Material 2.SM.1.2).
However, IAMs with spatially explicit land-use modelling include a fullaccounting of land-use change emissions comprising carbon stored
in the terrestrial biosphere and soils. Net CDR in the AFOLU sector,
including but not restricted to afforestation and reforestation, can thus
in principle be inferred by comparing AFOLU CO2 emissions between
a baseline scenario and a 1.5°C-consistent pathway from the samemodel and study. However, baseline AFOLU CO2 emissions can not only
be reduced by CDR in the AFOLU sector but also by measures to reduce
deforestation and preserve land carbon stocks. The pathway literatureand pathway data available to this assessment do not yet allow
separating the two contributions. As a conservative approximation, theadditional net negative AFOLU CO2 emissions below the baseline aretaken as a proxy for AFOLU CDR in this assessment. Because this does
not include CDR that was deployed before reaching net zero AFOLU
CO2 emissions, this approximation is a lower-bound for terrestrial CDR
in the AFOLU sector (including all mitigation-policy-related factors thatlead to net negative AFOLU CO2 emissions).
The scale and type of CDR deployment in 1.5°C-consistent pathways
varies widely (Figure 2.9 and 2.10). Overall CDR deployment over the
21st century is substantial in most of the pathways, and deployment
levels cover a wide range, on the order of 100–1000 Gt CO2 in 1.5°C
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2

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

pathways with no or limited overshoot (730 [260–1030] GtCO2, formedian and 5th–95th percentile range). Both BECCS (480 [0–1000]
GtCO2 in 1.5°C pathways with no or limited overshoot) and AFOLU
CDR measures including afforestation and reforestation (210 [10540] GtCO2 in 1.5°C pathways with no or limited overshoot) can playa major role,4 but for both cases pathways exist where they play norole at all. This shows the flexibility in substituting between individual
CDR measures, once a portfolio of options becomes available. The highend of the CDR deployment range is populated by high overshootpathways, as illustrated by pathway archetype S5 based on SSP5
(fossil-fuelled development, see Section 2.3.1.1) and characterized
by very large BECCS deployment to return warming to 1.5°C by 2100
(Kriegler et al., 2017). In contrast, the low end is populated by a few
pathways with no or limited overshoot that limit CDR to on the order of
100–200 GtCO2 over the 21st century, coming entirely from terrestrial
CDR measures with no or small use of BECCS. These are pathways
with very low energy demand facilitating the rapid phase-out offossil fuels and process emissions that exclude BECCS and CCS use(Grubler et al., 2018) and/or pathways with rapid shifts to sustainable

food consumption freeing up sufficient land areas for afforestation
and reforestation (Haberl et al., 2011; van Vuuren et al., 2018). Somepathways use neither BECCS nor afforestation but still rely on CDR
through considerable net negative CO2 emissions in the AFOLU sector
around mid-century (Holz et al., 2018b). We conclude that the role of
BECCS as a dominant CDR measure in deep mitigation pathways has
been reduced since the time of the AR5. This is related to three factors:
a larger variation of underlying assumptions about socio-economic
drivers (Riahi et al., 2017; Rogelj et al., 2018) and associated energy(Grubler et al., 2018) and food demand (van Vuuren et al., 2018);
the incorporation of a larger portfolio of mitigation and CDR options(Marcucci et al., 2017; Grubler et al., 2018; Lehtilä and Koljonen,
2018; Liu et al., 2018; van Vuuren et al., 2018); and targeted analysis
of deployment limits for (specific) CDR measures (Holz et al., 2018b;
Kriegler et al., 2018a; Strefler et al., 2018b), including the availabilityof bioenergy (Bauer et al., 2018), CCS (Krey et al., 2014a; Grubler etal., 2018) and afforestation (Popp et al., 2014b, 2017). As additional
CDR measures are being built into IAMs, the prevalence of BECCS is
expected to be further reduced.

Figure 2.9 | Cumulative CDR deployment in 1.5°C-consistent pathways in the literature as reported in the database collected for this assessment until
2050 (panel a) and until 2100 (panel b). Total CDR comprises all forms of CDR, including AFOLU CDR and BECCS, and, in a few pathways, other CDR measures like DACCS.
It does not include CCS combined with fossil fuels (which is not a CDR technology as it does not result in active removal of CO2 from the atmosphere). AFOLU CDR has not beenreported directly and is hence represented by means of a proxy: the additional amount of net negative CO2 emissions in the AFOLU sector compared to a baseline scenario (seetext for a discussion). ‘Compensatory CO2’ depicts the cumulative amount of CDR that is used to neutralize concurrent residual CO2 emissions. ‘Net negative CO2’ describes the
additional amount of CDR that is used to produce net negative CO2 emissions, once residual CO2 emissions are neutralized. The two quantities add up to total CDR for individual
pathways (not for percentiles and medians, see Footnote 4).

As discussed in Section 2.3.2, CDR can be used in two ways inmitigation pathways: (i) to move more rapidly towards the point ofcarbon neutrality and maintain it afterwards in order to stabilize global
mean temperature rise, and (ii) to produce net negative CO2 emissions,drawing down anthropogenic CO2 in the atmosphere in order to decline
global mean temperature after an overshoot peak (Kriegler et al., 2018b;
Obersteiner et al., 2018). Both uses are important in 1.5°C-consistentpathways (Figure 2.9 and 2.10). Because of the tighter remaining 1.5°C
4carbon budget, and because many pathways in the literature do not
restrict exceeding this budget prior to 2100, the relative weight of
the net negative emissions component of CDR increases compared to
2°C-consistent pathways. The amount of compensatory CDR remains
roughly the same over the century. This is the net effect of stronger
deployment of compensatory CDR until mid-century to accelerate
the approach to carbon neutrality and less compensatory CDR in thesecond half of the century due to deeper mitigation of end-use sectors

The median and percentiles of the sum of two quantities is in general not equal to the sum of the medians and percentiles, respectively, of the two quantitites.

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Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development
LegendAll amounts over 2018-2100 period

Total amount of CO2 producedby the economy
Amount not emitted
due to Fossil CCS

Gross CO2 emissions:
total amount of CO2 emissions
being vented into the atmosphere

Chapter 2

CDR compensating
residual CO2
emissions

Maximum (peak) CO2 emissions
peak amount emitted into the atmosphere
during the 21st century after subtracting
CDR (relates to peak warming)

BECCS

AFOLU CDR

Net CO2 emissions:amount that remains emitted
over the 2018-2100 period
after subtracting CDR(relates to year-2100 warming)

net negative CO2

Remaining 1.5°C carbon budget
from 2018 onwards
range: TCRE 50% and TCRE67%

2

1000

500

0

Pathway class:
S1
Median warming:
1.5°C-low-OS
peak : 1.5°C
estimated by MAGICC(AR5 setup)
year-2100 : 1.3°C

S2
1.5°C-low-OS
peak : 1.6°C
year-2100 : 1.3°C

S5
1.5°C-high-OS
peak : 1.8°C
year-2100 : 1.3°C

LED
1.5°C-low-OS
peak : 1.5°C
year-2100 : 1.4°C

Remaining
1.5°C carbon budget
accounting for addtional
Earth-system feedbacks

Cumulated CO2 2018−2100 (GtCO2)

1500

Figure 2.10 | Accounting of cumulative CO2 emissions for the four 1.5°C-consistent pathway archetypes. See top panel for explanation of the bar plots. Total
CDR is the difference between gross (red horizontal bar) and net (purple horizontal bar) cumulative CO2 emissions over the period 2018–2100, and it is equal to the sum of the
BECCS (grey) and AFOLU CDR (green) contributions. Cumulative net negative emissions are the difference between peak (orange horizontal bar) and net (purple) cumulative CO2
emissions. The blue shaded area depicts the estimated range of the remaining carbon budget for a two-in-three to one-in-two chance of staying below1.5°C. The grey shaded
area depicts the range when accounting for additional Earth system feedbacks.

in 1.5°C-consistent pathways (Luderer et al., 2018). Comparing medianlevels, end-of-century net cumulative CO2 emissions are roughly
600 GtCO2 smaller in 1.5°C compared to 2°C-consistent pathways,
with approximately two thirds coming from further reductions of grossCO2 emissions and the remaining third from increased CDR deployment.
As a result, median levels of total CDR deployment in 1.5°C-consistentpathways are larger than in 2°C-consistent pathways (Figure 2.9), butwith marked variations in each pathway class.
Ramp-up rates of individual CDR measures in 1.5°C-consistentpathways are provided in Table 2.4. BECCS deployment is still
limited in 2030, but ramps up to median levels of 3 (Below-1.5°C),
5 (1.5°C-low-OS) and 7 GtCO2 yr−1 (1.5°C-high-OS) in 2050, and to 6
(Below-1.5°C), 12 (1.5°C-low-OS) and 15 GtCO2 yr−1 (1.5°C-high-OS)
in 2100, respectively. In 1.5°C pathways with no or limited overshoot,this amounts to 0–1, 0–8, and 0–16 GtCO2 yr−1 in 2030, 2050, and2100, respectively (ranges refer to the union of the min-max rangeof the Below-1.5°C and the interquartile range of the 1.5°C-low-OS
class; see Table 2.4). Net CDR in the AFOLU sector reaches slightly
lower levels in 2050, and stays more constant until 2100. In 1.5°C
pathways with no or limited overshoot, AFOLU CDR amounts to 0–5,

1–11, and 1–5 GtCO2 yr−1 (see above for the definition of the ranges)
in 2030, 2050, and 2100, respectively. In contrast to BECCS, AFOLU
CDR is more strongly deployed in non-overshoot than overshootpathways. This indicates differences in the timing of the two CDR
approaches. Afforestation is scaled up until around mid-century, when
the time of carbon neutrality is reached in 1.5°C-consistent pathways,
while BECCS is projected to be used predominantly in the 2nd halfof the century (Figure 2.5). This reflects the fact that afforestation isa readily available CDR technology, while BECCS is more costly andmuch less mature a technology. As a result, the two options contributedifferently to compensating concurrent CO2 emissions (until 2050)
and to producing net negative CO2 emissions (post-2050). BECCS
deployment is particularly strong in pathways with high overshoots
but can also feature in pathways with low overshoot (see Figure 2.5
and 2.10). Annual deployment levels until mid-century are not foundto be significantly different between 2°C-consistent pathways and
1.5°C-consistent pathways with no or low overshoot. This suggests
similar implementation challenges for ramping up BECCS deployment
at the rates projected in the pathways (Honegger and Reiner, 2018;
Nemet et al., 2018). The feasibility and sustainability of upscaling CDR
at these rates is assessed in Chapter 4.3.7.
123Chapter 2

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Concerns have been raised that building expectations about largescale CDR deployment in the future can lead to an actual reductionof near-term mitigation efforts (Geden, 2015; Anderson and Peters,
2016; Dooley and Kartha, 2018). The pathway literature confirms that
CDR availability influences the shape of mitigation pathways critically(Krey et al., 2014a; Holz et al., 2018b; Kriegler et al., 2018a; Strefler
et al., 2018b). Deeper near-term emissions reductions are required toreach the 1.5°C–2°C target range if CDR availability is constrained. As
a result, the least-cost benchmark pathways to derive GHG emissions
gap estimates (UNEP, 2017) are dependent on assumptions about CDR

availability. Using GHG benchmarks in climate policy makes implicit
assumptions about CDR availability (Fuss et al., 2014; van Vuuren
et al., 2017a). At the same time, the literature also shows that rapid
and stringent mitigation as well as large-scale CDR deployment occur
simultaneously in 1.5°C pathways due to the tight remaining carbon
budget (Luderer et al., 2018). Thus, an emissions gap is identified evenfor high CDR availability (Strefler et al., 2018b), contradicting a waitand-see approach. There are significant trade-offs between near-term
action, overshoot and reliance on CDR deployment in the long-term
which are assessed in Section 2.3.5.

Box 2.1 | Bioenergy and BECCS Deployment in Integrated Assessment Modelling

2

Bioenergy can be used in various parts of the energy sector of IAMs, including for electricity, liquid fuel, biogas, and hydrogen production.
It is this flexibility that makes bioenergy and bioenergy technologies valuable for the decarbonization of energy use (Klein et al., 2014;
Krey et al., 2014a; Rose et al., 2014a; Bauer et al., 2017, 2018). Most bioenergy technologies in IAMs are also available in combination
with CCS (BECCS). Assumed capture rates differ between technologies, for example, about 90% for electricity and hydrogen production
and about 40–50% for liquid fuel production. Decisions about bioenergy deployment in IAMs are based on economic considerations tostay within a carbon budget that is consistent with a long-term climate goal. IAMs consider both the value of bioenergy in the energy
system and the value of BECCS in removing CO2 from the atmosphere. Typically, if bioenergy is strongly limited, BECCS technologies
with high capture rates are favoured. If bioenergy is plentiful IAMs tend to choose biofuel technologies with lower capture rates but
high value for replacing fossil fuels in transport (Kriegler et al., 2013a; Bauer et al., 2018). Most bioenergy use in IAMs is combined with
CCS if available (Rose et al., 2014a). If CCS is unavailable, bioenergy use remains largely unchanged or even increases due to the highvalue of bioenergy for the energy transformation (Bauer et al., 2018). As land impacts are tied to bioenergy use, the exclusion of BECCS
from the mitigation portfolio will not automatically remove the trade-offs with food, water and other sustainability objectives due tothe continued and potentially increased use of bioenergy.
IAMs assume bioenergy to be supplied mostly from second generation biomass feedstocks such as dedicated cellulosic crops (forexample Miscanthus or poplar) as well as agricultural and forest residues. Detailed process IAMs include land-use models that capturecompetition for land for different uses (food, feed, fiber, bioenergy, carbon storage, biodiversity protection) under a range of dynamic
factors including socio-economic drivers, productivity increases in crop and livestock systems, food demand, and land, environmental,
biodiversity, and carbon policies. Assumptions about these factors can vary widely between different scenarios (Calvin et al., 2014;
Popp et al., 2017; van Vuuren et al., 2018). IAMs capture a number of potential environmental impacts from bioenergy production, inparticular indirect land-use change emissions from land conversion and nitrogen and water use for bioenergy production (Kraxner et al.,2013; Bodirsky et al., 2014; Bonsch et al., 2014; Obersteiner et al., 2016; Humpenöder et al., 2018). The impact of bioenergy production
on soil degradation is an area of active IAM development and was not comprehensively accounted for in the mitigation pathways
assessed in this report (but is, for example, in Frank et al., 2017). Whether bioenergy has large adverse impacts on environmental andsocietal goals depends in large parts on the governance of land use (Haberl et al., 2013; Erb et al., 2016b; Obersteiner et al., 2016;
Humpenöder et al., 2018). Here IAMs often make idealized assumptions about effective land management, such as full protection of
the land carbon stock by conservation measures and a global carbon price, respectively, but variations on these assumptions have also
been explored (Calvin et al., 2014; Popp et al., 2014a).

2.3.4.2

Sustainability implications of CDR deployment in 1.5°C
pathways

Strong concerns about the sustainability implications of large-scale
CDR deployment in deep mitigation pathways have been raised in theliterature (Williamson and Bodle, 2016; Boysen et al., 2017b; Dooley andKartha, 2018; Heck et al., 2018), and a number of important knowledge
gaps have been identified (Fuss et al., 2016). An assessment of theliterature on implementation constraints and sustainable development
implications of CDR measures is provided in Chapter 4, Section 4.3.7 and
the Cross-chapter Box 7 in Chapter 3. An initial discussion of potential
124

environmental side effects of CDR deployment in 1.5°C-consistentpathways is provided in this section. Chapter 4, Section 4.3.7 thencontrasts CDR deployment in 1.5°C-consistent pathways with otherbranches of literature on limitations of CDR. Integrated modelling aims
to explore a range of developments compatible with specific climate
goals and often does not include the full set of broader environmental
and societal concerns beyond climate change. This has given rise to
the concept of sustainable development pathways (Cross-Chapter Box
1 in Chapter 1) (van Vuuren et al., 2015), and there is an increasing
body of work to extend integrated modelling to cover a broader rangeof sustainable development goals (Section 2.6). However, only some

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Developmentof the available 1.5°C-consistent pathways were developed within alarger sustainable development context (Bertram et al., 2018; Grubler
et al., 2018; Rogelj et al., 2018; van Vuuren et al., 2018). As discussed
in Section 2.3.4.1, those pathways are characterized by low energy
and/or food demand effectively limiting fossil-fuel substitution andalleviating land competition, respectively. They also include regulatory
policies for deepening early action and ensuring environmental
protection (Bertram et al., 2018). Overall sustainability implications of
1.5°C-consistent pathways are assessed in Section 2.5.3 and Chapter
5, Section 5.4.
Individual CDR measures have different characteristics and therefore
would carry different risks for their sustainable deployment at scale(Smith et al., 2015). Terrestrial CDR measures, BECCS and enhanced
weathering of rock powder distributed on agricultural lands requireland. Those land-based measures could have substantial impacts
on environmental services and ecosystems (Cross-Chapter Box 7 in
Chapter 3) (Smith and Torn, 2013; Boysen et al., 2016; Heck et al., 2016;
Krause et al., 2017). Measures like afforestation and bioenergy withand without CCS that directly compete with other land uses could havesignificant impacts on agricultural and food systems (Creutzig et al.,2012, 2015; Calvin et al., 2014; Popp et al., 2014b, 2017; Kreidenweis
et al., 2016; Boysen et al., 2017a; Frank et al., 2017; Stevanović et al.,2017; Strapasson et al., 2017; Humpenöder et al., 2018). BECCS using
dedicated bioenergy crops could substantially increase agricultural
water demand (Bonsch et al., 2014; Séférian et al., 2018) and nitrogen
fertilizer use (Bodirsky et al., 2014). DACCS and BECCS rely on CCS andwould require safe storage space in geological formations, including
management of leakage risks (Pawar et al., 2015) and induced
seismicity (Nicol et al., 2013). Some approaches like DACCS have highenergy demand (Socolow et al., 2011). Most of the CDR measurescurrently discussed could have significant impacts on either land,
energy, water, or nutrients if deployed at scale (Smith et al., 2015).
However, actual trade-offs depend on a multitude factors (Haberl etal., 2011; Erb et al., 2012; Humpenöder et al., 2018), including themodalities of CDR deployment (e.g., on marginal vs. productive land)(Bauer et al., 2018), socio-economic developments (Popp et al., 2017),
dietary choices (Stehfest et al., 2009; Popp et al., 2010; van Sluisveld etal., 2016; Weindl et al., 2017; van Vuuren et al., 2018), yield increases,
livestock productivity and other advances in agricultural technology(Havlik et al., 2013; Valin et al., 2013; Havlík et al., 2014; Weindl et al.,2015; Erb et al., 2016b), land policies (Schmitz et al., 2012; Calvin et al.,2014; Popp et al., 2014a), and governance of land use (Unruh, 2011;
Buck, 2016; Honegger and Reiner, 2018).
Figure 2.11 shows the land requirements for BECCS and afforestationin the selected 1.5°C-consistent pathway archetypes, including the LED(Grubler et al., 2018) and S1 pathways (Fujimori, 2017; Rogelj et al.,2018) following a sustainable development paradigm. As discussed,
these land-use patterns are heavily influenced by assumptions about,among other things, future population levels, crop yields, livestock
production systems, and food and livestock demand, which all vary
between the pathways (Popp et al., 2017) (Section 2.3.1.1). In pathways
that allow for large-scale afforestation in addition to BECCS, landdemand for afforestation can be larger than for BECCS (Humpenöder
et al., 2014). This follows from the assumption in the modelled
pathways that, unlike bioenergy crops, forests are not harvested to

Chapter 2

allow unabated carbon storage on the same patch of land. If wood
harvest and subsequent processing or burial are taken into account,
this finding can change. There are also synergies between the various
uses of land, which are not reflected in the depicted pathways. Trees
can grow on agricultural land (Zomer et al., 2016), and harvested
wood can be used with BECCS and pyrolysis systems (Werner et al.,2018). The pathways show a very substantial land demand for the two
CDR measures combined, up to the magnitude of the current global
cropland area. This is achieved in IAMs in particular by a conversion of
pasture land freed by intensification of livestock production systems,
pasture intensification and/or demand changes (Weindl et al., 2017),
and to a more limited extent, cropland for food production, as well
as expansion into natural land. However, pursuing such large-scalechanges in land use would pose significant food supply, environmental
and governance challenges, concerning both land management andtenure (Unruh, 2011; Erb et al., 2012, 2016b; Haberl et al., 2013;
Haberl, 2015; Buck, 2016), particularly if synergies between land
uses, the relevance of dietary changes for reducing land demand, andco-benefits with other sustainable development objectives are notfully recognized. A general discussion of the land-use transformation in
1.5°C-consistent pathways is provided in Section 2.4.4.
An important consideration for CDR which moves carbon from theatmosphere to the geological, oceanic or terrestrial carbon pools is thepermanence of carbon stored in these different pools (Matthews andCaldeira, 2008; NRC, 2015; Fuss et al., 2016; Jones et al., 2016) (seealso Chapter 4, Section 4.3.7 for a discussion). Terrestrial carbon canbe returned to the atmosphere on decadal time scales by a variety ofmechanisms, such as soil degradation, forest pest outbreaks and forest
fires, and therefore requires careful consideration of policy frameworks
to manage carbon storage, for example, in forests (Gren and Aklilu,2016). There are similar concerns about outgassing of CO2 from oceanstorage (Herzog et al., 2003), unless it is transformed to a substancethat does not easily exchange with the atmosphere, for example, ocean
alkalinity or buried marine biomass (Rau, 2011). Understanding of theassessment and management of the potential risk of CO2 release fromgeological storage of CO2 has improved since the IPCC Special Reporton Carbon Dioxide Capture and Storage (IPCC, 2005) with experienceand the development of management practices in geological storage
projects, including risk management to prevent sustentative leakage(Pawar et al., 2015). Estimates of leakage risk have been updated toinclude scenarios of unregulated drilling and limited wellbore integrity(Choi et al., 2013) and find that about 70% of stored CO2 would still
be retained after 10,000 years in these circumstances (Alcalde et al.,2018). The literature on the potential environmental impacts from theleakage of CO2 – and approaches to minimize these impacts shoulda leak occur – has also grown and is reviewed by Jones et al. (2015).
To the extent that non-permanence of terrestrial and geological carbon
storage is driven by socio-economic and political factors, there areparallels to questions of fossil-fuel reservoirs remaining in the ground(Scott et al., 2015).

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Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

2

Figure 2.11 | Land-use changes in 2050 and 2100 in the illustrative 1.5°C-consistent pathway archetypes (Fricko et al., 2017; Fujimori, 2017; Kriegler etal., 2017; Grubler et al., 2018; Rogelj et al., 2018). Changes in land for food crops, energy crops, forest, pasture and other natural land are shown, compared to 2010.

2.3.5

Implications of Near-Term Action in 1.5°C Pathways

Less CO2 emission reductions in the near term would require steeper
and deeper reductions in the longer term in order to meet specificwarming targets afterwards (Riahi et al., 2015; Luderer et al., 2016a).
This is a direct consequence of the quasi-linear relationship between
the total cumulative amount of CO2 emitted into the atmosphere andglobal mean temperature rise (Matthews et al., 2009; Zickfeld et al.,2009; Collins et al., 2013; Knutti and Rogelj, 2015). Besides this clear
geophysical trade-off over time, delaying GHG emissions reductions
over the coming years also leads to economic and institutional lock-in
into carbon-intensive infrastructure, that is, the continued investment
in and use of carbon-intensive technologies that are difficult or costly
to phase-out once deployed (Unruh and Carrillo-Hermosilla, 2006;
Jakob et al., 2014; Erickson et al., 2015; Steckel et al., 2015; Seto et al.,2016; Michaelowa et al., 2018). Studies show that to meet stringent
climate targets despite near-term delays in emissions reductions,models prematurely retire carbon-intensive infrastructure, in particularcoal without CCS (Bertram et al., 2015a; Johnson et al., 2015). The AR5reports that delaying mitigation action leads to substantially higher
rates of emissions reductions afterwards, a larger reliance on CDR
technologies in the long term, and higher transitional and long-term
economic impacts (Clarke et al., 2014). The literature mainly focuses
on delayed action until 2030 in the context of meeting a 2°C goal(den Elzen et al., 2010; van Vuuren and Riahi, 2011; Kriegler et al.,2013b; Luderer et al., 2013, 2016a; Rogelj et al., 2013b; Riahi et al.,2015; OECD/IEA and IRENA, 2017). However, because of the smaller
carbon budget consistent with limiting warming to 1.5°C and theabsence of a clearly declining long-term trend in global emissions
to date, these general insights apply equally, or even more so, to themore stringent mitigation context of 1.5°C-consistent pathways. This5

is further supported by estimates of committed emissions due to fossil
fuel-based infrastructure (Seto et al., 2016; Edenhofer et al., 2018).
All available 1.5°C pathways that explore consistent mitigation action
from 2020 onwards peak global Kyoto-GHG emissions in the next
decade and already decline Kyoto-GHG emissions to below 2010 levels
by 2030. The near-term emissions development in these pathways
can be compared with estimated emissions in 2030 implied by the
Nationally Determined Contributions (NDCs) submitted by Parties
to the Paris Agreement (Figure 2.12). Altogether, the unconditional(conditional) NDCs are assessed to result in global Kyoto-GHG
emissions on the order of 52–58 (50–54) GtCO2e yr−1 in 2030 (e.g.,den Elzen et al., 2016; Fujimori et al., 2016; UNFCCC, 2016; Rogelj etal., 2017; Rose et al., 2017b; Benveniste et al., 2018; Vrontisi et al.,2018; see Cross-Chapter Box 11 in Chapter 4 for detailed assessment).
In contrast, 1.5°C pathways with limited overshoot available to thisassessment show an interquartile range of about 26–31 (median 28)
GtCO2e yr−1 in 20305 (Table 2.4, Section 2.3.3). Based on these ranges,
this report assesses the emissions gap for a two-in-three chance oflimiting warming to 1.5°C to be 26 (19–29) and 28 (22–33) GtCO2e(median and interquartile ranges) for conditional and unconditional
NDCs, respectively (Cross-Chapter Box 11, applying GWP-100 values
from the IPCC Second Assessment Report).
The later emissions peak and decline, the more CO2 will haveaccumulated in the atmosphere. Peak cumulated CO2 emissions –and consequently peak temperatures – increase with higher 2030
emissions levels (Figure 2.12). Current NDCs (Cross-Chapter Box 11 in
Chapter 4) are estimated to lead to CO2 emissions of about 400–560
GtCO2 from 2018 to 2030 (Rogelj et al., 2016a). Available 1.5°C- and2°C-consistent pathways with 2030 emissions in the range estimatedNote that aggregated Kyoto-GHG emissions implied by the NDCs from Cross-Chapter Box 11 in Chapter 4 and Kyoto-GHG ranges from the pathway classes in Chapter 2
are only approximately comparable, because this chapter applies GWP-100 values from the IPCC Fourth Assessment Report while the NDC Cross-Chapter Box 11 applies
GWP-100 values from the IPCC Second Assessment Report. At a global scale, switching between GWP-100 values of the Second to the Fourth IPCC Assessment Report
would result in an increase in estimated aggregated Kyoto-GHG emissions of no more than about 3% in 2030 (UNFCCC, 2016).

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Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

1.8

1.7

1.6

1.5

NDC
range
1.4
15

20

25

30

35

40

45

50

55

60

Kyoto gas emissions in 2030 (GtCO2 eq yr )-post-2030 transition, and the assessment of carbon budgets in Section
2.2.2, global warming is assessed to exceed 1.5°C if emissions stay atthe levels implied by the NDCs until 2030 (Figure 2.12). The chancesof remaining below 1.5°C in these circumstances remain conditional
upon geophysical properties that are uncertain, but these Earthsystem response uncertainties would have to serendipitously alignbeyond current median estimates in order for current NDCs to become
consistent with limiting warming to 1.5°C.

Peak Cumulative CO2 Emissions from 2018 (GtCO2)

Below 1.5C
1.5C Low OS
1.5C High OS
NDC pathways(a)oMedian global warming since preindustrial ( C)for the NDCs rely on an assumed swift and widespread deployment of
CDR after 2030, and show peak cumulative CO2 emissions from 2018of about 800–1000 GtCO2, above the remaining carbon budget for a
one-in-two chance of remaining below 1.5°C. These emissions reflectthat no pathway is able to project a phase-out of CO2 emissions startingfrom year-2030 NDC levels of about 40 GtCO2 yr−1 (Fawcett et al., 2015;
Rogelj et al., 2016a) to net zero in less than about 15 years. Based on
the implied emissions until 2030, the high challenges of the assumed

Chapter 2

1000

900

2

800

700

Kriegler et al., 2018a

600

500

400
15

NDC
range

20

25

30

35

40

45

50

55

60

Kyoto gas emissions in 2030 (GtCO2 eq yr-)

Figure 2.12 | Median global warming estimated by MAGICC (panel a) and peak cumulative CO2 emissions (panel b) in 1.5°C-consistent pathways in theSR1.5 scenario database, as a function of CO2-equivalent emissions (based on AR4 GWP-100) of Kyoto-GHGs in 2030. Pathways that were forced to go throughthe NDCs or a similarly high emissions point in 2030 by design are highlighted by yellow marker edges (see caption of Figure 2.13 and text for further details on the designof these pathways). The combined range of global Kyoto-GHG emissions in 2030 for the conditional and unconditional NDCs assessed in Cross-Chapter Box 11 is shown by
the grey shaded area (adjusted to AR4 GWPs for comparison). As a second line of evidence, peak cumulative CO2 emissions derived from a 1.5°C pathway sensitivity analysis(Kriegler et al., 2018b) are shown by grey circles in the right-hand panel. Circles show gross fossil-fuel and industry emissions of the sensitivity cases, increased by assumptions
about the contributions from AFOLU (5 GtCO2 yr−1 until 2020, followed by a linear phase out until 2040) and non-CO2 Kyoto-GHGs (median non-CO2 contribution from
1.5°C-consistent pathways available in the database: 10 GtCO2e yr−1 in 2030), and reduced by assumptions about CDR deployment until the time of net zero CO2 emissions(limiting case for CDR deployment assumed in (Kriegler et al., 2018b) (logistic growth to 1, 4, 10 GtCO2 yr−1 in 2030, 2040, and 2050, respectively, leading to approximately
100 GtCO2 of CDR by mid-century).

It is unclear whether following NDCs until 2030 would still allow
global mean temperature to return to 1.5°C by 2100 after a temporary
overshoot, due to the uncertainty associated with the Earth system
response to net negative emissions after a peak (Section 2.2). Available
IAM studies are working with reduced-form carbon cycle–climate
models like MAGICC, which assume a largely symmetric Earthsystem response to positive and net negative CO2 emissions. The IAM
findings on returning warming to 1.5°C from NDCs after a temporary
temperature overshoot are hence all conditional on this assumption.
Two types of pathways with 1.5°C-consistent action starting in 2030
have been considered in the literature (Luderer et al., 2018) (Figure2.13): pathways aiming to obtain the same end-of-century carbon
budget as 1.5°C-consistent pathways starting in 2020 despite higher
emissions until 2030, and pathways assuming the same mitigation
stringency after 2030 as in 1.5°C-consistent pathways starting in2020 (approximated by using the same global price of emissions asfound in least-cost pathways starting from 2020). An IAM comparison
study found increasing challenges to implementing pathways with thesame end-of-century carbon budgets after following NDCs until 2030
(Luderer et al., 2018). The majority of model experiments (four out ofseven) failed to produce NDC pathways that would return cumulative
CO2 emissions over the 2016–2100 period to 200 GtCO2, indicating
limitations to the availability and timing of CDR. The few such
pathways that were identified show highly disruptive features in 2030
(including abrupt transitions from moderate to very large emissions
reduction and low carbon energy deployment rates) indicating a highrisk that the required post-2030 transformations are too steep andabrupt to be achieved by the mitigation measures in the models (highconfidence). NDC pathways aiming for a cumulative 2016–2100 CO2emissions budget of 800 GtCO2 were more readily obtained (Luderer etal., 2018), and some were classified as 1.5°C-high-OS pathways in thisassessment (Section 2.1).
127Chapter 2

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2

40

b
Additional global mean
temperature rise (°C)

a

50

Net CO2 emissions (GtCO2/yr)

Variations in global CO2 emissions
over next decade

30
20

A

10

P

LegendData from the same model forthe set of three pathways withnear-term emissions variations
0.2

MAGICC

B
A

FAIR

0.1

P
Change relative topathway with deep
CO2 reductions
from 2020

0

2020

2040

2060

2080

Other pathways witha similar design

2100

0
−10

Range of below-1.5°C
and 1.5°C-low-OS pathways

B−20
2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Year

1500

d

0.6

1000

0.4
0.2

500

0

−0.2

0

2030

2050

2100

f
1400
1200
1000
800
600

Remaining 1.5°C
carbon budget

400

Annual average FF & Ind CO2
emissions reductions (GtCO2/yr])e
Cumulative FF & Ind CO2
emissions from 2018 (GtCO2)

0.8

Annual average CDR addition(GtCO2/yr)

Cumulative CDR (GtCO2)c2020−2030

2030−2050

2050−2100

2020−2030

2030−2050

2050−2100

2
1.5
1
0.5
0

−0.5

200

2030

2050

2100

Figure 2.13 | Comparison of 1.5°C-consistent pathways starting action as of 2020 (A; light-blue diamonds) with pathways following the NDCs until 2030
and aiming to limit warming to 1.5°C thereafter. The 1.5°C pathways that follow the NDCs until 2030 either aim for the same cumulative CO2 emissions by 2100 as thepathways that start action as of 2020 (B; red diamonds) or assume the same mitigation stringency as reflected by the price of emissions in associated least-cost 1.5°C-consistentpathways starting from 2020 (P; black diamonds). Panels show (a) the underlying emissions pathways, (b) additional warming in the delay scenarios compared to 2020 action
case, (c) cumulated CDR, (d) CDR ramp-up rates, (e) cumulated gross CO2 emissions from fossil-fuel combustion and industrial (FFI) processes over the 2018–2100 period, and (f)gross FFI CO2 emissions reductions rates. Scenario pairs or triplets (circles and diamonds) with 2020 and 2030 action variants were calculated by six (out of seven) models in the
ADVANCE study symbols (Luderer et al., 2018) and five of them (passing near-term plausibility checks) are shown by symbols. Only two of five models could identify pathways
with post-2030 action leading to a 2016–2100 carbon budget of about 200 GtCO2 (red). The range of all 1.5°C pathways with no and low overshoot is shown by the boxplots.

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Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Chapter 2

NDC pathways that apply a post-2030 price of emissions as found inleast-cost pathways starting from 2020 show infrastructural carbon
lock-in as a result of following NDCs instead of least-cost action until
2030. A key finding is that carbon lock-ins persist long after 2030, withthe majority of additional CO2 emissions occurring during the 2030–
2050 period. Luderer et al. (2018) find 90 (80–120) GtCO2 additionalemissions until 2030, growing to 240 (190–260) GtCO2 by 2050 and
290 (200–200) GtCO2 by 2100. As a result, peak warming is about 0.2°C
higher and not all of the modelled pathways return warming to 1.5°C
by the end of the century. There is a four sided trade-off between (i)near-term ambition, (ii) degree of overshoot, (iii) transitional challenges
during the 2030–2050 period, and (iv) the amount of CDR deployment
required during the century (Figure 2.13) (Holz et al., 2018b; Strefler
et al., 2018b). Transition challenges, overshoot, and CDR requirements
can be significantly reduced if global emissions peak before 2030

and fall below levels in line with current NDCs by 2030. For example,
Strefler et al. (2018b) find that CDR deployment levels in the second
half of the century can be halved in 1.5°C-consistent pathways withsimilar CO2 emissions reductions rates during the 2030–2050 period
if CO2 emissions by 2030 are reduced by an additional 30% compared
to NDC levels. Kriegler et al. (2018a) investigate a global rollout ofselected regulatory policies and moderate carbon pricing policies.
They show that additional reductions of about 10 GtCO2e yr−1 can beachieved in 2030 compared to the current NDCs. Such a 20% reductionof year-2030 emissions compared to current NDCs would effectively
lower the disruptiveness of post-2030 action. The strengthening of
short-term policies in deep mitigation pathways has hence been
identified as a way of bridging options to keep the Paris climate goals
within reach (Bertram et al., 2015b; IEA, 2015a; Spencer et al., 2015;
Kriegler et al., 2018a).

2.4

2.4.1

Disentangling the Whole-System
Transformation

Mitigation pathways map out prospective transformations of theenergy, land and economic systems over this century (Clarke et al.,2014). There is a diversity of potential pathways consistent with 1.5°C,
yet they share some key characteristics summarized in Table 2.5. To
explore characteristics of 1.5°C pathways in greater detail, this section
focuses on changes in energy supply and demand, and changes in the
AFOLU sector.

Energy System Transformation

The energy system links energy supply (Section 2.4.2) with energy
demand (Section 2.4.3) through final energy carriers, including
electricity and liquid, solid or gaseous fuels, that are tailored totheir end-uses. To chart energy-system transformations in mitigation
pathways, four macro-level decarbonization indicators associated withfinal energy are useful: limits on the increase of final energy demand,
reductions in the carbon intensity of electricity, increases in the shareof final energy provided by electricity, and reductions in the carbon

Table 2.5 | Overview of Key Characteristics of 1.5°C Pathways.
1.5°C Pathway
Characteristic

Supporting Information

Reference

Rapid and profound near-term

Strong upscaling of renewables and sustainable biomass and reduction of unabated (no CCS) fossil fuels,

Section 2.4.1

decarbonisation of energy supply

along with the rapid deployment of CCS, lead to a zero-emission energy supply system by mid-century.

Section 2.4.2

Greater mitigation efforts

All end-use sectors show marked demand reductions beyond the reductions projected for 2°C pathways. Demandon the demand side

reductions from IAMs for 2030 and 2050 lie within the potential assessed by detailed sectoral bottom-up assessments.

Switching from fossil fuels toelectricity in end-use sectors
Comprehensive emission

Both in the transport and the residential sector, electricity covers markedly larger shares of total demand by mid-century.

neutrality around mid-century. In 2030, below-1.5°C and 1.5°C-low-OS pathways show maximum net CO2 emissions

in the coming decadeof 18 and 28 GtCO2 yr−1, respectively. GHG emissions in these scenarios are not higher than 34 GtCO2e yr−1 in 2030.

reductions from both CO2 andnon-CO2 required for 2°C,
are mainly from CO2

Section 2.4.3.2
Section 2.4.3.3

Virtually all 1.5°C-consistent pathways decline net annual CO2 emissions between 2020 and 2030, reaching carbon

reductions are implemented
Additional reductions, on top of

Section 2.4.3

Section 2.3.4

Both CO2 and the non-CO2 GHGs and aerosols are strongly reduced by 2030 and until 2050 in 1.5°C pathways.
The greatest difference to 2°C pathways, however, lies in additional reductions of CO2, as the non-CO2 mitigation

Section 2.3.1.2

potential that is currently included in integrated pathways is mostly already fully deployed for reaching a 2°C pathway.
Low-carbon investments in the energy supply side (energy production and refineries) are projected to average

Considerable shifts in

1.6–3.8 trillion 2010USD yr−1 globally to 2050. Investments in fossil fuels decline, with investments in unabated coalinvestment patterns

halted by 2030 in most available 1.5°C-consistent projections, while the literature is less conclusive for investments

Section 2.5.2

in unabated gas and oil. Energy demand investments are a critical factor for which total estimates are uncertain.
Options are available toalign 1.5°C pathways withsustainable development

Synergies can be maximized, and risks of trade-offs limited or avoided through an informed choice of mitigation
strategies. Particularly pathways that focus on a lowering of demand show many synergies and few trade-offs.

Section 2.5.3

By 2050, 1.5°C pathways project deployment of BECCS at a scale of 3–7 GtCO2yr−1 (range of medians across
CDR at scale before mid-century

1.5°C pathway classes), depending on the level of energy demand reductions and mitigation in other sectors.

Section 2.3.3, 2.3.4.1

Some 1.5°C pathways are available that do not use BECCS, but only focus terrestrial CDR in the AFOLU sector.

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Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

intensity of final energy other than electricity (referred to in this section
as the carbon intensity of the residual fuel mix). Figure 2.14 showschanges of these four indicators for the pathways in the scenario
database (Section 2.1.3 and Supplementary Material 2.SM.1.3) for
1.5°C and 2°C pathways (Table 2.1).
Pathways in both the 1.5°C and 2°C classes (Figure 2.14) generally
show rapid transitions until mid-century, with a sustained but slower
evolution thereafter. Both show an increasing share of electricity
accompanied by a rapid decline in the carbon intensity of electricity.
Both also show a generally slower decline in the carbon intensity of
the residual fuel mix, which arises from the decarbonization of liquids,
gases and solids provided to industry, residential and commercial
activities, and the transport sector.

The largest differences between 1.5°C and 2°C pathways are seen in thefirst half of the century (Figure 2.14), where 1.5°C pathways generally
show lower energy demand, a faster electrification of energy end-use,
and a faster decarbonization of the carbon intensity of electricity andthe residual fuel mix. There are very few pathways in the Below-1.5°Cclass (Figure 2.14). Those scenarios that are available, however, show
a faster decline in the carbon intensity of electricity generation andresidual fuel mix by 2030 than most pathways that are projected totemporarily overshoot 1.5°C and return by 2100 (or 2°C pathways).
The Below-1.5°C pathways also appear to differentiate themselvesfrom the other pathways as early as 2030 through reductions in final
energy demand and increases in electricity share (Figure 2.14).

2

Figure 2.14 | Decomposition of transformation pathways into (a) energy demand, (b) carbon intensity of electricity, (c) the electricity share in final energy,and (d) the carbon intensity of the residual (non-electricity) fuel mix. Box plots show median, interquartile range and full range of pathways. Pathway temperature classes(Table 2.1) and illustrative pathway archetypes are indicated in the legend. Values following the class labels give the number of available pathways in each class.

2.4.2

Energy Supply

Several energy supply characteristics are evident in 1.5°C pathways
assessed in this section: (i) growth in the share of energy derived
from low-carbon-emitting sources (including renewables, nuclear andfossil fuel with CCS) and a decline in the overall share of fossil fuels
without CCS (Section 2.4.2.1), (ii) rapid decline in the carbon intensityof electricity generation simultaneous with further electrification ofenergy end-use (Section 2.4.2.2), and (iii) the growth in the use of CCS
applied to fossil and biomass carbon in most 1.5°C pathways (Section
2.4.2.3).
130

2.4.2.1

Evolution of primary energy contributions over time

By mid-century, the majority of primary energy comes from non-fossilfuels (i.e., renewables and nuclear energy) in most 1.5°C pathways(Table 2.6). Figure 2.15 shows the evolution of primary energy supply
over this century across 1.5°C pathways, and in detail for the four
illustrative pathway archetypes highlighted in this chapter. Note that
this section reports primary energy using the direct equivalent methodon the basis of lower heating values (Bruckner et al., 2014).

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Chapter 22

Figure 2.15 | Primary energy supply for the four illustrative pathway archetypes plus the IEA’s Faster Transition Scenario (OECD/IEA and IRENA, 2017) (panela), and their relative location in the ranges for pathways limiting warming to 1.5°C with no or limited overshoot (panel b). The category ‘Other renewables’
includes primary energy sources not covered by the other categories, for example, hydro and geothermal energy. The number of pathways that have higher primary energy than thescale in the bottom panel are indicated by the numbers above the whiskers. Black horizontal dashed lines indicates the level of primary energy supply in 2015 (IEA, 2017e). Box
plots in the lower panel show the minimum–maximum range (whiskers), interquartile range (box), and median (vertical thin black line). Symbols in the lower panel show the four
pathway archetypes S1 (white square), S2 (yellow square), S5 (black square), LED (white disc), as well as the IEA–(red disc). Pathways with no or limited overshoot included theBelow-1.5°C and 1.5°C-low-OS classes.

The share of energy from renewable sources (including biomass, hydro,
solar, wind and geothermal) increases in all 1.5°C pathways with no or
limited overshoot, with the renewable energy share of primary energy
reaching 38–88% in 2050 (Table 2.6), with an interquartile range of
52–67%. The magnitude and split between bioenergy, wind, solar,
and hydro differ between pathways, as can be seen in the illustrative
pathway archetypes in Figure 2.15. Bioenergy is a major supplier ofprimary energy, contributing to both electricity and other forms of
final energy such as liquid fuels for transportation (Bauer et al., 2018).
In 1.5°C pathways, there is a significant growth in bioenergy used in
combination with CCS for pathways where it is included (Figure 2.15).
Nuclear power increases its share in most 1.5°C pathways with no or
limited overshoot by 2050, but in some pathways both the absolute
capacity and share of power from nuclear generators decrease (Table
2.15). There are large differences in nuclear power between models
and across pathways (Kim et al., 2014; Rogelj et al., 2018). One of
the reasons for this variation is that the future deployment of nuclear
can be constrained by societal preferences assumed in narratives
underlying the pathways (O’Neill et al., 2017; van Vuuren et al., 2017b).
Some 1.5°C pathways with no or limited overshoot no longer see a rolefor nuclear fission by the end of the century, while others project about
95 EJ yr−1 of nuclear power in 2100 (Figure 2.15).
The share of primary energy provided by total fossil fuels decreases from2020 to 2050 in all 1.5°C pathways, but trends for oil, gas and coal differ(Table 2.6). By 2050, the share of primary energy from coal decreases
to 0–11% across 1.5°C pathways with no or limited overshoot, withan interquartile range of 1–7%. From 2020 to 2050 the primary energy
supplied by oil changes by −93 to −9% (interquartile range −77 to−39%); natural gas changes by −88 to +85% (interquartile range−62 to −13%), with varying levels of CCS. Pathways with higher useof coal and gas tend to deploy CCS to control their carbon emissions(see Section 2.4.2.3). As the energy transition is accelerated by several
decades in 1.5°C pathways compared to 2°C pathways, residual fossilfuel use (i.e., fossil fuels not used for electricity generation) without
CCS is generally lower in 2050 than in 2°C pathways, while combinedhydro, solar, and wind power deployment is generally higher than in
2°C pathways (Figure 2.15).
In addition to the 1.5°C pathways included in the scenario database(Supplementary Material 2.SM.1.3), there are other analyses in the131Chapter 2

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

challenges related to its land use and impact on food supply (Burns
and Nicholson, 2017) (assessed in greater detail in Sections 2.3.4.2,
4.3.7 and 5.4). These analyses could, provided their assumptions proveplausible, expand the range of 1.5°C pathways.

literature including, for example, sector-based analyses of energy
demand and supply options. Even though they were not necessarily
developed in the context of the 1.5°C target, they explore in greater
detail some options for deep reductions in GHG emissions. For example,
there are analyses of transitions to up to 100% renewable energy by2050 (Creutzig et al., 2017; Jacobson et al., 2017), which describe
what is entailed for a renewable energy share largely from solar andwind (and electrification) that is above the range of 1.5°C pathways
available in the database, although there have been challenges to theassumptions used in high-renewable analyses (e.g., Clack et al., 2017).
There are also analyses that result in a large role for nuclear energy
in mitigation of GHGs (Hong et al., 2015; Berger et al., 2017a, b; Xiao
and Jiang, 2018). BECCS could also contribute a larger share, but faces

2

In summary, the share of primary energy from renewables increases
while that from coal decreases across 1.5°C pathways (highconfidence). This statement is true for all 1.5°C pathways in the
scenario database and associated literature (Supplementary Material
2.SM.1.3), and is consistent with the additional studies mentioned
above, an increase in energy supply from lower-carbon-intensity
energy supply, and a decrease in energy supply from higher-carbonintensity energy supply.

Table 2.6 | Global primary energy supply of 1.5°C pathways from the scenario database (Supplementary Material 2.SM.1.3).
Values given for the median (maximum, minimum) across the full range of 85 available 1.5°C pathways. Growth Factor = [(primary energy supply in 2050)/(primaryenergy supply in 2020) − 1]

Below1.5°C and
1.5°Clow-OS
pathways

1.5°Chigh-OS

132

Median(max, min)Count

total primaryPrimary Energy Supply (EJ)

Share in Primary Energy (%)

Growth (factor)
2020-2050

2020

2030

2050

2020

2030

2050

50

565.33(619.70, 483.22)

464.50(619.87, 237.37)

553.23(725.40, 289.02)

NA

NA

NA

–0.05
(0.48, –0.51)

renewables

50

87.14
(101.60, 60.16)

146.96(203.90, 87.75)

291.33(584.78, 176.77)

14.90(20.39, 10.60)

29.08(62.15, 18.24)

60.24(87.89, 38.03)

2.37 (6.71, 0.91)

biomass50

60.41(70.03, 40.54)

77.07(113.02, 44.42)

152.30(311.72, 40.36)

10.17(13.66, 7.14)

17.22(35.61, 9.08)

27.29(54.10, 10.29)

1.71(5.56, –0.42)

non-biomass

50

26.35
(36.57, 17.78)

62.58(114.41, 25.79)

146.23(409.94, 53.79)

4.37(7.19, 3.01)

13.67(26.54, 5.78)

27.98(61.61, 12.04)

4.28 (13.46, 1.45)

wind & solar

44

10.93
(20.16, 2.61)

40.14(82.66, 7.05)

121.82(342.77, 27.95)

1.81(3.66, 0.45)

9.73(19.56, 1.54)

21.13(51.52, 4.48)

10.00 (53.70, 3.71)

nuclear50

10.91(18.55, 8.52)

16.26(36.80, 6.80)

24.51(66.30, 3.09)

2.10(3.37, 1.45)

3.52(9.61, 1.32)

4.49(12.84, 0.44)

1.24 (5.01, –0.64)

fossil50462.95(520.41, 376.30)

310.36(479.13, 70.14)

183.79(394.71, 54.86)

82.53(86.65, 77.73)

66.58(77.30, 29.55)

32.79
60.84, 8.58)–0.59 (–0.21, –0.89)

coal

50

136.89(191.02, 83.23)

44.03(127.98, 5.97)

24.15(71.12, 0.92)

25.63(30.82, 17.19)

9.62 (20.65, 1.31)

5.08 (11.43, 0.15)–0.83 (–0.57, –0.99)

gas

50

132.95(152.80, 105.01)

112.51(173.56, 17.30)

76.03(199.18, 14.92)

23.10(28.39, 18.09)

22.52(35.05, 7.08)

13.23(34.83, 3.68)–0.40 (0.85, –0.88)

oil

50

197.26(245.15, 151.02)

156.16(202.57, 38.94)

69.94(167.52, 15.07)

34.81(42.24, 29.00)

31.24(39.84, 16.41)

12.89(27.04, 2.89)–0.66 (–0.09, –0.93)

total primary35

594.96(636.98, 510.55)

559.04(749.05, 419.28)

651.46(1012.50, 415.31)

NA

NA

NA

0.13 (0.59, –0.27)

renewables

35

89.84
(98.60, 66.57)

135.12(159.84, 87.93)

323.21(522.82, 177.66)

15.08(18.58, 11.04)

23.65(29.32, 13.78)

62.16(86.26, 28.47)

2.68 (4.81, 1.17)

biomass35

62.59(73.03, 48.42)

69.05(98.27, 56.54)

160.16(310.10, 71.17)

10.30(14.23, 8.03)

13.64(16.37, 9.03)

23.79(45.79, 10.64)

1.71 (3.71, 0.19)

non-biomass

35

28.46
(36.58, 17.60)

59.81(92.12, 27.39)

164.91(329.69, 55.72)

4.78(6.64, 2.84)

10.23(16.59, 4.49)

31.17(45.86, 9.87)

6.10 (10.63, 1.38)

wind & solar

26

11.32
(20.17, 1.91)

40.31(65.50, 8.14)

139.20(275.47, 30.92)

1.95 (3.66, 0.32)

7.31 (11.61, 1.83)

26.01(38.79, 6.33)

16.06 (63.34, 3.13)

nuclear35

10.94(14.27, 8.52)

16.12(41.73, 6.80)

22.98(115.80, 3.09)

1.86 (2.37, 1.45)

2.99 (5.57, 1.20)

4.17(13.60, 0.43)

1.49 (7.22, –0.64)

fossil35497.30(543.29, 407.49)

397.76(568.91, 300.63)

209.80(608.39, 43.87)

83.17(86.59, 79.39)

73.87(82.94, 68.00)

33.58(60.09, 7.70)–0.56 (0.12, –0.91)

coal

35

155.65(193.55, 118.40)

70.99(176.99, 19.15)

18.95(134.69, 0.36)

25.94(30.82, 19.10)

14.53(26.35, 3.64)

4.14 (13.30, 0.05)–0.87 (–0.30, –1.00)

gas

35

138.01(169.50, 107.07)

147.43(208.55, 76.45)

97.71(265.66, 15.96)

23.61(27.35, 19.26)

25.79(32.73, 14.69)

15.67(33.80, 2.80)–0.31 (0.99, –0.88)

oil

35

195.02(236.40, 154.66)

198.50(319.80, 102.10)

126.20(208.04, 24.68)

32.21(38.87, 28.07)

33.27(50.12, 24.35)

18.61(27.30, 4.51)–0.34 (0.06, –0.87)

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Chapter 2

Table 2.6 (continued)

Two aboveclassescombined

Median(max, min)Count

total primaryPrimary Energy Supply (EJ)

Share in Primary Energy (%)

2020

2030

2050

2020

2030

2050

Growth (factor)
2020-2050

85582.12(636.98, 483.22)

502.81(749.05, 237.37)

580.78(1012.50, 289.02)

-

-

-

0.03 (0.59, –0.51)

renewables

85

87.70
(101.60, 60.16)

139.48(203.90, 87.75)

293.80(584.78, 176.77)

15.03(20.39, 10.60)

27.90(62.15, 13.78)

60.80(87.89, 28.47)

2.62 (6.71, 0.91)

biomass85

61.35(73.03, 40.54)

75.28(113.02, 44.42)

154.13(311.72, 40.36)

10.27(14.23, 7.14)

14.38(35.61, 9.03)

26.38(54.10, 10.29)

1.71 (5.56, –0.42)

non-biomass

85

26.35
(36.58, 17.60)

61.60(114.41, 25.79)

157.37(409.94, 53.79)

4.40(7.19, 2.84)

11.87(26.54, 4.49)

28.60(61.61, 9.87)

4.63 (13.46, 1.38)

wind & solar

70

10.93
(20.17, 1.91)

40.17(82.66, 7.05)

125.31(342.77, 27.95)

1.81 (3.66, 0.32)

8.24 (19.56, 1.54)

22.10(51.52, 4.48)

11.64 (63.34, 3.13)

nuclear85

10.93(18.55, 8.52)

16.22(41.73, 6.80)

24.48(115.80, 3.09)

1.97 (3.37, 1.45)

3.27 (9.61, 1.20)

4.22(13.60, 0.43)

1.34 (7.22, –0.64)

fossil85489.52(543.29, 376.30)

343.48(568.91, 70.14)

198.58(608.39, 43.87)

83.05(86.65, 77.73)

69.19(82.94, 29.55)

33.06(60.84, 7.70)–0.58 (0.12, –0.91)

coal

85

147.09(193.55, 83.23)

49.46(176.99, 5.97)

23.84(134.69, 0.36)

25.72(30.82, 17.19)

10.76(26.35, 1.31)

4.99 (13.30, 0.05)–0.85 (–0.30, –1.00)

gas

85

135.58(169.50, 105.01)

127.99(208.55, 17.30)

88.97(265.66, 14.92)

23.28(28.39, 18.09)

24.02(35.05, 7.08)

13.46(34.83, 2.80)–0.37 (0.99, –0.88)

oil

85

195.02(245.15, 151.02)

175.69(319.80, 38.94)

93.48(208.04, 15.07)

33.79(42.24, 28.07)

32.01(50.12, 16.41)

16.22(27.30, 2.89)–0.54 (0.06, –0.93)

Table 2.7 | Global electricity generation of 1.5°C pathways from the scenarios database.
(Supplementary Material 2.SM.1.3). Values given for the median (maximum, minimum) values across the full range across 89 available 1.5°C pathways. Growth
Factor = [(primary energy supply in 2050)/(primary energy supply in 2020) – 1].

TBelow-1.5°C and
1.5°Clow-OS
pathways

1.5°Chigh-OS

Median(max, min)

Count

total
generation

Electricity Generation (EJ)

Share in Electricity Generation (%)

2020

2030

2050

2020

2030

2050

Growth (factor)
2020–2050

50

98.45
(113.98, 83.53)

115.82(152.40, 81.28)

215.58(354.48, 126.96)

NA

NA

NA

1.15 (2.55, 0.28)

renewables

50

26.28
(41.80, 18.50)

63.30(111.70, 32.41)

145.50(324.26, 90.66)

26.32(41.84, 18.99)

53.68(79.67, 37.30)

77.12(96.65, 58.89)

4.48 (10.88, 2.65)

biomass50

2.02 (7.00, 0.76)

4.29(11.96, 0.79)

20.35(39.28, 0.24)

1.97 (6.87, 0.82)

3.69 (13.29, 0.73)

8.77(30.28, 0.10)

6.42 (38.14, –0.93)

non-biomass

50

24.21
(35.72, 17.70)

57.12(101.90, 25.79)

135.04(323.91, 53.79)

24.38(40.43, 17.75)

49.88(78.27, 29.30)

64.68(96.46, 41.78)

4.64 (10.64, 1.45)

wind & solar

50

1.66 (6.60, 0.38)

8.91(48.04, 0.60)

39.04(208.97, 2.68)

1.62 (7.90, 0.38)

8.36 (41.72, 0.53)

19.10(60.11, 1.65)

26.31 (169.66, 5.23)

nuclear50

10.84(18.55, 8.52)

15.46(36.80, 6.80)

21.97(64.72, 3.09)

12.09(18.34, 8.62)

14.33(31.63, 5.24)

8.10(27.53, 1.02)

0.71 (4.97, –0.64)

fossil50

59.43(68.75, 39.48)

36.51(66.07, 2.25)

14.81(57.76, 0.00)

61.32(67.40, 47.26)

30.04(52.86, 1.95)

8.61 (25.18, 0.00)–0.74 (0.01, –1.00)

coal

50

31.02
(42.00, 14.40)

8.83(34.11, 0.00)

1.38(17.39, 0.00)

32.32
40.38, 17.23)

7.28 (27.29, 0.00)

0.82 (7.53, 0.00)–0.96 (–0.56, –1.00)

gas

50

24.70
(32.46, 13.44)

22.59(42.08, 2.01)

12.79(53.17, 0.00)

24.39(35.08, 11.80)

20.18(37.23, 1.75)

6.93 (24.87, 0.00)–0.47 (1.27, –1.00)

oil

50

2.48
(13.36, 1.12)

1.89 (7.56, 0.24)

0.10 (8.78, 0.00)

2.82(11.73, 1.01)

1.95 (5.67, 0.21)

0.05 (3.80, 0.00)–0.92 (0.36, –1.00)

total
generation

35

101.44(113.96, 88.55)

125.26(177.51, 89.60)

251.50(363.10, 140.65)

NA

NA

NA

1.38 (2.19, 0.39)

renewables

35

26.38
(31.83, 18.26)

53.32(86.85, 30.06)

173.29(273.92, 84.69)

28.37(32.96, 17.38)

42.73(65.73, 25.11)

82.39(94.66, 35.58)

5.97 (8.68, 2.37)

biomass35

1.23 (6.47, 0.66)

2.14 (7.23, 0.86)

10.49(40.32, 0.21)

1.22 (7.30, 0.63)

1.59 (6.73, 0.72)

3.75(28.09, 0.08)

7.93 (33.32, –0.81)

non-biomass

35

24.56
(30.70, 17.60)

47.96(85.83, 27.39)

144.13(271.17, 55.72)

26.77(31.79, 16.75)

40.07(64.96, 23.10)

69.72(94.58, 27.51)

5.78 (8.70, 1.38)

Table 2.7 (continued next page)133

2

Chapter 2

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Table 2.7 (continued)

1.5°Chigh-OS

2

Two aboveclassescombined

2.4.2.2

Median(max, min)Countwind & solar

Electricity Generation (EJ)

Growth (factor)
2020-2050

2030

2050

2020

2030

2050

35

2.24 (5.07, 0.42)

8.95(36.52, 1.18)

65.08(183.38, 13.79)

2.21 (5.25, 0.41)

7.48 (27.90, 0.99)

25.88(61.24, 8.71)

30.70 (106.95, 4.87)

nuclear35

10.84(14.08, 8.52)

16.12(41.73, 6.80)

22.91(115.80, 3.09)

10.91(13.67, 8.62)

14.65(23.51, 5.14)

11.19(39.61, 1.12)

1.49 (7.22, –0.64)

fossil35

62.49(76.76, 49.09)

48.08(87.54, 30.99)

11.84(118.12, 0.78)

61.58(71.03, 54.01)

42.02(59.48, 24.27)

6.33 (33.19, 0.27)–0.80 (0.54, –0.99)

coal

35

32.37
(46.20, 26.00)

16.22(43.12, 1.32)

1.18(46.72, 0.01)

32.39(40.88, 24.41)

14.23(29.93, 1.19)

0.55 (12.87, 0.00)–0.96 (0.01, –1.00)

gas

35

26.20
(41.20, 20.11)

26.45(51.99, 16.45)

10.66(67.94, 0.76)

26.97(39.20, 19.58)

22.29(43.43, 14.03)

5.29 (32.59, 0.26)–0.57 (1.63, –0.97)

oil

35

1.51 (6.28, 1.12)

0.61 (7.54, 0.36)

0.04 (7.47, 0.00)

1.51 (6.27, 1.01)

0.55 (6.20, 0.26)

0.02 (3.31, 0.00)–0.99 (0.98, –1.00)

total
generation

85

100.09(113.98, 83.53)

120.01(177.51, 81.28)

224.78(363.10, 126.96)

NA

NA

NA

1.31 (2.55, 0.28)

renewables

85

26.38
(41.80, 18.26)

59.50(111.70, 30.06)

153.72(324.26, 84.69)

27.95(41.84, 17.38)

51.51(79.67, 25.11)

77.52(96.65, 35.58)

5.08 (10.88, 2.37)

biomass85

1.52 (7.00, 0.66)

3.55(11.96, 0.79)

16.32(40.32, 0.21)

1.55 (7.30, 0.63)

2.77 (13.29, 0.72)

8.02(30.28, 0.08)

6.53 (38.14, –0.93)

non-biomass

85

24.48
(35.72, 17.60)

55.68(101.90, 25.79)

136.40(323.91, 53.79)

25.00(40.43, 16.75)

47.16(78.27, 23.10)

66.75(96.46, 27.51)

4.75 (10.64, 1.38)

wind & solar

85

1.66 (6.60, 0.38)

8.95(48.04, 0.60)

43.20(208.97, 2.68)

1.67(7.90, 0.38)

8.15(41.72, 0.53)

19.70(61.24, 1.65)

28.02 (169.66, 4.87)

nuclear85

10.84(18.55, 8.52)

15.49(41.73, 6.80)

22.64(115.80, 3.09)

10.91(18.34, 8.62)

14.34(31.63, 5.14)

8.87(39.61, 1.02)

1.21 (7.22, –0.64)

fossil8561.35(76.76, 39.48)

38.41(87.54, 2.25)

14.10(118.12, 0.00)

61.55(71.03, 47.26)

33.96(59.48, 1.95)

8.05 (33.19, 0.00)–0.76 (0.54, –1.00)

coal

85

32.37
(46.20, 14.40)

10.41(43.12, 0.00)

1.29(46.72, 0.00)

32.39(40.88, 17.23)

8.95 (29.93, 0.00)

0.59 (12.87, 0.00)–0.96 (0.01, –1.00)

gas

85

24.70
(41.20, 13.44)

25.00(51.99, 2.01)

11.92(67.94, 0.00)

24.71(39.20, 11.80)

21.03(43.43, 1.75)

6.78 (32.59, 0.00)–0.52 (1.63, –1.00)

oil

85

1.82
(13.36, 1.12)

0.92 (7.56, 0.24)

0.08 (8.78, 0.00)

2.04(11.73, 1.01)

0.71 (6.20, 0.21)

0.04 (3.80, 0.00)–0.97 (0.98, –1.00)

Evolution of electricity supply over time

Electricity supplies an increasing share of final energy, reaching
34–71% in 2050, across 1.5°C pathways with no or limited overshoot(Figure 2.14), extending the historical increases in electricity shareseen over the past decades (Bruckner et al., 2014). From 2020 to 2050,
the quantity of electricity supplied in most 1.5°C pathways with no or
limited overshoot more than doubles (Table 2.7). By 2050, the carbon
intensity of electricity has fallen rapidly to −92 to +11 gCO2 MJ−1
electricity across 1.5°C pathways with no or limited overshoot froma value of around 140 gCO2 MJ−1 (range: 88–181 gCO2 MJ−1) in 2020
(Figure 2.14). A negative contribution to carbon intensity is provided by
BECCS in most pathways (Figure 2.16).
By 2050, the share of electricity supplied by renewables increases from
23% in 2015 (IEA, 2017b) to 59–97% across 1.5°C pathways with noor limited overshoot. Wind, solar, and biomass together make a major
contribution in 2050, although the share for each spans a wide range
across 1.5°C pathways (Figure 2.16). Fossil fuels on the other hand
have a decreasing role in electricity supply, with their share falling to
0–25% by 2050 (Table 2.7).

134

Share in Electricity Generation (%)

2020

In summary, 1.5°C pathways include a rapid decline in the carbon
intensity of electricity and an increase in electrification of energy enduse (high confidence). This is the case across all 1.5°C pathways andtheir associated literature (Supplementary Material 2.SM.1.3), withpathway trends that extend those seen in past decades, and resultsthat are consistent with additional analyses (see Section 2.4.2.2).

2.4.2.3

Deployment of carbon capture and storage

Studies have shown the importance of CCS for deep mitigation pathways(Krey et al., 2014a; Kriegler et al., 2014b), based on its multiple roles tolimit fossil-fuel emissions in electricity generation, liquids production,and industry applications along with the projected ability to remove
CO2 from the atmosphere when combined with bioenergy. This remains
a valid finding for those 1.5°C and 2°C pathways that do not radicallyreduce energy demand or do not offer carbon-neutral alternatives toliquids and gases that do not rely on bioenergy.
There is a wide range of CCS that is deployed across 1.5°C pathways(Figure 2.17). A few 1.5°C pathways with very low energy demand
do not include CCS at all (Grubler et al., 2018). For example, the LED
pathway has no CCS, whereas other pathways, such as the S5 pathway,Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Chapter 22

Figure 2.16 | Electricity generation for the four illustrative pathway archetypes plus the IEA’s Faster Transition Scenario (IEA, 2017d) (panel a), and theirrelative location in the ranges for pathways limiting warming to 1.5°C with no or limited overshoot (panel b). The category ‘Other renewables’ includes electricity
generation not covered by the other categories, for example, hydro and geothermal. The number of pathways that have higher primary energy than the scale in the bottom panel
are indicated by the numbers above the whiskers. Black horizontal dashed lines indicate the level of primary energy supply in 2015 (IEA, 2017e). Box plots in the lower panel show
the minimum–maximum range (whiskers), interquartile range (box), and median (vertical thin black line). Symbols in the lower panel show the four pathway archetypes – S1 (whitesquare), S2 (yellow square), S5 (black square), LED (white disc) – as well as the IEA’s Faster Transition Scenario (red disc). Pathways with no or limited overshoot included the Below1.5°C and 1.5°C-low-OS classes.

rely on a large amount of BECCS to get to net-zero carbon emissions.
The cumulative fossil and biomass CO2 stored through 2050 ranges fromzero to 300 GtCO2 across 1.5°C pathways with no or limited overshoot,with zero up to 140 GtCO2 from biomass captured and stored. Somepathways have very low fossil-fuel use overall, and consequently little
CCS applied to fossil fuels. In 1.5°C pathways where the 2050 coal use
remains above 20 EJ yr−1 in 2050, 33–100% is combined with CCS.
While deployment of CCS for natural gas and coal vary widely acrosspathways, there is greater natural gas primary energy connected to
CCS than coal primary energy connected to CCS in many pathways(Figure 2.17).
CCS combined with fossil-fuel use remains limited in some 1.5°C
pathways (Rogelj et al., 2018), as the limited 1.5°C carbon budget
penalizes CCS if it is assumed to have incomplete capture rates or if
fossil fuels are assumed to continue to have significant lifecycle GHG
emissions (Pehl et al., 2017). However, high capture rates are technicallyachievable now at higher cost, although efforts to date have focussed
on reducing the costs of capture (IEAGHG, 2006; NETL, 2013).

The quantity of CO2 stored via CCS over this century in 1.5°C pathways
with no or limited overshoot ranges from zero to more than 1,200
GtCO2, (Figure 2.17). The IPCC Special Report on Carbon Dioxide
Capture and Storage (IPCC, 2005) found that that, worldwide, it islikely that there is a technical potential of at least about 2,000 GtCO2
of storage capacity in geological formations. Furthermore, the IPCC
(2005) recognized that there could be a much larger potential forgeological storage in saline formations, but the upper limit estimates
are uncertain due to lack of information and an agreed methodology.
Since IPCC (2005), understanding has improved and there have beendetailed regional surveys of storage capacity (Vangkilde-Pedersen
et al., 2009; Ogawa et al., 2011; Wei et al., 2013; Bentham et al.,2014; Riis and Halland, 2014; Warwick et al., 2014; NETL, 2015) and
improvement and standardization of methodologies (e.g., Bachu et al.
2007a, b). Dooley (2013) synthesized published literature on both theglobal geological storage resource as well as the potential demand
for geologic storage in mitigation pathways, and found that the
cumulative demand for CO2 storage was small compared to a practicalstorage capacity estimate (as defined by Bachu et al., 2007a) of 3,900
GtCO2 worldwide. Differences remain, however, in estimates of storage
capacity due to, for example, the potential storage limitations of135

Chapter 2

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

2

Figure 2.17 | CCS deployment in 1.5°C and 2°C pathways for (a) biomass, (b) coal and (c) natural gas (EJ of primary energy) and (d) the cumulative quantityof fossil (including from, e.g., cement production) and biomass CO2 stored via CCS (in GtCO2 stored). TBox plots show median, interquartile range and full range ofpathways in each temperature class. Pathway temperature classes (Table 2.1), illustrative pathway archetypes, and the IEA’s Faster Transition Scenario (IEA WEM) (OECD/IEA and
IRENA, 2017) are indicated in the legend.

subsurface pressure build-up (Szulczewski et al., 2014) and assumptions
on practices that could manage such issues (Bachu, 2015). Kearns etal. (2017) constructed estimates of global storage capacity of 8,000 to
55,000 GtCO2 (accounting for differences in detailed regional and local
estimates), which is sufficient at a global level for this century, but
found that at a regional level, robust demand for CO2 storage exceeds
their lower estimate of regional storage available for some regions.
However, storage capacity is not solely determined by the geologicalsetting, and Bachu (2015) describes storage engineering practices
that could further extend storage capacity estimates. In summary,the storage capacity of all of these global estimates is larger than the
cumulative CO2 stored via CCS in 1.5°C pathways over this century.
There is uncertainty in the future deployment of CCS given thelimited pace of current deployment, the evolution of CCS technologythat would be associated with deployment, and the current lack of
incentives for large-scale implementation of CCS (Bruckner et al., 2014;
Clarke et al., 2014; Riahi et al., 2017). Given the importance of CCS in
most mitigation pathways and its current slow pace of improvement,
the large-scale deployment of CCS as an option depends on the further
development of the technology in the near term. Chapter 4 discusses
how progress on CCS might be accelerated.

136

2.4.3

Energy End-Use Sectors

Since the power sector is almost decarbonized by mid-century in both
1.5°C and 2°C pathways, major differences come from CO2 emission
reductions in end-use sectors. Energy-demand reductions are key
and common features in 1.5˚C pathways, and they can be achievedby efficiency improvements and various specific demand-reduction
measures. Another important feature is end-use decarbonization
including by electrification, although the potential and challenges ineach end-use sector vary significantly.
In the following sections, the potential and challenges of CO2 emission
reductions towards 1.5°C and 2°C- consistent pathways are discussed
for each end-use energy sector (industry, buildings, and transport).
For this purpose, two types of pathways are analysed and compared:
IAM (integrated assessment modelling) studies and sectoral (detailed)studies. IAM data are extracted from the database that was compiled
for this assessment (see Supplementary Material 2.SM.1.3), and thesectoral data are taken from a recent series of publications; ‘Energy
Technology Perspectives’ (ETP) (IEA, 2014, 2015b, 2016a, 2017a), the
IEA/IRENA report (OECD/IEA and IRENA, 2017), and the Shell Sky report(Shell International B.V., 2018). The IAM pathways are categorizedaccording to their temperature rise in 2100 and the overshoot of
temperature during the century (see Table 2.1 in Section 2.1). Since
the number of Below-1.5°C pathways is small, the following analyses

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Chapter 2

focus only on the features of the 1.5°C-low-OS and 1.5°C-high-OS
pathways (hereafter denoted together as 1.5°C overshoot pathways
or IAM-1.5DS-OS) and 2°C-consistent pathways (IAM-2DS). In order toshow the diversity of IAM pathways, we again show specific data fromthe four illustrative pathways archetypes used throughout this chapter(see Sections 2.1 and 2.3).
IEA ETP-B2DS (‘Beyond 2 Degrees’) and ETP-2DS are pathways witha 50% chance of limiting temperature rise below 1.75°C and 2°C
by 2100, respectively (IEA, 2017a). The IEA-66%2DS pathway keeps
global mean temperature rise below 2°C, not just in 2100 but also
over the course of the 21st century, with a 66% chance of being below
2°C by 2100 (OECD/IEA and IRENA, 2017). The comparison of CO2
emission trajectories between ETP-B2DS and IAM-1.5DS-OS show thatthese are consistent up to 2060 (Figure 2.18). IEA scenarios assume
that only a very low level of BECCS is deployed to help offset emissions
in difficult-to-decarbonize sectors, and that global energy-related CO2
emissions do not turn net negative at any time but stay at zero from2060 to 2100 (IEA, 2017a). Therefore, although its temperature risein 2100 is below 1.75°C rather than below 1.5°C, this scenario cangive information related to a 1.5°C overshoot pathway up to 2050.
The trajectory of IEA-66%2DS (also referred to in other publications as
IEA’s ‘Faster Transition Scenario’) lies between IAM-1.5DS-OS and IAM2DS pathway ranges, and IEA-2DS stays in the range of 2°C-consistent
IAM pathways. The Shell-Sky scenario aims to hold the temperaturerise to well below 2°C, but it is a delayed action pathway relative toothers, as can be seen in Figure 2.18.
Energy-demand reduction measures are key to reducing CO2 emissions
from end-use sectors for low-carbon pathways. The upstream energy
reductions can be from several times to an order of magnitude larger
than the initial end-use demand reduction. There are interdependencies
among the end-use sectors and between energy-supply and end-use
sectors, which elevate the importance of a wide, systematic approach.
As shown in Figure 2.19, global final energy consumption grows by 30%
and 10% from 2010 to 2050 for 2°C-consistent and 1.5°C overshootpathways from IAMs, respectively, while much higher growth of 75% isprojected for reference scenarios. The ranges within a specific pathway
class are due to a variety of factors as introduced in Section 2.3.1, as
well as differences between modelling frameworks. The importantenergy efficiency and conservation improvements that facilitate manyof the 1.5°C pathways raise the issue of potential rebound effects(Saunders, 2015), which, while promoting development, can makethe achievement of low-energy demand futures more difficult thanmodelling studies anticipate (see Sections 2.5 and 2.6).
Final energy demand is driven by demand in energy services formobility, residential and commercial activities (buildings), andmanufacturing. Projections of final energy demand depend heavily on
assumptions about socio-economic futures as represented by the SSPs
(Bauer et al., 2017) (see Sections 2.1, 2.3 and 2.5). The structure of thisdemand drives the composition of final energy use in terms of energy
carriers (electricity, liquids, gases, solids, hydrogen etc.).

6

Figure 2.18 | Comparison of CO2 emission trajectories of sectoral pathways(IEA ETP-B2DS, ETP-2DS, IEA-66%2DS, Shell-Sky) with the ranges of IAM pathway (2DS
are 2°C-consistent pathways and 1.5DS-OS are1.5°C overshoot pathways). The CO2
emissions shown here are the energy-related emissions, including industrial process
emissions.

Figure 2.19 shows the structure of global final energy demand in 2030
and 2050, indicating the trend toward electrification and fossil fuelusage reduction. This trend is more significant in 1.5°C pathways than
2°C pathways. Electrification continues throughout the second half ofthe century, leading to a 3.5- to 6-fold increase in electricity demand(interquartile range; median 4.5) by the end of the century relative totoday (Grubler et al., 2018; Luderer et al., 2018). Since the electricity
sector is completely decarbonized by mid-century in 1.5°C pathways(see Figure 2.20), electrification is the primary means to decarbonize
energy end-use sectors.
The CO2 emissions6 of end-use sectors and carbon intensity are shownin Figure 2.20. The projections of IAMs and IEA studies show rather
different trends, especially in the carbon intensity. These differences
come from various factors, including the deployment of CCS, thelevel of fuel switching and efficiency improvements, and the effectof structural and behavioural changes. IAM projections are generally
optimistic for the industry sectors, but not for buildings and transport
sectors. Although GDP increases by a factor of 3.4 from 2010 to 2050,
the total energy consumption of end-use sectors grows by only about
30% and 20% in 1.5°C overshoot and 2°C-consistent pathways,
respectively. However, CO2 emissions would need to be reduced further
to achieve the stringent temperature limits. Figure 2.20 shows that the
reduction in CO2 emissions of end-use sectors is larger and more rapid
in 1.5°C overshoot than 2°C-consistent pathways, while emissions
from the power sector are already almost zero in 2050 in both setsof pathways, indicating that supply-side emissions reductions arealmost fully exploited already in 2°C-consistent pathways (see Figure2.20) (Rogelj et al., 2015b, 2018; Luderer et al., 2016b). The emission
reductions in end-use sectors are largely made possible by efficiency
improvements, demand reduction measures and electrification, but
the level of emissions reductions varies across end-use sectors. Whilethe carbon intensity of the industry and buildings sectors decreases

This section reports ‘direct’ CO2 emissions as reported for pathways in the database for the report. As shown below, the emissions from electricity are nearly zero around
2050, so the impact of indirect emissions on the whole emission contributions of each sector is very small in 2050.

137

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60

20

2DS

Sectoral

1.5DS-H
1.5DS-L

1.5DS-䠝䡈䡈

L2DS

H2DS

2DS-䠝䡈䡈

1.5DS-OS

1.5DS-OS
2050

2030

10
Max
75% Percentile

5

2DS

1.5DS-L

Sectoral

1.5DS-H

L2DS

1.5DS-䠝䡈䡈

H2DS

2DS-䠝䡈䡈

1.5DS-L

Sectoral

Sectoral
Shell-Sky
IEA-66%2DS
IEA-2DS
IEA-B2DS

1.5DS-OS

Coal

100
80

Biomass

Sectoral

120

Sectoral

SectoralLiquid

300

60

200

40
2010 level2DS 1.5DS 2DS 1.5DS

2030

2050

Sectoral

100
0

25% Percentile
Min

IAM-archetypes
S1
S2
S5
LED

2050

Electricity

d2DS

1.5DS-OS

2030
400

1.5DS-H

2DS-䠝䡈䡈

1.5DS-䠝䡈䡈

Median0

Final Energy (EJ)

2DS

2010 level

15

L2DS

2

25

0

Carbon Emissions

b

H2DS

CO2 Emissions (GtCO2)30

101.5DS-L

200

20

Sectoral2010 level

300

30

1.5DS-H

400

2010 level

40

L2DS

500

50

1.5DS-䠝䡈䡈

600

Carbon Intensity

c

H2DS

Final Energya2DS-䠝䡈䡈

700

Carbon Intensity (gCO2/MJ)

Final Energy Consumption (EJ)

Energy consumption and CO2 emissions of Demand-side20
2DS 1.5DS 2DS 1.5DS

2030

2050

0

2DS 1.5DS 2DS 1.5DS

2030

2050

2DS 1.5DS 2DS 1.5DS

2030

2050

Figure 2.19 | (a) Global final energy, (b) direct CO2 emissions from the all energy demand sectors, (c) carbon intensity, and (d) structure of final energy(electricity, liquid fuel, coal, and biomass). The squares and circles indicate the IAM archetype pathways and diamonds indicate the data of sectoral scenarios. The red dottedline indicates the 2010 level. H2DS = Higher-2°C, L2DS = Lower-2°C, 1.5DS-H = 1.5°C-high-OS, 1.5DS-L = 1.5°C-low-OS. The label 1.5DS combines both high and low overshoot
1.5°C-consistent pathway. See Section 2.1 for descriptions.

to a very low level of around 10 gCO2 MJ-1, the carbon intensity oftransport becomes the highest of any sector by 2040 due to its higher
reliance on oil-based fuels. In the following subsections, the potential
and challenges of CO2 emission reduction in each end-use sector are
discussed in detail.

2.4.3.1

Industry

The industry sector is the largest end-use sector, both in terms of
final energy demand and GHG emissions. Its direct CO2 emissions
currently account for about 25% of total energy-related and process
CO2 emissions, and emissions have increased at an average annual
rate of 3.4% between 2000 and 2014, significantly faster than total
CO2 emissions (Hoesly et al., 2018). In addition to emissions fromthe combustion of fossil fuels, non-energy uses of fossil fuels in the
petrochemical industry and metal smelting, as well as non-fossil fuelprocess emissions (e.g., from cement production) contribute a small
amount (~5%) to the sector’s CO2 emissions inventory. Material
industries are particularly energy and emissions intensive: together,
the steel, non-ferrous metals, chemicals, non-metallic minerals, and138

pulp and paper industries accounted for close to 66% of final energy
demand and 72% of direct industry-sector emissions in 2014 (IEA,
2017a). In terms of end-uses, the bulk of energy in manufacturingindustries is required for process heating and steam generation,
while most electricity (but smaller shares of total final energy) is used
for mechanical work (Banerjee et al., 2012; IEA, 2017a).
As shown in Figure 2.21, a major share of the additional emission
reductions required for 1.5°C-overshoot pathways compared tothose in 2°C-consistent pathways comes from industry. Final energy,
CO2 emissions, and carbon intensity are consistent in IAM andsectoral studies, but in IAM-1.5°C-overshoot pathways the share ofelectricity is higher than IEA-B2DS (40% vs. 25%) and hydrogen isalso considered to have a share of about 5% versus 0%. In 2050, final
energy is increased by 30% and 5% compared with the 2010 level(red dotted line) for 1.5°C-overshoot and 2°C-consistent pathways,
respectively, but CO2 emissions are decreased by 80% and 50%
and carbon intensity by 80% and 60%, respectively. This additional
decarbonization is brought by switching to low-carbon fuels and CCS
deployment.

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Chapter 22

300 (a)Carbon Intensity (gCO2/MJ)

250
200

Carbon Intensity
2010 level40

2010 level2DS

102010 level

Max
75% Percentile

1.5DS-L

Sectoral

1.5DS-H

1.5DS-䠝䡈䡈

2DS

1.5DS-OS2050

1.5DS-L

Sectoral

1.5DS-H

L2DS

1.5DS-䠝䡈䡈

H2DS

2DS-䠝䡈䡈

1.5DS-L

Sectoral

20

2DS
2030

1.5DS-OS

2DS

2050

Sectoral

L1.5DS

H1.5DS

1.5DS-䠝䡈䡈

L2DS-66

H2DS-5002DS-䠝䡈䡈

10
Sectoral

Sectoral

L1.5DS

H1.5DS

1.5DS-䠝䡈䡈

L2DS-66

2DS-䠝䡈䡈

H2DS-50

Sectoral

L1.5DS

H1.5DS

1.5DS-䠝䡈䡈

L2DS

H2DS

2DS-䠝䡈䡈

50

30

L1.5DS

75

40

H1.5DS100

1.5DS-OS

Sectoral
Shell-Sky
IEA-66%2DS
IEA-2DS
IEA-B2DS

Biomass

1.5DS-䠝䡈䡈

125

2030

IAM-archetypes
S1
S2
S5
LED

50

L2DS

Biomass consumption (EJ)Electricity

2DS

1.5DS-OS2050

1.5DS-OS

H2DS

150 (d)2050

25% PercentileMin

2DS-䠝䡈䡈

2DS

1.5DS-OS
2030

L2DS

H2DS

2DS-䠝䡈䡈

1.5DS-L

Sectoral

1.5DS-H

L2DS

1.5DS-䠝䡈䡈

H2DS

Median25

2DS

1.5DS-OS
2030

5

0

1.5DS-H

L2DS

0Carbon Emissions

1.5DS-䠝䡈䡈

Lore

50
15 (b)

20

H2DS

100

2DS-䠝䡈䡈

150

2DS

Electricity consumption (EJ)

Industry Sector(c)Final Energy602DS-䠝䡈䡈

(CO2 Emissions (GtCO2)

Final Energy Consumption (EJ)

Figure 2.20 | Comparison of (a) direct CO2 emissions and (b) carbon intensity of the power and energy end-use sectors (industry, buildings, and transport
sectors) between IAMs and sectoral studies (IEA-ETP and IEA/IRENA). Diamond markers in panel (b) show data for IEA-ETP scenarios (2DS and B2DS), and IEA/IRENA
scenario (66%2DS). Note: for the data from IAM studies, there is rather large variation of projections for each indicator. Please see the details in the following figures in each enduse sector section.

1.5DS-OSFigure 2.21 | Comparison of (a) final energy, (b) direct CO2 emissions, (c) carbon intensity, (d) electricity and biomass consumption in the industry sector
between IAM and sectoral studies. The squares and circles indicate the IAM archetype pathways and diamonds the data of sectoral scenarios. The red dotted line indicates the2010 level. H2DS = Higher-2°C, L2DS = Lower-2°C, 1.5DS-H = 1.5°C-high-OS, 1.5DS-L = 1.5°C-low-OS. The label 1.5DS combines both high and low overshoot 1.5°C-consistentpathways. Section 2.1 for descriptions.

139

Chapter 2

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Broadly speaking, the industry sector’s mitigation measures canbe categorized in terms of the following five strategies: (i) reducing
demand, (ii) energy efficiency, (iii) increasing electrification of energy
demand, (iv) reducing the carbon content of non-electric fuels, and(v) deploying innovative processes and application of CCS. IEA ETP
estimates the relative contribution of different measures for CO2
emission reduction in their B2DS scenario compared with their reference
scenario in 2050 as follows: energy efficiency 42%, innovative process
and CCS 37%, switching to low-carbon fuels and feedstocks 13% andmaterial efficiency (include efficient production and use to contributeto demand reduction) 8%. The remainder of this section delves moredeeply into the potential mitigation contributions of these strategies aswell as their limitations.

2

Reduction in the use of industrial materials, while delivering similar
services, or improving the quality of products could help to reduceenergy demand and overall system-level CO2 emissions. Strategies
include using materials more intensively, extending product lifetimes,
increasing recycling, and increasing inter-industry material synergies,
such as clinker substitution in cement production (Allwood et al., 2013;
IEA, 2017a). Related to material efficiency, use of fossil-fuel feedstocks
could shift to lower-carbon feedstocks, such as from oil to natural gas
and biomass, and end-uses could shift to more sustainable materials,
such as biomass-based materials, reducing the demand for energyintensive materials (IEA, 2017a).
Reaping energy efficiency potentials hinges critically on advanced
management practices, such as energy management systems, inindustrial facilities as well as targeted policies to accelerate adoption of
the best available technology (see Section 2.5). Although excess energy,
usually as waste heat, is inevitable, recovering and reusing this waste
heat under economically and technically viable conditions benefits
the overall energy system. Furthermore, demand-side management
strategies could modulate the level of industrial activity in line withthe availability of resources in the power system. This could imply ashift away from peak demand and as power supply decarbonizes, thisdemand-shaping potential could shift some load to times with highportions of low-carbon electricity generation (IEA, 2017a).
In the industry sector, energy demand increases more than 40%
between 2010 and 2050 in baseline scenarios. However, in the
1.5°C-overshoot and 2°C-consistent pathways from IAMs, the increaseis only 30% and 5%, respectively (Figure 2.21). These energy-demand
reductions encompass both efficiency improvements in production andreductions in material demand, as most IAMs do not discern these two
factors.

century, CO2 emissions per unit of electricity are projected to decrease
to near zero in both sets of pathways (see Figure 2.20). An accelerated
electrification of the industry sector thus becomes an increasingly
powerful mitigation option. In the IAM pathways, the share of electricity
increases up to 30% by 2050 in 1.5°C-overshoot pathways (Figure2.21) from 20% in 2010. Some industrial fuel uses are substantiallymore difficult to electrify than others, and electrification would haveother effects on the process, including impacts on plant design, costand available process integration options (IEA, 2017a).7
In 1.5°C-overshoot pathways, the carbon intensity of non-electric fuelsconsumed by industry decreases to 16 gCO2 MJ−1 by 2050, compared
to 25 gCO2 MJ−1 in 2°C-consistent pathways. Considerable carbon
intensity reductions are already achieved by 2030, largely via a rapid
phase-out of coal. Biomass becomes an increasingly important energy
carrier in the industry sector in deep-decarbonization pathways, but
primarily in the longer term (in 2050, biomass accounts for only 10%
of final energy consumption even in 1.5°C-overshoot pathways). In
addition, hydrogen plays a considerable role as a substitute for fossilbased non-electric energy demands in some pathways.
Without major deployment of new sustainability-oriented low-carbon
industrial processes, the 1.5°C-overshoot target is difficult to achieve.
Bringing such technologies and processes to commercial deployment
requires significant investment in research and development. Some
examples of innovative low-carbon process routes include: newsteelmaking processes such as upgraded smelt reduction and upgradeddirect reduced iron, inert anodes for aluminium smelting, and full oxyfuelling kilns for clinker production in cement manufacturing (IEA,
2017a).
CCS plays a major role in decarbonizing the industry sector in thecontext of 1.5°C and 2°C pathways, especially in industries withhigher process emissions, such as cement, iron and steel industries.
In 1.5°C-overshoot pathways, CCS in industry reaches 3 GtCO2 yr−1
by 2050, albeit with strong variations across pathways. Given the
projected long-lead times and need for technological innovation, early
scale-up of industry-sector CCS is essential to achieving the stringent
temperature target. Development and demonstration of such projects
has been slow, however. Currently, only two large-scale industrial CCS
projects outside of oil and gas processing are in operation (Global
CCS Institute, 2016). The estimated current cost8 of CO2 avoided (inUSD2015) ranges from $20–27 tCO2−1 for gas processing and bioethanol production, and $60–138 tCO2−1 for fossil fuel-fired power
generation up to $104–188 tCO2−1 for cement production (Irlam, 2017).

2.4.3.2CO2 emissions from industry increase by 30% in 2050 compared to2010 in baseline scenarios. By contrast, these emissions are reduced
by 80% and 50% relative to 2010 levels in 1.5°C-overshoot and
2°C-consistent pathways from IAMs, respectively (Figure 2.21). By mid-

Buildings

In 2014, the buildings sector accounted for 31% of total global final
energy use, 54% of final electricity demand, and 8% of energy-related
CO2 emissions (excluding indirect emissions due to electricity). When

7

Electrification can be linked with the heating and drying process by electric boilers and electro-thermal processes, and also with low-temperature heat demand by heatpumps. In the iron and steel industry, hydrogen produced by electrolysis can be used as a reduction agent of iron instead of coke. Excess resources, such as black liquor,
will provide the opportunity to increase the systematic efficiency to use for electricity generation.

8

These are first-of-a-kind (FOAK) cost data.

140

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Chapter 2

Buildings Sector
Final Energy

Carbon Intensity (gCO2/MJ)

200

30

5

2DS

4
2

Max
75% Percentile

1
1.5DS-L

Sectoral

1.5DS-H

1.5DS-䠝䡈䡈

50

1.5DS-OS

2DS

1.5DS-OS2050

1.5DS-L

Sectoral

1.5DS-H

1.5DS-䠝䡈䡈

L2DS

H2DS

2DS-䠝䡈䡈

1.5DS-L

Sectoral

1.5DS-H

2DS
2030

1.5DS-OS

2DS

Sectoral

1.5DS-L

1.5DS-H

1.5DS-䠝䡈䡈

L2DS

H2DS

0

2DS-䠝䡈䡈

10
Sectoral

Sectoral

1.5DS-L

1.5DS-H

1.5DS-䠝䡈䡈

L2DS

H2DS

2DS-䠝䡈䡈

Sectoral

1.5DS-L

1.5DS-H

1.5DS-䠝䡈䡈

L2DS

H2DS

2DS-䠝䡈䡈

50

20

1.5DS-L

75

30

1.5DS-䠝䡈䡈

100

40

L2DS

125

2030

2

Biomass

H2DS

Biomass consumption (EJ)Electricity

2DS

Sectoral
Shell-Sky
IEA-66%2DS
IEA-2DS
IEA-B2DS

1.5DS-OS

150

25

1.5DS-OS
2050

IAM-archetypes
S1
S2
S5
LED

1.5DS-H

L2DS

2050

25% PercentileMin

2DS-䠝䡈䡈

(d)

H2DS

2DS-䠝䡈䡈

1.5DS-L

Sectoral

2DS

1.5DS-OS2030
175

1.5DS-H

1.5DS-䠝䡈䡈

L2DS

Median2DS

2DS

1.5DS-OS
2030

2010 level

30

1.5DS-䠝䡈䡈

6

L2DS

0

Carbon Emissions

H2DS

(b)

2DS-䠝䡈䡈

10

H2DS

CO2Emissions (GtCO2)

2010 level20

2010 level

100
50

Carbon Intensity

40

150

7

Electricity consumption (EJ)

60 (c)
50

2DS-䠝䡈䡈

Final Energy Consumption (EJ)

250 (a)1.5DS-OS
2050

Figure 2.22 | Comparison of (a) final energy, (b) direct CO2 emissions, (c) carbon intensity, (d) electricity and biomass consumption in the buildings sector
between IAM and sectoral studies. The squares and circles indicate the IAM archetype pathways and diamonds the data of sectoral scenarios. The red dotted line indicates the2010 level. H2DS = Higher-2°C, L2DS = Lower-2°C, 1.5DS-H = 1.5°C-high-OS, 1.5DS-L = 1.5°C-low-OS. The label 1.5DS combines both high and low overshoot 1.5°C-consistentpathways. Section 2.1 for descriptions.

upstream electricity generation is taken into account, buildings were
responsible for 23% of global energy-related CO2 emissions, with onethird of those from direct fossil fuel consumption (IEA, 2017a).
Past growth of energy consumption has been mainly driven by
population and economic growth, with improved access to electricity,and higher use of electrical appliances and space cooling resulting
from increasing living standards, especially in developing countries(Lucon et al., 2014). These trends will continue in the future and in
2050, energy consumption is projected to increase by 20% and 50%
compared to 2010 in the IAM-1.5°C-overshoot and 2°C-consistentpathways, respectively (Figure 2.22). However, sectoral studies (IEAETP scenarios) show different trends. Energy consumption in 2050
decreases compared to 2010 in ETP-B2DS, and the reduction rate of
CO2 emissions is higher than in IAM pathways (Figure 2.22). Mitigation
options are often more widely covered in sectoral studies (Lucon et al.,2014), leading to greater reductions in energy consumption and CO2
emissions.
Emissions reductions are driven by a clear tempering of energy
demand and a strong electrification of the buildings sector. The shareof electricity in 2050 is 60% in 1.5°C-overshoot pathways, comparedwith 50% in 2°C-consistent pathways (Figure 2.22). Electrification
contributes to the reduction of direct CO2 emissions by replacing
carbon-intensive fuels, like oil and coal. Furthermore, when combinedwith a rapid decarbonization of the power system (see Section 2.4.1) italso enables further reduction of indirect CO2 emissions from electricity.
Sectoral bottom-up models generally estimate lower electrification
potentials for the buildings sector in comparison to global IAMs (seeFigure 2.22). Besides CO2 emissions, increasing global demand for
air conditioning in buildings may also lead to increased emissions of
HFCs in this sector over the next few decades. Although these gases
are currently a relatively small proportion of annual GHG emissions,
their use in the air conditioning sector is expected to grow rapidly overthe next few decades if alternatives are not adopted. However, theirprojected future impact can be significantly mitigated through better
servicing and maintenance of equipment and switching of coolinggases (Shah et al., 2015; Purohit and Höglund-Isaksson, 2017).
IEA-ETP (IEA, 2017a) analysed the relative importance of various
technology measures toward the reduction of energy and CO2
emissions in the buildings sector. The largest energy savings potential
is in heating and cooling demand, largely due to building envelope
improvements and high efficiency and renewable equipment. In the141Chapter 2

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

ETP-B2DS, energy demand for space heating and cooling is 33% lower
in 2050 than in the reference scenario, and these reductions accountfor 54% of total reductions from the reference scenario. Energy savings
from shifts to high-performance lighting, appliances, and water heating
equipment account for a further 24% of the total reduction. The longterm, strategic shift away from fossil-fuel use in buildings, alongside
the rapid uptake of energy efficient, integrated and renewableenergy technologies (with clean power generation), leads to a drastic
reduction of CO2 emissions. In ETP-B2DS, the direct CO2 emissions are
79% lower than the reference scenario in 2050, and the remaining
emissions come mainly from the continued use of natural gas.

2

The buildings sector is characterized by very long-living infrastructure,and immediate steps are hence important to avoid lock-in of inefficientcarbon and energy-intensive buildings. This applies both to new buildings
in developing countries where substantial new construction is expectedin the near future and to retrofits of existing building stock in developed
regions. This represents both a significant risk and opportunity for
mitigation.9 A recent study highlights the benefits of deploying the mostadvanced renovation technologies, which would avoid lock-in into lessefficient measures (Güneralp et al., 2017).Aside from the effect of building
envelope measures, adoption of energy-efficient technologies such asheat pumps and, more recently, light-emitting diodes is also important
for the reduction of energy and CO2 emissions (IEA, 2017a). Consumer
choices, behaviour and building operation can also significantly affectenergy consumption (see Chapter 4, Section 4.3).

2.4.3.3

Transport

Transport accounted for 28% of global final energy demand and 23%of global energy-related CO2 emissions in 2014. Emissions increased by
2.5% annually between 2010 and 2015, and over the past half century
the sector has witnessed faster emissions growth than any other. The
transport sector is the least diversified energy end-use sector; thesector consumed 65% of global oil final energy demand, with 92% oftransport final energy demand consisting of oil products (IEA, 2017a),suggesting major challenges for deep decarbonization.
Final energy, CO2 emissions, and carbon intensity for the transport
sector are shown in Figure 2.23. The projections of IAMs are more
pessimistic than IEA-ETP scenarios, though both clearly project deep
cuts in energy consumption and CO2 emissions by 2050. For example,
1.5°C-overshoot pathways from IAMs project a reduction of 15% inenergy consumption between 2015 and 2050, while ETP-B2DS projects
a reduction of 30% (Figure 2.23). Furthermore, IAM pathways are
generally more pessimistic in the projections of CO2 emissions andcarbon intensity reductions. In AR5 (Clarke et al., 2014; Sims et al.,2014), similar comparisons between IAMs and sectoral studies were
performed and these were in good agreement with each other. Since
the AR5, two important changes can be identified: rapid growth ofelectric vehicle sales in passenger cars, and more attention towards

structural changes in this sector. The former contributes to reductionof CO2 emissions and the latter to reduction of energy consumption.
Deep emissions reductions in the transport sector would be achieved byseveral means. Technology-focused measures such as energy efficiency
and fuel-switching are two of these. Structural changes that avoid or
shift transport activity are also important. While the former solutions(technologies) always tend to figure into deep decarbonization
pathways in a major way, this is not always the case with the latter,
especially in IAM pathways. Comparing different types of global
transport models, Yeh et al. (2016) find that sectoral (intensive) studies
generally envision greater mitigation potential from structural changes
in transport activity and modal choice. Though, even there, it is primarily
the switching of passengers and freight from less- to more-efficient
travel modes (e.g., cars, trucks and airplanes to buses and trains) that is
the main strategy; other actions, such as increasing vehicle load factors(occupancy rates) and outright reductions in travel demand (e.g., asa result of integrated transport, land-use and urban planning), figuremuch less prominently. Whether these dynamics accurately reflect theactual mitigation potential of structural changes in transport activity
and modal choice is a point of investigation. According to the recent
IEA-ETP scenarios, the share of avoid (reduction of mobility demand)and shift (shifting to more efficient modes) measures in the reduction of
CO2 emissions from the reference to B2DS scenarios in 2050 amounts
to 20% (IEA, 2017a).
The potential and strategies to reduce energy consumption and CO2
emissions differ significantly among transport modes. In ETP-B2DS,
the shares of energy consumption and CO2 emissions in 2050 for eachmode are rather different (see Table 2.8), indicating the challenge
of decarbonizing heavy-duty vehicles (HDV, trucks), aviation, andshipping. The reduction of CO2 emissions in the whole sector fromthe reference scenario to ETP-B2DS is 60% in 2050, with varying
contributions per mode (Table 2.8). Since there is no silver bullet forthis deep decarbonization, every possible measure would be required
to achieve this stringent emissions outcome. The contribution ofvarious measures for the CO2 emission reduction from the reference
scenario to the IEA-B2DS in 2050 can be decomposed to efficiency
improvement (29%), biofuels (36%), electrification (15%), and avoid/
shift (20%) (IEA, 2017a). It is noted that the share of electrification
becomes larger compared with older studies, reflected by the recent
growth of electric vehicle sales worldwide. Another new trend is the
allocation of biofuels to each mode of transport. In IEA-B2DS, the total
amount of biofuels consumed in the transport sector is 24EJ10 in 2060,
and allocated to LDV (light-duty vehicles, 17%), HDV (35%), aviation(28%), and shipping (21%), that is, more biofuels is allocated to thedifficult-to-decarbonize modes (see Table 2.8).
In road transport, incremental vehicle improvements (including
engines) are relevant, especially in the short to medium term. Hybrid
electric vehicles are also instrumental to enabling the transition from9

In this section, we only discuss the direct emissions from the sector, but the selection of building materials has a significant impact on the reduction of energy and emissions
during production, such as shift from the steel and concrete to wood-based materials.

10This is estimated for the biofuels produced in a “sustainable manner” from non-food crop feedstocks, which are capable of delivering significant lifecycle GHG emissions
savings compared with fossil fuel alternatives, and which do not directly compete with food and feed crops for agricultural land or cause adverse sustainability impacts.

142Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Chapter 2

Table 2.8 | Transport sector indicators by mode in 2050 (IEA, 2017a).
Share of energy consumption, biofuel consumption, CO2 emissions, and reduction of energy consumption and CO2 emissions from 2014. (CO2 emissions are wellto-wheel emissions, including the emission during the fuel production.), LDV: light duty vehicle, HDV: heavy duty vehicle.

Share of Each Mode (%)

Reduction from 2014 (%)

EnergyBiofuel

CO2

Energy

CO2

LDV

36

17

30

51

81

HDV

33

35

36

8

56

Rail

6

-

–1

–136

107

Aviation

12

28

14

14

56

Shipping

17

21

21

26

29

(a)

The share of low-carbon fuels in the total transport fuel mix
increases to 10% and 16% by 2030 and to 40% and 58% by 2050

Carbon Intensity (gCO2/MJ)

150

2010 level

40

10

2DS

8
4

Max
75% Percentile

1.5DS-L

Sectoral

1.5DS-H

1.5DS-䠝䡈䡈

1.5DS-OS

2DS

1.5DS-OS2050

1.5DS-L

Sectoral

1.5DS-H

1.5DS-䠝䡈䡈

L2DS

H2DS

1.5DS-L

Sectoral

2DS-䠝䡈䡈

Sectoral
Shell-Sky
IEA-66%2DS
IEA-2DS
IEA-B2DS

Biofuel

89

68

30
20

2DS

2DS

1.5DS-L

Sectoral

1.5DS-H

1.5DS-䠝䡈䡈

L2DS

H2DS

2DS-䠝䡈䡈

1.5DS-L

1.5DS-OS
2030

Sectoral

1.5DS-H

0

1.5DS-䠝䡈䡈

10

L2DS

Sectoral

1.5DS-L

1.5DS-H

1.5DS-䠝䡈䡈

L2DS

H2DS

2DS-䠝䡈䡈

Sectoral

1.5DS-L

1.5DS-H

1.5DS-䠝䡈䡈

L2DS

H2DS

2DS-䠝䡈䡈

10

61

40

H2DS

Biomass consumption (EJ)

20

2030

IAM-archetypes
S1
S2
S5
LED

1.5DS-OSElectricity

2DS

1.5DS-OS
2050

2050

30

0

25% PercentileMin

2DS-䠝䡈䡈

40 (d)

L2DS

2DS

1.5DS-OS

2030

H2DS

2DS-䠝䡈䡈

1.5DS-L

Sectoral

1.5DS-H

1.5DS-䠝䡈䡈

L2DS

H2DS

2DS-䠝䡈䡈

Median2DS

1.5DS-OS

2030

2010 level6

1.5DS-H

12

1.5DS-䠝䡈䡈

0
Carbon Emissions

L2DS

50
14 (b)

H2DS

20

2010 level

2DS-䠝䡈䡈

100

20

Carbon Intensity

60

2DS

Electricity consumption (EJ)

transport modes. Both modes would need to pursue highly ambitious
efficiency improvements and use of low-carbon fuels. In the near andmedium term, this would be advanced biofuels while in the long term
it could be hydrogen as direct use for shipping or an intermediate
product for synthetic fuels for both modes (IEA, 2017a).

Transport Sector(c)80

Final Energy

200(CO2 Emissions (GtCO2)

Final Energy Consumption (EJ)internal combustion engine vehicles to electric vehicles, especiallyplug-in hybrid electric vehicles. Electrification is a powerful measure todecarbonize short-distance vehicles (passenger cars and two and threewheelers) and the rail sector. In road freight transport (trucks), systemic
improvements (e.g., in supply chains, logistics, and routing) would be
effective measures in conjunction with efficiency improvement ofvehicles. Shipping and aviation are more challenging to decarbonize,
while their demand growth is projected to be higher than other

1.5DS-OS
2050

Figure 2.23 | Comparison of (a) final energy, (b) direct CO2 emissions, (c) carbon intensity, (d) electricity and biofuel consumption in the transport sector
between IAM and sectoral studies. The squares and circles indicate the IAM archetype pathways and diamonds the data of sectoral scenarios. The red dotted line indicates the2010 level. H2DS = Higher-2°C, L2DS = Lower-2°C, 1.5DS-H = 1.5°C-high-OS, 1.5DS-L = 1.5°C-low-OS. The label 1.5DS combines both high and low overshoot 1.5°C-consistentpathways. Section 2.1 for descriptions.

143

2

Chapter 2

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Developmentin 1.5°C-overshoot pathways from IAMs and the IEA-B2DS pathway,
respectively. The IEA-B2DS scenario is on the more ambitious side,
especially in the share of electricity. Hence, there is wide variation
among scenarios, including the IAM pathways, regarding changes
in the transport fuel mix over the first half of the century. As seen in
Figure 2.23, the projections of energy consumption, CO2 emissions andcarbon intensity are quite different between IAM and ETP scenarios.
These differences can be explained by more weight on efficiency
improvements and avoid/shift decreasing energy consumption, andthe higher share of biofuels and electricity accelerating the speed of
decarbonization in ETP scenarios. Although biofuel consumption andelectric vehicle sales have increased significantly in recent years, thegrowth rates projected in these pathways would be unprecedented
and far higher than has been experienced to date.

2

The 1.5°C pathways require an acceleration of the mitigation solutions
already featured in 2°C-consistent pathways (e.g., more efficient
vehicle technologies operating on lower-carbon fuels), as well as
those having received lesser attention in most global transport
decarbonization pathways up to now (e.g., mode-shifting and travel
demand management). Current-generation, global pathways generally
do not include these newer transport sector developments, whereby
technological solutions are related to shifts in traveller’s behaviour.

2.4.4

Land-Use Transitions and Changes
in the Agricultural Sector

The agricultural and land system described together under the umbrellaof the AFOLU (agriculture, forestry, and other land use) sector plays
an important role in 1.5°C pathways (Clarke et al., 2014; Smith andBustamante, 2014; Popp et al., 2017). On the one hand, its emissions
need to be limited over the course of this century to be in line withpathways limiting warming to 1.5°C (see Sections 2.2-3). On the other
hand, the AFOLU system is responsible for food and feed production;
for wood production for pulp and construction; for the production of
biomass that is used for energy, CDR or other uses; and for the supply of
non-provisioning (ecosystem) services (Smith and Bustamante, 2014).
Meeting all demands together requires changes in land use, as well as
in agricultural and forestry practices, for which a multitude of potential
options have been identified (Smith and Bustamante, 2014; Popp etal., 2017) (see also Supplementary Material 2.SM.1.2 and Chapter 4,
Section 4.3.1, 4.3.2 and 4.3.7).
This section assesses the transformation of the AFOLU system, mainly
making use of pathways from IAMs (see Section 2.1) that are based on
quantifications of the SSPs and that report distinct land-use evolutions
in line with limiting warming to 1.5°C (Calvin et al., 2017; Fricko etal., 2017; Fujimori, 2017; Kriegler et al., 2017; Popp et al., 2017; Riahi
et al., 2017; van Vuuren et al., 2017b; Doelman et al., 2018; Rogelj
et al., 2018). The SSPs were designed to vary mitigation challenges(O’Neill et al., 2014) (Cross-Chapter Box 1 in Chapter 1), including
for the AFOLU sector (Popp et al., 2017; Riahi et al., 2017). The SSP
pathway ensemble hence allows for a structured exploration of AFOLU
transitions in the context of climate change mitigation in line with
1.5°C, taking into account technological and socio-economic aspects.
Other considerations, like food security, livelihoods and biodiversity,
are also of importance when identifying AFOLU strategies. These are144

at present only tangentially explored by the SSPs. Further assessmentsof AFOLU mitigation options are provided in other parts of this reportand in the IPCC Special Report on Climate Change and Land (SRCCL).
Chapter 4 provides an assessment of bioenergy (including feedstocks,
see Section 4.3.1), livestock management (Section 4.3.1), reducing
rates of deforestation and other land-based mitigation options (asmitigation and adaptation option, see Section 4.3.2), and BECCS,
afforestation and reforestation options (including the bottom-up
literature of their sustainable potential, mitigation cost and side
effects, Section 4.3.7). Chapter 3 discusses impacts land-based CDR(Cross-Chapter Box 7 in Chapter 3). Chapter 5 assesses the sustainable
development implications of AFOLU mitigation, including impacts on
biodiversity (Section 5.4). Finally, the SRCCL will undertake a morecomprehensive assessment of land and climate change aspects. For
the sake of complementarity, this section focusses on the magnitude
and pace of land transitions in 1.5°C pathways, as well as on theimplications of different AFOLU mitigation strategies for different land
types. The interactions with other societal objectives and potential
limitations of identified AFOLU measures link to these large-scaleevolutions, but these are assessed elsewhere (see above).
Land-use changes until mid-century occur in the large majority of
SSP pathways, both under stringent mitigation and in absence of
mitigation (Figure 2.24). In the latter case, changes are mainly due
to socio-economic drivers like growing demands for food, feed andwood products. General transition trends can be identified for many
land types in 1.5°C pathways, which differ from those in baseline
scenarios and depend on the interplay with mitigation in othersectors (Figure 2.24) (Popp et al., 2017; Riahi et al., 2017; Rogelj etal., 2018). Mitigation that demands land mainly occurs at the expenseof agricultural land for food and feed production. Additionally, somebiomass is projected to be grown on marginal land or supplied fromresidues and waste, but at lower shares. Land for second-generationenergy crops (such as Miscanthus or poplar) expands by 2030
and 2050 in all available pathways that assume a cost-effective
achievement of a 1.5°C temperature goal in 2100 (Figure 2.24), but
the scale depends strongly on underlying socio-economic assumptions(see later discussion of land pathway archetypes). Reducing rates ofdeforestation restricts agricultural expansion, and forest cover can
expand strongly in 1.5°C and 2°C pathways alike compared to its
extent in no-climate-policy baselines due to reduced deforestation andafforestation and reforestation measures. However, the extent to which
forest cover expands varies highly across models in the literature,with some models projecting forest cover to stay virtually constant or
decline slightly. This is due to whether afforestation and reforestation is
included as a mitigation technology in these pathways and interactions
with other sectors.
As a consequence of other land-use changes, pasture land is generally
projected to be reduced compared to both baselines in which no climatechange mitigation action is undertaken and 2°C-consistent pathways.
Furthermore, cropland for food and feed production decreases inmost 1.5°C pathways, both compared to a no-climate baseline andrelative to 2010. These reductions in agricultural land for food and feed
production are facilitated by intensification on agricultural land and inlivestock production systems (Popp et al., 2017), as well as changes
in consumption patterns (Frank et al., 2017; Fujimori, 2017) (see

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Chapter 22

Figure 2.24 | Overview of land-use change transitions in 2030 and 2050, relative to 2010 based on pathways based on the Shared Socio-Economic Pathways (SSPs) (Popp etal., 2017; Riahi et al., 2017; Rogelj et al., 2018). Grey: no-climate-policy baseline; green: 2.6 W m−2 pathways; blue: 1.9 W m−2 pathways. Pink: 1.9 W m−2 pathways grouped per
underlying socio-economic assumption (from left to right: SSP1 sustainability, SSP2 middle-of-the-road, SSP5 fossil-fuelled development). Ranges show the minimum–maximum
range across the SSPs. Single pathways are shown with plus signs. Illustrative archetype pathways are highlighted with distinct icons. Each panel shows the changes for a different
land type. The 1.9 and 2.6 W m−2 pathways are taken as proxies for 1.5°C and 2°C pathways, respectively. The 2.6 W m−2 pathways are mostly consistent with the Lower-2°C andHigher-2°C pathway classes. The 1.9 W m−2 pathways are consistent with the 1.5°C-low-OS (mostly SSP1 and SSP2) and 1.5°C-high-OS (SSP5) pathway classes. In 2010, pasture
was estimated to cover about 3–3.5 103 Mha, food and feed crops about 1.5–1.6 103 Mha, energy crops about 0–14 Mha and forest about 3.7–4.2 103 Mha, across the models
that reported SSP pathways (Popp et al., 2017). When considering pathways limiting warming to 1.5°C with no or limited overshoot, the full set of scenarios shows a conversionof 50–1100 Mha of pasture into 0–600 Mha for energy crops, a 200 Mha reduction to 950 Mha increase forest, and a 400 Mha decrease to a 250 Mha increase in non-pasture
agricultural land for food and feed crops by 2050 relative to 2010. The large range across the literature and the understanding of the variations across models and assumptions
leads to medium confidence in the size of these ranges.

also Chapter 4, Section 4.3.2 for an assessment of these mitigation
options). For example, in a scenario based on rapid technologicalprogress (Kriegler et al., 2017), global average cereal crop yields in2100 are assumed to be above 5 tDM ha−1 yr−1 in mitigation scenarios
aiming at limiting end-of-century radiative forcing to 4.5 or 2.6 W m−2,
compared to 4 tDM ha−1 yr−1 in the SSP5 baseline to ensure the same
food production. Similar improvements are present in 1.5°C variantsof such scenarios. Historically, cereal crop yields are estimated at
1 tDM ha−1 yr−1 and about 3 tDM ha−1 yr−1 in 1965 and 2010,
respectively (calculations based on FAOSTAT, 2018). For aggregate
energy crops, models assume 4.2–8.9 tDM ha−1 yr−1 in 2010, increasing
to about 6.9–17.4 tDM ha−1 yr−1 in 2050, which fall within the range
found in the bottom-up literature yet depend on crop, climatic zone,
land quality and plot size (Searle and Malins, 2014).
The pace of projected land transitions over the coming decades candiffer strongly between 1.5°C and baseline scenarios without climatechange mitigation and from historical trends (Table 2.9). However,there is uncertainty in the sign and magnitude of these future landuse changes (Prestele et al., 2016; Popp et al., 2017; Doelman et al.,2018). The pace of projected cropland changes overlaps with historical
trends over the past four decades, but in several cases also goes wellbeyond this range. By the 2030–2050 period, the projected reductions

in pasture and potentially strong increases in forest cover imply areversed dynamic compared to historical and baseline trends. Thissuggests that distinct policy and government measures would beneeded to achieve forest increases, particularly in a context of projected
increased bioenergy use.
Changes in the AFOLU sector are driven by three main factors: demandchanges, efficiency of production, and policy assumptions (Smith etal., 2013; Popp et al., 2017). Demand for agricultural products andother land-based commodities is influenced by consumption patterns(including dietary preferences and food waste affecting demand forfood and feed) (Smith et al., 2013; van Vuuren et al., 2018), demand forforest products for pulp and construction (including less wood waste),and demand for biomass for energy production (Lambin and Meyfroidt,
2011; Smith and Bustamante, 2014). Efficiency of agricultural andforestry production relates to improvements in agricultural and forestry
practices (including product cascades, by-products and more waste- andresidue-based biomass for energy production), agricultural and forestry
yield increases, and intensification of livestock production systems
leading to higher feed efficiency and changes in feed composition(Havlík et al., 2014; Weindl et al., 2015). Policy assumptions relate to
the level of land protection, the treatment of food waste, policy choices
about the timing of mitigation action (early vs late), the choice and145

Chapter 2

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Table 2.9 | Annual pace of land-use change in baseline, 2°C and 1.5°C pathways.
All values in Mha yr−1. The 2.6 W m−2 pathways are mostly consistent with the Lower-2°C and Higher-2°C pathway classes. The 1.9 W m−2 pathways arebroadly consistent with the 1.5°C-low-OS (mostly SSP1 and SSP2) and 1.5°C-high-OS (SSP5) pathway classes. Baseline projections reflect land-use developments
projected by integrated assessment models under the assumptions of the Shared Socio-Economic Pathways (SSPs) in absence of climate policies (Popp et al., 2017;
Riahi et al., 2017; Rogelj et al., 2018). Values give the full range across SSP scenarios. According to the Food and Agriculture Organization of the United Nations(FAOSTAT, 2018), 4.9 billion hectares (approximately 40% of the land surface) was under agricultural use in 2005, either as cropland (1.5 billion hectares) or
pasture (3.4 billion hectares). FAO data in the table are equally from FAOSTAT (2018).

Annual Pace of Land-Use Change [Mha yr–1]
Land Type
Pasture2

Cropland for food,feed and material
Cropland for energy

Pathway

Time Window

Historical

2010–2030

2030–2050

1970–1990

1990–2010–2[–14.6/3.0][–28.7/–5.2]

2.6 W m–2[–9.3/4.1][–21.6/0.4]

8.7
Permanent meadows
and pastures (FAO)

0.9
Permanent meadows
and pastures (FAO)

4.6
Arable land andPermanent crops

0.9
Arable land andPermanent crops

N.A.
Forest (FAO)–5.6
Forest (FAO)

1.9 W mBaseline[–5.1/14.1][–9.6/9.0]

1.9 W m–2[–12.7/9.0][–18.5/0.1]

2.6 W m–2[–12.9/8.3][–16.8/2.3]

Baseline[–5.3/9.9][–2.7/6.7]

1.9 W m–2

[0.7/10.5][3.9/34.8]

2.6 W m–2

[0.2/8.8][2.0/22.9]

Baseline[0.2/4.2][–0.2/6.1]

Total cropland (Sumof cropland for foodand feed & energy)

1.9 W m–2[–6.8/12.8][–5.8/26.7]

2.6 W m–2[–8.4/9.3][–7.1/17.8]

Baseline[–3.0/11.3][0.6/11.0]

Forest

1.9 W m–2[–4.8/23.7][0.0/34.3]

2.6 W m–2[–4.7/22.2][–2.4/31.7]

Baseline[–13.6/3.3][–6.5/4.3]

preference of land-based mitigation options (for example, the inclusionof afforestation and reforestation as mitigation options), interactions
with other sectors (Popp et al., 2017), and trade (Schmitz et al., 2012;
Wiebe et al., 2015).
A global study (Stevanović et al., 2017) reported similar GHG reduction
potentials for both production-side (agricultural production measuresin combination with reduced deforestation) and consumption-side(diet change in combination with lower shares of food waste) measureson the order of 40% in 210011 (compared to a baseline scenario
without land-based mitigation). Lower consumption of livestock
products by 2050 could also substantially reduce deforestation andcumulative carbon losses (Weindl et al., 2017). On the supply side,minor productivity growth in extensive livestock production systems
is projected to lead to substantial CO2 emission abatement, but theemission-saving potential of productivity gains in intensive systems islimited, mainly due to trade-offs with soil carbon stocks (Weindl et al.,2017). In addition, even within existing livestock production systems, atransition from extensive to more productive systems bears substantial
GHG abatement potential, while improving food availability (Gerber etal., 2013; Havlík et al., 2014). Many studies highlight the capability ofagricultural intensification for reducing GHG emissions in the AFOLU
sector or even enhancing terrestrial carbon stocks (Valin et al., 2013;
Popp et al., 2014a; Wise et al., 2014). Also the importance of immediate
and global land-use regulations for a comprehensive reduction of
11land-related GHG emissions (especially related to deforestation)
has been shown by several studies (Calvin et al., 2017; Fricko et al.,2017; Fujimori, 2017). Ultimately, there are also interactions between
these three factors and the wider society and economy, for example,
if CDR technologies that are not land-based are deployed (like direct
air capture – DACCS, see Chapter 4, Section 4.3.7) or if other sectors
over- or underachieve their projected mitigation contributions (Clarke
et al., 2014). Variations in these drivers can lead to drastically different
land-use implications (Popp et al., 2014b) (Figure 2.24).
Stringent mitigation pathways inform general GHG dynamics in
the AFOLU sector. First, CO2 emissions from deforestation can beabated at relatively low carbon prices if displacement effects inother regions (Calvin et al., 2017) or other land-use types with highcarbon density (Calvin et al., 2014; Popp et al., 2014a; Kriegler etal., 2017) can be avoided. However, efficiency and costs of reducing
rates of deforestation strongly depend on governance performance,
institutions and macroeconomic factors (Wang et al., 2016). Secondly,
besides CO2 reductions, the land system can play an important rolefor overall CDR efforts (Rogelj et al., 2018) via BECCS, afforestation
and reforestation, or a combination of options. The AFOLU sector also
provides further potential for active terrestrial carbon sequestration,for example, via land restoration, improved management of forest andagricultural land (Griscom et al., 2017), or biochar applications (Smith,2016) (see also Chapter 4, Section 4.3.7). These options have so far

Land-based mitigation options on the supply and the demand side are assessed in 4.3.2, and CDR options with a land component in 4.3.7. Chapter 5 (Section 5.4) assesses
the implications of land-based mitigation for related SDGs, e.g., food security.

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Chapter 2

Figure 2.25 | Agricultural emissions in transformation pathways. Global agricultural (a) CH4 and (b) N2O emissions. Box plots show median, interquartile range and fullrange. Classes are defined in Section 2.1.

not been extensively integrated in the mitigation pathway literature(see Supplementary Material 2.SM.1.2), but in theory their availability
would impact the deployment of other CDR technologies, like BECCS(Section 2.3.4) (Strefler et al., 2018a). These interactions will be
discussed further in the SRCCL.
Residual agricultural non-CO2 emissions of CH4 and N2O play animportant role for temperature stabilization pathways, and their relative
importance increases in stringent mitigation pathways in which CO2 isreduced to net zero emissions globally (Gernaat et al., 2015; Popp et al.,2017; Stevanović et al., 2017; Rogelj et al., 2018), for example, throughtheir impact on the remaining carbon budget (Section 2.2). Although
agricultural non-CO2 emissions show marked reduction potentials
in 2°C-consistent pathways, complete elimination of these emission
sources does not occur in IAMs based on the evolution of agricultural
practice assumed in integrated models (Figure 2.25) (Gernaat et al.,2015). Methane emissions in 1.5°C pathways are reduced throughimproved agricultural management (e.g., improved management ofwater in rice production, manure and herds, and better livestock quality
through breeding and improved feeding practices) as well as dietaryshifts away from emissions-intensive livestock products. Similarly,
N2O emissions decrease due to improved N-efficiency and manure
management (Frank et al., 2018). However, high levels of bioenergy
production can also result in increased N2O emissions (Kriegler etal., 2017), highlighting the importance of appropriate management
approaches (Davis et al., 2013). Residual agricultural emissions can befurther reduced by limiting demand for GHG-intensive foods throughshifts to healthier and more sustainable diets (Tilman and Clark, 2014;
Erb et al., 2016b; Springmann et al., 2016) and reductions in food waste(Bajželj et al., 2014; Muller et al., 2017; Popp et al., 2017) (see also
Chapter 4 and SRCCL). Finally, several mitigation measures that could
affect these agricultural non-CO2 emissions are not, or only to a limited
degree, considered in the current integrated pathway literature (seeSupplementary Material 2.SM.1.2). Such measures (like plant-based
and synthetic proteins, methane inhibitors and vaccines in livestock,
alternate wetting and drying in paddy rice, or nitrification inhibitors)
are very diverse and differ in their development or deployment stages.
Their potentials have not been explicitly assessed here.
Pathways consistent with 1.5°C rely on one or more of the threestrategies highlighted above (demand changes, efficiency gains, andpolicy assumptions), and can apply these in different configurations.
For example, among the four illustrative archetypes used in thischapter (Section 2.1), the LED and S1 pathways focus on generally
low resource and energy consumption (including healthy diets withlow animal-calorie shares and low food waste) as well as significant
agricultural intensification in combination with high levels of nature
protection. Under such assumptions, comparably small amounts ofland are needed for land-demanding mitigation activities such as
BECCS and afforestation and reforestation, leaving the land footprint
for energy crops in 2050 virtually the same compared to 2010 levels for
the LED pathway. In contrast, future land-use developments can lookvery different under the resource- and energy-intensive S5 pathwaythat includes less healthy diets with high animal shares and highshares of food waste (Tilman and Clark, 2014; Springmann et al., 2016)combined with a strong orientation towards technology solutions tocompensate for high reliance on fossil-fuel resources and associatedhigh levels of GHG emissions in the baseline. In such pathways, climatechange mitigation strategies strongly depend on the availability of
CDR through BECCS (Humpenöder et al., 2014). As a consequence, theS5 pathway sources significant amounts of biomass through bioenergy
crop expansion in combination with agricultural intensification. Also,
further policy assumptions can strongly affect land-use developments,
highlighting the importance for land use of making appropriate
policy choices. For example, within the SSP set, some pathways rely
strongly on a policy to incentivize afforestation and reforestation for
CDR together with BECCS, which results in an expansion of forest area
and a corresponding increase in terrestrial carbon stock. Finally, thevariety of pathways illustrates how policy choices in the AFOLU andother sectors strongly affect land-use developments and associatedsustainable development interactions (Chapter 5, Section 5.4) in 1.5°C
pathways.
The choice of strategy or mitigation portfolio impacts the GHG
dynamics of the land system and other sectors (see Section 2.3), as wellas the synergies and trade-offs with other environmental and societal
objectives (see Section 2.5.3 and Chapter 5, Section 5.4). For example,
AFOLU developments in 1.5°C pathways range from strategies
that differ by almost an order of magnitude in their projected land
requirements for bioenergy (Figure 2.24), and some strategies would
allow an increase in forest cover over the 21st century compared to
strategies under which forest cover remains approximately constant.
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High agricultural yields and application of intensified animal husbandry,
implementation of best-available technologies for reducing non-CO2
emissions, or lifestyle changes including a less-meat-intensive diet andless CO2-intensive transport modes, have been identified as allowing
for such a forest expansion and reduced footprints from bioenergy
without compromising food security (Frank et al., 2017; Doelman et al.,2018; van Vuuren et al., 2018).

2

The IAMs used in the pathways underlying this assessment (Poppet al., 2017; Riahi et al., 2017; Rogelj et al., 2018) do not include all
potential land-based mitigation options and side-effects, and theirresults are hence subject to uncertainty. For example, recent research
has highlighted the potential impact of forest management practices
on land carbon content (Erb et al., 2016a; Naudts et al., 2016) andthe uncertainty surrounding future crop yields (Haberl et al., 2013;
Searle and Malins, 2014) and water availability (Liu et al., 2014).
These aspects are included in IAMs in varying degrees but were notassessed in this report. Furthermore, land-use modules of some IAMs
can depict spatially resolved climate damages to agriculture (Nelson etal., 2014), but this option was not used in the SSP quantifications (Riahi
et al., 2017). Damages (e.g., due to ozone exposure or varying indirect
fertilization due to atmospheric N and Fe deposition (e.g., Shindell etal., 2012; Mahowald et al., 2017) are also not included. Finally, thisassessment did not look into the literature of agricultural sector models
which could provide important additional detail and granularity to thediscussion presented here.12 This limits their ability to capture the full
mitigation potentials and benefits between scenarios. An in-depth
assessment of these aspects lies outside the scope of this Special
Report. However, their existence affects the confidence assessment of
the AFOLU transition in 1.5°C pathways.
Despite the limitations of current modelling approaches, there is highagreement and robust evidence across models and studies that the
AFOLU sector plays an important role in stringent mitigation pathways.
The findings from these multiple lines of evidence also result in highconfidence that AFOLU mitigation strategies can vary significantly
based on preferences and policy choices, facilitating the exploration ofstrategies that can achieve multiple societal objectives simultaneously(see also Section 2.5.3). At the same time, given the many uncertainties
and limitations, only low to medium confidence can be attributed bythis assessment to the more extreme AFOLU developments found in
the pathway literature, and low to medium confidence to the level ofresidual non-CO2 emissions.

2.5

Challenges, Opportunities and Co-Impactsof Transformative Mitigation Pathways

This section examines aspects other than climate outcomes of 1.5°C
mitigation pathways. Focus is given to challenges and opportunities
related to policy regimes, price of carbon and co-impacts, including
sustainable development issues, which can be derived from the existing
integrated pathway literature. Attention is also given to uncertainties
and critical assumptions underpinning mitigation pathways. The
12challenges and opportunities identified in this section are further
elaborated Chapter 4 (e.g., policy choice and implementation) and
Chapter 5 (e.g., sustainable development). The assessment indicates
unprecedented policy and geopolitical challenges.

2.5.1

Policy Frameworks and Enabling Conditions

Moving from a 2°C to a 1.5°C pathway implies bold integrated policies
that enable higher socio-technical transition speeds, larger deployment
scales, and the phase-out of existing systems that may lock inemissions for decades (high confidence) (Geels et al., 2017; Kuramochi
et al., 2017; Rockström et al., 2017; Vogt-Schilb and Hallegatte, 2017;
Kriegler et al., 2018a; Michaelowa et al., 2018). This requires higher
levels of transformative policy regimes in the near term, which allow
deep decarbonization pathways to emerge and a net zero carbon
energy–economy system to emerge in the 2040–2060 period (Rogelj
et al., 2015b; Bataille et al., 2016b). This enables accelerated levelsof technological deployment and innovation (Geels et al., 2017; IEA,
2017a; Grubler et al., 2018) and assumes more profound behavioural,
economic and political transformation (Sections 2.3, 2.4 and 4.4).
Despite inherent levels of uncertainty attached to modelling studies(e.g., related to climate and carbon cycle response), studies stress theurgency for transformative policy efforts to reduce emissions in the
short term (Riahi et al., 2015; Kuramochi et al., 2017; Rogelj et al.,2018).
The available literature indicates that mitigation pathways in linewith 1.5°C pathways would require stringent and integrated policy
interventions (very high confidence). Higher policy ambition often
takes the form of stringent economy-wide emission targets (andresulting peak-and-decline of emissions), larger coverage of NDCs tomore gases and sectors (e.g., land-use, international aviation), much
lower energy and carbon intensity rates than historically seen, carbon
prices much higher than the ones observed in real markets, increased
climate finance, global coordinated policy action, and implementationof additional initiatives (e.g., by non-state actors) (Sections 2.3, 2.4 and2.5.2). The diversity (beyond explicit carbon pricing) and effectivenessof policy portfolios are of prime importance, particularly in the shortterm (Mundaca and Markandya, 2016; Kuramochi et al., 2017; OECD,
2017; Kriegler et al., 2018a; Michaelowa et al., 2018). For instance,
deep decarbonization pathways in line with a 2˚C target (covering
74% of global energy-system emissions) include a mix of stringent
regulation (e.g., building codes, minimum performance standards),
carbon pricing mechanisms and R&D (research and development)
innovation policies (Bataille et al., 2016a). Explicit carbon pricing,
direct regulation and public investment to enable innovation are
critical for deep decarbonization pathways (Grubb et al., 2014).
Effective planning (including compact city measures) and integrated
regulatory frameworks are also key drivers in the IEA-ETP B2DS study
for the transport sector (IEA, 2017a). Effective urban planning canreduce GHG emissions from urban transport between 20% and 50%(Creutzig, 2016). Comprehensive policy frameworks would be needed
if the decarbonization of the power system is pursued while increasing
end-use electrification (including transport) (IEA, 2017a). Technology
policies (e.g., feed-in-tariffs), financing instruments, carbon pricing

For example, the GLEAM (http://www.fao.org/gleam/en/) model from the UN Food and Agricultural Organisation (FAO).

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and system integration management driving the rapid adoption ofrenewable energy technologies are critical for the decarbonizationof electricity generation (Bruckner et al., 2014; Luderer et al., 2014;
Creutzig et al., 2017; Pietzcker et al., 2017). Likewise, low-carbon andresilient investments are facilitated by a mix of coherent policies,
including fiscal and structural reforms (e.g., labour markets), public
procurement, carbon pricing, stringent standards, information schemes,
technology policies, fossil-fuel subsidy removal, climate risk disclosure,and land-use and transport planning (OECD, 2017). Pathways in which
CDR options are restricted emphasize the strengthening of near-term
policy mixes (Luderer et al., 2013; Kriegler et al., 2018a). Together withthe decarbonization of the supply side, ambitious policies targeting
fuel switching and energy efficiency improvements on the demandside play a major role across mitigation pathways (Clarke et al., 2014;
Kriegler et al., 2014b; Riahi et al., 2015; Kuramochi et al., 2017; Brown
and Li, 2018; Rogelj et al., 2018; Wachsmuth and Duscha, 2018).
The combined evidence suggests that aggressive policies addressing
energy efficiency are central in keeping 1.5°C within reach and lowering
energy system and mitigation costs (high confidence) (Luderer et al.,2013; Rogelj et al., 2013b, 2015b; Grubler et al., 2018). Demand-side
policies that increase energy efficiency or limit energy demand at ahigher rate than historically observed are critical enabling factors for
reducing mitigation costs in stringent mitigation pathways across theboard (Luderer et al., 2013; Rogelj et al., 2013b, 2015b; Clarke et al.,2014; Bertram et al., 2015a; Bataille et al., 2016b). Ambitious sectorspecific mitigation policies in industry, transportation and residential
sectors are needed in the short run for emissions to peak in 2030
(Méjean et al., 2018). Stringent demand-side policies (e.g., tightened
efficiency standards for buildings and appliances) driving the expansion,
efficiency and provision of high-quality energy services are essential
to meet a 1.5˚C mitigation target while reducing the reliance on CDR(Grubler et al., 2018). A 1.5˚C pathway for the transport sector is possibleusing a mix of additional and stringent policy actions preventing (orreducing) the need for transport, encouraging shifts towards efficientmodes of transport, and improving vehicle-fuel efficiency (Gota et al.,2018). Stringent demand-side policies also reduce the need for CCS(Wachsmuth and Duscha, 2018). Even in the presence of weak nearterm policy frameworks, increased energy efficiency lowers mitigation
costs noticeably compared to pathways with reference energy intensity(Bertram et al., 2015a). Common issues in the literature relate to therebound effect, the potential overestimation of the effectivenessof energy efficiency policy, and policies to counteract the rebound(Saunders, 2015; van den Bergh, 2017; Grubler et al., 2018) (Sections
2.4 and 4.4).
SSP-based modelling studies underline that socio-economic andclimate policy assumptions strongly influence mitigation pathway
characteristics and the economics of achieving a specific climate
target (very high confidence) (Bauer et al., 2017; Guivarch and Rogelj,
2017; Riahi et al., 2017; Rogelj et al., 2018). SSP assumptions related
to economic growth and energy intensity are critical determinantsof projected CO2 emissions (Marangoni et al., 2017). A multimodelinter-comparison study found that mitigation challenges in line witha 1.5˚C target vary substantially across SSPs and policy assumptions(Rogelj et al., 2018). Under SSP1-SPA1 (sustainability) and SSP2-SPA2(middle-of-the-road), the majority of IAMs were capable of producing

Chapter 2

1.5˚C pathways. On the contrary, none of the IAMs contained in theSR1.5 database could produce a 1.5°C pathway under SSP3-SPA3
assumptions. Preventing elements include, for instance, climate
policy fragmentation, limited control of land-use emissions, heavy
reliance on fossil fuels, unsustainable consumption and marked
inequalities (Rogelj et al., 2018). Dietary aspects of the SSPs are also
critical: climate-friendly diets were contained in ‘sustainability’ (SSP1)
and meat-intensive diets in SSP3 and SSP5 (Popp et al., 2017). CDR
requirements are reduced under ‘sustainability’ related assumptions(Strefler et al., 2018b). These are major policy-related reasons for why
SSP1-SPA1 translates into relatively low mitigation challenges whereas
SSP3-SPA3 and SSP5-SPA5 entail futures that pose the highest sociotechnical and economic challenges. SSPs/SPAs assumptions indicatethat policy-driven pathways that encompass accelerated change awayfrom fossil fuels, large-scale deployment of low-carbon energy supplies,
improved energy efficiency and sustainable consumption lifestyles
reduce the risks of climate targets becoming unreachable (Clarke etal., 2014; Riahi et al., 2015, 2017; Marangoni et al., 2017; Rogelj et al.,2017, 2018; Strefler et al., 2018b).
Policy assumptions that lead to weak or delayed mitigation action fromwhat would be possible in a fully cooperative world strongly influence
the achievability of mitigation targets (high confidence) (Luderer et al.,2013; Rogelj et al., 2013b; OECD, 2017; Holz et al., 2018a; Strefler et al.,2018b). Such regimes also include current NDCs (Fawcett et al., 2015;
Aldy et al., 2016; Rogelj et al., 2016a, 2017; Hof et al., 2017; van Soest etal., 2017), which have been reported to make achieving a 2°C pathway
unattainable without CDR (Strefler et al., 2018b). Not strengthening
NDCs would make it very challenging to keep 1.5°C within reach (seeSection 2.3 and Cross-Chapter Box 11 in Chapter 4). One multimodelinter-comparison study (Luderer et al., 2016b, 2018) explored the effects
on 1.5°C pathways assuming the implementation of current NDCs
until 2030 and stringent reductions thereafter. It finds that delays inglobally coordinated actions lead to various models reaching no 1.5°C
pathways during the 21st century. Transnational emission reduction
initiatives (TERIs) outside the UNFCCC have also been assessed andfound to overlap (70–80%) with NDCs and be inadequate to bridge
the gap between NDCs and a 2°C pathway (Roelfsema et al., 2018).
Weak and fragmented short-term policy efforts use up a large share of
the long-term carbon budget before 2030–2050 (Bertram et al., 2015a;
van Vuuren et al., 2016) and increase the need for the full portfolioof mitigation measures, including CDR (Clarke et al., 2014; Riahi
et al., 2015; Xu and Ramanathan, 2017). Furthermore, fragmented
policy scenarios also exhibit ‘carbon leakage’ via energy and capital
markets (Arroyo-Currás et al., 2015; Kriegler et al., 2015b). A lack ofintegrated policy portfolios can increase the risks of trade-offs between
mitigation approaches and sustainable development objectives (seeSections 2.5.3 and 5.4). However, more detailed analysis is needed
about realistic (less disruptive) policy trajectories until 2030 that canstrengthen near-term mitigation action and meaningfully decrease
post-2030 challenges (see Chapter 4, Section 4.4).
Whereas the policy frameworks and enabling conditions identified
above pertain to the ‘idealized’ dimension of mitigation pathways,
aspects related to 1.5°C mitigation pathways in practice are of prime
importance. For example, issues related to second-best stringency
levels, international cooperation, public acceptance, distributional
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consequences, multilevel governance, non-state actions, compliancelevels, capacity building, rebound effects, linkages across highly
heterogeneous policies, sustained behavioural change, finance andintra- and inter-generational issues need to be considered (see Chapter
4, Section 4.4) (Bataille et al., 2016a; Mundaca and Markandya, 2016;
Baranzini et al., 2017; MacDougall et al., 2017; van den Bergh, 2017;
Vogt-Schilb and Hallegatte, 2017; Chan et al., 2018; Holz et al., 2018a;
Klinsky and Winkler, 2018; Michaelowa et al., 2018; Patterson et al.,2018). Furthermore, policies interact with a wide portfolio of preexisting policy instruments that address multiple areas (e.g., technology
markets, economic growth, poverty alleviation, climate adaptation) anddeal with various market failures (e.g., information asymmetries) andbehavioural aspects (e.g., heuristics) that prevent or hinder mitigation
actions (Kolstad et al., 2014; Mehling and Tvinnereim, 2018). The sociotechnical transition literature points to multiple complexities in realworld settings that prevent reaching ‘idealized’ policy conditions butat the same time can still accelerate transformative change throughother co-evolutionary processes of technology and society (Geels etal., 2017; Rockström et al., 2017). Such co-processes are complex andgo beyond the role of policy (including carbon pricing) and comprisethe role of citizens, businesses, stakeholder groups or governments,
as well as the interplay of institutional and socio-political dimensions(Michaelowa et al., 2018; Veland et al., 2018). It is argued that large
system transformations, similar to those in 1.5°C pathways, requireprioritizing an evolutionary and behavioural framework in economic
theory rather than an optimization or equilibrium framework as iscommon in current IAMs (Grubb et al., 2014; Patt, 2017). Accumulated
know-how, accelerated innovation and public investment play a keyrole in (rapid) transitions (see Sections 4.2 and 4.4) (Geels et al., 2017;
Michaelowa et al., 2018).
In summary, the emerging literature supports the AR5 on the need for
integrated, robust and stringent policy frameworks targeting both the
supply and demand-side of energy-economy systems (high confidence).
Continuous ex-ante policy assessments provide learning opportunities
for both policy makers and stakeholders.

Cross-Chapter Box 5 | Economics of 1.5°C Pathways and the Social Cost of Carbon
Contributing Authors:
Luis Mundaca (Sweden/Chile), Mustafa Babiker (Sudan), Johannes Emmerling (Italy/Germany), Sabine Fuss (Germany), Jean-Charles
Hourcade (France), Elmar Kriegler (Germany), Anil Markandya (Spain/UK), Joyashree Roy (India), Drew Shindell (USA)Two approaches have been commonly used to assess alternative emissions pathways: cost-effectiveness analysis (CEA) andcost–benefit analysis (CBA). CEA aims at identifying emissions pathways minimising the total mitigation costs of achieving
a given warming or GHG limit (Clarke et al., 2014). CBA has the goal to identify the optimal emissions trajectory minimising thediscounted flows of abatement expenditures and monetized climate change damages (Boardman et al., 2006; Stern, 2007). A thirdconcept, the Social Cost of Carbon (SCC) measures the total net damages of an extra metric ton of CO2 emissions due to theassociated climate change (Nordhaus, 2014; Pizer et al., 2014; Rose et al., 2017a). Negative and positive impacts are monetized,
discounted and the net value is expressed as an equivalent loss of consumption today. The SCC can be evaluated for any emissions
pathway under policy consideration (Rose, 2012; NASEM, 2016, 2017).
Along the optimal trajectory determined by CBA, the SCC equals the discounted value of the marginal abatement cost of a metric tonof CO2 emissions. Equating the present value of future damages and marginal abatement costs includes a number of critical value
judgements in the formulation of the social welfare function (SWF), particularly in how non-market damages and the distribution ofdamages across countries and individuals and between current and future generations are valued (Kolstad et al., 2014). For example,
since climate damages accrue to a larger extent farther in the future and can persist for many years, assumptions and approaches
to determine the social discount rate (normative ‘prescriptive’ vs. positive ‘descriptive’) and social welfare function (e.g., discounted
utilitarian SWF vs. undiscounted prioritarian SWF) can heavily influence CBA outcomes and associated estimates of SCC (Kolstad etal., 2014; Pizer et al., 2014; Adler and Treich, 2015; Adler et al., 2017; NASEM, 2017; Nordhaus, 2017; Rose et al., 2017a).
In CEA, the marginal abatement cost of carbon is determined by the climate goal under consideration. It equals the shadow priceof carbon associated with the goal which in turn can be interpreted as the willingness to pay for imposing the goal as a politicalconstraint. Emissions prices are usually expressed in carbon (equivalent) prices using the GWP-100 metric as the exchange rate
for pricing emissions of non-CO2 GHGs controlled under internationally climate agreements (like CH4, N2O and fluorinated gases,
see Cross-Chapter Box 2 in Chapter 1).13 Since policy goals like the goals of limiting warming to 1.5°C or well below 2°C do notdirectly result from a money metric trade-off between mitigation and damages, associated shadow prices can differ from the SCC ina CBA. In CEA, value judgments are to a large extent concentrated in the choice of climate goal and related implications, while more
explicit assumptions about social values are required to perform CBA. For example, in CEA assumptions about the social discountrate no longer affect the overall abatement levels now set by the climate goal, but the choice and timing of investments in individual
measures to reach these levels.
13

Also other metrics to compare emissions have been suggested and adopted by governments nationally (Kandlikar, 1995; Marten et al., 2015; Shindell, 2015; IWG, 2016).

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Chapter 2

Cross Chapter Box 5 (continued)

Although CBA-based and CEA-based assessment are both subject to large uncertainty about socio-techno-economic trends, policy
developments and climate response, the range of estimates for the SCC along an optimal trajectory determined by CBA is far wider
than for estimates of the shadow price of carbon in CEA-based approaches. In CBA, the value judgments about inter- and intragenerational equity combined with uncertainties in the climate damage functions assumed, including their empirical basis, areimportant (Pindyck, 2013; Stern, 2013; Revesz et al., 2014). In a CEA-based approach, the value judgments about the aggregate
welfare function matter less, and uncertainty about climate response and impacts can be tied into various climate targets andrelated emissions budgets (Clarke et al., 2014).
The CEA- and CBA-based carbon cost estimates are derived with a different set of tools. They are all summarised as integrated
assessment models (IAMs) but in fact are of very different nature (Weyant, 2017). Detailed process IAMs such as AIM (Fujimori,2017), GCAM (Thomson et al., 2011; Calvin et al., 2017), IMAGE (van Vuuren et al., 2011b, 2017b), MESSAGE-GLOBIOM (Riahi
et al., 2011; Havlík et al., 2014; Fricko et al., 2017), REMIND-MAgPIE (Popp et al., 2010; Luderer et al., 2013; Kriegler et al., 2017)
and WITCH (Bosetti et al., 2006, 2008, 2009) include a process-based representation of energy and land systems, but in mostcases lack a comprehensive representation of climate damages, and are typically used for CEA. Diagnostic analyses across CBAIAMs indicate important dissimilarities in modelling assembly, implementation issues and behaviour (e.g., parametric uncertainty,
damage responses, income sensitivity) that need to be recognized to better understand SCC estimates (Rose et al., 2017a).

2

CBA-IAMs such as DICE (Nordhaus and Boyer, 2000; Nordhaus, 2013, 2017), PAGE (Hope, 2006) and FUND (Tol, 1999; Anthoff andTol, 2009) attempt to capture the full feedback from climate response to socio-economic damages in an aggregated manner, but areusually much more stylised than detailed process IAMs. In a nutshell, the methodological framework for estimating SCC involves
projections of population growth, economic activity and resulting emissions; computations of atmospheric composition and global
mean temperatures as a result of emissions; estimations of physical impacts of climate changes; monetization of impacts (positive
and negative) on human welfare; and the discounting of the future monetary value of impacts to year of emission (Kolstad et al.,2014; Revesz et al., 2014; NASEM, 2017; Rose et al., 2017a). There has been a discussion in the literature to what extent CBAIAMs underestimate the SCC due to, for example, a limited treatment or difficulties in addressing damages to human well-being,labour productivity, value of capital stock, ecosystem services and the risks of catastrophic climate change for future generations(Ackerman and Stanton, 2012; Revesz et al., 2014; Moore and Diaz, 2015; Stern, 2016). However, there has been progress in ‘bottomup’ empirical analyses of climate damages (Hsiang et al., 2017), the insights of which could be integrated into these models (Dell etal., 2014). Most of the models used in Chapter 2 on 1.5°C mitigation pathways are detailed process IAMs and thus deal with CEA.
An important question is how results from CEA- and CBA-type approaches can be compared and synthesized. Such synthesis needs
to be done with care, since estimates of the shadow price of carbon under the climate goal and SCC estimates from CBA might not
be directly comparable due to different tools, approaches and assumptions used to derive them. Acknowledging this caveat, theSCC literature has identified a range of factors, assumptions and value judgements that support SCC values above $100 tCO2−1 thatare also found as net present values of the shadow price of carbon in 1.5°C pathways. These factors include accounting for tipping
points in the climate system (Lemoine and Traeger, 2014; Cai et al., 2015; Lontzek et al., 2015), a low social discount rate (Nordhaus,
2007a; Stern, 2007) and inequality aversion (Schmidt et al., 2013; Dennig et al., 2015; Adler et al., 2017).
The SCC and the shadow price of carbon are not merely theoretical concepts but used in regulation (Pizer et al., 2014; Revesz et al.,2014; Stiglitz et al., 2017). As stated by the report of the High-Level Commission on Carbon Pricing (Stiglitz et al., 2017), in the realworld there is a distinction to be made between the implementable and efficient explicit carbon prices and the implicit (notional)
carbon prices to be retained for policy appraisal and the evaluation of public investments, as is already done in some jurisdictions
such as the USA, UK and France. Since 2008, the U.S. government has used SCC estimates to assess the benefits and costs related
to CO2 emissions resulting from federal policymaking (NASEM, 2017; Rose et al., 2017a).
The use of the SCC for policy appraisals is, however, not straightforward in an SDG context. There are suggestions that a broader
range of polluting activities than only CO2 emissions, for example emissions of air pollutants, and a broader range of impacts
than only climate change, such as impacts on air quality, health and sustainable development in general (see Chapter 5 for a
detailed discussion), would need to be included in social costs (Sarofim et al., 2017; Shindell et al., 2017a). Most importantly,
a consistent valuation of the SCC in a sustainable development framework would require accounting for the SDGs in the social
welfare formulation (see Chapter 5).

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2.5.2

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Economic and Investment Implications of 1.5°C
Pathways

2.5.2.1 Price of carbon emissions
The price of carbon assessed here is fundamentally different from theconcepts of optimal carbon price in a cost–benefit analysis, or the social
cost of carbon (see Cross-Chapter Box 5 in this chapter and Chapter
3, Section 3.5.2). Under a cost-effectiveness analysis (CEA) modelling
framework, prices for carbon (mitigation costs) reflect the stringency of
mitigation requirements at the margin (i.e., cost of mitigating one extra
unit of emission). Explicit carbon pricing is briefly addressed here to theextent it pertains to the scope of Chapter 2. For detailed policy issues
about carbon pricing see Section 4.4.5.

2

Based on data available for this special report, the price of carbon
varies substantially across models and scenarios, and their values
increase with mitigation efforts (see Figure 2.26) (high confidence).
For instance, undiscounted values under a Higher-2°C pathway range
from 15–220 USD2010 tCO2-eq−1 in 2030, 45–1050 USD2010 tCO2-eq−1
in 2050, 120–1100 USD2010 tCO2-eq−1 in 2070 and 175–2340 USD2010
tCO2-eq−1 in 2100. On the contrary, estimates for a Below-1.5°C
pathway range from 135–6050 USD2010 tCO2-eq−1 in 2030, 245–14300
USD2010 tCO2-eq−1 in 2050, 420–19300 USD2010 tCO2-eq−1 in 2070
and 690–30100 USD2010 tCO2-eq−1 in 2100. Values for 1.5°C-low-OS
pathway are relatively higher than 1.5°C-high-OS pathway in 2030,
but the difference decreases over time, particularly between 2050 and
2070. This is because in 1.5°C-high-OS pathways there is relatively
less mitigation activity in the first half of the century, but more in
the second half. The low energy demand (LED, P1 in the Summary for
Policymakers) scenario exhibits the lowest values across the illustrative
pathway archetypes. As a whole, the global average discounted priceof emissions across 1.5°C- and 2°C pathways differs by a factor offour across models (assuming a 5% annual discount rate, comparing toBelow-1.5°C and 1.5°C-low-OS pathways). If 1.5°C-high-OS pathways(with peak warming 0.1–0.4°C higher than 1.5°C) or pathways withvery large land-use sinks are also considered, the differential value isreduced to a limited degree, from a factor 4 to a factor 3. The increasein mitigation costs between 1.5°C and 2°C pathways is based on a
direct comparison of pathway pairs from the same model and thesame study in which the 1.5°C pathway assumes a significantly smaller
carbon budget compared to the 2°C pathway (e.g., 600 GtCO2 smallerin the CD-LINKS and ADVANCE studies). This assumption is the main
driver behind the increase in the price of carbon (Luderer et al., 2018;
McCollum et al., 2018).14
The wide range of values depends on numerous aspects, including
methodologies, projected energy service demands, mitigation targets,
fuel prices and technology availability (high confidence) (Clarke et al.,2014; Kriegler et al., 2015b; Rogelj et al., 2015c; Riahi et al., 2017;
Stiglitz et al., 2017). The characteristics of the technology portfolio,
particularly in terms of investment costs and deployment rates, play a
key role (Luderer et al., 2013, 2016a; Clarke et al., 2014; Bertram et al.,2015a; Riahi et al., 2015; Rogelj et al., 2015c). Models that encompass
14

a higher degree of technology granularity and that entail more
flexibility regarding mitigation response often produce relatively lower
mitigation costs than those that show less flexibility from a technology
perspective (Bertram et al., 2015a; Kriegler et al., 2015a). Pathways
providing high estimates often have limited flexibility of substituting
fossil fuels with low-carbon technologies and the associated needto compensate fossil-fuel emissions with CDR. The price of carbon is
also sensitive to the non-availability of BECCS (Bauer et al., 2018).
Furthermore, and due to the treatment of future price anticipation,
recursive-dynamic modelling approaches (with ‘myopic anticipation’)exhibit higher prices in the short term but modest increases in the longterm compared to optimization modelling frameworks with ‘perfectforesight’ that show exponential pricing trajectories (Guivarch andRogelj, 2017). The chosen social discount rate in CEA studies (range
of 2–8% per year in the reported data, varying over time and sectors)
can also affect the choice and timing of investments in mitigation
measures (Clarke et al., 2014; Kriegler et al., 2015b; Weyant, 2017).
However, the impacts of varying discount rates on 1.5°C (and 2°C)
mitigation strategies can only be assessed to a limited degree. The
above highlights the importance of sampling bias in pathway analysis
ensembles towards outcomes derived from models which are more
flexible, have more mitigation options and cheaper cost assumptions
and thus can provide feasible pathways in contrast to other who areunable to do so (Tavoni and Tol, 2010; Clarke et al., 2014; Bertram etal., 2015a; Kriegler et al., 2015a; Guivarch and Rogelj, 2017). All CEAbased IAM studies reveal no unique path for the price of emissions(Bertram et al., 2015a; Kriegler et al., 2015b; Akimoto et al., 2017; Riahi
et al., 2017).
Socio-economic conditions and policy assumptions also influence theprice of carbon (very high confidence) (Bauer et al., 2017; Guivarch andRogelj, 2017; Hof et al., 2017; Riahi et al., 2017; Rogelj et al., 2018). A
multimodel study (Riahi et al., 2017) estimated the average discounted
price of carbon (2010–2100, 5% discount rate) for a 2°C target tobe nearly three times higher in the SSP5 marker than in the SSP1
marker. Another multimodel study (Rogelj et al., 2018) estimated theaverage discounted price of carbon (2020–2100, 5%) to be 35–65%
lower in SSP1 compared to SSP2 in 1.5°C pathways. Delayed nearterm mitigation policies and measures, including the limited extent ofinternational global cooperation, result in increases in total economic
mitigation costs and corresponding prices of carbon (Luderer et al.,2013; Clarke et al., 2014). This is because stronger efforts are requiredin the period after the delay to counterbalance the higher emissions
in the near term. Staged accession scenarios also produce higher
mitigation costs than immediate action mitigation scenarios under thesame stringency level of emissions (Kriegler et al., 2015b).
It has been long argued that an explicit carbon pricing mechanism(whether via a tax or cap-and-trade scheme) can theoretically achieve
cost-effective emission reductions (Nordhaus, 2007b; Stern, 2007;
Aldy and Stavins, 2012; Goulder and Schein, 2013; Somanthan et al.,2014; Weitzman, 2014; Tol, 2017). Whereas the integrated assessment
literature is mostly focused on the role of carbon pricing to reduce
emissions (Clarke et al., 2014; Riahi et al., 2017; Weyant, 2017), there

Unlike AR5, which only included cost-effective scenarios for estimating discounted average carbon prices for 2015–2100 (also using a 5% discount rate) (see Clarke et al.,2014, p.450), please note that values shown in Figure 2.26b include delays or technology constraint cases (see Sections 2.1 and 2.3).

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Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

is an emerging body of studies (including bottom-up approaches) that
focuses on the interaction and performance of various policy mixes (e.g.,
regulation, subsidies, standards). Assuming global implementation ofa mix of regionally existing best-practice policies (mostly regulatory
policies in the electricity, industry, buildings, transport and agricultural
sectors) and moderate carbon pricing (between 5–20 USD2010 tCO2−1
in 2025 in most world regions and average prices around 25 USD2010
tCO2−1 in 2030), early action mitigation pathways are generated that
reduce global CO2 emissions by an additional 10 GtCO2e in 2030
compared to the NDCs (Kriegler et al., 2018a) (see Section 2.3.5).
Furthermore, a mix of stringent energy efficiency policies (e.g., minimum
performance standards, building codes) combined with a carbon tax(rising from 10 USD2010 tCO2−1 in 2020 to 27 USD2010 tCO2−1 in 2040)
is more cost-effective than a carbon tax alone (from 20 to 53 USD2010
tCO2−1) to generate a 1.5°C pathway for the U.S. electric sector (Brown
and Li, 2018). Likewise, a policy mix encompassing a moderate carbon
price (7 USD2010 tCO2−1 in 2015) combined with a ban on new coalbased power plants and dedicated policies addressing renewableelectricity generation capacity and electric vehicles reduces efficiency
losses compared with an optimal carbon pricing in 2030 (Bertram et al.,2015b). One study estimates the carbon prices in high energy-intensive
pathways to be 25–50% higher than in low energy-intensive pathways
that assume ambitious regulatory instruments, economic incentives(in addition to a carbon price) and voluntary initiatives (Méjean etal., 2018). A bottom-up approach shows that stringent minimum
performance standards (MEPS) for appliances (e.g., refrigerators) caneffectively complement explicit carbon pricing, as tightened MEPS canachieve ambitious efficiency improvements that cannot be assured bycarbon prices of 100 USD2010 tCO2−1 or higher (Sonnenschein et al.,2018). In addition, the revenue recycling effect of carbon pricing canreduce mitigation costs by displacing distortionary taxes (Baranzini etal., 2017; OECD, 2017; McFarland et al., 2018; Sands, 2018; Siegmeier
et al., 2018), and the reduction of capital tax (compared to a labourtax) can yield greater savings in welfare costs (Sands, 2018). The effecton public budgets is particularly important in the near term; however,it can decline in the long term as carbon neutrality is achieved (Sands,
2018). The literature indicates that explicit carbon pricing is relevant
but needs to be complemented with other policies to drive the requiredchanges in line with 1.5°C cost-effective pathways (low to medium
evidence, high agreement) (see Chapter 4, Section 4.4.5) (Stiglitz et al.,2017; Mehling and Tvinnereim, 2018; Méjean et al., 2018; Michaelowa
et al., 2018).
In summary, new analyses are consistent with AR5 and showthat the price of carbon increases significantly if a higher level ofstringency is pursued (high confidence). Values vary substantiallyacross models, scenarios and socio-economic, technology and policy
assumptions. While an explicit carbon pricing mechanism is central
to prompt mitigation scenarios compatible with 1.5°C pathways, acomplementary mix of stringent policies is required.

Chapter 22

Figure 2.26 | Global price of carbon emissions consistent with mitigation
pathways. Panels show (a) undiscounted price of carbon (2030–2100) and (b) average
price of carbon (2030–2100) discounted at a 5% discount rate to 2020 in USD2010.
AC: Annually compounded. NPV: Net present value. Median values in floating black line.
The number of pathways included in box plots is indicated in the legend. Number ofpathways outside the figure range is noted at the top.

et al., 2013; Bowen et al., 2014; Gupta and Harnisch, 2014; Marangoni
and Tavoni, 2014; OECD/IEA and IRENA, 2017).
Global energy-system investments in the year 2016 are estimated at
approximately 1.7 trillion USD2010 (approximately 2.2% of global GDP
and 10% of gross capital formation), of which 0.23 trillion USD2010
was for incremental end-use energy efficiency and the remainder for
supply-side capacity installations (IEA, 2017c). There is some uncertainty
surrounding this number because not all entities making investments
report them publicly, and model-based estimates show an uncertainty
range of about ±15% (McCollum et al., 2018). Notwithstanding, thetrend for global energy investments has been generally upward overthe last two decades: increasing about threefold between 2000 and2012, then levelling off for three years before declining in both 2015
and 2016 as a result of the oil price collapse and simultaneous capital
cost reductions for renewables (IEA, 2017c).

2.5.2.2 Investments
Realizing the transformations towards a 1.5°C world would require amajor shift in investment patterns (McCollum et al., 2018). Literature on
global climate change mitigation investments is relatively sparse, withmost detailed literature having focused on 2°C pathways (McCollum

Estimates of demand-side investments, either in total or for incremental
efficiency efforts, are more uncertain, mainly due to a lack of reliable
statistics and definitional issues about what exactly is counted towards
a demand-side investment and what the reference should be for
estimating incremental efficiency (McCollum et al., 2013). Grubler and153

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2

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Wilson (2014) use two working definitions (a broader and a narrowerone) to provide a first-order estimate of historical end-use technology
investments in total. The broad definition defines end-use technologies
as the technological systems purchasable by final consumers in orderto provide a useful service, for example, heating and air conditioning
systems, cars, freezers, or aircraft. The narrow definition sets the boundary
at the specific energy-using components or subsystems of the larger enduse technologies (e.g., compressor, car engine, heating element). Basedon these two definitions, demand-side energy investments for the year
2005 were estimated about 1–3.5 trillion USD2010 (central estimate 1.7
trillion USD2010) using the broad definition and 0.1–0.6 trillion USD2010(central estimate 0.3 trillion USD2010) using the narrower definition.
Due to these definitional issues, demand-side investment projections are
uncertain, often underreported, and difficult to compare. Global IAMs
often do not fully and explicitly represent all the various measures that
could improve end-use efficiency.
Research carried out by six global IAM teams found that 1.5°C-consistentclimate policies would require a marked upscaling of energy system
supply-side investments (resource extraction, power generation, fuelconversion, pipelines/transmission, and energy storage) between
now and mid-century, reaching levels of between 1.6–3.8 trillion
USD2010 yr−1 globally on average over the 2016–2050 timeframe(McCollum et al., 2018) (Figure 2.27). How these investment needs
compare to those in a policy baseline scenario is uncertain: they could
be higher, much higher, or lower. Investments in the policy baselines
from these same models are 1.6–2.7 trillion USD2010 yr−1. Much
hinges on the reductions in energy demand growth embodied in the
1.5°C pathways, which require investing in energy efficiency. Studies
suggest that annual supply-side investments by mid-century could belowered by around 10% (McCollum et al., 2018) and in some cases up
to 50% (Grubler et al., 2018) if strong policies to limit energy demand
growth are successfully implemented. However, the degree to whichthese supply-side reductions would be partially offset by an increase indemand-side investments is unclear.
Some trends are robust across scenarios (Figure 2.27). First, pursuing
1.5°C mitigation efforts requires a major reallocation of the investment
portfolio, implying a financial system aligned to mitigation challenges.
The path laid out by countries’ current NDCs until 2030 will not
drive these structural changes; and despite increasing low-carbon
investments in recent years (IEA, 2016b; Frankfurt School-UNEP Centre/
BNEF, 2017), these are not yet aligned with 1.5°C. Second, additional
annual average energy-related investments for the period 2016 to 2050
in pathways limiting warming to 1.5°C compared to the baseline (i.e.,pathways without new climate policies beyond those in place today)
are estimated by the models employed in McCollum et al. (2018) tobe around 830 billion USD2010 (range of 150 billion to 1700 billion
USD2010 across six models). This compares to total annual averageenergy supply investments in 1.5°C pathways of 1460 to 3510 billion
USD2010 and total annual average energy demand investments of
640 to 910 billion USD2010 for the period 2016 to 2050. Total energyrelated investments increase by about 12% (range of 3% to 24%) in
1.5°C pathways relative to 2°C pathways. Average annual investment
in low-carbon energy technologies and energy efficiency are upscaledby roughly a factor of six (range of factor of 4 to 10) by 2050 compared
to 2015. Specifically, annual investments in low-carbon energy are154

projected to average 0.8–2.9 trillion USD2010 yr−1 globally to 2050
in 1.5°C pathways, overtaking fossil investments globally already byaround 2025 (McCollum et al., 2018). The bulk of these investments
are projected to be for clean electricity generation, particularly solar
and wind power (0.09–1.0 trillion USD2010 yr−1 and 0.1–0.35 trillion
USD2010 yr−1, respectively) as well as nuclear power (0.1–0.25 trillion
USD2010 yr−1). Third, the precise apportioning of these investments
depends on model assumptions and societal preferences related tomitigation strategies and policy choices (see Sections 2.1 and 2.3).
Investments for electricity transmission and distribution and storage
are also scaled up in 1.5°C pathways (0.3–1.3 trillion USD2010 yr−1),
given their widespread electrification of the end-use sectors (seeSection 2.4). Meanwhile, 1.5°C pathways see a reduction in annual
investments for fossil-fuel extraction and unabated fossil electricity
generation (to 0.3–0.85 trillion USD2010 yr−1 on average over the
2016–2050 period). Investments in unabated coal are halted by 2030
in most 1.5°C projections, while the literature is less conclusive forinvestments in unabated gas (McCollum et al., 2018). This illustrates
how mitigation strategies vary between models, but in the real world
should be considered in terms of their societal desirability (see Section
2.5.3). Furthermore, some fossil investments made over the next few
years – or those made in the last few – will likely need to be retired prior
to fully recovering their capital investment or before the end of theiroperational lifetime (Bertram et al., 2015a; Johnson et al., 2015; OECD/
IEA and IRENA, 2017). How the pace of the energy transition will beaffected by such dynamics, namely with respect to politics and society,is not well captured by global IAMs at present. Modelling studies
have, however, shown how the reliability of institutions influences
investment risks and hence climate mitigation investment decisions(Iyer et al., 2015), finding that a lack of regulatory credibility or policy
commitment fails to stimulate low-carbon investments (Bosetti andVictor, 2011; Faehn and Isaksen, 2016).
Low-carbon supply-side investment needs are projected to be largest inOECD countries and those of developing Asia. The regional distributionof investments in 1.5°C pathways estimated by the multiple modelsin (McCollum et al., 2018) are the following (average over 2016–2050
timeframe): 0.30–1.3 trillion USD2010 yr−1(ASIA), 0.35–0.85 trillion
USD2010 yr−1 (OECD), 0.08–0.55 trillion USD2010 yr−1 (MAF), 0.07–0.25
trillion USD2010 yr−1 (LAM), and 0.05–0.15 trillion USD2010 yr−1 (REF)(regions are defined consistent with their use in AR5 WGIII, see Table
A.II.8 in Krey et al., 2014b).
Until now, IAM investment analyses of 1.5°C pathways have focusedon middle-of-the-road socio-economic and technological development
futures (SSP2) (Fricko et al., 2017). Consideration of a broader range
of development futures would yield different outcomes in terms ofthe magnitudes of the projected investment levels. Sensitivity analyses
indicate that the magnitude of supply-side investments as well as theinvestment portfolio do not change strongly across the SSPs for a given
level of climate policy stringency (McCollum et al., 2018). With only one
dedicated multimodel comparison study published, there is limited tomedium evidence available. For some features, there is high agreement
across modelling frameworks leading, for example, to medium to highconfidence that limiting global temperature increase to 1.5°C would
require a major reallocation of the investment portfolio. Given the limited
amount of sensitivity cases available compared to the default SSP2

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

assumptions, medium confidence can be assigned to the specific energy
and climate mitigation investment estimates reported here.
Assumptions in modelling studies indicate a number of challenges.
For instance, access to finance and mobilization of funds are critical(Fankhauser et al., 2016; OECD, 2017). In turn, policy efforts need to beeffective in redirecting financial resources (UNEP, 2015; OECD, 2017) andreducing transaction costs for bankable mitigation projects (i.e. projects
that have adequate future cash flow, collateral, etc. so lenders are willingto finance it), particularly on the demand side (Mundaca et al., 2013;
Brunner and Enting, 2014; Grubler et al., 2018). Assumptions also imply
that policy certainty, regulatory oversight mechanisms and fiduciary duty
need to be robust and effective to safeguard credible and stable financial

Chapter 2

markets and de-risk mitigation investments in the long term (Clarke etal., 2014; Mundaca et al., 2016; EC, 2017; OECD, 2017). Importantly,
the different time horizons that actors have in the competitive financeindustry are typically not explicitly captured by modelling assumptions(Harmes, 2011). See Chapter 4, Section 4.4.5 for details of climate
finance in practice.
In summary and despite inherent uncertainties, the emerging literature
indicates a gap between current investment patterns and thosecompatible with 1.5°C (or 2°C) pathways (limited to medium evidence,high agreement). Estimates and assumptions from modelling frameworks
suggest a major shift in investment patterns and entail a financial systemeffectively aligned with mitigation challenges (high confidence).

2

Figure 2.27 | Historical and projected global energy investments. (a) Historical investment estimates across six global models from (McCollum et al., 2018) (bars =model means, whiskers full model range) compared to historical estimates from IEA (International Energy Agency (IEA) 2016) (triangles). (b) Average annual investments over the
2016–2050 period in the “baselines” (i.e., pathways without new climate policies beyond those in place today), scenarios which implement the NDCs (‘NDC’, including conditional
NDCs), scenarios consistent with the Lower-2°C pathway class (‘2°C’), and scenarios in line with the 1.5°C-low-OS pathway class (‘1.5°C’). Whiskers show the range of models; widebars show the multimodel means; narrow bars represent analogous values from individual IEA scenarios (OECD/IEA and IRENA, 2017). (c) Average annual mitigation investments
and disinvestments for the 2016–2030 periods relative to the baseline. The solid bars show the values for ‘2°C’ pathways, while the hatched areas show the additional investments
for the pathways labelled with ‘1.5°C’. Whiskers show the full range around the multimodel means. T&D stands for transmission and distribution, and CCS stands for carbon captureand storage. Global cumulative carbon dioxide emissions, from fossil fuels and industrial processes (FF&I) but excluding land use, over the 2016-2100 timeframe range from 880 to1074 GtCO2 (multimodel mean: 952 GtCO2) in the ‘2°C’ pathway and from 206 to 525 GtCO2 (mean: 390 GtCO2) in the ‘1.5°C’ pathway.

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2.5.3

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Sustainable Development Featuresof 1.5°C Pathways

Potential synergies and trade-offs between 1.5°C mitigation pathways
and different sustainable development (SD) dimensions (see CrossChapter Box 4 in Chapter 1) are an emerging field of research. Chapter
5, Section 5.4 assesses interactions between individual mitigation
measures with other societal objectives, as well as the Sustainable

Development Goals (SDGs) (Table 5.1). This section synthesized
the Chapter 5 insights to assess how these interactions play out
in integrated 1.5°C pathways, and the four illustrative pathwayarchetypes of this chapter in particular (see Section 2.1). Informationfrom integrated pathways is combined with the interactions assessedin Chapter 5 and aggregated for each SDG, with a level of confidence
attributed to each interaction based on the amount and agreement of
the scientific evidence (see Chapter 5).

2

Figure 2.28 | Interactions of individual mitigation measures and alternative mitigations portfolios for 1.5°C with Sustainable Development Goals (SDGs).
The assessment of interactions between mitigation measures and individual SDGs is based on the assessment of Chapter 5, Section 5.4. Proxy indicators and synthesis method aredescribed in Supplementary Material 2.SM.1.5.

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Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Figure 2.28 shows how the scale and combination of individual
mitigation measures (i.e., their mitigation portfolios) influence theextent of synergies and trade-offs with other societal objectives. All
pathways generate multiple synergies with sustainable development
dimensions and can advance several other SDGs simultaneously. Some,however, show higher risks for trade-offs. An example is increased
biomass production and its potential to increase pressure on land andwater resources, food production, and biodiversity and to reduce air
quality when combusted inefficiently. At the same time, mitigation
actions in energy-demand sectors and behavioural response options
with appropriate management of rebound effects can advance multiple
SDGs simultaneously, more so than energy supply-side mitigation
actions (see Chapter 5, Section 5.4, Table 5.1 and Figure 5.3 for more
examples). Of the four pathway archetypes used in this chapter (LED,S1, S2, and S5, referred to as P1, P2, P3, and P4 in the Summary for
Policymakers), the S1 and LED pathways show the largest number ofsynergies and least number of potential trade-offs, while for the S5
pathway more potential trade-offs are identified. In general, pathways
with emphasis on demand reductions and policies that incentivize
behavioural change, sustainable consumption patterns, healthy dietsand relatively low use of CDR (or only afforestation) show relatively
more synergies with individual SDGs than other pathways.

interactions. Very limited literature suggests that achieving co-benefits
is not automatically assured but results from conscious and carefully
coordinated policies and implementation strategies (Shukla andChaturvedi, 2012; Clarke et al., 2014; McCollum et al., 2018).
Understanding these mitigation–SDG interactions is key for selecting
mitigation options that maximize synergies and minimize trade-offs
towards the 1.5°C and sustainable development objectives (van Vuuren
et al., 2015; Hildingsson and Johansson, 2016; Jakob and Steckel, 2016;von Stechow et al., 2016; Delponte et al., 2017).
In summary, the combined evidence indicates that the chosen
mitigation portfolio can have a distinct impact on the achievementof other societal policy objectives (high confidence); however, there is
uncertainty regarding the specific extent of climate–SDG interactions.

2
2.6

At the same time, the literature on climate–SDG interactions is still
an emergent field of research and hence there is low to medium
confidence in the precise magnitude of the majority of these

Knowledge Gaps

This section summarizes the knowledge gaps articulated in earlier
sections of the chapter.

2.6.1
There is robust evidence and high agreement in the pathway literaturethat multiple strategies can be considered to limit warming to 1.5°C (seeSections 2.1.3, 2.3 and 2.4). Together with the extensive evidence on
the existence of interactions of mitigation measures with other societal
objectives (Chapter 5, Section 5.4), this results in high confidence thatthe choice of mitigation portfolio or strategy can markedly affect theachievement of other societal objectives. For instance, action on SLCFs
has been suggested to facilitate the achievement of SDGs (Shindell etal., 2017b) and to reduce regional impacts, for example, from black
carbon sources on snow and ice loss in the Arctic and alpine regions(Painter et al., 2013), with particular focus on the warming sub-set of
SLCFs. Reductions in both surface aerosols and ozone through methane
reductions provide health and ecosystem co-benefits (Jacobson, 2002,
2010; Anenberg et al., 2012; Shindell et al., 2012; Stohl et al., 2015;
Collins et al., 2018). Public health benefits of stringent mitigation
pathways in line with 1.5°C pathways can be sizeable. For instance,a study examining a more rapid reduction of fossil-fuel usage toachieve 1.5°C relative to 2°C, similar to that of other recent studies(Grubler et al., 2018; van Vuuren et al., 2018), found that improved
air quality would lead to more than 100 million avoided premature
deaths over the 21st century (Shindell et al., 2018). These benefits are
assumed to be in addition to those occurring under 2°C pathways(e.g., Silva et al., 2016), and could in monetary terms offset either alarge portion or all of the initial mitigation costs (West et al., 2013;
Shindell et al., 2018). However, some sources of SLCFs with important
impacts for public health (e.g., traditional biomass burning) are only
mildly affected by climate policy in the available integrated pathways
and are more strongly impacted by baseline assumptions about futuresocietal development and preferences, and technologies instead (Raoet al., 2016, 2017).

Chapter 2

Geophysical Understanding

Knowledge gaps are associated with the carbon cycle response, therole of non-CO2 emissions and the evaluation of an appropriate historicbaseline.
Quantifying how the carbon cycle responds to negative emissions is
an important knowledge gap for strong mitigation pathways (Section
2.2). Earth system feedback uncertainties are important to consider forthe longer-term response, particularly in how permafrost melting might
affect the carbon budget (Section 2.2). Future research and ongoingobservations over the next years will provide a better indication as to
how the 2006-2015 base period compares with the long-term trends
and might at present bias the carbon budget estimates.
The future emissions of short-lived climate forcers and theirtemperature response are a large source of uncertainty in 1.5°C
pathways, having a greater relative uncertainty than in higher CO2
emission pathways. Their global emissions, their sectoral and regional
disaggregation, and their climate response are generally less wellquantified than for CO2 (Sections 2.2 and 2.3). Emissions from theagricultural sector, including land-use based mitigation options, in
1.5°C pathways constitute the main source of uncertainty here andare an important gap in understanding the potential achievement ofstringent mitigation scenarios (Sections 2.3 and 2.4). This also includes
uncertainties surrounding the mitigation potential of the long-lived
GHG nitrous oxide (Sections 2.3 and 2.4).
There is considerable uncertainty in how future emissions of aerosol
precursors will affect the effective radiative forcing from aerosol–cloudinteraction. The potential future warming from mitigation of theseemissions reduces remaining carbon budgets and increases peaktemperatures (Section 2.2). The potential co-benefits of mitigating air
pollutants and how the reduction in air pollution may affect the carbon
sink are also important sources of uncertainty (Sections 2.2 and 2.5).
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Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

The pathway classification employed in this chapter employs resultsfrom the MAGICC model with its AR5 parameter sets. The alternative
representation of the relationship between emissions and effective
radiative forcing and response in the FAIR model would lead to a different
classification that would make 1.5°C targets more achievable (Section
2.2 and Supplementary Material 2.SM.1.1). Such a revision would
significantly alter the temperature outcomes for the pathways and, if
the result is found to be robust, future research and assessments would
need to adjust their classifications accordingly. Any possible high bias in
the MAGICC response may be partly or entirely offset by missing Earthsystem feedbacks that are not represented in either climate emulator andthat would act to increase the temperature response (Section 2.2). For
this assessment report, any possible bias in the MAGICC setup appliedin this and earlier reports is not established enough in the literature tochange the classification approach. However, we only place medium
confidence in the classification adopted by the chapter.

2.6.2

industry, buildings, and transport sector in that realized by selected
pathways from IAMs, indicating the possibility to strengthen sectoral
decarbonization strategies compared to the IAM 1.5°C pathways
assessed in this chapter (Section 2.1).
Studies indicate that a major shift in investment patterns is required
to limit global warming to 1.5°C. This assessment would benefit froma more explicit representation and understanding of the financial
sector within the modelling approaches. Assumptions in modellingstudies imply low-to-zero transaction costs for market agents andthat regulatory oversight mechanisms and fiduciary duty need to behighly robust to guarantee stable and credible financial markets inthe long term. This area can be subject to high uncertainty, however.
The heterogeneity of actors (e.g., banks, insurance companies, asset
managers, or credit rating agencies) and financial products also needs
to be taken into account, as does the mobilization of capital andfinancial flows between countries and regions (Section 2.5).

Integrated Assessment Approaches

IAMs attempt to be as broad as possible in order to explore
interactions between various societal subsystems, like the economy,
land, and energy system. They hence include stylized and simplified
representations of these subsystems. Climate damages, avoided
impacts and societal co-benefits of the modelled transformations
remain largely unaccounted for and are important knowledge gaps.
Furthermore, rapid technological changes and uncertainties about
input data present continuous challenges.

The literature on interactions between 1.5˚C mitigation pathways
and SDGs is an emergent field of research (Section 2.3.5, 2.5 and Chapter5). Whereas the choice of mitigation strategies can noticeably affect theattainment of various societal objectives, there is uncertainty regarding
the extent of the majority of identified interactions. Understanding
climate–SDG interactions helps inform the choice of mitigation options
that minimize trade-offs and risks and maximize synergies towards
sustainable development objectives and the 1.5°C goal (Section 2.5).

2.6.3
The IAMs used in this report do not account for climate impacts(Section 2.1), and similarly, none of the Gross Domestic Product (GDP)projections in the mitigation pathway literature assessed in this chapter
included the feedback of climate damages on economic growth (Section
2.3). Although some IAMs do allow for climate impact feedbacks intheir modelling frameworks, particularly in their land components,
such feedbacks were by design excluded in pathways developed in thecontext of the SSP framework. The SSP framework aims at providing
an integrative framework for the assessment of climate change
adaptation and mitigation. IAMs are typically developed to informthe mitigation component of this question, while the assessment of
impacts is carried out by specialized impact models. However, the useof a consistent set of socio-economic drivers embodied by the SSPs
allows for an integrated assessment of climate change impacts andmitigation challenges at a later stage. Further integration of these
two strands of research will allow a better understanding of climate
impacts on mitigation studies.
Many of the IAMs that contributed mitigation pathways to thisassessment include a process-based description of the land system in
addition to the energy system, and several have been extended to cover
air pollutants and water use. These features make them increasingly fit
to explore questions beyond those that touch upon climate mitigation
only. The models do not, however, fully account for all constraints that
could affect realization of pathways (Section 2.1).
While the representation of renewable energy resource potentials,
technology costs and system integration in IAMs has been updated
since AR5, bottom-up studies find higher mitigation potentials in the158

Carbon Dioxide Removal (CDR)

Most 1.5°C and 2°C pathways are heavily reliant on CDR at a
speculatively large scale before mid-century. There are a numberof knowledge gaps associated which such technologies. Chapter 4
performs a detailed assessment of CDR technologies.
There is uncertainty in the future deployment of CCS given thelimited pace of current deployment, the evolution of CCS technologythat would be associated with deployment, and the current lack of
incentives for large-scale implementation of CCS (Chapter 4, Section
4.2.7). Technologies other than BECCS and afforestation have yet tobe comprehensively assessed in integrated assessment approaches. No
proposed technology is close to deployment at scale, and regulatory
frameworks are not established. This limits how they can be realistically
implemented within IAMs. (Section 2.3)
Evaluating the potential from BECCS is problematic due to large
uncertainties in future land projections due to differences in modelling
approaches in current land-use models, and these differences areat least as great as the differences attributed to climate scenario
variations. (Section 2.3)
There is substantial uncertainty about the adverse effects of largescale CDR deployment on the environment and societal sustainable
development goals. It is not fully understood how land-use andland-management choices for large-scale BECCS will affect various
ecosystem services and sustainable development, and how they furthertranslate into indirect impacts on climate, including GHG emissions
other than CO2. (Section 2.3, Section 2.5.3)

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Chapter 2

Frequently Asked Questions

FAQ 2.1 | What Kind of Pathways Limit Warming to 1.5°C and are we on Track?
Summary: There is no definitive way to limit global temperature rise to 1.5°C above pre-industrial levels. This
Special Report identifies two main conceptual pathways to illustrate different interpretations. One stabilizes
global temperature at, or just below, 1.5°C. Another sees global temperature temporarily exceed 1.5°C before
coming back down. Countries’ pledges to reduce their emissions are currently not in line with limiting global
warming to 1.5°C.
Scientists use computer models to simulate the emissions of greenhouse gases that would be consistent withdifferent levels of warming. The different possibilities are often referred to as ‘greenhouse gas emission
pathways’. There is no single, definitive pathway to limiting warming to 1.5°C.
This IPCC special report identifies two main pathways that explore global warming of 1.5°C. The first involves
global temperature stabilizing at or below before 1.5°C above pre-industrial levels. The second pathway sees
warming exceed 1.5°C around mid-century, remain above 1.5°C for a maximum duration of a few decades, andreturn to below 1.5°C before 2100. The latter is often referred to as an ‘overshoot’ pathway. Any alternative
situation in which global temperature continues to rise, exceeding 1.5°C permanently until the end of the 21st
century, is not considered to be a 1.5°C pathway.

2

The two types of pathway have different implications for greenhouse gas emissions, as well as for climate change
impacts and for achieving sustainable development. For example, the larger and longer an ‘overshoot’, thegreater the reliance on practices or technologies that remove CO2 from the atmosphere, on top of reducing
the sources of emissions (mitigation). Such ideas for CO2 removal have not been proven to work at scale and,
therefore, run the risk of being less practical, effective or economical than assumed. There is also the risk thatthe use of CO2 removal techniques ends up competing for land and water, and if these trade-offs are notappropriately managed, they can adversely affect sustainable development. Additionally, a larger and longer
overshoot increases the risk for irreversible climate impacts, such as the onset of the collapse of polar ice shelves
and accelerated sea level rise.
Countries that formally accept or ‘ratify’ the Paris Agreement submit pledges for how they intend to address
climate change. Unique to each country, these pledges are known as Nationally Determined Contributions(NDCs). Different groups of researchers around the world have analysed the combined effect of adding up all
the NDCs. Such analyses show that current pledges are not on track to limit global warming to 1.5°C above preindustrial levels. If current pledges for 2030 are achieved but no more, researchers find very few (if any) ways toreduce emissions after 2030 sufficiently quickly to limit warming to 1.5°C. This, in turn, suggests that with thenational pledges as they stand, warming would exceed 1.5°C, at least for a period of time, and practices andtechnologies that remove CO2 from the atmosphere at a global scale would be required to return warming to
1.5°C at a later date.
A world that is consistent with holding warming to 1.5°C would see greenhouse gas emissions rapidly decline
in the coming decade, with strong international cooperation and a scaling up of countries’ combined ambition
beyond current NDCs. In contrast, delayed action, limited international cooperation, and weak or fragmented
policies that lead to stagnating or increasing greenhouse gas emissions would put the possibility of limiting
global temperature rise to 1.5°C above pre-industrial levels out of reach.

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FAQ 2.1 (continued)2

FAQ 2.1, Figure 1 | Two main pathways for limiting global temperature rise to 1.5°C above pre-industrial levels are discussed in this Special Report. These are:stabilizing global temperature at, or just below, 1.5°C (left) and global temperature temporarily exceeding 1.5°C before coming back down later in the century(right). Temperatures shown are relative to pre-industrial but pathways are illustrative only, demonstrating conceptual not quantitative characteristics.

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Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

Chapter 2

Frequently Asked Questions

FAQ 2.2 | What do Energy Supply and Demand have to do with Limiting Warming
to 1.5°C?
Summary: Limiting global warming to 1.5°C above pre-industrial levels would require major reductions in greenhouse gas emissions in all sectors. But different sectors are not independent of each other, and making changes
in one can have implications for another. For example, if we as a society use a lot of energy, then this could
mean we have less flexibility in the choice of mitigation options available to limit warming to 1.5°C. If we use
less energy, the choice of possible actions is greater – for example, we could be less reliant on technologies thatremove carbon dioxide (CO2) from the atmosphere.
To stabilize global temperature at any level, ‘net’ CO2 emissions would need to be reduced to zero. This means theamount of CO2 entering the atmosphere must equal the amount that is removed. Achieving a balance between
CO2 ‘sources’ and ‘sinks’ is often referred to as ‘net zero’ emissions or ‘carbon neutrality’. The implication of net
zero emissions is that the concentration of CO2 in the atmosphere would slowly decline over time until a new
equilibrium is reached, as CO2 emissions from human activity are redistributed and taken up by the oceans andthe land biosphere. This would lead to a near-constant global temperature over many centuries.

2

Warming will not be limited to 1.5°C or 2°C unless transformations in a number of areas achieve the required
greenhouse gas emissions reductions. Emissions would need to decline rapidly across all of society’s main sectors,
including buildings, industry, transport, energy, and agriculture, forestry and other land use (AFOLU). Actions
that can reduce emissions include, for example, phasing out coal in the energy sector, increasing the amount ofenergy produced from renewable sources, electrifying transport, and reducing the ‘carbon footprint’ of the foodwe consume.
The above are examples of ‘supply-side’ actions. Broadly speaking, these are actions that can reduce greenhousegas emissions through the use of low-carbon solutions. A different type of action can reduce how much energy
human society uses, while still ensuring increasing levels of development and well-being. Known as ‘demand-side’
actions, this category includes improving energy efficiency in buildings and reducing consumption of energyand greenhouse-gas intensive products through behavioural and lifestyle changes, for example. Demand- andsupply-side measures are not an either-or question, they work in parallel with each other. But emphasis can begiven to one or the other.
Making changes in one sector can have consequences for another, as they are not independent of each other.
In other words, the choices that we make now as a society in one sector can either restrict or expand our
options later on. For example, a high demand for energy could mean we would need to deploy almost all known
options to reduce emissions in order to limit global temperature rise to 1.5°C above pre-industrial levels, withthe potential for adverse side-effects. In particular, a pathway with high energy demand would increase ourreliance on practices and technologies that remove CO2 from the atmosphere. As of yet, such techniques havenot been proven to work on a large scale and, depending on how they are implemented, could compete for land
and water. By leading to lower overall energy demand, effective demand-side measures could allow for greater
flexibility in how we structure our energy system. However, demand-side measures are not easy to implement
and barriers have prevented the most efficient practices being used in the past.

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FAQ 2.2 (continued)2

FAQ 2.2, Figure 1 | Having a lower energy demand increases the flexibility in choosing options for supplying energy. A larger energy demand means many more
low carbon energy supply options would need to be used.

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Impacts of 1.5°C
of Global Warming on
Natural and Human Systems
Coordinating Lead Authors:Ove Hoegh-Guldberg (Australia), Daniela Jacob (Germany), Michael Taylor (Jamaica)

Lead Authors:

Marco Bindi (Italy), Sally Brown (UK), Ines Camilloni (Argentina), Arona Diedhiou (Ivory Coast/Senegal),Riyanti Djalante (Japan/Indonesia), Kristie L. Ebi (USA), Francois Engelbrecht (South Africa), Joel Guiot(France), Yasuaki Hijioka (Japan), Shagun Mehrotra (USA/India), Antony Payne (UK), Sonia I. Seneviratne(Switzerland), Adelle Thomas (Bahamas), Rachel Warren (UK), Guangsheng Zhou (China)

Contributing Authors:

Sharina Abdul Halim (Malaysia), Michelle Achlatis (Australia/Greece), Lisa V. Alexander (Australia),
Myles R. Allen (UK), Peter Berry (Canada), Christopher Boyer (USA), Lorenzo Brilli (Italy), Marcos
Buckeridge (Brazil), Edward Byers (Austria/Brazil), William Cheung (Canada), Marlies Craig (South Africa),
Neville Ellis (Australia), Jason Evans (Australia), Hubertus Fischer (Switzerland), Klaus Fraedrich (Germany),
Sabine Fuss (Germany), Anjani Ganase (Australia/Trinidad and Tobago), Jean-Pierre Gattuso (France), Peter
Greve (Austria/Germany), Tania Guillén Bolaños (Germany/Nicaragua), Naota Hanasaki (Japan), Tomoko
Hasegawa (Japan), Katie Hayes (Canada), Annette Hirsch (Switzerland/Australia), Chris Jones (UK),Thomas Jung (Germany), Markku Kanninen (Finland), Gerhard Krinner (France), David Lawrence (USA), Tim
Lenton (UK), Debora Ley (Guatemala/Mexico), Diana Liverman (USA), Natalie Mahowald (USA), Kathleen
McInnes (Australia), Katrin J. Meissner (Australia), Richard Millar (UK), Katja Mintenbeck (Germany), Dann
Mitchell (UK), Alan C. Mix (US), Dirk Notz (Germany), Leonard Nurse (Barbados), Andrew Okem (Nigeria),
Lennart Olsson (Sweden), Michael Oppenheimer (USA), Shlomit Paz (Israel), Juliane Petersen (Germany),
Jan Petzold (Germany), Swantje Preuschmann (Germany), Mohammad Feisal Rahman (Bangladesh), Joeri
Rogelj (Austria/Belgium), Hanna Scheuffele (Germany), Carl-Friedrich Schleussner (Germany), Daniel
Scott (Canada), Roland Séférian (France), Jana Sillmann (Germany/Norway), Chandni Singh (India),
Raphael Slade (UK), Kimberly Stephenson (Jamaica), Tannecia Stephenson (Jamaica), Mouhamadou B.
Sylla (Senegal), Mark Tebboth (UK), Petra Tschakert (Australia/Austria), Robert Vautard (France), Richard
Wartenburger (Switzerland/Germany), Michael Wehner (USA), Nora M. Weyer (Germany), Felicia Whyte(Jamaica), Gary Yohe (USA), Xuebin Zhang (Canada), Robert B. Zougmoré (Burkina Faso/Mali)

Review Editors:

Jose Antonio Marengo (Brazil/Peru), Joy Pereira (Malaysia), Boris Sherstyukov (Russian Federation)

Chapter Scientist:

Tania Guillén Bolaños (Germany/Nicaragua)This chapter should be cited as:
Hoegh-Guldberg, O., D. Jacob, M. Taylor, M. Bindi, S. Brown, I. Camilloni, A. Diedhiou, R. Djalante, K.L. Ebi, F. Engelbrecht,
J. Guiot, Y. Hijioka, S. Mehrotra, A. Payne, S.I. Seneviratne, A. Thomas, R. Warren, and G. Zhou, 2018: Impacts of 1.5ºC Global
Warming on Natural and Human Systems. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global
warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context ofstrengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty[Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock,
S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In Press.

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Chapter 3

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Table of Contents
Executive Summary....................................................................177
3.1

About the Chapter........................................................182

3.2
		
		

How are Risks at 1.5°C and Higher Levelsof Global Warming Assessed inthis Chapter?...................................................................183

3.2.1
How are Changes in Climate and Weather			
at 1.5°C versus Higher Levels of Warming
			 Assessed?...................................................................183
3.2.2
			
			

3.3
		

Global and Regional Climate
Changes and Associated Hazards.............................186

3.4.7

Human Health.............................................................240

3.4.8

Urban Areas................................................................241

3.4.9

Key Economic Sectors and Services.............................242

3.4.10			Livelihoods and Poverty, and the Changing
Structure of Communities............................................244

3.4.11

Interacting and Cascading Risks..................................245

3.4.12
			

Summary of Projected Risks at 1.5°C and 2°Cof Global Warming......................................................245

3.4.13Synthesis of Key Elements of Risk...............................251

3.5
		
		

Avoided Impacts and Reduced Risks
at 1.5°C Compared with 2°C of
Global Warming.............................................................253

Global Changes in Climate..........................................186

3.5.1

Introduction................................................................253

3.3.2
Regional Temperatures on Land, Including			 Extremes.....................................................................189

3.5.2
			

Aggregated Avoided Impacts and Reduced
Risks at 1.5°C versus 2°C of Global Warming.............253

3.3.3
			

Regional Precipitation, Including Heavy
Precipitation and Monsoons........................................191

3.5.3
			

Regional Economic Benefit Analysis forthe 1.5°C versus 2°C Global Goals..............................258

3.3.4

Drought and Dryness...................................................196

3.5.4
			

Reducing Hotspots of Change for 1.5°C
and 2°C of Global Warming........................................258

3.5.5
			

Avoiding Regional Tipping Points by Achieving
More Ambitious Global Temperature Goals.................262

3.3.1

3

How are Potential Impacts on Ecosystems
Assessed at 1.5°C versus Higher Levelsof Warming?...............................................................185

Cross-Chapter Box 6: Food Security.......................................238

Box 3.1: Sub-Saharan Africa: Changes in Temperatureand Precipitation Extremes.....................................................197
Box 3.2: Droughts in the Mediterranean Basin and
the Middle East.........................................................................200

Box 3.6: Economic Damages from Climate Change.............264

3.3.5

Runoff and Fluvial Flooding........................................201

3.3.6

Tropical Cyclones and Extratropical Storms.................203

3.3.7

Ocean Circulation and Temperature............................204

3.6.1

Gradual versus Overshoot in 1.5°C Scenarios.............265

3.3.8

Sea Ice.........................................................................205

3.3.9

Sea Level.....................................................................206

3.6.2
			

Non-CO2 Implications and Projected Risks of
Mitigation Pathways...................................................265

Box 3.3: Lessons from Past Warm Climate Episodes............2083.3.10Ocean Chemistry.........................................................209

3.3.11

Global Synthesis..........................................................210

3.4
		

Observed Impacts and Projected Risks
in Natural and Human Systems.................................212

3.4.1

Introduction................................................................212

3.4.2

Freshwater Resources (Quantity and Quality)..............213

3.4.3

Terrestrial and Wetland Ecosystems............................216

3.4.4

Ocean Ecosystems.......................................................221

Box 3.4: Warm-Water (Tropical) Coral Reefs in a 1.5°C
Warmer World...........................................................................229
3.4.5

176

Cross-Chapter Box 7: Land-Based Carbon Dioxide
Removal in Relation to 1.5°C of Global Warming................268
3.6.3

3.7

Implications Beyond the End of the Century...............270

Knowledge Gaps............................................................272

3.7.1

Gaps in Methods and Tools.........................................272

3.7.2

Gaps in Understanding................................................272

Cross-Chapter Box 8: 1.5°C Warmer Worlds..........................274

Frequently Asked Questions
FAQ 3.1 What are the Impacts of 1.5°C and 2°Cof Warming?........................................................................282

Coastal and Low-Lying Areas, and Sea Level Rise........231

Box 3.5: Small Island Developing States (SIDS)...................234
3.4.6
			

3.6
Implications of Different 1.5°C and 2°C
		 Pathways..........................................................................265

Food, Nutrition Security and Food Production
Systems (Including Fisheries and Aquaculture)............236

References ....................................................................................284

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Executive Summary
This chapter builds on findings of AR5 and assesses new scientificevidence of changes in the climate system and the associated impacts
on natural and human systems, with a specific focus on the magnitude
and pattern of risks linked for global warming of 1.5°C above
temperatures in the pre-industrial period. Chapter 3 explores observed
impacts and projected risks to a range of natural and human systems,with a focus on how risk levels change from 1.5°C to 2°C of global
warming. The chapter also revisits major categories of risk (Reasons forConcern, RFC) based on the assessment of new knowledge that has
become available since AR5.

1.5°C and 2°C Warmer Worlds
The global climate has changed relative to the pre-industrial
period, and there are multiple lines of evidence that thesechanges have had impacts on organisms and ecosystems, aswell as on human systems and well-being (high confidence). The
increase in global mean surface temperature (GMST), which reached
0.87°C in 2006–2015 relative to 1850–1900, has increased thefrequency and magnitude of impacts (high confidence), strengthening
evidence of how an increase in GMST of 1.5°C or more could impact
natural and human systems (1.5°C versus 2°C). {3.3, 3.4, 3.5, 3.6,
Cross-Chapter Boxes 6, 7 and 8 in this chapter}
Human-induced global warming has already caused multiple
observed changes in the climate system (high confidence).
Changes include increases in both land and ocean temperatures, as well
as more frequent heatwaves in most land regions (high confidence).
There is also high confidence that global warming has resulted in an
increase in the frequency and duration of marine heatwaves. Further,
there is substantial evidence that human-induced global warming has
led to an increase in the frequency, intensity and/or amount of heavy
precipitation events at the global scale (medium confidence), as wellas an increased risk of drought in the Mediterranean region (medium
confidence). {3.3.1, 3.3.2, 3.3.3, 3.3.4, Box 3.4}
Trends in intensity and frequency of some climate and weather
extremes have been detected over time spans during which
about 0.5°C of global warming occurred (medium confidence).
This assessment is based on several lines of evidence, including
attribution studies for changes in extremes since 1950. {3.2, 3.3.1,
3.3.2, 3.3.3, 3.3.4}
Several regional changes in climate are assessed to occur with
global warming up to 1.5°C as compared to pre-industrial
levels, including warming of extreme temperatures in many
regions (high confidence), increases in frequency, intensity and/or
amount of heavy precipitation in several regions (high confidence),
and an increase in intensity or frequency of droughts in some regions(medium confidence). {3.3.1, 3.3.2, 3.3.3, 3.3.4, Table 3.2}

Chapter 3

There is no single ‘1.5°C warmer world’ (high confidence). In
addition to the overall increase in GMST, it is important to consider thesize and duration of potential overshoots in temperature. Furthermore,
there are questions on how the stabilization of an increase in GMST of
1.5°C can be achieved, and how policies might be able to influence theresilience of human and natural systems, and the nature of regionaland subregional risks. Overshooting poses large risks for natural andhuman systems, especially if the temperature at peak warming ishigh, because some risks may be long-lasting and irreversible, suchas the loss of some ecosystems (high confidence). The rate of change
for several types of risks may also have relevance, with potentiallylarge risks in the case of a rapid rise to overshooting temperatures,
even if a decrease to 1.5°C can be achieved at the end of the 21st
century or later (medium confidence). If overshoot is to be minimized,
the remaining equivalent CO2 budget available for emissions is verysmall, which implies that large, immediate and unprecedented global
efforts to mitigate greenhouse gases are required (high confidence).
{3.2, 3.6.2, Cross-Chapter Box 8 in this chapter}
Robust1 global differences in temperature means and extremes
are expected if global warming reaches 1.5°C versus 2°C above
the pre-industrial levels (high confidence). For oceans, regionalsurface temperature means and extremes are projected to be higher
at 2°C compared to 1.5°C of global warming (high confidence).
Temperature means and extremes are also projected to be higher at
2°C compared to 1.5°C in most land regions, with increases being
2–3 times greater than the increase in GMST projected for someregions (high confidence). Robust increases in temperature means andextremes are also projected at 1.5°C compared to present-day values(high confidence) {3.3.1, 3.3.2}. There are decreases in the occurrenceof cold extremes, but substantial increases in their temperature, inparticular in regions with snow or ice cover (high confidence) {3.3.1}.
Climate models project robust1 differences in regional climate
between present-day and global warming up to 1.5°C2, andbetween 1.5°C and 2°C2 (high confidence), depending on thevariable and region in question (high confidence). Large, robust
and widespread differences are expected for temperature
extremes (high confidence). Regarding hot extremes, the strongest
warming is expected to occur at mid-latitudes in the warm season (withincreases of up to 3°C for 1.5°C of global warming, i.e., a factor of two)and at high latitudes in the cold season (with increases of up to 4.5°C
at 1.5°C of global warming, i.e., a factor of three) (high confidence).
The strongest warming of hot extremes is projected to occur incentral and eastern North America, central and southern Europe, theMediterranean region (including southern Europe, northern Africa andthe Near East), western and central Asia, and southern Africa (medium
confidence). The number of exceptionally hot days are expected toincrease the most in the tropics, where interannual temperature
variability is lowest; extreme heatwaves are thus projected to emerge
earliest in these regions, and they are expected to already become
widespread there at 1.5°C global warming (high confidence). Limiting
global warming to 1.5°C instead of 2°C could result in around 420

1

Robust is used here to mean that at least two thirds of climate models show the same sign of changes at the grid point scale, and that differences in large regions are
statistically significant.

2

Projected changes in impacts between different levels of global warming are determined with respect to changes in global mean near-surface air temperature.

177

3Chapter 3

million fewer people being frequently exposed to extreme heatwaves,and about 65 million fewer people being exposed to exceptional
heatwaves, assuming constant vulnerability (medium confidence).
{3.3.1, 3.3.2, Cross-Chapter Box 8 in this chapter}
Limiting global warming to 1.5°C would limit risks of increases
in heavy precipitation events on a global scale and in several
regions compared to conditions at 2°C global warming(medium confidence). The regions with the largest increases in heavy
precipitation events for 1.5°C to 2°C global warming include: several
high-latitude regions (e.g. Alaska/western Canada, eastern Canada/
Greenland/Iceland, northern Europe and northern Asia); mountainous
regions (e.g., Tibetan Plateau); eastern Asia (including China and Japan);and eastern North America (medium confidence). Tropical cyclones areprojected to decrease in frequency but with an increase in the numberof very intense cyclones (limited evidence, low confidence). Heavy
precipitation associated with tropical cyclones is projected to be higher
at 2°C compared to 1.5°C of global warming (medium confidence).
Heavy precipitation, when aggregated at a global scale, is projected tobe higher at 2°C than at 1.5°C of global warming (medium confidence){3.3.3, 3.3.6}

3

Limiting global warming to 1.5°C is expected to substantiallyreduce the probability of extreme drought, precipitation deficits,
and risks associated with water availability (i.e., water stress) insome regions (medium confidence). In particular, risks associatedwith increases in drought frequency and magnitude are projected to be
substantially larger at 2°C than at 1.5°C in the Mediterranean region(including southern Europe, northern Africa and the Near East) andsouthern Africa (medium confidence). {3.3.3, 3.3.4, Box 3.1, Box 3.2}
Risks to natural and human systems are expected to be lower
at 1.5°C than at 2°C of global warming (high confidence). Thisdifference is due to the smaller rates and magnitudes of climatechange associated with a 1.5°C temperature increase, including lower
frequencies and intensities of temperature-related extremes. Lower
rates of change enhance the ability of natural and human systems
to adapt, with substantial benefits for a wide range of terrestrial,
freshwater, wetland, coastal and ocean ecosystems (including coral
reefs) (high confidence), as well as food production systems, humanhealth, and tourism (medium confidence), together with energy
systems and transportation (low confidence). {3.3.1, 3.4}
Exposure to multiple and compound climate-related risks is
projected to increase between 1.5°C and 2°C of global warmingwith greater proportions of people both exposed and susceptible topoverty in Africa and Asia (high confidence). For global warming from
1.5°C to 2°C, risks across energy, food, and water sectors could overlap
spatially and temporally, creating new – and exacerbating current –hazards, exposures, and vulnerabilities that could affect increasing
numbers of people and regions (medium confidence). Small islandstates and economically disadvantaged populations are particularly atrisk (high confidence). {3.3.1, 3.4.5.3, 3.4.5.6, 3.4.11, 3.5.4.9, Box 3.5}

3Impacts of 1.5°C of Global Warming on Natural and Human Systems

Global warming of 2°C would lead to an expansion of areas withsignificant increases in runoff, as well as those affected by flood
hazard, compared to conditions at 1.5°C (medium confidence).
Global warming of 1.5°C would also lead to an expansion of the global
land area with significant increases in runoff (medium confidence) andan increase in flood hazard in some regions (medium confidence)compared to present-day conditions. {3.3.5}
The probability of a sea-ice-free Arctic Ocean3 during summer
is substantially higher at 2°C compared to 1.5°C of global
warming (medium confidence). Model simulations suggest thatat least one sea-ice-free Arctic summer is expected every 10 years
for global warming of 2°C, with the frequency decreasing to one
sea-ice-free Arctic summer every 100 years under 1.5°C (medium
confidence). An intermediate temperature overshoot will have no longterm consequences for Arctic sea ice coverage, and hysteresis is notexpected (high confidence). {3.3.8, 3.4.4.7}
Global mean sea level rise (GMSLR) is projected to be around
0.1 m (0.04 – 0.16 m) less by the end of the 21st century in a
1.5°C warmer world compared to a 2°C warmer world (medium
confidence). Projected GMSLR for 1.5°C of global warming has anindicative range of 0.26 – 0.77m, relative to 1986–2005, (medium
confidence). A smaller sea level rise could mean that up to 10.4 million
fewer people (based on the 2010 global population and assuming no
adaptation) would be exposed to the impacts of sea level rise globallyin 2100 at 1.5°C compared to at 2°C. A slower rate of sea level rise
enables greater opportunities for adaptation (medium confidence).
There is high confidence that sea level rise will continue beyond 2100.
Instabilities exist for both the Greenland and Antarctic ice sheets, which
could result in multi-meter rises in sea level on time scales of century
to millennia. There is medium confidence that these instabilities could
be triggered at around 1.5°C to 2°C of global warming. {3.3.9, 3.4.5,
3.6.3}The ocean has absorbed about 30% of the anthropogenic
carbon dioxide, resulting in ocean acidification and changes tocarbonate chemistry that are unprecedented for at least thelast 65 million years (high confidence). Risks have been identified
for the survival, calcification, growth, development and abundance ofa broad range of marine taxonomic groups, ranging from algae to fish,with substantial evidence of predictable trait-based sensitivities (highconfidence). There are multiple lines of evidence that ocean warming
and acidification corresponding to 1.5°C of global warming would
impact a wide range of marine organisms and ecosystems, as well as
sectors such as aquaculture and fisheries (high confidence). {3.3.10,
3.4.4}
Larger risks are expected for many regions and systems for
global warming at 1.5°C, as compared to today, with adaptation
required now and up to 1.5°C. However, risks would be larger at 2°C ofwarming and an even greater effort would be needed for adaptation toa temperature increase of that magnitude (high confidence). {3.4, Box
3.4, Box 3.5, Cross-Chapter Box 6 in this chapter}

Ice free is defined for the Special Report as when the sea ice extent is less than 106 km2. Ice coverage less than this is considered to be equivalent to an ice-free Arctic Ocean
for practical purposes in all recent studies.

178

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Future risks at 1.5°C of global warming will depend on the
mitigation pathway and on the possible occurrence of atransient overshoot (high confidence). The impacts on naturaland human systems would be greater if mitigation pathways
temporarily overshoot 1.5°C and return to 1.5°C later in the century,
as compared to pathways that stabilize at 1.5°C without an overshoot(high confidence). The size and duration of an overshoot would also
affect future impacts (e.g., irreversible loss of some ecosystems) (highconfidence). Changes in land use resulting from mitigation choices
could have impacts on food production and ecosystem diversity. {3.6.1,
3.6.2, Cross-Chapter Boxes 7 and 8 in this chapter}

Climate Change Risks for Natural and Human systems
Terrestrial and Wetland Ecosystems

Chapter 3

Ocean Ecosystems
Ocean ecosystems are already experiencing large-scalechanges, and critical thresholds are expected to be reached at
1.5°C and higher levels of global warming (high confidence).
In the transition to 1.5°C of warming, changes to water temperatures
are expected to drive some species (e.g., plankton, fish) to relocate
to higher latitudes and cause novel ecosystems to assemble (highconfidence). Other ecosystems (e.g., kelp forests, coral reefs) are
relatively less able to move, however, and are projected to experiencehigh rates of mortality and loss (very high confidence). For example,
multiple lines of evidence indicate that the majority (70–90%) ofwarm water (tropical) coral reefs that exist today will disappear even
if global warming is constrained to 1.5°C (very high confidence).
{3.4.4, Box 3.4}

Risks of local species losses and, consequently, risks ofextinction are much less in a 1.5°C versus a 2°C warmer world(high confidence). The number of species projected to lose overhalf of their climatically determined geographic range at 2°C global
warming (18% of insects, 16% of plants, 8% of vertebrates) is
projected to be reduced to 6% of insects, 8% of plants and 4% of
vertebrates at 1.5°C warming (medium confidence). Risks associatedwith other biodiversity-related factors, such as forest fires, extreme
weather events, and the spread of invasive species, pests anddiseases, would also be lower at 1.5°C than at 2°C of warming (highconfidence), supporting a greater persistence of ecosystem services.
{3.4.3, 3.5.2}

Current ecosystem services from the ocean are expected to be
reduced at 1.5°C of global warming, with losses being evengreater at 2°C of global warming (high confidence). The risksof declining ocean productivity, shifts of species to higher latitudes,
damage to ecosystems (e.g., coral reefs, and mangroves, seagrassand other wetland ecosystems), loss of fisheries productivity (atlow latitudes), and changes to ocean chemistry (e.g., acidification,
hypoxia and dead zones) are projected to be substantially lower
when global warming is limited to 1.5°C (high confidence). {3.4.4,
Box 3.4}

Constraining global warming to 1.5°C, rather than to 2°C
and higher, is projected to have many benefits for terrestrial
and wetland ecosystems and for the preservation of theirservices to humans (high confidence). Risks for natural andmanaged ecosystems are higher on drylands compared to humid
lands. The global terrestrial land area projected to be affected byecosystem transformations (13%, interquartile range 8–20%) at 2°C
is approximately halved at 1.5°C global warming to 4% (interquartile
range 2–7%) (medium confidence). Above 1.5°C, an expansion ofdesert terrain and vegetation would occur in the Mediterranean
biome (medium confidence), causing changes unparalleled in the last
10,000 years (medium confidence). {3.3.2.2, 3.4.3.2, 3.4.3.5, 3.4.6.1,
3.5.5.10, Box 4.2}

The projected frequency and magnitude of floods and droughts
in some regions are smaller under 1.5°C than under 2°C ofwarming (medium confidence). Human exposure to increased
flooding is projected to be substantially lower at 1.5°C compared to
2°C of global warming, although projected changes create regionallydifferentiated risks (medium confidence). The differences in the risks
among regions are strongly influenced by local socio-economic
conditions (medium confidence). {3.3.4, 3.3.5, 3.4.2}

Many impacts are projected to be larger at higher latitudes,
owing to mean and cold-season warming rates above theglobal average (medium confidence). High-latitude tundra andboreal forest are particularly at risk, and woody shrubs are alreadyencroaching into tundra (high confidence) and will proceed withfurther warming. Constraining warming to 1.5°C would prevent thethawing of an estimated permafrost area of 1.5 to 2.5 million km2
over centuries compared to thawing under 2°C (medium confidence).
{3.3.2, 3.4.3, 3.4.4}

Water Resources

Risks of water scarcity are projected to be greater at 2°C than at
1.5°C of global warming in some regions (medium confidence).
Depending on future socio-economic conditions, limiting global
warming to 1.5°C, compared to 2°C, may reduce the proportion of
the world population exposed to a climate change-induced increasein water stress by up to 50%, although there is considerable variability
between regions (medium confidence). Regions with particularly
large benefits could include the Mediterranean and the Caribbean(medium confidence). Socio-economic drivers, however, are expected
to have a greater influence on these risks than the changes in climate(medium confidence). {3.3.5, 3.4.2, Box 3.5}
Land Use, Food Security and Food Production Systems
Limiting global warming to 1.5°C, compared with 2°C, isprojected to result in smaller net reductions in yields of maize,
rice, wheat, and potentially other cereal crops, particularly in

179

3

Chapter 3

sub-Saharan Africa, Southeast Asia, and Central and South America;
and in the CO2-dependent nutritional quality of rice and wheat(high confidence). A loss of 7–10% of rangeland livestock globallyis projected for approximately 2°C of warming, with considerable
economic consequences for many communities and regions (medium
confidence). {3.4.6, 3.6, Box 3.1, Cross-Chapter Box 6 in this chapter}
Reductions in projected food availability are larger at 2°C
than at 1.5°C of global warming in the Sahel, southern Africa,
the Mediterranean, central Europe and the Amazon (medium
confidence). This suggests a transition from medium to high risk ofregionally differentiated impacts on food security between 1.5°C and
2°C (medium confidence). Future economic and trade environments
and their response to changing food availability (medium confidence)are important potential adaptation options for reducing hunger risk
in low- and middle-income countries. {Cross-Chapter Box 6 in thischapter}3

Fisheries and aquaculture are important to global food security
but are already facing increasing risks from ocean warming
and acidification (medium confidence). These risks areprojected to increase at 1.5°C of global warming and impact
key organisms such as fin fish and bivalves (e.g., oysters),
especially at low latitudes (medium confidence). Small-scale
fisheries in tropical regions, which are very dependent on habitat
provided by coastal ecosystems such as coral reefs, mangroves,seagrass and kelp forests, are expected to face growing risks at 1.5°C
of warming because of loss of habitat (medium confidence). Risksof impacts and decreasing food security are projected to become
greater as global warming reaches beyond 1.5°C and both ocean
warming and acidification increase, with substantial losses likely forcoastal livelihoods and industries (e.g., fisheries and aquaculture)(medium to high confidence). {3.4.4, 3.4.5, 3.4.6, Box 3.1, Box 3.4,
Box 3.5, Cross-Chapter Box 6 in this chapter}
Land use and land-use change emerge as critical features ofvirtually all mitigation pathways that seek to limit global
warming to 1.5°C (high confidence). Most least-cost mitigation
pathways to limit peak or end-of-century warming to 1.5°C makeuse of carbon dioxide removal (CDR), predominantly employing
significant levels of bioenergy with carbon capture and storage(BECCS) and/or afforestation and reforestation (AR) in their portfolioof mitigation measures (high confidence). {Cross-Chapter Box 7 inthis chapter}
Large-scale deployment of BECCS and/or AR would havea far-reaching land and water footprint (high confidence).
Whether this footprint would result in adverse impacts, for example
on biodiversity or food production, depends on the existence andeffectiveness of measures to conserve land carbon stocks, measures
to limit agricultural expansion in order to protect natural ecosystems,
and the potential to increase agricultural productivity (medium
agreement). In addition, BECCS and/or AR would have substantial
direct effects on regional climate through biophysical feedbacks,
which are generally not included in Integrated Assessments Models(high confidence). {3.6.2, Cross-Chapter Boxes 7 and 8 in this chapter}

180

Impacts of 1.5°C of Global Warming on Natural and Human Systems

The impacts of large-scale CDR deployment could be greatly
reduced if a wider portfolio of CDR options were deployed, if aholistic policy for sustainable land management were adopted,
and if increased mitigation efforts were employed to strongly
limit the demand for land, energy and material resources,
including through lifestyle and dietary changes (medium
confidence). In particular, reforestation could be associated withsignificant co-benefits if implemented in a manner than helps restore
natural ecosystems (high confidence). {Cross-Chapter Box 7 in thischapter}
Human Health, Well-Being, Cities and Poverty
Any increase in global temperature (e.g., +0.5°C) is projected
to affect human health, with primarily negative consequences(high confidence). Lower risks are projected at 1.5°C than at 2°C
for heat-related morbidity and mortality (very high confidence), andfor ozone-related mortality if emissions needed for ozone formation
remain high (high confidence). Urban heat islands often amplify the
impacts of heatwaves in cities (high confidence). Risks for some
vector-borne diseases, such as malaria and dengue fever are projected
to increase with warming from 1.5°C to 2°C, including potential
shifts in their geographic range (high confidence). Overall for vectorborne diseases, whether projections are positive or negative depends
on the disease, region and extent of change (high confidence). Lower
risks of undernutrition are projected at 1.5°C than at 2°C (medium
confidence). Incorporating estimates of adaptation into projections
reduces the magnitude of risks (high confidence). {3.4.7, 3.4.7.1,
3.4.8, 3.5.5.8}
Global warming of 2°C is expected to pose greater risks to urban
areas than global warming of 1.5°C (medium confidence). The
extent of risk depends on human vulnerability and the effectivenessof adaptation for regions (coastal and non-coastal), informal
settlements and infrastructure sectors (such as energy, water andtransport) (high confidence). {3.4.5, 3.4.8}
Poverty and disadvantage have increased with recent warming(about 1°C) and are expected to increase for many populations
as average global temperatures increase from 1°C to 1.5°C
and higher (medium confidence). Outmigration in agriculturaldependent communities is positively and statistically significantly
associated with global temperature (medium confidence). Our
understanding of the links of 1.5°C and 2°C of global warming tohuman migration are limited and represent an important knowledge
gap. {3.4.10, 3.4.11, 5.2.2, Table 3.5}
Key Economic Sectors and Services
Risks to global aggregated economic growth due to climatechange impacts are projected to be lower at 1.5°C than at 2°C
by the end of this century (medium confidence). {3.5.2, 3.5.3}
The largest reductions in economic growth at 2°C comparedto 1.5°C of warming are projected for low- and middle-income
countries and regions (the African continent, Southeast Asia,
India, Brazil and Mexico) (low to medium confidence). Countries

Impacts of 1.5°C of Global Warming on Natural and Human Systems

in the tropics and Southern Hemisphere subtropics are projected toexperience the largest impacts on economic growth due to climatechange should global warming increase from 1.5°C to 2°C (medium
confidence). {3.5}
Global warming has already affected tourism, with increased
risks projected under 1.5°C of warming in specific geographic
regions and for seasonal tourism including sun, beach andsnow sports destinations (very high confidence). Risks will belower for tourism markets that are less climate sensitive, such asgaming and large hotel-based activities (high confidence). Risks forcoastal tourism, particularly in subtropical and tropical regions, will
increase with temperature-related degradation (e.g., heat extremes,
storms) or loss of beach and coral reef assets (high confidence).
{3.3.6, 3.4.4.12, 3.4.9.1, Box 3.4}

Chapter 3

Increased Reasons for Concern
There are multiple lines of evidence that since AR5 the assessed
levels of risk increased for four of the five Reasons for Concern(RFCs) for global warming levels of up to 2°C (high confidence).
The risk transitions by degrees of global warming are now: from highto very high between 1.5°C and 2°C for RFC1 (Unique and threatenedsystems) (high confidence); from moderate to high risk between 1°C and
1.5°C for RFC2 (Extreme weather events) (medium confidence); frommoderate to high risk between 1.5°C and 2°C for RFC3 (Distribution of
impacts) (high confidence); from moderate to high risk between 1.5°C
and 2.5°C for RFC4 (Global aggregate impacts) (medium confidence);
and from moderate to high risk between 1°C and 2.5°C for RFC5
(Large-scale singular events) (medium confidence). {3.5.2}
1.

The category ‘Unique and threatened systems’ (RFC1)
display a transition from high to very high risk which is
now located between 1.5°C and 2°C of global warming as
opposed to at 2.6°C of global warming in AR5, owing to new andmultiple lines of evidence for changing risks for coral reefs, theArctic and biodiversity in general (high confidence). {3.5.2.1}

2.

In ‘Extreme weather events’ (RFC2), the transition frommoderate to high risk is now located between 1.0°C and
1.5°C of global warming, which is very similar to the AR5
assessment but is projected with greater confidence (medium
confidence). The impact literature contains little information
about the potential for human society to adapt to extreme
weather events, and hence it has not been possible to locate
the transition from ‘high’ to ‘very high’ risk within the context ofassessing impacts at 1.5°C versus 2°C of global warming. Thereis thus low confidence in the level at which global warming could
lead to very high risks associated with extreme weather events inthe context of this report. {3.5}3.

With respect to the ‘Distribution of impacts’ (RFC3) atransition from moderate to high risk is now locatedbetween 1.5°C and 2°C of global warming, compared with
between 1.6°C and 2.6°C global warming in AR5, owing to newevidence about regionally differentiated risks to food security,
water resources, drought, heat exposure and coastal submergence(high confidence). {3.5}4.

In ‘global aggregate impacts’ (RFC4) a transition frommoderate to high levels of risk is now located between
1.5°C and 2.5°C of global warming, as opposed to at 3.6°C ofwarming in AR5, owing to new evidence about global aggregate
economic impacts and risks to Earth’s biodiversity (medium
confidence). {3.5}5.

Finally, ‘large-scale singular events’ (RFC5), moderate risk
is now located at 1°C of global warming and high risk islocated at 2.5°C of global warming, as opposed to at 1.6°C(moderate risk) and around 4°C (high risk) in AR5, because of new
observations and models of the West Antarctic ice sheet (medium
confidence). {3.3.9, 3.5.2, 3.6.3}

Small Islands, and Coastal and Low-lying areas
Small islands are projected to experience multiple interrelated risks at 1.5°C of global warming that will increase withwarming of 2°C and higher levels (high confidence). Climate
hazards at 1.5°C are projected to be lower compared to those at 2°C(high confidence). Long-term risks of coastal flooding and impacts on
populations, infrastructures and assets (high confidence), freshwater
stress (medium confidence), and risks across marine ecosystems (highconfidence) and critical sectors (medium confidence) are projected toincrease at 1.5°C compared to present-day levels and increase further
at 2°C, limiting adaptation opportunities and increasing loss anddamage (medium confidence). Migration in small islands (internallyand internationally) occurs for multiple reasons and purposes, mostly
for better livelihood opportunities (high confidence) and increasingly
owing to sea level rise (medium confidence). {3.3.2.2, 3.3.6–9,
3.4.3.2, 3.4.4.2, 3.4.4.5, 3.4.4.12, 3.4.5.3, 3.4.7.1, 3.4.9.1, 3.5.4.9,
Box 3.4, Box 3.5}
Impacts associated with sea level rise and changes to thesalinity of coastal groundwater, increased flooding anddamage to infrastructure, are projected to be criticallyimportant in vulnerable environments, such as small islands,
low-lying coasts and deltas, at global warming of 1.5°C and
2°C (high confidence). Localized subsidence and changes to riverdischarge can potentially exacerbate these effects. Adaptation isalready happening (high confidence) and will remain important overmulti-centennial time scales. {3.4.5.3, 3.4.5.4, 3.4.5.7, 5.4.5.4, Box
3.5}
Existing and restored natural coastal ecosystems may beeffective in reducing the adverse impacts of rising sea levels
and intensifying storms by protecting coastal and deltaic
regions (medium confidence). Natural sedimentation rates are
expected to be able to offset the effect of rising sea levels, given
the slower rates of sea level rise associated with 1.5°C of warming(medium confidence). Other feedbacks, such as landward migrationof wetlands and the adaptation of infrastructure, remain important(medium confidence). {3.4.4.12, 3.4.5.4, 3.4.5.7}

181

3

Chapter 3

About the Chapter

Chapter 3 uses relevant definitions of a potential 1.5°C warmer worldfrom Chapters 1 and 2 and builds directly on their assessment of gradualversus overshoot scenarios. It interacts with information presented in
Chapter 2 via the provision of specific details relating to the mitigation
pathways (e.g., land-use changes) and their implications for impacts.
Chapter 3 also includes information needed for the assessment andimplementation of adaptation options (presented in Chapter 4), as
well as the context for considering the interactions of climate changewith sustainable development and for the assessment of impacts onsustainability, poverty and inequalities at the household to subregional
level (presented in Chapter 5).

This chapter is necessarily transdisciplinary in its coverage of theclimate system, natural and managed ecosystems, and humansystems and responses, owing to the integrated nature of the naturaland human experience. While climate change is acknowledged as acentrally important driver, it is not the only driver of risks to human andnatural systems, and in many cases, it is the interaction between these
two broad categories of risk that is important (Chapter 1).
The flow of the chapter, linkages between sections, a list of chapterand cross-chapter boxes, and a content guide for reading according
to focus or interest are given in Figure 3.1. Key definitions used in thechapter are collected in the Glossary. Confidence language is usedthroughout this chapter and likelihood statements (e.g., likely, very
likely) are provided when there is high confidence in the assessment.

Section 3.1
Introduction

Section 3.2
Assessing 1.5°C

3Global and Regional Temperature
3.3.1 | 3.3.2 | 3.3.4 | Box 3.3 | 3.3.11

Drought
3.3.4 | Box 3.2 | 3.4.2 | 3.3.11

Floods
3.3.5 | 3.4.2 | 3.4.5 | 3.3.11

Extreme Weather
3.3.2 | 3.3.3 | 3.3.4 | 3.3.6 | 3.3.11 |3.4.4 | 3.5.2

Snow, Permafrost and Sea Ice
3.3.8 | 3.4.4 | 3.5.4 | 3.5.5 | 3.6.3 |3.3.11

Section 3.4
Observed Impacts andProjected Risks in Naturaland Human Systems

Section 3.3
Global and Regional
Climate Changes andAssociated Hazards

Quick Guide

Chapter Structure

Sea Level
3.3.9 | 3.4.4 | 3.4.5 | 3.4.12 | 3.5.2 |3.6.3

Section 3.5
Avoided Impacts andReduced Risks

Section 3.6
Implications of Different
1.5°C and 2°C Pathways

Ecosystems
3.4.3 | 3.4.4 | 3.4.5 | 3.4.12 | Box
3.4 | 3.5.2 | 3.5.5

Food Security
3.4.6 | 3.4.12 | 3.5.5 | 3.6.2 | X-Box 6 |X-Box 7

Freshwater
3.4.2 | 3.4.12

Oceans
3.3.7 | 3.3.10 | 3.3.11 | 3.4.4 | 3.4.12

Regional Outlooks
3.3.2 | 3.3.3 | 3.4.3 | Box 3.1 | Box
3.2 | 3.4.5 | Box 3.5 | 3.5.4 | 3.5.5 |3.3.11

Coastal and Low Lying Areas
3.3.5 | 3.4.5 | Box 3.5 | 3.5.4 | 3.4.12

Cities
3.4.5 | 3.4.8 | 3.4.9

Section 3.7
Knowledge Gaps

Chapter
Boxes

Global and Reg
Regional
Precipitation
3.3.1 | 3.3.3 | 3.3.4 | 3.3.11

Health
3.4.7 | 3.4.12 | 3.5.5

Key Economic Sectors and Services
3.4.9 | 3.4.12

Livelihoodss andd Poverty
3.4.6 | 3.4.10

RFCs, Hot Spots and Tipping Points
3.4.12 | 3.4.13 | 3.5.2 | 3.5.4 | 3.5.5

Box 3.1

Box 3.2

Box 3.3

Box 3.4

Box 3.5

Box 3.6

SubSaharanAfricaDroughts in the
Mediterranean
Basin and the
Middle East

Lessons from
Past WarmClimateEpisodesWarm Water
Coral Reefs ina 1.5°C
Warmer World

Small Island
Developing
States (SIDS)

EconomicDamage from
Climate Change

Cross Chapter
Boxes

3.1

Impacts of 1.5°C of Global Warming on Natural and Human Systems

X-Box 6

X-Box 7

X-Box 8Food
Security

Land-Based Carbon
Dioxide Removal in
Relation to 1.5°C of
Global Warming

1.5°C
WarmerWorldsFigure 3.1 | Chapter 3 structure and quick guide.

The underlying literature assessed in Chapter 3 is broad and includes alarge number of recent publications specific to assessments for 1.5°C
of warming. The chapter also utilizes information covered in prior
IPCC special reports, for example the Special Report on Managing theRisks of Extreme Events and Disasters to Advance Climate Change
Adaptation (SREX; IPCC, 2012), and many chapters from the IPCC
WGII Fifth Assessment Report (AR5) that assess impacts on naturaland managed ecosystems and humans, as well as adaptation options(IPCC, 2014b). For this reason, the chapter provides information based
182

on a broad range of assessment methods. Details about the approaches
used are presented in Section 3.2.
Section 3.3 gives a general overview of recent literature on observed
climate change impacts as the context for projected future risks. Witha few exceptions, the focus here is the analysis of transient responses
at 1.5°C and 2°C of global warming, with simulations of short-term
stabilization scenarios (Section 3.2) also assessed in some cases. In
general, long-term equilibrium stabilization responses could not beImpacts of 1.5°C of Global Warming on Natural and Human Systems

assessed owing to a lack of data and analysis. A detailed analysis ofdetection and attribution is not provided but will be the focus of the next
IPCC assessment report (AR6). Furthermore, possible interventions in
the climate system through radiation modification measures, which are
not tied to reductions of greenhouse gas emissions or concentrations,
are not assessed in this chapter.
Understanding the observed impacts and projected risks of climatechange is crucial to comprehending how the world is likely to change
under global warming of 1.5°C above temperatures in the pre-industrial
period (with reference to 2°C). Section 3.4 explores the new literatureand updates the assessment of impacts and projected risks for a large
number of natural and human systems. By also exploring adaptation
opportunities, where the literature allows, the section prepares thereader for discussions in subsequent chapters about opportunities totackle both mitigation and adaptation. The section is mostly globally
focused because of limited research on regional risks and adaptation
options at 1.5°C and 2°C. For example, the risks of 1.5°C and 2°C ofwarming in urban areas, as well as the risks of health outcomes underthese two warming scenarios (e.g. climate-related diseases, air quality
impacts and mental health problems), were not considered becauseof a lack of projections of how these risks might change in a 1.5°C or
2°C warmer world. In addition, the complexity of many interactionsof climate change with drivers of poverty, along with a paucity of
relevant studies, meant it was not possible to detect and attributemany dimensions of poverty and disadvantage to climate change. Even
though there is increasing documentation of climate-related impacts onplaces where indigenous people live and where subsistence-oriented
communities are found, relevant projections of the risks associatedwith warming of 1.5°C and 2°C are necessarily limited.
To explore avoided impacts and reduced risks at 1.5°C compared withat 2°C of global warming, the chapter adopts the AR5 ‘Reasons forConcern’ aggregated projected risk framework (Section 3.5). Updates
in terms of the aggregation of risks are informed by the most recent
literature and the assessments offered in Sections 3.3 and 3.4, witha focus on the impacts at 2°C of warming that could potentially be
avoided if warming were constrained to 1.5°C. Economic benefits thatwould be obtained (Section 3.5.3), climate change ‘hotspots’ that could
be avoided or reduced (Section 3.5.4 as guided by the assessments ofSections 3.3, 3.4 and 3.5), and tipping points that could be circumvented(Section 3.5.5) at 1.5°C compared to higher degrees of global warming
are all examined. The latter assessments are, however, constrained toregional analyses, and hence this particular section does not include anassessment of specific losses and damages.
Section 3.6 provides an overview on specific aspects of the mitigation
pathways considered compatible with 1.5°C of global warming,
including some scenarios involving temperature overshoot above
1.5°C global warming during the 21st century. Non-CO2 implications
and projected risks of mitigation pathways, such as changes to land
use and atmospheric compounds, are presented and explored. Finally,
implications for sea ice, sea level and permafrost beyond the end of the
century are assessed.
The exhaustive assessment of literature specific to global warmingof 1.5°C above the pre-industrial period, presented across all the

Chapter 3

sections in Chapter 3, highlights knowledge gaps resulting from theheterogeneous information available across systems, regions andsectors. Some of these gaps are described in Section 3.7.

3.2

How are Risks at 1.5°C andHigher Levels of Global Warming
Assessed in this Chapter?

The methods that are applied for assessing observed and projectedchanges in climate and weather are presented in Section 3.2.1, while
those used for assessing the observed impacts on and projected risks tonatural and managed systems, and to human settlements, are describedin Section 3.2.2. Given that changes in climate associated with 1.5°Cof global warming were not the focus of past IPCC reports, dedicatedapproaches based on recent literature that are specific to the present
report are also described. Background on specific methodologicalaspects (climate model simulations available for assessments at 1.5°C
global warming, attribution of observed changes in climate and theirrelevance for assessing projected changes at 1.5°C and 2°C global
warming, and the propagation of uncertainties from climate forcing
to impacts on ecosystems) are provided in the Supplementary Material
3.SM.

3.2.1

How are Changes in Climate and Weather at 1.5°C
versus Higher Levels of Warming Assessed?

Evidence for the assessment of changes to climate at 1.5°C versus
2°C can be drawn both from observations and model projections.
Global mean surface temperature (GMST) anomalies were about+0.87°C (±0.10°C likely range) above pre-industrial industrial (1850–
1900) values in the 2006-–2015 decade, with a recent warmingof about 0.2°C (±0.10°C) per decade (Chapter 1). Human-induced
global warming reached approximately 1°C (±0.2°C likely range) in2017 (Chapter 1). While some of the observed trends may be due
to internal climate variability, methods of detection and attribution
can be applied to assess which part of the observed changes may be
attributed to anthropogenic forcing (Bindoff et al., 2013b). Hence,
evidence from attribution studies can be used to assess changes
in the climate system that are already detectable at lower levels ofglobal warming and would thus continue to change with a further
0.5°C or 1°C of global warming (see Supplementary Material 3.SM.1
and Sections 3.3.1, 3.3.2, 3.3.3, 3.3.4 and 3.3.11). A recent study
identified significant changes in extremes for a 0.5°C difference inglobal warming based on the historical record (Schleussner et al.,2017). It should also be noted that attributed changes in extremes
since 1950 that were reported in the IPCC AR5 report (IPCC, 2013)
generally correspond to changes in global warming of about 0.5°C(see 3.SM.1)
Climate model simulations are necessary for the investigation of
the response of the climate system to various forcings, in particular
to forcings associated with higher levels of greenhouse gas
concentrations. Model simulations include experiments with global
and regional climate models, as well as impact models – driven with
output from climate models – to evaluate the risk related to climate
183

3

Chapter 3change for natural and human systems (Supplementary Material
3.SM.1). Climate model simulations were generally used in the context
of particular ‘climate scenarios’ from previous IPCC reports (e.g.,
IPCC, 2007, 2013). This means that emissions scenarios (IPCC, 2000)
were used to drive climate models, providing different projections
for given emissions pathways. The results were consequently used ina ‘storyline’ framework, which presents the development of climate
in the course of the 21st century and beyond for a given emissions
pathway. Results were assessed for different time slices within themodel projections such as 2016–2035 (‘near term’, which is slightly
below a global warming of 1.5°C according to most scenarios, Kirtman
et al., 2013), 2046–2065 (mid-21st century, Collins et al., 2013), and
2081–2100 (end of 21st century, Collins et al., 2013). Given that thisreport focuses on climate change for a given mean global temperature
response (1.5°C or 2°C), methods of analysis had to be developed and/or adapted from previous studies in order to provide assessments forthe specific purposes here.

3

Impacts of 1.5°C of Global Warming on Natural and Human Systems

C.

Most of the ‘1.5°C scenarios’, and some of the ‘2°C emissions
scenarios’ presented in Chapter 2 include a temperature
overshoot during the course of the 21st century. This means thatmedian temperature projections under these scenarios exceed
the target warming levels over the course of the century (typically
0.5°C–1°C higher than the respective target levels at most),
before warming returns to below 1.5°C or 2°C by 2100. During
the overshoot phase, impacts would therefore correspond tohigher transient temperature increases than 1.5°C or 2°C. For thisreason, impacts of transient responses at these higher warming
levels are also partly addressed in Cross-Chapter Box 8 in thischapter (on a 1.5°C warmer world), and some analyses forchanges in extremes are also presented for higher levels ofwarming in Section 3.3 (Figures 3.5, 3.6, 3.9, 3.10, 3.12 and 3.13).
Most importantly, different overshoot scenarios may have very
distinct impacts depending on (i) the peak temperature of
the overshoot, (ii) the length of the overshoot period, and (iii) the
associated rate of change in global temperature over thetime period of the overshoot. While some of these issues arebriefly addressed in Sections 3.3 and 3.6, and in the Cross-Chapter
Box 8, the definition of overshoot and related questions will need
to be more comprehensively addressed in the IPCC AR6 report.

D.

The levels of global warming that are the focus of this report(1.5°C and 2°C) are measured relative to the pre-industrial period.
This definition requires an agreement on the exact reference time
period (for 0°C of warming) and the time frame over which theglobal warming is assessed, typically 20 to 30 years in length. As
discussed in Chapter 1, a climate with 1.5°C global warming is
one in which temperatures averaged over a multi-decade timescale are 1.5°C above those in the pre-industrial reference period.
Greater detail is provided in Cross-Chapter Box 8 in this chapter.
Inherent to this is the observation that the mean temperature ofa ‘1.5°C warmer world’ can be regionally and temporally muchhigher (e.g., with regional annual temperature extremes involving
warming of more than 6°C; see Section 3.3 and Cross-Chapter
Box 8 in this chapter).

E.

The interference of factors unrelated to greenhouse gases with
mitigation pathways can strongly affect regional climate. For
example, biophysical feedbacks from changes in land use andirrigation (e.g., Hirsch et al., 2017; Thiery et al., 2017), or projectedchanges in short-lived pollutants (e.g., Z. Wang et al., 2017), canhave large influences on local temperatures and climate
conditions. While these effects are not explicitly integrated into thescenarios developed in Chapter 2, they may affect projectedchanges in climate under 1.5°C of global warming. These issues
are addressed in more detail in Section 3.6.2.2.

A major challenge in assessing climate change under 1.5°C, or 2°C(and higher levels), of global warming pertains to the definition ofa ‘1.5°C or 2°C climate projection’ (see also Cross-Chapter Box
8 in this chapter). Resolving this challenge includes the following
considerations:A. The need to distinguish between (i) transient climate responses(i.e., those that ‘pass through’ 1.5°C or 2°C of global warming),(ii) short-term stabilization responses (i.e., scenarios for the late
21st century that result in stabilization at a mean global warmingof 1.5°C or 2°C by 2100), and (iii) long-term equilibrium
stabilization responses (i.e., those occurring after several
millennia once climate (temperature) equilibrium at 1.5°C or 2°C
is reached). These responses can be very different in terms ofclimate variables and the inertia associated with a given climate
forcing. A striking example is sea level rise (SLR). In this case,projected increases within the 21st century are minimallydependent on the scenario considered, yet they stabilize at very
different levels for a long-term warming of 1.5°C versus 2°C(Section 3.3.9).
B.

184

The ‘1.5°C or 2°C emissions scenarios’ presented in Chapter
2 are targeted to hold warming below 1.5°C or 2°C with a certain
probability (generally two-thirds) over the course, or at theend, of the 21st century. These scenarios should be seen as theoperationalization of 1.5°C or 2°C warmer worlds. However,when these emission scenarios are used to drive climate models,some of the resulting simulations lead to warming above theserespective thresholds (typically with a probability of one-third, see
Chapter 2 and Cross-Chapter Box 8 in this chapter). This is due
both to discrepancies between models and to internal climate
variability. For this reason, the climate outcome for any of thesescenarios, even those excluding an overshoot (see next point, C.),
include some probability of reaching a global climate warmingof more than 1.5°C or 2°C. Hence, a comprehensive assessmentof climate risks associated with ‘1.5°C or 2°C climate scenarios’
needs to include consideration of higher levels of warming (e.g.,
up to 2.5°C to 3°C, see Chapter 2 and Cross-Chapter Box 8 in thischapter).

The assessment presented in the current chapter largely focuses on
the analysis of transient responses in climate at 1.5°C versus 2°C
and higher levels of global warming (see point A. above and Section
3.3). It generally uses the empirical scaling relationship (ESR) approach(Seneviratne et al., 2018c), also termed the ‘time sampling’ approach(James et al., 2017), which consists of sampling the response at 1.5°C
and other levels of global warming from all available global climate
model scenarios for the 21st century (e.g., Schleussner et al., 2016b;

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Seneviratne et al., 2016; Wartenburger et al., 2017). The ESR approach
focuses more on the derivation of a continuous relationship, while
the term ‘time sampling’ is more commonly used when comparing alimited number of warming levels (e.g., 1.5°C versus 2°C). A similar
approach in the case of regional climate model (RCM) simulations
consists of sampling the RCM model output corresponding to thetime frame at which the driving general circulation model (GCM)
reaches the considered temperature level, for example, as done within
IMPACT2C (Jacob and Solman, 2017), see description in Vautard etal. (2014). As an alternative to the ESR or time sampling approach,pattern scaling may be used. Pattern scaling is a statistical approachthat describes relationships of specific climate responses as a functionof global temperature change. Some assessments presented in thischapter are based on this method. The disadvantage of pattern scaling,
however, is that the relationship may not perfectly emulate the models’
responses at each location and for each global temperature level(James et al., 2017). Expert judgement is a third methodology that canbe used to assess probable changes at 1.5°C or 2°C of global warmingby combining changes that have been attributed to the observed time
period (corresponding to warming of 1°C or less if assessed over ashorter period) with known projected changes at 3°C or 4°C above
pre-industrial temperatures (Supplementary Material 3.SM.1). In order
to assess effects induced by a 0.5°C difference in global warming,
the historical record can be used at first approximation as a proxy,
meaning that conditions are compared for two periods that have a
0.5°C difference in GMST warming (such as 1991–2010 and 1960–
1979, e.g., Schleussner et al., 2017). This in particular also applies toattributed changes in extremes since 1950 that were reported in the
IPCC AR5 report (IPCC, 2013; see also 3.SM.1). Using observations,however, it is not possible to account for potential non-linear changes
that could occur above 1°C of global warming or as 1.5°C of warming
is reached.
In some cases, assessments of short-term stabilization responses
are also presented, derived using a subset of model simulations thatreach a given temperature limit by 2100, or driven by sea surface
temperature (SST) values consistent with such scenarios. This includes
new results from the ‘Half a degree additional warming, prognosis andprojected impacts’ (HAPPI) project (Section 1.5.2; Mitchell et al., 2017).
Notably, there is evidence that for some variables (e.g., temperatureand precipitation extremes), responses after short-term stabilization(i.e., approximately equivalent to the RCP2.6 scenario) are very similar
to the transient response of higher-emissions scenarios (Seneviratne etal., 2016, 2018c; Wartenburger et al., 2017; Tebaldi and Knutti, 2018).
This is, however, less the case for mean precipitation (e.g., Pendergrass
et al., 2015), for which other aspects of the emissions scenarios appear
relevant.
For the assessment of long-term equilibrium stabilization responses,
this chapter uses results from existing simulations where available(e.g., for sea level rise), although the available data for this type of
projection is limited for many variables and scenarios and will need tobe addressed in more depth in the IPCC AR6 report.
Supplementary Material 3.SM.1 of this chapter includes further detailsof the climate models and associated simulations that were used tosupport the present assessment, as well as a background on detection

Chapter 3

and attribution approaches of relevance to assessing changes inclimate at 1.5°C of global warming.

3.2.2

How are Potential Impacts on Ecosystems Assessed
at 1.5°C versus Higher Levels of Warming?

Considering that the impacts observed so far are for a global warming
lower than 1.5°C (generally up to the 2006–2015 decade, i.e., for a
global warming of 0.87°C or less; see above), direct information on
the impacts of a global warming of 1.5°C is not yet available. The
global distribution of observed impacts shown in AR5 (Cramer et al.,2014), however, demonstrates that methodologies now exist which
are capable of detecting impacts on systems strongly influenced byfactors (e.g., urbanization and human pressure in general) or whereclimate may play only a secondary role in driving impacts. Attributionof observed impacts to greenhouse gas forcing is more rarely
performed, but a recent study (Hansen and Stone, 2016) shows thatmost of the detected temperature-related impacts that were reported
in AR5 (Cramer et al., 2014) can be attributed to anthropogenic climatechange, while the signals for precipitation-induced responses are more
ambiguous.
One simple approach for assessing possible impacts on natural andmanaged systems at 1.5°C versus 2°C consists of identifying impacts ofa global 0.5°C of warming in the observational record (e.g., Schleussner
et al., 2017) assuming that the impacts would scale linearly for higher
levels of warming (although this may not be appropriate). Anotherapproach is to use conclusions from analyses of past climates combinedwith modelling of the relationships between climate drivers and natural
systems (Box 3.3). A more complex approach relies on laboratory or
field experiments (Dove et al., 2013; Bonal et al., 2016), which provide
useful information on the causal effect of a few factors, which can beas diverse as climate, greenhouse gases (GHG), management practices,and biological and ecological variables, on specific natural systems that
may have unusual physical and chemical characteristics (e.g., Fabricius
et al., 2011; Allen et al., 2017). This last approach can be importantin helping to develop and calibrate impact mechanisms and models
through empirical experimentation and observation.
Risks for natural and human systems are often assessed with
impact models where climate inputs are provided by representative
concentration pathway (RCP)-based climate projections. The numberof studies projecting impacts at 1.5°C or 2°C of global warming
has increased in recent times (see Section 3.4), even if the four RCP
scenarios used in AR5 are not strictly associated with these levelsof global warming. Several approaches have been used to extract
the required climate scenarios, as described in Section 3.2.1. As an
example, Schleussner et al. (2016b) applied a time sampling (or ESR)
approach, described in Section 3.2.1, to estimate the differential effectof 1.5°C and 2°C of global warming on water availability and impacts
on agriculture using an ensemble of simulations under the RCP8.5
scenario. As a further example using a different approach, Iizumi et al.
(2017) derived a 1.5°C scenario from simulations with a crop modelusing an interpolation between the no-change (approximately 2010)
conditions and the RCP2.6 scenario (with a global warming of 1.8°C in2100), and they derived the corresponding 2°C scenario from RCP2.6
and RCP4.5 simulations in 2100. The Inter-Sectoral Impact Model
185

3

Chapter 3

Integration and Intercomparison Project Phase 2 (ISIMIP2; Frieler etal., 2017) extended this approach to investigate a number of sectoral
impacts on terrestrial and marine ecosystems. In most cases, risks areassessed by impact models coupled offline to climate models after bias
correction, which may modify long-term trends (Grillakis et al., 2017).
Assessment of local impacts of climate change necessarily involvesa change in scale, such as from the global scale to that of naturalor human systems (Frieler et al., 2017; Reyer et al., 2017d; Jacob etal., 2018). An appropriate method of downscaling (Supplementary
Material 3.SM.1) is crucial for translating perspectives on 1.5°C and
2°C of global warming to scales and impacts relevant to humans andecosystems. A major challenge associated with this requirement is
the correct reproduction of the variance of local to regional changes,
as well as the frequency and amplitude of extreme events (Vautard
et al., 2014). In addition, maintaining physical consistency between
downscaled variables is important but challenging (Frost et al., 2011).

3

Another major challenge relates to the propagation of the uncertainties
at each step of the methodology, from the global forcings to the global
climate and from regional climate to impacts at the ecosystem level,
considering local disturbances and local policy effects. The risks fornatural and human systems are the result of complex combinations ofglobal and local drivers, which makes quantitative uncertainty analysis
difficult. Such analyses are partly done using multimodel approaches,such as multi-climate and multi-impact models (Warszawski et al.,2013, 2014; Frieler et al., 2017). In the case of crop projections, forexample, the majority of the uncertainty is caused by variation amongcrop models rather than by downscaling outputs of the climate models
used (Asseng et al., 2013). Error propagation is an important issue
for coupled models. Dealing correctly with uncertainties in a robust
probabilistic model is particularly important when considering thepotential for relatively small changes to affect the already small signal
associated with 0.5°C of global warming (Supplementary Material
3.SM.1). The computation of an impact per unit of climatic change,
based either on models or on data, is a simple way to present theprobabilistic ecosystem response while taking into account the various
sources of uncertainties (Fronzek et al., 2011).

Impacts of 1.5°C of Global Warming on Natural and Human Systems

However, impacts can only be partly inferred from these types ofobservations, given the strong possibility of non-linear changes, as wellas lag effects for some climate variables (e.g., sea level rise, snow andice melt). For the impact models, three challenges are noted about the
coupling procedure: (i) the bias correction of the climate model, which
may modify the simulated response of the ecosystem, (ii) the necessity
to downscale the climate model outputs to reach a pertinent scale forthe ecosystem without losing physical consistency of the downscaled
climate fields, and (iii) the necessity to develop an integrated study ofthe uncertainties.

3.3

This section provides the assessment of changes in climate at
1.5°C of global warming relative to changes at higher global mean
temperatures. Section 3.3.1 provides a brief overview of changes toglobal climate. Sections 3.3.2–3.3.11 provide assessments for specificaspects of the climate system, including regional assessments fortemperature (Section 3.3.2) and precipitation (Section 3.3.3) means
and extremes. Analyses of regional changes are based on the set of
regions displayed in Figure 3.2. A synthesis of the main conclusionsof this section is provided in Section 3.3.11. The section builds uponassessments from the IPCC AR5 WGI report (Bindoff et al., 2013a;
Christensen et al., 2013; Collins et al., 2013; Hartmann et al., 2013;
IPCC, 2013) and Chapter 3 of the IPCC Special Report on Managing
the Risks of Extreme Events and Disasters to Advance Climate Change
Adaptation (SREX; Seneviratne et al., 2012), as well as a substantial
body of new literature related to projections of climate at 1.5°C and 2°C
of warming above the pre-industrial period (e.g., Vautard et al., 2014;
Fischer and Knutti, 2015; Schleussner et al., 2016b, 2017; Seneviratne
et al., 2016, 2018c; Déqué et al., 2017; Maule et al., 2017; Mitchell etal., 2017, 2018a; Wartenburger et al., 2017; Zaman et al., 2017; Betts etal., 2018; Jacob et al., 2018; Kharin et al., 2018; Wehner et al., 2018b).
The main assessment methods are as already detailed in Section 3.2.

3.3.1
In summary, in order to assess risks at 1.5°C and higher levels ofglobal warming, several things need to be considered. Projectedclimates under 1.5°C of global warming differ depending on temporal
aspects and emission pathways. Considerations include whether global
temperature is (i) temporarily at this level (i.e., is a transient phase on its
way to higher levels of warming), (ii) arrives at 1.5°C, with or without
overshoot, after stabilization of greenhouse gas concentrations, or (iii)
is at this level as part of long-term climate equilibrium (complete only
after several millennia). Assessments of impacts of 1.5°C of warming
are generally based on climate simulations for these different possible
pathways. Most existing data and analyses focus on transient impacts(i). Fewer data are available for dedicated climate model simulations
that are able to assess pathways consistent with (ii), and very few data
are available for the assessment of changes at climate equilibrium (iii).
In some cases, inferences regarding the impacts of further warming of
0.5°C above present-day temperatures (i.e., 1.5°C of global warming)
can also be drawn from observations of similar sized changes (0.5°C)that have occurred in the past, such as during the last 50 years.
186

Global and Regional Climate
Changes and Associated Hazards

Global Changes in Climate

There is high confidence that the increase in global mean surface
temperature (GMST) has reached 0.87°C (±0.10°C likely range)
above pre-industrial values in the 2006–2015 decade (Chapter 1).
AR5 assessed that the globally averaged temperature (combinedover land and ocean) displayed a warming of about 0.85°C [0.65°C
to 1.06°C] during the period 1880–2012, with a large fraction of thedetected global warming being attributed to anthropogenic forcing(Bindoff et al., 2013a; Hartmann et al., 2013; Stocker et al., 2013).
While new evidence has highlighted that sampling biases and the
choice of approaches used to estimate GMST (e.g., using waterversus air temperature over oceans and using model simulations
versus observations-based estimates) can affect estimates of GMST
increase (Richardson et al., 2016; see also Supplementary Material
3.SM.2), the present assessment is consistent with that of AR5
regarding a detectable and dominant effect of anthropogenic forcing
on observed trends in global temperature (also confirmed in Ribes
et al., 2017). As highlighted in Chapter 1, human-induced warmingImpacts of 1.5°C of Global Warming on Natural and Human Systems

Chapter 3

ARC*
ALA

NEU

CGI
WNA

NAS

CEU
CNA

ENA

AMZ
MZ

TIB

EAS

WAS

SAH

CAR*

CAM

CAS

MED

SAS

WAF

NTP*
SEA

EAF

ETP*

WIO*

NEB

WSA

STP*

NAU

SAF
SSA
SA

SAU

ANT*

Abbreviation

Name

Abbreviation

Name

Abbreviation

Name

Abbreviation

Name

ALA

Alaska/N.W. Canada

CNA

Central North America

NEU

North Europe

TIB

Tibetan Plateau

AMZ

AmazonEAF

East Africa

NTP*

Pacific Islands region[2]

WAF

West Africa

ANT*

Antarctica

EAS

East Asia

SAF

Southern Africa

WASWest Asia

ARC*

ArcticENA

East North America

SAH

SaharaWIO*

West Indian Ocean

CAM

Central America/Mexico

ETP*

Pacific Islands region[3]SASSouth Asia

WNA

West North America

CAR*

small islands regions Caribbean

MED

South Europe/Mediterranean

SAU

South Australia/New Zealand

WSA

West Coast South America

CAS

Central Asia

NAS

North Asia

SEA

Southeast Asia

CEU

Central Europe

NAU

North Australia

SSA

Southeastern South America

CGI

Canada/Greenland/Iceland

NEB

North−East Brazil

STP*

Southern Topical Pacific3

Figure 3.2 | Regions used for regional analyses provided in Section 3.3. The choice of regions is based on the IPCC Fifth Assessment Report (AR5, Chapter 14, Christensen
et al., 2013 and Annex 1: Atlas) and the Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX, Chapter 3,
Seneviratne et al., 2012), with seven additional regions in the Arctic, Antarctic and islands not included in the IPCC SREX report (indicated with asterisks). Analyses for regionswith asterisks are provided in the Supplementary Material 3.SM.2

reached approximately 1°C (±0.2°C likely range) in 2017. Morebackground on recent observed trends in global climate is provided
in the Supplementary Material 3.SM.2.
A global warming of 1.5°C implies higher mean temperaturescompared to during pre-industrial times in almost all locations, bothon land and in oceans (high confidence) (Figure 3.3). In addition,a global warming of 2°C versus 1.5°C results in robust differencesin the mean temperatures in almost all locations, both on land andin the ocean (high confidence). The land–sea contrast in warming
is important and implies particularly large changes in temperature
over land, with mean warming of more than 1.5°C in most land
regions (high confidence; see Section 3.3.2 for more details). The
largest increase in mean temperature is found in the high latitudesof the Northern Hemisphere (high confidence; Figure 3.3, see Section
3.3.2 for more details). Projections for precipitation are more
uncertain, but they highlight robust increases in mean precipitationin the Northern Hemisphere high latitudes at 1.5°C global warming

versus pre-industrial conditions, as well as at 2°C global warming
versus pre-industrial conditions (high confidence) (Figure 3.3). Thereare consistent but less robust signals when comparing changes inmean precipitation at 2°C versus 1.5°C of global warming. Hence,
it is assessed that there is medium confidence in an increase ofmean precipitation in high-latitudes at 2°C versus 1.5°C of global
warming (Figure 3.3). For droughts, changes in evapotranspirationand precipitation timing are also relevant (see Section 3.3.4). Figure
3.4 displays changes in temperature extremes (the hottest daytime
temperature of the year, TXx, and the coldest night-time temperatureof the year, TNn) and heavy precipitation (the annual maximum
5-day precipitation, Rx5day). These analyses reveal distinct patternsof changes, with the largest changes in TXx occurring on mid-latitude
land and the largest changes in TNn occurring at high latitudes(both on land and in oceans). Differences in TXx and TNn compared
to pre-industrial climate are robust at both global warming levels.
Differences in TXx and TNn at 2°C versus 1.5°C of global warming
are robust across most of the globe. Changes in heavy precipitation
187

Chapter 3

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Mean temperature change
at 1.5°C GMST warming

Mean temperature change
at 2.0°C GMST warming

Temperature (°C)
Mean precipitation change
at 1.5°C GMST warming

Temperature (°C)
Mean precipitation change
at 2.0°C GMST warming

Precipitation (%)

3

Difference in mean temperaturechange (2.0°C - 1.5°C)

Difference in mean precipitation
change (2.0°C - 1.5°C)

Precipitation (%)

Figure 3.3 | Projected changes in mean temperature (top) and mean precipitation (bottom) at 1.5°C (left) and 2°C (middle) of global warming compared to the pre-industrial
period (1861–1880), and the difference between 1.5°C and 2°C of global warming (right). Cross-hatching highlights areas where at least two-thirds of the models agree on
the sign of change as a measure of robustness (18 or more out of 26). Values were assessed from the transient response over a 10-year period at a given warming level, basedon Representative Concentration Pathway (RCP)8.5 Coupled Model Intercomparison Project Phase 5 (CMIP5) model simulations (adapted from Seneviratne et al., 2016 andWartenburger et al., 2017, see Supplementary Material 3.SM.2 for more details). Note that the responses at 1.5°C of global warming are similar for RCP2.6 simulations (seeSupplementary Material 3.SM.2). Differences compared to 1°C of global warming are provided in the Supplementary Material 3.SM.2.

are less robust, but particularly strong increases are apparent at highlatitudes as well as in the tropics at both 1.5°C and 2°C of global
warming compared to pre-industrial conditions. The differences inheavy precipitation at 2°C versus 1.5°C global warming are generally
not robust at grid-cell scale, but they display consistent increases inmost locations (Figure 3.4). However, as addressed in Section 3.3.3,
statistically significant differences are found in several large regions andwhen aggregated over the global land area. We thus assess that there
is high confidence regarding global-scale differences in temperaturemeans and extremes at 2°C versus 1.5°C global warming, and medium
confidence regarding global-scale differences in precipitation means
and extremes. Further analyses, including differences at 1.5°C and 2°C
global warming versus 1°C (i.e., present-day) conditions are provided
in the Supplementary Material 3.SM.2.
These projected changes at 1.5°C and 2°C of global warming are
consistent with the attribution of observed historical global trends
in temperature and precipitation means and extremes (Bindoff et al.,2013a), as well as with some observed changes under the recent
global warming of 0.5°C (Schleussner et al., 2017). These comparisons
are addressed in more detail in Sections 3.3.2 and 3.3.3. Attribution
studies have shown that there is high confidence that anthropogenic
forcing has had a detectable influence on trends in global warming(virtually certain since the mid-20th century), in land warming on
all continents except Antarctica (likely since the mid-20th century),in ocean warming since 1970 (very likely), and in increases in hot
extremes and decreases in cold extremes since the mid-20th century
188(very likely) (Bindoff et al., 2013a). In addition, there is medium
confidence that anthropogenic forcing has contributed to increases
in mean precipitation at high latitudes in the Northern Hemispheresince the mid-20th century and to global-scale increases in heavy
precipitation in land regions with sufficient observations over thesame period (Bindoff et al., 2013a). Schleussner et al. (2017) showed,
through analyses of recent observed tendencies, that changes intemperature extremes and heavy precipitation indices are detectablein observations for the 1991–2010 period compared with thosefor 1960–1979, with a global warming of approximately 0.5°C
occurring between these two periods (high confidence). The observed
tendencies over that time frame are thus consistent with attributedchanges since the mid-20th century (high confidence).
The next sections assess changes in several different types of climaterelated hazards. It should be noted that the different types of hazards
are considered in isolation but some regions are projected to beaffected by collocated and/or concomitant changes in several typesof hazards (high confidence). Two examples are sea level rise andheavy precipitation in some regions, possibly leading together to more
flooding, and droughts and heatwaves, which can together increase
the risk of fire occurrence. Such events, also called compound events,
may substantially increase risks in some regions (e.g., AghaKouchak etal., 2014; Van Den Hurk et al., 2015; Martius et al., 2016; Zscheischler
et al., 2018). A detailed assessment of physically-defined compound
events was not possible as part of this report, but aspects related tooverlapping multi-sector risks are highlighted in Sections 3.4 and 3.5.

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Chapter 3

Change in temperature of hottest
days (TXx) at 1.5°C GMST warming

Change in temperature of hottest
days (TXx) at 2.0°C GMST warming

Difference in temperature of hottest
days (TXx) (2.0°C – 1.5°C)

Change in temperature of coldest
nights (TNn) at 1.5°C GMST warming

Change in temperature of coldest
nights (TNn) at 2.0°C GMST warming

Difference in temperature of coldest
nights (TNn) (2.0°C – 1.5°C)

Temperature (°C)

Temperature (°C)
Change in extreme precipitation(Rx5day) at 1.5°C GMST warming

Change in extreme precipitation(Rx5day) at 2.0°C GMST warming

Difference in change in extreme
precipitation (Rx5day) (2.0°C – 1.5°C)

3Precipitation (%)

Precipitation (%)

Figure 3.4 | Projected changes in extremes at 1.5°C (left) and 2°C (middle) of global warming compared to the pre-industrial period (1861–1880), and the difference between
1.5°C and 2°C of global warming (right). Cross-hatching highlights areas where at least two-thirds of the models agree on the sign of change as a measure of robustness(18 or more out of 26): temperature of annual hottest day (maximum temperature), TXx (top), and temperature of annual coldest night (minimum temperature), TNn (middle),
and annual maximum 5-day precipitation, Rx5day (bottom). The underlying methodology and data basis are the same as for Figure 3.3 (see Supplementary Material 3.SM.2
for more details). Note that the responses at 1.5°C of global warming are similar for Representative Concentration Pathway (RCP)2.6 simulations (see Supplementary Material
3.SM.2). Differences compared to 1°C of global warming are provided in the Supplementary Material 3.SM.2.

3.3.2

Regional Temperatures on Land, Including Extremes

3.3.2.1 Observed and attributed changes in regionaltemperature means and extremes
While the quality of temperature measurements obtained throughground observational networks tends to be high compared to that ofmeasurements for other climate variables (Seneviratne et al., 2012),
it should be noted that some regions are undersampled. Cowtan and
Way (2014) highlighted issues regarding undersampling, which ismost problematic at the poles and over Africa, and which may leadto biases in estimated changes in GMST (see also Supplementary
Material 3.SM.2 and Chapter 1). This undersampling also affects theconfidence of assessments regarding regional observed and projectedchanges in both mean and extreme temperature. Despite this partly
limited coverage, the attribution chapter of AR5 (Bindoff et al., 2013a)
and recent papers (e.g., Sun et al., 2016; Wan et al., 2018) assessed
that, over every continental region and in many sub-continental

regions, anthropogenic influence has made a substantial contribution
to surface temperature increases since the mid-20th century.
Based on the AR5 and SREX, as well as recent literature (seeSupplementary Material 3.SM), there is high confidence (very likely)that there has been an overall decrease in the number of cold days
and nights and an overall increase in the number of warm days andnights at the global scale on land. There is also high confidence (likely)that consistent changes are detectable on the continental scale inNorth America, Europe and Australia. There is high confidence thatthese observed changes in temperature extremes can be attributed toanthropogenic forcing (Bindoff et al., 2013a). As highlighted in Section
3.2, the observational record can be used to assess past changes
associated with a global warming of 0.5°C. Schleussner et al. (2017)used this approach to assess observed changes in extreme indices forthe 1991–2010 versus the 1960–1979 period, which corresponds tojust about a 0.5°C GMST difference in the observed record (based on
the Goddard Institute for Space Studies Surface Temperature Analysis
189

Chapter 3(GISTEMP) dataset, Hansen et al., 2010). They found that substantialchanges due to 0.5°C of warming are apparent for indices related to
hot and cold extremes, as well as for the Warm Spell Duration Indicator(WSDI). In particular, they identified that one-quarter of the land has
experienced an intensification of hot extremes (maximum temperatureon the hottest day of the year, TXx) by more than 1°C and a reduction in
the intensity of cold extremes by at least 2.5°C (minimum temperatureon the coldest night of the year, TNn). In addition, the same study
showed that half of the global land mass has experienced changes
in WSDI of more than six days, as well as an emergence of extremes
outside the range of natural variability (Schleussner et al., 2017).
Analyses from Schleussner et al. (2017) for temperature extremes areprovided in the Supplementary Material 3.SM, Figure 3.SM.6. It should
be noted that assessments of attributed changes in the IPCC SREX andAR5 reports were generally provided since 1950, for time frames also
approximately corresponding to a 0.5°C global warming (3.SM).

3.3.2.2 Projected changes in regional temperature means andextremes at 1.5°C versus 2°C of global warming

3

There are several lines of evidence available for providing a regional
assessment of projected changes in temperature means and extremes
at 1.5°C versus 2°C of global warming (see Section 3.2). These include:
analyses of changes in extremes as a function of global warming basedon existing climate simulations using the empirical scaling relationship(ESR) and variations thereof (e.g., Schleussner et al., 2017; Dosio andFischer, 2018; Seneviratne et al., 2018c; see Section 3.2 for details about
the methodology); dedicated simulations of 1.5°C versus 2°C of global
warming, for instance based on the Half a degree additional warming,
prognosis and projected impacts (HAPPI) experiment (Mitchell et al.,2017) or other model simulations (e.g., Dosio et al., 2018; Kjellström etal., 2018); and analyses based on statistical pattern scaling approaches(e.g., Kharin et al., 2018). These different lines of evidence lead toqualitatively consistent results regarding changes in temperaturemeans and extremes at 1.5°C of global warming compared to the preindustrial climate and 2°C of global warming.
There are statistically significant differences in temperature means andextremes at 1.5°C versus 2°C of global warming, both in the global
average (Schleussner et al., 2016b; Dosio et al., 2018; Kharin et al.,2018), as well as in most land regions (high confidence) (Wartenburger
et al., 2017; Seneviratne et al., 2018c; Wehner et al., 2018b). Projected
temperatures over oceans display significant increases in means andextremes between 1.5°C and 2°C of global warming (Figures 3.3 and3.4). A general background on the available evidence on regionalchanges in temperature means and extremes at 1.5°C versus 2°C of
global warming is provided in the Supplementary Material 3.SM.2. As
an example, Figure 3.5 shows regionally-based analyses for the IPCC
SREX regions (see Figure 3.2) of changes in the temperature of hot
extremes as a function of global warming (corresponding analyses
for changes in the temperature of cold extremes are provided in the
Supplementary Material 3.SM.2). As demonstrated in these analyses,
the mean response of the intensity of temperature extremes in climate
models to changes in the global mean temperature is approximately
linear and independent of the considered emissions scenario(Seneviratne et al., 2016; Wartenburger et al., 2017). Nonetheless, inthe case of changes in the number of days exceeding a given threshold,
190

Impacts of 1.5°C of Global Warming on Natural and Human Systems

changes are approximately exponential, with higher increases for rare
events (Fischer and Knutti, 2015; Kharin et al., 2018); see also Figure
3.6. This behaviour is consistent with a linear increase in absolutetemperature for extreme threshold exceedances (Whan et al., 2015).
As mentioned in Section 3.3.1, there is an important land–sea warming
contrast, with stronger warming on land (see also Christensen et al.,2013; Collins et al., 2013; Seneviratne et al., 2016), which implies that
regional warming on land is generally more than 1.5°C even whenmean global warming is at 1.5°C. As highlighted in Seneviratne et al.
(2016), this feature is generally stronger for temperature extremes(Figures 3.4 and 3.5; Supplementary Material 3.SM.2 ). For differencesin regional temperature extremes at a mean global warming of 1.5°C
versus 2°C, that is, a difference of 0.5°C in global warming, this implies
differences of as much as 1°C–1.5°C in some locations, which are twoto three times larger than the differences in global mean temperature.
For hot extremes, the strongest warming is found in central and eastern
North America, central and southern Europe, the Mediterranean,western and central Asia, and southern Africa (Figures 3.4 and 3.5)
(medium confidence). These regions are all characterized by a strong
soil-moisture–temperature coupling and projected increased dryness(Vogel et al., 2017), which leads to a reduction in evaporative coolingin the projections. Some of these regions also show a wide range of
responses to temperature extremes, in particular central Europe andcentral North America, owing to discrepancies in the representation of
the underlying processes in current climate models (Vogel et al., 2017).
For mean temperature and cold extremes, the strongest warming isfound in the northern high-latitude regions (high confidence). This isdue to substantial ice-snow-albedo-temperature feedbacks (Figure
3.3 and Figure 3.4, middle) related to the known ‘polar amplification’mechanism (e.g., IPCC, 2013; Masson-Delmotte et al., 2013).
Figure 3.7 displays maps of changes in the number of hot days
(NHD) at 1.5°C and 2°C of GMST increase. Maps of changes in thenumber of frost days (FD) can be found in Supplementary Material
3.SM.2. These analyses reveal clear patterns of changes between thetwo warming levels, which are consistent with analysed changes inheatwave occurrence (e.g., Dosio et al., 2018). For the NHD, the largest
differences are found in the tropics (high confidence), owing to the
low interannual temperature variability there (Mahlstein et al., 2011),
although absolute changes in hot temperature extremes tended tobe largest at mid-latitudes (high confidence) (Figures 3.4 and 3.5).
Extreme heatwaves are thus projected to emerge earliest in the tropics
and to become widespread in these regions already at 1.5°C of global
warming (high confidence). These results are consistent with otherrecent assessments. Coumou and Robinson (2013) found that 20%of the global land area, centred in low-latitude regions, is projected
to experience highly unusual monthly temperatures during Northern
Hemisphere summers at 1.5°C of global warming, with this number
nearly doubling at 2°C of global warming.
Figure 3.8 features an objective identification of ‘hotspots’ / keyrisks in temperature indices subdivided by region, based on the ESR
approach applied to Coupled Model Intercomparison Project Phase
5 (CMIP5) simulations (Wartenburger et al., 2017). Note that results
based on the HAPPI multimodel experiment (Mitchell et al., 2017)
are similar (Seneviratne et al., 2018c). The considered regions follow

Impacts of 1.5°C of Global Warming on Natural and Human Systems

the classification used in Figure 3.2 and also include the global land
areas. Based on these analyses, the following can be stated: significant
changes in responses are found in all regions for most temperature
indices, with the exception of i) the diurnal temperature range (DTR) in
most regions, ii) ice days (ID), frost days (FD) and growing season length(GSL) (mostly in regions where differences are zero, because, e.g., there
are no ice or frost days), iii) the minimum yearly value of the maximum
daily temperature (TXn) in very few regions. In terms of the sign ofthe changes, warm extremes display an increase in intensity, frequency
and duration (e.g., an increase in the temperature of the hottest day of
the year (TXx) in all regions, an increase in the proportion of days witha maximum temperature above the 90th percentile of Tmax (TX90p)
in all regions, and an increase in the length of the WSDI in all regions),
while cold extremes display a decrease in intensity, frequency andduration (e.g., an increase in the temperature of the coldest night of
the year (TNn) in all regions, a decrease in the proportion of days witha minimum temperature below the 10th percentile of Tmin (TN10p),
and a decrease in the cold spell duration index (CSDI) in all regions).
Hence, while warm extremes are intensified, cold extremes becomeless intense in affected regions.
Overall, large increases in hot extremes occur in many densely
inhabited regions (Figure 3.5), for both warming scenarios compared
to pre-industrial and present-day climate, as well as for 2°C versus
1.5°C GMST warming. For instance, Dosio et al. (2018) concluded,
based on a modelling study, that 13.8% of the world population would
be exposed to ‘severe heatwaves’ at least once every 5 years under
1.5°C of global warming, with a threefold increase (36.9%) under 2°Cof GMST warming, corresponding to a difference of about 1.7 billion
people between the two global warming levels. They also concluded
that limiting global warming to 1.5°C would result in about 420
million fewer people being frequently exposed to extreme heatwaves,and about 65 million fewer people being exposed to ‘exceptional
heatwaves’ compared to conditions at 2°C GMST warming. However,changes in vulnerability were not considered in their study. For thisreason, we assess that there is medium confidence in their conclusions.
In summary, there is high confidence that there are robust andstatistically significant differences in the projected temperature means
and extremes at 1.5°C versus 2°C of global warming, both in the global
average and in nearly all land regions4 (likely). Further, the observational
record reveals that substantial changes due to a 0.5°C GMST warming
are apparent for indices related to hot and cold extremes, as well as for
the WSDI (likely). A global warming of 2°C versus 1.5°C would lead tomore frequent and more intense hot extremes in all land regions4, as
well as longer warm spells, affecting many densely inhabited regions(very likely). The strongest increases in the frequency of hot extremes
are projected for the rarest events (very likely). On the other hand, cold
extremes would become less intense and less frequent, and cold spells
would be shorter (very likely). Temperature extremes on land would
generally increase more than the global average temperature (verylikely). Temperature increases of extreme hot days in mid-latitudes areprojected to be up to two times the increase in GMST, that is, 3°C at
1.5°C GMST warming (high confidence). The highest levels of warming
for extreme hot days are expected to occur in central and eastern North
4

Using the SREX definition of regions (Figure 3.2)

Chapter 3

America, central and southern Europe, the Mediterranean, western andcentral Asia, and southern Africa (medium confidence). These regions
have a strong soil-moisture-temperature coupling in common as wellas increased dryness and, consequently, a reduction in evaporative
cooling. However, there is a substantial range in the representationof these processes in models, in particular in central Europe andcentral North America (medium confidence). The coldest nights in highlatitudes warm by as much as 1.5°C for a 0.5°C increase in GMST,
corresponding to a threefold stronger warming (high confidence). NHD
shows the largest differences between 1.5°C and 2°C in the tropics,
because of the low interannual temperature variability there (highconfidence); extreme heatwaves are thus projected to emerge earliest
in these regions, and they are expected to become widespread alreadyat 1.5°C of global warming (high confidence). Limiting global warming
to 1.5°C instead of 2°C could result in around 420 million fewer people
being frequently exposed to extreme heatwaves, and about 65 million
fewer people being exposed to exceptional heatwaves, assuming
constant vulnerability (medium confidence).

3.3.3

Regional Precipitation, Including Heavy
Precipitation and Monsoons

This section addresses regional changes in precipitation on land, witha focus on heavy precipitation and consideration of changes to the key
features of monsoons.

3.3.3.1 Observed and attributed changes in regional
precipitation
Observed global changes in the water cycle, including precipitation,
are more uncertain than observed changes in temperature (Hartmann
et al., 2013; Stocker et al., 2013). There is high confidence thatmean precipitation over the mid-latitude land areas of the Northern
Hemisphere has increased since 1951 (Hartmann et al., 2013). For
other latitudinal zones, area-averaged long-term positive or negative
trends have low confidence because of poor data quality, incomplete
data or disagreement amongst available estimates (Hartmann et al.,2013). There is, in particular, low confidence regarding observed trends
in precipitation in monsoon regions, according to the SREX report(Seneviratne et al., 2012) and AR5 (Hartmann et al., 2013), as well as
more recent publications (Singh et al., 2014; Taylor et al., 2017; Bichet
and Diedhiou, 2018; see Supplementary Material 3.SM.2).
For heavy precipitation, AR5 (Hartmann et al., 2013) assessed thatobserved trends displayed more areas with increases than decreases in
the frequency, intensity and/or amount of heavy precipitation (likely).
In addition, for land regions where observational coverage is sufficientfor evaluation, it was assessed that there is medium confidence thatanthropogenic forcing has contributed to a global-scale intensificationof heavy precipitation over the second half of the 20th century (Bindoff
et al., 2013a).
Regarding changes in precipitation associated with global warmingof 0.5°C, the observed record suggests that increases in precipitation
extremes can be identified for annual maximum 1-day precipitation
Continued page 194 >191

3

Chapter 3
Alaska/N.W. Canada
(ALA)

1

S.E. South America
(SSA)

10

Impacts of 1.5°C of Global Warming on Natural and Human Systems
Canada/Greenl./Icel.
(CGI)

2

11

W. North America(WNA)

C. North America
(CNA)

3

E. North America
(ENA)

4

5

15

Amazon
(AMZ)

7

2

1
4

11

7

8

9

20 21

19

14

W. Coast South America(WSA)

C. Europe(CEU)

S. Europe/Mediterranean
(MED)

9

13

22

23

15

N.E. Brazil
(NEB)

18

12
13

5

W. Africa
(WAF)

8

12

6
14

6

N. Europe(NEU)

3

Sahara(SAH)

Central America/Mexico
(CAM)

24

16

16

E. Africa
(EAF)

17

S. Africa
(SAF)

25

17
10

26
∆ TXx (2°C – 1.5°C) (°C)

183

N. Asia(NAS)

19

W. Asia(WAS)

Global Land

0.2

0.3

Tibetan Plateau
(TIB)

21

0.4

0.5
22

0.6

E. Asia
(EAS)

0.7

0.8

0.9
23

Global Ocean#∆ TXx reg (°C)

Global Land + Ocean#

20

C. Asia
(CAS)

0.1

5
4
3
2
1
0
-1

1

S. Asia(SAS)

1.1

Legend24

S.E. Asia
(SEA)

25

N. Australia(NAU)

S. Australia/New Zealand
(SAU)

26

CMIP5 (ESR)
historical
RCP 8.5
HAPPI
1.5°C - pre-industrial

0 1 2 3
∆ T glob (°C)

2.0°C - pre-industrial

Figure 3.5 | Projected changes in annual maximum daytime temperature (TXx) as a function of global warming for IPCC Special Report on Managing the Risk of Extreme Eventsand Disasters to Advance Climate Change Adaptation (SREX) regions (see Figure 3.2), based on an empirical scaling relationship applied to Coupled Model Intercomparison
Project Phase 5 (CMIP5) data (adapted from Seneviratne et al., 2016 and Wartenburger et al., 2017) together with projected changes from the Half a degree additional warming,
prognosis and projected impacts (HAPPI) multimodel experiment (Mitchell et al., 2017; based on analyses in Seneviratne et al., 2018c) (bar plots on regional analyses and central
plot, respectively). For analyses for other regions from Figure 3.2 (with asterisks), see Supplementary Material 3.SM.2. (The stippling indicates significance of the differences inchanges between 1.5°C and 2°C of global warming based on all model simulations, using a two-sided paired Wilcoxon test (P = 0.01, after controlling the false discovery rateaccording to Benjamini and Hochberg, 1995). See Supplementary Material 3.SM.2 for details.

Figure 3.6 | Probability ratio (PR) of exceeding extreme temperature thresholds. (a) PR of exceeding the 99th (blue) and 99.9th (red) percentile of pre-industrial daily
temperatures at a given warming level, averaged across land (from Fischer and Knutti, 2015). (b) PR for the hottest daytime temperature of the year (TXx). (c) PR for the coldest
night of the year (TNn) for different event probabilities (with RV indicating return values) in the current climate (1°C of global warming). Shading shows the interquartile(25–75%) range (from Kharin et al., 2018).

192

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Chapter 3

Figure 3.7 | Projected changes in the number of hot days (NHD; 10% warmest days) at 1.5°C (left) and at 2°C (middle) of global warming compared to the pre-industrial
period (1861–1880), and the difference between 1.5°C and 2°C of warming (right). Cross-hatching highlights areas where at least two-thirds of the models agree on the sign ofchange as a measure of robustness (18 or more out of 26). The underlying methodology and the data basis are the same as for Figure 3.2 (see Supplementary Material 3.SM.2
for more details). Differences compared to 1°C global warming are provided in the Supplementary Material 3.SM.2.

3

Figure 3.8 | Significance of differences in regional mean temperature and range of temperature indices between the 1.5°C and 2°C global mean temperature targets (rows).
Definitions of indices: T: mean temperature; CSDI: cold spell duration index; DTR: diurnal temperature range; FD: frost days; GSL: growing season length; ID: ice days; SU: summer
days; TN10p: proportion of days with a minimum temperature (TN) lower than the 10th percentile of TN; TN90p: proportion of days with TN higher than the 90th percentile of
TN; TNn: minimum yearly value of TN; TNx: maximum yearly value of TN; TR: tropical nights; TX10p: proportion of days with a maximum temperature (TX) lower than the 10th
percentile of TX; TX90p: proportion of days with TX higher than the 90th percentile of TX; TXn: minimum yearly value of TX; TXx: maximum yearly value of TX; WSDI: warm spell
duration index. Columns indicate analysed regions and global land (see Figure 3.2 for definitions). Significant differences are shown in red shading, with increases indicatedwith + and decreases indicated with –, while non-significant differences are shown in grey shading. White shading indicates when an index is the same at the two global
warming levels (i.e., zero changes). Note that decreases in CSDI, FD, ID, TN10p and TX10p are linked to increased temperatures on cold days or nights. Significance was tested
using a two-sided paired Wilcoxon test (P=0.01, after controlling the false discovery rate according to Benjamini and Hochberg, 1995) (adapted from Wartenburger et al., 2017).

193

Chapter 3
3.3.3.1 (continued)(RX1day) and consecutive 5-day precipitation (RX5day) for GMST
changes of this magnitude (Supplementary Material 3.SM.2, Figure
3.SM.7; Schleussner et al., 2017). It should be noted that assessments
of attributed changes in the IPCC SREX and AR5 reports were generally
provided since 1950, for time frames also approximately corresponding
to a 0.5°C global warming (3.SM).

3.3.3.2 Projected changes in regional precipitation at 1.5°C
versus 2°C of global warming

3

Figure 3.3 in Section 3.3.1 summarizes the projected changes in mean
precipitation at 1.5°C and 2°C of global warming. Both warming
levels display robust differences in mean precipitation compared tothe pre-industrial period. Regarding differences at 2°C vs 1.5°C global
warming, some regions are projected to display changes in mean
precipitation at 2°C compared with that at 1.5°C of global warming in
the CMIP5 multimodel average, such as decreases in the Mediterraneanarea, including southern Europe, the Arabian Peninsula and Egypt, or
increases in high latitudes. The results, however, are less robust acrossmodels than for mean temperature. For instance, Déqué et al. (2017)
investigated the impact of 2°C of global warming on precipitation overtropical Africa and found that average precipitation does not show asignificant response, owing to two phenomena: (i) the number of days
with rain decreases whereas the precipitation intensity increases, and(ii) the rainy season occurs later during the year, with less precipitationin early summer and more precipitation in late summer. The resultsfrom Déqué et al. (2017) regarding insignificant differences between
1.5°C and 2°C scenarios for tropical Africa are consistent with theresults presented in Figure 3.3. For Europe, recent studies (Vautard etal., 2014; Jacob et al., 2018; Kjellström et al., 2018) have shown that
2°C of global warming was associated with a robust increase in mean
precipitation over central and northern Europe in winter but only overnorthern Europe in summer, and with decreases in mean precipitationin central/southern Europe in summer. Precipitation changes reaching
20% have been projected for the 2°C scenario (Vautard et al., 2014)
and are overall more pronounced than with 1.5°C of global warming(Jacob et al., 2018; Kjellström et al., 2018).
Regarding changes in heavy precipitation, Figure 3.9 displays projectedchanges in the 5-day maximum precipitation (Rx5day) as a functionof global temperature increase, using a similar approach as in Figure3.5. Further analyses are available in Supplementary Material 3.SM.2.
These analyses show that projected changes in heavy precipitation are
more uncertain than those for temperature extremes. However, themean response of model simulations is generally robust and linear(see also Fischer et al., 2014; Seneviratne et al., 2016). As observed fortemperature extremes, this response is also mostly independent of theconsidered emissions scenario (e.g., RCP2.6 versus RCP8.5; see also
Section 3.2). This feature appears to be specific to heavy precipitation,
possibly due to a stronger coupling with temperature, as the scaling of
projections of mean precipitation changes with global warming showssome scenario dependency (Pendergrass et al., 2015).
Robust changes in heavy precipitation compared to pre-industrial
conditions are found at both 1.5°C and 2°C global warming (Figure3.4). This is also consistent with results for, for example, the European
194

Impacts of 1.5°C of Global Warming on Natural and Human Systems

continent, although different indices for heavy precipitation changes
have been analysed. Based on regional climate simulations, Vautard
et al. (2014) found a robust increase in heavy precipitation everywherein Europe and in all seasons, except southern Europe in summer at 2°C
versus 1971–2000. Their findings are consistent with those of Jacobet al. (2014), who used more recent downscaled climate scenarios(EURO-CORDEX) and a higher resolution (12 km), but the change
is not so pronounced in Teichmann et al. (2018). There is consistentagreement in the direction of change in heavy precipitation at 1.5°Cof global warming over much of Europe, compared to 1971–2000(Jacob et al., 2018).
Differences in heavy precipitation are generally projected to besmall between 1.5°C and 2°C GMST warming (Figure 3.4 and 3.9
and Supplementary Material 3.SM.2, Figure 3.SM.10). Some regions
display substantial increases, for instance southern Asia, but generally
in less than two-thirds of the CMIP5 models (Figure 3.4, Supplementary
Material 3.SM.2, Figure 3.SM.10). Wartenburger et al. (2017) suggested
that there are substantial differences in heavy precipitation in eastern
Asia at 1.5°C versus 2°C. Overall, while there is variation among
regions, the global tendency is for heavy precipitation to increase at
2°C compared with at 1.5°C (see e.g., Fischer and Knutti, 2015 andKharin et al., 2018, as illustrated in Figure 3.10 from this chapter; seealso Betts et al., 2018).
AR5 assessed that the global monsoon, aggregated over all monsoon
systems, is likely to strengthen, with increases in its area and intensity,
while the monsoon circulation weakens (Christensen et al., 2013). A
few publications provide more recent evaluations of projections ofchanges in monsoons for high-emission scenarios (e.g., Jiang and Tian,
2013; Jones and Carvalho, 2013; Sylla et al., 2015, 2016; Supplementary
Material 3.SM.2 ). However, scenarios at 1.5°C or 2°C global warming
would involve a substantially smaller radiative forcing than those
assessed in AR5 and these more recent studies, and there appears
to be no specific assessment of changes in monsoon precipitation at
1.5°C versus 2°C of global warming in the literature. Consequently, thecurrent assessment is that there is low confidence regarding changes
in monsoons at these lower global warming levels, as well as regarding
differences in monsoon responses at 1.5°C versus 2°C.
Similar to Figure 3.8, Figure 3.11 features an objective identification of‘hotspots’ / key risks outlined in heavy precipitation indices subdivided
by region, based on the approach by Wartenburger et al. (2017). The
considered regions follow the classification used in Figure 3.2 and also
include global land areas. Hotspots displaying statistically significant
changes in heavy precipitation at 1.5°C versus 2°C global warming
are located in high-latitude (Alaska/western Canada, eastern Canada/
Greenland/Iceland, northern Europe, northern Asia) and high-elevation(e.g., Tibetan Plateau) regions, as well as in eastern Asia (including
China and Japan) and in eastern North America. Results are lessconsistent for other regions. Note that analyses for meteorologicaldrought (lack of precipitation) are provided in Section 3.3.4.
In summary, observations and projections for mean and heavy
precipitation are less robust than for temperature means and extremes(high confidence). Observations show that there are more areas withincreases than decreases in the frequency, intensity and/or amount ofImpacts of 1.5°C of Global Warming on Natural and Human Systems

Alaska/N.W. Canada
(ALA)

1

S.E. South America
(SSA)

10

Canada/Greenl./Icel.
(CGI)

2

11

W. North America(WNA)

C. North America
(CNA)

3

E. North America
(ENA)

4

2

1

15

6

Amazon
(AMZ)

7

4

5

7

8

9

20 21

19

23
16

15

N.E. Brazil(NEB)

W. Coast South America(WSA)

C. Europe(CEU)

S. Europe/Mediterranean
(MED)

9

13

18

14

W. Africa
(WAF)

8

12

11
12
13

6
14

Central America/Mexico
(CAM)

5

N. Europe(NEU)

3

Sahara(SAH)

Chapter 3

22
24
16

25

17

10

E. Africa
(EAF)

17

S. Africa
(SAF)

26

∆ Rx5day (2°C – 1.5°C) (mm)18N. Asia(NAS)

19

W. Asia(WAS)

20

C. Asia
(CAS)

-4

-2

Tibetan Plateau
(TIB)

21

-1

0
22

1

E. Asia
(EAS)

2

4

6
23

10

S. Asia(SAS)

24

S.E. Asia
(SEA)

25

N. Australia(NAU)

S. Australia/New Zealand
(SAU)

26

3
Global Land

Global Ocean#

Legend∆ Rx5dayreg (mm)Global Land + Ocean#

100
80
60
40
20
0

CMIP5 (ESR)
historical
RCP 8.5
HAPPI
1.5°C - pre-industrial

0 1 2 3
∆ T glob (°C)

2.0°C - pre-industrial

Figure 3.9 | Projected changes in annual 5-day maximum precipitation (Rx5day) as a function of global warming for IPCC Special Report on the Risk of Extreme Events andDisasters to Advance Climate Change Adaptation (SREX) regions (see Figure 3.2), based on an empirical scaling relationship applied to Coupled Model Intercomparison Project
Phase 5 (CMIP5) data together with projected changes from the HAPPI multimodel experiment (bar plots on regional analyses and central plot). The underlying methodology
and data basis are the same as for Figure 3.5 (see Supplementary Material 3.SM.2 for more details).

Figure 3.10 | Probability ratio (PR) of exceeding (heavy precipitation) thresholds. (a) PR of exceeding the 99th (blue) and 99.9th (red) percentile of pre-industrial daily
precipitation at a given warming level, averaged across land (from Fischer and Knutti, 2015). (b) PR for precipitation extremes (RX1day) for different event probabilities (with RV
indicating return values) in the current climate (1°C of global warming). Shading shows the interquartile (25–75%) range (from Kharin et al., 2018).

195

Chapter 3
3.3.3.2 (continued)

heavy precipitation (high confidence). Several large regions display
statistically significant differences in heavy precipitation at 1.5°C
versus 2°C GMST warming, with stronger increases at 2°C global
warming, and there is a global tendency towards increases in heavy
precipitation on land at 2°C compared with 1.5°C warming (highconfidence). Overall, regions that display statistically significant

Impacts of 1.5°C of Global Warming on Natural and Human Systems

changes in heavy precipitation between 1.5°C and 2°C of global
warming are located in high latitudes (Alaska/western Canada, eastern
Canada/Greenland/Iceland, northern Europe, northern Asia) and highelevation (e.g., Tibetan Plateau), as well as in eastern Asia (including
China and Japan) and in eastern North America (medium confidence).
There is low confidence in projected changes in heavy precipitation inother regions.

3

Figure 3.11 | Significance of differences in regional mean precipitation and range of precipitation indices between the 1.5°C and 2°C global mean temperature targets(rows). Definition of indices: PRCPTOT: mean precipitation; CWD: consecutive wet days; R10mm: number of days with precipitation >10 mm; R1mm: number of days with
precipitation >1 mm; R20mm: number of days with precipitation >20 mm; R95ptot: proportion of rain falling as 95th percentile or higher; R99ptot: proportion of rain falling as
99th percentile or higher; RX1day: intensity of maximum yearly 1-day precipitation; RX5day: intensity of maximum yearly 5-day precipitation; SDII: Simple Daily Intensity Index.
Columns indicate analysed regions and global land (see Figure 3.2 for definitions). Significant differences are shown in light blue (wetting tendency) or brown (drying tendency)
shading, with increases indicated with ‘+’ and decreases indicated with ‘–’, while non-significant differences are shown in grey shading. The underlying methodology and thedata basis are the same as in Figure 3.8 (see Supplementary Material 3.SM.2 for more details).

3.3.4

Drought and Dryness

3.3.4.1 Observed and attributed changes
The IPCC AR5 assessed that there was low confidence in the sign ofdrought trends since 1950 at the global scale, but that there was highconfidence in observed trends in some regions of the world, including
drought increases in the Mediterranean and West Africa and drought
decreases in central North America and northwest Australia (Hartmann
et al., 2013; Stocker et al., 2013). AR5 assessed that there was low
confidence in the attribution of global changes in droughts and did
not provide assessments for the attribution of regional changes indroughts (Bindoff et al., 2013a).
The recent literature does not suggest that the SREX and AR5
assessment of drought trends should be revised, except in the
Mediterranean region. Recent publications based on observational andmodelling evidence suggest that human emissions have substantially
increased the probability of drought years in the Mediterranean region(Gudmundsson and Seneviratne, 2016; Gudmundsson et al., 2017).
Based on this evidence, there is medium confidence that enhanced
196

greenhouse forcing has contributed to increased drying in the
Mediterranean region (including southern Europe, northern Africa andthe Near East) and that this tendency will continue to increase underhigher levels of global warming.

3.3.4.2 Projected changes in drought and dryness at 1.5°C
versus 2°C
There is medium confidence in projections of changes in droughtand dryness. This is partly consistent with AR5, which assessed theseprojections as being ‘likely (medium confidence)’ (Collins et al., 2013;
Stocker et al., 2013). However, given this medium confidence, thecurrent assessment does not include a likelihood statement, thereby
maintaining consistency with the IPCC uncertainty guidance document(Mastrandrea et al., 2010) and the assessment of the IPCC SREX report(Seneviratne et al., 2012). The technical summary of AR5 (Stocker etal., 2013) assessed that soil moisture drying in the Mediterranean,southwestern USA and southern African regions was consistent withprojected changes in the Hadley circulation and increased surface
temperatures, and it concluded that there was high confidence
in likely surface drying in these regions by the end of this century

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Chapter 3

Box 3.1 | Sub-Saharan Africa: Changes in Temperature and Precipitation Extremes
Sub-Saharan Africa has experienced the dramatic consequences of climate extremes becoming more frequent and more intense over the
past decades (Paeth et al., 2010; Taylor et al., 2017). In order to join international efforts to reduce climate change, all African countries
signed the Paris Agreement. In particular, through their nationally determined contributions (NDCs), they committed to contribute to theglobal effort to mitigate greenhouse gas (GHG) emissions with the aim to constrain global temperature increases to ‘well below 2°C’
and to pursue efforts to limit warming to ‘1.5°C above pre-industrial levels’. The target of limiting global warming to 1.5°C above preindustrial levels is useful for conveying the urgency of the situation. However, it focuses the climate change debate on a temperaturethreshold (Section 3.3.2), while the potential impacts of these global warming levels on key sectors at local to regional scales, such as
agriculture, energy and health, remain uncertain in most regions and countries of Africa (Sections 3.3.3, 3.3.4, 3.3.5 and 3.3.6).
Weber et al. (2018) found that at regional scales, temperature increases in sub-Saharan Africa are projected to be higher than the global
mean temperature increase (at global warming of 1.5°C and at 2°C; see Section 3.3.2 for further background and analyses of climate
model projections). Even if the mean global temperature anomaly is kept below 1.5°C, regions between 15°S and 15°N are projected toexperience an increase in hot nights, as well as longer and more frequent heatwaves (e.g., Kharin et al., 2018). Increases would be evenlarger if the global mean temperature were to reach 2°C of global warming, with significant changes in the occurrence and intensity oftemperature extremes in all sub-Saharan regions (Sections 3.3.1 and 3.3.2; Figures 3.4, 3.5 and 3.8).
West and Central Africa are projected to display particularly large increases in the number of hot days, both at 1.5°C and 2°C of global
warming (Section 3.3.2). This is due to the relatively small interannual present-day variability in this region, which implies that climatechange signals can be detected earlier there (Section 3.3.2; Mahlstein et al., 2011). Projected changes in total precipitation exhibit
uncertainties, mainly in the Sahel (Section 3.3.3 and Figure 3.8; Diedhiou et al., 2018). In the Guinea Coast and Central Africa, only asmall change in total precipitation is projected, although most models (70%) indicate a decrease in the length of wet periods and aslight increase in heavy rainfall. Western Sahel is projected by most models (80%) to experience the strongest drying, with a significant
increase in the maximum length of dry spells (Diedhiou et al., 2018). Above 2°C, this region could become more vulnerable to drought
and could face serious food security issues (Cross-Chapter Box 6 and Section 3.4.6 in this chapter; Salem et al., 2017; Parkes et al.,2018). West Africa has thus been identified as a climate-change hotspot with negative impacts from climate change on crop yields andproduction (Cross-Chapter Box 6 and Section 3.4.6; Sultan and Gaetani, 2016; Palazzo et al., 2017). Despite uncertainty in projections
for precipitation in West Africa, which is essential for rain-fed agriculture, robust evidence of yield loss might emerge. This yield loss
is expected to be mainly driven by increased mean temperature, while potential wetter or drier conditions – as well as elevated CO2
concentrations – could modulate this effect (Roudier et al., 2011; see also Cross-Chapter Box 6 and Section 3.4.6). Using Representative
Concentration Pathway (RCP)8.5 Coordinated Regional Climate Downscaling Experiment (CORDEX) scenarios from 25 regional climate
models (RCMs) forced with different general circulation models (GCMs), Klutse et al. (2018) noted a decrease in mean rainfall overWest Africa in models with stronger warming for this region at 1.5°C of global warming (Section 3.3.4). Mba et al. (2018) used a similar
approach and found a lack of consensus in the changes in precipitation over Central Africa (Figure 3.8 and Section 3.3.4), although there
was a tendency towards a decrease in the maximum number of consecutive wet days (CWD) and a significant increase in the maximum
number of consecutive dry days (CDD).

3

Over southern Africa, models agree on a positive sign of change for temperature, with temperature rising faster at 2°C (1.5°C–2.5°C) as
compared to 1.5°C (0.5°C–1.5°C) of global warming. Areas in the south-western region, especially in South Africa and parts of Namibia
and Botswana, are expected to experience the largest increases in temperature (Section 3.3.2; Engelbrecht et al., 2015; Maúre et al.,2018). The western part of southern Africa is projected to become drier with increasing drought frequency and number of heatwaves
towards the end of the 21st century (Section 3.3.4; Engelbrecht et al., 2015; Dosio, 2017; Maúre et al., 2018). At 1.5°C, a robust signalof precipitation reduction is found over the Limpopo basin and smaller areas of the Zambezi basin in Zambia, as well as over parts ofWestern Cape in South Africa, while an increase is projected over central and western South Africa, as well as in southern Namibia(Section 3.3.4). At 2°C, the region is projected to face robust precipitation decreases of about 10–20% and increases in the number of
CDD, with longer dry spells projected over Namibia, Botswana, northern Zimbabwe and southern Zambia. Conversely, the number of
CWD is projected to decrease, with robust signals over Western Cape (Maúre et al., 2018). Projected reductions in stream flow of 5–10%
in the Zambezi River basin have been associated with increased evaporation and transpiration rates resulting from a rise in temperature( Section 3.3.5; Kling et al., 2014), with issues for hydroelectric power across the region of southern Africa.
For Eastern Africa, Osima et al. (2018) found that annual rainfall projections show a robust increase in precipitation over Somalia anda less robust decrease over central and northern Ethiopia (Section 3.3.3). The number of CDD and CWD are projected to increase anddecrease, respectively (Section 3.3.4). These projected changes could impact the agricultural and water sectors in the region (CrossChapter Box 6 in this chapter and Section 3.4.6).

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under the RCP8.5 scenario. However, more recent assessments havehighlighted uncertainties in dryness projections due to a range offactors, including variations between the drought and dryness indicesconsidered, and the effects of enhanced CO2 concentrations on plantwater-use efficiency (Orlowsky and Seneviratne, 2013; Roderick etal., 2015). Overall, projections of changes in drought and dryness forhigh-emissions scenarios (e.g., RCP8.5, corresponding to about 4°C of
global warming) are uncertain in many regions, although a few regions
display consistent drying in most assessments (e.g., Seneviratne et al.,2012; Orlowsky and Seneviratne, 2013). Uncertainty is expected to beeven larger for conditions with a smaller signal-to-noise ratio, such as
for global warming levels of 1.5°C and 2°C.

3Some published literature is now available on the evaluation ofdifferences in drought and dryness occurrence at 1.5°C and 2°C of global
warming for (i) precipitation minus evapotranspiration (P–E, a generalmeasure of water availability; Wartenburger et al., 2017; Greve et al.,2018), (ii) soil moisture anomalies (Lehner et al., 2017; Wartenburger
et al., 2017), (iii) consecutive dry days (CDD) (Schleussner et al., 2016b;
Wartenburger et al., 2017), (iv) the 12-month standardized precipitationindex (Wartenburger et al., 2017), (v) the Palmer drought severity index(Lehner et al., 2017), and (vi) annual mean runoff (Schleussner et al.,2016b, see also next section). These analyses have produced consistentfindings overall, despite the known sensitivity of drought assessments tochosen drought indices (see above paragraph). These analyses suggestthat increases in drought, dryness or precipitation deficits are projected
at 1.5°C or 2°C global warming in some regions compared to the pre-Impacts of 1.5°C of Global Warming on Natural and Human Systems

industrial or present-day conditions, as well as between these two
global warming levels, although there is substantial variability in signals
depending on the considered indices or climate models (Lehner et al.,2017; Schleussner et al., 2017; Greve et al., 2018) (medium confidence).
Generally, the clearest signals are found for the Mediterranean region(medium confidence).
Greve et al. (2018, Figure 3.12) derives the sensitivity of regionalchanges in precipitation minus evapotranspiration to global
temperature changes. The simulations analysed span the full range ofavailable emission scenarios, and the sensitivities are derived usinga modified pattern scaling approach. The applied approach assumes
linear dependencies on global temperature changes while thoroughly
addressing associated uncertainties via resampling methods. Northernhigh-latitude regions display robust responses tending towards
increased wetness, while subtropical regions display a tendency
towards drying but with a large range of responses. While the internalvariability and the scenario choice play an important role in the overall
spread of the simulations, the uncertainty stemming from the climate
model choice usually dominates, accounting for about half of the total
uncertainty in most regions (Wartenburger et al., 2017; Greve et al.,2018). The sign of projections, that is, whether there might be increases
or decreases in water availability under higher global warming levels,
is particularly uncertain in tropical and mid-latitude regions. An
assessment of the implications of limiting the global mean temperature
increase to values below (i) 1.5°C or (ii) 2°C shows that constraining
global warming to the 1.5°C target might slightly influence the mean

Figure 3.12 | Summary of the likelihood of increases/decreases in precipitation minus evapotranspiration (P–E) in Coupled Model Intercomparison Project Phase 5 (CMIP5)
simulations considering all scenarios and a representative subset of 14 climate models (one from each modelling centre). Panel plots show the uncertainty distribution of thesensitivity of P–E to global temperature change, averaged for most IPCC Special Report on Managing the Risk of Extreme Events and Disasters to Advance Climate Change
Adaptation (SREX) regions (see Figure 3.2) outlined in the map (from Greve et al., 2018).

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Chapter 3

specific assessment of changes in the tails of the drought and dryness
distribution. The conclusions of Lehner et al. (2017) are that (i) ‘risks ofconsecutive drought years show little change in the US Southwest andCentral Plains, but robust increases in Europe and the Mediterranean’,and that (ii) ‘limiting warming to 1.5°C may have benefits for future
drought risk, but such benefits are regional, and in some cases highly
uncertain’.

response but could substantially reduce the risk of experiencing
extreme changes in regional water availability (Greve et al., 2018).
The findings from the analysis for the mean response by Greve et al.
(2018) are qualitatively consistent with results from Wartenburger etal. (2017), who used an ESR (Section 3.2) rather than a pattern scaling
approach for a range of drought and dryness indices. They are also
consistent with a study by Lehner et al. (2017), who assessed changes
in droughts based on soil moisture changes and the Palmer-Drought
Severity Index. Notably, these two publications do not provide a
Alaska/N.W. Canada
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Sahara(SAH)

Figure 3.13 features projected changes in CDD as a function of global
temperature increase, using a similar approach as for Figures 3.5 (based

24

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E. Africa
(EAF)

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(SAF)

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∆ CD D (2°C – 1.5°C) (days)
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historical50

RCP 8.5

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0
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1.5°C - pre-industrial
2.0°C - pre-industrial

0 1 2 3
∆ T glob (°C)

Figure 3.13 | Projected changes in consecutive dry days (CDD) as a function of global warming for IPCC Special Report on Managing the Risk of Extreme Events and Disasters
to Advance Climate Change Adaptation (SREX) regions, based on an empirical scaling relationship applied to Coupled Model Intercomparison Project Phase 5 (CMIP5) data
together with projected changes from the HAPPI multimodel experiment (bar plots on regional analyses and central plot, respectively). The underlying methodology and thedata basis are the same as for Figure 3.5 (see Supplementary Material 3.SM.2 for more details).
Global
ALA AMZ CAM CAS CEU CGI CNA EAF EAS ENA MED NAS NAU NEB NEU SAF SAH SAS SAU SEA
Land

SSA

T IB WAF WAS WNA WSA

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Figure 3.14 | Significance of differences in regional drought and dryness indices between the 1.5°C and 2°C global mean temperature targets (rows). Definition of indices:
CDD: consecutive dry days; P–E: precipitation minus evapotranspiration; SMA: soil moisture anomalies; SPI12: 12-month Standarized Precipitation Index. Columns indicate
analysed regions and global land (see Figure 3.2 for definitions). Significant differences are shown in light blue/brown shading (increases indicated with +, decreases indicatedwith –; light blue shading indicates decreases in dryness (decreases in CDD, or increases in P–E, SMA or SPI12) and light brown shading indicates increases in dryness (increases
in CDD, or decreases in P–E, SMA or SPI12). Non-significant differences are shown in grey shading. The underlying methodology and the data basis are the same as for Figure
3.7 (see Supplementary Material 3.SM.2 for more details).

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on Wartenburger et al., 2017). The figure also include results from the
HAPPI experiment (Mitchell et al., 2017). Again, the CMIP5-based ESR
estimates and the results of the HAPPI experiment agree well. Notethat the responses vary widely among the considered regions.
Similar to Figures 3.8 and 3.11, Figure 3.14 features an objective
identification of ‘hotspots’ / key risks in dryness indices subdivided
by region, based on the approach by Wartenburger et al. (2017). This
analysis reveals the following hotspots of drying (i.e. increases in CDD
and/or decreases in P–E, soil moisture anomalies (SMA) and 12-monthStandardized Precipitation Index (SPI12), with at least one of theindices displaying statistically significant drying): the Mediterranean
region (MED; including southern Europe, northern Africa, and the Near
East), northeastern Brazil (NEB) and southern Africa.

3

Consistent with this analysis, the available literature particularly
supports robust increases in dryness and decreases in water availability
in southern Europe and the Mediterranean with a shift from 1.5°C to
2°C of global warming (medium confidence) (Figure 3.13; Schleussner
et al., 2016b; Lehner et al., 2017; Wartenburger et al., 2017; Greve etal., 2018; Samaniego et al., 2018). This region is already displaying
substantial drying in the observational record (Seneviratne et al., 2012;
Sheffield et al., 2012; Greve et al., 2014; Gudmundsson and Seneviratne,
2016; Gudmundsson et al., 2017), which provides additional evidence
supporting this tendency and suggests that it will be a hotspot ofdryness change at global warming levels beyond 1.5°C (see also Box3.2). The other identified hotspots, southern Africa and northeastern

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Brazil, also consistently display drying trends under higher levels offorcing in other publications (e.g., Orlowsky and Seneviratne, 2013),
although no published studies could be found reporting observed
drying trends in these regions. There are substantial increases in
the risk of increased dryness (medium confidence) in both theMediterranean region and Southern Africa at 2°C versus 1.5°C of
global warming because these regions display significant changes
in two dryness indicators (CDD and SMA) between these two global
warming levels (Figure 3.14); the strongest effects are expected for
extreme droughts (medium confidence) (Figure 3.12). There is low
confidence elsewhere, owing to a lack of consistency in analyses
with different models or different dryness indicators. However, inmany regions there is medium confidence that most extreme risks ofchanges in dryness are avoided if global warming is constrained at
1.5°C instead of 2°C (Figure 3.12).
In summary, in terms of drought and dryness, limiting global warming
to 1.5°C is expected to substantially reduce the probability of extremechanges in water availability in some regions compared to changes
under 2°C of global warming (medium confidence). For shift from 1.5°C
to 2°C of GMST warming, the available studies and analyses suggest
strong increases in the probability of dryness and reduced water
availability in the Mediterranean region (including southern Europe,
northern Africa and the Near East) and in southern Africa (medium
confidence). Based on observations and modelling experiments, adrying trend is already detectable in the Mediterranean region, that is,
at global warming of less than 1°C (medium confidence).

Box 3.2 | Droughts in the Mediterranean Basin and the Middle East
Human society has developed in tandem with the natural environment of the Mediterranean basin over several millennia, laying
the groundwork for diverse and culturally rich communities. Even if advances in technology may offer some protection from climatic
hazards, the consequences of climatic change for inhabitants of this region continue to depend on the long-term interplay between anarray of societal and environmental factors (Holmgren et al., 2016). As a result, the Mediterranean is an example of a region with highvulnerability where various adaptation responses have emerged. Previous IPCC assessments and recent publications project regionalchanges in climate under increased temperatures, including consistent climate model projections of increased precipitation deficit
amplified by strong regional warming (Section 3.3.3; Seneviratne et al., 2012; Christensen et al., 2013; Collins et al., 2013; Greve andSeneviratne, 2015).
The long history of resilience to climatic change is especially apparent in the eastern Mediterranean region, which has experienced astrong negative trend in precipitation since 1960 (Mathbout et al., 2017) and an intense and prolonged drought episode between 2007
and 2010 (Kelley et al., 2015). This drought was the longest and most intense in the last 900 years (Cook et al., 2016). Some authors(e.g., Trigo et al., 2010; Kelley et al., 2015) assert that very low precipitation levels have driven a steep decline in agricultural productivity
in the Euphrates and Tigris catchment basins, and displaced hundreds of thousands of people, mainly in Syria. Impacts on the water
resources (Yazdanpanah et al., 2016) and crop performance in Iran have also been reported (Saeidi et al., 2017). Many historical periodsof turmoil have coincided with severe droughts, for example the drought which occurred at the end of the Bronze Age approximately
3200 years ago (Kaniewski et al., 2015). In this instance, a number of flourishing eastern Mediterranean civilizations collapsed, and rural
settlements re-emerged with agro-pastoral activities and limited long-distance trade. This illustrates how some vulnerable regions areforced to pursue drastic adaptive responses, including migration and societal structure changes.
The potential evolution of drought conditions under 1.5°C or 2°C of global warming (Section 3.3.4) can be analysed by comparing the2008 drought (high temperature, low precipitation) with the 1960 drought (low temperature, low precipitation) (Kelley et al., 2015).
Though the precipitation deficits were comparable, the 2008 drought was amplified by increased evapotranspiration induced by muchhigher temperatures (a mean increase of 1°C compared with the 1931–2008 period in Syria) and a large population increase (from200Impacts of 1.5°C of Global Warming on Natural and Human Systems

Chapter 3

Box 3.2 (continued)

5 million in 1960 to 22 million in 2008). Koutroulis et al. (2016) reported that only 6% out of the total 18% decrease in water availability
projected for Crete under 2°C of global warming at the end of the 21st century would be due to decreased precipitation, with theremaining 12% due to an increase in evapotranspiration. This study and others like it confirm an important risk of extreme drought
conditions for the Middle East under 1.5°C of global warming (Jacob et al., 2018), with risks being even higher in continental locations
than on islands; these projections are consistent with current observed changes (Section 3.3.4; Greve et al., 2014). Risks of drying in the
Mediterranean region could be substantially reduced if global warming is limited to 1.5°C compared to 2°C or higher levels of warming(Section 3.4.3; Guiot and Cramer, 2016). Higher warming levels may induce high levels of vulnerability exacerbated by large changes
in demography.

3.3.5

Runoff and Fluvial Flooding

3.3.5.1 Observed and attributed changes in runoff and riverflooding
There has been progress since AR5 in identifying historical changes
in streamflow and continental runoff. Using the available streamflow
data, Dai (2016) showed that long‐term (1948–2012) flow trends
are statistically significant only for 27.5% of the world’s 200 majorrivers, with negative trends outnumbering the positive ones. Althoughstreamflow trends are mostly not statistically significant, they are
consistent with observed regional precipitation changes. From 1950 to2012, precipitation and runoff have increased over southeastern SouthAmerica, central and northern Australia, the central and northeastern
United States, central and northern Europe, and most of Russia, andthey have decreased over most of Africa, East and South Asia, eastern
coastal Australia, the southeastern and northwestern United States,
western and eastern Canada, the Mediterranean region and someregions of Brazil (Dai, 2016).
A large part of the observed regional trends in streamflow and runoff
might have resulted from internal multi-decadal and multi-year climate
variations, especially the Pacific decadal variability (PDV), the Atlantic
Multi-Decadal Oscillation (AMO) and the El Niño–Southern Oscillation(ENSO), although the effect of anthropogenic greenhouse gases
and aerosols could also be important (Hidalgo et al., 2009; Gu andAdler, 2013, 2015; Chiew et al., 2014; Luo et al., 2016; Gudmundsson
et al., 2017). Additionally, other human activities can influence thehydrological cycle, such as land-use/land-cover change, modifications
in river morphology and water table depth, construction andoperation of hydropower plants, dikes and weirs, wetland drainage,
and agricultural practices such as water withdrawal for irrigation. Allof these activities can also have a large impact on runoff at the river
basin scale, although there is less agreement over their influence on
global mean runoff (Gerten et al., 2008; Sterling et al., 2012; Hall et al.,2014; Betts et al., 2015; Arheimer et al., 2017). Some studies suggestthat increases in global runoff resulting from changes in land coveror land use (predominantly deforestation) are counterbalanced bydecreases resulting from irrigation (Gerten et al., 2008; Sterling et al.,2012). Likewise, forest and grassland fires can modify the hydrologicalresponse at the watershed scale when the burned area is significant(Versini et al., 2013; Springer et al., 2015; Wine and Cadol, 2016).
Few studies have explored observed changes in extreme streamflow
and river flooding since the IPCC AR5. Mallakpour and Villarini (2015)

analysed changes of flood magnitude and frequency in the central
United States by considering stream gauge daily records with at least
50 years of data ending no earlier than 2011. They showed that flood
frequency has increased, whereas there was limited evidence of adecrease in flood magnitude in this region. Stevens et al. (2016) founda rise in the number of reported floods in the United Kingdom during
the period 1884–2013, with flood events appearing more frequently
towards the end of the 20th century. A peak was identified in 2012,
when annual rainfall was the second highest in over 100 years. Do et al.
(2017) computed the trends in annual maximum daily streamflow data
across the globe over the 1966–2005 period. They found decreasing
trends for a large number of stations in western North America andAustralia, and increasing trends in parts of Europe, eastern North
America, parts of South America, and southern Africa.
In summary, streamflow trends since 1950 are not statisticallysignificant in most of the world’s largest rivers (high confidence),
while flood frequency and extreme streamflow have increased in someregions (high confidence).

3.3.5.2 Projected changes in runoff and river flooding at 1.5°C
versus 2°C of global warming
Global-scale assessments of projected changes in freshwater systems
generally suggest that areas with either positive or negative changes
in mean annual streamflow are smaller for 1.5°C than for 2°C of
global warming (Betts et al., 2018; Döll et al., 2018). Döll et al. (2018)
found that only 11% of the global land area (excluding Greenland andAntarctica) shows a statistically significantly larger hazard at 2°C thanat 1.5°C. Significant decreases are found for 13% of the global landarea for both global warming levels, while significant increases areprojected to occur for 21% of the global land area at 1.5°C, and rise
to between 26% (Döll et al., 2018) and approximately 50% (Betts etal., 2018) at 2°C.
At the regional scale, projected runoff changes generally follow thespatial extent of projected changes in precipitation (see Section 3.3.3).
Emerging literature includes runoff projections for different warming
levels. For 2°C of global warming, an increase in runoff is projected
for much of the high northern latitudes, Southeast Asia, East Africa,
northeastern Europe, India, and parts of, Austria, China, Hungary,
Norway, Sweden, the northwest Balkans and Sahel (Schleussner etal., 2016b; Donnelly et al., 2017; Döll et al., 2018; Zhai et al., 2018).
Additionally, decreases are projected in the Mediterranean region,
southern Australia, Central America, and central and southern South
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America (Schleussner et al., 2016b; Donnelly et al., 2017; Döll et al.,2018). Differences between 1.5°C and 2°C would be most prominent
in the Mediterranean, where the median reduction in annual runoff
is expected to be about 9% (likely range 4.5–15.5%) at 1.5°C,
while at 2°C of warming runoff could decrease by 17% (likely range
8–25%) (Schleussner et al., 2016b). Consistent with these projections,Döll et al. (2018) found that statistically insignificant changes in themean annual streamflow around the Mediterranean region became
significant when the global warming scenario was changed from 1.5°C
to 2°C, with decreases of 10–30% between these two warming levels.
Donnelly et al. (2017) found an intense decrease in runoff along boththe Iberian and Balkan coasts with an increase in warming level.
Basin-scale projections of river runoff at different warming levels
are available for many regions. Betts et al. (2018) assessed runoffchanges in 21 of the world’s major river basins at 1.5°C and 2°C of
global warming (Figure 3.15). They found a general tendency towards
increased runoff, except in the Amazon, Orange, Danube and Guadianabasins where the range of projections indicate decreased mean flows(Figure 3.13). In the case of the Amazon, mean flows are projected
to decline by up to 25% at 2°C global warming (Betts et al., 2018).

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Gosling et al. (2017) analysed the impact of global warming of 1°C, 2°C
and 3°C above pre-industrial levels on river runoff at the catchment
scale, focusing on eight major rivers in different continents: Upper
Amazon, Darling, Ganges, Lena, Upper Mississippi, Upper Niger, Rhine
and Tagus. Their results show that the sign and magnitude of changewith global warming for the Upper Amazon, Darling, Ganges, Upper
Niger and Upper Mississippi is unclear, while the Rhine and Tagus may
experience decreases in projected runoff and the Lena may experience
increases. Donnelly et al. (2017) analysed the mean flow response todifferent warming levels for six major European rivers: Glomma, Wisla,
Lule, Ebro, Rhine and Danube. Consistent with the increases in mean
runoff projected for large parts of northern Europe, the Glomma, Wisla
and Lule rivers could experience increased discharges with global
warming while discharges from the Ebro could decrease, in part due
to a decrease in runoff in southern Europe. In the case of the Rhine
and Danube rivers, Donnelly et al. (2017) did not find clear results.
Mean annual runoff of the Yiluo River catchment in northern China
is projected to decrease by 22% at 1.5°C and by 21% at 2°C, while
the mean annual runoff for the Beijiang River catchment in southern
China is projected to increase by less than 1% at 1.5°C and 3% at
2°C in comparison to the studied baseline period (L. Liu et al., 2017).

3

Figure 3.15 | Runoff changes in twenty-one of the world’s major river basins at 1.5°C (blue) and 2°C (orange) of global warming, simulated by the Joint UK Land Environment
Simulator (JULES) ecosystem–hydrology model under the ensemble of six climate projections. Boxes show the 25th and 75th percentile changes, whiskers show the range, circles
show the four projections that do not define the ends of the range, and crosses show the ensemble means. Numbers in square brackets show the ensemble-mean flow in thebaseline (millimetres of rain equivalent) (Source: Betts et al., 2018).

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Impacts of 1.5°C of Global Warming on Natural and Human Systems

Chen et al. (2017) assessed the future changes in water resources in
the Upper Yangtze River basin for the same warming levels and founda slight decrease in the annual discharge at 1.5°C but a slight increaseat 2°C. Montroull et al. (2018) studied the hydrological impacts of themain rivers (Paraguay, Paraná, Iguazú and Uruguay) in La Plata basinin South America under 1.5°C and 2°C of global warming and for twoemissions scenarios. The Uruguay basin shows increases in streamflow
for all scenarios/warming targets except for the combination of
RCP8.5/1.5°C of warming. The increase is approximately 15% above
the 1981–2000 reference period for 2°C of global warming and the
RCP4.5 scenario. For the other three rivers the sign of the change inmean streamflow depends strongly on the RCP and GCM used.
Marx et al. (2018) analysed how hydrological low flows in Europe areaffected under different global warming levels (1.5°C, 2°C and 3°C).
The Alpine region showed the strongest low flow increase, from 22%
at 1.5°C to 30% at 2°C, because of the relatively large snow melt
contribution, while in the Mediterranean low flows are expected todecrease because of the decreases in annual precipitation projected
for that region. Döll et al. (2018) found that extreme low flows in thetropical Amazon, Congo and Indonesian basins could decrease by 10%
at 1.5°C, whereas they could increase by 30% in the southwestern partof Russia under the same warming level. At 2°C, projected increases inextreme low flows are exacerbated in the higher northern latitudes andin eastern Africa, India and Southeast Asia, while projected decreases
intensify in the Amazon basin, western United States, central Canada,and southern and western Europe, although not in the Congo basin orIndonesia, where models show less agreement.
Recent analyses of projections in river flooding and extreme runoff andflows are available for different global warming levels. At the global
scale, Alfieri et al. (2017) assessed the frequency and magnitude of riverfloods and their impacts under 1.5°C, 2°C and 4°C global warming
scenarios. They found that flood events with an occurrence interval
longer than the return period of present-day flood protections areprojected to increase in all continents under all considered warming
levels, leading to a widespread increment in the flood hazard. Döll et al.
(2018) found that high flows are projected to increase significantly on
11% and 21% of the global land area at 1.5°C and 2°C, respectively.
Significantly increased high flows are expected to occur in South andSoutheast Asia and Central Africa at 1.5°C, with this effect intensifying
and including parts of South America at 2°C.
Regarding the continental scale, Donnelly et al. (2017) and Thober etal. (2018) explored climate change impacts on European high flows
and/or floods under 1.5°C, 2°C and 3°C of global warming. Thober etal. (2018) identified the Mediterranean region as a hotspot of change,
with significant decreases in high flows of −11% and –13% at 1.5°C
and 2°C, respectively, mainly resulting from reduced precipitation (Box3.2). In northern regions, high flows are projected to rise by 1% and
5% at 1.5°C and 2°C, respectively, owing to increasing precipitation,although floods could decrease by 6% in both scenarios because ofless snowmelt. Donnelly et al. (2017) found that high runoff levels
could rise in intensity, robustness and spatial extent over large partsof continental Europe with an increasing warming level. At 2°C, flood
magnitudes are expected to increase significantly in Europe south of
60°N, except for some regions (Bulgaria, Poland and southern Spain);

Chapter 3

in contrast, they are projected to decrease at higher latitudes (e.g.,
in most of Finland, northwestern Russia and northern Sweden), withthe exception of southern Sweden and some coastal areas in Norway
where flood magnitudes may increase (Roudier et al., 2016). At thebasin scale, Mohammed et al. (2017) found that floods are projected tobe more frequent and flood magnitudes greater at 2°C than at 1.5°C
in the Brahmaputra River in Bangladesh. In coastal regions, increases
in heavy precipitation associated with tropical cyclones (Section
3.3.6) combined with increased sea levels (Section 3.3.9) may lead toincreased flooding (Section 3.4.5).
In summary, there is medium confidence that global warming of 2°C
above the pre-industrial period would lead to an expansion of thearea with significant increases in runoff, as well as the area affectedby flood hazard, compared to conditions at 1.5°C of global warming.
A global warming of 1.5°C would also lead to an expansion of the global
land area with significant increases in runoff (medium confidence) andto an increase in flood hazard in some regions (medium confidence)compared to present-day conditions.

3.3.6

Tropical Cyclones and Extratropical Storms

Most recent studies on observed trends in the attributes of tropical
cyclones have focused on the satellite era starting in 1979 (Rienecker
et al., 2011), but the study of observed trends is complicated by theheterogeneity of constantly advancing remote sensing techniques andinstrumentation during this period (e.g., Landsea, 2006; Walsh et al.,2016). Numerous studies leading up to and after AR5 have reported
a decreasing trend in the global number of tropical cyclones and/or
the globally accumulated cyclonic energy (Emanuel, 2005; Elsner et al.,2008; Knutson et al., 2010; Holland and Bruyère, 2014; Klotzbach andLandsea, 2015; Walsh et al., 2016). A theoretical physical basis for sucha decrease to occur under global warming was recently provided by
Kang and Elsner (2015). However, using a relatively short (20 year)
and relatively homogeneous remotely sensed record, Klotzbach (2006)
reported no significant trends in global cyclonic activity, consistent
with more recent findings of Holland and Bruyère (2014). Suchcontradictions, in combination with the fact that the almost fourdecade-long period of remotely sensed observations remains relatively
short to distinguish anthropogenically induced trends from decadal
and multi-decadal variability, implies that there is only low confidence
regarding changes in global tropical cyclone numbers under global
warming over the last four decades.
Studies in the detection of trends in the occurrence of very intense
tropical cyclones (category 4 and 5 hurricanes on the Saffir-Simpson
scale) over recent decades have yielded contradicting results. Most
studies have reported increases in these systems (Emanuel, 2005;
Webster et al., 2005; Klotzbach, 2006; Elsner et al., 2008; Knutson et al.,2010; Holland and Bruyère, 2014; Walsh et al., 2016), in particular for the
North Atlantic, North Indian and South Indian Ocean basins (e.g., Singhet al., 2000; Singh, 2010; Kossin et al., 2013; Holland and Bruyère, 2014;
Walsh et al., 2016). In the North Indian Ocean over the Arabian Sea, anincrease in the frequency of extremely severe cyclonic storms has beenreported and attributed to anthropogenic warming (Murakami et al.,2017). However, to the east over the Bay of Bengal, tropical cyclones
and severe tropical cyclones have exhibited decreasing trends over203

3

Chapter 3

3Impacts of 1.5°C of Global Warming on Natural and Human Systems

the period 1961–2010, although the ratio between severe tropical
cyclones and all tropical cyclones is increasing (Mohapatra et al.,2017). Moreover, studies that have used more homogeneous records,
but were consequently limited to rather short periods of 20 to 25 years,
have reported no statistically significant trends or decreases in the
global number of these systems (Kamahori et al., 2006; Klotzbach andLandsea, 2015). Likewise, CMIP5 model simulations of the historicalperiod have not produced anthropogenically induced trends in very
intense tropical cyclones (Bender et al., 2010; Knutson et al., 2010,2013; Camargo, 2013; Christensen et al., 2013), consistent with thefindings of Klotzbach and Landsea (2015). There is consequently low
confidence in the conclusion that the number of very intense cyclones
is increasing globally.

degrees of global warming on tropical cyclones over the southwest
Indian Ocean, using transient simulations that downscaled a number of
RCP8.5 GCM projections. Decreases in tropical cyclone frequencies areprojected under both 1.5°C and 2°C of global warming. The decreases
in cyclone frequencies under 2°C of global warming are somewhat
larger than under 1.5°C, but no further decreases are projected under
3°C. This suggests that 2°C of warming, at least in these downscaling
simulations, represents a type of stabilization level in terms of tropical
cyclone formation over the southwest Indian Ocean and landfall oversouthern Africa (Muthige et al., 2018). There is thus limited evidence
that the global number of tropical cyclones will be lower under 2°C
compared to 1.5°C of global warming, but with an increase in thenumber of very intense cyclones (low confidence).

General circulation model (GCM) projections of the changing
attributes of tropical cyclones under high levels of greenhouse gas
forcing (3°C to 4°C of global warming) consistently indicate decreases
in the global number of tropical cyclones (Knutson et al., 2010, 2015;
Sugi and Yoshimura, 2012; Christensen et al., 2013; Yoshida et al.,2017). A smaller number of studies based on statistical downscaling
methodologies contradict these findings, however, and indicate
increases in the global number of tropical cyclones under climatechange (Emanuel, 2017). Most studies also indicate increases in the
global number of very intense tropical cyclones under high levels ofglobal warming (Knutson et al., 2015; Sugi et al., 2017), consistent
with dynamic theory (Kang and Elsner, 2015), although a few studies
contradict this finding (e.g., Yoshida et al., 2017). Hence, it is assessedthat under 3°C to 4°C of warming that the global number of tropical
cyclones would decrease whilst the number of very intense cyclones
would increase (medium confidence).

The global response of the mid-latitude atmospheric circulation to
1.5°C and 2°C of warming was investigated using the HAPPI ensemble
with a focus on the winter season (Li et al., 2018). Under 1.5°C of
global warming a weakening of storm activity over North America,
an equatorward shift of the North Pacific jet exit and an equatorward
intensification of the South Pacific jet are projected. Under an additional
0.5°C of warming a poleward shift of the North Atlantic jet exit andan intensification on the flanks of the Southern Hemisphere storm
track are projected to become more pronounced. The weakening of
the Mediterranean storm track that is projected under low mitigation
emerges in the 2°C warmer world (Li et al., 2018). AR5 assessed that
under high greenhouse gas forcing (3°C or 4°C of global warming)
there is low confidence in projections of poleward shifts of theNorthern Hemisphere storm tracks, while there is high confidence that
there would be a small poleward shift of the Southern Hemispherestorm tracks (Stocker et al., 2013). In the context of this report, theassessment is that there is limited evidence and low confidence inwhether any projected signal for higher levels of warming would beclearly manifested under 2°C of global warming.

To date, only two studies have directly explored the changing tropical
cyclone attributes under 1.5°C versus 2°C of global warming. Usinga high resolution global atmospheric model, Wehner et al. (2018a)
concluded that the differences in tropical cyclone statistics under 1.5°C
versus 2°C stabilization scenarios, as defined by the HAPPI protocols(Mitchell et al., 2017) are small. Consistent with the majority of studies
performed for higher degrees of global warming, the total numberof tropical cyclones is projected to decrease under global warming,whilst the most intense (categories 4 and 5) cyclones are projected
to occur more frequently. These very intense storms are projected
to be associated with higher peak wind speeds and lower centralpressures under 2°C versus 1.5°C of global warming. The accumulated
cyclonic energy is projected to decrease globally from 1.5°C to 2°C, inassociation with a decrease in the global number of tropical cyclones
under progressively higher levels of global warming. It is also notedthat heavy rainfall associated with tropical cyclones was assessed in
the IPCC SREX as likely to increase under increasing global warming(Seneviratne et al., 2012). Two recent articles suggest that there ishigh confidence that the current level of global warming (i.e., about
1°C, see Section 3.3.1) increased the heavy precipitation associatedwith the 2017 Hurricane Harvey by about 15% or more (Risser andWehner, 2017; van Oldenborgh et al., 2017). Hence, it can be inferred,under the assumption of linear dynamics, that further increases inheavy precipitation would occur under 1.5°C, 2°C and higher levels ofglobal warming (medium confidence). Using a high resolution regional
climate model, Muthige et al. (2018) explored the effects of different
204

3.3.7

Ocean Circulation and Temperature

It is virtually certain that the temperature of the upper layers of theocean (0–700 m in depth) has been increasing, and that the global
mean for sea surface temperature (SST) has been changing at a rate
just behind that of GMST. The surfaces of three ocean basins has
warmed over the period 1950–2016 (by 0.11°C, 0.07°C and 0.05°C
per decade for the Indian, Atlantic and Pacific Oceans, respectively;
Hoegh-Guldberg et al., 2014), with the greatest changes occurringat the highest latitudes. Isotherms (i.e., lines of equal temperature) of
sea surface temperature (SST) are shifting to higher latitudes at rates
of up to 40 km per year (Burrows et al., 2014; García Molinos et al.,2015). Long-term patterns of variability make detecting signals due toclimate change complex, although the recent acceleration of changes
to the temperature of the surface layers of the ocean has made theclimate signal more distinct (Hoegh-Guldberg et al., 2014). There is also
evidence of significant increases in the frequency of marine heatwaves
in the observational record (Oliver et al., 2018), consistent withchanges in mean ocean temperatures (high confidence). Increasingclimate extremes in the ocean are associated with the general rise inglobal average surface temperature, as well as more intense patternsof climate variability (e.g., climate change intensification of ENSO)(Section 3.5.2.5). Increased heat in the upper layers of the ocean isImpacts of 1.5°C of Global Warming on Natural and Human Systems

also driving more intense storms and greater rates of inundation insome regions, which, together with sea level rise, are already driving
significant impacts to sensitive coastal and low-lying areas (Section
3.3.6).
Increasing land–sea temperature gradients have the potential tostrengthen upwelling systems associated with the eastern boundary
currents (Benguela, Canary, Humboldt and Californian Currents;
Bakun, 1990). Observed trends support the conclusion that a generalstrengthening of longshore winds has occurred (Sydeman et al., 2014),
but the implications of trends detected in upwelling currents themselves
are unclear (Lluch-Cota et al., 2014). Projections of the scale of changes
between 1°C and 1.5°C of global warming and between 1.5°C and
2°C are only informed by the changes during the past increase in GMST
of 0.5°C (low confidence). However, evidence from GCM projectionsof future climate change indicates that a general strengthening of the
Benguela, Canary and Humboldt upwelling systems under enhanced
anthropogenic forcing (D. Wang et al., 2015) is projected to occur(medium confidence). This strengthening is projected to be stronger
at higher latitudes. In fact, evidence from regional climate modellingis supportive of an increase in long-shore winds at higher latitudes,
whereas long-shore winds may decrease at lower latitudes as aconsequence of the poleward displacement of the subtropical highs
under climate change (Christensen et al., 2007; Engelbrecht et al.,2009).
It is more likely than not that the Atlantic Meridional Overturning
Circulation (AMOC) has been weakening in recent decades, given
the detection of the cooling of surface waters in the North Atlantic
and evidence that the Gulf Stream has slowed since the late 1950s(Rahmstorf et al., 2015b; Srokosz and Bryden, 2015; Caesar et al.,2018). There is only limited evidence linking the current anomalously
weak state of AMOC to anthropogenic warming (Caesar et al., 2018). It
is very likely that the AMOC will weaken over the 21st century. The best
estimates and ranges for the reduction based on CMIP5 simulations
are 11% (1– 24%) in RCP2.6 and 34% (12– 54%) in RCP8.5 (AR5).
There is no evidence indicating significantly different amplitudes of
AMOC weakening for 1.5°C versus 2°C of global warming.

3.3.8

Sea Ice

Summer sea ice in the Arctic has been retreating rapidly in recent
decades. During the period 1997 to 2014, for example, the monthly
mean sea ice extent during September (summer) decreased on averageby 130,000 km² per year (Serreze and Stroeve, 2015). This is about fourtimes as fast as the September sea ice loss during the period 1979
to 1996. Sea ice thickness has also decreased substantially, with anestimated decrease in ice thickness of more than 50% in the central
Arctic (Lindsay and Schweiger, 2015). Sea ice coverage and thickness
also decrease in CMIP5 simulations of the recent past, and areprojected to decrease in the future (Collins et al., 2013). However,
the modelled sea ice loss in most CMIP5 models is much smaller
than observed losses. Compared to observations, the simulations areless sensitive to both global mean temperature rise (Rosenblum and5

Chapter 3

Eisenman, 2017) and anthropogenic CO2 emissions (Notz and Stroeve,2016). This mismatch between the observed and modelled sensitivityof Arctic sea ice implies that the multi-model-mean responses of futuresea ice evolution probably underestimates the sea ice loss for a given
amount of global warming. To address this issue, studies estimating
the future evolution of Arctic sea ice tend to bias correct the model
simulations based on the observed evolution of Arctic sea ice in
response to global warming. Based on such bias correction, pre-AR5
and post-AR5 studies generally agree that for 1.5°C of global warming
relative to pre-industrial levels, the Arctic Ocean will maintain a sea ice
cover throughout summer in most years (Collins et al., 2013; Notz andStroeve, 2016; Screen and Williamson, 2017; Jahn, 2018; Niederdrenk
and Notz, 2018; Sigmond et al., 2018). For 2°C of global warming,chances of a sea ice-free Arctic during summer are substantially higher(Screen and Williamson, 2017; Jahn, 2018; Niederdrenk and Notz,
2018; Screen et al., 2018; Sigmond et al., 2018). Model simulations
suggest that there will be at least one sea ice-free Arctic5 summer after
approximately 10 years of stabilized warming at 2°C, as compared
to one sea ice-free summer after 100 years of stabilized warming at
1.5°C above pre-industrial temperatures (Jahn, 2018; Screen et al.,2018; Sigmond et al., 2018). For a specific given year under stabilized
warming of 2°C, studies based on large ensembles of simulations witha single model estimate the likelihood of ice-free conditions as 35%
without a bias correction of the underlying model (Sanderson et al.,2017; Jahn, 2018); as between 10% and >99% depending on theobservational record used to correct the sensitivity of sea ice decline
to global warming in the underlying model (Niederdrenk and Notz,2018); and as 19% based on a procedure to correct for biases in the
climatological sea ice coverage in the underlying model (Sigmond etal., 2018). The uncertainty of the first year of the occurrence of an icefree Arctic Ocean arising from internal variability is estimated to beabout 20 years (Notz, 2015; Jahn et al., 2016).
The more recent estimates of the warming necessary to produce an icefree Arctic Ocean during summer are lower than the ones given in AR5(about 2.6°C–3.1°C of global warming relative to pre-industrial levels
or 1.6°C–2.1°C relative to present-day conditions), which were similar
to the estimate of 3°C of global warming relative to pre-industrial
levels (or 2°C relative to present-day conditions) by Mahlstein and
Knutti (2012) based on bias-corrected CMIP3 models. Rosenblum andEisenman (2016) explained why the sensitivity estimated by Mahlstein
and Knutti (2012) might be too low, estimating instead that September
sea ice in the Arctic would disappear at 2°C of global warming
relative to pre-industrial levels (or about 1°C relative to present-day
conditions), in line with the other recent estimates. Notz and Stroeve(2016) used the observed correlation between September sea ice
extent and cumulative CO2 emissions to estimate that the Arctic Ocean
would become nearly free of sea ice during September with a further
1000 Gt of emissions, which also implies a sea ice loss at about 2°C of
global warming. Some of the uncertainty in these numbers stems fromthe possible impact of aerosols (Gagne et al., 2017) and of volcanic
forcing (Rosenblum and Eisenman, 2016). During winter, little Arctic
sea ice is projected to be lost for either 1.5°C or 2°C of global warming(Niederdrenk and Notz, 2018).

Ice free is defined for the Special Report as when the sea ice extent is less than 106 km2. Ice coverage less than this is considered to be equivalent to an ice-free Arctic Ocean
for practical purposes in all recent studies.

205

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Chapter 3

A substantial number of pre-AR5 studies found that there is no
indication of hysteresis behaviour of Arctic sea ice under decreasing
temperatures following a possible overshoot of a long-term
temperature target (Holland et al., 2006; Schröder and Connolley, 2007;
Armour et al., 2011; Sedláček et al., 2011; Tietsche et al., 2011; Boucher
et al., 2012; Ridley et al., 2012). In particular, the relationship between
Arctic sea ice coverage and GMST was found to be indistinguishable
between a warming scenario and a cooling scenario. These results havebeen confirmed by post-AR5 studies (Li et al., 2013; Jahn, 2018), which
implies high confidence that an intermediate temperature overshoothas no long-term consequences for Arctic sea ice coverage.

3

In the Antarctic, sea ice shows regionally contrasting trends, such as astrong decrease in sea ice coverage near the Antarctic peninsula but
increased sea ice coverage in the Amundsen Sea (Hobbs et al., 2016).
Averaged over these contrasting regional trends, there has been a slow
long-term increase in overall sea ice coverage in the Southern Ocean,although with comparably low ice coverage from September 2016
onwards. Collins et al. (2013) assessed low confidence in Antarctic
sea ice projections because of the wide range of model projections
and an inability of almost all models to reproduce observations suchas the seasonal cycle, interannual variability and the long-term slow
increase. No existing studies have robustly assessed the possible future
evolution of Antarctic sea ice under low-warming scenarios.
In summary, the probability of a sea-ice-free Arctic Ocean during
summer is substantially higher at 2°C compared to 1.5°C of global
warming relative to pre-industrial levels, and there is medium
confidence that there will be at least one sea ice-free Arctic summer
after about 10 years of stabilized warming at 2°C, while about
100 years are required at 1.5°C. There is high confidence that anintermediate temperature overshoot has no long-term consequences
for Arctic sea ice coverage with regrowth on decadal time scales.

3.3.9

Sea Level

Sea level varies over a wide range of temporal and spatial scales, which
can be divided into three broad categories. These are global mean sea
level (GMSL), regional variation about this mean, and the occurrence of
sea-level extremes associated with storm surges and tides. GMSL has
been rising since the late 19th century from the low rates of change thatcharacterized the previous two millennia (Church et al., 2013). Slowing
in the reported rate over the last two decades (Cazenave et al., 2014)
may be attributable to instrumental drift in the observing satellite
system (Watson et al., 2015) and increased volcanic activity (Fasullo
et al., 2016). Accounting for the former results in rates (1993 to mid2014) between 2.6 and 2.9 mm yr–1 (Watson et al., 2015). The relative
contributions from thermal expansion, glacier and ice-sheet mass loss,
and freshwater storage on land are relatively well understood (Church
et al., 2013; Watson et al., 2015) and their attribution is dominated byanthropogenic forcing since 1970 (15 ± 55% before 1950, 69 ± 31%
after 1970) (Slangen et al., 2016).
There has been a significant advance in the literature since AR5, which
has included the development of semi-empirical models (SEMs) into a
broader emulation-based approach (Kopp et al., 2014; Mengel et al.,2016; Nauels et al., 2017) that is partially based on the results from
206

Impacts of 1.5°C of Global Warming on Natural and Human Systems

more detailed, process-based modelling Church et al. (2013) assigned
low confidence to SEMs because these models assume that therelation between climate forcing and GMSL is the same in the past(calibration) and future (projection). Probable future changes in therelative contributions of thermal expansion, glaciers and (in particular)
ice sheets invalidate this assumption. However, recent emulationbased studies overcame this shortcoming by considering individual
GMSL contributors separately, and they are therefore employed inthis assessment. In this subsection, the process-based literature ofindividual contributors to GMSL is considered for scenarios close to
1.5°C and 2°C of global warming before emulation-based approaches
are assessed.
A limited number of processes-based studies are relevant to GMSL in
1.5°C and 2°C worlds. Marzeion et al. (2018) used a global glacier modelwith temperature-scaled scenarios based on RCP2.6 to investigate
the difference between 1.5°C and 2°C of global warming and foundlittle difference between scenarios in the glacier contribution to GMSL
for the year 2100 (54–97 mm relative to present-day levels for 1.5°C
and 63–112 mm for 2°C, using a 90% confidence interval). This arises
because glacier melt during the remainder of the century is dominatedby the response to warming from pre-industrial to present-day levels,
which is in turn a reflection of the slow response times of glaciers. Fürst
et al. (2015) made projections of the Greenland ice sheet’s contribution
to GMSL using an ice-flow model forced by the regional climate
model Modèle Atmosphérique Régional (MAR; considered by Church
et al. (2013) to be the ‘most realistic’ such model). They projected an
RCP2.6 range of 24–60 mm (1 standard deviation) by the end of the
century (relative to the year 2000 and consistent with the assessmentof Church et al. (2013); however, their projections do not allow thedifference between 1.5°C and 2°C worlds to be evaluated.
The Antarctic ice sheet can contribute both positively, through increases
in outflow (solid ice lost directly to the ocean), and negatively, throughincreases in snowfall (owing to the increased moisture-bearing capacityof a warmer atmosphere), to future GMSL rise. Frieler et al. (2015)
suggested a range of 3.5–8.7% °C–1 for this effect, which is consistent
with AR5. Observations from the Amundsen Sea sector of Antarctica
suggest an increase in outflow (Mouginot et al., 2014) over recent
decades associated with grounding line retreat (Rignot et al., 2014)
and the influx of relatively warm Circumpolar Deepwater (Jacobs et al.,2011). Literature on the attribution of these changes to anthropogenic
forcing is still in its infancy (Goddard et al., 2017; Turner et al., 2017a).
RCP2.6-based projections of Antarctic outflow (Levermann et al.,2014; Golledge et al., 2015; DeConto and Pollard, 2016, who include
snowfall changes) are consistent with the AR5 assessment of Church
et al. (2013) for end-of-century GMSL for RCP2.6, and do not support
substantial additional GMSL rise by Marine Ice Sheet Instability or
associated instabilities (see Section 3.6). While agreement is relatively
good, concerns about the numerical fidelity of these models still exist,
and this may affect the quality of their projections (Drouet et al., 2013;
Durand and Pattyn, 2015). An assessment of Antarctic contributions
beyond the end of the century, in particular related to the Marine Ice
Sheet Instability, can be found in Section 3.6.
While some literature on process-based projections of GMSL for theperiod up to 2100 is available, it is insufficient for distinguishingImpacts of 1.5°C of Global Warming on Natural and Human Systems

between emissions scenarios associated with 1.5°C and 2°C warmer
worlds. This literature is, however, consistent with the assessment by
Church et al. (2013) of a likely range of 0.28–0.61 m in 2100 (relative
to 1986–2005), suggesting that the AR5 assessment is still appropriate.

Chapter 3

Recent emulation-based studies show convergence towards thisAR5 assessment (Table 3.1) and offer the advantage of allowing acomparison between 1.5°C and 2°C warmer worlds. Table 3.1 features
a compilation of recent emulation-based and SEM studies.

Table 3.1 | Compilation of recent projections for sea level at 2100 (in cm) for Representative Concentration Pathway (RCP)2.6, and 1.5°C and 2°C scenarios. Upper and lower
limits are shown for the 17-84% and 5-95% confidence intervals quoted in the original papers.
Study
AR5
Kopp et al. (2014)
Jevrejeva et al. (2016)

Baseline

RCP2.667%

1986–2005

28–61

2000

37–65

1986–2005

1.5°C
90%

28–51

Mengel et al. (2016)

1986–2005

28–56

Nauels et al. (2017)

1986–2005

35–56

Goodwin et al. (2017)

1986–2005

31–59
45–70
45–72

Schaeffer et al. (2012)

2000

52–96

Schleussner et al. (2016b)

2000
2000
1986–2005

54–99
26–53

Rasmussen et al. (2018)2000
1986–2005

There is little consensus between the reported ranges of GMSL rise
(Table 3.1). Projections vary in the range 0.26–0.77 m and 0.35–0.93
m for 1.5°C and 2°C respectively for the 17–84% confidence interval(0.20–0.99 m and 0.24–1.17 m for the 5–95% confidence interval).
There is, however, medium agreement that GMSL in 2100 would be
0.04–0.16 m higher in a 2°C warmer world compared to a 1.5°C
warmer world based on the 17–84% confidence interval (0.00–0.24
m based on 5–95% confidence interval) with a value of around 0.1
m. There is medium confidence in this assessment because of issues
associated with projections of the Antarctic contribution to GMSL
that are employed in emulation-based studies (see above) and theissues previously identified with SEMs (Church et al., 2013).
Translating projections of GMSL to the scale of coastlines andislands requires two further steps. The first step accounts for regionalchanges associated with changing water and ice loads (such as
Earth’s gravitational field and rotation, and vertical land movement),
as well as spatial differences in ocean heat uptake and circulation.
The second step maps regional sea level to changes in the return
periods of particular flood events to account for effects not included
in global climate models, such as tides, storm surges, and wave setup
and runup. Kopp et al. (2014) presented a framework to do this andgave an example application for nine sites located in the US, Japan,
northern Europe and Chile. Of these sites, seven (all except those innorthern Europe) were found to experience at least a quadruplingin the number of years in the 21st century with 1-in-100-year floods
under RCP2.6 compared to under no future sea level rise. Rasmussen

56–105
36–65

29–46
30–58
40–77

1986–2005

Goodwin et al. (2018)

90%

24–61

Sanderson et al. (2017)
Nicholls et al. (2018)

67%

29–58

2000

Bittermann et al. (2017)

90%

29–82

Kopp et al. (2016)

Jackson et al. (2018)

67%

2°C

35–64

20–67
28–93

39–61
35–64
47–93

50–80

60–90

24–54

31–65

28–82
26–62

39–76

3

24–74
32–117

28–96
30–69

et al. (2018) used this approach to investigate the difference
between 1.5°C and 2°C warmer worlds up to 2200. They found thatthe reduction in the frequency of 1-in-100-year floods in a 1.5°C
compared to a 2°C warmer world would be greatest in the eastern
USA and Europe, with ESL event frequency amplification being
reduced by about a half and with smaller reductions for small island
developing states (SIDS). This last result contrasts with the findingof Vitousek et al. (2017) that regions with low variability in extreme
water levels (such as SIDS in the tropics) are particularly sensitive to
GMSL rise, such that a doubling of frequency may be expected foreven small (0.1–0.2 m) rises. Schleussner et al. (2011) emulated the
AMOC based on a subset of CMIP-class climate models. When forced
using global temperatures appropriate for the CP3-PD scenario (1°Cof warming in 2100 relative to 2000 or about 2°C of warming relative
to pre-industrial) the emulation suggests an 11% median reduction
in AMOC strength at 2100 (relative to 2000) with an associated0.04 m dynamic sea level rise along the New York City coastline.
In summary, there is medium confidence that GMSL rise will be about
0.1 m (within a 0.00–0.20 m range based on 17–84% confidenceinterval projections) less by the end of the 21st century in a 1.5°C
compared to a 2°C warmer world. Projections for 1.5°C and 2°C
global warming cover the ranges 0.2–0.8 m and 0.3–1.00 m relative
to 1986–2005, respectively (medium confidence). Sea level rise
beyond 2100 is discussed in Section 3.6; however, recent literaturestrongly supports the assessment by Church et al. (2013) that sea
level rise will continue well beyond 2100 (high confidence).
207

Chapter 3

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Box 3.3 | Lessons from Past Warm Climate Episodes
Climate projections and associated risk assessments for a future warmer world are based on climate model simulations. However,Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models do not include all existing Earth system feedbacks andmay therefore underestimate both rates and extents of changes (Knutti and Sedláček, 2012). Evidence from natural archives of threemoderately warmer (1.5°C–2°C) climate episodes in Earth’s past help to assess such long-term feedbacks (Fischer et al., 2018).
While evidence over the last 2000 years and during the Last Glacial Maximum (LGM) was discussed in detail in the IPCC Fifth
Assessment Report (Masson-Delmotte et al., 2013), the climate system response during past warm intervals was the focus of a recent
review paper (Fischer et al., 2018) summarized in this Box. Examples of past warmer conditions with essentially modern physicalgeography include the Holocene Thermal Maximum (HTM; broadly defined as about 10–5 kyr before present (BP), where present
is defined as 1950), the Last Interglacial (LIG; about 129–116 kyr BP) and the Mid Pliocene Warm Period (MPWP; 3.3–3.0 Myr BP).
Changes in insolation forcing during the HTM (Marcott et al., 2013) and the LIG (Hoffman et al., 2017) led to a global temperature
up to 1°C higher than that in the pre-industrial period (1850–1900); high-latitude warming was 2°C–4°C (Capron et al., 2017), whiletemperature in the tropics changed little (Marcott et al., 2013). Both HTM and LIG experienced atmospheric CO2 levels similar topre-industrial conditions (Masson-Delmotte et al. 2013). During the MPWP, the most recent time period when CO2 concentrations
were similar to present-day levels, the global temperature was >1°C and Arctic temperatures about 8°C warmer than pre-industrial(Brigham-Grette et al., 2013).
Although imperfect as analogues for the future, these regional changes can inform risk assessments such as the potential forcrossing irreversible thresholds or amplifying anthropogenic changes (Box 3.3, Figure 1). For example, HTM and LIG greenhouse gas(GHG) concentrations show no evidence of runaway greenhouse gas releases under limited global warming. Transient releases of
CO2 and CH4 may follow permafrost melting, but these occurrences may be compensated by peat growth over longer time scales (Yuet al., 2010). Warming may release CO2 by enhancing soil respiration, counteracting CO2 fertilization of plant growth (Frank et al.,2010). Evidence of a collapse of the Atlantic Meridional Overturning Circulation (AMOC) during these past events of limited global
warming could not be found (Galaasen et al., 2014).

3

The distribution of ecosystems and biomes (major ecosystem types) changed significantly during past warming events, both inthe ocean and on land. For example, some tropical and temperate forests retreated because of increased aridity, while savannas
expanded (Dowsett et al., 2016). Further, poleward shifts of marine and terrestrial ecosystems, upward shifts in alpine regions, andreorganizations of marine productivity during past warming events are recorded in natural archives (Williams et al., 2009; Haywood
et al., 2016). Finally, past warming events are associated with partial sea ice loss in the Arctic. The limited amount of data collected
so far on Antarctic sea ice precludes firm conclusions about Southern Hemisphere sea ice losses (de Vernal et al., 2013).
Reconstructed global sea level rise of 6–9 m during the LIG and possibly >6 m during the MPWP requires a retreat of either theGreenland or Antarctic ice sheets or both (Dutton et al., 2015). While ice sheet and climate models suggest a substantial retreatof the West Antarctic ice sheet (WAIS) and parts of the East Antarctic ice sheet (DeConto and Pollard, 2016) during these periods,
direct observational evidence is still lacking. Evidence for ice retreat in Greenland is stronger, although a complete collapse of theGreenland ice sheet during the LIG can be excluded (Dutton et al., 2015). Rates of past sea level rises under modest warming weresimilar to or up to two times larger than rises observed over the past two decades (Kopp et al., 2013). Given the long time scales
required to reach equilibrium in a warmer world, sea level rise will likely continue for millennia even if warming is limited to 2°C.
Finally, temperature reconstructions from these past warm intervals suggest that current climate models underestimate regional
warming at high latitudes (polar amplification) and long-term (multi-millennial) global warming. None of these past warm climate
episodes involved the high rate of change in atmospheric CO2 and temperatures that we are experiencing today (Fischer et al., 2018).

208

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Chapter 3

Box 3.3 (continued)
Arctic sea ice:
HTM: reduced
LIG: reduced
MPWP: reduced

GIS:
HTM: deglacial reequilibration
LIG: partial retreat
MPWP: smaller

boreal forests:
HTM: northward expansion
LIG: expansion
MPWP: northward expansion

Savanna:
HTM: expansion
LIG: expansion likely
MPWP: expansion

marine ecosystems:
HTM: rather unchanged
LIG: poleward shift
MPWP: poleward shift

Antarctic sea ice:
HTM: limited evidence
LIG: reduced
MPWP: reduced
WAIS
HTM: deglacial reequilibration
LIG: partial retreat likely
MPWP: retreat likely

marine ecosystems:
HTM: rather unchanged
LIG: poleward shift
MPWP: poleward shift

3

EAIS:
HTM: deglacial reequilibration
LIG: partial retreat possible
MPWP: partial retreat possible

Box 3.3, Figure 1 | Impacts and responses of components of the Earth System. Summary of typical changes found for warmer periods in the paleorecord, as discussed
by Fischer et al. (2018). All statements are relative to pre-industrial conditions. Statements in italics indicate that no conclusions can be drawn for the future. Note thatsignificant spatial variability and uncertainty exists in the assessment of each component, and this figure therefore should not be referred to without reading the
publication in detail. HTM: Holocene Thermal Maximum, LIG: Last Interglacial, MPWP: Mid Pliocene Warm Period. (Adapted from Fischer et al., 2018).

3.3.10Ocean Chemistry

Ocean chemistry includes pH, salinity, oxygen, CO2, and a range of otherions and gases, which are in turn affected by precipitation, evaporation,
storms, river runoff, coastal erosion, up-welling, ice formation, and theactivities of organisms and ecosystems (Stocker et al., 2013). Ocean
chemistry is changing alongside increasing global temperature, with
impacts projected at 1.5°C and, more so, at 2°C of global warming(Doney et al., 2014) (medium to high confidence). Projected changes in
the upper layers of the ocean include altered pH, oxygen content andsea level. Despite its many component processes, ocean chemistry has
been relatively stable for long periods of time prior to the industrial
period (Hönisch et al., 2012). Ocean chemistry is changing under the
influence of human activities and rising greenhouse gases (virtuallycertain; Rhein et al., 2013; Stocker et al., 2013). About 30% of CO2
emitted by human activities, for example, has been absorbed by
the upper layers of the ocean, where it has combined with water toproduce a dilute acid that dissociates and drives ocean acidification(high confidence) (Cao et al., 2007; Stocker et al., 2013). Ocean pH has
decreased by 0.1 pH units since the pre-industrial period, a shift thatis unprecedented in the last 65 Ma (high confidence) (Ridgwell andSchmidt, 2010) or even 300 Ma of Earth’s history (medium confidence)(Hönisch et al., 2012).
Ocean acidification is a result of increasing CO2 in the atmosphere(very high confidence) and is most pronounced where temperatures
are lowest (e.g., polar regions) or where CO2-rich water is brought tothe ocean surface by upwelling (Feely et al., 2008). Acidification canalso be influenced by effluents from natural or disturbed coastal landuse (Salisbury et al., 2008), plankton blooms (Cai et al., 2011), andthe atmospheric deposition of acidic materials (Omstedt et al., 2015).
These sources may not be directly attributable to climate change,
but they may amplify the impacts of ocean acidification (Bates andPeters, 2007; Duarte et al., 2013). Ocean acidification also influences
the ionic composition of seawater by changing the organic andinorganic speciation of trace metals (e.g., 20-fold increases in free ion
209Chapter 3

Impacts of 1.5°C of Global Warming on Natural and Human Systems

concentrations of metals such as aluminium) – with changes expected
to have impacts although they are currently poorly documented andunderstood (low confidence) (Stockdale et al., 2016).
Oxygen varies regionally and with depth; it is highest in polar regions
and lowest in the eastern basins of the Atlantic and Pacific Oceans andin the northern Indian Ocean (Doney et al., 2014; Karstensen et al.,2015; Schmidtko et al., 2017). Increasing surface water temperatures
have reduced oxygen in the ocean by 2% since 1960, with othervariables such as ocean acidification, sea level rise, precipitation, windand storm patterns playing roles (Schmidtko et al., 2017). Changes
to ocean mixing and metabolic rates, due to increased temperatureand greater supply of organic carbon to deep areas, has increased thefrequency of ‘dead zones’, areas where oxygen levels are so low thatthey no longer support oxygen dependent life (Diaz and Rosenberg,2008). The changes are complex and include both climate change andother variables (Altieri and Gedan, 2015), and are increasing in tropicalas well as temperate regions (Altieri et al., 2017).

3

Ocean salinity is changing in directions that are consistent withsurface temperatures and the global water cycle (i.e., precipitation
versus evaporation). Some regions, such as northern oceans and theArctic, have decreased in salinity, owing to melting glaciers and ice
sheets, while others have increased in salinity, owing to higher sea
surface temperatures and evaporation (Durack et al., 2012). Thesechanges in salinity (i.e., density) are also potentially contributing tolarge-scale changes in water movement (Section 3.3.8).

3.3.11

Global Synthesis

Table 3.2 features a summary of the assessments of global andregional climate changes and associated hazards described in thischapter, based on the existing literature. For more details about
observation and attribution in ocean and cryosphere systems,
please refer to the upcoming IPCC Special Report on the Ocean and
Cryosphere in a Changing Climate (SROCC) due to be released in
2019.

Table 3.2 | Summary of assessments of global and regional climate changes and associated hazards. Confidence and likelihood statements are quoted from the relevant
chapter text and are omitted where no assessment was made, in which case the IPCC Fifth Assessment Report (AR5) assessment is given where available.
GMST: global mean surface temperature, AMOC: Atlantic Meridional Overturning Circulation, GMSL: global mean sea level.

Observed change(recent past versus
pre-industrial)

GMST
anomaly

GMST anomalies were 0.87°C(±0.10°C likely range) above
pre-industrial (1850–1900)
values in the 2006–2015
decade, with a recent warmingof about 0.2°C (±0.10°C) per
decade (high confidence)[Chapter 1]

Attribution of observedchange to humaninduced forcing(present-day versus
pre-industrial)

Projected change
at 1.5°C of global
warming compared
to pre-industrial(1.5°C versus 0°C)

Projected change
at 2°C of global
warming compared
to pre-industrial(2°C versus 0°C)

Differences between
2°C and 1.5°C of
global warming

The observed 0.87°C GMST
increase in the 2006–2015
decade compared topre-industrial (1850–1900)
conditions was mostly humaninduced (high confidence)

1.5°C

2°C

0.5°C

Global-scale increased intensityand frequency of hot days
and nights, and decreased
intensity and frequency of cold
days and nights (very likely)

Global-scale increased intensityand frequency of hot days
and nights, and decreased
intensity and frequency of cold
days and nights (very likely)

Global-scale increased intensityand frequency of hot days andnights, and decreased intensityand frequency of cold days
and nights (high confidence)

Warming of temperature
extremes highest over land,
including many inhabitedregions (high confidence), withincreases of up to 3°C in the
mid-latitude warm season and
up to 4.5°C in the high-latitude
cold season (high confidence)

Warming of temperature
extremes highest over land,
including many inhabitedregions (high confidence), withincreases of up to 4°C in the
mid-latitude warm season and
up to 6°C in the high-latitude
cold season (high confidence)

Global-scale increase inlength of warm spells anddecrease in length of cold
spells (high confidence)

Largest increase infrequency of unusually
hot extremes in tropicalregions (high confidence)

Largest increase infrequency of unusually
hot extremes in tropicalregions (high confidence)

Particularly large increases
in hot extremes in inhabitedregions (high confidence)[Section 3.3.2][Section 3.3.2][Section 3.3.2]

Human-induced warming
reached about 1°C (±0.2°C
likely range) above preindustrial levels in 2017
[Chapter 1]

Overall decrease in thenumber of cold days andnights and overall increasein the number of warm days
and nights at the global
scale on land (very likely)

Temperature
extremes

Continental-scale increase inintensity and frequency of hotdays and nights, and decrease
in intensity and frequency
of cold days and nights, inNorth America, Europe andAustralia (very likely)
Increases in frequency or
duration of warm spell lengths
in large parts of Europe, Asia
and Australia (high confidence(likely)), as well as at the global
scale (medium confidence)[Section 3.3.2]

210

Anthropogenic forcing has
contributed to the observedchanges in frequency andintensity of daily temperature
extremes on the global
scale since the mid-20th
century (very likely)[Section 3.3.2]

Strongest increase infrequency for the rarest
and most extreme events(high confidence)Impacts of 1.5°C of Global Warming on Natural and Human Systems

Chapter 3

Table 3.2 (continued)

Observed change(recent past versus
pre-industrial)
More areas with increases thandecreases in the frequency,
intensity and/or amount ofheavy precipitation (likely)[Section 3.3.3]

Heavy
precipitation

Attribution of observedchange to humaninduced forcing(present-day versus
pre-industrial)

Projected change
at 1.5°C of global
warming compared
to pre-industrial(1.5°C versus 0°C)

Projected change
at 2°C of global
warming compared
to pre-industrial(2°C versus 0°C)

Differences between
2°C and 1.5°C of
global warming

Human influence contributed to the global-scaletendency towards increases in
the frequency, intensity and/or
amount of heavy precipitation
events (medium confidence)

Increases in frequency,
intensity and/or amountheavy precipitation when
averaged over global land,with positive trends in several
regions (high confidence)

Increases in frequency,
intensity and/or amountheavy precipitation when
averaged over global land,with positive trends in several
regions (high confidence)

Higher frequency, intensity
and/or amount of heavy
precipitation when averaged
over global land, with positive
trends in several regions(medium confidence)[Section 3.3.3; AR5 Chapter
10 (Bindoff et al., 2013a)][Section 3.3.3][Section 3.3.3]

Several regions are projected
to experience increases
in heavy precipitation at
2°C versus 1.5°C (medium
confidence), in particular in
high-latitude and mountainous
regions, as well as in eastern
Asia and eastern North
America (medium confidence)[Section 3.3.3]

High confidence in dryness
trends in some regions,especially drying in the Mediterranean region (including
southern Europe, northern
Africa and the Near East)
Low confidence in droughtand dryness trends at
the global scale
Drought anddryness[Section 3.3.4]

Medium confidence inattribution of drying
trends in southern Europe(Mediterranean region)
Low confidence elsewhere, inpart due to large interannual
variability and longer duration(and thus lower frequency) of
drought events, as well as todependency on the dryness
index definition applied[Section 3.3.4]

Streamflow trends mostly
not statistically significant(high confidence)
Runoff andriver flooding

Not assessed in this report

Increase in flood frequency andextreme streamflow in someregions (high confidence)[Section 3.3.5]
Low confidence inthe robustness ofobserved changes[Section 3.3.6]

Tropical andextra-tropical
cyclones

Not meaningful to assess given
low confidence in changes,
due to large interannual
variability, heterogeneityof the observational recordand contradictory findings
regarding trends in theobservational record

Medium confidence instronger drying trends in
the Mediterranean region
and Southern Africa

Medium confidence
in drying trends in the
Mediterranean region

Medium confidence in drying
trends in the Mediterranean
region and Southern Africa

Low confidence elsewhere, inpart due to large interannual
variability and longer duration(and thus lower frequency) of
drought events, as well as todependency on the dryness
index definition applied

Low confidence elsewhere, inpart due to large interannual
variability and longer duration(and thus lower frequency) of
drought events, as well as todependency on the dryness
index definition applied

Increases in drought, dryness
or precipitation deficits
projected in some regions
compared to the pre-industrial
or present-day conditions,but substantial variability
in signals depending on
considered indices or climate
model (medium confidence)

Increases in drought, dryness
or precipitation deficits
projected in some regions
compared to the pre-industrial
or present-day conditions,but substantial variability
in signals depending on
considered indices or climate
model (medium confidence).

[Section 3.3.4][Section 3.3.4]

Expansion of the global landarea with a significant increasein runoff (medium confidence)

Expansion of the global landarea with a significant increasein runoff (medium confidence)

Expansion of the global landarea with significant increasein runoff (medium confidence)

Increase in flood
hazard in some regions(medium confidence)

Increase in flood
hazard in some regions(medium confidence)

Expansion in the area
affected by flood hazard(medium confidence)

Low confidence elsewhere, inpart due to large interannual
variability and longer duration(and thus lower frequency) of
drought events, as well as todependency on the dryness
index definition applied[Section 3.3.4][Section 3.3.5][Section 3.3.5][Section 3.3.5]

Increases in heavy precipitation
associated with tropical
cyclones (medium confidence)

Further increases in heavy
precipitation associatedwith tropical cyclones(medium confidence)

Heavy precipitation associatedwith tropical cyclones is
projected to be higher at
2°C compared to 1.5°C
global warming (medium
confidence). Limited evidence
that the global number oftropical cyclones will be lower
under 2°C of global warming
compared to under 1.5°C ofwarming, but an increase in
the number of very intense
cyclones (low confidence)

211

3

Chapter 3

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Table 3.2 (continued)
Observed change(recent past versus
pre-industrial)

Ocean
circulation andtemperature

Attribution of observedchange to humaninduced forcing(present-day versus
pre-industrial)

Observed warming of theupper ocean, with slightly
lower rates than global
warming (virtually certain)

Limited evidence attributing
the weakening of AMOC
in recent decades toanthropogenic forcing

Increased occurrence of marine
heatwaves (high confidence)[Section 3.3.7]

Projected change
at 1.5°C of global
warming compared
to pre-industrial(1.5°C versus 0°C)

Projected change
at 2°C of global
warming compared
to pre-industrial(2°C versus 0°C)

Differences between
2°C and 1.5°C of
global warming

Further increases in ocean temperatures, including more frequent marine heatwaves (high confidence)
AMOC will weaken over the 21st century and substantially so under high levels (more than 2°C) of
global warming (very likely)[Section 3.3.7]

AMOC has been weakening
over recent decades(more likely than not)[Section 3.3.7]

Sea iceContinuing the trends reported
in AR5, the annual Arctic sea
ice extent decreased overthe period 1979–2012. The
rate of this decrease wasvery likely between 3.5 and4.1% per decade (0.45 to
0.51 million km2 per decade)

Anthropogenic forcings arevery likely to have contributed
to Arctic sea ice loss since 1979
[AR5 Chapter 10(Bindoff et al., 2013a)]

Sea level

It is likely that the rate of
GMSL rise has continued toincrease since the early 20th
century, with estimates that
range from 0.000 [–0.002
to 0.002] mm yr–2 to 0.013[0.007 to 0.019] mm yr–2

At least one sea-ice-free
Arctic summer after about
10 years of stabilized warming(medium confidence)

Probability of sea-ice-free
Arctic summer greatly reduced
at 1.5°C versus 2°C of global
warming (medium confidence)[Section 3.3.8][Section 3.3.8][Section 3.3.8]

Intermediate temperature overshoot has no long-term consequences for Arctic sea ice cover(high confidence)[AR5 Chapter 4 (Vaughan
et al., 2013)]

3

At least one sea-ice-free Arctic
summer after about 100 yearsof stabilized warming(medium confidence)[3.3.8]It is very likely that there isa substantial contribution
from anthropogenic forcings
to the global mean sea
level rise since the 1970s

Not assessed in this report

Not assessed in this report

GMSL rise will be about
0.1 m (0.00–0.20 m) lessat 1.5°C versus 2°C global
warming (medium confidence)[Section 3.3.9][AR5 Chapter 10 (Bindoff
et al., 2013a)][AR5 Chapter 13(Church et al., 2013)]

Ocean
chemistry

Ocean acidification due toincreased CO2 has resulted ina 0.1 pH unit decrease since
the pre-industrial period, whichis unprecedented in the last
65 Ma (high confidence)

The oceanic uptake ofanthropogenic CO2 has resulted
in acidification of surface
waters (very high confidence).

Ocean chemistry is changing with global temperature increases, with impacts
projected at 1.5°C and, more so, at 2°C of warming (high confidence)[Section 3.3.10][Section 3.3.10][Section 3.3.10]

3.4

Observed Impacts and Projected Risks
in Natural and Human Systems

3.4.1

Introduction

In Section 3.4, new literature is explored and the assessment of impacts
and projected risks is updated for a large number of natural andhuman systems. This section also includes an exploration of adaptation
opportunities that could be important steps towards reducing climatechange, thereby laying the ground for later discussions on opportunities
to tackle both mitigation and adaptation while at the same time
recognising the importance of sustainable development and reducing
the inequities among people and societies facing climate change.
Working Group II (WGII) of the IPCC Fifth Assessment Report (AR5)provided an assessment of the literature on the climate risk for naturaland human systems across a wide range of environments, sectors
and greenhouse gas scenarios, as well as for particular geographic
212

regions (IPCC, 2014a, b). The comprehensive assessment undertaken
by AR5 evaluated the evidence of changes to natural systems, andthe impact on human communities and industry. While impacts varied
substantially among systems, sectors and regions, many changes
over the past 50 years could be attributed to human driven climatechange and its impacts. In particular, AR5 attributed observed impacts
in natural ecosystems to anthropogenic climate change, includingchanges in phenology, geographic and altitudinal range shifts in floraand fauna, regime shifts and increased tree mortality, all of which can
reduce ecosystem functioning and services thereby impacting people.
AR5 also reported increasing evidence of changing patterns of disease
and invasive species, as well as growing risks for communities andindustry, which are especially important with respect to sea level riseand human vulnerability.
One of the important themes that emerged from AR5 is that previous
assessments may have under-estimated the sensitivity of natural andhuman systems to climate change. A more recent analysis of attribution

Impacts of 1.5°C of Global Warming on Natural and Human Systems

to greenhouse gas forcing at the global scale (Hansen and Stone,2016) confirmed that many impacts related to changes in regional
atmospheric and ocean temperature can be confidently attributed toanthropogenic forcing, while attribution to anthropogenic forcing ofchanges related to precipitation are by comparison less clear. Moreover,there is no strong direct relationship between the robustness of climate
attribution and that of impact attribution (Hansen and Stone, 2016).
The observed changes in human systems are amplified by the lossof ecosystem services (e.g., reduced access to safe water) that aresupported by biodiversity (Oppenheimer et al., 2014). Limited researchon the risks of warming of 1.5°C and 2°C was conducted following
AR5 for most key economic sectors and services, for livelihoods andpoverty, and for rural areas. For these systems, climate is one of many
drivers that result in adverse outcomes. Other factors include patternsof demographic change, socio-economic development, trade andtourism. Further, consequences of climate change for infrastructure,
tourism, migration, crop yields and other impacts interact with
underlying vulnerabilities, such as for individuals and communities
engaged in pastoralism, mountain farming and artisanal fisheries, toaffect livelihoods and poverty (Dasgupta et al., 2014).
Incomplete data and understanding of these lower-end climate
scenarios have increased the need for more data and an improved
understanding of the projected risks of warming of 1.5°C and 2°C forreference. In this section, the available literature on the projected risks,
impacts and adaptation options is explored, supported by additional
information and background provided in Supplementary Material
3.SM.3.1, 3.SM.3.2, 3.SM.3.4, and 3.SM.3.5. A description of the main
assessment methods of this chapter is given in Section 3.2.2.

3.4.2

Freshwater Resources (Quantity and Quality)

3.4.2.1 Water availability
Working Group II of AR5 concluded that about 80% of the world’s
population already suffers from serious threats to its water security, as
measured by indicators including water availability, water demand andpollution (Jiménez Cisneros et al., 2014). UNESCO (2011) concluded
that climate change can alter the availability of water and threaten
water security.
Although physical changes in streamflow and continental runoff thatare consistent with climate change have been identified (Section
3.3.5), water scarcity in the past is still less well understood because
the scarcity assessment needs to take into account various factors, suchas the operations of water supply infrastructure and human water use
behaviour (Mehran et al., 2017), as well as green water, water quality
and environmental flow requirements (J. Liu et al., 2017). Over the past
century, substantial growth in populations, industrial and agriculturalactivities, and living standards have exacerbated water stress in many
parts of the world, especially in semi-arid and arid regions such as
California in the USA (AghaKouchak et al., 2015; Mehran et al., 2015).
Owing to changes in climate and water consumption behaviour, andparticularly effects of the spatial distribution of population growth
relative to water resources, the population under water scarcity
increased from 0.24 billion (14% of the global population) in the
1900s to 3.8 billion (58%) in the 2000s. In that last period (2000s), 1.1

Chapter 3billion people (17% of the global population) who mostly live in Southand East Asia, North Africa and the Middle East faced serious water
shortage and high water stress (Kummu et al., 2016).
Over the next few decades, and for increases in global mean
temperature less than about 2°C, AR5 concluded that changes in
population will generally have a greater effect on water resource
availability than changes in climate. Climate change, however, willregionally exacerbate or offset the effects of population pressure(Jiménez Cisneros et al., 2014).
The differences in projected changes to levels of runoff under 1.5°C
and 2°C of global warming, particularly those that are regional, aredescribed in Section 3.3.5. Constraining warming to 1.5°C insteadof 2°C might mitigate the risks for water availability, althoughsocio-economic drivers could affect water availability more than therisks posed by variation in warming levels, while the risks are nothomogeneous among regions (medium confidence) (Gerten et al.,2013; Hanasaki et al., 2013; Arnell and Lloyd-Hughes, 2014; Schewe etal., 2014; Karnauskas et al., 2018). Assuming a constant population in
the models used in his study, Gerten et al. (2013) determined that anadditional 8% of the world population in 2000 would be exposed to
new or aggravated water scarcity at 2°C of global warming. This value
was almost halved – with 50% greater reliability – when warming wasconstrained to 1.5°C. People inhabiting river basins, particularly in the
Middle East and Near East, are projected to become newly exposed
to chronic water scarcity even if global warming is constrained toless than 2°C. Many regions, especially those in Europe, Australia
and southern Africa, appear to be affected at 1.5°C if the reduction
in water availability is computed for non-water-scarce basins as well
as for water-scarce regions. Out of a contemporary population of
approximately 1.3 billion exposed to water scarcity, about 3% (North
America) to 9% (Europe) are expected to be prone to aggravatedscarcity at 2°C of global warming (Gerten et al., 2013). Under the
Shared Socio-Economic Pathway (SSP)2 population scenario, about 8%of the global population is projected to experience a severe reduction
in water resources under warming of 1.7°C in 2021–2040, increasing
to 14% of the population under 2.7°C in 2043–2071, based on thecriteria of discharge reduction of either >20% or >1 standard deviation(Schewe et al., 2014). Depending on the scenarios of SSP1–5, exposure
to the increase in water scarcity in 2050 will be globally reduced by
184–270 million people at about 1.5°C of warming compared to theimpacts at about 2°C. However, the variation between socio-economic
levels is larger than the variation between warming levels (Arnell and
Lloyd-Hughes, 2014).
On many small islands (e.g., those constituting SIDS), freshwater stressis expected to occur as a result of projected aridity change. Constraining
warming to 1.5°C, however, could avoid a substantial fraction ofwater stress compared to 2°C, especially across the Caribbean region,
particularly on the island of Hispaniola (Dominican Republic and Haiti)(Karnauskas et al., 2018). Hanasaki et al. (2013) concluded that the
projected range of changes in global irrigation water withdrawal(relative to the baseline of 1971–2000), using human configuration
fixing non-meteorological variables for the period around 2000, are
1.1–2.3% and 0.6–2.0% lower at 1.5°C and 2°C, respectively. In thesame study, Hanasaki et al. (2013) highlighted the importance of water
213

3

Chapter 3

use scenarios in water scarcity assessments, but neither quantitative
nor qualitative information regarding water use is available.
When the impacts on hydropower production at 1.5°C and 2°C arecompared, it is found that mean gross potential increases in northern,eastern and western Europe, and decreases in southern Europe (Jacobet al., 2018; Tobin et al., 2018). The Baltic and Scandinavian countries
are projected to experience the most positive impacts on hydropower
production. Greece, Spain and Portugal are expected to be the mostnegatively impacted countries, although the impacts could be reducedby limiting warming to 1.5°C (Tobin et al., 2018). In Greece, Spain andPortugal, warming of 2°C is projected to decrease hydropower potential
below 10%, while limiting global warming to 1.5°C would keep the
reduction to 5% or less. There is, however, substantial uncertainty
associated with these results due to a large spread between the
climate models (Tobin et al., 2018).

3

Due to a combination of higher water temperatures and reduced
summer river flows, the usable capacity of thermoelectric power plants
using river water for cooling is expected to reduce in all European
countries (Jacob et al., 2018; Tobin et al., 2018), with the magnitudeof decreases being about 5% for 1.5°C and 10% for 2°C of global
warming for most European countries (Tobin et al., 2018). Greece,
Spain and Bulgaria are projected to have the largest reduction at 2°C
of warming (Tobin et al., 2018).
Fricko et al. (2016) assessed the direct water use of the global energy
sector across a broad range of energy system transformation pathways
in order to identify the water impacts of a 2°C climate policy. This
study revealed that there would be substantial divergence in water
withdrawal for thermal power plant cooling under conditions in which
the distribution of future cooling technology for energy generation isfixed, whereas adopting alternative cooling technologies and water
resources would make the divergence considerably smaller.

3.4.2.2 Extreme hydrological events (floods and droughts)
Working Group II of AR5 concluded that socio-economic losses fromflooding since the mid-20th century have increased mainly becauseof greater exposure and vulnerability (high confidence) (Jiménez
Cisneros et al., 2014). There was low confidence due to limited
evidence, however, that anthropogenic climate change has affected
the frequency and magnitude of floods. WGII AR5 also concluded that
there is no evidence that surface water and groundwater drought
frequency has changed over the last few decades, although impactsof drought have increased mostly owing to increased water demand(Jiménez Cisneros et al., 2014).
Since AR5, the number of studies related to fluvial flooding andmeteorological drought based on long-term observed data has been
gradually increasing. There has also been progress since AR5 in
identifying historical changes in streamflow and continental runoff(Section 3.3.5). As a result of population and economic growth,
increased exposure of people and assets has caused more damage
due to flooding. However, differences in flood risks among regions
reflect the balance among the magnitude of the flood, the populations,
their vulnerabilities, the value of assets affected by flooding, and the214

Impacts of 1.5°C of Global Warming on Natural and Human Systems

capacity to cope with flood risks, all of which depend on socio-economic
development conditions, as well as topography and hydro-climaticconditions (Tanoue et al., 2016). AR5 concluded that there was low
confidence in the attribution of global changes in droughts (Bindoff etal., 2013b). However, recent publications based on observational andmodelling evidence assessed that human emissions have substantially
increased the probability of drought years in the Mediterranean region(Section 3.3.4).
WGII AR5 assessed that global flood risk will increase in the future,
partly owing to climate change (low to medium confidence), withprojected changes in the frequency of droughts longer than 12 months
being more uncertain because of their dependence on accumulated
precipitation over long periods (Jiménez Cisneros et al., 2014).
Increases in the risks associated with runoff at the global scale(medium confidence), and in flood hazard in some regions (medium
confidence), can be expected at global warming of 1.5°C, with an
overall increase in the area affected by flood hazard at 2°C (medium
confidence) (Section 3.3.5). There are studies, however, that indicatethat socio-economic conditions will exacerbate flood impacts
more than global climate change, and that the magnitude of these
impacts could be larger in some regions (Arnell and Lloyd-Hughes,
2014; Winsemius et al., 2016; Alfieri et al., 2017; Arnell et al., 2018;
Kinoshita et al., 2018). Assuming constant population sizes, countries
representing 73% of the world population will experience increasing
flood risk, with an average increase of 580% at 4°C compared to theimpact simulated over the baseline period 1976–2005. This impact
is projected to be reduced to a 100% increase at 1.5°C and a 170%
increase at 2°C (Alfieri et al., 2017). Alfieri et al. (2017) additionallyconcluded that the largest increases in flood risks would be found in
the US, Asia, and Europe in general, while decreases would be found in
only a few countries in eastern Europe and Africa. Overall, Alfieri et al.
(2017) reported that the projected changes are not homogeneously
distributed across the world land surface. Alfieri et al. (2018) studied
the population affected by flood events using three case studies in
European states, specifically central and western Europe, and foundthat the population affected could be limited to 86% at 1.5°C ofwarming compared to 93% at 2°C. Under the SSP2 population
scenario, Arnell et al. (2018) found that 39% (range 36–46%) of
impacts on populations exposed to river flooding globally could be
avoided at 1.5°C compared to 2°C of warming.
Under scenarios SSP1–5, Arnell and Lloyd-Hughes (2014) foundthat the number of people exposed to increased flooding in 2050
under warming of about 1.5°C could be reduced by 26–34 million
compared to the number exposed to increased flooding associatedwith 2°C of warming. Variation between socio-economic levels,
however, is projected to be larger than variation between the twolevels of global warming. Kinoshita et al. (2018) found that a serious
increase in potential flood fatality (5.7%) is projected without any
adaptation if global warming increases from 1.5°C to 2°C, whereas
the projected increase in potential economic loss (0.9%) is relatively
small. Nevertheless, their study indicates that socio-economic changes
make a larger contribution to the potentially increased consequencesof future floods, and about half of the increase in potential economic
losses could be mitigated by autonomous adaptation.

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There is limited information about the global and regional
projected risks posed by droughts at 1.5°C and 2°C of global
warming. However, hazards by droughts at 1.5°C could be reduced
compared to the hazards at 2°C in some regions, in particular in the
Mediterranean region and southern Africa (Section 3.3.4). Under
constant socio-economic conditions, the population exposed todrought at 2°C of warming is projected to be larger than at 1.5°C(low to medium confidence) (Smirnov et al., 2016; Sun et al., 2017;
Arnell et al., 2018; Liu et al., 2018). Under the same scenario, theglobal mean monthly number of people expected to be exposed to
extreme drought at 1.5°C in 2021–2040 is projected to be 114.3
million, compared to 190.4 million at 2°C in 2041–2060 (Smirnov etal., 2016). Under the SSP2 population scenario, Arnell et al. (2018)
projected that 39% (range 36–51%) of impacts on populations
exposed to drought could be globally avoided at 1.5°C comparedto 2°C warming.

3.4.2.3 Groundwater

Liu et al. (2018) studied the changes in population exposure to severe
droughts in 27 regions around the globe for 1.5°C and 2°C of warming
using the SSP1 population scenario compared to the baseline periodof 1986–2005 based on the Palmer Drought Severity Index (PDSI).
They concluded that the drought exposure of urban populations inmost regions would be decreased at 1.5°C (350.2 ± 158.8 million
people) compared to 2°C (410.7 ± 213.5 million people). Liu et al.
(2018) also suggested that more urban populations would be exposed
to severe droughts at 1.5°C in central Europe, southern Europe, theMediterranean, West Africa, East and West Asia, and Southeast Asia,and that number of affected people would increase further in theseregions at 2°C. However, it should be noted that the PDSI is known
to have limitations (IPCC SREX, Seneviratne et al., 2012), and drought
projections strongly depend on considered indices (Section 3.3.4); thus
only medium confidence is assigned to these projections. In the Haihe
River basin in China, a study has suggested that the proportion of the
population exposed to droughts is projected to be reduced by 30.4%
at 1.5°C but increased by 74.8% at 2°C relative to the baseline valueof 339.65 million people in the 1986–2005 period, when assessingchanges in droughts using the Standardized Precipitation-Evaporation
Index, using a Penman–Monteith estimate of potential evaporation(Sun et al., 2017) .

In some regions, groundwater is often intensively used to supplement
the excess demand, often leading to groundwater depletion. Climatechange adds further pressure on water resources and exaggerates
human water demands by increasing temperatures over agriculturallands (Wada et al., 2017). Very few studies have projected the risks ofgroundwater depletion under 1.5°C and 2°C of global warming. Under
2°C of warming, impacts posed on groundwater are projected to begreater than at 1.5°C (low confidence) (Portmann et al., 2013; Salem
et al., 2017).

Alfieri et al. (2018) estimated damage from flooding in Europe forthe baseline period (1976–2005) at 5 billion euro of losses annually,
with projections of relative changes in flood impacts that will rise withwarming levels, from 116% at 1.5°C to 137% at 2°C.

Working Group II of AR5 concluded that most observed changes towater quality from climate change are from isolated studies, mostlyof rivers or lakes in high-income countries, using a small number ofvariables (Jiménez Cisneros et al., 2014). AR5 assessed that climatechange is projected to reduce raw water quality, posing risks todrinking water quality with conventional treatment (medium to highconfidence) (Jiménez Cisneros et al., 2014).

Kinoshita et al. (2018) studied the increase of potential economic loss
under SSP3 and projected that the smaller loss at 1.5°C comparedto 2°C (0.9%) is marginal, regardless of whether the vulnerability isfixed at the current level or not. By analysing the differences in resultswith and without flood protection standards, Winsemius et al. (2016)
showed that adaptation measures have the potential to greatly reduce
present-day and future flood damage. They concluded that increases inflood-induced economic impacts (% gross domestic product, GDP) in
African countries are mainly driven by climate change and that Africa’s
growing assets would become increasingly exposed to floods. Hence,
there is an increasing need for long-term and sustainable investments
in adaptation in Africa.

Working Group II of AR5 concluded that the detection of changes ingroundwater systems, and attribution of those changes to climaticchanges, are rare, owing to a lack of appropriate observation wells
and an overall small number of studies (Jiménez Cisneros et al., 2014).
Since AR5, the number of studies based on long-term observed data
continues to be limited. The groundwater-fed lakes in northeastern
central Europe have been affected by climate and land-use changes,
and they showed a predominantly negative lake-level trend in 1999–
2008 (Kaiser et al., 2014).
WGII AR5 concluded that climate change is projected to reducegroundwater resources significantly in most dry subtropical regions(high confidence) (Jiménez Cisneros et al., 2014).

Portmann et al. (2013) indicated that 2% (range 1.1–2.6%) of the
global land area is projected to suffer from an extreme decrease inrenewable groundwater resources of more than 70% at 2°C, with aclear mitigation at 1.5°C. These authors also projected that 20% of
the global land surface would be affected by a groundwater reductionof more than 10% at 1.5°C of warming, with the percentage of land
impacted increasing at 2°C. In a groundwater-dependent irrigatedregion in northwest Bangladesh, the average groundwater level during
the major irrigation period (January–April) is projected to decrease inaccordance with temperature rise (Salem et al., 2017).

3.4.2.4 Water quality

Since AR5, studies have detected climate change impacts on several
indices of water quality in lakes, watersheds and regions (e.g., Patiño
et al., 2014; Aguilera et al., 2015; Watts et al., 2015; Marszelewski
and Pius, 2016; Capo et al., 2017). The number of studies utilising
RCP scenarios at the regional or watershed scale have gradually
increased since AR5 (e.g., Boehlert et al., 2015; Teshager et al., 2016;
Marcinkowski et al., 2017). Few studies, have explored projected
impacts on water quality under 1.5°C versus 2°C of warming,
however, the differences are unclear (low confidence) (Bonte and215

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3

Zwolsman, 2010; Hosseini et al., 2017). The daily probability ofexceeding the chloride standard for drinking water taken from Lake
IJsselmeer (Andijk, the Netherlands) is projected to increase bya factor of about five at 2°C relative to the present-day warming
level of 1°C since 1990 (Bonte and Zwolsman, 2010). Mean monthly
dissolved oxygen concentrations and nutrient concentrations in
the upper Qu’Appelle River (Canada) in 2050–2055 are projected
to decrease less at about 1.5°C of warming (RCP2.6) compared toconcentrations at about 2°C (RCP4.5) (Hosseini et al., 2017). In threeriver basins in Southeast Asia (Sekong, Sesan and Srepok), about 2°C
of warming (corresponding to a 1.05°C increase in the 2030s relative
to the baseline period 1981–2008, RCP8.5), impacts posed by landuse change on water quality are projected to be greater than at 1.5°C(corresponding to a 0.89°C increase in the 2030s relative to thebaseline period 1981–2008, RCP4.5) (Trang et al., 2017). Under thesame warming scenarios, Trang et al. (2017) projected changes in theannual nitrogen (N) and phosphorus (P) yields in the 2030s, as well aswith combinations of two land-use change scenarios: (i) conversionof forest to grassland, and (ii) conversion of forest to agriculturalland. The projected changes in N (P) yield are +7.3% (+5.1%) under
a 1.5°C scenario and –6.6% (–3.6%) under 2°C, whereas changes
under the combination of land-use scenarios are (i) +5.2% (+12.6%)
at 1.5°C and +8.8% (+11.7%) at 2°C, and (ii) +7.5% (+14.9%) at
1.5°C and +3.7% (+8.8%) at 2°C (Trang et al., 2017).

3.4.2.5 Soil erosion and sediment load
Working Group II of AR5 concluded that there is little or noobservational evidence that soil erosion and sediment load have beenaltered significantly by climate change (low to medium confidence)(Jiménez Cisneros et al., 2014). As the number of studies on climatechange impacts on soil erosion has increased where rainfall is an
important driver (Lu et al., 2013), studies have increasingly consideredother factors, such as rainfall intensity (e.g., Shi and Wang, 2015;
Li and Fang, 2016), snow melt, and change in vegetation cover
resulting from temperature rise (Potemkina and Potemkin, 2015),
as well as crop management practices (Mullan et al., 2012). WGII
AR5 concluded that increases in heavy rainfall and temperature areprojected to change soil erosion and sediment yield, although theextent of these changes is highly uncertain and depends on rainfall
seasonality, land cover, and soil management practices (Jiménez
Cisneros et al., 2014).
While the number of published studies of climate change impacts on
soil erosion have increased globally since 2000 (Li and Fang, 2016),
few articles have addressed impacts at 1.5°C and 2°C of global
warming. The existing studies have found few differences in projectedrisks posed on sediment load under 1.5°C and 2°C (low confidence)(Cousino et al., 2015; Shrestha et al., 2016). The differences between
average annual sediment load under 1.5°C and 2°C of warming are
not clear, owing to complex interactions among climate change, land
cover/surface and soil management (Cousino et al., 2015; Shrestha
et al., 2016). Averages of annual sediment loads are projected tobe similar under 1.5°C and 2°C of warming, in particular in the
Great Lakes region in the USA and in the Lower Mekong region inSoutheast Asia (Cross-Chapter Box 6 in this chapter, Cousino et al.,2015; Shrestha et al., 2016).
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Impacts of 1.5°C of Global Warming on Natural and Human Systems

3.4.3

Terrestrial and Wetland Ecosystems

3.4.3.1 Biome shifts
Latitudinal and elevational shifts of biomes (major ecosystem
types) in boreal, temperate and tropical regions have been detected(Settele et al., 2014) and new studies confirm these changes (e.g.,
shrub encroachment on tundra; Larsen et al., 2014). Attribution
studies indicate that anthropogenic climate change has made agreater contribution to these changes than any other factor (medium
confidence) (Settele et al., 2014).
An ensemble of seven Dynamic Vegetation Models driven by projectedclimates from 19 alternative general circulation models (GCMs)(Warszawski et al., 2013) shows 13% (range 8–20%) of biomes
transforming at 2°C of global warming, but only 4% (range 2–7%)
doing so at 1°C, suggesting that about 6.5% may be transformed at
1.5°C; these estimates indicate a doubling of the areal extent of biome
shifts between 1.5°C and 2°C of warming (medium confidence) (Figure
3.16a). A study using the single ecosystem model LPJmL (Gerten etal., 2013) illustrated that biome shifts in the Arctic, Tibet, Himalayas,
southern Africa and Australia would be avoided by constraining
warming to 1.5°C compared with 2°C (Figure 3.16b). Seddon et al.
(2016) quantitatively identified ecologically sensitive regions to climatechange in most of the continents from tundra to tropical rainforest.
Biome transformation may in some cases be associated with novel
climates and ecological communities (Prober et al., 2012).

3.4.3.2 Changes in phenology
Advancement in spring phenology of 2.8 ± 0.35 days per decade has
been observed in plants and animals in recent decades in most Northern
Hemisphere ecosystems (between 30°N and 72°N), and these shifts
have been attributed to changes in climate (high confidence) (Settele
et al., 2014). The rates of change are particularly high in the Arctic
zone owing to the stronger local warming (Oberbauer et al., 2013),
whereas phenology in tropical forests appears to be more responsive
to moisture stress (Zhou et al., 2014). While a full review cannot be
included here, trends consistent with this earlier finding continue tobe detected, including in the flowering times of plants (Parmesan
and Hanley, 2015), in the dates of egg laying and migration in birds(newly reported in China; Wu and Shi, 2016), in the emergence datesof butterflies (Roy et al., 2015), and in the seasonal greening-up ofvegetation as detected by satellites (i.e., in the normalized difference
vegetation index, NDVI; Piao et al., 2015).
The potential for decoupling species–species interactions owing todiffering phenological responses to climate change is well established(Settele et al., 2014), for example for plants and their insect pollinators(Willmer, 2012; Scaven and Rafferty, 2013). Mid-century projectionsof plant and animal phenophases in the UK clearly indicate thatthe timing of phenological events could change more for primaryconsumers (6.2 days earlier on average) than for higher trophic
levels (2.5–2.9 days earlier on average) (Thackeray et al., 2016). This
indicates the potential for phenological mismatch and associatedrisks for ecosystem functionality in the future under global warmingof 2.1°C–2.7°C above pre-industrial levels. Further, differing responses

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Chapter 3

(a)

3

(b)

Figure 3.16 | (a) Fraction of global natural vegetation (including managed forests) at risk of severe ecosystem change as a function of global mean temperature change forall ecosystems, models, global climate change models and Representative Concentration Pathways (RCPs). The colours represent the different ecosystem models, which are also
horizontally separated for clarity. Results are collated in unit-degree bins, where the temperature for a given year is the average over a 30-year window centred on that year.
The boxes span the 25th and 75th percentiles across the entire ensemble. The short, horizontal stripes represent individual (annual) data points, the curves connect the meanvalue per ecosystem model in each bin. The solid (dashed) curves are for models with (without) dynamic vegetation composition changes. Source: (Warszawski et al., 2013)(b) Threshold level of global temperature anomaly above pre-industrial levels that leads to significant local changes in terrestrial ecosystems. Regions with severe (coloured) or
moderate (greyish) ecosystem transformation; delineation refers to the 90 biogeographic regions. All values denote changes found in >50% of the simulations. Source: (Gerten
et al., 2013). Regions coloured in dark red are projected to undergo severe transformation under a global warming of 1.5°C while those coloured in light red do so at 2°C; othercolours are used when there is no severe transformation unless global warming exceeds 2°C.

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could alter community structure in temperate forests (Roberts et al.,2015). Specifically, temperate forest phenology is projected to advanceby 14.3 days in the near term (2010–2039) and 24.6 days in themedium term (2040–2069), so as a first approximation the difference
between 2°C and 1.5°C of global warming is about 10 days (Roberts etal., 2015). This phenological plasticity is not always adaptive and must
be interpreted cautiously (Duputié et al., 2015), and considered in thecontext of accompanying changes in climate variability (e.g., increased
risk of frost damage for plants or earlier emergence of insects resulting inmortality during cold spells). Another adaptive response of some plants isrange expansion with increased vigour and altered herbivore resistancein their new range, analogous to invasive plants (Macel et al., 2017).
In summary, limiting warming to 1.5°C compared with 2°C may avoid
advance in spring phenology (high confidence) by perhaps a few days
(medium confidence) and hence decrease the risks of loss of ecosystem
functionality due to phenological mismatch between trophic levels,and also of maladaptation coming from the sensitivity of many species
to increased climate variability. Nevertheless, this difference between
1.5°C and 2°C of warming might be limited for plants that are able toexpand their range.

3

3.4.3.3 Changes in species range, abundance and extinction
AR5 (Settele et al., 2014) concluded that the geographical ranges ofmany terrestrial and freshwater plant and animal species have moved
over the last several decades in response to warming: approximately 17
km poleward and 11 m up in altitude per decade. Recent trends confirm
this finding; for example, the spatial and interspecific variance in bird
populations in Europe and North America since 1980 were found to bewell predicted by trends in climate suitability (Stephens et al., 2016).
Further, a recent meta-analysis of 27 studies concerning a total of 976
species (Wiens, 2016) found that 47% of local extinctions (extirpations)
reported across the globe during the 20th century could be attributed toclimate change, with significantly more extinctions occurring in tropicalregions, in freshwater habitats and for animals. IUCN (2018) lists 305
terrestrial animal and plant species from Pacific Island developing nations
as being threatened by climate change and severe weather. Owing
to lags in the responses of some species to climate change, shifts in
insect pollinator ranges may result in novel assemblages with unknown
implications for biodiversity and ecosystem function (Rafferty, 2017).
Warren et al. (2013) simulated climatically determined geographic range
loss under 2°C and 4°C of global warming for 50,000 plant and animal
species, accounting for uncertainty in climate projections and for the
potential ability of species to disperse naturally in an attempt to track theirgeographically shifting climate envelope. This earlier study has now been
updated and expanded to incorporate 105,501 species, including 19,848
insects, and new findings indicate that warming of 2°C by 2100 would
lead to projected bioclimatic range losses of >50% in 18% (6–35%) of
the 19,848 insects species, 8% (4–16%) of the 12,429 vertebrate species,
and 16% (9–28%) of the 73,224 plant species studied (Warren et al.,2018a). At 1.5°C of warming, these values fall to 6% (1–18%) of theinsects, 4% (2–9%) of the vertebrates and 8% (4–15%) of the plants
studied. Hence, the number of insect species projected to lose over halfof their geographic range is reduced by two-thirds when warming islimited to 1.5°C compared with 2°C, while the number of vertebrate
218

Impacts of 1.5°C of Global Warming on Natural and Human Systems

and plant species projected to lose over half of their geographic range
is halved (Warren et al., 2018a) (medium confidence). These findings are
consistent with estimates made from an earlier study suggesting that
range losses at 1.5°C were significantly lower for plants than those at
2°C of warming (Smith et al., 2018). It should be noted that at 1.5°C
of warming, and if species’ ability to disperse naturally to track theirpreferred climate geographically is inhibited by natural or anthropogenic
obstacles, there would still remain 10% of the amphibians, 8% of thereptiles, 6% of the mammals, 5% of the birds, 10% of the insects and
8% of the plants which are projected to lose over half their range, while
species on average lose 20–27% of their range (Warren et al., 2018a).
Given that bird and mammal species can disperse more easily thanamphibians and reptiles, a small proportion can expand their range
as climate changes, but even at 1.5°C of warming the total range loss
integrated over all birds and mammals greatly exceeds the integrated
range gain (Warren et al., 2018a).
A number of caveats are noted for studies projecting changes to climatic
range. This approach, for example, does not incorporate the effects ofextreme weather events and the role of interactions between species.
As well, trophic interactions may locally counteract the range expansionof species towards higher altitudes (Bråthen et al., 2018). There is also
the potential for highly invasive species to become established in new
areas as the climate changes (Murphy and Romanuk, 2014), but there isno literature that quantifies this possibility for 1.5°C of global warming.
Pecl et al. (2017) summarized at the global level the consequencesof climate-change-induced species redistribution for economic
development, livelihoods, food security, human health and culture.
These authors concluded that even if anthropogenic greenhouse gas
emissions stopped today, the effort for human systems to adapt tothe most crucial effects of climate-driven species redistribution will
be far-reaching and extensive. For example, key insect crop pollinatorfamilies (Apidae, Syrphidae and Calliphoridae; i.e., bees, hoverflies
and blowflies) are projected to retain significantly greater geographic
ranges under 1.5°C of global warming compared with 2°C (Warren
et al., 2018a). In some cases, when species (such as pest and disease
species) move into areas which have become climatically suitable
they may become invasive or harmful to human or natural systems(Settele et al., 2014). Some studies are beginning to locate ‘refugial’
areas where the climate remains suitable in the future for most of thespecies currently present. For example, Smith et al. (2018) estimatedthat 5.5–14% more of the globe’s terrestrial land area could act asclimatic refugia for plants under 1.5°C of warming compared to 2°C.
There is no literature that directly estimates the proportion of species atincreased risk of global (as opposed to local) commitment to extinction
as a result of climate change, as this is inherently difficult to quantify.
However, it is possible to compare the proportions of species at riskof very high range loss; for example, a discernibly smaller number ofterrestrial species are projected to lose over 90% of their range at
1.5°C of global warming compared with 2°C (Figure 2 in Warren etal., 2018a). A link between very high levels of range loss and greatly
increased extinction risk may be inferred (Urban, 2015). Hence, limiting
global warming to 1.5°C compared with 2°C would be expected toreduce both range losses and associated extinction risks in terrestrial
species (high confidence).

Impacts of 1.5°C of Global Warming on Natural and Human Systems

3.4.3.4 Changes in ecosystem function, biomass
and carbon stocks
Working Group II of AR5 (Settele et al., 2014) concluded that there ishigh confidence that net terrestrial ecosystem productivity at the global
scale has increased relative to the pre-industrial era and that rising
CO2 concentrations are contributing to this trend through stimulationof photosynthesis. There is, however, no clear and consistent signalof a climate change contribution. In northern latitudes, the change inproductivity has a lower velocity than the warming, possibly because ofa lack of resource and vegetation acclimation mechanisms (M. Huang
et al., 2017). Biomass and soil carbon stocks in terrestrial ecosystems
are currently increasing (high confidence), but they are vulnerable toloss of carbon to the atmosphere as a result of projected increases in
the intensity of storms, wildfires, land degradation and pest outbreaks(Settele et al., 2014; Seidl et al., 2017). These losses are expected tocontribute to a decrease in the terrestrial carbon sink. Anderegg et al.
(2015) demonstrated that total ecosystem respiration at the global
scale has increased in response to increases in night-time temperature(1 PgC yr–1 °C–1, P=0.02).
The increase in total ecosystem respiration in spring and autumn,
associated with higher temperatures, may convert boreal forestsfrom carbon sinks to carbon sources (Hadden and Grelle, 2016). In
boreal peatlands, for example, increased temperature may diminish
carbon storage and compromise the stability of the peat (Dieleman
et al., 2016). In addition, J. Yang et al. (2015) showed that fires reducethe carbon sink of global terrestrial ecosystems by 0.57 PgC yr–1 inecosystems with large carbon stores, such as peatlands and tropicalforests. Consequently, for adaptation purposes, it is necessary toenhance carbon sinks, especially in forests which are prime regulators
within the water, energy and carbon cycles (Ellison et al., 2017). Soil canalso be a key compartment for substantial carbon sequestration (Lal,2014; Minasny et al., 2017), depending on the net biome productivity
and the soil quality (Bispo et al., 2017).
AR5 assessed that large uncertainty remains regarding the land carbon
cycle behaviour of the future (Ciais et al., 2013), with most, but not all,CMIP5 models simulating continued terrestrial carbon uptake under
all four RCP scenarios (Jones et al., 2013). Disagreement between
models outweighs differences between scenarios even up to the year
2100 (Hewitt et al., 2016; Lovenduski and Bonan, 2017). Increased
atmospheric CO2 concentrations are expected to drive further increases
in the land carbon sink (Ciais et al., 2013; Schimel et al., 2015), which
could persist for centuries (Pugh et al., 2016). Nitrogen, phosphorus andother nutrients will limit the terrestrial carbon cycle response to both
elevated CO2 and altered climate (Goll et al., 2012; Yang et al., 2014;
Wieder et al., 2015; Zaehle et al., 2015; Ellsworth et al., 2017). Climatechange may accelerate plant uptake of carbon (Gang et al., 2015)
but also increase the rate of decomposition (Todd-Brown et al., 2014;
Koven et al., 2015; Crowther et al., 2016). Ahlström et al. (2012) founda net loss of carbon in extra-tropical regions and the largest spread
across model results in the tropics. The projected net effect of climatechange is to reduce the carbon sink expected under CO2 increase alone(Settele et al., 2014). Friend et al. (2014) found substantial uptake ofcarbon by vegetation under future scenarios when considering theeffects of both climate change and elevated CO2.

Chapter 3

There is limited published literature examining modelled land carbonchanges specifically under 1.5°C of warming, but existing CMIP5
models and published data are used in this report to draw someconclusions. For systems with significant inertia, such as vegetation orsoil carbon stores, changes in carbon storage will depend on the rateof change of forcing and thus depend on the choice of scenario (Joneset al., 2009; Ciais et al., 2013; Sihi et al., 2017). To avoid legacy effectsof the choice of scenario, this report focuses on the response of grossprimary productivity (GPP) – the rate of photosynthetic carbon uptake– by the models, rather than by changes in their carbon store.
Figure 3.17 shows different responses of the terrestrial carbon cycle
to climate change in different regions. The models show a consistent
response of increased GPP in temperate latitudes of approximately 2
GtC yr–1 °C–1. Similarly, Gang et al. (2015) projected a robust increasein the net primary productivity (NPP) of temperate forests. However,Ahlström et al. (2012) showed that this effect could be offset or reversed
by increases in decomposition. Globally, most models project that GPP
will increase or remain approximately unchanged (Hashimoto et al.,2013). This projection is supported by findings by Sakalli et al. (2017)
for Europe using Euro-CORDEX regional models under a 2°C global
warming for the period 2034–2063, which indicated that storage
will increase by 5% in soil and by 20% in vegetation. However, using
the same models Jacob et al. (2018) showed that limiting warming
to 1.5°C instead of 2°C avoids an increase in ecosystem vulnerability(compared to a no-climate change scenario) of 40–50%.
At the global level, linear scaling is acceptable for net primary production,
biomass burning and surface runoff, and impacts on terrestrial carbon
storage are projected to be greater at 2°C than at 1.5°C (Tanaka etal., 2017). If global CO2 concentrations and temperatures stabilize, or
peak and decline, then both land and ocean carbon sinks – which areprimarily driven by the continued increase in atmospheric CO2 – will
also decline and may even become carbon sources (Jones et al., 2016).
Consequently, if a given amount of anthropogenic CO2 is removed fromthe atmosphere, an equivalent amount of land and ocean anthropogenic
CO2 will be released to the atmosphere (Cao and Caldeira, 2010).
In conclusion, ecosystem respiration is expected to increase withincreasing temperature, thus reducing soil carbon storage. Soil carbon
storage is expected to be larger if global warming is restricted to
1.5°C, although some of the associated changes will be countered byenhanced gross primary production due to elevated CO2 concentrations(i.e., the ‘fertilization effect’) and higher temperatures, especially at
mid- and high latitudes (medium confidence).

3.4.3.5 Regional and ecosystem-specific risks
A large number of threatened systems, including mountainecosystems, highly biodiverse tropical wet and dry forests, deserts,
freshwater systems and dune systems, were assessed in AR5. These
include Mediterranean areas in Europe, Siberian, tropical and desert
ecosystems in Asia, Australian rainforests, the Fynbos and succulent
Karoo areas of South Africa, and wetlands in Ethiopia, Malawi, Zambia
and Zimbabwe. In all these systems, it has been shown that impacts
accrue with greater warming, and thus impacts at 2°C are expected to
be greater than those at 1.5°C (medium confidence).
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Global

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0
-10
-20
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60°N - 90°N

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GPP change (GtC yr-1)

530°N - 60°N

20

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30°S - 30°N

20

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GPP change (GtC yr-1)

40

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-1

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Figure 3.17 | The response of terrestrial productivity (gross primary productivity, GPP) to climate change, globally (top left) and for three latitudinal regions: 30°S–30°N;
30–60°N and 60–90°N. Data come from the Coupled Model Intercomparison Project Phase 5 (CMIP5) archive (http://cmip-pcmdi.llnl.gov/cmip5/). Seven Earth System
Models were used: Norwegian Earth System Model (NorESM-ME, yellow); Community Earth System Model (CESM, red); Institute Pierre Simon Laplace (IPLS)-CM5-LR (darkblue); Geophysical Fluid Dynamics Laboratory (GFDL, pale blue); Max Plank Institute-Earth System Model (MPI-ESM, pink); Hadley Centre New Global Environmental Model
2-Earth System (HadGEM2-ES, orange); and Canadian Earth System Model 2 (CanESM2, green). Differences in GPP between model simulations with (‘1pctCO2’) and without(‘esmfixclim1’) the effects of climate change are shown. Data are plotted against the global mean temperature increase above pre-industrial levels from simulations with a 1%
per year increase in CO2 (‘1pctCO2’).

The High Arctic region, with tundra-dominated landscapes, has warmed
more than the global average over the last century (Section 3.3; Settele
et al., 2014). The Arctic tundra biome is experiencing increasing fire
disturbance and permafrost degradation (Bring et al., 2016; DeBeer etal., 2016; Jiang et al., 2016; Yang et al., 2016). Both of these processes
facilitate the establishment of woody species in tundra areas. Arctic
terrestrial ecosystems are being disrupted by delays in winter onset
and mild winters associated with global warming (high confidence)(Cooper, 2014). Observational constraints suggest that stabilization
at 1.5°C of warming would avoid the thawing of approximately 1.5
to 2.5 million km2 of permafrost (medium confidence) comparedwith stabilization at 2°C (Chadburn et al., 2017), but the time scale
for release of thawed carbon as CO2 or CH4 should be many centuries(Burke et al., 2017). In northern Eurasia, the growing season length isprojected to increase by about 3–12 days at 1.5°C and 6–16 days at
2°C of warming (medium confidence) (Zhou et al., 2018). Aalto et al.
(2017) predicted a 72% reduction in cryogenic land surface processes
in northern Europe for RCP2.6 in 2040–2069 (corresponding to a global
warming of approximately 1.6°C), with only slightly larger losses for
RCP4.5 (2°C of global warming).
220

Projected impacts on forests as climate change occurs include increases
in the intensity of storms, wildfires and pest outbreaks (Settele et al.,2014), potentially leading to forest dieback (medium confidence).
Warmer and drier conditions in particular facilitate fire, drought and insect
disturbances, while warmer and wetter conditions increase disturbancesfrom wind and pathogens (Seidl et al., 2017). Particularly vulnerable
regions are Central and South America, Mediterranean Basin, South
Africa, South Australia where the drought risk will increase (see Figure3.12). Including disturbances in simulations may influence productivity
changes in European forests in response to climate change (Reyer etal., 2017b). There is additional evidence for the attribution of increased
forest fire frequency in North America to anthropogenic climate change
during 1984–2015, via the mechanism of increasing fuel aridity almost
doubling the western USA forest fire area compared to what would
have been expected in the absence of climate change (Abatzoglou andWilliams, 2016). This projection is in line with expected fire risks, whichindicate that fire frequency could increase over 37.8% of the global landarea during 2010–2039 (Moritz et al., 2012), corresponding to a global
warming level of approximately 1.2°C, compared with over 61.9% of
the global land area in 2070–2099, corresponding to a warming ofImpacts of 1.5°C of Global Warming on Natural and Human Systems

approximately 3.5°C.6 The values in Table 26-1 in a recent paper by
Romero-Lankao et al. (2014) also indicate significantly lower wildfire
risks in North America for near-term warming (2030–2040, considered a
proxy for 1.5°C of warming) than at 2°C (high confidence).
The Amazon tropical forest has been shown to be close to its climatic
limits (Hutyra et al., 2005), but this threshold may move under elevated
CO2 (Good et al., 2011). Future changes in rainfall, especially dry season
length, will determine responses of the Amazon forest (Good et al.,2013). The forest may be especially vulnerable to combined pressure
from multiple stressors, namely changes in climate and continued
anthropogenic disturbance (Borma et al., 2013; Nobre et al., 2016).
Modelling (Huntingford et al., 2013) and observational constraints(Cox et al., 2013) suggest that large-scale forest dieback is less likely
than suggested under early coupled modelling studies (Cox et al., 2000;
Jones et al., 2009). Nobre et al. (2016) estimated a climatic threshold of
4°C of warming and a deforestation threshold of 40%.
In many places around the world, the savanna boundary is moving
into former grasslands. Woody encroachment, including increased
tree cover and biomass, has increased over the past century, owing
to changes in land management, rising CO2 levels, and climate
variability and change (often in combination) (Settele et al., 2014). For
plant species in the Mediterranean region, shifts in phenology, range
contraction and health decline have been observed with precipitation
decreases and temperature increases (medium confidence) (Settele
et al., 2014). Recent studies using independent complementary
approaches have shown that there is a regional-scale threshold in the
Mediterranean region between 1.5°C and 2°C of warming (Guiot andCramer, 2016; Schleussner et al., 2016b). Further, Guiot and Cramer(2016) concluded that biome shifts unprecedented in the last 10,000
years can only be avoided if global warming is constrained to 1.5°C(medium confidence) – whilst 2°C of warming will result in a decreaseof 12–15% of the Mediterranean biome area. The Fynbos biome insouthwestern South Africa is vulnerable to the increasing impact offires under increasing temperatures and drier winters. It is projected
to lose about 20%, 45% and 80% of its current suitable climate area
under 1°C, 2°C and 3°C of global warming, respectively, compared to
1961–1990 (high confidence) (Engelbrecht and Engelbrecht, 2016). In
Australia, an increase in the density of trees and shrubs at the expenseof grassland species is occurring across all major ecosystems and is
projected to be amplified (NCCARF, 2013). Regarding Central America,
Lyra et al. (2017) showed that the tropical rainforest biomass would be
reduced by about 40% under global warming of 3°C, with considerable
replacement by savanna and grassland. With a global warming of close
to 1.5°C in 2050, a biomass decrease of 20% is projected for tropicalrainforests of Central America (Lyra et al., 2017). If a linear response isassumed, this decrease may reach 30% (medium confidence).
Freshwater ecosystems are considered to be among the most threatenedon the planet (Settele et al., 2014). Although peatlands cover only about
3% of the land surface, they hold one-third of the world’s soil carbon
stock (400 to 600 Pg) (Settele et al., 2014). When drained, this carbon
is released to the atmosphere. At least 15% of peatlands have drained,
6

Chapter 3

mostly in Europe and Southeast Asia, and are responsible for 5% of
human derived CO2 emissions (Green and Page, 2017). Moreover, in theCongo basin (Dargie et al., 2017) and in the Amazonian basin (Draper etal., 2014), the peatlands store the equivalent carbon as that of a tropicalforest. However, stored carbon is vulnerable to land-use change andfuture risk of drought, for example in northeast Brazil (high confidence)(Figure 3.12, Section 3.3.4.2). At the global scale, these peatlands areundergoing rapid major transformations through drainage and burningin preparation for oil palm and other crops or through unintentionalburning (Magrin et al., 2014). Wetland salinization, a widespreadthreat to the structure and ecological functioning of inland and coastal
wetlands, is occurring at a high rate and large geographic scale (Section
3.3.6; Herbert et al., 2015). Settele et al. (2014) found that rising watertemperatures are projected to lead to shifts in freshwater species
distributions and worsen water quality. Some of these ecosystems
respond non-linearly to changes in temperature. For example, Johnson
and Poiani (2016) found that the wetland function of the Prairie Potholeregion in North America is projected to decline at temperatures beyond
a local warming of 2°C–3°C above present-day values (1°C local
warming, corresponding to 0.6°C of global warming). If the ratio of local
to global warming remains similar for these small levels of warming,
this would indicate a global temperature threshold of 1.2°C–1.8°C
of warming. Hence, constraining global warming to approximately
1.5°C would maintain the functioning of prairie pothole ecosystems interms of their productivity and biodiversity, although a 20% increaseof precipitation could offset 2°C of global warming (high confidence)(Johnson and Poiani, 2016).

3.4.3.6 Summary of implications for ecosystem services
In summary, constraining global warming to 1.5°C rather than 2°C
has strong benefits for terrestrial and wetland ecosystems and theirservices (high confidence). These benefits include avoidance or
reduction of changes such as biome transformations, species range
losses, increased extinction risks (all high confidence) and changes
in phenology (high confidence), together with projected increases
in extreme weather events which are not yet factored into theseanalyses (Section 3.3). All of these changes contribute to disruption of
ecosystem functioning and loss of cultural, provisioning and regulating
services provided by these ecosystems to humans. Examples of suchservices include soil conservation (avoidance of desertification), flood
control, water and air purification, pollination, nutrient cycling, sourcesof food, and recreation.

3.4.4

Ocean Ecosystems

The ocean plays a central role in regulating atmospheric gas
concentrations, global temperature and climate. It also provides
habitat to a large number of organisms and ecosystems that provide
goods and services worth trillions of USD per year (e.g., Costanza etal., 2014; Hoegh-Guldberg et al., 2015). Together with local stresses(Halpern et al., 2015), climate change poses a major threat to anincreasing number of ocean ecosystems (e.g., warm water or tropicalcoral reefs: virtually certain, WGII AR5) and consequently to many

The approximate temperatures are derived from Figure 10.5a in Meehl et al. (2007), which indicates an ensemble average projection of 0.7°C or 3°C above 1980–1999
temperatures, which were already 0.5°C above pre-industrial values.

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Chapter 3

coastal communities that depend on marine resources for food,livelihoods and a safe place to live. Previous sections of this report
have described changes in the ocean, including rapid increases
in ocean temperature down to a depth of at least 700 m (Section
3.3.7). In addition, anthropogenic carbon dioxide has decreased
ocean pH and affected the concentration of ions in seawater suchas carbonate (Sections 3.3.10 and 3.4.4.5), both over a similar depth
range. Increased ocean temperatures have intensified storms in someregions (Section 3.3.6), expanded the ocean volume and increased
sea levels globally (Section 3.3.9), reduced the extent of polarsummer sea ice (Section 3.3.8), and decreased the overall solubilityof the ocean for oxygen (Section 3.3.10). Importantly, changes in the
response to climate change rarely operate in isolation. Consequently,
the effect of global warming of 1.5°C versus 2°C must be consideredin the light of multiple factors that may accumulate and interact overtime to produce complex risks, hazards and impacts on human andnatural systems.

3.4.4.1 Observed impacts

3

Physical and chemical changes to the ocean resulting from increasing
atmospheric CO2 and other GHGs are already driving significant changes
to ocean systems (very high confidence) and will continue to do so at
1.5°C, and more so at 2°C, of global warming above pre-industrial
temperatures (Section 3.3.11). These changes have been accompanied
by other changes such as ocean acidification, intensifying storms anddeoxygenation (Levin and Le Bris, 2015). Risks are already significant
at current greenhouse gas concentrations and temperatures, and theyvary significantly among depths, locations and ecosystems, with impacts
being singular, interactive and/or cumulative (Boyd et al., 2015).

3.4.4.2 Warming and stratification of the surface ocean
As atmospheric greenhouse gases have increased, the global meansurface temperature (GMST) has reached about 1°C above the preindustrial period, and oceans have rapidly warmed from the ocean
surface to the deep sea (high confidence) (Sections 3.3.7; Hughes
and Narayanaswamy, 2013; Levin and Le Bris, 2015; Yasuhara andDanovaro, 2016; Sweetman et al., 2017). Marine organisms are
already responding to these changes by shifting their biogeographicalranges to higher latitudes at rates that range from approximately 0
to 40 km yr–1 (Burrows et al., 2014; Chust, 2014; Bruge et al.,2016; Poloczanska et al., 2016), which has consequently affected
the structure and function of the ocean, along with its biodiversity
and foodwebs (high confidence). Movements of organisms does
not necessarily equate to the movement of entire ecosystems. For
example, species of reef-building corals have been observed to shift
their geographic ranges, yet this has not resulted in the shift of entire
coral ecosystems (high confidence) (Woodroffe et al., 2010; Yamano
et al., 2011). In the case of ‘less mobile’ ecosystems (e.g., coral reefs,
kelp forests and intertidal communities), shifts in biogeographicalranges may be limited, with mass mortalities and disease outbreaks
increasing in frequency as the exposure to extreme temperatures
increases (very high confidence) (Hoegh-Guldberg, 1999; Garrabou
et al., 2009; Rivetti et al., 2014; Maynard et al., 2015; Krumhansl etal., 2016; Hughes et al., 2017b; see also Box 3.4). These trends areprojected to become more pronounced at warming of 1.5°C, and222

Impacts of 1.5°C of Global Warming on Natural and Human Systems

more so at 2°C, above the pre-industrial period (Hoegh-Guldberg etal., 2007; Donner, 2009; Frieler et al., 2013; Horta E Costa et al., 2014;
Vergés et al., 2014, 2016; Zarco-Perello et al., 2017) and are likely toresult in decreases in marine biodiversity at the equator but increases
in biodiversity at higher latitudes (Cheung et al., 2009; Burrows etal., 2014).
While the impacts of species shifting their ranges are mostly negative
for human communities and industry, there are instances of shortterm gains. Fisheries, for example, may expand temporarily at highlatitudes in the Northern Hemisphere as the extent of summer sea ice
recedes and NPP increases (medium confidence) (Cheung et al., 2010;
Lam et al., 2016; Weatherdon et al., 2016). High-latitude fisheries are
not only influenced by the effect of temperature on NPP but are alsostrongly influenced by the direct effects of changing temperatures onfish and fisheries (Section 3.4.4.9; Barange et al., 2014; Pörtner et al.,2014; Cheung et al., 2016b; Weatherdon et al., 2016). Temporary gains
in the productivity of high-latitude fisheries are offset by a growing
number of examples from low and mid-latitudes where increases in
sea temperature are driving decreases in NPP, owing to the direct
effects of elevated temperatures and/or reduced ocean mixing fromreduced ocean upwelling, that is, increased stratification (low-medium
confidence) (Cheung et al., 2010; Ainsworth et al., 2011; Lam et al.,2012, 2014, 2016; Bopp et al., 2013; Boyd et al., 2014; Chust et al., 2014;
Hoegh-Guldberg et al., 2014; Poloczanska et al., 2014; Pörtner et al.,2014; Signorini et al., 2015). Reduced ocean upwelling has implications
for millions of people and industries that depend on fisheries for foodand livelihoods (Bakun et al., 2015; FAO, 2016; Kämpf and Chapman,2016), although there is low confidence in the projection of the sizeof the consequences at 1.5°C. It is also important to appreciate thesechanges in the context of large-scale ocean processes such as theocean carbon pump. The export of organic carbon to deeper layers of
the ocean increases as NPP changes in the surface ocean, for example,
with implications for foodwebs and oxygen levels (Boyd et al., 2014;
Sydeman et al., 2014; Altieri and Gedan, 2015; Bakun et al., 2015;
Boyd, 2015).

3.4.4.3 Storms and coastal runoff
Storms, wind, waves and inundation can have highly destructive impacts
on ocean and coastal ecosystems, as well as the human communities
that depend on them (IPCC, 2012; Seneviratne et al., 2012). The intensityof tropical cyclones across the world’s oceans has increased, although theoverall number of tropical cyclones has remained the same or decreased(medium confidence) (Section 3.3.6; Elsner et al., 2008; Holland andBruyère, 2014). The direct force of wind and waves associated withlarger storms, along with changes in storm direction, increases the risksof physical damage to coastal communities and to ecosystems such as
mangroves (low to medium confidence) (Long et al., 2016; Primavera etal., 2016; Villamayor et al., 2016; Cheal et al., 2017) and tropical coral
reefs (De’ath et al., 2012; Bozec et al., 2015; Cheal et al., 2017). Thesechanges are associated with increases in maximum wind speed, wave
height and the inundation, although trends in these variables vary fromregion to region (Section 3.3.5). In some cases, this can lead to increased
exposure to related impacts, such as flooding, reduced water quality andincreased sediment runoff (medium-high confidence) (Brodie et al., 2012;
Wong et al., 2014; Anthony, 2016; AR5, Table 5.1).

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Sea level rise also amplifies the impacts of storms and wave action(Section 3.3.9), with robust evidence that storm surges and damage
are already penetrating farther inland than a few decades ago,changing conditions for coastal ecosystems and human communities.
This is especially true for small islands (Box 3.5) and low-lying coastal
communities, where issues such as storm surges can transform coastal
areas (Section 3.4.5; Brown et al., 2018a). Changes in the frequency of
extreme events, such as an increase in the frequency of intense storms,
have the potential (along with other factors, such as disease, food webchanges, invasive organisms and heat stress-related mortality; Burgeet al., 2014; Maynard et al., 2015; Weatherdon et al., 2016; Clements
et al., 2017) to overwhelm the capacity for natural and human systems
to recover following disturbances. This has recently been seen for key
ecosystems such as tropical coral reefs (Box 3.4), which have changed
from coral-dominated ecosystems to assemblages dominated by otherorganisms such as seaweeds, with changes in associated organisms
and ecosystem services (high confidence) (De’ath et al., 2012; Bozec etal., 2015; Cheal et al., 2017; Hoegh-Guldberg et al., 2017; Hughes et al.,2017a, b). The impacts of storms are amplified by sea level rise (Section
3.4.5), leading to substantial challenges today and in the future for
cities, deltas and small island states in particular (Sections 3.4.5.2 to
3.4.5.4), as well as for coastlines and their associated ecosystems(Sections 3.4.5.5 to 3.4.5.7).

3.4.4.4 Ocean circulation
The movement of water within the ocean is essential to its biology
and ecology, as well to the circulation of heat, water and nutrientsaround the planet (Section 3.3.7). The movement of these factors
drives local and regional climates, as well as primary productivity andfood production. Firmly attributing recent changes in the strength anddirection of ocean currents to climate change, however, is complicatedby long-term patterns and variability (e.g., Pacific decadal oscillation,
PDO; Signorini et al., 2015) and a lack of records that match the longterm nature of these changes in many cases (Lluch-Cota et al., 2014). An
assessment of the literature since AR5 (Sydeman et al., 2014), however,concluded that (overall) upwelling-favourable winds have intensified
in the California, Benguela and Humboldt upwelling systems, buthave weakened in the Iberian system and have remained neutral in
the Canary upwelling system in over 60 years of records (1946–2012)(medium confidence). These conclusions are consistent with a growing
consensus that wind-driven upwelling systems are likely to intensify
under climate change in many upwelling systems (Sydeman et al.,2014; Bakun et al., 2015; Di Lorenzo, 2015), with potentially positive
and negative consequences (Bakun et al., 2015).
Changes in ocean circulation can have profound impacts on marine
ecosystems by connecting regions and facilitating the entry andestablishment of species in areas where they were unknown before (e.g.,‘tropicalization’ of temperate ecosystems; Wernberg et al., 2012; Vergés
et al., 2014, 2016; Zarco-Perello et al., 2017), as well as the arrival of novel
disease agents (low-medium confidence) (Burge et al., 2014; Maynard
et al., 2015; Weatherdon et al., 2016). For example, the herbivorous sea
urchin Centrostephanus rodgersii has been reached Tasmania from theAustralian mainland, where it was previously unknown, owing to astrengthening of the East Australian Current (EAC) that connects thetwo regions (high confidence) (Ling et al., 2009). As a consequence, theChapter 3

distribution and abundance of kelp forests has rapidly decreased, with
implications for fisheries and other ecosystem services (Ling et al., 2009).
These risks to marine ecosystems are projected to become greater at
1.5°C, and more so at 2°C (medium confidence) (Cheung et al., 2009;
Pereira et al., 2010; Pinsky et al., 2013; Burrows et al., 2014).
Changes to ocean circulation can have even larger influence in terms ofscale and impacts. Weakening of the Atlantic Meridional Overturning
Circulation (AMOC), for example, is projected to be highly disruptive tonatural and human systems as the delivery of heat to higher latitudes
via this current system is reduced (Collins et al., 2013). Evidence ofa slowdown of AMOC has increased since AR5 (Smeed et al., 2014;
Rahmstorf et al., 2015a, b; Kelly et al., 2016), yet a strong causal
connection to climate change is missing (low confidence) (Section
3.3.7).

3.4.4.5 Ocean acidification
Ocean chemistry encompasses a wide range of phenomena and chemical
species, many of which are integral to the biology and ecology of theocean (Section 3.3.10; Gattuso et al., 2014, 2015; Hoegh-Guldberg etal., 2014; Pörtner et al., 2014). While changes to ocean chemistry arelikely to be of central importance, the literature on how climate change
might influence ocean chemistry over the short and long term is limited(medium confidence). By contrast, numerous risks from the specificchanges associated with ocean acidification have been identified (Doveet al., 2013; Kroeker et al., 2013; Pörtner et al., 2014; Gattuso et al.,2015; Albright et al., 2016), with the consensus that resulting changes
to the carbonate chemistry of seawater are having, and are likely tocontinue to have, fundamental and substantial impacts on a wide varietyof organisms (high confidence). Organisms with shells and skeletons
made out of calcium carbonate are particularly at risk, as are the earlylife history stages of a large number of organisms and processes suchas de-calcification, although there are some taxa that have not shownhigh-sensitivity to changes in CO2, pH and carbonate concentrations(Dove et al., 2013; Fang et al., 2013; Kroeker et al., 2013; Pörtner etal., 2014; Gattuso et al., 2015). Risks of these impacts also vary withlatitude and depth, with the greatest changes occurring at high latitudes
as well as deeper regions. The aragonite saturation horizon (i.e., whereconcentrations of calcium and carbonate fall below the saturation point
for aragonite, a key crystalline form of calcium carbonate) is decreasing
with depth as anthropogenic CO2 penetrates deeper into the ocean overtime. Under many models and scenarios, the aragonite saturation is
projected to reach the surface by 2030 onwards, with a growing list of
impacts and consequences for ocean organisms, ecosystems and people(Orr et al., 2005; Hauri et al., 2016).
Further, it is difficult to reliably separate the impacts of ocean warming
and acidification. As ocean waters have increased in sea surface
temperature (SST) by approximately 0.9°C they have also decreased
by 0.2 pH units since 1870–1899 (‘pre-industrial’; Table 1 in Gattuso etal., 2015; Bopp et al., 2013). As CO2 concentrations continue to increase
along with other GHGs, pH will decrease while sea temperature will
increase, reaching 1.7°C and a decrease of 0.2 pH units (by 2100
under RCP4.5) relative to the pre-industrial period. These changes arelikely to continue given the negative correlation of temperature andpH. Experimental manipulation of CO2, temperature and consequently
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While many risks have been defined through laboratory and mesocosm
experiments, there is a growing list of impacts from the field (medium
confidence) that include community-scale impacts on bacterial
assemblages and processes (Endres et al., 2014), coccolithophores(K.J.S. Meier et al., 2014), pteropods and polar foodwebs (Bednaršek etal., 2012, 2014), phytoplankton (Moy et al., 2009; Riebesell et al., 2013;
Richier et al., 2014), benthic ecosystems (Hall-Spencer et al., 2008;
Linares et al., 2015), seagrass (Garrard et al., 2014), and macroalgae(Webster et al., 2013; Ordonez et al., 2014), as well as excavating
sponges, endolithic microalgae and reef-building corals (Dove et al.,2013; Reyes-Nivia et al., 2013; Fang et al., 2014), and coral reefs (Box3.4; Fabricius et al., 2011; Allen et al., 2017). Some ecosystems, such as
those from bathyal areas (i.e., 200–3000 m below the surface), are likely
to undergo very large reductions in pH by the year 2100 (0.29 to 0.37
pH units), yet evidence of how deep-water ecosystems will respond iscurrently limited despite the potential planetary importance of theseareas (low to medium confidence) (Hughes and Narayanaswamy,
2013; Sweetman et al., 2017).

zones (OMZ) (Turner et al., 2008; Carstensen et al., 2014; Acharya andPanigrahi, 2016; Lachkar et al., 2018), physiological impacts (Pörtner
et al., 2014), and mortality and/or displacement of oxygen dependent
organisms such as fish (Hamukuaya et al., 1998; Thronson and Quigg,
2008; Jacinto, 2011) and invertebrates (Hobbs and Mcdonald, 2010;
Bednaršek et al., 2016; Seibel, 2016; Altieri et al., 2017). In addition,
deoxygenation interacts with ocean acidification to present substantial
separate and combined challenges for fisheries and aquaculture(medium confidence) (Hamukuaya et al., 1998; Bakun et al., 2015;
Rodrigues et al., 2015; Feely et al., 2016; S. Li et al., 2016; Asiedu et al.,2017a; Clements and Chopin, 2017; Clements et al., 2017; Breitburg etal., 2018). Deoxygenation is expected to have greater impacts as ocean
warming and acidification increase (high confidence), with impacts
being larger and more numerous than today (e.g., greater challenges
for aquaculture and fisheries from hypoxia), and as the number ofhypoxic areas continues to increase. Risks from deoxygenation are
virtually certain to increase as warming continues, although ourunderstanding of risks at 1.5°C versus 2°C is incomplete (medium
confidence). Reducing coastal pollution, and consequently thepenetration of organic carbon into deep benthic habitats, is expected
to reduce the loss of oxygen in coastal waters and hypoxic areas ingeneral (high confidence) (Breitburg et al., 2018).

3.4.4.6 Deoxygenation

3.4.4.7 Loss of sea ice

Oxygen levels in the ocean are maintained by a series of processes
including ocean mixing, photosynthesis, respiration and solubility(Boyd et al., 2014, 2015; Pörtner et al., 2014; Breitburg et al., 2018).
Concentrations of oxygen in the ocean are declining (high confidence)owing to three main factors related to climate change: (i) heat-related
stratification of the water column (less ventilation and mixing), (ii)reduced oxygen solubility as ocean temperature increases, and (iii)
impacts of warming on biological processes that produce or consume
oxygen such as photosynthesis and respiration (high confidence) (Boppet al., 2013; Pörtner et al., 2014; Altieri and Gedan, 2015; Deutsch etal., 2015; Schmidtko et al., 2017; Shepherd et al., 2017; Breitburg etal., 2018). Further, a range of processes (Section 3.4.11) are actingsynergistically, including factors not related to climate change, suchas runoff and coastal eutrophication (e.g., from coastal farming
and intensive aquaculture). These changes can lead to increased
phytoplankton productivity as a result of the increased concentrationof dissolved nutrients. Increased supply of organic carbon moleculesfrom coastal run-off can also increase the metabolic activity of coastal
microbial communities (Altieri and Gedan, 2015; Bakun et al., 2015;
Boyd, 2015). Deep sea areas are likely to experience some of thegreatest challenges, as abyssal seafloor habitats in areas of deep-water
formation are projected to experience decreased water column oxygen
concentrations by as much as 0.03 mL L–1 by 2100 (Levin and Le Bris,
2015; Sweetman et al., 2017).

Sea ice is a persistent feature of the planet’s polar regions (Polyak et al.,2010) and is central to marine ecosystems, people (e.g., food, culture
and livelihoods) and industries (e.g., fishing, tourism, oil and gas, andshipping). Summer sea ice in the Arctic, however, has been retreating
rapidly in recent decades (Section 3.3.8), with an assessment of theliterature revealing that a fundamental transformation is occurringin polar organisms and ecosystems, driven by climate change (highconfidence) (Larsen et al., 2014). These changes are strongly affecting
people in the Arctic who have close relationships with sea ice andassociated ecosystems, and these people are facing major adaptation
challenges as a result of sea level rise, coastal erosion, the accelerated
thawing of permafrost, changing ecosystems and resources, and manyother issues (Ford, 2012; Ford et al., 2015).

acidification indicate that these impacts will continue to increase insize and scale as CO2 and SST continue to increase in tandem (Dove etal., 2013; Fang et al., 2013; Kroeker et al., 2013).

3

Impacts of 1.5°C of Global Warming on Natural and Human Systems

The number of ‘dead zones’ (areas where oxygenated waters havebeen replaced by hypoxic conditions) has been growing strongly
since the 1990s (Diaz and Rosenberg, 2008; Altieri and Gedan, 2015;
Schmidtko et al., 2017). While attribution can be difficult because of
the complexity of the processes involved, both related and unrelated
to climate change, some impacts associated to deoxygenation (lowmedium confidence) include the expansion of oxygen minimum
224

There is considerable and compelling evidence that a further increaseof 0.5°C beyond the present-day average global surface temperature
will lead to multiple levels of impact on a variety of organisms, fromphytoplankton to marine mammals, with some of the most dramaticchanges occurring in the Arctic Ocean and western Antarctic Peninsula(Turner et al., 2014, 2017b; Steinberg et al., 2015; Piñones and Fedorov,2016).
The impacts of climate change on sea ice are part of the focusof the IPCC Special Report on the Ocean and Cryosphere in a
Changing Climate (SROCC), due to be released in 2019, and hence
are not covered comprehensively here. However, there is a range of
responses to the loss of sea ice that are occurring and which increaseat 1.5°C and further so with 2°C of global warming. Some of thesechanges are described briefly here. Photosynthetic communities,
such macroalgae, phytoplankton and microalgae dwelling on theunderside of floating sea ice are changing, owing to increased
temperatures, light and nutrient levels. As sea ice retreats, mixing ofImpacts of 1.5°C of Global Warming on Natural and Human Systems

the water column increases, and phototrophs have increased accessto seasonally high levels of solar radiation (medium confidence)(Dalpadado et al., 2014; W.N. Meier et al., 2014). These changes are
expected to stimulate fisheries productivity in high-latitude regions
by mid-century (high confidence) (Cheung et al., 2009, 2010, 2016b;
Lam et al., 2014), with evidence that this is already happening forseveral high-latitude fisheries in the Northern Hemisphere, such as the
Bering Sea, although these ‘positive’ impacts may be relatively shortlived (Hollowed and Sundby, 2014; Sundby et al., 2016). In addition to
the impact of climate change on fisheries via impacts on net primary
productivity (NPP), there are also direct effects of temperature on
fish, which may in turn have a range of impacts (Pörtner et al., 2014).
Sea ice in Antarctica is undergoing changes that exceed those seenin the Arctic (Maksym et al., 2011; Reid et al., 2015), with increases
in sea ice coverage in the western Ross Sea being accompanied bystrong decreases in the Bellingshausen and Amundsen Seas (Hobbs
et al., 2016). While Antarctica is not permanently populated, theramifications of changes to the productivity of vast regions, suchas the Southern Ocean, have substantial implications for ocean
foodwebs and fisheries globally.

3.4.4.8

Sea level rise

Mean sea level is increasing (Section 3.3.9), with substantial impacts
already being felt by coastal ecosystems and communities (Wong etal., 2014) (high confidence). These changes are interacting with otherfactors, such as strengthening storms, which together are driving larger
storm surges, infrastructure damage, erosion and habitat loss (Church etal., 2013; Stocker et al., 2013; Blankespoor et al., 2014). Coastal wetland
ecosystems such as mangroves, sea grasses and salt marshes are underpressure from rising sea level (medium confidence) (Section 3.4.5; Di
Nitto et al., 2014; Ellison, 2014; Lovelock et al., 2015; Mills et al., 2016;
Nicholls et al., 2018), as well as from a wide range of other risks andimpacts unrelated to climate change, with the ongoing loss of wetlands
recently estimated at approximately 1% per annum across a large
number of countries (Blankespoor et al., 2014; Alongi, 2015). While someecosystems (e.g., mangroves) may be able to shift shoreward as sea levels
increase, coastal development (e.g., buildings, seawalls and agriculture)often interrupts shoreward shifts, as well as reducing sediment supplies
down some rivers (e.g., dams) due to coastal development (Di Nitto et al.,2014; Lovelock et al., 2015; Mills et al., 2016).
Responses to sea level rise challenges for ocean and coastal systems
include reducing the impact of other stresses, such as those arisingfrom tourism, fishing, coastal development, reduced sedimentsupply and unsustainable aquaculture/agriculture, in order to build
ecological resilience (Hossain et al., 2015; Sutton-Grier and Moore,
2016; Asiedu et al., 2017a). The available literature largely concludes
that these impacts will intensify under a 1.5°C warmer world but will
be even higher at 2°C, especially when considered in the context ofchanges occurring beyond the end of the current century. In somecases, restoration of coastal habitats and ecosystems may be a costeffective way of responding to changes arising from increasing levelsof exposure to rising sea levels, intensifying storms, coastal inundationand salinization (Section 3.4.5 and Box 3.5; Arkema et al., 2013),
although limitations of these strategies have been identified (e.g.,
Lovelock et al., 2015; Weatherdon et al., 2016).

Chapter 3

3.4.4.9 Projected risks and adaptation options for oceans underglobal warming of 1.5°C or 2°C above pre-industrial levels
A comprehensive discussion of risk and adaptation options for all
natural and human systems is not possible in the context and lengthof this report, and hence the intention here is to illustrate key risks
and adaptation options for ocean ecosystems and sectors. This
assessment builds on the recent expert consensus of Gattuso et al.
(2015) by assessing new literature from 2015–2017 and adjusting
the levels of risk from climate change in the light of literature since2014. The original expert group’s assessment (Supplementary Material
3.SM.3.2) was used as input for this new assessment, which focuseson the implications of global warming of 1.5°C as compared to 2°C. A
discussion of potential adaptation options is also provided, the detailsof which will be further explored in later chapters of this special report.
The section draws on the extensive analysis and literature presented in
the Supplementary Material of this report (3.SM.3.2, 3.SM.3.3) and has
a summary in Figures 3.18 and 3.20 which outline the added relative
risks of climate change.

3.4.4.10 Framework organisms (tropical corals, mangroves
and seagrass)
Marine organisms (‘ecosystem engineers’), such as seagrass, kelp,
oysters, salt marsh species, mangroves and corals, build physicalstructures or frameworks (i.e., sea grass meadows, kelp forests, oyster
reefs, salt marshes, mangrove forests and coral reefs) which form thehabitat for a large number of species (Gutiérrez et al., 2012). These
organisms in turn provide food, livelihoods, cultural significance, andservices such as coastal protection to human communities (Bell et al.,2011, 2018; Cinner et al., 2012; Arkema et al., 2013; Nurse et al., 2014;
Wong et al., 2014; Barbier, 2015; Bell and Taylor, 2015; Hoegh-Guldberg
et al., 2015; Mycoo, 2017; Pecl et al., 2017).
Risks of climate change impacts for seagrass and mangrove ecosystems
were recently assessed by an expert group led by Short et al. (2016).
Impacts of climate change were assessed to be similar across a rangeof submerged and emerged plants. Submerged plants such as seagrass were affected mostly by temperature extremes (Arias-Ortiz et al.,2018), and indirectly by turbidity, while emergent communities suchas mangroves and salt marshes were most susceptible to sea levelvariability and temperature extremes, which is consistent with otherevidence (Di Nitto et al., 2014; Sierra-Correa and Cantera Kintz, 2015;
Osorio et al., 2016; Sasmito et al., 2016), especially in the context ofhuman activities that reduce sediment supply (Lovelock et al., 2015)
or interrupt the shoreward movement of mangroves though theconstruction of coastal infrastructure. This in turn leads to ‘coastalsqueeze’ where coastal ecosystems are trapped between changing
ocean conditions and coastal infrastructure (Mills et al., 2016).
Projections of the future distribution of seagrasses suggest a poleward
shift, which raises concerns that low-latitude seagrass communities
may contract as a result of increasing stress levels (Valle et al., 2014).
Climate change (e.g., sea level rise, heat stress, storms) presents risk
for coastal ecosystems such as seagrass (high confidence) and reefbuilding corals (very high confidence) (Figure 3.18, Supplementary
Material 3.SM.3.2), with evidence of increasing concern since AR5 and225

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3

the conclusion that tropical corals may be even more vulnerable toclimate change than indicated in assessments made in 2014 (HoeghGuldberg et al., 2014; Gattuso et al., 2015). The current assessment
also considered the heatwave-related loss of 50% of shallow-watercorals across hundreds of kilometres of the world’s largest continuous
coral reef system, the Great Barrier Reef. These large-scale impacts,
plus the observation of back-to-back bleaching events on the Great
Barrier Reef (predicted two decades ago, Hoegh-Guldberg, 1999) andarriving sooner than predicted (Hughes et al., 2017b, 2018), suggestthat the research community may have underestimated climate risks
for coral reefs (Figure 3.18). The general assessment of climate risks for
mangroves prior to this special report was that they face greater risks
from deforestation and unsustainable coastal development than fromclimate change (Alongi, 2008; Hoegh-Guldberg et al., 2014; Gattuso etal., 2015). Recent large-scale die-offs (Duke et al., 2017; Lovelock et al.,2017), however, suggest that risks from climate change may have been
underestimated for mangroves as well. With the events of the last past
three years in mind, risks are now considered to be undetectable tomoderate (i.e., moderate risks now start at 1.3°C as opposed to 1.8°C;
medium confidence). Consequently, when average global warming
reaches 1.3°C above pre-industrial levels, the risk of climate change tomangroves are projected to be moderate (Figure 3.18) while tropicalcoral reefs will have reached a high level of risk as examplified byincreasing damage from heat stress since the early 1980s. At global
warming of 1.8°C above pre-industrial levels, seagrasses are projected
to reach moderate to high levels of risk (e.g., damage resulting fromsea level rise, erosion, extreme temperatures, and storms), while risks
to mangroves from climate change are projected to remain moderate(e.g., not keeping up with sea level rise, and more frequent heat stressmortality) although there is low certainty as to when or if this important
ecosystem is likely to transition to higher levels of additional risk fromclimate change (Figure 3.18).
Warm water (tropical) coral reefs are projected to reach a very highrisk of impact at 1.2°C (Figure 3.18), with most available evidence
suggesting that coral-dominated ecosystems will be non-existent at thistemperature or higher (high confidence). At this point, coral abundance
will be near zero at many locations and storms will contribute to‘flattening’ the three-dimensional structure of reefs without recovery,as already observed for some coral reefs (Alvarez-Filip et al., 2009). The
impacts of warming, coupled with ocean acidification, are expected
to undermine the ability of tropical coral reefs to provide habitat for
thousand of species, which together provide a range of ecosystemservices (e.g., food, livelihoods, coastal protection, cultural services)that are important for millions of people (high confidence) (Burke etal., 2011).
Strategies for reducing the impact of climate change on framework
organisms include reducing stresses not directly related to climatechange (e.g., coastal pollution, overfishing and destructive coastal
development) in order to increase their ecological resilience in the faceof accelerating climate change impacts (World Bank, 2013; Ellison,
2014; Anthony et al., 2015; Sierra-Correa and Cantera Kintz, 2015;
Kroon et al., 2016; O’Leary et al., 2017), as well as protecting locations
where organisms may be more robust (Palumbi et al., 2014) or lessexposed to climate change (Bongaerts et al., 2010; van Hooidonk etal., 2013; Beyer et al., 2018). This might involve cooler areas due to226

Impacts of 1.5°C of Global Warming on Natural and Human Systems

upwelling, or involve deep-water locations that experience less extreme
conditions and impacts. Given the potential value of such locations forpromoting the survival of coral communities under climate change,
efforts to prevent their loss resulting from other stresses are important(Bongaerts et al., 2010, 2017; Chollett et al., 2010, 2014; Chollett andMumby, 2013; Fine et al., 2013; van Hooidonk et al., 2013; Cacciapaglia
and van Woesik, 2015; Beyer et al., 2018). A full understanding of
the role of refugia in reducing the loss of ecosystems has yet to be
developed (low to medium confidence). There is also interest in exsitu conservation approaches involving the restoration of corals via
aquaculture (Shafir et al., 2006; Rinkevich, 2014) or the use of ‘assisted
evolution’ to help corals adapt to changing sea temperatures (vanOppen et al., 2015, 2017), although there are numerous challenges
that must be surpassed if these approaches are to be cost-effective
responses to preserving coral reefs under rapid climate change (lowconfidence) (Hoegh-Guldberg, 2012, 2014a; Bayraktarov et al., 2016).
High levels of adaptation are expected to be required to prevent
impacts on food security and livelihoods in coastal populations(medium confidence). Integrating coastal infrastructure with changing
ecosystems such as mangroves, seagrasses and salt marsh, may offer
adaptation strategies as they shift shoreward as sea levels rise (highconfidence). Maintaining the sediment supply to coastal areas would
also assist mangroves in keeping pace with sea level rise (Shearman etal., 2013; Lovelock et al., 2015; Sasmito et al., 2016). For this reason,
habitat for mangroves can be strongly affected by human actions suchas building dams which reduce the sediment supply and hence theability of mangroves to escape ‘drowning’ as sea level rises (Lovelock
et al., 2015). In addition, integrated coastal zone management should
recognize the importance and economic expediency of using natural
ecosystems such as mangroves and tropical coral reefs to protect
coastal human communities (Arkema et al., 2013; Temmerman et al.,2013; Ferrario et al., 2014; Hinkel et al., 2014; Elliff and Silva, 2017).
Adaptation options include developing alternative livelihoods andfood sources, ecosystem-based management/adaptation such as
ecosystem restoration, and constructing coastal infrastructure that
reduces the impacts of rising seas and intensifying storms (Rinkevich,
2015; Weatherdon et al., 2016; Asiedu et al., 2017a; Feller et al.,2017). Clearly, these options need to be carefully assessed in termsof feasibility, cost and scalability, as well as in the light of the coastal
ecosystems involved (Bayraktarov et al., 2016).

3.4.4.11 Ocean foodwebs (pteropods, bivalves, krill and fin fish)
Ocean foodwebs are vast interconnected systems that transfer solar
energy and nutrients from phytoplankton to higher trophic levels,
including apex predators and commercially important species suchas tuna. Here, we consider four representative groups of marineorganisms which are important within foodwebs across the ocean, andwhich illustrate the impacts and ramifications of 1.5°C or higher levelsof warming.
The first group of organisms, pteropods, are small pelagic molluscs
that suspension feed and produce a calcium carbonate shell. They arehighly abundant in temperate and polar waters where they are an
important link in the foodweb between phytoplankton and a rangeof other organisms including fish, whales and birds. The second group,Impacts of 1.5°C of Global Warming on Natural and Human Systems

bivalve molluscs (e.g., clams, oysters and mussels), are filter-feeding
invertebrates. These invertebrate organisms underpin importantfisheries and aquaculture industries, from polar to tropical regions, andare important food sources for a range of organisms including humans.
The third group of organisms considered here is a globally significant
group of invertebrates known as euphausiid crustaceans (krill), which
are a key food source for many marine organisms and hence a major
link between primary producers and higher trophic levels (e.g., fish,mammals and sea birds). Antarctic krill, Euphausia superba, are among
the most abundant species in terms of mass and are consequently anessential component of polar foodwebs (Atkinson et al., 2009). The lastgroup, fin fishes, is vitally important components of ocean foodwebs,
contribute to the income of coastal communities, industries and nations,and are important to the foodsecurity and livelihood of hundreds ofmillions of people globally (FAO, 2016). Further background for thissection is provided in Supplementary Material 3.SM.3.2.
There is a moderate risk to ocean foodwebs under present-day
conditions (medium to high confidence) (Figure 3.18). Changing
water chemistry and temperature are already affecting the ability ofpteropods to produce their shells, swim and survive (Bednaršek etal., 2016). Shell dissolution, for example, has increased by 19–26%
in both nearshore and offshore populations since the pre-industrial
period (Feely et al., 2016). There is considerable concern as towhether these organisms are declining further, especially given
the central importance in ocean foodwebs (David et al., 2017).
Reviewing the literature reveals that pteropods are projected to
face high risks of impact at average global temperatures 1.5°C
above pre-industrial levels and increasing risks of impacts at 2°C(medium confidence).
As GMST increases by 1.5°C and more, the risk of impacts from ocean
warming and acidification are expected to be moderate to high, except
in the case of bivalves (mid-latitudes) where the risks of impacts areprojected to be high to very high (Figure 3.18). Ocean warming andacidification are already affecting the life history stages of bivalve
molluscs (e.g., Asplund et al., 2014; Mackenzie et al., 2014; Waldbusser
et al., 2014; Zittier et al., 2015; Shi et al., 2016; Velez et al., 2016; Q.
Wang et al., 2016; Castillo et al., 2017; Lemasson et al., 2017; Ong et al.,2017; X. Zhao et al., 2017). Impacts on adult bivalves include decreased
growth, increased respiration and reduced calcification, whereas
larval stages tend to show greater developmental abnormalities andincreased mortality after exposure to these conditions (medium to highconfidence) (Q. Wang et al., 2016; Lemasson et al., 2017; Ong et al.,2017; X. Zhao et al., 2017). Risks are expected to accumulate at higher
temperatures for bivalve molluscs, with very high risks expected at
1.8°C of warming or more. This general pattern applies to low-latitude
fin fish, which are expected to experience moderate to high risks ofimpact at 1.3°C of global warming (medium confidence), and very highrisks at 1.8°C at low latitudes (medium confidence) (Figure 3.18).
Large-scale changes to foodweb structure are occurring in all oceans. For
example, record levels of sea ice loss in the Antarctic (Notz and Stroeve,
2016; Turner et al., 2017b) translate into a loss of habitat and hence
reduced abundance of krill (Piñones and Fedorov, 2016), with negative
ramifications for the seabirds and whales which feed on krill (Croxall,
1992; Trathan and Hill, 2016) (low-medium confidence). Other influences,

Chapter 3

such as high rates of ocean acidification coupled with shoaling of thearagonite saturation horizon, are likely to also play key roles (Kawaguchi
et al., 2013; Piñones and Fedorov, 2016). As with many risks associatedwith impacts at the ecosystem scale, most adaptation options focus on
the management of stresses unrelated to climate change but resulting
from human activities, such as pollution and habitat destruction.
Reducing these stresses will be important in efforts to maintain importantfoodweb components. Fisheries management at local to regional scales
will be important in reducing stress on foodweb organisms, such as
those discussed here, and in helping communities and industries adapt
to changing foodweb structures and resources (see further discussion offisheries per se below; Section 3.4.6.3). One strategy is to maintain larger
population levels of fished species in order to provide more resilient
stocks in the face of challenges that are increasingly driven by climatechange (Green et al., 2014; Bell and Taylor, 2015).

3.4.4.12 Key ecosystem services (e.g., carbon uptake, coastal
protection, and tropical coral reef recreation)
The ocean provides important services, including the regulation of
atmospheric composition via gas exchange across the boundary
between ocean and atmosphere, and the storage of carbon in vegetationand soils associated with ecosystems such as mangroves, salt marshes
and coastal peatlands. These services involve a series of physicochemical
processes which are influenced by ocean chemistry, circulation, biology,
temperature and biogeochemical components, as well as by factors otherthan climate (Boyd, 2015). The ocean is also a net sink for CO2 (anotherimportant service), absorbing approximately 30% of human emissions
from the burning of fossil fuels and modification of land use (IPCC, 2013).
Carbon uptake by the ocean is decreasing (Iida et al., 2015), and there isincreasing concern from observations and models regarding associatedchanges to ocean circulation (Sections 3.3.7 and 3.4.4., Rahmstorf etal., 2015b);. Biological components of carbon uptake by the ocean are
also changing, with observations of changing net primary productivity(NPP) in equatorial and coastal upwelling systems (medium confidence)(Lluch-Cota et al., 2014; Sydeman et al., 2014; Bakun et al., 2015), as
well as subtropical gyre systems (low confidence) (Signorini et al., 2015).
There is general agreement that NPP will decline as ocean warming andacidification increase (medium confidence) (Bopp et al., 2013; Boyd et al.,2014; Pörtner et al., 2014; Boyd, 2015).
Projected risks of impacts from reductions in carbon uptake, coastal
protection and services contributing to coral reef recreation suggest
a transition from moderate to high risks at 1.5°C and higher (lowconfidence). At 2°C, risks of impacts associated with changes tocarbon uptake are high (high confidence), while the risks associatedwith reduced coastal protection and recreation on tropical coral
reefs are high, especially given the vulnerability of this ecosystem
type, and others (e.g., seagrass and mangroves), to climate change(medium confidence) (Figure 3.18). Coastal protection is a service
provided by natural barriers such as mangroves, seagrass meadows,
coral reefs, and other coastal ecosystems, and it is important forprotecting human communities and infrastructure against the impacts
associated with rising sea levels, larger waves and intensifying
storms (high confidence) (Gutiérrez et al., 2012; Kennedy et al.,2013; Ferrario et al., 2014; Barbier, 2015; Cooper et al., 2016; Hauer
et al., 2016; Narayan et al., 2016). Both natural and human coastal
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protection have the potential to reduce these impacts (Fu and Song,2017). Tropical coral reefs, for example, provide effective protection
by dissipating about 97% of wave energy, with 86% of the energy
being dissipated by reef crests alone (Ferrario et al., 2014; Narayan
et al., 2016). Mangroves similarly play an important role in coastal
protection, as well as providing resources for coastal communities,

Impacts of 1.5°C of Global Warming on Natural and Human Systems

but they are already under moderate risk of not keeping up with sea
level rise due to climate change and to contributing factors, such as
reduced sediment supply or obstacles to shoreward shifts (Saunders
et al., 2014; Lovelock et al., 2015). This implies that coastal areas
currently protected by mangroves may experience growing risks over
time.

3

Figure 3.18 | Summary of additional risks of impacts from ocean warming (and associated climate change factors such ocean acidification) for a range of ocean organisms,
ecosystems and sectors at 1.0°C, 1.5°C and 2.0°C of warming of the average sea surface temperature (SST) relative to the pre-industrial period. The grey bar represents therange of GMST for the most recent decade: 2006–2015. The assessment of changing risk levels and associated confidence were primarily derived from the expert judgementof Gattuso et al. (2015) and the lead authors and relevant contributing authors of Chapter 3 (SR1.5), while additional input was received from the many reviewers of theocean systems section of SR1.5. Notes: (i) The analysis shown here is not intended to be comprehensive. The examples of organisms, ecosystems and sectors included here areintended to illustrate the scale, types and projection of risks for representative natural and human ocean systems. (ii) The evaluation of risks by experts did not consider genetic
adaptation, acclimatization or human risk reduction strategies (mitigation and societal adaptation). (iii) As discussed elsewhere (Sections 3.3.10 and 3.4.4.5, Box 3.4; Gattuso
et al., 2015), ocean acidification is also having impacts on organisms and ecosystems as carbon dioxide increases in the atmosphere. These changes are part of the responses
reported here, although partitioning the effects of the two drivers is difficult at this point in time and hence was not attempted. (iv) Confidence levels for location of transition
points between levels of risk (L = low, M = moderate, H = high and VH = very high) are assessed and presented here as in the accompanying study by Gattuso et al. (2015).
Three transitions in risk were possible: W–Y (white to yellow), Y–R (yellow to red), and R–P (red to purple), with the colours corresponding to the level of additional risk posedby climate change. The confidence levels for these transitions were assessed, based on level of agreement and extent of evidence, and appear as letters associated with eachtransition (see key in diagram).

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Impacts of 1.5°C of Global Warming on Natural and Human Systems

Tourism is one of the largest industries globally (Rosselló-Nadal, 2014;
Markham et al., 2016; Spalding et al., 2017). A substantial part of the
global tourist industry is associated with tropical coastal regions andislands, where tropical coral reefs and related ecosystems play importantroles (Section 3.4.9.1) (medium confidence). Coastal tourism can be adominant money earner in terms of foreign exchange for many countries,
particularly small island developing states (SIDS) (Section 3.4.9.1, Box
3.5; Weatherdon et al., 2016; Spalding et al., 2017). The direct relationshipbetween increasing global temperatures, intensifying storms, elevatedthermal stress, and the loss of tropical coral reefs has raised concern
about the risks of climate change for local economies and industries
based on tropical coral reefs. Risks to coral reef recreational services fromclimate change are considered here, as well as in Box 3.5, Section 3.4.9
and Supplementary Material 3.SM.3.2.
Adaptations to the broad global changes in carbon uptake by the ocean
are limited and are discussed later in this report with respect to changes
in NPP and implications for fishing industries. These adaptation options
are broad and indirect, and the only other solution at large scale isto reduce the entry of CO2 into the ocean. Strategies for adapting to
reduced coastal protection involve (a) avoidance of vulnerable areasand hazards, (b) managed retreat from threatened locations, and/or (c)accommodation of impacts and loss of services (Bell, 2012; André et al.,2016; Cooper et al., 2016; Mills et al., 2016; Raabe and Stumpf, 2016; Fu
and Song, 2017). Within these broad options, there are some strategies
that involve direct human intervention, such as coastal hardening andthe construction of seawalls and artificial reefs (Rinkevich, 2014, 2015;
André et al., 2016; Cooper et al., 2016; Narayan et al., 2016), whileothers exploit opportunities for increasing coastal protection by involving
naturally occurring oyster banks, coral reefs, mangroves, seagrass andother ecosystems (UNEP-WCMC, 2006; Scyphers et al., 2011; Zhang et

Chapter 3al., 2012; Ferrario et al., 2014; Cooper et al., 2016). Natural ecosystems,
when healthy, also have the ability to repair themselves after being
damaged, which sets them apart from coastal hardening and otherhuman structures that require constant maintenance (Barbier, 2015; Elliffand Silva, 2017). In general, recognizing and restoring coastal ecosystems
may be more cost-effective than installing human structures, in thatcreating and maintaining structures is typically expensive (Temmerman
et al., 2013; Mycoo, 2017).
Recent studies have increasingly stressed the need for coastal protection
to be considered within the context of coastal land management,
including protecting and ensuring that coastal ecosystems are able toundergo shifts in their distribution and abundance as climate change
occurs (Clausen and Clausen, 2014; Martínez et al., 2014; Cui et al.,2015; André et al., 2016; Mills et al., 2016). Facilitating these changes
will require new tools in terms of legal and financial instruments, aswell as integrated planning that involves not only human communities
and infrastructure, but also associated ecosystem responses and values(Bell, 2012; Mills et al., 2016). In this regard, the interactions between
climate change, sea level rise and coastal disasters are increasingly
being informed by models (Bosello and De Cian, 2014) with a widening
appreciation of the role of natural ecosystems as an alternative tohardened coastal structures (Cooper et al., 2016). Adaptation options
for tropical coral reef recreation include: (i) protecting and improving
biodiversity and ecological function by minimizing the impact ofstresses unrelated to climate change (e.g., pollution and overfishing),(ii) ensuring adequate levels of coastal protection by supporting andrepairing ecosystems that protect coastal regions, (iii) ensuring fairand equitable access to the economic opportunities associated withrecreational activities, and (iv) seeking and protecting supplies of water
for tourism, industry and agriculture alongside community needs.

Box 3.4 | Warm-Water (Tropical) Coral Reefs in a 1.5°C Warmer World
Warm-water coral reefs face very high risks (Figure 3.18) from climate change. A world in which global warming is restricted to 1.5°C
above pre-industrial levels would be a better place for coral reefs than that of a 2°C warmer world, in which coral reefs would mostly
disappear (Donner et al., 2005; Hoegh-Guldberg et al., 2014; Schleussner et al., 2016b; van Hooidonk et al., 2016; Frieler et al., 2017;
Hughes et al., 2017a). Even with warming up until today (GMST for decade 2006–2015: 0.87°C; Chapter 1), a substantial proportionof coral reefs have experienced large-scale mortalities that have lead to much reduced coral populations (Hoegh-Guldberg et al.,2014). In the last three years alone (2016–2018), large coral reef systems such as the Great Barrier Reef (Australia) have lost as
much as 50% of their shallow water corals (Hughes et al., 2017b).
Coral-dominated reefs are found along coastlines between latitudes 30°S and 30°N, where they provide habitat for over a million
species (Reaka-Kudla, 1997) and food, income, coastal protection, cultural context and many other services for millions of people
in tropical coastal areas (Burke et al., 2011; Cinner et al., 2012; Kennedy et al., 2013; Pendleton et al., 2016). Ultimately, coral reefs
are underpinned by a mutualistic symbiosis between reef-building corals and dinoflagellates from the genus Symbiodinium (HoeghGuldberg et al., 2017). Warm-water coral reefs are found down to depths of 150 m and are dependent on light, making them distinct
from the cold deep-water reef systems that extend down to depths of 2000 m or more. The difficulty in accessing deep-water reefs
also means that the literature on the impacts of climate change on these systems is very limited by comparison to those on warmwater coral reefs (Hoegh-Guldberg et al., 2017). Consequently, this Box focuses on the impacts of climate change on warm-water(tropical) coral reefs, particularly with respect to their prospects under average global surface temperatures of 1.5°C and 2°C above
the pre-industrial period.

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Impacts of 1.5°C of Global Warming on Natural and Human Systems

Box 3.4 (continued)

The distribution and abundance of coral reefs has decreased by approximately 50% over the past 30 years (Gardner et al., 2005;
Bruno and Selig, 2007; De’ath et al., 2012) as a result of pollution, storms, overfishing and unsustainable coastal development (Burkeet al., 2011; Halpern et al., 2015; Cheal et al., 2017). More recently, climate change (i.e., heat stress; Hoegh-Guldberg, 1999; Baker
et al., 2008; Spalding and Brown, 2015; Hughes et al., 2017b) has emerged as the greatest threat to coral reefs, with temperaturesof just 1°C above the long-term summer maximum for an area (reference period 1985–1993) over 4–6 weeks being enough tocause mass coral bleaching (loss of the symbionts) and mortality (very high confidence) (WGII AR5, Box 18-2; Cramer et al., 2014).
Ocean warming and acidification can also slow growth and calcification, making corals less competitive compared to other benthic
organisms such as macroalgae or seaweeds (Dove et al., 2013; Reyes-Nivia et al., 2013, 2014). As corals disappear, so do fish andmany other reef-dependent species, which directly impacts industries such as tourism and fisheries, as well as the livelihoods for
many, often disadvantaged, coastal people (Wilson et al., 2006; Graham, 2014; Graham et al., 2015; Cinner et al., 2016; Pendleton etal., 2016). These impacts are exacerbated by increasingly intense storms (Section 3.3.6), which physically destroy coral communities
and hence reefs (Cheal et al., 2017), and by ocean acidification (Sections 3.3.10 and 3.4.4.5), which can weaken coral skeletons,
contribute to disease, and slow the recovery of coral communities after mortality events (low to medium confidence) (Gardner etal., 2005; Dove et al., 2013; Kennedy et al., 2013; Webster et al., 2013; Hoegh-Guldberg, 2014b; Anthony, 2016). Ocean acidification
also leads to enhanced activity by decalcifying organisms such as excavating sponges (Kline et al., 2012; Dove et al., 2013; Fang etal., 2013, 2014; Reyes-Nivia et al., 2013, 2014).
The predictions of back-to-back bleaching events (Hoegh-Guldberg, 1999) have become the reality in the summers of 2016–2017(e.g., Hughes et al., 2017b), as have projections of declining coral abundance (high confidence). Models have also become increasingly
capable and are currently predicting the large-scale loss of coral reefs by mid-century under even low-emissions scenarios (HoeghGuldberg, 1999; Donner et al., 2005; Donner, 2009; van Hooidonk and Huber, 2012; Frieler et al., 2013; Hoegh-Guldberg et al.,2014; van Hooidonk et al., 2016). Even achieving emissions reduction targets consistent with the ambitious goal of 1.5°C of global
warming under the Paris Agreement will result in the further loss of 70–90% of reef-building corals compared to today, with 99%
of corals being lost under warming of 2°C or more above the pre-industrial period (Frieler et al., 2013; Hoegh-Guldberg, 2014b;
Hoegh-Guldberg et al., 2014; Schleussner et al., 2016b; Hughes et al., 2017a).

3

The assumptions underpinning these assessments are considered to be highly conservative. In some cases, ‘optimistic’ assumptions
in models include rapid thermal adaptation by corals of 0.2°C–1°C per decade (Donner et al., 2005) or 0.4°C per decade (Schleussner
et al., 2016b), as well as very rapid recovery rates from impacts (e.g., five years in the case of Schleussner et al., 2016b). Adaptation
to climate change at these high rates, has not been documented, and recovery from mass mortality tends to take much longer(>15 years; Baker et al., 2008). Probability analysis also indicates that the underlying increases in sea temperatures that drive coralbleaching and mortality are 25% less likely under 1.5°C when compared to 2°C (King et al., 2017). Spatial differences between
the rates of heating suggest the possibility of temporary climate refugia (Caldeira, 2013; van Hooidonk et al., 2013; Cacciapaglia
and van Woesik, 2015; Keppel and Kavousi, 2015), which may play an important role in terms of the regeneration of coral reefs,
especially if these refuges are protected from risks unrelated to climate change. Locations at higher latitudes are reporting the arrivalof reef-building corals, which may be valuable in terms of the role of limited refugia and coral reef structures but will have low
biodiversity (high confidence) when compared to present-day tropical reefs (Kersting et al., 2017). Similarly, deep-water (30–150m) or mesophotic coral reefs (Bongaerts et al., 2010; Holstein et al., 2016) may play an important role because they avoid shallow
water extremes (i.e., heat and storms) to some extent, although the ability of these ecosystems to assist in repopulating damaged
shallow water areas may be limited (Bongaerts et al., 2017).
Given the sensitivity of corals to heat stress, even short periods of overshoot (i.e., decades) are expected to be extremely damaging
to coral reefs. Losing 70–90% of today’s coral reefs, however, will remove resources and increase poverty levels across the world’s
tropical coastlines, highlighting the key issue of equity for the millions of people that depend on these valuable ecosystems(Cross-Chapter Box 6; Spalding et al., 2014; Halpern et al., 2015). Anticipating these challenges to food and livelihoods for coastal
communities will become increasingly important, as will adaptation options, such as the diversification of livelihoods and thedevelopment of new sustainable industries, to reduce the dependency of coastal communities on threatened ecosystems such ascoral reefs (Cinner et al., 2012, 2016; Pendleton et al., 2016). At the same time, coastal communities will need to pre-empt changes
to other services provided by coral reefs such as coastal protection (Kennedy et al., 2013; Hoegh-Guldberg et al., 2014; Pörtner etal., 2014; Gattuso et al., 2015). Other threats and challenges to coastal living, such as sea level rise, will amplify challenges fromdeclining coral reefs, specially for SIDS and low-lying tropical nations. Given the scale and cost of these interventions, implementing
them earlier rather than later would be expedient.

230

Impacts of 1.5°C of Global Warming on Natural and Human Systems

3.4.5

Coastal and Low-Lying Areas, and Sea Level Rise

Sea level rise (SLR) is accelerating in response to climate change(Section 3.3.9; Church et al., 2013) and will produce significant impacts(high confidence). In this section, impacts and projections of SLR arereported at global and city scales (Sections 3.4.5.1 and 3.4.5.2) and forcoastal systems (Sections 3.4.5.3 to 3.4.5.6). For some sectors, there
is a lack of precise evidence of change at 1.5°C and 2°C of global
warming. Adaptation to SLR is discussed in Section 3.4.5.7.

3.4.5.1

Global / sub-global scale

Sea level rise (SLR) and other oceanic climate changes are alreadyresulting in salinization, flooding, and erosion and in the future areprojected to affect human and ecological systems, including health,heritage, freshwater availability, biodiversity, agriculture, fisheries andother services, with different impacts seen worldwide (high confidence).
Owing to the commitment to SLR, there is an overlapping uncertainty
in projections at 1.5°C and 2°C (Schleussner et al., 2016b; Sanderson
et al., 2017; Goodwin et al., 2018; Mengel et al., 2018; Nicholls et al.,2018; Rasmussen et al., 2018) and about 0.1 m difference in global
mean sea level (GMSL) rise between 1.5°C and 2°C worlds in the year
2100 (Section 3.3.9, Table 3.3). Exposure and impacts at 1.5°C and 2°C
differ at different time horizons (Schleussner et al., 2016b; Brown etal., 2018a, b; Nicholls et al., 2018; Rasmussen et al., 2018). However,these are distinct from impacts associated with higher increases intemperature (e.g., 4°C or more, as discussed in Brown et al., 2018a)over centennial scales. The benefits of climate change mitigation
reinforce findings of earlier IPCC reports (e.g., Wong et al., 2014).
Table 3.3 shows the land and people exposed to SLR (assuming there
is no adaptation or protection at all) using the Dynamic Interactive
Vulnerability Assessment (DIVA) model (extracted from Brown et al.,2018a and Goodwin et al., 2018; see also Supplementary Material
3.SM, Table 3.SM.4). Thus, exposure increases even with temperaturestabilization. The exposed land area is projected to at least double by2300 using a RCP8.5 scenario compared with a mitigation scenario(Brown et al., 2018a). In the 21st century, land area exposed tosea level rise (assuming there is no adaptation or protection at all)is projected to be at least an order of magnitude larger than the
cumulative land loss due to submergence (which takes into accountdefences) (Brown et al., 2016, 2018a) regardless of the SLR scenario
applied. Slower rates of rise due to climate change mitigation may
provide a greater opportunity for adaptation (medium confidence),
which could substantially reduce impacts.
In agreement with the assessment in WGII AR5 Section 5.4.3.1 (Wonget al., 2014), climate change mitigation may reduce or delay coastal
exposure and impacts (very high confidence). Adaptation has thepotential to substantially reduce risk through a portfolio of available
options (Sections 5.4.3.1 and 5.5 of Wong et al., 2014; Sections 6.4.2.3
and 6.6 of Nicholls et al., 2007). At 1.5°C in 2100, 31–69 million people(2010 population values) worldwide are projected to be exposed toflooding, assuming no adaptation or protection at all, comparedwith 32–79 million people (2010 population values) at 2°C in 2100
(Supplementary Material 3.SM, Table 3.SM.4; Rasmussen et al., 2018).
As a result, up to 10.4 million more people would be exposed to sea

Chapter 3

level rise at 2°C compared with 1.5°C in 2100 (medium confidence).
With a 1.5°C stabilization scenario in 2100, 62.7 million people per year
are at risk from flooding, with this value increasing to 137.6 million
people per year in 2300 (50th percentile, average across SSP1­­–­5, nosocio-economic change after 2100). These projections assume that no
upgrade to current protection levels occurs (Nicholls et al., 2018). The
number of people at risk increases by approximately 18% in 2030 if
a 2°C scenario is used and by 266% in 2300 if an RCP8.5 scenario
is considered (Nicholls et al., 2018). Through prescribed IPCC Special
Report on Emissions Scenarios (SRES) SLR scenarios, Arnell et al.
(2016) also found that the number of people exposed to flooding
increased substantially at warming levels higher than 2°C, assuming
no adaptation beyond current protection levels. Additionally, impacts
increased in the second half of the 21st century.
Coastal flooding is projected to cost thousands of billions of USD
annually, with damage costs under constant protection estimatedat 0.3–5.0% of global gross domestic product (GDP) in 2100 underan RCP2.6 scenario (Hinkel et al., 2014). Risks are projected to behighest in South and Southeast Asia, assuming there is no upgrade
to current protection levels, for all levels of climate warming (Arnell etal., 2016; Brown et al., 2016). Countries with at least 50 million people
exposed to SLR (assuming no adaptation or protection at all) based on
a 1,280 Pg C emissions scenario (approximately a 1.5°C temperaturerise above today’s level) include China, Bangladesh, Egypt, India,
Indonesia, Japan, Philippines, United States and Vietnam (Clark et al.,2016). Rasmussen et al. (2018) and Brown et al. (2018a) project thatsimilar countries would have high exposure to SLR in the 21st century
using 1.5°C and 2°C scenarios. Thus, there is high confidence that SLR
will have significant impacts worldwide in this century and beyond.

3.4.5.2

CitiesObservations of the impacts of SLR in cities are difficult to record
because multiple drivers of change are involved. There are observationsof ongoing and planned adaptation to SLR and extreme water levels
in some cities (Araos et al., 2016; Nicholls et al., 2018), whilst other
cities have yet to prepare for these impacts (high confidence) (seeSection 3.4.8 and Cross-Chapter Box 9 in Chapter 4). There are limited
observations and analyses of how cities will cope with higher and/or
multi-centennial SLR, with the exception of Amsterdam, New York andLondon (Nicholls et al., 2018).
Coastal urban areas are projected to see more extreme water levels
due to rising sea levels, which may lead to increased flooding anddamage of infrastructure from extreme events (unless adaptation isundertaken), plus salinization of groundwater. These impacts may beenhanced through localized subsidence (Wong et al., 2014), whichcauses greater relative SLR. At least 136 megacities (port cities witha population greater than 1 million in 2005) are at risk from flooding
due to SLR (with magnitudes of rise possible under 1.5°C or 2°C in the
21st century, as indicated in Section 3.3.9) unless further adaptationis undertaken (Hanson et al., 2011; Hallegatte et al., 2013). Many ofthese cities are located in South and Southeast Asia (Hallegatte etal., 2013; Cazenave and Cozannet, 2014; Clark et al., 2016; Jevrejeva
et al., 2016). Jevrejeva et al. (2016) projected that more than 90% of
global coastlines could experience SLR greater than 0.2 m with 2°C
2313Chapter 3of warming by 2040 (RCP8.5). However, for scenarios where 2°C isstabilized or occurs later in time, this figure is likely to differ becauseof the commitment to SLR. Raising existing dikes helps protect against
SLR, substantially reducing risks, although other forms of adaptation
exist. By 2300, dike heights under a non-mitigation scenario (RCP8.5)
could be more than 2 m higher (on average for 136 megacities) thanunder climate change mitigation scenarios at 1.5°C or 2°C (Nicholls
et al., 2018). Thus, rising sea levels commit coastal cities to long-term
adaptation (high confidence).

3.4.5.3

3

Small islands

Qualitative physical observations of SLR (and other stresses) include
inundation of parts of low-lying islands, land degradation due tosaltwater intrusion in Kiribati and Tuvalu (Wairiu, 2017), and shorelinechange in French Polynesia (Yates et al., 2013), Tuvalu (Kench et al.,2015, 2018) and Hawaii (Romine et al., 2013). Observations, models
and other evidence indicate that unconstrained Pacific atolls have kept
pace with SLR, with little reduction in size or net gain in land (Kenchet al., 2015, 2018; McLean and Kench, 2015; Beetham et al., 2017).
Whilst islands are highly vulnerable to SLR (high confidence), they arealso reactive to change. Small islands are impacted by multiple climaticstressors, with SLR being a more important stressor to some islands
than others (Sections 3.4.10, 4.3.5.6, 5.2.1, 5.5.3.3, Boxes 3.5, 4.3 and
5.3).
Observed adaptation to multiple drivers of coastal change, including
SLR, includes retreat (migration), accommodation and defence.
Migration (internal and international) has always been importanton small islands (Farbotko and Lazrus, 2012; Weir et al., 2017), withchanging environmental and weather conditions being just one factor in
the choice to migrate (Sections 3.4.10, 4.3.5.6 and 5.3.2; Campbell and
Warrick, 2014). Whilst flooding may result in migration or relocation,for example in Vunidogoloa, Fiji (McNamara and Des Combes, 2015;
Gharbaoui and Blocher, 2016) and the Solomon Islands (Albert et al.,2017), in situ adaptation may be tried or preferred, for example stilted
housing or raised floors in Tubigon, Bohol, Philippines (Jamero et al.,2017), raised roads and floors in Batasan and Ubay, Philippines (Jamero
et al., 2018), and raised platforms for faluw in Leang, Federated Statesof Micronesia (Nunn et al., 2017). Protective features, such as seawalls
or beach nourishment, are observed to locally reduce erosion and floodrisk but can have other adverse implications (Sovacool, 2012; Mycoo,
2014, 2017; Nurse et al., 2014; AR5 Section 29.6.22).
There is a lack of precise, quantitative studies of projected impacts
of SLR at 1.5°C and 2°C. Small islands are projected to be at risk
and very sensitive to coastal climate change and other stressors(high confidence) (Nurse et al., 2014; Benjamin and Thomas, 2016;
Ourbak and Magnan, 2017; Brown et al., 2018a; Nicholls et al., 2018;
Rasmussen et al., 2018; AR5 Sections 29.3 and 29.4), such as oceanic
warming, SLR (resulting in salinization, flooding and erosion), cyclones
and mass coral bleaching and mortality (Section 3.4.4, Boxes 3.4 and3.5). These impacts can have significant socio-economic and ecologicalimplications, such as on health, agriculture and water resources, which
in turn have impacts on livelihoods (Sovacool, 2012; Mycoo, 2014,2017; Nurse et al., 2014). Combinations of drivers causing adverse
impacts are important. For example, Storlazzi et al. (2018) found that
232

Impacts of 1.5°C of Global Warming on Natural and Human Systems

the impacts of SLR and wave-induced flooding (within a temperature
horizon equivalent of 1.5°C), could affect freshwater availability on
Roi-Namur, Marshall Islands, but is also dependent on other extreme
weather events. Freshwater resources may also be affected by
a 0.40 m rise in sea level (which may be experienced with a 1.5°C
warming) in other Pacific atolls (Terry and Chui, 2012). Whilst SLR isa major hazard for atolls, islands reaching higher elevations are also
threatened given that there is often a lot of infrastructure located near
the coast (high confidence) (Kumar and Taylor, 2015; Nicholls et al.,2018). Tens of thousands of people on small islands are exposed to
SLR (Rasmussen et al., 2018). Giardino et al. (2018) found that hard
defence structures on the island of Ebeye in the Marshall Islands wereeffective in reducing damage due to SLR at 1.5°C and 2°C. Additionally,
damage was also reduced under mitigation scenarios compared with
non-mitigation scenarios. In Jamaica and St Lucia, SLR and extreme
sea levels are projected to threaten transport system infrastructure at
1.5°C unless further adaptation is undertaken (Monioudi et al., 2018).
Slower rates of SLR will provide a greater opportunity for adaptation
to be successful (medium confidence), but this may not be substantial
enough on islands with a very low mean elevation. Migration and/or
relocation may be an adaptation option (Section 3.4.10). Thomas andBenjamin (2017) highlight three areas of concern in the context of loss
and damage at 1.5°C: a lack of data, gaps in financial assessments,
and a lack of targeted policies or mechanisms to address these issues(Cross-Chapter Box 12 in Chapter 5). Small islands are projected toremain vulnerable to SLR (high confidence).

3.4.5.4

Deltas and estuaries

Observations of SLR and human influence are felt through salinization,
which leads to mixing in deltas and estuaries, aquifers, leading toflooding (also enhanced by precipitation and river discharge), landdegradation and erosion. Salinization is projected to impact freshwater
sources and pose risks to ecosystems and human systems (Section
5.4; Wong et al., 2014). For instance, in the Delaware River estuary on
the east coast of the USA, upward trends of salinity (measured sincethe 1900s), accounting for the effects of streamflow and seasonalvariations, have been detected and SLR is a potential cause (Ross etal., 2015).
Z. Yang et al. (2015) found that future climate scenarios for the USA(A1B 1.6°C and B1 2°C in the 2040s) had a greater effect on salinity
intrusion than future land-use/land-cover change in the Snohomish
River estuary in Washington state (USA). This resulted in a shift inthe salinity both upstream and downstream in low flow conditions.
Projecting impacts in deltas needs an understanding of both fluvial
discharge and SLR, making projections complex because the drivers
operate on different temporal and spatial scales (Zaman et al., 2017;
Brown et al., 2018b). The mean annual flood depth when 1.5°C is firstprojected to be reached in the Ganges-Brahmaputra delta may be lessthan the most extreme annual flood depth seen today, taking into
account SLR, surges, tides, bathymetry and local river flows (Brown etal., 2018b). Further, increased river salinity and saline intrusion in theGanges-Brahmaputra-Meghna is likely with 2°C of warming (Zaman
et al., 2017). Salinization could impact agriculture and food security(Cross-Chapter Box 6 in this chapter). For 1.5°C or 2°C stabilization
conditions in 2200 or 2300 plus surges, a minimum of 44% of theImpacts of 1.5°C of Global Warming on Natural and Human Systems

Bangladeshi Ganges-Brahmaputra, Indian Bengal, Indian Mahanadi
and Ghanese Volta delta land area (without defences) would be
exposed unless sedimentation occurs (Brown et al., 2018b). Other
deltas are similarly vulnerable. SLR is only one factor affecting deltas,
and assessment of numerous geophysical and anthropogenic driversof geomorphic change is important (Tessler et al., 2018). For example,
dike building to reduce flooding and dam building (Gupta et al., 2012)
restricts sediment movement and deposition, leading to enhanced
subsidence, which can occur at a greater rate than SLR (Auerbach
et al., 2015; Takagi et al., 2016). Although dikes remain essential for
reducing flood risk today, promoting sedimentation is an advisable
strategy (Brown et al., 2018b) which may involve nature-based
solutions. Transformative decisions regarding the extent of sediment
restrictive infrastructure may need to be considered over centennial
scales (Brown et al., 2018b). Thus, in a 1.5°C or 2°C warmer world,
deltas, which are home to millions of people, are expected to be highly
threatened from SLR and localized subsidence (high confidence).

3.4.5.5

Wetlands

Observations indicate that wetlands, such as saltmarshes and mangrove
forests, are disrupted by changing conditions (Sections 3.4.4.8; Wong etal., 2014; Lovelock et al., 2015), such as total water levels and sedimentavailability. For example, saltmarshes in Connecticut and New York,USA, measured from 1900 to 2012, have accreted with SLR but havelost marsh surface relative to tidal datums, leading to increased marsh
flooding and further accretion (Hill and Anisfeld, 2015). This change
stimulated marsh carbon storage and aided climate change mitigation.
Salinization may lead to shifts in wetland communities and theirecosystem functions (Herbert et al., 2015). Some projections of wetlandchange, with magnitudes (but not necessarily rates or timing) of SLR
analogous to 1.5°C and 2°C of global warming, indicate a net loss of
wetlands in the 21st century (e.g., Blankespoor et al., 2014; Cui et al.,2015; Arnell et al., 2016; Crosby et al., 2016), whilst others report a net
gain with wetland transgression (e.g., Raabe and Stumpf, 2016 in the
Gulf of Mexico). However, the feedback between wetlands and sea
level is complex, with parameters such as a lack of accommodation
space restricting inland migration, or sediment supply and feedbacks
between plant growth and geomorphology (Kirwan and Megonigal,
2013; Ellison, 2014; Martínez et al., 2014; Spencer et al., 2016) still
being explored. Reducing global warming from 2°C to 1.5°C will
deliver long-term benefits, with natural sedimentation rates more likely
keep up with SLR. It remains unclear how wetlands will respond andunder what conditions (including other climate parameters) to a global
temperature rise of 1.5°C and 2°C. However, they have great potential
to aid and benefit climate change mitigation and adaptation (medium
confidence) (Sections 4.3.2.2 and 4.3.2.3).

3.4.5.6

Other coastal settings

Numerous impacts have not been quantified at 1.5°C or 2°C but remain
important. This includes systems identified in WGII AR5 (AR5 – Section
5.4 of Wong et al., 2014), such as beaches, barriers, sand dunes, rocky
coasts, aquifers, lagoons and coastal ecosystems (for the last system,
see Section 3.4.4.12). For example, SLR potentially affects erosion andaccretion, and therefore sediment movement, instigating shoreline

Chapter 3change (Section 5.4.2.1 of Wong et al., 2014), which could affect landbased ecosystems. Global observations indicate no overall clear effectof SLR on shoreline change (Le Cozannet et al., 2014), as it is highlysite specific (e.g., Romine et al., 2013). Infrastructure and geologicalconstraints reduce shoreline movement, causing coastal squeeze. In
Japan, for example, SLR is projected to cause beach losses under an
RCP2.6 scenario, which will worsen under RCP8.5 (Udo and Takeda,2017). Further, compound flooding (the combined risk of flooding frommultiple sources) has increased significantly over the past century inmajor coastal cities (Wahl et al., 2015) and is likely to increase withfurther development and SLR at 1.5°C and 2°C unless adaptation isundertaken. Thus, overall SLR will have a wide range of adverse effects
on coastal zones (medium confidence).

3.4.5.7

Adapting to coastal change

Adaptation to coastal change from SLR and other drivers is occurring
today (high confidence) (see Cross-Chapter Box 9 in Chapter4), including migration, ecosystem-based adaptation, raisinginfrastructure and defences, salt-tolerant food production, early
warning systems, insurance and education (Section 5.4.2.1 of Wong etal., 2014). Climate change mitigation will reduce the rate of SLR thiscentury, decreasing the need for extensive and, in places, immediate
adaptation. Adaptation will reduce impacts in human settings (highconfidence) (Hinkel et al., 2014; Wong et al., 2014), although there isless certainty for natural ecosystems (Sections 4.3.2 and 4.3.3.3). Whilesome ecosystems (e.g., mangroves) may be able to move shoreward
as sea levels increase, coastal development (e.g., coastal building,seawalls and agriculture) often interrupt these transitions (Saunders etal., 2014). Options for responding to these challenges include reducing
the impact of other stresses such as those arising from tourism, fishing,coastal development and unsustainable aquaculture/agriculture. In
some cases, restoration of coastal habitats and ecosystems can be acost-effective way of responding to changes arising from increasinglevels of exposure from rising sea levels, changes in storm conditions,coastal inundation and salinization (Arkema et al., 2013; Temmerman
et al., 2013; Ferrario et al., 2014; Hinkel et al., 2014; Spalding et al.,2014; Elliff and Silva, 2017).
Since AR5, planned and autonomous adaptation and forward planninghave become more widespread (Araos et al., 2016; Nicholls et al.,2018), but continued efforts are required as many localities are in theearly stages of adapting or are not adapting at all (Cross-Chapter Box
9 in Chapter 4; Araos et al., 2016). This is region and sub-sector specific,
and also linked to non-climatic factors (Ford et al., 2015; Araos et al.,2016; Lesnikowski et al., 2016). Adaptation pathways (e.g., Ranger etal., 2013; Barnett et al., 2014; Rosenzweig and Solecki, 2014; Buurman
and Babovic, 2016) assist long-term planning but are not widespreadpractices despite knowledge of long-term risks (Section 4.2.2).
Furthermore, human retreat and migration are increasingly being
considered as an adaptation response (Hauer et al., 2016; Geisler andCurrens, 2017), with a growing emphasis on green adaptation. There
are few studies on the adaptation limits to SLR where transformation
change may be required (AR5-Section 5.5 of Wong et al., 2014; Nicholls
et al., 2015). Sea level rise poses a long-term threat (Section 3.3.9), andadaptation will remain essential at the centennial scale under 1.5°C
and 2°C of warming (high confidence).
233

3

Chapter 3

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Table 3.3 | Land and people exposed to sea level rise (SLR), assuming no protection at all. Extracted from Brown et al. (2018a) and Goodwin et al. (2018). SSP: Shared SocioEconomic Pathway; wrt: with respect to; *:Population held constant at 2100 level.

Climate scenario
1.5°C

2°C

Impact factor, assuming there isno adaptation or protection at all(50th, [5th-95th percentiles])

Year
2050

2100

2200

2300

Temperature rise wrt 1850–1900 (°C)

1.71 (1.44–2.16)

1.60 (1.26–2.33)

1.41 (1.15–2.10)

1.32 (1.12–1.81)

SLR (m) wrt 1986–2005

0.20 (0.14–0.29)

0.40 (0.26–0.62)

0.73 (0.47–1.25)

1.00 (0.59–1.55)

Land exposed (x103 km2)

574 [558–597]

620 [575–669]

666 [595–772]

702 [666–853]

People exposed, SSP1–5 (millions)

127.9–139.0[123.4–134.0,
134.5–146.4]

102.7–153.5[94.8–140.7,
102.7–153.5]--

133.8–207.1[112.3–169.6,
165.2–263.4]*

Temperature rise wrt 1850–1900 (° C)

1.76 (1.51–2.16)

2.03 (1.72–2.64)

1.90 (1.66–2.57)

1.80 (1.60–2.20)

SLR (m) wrt 1986-2005

0.20 (0.14–0.29)

0.46 (0.30–0.69)

0.90 (0.58–1.50)

1.26 (0.74–1.90)

Land exposed (x103 km2)

575 [558–598]

637 [585–686]

705 [618–827]

767 [642–937]

People exposed, SSP1–5 (millions)

128.1–139.2[123.6–134.2,
134.7–146.6]

105.5–158.1[97.0–144.1,
118.1–179.0]--

148.3–233.0[120.3–183.4,
186.4–301.8]*

Box 3.5 | Small Island Developing States (SIDS)

3

Global warming of 1.5°C is expected to prove challenging for small island developing states (SIDS) that are already experiencing
impacts associated with climate change (high confidence). At 1.5°C, compounding impacts from interactions between climate drivers
may contribute to the loss of, or change in, critical natural and human systems (medium to high confidence). There are a number of
reduced risks at 1.5°C versus 2°C, particularly when coupled with adaptation efforts (medium to high confidence).
Changing climate hazards for SIDS at 1.5°C
Mean surface temperature is projected to increase in SIDS at 1.5°C of global warming (high confidence). The Caribbean region
will experience 0.5°C–1.5°C of warming compared to a 1971–2000 baseline, with the strongest warming occurring over larger
land masses (Taylor et al., 2018). Under the Representative Concentration Pathway (RCP)2.6 scenario, the western tropical Pacificis projected to experience warming of 0.5°C–1.7°C relative to 1961–1990. Extreme temperatures will also increase, with potential
for elevated impacts as a result of comparably small natural variability (Reyer et al., 2017a). Compared to the 1971–2000 baseline,
up to 50% of the year is projected to be under warm spell conditions in the Caribbean at 1.5°C, with a further increase of up to
70 days at 2°C (Taylor et al., 2018).
Changes in precipitation patterns, freshwater availability and drought sensitivity differ among small island regions (medium to highconfidence). Some western Pacific islands and those in the northern Indian Ocean may see increased freshwater availability, whileislands in most other regions are projected to see a substantial decline (Holding et al., 2016; Karnauskas et al., 2016). For several
SIDS, approximately 25% of the overall freshwater stress projected under 2°C at 2030 could be avoided by limiting global warming
to 1.5°C (Karnauskas et al., 2018). In accordance with an overall drying trend, an increasing drought risk is projected for Caribbean
SIDS (Lehner et al., 2017), and moderate to extreme drought conditions are projected to be about 9% longer on average at 2°C
versus 1.5°C for islands in this region (Taylor et al., 2018).
Projected changes in the ocean system at higher warming targets (Section 3.4.4), including potential changes in circulation (Section
3.3.7) and increases in both surface temperatures (Section 3.3.7) and ocean acidification (Section 3.3.10), suggest increasing risks
for SIDS associated with warming levels close to and exceeding 1.5°C.
Differences in global sea level between 1.5°C and 2°C depend on the time scale considered and are projected to fully materialize
only after 2100 (Section 3.3.9). Projected changes in regional sea level are similarly time dependent, but generally found to be
above the global average for tropical regions including small islands (Kopp et al., 2014; Jevrejeva et al., 2016). Threats related tosea level rise (SLR) for SIDS, for example from salinization, flooding, permanent inundation, erosion and pressure on ecosystems,
will therefore persist well beyond the 21st century even under 1.5°C of warming (Section 3.4.5.3; Nicholls et al., 2018). Prolonged
interannual sea level inundations may increase throughout the tropical Pacific with ongoing warming and in the advent of an234

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Chapter 3

Box 3.5 (continued)

increased frequency of extreme La Niña events, exacerbating coastal impacts of projected global mean SLR (Widlansky et al., 2015).
Changes to the frequency of extreme El Niño and La Niña events may also increase the frequency of droughts and floods in South
Pacific islands (Box 4.2, Section 3.5.2; Cai et al., 2012).
Extreme precipitation in small island regions is often linked to tropical storms and contributes to the climate hazard (Khouakhi etal., 2017). Similarly, extreme sea levels for small islands, particularly in the Caribbean, are linked to tropical cyclone occurrence(Khouakhi and Villarini, 2017). Under a 1.5°C stabilization scenario, there is a projected decrease in the frequency of weaker tropicalstorms and an increase in the number of intense cyclones (Section 3.3.6; Wehner et al., 2018a). There are not enough studies
to assess differences in tropical cyclone statistics for 1.5°C versus 2°C (Section 3.3.6). There are considerable differences in theadaptation responses to tropical cyclones across SIDS (Cross-Chapter Box 11 in Chapter 4).
Impacts on key natural and human systems
Projected increases in aridity and decreases in freshwater availability at 1.5°C of warming, along with additional risks from SLR
and increased wave-induced run-up, might leave several atoll islands uninhabitable (Storlazzi et al., 2015; Gosling and Arnell,2016). Changes in the availability and quality of freshwater, linked to a combination of changes to climate drivers, may adversely
impact SIDS’ economies (White and Falkland, 2010; Terry and Chui, 2012; Holding and Allen, 2015; Donk et al., 2018). Growth-rate
projections based on temperature impacts alone indicate robust negative impacts on gross domestic product (GDP) per capita
growth for SIDS (Sections 3.4.7.1, 3.4.9.1 and 3.5.4.9; Pretis et al., 2018). These impacts would be reduced considerably under 1.5°C
but may be increased by escalating risks from climate-related extreme weather events and SLR (Sections 3.4.5.3, 3.4.9.4 and 3.5.3)

3

Marine systems and associated livelihoods in SIDS face higher risks at 2°C compared to 1.5°C (medium to high confidence).
Mass coral bleaching and mortality are projected to increase because of interactions between rising ocean temperatures, ocean
acidification, and destructive waves from intensifying storms (Section 3.4.4 and 5.2.3, Box 3.4). At 1.5°C, approximately 70–90% of
global coral reefs are projected to be at risk of long-term degradation due to coral bleaching, with these values increasing to 99% at
2°C (Frieler et al., 2013; Schleussner et al., 2016b). Higher temperatures are also related to an increase in coral disease development,
leading to coral degradation (Maynard et al., 2015). For marine fisheries, limiting warming to 1.5°C decreases the risk of species
extinction and declines in maximum catch potential, particularly for small islands in tropical oceans (Cheung et al., 2016a).
Long-term risks of coastal flooding and impacts on populations, infrastructure and assets are projected to increase with higher levelsof warming (high confidence). Tropical regions including small islands are expected to experience the largest increases in coastal
flooding frequency, with the frequency of extreme water-level events in small islands projected to double by 2050 (Vitousek et al.,2017). Wave-driven coastal flooding risks for reef-lined islands may increase as a result of coral reef degradation and SLR (Quataert
et al., 2015). Exposure to coastal hazards is particularly high for SIDS, with a significant share of population, infrastructure and assets
at risk (Sections 3.4.5.3 and 3.4.9; Scott et al., 2012; Kumar and Taylor, 2015; Rhiney, 2015; Byers et al., 2018). Limiting warming to
1.5°C instead of 2°C would spare the inundation of lands currently home to 60,000 individuals in SIDS by 2150 (Rasmussen et al.,2018). However, such estimates do not consider shoreline response (Section 3.4.5) or adaptation.
Risks of impacts across sectors are projected to be higher at 1.5°C compared to the present, and will further increase at 2°C (medium
to high confidence). Projections indicate that at 1.5°C there will be increased incidents of internal migration and displacement(Sections 3.5.5, 4.3.6 and 5.2.2; Albert et al., 2017), limited capacity to assess loss and damage (Thomas and Benjamin, 2017)
and substantial increases in the risk to critical transportation infrastructure from marine inundation (Monioudi et al., 2018). The
difference between 1.5°C and 2°C might exceed limits for normal thermoregulation of livestock animals and result in persistent heatstress for livestock animals in SIDS (Lallo et al., 2018).
At 1.5°C, limits to adaptation will be reached for several key impacts in SIDS, resulting in residual impacts, as well as loss anddamage (Section 1.1.1, Cross-Chapter Box 12 in Chapter 5). Limiting temperature increase to 1.5°C versus 2°C is expected to reduce
a number of risks, particularly when coupled with adaptation efforts that take into account sustainable development (Section 3.4.2
and 5.6.3.1, Box 4.3 and 5.3, Mycoo, 2017; Thomas and Benjamin, 2017). Region-specific pathways for SIDS exist to address climatechange (Section 5.6.3.1, Boxes 4.6 and 5.3, Cross-Chapter Box 11 in Chapter 4).

235Chapter 3

3.4.6

Food, Nutrition Security and Food Production
Systems (Including Fisheries and Aquaculture)

3.4.6.1

Crop production

Quantifying the observed impacts of climate change on food security
and food production systems requires assumptions about the many
non-climate variables that interact with climate change variables.
Implementing specific strategies can partly or greatly alleviate theclimate change impacts on these systems (Wei et al., 2017), whilst thedegree of compensation is mainly dependent on the geographical area
and crop type (Rose et al., 2016). Despite these uncertainties, recent
studies confirm that observed climate change has already affected crop
suitability in many areas, resulting in changes in the production levelsof the main agricultural crops. These impacts are evident in many areasof the world, ranging from Asia (C. Chen et al., 2014; Sun et al., 2015;
He and Zhou, 2016) to America (Cho and McCarl, 2017) and Europe(Ramirez-Cabral et al., 2016), and they particularly affect the typical
local crops cultivated in specific climate conditions (e.g., Mediterranean
crops like olive and grapevine, Moriondo et al., 2013a, b).

3

Temperature and precipitation trends have reduced crop production
and yields, with the most negative impacts being on wheat and maize(Lobell et al., 2011), whilst the effects on rice and soybean yields areless clear and may be positive or negative (Kim et al., 2013; van Oort
and Zwart, 2018). Warming has resulted in positive effects on crop yieldin some high-latitude areas (Jaggard et al., 2007; Supit et al., 2010;
Gregory and Marshall, 2012; C. Chen et al., 2014; Sun et al., 2015; He
and Zhou, 2016; Daliakopoulos et al., 2017), and may make it possible
to have more than one harvest per year (B. Chen et al., 2014; Sun etal., 2015). Climate variability has been found to explain more than
60% of the of maize, rice, wheat and soybean yield variations in themain global breadbaskets areas (Ray et al., 2015), with the percentage
varying according to crop type and scale (Moore and Lobell, 2015; Kentet al., 2017). Climate trends also explain changes in the length of thegrowing season, with greater modifications found in the northern highlatitude areas (Qian et al., 2010; Mueller et al., 2015).
The rise in tropospheric ozone has already reduced yields of wheat,rice, maize and soybean by 3–16% globally (Van Dingenen et al.,2009). In some studies, increases in atmospheric CO2 concentrations
were found to increase yields by enhancing radiation and water use
efficiencies (Elliott et al., 2014; Durand et al., 2018). In open-top
chamber experiments with a combination of elevated CO2 and 1.5°C ofwarming, maize and potato yields were observed to increase by 45.7%
and 11%, respectively (Singh et al., 2013; Abebe et al., 2016). However,observations of trends in actual crop yields indicate that reductions
as a result of climate change remain more common than crop yield
increases, despite increased atmospheric CO2 concentrations (Porter
et al., 2014). For instance, McGrath and Lobell (2013) indicated that
production stimulation at increased atmospheric CO2 concentrations
was mostly driven by differences in climate and crop species, whilst
yield variability due to elevated CO2 was only about 50–70% of thevariability due to climate. Importantly, the faster growth rates induced
by elevated CO2 have been found to coincide with lower protein content
in several important C3 cereal grains (Myers et al., 2014), although thismay not always be the case for C4 grains, such as sorghum, under236

Impacts of 1.5°C of Global Warming on Natural and Human Systems

drought conditions (De Souza et al., 2015). Elevated CO2 concentrationsof 568–590 ppm (a range that corresponds approximately to RCP6 in
the 2080s and hence a warming of 2.3°C–3.3°C (van Vuuren et al.,2011a, AR5 WGI Table 12.2 ) alone reduced the protein, micronutrientand B vitamin content of the 18 rice cultivars grown most widely inSoutheast Asia, where it is a staple food source, by an amount sufficient
to create nutrition-related health risks for 600 million people (Zhu etal., 2018). Overall, the effects of increased CO2 concentrations alone
during the 21st century are therefore expected to have a negative
impact on global food security (medium confidence).
Crop yields in the future will also be affected by projected changes intemperature and precipitation. Studies of major cereals showed thatmaize and wheat yields begin to decline with 1°C–2°C of local warming
and under nitrogen stress conditions at low latitudes (high confidence)(Porter et al., 2014; Rosenzweig et al., 2014). A few studies since AR5have focused on the impacts on cropping systems for scenarios where
the global mean temperature increase is within 1.5°C. Schleussner etal. (2016b) projected that constraining warming to 1.5°C rather than
2°C would avoid significant risks of declining tropical crop yield inWest Africa, Southeast Asia, and Central and South America. Ricke et al.
(2016) highlighted that cropland stability declines rapidly between 1°C
and 3°C of warming, whilst Bassu et al. (2014) found that an increasein air temperature negatively influences the modelled maize yield
response by –0.5 t ha−1 °C–1 and Challinor et al. (2014) reported similareffect for tropical regions. Niang et al. (2014) projected significantly
lower risks to crop productivity in Africa at 1.5°C compared to 2°C ofwarming. Lana et al. (2017) indicated that the impact of temperature
increases on crop failure of maize hybrids would be much greater as
temperatures increase by 2°C compared to 1.5°C (high confidence). J.
Huang et al. (2017) found that limiting warming to 1.5°C comparedto 2°C would reduce maize yield losses over drylands. Although
Rosenzweig et al. (2017, 2018) did not find a clear distinction between
yield declines or increases in some breadbasket regions between the
two temperature levels, they generally did find projections of decreasing
yields in breadbasket regions when the effects of CO2 fertilization were
excluded. Iizumi et al. (2017) found smaller reductions in maize andsoybean yields at 1.5°C than at 2°C of projected warming, higher riceproduction at 2°C than at 1.5°C, and no clear differences for wheat
on a global mean basis. These results are largely consistent with thoseof other studies (Faye et al., 2018; Ruane et al., 2018). In the western
Sahel and southern Africa, moving from 1.5°C to 2°C of warming has
been projected to result in a further reduction of the suitability of maize,sorghum and cocoa cropping areas and yield losses, especially for C3
crops, with rainfall change only partially compensating these impacts(Läderach et al., 2013; World Bank, 2013; Sultan and Gaetani, 2016).
A significant reduction has been projected for the global production ofwheat (by 6.0 ± 2.9%), rice (by 3.2 ± 3.7%), maize (by 7.4 ± 4.5%),and soybean, (by 3.1%) for each degree Celsius increase in global
mean temperature (Asseng et al., 2015; C. Zhao et al., 2017). Similarly,
Li et al. (2017) indicated a significant reduction in rice yields for each
degree Celsius increase, by about 10.3%, in the greater Mekong
subregion (medium confidence; Cross-Chapter Box 6: Food Security
in this chapter). Large rice and maize yield losses are to be expected
in China, owing to climate extremes (medium confidence) (Wei et al.,2017; Zhang et al., 2017).

Impacts of 1.5°C of Global Warming on Natural and Human Systems

While not often considered, crop production is also negatively affectedby the increase in both direct and indirect climate extremes. Direct
extremes include changes in rainfall extremes (Rosenzweig et al.,2014), increases in hot nights (Welch et al., 2010; Okada et al., 2011),
extremely high daytime temperatures (Schlenker and Roberts, 2009;
Jiao et al., 2016; Lesk et al., 2016), drought (Jiao et al., 2016; Lesk etal., 2016), heat stress (Deryng et al., 2014, Betts et al., 2018), flooding(Betts et al., 2018; Byers et al., 2018), and chilling damage (Jiao et al.,2016), while indirect effects include the spread of pests and diseases(Jiao et al., 2014; van Bruggen et al., 2015), which can also havedetrimental effects on cropping systems.
Taken together, the findings of studies on the effects of changes intemperature, precipitation, CO2 concentration and extreme weather
events indicate that a global warming of 2°C is projected to result in agreater reduction in global crop yields and global nutrition than global
warming of 1.5°C (high confidence; Section 3.6).

3.4.6.2

Livestock production

Studies of climate change impacts on livestock production are few innumber. Climate change is expected to directly affect yield quantity andquality (Notenbaert et al., 2017), as well as indirectly impacting the
livestock sector through feed quality changes and spread of pests anddiseases (Kipling et al., 2016) (high confidence). Increased warming andits extremes are expected to cause changes in physiological processes
in livestock (i.e., thermal distress, sweating and high respiratory rates)(Mortola and Frappell, 2000) and to have detrimental effects on animal
feeding, growth rates (André et al., 2011; Renaudeau et al., 2011; Collier
and Gebremedhin, 2015) and reproduction (De Rensis et al., 2015). Wallet al. (2010) observed reduced milk yields and increased cow mortality
as the result of heat stress on dairy cow production over some UK
regions.
Further, a reduction in water supply might increase cattle water demand(Masike and Urich, 2008). Generally, heat stress can be responsiblefor domestic animal mortality increase and economic losses (Vitali etal., 2009), affecting a wide range of reproductive parameters (e.g.,embryonic development and reproductive efficiency in pigs, Barati et al.,2008; ovarian follicle development and ovulation in horses, Mortensen
et al., 2009). Much attention has also been dedicated to ruminantdiseases (e.g., liver fluke, Fox et al., 2011; blue-tongue virus, Guis et al.,2012; foot-and-mouth disease (FMD), Brito et al. (2017); and zoonotic
diseases, Njeru et al., 2016; Simulundu et al., 2017).
Climate change impacts on livestock are expected to increase. In
temperate climates, warming is expected to lengthen the forage
growing season but decrease forage quality, with important variations
due to rainfall changes (Craine et al., 2010; Hatfield et al., 2011;
Izaurralde et al., 2011). Similarly, a decrease in forage quality is expected
for both natural grassland in France (Graux et al., 2013) and sown
pastures in Australia (Perring et al., 2010). Water resource availability
for livestock is expected to decrease owing to increased runoff andreduced groundwater resources. Increased temperature will likely
induce changes in river discharge and the amount of water in basins,
leading human and livestock populations to experience water stress,
especially in the driest areas (i.e., sub-Saharan Africa and South Asia)

Chapter 3(medium confidence) (Palmer et al., 2008). Elevated temperatures arealso expected to increase methane production (Knapp et al., 2014; M.A.
Lee et al., 2017). Globally, a decline in livestock of 7–10% is expected atabout 2°C of warming, with associated economic losses between $9.7
and $12.6 billion (Boone et al., 2018).

3.4.6.3

Fisheries and aquaculture production

Global fisheries and aquaculture contribute a total of 88.6 and 59.8
million tonnes of fish and other products annually (FAO, 2016),
and play important roles in the food security of a large number ofcountries (McClanahan et al., 2015; Pauly and Charles, 2015) as well
as being essential for meeting the protein demand of a growing
global population (Cinner et al., 2012, 2016; FAO, 2016; Pendleton
et al., 2016). A steady increase in the risks associated with bivalve
fisheries and aquaculture at mid-latitudes is coincident with increases
in temperature, ocean acidification, introduced species, disease andother drivers ( Lacoue-Labarthe et al., 2016; Clements and Chopin,
2017; Clements et al., 2017; Parker et al., 2017). Sea level rise andstorm intensification pose a risk to hatcheries and other infrastructure(Callaway et al., 2012; Weatherdon et al., 2016), whilst others risks
are associated with the invasion of parasites and pathogens (Asplund
et al., 2014; Castillo et al., 2017). Specific human strategies have
reduced these risks, which are expected to be moderate under RCP2.6
and very high under RCP8.5 (Gattuso et al., 2015). The risks related
to climate change for fin fish (Section 3.4.4) are producing a numberof challenges for small-scale fisheries (e.g., Kittinger, 2013; Pauly andCharles, 2015; Bell et al., 2018). Recent literature from 2015 to 2017
has described growing threats from rapid shifts in the biogeographyof key species (Poloczanska et al., 2013, 2016; Burrows et al., 2014;
García Molinos et al., 2015) and the ongoing rapid degradation of
key ecosystems such as coral reefs, seagrass and mangroves (Section
3.4.4, Box 3.4). The acceleration of these changes, coupled with nonclimate stresses (e.g., pollution, overfishing and unsustainable coastal
development), are driving many small-scale fisheries well below thesustainable harvesting levels required to maintain these resources
as a source of food (McClanahan et al., 2009, 2015; Cheung et al.,2010; Pendleton et al., 2016). As a result, future scenarios surrounding
climate change and global population growth increasingly project
shortages of fish protein for many regions, such as the Pacific Ocean(Bell et al., 2013, 2018) and Indian Ocean (McClanahan et al., 2015).
Mitigation of these risks involves marine spatial planning, fisheries
repair, sustainable aquaculture, and the development of alternative
livelihoods (Kittinger, 2013; McClanahan et al., 2015; Song and
Chuenpagdee, 2015; Weatherdon et al., 2016). Other threats concern
the increasing incidence of alien species and diseases (Kittinger et al.,2013; Weatherdon et al., 2016).
Risks of impacts related to climate change on low-latitude small-scale
fin fisheries are moderate today but are expected to reach very highlevels by 1.1°C of global warming. Projections for mid- to high-latitude
fisheries include increases in fishery productivity in some cases (Cheung
et al., 2013; Hollowed et al., 2013; Lam et al., 2014; FAO, 2016). These
projections are associated with the biogeographical shift of species
towards higher latitudes (Fossheim et al., 2015), which brings benefits
as well as challenges (e.g., increased production yet a greater risk of
disease and invasive species; low confidence). Factors underpinning237

3

Chapter 3

the expansion of fisheries production to high-latitude locations include
warming, increased light levels and mixing due to retreating sea ice(Cheung et al., 2009), which result in substantial increases in primary
productivity and fish harvesting in the North Pacific and North Atlantic(Hollowed and Sundby, 2014).
Present-day risks for mid-latitude bivalve fisheries and aquaculture
become undetectible up to 1.1°C of global warming, moderate at
1.3°C, and moderate to high up to 1.9°C (Figure 3.18). For instance,
Cheung et al. (2016a), simulating the loss in fishery productivity
at 1.5°C, 2°C and 3.5°C above the pre-industrial period, found thatthe potential global catch for marine fisheries will likely decrease by
more than three million metric tonnes for each degree of warming.
Low-latitude fin-fish fisheries have higher risks of impacts, with risks
being moderate under present-day conditions and becoming highabove 0.9°C and very high at 2°C of global warming. High-latitude

Impacts of 1.5°C of Global Warming on Natural and Human Systems

fisheries are undergoing major transformations, and while production
is increasing, present-day risk is moderate and is projected to remain
moderate at 1.5°C and 2°C (Figure 3.18).
Adaptation measures can be applied to shellfish, large pelagic fish
resources and biodiversity, and they include options such as protecting
reproductive stages and brood stocks from periods of high ocean
acidification (OA), stock selection for high tolerance to OA (highconfidence) (Ekstrom et al., 2015; Rodrigues et al., 2015; Handisyde
et al., 2016; Lee, 2016; Weatherdon et al., 2016; Clements and Chopin,
2017), redistribution of highly migratory resources (e.g., Pacific tuna)(high confidence), governance instruments such as international
fisheries agreements (Lehodey et al., 2015; Matear et al., 2015),
protection and regeneration of reef habitats, reduction of coral reef
stresses, and development of alternative livelihoods (e.g., aquaculture;
Bell et al., 2013, 2018).

Cross-Chapter Box 6 | Food Security
Lead Authors:Ove Hoegh-Guldberg (Australia), Sharina Abdul Halim (Malaysia), Marco Bindi (Italy), Marcos Buckeridge (Brazil), Arona Diedhiou (Ivory
Coast/Senegal), Kristie L. Ebi (USA), Deborah Ley (Guatemala/Mexico), Diana Liverman (USA), Chandni Singh (India), Rachel Warren(UK), Guangsheng Zhou (China).

3

Contributing Author:
Lorenzo Brilli (Italy)
Climate change influences food and nutritional security through its effects on food availability, quality, access and distribution (Patersonand Lima, 2010; Thornton et al., 2014; FAO, 2016). More than 815 million people were undernourished in 2016, and 11% of the world’s
population has experienced recent decreases in food security, with higher percentages in Africa (20%), southern Asia (14.4%) and the
Caribbean (17.7%) (FAO et al., 2017). Overall, food security is expected to be reduced at 2°C of global warming compared to 1.5°C,
owing to projected impacts of climate change and extreme weather on yields, crop nutrient content, livestock, fisheries and aquaculture
and land use (cover type and management) (Sections 3.4.3.6, 3.4.4.12 and 3.4.6), (high confidence). The effects of climate change
on crop yield, cultivation area, presence of pests, food price and supplies are projected to have major implications for sustainable
development, poverty eradication, inequality and the ability of the international community to meet the United Nations sustainable
development goals (SDGs; Cross-Chapter Box 4 in Chapter 1).
Goal 2 of the SDGs is to end hunger, achieve food security, improve nutrition and promote sustainable agriculture by 2030. This goalbuilds on the first millennium development goal (MDG-1) which focused on eradicating extreme poverty and hunger, through efforts
that reduced the proportion of undernourished people in low- and middle-income countries from 23.3% in 1990 to 12.9% in 2015.
Climate change threatens the capacity to achieve SDG 2 and could reverse the progress made already. Food security and agriculture
are also critical to other aspects of sustainable development, including poverty eradication (SDG 1), health and well-being (SDG 3),
clean water (SDG 6), decent work (SDG 8), and the protection of ecosystems on land (SDG 14) and in water (SDG 15) (UN, 2015, 2017;
Pérez-Escamilla, 2017).
Increasing global temperature poses large risks to food security globally and regionally, especially in low-latitude areas (medium
confidence) (Cheung et al., 2010; Rosenzweig et al., 2013; Porter et al., 2014; Rosenzweig and Hillel, 2015; Lam et al., 2016), with warmingof 2°C projected to result in a greater reduction in global crop yields and global nutrition than warming of 1.5°C (high confidence) (Section
3.4.6), owing to the combined effects of changes in temperature, precipitation and extreme weather events, as well as increasing CO2
concentrations. Climate change can exacerbate malnutrition by reducing nutrient availability and the quality of food products (medium
confidence) (Cramer et al., 2014; Zhu et al., 2018). Generally, vulnerability to decreases in water and food availability is projected to be
reduced at 1.5°C versus 2°C (Cheung et al., 2016a; Betts et al., 2018), especially in regions such as the African Sahel, the Mediterranean,
central Europe, the Amazon, and western and southern Africa (medium confidence) (Sultan and Gaetani, 2016; Lehner et al., 2017; Betts
et al., 2018; Byers et al., 2018; Rosenzweig et al., 2018).

238

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Chapter 3

Cross-Chapter Box 6 (continued)

Rosenzweig et al. (2018) and Ruane et al. (2018) reported that the higher CO2 concentrations associated with 2°C as compared tothose at 1.5°C of global warming are projected to drive positive effects in some regions. Production can also benefit from warming inhigher latitudes, with more fertile soils, favouring crops, and grassland production, in contrast to the situation at low latitudes (Section
3.4.6), and similar benefits could arise for high-latitude fisheries production (high confidence) (Section 3.4.6.3). Studies exploring
regional climate change risks on crop production are strongly influenced by the use of different regional climate change projections
and by the assumed strength of CO2 fertilization effects (Section 3.6), which are uncertain. For C3 crops, theoretically advantageous
CO2 fertilization effects may not be realized in the field; further, they are often accompanied by losses in protein and nutrient content ofcrops (Section 3.6), and hence these projected benefits may not be realized. In addition, some micronutrients such as iron and zinc willaccumulate less and be less available in food (Myers et al., 2014). Together, the impacts on protein availability may bring as many as
150 million people into protein deficiency by 2050 (Medek et al., 2017). However, short-term benefits could arise for high-latitude
fisheries production as waters warm, sea ice contracts and primary productivity increases under climate change (high confidence)(Section 3.4.6.3; Cheung et al., 2010; Hollowed and Sundby, 2014; Lam et al., 2016; Sundby et al., 2016; Weatherdon et al., 2016).
Factors affecting the projections of food security include variability in regional climate projections, climate change mitigation (whereland use is involved; see Section 3.6 and Cross-Chapter Box 7 in this chapter) and biological responses (medium confidence) (Section
3.4.6.1; McGrath and Lobell, 2013; Elliott et al., 2014; Pörtner et al., 2014; Durand et al., 2018), extreme events such as droughts
and floods (high confidence) (Sections 3.4.6.1, 3.4.6.2; Rosenzweig et al., 2014; Wei et al., 2017), financial volatility (Kannan et al.,
2000; Ghosh, 2010; Naylor and Falcon, 2010; HLPE, 2011), and the distributions of pests and disease (Jiao et al., 2014; van Bruggen
et al., 2015). Changes in temperature and precipitation are projected to increase global food prices by 3–84% by 2050 (IPCC, 2013).
Differences in price impacts of climate change are accompanied by differences in land-use change (Nelson et al., 2014b), energy policies
and food trade (Mueller et al., 2011; Wright, 2011; Roberts and Schlenker, 2013). Fisheries and aquatic production systems (aquaculture)face similar challenges to those of crop and livestock sectors (Section 3.4.6.3; Asiedu et al., 2017a, b; Utete et al., 2018). Human
influences on food security include demography, patterns of food waste, diet shifts, incomes and prices, storage, health status, tradepatterns, conflict, and access to land and governmental or other assistance (Chapters 4 and 5). Across all these systems, the efficiencyof adaptation strategies is uncertain because it is strongly linked with future economic and trade environments and their response tochanging food availability (medium confidence) (Lobell et al., 2011; von Lampe et al., 2014; d’Amour et al., 2016; Wei et al., 2017).

3

Climate change impacts on food security can be reduced through adaptation (Hasegawa et al., 2014). While climate change is projected
to decrease agricultural yield, the consequences could be reduced substantially at 1.5°C versus 2°C with appropriate investment (highconfidence) (Neumann et al., 2010; Muller, 2011; Roudier et al., 2011), awareness-raising to help inform farmers of new technologies formaintaining yield, and strong adaptation strategies and policies that develop sustainable agricultural choices (Sections 4.3.2 and 4.5.3).
In this regard, initiatives such as ‘climate-smart’ food production and distribution systems may assist via technologies and adaptation
strategies for food systems (Lipper et al., 2014; Martinez-Baron et al., 2018; Whitfield et al., 2018), as well as helping meet mitigation
goals (Harvey et al., 2014).
K.R. Smith et al. (2014) concluded that climate change will exacerbate current levels of childhood undernutrition and stunting throughreduced food availability. As well, climate change can drive undernutrition-related childhood mortality, and increase disability-adjusted
life years lost, with the largest risks in Asia and Africa (Supplementary Material 3.SM, Table 3.SM.12; Ishida et al., 2014; Hasegawa et al.,2016; Springmann et al., 2016). Studies comparing the health risks associated with reduced food security at 1.5°C and 2°C concluded
that risks would be higher and the globally undernourished population larger at 2°C (Hales et al., 2014; Ishida et al., 2014; Hasegawa
et al., 2016). Climate change impacts on dietary and weight-related risk factors are projected to increase mortality, owing to global
reductions in food availability and consumption of fruit, vegetables and red meat (Springmann et al., 2016). Further, temperature
increases are projected to reduce the protein and micronutrient content of major cereal crops, which is expected to further affect foodand nutritional security (Myers et al., 2017; Zhu et al., 2018).
Strategies for improving food security often do so in complex settings such as the Mekong River basin in Southeast Asia. The Mekong isa major food bowl (Smajgl et al., 2015) but is also a climate change hotspot (de Sherbinin, 2014; Lebel et al., 2014). This area is also a
useful illustration of the complexity of adaptation choices and actions in a 1.5°C warmer world. Climate projections include increased
annual average temperatures and precipitation in the Mekong (Zhang et al., 2017), as well as increased flooding and related disaster risks(T.F. Smith et al., 2013; Ling et al., 2015; Zhang et al., 2016). Sea level rise and saline intrusion are ongoing risks to agricultural systems
in this area by reducing soil fertility and limiting the crop productivity (Renaud et al., 2015). The main climate impacts in the Mekong areexpected to be on ecosystem health, through salinity intrusion, biomass reduction and biodiversity losses (Le Dang et al., 2013; Smajgl
et al., 2015); agricultural productivity and food security (Smajgl et al., 2015); livelihoods such as fishing and farming (D. Wu et al., 2013);
and disaster risk (D. Wu et al., 2013; Hoang et al., 2016), with implications for human mortality and economic and infrastructure losses.

239

Chapter 3

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Cross-Chapter Box 6 (continued)

Adaptation imperatives and costs in the Mekong will be higher under higher temperatures and associated impacts on agriculture
and aquaculture, hazard exposure, and infrastructure. Adaptation measures to meet food security include greater investment in crop
diversification and integrated agriculture–aquaculture practices (Renaud et al., 2015), improvement of water-use technologies (e.g.,
irrigation, pond capacity improvement and rainwater harvesting), soil management, crop diversification, and strengthening allied
sectors such as livestock rearing and aquaculture (ICEM, 2013). Ecosystem-based approaches, such as integrated water resourcesmanagement, demonstrate successes in mainstreaming adaptation into existing strategies (Sebesvari et al., 2017). However, some ofthese adaptive strategies can have negative impacts that deepen the divide between land-wealthy and land-poor farmers (Chapman
et al., 2016). Construction of high dikes, for example, has enabled triple-cropping, which benefits land-wealthy farmers but leads toincreasing debt for land-poor farmers (Chapman and Darby, 2016).
Institutional innovation has happened through the Mekong River Commission (MRC), which is an intergovernmental body between
Cambodia, Lao PDR, Thailand and Viet Nam that was established in 1995. The MRC has facilitated impact assessment studies, regional
capacity building and local project implementation (Schipper et al., 2010), although the mainstreaming of adaptation into development
policies has lagged behind needs (Gass et al., 2011). Existing adaptation interventions can be strengthened through greater flexibilityof institutions dealing with land-use planning and agricultural production, improved monitoring of saline intrusion, and the installationof early warning systems that can be accessed by the local authorities or farmers (Renaud et al., 2015; Hoang et al., 2016; Tran et al.,2018). It is critical to identify and invest in synergistic strategies from an ensemble of infrastructural options (e.g., building dikes); soft
adaptation measures (e.g., land-use change) (Smajgl et al., 2015; Hoang et al., 2018); combinations of top-down government-led (e.g.,
relocation) and bottom-up household strategies (e.g., increasing house height) (Ling et al., 2015); and community-based adaptation
initiatives that merge scientific knowledge with local solutions (Gustafson et al., 2016, 2018; Tran et al., 2018). Special attention needs
to be given to strengthening social safety nets and livelihood assets whilst ensuring that adaptation plans are mainstreamed into
broader development goals (Sok and Yu, 2015; Kim et al., 2017). The combination of environmental, social and economic pressures on
people in the Mekong River basin highlights the complexity of climate change impacts and adaptation in this region, as well as the factthat costs are projected to be much lower at 1.5°C than 2°C of global warming.

3

3.4.7

Human Health

Climate change adversely affects human health by increasing exposureand vulnerability to climate-related stresses, and decreasing thecapacity of health systems to manage changes in the magnitude andpattern of climate-sensitive health outcomes (Cramer et al., 2014; Hales
et al., 2014). Changing weather patterns are associated with shifts in
the geographic range, seasonality and transmission intensity of selectedclimate-sensitive infectious diseases (e.g., Semenza and Menne, 2009),
and increasing morbidity and mortality are associated with extreme
weather and climate events (e.g., K.R. Smith et al., 2014). Healthdetection and attribution studies conducted since AR5 have provided
evidence, using multistep attribution, that climate change is negatively
affecting adverse health outcomes associated with heatwaves,
Lyme disease in Canada, and Vibrio emergence in northern Europe(Mitchell, 2016; Mitchell et al., 2016; Ebi et al., 2017). The IPCC AR5
concluded there is high to very high confidence that climate change
will lead to greater risks of injuries, disease and death, owing to more
intense heatwaves and fires, increased risks of undernutrition, andconsequences of reduced labour productivity in vulnerable populations(K.R. Smith et al., 2014).

3.4.7.1

Projected risk at 1.5°C and 2°C of global warming

The projected risks to human health of warming of 1.5°C and 2°C,
based on studies of temperature-related morbidity and mortality,
air quality and vector borne diseases assessed in and since AR5, aresummarized in Supplementary Material 3.SM, Tables 3.SM.8, 3.SM.9
240

and 3.SM.10 (based on Ebi et al., 2018). Other climate-sensitive
health outcomes, such as diarrheal diseases, mental health issues
and the full range of sources of poor air quality, were not considered
because of the lack of projections of how risks could change at 1.5°C
and 2°C. Few projections were available for specific temperatures
above pre-industrial levels; Supplementary Material 3.SM, Table
3.SM.7 provides the conversions used to translate risks projected forparticular time slices to those for specific temperature changes (Ebiet al., 2018).
Temperature-related morbidity and mortality: The magnitude of
projected heat-related morbidity and mortality is greater at 2°C thanat 1.5°C of global warming (very high confidence)(Doyon et al., 2008;
Jackson et al., 2010; Hanna et al., 2011; Huang et al., 2012; Petkova
et al., 2013; Hajat et al., 2014; Hales et al., 2014; Honda et al., 2014;
Vardoulakis et al., 2014; Garland et al., 2015; Huynen and Martens,
2015; Li et al., 2015; Schwartz et al., 2015; L. Wang et al., 2015;
Guo et al., 2016; T. Li et al., 2016; Chung et al., 2017; Kendrovski
et al., 2017; Mishra et al., 2017; Arnell et al., 2018; Mitchell et al.,2018b). The number of people exposed to heat events is projected
to be greater at 2°C than at 1.5°C (Russo et al., 2016; Mora et al.,2017; Byers et al., 2018; Harrington and Otto, 2018; King et al.,2018). The extent to which morbidity and mortality are projected
to increase varies by region, presumably because of differences inacclimatization, population vulnerability, the built environment,
access to air conditioning and other factors (Russo et al., 2016; Moraet al., 2017; Byers et al., 2018; Harrington and Otto, 2018; King etal., 2018). Populations at highest risk include older adults, children,Impacts of 1.5°C of Global Warming on Natural and Human Systems

women, those with chronic diseases, and people taking certain
medications (very high confidence). Assuming adaptation takes place
reduces the projected magnitude of risks (Hales et al., 2014; Huynen
and Martens, 2015; T. Li et al., 2016).
In some regions, cold-related mortality is projected to decrease withincreasing temperatures, although increases in heat-related mortality
generally are projected to outweigh any reductions in cold-related
mortality with warmer winters, with the heat-related risks increasing
with greater degrees of warming (Huang et al., 2012; Hajat et al., 2014;
Vardoulakis et al., 2014; Gasparrini et al., 2015; Huynen and Martens,
2015; Schwartz et al., 2015).
Occupational health: Higher ambient temperatures and humidity levels
place additional stress on individuals engaging in physical activity. Safe
work activity and worker productivity during the hottest months of theyear would be increasingly compromised with additional climate change(medium confidence) (Dunne et al., 2013; Kjellstrom et al., 2013, 2018;
Sheffield et al., 2013; Habibi Mohraz et al., 2016). Patterns of change may
be complex; for example, at 1.5°C, there could be about a 20% reductionin areas experiencing severe heat stress in East Asia, compared tosignificant increases in low latitudes at 2°C (Lee and Min, 2018). The costsof preventing workplace heat-related illnesses through worker breaks
suggest that the difference in economic loss between 1.5°C and 2°C could
be approximately 0.3% of global gross domestic product (GDP) in 2100
(Takakura et al., 2017). In China, taking into account population growthand employment structure, high temperature subsidies for employees
working on extremely hot days are projected to increase from 38.6 billion
yuan yr–1 in 1979–2005 to 250 billion yuan yr–1 in the 2030s (about 1.5°C)(Zhao et al., 2016).
Air quality: Because ozone formation is temperature dependent,
projections focusing only on temperature increase generally conclude
that ozone-related mortality will increase with additional warming, withthe risks higher at 2°C than at 1.5°C (high confidence) (Supplementary
Material 3.SM, Table 3.SM.9; Heal et al., 2013; Tainio et al., 2013;
Likhvar et al., 2015; Silva et al., 2016; Dionisio et al., 2017; J.Y. Lee
et al., 2017). Reductions in precursor emissions would reduce futureozone concentrations and associated mortality. Mortality associatedwith exposure to particulate matter could increase or decrease in the
future, depending on climate projections and emissions assumptions(Supplementary Material 3.SM, Table 3.SM.8; Tainio et al., 2013;
Likhvar et al., 2015; Silva et al., 2016).
Malaria: Recent projections of the potential impacts of climatechange on malaria globally and for Asia, Africa, and South America(Supplementary Material 3.SM, Table 3.SM.10) confirm that weather
and climate are among the drivers of the geographic range, intensity oftransmission, and seasonality of malaria, and that the relationships are
not necessarily linear, resulting in complex patterns of changes in risk
with additional warming (very high confidence) (Ren et al., 2016; Songet al., 2016; Semakula et al., 2017). Projections suggest that the burdenof malaria could increase with climate change because of a greater
geographic range of the Anopheles vector, longer season, and/or
increase in the number of people at risk, with larger burdens at higher
levels of warming, but with regionally variable patterns (medium tohigh confidence). Vector populations are projected to shift with climate

Chapter 3change, with expansions and reductions depending on the degree of
local warming, the ecology of the mosquito vector, and other factors(Ren et al., 2016).
Aedes (mosquito vector for dengue fever, chikungunya, yellow
fever and Zika virus): Projections of the geographic distribution of
Aedes aegypti and Ae. albopictus (principal vectors) or of the prevalenceof dengue fever generally conclude that there will be an increase in thenumber of mosquitos and a larger geographic range at 2°C than at
1.5°C, and they suggest that more individuals will be at risk of dengue
fever, with regional differences (high confidence) (Fischer et al., 2011,2013; Colón-González et al., 2013, 2018; Bouzid et al., 2014; Ogden
et al., 2014a; Mweya et al., 2016). The risks increase with greater
warming. Projections suggest that climate change is projected to
expand the geographic range of chikungunya, with greater expansions
occurring at higher degrees of warming (Tjaden et al., 2017).
Other vector-borne diseases: Increased warming in North
America and Europe could result in geographic expansions ofregions (latitudinally and altitudinally) climatically suitable for West
Nile virus transmission, particularly along the current edges of its
transmission areas, and extension of the transmission season, withthe magnitude and pattern of changes varying by location and levelof warming (Semenza et al., 2016). Most projections conclude thatclimate change could expand the geographic range and seasonalityof Lyme and other tick-borne diseases in parts of North America andEurope (Ogden et al., 2014b; Levi et al., 2015). The projected changes
are larger with greater warming and under higher greenhouse gas
emissions pathways. Projections of the impacts of climate change onleishmaniosis and Chagas disease indicate that climate change could
increase or decrease future health burdens, with greater impacts
occurring at higher degrees of warming (González et al., 2014;
Ceccarelli and Rabinovich, 2015).
In summary, warming of 2°C poses greater risks to human health thanwarming of 1.5°C, often with the risks varying regionally, with a few
exceptions (high confidence). There is very high confidence that each
additional unit of warming could increase heat-related morbidity andmortality, and that adaptation would reduce the magnitude of impacts.
There is high confidence that ozone-related mortality could increase if
precursor emissions remain the same, and that higher temperatures
could affect the transmission of some infectious diseases, withincreases and decreases projected depending on the disease (e.g.,malaria, dengue fever, West Nile virus and Lyme disease), region anddegree of temperature change.

3.4.8

Urban Areas

There is new literature on urban climate change and its differential
impacts on and risks for infrastructure sectors – energy, water, transportand buildings – and vulnerable populations, including those living in
informal settlements (UCCRN, 2018). However, there is limited literatureon the risks of warming of 1.5°C and 2°C in urban areas. Heat-related
extreme events (Matthews et al., 2017), variability in precipitation (Yuet al., 2018) and sea level rise can directly affect urban areas (Section
3.4.5, Bader et al., 2018; Dawson et al., 2018). Indirect risks may arise
from interactions between urban and natural systems.
2413

Chapter 3

3

Future warming and urban expansion could lead to more extreme
heat stress (Argüeso et al., 2015; Suzuki-Parker et al., 2015). At 1.5°C
of warming, twice as many megacities (such as Lagos, Nigeria andShanghai, China) could become heat stressed, exposing more than
350 million more people to deadly heat by 2050 under midrange
population growth. Without considering adaptation options, suchas cooling from more reflective roofs, and overall characteristics ofurban agglomerations in terms of land use, zoning and building codes(UCCRN, 2018), Karachi (Pakistan) and Kolkata (India) could experience
conditions equivalent to the deadly 2015 heatwaves on an annual
basis under 2°C of warming (Akbari et al., 2009; Oleson et al., 2010;
Matthews et al., 2017). Warming of 2°C is expected to increase therisks of heatwaves in China’s urban agglomerations (Yu et al., 2018).
Stabilizing at 1.5°C of warming instead of 2°C could decrease mortality
related to extreme temperatures in key European cities, assuming no
adaptation and constant vulnerability (Jacob et al., 2018; Mitchell etal., 2018a). Holding temperature change to below 2°C but taking urban
heat islands (UHI) into consideration, projections indicate that there
could be a substantial increase in the occurrence of deadly heatwaves incities. The urban impacts of these heatwaves are expected to be similar
at 1.5°C and 2°C and substantially larger than under the present climate(Matthews et al., 2017; Yu et al., 2018). Increases in the intensity of
UHI could exacerbate warming of urban areas, with projections ranging
from a 6% decrease to a 30% increase for a doubling of CO2 (McCarthy
et al., 2010). Increases in population and city size, in the context of a
warmer climate, are projected to increase UHI (Georgescu et al., 2012;
Argüeso et al., 2014; Conlon et al., 2016; Kusaka et al., 2016; GrossmanClarke et al., 2017).
For extreme heat events, an additional 0.5°C of warming implies
a shift from the upper bounds of observed natural variability to a
new global climate regime (Schleussner et al., 2016b), with distinct
implications for the urban poor (Revi et al., 2014; Jean-Baptiste et al.,2018; UCCRN, 2018). Adverse impacts of extreme events could arisein tropical coastal areas of Africa, South America and Southeast Asia
(Schleussner et al., 2016b). These urban coastal areas in the tropics
are particularly at risk given their large informal settlements and othervulnerable urban populations, as well as vulnerable assets, including
businesses and critical urban infrastructure (energy, water, transportand buildings) (McGranahan et al., 2007; Hallegatte et al., 2013; Reviet al., 2014; UCCRN, 2018). Mediterranean water stress is projected
to increase from 9% at 1.5°C to 17% at 2°C compared to values in
1986–2005 period. Regional dry spells are projected to expand from
7% at 1.5°C to 11% at 2°C for the same reference period. Sea level riseis expected to be lower at 1.5°C than 2°C, lowering risks for coastal
metropolitan agglomerations (Schleussner et al., 2016b).

Impacts of 1.5°C of Global Warming on Natural and Human Systems

depending on the vulnerability of the location (coastal or non-coastal)(high confidence), businesses, infrastructure sectors (energy, waterand transport), levels of poverty, and the mix of formal and informal
settlements.

3.4.9

Key Economic Sectors and Services

Climate change could affect tourism, energy systems and transportation
through direct impacts on operations (e.g., sea level rise) and throughimpacts on supply and demand, with the risks varying significantly withgeographic region, season and time. Projected risks also depend on
assumptions with respect to population growth, the rate and patternof urbanization, and investments in infrastructure. Table 3.SM.11 in
Supplementary Material 3.SM summarizes the cited publications.

3.4.9.1

Tourism

The implications of climate change for the global tourism sector are
far-reaching and are impacting sector investments, destination assets(environment and cultural), operational and transportation costs, andtourist demand patterns (Scott et al., 2016a; Scott and Gössling, 2018).
Since AR5, observed impacts on tourism markets and destination
communities continue to be not well analysed, despite the manyanalogue conditions (e.g., heatwaves, major hurricanes, wild fires,
reduced snow pack, coastal erosion and coral reef bleaching) thatare anticipated to occur more frequently with climate change. Thereis some evidence that observed impacts on tourism assets, such asenvironmental and cultural heritage, are leading to the development of‘last chance to see’ tourism markets, where travellers visit destinations
before they are substantially degraded by climate change impacts or
to view the impacts of climate change on landscapes (Lemelin et al.,2012; Stewart et al., 2016; Piggott-McKellar and McNamara, 2017).

Climate models are better at projecting implications of greenhousegas forcing on physical systems than at assessing differential risks
associated with achieving a specific temperature target (James etal., 2017). These challenges in managing risks are amplified whencombined with the scale of urban areas and assumptions about socioeconomic pathways (Krey et al., 2012; Kamei et al., 2016; Yu et al.,2016; Jiang and Neill, 2017).

There is limited research on the differential risks of a 1.5° versus
2°C temperature increase and resultant environmental and socioeconomic impacts in the tourism sector. The translation of thesechanges in climate resources for tourism into projections of tourism
demand remains geographically limited to Europe. Based on analysesof tourist comfort, summer and spring/autumn tourism in muchof western Europe may be favoured by 1.5°C of warming, but withnegative effects projected for Spain and Cyprus (decreases of 8% and2%, respectively, in overnight stays) and most coastal regions of theMediterranean (Jacob et al., 2018). Similar geographic patterns ofpotential tourism gains (central and northern Europe) and reduced
summer favourability (Mediterranean countries) are projected under2°C (Grillakis et al., 2016). Considering potential changes in naturalsnow only, winter overnight stays at 1.5°C are projected to decline
by 1–2% in Austria, Italy and Slovakia, with an additional 1.9 million
overnight stays lost under 2°C of warming (Jacob et al., 2018). Usingan econometric analysis of the relationship between regional tourism
demand and climate conditions, Ciscar et al. (2014) projected that a
2°C warmer world would reduce European tourism by 5% (€15 billion
yr–1), with losses of up to 11% (€6 billion yr–1) for southern Europe anda potential gain of €0.5 billion yr–1 in the UK.

In summary, in the absence of adaptation, in most cases, warmingof 2°C poses greater risks to urban areas than warming of 1.5°C,

There is growing evidence that the magnitude of projected impacts istemperature dependent and that sector risks could be much greater242

Impacts of 1.5°C of Global Warming on Natural and Human Systems

with higher temperature increases and resultant environmental
and socio-economic impacts (Markham et al., 2016; Scott et al.,2016a; Jones, 2017; Steiger et al., 2017). Studies from 27 countries
consistently project substantially decreased reliability of ski areas thatare dependent on natural snow, increased snowmaking requirements
and investment in snowmaking systems, shortened and more variable
ski seasons, a contraction in the number of operating ski areas,altered competitiveness among and within regional ski markets,and subsequent impacts on employment and the value of vacation
properties (Steiger et al., 2017). Studies that omit snowmaking do
not reflect the operating realities of most ski areas and overestimate
impacts at 1.5°C–2°C. In all regional markets, the extent and timingof these impacts depend on the magnitude of climate change and thetypes of adaptive responses by the ski industry, skiers and destination
communities. The decline in the number of former Olympic WinterGames host locations that could remain climatically reliable for future
Olympic and Paralympic Winter Games has been projected to be muchgreater under scenarios warmer than 2°C (Scott et al., 2015; Jacob etal., 2018).
The tourism sector is also affected by climate-induced changes inenvironmental assets critical for tourism, including biodiversity,
beaches, glaciers and other features important for environmental andcultural heritage. Limited analyses of projected risks associated with
1.5°C versus 2°C are available (Section 3.4.4.12). A global analysis ofsea level rise (SLR) risk to 720 UNESCO Cultural World Heritage sites
projected that about 47 sites might be affected under 1°C of warming,with this number increasing to 110 and 136 sites under 2°C and 3°C,
respectively (Marzeion and Levermann, 2014). Similar risks to vast
worldwide coastal tourism infrastructure and beach assets remainunquantified for most major tourism destinations and small island
developing states (SIDS) that economically depend on coastal tourism.
One exception is the projection that an eventual 1 m SLR could
partially or fully inundate 29% of 900 coastal resorts in 19 Caribbean
countries, with a substantially higher proportion (49–60%) vulnerable
to associated coastal erosion (Scott and Verkoeyen, 2017).
A major barrier to understanding the risks of climate change for tourism,
from the destination community scale to the global scale, has been
the lack of integrated sectoral assessments that analyse the full rangeof potential compounding impacts and their interactions with othermajor drivers of tourism (Rosselló-Nadal, 2014; Scott et al., 2016b).
When applied to 181 countries, a global vulnerability index including
27 indicators found that countries with the lowest risk are located inwestern and northern Europe, central Asia, Canada and New Zealand,
while the highest sector risks are projected for Africa, the Middle
East, South Asia and SIDS in the Caribbean, Indian and Pacific Oceans(Scott and Gössling, 2018). Countries with the highest risks and where
tourism represents a significant proportion of the national economy(i.e., more than 15% of GDP) include many SIDS and least developed
countries. Sectoral climate change risk also aligns strongly with regions
where tourism growth is projected to be the strongest over the coming
decades, including sub-Saharan Africa and South Asia, pointing to an
important potential barrier to tourism development. The transnational
implications of these impacts on the highly interconnected global
tourism sector and the contribution of tourism to achieving the 2030
sustainable development goals (SDGs) remain important uncertainties.

Chapter 3

In summary, climate is an important factor influencing the geography
and seasonality of tourism demand and spending globally (very highconfidence). Increasing temperatures are projected to directly impact
climate-dependent tourism markets, including sun, beach and snow
sports tourism, with lesser risks for other tourism markets that are lessclimate sensitive (high confidence). The degradation or loss of beachand coral reef assets is expected to increase risks for coastal tourism,
particularly in subtropical and tropical regions (high confidence).

3.4.9.2

Energy systems

Climate change is projected to lead to an increased demand for air
conditioning in most tropical and subtropical regions (Arent et al.,2014; Hong and Kim, 2015) (high confidence). Increasing temperatures
will decrease the thermal efficiency of fossil, nuclear, biomass andsolar power generation technologies, as well as buildings and otherinfrastructure (Arent et al., 2014). For example, in Ethiopia, capital
expenditures through 2050 might either decrease by approximately
3% under extreme wet scenarios or increase by up to 4% under asevere dry scenario (Block and Strzepek, 2012).
Impacts on energy systems can affect gross domestic product (GDP).
The economic damage in the United States from climate change isestimated to be, on average, roughly 1.2% cost of GDP per year per
1°C increase under RCP8.5 (Hsiang et al., 2017). Projections of GDP
indicate that negative impacts of energy demand associated withspace heating and cooling in 2100 will be greatest (median: –0.94%
change in GDP) under 4°C (RCP8.5) compared with under 1.5°C(median: –0.05%), depending on the socio-economic conditions (Parket al., 2018). Additionally, projected total energy demands for heating
and cooling at the global scale do not change much with increases in
global mean surface temperature (GMST) of up to 2°C. A high degreeof variability is projected between regions (Arnell et al., 2018).
Evidence for the impact of climate change on energy systems since AR5
is limited. Globally, gross hydropower potential is projected to increase(by 2.4% under RCP2.6 and by 6.3% under RCP8.5 for the 2080s), withthe most growth expected in Central Africa, Asia, India and northernhigh latitudes (van Vliet et al., 2016). Byers et al. (2018) found thatenergy impacts at 2°C increase, including more cooling degree days,
especially in tropical regions, as well as increased hydro-climatic risk
to thermal and hydropower plants predominantly in Europe, North
America, South and Southeast Asia and southeast Brazil. Donk et al.
(2018) assessed future climate impacts on hydropower in Suriname
and projected a decrease of approximately 40% in power capacity
for a global temperature increase in the range of 1.5°C. At minimum
and maximum increases in global mean temperature of 1.35°C and
2°C, the overall stream flow in Florida, USA is projected to increaseby an average of 21%, with pronounced seasonal variations, resulting
in increases in power generation in winter (+72%) and autumn(+15%) and decreases in summer (–14%; Chilkoti et al., 2017). Greaterchanges are projected at higher temperature increases. In a reference
scenario with global mean temperatures rising by 1.7°C from 2005
to 2050, U.S. electricity demand in 2050 was 1.6–6.5% higher thanin a control scenario with constant temperatures (McFarland et al.,2015). Decreased electricity generation of –15% is projected for Brazil
starting in 2040, with values expected to decline to –28% later in the
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3Chapter 3

3Impacts of 1.5°C of Global Warming on Natural and Human Systems

century (de Queiroz et al., 2016). In large parts of Europe, electricity
demand is projected to decrease, mainly owing to reduced heating
demand (Jacob et al., 2018).

signal was limited, with more evidence of impacts of climate change on
the places where indigenous people live and use traditional ecologicalknowledge (Olsson et al., 2014).

In Europe, no major differences in large-scale wind energy resources
or in inter- or intra-annual variability are projected for 2016–2035
under RCP8.5 and RCP4.5 (Carvalho et al., 2017). However, in 2046–
2100, wind energy density is projected to decrease in eastern Europe(–30%) and increase in Baltic regions (+30%). Intra-annual variability
is expected to increase in northern Europe and decrease in southern
Europe. Under RCP4.5 and RCP8.5, the annual energy yield of European
wind farms as a whole, as projected to be installed by 2050, will remain
stable (±5 yield for all climate models). However, wind farm yields areprojected to undergo changes of up to 15% in magnitude at country
and local scales and of 5% at the regional scale (Tobin et al., 2015,2016). Hosking et al. (2018) assessed wind power generation over
Europe for 1.5°C of warming and found the potential for wind energy
to be greater than previously assumed in northern Europe. Additionally,
Tobin et al. (2018) assessed impacts under 1.5°C and 2°C of warmingon wind, solar photovoltaic and thermoelectric power generation
across Europe. These authors found that photovoltaic and wind power
might be reduced by up to 10%, and hydropower and thermoelectric
generation might decrease by up to 20%, with impacts being limited
at 1.5°C of warming but increasing as temperature increases (Tobin etal., 2018).

3.4.10.1 Livelihoods and poverty

3.4.9.3

Transportation

Road, air, rail, shipping and pipeline transportation can be impacted
directly or indirectly by weather and climate, including increases inprecipitation and temperature; extreme weather events (flooding andstorms); SLR; and incidence of freeze–thaw cycles (Arent et al., 2014).
Much of the published research on the risks of climate change for thetransportation sector has been qualitative.
The limited new research since AR5 supports the notion that increases
in global temperatures will impact the transportation sector. Warming
is projected to result in increased numbers of days of ice-free navigation
and a longer shipping season in cold regions, thus affecting shipping
and reducing transportation costs (Arent et al., 2014). In the North Sea
Route, large-scale commercial shipping might not be possible until
2030 for bulk shipping and until 2050 for container shipping under
RCP8.5. A 0.05% increase in mean temperature is projected from anincrease in short-lived pollutants, as well as elevated CO2 and non-CO2
emissions, associated with additional economic growth enabled by theNorth Sea Route. (Yumashev et al., 2017). Open water vessel transit
has the potential to double by mid-century, with a two to four month
longer season (Melia et al., 2016).

3.4.10

Livelihoods and Poverty, and the Changing
Structure of Communities

Multiple drivers and embedded social processes influence themagnitude and pattern of livelihoods and poverty, as well as thechanging structure of communities related to migration, displacement
and conflict (Adger et al., 2014). In AR5, evidence of a climate change

244

At approximately 1.5°C of global warming (2030), climate change is
expected to be a poverty multiplier that makes poor people poorer andincreases the poverty head count (Hallegatte et al., 2016; Hallegatteand Rozenberg, 2017). Poor people might be heavily affected by climatechange even when impacts on the rest of population are limited.
Climate change alone could force more than 3 million to 16 million
people into extreme poverty, mostly through impacts on agriculture
and food prices (Hallegatte et al., 2016; Hallegatte and Rozenberg,2017). Unmitigated warming could reshape the global economy laterin the century by reducing average global incomes and widening
global income inequality (Burke et al., 2015b). The most severe impacts
are projected for urban areas and some rural regions in sub-SaharanAfrica and Southeast Asia.

3.4.10.2 The changing structure of communities:
migration, displacement and conflict
Migration: In AR5, the potential impacts of climate change on migrationand displacement were identified as an emerging risk (Oppenheimer etal., 2014). The social, economic and environmental factors underlying
migration are complex and varied; therefore, detecting the effect ofobserved climate change or assessing its possible magnitude with any
degree of confidence is challenging (Cramer et al., 2014).
No studies have specifically explored the difference in risks between
1.5°C and 2°C of warming on human migration. The literature
consistently highlights the complexity of migration decisions and the
difficulties in attributing causation (e.g., Nicholson, 2014; Baldwin and
Fornalé, 2017; Bettini, 2017; Constable, 2017; Islam and Shamsuddoha,
2017; Suckall et al., 2017). The studies on migration that havemost closely explored the probable impacts of 1.5°C and 2°C havemainly focused on the direct effects of temperature and precipitation
anomalies on migration or the indirect effects of these climatic changes
through changing agriculture yield and livelihood sources (Mueller etal., 2014; Piguet and Laczko, 2014; Mastrorillo et al., 2016; SudmeierRieux et al., 2017).
Temperature has had a positive and statistically significant effecton outmigration over recent decades in 163 countries, but only for
agriculture-dependent countries (R. Cai et al., 2016). A 1°C increasein average temperature in the International Migration Database of theOrganisation for Economic Co-operation and Development (OECD)
was associated with a 1.9% increase in bilateral migration flows from142 sending countries and 19 receiving countries, and an additional
millimetre of average annual precipitation was associated with anincrease in migration by 0.5% (Backhaus et al., 2015). In anotherstudy, an increase in precipitation anomalies from the long-term mean,
was strongly associated with an increase in outmigration, whereas no
significant effects of temperature anomalies were reported (Coniglio
and Pesce, 2015).

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Internal and international migration have always been important forsmall islands (Farbotko and Lazrus, 2012; Weir et al., 2017). There israrely a single cause for migration (Constable, 2017). Numerous factors
are important, including work, education, quality of life, family ties,
access to resources, and development (Bedarff and Jakobeit, 2017;
Speelman et al., 2017; Nicholls et al., 2018). Depending on the situation,changing weather, climate or environmental conditions might each bea factor in the choice to migrate (Campbell and Warrick, 2014).
Displacement: At 2°C of warming, there is a potential for significant
population displacement concentrated in the tropics (Hsiang and Sobel,2016). Tropical populations may have to move distances greater than
1000 km if global mean temperature rises by 2°C from 2011–2030 tothe end of the century. A disproportionately rapid evacuation from thetropics could lead to a concentration of population in tropical margins
and the subtropics, where population densities could increase by 300%
or more (Hsiang and Sobel, 2016).
Conflict: A recent study has called for caution in relating conflictto climate change, owing to sampling bias (Adams et al., 2018).
Insufficient consideration of the multiple drivers of conflict often leads
to inconsistent associations being reported between climate change
and conflict (e.g., Hsiang et al., 2013; Hsiang and Burke, 2014; Buhaug,
2015, 2016; Carleton and Hsiang, 2016; Carleton et al., 2016). Therealso are inconsistent relationships between climate change, migrationand conflict (e.g., Theisen et al., 2013; Buhaug et al., 2014; Selby, 2014;
Christiansen, 2016; Brzoska and Fröhlich, 2016; Burrows and Kinney,
2016; Reyer et al., 2017c; Waha et al., 2017). Across world regions andfrom the international to micro level, the relationship between drought
and conflict is weak under most circumstances (Buhaug, 2016; von
Uexkull et al., 2016). However, drought significantly increases thelikelihood of sustained conflict for particularly vulnerable nations orgroups, owing to the dependence of their livelihood on agriculture.
This is particularly relevant for groups in the least developed countries(von Uexkull et al., 2016), in sub-Saharan Africa (Serdeczny et al., 2016;
Almer et al., 2017) and in the Middle East (Waha et al., 2017). Hsiang
et al. (2013) reported causal evidence and convergence across studies
that climate change is linked to human conflicts across all major
regions of the world, and across a range of spatial and temporal scales.
A 1°C increase in temperature or more extreme rainfall increases
the frequency of intergroup conflicts by 14% (Hsiang et al., 2013). If
the world warms by 2°C–4°C by 2050, rates of human conflict could
increase. Some causal associations between violent conflict andsocio-political instability were reported from local to global scales
and from hour to millennium time frames (Hsiang and Burke, 2014).
A temperature increase of one standard deviation increased the riskof interpersonal conflict by 2.4% and intergroup conflict by 11.3%(Burke et al., 2015a). Armed-conflict risks and climate-related disasters
are both relatively common in ethnically fractionalized countries,
indicating that there is no clear signal that environmental disasters
directly trigger armed conflicts (Schleussner et al., 2016a).

Chapter 3

poorer and increases poverty head count, and the association between
temperature and economic productivity is not linear (high confidence).
Temperature has a positive and statistically significant effect onoutmigration for agriculture-dependent communities (medium
confidence).

3.4.11

Interacting and Cascading Risks

The literature on compound as well as interacting and cascading risks
at warming of 1.5°C and 2°C is limited. Spatially compound risks,
often referred to as hotspots, involve multiple hazards from different
sectors overlapping in location (Piontek et al., 2014). Global exposures
were assessed for 14 impact indicators, covering water, energy andland sectors, from changes including drought intensity and water
stress index, cooling demand change and heatwave exposure, habitat
degradation, and crop yields using an ensemble of climate and impact
models (Byers et al., 2018). Exposures are projected to approximately
double between 1.5°C and 2°C, and the land area affected by climate
risks is expected to increase as warming progresses. For populations
vulnerable to poverty, the exposure to climate risks in multiple sectors
could be an order of magnitude greater (8–32 fold) in the high poverty
and inequality scenarios (SSP3; 765–1,220 million) compared to undersustainable socio-economic development (SSP1; 23–85 million). Asianand African regions are projected to experience 85–95% of global
exposure, with 91–98% of the exposed and vulnerable population(depending on SSP/GMT combination), approximately half of which
are in South Asia. Figure 3.19 shows that moderate and large multisector impacts are prevalent at 1.5°C where vulnerable people live,
predominantly in South Asia (mostly Pakistan, India and China), but that
impacts spread to sub-Saharan Africa, the Middle East and East Asia at
higher levels of warming. Beyond 2°C and at higher risk thresholds,
the world’s poorest populations are expected to be disproportionately
impacted, particularly in cases (SSP3) of great inequality in Africa andsouthern Asia. Table 3.4 shows the number of exposed and vulnerable
people at 1.5°C and 2°C of warming, with 3°C shown for context, forselected multi-sector risks.

3.4.12

Summary of Projected Risks at 1.5°C and 2°Cof Global Warming

The information presented in Section 3.4 is summarized below in Table
3.5, which illustrates the growing evidence of increasing risks across a
broad range of natural and human systems at 1.5°C and 2°C of global
warming.

In summary, average global temperatures that extend beyond 1.5°C areprojected to increase poverty and disadvantage in many populations
globally (medium confidence). By the mid- to late 21st century, climatechange is projected to be a poverty multiplier that makes poor people

245

3Chapter 3

Impacts of 1.5°C of Global Warming on Natural and Human Systems

3

Figure 3.19 | Multi-sector risk maps for 1.5°C (top), 2°C (middle), and locations where 2°C brings impacts not experienced at 1.5°C (2°C–1.5°C; bottom). The maps in theleft column show the full range of the multi-sector risk (MSR) score (0–9), with scores ≤5.0 shown with a transparency gradient and scores >5.0 shown with a colour gradient.
Score must be >4.0 to be considered ‘multi-sector’. The maps in the right column overlay the 2050 vulnerable populations (low income) under Shared Socio-Economic Pathway(SSP)2 (greyscale) with the multi-sector risk score >5.0 (colour gradient), thus indicating the concentrations of exposed and vulnerable populations to risks in multiple sectors.
Source: Byers et al. (2018).

Table 3.4 | Number of exposed and vulnerable people at 1.5°C, 2°C, and 3°C for selected multi-sector risks under shared socioeconomic pathways (SSPs).
Source: Byers et al., 2018
SSP2
(SSP1 to SSP3 range), millions

1.5°C

2°C

3°C

Exposed

Exposedand vulnerable

Exposed

Exposedand vulnerable

Exposed

Exposedand vulnerable

Water stress index

3340 (3032–3584)

496 (103–1159)

3658 (3080–3969)

586 (115–1347)

3920 (3202–4271)

662 (146–1480)

Heatwave event exposure3960 (3546–4508)

1187 (410–2372)

5986 (5417–6710)

1581 (506–3218)

7909 (7286–8640)

1707 (537–3575)

334 (326–337)

30 (6–76)

385 (374–389)

38 (9–94)

742 (725–739)

72 (16–177)

Crop yield change

35 (32–36)

8 (2–20)362 (330–396)

81 (24–178)

1817 (1666–1992)

406 (118–854)

Habitat degradation

91 (92–112)

10 (4–31)

680 (314–706)

102 (23–234)

1357 (809–1501)

248 (75–572)

Two indicators

1129 (1019–1250)

203 (42–487)

2726 (2132–2945)

562 (117–1220)

3500 (3212–3864)

707 (212–1545)

Three indicators

66 (66–68)

7 (0.9–19)

422 (297–447)

54 (8–138)1472 (1177–1574)

237 (48–538)

Four indicators

5 (0.3–5.7)

0.3 (0–1.2)

11 (5–14)

0.5 (0–2)

258 (104–280)

33 (4–86)

Indicator

Hydroclimate risk to power production

Multi-sector exposure

246SectorPhysicalclimatechangedrivers

18% insects,
8% vertebrates,
16% plants lose
>50% range4

350.2 ± 158.8
million, changes
in urban
population
exposure tosevere drought at
the globe scale3
6% insects, 4%vertebrates,
8% plants, lose
>50% range4m

About 7%transformed5

Fluvial flood

Drought

Speciesrange loss

Loss ofecosystemfunctioning
and services

Shifts of biomes(major ecosystem
types)

h60.5 ± 84.1
million(±84.1 basedon the SSP1
scenario)(based on PDSI
estimate)

410.7 ± 213.5
million, changes
in urban
population
exposure tosevere drought at
the globe scale3

100% increasein the population
affected
compared to theimpact simulated
over thebaseline period
1976–20052

Wildfire

70% increase

170% increasein the population
affected
compared to theimpact simulated
over thebaseline period
1976–20052

Around half
compared to therisks at 2°C1

Water Stressh13% (range
8–20%)
transformed5h

Up to 100%
increase

Additional 8%of the world
population in2000 exposed
to new or
aggravatedwater scarcity1

Natureof risk

Increased risk

About double

Double or triple

Change inrisk whenmoving from
1.5°C to 2°C
of warming

Globalrisks at 2°Cof global
warming
above preindustrial

Global risks
at 1.5°Cof global
warming
above preindustrial

M

M

M

M

M

M

M

Confidence
in riskstatements

Canada, USA and
MediterraneanCentral Europe,
southern Europe,
Mediterranean,
West Africa, Eastand West Asia,
Southeast Asia(based on PDSI
estimate#)

USA, Asia,
EuropeRegionswhererisks areparticularlyhigh with
2°C of global
warming

Mediterranean

Arctic, Tibet,
Himalayas, South
Africa, Australia

Amazon, Europe,
southern Africa

Europe, Australia,
southern Africa

Regions
where thechange in risk
when movingfrom 1.5°C
to 2°C are
particularly
highCentral andSouth America,
Australia,
Russia, China,
AfricaAfrica,Oceania

Regionswith little
or noinformation

1, 2,
4, 5

4

4

l

m

l/m

2

1,4

l/m

l

2

3

RFC*

Adaptation
potential
at 1.5°C

l

l

l/m

l/m

l

Adaptation
potential
at 2°C

M

H

L

M

M

Confidence
in assigning
adaptation
potential

Table 3.5 | Summary of projected risks to natural and human systems at 1.5°C and 2°C of global warming, and of the potential to adapt to these risks. Table summarizes the chapter text and with references supporting table entries
found in the main chapter text. Risk magnitude is provided either as assessed levels of risk (very high: vh, high: h, medium: m, or low: l) or as quantitative examples of risk levels taken from the literature. Further compilationsof quantified levels of risk taken from the literature may be found Tables 3.SM1-5 in the Supplementary Material. Similarly, potential to adapt is assessed from the literature by expert judgement as either high (h), medium(m), or low (l). Confidence in each assessed level/quantification of risk, or in each assessed adaptation potential, is indicated as very high (VH), high (H), medium (M), or low (L). Note that the use of l, m, h and vh here isdistinct from the use of L, M, H and VH in Figures 3.18, 3.20 and 3.21.

Freshwater

Terrestrial ecosystems

Precipitation, temperature, snowmelt

Temperature, precipitation

Heat and cold
stress, warming,
precipitation
drought

Impacts of 1.5°C of Global Warming on Natural and Human Systems
Chapter 33247

Sector

Ocean

Physicalclimatechangedrivers

Table 3.5 (continued)

Warming and stratification of the surface ocean

248

Ocean acidification andelevated sea temperatures

Global risks
at 1.5°Cof global
warming
above preindustrial

vh

m

m

h

m

h

m

m/h

l/m

l

l

Natureof risk

Loss offramework
species (coral
reefs)

Loss offramework
species(seagrass)

Loss offramework
species(mangroves)

Disruption ofmarine foodwebs

Range migrationof marine species
and ecosystems

Loss of fin fish
and fisheries

Loss of coastal
ecosystems andprotection

Loss of bivalves
and bivalve
fisheries

Changes tophysiology
and ecology ofmarine species

Increased
hypoxic deadzones

Changes to
upwelling
productivity

m

l/m

m

h/vh

h

h/vh

h

vh

m

h

vh

Globalrisks at 2°Cof global
warming
above preindustrial

Increase in risk

Large increasein risk

Increase in risk

Large increasein risk

Increase in risk

Large increasein risk

Large increasein risk

Large increasein risk

Uncertain anddepends on otherhuman activities

L/M

L/M

H

H

M

H

H

M

M/H

M

H/very H

Greater rateof loss: from
70–90% loss at
1.5°C to 99% loss
at 2°C and above

Increase in risk

Confidence
in riskstatements

Change inrisk whenmoving from
1.5°C to 2°C
of warming

Most upwelling
regions

Temperate
countries withupwelling

GlobalMost upwelling
regions

Temperate
countries withupwelling

GlobalTemperate
countries withupwelling

Low-latitude
tropical/subtropical countries

Low-latitude
tropical/subtropicalcountries
Temperate
countries withupwelling

GlobalGlobalGlobalTropical/subtropicalcountries

Tropical/subtropicalcountries

Tropical/subtropicalcountries

Regions
where thechange inrisk whenmovingfrom 1.5°C
to2°C areparticularly
highGlobalGlobalGlobalTropical/subtropicalcountries

Tropical/subtropicalcountries

Tropical/subtropicalcountries

Regionswhererisks areparticularlyhigh with
2°C of global
warming

3

Reduced bulk ocean
circulation andde-oxygenationSome
upwellingsystems

Deep sea

Most regions– risks notwell defined

Most regions– risks notwell defined

Most regions– risks notwell defined

Deep sea, upwelling systems

Deep sea

Deep sea

Southern Red
Sea, Somalia,
Yemen,
MyanmarSouthern Red
Sea, Somalia,
Yemen,
MyanmarSouthern Red
Sea, Somalia,
Yemen,
deep watercoral reefs

Regionswith little
or noinformation

4

4

4

4

1

4

1

4

1,3

1,2

1,2

RFC*

l

m

l

m/h

m

m

m

m

m

m

h

Adaptation
potential
at 1.5°C

l

l

l

l/m

m/l

m/l

l

l

l

l

l

Adaptation
potential
at 2°C

M

M

M/H

M/H

M

M/H

H

M/H

L/M

M/H

H

Confidence
in assigning
adaptation
potential

Chapter 3
Impacts of 1.5°C of Global Warming on Natural and Human Systems

Coastal

Ocean

Sector

Sea level rise, increased storminess

Loss of sea ice

Intensified storms,
precipitation plus
sea level rise

Physicalclimatechangedrivers

Table 3.5 (continued)

Area exposed(assuming no
defences)

Increasing withtime, but highly
dependent on
adaptation9590–613th km2
when 2°C firstreached6,7,8

Increasing; 13–8
million whentemperatures are
first reached, 0–6
million people in
2100, increasing
to 35–95 million
people in 23006

Increasing;
25–38thkm2 whentemperatures
are first reached,
10–17th km2 in
2100 increasing
to 16–230th
km2 in 23006,7,8(dependent on
adaptation)

M/H(dependent
on population
datasets)

M/H(dependent
on population
datasets)

M/Hvery HH

Large increasein risk
Large increasein risk

H

H

Confidence
in riskstatements

Large increasein risk

Large increasein risk

Change inrisk whenmoving from
1.5°C to 2°C
of warming

m/h

15–53 million
people yr–1 ifdefences are not
upgraded fromthe modelled1995 baseline9562–575th km2
when 1.5°C firstreached6,7,8

Increased
productivity
but changing
fisheriesvh

2–28 million
people yr–1 ifdefences are not
upgraded fromthe modelled1995 baseline9

l/m

Loss of habitat

h/vh

People at risk
accounting
for defences(modelled
in 1995)hInundation anddestruction ofhuman/coastal
infrastructure
and livelihoods

h/vh

141–151 million
when 2°C firstreachedhLoss of coastal
ecosystems

128–143 million
when 1.5°C
first reachedhNatureof riskGlobalrisks at 2°Cof global
warming
above preindustrial

Population
exposed(assuming no
defences)

Global risks
at 1.5°Cof global
warming
above preindustrial

1

1, 4

2, 3

Small islands

Small islands

Small islands

Polar regions

Polar regions

Asia, small
islands

Asia, small
islands

Asia, small
islands

Asia, small
islands,
potentially
African nations

Asia, small
islands

Asia, small
islands

Polar regions

Polar regions

2, 3, 4

2, 3

1, 5

GlobalGlobal

1, 4

Tropical/subtropicalcountries

RFC*

Tropical/subtropicalcountries

Regionswith little
or noinformation

Regions
where thechange inrisk whenmovingfrom 1.5°C
to2°C areparticularlyhighRegionswhererisks areparticularlyhigh with
2°C of global
warming

m

m

m

l

l

m/h

m

Adaptation
potential
at 1.5°C

m

m

m

m/l

very lml

Adaptation
potential
at 2°C

M

M

M

H

H

M/L

M

Confidence
in assigning
adaptation
potential

Impacts of 1.5°C of Global Warming on Natural and Human Systems
Chapter 33249

SectorFood security andfood production systems

Human healthm/h (if precursor
emissions remain
the same)mmm (if precursor
emissions remain
the same)

Shift andcompositionchange ofbiomes (major
ecosystem types)

Heat-related
morbidity andmortality

Occupational
heat stressOzone-related
mortality

Undernutrition

Tourism(sun, beach, andsnow sports)
m/h

m

m/h

m/h

Changes inecosystem
production

h

m/h

m/h

h

h

m/h

Natureof riskGlobalrisks at 2°Cof global
warming
above preindustrial

Risk increased

Risk increased

Risk increased

Risk increased

Risk increased

Moderateincrease

Large increase

Change inrisk whenmoving from
1.5°C to 2°C
of warming

VH

H

H

M

VH

L/M

M/H

Confidence
in riskstatements

Low-income
countries inAfrica and Asia
Coastal tourism,
particularly insubtropical andtropical regions

Coastal tourism,
particularly insubtropical andtropical regions

High income
and emerging
economies

Tropical regions

All regions

Global,tropical areas,
Mediterranean

North America,
Central andSouth America,
Mediterraneanbasin, South
Africa, Australia,
Asia

Regions
where thechange inrisk whenmovingfrom 1.5°C
to2°C areparticularlyhigh

Low-income
countries inAfrica and Asia

High income
and emerging
economies

Tropical regions

All regions at risk

GlobalGlobalRegionswhererisks areparticularlyhigh with
2°C of global
warming

AfricaSmall islands

Africa, partsof Asia

AfricaAfricaAfrica, Asia

Regionswith little
or noinformation

1, 2, 32, 3, 42, 3, 42, 3, 42, 3, 4

1, 2,
3, 4

2, 4, 5

RFC*

m

m

l

h

h

l/m

h

Adaptation
potential
at 1.5°C

l

l

l

m

h

l

m/h

Adaptation
potential
at 2°C

H

M

M

M

H

L/M

M/H

Confidence
in assigning
adaptation
potential

91

Gerten et al., 2013; 2 Alfieri et al., 2017; 3 Liu et al., 2018; 4 Warren et al., 2018a; 5 Warzawski et al., 2013; 6 Brown et al., 2018a; 7 Rasmussen et al., 2018; 8 Yokoki et al., 2018;
Nicholls et al., 2018.

# PDSI-based drought estimates tend to overestimate drought impacts (see Section 3.3.4); hence projections with other drought indices may differ. Further quantifications may be found in
Table 3.SM.1*RFC: 1 = unique and threatened systems, 2 = extreme events, 3 = unequal distribution of impacts, 4 = global aggregate impacts (economic + biodiversity), 5 = large-scale singular events.

Key
economic
sectors

Physicalclimatechangedrivers

Global risks
at 1.5°Cof global
warming
above preindustrial

Table 3.5 (continued)

Heat andcold stress, warming,
precipitation, drought

Heat and cold
stress, warming,
precipitation
drought

Air
quality

Temperature,
precipitation

250

Temperature3

Temperature

Chapter 3
Impacts of 1.5°C of Global Warming on Natural and Human Systems

Impacts of 1.5°C of Global Warming on Natural and Human Systems

3.4.13Synthesis of Key Elements of Risk

Some elements of the assessment in Section 3.4 were synthesized into
Figure 3.18 and 3.20, indicating the overall risk for a representative setof natural and human systems from increases in global mean surface
temperature (GMST) and anthropogenic climate change. The elements
included are supported by a substantive enough body of literatureproviding at least medium confidence in the assessment. The format for
Figures 3.18 and 3.20 match that of Figure 19.4 of WGII AR5 Chapter
19 (Oppenheimer et al., 2014) indicating the levels of additional risk
as colours: undetectable (white) to moderate (detected and attributed;
yellow), from moderate to high (severe and widespread; red), andfrom high to very high (purple), the last of which indicates significant
irreversibility or persistence of climate-related hazards combinedwith a much reduced capacity to adapt. Regarding the transition
from undetectable to moderate, the impact literature assessed in AR5
focused on describing and quantifying linkages between weather andclimate patterns and impact outcomes, with limited detection andattribution to anthropogenic climate change (Cramer et al., 2014). A
more recent analysis of attribution to greenhouse gas forcing at the
global scale (Hansen and Stone, 2016) confirmed that the impacts
related to changes in regional atmospheric and ocean temperature canbe confidently attributed to anthropogenic forcing, while attribution
to anthropogenic forcing of those impacts related to precipitation is
only weakly evident or absent. Moreover, there is no strong direct
relationship between the robustness of climate attribution and that of
impact attribution (Hansen and Stone, 2016).
The current synthesis is complementary to the synthesis in Section 3.5.2
that categorizes risks into ‘Reasons for Concern’ (RFCs), as described in
Oppenheimer et al. (2014). Each element, or burning ember, presented
here (Figures 3.18, 3.20) maps to one or more RFCs (Figure 3.21). It
should be emphasized that risks to the elements assessed here are
only a subset of the full range of risks that contribute to the RFCs.
Figures 3.18 and 3.20 are not intended to replace the RFCs but rather
to indicate how risks to particular elements of the Earth system accrue
with global warming, through the visual burning embers format,
with a focus on levels of warming of 1.5°C and 2°C. Key evidence
assessed in earlier parts of this chapter is summarized to indicate thetransition points between the levels of risk. In this regard, the assessed
confidence in assigning the transitions between risk levels are as
follows: L=Low, M=Medium, H=High, and VH=Very high levels ofconfidence. A detailed account of the procedures involved is provided
in the Supplementary Material (3.SM.3.2 and 3.SM.3.3).
In terrestrial ecosystems (feeding into RFC1 and RFC4), detection andattribution studies show that impacts of climate change on terrestrial
ecosystems began to take place over the past few decades, indicatinga transition from no risk (white areas in Figure 3.20) to moderate risk
below recent temperatures (high confidence) (Section 3.4.3). Risks tounique and threatened terrestrial ecosystems are generally projected tobe higher under warming of 2°C compared to 1.5°C (Section 3.5.2.1),
while at the global scale severe and widespread risks are projected
to occur by 2°C of warming. These risks are associated with biome
shifts and species range losses (Sections 3.4.3 and 3.5.2.4); however,
because many systems and species are projected to be unable to adapt
to levels of warming below 2°C, the transition to high risk (red areas

Chapter 3

in Figure 3.20) is located below 2°C (high confidence). With 3°C ofwarming, however, biome shifts and species range losses are expected
to escalate to very high levels, and the systems are projected to havevery little capacity to adapt (Figure 3.20) (high confidence) (Section
3.4.3).
In the Arctic (related to RFC1), the increased rate of summer sea ice
melt was detected and attributed to climate change by the year 2000
(corresponding to warming of 0.7°C), indicating moderate risk. At
1.5°C of warming an ice-free Arctic Ocean is considered unlikely, whilst
by 2°C of warming it is considered likely and this unique ecosystem isprojected to be unable to adapt. Hence, a transition from high to very
high risk is expected between 1.5°C and 2°C of warming.
For warm-water coral reefs, there is high confidence in the transitions
between risk levels, especially in the growing impacts in thetransition of warming from non-detectable (0.2°C to 0.4°C), and thensuccessively higher levels risk until high and very high levels of risks
by 1.2°C (Section 3.4.4 and Box 3.4). This assessment considered theheatwave-related loss of 50% of shallow water corals across hundredsof kilometres of the world’s largest continuous coral reef system,
the Great Barrier Reef, as well as losses at other sites globally. The
major increase in the size and loss of coral reefs over the past three
years, plus sequential mass coral bleaching and mortality events on
the Great Barrier Reef, (Hoegh-Guldberg, 1999; Hughes et al., 2017b,
2018), have reinforced the scale of climate-change related risks tocoral reefs. General assessments of climate-related risks for mangroves
prior to this special report concluded that they face greater risks fromdeforestation and unsustainable coastal development than fromclimate change (Alongi, 2008; Hoegh-Guldberg et al., 2014; Gattuso etal., 2015). Recent climate-related die-offs (Duke et al., 2017; Lovelock
et al., 2017), however, suggest that climate change risks may havebeen underestimated for mangroves as well, and risks have thus beenassessed as undetectable to moderate, with the transition now startingat 1.3°C as opposed to 1.8°C as assessed in 2015 (Gattuso et al., 2015).
Risks of impacts related to climate change on small-scale fisheries at
low latitudes, many of which are dependent on ecosystems such ascoral reefs and mangroves, are moderate today but are expected toreach high levels of risk around 0.9°C–1.1°C (high confidence) (Section
3.4.4.10).
The transition from undetectable to moderate risk (related to RFCs 3
and 4), shown as white to yellow in Figure 3.20, is based on AR5 WGII
Chapter 7, which indicated with high confidence that climate change
impacts on crop yields have been detected and attributed to climatechange, and the current assessment has provided further evidence
to confirm this (Section 3.4.6). Impacts have been detected in thetropics (AR5 WGII Chapters 7 and 18), and regional risks are projected
to become high in some regions by 1.5°C of warming, and in many
regions by 2.5°C, indicating a transition from moderate to high risk
between 1.5°C and 2.5°C of warming (medium confidence).
Impacts from fluvial flooding (related to RFCs 2, 3 and 4) depend on
the frequency and intensity of the events, as well as the extent of
exposure and vulnerability of society (i.e., socio-economic conditions
and the effect of non-climate stressors). Moderate risks posed by
1.5°C of warming are expected to continue to increase with higher
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3Chapter 3

Impacts of 1.5°C of Global Warming on Natural and Human Systems

risk (Section 3.4.5). With 2.5°C of warming, adaptation limits are
expected to be exceeded in sensitive areas, and hence a transition tovery high risk is projected. Additionally, at this temperature, sea level
rise could have adverse effects for centuries, posing significant risk to
low-lying areas (high confidence) (Sections 3.4.5.7 and 3.5.2.5).

levels of warming (Sections 3.3.5 and 3.4.2), with projected risks being
threefold the current risk in economic damages due to flooding in 19
countries for warming of 2°C, indicating a transition to high risk at
this level (medium confidence). Because few studies have assessed thepotential to adapt to these risks, there was insufficient evidence tolocate a transition to very high risk (purple).

For heat-related morbidity and mortality (related to RFCs 2, 3 and 4),
detection and attribution studies show heat-related mortality in somelocations increasing with climate change (high confidence) (Section
3.4.7; Ebi et al., 2017). The projected risks of heat-related morbidity andmortality are generally higher under warming of 2°C than 1.5°C (highconfidence), with projections of greater exposure to high ambient
temperatures and increased morbidity and mortality (Section 3.4.7).
Risk levels will depend on the rate of warming and the (related) level ofadaptation, so a transition in risk from moderate (yellow) to high (red)is located between 1°C and 3°C (medium confidence).

Climate-change induced sea level rise (SLR) and associated coastal
flooding (related to RFCs 2, 3 and 4) have been detectable andattributable since approximately 1970 (Slangen et al., 2016), during
which time temperatures have risen by 0.3°C (medium confidence)(Section 3.3.9). Analysis suggests that impacts could be morewidespread in sensitive systems such as small islands (high confidence)(Section 3.4.5.3) and increasingly widespread by the 2070s (Brown
et al., 2018a) as temperatures rise from 1.5°C to 2°C, even when
adaptation measures are considered, suggesting a transition to highRisks and/or impacts for specific natural, managed and human systems

Global mean surface temperature change
relative to pre-industrial levels (0C)

3

Global mean surface temperature change
relative to pre-industrial levels (0C)The key elements are presented here as a function of the risk level assessed
between 1.5°C and 2°C.
H

Very high

H

2.0
H

H

1.5
1.0

H

VH
H

0

Warm-watercorals,Coral reefs

H

M

H

VH

High

Moderate

2006-2015
H
H

Artic
(including
ocean areaand sea ice)

Coastalflooding

M

2.0

H

M

Undetectable

Index: Level of additionalrisk due to climate change

Small scale
Terrestrial
fisheries
Ecosystems(low latitude)

M

M

M

Purple indicates very highrisks of severe impacts andthe presence of significant
irreversibility or thepersistence of climate-related
hazards, combined withlimited ability to adapt due tothe nature of the hazard or
impacts/risks.
Red indicates severe andwidespread impacts/risks.
Yellow indicates that
impacts/risks are detectable
and attributable to climatechange with at least medium
confidence.
White indicates that no
impacts are detectable andattributable to climatechange.

M
1.5

M
M

H

1.0
M

H

2006-2015

H

0

FluvialFlooding

Crop
YieldsHeat-related
morbidity
and mortality

Tourism

Ability to achieve
Sustainable
Development
Goals (SDGs)

Mangroves

Confidence level for transition: L=Low, M=Medium, H=High and VH=Very highFigure 3.20 | The dependence of risks and/or impacts associated with selected elements of human and natural systems on the level of climate change, adapted from Figure
3.21 and from AR5 WGII Chapter 19, Figure 19.4, and highlighting the nature of this dependence between 0°C and 2°C warming above pre-industrial levels. The selection of
impacts and risks to natural, managed and human systems is illustrative and is not intended to be fully comprehensive. Following the approach used in AR5, literature was used
to make expert judgements to assess the levels of global warming at which levels of impact and/or risk are undetectable (white), moderate (yellow), high (red) or very high(purple). The colour scheme thus indicates the additional risks due to climate change. The transition from red to purple, introduced for the first time in AR4, is defined by a very
high risk of severe impacts and the presence of significant irreversibility or persistence of climate-related hazards combined with limited ability to adapt due to the nature of thehazard or impact. Comparison of the increase of risk across RFCs indicates the relative sensitivity of RFCs to increases in GMST. As was done previously, this assessment takes
autonomous adaptation into account, as well as limits to adaptation independently of development pathway. The levels of risk illustrated reflect the judgements of the authorsof Chapter 3 and Gattuso et al. (2015; for three marine elements). The grey bar represents the range of GMST for the most recent decade: 2006–2015.

252

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Chapter 3of Oppenheimer et al. (2014) was adopted, with updates to theaggregation of risk informed by the most recent literature, for the
analysis of avoided impacts at 1.5°C compared to 2°C of global
warming presented in this section.

For tourism (related to RFCs 3 and 4), changing weather patterns,
extreme weather and climate events, and sea level rise are affecting
many – but not all – global tourism investments, as well as
environmental and cultural destination assets (Section 3.4.4.12), with‘last chance to see’ tourism markets developing based on observed
impacts on environmental and cultural heritage (Section 3.4.9.1),
indicating a transition from undetectable to moderate risk between
0°C and 1.5°C of warming (high confidence). Based on limited
analyses, risks to the tourism sector are projected to be larger at 2°C
than at 1.5°C, with impacts on climate-sensitive sun, beach and snow
sports tourism markets being greatest. The degradation or loss ofcoral reef systems is expected to increase the risks to coastal tourism
in subtropical and tropical regions. A transition in risk from moderate
to high levels of added risk from climate change is projcted to occur
between 1.5°C and 3°C (medium confidence).

3.5.2

Climate change is already having large scale impacts on ecosystems,
human health and agriculture, which is making it much more difficult
to reach goals to eradicate poverty and hunger, and to protect healthand life on land (Sections 5.1 and 5.2.1 in Chapter 5), suggesting atransition from undetectable to moderate risk for recent temperaturesat 0.5°C of warming (medium confidence). Based on the limited
analyses available, there is evidence and agreement that the risks
to sustainable development are considerably less at 1.5°C than 2°C(Section 5.2.2), including impacts on poverty and food security. It iseasier to achieve many of the sustainable development goals (SDGs) at
1.5°C, suggesting that a transition to higher risk will not begin yet at
this level. At 2°C and higher levels of warming (e.g., RCP8.5), however,there are high risks of failure to meet SDGs such as eradicating
poverty and hunger, providing safe water, reducing inequality andprotecting ecosystems, and these risks are projected to become severe
and widespread if warming increases further to about 3°C (medium
confidence) (Section 5.2.3).

A brief summary of the accrual of RFCs with global warming, asassessed in WGII AR5, is provided in the following sections, whichleads into an update of relevant literature published since AR5. The
new literature is used to confirm the levels of global warming at whichrisks are considered to increase from undetectable to moderate, frommoderate to high, and from high to very high. Figure 3.21 modifies
Figure 19.4 from AR5 WGII, and the following text in this subsection
provides justification for the modifications. O’Neill et al. (2017)presented a very similar assessment to that of WGII AR5, but withfurther discussion of the potential to create ‘embers’ specific to socioeconomic scenarios in the future. There is insufficient literature to
do this at present, so the original, simple approach has been used
here. As the focus of the present assessment is on the consequencesof global warming of 1.5°C–2°C above the pre-industrial period, noassessment for global warming of 3°C or more is included in the
figure (i.e., analysis is discontinued at 2.5°C).

Disclosure statement: The selection of elements depicted in Figures
3.18 and 3.20 is not intended to be fully comprehensive and does notnecessarily include all elements for which there is a substantive bodyof literature, nor does it necessarily include all elements which are ofparticular interest to decision-makers.

3.5

Avoided Impacts and Reduced Risks
at 1.5°C Compared with 2°C
of Global Warming

3.5.1

Introduction

Oppenheimer et al. (2014, AR5 WGII Chapter 19) provided a frameworkthat aggregates projected risks from global mean temperaturechange into five categories identified as ‘Reasons for Concern’. Risks
are classified as moderate, high or very high and coloured yellow,
red or purple, respectively, in Figure 19.4 of that chapter (AR5 WGII
Chapter 19 for details and findings). The framework’s conceptualbasis and the risk judgements made by Oppenheimer et al. (2014)
were recently reviewed, and most judgements were confirmed in thelight of more recent literature (O’Neill et al., 2017). The approach

The regional economic benefits that could be obtained by limiting the
global temperature increase to 1.5°C of warming, rather than 2°C
or higher levels, are discussed in Section 3.5.3 in the light of the five
RFCs explored in Section 3.5.2. Climate change hotspots that could
be avoided or reduced by achieving the 1.5°C target are summarized
in Section 3.5.4. The section concludes with a discussion of regional
tipping points that could be avoided at 1.5°C compared to higher
degrees of global warming (Section 3.5.5).

3.5.2.1

Aggregated Avoided Impacts and Reduced
Risks at 1.5°C versus 2°C of Global Warming

RFC 1 – Unique and threatened systems

WGII AR5 Chapter 19 found that some unique and threatenedsystems are at risk from climate change at current temperatures,with increasing numbers of systems at potential risk of severe
consequences at global warming of 1.6°C above pre-industrial levels.
It was also observed that many species and ecosystems have a limited
ability to adapt to the very large risks associated with warming of
2.6°C or more, particularly Arctic sea ice and coral reef systems (highconfidence). In the AR5 analysis, a transition from white to yellow
indicated that the onset of moderate risk was located below presentday global temperatures (medium confidence); a transition fromyellow to red indicated that the onset of high risk was located at
1.6°C, and a transition from red to purple indicated that the onsetof very high risk was located at about 2.6°C. This WGII AR5 analysis
already implied that there would be a significant reduction in risks
to unique and threatened systems if warming were limited to 1.5°C
compared with 2°C. Since AR5, evidence of present-day impacts inthese systems has continued to grow (Sections 3.4.2, 3.4.4 and 3.4.
5), whilst new evidence has also accumulated for reduced risks at
1.5°C compared to 2°C of warming in Arctic ecosystems (Section
3.3.9), coral reefs (Section 3.4.4) and some other unique ecosystems(Section 3.4.3), as well as for biodiversity.
253

3

Chapter 3

3Impacts of 1.5°C of Global Warming on Natural and Human Systems

Figure 3.21 | The dependence of risks and/or impacts associated with the Reasons for Concern (RFCs) on the level of climate change, updated and adapted from WGII AR5
Ch 19, Figure 19.4 and highlighting the nature of this dependence between 0°C and 2°C warming above pre-industrial levels. As in the AR5, literature was used to makeexpert judgements to assess the levels of global warming at which levels of impact and/or risk are undetectable (white), moderate (yellow), high (red) or very high (purple).
The colour scheme thus indicates the additional risks due to climate change. The transition from red to purple, introduced for the first time in AR4, is defined by very high riskof severe impacts and the presence of significant irreversibility, or persistence of climate-related hazards combined with a limited ability to adapt due to the nature of thehazard or impact. Comparison of the increase of risk across RFCs indicates the relative sensitivity of RFCs to increases in GMST. As was done previously, this assessment takes
autonomous adaptation into account, as well as limits to adaptation (RFC 1, 3, 5) independently of development pathway. The rate and timing of impacts were taken into
account in assessing RFC 1 and 5. The levels of risk illustrated reflect the judgements of the Ch 3 authors. RFC1 Unique and threatened systems: ecological and humansystems that have restricted geographic ranges constrained by climate related conditions and have high endemism or other distinctive properties. Examples include coral reefs,
the Arctic and its indigenous people, mountain glaciers and biodiversity hotspots. RFC2 Extreme weather events: risks/impacts to human health, livelihoods, assets andecosystems from extreme weather events such as heatwaves, heavy rain, drought and associated wildfires, and coastal flooding. RFC3 Distribution of impacts: risks/impacts
that disproportionately affect particular groups due to uneven distribution of physical climate change hazards, exposure or vulnerability. RFC4 Global aggregate impacts:
global monetary damage, global scale degradation and loss of ecosystems and biodiversity. RFC5 Large-scale singular events: are relatively large, abrupt and sometimes
irreversible changes in systems that are caused by global warming. Examples include disintegration of the Greenland and Antarctic ice sheets. The grey bar represents the rangeof GMST for the most recent decade: 2006–2015.

New literature since AR5 has provided a closer focus on the comparative
levels of risk to coral reefs at 1.5°C versus 2°C of global warming. As
assessed in Section 3.4.4 and Box 3.4, reaching 2°C will increase thefrequency of mass coral bleaching and mortality to a point at which it
will result in the total loss of coral reefs from the world’s tropical andsubtropical regions. Restricting overall warming to 1.5°C will still see
a downward trend in average coral cover (70–90% decline by midcentury) but will prevent the total loss of coral reefs projected withwarming of 2°C (Frieler et al., 2013). The remaining reefs at 1.5°C will
also benefit from increasingly stable ocean conditions by the mid-tolate 21st century. Limiting global warming to 1.5°C during the courseof the century may, therefore, open the window for many ecosystems
to adapt or reassort geographically. This indicates a transition in riskin this system from high to very high (high confidence) at 1.5°C ofwarming and contributes to a lowering of the transition from high tovery high (Figure 3.21) in this RFC1 compared to in AR5. Further detailsof risk transitions for ocean systems are described in Figure 3.18.
Substantial losses of Arctic Ocean summer ice were projected in
WGI AR5 for global warming of 1.6°C, with a nearly ice-free Arctic
Ocean being projected for global warming of more than 2.6°C.
Since AR5, the importance of a threshold between 1°C and 2°C has
been further emphasized in the literature, with sea ice projected topersist throughout the year for a global warming of less than 1.5°C,
254

yet chances of an ice-free Arctic during summer being high at 2°C ofwarming (Section 3.3.8). Less of the permafrost in the Arctic is projectedto thaw under 1.5°C of warming (17–44%) compared with under 2°C(28–53%) (Section 3.3.5.2; Chadburn et al., 2017), which is expected
to reduce risks to both social and ecological systems in the Arctic. This
indicates a transition in the risk in this system from high to very highbetween 1.5°C and 2°C of warming and contributes to a lowering of
the transition from high to very high in this RFC1 compared to in AR5.
AR5 identified a large number of threatened systems, including mountainecosystems, highly biodiverse tropical wet and dry forests, deserts,
freshwater systems and dune systems. These include Mediterranean
areas in Europe, Siberian, tropical and desert ecosystems in Asia,
Australian rainforests, the Fynbos and succulent Karoo areas of South
Africa, and wetlands in Ethiopia, Malawi, Zambia and Zimbabwe. In allthese systems, impacts accrue with greater warming and impacts at 2°C
are expected to be greater than those at 1.5°C (medium confidence).
One study since AR5 has shown that constraining global warming to
1.5°C would maintain the functioning of prairie pothole ecosystems
in North America in terms of their productivity and biodiversity, whilst
warming of 2°C would not do so (Johnson et al., 2016). The large
proportion of insects projected to lose over half their range at 2°C ofwarming (25%) compared to at 1.5°C (9%) also suggests a significant
loss of functionality in these threatened systems at 2°C of warming,

Impacts of 1.5°C of Global Warming on Natural and Human Systems

owing to the critical role of insects in nutrient cycling, pollination,
detritivory and other important ecosystem processes (Section 3.4.3).
Unique and threatened systems in small island states and in systems
fed by glacier meltwater were also considered to contribute to this
RFC in AR5, but there is little new information about these systems
that pertains to 1.5°C or 2°C of global warming. Taken together, theevidence suggests that the transition from high to very high risk inunique and threatened systems occurs at a lower level of warming,
between 1.5°C and 2°C (high confidence), than in AR5, where thistransition was located at 2.6°C. The transition from moderate to highrisk relocates very slightly from 1.6°C to 1.5°C (high confidence). Thereis also high confidence in the location of the transition from low tomoderate risk below present-day global temperatures.

3.5.2.2

RFC 2 – Extreme weather events

Reduced risks in terms of the likelihood of occurrence of extreme
weather events are discussed in this sub-subsection for 1.5°C as
compared to 2°C of global warming, for those extreme events where
evidence is currently available based on the assessments of Section 3.3.
AR5 assigned a moderate level of risk from extreme weather events atrecent temperatures (1986–2005) owing to the attribution of heat andprecipitation extremes to climate change, and a transition to high risk
beginning below 1.6°C of global warming based on the magnitude,
likelihood and timing of projected changes in risk associated withextreme events, indicating more severe and widespread impacts.
The AR5 analysis already suggested a significant benefit of limiting
warming to 1.5°C, as doing so might keep risks closer to the moderatelevel. New literature since AR5 has provided greater confidence in a
reduced level of risks due to extreme weather events at 1.5°C versus
2°C of warming for some types of extremes (Section 3.3 and below;
Figure 3.21).
Temperature: It is expected that further increases in the number ofwarm days/nights and decreases in the number of cold days/nights,
and an increase in the overall temperature of hot and cold extremes
would occur under 1.5°C of global warming relative to pre-industrial
levels (high confidence) compared to under the present-day climate(1°C of warming), with further changes occurring towards 2°C of
global warming (Section 3.3). As assessed in Sections 3.3.1 and 3.3.2,
impacts of 0.5°C of global warming can be identified for temperature
extremes at global scales, based on observations and the analysis ofclimate models. At 2°C of global warming, it is likely that temperature
increases of more than 2°C would occur over most land regions interms of extreme temperatures (up to 4°C–6°C depending on region
and considered extreme index) (Section 3.3.2, Table 3.2). Regional
increases in temperature extremes can be robustly limited if global
warming is constrained to 1.5°C, with regional warmings of up to
3°C–4.5°C (Section 3.3.2, Table 3.2). Benefits obtained from thisgeneral reduction in extremes depend to a large extent on whether the
lower range of increases in extremes at 1.5°C is sufficient for criticalthresholds to be exceeded, within the context of wide-ranging aspects
such as crop yields, human health and the sustainability of ecosystems.
Heavy precipitation: AR5 assessed trends in heavy precipitation
for land regions where observational coverage was sufficient for

Chapter 3

assessment. It concluded with medium confidence that anthropogenic
forcing has contributed to a global-scale intensification of heavy
precipitation over the second half of the 20th century, for a global
warming of approximately 0.5°C (Section 3.3.3). A recent observationbased study likewise showed that a 0.5°C increase in global mean
temperature has had a detectable effect on changes in precipitation
extremes at the global scale (Schleussner et al., 2017), thus suggestingthat there would be detectable differences in heavy precipitation at
1.5°C and 2°C of global warming. These results are consistent withanalyses of climate projections, although they also highlight a large
amount of regional variation in the sensitivity of changes in heavy
precipitation (Section 3.3.3).
Droughts: When considering the difference between precipitation andevaporation (P–E) as a function of global temperature changes, thesubtropics generally display an overall trend towards drying, whilst thenorthern high latitudes display a robust response towards increased
wetting (Section 3.3.4, Figure 3.12). Limiting global mean temperature
increase to 1.5°C as opposed to 2°C could substantially reduce the riskof reduced regional water availability in some regions (Section 3.3.4).
Regions that are projected to benefit most robustly from restrictedwarming include the Mediterranean and southern Africa (Section
3.3.4).
Fire: Increasing evidence that anthropogenic climate change has
already caused significant increases in fire area globally (Section
3.4.3) is in line with projected fire risks. These risks are projected toincrease further under 1.5°C of global warming relative to the presentday (Section 3.4.3). Under 1.2°C of global warming, fire frequency
has been estimated to increase by over 37.8% of global land areas,
compared to 61.9% of global land areas under 3.5°C of warming. For
in-depth discussion and uncertainty estimates, see Meehl et al. (2007),Moritz et al. (2012) and Romero-Lankao et al. (2014).
Regarding extreme weather events (RFC2), the transition frommoderate to high risk is located between 1°C and 1.5°C of global
warming (Figure 3.21), which is very similar to the AR5 assessment but
is assessed with greater confidence (medium confidence). The impact
literature contains little information about the potential for humansociety to adapt to extreme weather events, and hence it has not beenpossible to locate the transition from high to very high risk within thecontext of assessing impacts at 1.5°C and 2°C of global warming.
There is thus low confidence in the level at which global warming could
lead to very high risks associated with extreme weather events in thecontext of this report.

3.5.2.3

RFC 3 – Distribution of impacts

Risks due to climatic change are unevenly distributed and are
generally greater at lower latitudes and for disadvantaged people andcommunities in countries at all levels of development. AR5 locatedthe transition from undetectable to moderate risk below recent
temperatures, owing to the detection and attribution of regionallydifferentiated changes in crop yields (medium to high confidence;
Figure 3.20), and new literature has continued to confirm this finding.
Based on the assessment of risks to regional crop production andwater resources, AR5 located the transition from moderate to high risk
255

3

Chapter 3

3

between 1.6°C and 2.6°C above pre-industrial levels. Cross-Chapter
Box 6 in this chapter highlights that at 2°C of warming, new literatureshows that risks of food shortage are projected to emerge in the African
Sahel, the Mediterranean, central Europe, the Amazon, and western andsouthern Africa, and that these are much larger than the corresponding
risks at 1.5°C. This suggests a transition from moderate to high risk ofregionally differentiated impacts between 1.5°C and 2°C above preindustrial levels for food security (medium confidence) (Figure 3.20).
Reduction in the availability of water resources at 2°C is projected to
be greater than 1.5°C of global warming, although changes in socioeconomics could have a greater influence (Section 3.4.2), with larger
risks in the Mediterranean (Box 3.2); estimates of the magnitude of therisks remain similar to those cited in AR5. Globally, millions of people
may be at risk from sea level rise (SLR) during the 21st century (Hinkel
et al., 2014; Hauer et al., 2016), particularly if adaptation is limited. At
2°C of warming, more than 90% of global coastlines are projected toexperience SLR greater than 0.2 m, suggesting regional differences in
the risks of coastal flooding. Regionally differentiated multi-sector risks
are already apparent at 1.5°C of warming, being more prevalent where
vulnerable people live, predominantly in South Asia (mostly Pakistan,
India and China), but these risks are projected to spread to sub-SaharanAfrica, the Middle East and East Asia as temperature rises, with theworld’s poorest people disproportionately impacted at 2°C of warming(Byers et al., 2018). The hydrological impacts of climate change inEurope are projected to increase in spatial extent and intensity acrossincreasing global warming levels of 1.5°C, 2°C and 3°C (Donnelly etal., 2017). Taken together, a transition from moderate to high risk is
now located between 1.5°C and 2°C above pre-industrial levels, basedon the assessment of risks to food security, water resources, drought,
heat exposure and coastal submergence (high confidence; Figure 3.21).

3.5.2.4

RFC 4 – Global aggregate impacts

Oppenheimer et al. (2014) explained the inclusion of non-economic
metrics related to impacts on ecosystems and species at the global
level, in addition to economic metrics in global aggregate impacts.
The degradation of ecosystem services by climate change and ocean
acidification have generally been excluded from previous global
aggregate economic analyses.
Global economic impacts: WGII AR5 found that overall global
aggregate impacts become moderate at 1°C–2°C of warming, and thetransition to moderate risk levels was therefore located at 1.6°C above
pre-industrial levels. This was based on the assessment of literatureusing model simulations which indicated that the global aggregate
economic impact will become significantly negative between 1°C and
2°C of warming (medium confidence), whilst there will be a further
increase in the magnitude and likelihood of aggregate economic risks
at 3°C of warming (low confidence).
Since AR5, three studies have emerged using two entirely different
approaches which indicate that economic damages are projected tobe higher by 2100 if warming reaches 2°C than if it is constrained
to 1.5°C. The study by Warren et al. (2018c) used the integrated
assessment model PAGE09 to estimate that avoided global economic
damages of 22% (10–26%) accrue from constraining warming to
1.5°C rather than 2°C, 90% (77–93%) from 1.5°C rather than 3.66°C,
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Impacts of 1.5°C of Global Warming on Natural and Human Systems

and 87% (74–91%) from 2°C rather than 3.66°C. In the second
study, Pretis et al. (2018) identified several regions where economic
damages are projected to be greater at 2°C compared to 1.5°C ofwarming, further estimating that projected damages at 1.5°C remainsimilar to today’s levels of economic damage. The third study, by M.
Burke et al. (2018) used an empirical, statistical approach and foundthat limiting warming to 1.5°C instead of 2°C would save 1.5–2.0%
of the gross world product (GWP) by mid-century and 3.5% of the
GWP by end-of-century (see Figure 2A in M. Burke et al., 2018).
Based on a 3% discount rate, this corresponds to 8.1–11.6 trillion
USD and 38.5 trillion USD in avoided damages by mid- and end-ofcentury, respectively, agreeing closely with the estimate by Warren etal. (2018c) of 15 trillion USD. Under the no-policy baseline scenario,
temperature rises by 3.66°C by 2100, resulting in a global grossdomestic product (GDP) loss of 2.6% (5–95% percentile range 0.5–8.2%), compared with 0.3% (0.1–0.5%) by 2100 under the 1.5°C
scenario and 0.5% (0.1–1.0%) in the 2°C scenario. Limiting warming
to 1.5°C rather than 2°C by 2060 has also been estimated to result
in co-benefits of 0.5–0.6% of the world GDP, owing to reductions in
air pollution (Shindell et al., 2018), which is similar to the avoided
damages identified for the USA (Box 3.6).
Two studies focusing only on the USA found that economic damages
are projected to be higher by 2100 if warming reaches 2°C than if it
is constrained to 1.5°C. Hsiang et al. (2017) found a mean differenceof 0.35% GDP (range 0.2–0.65%), while Yohe (2017) identified a GDP
loss of 1.2% per degree of warming, hence approximately 0.6% for halfa degree. Further, the avoided risks compared to a no-policy baseline
are greater in the 1.5°C case (4%, range 2–7%) compared to the 2°C
case (3.5%, range 1.8–6.5%). These analyses suggest that the point
at which global aggregates of economic impacts become negative isbelow 2°C (medium confidence), and that there is a possibility that itis below 1.5°C of warming.
Oppenheimer et al. (2014) noted that the global aggregated damages
associated with large-scale singular events has not been explored, andreviews of integrated modelling exercises have indicated a potential
underestimation of global aggregate damages due to the lack ofconsideration of the potential for these events in many studies. Since
AR5, further analyses of the potential economic consequences oftriggering these large-scale singular events have indicated a two toeight fold larger economic impact associated with warming of 3°C
than estimated in most previous analyses, with the extent of increasedepending on the number of events incorporated. Lemoine and Traeger(2016) included only three known singular events whereas Y. Cai et al.
(2016) included five.
Biome shifts, species range loss, increased risks of species
extinction and risks of loss of ecosystem functioning andservices: 13% (range 8–20%) of Earth’s land area is projected toundergo biome shifts at 2°C of warming compared to approximately
7% at 1.5°C (medium confidence) (Section 3.4.3; Warszawski et al.,2013), implying a halving of biome transformations. Overall levels of
species loss at 2°C of warming are similar to values found in previous
studies for plants and vertebrates (Warren et al., 2013, 2018a), but
insects have been found to be more sensitive to climate change, with18% (6–35%) projected to lose over half their range at 2°C of warmingImpacts of 1.5°C of Global Warming on Natural and Human Systems

compared to 6% (1–18%) under 1.5°C of warming, corresponding
to a difference of 66% (Section 3.4.3). The critical role of insects inecosystem functioning therefore suggests that there will be impacts
on global ecosystem functioning already at 2°C of warming, whilst
species that lose large proportions of their range are considered to
be at increased risk of extinction (Section 3.4.3.3). Since AR5, newliterature has indicated that impacts on marine fish stocks and fisheries
are lower under 1.5°C–2°C of global warming relative to pre-industrial
levels compared to under higher warming scenarios (Section 3.4.6),
especially in tropical and polar systems.
In AR5, the transition from undetectable to moderate impacts wasconsidered to occur between 1.6°C and 2.6°C of global warming
reflecting impacts on the economy and on biodiversity globally, whereas
high risks were associated with 3.6°C of warming to reflect the highrisks to biodiversity and accelerated effects on the global economy.
New evidence suggests moderate impacts on the global aggregate
economy and global biodiversity by 1.5°C of warming, suggesting alowering of the temperature level for the transition to moderate risk
to 1.5°C (Figure 3.21). Further, recent literature points to higher risks
than previously assessed for the global aggregate economy and global
biodiversity by 2°C of global warming, suggesting that the transition
to a high risk level is located between 1.5°C and 2.5°C of warming(Figure 3.21), as opposed to at 3.6°C as previously assessed (medium
confidence).

3.5.2.5

RFC 5 – Large-scale singular events

Large-scale singular events are components of the global Earth systemthat are thought to hold the risk of reaching critical tipping points
under climate change, and that can result in or be associated withmajor shifts in the climate system. These components include:• the cryosphere: West Antarctic ice sheet, Greenland ice sheet• the thermohaline circulation: slowdown of the Atlantic Meridional
Overturning Circulation (AMOC)• the El Niño–Southern Oscillation (ENSO) as a global mode ofclimate variability• role of the Southern Ocean in the global carbon cycle
AR5 assessed that the risks associated with these events become
moderate between 0.6°C and 1.6°C above pre-industrial levels, basedon early warning signs, and that risk was expected to become highbetween 1.6°C and 4.6°C based on the potential for commitment tolarge irreversible sea level rise from the melting of land-based ice sheets(low to medium confidence). The increase in risk between 1.6°C and2.6°C above pre-industrial levels was assessed to be disproportionately
large. New findings since AR5 are described in detail below.
Greenland and West Antarctic ice sheets and marine ice sheet
instability (MISI): Various feedbacks between the Greenland ice
sheet and the wider climate system, most notably those related tothe dependence of ice melt on albedo and surface elevation, makeirreversible loss of the ice sheet a possibility. Church et al. (2013)assessed this threshold to be at 2°C of warming or higher levels relative
to pre-industrial temperature. Robinson et al. (2012) found a range forthis threshold of 0.8°C–3.2°C (95% confidence). The threshold of global

Chapter 3

temperature increase that may initiate irreversible loss of the West
Antarctic ice sheet and marine ice sheet instability (MISI) is estimatedto lie be between 1.5°C and 2°C. The time scale for eventual loss of theice sheets varies between millennia and tens of millennia and assumes
constant surface temperature forcing during this period. If temperature
were to decline subsequently the ice sheets might regrow, although
the amount of cooling required is likely to be highly dependent on the
duration and rate of the previous retreat. The magnitude of global sea
level rise that could occur over the next two centuries under 1.5°C–2°C
of global warming is estimated to be in the order of several tenths of ametre according to most studies (low confidence) (Schewe et al., 2011;
Church et al., 2013; Levermann et al., 2014; Marzeion and Levermann,
2014; Fürst et al., 2015; Golledge et al., 2015), although a smaller
number of investigations (Joughin et al., 2014; Golledge et al., 2015;
DeConto and Pollard, 2016) project increases of 1–2 m. This body ofevidence suggests that the temperature range of 1.5°C–2°C may be
regarded as representing moderate risk, in that it may trigger MISI in
Antarctica or irreversible loss of the Greenland ice sheet and it may be
associated with sea level rise by as much as 1–2 m over a period of
two centuries.
Thermohaline circulation (slowdown of AMOC): It is more likely
than not that the AMOC has been weakening in recent decades,
given the detection of cooling of surface waters in the North Atlantic
and evidence that the Gulf Stream has slowed since the late 1950s(Rahmstorf et al., 2015b; Srokosz and Bryden, 2015; Caesar et al.,2018). There is limited evidence linking the recent weakening of the
AMOC to anthropogenic warming (Caesar et al., 2018). It is very likelythat the AMOC will weaken over the 21st century. Best estimates andranges for the reduction based on CMIP5 simulations are 11% (1–24%)
in RCP2.6 and 34% (12–54%) in RCP8.5 (AR5). There is no evidence
indicating significantly different amplitudes of AMOC weakening for
1.5°C versus 2°C of global warming, or of a shutdown of the AMOC atthese global temperature thresholds. Associated risks are classified as
low to moderate.
El Niño–Southern Oscillation (ENSO): Extreme El Niño events areassociated with significant warming of the usually cold eastern Pacific
Ocean, and they occur about once every 20 years (Cai et al., 2015). Such
events reorganize the distribution of regions of organized convection
and affect weather patterns across the globe. Recent research indicatesthat the frequency of extreme El Niño events increases linearly with theglobal mean temperature, and that the number of such events mightdouble (one event every ten years) under 1.5°C of global warming (G.
Wang et al., 2017). This pattern is projected to persist for a century after
stabilization at 1.5°C, thereby challenging the limits to adaptation, andthus indicates high risk even at the 1.5°C threshold. La Niña event(the opposite or balancing event to El Niño) frequency is projected toremain similar to that of the present day under 1.5°C–2°C of global
warming.
Role of the Southern Ocean in the global carbon cycle: The criticalrole of the Southern Ocean as a net sink of carbon might decline underglobal warming, and assessing this effect under 1.5°C compared to
2°C of global warming is a priority. Changes in ocean chemistry (e.g.,oxygen content and ocean acidification), especially those associatedwith the deep sea, are associated concerns (Section 3.3.10).
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Chapter 3

For large-scale singular events (RFC5), moderate risk is now locatedat 1°C of warming and high risk is located at 2.5°C (Figure 3.21), as
opposed to at 1.6°C (moderate risk) and around 4°C (high risk) in
AR5, because of new observations and models of the West Antarctic
ice sheet (medium confidence), which suggests that the ice sheet
may be in the early stages of marine ice sheet instability (MISI).
Very high risk is assessed as lying above 5°C because the growing
literature on process-based projections of the West Antarctic ice sheet
predominantly supports the AR5 assessment of an MISI contribution ofseveral additional tenths of a metre by 2100.

3.5.3

3

Regional Economic Benefit Analysis for the 1.5°C
versus 2°C Global Goals

This section reviews recent literature that has estimated the economic
benefits of constraining global warming to 1.5°C compared to 2°C.
The focus here is on evidence pertaining to specific regions, rather
than on global aggregated benefits (Section 3.5.2.4). At 2°C of global
warming, lower economic growth is projected for many countries thanat 1.5°C of global warming, with low-income countries projected toexperience the greatest losses (low to medium confidence) (M. Burkeet al., 2018; Pretis et al., 2018). A critical issue for developing countries
in particular is that advantages in some sectors are projected to beoffset by increasing mitigation costs (Rogelj et al., 2013; M. Burke etal., 2018), with food production being a key factor. That is, although
restraining the global temperature increase to 2°C is projected toreduce crop losses under climate change relative to higher levels ofwarming, the associated mitigation costs may increase the risk ofhunger in low-income countries (low confidence) (Hasegawa et al.,2016). It is likely that the even more stringent mitigation measuresrequired to restrict global warming to 1.5°C (Rogelj et al., 2013) willfurther increase these mitigation costs and impacts. International
trade in food might be a key response measure for alleviating hunger
in developing countries under 1.5°C and 2°C stabilization scenarios(IFPRI, 2018).
Although warming is projected to be the highest in the Northern
Hemisphere under 1.5°C or 2°C of global warming, regions in
the tropics and Southern Hemisphere subtropics are projected toexperience the largest impacts on economic growth (low to medium
confidence) (Gallup et al., 1999; M. Burke et al., 2018; Pretis et al.,2018). Despite the uncertainties associated with climate change
projections and econometrics (e.g., M. Burke et al., 2018), it is more
likely than not that there will be large differences in economic
growth under 1.5°C and 2°C of global warming for developing
versus developed countries (M. Burke et al., 2018; Pretis et al.,2018). Statistically significant reductions in gross domestic product(GDP) per capita growth are projected across much of the African
continent, Southeast Asia, India, Brazil and Mexico (low to medium
confidence). Countries in the western parts of tropical Africa areprojected to benefit most from restricting global warming to 1.5°C,
as opposed to 2°C, in terms of future economic growth (Pretis et al.,2018). An important reason why developed countries in the tropics
and subtropics are projected to benefit substantially from restricting
global warming to 1.5°C is that present-day temperatures in theseregions are above the threshold thought to be optimal for economic
production (M. Burke et al., 2015b, 2018).
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Impacts of 1.5°C of Global Warming on Natural and Human Systems

The world’s largest economies are also projected to benefit fromrestricting warming to 1.5°C as opposed to 2°C (medium confidence),with the likelihood of such benefits being realized estimated at
76%, 85% and 81% for the USA, China and Japan, respectively (M.
Burke et al., 2018). Two studies focusing only on the USA found that
economic damages are projected to be higher by 2100 if warming
reaches 2°C than if it is constrained to 1.5°C. Yohe (2017) found amean difference of 0.35% GDP (range 0.2–0.65%), while Hsiang
et al. (2017) identified a GDP loss of 1.2% per degree of warming,
hence approximately 0.6% for half a degree. Overall, no statisticallysignificant changes in GDP are projected to occur over most of thedeveloped world under 1.5°C of global warming in comparison topresent-day conditions, but under 2°C of global warming impacts on
GDP are projected to be generally negative (low confidence) (Pretis
et al., 2018).
A caveat to the analyses of Pretis et al. (2018) and M. Burke et al.
(2018) is that the effects of sea level rise were not included in theestimations of damages or future economic growth, implying a potential
underestimation of the benefits of limiting warming to 1.5°C for thecase where significant sea level rise is avoided at 1.5°C but not at 2°C.

3.5.4

Reducing Hotspots of Change for 1.5°C and 2°Cof Global WarmingThis subsection integrates Sections 3.3 and 3.4 in terms of climatechange-induced hotspots that occur through interactions across thephysical climate system, ecosystems and socio-economic humansystems, with a focus on the extent to which risks can be avoided or
reduced by achieving the 1.5°C global warming goal (as opposed to
the 2°C goal). Findings are summarized in Table 3.6.

3.5.4.1

Arctic sea ice

Ice-free Arctic Ocean summers are very likely at levels of global
warming higher than 2°C (Notz and Stroeve, 2016; Rosenblum andEisenman, 2016; Screen and Williamson, 2017; Niederdrenk andNotz, 2018). Some studies even indicate that the entire Arctic Ocean
summer period will become ice free under 2°C of global warming,whilst others more conservatively estimate this probability to be in theorder of 50% (Section 3.3.8; Sanderson et al., 2017). The probabilityof an ice-free Arctic in September at 1.5°C of global warming is low
and substantially lower than for the case of 2°C of global warming(high confidence) (Section 3.3.8; Screen and Williamson, 2017; Jahn,
2018; Niederdrenk and Notz, 2018). There is, however, a single
study that questions the validity of the 1.5°C threshold in terms of
maintaining summer Arctic Ocean sea ice (Niederdrenk and Notz,2018). In contrast to summer, little ice is projected to be lost during
winter for either 1.5°C or 2°C of global warming (medium confidence)(Niederdrenk and Notz, 2018). The losses in sea ice at 1.5°C and
2°C of warming will result in habitat losses for organisms such as
seals, polar bears, whales and sea birds (e.g., Larsen et al., 2014).
There is high agreement and robust evidence that photosynthetic
species will change because of sea ice retreat and related changes
in temperature and radiation (Section 3.4.4.7), and this is very likely
to benefit fisheries productivity in the Northern Hemisphere spring
bloom system (Section 3.4.4.7).

Impacts of 1.5°C of Global Warming on Natural and Human Systems

3.5.4.2

Arctic land regions

In some Arctic land regions, the warming of cold extremes and theincrease in annual minimum temperature at 1.5°C are stronger than
the global mean temperature increase by a factor of two to three,meaning 3°C–4.5°C of regional warming at 1.5°C of global warming(e.g., northern Europe in Supplementary Material 3.SM, Figure 3.SM.5
see also Section 3.3.2.2 and Seneviratne et al., 2016). Moreover, overmuch of the Arctic, a further increase of 0.5°C in the global surface
temperature, from 1.5°C to 2°C, may lead to further temperature
increases of 2°C–2.5°C (Figure 3.3). As a consequence, biome (major
ecosystem type) shifts are likely in the Arctic, with increases in fire
frequency, degradation of permafrost, and tree cover likely to occur at
1.5°C of warming and further amplification of these changes expectedunder 2°C of global warming (e.g., Gerten et al., 2013; Bring et al.,2016). Rising temperatures, thawing permafrost and changing weather
patterns are projected to increasingly impact people, infrastructure andindustries in the Arctic (W.N. Meier et al., 2014) with these impacts
larger at 2°C than at 1.5°C of warming (medium confidence).

3.5.4.3

Alpine regions

Alpine regions are generally regarded as climate change hotspots
given that rich biodiversity has evolved in their cold and harsh climate,
but with many species consequently being vulnerable to increases intemperature. Under regional warming, alpine species have been foundto migrate upwards on mountain slopes (Reasoner and Tinner, 2009),
an adaptation response that is obviously limited by mountain heightand habitability. Moreover, many of the world’s alpine regions areimportant from a water security perspective through associated glaciermelt, snow melt and river flow (see Section 3.3.5.2 for a discussion ofthese aspects). Projected biome shifts are likely to be severe in alpine
regions already at 1.5°C of warming and to increase further at 2°C(Gerten et al., 2013, Figure 1b; B. Chen et al., 2014).

3.5.4.4

Southeast Asia

Southeast Asia is a region highly vulnerable to increased flooding in
the context of sea level rise (Arnell et al., 2016; Brown et al., 2016,2018a). Risks from increased flooding are projected to rise from 1.5°C
to 2°C of warming (medium confidence), with substantial increases
projected beyond 2°C (Arnell et al., 2016). Southeast Asia displays
statistically significant differences in projected changes in heavy
precipitation, runoff and high flows at 1.5°C versus 2°C of warming,with stronger increases occurring at 2°C (Section 3.3.3; Wartenburger
et al., 2017; Döll et al., 2018; Seneviratne et al., 2018c); thus, this region
is considered a hotspot in terms of increases in heavy precipitation
between these two global temperature levels (medium confidence)(Schleussner et al., 2016b; Seneviratne et al., 2016). For Southeast Asia,
2°C of warming by 2040 could lead to a decline by one-third in percapita crop production associated with general decreases in crop yields(Nelson et al., 2010). However, under 1.5°C of warming, significant
risks for crop yield reduction in the region are avoided (Schleussner etal., 2016b). These changes pose significant risks for poor people in bothrural regions and urban areas of Southeast Asia (Section 3.4.10.1), withthese risks being larger at 2°C of global warming compared to 1.5°C(medium confidence).

Chapter 3

3.5.4.5

Southern Europe and the Mediterranean

The Mediterranean is regarded as a climate change hotspot, both interms of projected stronger warming of the regional land-based hot
extremes compared to the mean global temperature increase (e.g.,
Seneviratne et al., 2016) and in terms of of robust increases in theprobability of occurrence of extreme droughts at 2°C vs 1.5°C global
warming (Section 3.3.4). Low river flows are projected to decrease in
the Mediterranean under 1.5°C of global warming (Marx et al., 2018),
with associated significant decreases in high flows and floods (Thober
et al., 2018), largely in response to reduced precipitation. The median
reduction in annual runoff is projected to almost double from about
9% (likely range 4.5–15.5%) at 1.5°C to 17% (likely range 8–25%)
at 2°C (Schleussner et al., 2016b). Similar results were found by Döll
et al. (2018). Overall, there is high confidence that strong increases indryness and decreases in water availability in the Mediterranean andsouthern Europe would occur from 1.5°C to 2°C of global warming. Sea
level rise is expected to be lower for 1.5°C versus 2°C, lowering risks
for coastal metropolitan agglomerations. The risks (assuming current
adaptation) related to water deficit in the Mediterranean are high for
global warming of 2°C but could be substantially reduced if global
warming were limited to 1.5°C (Section 3.3.4; Guiot and Cramer, 2016;
Schleussner et al., 2016b; Donnelly et al., 2017).

3.5.4.6

West Africa and the Sahel

West Africa and the Sahel are likely to experience increases in thenumber of hot nights and longer and more frequent heatwaves
even if the global temperature increase is constrained to 1.5°C, withfurther increases expected at 2°C of global warming and beyond(e.g., Weber et al., 2018). Moreover, daily rainfall intensity and runoff
is expected to increase (low confidence) towards 2°C and higher
levels of global warming (Schleussner et al., 2016b; Weber et al.,2018), with these changes also being relatively large compared tothe projected changes at 1.5°C of warming. Moreover, increased risks
are projected in terms of drought, particularly for the pre-monsoon
season (Sylla et al., 2015), with both rural and urban populations
affected, and more so at 2°C of global warming as opposed to 1.5°C(Liu et al., 2018). Based on a World Bank (2013) study for sub-SaharanAfrica, a 1.5°C warming by 2030 might reduce the present maize
cropping areas by 40%, rendering these areas no longer suitablefor current cultivars. Substantial negative impacts are also projected
for sorghum suitability in the western Sahel (Läderach et al., 2013;
Sultan and Gaetani, 2016). An increase in warming to 2°C by 2040
would result in further yield losses and damages to crops (i.e., maize,sorghum, wheat, millet, groundnut and cassava). Schleussner et al.
(2016b) found consistently reduced impacts on crop yield for West
Africa under 2°C compared to 1.5°C of global warming. There is
medium confidence that vulnerabilities to water and food security in
the African Sahel will be higher at 2°C compared to 1.5°C of global
warming (Cheung et al., 2016a; Betts et al., 2018), and at 2°C thesevulnerabilities are expected to be worse (high evidence) (Sultan andGaetani, 2016; Lehner et al., 2017; Betts et al., 2018; Byers et al.,2018; Rosenzweig et al., 2018). Under global warming of more than2°C, the western Sahel might experience the strongest drying andexperience serious food security issues (Ahmed et al., 2015; Parkes
et al., 2018).
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Chapter 3

3.5.4.7

Southern Africa

The southern African region is projected to be a climate change hotspot
in terms of both hot extremes (Figures 3.5 and 3.6) and drying (Figure3.12). Indeed, temperatures have been rising in the subtropical regionsof southern Africa at approximately twice the global rate over the last
five decades (Engelbrecht et al., 2015). Associated elevated warmingof the regional land-based hot extremes has occurred (Section 3.3;
Seneviratne et al., 2016). Increases in the number of hot nights, aswell as longer and more frequent heatwaves, are projected even if the
global temperature increase is constrained to 1.5°C (high confidence),with further increases expected at 2°C of global warming and beyond(high confidence) (Weber et al., 2018).

3

Moreover, southern Africa is likely to generally become drier with
reduced water availability under low mitigation (Niang et al., 2014;
Engelbrecht et al., 2015; Karl et al., 2015; James et al., 2017), withthis particular risk being prominent under 2°C of global warming andeven under 1.5°C (Gerten et al., 2013). Risks are significantly reduced,
however, under 1.5°C of global warming compared to under higher
levels (Schleussner et al., 2016b). There are consistent and statisticallysignificant increases in projected risks of increased meteorologicaldrought in southern Africa at 2°C versus 1.5°C of warming (medium
confidence). Despite the general rainfall reductions projected forsouthern Africa, daily rainfall intensities are expected to increase overmuch of the region (medium confidence), and increasingly so withhigher levels of global warming. There is medium confidence thatlivestock in southern Africa will experience increased water stressunder both 1.5°C and 2°C of global warming, with negative economic
consequences (e.g., Boone et al., 2018). The region is also projected
to experience reduced maize, sorghum and cocoa cropping area
suitability, as well as yield losses under 1.5°C of warming, with further
decreases occurring towards 2°C of warming (World Bank, 2013).
Generally, there is high confidence that vulnerability to decreases inwater and food availability is reduced at 1.5°C versus 2°C for southern
Africa (Betts et al., 2018), whilst at 2°C these are expected to be higher(high confidence) (Lehner et al., 2017; Betts et al., 2018; Byers et al.,2018; Rosenzweig et al., 2018).

3.5.4.8

Tropics

Worldwide, the largest increases in the number of hot days areprojected to occur in the tropics (Figure 3.7). Moreover, the largest
differences in the number of hot days for 1.5°C versus 2°C of global
warming are projected to occur in the tropics (Mahlstein et al., 2011).
In tropical Africa, increases in the number of hot nights, as well as
longer and more frequent heatwaves, are projected under 1.5°C of
global warming, with further increases expected under 2°C of global
warming (Weber et al., 2018). Impact studies for major tropical cereals
reveal that yields of maize and wheat begin to decline with 1°C to 2°C
of local warming in the tropics. Schleussner et al. (2016b) project that
constraining warming to 1.5°C rather than 2°C would avoid significant
risks of tropical crop yield declines in West Africa, Southeast Asia, andCentral and South America. There is limited evidence and thus low
confidence that these changes may result in significant population
displacement from the tropics to the subtropics (e.g., Hsiang and Sobel,2016).
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Impacts of 1.5°C of Global Warming on Natural and Human Systems

3.5.4.9

Small islands

It is widely recognized that small islands are very sensitive to climatechange impacts such as sea level rise, oceanic warming, heavy
precipitation, cyclones and coral bleaching (high confidence) (Nurse etal., 2014; Ourbak and Magnan, 2017). Even at 1.5°C of global warming,
the compounding impacts of changes in rainfall, temperature, tropical
cyclones and sea level are likely to be significant across multiple
natural and human systems. There are potential benefits to small
island developing states (SIDS) from avoided risks at 1.5°C versus
2°C, especially when coupled with adaptation efforts. In terms of sea
level rise, by 2150, roughly 60,000 fewer people living in SIDS will be
exposed in a 1.5°C world than in a 2°C world (Rasmussen et al., 2018).
Constraining global warming to 1.5°C may significantly reduce water
stress (by about 25%) compared to the projected water stress at 2°C,
for example in the Caribbean region (Karnauskas et al., 2018), and may
enhance the ability of SIDS to adapt (Benjamin and Thomas, 2016). Up
to 50% of the year is projected to be very warm in the Caribbean at
1.5°C, with a further increase by up to 70 days at 2°C versus 1.5°C(Taylor et al., 2018). By limiting warming to 1.5°C instead of 2°C in
2050, risks of coastal flooding (measured as the flood amplification
factors for 100-year flood events) are reduced by 20–80% for SIDS
(Rasmussen et al., 2018). A case study of Jamaica with lessons forother Caribbean SIDS demonstrated that the difference between 1.5°C
and 2°C is likely to challenge livestock thermoregulation, resulting inpersistent heat stress for livestock (Lallo et al., 2018).

3.5.4.10 Fynbos and shrub biomes
The Fynbos and succulent Karoo biomes of South Africa arethreatened systems that were assessed in AR5. Similar shrublands
exist in the semi-arid regions of other continents, with the SonoraMojave creosotebush-white bursage desert scrub ecosystem in the
USA being a prime example. Impacts accrue across these systems
with greater warming, with impacts at 2°C likely to be greater thanthose at 1.5°C (medium confidence). Under 2°C of global warming,
regional warming in drylands is projected to be 3.2°C–4°C, and under
1.5°C of global warming, mean warming in drylands is projected tostill be about 3°C. The Fynbos biome in southwestern South Africa
is vulnerable to the increasing impact of fires under increasing
temperatures and drier winters (high confidence). The Fynbos biome
is projected to lose about 20%, 45% and 80% of its current suitableclimate area relative to its present-day area under 1°C, 2°C and
3°C of warming, respectively (Engelbrecht and Engelbrecht, 2016),
demonstrating the value of climate change mitigation in protecting
this rich centre of biodiversity.

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Chapter 3

Table 3.6 | Emergence and intensity of climate change hotspots under different degrees of global warming.
Region and/or
Phenomenon

Arctic sea ice

Arctic land regions

Alpine regions

Southeast Asia

MediterraneanWest Africa andthe SahelWarming of 1.5°C or less

The risk of an ice-free Arctic in summer is about 50%
or higher

The Arctic is very likely to be ice free in summer

Habitat losses for organisms such as polar bears,
whales, seals and sea birds

Habitat losses for organisms such as polar bears,
whales,seals and sea birds may be critical if
summers are ice free

Critical habitat losses for organisms such aspolar bears, whales, seals and sea birds

Benefits for Arctic fisheries

Benefits for Arctic fisheries

Benefits for Arctic fisheries

Cold extremes warm by a factor of 2–3, reaching
up to 4.5°C (high confidence)

Cold extremes warm by as much as 8°C(high confidence)

Drastic regional warming is very likely

Biome shifts in the tundra and permafrost
deterioration are likely

Larger intrusions of trees and shrubs in the tundra
than under 1.5°C of warming are likely; larger
but constrained losses in permafrost are likely

A collapse in permafrost may occur (lowconfidence); a drastic biome shift from tundra
to boreal forest is possible (low confidence)

Severe shifts in biomes are likely

Even more severe shifts are likely

Critical losses in alpine habitats are likely

Risks for increased flooding related to sea level rise

Higher risks of increased flooding related
to sea level rise (medium confidence)

Substantial increases in risks related to flooding
from sea level rise

Increases in heavy precipitation events

Stronger increases in heavy precipitation events(medium confidence)

Substantial increase in heavy precipitationand high-flow events

Significant risks of crop yield reductions are avoided

One-third decline in per capita crop production(medium confidence)

Substantial reductions in crop yield

Increase in probability of extreme
drought (medium confidence)

Robust increase in probability of extreme
drought (medium confidence)

Medium confidence in reduction in runoffof about 9% (likely range 4.5–15.5%)

Medium confidence in further reductions(about 17%) in runoff (likely range 8–28%)

Robust and large increases in extreme
drought. Substantial reductions in precipitationand in runoff (medium confidence)

Risk of water deficit (medium confidence)

Higher risks of water deficit (medium confidence)

Very high risks of water deficit (medium confidence)

Increases in the number of hot nights and longer
and more frequent heatwaves are likely

Further increases in number of hot nights andlonger and more frequent heatwaves are likely

Substantial increases in the number of hot nights
and heatwave duration and frequency (very likely)

Reduced maize and sorghum production is likely,
with area suitable for maize production reducedby as much as 40%

Negative impacts on maize and sorghum production
likely larger than at 1.5°C; medium confidence
that vulnerabilities to food security in the African
Sahel will be higher at 2°C compared to 1.5°C

Negative impacts on crop yield may result in major
regional food insecurities (medium confidence)

Increased risks of undernutrition

Higher risks of undernutrition

High risks of undernutrition

Reductions in water availability (medium confidence)

Larger reductions in rainfall and water
availability (medium confidence)

Large reductions in rainfall and water
availability (medium confidence)

Increases in number of hot nights and longer andmore frequent heatwaves (high confidence)

Further increases in number of hot nights andlonger and more frequent heatwaves (highconfidence), associated increases in risks ofincreased mortality from heatwaves comparedto 1.5°C warming (high confidence)

Drastic increases in the number of hot nights, hotdays and heatwave duration and frequency toimpact substantially on agriculture, livestock andhuman health and mortality (high confidence)High risk of undernutrition in communities
dependent on dryland agriculture and livestock

Higher risks of undernutrition in communities
dependent on dryland agriculture and livestock

Very high risks of undernutrition in communities
dependent on dryland agriculture and livestock

Increases in the number of hot days and hot nights
as well as longer and more frequent heatwaves(high confidence)

The largest increase in hot days under 2°C comparedto 1.5°C is projected for the tropics.

Oppressive temperatures and accumulated
heatwave duration very likely to directly impact
human health, mortality and productivity

Risks to tropical crop yields in West Africa,
Southeast Asia and Central and South America
are significantly less than under 2°C of warming

Risks to tropical crop yields in West Africa,
Southeast Asia and Central and South America
could be extensive

Substantial reductions in crop yield very likely

Land of 60,000 less people exposed by 2150 on SIDS
compared to impacts under 2°C of global warming

Tens of thousands of people displaced owing toinundation of SIDS

Risks for coastal flooding reduced by 20–80%
for SIDS compared to 2°C of global warmingHigh risks for coastal flooding

Substantial and widespread impacts
through inundation of SIDS, coastal flooding,freshwater stress, persistent heat stress andloss of most coral reefs (very likely)

Freshwater stress reduced by 25%

Freshwater stress reduced by 25% compared to
2°C of global warmingHigh risks of increased mortality from heatwaves

Small islands

Fynbos biome

Warming of 2°C–3°C

Arctic summer sea ice is likely to be maintained

Southern Africa

Tropics

Warming of 1.5°C–2°C

Freshwater stress from projected aridity
Increase in the number of warm days for SIDS
in the tropics

Further increase of about 70 warm days per year

Persistent heat stress in cattle avoided

Persistent heat stress in cattle in SIDS

Loss of 70–90% of coral reefs

Loss of most coral reefs and weaker remaining
structures owing to ocean acidification

About 30% of suitable climate area lost
(medium confidence)

Increased losses (about 45%) of suitable climate
area (medium confidence)

Up to 80% of suitable climate area lost
(medium confidence)261

3

Chapter 3

3.5.5

Avoiding Regional Tipping Points by Achieving
More Ambitious Global Temperature Goals

Tipping points refer to critical thresholds in a system that, when exceeded,
can lead to a significant change in the state of the system, often with anunderstanding that the change is irreversible. An understanding of thesensitivities of tipping points in the physical climate system, as well as inecosystems and human systems, is essential for understanding the risks
associated with different degrees of global warming. This subsection
reviews tipping points across these three areas within the context
of the different sensitivities to 1.5°C versus 2°C of global warming.
Sensitivities to less ambitious global temperature goals are also briefly
reviewed. Moreover, an analysis is provided of how integrated risks
across physical, natural and human systems may accumulate to leadto the exceedance of thresholds for particular systems. The emphasis in
this section is on the identification of regional tipping points and their
sensitivity to 1.5°C and 2°C of global warming, whereas tipping points
in the global climate system, referred to as large-scale singular events,
were already discussed in Section 3.5.2. A summary of regional tipping
points is provided in Table 3.7.

3.5.5.1

3

Arctic sea ice

Collins et al. (2013) discussed the loss of Arctic sea ice in the context
of potential tipping points. Climate models have been used to assess
whether a bifurcation exists that would lead to the irreversible lossof Arctic sea ice (Armour et al., 2011; Boucher et al., 2012; Ridley etal., 2012) and to test whether the summer sea ice extent can recoverafter it has been lost (Schröder and Connolley, 2007; Sedláček et al.,2011; Tietsche et al., 2011). These studies did not find e vidence o fbifurcation or indicate that sea ice returns within a few years of its loss,
leading Collins et al. (2013) to conclude that there is little evidence
for a tipping point in the transition from perennial to seasonal ice
cover. No evidence has been found for irreversibility or tipping points,suggesting that year-round sea ice will return given a suitable climate(medium confidence) (Schröder and Connolley, 2007; Sedláček et al.,2011; Tietsche et al., 2011).

3.5.5.2

TundraTree growth in tundra-dominated landscapes is strongly constrained by
the number of days with mean air temperature above 0°C. A potential
tipping point exists where the number of days below 0°C decreases
to the extent that the tree fraction increases significantly. T undradominated landscapes have warmed more than the global averageover the last century (Settele et al., 2014), with associated increases
in fires and permafrost degradation (Bring et al., 2016; DeBeer et al.,2016; Jiang et al., 2016; Yang et al., 2016). These processes facilitate
conditions for woody species establishment in tundra areas, and forthe eventual transition of the tundra to boreal forest. The number ofinvestigations into how the tree fraction may respond in the Arctic todifferent degrees of global warming is limited, and studies generallyindicate that substantial increases will likely occur gradually (e.g.,
Lenton et al., 2008). Abrupt changes are only plausible at levels ofwarming significantly h igher t han 2 °C ( low c onfidence) an d wo uld
occur in conjunction with a collapse in permafrost (Drijfhout et al.,2015).
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Impacts of 1.5°C of Global Warming on Natural and Human Systems

3.5.5.3

Permafrost

Widespread thawing of permafrost potentially makes a large carbon
store (estimated to be twice the size of the atmospheric store; Dolman
et al., 2010) vulnerable to decomposition, which could lead to further
increases in atmospheric carbon dioxide and methane and hence tofurther global warming. This feedback loop between warming and therelease of greenhouse gas from thawing tundra represents a potential
tipping point. However, the carbon released to the atmosphere fromthawing permafrost is projected to be restricted to 0.09–0.19 Gt C yr–1
at 2°C of global warming and to 0.08–0.16 Gt C yr–1 at 1.5°C (E.J.
Burke et al., 2018), which does not indicate a tipping point (medium
confidence). At higher degrees of global warming, in the order of
3°C, a different type of tipping point in permafrost may be reached.
A single model projection (Drijfhout et al., 2015) suggested thathigher temperatures may induce a smaller ice fraction in soils in thetundra, leading to more rapidly warming soils and a positive feedback
mechanism that results in permafrost collapse (low confidence). The
disparity between the multi-millennial time scales of soil carbon
accumulation and potentially rapid decomposition in a warming
climate implies that the loss of this carbon to the atmosphere would
be essentially irreversible (Collins et al., 2013).

3.5.5.4

Asian monsoon

At a fundamental level, the pressure gradient between the Indian Ocean
and Asian continent determines the strength of the Asian monsoon. As
land masses warm faster than the oceans, a general strengthening of
this gradient, and hence of monsoons, may be expected under global
warming (e.g., Lenton et al., 2008). Additional factors such as changes
in albedo induced by aerosols and snow-cover change may also affecttemperature gradients and consequently pressure gradients and thestrength of the monsoon. In fact, it has been estimated that an increaseof the regional land mass albedo to 0.5 over India would represent atipping point resulting in the collapse of the monsoon system (Lenton
et al., 2008). The overall impacts of the various types of radiative
forcing under different emissions scenarios are more subtle, with aweakening of the monsoon north of about 25°N in East Asia but astrengthening south of this latitude projected by Jiang and Tian (2013)
under high and modest emissions scenarios. Increases in the intensityof monsoon precipitation are likely under low mitigation (AR5). Given
that scenarios of 1.5°C or 2°C of global warming would include a
substantially smaller radiative forcing than those assessed in the study
by Jiang and Tian (2013), there is low confidence regarding changes inmonsoons at these low global warming levels, as well as regarding thedifferences between responses at 1.5°C versus 2°C of warming.

3.5.5.5

West African monsoon and the Sahel

Earlier work has identified 3°C of global warming as the tipping point
leading to a significant strengthening of the West African monsoon andsubsequent wettening (and greening) of the Sahel and Sahara (Lenton
et al., 2008). AR5 (Niang et al., 2014), as well as more recent researchthrough the Coordinated Regional Downscaling Experiment for Africa(CORDEX–AFRICA), provides a more uncertain view, however, in termsof the rainfall futures of the Sahel under low mitigation futures. Evenif a wetter Sahel should materialize under 3°C of global warming (lowImpacts of 1.5°C of Global Warming on Natural and Human Systems

confidence), it should be noted that there would be significant offsets
in the form of strong regional warming and related adverse impacts
on crop yield, livestock mortality and human health under such low
mitigation futures (Engelbrecht et al., 2015; Sylla et al., 2016; Weberet al., 2018).

3.5.5.6

Rainforests

A large portion of rainfall over the world’s largest rainforests isrecirculated (e.g., Lenton et al., 2008), which raises the concern that
deforestation may trigger a threshold in reduced forest cover, leading
to pronounced forest dieback. For the Amazon, this deforestationthreshold has been estimated to be 40% (Nobre et al., 2016). Global
warming of 3°C–4°C may also, independent of deforestation, represent
a tipping point that results in a significant dieback of the Amazon
forest, with a key forcing mechanism being stronger El Niño events
bringing more frequent droughts to the region (Nobre et al., 2016).
Increased fire frequencies under global warming may interact with andaccelerate deforestation, particularly during periods of El Niño-induced
droughts (Lenton et al., 2008; Nobre et al., 2016). Global warming of
3°C is projected to reduce the extent of tropical rainforest in Central
America, with biomass being reduced by about 40%, which can leadto a large replacement of rainforest by savanna and grassland (Lyra etal., 2017). Overall, modelling studies (Huntingford et al., 2013; Nobreet al., 2016) and observational constraints (Cox et al., 2013) suggestthat pronounced rainforest dieback may only be triggered at 3°C–4°C(medium confidence), although pronounced biomass losses may occur
at 1.5°C– 2°C of global warming.

3.5.5.7

Boreal forests

Boreal forests are likely to experience stronger local warming than the
global average (WGII AR5; Collins et al., 2013). Increased disturbancefrom fire, pests and heat-related mortality may affect, in particular, thesouthern boundary of boreal forests (medium confidence) (Gauthieret al., 2015), with these impacts accruing with greater warming andthus impacts at 2°C would be expected to be greater than those at
1.5°C (medium confidence). A tipping point for significant dieback of
the boreal forests is thought to exist, where increased tree mortality
would result in the creation of large regions of open woodlands
and grasslands, which would favour further regional warming andincreased fire frequencies, thus inducing a powerful positive feedback
mechanism (Lenton et al., 2008; Lenton, 2012). This tipping point has
been estimated to exist between 3°C and 4°C of global warming(low confidence) (Lucht et al., 2006; Kriegler et al., 2009), but given
the complexities of the various forcing mechanisms and feedback
processes involved, this is thought to be an uncertain estimate.

3.5.5.8

Heatwaves, unprecedented heat and human health

Chapter 3

in the occurrence of deadly heatwaves in cities if urban heat islandeffects are considered, with impacts being similar at 1.5°C and 2°C but
substantially larger than under the present climate (Matthews et al.,2017). At 1.5°C of warming, twice as many megacities (such as Lagos,
Nigeria, and Shanghai, China) than at present are likely to become heatstressed, potentially exposing more than 350 million more people todeadly heat stress by 2050. At 2°C of warming, Karachi (Pakistan) andKolkata (India) could experience conditions equivalent to their deadly2015 heatwaves on an annual basis (medium confidence). Thesestatistics imply a tipping point in the extent and scale of heatwave
impacts. However, these projections do not integrate adaptation toprojected warming, for instance cooling that could be achieved withmore reflective roofs and urban surfaces in general (Akbari et al., 2009;
Oleson et al., 2010).

3.5.5.9

Agricultural systems: key staple crops

A large number of studies have consistently indicated that maize crop
yield will be negatively affected under increased global warming, withnegative impacts being higher at 2°C of warming than at 1.5°C (e.g.,
Niang et al., 2014; Schleussner et al., 2016b; J. Huang et al., 2017;
Iizumi et al., 2017). Under 2°C of global warming, losses of 8–14%
are projected in global maize production (Bassu et al., 2014). Underglobal warming of more than 2°C, regional losses are projected to
be about 20% if they co-occur with reductions in rainfall (Lana et al.,2017). These changes may be classified as incremental rather thanrepresenting a tipping point. Large-scale reductions in maize crop yield,
including the potential collapse of this crop in some regions, may exist
under 3°C or more of global warming (low confidence) (e.g., Thornton
et al., 2011).

3.5.5.10 Agricultural systems: livestock in the tropics andsubtropics
The potential impacts of climate change on livestock (Section 3.4.6),
in particular the direct impacts through increased heat stress, havebeen less well studied than impacts on crop yield, especially fromthe perspective of critical thresholds being exceeded. A case study
from Jamaica revealed that the difference in heat stress for livestock
between 1.5°C and 2°C of warming is likely to exceed the limits fornormal thermoregulation and result in persistent heat stress for theseanimals (Lallo et al., 2018). It is plausible that this finding holds forlivestock production in both tropical and subtropical regions moregenerally (medium confidence) (Section 3.4.6). Under 3°C of global
warming, significant reductions in the areas suitable for livestock
production could occur (low confidence), owing to strong increases inregional temperatures in the tropics and subtropics (high confidence).
Thus, regional tipping points in the viability of livestock production may
well exist, but little evidence quantifying such changes exists.

Increases in ambient temperature are linearly related to hospitalizations
and deaths once specific thresholds are exceeded (so there is not a
tipping point per se). It is plausible that coping strategies will not
be in place for many regions, with potentially significant impacts oncommunities with low adaptive capacity, effectively representing theoccurrence of a local/regional tipping point. In fact, even if global
warming is restricted to below 2°C, there could be a substantial increase263

3Chapter 3

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Table 3.7 | Summary of enhanced risks in the exceedance of regional tipping points under different global temperature goals.
Tipping point

Arctic sea ice

TundraWarming of 1.5°C or lessWarming of 1.5°C–2°C

Arctic summer sea ice is likely to be maintained

The risk of an ice-free Arctic in summer is about
50% or higher

Arctic is very likely to be ice free in summer

Sea ice changes reversible under suitable climate
restoration

Sea ice changes reversible under suitable climate
restoration

Sea ice changes reversible under suitable climate
restoration

Decrease in number of growing degree days
below 0°C

Further decreases in number of growing degreedays below 0°C

Abrupt increases in tree cover are unlikely

Abrupt increased in tree cover are unlikely

Potential for an abrupt increase in tree fraction(low confidence)

17–44% reduction in permafrost

28–53% reduction in permafrost

Potential for permafrost collapse (low confidence)

Approximately 2 million km morepermafrost maintained than under 2°C of
global warming (medium confidence)
Irreversible loss of stored carbon

Irreversible loss of stored carbon

Low confidence in projected changes

Low confidence in projected changes

Increases in the intensity of
monsoon precipitation likely

Uncertain changes; unlikely that a tipping point isreached

Uncertain changes; unlikely that tipping point isreachedStrengthening of monsoon withwettening and greening of the Saheland Sahara (low confidence)2

Permafrost

Asian monsoon

West African monsoon
and the Sahel

Negative associated impacts through increases
in extreme temperature events
Reduced biomass, deforestation and fireincreases pose uncertain risks to forest dieback

3

Warming of up to 3°C

Rainforests

Larger biomass reductions than under 1.5°C ofwarming; deforestation and fire increases pose
uncertain risk to forest dieback

Reduced extent of tropical rainforest in Central
America and large replacement of rainforestby savanna and grassland
Potential tipping point leading to pronounced
forest dieback (medium confidence)

Boreal forests

Heatwaves, unprecedented
heat and human health

Increased tree mortality at southern boundary of
boreal forest (medium confidence)

Further increases in tree mortality at southernboundary of boreal forest (medium confidence)

Potential tipping point at 3°C–4°C for significant
dieback of boreal forest (low confidence)

Substantial increase in occurrence of potentiallydeadly heatwaves (likely)

Substantial increase in potentially deadly
heatwaves (likely)

Substantial increase in potentially deadly
heatwaves very likely

More than 350 million more people exposed todeadly heat by 2050 under a midrange
population growth scenario (likely)

Annual occurrence of heatwaves similar to thedeadly 2015 heatwaves in India and Pakistan(medium confidence)

Global maize crop reductions of about 10%

Larger reductions in maize crop production thanunder 1.5°C of about 15%

Agricultural systems:
key staple crops
Livestock in the tropics
and subtropics

Drastic reductions in maize crop globallyand in Africa (high confidence)
Potential tipping point for collapse of maizecrop in some regions (low confidence)

Increased heat stress

Onset of persistent heat stress(medium confidence)

Persistent heat stress likely

Box 3.6 | Economic Damages from Climate Change
Balancing the costs and benefits of mitigation is challenging because estimating the value of climate change damages depends on
multiple parameters whose appropriate values have been debated for decades (for example, the appropriate value of the discount rate)or that are very difficult to quantify (for example, the value of non-market impacts; the economic effects of losses in ecosystem services;and the potential for adaptation, which is dependent on the rate and timing of climate change and on the socio-economic content). See
Cross-Chapter Box 5 in Chapter 2 for the definition of the social cost of carbon and for a discussion of the economics of 1.5°C-consistentpathways and the social cost of carbon, including the impacts of inequality on the social cost of carbon.
Global economic damages of climate change are projected to be smaller under warming of 1.5°C than 2°C in 2100 (Warren et al.,2018c). The mean net present value of the costs of damages from warming in 2100 for 1.5°C and. 2°C (including costs associatedwith climate change-induced market and non-market impacts, impacts due to sea level rise, and impacts associated with large-scalediscontinuities) are $54 and $69 trillion, respectively, relative to 1961–1990.

264

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Chapter 3

Box 3.6 (continued)

Values of the social cost of carbon vary when tipping points are included. The social cost of carbon in the default setting of the Dynamic
Integrated Climate-Economy (DICE) model increases from $15 tCO2–1 to $116 (range 50–166) tCO2–1 when large-scale singularities or‘tipping elements’ are incorporated (Y. Cai et al., 2016; Lemoine and Traeger, 2016). Lemoine and Traeger (2016) included optimization
calculations that minimize welfare impacts resulting from the combination of climate change risks and climate change mitigation costs,
showing that welfare is minimized if warming is limited to 1.5°C. These calculations excluded the large health co-benefits that accrue
when greenhouse gas emissions are reduced (Section 3.4.7.1; Shindell et al., 2018).
The economic damages of climate change in the USA are projected to be large (Hsiang et al., 2017; Yohe, 2017). Hsiang et al. (2017)shows that the USA stand to lose -0.1 to 1.7% of the Gross Domestic Product (GDP) at 1.5°C warming. Yohe (2017) calculated transient
temperature trajectories from a linear relationship with contemporaneous cumulative emissions under a median no-policy baseline
trajectory that brings global emissions to roughly 93 GtCO2 yr–1 by the end of the century (Fawcett et al., 2015), with 1.75°C per 1000
GtCO2 as the median estimate. Associated aggregate economic damages in decadal increments through the year 2100 are estimatedin terms of the percentage loss of GDP at the median, 5th percentile and 95th percentile transient temperature (Hsiang et al., 2017).
The results for the baseline no-policy case indicate that economic damages along median temperature change and median damages(median-median) reach 4.5% of GDP by 2100, with an uncertainty range of 2.5% and 8.5% resulting from different combinations oftemperature change and damages. Avoided damages from achieving a 1.5°C temperature limit along the median-median case arenearly 4% (range 2–7%) by 2100. Avoided damages from achieving a 2°C temperature limit are only 3.5% (range 1.8–6.5%). Avoided
damages from achieving 1.5°C versus 2°C are modest at about 0.35% (range 0.20–0.65%) by 2100. The values of achieving the twotemperature limits do not diverge significantly until 2040, when their difference tracks between 0.05 and 0.13%; the differences
between the two temperature targets begin to diverge substantially in the second half of the century.

3
3.6

Implications of Different 1.5°C and 2°C
PathwaysThis section provides an overview on specific aspects of the mitigation
pathways considered compatible with 1.5°C of global warming. Someof these aspects are also addressed in more detail in Cross-Chapter
Boxes 7 and 8 in this chapter.

3.6.1

Gradual versus Overshoot in 1.5°C Scenarios

All 1.5°C scenarios from Chapter 2 include some overshoot above
1.5°C of global warming during the 21st century (Chapter 2 and CrossChapter Box 8 in this chapter). The level of overshoot may also depend
on natural climate variability. An overview of possible outcomes of
1.5°C-consistent mitigation scenarios for changes in the physicalclimate at the time of overshoot and by 2100 is provided in CrossChapter Box 8 on ‘1.5°C warmer worlds’. Cross-Chapter Box 8 also
highlights the implications of overshoots.

3.6.2
3.6.2.1

Non-CO2 Implications and Projected Risks of
Mitigation Pathways
Risks arising from land-use changes
in mitigation pathways

In mitigation pathways, land-use change is affected by many different
mitigation options. First, mitigation of non-CO2 emissions fromagricultural production can shift agricultural production between
regions via trade of agricultural commodities. Second, protection ofcarbon-rich ecosystems such as tropical forests constrains the area
for agricultural expansion. Third, demand-side mitigation measures,such as less consumption of resource-intensive commodities (animal
products) or reductions in food waste, reduce pressure on land (Poppet al., 2017; Rogelj et al., 2018). Finally, carbon dioxide removal(CDR) is a key component of most, but not all, mitigation pathways
presented in the literature to date which constrain warming to 1.5°C
or 2°C. Carbon dioxide removal measures that require land include
bioenergy with carbon capture and storage (BECCS), afforestation andreforestation (AR), soil carbon sequestration, direct air capture, biochar
and enhanced weathering (see Cross-Chapter Box 7 in this chapter).
These potential methods are assessed in Section 4.3.7.
In cost-effective integrated assessment modelling (IAM) pathways
recently developed to be consistent with limiting warming to 1.5°C,
use of CDR in the form of BECCS and AR are fundamental elements(Chapter 2; Popp et al., 2017; Hirsch et al., 2018; Rogelj et al., 2018;
Seneviratne et al., 2018c). The land-use footprint of CDR deployment
in 1.5°C-consistent pathways can be substantial (Section 2.3.4, Figure2.11), even though IAMs predominantly rely on second-generation
biomass and assume future productivity increases in agriculture.
A body of literature has explored potential consequences of large-scaleuse of CDR. In this case, the corresponding land footprint by the endof the century could be extremely large, with estimates including: up
to 18% of the land surface being used (Wiltshire and Davies-Barnard,2015); vast acceleration of the loss of primary forest and natural
grassland (Williamson, 2016) leading to increased greenhouse gas
emissions (P. Smith et al., 2013, 2015); and potential loss of up to 10%of the current forested lands to biofuels (Yamagata et al., 2018). Other
estimates reach 380–700 Mha or 21–64% of current arable cropland(Section 4.3.7). Boysen et al. (2017) found that in a scenario in whichemissions reductions were sufficient only to limit warming to 2.5°C,
265

Chapter 3

use of CDR to further limit warming to 1.7°C would result in theconversion of 1.1–1.5 Gha of land – implying enormous losses of both
cropland and natural ecosystems. Newbold et al. (2015) found thatbiodiversity loss in the Representative Concentration Pathway (RCP)2.6
scenario could be greater than that in RCP4.5 and RCP6, in which there
is more climate change but less land-use change. Risks to biodiversity
conservation and agricultural production are therefore projected toresult from large-scale bioenergy deployment pathways (P. Smith etal., 2013; Tavoni and Socolow, 2013). One study explored an extreme
mitigation strategy encouraging biofuel expansion sufficient to limit
warming to 1.5°C and found that this would be more disruptive to land
use and crop prices than the impacts of a 2°C warmer world which
has a larger climate signal and lower mitigation requirement (Ruane
et al., 2018). However, it should again be emphasized that many of thepathways explored in Chapter 2 of this report follow strategies that
explore how to reduce these issues. Chapter 4 provides an assessmentof the land footprint of various CDR technologies (Section 4.3.7).

3The degree to which BECCS has these large land-use footprints
depends on the source of the bioenergy used and the scale at which
BECCS is deployed. Whether there is competition with food production
and biodiversity depends on the governance of land use, agriculturalintensification, trade, demand for food (in particular meat), feed andtimber, and the context of the whole supply chain (Section 4.3.7,
Fajardy and Mac Dowell, 2017; Booth, 2018; Sterman et al., 2018).
The more recent literature reviewed in Chapter 2 explores pathways
which limit warming to 2°C or below and achieve a balance between
sources and sinks of CO2 by using BECCS that relies on secondgeneration (or even third-generation) biofuels, changes in diet or moregenerally, management of food demand, or CDR options such as forest
restoration (Chapter 2; Bajželj et al., 2014). Overall, this literature
explores how to reduce the issues of competition for land with foodproduction and with natural ecosystems (in particular forests) (CrossChapter Box 1 in Chapter 1; van Vuuren et al., 2009; Haberl et al.,2010, 2013; Bajželj et al., 2014; Daioglou et al., 2016; Fajardy and Mac
Dowell, 2017).
Some IAMs manage this transition by effectively protecting carbon
stored on land and focusing on the conversion of pasture area
into both forest area and bioenergy cropland. Some IAMs explore
1.5°C-consistent pathways with demand-side measures such as dietarychanges and efficiency gains such as agricultural changes (Sections
2.3.4 and 2.4.4), which lead to a greatly reduced CDR deployment andconsequently land-use impacts (van Vuuren et al., 2018). In reality,
however, whether this CDR (and bioenergy in general) has large
adverse impacts on environmental and societal goals depends in largepart on the governance of land use (Section 2.3.4; Obersteiner et al.,2016; Bertram et al., 2018; Humpenöder et al., 2018).
Rates of sequestration of 3.3 GtC ha–1 require 970 Mha of afforestation
and reforestation (Smith et al., 2015). Humpenöder et al. (2014)
estimated that in least-cost pathways afforestation would cover 2800
Mha by the end of the century to constrain warming to 2°C. Hence,
the amount of land considered if least-cost mitigation is implemented
by afforestation and reforestation could be up to three to fivetimes greater than that required by BECCS, depending on the forest
266

Impacts of 1.5°C of Global Warming on Natural and Human Systems

management used. However, not all of the land footprint of CDR isnecessarily to be in competition with biodiversity protection. Where
reforestation is the restoration of natural ecosystems, it benefits bothcarbon sequestration and conservation of biodiversity and ecosystemservices (Section 4.3.7) and can contribute to the achievement of
the Aichi targets under the Convention on Biological Diversity (CBD)(Leadley et al., 2016). However, reforestation is often not defined inthis way (Section 4.3.8; Stanturf et al., 2014) and the ability to deliverbiodiversity benefits is strongly dependent on the precise natureof the reforestation, which has different interpretations in different
contexts and can often include agroforestry rather than restorationof pristine ecosystems (Pistorious and Kiff, 2017). However, ‘natural
climate solutions’, defined as conservation, restoration, and improved
land management actions that increase carbon storage and/or avoid
greenhouse gas emissions across global forests, wetlands, grasslands
and agricultural lands, are estimated to have the potential to provide
37% of the cost-effective CO2 mitigation needed by southern Europe
and the Mediterranean by 2030 – in order to have a >66% chance ofholding warming to below 2°C (Griscom et al., 2017).
Any reductions in agricultural production driven by climate change
and/or land management decisions related to CDR may (e.g., Nelson
et al., 2014a; Dalin and Rodríguez-Iturbe, 2016) or may not (Muratori
et al., 2016) affect food prices. However, these studies did not consider
the deployment of second-generation (instead of first-generation)
bioenergy crops, for which the land footprint can be much smaller.
Irrespective of any mitigation-related issues, in order for ecosystems
to adapt to climate change, land use would also need to be carefully
managed to allow biodiversity to disperse to areas that become
newly climatically suitable for it (Section 3.4.1) and to protect theareas where the future climate will still remain suitable. This implies
a need for considerable expansion of the protected area network(Warren et al., 2018b), either to protect existing natural habitat or
to restore it (perhaps through reforestation, see above). At the sametime, adaptation to climate change in the agricultural sector (Rippke
et al., 2016) can require transformational as well as new approaches
to land-use management; in order to meet the rising food demandof a growing human population, it is projected that additional
land will need to be brought into production unless there are large
increases in agricultural productivity (Tilman et al., 2011). However,
future rates of deforestation may be underestimated in the existing
literature (Mahowald et al., 2017a), and reforestation may therefore
be associated with significant co-benefits if implemented to restore
natural ecosystems (high confidence).

3.6.2.2

Biophysical feedbacks on regional climate
associated with land-use changes

Changes in the biophysical characteristics of the land surface are known
to have an impact on local and regional climates through changes inalbedo, roughness, evapotranspiration and phenology, which can leadto a change in temperature and precipitation. This includes changes inland use through agricultural expansion/intensification (e.g., Mueller
et al., 2016), reforestation/revegetation endeavours (e.g., Feng et al.,2016; Sonntag et al., 2016; Bright et al., 2017) and changes in land
management (e.g., Luyssaert et al., 2014; Hirsch et al., 2017) that canImpacts of 1.5°C of Global Warming on Natural and Human Systems

Chapter 3

Figure 3.22 | Regional temperature scaling with carbon dioxide (CO2) concentration (ppm) from 1850 to 2099 for two different regions defined in the Special Report on
Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) for central Europe (CEU) (a) and central North America (CNA) (b). Solidlines correspond to the regional average annual maximum daytime temperature (TXx) anomaly, and dashed lines correspond to the global mean temperature anomaly, where
all temperature anomalies are relative to 1850–1870 and units are degrees Celsius. The black line in all panels denotes the three-member control ensemble mean, with the grey
shaded regions corresponding to the ensemble range. The coloured lines represent the three-member ensemble means of the experiments corresponding to albedo +0.02 (cyan),
albedo +0.04 (purple), albedo + 0.08 (orange), albedo +0.10 (red), irrigation (blue), and irrigation with albedo +0.10 (green). Adapted from Hirsch et al. (2017).

involve double cropping (e.g., Jeong et al., 2014; Mueller et al., 2015;
Seifert and Lobell, 2015), irrigation (e.g., Lobell et al., 2009; Sacks etal., 2009; Cook et al., 2011; Qian et al., 2013; de Vrese et al., 2016;
Pryor et al., 2016; Thiery et al., 2017), no-till farming and conservation
agriculture (e.g., Lobell et al., 2006; Davin et al., 2014), and wood
harvesting (e.g., Lawrence et al., 2012). Hence, the biophysical impactsof land-use changes are an important topic to assess in the context of
low-emissions scenarios (e.g., van Vuuren et al., 2011b), in particular
for 1.5°C warming levels (see also Cross-Chapter Box 7 in this chapter).
The magnitude of the biophysical impacts is potentially large fortemperature extremes. Indeed, changes induced both by modifications
in moisture availability and irrigation and by changes in surface albedo
tend to be larger (i.e., stronger cooling) for hot extremes than for mean
temperatures (e.g., Seneviratne et al., 2013; Davin et al., 2014; Wilhelm
et al., 2015; Hirsch et al., 2017; Thiery et al., 2017). The reasons for
reduced moisture availability are related to a strong contribution ofmoisture deficits to the occurrence of hot extremes in mid-latitude
regions (Mueller and Seneviratne, 2012; Seneviratne et al., 2013). In
the case of surface albedo, cooling associated with higher albedo (e.g.,in the case of no-till farming) is more effective at cooling hot days
because of the higher incoming solar radiation for these days (Davinet al., 2014). The overall effect of either irrigation or albedo has beenfound to be at the most in the order of about 1°C–2°C regionally fortemperature extremes. This can be particularly important in the context
of low-emissions scenarios because the overall effect is in this caseof similar magnitude to the response to the greenhouse gas forcing(Figure 3.22; Hirsch et al., 2017; Seneviratne et al., 2018a,c).
In addition to the biophysical feedbacks from land-use change and land
management on climate, there are potential consequences for particular

ecosystem services. This includes climate change-induced changes incrop yield (e.g., Schlenker and Roberts, 2009; van der Velde et al., 2012;
Asseng et al., 2013, 2015; Butler and Huybers, 2013; Lobell et al., 2014)
which may be further exacerbated by competing demands for arable
land between reforestation mitigation activities, crop growth for BECCS(Chapter 2), increasing food production to support larger populations,and urban expansion (see review by Smith et al., 2010). In particular,some land management practices may have further implications forfood security, for instance throughincreases or decreases in yield whentillage is ceased in some regions (Pittelkow et al., 2014).
We note that the biophysical impacts of land use in the context of
mitigation pathways constitute an emerging research topic. This
topic, as well as the overall role of land-use change in climate change
projections and socio-economic pathways, will be addressed in depth
in the upcoming IPCC Special Report on Climate Change and Land Use
due in 2019.

3.6.2.3

Atmospheric compounds (aerosols and methane)

There are multiple pathways that could be used to limit anthropogenic
climate change, and the details of the pathways will influence theimpacts of climate change on humans and ecosystems. Anthropogenicdriven changes in aerosols cause important modifications to theglobal climate (Bindoff et al., 2013a; Boucher et al., 2013b; P. Wu etal., 2013; Sarojini et al., 2016; H. Wang et al., 2016). Enforcement ofstrict air quality policies may lead to a large decrease in cooling aerosol
emissions in the next few decades. These aerosol emission reductions
may cause a warming comparable to that resulting from the increasein greenhouse gases by mid-21st century under low CO2 pathways(Kloster et al., 2009; Acosta Navarro et al., 2017). Further background267

3

Chapter 3

is provided in Sections 2.2.2 and 2.3.1; Cross Chapter Box 1 in Chapter1). Because aerosol effects on the energy budget are regional, strong
regional changes in precipitation from aerosols may occur if aerosol
emissions are reduced for air quality reasons or as a co-benefit fromswitches to sustainable energy sources (H. Wang et al., 2016). Thus,
regional impacts, especially on precipitation, are very sensitive to
1.5°C-consistent pathways (Z. Wang et al., 2017).
Pathways which rely heavily on reductions in methane (CH4) insteadof CO2 will reduce warming in the short term because CH4 is such astronger and shorter-lived greenhouse gas than CO2, but will leadto stronger warming in the long term because of the much longer
residence time of CO2 (Myhre et al., 2013; Pierrehumbert, 2014). In
addition, the dominant loss mechanism for CH4 is atmospheric photooxidation. This conversion modifies ozone formation and destruction in
the troposphere and stratosphere, therefore modifying the contributionof ozone to radiative forcing, as well as feedbacks on the oxidation
rate of methane itself (Myhre et al., 2013). Focusing on pathways andpolicies which both improve air quality and reduce impacts of climate

Impacts of 1.5°C of Global Warming on Natural and Human Systems

change can provide multiple co-benefits (Shindell et al., 2017). Thesepathways are discussed in detail in Sections 4.3.7 and 5.4.1 and in
Cross-Chapter Box 12 in Chapter 5.
Atmospheric aerosols and gases can also modify the land and ocean
uptake of anthropogenic CO2; some compounds enhance uptake whileothers reduce it (Section 2.6.2; Ciais et al., 2013). While CO2 emissions
tend to encourage greater uptake of carbon by the land and theocean (Ciais et al., 2013), CH4 emissions can enhance ozone pollution,
depending on nitrogen oxides, volatile organic compounds and otherorganic species concentrations, and ozone pollution tends to reduce
land productivity (Myhre et al., 2013; B. Wang et al., 2017). Aside from
inhibiting land vegetation productivity, ozone may also alter the CO2,CH4 and nitrogen (N2O) exchange at the land–atmosphere interface
and transform the global soil system from a sink to a source ofcarbon (B. Wang et al., 2017). Aerosols and associated nitrogen-based
compounds tend to enhance the uptake of CO2 in land and oceansystems through deposition of nutrients and modification of climate(Ciais et al., 2013; Mahowald et al., 2017b).

Cross-Chapter Box 7 | Land-Based Carbon Dioxide Removal in Relation to 1.5°C of Global Warming

3

Lead Authors:
Rachel Warren (United Kingdom), Marcos Buckeridge (Brazil), Sabine Fuss (Germany), Markku Kanninen (Finland), Joeri Rogelj(Austria/Belgium), Sonia I. Seneviratne (Switzerland), Raphael Slade (United Kingdom)
Climate and land form a complex system characterized by multiple feedback processes and the potential for non-linear responses toperturbation. Climate determines land cover and the distribution of vegetation, affecting above- and below-ground carbon stocks.
At the same time, land cover influences global climate through altered biogeochemical processes (e.g., atmospheric compositionand nutrient flow into oceans), and regional climate through changing biogeophysical processes including albedo, hydrology,transpiration and vegetation structure (Forseth, 2010).
Greenhouse gas (GHG) fluxes related to land use are reported in the ‘agriculture, forestry and other land use’ sector (AFOLU) andcomprise about 25% (about 10–12 GtCO2eq yr–1) of anthropogenic GHG emissions (P. Smith et al., 2014). Reducing emissions fromland use, as well as land-use change, are thus an important component of low-emissions mitigation pathways (Clarke et al., 2014),
particularly as land-use emissions can be influenced by human actions such as deforestation, afforestation, fertilization, irrigation,
harvesting, and other aspects of cropland, grazing land and livestock management (Paustian et al., 2006; Griscom et al., 2017;
Houghton and Nassikas, 2018).
In the IPCC Fifth Assessment Report, the vast majority of scenarios assessed with a 66% or better chance of limiting global warming
to 2°C by 2100 included carbon dioxide removal (CDR) – typically about 10 GtCO2 yr–1 in 2100 or about 200–400 GtCO2 overthe course of the century (Smith et al., 2015; van Vuuren et al., 2016). These integrated assessment model (IAM) results werepredominately achieved by using bioenergy with carbon capture and storage (BECCS) and/or afforestation and reforestation (AR).
Virtually all scenarios that limit either peak or end-of-century warming to 1.5°C also use land-intensive CDR technologies (Rogelj
et al., 2015; Holz et al., 2017; Kriegler et al., 2017; Fuss et al., 2018; van Vuuren et al., 2018). Again, AR (Sections 2.3 and 4.3.7)
and BECCS (Sections 4.3.2. and 4.3.7) predominate. Other CDR options, such as the application of biochar to soil, soil carbon
sequestration, and enhanced weathering (Section 4.3.7) are not yet widely incorporated into IAMs, but their deployment would also
necessitate the use of land and/or changes in land management.
Integrated assessment models provide a simplified representation of land use and, with only a few exceptions, do not include
biophysical feedback processes (e.g., albedo and evapotranspiration effects) (Kreidenweis et al., 2016) despite the importance of
these processes for regional climate, in particular hot extremes (Section 3.6.2.2; Seneviratne et al., 2018c). The extent, location andimpacts of large-scale land-use change described by existing IAMs can also be widely divergent, depending on model structure,scenario parameters, modelling objectives and assumptions (including regarding land availability and productivity) (Prestele et268

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Chapter 3

Cross-Chapter Box 7 (continued)al., 2016; Alexander et al., 2017; Popp et al., 2017; Seneviratne et al., 2018c). Despite these limitations, IAM scenarios effectively
highlight the extent and nature of potential land-use transitions implicit in limiting warming to 1.5°C.
Cross-Chapter Box 7 Table 1 presents a comparison of the five CDR options assessed in this report. This illustrates that if BECCS
and AR were to be deployed at a scale of 12 GtCO2 yr–1 in 2100, for example, they would have a substantial land and water
footprint. Whether this footprint would result in adverse impacts, for example on biodiversity or food production, depends on theexistence and effectiveness of measures to conserve land carbon stocks, limit the expansion of agriculture at the expense of natural
ecosystems, and increase agriculture productivity (Bonsch et al., 2016; Obersteiner et al., 2016; Bertram et al., 2018; Humpenöder etal., 2018). In comparison, the land and water footprints of enhanced weathering, soil carbon sequestration and biochar application
are expected to be far less per GtCO2 sequestered. These options may offer potential co-benefits by providing an additional source ofnutrients or by reducing N2O emissions, but they are also associated with potential side effects. Enhanced weathering would requiremassive mining activity, and providing feedstock for biochar would require additional land, even though a proportion of the required
biomass is expected to come from residues (Woolf et al., 2010; Smith, 2016). For the terrestrial CDR options, permanence andsaturation are important considerations, making their viability and long-term contributions to carbon reduction targets uncertain.
The technical, political and social feasibility of scaling up and implementing land-intensive CDR technologies (Cross-Chapter Box 3
in Chapter 1) is recognized to present considerable potential barriers to future deployment (Boucher et al., 2013a; Fuss et al., 2014,2018; Anderson and Peters, 2016; Vaughan and Gough, 2016; Williamson, 2016; Minx et al., 2017, 2018; Nemet et al., 2018; Strefler
et al., 2018; Vaughan et al., 2018). To investigate the implications of restricting CDR options should these barriers prove difficult toovercome, IAM studies (Section 2.3.4) have developed scenarios that limit – either implicitly or explicitly – the use of BECCS andbioenergy (Krey et al., 2014; Bauer et al., 2018; Rogelj et al., 2018) or the use of BECCS and afforestation (Strefler et al., 2018).
Alternative strategies to limit future reliance on CDR have also been examined, including increased electrification, agriculturalintensification, behavioural change, and dramatic improvements in energy and material efficiency (Bauer et al., 2018; Grubler etal., 2018; van Vuuren et al., 2018). Somewhat counterintuitively, scenarios that seek to limit the deployment of BECCs may result inincreased land use, through greater deployment of bioenergy, and afforestation (Chapter 2, Box 2.1; Krey et al., 2014; Krause et al.,2017; Bauer et al., 2018; Rogelj et al., 2018). Scenarios aiming to minimize the total human land footprint (including land for food,energy and climate mitigation) also result in land-use change, for example by increasing agricultural efficiency and dietary change(Grubler et al., 2018).

3

The impacts of changing land use are highly context, location and scale dependent (Robledo‐Abad et al., 2017). The supply of
biomass for CDR (e.g., energy crops) has received particular attention. The literature identifies regional examples of where the useof land to produce biofuels might be sustainably increased (Jaiswal et al., 2017), where biomass markets could contribute to theprovision of ecosystem services (Dale et al., 2017), and where bioenergy could increase the resilience of production systems andcontribute to rural development (Kline et al., 2017). However, studies of global biomass potential provide only limited insight into
the local feasibility of supplying large quantities of biomass on a global scale (Slade et al., 2014). Concerns about large-scale useof biomass for CDR include a range of potential consequences including greatly increased demand for freshwater use, increased
competition for land, loss of biodiversity and/or impacts on food security (Section 3.6.2.1; Heck et al., 2018). The short- versus longterm carbon impacts of substituting biomass for fossil fuels, which are largely determined by feedstock choice, also remain a sourceof contention (Schulze et al., 2012; Jonker et al., 2014; Booth, 2018; Sterman et al., 2018).
Afforestation and reforestation can also present trade-offs between biodiversity, carbon sequestration and water use, and thesestrategies have a higher land footprint per tonne of CO2 removed (Cunningham, 2015; Naudts et al., 2016; Smith et al., 2018).
For example, changing forest management to strategies favouring faster growing species, greater residue extraction and shorterrotations may have a negative impact on biodiversity (de Jong et al., 2014). In contrast, reforestation of degraded land with native
trees can have substantial benefits for biodiversity (Section 3.6). Despite these constraints, the potential for increased carbon
sequestration through improved land stewardship measures is considered to be substantial (Griscom et al., 2017).
Evaluating the synergies and trade-offs between mitigation and adaptation actions, resulting land and climate impacts, and the
myriad issues related to land-use governance will be essential to better understand the future role of CDR technologies. This topic
will be addressed further in the IPCC Special Report on Climate Change and Land (SRCCL) due to be published in 2019.
Cross-Chapter Box 7 (continued next page)269

Chapter 3

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Cross-Chapter Box 7 (continued)

Key messages:
Cost-effective strategies to limit peak or end-of-century warming to 1.5°C all include enhanced GHG removals in the AFOLU sector
as part of their portfolio of measures (high confidence).
Large-scale deployment of land-based CDR would have far-reaching implications for land and water availability (high confidence).
This may impact food production, biodiversity and the provision of other ecosystem services (high confidence).
The impacts of deploying land-based CDR at large scales can be reduced if a wider portfolio of CDR options is deployed, and if
increased mitigation effort focuses on strongly limiting demand for land, energy and material resources, including through lifestyleand dietary changes (medium confidence).
Afforestation and reforestation may be associated with significant co-benefits if implemented appropriately, but they feature large
land and water footprints if deployed at large scales (medium confidence).
Cross-Chapter Box 7, Table 1 | Comparison of land-based carbon removal options.
Sources: a assessed ranges by Fuss et al. (2018), see Figures in Section 4.3.7 for full literature range; b based on the 2100 estimate for mean potentials by Smith etal. (2015). Note that biophysical impacts of land-based CDR options besides albedo changes (e.g., through changes in evapotranspiration related to irrigation orland cover/use type) are not displayed.

3

OptionBECCS

Afforestation& reforestation

Enhancedweathering

Potentials a

Cost aRequiredland bRequiredwater bImpact on
nutrients b

Impact on
albedo b

Saturationand permanence a

GtCO2 y−1

$ tCO2−1

Mha GtCO2−1

km3 GtCO2−1

Mt N, P, K y−1

No units

No units

Variable

Variable; depends on sourceof biofuel (higher albedo forcrops than for forests) andon land management (e.g.,
no-till farming for crops)

Long-term governance ofstorage; limits on rates ofbioenergy production andcarbon sequestration
Saturation of forests;
vulnerable to disturbance;
post-AR forest
management essential

0.5–5

100–200

31–58

60

0.5–3.6

5–50

80

92

0.5

Negative, or reduced GHG
benefit where not negative

2–4

50–200

3

0.4

0

0

Saturation of soil; residence
time from months togeological timescale

N: 8.2,
P: 2.7,
K: 19.1

0.08–0.12

Mean residence times
between decades tocenturies, depending on
soil type, management andenvironmental conditions

0

Soil sinks saturate and canreverse if poor management
practices resume

Biochar0.3–2

30–120

16–1000Soil carbon
sequestration

2.3–5

0–100

0

0

3.6.3

Implications Beyond the End of the Century

3.6.3.1

Sea ice

Sea ice is often cited as a tipping point in the climate system (Lenton,2012). Detailed modelling of sea ice (Schröder and Connolley, 2007;
Sedláček et al., 2011; Tietsche et al., 2011), however, suggests thatsummer sea ice can return within a few years after its artificial removal
270

N: 21.8,
P: 5.5,
K: 4.1for climates in the late 20th and early 21st centuries. Further studies(Armour et al., 2011; Boucher et al., 2012; Ridley et al., 2012) modelled
the removal of sea ice by raising CO2 concentrations and studied
subsequent regrowth by lowering CO2. These studies suggest thatchanges in Arctic sea ice are neither irreversible nor exhibit bifurcation
behaviour. It is therefore plausible that the extent of Arctic sea ice may
quickly re-equilibrate to the end-of-century climate under an overshootscenario.

Impacts of 1.5°C of Global Warming on Natural and Human Systems

3.6.3.2

Sea level

Policy decisions related to anthropogenic climate change will have a
profound impact on sea level, not only for the remainder of this century
but for many millennia to come (Clark et al., 2016). On these long timescales, 50 m of sea level rise (SLR) is possible (Clark et al., 2016). While it
is virtually certain that sea level will continue to rise well beyond 2100,
the amount of rise depends on future cumulative emissions (Church etal., 2013) as well as their profile over time (Bouttes et al., 2013; Mengel
et al., 2018). Marzeion et al. (2018) found that 28–44% of present-day
glacier volume is unsustainable in the present-day climate and that it
would eventually melt over the course of a few centuries, even if there
were no further climate change. Some components of SLR, such as
thermal expansion, are only considered reversible on centennial timescales (Bouttes et al., 2013; Zickfeld et al., 2013), while the contribution
from ice sheets may not be reversible under any plausible futurescenario (see below).
Based on the sensitivities summarized by Levermann et al. (2013), thecontributions of thermal expansion (0.20–0.63 m °C–1) and glaciers(0.21 m °C–1 but falling at higher degrees of warming mostly becauseof the depletion of glacier mass, with a possible total loss of about
0.6 m) amount to 0.5–1.2 m and 0.6–1.7 m in 1.5°C and 2°C warmer
worlds, respectively. The bulk of SLR on greater than centennial timescales will therefore be caused by contributions from the continental
ice sheets of Greenland and Antarctica, whose existence is threatenedon multi-millennial time scales.
For Greenland, where melting from the ice sheet’s surface is important,a well-documented instability exists where the surface of a thinning
ice sheet encounters progressively warmer air temperatures thatfurther promote melting and thinning. A useful indicator associatedwith this instability is the threshold at which annual mass loss fromthe ice sheet by surface melt exceeds mass gain by snowfall. Previous
estimates put this threshold at about 1.9°C to 5.1°C above preindustrial temperatures (Gregory and Huybrechts, 2006). More recent
analyses, however, suggest that this threshold sits between 0.8°C
and 3.2°C, with a best estimate at 1.6°C (Robinson et al., 2012). The
continued decline of the ice sheet after this threshold has been passed
is highly dependent on the future climate and varies between about
80% loss after 10,000 years to complete loss after as little as 2000
years (contributing about 6 m to SLR). Church et al. (2013) were unable
to quantify a likely range for this threshold. They assigned medium
confidence to a range greater than 2°C but less than 4°C, and had
low confidence in a threshold of about 1°C. There is insufficient newliterature to change this assessment.
The Antarctic ice sheet, in contrast, loses the mass gained by snowfall
as outflow and subsequent melt to the ocean, either directly from theunderside of floating ice shelves or indirectly by the melting of calved
icebergs. The long-term existence of this ice sheet will also be affectedby a potential instability (the marine ice sheet instability, MISI), whichlinks outflow (or mass loss) from the ice sheet to water depth at the
grounding line (i.e., the point at which grounded ice starts to float andbecomes an ice shelf) so that retreat into deeper water (the bedrockunderlying much of Antarctica slopes downwards towards the centreof the ice sheet) leads to further increases in outflow and promotes

Chapter 3

yet further retreat (Schoof, 2007). More recently, a variant on thismechanism was postulated in which an ice cliff forms at the grounding
line and retreats rapidly though fracture and iceberg calving (DeContoand Pollard, 2016). There is a growing body of evidence (Golledge etal., 2015; DeConto and Pollard, 2016) that large-scale retreat may be
avoided in emissions scenarios such as Representative Concentration
Pathway (RCP)2.6 but that higher-emissions RCP scenarios could leadto the loss of the West Antarctic ice sheet and sectors in East Antarctica,
although the duration (centuries or millennia) and amount of mass loss
during such a collapse is highly dependent on model details and noconsensus exists yet. Schoof (2007) suggested that retreat may beirreversible, although a rigorous test has yet to be made. In this context,
overshoot scenarios, especially of higher magnitude or longer duration,
could increase the risk of such irreversible retreat.
Church et al. (2013) noted that the collapse of marine sectors of the
Antarctic ice sheet could lead to a global mean sea level (GMSL) rise
above the likely range, and that there was medium confidence that thisadditional contribution ‘would not exceed several tenths of a metre
during the 21st century’.
The multi-centennial evolution of the Antarctic ice sheet has been
considered in papers by DeConto and Pollard (2016) and Golledge etal. (2015). Both suggest that RCP2.6 is the only RCP scenario leading
to long-term contributions to GMSL of less than 1.0 m. The long-term
committed future of Antarctica and the GMSL contribution at 2100
are complex and require further detailed process-based modelling;
however, a threshold in this contribution may be located close to 1.5°C
to 2°C of global warming.
In summary, there is medium confidence that a threshold in the longterm GMSL contribution of both the Greenland and Antarctic ice sheets
lies around 1.5°C to 2°C of global warming relative to pre-industrial;
however, the GMSL associated with these two levels of global warming
cannot be differentiated on the basis of the existing literature.

3.6.3.3

Permafrost

The slow rate of permafrost thaw introduces a lag between thetransient degradation of near-surface permafrost and contemporary
climate, so that the equilibrium response is expected to be 25–38%
greater than the transient response simulated in climate models (Slaterand Lawrence, 2013). The long-term, equilibrium Arctic permafrost loss
to global warming was analysed by Chadburn et al. (2017). They used
an empirical relation between recent mean annual air temperatures
and the area underlain by permafrost coupled to Coupled Model
Intercomparison Project Phase 5 (CMIP5) stabilization projections
to 2300 for RCP2.6 and RCP4.5. Their estimate of the sensitivity ofpermafrost to warming is 2.9–5.0 million km2 °C–1 (1 standard deviation
confidence interval), which suggests that stabilizing climate at 1.5°C as
opposed to 2°C would reduce the area of eventual permafrost loss by
1.5 to 2.5 million km2 (stabilizing at 56–83% as opposed to 43–72% of
1960–1990 levels). This work, combined with the assessment of Collins
et al. (2013) on the link between global warming and permafrost loss,
leads to the assessment that permafrost extent would be appreciably
greater in a 1.5°C warmer world compared to in a 2°C warmer world(low to medium confidence).
271

3Chapter 3

3.7

Knowledge Gaps

Most scientific literature specific to global warming of 1.5°C is only
just emerging. This has led to differences in the amount of information
available and gaps across the various sections of this chapter. In
general, the number of impact studies that specifically focused on
1.5°C lags behind climate-change projections in general, due in part tothe dependence of the former on the latter. There are also insufficientstudies focusing on regional changes, impacts and consequences at
1.5°C and 2°C of global warming.
The following gaps have been identified with respect to tools,
methodologies and understanding in the current scientific literature
specific to Chapter 3. The gaps identified here are not comprehensive
but highlight general areas for improved understanding, especiallyregarding global warming at 1.5°C compared to 2°C and higher levels.

3.7.1

3

Impacts of 1.5°C of Global Warming on Natural and Human Systems•

The distinct impacts of different overshoot scenarios, dependingon (i) the peak temperature of the overshoot, (ii) the length of theovershoot period, and (iii) the associated rate of change in global
temperature over the time period of the overshoot.

3.7.2.2•

Critical thresholds for extreme events (e.g., drought and inundation)
between 1.5°C and 2°C of warming for different climate models
and projections. All aspects of storm intensity and frequency as afunction of climate change, especially for 1.5°C and 2°C warmer
worlds, and the impact of changing storminess on storm surges,
damage, and coastal flooding at regional and local scales.

•

The timing and implications of the release of stored carbon in
Arctic permafrost in a 1.5°C warmer world and for climate
stabilization by the end of the century.

•

Antarctic ice sheet dynamics, global sea level, and links between
seasonal and year-long sea ice in both polar regions.

Gaps in Methods and Tools•

Regional and global climate model simulations for low-emissions
scenarios such as a 1.5°C warmer world.

•

Robust probabilistic models which separate the relatively small
signal between 1.5°C versus 2°C from background noise, andwhich handle the many uncertainties associated with nonlinearities, innovations, overshoot, local scales, and latent or
lagging responses in climate.

3.7.2.3•

Physical and chemical characteristics of a 1.5°C
warmer world

Projections of risks under a range of climate and development
pathways required to understand how development choices
affect the magnitude and pattern of risks, and to provide better
estimates of the range of uncertainties.

•

More complex and integrated socio-ecological models for predicting
the response of terrestrial as well as coastal and oceanic ecosystems
to climate and models which are more capable of separating climate
effects from those associated with human activities.

•

Tools for informing local and regional decision-making, especiallywhen the signal is ambiguous at 1.5°C and/or reverses sign at
higher levels of global warming.

Terrestrial and freshwater systems•

The dynamics between climate change, freshwater resources andsocio-economic impacts for lower levels of warming.

•

How the health of vegetation is likely to change, carbon storage inplant communities and landscapes, and phenomena such as thefertilization effect.

•

The risks associated with species’ maladaptation in response to
climatic changes (e.g., effects of late frosts). Questions associatedwith issues such as the consequences of species advancing theirspring phenology in response to warming, as well as the interaction
between climate change, range shifts and local adaptation in a
1.5°C warmer world.

•

The biophysical impacts of land use in the context of mitigation
pathways.

3.7.2.4

Ocean Systems

3.7.2

Gaps in Understanding•

Deep sea processes and risks to deep sea habitats and ecosystems.

3.7.2.1

Earth systems and 1.5°C of global warming•

How changes in ocean chemistry in a 1.5°C warmer world,
including decreasing ocean oxygen content, ocean acidificationand changes in the activity of multiple ion species, will affectnatural and human systems.

•

How ocean circulation is changing towards 1.5°C and 2°C warmer
worlds, including vertical mixing, deep ocean processes, currents,
and their impacts on weather patterns at regional to local scales.

•

The impacts of changing ocean conditions at 1.5°C and 2°C ofwarming on foodwebs, disease, invading species, coastal protection,
fisheries and human well-being, especially as organisms modify

•

•

272

The cumulative effects of multiple stresses and risks (e.g.,increased storm intensity interacting with sea level rise and theeffect on coastal people; feedbacks on wetlands due to climatechange and human activities).
Feedbacks associated with changes in land use/cover for lowemissions scenarios, for example feedback from changes inforest cover, food production, biofuel production, bio-energy withcarbon capture and storage (BECCS), and associated unquantified
biophysical impacts.

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Chapter 3

for natural and human systems, within cities and in interaction
with surrounding areas. For example, current projections do not
integrate adaptation to projected warming by considering coolingthat could be achieved through a combination of revised building
codes, zoning and land use to build more reflective roofs andurban surfaces that reduce urban heat island effects.

their biogeographical ranges within a changing ocean.
•Specific linkages between food security and changing coastal andocean resources.

3.7.2.5

Human systems•

The impacts of global and regional climate change at 1.5°C on
food distribution, nutrition, poverty, tourism, coastal infrastructure
and public health, particularly for developing nations.

•

Implications of climate change at 1.5°C on livelihoods andpoverty, as well as on rural communities, indigenous groups andmarginalized people.

•

Health and well-being risks in the context of socio-economic
and climate change at 1.5°C, especially in key areas such as
occupational health, air quality and infectious disease.

•

The changing levels of risk in terms of extreme events, includingstorms and heatwaves, especially with respect to people being
displaced or having to migrate away from sensitive and exposedsystems such as small islands, low-lying coasts and deltas.

•

Micro-climates at urban/city scales and their associated risks

3273

Chapter 3

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Cross-Chapter Box 8 | 1.5°C Warmer Worlds
Lead Authors:
Sonia I. Seneviratne (Switzerland), Joeri Rogelj (Austria/Belgium), Roland Séférian (France), Myles R. Allen (United Kingdom), Marcos
Buckeridge (Brazil), Kristie L. Ebi (United States of America), Ove Hoegh-Guldberg (Australia), Richard J. Millar (United Kingdom),Antony J. Payne (United Kingdom), Petra Tschakert (Australia), Rachel Warren (United Kingdom)

Contributing Authors:
Neville Ellis (Australia), Richard Wartenburger (Germany/Switzerland)

Introduction
The Paris Agreement includes goals of stabilizing global mean surface temperature (GMST) well below 2°C and 1.5°C above preindustrial levels in the longer term. There are several aspects, however, that remain open regarding what a ‘1.5°C warmer world’
could be like, in terms of mitigation (Chapter 2) and adaptation (Chapter 4), as well as in terms of projected warming and associatedregional climate change (Chapter 3), which are overlaid on anticipated and differential vulnerabilities (Chapter 5). Alternative‘1.5°C warmer worlds’ resulting from mitigation and adaptation choices, as well as from climate variability (climate‘noise’), can be vastly different, as highlighted in this Cross-Chapter Box. In addition, the range of models underlying 1.5°C
projections can be substantial and needs to be considered.

Key questions7:

3•

What is a 1.5°C global mean warming, how is it measured, and what temperature increase does it imply forsingle locations and at specific times? Global mean surface temperature (GMST) corresponds to the globally averaged
temperature of Earth derived from point-scale ground observations or computed in climate models (Chapters 1 and 3). Global
mean surface temperature is additionally defined over a given time frame, for example averaged over a month, a year, or
multiple decades. Because of climate variability, a climate-based GMST typically needs to be defined over several decades(typically 20 or 30 years; Chapter 3, Section 3.2). Hence, whether or when global warming reaches 1.5°C depends to someextent on the choice of pre-industrial reference period, whether 1.5°C refers to total or human-induced warming, and which
variables and coverage are used to define GMST change (Chapter 1). By definition, because GMST is an average in time andspace, there will be locations and time periods in which 1.5°C of warming is exceeded, even if the global mean warming is at
1.5°C. In some locations, these differences can be particularly large (Cross-Chapter Box 8, Figure 1).

•

What is the impact of different climate models for projected changes in climate at 1.5°C of global warming?
The range between single model simulations of projected regional changes at 1.5°C GMST increase can be substantial forregional responses (Chapter 3, Section 3.3). For instance, for the warming of cold extremes in a 1.5°C warmer world, somemodel simulations project a 3°C warming while others project more than 6°C of warming in the Arctic land areas (CrossChapter Box 8, Figure 2). For hot temperature extremes in the contiguous United States, the range of model simulations
includes temperatures lower than pre-industrial values (–0.3°C) and a warming of 3.5°C (Cross-Chapter Box 8, Figure 2). Someregions display an even larger range (e.g., 1°C–6°C regional warming in hot extremes in central Europe at 1.5°C of warming;
Chapter 3, Sections 3.3.1 and 3.3.2). This large spread is due to both modelling uncertainty and internal climate variability.
While the range is large, it also highlights risks that can be avoided with near certainty in a 1.5°C warmer world compared
to worlds at higher levels of warming (e.g., an 8°C warming of cold extremes in the Arctic is not reached at 1.5°C of global
warming in the multimodel ensemble but could happen at 2°C of global warming; Cross-Chapter Box 8, Figure 2). Inferredprojected ranges of regional responses (mean value, minimum and maximum) for different mitigation scenarios from Chapter
2 are displayed in Cross-Chapter Box 8, Table 1.

•

What is the impact of emissions pathways with, versus without, an overshoot? All mitigation pathways projecting lessthan 1.5°C of global warming over or at the end of the 21st century include some probability of overshooting 1.5°C. Thesepathways include some periods with warming stronger than 1.5°C in the course of the coming decades and/or some probabilityof not reaching 1.5°C (Chapter 2, Section 2.2). This is inherent to the difficulty of limiting global warming to 1.5°C, given that
we are already very close to this warming level. The implications of overshooting are large for risks to natural and human

7274Part of this discussion is based on Seneviratne et al. (2018b).

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Chapter 3

Cross-Chapter Box 8 (continued)

3

Cross-Chapter Box 8, Figure 1 | Range of projected realized temperatures at 1.5°C of global warming (due to stochastic noise and model-based spread).
Temperatures with a 25% chance of occurrence at any location within a 10-year time frame are shown, corresponding to GMST anomalies of 1.5°C (Coupled Model
Intercomparison Project Phase 5 (CMIP5) multimodel ensemble). The plots display the 25th percentile (Q25, left) and 75th percentile (Q75, right) values of mean
temperature (Tmean), yearly maximum daytime temperature (TXx) and yearly minimum night-time temperature (TNn), sampled from all time frames with GMST
anomalies of 1.5°C in Representative Concentration Pathway (RCP)8.5 model simulations of the CMIP5 ensemble. From Seneviratne et al. (2018b).

Cross-Chapter Box 8 (continued next page)275

Chapter 3

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Cross-Chapter Box 8 (continued)

Global mean temperature anomaly relative to pre-industrial conditions (°C)

Cross-Chapter Box 8, Figure 2 | Spread of projected multimodel changes in minimum annual night-time temperature (TNn) in Arctic land (left) and in maximum
annual daytime temperature (TXx) in the contiguous United States as a function of mean global warming in climate simulations. The multimodel range (due tomodel spread and internal climate variability) is indicated in red shading (minimum and maximum value based on climate model simulations). The multimodel
mean value is displayed with solid red and blue lines for two emissions pathways (blue: Representative Concentration Pathway (RCP)4.5; red: RCP8.5). The dashed
red line indicates projections for a 1.5°C warmer world. The dashed black line displays the 1:1 line. The figure is based on Figure 3 of Seneviratne et al. (2016).

3

systems, especially if the temperature at peak warming is high, because some risks may be long lasting and irreversible, suchas the loss of some ecosystems (Chapter 3, Box 3.4). The chronology of emissions pathways and their implied warming is also
important for the more slowly evolving parts of the Earth system, such as those associated with sea level rise. In addition, forseveral types of risks the rate of change may be most relevant (Loarie et al., 2009; LoPresti et al., 2015), with potentially large
risks occurring in the case of a rapid rise to overshooting temperatures, even if a decrease to 1.5°C may be achieved at the endof the 21st century or later. On the other hand, if overshoot is to be minimized, the remaining equivalent CO2 budget available
for emissions has to be very small, which implies that large, immediate and unprecedented global efforts to mitigate GHGs are
required (Cross-Chapter Box 8, Table 1; Chapter 4).

276•

What is the probability of reaching 1.5°C of global warming if emissions compatible with 1.5°C pathways arefollowed? Emissions pathways in a ‘prospective scenario’ (see Chapter 1, Section 1.2.3, and Cross-Chapter Box 1 in Chapter 1
on ‘Scenarios and pathways’) compatible with 1.5°C of global warming are determined based on their probability of reaching
1.5°C by 2100 (Chapter 2, Section 2.1), given current knowledge of the climate system response. These probabilities cannot
be quantified precisely but are typically 50–66% in 1.5°C-consistent pathways (Section 1.2.3). This implies a one-in-two to
one-in-three probability that global warming would exceed 1.5°C even under a 1.5°C-consistent pathway, including somepossibility that global warming would be substantially over this value (generally about 5–10% probability; see Cross-Chapter
Box 8, Table 1 and Seneviratne et al., 2018b). These alternative outcomes need to be factored into the decision-making process.
To address this issue, ‘adaptive’ mitigation scenarios have been proposed in which emissions are continually adjusted toachieve a temperature goal (Millar et al., 2017). The set of dimensions involved in mitigation options (Chapter 4) is complex
and need system-wide approaches to be successful. Adaptive scenarios could be facilitated by the global stocktake mechanism
established in the Paris Agreement, and thereby transfer the risk of higher-than-expected warming to a risk of faster-thanexpected mitigation efforts. However, there are some limits to the feasibility of such approaches because some investments, forexample in infrastructure, are long term and also because the actual departure from an aimed pathway will need to be detectedagainst the backdrop of internal climate variability, typically over several decades (Haustein et al., 2017; Seneviratne et al.,2018b). Avoiding impacts that depend on atmospheric composition as well as GMST (Baker et al., 2018) would also require
limits on atmospheric CO2 concentrations in the event of a lower-than-expected GMST response.

•

How can the transformation towards a 1.5°C warmer world be implemented? This can be achieved in a variety ofways, such as decarbonizing the economy with an emphasis on demand reductions and sustainable lifestyles, or, alternatively,
with an emphasis on large-scale technological solutions, amongst many other options (Chapter 2, Sections 2.3 and 2.4;
Chapter 4, Sections 4.1 and 4.4.4). Different portfolios of mitigation measures come with distinct synergies and trade-offs withrespect to other societal objectives. Integrated solutions and approaches are required to achieve multiple societal objectives
simultaneously (see Chapter 4, Section 4.5.4 for a set of synergies and trade-offs).

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Chapter 3

Cross-Chapter Box 8 (continued)•

What determines risks and opportunities in a 1.5°C warmer world? The risks to natural, managed and human systems ina 1.5°C warmer world will depend not only on uncertainties in the regional climate that results from this level of warming, butalso very strongly on the methods that humanity uses to limit global warming to 1.5°C. This is particularly the case for natural
ecosystems and agriculture (see Cross-Chapter Box 7 in this chapter and Chapter 4, Section 4.3.2). The risks to human systems
will also depend on the magnitude and effectiveness of policies and measures implemented to increase resilience to the risksof climate change and on development choices over coming decades, which will influence the underlying vulnerabilities andcapacities of communities and institutions for responding and adapting.

•

Which aspects are not considered, or only partly considered, in the mitigation scenarios from Chapter 2? These
include biophysical impacts of land use, water constraints on energy infrastructure, and regional implications of choices of
specific scenarios for tropospheric aerosol concentrations or the modulation of concentrations of short-lived climate forcers,that is, greenhouse gases (Chapter 3, Section 3.6.3). Such aspects of development pathways need to be factored into
comprehensive assessments of the regional implications of mitigation and adaptation measures. On the other hand, some ofthese aspects are assessed in Chapter 4 as possible options for mitigation and adaptation to a 1.5°C warmer world.

•

Are there commonalities to all alternative 1.5°C warmer worlds? Human-driven warming linked to CO2 emissions is nearlyirreversible over time frames of 1000 years or more (Matthews and Caldeira, 2008; Solomon et al., 2009). The GSMT of the Earthresponds to the cumulative amount of CO2 emissions. Hence, all 1.5°C stabilization scenarios require both net CO2 emissions andmulti-gas CO2-forcing-equivalent emissions to be zero at some point (Chapter 2, Section 2.2). This is also the case for stabilization
scenarios at higher levels of warming (e.g., at 2°C); the only difference is the projected time at which the net CO2 budget is zero.

3

Hence, a transition to decarbonization of energy use is necessary in all scenarios. It should be noted that all scenarios
of Chapter 2 include approaches for carbon dioxide removal (CDR) in order to achieve the net zero CO2 emissions budget.
Most of these use carbon capture and storage (CCS) in addition to reforestation, although to varying degrees (Chapter
4, Section 4.3.7). Some potential pathways to 1.5°C of warming in 2100 would minimize the need for CDR (Obersteiner etal., 2018; van Vuuren et al., 2018). Taking into account the implementation of CDR, the CO2-induced warming by 2100 is
determined by the difference between the total amount of CO2 generated (that can be reduced by early decarbonization) andthe total amount permanently stored out of the atmosphere, for example by geological sequestration (Chapter 4, Section 4.3.7).
•

What are possible storylines of ‘warmer worlds’ at 1.5°C versus higher levels of global warming? Cross-Chapter Box
8, Table 2 features possible storylines based on the scenarios of Chapter 2, the impacts of Chapters 3 and 5, and the options of
Chapter 4. These storylines are not intended to be comprehensive of all possible future outcomes. Rather, they are intended as
plausible scenarios of alternative warmer worlds, with two storylines that include stabilization at 1.5°C (Scenario 1) or close to
1.5°C (Scenario 2), and one storyline missing this goal and consequently only including reductions of CO2 emissions and efforts
towards stabilization at higher temperatures (Scenario 3).

Summary:
There is no single ‘1.5°C warmer world’. Impacts can vary strongly for different worlds characterized by a 1.5°C global
warming. Important aspects to consider (besides the changes in global temperature) are the possible occurrenceof an overshoot and its associated peak warming and duration, how stabilization of the increase in global surface
temperature at 1.5°C could be achieved, how policies might be able to influence the resilience of human and natural
systems, and the nature of regional and subregional risks.
The implications of overshooting are large for risks to natural and human systems, especially if the temperature at peak warming
is high, because some risks may be long lasting and irreversible, such as the loss of some ecosystems. In addition, for several typesof risks, the rate of change may be most relevant, with potentially large risks occurring in the case of a rapid rise to overshooting
temperatures, even if a decrease to 1.5°C may be achieved at the end of the 21st century or later. If overshoot is to be minimized, theremaining equivalent CO2 budget available for emissions has to be very small, which implies that large, immediate and unprecedented
global efforts to mitigate GHGs are required.
The time frame for initiating major mitigation measures is essential in order to reach a 1.5°C (or even a 2°C) global stabilizationof climate warming (see consistent cumulative CO2 emissions up to peak warming in Cross-Chapter Box 8, Table 1). If mitigation
pathways are not rapidly activated, much more expensive and complex adaptation measures will have to be taken to avoid theimpacts of higher levels of global warming on the Earth system.
Cross-Chapter Box 8 (continued next page)277

Chapter 3

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Cross-Chapter Box 8 (continued)

General characteristics
of pathway

Cross-Chapter Box 8, Table 1 | Different worlds resulting from 1.5°C and 2°C mitigation (prospective) pathways, including 66% (probable) best-case outcome,and 5% worst-case outcome, based on Chapter 2 scenarios and Chapter 3 assessments of changes in regional climate. Note that the pathway characteristics
estimates are based on computations with the MAGICC model (Meinshausen et al., 2011) consistent with the set-up used in AR5 WGIII (Clarke et al., 2014),
but are uncertain and will be subject to updates and adjust-ments (see Chapter 2 for details). Updated from Seneviratne et al. (2018b).
B1.5_LOS (below 1.5°C
with low overshoot)with 2/3 ´probablebest-case outcome´aB1.5_LOS (below 1.5°C
with low overshoot)with 1/20 ´worst-caseoutcome´b

L20 (lower than 2°C)
with 2/3 ´probablebest-case outcome´a

L20 (lower than 2°C)
with 1/20 ´worst-caseoutcome´bOvershoot > 1.5°C in 21st centuryc

Yes (51/51)

Yes (51/51)

Yes (72/72)

Yes (72/72)

Overshoot > 2°C in 21st century

No (0/51)

Yes (37/51)

No (72/72)

Yes (72/72)

Cumulative CO2 emissions up to peakwarming (relative to 2016)d [GtCO2]

610–760

590–750

1150–1460

1130–1470

Cumulative CO2 emissions up to2100 (relative to 2016)d [GtCO2]
Years of global net zero CO2 emissions

Possible climate range at peakwarming (regional+global)
Possible climate range
in 2100 (regional+global)

1030–1440

19–23

31–38

2055–2066

2082–2090

Global GHG emissions in 2030d [GtCO2 y-1]d3

170–560

Global mean temperature
anomaly at peak warming

1.7°C (1.66°C–1.72°C)

2.05°C (2.00°C–2.09°C)

2.11°C (2.05°C–2.17°C)

2.67°C (2.59°C–2.76°C)

Warming in the Arctice (TNnf)

4.93°C (4.36, 5.52)

6.02°C (5.12, 6.89)

6.24°C (5.39, 7.21)

7.69°C (6.69, 8.93)

Warming in Central North Americae (TXxg)

2.65°C (1.92, 3.15)

3.11°C (2.37, 3.63)

3.18°C (2.50, 3.71)

4.06°C (3.35, 4.63)

Warming in Amazon regione (TXx)

2.55°C (2.23, 2.83)

3.07°C (2.74, 3.46)

3.16°C (2.84, 3.57)

4.05°C (3.62, 4.46)

Drying in the Mediterranean region–1.11 (–2.24, –0.41)–1.28 (–2.44, –0.51)–1.38 (–2.58, –0.53)–1.56 (–3.19, –0.67)

Increase in heavy precipitation eventse in Southern Asiai

9.94% (6.76, 14.00)

11.94% (7.52, 18.86)

12.68% (7.71, 22.39)

19.67% (11.56, 27.24)

Global mean temperature
warming in 2100

1.46°C (1.41°C–1.51°C)

1.87°C (1.81°C–1.94°C)

2.06°C (1.99°C–2.15°C)

2.66°C (2.56°C–2.76°C)e,hWarming in the Arcticj (TNn)

4.28°C (3.71, 4.77)

5.50°C (4.74, 6.21)

6.08°C (5.20, 6.94)

7.63°C (6.66, 8.90)

Warming in Central North Americaj (TXx)

2.31°C (1.56, 2.66)

2.83°C (2.03, 3.49)

3.12°C (2.38, 3.67)

4.06°C (3.33, 4.59)

Warming in Amazon regionj (TXx)

2.22°C (2.00, 2.45)

2.76°C (2.50, 3.07)

3.10°C (2.75, 3.49)

4.03°C (3.62, 4.45)

Drying in the Mediterranean region–0.95 (–1.98, –0.30)–1.10 (–2.17, –0.51)–1.26 (–2.43, –0.52)–1.55 (–3.17, –0.67)

Increase in heavy precipitation events
in Southern Asiaj

8.38% (4.63, 12.68)

10.34% (6.64, 16.07)

12.02% (7.41, 19.62)

19.72% (11.34, 26.95)

jNotes:a) 66th percentile for global temperature (that is, 66% likelihood of being at or below values)b) 95th percentile for global temperature (that is, 5% likelihood of being at or above values)c) All 1.5°C scenarios include a substantial probability of overshooting above 1.5°C global warming before returning to 1.5°C.
d) Interquartile range (25th percentile, q25, and 75th percentile, q75)e) The regional projections in these rows provide the median and the range [q25, q75] associated with the median global temperature outcomes of the considered mitigation
scenarios at peak warming.
f) TNn: Annual minimum night-time temperatureg) TXx: Annual maximum day-time temperatureh) Indicates drying of soil moisture expressed in units of standard deviations of pre-industrial climate (1861–1880) variability (where −1 is dry; −2 is severely dry; and −3 is very
severely dry);i) Rx5day: the annual maximum consecutive 5-day precipitation.
j) As for footnote e, but for the regional responses associated with the median global temperature outcomes of the considered mitigation scenarios in 2100

278

Impacts of 1.5°C of Global Warming on Natural and Human Systems

Chapter 3

Cross-Chapter Box 8 (continued)
Cross-Chapter Box 8, Table 2 | Storylines of possible worlds resulting from different mitigation options. The storylines build upon Cross-Chapter Box 8, Table
1 and the assessments of Chapters 1–5. Only a few of the many possible storylines were chosen and they are presented for illustrative purposes.
Scenario 1 [one possible storyline
among best-case scenarios]:
Mitigation:
early move to decarbonization,
decarbonization designed to minimize
land footprint, coordination andrapid action of the world’s nations
towards 1.5°C goal by 2100
Internal climate variability:
probable (66%) best-case outcome for
global and regional climate responses

In 2020, strong participation and support for the Paris Agreement and its ambitious goals for reducing CO2 emissions
by an almost unanimous international community led to a time frame for net zero emissions that is compatible withhalting global warming at 1.5°C by 2100.
There is strong participation in all major world regions at the national, state and/or city levels. Transport is strongly decarbonizedthrough a shift to electric vehicles, with more cars with electric than combustion engines being sold by 2025 (Chapter 2, Section
2.4.3; Chapter 4, Section 4.3.3). Several industry-sized plants for carbon capture and storage are installed and tested in the 2020s(Chapter 2, Section 2.4.2; Chapter 4, Sections 4.3.4 and 4.3.7). Competition for land between bioenergy cropping, food production,and biodiversity conservation is minimized by sourcing bioenergy for carbon capture and storage from agricultural wastes, algae
and kelp farms (Cross-Chapter Box 7 in Chapter 3; Chapter 4, Section 4.3.2). Agriculture is intensified in countries with coordinatedplanning associated with a drastic decrease in food waste (Chapter 2, Section 2.4.4; Chapter 4, Section 4.3.2). This leaves many
natural ecosystems relatively intact, supporting continued provision of most ecosystem services, although relocation of species
towards higher latitudes and elevations still results in changes in local biodiversity in many regions, particularly in mountain,
tropical, coastal and Arctic ecosystems (Chapter 3, Section 3.4.3). Adaptive measures such as the establishment of corridors forthe movement of species and parts of ecosystems become a central practice within conservation management (Chapter 3, Section
3.4.3; Chapter 4, Section 4.3.2). The movement of species presents new challenges for resource management as novel ecosystems,
as well as pests and disease, increase (Cross-Chapter Box 6 in Chapter 3). Crops are grown on marginal land, no-till agriculture is
deployed, and large areas are reforested with native trees (Chapter 2, Section 2.4.4; Chapter 3, Section 3.6.2; Cross-Chapter Box
7 in Chapter 3; Chapter 4, Section 4.3.2). Societal preference for healthy diets reduces meat consumption and associated GHG
emissions (Chapter 2, Section 2.4.4; Chapter 4, Section 4.3.2; Cross-Chapter Box 6 in Chapter 3).
By 2100, global mean temperature is on average 0.5°C warmer than it was in 2018 (Chapter 1, Section 1.2.1). Only a minor
temperature overshoot occurs during the century (Chapter 2, Section 2.2). In mid-latitudes, frequent hot summers and precipitation
events tend to be more intense (Chapter 3, Section 3.3). Coastal communities struggle with increased inundation associatedwith rising sea levels and more frequent and intense heavy rainfall (Chapter 3, Sections 3.3.2 and 3.3.9; Chapter 4, Section
4.3.2; Chapter 5, Box 5.3 and Section 5.3.2; Cross-Chapter Box 12 in Chapter 5), and some respond by moving, in many caseswith consequences for urban areas. In the tropics, in particular in megacities, there are frequent deadly heatwaves whose risks
are reduced by proactive adaptation (Chapter 3, Sections 3.3.1 and 3.4.8; Chapter 4, Section 4.3.8), overlaid on a suite ofdevelopment challenges and limits in disaster risk management (Chapter 4, Section 4.3.3; Chapter 5, Sections 5.2.1 and 5.2.2;
Cross-Chapter Box 12 in Chapter 5). Glaciers extent decreases in most mountainous areas (Chapter 3, Sections 3.3.5 and 3.5.4).
Reduced Arctic sea ice opens up new shipping lanes and commercial corridors (Chapter 3, Section 3.3.8; Chapter 4, Box 4.3).
Small island developing states (SIDS), as well as coastal and low-lying areas, have faced significant changes but have largely
persisted in most regions (Chapter 3, Sections 3.3.9 and 3.5.4, Box 3.5). The Mediterranean area becomes drier (Chapter 3,
Section 3.3.4 and Box 3.2) and irrigation of crops expands, drawing the water table down in many areas (Chapter 3, Section
3.4.6). The Amazon is reasonably well preserved, through avoided risk of droughts (Chapter 3, Sections 3.3.4 and 3.4.3; Chapter
4, Box 4.3) and reduced deforestation (Chapter 2, Section 2.4.4; Cross-Chapter Box 7 in Chapter 3; Chapter 4, Section 4.3.2), andthe forest services are working with the pattern observed at the beginning of the 21st century (Chapter 4, Box 4.3). While someclimate hazards become more frequent (Chapter 3, Section 3.3), timely adaptation measures help reduce the associated risks
for most, although poor and disadvantaged groups continue to experience high climate risks to their livelihoods and well-being(Chapter 5, Section 5.3.1; Cross-Chapter Box 12 in Chapter 5; Chapter 3, Boxes 3.4 and 3.5; Cross-Chapter Box 6 in Chapter 3).
Summer sea ice has not completely disappeared from the Arctic (Chapter 3, Section 3.4.4.7) and coral reefs, having been driven toa low level (10–30% of levels in 2018), have partially recovered by 2100 after extensive dieback (Chapter 3, Section 3.4.4.10 andBox 3.4). The Earth system, while warmer, is still recognizable compared to the 2000s, and no major tipping points are reached(Chapter 3, Section 3.5.2.5). Crop yields remain relatively stable (Chapter 3, Section 3.4). Aggregate economic damage of climatechange impacts is relatively small, although there are some local losses associated with extreme weather events (Chapter 3,
Section 3.5; Chapter 4). Human well-being remains overall similar to that in 2020 (Chapter 5, Section 5.2.2).

Scenario 2 [one possible storyline
among mid-case scenarios]:
Mitigation:
delayed action (ambitious targets
reached only after warmer decadein the 2020s due to internal climate
variability), overshoot at 2°C, decrease
towards 1.5°C afterward, no efforts tominimize the land and water footprintsof bioenergy
Internal climate variability:
10% worst-case outcome (2020s)
followed by normal internal climate
variability

3

The international community continues to largely support the Paris Agreement and agrees in 2020 on reduction
targets for CO2 emissions and time frames for net zero emissions. However, these targets are not ambitious enoughto reach stabilization at 2°C of warming, let alone 1.5°C.
In the 2020s, internal climate variability leads to higher warming than projected, in a reverse development to what
happened in the so-called ‘hiatus’ period of the 2000s. Temperatures are regularly above 1.5°C of warming, althoughradiative forcing is consistent with a warming of 1.2°C or 1.3°C. Deadly heatwaves in major cities (Chicago, Kolkata, Beijing,
Karachi, São Paulo), droughts in southern Europe, southern Africa and the Amazon region, and major flooding in Asia, allintensified by the global and regional warming (Chapter 3, Sections 3.3.1, 3.3.2, 3.3.3, 3.3.4 and 3.4.8; Cross-Chapter
Box 11 in Chapter 4), lead to increasing levels of public unrest and political destabilization (Chapter 5, Section 5.2.1). An
emergency global summit in 2025 moves to much more ambitious climate targets. Costs for rapidly phasing out fossil fuel use andinfrastructure, while rapidly expanding renewables to reduce emissions, are much higher than in Scenario 1, owing to a failure to
support economic measures to drive the transition (Chapter 4). Disruptive technologies become crucial to face up to the adaptation
measures needed (Chapter 4, Section 4.4.4).

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Cross-Chapter Box 8 (continued)
Cross-Chapter Box 8, Table 2 (continued)
Scenario 2 [one possible storyline
among mid-case scenarios]:
Mitigation:
delayed action (ambitious targets
reached only after warmer decadein the 2020s due to internal climate
variability), overshoot at 2°C, decrease
towards 1.5°C afterward, no efforts tominimize the land and water footprintsof bioenergy
Internal climate variability:
10% worst-case outcome (2020s)
followed by normal internal
climate variability

Temperature peaks at 2°C of warming by the middle of the century before decreasing again owing to intensive implementationof bioenergy plants with carbon capture and storage (Chapter 2), without efforts to minimize the land and water footprint of
bioenergy production (Cross-Chapter Box 7 in Chapter 3). Reaching 2°C of warming for several decades eliminates or severely
damages key ecosystems such as coral reefs and tropical forests (Chapter 3, Section 3.4). The elimination of coral reef ecosystems
and the deterioration of their calcified frameworks, as well as serious losses of coastal ecosystems such as mangrove forests andseagrass beds (Chapter 3, Boxes 3.4 and 3.5, Sections 3.4.4.10 and 3.4.5), leads to much reduced levels of coastal defence fromstorms, winds and waves. These changes increase the vulnerability and risks facing communities in tropical and subtropical regions,with consequences for many coastal communities (Cross-Chapter Box 12 in Chapter 5). These impacts are being amplified bysteadily rising sea levels (Chapter 3, Section 3.3.9) and intensifying storms (Chapter 3, Section 3.4.4.3). The intensive area required
for the production of bioenergy, combined with increasing water stress, puts pressure on food prices (Cross-Chapter Box 6 in
Chapter 3), driving elevated rates of food insecurity, hunger and poverty (Chapter 4, Section 4.3.2; Cross-Chapter Box 6 in Chapter
3; Cross-Chapter Box 11 in Chapter 4). Crop yields decline significantly in the tropics, leading to prolonged famines in some Africancountries (Chapter 3, Section 3.4; Chapter 4, Section 4.3.2). Food trumps environment in terms of importance in most countries, withthe result that natural ecosystems decrease in abundance, owing to climate change and land-use change (Cross-Chapter Box 7 in
Chapter 3). The ability to implement adaptive action to prevent the loss of ecosystems is hindered under the circumstances andis consequently minimal (Chapter 3, Sections 3.3.6 and 3.4.4.10). Many natural ecosystems, in particular in the Mediterranean,
are lost because of the combined effects of climate change and land-use change, and extinction rates increase greatly (Chapter
3, Section 3.4 and Box 3.2).
By 2100, warming has decreased but is still stronger than 1.5°C, and the yields of some tropical crops are recovering (Chapter
3, Section 3.4.3). Several of the remaining natural ecosystems experience irreversible climate change-related damages whilst
others have been lost to land-use change, with very rapid increases in the rate of species extinctions (Chapter 3, Section 3.4;
Cross-Chapter Box 7 in Chapter 3; Cross-Chapter Box 11 in Chapter 4). Migration, forced displacement, and loss of identity areextensive in some countries, reversing some achievements in sustainable development and human security (Chapter 5, Section
5.3.2). Aggregate economic impacts of climate change damage are small, but the loss in ecosystem services creates large economic
losses (Chapter 4, Sections 4.3.2 and 4.3.3). The health and well-being of people generally decrease from 2020, while the levels ofpoverty and disadvantage increase considerably (Chapter 5, Section 5.2.1).

3

Scenario 3 [one possible storyline
among worst-case scenarios]:

In 2020, despite past pledges, the international support for the Paris Agreement starts to wane. In the years that
follow, CO2 emissions are reduced at the local and national level but efforts are limited and not always successful.

Mitigation:
uncoordinated action, major
actions late in the 21st century,
3°C of warming in 2100

Radiative forcing increases and, due to chance, the most extreme events tend to happen in less populated regions and thus do notincrease global concerns. Nonetheless, there are more frequent heatwaves in several cities and less snow in mountain resorts in
the Alps, Rockies and Andes (Chapter 3, Section 3.3). Global warming of 1.5°C is reached by 2030 but no major changes in policies
occur. Starting with an intense El Niño–La Niña phase in the 2030s, several catastrophic years occur while global warming starts
to approach 2°C. There are major heatwaves on all continents, with deadly consequences in tropical regions and Asian megacities,
especially for those ill-equipped for protecting themselves and their communities from the effects of extreme temperatures(Chapter 3, Sections 3.3.1, 3.3.2 and 3.4.8). Droughts occur in regions bordering the Mediterranean Sea, central North America,
the Amazon region and southern Australia, some of which are due to natural variability and others to enhanced greenhouse gas
forcing (Chapter 3, Section 3.3.4; Chapter 4, Section 4.3.2; Cross-Chapter Box 11 in Chapter 4). Intense flooding occurs in highlatitude and tropical regions, in particular in Asia, following increases in heavy precipitation events (Chapter 3, Section 3.3.3).
Major ecosystems (coral reefs, wetlands, forests) are destroyed over that period (Chapter 3, Section 3.4), with massive disruption
to local livelihoods (Chapter 5, Section 5.2.2 and Box 5.3; Cross-Chapter Box 12 in Chapter 5). An unprecedented drought leads
to large impacts on the Amazon rainforest (Chapter 3, Sections 3.3.4 and 3.4), which is also affected by deforestation (Chapter 2).
A hurricane with intense rainfall and associated with high storm surges (Chapter 3, Section 3.3.6) destroys a large part of Miami.
A two-year drought in the Great Plains in the USA and a concomitant drought in eastern Europe and Russia decrease global crop
production (Chapter 3, Section 3.3.4), resulting in major increases in food prices and eroding food security. Poverty levels increase
to a very large scale, and the risk and incidence of starvation increase considerably as food stores dwindle in most countries; humanhealth suffers (Chapter 3, Section 3.4.6.1; Chapter 4, Sections 4.3.2 and 4.4.3; Chapter 5, Section 5.2.1).

Internal climate variability:
unusual (ca. 10%) best-case scenario
for one decade, followed by normalinternal climate variability

There are high levels of public unrest and political destabilization due to the increasing climatic pressures, resulting in somecountries becoming dysfunctional (Chapter 4, Sections 4.4.1 and 4.4.2). The main countries responsible for the CO2 emissions
design rapidly conceived mitigation plans and try to install plants for carbon capture and storage, in some cases without sufficient
prior testing (Chapter 4, Section 4.3.6). Massive investments in renewable energy often happen too late and are uncoordinated;energy prices soar as a result of the high demand and lack of infrastructure. In some cases, demand cannot be met, leading
to further delays. Some countries propose to consider sulphate-aerosol based Solar Radiation Modification (SRM) (Chapter 4,
Section 4.3.8); however, intensive international negotiations on the topic take substantial time and are inconclusive because ofoverwhelming concerns about potential impacts on monsoon rainfall and risks in case of termination (Cross-Chapter Box 10 in
Chapter 5). Global and regional temperatures continue to increase strongly while mitigation solutions are being developed andimplemented.

280Impacts of 1.5°C of Global Warming on Natural and Human Systems

Chapter 3

Cross-Chapter Box 8 (continued)
Cross-Chapter Box 8, Table 2 (continued)
Scenario 3 [one possible storyline
among worst-case scenarios]:
Mitigation:
uncoordinated action, major
actions late in the 21st century,
3°C of warming in 2100
Internal climate variability:
unusual (ca. 10%) best-case scenario
for one decade, followed by normalinternal climate variability

Global mean warming reaches 3°C by 2100 but is not yet stabilized despite major decreases in yearly CO2 emissions, as a net zeroCO2 emissions budget could not yet be achieved and because of the long lifetime of CO2 concentrations (Chapters 1, 2 and 3).
The world as it was in 2020 is no longer recognizable, with decreasing life expectancy, reduced outdoor labour productivity, andlower quality of life in many regions because of too frequent heatwaves and other climate extremes (Chapter 4, Section 4.3.3).
Droughts and stress on water resources renders agriculture economically unviable in some regions (Chapter 3, Section 3.4; Chapter
4, Section 4.3.2) and contributes to increases in poverty (Chapter 5, Section 5.2.1; Cross-Chapter Box 12 in Chapter 5). Progress on
the sustainable development goals is largely undone and poverty rates reach new highs (Chapter 5, Section 5.2.3). Major conflicts
take place (Chapter 3, Section 3.4.9.6; Chapter 5, Section 5.2.1). Almost all ecosystems experience irreversible impacts, species
extinction rates are high in all regions, forest fires escalate, and biodiversity strongly decreases, resulting in extensive losses toecosystem services. These losses exacerbate poverty and reduce quality of life (Chapter 3, Section 3.4; Chapter 4, Section 4.3.2).
Life for many indigenous and rural groups becomes untenable in their ancestral lands (Chapter 4, Box 4.3; Cross-Chapter Box 12
in Chapter 5). The retreat of the West Antarctic ice sheet accelerates (Chapter 3, Sections 3.3 and 3.6), leading to more rapid sea
level rise (Chapter 3, Section 3.3.9; Chapter 4, Section 4.3.2). Several small island states give up hope of survival in their locations
and look to an increasingly fragmented global community for refuge (Chapter 3, Box 3.5; Cross-Chapter Box 12 in Chapter 5).
Aggregate economic damages are substantial, owing to the combined effects of climate changes, political instability, and lossesof ecosystem services (Chapter 4, Sections 4.4.1 and 4.4.2; Chapter 3, Box 3.6 and Section 3.5.2.4). The general health and wellbeing of people is substantially reduced compared to the conditions in 2020 and continues to worsen over the following decades(Chapter 5, Section 5.2.3).

3281

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Impacts of 1.5°C of Global Warming on Natural and Human Systems

Frequently Asked Questions

FAQ 3.1 | What are the Impacts of 1.5°C and 2°C of Warming?
Summary: The impacts of climate change are being felt in every inhabited continent and in the oceans. However,they are not spread uniformly across the globe, and different parts of the world experience impacts differently.
An average warming of 1.5°C across the whole globe raises the risk of heatwaves and heavy rainfall events,
amongst many other potential impacts. Limiting warming to 1.5°C rather than 2°C can help reduce these risks,
but the impacts the world experiences will depend on the specific greenhouse gas emissions ‘pathway’ taken.
The consequences of temporarily overshooting 1.5°C of warming and returning to this level later in the century,for example, could be larger than if temperature stabilizes below 1.5°C. The size and duration of an overshootwill also affect future impacts.
Human activity has warmed the world by about 1°C since pre-industrial times, and the impacts of this warming
have already been felt in many parts of the world. This estimate of the increase in global temperature is theaverage of many thousands of temperature measurements taken over the world’s land and oceans. Temperatures
are not changing at the same speed everywhere, however: warming is strongest on continents and is particularly
strong in the Arctic in the cold season and in mid-latitude regions in the warm season. This is due to selfamplifying mechanisms, for instance due to snow and ice melt reducing the reflectivity of solar radiation at thesurface, or soil drying leading to less evaporative cooling in the interior of continents. This means that some partsof the world have already experienced temperatures greater than 1.5°C above pre-industrial levels.
Extra warming on top of the approximately 1°C we have seen so far would amplify the risks and associatedimpacts, with implications for the world and its inhabitants. This would be the case even if the global warmingis held at 1.5°C, just half a degree above where we are now, and would be further amplified at 2°C of global
warming. Reaching 2°C instead of 1.5°C of global warming would lead to substantial warming of extreme hotdays in all land regions. It would also lead to an increase in heavy rainfall events in some regions, particularly inthe high latitudes of the Northern Hemisphere, potentially raising the risk of flooding. In addition, some regions,such as the Mediterranean, are projected to become drier at 2°C versus 1.5°C of global warming. The impacts ofany additional warming would also include stronger melting of ice sheets and glaciers, as well as increased sea
level rise, which would continue long after the stabilization of atmospheric CO2 concentrations.

3

Change in climate means and extremes have knock-on effects for the societies and ecosystems living on theplanet. Climate change is projected to be a poverty multiplier, which means that its impacts are expected to makethe poor poorer and the total number of people living in poverty greater. The 0.5°C rise in global temperaturesthat we have experienced in the past 50 years has contributed to shifts in the distribution of plant and animal
species, decreases in crop yields and more frequent wildfires. Similar changes can be expected with further rises
in global temperature.
Essentially, the lower the rise in global temperature above pre-industrial levels, the lower the risks to humansocieties and natural ecosystems. Put another way, limiting warming to 1.5°C can be understood in terms of‘avoided impacts’ compared to higher levels of warming. Many of the impacts of climate change assessed in thisreport have lower associated risks at 1.5°C compared to 2°C.
Thermal expansion of the ocean means sea level will continue to rise even if the increase in global temperature
is limited to 1.5°C, but this rise would be lower than in a 2°C warmer world. Ocean acidification, the process bywhich excess CO2 is dissolving into the ocean and increasing its acidity, is expected to be less damaging in a world
where CO2 emissions are reduced and warming is stabilized at 1.5°C compared to 2°C. The persistence of coral
reefs is greater in a 1.5°C world than that of a 2°C world, too.
The impacts of climate change that we experience in future will be affected by factors other than the change
in temperature. The consequences of 1.5°C of warming will additionally depend on the specific greenhouse gas
emissions ‘pathway’ that is followed and the extent to which adaptation can reduce vulnerability. This IPCC
Special Report uses a number of ‘pathways’ to explore different possibilities for limiting global warming to
1.5°C above pre-industrial levels. One type of pathway sees global temperature stabilize at, or just below, 1.5°C.
Another sees global temperature temporarily exceed 1.5°C before declining later in the century (known as an‘overshoot’ pathway).
(continued on next page)282

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Chapter 3

Such pathways would have different associated impacts, so it is important to distinguish between them forplanning adaptation and mitigation strategies. For example, impacts from an overshoot pathway could be larger
than impacts from a stabilization pathway. The size and duration of an overshoot would also have consequences
for the impacts the world experiences. For instance, pathways that overshoot 1.5°C run a greater risk of passing
through ‘tipping points’, thresholds beyond which certain impacts can no longer be avoided even if temperatures
are brought back down later on. The collapse of the Greenland and Antarctic ice sheets on the time scale ofcenturies and millennia is one example of a tipping point.

FAQ3.1:Impact of 1.5°C and 2.0°C global warming

Temperature rise is not uniform across the world. Some regions will experience greater increases in the temperature of
hot days and cold nights than others.
+ 1.5°C: Change in average temperature of hottest days

+ 2.0°C: Change in average temperature of hottest days3
+ 1.5°C: Change in average temperature of coldest nights+ 2.0°C: Change in average temperature of coldest nights

°C
0.0

0.5

1.0

1.5

2.0

3.0

4.0

6.0

8.0

10.0

FAQ 3.1, Figure 1 | Temperature change is not uniform across the globe. Projected changes are shown for the average temperature of the annual hottest day (top)and the annual coldest night (bottom) with 1.5°C of global warming (left) and 2°C of global warming (right) compared to pre-industrial levels.

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Strengthening and
Implementing the
Global Response
Coordinating Lead Authors:
Heleen de Coninck (Netherlands/EU), Aromar Revi (India)

Lead Authors:
Mustafa Babiker (Sudan), Paolo Bertoldi (Italy), Marcos Buckeridge (Brazil), Anton Cartwright (South
Africa), Wenjie Dong (China), James Ford (UK/Canada), Sabine Fuss (Germany), Jean-Charles Hourcade(France), Debora Ley (Guatemala/Mexico), Reinhard Mechler (Germany), Peter Newman (Australia),
Anastasia Revokatova (Russian Federation), Seth Schultz (USA), Linda Steg (Netherlands), Taishi Sugiyama(Japan)Contributing Authors:
Malcolm Araos (Canada), Stefan Bakker (Netherlands), Amir Bazaz (India), Ella Belfer (Canada), Tim Benton(UK), Sarah Connors (France/UK), Joana Correia de Oliveira de Portugal Pereira (UK/Portugal), Dipak
Dasgupta (India), Kiane de Kleijne (Netherlands/EU), Maria del Mar Zamora Dominguez (Mexico), Michel
den Elzen (Netherlands), Kristie L. Ebi (USA), Dominique Finon (France), Piers Forster (UK), Jan Fuglestvedt(Norway), Frédéric Ghersi (France), Adriana Grandis (Brazil), Eamon Haughey (Ireland), Bronwyn Hayward(New Zealand), Ove Hoegh-Guldberg (Australia), Daniel Huppmann (Austria), Kejun Jiang (China), Richard
Klein (Netherlands/Germany), Shagun Mehrotra (USA/India), Luis Mundaca (Sweden/Chile), Carolyn Opio
(Uganda), Maxime Plazzotta (France), Andy Reisinger (New Zealand), Kevon Rhiney (Jamaica), Timmons
Roberts (USA), Joeri Rogelj (Austria/Belgium), Arjan van Rooij (Netherlands), Roland Séférian (France),
Drew Shindell (USA), Jana Sillmann (Germany/Norway), Chandni Singh (India), Raphael Slade (UK), Gerd
Sparovek (Brazil), Pablo Suarez (Argentina), Adelle Thomas (Bahamas), Evelina Trutnevyte (Switzerland/
Lithuania), Anne van Valkengoed (Netherlands), Maria Virginia Vilariño (Argentina), Eva Wollenberg (USA)

Review Editors:
Amjad Abdulla (Maldives), Rizaldi Boer (Indonesia), Mark Howden (Australia), Diana Ürge-Vorsatz(Hungary)

Chapter Scientists:
Kiane de Kleijne (Netherlands/EU), Chandni Singh (India)
This chapter should be cited as:de Coninck, H., A. Revi, M. Babiker, P. Bertoldi, M. Buckeridge, A. Cartwright, W. Dong, J. Ford, S. Fuss, J.-C. Hourcade, D. Ley,
R. Mechler, P. Newman, A. Revokatova, S. Schultz, L. Steg, and T. Sugiyama, 2018: Strengthening and Implementing the
Global Response. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C
above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the
global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [MassonDelmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock,
S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In
Press.

313

Chapter 4

Strengthening and Implementing the Global Response

Table of Contents
Executive Summary....................................................................315
4.1
		

Accelerating the Global Response
to Climate Change.........................................................319

4.2
		

Pathways Compatible with 1.5°C: Starting
Points for Strengthening Implementation.............320

4.2.1
			

Implications for Implementation of
1.5°C-Consistent Pathways.........................................320

4.2.2

System Transitions and Rates of Change.....................322

4.3
		

Systemic Changes
for 1.5°C-Consistent Pathways.................................323

Enabling Lifestyle and Behavioural Change................362

Box 4.5 | How Pricing Policy has Reduced Car Usein Singapore, Stockholm and London....................................366
Box 4.6 | Bottom-up Initiatives: Adaptation
Responses Initiated by Individuals and Communities.........368
4.4.4

Enabling Technological Innovation..............................369

Box 4.7 | Bioethanol in Brazil: Innovation andLessons for Technology Transfers...........................................371
4.4.5
			

Strengthening Policy Instruments and Enabling
Climate Finance...........................................................372

Box 4.8 | Investment Needs and the Financial
Challenge of Limiting Warming to 1.5°C...............................373
Box 4.9 | Emerging Cities and ‘Peak Car Use’:
Evidence of Decoupling in Beijing.........................................376

4.3.1

Energy System Transitions...........................................324

4.3.2

Land and Ecosystem Transitions..................................327

4.3.3

Urban and Infrastructure System Transitions...............330

4.5

4.3.4

Industrial Systems Transitions......................................324

4.3.5
			

Overarching Adaptation Options Supporting
Adaptation Transitions................................................336

4.5.1
Assessing Feasibility of Options for Accelerated
			 Transitions...................................................................380

Cross-Chapter Box 9 | Risks, Adaptation Interventions,and Implications for Sustainable Development
and Equity Across Four Social-Ecological Systems:
Arctic, Caribbean, Amazon, and Urban..................................338

4

4.4.3

4.3.6

Short-Lived Climate Forcers.........................................341

4.3.7

Carbon Dioxide Removal (CDR)...................................342

4.3.8

Solar Radiation Modification (SRM)............................347

Cross-Chapter Box 10 | Solar Radiation Modificationin the Context of 1.5°C Mitigation Pathways.......................349

4.4

Implementing Far-Reaching and Rapid Change...352

4.4.1

Enhancing Multilevel Governance...............................352

Box 4.1 | Multilevel Governance in the EU Covenantof Mayors: Example of the Provincia di Foggia....................355
Box 4.2 | Watershed Management in a 1.5˚C World.............356
Cross-Chapter Box 11 | Consistency Between
Nationally Determined Contributions and
1.5°C Scenarios.........................................................................357
4.4.2

Enhancing Institutional Capacities..............................359

Box 4.3 | Indigenous Knowledge and Community
Adaptation................................................................................360
Box 4.4 | Manizales, Colombia: Supportive National
Government and Localized Planning and Integration
as an Enabling Condition for Managing Climate
and Development Risks...........................................................361

314

Integration and Enabling Transformation..............380

4.5.2

Implementing Mitigation............................................381

4.5.3

Implementing Adaptation...........................................383

4.5.4
			

Synergies and Trade-Offs between
Adaptation and Mitigation..........................................386

Box 4.10 | Bhutan: Synergies and Trade-Offs in Economic
Growth, Carbon Neutrality and Happiness...........................387

4.6

Knowledge Gaps and Key Uncertainties................387

Frequently Asked Questions
FAQ 4.1: What Transitions Could Enable Limiting
Global Warming to 1.5°C?...................................................392
FAQ 4.2: What are Carbon Dioxide Removal and
Negative Emissions?...........................................................394
FAQ 4.3: Why is Adaptation Important in a
1.5°C-Warmer World?..........................................................396

References ....................................................................................398

Strengthening and Implementing the Global Response

Executive Summary
Limiting warming to 1.5°C above pre-industrial levels would
require transformative systemic change, integrated withsustainable development. Such change would require theupscaling and acceleration of the implementation of farreaching, multilevel and cross-sectoral climate mitigation
and addressing barriers. Such systemic change would need
to be linked to complementary adaptation actions, includingtransformational adaptation, especially for pathways that
temporarily overshoot 1.5°C (medium evidence, high agreement){Chapter 2, Chapter 3, 4.2.1, 4.4.5, 4.5}. Current national pledges
on mitigation and adaptation are not enough to stay below the Paris
Agreement temperature limits and achieve its adaptation goals. Whiletransitions in energy efficiency, carbon intensity of fuels, electrificationand land-use change are underway in various countries, limiting
warming to 1.5°C will require a greater scale and pace of change totransform energy, land, urban and industrial systems globally. {4.3, 4.4,
Cross-Chapter Box 9 in this Chapter}
Although multiple communities around the world aredemonstrating the possibility of implementation consistent with
1.5°C pathways {Boxes 4.1-4.10}, very few countries, regions,cities, communities or businesses can currently make sucha claim (high confidence). To strengthen the global response,
almost all countries would need to significantly raise their levelof ambition. Implementation of this raised ambition would
require enhanced institutional capabilities in all countries,
including building the capability to utilize indigenous and local
knowledge (medium evidence, high agreement). In developing
countries and for poor and vulnerable people, implementing the
response would require financial, technological and other forms of
support to build capacity, for which additional local, national andinternational resources would need to be mobilized (high confidence).
However, public, financial, institutional and innovation capabilities
currently fall short of implementing far-reaching measures at scale inall countries (high confidence). Transnational networks that support
multilevel climate action are growing, but challenges in their scale-up
remain. {4.4.1, 4.4.2, 4.4.4, 4.4.5, Box 4.1, Box 4.2, Box 4.7}
Adaptation needs will be lower in a 1.5°C world compared to
a 2°C world (high confidence) {Chapter 3; Cross-Chapter Box 11
in this chapter}. Learning from current adaptation practices andstrengthening them through adaptive governance {4.4.1}, lifestyleand behavioural change {4.4.3} and innovative financing mechanisms{4.4.5} can help their mainstreaming within sustainable development
practices. Preventing maladaptation, drawing on bottom-up approaches{Box 4.6} and using indigenous knowledge {Box 4.3} would effectively
engage and protect vulnerable people and communities. Whileadaptation finance has increased quantitatively, significant further
expansion would be needed to adapt to 1.5°C. Qualitative gaps in the
distribution of adaptation finance, readiness to absorb resources, andmonitoring mechanisms undermine the potential of adaptation financeto reduce impacts. {Chapter 3, 4.4.2, 4.4.5, 4.6}

Chapter 4

System Transitions
The energy system transition that would be required to limit
global warming to 1.5°C above pre-industrial conditions isunderway in many sectors and regions around the world(medium evidence, high agreement). The political, economic, social
and technical feasibility of solar energy, wind energy and electricity
storage technologies has improved dramatically over the past few
years, while that of nuclear energy and carbon dioxide captureand storage (CCS) in the electricity sector have not shown similar
improvements. {4.3.1}
Electrification, hydrogen, bio-based feedstocks and substitution,
and, in several cases, carbon dioxide capture, utilization andstorage (CCUS) would lead to the deep emissions reductions
required in energy-intensive industries to limit warming to
1.5°C. However, those options are limited by institutional, economic andtechnical constraints, which increase financial risks to many incumbent
firms (medium evidence, high agreement). Energy efficiency in industry
is more economically feasible and helps enable industrial systemtransitions but would have to be complemented with greenhouse gas(GHG)-neutral processes or carbon dioxide removal (CDR) to makeenergy-intensive industries consistent with 1.5°C (high confidence).
{4.3.1, 4.3.4}
Global and regional land-use and ecosystems transitions andassociated changes in behaviour that would be required tolimit warming to 1.5°C can enhance future adaptation andland-based agricultural and forestry mitigation potential. Suchtransitions could, however, carry consequences for livelihoods
that depend on agriculture and natural resources {4.3.2, CrossChapter Box 6 in Chapter 3}. Alterations of agriculture and forest
systems to achieve mitigation goals could affect current ecosystems
and their services and potentially threaten food, water and livelihood
security. While this could limit the social and environmental feasibilityof land-based mitigation options, careful design and implementation
could enhance their acceptability and support sustainable development
objectives (medium evidence, medium agreement). {4.3.2, 4.5.3}
Changing agricultural practices can be an effective climate
adaptation strategy. A diversity of adaptation options exists,
including mixed crop-livestock production systems which can be acost-effective adaptation strategy in many global agriculture systems(robust evidence, medium agreement). Improving irrigation efficiency
could effectively deal with changing global water endowments,
especially if achieved via farmers adopting new behaviours and waterefficient practices rather than through large-scale infrastructuralinterventions (medium evidence, medium agreement). Well-designed
adaptation processes such as community-based adaptation can beeffective depending upon context and levels of vulnerability. {4.3.2,
4.5.3}
Improving the efficiency of food production and closing yieldgaps have the potential to reduce emissions from agriculture,
reduce pressure on land, and enhance food security and future

315

4Chapter 4

mitigation potential (high confidence). Improving productivity ofexisting agricultural systems generally reduces the emissions intensityof food production and offers strong synergies with rural development,poverty reduction and food security objectives, but options to reduce
absolute emissions are limited unless paired with demand-side
measures. Technological innovation including biotechnology, withadequate safeguards, could contribute to resolving current feasibility
constraints and expand the future mitigation potential of agriculture.
{4.3.2, 4.4.4}
Shifts in dietary choices towards foods with lower emissions
and requirements for land, along with reduced food loss andwaste, could reduce emissions and increase adaptation options(high confidence). Decreasing food loss and waste and changing
dietary behaviour could result in mitigation and adaptation (highconfidence) by reducing both emissions and pressure on land, withsignificant co-benefits for food security, human health and sustainable
development {4.3.2, 4.4.5, 4.5.2, 4.5.3, 5.4.2}, but evidence of
successful policies to modify dietary choices remains limited.

Mitigation and Adaptation Options and Other Measures

4

A mix of mitigation and adaptation options implemented in aparticipatory and integrated manner can enable rapid, systemictransitions – in urban and rural areas – that are necessary
elements of an accelerated transition consistent with limiting
warming to 1.5°C. Such options and changes are most effective
when aligned with economic and sustainable development,
and when local and regional governments are supported bynational governments {4.3.3, 4.4.1, 4.4.3}. Various mitigation
options are expanding rapidly across many geographies. Althoughmany have development synergies, not all income groups have so
far benefited from them. Electrification, end-use energy efficiency
and increased share of renewables, amongst other options, arelowering energy use and decarbonizing energy supply in the built
environment, especially in buildings. Other rapid changes needed inurban environments include demotorization and decarbonization oftransport, including the expansion of electric vehicles, and greater useof energy-efficient appliances (medium evidence, high agreement).
Technological and social innovations can contribute to limiting
warming to 1.5°C, for example, by enabling the use of smart grids,
energy storage technologies and general-purpose technologies, suchas information and communication technology (ICT) that can be
deployed to help reduce emissions. Feasible adaptation options include
green infrastructure, resilient water and urban ecosystem services,
urban and peri-urban agriculture, and adapting buildings and land use
through regulation and planning (medium evidence, medium to highagreement). {4.3.3, 4.4.3, 4.4.4}
Synergies can be achieved across systemic transitions throughseveral overarching adaptation options in rural and urban areas.
Investments in health, social security and risk sharing and spreading
are cost-effective adaptation measures with high potential for scalingup (medium evidence, medium to high agreement). Disaster risk
management and education-based adaptation have lower prospects ofscalability and cost-effectiveness (medium evidence, high agreement)
but are critical for building adaptive capacity. {4.3.5, 4.5.3}
316

Strengthening and Implementing the Global Response

Converging adaptation and mitigation options can lead tosynergies and potentially increase cost-effectiveness, but
multiple trade-offs can limit the speed of and potential for
scaling up. Many examples of synergies and trade-offs exist in
all sectors and system transitions. For instance, sustainable water
management (high evidence, medium agreement) and investment ingreen infrastructure (medium evidence, high agreement) to deliver
sustainable water and environmental services and to support urban
agriculture are less cost-effective than other adaptation options but
can help build climate resilience. Achieving the governance, financeand social support required to enable these synergies and to avoid
trade-offs is often challenging, especially when addressing multiple
objectives, and attempting appropriate sequencing and timing ofinterventions. {4.3.2, 4.3.4, 4.4.1, 4.5.2, 4.5.3, 4.5.4}
Though CO2 dominates long-term warming, the reduction ofwarming short-lived climate forcers (SLCFs), such as methaneand black carbon, can in the short term contribute significantly tolimiting warming to 1.5°C above pre-industrial levels. Reductionsof black carbon and methane would have substantial co-benefits(high confidence), including improved health due to reduced air
pollution. This, in turn, enhances the institutional and sociocultural feasibility of such actions. Reductions of several warming
SLCFs are constrained by economic and social feasibility (low evidence,high agreement). As they are often co-emitted with CO2, achieving theenergy, land and urban transitions necessary to limit warming to 1.5°C
would see emissions of warming SLCFs greatly reduced. {2.3.3.2, 4.3.6}
Most CDR options face multiple feasibility constraints, which
differ between options, limiting the potential for any single
option to sustainably achieve the large-scale deployment
required in the 1.5°C-consistent pathways described in
Chapter 2 (high confidence). Those 1.5°C pathways typically relyon bioenergy with carbon capture and storage (BECCS), afforestation
and reforestation (AR), or both, to neutralize emissions that are
expensive to avoid, or to draw down CO2 emissions in excess of the
carbon budget {Chapter 2}. Though BECCS and AR may be technicallyand geophysically feasible, they face partially overlapping yet different
constraints related to land use. The land footprint per tonne of CO2
removed is higher for AR than for BECCS, but given the low levels of
current deployment, the speed and scales required for limiting warming
to 1.5°C pose a considerable implementation challenge, even if theissues of public acceptance and absence of economic incentives were
to be resolved (high agreement, medium evidence). The large potentialof afforestation and the co-benefits if implemented appropriately (e.g.,
on biodiversity and soil quality) will diminish over time, as forests
saturate (high confidence). The energy requirements and economic
costs of direct air carbon capture and storage (DACCS) and enhanced
weathering remain high (medium evidence, medium agreement). At the
local scale, soil carbon sequestration has co-benefits with agriculture
and is cost-effective even without climate policy (high confidence). Its
potential feasibility and cost-effectiveness at the global scale appears
to be more limited. {4.3.7}
Uncertainties surrounding solar radiation modification(SRM) measures constrain their potential deployment. These
uncertainties include: technological immaturity; limited physicalStrengthening and Implementing the Global Response

understanding about their effectiveness to limit global warming; anda weak capacity to govern, legitimize, and scale such measures. Some
recent model-based analysis suggests SRM would be effective but that
it is too early to evaluate its feasibility. Even in the uncertain case thatthe most adverse side-effects of SRM can be avoided, public resistance,
ethical concerns and potential impacts on sustainable development
could render SRM economically, socially and institutionally undesirable(low agreement, medium evidence). {4.3.8, Cross-Chapter Box 10 inthis chapter}

Enabling Rapid and Far-Reaching Change
The speed of transitions and of technological change required
to limit warming to 1.5°C above pre-industrial levels has been
observed in the past within specific sectors and technologies{4.2.2.1}. But the geographical and economic scales at which
the required rates of change in the energy, land, urban,
infrastructure and industrial systems would need to take place
are larger and have no documented historic precedent (limited
evidence, medium agreement). To reduce inequality and alleviate
poverty, such transformations would require more planning andstronger institutions (including inclusive markets) than observed in thepast, as well as stronger coordination and disruptive innovation acrossactors and scales of governance. {4.3, 4.4}
Governance consistent with limiting warming to 1.5°C and thepolitical economy of adaptation and mitigation can enable andaccelerate systems transitions,behavioural change,innovation andtechnology deployment (medium evidence, medium agreement).
For 1.5°C-consistent actions, an effective governance framework
would include: accountable multilevel governance that includes nonstate actors, such as industry, civil society and scientific institutions;coordinated sectoral and cross-sectoral policies that enable collaborative
multi-stakeholder partnerships; strengthened global-to-local financial
architecture that enables greater access to finance and technology;
addressing climate-related trade barriers; improved climate educationand greater public awareness; arrangements to enable accelerated
behaviour change; strengthened climate monitoring and evaluation
systems; and reciprocal international agreements that are sensitive
to equity and the Sustainable Development Goals (SDGs). Systemtransitions can be enabled by enhancing the capacities of public, privateand financial institutions to accelerate climate change policy planningand implementation, along with accelerated technological innovation,
deployment and upkeep. {4.4.1, 4.4.2, 4.4.3, 4.4.4}
Behaviour change and demand-side management cansignificantly reduce emissions, substantially limiting thereliance on CDR to limit warming to 1.5°C {Chapter 2, 4.4.3}.
Political and financial stakeholders may find climate actions more costeffective and socially acceptable if multiple factors affecting behaviour
are considered, including aligning these actions with people’s corevalues (medium evidence, high agreement). Behaviour- and lifestylerelated measures and demand-side management have already led
to emission reductions around the world and can enable significant
future reductions (high confidence). Social innovation through bottom1

Chapter 4

up initiatives can result in greater participation in the governance ofsystems transitions and increase support for technologies, practices
and policies that are part of the global response to limit warming to
1.5°C . {Chapter 2, 4.4.1, 4.4.3, Figure 4.3}
This rapid and far-reaching response required to keep warming
below 1.5°C and enhance the capacity to adapt to climate risks
would require large increases of investments in low-emission
infrastructure and buildings, along with a redirection of financial
flows towards low-emission investments (robust evidence, highagreement). An estimated mean annual incremental investment ofaround 1.5% of global gross fixed capital formation (GFCF) for theenergy sector is indicated between 2016 and 2035, as well as about
2.5% of global GFCF for other development infrastructure that could
also address SDG implementation. Though quality policy design andeffective implementation may enhance efficiency, they cannot fully
substitute for these investments. {2.5.2, 4.2.1, 4.4.5}
Enabling this investment requires the mobilization and better
integration of a range of policy instruments that include thereduction of socially inefficient fossil fuel subsidy regimes and innovative
price and non-price national and international policy instruments. These
would need to be complemented by de-risking financial instruments
and the emergence of long-term low-emission assets. These instruments
would aim to reduce the demand for carbon-intensive services and shift
market preferences away from fossil fuel-based technology. Evidence
and theory suggest that carbon pricing alone, in the absence ofsufficient transfers to compensate their unintended distributional crosssector, cross-nation effects, cannot reach the incentive levels needed
to trigger system transitions (robust evidence, medium agreement).
But, embedded in consistent policy packages, they can help mobilize
incremental resources and provide flexible mechanisms that help reduce
the social and economic costs of the triggering phase of the transition(robust evidence, medium agreement). {4.4.3, 4.4.4, 4.4.5}
Increasing evidence suggests that a climate-sensitive
realignment of savings and expenditure towards low-emission,
climate-resilient infrastructure and services requires an
evolution of global and national financial systems. Estimatessuggest that, in addition to climate-friendly allocation of public
investments, a potential redirection of 5% to 10% of the annual
capital revenues1 is necessary for limiting warming to 1.5°C {4.4.5,
Table 1 in Box 4.8}. This could be facilitated by a change of incentives
for private day-to-day expenditure and the redirection of savings
from speculative and precautionary investments towards longterm productive low-emission assets and services. This implies themobilization of institutional investors and mainstreaming of climate
finance within financial and banking system regulation. Access bydeveloping countries to low-risk and low-interest finance throughmultilateral and national development banks would have to be
facilitated (medium evidence, high agreement). New forms of public–
private partnerships may be needed with multilateral, sovereign andsub-sovereign guarantees to de-risk climate-friendly investments,
support new business models for small-scale enterprises and help
households with limited access to capital. Ultimately, the aim is to

Annual capital revenues are paid interests plus an increase of asset value.

317

4Chapter 4

promote a portfolio shift towards long-term low-emission assets thatwould help redirect capital away from potentially stranded assets(medium evidence, medium agreement). {4.4.5}

Knowledge Gaps
Knowledge gaps around implementing and strengthening the
global response to climate change would need to be urgently
resolved if the transition to a 1.5°C world is to become reality.
Remaining questions include: how much can be realistically expectedfrom innovation and behavioural and systemic political and economicchanges in improving resilience, enhancing adaptation and reducing
GHG emissions? How can rates of changes be accelerated and scaled
up? What is the outcome of realistic assessments of mitigation andadaptation land transitions that are compliant with sustainable
development, poverty eradication and addressing inequality? What arelife-cycle emissions and prospects of early-stage CDR options? How
can climate and sustainable development policies converge, and how
can they be organised within a global governance framework andfinancial system, based on principles of justice and ethics (including‘common but differentiated responsibilities and respective capabilities’(CBDR-RC)), reciprocity and partnership? To what extent would
limiting warming to 1.5°C require a harmonization of macro-financial
and fiscal policies, which could include financial regulators such as
central banks? How can different actors and processes in climate
governance reinforce each other, and hedge against the fragmentationof initiatives? {4.1, 4.3.7, 4.4.1, 4.4.5, 4.6}

4318

Strengthening and Implementing the Global Response

Strengthening and Implementing the Global Response

4.1

Accelerating the Global Response
to Climate ChangeThis chapter discusses how the global economy and socio-technicaland socio-ecological systems can transition to 1.5°C-consistentpathways and adapt to warming of 1.5°C above pre-industrial levels.
In the context of systemic transitions, the chapter assesses adaptationand mitigation options, including carbon dioxide removal (CDR), andpotential solar radiation modification (SRM) remediative measures(Section 4.3), as well as the enabling conditions that would be requiredfor implementing the rapid and far-reaching global response of limiting
warming to 1.5°C (Section 4.4), and render the options more or lessfeasible (Section 4.5).
The impacts of a 1.5°C-warmer world, while less than in a 2°C-warmer
world, would require complementary adaptation and development
action, typically at local and national scale. From a mitigation
perspective, 1.5°C-consistent pathways require immediate action ona greater and global scale so as to achieve net zero emissions by midcentury, or earlier (Chapter 2). This chapter and Chapter 5 highlightthe potential that combined mitigation, development and poverty
reduction offer for accelerated decarbonization.
The global context is an increasingly interconnected world, with thehuman population growing from the current 7.6 billion to over 9 billion
by mid-century (UN DESA, 2017). There has been a consistent growth ofglobal economic output, wealth and trade with a significant reduction
in extreme poverty. These trends could continue for the next few
decades (Burt et al., 2014), potentially supported by new and disruptive
information and communication, and nano- and bio-technologies.
However, these trends co-exist with rising inequality (Piketty, 2014),
exclusion and social stratification, and regions locked in poverty traps(Deaton, 2013) that could fuel social and political tensions.
The aftermath of the 2008 financial crisis generated a challenging
environment in which leading economists have issued repeated alerts
about the ‘discontents of globalisation’ (Stiglitz, 2002), ‘depression
economics’ (Krugman, 2009), an excessive reliance of export-led
development strategies (Rajan, 2011), and risks of ‘secular stagnation’due to the ‘saving glut’ that slows down the flow of global savings
towards productive 1.5°C-consistent investments (Summers, 2016).
Each of these affects the implementation of both 1.5°C-consistentpathways and sustainable development (Chapter 5).
The range of mitigation and adaptation actions that can be deployed in
the short run are well-known: for example, low-emission technologies,
new infrastructure, and energy efficiency measures in buildings,
industry and transport; transformation of fiscal structures; reallocationof investments and human resources towards low-emission assets;sustainable land and water management; ecosystem restoration;enhancement of adaptive capacities to climate risks and impacts;
disaster risk management; research and development; and mobilizationof new, traditional and indigenous knowledge.
The convergence of short-term development co-benefits frommitigation and adaptation to address ‘everyday development failures’Chapter 4(e.g., institutions, market structures and political processes) (Hallegatteet al., 2016; Pelling et al., 2018) could enhance the adaptive capacityof key systems at risk (e.g., water, energy, food, biodiversity, urban,
regional and coastal systems) to 1.5°C climate impacts (Chapter
3). The issue is whether aligning 1.5°C-consistent pathways with
the Sustainable Development Goals (SDGs) will secure support for
accelerated change and a new growth cycle (Stern, 2013, 2015). It isdifficult to imagine how a 1.5°C world would be attained unless the
SDG on cities and sustainable urbanization is achieved in developing
countries (Revi, 2016), or without reforms in the global financial
intermediation system.
Unless affordable and environmentally and socially acceptable
CDR becomes feasible and available at scale well before 2050,
1.5°C-consistent pathways will be difficult to realize, especially inovershoot scenarios. The social costs and benefits of 1.5°C-consistentpathways depend on the depth and timing of policy responses andtheir alignment with short term and long-term development objectives,
through policy packages that bring together a diversity of policy
instruments, including public investment (Grubb et al., 2014; Winkler
and Dubash, 2015; Campiglio, 2016).
Whatever its potential long-term benefits, a transition to a 1.5°C
world may suffer from a lack of broad political and public support,
if it exacerbates existing short-term economic and social tensions,
including unemployment, poverty, inequality, financial tensions,competitiveness issues and the loss of economic value of carbonintensive assets (Mercure et al., 2018). The challenge is therefore how
to strengthen climate policies without inducing economic collapse or
hardship, and to make them contribute to reducing some of the ‘fault
lines’ of the world economy (Rajan, 2011).
This chapter reviews literature addressing the alignment of climate
with other public policies (e.g., fiscal, trade, industrial, monetary, urban
planning, infrastructure, and innovation) and with a greater access tobasic needs and services, defined by the SDGs. It also reviews how
de-risking low-emission investments and the evolution of the financial
intermediation system can help reduce the ‘savings glut’ (Arezki etal., 2016) and the gap between cash balances and long-term assets(Aglietta et al., 2015b) to support more sustainable and inclusive
growth.
As the transitions associated with 1.5°C-consistent pathways require
accelerated and coordinated action, in multiple systems across all
world regions, they are inherently exposed to risks of freeriding andmoral hazards. A key governance challenge is how the convergenceof voluntary domestic policies can be organized via aligned global,
national and sub-national governance, based on reciprocity (Ostrom
and Walker, 2005) and partnership (UN, 2016), and how different
actors and processes in climate governance can reinforce each otherto enable this (Gupta, 2014; Andonova et al., 2017). The emergence ofpolycentric sources of climate action and transnational and subnational
networks that link these efforts (Abbott, 2012) offer the opportunity toexperiment and learn from different approaches, thereby accelerating
approaches led by national governments (Cole, 2015; Jordan et al.,2015).

319

4

Chapter 4

Strengthening and Implementing the Global Response

Section 4.2 of this chapter outlines existing rates of change andattributes of accelerated change. Section 4.3 identifies global systems,
and their components, that offer options for this change. Section 4.4
documents the enabling conditions that influence the feasibility of
those options, including economic, financial and policy instruments that

could trigger the transition to 1.5°C-consistent pathways. Section 4.5
assesses mitigation and adaptation options for feasibility, strategies for
implementation and synergies and trade-offs between mitigation andadaptation.

4.2

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

4.2.1

Implications for Implementation of
1.5°C-Consistent Pathways

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-consistentpathways (see Table 4.1).

The 1.5°C-consistent pathways assessed in Chapter 2 form thebasis 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 sharedby 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

4

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 forthe 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). 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 offeasibility and enablers, including governance, institutions, and policy
instruments addressed in Sections 4.3 and 4.4.

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’ and ‘1.5C-highOS’ 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).
EnergyOther
Studies
2030

IAM
Pathways
2050

Other
Studies
2050

320Transport

Industry

Changein energy
demand forbuildings (2010baseline) [%]Share of lowcarbon fuels(electricity,hydrogen andbiofuel) intransport [%]Share ofelectricity intransport [%]54 (65; 47)

0 (7; −7) [42]12 (18; 9) [29]5 (7; 3) [49]42 (55; 34) [42]43 (54; 37)−17 (−12; −20) [29]7 (8; 6) [23]3 (5; 3)18 (28; −13) [29]Numberofscenarios

Share ofrenewables
in primaryenergy [%]Share ofrenewables inelectricity [%]1.5°C-no or low-OS

50

29 (37; 26)

1.5°C-high-OS

35

24 (27; 20)

S1

29

58

−8

S2

29

48

−14

S5

14

25

Pathways

IAM
Pathways
2030

Buildings

LED

37

60

Löffler et al. (2017)

46

79

IEA (2017c) (ETP)

31

47

2

IEA (2017g) (WEM)

27

50

–6

Industrial
emissions
reductions(2010 baseline)
[%]

4

49

5

4

19

3

1

30

21

42

14

5

22

17

6

15

1.5°C-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]1.5°C-high-OS

35

38 (44; 27) [23]18 (23; 14)

68 (81; 54) [29]

34

74

26

23

73

53

10

62 (68; 47)

82 (88; 64)−37 (−13; −51) [29]

S1

58

81

−21

S2

53

63

−25

S5

67

70

LED

73

77

Löffler et al. (2017)

100

100

IEA (2017c) (ETP)

58

74

5

IEA (2017g) (WEM)

47

69

−5

45

59

91

55

30

57

58

32

55

Strengthening and Implementing the Global Response

4.2.1.1

Challenges and Opportunities for Mitigation Alongthe Reviewed Pathways

Greater scale, speed and change in investment patterns. Thereis agreement in the literature reviewed by Chapter 2 that staying
below 1.5°C would entail significantly greater transformation in termsof energy systems, lifestyles and investments patterns comparedto 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−1 globally with an average of about 3.5 trillion
USD yr−1 over 2016–2050 (see Figure 2.27). This can be compared toan average of about 3.0 trillion USD yr−1 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−1), wind (0.1–0.35 trillion USD yr−1),
nuclear (0.1–0.25 trillion USD yr−1), and transmission, distribution,and storage (0.3–1.3 trillion USD yr−1). In contrast, investments infossil 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−1 over the period 2016–2050, with investments in unabatedcoal generation projected to halt by 2030 in most 1.5°C-consistentpathways (Chapter 2, Section 2.5.2). Estimates of investments inother infrastructure are currently unavailable, but they could beconsiderably 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 paceof 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-consistentpathways would require robust, stringent and urgent transformative
policy interventions targeting the decarbonization of energy supply,
electrification, fuel switching, energy efficiency, land-use change, andlifestyles (Chapter 2, Section 2.5, 4.4.2, 4.4.3). Examples of effective
approaches to integrate mitigation with adaptation in the context ofsustainable 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) andtransdisciplinary knowledge systems (Bendito and Barrios, 2016).
Yet, even with good policy design and effective implementation,
1.5°C-consistent pathways would incur higher costs. Projections of themagnitudes of global economic costs associated with 1.5°C-consistentpathways and their sectoral and regional distributions from the

Chapter 4

currently assessed literature are scant, yet suggestive. For example, IAM
simulations assessed in Chapter 2 project (with a probability greaterthan 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-consistentpathway (Chapter 2, Section 2.5.2, Figure 2.26). Managing thesecosts 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., 2017;
Pollitt, 2018; Sands, 2018; Siegmeier et al., 2018).
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). Nonetheless, literature on potential synergies
and trade-offs between 1.5°C-consistent mitigation pathways andsustainable 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 theinstitutional and policy responses to deal with these challenges isdiscussed in Section 4.4 together with the linkage between disruptivechanges in the energy sector and structural changes in otherinfrastructure (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 ispresented in Chapter 5.

4.2.1.2

Implications for Adaptation Along the Reviewed
Pathways

4

Climate variability and uncertainties in the underlying assumptionsin Chapter 2’s IAMs as well as in model comparisons complicate
discerning the implications for climate impacts, adaptation options andavoided adaptation investments at the global level of 2°C compared to
1.5°C warming (James et al., 2017; Mitchell et al., 2017).
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 theemissions 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 andthe lengthening of regional dry spells have negative implications for
agricultural yields depending on crop types and world regions (see forexample 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 temperatureinduced bleaching (Chapter 3, Box 3.4).
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4.2.2

System Transitions and Rates of Change

Society-wide transformation involves socio-technical transitions
and social-ecological resilience (Gillard et al., 2016). 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). In this chapter, transformativechange is framed in mitigation around socio-technical transitions, andin 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-consistentpathways and adaptation needs associated with warming of 1.5°C
imply both incremental and rapid, disruptive and transformativechanges.

4.2.2.1

Mitigation: historical rates of change and stateof decoupling

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

4

Some studies conduct a de-facto validation of IAM projections. For CO2
emission intensity over 1990–2010, this resulted in the IAMs projecting
declining emission intensities while actual observations showed anincrease. For individual technologies (in particular solar energy), IAM
projections have been conservative regarding deployment rates andcost reductions (Creutzig et al., 2017), suggesting that IAMs do notalways impute actual rates of technological change resulting frominfluence 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).
Other studies extrapolate historical trends into the future (Höök et al.,2011; Fouquet, 2016), or contrast the rates of change associated withspecific temperature limits in IAMs (such as those in Chapter 2) withhistorical trends to investigate plausibility of emission pathways andassociated temperature limits (Wilson et al., 2013; Gambhir et al., 2017;
Napp et al., 2017). 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; vanSluisveld et al., 2015). This bias is consistent with the findings fromthe ‘validation’ studies cited above, suggesting that IAMs may underreport the potential for supply-side technological change assumed
in 1.5°-consistent pathways, but may be more optimistic about thesystemic ability to realize incremental changes in reduction of emission
intensity as a consequence of favourable energy efficiency paybacktimes (Wilson et al., 2013). This finding suggests that barriers andenablers other than costs and climate limits play a role in technologicalchange, as also found in the innovation literature (Hekkert et al., 2007;
Bergek et al., 2008; Geels et al., 2016b).
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Strengthening and Implementing the Global Response

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). 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 etal., 2013), but in 2015 and 2016 total global GHG emissions havedecoupled absolutely from economic growth (IEA, 2017g; Peters
et al., 2017). A longer data trend would be needed before stabledecoupling 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 UnitedStates and Australia, and more recently in China (Newman, 2017).
In 2017, decoupling in China reversed by 2% due to a drought
and subsequent replacement of hydropower with coal-fired power(Tollefson, 2017), but this reversal is expected to be temporary (IEA,
2017c). Oil consumption in China is still rising slowly, but absolute
decoupling is ongoing in megacities like Beijing (Gao and Newman,2018) (see Box 4.9).

4.2.2.2

Transformational adaptation

In some regions and places, incremental adaptation would not
be sufficient to mitigate the impacts of climate change on socialecological 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). Transformational adaptation refers to actions aiming
at adapting to climate change resulting in significant changes instructure or function that go beyond adjusting existing practices(Dowd et al., 2014; IPCC, 2014a; Few et al., 2017), including
approaches that enable new ways of decision-making on adaptation(Colloff et al., 2017). Few studies have assessed the potentiallytransformative character of adaptation options (Pelling et al., 2015;
Rippke et al., 2016; Solecki et al., 2017), especially in the context ofwarming of 1.5°C.
Transformational adaptation can be adopted at a large scale, can leadto new strategies in a region or resource system, transform places
and potentially shift locations (Kates et al., 2012). Some systems
might require transformational adaptation at 1.5°C. Implementing
adaptation policies in anticipation of 1.5°C would requiretransformation and flexible planning of adaptation (sometimes
called adaptation pathways) (Rothman et al., 2014; Smucker etal., 2015; Holland, 2017; Gajjar et al., 2018), an understanding of
the varied stakeholders involved and their motives, and knowledgeof less visible aspects of vulnerability based on social, cultural,political, and economic factors (Holland, 2017). Transformational
adaptation would seek deep and long-term societal changes that
influence sustainable development (Chung Tiam Fook, 2017; Few
et al., 2017).
Adaptation requires multidisciplinary approaches integrating
scientific, technological and social dimensions. For example, aStrengthening and Implementing the Global Response

framework for transformational adaptation and the integrationof mitigation and adaptation pathways can transform rural
indigenous communities to address risks of climate change andother stressors (Thornton and Comberti, 2017). 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 degradationof natural resources (Thornton and Comberti, 2017). Instead, they
are now opening stores, hotels, and tea shops. In another case, thearrival of an oil pipeline altered traditional Alaskan communities’
livelihoods. With growth of oil production, investments were madefor rural development. A later drop in oil production decreased theseinvestments. 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).

4.2.2.3

Chapter 4

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). This may have consequences for fossil
fuels that are rendered ‘unburnable’ (McGlade and Ekins, 2015) andfossil fuel-fired power and industry assets that would become obsolete(Caldecott, 2017; Farfan and Breyer, 2017). 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)or large-scale disruptions, is seldom an insular or discrete process(Sterling et al., 2017). This finding informs the multidimensional natureof 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). Also political, social and technological influences onenergy transitions, for example, can accelerate them faster than narrow
techno-economic analysis suggests is possible (Kern and Rogge, 2016),
but could also introduce new constraints and risks (Geels et al., 2016b;
Sovacool, 2016; Eyre et al., 2018).

Disruptive innovation

Demand-driven disruptive innovations that emerge as the productof political and social changes across multiple scales can betransformative (Seba, 2014; Christensen et al., 2015; Green andNewman, 2017a). 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). Rapid sociotechnical change has been observed in the solar industry (Creutzig etal. (2017). Similar changes to socio-ecological systems can stimulate
adaptation and mitigation options that lead to more climate-resilientsystems (Adger et al., 2005; Ostrom, 2009; Gillard et al., 2016) (seethe Alaska and Nepal examples in Section 4.2.2.2). The increase inroof-top solar and energy storage technology as well as the increase inpassive housing and net zero-emissions buildings are further examples
of such disruptions (Green and Newman, 2017b). Both roof-top solarand energy storage have benefitted from countries’ economic growthstrategies and associated price declines in photovoltaic technologies,
particularly in China (Shrivastava and Persson, 2018), as well as fromnew information and communication technologies (Koomey et al.,2013), 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).
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), and for rapid andtransformational change (Cole, 2015; Geels et al., 2016b; Hallegatteand Mach, 2016). Examples of co-benefits include gender equality,
agricultural productivity (Nyantakyi-Frimpong and Bezner-Kerr, 2015),
reduced indoor air pollution (Satterthwaite and Bartlett, 2017), flood
buffering (Colenbrander et al., 2017), livelihood support (Shaw etal., 2014; Ürge-Vorsatz et al., 2014), economic growth (GCEC, 2014;
Stiglitz et al., 2017), social progress (Steg et al., 2015; Hallegatte andMach, 2016) and social justice (Ziervogel et al., 2017; Patterson etal., 2018).

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

4.3

Systemic Changes for 1.5°C-Consistent
Pathways

Section 4.2 emphasizes the importance of systemic change for
1.5°C-consistent pathways. This section translates this into fourmain system transitions: energy, land and ecosystem, urban andinfrastructure, and industrial system transitions. This section assesses
the mitigation, adaptation and carbon dioxide removal options thatoffer the potential for such change within those systems, based on
options identified by Chapter 2 and risks and impacts in Chapter 3.
The section puts more emphasis on those adaptation options (Sections
4.3.1–4.3.5) and mitigation options (Sections 4.3.1–4.3.4, 4.3.6
and 4.3.7) that are 1.5°C-relevant and have developed considerably
since AR5. They also form the basis for the mitigation and adaptation
feasibility assessments in Section 4.5. Section 4.3.8 discusses solar
radiation modification methods.
This section emphasizes that no single solution or option can enable a
global transition to 1.5°C-consistent pathways or adapting to projected
impacts. Rather, accelerating change, much of which is already startingor underway, in multiple global systems, simultaneously and at different
scales, could provide the impetus for these system transitions. The
feasibility of individual options as well as the potential for synergies
and reducing trade-offs will vary according to context and the local
enabling conditions. These are explored at a high level in Section 4.5.
Policy packages that bring together multiple enabling conditions canprovide building blocks for a strategy to scale up implementation andintervention impacts.

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Chapter 4

4.3.1

Energy System Transitions

This section discusses the feasibility of mitigation and adaptation
options related to the energy system transition. Only options relevant
to 1.5°C and with significant changes since AR5 are discussed, whichmeans that for options like hydropower and geothermal energy,
the chapter refers to AR5 and does not provide a discussion. Sociotechnical inertia of energy options for 1.5°C-consistent pathways are
increasingly being surmounted as fossil fuels start to be phased out.
Supply-side mitigation and adaptation options and energy demandside options, including energy efficiency in buildings and transportation,
are discussed in Section 4.3.3; options around energy use in industry
are discussed in Section 4.3.4.
Section 4.5 assesses the feasibility in a systematic manner based on
the approach outlined in Cross-Chapter Box 3 in Chapter 1.

4.3.1.1

Renewable electricity: solar and wind

All renewable energy options have seen considerable advances over
the years since AR5, but solar energy and both onshore and offshore
wind energy have had dramatic growth trajectories. They appear wellunderway to contribute to 1.5°C-consistent pathways (IEA, 2017c;
IRENA, 2017b; REN21, 2017).

4

Strengthening and Implementing the Global Response(Yenneti and Day, 2016; Rand and Hoen, 2017; Gorayeb et al., 2018)that raise landscape management (Nadaï and Labussière, 2017) anddistributional justice (Yenneti and Day, 2016) challenges. Research
indicates that financial participation and community engagement canbe effective in mitigating resistance (Brunes and Ohlhorst, 2011; Rand
and Hoen, 2017) (see Section 4.4.3).
Bottom-up studies estimating the use of renewable energy in the future,
either at the global or at the national level, are plentiful, especially in
the grey literature. It is hotly debated whether a fully renewable energy
or electricity system, with or without biomass, is possible (Jacobson etal., 2015, 2017) or not (Clack et al., 2017; Heard et al., 2017), and by
what year. Scale-up estimates vary with assumptions about costs andtechnological maturity, as well as local geographical circumstances
and the extent of storage used (Ghorbani et al., 2017; REN21, 2017).
Several countries have adopted targets of 100% renewable electricity(IEA, 2017c) as this meets multiple social, economic and environmental
goals and contributes to mitigation of climate change (REN21, 2017).

4.3.1.2

Bioenergy and biofuels

The feasibility of renewable energy options depends to a large
extent on geophysical characteristics of the area where the option is
implemented. However, technological advances and policy instruments
make renewable energy options increasingly attractive in other areas.
For example, solar PV is deployed commercially in areas with low solar
insolation, like northwest Europe (Nyholm et al., 2017). Feasibility also
depends on grid adaptations (e.g., storage, see below) as renewables
grow (IEA, 2017c). For regions with high energy needs, such asindustrial areas (see Section 4.3.4), high-voltage DC transmission
across long distances would be needed (MacDonald et al., 2016).

Bioenergy is renewable energy from biomass. Biofuel is biomass-basedenergy used in transport. Chapter 2 suggests that pathways limiting
warming to 1.5°C would enable supply of 67–310 (median 150)
EJ yr−1 (see Table 2.8) from biomass. Most scenarios find that bioenergy
is combined with carbon dioxide capture and storage (CCS, BECCS) if it
is available but also find robust deployment of bioenergy independentof the availability of CCS (see Chapter 2, Section 2.3.4.2 and Section
4.3.7 for a discussion of BECCS). Detailed assessments indicate that
deployment is similar for pathways limiting global warming to below
2°C (Chum et al., 2011; P. Smith et al., 2014; Creutzig et al., 2015b).
There is however high agreement that the sustainable bioenergy
potential in 2050 would be restricted to around 100 EJ yr−1 (Sladeet al., 2014; Creutzig et al., 2015b). Sustainable deployment at suchor higher levels envisioned by 1.5°C-consistent pathways may put
significant pressure on available land, food production and prices(Popp et al., 2014b; Persson, 2015; Kline et al., 2017; Searchinger etal., 2017), preservation of ecosystems and biodiversity (Creutzig etal., 2015b; Holland et al., 2015; Santangeli et al., 2016), and potential
water and nutrient constraints (Gerbens-Leenes et al., 2009; Gheewala
et al., 2011; Bows and Smith, 2012; Smith and Torn, 2013; Bonsch etal., 2016; Lampert et al., 2016; Mouratiadou et al., 2016; Smith et al.,2016b; Wei et al., 2016; Mathioudakis et al., 2017); but there is still low
agreement on these interactions (Robledo-Abad et al., 2017). Someof the disagreement on the sustainable capacity for bioenergy stems
from global versus local assessments. Global assessments may mask
local dynamics that exacerbate negative impacts and shortages whileat the same time niche contexts for deployment may avoid trade-offs
and exploit co-benefits more effectively. In some regions of the world(e.g., the case of Brazilian ethanol, see Box 4.7, where land may be lessof a constraint, the use of bioenergy is mature and the industry is welldeveloped), land transitions could be balanced with food production
and biodiversity to enable a global impact on CO2 emissions (Jaiswal
et al., 2017).

Another important factor affecting feasibility is public acceptance, inparticular for wind energy and other large-scale renewable facilities

The carbon intensity of bioenergy, key for both bioenergy as anemission-neutral energy option and BECCS as a CDR measure, is

The largest growth driver for renewable energy since AR5 has been
the dramatic reduction in the cost of solar photovoltaics (PV) (REN21,
2017). This has made rooftop solar competitive in sunny areas between
45° north and south latitude (Green and Newman, 2017b), though
IRENA (2018) suggests it is cost effective in many other places too. Solar
PV with batteries has been cost effective in many rural and developing
areas (Pueyo and Hanna, 2015; Szabó et al., 2016; Jimenez, 2017),
for example 19 million people in Bangladesh now have solar-battery
electricity in remote villages and are reporting positive experiences onsafety and ease of use (Kabir et al., 2017). Small-scale distributed energy
projects are being implemented in developed and developing cities
where residential and commercial rooftops offer potential for consumers
becoming producers (called prosumers) (ACOLA, 2017; Kotilainen and
Saari, 2018). Such prosumers could contribute significantly to electricity
generation in sun-rich areas like California (Kurdgelashvili et al., 2016)or sub-Saharan Africa in combination with micro-grids and mini-grids(Bertheau et al., 2017). It could also contribute to universal energy
access (SDG 7) as shown by (IEA, 2017c).

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Strengthening and Implementing the Global Response

still a matter of debate (Buchholz et al., 2016; Liu et al., 2018) anddepends on management (Pyörälä et al., 2014; Torssonen et al., 2016;
Baul et al., 2017; Kilpeläinen et al., 2017); direct and indirect land-use
change emissions (Plevin et al., 2010; Schulze et al., 2012; Harris etal., 2015; Repo et al., 2015; DeCicco et al., 2016; Qin et al., 2016)2; thefeedstock considered; and time frame (Zanchi et al., 2012; Daioglou etal., 2017; Booth, 2018; Sterman et al., 2018), as well as the availabilityof coordinated policies and management to minimize negativeside effects and trade-offs, particularly those around food security(Stevanović et al., 2017) and livelihood and equity considerations(Creutzig et al., 2013; Calvin et al., 2014) .
Biofuels are a part of the transport sector in some cities and countries,
and may be deployed as a mitigation option for aviation, shippingand freight transport (see Section 4.3.3.5) as well as industrial
decarbonization (IEA, 2017g) (Section 4.3.4), though only Brazil has
mainstreamed ethanol as a substantial, commercial option. Lower
emissions and reduced urban air pollution have been achieved thereby use of ethanol and biodiesel as fuels (Hill et al., 2006; Salvo et al.,2017) (see Box 4.7).

4.3.1.3

Nuclear energy

Many scenarios in Chapter 2 and in AR5 (Bruckner et al., 2014)
project an increase in the use of nuclear power, while others projecta decrease. The increase can be realized through existing mature
nuclear technologies or new options (generation III/IV reactors,
breeder reactors, new uranium and thorium fuel cycles, small reactors
or nuclear cogeneration).
Even though scalability and speed of scaling of nuclear plants havehistorically been high in many nations, such rates are currently notachieved anymore. In the 1960s and 1970s, France implemented a
programme to rapidly get 80% of its power from nuclear in about
25 years (IAEA, 2018), but the current time lag between the decisiondate and the commissioning of plants is observed to be 10-19 years(Lovins et al., 2018). The current deployment pace of nuclear energy isconstrained by social acceptability in many countries due to concerns
over risks of accidents and radioactive waste management (Bruckner
et al., 2014). Though comparative risk assessment shows health risks
are low per unit of electricity production (Hirschberg et al., 2016), andland requirement is lower than that of other power sources (Cheng andHammond, 2017), the political processes triggered by societal concerns
depend on the country-specific means of managing the politicaldebates around technological choices and their environmental impacts(Gregory et al., 1993). Such differences in perception explain why the2011 Fukushima incident resulted in a confirmation or acceleration ofphasing out nuclear energy in five countries (Roh, 2017) while 30 othercountries have continued using nuclear energy, amongst which 13 arebuilding new nuclear capacity, including China, India and the United
Kingdom (IAEA, 2017; Yuan et al., 2017).
Costs of nuclear power have increased over time in some developed
nations, principally due to market conditions where increased2

Chapter 4

investment risks of high-capital expenditure technologies havebecome significant. ‘Learning by doing’ processes often failed tocompensate for this trend because they were slowed down by theabsence of standardization and series effects (Grubler, 2010). What
the costs of nuclear power are and have been is debated in theliterature (Lovering et al., 2016; Koomey et al., 2017). Countries withliberalized markets that continue to develop nuclear employ de-risking
instruments through long-term contracts with guaranteed sale prices(Finon and Roques, 2013). For instance, the United Kingdom works
with public guarantees covering part of the upfront investment costsof newly planned nuclear capacity. This dynamic differs in countries
such as China and South Korea, where monopolistic conditions in
the electric system allow for reducing investment risks, deploying
series effects and enhancing the engineering capacities of users
due to stable relations between the security authorities and builders(Schneider et al., 2017).
The safety of nuclear plants depends upon the public authorities ofeach country. However, because accidents affect worldwide public
acceptance of this industry, questions have been raised about the riskof economic and political pressures weakening the safety of the plants(Finon, 2013; Budnitz, 2016). This raises the issue of international
governance of civil nuclear risks and reinforced international
cooperation involving governments, companies and engineering(Walker and Lönnroth, 1983; Thomas, 1988; Finon, 2013), based on theexperience of the International Atomic Energy Agency.

4.3.1.4

Energy storage

The growth in electricity storage for renewables has been around grid
flexibility resources (GFR) that would enable several places to source
more than half their power from non-hydro renewables (Komarnicki,
2016). Ten types of GFRs within smart grids have been developed(largely since AR5)(Blaabjerg et al., 2004; IRENA, 2013; IEA, 2017d;
Majzoobi and Khodaei, 2017), though how variable renewables
can be balanced without hydro or natural gas-based power backup at a larger scale would still need demonstration. Pumped hydrocomprised 150 GW of storage capacity in 2016, and grid-connectedbattery storage just 1.7 GW, but the latter grew between 2015 to2016 by 50% (REN21, 2017). Battery storage has been the main
growth feature in energy storage since AR5 (Breyer et al., 2017).
This appears to the result of significant cost reductions due to mass
production for electric vehicles (EVs) (Nykvist and Nilsson, 2015; Dharet al., 2017). Although costs and technical maturity look increasingly
positive, the feasibility of battery storage is challenged by concerns
over the availability of resources and the environmental impacts of
its production (Peters et al., 2017). Lithium, a common element in
the earth’s crust, does not appear to be restricted and large increases
in production have happened in recent years with eight new minesin Western Australia where most lithium is produced (GWA, 2016).
Emerging battery technologies may provide greater efficiency andrecharge rates (Belmonte et al., 2016) but remain significantly more
expensive due to speed and scale issues compared to lithium ion
batteries (Dhar et al., 2017; IRENA, 2017a).

While there is high agreement that indirect land use change (iLUC) could occur, there is low agreement about the actual extent of iLUC (P. Smith et al., 2014; Verstegen etal., 2015; Zilberman, 2017)

325

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Chapter 4

Research and demonstration of energy storage in the form of thermaland chemical systems continues, but large-scale commercial systems
are rare (Pardo et al., 2014). Renewably derived synthetic liquid (likemethanol and ammonia) and gas (like methane and hydrogen) are
increasingly being seen as a feasible storage options for renewableenergy (producing fuel for use in industry during times when solar
and wind are abundant) (Bruce et al., 2010; Jiang et al., 2010; Ezeji,2017) but, in the case of carbonaceous storage media, would need arenewable source of carbon to make a positive contribution to GHG
reduction (von der Assen et al., 2013; Abanades et al., 2017) (see also
Section 4.3.4.5). The use of electric vehicles as a form of storage has
been modelled and evaluated as an opportunity, and demonstrations
are emerging (Dhar et al., 2017; Green and Newman, 2017a), butchallenges to upscaling remain.

4.3.1.5

Strengthening and Implementing the Global Response

plants can reduce emissions and water needs (Eisenack and Stecker,
2012; van Vliet et al., 2016), but applying CCS would increase water
consumption (Koornneef et al., 2012). The technological, economic,
social and institutional feasibility of efficiency improvements is high,but insufficient to limit temperature rise to 1.5°C (van Vliet et al., 2016).
In addition, a number of options for water cooling management
systems have been proposed, such as hydraulic measures (Eisenackand Stecker, 2012) and alternative cooling technologies (Chandel et al.,2011; Eisenack and Stecker, 2012; Bartos and Chester, 2015; Murrant
et al., 2015; Bustamante et al., 2016; van Vliet et al., 2016; Huang et al.,2017b). There is high agreement on the technological and economic
feasibility of these technologies, as their absence can severely impact
the functioning of the power plant as well as safety and security
standards.

Options for adapting electricity systems to 1.5°C
4.3.1.6

Climate change has started to disrupt electricity generation and, if
climate change adaptation options are not considered, it is predictedthat these disruptions will be lengthier and more frequent (JahandidehTehrani et al., 2014; Bartos and Chester, 2015; Kraucunas et al., 2015;
van Vliet et al., 2016). Adaptation would both secure vulnerable
infrastructure and ensure the necessary generation capacity (Minvilleet al., 2009; Eisenack and Stecker, 2012; Schaeffer et al., 2012; Cortekar
and Groth, 2015; Murrant et al., 2015; Panteli and Mancarella, 2015;
Goytia et al., 2016). The literature shows high agreement that climatechange impacts need to be planned for in the design of any kind of
infrastructure, especially in the energy sector (Nierop, 2014), includinginterdependencies with other sectors that require electricity to function,
including water, data, telecommunications and transport (Fryer, 2017).

4

Recent research has developed new frameworks and models that
aim to assess and identify vulnerabilities in energy infrastructure
and create more proactive responses (Francis and Bekera, 2014;
Ouyang and Dueñas-Osorio, 2014; Arab et al., 2015; Bekera andFrancis, 2015; Knight et al., 2015; Jeong and An, 2016; Panteli et al.,2016; Perrier, 2016; Erker et al., 2017; Fu et al., 2017). Assessments ofenergy infrastructure adaptation, while limited, emphasize the needfor redundancy (Liu et al., 2017). The implementation of controllable
and islandable microgrids, including the use of residential batteries,
can increase resiliency, especially after extreme weather events (Qazi
and Young Jr., 2014; Liu et al., 2017). Hybrid renewables-based powersystems with non-hydro capacity, such as with high-penetration wind
generation, could provide the required system flexibility (Canales etal., 2015). Overall, there is high agreement that hybrid systems, taking
advantage of an array of sources and time of use strategies, can help
make electricity generation more resilient (Parkinson and Djilali, 2015),
given that energy security standards are in place (Almeida Prado etal., 2016).
Interactions between water and energy are complex (IEA, 2017g).
Water scarcity patterns and electricity disruptions will differ acrossregions. There is high agreement that mitigation and adaptation
options for thermal electricity generation (if that remains fitted with
CCS) need to consider increasing water shortages, taking into accountother factors such as ambient water resources and demand changes in
irrigation water (Hayashi et al., 2018). Increasing the efficiency of power
326

Carbon dioxide capture and storage in the power sector

The AR5 (IPCC, 2014b) as well as Chapter 2, Section 2.4.2, assign
significant emission reductions over the course of this century to CO2
capture and storage (CCS) in the power sector. This section focuseson CCS in the fossil-fuelled power sector; Section 4.3.4 discusses
CCS in non-power industry, and Section 4.3.7 discusses bioenergy
with CCS (BECCS). Section 2.4.2 puts the cumulative CO2 storedfrom fossil-fuelled power at 410 (199–470 interquartile range) GtCO2
over this century. Such modelling suggests that CCS in the power
sector can contribute to cost-effective achievement of emission
reduction requirements for limiting warming to 1.5°C. CCS may alsooffer employment and political advantages for fossil fuel-dependent
economies (Kern et al., 2016), but may entail more limited co-benefits
than other mitigation options (that, e.g., generate power) and therefore
relies on climate policy incentives for its business case and economic
feasibility. Since 2017, two CCS projects in the power sector capture
2.4 MtCO2 annually, while 30 MtCO2 is captured annually in all CCS
projects (Global CCS Institute, 2017).
The technological maturity of CO2 capture options in the power sectors
has improved considerably (Abanades et al., 2015; Bui et al., 2018),
but costs have not come down between 2005 and 2015 due to limited
learning in commercial settings and increased energy and resources
costs (Rubin et al., 2015). Storage capacity estimates vary greatly, but
Section 2.4.2 as well as literature (V. Scott et al., 2015) indicate thatperhaps 10,000 GtCO2 could be stored in underground reservoirs.
Regional availability of this may not be sufficient, and it requires
efforts to have this storage and the corresponding infrastructure
available at the necessary rates and times (de Coninck and Benson,2014). CO2 retention in the storage reservoir was recently assessed
as 98% over 10,000 years for well-managed reservoirs, and 78%
for poorly regulated ones (Alcalde et al., 2018). A paper reviewing
42 studies on public perception of CCS (Seigo et al., 2014) found thatsocial acceptance of CCS is predicted by trust, perceived risks andbenefits. The technology itself mattered less than the social context ofthe project. Though insights on communication of CCS projects to thegeneral public and inhabitants of the area around the CO2 storage sites
have been documented over the years, project stakeholders are notconsistently implementing these lessons, although some projects haveobserved good practices (Ashworth et al., 2015).

Strengthening and Implementing the Global Response

CCS in the power sector is hardly being realized at scale, mainly
because the incremental costs of capture, and the development oftransport and storage infrastructures are not sufficiently compensatedby market or government incentives (IEA, 2017c). In the two full-scale
projects in the power sector mentioned above, part of the capture costs
are compensated for by revenues from enhanced oil recovery (EOR)(Global CCS Institute, 2017), demonstrating that EOR helps developing
CCS further. EOR is a technique that uses CO2 to mobilize more oil
out of depleting oil fields, leading to additional CO2 emissions by
combusting the additionally recovered oil (Cooney et al., 2015).

4.3.2

Land and Ecosystem Transitions

This section assesses the feasibility of mitigation and adaptation options
related to land use and ecosystems. Land transitions are grouped aroundagriculture and food, ecosystems and forests, and coastal systems.

4.3.2.1

Agriculture and food

In a 1.5°C world, local yields are projected to decrease in tropicalregions that are major food producing areas of the world (West Africa,
Southeast Asia, South Asia, and Central and northern South America)(Schleussner et al., 2016). Some high-latitude regions may benefit fromthe combined effects of elevated CO2 and temperature because theiraverage temperatures are below optimal temperature for crops. In bothcases there are consequences for food production and quality (CrossChapter Box 6 in Chapter 3 on Food Security), conservation agriculture,irrigation, food wastage, bioenergy and the use of novel technologies.
Food production and quality. Increased temperatures, including
1.5°C warming, would affect the production of cereals such as wheat
and rice, impacting food security (Schleussner et al., 2016). There is
medium agreement that elevated CO2 concentrations can change foodcomposition, with implications for nutritional security (Taub et al.,2008; Högy et al., 2009; DaMatta et al., 2010; Loladze, 2014; De Souzaet al., 2015), with the effects being different depending on the region(Medek et al., 2017).
Meta-analyses of the effects of drought, elevated CO2, and temperatureconclude that at 2°C local warming and above, aggregate production ofwheat, maize, and rice are expected to decrease in both temperate andtropical areas (Challinor et al., 2014). These production losses could belowered if adaptation measures are taken (Challinor et al., 2014), suchas developing varieties better adapted to changing climate conditions.
Adaptation options can help ensure access to sufficient, quality food.
Such options include conservation agriculture, improved livestock
management, increasing irrigation efficiency, agroforestry andmanagement of food loss and waste. Complementary adaptation andmitigation options, for example, the use of climate services (Section
4.3.5), bioenergy (Section 4.3.1) and biotechnology (Section 4.4.4) can
also serve to reduce emissions intensity and the carbon footprint of foodproduction.
Conservation agriculture (CA) is a soil management approachthat reduces the disruption of soil structure and biotic processes byminimising tillage. A recent meta-analysis showed that no-till practices

Chapter 4

work well in water-limited agroecosystems when implemented jointlywith residue retention and crop rotation, but when used independently,
may decrease yields in other situations (Pittelkow et al., 2014).
Additional climate adaptations include adjusting planting times andcrop varietal selection and improving irrigation efficiency. Adaptations
such as these may increase wheat and maize yields by 7–12% underclimate change (Challinor et al., 2014). CA can also help build adaptive
capacity (medium evidence, medium agreement) (H. Smith et al., 2017;
Pradhan et al., 2018) and have mitigation co-benefits through improved
fertiliser use or efficient use of machinery and fossil fuels (Harvey et al.,2014; Cui et al., 2018; Pradhan et al., 2018). CA practices can also raisesoil carbon and therefore remove CO2 from the atmosphere (Aguilera
et al., 2013; Poeplau and Don, 2015; Vicente-Vicente et al., 2016).
However, CA adoption can be constrained by inadequate institutionalarrangements and funding mechanisms (Harvey et al., 2014; Baudron
et al., 2015; Li et al., 2016; Dougill et al., 2017; H. Smith et al., 2017).
Sustainable intensification of agriculture consists of agricultural
systems with increased production per unit area but with managementof the range of potentially adverse impacts on the environment (Pretty
and Bharucha, 2014). Sustainable intensification can increase theefficiency of inputs and enhance health and food security (Ramankutty
et al., 2018).
Livestock management. Livestock are responsible for more GHG
emissions than all other food sources. Emissions are caused by feed
production, enteric fermentation, animal waste, land-use change
and livestock transport and processing. Some estimates indicate thatlivestock supply chains could account for 7.1 GtCO2 per year, equivalent
to 14.5% of global anthropogenic greenhouse gas emissions (Gerber
et al., 2013). Cattle (beef, milk) are responsible for about two-thirdsof that total, largely due to methane emissions resulting from rumen
fermentation (Gerber et al., 2013; Opio et al., 2013).
Despite ongoing gains in livestock productivity and volumes, theincrease of animal products in global diets is restricting overall
agricultural efficiency gains because of inefficiencies in the conversionof agricultural primary production (e.g., crops) in the feed-animal
products pathway (Alexander et al., 2017), offsetting the benefits ofimprovements in livestock production systems (Clark and Tilman, 2017).
There is increasing agreement that overall emissions from food systems
could be reduced by targeting the demand for meat and other livestock
products, particularly where consumption is higher than suggested
by human health guidelines. Adjusting diets to meet nutritionaltargets could bring large co-benefits, through GHG mitigation andimprovements in the overall efficiency of food systems (Erb et al., 2009;
Tukker et al., 2011; Tilman and Clark, 2014; van Dooren et al., 2014;
Ranganathan et al., 2016). Dietary shifts could contribute one-fifth ofthe mitigation needed to hold warming below 2°C, with one-quarter of
low-cost options (Griscom et al., 2017). There, however, remains limited
evidence of effective policy interventions to achieve such large-scale
shifts in dietary choices, and prevailing trends are for increasing rather
than decreasing demand for livestock products at the global scale(Alexandratos and Bruinsma, 2012; OECD/FAO, 2017). How the roleof dietary shift could change in 1.5°C-consistent pathways is also not
clear (see Chapter 2).
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Chapter 4

Adaptation of livestock systems can include a suite of strategies suchas using different breeds and their wild relatives to develop a genetic
pool resilient to climatic shocks and longer-term temperature shifts(Thornton and Herrero, 2014), improving fodder and feed management(Bell et al., 2014; Havet et al., 2014) and disease prevention and control(Skuce et al., 2013; Nguyen et al., 2016). Most interventions that
improve the productivity of livestock systems and enhance adaptation
to climate changes would also reduce the emissions intensity of foodproduction, with significant co-benefits for rural livelihoods and the
security of food supplies (Gerber et al., 2013; FAO and NZAGRC, 2017a,b, c). Whether such reductions in emission intensity result in lower
or higher absolute GHG emissions depends on overall demand forlivestock products, indicating the relevance of integrating supply-side
with demand-side measures within food security objectives (Gerber
et al., 2013; Bajželj et al., 2014). Transitions in livestock production
systems (e.g., from extensive to intensive) can also result in significant
emission reductions as part of broader land-based mitigation strategies(Havlik et al., 2014).

4

Overall, there is high agreement that farm strategies that integrate
mixed crop–livestock systems can improve farm productivity andhave positive sustainability outcomes (Havet et al., 2014; Thorntonand Herrero, 2014; Herrero et al., 2015; Weindl et al., 2015). Shifting
towards mixed crop–livestock systems is estimated to reduce
agricultural adaptation costs to 0.3% of total production costs while
abating deforestation by 76 Mha globally, making it a highly costeffective adaptation option with mitigation co-benefits (Weindl etal., 2015). Evidence from various regions supports this (Thornton andHerrero, 2015), although the feasible scale varies between regions andsystems, as well as being moderated by overall demand in specificfood products. In Australia, some farmers have successfully shiftedto crop–livestock systems where, each year, they allocate land andforage resources in response to climate and price trends (Bell et al.,2014) . However, there can be some unintended negative impactsof such integration, including increased burdens on women, higher
requirements of capital, competing uses of crop residues (e.g., feed
vs. mulching vs. carbon sequestration) and higher requirementsof management skills, which can be a challenge across several low
income countries (Thornton and Herrero, 2015; Thornton et al., 2018).
Finally, the feasibility of improving livestock efficiency is dependent
on socio-cultural context and acceptability: there remain significant
issues around widespread adoption of crossbred animals, especially bysmallholders (Thornton et al., 2018).
Irrigation efficiency. Irrigation efficiency is especially critical since
water endowments are expected to change, with 20–60 Mha ofglobal cropland being projected to revert from irrigated to rain-fed
land, while other areas will receive higher precipitation in shorter timespans, thus affecting irrigation demand (Elliott et al., 2014). While
increasing irrigation system efficiency is necessary, there is mixed
evidence on how to enact efficiency improvements (Fader et al., 2016;
Herwehe and Scott, 2018). Physical and technical strategies include
building large-scale reservoirs or dams, renovating or deepening
irrigation channels, building on-farm rainwater harvesting structures,
lining ponds, channels and tanks to reduce losses through percolationand evaporation, and investing in small infrastructure such as sprinkler
or drip irrigation sets (Varela-Ortega et al., 2016; Sikka et al., 2018).
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Strengthening and Implementing the Global Response

Each strategy has differing costs and benefits relating to uniquebiophysical, social, and economic contexts. Also, increasing irrigation
efficiency may foster higher dependency on irrigation, resulting in aheightened sensitivity to climate that may be maladaptive in the longterm (Lindoso et al., 2014).
Improvements in irrigation efficiency would need to be supplemented
with ancillary activities, such as shifting to crops that require less waterand improving soil and moisture conservation (Fader et al., 2016;
Hong and Yabe, 2017; Sikka et al., 2018). Currently, the feasibility of
improving irrigation efficiency is constrained by issues of replicability
across scale and sustainability over time (Burney and Naylor, 2012),
institutional barriers and inadequate market linkages (Pittock et al.,2017).
Growing evidence suggests that investing in behavioural shifts
towards using irrigation technology such as micro-sprinklers or drip
irrigation, is an effective and quick adaptation strategy (Varela-Ortega
et al., 2016; Herwehe and Scott, 2018; Sikka et al., 2018) as opposedto large dams which have high financial, ecological and social costs(Varela-Ortega et al., 2016). While improving irrigation efficiency istechnically feasible (R. Fishman et al., 2015) and has clear benefits forenvironmental values (Pfeiffer and Lin, 2014; R. Fishman et al., 2015),
feasibility is regionally differentiated as shown by examples as diverse
as Kansas (Jägermeyr et al., 2015), India (R. Fishman et al., 2015) and
Africa (Pittock et al., 2017).
Agroforestry. The integration of trees and shrubs into crop andlivestock systems, when properly managed, can potentially restrict soil
erosion, facilitate water infiltration, improve soil physical properties
and buffer against extreme events (Lasco et al., 2014; Mbow et al.,2014; Quandt et al., 2017; Sida et al., 2018). There is medium evidence
and high agreement on the feasibility of agroforestry practices thatenhance productivity, livelihoods and carbon storage (Lusiana et al.,2012; Murthy, 2013; Coulibaly et al., 2017; Sida et al., 2018), includingfrom indigenous production systems (Coq-Huelva et al., 2017), withvariation by region, agroforestry type, and climatic conditions (Placeet al., 2012; Coe et al., 2014; Mbow et al., 2014; Iiyama et al., 2017;
Abdulai et al., 2018). Long-term studies examining the success ofagroforestry, however, are rare (Coe et al., 2014; Meijer et al., 2015;
Brockington et al., 2016; Zomer et al., 2016).
The extent to which agroforestry practices employed at the farm level
could be scaled up globally while satisfying growing food demandis relatively unknown. Agroforestry adoption has been relatively low
and uneven (Jacobi et al., 2017; Hernández-Morcillo et al., 2018), withconstraints including the expense of establishment and lack of reliablefinancial support, insecure land tenure, landowner’s lack of experience
with trees, complexity of management practices, fluctuating market
demand and prices for different food and fibre products, the time andknowledge required for management, low intermediate benefits tooffset revenue lags, and inadequate market access (Pattanayak et al.,
2003; Mercer, 2004; Sendzimir et al., 2011; Valdivia et al., 2012; Coe etal., 2014; Meijer et al., 2015; Coulibaly et al., 2017; Jacobi et al., 2017).
Managing food loss and waste. The way food is produced,
processed and transported strongly influences GHG emissions. AroundStrengthening and Implementing the Global Response

Chapter 4

one-third of the food produced on the planet is not consumed (FAO,
2013), affecting food security and livelihoods (See Cross-Chapter Box
6 on Food Security in Chapter 3). Food wastage is a combinationof food loss – the decrease in mass and nutritional value of food
due to poor infrastructure, logistics, and lack of storage technologies
and management – and food waste that derives from inappropriate
human consumption that leads to food spoilage associated withinferior quality or overproduction. Food wastage could lead to anincrease in emissions estimated to 1.9–2.5 GtCO2-eq yr−1 (Hiç et al.,2016).

fibre resources, and could reduce impacts on land and conventional
agriculture (Greene et al., 2017).

Decreasing food wastage has high mitigation and adaptation potential
and could play an important role in land transitions towards 1.5°C,
provided that reduced food waste results in lower production-side
emissions rather than increased consumption (Foley et al., 2011). Thereis medium agreement that a combination of individual–institutional
behaviour (Refsgaard and Magnussen, 2009; Thornton and Herrero,2014), and improved technologies and management (Lin et al., 2013;
Papargyropoulou et al., 2014) can transform food waste into productswith marketable value. Institutional behaviour depends on investment
and policies, which if adequately addressed could enable mitigation
and adaptation co-benefits in a relatively short time.

4.3.2.2

Novel technologies. New molecular biology tools have beendeveloped that can lead to fast and precise genome modification (DeSouza et al., 2016; Scheben et al., 2016) (e.g., CRISPR Cas9; Ran etal., 2013; Schaeffer and Nakata, 2015). Such genome editing tools
may moderately assist in mitigation and adaptation of agriculture
in relation to climate changes, elevated CO2, drought and flooding(DaMatta et al., 2010; De Souza et al., 2015, 2016). These tools could
contribute to developing new plant varieties that can adapt to warmingof 1.5°C and overshoot, potentially avoiding some of the costs of crop
shifting (Schlenker and Roberts, 2009; De Souza et al., 2016). However,biosafety concerns and government regulatory systems can be a major
barrier to the use of these tools as this increases the time and costof turning scientific discoveries into ready applicable technologies(Andow and Zwahlen, 2006; Maghari and Ardekani, 2011).
The strategy of reducing enteric methane emissions by ruminants
through the development of inhibitors or vaccines has already been
attempted with some successes, although the potential for application
at scale and in different situations remains uncertain. A methane
inhibitor has been demonstrated to reduce methane from feedlot
systems by 30% over a 12-week period (Hristov et al., 2015) withsome productivity benefits, but the ability to apply it in grazing systems
will depend on further technological developments as well as costs
and incentives. A vaccine could potentially modify the microbiota of
the rumen and be applicable even in extensive grazing systems by
reducing the presence of methanogenic micro-organisms (Wedlock etal., 2013) but has not yet been successfully demonstrated to reduce
emissions in live animals. Selective breeding for lower-emitting
ruminants is becoming rapidly feasible, offering small but cumulativeemissions reductions without requiring substantial changes in farmsystems (Pickering et al., 2015).
Technological innovation in culturing marine and freshwater micro
and macro flora has significant potential to expand food, fuel andTechnological innovation could assist in increased agricultural efficiency(e.g., via precision agriculture), decrease food wastage and genetics
that enhance plant adaptation traits (Section 4.4.4). Technological andassociated management improvements may be ways to increase theefficiency of contemporary agriculture to help produce enough foodto cope with population increases in a 1.5°C warmer world, and help
reduce the pressure on natural ecosystems and biodiversity.

Forests and other ecosystems

Ecosystem restoration. Biomass stocks in tropical, subtropical,
temperate and boreal biomes currently hold 1085, 194, 176, 190 Gt CO2,
respectively. Conservation and restoration can enhance these naturalcarbon sinks (Erb et al., 2017).
Recent studies explore options for conservation, restoration andimproved land management estimating up to 23 GtCO2 (Griscom etal., 2017). Mitigation potentials are dominated by reduced rates ofdeforestation, reforestation and forest management, and concentrated
in tropical regions (Houghton, 2013; Canadell and Schulze, 2014; Graceet al., 2014; Houghton et al., 2015; Griscom et al., 2017). Much of theliterature focuses on REDD+ (reducing emissions from deforestation andforest degradation) as an institutional mechanism. However, restorationand management activities need not be limited to REDD+, and locally
adapted implementation may keep costs low, capitalize on co-benefits
and ensure consideration of competing for socio-economic goals (Jantke
et al., 2016; Ellison et al., 2017; Perugini et al., 2017; Spencer et al., 2017).
Half of the estimated potential can be achieved at <100 USD/tCO2; anda third of the cost-effective potential at <10 USD/tCO2 (Griscom et al.,2017). Variation of costs in projects aiming to reduce emissions fromdeforestation is high when considering opportunity and transaction
costs (Dang Phan et al., 2014; Overmars et al., 2014; Ickowitz et al.,2017; Rakatama et al., 2017).
However, the focus on forests raises concerns of cross-biome leakage(medium evidence, low agreement) (Popp et al., 2014a; Strassburg
et al., 2014; Jayachandran et al., 2017) and encroachment on otherecosystems (Veldman et al., 2015). Reducing rates of deforestation
constrains the land available for agriculture and grazing, with tradeoffs between diets, higher yields and food prices (Erb et al., 2016a;
Kreidenweis et al., 2016). Forest restoration and conservation arecompatible with biodiversity (Rey Benayas et al., 2009; Jantke et al.,2016) and available water resources; in the tropics, reducing rates ofdeforestation maintains cooler surface temperatures (Perugini et al.,2017) and rainfall (Ellison et al., 2017).
Its multiple potential co-benefits have made REDD+ important for local
communities, biodiversity and sustainable landscapes (Ngendakumana
et al., 2017; Turnhout et al., 2017). There is low agreement on whether
climate impacts will reverse mitigation benefits of restoration (Le Pageet al., 2013) by increasing the likelihood of disturbance (Anderegg etal., 2015), or reinforce them through carbon fertilization (P. Smith etal., 2014).
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4Strengthening and Implementing the Global Response

Emerging regional assessments offer new perspectives for upscaling.
Strengthening coordination, additional funding sources, and accessand disbursement points increase the potential of REDD+ in working
towards 2°C and 1.5°C limits (Well and Carrapatoso, 2017). While
there are indications that land tenure has a positive impact (Sunderlin
et al., 2014), a meta-analysis by Wehkamp et al. (2018a) shows that
there is medium evidence and low agreement on which aspects ofgovernance improvements are supportive of conservation. Local
benefits, especially for indigenous communities, will only be accrued if
land tenure is respected and legally protected, which is not often thecase (Sunderlin et al., 2014; Brugnach et al., 2017). Although payments
for reduced rates of deforestation may benefit the poor, the mostvulnerable populations could have limited, uneven access (Atela et al.,2014) and face lower opportunity costs from deforestation (Ickowitz
et al., 2017).

hardening through the building of seawalls and the re-establishmentof coastal ecosystems such as mangroves (André et al., 2016; Cooper
et al., 2016). While the feasibility of the solutions is high, they are
expensive to scale (robust evidence, medium agreement).

Community-based adaptation (CbA). There is medium evidence
and high agreement for the use of CbA. The specific actions to takewill depend upon the location, context, and vulnerability of the specificcommunity. CbA is defined as ‘a community-led process, based oncommunities’ priorities, needs, knowledge, and capacities, which aim
to empower people to plan for and cope with the impacts of climatechange’ (Reid et al., 2009). The integration of CbA with ecosystemsbased adaptation (EbA) has been increasingly promoted, especially inefforts to alleviate poverty (Mannke, 2011; Reid, 2016).

Working with coastal communities has the potential for improving
the resilience of coastal ecosystems. Combined with the advantagesof using indigenous knowledge to guide transitions, solutions can be
more effective when undertaken in partnership with local communities,
cultures, and knowledge (See Box 4.3).

Despite the potential and advantages of both CbA and EbA, including
knowledge exchange, information access and increased social capitaland equity; institutional and governance barriers still constitute achallenge for local adaptation efforts (Wright et al., 2014; FernándezGiménez et al., 2015).
Wetland management. In wetland ecosystems, temperature rise has
direct and irreversible impacts on species functioning and distribution,
ecosystem equilibrium and services, and second-order impacts on local
livelihoods (see Chapter 3, Section 3.4.3). The structure and functionof wetland systems are changing due to climate change. Wetland
management strategies, including adjustments in infrastructural,behavioural, and institutional practices have clear implications foradaptation (Colloff et al., 2016b; Finlayson et al., 2017; Wigand et al.,2017)
Despite international initiatives on wetland restoration andmanagement through the Ramsar Convention on Wetlands, policies
have not been effective (Finlayson, 2012; Finlayson et al., 2017).
Institutional reform, such as flexible, locally relevant governance,
drawing on principles of adaptive co-management, and multistakeholder participation becomes increasingly necessary for effective
wetland management (Capon et al., 2013; Finlayson et al., 2017).

4.3.2.3

Coastal systems

Managing coastal stress. Particularly to allow for the landward
relocation of coastal ecosystems under a transition to a 1.5°C warmer
world, planning for climate change would need to be integrated withthe use of coastlines by humans (Saunders et al., 2014; Kelleway et al.,2017). Adaptation options for managing coastal stress include coastal
330

There is low evidence and high agreement that reducing the impactof local stresses (Halpern et al., 2015) will improve the resilience ofmarine ecosystems as they transition to a 1.5°C world (O’Leary etal., 2017). Approaches to reducing local stresses are consideredfeasible, cost-effective and highly scalable. Ecosystem resilience
may be increased through alternative livelihoods (e.g., sustainableaquaculture), which are among a suite of options for building resiliencein coastal ecosystems. These options enjoy high levels of feasibility yet
are expensive, which stands in the way of scalability (robust evidence,
medium agreement) (Hiwasaki et al., 2015; Brugnach et al., 2017).

Restoration of coastal ecosystems and fisheries. Marine
restoration is expensive compared to terrestrial restoration, and thesurvival of projects is currently low, with success depending on theecosystem and site, rather than the size of the financial investment(Bayraktarov et al., 2016). Mangrove replanting shows evidenceof success globally, with numerous examples of projects that haveestablished forests (Kimball et al., 2015; Bayraktarov et al., 2016).
Efforts with reef-building corals have been attempted with a low levelof success (Bayraktarov et al., 2016). Technologies to help re-establish
coral communities are limited (Rinkevich, 2014), as are largely
untested disruptive technologies (e.g., genetic manipulation, assisted
evolution) (van Oppen et al., 2015). Current technologies also havetrouble scaling given the substantial costs and investment required(Bayraktarov et al., 2016).
Johannessen and Macdonald (2016) report the ‘blue carbon’ sink to
be 0.4–0.8% of global anthropogenic emissions. However, this does
not adequately account for post-depositional processes and could
overestimate removal potentials, subject to a risk of reversal. Seagrassbeds will thus not contribute significantly to enabling 1.5°C-consistentpathways.

4.3.3

Urban and Infrastructure System Transitions

There will be approximately 70 million additional urban residents every
year through to the middle part of this century (UN DESA, 2014). The
majority of these new urban citizens will reside in small and mediumsized cities in low- and middle-income countries (Cross-Chapter Box
13 in Chapter 5). The combination of urbanization and economic
and infrastructure development could account for an additional
226 GtCO2 by 2050 (Bai et al. 2018). However, urban systems canharness the mega-trends of urbanization, digitalization, financializationand growing sub-national commitment to smart cities, green cities,
resilient cities, sustainable cities and adaptive cities, for the type ofStrengthening and Implementing the Global Responsetransformative change required by 1.5°C-consistent pathways (SDSN,
2013; Parag and Sovacool, 2016; Roberts, 2016; Wachsmuth et al., 2016;
Revi, 2017; Solecki et al., 2018). There is a growing number of urban
climate responses driven by cost-effectiveness, development, work
creation and inclusivity considerations (Solecki et al., 2013; Ahern etal., 2014; Floater et al., 2014; Revi et al., 2014a; Villarroel Walker etal., 2014; Kennedy et al., 2015; Rodríguez, 2015; McGranahan et al.,2016; Dodman et al., 2017a; Newman et al., 2017; UN-Habitat, 2017;
Westphal et al., 2017).
In addition, low-carbon cities could reduce the need to deploy carbon
dioxide removal (CDR) and solar radiation modification (SRM) (Fink,
2013; Thomson and Newman, 2016).
Cities are also places in which the risks associated with warming of
1.5°C, such as heat stress, terrestrial and coastal flooding, new disease
vectors, air pollution and water scarcity, will coalesce (see Chapter 3,
Section 3.3) (Dodman et al., 2017a; Satterthwaite and Bartlett, 2017).
Unless adaptation and mitigation efforts are designed around the needto decarbonize urban societies in the developed world and provide
low-carbon solutions to the needs of growing urban populations indeveloping countries, they will struggle to deliver the pace or scaleof change required by 1.5°C-consistent pathways (Hallegatte et al.,2013; Villarroel Walker et al., 2014; Roberts, 2016; Solecki et al., 2018).
The pace and scale of urban climate responses can be enhanced byattention to social equity (including gender equity), urban ecology(Brown and McGranahan, 2016; Wachsmuth et al., 2016; Ziervogel
et al., 2016a) and participation in sub-national networks for climate
action (Cole, 2015; Jordan et al., 2015).
The long-lived urban transport, water and energy systems that will be
constructed in the next three decades to support urban populations indeveloping countries and to retrofit cities in developed countries will
have to be different to those built in Europe and North America in the20th century, if they are to support the required transitions (Freire et al.,2014; Cartwright, 2015; McPhearson et al., 2016; Roberts, 2016; Lwasa,
2017). Recent literature identifies energy, infrastructure, appliances,urban planning, transport and adaptation options as capable offacilitating systemic change. It is these aspects of the urban system thatare discussed below and from which options in Section 4.5 are selected.

4.3.3.1

Urban energy systems

Urban economies tend to be more energy intensive than national
economies due to higher levels of per capita income, mobility andconsumption (Kennedy et al., 2015; Broto, 2017; Gota et al., 2018).
However, some urban systems have begun decoupling developmentfrom the consumption of fossil fuel-powered energy through energy
efficiency, renewable energy and locally managed smart grids(Dodman, 2009; Freire et al., 2014; Eyre et al., 2018; Glazebrook andNewman, 2018).
The rapidly expanding cities of Africa and Asia, where energy poverty
currently undermines adaptive capacity (Westphal et al., 2017;
Satterthwaite et al., 2018), have the opportunity to benefit from recent
3

Chapter 4

price changes in renewable energy technologies to enable clean
energy access to citizens (SDG 7) (Cartwright, 2015; Watkins, 2015;
Lwasa, 2017; Kennedy et al., 2018; Teferi and Newman, 2018). This will
require strengthened energy governance in these countries (Eberhard
et al., 2017). Where renewable energy displaces paraffin, wood fuelor charcoal feedstocks in informal urban settlements, it provides
the co-benefits of improved indoor air quality, reduced fire risk andreduced deforestation, all of which can enhance adaptive capacity
and strengthen demand for this energy (Newham and Conradie, 2013;
Winkler, 2017; Kennedy et al., 2018; Teferi and Newman, 2018).

4.3.3.2

Urban infrastructure, buildings and appliances

Buildings are responsible for 32% of global energy consumption (IEA,
2016c) and have a large energy saving potential with available anddemonstrated technologies such as energy efficiency improvements
in technical installations and in thermal insulation (Toleikyte et al.,2018) and energy sufficiency (Thomas et al., 2017). Kuramochi etal. (2018) show that 1.5°C-consistent pathways require building
emissions to be reduced by 80–90% by 2050, new construction to
be fossil-free and near-zero energy by 2020, and an increased rate ofenergy refurbishment of existing buildings to 5% per annum in OECD
(Organisation for Economic Co-operation and Development) countries(see also Section 4.2.1).
Based on the IEA-ETP (IEA, 2017g), Chapter 2 identifies large saving
potential in heating and cooling through improved building design,efficient equipment, lighting and appliances. Several examples ofnet zero energy in buildings are now available (Wells et al., 2018).
In existing buildings, refurbishment enables energy saving (Semprini
et al., 2017; Brambilla et al., 2018; D’Agostino and Parker, 2018; Sunet al., 2018) and cost savings (Toleikyte et al., 2018; Zangheri et al.,2018).
Reducing the energy embodied in building materials provides further
energy and GHG savings (Cabeza et al., 2013; Oliver and Morecroft,
2014; Koezjakov et al., 2018), in particular through increased use of biobased materials (Lupíšek et al., 2015) and wood construction (Ramage
et al., 2017). The United Nations Environment Programme (UNEP3)
estimates that improving embodied energy, thermal performance, anddirect energy use of buildings can reduce emissions by 1.9 GtCO2e yr −1(UNEP, 2017b), with an additional reduction of 3 GtCO2e yr−1 throughenergy efficient appliances and lighting (UNEP, 2017b). Further
increasing the energy efficiency of appliances and lighting, heating
and cooling offers the potential for further savings (Parikh and Parikh,
2016; Garg et al., 2017).
Smart technology, drawing on the internet of things (IoT) and building
information modelling, offers opportunities to accelerate energy
efficiency in buildings and cities (Moreno-Cruz and Keith, 2013; Hoy,
2016) (see also Section 4.4.4). Some cities in developing countries
are drawing on these technologies to adopt ‘leapfrog’ infrastructure,buildings and appliances to pursue low-carbon development (Newman
et al., 2017; Teferi and Newman, 2017) (Cross-Chapter Box 13 in
Chapter 5).

Currently called UN Environment.

331

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Chapter 4

4.3.3.3

Urban transport and urban planning

Urban form impacts demand for energy (Sims et al., 2014) and otherwelfare related factors: a meta-analysis of 300 papers reported
energy savings of 26 USD per person per year attributable to a 10%
increase in urban population density (Ahlfeldt and Pietrostefani,2017). Significant reductions in car use are associated with dense,
pedestrianized cities and towns and medium-density transit corridors(Newman and Kenworthy, 2015; Newman et al., 2017) relative to lowdensity cities in which car dependency is high (Schiller and Kenworthy,
2018). Combined dense urban forms and new mass transit systems
in Shanghai and Beijing have yielded less car use (Gao and Newman,2018) (see Box 4.9). Compact cities also create the passenger density
required to make public transport more financially viable (Rode et al.,2017; Ahlfeldt and Pietrostefani, 2017) and enable combinations ofcleaner fuel feedstocks and urban smart grids, in which vehicles form
part of the storage capacity (Oldenbroek et al., 2017). Similarly, thespatial organization of urban energy influenced the trajectories ofurban development in cities as diverse as Hong Kong, Bengaluru and
Maputo (Broto, 2017).
The informal settlements of middle- and low-income cities, where urban
density is more typically associated with a range of water- and vectorborne health risks, may provide a notable exception to the adaptive
advantages of urban density (Mitlin and Satterthwaite, 2013; Lilford etal., 2017) unless new approaches and technologies are harnessed toaccelerate slum upgrading (Teferi and Newman, 2017).

4

Scenarios consistent with 1.5°C depend on a roughly 15% reduction
in final energy use by the transport sector by 2050 relative to 2015
(Chapter 2, Figure 2.12). In one analysis the phasing out of fossil fuelpassenger vehicle sales by 2035–2050 was identified as a benchmark
for aligning with 1.5°C-consistent pathways (Kuramochi et al., 2018).
Reducing emissions from transport has lagged the power sector (Simset al., 2014; Creutzig et al., 2015a), but evidence since AR5 suggeststhat cities are urbanizing and re-urbanizing in ways that coordinatetransport sector adaptation and mitigation (Colenbrander et al., 2017;
Newman et al., 2017; Salvo et al., 2017; Gota et al., 2018). The global
transport sector could reduce 4.7 GtCO2e yr−1 (4.1–5.3) by 2030.
This is significantly more than is predicted by integrated assessment
models (UNEP, 2017b). Such a transition depends on cities thatenable modal shifts and avoided journeys and that provide incentives
for uptake of improved fuel efficiency and changes in urban designthat encourage walkable cities, non-motorized transport and shorter
commuter distances (IEA, 2016a; Mittal et al., 2016; Zhang et al.,2016; Li and Loo, 2017). In at least 4 African cities, 43 Asian cities
and 54 Latin American cities, transit-oriented development (TOD),
has emerged as an organizing principle for urban growth and spatialplanning (Colenbrander et al., 2017; Lwasa, 2017; BRTData, 2018).
This trend is important to counter the rising demand for private cars indeveloping-country cities (AfDB/OECD/UNDP, 2016). In India, TOD has
been combined with localized solar PV installations and new ways offinancing rail expansion (Sharma, 2018).
Cities pursuing sustainable transport benefit from reduced air pollution,congestion and road fatalities and are able to harness the relationshipbetween transport systems, urban form, urban energy intensity
332

Strengthening and Implementing the Global Response

and social cohesion (Goodwin and Van Dender, 2013; Newman and
Kenworthy, 2015; Wee, 2015).
Technology and electrification trends since AR5 make carbon-efficienturban transport easier (Newman et al., 2016), but realizing urban
transport’s contribution to a 1.5°C-consistent pathways will require
the type of governance that can overcome the financial, institutional,
behavioural and legal barriers to change (Geels, 2014; Bakker et al.,2017).
Adaptation to a 1.5°C world is enabled by urban design and spatialplanning policies that consider extreme weather conditions and reduce
displacement by climate related disasters (UNISDR, 2009; UN-Habitat,
2011; Mitlin and Satterthwaite, 2013).
Building codes and technology standards for public lighting,including traffic lights (Beccali et al., 2015), play a critical role in
reducing carbon emissions, enhancing urban climate resilience andmanaging climate risk (Steenhof and Sparling, 2011; Parnell, 2015;
Shapiro, 2016; Evans et al., 2017). Building codes can support theconvergence to zero emissions from buildings (Wells et al., 2018) andcan be used retrofit the existing building stock for energy efficiency(Ruparathna et al., 2016).
The application of building codes and standards for 1.5°C-consistentpathways will require improved enforcement, which can be a challenge
in developing countries where inspection resources are often limited
and codes are poorly tailored to local conditions (Ford et al., 2015c;
Chandel et al., 2016; Eisenberg, 2016; Shapiro, 2016; Hess and Kelman,
2017; Mavhura et al., 2017). In all countries, building codes can beundermined by industry interests and can be maladaptive if they
prevent buildings or land use from evolving to reduce climate impacts(Eisenberg, 2016; Shapiro, 2016).
The deficit in building codes and standards in middle-income
and developing-country cities need not be a constraint to moreenergy-efficient and resilient buildings (Tait and Euston-Brown,2017). For example, the relatively high price that poor households
pay for unreliable and at times dangerous household energy
in African cities has driven the uptake of renewable energy andenergy efficiency technologies in the absence of regulations or
fiscal incentives (Eberhard et al., 2011, 2016; Cartwright, 2015;
Watkins, 2015). The Kuyasa Housing Project in Khayelitsha, one ofCape Town’s poorest suburbs, created significant mitigation andadaptation benefits by installing ceilings, solar water heaters andenergy-efficient lightbulbs in houses independent of the formal
housing or electrification programme (Winkler, 2017).

4.3.3.4

Electrification of cities and transport

The electrification of urban systems, including transport, has shown
global progress since AR5 (IEA, 2016a; Kennedy et al., 2018; Schiller
and Kenworthy, 2018). High growth rates are now appearing inelectric vehicles (Figure 4.1), electric bikes and electric transit (IEA,
2018), which would need to displace fossil fuel-powered passenger
vehicles by 2035–2050 to remain in line with 1.5°C-consistentpathways. China’s 2017 Road Map calls for 20% of new vehicle

Strengthening and Implementing the Global Response

Chapter 4

in IAMs (Bows-Larkin, 2015), but could be reduced by between a third
and two-thirds through energy efficiency measures and operationalchanges (Dahlmann et al., 2016). On shorter intercity trips, aviation
could be replaced by high-speed electric trains drawing on renewableenergy (Åkerman, 2011). Some progress has been made on the useof electricity in planes and shipping (Grewe et al., 2017) though nocommercial applications have arisen. Studies indicate that biofuels are
the most viable means of decarbonizing intercontinental travel, given
their technical characteristics, energy content and affordability (Wiseet al., 2017). The lifecycle emissions of bio-based jet fuels and marine
fuels can be considerable (Cox et al., 2014; IEA, 2017g) depending on
their location (Elshout et al., 2014), but can be reduced by feedstockand conversion technology choices (de Jong et al., 2017).
Figure 4.1 | Increase of the global electric car stock by country (2013–2017). The grey line is battery electric vehicles (BEV) only while the black line includes
both BEV and plug-in hybrid vehicles (PHEV). Source: (IEA, 2018). Based on IEA datafrom Global EV Outlook 2018 © OECD/IEA 2018, IEA Publishing.

sales to be electric. India is aiming for exclusively electric vehicles(EVs) by 2032 (NITI Aayog and RMI, 2017). Globally, EV sales wereup 42% in 2016 relative to 2015, and in the United States EV sales
were up 36% over the same period (Johnson and Walker, 2016).
The extent of electric railways in and between cities has expanded
since AR5 (IEA, 2016a; Mittal et al., 2016; Zhang et al., 2016; Li andLoo, 2017). In high-income cities there is medium evidence for thedecoupling of car use and wealth since AR5 (Newman, 2017). In cities
where private vehicle ownership is expected to increase, less carbonintensive fuel sources and reduced car journeys will be necessary as
well as electrification of all modes of transport (Mittal et al., 2016;
van Vuuren et al., 2017). Some recent urban data show a decouplingof urban growth and GHG emissions (Newman and Kenworthy, 2015)
and that ‘peak car’ has been reached in Shanghai and Beijing (Gao and
Kenworthy, 2017) and beyond (Manville et al., 2017) (also see Box 4.9).
An estimated 800 cities globally have operational bike-share schemes (E.
Fishman et al., 2015), and China had 250 million electric bicycles in 2017
(Newman et al., 2017). Advances in information and communication
technologies (ICT) offer cities the chance to reduce urban transportcongestion and fuel consumption by making better use of the urban
vehicle fleet through car sharing, driverless cars and coordinated public
transport, especially when electrified (Wee, 2015; Glazebrook andNewman, 2018). Advances in ‘big-data’ can assist in creating a better
understanding of the connections between cities, green infrastructure,environmental services and health (Jennings et al., 2016) and improve
decision-making in urban development (Lin et al., 2017).

4.3.3.5

Shipping, freight and aviation

International transport hubs, including airports and ports, and theassociated mobility of people are major economic contributors to mostlarge cities even while under the governance of national authorities
and international legislation. Shipping, freight and aviation systems
have grown rapidly, and little progress has been made since AR5 on
replacing fossil fuels, though some trials are continuing (Zhang, 2016;
Bouman et al., 2017; EEA, 2017). Aviation emissions do not yet feature

In recent years the potential for transport to use synfuels, such as
ethanol, methanol, methane, ammonia and hydrogen, created fromrenewable electricity and CO2, has gained momentum but has not yet
demonstrated benefits on a scale consistent with 1.5°C pathways (Ezeji,
2017; Fasihi et al., 2017). Decarbonizing the fuel used by the world’s
60,000 large ocean vessels faces governance barriers and the need fora global policy (Bows and Smith, 2012; IRENA, 2015; Rehmatulla andSmith, 2015). Low-emission marine fuels could simultaneously addresssulphur and black carbon issues in ports and around waterways andaccelerate the electrification of all large ports (Bouman et al., 2017;
IEA, 2017g).

4.3.3.6

Climate-resilient land use

Urban land use influences energy intensity, risk exposure and adaptive
capacity (Carter et al., 2015; Araos et al., 2016a; Ewing et al., 2016;
Newman et al., 2016; Broto, 2017). Accordingly, urban land-use
planning can contribute to climate mitigation and adaptation (Parnell,
2015; Francesch-Huidobro et al., 2017) and the growing number ofurban climate adaptation plans provide instruments to do this (Carter
et al., 2015; Dhar and Khirfan, 2017; Siders, 2017; Stults and Woodruff,2017). Adaptation plans can reduce exposure to urban flood risk
(which, in a 1.5°C world, could double relative to 1976–2005; Alfieri
et al., 2017), heat stress (Chapter 3, Section 3.5.5.8), fire risk (Chapter
3, Section 3.4.3.4) and sea level rise (Chapter 3, Section 3.4.5.1)(Schleussner et al., 2016).
Cities can reduce their risk exposure by considering investment ininfrastructure and buildings that are more resilient to warming of
1.5°C or beyond. Where adaptation planning and urban planninggenerate the type of local participation that enhances capacity to copewith risks, they can be mutually supportive processes (Archer et al.,2014; Kettle et al., 2014; Campos et al., 2016; Chu et al., 2017; Siders,
2017; Underwood et al., 2017). Not all adaptation plans are reported
as effective (Measham et al., 2011; Hetz, 2016; Woodruff and Stults,
2016; Mahlkow and Donner, 2017), especially in developing-country
cities (Kiunsi, 2013). In cases where adaptation planning may further
marginalize poor citizens, either through limited local control overadaptation priorities or by displacing impacts onto poorer communities,
successful urban risk management would need to consider factors
such as justice, equity, and inclusive participation, as well as recognize
the political economy of adaptation (Archer, 2016; Shi et al., 2016;
Ziervogel et al., 2016a, 2017; Chu et al., 2017).
333

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Chapter 4

4.3.3.7

Strengthening and Implementing the Global Response

Green urban infrastructure and ecosystem services

Integrating and promoting green urban infrastructure (including
street trees, parks, green roofs and facades, and water features) into
city planning can be difficult (Leck et al., 2015) but increases urban
resilience to impacts of 1.5°C warming (Table 4.2) in ways that can be
more cost-effective than conventional infrastructure (Cartwright et al.,2013; Culwick and Bobbins, 2016).
Realizing climate benefits from urban green infrastructure sometimes
requires a city-region perspective (Wachsmuth et al., 2016). Where
the urban impact on ecological systems in and beyond the city isappreciated, the potential for transformative change exists (Soderlund
and Newman, 2015; Ziervogel et al., 2016a), and a locally appropriate
combination of green space, ecosystem goods and services and thebuilt environment can increase the set of urban adaptation options(Puppim de Oliveira et al., 2013).
Milan, Italy, a city with deliberate urban greening policies, planted
10,000 hectares of new forest and green areas over the last two
decades (Sanesi et al., 2017). The accelerated growth of urban trees,
relative to rural trees, in several regions of the world is expected todecrease tree longevity (Pretzsch et al., 2017), requiring monitoring
and additional management of urban trees if their contribution tourban ecosystem-based adaptation and mitigation is to be maintainedin a 1.5°C world (Buckeridge, 2015; Pretzsch et al., 2017).

4.3.3.8

4

Sustainable urban water and environmental services

Urban water supply and wastewater treatment is energy intensive andcurrently accounts for significant GHG emissions (Nair et al., 2014).
Cities can integrate sustainable water resource management and the
supply of water services in ways that support mitigation, adaptationand development through waste water recycling and storm water
diversion (Xue et al., 2015; Poff et al., 2016). Governance and financechallenges complicate balancing sustainable water supply and risingurban demand, particularly in low-income cities (Bettini et al., 2015;
Deng and Zhao, 2015; Hill Clarvis and Engle, 2015; Lemos, 2015;
Margerum and Robinson, 2015).

Urban surface-sealing with impervious materials affects the volume
and velocity of runoff and flooding during intense rainfall (Skougaard
Kaspersen et al., 2015), but urban design in many cities now seeks
to mediate runoff, encourage groundwater recharge and enhancewater quality (Liu et al., 2014; Lamond et al., 2015; Voskamp and
Van de Ven, 2015; Costa et al., 2016; Mguni et al., 2016; Xie et al.,2017). Challenges remain for managing intense rainfall events that arereported to be increasing in frequency and intensity in some locations(Ziervogel et al., 2016b), and urban flooding is expected to increase at
1.5°C of warming (Alfieri et al., 2017). This risk falls disproportionately
on women and poor people in cities (Mitlin, 2005; Chu et al., 2016;
Ziervogel et al., 2016b; Chant et al., 2017; Dodman et al., 2017a, b).
Nexus approaches that highlight urban areas as socio-ecologicalsystems can support policy coherence (Rasul and Sharma, 2016) andsustainable urban livelihoods (Biggs et al., 2015). The water–energy–
food (WEF) nexus is especially important to growing urban populations(Tacoli et al., 2013; Lwasa et al., 2014; Villarroel Walker et al., 2014).

4.3.4

Industrial Systems Transitions

Industry consumes about one-third of global final energy and contributes,
directly and indirectly, about one-third of global GHG emissions (IPCC,
2014b). If the increase in global mean temperature is to remain under
1.5°C, modelling indicates that industry cannot emit more than 2
GtCO2 in 2050, corresponding to a reduction of between 67 and 91%(interquartile range) in GHG emissions compared to 2010 (see Chapter
2, Figures 2.20 and 2.21, and Table 4.1). Moreover, the consequencesof warming of 1.5°C or more pose substantial challenges for industrial
diversity. This section will first briefly discuss the limited literature on
adaptation options for industry. Subsequently, new literature since AR5on the feasibility of industrial mitigation options will be discussed.
Research assessing adaptation actions by industry indicates that onlya small fraction of corporations has developed adaptation measures.
Studies of adaptation in the private sector remain limited (Agrawala etal., 2011; Linnenluecke et al., 2015; Averchenkova et al., 2016; Bremer
and Linnenluecke, 2016; Pauw et al., 2016a) and for 1.5°C are largely

Table 4.2 | Green urban infrastructure and benefits
Green
Infrastructure

Adaptation
Benefits

Mitigation
Benefits

Urban tree planting,urban parks

Reduced heat islandeffect, psychologicalbenefits

Less cement, reduced
air-conditioning use

Permeable surfaces

Water rechargeLess cement in city,some bio-sequestration,less water pumping

Liu et al., 2014; Lamond et al., 2015; Skougaard Kaspersen et al., 2015; Voskamp
and Van de Ven, 2015; Costa et al., 2016; Mguni et al., 2016; Xie et al., 2017

Forest retention, urban
agricultural land

Flood mediation,
healthy lifestyles

Reduced air pollution

Nowak et al., 2006; Tallis et al., 2011; Elmqvist et al., 2013; Buckeridge, 2015; Culwick andBobbins, 2016; Panagopoulos et al., 2016; Stevenson et al., 2016; R. White et al., 2017

Wetland restoration,
riparian buffer zones

Reduced urban flooding, low-skilled local
work, sense of place

Some bio-sequestration,less energy spent onwater treatment

Cartwright et al., 2013; Elmqvist et al., 2015; Brown and McGranahan, 2016;
Camps-Calvet et al., 2016; Culwick and Bobbins, 2016; McPhearson et al.,2016; Ziervogel et al., 2016b; Collas et al., 2017; F. Li et al., 2017

Biodiverse urban habitat

Psychological benefits,
inner-city recreation

Carbon sequestration

Beatley, 2011; Elmqvist et al., 2015; Brown and McGranahan, 2016; Camps-Calvet et al., 2016;
McPhearson et al., 2016; Collas et al., 2017; F. Li et al., 2017334

References
Demuzere et al., 2014; Mullaney et al., 2015; Soderlund and Newman, 2015;
Beaudoin and Gosselin, 2016; Green et al., 2016; Lin et al., 2017

Strengthening and Implementing the Global Response

Chapter 4

absent. This knowledge gap is particularly evident for medium-sizedenterprises and in low- and middle-income nations (Surminski, 2013).
Depending on the industrial sector, mitigation consistent with 1.5°C
would mean, across industries, a reduction of final energy demandby one-third, an increase of the rate of recycling of materials and thedevelopment of a circular economy in industry (Lewandowski, 2016;
Linder and Williander, 2017), the substitution of materials in highcarbon products with those made up of renewable materials (e.g., woodinstead of steel or cement in the construction sector, natural textile
fibres instead of plastics), and a range of deep emission reduction
options, including use of bio-based feedstocks, low-emission heatsources, electrification of production processes, and/or capture andstorage of all CO2 emissions by 2050 (Åhman et al., 2016). Some of the
choices for mitigation options and routes for GHG-intensive industry
are discrete and potentially subject to path dependency: if an industry
goes one way (e.g., in keeping existing processes), it will be harder totransition to process change (e.g., electrification) (Bataille et al., 2018).
In the context of rising demand for construction, an increasing shareof industrial production may be based in developing countries (N. Li etal., 2017), where current efficiencies may be lower than in developed
countries, and technical and institutional feasibility may differ (Ma etal., 2015).
Except for energy efficiency, costs of disruptive change associatedwith hydrogen- or electricity-based production, bio-based feedstocks
and carbon dioxide capture, (utilization) and storage (CC(U)S) for
trade-sensitive industrial sectors (in particular the iron and steel,
petrochemical and refining industries) make policy action by individual
countries challenging because of competitiveness concerns (Åhman etal., 2016; Nabernegg et al., 2017).
Table 4.3 provides an overview of applicable mitigation options for key
industrial sectors.

4.3.4.1

Energy efficiency

are available. In general, their feasibility depends on lowering capitalcosts and raising awareness and expertise (Wesseling et al., 2017).
General-purpose technologies, such as ICT, and energy management
tools can improve the prospects of energy efficiency in industry (seeSection 4.4.4).
Cross-sector technologies and practices, which play a role in allindustrial sectors including small- and medium-sized enterprises (SMEs)
and non-energy intensive industry, also offer potential for considerable
energy efficiency improvements. They include: (i) motor systems (forexample electric motors, variable speed drives, pumps, compressors
and fans), responsible for about 10% of worldwide industrial energy
consumption, with a global energy efficiency improvement potential ofaround 20–25% (Napp et al., 2014); and (ii) steam systems, responsible
for about 30% of industrial energy consumption and energy saving
potentials of about 10% (Hasanbeigi et al., 2014; Napp et al., 2014).
Waste heat recovery from industry has substantial potential for energy
efficiency and emission reduction (Forman et al., 2016). Low awarenessand competition from other investments limit the feasibility of such
options (Napp et al., 2014).

4.3.4.2

Recycling materials and developing a circular economy can be
institutionally challenging, as it requires advanced capabilities (Henry etal., 2006) and organizational changes (Cooper-Searle et al., 2018), but
has advantages in terms of cost, health, governance and environment(Ali et al., 2017). An assessment of the impacts on energy use andenvironmental issues is not available, but substitution could play a largerole in reducing emissions (Åhman et al., 2016) although its potential
depends on the demand for material and the turnover rate of, forexample, buildings (Haas et al., 2015). Material substitution and CO2
storage options are under development, for example, the use of algae
and renewable energy for carbon fibre production, which could becomea net sink of CO2 (Arnold et al., 2018).

4.3.4.3
Isolated efficiency implementation in energy-intensive industries is anecessary but insufficient condition for deep emission reductions (Nappet al., 2014; Aden, 2018). Various options specific to different industries

Substitution and circularity

Bio-based feedstocks

Bio-based feedstock processes could be seen as part of the circularmaterials economy (see section above). In several sectors, bio-based

Table 4.3 | Overview of different mitigation options potentially consistent with limiting warming to 1.5°C and applicable to main industrial sectors, including examples ofapplication (Napp et al., 2014; Boulamanti and Moya, 2017; Wesseling et al., 2017).
Industrial mitigation option
Process and Energy
Efficiency
Bio-based
Circularity & Substitution
Electrification & Hydrogen
Carbon dioxide capture,utilization and storage

Iron/Steel

CementRefineries andPetrochemicals

Chemicals

Can make a difference of between 10% and 50%, depending on the plant. Relevant but not enough for 1.5°C
Coke can be made from biomass
instead of coalPartial (only energy-related
emissions)

More recycling and replacement by low-emission materials,
including alternative chemistries for cement
Direct reduction with hydrogen
Heat generation through electricity

Partial (only electrified heatgeneration)

Possible for process emissions and energy. Reduces
emissions by 80–95%, and net emissions can become
negative when combined with biofuel

Biomass can replace fossil feedstocks
Limited potential
Electrified heat and hydrogen generation
Can be applied to energy emissions and different stacks but not onemissions of products in the use phase (e.g., gasoline)

335

4Chapter 4

feedstocks would leave the production process of materials relatively
untouched, and a switch would not affect the product quality,
making the option more attractive. However, energy requirementsfor processing bio-based feedstocks are often high, costs are also
still higher, and the emissions over the full life cycle, both upstream
and downstream, could be significant (Wesseling et al., 2017). Biobased feedstocks may put pressure on natural resources by increasing
land demand by biodiversity impacts beyond bioenergy demand forelectricity, transport and buildings (Slade et al., 2014), and, partly as aresult, face barriers in public acceptance (Sleenhoff et al., 2015).

4.3.4.4

Electrification and hydrogen

Electrification of manufacturing processes would constitute asignificant technological challenge and would entail a more disruptive
innovation in industry than bio-based or CCS options to get to very low
or zero emissions, except potentially in steel-making (Philibert, 2017).
The disruptive characteristics could potentially lead to stranded assets,
and could reduce political feasibility and industry support (Åhman
et al., 2016). Electrification of manufacturing would require further
technological development in industry, as well as an ample supply of
cost-effective low-emission electricity (Philibert, 2017).

4

Low-emission hydrogen can be produced by natural gas with CCS,
by electrolysis of water powered by zero-emission electricity, or
potentially in the future by generation IV nuclear reactors. Feasibility ofelectrification and use of hydrogen in production processes or fuel cells
is affected by technical development (in terms of efficient hydrogenproduction and electrification of processes), by geophysical factors
related to the availability of low-emission electricity (MacKay, 2013),
by associated public perception and by economic feasibility, except
in areas with ample solar and/or wind resources (Philibert, 2017;
Wesseling et al., 2017).

4.3.4.5

CO2 capture, utilization and storage in industry

CO2 capture in industry is generally considered more feasible than CCS
in the power sector (Section 4.3.1) or from bioenergy sources (Section
4.3.7), although CCS in industry faces similar barriers. Almost all of
the current full-scale (>1MtCO2 yr−1) CCS projects capture CO2 fromindustrial sources, including the Sleipner project in Norway, which has
been injecting CO2 from a gas facility in an offshore saline formation
since 1996 (Global CCS Institute, 2017). Compared to the power
sector, retrofitting CCS on existing industrial plants would leave theproduction process of materials relatively untouched (Åhman et al.,2016), though significant investments and modifications still have to
be made. Some industries, in particular cement, emit CO2 as inherent
process emissions and can therefore not reduce emissions to zero
without CC(U)S. CO2 stacks in some industries have a high economic
and technical feasibility for CO2 capture as the CO2 concentration in
the exhaust gases is relatively high (IPCC, 2005b; Leeson et al., 2017),
but others require strong modifications in the production process,limiting technical and economic feasibility, though costs remain
lower than other deep GHG reduction options (Rubin et al., 2015).
There are indications that the energy use in CO2 capture throughamine solvents (for solvent regeneration) can decrease by around60%, from 5 GJ tCO2−1 in 2005 to 2 GJ tCO2−1 in the best-performing
336

Strengthening and Implementing the Global Response

current pilot plants (Idem et al., 2015), increasing both technical andeconomic potential for this option. The heterogeneity of industrial
production processes might point to the need for specific institutionalarrangements to incentivize industrial CCS (Mikunda et al., 2014), andmay decrease institutional feasibility.
Whether carbon dioxide utilization (CCU) can contribute to limiting
warming to 1.5°C depends on the origin of the CO2 (fossil, biogenic
or atmospheric), the source of electricity for converting the CO2or regenerating catalysts, and the lifetime of the product. Review
studies indicate that CO2 utilization in industry has a small role toplay in limiting warming to 1.5°C because of the limited potential ofreusing CO2 with currently available technologies and the re-emissionof CO2 when used as a fuel (IPCC, 2005b; Mac Dowell et al., 2017).
However, new developments could make CCU more feasible, inparticular in CO2 use as a feedstock for carbon-based materials thatwould isolate CO2 from the atmosphere for a long time, and in lowcost, low-emission electricity that would make the energy use of CO2
capture more sustainable. The conversion of CO2 to fuels using zeroemission electricity has a lower technical, economic and environmental
feasibility than direct CO2 capture and storage from industry (Abanades
et al., 2017), although the economic prospects have improved recently(Philibert, 2017).

4.3.5

Overarching Adaptation Options Supporting
Adaptation Transitions

This section assesses overarching adaptation options –specific solutions
from which actors can choose and make decisions to reduce climate
vulnerability and build resilience. We examine their feasibility inthe context of transitions of energy, land and ecosystem, urban andinfrastructure, and industrial systems here, and further in Section 4.5.
These options can contribute to creating an enabling environment foradaptation (see Table 4.4 and Section 4.4).

4.3.5.1

Disaster risk management (DRM)

DRM is a process for designing, implementing and evaluating strategies,
policies and measures to improve the understanding of disaster risk,
and promoting improvement in disaster preparedness, response andrecovery (IPCC, 2012). There is increased demand to integrate DRM andadaptation (Howes et al., 2015; Kelman et al., 2015; Serrao-Neumann
et al., 2015; Archer, 2016; Rose, 2016; van der Keur et al., 2016; Kelman,
2017; Wallace, 2017) to reduce vulnerability, but institutional, technicaland financial capacity challenges in frontline agencies constituteconstraints (medium evidence, high agreement) (Eakin et al., 2015; Kita,
2017; Wallace, 2017).

4.3.5.2

Risk sharing and spreading

Risks associated with 1.5°C warming (Chapter 3, Section 3.4) may
increase the demand for options that share and spread financial
burdens. Formal, market-based (re)insurance spreads risk andprovides a financial buffer against the impacts of climate hazards(Linnerooth-Bayer and Hochrainer-Stigler, 2015; Wolfrom and YokoiArai, 2015; O’Hare et al., 2016; Glaas et al., 2017; Patel et al., 2017).
As an alternative to traditional indemnity-based insurance, index-

Strengthening and Implementing the Global Response

based micro-crop and livestock insurance programmes have beenrolled out in regions with less developed insurance markets (Akter etal., 2016, 2017; Jensen and Barrett, 2017). There is medium evidence
and medium agreement on the feasibility of insurance for adaptation,with financial, social, and institutional barriers to implementation anduptake, especially in low-income nations (García Romero and Molina,
2015; Joyette et al., 2015; Lashley and Warner, 2015; Jin et al., 2016).
Social protection programmes include cash and in-kind transfers toprotect poor and vulnerable households from the impact of economic
shocks, natural disasters and other crises (World Bank, 2017b), andcan build generic adaptive capacity and reduce vulnerability whencombined with a comprehensive climate risk management approach(medium evidence, medium agreement) (Devereux, 2016; Lemos et al.,2016).

4.3.5.3

Education and learning

Educational adaptation options motivate adaptation through building
awareness (Butler et al., 2016; Myers et al., 2017), leveraging multiple
knowledge systems (Pearce et al., 2015; Janif et al., 2016), developing
participatory action research and social learning processes (Butler andAdamowski, 2015; Ensor and Harvey, 2015; Butler et al., 2016; ThiHong Phuong et al., 2017; Ford et al., 2018), strengthening extension
services, and building mechanisms for learning and knowledge sharing
through community-based platforms, international conferences andknowledge networks (Vinke-de Kruijf and Pahl-Wostl, 2016) (medium
evidence, high agreement).

4.3.5.4

Population health and health system adaptation options

Climate change will exacerbate existing health challenges (Chapter 3,
Section 3.4.7). Options for enhancing current health services include
providing access to safe water and improved sanitation, enhancing
access to essential services such as vaccination, and developing or
strengthening integrated surveillance systems (WHO, 2015). Combining
these with iterative management can facilitate effective adaptation(medium evidence, high agreement).

4.3.5.5

Chapter 4

4.3.5.6

Human migration

Human migration, whether planned, forced or voluntary, is increasingly
gaining attention as a response, particularly where climatic risks are
becoming severe (Chapter 3, Section 3.4.10.2). There is medium
evidence and low agreement as to whether migration is adaptive,
in relation to cost effectiveness concerns (Grecequet et al., 2017)
and scalability (Brzoska and Fröhlich, 2016; Gemenne and Blocher,2017; Grecequet et al., 2017). Migrating can have mixed outcomeson reducing socio-economic vulnerability (Birk and Rasmussen,
2014; Kothari, 2014; Adger et al., 2015; Betzold, 2015; Kelman, 2015;
Grecequet et al., 2017; Melde et al., 2017; World Bank, 2017a; Kumari
Rigaud et al., 2018) and its feasibility is constrained by low politicaland legal acceptability and inadequate institutional capacity (Betzold,2015; Methmann and Oels, 2015; Brzoska and Fröhlich, 2016; Gemenne
and Blocher, 2017; Grecequet et al., 2017; Yamamoto et al., 2017).

4.3.5.7

Climate services

There is medium evidence and high agreement that climate services
can play a critical role in aiding adaptation decision-making (Vaughan
and Dessai, 2014; Wood et al., 2014; Lourenço et al., 2016; Trenberth etal., 2016; Singh et al., 2017; Vaughan et al., 2018). The higher uptakeof short-term climate information such as weather advisories anddaily forecasts contrast with lesser use of longer-term informationsuch as seasonal forecasts and multi-decadal projections (Singh et al.,2017; Vaughan et al., 2018). Climate service interventions have met
challenges with scaling up due to low capacity, inadequate institutions,and difficulties in maintaining systems beyond pilot project stage(Sivakumar et al., 2014; Tall et al., 2014; Gebru et al., 2015; Singh etal., 2016b), and technical, institutional, design, financial and capacity
barriers to the application of climate information for better decisionmaking remain (Briley et al., 2015; WMO, 2015; L. Jones et al., 2016;
Lourenço et al., 2016; Snow et al., 2016; Harjanne, 2017; Singh et al.,2017; C.J. White et al., 2017).

Indigenous knowledge

There is medium evidence and high agreement that indigenous
knowledge is critical for adaptation, underpinning adaptive capacity
through the diversity of indigenous agro-ecological and forest
management systems, collective social memory, repository ofaccumulated experience and social networks (Hiwasaki et al., 2015;
Pearce et al., 2015; Mapfumo et al., 2016; Sherman et al., 2016; Ingty,
2017) (Box 4.3). Indigenous knowledge is threatened by acculturation,
dispossession of land rights and land grabbing, rapid environmental
changes, colonization and social change, resulting in increasing
vulnerability to climate change – which climate policy can exacerbate
if based on limited understanding of indigenous worldviews (Thornton
and Manasfi, 2010; Ford, 2012; Nakashima et al., 2012; McNamara
and Prasad, 2014). Many scholars argue that recognition of indigenous
rights, governance systems and laws is central to adaptation, mitigationand sustainable development (Magni, 2017; Thornton and Comberti,
2017; Pearce, 2018).

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Table 4.4 | Assessment of overarching adaptation options in relation to enabling conditions. For more details, see Supplementary Material 4.SM.2.
OptionEnabling Conditions

ExamplesDisaster risk
management(DRM)

Governance and institutional capacity: supports post-disasterrecovery and reconstruction (Kelman et al., 2015; Kull et al., 2016).

Early warning systems (Anacona et al., 2015), and monitoring of dangerous lakes andsurrounding slopes (including using remote sensing) offer DRM opportunities(Emmer et al., 2016; Milner et al., 2017).

Risk sharing
and spreading:insurance

Institutional capacity and finance: buffers climate risk
(Wolfrom and Yokoi-Arai, 2015; O’Hare et al., 2016; Glaas
et al., 2017; Jenkins et al., 2017; Patel et al., 2017).

In 2007, the Caribbean Catastrophe Risk Insurance Facility was formed to pool risk fromtropical cyclones, earthquakes, and excess rainfalls (Murphy et al., 2012; CCRIF, 2017).

Social safety nets

Institutional capacity and finance: builds generic adaptive capacity
and reduces social vulnerability (Weldegebriel and Prowse, 2013;
Eakin et al., 2014; Lemos et al., 2016; Schwan and Yu, 2017).

In sub-Saharan Africa, cash transfer programmes targeting poor communities have provensuccessful in smoothing household welfare and food security during droughts, strengthening
community ties, and reducing debt levels (del Ninno et al., 2016; Asfaw et al., 2017;
Asfaw and Davis, 2018).

Education andlearning

Behavioural change and institutional capacity: social learning
strengthens adaptation and affects longer-term change (Clemens
et al., 2015; Ensor and Harvey, 2015; Henly-Shepard et al., 2015).

Participatory scenario planning is a process by which multiple stakeholders work together
to envision future scenarios under a range of climatic conditions (Oteros-Rozas et al.,2015; Butler et al., 2016; Flynn et al., 2018).

Population healthand health system

Institutional capacity: 1.5°C warming will primarily exacerbate
existing health challenges (K.R. Smith et al., 2014), which canbe targeted by enhancing health services.

Heatwave early warning and response systems coordinate the implementation of multiple
measures in response to predicted extreme temperatures (e.g., public announcements,
opening public cooling shelters, distributing information on heat stress symptoms)(Knowlton et al., 2014; Takahashi et al., 2015; Nitschke et al., 2016, 2017).

Indigenous
knowledge

Institutional capacity and behavioural change: knowledge ofenvironmental conditions helps communities detect and monitorchange (Johnson et al., 2015; Mistry and Berardi, 2016;
Williams et al., 2017).

Options such as integration of indigenous knowledge into resource management systems
and school curricula, are identified as potential adaptations (Cunsolo Willox et al., 2013;
McNamara and Prasad, 2014; MacDonald et al., 2015; Pearce et al., 2015; Chambers
et al., 2017; Inamara and Thomas, 2017).

Human migration

Governance: revising and adopting migration issues in national
disaster risk management policies, National Adaptation Plans
and NDCs (Kuruppu and Willie, 2015; Yamamoto et al., 2017).

In dryland India, populations in rural regions already experiencing 1.5°C warming are
migrating to cities (Gajjar et al., 2018) but are inadequately covered by existing
policies (Bhagat, 2017).

Climate services

Technological innovation: rapid technical development (due toincreased financial inputs and growing demand) is improving
quality of climate information provided (Rogers and Tsirkunov,
2010; Clements et al., 2013; Perrels et al., 2013; Gasc et al., 2014;
WMO, 2015; Roudier et al., 2016).

Climate services are seeing wide application in sectors such as agriculture, health,disaster management and insurance (Lourenço et al., 2016; Vaughan et al., 2018),
with implications for adaptation decision-making (Singh et al., 2017).

4
Cross-Chapter Box 9 | Risks, Adaptation Interventions, and Implications for Sustainable Development
and Equity Across Four Social-Ecological Systems: Arctic, Caribbean, Amazon, and Urban
Authors:
Debora Ley (Guatemala/Mexico), Malcolm E. Araos (Canada), Amir Bazaz (India), Marcos Buckeridge (Brazil), Ines Camilloni(Argentina), James Ford (UK/Canada), Bronwyn Hayward (New Zealand), Shagun Mehrotra (USA/India), Antony Payne (UK), Patricia
Pinho (Brazil), Aromar Revi (India), Kevon Rhiney (Jamaica), Chandni Singh (India), William Solecki (USA), Avelino Suarez (Cuba),
Michael Taylor (Jamaica), Adelle Thomas (Bahamas).
This box presents four case studies from different social-ecological systems as examples of risks of 1.5°C warming and higher(Chapter 3); adaptation options that respond to these risks (Chapter 4); and their implications for poverty, livelihoods andsustainability (Chapter 5). It is not yet possible to generalize adaptation effectiveness across regions due to a lack of empiricalstudies and monitoring and evaluation of current efforts.
ArcticThe Arctic is undergoing the most rapid climate change globally (Larsen et al., 2014), warming by 1.9°C over the last 30 years(Walsh, 2014; Grosse et al., 2016). For 2°C of global warming relative to pre-industrial levels, chances of an ice-free Arctic during
summer are substantially higher than at 1.5°C (see Chapter 3, Sections 3.3.5 and 3.3.8), with permafrost melt, increased instancesof storm surge, and extreme weather events anticipated along with later ice freeze up, earlier break up, and a longer ice-free open
water season (Bring et al., 2016; DeBeer et al., 2016; Jiang et al., 2016; Chadburn et al., 2017; Melvin et al., 2017). Negative impacts
on health, infrastructure, and economic sectors (AMAP, 2017a, b, 2018) are projected, although the extension of the summer oceanshipping season has potential economic opportunities (Ford et al., 2015b; Dawson et al., 2016; Ng et al., 2018).

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Strengthening and Implementing the Global Response

Chapter 4

Cross Chapter Box 9 (continued)

Communities, many with indigenous roots, have adapted to environmental change, developing or shifting harvesting activities andpatterns of travel and transitioning economic systems (Forbes et al., 2009; Wenzel, 2009; Ford et al., 2015b; Pearce et al., 2015),
although emotional and psychological effects have been documented (Cunsolo Willox et al., 2012; Cunsolo and Ellis, 2018). Besides
climate change (Keskitalo et al., 2011; Loring et al., 2016), economic and social conditions can constrain the capacity to adapt unlessresources and cooperation are available from public and private sector actors (AMAP, 2017a, 2018) (see Chapter 5, Box 5.3). In
Alaska, the cumulative economic impacts of climate change on public infrastructure are projected at 4.2 billion USD to 5.5 billion
USD from 2015 to 2099, with adaptation efforts halving these estimates (Melvin et al., 2017). Marginalization, colonization, andland dispossession provide broader underlying challenges facing many communities across the circumpolar north in adapting tochange (Ford et al., 2015a; Sejersen, 2015) (see Section 4.3.5).
Adaptation opportunities include alterations to building codes and infrastructure design, disaster risk management, and surveillance(Ford et al., 2014a; AMAP, 2017a, b; Labbé et al., 2017). Most adaptation initiatives are currently occurring at local levels in response
to both observed and projected environmental changes as well as social and economic stresses (Ford et al., 2015a). In a recent studyof Canada, most adaptations were found to be in the planning stages (Labbé et al., 2017). Studies have suggested that a number of
the adaptation actions are not sustainable, lack evaluation frameworks, and hold potential for maladaptation (Loboda, 2014; Ford etal., 2015a; Larsson et al., 2016). Utilizing indigenous and local knowledge and stakeholder engagement can aid the development ofadaptation policies and broader sustainable development, along with more proactive and regionally coherent adaptation plans andactions, and regional cooperation (e.g., through the Arctic Council) (Larsson et al., 2016; AMAP, 2017a; Melvin et al., 2017; Forbis Jr
and Hayhoe, 2018) (see Section 4.3.5).
Caribbean Small Island Developing States (SIDS) and Territories
Extreme weather, linked to tropical storms and hurricanes, represent one of the largest risks facing Caribbean island nations(Chapter 3, Section 3.4.5.3). Non-economic damages include detrimental health impacts, forced displacement and destruction ofcultural heritages. Projections of increased frequency of the most intense storms at 1.5°C and higher warming levels (Wehner etal., 2018; Chapter 3, Section 3.3.6; Box 3.5) are a significant cause for concern, making adaptation a matter of survival (Mycoo andDonovan, 2017).
Despite a shared vulnerability arising from commonalities in location, circumstance and size (Bishop and Payne, 2012; Nurse et al.,2014), adaptation approaches are nuanced by differences in climate governance, affecting vulnerability and adaptive capacity (seeSection 4.4.1). Three cases exemplify differences in disaster risk management.

4

Cuba: Together with a robust physical infrastructure and human-resource base (Kirk, 2017), Cuba has implemented an effective
civil defence system for emergency preparedness and disaster response, centred around community mobilization and preparedness(Kirk, 2017). Legislation to manage disasters, an efficient and robust early warning system, emergency stockpiles, adequate shelter
system and continuous training and education of the population help create a ‘culture of risk’ (Isayama and Ono, 2015; Lizarralde
et al., 2015) which reduces vulnerability to extreme events (Pichler and Striessnig, 2013). Cuba’s infrastructure is still susceptible todevastation, as seen in the aftermath of the 2017 hurricane season.
United Kingdom Overseas Territories (UKOT): All UKOT have developed National Disaster Preparedness Plans (PAHO/WHO,
2016) and are part of the Caribbean Disaster Risk Management Program which aims to improve disaster risk management
within the health sector. Different vulnerability levels across the UKOT (Lam et al., 2015) indicate the benefits of greater regional
cooperation and capacity-building, not only within UKOT, but throughout the Caribbean (Forster et al., 2011). While sovereign statesin the region can directly access climate funds and international support, Dependent Territories are reliant on their controllingstates (Bishop and Payne, 2012). There tends to be low-scale management for environmental issues in UKOT, which increases
UKOT’s vulnerability. Institutional limitations, lack of human and financial resources, and limited long-term planning are identified
as barriers to adaptation (Forster et al., 2011).
Jamaica: Disaster management is coordinated through a hierarchy of national, parish and community disaster committees under theleadership of the Office of Disaster Preparedness and Emergency Management (ODPEM). ODPEM coordinates disaster preparedness
and risk-reduction efforts among key state and non-state agencies (Grove, 2013). A National Disaster Committee provides technicaland policy oversight to the ODPEM and is composed of representatives from multiple stakeholders (Osei, 2007). Most initiatives
are primarily funded through a mix of multilateral and bilateral loan and grant funding focusing on strengthening technical andinstitutional capacities of state- and research-based institutions and supporting integration of climate change considerations into
national and sectoral development plans (Robinson, 2017).

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Cross Chapter Box 9 (continued)

To improve climate change governance in the region, Pittman et al. (2015) suggest incorporating holistic and integrated management
systems, improving flexibility in collaborative processes, implementing monitoring programs, and increasing the capacity of local
authorities. Implementation of the 2030 Sustainable Development Agenda and the Sustainable Development Goals (SDGs) cancontribute to addressing the risks related with extreme events (Chapter 5, Box 5.3).
The Amazon
Terrestrial forests, such as the Amazon, are sensitive to changes in the climate, particularly drought (Laurance and Williamson, 2001)
which might intensify through the 21st century (Marengo and Espinoza, 2016) (Chapter 3, Section 3.5.5.6).
The poorest communities in the region face substantial risks with climate change, and barriers and limits to adaptive capacity (Maruet al., 2014; Pinho et al., 2014, 2015; Brondízio et al., 2016). The Amazon is considered a hotspot, with interconnections between
increasing temperature, decreased precipitation and hydrological flow (Betts et al., 2018) (Sections 3.3.2.2, 3.3.3.2 and 3.3.5); lowlevels of socio-economic development (Pinho et al., 2014); and high levels of climate vulnerability (Darela et al., 2016). Limiting
global warming to 1.5°C could increase food and water security in the region compared to 2°C (Betts et al., 2018), reduce the impact
on poor people and sustainable development, and make adaptation easier (O’Neill et al., 2017), particularly in the Amazon (Bathiany
et al., 2018) (Chapter 5, Section 5.2.2).
Climate policy in many Amazonian nations has focused on forests as carbon sinks (Soares-Filho et al., 2010). In 2009, the Brazilian
National Policy on Climate Change acknowledged adaptation as a concern, and the government sought to mainstream adaptation
into public administration. Brazil’s National Adaptation Plan sets guidelines for sectoral adaptation measures, primarily by developing
capacity building, plans, assessments and tools to support adaptive decision-making. Adaptation is increasingly being presented
as having mitigation co-benefits in the Brazilian Amazon (Gregorio et al., 2016), especially within ecosystem-based adaptation(Locatelli et al., 2011). In Peru’s Framework Law for Climate Change, every governmental sector will consider climatic conditions as
potential risks and/or opportunities to promote economic development and to plan adaptation.
Drought and flood policies have had limited effectiveness in reducing vulnerability (Marengo et al., 2013). In the absence of effective
adaptation, achieving the SDGs will be challenging, mainly in poverty, health, water and sanitation, inequality and gender equality(Chapter 5, Section 5.2.3).

4

Urban systems
Around 360 million people reside in urban coastal areas where precipitation variability is exposing inadequacies of urban infrastructureand governance, with the poor being especially vulnerable (Reckien et al., 2017) (Cross-Chapter Box 13 in Chapter 5). Urban systems
have seen growing adaptation action (Revi et al., 2014b; Araos et al., 2016b; Amundsen et al., 2018). Developing cities spend more
on health and agriculture-related adaptation options while developed cities spend more on energy and water (Georgeson et al.,2016). Current adaptation activities are lagging in emerging economies, which are major centres of population growth facing complex
interrelated pressures on investment in health, housing and education (Georgeson et al., 2016; Reckien et al., 2017).
New York, United States: Adaptation plans are undertaken across government levels, sectors and departments (NYC Parks, 2010;
Vision 2020 Project Team, 2011; PlaNYC, 2013), and have been advanced by an expert science panel that is obligated by local city
law to provide regular updates on policy-relevant climate science (NPCC, 2015). Federal initiatives include 2013’s Rebuild By Designcompetition to promote resilience through infrastructural projects (HSRTF, 2013). In 2013 the Mayor’s office, in response to Hurricane
Sandy, published the city’s adaptation strategy (PlaNYC, 2013). In 2015, the OneNYC Plan for a Strong and Just City (OneNYC Team,
2015) laid out a strategy for urban planning through a justice and equity lens. In 2017, new climate resiliency guidelines proposedthat new construction must include sea level rise projections into planning and development (ORR, 2018). Although this attention
to climate-resilient development may help reduce income inequality, its full effect could be constrained if a policy focus on resilienceobscures analysis of income redistribution for the poor (Fainstein, 2018).
Kampala, Uganda: Kampala Capital City Authority (KCCA) has the statutory responsibility for managing the city. The Kampala
Climate Change Action Strategy (KCCAS) is responding to climatic impacts of elevated temperature and more intense, erratic rain.
KCCAS has considered multi-scale and temporal aspects of response (Chelleri et al., 2015; Douglas, 2017; Fraser et al., 2017),
strengthened community adaptation (Lwasa, 2010; Dobson, 2017), responded to differential adaptive capacities (Waters andAdger, 2017) and believes in participatory processes and bridging of citywide linkages (KCCA, 2016). Analysis of the implicationsof uniquely adapted local solutions (e.g., motorcycle taxis) suggests sustainability can be enhanced when planning recognizes theneed to adapt to uniquely local solutions (Evans et al., 2018).

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Chapter 4

Cross Chapter Box 9 (continued)

Rotterdam, The Netherlands: The Rotterdam Climate Initiative (RCI) was launched to reduce greenhouse gas emissions andclimate-proof Rotterdam (RCI, 2017). Rotterdam has an integrated adaptation strategy, built on flood management, accessibility,
adaptive building, urban water systems and urban climate, defined through the Rotterdam Climate Proof programme and theRotterdam Climate Change Adaptation Strategy (RCI, 2008, 2013). Governance mechanisms that enabled integration of floodrisk management plans with other policies, citizen participation, institutional eco-innovation, and focusing on green infrastructure(Albers et al., 2015; Dircke and Molenaar, 2015; de Boer et al., 2016a; Huang-Lachmann and Lovett, 2016) have contributed toeffective adaptation (Ward et al., 2013). Entrenched institutional characteristics constrain the response framework (FranceschHuidobro et al., 2017), but emerging evidence suggests that new governance arrangements and structures can potentially overcome
these barriers in Rotterdam (Hölscher et al., 2018).

4.3.6

Short-Lived Climate Forcers

The main short-lived climate forcer (SLCF) emissions that cause
warming are methane (CH4), other precursors of tropospheric ozone(i.e., carbon monoxide (CO), non-methane volatile organic compounds(NMVOC), black carbon (BC) and hydrofluorocarbons (HFCs); Myhre etal., 2013). SLCFs also include emissions that lead to cooling, such assulphur dioxide (SO2) and organic carbon (OC). Nitrogen oxides (NOx)can have both warming and cooling effects, by affecting ozone (O3)and CH4, depending on time scale and location (Myhre et al., 2013).
Cross-Chapter Box 2 in Chapter 1 provides a discussion of role of
SLCFs in comparison to long-lived GHGs. Chapter 2 shows that
1.5°C-consistent pathways require stringent reductions in CO2 andCH4, and that non-CO2 climate forcers reduce carbon budgets byabout 2200 GtCO2 per degree of warming attributed to them (see theSupplementary Material to Chapter 2).
Reducing non-CO2 emissions is part of most mitigation pathways(IPCC, 2014c). All current GHG emissions and other forcing agentsaffect the rate and magnitude of climate change over the next few
decades, while long-term warming is mainly driven by CO2 emissions.
CO2 emissions result in a virtually permanent warming, whiletemperature change from SLCFs disappears within decades after
emissions of SLCFs are ceased. Any scenario that fails to reduce CO2
emissions to net zero would not limit global warming, even if SLCFs are
reduced, due to accumulating CO2-induced warming that overwhelms
SLCFs’ mitigation benefits in a couple of decades (Shindell et al., 2012;
Schmale et al., 2014) (and see Chapter 2, Section 2.3.3.2).
Mitigation options for warming SLCFs often overlap with othermitigation options, especially since many warming SLCFs areco-emitted with CO2. SLCFs are generally mitigated in 1.5°C- or
2°C-consistent pathways as an integral part of an overall mitigation
strategy (Chapter 2). For example, Section 2.3 indicates that most verylow-emissions pathways include a transition away from the use of coaland natural gas in the energy sector and oil in transportation, whichcoincides with emission-reduction strategies related to methane fromthe fossil fuel sector and BC from the transportation sector. Much SLCF
emission reduction aims at BC-rich sectors and considers the impactsof several co-emitted SLCFs (Bond et al., 2013; Sand et al., 2015; Stohl
et al., 2015). The benefits of such strategies depend greatly upon theassumed level of progression of access to modern energy for thepoorest populations who still rely on biomass fuels, as this affects thereference level of BC emissions (Rogelj et al., 2014).
Some studies have evaluated the focus on SLCFs in mitigation strategies
and point towards trade-offs between short-term SLCF benefits
and lock-in of long-term CO2 warming (Smith and Mizrahi, 2013;
Pierrehumbert, 2014). Reducing fossil fuel combustion will reduceaerosols levels, and thereby cause warming from removal of aerosol
cooling effects (Myhre et al., 2013; Xu and Ramanathan, 2017; Samset
et al., 2018). While some studies have found a lower temperature effectfrom BC mitigation, thus questioning the effectiveness of targeted BC
mitigation for climate change mitigation (Myhre et al., 2013; Baker etal., 2015; Stjern et al., 2017; Samset et al., 2018), other models andobservationally constrained estimates suggest that these widely-usedmodels do not fully capture observed effects of BC and co-emissions
on climate (e.g., Bond et al., 2013; Cui et al., 2016; Peng et al., 2016).
Table 4.5 provides an overview of three warming SLCFs and theiremission sources, with examples of options for emission reductions
and associated co-benefits.
A wide range of options to reduce SLCF emissions was extensively
discussed in AR5 (IPCC, 2014b). Fossil fuel and waste sector methane
mitigation options have high cost-effectiveness, producing a net profit
over a few years, considering market costs only. Moreover, reducing
roughly one-third to one-half of all human-caused emissions has
societal benefits greater than mitigation costs when considering
environmental impacts only (UNEP, 2011; Höglund-Isaksson, 2012; IEA,
2017b; Shindell et al., 2017a). Since AR5, new options for methane,such as those related to shale gas, have been included in mitigation
portfolios (e.g., Shindell et al., 2017a).
Reducing BC emissions and co-emissions has sustainable development
co-benefits, especially around human health (Stohl et al., 2015;
Haines et al., 2017; Aakre et al., 2018), avoiding premature deaths
and increasing crop yields (Scovronick et al., 2015; Peng et al., 2016).
Additional benefits include lower likelihood of non-linear climatechanges and feedbacks (Shindell et al., 2017b) and temporarily slowing
down the rate of sea level rise (Hu et al., 2013). Interventions to reduce
BC offer tangible local air quality benefits, increasing the likelihood of
local public support (Eliasson, 2014; Venkataraman et al., 2016) (seeChapter 5, Section 5.4.2.1). Limited interagency co-ordination, poor
science-policy interactions (Zusman et al., 2015), and weak policy and341

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Table 4.5 | Overview of main characteristics of three warming short-lived climate forcers (SLCFs) (core information based on Pierrehumbert, 2014 and Schmale et al., 2014;
rest of the details as referenced).
SLCF
CompoundAnnual Global
Emission

Main Anthropogenic
Emission Sources

Examples of Options
to Reduce Emissions
Consistent with 1.5°C
Managing manure from livestock;
Intermittent irrigation of rice;
Capture and usage of fugitive
methane;
Dietary change;
For more: see Section 4.3.2

Reduction of tropospheric ozone(Shindell et al., 2017a);
Health benefits of dietary changes;
Increased crop yields;
Improved access to drinking water

MethaneOn the orderof 10 years

0.3 GtCH4 (2010)(Pierrehumbert, 2014)

HFCs

Months to decades,
depending on the gas

0.35 GtCO2-eq (2010)(Velders et al., 2015)

Air conditioning; Refrigeration;
Construction material

Alternatives to HFCs inair-conditioning and refrigeration
applications

Greater energy efficiency(Mota-Babiloni et al., 2017)~7 Mt (2010)(Klimont et al., 2017)

Incomplete combustion of fossil
fuels or biomass in vehicles (esp.
diesel), cook stoves or kerosene
lamps;
Field and biomass burning

Fewer and cleaner vehicles; Reducing
agricultural biomass burning;
Cleaner cook stoves, gas-basedor electric cooking;Replacing brick and coke ovens;
Solar lamps;
For more see Section 4.3.3

Health benefits of better air quality;
Increased education opportunities;
Reduced coal consumption for modern
brick kilns;
Reduced deforestationDays

absence of inspections and enforcement (Kholod and Evans, 2016) areamong barriers that reduce the institutional feasibility of options to
reduce vehicle-induced BC emissions. A case study for India shows thatswitching from biomass cook stoves to cleaner gas stoves (based onliquefied petroleum gas or natural gas) or to electric cooking stoves istechnically and economically feasible in most areas, but faces barriers
in user preferences, costs and the organization of supply chains(Jeuland et al., 2015). Similar feasibility considerations emerge inswitching from kerosene wick lamps for lighting to solar lanterns, fromcurrent low-efficiency brick kilns and coke ovens to cleaner production
technologies; and from field burning of crop residues to agricultural
practices using deep-sowing and mulching technologies (Williams etal., 2011; Wong, 2012).
The radiative forcing from HFCs are currently small but have been
growing rapidly (Myhre et al., 2013). The Kigali Amendment (from2016) to the Montreal Protocol set out a global accord for phasingout these compounds (Höglund-Isaksson et al., 2017). HFC mitigation
options include alternatives with reduced warming effects, ideallycombined with improved energy efficiency so as to simultaneously
reduce CO2 and co-emissions (Shah et al., 2015). Costs for mostof HFC’s mitigation potential are estimated to be below USD2010
60 tCO2-eq−1, and the remainder below roughly double that number(Höglund-Isaksson et al., 2017).
Reductions in SLCFs can provide large benefits towards sustainable
development, beneficial for social, institutional and economic
feasibility. Strategies that reduce SLCFs can provide benefits that
include improved air quality (e.g., Anenberg et al., 2012) and crop yields(e.g., Shindell et al., 2012), energy access, gender equality and poverty
eradication (e.g.,Shindell et al., 2012; Haines et al., 2017). Institutional
feasibility can be negatively affected by an information deficit, with
4

Examples of Co-Benefits
Based on Haines et al. (2017)
Unless Specified Otherwise

Fossil fuel extraction andtransportation;
Land-use change;
Livestock and rice cultivation;
Waste and wastewaterBlack Carbon

4

Atmospheric
Lifetime

the absence of international frameworks for integrating SLCFs into
emissions accounting and reporting mechanisms being a barrier todeveloping policies for addressing SLCF emissions (Venkataraman etal., 2016). The incentives for reducing SLCFs are particularly strong forsmall groups of countries, and such collaborations could increase thefeasibility and effectiveness of SLCF mitigation options (Aakre et al.,2018).

4.3.7

Carbon Dioxide Removal (CDR)

CDR methods refer to a set of techniques for removing CO2 from theatmosphere. In the context of 1.5°C-consistent pathways (Chapter 2),
they serve to offset residual emissions and, in most cases, achieve net
negative emissions to return to 1.5°C from an overshoot. See CrossChapter Box 7 in Chapter 3 for a synthesis of land-based CDR options.
Cross-cutting issues and uncertainties are summarized in Table 4.6.

4.3.7.1

Bioenergy with carbon capture and storage (BECCS)

BECCS has been assessed in previous IPCC reports (IPCC, 2005b,
2014b; P. Smith et al., 2014; Minx et al., 2017) and has been
incorporated into integrated assessment models (Clarke et al., 2014),
but also 1.5°C-consistent pathways without BECCS have emerged(Bauer et al., 2018; Grubler et al., 2018; Mousavi and Blesl, 2018; vanVuuren et al., 2018). Still, the overall set of pathways limiting global
warming to 1.5°C with limited or no overshoot indicates that 0–1, 0–8,
and 0–16 GtCO2 yr−1 would be removed by BECCS by 2030, 2050 and
2100, respectively (Chapter 2, Section 2.3.4). BECCS is constrained bysustainable bioenergy potentials (Section 4.3.1.2, Chapter 5, Section
5.4.1.3 and Cross-Chapter Box 6 in Chapter 3), and availability ofsafe storage for CO2 (Section 4.3.1.6). Literature estimates for BECCS
mitigation potentials in 2050 range from 1–85 GtCO24. Fuss et al.

As more bottom-up literature exists on bioenergy potentials, this exercise explored the bioenergy literature and converted those estimates to BECCS potential with 1EJ of
bioenergy yielding 0.02–0.05 GtCO2 emission reduction. For the bottom-up literature references for the potentials range, please refer to Supplementary Material 4.SM.3
Table 1.

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Strengthening and Implementing the Global Response(2018) narrow this range to 0.5–5 GtCO2 yr−1 (medium agreement,high evidence) (Figure 4.3), meaning that BECCS mitigation potentials
are not necessarily sufficient for 1.5°C-consistent pathways. This is,among other things, related to sustainability concerns (Boysen et al.,2017; Heck et al., 2018; Henry et al., 2018).
Assessing BECCS deployment in 2°C pathways (of about 12
GtCO2-eq yr−1 by 2100, considered as a conservative deployment
estimate for BECCS-accepting pathways consistent with 1.5°C), Smithet al. (2016b) estimate a land-use intensity of 0.3–0.5 ha tCO2-eq−1 yr−1
using forest residues, 0.16 ha CO2-eq−1 yr−1 for agricultural residues,
and 0.03–0.1 ha tCO2-eq−1 yr−1 for purpose-grown energy crops. The
average amount of BECCS in these pathways requires 25–46% ofarable and permanent crop area in 2100. Land area estimates differ
in scale and are not necessarily a good indicator of competition with,for example, food production, because requiring a smaller land area forthe same potential could indicate that high-productivity agriculturalland is used. In general, the literature shows low agreement on theavailability of land (Fritz et al., 2011; see Erb et al., 2016b for recent
advances). Productivity, food production and competition with otherecosystem services and land use by local communities are importantfactors for designing regulation. These potentials and trade-offs are nothomogenously distributed across regions. However, Robledo-Abad etal. (2017) find that regions with higher potentials are understudied,
given their potential contribution. Researchers have expressed theneed to complement global assessments with regional, geographically
explicit bottom-up studies of biomass potentials and socio-economic
impacts (e.g., de Wit and Faaij, 2010; Kraxner et al., 2014; Baik et al.,2018).
Energy production and land and water footprints show wide ranges
in bottom-up assessments due to differences in technology, feedstockand other parameters (−1–150 EJ yr−1 of energy, 109–990 Mha, 6–79
MtN, 218–4758 km3 yr−1 of water per GtCO2 yr−1; Smith and Torn,
2013; Smith et al., 2016b; Fajardy and Mac Dowell, 2017) and are notcomparable to IAM pathways which consider system effects (Baueret al., 2018). Global impacts on nutrients and albedo are difficult toquantify (Smith et al., 2016b). BECCS competes with other land-based
CDR and mitigation measures for resources (Chapter 2).
There is uncertainty about the feasibility of timely upscaling (Nemet etal., 2018). CCS (see Section 4.3.1) is largely absent from the Nationally
Determined Contributions (Spencer et al., 2015) and lowly ranked ininvestment priorities (Fridahl, 2017). Although there are dozens of smallscale BECCS demonstrations (Kemper, 2015) and a full-scale project
capturing 1 MtCO2 exists (Finley, 2014), this is well below the numbers
associated with 1.5°C or 2°C-compatible pathways (IEA, 2016a;
Peters et al., 2017). Although the majority of BECCS cost estimates are
below 200 USD tCO2−1 (Figure 4.2), estimates vary widely. Economic
incentives for ramping up large CCS or BECCS infrastructure are weak
(Bhave et al., 2017). The 2050 average investment costs for such a
BECCS infrastructure for bio-electricity and biofuels are estimated at
138 and 123 billion USD yr−1, respectively (Smith et al., 2016b).
BECCS deployment is further constrained by bioenergy’s carbon
accounting, land, water and nutrient requirements (Section 4.3.1), its
compatibility with other policy goals and limited public acceptance ofChapter 4both bioenergy and CCS (Section 4.3.1). Current pathways are believed
to have inadequate assumptions on the development of societal
support and governance structures (Vaughan and Gough, 2016).
However, removing BECCS and CCS from the portfolio of available
options significantly raises modelled mitigation costs (Kriegler et al.,2013; Bauer et al., 2018).

4.3.7.2

Afforestation and reforestation (AR)Afforestation implies planting trees on land not forested for a longtime (e.g., over the last 50 years in the context of the Kyoto Protocol),while reforestation implies re-establishment of forest formations after
a temporary condition with less than 10% canopy cover due to humaninduced or natural perturbations. Houghton et al. (2015) estimate
about 500 Mha could be available for the re-establishment of forests
on lands previously forested, but not currently used productively. This
could sequester at least 3.7 GtCO2 yr−1 for decades. The full literature
range gives 2050 potentials of 1–7 GtCO2 yr−1 (low evidence, medium
agreement), narrowed down to 0.5–3.6 GtCO2 yr−1 based on a numberof constraints (Fuss et al., 2018). Abatement costs are estimated to
be low compared to other CDR options, 5–50 USD tCO2-eq−1 (robust
evidence, high agreement). Yet, realizing such large potentials comesat higher land and water footprints than BECCS, although there would
be a positive impact on nutrients and the energy requirement would
be negligible (Smith et al., 2016b; Cross-Chapter Box 7 in Chapter 3).
The 2030 estimate by Griscom et al. (2017) is up to 17.9 GtCO2 yr−1
for reforestation with significant co-benefits (Cross-Chapter Box 7 in
Chapter 3).
Biogenic storage is not as permanent as emission reductions bygeological storage. In addition, forest sinks saturate, a process which
typically occurs in decades to centuries compared to the thousandsof years of residence time of CO2 stored geologically (Smith et al.,2016a) and is subject to disturbances that can be exacerbated byclimate change (e.g., drought, forest fires and pests) (Seidl et al., 2017).
Handling these challenges requires careful forest management. Thereis much practical experience with AR, facilitating upscaling but withtwo caveats: AR potentials are heterogeneously distributed (Bala et al.,2007), partly because the planting of less reflective forests results inhigher net absorbed radiation and localised surface warming in higher
latitudes (Bright et al., 2015; Jones et al., 2015), and forest governancestructures and monitoring capacities can be bottlenecks and areusually not considered in models (Wang et al., 2016; Wehkamp et al.,2018b). There is medium agreement on the positive impacts of AR on
ecosystems and biodiversity due to different forms of afforestation
discussed in the literature: afforestation of grassland ecosystems ordiversified agricultural landscapes with monocultures or invasive alien
species can have significant negative impacts on biodiversity, water
resources, etc. (P. Smith et al., 2014), while forest ecosystem restoration(forestry and agroforestry) with native species can have positive socialand environmental impacts (Cunningham et al., 2015; Locatelli et al.,2015; Paul et al., 2016; See Section 4.3.2).
Synergies with other policy goals are possible (see also Section 4.5.4);
for example, land spared by diet shifts could be afforested (Röös et al.,2017) or used for energy crops (Grubler et al., 2018). Such land-sparing
strategies could also benefit other land-based CDR options.
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Strengthening and Implementing the Global Response

4

Figure 4.2 | Evidence on carbon dioxide removal (CDR) abatement costs, 2050 deployment potentials, and key side effects. Panel A presents estimates
based on a systematic review of the bottom up literature (Fuss et al., 2018), corresponding to dashed blue boxes in Panel B. Dashed lines represent saturation limits for thecorresponding technology. Panel B shows the percentage of papers at a given cost or potential estimate. Reference year for all potential estimates is 2050, while all costestimates preceding 2050 have been included (as early as 2030, older estimates are excluded if they lack a base year and thus cannot be made comparable). Ranges havebeen trimmed to show detail (see Fuss et al., 2018 for the full range). Costs refer only to abatement costs. Icons for side-effects are allocated only if a critical mass of papers
corroborates their occurrence
Notes: For references please see Supplementary Material Table 4.SM.3. Direct air carbon dioxide capture and storage (DACCS) is theoretically only constrained by geological storage
capacity, estimates presented are considering upscaling and cost challenges (Nemet et al., 2018). BECCS potential estimates are based on bioenergy estimates in the literature(EJ yr−1), converted to GtCO2 following footnote 4. Potentials cannot be added up, as CDR options would compete for resources (e.g., land). SCS - soil carbon sequestration; OA ocean alkalinization; EW- enhanced weathering; DACCS - direct air carbon dioxide capture and storage; BECCS - bioenergy with carbon capture and storage; AR - afforestation.

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Strengthening and Implementing the Global Response

4.3.7.3

Soil carbon sequestration and biochar

Chapter 4

4.3.7.4

Enhanced weathering (EW) and ocean alkalinization

At local scales there is robust evidence that soil carbon sequestration(SCS, e.g., agroforestry, De Stefano and Jacobson, 2018), restorationof degraded land (Griscom et al., 2017), or conservation agriculture
management practices (Aguilera et al., 2013; Poeplau and Don, 2015;
Vicente-Vicente et al., 2016) have co-benefits in agriculture and thatmany measures are cost-effective even without supportive climate
policy. Evidence at global scale for potentials and especially costs ismuch lower. The literature spans cost ranges of −45–100 USD tCO2−1(negative costs relating to the multiple co-benefits of SCS, such asincreased productivity and resilience of soils; P. Smith et al., 2014),
and 2050 potentials are estimated at between 0.5 and 11 GtCO2 yr−1,
narrowed down to 2.3–5.3 GtCO2 yr−1 considering that studies above
5 GtCO2 yr−1 often do not apply constraints, while estimates lower than
2 GtCO2 yr−1 mostly focus on single practices (Fuss et al., 2018).

Weathering is the natural process of rock decomposition via chemicaland physical processes in which CO2 is spontaneously consumed andconverted to solid or dissolved alkaline bicarbonates and/or carbonates(IPCC, 2005a). The process is controlled by temperature, reactive
surface area, interactions with biota and, in particular, water solution
composition. CDR can be achieved by accelerating mineral weathering
through the distribution of ground-up rock material over land(Hartmann and Kempe, 2008; Wilson et al., 2009; Köhler et al., 2010;
Renforth, 2012; ten Berge et al., 2012; Manning and Renforth, 2013;
Taylor et al., 2016), shorelines (Hangx and Spiers, 2009; Montserrat etal., 2017) or the open ocean (House et al., 2007; Harvey, 2008; Köhler
et al., 2013; Hauck et al., 2016). Ocean alkalinization adds alkalinity tomarine areas to locally increase the CO2 buffering capacity of the ocean(González and Ilyina, 2016; Renforth and Henderson, 2017).

SCS has negligible water and energy requirements (Smith, 2016),
affects nutrients and food security favourably (high agreement, robust
evidence) and can be applied without changing current land use, thus
making it socially more acceptable than CDR options with a high land
footprint. However, soil sinks saturate after 10–100 years, dependingon the SCS option, soil type and climate zone (Smith, 2016).

In the case of land application of ground minerals, the estimated CDR
potential range is 0.72–95 GtCO2 yr−1 (low evidence, low agreement)(Hartmann and Kempe, 2008; Köhler et al., 2010; Hartmann et al.,2013; Taylor et al., 2016; Strefler et al., 2018a). Marine applicationof ground minerals is limited by feasible rates of mineral extraction,
grinding and delivery, with estimates of 1–6 GtCO2 yr−1 (low evidence,
low agreement) (Köhler et al., 2013; Hauck et al., 2016; Renforth andHenderson, 2017). Agreement is low due to a variety of assumptions
and unknown parameter ranges in the applied modelling proceduresthat would need to be verified by field experiments (Fuss et al., 2018).
As with other CDR options, scaling and maturity are challenges, with
deployment at scale potentially requiring decades (NRC, 2015a),considerable costs in transport and disposal (Hangx and Spiers, 2009;
Strefler et al., 2018a) and mining (NRC, 2015a; Strefler et al., 2018a)6.

Biochar is formed by recalcitrant (i.e., very stable) organic carbon
obtained from pyrolysis, which, applied to soil, can increase soil carbon
sequestration leading to improved soil fertility properties.5 Looking at
the full literature range, the global potential in 2050 lies between 1
and 35 Gt CO2 yr−1 (low agreement, low evidence), but considering
limitations in biomass availability and uncertainties due to a lack oflarge-scale trials of biochar application to agricultural soils under field
conditions, Fuss et al. (2018) lower the 2050 range to 0.3–2 GtCO2 yr−1.
This potential is below previous estimates (e.g., Woolf et al., 2010),
which additionally consider the displacement of fossil fuels throughbiochar. Permanence depends on soil type and biochar production
temperatures, varying between a few decades and several centuries(Fang et al., 2014). Costs are 30– 120 USD tCO2−1 (medium agreement,
medium evidence) (McCarl et al., 2009; McGlashan et al., 2012;
McLaren, 2012; Smith, 2016).
Water requirements are low and at full theoretical deployment, up
to 65 EJ yr−1 of energy could be generated as a side product (Smith,2016). Positive side effects include a favourable effect on nutrients andreduced N2O emissions (Cayuela et al., 2014; Kammann et al., 2017).
However, 40–260 Mha are needed to grow the biomass for biochar
for implementation at 0.3 GtCO2-eq yr−1 (Smith, 2016), even though
it is also possible to use residues (e.g., Windeatt et al., 2014). Biochar
is further constrained by the maximum safe holding capacity of soils(Lenton, 2010) and the labile nature of carbon sequestrated in plants
and soil at higher temperatures (Wang et al., 2013).

Site-specific cost estimates vary depending on the chosen technology
for rock grinding (an energy-intensive process; Köhler et al., 2013;
Hauck et al., 2016), material transport, and rock source (Renforth,
2012; Hartmann et al., 2013), and range from 15–40 USD tCO2−1 to
3,460 USD tCO2−1 (limited evidence, low agreement; Figure 4.2)
(Schuiling and Krijgsman, 2006; Köhler et al., 2010; Taylor et al.,2016). The evidence base for costs of ocean alkalinization and marine
enhanced weathering is sparser than the land applications. The ocean
alkalinization potential is assessed to be 0.1–10 GtCO2 yr−1 with costsof 14– >500 USD tCO2−1 (Renforth and Henderson, 2017).
The main side effects of terrestrial EW are an increase in water pH(Taylor et al., 2016), the release of heavy metals like Ni and Cr and plant
nutrients like K, Ca, Mg, P and Si (Hartmann et al., 2013), and changes inhydrological soil properties. Respirable particle sizes, though resulting inhigher potentials, can have impacts on health (Schuiling and Krijgsman,
2006; Taylor et al., 2016); utilization of wave-assisted decomposition
through deployment on coasts could avert the need for fine grinding(Hangx and Spiers, 2009; Schuiling and de Boer, 2010). Side effects

5

Other pyrolysis products that can achieve net CO2 removals are bio-oil (pumped into geological storages) and permanent-pyrogas (capture and storage of CO2 from gas
combustion) (Werner et al., 2018)6

It has also been suggested that ocean alkalinity can be increased through accelerated weathering of limestone (Rau and Caldeira, 1999; Rau, 2011; Chou et al., 2015) orelectrochemical processes (House et al., 2007; Rau, 2008; Rau et al., 2013; Lu et al., 2015). However, these techniques have not been proven at large scale either(Renforth and Henderson, 2017).

345

4

Chapter 4of marine EW and ocean alkalinization are the potential release ofheavy metals like Ni and Cr (Montserrat et al., 2017). Increasing ocean
alkalinity helps counter ocean acidification (Albright et al., 2016; Fenget al., 2016). Ocean alkalinization could affect ocean biogeochemicalfunctioning (González and Ilyina, 2016). A further caveat of relates tosaturation state and the potential to trigger spontaneous carbonate
precipitation.7 While the geochemical potential to remove and store
CO2 is quite large, limited evidence on the preceding topics makes it
difficult to assess the true capacity, net benefits and desirability of EW
and ocean alkalinity addition in the context of CDR.

4.3.7.5

Direct air carbon dioxide capture and storage (DACCS)

Capturing CO2 from ambient air through chemical processes withsubsequent storage of the CO2 in geological formations is independentof source and timing of emissions and can avoid competition for land.
Yet, this is also the main challenge: while the theoretical potential
for DACCS is mainly limited by the availability of safe and accessible
geological storage, the CO2 concentration in ambient air is 100–300
times lower than at gas- or coal-fired power plants (Sanz-Pérez et al.,2016) thus requiring more energy than flue gas CO2 capture (Pritchard
et al., 2015). This appears to be the main challenge to DACCS (SanzPérez et al., 2016; Barkakaty et al., 2017).
Studies explore alternative techniques to reduce the energy penalty
of DACCS (van der Giesen et al., 2017). Energy consumption could be
up to 12.9 GJ tCO2-eq−1; translating into an average of 156 EJ yr−1 by
2100 (current annual global primary energy supply is 600 EJ); waterrequirements are estimated to average 0.8–24.8 km3 GtCO2-eq−1 yr−1
(Smith et al., 2016b, based on Socolow et al., 2011).

4

However, the literature shows low agreement and is fragmented(Broehm et al., 2015). This fragmentation is reflected in a large rangeof cost estimates: from 20–1,000 USD tCO2−1 (Keith et al., 2006; Pielke,
2009; House et al., 2011; Ranjan and Herzog, 2011; Simon et al., 2011;
Goeppert et al., 2012; Holmes and Keith, 2012; Zeman, 2014; SanzPérez et al., 2016; Sinha et al., 2017). There is lower agreement and asmaller evidence base at the lower end of the cost range. Fuss et al.
(2018) narrow this range to 100–300 USD tCO2-1.
Research and efforts by small-scale commercialization projects focus
on utilization of captured CO2 (Wilcox et al., 2017). Given that only
a few IAM scenarios incorporate DACCS (e.g., Chen and Tavoni,2013; Strefler et al., 2018b) its possible role in cost-optimized 1.5°C
scenarios is not yet fully explored. Given the technology’s early stageof development (McLaren, 2012; NRC, 2015a; Nemet et al., 2018)
and few demonstrations (Holmes et al., 2013; Rau et al., 2013; Agee

7

Strengthening and Implementing the Global Response

et al., 2016), deploying the technology at scale is still a considerable
challenge, though both optimistic (Lackner et al., 2012) and pessimistic
outlooks exist (Pritchard et al., 2015).

4.3.7.6

Ocean fertilization

Nutrients can be added to the ocean resulting in increased biologic
production, leading to carbon fixation in the sunlit ocean andsubsequent sequestration in the deep ocean or sea floor sediments.
The added nutrients can be either micronutrients (such as iron) or
macronutrients (such as nitrogen and/or phosphorous) (Harrison,2017). There is limited evidence and low agreement on the readiness of
this technology to contribute to rapid decarbonization (Williamson etal., 2012). Only small-scale field experiments and theoretical modelling
have been conducted (e.g., McLaren, 2012). The full range of CDR
potential estimates is from 15.2 ktCO2 yr−1 (Bakker et al., 2001) for a
spatially constrained field experiment up to 44 GtCO2 yr−1 (Sarmiento
and Orr, 1991) following a modelling approach, but Fuss et al. (2018)consider the potential to be extremely limited given the evidence andexisting barriers. Due to scavenging of iron, the iron addition only leads
to inefficient use of the nitrogen in exporting carbon (Zeebe, 2005;
Aumont and Bopp, 2006; Zahariev et al., 2008).
Cost estimates range from 2 USD tCO2−1 (for iron fertilization) (Boydand Denman, 2008) to 457 USD tCO2−1 (Harrison, 2013). Jones (2014)
proposed values greater than 20 USD tCO2−1 for nitrogen fertilization.
Fertilization is expected to impact food webs by stimulating its baseorganisms (Matear, 2004), and extensive algal blooms may cause
anoxia (Sarmiento and Orr, 1991; Matear, 2004; Russell et al., 2012)and deep water oxygen decline (Matear, 2004), with negative impacts
on biodiversity. Nutrient inputs can shift ecosystem production froman iron-limited system to a P, N-, or Si-limited system depending on
the location (Matear, 2004; Bertram, 2010) and non-CO2 GHGs may
increase (Sarmiento and Orr, 1991; Matear, 2004; Bertram, 2010). The
greatest theoretical potential for this practice is the Southern Ocean,posing challenges for monitoring and governance (Robinson et al.,2014). The London Protocol of the International Maritime Organization
has asserted authority for regulation of ocean fertilization (Strong et al.,2009), which is widely viewed as a de facto moratorium on commercial
ocean fertilization activities.
There is low agreement in the technical literature on the permanenceof CO2 in the ocean, with estimated residence times of 1,600 years
to millennia, especially if injected or buried in or below the sea floor(Williams and Druffel, 1987; Jones, 2014). Storage at the surface would
mean that the carbon would be rapidly released after cessation (Zeebe,
2005; Aumont and Bopp, 2006).

This analysis relies on the assessment in Fuss et al. (2018), which provides more detail on saturation and permanence.

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Chapter 4

Table 4.6 | Cross-cutting issues and uncertainties across carbon dioxide removal (CDR) options, aspects and uncertainties
Area of Uncertainty

Cross-Cutting Issues and Uncertainties

Technology upscaling• CDR options are at different stages of technological readiness (McLaren, 2012) and differ with respect to scalability.
• Nemet et al. (2018) find >50% of the CDR innovation literature concerned with the earliest stages of the innovation process (R&D), identifying a
dissonance between the large CO2 removals needed in 1.5°C pathways and the long -time periods involved in scaling up novel technologies.
• Lack of post-R&D literature, including incentives for early deployment, niche markets, scale up, demand, and public acceptance.

Emerging and nichetechnologies• For BECCS, there are niche opportunities with high efficiencies and fewer trade-offs, for example, sugar and paper processing facilities (Möllersten et al., 2003),
district heating (Kärki et al., 2013; Ericsson and Werner, 2016), and industrial and municipal waste (Sanna et al., 2012). Turner et al. (2018) constrain potential using
sustainability considerations and overlap with storage basins to avoid the CO2 transportation challenge, providing a possible, though limited entry point for BECCS.
• The impacts on land use, water, nutrients and albedo of BECCS could be alleviated using marine sources of biomass that could include aquacultured micro
and macro flora (Hughes et al., 2012; Lenton, 2014).
• Regarding captured CO2 as a resource is discussed as an entry point for CDR. However, this does not necessarily lead to carbon removals, particularly if
the CO2 is sourced from fossil fuels and/or if the products do not store the CO2 for climate-relevant horizons (von der Assen et al., 2013) (see also Section 4.3.4.5).
• Methane8 is a much more potent GHG than CO2 (Montzka et al., 2011), associated with difficult-to-abate emissions in industry and agriculture and with
outgassing from lakes, wetlands, and oceans (Lockley, 2012; Stolaroff et al., 2012). Enhancing processes that naturally remove methane, either by chemical
or biological decomposition (Sundqvist et al., 2012), has been proposed to remove CH4. There is low confidence that existing technologies for CH4
removal are economically or energetically suitable for large-scale air capture (Boucher and Folberth, 2010). Methane removal potentials are limited due to
its low atmospheric concentration and its low chemical reactivity at ambient conditions.

Ethical aspects• Preston (2013) identifies distributive and procedural justice, permissibility, moral hazard (Shue, 2018), and hubris as ethical aspects that could apply tolarge-scale CDR deployment.
• There is a lack of reflection on the climate futures produced by recent modelling and implying very different ethical costs/risks and benefits (Minx et al., 2018).

Governance• Existing governance mechanisms are scarce and either targeted at particular CDR options (e.g., ocean-based) or aspects (e.g., concerning indirect land-use
change (iLUC)) associated with bioenergy upscaling, and often the mechanisms are at national or regional scale (e.g., EU). Regulation accounting for iLUC
by formulating sustainability criteria (e.g., the EU Renewable Energy Directive) has been assessed as insufficient in avoiding leakage (e.g., Frank et al., 2013).
• An international governance mechanism is only in place for R&D of ocean fertilization within the Convention on Biological Diversity (IMO, 1972, 1996; CBD, 2008, 2010).
• Burns and Nicholson (2017) propose a human rights-based approach to protect those potentially adversely impacted by CDR options.

Policy• The CDR potentials that can be realized are constrained by the lack of policy portfolios incentivising large-scale CDR (Peters and Geden, 2017).
• Near-term opportunities could be supported through modifying existing policy mechanisms (Lomax et al., 2015).
• Scott and Geden (2018) sketch three possible routes for limited progress, (i) at EU-level, (ii) at EU Member State level, and (iii) at private sector level, noting
the implied paradigm shift this would entail.
• EU may struggle to adopt policies for CDR deployment on the scale or time-frame envisioned by IAMs (Geden et al., 2018).
• Social impacts of large-scale CDR deployment (Buck, 2016) require policies taking these into account.

Carbon cycle• On long time scales, natural sinks could reverse (C.D. Jones et al., 2016)• No robust assessments yet of the effectiveness of CDR in reverting climate change (Tokarska and Zickfeld, 2015; Wu et al., 2015; Keller et al., 2018),
see also Chapter 2, Section 2.2.2.2.

4.3.8

Solar Radiation Modification (SRM)

This report refrains from using the term ‘geoengineering’ and separates
SRM from CDR and other mitigation options (see Chapter 1, Section
1.4.1 and Glossary).
Table 4.7 gives an overview of SRM methods and characteristics. For a
more comprehensive discussion of currently proposed SRM methods,
and their implications for geophysical quantities and sustainable
development, see also Cross-Chapter Box 10 in this Chapter. Thissection assesses the feasibility, from an institutional, technological,
economic and social-cultural viewpoint, focusing on stratosphericaerosol injection (SAI) unless otherwise indicated, as most available
literature is about SAI.
Some of the literature on SRM appears in the forms of commentaries,
policy briefs, viewpoints and opinions (e.g., (Horton et al., 2016; Keith etal., 2017; Parson, 2017). This assessment covers original research rather
than viewpoints, even if the latter appear in peer-reviewed journals.

84

SRM could reduce some of the global risks of climate change related
to temperature rise (Izrael et al., 2014; MacMartin et al., 2014), rate ofsea level rise (Moore et al., 2010), sea-ice loss (Berdahl et al., 2014) andfrequency of extreme storms in the North Atlantic and heatwaves in
Europe (Jones et al., 2018). SRM also holds risks of changing precipitationand ozone concentrations and potentially reductions in biodiversity(Pitari et al., 2014; Visioni et al., 2017a; Trisos et al., 2018). Literature
only supports SRM as a supplement to deep mitigation, for example inovershoot scenarios (Smith and Rasch, 2013; MacMartin et al., 2018).

4.3.8.1

Governance and institutional feasibility

There is robust evidence but medium agreement for unilateral action
potentially becoming a serious SRM governance issue (Weitzman,
2015; Rabitz, 2016), as some argue that enhanced collaboration
might emerge around SRM (Horton, 2011). An equitable institutionalor governance arrangement around SRM would have to reflect
views of different countries (Heyen et al., 2015) and be multilateralbecause of the risk of termination, and risks that implementation or
unilateral action by one country or organization will produce negative
precipitation or extreme weather effects across borders (Lempert andCurrent work (e.g., de Richter et al., 2017) examines other technologies considering non-CO2 GHGs like N2O.

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Table 4.7 | Overview of the main characteristics of the most-studied SRM methods.
SRM indicator

Stratospheric Aerosol
injection (SAI)

Marine Cloud
Brightening (MCB)

Cirrus Cloud
Thinning (CCT)

Ground-Based Albedo
Modification (GBAM)

Description of
SRM method

Injection of a gas in thestratosphere, which then converts
to aerosols. Injection of otherparticles also considered.

Spraying sea salt or otherparticles into marine clouds,
making them more reflective.

Seeding to promote nucleation, reducing
optical thickness and cloud lifetime,to allow more outgoing longwave
radiation to escape into space.

Whitening roofs, changes in land use
management (e.g., no-till farming),change of albedo at a larger scale(covering glaciers or deserts with reflective
sheeting and changes in ocean albedo).

Radiative forcing
efficiencies

1–4 TgS W−1 m2 yr−1

100–295 Tg dry sea
salt W−1 m2 yr−1

Not known

Small on global scale, up to 1°C–3°C
on regional scale

Amount neededfor 1°C overshoot

2–8 TgS yr−1

70 Tg dry sea salt yr−1

Not known

0.04–0.1 albedo change in agriculturaland urban areas

SRM specificimpacts on climate
variables

Changes in precipitation patterns
and circulation regimes; in caseof SO2 injection, disruption to
stratospheric chemistry (forinstance NOx depletion andchanges in methane lifetime);
increase in stratospheric water
vapour and troposphericstratospheric ice formation
affecting cloud microphysics

Regional rainfall responses;
reduction in hurricane intensity

Low-level cloud changes;
tropospheric drying; intensificationof the hydrological cycle

Impacts on precipitation in monsoon areas;
could target hot extremes

SRM specificimpacts on human/natural systems

In case of SO2 injection,
stratospheric ozone loss (which
could also have a positiveeffect – a net reduction in global
mortality due to competing
health impact pathways) andsignificant increase of surface UVReduction in the numberof mild crop failures

Not known

Not known

Maturity of science

Volcanic analogues; highagreement amongst simulations;robust evidence on ethical,
governance and sustainable
development limitations

Observed in ships tracks;
several simulations confirm
mechanism;
regionally limited

No clear physical mechanism;
limited evidence and low agreement;
several simulations

Natural and land-use analogues;
several simulations confirm mechanism;high agreement to influence on regionaltemperature; land use costly

Key references

Robock et al., 2008;
Heckendorn et al., 2009;
Tilmes et al., 2012, 2016;
Pitari et al., 2014;
Crook et al., 2015;
C.J. Smith et al., 2017;
Visioni et al., 2017a, b;
Eastham et al., 2018;
Plazzotta et al., 2018

Salter et al., 2008;
Alterskjær et al., 2012;
Jones and Haywood, 2012;
Latham et al., 2012, 2013;
Kravitz et al., 2013;
Crook et al., 2015;
Parkes et al., 2015;
Ahlm et al., 2017

Storelvmo et al., 2014;
Kristjánsson et al., 2015;
Jackson et al., 2016;
Kärcher, 2017;
Lohmann and Gasparini, 2017

Irvine et al., 2011;
Akbari et al., 2012;
Jacobson and Ten Hoeve, 2012;
Davin et al., 2014;
Crook et al., 2015, 2016;
Seneviratne et al., 2018

4

Prosnitz, 2011; Dilling and Hauser, 2013; NRC, 2015b). Some havesuggested that the governance of research and field experimentation
can help clarify uncertainties surrounding deployment of SRM (Longand Shepherd, 2014; Parker, 2014; NRC, 2015c; Caldeira and Bala,
2017; Lawrence and Crutzen, 2017), and that SRM is compatible withdemocratic processes (Horton et al., 2018) or not (Szerszynski et al.,2013; Owen, 2014).

Alongside SBSTA, the WMO, UNESCO and UN Environment could playa role in governance of SRM (Nicholson et al., 2018). Each of theseorganizations has relevance with respect to the regulatory framework(Bodle et al., 2012; Williamson and Bodle, 2016). The UNCBD gives
guidance that ‘that no climate-related geo-engineering activities that
may affect biodiversity take place’ (CBD, 2010).

4.3.8.2
Several possible institutional arrangements have been considered
for SRM governance: under the UNFCCC (in particular under theSubsidiary Body on Scientific and Technological Advice (SBSTA)) or theUnited Nations Convention on Biological Diversity (UNCBD) (Honegger
et al., 2013; Nicholson et al., 2018), or through a consortium ofstates (Bodansky, 2013; Sandler, 2017). Reasons for states to join aninternational governance framework for SRM include having a voice in
SRM diplomacy, prevention of unilateral action by others and benefits
from research collaboration (Lloyd and Oppenheimer, 2014).
348

Economic and technological feasibility

The literature on the engineering costs of SRM is limited and may
be unreliable in the absence of testing or deployment. There is highagreement that costs of SAI (not taking into account indirect and social
costs, research and development costs and monitoring expenses) may
be in the range of 1–10 billion USD yr−1 for injection of 1–5 MtS to
achieve cooling of 1–2 W m−2 (Robock et al., 2009; McClellan et al.,2012; Ryaboshapko and Revokatova, 2015; Moriyama et al., 2016),
suggesting that cost-effectiveness may be high if side-effects are low

Strengthening and Implementing the Global Response

Chapter 4

or neglected (McClellan et al., 2012). The overall economic feasibilityof SRM also depends on externalities and social costs (Moreno-Cruzand Keith, 2013; Mackerron, 2014), climate sensitivity (Kosugi, 2013),
option value (Arino et al., 2016), presence of climate tipping points(Eric Bickel, 2013) and damage costs as a function of the level of SRM(Bahn et al., 2015; Heutel et al., 2018). Modelling of game-theoretic,strategic interactions of states under heterogeneous climatic impacts
shows low agreement on the outcome and viability of a cost-benefit
analysis for SRM (Ricke et al., 2015; Weitzman, 2015).

shows low agreement on whether SRM research and deployment may
lead policy-makers to reduce mitigation efforts and thus imply a moral
hazard (Linnér and Wibeck, 2015). SRM might motivate individuals(as opposed to policymakers) to reduce their GHG emissions, but evena subtle difference in the articulation of information about SRM can
influence subsequent judgements of favourability (Merk et al., 2016).
The argument that SRM research increases the likelihood of deployment(the ‘slippery slope’ argument), is also made (Quaas et al., 2017), butsome also found an opposite effect (Bellamy and Healey, 2018).

For SAI, there is high agreement that aircrafts could, after somemodifications, inject millions of tons of SO2 in the lower stratosphere(at approximately 20 km; (Davidson et al., 2012; McClellan et al., 2012;
Irvine et al., 2016).

Unequal representation and deliberate exclusion are plausible indecision-making on SRM, given diverging regional interests and theanticipated low resource requirements to deploy SRM (Ricke et al.,2013). Whyte (2012) argues that the concerns, sovereignties, andexperiences of indigenous peoples may particularly be at risk.

4.3.8.3

Social acceptability and ethics

Ethical questions around SRM include those of international
responsibilities for implementation, financing, compensation for
negative effects, the procedural justice questions of who is involved
in decisions, privatization and patenting, welfare, informed consent
by affected publics, intergenerational ethics (because SRM requires
sustained action in order to avoid termination hazards), and theso-called ‘moral hazard’ (Burns, 2011; Whyte, 2012; Gardiner, 2013;
Lin, 2013; Buck et al., 2014; Klepper and Rickels, 2014; Morrow, 2014;
Wong, 2014; Reynolds, 2015; Lockley and Coffman, 2016; McLaren,
2016; Suarez and van Aalst, 2017; Reynolds et al., 2018). The literature

The general public can be characterized as oblivious to and worried
about SRM (Carr et al., 2013; Parkhill et al., 2013; Wibeck et al., 2017).
An emerging literature discusses public perception of SRM, showing alack of knowledge and unstable opinions (Scheer and Renn, 2014). The
perception of controllability affects legitimacy and public acceptabilityof SRM experiments (Bellamy et al., 2017). In Germany, laboratory
work on SRM is generally approved of, field research much less so,and immediate deployment is largely rejected (Merk et al., 2015; Braunet al., 2017). Various factors could explain variations in the degree of
rejection of SRM between Canada, China, Germany, Switzerland, theUnited Kingdom, and the United States (Visschers et al., 2017).

Cross-Chapter Box 10 | Solar Radiation Modification in the Context of 1.5°C Mitigation Pathways

4
Contributing Authors:
Anastasia Revokatova (Russian Federation), Heleen de Coninck (Netherlands/EU), Piers Forster (UK), Veronika Ginzburg (RussianFederation), Jatin Kala (Australia), Diana Liverman (USA), Maxime Plazzotta (France), Roland Séférian (France), Sonia I. Seneviratne(Switzerland), Jana Sillmann (Norway).
Solar radiation modification (SRM) refers to a range of radiation modification measures not related to greenhouse gas (GHG)
mitigation that seek to limit global warming (see Chapter 1, Section 1.4.1). Most methods involve reducing the amount of incoming
solar radiation reaching the surface, but others also act on the longwave radiation budget by reducing optical thickness and cloud
lifetime (see Table 4.7). In the context of this report, SRM is assessed in terms of its potential to limit warming below 1.5°C in
temporary overshoot scenarios as a way to reduce elevated temperatures and associated impacts (Irvine et al., 2016; Keith andIrvine, 2016; Chen and Xin, 2017; Sugiyama et al., 2017a; Visioni et al., 2017a; MacMartin et al., 2018). The inherent variability of theclimate system would make it difficult to detect the efficacy or side-effects of SRM intervention when deployed in such a temporary
scenario (Jackson et al., 2015).

A. Potential SRM timing and magnitude
Published SRM approaches are summarized in Table 4.7. The timing and magnitude of potential SRM deployment depends on
the temperature overshoot associated with mitigation pathways. All overshooting pathways make use of carbon dioxide removal.
Therefore, if considered, SRM would only be deployed as a supplemental measure to large-scale carbon dioxide removal (Chapter
2, Section 2.3).
Cross-Chapter Box 10, Figure 1 below illustrates an example of how a hypothetical SRM deployment based on stratospheric aerosols
injection (SAI) could be used to limit warming below 1.5°C using an ‘adaptive SRM’ approach (e.g., Kravitz et al., 2011; Tilmes et al.,2016), where global mean temperature rise exceeds 1.5°C compared to pre-industrial level by mid-century and returns below 1.5°C
before 2100 with a 66% likelihood (see Chapter 2). In all such limited adaptive deployment scenarios, deployment of SRM only349

Chapter 4

Strengthening and Implementing the Global Response

Cross Chapter Box 10 (continued)

commences under conditions in which CO2 emissions have already fallen substantially below their peak level and are continuing tofall. In order to hold warming to 1.5°C, a hypothetical SRM deployment could span from one to several decades, with the earliest
possible threshold exceedance occurring before mid-century. Over this duration, SRM has to compensate for warming that exceeds
1.5°C (displayed with hatching on panel a) with a decrease in radiative forcing (panel b) which could be achieved with a rate of SAI
varying between 0–5.9 MtSO2 yr−1 (panel c) (Robock et al., 2008; Heckendorn et al., 2009).

4

Cross-Chapter Box 10, Figure 1 | Evolution of hypothetical SRM deployment (based on stratospheric aerosols injection, or SAI) in the context of
1.5°C-consistent pathways. (a) Range of median temperature outcomes as simulated by MAGICC (see in Chapter 2, Section 2.2) given the range of CO2 emissionsand (b) other climate forcers for mitigation pathways exceeding 1.5°C at mid-century and returning below by 2100 with a 66% likelihood. Geophysical characteristics are
represented by (c) the magnitude of radiative forcing and (d) the amount of stratospheric SO2 injection that are required to keep the global median temperature below
1.5°C during the temperature overshoot (given by the blue hatching on panel a). SRM surface radiative forcing has been diagnosed using a mean cooling efficiency of
0.3°C (W− m2) of Plazzotta et al. (2018). Magnitude and timing of SO2 injection have been derived from published estimates of Heckendorn et al. (2009) and Robock
et al. (2008).

SAI is the most-researched SRM method, with high agreement that it could limit warming to below 1.5°C (Tilmes et al., 2016;
Jones et al., 2018). The response of global temperature to SO2 injection, however, is uncertain and varies depending on the model
parametrization and emission scenarios (Jones et al., 2011; Kravitz et al., 2011; Izrael et al., 2014; Crook et al., 2015; Niemeier andTimmreck, 2015; Tilmes et al., 2016; Kashimura et al., 2017). Uncertainty also arises due to the nature and the optical properties of
injected aerosols.

350

Strengthening and Implementing the Global Response

Chapter 4

Cross Chapter Box 10 (continued)

Other approaches are less well researched, but the literature suggests that ground-based albedo modification (GBAM), marine cloudbrightening (MCB) or cirrus cloud thinning (CCT) are not assessed to be able to substantially reduce overall global temperature(Irvine et al., 2011; Seneviratne et al., 2018). However, these SRM approaches are known to create spatially heterogeneous forcing
and potentially more spatially heterogeneous climate effects, which may be used to mitigate regional climate impacts. This may
be of most relevance in the case of GBAM when applied to crop and urban areas (Seneviratne et al., 2018). Most of the literatureon regional mitigation has focused on GBAM in relationship with land-use and land-cover change scenarios. Both models andobservations suggest that there is a high agreement that GBAM would result in cooling over the region of changed albedo, and inparticular would reduce hot extremes (Irvine et al., 2011; Akbari et al., 2012; Jacobson and Ten Hoeve, 2012; Davin et al., 2014; Crooket al., 2015, 2016; Alkama and Cescatti, 2016; Seneviratne et al., 2018). In comparison, there is a limited evidence on the ability of
MCB or CCT to mitigate regional climate impacts of 1.5°C warming because the magnitude of the climate response to MCB or CCT
remains uncertain and the processes are not fully understood (Lohmann and Gasparini, 2017).

B.

General consequences and impacts of solar radiation modification

It has been proposed that deploying SRM as a supplement to mitigation may reduce increases in global temperature-related
extremes and rainfall intensity, and lessen the loss of coral reefs from increasing sea-surface temperatures (Keith and Irvine, 2016),
but it would not address, or could even worsen (Tjiputra et al., 2016), negative effects from continued ocean acidification.
Another concern with SRM is the risk of a ‘termination shock’ or ‘termination effect’ when suddenly stopping SRM, which mightcause rapid temperature rise and associated impacts (Jones et al., 2013; Izrael et al., 2014; McCusker et al., 2014), most noticeably
biodiversity loss (Trisos et al., 2018). The severity of the termination effect has recently been debated (Parker and Irvine, 2018) anddepends on the degree of SRM cooling. This report only considers limited SRM in the context of mitigation pathways to 1.5°C. Otherrisks of SRM deployment could be associated with the lack of testing of the proposed deployment schemes (e.g., Schäfer et al.,2013). Ethical aspects and issues related to the governance and economics are discussed in Section 4.3.8.

C.

Consequences and impacts of SRM on the carbon budget

Because of its effects on surface temperature, precipitation and surface shortwave radiation, SRM would also alter the carbon
budget pathways to 1.5°C or 2°C (Eliseev, 2012; Keller et al., 2014; Keith et al., 2017; Lauvset et al., 2017).
Despite the large uncertainties in the simulated climate response to SRM, current model simulations suggest that SRM would
lead to altered carbon budgets compatible with 1.5°C or 2°C. The 6 CMIP5 models investigated simulated an increase of naturalcarbon uptake by land biosphere and, to a smaller extent, by the oceans (high agreement). The multimodel mean of this response
suggests an increase of the RCP4.5 carbon budget of about 150 GtCO2 after 50 years of SO2 injection with a rate of 4 TgS yr−1, which
represents about 4 years of CO2 emissions at the current rate (36 GtCO2 yr−1). However, there is uncertainty around quantitative
determination of the effects that SRM or its cessation has on the carbon budget due to a lack of understanding of the radiative
processes driving the global carbon cycle response to SRM (Ramachandran et al., 2000; Mercado et al., 2009; Eliseev, 2012; Xia etal., 2016), uncertainties about how the carbon cycle will respond to termination effects of SRM, and uncertainties in climate–carbon
cycle feedbacks (Friedlingstein et al., 2014).

4

D. Sustainable development and SRM
There are few studies investigating potential implications of SRM for sustainable development. These are based on a limited
number of scenarios and hypothetical considerations, mainly referring to benefits from lower temperatures (Irvine et al., 2011;
Nicholson, 2013; Anshelm and Hansson, 2014; Harding and Moreno-Cruz, 2016). Other studies suggest negative impacts from SRM
implementation concerning issues related to regional disparities (Heyen et al., 2015), equity (Buck, 2012), fisheries, ecosystems,
agriculture, and termination effects (Robock, 2012; Morrow, 2014; Wong, 2014). If SRM is initiated by the richer nations, there might
be issues with local agency, and possibly worsening conditions for those suffering most under climate change (Buck et al., 2014).
In addition, ethical issues related to testing SRM have been raised (e.g., Lenferna et al., 2017). Overall, there is high agreement that
SRM would affect many development issues but limited evidence on the degree of influence, and how it manifests itself acrossregions and different levels of society.

E.

Overall feasibility of SRM

If mitigation efforts do not keep global mean temperature below 1.5°C, SRM can potentially reduce the climate impacts of atemporary temperature overshoot, in particular extreme temperatures, rate of sea level rise and intensity of tropical cyclones,
alongside intense mitigation and adaptation efforts. While theoretical developments show that SRM is technically feasible (seeSection 4.3.8.2), global field experiments have not been conducted and most of the knowledge about SRM is based on imperfect351

Chapter 4

Strengthening and Implementing the Global Response

Cross Chapter Box 10 (continued)

model simulations and some natural analogues. There are also considerable challenges to the implementation of SRM associatedwith disagreements over the governance, ethics, public perception, and distributional development impacts (see Section 4.3.8) (Boyd,2016; Preston, 2016; Asayama et al., 2017; Sugiyama et al., 2017b; Svoboda, 2017; McKinnon, 2018; Talberg et al., 2018). Overall,the combined uncertainties surrounding the various SRM approaches, including technological maturity, physical understanding,potential impacts, and challenges of governance, constrain the ability to implement SRM in the near future.

4.4

Implementing Far-Reaching
and Rapid Change

The feasibility of 1.5°C-compatible pathways is contingent uponenabling conditions for systemic change (see Cross Chapter Box 3 in
Chapter 1). Section 4.3 identifies the major systems, and options withinthose systems, that offer the potential for change to align with 1.5°C
pathways.
AR5 identifies enabling conditions as influencing the feasibilityof climate responses (Kolstad et al., 2014). This section draws on
1.5°C-specific and related literature on rapid and scaled up change
to identify the enabling conditions that influence the feasibility ofadaptation and mitigation options assessed in Section 4.5. Examples
from diverse regions and sectors are provided in Boxes 4.1 to 4.10
to illustrate how these conditions could enable or constrain theimplementation of incremental, rapid, disruptive and transformative
mitigation and adaptation consistent with 1.5°C pathways.

4

Coherence between the enabling conditions holds potential to enhance
the feasibility of 1.5°C-consistent pathways and adapting to theconsequences. This includes better alignment across governance scales(OECD, 2015a; Geels et al., 2017), enabling multilevel governance(Cheshmehzangi, 2016; Revi, 2017; Tait and Euston-Brown, 2017) andnested institutions (Abbott, 2012). It also includes interdisciplinary
actions, combined adaptation and mitigation action (Göpfert et al.,2018), and science–policy partnerships (Vogel et al., 2007; Hering et al.,2014; Roberts, 2016; Figueres et al., 2017; Leal Filho et al., 2018). These
partnerships are difficult to establish and sustain, but can generate
trust (Cole, 2015; Jordan et al., 2015) and inclusivity that ultimately canprovide durability and the realization of co-benefits for sustained rapidchange (Blanchet, 2015; Ziervogel et al., 2016a).

4.4.1

Enhancing Multilevel Governance

Addressing climate change and implementing responses to
1.5°C-consistent pathways would require engagement between
various levels and types of governance (Betsill and Bulkeley, 2006;
Kern and Alber, 2009; Christoforidis et al., 2013; Romero-Lankao et al.,2018). AR5 highlighted the significance of governance as a means ofstrengthening adaptation and mitigation and advancing sustainable
development (Fleurbaey et al., 2014). Governance is defined in the
broadest sense as the ‘processes of interaction and decision-making
among actors involved in a common problem’ (Kooiman, 2003; Hufty,
2011; Fleurbaey et al., 2014). This definition goes beyond notions offormal government or political authority and integrates other actors,
networks, informal institutions and communities.
352

4.4.1.1

Institutions and their capacity to invoke far-reaching
and rapid change

Institutions – the rules and norms that guide human interactions(Section 4.4.2) – enable or impede the structures, mechanisms
and measures that guide mitigation and adaptation. Institutions,
understood as the ‘rules of the game’ (North, 1990), exert direct andindirect influence over the viability of 1.5°C-consistent pathways(Munck et al., 2014; Willis, 2017). Governance would be needed tosupport wide-scale and effective adoption of mitigation and adaptation
options. Institutions and governance structures are strengthened
when the principle of the ‘commons’ is explored as a way of sharing
management and responsibilities (Ostrom et al., 1999; Chaffin etal., 2014; Young, 2016). Institutions would need to be strengthened
to interact amongst themselves, and to share responsibilities for thedevelopment and implementation of rules, regulations and policies(Ostrom et al., 1999; Wejs et al., 2014; Craig et al., 2017), with the goalof ensuring that these embrace equity, justice, poverty alleviation andsustainable development, enabling a 1.5°C world (Reckien et al., 2017;
Wood et al., 2017).
Several authors have identified different modes of cross-stakeholderinteraction in climate policy, including the role played by large
multinational corporations, small enterprises, civil society and nonstate actors. Ciplet et al. (2015) argue that civil society is to a great
extent the only reliable motor for driving institutions to change at
the pace required. Kern and Alber (2009) recognize different forms ofcollaboration relevant to successful climate policies beyond the local
level. Horizontal collaboration (e.g., transnational city networks) andvertical collaboration within nation-states can play an enabling role
(Ringel, 2017). Vertical and horizontal collaboration requires synergistic
relationships between stakeholders (Ingold and Fischer, 2014; Hsu etal., 2017). The importance of community participation is emphasized
in literature, and in particular the need to take into account equity
and gender considerations (Chapter 5) (Graham et al., 2015; Bryan
et al., 2017; Wangui and Smucker, 2017). Participation often faces
implementation challenges and may not always result in better policy
outcomes. Stakeholders, for example, may not view climate change asa priority and may not share the same preferences, potentially creatinga policy deadlock (Preston et al., 2013, 2015; Ford et al., 2016).

4.4.1.2

International governanceInternational treaties help strengthen policy implementation, providing
a medium- and long-term vision (Obergassel et al., 2016). International
climate governance is organized via many mechanisms, includinginternational organizations, treaties and conventions, for example,

Strengthening and Implementing the Global Response

UNFCCC, the Paris Agreement and the Montreal Protocol. Other
multilateral and bilateral agreements, such as trade agreements, also
have a bearing on climate change.
There are significant differences between global mitigation andadaptation governance frames. Mitigation tends to be global by its
nature and based on the principle of the climate system as a global
commons (Ostrom et al., 1999). Adaptation has traditionally beenviewed as a local process, involving local authorities, communities,
and stakeholders (Khan, 2013; Preston et al., 2015), although it is now
recognized to be a multi-scaled, multi-actor process that transcends
scales from local and sub-national to national and international(Mimura et al., 2014; UNEP, 2017a). National governments provide a
central pivot for coordination, planning, determining policy priorities
and distributing resources. National governments are accountable
to the international community through international agreements.
Yet, many of the impacts of climate change are transboundary, sothat bilateral and multilateral cooperation are needed (Nalau et al.,2015; Donner et al., 2016; Magnan and Ribera, 2016; Tilleard and Ford,
2016; Lesnikowski et al., 2017). The Kigali Amendment to the Montreal
Protocol demonstrates that a global environmental agreement
facilitating common but differentiated responsibilities is possible(Sharadin, 2018). This was operationalized by developed countries
acting first, with developing countries following and benefiting fromleap-frogging the trial-and-error stages of innovative technology
development.
Work on international climate governance has focused on the natureof ‘climate regimes’ and coordinating the action of nation-states(Aykut, 2016) organized around a diverse set of instruments: (i) binding
limits allocated by principles of historical responsibility and equity, (ii)carbon prices, emissions quotas, (iii) pledges and review of policies andmeasures or (iv) a combination of these options (Stavins, 1988; Grubb,
1990; Pizer, 2002; Newell and Pizer, 2003).
Literature on the Kyoto Protocol provides two important insights forthe 1.5°C transition: the challenge of agreeing on rules to allocate
emissions quotas (Shukla, 2005; Caney, 2012; Winkler et al., 2013;
Gupta, 2014; Méjean et al., 2015) and a climate-centric vision (Shukla,
2005; BASIC experts, 2011), separated from development issues which
drove resistance from many developing nations (Roberts and Parks,
2006). For the former, a burden-sharing approach led to an adversarial
process among nations to decide who should be allocated ‘how much’of the remainder of the emissions budget (Caney, 2014; Ohndorf et al.,2015; Roser et al., 2015; Giménez-Gómez et al., 2016). Industry group
lobbying further contributed to reducing space for manoeuvre of somemajor emitting nations (Newell and Paterson, 1998; Levy and Egan,
2003; Dunlap and McCright, 2011; Michaelowa, 2013; Geels, 2014).
Given the political unwillingness to continue with the Kyoto Protocol
approach a new approach was introduced in the Copenhagen Accord,
the Cancun Agreements, and finally in the Paris Agreement. The
transition to 1.5°C requires carbon neutrality and thus going beyond
the traditional framing of climate as a ‘tragedy of the commons’ to beaddressed via cost-optimal allocation rules, which demonstrated a low
probability of enabling a transition to 1.5°C-consistent pathways (Patt,
2017). The Paris Agreement, built on a ‘pledge and review’ system,

Chapter 4

is thought be more effective in securing trust (Dagnet et al., 2016)
and enables effective monitoring and timely reporting on national
actions (including adaptation), allowing for international scrutiny andpersistent efforts of civil society and non-state actors to encourage
action in both national and international contexts (Allan and Hadden,
2017; Bäckstrand and Kuyper, 2017; Höhne et al., 2017; Lesnikowski etal., 2017; Maor et al., 2017; UNEP, 2017a), with some limitations (Nieto
et al., 2018).
The paradigm shift enabled at Cancun succeeded by focusing on theobjective of ‘equitable access to sustainable development’ (Hourcade
et al., 2015). The use of ‘pledge and review’ now underpins the Paris
Agreement. This consolidates multiple attempts to define a governanceapproach that relies on Nationally Determined Contributions (NDCs)
and on means for a ‘facilitative model’ (Bodansky and Diringer, 2014)
to reinforce them. This enables a regular, iterative, review of NDCs
allowing countries to set their own ambitions after a global stocktake
and more flexible, experimental forms of climate governance, which may
provide room for higher ambition and be consistent with the needs ofgoverning for a rapid transition to close the emission gap (Clémençon,
2016; Falkner, 2016) (Cross-Chapter Box 11 in this chapter). Beyond
a general consensus on the necessity of measurement, reporting andverification (MRV) mechanisms as a key element of a climate regime(Ford et al., 2015b; van Asselt et al., 2015), some authors emphasize
different governance approaches to implement the Paris Agreement.
Through the new proposed sustainable development mechanism in
Article 6, the Paris Agreement allows the space to harness the lowest
cost mitigation options worldwide. This may incentivize policymakers
to enhance mitigation ambition by speeding up climate action as partof a ‘climate regime complex’ (Keohane and Victor, 2011) of looselyinterrelated global governance institutions. In the Paris Agreement, the‘common but differentiated responsibilities and respective capabilities’(CBDR-RC) principle could be expanded and revisited under a ‘sharing
the pie’ paradigm (Ji and Sha, 2015) as a tool to open innovation
processes towards alternative development pathways (Chapter 5).
COP 16 in Cancun was also the first time in the UNFCCC that
adaptation was recognized to have similar priority as mitigation. The
Paris Agreement recognizes the importance of adaptation action andcooperation to enhance such action. Chung Tiam Fook (2017) andLesnikowski et al. (2017) suggest that the Paris Agreement is explicit
about multilevel adaptation governance, outlines stronger transparency
mechanisms, links adaptation to development and climate justice, andis therefore suggestive of greater inclusiveness of non-state voices andthe broader contexts of social change.
1.5°C-consistent pathways require further exploration of conditions oftrust and reciprocity amongst nation states (Schelling, 1991; Ostrom
and Walker, 2005). Some authors (Colman et al., 2011; Courtois et al.,2015) suggest a departure from the vision of actors acting individuallyin the pursuit of self-interest to that of iterated games with actors
interacting over time showing that reciprocity, with occasional
forgiveness and initial good faith, can lead to win-win outcomes andto cooperation as a stable strategy (Axelrod and Hamilton, 1981).
Regional cooperation plays an important role in the context ofglobal governance. Literature on climate regimes has only started
353

4

Chapter 4

exploring innovative governance arrangements, including coalitionsof transnational actors including state, market and non-state actors(Bulkeley et al., 2012; Hovi et al., 2016; Hagen et al., 2017; Hermwilleet al., 2017; Roelfsema et al., 2018) and groupings of countries, asa complement to the UNFCCC (Abbott and Snidal, 2009; Biermann,
2010; Zelli, 2011; Nordhaus, 2015). Climate action requires multilevelgovernance from the local and community level to national, regional
and international levels. Box 4.1 shows the role of sub-national
authorities (e.g., regions and provinces) in facilitating urban climate
action, while Box 4.2 shows that climate governance can be organizedacross hydrological as well as political units.

4.4.1.3

4Sub-national governance

Local governments can play a key role (Melica et al., 2018; RomeroLankao et al., 2018) in influencing mitigation and adaptation
strategies. It is important to understand how rural and urban
areas, small islands, informal settlements and communities mightintervene to reduce climate impacts (Bulkeley et al., 2011), either byimplementing climate objectives defined at higher government levels
or by taking initiative autonomously or collectively (Aall et al., 2007;
Reckien et al., 2014; Araos et al., 2016a; Heidrich et al., 2016). Local
governance faces the challenge of reconciling local concerns with
global objectives. Local governments could coordinate and develop
effective local responses, and could pursue procedural justice inensuring community engagement and more effective policies aroundenergy and vulnerability reduction (Moss et al., 2013; Fudge et al.,2016). They can enable more participative decision-making (Barrett,2015; Hesse, 2016). Fudge et al. (2016) argue that local authorities
are well-positioned to involve the wider community in: designingand implementing climate policies, engaging with sustainable energy
generation (e.g., by supporting energy communities) (Slee, 2015), andthe delivery of demand-side measures and adaptation implementation.
By 2050, it is estimated three billion people will be living in slums andinformal settlements: neighbourhoods without formal governance, on
un-zoned land developments and in places that are exposed to climaterelated hazards (Bai et al., 2018). Emerging research is examining how
citizens can contribute informally to governance with rapid urbanization
and weaker government regulation (Sarmiento and Tilly, 2018). It
remains to be seen how the possibilities and consequences of alternative
urban governance models will be managed for large, complex problems
and for addressing inequality and urban adaptation (Amin and Cirolia,
2018; Bai et al., 2018; Sarmiento and Tilly, 2018).
Expanding networks of cities are sharing experiences on coping withclimate change and drawing economic and development benefits fromclimate change responses – a recent institutional innovation. This could
be complemented by efforts of national governments to enhance local
climate action through national urban policies (Broekhoff et al., 2018).
Over the years, non-state actors have set up several transnational
climate governance initiatives to accelerate the climate response, forexample, ICLEI (1990), C–40 (2005), the Global Island Partnership(2006) and the Covenant of Mayors (2008) (Gordon and Johnson,
2017; Hsu et al., 2017; Ringel, 2017; Kona et al., 2018; Melica et al.,2018) and to exert influence on national governments and the UNFCCC

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Strengthening and Implementing the Global Response(Bulkeley, 2005). However, Michaelowa and Michaelowa (2017) find
low effectiveness for over 100 of such mitigation initiatives.

4.4.1.4

Interactions and processes for multilevel governance

Literature has proposed multilevel governance in climate change as
an enabler for systemic transformation and effective governance,as the concept is thought to allow for combining decisions acrosslevels and sectors and across institutional types at the same level(Romero-Lankao et al., 2018), with multilevel reinforcement and themobilization of economic interests at different levels of governance(Jänicke and Quitzow, 2017). These governance mechanisms are
based on accountability and transparency rules and participation andcoordination across and within these levels.
A study of 29 European countries showed that the rapid adoption
and diffusion of adaptation policymaking is largely driven by internal
factors, at the national and sub-national levels (Massey et al., 2014).
An assessment of national-level adaptation in 117 countries (BerrangFord et al., 2014) found good governance to be the one of the strongest
predictors of national adaptation policy. An analysis of the climate
responses of 200 large and medium-sized cities across eleven European
countries found that factors such as membership of climate networks,
population size, gross domestic product (GDP) per capita and adaptive
capacity act as drivers of mitigation and adaptation plans (Reckien etal., 2015).
Adaptation policy has seen growth in some areas (Massey et al.,2014; Lesnikowski et al., 2016), although efforts to track adaptation
progress are constrained by an absence of data sources on adaptation(Berrang-Ford et al., 2011; Ford and Berrang-Ford, 2016; Magnan,2016; Magnan and Ribera, 2016). Many developing countries havemade progress in formulating national policies, plans and strategies on
responding to climate change. The NDCs have been identified as one
such institutional mechanism (Cross-Chapter Box 11 in this Chapter)(Magnan et al., 2015; Kato and Ellis, 2016; Peters et al., 2017).
To overcome barriers to policy implementation, local conflicts ofinterest or vested interests, strong leadership and agency is needed bypolitical leaders. As shown by the Covenant of Mayors initiative (Box4.1), political leaders with a vision for the future of the local community
can succeed in reducing GHG emissions, when they are supported bycivil society (Rivas et al., 2015; Croci et al., 2017; Kona et al., 2018).
Any political vision would need to be translated into an action plan,
which could include elements describing policies and measures neededto achieve transition, the human and financial resources needed,
milestones, and appropriate measurement and verification processes(Azevedo and Leal, 2017). Discussing the plan with stakeholders
and civil society, including citizens and allowing for participation forminorities, and having them provide input and endorse it, has beenfound to increase the likelihood of success (Rivas et al., 2015; Wamsler,2017). However, as described by Nightingale (2017) and Green (2016),
struggles over natural resources and adaptation governance both at
the national and community levels would also need to be addressed‘in politically unstable contexts, where power and politics shapeadaptation outcomes’.

Strengthening and Implementing the Global Response

Multilevel governance includes adaptation across local, regional, andnational scales (Adger et al., 2005). The whole-of-government approachto understanding and influencing climate change policy design andimplementation puts analytical emphasis on how different levels ofgovernment and different types of actors (e.g., public and private)
can constrain or support local adaptive capacity (Corfee-Morlot et al.,2011), including the role of the civil society. National governments,for example, have been associated with enhancing adaptive capacity
through building awareness of climate impacts, encouraging economic
growth, providing incentives, establishing legislative frameworks
conducive to adaptation, and communicating climate change
information (Berrang-Ford et al., 2014; Massey et al., 2014; Austin et al.,2015; Henstra, 2016; Massey and Huitema, 2016). Local governments,on the other hand, are responsible for delivering basic services andutilities to the urban population, and protecting their integrity fromthe impacts of extreme weather (Austin et al., 2015; Cloutier et al.,2015; Nalau et al., 2015; Araos et al., 2016b). National policies andtransnational governance could be seen as complementary, rather
than competitors, and strong national policies favour transnational
engagement of sub- and non-state actors (Andonova et al., 2017).
Local initiatives are complementary with higher level policies and canbe integrated in the multilevel governance system (Fuhr et al., 2018).

Chapter 4

A multilevel approach considers that adaptation planning is affectedby scale mismatches between the local manifestation of climate
impacts and the diverse scales at which the problem is driven (Shiet al., 2016). Multilevel approaches may be relevant in low-income
countries where limited financial resources and human capabilities
within local governments often lead to greater dependency onnational governments and other (donor) organizations, to strengthen
adaptation responses (Donner et al., 2016; Adenle et al., 2017).
National governments or international organizations may motivate
urban adaptation externally through broad policy directives or projects
by international donors. Municipal governments on the other hand
work within the city to spur progress on adaptation. Individual politicalleadership in municipal government, for example, has been cited asa factor driving the adaptation policies of early adapters in Quito,
Ecuador, and Durban, and South Africa (Anguelovski et al., 2014),
and for adaptation more generally (Smith et al., 2009). Adaptation
pathways can help identify maladaptive actions (Juhola et al., 2016;
Magnan et al., 2016; Gajjar et al., 2018) and encourage social learning
approaches across multiple levels of stakeholders in sectors such as
marine biodiversity and water supply (Bosomworth et al., 2015; Butler
et al., 2015; van der Brugge and Roosjen, 2015).

Box 4.1 | Multilevel Governance in the EU Covenant of Mayors: Example of the Provincia di Foggia
Since 2005, cities have emerged as a locus of institutional and governance climate innovation (Melica et al., 2018) and are driving
responses to climate change (Roberts, 2016). Many cities have adopted more ambitious greenhouse gas (GHG) emission reduction
targets than countries (Kona et al., 2018), with an overall commitment of GHG emission reduction targets by 2020 of 27%, almost
7 percentage points higher than the minimum target for 2020 (Kona et al., 2018). The Covenant of Mayors (CoM) is an initiativein which municipalities voluntarily commit to CO2 emission reduction. The participation of small municipalities has been facilitatedby the development and testing of a new multilevel governance model involving Covenant Territorial Coordinators (CTCs), i.e.,
provinces and regions, which commit to providing strategic guidance and financial and technical support to municipalities in theirterritories. Results from the 315 monitoring inventories submitted show an achievement of 23% reduction in emissions (compared
to an average year 2005) for more than half of the cities under a CTC schema (Kona et al., 2018).

4

The Province of Foggia, acting as a CTC, gave support to 36 municipalities to participate in the CoM and to prepare Sustainable
Energy Action Plans (SEAPs). The Province developed a common approach to prepare SEAPs, provided data to compile municipal
emission inventories (Bertoldi et al., 2018) and guided the signatory to identify an appropriate combination of measures to curb
GHG emissions. The local Chamber of Commerce also had a key role in the implementation of these projects by the municipalities(Lombardi et al., 2016). The joint action by the province and the municipalities in collaboration with the local business community
could be seen as an example of multilevel governance (Lombardi et al., 2016).
Researchers have investigated local forms of collaboration within local government, with the active involvement of citizens andstakeholders, and acknowledge that public acceptance is key to the successful implementation of policies (Larsen and GunnarssonÖstling, 2009; Musall and Kuik, 2011; Pollak et al., 2011; Christoforidis et al., 2013; Pasimeni et al., 2014; Lee and Painter, 2015).
Achieving ambitious targets would need leadership, enhanced multilevel governance, vision and widespread participation intransformative change (Castán Broto and Bulkeley, 2013; Rosenzweig et al., 2015; Castán Broto, 2017; Fazey et al., 2017; Wamsler,
2017; Romero-Lankao et al., 2018). The Chapter 5, Section 5.6.4 case studies of climate-resilient development pathways, at stateand community scales, show that participation, social learning and iterative decision-making are governance features of strategies
that deliver mitigation, adaptation, and sustainable development in a fair and equitable manner. Another insight is the findingthat incremental voluntary changes are amplified through community networking, polycentric governance (Dorsch and Flachsland,2017), partnerships, and long-term change to governance systems at multiple levels (Stevenson and Dryzek, 2014; Lövbrand et al.,2017; Pichler et al., 2017; Termeer et al., 2017).

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Box 4.2 | Watershed Management in a 1.5˚C World
Water management is necessary in order for the global community to adapt to 1.5°C-consistent pathways. Cohesive planningthat includes numerous stakeholders would be required to improve access, utilization and efficiency of water use and to ensurehydrologic viability.
Response to drought and El Niño–Southern Oscillation (ENSO) in southern Guatemala
Hydro-meteorological events, including ENSO, have impacted Central America (Steinhoff et al., 2014; Chang et al., 2015; Maggioni etal., 2016) and are projected to increase in frequency during a 1.5°C transition (Wang et al., 2017). The 2014–2016 ENSO damaged
agriculture, seriously impacting rural communities.
In 2016, the Climate Change Institute, in conjunction with local governments, the private sector, communities and human rights
organizations, established dialogue tables for different watersheds to discuss water usage amongst stakeholders and plans to
mitigate the effects of drought, alleviate social tension, and map water use of watersheds at risk. The goal was to encourage better
water resource management and to enhance ecological flow through improved communication, transparency, and coordination
amongst users. These goals were achieved in 2017 when each previously affected river reached the Pacific Ocean with at least its
minimum ecological flow (Guerra, 2017).
Drought management through the Limpopo Watercourse Commission
The governments sharing the Limpopo river basin (Botswana, Mozambique, South Africa and Zimbabwe) formed the Limpopo
Watercourse Commission in 2003 (Nyagwambo et al., 2008; Mitchell, 2013). It has an advisory body composed of working groups
that assess water use and sustainability, decide national level distribution of water access, and support disaster and emergency
planning. The Limpopo basin delta is highly vulnerable (Tessler et al., 2015), and is associated with a lack of infrastructure andinvestment capacity, requiring increased economic development together with plans for vulnerability reduction (Tessler et al.,2015) and water rights (Swatuk, 2015). The high vulnerability is influenced by gender inequality, limited stakeholder participationand limited institutional capacity to address unequal water access (Mehta et al., 2014). The implementation of integrated water
resources management (IWRM) would need to consider pre-existing social, economic, historical and cultural contexts (Merrey,
2009; Mehta et al., 2014). The Commission therefore could play a role in improving participation and in providing an adaptable andequitable strategy for cross-border water sharing (Ekblom et al., 2017).

4

Flood management in the Danube
The Danube River Protection Convention is the official instrument for cooperation on transboundary water governance between
the countries that share the Danube Basin. The International Commission for the Protection of the Danube River (ICPDR) provides
a strong science–policy link through expert working groups dealing with issues including governance, monitoring and assessment,
and flood protection (Schmeier, 2014). The Trans-National Monitoring Network (TNMN) was developed to undertake comprehensive
monitoring of water quality (Schmeier, 2014). Monitoring of water quality constitutes almost 50% of ICPDR’s scientific publications,although ICPDR also works on governance, basin planning, monitoring, and IWRM, indicating its importance. The ICPDR is an
example of IWRM ‘coordinating groundwater, surface water abstractions, flood management, energy production, navigation, andwater quality’ (Hering et al., 2014).

Box 4.2 exemplifies how multilevel governance has been used forwatershed management in different basins, given the impacts on water
sources (Chapter 3, Section 3.4.2).

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Chapter 4

Cross-Chapter Box 11 | Consistency Between Nationally Determined Contributions and 1.5°C Scenarios
Contributing Authors:
Paolo Bertoldi (Italy), Michel den Elzen (Netherlands), James Ford (Canada/UK), Richard Klein (Netherlands/Germany), Debora Ley(Guatemala/Mexico), Timmons Roberts (USA), Joeri Rogelj (Austria/Belgium).

Mitigation
1. Introduction
There is high agreement that Nationally Determined Contributions (NDCs) are important for the global response to climate change
and represent an innovative bottom-up instrument in climate change governance (Section 4.4.1), with contributions from all
signatory countries (den Elzen et al., 2016; Rogelj et al., 2016; Vandyck et al., 2016; Luderer et al., 2018; Vrontisi et al., 2018). The
global emission projections resulting from full implementation of the NDCs represent an improvement compared to business as
usual (Rogelj et al., 2016) and current policies scenarios to 2030 (den Elzen et al., 2016; Vrontisi et al., 2018). Most G20 economies
would require new policies and actions to achieve their NDC targets (den Elzen et al., 2016; Vandyck et al., 2016; UNEP, 2017b;
Kuramochi et al., 2018).
2. The effect of NDCs on global greenhouse gas (GHG) emissions
Several studies estimate global emission levels that would be achieved under the NDCs (e.g., den Elzen et al., 2016; Luderer et al.,2016; Rogelj et al., 2016, 2017; Vandyck et al., 2016; Rose et al., 2017; Vrontisi et al., 2018). Rogelj et al. (2016) and UNEP (2017b)
concluded that the full implementation of the unconditional and conditional NDCs are expected to result in global GHG emissionsof about 55 (52–58) and 53 (50–54) GtCO2-eq yr−1, respectively (Cross-Chapter Box 11, Figure 1 below).

Global greenhouse gas emissions, including LULUCFemissions (GtCO2-eq yr-1)

65

60– 25th percentile– Median– 75th percentile

55

54
53
52

50

48
47

45

61
60
56

56

57

52
51

52

53
52

49

40
35
30

56
53
50
48

50

58

NDC emission projections:
Unconditional NDCs

55

52

Conditional NDCs

44

44

Range of emissions (25thpercentile, median, 75th
percentile) in scenarios that
hold global mean
temperature increase below
1.5oC and 2˚C

38

50

4

48

47
42

39

37

30

35
30
27
25

25
20

54
53

22
2010

2020
Current
policies

2020
Higher-2°C

2020
Lower-2°C

2020
2020
1.5°C-high-OS 1.5°C-low-OS

2020
Below-1.5°C

2030
Current
policies

2030
Higher-2°C

2030
Lower-2°C

2030
2030
1.5°C-high-OS 1.5°C-low-OS

21

2030
Below-1.5°C

Cross-Chapter Box 11, Figure 1 | GHG emissions are all expressed in units of CO2-equivalence computed with 100-year global warming potentials (GWPs) reported
in IPCC SAR, while the emissions for the 1.5°C and 2°C scenarios in Table 2.4 are reported using the 100-year GWPs reported in IPCC AR4, and are hence about 3%
higher. Using IPCC AR4 instead of SAR GWP values is estimated to result in a 2–3% increase in estimated 1.5°C and 2°C emissions levels in 2030. Source: based on
Rogelj et al. (2016) and UNEP (2017b).

3. The effect of NDCs on temperature increase and carbon budget
Estimates of global average temperature increase are 2.9°C–3.4°C above preindustrial levels with a greater than 66% probability
by 2100 (Rogelj et al., 2016; UNEP, 2017b), under a full implementation of unconditional NDCs and a continuation of climate action
similar to that of the NDCs. Full implementation of the conditional NDCs would lower the estimates by about 0.2°C by 2100. As
an indication of the carbon budget implications of NDC scenarios, Rogelj et al. (2016) estimated cumulative emissions in the range

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Cross Chapter Box 11 (continued)of 690 to 850 GtCO2 for the period 2011–2030 if the NDCs are successfully implemented. The carbon budget for post-2010 till 2100
compatible with staying below 1.5°C with a 50–66% probability was estimated at 550–600 GtCO2 (Clarke et al., 2014; Rogelj et al.,2016), which will be well exceeded by 2030 at full implementation of the NDCs (Chapter 2, Section 2.2 and Section 2.3.1).
4. The 2030 emissions gap with 1.5°C and urgency of action
As the 1.5°C pathways require reaching carbon neutrality by mid-century, the NDCs alone are not sufficient, as they have a time
horizon until 2030. Rogelj et al. (2016) and Hof et al. (2017) have used results or compared NDC pathways with emissions pathways
produced by integrated assessment models (IAMs) assessing the contribution of NDCs to achieve the 1.5°C targets. There is highagreement that current NDC emissions levels are not in line with pathways that limit warming to 1.5°C by the end of the century(Rogelj et al., 2016, 2017; Hof et al., 2017; UNEP, 2017b; Vrontisi et al., 2018). The median 1.5°C emissions gap (>66% chance) for
the full implementation of both the conditional and unconditional NDCs for 2030 is 26 (19–29) to 28 (22–33) GtCO2-eq (CrossChapter Box 11, Figure 1 above).
Studies indicate important trade-offs of delaying global emissions reductions (Chapter 2, Sections 2.3.5 and 2.5.1). AR5 identified
flexibility in 2030 emission levels when pursuing a 2°C objective (Clarke et al., 2014) indicating that strongest trade-offs for 2°C
pathways could be avoided if emissions are limited to below 50 GtCO2-eq yr−1 in 2030 (here computed with the GWP–100 metricof the IPCC SAR). New scenario studies show that full implementation of the NDCs by 2030 would imply the need for deeper andfaster emission reductions beyond 2030 in order to meet 2°C, and also higher costs and efforts of negative emissions (Fujimori etal., 2016; Sanderson et al., 2016; Rose et al., 2017; van Soest et al., 2017; Luderer et al., 2018). However, no flexibility has been foundfor 1.5°C-consistent pathways (Luderer et al., 2016; Rogelj et al., 2017), indicating that if emissions through 2030 are at NDC levels,
the resulting post-2030 reductions required to remain within a 1.5°C-consistent carbon budget during the 21st century (Chapter 2,
Section 2.2) are not within the feasible operating space of IAMs. This indicates that the chances of failing to reach a 1.5°C pathway
are significantly increased (Riahi et al., 2015), if near-term ambition is not strengthened beyond the level implied by current NDCs.
Accelerated and stronger short-term action and enhanced longer-term national ambition going beyond the NDCs would be neededfor 1.5°C-consistent pathways. Implementing deeper emissions reductions than current NDCs would imply action towards levels
identified in Chapter 2, Section 2.3.3, either as part of or over-delivering on NDCs.
5. The impact of uncertainties on NDC emission levels
The measures proposed in NDCs are not legally binding (Nemet et al., 2017), further impacting estimates of anticipated 2030
emission levels. The aggregation of targets results in high uncertainty (Rogelj et al., 2017), which could be reduced with clearer
guidelines for compiling future NDCs focused more on energy accounting (Rogelj et al., 2017) and increased transparency andcomparability (Pauw et al., 2018).

4

Many factors would influence NDCs global aggregated effects, including: (1) variations in socio-economic conditions (GDP and
population growth), (2) uncertainties in historical emission inventories, (3) conditionality of certain NDCs, (4) definition of NDCtargets as ranges instead of single values, (5) the way in which renewable energy targets are expressed, and (6) the way in which
traditional biomass use is accounted for. Additionally, there are land-use mitigation uncertainties (Forsell et al., 2016; Grassi et al.,2017). Land-use options play a key role in many country NDCs; however, many analyses on NDCs do not use country estimates onland-use emissions, but use model estimates, mainly because of the large difference in estimating the ‘anthropogenic’ forest sinkbetween countries and models (Grassi et al., 2017).
6. Comparing countries’ NDC ambition (equity, cost optimal allocation and other indicators)
Various assessment frameworks have been proposed to analyse, benchmark and compare NDCs, and indicate possible strengthening,based on equity and other indicators (Aldy et al., 2016; den Elzen et al., 2016; Höhne et al., 2017; Jiang et al., 2017; Holz et al.,2018).There is large variation in conformity/fulfilment with equity principles across NDCs and countries. Studies use assessment
frameworks based on six effort sharing categories in the AR5 (Clarke et al., 2014) with the principles of ‘responsibility’, ‘capability’and ‘equity’ (Höhne et al., 2017; Pan et al., 2017; Robiou du Pont et al., 2017). There is an important methodological gap in
the assessment of the NDCs’ fairness and equity implications, partly due to lack of information on countries’ own assessments(Winkler et al., 2017). Implementation of Article 2.2 of the Paris Agreement could reflect equity and the principle of ‘common but
differentiated responsibilities and respective capabilities’, due to different national circumstances and different interpretations ofequity principles (Lahn and Sundqvist, 2017; Lahn, 2018).

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Chapter 4

Cross Chapter Box 11 (continued)

Adaptation
The Paris Agreement recognizes adaptation by establishing a global goal for adaptation (Kato and Ellis, 2016; Rajamani, 2016;
Kinley, 2017; Lesnikowski et al., 2017; UNEP, 2017a). This is assessed qualitatively, as achieving a temperature goal would determine
the level of adaptation ambition required to deal with the consequent risks and impacts (Rajamani, 2016). Countries can include
domestic adaptation goals in their NDCs, which together with national adaptation plans (NAPs) give countries flexibility to design
and adjust their adaptation trajectories as their needs evolve and as progress is evaluated over time. A challenge for assessing
progress on adaptation globally is the aggregation of many national adaptation actions and approaches. Knowledge gaps still
remain about how to design measurement frameworks that generate and integrate national adaptation data without placing undue
burdens on countries (UNEP, 2017a).
The Paris Agreement stipulates that adaptation communications shall be submitted as a component of or in conjunction withother communications, such as an NDC, a NAP, or a national communication. Of the 197 Parties to the UNFCCC, 140 NDCs havean adaptation component, almost exclusively from developing countries. NDC adaptation components could be an opportunity
for enhancing adaptation planning and implementation by highlighting priorities and goals (Kato and Ellis, 2016). At the national
level they provide momentum for the development of NAPs and raise the profile of adaptation (Pauw et al., 2016b, 2018). The Paris
Agreement’s transparency framework includes adaptation, through which ‘adaptation communication’ and accelerated adaptation
actions are submitted and reviewed every five years (Hermwille, 2016; Kato and Ellis, 2016). This framework, unlike others used in
the past, is applicable to all countries taking into account differing capacities amongst Parties (Rajamani, 2016).
Adaptation measures presented in qualitative terms include sectors, risks and vulnerabilities that are seen as priorities by the Parties.
Sectoral coverage of adaptation actions identified in NDCs is uneven, with adaptation primarily reported to focus on the watersector (71% of NDCs with adaptation component), agriculture (63%), health (54%), and biodiversity/ecosystems (50%) (Pauw etal., 2016b, 2018).

4.4.2

Enhancing Institutional Capacities

The implementation of sound responses and strategies to enable atransition to 1.5°C world would require strengthening governanceand scaling up institutional capacities, particularly in developing
countries (Adenle et al., 2017; Rosenbloom, 2017). Building on thecharacterization of governance in Section 4.4.1, this section examines
the necessary institutional capacity to implement actions to limit
warming to 1.5°C and adapt to the consequences. This takes into
account a plurality of regional and local responses, as institutional
capacity is highly context-dependent (North, 1990; Lustick et al.,2011).
Institutions would need to interact with one another and align acrossscales to ensure that rules and regulations are followed (Chaffin
and Gunderson, 2016; Young, 2016). The institutional architecture
required for a 1.5°C world would include the growing proportion of
the world’s population that live in peri-urban and informal settlements
and engage in informal economic activity (Simone and Pieterse, 2017).
This population, amongst the most exposed to perturbed climates in
the world (Hallegatte et al., 2017), is also beyond the direct reachof some policy instruments (Jaglin, 2014; Thieme, 2018). Strategies
that accommodate the informal rules of the game adopted by these
populations have large chances of success (McGranahan et al., 2016;
Kaika, 2017).
The goal for strengthening implementation is to ensure that these rules
and regulations embrace equity, equality and poverty alleviation along

1.5°C-consistent pathways (mitigation) and enables the building ofadaptive capacity that together, will enable sustainable development
and poverty reduction.
Rising to the challenge of a transition to a 1.5°C world would require
enhancing institutional climate change capacities along multiple
dimensions presented below.

4.4.2.1

Capacity for policy design and implementation

The enhancement of institutional capacity for integrated policy design
and implementation has long been among the top items on the UN
agenda of addressing global environmental problems and sustainable
development (see Chapter 5, Section 5.5) (UNEP, 2005).
Political stability, an effective regulatory and enforcement framework(e.g., institutions to impose sanctions, collect taxes and to verify
building codes), access to a knowledge base and the availability ofresources, would be needed at various governance levels to addressa wide range of stakeholders and their concerns. The strengtheningof the global response would need to support these with different
interventions, in the context of sustainable development (Chapter 5,
Section 5.5.1) (Pasquini et al., 2015).
Given the scale of change needed to limit warming to 1.5°C,
strengthening the response capacity of relevant institutions is best
addressed in ways that take advantage of existing decision-making
processes in local and regional governments and within cities and359

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Strengthening and Implementing the Global Response

communities (Romero-Lankao et al., 2013), and draws upon diverse
knowledge sources including indigenous and local knowledge(Nakashima et al., 2012; Smith and Sharp, 2012; Mistry and Berardi,
2016; Tschakert et al., 2017). Examples of successful local institutional
processes and the integration of local knowledge in climate-related
decision-making are provided in Box 4.3 and Box 4.4.

settlements which are vulnerable to climate impacts. It is common
for 30–50% of urban populations in low-income nations to live in
informal settlements with no regulatory infrastructure (Revi et al.,2014b). For example, in Huambo (Angola), a classified ‘urban’ area
extends 20 km west of the city and is predominantly made up of‘unplanned’ urban settlements (Smith and Jenkins, 2015).

Implementing 1.5°C-consistent strategies would require wellfunctioning legal frameworks to be in place, in conjunction withclearly defined mandates, rights and responsibilities to enable theinstitutional capacity to deliver (Romero-Lankao et al., 2013). As
an example, current rates of urbanization occurring in cities with alack of institutional capacity for effective land-use planning, zoning
and infrastructure development result in unplanned, informal urban

Internationally, the Paris Agreement process has aimed at enhancing
the capacity of decision-making institutions in developing countries
to support effective implementation. These efforts are particularlyreflected in Article 11 of the Paris Agreement on capacity building(the creation of the Paris Committee on Capacity Building), Article 13(the creation of the Capacity Building Initiative on Transparency), andArticle 15 on compliance (UNFCCC, 2016).

Box 4.3 | Indigenous Knowledge and Community Adaptation
Indigenous knowledge refers to the understandings, skills and philosophies developed by societies with long histories of interaction
with their natural surroundings (UNESCO, 2017). This knowledge can underpin the development of adaptation and mitigation
strategies (Ford et al., 2014b; Green and Minchin, 2014; Pearce et al., 2015; Savo et al., 2016).
Climate change is an important concern for the Maya, who depend on climate knowledge for their livelihood. In Guatemala, thecollaboration between the Mayan K’iché population of the Nahualate river basin and the Climate Change Institute has resulted ina catalogue of indigenous knowledge, used to identify indicators for watershed meteorological forecasts (López and Álvarez, 2016).
These indicators are relevant but would need continuous assessment if their continued reliability is to be confirmed (Nyong et al.,2007; Alexander et al., 2011; Mistry and Berardi, 2016). For more than ten years, Guatemala has maintained an ‘Indigenous Table
for Climate Change’, to enable the consideration of indigenous knowledge in disaster management and adaptation development.

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In Tanzania, increased variability of rainfall is challenging indigenous and local communities (Mahoo et al., 2015; Sewando etal., 2016). The majority of agro-pastoralists use indigenous knowledge to forecast seasonal rainfall, relying on observations ofplant phenology, bird, animal, and insect behaviour, the sun and moon, and wind (Chang’a et al., 2010; Elia et al., 2014; Shaffer,2014). Increased climate variability has raised concerns about the reliability of these indicators (Shaffer, 2014); therefore, initiatives
have focused on the co-production of knowledge by involving local communities in monitoring and discussing the implications ofindigenous knowledge and meteorological forecasts (Shaffer, 2014), and creating local forecasts by utilizing the two sources ofknowledge (Mahoo et al., 2013). This has resulted in increased documentation of indigenous knowledge, understanding of relevant
climate information amongst stakeholders, and adaptive capacity at the community level (Mahoo et al., 2013, 2015; Shaffer, 2014).
The Pacific Islands and small island developing states (SIDS) are vulnerable to the effects of climate change, but the cultural resilienceof Pacific Island inhabitants is also recognized (Nunn et al., 2017). In Fiji and Vanuatu, strategies used to prepare for cyclones include
building reserve emergency supplies and utilizing farming techniques to ensure adequate crop yield to combat potential losses
from a cyclone or drought (McNamara and Prasad, 2014; Granderson, 2017; Pearce et al., 2017). Social cohesion and kinship areimportant in responding and preparing for climate-related hazards, including the role of resource sharing, communal labour, andremittances (McMillen et al., 2014; Gawith et al., 2016; Granderson, 2017). There is a concern that indigenous knowledge will
weaken, a process driven by westernization and disruptions in established bioclimatic indicators and traditional planning calendars(Granderson, 2017). In some urban settlements, it has been noted that cultural practices (e.g., prioritizing the quantity of food overthe quality of food) can lower food security through dispersing limited resources and by encouraging the consumption of cheap
but nutrient-poor foods (Mccubbin et al., 2017) (See Cross-Chapter Box 6 on Food Security in Chapter 3). Indigenous practices also
encounter limitations, particularly in relation to sea level rise (Nunn et al., 2017).

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Chapter 4

Box 4.4 | Manizales, Colombia: Supportive National Government and Localized Planning and Integration
as an Enabling Condition for Managing Climate and Development Risks
Institutional reform in the city of Manizales, Colombia, helps identify three important features of an enabling environment:
integrating climate change adaptation, mitigation and disaster risk management at the city-scale; the importance of decentralizedplanning and policy formulation within a supportive national policy environment; and the role of a multi-sectoral framework inmainstreaming climate action in development activities.
Manizales is exposed to risks caused by rapid development and expansion in a mountainous terrain exposed to seismic activity andperiodic wet and dry spells. Local assessments expect climate change to amplify the risk of disasters (Carreño et al., 2017). The city
is widely recognized for its longstanding urban environmental policy (Biomanizales) and local environmental action plan (Bioplan),and has been integrating environmental planning in its development agenda for nearly two decades (Velásquez Barrero, 1998;
Hardoy and Velásquez Barrero, 2014). When the city’s environmental agenda was updated in 2014 to reflect climate change risks,assessments were conducted in a participatory manner at the street and neighbourhood level (Hardoy and Velásquez Barrero, 2016).
The creation of a new Environmental Secretariat assisted in coordination and integration of environmental policies, disaster risk
management, development and climate change (Leck and Roberts, 2015). Planning in Manizales remains mindful of steep gradients
through its longstanding Slope Guardian programme that trains women and keeps records of vulnerable households. Planning also
looks to include mitigation opportunities and enhance local capacity through participatory engagement (Hardoy and Velásquez
Barrero, 2016).
Manizales’ mayors were identified as important champions for much of these early integration and innovation efforts. Theirrole may have been enabled by Colombia’s history of decentralized approaches to planning and policy formulation, including
establishing environmental observatories (for continuous environmental assessment) and participatory tracking of environmental
indicators. Multi-stakeholder involvement has both enabled and driven progress, and has enabled the integration of climate risks indevelopment planning (Hardoy and Velásquez Barrero, 2016).

4.4.2.2

Monitoring, reporting, and review institutions

One of the novel features of the new climate governance architecture
emerging from the 2015 Paris Agreement is the transparency
framework in Article 13 committing countries, based on capacity,
to provide regular progress reports on national pledges to address
climate change (UNFCCC, 2016). Many countries will rely on public
policies and existing national reporting channels to deliver on their
NDCs under the Paris Agreement. Scaling up the mitigation andadaptation efforts in these countries to be consistent with 1.5°C
would put significant pressure on the need to develop, enhance andstreamline local, national and international climate change reportingand monitoring methodologies and institutional capacity in relation
to mitigation, adaptation, finance, and GHG inventories (Ford et al.,2015b; Lesnikowski et al., 2015; Schoenefeld et al., 2016). Consistent
with this direction, the provision of the information to the stocktake
under Article 14 of the Paris Agreement would contribute to enhancing
reporting and transparency (UNFCCC, 2016). Nonetheless, approaches,
reporting procedures, reference points, and data sources to assess
progress on implementation across and within nations are still largely
underdeveloped (Ford et al., 2015b; Araos et al., 2016b; Magnan andRibera, 2016; Lesnikowski et al., 2017). The availability of independent
private and public reporting and statistical institutions are integral tooversight, effective monitoring, reporting and review. The creation andenhancement of these institutions would be an important contribution
to an effective transition to a low-emission world.

4.4.2.3

Financial institutions

4

IPCC AR5 assessed that in order to enable a transition to a 2°C pathway,
the volume of climate investments would need to be transformed alongwith changes in the pattern of general investment behaviour towards
low emissions. The report argued that, compared to 2012, annually up
to a trillion dollars in additional investment in low-emission energy andenergy efficiency measures may be required until 2050 (Blanco et al.,2014; IEA, 2014a). Financing of 1.5°C would present an even greater
challenge, addressing financing of both existing and new assets, which
would require significant transitions to the type and structure of financial
institutions as well as to the method of financing (Cochrani et al., 2014;
Ma, 2014). Both public and private financial institutions would be needed
to contribute to the large resource mobilization needed for 1.5°C, yet, inthe ordinary course of business, these transitions may not be expected.
On the one hand, private financial institutions could face scale-up risk,for example, the risks associated with commercialization and scaling
up of renewable technologies to accelerate mitigation (Wilson, 2012;
Hartley and Medlock, 2013) and/or price risk, such as carbon pricevolatility that carbon markets could face. In contrast, traditional publicfinancial institutions are limited by both structure and instruments, while
concessional financing would require taxpayer support for subsidization.
Special efforts and innovative approaches would be needed to addressthese challenges, for example the creation of special institutions that
underwrite the value of emission reductions using auctioned price floors(Bodnar et al., 2018) to deal with price volatility.

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Strengthening and Implementing the Global Response

Financial institutions are equally important for adaptation.
Linnerooth-Bayer and Hochrainer-Stigler (2015) discussed thebenefits of financial instruments in adaptation, including theprovision of post-disaster finances for recovery and pre-disastersecurity necessary for climate adaptation and poverty reduction.
Pre-disaster financial instruments and options include insurance,such as index-based weather insurance schemes, catastrophe bonds,
and laws to encourage insurance purchasing. The development andenhancement of microfinance institutions to ensure social resilienceand smooth transitions in the adaptation to climate change impacts
could be an important local institutional innovation (Hammill et al.,2008).

4.4.2.4

Co-operative institutions and social safety nets

Effective cooperative institutions and social safety nets may help
address energy access and adaptation, as well as distributional impacts
during the transition to 1.5°C-consistent pathways and enablingsustainable development. Not all countries have the institutional
capabilities to design and manage these. Social capital for adaptationin the form of bonding, bridging, and linking social institutions has
proved to be effective in dealing with climate crises at the local,
regional and national levels (Aldrich et al., 2016).

4

The shift towards sustainable energy systems in transitioning
economies could impact the livelihoods of large populations in
traditional and legacy employment sectors. The transition of selected
EU Member States to biofuels, for example, caused anxiety among
farmers, who lacked confidence in the biofuel crop market. Enabling
contracts between farmers and energy companies, involving local
governments, helped create an atmosphere of confidence during thetransition (McCormick and Kåberger, 2007).
How do broader socio-economic processes influence urban
vulnerabilities and thereby underpin climate change adaptation?
This is a systemic challenge originating from a lack of collective
societal ownership of the responsibility for climate risk management.
Explanations for this situation include competing time-horizons due

to self-interest of stakeholders to a more ‘rational’ conception of risk
assessment, measured across a risk-tolerance spectrum (Moffatt, 2014).
Self-governing and self-organ¬ised institutional settings, where
equipment and resource systems are commonly owned and managed,
can poten¬tially generate a much higher diversity of administration
solutions, than other institutional arrangements, where energy
technology and resource systems are either owned and administeredindividually in market settings or via a central authority (e.g., thestate). They can also increase the adaptability of technological systems
while reducing their burden on the environment (Labanca, 2017).
Educational, learning and awareness-building institutions can help
strengthen the societal response to climate change (Butler et al., 2016;
Thi Hong Phuong et al., 2017).

4.4.3

Enabling Lifestyle and Behavioural Change

Humans are at the centre of global climate change: their actions cause
anthropogenic climate change, and social change is key to effectively
responding to climate change (Vlek and Steg, 2007; Dietz et al., 2013;
ISSC and UNESCO, 2013; Hackmann et al., 2014). Chapter 2 showsthat 1.5°C-consistent pathways assume substantial changes inbehaviour. This section assesses the potential of behaviour change, as
the integrated assessment models (IAMs) applied in Chapter 2 do notcomprehensively asses this potential.
Table 4.8 shows examples of mitigation and adaption actions relevantfor 1.5°C-consistent pathways. Reductions in population growth can
reduce overall carbon demand and mitigate climate change (Bridgeman,2017), particularly when population growth is accompanied by increases
in affluence and carbon-intensive consumption (Rosa and Dietz, 2012;
Clayton et al., 2017). Mitigation actions with a substantial carbon
emission reduction potential (see Figure 4.3) that individuals may
readily adopt would have the most climate impact (Dietz et al., 2009).
Various policy approaches and strategies can encourage and enableclimate actions by individuals and organizations. Policy approaches
would be more effective when they address key contextual and psycho-

Table 4.8 | Examples of mitigation and adaptation behaviours relevant for 1.5°C (Dietz et al., 2009; Jabeen, 2014; Taylor et al., 2014; Araos et al., 2016b; Steg, 2016; Stern etal., 2016b; Creutzig et al., 2018)
Climate action

Mitigation

362

Type of action

ExamplesImplementing resource efficiency in buildings

Insulation
Low-carbon building materials

Adopting low-emission innovations

Electric vehicles
Heat pumps, district heating and cooling

Adopting energy efficient appliances

Energy-efficient heating or cooling
Energy-efficient appliances

Energy-saving behaviour

Walking or cycling rather than drive short distances
Using mass transit rather than flying
Lower temperature for space heatingLine drying of laundry
Reducing food waste

Buying products and materials with low GHG
emissions during production and transport

Reducing meat and dairy consumption
Buying local, seasonal food
Replacing aluminium products by low-GHG alternatives

Organisational behaviour

Designing low-emission products and procedures
Replacing business travel by videoconferencing

Strengthening and Implementing the Global Response

Chapter 4

Table 4.8 (continued)
Climate action

Type of action

Using crops with higher tolerance for higher temperatures or CO2 elevation

Flood protective behaviour

Elevating barriers between rooms
Building elevated storage spaces
Building drainage channels outside the homeHeat protective behaviour

Staying hydrated
Moving to cooler places
Installing green roofs

Efficient water use during water shortage crisis

Rationing water
Constructing wells or rainwater tanks

Adoption of renewable energy sources

Solar PV
Solar water heaters

Citizenship behaviour

Engage through civic channels to encourage or support planning for low-carbon
climate-resilient development

Adaptation

Mitigation &adaptation

ExamplesGrowing different crops and raising different animal varieties

4

Figure 4.3 | Examples of mitigation behaviour and their GHG emission reduction potential. Mitigation potential assessments are printed in different
units. Based on [1] Carlsson-Kanyama and González (2009); [2] Tuomisto and Teixeira de Mattos (2011); [3] Springmann et al. (2016); [4] Nijland and Meerkerk (2017); [5]Woodcock et al. (2009); [6] Salon et al. (2012); [7] Dietz et al. (2009); [8] Mulville et al. (2017); [9] Huebner and Shipworth (2017); [10] Jaboyedoff et al. (2004); [11] Pellegrino
et al. (2016); [12] Nägele et al. (2017).

social factors influencing climate actions, which differ across contexts
and individuals (Steg and Vlek, 2009; Stern, 2011). This suggeststhat diverse policy approaches would be needed in 1.5°C-consistentpathways in different contexts and regions. Combinations of policies
that target multiple barriers and enabling factors simultaneously canbe more effective (Nissinen et al., 2015).

In the United States and Europe, GHG emissions are lower when
legislators have strong environmental records (Jensen and Spoon, 2011;
Dietz et al., 2015). Political elites affect public concern about climatechange: pro-climate action statements increased concern, while anticlimate action statements and anti-environment voting reduced publicconcern about climate change (Brulle et al., 2012). In the European
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Union (EU), individuals worry more about climate change and engage
more in climate actions in countries where political party elites areunited rather than divided in their support for environmental issues(Sohlberg, 2017).
This section discusses how to enable and encourage behaviour andlifestyle changes that strengthen implementation of 1.5°C-consistentpathways by assessing psycho-social factors related to climate action,
as well as the effects and acceptability of policy approaches targetingclimate actions that are consistent with 1.5°C. Box 4.5 and Box 4.6
illustrate how these have worked in practice.

4.4.3.1

Factors related to climate actions

Mitigation and adaptation behaviour is affected by many factors thatshape which options are feasible and considered by individuals. Besides
contextual factors (see other sub-sections in Section 4.4), these include
abilities and different types of motivation to engage in behaviour.

4

Ability to engage in climate action. Individuals more often engage
in adaptation (Gebrehiwot and van der Veen, 2015; Koerth et al.,2017) and mitigation behaviour (Pisano and Lubell, 2017) when they
are or feel more capable to do so. Hence, it is important to enhanceability to act on climate change, which depends on income andknowledge, among other things. A higher income is related to higher
CO2 emissions; higher income groups can afford more carbon-intensive
lifestyles (Lamb et al., 2014; Dietz et al., 2015; Wang et al., 2015). Yet
low-income groups may lack resources to invest in energy-efficienttechnology and refurbishments (Andrews-Speed and Ma, 2016) andadaptation options (Wamsler, 2007; Fleming et al., 2015b; Takahashi etal., 2016). Adaptive capacity further depends on gender roles (Jabeen,
2014; Bunce and Ford, 2015), technical capacities and knowledge(Feola et al., 2015; Eakin et al., 2016; Singh et al., 2016b).
Knowledge of the causes and consequences of climate change andof ways to reduce GHG emissions is not always accurate (Bord et al.,
2000; Whitmarsh et al., 2011; Tobler et al., 2012), which can inhibit
climate actions, even when people would be motivated to act. For
example, people overestimate savings from low-energy activities,
and underestimate savings from high-energy activities (Attari et al.,2010). They know little about ‘embodied’ energy (i.e., energy needed
to produce products; Tobler et al., 2011), including meat (de Boer etal., 2016b). Some people mistake weather for climate (Reynolds et al.,2010), or conflate climate risks with other hazards, which can inhibit
adequate adaptation (Taylor et al., 2014).
More knowledge on adaptation is related to higher engagement in
adaptation actions in some circumstances (Bates et al., 2009; vanKasteren, 2014; Hagen et al., 2016). How adaptation is framed in
the media can influence the types of options viewed as important indifferent contexts (Boykoff et al., 2013; Moser, 2014; Ford and King,
2015).
Knowledge is important, but is often not sufficient to motivate action(Trenberth et al., 2016). Climate change knowledge and perceptions
are not strongly related to mitigation actions (Hornsey et al., 2016).
Direct experience of events related to climate change influences
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Strengthening and Implementing the Global Responseclimate concerns and actions (Blennow et al., 2012; Taylor et al., 2014),
more so than second-hand information (Spence et al., 2011; Myers etal., 2012; Demski et al., 2017); high impact events with low frequency
are remembered more than low impact regular events (Meze-Hausken,
2004; Singh et al., 2016b; Sullivan-Wiley and Short Gianotti, 2017).
Personal experience with climate hazards strengthens motivation toprotect oneself (Jabeen, 2014) and enhances adaptation actions (Bryan
et al., 2009; Berrang-Ford et al., 2011; Demski et al., 2017), although
this does not always translate into proactive adaptation (Taylor etal., 2014). Collectively constructed notions of risk and expectationsof future climate variability shape risk perception and adaptation
behaviour (Singh et al., 2016b). People with particular political views
and those who emphasize individual autonomy may reject climate
science knowledge and believe that there is widespread scientificdisagreement about climate change (Kahan, 2010; O’Neill et al., 2013),
inhibiting support for climate policy (Ding et al., 2011; McCright et al.,2013). This may explain why extreme weather experiences enhances
preparedness to reduce energy use among left- but not right-leaning
voters (Ogunbode et al., 2017).
Motivation to engage in climate action. Climate actions are
more strongly related to motivational factors than to knowledge,
reflecting individuals’ reasons for actions, such as values, ideology
and worldviews (Hornsey et al., 2016). People consider various typesof costs and benefits of actions (Gölz and Hahnel, 2016) and focus
on consequences that have implications for the values they find mostimportant (Dietz et al., 2013; Hahnel et al., 2015; Steg, 2016). Thisimplies that different individuals consider different consequences when
making choices. People who strongly value protecting the environmentand other people generally more strongly consider climate impacts andact more on climate change than those who strongly endorse hedonic
and egoistic values (Taylor et al., 2014; Steg, 2016). People are more
prone to adopt sustainable innovations when they are more open to
new ideas (Jansson, 2011; Wolske et al., 2017). Further, a free-market
ideology is associated with weaker climate change beliefs (McCrightand Dunlap, 2011; Hornsey et al., 2016), and a capital-oriented culture
tends to promote activity associated with GHG emissions (Kasser etal., 2007).
Some indigenous populations believe it is arrogant to predict the
future, and some cultures have belief systems that interpret natural
phenomena as sentient, where thoughts and words are believed toinfluence the future, with people reluctant to talk about negative future
possibilities (Natcher et al., 2007; Flynn et al., 2018). Integrating these
considerations into the design of adaptation and mitigation policy isimportant (Cochran et al., 2013; Chapin et al., 2016; Brugnach et al.,2017; Flynn et al., 2018).
People are more prone to act on climate change when individual benefitsof actions exceed costs (Steg and Vlek, 2009; Kardooni et al., 2016;
Wolske et al., 2017). For this reason, people generally prefer adoption ofenergy-efficient appliances above energy-consumption reductions; thelatter is perceived as more costly (Poortinga et al., 2003; Steg et al.,2006), although transaction costs can inhibit the uptake of mitigation
technology (Mundaca, 2007). Decentralized renewable energy systems
are evaluated most favourably when they guarantee independence,autonomy, control and supply security (Ecker et al., 2017).

Strengthening and Implementing the Global Response

Besides, social costs and benefits affect climate action (Farrow et al.,2017). People engage more in climate actions when they think others
expect them to do so and when others act as well (Nolan et al., 2008;
Le Dang et al., 2014; Truelove et al., 2015; Rai et al., 2016), and when
they experience social support (Singh et al., 2016a; Burnham and Ma,
2017; Wolske et al., 2017). Discussing effective actions with peers also
encourages climate action (Esham and Garforth, 2013), particularly
when individuals strongly identify with their peers (Biddau et al.,2012; Fielding and Hornsey, 2016). Further, individuals may engage
in mitigation actions when they think doing so would enhance theirreputation (Milinski et al., 2006; Noppers et al., 2014; Kastner andStern, 2015). Such social costs and benefits can be addressed in climate
policy (see Section 4.4.3.2).
Feelings affect climate action (Brosch et al., 2014). Negative feelings
related to climate change can encourage adaptation action (Kerstholt
et al., 2017; Zhang et al., 2017), while positive feelings associated withclimate risks may inhibit protective behaviour (Lefevre et al., 2015).
Individuals are more prone to engage in mitigation actions when they
worry about climate change (Verplanken and Roy, 2013) and when
they expect to derive positive feelings from such actions (Pelletier etal., 1998; Taufik et al., 2016).
Furthermore, collective consequences affect climate actions(Balcombe et al., 2013; Dóci and Vasileiadou, 2015; Kastner andStern, 2015). People are motivated to see themselves as morally
right, which encourages mitigation actions (Steg et al., 2015),
particularly when long-term goals are salient (Zaval et al., 2015) andbehavioural costs are not too high (Diekmann and Preisendörfer,2003). Individuals are more prone to engage in climate actions when
they believe climate change is occurring, when they are aware of
threats caused by climate change and by their inaction, and when
they think they can engage in actions that will reduce these threats(Esham and Garforth, 2013; Arunrat et al., 2017; Chatrchyan et al.,2017). The more individuals are concerned about climate change andaware of the negative climate impact of their behaviour, the morethey feel responsible for their actions and think that their actions canhelp reduce such negative impacts, which can strengthen their moral
norms to act accordingly (Steg and de Groot, 2010; Jakovcevic andSteg, 2013; Chen, 2015; Ray et al., 2017; Wolske et al., 2017; Woods
et al., 2017). Individuals may engage in mitigation actions when
they see themselves as supportive of the environment (i.e., strong
environmental self-identity) (Fielding et al., 2008; van der Werff etal., 2013b; Kashima et al., 2014; Barbarossa et al., 2017); a strong
environmental identity strengthens intrinsic motivation to engage
in mitigation actions both at home (van der Werff et al., 2013a)
and at work (Ruepert et al., 2016). Environmental self-identity isstrengthened when people realize they have engaged in mitigation
actions, which can in turn promote further mitigation actions (van derWerff et al., 2014b).
Individuals are less prone to engage in adaptation behaviour
themselves when they rely on external measures such as government
interventions (Grothmann and Reusswig, 2006; Wamsler and Brink,
2014a; Armah et al., 2015; Burnham and Ma, 2017) or perceive
themselves as protected by god (Gandure et al., 2013; Dang et al.,2014; Cannon, 2015).

Chapter 4

Habits, heuristics and biases. Decisions are often not based onweighing costs and benefits, but on habit or automaticity, both ofindividuals (Aarts and Dijksterhuis, 2000; Kloeckner et al., 2003)
and within organizations (Dooley, 2017) and institutions (Muncket al., 2014). When habits are strong, individuals are less perceptiveof information (Verplanken et al., 1997; Aarts et al., 1998) and may
not consider alternatives as long as outcomes are good enough(Maréchal, 2010). Habits are mostly only reconsidered when thesituation changed significantly (Fujii and Kitamura, 2003; Maréchal,
2010; Verplanken and Roy, 2016). Hence, strategies that create theopportunity for reflection and encourage active decisions can breakhabits (Steg et al., 2018).
Individuals can follow heuristics, or ‘rules of thumb’, in making
inferences, which demand less cognitive resources, knowledge
and time than thinking through all implications of actions (Preston
et al., 2013; Frederiks et al., 2015; Gillingham and Palmer, 2017).
For example, people tend to think that larger and more visible
appliances use more energy, which is not always accurate (Cowen
and Gatersleben, 2017). They underestimate energy used for water
heating and overestimate energy used for lighting (Stern, 2014).
When facing choice overload, people may choose the easiest or firstavailable option, which can inhibit energy-saving behaviour (Sternand Gardner, 1981; Frederiks et al., 2015). As a result, individuals
and firms often strive for satisficing (‘good enough’) outcomes withregard to energy decisions (Wilson and Dowlatabadi, 2007; Klotz,2011), which can inhibit investments in energy efficiency (Decanio,
1993; Frederiks et al., 2015).
Biases also play a role. In Mozambique, farmers displayed omission
biases (unwillingness to take adaptation actions with potentiallynegative consequences to avoid personal responsibility for losses),
while policymakers displayed action biases (wanting to demonstrate
positive action despite potential negative consequences; Patt and
Schröter, 2008). People tend to place greater value on relative losses
than gains (Kahneman, 2003). Perceived gains and losses depend on
the reference point or status-quo (Kahneman, 2003). Loss aversion
and the status-quo bias prevent consumers from switching electricity
suppliers (Ek and Söderholm, 2008), to time-of-use electricity tariffs(Nicolson et al., 2017), and to accept new energy systems (Leijten etal., 2014).
Owned inefficient appliances and fossil fuel-based electricity can act asendowments, increasing their value compared to alternatives (Pichert
and Katsikopoulos, 2008; Dinner et al., 2011). Uncertainty and loss
aversion lead consumers to undervalue future energy savings (Greene,2011) and savings from energy efficient technologies (Kolstad et al.,2014). Uncertainties about the performance of products and illiquidityof investments can drive consumers to postpone (profitable) energyefficient investments (Sutherland, 1991; van Soest and Bulte, 2001).
People with a higher tendency to delay decisions may engage lessin energy saving actions (Lillemo, 2014). Training energy auditors inloss-aversion increased their clients’ investments in energy efficiency
improvements (Gonzales et al., 1988). Engagement in energy saving
and renewable energy programmes can be enhanced if participation isset as a default option (Pichert and Katsikopoulos, 2008; Ölander andThøgersen, 2014; Ebeling and Lotz, 2015).
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4

Chapter 4

4.4.3.2

Strategies and policies to promote actions
on climate change

Policy can enable and strengthen motivation to act on climate change
via top-down or bottom-up approaches, through informational
campaigns, regulatory measures, financial (dis)incentives, andinfrastructural and technological changes (Adger et al., 2003; Steg and
Vlek, 2009; Henstra, 2016).
Adaptation efforts tend to focus on infrastructural and technologicalsolutions (Ford and King, 2015) with lower emphasis on socio-cognitiveand finance aspects of adaptation. For example, flooding policies incities focus on infrastructure projects and regulation such as building
codes, and hardly target individual or household behaviour (Araos etal., 2016b; Georgeson et al., 2016).
Current mitigation policies emphasize infrastructural and technology
development, regulation, financial incentives and information
provision (Mundaca and Markandya, 2016) that can create conditions
enabling climate action, but target only some of the many factors
influencing climate actions (see Section 4.4.5.1). They fall short oftheir true potential if their social and psychological implications areStrengthening and Implementing the Global Response

overlooked (Stern et al., 2016a). For example, promising energysaving or low-carbon technology may not be adopted or not be used
as intended (Pritoni et al., 2015) when people lack resources andtrustworthy information (Stern, 2011; Balcombe et al., 2013).
Financial incentives or feedback on financial savings can encourage
climate action (Santos, 2008; Bolderdijk et al., 2011; Maki et al.,2016) (see Box 4.5), but are not always effective (Delmas et al.,2013) and can be less effective than social rewards (Handgraaf etal., 2013) or emphasising benefits for people and the environment(Bolderdijk et al., 2013b; Asensio and Delmas, 2015; Schwartz etal., 2015). The latter can happen when financial incentives reducea focus on environmental considerations and weaken intrinsic
motivation to engage in climate action (Evans et al., 2012; Agrawal
et al., 2015; Schwartz et al., 2015). In addition, pursuing small
financial gains is perceived to be less worth the effort than pursuingequivalent CO2 emission reductions (Bolderdijk et al., 2013b; Doganet al., 2014). Also, people may not respond to financial incentives(e.g., to improve energy efficiency) because they do not trust theorganization sponsoring incentive programmes (Mundaca, 2007) or
when it takes too much effort to receive the incentive (Stern et al.,2016a).

Box 4.5 | How Pricing Policy has Reduced Car Use in Singapore, Stockholm and London
In Singapore, Stockholm and London, car ownership, car use, and GHG emissions have reduced because of pricing and regulatory
policies and policies facilitating behaviour change. Notably, acceptability of these policies has increased as people experienced theirpositive effects.

4Singapore implemented electronic road pricing in the central business district and at major expressways, a vehicle quota andregistration fee system, and investments in mass transit. In the vehicle quota system introduced in 1990, registration of new vehicles
is conditional upon a successful bid (via auctioning) (Chu, 2015), costing about 50,000 USD in 2014 (LTA, 2015). The registration
tax incentivizes purchases of low-emission vehicles via a feebate system. As a result, per capita transport emissions (approximately
1.25 tCO2yr−1) and car ownership (107 vehicles per 1000 capita) (LTA, 2017) are substantially lower than in cities with comparable
income levels. Modal share of public transport was 63% during peak hours in 2013 (LTA, 2013).
The Stockholm congestion charge implemented in 2007 (after a trial in 2006) reduced kilometres driven in the inner city by 16%, andoutside the city by 5%; traffic volumes reduced by 20% and remained constant over time despite economic and population growth(Eliasson, 2014). CO2 emissions from traffic reduced by 2–3% in Stockholm county. Vehicles entering or leaving the city centre
were charged during weekdays (except for holidays). Charges were 1–2€ (maximum 6€ per day), being higher during peak hours;
taxis, emergency vehicles and buses were exempted. Before introducing the charge, public transport and parking places near masstransit stations were extended. The aim and effects of the charge were extensively communicated to the public. Acceptability of thecongestion charge was initially low, but the scheme gained support of about two-thirds of the population and all political parties
after it was implemented (Eliasson, 2014), which may be related to the fact that the revenues were earmarked for constructing
a motorway tunnel. After the trial, people believed that the charge had more positive effects on environmental, congestion andparking problems while costs increased less than they anticipated beforehand (Schuitema et al., 2010a). The initially hostile media
eventually declared the scheme to be a success.
In 2003, a congestion charge was implemented in the Greater London area, with an enforcement and compliance scheme and an
information campaign on the functioning of the scheme. Vehicles entering, leaving, driving or parking on a public road in the zone
at weekdays at daytime pay a congestion charge of 8£ (until 2005, 5£), with some exemptions. Revenues were invested in London’sbus network (80%), cycling facilities, and road safety measures (Leape, 2006). The number of cars entering the zone decreased by
18% in 2003 and 2004. In the charging zone, vehicle kilometres driven decreased by 15% in the first year and a further 6% a year
later, while CO2 emissions from road traffic reduced by 20% (Santos, 2008).

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Strengthening and Implementing the Global Response

While providing information on the causes and consequences of climatechange or on effective climate actions generally increases knowledge,
it often does not encourage engagement in climate actions by
individuals (Abrahamse et al., 2005; Ünal et al., 2017) or organizations(Anderson and Newell, 2004). Similarly, media coverage on the UN
Climate Summit slightly increased knowledge about the conference
but did not enhance motivation to engage personally in climate
protection (Brüggemann et al., 2017). Fear-inducing representations ofclimate change may inhibit action when they make people feel helplessand overwhelmed (O’Neill and Nicholson-Cole, 2009). Energy-related
recommendations and feedback (e.g., via performance contracts,
energy audits, smart metering) are more effective for promoting energy
conservation, load shifting in electricity use and sustainable travel
choices when framed in terms of losses rather than gains (Gonzales etal., 1988; Wolak, 2011; Bradley et al., 2016; Bager and Mundaca, 2017).
Credible and targeted information at the point of decision can promoteclimate action (Stern et al., 2016a). For example, communicating theimpacts of climate change is more effective when provided rightbefore adaptation decisions are taken (e.g., before the agricultural
season) and when bundled with information on potential actions toameliorate impacts, rather than just providing information on climate
projections with little meaning to end users (e.g., weather forecasts,
seasonal forecasts, decadal climate trends) (Dorward et al., 2015;
Singh et al., 2017). Similarly, heat action plans that provide early alerts
and advisories combined with emergency public health measures can
reduce heat-related morbidity and mortality (Benmarhnia et al., 2016).
Information provision is more effective when tailored to the personal
situation of individuals, demonstrating clear impacts, and resonatingwith individuals’ core values (Daamen et al., 2001; Abrahamse et al.,2007; Bolderdijk et al., 2013a; Dorward et al., 2015; Singh et al., 2017).
Tailored information prevents information overload, and people are
more motivated to consider and act upon information that aligns with
their core values and beliefs (Campbell and Kay, 2014; Hornsey et al.,2016). Also, tailored information can remove barriers to receive andinterpret information faced by vulnerable groups, such as the elderly
during heatwaves (Vandentorren et al., 2006; Keim, 2008). Further,
prompts can be effective when they serve as reminders to perform aplanned action (Osbaldiston and Schott, 2012).
Feedback provision is generally effective in promoting mitigation
behaviour within households (Abrahamse et al., 2005; Delmas et al.,2013; Karlin et al., 2015) and at work (Young et al., 2015), particularly
when provided in real-time or immediately after the action (Abrahamse
et al., 2005), which makes the implications of one’s behaviour moresalient (Tiefenbeck et al., 2016). Simple information is more effective
than detailed and technical data (Wilson and Dowlatabadi, 2007; Ek
and Söderholm, 2010; Frederiks et al., 2015). Energy labels (Banerjee
and Solomon, 2003; Stadelmann, 2017), visualization techniques (Pahl
et al., 2016), and ambient persuasive technology (Midden and Ham,
2012) can encourage mitigation actions by providing informationand feedback in a format that immediately makes sense and hardly
requires users’ conscious attention.
Social influence approaches that emphasize what other people do or
think can encourage climate action (Clayton et al., 2015), particularly

Chapter 4

when they involve face-to-face interaction (Abrahamse and Steg, 2013).
For example, community approaches, where change is initiated fromthe bottom-up, can promote adaptation (see Box 4.6) and mitigation
actions (Middlemiss, 2011; Seyfang and Haxeltine, 2012; Abrahamse
and Steg, 2013), especially when community ties are strong (Weenig
and Midden, 1991). Furthermore, providing social models of desired
actions can encourage mitigation action (Osbaldiston and Schott,2012; Abrahamse and Steg, 2013). Social influence approaches that do
not involve social interaction, such as social norm, social comparisonand group feedback, are less effective, but can be easily administeredon a large scale at low costs (Allcott, 2011; Abrahamse and Steg, 2013).
Goal setting can promote mitigation action when goals are not set
too low or too high (Loock et al., 2013). Commitment strategies where
people make a pledge to engage in climate actions can encourage
mitigation behaviour (Abrahamse and Steg, 2013; Lokhorst et al., 2013),
particularly when individuals also indicate how and when they will
perform the relevant action and anticipate how to cope with possible
barriers (i.e., implementation intentions) (Bamberg, 2000, 2002). Such
strategies take advantage of individuals’ desire to be consistent (Steg,2016). Similarly, hypocrisy-related strategies that make people aware ofinconsistencies between their attitudes and behaviour can encourage
mitigation actions (Osbaldiston and Schott, 2012).
Actions that reduce climate risks can be rewarded and facilitated, while
actions that increase climate risks can be punished and inhibited, andbehaviour change can be voluntary (e.g., information provision) or
imposed (e.g., by law); voluntary changes that involve rewards are
more acceptable than imposed changes that restrict choices (Eriksson
et al., 2006, 2008; Steg et al., 2006; Dietz et al., 2007). Policies punishingmaladaptive behaviour can increase vulnerability when they reinforce
socio-economic inequalities that typically produce the maladaptive
behaviour in the first place (Adger et al., 2003). Change can be initiatedby governments at various levels, but also by individuals, communities,
profit-making organizations, trade organizations, and other nongovernmental actors (Lindenberg and Steg, 2013; Robertson andBarling, 2015; Stern et al., 2016b).
Strategies can target intrinsic versus extrinsic motivation. It may beparticularly important to enhance intrinsic motivation so that people
voluntarily engage in climate action over and again (Steg, 2016).
Endorsement of mitigation and adaptation actions are positively
related (Brügger et al., 2015; Carrico et al., 2015); both are positively
related to concern about climate change (Brügger et al., 2015).
Strategies that target general antecedents that affect a wide rangeof actions, such as values, identities, worldviews, climate change
beliefs, awareness of the climate impacts of one’s actions, and feelingsof responsibility to act on climate change, can encourage consistentactions on climate change (van Der Werff and Steg, 2015; Hornsey
et al., 2016; Steg, 2016). Initial climate actions can lead to further
commitment to climate action (Juhl et al., 2017), when people learn
that such actions are easy and effective (Lauren et al., 2016), when they
engaged in the initial behaviour for environmental reasons (Peters etal., 2018), hold strong pro-environmental values and norms (Thøgersenand Ölander, 2003), and when initial actions make them realise they
are an environmentally sensitive person, motivating them to act on
climate change in subsequent situations so as to be consistent (van der367

4

Chapter 4

Strengthening and Implementing the Global Response

Box 4.6 | Bottom-up Initiatives: Adaptation Responses Initiated by Individuals and Communities
To effectively adapt to climate change, bottom-up initiatives by individuals and communities are essential, in addition to effortsof governments, organizations, and institutions (Wamsler and Brink, 2014a). This box presents examples of bottom-up adaptation
responses and behavioural change.
Fiji increasingly faces a lack of freshwater due to decreasing rainfall and rising temperatures (Deo, 2011; IPCC, 2014a). Whilesome villages have access to boreholes, these are not sufficient to supply the population with freshwater. Villagers are adapting
by rationing water, changing diets, and setting up inter-village sharing networks (Pearce et al., 2017). Some villagers take up wage
employment to buy food instead of growing it themselves (Pearce et al., 2017). In Kiribati, residents adapt to drought by purchasing
rainwater tanks and constructing additional wells (Kuruppu and Liverman, 2011). An important factor that motivated residents ofKiribati to adapt to drought was the perception that they could effectively adapt to the negative consequences of climate change(Kuruppu and Liverman, 2011).
In the Philippines, seismic activity has caused some islands to flood during high tide. While the municipal government offered affectedisland communities the possibility to relocate to the mainland, residents preferred to stay and implement measures themselves intheir local community to reduce flood damage (Laurice Jamero et al., 2017). Migration is perceived as undesirable because island
communities have strong place-based identities (Mortreux and Barnett, 2009). Instead, these island communities have adapted toflooding by constructing stilted houses and raising floors, furniture, and roads to prevent water damage (Laurice Jamero et al., 2017).
While inundation was in this case caused by seismic activity, this example indicates how island-based communities may respond torising sea levels caused by climate change.
Adaptation initiatives by individuals may temporarily reduce the impacts of climate change and enable residents to cope withchanging environmental circumstances. However, they may not be sufficient to sustain communities’ way of life in the long term. For
instance, in Fiji and Kiribati, freshwater and food are projected to become even scarcer in the future, rendering individual adaptations
ineffective. Moreover, individuals can sometimes engage in behaviour that may be maladaptive over larger spatio-temporal scales.
For example, in the Philippines, many islanders adapt to flooding by elevating their floors using coral stone (Laurice Jamero etal., 2017). Over time, this can harm the survivability of their community, as coral reefs are critical for reducing flood vulnerability(Ferrario et al., 2014). In Maharashtra, India, on-farm ponds are promoted as rainwater harvesting structures to adapt to dry spells
during the monsoon season. However, some individuals fill these ponds with groundwater, leading to depletion of water tables andpotentially maladaptive outcomes in the long run (Kale, 2015).

4

Integration of individuals’ adaptation initiatives with top-down adaptation policy is critical (Butler et al., 2015), as failing to do so
may lead individual actors to mistrust authority and can discourage them from undertaking adequate adaptive actions (Wamsler
and Brink, 2014a).

Werff et al., 2014a; Lacasse, 2015, 2016). Yet some studies suggest thatpeople may feel licensed not to engage in further mitigation actions
when they believe they have already done their part (Truelove et al.,2014).

4.4.3.3

Acceptability of policy and system changes

Public acceptability can shape, enable or prevent policy and systemchanges. Acceptability reflects the extent to which policy or systemchanges are evaluated (un)favourably. Acceptability is higher whenpeople expect more positive and less negative effects of policy
and system changes (Perlaviciute and Steg, 2014; Demski et al.,2015; Drews and Van den Bergh, 2016), including climate impacts(Schuitema et al., 2010b). Because of this, policy ‘rewarding’
climate actions is more acceptable than policy ‘punishing’ actions
that increase climate risks (Steg et al., 2006; Eriksson et al., 2008).
Pricing policy is more acceptable when revenues are earmarked forenvironmental purposes (Steg et al., 2006; Sælen and Kallbekken,
3682011) or redistributed towards those affected (Schuitema and Steg,2008). Acceptability can increase when people experience positiveeffects after a policy has been implemented (Schuitema et al., 2010a;
Eliasson, 2014; Weber, 2015); effective policy trials can thus build
public support for climate policy (see Box 4.8).
Climate policy and renewable energy systems are more acceptable
when people strongly value other people and the environment, or
support egalitarian worldviews, left-wing or green political ideologies(Drews and Van den Bergh, 2016), and less acceptable when people
strongly endorse self-enhancement values, or support individualisticand hierarchical worldviews (Dietz et al., 2007; Perlaviciute and Steg,
2014; Drews and Van den Bergh, 2016). Solar radiation modification
is more acceptable when people strongly endorse self-enhancement
values, and less acceptable when they strongly value other people
and the environment (Visschers et al., 2017). Climate policy is more
acceptable when people believe climate change is real, when they
are concerned about climate change (Hornsey et al., 2016), whenStrengthening and Implementing the Global Response

they think their actions may reduce climate risks, and when they feel
responsible to act on climate change (Steg et al., 2005; Eriksson etal., 2006; Jakovcevic and Steg, 2013; Drews and Van den Bergh, 2016;
Kim and Shin, 2017). Stronger environmental awareness is associatedwith a preference for governmental regulation and behaviour change
rather than free-market and technological solutions (Poortinga et al.,2002).
Climate policy is more acceptable when costs and benefits are
distributed equally, when nature and future generations areprotected (Sjöberg and Drottz-Sjöberg, 2001; Schuitema et al., 2011;
Drews and Van den Bergh, 2016), and when fair procedures havebeen followed, including participation by the public (Dietz, 2013;
Bernauer et al., 2016a; Bidwell, 2016) or public society organizations(Bernauer and Gampfer, 2013). Providing benefits to compensate
affected communities for losses due to policy or systems changes
enhanced public acceptability in some cases (Perlaviciute and Steg,2014), although people may disagree on what would be a worthwhile
compensation (Aitken, 2010; Cass et al., 2010), or feel they are being
bribed (Cass et al., 2010; Perlaviciute and Steg, 2014).
Public support is higher when individuals trust responsible parties(Perlaviciute and Steg, 2014; Drews and Van den Bergh, 2016). Yet,public support for multilateral climate policy is not higher than forunilateral policy (Bernauer and Gampfer, 2015); public support forunilateral, non-reciprocal climate policy is rather strong and robust(Bernauer et al., 2016b). Public opposition may result from a culturally
valued landscape being affected by adaptation or mitigation options,such as renewable energy development (Warren et al., 2005; Devinewright and Howes, 2010) or coastal protection measures (Kimura,2016), particularly when people have formed strong emotional bonds
with the place (Devine-Wright, 2009, 2013).
Climate actions may reduce human well-being when such actions
involve more costs, effort or discomfort. Yet some climate actions
enhance well-being, such as technology that improves daily comfort
and nature-based solutions for climate adaptation (Wamsler and Brink,
2014b). Further, climate action may enhance well-being (Kasser and
Sheldon, 2002; Xiao et al., 2011; Schmitt et al., 2018) because pursuingmeaning by acting on climate change can make people feel good
(Venhoeven et al., 2013, 2016; Taufik et al., 2015), more so than merely
pursuing pleasure.

4.4.4

Enabling Technological Innovation

This section focuses on the role of technological innovation in limiting
warming to 1.5°C, and how innovation can contribute to strengthening
implementation to move towards or to adapt to 1.5°C worlds. This
assessment builds on information of technological innovation andrelated policy debates in and after AR5 (Somanathan et al., 2014).

4.4.4.1

The nature of technological innovations

Technological systems have their own dynamics. New technologies
have been described as emerging as part of a ‘socio-technical system’
that is integrated with social structures and that itself evolves over time(Geels and Schot, 2007). This progress is cumulative and accelerating

Chapter 4(Kauffman, 2002; Arthur, 2009). To illustrate such a process ofco-evolution: the progress of computer simulation enables us to better
understand climate, agriculture, and material sciences, contributing
to upgrading food production and quality, microscale manufacturing
techniques, and leading to much faster computing technologies,
resulting, for instance, in better performing photovoltaic (PV) cells.
A variety of technological developments have and will contribute to
1.5°C-consistent climate action or the lack of it. They can do this, forexample, in the form of applications such as smart lighting systems,
more efficient drilling techniques that make fossil fuels cheaper, orprecision agriculture. As discussed in Section 4.3.1, costs of PV (IEA,
2017f) and batteries (Nykvist and Nilsson, 2015) have sharply dropped.
In addition, costs of fuel cells (Iguma and Kidoshi, 2015; Wei et al., 2017)
and shale gas and oil (Wang et al., 2014; Mills, 2015) have come down
as a consequence of innovation.

4.4.4.2

Technologies as enablers of climate action

Since AR5, literature has emerged as to how much future GHG emission
reductions can be enabled by the rapid progress of general purpose
technologies (GPTs), consisting of information and communication
technologies (ICT), including artificial intelligence (AI) and the internetof things (IoT), nanotechnologies, biotechnologies, robotics, and so forth(WEF, 2015; OECD, 2017c). Although these may contribute to limiting
warming to 1.5°C, the potential environmental, social and economic
impacts of new technologies are uncertain.
Rapid improvement of performance and cost reduction is observed
for many GPTs. They include AI, sensors, internet, memory storageand microelectromechanical systems. The latter GPTs are not usuallycategorized as climate technologies, but they can impact GHG emissions.
Progress of GPT could help reduce GHG emissions more costeffectively. Examples are shown in Table 4.9. It may however, result in
more emissions by increasing the volume of economic activities, with
unintended negative consequence on sustainable development. While
ICT increases electricity consumption (Aebischer and Hilty, 2015), theenergy consumption of ICT is usually dwarfed by the energy saving by
ICT (Koomey et al., 2013; Malmodin et al., 2014), but rebound effects
and other sustainable development impacts may be significant. An
appropriate policy framework that accommodates such impacts andtheir uncertainties could address the potential negative impacts by GPT(Jasanoff, 2007).
GHG emission reduction potentials in relation to GPTs were estimatedfor passenger cars using a combination of three emerging technologies:
electric vehicles, car sharing, and self-driving. GHG emission reduction
potential is reported, assuming generation of electricity with low GHG
emissions (Greenblatt and Saxena, 2015; ITF, 2015; Viegas et al., 2016;
Fulton et al., 2017). It is also possible that GHG emissions increase due
to an incentive to car use. Appropriate policies such as urban planningand efficiency regulations could contain such rebound effects (Wadudet al., 2016).
Estimating emission reductions by GPT is difficult due to substantial
uncertainties, including projections of future technological performance,
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4Chapter 4

Strengthening and Implementing the Global Response

Table 4.9 | Examples of technological innovations relevant to 1.5°C enabled by general purpose technologies (GPT). Note: lists of enabling GPT or adaptation/mitigation
options are not exhaustive, and the GPTs by themselves do not reduce emissions or increase climate change resilience.
SectorBuildings

Industry

Examples of Mitigation/Adaptation Technological Innovation

IoT, AI

Smart lighting and air conditioning (IEA, 2016b, 2017a)

IoT, AI

Energy efficiency improvement by industrial process optimization (IEA, 2017a)

Robots, IoT

Bio-based plastic production by biorefinery (OECD, 2017c)

Biotechnology

New materials from biorefineries (Fornell et al., 2013; McKay et al., 2016)

ICT, biotechnology

Electric vehicles, car sharing, automation (Greenblatt and Saxena, 2015; Fulton et al., 2017)
Bio-based diesel fuel by biorefinery (OECD, 2017c)

Logistical optimization, and electrification of trucks by overhead line (IEA, 2017e)

Biotechnology
ICT, biotechnology

Reduction of transport needs by remote education, health and other services (GeSI, 2015; IEA, 2017a)

Biotechnology

Energy saving by lightweight aircraft components (Beyer, 2014; Faludi et al., 2015; Verhoef et al., 2018)

Additive manufacturing(3D printing)Solar PV manufacturing (Nemet, 2014)
Electricity

Biotechnology
ICT, biotechnology

Second generation bioethanol potentially coupled to carbon capture systems (De Souza et al., 2014; Rochedo et al., 2016)
Transport

Enabling GPTEnergy and CO2 efficiency of logistics, warehouse and shops (GeSI, 2015; IEA, 2017a)

Nanotechnology

Smart grids and grid flexibility to accommodate intermittent renewables (Heard et al., 2017)

IoT, AI

Plasma confinement for nuclear fusion (Baltz et al., 2017)AI

Precision agriculture (improvement of energy and resource efficiency including reduction of fertilizer use and N2O emissions)(Pierpaoli et al., 2013; Brown et al., 2016; Schimmelpfennig and Ebel, 2016)
Agriculture

Disaster Reduction
and Adaptation

4

Methane inhibitors (and methane-suppressing vaccines) that reduce livestock emissions from enteric fermentation (Wedlock et al.
2013; Hristov et al. 2015; Wollenberg et al. 2016)

Biotechnology

Engineering C3 into C4 photosynthesis to improve agricultural production and productivity (Schuler et al., 2016)

Biotechnology

Genome editing using CRISPR to improve/adapt crops to a changing climate (Gao, 2018)

Biotechnology

Weather forecasting and early warning systems, in combination with user knowledge (Hewitt et al., 2012; Lourenço et al., 2016)

ICT

Climate risk reduction (Upadhyay and Bijalwan, 2015)

ICT

Rapid assessment of disaster damage (Kryvasheyeu et al., 2016)

ICT

costs, penetration rates, and induced human activity. Even if atechnology is available, the establishment of business models might
not be feasible (Linder and Williander, 2017). Indeed, studies show awide range of estimates, ranging from deep emission reductions topossible increases in emissions due to the rebound effect (Larson andZhao, 2017).
GPT could also enable climate adaptation, in particular through more
effective climate disaster risk management and improved weather
forecasting.
Government policy usually plays a role in promoting or limiting
GPTs, or science and technology in general. It has impacts on climate
action, because the performance of further climate technologies
will partly depend on the progress of GPTs. Governments haveestablished institutions for achieving many social, and sometimes
conflicting goals, including economic growth and addressing climatechange (OECD, 2017c), which include investment in basic researchand development (R&D) that can help develop game-changing
technologies (Shayegh et al., 2017). Governments are also needed
to create an enabling environment for the growth of scientific andtechnological ecosystems necessary for GPT development (Tassey,2014).

370

Biotechnology ICT, AI

4.4.4.3

The role of government in 1.5°C-consistentclimate technology policy

While literature on 1.5°C-specific innovation policy is absent, a growing
body of literature indicates that governments aim to achieve social,economic and environmental goals by promoting science and a broad
range of technologies through ‘mission-driven’ innovation policies,
based on differentiated national priorities (Edler and Fagerberg,2017). Governments can play a role in advancing climate technology
via a ‘technology push’ policy on the technology supply side (e.g.,
R&D subsidies), and by ‘demand pull’ policy on the demand side (e.g.,energy-efficiency regulation), and these policies can be complemented
by enabling environments (Somanathan et al., 2014). Governments may
also play a role in removing existent support for incumbents (Kivimaa
and Kern, 2016). A growing literature indicates that policy mixes, rather
than single policy instruments, are more effective in addressing climate
innovation challenges ranging from technologies in the R&D phase to
those ready for diffusion (Veugelers, 2012; Quitzow, 2015; Rogge et al.,2017; Rosenow et al., 2017). Such innovation policies can help address
two kinds of externalities: environmental externalities and proprietary
problems (GEA, 2012; IPCC, 2014b; Mazzucato and Semieniuk, 2017). To
avoid ‘picking winners’, governments often maintain a broad portfolioof technological options (Kverndokk and Rosendahl, 2007) and work inStrengthening and Implementing the Global Response

close collaboration with the industrial sector and society in general. Somegovernments have achieved relative success in supporting innovation
policies (Grubler et al., 2012; Mazzucato, 2013) that addressed climaterelated R&D (see Box 4.7 on bioethanol in Brazil).
Funding for R&D could come from various sources, including the generalbudget, energy or resource taxation, or emission trading schemes (seeSection 4.4.5). Investing in climate-related R&D has as an additional
benefit of building capabilities to implement climate mitigation andadaptation technologies (Ockwell et al., 2015). Countries regard
innovation in general and climate technology specifically as a national
interests issue and addressing climate change primarily as being in
the global interest. Reframing part of climate policy as technology orindustrial policy might therefore contribute to resolving the difficulties
that continue to plague emission target negotiations (Faehn andIsaksen, 2016; Fischer et al., 2017; Lachapelle et al., 2017).
Climate technology transfer to emerging economies has happened
regardless of international treaties, as these countries have been keen
to acquire them, and companies have an incentive to access emerging
markets to remain competitive (Glachant and Dechezleprêtre, 2016).

Chapter 4

However, the complexity of these transfer processes is high, andthey have to be conducted carefully by governments and institutions(Favretto et al., 2017). It is noticeable that the impact of the EU emission
trading scheme (EU ETS) on innovation is contested; recent work(based on lower carbon prices than anticipated for 1.5°C-consistentpathways) indicates that it is limited (Calel and Dechezleprêtre, 2016),
but earlier assessments (Blanco et al., 2014) indicate otherwise.

4.4.4.4

Technology transfer in the Paris Agreement

Technology development and transfer is recognized as an enabler of both
mitigation and adaptation in Article 10 in the Paris Agreement (UNFCCC,
2016) as well as in Article 4.5 of the original text of the UNFCCC (UNFCCC,
1992). As previous sections have focused on technology development
and diffusion, this section focuses on technology transfer. Technology
transfer can adapt technologies to local circumstances, reduce financing
costs, develop indigenous technology, and build capabilities to operate,
maintain, adapt and innovate on technology globally (Ockwell et al.,2015; de Coninck and Sagar, 2017). Technology cooperation could
decrease global mitigation cost, and enhance developing countries’
mitigation contributions (Huang et al., 2017a).

Box 4.7 | Bioethanol in Brazil: Innovation and Lessons for Technology Transfer
The use of sugarcane as a bioenergy source started in Brazil in the 1970s. Government and multinational car factories modified
car engines nationwide so that vehicles running only on ethanol could be produced. As demand grew, production and distribution
systems matured and costs came down (Soccol et al., 2010). After a transition period in which both ethanol-only and gasoline-onlycars were used, the flex-fuel era started in 2003, when all gasoline was blended with 25% ethanol (de Freitas and Kaneko, 2011). By
2010, around 80% of the car fleet in Brazil had been converted to use flex-fuel (Goldemberg, 2011; Su et al., 2015).

4

More than forty years of combining technology push and market pull measures led to the deployment of ethanol production,transportation and distribution systems across Brazil, leading to a significant decrease in CO2 emissions (Macedo et al., 2008).
Examples of innovations include: (i) the development of environmentally well-adapted varieties of sugarcane; (ii) the development
and scaling up of sugar fermentation in a non-sterile environment, and (iii) the development of adaptations of car engines to use
ethanol as a fuel in isolation or in combination with gasoline (Amorim et al., 2011; de Freitas and Kaneko, 2011; De Souza et al.,2014). Public procurement, public investment in R&D and mandated fuel blends accompanying these innovations were also crucial(Hogarth, 2017). In the future, innovation could lead to viable partial CO2 removal through deployment of BECCS associated withthe bioethanol refineries (Fuss et al., 2014; Rochedo et al., 2016) (see Section 4.3.7).
Ethanol appears to reduce urban car emission of health-affecting ultrafine particles by 30% compared to gasoline-based cars,
but increases ozone (Salvo et al., 2017). During the 1990s, when sugarcane burning was still prevalent, particulate pollution had
negative consequences for human health and the environment (Ribeiro, 2008; Paraiso and Gouveia, 2015). While Jaiswal et al.
(2017) report bioethanol’s limited impact on food production and forests in Brazil, despite the large scale, and attribute this tospecific agro-ecological zoning legislation, various studies report adverse effects of bioenergy production through forest substitution
by croplands (Searchinger et al., 2008), as well as impacts on biodiversity, water resources and food security (Rathore et al., 2016).
For new generation biofuels, feasibility and life cycle assessment studies can provide information on their impacts on environmental,
economic and social factors (Rathore et al., 2016).
Brazil and the European Union have tried to replicate Brazil’s bioethanol experience in climatically suitable African countries.
Although such technology transfer achieved relative success in Angola and Sudan, the attempts to set up bioethanol value chainsdid not pass the phase of political deliberations and feasibility studies elsewhere in Africa. Lessons learned include the need forpolitical and economic stability of the donor country (Brazil) and the necessity for market creation to attract investments in firstgeneration biofuels alongside a safe legal and policy environment for improved technologies (Afionis et al., 2014; Favretto et al.,2017).

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Chapter 4

The international institutional landscape around technology
development and transfer includes the UNFCCC (via its technology
framework and Technology Mechanism including the Climate
Technology Centre and Network (CTCN)), the United Nations (atechnology facilitation mechanism for the SDGs) and a variety of
non-UN multilateral and bilateral cooperation initiatives such as theConsultative Group on International Agricultural Research (CGIAR,
founded in the 1970s), and numerous initiatives of companies,
foundations, governments and non-governmental and academicorganizations. Moreover, in 2015, twenty countries launched an
initiative called ‘Mission Innovation’, seeking to double their energy
R&D funding. At this point it is difficult to evaluate whether Mission
Innovation achieved its objective (Sanchez and Sivaram, 2017). At
the same time, the private sector started an innovation initiative
called the ‘Breakthrough Energy Coalition’.

4

Most technology transfer is driven by through markets by theinterests of technology seekers and technology holders, particularly
in regions with well-developed institutional and technologicalcapabilities such as developed and emerging nations (Glachant andDechezleprêtre, 2016). However, the current international technology
transfer landscape has gaps, in particular in reaching out to leastdeveloped countries, where institutional and technology capabilities
are limited (de Coninck and Puig, 2015; Ockwell and Byrne, 2016).
On the one hand, literature suggests that the management or evenmonitoring of all these UN, bilateral, private and public initiatives
may fail to lead to better results. On the other hand, it is probably
more cost-effective to adopt a strategy of ‘letting a thousand flowers
bloom’, by challenging and enticing researchers in the public andthe private sector to direct innovation towards low-emission andadaptation options (Haselip et al., 2015). This can be done at thesame time as mission-oriented research is adopted in parallel by thescientific community (Mazzucato, 2018).
At COP 21, the UNFCCC requested the Subsidiary Body for Scientificand Technological Advice (SBSTA) to initiate the elaboration of
the technology framework established under the Paris Agreement(UNFCCC, 2016). Among other things, the technology framework
would ‘provide overarching guidance for the work of the Technology
Mechanism in promoting and facilitating enhanced action ontechnology development and transfer in order to support theimplementation of this Agreement’ (this Agreement being theParis Agreement). An enhanced guidance issued by the Technology
Executive Committee (TEC) for preparing a technology action plan
(TAP) supports the new technology framework as well as the Parties’
long-term vision on technology development and transfer, reflectedin the Paris Agreement (TEC, 2016).

9

Strengthening and Implementing the Global Response

4.4.5

Strengthening Policy Instruments and Enabling
Climate Finance

Triggering rapid and far-reaching change in technical choices andinstitutional arrangements, consumption and lifestyles, infrastructure,
land use, and spatial patterns implies the ability to scale up policy signals
to enable the decoupling of GHGs emission, and economic growth anddevelopment (Section 4.2.2.3). Such a scale-up would also imply that
potential short-term negative responses by populations and interest
groups, which could block these changes from the outset, would need
to be prevented or overcome. This section describes the size and natureof investment needs and the financial challenge over the coming two
decades in the context of 1.5°C warmer worlds, assesses the potential
and constraints of three categories of policy instruments that respond tothe challenge, and explains the conditions for using them synergistically.
The policy and finance instruments discussed in this section relate to
Section 4.4.1 (on governance) and other Sections in 4.4.

4.4.5.1

The core challenge: cost-efficiency, coordinationof expectations and distributive effects

Box 4.8 shows that the average estimate by seven models of annual
investment needs in the energy system is around 2.38 trillion USD2010(1.38 to 3.25) between 2016 and 2035. This represents between 2.53%(1.6–4%) of the world GDP in market exchange rates (MER) and 1.7%of the world GDP in purchasing power parity (PPP). OECD investment
assessments for a 2°C-consistent transition suggest that including
investments in transportation and in other infrastructure would increase
the investment needs by a factor of three. Other studies not included inBox 4.8, in particular by the World Economic Forum (WEF, 2013) and the
Global Commission on the Economy and Climate (GCEC, 2014) confirm
these orders of magnitude of investment.
The average increase of investment in the energy sector resulting fromBox 4.8 represents a mean value of 1.5% of the total world investmentcompared with the baselines scenario in MER and a little over 1% in
PPP. Including infrastructure investments would raise this to 2.5% and1.7% respectively.9
These incremental investments could be funded through a drain on
consumption (Bowen et al., 2017), which would necessitate between
0.68% and 0.45% lower global consumption than in the baseline. But,
consumption at a constant savings/consumption ratio can alternatively
be funded by shifting savings towards productive adaptation andmitigation investments, instead of real-estate sector and liquid financial
products. This response depends upon whether it is possible to close theglobal investment funding gap for infrastructure that potentially inhibits
growth, through structural changes in the global economy. In this case,investing more in infrastructure would not be an incremental cost interms of development and welfare (IMF, 2014; Gurara et al., 2017)

A calculation in MER tends indeed to underestimate the world GDP and its growth by giving a lower weight to fast-growing developing countries, whereas a calculationin PPP tends to overestimate it. The difference between the value of two currencies in PPP and MER should vanish as the gap of the income levels of the two concernedcountries decreases. Accounting for this trend in modelling is challenging.

372

Strengthening and Implementing the Global Response

Chapter 4

Box 4.8 | Investment Needs and the Financial Challenge of Limiting Warming to 1.5°C
Peer-reviewed literature that estimates the investment needs over the next two decades to scale up the response to limit warming
to 1.5°C is very limited (see Section 4.6). This box attempts to bring together available estimates of the order of magnitude ofthese investments, after consultation with the makers of those estimates, to provide the context for global and national financial
mobilization policy and related institutional arrangements.
Table 1 in this box presents mean annual investments up to 2035, based on three studies (after clarifying their scope and harmonizing
their metrics): an ensemble of four integrated assessment models (here denoted IAM, see Chapter 2), an Organization for Economic
Co-operation and Development (OECD) scenario for a 2°C limit (OECD, 2017a) and scenarios from the International Energy Agency(IEA, 2016c). All three sources provide estimates for the energy sector for various mitigation scenarios. They give a mean valueof 2.38 trillion USD of yearly investments in the energy sector over the period, with minimum and maximum values of 1.38 and3.25 respectively. We also report the OECD estimate for 2°C because it also covers transportation and other infrastructure (water,
sanitation, and telecommunication), which are essential to deliver the Sustainable Development Goals (SDGs), including SDG 7 onclean energy access, and enhance the adaptive capacity to climate change.
Box 4.8, Table 1 | Estimated annualized world mitigation investment needed to limit global warming to 2°C or 1.5°C (2015–2035 in trillions of USD at market
exchange rates) from different sources. The top four lines indicate the results of Integrated Assessment Models (IAMs) as reported in Chapter 2
for their Baseline, Nationally Determined Contributions (NDC), 2°C- and 1.5°C-consistent pathways. These numbers only cover the energy
sector and the second row includes energy efficiency in all sectors. The final two rows indicate the mitigation investment needs for the energy,transport and other infrastructure according to the Organization for Economic Co-operation and Development (OECD) for a Baseline pathway
and a 2°C-consistent pathway. Sources: IEA, 2016c; OECD, 2017a.

EnergyInvestments

Of which
Demand Side

Transport

Other Infrastructures

Total

Ratio to
MER GDPIAM Baseline (mean)

1.96

0.24

1.96

1.8%

IAM NDC (mean)

2.04

0.28

2.04

1.9%

IAM 2°C (mean)

2.19

0.38

2.19

2.1%

IAM 1.5°C (mean)

2.32

0.45

2.32

2.2%

IEA NDC

2.40

0.72

2.40

2.3%

1.13

2.76

2.7%

0.54

2.38

2.53%

IEA 1.5°C

2.76

Mean IAM-IEA, 1.5°C

2.38

Min IAM-IEA, 1.5°C

1.38

0.38

1.38

1.6%

Max IAM-IEA, 1.5°C

3.25

1.13

3.25

4.0%

5.74

5.4%

2.13

0.40

6.38

6.0%

OECD Baseline

OECD 2°C

2.73

1.52

4

The mean incremental share of annual energy investments to stay well below 2°C is 0.36% (between 0.2–1%) of global GDP
between 2016 and 2035. Since total world investment (also called gross fixed capital formation (GFCF)) is about 24% of global
GDP, the estimated incremental energy investments between a baseline and a 1.5°C transition would be approximately 1.5%
(between 0.8–4.2%) of projected total world investments. As the higher ends of these ranges reflect pessimistic assumptions
in 1.5°C-consistent pathways on technological change, the implementation of policies to accelerate technical change (see theremainder of Section 4.4.5) could lower the probability of higher incremental investment.
If we assume the amounts of investments given by the OECD for transportation and other infrastructure for warming of 2°C to bea lower limit for an 1.5°C pathway, then total incremental investments for all sectors for a 1.5°C-consistent pathway would be
estimated at 2.4% of total world investments. This total incremental investment reaches 2.53% if the investments in transportation
are scaled up proportionally with the investments in the energy sector and if all other investments are kept constant. Comparing this
2.4% or 2.53% number for all sectors to the 1.5% number for energy only (see previous paragraph) suggests that the investments in
sectors other than energy contribute significantly to incremental world investments, even though a comprehensive study or estimateof these investments for a 1.5°C limit is not available.
The issue, from a macroeconomic perspective, is whether these investments would be funded by higher savings at the costs of lower
consumption. This would mean a 0.5% reduction in consumption for the energy sector for 1.5°C. Note that for a 2°C scenario, this373

Chapter 4

Strengthening and Implementing the Global Response

Box 4.8 (continued)

reduction would be 0.8% if we account for the investment needs of all infrastructure sectors. Assuming conversely a constantsavings ratio, this would necessitate reallocating existing capital flows towards infrastructure. In addition to these incremental
investments, the amount of redirected investments is relevant from a financial perspective. In the reported IAM energy sector
scenarios, about three times the incremental investments is redirected. There is no such assessment for the other sectors. The OECD
report suggests that these ratios might be higher.
These orders of magnitude of investment can be compared to the available statistics of the global stock of 386 trillion USD offinancial capital, which consists of 100 trillion USD in bonds (SIFMA, 2017), around 60 trillion USD in equity (World Bank, 2018b),and 226 trillion USD of loans managed by the banking system (IIF, 2017; World Bank, 2018a). The long-term rate of return (interest
plus increase of shareholder value) is about 3% on bonds, 5% on bank lending and 7% on equity, leading to a weighted mean returnon capital of 3.4% in real terms (5.4% in nominal terms). Using 3.4% as a lower bound and 5% as a higher bound (following Piketty,2014) and taking a conservative assumption that global financial capital grows at the same rate as global GDP, the estimated yearlyfinancial capital revenues would be between 16.8 and 25.4 trillion USD.
Assuming that a quarter of these investments comes from public funds (as estimated by the World Bank; World Bank, 2018a), theamount of private resources needed to enable an energy sector transition is between 3.3% and 5.3% of annual capital income andbetween 5.6% and 8.3% of these revenues for all infrastructure to meet the 2°C limit and the SDGs.
Since the financial system has limited fungibility across budget lines, changing the partitioning of investments is not a zero-sum
game. An effective policy regime could encourage investment managers to change their asset allocation. Part of the challenge maylie in increasing the pace of financing of low-emission assets to compensate for a possible 38% decrease, by 2035, in the value offossil fuel assets (energy sector and indirect holdings in downstream uses like automobiles) (Mercure et al., 2018).

4

Investments in other (non-energy system) infrastructure to meetdevelopment and poverty-reduction goals can strengthen the adaptive
capacity to address climate change, and are difficult to separate fromoverall sustainable development and poverty-alleviation investments(Hallegatte and Rozenberg, 2017). The magnitude of potential climatechange damages is related to pre-existing fragility of impacted
societies (Hallegatte et al., 2007). Enhancing infrastructure and service
provision would lower this fragility, for example, through the provisionof universal (water, sanitation, telecommunication) service access(Arezki et al., 2016).
The main challenge is thus not just a lack of mobilization of aggregate
resources but of redirection of savings towards infrastructure, andthe further redirection of these infrastructure investments towards
low-emission options. If emission-free assets emerge fast enough tocompensate for the devaluation of high-emission assets, the sum of
the required incremental and redirected investments in the energy
sector would (up to 2035) be equivalent to between 3.3% and 5.3%of the average annual revenues of the private capital stock (see Box4.8) and to between 5.6% and 8.3%, including all infrastructure
investments.
The interplay between mechanisms of financial intermediationand the private risk-return calculus is a major barrier to realizingthese investments (Sirkis et al., 2015). This obstacle is not specific toclimate mitigation investments but also affects infrastructure andhas been characterised as the gap between the ‘propensity to save’and the ‘propensity to invest’ (Summers, 2016). The issue is whether
new financial instruments could close this gap and inject liquidity
into the low-emission transition, thereby unlocking new economic
374

opportunities (GCEC, 2014; NCE, 2016). By offsetting the crowding-out
of other private and public investments (Pollitt and Mercure, 2017), theensuing ripple effect could reinforce growth and the sustainability ofdevelopment (King, 2011; Teulings and Baldwin, 2014) and potentiallytrigger a new growth cycle (Stern, 2013, 2015). In this case, a massive
mobilization of low-emission investments would require a significant
effort but may be complementary to sustainable development
investments.
This uncertain but potentially positive outcome might be constrainedby the higher energy costs of low-emission options in the energy andtransportation sectors. The envelope of worldwide marginal abatement
costs for 1.5°C-consistent pathways reported in Chapter 2 is 135–5500
USD2010 tCO2−1 in 2030 and 245–13000 USD2010 tCO2−1 in 2050,
which is between three to four times higher than for a 2°C limit.
These figures are consistent with the dramatic reduction in the unit
costs of some low-emission technical options (for example solar
PV, LED lighting) over the past decade (see Section 4.3.1) (OECD,
2017c). Yet there are multiple constraints to a system-wide energy
transition. Lower costs of some supply- and demand-side options do
not always result in a proportional decrease in energy system costs.
The adoption of alternative options can be slowed down by increasing
costs of decommissioning existing infrastructure, the inertia of market
structures, cultural habits and risk-adverse user behaviour (see Sections
4.4.1 to 4.4.3). Learning-by-doing processes and R&D can accelerate
the cost-efficiency of low-emission technology but often imply higher
early-phase costs. The German energy transition resulted in highconsumer prices for electricity in Germany (Kreuz and Müsgens, 2017)
and needed strong accompanying measures to succeed.

Strengthening and Implementing the Global Response

One key issue is that energy costs can propagate across sectors andamplify overall production costs. During the early stage of a lowemission transition, an increase in the prices of non-energy goods could
reduce consumer purchasing power and final demand. A rise in energy
prices has a proportionally greater impact in developing countries
that are in a catch-up phase, as they have a stronger dependence onenergy-intensive sectors (Crassous et al., 2006; Luderer et al., 2012)
and a higher ratio of energy to labour cost (Waisman et al., 2012). This
explains why with lower carbon prices, similar emission reductions arereached in South Africa (Altieri et al., 2016) and Brazil (La Rovere et al.,2017a) compared to developed countries. However, three distributional
issues emerge.
First, in the absence of countervailing policies, higher energy costs
have an adverse effect on the distribution of welfare (see also
Chapter 5). The negative impact is inversely correlated with thelevel of income (Harberger, 1984; Fleurbaey and Hammond, 2004)
and positively correlated with the share of energy in the households
budget, which is high for low- and middle-income households(Proost and Van Regemorter, 1995; Barker and Kohler, 1998; Westand Williams, 2004; Chiroleu-Assouline and Fodha, 2011). Moreover,climatic conditions and the geographical conditions of humansettlements matter for heating and mobility needs (see Chapter 5).
Medium-income populations in the suburbs, in remote areas, and in
low-density regions can be as vulnerable as residents of low-income
urban areas. Poor households with low levels of energy consumption
are also impacted by price increases of non-energy goods caused by
the propagation of energy costs (Combet et al., 2010; Dubois, 2012).
These impacts are generally not offset by non-market co-benefits ofclimate policies for the poor (Baumgärtner et al., 2017).
A second matter of concern is the distortion of international competitionand employment implications in the case of uneven carbon constraints,
especially for energy-intensive industries (Demailly and Quirion, 2008).
Some of these industries are not highly exposed to international
competition because of their very high transportation costs per unit
value added (Sartor, 2013; Branger et al., 2016), but other industries
could suffer severe shocks, generate ‘carbon leakage’ through cheaper
imports from countries with lower carbon constraints (Branger andQuirion, 2014), and weaken the surrounding regional industrial fabricwith economy-wide and employment implications.
A third challenge is the depreciation of assets whose value is based on
the valuation of fossil energy resources, of which future revenues may
decline precipitously with higher carbon prices (Waisman et al., 2013;
Jakob and Hilaire, 2015; McGlade and Ekins, 2015), and on emissionintensive capital stocks (Guivarch and Hallegatte, 2011; OECD, 2015a;
Pfeiffer et al., 2016). This raises issues of changes in industrial structure,adaptation of worker skills, and of stability of financial, insurance andsocial security systems. These systems are in part based on current
holdings of carbon-based assets whose value might decrease by about
38% by the mid-2030s (Mercure et al., 2018). This stranded asset
challenge may be exacerbated by a decline of export revenues of fossil
fuel producing countries and regions (Waisman et al., 2013; Jakob andHilaire, 2015; McGlade and Ekins, 2015).

Chapter 4

These distributional issues, if addressed carefully and expeditiously, could
affect popular sensitivity towards climate policies. Addressing them
could mitigate adverse macroeconomic effects on economic growth andemployment that could undermine the potential benefits of a redirectionof savings and investments towards 1.5°C-consistent pathways.
Strengthening policy instruments for a low-emission transition would
thus need to reconcile three objectives: (i) handling the short-term
frictions inherent to this transition in an equitable way, (ii) minimizingthese frictions by lowering the cost of avoided GHGs emissions, and (iii)
coordinating expectations of multiple stakeholders at various decisionmaking levels to accelerate the decline in costs of emission reduction,
efficiency and decoupling options and maximizing their co-benefits(see the practical example of lowering car use in cities in Box 4.9).
Three categories of policy tools would be available to meet the
distributional challenges: carbon pricing, regulatory instruments andinformation and financial tools. Each of them has its own strengths
and weaknesses, from a 1.5°C perspective, policy tools would have to
be both scaled up and better coordinated in packages in a synergisticmanner.

4.4.5.2

Carbon pricing: necessity and constraints

Economic literature has long argued that climate and energy policy
grounded only in regulation, standards and public funding of R&D is
at risk of being influenced by political and administrative arbitrariness,
which could raise the costs of implementation. This literature has argued
that it may be more efficient to make these costs explicit through carbon
taxes and carbon trading, securing the abatement of emissions in places
and sectors where it is cheapest (IPCC, 1995, 2001; Gupta et al., 2007;
Somanathan et al., 2014).
In a frictionless world, a uniform world carbon price could minimize thesocial costs of the low-carbon transition by equating the marginal costsof abatement across all sources of emissions. This implies that investors
will be able to make the right choices under perfect foresight and thatdomestic and international compensatory transfers offset the adverse
distributional impacts of higher energy prices and their consequences on
economic activity. In the absence of such transfers, carbon prices would
have to be differentiated by jurisdiction (Chichilnisky and Heal, 2000;
Sheeran, 2006; Böhringer et al., 2009; Böhringer and Alexeeva-Talebi,2013). This differentiation could in turn raise concerns of distortions ininternational competition (Hourcade et al., 2001; Stavins et al., 2014).
Obstacles to enforcing a uniform world carbon price in the short run
would not necessarily crowd out explicit national carbon pricing, forthree reasons. First, a uniform carbon price would limit an emissions
rebound resulting from a higher consumption of energy services
enabled by efficiency gains, if energy prices do not change (Greening etal., 2000; Fleurbaey and Hammond, 2004; Sorrell et al., 2009; Guivarchand Hallegatte, 2011; Chitnis and Sorrell, 2015; Freire-González, 2017).
Second, it could hedge against the arbitrariness of regulatory policies.
Third, ‘revenue neutral’ recycling, at a constant share of taxes on GDP,
into lowering some existing taxes would compensate for at least partof the propagation effect of higher energy costs (Stiglitz et al., 2017).
The substitution by carbon taxes of taxes that cause distortions on the375

4Chapter 4

Strengthening and Implementing the Global Response

Box 4.9 | Emerging Cities and ‘Peak Car Use’: Evidence of Decoupling in Beijing
The phenomenon of ‘peak car use’, or reductions in per capita car use, provides hope for continuing reductions in greenhousegases from oil consumption (Millard-Ball and Schipper, 2011; Newman and Kenworthy, 2011; Goodwin and Van Dender, 2013). The
phenomenon has been mostly associated with developed cities apart from some early signs in Eastern Europe, Latin America andChina (Newman and Kenworthy, 2015). New research indicates that peak car is now also underway in China (Gao and Newman,2018).

Modal split of daily trips (%)

China’s rapid urban motorization was a result of strong economic growth, fast urban development and the prosperity of the Chinese
automobile industry (Gao et al., 2015). However, recent data (Gao and Newman, 2018) (expressed as a percentage of daily trips)
suggest the first signs of a break in the growth of car use along with the growth in mass transit, primarily the expansion of Metrosystems (see Box 4.9, Figure 1).
70

TransitPrivate Vehicles

Bicycle

1995

2000

2005

60

50
40
30
20
10

0
1985

1990

2010

2015

Year
Box 4.9, Figure 1 | The modal split data in Beijing between 1986 and 2014. Source: (Gao and Newman, 2018).

Chinese urban fabrics, featuring traditional dense linear forms and mixed land use, favour mass transit systems over automobiles(Gao and Newman, 2018). The data show that the decline in car use did not impede economic development, but the growth invehicle kilometres of travel (VKT) has decoupled absolutely from GDP as shown in Box 4.9, Figure 2 below.
5000

per capita GDP (USD)

20000

4000

15000

3000

10000

2000

per capita GDP (USD)5000

per capita VKT (km)
0
2002

2004

2006

2008

2010

2012

2014

1000

per capita VKT (km)

4

0
2016

Year
Box 4.9, Figure 2 | Peak car in Beijing: relationships between economic performance and private automobile use in Beijing from 1986 to 2014.
VKT is vehicle kilometres of travel. Source: (Gao and Newman, 2018).

economy can counteract the regressive effect of higher energy prices. For
example, offsetting increased carbon prices with lower labour taxes canpotentially decrease labour costs (without affecting salaries), enhance
employment and reduce the attractiveness of informal economic activity(Goulder, 2013).
The conditions under which an economic gain along with climate
benefit (a ‘double dividend’) can be expected are well documented
376(Goulder, 1995; Bovenberg, 1999; Mooij, 2000). In the context of OECD
countries, the literature examines how carbon taxation could substitutefor other taxes to fund the social security system (Combet, 2013). The
same general principles apply for countries that are building their social
welfare system, such as China (Li and Wang, 2012) or Brazil (La Rovere
et al., 2017a), but an optimal recycling scheme could differ based on the
structure of the economy (Lefèvre et al., 2018).

Strengthening and Implementing the Global Response

In every country the design of carbon pricing policy implies a balance
between incentivizing low-carbon behaviour and mitigating theadverse distributional consequences of higher energy prices (Combet
et al., 2010). Carbon taxes can offset these effects if their revenues
are redistributed through rebates to poor households. Other options
include the reduction of value-added taxes for basic products or direct
benefit transfers to enable poverty reduction (see Winkler et al. (2017)
for South Africa and Grottera et al. (2016) for Brazil). This is possible
because higher-income households pay more in absolute terms, eventhough their carbon tax burden is a relatively smaller share of theirincome (Arze del Granado et al., 2012).
Ultimately, the pace of increase of carbon prices would depend on thepace at which they can be embedded in a consistent set of fiscal andsocial policies. This is specifically critical in the context of the 1.5°C
limit (Michaelowa et al., 2018). This is why, after a quarter century ofacademic debate and experimentation (see IPCC WGIII reports since
the SAR), a gap persists with respect to ‘switching carbon prices’
needed to trigger rapid changes. In 2016, only 15% of global emissions
are covered by carbon pricing, three-quarters of which with prices
below 10 USD tCO2−1 (World Bank, 2016). This is too low to outweighthe ‘noise’ from the volatility of oil markets (in the range of 100 USD
tCO2−1 over the past decade), of other price dynamics (interest rates,
currency exchange rates and real estate prices) and of regulatory
policies in energy, transportation and industry. For example, thedynamics of mobility depend upon a trade-off between housing prices
and transportation costs in which the price of real estate and the inert
endowments in public transport play as important a role as liquid fuelprices (Lampin et al., 2013).
These considerations apply to attempts to secure a minimum price incarbon trading systems (Wood and Jotzo, 2011; Fell et al., 2012; Fusset al., 2018) and to the reduction of fossil fuel subsidies. Estimated at
650 billion USD in 2015 (Coady et al., 2017), these subsidies represent
25–30% of government expenditures in forty (mostly developing)countries (IEA, 2014b). Reducing these subsidies would contribute toreaching 1.5°C-consistent pathways, but raises similar issues as carbon
pricing around long-term benefits and short-term costs (Jakob et al.,2015; Zeng and Chen, 2016), as well as social impacts.
Explicit carbon prices remain a necessary condition of ambitious
climate policies, and some authors highlight the potential benefit
brought by coordination among groups of countries (Weischer et al.,2012; Hermwille et al., 2017; Keohane et al., 2017). They could take theform of carbon pricing corridors (Bhattacharya et al., 2015). They area necessary ‘lubricant’ through fiscal reforms or direct compensatingtransfers to accommodate the general equilibrium effects of higher
energy prices but may not suffice to trigger the low-carbon transition
because of a persistent ‘implementation gap’ between the aspirational
carbon prices and those that can practically be enforced. When systemicchanges, such as those needed for 1.5°C-consistent pathways, are atplay on many dimensions of development, price levels ‘depend on thepath and the path depends on political decisions’ (Drèze and Stern,1990).

Chapter 4

4.4.5.3

Regulatory measures and information flows

Regulatory instruments are a common tool for improving energy
efficiency and enhancing renewable energy in OECD countries (e.g., theUSA, Japan, Korea, Australia, the EU) and, more recently, in developing
countries (M.J. Scott et al., 2015; Brown et al., 2017). Such instruments
include constraints on the import of products banned in other countries(Knoop and Lechtenböhmer, 2017).
For energy efficiency, these instruments include end-use standards andlabelling for domestic appliances, lighting, electric motors, water heaters
and air-conditioners. They are often complemented by mandatory
efficiency labels to attract consumers’ attention and stimulate the
manufacture of more efficient products (Girod et al., 2017). Experience
shows that these policy instruments are effective only if they are
regularly reviewed to follow technological developments, as in the ‘Top
Runner’ programme for domestic appliances in Japan (Sunikka-Blank
and Iwafune, 2011).
In four countries, efficiency standards (e.g. miles per gallon or level of
CO2 emission per kilometre) have been used in the transport sector,
for light- and heavy-duty vehicles, which have spillovers for the global
car industry. In the EU (Ajanovic and Haas, 2017) and the USA (Senet al., 2017), vehicle manufacturers need to meet an annual CO2
emission target for their entire new vehicle fleet. This allows them tocompensate through the introduction of low-emission vehicles for thehigh-emission ones in the fleet. This leads to increasingly efficient fleetsof vehicles over time but does not necessarily limit the driven distance.
Building codes that prescribe efficiency requirements for new andexisting buildings have been adopted in many OECD countries (Evanset al., 2017) and are regularly revised to increase their efficiency perunit of floor space. Building codes can avoid locking rapidly urbanizing
countries into poorly performing buildings that remain in use for the
next 50–100 years (Ürge-Vorsatz et al., 2014). In OECD countries,
however, their main role is to incentivize the retrofit of existingbuildings. In addition of the convergence of these codes to net zeroenergy buildings (D’Agostino, 2015), a new focus should be placed,in the context of 1.5°C-consistent pathways, on public and private
coordination to achieve better integration of building policies with thepromotion of low-emission transportation modes (Bertoldi, 2017).
The efficacy of regulatory instruments can be reinforced by economic
incentives, such as feed-in tariffs based on the quantity of renewableenergy produced, subsidies or tax exemptions for energy savings(Bertoldi et al., 2013; Ritzenhofen and Spinler, 2016; García-Álvarez etal., 2017; Pablo-Romero et al., 2017), fee-bates, and ‘bonus-malus’ that
foster the penetration of low-emission options (Butler and Neuhoff,2008). Economic incentives can also be combined with direct-use
market-based instruments, for example combining, in the UnitedStates and, in some EU countries, carbon trading schemes with energy
savings obligations for energy retailers (Haoqi et al., 2017), or withgreen certificates for renewable energy portfolio standards (Upton and
Snyder, 2017). Scholars have investigated caps on utilities’ energy sales(Thomas et al., 2017) and emission caps implemented at a personallevel (Fawcett et al., 2010).

377

4

Chapter 4

In combination with the funding of public research institutes, grants
or subsidies also support R&D, where risk and the uncertainty about
long-term perspectives can reduce the private sector’s willingness toinvest in low-emission innovation (see also Section 4.4.4). Subsidies cantake the form of rebates on value-added tax (VAT), of direct support toinvestments (e.g., renewable energy or refurbishment of buildings) or
feed-in tariffs (Mir-Artigues and del Río, 2014). They can be provided
by the public budget, via consumption levies, or via the revenues ofcarbon taxes or pricing. Fee-bates, introduced in some countries (e.g.,
for cars), have had a neutral impact on public budgets by incentivizing
low-emission products and penalizing high-emission ones (de Haan etal., 2009).
All policy instruments can benefit from information campaigns (e.g., TVads) tailored to specific end-users. A vast majority of public campaigns onenergy and climate have been delivered through mass-media channels
and advertising-based approaches (Corner and Randall, 2011; Doyle,2011). Although some authors report large savings obtained by suchcampaigns, most agree that the effects are short-lived and decreaseover time (Bertoldi et al., 2016). Recently, focus has been placed on theuse of social norms to motivate behavioural changes (Allcott, 2011; Allóand Loureiro, 2014). More on strategies to change behaviour can befound in Section 4.4.3.

4.4.5.4

4

Scaling up climate finance and de-risking
low-emission investments

The redirection of savings towards low-emission investments may beconstrained by enforceable carbon prices, implementation of technicalstandards and the short-term bias of financial systems (Miles, 1993;
Bushee, 2001; Black and Fraser, 2002). The many causes of this bias areextensively analysed in economic literature (Tehranian and Waegelein,
1985; Shleifer and Vishny, 1990; Bikhchandani and Sharma, 2000),
including their link with prevailing patterns of economic globalization(Krugman, 2009; Rajan, 2011) and the chronic underinvestment inlong-term infrastructure (IMF, 2014). Emerging literature explores how
to overcome this through reforms targeted to bridge the gap between
short-term cash balances and long-term low-emission assets and to
reduce the risk-weighted capital costs of climate-resilient investments.
This gap, which was qualified by the Governor of the Bank of England asa ‘tragedy of the horizon’ (Carney, 2016) that constitutes a threat to thestability of the financial system, is confirmed by the literature (Arezki etal., 2016; Christophers, 2017). This potential threat would encompass theimpact of climate events on the value of assets (Battiston et al., 2017),
liability risks (Heede, 2014) and the transition risk due to devaluation ofcertain classes of assets (Platinga and Scholtens, 2016).
The financial community’s attention to climate change grew after COP
15 (ESRB ASC, 2016). This led to the introduction of climate-related risk
disclosure in financial portfolios (UNEP, 2015), placing it on the agendaof G20 Green Finance Study Group and of the Financial Stability Board.
This led to the creation of low-carbon financial indices that investors
could consider as a ‘free option on carbon’ to hedge against risks of
stranded carbon-intensive assets (Andersson et al., 2016). This could also
accelerate the emergence of climate-friendly financial products such as
10Strengthening and Implementing the Global Response

green or climate bonds. The estimated value of the green bonds market
in 2017 is 155 billion USD (BNEF, 2018). The bulk of these investments
are in renewable energy, energy efficiency and low-emission transport(Lazurko and Venema, 2017), with only 4% for adaptation (OECD,
2017b). One major question is whether individual strategies based on
improved climate-related information alone will enable the financial
system to allocate capital in an optimal way (Christophers, 2017) sinceclimate change is a systemic risk (CISL, 2015; Schoenmaker and van
Tilburg, 2016).
The readiness of financial actors to reduce investments in fossil fuels
is a real trend (Platinga and Scholtens, 2016; Ayling and Gunningham,
2017), but they may not resist the attractiveness of carbon-intensive
investments in many regions. Hence, decarbonizing an investment
portfolio is not synonymous with investing massively in low-emission
infrastructure. Scaling up climate-friendly financial products may
depend upon a business context conducive to the reduction of the riskweighted capital costs of low-emission projects. The typical leverage of
public funding mechanisms for low-emission investment is low (2 to 4)compared with other sectors (10 to 15) (Maclean et al., 2008; Ward etal., 2009; MDB, 2016). This is due to the interplay of the uncertainty ofemerging low-emission technologies in the midst of their learning-bydoing cycle with uncertain future revenues due to volatility of fossil fuelprices (Roques et al., 2008; Gross et al., 2010) as well as uncertainty
around regulatory policies. This inhibits low-emission investments bycorporations functioning under a ‘shareholder value business regime’(Berle and Means, 1932; Roe, 1996; Froud et al., 2000) and actors
with restricted access to capital (e.g. cities, local authorities, SMEs andhouseholds).
De-risking policy instruments to enable low-emission investment
encompasses interest rate subsidies, fee-bates, tax breaks, concessionalloans from development banks, and public investment funds, including
revolving funds. Given the constraints on public budgets, publicguarantees can be used to increase the leverage effect of publicfinancing on private financing. Such de-risking instruments imply
indeed a full direct burden on public budgets only in case of defaultof the project. They could back for example various forms of green
infrastructure funds (de Gouvello and Zelenko, 2010; Emin et al., 2014;
Studart and Gallagher, 2015).10
The risk of defaulting can be mitigated by strong measurement, reportingand verifying (MRV) systems (Bellassen et al., 2015) and by the use ofnotional prices recommended in public economics (and currently in use
in France and the UK) to calibrate public support to the provision of
public goods in case of persisting distortions in pricing (Stiglitz et al.,2017). Some suggest linking these notional prices to ‘social, economicand environmental value of voluntary mitigation actions’ recognized by
the COP 21 Decision accompanying the Paris Agreement (paragraph108) (Hourcade et al., 2015; La Rovere et al., 2017b; Shukla et al., 2017),
in order to incorporate the co-benefits of mitigation.
Such public guarantees ultimately amount to money issuance backed by
low-emission projects as collateral. This explains the potentially stronglink between global climate finance and the evolution of the financial

One prototype is the World Bank’s Pilot Auction Facility on Methane and Climate Change

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Strengthening and Implementing the Global Response

and monetary system. Amongst suggested mechanisms for thisevolution are the use of International Monetary Fund’s (IMF’s) Special
Drawing Rights to fund the paid-in capital of the Green Climate Fund
(Bredenkamp and Pattillo, 2010) and the creation of carbon remediation
assets at a predetermined face value per avoided tonne of emissions(Aglietta et al., 2015a, b). Such a predetermined value could hedge
against the fragmentation of climate finance initiatives and support the
emergence of financial products backed by a new class of long-term
assets.
Combining public guarantees at a predetermined value of avoided
emissions, in addition to improving the consistency of non-pricemeasures, could support the emergence of financial products backed
by a new class of certified assets to attract savers in search of safe andethical investments (Aglietta et al., 2015b). It could hedge against the
fragmentation of climate finance initiatives and provide a mechanism tocompensate for the ‘stranded’ assets caused by divestment in carbonbased activities and in lowering the systemic risk of stranded assets(Safarzyńska and van den Bergh, 2017). These new assets could also
facilitate a low-carbon transition for fossil fuel producers and help themto overcome the ‘resource curse’ (Ross, 2015; Venables, 2016).
Blended injection of liquidity has monetary implications. Some argue
that this questions the premise that money should remain neutral(Annicchiarico and Di Dio, 2015, 2016; Nikiforos and Zezza, 2017).
Central banks or financial regulators could act as a facilitator of lastresort for low-emission financing instruments, which could in turn lower
the systemic risk of stranded assets (Safarzyńska and van den Bergh,2017). This may, in time, lead to the use of carbon-based monetary
instruments to diversify reserve currencies (Jaeger et al., 2013) anddifferentiate reserve requirements (Rozenberg et al., 2013) in thecontext of a climate-friendly Bretton Woods (Sirkis et al., 2015; Stua,
2017).

4.4.5.5

Financial challenge for basic needs and adaptation
finance

Adaptation finance is difficult to quantify for two reasons. The first isthat it is very difficult to isolate specific investment needs to enhance
climate resilience from the provision of basic infrastructure that are
currently underinvested (IMF, 2014; Gurara et al., 2017). The UNEP
(2016) estimate of investment needs on adaptation in developing
countries between 140–300 billion USD yr−1 in 2030, a major partbeing investment expenditures that are complementary with SDGrelated investments focused on universal access to infrastructure andservices and meeting basic needs. Many climate-adaptation-centricfinancial incentives are relevant to non-market services, offering fewer
opportunities for market revenues while they contribute to creating
resilience to climate impacts.
Hence, adaptation investments and the provision of basic needs would
typically have to be supported by national and sub-national government
budgets together with support from overseas development assistanceand multilateral development banks (Fankhauser and Schmidt-Traub,
2011; Adenle et al., 2017; Robinson and Dornan, 2017), and a slow
increase of dedicated NGO and private climate funds (Nakhooda
and Watson, 2016). Even though the UNEP estimates of the costs ofChapter 4

adaptation might be lower in a 1.5°C world (UNEP/Climate Analytics,
2015) they would be higher than the UNEP estimate of 22.5 billion
USD of bilateral and multilateral funding for climate change adaptationin 2014. Currently, 18–25% of climate finance flows to adaptation indeveloping countries (OECD, 2015b, 2016; Shine and Campillo, 2016). It
remains fragmented, with small proportions flowing through UNFCCC
channels (AdaptationWatch, 2015; Roberts and Weikmans, 2017).
Means of raising resources for adaptation, achieving the SDGs andmeeting basic needs (Durand et al., 2016; Roberts et al., 2017) include
the reduction of fossil fuel subsidies (Jakob et al., 2016), increasing
revenues from carbon taxes (Jakob et al., 2016), levies on international
aviation and maritime transport, and sharing of the proceeds of financial
arrangements supporting mitigation activities (Keen et al., 2013). Eachhave different redistribution implications. Challenges, however, include
the efficient use of resources, the emergence of long-term assets using
infrastructure as collateral and the capacity to implement small-scale
adaptation and the mainstreaming of adaptation in overall development
policies. There is thus a need for greater policy coordination (Fankhauser
and McDermott, 2014; Morita and Matsumoto, 2015; Sovacool et al.,2015, 2017; Lemos et al., 2016; Adenle et al., 2017; Peake and Ekins,2017) that includes robust mechanisms for tracking, reporting andensuring transparency of adaptation finance (Donner et al., 2016; Pauw
et al., 2016a; Roberts and Weikmans, 2017; Trabacchi and Buchner,2017) and its consistency with the provision of basic needs (Hallegatteet al., 2016).

4.4.5.6

Towards integrated policy packages and innovative
forms of financial cooperation

Carbon prices, regulation and standards, improved information andappropriate financial instruments can work synergistically to meet thechallenge of ‘making finance flows consistent with a pathway towards
low greenhouse gas emissions and climate-resilient development’, as inArticle 2 in the Paris Agreement.
There is growing attention to the combination of policy instrumentsthat address three domains of action: behavioural changes, economic
optimization and long-term strategies (Grubb et al., 2014). For example,
de-risking low-emission investments would result in higher volumes of
low-emission investments, and would in turn lead to a lower switching
price for the same climate ambition (Hirth and Steckel, 2016). In thereverse direction, higher explicit carbon prices may generate more
low-emission projects for a given quantum of de-risking. For example,
efficiency standards for housing can increase the efficacy of carbon prices
and overcome the barriers coming from the high discount rates used byhouseholds (Parry et al., 2014), while explicit and notional carbon prices
can lower the risk of arbitrary standards. The calibration of innovative
financial instruments to notional carbon prices could encourage large
multinational companies to increase their level of internal carbon prices(UNEP, 2016). These notional prices could be higher than explicit carbon
prices because they redirect new hardware investments without animmediate impact on existing capital stocks and associated interests.
Literature, however, shows that conflicts between poorly articulated
policy instruments can undermine their efficiency (Lecuyer andQuirion, 2013; Bhattacharya et al., 2017; García-Álvarez et al., 2017).
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Chapter 4

As has been illustrated in Europe, commitment uncertainty and lack ofcredibility of regulation have consistently led to low carbon prices in
the case of the EU Emission Trading System (Koch et al., 2014, 2016). A
comparative study shows how these conflicts can be avoided by policy
packages that integrate many dimensions of public policies and aredesigned to match institutional and social context of each country andregion (Bataille et al., 2015).
Even though policy packages depend upon domestic politicalprocesses, they might not reinforce the NDCs at a level consistent withthe 1.5°C transition without a conducive international setting whereinternational development finance plays a critical role. Section 4.4.1
explores the means of mainstreaming climate finance in the current
evolution of the lending practices of national and multilateral banks(Badré, 2018). This could facilitate the access of developing countries
to loans via bond markets at low interest rates, encouragementof the emergence of new business models for infrastructure,and encouragement of financial markets to support small-scale
investments (Déau and Touati, 2017).
These financial innovations may involve non-state public actors
like cities and regional public authorities that govern infrastructure
investment, enable energy and food systems transitions and manage
urban dynamics (Cartwright, 2015). They would help, for example, inraising the 4.5–5.4 trillion USD yr−1 from 2015 to 2030 announced
by the Cities Climate Finance Leadership Alliance (CCFLA, 2016) toachieve the commitments by the Covenant of Mayors of many cities tolong-term climate targets (Kona et al., 2018).

4

The evolution of global climate financial cooperation may involve
central banks, financial regulatory authorities, and multilateral andcommercial banks. There are still knowledge gaps about the form,
structure and potential of these arrangements. They could be viewed
as a form of a burden-sharing between high-, medium- and lowincome countries to enhance the deployment of ambitious Nationally
Determined Contributions (NDCs) and new forms of ‘common but
differentiated responsibility and respective capabilities’ (Edenhofer etal., 2015; Hourcade et al., 2015; Ji and Sha, 2015).

4.5

Integration and Enabling Transformation

4.5.1

Assessing Feasibility of Options for Accelerated
Transitions

Chapter 2 shows that 1.5°C-consistent pathways involve rapid, global
climate responses to reach net zero emissions by mid-century or earlier.
Chapter 3 identifies climate change risks and impacts to which theworld would need to adapt during these transitions and additional risks
and impacts during potential 1.5°C overshoot pathways. The feasibilityof these pathways is contingent upon systemic change (Section 4.3)
and enabling conditions (Section 4.4), including policy packages. Thissection assesses the feasibility of options (technologies, actions andmeasures) that form part of global systems under transition that makeup 1.5°C-consistent pathways.

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Strengthening and Implementing the Global Response

Following the assessment framework developed in Chapter 1, economic
and technological, institutional and socio-cultural, and environmental andgeophysical feasibility are considered and applied to system transitions(Sections 4.3.1–4.3.4), overarching adaptation options (Section 4.3.5)
and carbon dioxide removal (CDR) options (Section 4.3.7). This is doneto assess the multidimensional feasibility of mitigation and adaptation
options that have seen considerable development and change sinceAR5. In the case of adaptation, the assessed AR5 options are typicallyclustered. For example, all options related to energy infrastructure
resilience, independently of the generation source, are categorized as‘resilience of power infrastructure’.
Table 4.10 presents sets of indicators against which the multidimensional
feasibility of individual adaptation options relevant to warming of 1.5°C,
and mitigation options along 1.5°C-consistent pathways, is assessed.
The feasibility assessment takes the following steps. First, each of
the mitigation and adaptation options is assessed along the relevant
indicators grouped around six feasibility dimensions: economic,
technological, institutional, socio-cultural, environmental/ecologicaland geophysical. Three types of feasibility groupings were assessedfrom the underlying literature: first, if the indicator could block thefeasibility of this option; second, if the indicator has neither a positive
nor a negative effect on the feasibility of the option or the evidenceis mixed; and third, if the indicator does not pose any barrier to thefeasibility of this option. The full assessment of each option under eachindicator, including the literature references on which the assessment
is based, can be found in supplementary materials 4.SM.4.2 and4.SM.4.3. When appropriate, it is indicated that there is no evidence(NE), limited evidence (LE) or that the indicator is not applicable to theoption (NA).
Next, for each feasibility dimension and option, the overall feasibility
for a given dimension is assessed as the mean of combined scoresof the relevant underlying indicators and classified into ‘insignificant
barriers’ (2.5 to 3), ‘mixed or moderate but still existent barriers’ (1.5
to 2.5) or ‘significant barriers’ (below 1.5) to feasibility. Indicators
assessed as NA, LE or NE are not included in this overall assessment(see supplementary material 4.SM.4.1 for the averaging and weighing
guidance).
The results are summarized in Table 4.11 (for mitigation options)and Table 4.12 (for adaptation options) for each of the six feasibility
dimensions: where dark shading indicates few feasibility barriers;
moderate shading indicates that there are mixed or moderate but still
existent barriers, and light shading indicates that multiple barriers, inthis dimension, may block implementation.
A three-step process of independent validation and discussion byauthors was undertaken to make this assessment as robust as possible
within the scope of this Special Report. It must, however, be recognizedthat this is an indicative assessment at global scale, and both policy
and implementation at regional, national and local level would need toadapt and build on this knowledge, within the particular local context
and constraints. Some contextual factors are indicated in the rightmost
column in Tables 4.11 and 4.12.

Strengthening and Implementing the Global Response

Chapter 4

Table 4.10 | Sets of indicators against which the feasibility of adaptation and mitigation options are assessed for each feasibility dimension. The options are discussed in
Sections 4.3.1-4.3.5 and 4.3.7.
Feasibility Dimensions

Adaptation Indicators

Mitigation Indicators

Economic

Microeconomic viability
Macroeconomic viability
Socio-economic vulnerability reduction potential
Employment & productivity enhancement potential

Cost-effectiveness
Absence of distributional effects
Employment & productivity enhancement potential

TechnologicalTechnical resource availability
Risks mitigation potential

Technical scalability
Maturity
Simplicity
Absence of risk

Institutional

Political acceptability
Legal & regulatory feasibility
Institutional capacity & administrative feasibility
Transparency & accountability potential

Political acceptability
Legal & administrative feasibility
Institutional capacity
Transparency & accountability potential

Socio-cultural

Social co-benefits (health, education)
Socio-cultural acceptability
Social & regional inclusivenessIntergenerational equity

Social co-benefits (health, education)
Public acceptanceSocial & regional inclusivenessIntergenerational equity
Human capabilities

Environmental/Ecological

Ecological capacity
Adaptive capacity/ resilience building potential

Reduction of air pollution
Reduction of toxic waste
Reduction of water use
Improved biodiversity

GeophysicalPhysical feasibility
Land use change enhancement potential
Hazard risk reduction potential

Physical feasibility (physical potentials)
Limited use of land
Limited use of scarce (geo)physical resources
Global spread

4.5.2

Implementing Mitigation

This section builds on the insights on mitigation options in Section 4.3,
applies the assessment methodology along feasibility dimensions andindicators explained in Section 4.5.1, and synthesizes the assessmentof the enabling conditions in Section 4.4.

section (Section 4.6). Nevertheless, in Table 4.11, an indicative attempt
has been made to capture relevant contextual information. The ‘context’
column indicates which contextual factors may affect the feasibility ofan option, including regional differences. For instance, solar irradiationin an area impacts the cost-effectiveness of solar photovoltaic energy,
so solar irradiation is mentioned in this column.

4.5.2.1

4.5.2.2

Assessing mitigation options for limiting warming
to 1.5˚C against feasibility dimensions

Enabling conditions for implementationof mitigation options towards 1.5˚CAn assessment of the degree to which examples of 1.5°C-relevant
mitigation options face barriers to implementation, and on which
contexts this depends, is summarized in Table 4.11. An explanation of
the approach is given in Section 4.5.1 and in supplementary material
4.SM.4.1. Selected options were mapped onto system transitions
and clustered through an iterative process of literature review,expert feedback, and responses to reviewer comments. The detailed
assessment and the literature underpinning the assessment can befound in supplementary material 4.SM.4.2.

The feasibility assessment highlights six dimensions that could help
inform an agenda that could be addressed by the areas discussed in
Section 4.4: governance, behaviour and lifestyles, innovation, enhancing
institutional capacities, policy and finance. For instance, Section 4.4.3 on
behaviour offers strategies for addressing public acceptance problems,and how changes can be more effective when communication andactions relate to people’s values. This section synthesizes the findings in
Section 4.4 in an attempt to link them to the assessment in Table 4.11.
The literature on which the discussion is based is found in Section 4.4.

The feasibility framework in Cross-Chapter Box 3 in Chapter 1 highlightsthat the feasibility of mitigation and adaptation options depends on
many factors. Many of those are captured in the indicators in Table 4.10,
but many depend on the specific context in which an option features. This
Special Report did not have the mandate, space or the literature baseto undertake a regionally specific assessment. Hence the assessment iscaveated as providing a broad indication of the likely global barriers,
ignoring significant regional diversity. Regional and context-specificliterature is also just emerging as is noted in the knowledge gaps

From Section 4.4, including the case studies presented in the Boxes
4.1 to 4.10, several main messages can be constructed. For instance,
governance would have to be multilevel and engaging different actors,
while being efficient, and choosing the form of cooperation based on
the specific systemic challenge or option at hand. If institutional capacity
for financing and governing the various transitions is not urgently built,
many countries would lack the ability to change pathways from ahigh-emission scenario to a low- or zero-emission scenario. In terms ofinnovation, governments, both national and multilateral, can contribute
381

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Chapter 4

Strengthening and Implementing the Global Response

Table 4.11 | Feasibility assessment of examples of 1.5°C-relevant mitigation options, with dark shading signifying the absence of barriers in the feasibility dimension, moderate
shading indicating that, on average, the dimension does not have a positive or negative effect on the feasibility of the option, or the evidence is mixed, and faintshading the presence of potentially blocking barriers. No shading means that the literature found was not sufficient to make an assessment. Evidence and agreement
assessment is undertaken at the option level. The context column on the far right indicates how the assessment might change if contextual factors were different. For
the methodology and literature basis, see supplementary material 4.SM.4.1 and 4.SM.4.2.
Abbreviations used: Ec: Economic - Tec: Technological - Inst: Institutional - Soc: Socio-cultural - Env: Environmental/Ecological - Geo: Geophysical
SystemMitigation Option
Wind energy (on-shore& off-shore)

Solar PV

EnergySystemTransitions

4

Land &
Ecosystem
Transitions

Urban &
Infra
structure
SystemTransitions

382

Evidence
RobustRobustAgreement

Ec

Tec

Inst

Soc

Env

Geo

Context

MediumWind regime, economic status, space for windfarms, and the existence of a legal framework
for independent power producers affect uptake;cost-effectiveness affected by incentive regimeHigh

Cost-effectiveness affected by solar irradiationand incentive regime. Also enhanced by legal
framework for independent power producers,which affects uptake

Bioenergy

RobustMediumDepends on availability of biomass and land and thecapability to manage sustainable land use.
Distributional effects depend on the agrarian(or other) system used to produce feedstock

Electricity storage

RobustHigh

Batteries universal, but grid-flexible resources
vary with area’s level of development

Power sector carbon
dioxide captureand storage

RobustHigh

Varies with local CO2 storage capacity, presence oflegal framework, level of development andquality of public engagement

Nuclear energy

RobustHigh

Electricity market organization, legal framework,
standardization & know-how, country’s ‘democratic
fabric’, institutional and technical capacity, andsafety culture of public and private institutions

Reduced foodwastage & efficient
food production

RobustHigh

Will depend on the combination ofindividual and institutional behaviour

Dietary shifts

MediumHigh

Depends on individual behaviour, education,
cultural factors and institutional support

Sustainable
intensificationof agriculture

MediumHigh

Depends on development and deploymentof new technologies

Ecosystems restoration

MediumHigh

Depends on location and institutional factors

Land-use & urban
planning

RobustMediumVaries with urban fabric, not geography or economy;
requires capacitated local government and legitimate
tenure system

Electric cars and buses

MediumHigh

Varies with degree of government intervention;requires capacity to retrofit “fuelling” stations

Sharing schemes

LimitedMediumHistoric schemes universal, but new ones dependon ICT status; undermined by high crime and low
levels of law enforcement

Public transport

RobustMediumDepends on presence of existing ‘informal’ taxi
systems, which may be more cost-effective andaffordable than capital-intensive new build schemes,
as well as (local) government capabilities

Non-motorizedtransport

RobustHigh

Viability rests on linkages with public transport,
cultural factors, climate and geography

Aviation & shipping

MediumMediumVaries with technology, governanceand accountability

Smart grids

MediumMediumVaries with economic status and presence or qualityof existing grid

Efficient appliances

MediumHigh

Adoption varies with economic status and policy
framework

Low/zero-energy
buildings

MediumHigh

Depends on size of existing building stock and growthof building stockStrengthening and Implementing the Global Response

Chapter 4

Table 4.11 (continued)
SystemIndustrial
SystemTransitions

CarbonDioxideRemovalMitigation Option

Evidence

Agreement

Ec

Tec

Inst

Soc

Env

Geo

Context

Energy efficiency

RobustHigh

Potential and adoption depend on existing efficiency,
energy prices and interest rates, as well as
government incentives

Bio-based & circularity

MediumMediumFaces barriers in terms of pressure on natural
resources and biodiversity. Product substitution
depends on market organization and government
incentivization

Electrification& hydrogen

MediumHigh

Depends on availability of large-scale, cheap,
emission-free electricity (electrification, hydrogen)or CO2 storage nearby (hydrogen). Manufacturers’
appetite to embrace disruptive innovations

Industrial carbon
dioxide capture,utilization and storage

RobustHigh

High concentration of CO2 in exhaust gas improve
economic and technical feasibility of CCUS in
industry. CO2 storage or reuse possibilities

Bioenergy and carbon
dioxide captureand storage

RobustMediumDepends on biomass availability, CO2 storage
capacity, legal framework, economic status andsocial acceptanceDirect air carbon
dioxide captureand storage

MediumMediumDepends on CO2-free energy, CO2 storage capacity,
legal framework, economic status and social
acceptance

Afforestation &reforestation

RobustHigh

Depends on location, mode of implementation,and economic and institutional factors

Soil carbon
sequestration & biochar

RobustHigh

Depends on location, soil properties, time span

Enhanced weathering

MediumLow

Depends on CO2-free energy, economic
status and social acceptanceto applying general-purpose technologies to mitigation purposes.
If this is not managed, some reduction in emissions could happen
autonomously, but it may not lead to a 1.5°C-consistent pathway.
International cooperation on technology, including technology transfer
where this does not happen autonomously, is needed and can help
create innovation capabilities in all countries that allow them to operate,
maintain, adapt and regulate a portfolio of mitigation technologies.
Case studies in the various subsections highlight the opportunities andchallenges of doing this in practice. They indicate that it can be done in
specific circumstances, which can be created.
A combination of behaviour-oriented pricing policies and financing
options can help change technologies and social behaviour as it would
challenge the existing, high-emission socio-technical regime on multiple
levels across feasibility characteristics. For instance, for dietary change,
combining supply-side measures with value-driven communication andeconomic instruments may help make a lasting transition, while aneconomic instrument, such as enhanced prices or taxation, on its own
may not be as robust.
Governments could benefit from enhanced carbon prices, as a price andinnovation incentive and also a source of additional revenue to correct
distributional effects and subsidize the development of new, costeffective negative-emission technology and infrastructure. However,there is high evidence and medium agreement that pricing alone isinsufficient. Even if prices rise significantly, they typically incentivize
incremental change, but typically fail to provide the impetus for private
actors to take the risk of engaging in the transformational changes
that would be needed to limit warming to 1.5°C. Apart from the

incentives to change behaviour and technology, financial systems are
an indispensable element of a systemic transition. If financial markets
do not acknowledge climate risk and the risk of transitions, regulatory
financial institutions, such as central banks, could intervene.
Strengthening implementation revolves around more than addressing
barriers to feasibility. A system transition, be it in energy, industry, land
or a city, requires changing the core parameters of a system. These relate,
as introduced in Section 4.2 and further elaborated in Section 4.4, to
how actors cooperate, how technologies are embedded, how resources
are linked, how cultures relate and what values people associate withthe transition and the current regime.

4.5.3

Implementing Adaptation

Article 7 of the Paris Agreement provides an aspirational global goal for
adaptation, of ‘enhancing adaptive capacity, strengthening resilience,and reducing vulnerability’ (UNFCCC, 2016). Adaptation implementation
is gathering momentum in many regions, guided by national NDC’s andnational adaptation plans (see Cross-Chapter Box 11 in this Chapter).
Operationalizing adaptation in a set of regional environments on
pathways to a 1.5°C world requires strengthened global and differentiatedregional and local capacities. It also needs rapid and decisive adaptation
actions to reduce the costs and magnitude of potential climate impacts(Vergara et al., 2015).
This could be facilitated by: (i) enabling conditions, especially improved
governance, economic measures and financing (Section 4.4); (ii)383

4

Chapter 4enhanced clarity on adaptation options to help identify strategic
priorities, sequencing and timing of implementation (Section 4.3);(iii) robust monitoring and evaluation frameworks; and (iv) politicalleadership (Magnan et al., 2015; Magnan and Ribera, 2016; Lesnikowski
et al., 2017; UNEP, 2017a).

4.5.3.1

Feasible adaptation options

This section summarizes the feasibility (defined in Cross-Chapter Box 3,
Table 1 in Chapter 1 and Table 4.4) of select adaptation options using
evidence presented across this chapter and in supplementary material
4.SM.4.3 and the expert-judgement of its authors (Table 4.12). The
options assessed respond to risks and impacts identified in Chapter 3.
They were selected based on options identified in AR5 (Noble et al.,2014), focusing on those relevant to 1.5°C-compatible pathways, where
sufficient literature exists. Selected options were mapped onto systemtransitions and clustered through an iterative process of literature
review, expert feedback, and responses to reviewer comments.
Besides gaps in the literature around crucial adaptation questions
on the transition to a 1.5°C world (Section 4.6), there is inadequate
current literature to undertake a spatially differentiated assessment(Cross-Chapter Box 3 in Chapter 1). There are also limited baselines
for exposure, vulnerability and risk to help policy and implementation
prioritization. Hence, the compiled results can at best provide a broad
framework to inform policymaking. Given the bottom-up nature ofmost adaptation implementation evidence, care needs to be taken ingeneralizing these findings.

4

Options are considered as part of a systemic approach, recognizing thatno single solution exists to limit warming to 1.5°C and adapting to its
impacts. To respond to the local and regional context – and to synergies
and trade-offs between adaptation, mitigation and sustainable
development – packages of options suited to local enabling conditions
can be implemented.
Table 4.12 summarizes the feasibility assessment through its six
dimensions with levels of evidence and agreement and indicates how
the feasibility of an adaptation option may be differentiated by certain
contextual factors (last column).
When considered jointly, the description of adaptation options (Section4.3), the feasibility assessment (summarized in Table 4.12), anddiscussion of enabling conditions (Section 4.4) show us how options
can be implemented and lead towards transformational adaptation if
and when needed.
The adaptation options for energy system transitions focus on existing
power infrastructure resilience and water management, when required,for any type of generation source. These options are not sufficient for
the far-reaching transformations required in the energy sector, whichhave tended to focus on technologies to shift from a fossil-based to arenewable energy system (Erlinghagen and Markard, 2012; Muench
et al., 2014; Brand and von Gleich, 2015; Monstadt and Wolff, 2015;
Child and Breyer, 2017; Hermwille et al., 2017). There is also need for
integration of such energy system transitions with social-ecologicalsystems transformations to increase the resilience of the energy sector,
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Strengthening and Implementing the Global Response

for which appropriate enabling conditions, such as for technologicalinnovations, are fundamentally important. Institutional capacities
can be enhanced by expanding the role of actors as transformation
catalysts (Erlinghagen and Markard, 2012). The integration of ethics
and justice within these transformations can help attain SDG7 on clean
energy access (Jenkins et al., 2018), while inclusion of the culturaldimension and cultural legitimacy (Amars et al., 2017) can provide amore substantial base for societal transformation. Strengthening policy
instruments and regulatory frameworks and enhancing multilevelgovernance that focuses on resilience components can help securethese transitions (Exner et al., 2016).
For land and ecosystem transitions, the options of conservation
agriculture, efficient irrigation, agroforestry, ecosystem restoration
and avoided deforestation, and coastal defence and hardening have
between medium and robust evidence with medium to high agreement.
The other options assessed have limited or no evidence across one
or more of the feasibility dimensions. Community-based adaptation
is assessed as having medium evidence and high agreement to facescaling barriers. Scaling community-based adaptation may require
structural changes, implying the need for transformational adaptation insome regions. This would involve enhanced multilevel governance andinstitutional capacities by enabling anticipatory and flexible decisionmaking systems that access and develop collaborative networks(Dowd et al., 2014), tackling root causes of vulnerability (Chung Tiam
Fook, 2017), and developing synergies between development andclimate change (Burch et al., 2017). Case studies show the use oftransformational adaptation approaches for fire management (Colloff
et al., 2016a), floodplain and wetland management (Colloff et al.,2016b), and forest management (Chung Tiam Fook, 2017), in which
the strengthening of policy instruments and climate finance are also
required.
There is growing recognition of the need for transformational
adaptation within the agricultural sector but limited evidence on
how to facilitate processes of deep, systemic change (Dowd et al.,2014). Case studies demonstrate that transformational adaptation inagriculture requires a sequencing and overlap between incremental andtransformational adaptation actions (Hadarits et al., 2017; Termeer etal., 2017), e.g., incremental improvements to crop management while
new crop varieties are being researched and field-tested (Rippke et al.,2016). Broader considerations include addressing stakeholder values
and attitudes (Fleming et al., 2015a), understanding and leveraging therole of social capital, collaborative networks, and information (Dowd etal., 2014), and being inclusive with rural and urban communities, andthe social, political, and cultural environment (Rickards and Howden,2012). Transformational adaptation in agriculture systems could havesignificant economic and institutional costs (Mushtaq, 2016), along withpotential unintended negative consequences (Davidson, 2016; Rippke
et al., 2016; Gajjar et al., 2018; Mushtaq, 2018), and a need to focus
on the transitional space between incremental and transformational
adaptation (Hadarits et al., 2017), as well as the timing of the shift from
one to the other (Läderach et al., 2017).
Within urban and infrastructure transitions, green infrastructure andsustainable water management are assessed as the most feasible
options, followed by sustainable land-use and urban planning. The

Strengthening and Implementing the Global Response

Chapter 4

Table 4.12 | Feasibility assessment of examples of 1.5°C-relevant adaptation options, with dark shading signifying the absence of barriers in the feasibility dimension,moderate shading indicating that, on average, the dimension does not have a positive or negative effect on the feasibility of the option, or the evidence is mixed,
and light shading indicating the presence of potentially blocking barriers. No shading means that sufficient literature could not be found to make theassessment. NA signifies that the dimension is not applicable to that adaptation option. For methodology and literature basis, see supplementary material
4.SM.4.
Abbreviations used: Ec: Economic - Tec: Technological - Inst: Institutional - Soc: Socio-cultural - Env: Environmental/Ecological - Geo: Geophysical
SystemEnergy System
Transitions

Land &
Ecosystem
Transitions

Urban &
Infrastructure
SystemTransitions

Industrial
SystemTransitions

Overarching
Adaptation
Options

Adaptation Option

Evidence

Agreement

Ec

Tec

Inst

Soc

Env

Geo

Context

Power infrastructure,including water

MediumHigh

Depends on existing power infrastructure,
all generation sources and those withintensive water requirements

Conservation
agriculture

MediumMediumDepends on irrigated/rainfed system, ecosystemcharacteristics, crop type, other farming practices

Efficient irrigation

MediumMediumDepends on agricultural system, technology used,regional institutional and biophysical context

Efficient livestock
systems

LimitedHigh

Dependent on livestock breeds, feed practices,and biophysical context (e.g., carrying capacity)

Agroforestry

MediumHigh

Depends on knowledge, financial support, and market
conditions

Community-based
adaptation

MediumHigh

Focus on rural areas and combined with ecosystemsbased adaptation, does not include urban settings

Ecosystem restoration& avoided deforestation

RobustMediumMostly focused on existing and evaluated REDD+
projects

Biodiversity
management

MediumMediumFocus on hotspots of biodiversity vulnerability andhigh connectivity

Coastal defence& hardening

RobustMediumDepends on locations that require it as a first
adaptation option

Sustainable aquaculture

LimitedMediumDepends on locations at risk and socio-cultural context

Sustainable land-use& urban planningMediumMediumDepends on nature of planning systems
and enforcement mechanisms

Sustainable water
management

RobustMediumBalancing sustainable water supply and risingdemand, especially in low-income countries

Green infrastructure& ecosystem services

MediumHigh

Depends on reconciliation of urban developmentwith green infrastructure

Building codes& standards

LimitedMediumAdoption requires legal, educational, andenforcement mechanisms to regulate buildings

Intensive industry
infrastructure resilienceand water management

LimitedHigh

Depends on intensive industry, existing infrastructure
and using or requiring high demand of water

Disaster risk
management

MediumHigh

Requires institutional, technical, and financial
capacity in frontline agencies and government

Risk spreading andsharing: insurance

MediumMediumRequires well-developed financial structures and public
understanding

Social safety nets

MediumMediumType and mechanism of safety net, political priorities,
institutional transparency

Climate services

MediumHigh

Depends on climate information availability and usability, local infrastructure
and institutions, national priorities

Indigenous knowledge

MediumHigh

Dependent on recognition of indigenous
rights, laws, and governance systems

Education and learning

MediumHigh

Existing education system, funding

Population healthand health system

MediumHigh

Human migration

MediumLow

NA

4

Requires basic health services and infrastructure
Hazard exposure, political and socio-culturalacceptability (in destination), migrant skills andsocial networks

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Chapter 4

need for transformational adaptation in urban settings arises from theroot causes of poverty, failures in sustainable development, and a lackof focus on social justice (Revi et al., 2014a; Parnell, 2015; Simon andLeck, 2015; Shi et al., 2016; Ziervogel et al., 2016a; Burch et al., 2017),
and necessitates a focus on governance structures and the inclusion ofequity and justice concerns (Bos et al., 2015; Shi et al., 2016; Hölscher
et al., 2018).
Current implementation of urban ecosystems-based adaptation (EbA)
lacks a systems perspective of transformations and consideration of
the normative and ethical aspects of EbA (Brink et al., 2016). Flexibility
within urban planning could help deal with the multiple uncertainties
of implementing adaptation (Rosenzweig and Solecki, 2014;
Radhakrishnan et al., 2018), for example, urban adaptation pathways
were implemented in the aftermath of Superstorm Sandy in New York,
which is considered as tipping point that led to the implementation oftransformational adaptation practices.

4

Adaptation options for industry focus on infrastructure resilienceand water management. Like with energy system transitions,
technological innovation would be required, but also the enhancementof institutional capacities. Recent research illustrates transformational
adaptation within industrial transitions focusing on the role ofdifferent actors and tools driving innovation, and points to the roleof nationally appropriate mitigation actions in avoiding lock-ins andpromoting system innovation (Boodoo and Olsen, 2017), the role ofprivate sector in sustainability governance in the socio-political context(Burch et al., 2016), and of green entrepreneurs driving transformativechange in the green economy (Gibbs and O’Neill, 2014). Lim-Camacho
et al. (2015) suggest an analysis of the complete lifecycle of supply
chains as a means of identifying additional adaptation strategies, as
opposed to the current focus on a part of the supply chain. Chain-wide
strategies can modify the rest of the chain and present a win-win withcommercial objectives.
The assessed adaptation options also have mitigation synergies
and trade-offs (assessed in Section 4.5.4) that need to be carefully
considered, while planning climate action.

4.5.3.2

adaptation for the purposes of assessing progress (Dupuis andBiesbroek, 2013; Biesbroek et al., 2015), an absence of comprehensive
and systematically collected data on adaptation to support longitudinalassessment and comparison (Ford et al., 2015b; Lesnikowski et al.,2016), a lack of agreement on indicators to measure (Brooks et al.,2013; Bours et al., 2015; Lesnikowski et al., 2015), and challenges of
attributing altered vulnerability to adaptation actions (Ford et al., 2013;
Bours et al., 2015; UNEP, 2017a).

4.5.4

Synergies and Trade-Offs between
Adaptation and Mitigation

Implementing a particular mitigation or adaptation option may affect thefeasibility and effectiveness of other mitigation and adaptation options.
Supplementary Material 4.SM.5.1 provides examples of possible positive
impacts (synergies) and negative impacts (trade-offs) of mitigation
options for adaptation. For example, renewable energy sources such as
wind energy and solar PV combined with electricity storage can increase
resilience due to distributed grids, thereby enhancing both mitigation
and adaptation. Yet, as another example, urban densification may reduce
GHG emissions, enhancing mitigation, but can also intensify heat islandeffects and inhibit restoration of local ecosystems if not accounted for,thereby increasing adaptation challenges.
The table in Supplementary Material 4.SM.5.2 provides examplesof synergies and trade-offs of adaptation options for mitigation. Itshows, for example, that conservation agriculture can reduce some
GHG emissions and thus enhance mitigation, but at the same time canincrease other GHG emissions, thereby reducing mitigation potential.
As another example, agroforestry can reduce GHG emissions throughreduced deforestation and fossil fuel consumption but has a lower
carbon sequestration potential compared with natural and secondary
forest.
Maladaptive actions could increase the risk of adverse climate-related
outcomes. For example, biofuel targets could lead to indirect land usechange and influence local food security, through a shift in land use
abroad in response to increased domestic biofuel demand, increasing
global GHG emissions rather than decreasing them.

Monitoring and evaluation

Monitoring and evaluation (M&E) in adaptation implementation canpromote accountability and transparency of adaptation financing,facilitate policy learning and sharing good practices, pressure laggards,
and guide adaptation planning. The majority of research on M&E focuseson specific policies or programmes, and has typically been driven by
the needs of development organizations, donors, and governments tomeasure the impact and attribution of adaptation initiatives (Ford andBerrang-Ford, 2016). There is growing research examining adaptation
progress across nations, sectors, and scales (Reckien et al., 2014; Araos
et al., 2016a, b; Austin et al., 2016; Heidrich et al., 2016; Lesnikowski etal., 2016; Robinson, 2017). In response to a need for global, regional
and local adaptation, the development of indicators and standardized
approaches to evaluate and compare adaptation over time andacross regions, countries, and sectors would enhance comparability
and learning. A number of constraints continue to hamper progresson adaptation M&E, including a debate on what actually constitutes
386

Strengthening and Implementing the Global Response

Various options enhance both climate change mitigation andadaptation, and would hence serve two 1.5°C-related goals: reducing
emissions while adapting to the associated climate change. Examples
of such options are reforestation, urban and spatial planning, and land
and water management.
Synergies between mitigation and adaptation may be enhanced, andtrade-offs reduced, by considering enabling conditions (Section 4.4),
while trade-offs can be amplified when enabling conditions are notconsidered (C.A. Scott et al., 2015). For example, information thatis tailored to the personal situation of individuals and communities,
including climate services that are credible and targeted at the pointof decision-making, can enable and promote both mitigation andadaptation actions (Section 4.4.3). Similarly, multilevel governanceand community participation, respectively, can enable and promote
both adaptation and mitigation actions (Section 4.4.1). Governance,
policies and institutions can facilitate the implementation of the water–

Strengthening and Implementing the Global Response

energy–food (WEF) nexus (Rasul and Sharma, 2016). The WEF nexus
can enhance food, water and energy security, particularly in cities with
agricultural production areas (Biggs et al., 2015), electricity generation
with intensive water requirements (Conway et al 2015), and in
agriculture (El Gafy et al., 2017) and livelihoods (Biggs et al., 2015). Sucha nexus approach can reduce the transport energy that is embedded
in food value chains (Villarroel Walker et al., 2014), providing diverse
sources of food in the face of changing climates (Tacoli et al., 2013).
Urban agriculture, where integrated, can mitigate climate change andsupport urban flood management (Angotti, 2015; Bell et al., 2015; Biggs

Chapter 4

et al., 2015; Gwedla and Shackleton, 2015; Lwasa et al., 2015; Yanget al., 2016; Sanesi et al., 2017). In the case of electricity generation,
enabling conditions through a combination of carefully selected policy
instruments can maximize the synergic benefits between low GHG
energy production and water for energy (Shang et al., 2018). Despite
the multiple benefits of maximizing synergies between mitigation
and adaptations options through the WEF nexus approach (Chen andChen, 2016), there are implementation challenges given institutional
complexity, political economy, and interdependencies between actors(Leck et al., 2015).

Box 4.10 | Bhutan: Synergies and Trade-Offs in Economic Growth, Carbon Neutrality and Happiness
Bhutan has three national goals: improving its gross national happiness index (GNHI), improving its economic growth (grossdomestic product, GDP) and maintaining its carbon neutrality. These goals increasingly interact and raise questions about whether
they can be sustainably maintained into the future. Interventions in this enabling environment are required to comply with all threegoals.
Bhutan is well known for its GNHI, which is based on a variety of indicators covering psychological well-being, health, education,
cultural and community vitality, living standards, ecological issues and good governance (RGoB, 2012; Schroeder and Schroeder,
2014; Ura, 2015). The GNHI is a precursor to the Sustainable Development Goals (SDGs) (Allison, 2012; Brooks, 2013) and reflects
local enabling environments. The GNHI has been measured twice, in 2010 and 2015, and this showed an increase of 1.8% (CBS &GNH, 2016). Like most emerging countries, Bhutan wants to increase its wealth and become a middle-income country (RGoB, 2013,2016), while remaining carbon-neutral – a goal which has been in place since 2009 at COP15 and was reiterated in its Intended
Nationally Determined Contribution (NEC, 2015). Bhutan achieves its current carbon-neutral status through hydropower and forest
cover (Yangka and Diesendorf, 2016), which are part of its resilience and adaptation strategy.
Nevertheless, Bhutan faces rising GHG emissions. Transport and industry are the largest growth areas (NEC, 2011). Bhutan’s carbonneutral status would be threatened by 2044 with business-as-usual approaches to economic growth (Yangka and Newman, 2018).
Increases in hydropower are being planned based on climate change scenarios that suggest sufficient water supply will be available(NEC, 2011). Forest cover is expected to remain sufficient to maintain co-benefits. The biggest challenge is to electrify both freightand passenger transport (ADB, 2013). Bhutan wants to be a model for achieving economic growth consistent with limiting climatechange to 1.5°C and improving its GNHI (Michaelowa et al., 2018) through synthesizing all three goals and improving its adaptive
capacity.

4.6

Knowledge Gaps and Key Uncertainties

The global response to limiting warming to 1.5°C is a new knowledgearea, which has emerged after the Paris Agreement. This section
presents a number of knowledge gaps that have emerged from theassessment of mitigation, adaptation and carbon dioxide removal(CDR) options and solar radiation modification (SRM) measures;
enabling conditions; and synergies and trade-offs. Illustrative questions
that emerge synthesizing the more comprehensive Table 4.13 below
include: how much can be realistically expected from innovation,
behaviour and systemic political and economic change in improving
resilience, enhancing adaptation and reducing GHG emissions?
How can rates of changes be accelerated and scaled up? What is
the outcome of realistic assessments of mitigation and adaptation

4

land transitions that are compliant with sustainable development,poverty eradication and addressing inequality? What are life-cycle
emissions and prospects of early-stage CDR options? How can climate
and sustainable development policies converge, and how can they
be organized within a global governance framework and financial
system, based on principles of justice and ethics (CBDR-RC), reciprocity
and partnership? To what extent would limiting warming to 1.5°C
require a harmonization of macro-financial and fiscal policies, which
could include central banks? How can different actors and processes
in climate governance reinforce each other, and hedge against the
fragmentation of initiatives?
These knowledge gaps are highlighted in Table 4.13 along with a crossreference to the respective sections in the last column.

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Strengthening and Implementing the Global Response

Table 4.13 | Knowledge gaps and uncertainties
Knowledge Area

1.5°C Pathways andEnsuing Change

EnergySystems

4
Options toAchieveand Adapt
to 1.5°C
Land &
ecosystems

Urban &
infrastructure
systems

388

Mitigation

Adaptation

Reference• Lack of literature specific to 1.5°C on investment costs withdetailed breakdown by technology• Lack of literature specific to 1.5°C on mitigation costs interms of GDP and welfare• Lack of literature on distributional implications of 1.5°C
compared to 2°C or business-as-usual at sectoraland regional levels• Limited 1.5°C-specific case studies for mitigation• Limited knowledge on the systemic and dynamic aspects oftransitions to 1.5°C, including how vicious or virtuous circles
might work, how self-reinforcing aspects can be actively
introduced and managed• Lack of literature specific to 1.5°C on adaptation costs
and need• Lack of literature on what overshoot means for adaptation• Lack of knowledge on avoided adaptation investments
associated with limiting warming to 1.5°C, 2°C or
business-as-usual• Limited 1.5°C-specific case studies for adaptation• Scant literature examining current or future adaptation options,or examining what different climate pathways mean foradaptation success• Need for transformational adaptation at 1.5°C and beyond
remains largely unexplored

4.2• The shift to variable renewables that many countries are
implementing is just reaching a level where large-scalestorage systems or other grid flexibility options, e.g., demand
response, are required to enable resilient grid systems. Thus,
new knowledge on the opportunities and issues associatedwith scaling up zero-carbon grids would be needed, including
knowledge about how zero-carbon electric grids can integratewith the full-scale electrification of transport systems• CCS suffers mostly from uncertainty about the feasibilityof timely upscaling, both due to lack of regulatory capacity
and concerns about storage safety and cost
• There is not much literature on the distributional implicationsof large-scale bioenergy deployment, the assessment ofenvironmental feasibility is hampered by a diversity of contexts
of individual studies (type of feedstock, technology, landavailability), which could be improved through emerging
meta-studies• Relatively little literature on individual adaptation options
since AR5
• No evidence on socio-cultural acceptability of adaptation
options• Lack of regional research on the implementation of adaptation
options• More knowledge would be needed on how land-based
mitigation can be reconciled with land demands foradaptation and development• While there is now more literature on the underlying
mechanisms of land transitions, data is often insufficient
to draw robust conclusions, and there is uncertainty about
land availability• The lack of data on social and institutional information(largest knowledge gap indicated for ecosystems restorationin Table 4.11), which are therefore not widely integrated inland use modelling• Examples of successful policy implementation and institutions
related to land-based mitigation leading to co-benefits foradaptation and development are missing from the literature• There is relatively little scientific literature on the effects
of dietary shifts and reduction of food wastage on mitigation,
especially regarding the institutional, technical andenvironmental concerns• Regional information on some options does not exist,
especially in the case of land-use transitions• Limited research examining socio-cultural perspectives andimpacts of adaptation options, especially for efficient irrigation,
coastal defence and hardening, agroforestry and biodiversity
management• Lack of longitudinal, regional studies assessing the impacts ofcertain adaptation options, such as conservation agriculture
and shifting to efficient livestock systems• More knowledge is needed on the cost-effectiveness andscalability of various adaptation options. For example, there
is no evidence for the macro-economic viability of communitybased adaptation (CbA) and biodiversity management, or on
employment and productivity enhancement potential forbiodiversity management and coastal defence and hardening.
• More knowledge is needed on risk mitigation and the potentialof biodiversity management• Lack of evidence of the political acceptability of efficient
livestock systems• Limited evidence on legal and regulatory feasibility ofconservation agriculture and no evidence on coastal
defence and hardening• For transparency and accountability potential, there is limited
evidence for conservation agriculture and no evidence forbiodiversity management, coastal defence and hardening andsustainable aquaculture• No evidence on hazard risk reduction potential of conservation
agriculture and biodiversity management• Limited evidence of effective land-use planning in low-income
cities where tenure and land zoning are contested, and therisks of trying to implement land-use planning undercommunal tenure• Limited evidence on the governance of public transport froman accountability and transparency perspective• Regional and sectoral adaptation cost assessments are missing,particularly in the context of welfare losses of households,
across time and space• More knowledge is needed on the political economy ofadaptation, particularly on how to impute different types ofcost and benefit in a consistent manner, on adaptation
performance indicators that could stimulate investment,
and the impact of adaptation interventions on socio-economic
and other types of inequality

4.3.1

4.3.2

4.3.3

Strengthening and Implementing the Global Response

Chapter 4

Table 4.13 (continued)
Knowledge Area

Urban &
infrastructure
systems

Options toAchieveand Adapt
to 1.5°C
Industrial
systems

Overarching
adaptation
options

Short-lived
climateforcers

Mitigation

Adaptation• Limited evidence on relationship between toxic waste
and public transport• Limited evidence on the impacts of electric vehicles andnon-motorized urban transport, as most schemes are too new• As changes in shipping and aviation have been limited todate, limited evidence of social impacts• Knowledge about how to facilitate disruptive, demand-based
innovations that may be transformative in urban systems,
is needed• Understanding of the urban form implications of combinedchanges from electric, autonomous and shared/public mobility
systems, is needed• Considering distributional consequences of climate responses
is an on-going need
• Knowledge gaps in the application and scale up of
combinations of new smart technologies, sustainable design,advanced construction techniques and new insulationmaterials, renewable energy and behaviour change in urban
settlements• The potential for leapfrog technologies to be applied to slums
and new urban developments in developing countries is weak.

• More evidence would be needed on hot-spots, for example
the growth of peri-urban areas populated by large informal
settlements• Major uncertainties emanate from the lack of knowledge on
the integration of climate adaptation and mitigation, disasterrisk management, and urban poverty alleviation• There is limited evidence on the institutional, technologicaland economic feasibility of green infrastructure andenvironmental services and for socio-cultural andenvironmental feasibility of codes and standards• In general, there is no evidence for the employment andproductivity enhancement potential of most adaptation options.
• There is limited evidence on the economic feasibility ofsustainable water management• Lack of knowledge on potential for scaling up and global
diffusion of zero- and low-emission technologies in industry• Questions remain on the socio-cultural feasibility of industry
options, including human capacity and private sector
acceptance of new, radically different technologies fromcurrent well-developed practices, as well as distributional
effects of potential new business models• As the industrial transition unfolds, lack of knowledge on
its dynamic interactions with other sectors, in particular withthe power sector (and infrastructure) for electrification ofindustry, with food production and other users of biomass
in case of bio-based industry developments, and with
CDR technologies in the case of CC(U)S• Life-cycle assessment-based comparative analyses of CCUS
options are missing, as well as life-cycle information onelectrification and hydrogen• Impacts of industrial system transitions are not wellunderstood, especially on employment, identity and well-being,in particular in the case of substitution of conventional,
high-carbon industrial products with lower-carbon alternatives,
as well as electrification and use of hydrogen• Very limited evidence on how industry would adapt to theconsequences of 1.5°C or 2°C temperature increases, inparticular large and immobile industrial clusters in low-lying
areas as well as availability of transportation and (cooling)water resources and infrastructure• There is limited evidence on the economic, institutional andsocio-cultural feasibility of adaptation options available
to industry• There is no evidence on technical and institutional feasibility of educational options• There is limited evidence on employment and productivity enforcement potential of climate services• There is limited evidence on socio-cultural acceptability of social safety nets
• There is a small but growing literature on human migration as an adaptation strategy. Scant literature on the cost-effectivenessof migration• Limited evidence of co-benefits and trade-offs of SLCF
reduction (e.g., better health outcomes, agricultural
productivity improvements)• Integration of SLCFs into emissions accounting andinternational reporting mechanisms enabling a better
understanding of the links between black carbon, air
pollution, climate change and agricultural productivity

Reference

4.3.3

4.3.4

4

4.3.5

4.3.6

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Table 4.13 (continued)
Knowledge Area

Options
to Achieve
and Adapt
to 1.5°C

CarbondioxideremovalSolar radiation
modification (SRM)

390

Adaptation

Reference

4.3.7• In spite of increasing attention to the different SRM measures and their potential to keep global temperature below 1.5°C,
knowledge gaps remain, not only with respect to the physical understanding of SRM measures but also concerning ethical issues• We do not know how to govern SRM in order to avoid unilateral action and how to prevent possible reductions
in mitigation (‘moral hazard’)

4.3.8• The ability to identify explanatory factors affecting the progressof climate policy is constrained by a lack of data on adaptation
actions across nations, regions, and sectors, compounded by anabsence of frameworks for assessing progress. Most
hypotheses on what drives adaptation remain untested• Limited empirical assessment of how governance affects
adaptation across cases• Focus on ‘success’ stories and leading adaptors overlooks
lessons from situations where no or unsuccessful
adaptation is taking place

Governance• As technological changes have begun to accelerate, there isa lack of knowledge on new mechanisms that can enableprivate enterprise to mainstream this activity, and reasonsfor success and failure need to be researched• Research is thin on effective multilevel governance, inparticular in developing countries, including participation
by civil society, women and minorities• Gaps in knowledge remain pertaining to partnerships within
local governance arrangements that may act as mediatorsand drivers for achieving global ambition and local action• Methods for assessing contribution and aggregation of
non-state actors in limiting warming to 1.5°C• Knowledge gap on an enhanced framework for assessmentof the ambition of NDCs

Institutions• Lack of 1.5°C-specific literature• Role of regulatory financial institutions and their capacity to guarantee financial stability of economies when investments potentiallyface risks, both because of climate impacts and because of the systems transitions if lower temperature scenarios are pursued• Knowledge gaps on how to build capabilities across all countries and regions globally to implement, maintain, manage, govern andfurther develop mitigation options for 1.5°C.
• While importance of indigenous and local knowledge is recognized, the ability to scale up beyond the local remains challenging
and little examined• There is a lack of monitoring and evaluation (M&E) of adaptation measures, with most studies enumerating M&E challenges andemphasising the importance of context and social learning. Very few studies evaluate whether and why an adaptation initiative
has been effective. One of the challenges of M&E for both mitigation and adaptation is a lack of high quality information formodelling. Adaptation M&E is additionally challenged by limited understanding on what indicators to measure and how to attributealtered vulnerability to adaptation actions

Lifestyle andbehaviouralchange• Whereas mitigation pathways studies address (implicitly or
explicitly) the reduction or elimination of market failures(e.g., external costs, information asymmetries) via climate orenergy policies, no study addresses behavioural change
strategies in the relationship with mitigation and adaptation
actions in the 1.5°C context• Limited knowledge on GHG emissions reduction potential ofdiverse mitigation behaviour across the world• Most studies on factors enabling lifestyle changes have been
conducted in high-income countries, more knowledge neededfrom low- and middle-income countries, and the focus istypically on enabling individual behaviour change, far less on
enabling change in organizations and political systems

4

Enabling
Conditions

Mitigation• A bottom-up analysis of CDR options indicates that there
are still key uncertainties around the individual technologies.
Ocean-based options will be assessed in depth in the IPCC
Special Report on the Ocean and Cryosphere in a Changing
Climate (SROCC)• Assessments of environmental aspects are missing,
especially for ‘newer’ options like enhanced weatheringor direct air carbon capture• In order to obtain more information on realistically available
and sustainable removal potentials, more bottom-up, regional
studies, also taking into account also social issues, would
be needed. These can better inform the modelling of 1.5°C
pathways• Knowledge gaps on issues of governance and public
acceptance, the impacts of large-scale removals on the
carbon cycle, the potential to accelerate deployment andupscaling, and means of incentivization• Knowledge gaps on integrated systems of renewable energy
and CDR technologies such as enhanced weathering and
DACCS• Knowledge gaps on under which conditions the use of
captured CO2 is generating negative emissions and would
qualify as a mitigation option

4.4.1

4.4.2

• Knowledge gaps on factors enabling adaptation behaviour,
except for behaviour in agriculture.
• Little is known about cognitive and motivational factors
promoting adaptive behaviour.
• Little is known about how potential adaptation actions might
affect behaviour to influence vulnerability outcomes
4.4.3

Strengthening and Implementing the Global Response

Chapter 4

Table 4.13 (continued)
Knowledge Area
Lifestyle andbehaviouralchangeEnabling
Conditions

Mitigation

Adaptation• Limited understanding and treatment of behavioural change
and the potential effects of related policies in ambitious
mitigation pathways, e.g., in Integrated Assessment Models

Reference

4.4.3

Lack of insight on what can enable changes in adaptation and mitigation behaviour in organizations and political systems

Technologicalinnovation• Quantitative estimates for mitigation and adaptation potentials at economy or sector scale as a result of the combination ofgeneral purpose technologies and mitigation technologies have been scarce, except for some evidence in the transport sector• Evidence on the role of international organizations, including the UNFCCC, in building capabilities and enhancing technologicalinnovation for 1.5°C, except for some parts of the transport sector• Technology transfer trials to enable leapfrog applications in developing countries have limited evidence

4.4.4

Policy• More empirical research would be needed to deriverobust conclusions on effectiveness of policies forenabling transitions to 1.5°C and on which factors aid
decision-makers seeking to ratchet up their NDCs

4.4.5

Finance

Knowledge gaps persist with respect to the instruments to match finance to its most effective use in mitigation and adaptation

4.4.5• Strong claims are made with respect to synergies and trade-offs, but there is little knowledge to underpin these, especially of
co-benefits by region• Water–energy conservation relationships of individual conservation measures in industries other than the water and energy sectors
have not been investigated in detail• There is no evidence on synergies with adaptation of CCS in the power sector and of enhanced weathering under carbon
dioxide removal• There is no evidence on trade-offs with adaptation of low- and zero-energy buildings, and circularity and substitution andbio-based industrial system transitions• There is no evidence of synergies or trade-offs with mitigation of CbA• There is no evidence of trade-offs with mitigation of the built environment, on adaptation options for industrial energy,
and climate services

4.5.4

Synergies and Trade-Offs
Between Adaptationand Mitigation• Understanding of what policies work (and do not work) is
limited for adaptation in general and for 1.5°C in
particular, beyond specific case studies

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Frequently Asked Questions

FAQ 4.1 | What Transitions could Enable Limiting Global Warming to 1.5°C?
Summary: In order to limit warming to 1.5°C above pre-industrial levels, the world would need to transformin a number of complex and connected ways. While transitions towards lower greenhouse gas emissions areunderway in some cities, regions, countries, businesses and communities, there are few that are currently
consistent with limiting warming to 1.5°C. Meeting this challenge would require a rapid escalation in the current
scale and pace of change, particularly in the coming decades. There are many factors that affect the feasibilityof different adaptation and mitigation options that could help limit warming to 1.5°C and with adapting to theconsequences.
There are actions across all sectors that can substantially reduce greenhouse gas emissions. This Special Reportassesses energy, land and ecosystems, urban and infrastructure, and industry in developed and developing
nations to see how they would need to be transformed to limit warming to 1.5°C. Examples of actions include
shifting to low- or zero-emission power generation, such as renewables; changing food systems, such as dietchanges away from land-intensive animal products; electrifying transport and developing ‘green infrastructure’,such as building green roofs, or improving energy efficiency by smart urban planning, which will change thelayout of many cities.
Because these different actions are connected, a ‘whole systems’ approach would be needed for the type oftransformations that could limit warming to 1.5°C. This means that all relevant companies, industries andstakeholders would need to be involved to increase the support and chance of successful implementation. As
an illustration, the deployment of low-emission technology (e.g., renewable energy projects or a bio-based
chemical plants) would depend upon economic conditions (e.g., employment generation or capacity to mobilize
investment), but also on social/cultural conditions (e.g., awareness and acceptability) and institutional conditions(e.g., political support and understanding).
To limit warming to1.5°C, mitigation would have to be large-scale and rapid. Transitions can be transformative or
incremental, and they often, but not always, go hand in hand. Transformative change can arise from growth indemand for a new product or market, such that it displaces an existing one. This is sometimes called ‘disruptive
innovation’. For example, high demand for LED lighting is now making more energy-intensive, incandescentlighting near-obsolete, with the support of policy action that spurred rapid industry innovation. Similarly, smart
phones have become global in use within ten years. But electric cars, which were released around the sametime, have not been adopted so quickly because the bigger, more connected transport and energy systems are
harder to change. Renewable energy, especially solar and wind, is considered to be disruptive by some as it
is rapidly being adopted and is transitioning faster than predicted. But its demand is not yet uniform. Urban
systems that are moving towards transformation are coupling solar and wind with battery storage and electricvehicles in a more incremental transition, though this would still require changes in regulations, tax incentives,
new standards, demonstration projects and education programmes to enable markets for this system to work.

4

Transitional changes are already underway in many systems, but limiting warming to 1.5°C would require a
rapid escalation in the scale and pace of transition, particularly in the next 10–20 years. While limiting warming
to 1.5°C would involve many of the same types of transitions as limiting warming to 2°C, the pace of change
would need to be much faster. While the pace of change that would be required to limit warming to 1.5°C canbe found in the past, there is no historical precedent for the scale of the necessary transitions, in particular in asocially and economically sustainable way. Resolving such speed and scale issues would require people’s support,
public-sector interventions and private-sector cooperation.
Different types of transitions carry with them different associated costs and requirements for institutional or
governmental support. Some are also easier to scale up than others, and some need more government support
than others. Transitions between, and within, these systems are connected and none would be sufficient on its
own to limit warming to 1.5°C.
The ‘feasibility’ of adaptation and mitigation options or actions within each system that together can limit
warming to 1.5°C within the context of sustainable development and efforts to eradicate poverty requires carefulconsideration of multiple different factors. These factors include: (i) whether sufficient natural systems andresources are available to support the various options for transitioning (known as environmental feasibility); (ii)the degree to which the required technologies are developed and available (known as technological feasibility);

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FAQ 4.1 (continued)(iii) the economic conditions and implications (known as economic feasibility); (iv) what are the implications forhuman behaviour and health (known as social/cultural feasibility); and (v) what type of institutional support would
be needed, such as governance, institutional capacity and political support (known as institutional feasibility).
An additional factor (vi – known as the geophysical feasibility) addresses the capacity of physical systems to carry
the option, for example, whether it is geophysically possible to implement large-scale afforestation consistent
with 1.5°C.
Promoting enabling conditions, such as finance, innovation and behaviour change, would reduce barriers to theoptions, make the required speed and scale of the system transitions more likely, and therefore would increase
the overall feasibility limiting warming to 1.5°C.

4

FAQ 4.1, Figure 1 | The different dimensions to consider when assessing the ‘feasibility’ of adaptation and mitigation options or actions withineach system that can help to limit warming to 1.5°C. These are: (i) the environmental feasibility; (ii) the technological feasibility; (iii) the economic feasibility; (iv)the social/cultural feasibility; (v) the institutional feasibility; and (vi) the geophysical feasibility.

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Frequently Asked Questions

FAQ 4.2 | What are Carbon Dioxide Removal and Negative Emissions?
Summary: Carbon dioxide removal (CDR) refers to the process of removing CO2 from the atmosphere. Since this is
the opposite of emissions, practices or technologies that remove CO2 are often described as achieving ‘negativeemissions’. The process is sometimes referred to more broadly as greenhouse gas removal if it involves removinggases other than CO2. There are two main types of CDR: either enhancing existing natural processes that remove
carbon from the atmosphere (e.g., by increasing its uptake by trees, soil, or other ‘carbon sinks’) or using chemical
processes to, for example, capture CO2 directly from the ambient air and store it elsewhere (e.g., underground).
All CDR methods are at different stages of development and some are more conceptual than others, as they havenot been tested at scale.
Limiting warming to 1.5°C above pre-industrial levels would require unprecedented rates of transformationin many areas, including in the energy and industrial sectors, for example. Conceptually, it is possible thattechniques to draw CO2 out of the atmosphere (known as carbon dioxide removal, or CDR) could contribute tolimiting warming to 1.5°C. One use of CDR could be to compensate for greenhouse gas emissions from sectorsthat cannot completely decarbonize, or which may take a long time to do so.
If global temperature temporarily overshoots 1.5°C, CDR would be required to reduce the atmosphericconcentration of CO2 to bring global temperature back down. To achieve this temperature reduction, the amountof CO2 drawn out of the atmosphere would need to be greater than the amount entering the atmosphere,
resulting in ‘net negative emissions’. This would involve a greater amount of CDR than stabilizing atmospheric
CO2 concentration – and, therefore, global temperature – at a certain level. The larger and longer an overshoot,the greater the reliance on practices that remove CO2 from the atmosphere.
There are a number of CDR methods, each with different potentials for achieving negative emissions, as well
as different associated costs and side effects. They are also at differing levels of development, with some moreconceptual than others. One example of a CDR method in the demonstration phase is a process known asbioenergy with carbon capture and storage (BECCS), in which atmospheric CO2 is absorbed by plants and trees
as they grow, and then the plant material (biomass) is burned to produce bioenergy. The CO2 released in the
production of bioenergy is captured before it reaches the atmosphere and stored in geological formations deep
underground on very long time scales. Since the plants absorb CO2 as they grow and the process does not emit
CO2, the overall effect can be to reduce atmospheric CO2.

4

Afforestation (planting new trees) and reforestation (replanting trees where they previously existed) are also
considered forms of CDR because they enhance natural CO2 ‘sinks’. Another category of CDR techniques uses
chemical processes to capture CO2 from the air and store it away on very long time scales. In a process known
as direct air carbon capture and storage (DACCS), CO2 is extracted directly from the air and stored in geologicalformations deep underground. Converting waste plant material into a charcoal-like substance called biochar andburying it in soil can also be used to store carbon away from the atmosphere for decades to centuries.
There can be beneficial side effects of some types of CDR, other than removing CO2 from the atmosphere. For
example, restoring forests or mangroves can enhance biodiversity and protect against flooding and storms. But
there could also be risks involved with some CDR methods. For example, deploying BECCS at large scale would
require a large amount of land to cultivate the biomass required for bioenergy. This could have consequences
for sustainable development if the use of land competes with producing food to support a growing population,
biodiversity conservation or land rights. There are also other considerations. For example, there are uncertainties
about how much it would cost to deploy DACCS as a CDR technique, given that removing CO2 from the air
requires considerable energy.

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FAQ 4.2 (continued)

FAQ 4.2, Figure 1 | Carbon dioxide removal (CDR) refers to the process of removing CO2 from the atmosphere. There are a number of CDR techniques,
each with different potential for achieving ‘negative emissions’, as well as different associated costs and side effects.

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Frequently Asked Questions

FAQ 4.3 | Why is Adaptation Important in a 1.5°C-Warmer World?
Summary: Adaptation is the process of adjusting to current or expected changes in climate and its effects. Even
though climate change is a global problem, its impacts are experienced differently across the world. This meansthat responses are often specific to the local context, and so people in different regions are adapting in different
ways. A rise in global temperature from the current 1°C above pre-industrial levels to 1.5°C, and beyond, increases
the need for adaptation. Therefore, stabilizing global temperatures at 1.5°C above pre-industrial levels would
require a smaller adaptation effort than at 2°C. Despite many successful examples around the world, progress in
adaptation is, in many regions, in its infancy and unevenly distributed globally.
Adaptation refers to the process of adjustment to actual or expected changes in climate and its effects. Since
different parts of the world are experiencing the impacts of climate change differently, there is similar diversity
in how people in a given region are adapting to those impacts.
The world is already experiencing the impacts from 1°C of global warming above pre-industrial levels, and there
are many examples of adaptation to impacts associated with this warming. Examples of adaptation efforts taking
place around the world include investing in flood defences such as building sea walls or restoring mangroves,efforts to guide development away from high risk areas, modifying crops to avoid yield reductions, and usingsocial learning (social interactions that change understanding on the community level) to modify agricultural
practices, amongst many others. Adaptation also involves building capacity to respond better to climate change
impacts, including making governance more flexible and strengthening financing mechanisms, such as byproviding different types of insurance.
In general, an increase in global temperature from present day to 1.5°C or 2°C (or higher) above pre-industrial
temperatures would increase the need for adaptation. Stabilizing global temperature increase at 1.5°C would
require a smaller adaptation effort than for 2°C.
Since adaptation is still in early stages in many regions, there are questions about the capacity of vulnerable
communities to cope with any amount of further warming. Successful adaptation can be supported at
the national and sub-national levels, with national governments playing an important role in coordination,planning, determining policy priorities, and distributing resources and support. However, given that the needfor adaptation can be very different from one community to the next, the kinds of measures that can successfully
reduce climate risks will also depend heavily on the local context.

4

When done successfully, adaptation can allow individuals to adjust to the impacts of climate change in ways thatminimize negative consequences and to maintain their livelihoods. This could involve, for example, a farmer
switching to drought-tolerant crops to deal with increasing occurrences of heatwaves. In some cases, however,
the impacts of climate change could result in entire systems changing significantly, such as moving to an entirely
new agricultural system in areas where the climate is no longer suitable for current practices. Constructing
sea walls to stop flooding due to sea level rise from climate change is another example of adaptation, butdeveloping city planning to change how flood water is managed throughout the city would be an exampleof transformational adaptation. These actions require significantly more institutional, structural, and financial
support. While this kind of transformational adaptation would not be needed everywhere in a 1.5°C world, thescale of change needed would be challenging to implement, as it requires additional support, such as throughfinancial assistance and behavioural change. Few empirical examples exist to date.
Examples from around the world show that adaptation is an iterative process. Adaptation pathways describe
how communities can make decisions about adaptation in an ongoing and flexible way. Such pathways allow
for pausing, evaluating the outcomes of specific adaptation actions, and modifying the strategy as appropriate.
Due to their flexible nature, adaptation pathways can help to identify the most effective ways to minimise theimpacts of present and future climate change for a given local context. This is important since adaptation cansometimes exacerbate vulnerabilities and existing inequalities if poorly designed. The unintended negative
consequences of adaptation that can sometimes occur are known as ‘maladaptation’. Maladaptation can be seen
if a particular adaptation option has negative consequences for some (e.g., rainwater harvesting upstream might
reduce water availability downstream) or if an adaptation intervention in the present has trade-offs in the future(e.g., desalination plants may improve water availability in the present but have large energy demands over time).

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FAQ 4.3 (continued)

While adaptation is important to reduce the negative impacts from climate change, adaptation measures on
their own are not enough to prevent climate change impacts entirely. The more global temperature rises, themore frequent, severe, and erratic the impacts will be, and adaptation may not protect against all risks. Examplesof where limits may be reached include substantial loss of coral reefs, massive range losses for terrestrial species,
more human deaths from extreme heat, and losses of coastal-dependent livelihoods in low lying islands andcoasts.

4

FAQ 4.3, Figure 1 | Why is adaptation important in a world with global warming of 1.5°C? Examples of adaptation and transformational adaptation.
Adapting to further warming requires action at national and sub-national levels and can mean different things to different people in different contexts. Whiletransformational adaptation would not be needed everywhere in a world limited to 1.5°C warming, the scale of change needed would be challenging to implement.

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Sustainable Development,
Poverty Eradication andReducing Inequalities
Coordinating Lead Authors:
Joyashree Roy (India), Petra Tschakert (Australia/Austria), Henri Waisman (France)

Lead Authors:
Sharina Abdul Halim (Malaysia), Philip Antwi-Agyei (Ghana), Purnamita Dasgupta (India), Bronwyn
Hayward (New Zealand), Markku Kanninen (Finland), Diana Liverman (USA), Chukwumerije
Okereke (UK/Nigeria), Patricia Fernanda Pinho (Brazil), Keywan Riahi (Austria), Avelino G. Suarez
Rodriguez (Cuba)

Contributing Authors:
Fernando Aragón-Durand (Mexico), Mustapha Babiker (Sudan), Mook Bangalore (USA), Paolo
Bertoldi (Italy), Bishwa Bhaskar Choudhary (India), Edward Byres (Austria/Brazil), Anton Cartwright(South Africa), Riyanti Djalante (Japan/Indonesia), Kristie L. Ebi (USA), Neville Ellis (Australia),
Francois Engelbrecht (South Africa), Maria Figueroa (Denmark/Venezuela), Mukesh Gupta (India),
Diana Hinge Salili (Vanuatu), Daniel Huppmann (Austria), Saleemul Huq (Bangladesh/UK), Daniela
Jacob (Germany), Rachel James (UK), Debora Ley (Guatemala/Mexico), Peter Marcotullio (USA),
Omar Massera (Mexico), Reinhard Mechler (Germany), Haileselassie Amaha Medhin (Ethiopia),
Shagun Mehrotra (USA/India), Peter Newman (Australia), Karen Paiva Henrique (Brazil), Simon
Parkinson (Canada), Aromar Revi (India), Wilfried Rickels (Germany), Lisa Schipper (UK/Sweden),
Jörn Schmidt (Germany), Seth Schultz (USA), Pete Smith (UK), William Solecki (USA), Shreya Some
(India), Nenenteiti Teariki-Ruatu (Kiribati), Adelle Thomas (Bahamas), Penny Urquhart (South
Africa), Margaretha Wewerinke-Singh (Netherlands)

Review Editors:
Svitlana Krakovska (Ukraine), Ramon Pichs Madruga (Cuba), Roberto Sanchez (Mexico)

Chapter Scientist:
Neville Ellis (Australia)

This chapter should be cited as:
Roy, J., P. Tschakert, H. Waisman, S. Abdul Halim, P. Antwi-Agyei, P. Dasgupta, B. Hayward, M. Kanninen, D. Liverman,
C. Okereke, P.F. Pinho, K. Riahi, and A.G. Suarez Rodriguez, 2018: Sustainable Development, Poverty Eradication
and Reducing Inequalities. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warmingof 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context ofstrengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate
poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia,
C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, andT. Waterfield (eds.)]. In Press.

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Chapter 5

Sustainable Development, Poverty Eradication and Reducing Inequalities

Table of Contents
Executive Summary....................................................................447

5.5

5.1

5.5.1
			

Integration of Adaptation, Mitigationand Sustainable Development.....................................467

5.5.2
			

Pathways for Adaptation, Mitigationand Sustainable Development.....................................467

5.5.3

Climate-Resilient Development Pathways...................468

Scope and Delineations...............................................450

5.1.1
			

Sustainable Development, SDGs, Poverty
Eradication and Reducing Inequalities........................450

5.1.2

Pathways to 1.5°C.......................................................450

5.1.3

Types of evidence........................................................451

5.2
		

Poverty, Equality and Equity Implicationsof a 1.5°C Warmer World.............................................451

5.2.1
			

Impacts and Risks of a 1.5°C Warmer World:
Implications for Poverty and Livelihoods.....................452

5.2.2
			

Avoided Impacts of 1.5°C Versus 2°C
Warming for Poverty and Inequality............................452

5.2.3
			

Risks from 1.5°C Versus 2°C Global Warming
and the Sustainable Development Goals.....................453

Cross-Chapter Box 12: Residual Risks,
Limits to Adaptation and Loss and Damage.........................454

5.3
		

Climate Adaptation andSustainable Development...........................................456

Box 5.3: Republic of Vanuatu – National Planningfor Development and Climate Resilience..............................471
Cross-Chapter Box 13: Cities and Urban Transformation....472

5.6
		
		
		

Conditions for Achieving Sustainable
Development, Eradicating Poverty
and Reducing Inequalities in
1.5°C Warmer Worlds....................................................474

5.6.1

Finance and Technology Aligned with Local Needs......474

5.6.2

Integration of Institutions...........................................474

5.6.3

Inclusive Processes......................................................475

5.6.4

Attention to Issues of Power and Inequality................475

5.6.5

Reconsidering Values..................................................475

5.3.1
			

Sustainable Development in Supportof Climate Adaptation.................................................456

5.7

5.3.2
			

Synergies and Trade-Offs between Adaptation
Options and Sustainable Development.......................457

Frequently Asked Questions

5.3.3
			

Adaptation Pathways towards a 1.5°C Warmer
World and Implications for Inequalities.......................458

Box 5.1 : Ecosystem- and Community-Based
Practices in Drylands................................................................459

5.4

Mitigation and Sustainable Development.............459

5.4.1
			

5

Sustainable Development Pathways to 1.5°C......466

Synergies and Trade-Offs between Mitigation
Options and Sustainable Development.......................459

Box 5.2: Challenges and Opportunities of
Low-Carbon Pathways in Gulf Cooperative
Council Countries.....................................................................462
5.4.2
			

446

Sustainable Development Implications of 1.5°C
and 2°C Mitigation Pathways......................................463

Synthesis and Research Gaps....................................475

FAQ 5.1
			
			
			

What are the Connections between
Sustainable Development and Limiting
Global Warming to 1.5°C above
Pre-Industrial Levels?...........................................477

FAQ 5.2 What are the Pathways to Achieving			Poverty Reduction and Reducing Inequalities			
while Reaching a 1.5°C World?...............................479

References ....................................................................................510

Sustainable Development, Poverty Eradication and Reducing Inequalities

Executive Summary
This chapter takes sustainable development as the starting pointand focus for analysis. It considers the broad and multifacetedbi-directional interplay between sustainable development, including
its focus on eradicating poverty and reducing inequality in theirmultidimensional aspects, and climate actions in a 1.5°C warmer world.
These fundamental connections are embedded in the Sustainable
Development Goals (SDGs). The chapter also examines synergies
and trade-offs of adaptation and mitigation options with sustainable
development and the SDGs and offers insights into possible pathways,
especially climate-resilient development pathways towards a 1.5°C
warmer world.

Sustainable Development, Poverty and Inequality
in a 1.5°C Warmer World
Limiting global warming to 1.5°C rather than 2°C above preindustrial levels would make it markedly easier to achieve many
aspects of sustainable development, with greater potential toeradicate poverty and reduce inequalities (medium evidence,high agreement). Impacts avoided with the lower temperature
limit could reduce the number of people exposed to climate risks andvulnerable to poverty by 62 to 457 million, and lessen the risks ofpoor people to experience food and water insecurity, adverse healthimpacts, and economic losses, particularly in regions that already facedevelopment challenges (medium evidence, medium agreement).
{5.2.2, 5.2.3} Avoided impacts expected to occur between 1.5°C and
2°C warming would also make it easier to achieve certain SDGs, such as
those that relate to poverty, hunger, health, water and sanitation, cities
and ecosystems (SDGs 1, 2, 3, 6, 11, 14 and 15) (medium evidence,high agreement). {5.2.3, Table 5.2 available at the end of the chapter}
Compared to current conditions, 1.5°C of global warming would
nonetheless pose heightened risks to eradicating poverty,
reducing inequalities and ensuring human and ecosystem wellbeing (medium evidence, high agreement). Warming of 1.5°C is
not considered ‘safe’ for most nations, communities, ecosystems andsectors and poses significant risks to natural and human systems ascompared to the current warming of 1°C (high confidence). {CrossChapter Box 12 in Chapter 5} The impacts of 1.5°C of warming would
disproportionately affect disadvantaged and vulnerable populations
through food insecurity, higher food prices, income losses, lost
livelihood opportunities, adverse health impacts and population
displacements (medium evidence, high agreement). {5.2.1} Some of
the worst impacts on sustainable development are expected to befelt among agricultural and coastal dependent livelihoods, indigenous
people, children and the elderly, poor labourers, poor urban dwellers in
African cities, and people and ecosystems in the Arctic and Small Island
Developing States (SIDS) (medium evidence, high agreement). {5.2.1,
Box 5.3, Chapter 3, Box 3.5, Cross-Chapter Box 9 in Chapter 4}

Climate Adaptation and Sustainable Development
Prioritization of sustainable development and meeting theSDGs is consistent with efforts to adapt to climate change (highChapter 5

confidence). Many strategies for sustainable development enabletransformational adaptation for a 1.5°C warmer world, provided
attention is paid to reducing poverty in all its forms and to promoting
equity and participation in decision-making (medium evidence, highagreement). As such, sustainable development has the potential
to significantly reduce systemic vulnerability, enhance adaptive
capacity, and promote livelihood security for poor and disadvantaged
populations (high confidence). {5.3.1}
Synergies between adaptation strategies and the SDGs are
expected to hold true in a 1.5°C warmer world, across sectors
and contexts (medium evidence, medium agreement). Synergies
between adaptation and sustainable development are significant
for agriculture and health, advancing SDGs 1 (extreme poverty),
2 (hunger), 3 (healthy lives and well-being) and 6 (clean water) (robust
evidence, medium agreement). {5.3.2} Ecosystem- and communitybased adaptation, along with the incorporation of indigenous andlocal knowledge, advances synergies with SDGs 5 (gender equality),
10 (reducing inequalities) and 16 (inclusive societies), as exemplified
in drylands and the Arctic (high evidence, medium agreement). {5.3.2,
Box 5.1, Cross-Chapter Box 10 in Chapter 4}
Adaptation strategies can result in trade-offs with and among
the SDGs (medium evidence, high agreement). Strategies that
advance one SDG may create negative consequences for other
SDGs, for instance SDGs 3 (health) versus 7 (energy consumption)
and agricultural adaptation and SDG 2 (food security) versus SDGs 3(health), 5 (gender equality), 6 (clean water), 10 (reducing inequalities),
14 (life below water) and 15 (life on the land) (medium evidence,
medium agreement). {5.3.2}
Pursuing place-specific adaptation pathways towards a 1.5°C
warmer world has the potential for significant positive outcomesfor well-being in countries at all levels of development (medium
evidence, high agreement). Positive outcomes emerge when
adaptation pathways (i) ensure a diversity of adaptation options basedon people’s values and the trade-offs they consider acceptable, (ii)maximize synergies with sustainable development through inclusive,
participatory and deliberative processes, and (iii) facilitate equitabletransformation. Yet such pathways would be difficult to achieve
without redistributive measures to overcome path dependencies,
uneven power structures, and entrenched social inequalities (medium
evidence, high agreement). {5.3.3}

Mitigation and Sustainable Development
The deployment of mitigation options consistent with 1.5°C
pathways leads to multiple synergies across a range ofsustainable development dimensions. At the same time, therapid pace and magnitude of change that would be required
to limit warming to 1.5°C, if not carefully managed, would leadto trade-offs with some sustainable development dimensions(high confidence). The number of synergies between mitigation
response options and sustainable development exceeds the numberof trade-offs in energy demand and supply sectors; agriculture, forestry
and other land use (AFOLU); and for oceans (very high confidence).
{Figure 5.2, Table 5.2 available at the end of the chapter} The 1.5°C
4475Chapter 5

Sustainable Development, Poverty Eradication and Reducing Inequalities

pathways indicate robust synergies, particularly for the SDGs 3 (health),
7 (energy), 12 (responsible consumption and production) and 14(oceans) (very high confidence). {5.4.2, Figure 5.3} For SDGs 1 (poverty),
2 (hunger), 6 (water) and 7 (energy), there is a risk of trade-offs ornegative side effects from stringent mitigation actions compatible with
1.5°C of warming (medium evidence, high agreement). {5.4.2}
Appropriately designed mitigation actions to reduce energy
demand can advance multiple SDGs simultaneously. Pathways
compatible with 1.5°C that feature low energy demand show themost pronounced synergies and the lowest number of trade-offs
with respect to sustainable development and the SDGs (very highconfidence). Accelerating energy efficiency in all sectors has synergies
with SDGs 7 (energy), 9 (industry, innovation and infrastructure),
11 (sustainable cities and communities), 12 (responsible consumption
and production), 16 (peace, justice and strong institutions), and17 (partnerships for the goals) (robust evidence, high agreement).
{5.4.1, Figure 5.2, Table 5.2} Low-demand pathways, which would
reduce or completely avoid the reliance on bioenergy with carbon
capture and storage (BECCS) in 1.5°C pathways, would result insignificantly reduced pressure on food security, lower food prices andfewer people at risk of hunger (medium evidence, high agreement).
{5.4.2, Figure 5.3}
The impacts of carbon dioxide removal options on SDGs dependon the type of options and the scale of deployment (highconfidence). If poorly implemented, carbon dioxide removal (CDR)
options such as bioenergy, BECCS and AFOLU would lead to tradeoffs. Appropriate design and implementation requires considering
local people’s needs, biodiversity and other sustainable development
dimensions (very high confidence). {5.4.1.3, Cross-Chapter Box 7 in
Chapter 3}

5The design of the mitigation portfolios and policy instruments
to limit warming to 1.5°C will largely determine the overall
synergies and trade-offs between mitigation and sustainable
development (very high confidence). Redistributive policies
that shield the poor and vulnerable can resolve trade-offs fora range of SDGs (medium evidence, high agreement). Individual
mitigation options are associated with both positive and negativeinteractions with the SDGs (very high confidence). {5.4.1} However,appropriate choices across the mitigation portfolio can help tomaximize positive side effects while minimizing negative side effects(high confidence). {5.4.2, 5.5.2} Investment needs for complementarypolicies resolving trade-offs with a range of SDGs are only a small
fraction of the overall mitigation investments in 1.5°C pathways(medium evidence, high agreement). {5.4.2, Figure 5.4} Integration of
mitigation with adaptation and sustainable development compatible
with 1.5°C warming requires a systems perspective (high confidence).
{5.4.2, 5.5.2}
Mitigation consistent with 1.5°C of warming create high risks
for sustainable development in countries with high dependency
on fossil fuels for revenue and employment generation (highconfidence). These risks are caused by the reduction of global demandaffecting mining activity and export revenues and challenges to rapidly
decrease high carbon intensity of the domestic economy (robust
448

evidence, high agreement). {5.4.1.2, Box 5.2} Targeted policies that
promote diversification of the economy and the energy sector could
ease this transition (medium evidence, high agreement). {5.4.1.2,
Box 5.2}

Sustainable Development Pathways to 1.5°C
Sustainable development broadly supports and often enables
the fundamental societal and systems transformations thatwould be required for limiting warming to 1.5°C above preindustrial levels (high confidence). Simulated pathways thatfeature the most sustainable worlds (e.g., Shared Socio-Economic
Pathways (SSP) 1) are associated with relatively lower mitigation andadaptation challenges and limit warming to 1.5°C at comparatively
lower mitigation costs. In contrast, development pathways with highfragmentation, inequality and poverty (e.g., SSP3) are associated withcomparatively higher mitigation and adaptation challenges. In such
pathways, it is not possible to limit warming to 1.5°C for the vast
majority of the integrated assessment models (medium evidence,high agreement). {5.5.2} In all SSPs, mitigation costs substantially
increase in 1.5°C pathways compared to 2°C pathways. No pathway
in the literature integrates or achieves all 17 SDGs (high confidence).
{5.5.2} Real-world experiences at the project level show that theactual integration between adaptation, mitigation and sustainable
development is challenging as it requires reconciling trade-offs acrosssectors and spatial scales (very high confidence). {5.5.1}
Without societal transformation and rapid implementationof ambitious greenhouse gas reduction measures, pathways
to limiting warming to 1.5°C and achieving sustainable
development will be exceedingly difficult, if not impossible,
to achieve (high confidence). The potential for pursuing such
pathways differs between and within nations and regions, due todifferent development trajectories, opportunities and challenges (veryhigh confidence). {5.5.3.2, Figure 5.1} Limiting warming to 1.5°C
would require all countries and non-state actors to strengthen theircontributions without delay. This could be achieved through sharing
efforts based on bolder and more committed cooperation, with support
for those with the least capacity to adapt, mitigate and transform(medium evidence, high agreement). {5.5.3.1, 5.5.3.2} Current
efforts towards reconciling low-carbon trajectories and reducing
inequalities, including those that avoid difficult trade-offs associatedwith transformation, are partially successful yet demonstrate notableobstacles (medium evidence, medium agreement). {5.5.3.3, Box 5.3,
Cross-Chapter Box 13 in this chapter}
Social justice and equity are core aspects of climate-resilient
development pathways for transformational social change.
Addressing challenges and widening opportunities between
and within countries and communities would be necessary
to achieve sustainable development and limit warming to
1.5°C, without making the poor and disadvantaged worse off(high confidence). Identifying and navigating inclusive and sociallyacceptable pathways towards low-carbon, climate-resilient futures is achallenging yet important endeavour, fraught with moral, practical andpolitical difficulties and inevitable trade-offs (very high confidence).
{5.5.2, 5.5.3.3, Box 5.3} It entails deliberation and problem-solving

Sustainable Development, Poverty Eradication and Reducing Inequalities

Chapter 5

processes to negotiate societal values, well-being, risks and resilienceand to determine what is desirable and fair, and to whom (medium
evidence, high agreement). Pathways that encompass joint, iterativeplanning and transformative visions, for instance in Pacific SIDS
like Vanuatu and in urban contexts, show potential for liveable andsustainable futures (high confidence). {5.5.3.1, 5.5.3.3, Figure 5.5,
Box 5.3, Cross-Chapter Box 13 in this chapter}
The fundamental societal and systemic changes to achievesustainable development, eradicate poverty and reduce
inequalities while limiting warming to 1.5°C would require
meeting a set of institutional, social, cultural, economic andtechnological conditions (high confidence). The coordinationand monitoring of policy actions across sectors and spatial scales
is essential to support sustainable development in 1.5°C warmer
conditions (very high confidence). {5.6.2, Box 5.3} External funding
and technology transfer better support these efforts when they
consider recipients’ context-specific needs (medium evidence, highagreement). {5.6.1} Inclusive processes can facilitate transformations
by ensuring participation, transparency, capacity building and iterativesocial learning (high confidence). {5.5.3.3, Cross-Chapter Box 13,
5.6.3} Attention to power asymmetries and unequal opportunities
for development, among and within countries, is key to adopting
1.5°C-compatible development pathways that benefit all populations(high confidence). {5.5.3, 5.6.4, Box 5.3} Re-examining individual andcollective values could help spur urgent, ambitious and cooperativechange (medium evidence, high agreement). {5.5.3, 5.6.5}

5449

Chapter 5

5.1

Sustainable Development, Poverty Eradication and Reducing Inequalities

Scope and Delineations

This chapter takes sustainable development as the starting point andfocus for analysis, considering the broader bi-directional interplay
and multifaceted interactions between development patterns andclimate actions in a 1.5°C warmer world and in the context oferadicating poverty and reducing inequality. It assesses the impactsof keeping temperatures at or below 1.5°C of global warming above
pre-industrial levels on sustainable development and compares theimpacts avoided at 1.5°C compared to 2°C (Section 5.2). It thenexamines the interactions, synergies and trade-offs of adaptation(Section 5.3) and mitigation (Section 5.4) measures with sustainable
development and the Sustainable Development Goals (SDGs). The
chapter offers insights into possible pathways towards a 1.5°C
warmer world, especially through climate-resilient development
pathways providing a comprehensive vision across different contexts(Section 5.5). The chapter also identifies the conditions that would beneeded to simultaneously achieve sustainable development, poverty
eradication, the reduction of inequalities, and the 1.5°C climate
objective (Section 5.6).

5.1.1

Sustainable Development, SDGs, Poverty
Eradication and Reducing Inequalities

Chapter 1 (see Cross-Chapter Box 4 in Chapter 1) defines sustainable
development as ‘development that meets the needs of the present
and future generations’ through balancing economic, social andenvironmental considerations, and then introduces the United Nations(UN) 2030 Agenda for Sustainable Development, which sets out
17 ambitious goals for sustainable development for all countries by2030. These SDGs are: no poverty (SDG 1), zero hunger (SDG 2), good
health and well-being (SDG 3), quality education (SDG 4), genderequality (SDG 5), clean water and sanitation (SDG 6), affordable andclean energy (SDG 7), decent work and economic growth (SDG 8),
industry, innovation and infrastructure (SDG 9), reduced inequalities(SDG 10), sustainable cities and communities (SDG 11), responsible
consumption and production (SDG 12), climate action (SDG 13), life
below water (SDG 14), life on land (SDG 15), peace, justice and strong
institutions (SDG 16) and partnerships for the goals (SDG 17).

5

The IPCC Fifth Assessment Report (AR5) included extensive discussionof links between climate and sustainable development, especially in
Chapter 13 (Olsson et al., 2014) and Chapter 20 (Denton et al., 2014)
in Working Group II and Chapter 4 (Fleurbaey et al., 2014) in Working
Group III. However, the AR5 preceded the 2015 adoption of the SDGs
and the literature that argues for their fundamental links to climate(Wright et al., 2015; Salleh, 2016; von Stechow et al., 2016; Hammill
and Price-Kelly, 2017; ICSU, 2017; Maupin, 2017; Gomez-Echeverri,2018).
The SDGs build on efforts under the UN Millennium Development Goals
to reduce poverty, hunger, and other deprivations. According to the UN,
the Millennium Development Goals were successful in reducing poverty
and hunger and improving water security (UN, 2015a). However, critics
argued that they failed to address within-country disparities, humanrights and key environmental concerns, focused only on developing
countries, and had numerous measurement and attribution problems
450(Langford et al., 2013; Fukuda-Parr et al., 2014). While improvements
in water security, slums and health may have reduced some aspects
of climate vulnerability, increases in incomes were linked to risinggreenhouse gas (GHG) emissions and thus to a trade-off between
development and climate change (Janetos et al., 2012; UN, 2015a;
Hubacek et al., 2017).
While the SDGs capture many important aspects of sustainable
development, including the explicit goals of poverty eradication
and reducing inequality, there are direct connections fromclimate to other measures of sustainable development including
multidimensional poverty, equity, ethics, human security, wellbeing and climate-resilient development (Bebbington and
Larrinaga, 2014; Robertson, 2014; Redclift and Springett, 2015;
Barrington-Leigh, 2016; Helliwell et al., 2018; Kirby and O’Mahony,2018) (see Glossary). The UN proposes sustainable development
as ‘eradicating poverty in all its forms and dimensions, combating
inequality within and among countries, preserving the planet,creating sustained, inclusive and sustainable economic growth andfostering social inclusion’ (UN, 2015b). There is robust evidenceof the links between climate change and poverty (see Chapter 1,
Cross-Chapter Box 4). The AR5 concluded with high confidence
that disruptive levels of climate change would preclude reducing
poverty (Denton et al., 2014; Fleurbaey et al., 2014). Internationalorganizations have since stated that climate changes ‘undermine
the ability of all countries to achieve sustainable development’ (UN,2015b) and can reverse or erase improvements in living conditions
and decades of development (Hallegatte et al., 2016).
Climate warming has unequal impacts on different people and places
as a result of differences in regional climate changes, vulnerabilities
and impacts, and these differences then result in unequal impacts
on sustainable development and poverty (Section 5.2). Responses toclimate change also interact in complex ways with goals of poverty
reduction. The benefits of adaptation and mitigation projects andfunding may accrue to some and not others, responses may be costly
and unaffordable to some people and countries, and projects may
disadvantage some individuals, groups and development initiatives(Sections 5.3 and 5.4, Cross-Chapter Box 11 in Chapter 4).

5.1.2

Pathways to 1.5°C

Pathways to 1.5°C (see Chapter 1, Cross-Chapter Box 1 in Chapter 1,
Glossary) include ambitious reductions in emissions and strategies foradaptation that are transformational, as well as complex interactions
with sustainable development, poverty eradication and reducing
inequalities. The AR5 WGII introduced the concept of climateresilient development pathways (CRDPs) (see Glossary) which
combine adaptation and mitigation to reduce climate change andits impacts, and emphasize the importance of addressing structuraland intersecting inequalities, marginalization and multidimensional
poverty to ‘transform […] the development pathways themselves
towards greater social and environmental sustainability, equity,
resilience, and justice’ (Olsson et al., 2014). This chapter assesses
literature on CRDPs relevant to 1.5°C global warming (Section 5.5.3),
to understand better the possible societal and systems transformations(see Glossary) that reduce inequality and increase well-being

Sustainable Development, Poverty Eradication and Reducing Inequalities(Figure 5.1). It also summarizes the knowledge on conditions toachieve such transformations, including changes in technologies,

Chapter 5

culture, values, financing and institutions that support low-carbon
and resilient pathways and sustainable development (Section 5.6).

Figure 5.1 | Climate-resilient development pathways (CRDPs) (green arrows) between a current world in which countries and communities exist at different levels ofdevelopment (A) and future worlds that range from climate-resilient (bottom) to unsustainable (top) (D). CRDPs involve societal transformation rather than business-as-usualapproaches, and all pathways involve adaptation and mitigation choices and trade-offs (B). Pathways that achieve the Sustainable Development Goals by 2030 and beyond,strive for net zero emissions around mid-21st century, and stay within the global 1.5°C warming target by the end of the 21st century, while ensuring equity and well-being for
all, are best positioned to achieve climate-resilient futures (C). Overshooting on the path to 1.5°C will make achieving CRDPs and other sustainable trajectories more difficult;yet, the limited literature does not allow meaningful estimates.

5.1.3

Types of Evidence

A variety of sources of evidence are used to assess the interactionsof sustainable development and the SDGs with the causes, impacts
and responses to climate change of 1.5°C warming. This chapter builds
on Chapter 3 to assess the sustainable development implications ofimpacts at 1.5°C and 2°C, and on Chapter 4 to examine the implicationsof response measures. Scientific and grey literature, with a postAR5 focus, and data that evaluate, measure and model sustainable
development–climate links from various perspectives, quantitatively
and qualitatively, across scales, and through well-documented casestudies are assessed.
Literature that explicitly links 1.5°C global warming to sustainable
development across scales remains scarce; yet we find relevant insights
in many recent publications on climate and development that assess
impacts across warming levels, the effects of adaptation and mitigation
response measures, and interactions with the SDGs. Relevant evidence
also stems from emerging literature on possible pathways, overshootand enabling conditions (see Glossary) for integrating sustainable
development, poverty eradication and reducing inequalities in thecontext of 1.5°C.

5.2

Poverty, Equality and Equity Implicationsof a 1.5°C Warmer World

Climate change could lead to significant impacts on extreme poverty
by 2030 (Hallegatte et al., 2016; Hallegatte and Rozenberg, 2017).
The AR5 concluded, with very high confidence, that climate change
and climate variability worsen existing poverty and exacerbate
inequalities, especially for those disadvantaged by gender, age, race,class, caste, indigeneity and (dis)ability (Olsson et al., 2014). New
literature on these links is substantial, showing that the poor willcontinue to experience climate change severely, and climate change
will exacerbate poverty (very high confidence) (Fankhauser andStern, 2016; Hallegatte et al., 2016; O’Neill et al., 2017a; Winsemius
et al., 2018). The understanding of regional impacts and risks of
1.5°C global warming and interactions with patterns of societal
451

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Chapter 5

Sustainable Development, Poverty Eradication and Reducing Inequalities

vulnerability and poverty remains limited. Yet identifying andaddressing poverty and inequality is at the core of staying withina safe and just space for humanity (Raworth, 2017; Bathiany et al.,2018). Building on relevant findings from Chapter 3 (see Section 3.4),
this section examines anticipated impacts and risks of 1.5°C andhigher warming on sustainable development, poverty, inequality andequity (see Glossary).

5.2.1

Impacts and Risks of a 1.5°C Warmer World:
Implications for Poverty and Livelihoods

Global warming of 1.5°C will have consequences for sustainable
development, poverty and inequalities. This includes residual risks,
limits to adaptation, and losses and damages (Cross-Chapter Box 12
in this chapter; see Glossary). Some regions have already experienced
a 1.5°C warming, with impacts on food and water security, health andother components of sustainable development (medium evidence,
medium agreement) (see Chapter 3, Section 3.4). Climate change is also
already affecting poorer subsistence communities through decreases
in crop production and quality, increases in crop pests and diseases,
and disruption to culture (Savo et al., 2016). It disproportionally affects
children and the elderly and can increase gender inequality (Kaijser
and Kronsell, 2014; Vinyeta et al., 2015; Carter et al., 2016; Hanna andOliva, 2016; Li et al., 2016).

5

At 1.5°C warming, compared to current conditions, further negative
consequences are expected for poor people, and inequality andvulnerability (medium evidence, high agreement). Hallegatte andRozenberg (2017) report that by 2030 (roughly approximating a 1.5°C
warming), 122 million additional people could experience extreme
poverty, based on a ‘poverty scenario’ of limited socio-economic
progress, comparable to the Shared Socio-Economic Pathway (SSP)
4 (inequality), mainly due to higher food prices and declining health,with substantial income losses for the poorest 20% across 92 countries.
Pretis et al. (2018) estimate negative impacts on economic growth
in lower-income countries at 1.5°C warming, despite uncertainties.
Impacts are likely to occur simultaneously across livelihood, food,human, water and ecosystem security (limited evidence, highagreement) (Byers et al., 2018), but the literature on interacting andcascading effects remains scarce (Hallegatte et al., 2014; O’Neill etal., 2017b; Reyer et al., 2017a, b).
Chapter 3 outlines future impacts and risks for ecosystems andhuman systems, many of which could also undermine sustainable
development and efforts to eradicate poverty and hunger, andto protect health and ecosystems. Chapter 3 findings (see Section
3.5.2.1) suggest increasing Reasons for Concern from moderate tohigh at a warming of 1.1° to 1.6°C, including for indigenous people
and their livelihoods, and ecosystems in the Arctic (O’Neill et al.,2017b). In 2050, based on the Hadley Centre Climate Prediction
Model 3 (HadCM3) and the Special Report on Emission Scenarios A1b
scenario (roughly comparable to 1.5°C warming), 450 million more
flood-prone people would be exposed to doubling in flood frequency,
and global flood risk would increase substantially (Arnell and
Gosling, 2016). For droughts, poor people are expected to be more
exposed (85% in population terms) in a warming scenario greaterthan 1.5°C for several countries in Asia and southern and western
452

Africa (Winsemius et al., 2018). In urban Africa, a 1.5°C warming
could expose many households to water poverty and increased
flooding (Pelling et al., 2018). At 1.5ºC warming, fisheries-dependentand coastal livelihoods, of often disadvantaged populations, would
suffer from the loss of coral reefs (see Chapter 3, Box 3.4).
Global heat stress is projected to increase in a 1.5°C warmer world,and by 2030, compared to 1961–1990, climate change could be
responsible for additional annual deaths of 38,000 people from heatstress, particularly among the elderly, and 48,000 from diarrhoea,
60,000 from malaria, and 95,000 from childhood undernutrition (WHO,2014). Each 1°C increase could reduce work productivity by 1 to 3%
for people working outdoors or without air conditioning, typically thepoorer segments of the workforce (Park et al., 2015).
The regional variation in the ‘warming experience at 1.5°C’ (see Chapter
1, Section 1.3.1) is large (see Chapter 3, Section 3.3.2). Declines in crop
yields are widely reported for Africa (60% of observations), with serious
consequences for subsistence and rain-fed agriculture and food security(Savo et al., 2016). In Bangladesh, by 2050, damages and losses are
expected for poor households dependent on freshwater fish stocks due
to lack of mobility, limited access to land and strong reliance on local
ecosystems (Dasgupta et al., 2017). Small Island Developing States(SIDS) are expected to experience challenging conditions at 1.5°C
warming due to increased risk of internal migration and displacement
and limits to adaptation (see Chapter 3, Box 3.5, Cross-Chapter Box
12 in this chapter). An anticipated decline of marine fisheries of
3 million metric tonnes per degree warming would have serious
regional impacts for the Indo-Pacific region and the Arctic (Cheung etal., 2016).

5.2.2

Avoided Impacts of 1.5°C versus 2°C
Warming for Poverty and Inequality

Avoided impacts between 1.5°C and 2°C warming are expected tohave significant positive implications for sustainable development,
and reducing poverty and inequality. Using the SSPs (see Chapter 1,
Cross-Chapter Box 1 in Chapter 1, Section 5.5.2), Byers et al. (2018)
model the number of people exposed to multi-sector climate risks
and vulnerable to poverty (income < $10/day), comparing 2°C and
1.5°C; the respective declines are from 86 million to 24 million for
SSP1 (sustainability), from 498 million to 286 million for SSP2 (middleof the road), and from 1220 million to 763 million for SSP3 (regional
rivalry), which suggests overall 62–457 million fewer people exposedand vulnerable at 1.5°C warming. Across the SSPs, the largest
populations exposed and vulnerable are in South Asia (Byers etal., 2018). The avoided impacts on poverty at 1.5°C relative to 2°C
are projected to depend at least as much or more on development
scenarios than on warming (Wiebe et al., 2015; Hallegatte andRozenberg, 2017).
Limiting warming to 1.5°C is expected to reduce the number of people
exposed to hunger, water stress and disease in Africa (Clements,2009). It is also expected to limit the number of poor people exposed
to floods and droughts at higher degrees of warming, especially in
African and Asian countries (Winsemius et al., 2018). Challenges forpoor populations – relating to food and water security, clean energySustainable Development, Poverty Eradication and Reducing Inequalities

access and environmental well-being – are projected to be less at
1.5°C, particularly for vulnerable people in Africa and Asia (Byers etal., 2018). The overall projected socio-economic losses compared to thepresent day are less at 1.5°C (8% loss of gross domestic product percapita) compared to 2°C (13%), with lower-income countries projected
to experience greater losses, which may increase economic inequality
between countries (Pretis et al., 2018).

5.2.3

Risks from 1.5°C versus 2°C Global Warming
and the Sustainable Development Goals

The risks that can be avoided by limiting global warming to 1.5ºC rather
than 2°C have many complex implications for sustainable development(ICSU, 2017; Gomez-Echeverri, 2018). There is high confidence that
constraining warming to 1.5°C rather than 2°C would reduce risks
for unique and threatened ecosystems, safeguarding the services they
provide for livelihoods and sustainable development and making
adaptation much easier (O’Neill et al., 2017b), particularly in Central
America, the Amazon, South Africa and Australia (Schleussner et al.,2016; O’Neill et al., 2017b; Reyer et al., 2017b; Bathiany et al., 2018).
In places that already bear disproportionate economic and social
challenges to their sustainable development, people will face lower
risks at 1.5°C compared to 2°C. These include North Africa andthe Levant (less water scarcity), West Africa (less crop loss), SouthAmerica and Southeast Asia (less intense heat), and many othercoastal nations and island states (lower sea level rise, less coral reefloss) (Schleussner et al., 2016; Betts et al., 2018). The risks for food,water and ecosystems, particularly in subtropical regions such as
Central America and countries such as South Africa and Australia,
are expected to be lower at 1.5°C than at 2°C warming (Schleussner
et al., 2016). Fewer people would be exposed to droughts andChapter 5heat waves and the associated health impacts in countries such as
Australia and India (King et al., 2017; Mishra et al., 2017).
Limiting warming to 1.5°C would make it markedly easier to achieve
the SDGs for poverty eradication, water access, safe cities, foodsecurity, healthy lives and inclusive economic growth, and would help
to protect terrestrial ecosystems and biodiversity (medium evidence,high agreement) (Table 5.2 available at the end of the chapter). For
example, limiting species loss and expanding climate refugia will
make it easier to achieve SDG 15 (see Chapter 3, Section 3.4.3). One
indication of how lower temperatures benefit the SDGs is to compare
the impacts of Representative Concentration Pathway (RCP) 4.5 (lower
emissions) and RCP8.5 (higher emissions) on the SDGs (Ansuategi
et al., 2015). A low emissions pathway allows for greater success inachieving SDGs for reducing poverty and hunger, providing accessto clean energy, reducing inequality, ensuring education for all andmaking cities more sustainable. Even at lower emissions, a medium
risk of failure exists to meet goals for water and sanitation, and marine
and terrestrial ecosystems.
Action on climate change (SDG 13), including slowing the rate ofwarming, would help reach the goals for water, energy, food andland (SDGs 6, 7, 2 and 15) (Obersteiner et al., 2016; ICSU, 2017)and contribute to poverty eradication (SDG 1) (Byers et al., 2018).
Although the literature that connects 1.5°C to the SDGs is limited, apathway that stabilizes warming at 1.5°C by the end of the century is
expected to increase the chances of achieving the SDGs by 2030, withgreater potential to eradicate poverty, reduce inequality and foster
equity (limited evidence, medium agreement). There are no studies
on overshoot and dimensions of sustainable development, although
literature on 4°C of warming suggests the impacts would be severe(Reyer et al., 2017b).

Table 5.1 | Sustainable development implications of avoided impacts between 1.5°C and 2°C global warming.

Impacts

Chapter 3
Section4% more people exposed to water stress

8% more people exposed to water stress,
with 184–270 million people more exposed

Table 3.4

496 (range 103–1159) million people exposedand vulnerable to water stress

586 (range 115–1347) million people exposedand vulnerable to water stress3.4.3,
Table 3.4

Around 7% of land area experiences biome
shiftsAround 13% (range 8–20%) of land area
experiences biome shifts

Box 3.5

70–90% of coral reefs at risk from bleaching

99% of coral reefs at risk from bleaching

3.4.5.1

31–69 million people exposed to coastal
flooding

32–79 million exposed to coastal flooding

3.4.5.2

Fewer cities and coasts exposed to sea level riseand extreme events

More people and cities exposed to flooding

3.4.6,
Box 3.1

Significant declines in crop yields avoided,
some yields may increase

Average crop yields decline

Table 3.4

32–36 million people exposed to lower yields

330–396 million people exposed to lower yields

3.4.5.1

Lower risk of temperature-related morbidity
and smaller mosquito range

Higher risks of temperature-related morbidity
and mortality and larger geographic rangeof mosquitoes

3.4.5.2

3546–4508 million people exposed to heat waves

5417–6710 million people exposed to heat waves

Coastal cities

Food systems

Health2°C

3.4.2.1
Water scarcity

Ecosystems

1.5°C

Sustainable Development Goals(SDGs) More Easily Achieved
when Limiting Warming to 1.5°C5

SDG 6 water availability for all

SDG 15 to protect terrestrial ecosystems
and halt biodiversity loss

SDG 11 to make cities and humansettlements safe and resilient

SDG 2 to end hunger andachieve food security

SDG 3 to ensure healthy lives for all

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Sustainable Development, Poverty Eradication and Reducing Inequalities

Cross-Chapter Box 12 | Residual Risks, Limits to Adaptation and Loss and Damage
Lead Authors:
Riyanti Djalante (Japan/Indonesia), Kristie L. Ebi (USA), Debora Ley (Guatemala/Mexico), Reinhard Mechler (Germany), Patricia
Fernanda Pinho (Brazil), Aromar Revi (India), Petra Tschakert (Australia/Austria)

Contributing Authors:
Karen Paiva Henrique (Brazil), Saleemul Huq (Bangladesh/UK), Rachel James (UK), Adelle Thomas (Bahamas), Margaretha
Wewerinke-Singh (Netherlands)

Introduction
Residual climate-related risks, limits to adaptation, and loss and damage (see Glossary) are increasingly assessed in the scientific
literature (van der Geest and Warner, 2015; Boyd et al., 2017; Mechler et al., 2019). The AR5 (IPCC, 2013; Oppenheimer et al., 2014)
documented impacts that have been detected and attributed to climate change, projected increasing climate-related risks with continued global warming, and recognized barriers and limits to adaptation. It recognized that adaptation is constrained by biophysical, institutional, financial, social and cultural factors, and that the interaction of these factors with climate change can lead to soft
adaptation limits (adaptive actions currently not available) and hard adaptation limits (adaptive actions appear infeasible leading
to unavoidable impacts) (Klein et al., 2014).

Loss and damage: concepts and perspectives‘Loss and Damage’ (L&D) has been discussed in international climate negotiations for three decades (INC, 1991; Calliari, 2016;
Vanhala and Hestbaek, 2016). A work programme on L&D was established as part of the Cancun Adaptation Framework in 2010
supporting developing countries particularly vulnerable to climate change impacts (UNFCCC, 2011a). In 2013, the Conference of
the Parties (COP) 19 established the Warsaw International Mechanism for Loss and Damage (WIM) as a formal part of the United
Nations Framework Convention on Climate Change (UNFCCC) architecture (UNFCCC, 2014). It acknowledges that L&D ‘includes,
and in some cases involves more than, that which can be reduced by adaptation’ (UNFCCC, 2014). The Paris Agreement recognized‘the importance of averting, minimizing and addressing loss and damage associated with the adverse effects of climate change’
through Article 8 (UNFCCC, 2015).
There is no one definition of L&D in climate policy, and analysis of policy documents and stakeholder views has demonstrated ambiguity (Vanhala and Hestbaek, 2016; Boyd et al., 2017). UNFCCC documents suggest that L&D is associated with adverse impactsof climate change on human and natural systems, including impacts from extreme events and slow-onset processes (UNFCCC,
2011b, 2014, 2015). Some documents focus on impacts in developing or particularly vulnerable countries (UNFCCC, 2011b, 2014).
They refer to economic (loss of assets and crops) and non-economic (biodiversity, culture, health) impacts, the latter also being anaction area under the WIM workplan, and irreversible and permanent loss and damage. Lack of clarity of what the term addresses(avoidance through adaptation and mitigation, unavoidable losses, climate risk management, existential risk) was expressed among
stakeholders, with further disagreement ensuing about what constitutes anthropogenic climate change versus natural climate variability (Boyd et al., 2017).

5

Limits to adaptation and residual risks
The AR5 described adaptation limits as points beyond which actors’ objectives are compromised by intolerable risks threatening key
objectives such as good health or broad levels of well-being, thus requiring transformative adaptation for overcoming soft limits(see Chapter 4, Sections 4.2.2.3, 4.5.3 and Cross-Chapter Box 9, Section 5.3.1) (Dow et al., 2013; Klein et al., 2014). The AR5 WGII
risk tables, based on expert judgment, depicted the potential for, and the limits of, additional adaptation to reduce risk. Near-term(2030–2040) risks can be used as a proxy for 1.5°C warming by the end of the century and compared to longer-term (2080–2100)
risks associated with an approximate 2°C warming. Building on the AR5 risk approach, Cross-Chapter Box 12, Figure 1 provides a
stylised application example to poverty and inequality.

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Sustainable Development, Poverty Eradication and Reducing Inequalities

Chapter 5

Cross-Chapter Box 12 (continued)

Cross-Chapter Box 12, Figure 1 | Stylized reduced risk levels due to avoided impacts between 2°C and 1.5°C warming (in solid red-orange), additional
avoided impacts with adaptation under 2°C (striped orange) and under 1.5°C (striped yellow), and unavoidable impacts (losses) with no or very limited
potential for adaptation (grey), extracted from the AR5 WGII risk tables (Field et al., 2014), and underlying chapters by Adger et al. (2014) and Olsson et al.
(2014). For some systems and sectors (A), achieving 1.5°C could reduce risks to low (with adaptation) from very high (without adaptation) and high (withadaptation) under 2°C. For other areas (C), no or very limited adaptation potential is anticipated, suggesting limits, with the same risks for 1.5°C and 2°C.
Other risks are projected to be medium under 2°C with further potential for reduction, especially with adaptation, to very low levels (B).

Limits to adaptation, residual risks, and losses in a 1.5°C warmer world
The literature on risks at 1.5°C (versus 2°C and more) and potentials for adaptation remains limited, particularly for specific regions,sectors, and vulnerable and disadvantaged populations. Adaptation potential at 1.5°C and 2°C is rarely assessed explicitly, making
an assessment of residual risk challenging. Substantial progress has been made since the AR5 to assess which climate change
impacts on natural and human systems can be attributed to anthropogenic emissions (Hansen and Stone, 2016) and to examine
the influence of anthropogenic emissions on extreme weather events (NASEM, 2016), and on consequent impacts on human life
(Mitchell et al., 2016), but less so on monetary losses and risks (Schaller et al., 2016). There has also been some limited research to
examine local-level limits to adaptation (Warner and Geest, 2013; Filho and Nalau, 2018). What constitutes losses and damages
is context-dependent and often requires place-based research into what people value and consider worth protecting (Barnett etal., 2016; Tschakert et al., 2017). Yet assessments of non-material and intangible losses are particularly challenging, such as lossof sense of place, belonging, identity, and damage to emotional and mental well-being (Serdeczny et al., 2017; Wewerinke-Singh,
2018a). Warming of 1.5°C is not considered ‘safe’ for most nations, communities, ecosystems and sectors, and poses significant risks
to natural and human systems as compared to the current warming of 1°C (high confidence) (see Chapter 3, Section 3.4, Box 3.4,
Box 3.5, Table 3.5, Cross-Chapter Box 6 in Chapter 3). Table 5.2, drawing on findings from Chapters 3, 4 and 5, presents examples
of soft and hard limits in natural and human systems in the context of 1.5°C and 2°C of warming.

5

Cross-Chapter Box 12, Table 1 | Soft and hard adaptation limits in the context of 1.5°C and 2°C of global warming.
System/Region

ExampleSoft Limit

Hard Limit

Coral reefs

Loss of 70–90% of tropical coral reefs by mid-century under 1.5°C scenario (total loss under 2°C
scenario) (see Chapter 3, Sections 3.4.4 and 3.5.2.1, Box 3.4)✓

Biodiversity

6% of insects, 8% of plants and 4% of vertebrates lose over 50% of the climatically determined
geographic range at 1.5°C (18% of insects, 16% of plants and 8% of vertebrates at 2°C)
(see Chapter 3, Section 3.4.3.3)✓

Poverty

24–357 million people exposed to multi-sector climate risks and vulnerable to poverty at 1.5°C(86–1220 million at 2°C) (see Section 5.2.2)✓

Human health

Twice as many megacities exposed to heat stress at 1.5°C compared to present, potentially exposing
350 million additional people to deadly heat wave conditions by 2050 (see Chapter 3, Section 3.4.8)✓✓

Coastal livelihoods

Large-scale changes in oceanic systems (temperature and acidification) inflict damage and losses tolivelihoods, income, cultural identity and health for coastal-dependent communities at 1.5°C (potential
higher losses at 2°C) (see Chapter 3, Sections 3.4.4, 3.4.5, 3.4.6.3, Box 3.4, Box 3.5, Cross-Chapter
Box 6, Chapter 4, Section 4.3.5; Section 5.2.3)✓✓

Small Island Developing States

Sea level rise and increased wave run up combined with increased aridity and decreased
freshwater availability at 1.5°C warming potentially leaving several atoll islands uninhabitable(see Chapter 3, Sections 3.4.3, 3.4.5, Box 3.5, Chapter 4, Cross-Chapter Box 9)✓455

Chapter 5

Sustainable Development, Poverty Eradication and Reducing Inequalities

Cross-Chapter Box 12 (continued)

Approaches and policy options to address residual risk and loss and damage
Conceptual and applied work since the AR5 has highlighted the synergies and differences with adaptation and disaster risk reduction
policies (van der Geest and Warner, 2015; Thomas and Benjamin, 2017), suggesting more integration of existing mechanisms, yetcareful consideration is advised for slow-onset and potentially irreversible impacts and risk (Mechler and Schinko, 2016). Scholarshipon justice and equity has provided insight on compensatory, distributive and procedural equity considerations for policy and practiceto address loss and damage (Roser et al., 2015; Wallimann-Helmer, 2015; Huggel et al., 2016). A growing body of legal literature
considers the role of litigation in preventing and addressing loss and damage and finds that litigation risks for governments andbusiness are bound to increase with improved understanding of impacts and risks as climate science evolves (high confidence)(Mayer, 2016; Banda and Fulton, 2017; Marjanac and Patton, 2018; Wewerinke-Singh, 2018b). Policy proposals include international
support for experienced losses and damages (Crosland et al., 2016; Page and Heyward, 2017), addressing climate displacement,
donor-supported implementation of regional public insurance systems (Surminski et al., 2016) and new global governance systems
under the UNFCCC (Biermann and Boas, 2017).

5.3

5Climate Adaptation andSustainable Development

Adaptation will be extremely important in a 1.5°C warmer world
since substantial impacts will be felt in every region (high confidence)(Chapter 3, Section 3.3), even if adaptation needs will be lower thanin a 2°C warmer world (see Chapter 4, Sections 4.3.1 to 4.3.5, 4.5.3,
Cross-Chapter Box 10 in Chapter 4). Climate adaptation options
comprise structural, physical, institutional and social responses, with
their effectiveness depending largely on governance (see Glossary),
political will, adaptive capacities and availability of finance (seeChapter 4, Sections 4.4.1 to 4.4.5) (Betzold and Weiler, 2017; Sonwa
et al., 2017; Sovacool et al., 2017). Even though the literature is scarceon the expected impacts of future adaptation measures on sustainable
development specific to warming experiences of 1.5°C, this section
assesses available literature on how (i) prioritising sustainable
development enhances or impedes climate adaptation efforts(Section 5.3.1); (ii) climate adaptation measures impact sustainable
development and the SDGs in positive (synergies) or negative (tradeoffs) ways (Section 5.3.2); and (iii) adaptation pathways towards a 1.5°C
warmer world affect sustainable development, poverty and inequalities(Section 5.3.3). The section builds on Chapter 4 (see Section 4.3.5)
regarding available adaptation options to reduce climate vulnerability
and build resilience (see Glossary) in the context of 1.5°C-compatible
trajectories, with emphasis on sustainable development implications.

5.3.1

Sustainable Development in Supportof Climate Adaptation

Making sustainable development a priority, and meeting the SDGs,
is consistent with efforts to adapt to climate change (very highconfidence). Sustainable development is effective in building adaptive
capacity if it addresses poverty and inequalities, social and economic
exclusion, and inadequate institutional capacities (Noble et al., 2014;
Abel et al., 2016; Colloff et al., 2017). Four ways in which sustainable
development leads to effective adaptation are described below.
First, sustainable development enables transformational adaptation(see Chapter 4, Section 4.2.2.2) when an integrated approach is456

adopted, with inclusive, transparent decision-making, rather thanaddressing current vulnerabilities as stand-alone climate problems(Mathur et al., 2014; Arthurson and Baum, 2015; Shackleton et al.,2015; Lemos et al., 2016; Antwi-Agyei et al., 2017b). Ending poverty
in its multiple dimensions (SDG 1) is often a highly effective form ofclimate adaptation (Fankhauser and McDermott, 2014; Leichenko
and Silva, 2014; Hallegatte and Rozenberg, 2017). However, ending
poverty is not sufficient, and the positive outcome as an adaptation
strategy depends on whether increased household wealth is actually
directed towards risk reduction and management strategies (Nelson
et al., 2016), as shown in urban municipalities (Colenbrander et al.,2017; Rasch, 2017) and agrarian communities (Hashemi et al., 2017),
and whether finance for adaptation is made available (Section 5.6.1).
Second, local participation is effective when wider socio-economic
barriers are addressed via multiscale planning (McCubbin et al.,2015; Nyantakyi-Frimpong and Bezner-Kerr, 2015; Toole et al., 2016).
This is the case, for instance, when national education efforts (SDG 4)(Muttarak and Lutz, 2014; Striessnig and Loichinger, 2015) andindigenous knowledge (Nkomwa et al., 2014; Pandey and Kumar, 2018)enhance information sharing, which also builds resilience (Santos et al.,2016; Martinez-Baron et al., 2018) and reduces risks for maladaptation(Antwi-Agyei et al., 2018; Gajjar et al., 2018).
Third, development promotes transformational adaptation whenaddressing social inequalities (Section 5.5.3, 5.6.4), as in SDGs
4, 5, 16 and 17 (O’Brien, 2016; O’Brien, 2017). For example, SDG 5
supports measures that reduce women’s vulnerabilities and allow
women to benefit from adaptation (Antwi-Agyei et al., 2015; Van Aelst
and Holvoet, 2016; Cohen, 2017). Mobilization of climate finance,
carbon taxation and environmentally motivated subsidies can reduce
inequalities (SDG 10), advance climate mitigation and adaptation(Chancel and Picketty, 2015), and be conducive to strengthening andenabling environments for resilience building (Nhamo, 2016; Halonen
et al., 2017).
Fourth, when sustainable development promotes livelihood security,
it enhances the adaptive capacities of vulnerable communities andhouseholds. Examples include SDG 11 supporting adaptation in cities

Sustainable Development, Poverty Eradication and Reducing Inequalities

Chapter 5

to reduce harm from disasters (Kelman, 2017; Parnell, 2017); access towater and sanitation (SDG 6) with strong institutions (SDG 16) (Rasul
and Sharma, 2016); SDG 2 and its targets that promote adaptationin agricultural and food systems (Lipper et al., 2014); and targets for
SDG 3 such as reducing infectious diseases and providing health coverare consistent with health-related adaptation (ICSU, 2017; GomezEcheverri, 2018).

et al., 2017a; Carr and Onzere, 2018). Agricultural adaptation measures
may increase workloads, especially for women, while changes in crop
mix can result in loss of income or culturally inappropriate food (Carrand Thompson, 2014; Thompson-Hall et al., 2016; Bryan et al., 2017),
and they may benefit farmers with more land to the detriment of landpoor farmers, as seen in the Mekong River Basin (see Chapter 3, CrossChapter Box 6 in Chapter 3).

Sustainable development has the potential to significantly reduce
systemic vulnerability, enhance adaptive capacity and promote
livelihood security for poor and disadvantaged populations (highconfidence). Transformational adaptation (see Chapter 4, Sections
4.2.2.2 and 4.5.3) would require development that takes into
consideration multidimensional poverty and entrenched inequalities,
local cultural specificities and local knowledge in decision-making,thereby making it easier to achieve the SDGs in a 1.5°C warmer world(medium evidence, high agreement).

Adaptation to protect human health: Adaptation options in the healthsector are expected to reduce morbidity and mortality (Arbuthnott
et al., 2016; Ebi and Otmani del Barrio, 2017). Heat-early-warning
systems help lower injuries, illnesses and deaths (Hess and Ebi, 2016),
with positive impacts for SDG 3. Institutions better equipped toshare information, indicators for detecting climate-sensitive diseases,
improved provision of basic health care services and coordination
with other sectors also improve risk management, thus reducing
adverse health outcomes (Dasgupta et al., 2016; Dovie et al., 2017).
Effective adaptation creates synergies via basic public health measures(K.R. Smith et al., 2014; Dasgupta, 2016) and health infrastructure
protected from extreme weather events (Watts et al., 2015). Yet tradeoffs can occur when adaptation in one sector leads to negative impacts
in another sector. Examples include the creation of urban wetlands
through flood control measures which can breed mosquitoes, andmigration eroding physical and mental well-being, hence adversely
affecting SDG 3 (K.R. Smith et al., 2014; Watts et al., 2015). Similarly,
increased use of air conditioning enhances resilience to heat stress(Petkova et al., 2017), yet it can result in higher energy consumption,
undermining SDG 13.

5.3.2

Synergies and Trade-Offs between Adaptation
Options and Sustainable Development

There are short-, medium-, and long-term positive impacts (synergies)
and negative impacts (trade-offs) between the dual goals of keeping
temperatures below 1.5°C global warming and achieving sustainable
development. The extent of synergies between development andadaptation goals will vary by the development process adopted for aparticular SDG and underlying vulnerability contexts (medium evidence,high agreement). Overall, the impacts of adaptation on sustainable
development, poverty eradication and reducing inequalities in general,and the SDGs specifically, are expected to be largely positive, giventhat the inherent purpose of adaptation is to lower risks. Building on
Chapter 4 (see Section 4.3.5), this section examines synergies andtrade-offs between adaptation and sustainable development for somekey sectors and approaches.
Agricultural adaptation: The most direct synergy is between SDG 2(zero hunger) and adaptation in cropping, livestock and food systems,
designed to maintain or increase production (Lipper et al., 2014;
Rockström et al., 2017). Farmers with effective adaptation strategies
tend to enjoy higher food security and experience lower levels ofpoverty (FAO, 2015; Douxchamps et al., 2016; Ali and Erenstein, 2017).
Vermeulen et al. (2016) report strong positive returns on investment
across the world from agricultural adaptation with side benefits forenvironment and economic well-being. Well-adapted agricultural
systems contribute to safe drinking water, health, biodiversity andequity goals (DeClerck et al., 2016; Myers et al., 2017). Climate-smart
agriculture has synergies with food security, though it can be biased
towards technological solutions, may not be gender sensitive, and cancreate specific challenges for institutional and distributional aspects(Lipper et al., 2014; Arakelyan et al., 2017; Taylor, 2017).
At the same time, adaptation options increase risks for humanhealth, oceans and access to water if fertiliser and pesticides are usedwithout regulation or when irrigation reduces water availability forother purposes (Shackleton et al., 2015; Campbell et al., 2016). Whenagricultural insurance and climate services overlook the poor, inequality
may rise (Dinku et al., 2014; Carr and Owusu-Daaku, 2015; Georgeson

Coastal adaptation: Adaptation to sea level rise remains essential
in coastal areas even under a climate stabilization scenario of 1.5°C(Nicholls et al., 2018). Coastal adaptation to restore ecosystems (forinstance by planting mangrove forests) supports SDGs for enhancing
life and livelihoods on land and oceans (see Chapter 4, Sections
4.3.2.3). Synergistic outcomes between development and relocationof coastal communities are enhanced by participatory decision-making
and settlement designs that promote equity and sustainability (van der
Voorn et al., 2017). Limits to coastal adaptation may rise, for instancein low-lying islands in the Pacific, Caribbean and Indian Ocean, withattendant implications for loss and damage (see Chapter 3 Box 3.5,
Chapter 4, Cross-Chapter Box 9 in Chapter 4, Cross-Chapter Box 12 in
Chapter 5, Box 5.3).
Migration as adaptation: Migration has been used in various contexts
to protect livelihoods from challenges related to climate change(Marsh, 2015; Jha et al., 2017), including through remittances (Betzold
and Weiler, 2017). Synergies between migration and the achievementof sustainable development depend on adaptive measures andconditions in both sending and receiving regions (Fatima et al., 2014;
McNamara, 2015; Entzinger and Scholten, 2016; Ober and Sakdapolrak,
2017; Schwan and Yu, 2017). Adverse developmental impacts arise
when vulnerable women or the elderly are left behind or if migration
is culturally disruptive (Wilkinson et al., 2016; Albert et al., 2017; Islamand Shamsuddoha, 2017).
Ecosystem-based adaptation: Ecosystem-based adaptation (EBA) canoffer synergies with sustainable development (Morita and Matsumoto,457

5

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Sustainable Development, Poverty Eradication and Reducing Inequalities

2015; Ojea, 2015; Szabo et al., 2015; Brink et al., 2016; Butt et al.,2016; Conservation International, 2016; Huq et al., 2017), althoughassessments remain difficult (see Chapter 4, Section 4.3.2.2) (Doswald
et al., 2014). Examples include mangrove restoration reducing
coastal vulnerability, protecting marine and terrestrial ecosystems,
and increasing local food security, as well as watershed management
reducing flood risks and improving water quality (Chong, 2014).
In drylands, EBA practices, combined with community-based
adaptation, have shown how to link adaptation with mitigation to
improve livelihood conditions of poor farmers (Box 5.1). Synergistic
developmental outcomes arise where EBA is cost effective, inclusiveof indigenous and local knowledge and easily accessible by the poor
(Ojea, 2015; Daigneault et al., 2016; Estrella et al., 2016). Payment forecosystem services can provide incentives to land owners and natural
resource managers to preserve environmental services with synergies
with SDGs 1 and 13 (Arriagada et al., 2015), when implementation
challenges are overcome (Calvet-Mir et al., 2015; Wegner, 2016; Chanet al., 2017). Trade-offs include loss of other economic land use types,
tension between biodiversity and adaptation priorities, and conflicts
over governance (Wamsler et al., 2014; Ojea, 2015).
Community-based adaptation: Community-based adaptation (CBA)(see Chapter 4, Sections 4.3.3.2) enhances resilience and sustainabilityof adaptation plans (Ford et al., 2016; Fernandes-Jesus et al., 2017;
Grantham and Rudd, 2017; Gustafson et al., 2017). Yet negative
impacts occur if it fails to fairly represent vulnerable populations
and to foster long-term social resilience (Ensor, 2016; Taylor Aiken
et al., 2017). Mainstreaming CBA into planning and decision-making
enables the attainment of SDGs 5, 10 and 16 (Archer et al., 2014;
Reid and Huq, 2014; Vardakoulias and Nicholles, 2014; Cutter, 2016;
Kim et al., 2017). Incorporating multiple forms of indigenous andlocal knowledge is an important element of CBA, as shown for
instance in the Arctic region (see Chapter 4, Section 4.3.5.5, Box 4.3,
Cross-Chapter Box 9) (Apgar et al., 2015; Armitage, 2015; Pearce
et al., 2015; Chief et al., 2016; Cobbinah and Anane, 2016; Ford etal., 2016). Indigenous and local knowledge can be synergistic withachieving SDGs 2, 6 and 10 (Ayers et al., 2014; Lasage et al., 2015;
Regmi and Star, 2015; Berner et al., 2016; Chief et al., 2016; Murtinho,
2016; Reid, 2016).

5

There are clear synergies between adaptation options and several
SDGs, such as poverty eradication, elimination of hunger, clean waterand health (robust evidence, high agreement), as well-integrated
adaptation supports sustainable development (Eakin et al., 2014;
Weisser et al., 2014; Adam, 2015; Smucker et al., 2015). Substantial
synergies are observed in the agricultural and health sectors, andin ecosystem-based adaptations. However, particular adaptation
strategies can lead to adverse consequences for developmental
outcomes (medium evidence, high agreement). Adaptation strategies
that advance one SDG can result in trade-offs with other SDGs; forinstance, agricultural adaptation to enhance food security (SDG 2)
causing negative impacts for health, equality and healthy ecosystems(SDGs 3, 5, 6, 10, 14 and 15), and resilience to heat stress increasingenergy consumption (SDGs 3 and 7) and high-cost adaptationin resource-constrained contexts (medium evidence, medium
agreement).

458

5.3.3

Adaptation Pathways towards a 1.5°C Warmer
World and Implications for Inequalities

In a 1.5°C warmer world, adaptation measures and options would
need to be intensified, accelerated and scaled up. This entails not only
the right ‘mix’ of options (asking ‘right for whom and for what?’) but
also a forward-looking understanding of dynamic trajectories, that is
adaptation pathways (see Chapter 1, Cross-Chapter Box 1 in Chapter1), best understood as decision-making processes over sets of potential
action sequenced over time (Câmpeanu and Fazey, 2014; Wise et al.,2014). Given the scarcity of literature on adaptation pathways thatnavigate place-specific warming experiences at 1.5°C, this section
presents insights into current local decision-making for adaptation
futures. This grounded evidence shows that choices between possible
pathways, at different scales and for different groups of people, areshaped by uneven power structures and historical legacies that create
their own, often unforeseen change (Fazey et al., 2016; Bosomworth
et al., 2017; Lin et al., 2017; Murphy et al., 2017; Pelling et al., 2018).
Pursuing a place-specific adaptation pathway approach towards a
1.5°C warmer world harbours the potential for significant positive
outcomes, with synergies for well-being possibilities to ‘leap-frog the
SDGs’ (J.R.A. Butler et al., 2016), in countries at all levels of development(medium evidence, high agreement). It allows for identifying local,socially salient tipping points before they are crossed, based on what
people value and trade-offs that are acceptable to them (Barnett et al.,2014, 2016; Gorddard et al., 2016; Tschakert et al., 2017). Yet evidence
also reveals adverse impacts that reinforce rather than reduce existingsocial inequalities and hence may lead to poverty traps (medium
evidence, high agreement) (Nagoda, 2015; Warner et al., 2015; Barnett
et al., 2016; J.R.A. Butler et al., 2016; Godfrey-Wood and Naess, 2016;
Pelling et al., 2016; Albert et al., 2017; Murphy et al., 2017).
Past development trajectories as well as transformational adaptationplans can constrain adaptation futures by reinforcing dominant
political-economic structures and processes, and narrowing option
spaces; this leads to maladaptive pathways that preclude alternative,
locally relevant and sustainable development initiatives and increase
vulnerabilities (Warner and Kuzdas, 2017; Gajjar et al., 2018). Suchdominant pathways tend to validate the practices, visions andvalues of existing governance regimes and powerful members of acommunity while devaluing those of less privileged stakeholders.
Examples from Romania, the Solomon Islands and Australia illustratesuch pathway dynamics in which individual economic gains andprosperity matter more than community cohesion and solidarity; this
discourages innovation, exacerbates inequalities and further erodes
adaptive capacities of the most vulnerable (Davies et al., 2014; Fazey
et al., 2016; Bosomworth et al., 2017). In the city of London, United
Kingdom, the dominant adaptation and disaster risk management
pathway promotes resilience that emphasizes self-reliance; yet it
intensifies the burden on low-income citizens, the elderly, migrants
and others unable to afford flood insurance or protect themselves
against heat waves (Pelling et al., 2016). Adaptation pathways in the
Bolivian Altiplano have transformed subsistence farmers into worldleading quinoa producers, but loss of social cohesion and traditional
values, dispossession and loss of ecosystem services now constituteundesirable trade-offs (Chelleri et al., 2016).

Sustainable Development, Poverty Eradication and Reducing Inequalities

A narrow view of adaptation decision-making, for example focused ontechnical solutions, tends to crowd out more participatory processes(Lawrence and Haasnoot, 2017; Lin et al., 2017), obscures contested
values and reinforces power asymmetries (Bosomworth et al., 2017;
Singh, 2018). A situated and context-specific understanding ofadaptation pathways that galvanizes diverse knowledge, values andjoint initiatives helps to overcome dominant path dependencies, avoid
trade-offs that intensify inequities and challenge policies detached

Chapter 5

from place (Fincher et al., 2014; Wyborn et al., 2015; Murphy et al.,2017; Gajjar et al., 2018). These insights suggest that adaptation
pathway approaches to prepare for 1.5°C warmer futures would be
difficult to achieve without considerations for inclusiveness, placespecific trade-off deliberations, redistributive measures and procedural
justice mechanisms to facilitate equitable transformation (medium
evidence, high agreement).

Box 5.1 | Ecosystem- and Community-Based Practices in Drylands
Drylands face severe challenges in building climate resilience (Fuller and Lain, 2017), yet small-scale farmers can play a crucialrole as agents of change through ecosystem- and community-based practices that combine adaptation, mitigation and sustainable
development.
Farmer managed natural regeneration (FMNR) of trees in cropland is practised in 18 countries across sub-Saharan Africa, Southeast
Asia, Timor-Leste, India and Haiti and has, for example, permitted the restoration of over five million hectares of land in the Sahel(Niang et al., 2014; Bado et al., 2016). In Ethiopia, the Managing Environmental Resources to Enable Transitions programme,
which entails community-based watershed rehabilitation in rural landscapes, supported around 648,000 people, resulting in
the rehabilitation of 25,400,000 hectares of land in 72 severely food-insecure districts across Ethiopia between 2012 and 2015
(Gebrehaweria et al., 2016). In India, local farmers have benefitted from watershed programmes across different agro-ecologicalregions (Singh et al., 2014; Datta, 2015).
These low-cost, flexible community-based practices represent low-regrets adaptation and mitigation strategies. These strategies
often contribute to strengthened ecosystem resilience and biodiversity, increased agricultural productivity and food security,
reduced household poverty and drudgery for women, and enhanced agency and social capital (Niang et al., 2014; Francis et al.,2015; Kassie et al., 2015; Mbow et al., 2015; Reij and Winterbottom, 2015; Weston et al., 2015; Bado et al., 2016; Dumont et al.,2017). Small check dams in dryland areas and conservation agriculture can significantly increase agricultural output (Kumar et al.,2014; Agoramoorthy and Hsu, 2016; Pradhan et al., 2018). Mitigation benefits have also been quantified (Weston et al., 2015); forexample, FMNR of more than five million hectares in Niger has sequestered 25–30 Mtonnes of carbon over 30 years (Stevens etal., 2014).
However, several constraints hinder scaling-up efforts: inadequate attention to the socio-technical processes of innovation (Gristet al., 2017; Scoones et al., 2017), difficulties in measuring the benefits of an innovation (Coe et al., 2017), farmers’ inability todeal with long-term climate risk (Singh et al., 2017), and difficulties for matching practices with agro-ecological conditions andcomplementary modern inputs (Kassie et al., 2015). Key conditions to overcome these challenges include: developing agroforestry
value chains and markets (Reij and Winterbottom, 2015) and adaptive planning and management (Gray et al., 2016). Others include
inclusive processes giving greater voice to women and marginalized groups (MRFCJ, 2015a; UN Women and MRFCJ, 2016; Dumont
et al., 2017), strengthening community land and forest rights (Stevens et al., 2014; Vermeulen et al., 2016), and co-learning amongcommunities of practice at different scales (Coe et al., 2014; Reij and Winterbottom, 2015; Sinclair, 2016; Binam et al., 2017; Dumont
et al., 2017; Epule et al., 2017).

5.4

Mitigation and Sustainable Development

The AR5 WGIII examined the potential of various mitigation options
for specific sectors (energy supply, industry, buildings, transport, andagriculture, forestry, and other land use; AFOLU); it provided a narrativeof dimensions of sustainable development and equity as a framing forevaluating climate responses and policies, respectively, in Chapters 4,
7, 8, 9, 10 and 11 (IPCC, 2014a). This section builds on the analyses of
Chapters 2 and 4 of this report to re-assess mitigation and sustainable
development in the context of 1.5°C global warming as well as the
SDGs.

5.4.1

5

Synergies and Trade-Offs between Mitigation
Options and Sustainable Development

Adopting stringent climate mitigation options can generate multiple
positive non-climate benefits that have the potential to reduce thecosts of achieving sustainable development (IPCC, 2014b; ÜrgeVorsatz et al., 2014, 2016; Schaeffer et al., 2015; von Stechow et al.,2015). Understanding the positive impacts (synergies) but also thenegative impacts (trade-offs) is key for selecting mitigation options
and policy choices that maximize the synergies between mitigationand developmental actions (Hildingsson and Johansson, 2015; Nilsson
459

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Sustainable Development, Poverty Eradication and Reducing Inequalities

et al., 2016; Delponte et al., 2017; van Vuuren et al., 2017b; McCollum
et al., 2018b). Aligning mitigation response options to sustainable
development objectives can ensure public acceptance (IPCC, 2014a),
encourage faster action (Lechtenboehmer and Knoop, 2017) andsupport the design of equitable mitigation (Holz et al., 2018; Winkler
et al., 2018) that protect human rights (MRFCJ, 2015b) (Section 5.5.3).
This sub-section assesses available literature on the interactions ofindividual mitigation options (see Chapter 2, Section 2.3.1.2, Chapter
4, Sections 4.2 and 4.3) with sustainable development and the SDGs
and underlying targets. Table 5.2 presents an assessment of thesesynergies and trade-offs and the strength of the interaction using an
SDG-interaction score (see Glossary) (McCollum et al., 2018b), withevidence and agreements levels. Figure 5.2 presents the informationof Table 5.2, showing gross (not net) interactions with the SDGs. Thisdetailed assessment of synergies and trade-offs of individual mitigation
options with the SDGs (Table 5.2 a–d and Figure 5.2) reveals that the
number of synergies exceeds that of trade-offs. Mitigation response
options in the energy demand sector, AFOLU and oceans have morepositive interactions with a larger number of SDGs compared to thoseon the energy supply side (robust evidence, high agreement).

5.4.1.1 Energy Demand: Mitigation Options to Accelerate
Reduction in Energy Use and Fuel Switch
For mitigation options in the energy demand sectors, the numberof synergies with all sixteen SDGs exceeds the number of trade-offs(Figure 5.2 and Table 5.2) (robust evidence, high agreement). Mostof the interactions are of a reinforcing nature, hence facilitating theachievement of the goals.

5

Accelerating energy efficiency in all sectors, which is a necessary
condition for a 1.5°C warmer world (see Chapters 2 and 4), has
synergies with a large number of SDGs (robust evidence, highagreement) (Figure 5.2 and Table 5.2). The diffusion of efficientequipment and appliances across end use sectors has synergies withinternational partnership (SDG 17) and participatory and transparent
institutions (SDG 16) because innovations and deployment of newtechnologies require transnational capacity building and knowledge
sharing. Resource and energy savings support sustainable production
and consumption (SDG 12), energy access (SDG 7), innovation andinfrastructure development (SDG 9) and sustainable city development(SDG 11). Energy efficiency supports the creation of decent jobs by newservice companies providing services for energy efficiency, but the net
employment effect of efficiency improvement remains uncertain due tomacro-economic feedback (SDG 8) (McCollum et al., 2018b).
In the buildings sector, accelerating energy efficiency by way of,for example, enhancing the use of efficient appliances, refrigeranttransition, insulation, retrofitting and low- or zero-energy buildings
generates benefits across multiple SDG targets. For example,
improved cook stoves make fuel endowments last longer andhence reduce deforestation (SDG 15), support equal opportunity by
reducing school absences due to asthma among children (SDGs 3
and 4) and empower rural and indigenous women by reducing drudgery(SDG 5) (robust evidence, high agreement) (Derbez et al., 2014; Luconet al., 2014; Maidment et al., 2014; Scott et al., 2014; Cameron et al.,460

2015; Fay et al., 2015; Liddell and Guiney, 2015; Shah et al., 2015;
Sharpe et al., 2015; Wells et al., 2015; Willand et al., 2015; Hallegatteet al., 2016; Kusumaningtyas and Aldrian, 2016; Berrueta et al., 2017;
McCollum et al., 2018a).
In energy-intensive processing industries, 1.5ºC-compatible trajectories
require radical technology innovation through maximum electrification,
shift to other low emissions energy carriers such as hydrogen or
biomass, integration of carbon capture and storage (CCS) andinnovations for carbon capture and utilization (CCU) (see Chapter 4,
Section 4.3.4.5). These transformations have strong synergies with
innovation and sustainable industrialization (SDG 9), supranational
partnerships (SDGs 16 and 17) and sustainable production (SDG 12).
However, possible trade-offs due to risks of CCS-based carbon
leakage, increased electricity demands, and associated price impacts
affecting energy access and poverty (SDGs 7 and 1) would need careful
regulatory attention (Wesseling et al., 2017). In the mining industry,
energy efficiency can be synergetic or face trade-offs with sustainable
management (SDG 6), depending on the option retained for water
management (Nguyen et al., 2014). Substitution and recycling arealso an important driver of 1.5ºC-compatible trajectories in industrial
systems (see Chapter 4, Section 4.3.4.2). Structural changes andreorganization of economic activities in industrial park/clusters
following the principles of industrial symbiosis (circular economy)
improves the overall sustainability by reducing energy and waste(Fan et al., 2017; Preston and Lehne, 2017) and reinforces responsible
production and consumption (SDG 12) through recycling, water use
efficiency (SDG 6), energy access (SDG 7) and ecosystem protection
and restoration (SDG 15) (Karner et al., 2015; Zeng et al., 2017).
In the transport sector, deep electrification may trigger increases ofelectricity prices and adversely affect poor populations (SDG 1), unlesspro-poor redistributive policies are in place (Klausbruckner et al., 2016). In
cities, governments can lay the foundations for compact, connected lowcarbon cities, which are an important component of 1.5ºC-compatible
transformations (see Chapter 4, Section 4.3.3) and show synergies withsustainable cities (SDG 11) (Colenbrander et al., 2016).
Behavioural responses are important determinants of the ultimate
outcome of energy efficiency on emission reductions and energy access(SDG 7) and their management requires a detailed understandingof the drivers of consumption and the potential for and barriers toabsolute reductions (Fuchs et al., 2016). Notably, the rebound effecttends to offset the benefits of efficiency for emissions reductions
through growing demand for energy services (Sorrell, 2015; Suffolk andPoortinga, 2016). However, high rebound can help in providing faster
access to affordable energy (SDG 7.1) where the goal is to reduce energy
poverty and unmet energy demand (see Chapter 2, Section 2.4.3)(Chakravarty et al., 2013). Comprehensive policy design – including
rebound supressing policies, such as carbon pricing and policies thatencourage awareness building and promotional material design – isneeded to tap the full potential of energy savings, as applicable to a
1.5°C warming context (Chakravarty and Tavoni, 2013; IPCC, 2014b;
Karner et al., 2015; Zhang et al., 2015; Altieri et al., 2016; Santarius
et al., 2016) and to address policy-related trade-offs and welfareenhancing benefits (robust evidence, high agreement) (Chakravarty etal., 2013; Chakravarty and Roy, 2016; Gillingham et al., 2016).

Sustainable Development, Poverty Eradication and Reducing Inequalities

Other behavioural responses will affect the interplay between energy
efficiency and sustainable development. Building occupants reluctant
to change their habits may miss out on welfare-enhancing energy
efficiency opportunities (Zhao et al., 2017). Preferences for new
products and premature obsolescence for appliances is expected toadversely affect sustainable consumption and production (SDG 12) withramifications for resource use efficiency (Echegaray, 2016). Changes
in user behaviour towards increased physical activity, less reliance on
motorized travel over short distances and the use of public transport
would help to decarbonize the transport sector in a synergetic mannerwith SDGs 3, 11 and 12 (Shaw et al., 2014; Ajanovic, 2015; Chakrabarti
and Shin, 2017), while reducing inequality in access to basic facilities(SDG 10) (Lucas and Pangbourne, 2014; Kagawa et al., 2015). However,infrastructure design and regulations would need to ensure road safety
and address risks of road accidents for pedestrians (Hwang et al.,2017; Khreis et al., 2017) to ensure sustainable infrastructure growthin human settlements (SDGs 9 and 11) (Lin et al., 2015; SLoCaT, 2017).

5.4.1.2 Energy Supply: Accelerated Decarbonization
Decreasing the share of coal in energy supply in line with 1.5ºC-compatible
scenarios (see Chapter 2, Section 2.4.2) reduces adverse impacts of
upstream supply-chain activities, in particular air and water pollution andcoal mining accidents, and enhances health by reducing air pollution,
notably in cities, showing synergies with SDGs 3, 11 and 12 (Yang et al.,2016; UNEP, 2017).
Fast deployment of renewables such as solar, wind, hydro and modern
biomass, together with the decrease of fossil fuels in energy supply (seeChapter 2, Section 2.4.2.1), is aligned with the doubling of renewablesin the global energy mix (SDG 7.2). Renewables could also support
progress on SDGs 1, 10, 11 and 12 and supplement new technology(robust evidence, high agreement) (Chaturvedi and Shukla, 2014; Roseet al., 2014; Smith and Sagar, 2014; Riahi et al., 2015; IEA, 2016; vanVuuren et al., 2017a; McCollum et al., 2018a). However, some tradeoffs with the SDGs can emerge from offshore installations, particularly
SDG 14 in local contexts (McCollum et al., 2018a). Moreover, tradeoffs between renewable energy production and affordability (SDG 7)(Labordena et al., 2017) and other environmental objectives would
need to be scrutinised for potential negative social outcomes. Policy
interventions through regional cooperation-building (SDG 17) andinstitutional capacity (SDG 16) can enhance affordability (SDG 7)(Labordena et al., 2017). The deployment of small-scale renewables, or
off-grid solutions for people in remote areas (Sánchez and Izzo, 2017),
has strong potential for synergies with access to energy (SDG 7), but
the actualization of these potentials requires measures to overcome
technology and reliability risks associated with large-scale deploymentof renewables (Giwa et al., 2017; Heard et al., 2017). Bundling energyefficient appliances and lighting with off-grid renewables can leadto substantial cost reduction while increasing reliability (IEA, 2017).
Low-income populations in industrialized countries are often left out ofrenewable energy generation schemes, either because of high start-up
costs or lack of home ownership (UNRISD, 2016).

Chapter 5(e.g., for water use; SDG 6) and have mixed effects for human healthwhen replacing fossil fuels (SDGs 7 and 3) (see Table 5.2). The use offossil CCS, which plays an important role in deep mitigation pathways(see Chapter 2, Section 2.4.2.3), implies continued adverse impacts
of upstream supply-chain activities in the coal sector, and because oflower efficiency of CCS coal power plants (SDG 12), upstream impacts
and local air pollution are likely to be exacerbated (SDG 3). Furthermore,
there is a non-negligible risk of carbon dioxide leakage from geologicalstorage and the carbon dioxide transport infrastructure (SDG 3)(Table 5.2).
Economies dependent upon fossil fuel-based energy generation and/or
export revenue are expected to be disproportionally affected by future
restrictions on the use of fossil fuels under stringent climate goals andhigher carbon prices; this includes impacts on employment, stranded
assets, resources left underground, lower capacity use and early phasingout of large infrastructure already under construction (robust evidence,high agreement) (Box 5.2) (Johnson et al., 2015; McGlade and Ekins,
2015; UNEP, 2017; Spencer et al., 2018). Investment in coal continues
to be attractive in many countries as it is a mature technology andprovides cheap energy supplies, large-scale employment and energy
security (Jakob and Steckel, 2016; Vogt-Schilb and Hallegatte, 2017;
Spencer et al., 2018). Hence, accompanying policies and measureswould be required to ease job losses and correct for relatively higher
prices of alternative energy (Oosterhuis and Ten Brink, 2014; Oei and
Mendelevitch, 2016; Garg et al., 2017; HLCCP, 2017; Jordaan et al.,2017; OECD, 2017; UNEP, 2017; Blondeel and van de Graaf, 2018;
Green, 2018). Research on historical transitions shows that managing
the impacts on workers through retraining programmes is essential
in order to align the phase-down of mining industries with meeting
ambitious climate targets, and the objectives of a ‘just transition’(Galgóczi, 2014; Caldecott et al., 2017; Healy and Barry, 2017). This
aspect is even more important in developing countries where themining workforce is largely semi- or unskilled (Altieri et al., 2016; Tung,2016). Ambitious emissions reduction targets can unlock very strong
decoupling potentials in industrialized fossil exporting economies(Hatfield-Dodds et al., 2015).

5

Nuclear energy, the share of which increases in most of the
1.5ºC-compatible pathways (see Chapter 2, Section 2.4.2.1), can increase
the risks of proliferation (SDG 16), have negative environmental effects
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Box 5.2 | Challenges and Opportunities of Low-Carbon Pathways in Gulf Cooperative Council Countries
The Gulf Cooperative Council (GCC) region (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and United Arab Emirates) is characterizedby high dependency on hydrocarbon resources (natural oil and gas), with high risks of socio-economic impacts of policies andresponse measures to address climate change. The region is also vulnerable to the decrease of the global demand and price ofhydrocarbons as a result of climate change response measures. The projected declining use of oil and gas under low emissions
pathways creates risks of significant economic losses for the GCC region (e.g., Waisman et al., 2013; Van de Graaf and Verbruggen,2015; Al-Maamary et al., 2016; Bauer et al., 2016), given that natural gas and oil revenues contributed to about 70% of government
budgets and > 35% of the gross domestic product in 2010 (Callen et al., 2014).
The current high energy intensity of the domestic economies (Al-Maamary et al., 2017), triggered mainly by low domestic energy
prices (Alshehry and Belloumi, 2015), suggests specific challenges for aligning mitigation towards 1.5°C-consistent trajectories,
which would require strong energy efficiency and economic development for the region.
The region’s economies are highly reliant on fossil fuel for their domestic activities. Yet the renewables deployment potentials arelarge, deployment is already happening (Cugurullo, 2013; IRENA, 2016) and positive economic benefits can be envisaged (Sgouridis
et al., 2016). Nonetheless, the use of renewables is currently limited by economics and structural challenges (Lilliestam and Patt,
2015; Griffiths, 2017a). Carbon capture and storage (CCS) is also envisaged with concrete steps towards implementation (Alsheyab,
2017; Ustadi et al., 2017); yet the real potential of this technology in terms of scale and economic dimensions is still uncertain.
Beyond the above mitigation-related challenges, the region’s human societies and fragile ecosystems are highly vulnerable to theimpacts of climate change, such as water stress (Evans et al., 2004; Shaffrey et al., 2009), desertification (Bayram and Öztürk, 2014),
sea level rise affecting vast low coastal lands, and high temperature and humidity with future levels potentially beyond adaptive
capacities (Pal and Eltahir, 2016). A low-carbon pathway that manages climate-related risks within the context of sustainable
development requires an approach that jointly addresses both types of vulnerabilities (Al Ansari, 2013; Lilliestam and Patt, 2015;
Babiker, 2016; Griffiths, 2017b).
The Nationally Determined Contributions (NDCs) for GCC countries identified energy efficiency, deployment of renewables andtechnology transfer to enhance agriculture, food security, protection of marine resources, and management of water and costal zones(Babiker, 2016). Strategic vision documents, such as Saudi Arabia’s ‘Vision 2030’, identify emergent opportunities for energy pricereforms, energy efficiency, turning emissions into valuable products, and deployment of renewables and other clean technologies, if
accompanied with appropriate policies to manage the transition and in the context of economic diversification (Luomi, 2014; Atalay
et al., 2016; Griffiths, 2017b; Howarth et al., 2017).

55.4.1.3 Land-based agriculture, forestry and ocean: mitigation
response options and carbon dioxide removal
In the AFOLU sector, dietary change towards global healthy diets, that
is, a shift from over-consumption of animal-related to plant-related
diets, and food waste reduction (see Chapter 4, Section 4.3.2.1) arein synergy with SDGs 2 and 6, and SDG 3 through lower consumptionof animal products and reduced losses and waste throughout the foodsystem, contributing to achieving SDGs 12 and 15 (Bajželj et al., 2014;
Bustamante et al., 2014; Tilman and Clark, 2014; Hiç et al., 2016).
Power dynamics play an important role in achieving behavioural change
and sustainable consumption (Fuchs et al., 2016). In forest management(see Chapter 4, Section 4.3.2.2), encouraging responsible sourcing offorest products and securing indigenous land tenure has the potential toincrease economic benefits by creating decent jobs (SDG 8), maintaining
biodiversity (SDG 15), facilitating innovation and upgrading technology(SDG 9), and encouraging responsible and just decision-making(SDG 16) (medium evidence, high agreement) (Ding et al., 2016; WWF,2017).
462

Emerging evidence indicates that future mitigation efforts that would
be required to reach stringent climate targets, particularly those
associated with carbon dioxide removal (CDR) (e.g., afforestation andreforestation and bioenergy with carbon capture and storage; BECCS),
may also impose significant constraints upon poor and vulnerable
communities (SDG 1) via increased food prices and competition forarable land, land appropriation and dispossession (Cavanagh andBenjaminsen, 2014; Hunsberger et al., 2014; Work, 2015; Muratori etal., 2016; Smith et al., 2016; Burns and Nicholson, 2017; Corbera etal., 2017) with disproportionate negative impacts upon rural poor andindigenous populations (SDG 1) (robust evidence, high agreement)(Section 5.4.2.2, Table 5.2, Figure 5.2) (Grubert et al., 2014; Grill et al.,2015; Zhang and Chen, 2015; Fricko et al., 2016; Johansson et al., 2016;
Aha and Ayitey, 2017; De Stefano et al., 2017; Shi et al., 2017). Cropsfor bioenergy may increase irrigation needs and exacerbate water
stress with negative associated impacts on SDGs 6 and 10 (Boysen etal., 2017).
Ocean iron fertilization and enhanced weathering have two-way
interactions with life under water and on land and food security (SDGs

Sustainable Development, Poverty Eradication and Reducing Inequalities

2, 14 and 15) (Table 5.2). Development of blue carbon resources throughcoastal (mangrove) and marine (seaweed) vegetative ecosystems
encourages: integrated water resource management (SDG 6) (Vierros,2017); promotes life on land (SDG 15) (Potouroglou et al., 2017); poverty

Chapter 5

reduction (SDG 1) (Schirmer and Bull, 2014; Lamb et al., 2016); and foodsecurity (SDG 2) (Ahmed et al., 2017a, b; Duarte et al., 2017; Sondak etal., 2017; Vierros, 2017; Zhang et al., 2017).

Figure 5.2 | Synergies and trade-offs and gross Sustainable Development Goal (SDG)-interaction with individual mitigation options. The top three wheels represent synergies
and the bottom three wheels show trade-offs. The colours on the border of the wheels correspond to the SDGs listed above, starting at the 9 o’clock position, with reading
guidance in the top-left corner with the quarter circle (Note 1). Mitigation (climate action, SDG 13) is at the centre of the circle. The coloured segments inside the circles can becounted to arrive at the number of synergies (green) and trade-offs (red). The length of the coloured segments shows the strength of the synergies or trade-offs (Note 3) andthe shading indicates confidence (Note 2). Various mitigation options within the energy demand sector, energy supply sector, and land and ocean sector, and how to read themwithin a segment are shown in grey (Note 4). See also Table 5.2.

5.4.2

Sustainable Development Implications of
1.5°C and 2°C Mitigation Pathways

While previous sections have focused on individual mitigation options
and their interaction with sustainable development and the SDGs,
this section takes a systems perspective. Emphasis is on quantitative
pathways depicting path-dependent evolutions of human andnatural systems over time. Specifically, the focus is on fundamental
transformations and thus stringent mitigation policies consistent with
1.5°C or 2°C, and the differential synergies and trade-offs with respect
to the various sustainable development dimensions.

Both 1.5°C and 2°C pathways would require deep cuts in greenhousegas (GHG) emissions and large-scale changes of energy supply anddemand, as well as in agriculture and forestry systems (see Chapter
2, Section 2.4). For the assessment of the sustainable development
implications of these pathways, this chapter draws upon studies thatshow the aggregated impact of mitigation for multiple sustainable
development dimensions (Grubler et al., 2018; McCollum et al.,2018b; Rogelj et al., 2018) and across multiple integrated assessment
modelling (IAM) frameworks. Often these tools are linked to
disciplinary models covering specific SDGs in more detail (Cameron
et al., 2016; Rao et al., 2017; Grubler et al., 2018; McCollum et al.,463

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2018b). Using multiple IAMs and disciplinary models is important
for a robust assessment of the sustainable development implicationsof different pathways. Emphasis is on multi-regional studies, which
can be aggregated to the global scale. The recent literature on 1.5°C
mitigation pathways has begun to provide quantifications for a rangeof sustainable development dimensions, including air pollution andhealth, food security and hunger, energy access, water security, andmultidimensional poverty and equity.

5.4.2.1 Air pollution and health
GHGs and air pollutants are typically emitted by the same sources.
Hence, mitigation strategies that reduce GHGs or the use of fossil fuelstypically also reduce emissions of pollutants, such as particulate matter(e.g., PM2.5 and PM10), black carbon (BC), sulphur dioxide (SO2),
nitrogen oxides (NOx) and other harmful species (Clarke et al., 2014)(Figure 5.3), causing adverse health and ecosystem effects at various
scales (Kusumaningtyas and Aldrian, 2016).
Mitigation pathways typically show that there are significant synergies
for air pollution, and that the synergies increase with the stringency ofthe mitigation policies (Amann et al., 2011; Rao et al., 2016; Klimont
et al., 2017; Shindell et al., 2017; Markandya et al., 2018). Recent
multimodel comparisons indicate that mitigation pathways consistent
with 1.5°C would result in higher synergies with air pollution comparedto pathways that are consistent with 2°C (Figures 5.4 and 5.5). Shindell
et al. (2018) indicate that health benefits worldwide over the centuryof 1.5°C pathways could be in the range of 110 to 190 million fewerpremature deaths compared to 2°C pathways. The synergies for air
pollution are highest in the developing world, particularly in Asia. In
addition to significant health benefits, there are also economic benefits
from mitigation, reducing the investment needs in air pollution control
technologies by about 35% globally (or about 100 billion USD2010 per
year to 2030 in 1.5°C pathways; McCollum et al., 2018b) (Figure 5.4).

5.4.2.2 Food security and hunger

5

Stringent climate mitigation pathways in line with ‘well below 2°C’ or‘1.5°C’ goals often rely on the deployment of large-scale land-related
measures, like afforestation and/or bioenergy supply (Popp et al., 2014;
Rose et al., 2014; Creutzig et al., 2015). These land-related measures
can compete with food production and hence raise food security
concerns (Section 5.4.1.3) (P. Smith et al., 2014). Mitigation studies
indicate that so-called ‘single-minded’ climate policy, aiming solely
at limiting warming to 1.5°C or 2°C without concurrent measures in
the food sector, can have negative impacts for global food security(Hasegawa et al., 2015; McCollum et al., 2018b). Impacts of 1.5°C
mitigation pathways can be significantly higher than those of 2°C
pathways (Figures 5.4 and 5.5). An important driver of the food security
impacts in these scenarios is the increase of food prices and the effectof mitigation on disposable income and wealth due to GHG pricing. A
recent study indicates that, on aggregate, the price and income effects
on food may be bigger than the effect due to competition over land
between food and bioenergy (Hasegawa et al., 2015).
In order to address the issue of trade-offs with food security, mitigation
policies would need to be designed in a way that shields the population464

at risk of hunger, including through the adoption of different
complementary measures, such as food price support. The investment
needs of complementary food price policies are found to be globally
relatively much smaller than the associated mitigation investmentsof 1.5°C pathways (Figure 5.3) (McCollum et al., 2018b). Besides
food support price, other measures include improving productivity
and efficiency of agricultural production systems (FAO and NZAGRC,
2017a, b; Frank et al., 2017) and programmes focusing on forest landuse change (Havlík et al., 2014). All these lead to additional benefits ofmitigation, improving resilience and livelihoods.
Van Vuuren et al. (2018) and Grubler et al. (2018) show that 1.5°C
pathways without reliance on BECCS can be achieved through afundamental transformation of the service sectors which would
significantly reduce energy and food demand (see Chapter 2, Sections
2.1.1, 2.3.1 and 2.4.3). Such low energy demand (LED) pathways
would result in significantly reduced pressure on food security, lower
food prices and fewer people at risk of hunger. Importantly, the tradeoffs with food security would be reduced by the avoided impacts in the
agricultural sector due to the reduced warming associated with the
1.5°C pathways (see Chapter 3, Section 3.5). However, such feedbacks
are not comprehensively captured in the studies on mitigation.

5.4.2.3 Lack of energy access/energy poverty
A lack of access to clean and affordable energy (especially for cooking)is a major policy concern in many countries, especially in those in South
Asia and Africa where major parts of the population still rely primarily
on solid fuels for cooking (IEA and World Bank, 2017). Scenario studies
which quantify the interactions between climate mitigation and energy
access indicate that stringent climate policy which would affect energy
prices could significantly slow down the transition to clean cooking
fuels, such as liquefied petroleum gas or electricity (Cameron et al.,2016).
Estimates across six different IAMs (McCollum et al., 2018b) indicate
that, in the absence of compensatory measures, the number of people
without access to clean cooking fuels may increase. Redistributional
measures, such as subsidies on cleaner fuels and stoves, could
compensate for the negative effects of mitigation on energy access.
Investment costs of the redistributional measures in 1.5°C pathways(on average around 120 billion USD2010 per year to 2030; Figure 5.4)
are much smaller than the mitigation investments of 1.5°C pathways(McCollum et al., 2018b). The recycling of revenues from climate policy
might act as a means to help finance the costs of providing energy
access to the poor (Cameron et al., 2016).

5.4.2.4 Water security
Transformations towards low emissions energy and agricultural
systems can have major implications for freshwater demand as well aswater pollution. The scaling up of renewables and energy efficiency as
depicted by low emissions pathways would, in most instances, lower
water demands for thermal energy supply facilities (‘water-for-energy’)
compared to fossil energy technologies, and thus reinforce targets
related to water access and scarcity (see Chapter 4, Section 4.2.1).
However, some low-carbon options such as bioenergy, centralized solar

Sustainable Development, Poverty Eradication and Reducing Inequalities

Chapter 55Figure 5.3 | Sustainable development implications of mitigation actions in 1.5°C pathways. Panel (a) shows ranges for 1.5°C pathways for selected sustainable development
dimensions compared to the ranges of 2°C pathways and baseline pathways. The panel (a) depicts interquartile and the full range across the scenarios for Sustainable
Development Goal (SDG) 2 (hunger), SDG 3 (health), SDG 6 (water), SDG 7 (energy), SDG 12 (resources), SDG 13/14 (climate/ocean) and SDG 15 (land). Progress towards
achieving the SDGs is denoted by arrow symbols (increase or decrease of indicator). Black horizontal lines show 2015 values for comparison. Note that sustainable development
effects are estimated for the effect of mitigation and do not include benefits from avoided impacts (see Chapter 3, Section 3.5). Low energy demand (LED) denotes estimatesfrom a pathway with extremely low energy demand reaching 1.5°C without bioenergy with carbon capture and storage (BECCS). Panel (b) presents the resulting full range
for synergies and trade-offs of 1.5°C pathways compared to the corresponding baseline scenarios. The y-axis in panel (b) indicates the factor change in the 1.5°C pathway
compared to the baseline. Note that the figure shows gross impacts of mitigation and does not include feedbacks due to avoided impacts. The realization of the side effects
will critically depend on local circumstances and implementation practice. Trade-offs across many sustainable development dimensions can be reduced through complementary/
re-distributional measures. The figure is not comprehensive and focuses on those sustainable development dimensions for which quantifications across models are available.
Sources: 1.5°C pathways database from Chapter 2 (Grubler et al., 2018; McCollum et al., 2018b).

465

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Sustainable Development, Poverty Eradication and Reducing Inequalities

power, nuclear and hydropower technologies could, if not managed
properly, have counteracting effects that compound existing waterrelated problems in a given locale (Byers et al., 2014; Fricko et al., 2016;
IEA, 2016; Fujimori et al., 2017a; Wang, 2017; McCollum et al., 2018a).
Under stringent mitigation efforts, the demand for bioenergy can
result in a substantial increase of water demand for irrigation, thereby
potentially contributing to water scarcity in water-stressed regions(Berger et al., 2015; Bonsch et al., 2016; Jägermeyr et al., 2017).
However, this risk can be reduced by prioritizing rain-fed production of
bioenergy (Hayashi et al., 2015, 2018; Bonsch et al., 2016), but might
have adverse effects for food security (Boysen et al., 2017).
Reducing food and energy demand without compromising the needsof the poor emerges as a robust strategy for both water conservation
and GHG emissions reductions (von Stechow et al., 2015; IEA, 2016;
Parkinson et al., 2016; Grubler et al., 2018). The results underscore theimportance of an integrated approach when developing water, energy
and climate policy (IEA, 2016).
Estimates across different models for the impacts of stringent
mitigation pathways on energy-related water uses seem ambiguous.
Some pathways show synergies (Mouratiadou et al., 2018) whileothers indicate trade-offs and thus increases of water use due tomitigation (Fricko et al., 2016). The synergies depend on the adopted
policy implementation or mitigation strategies and technology
portfolio. A number of adaptation options exist (e.g., dry cooling),
which can effectively reduce electricity-related water trade-offs (Fricko
et al., 2016; IEA, 2016). Similarly, irrigation water use will depend on
the regions where crops are produced, the sources of bioenergy (e.g.,
agriculture vs. forestry) and dietary change induced by climate policy.
Overall, and also considering other water-related SDGs, including
access to safe drinking water and sanitation as well as waste-water
treatment, investments into the water sector seem to be only modestly
affected by stringent climate policy compatible with 1.5°C (Figure 5.4)
(McCollum et al., 2018b).

5

In summary, the assessment of mitigation pathways shows that tomeet the 1.5°C target, a wide range of mitigation options would need
to be deployed (see Chapter 2, Sections 2.3 and 2.4). While pathways
aiming at 1.5°C are associated with high synergies for some sustainable
development dimensions (such as human health and air pollution, forest
preservation), the rapid pace and magnitude of the required changes
would also lead to increased risks for trade-offs for other sustainable
development dimensions (particularly food security) (Figures 5.4 and5.5). Synergies and trade-offs are expected to be unevenly distributed
between regions and nations (Box 5.2), though little literature has
formally examined such distributions under 1.5°C-consistent mitigation
scenarios. Reducing these risks requires smart policy designs andmechanisms that shield the poor and redistribute the burden so that themost vulnerable are not disproportionately affected. Recent scenario
analyses show that associated investments for reducing the trade-offs
for, for example, food, water and energy access to be significantly lower
than the required mitigation investments (McCollum et al., 2018b).
Fundamental transformation of demand, including efficiency andbehavioural changes, can help to significantly reduce the reliance onrisky technologies, such as BECCS, and thus reduce the risk of potential
466

Figure 5.4 | Investment into mitigation up until 2030 and implications forinvestments for four sustainable development dimensions. Cross-hatched bars show
the median investment in 1.5°C pathways across results from different models, andsolid bars for 2°C pathways, respectively. Whiskers on bars represent minima andmaxima across estimates from six models. Clean water and air pollution investments
are available only from one model. Mitigation investments show the change ininvestments across mitigation options compared to the baseline. Negative mitigation
investments (grey bars) denote disinvestment (reduced investment needs) into
fossil fuel sectors compared to the baseline. Investments for different sustainable
development dimensions denote the investment needs for complementary measuresin order to avoid trade-offs (negative impacts) of mitigation. Negative sustainable
development investments for air pollution indicate cost savings, and thus synergiesof mitigation for air pollution control costs. The values compare to about 2 trillion
USD2010 (range of 1.4 to 3 trillion) of total energy-related investments in the 1.5°C
pathways. Source: Estimates from CD-LINKS scenarios summarised by McCollum etal., 2018b.

trade-offs between mitigation and other sustainable development
dimensions (von Stechow et al., 2015; Grubler et al., 2018; van Vuuren
et al., 2018). Reliance on demand-side measures only, however, would
not be sufficient for meeting stringent targets, such as 1.5°C and 2°C(Clarke et al., 2014).

5.5

Sustainable Development
Pathways to 1.5°C

This section assesses what is known in the literature on development
pathways that are sustainable and climate-resilient and relevant toa 1.5°C warmer world. Pathways, transitions from today’s world toachieving a set of future goals (see Chapter 1, Section 1.2.3, CrossChapter Box 1), follow broadly two main traditions: first, as integrated
pathways describing the required societal and systems transformations,combining quantitative modelling and qualitative narratives at multiple
spatial scales (global to sub-national); and second, as country- andcommunity-level, solution-oriented trajectories and decision-making
processes about context- and place-specific opportunities, challenges
and trade-offs. These two notions of pathways offer different, thoughcomplementary, insights into the nature of 1.5°C-relevant trajectories
and the short-term actions that enable long-term goals. Both highlightto varying degrees the urgency, ethics and equity dimensions ofpossible trajectories and society- and system-wide transformations, yetat different scales, building on Chapter 2 (see Section 2.4) and Chapter
4 (see Section 4.5).

Sustainable Development, Poverty Eradication and Reducing Inequalities

5.5.1

Integration of Adaptation, Mitigationand Sustainable Development

Insights into climate-compatible development (see Glossary)illustrate how integration between adaptation, mitigation andsustainable development works in context-specific projects, howsynergies are achieved and what challenges are encountered during
implementation (Stringer et al., 2014; Suckall et al., 2014; Antwi-Agyeiet al., 2017a; Bickersteth et al., 2017; Kalafatis, 2017; Nunan, 2017).
The operationalization of climate-compatible development, including
climate-smart agriculture and carbon-forestry projects (Lipper et al.,2014; Campbell et al., 2016; Quan et al., 2017), shows multileveland multisector trade-offs involving ‘winners’ and ‘losers’ acrossgovernance levels (high confidence) (Kongsager and Corbera, 2015;
Naess et al., 2015; Karlsson et al., 2017; Tanner et al., 2017; Taylor,2017; Wood, 2017; Ficklin et al., 2018). Issues of power, participation,
values, equity, inequality and justice transcend case study examples ofattempted integrated approaches (Nunan, 2017; Phillips et al., 2017;
Stringer et al., 2017; Wood, 2017), also reflected in policy frameworks
for integrated outcomes (Stringer et al., 2014; Di Gregorio et al., 2017;
Few et al., 2017; Tanner et al., 2017).
Ultimately, reconciling trade-offs between development needs andemissions reductions towards a 1.5°C warmer world requires a
dynamic view of the interlinkages between adaptation, mitigationand sustainable development (Nunan, 2017). This entails recognitionof the ways in which development contexts shape the choice andeffectiveness of interventions, limit the range of responses afforded
to communities and governments, and potentially impose injustices
upon vulnerable groups (UNRISD, 2016; Thornton and Comberti, 2017).
A variety of approaches, both quantitative and qualitative, exist to
examine possible sustainable development pathways under whichclimate and sustainable development goals can be achieved, andsynergies and trade-offs for transformation identified (Sections 5.3
and 5.4).

5.5.2

Pathways for Adaptation, Mitigationand Sustainable Development

This section focuses on the growing body of pathways literaturedescribing the dynamic and systemic integration of mitigation
and adaptation with sustainable development in the context of a
1.5°C warmer world. These studies are critically important for theidentification of ‘enabling’ conditions under which climate and theSDGs can be achieved, and thus help the design of transformation
strategies that maximize synergies and avoid potential trade-offs(Sections 5.3 and 5.4). Full integration of sustainable development
dimensions is, however, challenging, given their diversity and the needfor high temporal, spatial and social resolution to address local effects,
including heterogeneity related to poverty and equity (von Stechow
et al., 2015). Research on long-term climate change mitigation andadaptation pathways has covered individual SDGs to different degrees.
Interactions between climate and other SDGs have been explored for
SDGs 2, 3, 4, 6, 7, 8, 12, 14 and 15 (Clarke et al., 2014; Abel et al., 2016;von Stechow et al., 2016; Rao et al., 2017), while interactions with
SDGs 1, 5, 11 and 16 remain largely underexplored in integrated longterm scenarios (Zimm et al., 2018).

Chapter 5

Quantitative pathways studies now better represent ‘nexus’
approaches to assess sustainable development dimensions. In suchapproaches (see Chapter 4, Section 4.3.3.8), a subset of sustainable
development dimensions are investigated together because of theirclose relationships (Welsch et al., 2014; Conway et al., 2015; Keairns
et al., 2016; Parkinson et al., 2016; Rasul and Sharma, 2016; Howarthand Monasterolo, 2017). Compared to single-objective climate–SDG
assessments (Section 5.4.2), nexus solutions attempt to integratecomplex interdependencies across diverse sectors in a systems
approach for consistent analysis. Recent pathways studies show how
water, energy and climate (SDGs 6, 7 and 13) interact (Parkinson et al.,2016; McCollum et al., 2018b) and call for integrated water–energy
investment decisions to manage systemic risks. For instance, theprovision of bioenergy, important in many 1.5°C-consistent pathways,
can help resolve ‘nexus challenges’ by alleviating energy security
concerns, but can also have adverse ‘nexus impacts’ on food security,
water use and biodiversity (Lotze-Campen et al., 2014; Bonsch et al.,2016). Policies that improve resource use efficiency across sectors canmaximize synergies for sustainable development (Bartos and Chester,
2014; McCollum et al., 2018b; van Vuuren et al., 2018). Mitigationcompatible with 1.5°C can significantly reduce impacts and adaptationneeds in the nexus sectors compared to 2°C (Byers et al., 2018). In
order to avoid trade-offs due to high carbon pricing of 1.5°C pathways,
regulation in specific areas may complement price-based instruments.
Such combined policies generally lead also to more early action
maximizing synergies and avoiding some of the adverse climate effects
for sustainable development (Bertram et al., 2018).
The comprehensive analysis of climate change in the context ofsustainable development requires suitable reference scenarios that
lend themselves to broader sustainable development analyses.
The Shared Socio-Economic Pathways (SSPs) (Chapter 1, CrossChapter Box 1 in Chapter 1) (O’Neill et al., 2017a; Riahi et al., 2017)
constitute an important first step in providing a framework forthe integrated assessment of adaptation and mitigation and theirclimate–development linkages (Ebi et al., 2014). The five underlying
SSP narratives (O’Neill et al., 2017a) map well into some of the key
SDG dimensions, with one of the pathways (SSP1) explicitly depicting
sustainability as the main theme (van Vuuren et al., 2017b).
To date, no pathway in the literature proves to achieve all 17 SDGs
because several targets are not met or not sufficiently covered in theanalysis, hence resulting in a sustainability gap (Zimm et al., 2018).
The SSPs facilitate the systematic exploration of different sustainabledimensions under ambitious climate objectives. SSP1 proves to be inline with eight SDGs (3, 7, 8, 9, 10, 11, 13 and 15) and several of theirtargets in a 2°C warmer world (van Vuuren et al., 2017b; Zimm et al.,2018). However, important targets for SDGs 1, 2 and 4 (i.e., people
living in extreme poverty, people living at the risk of hunger and gender
gap in years of schooling) are not met in this scenario.
The SSPs show that sustainable socio-economic conditions will play a
key role in reaching stringent climate targets (Riahi et al., 2017; Rogelj
et al., 2018). Recent modelling work has examined 1.5°C-consistent,
stringent mitigation scenarios for 2100 applied to the SSPs, using
six different IAMs. Despite the limitations of these models, which
are coarse approximations of reality, robust trends can be identified
467

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Chapter 5

Sustainable Development, Poverty Eradication and Reducing Inequalities(Rogelj et al., 2018). SSP1 – which depicts broader ‘sustainability’ aswell as enhancing equity and poverty reductions – is the only pathway
where all models could reach 1.5°C and is associated with the lowest
mitigation costs across all SSPs. A decreasing number of models was
successful for SSP2, SSP4 and SSP5, respectively, indicating distinctly
higher risks of failure due to high growth and energy intensity as
well as geographical and social inequalities and uneven regional
development. And reaching 1.5°C has even been found infeasible in
the less sustainable SSP3 – ‘regional rivalry’ (Fujimori et al., 2017b;
Riahi et al., 2017). All these conclusions hold true if a 2°C objective isconsidered (Calvin et al., 2017; Fujimori et al., 2017b; Popp et al., 2017;
Riahi et al., 2017). Rogelj et al. (2018) also show that fewer scenarios
are, however, feasible across different SSPs in case of 1.5°C, andmitigation costs substantially increase in 1.5°C pathways comparedto 2°C pathways.

5

There is a wide range of SSP-based studies focusing on the connections
between adaptation/impacts and different sustainable development
dimensions (Hasegawa et al., 2014; Ishida et al., 2014; Arnell et al.,2015; Bowyer et al., 2015; Burke et al., 2015; Lemoine and Kapnick,
2016; Rozenberg and Hallegatte, 2016; Blanco et al., 2017; Hallegatteand Rozenberg, 2017; O’Neill et al., 2017a; Rutledge et al., 2017;
Byers et al., 2018). New methods for projecting inequality and poverty(downscaled to sub-national rural and urban levels as well as spatially
explicit levels) have enabled advanced SSP-based assessments oflocally sustainable development implications of avoided impacts
and related adaptation needs. For instance, Byers et al. (2018) findthat, in a 1.5°C warmer world, a focus on sustainable development
can reduce the climate risk exposure of populations vulnerable topoverty by more than an order of magnitude (Section 5.2.2). Moreover,aggressive reductions in between-country inequality may decrease
the emissions intensity of global economic growth (Rao and Min,2018). This is due to the higher potential for decoupling of energy
from income growth in lower-income countries, due to high potential
for technological advancements that reduce the energy intensity ofgrowth of poor countries – critical also for reaching 1.5°C in a sociallyand economically equitable way. Participatory downscaling of SSPs inseveral European Union countries and in Central Asia shows numerous
possible pathways of solutions to the 2°C–1.5°C goal, depending on
differential visions (Tàbara et al., 2018). Other participatory applicationsof the SSPs, for example in West Africa (Palazzo et al., 2017) and thesoutheastern United States (Absar and Preston, 2015), illustrate thepotentially large differences in adaptive capacity within regions andbetween sectors.
Harnessing the full potential of the SSP framework to inform sustainable
development requires: (i) further elaboration and extension of thecurrent SSPs to cover sustainable development objectives explicitly; (ii)the development of new or variants of current narratives that would
facilitate more SDG-focused analyses with climate as one objective(among other SDGs) (Riahi et al., 2017); (iii) scenarios with high regional
resolution (Fujimori et al., 2017b); (iv) a more explicit representationof institutional and governance change associated with the SSPs
(Zimm et al., 2018); and (v) a scale-up of localized and spatially explicit
vulnerability, poverty and inequality estimates, which have emergedin recent publications based on the SSPs (Byers et al., 2018) and areessential to investigate equity dimensions (Klinsky and Winkler, 2018).
468

5.5.3

Climate-Resilient Development Pathways

This section assesses the literature on pathways as solutionoriented trajectories and decision-making processes for attaining
transformative visions for a 1.5°C warmer world. It builds on climateresilient development pathways (CRDPs) introduced in the AR5(Section 5.1.2) (Olsson et al., 2014) as well as growing literature(e.g., Eriksen et al., 2017; Johnson, 2017; Orindi et al., 2017; Kirby andO’Mahony, 2018; Solecki et al., 2018) that uses CRDPs as a conceptualand aspirational idea for steering societies towards low-carbon,
prosperous and ecologically safe futures. Such a notion of pathways
foregrounds decision-making processes at local to national levels tosituate transformation, resilience, equity and well-being in the complex
reality of specific places, nations and communities (Harris et al., 2017;
Ziervogel et al., 2017; Fazey et al., 2018; Gajjar et al., 2018; Klinsky and
Winkler, 2018; Patterson et al., 2018; Tàbara et al., 2018).
Pathways compatible with 1.5°C warming are not merely scenarios
to envision possible futures but processes of deliberation andimplementation that address societal values, local priorities andinevitable trade-offs. This includes attention to politics and power thatperpetuate business-as-usual trajectories (O’Brien, 2016; Harris et al.,2017), the politics that shape sustainability and capabilities of everyday
life (Agyeman et al., 2016; Schlosberg et al., 2017), and ingredientsfor community resilience and transformative change (Fazey et al.,2018). Chartering CRDPs encourages locally situated and problemsolving processes to negotiate and operationalize resilience ‘on the
ground’ (Beilin and Wilkinson, 2015; Harris et al., 2017; Ziervogel etal., 2017). This entails contestation, inclusive governance and iterative
engagement of diverse populations with varied needs, aspirations,
agency and rights claims, including those most affected, to deliberate
trade-offs in a multiplicity of possible pathways (high confidence) (seeFigure 5.5) (Stirling, 2014; Vale, 2014; Walsh-Dilley and Wolford, 2015;
Biermann et al., 2016; J.R.A. Butler et al., 2016; O’Brien, 2016, 2018;
Harris et al., 2017; Jones and Tanner, 2017; Mapfumo et al., 2017;
Rosenbloom, 2017; Gajjar et al., 2018; Klinsky and Winkler, 2018; Lyon,
2018; Tàbara et al., 2018).

5.5.3.1 Transformations, equity and well-being
Most literature related to CRDPs invokes the concept of transformation,
underscoring the need for urgent and far-reaching changes in practices,institutions and social relations in society. Transformations towards a
1.5°C warmer world would need to address considerations for equity
and well-being, including in trade-off decisions (see Figure 5.1).
To attain the anticipated transformations, all countries as well as nonstate actors would need to strengthen their contributions, throughbolder and more committed cooperation and equitable effort-sharing(medium evidence, high agreement) (Rao, 2014; Frumhoff et al., 2015;
Ekwurzel et al., 2017; Millar et al., 2017; Shue, 2017; Holz et al., 2018;
Robinson and Shine, 2018). Sustaining decarbonization rates at a
1.5°C-compatible level would be unprecedented and not possible
without rapid transformations to a net-zero-emissions global economy
by mid-century or the later half of the century (see Chapters 2 and
4). Such efforts would entail overcoming technical, infrastructural,institutional and behavioural barriers across all sectors and levels

Sustainable Development, Poverty Eradication and Reducing Inequalities

Chapter 5

Figure 5.5 | Pathways into the future, with path dependencies and iterative problem-solving and decision-making (after Fazey et al., 2016).

of society (Pfeiffer et al., 2016; Seto et al., 2016) and defeating pathdependencies, including poverty traps (Boonstra et al., 2016; Enqvist
et al., 2016; Lade et al., 2017; Haider et al., 2018). Transformation alsoentails ensuring that 1.5°C-compatible pathways are inclusive anddesirable, build solidarity and alliances, and protect vulnerable groups,
including against disruptions of transformation (Patterson et al., 2018).
There is growing emphasis on the role of equity, fairness and justice (seeGlossary) regarding context-specific transformations and pathways
to a 1.5°C warmer world (medium evidence, high agreement) (Shue,
2014; Thorp, 2014; Dennig et al., 2015; Moellendorf, 2015; Klinsky etal., 2017b; Roser and Seidel, 2017; Sealey-Huggins, 2017; Klinsky and
Winkler, 2018; Robinson and Shine, 2018). Consideration for what is
equitable and fair suggests the need for stringent decarbonization
and up-scaled adaptation that do not exacerbate social injustices,
locally and at national levels (Okereke and Coventry, 2016), upholdhuman rights (Robinson and Shine, 2018), are socially desirable andacceptable (von Stechow et al., 2016; Rosenbloom, 2017), address
values and beliefs (O’Brien, 2018), and overcome vested interests(Normann, 2015; Patterson et al., 2016). Attention is often drawn tohuge disparities in the cost, benefits, opportunities and challenges
involved in transformation within and between countries, and thefact that the suffering of already poor, vulnerable and disadvantaged
populations may be worsened, if care to protect them is not taken(Holden et al., 2017; Klinsky and Winkler, 2018; Patterson et al., 2018).
Well-being for all (Dearing et al., 2014; Raworth, 2017) is at thecore of an ecologically safe and socially just space for humanity,
including health and housing, peace and justice, social equity, genderequality and political voices (Raworth, 2017). It is in alignment withtransformative social development (UNRISD, 2016) and the 2030
Agenda of ‘leaving no one behind’. The social conditions to enable wellbeing for all are to reduce entrenched inequalities within and between
countries (Klinsky and Winkler, 2018); rethink prevailing values, ethics
and behaviours (Holden et al., 2017); allow people to live a life indignity while avoiding actions that undermine capabilities (Klinsky
and Golub, 2016); transform economies (Popescu and Ciurlau, 2016;
Tàbara et al., 2018); overcome uneven consumption and production
patterns (Dearing et al., 2014; Häyhä et al., 2016; Raworth, 2017) andconceptualize development as well-being rather than mere economic
growth (medium evidence, high agreement) (Gupta and Pouw, 2017).

5.5.3.2 Development trajectories, sharingof efforts and cooperation
The potential for pursuing sustainable and climate-resilient development
pathways towards a 1.5°C warmer world differs between and within
nations, due to differential development achievements and trajectories,
and opportunities and challenges (very high confidence) (Figure 5.1).
There are clear differences between high-income countries where
social achievements are high, albeit often with negative effects on
the environment, and most developing nations where vulnerabilities
to climate change are high and social support and life satisfaction
are low, especially in the Least Developed Countries (LDCs) (Sachs etal., 2017; O’Neill et al., 2018). Differential starting points for CRDPs
between and within countries, including path dependencies (Figure5.5), call for sensitivity to context (Klinsky and Winkler, 2018). For thedeveloping world, limiting warming to 1.5°C also means potentially469

5Chapter 5

Sustainable Development, Poverty Eradication and Reducing Inequalities

severely curtailed development prospects (Okereke and Coventry,
2016) and risks to human rights from both climate action and inaction
to achieve this goal (Robinson and Shine, 2018) (Section 5.2). Withincountry development differences remain, despite efforts to ensure
inclusive societies (Gupta and Arts, 2017; Gupta and Pouw, 2017). Coleet al. (2017), for instance, show how differences between provinces inSouth Africa constitute barriers to sustainable development trajectories
and for operationalising nation-level SDGs, across various dimensionsof social deprivation and environmental stress, reflecting historicdisadvantages.
Moreover, various equity and effort- or burden-sharing approaches toclimate stabilization in the literature describe how to sketch national
potentials for a 1.5°C warmer world (e.g., Anand, 2004; CSO Equity
Review, 2015; Meinshausen et al., 2015; Okereke and Coventry, 2016;
Bexell and Jönsson, 2017; Otto et al., 2017; Pan et al., 2017; Robiou duPont et al., 2017; Holz et al., 2018; Kartha et al., 2018; Winkler et al.,2018;). Many approaches build on the AR5 ‘responsibility – capacity –need’ assessment (Clarke et al., 2014), complement other proposed
national-level metrics for capabilities, equity and fairness (Heyward
and Roser, 2016; Klinsky et al., 2017a), or fall under the wider umbrellaof fair share debates on responsibility, capability and the right todevelopment in climate policy (Fuglestvedt and Kallbekken, 2016).
Importantly, different principles and methodologies generate different
calculated contributions, responsibilities and capacities (Skeie et al.,2017).

5

The notion of nation-level fair shares is now also discussed in thecontext of limiting global warming to 1.5°C and the Nationally
Determined Contributions (NDCs) (see Chapter 4, Cross-Chapter Box
11 in Chapter 4) (CSO Equity Review, 2015; Mace, 2016; Pan et al.,2017; Robiou du Pont et al., 2017; Holz et al., 2018; Kartha et al., 2018;
Winkler et al., 2018). A study by Pan et al. (2017) concluded that all
countries would need to contribute to ambitious emissions reductions
and that current pledges for 2030 by seven out of eight high-emitting
countries would be insufficient to meet 1.5°C. Emerging literature on
justice-centred pathways to 1.5°C points towards ambitious emissions
reductions domestically and committed cooperation internationallywhereby wealthier countries support poorer ones, technologically,financially and otherwise to enhance capacities (Okereke and Coventry,
2016; Holz et al., 2018; Robinson and Shine, 2018; Shue, 2018). Thesefindings suggest that equitable and 1.5°C-compatible pathways would
require fast action across all countries at all levels of development
rather than late accession of developing countries (as assumed under
SSP3, see Chapter 2), with external support for prompt mitigation andresilience-building efforts in the latter (medium evidence, medium
agreement).
Scientific advances since the AR5 now also make it possible to determine
contributions to climate change for non-state actors (see Chapter 4,
Section 4.4.1) and their potential to contribute to CRDPs (medium
evidence, medium agreement). These non-state actors includes cities(Bulkeley et al., 2013, 2014; Byrne et al., 2016), businesses (Heede,
2014; Frumhoff et al., 2015; Shue, 2017), transnational initiatives(Castro, 2016; Andonova et al., 2017) and industries. Recent work
demonstrates the contributions of 90 industrial carbon producers to
global temperature and sea level rise, and their responsibilities to470

contribute to investments in and support for mitigation and adaptation(Heede, 2014; Ekwurzel et al., 2017; Shue, 2017) (Sections 5.6.1 and5.6.2).
At the level of groups and individuals, equity in pursuing climate
resilience for a 1.5°C warmer world means addressing disadvantage,
inequities and empowerment that shape transformative processes
and pathways (Fazey et al., 2018), and deliberate efforts to strengthen
the capabilities, capacities and well-being of poor, marginalized andvulnerable people (Byrnes, 2014; Tokar, 2014; Harris et al., 2017;
Klinsky et al., 2017a; Klinsky and Winkler, 2018). Community-driven
CRDPs can flag potential negative impacts of national trajectories on
disadvantaged groups, such as low-income families and communities
of colour (Rao, 2014). They emphasize social equity, participatory
governance, social inclusion and human rights, as well as innovation,
experimentation and social learning (see Glossary) (medium evidence,high agreement) (Sections 5.5.3.3 and 5.6).

5.5.3.3 Country and community strategies and experiences
There are many possible pathways towards climate-resilient futures(O’Brien, 2018; Tàbara et al., 2018). Literature depicting different
sustainable development trajectories in line with CRDPs is growing, withsome of it being specific to 1.5°C global warming. Most experiences
to date are at local and sub-national levels (Cross-Chapter Box 13 inthis chapter), while state-level efforts align largely with green economy
trajectories or planning for climate resilience (Box 5.3). Due to the factthat these strategies are context-specific, the literature is scarce oncomparisons, efforts to scale up and systematic monitoring.
States can play an enabling or hindering role in a transition to a 1.5°C
warmer world (Patterson et al., 2018). The literature on strategies toreconcile low-carbon trajectories with sustainable development andecological sustainability through green growth, inclusive growth,de-growth, post-growth and development as well-being shows low
agreement (see Chapter 4, Section 4.5). Efforts that align best with
CRDPs are described as ‘transformational’ and ‘strong’ (Ferguson,
2015). Some view ‘thick green’ perspectives as enabling equity,
democracy and agency building (Lorek and Spangenberg, 2014; Stirling,
2014; Ehresman and Okereke, 2015; Buch-Hansen, 2018), others show
how green economy and sustainable development pathways can align(Brown et al., 2014; Georgeson et al., 2017b), and how a green economy
can help link the SDGs with NDCs, for instance in Mongolia, Kenya andSweden (Shine, 2017). Others still critique the continuous reliance onmarket mechanisms (Wanner, 2014; Brockington and Ponte, 2015) anddisregard for equity and distributional and procedural justice (Stirling,
2014; Bell, 2015).
Country-level pathways and achievements vary significantly (robust
evidence, medium agreement). For instance, the Scandinavian countries
rank at the top of the Global Green Economy Index (Dual Citizen LLC,
2016), although they also tend to show high spill-over effects (Holz et al.,2018) and transgress their biophysical boundaries (O’Neill et al., 2018).
State-driven efforts in non-member countries of the Organisation for
Economic Co-operation and Development include Ethiopia’s ‘Climateresilient Green Economy Strategy’, Mozambique’s ‘Green Economy
Action Plan’ and Costa Rica’s ecosystem- and conservation-driven

Sustainable Development, Poverty Eradication and Reducing Inequalities

green transition paths. China and India have adopted technology andrenewables pathways (Brown et al., 2014; Death, 2014, 2015, 2016;
Khanna et al., 2014; Chen et al., 2015; Kim and Thurbon, 2015; Wanget al., 2015; Weng et al., 2015). Brazil promotes low per capita GHG
emissions, clean energy sources, green jobs, renewables and sustainabletransportation, while slowing rates of deforestation (see Chapter 4, Box4.7) (Brown et al., 2014; La Rovere, 2017). Yet concerns remain regarding
persistent inequalities, ecosystem monetization, lack of participationin green-style projects (Brown et al., 2014) and labour conditions andrisk of displacement in the sugarcane ethanol sector (McKay et al.,2016). Experiences with low-carbon development pathways in LDCs
highlight the crucial role of identifying synergies across scale, removing
institutional barriers and ensuring equity and fairness in distributing
benefits as part of the right to development (Rai and Fisher, 2017).
In small islands states, for many of which climate change hazards andimpacts at 1.5°C pose significant risks to sustainable development (seeChapter 5

Chapter 3 Box 3.5, Chapter 4 Box 4.3, Box 5.3), examples of CRDPs
have emerged since the AR5. This includes the SAMOA Pathway: SIDS
Accelerated Modalities of Action (see Chapter 4, Box 4.3) (UNGA, 2014;
Government of Kiribati, 2016; Steering Committee on Partnerships for
SIDS and UN DESA, 2016; Lefale et al., 2017) and the Framework for
Resilient Development in the Pacific, a leading example of integrated
regional climate change adaptation planning for mitigation andsustainable development, disaster risk management and low-carbon
economies (SPC, 2016). Small islands of the Pacific vary significantlyin their capacity and resources to support effective integrated planning(McCubbin et al., 2015; Barnett and Walters, 2016; Cvitanovic et al.,2016; Hemstock et al., 2017; Robinson and Dornan, 2017). Vanuatu (Box5.3) has developed a significant coordinated national adaptation plan
to advance the 2030 Agenda for Sustainable Development, respond to
the Paris Agreement and reduce the risk of disasters in line with the
Sendai targets (UNDP, 2016; Republic of Vanuatu, 2017).

Box 5.3 | Republic of Vanuatu – National Planning for Development and Climate Resilience
The Republic of Vanuatu is leading Pacific Small Island Developing States (SIDS) to develop a nationally coordinated plan for climateresilient development in the context of high exposure to hazard risk (MoCC, 2016; UNU-EHS, 2016). The majority of the population
depends on subsistence, rain-fed agriculture and coastal fisheries for food security (Sovacool et al., 2017). Sea level rise, increased
prolonged drought, water shortages, intense storms, cyclone events and degraded coral reef environments threaten human security
in a 1.5°C warmer world (see Chapter 3, Box 3.5) (SPC, 2015; Aipira et al., 2017). Given Vanuatu’s long history of climate hazards
and disasters, local adaptive capacity is relatively high, despite barriers to the use of local knowledge and technology, and low rates
of literacy and women’s participation (McNamara and Prasad, 2014; Aipira et al., 2017; Granderson, 2017). However, the adaptive
capacity of Vanuatu and other SIDS is increasingly constrained due to more frequent severe weather events (see Chapter 3, Box
3.5, Chapter 4, Cross-Chapter Box 9 in Chapter 4) (Gero et al., 2013; Kuruppu and Willie, 2015; SPC, 2015; Sovacool et al., 2017).
Vanuatu has developed a national sustainable development plan for 2016–2030: the People’s Plan (Republic of Vanuatu, 2016).
This coordinated, inclusive plan of action on economy, environment and society aims to strengthen adaptive capacity and resilience
to climate change and disasters. It emphasizes rights of all Ni-Vanuatu, including women, youth, the elderly and vulnerable groups(Nalau et al., 2016). Vanuatu has also developed a Coastal Adaptation Plan (Republic of Vanuatu, 2016), an integrated Climate
Change and Disaster Risk Reduction Policy (2016–2030) (SPC, 2015) and the first South Pacific National Advisory Board on Climate
Change & Disaster Risk Reduction (SPC, 2015; UNDP, 2016).

5

Vanuatu aims to integrate planning at multiple scales, and increase climate resilience by supporting local coping capacities anditerative processes of planning for sustainable development and integrated risk assessment (Aipira et al., 2017; Eriksson et al.,2017; Granderson, 2017). Climate-resilient development is also supported by non-state partnerships, for example, the ‘Yumi stap
redi long climate change’–the Vanuatu non-governmental organization Climate Change Adaptation Program (Maclellan, 2015).
This programme focuses on equitable governance, with particular attention to supporting women’s voices in decision-making
through allied programmes addressing domestic violence, and rights-based education to reduce social marginalization; alongsideinstitutional reforms for greater transparency, accountability and community participation in decision-making (Davies, 2015;
Maclellan, 2015; Sterrett, 2015; Ensor, 2016; UN Women, 2016).
Power imbalances embedded in the political economy of development (Nunn et al., 2014), gender discrimination (Aipira et al., 2017)
and the priorities of climate finance (Cabezon et al., 2016) may marginalize the priorities of local communities and influence how
local risks are understood, prioritised and managed (Kuruppu and Willie, 2015; Baldacchino, 2017; Sovacool et al., 2017). However,
the experience of the low death toll after Cyclone Pam suggests effective use of local knowledge in planning and early warning may
support resilience at least in the absence of storm surge flooding (Handmer and Iveson, 2017; Nalau et al., 2017). Nevertheless, thevery severe infrastructure damage of Cyclone Pam 2015 highlights the limits of individual Pacific SIDS efforts and the need for global
and regional responses to a 1.5°C warmer world (see Chapter 3, Box 3.5, Chapter 4, Box 4.3) (Dilling et al., 2015; Ensor, 2016; Shultz
et al., 2016; Rey et al., 2017).

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Communities, towns and cities also contribute to low-carbon pathways,
sustainable development and fair and equitable climate resilience,
often focused on processes of power, learning and contestation as entry
points to more localised CRDPs (medium evidence, high agreement)(Cross-Chapter Box 13 in this chapter, Box 5.2). In the Scottish Borders
Climate Resilient Communities Project (United Kingdom), local flood
management is linked with national policies to foster cross-scalarand inclusive governance, with attention to systemic disadvantages,
shocks and stressors, capacity building, learning for change and climate
narratives to inspire hope and action, all of which are essential forcommunity resilience in a 1.5°C warmer world (Fazey et al., 2018).
Narratives and storytelling are vital for realizing place-based 1.5°C
futures as they create space for agency, deliberation, co-constructing
meaning, imagination and desirable and dignified pathways (Veland
et al., 2018). Engagement with possible futures, identity and selfreliance is also documented for Alaska, where warming has alreadyexceeded 1.5°C and indigenous communities invest in renewableenergy, greenhouses for food security and new fishing practices toovercome loss of sea ice, flooding and erosion (Chapin et al., 2016;
Fazey et al., 2018). The Asian Cities Climate Change Resilience Network
facilitates shared learning dialogues, risk-to-resilience workshops, anditerative, consultative planning in flood-prone cities in India; vulnerable
communities, municipal governmental agents, entrepreneurs andtechnical experts negotiate different visions, trade-offs and local politics
to identify desirable pathways (Harris et al., 2017).
Transforming our societies and systems to limit global warming to
1.5°C and ensuring equity and well-being for human populations
and ecosystems in a 1.5°C warmer world would require ambitious
and well-integrated adaptation–mitigation–development pathways
that deviate fundamentally from high-carbon, business-as-usual
futures (Okereke and Coventry, 2016; Arts, 2017; Gupta and Arts,
2017; Sealey-Huggins, 2017). Identifying and negotiating sociallyacceptable, inclusive and equitable pathways towards climateresilient futures is a challenging, yet important, endeavour, fraught
with complex moral, practical and political difficulties and inevitabletrade-offs (very high confidence). The ultimate questions are: what
futures do we want (Bai et al., 2016; Tàbara et al., 2017; Klinsky and
Winkler, 2018; O’Brien, 2018; Veland et al., 2018), whose resilience
matters, for what, where, when and why (Meerow and Newell, 2016),
and ‘whose vision … is being pursued and along which pathways’(Gillard et al., 2016).

Cross-Chapter Box 13 | Cities and Urban Transformation
Lead Authors:
Fernando Aragon-Durand (Mexico), Paolo Bertoldi (Italy), Anton Cartwright (South Africa), François Engelbrecht (South Africa),
Bronwyn Hayward (New Zealand), Daniela Jacob (Germany), Debora Ley (Guatemala/Mexico), Shagun Mehrotra (USA/India), Peter
Newman (Australia), Aromar Revi (India), Seth Schultz (USA), William Solecki (USA), Petra Tschakert (Australia/Austria)

Contributor:Peter Marcotullio (USA)Global Urbanization in a 1.5°C Warmer WorldThe concentration of economic activity, dense social networks, human resource capacity, investment in infrastructure and buildings,
relatively nimble local governments, close connection to surrounding rural and natural environments, and a tradition of innovation
provide urban areas with transformational potential (see Chapter 4, Section 4.3.3) (Castán Broto, 2017). In this sense, the urbanization
megatrend that will take place over the next three decades, and add approximately 2 billion people to the global urban population(UN, 2014), offers opportunities for efforts to limit warming to 1.5°C.

5Cities can also, however, concentrate the risks of flooding, landslides, fire and infectious and parasitic disease that are expected toheighten in a 1.5°C warmer world (Chapter 3). In African and Asian countries where urbanization rates are highest, these risks could
expose and amplify pre-existing stresses related to poverty, exclusion, and governance (Gore, 2015; Dodman et al., 2017; Jiang andO’Neill, 2017; Pelling et al., 2018; Solecki et al., 2018). Through its impact on economic development and investment, urbanization
often leads to increased consumption and environmental degradation and enhanced vulnerability and risk (Rosenzweig et al., 2018).
In the absence of innovation, the combination of urbanization and urban economic development could contribute 226 GtCO2 inemissions by 2050 (Bai et al., 2018). At the same time, some new urban developments are demonstrating combined carbon andSustainable Development Goals (SDG) benefits (Wiktorowicz et al., 2018), and it is in towns and cities that building renovation rates
can be most easily accelerated to support the transition to 1.5°C pathways (Kuramochi et al., 2018), including through voluntary
programmes (Van der Heijden, 2018).

Urban transformations and emerging climate-resilient development pathways
The 1.5°C pathways require action in all cities and urban contexts. Recent literature emphasizes the need to deliberate and negotiate
how resilience and climate-resilient pathways can be fostered in the context of people’s daily lives, including the failings of everyday
development such as unemployment, inadequate housing and a growing informal sector and settlements (informality), in order472

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Chapter 5

Cross-Chapter Box 13 (continued)

to acknowledge local priorities and foster transformative learning (Vale, 2014; Shi et al., 2016; Harris et al., 2017; Ziervogel et al.,2017; Fazey et al., 2018; Macintyre et al., 2018). Enhancing deliberate transformative capacities in urban contexts also entails new
and relational forms of envisioning agency, equity, resilience, social cohesion and well-being (Section 5.5.3) (Gillard et al., 2016;
Ziervogel et al., 2016). Two examples of urban transformation are explored here.
The built environment, spatial planning, infrastructure, energy services, mobility and urban–rural linkages necessary in rapidly
growing cities in South Asia and Africa in the next three decades present mitigation, adaptation and development opportunities
that are crucial for a 1.5°C world (Newman et al., 2017; Lwasa et al., 2018; Teferi and Newman, 2018). Realizing these opportunities
would require the structural challenges of poverty, weak and contested local governance, and low levels of local government
investment to be addressed on an unprecedented scale (Wachsmuth et al., 2016; Chu et al., 2017; van Noorloos and Kloosterboer,
2017; Pelling et al., 2018).
Urban governance is critical to ensuring that the necessary urban transitions deliver economic growth and equity (Hughes et al.,2018). The proximity of local governments to citizens and their needs can make them powerful agents of climate action (Melica etal., 2018), but urban governance is enhanced when it involves multiple actors (Ziervogel et al., 2016; Pelling et al., 2018), supportive
national governments (Tait and Euston-Brown, 2017), and sub-national climate networks (see Chapter 4, Section 4.4.1). Governanceis complicated for the urban population currently living in informality. This population is expected to triple, to three billion, by
2050 (Satterthwaite et al., 2018), placing a significant portion of the world’s population beyond the direct reach of formal climate
mitigation and adaptation policies (Revi et al., 2014). How to address the co-evolved and structural conditions that lead to urban
informality and associated vulnerability to 1.5°C of warming is a central question for this report. Brown and McGranahan (2016)cite evidence that the informal urban ‘green economy’ that has emerged out of necessity in the absence of formal service provisions
is frequently low-carbon and resource-efficient.
Realising the potential for low carbon transitions in informal urban settlements would require an express recognition of the unpaidfor contributions of women in the informal economy, and new partnerships between the state and communities (Ziervogel et al.,2017; Pelling et al., 2018; Satterthwaite et al., 2018). There is no guarantee that these partnerships will evolve or cohere into thetype of service delivery and climate governance system that could steer the change on a scale required to limit to warming to 1.5°C(Jaglin, 2014). However, work by transnational networks, such as Shack/Slum Dwellers International, C40, the Global Covenantof Mayors, and the International Council for Local Environmental Initiatives, as well as efforts to combine in-country planning for
Nationally Determined Contributions (NDCs) (Andonova et al., 2017; Fuhr et al., 2018) with those taking place to support the New
Urban Agenda and National Urban Policies, represent one step towards realizing the potential (Tait and Euston-Brown, 2017).
So too do ‘old urban agendas’, such as slum upgrading and universal water and sanitation provision (McGranahan et al., 2016;
Satterthwaite, 2016; Satterthwaite et al., 2018).
Transition Towns (TTs) are a type of urban transformation that have emerged mainly in high-income countries. The grassroots TT
movement (origin in the United Kingdom) combines adaptation, mitigation and just transitions, mainly at the level of communities
and small towns. It now has more than 1,300 registered local initiatives in more than 40 countries (Grossmann and Creamer,2017), many of them in the United Kingdom, the United States, and other high-income countries. TTs are described as ‘progressive
localism’ (Cretney et al., 2016), aiming to foster a ‘communitarian ecological citizenship’ that goes beyond changes in consumption
and lifestyle (Kenis, 2016). They aspire to promote equitable communities resilient to the impacts of climate change, peak oil andunstable global markets; re-localization of production and consumption; and transition pathways to a post-carbon future (Feola andNunes, 2014; Evans and Phelan, 2016; Grossmann and Creamer, 2017).

5

TT initiatives typically pursue lifestyle-related low-carbon living and economies, food self-sufficiency, energy efficiency throughrenewables, construction with locally sourced material and cottage industries (Barnes, 2015; Staggenborg and Ogrodnik, 2015;
Taylor Aiken, 2016). Social and iterative learning through the collective involves dialogue, deliberation, capacity building, citizen
science engagements, technical re-skilling to increase self-reliance, for example canning and preserving food and permaculture,
future visioning and emotional training to share difficulties and loss (Feola and Nunes, 2014; Barnes, 2015; Boke, 2015; Taylor Aiken,
2015; Kenis, 2016; Mehmood, 2016; Grossmann and Creamer, 2017).
Important conditions for successful transition groups include flexibility, participatory democracy, care ethics, inclusiveness andconsensus-building, assuming bridging or brokering roles, and community alliances and partnerships (Feola and Nunes, 2014;
Mehmood, 2016; Taylor Aiken, 2016; Grossmann and Creamer, 2017). Smaller scale rural initiatives allow for more experimentation

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Cross-Chapter Box 13 (continued)(Cretney et al., 2016), while those in urban centres benefit from stronger networks and proximity to power structures (North andLonghurst, 2013; Nicolosi and Feola, 2016). Increasingly, TTs recognize the need to participate in policymaking (Kenis and Mathijs,
2014; Barnes, 2015).
Despite high self-ratings of success, some TT initiatives are too inwardly focused and geographically isolated (Feola and Nunes,2014), while others have difficulties in engaging marginalized, non-white, non-middle-class community members (Evans andPhelan, 2016; Nicolosi and Feola, 2016; Grossmann and Creamer, 2017). In the United Kingdom, expectations of innovations
growing in scale (Taylor Aiken, 2015) and carbon accounting methods required by funding bodies (Taylor Aiken, 2016)
undermine local resilience building. Tension between explicit engagements with climate change action and efforts to appeal
to more people have resulted in difficult trade-offs and strained member relations (Grossmann and Creamer, 2017) though thecontribution to changing an urban culture that prioritizes climate change is sometimes underestimated (Wiktorowicz et al., 2018).
Urban actions that can highlight the 1.5°C agenda include individual actions within homes (Werfel, 2017; Buntaine and Prather,2018); demonstration zero carbon developments (Wiktorowicz et al., 2018); new partnerships between communities, governmentand business to build mass transit and electrify transport (Glazebrook and Newman, 2018); city plans to include climate outcomes(Millard-Ball, 2013); and support for transformative change across political, professional and sectoral divides (Bai et al., 2018).

5.6

Conditions for Achieving Sustainable
Development, Eradicating Poverty
and Reducing Inequalities in
1.5°C Warmer Worlds

This chapter has described the fundamental, urgent and systemictransformations that would be needed to achieve sustainable
development, eradicate poverty and reduce inequalities in a 1.5°C
warmer world, in various contexts and across scales. In particular, it
has highlighted the societal dimensions, putting at the centre people’s
needs and aspirations in their specific contexts. Here we synthesize
some of the most pertinent enabling conditions (see Glossary) tosupport these profound transformations. These conditions are closelyinterlinked and connected by the overarching concept of governance,
which broadly includes institutional, socio-economic, cultural andtechnological elements (see Chapter 1, Cross-Chapter Box 4 in
Chapter 1).

5

5.6.1

Finance and Technology Aligned with Local Needs

Significant gaps in green investment constrain transitions to a lowcarbon economy aligned with development objectives (Volz et al.,2015; Campiglio, 2016). Hence, unlocking new forms of public, private
and public–private financing is essential to support environmental
sustainability of the economic system (Croce et al., 2011; Blyth et al.,2015; Falcone et al., 2018) (see Chapter 4, Section 4.4.5). To avoid risks
of undesirable trade-offs with the SDGs caused by national budget
constraints, improved access to international climate finance is essential
for supporting adaptation, mitigation and sustainable development,
especially for LDCs and SIDS (medium evidence, high agreement)(Shine and Campillo, 2016; Wood, 2017). Care needs to be taken wheninternational donors or partnership arrangements influence project
financing structures (Kongsager and Corbera, 2015; Purdon, 2015;
Phillips et al., 2017; Ficklin et al., 2018). Conventional climate funding
schemes, especially the Clean Development Mechanism (CDM), have474

shown positive effects on sustainable development but also adverse
consequences, for example, on adaptive capacities of rural households
and uneven distribution of costs and benefits, often exacerbating
inequalities (robust evidence, high agreement) (Aggarwal, 2014;
Brohé, 2014; He et al., 2014; Schade and Obergassel, 2014; Smits andMiddleton, 2014; Wood et al., 2016a; Horstmann and Hein, 2017;
Kreibich et al., 2017). Close consideration of recipients’ contextspecific needs when designing financial support helps to overcome
these limitations as it better aligns community needs, national policy
objectives and donors’ priorities; puts the emphasis on the increase oftransparency and predictability of support; and fosters local capacity
building (medium evidence, high agreement) (Barrett, 2013; Boyle etal., 2013; Shine and Campillo, 2016; Ley, 2017; Sánchez and Izzo, 2017).
The development and transfer of technologies is another enabler for
developing countries to contribute to the requirements of the 1.5°C
objective while achieving climate resilience and their socio-economic
development goals (see Chapter 4, Section 4.4.4). Internationallevel governance would be needed to boost domestic innovationand the deployment of new technologies, such as negative emission
technologies, towards the 1.5°C objective (see Chapter 4, Section 4.3.7),
but the alignment with local needs depends on close considerationof the specificities of the domestic context in countries at all levelsof development (de Coninck and Sagar, 2015; IEA, 2015; Parikh et al.,2018). Technology transfer supporting development in developing
countries would require an understanding of local and national actors
and institutions (de Coninck and Puig, 2015; de Coninck and Sagar,
2017; Michaelowa et al., 2018), careful attention to the capacities inthe entire innovation chain (Khosla et al., 2017; Olawuyi, 2017) andtransfer of not only equipment but also knowledge (medium evidence,high agreement) (Murphy et al., 2015).

5.6.2

Integration of Institutions

Multilevel governance in climate change has emerged as a key enabler
for systemic transformation and effective governance (see Chapter 4,

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Chapter 5

Section 4.4.1). On the one hand, low-carbon and climate-resilient
development actions are often well aligned at the lowest scalepossible (Suckall et al., 2015; Sánchez and Izzo, 2017), and informal,
local institutions are critical in enhancing the adaptive capacityof countries and marginalized communities (Yaro et al., 2015). On
the other hand, international and national institutions can provide
incentives for projects to harness synergies and avoid trade-offs(Kongsager et al., 2016).

al., 2014; Fazey et al., 2016; Tschakert et al., 2016; Winkler and Dubash,
2016; Wood et al., 2016b; Karlsson et al., 2017; Quan et al., 2017;
Tanner et al., 2017). Each development pathway, including legacies andpath dependencies, creates its own set of opportunities and challenges
and winners and losers, both within and across countries (Figure 5.5)
(robust evidence, high agreement) (Mathur et al., 2014; Phillips et al.,2017; Stringer et al., 2017; Wood, 2017; Ficklin et al., 2018; Gajjar etal., 2018).

Governance approaches that coordinate and monitor multiscale
policy actions and trade-offs across sectoral, local, national, regional
and international levels are therefore best suited to implement goals
towards 1.5°C warmer conditions and sustainable development (Ayers
et al., 2014; Stringer et al., 2014; von Stechow et al., 2016; Gwimbi,
2017; Hayward, 2017; Maor et al., 2017; Roger et al., 2017; Michaelowa
et al., 2018). Vertical and horizontal policy integration and coordination
is essential to take into account the interplay and trade-offs between
sectors and spatial scales (Duguma et al., 2014; Naess et al., 2015; vonStechow et al., 2015; Antwi-Agyei et al., 2017a; Di Gregorio et al., 2017;
Runhaar et al., 2018), enable the dialogue between local communities
and institutional bodies (Colenbrander et al., 2016), and involve nonstate actors such as business, local governments and civil society
operating across different scales (robust evidence, high agreement)(Hajer et al., 2015; Labriet et al., 2015; Hale, 2016; Pelling et al., 2016;
Kalafatis, 2017; Lyon, 2018).

Addressing the uneven distribution of power is critical to ensurethat societal transformation towards a 1.5°C warmer world does
not exacerbate poverty and vulnerability or create new injustices but
rather encourages equitable transformational change (Patterson etal., 2018). Equitable outcomes are enhanced when they pay attention
to just outcomes for those negatively affected by change (Newell etal., 2014; Dilling et al., 2015; Naess et al., 2015; Sovacool et al., 2015;
Cervigni and Morris, 2016; Keohane and Victor, 2016) and promote
human rights, increase equality and reduce power asymmetries withinsocieties (robust evidence, high agreement) (UNRISD, 2016; Robinson
and Shine, 2018).

5.6.3

Inclusive Processes

Inclusive governance processes are critical for preparing for a 1.5°C
warmer world (Fazey et al., 2018; O’Brien, 2018; Patterson et al., 2018).
These processes have been shown to serve the interests of diverse
groups of people and enhance empowerment of often excluded
stakeholders, notably women and youth (MRFCJ, 2015a; Dumont etal., 2017). They also enhance social- and co-learning which, in turn,facilitates accelerated and adaptive management and the scaling up
of capacities for resilience building (Ensor and Harvey, 2015; Reij andWinterbottom, 2015; Tschakert et al., 2016; Binam et al., 2017; Dumont
et al., 2017; Fazey et al., 2018; Lyon, 2018; O’Brien, 2018), and provides
opportunities to blend indigenous, local and scientific knowledge(robust evidence, high agreement) (see Chapter 4, Section 4.3.5.5,
Box 4.3, Section 5.3) (Antwi-Agyei et al., 2017a; Coe et al., 2017;
Thornton and Comberti, 2017) . Such co-learning has been effectivein improving deliberative decision-making processes that incorporate
different values and world views (Cundill et al., 2014; C. Butler et al.,2016; Ensor, 2016; Fazey et al., 2016; Gorddard et al., 2016; Aipira etal., 2017; Chung Tiam Fook, 2017; Maor et al., 2017), and create spacefor negotiating diverse interests and preferences (robust evidence, highagreement) (O’Brien et al., 2015; Gillard et al., 2016; DeCaro et al.,2017; Harris et al., 2017; Lahn, 2018).

5.6.4

Attention to Issues of Power and Inequality

Societal transformations to limit global warming to 1.5°C and strive
for equity and well-being for all are not power neutral (Section 5.5.3).
Development preferences are often shaped by powerful interests that
determine the direction and pace of change, anticipated benefits andbeneficiaries, and acceptable and unacceptable trade-offs (Newell et

5.6.5

Reconsidering Values

The profound transformations that would be needed to integratesustainable development and 1.5°C-compatible pathways call for
examining the values, ethics, attitudes and behaviours that underpin
societies (Hartzell-Nichols, 2017; O’Brien, 2018; Patterson et al., 2018).
Infusing values that promote sustainable development (Holden et al.,2017), overcome individual economic interests and go beyond economic
growth (Hackmann, 2016), encourage desirable and transformative
visions (Tàbara et al., 2018), and care for the less fortunate (Howelland Allen, 2017) is part and parcel of climate-resilient and sustainable
development pathways. This entails helping societies and individuals
to strive for sufficiency in resource consumption within planetary
boundaries alongside sustainable and equitable well-being (O’Neillet al., 2018). Navigating 1.5°C societal transformations, characterized
by action from local to global, stresses the core commitment tosocial justice, solidarity and cooperation, particularly regarding the
distribution of responsibilities, rights and mutual obligations between
nations (medium evidence, high agreement) (Patterson et al., 2018;
Robinson and Shine, 2018).

5.7

Synthesis and Research Gaps

The assessment in Chapter 5 illustrates that limiting global warming
to 1.5°C above pre-industrial levels is fundamentally connected withachieving sustainable development, poverty eradication and reducing
inequalities. It shows that avoided impacts between 1.5°C and 2°Ctemperature stabilization would make it easier to achieve many aspectsof sustainable development, although important risks would remain
at 1.5°C (Section 5.2). Synergies between adaptation and mitigation
response measures with sustainable development and the SDGs canoften be enhanced when attention is paid to well-being and equity
while, when unaddressed, poverty and inequalities may be exacerbated(Section 5.3 and 5.4). Climate-resilient development pathways (CRDPs)
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open up routes towards socially desirable futures that are sustainable
and liveable, but concrete evidence reveals complex trade-offs along
a continuum of different pathways, highlighting the role of societal
values, internal contestations and political dynamics (Section 5.5). The
transformations towards sustainable development in a 1.5°C warmer
world, in all contexts, involve fundamental societal and systemicchanges over time and across scale, and a set of enabling conditions
without which the dual goal is difficult if not impossible to achieve(Sections 5.5 and 5.6).
This assessment is supported by growing knowledge on the linkages
between a 1.5°C warmer world and different dimensions of sustainable
development. However, several gaps in the literature remain:
Limited evidence exists that explicitly examines the real-world
implications of a 1.5°C warmer world (and overshoots) as well as
avoided impacts between 1.5°C versus 2°C for the SDGs and sustainable
development more broadly. Few projections are available forhouseholds, livelihoods and communities. And literature on differential
localized impacts and their cross-sector interacting and cascading
effects with multidimensional patterns of societal vulnerability, poverty
and inequalities remains scarce. Hence, caution is needed when globallevel conclusions about adaptation and mitigation measures in a 1.5°C
warmer world are applied to sustainable development in local, nationaland regional settings.
Limited literature has systematically evaluated context-specificsynergies and trade-offs between and across adaptation and mitigation
response measures in 1.5°C-compatible pathways and the SDGs. This5

476

hampers the ability to inform decision-making and fair and robust policy
packages adapted to different local, regional or national circumstances.
More research is required to understand how trade-offs and synergies
will intensify or decrease, differentially across geographic regions andtime, in a 1.5°C warmer world and as compared to higher temperatures.
Limited availability of interdisciplinary studies also poses a challenge
for connecting the socio-economic transformations and the governanceaspects of low emissions, climate-resilient transformations. For
example, it remains unclear how governance structures enable orhinder different groups of people and countries to negotiate pathway
options, values and priorities.
The literature does not demonstrate the existence of 1.5°C-compatible
pathways achieving the ‘universal and indivisible’ agenda of the
17 SDGs, and hence does not show whether and how the natureand pace of changes that would be required to meet 1.5°C climate
stabilization could be fully synergetic with all the SDGs.
The literature on low emissions and CRDPs in local, regional and national
contexts is growing. Yet the lack of standard indicators to monitor such
pathways makes it difficult to compare evidence grounded in specificcontexts with differential circumstances, and therefore to derive
generic lessons on the outcome of decisions on specific indicators. Thisknowledge gap poses a challenge for connecting local-level visions
with global-level trajectories to better understand key conditions forsocietal and systems transformations that reconcile urgent climate
action with well-being for all.

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Chapter 5

Frequently Asked Questions

FAQ 5.1 | What are the Connections between Sustainable Development and Limiting Global
Warming to 1.5°C above Pre-Industrial Levels?
Summary: Sustainable development seeks to meet the needs of people living today without compromising theneeds of future generations, while balancing social, economic and environmental considerations. The 17 UN
Sustainable Development Goals (SDGs) include targets for eradicating poverty; ensuring health, energy and foodsecurity; reducing inequality; protecting ecosystems; pursuing sustainable cities and economies; and a goal forclimate action (SDG 13). Climate change affects the ability to achieve sustainable development goals, and limiting
warming to 1.5°C will help meet some sustainable development targets. Pursuing sustainable development will
influence emissions, impacts and vulnerabilities. Responses to climate change in the form of adaptation andmitigation will also interact with sustainable development with positive effects, known as synergies, or negative
effects, known as trade-offs. Responses to climate change can be planned to maximize synergies and limit tradeoffs with sustainable development.
For more than 25 years, the United Nations (UN) and other international organizations have embraced theconcept of sustainable development to promote well-being and meet the needs of today’s population withoutcompromising the needs of future generations. This concept spans economic, social and environmental objectives
including poverty and hunger alleviation, equitable economic growth, access to resources, and the protection ofwater, air and ecosystems. Between 1990 and 2015, the UN monitored a set of eight Millennium Development
Goals (MDGs). They reported progress in reducing poverty, easing hunger and child mortality, and improving
access to clean water and sanitation. But with millions remaining in poor health, living in poverty and facing
serious problems associated with climate change, pollution and land-use change, the UN decided that moreneeded to be done. In 2015, the UN Sustainable Development Goals (SDGs) were endorsed as part of the 2030
Agenda for Sustainable Development. The 17 SDGs (Figure FAQ 5.1) apply to all countries and have a timeline
for success by 2030. The SDGs seek to eliminate extreme poverty and hunger; ensure health, education, peace,safe water and clean energy for all; promote inclusive and sustainable consumption, cities, infrastructure andeconomic growth; reduce inequality including gender inequality; combat climate change and protect oceans andterrestrial ecosystems.
Climate change and sustainable development are fundamentally connected. Previous IPCC reports found thatclimate change can undermine sustainable development, and that well-designed mitigation and adaptation
responses can support poverty alleviation, food security, healthy ecosystems, equality and other dimensions ofsustainable development. Limiting global warming to 1.5°C would require mitigation actions and adaptation
measures to be taken at all levels. These adaptation and mitigation actions would include reducing emissions andincreasing resilience through technology and infrastructure choices, as well as changing behaviour and policy.
These actions can interact with sustainable development objectives in positive ways that strengthen sustainable
development, known as synergies. Or they can interact in negative ways, where sustainable development ishindered or reversed, known as trade-offs.

5

An example of a synergy is sustainable forest management, which can prevent emissions from deforestationand take up carbon to reduce warming at reasonable cost. It can work synergistically with other dimensions ofsustainable development by providing food (SDG 2) and clean water (SDG 6) and protecting ecosystems (SDG 15).
Other examples of synergies are when climate adaptation measures, such as coastal or agricultural projects,
empower women and benefit local incomes, health and ecosystems.
An example of a trade-off can occur if ambitious climate change mitigation compatible with 1.5°C changes
land use in ways that have negative impacts on sustainable development. An example could be turning naturalforests, agricultural areas, or land under indigenous or local ownership to plantations for bioenergy production.
If not managed carefully, such changes could undermine dimensions of sustainable development by threatening
food and water security, creating conflict over land rights and causing biodiversity loss. Another trade-off could
occur for some countries, assets, workers and infrastructure already in place if a switch is made from fossil fuels toother energy sources without adequate planning for such a transition. Trade-offs can be minimized if effectively
managed, as when care is taken to improve bioenergy crop yields to reduce harmful land-use change or where
workers are retrained for employment in lower carbon sectors.
(continued on next page)477

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FAQ 5.1 (continued)

Limiting temperature increase to 1.5°C can make it much easier to achieve the SDGs, but it is also possible that
pursuing the SDGs could result in trade-offs with efforts to limit climate change. There are trade-offs whenpeople escaping from poverty and hunger consume more energy or land and thus increase emissions, or if
goals for economic growth and industrialization increase fossil fuel consumption and greenhouse gas emissions.
Conversely, efforts to reduce poverty and gender inequalities and to enhance food, health and water security can
reduce vulnerability to climate change. Other synergies can occur when coastal and ocean ecosystem protection
reduces the impacts of climate change on these systems. The sustainable development goal of affordable andclean energy (SDG 7) specifically targets access to renewable energy and energy efficiency, which are important
to ambitious mitigation and limiting warming to 1.5°C.
The link between sustainable development and limiting global warming to 1.5°C is recognized by the SDG forclimate action (SDG 13), which seeks to combat climate change and its impacts while acknowledging that the United
Nations Framework Convention on Climate Change (UNFCCC) is the primary international, intergovernmental
forum for negotiating the global response to climate change.
The challenge is to put in place sustainable development policies and actions that reduce deprivation, alleviate
poverty and ease ecosystem degradation while also lowering emissions, reducing climate change impacts andfacilitating adaptation. It is important to strengthen synergies and minimize trade-offs when planning climatechange adaptation and mitigation actions. Unfortunately, not all trade-offs can be avoided or minimized, but
careful planning and implementation can build the enabling conditions for long-term sustainable development.

5

FAQ 5.1, Figure 1 | Climate change action is one of the United Nations Sustainable Development Goals (SDGs) and is connected to sustainable development
more broadly. Actions to reduce climate risk can interact with other sustainable development objectives in positive ways (synergies) and negative ways (trade-offs).

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Frequently Asked Questions

FAQ 5.2 | What are the Pathways to Achieving Poverty Reduction and Reducing Inequalities
while Reaching a 1.5°C World?
Summary: There are ways to limit global warming to 1.5°C above pre-industrial levels. Of the pathways thatexist, some simultaneously achieve sustainable development. They entail a mix of measures that lower emissions
and reduce the impacts of climate change, while contributing to poverty eradication and reducing inequalities.
Which pathways are possible and desirable will differ between and within regions and nations. This is due tothe fact that development progress to date has been uneven and climate-related risks are unevenly distributed.
Flexible governance would be needed to ensure that such pathways are inclusive, fair and equitable to avoid
poor and disadvantaged populations becoming worse off. Climate-resilient development pathways (CRDPs) offer
possibilities to achieve both equitable and low-carbon futures.
Issues of equity and fairness have long been central to climate change and sustainable development. Equity,
like equality, aims to promote justness and fairness for all. This is not necessarily the same as treating everyone
equally, since not everyone comes from the same starting point. Often used interchangeably with fairness andjustice, equity implies implementing different actions in different places, all with a view to creating an equal
world that is fair for all and where no one is left behind.
The Paris Agreement states that it ‘will be implemented to reflect equity… in the light of different national
circumstances’ and calls for ‘rapid reductions’ of greenhouse gases to be achieved ‘on the basis of equity, and in
the context of sustainable development and efforts to eradicate poverty’. Similarly, the UN SDGs include targets
to reduce poverty and inequalities, and to ensure equitable and affordable access to health, water and energy
for all.
Equity and fairness are important for considering pathways that limit warming to 1.5°C in a way that is liveable
for every person and species. They recognize the uneven development status between richer and poorer nations,the uneven distribution of climate impacts (including on future generations) and the uneven capacity of different
nations and people to respond to climate risks. This is particularly true for those who are highly vulnerable toclimate change, such as indigenous communities in the Arctic, people whose livelihoods depend on agriculture
or coastal and marine ecosystems, and inhabitants of small island developing states. The poorest people willcontinue to experience climate change through the loss of income and livelihood opportunities, hunger, adverse
health effects and displacement.
Well-planned adaptation and mitigation measures are essential to avoid exacerbating inequalities or creating
new injustices. Pathways that are compatible with limiting warming to 1.5°C and aligned with the SDGs consider
mitigation and adaptation options that reduce inequalities in terms of who benefits, who pays the costs and who
is affected by possible negative consequences. Attention to equity ensures that disadvantaged people can secure
their livelihoods and live in dignity, and that those who experience mitigation or adaptation costs have financial
and technical support to enable fair transitions.

5

CRDPs describe trajectories that pursue the dual goal of limiting warming to 1.5°C while strengthening sustainable
development. This includes eradicating poverty as well as reducing vulnerabilities and inequalities for regions,countries, communities, businesses and cities. These trajectories entail a mix of adaptation and mitigation
measures consistent with profound societal and systems transformations. The goals are to meet the short-term
SDGs, achieve longer-term sustainable development, reduce emissions towards net zero around the middle ofthe century, build resilience and enhance human capacities to adapt, all while paying close attention to equity
and well-being for all.
The characteristics of CRDPs will differ across communities and nations, and will be based on deliberations witha diverse range of people, including those most affected by climate change and by possible routes towards
transformation. For this reason, there are no standard methods for designing CRDPs or for monitoring theirprogress towards climate-resilient futures. However, examples from around the world demonstrate that flexible
and inclusive governance structures and broad participation often help support iterative decision-making,continuous learning and experimentation. Such inclusive processes can also help to overcome weak institutionalarrangements and power structures that may further exacerbate inequalities.
(continued on next page)479

Chapter 5

Sustainable Development, Poverty Eradication and Reducing Inequalities

FAQ 5.2 (continued)

FAQ 5.2, Figure 1 | Climate-resilient development pathways (CRDPs) describe trajectories that pursue the dual goals of limiting warming to 1.5°C whilestrengthening sustainable development. Decision-making that achieves the SDGs, lowers greenhouse gas emissions and limits global warming could help lead toa climate-resilient world, within the context of enhancing adaptation.

Ambitious actions already underway around the world can offer insight into CRDPs for limiting warming to 1.5°C.
For example, some countries have adopted clean energy and sustainable transport while creating environmentallyfriendly jobs and supporting social welfare programmes to reduce domestic poverty. Other examples teach us
about different ways to promote development through practices inspired by community values. For instance,
Buen Vivir, a Latin American concept based on indigenous ideas of communities living in harmony with nature,is aligned with peace; diversity; solidarity; rights to education, health, and safe food, water, and energy; andwell-being and justice for all. The Transition Movement, with origins in Europe, promotes equitable and resilient
communities through low-carbon living, food self-sufficiency and citizen science. Such examples indicate thatpathways that reduce poverty and inequalities while limiting warming to 1.5°C are possible and that they canprovide guidance on pathways towards socially desirable, equitable and low-carbon futures.

5480

Industry

[+2]

↑





Evidence
Agreement
Reduces Poverty
Confidence[0]

No direct interaction[0]

No direct interaction

Altieri et al., 2016

% of people living below poverty line declines from 49% to 18% in
South African context.

Score

Interaction

Low-carbon Fuel Switch

Decarbonization/CCS/CCU

Accelerating Energy Efficiency Improvement

Table 5.2 | Mitigation – SDG table
Social-DemandInteraction

[0]

[0]

[0]

Score

Agreement

No direct interaction

No direct interaction

No direct interaction

Evidence

Confidence

[+2]






↑

[+1]







Interaction
Score
Evidence
Agreement Confidence
Technical Education, Vocational Training, Education for
Sustainability (4.3/4.4/4.5/4.7)

[+2]







Technical Education, Vocational Training, Education for
Sustainability (4.b/4.7)
↑
[+1]




[‐1]







Wang and Jaffe, 2004; Hertwich et al., 2008; Apps et al., 2010; Veltman
et al., 2010; Koornneef et al., 2011; Singh et al., 2011; Siirila et al.,2012; Atchley et al., 2013; Corsten et al., 2013; IPCC, 2014

There is a risk of CO2 leakage both from geological formations as well
as from the transportation infrastructure from source to sequestration
locations.

↓

Disease and Mortality (3.1/3.2/3.3/3.4)

Vassolo and Döll, 2005; Nguyen et al., 2014; Holland et al., 2015;
Karner et al., 2015; Fricko et al., 2016[0]
No direct interaction

Apeaning and Thollander, 2013; Fernando and Evans, 2015; Roy et al.,2018

Industries are becoming suppliers of energy, waste heat, water and roof New technology deployment creates demand for awareness andtops for solar energy generation, and hence helping to improve air and knowledge with technical and managerial capability; otherwise acts as
barrier for rapid expansion.
water quality.

↑

Water and Air Pollution Reduction and Better Health (3.9)

Vassolo and Döll, 2005; Xi et al., 2013; Nguyen et al., 2014; Holland et Apeaning and Thollander, 2013; Fernando and Evans, 2015; Roy et al.,al., 2015; Zhang et al., 2015; Fricko et al., 20162018

People living in deprived communities feel positive and predict
Awareness, knowledge, technical and managerial capability are closely
considerable financial savings. Efficiency changes in the industrial sector linked, energy audit, information for trade unions, product/appliancethat lead to reduced energy demand can lead to reduced requirements labeling help in sustainability education.
on energy supply. As water is used to convert energy into useful forms,
the reduction in industrial demand is anticipated to reduce water
consumption and wastewater, resulting in more clean water for othersectors and the environment. In extractive industries there are trade-off
unless strategically managed. Behavioural changes in the industrial
sector that lead to reduced energy demand can lead to reduced
requirements on energy supply. As water is used to convert energy into
useful forms, the reduction in industrial demand is anticipated to reducewater consumption and wastewater, resulting in more clean water forother sectors and the environment.

↑Interaction
Score
Evidence
Agreement Confidence
Air, Water Pollution Reduction and Better Health (3.9)

Sustainable Development, Poverty Eradication and Reducing Inequalities
Chapter 55481

Evidence

Agreement

[+2]





[+2,‐1]

Poverty and Development (1.1/1.2/1.3/1.4)
↑Interaction

[+2]

[0]

Score



Food Security (2.1)


Agreement

No direct interaction

EvidenceConfidence

[+2]







Poverty and Development (1.1/1.2/1.3/1.4)[0,‐1]


Evidence

Agreement

[+2]






Confidence

[0]

Score

Agreement

No direct interaction

Evidence

Confidence

↑

[+2]





Disease and Mortality (3.1/3.2/3.3/3.4)


↑

[+1]







Equal Access to Educational Institutions (4.1/4.2/4.3/4.5)

Djamila et al., 2013; Huebner et al., 2013; Yue et al., 2013; Bhojvaid et Maidment et al., 2014
al., 2014; Derbez et al., 2014; Maidment et al., 2014; Scott et al., 2014;
Cameron et al., 2015; Liddell and Guiney, 2015; Sharpe et al., 2015;
Wells et al., 2015; Willand et al., 2015; Berrueta et al., 2017; Zhao etal., 2017

Huebner et al., 2013; Yue et al., 2013; Scott et al., 2014; Zhao et al.,2017
Equal Access to Educational Institutions (4.1/4.2/4.3/4.5)
Healthy Lives and Well-being for All at All Ages (3.2/3.9)

↑
[+2]


↑
[+2]




Efficient stoves improve health, especially for indigenous and poor rural Household energy efficiency measures reduce school absences forcommunities. Household energy efficiency has positive health impacts children with asthma due to indoor pollution.
on children’s respiratory health, weight and susceptibility to illness, andthe mental health of adults. Household energy efficiency improves
winter warmth, lowers relative humidity with benefits for cardiovascularand respiratory health. Further improved indoor air quality by thermal
regulation and occupant comfort are realised. However, in one instance,negative health impacts (asthma) of increased household energy
efficiency were also noted when housing upgrades took place withoutchanges in occupant behaviours. Home occupants reported warmth as
the most important aspect of comfort which was largely temperaturerelated and low in energy costs. Residents living in the deprived areas
expect improved warmth in their properties after energy efficiency
measures are employed.

Interaction

Lipscomb et al., 2013; van de Walle et al., 2013; McCollum et al., 2018

Modern energy access is critical to enhance agricultural
Access to modern energy services can contribute to fewer injuries and Access to modern energy is necessary for schools to have quality
yields/productivity, decrease post-harvest losses and mechanize agridiseases related to traditional solid fuel collection and burning, as well lighting and thermal comfort, as well as modern information andprocessing – all of which can aid food security. However, large-scale
as utilization of kerosene lanterns. Access to modern energy services
communication technologies. Access to modern lighting and energy
bioenergy and food production may compete for scarce land and other can facilitate improved health care provision, medicine and vaccine
allows for studying after sundown and frees constraints on time
inputs (e.g., water, fertilizers), depending on how and where biomass
storage, utilization of powered medical equipment, and dissemination of management that allow for higher school enrolment rates and bettersupplies are grown and the indirect land use change impacts that result. health-related information and education. Such services can also enable literacy outcomes. (Quote from McCollum et al., 2018)
If not implemented thoughtfully, this could lead to higher food prices
thermal comfort in homes and contribute to food preservation andglobally, and thus reduce access to affordable food for the poor.
safety. (Quote from McCollum et al., 2018)
Enhanced agricultural productivities can ameliorate the situation by
allowing as much bioenergy to be produced on as little land as possible.

~ /↓Food Security and Agricultural Productivity (2.1/2.4)

Berrueta et al., 2017

Score
Improved Warmth and Comforts

Home occupants reported warmth as the most important aspect of
comfort which was largely temperature-related and low in energy costs.
Residents living in deprived areas expect improved warmth in theirproperties after energy efficiency measures are employed.

↑

Interaction

Kirubi et al., 2009; Casillas and Kammen, 2010; Cook, 2011; Pachauri et Cabraal et al., 2005; Tilman et al., 2009; van Vuuren et al., 2009;
Lam et al., 2012; Lim et al., 2012; Smith et al., 2013; Aranda et al.,al., 2012; Pode, 2013; Pueyo et al., 2013; Zulu and Richardson, 2013; Asaduzzaman et al., 2010; Finco and Doppler, 2010; Msangi et al.,2014; McCollum et al., 2018
Bonan et al., 2014; Rao et al., 2014; Burlig and Preonas, 2016;2010; Smith et al., 2013, 2014; Lotze-Campen et al., 2014; Hasegawa et
McCollum et al., 2018
al., 2015; Sola et al., 2016; McCollum et al., 2018

Access to modern energy forms (electricity, clean stoves, high-quality
lighting) is fundamental to human development since the energy
services made possible by them help alleviate chronic and persistentpoverty. Strength of the impact varies in the literature. (Quote from
McCollum et al., 2018)↑

Casillas and Kammen, 2012; Hirth and Ueckerdt, 2013; Jakob andSteckel, 2014; Maidment et al., 2014; Scott et al., 2014; Fay et al.,2015; Cameron et al., 2016; Hallegatte et al., 2016b; Berrueta et al.,2017; McCollum et al., 2018

Energy efficiency interventions lead to cost savings which are realized Using the improved stoves supports local food security and has
due to reduced energy bills that further lead to poverty reduction.
significantly impacted on food security. By making fuel last longer, theParticipants with low incomes experience greater benefits. 'Energy
improved stoves help improve food security and also provide a better
efficiency and biomass strategies benefitted the poor more than windbuffer against fuel shortages induced by climate change-related eventsand solar, whose benefits are captured by industry. Carbon mitigation such as droughts, floods or hurricanes (Berrueta et al. 2017).
can increase or decrease inequalities. The distributional costs of newenergy policies (e.g., supporting renewables and energy efficiency) aredependent on instrument design. If costs fall disproportionately on thepoor, then this could impair progress towards universal energy access
and, by extension, counteract the fight to eliminate poverty. (Quote from
McCollum et al., 2018).

↑/↓

Scott et al., 2014

Confidence

People living in deprived communities feel positive and predict
considerable financial savings.

↑

Score

Poverty Reduction via Financial Savings (1.1)

Interaction

Social-Demand (continued)

Buildings

Behavioural Response482

Accelerating Energy Efficiency Improvement

5

Improved Access and Fuel Switch to Modern Lowcarbon Energy

Chapter 5
Sustainable Development, Poverty Eradication and Reducing Inequalities

Improved Access and Fuel Switch to Modern Lowcarbon Energy

Accelerating Energy Efficiency Improvement

Transport

Behavioural Response[+2,‐1]







↑

[+2]






Interaction
Score
Evidence
Agreement Confidence
Ensure Access to Safe Nutritious Food (2.1/2.2)[+2,‐1]











[+2]









[0]






[+2]







Increasingly volatile global oil prices have raised concerns for the
21 projects aiming at resilient transport infrastructure development to Projects aiming at resilient transport infrastructure development (e.g.,vulnerability of households to fuel price increases. Pricing measures as a improve access (e.g., C40 Cities Clean Bus Declaration, UITP Declaration C40 Cities Clean Bus Declaration, UITP Declaration on Climate
key component of sustainable transport policy need to consider equity. on Climate Leadership, Cycling Delivers on the Global Goals, Global
Leadership, Cycling Delivers on the Global Goals, Global Sidewalk
Pro-poor mitigation policies are needed to reduce climate impact and
Sidewalk Challenge) do not substantially contribute to realizing the
Challenge) are targeted at reducing air pollution; electric vehicles using
reduce threat; for example, investing more and better in infrastructure (indirect) transport targets with mostly a rural focus: agriculturalelectricity from renewables or low carbon sources combined with eby leveraging private resources and using designs that account for
productivity (SDG 2) and access to safe drinking water (SDG 6).
mobility options such as trolley buses, metros, trams and electro buses,
future climate change and the related uncertainty. Communities in poor
as well as promoting walking and biking, especially for short distances,areas cope with and adapt to multiple-stressors including climate
need consideration.
change. Coping strategies provide short-term relief but in the long-term
may negatively affect development goals. And responses generate a
trade-off between adaptation, mitigation and development. For African
cities with slums, due to high commuting costs, many walk to work
places which limit access. In Latin America triple informality leading to
low productivity and living standards.
Dodson and Sipe, 2008; Suckall et al., 2014; Hallegatte et al., 2016a;
SLoCaT, 2017
Ajanovic, 2015; SLoCaT, 2017
Klausbruckner et al., 2016; CAF, 2017; Lall et al., 2017Reduce Illnesses from Hazardous Air Pollution (3.9)

↑[+2,‐1]

Ensure Access to Food Security (2.1/2.3/2.a/2.b/2.c)Figueroa et al., 2014; Schucht et al., 2015; Klausbruckner et al., 2016;
Peng et al., 2017

Locally relevant policies targeting traffic reductions and ambitious
diffusion of electric vehicles results in measured changes in non-climatic
exposure for population, including ambient air pollution, physicalactivity and noise. The transition to low-carbon equitable andsustainable transport can be fostered by numerous short- and mediumterm strategies that would benefit energy security, health, productivity
and sustainability. An evidence-based approach that takes into account
GHG emissions, ambient air pollutants, economic factors (affordability,
cost optimization), social factors (poverty alleviations, public healthbenefits) and political acceptability is needed to tackle these challenges.

↑

Reduce Illnesses from Hazardous Air, Water and Soil Pollution(3.9)

Woodcock et al., 2009; Creutzig et al., 2012; Haines and Dora, 2012;
Saunders et al., 2013; Shaw et al., 2014, 2017; Chakrabarti and Shin,
2017; Hwang et al., 2017; McCollum et al., 2018~

No direct interaction

Confidence

End Poverty in all its Forms Everywhere (1.1/1.4/1.a/1.b)[0]

Clifton, 2004; Hillier, 2011; Krukowski et al., 2013; LeDoux and
Vojnovic, 2013; Ghosh-Dastidar et al., 2014; Zenk et al., 2015; Lowery
et al., 2016[+2,‐1]

Score
Evidence
Agreement
Road Traffic Accidents (3.4/3.6)
↑

[+1]







Interaction
Score
Evidence
Agreement Confidence
Equal Safe Access to Educational Institutions (4.1/4.2/4.3/4.5)

Yu, 2015[0][0]No direct interaction

No direct interaction

Active travel modes, such as walking and cycling, represent strategies Poor road quality affects school travel safety, so collaborative efforts
not only for boosting energy efficiency but also, potentially, forneed to address safety issues from a dual perspective, first by working
improving health and well-being (e.g., lowering rates of diabetes,to change the existing infrastructure and use of roads to better address
obesity, heart disease, dementia and some cancers). However, a risk
the traffic problems that children currently face walking to school, andassociated with these measures is that they could increase rates of road then to better situate schools and control the roadways and land uses
traffic accidents, if the existing infrastructure is unsatisfactory. Overall around them in the future.
health effects will depend on the severity of the injuries sustained fromthese potential accidents relative to the health benefits accruing fromincreased exercise (McCollum et al., 2018).

↑/↓

Interaction↑/↓

Xylia and Silveira, 2017

Decarbonization of public buses in Sweden is receiving attention morethan efficiency improvement. With more electrification, electricity prices
go up and affordability can worsen for the poor unless redistributive
policies are in place.

↑/↓

End Poverty in all its Forms Everywhere (1.1/1.4/1.a/1.b)

Dodson et al., 2004; Cascajo et al., 2017

The costs of daily mobility can have important economic stress impacts, Low-income community residents (non-white) who lack local access to
not only impacting carless families with low-mobility, but in countries affordable, quality sources of nutrition have to travel outside theirwith highimmediate neighbourhood to find better sources of food to feed
levels of car dependence, the costs of motoring can be burdensome,themselves and their families. Lack of locally available healthy foodraising questions of affordability for households with limited economic often exacerbates the rates of obesity in many of these communities
resources. During economic crisis, public transport authorities may react since it is often difficult or expensive to travel long distances on aby reducing levels of service and increasing fares, likely exacerbating the regular basis to shop for food.
situation for low-income households.

↑/↓

Interaction
Score
Evidence
Agreement Confidence
Equal Right to Economic Resources Access Basic Services(1.1/1.4/1.a/1.b)

Social-Demand (continued)

Sustainable Development, Poverty Eradication and Reducing Inequalities
Chapter 5

5

483

↑

[+2]







Interaction
Score
Evidence
Agreement Confidence
Poverty and Development (1.1/1.2/1.3/1.4)[+2,‐2]







↑/↓

Interaction

Agreement

No direct interaction

Evidence[+2,‐2]
Farm Employment and Incomes (2.3)

[0]

Score



Confidence
[+2]

Score




Evidence
Agreement
Air Pollution (3.9)
Confidence
↑

[+1]







Interaction
Score
Evidence
Agreement Confidence
Vocational Trainig, Education for Sustainability (4.b/4.7)

↑

[+2]







Disease and Mortality (3.1/3.2/3.3/3.4), Air Pollution (3.9)

Balishter and Singh, 1991; Gohin, 2008; de Moraes et al., 2010; van der Balishter and Singh, 1991; Gohin, 2008; de Moraes et al., 2010; van der IPCC, 2005, 2014; Miller et al., 2007; Hertwich et al., 2008; de BestHorst and Vermeylen, 2011; Corbera and Pascual, 2012; Rud, 2012;
Horst and Vermeylen, 2011; Corbera and Pascual, 2012; Rud, 2012;
Waldhober et al., 2009; Shackley et al., 2009; Wallquist et al., 2009,
Creutzig et al., 2013; Davis et al., 2013; Satolo and Bacchi, 2013; Muys Creutzig et al., 2013; Davis et al., 2013; Satolo and Bacchi, 2013; Muys 2010; Wong-Parodi and Ray, 2009; Chan and Griffiths, 2010; Veltman etet al., 2014; Ertem et al., 2017; McCollum et al., 2018
et al., 2014; Ertem et al., 2017; McCollum et al., 2018
al., 2010; Epstein et al., 2011; Koornneef et al., 2011; Reiner andNuttall, 2011; Singh et al., 2011; Ashworth et al., 2012; Burgherr et al.,2012; Chen et al., 2012; Asfaw et al., 2013; Corsten et al., 2013;
Einsiedel et al., 2013

[0]

Haines et al., 2007; Nemet et al., 2010; Kaygusuz, 2011; Riahi et al.,Anderson et al., 20172012; van Vliet et al., 2012; Anenberg et al., 2013; Rafaj et al., 2013;
Rao et al., 2013, 2016; West et al., 2013; Chaturvedi and Shukla, 2014;
Rose et al., 2014; Smith and Sagar, 2014; IEA, 2016; McCollum et al.,2018

No direct interaction

Promoting most types of renewables and boosting efficiency greatly aids Decentralized renewable energy systems (e.g., home- or village-scale
the achievement of targets to reduce local air pollution and improve air solar power) can support education and vocational training.
quality; however, the order of magnitude of the effects, both in terms of
avoided emissions and monetary valuation, varies significantly between
different parts of the world. Benefits would especially accrue to thoseliving in the dense urban centres of rapidly developing countries.
Utilization of biomass and biofuels might not lead to any air pollution
benefits, however, depending on the control measures applied. In
addition, household air quality can be significantly improved throughlowered particulate emissions from access to modern energy services(McCollum et al., 2018).

↑

Interaction

Large-scale bioenergy production could lead to the creation of
Large-scale bioenergy production could lead to the creation of
Replacing coal by biomass can reduce adverse impacts of upstream
agricultural jobs, as well as higher farm wages and more diversified
agricultural jobs, as well as higher farm wages and more diversified
supply-chain activities, in particular local air and water pollution, andincome streams for farmers. Modern energy access can make marginal income streams for farmers. Modern energy access can make marginal prevent coal mining accidents. Improvements to local air pollution inlands more cultivable, thus potentially generating on-farm jobs andlands more cultivable, thus potentially generating on-farm jobs andpower generation compared to coal-fired power plants depend on theincomes; on the other hand, greater farm mechanization can also
incomes; on the other hand, greater farm mechanization can also
technology and fuel of biomass power plants, but could be significant
displace labour. However, large-scale bioenergy production could alter displace labour. However, large-scale bioenergy production could alter when switching from outdated coal combustion technologies to state-ofthe structure of global agricultural markets in a way that is, potentially, the structure of global agricultural markets in a way that is, potentially, the-art biogas power generation.
unfavourable to small-scale food producers. See SDG2 (McCollum et al., unfavourable to small-scale food producers. The distributional effects of2018).
bioenergy production are underexplored in the literature (McCollum etal., 2018).

↑/↓

Poverty and Development (1.1/1.2/1.3/1.4)

Riahi et al., 2012; IPCC, 2014; Hallegatte et al., 2016b; McCollum et al.,2018

Deployment of renewable energy and improvements in energy efficiency
globally will aid climate change mitigation efforts, and this, in turn, canhelp to reduce the exposure of the world’s poor to climate-related
extreme events, negative health impacts and other environmental
shocks (McCollum et al., 2018).

Social-Supply

Replacing Coal484

Non-biomass Renewables - solar, wind, hydro5Increased Use of Biomass

Chapter 5
Sustainable Development, Poverty Eradication and Reducing Inequalities

CCS: Bioenergy

CCS: Fossil

Replacing Coal

Advanced Coal

Nuclear/Advanced Nuclear

Agreement

No direct interaction

Evidence[+2,‐2]

[0]

No direct interactionConfidence

Poverty and Development (1.1/1.2/1.3/1.4)

[0]

Score

See effects of increased bioenergy use.

↑/↓

Interaction

Social-Supply (continued)

Agreement

No direct interaction

Evidence[+1,‐2]
Farm Employment and Incomes (2.3)

[0]

Score



Confidence
[‐1]






Score
Evidence
Agreement Confidence
Disease and Mortality (3.1/3.2/3.3/3.4)↑/↓[+2,‐1]Disease and Mortality (3.1/3.2/3.3/3.4)
Abdelouas, 2006; Cardis et al., 2006; Kaatsch et al., 2008; Al-Zoughool
and Krewski, 2009; Heinävaara et al., 2010; Schnelzer et al., 2010;
Brugge and Buchner, 2011; Møller and Mousseau, 2011; Møller et al.,2011, 2012; Moomaw et al., 2011; UNSCEAR, 2011; Sermage-Faure etal., 2012; Ten Hoeve and Jacobson, 2012; Tirmarche et al., 2012;
Hiyama et al., 2013; Mousseau and Møller, 2013; Smith et al., 2013;
WHO, 2013; IPCC, 2014; von Stechow et al., 2016

In spite of the industry's overall safety track record, a non-negligible risk
for accidents in nuclear power plants and waste treatment facilities
remains. The long-term storage of nuclear waste is a politically fraught
subject, with no large-scale long-term storage operational worldwide.
Negative impacts from upstream uranium mining and milling arecomparable to those of coal, hence replacing fossil fuel combustion by
nuclear power would be neutral in that aspect. Increased occurrence ofchildhood leukaemia in populations living within 5 km of nuclear power
plants was identified by some studies, even though a direct causal
relation to ionizing radiation could not be established and other studies
could not confirm any correlation (low evidence/agreement on thisissue).

↓

Interaction[0]

No direct interaction

See literature on increased biomass use: IPCC, 2014; Muratori et al.,2016; Dooley and Kartha, 2018

[‐1]







Wang and Jaffe, 2004; Hertwich et al., 2008; Apps et al., 2010; Veltman
et al., 2010; Koornneef et al., 2011; Singh et al., 2011; Siirila et al.,2012; Atchley et al., 2013; Corsten et al., 2013; IPCC, 2014

The use of fossil CCS implies continued adverse impacts of upstream
supply-chain activities in the coal sector, and because of lower efficiencyof CCS coal power plants, upstream impacts and local air pollution arelikely to be exacerbated. Furthermore, there is a non-negligible risk of
CO2 leakage from geological storage or the CO2 transport infrastructurefrom source to sequestration location.

↓

Disease and Mortality (3.1/3.2/3.3/3.4)

Wang and Jaffe, 2004; Hertwich et al., 2008; Apps et al., 2010; Veltman
et al., 2010; Koornneef et al., 2011; Singh et al., 2011; Siirila et al.,2012; Atchley et al., 2013; Corsten et al., 2013; IPCC, 2014

See increased use of biomass effects. In addition, the concern that more See positive impacts of increased biomass use. At the same time, there
bioenergy (for BECCS) necessarily leads to unacceptably high food prices is a non-negligible risk of CO2 leakage both from geological formations
is not founded on large agreement in the literature. AR5, for example, as well as from the transportation infrastructure from source tofinds a significantly lower effect of large-scale bioenergy deployment on sequestration locations.
food prices by mid-century than the effect of climate change on crop
yields. Also, Muratori et al. (2016) show that BECCS reduces the upward
pressure on food crop prices by lowering carbon prices and lowering thetotal biomass demand in climate change mitigation scenarios. On theother hand, competition for land use may increase food prices andthereby increase risk of hunger. Use of agricultural residue for bioenergy
can reduce soil carbon, thereby threatening agricultural productivity.

↑/↓

Interaction

Interaction

[0]

[0]

[0]

Score

Agreement

No direct interaction

No direct interaction

No direct interaction

Evidence

Confidence

Sustainable Development, Poverty Eradication and Reducing Inequalities
Chapter 55485Social-Other

Agriculture and Livestock

Behavioural Response: Sustainable
Healthy Diets and Reduced Food Waste

486

Land-based GHG Reduction and Soil Carbon Sequestration[0,‐1]



Poverty and Development (1.1/1.2/1.3/1.4)

Score
Evidence
Agreement
Tobacco Control (3.a/3.a.1)

[+2]

&

J

«

Sansoucy, 1995

With mixed-farming systems farmers can not only mitigate risks byproducing a multitude of commodities, but they can also increase theproductivity of both crops and animals in a more profitable andsustainable way.

↑

Poverty Reduction and Minimize Exposure to Risk (1.5)

Confidence[+2]

Ensure Healthy Lives (3.c)

[0]

Score

Agreement

No direct interaction

Evidence

Confidence

Ensure Inclusive and Quality Education (4.4/4.7)

Interaction

[+2]

&&&&

JJJJ

««««

↑/↓

&&

JJ

Ensure Healthy Lives (3.c)[+2,-2]««

Fostering transitions towards more productive livestock production
Biodigestion, which has positive public health aspects, particularly
systems targeting land-use change appears to be the most efficient lever where toilets are coupled with the biodigester; anaerobic conditions killto deliver food availability outcomes. Genomic selection should be able pathogenic organisms as well as digestive toxins. Separation processes
to at least double the rate of genetic gain in the dairy industry. Given
can improve or worsen health risks related to food crops or to livestock.
the prevalence of mixed crop–livestock systems in many parts of theworld, closer integration of crops and livestock in such systems can giverise to increased productivity and increased soil fertility (Thornton,2010). Managing the indirect effects of livestock systems intensificationis critical for the sustainability of the global food system: such as
improving productivity and the close link to land sparing (Herrero andThornton, 2013). In East Africa pastoralists have shifted from cows tocamels, which are better adapted to survive periods of water scarcity
and able to consistently provide more milk (Steenwerth et al., 2014).
Scenarios where zero human-edible concentrate feed is used forlivestock, soil erosion potential reduces by 12%.
Thornton, 2010, 2013; Herrero and Thornton, 2013; Havlík et al., 2014; Sansoucy, 1995; Burton, 2007
Steenwerth et al., 2014; Schader et al., 2015↑

Food Security, Promoting Sustainable Agriculture (2.1/2.4/2a)[0]

No direct interaction

Steenwerth et al., 2014; Lamb et al., 2016




↑/↓
[+2,‐2]



↑/↓
[+2,‐2]



↑
[+2]

Safe application of biotechnology, both conventional and modern
Growing crops such as cassava, sorghum and millet, even in harsh
Science-based action within CSA is required to integrate data sets andmethods, can help to improve agricultural productivity, improving crop conditions, is important to the diets of very poor people. Policy
sound metrics for testing hypotheses about feedback regarding climate,
adaptability and thereby catering to food security. Reducing tillage,
scenarios show that reduced research support, delayed industrialization, weather data products and agricultural productivity, such as theeliminating fallow and keeping the soil covered with residue, coverdelayed biotechnology and climate change will delay progress innonlinearity of temperature effects on crop yield and the assessment ofcrops or perennial vegetation helps prevent soil erosion and has the
reducing childhood malnutrition. The global effects are small, but local trade-offs and synergies that arise from different agriculturalpotential to increase soil organic matter. Efficient land-management
effects for some countries, e.g., Bangladesh and Nigeria, are significant intensification strategies (Steenwerth et al., 2014). Low commodity
techniques can help in increasing crop yields, and so food security issues (Evenson, 1999).
prices have led to declining investment in research and development,
can be addressed. Yield projections are actually higher for developing
farmer education, etc. (Lamb et al., 2016).
countries than for developed countries, reflecting the fact that they havemore 'catch-up' potential (Evenson, 1999). Action is needed throughout
the food system on moderating demand, reducing waste, improving
governance and producing more food (Godfray and Garnett, 2014).
Improving cropland management is the key to increase crop productivity
without further degrading soil and water resources (Branca et al., 2011).
CSA practices increase productivity and prioritize food security.

Food Security, Promoting Sustainable Agriculture (2.1/2.4/2a)

↑




↑
[+1]




Curbing consumer waste of major food crops (i.e., wheat, rice andConsume fewer foods with low nutritional value, e.g., alcohol (Garnett,
vegetables) and meats (i.e., beef, pork and poultry) in China, USA and 2011). Demand-side measures aimed at reducing the proportion ofIndia alone could feed ~413 million people per year (West et al., 2014). livestock products in human diets, where the consumption of animal
One billion extra people could be fed if food crop losses could be halved products is higher than recommended, are associated with multiple(Kummu et al., 2012). Reducing waste, especially from meat and dairy, health benefits, especially in industrialized countries (Bustamante et al.,could play a role in delivering food security and reduce the need for2014).
sustainable intensification (Smith, 2013). Dietary change toward global
healthy diets could improve nutritional health, food security and reduce
emissions.
Garnett, 2011; Beddington et al., 2012; Kummu et al., 2012; Smith,Garnett, 2011; Bustamante et al., 20142013; Bajželj et al., 2014; Tilman and Clark, 2014; West et al., 2014;
Lamb et al., 2016

Interaction

Branca et al., 2011; Bogdanski, 2012; Scherr et al., 2012; Vermeulen et Evenson, 1999; West and Post, 2002; Johnson et al., 2007; Branca et al., Evenson, 1999; Godfray and Garnett, 2014
al., 2012; Campbell et al., 2014; Lipper et al., 2014; Mbow et al., 2014; 2011; McCarthy et al., 2011; Behnassi et al., 2014; Campbell et al.,Steenwerth et al., 2014; Hammond et al., 20172014; Godfray and Garnett, 2014; Harvey et al., 2014; Lipper et al.,
2014


↑
[+2]



Many CSA interventions aim to improve rural livelihoods, thereby
contributing to poverty alleviation. Agroforestry or integrated
crop–livestock–biogas systems can substitute costly, external inputs,
saving on household expenditures – or even lead to the selling of someof the products, providing the farmer with extra income, leading toincreased adaptive capacity (Bogdanski, 2012).

IPCC, 2014

Cutting livestock consumption can increase food security for some ifland grows food not feed, but can also undermine livelihoods andculture where livestock has long been the best use of land, such as inparts of Sub-Saharan Africa.

~ /↓Interaction
Score
Evidence
Agreement Confidence Interaction
Score
Evidence
Agreement Confidence
Poverty and Development (1.1/1.2/1.3/1.4)
Food Security, Promoting Sustainable Agriculture (2.1/2.4/2a)5Greenhouse Gas Reduction from Improved Livestock
Production and Manure Management Systems

Chapter 5
Sustainable Development, Poverty Eradication and Reducing Inequalities

[+2]





Score
Evidence
Agreement
Poverty Reduction (1.5)
Confidence↑/↓[+1,‐2]Interaction
Score
Evidence
Agreement Confidence
Food Security, Promoting Sustainable Agriculture (2.1/2.4/2a)[+2,‐2]

Poverty and Development (1.1/1.2/1.3/1.4)


↑/↓

[+1,‐1]
Food Security (2.1)


Turpie et al., 2015; Epstein and Theuer, 2017; Katila et al., 2017; Dooley
and Kartha, 2018

[+3]





Poverty and Development (1.1/1.2/1.5)

No direct interaction

No direct interaction[+1,‐1]
Food Security (2.2/2.3)

No direct interaction





↑

[+3]




Food Production (2.3/2.4)
Lampitt et al., 2008; Smetacek and Naqvi, 2008; Williamson et al., 2012

OIF can have different implications on fish stocks and aquaculture, andit might actually increase food availability for fish stocks (increasing
yields); but potentially at the cost of reducing the yields of fisheriesoutside the enhancement region by depleting other nutrients.

↑/↓

[0]

[0]

No direct interaction

Zomer et al., 2008; Schirmer and Bull, 2014; Lamb et al., 2016[0]
No direct interaction

Brander et al., 2012; Ahmed et al., 2017a, 2017b; Duarte et al., 2017;
Sondak et al., 2017; Vierros, 2017; Zhang et al., 2017

Avoiding loss of mangroves and maintaining the 2000 stock could save Avoiding loss of mangroves and maintaining the 2000 stock could savea value of ecosystem services from mangroves in South East Asia ofa value of ecosystem services from mangroves in South East Asia
approximately 2.16 billion USD until 2050 (2007 prices), with a 95%
including fisheries; seaweed aquaculture will provide employment;
prediction interval of 1.58–2.76 billion USD (case study area South East traditional management systems provide livelihoods for local
Asia); seaweed aquaculture will enhance carbon uptake and provide
communities; greening of aquaculture can increase income and wellemployment; traditional management systems provide benefits for blue being; and mariculture is a promising approach for China.
carbon and support livelihoods for local communities; greening of
aquaculture can significantly enhance carbon storage; PES schemes
could help capture the benefits derived from multiple ecosystem services
beyond carbon sequestration.

↑

[0]

[0]

Clean Development Mechanism (CDM) can have different implications CDM can have different implications on local to regional food security
on local community livelihoods. For example, willingness to adopt
and local community livelihoods.
afforestation is influenced in particular by Australian landholder’s
perceptions of its potential to provide a diversified income stream, andits impacts on flexibility of land management ; land sparing would have
far reaching implications for the UK countryside and would affectlandowners and rural communities ; and livelihoods could be threatened
if subsistence agriculture is targeted .
Zomer et al., 2008; Schirmer and Bull, 2014; Lamb et al., 2016; Dooley Zomer et al., 2008; Dooley and Kartha, 2018
and Kartha, 2018↑/↓

Katila et al., 2017

Partnerships between local forest managers, community enterprises and Food security may lead to the conversion of productive land under
private sector companies can support local economies and livelihoods, forest, including community forests, into agricultural production. In a
and boost regional and national economic growth.
similar fashion, the production of biomass for energy purposes (SDG 7)
may reduce land available for food production and/or for community
forest activities . Efforts by the Government of Zambia to reduce
emissions by REDD+ have contributed erosion control, ecotourism andpollination valued at 2.5% of the country's GDP.

↑

Interaction

Social-Other (continued)

ForestOceansReduced Deforestation, REDD+

Afforestation and Reforestation

Behavioural
Response(Responsible

Ocean Iron Fertilization

Blue Carbon

Enhanced
Weathering

Agreement

No direct interaction

Evidence









↓

[‐1]







Promote Knowledge and Skill to Promote SD (4.7)

Katila et al., 2017

[0]

[0]

[0]

[0]

No direct interaction

No direct interaction

No direct interaction

No direct interaction

[0]

[0]

[0]

[0]

No direct interaction

No direct interaction

No direct interaction

No direct interaction

Schirmer and Bull, 2014



[+1]

Local forest users learn to understand laws, regulations and policies
which facilitate their participation in society. Education and capacity
building provide technical skill and knowledge (Katila et al., 2017).

↑

Interaction
Score
Evidence
Agreement Confidence
Ensure Inclusive and Quality Education (4.4/4.7)

Jones and McDermott, 2018

Confidence

Most landholders reported having low levels of knowledge about tree
planting for carbon sequestration – particularly available programmes,
prices and markets, and government rules and regulations .

[+1]

Ensure Healthy Lives (3.c)

[0]

Score

Urban trees are increasingly seen as a way to reduce harmful air
pollutants and therefore improve cardio-respiratory health.

↑Interaction

Sustainable Development, Poverty Eradication and Reducing Inequalities
Chapter 55487

Interaction

Social 2-Demand

Industry

Accelerating Energy
Efficiency Improvement488

Low-carbon FuelSwitch

[0]

[0]

[0]

Score

Agreement

No direct interaction

No direct interaction

No direct interaction

Evidence

Confidence
[+1]





[0]

[0]

No direct interaction

No direct interaction

Apeaning and Thollander, 2013; Johansson and Thollander, 2018

There is need for skill in managing in-house energy efficiency.
Sometimes ESCOs also help. Energy audits, but many times absence ofskill acts as barrier for energy efficiency improvement. In many
countries, especially developing countries, these act as barriers.

↑

Interaction
Score
Evidence
Agreement Confidence
Knowledge and Skills Needed to Promote SD (4.7)

5Decarbonization/CCS/CCU

Interaction

[0]

[0]

[0]

Score

Agreement

No direct interaction

No direct interaction

No direct interaction

Evidence

Confidence
[+2]





Score
Evidence
Agreement
Global Partnership (17.6/17.7)

Confidence

[+2]





Global Partnership (17.6/17.7)


[+2]





Global Partnership (17.6/17.7)


Wesseling et al., 2017; Griffin et al., 2018

EPI plants are capital intensive and are mostly operated bymultinationals with long investment cycles. In developed countries new
innovation investments are happening in brown fields. Such large
innovation investments need strong collaboration among
partners/competitors which can be facilitated by public funds. They
happen at national and supranational scales and across sectors, needs
fresh revisit at Intellectual Property Rights issues. Global production of
bio-based polymers increasingly need public support and incentives topush forward.

↑

Abdul Quader et al., 2016

Ultra-low carbon steel making and breakthrough technologies are undertrial across many countries and helping in enhancing the learning.

↑Thollander, 2018; Lawrence et al., 2018

A driving force for energy efficiency is collaboration among companies,
networks, experience sharing and management tools. Sharing among
countries can help accelerate managerial action. Absence of
information, budgetary funding, lack of access to capital, etc. areit t andb iThollander,tti 2013;
C Griffinti et al.,t 2018;ilJohanssonllApeaning
and↑Interaction

Chapter 5
Sustainable Development, Poverty Eradication and Reducing Inequalities

Improved Access and Fuel Switch to Modern Lowcarbon Energy

Accelerating Energy Efficiency Improvement

Buildings

Behavioural Response

[0]

Score

Agreement

No direct interaction

Evidence

Confidence



↑/↓

[1,‐1]






[+2]




Score
Evidence
Agreement
Environmental Justice (16.7)
Confidence

↑

[+2]






Capacity and Accountability (16.1/16.3/16.5/16.6/16.7/16.8)

Consumption perspectives strengthen environmental justice discourse(as it claims to be a more just way of calculating global and local
environmental effects) while possibly also increasing the participatoryenvironmental discourse.
Hult and Larsson, 2016↑

Interaction

[0]

Score

Agreement

No direct interaction

Evidence

Confidence

↑

[+2]







Enhance Policy Coherence for Sustainable Development(17.4)

Interaction

Chowdhury, 2010; Dinkelman, 2011; Kaygusuz, 2011; Köhlin et al.,2011; Clancy et al., 2012; Haves, 2012; Matinga, 2012; Anenberg et al.,2013; Pachauri and Rao, 2013; Burney et al., 2017; McCollum et al.,2018

Improved access to electric lighting can improve women's safety andgirls' school enrolment. Cleaner cooking fuel and lighting access can
reduce health risks and drudgery, which women disproportionately face.
Access to modern energy services has the potential to empower womenby improving their income-earning and entrepreneurial opportunities
and reducing drudgery. Participating in energy supply chains canincrease women's opportunities and agency and improve businessoutcomes.

Women's Safety and Worth (5.1/5.2/5.4)/Opportunities for
Women (5.1/5.5)
↑
[+1]



[0]

No direct interaction

[+2]







↑

[+2]







Promote Transfer and Diffusion of Technology (17.6/17.7)

Acemoglu, 2009; Tabellini, 2010; Acemoglu et al., 2014; ICSU and ISSC, Kim and Sun, 20172015; McCollum et al., 2018

Institutions that are effective, accountable and transparent are needed Green building technology in Kazakhstan was based on transfer ofat all levels of government (local to national to international) for
knowledge among various parties.
providing energy access, promoting modern renewables and boosting
efficiency. Strengthening the participation of developing countries ininternational institutions (e.g., international energy agencies, UN
organizations, WTO, regional development banks and beyond) will be
important for issues related to energy trade, foreign direct investment,labour migration, and knowledge and technology transfer. Reducing
corruption, where it exists, will help these bodies and related domestic
institutions maximize their societal impacts. Limiting armed conflict andviolence will aid most efforts related to sustainable development,
including progress in the energy dimension.

↑

Capacity and Accountability (16.1/16.3/16.5/16.6/16.7/16.8)

Acemoglu, 2009; Tabellini, 2010; Acemoglu et al., 2014; ICSU and ISSC, Shah et al., 2015
2015; McCollum et al., 2018

Dinkelman, 2011; Casillas and Kammen, 2012; Pachauri et al., 2012;
Cayla and Osso, 2013; Hirth and Ueckerdt, 2013; Pueyo et al., 2013;
Jakob and Steckel, 2014; Fay et al., 2015; Cameron et al., 2016;
Hallegatte et al., 2016b; McCollum et al., 2018

Confidence

Bhojvaid et al., 2014; Berrueta et al., 2017

Agreement

No direct interaction

Evidence

Energy efficiency measures and the provision of energy access can free Institutions that are effective, accountable and transparent are needed Implementing refrigerant transition and energy efficiency improvement
policies in parallel for room ACs, roughly doubles the benefit of either
up resources that can then be put towards other productive uses (e.g., at all levels of government (local to national to international) for
policy implemented in isolation.
educational and employment opportunities), especially for women and providing energy access, promoting modern renewables and boostingchildren in poor, rural areas. The distributional costs of new energy
efficiency. Strengthening the participation of developing countries inpolicies are dependent on instrument design. If costs fallinternational institutions (e.g., international energy agencies, UN
disproportionately on the poor, then this could work against theorganizations, WTO, regional development banks and beyond) will bepromotion of social, economic and political equality for all. The impacts important for issues related to energy trade, foreign direct investment,of energy efficiency measures and policies on inequality can be bothlabour migration and knowledge and technology transfer. Reducing
positive, if they reduce energy costs, or negative, if mandatory standards corruption, where it exists, will help these bodies and related domestic
increase the need for purchasing more expensive equipment andinstitutions maximize their societal impacts. Limiting armed conflict andappliances.
violence will aid most efforts related to sustainable development,
including progress in the energy dimension.

[+1]

[0]

Score

Empowerment and Inclusion (10.1/10.2/10.3/10.4)

Interaction

Efficient stoves lead to empowerment of rural and indigenous women.

↑

Gender Equality and Women's Empowerment (5.1/5.4)

Interaction

Social 2-Demand (continued)

Sustainable Development, Poverty Eradication and Reducing Inequalities
Chapter 5

5

489

[+1]







↑

Interaction

[+2]




Score
Evidence
Agreement
Reduce Inequality (10.2)
Confidence↑/↓[+1, ‐1]Interaction
Score
Evidence
Agreement Confidence
Accountable and Transparent Institutions at All Levels(16.6/16.8)

[0]

[0]

No direct interaction

No direct interaction





↑

[+2]







Help Promote Global Partnership (17.1/17.3/17.5/17.6/17.7)

SLoCaT, 2017↑/↓[+1, ‐1]Responsive, Inclusive, Participatory Decision-making (16.7)

Aggarwal, 2017; AlSabbagh et al., 2017

↑

[+2]







Help Promote Global Partnership (17.1/17.3/17.5/17.6/17.7)

SLoCaT, 2017

Colenbrander et al., 2016

SLoCaT, 2017







Figueroa et al., 2014; Lucas and Pangbourne, 2014







Projects aiming at resilient transport infrastructure development (e.g.
C40 Cities Clean Bus Declaration, UITP Declaration on Climate
Leadership, Cycling Delivers on the Global Goals, Global Sidewalk
Challenge) are happening through multi-stakeholder coalitions.

[+2]

[+2]



In transport mitigation it is necessary to conduct needs assessments and Projects aiming at resilient transport infrastructure development andstakeholder consultation to determine plausible challenges, prior totechnology adoption (e.g. C40 Cities Clean Bus Declaration, UITP
introducing desired planning reforms. Further, the involved personnel
Declaration on Climate Leadership, Cycling Delivers on the Global
should actively engage transport-based stakeholders during policy
Goals, Global Sidewalk Challenge) are happening through multiidentification and its implementation to achieve the desired results. User stakeholder coalitions.
behaviour and stakeholder integration are key for successful transport
policy implementation.

↑

Responsive, Inclusive, Participatory Decision-making (16.7)[+2]

Projects aiming at resilient transport infrastructure development (e.g.,
C40 Cities Clean Bus Declaration, UITP Declaration on Climate
Leadership, Cycling Delivers on the Global Goals, Global Sidewalk
Challenge) are happening through multi-stakeholder coalitions.

↑Interaction
Score
Evidence
Agreement Confidence
Help Promote Global Partnership (17.1/17.3/17.5/17.6/17.7)

The equity impacts of climate change mitigation measures for transport, Formal transport infrastructure improvement in many cities inand indeed of transport policy intervention overall, are poorly
developing countries leads to eviction from informal settlements; needunderstood by policymakers. This is in large part because standard
for appropriate redistributive policies and cooperation and partnerships
assessment of these impacts is not a statutory requirement of current
with all stakeholders.
policymaking. Managing transport energy demand growth will have tobe advanced alongside efforts in passenger travel towards reducing thedeep inequalities in access to transport services that currently affect thepoor worldwide.

↑

No direct interaction

Reduce Inequality (10.2)[0]The equity impacts of climate change mitigation measures for transport, With behavioural change towards walking for short distances,
The woman's average trip to work differs markedly from the man'sand indeed of transport policy intervention overall, are poorly
pedestrian safety on the road might reduce, unless public policy isaverage trip. Working-poor women rely on extensive social networks
appropriately formulated. Prevalence of high levels of triple forms ofcreating communities of spatial necessity, bartering for basic needs to understood by policymakers. This is in large part because standard
assessmentof
these
impactsis
notastatutory
requirementofcurrent
informality, in jobs, housing and transportation, are responsible for low
overcome transportation constraints. Women earn lower wages and so
are less likely to justify longer commutes. Many women need to manage policymaking. Managing transport energy demand growth will have to productivity and low standards of living, and are a major challenge forbe advanced alongside efforts in passenger travel towards reducing the policies targeting urban growth in Latin America.
dual roles as workers and mothers. Women tend to perform multideep inequalities in access to transport services that currently affect the
purpose commuting, combining both work and household needs .
poor worldwide. Free provision of roads and parking spaces converts
vast amounts of public land and capital into under-priced space for cars,
in extreme cases like Los Angeles, USA, roads and streets free forparking and driving are 20% of land area; as governments give drivers
free land, people drive more than they would otherwise. High levels ofcar dependence and the costs of motoring can be burdensome, and leadto increasing debt, raising questions of affordability for households withlimited resources, particularly low-income houses located in suburban
areas
Crane, 2007; Rogalsky, 2010
Figueroa et al., 2014; Lucas and Pangbourne, 2014; Walks, 2015;
CAF, 2017; SLoCaT, 2017
Manville, 2017; Belton Chevallier et al., 2018↑

Interaction
Score
Evidence
Agreement Confidence
Recognize Women's Unpaid Work (5.1/5.4)/Opportunities for
Women (5.1/5.5)

Social 2-Demand (continued)

Transport

Behavioural response

490

Accelerating Energy Efficiency
Improvement

5

Improved Access and Fuel Switch
to Modern Low-carbon Energy

Chapter 5
Sustainable Development, Poverty Eradication and Reducing Inequalities

CCS: Fossil

Replacing Coal

Advanced Coal

CCS:
Bioenergy

Nuclear/Advanced Nuclear

Increased useof Biomass

Non-biomass Renewables - solar, wind, hydro

[+1]






[+2]

↑






↑/~

[+2,0]







Score
Evidence
Agreement ConfidenceInternational Cooperation (All Goals)

[0]

[0]

[0]

[0]

No direct interaction

No direct interaction

No direct interaction

No direct interaction

[0]

[0]

[0]

[0]

No direct interaction

No direct interaction

No direct interaction

No direct interaction

[‐1]





Reduce Illicit Arms Trade (16.4)

No direct interaction



[0]

[0]

No direct interaction

No direct interaction

Adamantiades and Kessides, 2009; Rogner, 2010; Sagan, 2011; vonHippel et al., 2011, 2012; Yim and Li, 2013; IPCC, 2014

Continued use of nuclear power poses a constant risk of proliferation.

↓

[0]

[0]

[0]

[0]

[0]

No direct interaction

No direct interaction

No direct interaction

No direct interaction

UN, 1989; Ramaker et al., 2003; Clarke et al., 2009; NCE, 2015; Riahi etal., 2015, 2017; Eis et al., 2016; O’Neill et al., 2017; McCollum et al.,2018

Islar et al., 2017↑

Interaction

Walker and Devine-Wright, 2008; Cass et al., 2010; Cumbers, 2012;
Kunze and Becker, 2015; McCollum et al., 2018

Confidence

Schwerhoff and Sy, 2017



Evidence
Agreement
Energy Justice

The energy justice framework serves as an important decision-making International cooperation (in policy) and collaboration (in science) is
tool in order to understand how different principles of justice can inform required for the protection of shared resources. Fragmented approaches
have been shown to be more costly. Specific to SDG7, to achieve theenergy systems and policies. Islar et al. (2017) state that off-grid andmicro-scale energy development offers an alternative path to fossil-fuel targets for energy access, renewables and efficiency, it will be criticaluse and top-down resource management as they democratize the grid that all countries: (i) are able to mobilize the necessary financial
resources (e.g., via taxes on fossil energy, sustainable financing, foreign
and increase marginalized communities' access to renewable energy,
direct investment, financial transfers from industrialized to developing
education and health care.
countries); (ii) are willing to disseminate knowledge and shareinnovative technologies between each other; (iii) follow recognizedinternational trade rules while at the same time ensuring that the least
developed countries are able to take part in that trade; (iv) respect eachother’s policy space and decisions; (v) forge new partnerships between
their public and private entities and within civil society; and (vi) support
the collection of high-quality, timely and reliable data relevant tofurthering their missions. There is some disagreement in the literatureon the effect of some of the above strategies, such as free trade.
Regarding international agreements, 'no-regrets options', where allsides gain through cooperation, are seen as particularly beneficial (e.g.,
nuclear test ban treaties) (McCollum et al., 2018).



Score

Interaction

Decentralized renewable energy systems (e.g., home- or village-scalesolar power) can enable a more participatory, democratic process for
managing energy-related decisions within communities.

[+1]

Interaction
Score
Evidence
Agreement Confidence
Empowerment and Inclusion (10.1/10.2/10.3/10.4)

Decentralized renewable energy systems (e.g., home- or village-scalesolar power) can reduce the burden on girls and women of procuring
traditional biomass.

↑Interaction
Score
Evidence
Agreement Confidence
Gender Equality and Women's Empowerment (5.1/5.4)

Social 2-Supply

Sustainable Development, Poverty Eradication and Reducing Inequalities
Chapter 5

5

491

↑/~

ConfidenceInteraction
[0]

Score

Evidence

Agreement

Confidence[+2,0]







↑/~

[+1,0]


Empower Economic and Political Inclusion of All, Irrespectiveof Sex (10.2)

Agreement

Equal Access, Empowerment of Women (5.5)

Evidence

No direct interaction

[0]

Score

No direct interaction

Interaction

Evidence

Agreementp[+1,‐1]making (16.6/16.7/16.a)

Score


ConfidenceInteraction
Score
Evidence
Agreement Confidence
Resource Mobilization and Strengthen Partnership(17 1/17 14)↑/↓[+1,‐1]




~ /↓

[0,‐1]







Build Effective, Accountable and Inclusive Institutions(16.6/16.7/16.8)

↑

[+2]







Resource Mobilization and Strengthen Multi-stakeholder
Partnership(17.1/ 17.3/17.5/17.17)

Garnett, 2011; Dagevos and Voordouw, 2013; Bajželj et al., 2014; Lamb Garnett, 2011; Dagevos and Voordouw, 2013
et al., 2016

Appropriate incentives to reduce food waste may require some policy
Decision makers should try to integrate agricultural, environmental andinnovation and experimentation, but a strong commitment for devising nutritional objectives through appropriate policy measures to achieve
and monitoring them seems essential.
sustainable healthy diets coupled with reduction in food waste. It is
A financial incentive to minimize waste could be created throughsurprising that politicians and policymakers demonstrate little regarding
effective taxation (e.g., by taxing foods with the highest wastage rates, the need to have strategies to reduce meat consumption and toor by increasing taxes on waste disposal). Decision makers should try to encourage more sustainable eating practices .
integrate agricultural, environmental and nutritional objectives throughappropriate policy measures to achieve sustainable healthy diets
coupled with reduction in food waste. It is surprising that politicians andpolicymakers demonstrate little regarding the need to have strategies toreduce meat consumption and to encourage more sustainable eating
practices.

↑/↓

Interactiong

Nelson et al., 2002; Demetriades and Esplen, 2009; Terry, 2009

↑/~[+1,0]


Patel et al., 2016



Patel et al., 2016



Livestock ownership is increasing women’s decision-making andeconomic power within both the household and the community. Accessto and control and management of small ruminants, grazing areas andfeed resources empower women and lead to an overall positive impact
on the welfare of the household.

[+2,0]

Most of the animal farming activities such as fodder collection andfeeding are performed by women. Alongside the considerable
involvement and contribution of women, gender inequalities arepervasive in Indian villages in terms of accessing natural resources,
extension services, marketing opportunities and financial services aswell as in exercising their decision-making powers. Therefore, there is aneed to correct gender bias in the farming sector. Efforts are needed toincrease the capacity of women to negotiate with confidence and meettheir strategic needs. Access to and control and management of small
ruminants, grazing areas and feed resources empower women and leadto an overall positive impact on the welfare of the household.

↑/~

Equal Access to Economic Resources, Promote Empowerment Empower Economic and Political Inclusion of All, Irrespectiveof Women (5.5/5.a/5.b)of Sex (10.2)

Denton, 2002; Nelson et al., 2002; Morton, 2007; Demetriades andEsplen, 2009; Terry, 2009; Bernier et al., 2013; Jost et al., 2016



↑

[+2]







Herrero and Thornton, 2013; Havlík et al., 2014

Havlík et al., 2014

The role of livestock system transitions in emission reductions depends
on the level of the carbon price and which emissions sector is targetedby the policies (Havlík et al., 2014). Mechanisms for affecting
behavioural change in livestock systems need to be better understood byimplementing combinations of incentives and taxes simultaneously in
different parts of the world (Herrero and Thornton, 2013).

[+1]

Improve Domestic Capacity for Tax Collection (17.1)

Behnassi et al., 2014; Lipper et al., 2014; Steenwerth et al., 2014

To minimize the economic and social cost, policies should target
emissions at their source—on the supply side—rather than on the
demand side as supply-side policies have lower calorie cost thandemand-side policies. The role of livestock system transitions inemission reductions depends on the level of the carbon price and whichemissions sector is targeted by the policies.

↑

Responsible Decision-making (16.7)

Behnassi et al., 2014; Bustamante et al., 2014; Godfray and Garnett,
2014; Lipper et al., 2014; Steenwerth et al., 2014

Many programmes for CSA have been used to empower women and to In many rural societies women are side-lined from decisions regarding Action is needed throughout the food system for improving governance CSA requires more careful adjustment of agricultural practices to natural
improve gender equality. Women often have an especially important
agriculture even when male household heads are absent, and they often and producing more food (Godfray and Garnett, 2014). CSA requires
conditions, a knowledge-intensive approach, huge financial investmentrole to play in adaptation, because of their gendered indigenous
lack access to important inputs such as irrigation water, credit, tools
policy intervention for careful adjustment of agricultural practices toand policy and institutional innovation, etc. Besides private investment,
knowledge on matters such as agriculture (Terry, 2009). Without access and fertilizer. To be effective, agricultural mitigation strategies need to natural conditions, a knowledge-intensive approach, huge financial
quality of public investment is also important (Behnass et al., 2014).
to land, credit and agricultural technologies, women farmers face major take these and other aspects of local gender relations into accountinvestment, etc., so having strong institutional frameworks is very
Sources of climate finance for CSA in developing countries include
constraints in their capacity to diversify into alternative livelihoods(Terry, 2009). Women's key role in maintaining biodiversity, throughimportant. The main source of climate finance for CSA in developing
bilateral donors and multilateral financial institutions, besides public(Demetriades and Esplen, 2008).
conserving and domesticating wild edible plant seed, and in food crop countries is the public sector. Lack of institutional capacity (as a means sector finance. CSA is committed to new ways of engaging in
breeding, is not sufficiently recognized in agricultural and economic
for securing creation of equal institutions among social groups andparticipatory research and partnerships with producers (Steenwerth etpolicymaking; nor is the importance of biodiversity to sustainable rural individuals) can reduce feasibility of AFOLU mitigation measures in the al., 2014).
livelihoods in the face of predicted climate changes (Nelson et al.,near future, especially in areas where small-scale farmers or forest users
2002).
are the main stakeholders (Bustamante et al., 2014).

Social 2-Other

Agriculture and Livestock

Behavioural Response: Sustainable Healthy
Diets and Reduced Food Waste

492

Land-based Greenhouse Gas Reduction and Soil
Carbon Sequestration

5

Greenhouse Gas Reduction from Improved
Livestock Production and Manure
Management Systems

Chapter 5
Sustainable Development, Poverty Eradication and Reducing Inequalities

Behavioural Response (Responsible
Sourcing)

Afforestation and Reforestation

ForestReduced Deforestation, REDD+



↑

[+2]






↑

[+2]






Interaction
Score
Evidence
Agreement
Confidencegp
Build Effective, Accountable and Inclusive Institutions,
Responsible Decision-making (16.6/16.7/16.8)↑/↓[+1,‐1]Interaction
Score
Evidence
Agreement Confidence
Resource Mobilization and Strengthen Multi-stakeholder
Partnership(17.1/ 17.3/17.5/17.17)

[+1]







↑

[+1]






Empower Economic and Political Inclusion of All, Irrespectiveof Sex (10.2)[0]

UN-Women et al., 2015

No direct interaction[0]

UN-Women et al., 2015

No direct interaction

Many women in developing countries are already prominently engaged Women’s participation in the decision-making process of forest
in economic sectors related to climate adaptation and mitigation efforts management, for example, has been shown to increase rates ofsuch as agriculture, renewable energy and forest management and are reforestation while decreasing the illegal extraction of forest products.
important drivers and leaders in climate responses that are innovativeand effective, benefitting not only their families but also their wider
communities. Women’s participation in the decision-making process offorest management, for example, has been shown to increase rates ofreforestation while decreasing the illegal extraction of forest products.

↑

Opportunities for Women (5.1/5.5)



↑

[+2]







[+1]







↑

[+1]






Private certification initiatives for wood product and biomass sourcing
Indonesian factories may seek advantages through non-pricecompetition—perhaps by highlighting decent working conditions or the may extend their schemes with criteria for 'leakage' (external GHG
effects). Also recycling of waste wood in pellets is not yet practiced, due
existence of a union—or to see trade associations or government
to unclear rules in the EU Waste Directive about overseas shipping
agencies promoting the country as a responsible sourcing location(Bartley, 2010). In the absence of domestic legal instruments providing (Sikkema et al., 2014). Engagement of Chinese government and private
sector stakeholders in supply-country sustainability initiatives may be
incentives to improve sustainability of sourcing, it appears thatthe best way to support this gradual process of improvement. Althoughinitiatives to engage the major importing enterprises in developing
carrying out due diligence in timber sourcing can require considerable
responsible sourcing practices and policies is a practical approach.
Unless initiatives involve all the major importers, they are unlikely to be internal resources, it may be substantially less of a financial burden thanthe potential fines and reputational damage resulting from sourcing
successful since the high costs associated with accreditation would
unknown or controversial timber (Huang et al., 2013).
increase production costs for these firms relative to their competitors(Huang et al., 2013).
Bartley, 2010; Huang et al., 2013
Huang et al., 2013; Sikkema et al., 2014↑

Epstein and Theuer, 2017

Finance and Trade (17.1/17.10)

Responsible Decision-making (16.7)

Financing at the national and international level is required to growmore seedlings/sapling, restore land, create awareness and education
factsheets, provide training to local communities regarding the benefits
of afforestation and reforestation. Article 12 of the Kyoto Protocol
further sets a Clean Development Mechanism through which countries
in Annex I earn ‘certified emissions reductions’ through projects
implemented in developing countries (Montanarella and Alva, 2015).
Afforestation and reforestation in India are being carried out undervarious programmes, namely social forestry initiated in the early 1980s,
the Joint Forest Management Programme initiated in 1990, afforestation
under National Afforestation and Eco-development Board programmes
since 1992, and private farmer and industry initiated plantation forestry.
If the current rate of afforestation and reforestation is assumed tocontinue, the carbon stock could increase by 11% by 2030
(Ravindranath et al 2008)
Ravindranath et al., 2008; Kibria, 2015; Montanarella and Alva, 2015[+1]

Resource Mobilization and Strengthen Partnership(17.1/17.14)

Miles and Kapos, 2008; Bustamante et al., 2014; Andrew, 2017; Bastos
Lima et al., 2017; Katila et al., 2017

Land-related mitigation, such as biofuel production, as well as
conservation and reforestation action can increase competition for landand natural resources, so these measures should be accompanied bycomplementary policies. (Quoted from Epstein and Theur, 2017)↑

Responsible Decision-making (16.7)

Bustamante et al., 2014; Bastos Lima et al., 2015, 2017

Bastos Lima et al., 2017; Katila et al., 2017Interaction
Score
Evidence
Agreement Confidence
Reduced Inequality, Empowerment and Inclusion(10.1/10.2/10.3/10.4)

Brown, 2011; Larson et al., 2014; Katila et al., 2017

Confidence

Urges developed countries to support, through multilateral and bilateral Institutional building (National Forest Monitoring Systems, Safeguard
To provide finance and technology to developing countries to supportchannels, the development of REDD+ national strategies or action plans Information Systems, etc.), with full and effective participation of allemissions reductions. Be supported by adequate and predictable
and implementation . Girls and women have an important role in
relevant countries . REDD+ actions also deliver non-carbon benefits (e.g. financial and technology support, including support for capacity building
forestry activities, related to fuel-wood, forest-food and medicine. Their local socioeconomic benefits, governance improvements). Forest. Partnerships in the form of significant aid money from, e.g., Norway,
empowerment contributes to sustainable forestry as well as reducing
governance is another central aspect in recent studies, including theother bilateral donors and the World Bank’s Forest Carbon Partnershipinequality .
debate on decentralization of forest management, logging concessions Facility (FCPF) are forthcoming . Estimates of opportunity cost forin public-owned commercially valuable forests and timber certification, REDD+ are very low. Lower costs and/or higher carbon prices could
primarily in temperate forests .
combine to protect more forests, including those with lower carbon
content. Conversely, where the cost of action is high, a large amount ofadditional funding would be required for the forest to be protected(Miles and Kapos, 2008). Forest governance is another central aspect inrecent studies, including debate on decentralization of forest
management, logging concessions in public-owned commerciallyvaluable forests and timber certification, primarily in temperate forests .
Partnerships between local forest managers, community enterprises andprivate sector companies can support local economies and livelihoods
and boost regional and national economic growth .

[+1,‐1]

Score
Evidence
Agreement
Opportunities for Women (5.1/5.5)

Women have been less involved in REDD+ initiative (pilot project)design decisions and processes than men. Girls and women have an
important role in forestry activities, related to fuel-wood, forest-foodand pharmaceutical. Their empowerment contributes to sustainable
forestry as well as reducing inequality .

↑/↓Interaction

Social 2-Other (continued)

Sustainable Development, Poverty Eradication and Reducing Inequalities
Chapter 55493

Blue Carbon

Oceans494

Enhanced
Weathering

Interaction

[0]

[0]

[0]

Score

Agreement

No direct interaction

No direct interaction

No direct interaction

Evidence

ConfidenceInteraction

5

Ocean Iron
Fertilization

Social 2-Other (continued)

[0]

[0]

[0]

Score

Agreement

No direct interaction

No direct interaction

No direct interaction

Evidence

ConfidenceInteraction
g

[0]

[0]

[0]

Score

Agreementp

No direct interaction

No direct interaction

No direct interaction

Evidence

ConfidenceInteraction

[0]

[0]

[0]

Score

Agreement

No direct interaction

No direct interaction

No direct interaction

Evidence

Confidence

Chapter 5
Sustainable Development, Poverty Eradication and Reducing Inequalities

Decarbonisation/CCS/CCU

Low-carbon Fuel Switch

Industry

Accelerating Energy Efficiency
Improvement[+2,‐1]






[+1]






↑

Sustainable Production (12.2/12.3/12.a)



↑

[+2]






↓

[‐1]







Griffin et al., 2018

Wesseling et al., 2017

No direct interaction

Confidence

Meldrum et al., 2013; Byers et al., 2016; Fricko et al., 2016; Brandl etal., 2017

Agreement

No direct interaction

Evidence

Conserve and Sustainably Use Ocean (14.1/14.5)

[0]

[0]

Score

EPI plants are capital intensive and are mostly operated by
CCU/S in the chemical industry faces challenges for transport costs andmultinationals with long investment cycles. In developed countries new storage. In the UK cluster region have been identified for storage underinvestments are happening in brown fields, while in developingsea.
countries these are in green fields. Collaboration among partners anduser demand change, policy change is essential for encouraging theselarge risky investments.

[+1,‐1]

Sustainable Production and Consumption (12.1/12.6/12.a)[+2]

Interaction

CCU/S requires access to water for cooling and processing which could
contribute to localized water stress. CCS/U processes can potentially be
configured for increased water efficiency compared to a system withoutcarbon capture via process integration.

↑/↓

Water Efficiency and Pollution Prevention (6.3/6.4/6.6)



Liu and Bai, 2014; Lieder and Rashid, 2016; Stahel, 2016; Supino et al.,2016; Fan et al., 2017; Shi et al., 2017; Zeng et al., 2017

Hejazi et al., 2015; Fricko et al., 2016; Song et al., 2016[+2,‐2]

A switch to low-carbon fuels can lead to a reduction in water demand
and waste water if the existing higher-carbon fuel is associated with ahigher water intensity than the lower-carbon fuel. However, in somesituations the switch to a low-carbon fuel such as, for example, biofuel
could increase water use compared to existing conditions if the biofuel
comes from a water-intensive feedstock.

↑/↓





A circular economy instead of linear global economy can achieve
climate goals and can help in economic growth through industrialization
which saves on resources, the environment and supports small, medium
and even large industries, and can lead to employment generation. So
new regulations, incentives and a tax regime can help in achieving thegoal, especially in newly emerging developing countries - although also
applicable for large industrialized countries.

Water Efficiency and Pollution Prevention (6.3/6.4/6.6)

Once started leads to chain of actions within the sector and policy space
to sustain the effort. Helps in expansion of sustainable industrial
production (Ghana).

↑

Interaction
Score
Evidence
Agreement Confidence
Sustainable and Efficient Resource (12.2/12.5/12.6/12.7/12.a)

Vassolo and Döll, 2005; Nguyen et al., 2014; Holland et al., 2015; Fricko Apeaning and Thollander, 2013; Fernando et al., 2017
et al., 2016

Efficiency and behavioural changes in the industrial sector that lead to
reduced energy demand can lead to reduced requirements on energy
supply. As water is used to convert energy into useful forms, thereduction in industrial demand is anticipated to reduce water
consumption and waste water, resulting in more clean water for othersectors and the environment. Likewise, reducing material inputs forindustrial processes through efficiency and behavioural changes will
reduce water inputs in the material supply chains. In extractive
industries there can be a trade-off with production unless strategicallymanaged.

↑/↓Interaction
Score
Evidence
Agreement Confidence
Water Efficiency and Pollution Prevention (6.3/6.4/6.6)

Environment-Demand

Agreement

No direct interaction

Evidence[+1,‐1]Confidence

Sustainable Production (15.1/15.5/15.9/15.10)

[0]

Score

Shi et al., 2017[0]

No direct interaction

A circular economy help in managing local biodiversity better by having
less resource use footprint↑

Interaction

Sustainable Development, Poverty Eradication and Reducing Inequalities
Chapter 5

5

495



↑

[+2]






Agreement

[0]

Score

Agreement

No direct interaction

Evidence







[+1]






Bilton et al., 2011; Scott, 2011; Kumar et al., 2012; Meldrum et al.,2013; Bartos and Chester, 2014; Hendrickson and Horvath, 2014; Kernet al., 2014; Holland et al., 2015; Fricko et al., 2016; Kim et al., 2017

Stefan and Paul, 2008; ECF, 2014; CDP, 2015; Khan et al., 2015; NCE,
2015; McCollum et al., 2018

Efficiency changes in the residential sector that lead to reduced energy Sustainable practices adopted by public and private bodies in theirdemand can lead to reduced requirements on energy supply. As water is operations (e.g., for goods procurement, supply chain management andused to convert energy into useful forms, the reduction in residential
accounting) create an enabling environment in which renewable energy
demand is anticipated to reduce water consumption and waste water, and energy efficiency measures may gain greater traction (McCollum etresulting in more clean water for other sectors and the environment. A al., 2018).
switch to low-carbon fuels in the residential sector can lead to a
reduction in water demand and waste water if the existing highercarbon fuel is associated with a higher water intensity than the lowercarbon fuel. However, in some situations the switch to a low-carbon fuelsuch as, for example, biofuel could increase water use compared toexisting conditions if the biofuel comes from a water-intensive
feedstock. As water is used to convert energy into useful forms, energy
efficiency is anticipated to reduce water consumption and waste water,
resulting in more clean water for other sectors and the environment.
Subsidies for renewables are anticipated to lead to the benefits andtrade-offs outlined when deploying renewables. Subsidies forrenewables could lead to improved water access and treatment if
subsidies support projects that provide both water and energy services(e.g., solar desalination).

[+2]



Bhojvaid et al., 2014

Confidence

Improved stoves has helped halt deforestation in rural India.



[+2]

↑

Interaction

Reduced Deforestation (15.2)

Confidence

Sustainable Practices and Lifestyles (12.6/12.7/12.8)

No direct interaction

No direct interaction

Evidence

↑

[0]

[0]

Score

↑

Interaction

Water Efficiency and Pollution Prevention (6.3/6.4/6.6)

Sweeney et al., 2013; Webb et al., 2013; Allen et al., 2015; Echegaray(2015); He et al., 2016; Hult and Larsson, 2016; Isenhour and Feng,
2016; van Sluisveld et al., 2016; Zhao et al., 2017; Liu et al., 2017;
Sommerfeld et al., 2017

Bartos and Chester (2014); Fricko et al. (2016); Holland et al. (2016)


Technological improvements alone are not sufficient to increase energy
savings. Zhao et al. (2017) found that building technology and occupant
behaviours interact with each other and finally affect energy
consumption from home. They found that occupant habits could nottake advantage of more than 50% of energy efficiency potential allowedby an efficient building. In the electronic segment, product obsolescence
represents a key challenge for sustainability. Echegaray (2016) discusses
the dissonance between consumers' product durability experience,orientations to replace devices before terminal technical failure, andperceptions of industry responsibility and performance. The results fromtheir urban sample survey indicate that technical failure is far surpassedby subjective obsolescence as a cause for fast product replacement. At
the same time Liu et al. (2017) suggest that we need to go beyond
individualist and structuralist perspectives to analyse sustainableconsumption (i.e., combines both human agency paradigm and social
structural perspective).

[+2]

Interaction
Score
Evidence
Agreement Confidence
Responsible and Sustainable Consumption

Behavioural changes in the residential sector that lead to reduced
energy demand can lead to reduced requirements on energy supply. As
water is used to convert energy into useful forms, the reduction inresidential demand is anticipated to reduce water consumption andwaste water, resulting in more clean water for other sectors and theenvironment.

↑Interaction
Score
Evidence
Agreement Confidence
Water Efficiency and Pollution Prevention (6.3/6.4/6.6)

Environment-Demand (continued)

Buildings

496

Behavioural Response5

Accelerating Energy Efficiency Improvement

Chapter 5
Sustainable Development, Poverty Eradication and Reducing Inequalities

Accelerating Energy Efficiency Improvement

Buildings

Transport

Behavioural Response

Improved Access and Fuel Switch to Modern
Low-carbon Energy[+2,‐1]







↑/↓

[+2,‐1]






Interaction
Score
Evidence
Agreement Confidence
Sustainable Use and Management of Natural Resource (12.2)

[+2]







↑

[+2]






Ensure Sustainable Consumption amd Production Patterns(12.3)

Hejazi et al., 2015; Cibin et al., 2016; Fricko et al., 2016; Song et al.,2016; Rao and Pachauri, 2017

[+2]






[+2]




Sustainable Consumption (12.2/12.8)
Relational complex transport behaviour resulting in significant growth inenergy-inefficient car choices, as well as differences in mobility patterns(distances driven, driving styles) and actual fuel consumption between
different car segments all affect non-progress on transport
decarbonization. Consumption choices and individual lifestyles are
situated and tied to the form of the surrounding urbanization. Major
behavioural changes and emissions reductions require understanding of
this relational complexity, consideration of potential interactions withother policies, and the local context and implementation of bothcommand-and-control as well as market-based measures.

↑

Vidic et al., 2013; Holland et al., 2015; Fricko et al., 2016; Tiedeman et Stanley et al., 2011; Gallego et al., 2013; Heinonen et al., 2013; Aamaasal., 2016
and Peters, 2017; Azevedo and Leal, 2017; Gössling and Metzler, 2017

Similar to behavioural changes, efficiency measures in the transport
sector that lead to reduced transport demand can lead to reduced
transport energy supply. As water is used to produce a number ofimportant transport fuels, the reduction in transport demand isanticipated to reduce water consumption and waste water, resulting in
more clean water for other sectors and the environment.

↑

Water Efficiency and Pollution Prevention (6.3/6.4/6.6)

Vidic et al., 2013; Holland et al., 2015; Fricko et al., 2016; Tiedeman et Kagawa et al., 2015; Lin et al., 2015; Creutzig et al., 2016
al., 2016

Behavioural changes in the transport sector that lead to reduced
Urban carbon mitigation must consider the supply chain management oftransport demand can lead to reduced transport energy supply. As water imported goods, the production efficiency within the city, theis used to produce a number of important transport fuels, the reduction consumption patterns of urban consumers, and the responsibility of thein transport demand is anticipated to reduce water consumption andultimate consumers outside the city. Important for climate policy ofwaste water, resulting in more clean water for other sectors and themonitoring the CO2 clusters that dominate CO2 emissions in global
environment.
supply chains, because they offer insights on where climate policy canbe effectively directed.

↑

Water Efficiency and Pollution Prevention (6.3/6.4/6.6)

Hejazi et al., 2015; Cibin et al., 2016; Fricko et al., 2016; Song et al.,2016; Rao and Pachauri, 2017

A switch to low-carbon fuels in the residential sector can lead to a
A switch to low-carbon fuels in the residential sector can lead to a
reduction in water demand and waste water if the existing higherreduction in water demand and waste water if the existing highercarbon fuel is associated with a higher water intensity than the lower- carbon fuel is associated with a higher water intensity than the lowercarbon fuel. However, in some situations the switch to a low-carbon fuel carbon fuel. However, in some situations the switch to a low-carbon fuelsuch as, for example, biofuel could increase water use compared tosuch as, for example, biofuel could increase water use compared toexisting conditions if the biofuel comes from a water-intensive
existing conditions if the biofuel comes from a water-intensive
feedstock. Improved access to energy can support clean water andfeedstock. Improved access to energy can support clean water andsanitation technologies. If energy access is supported with watersanitation technologies. If energy access is supported with waterintensive energy sources, there could be trade-offs with water efficiency intensive energy sources, there could be trade-offs with water efficiency
targets.
targets.

↑/↓

Interaction
Score
Evidence
Agreement Confidence
Access to Improved Water and Sanitation (6.1/6.2), Water
Efficiency and Pollution Prevention (6.3/6.4/6.6)

Environment-Demand (continued)

Interaction

[0]

[0]

[0]

Score

Agreement

No direct interaction

No direct interaction

No direct interaction

Evidence

Confidence

[+2]







[0]

[0]

No direct interaction

No direct interaction

Bazilian et al., 2011; Karekezi et al., 2012; Bailis et al., 2015; Winter etal., 2015; McCollum et al., 2018

Ensuring that the world’s poor have access to modern energy services
would reinforce the objective of halting deforestation, since firewood
taken from forests is a commonly used energy resource among the poor
(McCollum et al., 2018).

↑

Interaction
Score
Evidence
Agreement Confidence
Healthy Terrestrial Ecosystems (15.1/15.2/15.4/15.5/15.8)

Sustainable Development, Poverty Eradication and Reducing Inequalities
Chapter 5

5

497

498

Transport[+2,‐1]







↑

[+2]






Interaction
Score
Evidence
Agreement Confidence
Ensure Sustainable Consumption and Production Patterns(12.3)

Hejazi et al., 2015; Fricko et al., 2016; Song et al., 2016

Figueroa et al., 2014; IPCC, 2014; Pietzcker et al., 2014; Creutzig et al.,2015

A switch to low-carbon fuels in the transport sector can lead to a
Due to persistent reliance on fossil fuels, it is posited that transport isreduction in water demand and waste water if the existing highermore difficult to decarbonize than other sectors. This study partiallycarbon fuel is associated with a higher water intensity than the lower- confirms that transport is less reactive to a given carbon tax than the
carbon fuel. However, in some situations the switch to a low-carbon fuel non-transport sectors: in the first half of the century, transport
such as, for example, biofuel could increase water use compared tomitigation is delayed by 10–30 years compared to non-transport
existing conditions if the biofuel comes from a water-intensive
mitigation. The extent to which earlier mitigation is possible strongly
feedstock. Transport electrification could lead to trade-offs with water depends on implemented technologies and model structures.
use if the electricity is provided with water intensive power generation.

↑/↓

Interaction
Score
Evidence
Agreement Confidence
Water Efficiency and Pollution Prevention (6.3/6.4/6.6)

5

Improved Access and Fuel Switch toModern Low-carbon Energy

Environment-Demand (continued)

Interaction

[0]

Score

Agreement

No direct interaction

Evidence

ConfidenceInteraction

[0]

Score

Agreement

No direct interaction

Evidence

Confidence

Chapter 5
Sustainable Development, Poverty Eradication and Reducing Inequalities

Increased Use of Biomass

Non-biomass Renewables - solar, wind hydroReplacing CoalInteraction
Score
Evidence
Agreement Confidence
Marine Economies (14.7)/ Marine Protection(14.1/14.2/14.4/14.5)

Interaction
Score
Evidence
Agreement Confidence
Healthy Terrestrial Ecosystems (15.1/15.2/15.4/15.5/15.8)

Natural Resource Protection (12.2/12.3/12.4/12.5)

Hejazi et al., 2015; Bonsch et al., 2016; Cibin et al., 2016; Song et al.,2016; Gao and Bryan, 2017; Griffiths et al., 2017; Ha and Wu, 2017;
Taniwaki et al., 2017; Woodbury et al., 2018

Banerjee et al., 2012; Riahi et al., 2012; Schwanitz et al., 2014;
Bhattacharyya et al., 2016; Cameron et al., 2016; McCollum et al., 2018[0]

No direct interaction[+1,‐2]







Smith et al., 2010, 2014; Acheampong et al., 2017; McCollum et al.,2018

Protecting terrestrial ecosystems, sustainably managing forests, halting
deforestation, preventing biodiversity loss and controlling invasive alien
species could potentially clash with renewable energy expansion, if thatwould mean constraining large-scale utilization of bioenergy orhydropower. Good governance, cross-jurisdictional coordination andsound implementation practices are critical for minimizing trade-offs(McCollum et al., 2018).

↑/↓

Healthy Terrestrial Ecosystems (15.1/15.2/15.4/15.5/15.8)

Banerjee et al., 2012; Riahi et al., 2012; Schwanitz et al., 2014;
Inger et al., 2009; Michler-Cieluch et al., 2009; Buck and Krause, 2012; Alho, 2011; Garvin et al., 2011; Grodsky et al., 2011; Jain et al., 2011;
Bhattacharyya et al., 2016; Cameron et al., 2016; McCollum et al., 2018 WBGU, 2013; Cooke et al., 2016; Matthews and McCartney, 2018;
Kumar et al., 2011; Kunz et al., 2011; Wiser et al., 2011; Dahl et al.,
McCollum et al., 20182012; de Lucas et al., 2012; Ziv et al., 2012; Lovich and Ennen, 2013;
Smith et al., 2013; Matthews and McCartney, 2018↑/↓[+1,‐2]


↑
[+2]




Biomass expansion could lead to increased water stress when irrigated Switching to renewable energy reduces the depletion of finite natural
feedstocks and water-intensive processing steps are used. Bioenergy
resources.
crops can alter flow over land and through soils as well as require
fertilizer, and this can reduce water availability and quality. Planting
bioenergy crops on marginal lands or in some situations to replace
existing crops can lead to reductions in soil erosion and fertilizer inputs,
improving water quality.

Water Efficiency and Pollution Prevention (6.3/6.4/6.6)

Bilton et al., 2011; Scott et al., 2011; Kumar et al., 2012; Ziv et al.,2012; Meldrum et al., 2013; Kern et al., 2014; Grill et al., 2015; Fricko
et al., 2016; Grubert, 2016; De Stefano et al., 2017↑/↓[+2,‐2]


↑
[+2]



↑/↓
[2,‐1]



↓
[‐1]




Ocean-based energy from renewable sources (e.g., offshore wind farms, Landscape and wildlife impact for wind; habitat impact for hydropower.
Wind/solar renewable energy technologies are associated with very low Renewable energy and energy efficiency slow the depletion of several
water requirements compared to existing thermal power planttypes of natural resources, namely coal, oil, natural gas and uranium. In wave and tidal power) are potentially significant energy resource bases
for island countries and countries situated along coastlines. Multi-use
technologies. Widespread deployment is therefore anticipated to lead to addition, the phasing-out of fossil fuel subsidies encourages lessplatforms combining renewable energy generation, aqua-culture,
improved water efficiency and avoided thermal pollution. However,wasteful energy consumption; but if that is done, then the policies
managing wind and solar variability can increase water use at thermal implemented must take care to minimize any counteracting adverse side- transport services and leisure activities can lay the groundwork for more
effects on the poor (e.g., fuel price rises). (Quote from McCollum et al., diversified marine economies. Depending on the local context andpower plants and can cause poor water quality downstream fromprevailing regulations, ocean-based energy installations could eitherhydropower plants. Access to distributed renewables can provide power 2018)
induce spatial competition with other marine activities, such as tourism,
to improve water access, but could also lead to increased groundwater
shipping, resources exploitation, and marine and coastal habitats andpumping and stress if mismanaged. Developing dams to support reliable
protected areas, or provide further grounds for protecting those exacthydropower production can fragment rivers and alter natural flows
habitats, therefore enabling marine protection. (Quote from McCollum
reducing water and ecosystem quality. Developing dams to support
et al., 2018) Hydropower disrupts the integrity and connectivity of
reliable hydropower production can result in disputes for water in basinsaquatic habitats and impacts the productivity of inland waters and theirwith up- and down-stream users. Storing water in reservoirs increases
fisheries.
evaporation, which could offset water conservation targets and reduce
availability of water downstream. However, hydropower plays animportant role in energy access for water supply in developing regions,can support water security, and has the potential to reduce water
demands if used without reservoir storage to displace other water
intensive energy processes.

Interaction
Score
Evidence
Agreement Confidence Interaction
Score
Evidence
Agreement Confidence
Water Efficiency and Pollution Prevention (6.3/6.4/6.6)/ Access
Natural Resource Protection (12.2/12.3/12.4/12.5)
to Improved Water and Sanitation (6.1/6.2)

Environement-Supply

Sustainable Development, Poverty Eradication and Reducing Inequalities
Chapter 55499

[+2,-1]

&&&

JJJ

«««



↑

[+1]






Natural Resource Protection (12.2/12.3/12.4/12.5)

Confidence[+1,‐2]







Meldrum et al., 2013; Byers et al., 2016; Fricko et al., 2016; Brandl etal., 2017

CCU/S requires access to water for cooling and processing which could
contribute to localized water stress. However, CCS/U processes canpotentially be configured for increased water efficiency compared to asystem without carbon capture via process integration. Coal mining tosupport clean coal CCS will negatively impact water resources due tothe associated water demands, waste water and land-use requirements.

↑/↓

Water Efficiency and Pollution Prevention (6.3/6.4/6.6)[0]

No direct interaction

Banerjee et al., 2012; Riahi et al., 2012; Schwanitz et al., 2014;
Bhattacharyya et al., 2016; Cameron et al., 2016; McCollum et al., 2018

Agreement

No direct interaction

Evidence

Meldrum et al., 2013; Byers et al., 2016; Fricko et al., 2016; Brandl etal., 2017; Dooley and Kartha, 2018



[0]

Score

Switching to renewable energy reduces the depletion of finite natural
resources. On the other hand, the availability of underground storage islimited and therefore reduces the benefits of switching from finite
resources to bioenergy.

[+1,‐2]

Interaction

CCU/S requires access to water for cooling and processing which could
contribute to localized water stress. However, CCS/U processes canpotentially be configured for increased water efficiency compared to asystem without carbon capture via process integration. The bioenergy
component adds the additional trade-offs associated with bioenergy
use. Large-scale bioenergy increases input demand, resulting inenvironmental degradation and water stress.

↑/↓

Water Efficiency and Pollution Prevention (6.3/6.4/6.6)

Webster et al., 2013; Holland et al., 2015; Fricko et al., 2016; Raptis etal., 2016

Nuclear power generation requires water for cooling which can lead to
localized water stress and the resulting cooling effluents can cause
thermal pollution in rivers and oceans.

↑/↓Interaction
Score
Evidence
Agreement Confidence
Water Efficiency and Pollution Prevention (6.3/6.4/6.6)

Environement-Supply (continued)

Replacing Coal

Advanced Coal

Nuclear/Advanced
Nuclear

500

CCS: Bioenergy

5

CCS: Fossil

Interaction

[0]

[0]

[0]

Score

Agreement

No direct interaction

No direct interaction

No direct interaction

Evidence

Confidence
[-1]

&&

JJ

««

[+1,‐2]







[0]

No direct interaction

Smith et al., 2010, 2014; Acheampong et al., 2017; Dooley and Kartha,
2018, McCollum et al., 2018)

Protecting terrestrial ecosystems, sustainably managing forests, halting
deforestation, preventing biodiversity loss and controlling invasive alien
species could potentially clash with renewable energy expansion, if thatwould mean constraining large-scale utilization of bioenergy orhydropower. Good governance, cross-jurisdictional coordination andsound implementation practices are critical for minimizing trade-offs(McCollum et al., 2018). Large-scale bioenergy increases input demand,
resulting in environmental degradation and water stress.

↑/↓

Healthy Terrestrial Ecosystems (15.1/15.2/15.4/15.5/15.8)

Bickerstaff et al., 2008; Sjoberg and Sjoberg, 2009; Ahearne, 2011;
Corner et al., 2011; Visschers and Siegrist, 2012; IPCC, 2014

Safety and waste concerns from uranium mining and milling.

↓

Interaction
Score
Evidence
Agreement Confidence
Healthy Terrestrial Ecosystems (15.1/15.2/15.4/15.5/15.8)

Chapter 5
Sustainable Development, Poverty Eradication and Reducing Inequalities

Land-based Greenhouse Gas Reduction and Soil Carbon
Sequestration

Behavioural Response: Sustainable Healthy Diets and Reduced Food Waste

Agriculture and Livestock



↑

[+1]




Campbell et al., 2014; Lakshmi et al., 2015



Behnassi et al., 2014; Bustamante et al., 2014; P. Smith et al., 2016b


Millet or sorghum yield can double as compared with unimproved landby more than 1 tonne per hectare due to sustainable intensification. An
integrated approach to safe applications of both conventional andmodern agricultural biotechnologies will contribute to increased yield(Lakshmi et al., 2015).

[+1,‐1]

Ensure Sustainable Production Patterns (12.3)

Soil carbon sequestration can alter the capacity of soils to store water,which impacts the hydrological cycle and could be positive or negativefrom a water perspective, dependent on existing conditions. CSA enrichlinkages across sectors including management of water resources.
Minimum tillage systems have been reported to reduce water erosion
and thus sedimentation of water courses (Bustamante et al., 2014).

↑/↓

Water Efficiency and Pollution Prevention (6.3/6.4/6.6)
Stehfest et al., 2009; Steinfeld and Gerber, 2010; Garnett, 2011; Ingram,
2011; Beddington et al., 2012; Kummu et al., 2012; Bellarby et al.,2013; Dagevos and Voordouw, 2013; Smith, 2013; Bajželj et al., 2014;
Hedenus et al., 2014; Tilman and Clark, 2014; West et al., 2014; Hiç etal., 2016; Lamb et al., 2016[+2]

Khan et al., 2009; Ingram, 2011; Kummu et al., 2012; Haileselassie etal., 2013; Bajželj et al., 2014; Tilman and Clark, 2014; Walker et al.,2014; Ran et al., 2016

↑





Reduce loss and waste in food systems, processing, distribution and bychanging household habits. To reduce environmental impact of livestock
both production and consumption trends in this sector should be traced.
Livestock production needs to be intensified in a responsible way (i.e.,
be made more efficient in the way that it uses natural resources).
Wasted food represents a waste of all the emissions generated during
the course of producing and distributing that food. Mitigation measures
include: eat no more than needed to maintain a healthy body weight;
eat seasonal, robust, field-grown vegetables rather than protected,
fragile foods prone to spoilage and requiring heating and lighting intheir cultivation, refrigeration stage; consume fewer foods with low
nutritional value e.g., alcohol, tea, coffee, chocolate and bottled water(these foods are not needed in our diet and need not be produced); shop
on foot or over the Internet (reduced energy use). Reduction in foodwaste will not only pave the path for sustainable production but will
also help in achieving sustainable consumption (Garnett, 2011). Reducemeat consumption to encourage more sustainable eating practices.

[+2,‐1]





Reduced food waste avoids direct water demand and waste water forcrops and food processing, and avoids water used for energy supply by
reducing agricultural, food processing and waste management energy
inputs. Healthy diets will support water efficiency targets if the shift
towards healthy foods results in food supply chains that are less water
intensive than the supply chains supporting the historical dietary
pattern.

↑/↓

Interaction
Score
Evidence
Agreement Confidence
Ensure Sustainable Consumption and Production Patterns,
Sustainable Practices and Lifestyle (12.3/12.4/12.6/12.7/12.8)

Interaction
Score
Evidence
Agreement Confidence
Water Efficiency and Pollution Prevention (6.3/6.4/6.6)

Environement-Other

Interaction

[0]

[0]

Score

Agreement

No direct interaction

No direct interaction

Evidence

Confidence

[+1]







[+1,‐1]







Lybbert and Sumner, 2010; Behnassi et al., 2014; Harvey et al., 2014;
IPCC, 2014; Lamb et al., 2016)

Agricultural intensification can promote conservation of biologicaldiversity by reducing deforestation, and by rehabilitation and restorationof biodiverse communities on previously developed farm or pastureland. However, planting monocultures on biodiversity hot spots canhave adverse side-effects, reducing biodiversity. Genetically modified
crops reduce demand for cultivated land. Adaptation of integrated
landscape approaches can provide various ecosystem services. CSA
enrich linkages across sectors including management of land and bioresources. Land sparing has the potential to be beneficial forbiodiversity, including for many species of conservation concern, but
benefits will depend strongly on the use of spared land. In addition,high yield farming involves trade-offs and is likely to be detrimental forwild species associated with farm land (Lamb et al., 2016).

↑/↓

Conservation of Biodiversity and Restoration of Land(15.1/15.5/15.9)

Stehfest et al., 2009; Kummu et al., 2012

Reducing food waste has secondary benefits like protecting soil fromdegradation, and decreasing pressure for land conversion into
agriculture and thereby protecting biodiversity.
The agricultural area that becomes redundant through the dietarytransitions can be used for other agricultural purposes such as energy
crop production, or will revert to natural vegetation. A global foodtransition to less meat, or even a complete switch to plant-based protein
food, could have a dramatic effect on land use. Up to 2,700 Mha ofpasture and 100 Mha of crop land could be abandoned (Quoted from
Stehfest et al., 2009)↑

Interaction
Score
Evidence
Agreement Confidence
Conservation of Biodiversity and Restoration of Land (15.1/
15.5/15.9)

Sustainable Development, Poverty Eradication and Reducing Inequalities
Chapter 55501

[+2,‐1]







[+1]






In the future, many developed countries will see a continuing trend inwhich livestock breeding focuses on other attributes in addition to
production and productivity, such as product quality, increasing animal
welfare, disease resistance (Thornton, 2010). Diet composition andquality are key determinants of the productivity and feed-use efficiency
of farm animals (Herrero, et al., 2013). Mechanisms for effecting
behavioural change in livestock systems need to be better understood byimplementing combinations of incentives and taxes simultaneously in
different parts of the world (Herrero and Thornton, 2013). Reducing theamount of human-edible crops that are fed to livestock represents areversal of the current trend of steep increases in livestock production,and especially of monogastrics, so would require drastic changes in
production and consumption (Schader et al., 2015).

↑

Interaction
Score
Evidence
Agreement Confidence
Ensure Sustainable Production Patterns and Restructing
Taxation (12.3/12c)[+1,‐1]







↑

[+1]




Ensure Sustainable Consumption (12.3)
[+2,‐1]







Zomer et al., 2008; Bustamante et al., 2014; Kibria, 2015; Lamb et al.,2016; Dooley and Kartha, 2018

Similar to REDD+, forest management alters the hydrological cycle
which could be positive or negative from a water perspective and isdependent on existing conditions. Forest landscape restoration can havea large impact on water cycles. Strategic placement of tree belts in lands
affected by dryland salinity can remediate the affected lands bymodifying landscape water balances. Watershed scale reforestation can
result in the restoration of water quality. Fast-growing species canincrease nutrient input and water inputs that can cause ecologicaldamage and alter local hydrological patterns. Reforestation of mixed
native species and in carefully chosen sites could increase biodiversity
and restore waterways, reducing run-off and erosion (Dooley andKartha, 2018).

↑/↓

Enhance Water Quality (6.3)

Zomer et al., 2008; Kibria, 2015; Bonsch et al., 2016; Gao and Bryan,
2017; Griffiths et al., 2017; Katila et al., 2017

[0]

Bastos Lima et al., 2017

No direct interaction

Forest management alters the hydrological cycle which could be positive Reduce the human pressure on forests, including actions to addressor negative from a water perspective and is dependent on existing
drivers of deforestation.
conditions. Conservation of ecosystem services indirectly could help
countries maintain watershed integrity. Forests provide sustainable andregulated provision and help in water purification.

↑/↓

Water Efficiency and Pollution Prevention (6.3/6.4/6.6)

Haileselassie et al., 2013; Schader et al., 2015; Kong et al., 2016; Ran et Thornton, 2010; Herrero and Thornton, 2013; Herrero et al., 2013;
al., 2016
Schader et al., 2015

Livestock efficiency measures are expected to reduce water required forlivestock systems as well as associated livestock waste water flows.
However, efficiency measures that include agricultural intensification
could increase water demands locally, leading to increased water stress
if the intensification is mismanaged. In scenarios where zero humanedible concentrate feed is used for livestock, freshwater use reduces by
21%.

↑/↓

Interaction
Score
Evidence
Agreement Confidence
Water Efficiency and Pollution Prevention (6.3/6.4/6.6)

Environement-Other (continued)

Agriculture and Livestock

ForestGreenhouse Gas Reduction from Improved Livestock
Production and Manure Management Systems

502

Reduced Deforestation, REDD+

5

Afforestation and Reforestation

[0]

[0]

Score

Agreement

No direct interaction

No direct interaction

Evidence

Confidence

[+2]







Kibria, 2015

Mangroves would help to enhance fisheries and tourism businesses.

↑

Marine Economies (14.7)/Marine Protection and Income
Generation (14.1/14.2/14.4/14.5)

Interaction

[+1]





Score
Evidence
Agreement
Restoration of Land (15.1)

Confidence

[+1]










↑
[+2]

Identified large amounts of land (749 Mha) globally as biophysicallysuitable and meeting the CDM eligibility criteria . Forest landscaperestoration can conserve biodiversity and reduce land degradation.
Mangroves reduce impacts of disasters (cyclones/storms/floods) acting
as live seawalls and enhance forest resources/biodiversity. Forest goal
can conserve/restore 3.9–8.8 m ha/year average, 77.2–176.9 m ha intotal and 7.7–17.7 m ha /year in 2030 of forest area by 2030 (Wolosin,2014). Forest and biodiversity conservation, protected area formationand forestry-based afforestation are practices that enhance resilience offorest ecosystems to climate change (IPCC, 2014). Strategic placementof tree belts in lands affected by dryland salinity can remediate theaffected lands by modifying landscape water balances and protect
livestock. It can restore biologically diverse communities on previously
developed farmland . Large-scale restoration is likely to benefit
ecosystem service provision, including recreation, biodiversity,
conservation and flood mitigation. Reforestation of mixed native species
and in carefully chosen sites could increase biodiversity, reducing run-off
and erosion .
Zomer et al., 2008; Bustamante et al., 2014; IPCC, 2014; Kibria, 2015;
Lamb et al., 2016; Epstein and Theuer, 2017; Dooley and Kartha, 2018

Conservation of Biodiversity and Restoration of Land(15.1/15.5/15.9)

Miles and Kapos, 2008; IPCC, 2014; Bastos Lima et al., 2015; Turpie etal., 2015; Epstein and Theuer, 2017; Katila et al., 2017

Policies and programmes for reducing deforestation and forest
degradation for rehabilitation and restoration of degraded lands canpromote conservation of biological diversity. Reduce the humanpressure on forests, including actions to address drivers of deforestation.
Efforts by the Government of Zambia to reduce emissions by REDD+
have contributed erosion control, ecotourism and pollination valued at
2.5% of the country's GDP.

↑

Conservation of Biodiversity, Sustainability of Terrestrial
Ecosystems (15.2/15.3/15.4/15.5/15.9)

Herrero et al., 2013; Schader et al., 2015

Grasslands are valuable, but improved management is required as grassaccounts for close to 50% of feed use in livestock systems . The scenario
with 100% reduction of food-competing-feedstuffs resulted in a 335
Mha decrease in arable land area, which corresponds to a decrease of
22% in arable and 7% in the total agricultural area .

↑Interaction

Chapter 5
Sustainable Development, Poverty Eradication and Reducing Inequalities

Enhanced WeatheringBlue Carbon

ForestOceans

Ocean Iron Fertilization

Behavioural Response(Responsible Sourcing)[+2,‐1]







↑

[+1]






Interaction
Score
Evidence
Agreement Confidence
Ensure Sustainable Production Patterns (12.3)

No direct interaction

[+2]







Vierros et al., 2015[0]

No direct interaction

Development of blue carbon resources (coastal and marine vegetatedecosystems) can lead to coordinated management of water in coastal
areas.

↑

Integrated Water Resources Management (6.3/6.5)[0]van Oel and Hoekstra, 2012; Launiainen et al., 2014; Hontelez, 2016

Hontelez, 2016

[0]

[0]

[0]

No direct interaction

No direct interaction

No direct interaction

At local levels, forest certification programmes and practicing
Responsible sourcing will have co-benefits for water efficiency andpollution prevention if the sourcing strategies incorporate water metrics. sustainable forest management provide the provision of raw materials
There is a risk that shifting supply sources could lead to increased water for a ‘low ecological footprint’ economy.
use in another part of the economy. At local levels, forest certification
programmes and practicing sustainable forest management provide
freshwater supplies.

↑/↓

Interaction
Score
Evidence
Agreement Confidence
Water Efficiency and Pollution Prevention (6.3/6.4/6.6)

Environement-Other (continued)

[0]

Score

Agreement

No direct interaction

Evidence

Confidence[+1,‐2]










No direct interaction

↑

[+3]







Conservation of Biodiversity and Restoration of Land(15.1/15.2/15.3/15.4/15.9)[0]Bartley, 2010; Hontelez, 2016[+2,‐1]







↓

[‐2]





Protect Inland Freshwater Systems (14.1)
Enhanced weathering (either by spreading lime or quicklime, inOlivine can contain toxic metals such as nickel which could accumulate
combination with CCS, over the ocean or olivine at beaches or thein the environment or disrupt the local ecosystem by changing the pH ofcatchment area of rivers) opposes ocean acidification. "End-of-century the water (in case of spreading in the catchment area of rivers).
ocean acidification is reversed under RCP4.5 and reduced by about twothirds under RCP8.5; additionally, surface ocean aragonite saturation
state, a key control on coral calcification rates, is maintained above 3.5
throughout the low latitudes, thereby helping maintain the viability oftropical coral reef ecosystems ." However, marine biology would also beaffected, in particular if spreading olivine is used, which works likeocean (iron) fertilization.
Köhler et al., 2010, 2013; Hartmann et al., 2013; Paquay and Zeebe,
Hartmann et al., 20132013; P. Smith et al., 2016a; Taylor et al., 2016↑/↓

Ocean Acidification, Nutrient Pollution (14.3/14.1)

Alongi, 2012; Potouroglou et al., 2017



Pettit et al., 2015; Sippo et al., 2016



Average difference of 31 mm per year in elevation rates between areas
with seagrass and unvegetated areas (case study areas: Scotland,
Kenya, Tanzania and Saudi Arabia); mangroves fostering sedimentaccretion of about 5mm a year.

[+2,0][+1,‐1]At the macro level, forest certification has done little to stem the tide offorest degradation, conversion of forest land to agriculture, and illegal
logging—all of which remain serious threats to Indonesian forests(Bartley, 2010). At local levels, forest certification programmes andpracticing sustainable forest management help in biodiversity
protection.

↑/↓

Interaction
Score
Evidence
Agreement Confidence
Sustainability and Conservation (15.1/15.2/15.3)

Mangroves could buffer acidification in their immediate vicinity;
seaweeds have not been able to mitigate the effect on ocean
foraminifera.

↑/~

Ocean Acidification, Nutrient Pollution (14.3/14.1)

Gnanadesikan et al., 2003; Jin and Gruber, 2003; Denman, 2008;
Lampitt et al., 2008; Smetacek and Naqvi, 2008; Güssow et al., 2010;
Oschlies et al., 2010; Trick et al., 2010; Williamson et al., 2012

OIF could exacerbate or reduce nutrient pollution, increase thelikelihood of mid-water deoxygenation, increase ocean acidification,
might contribute to the rebuilding of fish stocks in producing plankton,
therefore generating benefits for SISD, but might also be in conflict withdesigning MPAs.

↑/↓

Nutrient Pollution, Ocean Acidification, Fish Stocks, MPAs,
SISD (14.1/14.3/14.4/14.5/14.7)

Interaction

Sustainable Development, Poverty Eradication and Reducing Inequalities
Chapter 55503

ScoreEvidence
Agreement
Energy Savings (7.1/7.3/7.a/7.b)

ConfidenceInteraction
Score
Evidence
Agreement Confidence
Reduces Unemployment (8.2/8.3/8.4/8.5/8.6)

Interaction

Score
Evidence
Agreement Confidence
Infrastructure Renewal (9.1/9.3/9.5/9.a)

[+2]







↑

[+2]






↑

[+2]







Denis-Ryan et al., 2016; Åhman et al., 2017; Wesseling et al., 2017

Denis-Ryan et al., 2016; Åhman et al., 2017; Wesseling et al., 2017;
Griffin et al., 2018



Griffin et al., 2017; Wesseling et al., 2017

Innovation and New Infrastructure (9.2/9.4/9.5)

Deep decarbonization through radical technological change in EPI willlead to radical innovations, for example, in completely changing
industries' innovation strategies, plants and equipment, skills,
production techniques, design, etc. Radical CCS will need new
infrastructure to transport CO2.

[+2,‐2]

Decouple Growth from Environmental Degradation(8.1/8.2/8.4)

EPI s are important players for economic growth. Deep decarbonization
CCS for EPIs can be incremental, but need additional space and canneed additional energy, sometimes compensating for higher efficiency. of EPIs through radical innovation is consistent with well-below 2°C
For example, recirculating blast R furnace and CCS for iron steel means scenarios.
high energy demand; electric melting in glass can mean higher
electricity prices; in the paper industry, new separation and drying
technologies are key to reducing the energy intensity, allowing forcarbon neutral operation in the future; bio-refineries can reduce petrorefineries; DRI in iron and steel with H2 encourages innovation inhydrogen infrastructure; and the chemicals industry also encourage
renewable electricity and hydrogen as bio-based polymers can increasebiomass price.

↑/↓

Affordable and Sustainable Energy Sources

Stahel, 2013, 2017; Liu et al., 2014; Leider et al., 2015; Supino et al.,2015; Zheng et al., 2016; Fan et al., 2017; Shi et al., 2017

Karner et al., 2015[0]

No direct interaction

Stahel, 2013, 2017; Liu et al., 2014; Leider et al., 2015; Supino et al.,2015; Zheng et al., 2016; Fan et al., 2017; Shi et al., 2017

Sustainable Cities (15.6/15.8/15.9)

Karner et al., 2015

Karner et al., 2015; Griffin et al., 2018

Innovation and New Infrastructure (9.2/9.3/9.4/9.5/9.a)Riahi et al., 2012; Apeaning and Thollander, 2013; Goldthau, 2014;
Bhattacharyya et al., 2016; Meltzer, 2016; McCollum et al., 2018

Confidence




↑
[+2]



↑
[+2]

Industries are becoming suppliers of energy, waste heat, water and roof
A circular economy instead of linear global economy is helping new
innovation, and infrastructure can achieve climate goals and can help in tops used for solar energy generation, and supply to neighbourial
human settlements, therefore reducing primary energy demand, which
economic growth through industrialization which saves on resources
also makes towns and cities grow sustainably.
and the environment and supports small, medium and even large
industries, which can lead to employment generation. So new
regulations, incentives and revised tax regime can help in achieving thegoal.

Economic Growth with Decent Employment (8.1/8.2/8.3/8.4)

Altieri et al., 2016; Fernando et al., 2017; Johansson and Thollander,
2018

[+2]

Score
Evidence
Agreement
Sustainable Cities (15.6/15.8/15.9)

Industries are becoming suppliers of energy, waste heat and water toneighbourial human settlements, and therefore there is a reduced
primary energy demand, which also makes towns and cities growsustainably.

↑Interaction




↑
[+2]



↑
[+2]

Industries are becoming suppliers of energy, waste heat, water and roof The circular economy instead of linear global economy can achieve
tops used for solar energy generation, and therefore helping to reduce climate goals and can help in economic growth throughindustrialization, which saves on resources and the environment andprimary energy demand. CHP in chemical industries can help in
supports small, medium and even large industries, which can lead toproviding surplus power in the grid.
employment generation. So new regulations, incentives and a revised
tax regime can help in achieving the goal.

Sustainable and Modern (7.2/7.a)Apeaning and Thollander, 2013; Chakravarty et al., 2013; IPCC, 2014;
Karner et al., 2015; Zhang et al., 2015; Li et al., 2016; Fernando et al.,2017; Wesseling et al., 2017

↑




↑
[+1]



↑
[+1]




Transitioning to a more renewables-based energy system that is highly
Unemployment rate reduction from 25% to 12% in South Africa.
Energy efficiency leads to reduced energy demand and hence energy
supply and energy security, reduces import. Positive rebound effect can Enhances firm productivity and technical and managerial capacity of the energy efficient is well-aligned with the goal of upgrading energy
infrastructure and making the energy industry more sustainable. At theemployees. New jobs for managing energy efficiency opens up
raise demand but to a very less extent due to low rebound effect insame time, infrastructure upgrades in other parts of the economy, suchindustry sector in many countries and by appropriate mix of industries opportunities in energy service delivery sector.
as modernized telecommunications networks, can create the conditions(China) can maintain energy savings gain. Supplying surplus energy tofor a successful expansion of renewable energy and energy efficiency
cities is also happening, proving maintenance culture, switching off idle
measures (e.g., smart metering and demand-side management;
equipment helps in saving energy (e.g Ghana).
McCollum et al., 2018).

Interaction

Economic-Demand

Industry

Accelerating Energy Efficiency
Improvement504

Low-Carbon Fuel Switch

5

Decarbonization/CCS/CCU

Chapter 5
Sustainable Development, Poverty Eradication and Reducing Inequalities

Behavioural Response

Buildings

Accelerating Energy Efficiency Improvement



↑/↓

[+2,‐1]








↑

[+2]







↑

[+2]







Innovation and New Infrastructure (9.2/9.4/9.5)

Anda and Temmen, 2014; Roy et al., 2018

↑

[+2]







Urban Environmental Sustainability (11.3/11.6/11.b/11.c)

Anda and Temmen, 2014; Roy et al., 2018

Creutzig et al., 2012; Kahn Ribeiro et al., 2012; Riahi et al., 2012;
Bongardt et al., 2013; Grubler and Fisk, 2013; Raji et al., 2015; Kim etal., 2017; McCollum et al., 2018





Confidence

Anda and Temmen, 2014; Roy et al., 2018
Babiker and Eckaus, 2007; Fankhauser and Tepic, 2007; Gohin, 2008;
McLeod et al., 2013; Noris et al., 2013; Bhojvaid et al., 2014;
Holopainen et al., 2014; Kwong et al., 2014; Yang et al., 2014; Cameron Frondel et al., 2010; Dinkelman, 2011; Guivarch et al., 2011; Jackson
et al., 2015; Liddell and Guiney, 2015; Shah et al., 2015; Berrueta et al., and Senker, 2011; Borenstein, 2012; Creutzig et al., 2013; Blyth et al.,2014; Clarke et al., 2014; Dechezleprêtre and Sato, 2014; Bertram et al.,2017; Kim et al., 2017; Salvalai et al., 20172015; Johnson et al., 2015; IRENA, 2016; A. Smith et al., 2016; Berrueta
et al., 2017; McCollum et al., 2018





Score
Evidence
Agreement
Sustainable Cities (15.6/15.8/15.9)

Renewable energy technologies and energy efficient urban infrastructure
solutions (e.g., public transit) can also promote urban environmental
sustainability by improving air quality and reducing noise. Efficienttransportation technologies powered by renewably based energy carriers
will be a key building block of any sustainable transport system(McCollum et al., 2018). Green buildings help in sustainable
construction.

[+2]

Employment Opportunities (8.2/8.3/8.5/8.6)/Strong Financial
Institutions (8.10)[+2]Interaction

Adoption of smart meters and smart grids following community-based Behavioural change programmes help in making cities more sustainable.
social marketing help with infrastructure expansion. People are adopting
solar rooftops, white roof/vertical garden/green roofs at much fasterrates due to new innovations and regulations.

↑Interaction
Score
Evidence
Agreement Confidence
Innovation and New Infrastructure (9.2/9.4/9.5)

Deploying renewables and energy efficient technologies, when
There is high agreement among researchers based on a great deal of
Adoption of smart meters and smart grids following community-based
combined with other targeted monetary and fiscal policies, can help
evidence across various countries that energy efficiency improvementsocial marketing help in infrastructure expansion. Statutory norms to
reduces energy consumption and therefore leads to energy savings (e.g., spur innovation and reinforce local, regional and national industrial and enhance energy and resource efficiency in buildings is encouraging
efficient stoves save bioenergy). Countries with higher hours of use due employment objectives. Gross employment effects seem likely to begreen building projects.
positive; however, uncertainty remains regarding the net employment
to higher ambient temperatures or more carbon intensive electricity
grids benefit more from available improvements in energy efficiency and effects due to several uncertainties surrounding macro-economic
feedback loops playing out at the global level. Moreover, theuse of refrigerant transition.
distributional effects experienced by individual actors may varysignificantly. Strategic measures may need to be taken to ensure that alarge-scale switch to renewable energy minimizes any negative impacts
on those currently engaged in the business of fossil fuels (e.g.,
government support could help businesses re-tool and workers re-train).
To support clean energy and energy efficiency efforts, strengthened
financial institutions in developing country communities are necessary
for providing capital, credit and insurance to local entrepreneurs
attempting to enact change (McCollum et al., 2018).

↑

Increase in Energy Savings (7.3)

Chakravarty et al., 2013; Gyamfi et al., 2013; Hori et al., 2013; Huebner Anda and Temmen, 2014
et al., 2013; Jain et al., 2013; Sweeney et al., 2013; Webb et al., 2013;
Yue et al., 2013; Anda and Temmen, 2014; Allen et al., 2015; Noonan
et al., 2015; de Koning et al., 2016; Isenhour and Feng, 2016; Santarius
et al., 2016; Song et al., 2016; van Sluisveld et al., 2016; Sommerfeld etal., 2017; Zhao et al., 2017; Roy et al., 2018




↑
[+2]



↑
[+2]

Lifestyle change measures and adoption behaviour affect residential
Behavioural change programmes help in sustaining energy savings
energy use and implementation of efficient technologies as residential through new infrastructure developments.
HVAC systems. Also, social influence can drive energy savings in users
exposed to energy consumption feedback. Effect of autonomous
motivation on energy savings behaviour is greater than that of othermore established predictors, such as intentions, subjective norms,
perceived behavioural control and past behaviour. Use of a hybrid
engineering approach using social psychology and economic behaviourmodels are suggested for residential peak electricity demand response.
However, some take-back in energy savings can happen due to reboundeffects unless managed appropriately or accounted for welfare
improvement. Adjusting thermostats helps in saving energy. Uptake ofenergy efficient appliances by households with an introduction toappliance standards, training, promotional material dissemination andthe desire to save on energy bills are helping to change acquisition
behaviour.

Interaction
Score
Evidence
Agreement Confidence Interaction
Score
Evidence
Agreement Confidence
Saving Energy, Improvement in Energy Efficiency (7.3/7.a/7.b) Progressively Improve Resource Efficiency (8.4), Employment
Opportunities (8.2/8.3/8.5/8.6)

Economic-Demand (continued)

Sustainable Development, Poverty Eradication and Reducing Inequalities
Chapter 5

5

505

[+2]





Score
Evidence
Agreement
Meeting Energy DemandConfidence
[+2]






↑

[+2]




Promote Sustained, Inclusive Economic Growth (8.3)
Build Resilient Infrastructure (9.1)

[+3]

↑





Evidence
Agreement
Housing (11.1)

Confidence

Gouldson et al., 2015; Karkatsoulis et al., 2016

Figueroa et al., 2013; Gouldson et al., 2015; Vasconcellos andMendonça, 2016; Alahakoon, 2017

Ajanovic, 2015; Månsson, 2016; Alahakoon, 2017; Wolfram et al., 2017 IPCC, 2014; Creutzig et al., 2015; Carrara and Longden, 2017

Figueroa et al., 2013; Gouldson et al., 2015; Vasconcellos andMendonça, 2016; Lall et al., 2017

Make Cities and Human Settlements Inclusive, Safe, Resilient
↑
[+2]



In rapidly growing cities, the carbon savings from investments at scale,in cost-effective low-carbon measures, could be quickly overwhelmed –in as little as 7 years – by the impacts of sustained population andeconomic growth, highlighting the need to build capacities that enable
the exploitation not only of the economically attractive options in the
short term but also of those deeper and more structural changes that arelikely to be needed in the longer term. With hybrid electric vehicles andplug-in electric vehicles, there is the emergence of new concepts intransportation, such as electric highways.

Increase Share of Renewable (7.2)
Promote Sustained, Inclusive Economic Growth (8.3)
Help Building Inclusive Infrastructure (9.1/9.a)
↑
[+2]



↑/↓
[+2,‐2]



↑
[+2]



Lack of appropriate infrastructure leads to limited access to jobs for the
Biofuel increases share of the renewables but can perform poorly if too The decarbonization of the freight sector tends to occur in the second
part of the century, and the sector decarbonizes by a lower extent than urban poor (Africa, Latin America, India).
many countries increase their use of biofuel, whereas electrification
the rest of the economy. Decarbonizing road freight on a global scale
performs best when many other countries implement this technology.
remains a challenge even when notable progress in biofuels and electric
The strategies are not mutually exclusive and simultaneous
implementation of some provides synergies for national energy security. vehicles has been accounted for.
Therefore, it is important to consider the results of material andcontextual factors that co-evolve. Electric vehicles using electricity fromrenewables or low carbon sources combined with e-mobility options
such as trolley buses, metros, trams and electro buses, as well as
promote walking and biking, especially for short distances, needconsideration.

Dulac, 2013; Aamaas and Peters, 2017; Martínez-Jaramillo et al., 2017; Song et al., 2016
Xylia and Silveira, 2017

↑
[+2]



The two most important elements of making cities sustainable are
efficient buildings and transport (e.g., Macau).

Shukxin et al., 2016

↑
[+2]



Combining promotion of mass transportation – train lines, tram lines,
BRTs, gondola lift systems, bicycle-sharing systems and hybrid buses –and telecommuting reduces traffic and significantly contributes tomeeting climate targets. A comprehensive package of complementary
mitigation options is necessary for deep and sustained emissions
reductions. In Sweden, a public bus fleet is aiming more towards
decarbonization than efficiency.

↑/↓[+2,‐2]Significant opportunities to slow travel growth and improve efficiency
exist and, similarly, alternatives to petroleum exist but have different
characteristics in terms of availability, cost, distribution, infrastructure,storage and public acceptability. Production of new technologies, fuels
and infrastructure can favour economic growth; however, efficientfinancing of increased capital spending and infrastructure is critical.

↑
[+2]



Accelerating efficiency in tourism transport reduces energy demand(China).

Energy Savings ( 7.3/7.a/7.b)

Lucas and Pangbourne, 2014; Suckall et al., 2014; Klausbruckner et al., Dulac, 2013; Aamaas and Peters, 2017; Martínez-Jaramillo et al., 2017; Ahmad and Puppim de Oliveira, 2016; Hallegatte et al., 2016a2016
Xylia and Silveira, 2017
Promote Sustained, Inclusive Economic Growth (8.3)
Build Resilient Infrastructure (9.1)
Make Cities Sustainable (11.2/11.3)

↑
[+2]



Climate change threatens to worsen poverty, therefore pro-poor
mitigation policies are needed to reduce this threat; for example,
investing more and better in infrastructure by leveraging private
resources and using designs that account for future climate change andthe related uncertainty.

Make Cities and Human Settlements Inclusive, Safe, Resilient

Bhattacharyya et al., 2016; Song et al., 2016; UN, 2016; McCollum etal., 2018; Roy et al., 2018

Ensuring access to basic housing services implies that households haveaccess to modern energy forms. (Quote from McCollum et al., 2018)
Solar roof tops in Macau make cities sustainable. Introduction of
incentives and norms for solar/white/green rooftops in cities are helping
to accelerate the expansion of the infrastructure.

Score

Interaction

Figueroa and Ribeiro, 2013; Ahmad and Puppim de Oliveira, 2016

↑
[+2]



↓
[‐2]



↑
[+2]



Behavioural responses will reduce the volume of transport needs and, by Policy contradictions (e.g., standards, efficient technologies leading to As people prefer more mass transportation – train lines, tram lines,
increased electricity prices leading the poor to switch away fromextension, energy demand.
BRTs, gondola lift systems, bicycle-sharing systems and hybrid buses –clean(er) fuels) and unintended outcomes (e.g., redistribution of income and telecommuting, the need for new infrastructure increases.
generated by carbon taxes) results in contradictions of the primary aimsof (productive) job creation and poverty alleviation, and in trade-offs
between mitigation, adaptation and development policies. Detailed
assessments of mitigation policies consequences requires developing
methods and reliable evidence to enable policymakers to more
systematically identify how different social groups may be affected by
the different available policy options.

Energy Savings ( 7.3/7.a/7.b)Interaction
Score
Evidence
Agreement Confidence
Innovation and New Infrastructure (9.2/9.4/9.5)

Creutzig et al. (2014) assessed the potential for renewable energies in Adoption of smart meters and smart grids following community-based
the European region. They found that a European energy transition with social marketing help in infrastructure expansion. Statutory norms toa high-level of renewable energy installations in the periphery could act enhance energy and resource efficiency in buildings is encouraging
as an economic stimulus, decrease trade deficits and possibly havegreen building projects. Introduction of incentives and norms for solar
positive employment effects. Provision of energy access can play arooftops/white/green roofs in cities are helping to accelerate innovation
critical enabling role for new productive activities, livelihoods andand the expansion of infrastructure.
employment. Reliable access to modern energy services can have an
important influence on productivity and earnings (McCollum et al.,2018).

↑

Interaction
Score
Evidence
Agreement Confidence
Sustainable Economic Growth and Employment

Li et al., 2013; Peng and Lu, 2013; Pietzcker, 2013; Pode, 2013; Yanine Grogan and Sadanand, 2013; Pueyo et al., 2013; Rao et al., 2013;
Roy et al., 2018; Anda and Temmen, 2014
Chakravorty et al., 2014; Creutzig et al., 2014; Ali et al., 2015; Bernard
and Sauma, 2013; Zulu and Richardson, 2013; Connolly et al., 2014;
Creutzig et al., 2014; Pietzcker et al., 2014; Ali et al., 2015; O’Mahony and Torero, 2015; Byravan et al., 2017; McCollum et al., 2018and Dufour, 2015; Abanda et al., 2016; Mittlefehldt, 2016; Biligii et al.,2017; Byravan et al., 2017; Islar et al., 2017; Ozturk et al., 2017

Renewable energies could potentially serve as the main source to meetenergy demand in rapidly growing developing country cities. Ali et al.
(2015) estimated the potential of solar, wind and biomass renewableenergy options to meet part of the electricity demand in Karachi,
Pakistan.

↑Interaction

Economic-Demand (continued)

Buildings

Transport

Improved Access and Fuel Switch to Modern
Low-carbon Energy

Behavioural Response

506

Accelerating Energy Efficiency
Improvement

5

Improved Access and Fuel Switch toModern Low-carbon Energy

Chapter 5
Sustainable Development, Poverty Eradication and Reducing Inequalities

CCS: Fossil

Replacing Coal

Advanced Coal

CCS: Bioenergy

Nuclear/Advanced
NuclearIncreased Use of
Biomass

Non-biomass Renewables - solar,
wind, hydroScore

Evidence

Agreement

[+3]






Confidence

Sustainable and Modern Energy (7.2/7.a)~Interaction
[0]




Score
Evidence
Agreement
Innovation and Growth (8.1/8.2/8.4)
Confidence

[+3]





Sustainable and Modern Energy (7.2/7.a)


[+1]




Innovation and Growth (8.1/8.2/8.4)


↑

[+1]






Innovation and New Infrastructure (9.2/9.4/9.5)

[1]




Innovation and Growth (8.1/8.2/8.4)

[+2]





Sustainable and Modern Energy (7.2/7.a)


[+2]






[+1]


↓

[‐1]




Innovation and Growth (8.1/8.2/8.4)

Innovation and Growth (8.1/8.2/8.4)
See positive impacts of bioenergy use.

↑

IPCC, 2014

IPCC, 2005, 2014; Benson and Cole, 2008; Fankhaeser et al., 2008;
Vergragt et al., 2011; Markusson et al., 2012; Shackley and Thompson,
2012; Bertram et al., 2015; Johnson et al., 2015

Advanced and cleaner fossil fuel technology is in line with the targets of Lock-in of human and physical capital in the fossil resources industry.
SDG7.

↑

Ensure energy access and promote investment in newtechnologies (7.1/7.b)

IPCC, 2014

Increased use of modern biomass will facilitate access to clean,affordable and reliable energy.

↑




↓

[‐1]







Innovation and New Infrastructure (9.2/9.4/9.5)

[+1]





Innovation and New Infrastructure (9.2/9.4/9.5)

[+1]




See positive impacts of CCS/CCU in industrial demand.

↑

Innovation and New Infrastructure (9.2/9.4/9.5)

See positive impacts of bioenergy use and CCS/CCU in industrial
demand.

↑Marra and Palmer, 2011; Greenberg et al., 2013; Schwenk-Ferrero,
2013; Skipperud et al., 2013; Tyler et al., 2013; IPCC, 2014↑

Decarbonization of the energy system through an upscaling ofAccess to modern and sustainable energy will be critical to sustain
renewables will greatly facilitate access to clean, affordable and reliable economic growth.
energy.
Jingura and Kamusoko, 2016
Jingura and Kamusoko, 2016; Shahbaz et al., 2016↑

Jackson and Senker, 2011; Bonan et al., 2014; Clarke et al., 2014; NCE, Fankhaeser et al., 2008; McCollum et al., 2008; Guivarch et al., 2011;2014; OECD, 2017; York and McGee, 2017; McCollum et al., 2018
Bertram et al., 2015; Johnson et al., 2015

IPCC, 2014



IPCC, 2014

Legacy cost of waste and abandoned reactors.



Local employment impact and reduced price volatility.

[1]

Sustainable and Modern Energy (7.2/7.a)[0,‐1]

A rapid upscaling of renewable energies could necessitate the early
retirement of fossil energy infrastructure (e.g., power plants, refineries,
pipelines) on a large scale. The implications of this could in some cases
be negative, unless targeted policies can help alleviate the burden on
industry (McCollum et al., 2018).

~ /↓Interaction
Score
Evidence
Agreement Confidence
Inclusive and Sustainable Industrialization (9.2/9.4)

Increased use of nuclear power can provide stable baseload power
supply and reduce price volatility.

↑

Increased use of modern biomass will facilitate access to clean,affordable and reliable energy. This mitigation option is in line with thetargets of SDG7.
Rogelj et al., 2013; Cherian, 2015; Jingura and Kamusoko, 2016↑

Rogelj et al., 2013; Cherian, 2015; Jingura and Kamusoko, 2016

Decarbonization of the energy system through an upscaling of
Decarbonization of the energy system through an upscaling of
renewables will greatly facilitate access to clean, affordable and reliable renewables and energy efficiency is consistent with sustained economic
energy. Hydropower plays an increasingly important role for the global growth and resource decoupling. Long-term scenarios point towards
electricity supply. This mitigation option is in line with the targets ofslight consumption losses caused by a rapid and pervasive expansion of
SDG7 under the caveat of a transition to modern biomass.
such energy solutions. Whether sustainable growth, as an overarching
concept, is attainable or not is more disputed in the literature. Existing
literature is also undecided as to whether or not access to modern
energy services causes economic growth (McCollum et al., 2018).

↑

Interaction

Economic-Supply

[+2]







[0]

[0]

[0]

[0]

No direct interaction

No direct interaction

No direct interaction

No direct interaction

Tully, 2006; Riahi et al., 2012; Daut et al., 2013; IPCC, 2014; Hallegatteet al., 2016b; McCollum et al., 2018

Deployment of renewable energy and improvements in energy efficiency
globally will aid climate change mitigation efforts, and this, in turn, canhelp to reduce the exposure of people to certain types of disasters andextreme events (McCollum et al., 2018).

↑

Interaction
Score
Evidence
Agreement Confidence
Disaster Preparedness and Prevention (11.5)

Sustainable Development, Poverty Eradication and Reducing Inequalities
Chapter 5

5

507







↑

[+1]






[+1]






↑/↓

[+2,‐1]






«

↑

[+1]

&

J

«

Herrero and Thornton, 2013; Herrero et al., 2013

J

Schader et al., 2015&

Exploiting the increasingly decoupled interactions between crops andlivestock could be beneficial for promoting structural changes in the
livestock sector and is a prerequisite for the sustainable growth of thesector. (Quoted from Herrero et al., 2013)[+1]

Sustainable Economic Growth (8.4)

Behnassi et al., 2014; Lamb et al., 2016

Scenarios where zero human-edible concentrate feed is used forlivestock, non-renewable energy use is reduced by 36%.

↑

Johnson et al., 2007; Sarin et al., 2007; Treasury, 2009; Jain andSharma, 2010; Lybbert and Sumner, 2010; Mtui, 2011; Lakshmi et al.,2015
Energy Efficiency (7.3)

Conventional agricultural biotechnology methods such as energy
Many developing countries including Gulf States will benefit from CSA
efficient farming can help in sequestration of soil carbon. Modern
given the central role of agriculture in their economic and social
biotechnologies such as green energy and N-efficient GM crops can also development. (Quoted from Behnassi et al. 2014). Low commodity
help in C-sequestration. Biotech crops allow farmers to use less – and prices have reduced the incentive to invest in yield growth and have led
environmentally friendly – energy and practice soil carbon
to declining farm labour and farm capital investment. (Quoted fromsequestration. Biofuels, both from traditional and GMO crops, such as Lamb et al., 2016)sugar cane, oilseed, rapeseed and jatropha, can be produced. Greenenergy programmes through plantations of perennial nonedible oilseed
producing plants and production of biodiesel for direct use in the energy
sector or blending biofuels with fossil fuels in certain proportions canthereby minimize fossil fuel use. (Quoted from Lakshmi et al., 2015) GM
crops reduce demand for fossil fuel-based inputs.

↑

Sustainable and Modern Energy (7.b)[+2,‐2]






[+2]







Technological Upgradation and Innovation (9.2)

Complete genome maps for poultry and cattle now exist, and these
open up the way to possible advances in evolutionary biology, animal
breeding and animal models for human diseases. Genomic selection
should be able to at least double the rate of genetic gain in the dairy
industry. (Quoted from Thornton, 2010) Nanotechnology, biogas
technology and separation technologies are disruptive technologies thatenhance biogas production from anaerobic digesters or to reduceodoursSansoucy, 1995; Burton, 2007; Thornton, 2010↑

et al., 2015; Lamb et al., 2016; Long et al., 2016

Reduced research support and delayed industrialization will have an
adverse effect on food security and nourishment of children. Organic
farming technologies utilizing bio-based fertilizers (composted humanand animal manure) are some of the conventional biotechnologicaloptions for reducing artificial fertilizer use (Lakshmi et al., 2015). CSA
requires huge financial investment and institutional innovation. CSA iscommitted to new ways of engaging in participatory research andpartnerships with producers (Steenwerth et al., 2014). Technologies
used on-farm and during food processing to increase productivity which
also helps in adaptation and/or mitigation are new, so convincing
potential customers is difficult. Also, low-awareness of CSA,
inaccessible language, high costs, lack of verified impact oftechnologies, hard to reach and train farmers, low consumer demand
and unequal distribution of costs/benefits across supply chains arebarriers to CSA technology adoption (Long et al., 2016). Low commodity
prices have reduced the incentive to invest in yield growth and have led
to declining investment in research and development (Lamb et al.,2016) 1999; Behnassi et al., 2014; Steenwerth et al., 2014; Lakshmi
Evenson,↑/↓Infrastructure Building, Promotion of Inclusive
Industrialization and Innovation (9.1/9.2/9.5/9.b)[+1]

Sustainable Growth (8.2)↑

Kummu et al., 2012; Hiç et al., 2016



Kummu et al., 2012
By targeting infrastructure, processing and distribution losses, wastage
in food systems can be minimized. 23–24% of total cropland andfertilizers are used to produce losses. So reduction in food losses will
help to diversify these valuable resources into other productive
activities.
Ingram, 2011; Beddington et al., 2012; Kummu et al., 2012; Hiç et al.,2016; Lamb et al., 2016

Interaction
Score
Evidence
Agreement Confidence
Infrastructure Building and Promotion of Inclusive
Industrialization (9.1/9.2)

23–24% of total cropland and fertilizers are used to produce losses. So
reduction in food losses will help to diversify these valuable resources
into other productive activities.

[+1]

Interaction
Score
Evidence
Agreement Confidence
Sustained and Inclusive Economic Growth (8.2)

Reducing global food supply chain losses have several importantsecondary benefits like conserving energy.

↑Interaction
Score
Evidence
Agreement Confidence
Energy Efficiency, Universal Access (7.1/7.3)

Economic-Other

Agriculture and Livestock

Behavioural Response:
Sustainable Healthy Diets andReduced Food Waste

508

Land-based Greenhouse Gas Reduction and Soil Carbon
Sequestration

5

Greenhouse Gas Reduction
from Improved Livestock
Production and Manure

Interaction

[0]

[0]

[0]

Score

Agreement

No direct interaction

no direct interaction

No interaction

Evidence

Confidence

Chapter 5
Sustainable Development, Poverty Eradication and Reducing Inequalities[+1,‐1]





Score
Evidence
Agreement
Energy Efficiency (7.3)
Confidence↑Interaction

[+1]




Score
Evidence
Agreement
Sustainable Economic Growth (8.4)
Confidence



↑

[+2]






Decent Job Creation and Sustainable Economic Growth(8.3/8.4)[+1,‐1]No direct interaction

Technological Upgradation and Innovation, Promotion of
Inclusive Industrialization (9.1/9.2/9.5)[0]Expanding road networks are recognized as one of the main drivers ofdeforesting and forest degradation, diminishing forest benefits tocommunities, On the other hand, roads can enhance market access,thereby boosting local benefits (SDG 1) from the commercialization offorest products. (Quoted from Katila et al., 2017). Efforts by theGovernment of Zambia to reduce emissions by REDD+ have contributed
to erosion control, ecotourism and pollination valued at 2.5% of thecountry's GDP
Turpie et al., 2015; Epstein and Theuer, 2017; Katila et al., 2017↑/↓

Interaction
Score
Evidence
Agreement Confidence
Infrastructure, Promotion of Inclusive Industrialization(9.1/9.2/9.5)

Agreement

No direct interaction

Evidence

Confidence

Improving Air Quality, Green and Public Spaces, Peri-urban
Spaces (11.6/11.7/11.a/11.b)Chen and Qi, 2018; Fu et al., 2018; Kowarik, 2018; McKinney and Ingo,
2018; McPherson et al., 2018; Pei et al., 2018

[+2]





Many urban tree plantations worldwide are created with a focus on
multiple benefits, like air quality improvement, cultural preference forgreen nature, healthy community interaction as well as temperature
control and biodiversity enhancement goals.
↑

[0]

Score

Improving Air Quality, Green and Public Spaces(11.6/11.7/11.a/11.b)Interaction

[0]

[0]

[0]

No direct interaction

No direct interaction

No direct interaction

[0]

[0]

[0]

No direct interaction

No direct interaction

No direct interaction

[0]

[0]

[0]

No direct interaction

No direct interaction

No direct interaction

[0]

[0]

[0]

No direct interaction

No direct interaction

No direct interaction




↑
[+1]



↑
[+2]



↑
[+2]



↑
[+2]

The trade of wood pellets from clean wood waste should be facilitated Some standards seek primarily to coordinate global trade, many purport Capacity for processing certified timber is often underutilized, due to the Many urban tree plantations worldwide are created with a focus on
limited supply available. As a result, manufacturing firms that arewith less administrative import barriers by the EU, in order to have this to promote ecological sustainability and social justice or tomultiple benefits, like air quality improvement, cultural preference forseeking to tap into green markets often turn to other sources of timber. green nature, healthy community interaction as well as temperature
new option seriously accounted for as a future resource for energy.
institutionalize CSR, for example, labour standards developed in the(Quoted from Sikkema et al., 2014) Recommends further harmonization wake of sweatshops and child labour scandals. Environmental standards (Quoted from Bartley, 2010) Responsible sourcing, when integrated into control and biodiversity enhancement goals. People's preference forbusiness practices, can enable retailers to better manage brand valueof legal harvesting, sustainable sourcing and cascaded use requirements for pollution control, etc. Indonesian factories may seek advantages
urban forest gardens are encouraging new urban green spaces, and tree
and reputation by avoiding negative public relations, as well as
for woody biomass for energy with the current requirements of
selection helps in building resilience to disaster.
through non-price competition—perhaps by highlighting decent
maintaining and enhancing brand integrity (Huang et al., 2013).
voluntary SFM certification schemes.
working conditions or the existence of a union—or to see tradeassociations or government promoting the country as a responsible
sourcing location.
Sikkema et al., 2014
Bartley, 2010
Bartley, 2010; Huang et al., 2013
Chen and Qi, 2018; Fu et al., 2018; Kowarik, 2018; McKinney and Ingo,
2018; McPherson et al., 2018; Pei et al., 2018

Universal Access (7.3)

Zomer et al., 2008; Kibria, 2015

Jones and McDermott, 2018Many tree plantations worldwide have higher growth rates which canprovide higher rates of returns for investors. Agroforestry initiatives thatoffer significant opportunities for projects to provide benefits tosmallholder farmers can also help address land degradation throughcommunity-based efforts in more marginal areas. Mangroves reduce
impacts of disasters (cyclones/storms/floods) and enhance water quality,
fisheries, tourism businesses and livelihoods.

[+1]

Decent Job Creation and Sustainable Economic Growth(8.3/8.4)

Turpie et al., 2015; Epstein and Theuer, 2017; Katila et al., 2017

The US Forest Service estimates that an average NYC street tree (urban
afforestation) produces 209 USD in annual benefits, which is primarily
driven by aesthetic (90 USD per tree) and energy savings (from shade)benefits (47.63 USD per tree).

↑

Energy Conservation (7.3/7.b)

Bastos Lima et al., 2017; Katila et al., 2017

Consider the entire sinks and reservoirs of GHG while developing theEfforts by the Government of Zambia to reduce emissions by REDD+
nationally appropriate mitigation actions. For countries with ahave contributed to erosion control, ecotourism and pollination valued
significant contribution of forest degradation (and GHG emissions) from at 2.5% of the country's GDP. Partnerships between local forest
wood fuels, this should be considered. (Quoted from Bastos Lima et al., managers, community enterprises and private sector companies can2017). Biomass for energy is recognized as often being inefficient, and is support local economies and livelihoods, and boost regional andoften harvested in an unsustainable manner, but is a renewable energy national economic growth.
source.

↑/↓Interaction

Economic-Other (continued)

ForestOceansReduced Deforestation, REDD+

Afforestation and Reforestation

Behavioural Response (Responsible
Sourcing)

Ocean Iron
Fertilization

Blue Carbon

Enhanced
Weathering

Sustainable Development, Poverty Eradication and Reducing Inequalities
Chapter 55509

Chapter 5

Sustainable Development, Poverty Eradication and Reducing Inequalities

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Annexes

AI Annex I: Glossary
Coordinating Editor:
J.B. Robin Matthews (France/UK)

Editorial Team:
Mustafa Babiker (Sudan), Heleen de Coninck (Netherlands/EU), Sarah Connors (France/UK), Renée
van Diemen (UK/Netherlands), Riyanti Djalante (Japan/Indonesia), Kristie L. Ebi (USA), Neville
Ellis (Australia), Andreas Fischlin (Switzerland), Tania Guillén Bolaños (Germany/Nicaragua),
Kiane de Kleijne (Netherlands/EU), Valérie Masson-Delmotte (France), Richard Millar (UK), Elvira
S. Poloczanska (Germany/UK), Hans-Otto Pörtner (Germany), Andy Reisinger (New Zealand), Joeri
Rogelj (Austria/Belgium), Sonia I. Seneviratne (Switzerland), Chandni Singh (India), Petra Tschakert(Australia/Austria), Nora M. Weyer (Germany)

Notes:
Note that subterms are in italics beneath main terms.
This glossary defines some specific terms as the Lead Authors intend them to be interpreted in thecontext of this report. Blue, italicized words indicate that the term is defined in the Glossary.

This annex should be cited as:
IPCC, 2018: Annex I: Glossary [Matthews, J.B.R. (ed.)]. In: Global Warming of 1.5°C. An IPCC Special Report on
the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission
pathways, in the context of strengthening the global response to the threat of climate change, sustainable
development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea,
P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou,
M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In Press541

Annex I

1.5°C pathway

AI

Glossary

See Pathways.

1.5°C warmer worlds Projected worlds in which global warming
has reached and, unless otherwise indicated, been limited to 1.5°C
above pre-industrial levels. There is no single 1.5°C warmer world,and projections of 1.5°C warmer worlds look different depending onwhether it is considered on a near-term transient trajectory or at climate
equilibrium after several millennia, and, in both cases, if it occurs with orwithout overshoot. Within the 21st century, several aspects play a role
for the assessment of risk and potential impacts in 1.5°C warmer worlds:
the possible occurrence, magnitude and duration of an overshoot; theway in which emissions reductions are achieved; the ways in which
policies might be able to influence the resilience of human and natural
systems; and the nature of the regional and sub-regional risks. Beyond
the 21st century, several elements of the climate system would continue
to change even if the global mean temperatures remain stable, including
further increases of sea level.
2030 Agenda for Sustainable Development A UN resolution
in September 2015 adopting a plan of action for people, planet andprosperity in a new global development framework anchored in 17
Sustainable Development Goals (UN, 2015). See also Sustainable
Development Goals (SDGs).
Acceptability of policy or system change The extent to whicha policy or system change is evaluated unfavourably or favourably,or rejected or supported, by members of the general public (public
acceptability) or politicians or governments (political acceptability).
Acceptability may vary from totally unacceptable/fully rejected to totally
acceptable/fully supported; individuals may differ in how acceptablepolicies or system changes are believed to be.
Adaptability

See Adaptive capacity.

Adaptation In human systems, the process of adjustment to actualor expected climate and its effects, in order to moderate harm or exploit
beneficial opportunities. In natural systems, the process of adjustment
to actual climate and its effects; human intervention may facilitateadjustment to expected climate and its effects.
Incremental adaptation
Adaptation that maintains the essence and integrity of a system orprocess at a given scale. In some cases, incremental adaptation can
accrue to result in transformational adaptation (Termeer et al., 2017;
Tàbara et al., 2018).
Transformational adaptation
Adaptation that changes the fundamental attributes of a socioecological system in anticipation of climate change and its impacts.
Adaptation limits
The point at which an actor’s objectives (or system needs) cannot be
secured from intolerable risks through adaptive actions.
• Hard adaptation limit: No adaptive actions are possible to avoid
intolerable risks.
• Soft adaptation limit: Options are currently not available to avoid
intolerable risks through adaptive action.
See also Adaptation options, Adaptive capacity and Maladaptive
actions (Maladaptation).
Adaptation behaviour
Adaptation limits

See Human behaviour.

See Adaptation.

Adaptation options The array of strategies and measures that areavailable and appropriate for addressing adaptation. They include awide range of actions that can be categorized as structural, institutional,542

ecological or behavioural. See also Adaptation, Adaptive capacity andMaladaptive actions (Maladaptation).
Adaptation pathways See Pathways.
Adaptive capacity The ability of systems, institutions, humans andother organisms to adjust to potential damage, to take advantage ofopportunities, or to respond to consequences. This glossary entry builds
from definitions used in previous IPCC reports and the Millennium
Ecosystem Assessment (MEA, 2005). See also Adaptation, Adaptation
options and Maladaptive actions (Maladaptation).
Adaptive governance See Governance.
Aerosol A suspension of airborne solid or liquid particles, with atypical size between a few nanometres and 10 μm that reside in theatmosphere for at least several hours. The term aerosol, which includes
both the particles and the suspending gas, is often used in this reportin its plural form to mean aerosol particles. Aerosols may be of either
natural or anthropogenic origin. Aerosols may influence climate inseveral ways: through both interactions that scatter and/or absorb
radiation and through interactions with cloud microphysics and othercloud properties, or upon deposition on snow- or ice-covered surfaces
thereby altering their albedo and contributing to climate feedback.
Atmospheric aerosols, whether natural or anthropogenic, originate fromtwo different pathways: emissions of primary particulate matter (PM),and formation of secondary PM from gaseous precursors. The bulk ofaerosols are of natural origin. Some scientists use group labels that referto the chemical composition, namely: sea salt, organic carbon, black
carbon (BC), mineral species (mainly desert dust), sulphate, nitrate, andammonium. These labels are, however, imperfect as aerosols combine
particles to create complex mixtures. See also Short-lived climate forcers(SLCF) and Black carbon (BC).
Afforestation Planting of new forests on lands that historically havenot contained forests. For a discussion of the term forest and related
terms such as afforestation, reforestation and deforestation, see the IPCC
Special Report on Land Use, Land-Use Change, and Forestry (IPCC, 2000),
information provided by the United Nations Framework Convention
on Climate Change (UNFCCC, 2013) and the report on Definitions andMethodological Options to Inventory Emissions from Direct Humaninduced Degradation of Forests and Devegetation of Other Vegetation
Types (IPCC, 2003). See also Reforestation, Deforestation, and Reducing
Emissions from Deforestation and Forest Degradation (REDD+).
Agreement In this report, the degree of agreement within thescientific body of knowledge on a particular finding is assessed based on
multiple lines of evidence (e.g., mechanistic understanding, theory, data,
models, expert judgement) and expressed qualitatively (Mastrandrea etal., 2010). See also Evidence, Confidence, Likelihood and Uncertainty.
Air pollution Degradation of air quality with negative effects
on human health or the natural or built environment due to theintroduction, by natural processes or human activity, into the atmosphereof substances (gases, aerosols) which have a direct (primary pollutants)
or indirect (secondary pollutants) harmful effect. See also Aerosol andShort-lived climate forcers (SLCF).
Albedo The fraction of solar radiation reflected by a surface orobject, often expressed as a percentage. Snow-covered surfaces have ahigh albedo, the surface albedo of soils ranges from high to low, andvegetation-covered surfaces and the oceans have a low albedo. The
Earth’s planetary albedo changes mainly through varying cloudiness andchanges in snow, ice, leaf area and land cover.
Ambient persuasive technology Technological systems andenvironments that are designed to change human cognitive processing,Glossary

Annex I

attitudes and behaviours without the need for the user’s conscious
attention.
Anomaly The deviation of a variable from its value averaged over areference period.
Anthropocene The ‘Anthropocene’ is a proposed new geologicalepoch resulting from significant human-driven changes to the structureand functioning of the Earth System, including the climate system.
Originally proposed in the Earth System science community in 2000, theproposed new epoch is undergoing a formalization process within thegeological community based on the stratigraphic evidence that humanactivities have changed the Earth System to the extent of forming
geological deposits with a signature that is distinct from those of the
Holocene, and which will remain in the geological record. Both thestratigraphic and Earth System approaches to defining the Anthropocene
consider the mid-20th Century to be the most appropriate starting date,although others have been proposed and continue to be discussed. The
Anthropocene concept has been taken up by a diversity of disciplines
and the public to denote the substantive influence humans have had on
the state, dynamics and future of the Earth System. See also Holocene.
Anthropogenic Resulting from or produced by human activities. See
also Anthropogenic emissions and Anthropogenic removals.
Anthropogenic emissions Emissions of greenhouse gases (GHGs),
precursors of GHGs and aerosols caused by human activities. These
activities include the burning of fossil fuels, deforestation, land use
and land-use changes (LULUC), livestock production, fertilisation, waste
management and industrial processes. See also Anthropogenic andAnthropogenic removals.
Anthropogenic removals Anthropogenic removals refer to thewithdrawal of GHGs from the atmosphere as a result of deliberate
human activities. These include enhancing biological sinks of CO2 andusing chemical engineering to achieve long-term removal and storage.
Carbon capture and storage (CCS) from industrial and energy-related
sources, which alone does not remove CO2 in the atmosphere, can reduce
atmospheric CO2 if it is combined with bioenergy production (BECCS). See
also Anthropogenic emissions, Bioenergy with carbon dioxide captureand storage (BECCS) and Carbon dioxide capture and storage (CCS).
Artificial intelligence (AI) Computer systems able to perform tasks
normally requiring human intelligence, such as visual perception andspeech recognition.
Atmosphere The gaseous envelope surrounding the earth, divided
into five layers – the troposphere which contains half of the Earth’s
atmosphere, the stratosphere, the mesosphere, the thermosphere,
and the exosphere, which is the outer limit of the atmosphere. The dry
atmosphere consists almost entirely of nitrogen (78.1% volume mixing
ratio) and oxygen (20.9% volume mixing ratio), together with a numberof trace gases, such as argon (0.93 % volume mixing ratio), helium andradiatively active greenhouse gases (GHGs) such as carbon dioxide (CO2)(0.04% volume mixing ratio) and ozone (O3). In addition, the atmosphere
contains the GHG water vapour (H2O), whose amounts are highlyvariable but typically around 1% volume mixing ratio. The atmospherealso contains clouds and aerosols. See also Troposphere, Stratosphere,
Greenhouse gas (GHG) and Hydrological cycle.
Atmosphere–ocean general circulation model (AOGCM) See
Climate model.
Attribution

See Detection and attribution.

Baseline scenario In much of the literature the term is alsosynonymous with the term business-as-usual (BAU) scenario, although

the term BAU has fallen out of favour because the idea of business as
usual in century-long socio-economic projections is hard to fathom. In
the context of transformation pathways, the term baseline scenarios
refers to scenarios that are based on the assumption that no mitigation
policies or measures will be implemented beyond those that are alreadyin force and/or are legislated or planned to be adopted. Baseline
scenarios are not intended to be predictions of the future, but rather
counterfactual constructions that can serve to highlight the level ofemissions that would occur without further policy effort. Typically,
baseline scenarios are then compared to mitigation scenarios that are
constructed to meet different goals for greenhouse gas (GHG) emissions,
atmospheric concentrations or temperature change. The term baseline
scenario is often used interchangeably with reference scenario and nopolicy scenario. See also Emission scenario and Mitigation scenario.
Battery electric vehicle (BEV)

See Electric vehicle (EV).

Biochar Stable, carbon-rich material produced by heating biomassin an oxygen-limited environment. Biochar may be added to soils toimprove soil functions and to reduce greenhouse gas emissions frombiomass and soils, and for carbon sequestration. This definition builds
from IBI (2018).
Biodiversity Biological diversity means the variability among livingorganisms from all sources, including, inter alia, terrestrial, marine andother aquatic ecosystems and the ecological complexes of which they
are part; this includes diversity within species, between species and of
ecosystems (UN, 1992).
Bioenergy Energy derived from any form of biomass or its metabolic
by-products. See also Biomass and Biofuel.
Bioenergy with carbon dioxide capture and storage(BECCS) Carbon dioxide capture and storage (CCS) technology applied
to a bioenergy facility. Note that depending on the total emissions of
the BECCS supply chain, carbon dioxide (CO2) can be removed from theatmosphere. See also Bioenergy and Carbon dioxide capture and storage(CCS).
Biofuel A fuel, generally in liquid form, produced from biomass.
Biofuels currently include bioethanol from sugarcane or maize, biodiesel
from canola or soybeans, and black liquor from the paper-manufacturing
process. See also Biomass and Bioenergy.
Biomass Living or recently dead organic material. See also Bioenergy
and Biofuel.
Biophilic urbanism Designing cities with green roofs, green walls
and green balconies to bring nature into the densest parts of cities inorder to provide green infrastructure and human health benefits. See also
Green infrastructure.
Black carbon (BC) Operationally defined aerosol species based on
measurement of light absorption and chemical reactivity and/or thermal
stability. It is sometimes referred to as soot. BC is mostly formed by the
incomplete combustion of fossil fuels, biofuels and biomass but it also
occurs naturally. It stays in the atmosphere only for days or weeks. It is
the most strongly light-absorbing component of particulate matter (PM)and has a warming effect by absorbing heat into the atmosphere andreducing the albedo when deposited on snow or ice. See also Aerosol.
Blue carbon Blue carbon is the carbon captured by living organisms
in coastal (e.g., mangroves, salt marshes, seagrasses) and marine
ecosystems, and stored in biomass and sediments.
Burden sharing (also referred to as Effort sharing) In thecontext of mitigation, burden sharing refers to sharing the effort of
reducing the sources or enhancing the sinks of greenhouse gases (GHGs)
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Glossary

from historical or projected levels, usually allocated by some criteria, as
well as sharing the cost burden across countries.
Business as usual (BAU)AI

See Baseline scenario.

Carbon budget This term refers to three concepts in the literature:(1) an assessment of carbon cycle sources and sinks on a global level,
through the synthesis of evidence for fossil fuel and cement emissions,land-use change emissions, ocean and land CO2 sinks, and the resulting
atmospheric CO2 growth rate. This is referred to as the global carbon
budget; (2) the estimated cumulative amount of global carbon dioxide
emissions that that is estimated to limit global surface temperature
to a given level above a reference period, taking into account global
surface temperature contributions of other GHGs and climate forcers; (3)the distribution of the carbon budget defined under (2) to the regional,
national, or sub-national level based on considerations of equity, costs orefficiency. See also Remaining carbon budget.
Carbon cycle The term used to describe the flow of carbon (invarious forms, e.g., as carbon dioxide (CO2), carbon in biomass, andcarbon dissolved in the ocean as carbonate and bicarbonate) throughthe atmosphere, hydrosphere, terrestrial and marine biosphere andlithosphere. In this report, the reference unit for the global carbon cycle
is GtCO2 or GtC (Gigatonne of carbon = 1 GtC = 1015 grams of carbon.
This corresponds to 3.667 GtCO2).
Carbon dioxide (CO2) A naturally occurring gas, CO2 is also a
by-product of burning fossil fuels (such as oil, gas and coal), of burning
biomass, of land-use changes (LUC) and of industrial processes (e.g.,cement production). It is the principal anthropogenic greenhouse gas(GHG) that affects the Earth’s radiative balance. It is the reference gas
against which other GHGs are measured and therefore has a global
warming potential (GWP) of 1. See also Greenhouse gas (GHG).
Carbon dioxide capture and storage (CCS) A process in whicha relatively pure stream of carbon dioxide (CO2) from industrial andenergy-related sources is separated (captured), conditioned, compressed
and transported to a storage location for long-term isolation from theatmosphere. Sometimes referred to as Carbon capture and storage. See
also Carbon dioxide capture and utilisation (CCU), Bioenergy with carbon
dioxide capture and storage (BECCS) and Uptake.
Carbon dioxide capture and utilisation (CCU) A process in which
CO2 is captured and then used to produce a new product. If the CO2 isstored in a product for a climate-relevant time horizon, this is referred
to as carbon dioxide capture, utilisation and storage (CCUS). Only then,and only combined with CO2 recently removed from the atmosphere, can
CCUS lead to carbon dioxide removal. CCU is sometimes referred to ascarbon dioxide capture and use. See also Carbon dioxide capture andstorage (CCS).
Carbon dioxide capture, utilisation and storage (CCUS) See
Carbon dioxide capture and utilisation (CCU).
Carbon dioxide removal (CDR) Anthropogenic activities removing
CO2 from the atmosphere and durably storing it in geological, terrestrial,or ocean reservoirs, or in products. It includes existing and potential
anthropogenic enhancement of biological or geochemical sinks anddirect air capture and storage, but excludes natural CO2 uptake notdirectly caused by human activities. See also Mitigation (of climatechange), Greenhouse gas removal (GGR), Negative emissions, Direct air
carbon dioxide capture and storage (DACCS) and Sink.
Carbon intensity The amount of emissions of carbon dioxide (CO2)
released per unit of another variable such as gross domestic product(GDP), output energy use or transport.

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Carbon neutrality

See Net zero CO2 emissions.

Carbon price The price for avoided or released carbon dioxide (CO2)or CO2-equivalent emissions. This may refer to the rate of a carbon tax,
or the price of emission permits. In many models that are used to assess
the economic costs of mitigation, carbon prices are used as a proxy torepresent the level of effort in mitigation policies.
Carbon sequestration The process of storing carbon in a carbon
pool. See also Blue carbon, Carbon dioxide capture and storage (CCS),
Uptake and Sink.
Carbon sink See Sink.
Clean Development Mechanism (CDM) A mechanism defined
under Article 12 of the Kyoto Protocol through which investors(governments or companies) from developed (Annex B) countries may
finance greenhouse gas (GHG) emission reduction or removal projects
in developing countries (Non-Annex B), and receive Certified Emission
Reduction Units (CERs) for doing so. The CERs can be credited towards thecommitments of the respective developed countries. The CDM is intended
to facilitate the two objectives of promoting sustainable development(SD) in developing countries and of helping industrialised countries toreach their emissions commitments in a cost-effective way.
Climate Climate in a narrow sense is usually defined as the average
weather, or more rigorously, as the statistical description in terms ofthe mean and variability of relevant quantities over a period of time
ranging from months to thousands or millions of years. The classicalperiod for averaging these variables is 30 years, as defined by the World
Meteorological Organization. The relevant quantities are most often
surface variables such as temperature, precipitation and wind. Climate
in a wider sense is the state, including a statistical description, of theclimate system.
Climate change Climate change refers to a change in the state of theclimate that can be identified (e.g., by using statistical tests) by changes
in the mean and/or the variability of its properties and that persists for an
extended period, typically decades or longer. Climate change may be due
to natural internal processes or external forcings such as modulationsof the solar cycles, volcanic eruptions and persistent anthropogenic
changes in the composition of the atmosphere or in land use. Note that
the Framework Convention on Climate Change (UNFCCC), in its Article
1, defines climate change as: ‘a change of climate which is attributed
directly or indirectly to human activity that alters the composition of the
global atmosphere and which is in addition to natural climate variability
observed over comparable time periods.’ The UNFCCC thus makes a
distinction between climate change attributable to human activities
altering the atmospheric composition and climate variability attributableto natural causes. See also Climate variability, Global warming, Ocean
acidification (OA) and Detection and attribution.
Climate change commitment Climate change commitment isdefined as the unavoidable future climate change resulting from inertia
in the geophysical and socio-economic systems. Different types of climatechange commitment are discussed in the literature (see subterms).
Climate change commitment is usually quantified in terms of the further
change in temperature, but it includes other future changes, for example
in the hydrological cycle, in extreme weather events, in extreme climate
events, and in sea level.
Constant composition commitment
The constant composition commitment is the remaining climate changethat would result if atmospheric composition, and hence radiative forcing,were held fixed at a given value. It results from the thermal inertia of theocean and slow processes in the cryosphere and land surface.

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Annex I

Constant emissions commitment
The constant emissions commitment is the committed climate changethat would result from keeping anthropogenic emissions constant.
Zero emissions commitment
The zero emissions commitment is the climate change commitmentthat would result from setting anthropogenic emissions to zero. It isdetermined by both inertia in physical climate system components(ocean, cryosphere, land surface) and carbon cycle inertia.
Feasible scenario commitment
The feasible scenario commitment is the climate change that corresponds
to the lowest emission scenario judged feasible.
Infrastructure commitment
The infrastructure commitment is the climate change that would result
if existing greenhouse gas and aerosol emitting infrastructure were useduntil the end of its expected lifetime.
Climate-compatible development (CCD) A form of developmentbuilding on climate strategies that embrace development goals anddevelopment strategies that integrate climate risk management,
adaptation and mitigation. This definition builds from Mitchell andMaxwell (2010).
Climate extreme (extreme weather or climate event) The
occurrence of a value of a weather or climate variable above (or below) athreshold value near the upper (or lower) ends of the range of observed
values of the variable. For simplicity, both extreme weather events andextreme climate events are referred to collectively as ‘climate extremes’.
See also Extreme weather event.
Climate feedback An interaction in which a perturbation in one
climate quantity causes a change in a second and the change in thesecond quantity ultimately leads to an additional change in the first. A
negative feedback is one in which the initial perturbation is weakened
by the changes it causes; a positive feedback is one in which the initial
perturbation is enhanced. The initial perturbation can either be externallyforced or arise as part of internal variability.
Climate governance
Climate justiceSee Governance.

See Justice.

Climate model A numerical representation of the climate system
based on the physical, chemical and biological properties of its
components, their interactions and feedback processes, and accounting
for some of its known properties. The climate system can be represented
by models of varying complexity; that is, for any one component or
combination of components a spectrum or hierarchy of models can be
identified, differing in such aspects as the number of spatial dimensions,the extent to which physical, chemical or biological processes are
explicitly represented, or the level at which empirical parametrizations
are involved. There is an evolution towards more complex models
with interactive chemistry and biology. Climate models are applied asa research tool to study and simulate the climate and for operational
purposes, including monthly, seasonal and interannual climate
predictions. See also Earth system model (ESM).

greenhouse gases (GHGs) and aerosols, generally derived using climate
models. Climate projections are distinguished from climate predictions
by their dependence on the emission/concentration/radiative forcing
scenario used, which is in turn based on assumptions concerning, forexample, future socioeconomic and technological developments that
may or may not be realized.
Climate-resilient development pathways (CRDPs) Trajectories
that strengthen sustainable development and efforts to eradicate
poverty and reduce inequalities while promoting fair and cross-scalar
adaptation to and resilience in a changing climate. They raise the ethics,
equity and feasibility aspects of the deep societal transformation neededto drastically reduce emissions to limit global warming (e.g., to 1.5°C)
and achieve desirable and liveable futures and well-being for all.
Climate-resilient pathways Iterative processes for managing
change within complex systems in order to reduce disruptions andenhance opportunities associated with climate change. See also
Development pathways (under Pathways), Transformation pathways(under Pathways), and Climate-resilient development pathways (CRDPs).
Climate sensitivity Climate sensitivity refers to the change in theannual global mean surface temperature in response to a change in theatmospheric CO2 concentration or other radiative forcing.
Equilibrium climate sensitivity
Refers to the equilibrium (steady state) change in the annual global
mean surface temperature following a doubling of the atmosphericcarbon dioxide (CO2) concentration. As a true equilibrium is challenging
to define in climate models with dynamic oceans, the equilibrium climate
sensitivity is often estimated through experiments in AOGCMs where CO2levels are either quadrupled or doubled from pre-industrial levels andwhich are integrated for 100-200 years. The climate sensitivity parameter(units: °C (W m–2)–1) refers to the equilibrium change in the annual global
mean surface temperature following a unit change in radiative forcing.
Effective climate sensitivity
An estimate of the global mean surface temperature response to adoubling of the atmospheric carbon dioxide (CO2) concentration that isevaluated from model output or observations for evolving non-equilibrium
conditions. It is a measure of the strengths of the climate feedbacks at aparticular time and may vary with forcing history and climate state, andtherefore may differ from equilibrium climate sensitivity.
Transient climate response
The change in the global mean surface temperature, averaged over a
20-year period, centered at the time of atmospheric CO2 doubling, in a
climate model simulation in which CO2 increases at 1% yr-1 from preindustrial. It is a measure of the strength of climate feedbacks and thetimescale of ocean heat uptake.
Climate services Climate services refers to information and productsthat enhance users’ knowledge and understanding about the impacts ofclimate change and/or climate variability so as to aid decision-making ofindividuals and organizations and enable preparedness and early climatechange action. Products can include climate data products.

Climate neutrality Concept of a state in which human activities
result in no net effect on the climate system. Achieving such a state would
require balancing of residual emissions with emission (carbon dioxide)
removal as well as accounting for regional or local biogeophysical effectsof human activities that, for example, affect surface albedo or local
climate. See also Net zero CO2 emissions.

Climate-smart agriculture (CSA) Climate-smart agriculture (CSA)
is an approach that helps to guide actions needed to transform andreorient agricultural systems to effectively support development andensure food security in a changing climate. CSA aims to tackle three main
objectives: sustainably increasing agricultural productivity and incomes,
adapting and building resilience to climate change, and reducing and/or
removing greenhouse gas emissions, where possible (FAO, 2018).

Climate projection A climate projection is the simulated responseof the climate system to a scenario of future emission or concentration of

Climate system The climate system is the highly complex system
consisting of five major components: the atmosphere, the hydrosphere,
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the cryosphere, the lithosphere and the biosphere and the interactions
between them. The climate system evolves in time under the influenceof its own internal dynamics and because of external forcings such as
volcanic eruptions, solar variations and anthropogenic forcings such as
the changing composition of the atmosphere and land-use change.

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Climate target Climate target refers to a temperature limit,
concentration level, or emissions reduction goal used towards the aim of
avoiding dangerous anthropogenic interference with the climate system.
For example, national climate targets may aim to reduce greenhouse gas
emissions by a certain amount over a given time horizon, for example
those under the Kyoto Protocol.
Climate variability Climate variability refers to variations in
the mean state and other statistics (such as standard deviations, theoccurrence of extremes, etc.) of the climate on all spatial and temporalscales beyond that of individual weather events. Variability may be due tonatural internal processes within the climate system (internal variability),or to variations in natural or anthropogenic external forcing (externalvariability). See also Climate change.
CO2 equivalent (CO2-eq) emission The amount of carbon dioxide(CO2) emission that would cause the same integrated radiative forcing
or temperature change, over a given time horizon, as an emitted amountof a greenhouse gas (GHG) or a mixture of GHGs. There are a numberof ways to compute such equivalent emissions and choose appropriate
time horizons. Most typically, the CO2-equivalent emission is obtained bymultiplying the emission of a GHG by its global warming potential (GWP)
for a 100-year time horizon. For a mix of GHGs it is obtained by summing
the CO2-equivalent emissions of each gas. CO2-equivalent emission is
a common scale for comparing emissions of different GHGs but does
not imply equivalence of the corresponding climate change responses.
There is generally no connection between CO2-equivalent emissions andresulting CO2-equivalent concentrations.
Co-benefits The positive effects that a policy or measure aimed at
one objective might have on other objectives, thereby increasing the total
benefits for society or the environment. Co-benefits are often subject
to uncertainty and depend on local circumstances and implementation
practices, among other factors. Co-benefits are also referred to as
ancillary benefits.
Common but Differentiated Responsibilities and Respective
Capabilities (CBDR-RC) Common but Differentiated Responsibilities
and Respective Capabilities (CBDR–RC) is a key principle in the United
Nations Framework Convention on Climate Change (UNFCCC) thatrecognises the different capabilities and differing responsibilities ofindividual countries in tacking climate change. The principle of CBDR–
RC is embedded in the 1992 UNFCCC treaty. The convention states:“… the global nature of climate change calls for the widest possible
cooperation by all countries and their participation in an effective andappropriate international response, in accordance with their common but
differentiated responsibilities and respective capabilities and their socialand economic conditions.” Since then the CBDR-RC principle has guided
the UN climate negotiations.
Conference of the Parties (COP) The supreme body of UN
conventions, such as the United Nations Framework Convention on
Climate Change (UNFCCC), comprising parties with a right to vote thathave ratified or acceded to the convention. See also United Nations
Framework Convention on Climate Change (UNFCCC).
Confidence The robustness of a finding based on the type, amount,quality and consistency of evidence (e.g., mechanistic understanding,theory, data, models, expert judgment) and on the degree of agreement
across multiple lines of evidence. In this report, confidence is expressed
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qualitatively (Mastrandrea et al., 2010). See Section 1.6 for the list of
confidence levels used. See also Agreement, Evidence, Likelihood andUncertainty.
Conservation agriculture A coherent group of agronomic and soil
management practices that reduce the disruption of soil structure andbiota.
Constant compositioncommitment.

commitment

See Climate change

Constant emissions commitment See Climate change commitment.
Coping capacity The ability of people, institutions, organizations,and systems, using available skills, values, beliefs, resources, andopportunities, to address, manage, and overcome adverse conditions inthe short to medium term. This glossary entry builds from the definition
used in UNISDR (2009) and IPCC (2012a). See also Resilience.
Cost–benefit analysis Monetary assessment of all negative andpositive impacts associated with a given action. Cost–benefit analysis
enables comparison of different interventions, investments or strategies
and reveals how a given investment or policy effort pays off for a particularperson, company or country. Cost–benefit analyses representing society’s
point of view are important for climate change decision-making, butthere are difficulties in aggregating costs and benefits across different
actors and across timescales. See also Discounting.
Cost-effectiveness A measure of the cost at which policy goal oroutcome is achieved. The lower the cost the greater the cost-effectiveness.
Coupled Model Intercomparison Project (CMIP) The Coupled
Model Intercomparison Project (CMIP) is a climate modelling activity
from the World Climate Research Programme (WCRP) which coordinates
and archives climate model simulations based on shared model inputs bymodelling groups from around the world. The CMIP3 multimodel data set
includes projections using SRES scenarios. The CMIP5 data set includes
projections using the Representative Concentration Pathways (RCPs). The
CMIP6 phase involves a suite of common model experiments as well as
an ensemble of CMIP-endorsed model intercomparison projects (MIPs).
Cumulative emissions The total amount of emissions released overa specified period of time. See also Carbon budget, and Transient climate
response to cumulative CO2 emissions (TCRE).
Decarbonization The process by which countries, individuals orother entities aim to achieve zero fossil carbon existence. Typicallyrefers to a reduction of the carbon emissions associated with electricity,
industry and transport.
Decoupling Decoupling (in relation to climate change) is where
economic growth is no longer strongly associated with consumption offossil fuels. Relative decoupling is where both grow but at different rates.
Absolute decoupling is where economic growth happens but fossil fuelsdecline.
Deforestation Conversion of forest to non-forest. For a discussionof the term forest and related terms such as afforestation, reforestationand deforestation, see the IPCC Special Report on Land Use, Land-Use
Change, and Forestry (IPCC, 2000). See also information provided by theUnited Nations Framework Convention on Climate Change (UNFCCC,2013) and the report on Definitions and Methodological Options toInventory Emissions from Direct Human-induced Degradation of Forests
and Devegetation of Other Vegetation Types (IPCC, 2003). See also
Afforestation, Reforestation and Reducing Emissions from Deforestation
and Forest Degradation (REDD+).
Deliberative governance See Governance.

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Annex I

Demand- and supply-side measures
Demand-side measures
Policies and programmes for influencing the demand for goods and/or services. In the energy sector, demand-side management aims at
reducing the demand for electricity and other forms of energy required
to deliver energy services.
Supply-side measures
Policies and programmes for influencing how a certain demand forgoods and/or services is met. In the energy sector, for example, supplyside mitigation measures aim at reducing the amount of greenhouse gas
emissions emitted per unit of energy produced.
See also Mitigation measures.
Demand-side measures
Detection

See Demand- and supply-side measures.

See Detection and attribution.

Detection and attribution Detection of change is defined as theprocess of demonstrating that climate or a system affected by climate
has changed in some defined statistical sense, without providing areason for that change. An identified change is detected in observations
if its likelihood of occurrence by chance due to internal variability alone isdetermined to be small, for example, <10%. Attribution is defined as theprocess of evaluating the relative contributions of multiple causal factors
to a change or event with a formal assessment of confidence.
Development pathways

See Pathways.

Direct air carbon dioxide capture and storage (DACCS) Chemical
process by which CO2 is captured directly from the ambient air, withsubsequent storage. Also known as direct air capture and storage (DACS).
Disaster Severe alterations in the normal functioning of a community
or a society due to hazardous physical events interacting with vulnerablesocial conditions, leading to widespread adverse human, material,
economic or environmental effects that require immediate emergency
response to satisfy critical human needs and that may require external
support for recovery. See also Hazard and Vulnerability.
Disaster risk management (DRM) Processes for designing,
implementing, and evaluating strategies, policies, and measures toimprove the understanding of disaster risk, foster disaster risk reduction
and transfer, and promote continuous improvement in disaster
preparedness, response, and recovery practices, with the explicit purposeof increasing human security, well-being, quality of life, and sustainable
development.
Discount rate

See Discounting.

Discounting A mathematical operation that aims to make monetary(or other) amounts received or expended at different times (years)
comparable across time. The discounter uses a fixed or possibly timevarying discount rate from year to year that makes future value worth
less today (if the discount rate is positive). The choice of discount rate(s)is debated as it is a judgement based on hidden and/or explicit values.
(Internal) Displacement Internal displacement refers to the forced
movement of people within the country they live in. Internally displaced
persons (IDPs) are ‘Persons or groups of persons who have been forced or
obliged to flee or to leave their homes or places of habitual residence, inparticular as a result of or in order to avoid the effects of armed conflict,situations of generalized violence, violations of human rights or naturalor human-made disasters, and who have not crossed an internationallyrecognized State border.’ (UN, 1998). See also Migration.

Disruptive innovation Disruptive innovation is demand-led
technological change that leads to significant system change and ischaracterized by strong exponential growth.
Distributive equity

See Equity.

Distributive justiceSee Justice.

Double dividend The extent to which revenues generated by policy
instruments, such as carbon taxes or auctioned (tradeable) emission
permits can (1) contribute to mitigation and (2) offset part of thepotential welfare losses of climate policies through recycling the revenue
in the economy by reducing other distortionary taxes.
Downscaling
Downscaling is a method that derives local- toregional-scale (up to 100 km) information from larger-scale models ordata analyses. Two main methods exist: dynamical downscaling andempirical/statistical downscaling. The dynamical method uses the output
of regional climate models, global models with variable spatial resolution,or high-resolution global models. The empirical/statistical methods are
based on observations and develop statistical relationships that link thelarge-scale atmospheric variables with local/regional climate variables. In
all cases, the quality of the driving model remains an important limitation
on quality of the downscaled information. The two methods can be
combined, e.g., applying empirical/statistical downscaling to the output
of a regional climate model, consisting of a dynamical downscaling of a
global climate model.
Drought A period of abnormally dry weather long enough to cause aserious hydrological imbalance. Drought is a relative term, therefore any
discussion in terms of precipitation deficit must refer to the particularprecipitation-related activity that is under discussion. For example,
shortage of precipitation during the growing season impinges on crop
production or ecosystem function in general (due to soil moisture drought,
also termed agricultural drought), and during the runoff and percolationseason primarily affects water supplies (hydrological drought). Storagechanges in soil moisture and groundwater are also affected by increases
in actual evapotranspiration in addition to reductions in precipitation.
A period with an abnormal precipitation deficit is defined as a
meteorological drought. See also Soil moisture.
Megadrought
A megadrought is a very lengthy and pervasive drought, lasting much
longer than normal, usually a decade or more.
Early warning systems (EWS) The set of technical, financial andinstitutional capacities needed to generate and disseminate timely andmeaningful warning information to enable individuals, communities andorganizations threatened by a hazard to prepare to act promptly andappropriately to reduce the possibility of harm or loss. Dependent upon
context, EWS may draw upon scientific and/or Indigenous knowledge.
EWS are also considered for ecological applications e.g., conservation,
where the organization itself is not threatened by hazard but theecosystem under conservation is (an example is coral bleaching alerts),
in agriculture (for example, warnings of ground frost, hailstorms) and infisheries (storm and tsunami warnings). This glossary entry builds fromthe definitions used in UNISDR (2009) and IPCC (2012a).
Earth system feedbacks

See Climate feedback.

Earth system model (ESM) A coupled atmosphere–ocean generalcirculation model in which a representation of the carbon cycle isincluded, allowing for interactive calculation of atmospheric CO2or compatible emissions. Additional components (e.g., atmosphericchemistry, ice sheets, dynamic vegetation, nitrogen cycle, but also urban
or crop models) may be included. See also Climate model.

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Ecosystem An ecosystem is a functional unit consisting of livingorganisms, their non-living environment and the interactions within andbetween them. The components included in a given ecosystem and its
spatial boundaries depend on the purpose for which the ecosystem isdefined: in some cases they are relatively sharp, while in others they
are diffuse. Ecosystem boundaries can change over time. Ecosystems
are nested within other ecosystems and their scale can range from verysmall to the entire biosphere. In the current era, most ecosystems either
contain people as key organisms, or are influenced by the effects ofhuman activities in their environment. See also Ecosystem services.
Ecosystem services Ecological processes or functions having
monetary or non-monetary value to individuals or society at large. These
are frequently classified as (1) supporting services such as productivity
or biodiversity maintenance, (2) provisioning services such as foodor fibre, (3) regulating services such as climate regulation or carbon
sequestration, and (4) cultural services such as tourism or spiritual andaesthetic appreciation.
Effective climate sensitivity
Effective radiative forcing

See Climate sensitivity.
See Radiative forcing.

El Niño-Southern Oscillation (ENSO) The term El Niño wasinitially used to describe a warm-water current that periodically flows
along the coast of Ecuador and Peru, disrupting the local fishery. It has
since become identified with warming of the tropical Pacific Ocean eastof the dateline. This oceanic event is associated with a fluctuation ofa global-scale tropical and subtropical surface pressure pattern called
the Southern Oscillation. This coupled atmosphere–ocean phenomenon,
with preferred time scales of two to about seven years, is known as the
El Niño-Southern Oscillation (ENSO). It is often measured by the surfacepressure anomaly difference between Tahiti and Darwin and/or the sea
surface temperatures in the central and eastern equatorial Pacific. During
an ENSO event, the prevailing trade winds weaken, reducing upwelling
and altering ocean currents such that the sea surface temperatures
warm, further weakening the trade winds. This phenomenon has a great
impact on the wind, sea surface temperature and precipitation patterns
in the tropical Pacific. It has climatic effects throughout the Pacific region
and in many other parts of the world, through global teleconnections. The
cold phase of ENSO is called La Niña.
Electric vehicle (EV)mostly by electricity.

A vehicle whose propulsion is powered fully orBattery electric vehicle (BEV)A vehicle whose propulsion is entirely electric without any internal
combustion engine.
Plug-in hybrid electric vehicle (PHEV)
A vehicle whose propulsion is mostly electric with batteries re-charged
from an electric source but extra power and distance are provided by ahybrid internal combustion engine.
Emission pathways See Pathways.
Emission scenario A plausible representation of the future
development of emissions of substances that are radiatively active (e.g.,greenhouse gases (GHGs), aerosols) based on a coherent and internallyconsistent set of assumptions about driving forces (such as demographic
and socio-economic development, technological change, energy and landuse) and their key relationships. Concentration scenarios, derived fromemission scenarios, are often used as input to a climate model to compute
climate projections. See also Baseline scenario, Mitigation scenario,
Socio-economic scenario, Scenario, Representative Concentration
Pathways (RCPs) (under Pathways), Shared Socio-economic Pathways(SSPs) (under Pathways) and Transformation pathways (under Pathways).
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Emission trajectories A projected development in time of theemission of a greenhouse gas (GHG) or group of GHGs, aerosols, and
GHG precursors. See also Emission pathways (under Pathways).
Emissions trading A market-based instrument aiming at meetinga mitigation objective in an efficient way. A cap on GHG emissions isdivided in tradeable emission permits that are allocated by a combinationof auctioning and handing out free allowances to entities within thejurisdiction of the trading scheme. Entities need to surrender emission
permits equal to the amount of their emissions (e.g., tonnes of CO2).
An entity may sell excess permits to entities that can avoid the sameamount of emissions in a cheaper way. Trading schemes may occur at
the intra-company, domestic, or international level (e.g., the flexibility
mechanisms under the Kyoto Protocol and the EU-ETS) and may apply
to carbon dioxide (CO2), other greenhouse gases (GHGs), or othersubstances.
Enabling conditions Conditions that affect the feasibility ofadaptation and mitigation options, and can accelerate and scale-up
systemic transitions that would limit temperature increase to 1.5°C andenhance capacities of systems and societies to adapt to the associatedclimate change, while achieving sustainable development, eradicating
poverty and reducing inequalities. Enabling conditions include finance,technological innovation, strengthening policy instruments, institutional
capacity, multilevel governance, and changes in human behaviour
and lifestyles. They also include inclusive processes, attention to
power asymmetries and unequal opportunities for development andreconsideration of values. See also Feasibility.
Energy efficiency The ratio of output or useful energy or energy
services or other useful physical outputs obtained from a system,
conversion process, transmission or storage activity to the input of energy(measured as kWh kWh-1, tonnes kWh-1 or any other physical measureof useful output like tonne-km transported). Energy efficiency is often
described by energy intensity. In economics, energy intensity describes
the ratio of economic output to energy input. Most commonly energy
efficiency is measured as input energy over a physical or economic unit,i.e., kWh USD-1 (energy intensity), kWh tonne-1. For buildings, it is often
measured as kWh m-2, and for vehicles as km liter-1 or liter km-1. Very often
in policy ‘energy efficiency’ is intended as the measures to reduce energy
demand through technological options such as insulating buildings, more
efficient appliances, efficient lighting, efficient vehicles, etc.
Energy security The goal of a given country, or the global community
as a whole, to maintain an adequate, stable and predictable energy supply.
Measures encompass safeguarding the sufficiency of energy resources tomeet national energy demand at competitive and stable prices and theresilience of the energy supply; enabling development and deploymentof technologies; building sufficient infrastructure to generate, store andtransmit energy supplies; and ensuring enforceable contracts of delivery.
Enhanced weathering Enhancing the removal of carbon dioxide(CO2) from the atmosphere through dissolution of silicate and carbonate
rocks by grinding these minerals to small particles and actively applying
them to soils, coasts or oceans.
(Model) Ensemble A group of parallel model simulations
characterising historical climate conditions, climate predictions, orclimate projections. Variation of the results across the ensemble members
may give an estimate of modelling-based uncertainty. Ensembles madewith the same model but different initial conditions only characterize
the uncertainty associated with internal climate variability, whereas
multimodel ensembles including simulations by several models
also include the impact of model differences. Perturbed parameter
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manner, aim to assess the uncertainty resulting from internal model
specifications within a single model. Remaining sources of uncertainty
unaddressed with model ensembles are related to systematic model
errors or biases, which may be assessed from systematic comparisonsof model simulations with observations wherever available. See also
Climate projection.
Equality A principle that ascribes equal worth to all human beings,
including equal opportunities, rights, and obligations, irrespective of origins.
Inequality
Uneven opportunities and social positions, and processes of discrimination
within a group or society, based on gender, class, ethnicity, age, and (dis)ability, often produced by uneven development. Income inequality refers
to gaps between highest and lowest income earners within a country
and between countries. See also Equity, Ethics and Fairness.
Equilibrium climate sensitivity

See Climate sensitivity.

Equity Equity is the principle of fairness in burden sharing and is abasis for understanding how the impacts and responses to climatechange, including costs and benefits, are distributed in and by society inmore or less equal ways. It is often aligned with ideas of equality, fairness
and justice and applied with respect to equity in the responsibility for, anddistribution of, climate impacts and policies across society, generations,and gender, and in the sense of who participates and controls theprocesses of decision-making.
Distributive equity
Equity in the consequences, outcomes, costs and benefits of actions or
policies. In the case of climate change or climate policies for different
people, places and countries, including equity aspects of sharing burdens
and benefits for mitigation and adaptation.
Gender equity
Ensuring equity in that women and men have the same rights, resourcesand opportunities. In the case of climate change gender equity recognizes
that women are often more vulnerable to the impacts of climate change
and may be disadvantaged in the process and outcomes of climate
policy.
Inter-generational equity
Equity between generations that acknowledges that the effects of pastand present emissions, vulnerabilities and policies impose costs andbenefits for people in the future and of different age groups.
Procedural equity
Equity in the process of decision-making, including recognition andinclusiveness in participation, equal representation, bargaining power,
voice and equitable access to knowledge and resources to participate.

Extratropical cyclone Any cyclonic-scale storm that is not a tropical
cyclone. Usually refers to a middle- or high-latitude migratory storm systemformed in regions of large horizontal temperature variations. Sometimes
called extratropical storm or extratropical low. See also Tropical cyclone.
Extreme weather event An extreme weather event is an event thatis rare at a particular place and time of year. Definitions of rare vary, but
an extreme weather event would normally be as rare as or rarer than the10th or 90th percentile of a probability density function estimated fromobservations. By definition, the characteristics of what is called extreme
weather may vary from place to place in an absolute sense. When apattern of extreme weather persists for some time, such as a season,
it may be classed as an extreme climate event, especially if it yields anaverage or total that is itself extreme (e.g., drought or heavy rainfall overa season). See also Heatwave and Climate extreme (extreme weather or
climate event).
Extreme weather or climate event
weather or climate event).

See Climate extreme (extreme

Fairness Impartial and just treatment without favouritism or
discrimination in which each person is considered of equal worth withequal opportunity. See also Equity, Equality and Ethics.
Feasibility The degree to which climate goals and response options
are considered possible and/or desirable. Feasibility depends on
geophysical, ecological, technological, economic, social and institutional
conditions for change. Conditions underpinning feasibility are dynamic,
spatially variable, and may vary between different groups. See also
Enabling conditions.
Feasible scenario commitment

See Climate change commitment.

Feedback See Climate feedback.
Flexible governance See Governance.
Flood The overflowing of the normal confines of a stream or otherbody of water, or the accumulation of water over areas that are notnormally submerged. Floods include river (fluvial) floods, flash floods,
urban floods, pluvial floods, sewer floods, coastal floods, and glacial lake
outburst floods.
Food security A situation that exists when all people, at all times,
have physical, social and economic access to sufficient, safe andnutritious food that meets their dietary needs and food preferences foran active and healthy life (FAO, 2001).
Food wastage Food wastage encompasses food loss (the loss offood during production and transportation) and food waste (the waste offood by the consumer) (FAO, 2013).

See also Equality, Ethics and Fairness.

Forcing See Radiative forcing.

Ethics Ethics involves questions of justice and value. Justice isconcerned with right and wrong, equity and fairness, and, in general,with the rights to which people and living beings are entitled. Value is amatter of worth, benefit, or good. See also Equality, Equity and Fairness.

Forest A vegetation type dominated by trees. Many definitions of theterm forest are in use throughout the world, reflecting wide differences inbiogeophysical conditions, social structure and economics. For a discussionof the term forest and related terms such as afforestation, reforestationand deforestation, see the IPCC Special Report on Land Use, Land-Use
Change, and Forestry (IPCC, 2000). See also information provided by theUnited Nations Framework Convention on Climate Change (UNFCCC,2013) and the Report on Definitions and Methodological Options toInventory Emissions from Direct Human-induced Degradation of Forests
and Devegetation of Other Vegetation Types (IPCC, 2003). See also
Afforestation, Deforestation and Reforestation.

Evidence Data and information used in the scientific process toestablish findings. In this report, the degree of evidence reflects theamount, quality and consistency of scientific/technical information onwhich the Lead Authors are basing their findings. See also Agreement,
Confidence, Likelihood and Uncertainty.
Exposure The presence of people; livelihoods; species or ecosystems;
environmental functions, services, and resources; infrastructure; or
economic, social, or cultural assets in places and settings that could be
adversely affected. See also Hazard, Risk and Vulnerability.

Fossil fuels Carbon-based fuels from fossil hydrocarbon deposits,
including coal, oil, and natural gas.

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Glossary

Framework Convention on Climate Change See United Nations
Framework Convention on Climate Change (UNFCCC).

ecosystem services and common pool natural resources, particularly insituations of complexity and uncertainty.

Gender equity See Equity.

Climate governancePurposeful mechanisms and measures aimed at steering social systems
towards preventing, mitigating, or adapting to the risks posed by climatechange (Jagers and Stripple, 2003).

General purpose technologies (GPT) General purpose technologies
can be or are used pervasively in a wide range of sectors in ways that
fundamentally change the modes of operation of those sectors (Helpman,1998). Examples include the steam engine, power generator and motor,
ICT, and biotechnology.
Geoengineering In this report, separate consideration is given to
the two main approaches considered as ‘geoengineering’ in some of theliterature: solar radiation modification (SRM) and carbon dioxide removal(CDR). Because of this separation, the term ‘geoengineering’ is not usedin this report. See also Carbon dioxide removal (CDR) and Solar radiation
modification (SRM).
Glacier A perennial mass of ice, and possibly firn and snow,
originating on the land surface by the recrystallisation of snow andshowing evidence of past or present flow. A glacier typically gains massby accumulation of snow, and loses mass by melting and ice discharge
into the sea or a lake if the glacier terminates in a body of water. Land ice
masses of continental size (>50,000 km2) are referred to as ice sheets.
See also Ice sheet.
Global climate model (also referred to as general circulation
model, both abbreviated as GCM) See Climate model.
Global mean surface temperature (GMST) Estimated global
average of near-surface air temperatures over land and sea-ice, andsea surface temperatures over ice-free ocean regions, with changes
normally expressed as departures from a value over a specified reference
period. When estimating changes in GMST, near-surface air temperature
over both land and oceans are also used.1 See also Land surface airtemperature, Sea surface temperature (SST) and Global mean surface airtemperature (GSAT).
Global mean surface air temperature (GSAT) Global average ofnear-surface air temperatures over land and oceans. Changes in GSAT areoften used as a measure of global temperature change in climate models
but are not observed directly. See also Global mean surface temperature(GMST) and Land surface air temperature.
Global warming The estimated increase in global mean surface
temperature (GMST) averaged over a 30-year period, or the 30-year
period centered on a particular year or decade, expressed relative topre-industrial levels unless otherwise specified. For 30-year periods thatspan past and future years, the current multi-decadal warming trend isassumed to continue. See also Climate change and Climate variability.
Governance A comprehensive and inclusive concept of the full rangeof means for deciding, managing, implementing and monitoring policies
and measures. Whereas government is defined strictly in terms of thenation-state, the more inclusive concept of governance recognizes thecontributions of various levels of government (global, international,
regional, sub-national and local) and the contributing roles of the private
sector, of nongovernmental actors, and of civil society to addressing themany types of issues facing the global community.
Adaptive governanceAn emerging term in the literature for the evolution of formal andinformal institutions of governance that prioritize social learning inplanning, implementation and evaluation of policy through iterativesocial learning to steer the use and protection of natural resources,
1

Deliberative governance
Deliberative governance involves decision-making through inclusive
public conversation, which allows opportunity for developing policy
options through public discussion rather than collating individual
preferences through voting or referenda (although the latter governancemechanisms can also be proceeded and legitimated by public deliberation
processes).
Flexible governance
Strategies of governance at various levels, which prioritize the useof social learning and rapid feedback mechanisms in planning andpolicy making, often through incremental, experimental and iterative
management processes.
Governance capacity
The ability of governance institutions, leaders, and non-state and civilsociety to plan, co-ordinate, fund, implement, evaluate and adjust
policies and measures over the short, medium and long term, adjustingfor uncertainty, rapid change and wide-ranging impacts and multiple
actors and demands.
Multilevel governance
Multilevel governance refers to negotiated, non-hierarchical exchanges
between institutions at the transnational, national, regional and local
levels. Multilevel governance identifies relationships among governanceprocesses at these different levels. Multilevel governance does include
negotiated relationships among institutions at different institutional
levels and also a vertical ‘layering’ of governance processes atdifferent levels. Institutional relationships take place directly between
transnational, regional and local levels, thus bypassing the state level(Peters and Pierre, 2001)
Participatory governance
A governance system that enables direct public engagement in decisionmaking using a variety of techniques for example, referenda, community
deliberation, citizen juries or participatory budgeting. The approach can beapplied in formal and informal institutional contexts from national to local,
but is usually associated with devolved decision-making. This definitionbuilds from Fung and Wright (2003) and Sarmiento and Tilly (2018).
Governance capacity

Green infrastructure The interconnected set of natural andconstructed ecological systems, green spaces and other landscapefeatures. It includes planted and indigenous trees, wetlands, parks,
green open spaces and original grassland and woodlands, as well as
possible building and street-level design interventions that incorporate
vegetation. Green infrastructure provides services and functions in thesame way as conventional infrastructure. This definition builds from
Culwick and Bobbins (2016).
Greenhouse gas (GHG) Greenhouse gases are those gaseous
constituents of the atmosphere, both natural and anthropogenic, thatabsorb and emit radiation at specific wavelengths within the spectrum of
terrestrial radiation emitted by the Earth’s surface, the atmosphere itself
and by clouds. This property causes the greenhouse effect. Water vapour(H2O), carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4) and

Past IPCC reports, reflecting the literature, have used a variety of approximately equivalent metrics of GMST change.

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See Governance.

Glossaryozone (O3) are the primary GHGs in the Earth’s atmosphere. Moreover,there are a number of entirely human-made GHGs in the atmosphere,
such as the halocarbons and other chlorine- and bromine-containing
substances, dealt with under the Montreal Protocol. Beside CO2, N2O andCH4, the Kyoto Protocol deals with the GHGs sulphur hexafluoride (SF6),hydrofluorocarbons (HFCs) and perfluorocarbons (PFCs). See also Carbon
dioxide (CO2), Methane (CH4), Nitrous oxide (N2O) and Ozone (O3).
Greenhouse gas removal (GGR) Withdrawal of a GHG and/or
a precursor from the atmosphere by a sink. See also Carbon dioxide
removal (CDR) and Negative emissions.
Gross domestic product (GDP) The sum of gross value added,
at purchasers’ prices, by all resident and non-resident producers in theeconomy, plus any taxes and minus any subsidies not included in thevalue of the products in a country or a geographic region for a given
period, normally one year. GDP is calculated without deducting fordepreciation of fabricated assets or depletion and degradation of natural
resources.
Gross fixed capital formation (GFCF) One component of the GDP
that corresponds to the total value of acquisitions, minus disposals of
fixed assets during one year by the business sector, governments andhouseholds, plus certain additions to the value of non-produced assets(such as subsoil assets or major improvements in the quantity, quality or
productivity of land).
Halocarbons A collective term for the group of partially halogenatedorganic species, which includes the chlorofluorocarbons (CFCs),
hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs), halons,
methyl chloride and methyl bromide. Many of the halocarbons havelarge global warming potentials. The chlorine and bromine-containing
halocarbons are also involved in the depletion of the ozone layer.
Hazard The potential occurrence of a natural or human-induced
physical event or trend that may cause loss of life, injury, or other
health impacts, as well as damage and loss to property, infrastructure,
livelihoods, service provision, ecosystems and environmental resources.
See also Disaster, Exposure, Risk, and Vulnerability.
Heatwave A period of abnormally hot weather. Heatwaves and warm
spells have various and in some cases overlapping definitions. See also
Extreme weather event.
Heating, ventilation, and air conditioning (HVAC) Heating,ventilation and air conditioning technology is used to control temperatureand humidity in an indoor environment, be it in buildings or in vehicles,
providing thermal comfort and healthy air quality to the occupants. HVAC
systems can be designed for an isolated space, an individual building ora distributed heating and cooling network within a building structure ora district heating system. The latter provides economies of scale and also
scope for integration with solar heat, natural seasonal cooling/heating etc.
Holocene The Holocene is the current interglacial geological epoch,
the second of two epochs within the Quaternary period, the preceding
being the Pleistocene. The International Commission on Stratigraphydefines the start of the Holocene at 11,650 years before 1950. See also
Anthropocene.
Human behaviour The way in which a person acts in response to aparticular situation or stimulus. Human actions are relevant at different
levels, from international, national, and sub-national actors, to NGO, firmlevel actors, and communities, households, and individual actions.
Adaptation behaviour
Human actions that directly or indirectly affect the risks of climatechange impacts.

Annex I

Mitigation behaviour
Human actions that directly or indirectly influence mitigation.
Human behavioural change A transformation or modification ofhuman actions. Behaviour change efforts can be planned in ways thatmitigate climate change and/or reduce negative consequences of climatechange impacts.
Human rights Rights that are inherent to all human beings, universal,
inalienable, and indivisible, typically expressed and guaranteed by law.
They include the right to life; economic, social, and cultural rights;
and the right to development and self-determination. Based upon thedefinition by the UN Office of the High Commissioner for Human Rights(UNOHCHR, 2018).
Procedural rights
Rights to a legal procedure to enforce substantive rights.
Substantive rights
Basic human rights, including the right to the substance of being humansuch as life itself, liberty and happiness.
Human security A condition that is met when the vital core of human
lives is protected, and when people have the freedom and capacity to livewith dignity. In the context of climate change, the vital core of human
lives includes the universal and culturally specific, material and nonmaterial elements necessary for people to act on behalf of their interests
and to live with dignity.
Human system Any system in which human organizations andinstitutions play a major role. Often, but not always, the term issynonymous with society or social system. Systems such as agricultural
systems, urban systems, political systems, technological systems andeconomic systems are all human systems in the sense applied in thisreport.
Hydrological cycle The cycle in which water evaporates from theoceans and the land surface, is carried over the earth in atmosphericcirculation as water vapour, condenses to form clouds, precipitates asrain or snow, which on land can be intercepted by trees and vegetation,
potentially accumulates as snow or ice, provides runoff on the land
surface, infiltrates into soils, recharges groundwater, discharges into
streams, flows out into the oceans, and ultimately evaporates again
from the ocean or land surface. The various systems involved in thehydrological cycle are usually referred to as hydrological systems.
Ice sheet A mass of land ice of continental size that is sufficiently
thick to cover most of the underlying bed, so that its shape is mainly
determined by its dynamics (the flow of the ice as it deforms internally
and/or slides at its base). An ice sheet flows outward from a high central
ice plateau with a small average surface slope. The margins usually slope
more steeply, and most ice is discharged through fast flowing ice streams
or outlet glaciers, in some cases into the sea or into ice shelves floatingon the sea. There are only two ice sheets in the modern world, one on
Greenland and one on Antarctica. During glacial periods there wereothers. See also Glacier.
(climate change) Impact assessment The practice of identifying
and evaluating, in monetary and/or non-monetary terms, the effects ofclimate change on natural and human systems.
Impacts (consequences, outcomes) The consequences ofrealized risks on natural and human systems, where risks result fromthe interactions of climate-related hazards (including extreme weather
and climate events), exposure, and vulnerability. Impacts generally
refer to effects on lives; livelihoods; health and well-being; ecosystems
and species; economic, social and cultural assets; services (including

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Glossary

ecosystem services); and infrastructure. Impacts may be referred toas consequences or outcomes, and can be adverse or beneficial. See
also Adaptation, Exposure, Hazard, Loss and Damage, and losses anddamages, and Vulnerability.

and management training to support integrated planning and decisionmaking processes between organizations and people, as well as
empowerment, social capital, and an enabling environment, including
the culture, values and power relations (Willems and Baumert, 2003).

Incremental adaptation

Integrated assessment A method of analysis that combines results
and models from the physical, biological, economic and social sciences
and the interactions among these components in a consistent framework
to evaluate the status and the consequences of environmental change
and the policy responses to it. See also Integrated assessment model(IAM).

See Adaptation.

Indigenous knowledge Indigenous knowledge refers to theunderstandings, skills and philosophies developed by societies withlong histories of interaction with their natural surroundings. For many
Indigenous peoples, Indigenous knowledge informs decision-making
about fundamental aspects of life, from day-to-day activities to longer
term actions. This knowledge is integral to cultural complexes, which also
encompass language, systems of classification, resource use practices,
social interactions, values, ritual and spirituality. These distinctive waysof knowing are important facets of the world’s cultural diversity. Thisdefinition builds on UNESCO (2018).
Indirect land-use change (iLUC)

See Land-use change (LUC).

Industrial revolution A period of rapid industrial growth with farreaching social and economic consequences, beginning in Britain during
the second half of the 18th century and spreading to Europe and later toother countries, including the United States. The invention of the steam
engine was an important trigger of this development. The industrial
revolution marks the beginning of a strong increase in the use of fossil
fuels, initially coal, and hence emission of carbon dioxide (CO2). See also
Pre-industrial.
Industrialized/developed/developing countries There are adiversity of approaches for categorizing countries on the basis of theirlevel of development, and for defining terms such as industrialized,
developed, or developing. Several categorizations are used in this report.
(1) In the United Nations system, there is no established convention for
designation of developed and developing countries or areas. (2) The
United Nations Statistics Division specifies developed and developing
regions based on common practice. In addition, specific countries aredesignated as Least Developed Countries (LDC), landlocked developing
countries, small island developing states, and transition economies. Many
countries appear in more than one of these categories. (3) The WorldBank uses income as the main criterion for classifying countries as low,
lower middle, upper middle and high income. (4) The UNDP aggregatesindicators for life expectancy, educational attainment, and income into asingle composite Human Development Index (HDI) to classify countries
as low, medium, high or very high human development.
Inequality

See Equality.

Information and communication technology (ICT) An umbrellaterm that includes any information and communication device orapplication, encompassing: computer systems, network hardware andsoftware, cell phones, etc.
Infrastructure commitment

See Climate change commitment.

Institution Institutions are rules and norms held in common by social
actors that guide, constrain and shape human interaction. Institutions
can be formal, such as laws and policies, or informal, such as norms andconventions. Organizations – such as parliaments, regulatory agencies,
private firms and community bodies – develop and act in response to
institutional frameworks and the incentives they frame. Institutions canguide, constrain and shape human interaction through direct control,
through incentives, and through processes of socialization. See also
Institutional capacity.
Institutional capacity Institutional capacity comprises building
and strengthening individual organizations and providing technical552

Integrated assessment model (IAM) Integrated assessment
models (IAMs) integrate knowledge from two or more domains intoa single framework. They are one of the main tools for undertaking
integrated assessments.
One class of IAM used in respect of climate change mitigation may
include representations of: multiple sectors of the economy, such asenergy, land use and land-use change; interactions between sectors; theeconomy as a whole; associated GHG emissions and sinks; and reduced
representations of the climate system. This class of model is used to assess
linkages between economic, social and technological development andthe evolution of the climate system.
Another class of IAM additionally includes representations of the costs
associated with climate change impacts, but includes less detailed
representations of economic systems. These can be used to assess
impacts and mitigation in a cost–benefit framework and have been used
to estimate the social cost of carbon.
Integrated water resources management (IWRM) A process
which promotes the coordinated development and management ofwater, land and related resources in order to maximize economic andsocial welfare in an equitable manner without compromising thesustainability of vital ecosystems.
Inter-generational equity

See Equity.

Inter-generational justiceSee Justice.

Internal variability

See Climate variability.

Internet of Things (IoT) The network of computing devices
embedded in everyday objects such as cars, phones and computers,connected via the internet, enabling them to send and receive data.
Iron fertilization

See Ocean fertilization.

Irreversibility A perturbed state of a dynamical system is defined as
irreversible on a given timescale, if the recovery time scale from this state
due to natural processes is substantially longer than the time it takes forthe system to reach this perturbed state. See also Tipping point.
Justice Justice is concerned with ensuring that people get what is dueto them, setting out the moral or legal principles of fairness and equity inthe way people are treated, often based on the ethics and values of society.
Climate justiceJustice that links development and human rights to achieve a humancentred approach to addressing climate change, safeguarding the rightsof the most vulnerable people and sharing the burdens and benefits ofclimate change and its impacts equitably and fairly. This definition builds
upon the one used by the Mary Robinson Foundation – Climate Justice(MRFCJ, 2018).
Distributive justiceJustice in the allocation of economic and non-economic costs andbenefits across society.

Glossary

Inter-generational justiceJustice in the distribution of economic and non-economic costs andbenefits across generations.
Procedural justiceJustice in the way outcomes are brought about including who participates
and is heard in the processes of decision-making.
Social justice
Just or fair relations within society that seek to address the distributionof wealth, access to resources, opportunity, and support according toprinciples of justice and fairness.
See also Equity, Ethics, Fairness, and Human rights.
Kyoto Protocol The Kyoto Protocol to the United Nations Framework
Convention on Climate Change (UNFCCC) is an international treaty
adopted in December 1997 in Kyoto, Japan, at the Third Session of theConference of the Parties (COP3) to the UNFCCC. It contains legallybinding commitments, in addition to those included in the UNFCCC.
Countries included in Annex B of the Protocol (mostly OECD countries
and countries with economies in transition) agreed to reduce theiranthropogenic greenhouse gas (GHG) emissions (carbon dioxide(CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs),
perfluorocarbons (PFCs), and sulphur hexafluoride (SF6)) by at least 5%
below 1990 levels in the first commitment period (2008–2012). The
Kyoto Protocol entered into force on 16 February 2005 and as of May
2018 had 192 Parties (191 States and the European Union). A second
commitment period was agreed in December 2012 at COP18, known as
the Doha Amendment to the Kyoto Protocol, in which a new set of Parties
committed to reduce GHG emissions by at least 18% below 1990 levelsin the period from 2013 to 2020. However, as of May 2018, the Doha
Amendment had not received sufficient ratifications to enter into force.
See also United Nations Framework Convention on Climate Change(UNFCCC) and Paris Agreement.
Land surface air temperature The near-surface air temperature
over land, typically measured at 1.25–2 m above the ground using
standard meteorological equipment.
Land use Land use refers to the total of arrangements, activities andinputs undertaken in a certain land cover type (a set of human actions).
The term land use is also used in the sense of the social and economic
purposes for which land is managed (e.g., grazing, timber extraction,conservation and city dwelling). In national greenhouse gas inventories,
land use is classified according to the IPCC land use categories of forest
land, cropland, grassland, wetland, settlements, other. See also Land-use
change (LUC).
Land-use change (LUC) Land-use change involves a change fromone land use category to another.
Indirect land-use change (iLUC)
Refers to market-mediated or policy-driven shifts in land use that cannot
be directly attributed to land-use management decisions of individuals
or groups. For example, if agricultural land is diverted to fuel production,
forest clearance may occur elsewhere to replace the former agricultural
production.
Land use, land-use change and forestry (LULUCF)
In the context of national greenhouse gas (GHG) inventories under the
UNFCCC, LULUCF is a GHG inventory sector that covers anthropogenic
emissions and removals of GHG from carbon pools in managed lands,
excluding non-CO2 agricultural emissions. Following the 2006 IPCC
Guidelines for National GHG Inventories, ‘anthropogenic’ land-related
GHG fluxes are defined as all those occurring on ‘managed land’, i.e.,‘where human interventions and practices have been applied to perform

Annex I

production, ecological or social functions’. Since managed land may
include CO2 removals not considered as ‘anthropogenic’ in some of thescientific literature assessed in this report (e.g., removals associated with
CO2 fertilization and N deposition), the land-related net GHG emission
estimates included in this report are not necessarily directly comparablewith LULUCF estimates in National GHG Inventories.
See also Afforestation, Deforestation, Reforestation, and the IPCC Special
Report on Land Use, Land-Use Change, and Forestry (IPCC, 2000).
Land use, land-use change and forestry (LULUCF) See Land-use
change (LUC).
Life cycle assessment (LCA) Compilation and evaluation of the
inputs, outputs and the potential environmental impacts of a product orservice throughout its life cycle. This definition builds from ISO (2018).
Likelihood The chance of a specific outcome occurring, where thismight be estimated probabilistically. Likelihood is expressed in this reportusing a standard terminology (Mastrandrea et al., 2010). See Section 1.6
for the list of likelihood qualifiers used. See also Agreement, Evidence,
Confidence and Uncertainty.
Livelihood The resources used and the activities undertaken in order
to live. Livelihoods are usually determined by the entitlements and assets
to which people have access. Such assets can be categorised as human,social, natural, physical or financial.
Local knowledge Local knowledge refers to the understandings
and skills developed by individuals and populations, specific to theplaces where they live. Local knowledge informs decision-making about
fundamental aspects of life, from day-to-day activities to longer-term
actions. This knowledge is a key element of the social and cultural systems
which influence observations of, and responses to climate change; it also
informs governance decisions. This definition builds on UNESCO (2018).
Lock-in A situation in which the future development of a system,
including infrastructure, technologies, investments, institutions, andbehavioural norms, is determined or constrained (‘locked in’) by historicdevelopments.
Long-lived climate forcers (LLCF) Long-lived climate forcers referto a set of well-mixed greenhouse gases with long atmospheric lifetimes.
This set of compounds includes carbon dioxide (CO2) and nitrous oxide(N2O), together with some fluorinated gases. They have a warming effecton climate. These compounds accumulate in the atmosphere at decadal
to centennial time scales, and their effect on climate hence persists for
decades to centuries after their emission. On time scales of decades toa century, already emitted emissions of long-lived climate forcers canonly be abated by greenhouse gas removal (GGR). See also Short-lived
climate forcers (SLCF).
Loss and Damage, and losses and damages Research has taken
Loss and Damage (capitalized letters) to refer to political debate under
the UNFCCC following the establishment of the Warsaw Mechanism
on Loss and Damage in 2013, which is to ‘address loss and damage
associated with impacts of climate change, including extreme eventsand slow onset events, in developing countries that are particularly
vulnerable to the adverse effects of climate change.’ Lowercase letters(losses and damages) have been taken to refer broadly to harm from(observed) impacts and (projected) risks (see Mechler et al., in press).
Maladaptive actions (Maladaptation) Actions that may leadto increased risk of adverse climate-related outcomes, including via
increased GHG emissions, increased vulnerability to climate change, or
diminished welfare, now or in the future. Maladaptation is usually anunintended consequence.

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Glossary

Market exchange rate (MER) The rate at which a currency of
one country can be exchanged with the currency of another country. In
most economies such rates evolve daily while in others there are official
conversion rates that are adjusted periodically. See also Purchasing
power parity (PPP).

AI

Market failure When private decisions are based on market pricesthat do not reflect the real scarcity of goods and services but rather
reflect market distortions, they do not generate an efficient allocationof resources but cause welfare losses. A market distortion is any eventin which a market reaches a market clearing price that is substantiallydifferent from the price that a market would achieve while operating
under conditions of perfect competition and state enforcement of legal
contracts and the ownership of private property. Examples of factors
causing market prices to deviate from real economic scarcity areenvironmental externalities, public goods, monopoly power, information
asymmetry, transaction costs and non-rational behaviour.
Measurement, Reporting and Verification (MRV)
Measurement‘Processes of data collection over time, providing basic datasets, including
associated accuracy and precision, for the range of relevant variables.
Possible data sources are field measurements, field observations,detection through remote sensing and interviews.’ (UN-REDD, 2009).
Reporting‘The process of formal reporting of assessment results to the UNFCCC,
according to predetermined formats and according to establishedstandards, especially the IPCC [Intergovernmental Panel on Climate
Change] Guidelines and GPG [Good Practice Guidance].’ (UN-REDD,
2009)
Verification‘The process of formal verification of reports, for example the establishedapproach to verify national communications and national inventory
reports to the UNFCCC.’ (UN-REDD, 2009)
Megadrought

See Drought.

Methane (CH4) One of the six greenhouse gases (GHGs) to be
mitigated under the Kyoto Protocol and is the major component of
natural gas and associated with all hydrocarbon fuels. Significant
emissions occur as a result of animal husbandry and agriculture, andtheir management represents a major mitigation option.
Migrant See Migration.
Migration The International Organization for Migration (IOM)
defines migration as ‘The movement of a person or a group of persons,either across an international border, or within a State. It is a population
movement, encompassing any kind of movement of people, whatever
its length, composition and causes; it includes migration of refugees,
displaced persons, economic migrants, and persons moving for otherpurposes, including family reunification.’ (IOM, 2018).
Migrant
The International Organization for Migration (IOM) defines a migrantas ‘any person who is moving or has moved across an international
border or within a State away from his/her habitual place of residence,
regardless of (1) the person’s legal status; (2) whether the movement isvoluntary or involuntary; (3) what the causes for the movement are; or(4) what the length of the stay is.’ (IOM, 2018).
See also (Internal) Displacement.
Millennium Development Goals (MDGs) A set of eight timebound and measurable goals for combating poverty, hunger, disease,554

illiteracy, discrimination against women and environmental degradation.
These goals were agreed at the UN Millennium Summit in 2000 together
with an action plan to reach the goals by 2015.
Mitigation (of climate change) A human intervention to reduce
emissions or enhance the sinks of greenhouse gases.
Mitigation behaviour See Human behaviour.
Mitigation measures In climate policy, mitigation measures aretechnologies, processes or practices that contribute to mitigation, forexample, renewable energy (RE) technologies, waste minimization
processes and public transport commuting practices. See also Mitigation
option, and Policies (for climate change mitigation and adaptation).
Mitigation option A technology or practice that reduces GHG
emissions or enhances sinks.
Mitigation pathways See Pathways.
Mitigation scenario A plausible description of the future that
describes how the (studied) system responds to the implementation of
mitigation policies and measures. See also Emission scenario, Pathways,
Socio-economic scenario and Stabilization (of GHG or CO2-equivalentconcentration).
Monitoring and evaluation (M&E) Monitoring and evaluationrefers to mechanisms put in place at national to local scales to respectively
monitor and evaluate efforts to reduce greenhouse gas emissions and/or adapt to the impacts of climate change with the aim of systematically
identifying, characterizing and assessing progress over time.
Motivation (of an individual) An individual’s reason or reasons for
acting in a particular way; individuals may consider various consequencesof actions, including financial, social, affective and environmental
consequences. Motivation can come from outside (extrinsic) or frominside (intrinsic) the individual.
Multilevel governance See Governance.
Narratives Qualitative descriptions of plausible future world
evolutions, describing the characteristics, general logic and developments
underlying a particular quantitative set of scenarios. Narratives are also
referred to in the literature as ‘storylines’. See also Scenario, Scenario
storyline and Pathways.
Nationally Determined Contributions (NDCs) A term used under
the United Nations Framework Convention on Climate Change (UNFCCC)
whereby a country that has joined the Paris Agreement outlines its plans
for reducing its emissions. Some countries’ NDCs also address how
they will adapt to climate change impacts, and what support they needfrom, or will provide to, other countries to adopt low-carbon pathways
and to build climate resilience. According to Article 4 paragraph 2of the Paris Agreement, each Party shall prepare, communicate andmaintain successive NDCs that it intends to achieve. In the lead up to
21st Conference of the Parties in Paris in 2015, countries submitted
Intended Nationally Determined Contributions (INDCs). As countries join
the Paris Agreement, unless they decide otherwise, this INDC becomes
their first Nationally Determined Contribution (NDC). See also United
Nations Framework Convention on Climate Change (UNFCCC) and Paris
Agreement.
Negative emissions Removal of greenhouse gases (GHGs) fromthe atmosphere by deliberate human activities, i.e., in addition to theremoval that would occur via natural carbon cycle processes. See also
Net negative emissions, Net zero emissions, Carbon dioxide removal(CDR) and Greenhouse gas removal (GGR).

Glossary

Annex INet negative emissions A situation of net negative emissions isachieved when, as result of human activities, more greenhouse gases
are removed from the atmosphere than are emitted into it. Where
multiple greenhouse gases are involved, the quantification of negativeemissions depends on the climate metric chosen to compare emissionsof different gases (such as global warming potential, global temperaturechange potential, and others, as well as the chosen time horizon). See
also Negative emissions, Net zero emissions and Net zero CO2 emissions.
Net zero CO2 emissions Net zero carbon dioxide (CO2) emissions
are achieved when anthropogenic CO2 emissions are balanced globallyby anthropogenic CO2 removals over a specified period. Net zero CO2emissions are also referred to as carbon neutrality. See also Net zero
emissions and Net negative emissions.
Net zero emissions Net zero emissions are achieved whenanthropogenic emissions of greenhouse gases to the atmosphere arebalanced by anthropogenic removals over a specified period. Where
multiple greenhouse gases are involved, the quantification of net zero
emissions depends on the climate metric chosen to compare emissionsof different gases (such as global warming potential, global temperaturechange potential, and others, as well as the chosen time horizon). See
also Net zero CO2 emissions, Negative emissions and Net negativeemissions.
Nitrous oxide (N2O) One of the six greenhouse gases (GHGs) to be
mitigated under the Kyoto Protocol. The main anthropogenic source of
N2O is agriculture (soil and animal manure management), but important
contributions also come from sewage treatment, fossil fuel combustion,and chemical industrial processes. N2O is also produced naturally from awide variety of biological sources in soil and water, particularly microbial
action in wet tropical forests.
Non-CO2 emissions and radiative forcing Non-CO2 emissions
included in this report are all anthropogenic emissions other than CO2that result in radiative forcing. These include short-lived climate forcers,
such as methane (CH4), some fluorinated gases, ozone (O3) precursors,
aerosols or aerosol precursors, such as black carbon and sulphur dioxide,
respectively, as well as long-lived greenhouse gases, such as nitrous
oxide (N2O) or other fluorinated gases. The radiative forcing associatedwith non-CO2 emissions and changes in surface albedo is referred to as
non-CO2 radiative forcing.
Non-overshoot pathways

See Pathways.

Ocean acidification (OA) Ocean acidification refers to a reductionin the pH of the ocean over an extended period, typically decades or
longer, which is caused primarily by uptake of carbon dioxide (CO2) fromthe atmosphere, but can also be caused by other chemical additions or
subtractions from the ocean. Anthropogenic ocean acidification refers to
the component of pH reduction that is caused by human activity (IPCC,
2011, p. 37).
Ocean fertilization Deliberate increase of nutrient supply tothe near-surface ocean in order to enhance biological production
through which additional carbon dioxide (CO2) from the atmosphere issequestered. This can be achieved by the addition of micro-nutrients or
macro-nutrients. Ocean fertilization is regulated by the London Protocol.
Overshoot See Temperature overshoot.
Overshoot pathways See Pathways.
Ozone (O3) Ozone, the triatomic form of oxygen (O3), is a gaseousatmospheric constituent. In the troposphere, it is created both naturally
and by photochemical reactions involving gases resulting from humanactivities (smog). Tropospheric ozone acts as a greenhouse gas. In thestratosphere, it is created by the interaction between solar ultraviolet
radiation and molecular oxygen (O2). Stratospheric ozone plays adominant role in the stratospheric radiative balance. Its concentration ishighest in the ozone layer.
Paris Agreement The Paris Agreement under the United Nations
Framework Convention on Climate Change (UNFCCC) was adopted on
December 2015 in Paris, France, at the 21st session of the Conferenceof the Parties (COP) to the UNFCCC. The agreement, adopted by 196
Parties to the UNFCCC, entered into force on 4 November 2016 and asof May 2018 had 195 Signatories and was ratified by 177 Parties. Oneof the goals of the Paris Agreement is ‘Holding the increase in the global
average temperature to well below 2°C above pre-industrial levels
and pursuing efforts to limit the temperature increase to 1.5°C above
pre-industrial levels’, recognising that this would significantly reduce
the risks and impacts of climate change. Additionally, the Agreement
aims to strengthen the ability of countries to deal with the impactsof climate change. The Paris Agreement is intended to become fully
effective in 2020. See also United Nations Framework Convention on
Climate Change (UNFCCC), Kyoto Protocol and Nationally Determined
Contributions (NDCs).
Participatory governance See Governance.
Pathways The temporal evolution of natural and/or human systems
towards a future state. Pathway concepts range from sets of quantitativeand qualitative scenarios or narratives of potential futures to solutionoriented decision-making processes to achieve desirable societal goals.
Pathway approaches typically focus on biophysical, techno-economic,
and/or socio-behavioural trajectories and involve various dynamics, goals
and actors across different scales.
1.5°C pathway
A pathway of emissions of greenhouse gases and other climate forcers
that provides an approximately one-in-two to two-in-three chance, given
current knowledge of the climate response, of global warming either
remaining below 1.5°C or returning to 1.5°C by around 2100 following
an overshoot. See also Temperature overshoot.
Adaptation pathways
A series of adaptation choices involving trade-offs between short-termand long-term goals and values. These are processes of deliberation to
identify solutions that are meaningful to people in the context of their
daily lives and to avoid potential maladaptation.
Development pathways
Development pathways are trajectories based on an array of social,economic, cultural, technological, institutional and biophysical featuresthat characterise the interactions between human and natural systems
and outline visions for the future, at a particular scale.
Emission pathways
Modelled trajectories of global anthropogenic emissions over the 21st
century are termed emission pathways.
Mitigation pathways
A mitigation pathway is a temporal evolution of a set of mitigation
scenario features, such as greenhouse gas emissions and socio-economic
development.
Overshoot pathways
Pathways that exceed the stabilization level (concentration, forcing, ortemperature) before the end of a time horizon of interest (e.g., before2100) and then decline towards that level by that time. Once the target
level is exceeded, removal by sinks of greenhouse gases is required. See
also Temperature overshoot.

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Non-overshoot pathways
Pathways that stay below the stabilization level (concentration, forcing,or temperature) during the time horizon of interest (e.g., until 2100).

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Representative Concentration Pathways (RCPs)
Scenarios that include time series of emissions and concentrations of
the full suite of greenhouse gases (GHGs) and aerosols and chemically
active gases, as well as land use/land cover (Moss et al., 2008). The
word representative signifies that each RCP provides only one of many
possible scenarios that would lead to the specific radiative forcing
characteristics. The term pathway emphasizes the fact that not only thelong-term concentration levels but also the trajectory taken over time toreach that outcome are of interest (Moss et al., 2010). RCPs were used todevelop climate projections in CMIP5.
• RCP2.6: One pathway where radiative forcing peaks at
approximately 3 W m-2 and then declines to be limited at 2.6 W m-2
in 2100 (the corresponding Extended Concentration Pathway, or
ECP, has constant emissions after 2100).
• RCP4.5 and RCP6.0: Two intermediate stabilization pathways
in which radiative forcing is limited at approximately 4.5 W m-2
and 6.0 W m-2 in 2100 (the corresponding ECPs have constantconcentrations after 2150).
• RCP8.5: One high pathway which leads to >8.5 W m-2 in 2100
(the corresponding ECP has constant emissions after 2100 until
2150 and constant concentrations after 2250).
See also Coupled Model Intercomparison Project (CMIP) and Shared
Socio-economic Pathways (SSPs).
Shared Socio-economic Pathways (SSPs)
Shared Socio-economic Pathways (SSPs) were developed to complement
the RCPs with varying socio-economic challenges to adaptation andmitigation (O’Neill et al., 2014). Based on five narratives, the SSPs
describe alternative socio-economic futures in the absence of climate
policy intervention, comprising sustainable development (SSP1), regional
rivalry (SSP3), inequality (SSP4), fossil–fuelled development (SSP5) andmiddle-of-the-road development (SSP2) (O’Neill, 2000; O’Neill et al.,2017; Riahi et al., 2017). The combination of SSP-based socio-economic
scenarios and Representative Concentration Pathway (RCP)-based
climate projections provides an integrative frame for climate impact andpolicy analysis.
Transformation pathways
Trajectories describing consistent sets of possible futures of greenhousegas (GHG) emissions, atmospheric concentrations, or global meansurface temperatures implied from mitigation and adaptation actions
associated with a set of broad and irreversible economic, technological,
societal and behavioural changes. This can encompass changes in theway energy and infrastructure are used and produced, natural resources
are managed and institutions are set up and in the pace and direction oftechnological change.
See also Scenario, Scenario storyline, Emission scenario, Mitigation
scenario, Baseline scenario, Stabilization (of GHG or CO2-equivalentconcentration) and Narratives.
Peri-urban areas Peri-urban areas are those parts of a city thatappear to be quite rural but are in reality strongly linked functionally tothe city in its daily activities.
Permafrost Ground (soil or rock and included ice and organic
material) that remains at or below 0°C for at least two consecutive years.
pH pH is a dimensionless measure of the acidity of a solution given byits concentration of hydrogen ions ([H+]). pH is measured on a logarithmic
scale where pH = -log10[H+]. Thus, a pH decrease of 1 unit corresponds toa 10-fold increase in the concentration of H+, or acidity.
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Glossary

Plug-in hybrid electric vehicle (PHEV) See Electric vehicle (EV).
Policies (for climate change mitigation and adaptation)
Policies are taken and/or mandated by a government – often in conjunction
with business and industry within a single country, or collectively with othercountries – to accelerate mitigation and adaptation measures. Examples ofpolicies are support mechanisms for renewable energy supplies, carbon orenergy taxes, fuel efficiency standards for automobiles, etc.
Political economy The set of interlinked relationships between
people, the state, society and markets as defined by law, politics,
economics, customs and power that determine the outcome of trade andtransactions and the distribution of wealth in a country or economy.
Poverty Poverty is a complex concept with several definitions
stemming from different schools of thought. It can refer to material
circumstances (such as need, pattern of deprivation or limited resources),
economic conditions (such as standard of living, inequality or economic
position) and/or social relationships (such as social class, dependency,
exclusion, lack of basic security or lack of entitlement). See also Poverty
eradication.
Poverty eradication A set of measures to end poverty in all its
forms everywhere. See also Sustainable Development Goals (SDGs).
Precursors Atmospheric compounds that are not greenhousegases (GHGs) or aerosols, but that have an effect on GHG or aerosol
concentrations by taking part in physical or chemical processes regulating
their production or destruction rates. See also Aerosol and Greenhousegas (GHG).
Pre-industrial The multi-century period prior to the onset of largescale industrial activity around 1750. The reference period 1850–1900
is used to approximate pre-industrial global mean surface temperature(GMST). See also Industrial revolution.
Procedural equity See Equity.
Procedural justice See Justice.
Procedural rights

See Human rights.

Projection A projection is a potential future evolution of a quantity
or set of quantities, often computed with the aid of a model. Unlike
predictions, projections are conditional on assumptions concerning, forexample, future socio-economic and technological developments that
may or may not be realized. See also Climate projection, Scenario andPathways.
Purchasing power parity (PPP) The purchasing power of a currency
is expressed using a basket of goods and services that can be boughtwith a given amount in the home country. International comparison of,for example, gross domestic products (GDPs) of countries can be basedon the purchasing power of currencies rather than on current exchange
rates. PPP estimates tend to lower the gap between the per capita GDP inindustrialized and developing countries. See also Market exchange rate(MER).
Radiative forcing Radiative forcing is the change in the net,
downward minus upward, radiative flux (expressed in W m-2) at thetropopause or top of atmosphere due to a change in a driver of climatechange, such as a change in the concentration of carbon dioxide (CO2)or the output of the Sun. The traditional radiative forcing is computedwith all tropospheric properties held fixed at their unperturbed values,
and after allowing for stratospheric temperatures, if perturbed, toreadjust to radiative-dynamical equilibrium. Radiative forcing is called
instantaneous if no change in stratospheric temperature is accounted for.
The radiative forcing once rapid adjustments are accounted for is termedGlossary

Annex Ithe effective radiative forcing. Radiative forcing is not to be confusedwith cloud radiative forcing, which describes an unrelated measure of
the impact of clouds on the radiative flux at the top of the atmosphere.

identity and structure while also maintaining the capacity for adaptation,
learning and transformation. This definition builds from the definition
used by Arctic Council (2013). See also Hazard, Risk and Vulnerability.

Reasons for Concern (RFCs) Elements of a classification
framework, first developed in the IPCC Third Assessment Report, whichaims to facilitate judgments about what level of climate change may be
dangerous (in the language of Article 2 of the UNFCCC) by aggregating
risks from various sectors, considering hazards, exposures, vulnerabilities,
capacities to adapt, and the resulting impacts.

Risk The potential for adverse consequences where something ofvalue is at stake and where the occurrence and degree of an outcome
is uncertain. In the context of the assessment of climate impacts, theterm risk is often used to refer to the potential for adverse consequencesof a climate-related hazard, or of adaptation or mitigation responses tosuch a hazard, on lives, livelihoods, health and well-being, ecosystems
and species, economic, social and cultural assets, services (including
ecosystem services), and infrastructure. Risk results from the interactionof vulnerability (of the affected system), its exposure over time (to thehazard), as well as the (climate-related) hazard and the likelihood of its
occurrence.

Reducing Emissions from Deforestation and Forest Degradation(REDD+) An effort to create financial value for the carbon stored inforests, offering incentives for developing countries to reduce emissions
from forested lands and invest in low-carbon paths to sustainable
development (SD). It is therefore a mechanism for mitigation that resultsfrom avoiding deforestation. REDD+ goes beyond deforestation andforest degradation, and includes the role of conservation, sustainable
management of forests and enhancement of forest carbon stocks.
The concept was first introduced in 2005 in the 11th Session of theConference of the Parties (COP) in Montreal and later given greater
recognition in the 13th Session of the COP in 2007 at Bali and inclusionin the Bali Action Plan, which called for ‘policy approaches and positive
incentives on issues relating to reducing emissions from deforestationand forest degradation in developing countries (REDD) and the role ofconservation, sustainable management of forests and enhancementof forest carbon stock in developing countries.’ Since then, support for
REDD has increased and has slowly become a framework for action
supported by a number of countries.
Reference period The period relative to which anomalies arecomputed. See also Anomaly.
Reference scenario

See Baseline scenario.

Reforestation Planting of forests on lands that have previously
contained forests but that have been converted to some other use. Fora discussion of the term forest and related terms such as afforestation,
reforestation and deforestation, see the IPCC Special Report on Land Use,
Land-Use Change, and Forestry (IPCC, 2000), information provided by
the United Nations Framework Convention on Climate Change (UNFCCC,2013), the report on Definitions and Methodological Options toInventory Emissions from Direct Human-induced Degradation of Forests
and Devegetation of Other Vegetation Types (IPCC, 2003). See also
Deforestation, Afforestation and Reducing Emissions from Deforestation
and Forest Degradation (REDD+).
Region A region is a relatively large-scale land or ocean area
characterized by specific geographical and climatological features. The
climate of a land-based region is affected by regional and local scalefeatures like topography, land use characteristics and large water
bodies, as well as remote influences from other regions, in addition to
global climate conditions. The IPCC defines a set of standard regions foranalyses of observed climate trends and climate model projections (seeFigure 3.2; AR5, SREX).
Remaining carbon budget Estimated cumulative net global
anthropogenic CO2 emissions from the start of 2018 to the time thatanthropogenic CO2 emissions reach net zero that would result, at someprobability, in limiting global warming to a given level, accounting for the
impact of other anthropogenic emissions.
Representative Concentration Pathways (RCPs) See Pathways.
Resilience The capacity of social, economic and environmental
systems to cope with a hazardous event or trend or disturbance,
responding or reorganizing in ways that maintain their essential function,

Risk assessment The qualitative and/or quantitative scientificestimation of risks. See also Risk, Risk management and Risk perception.
Risk management Plans, actions, strategies or policies to reduce thelikelihood and/or consequences of risks or to respond to consequences.
See also Risk, Risk assessment and Risk perception.
Risk perception The subjective judgment that people make about
the characteristics and severity of a risk. See also Risk, Risk assessment
and Risk management.
Runoff The flow of water over the surface or through the subsurface,which typically originates from the part of liquid precipitation and/or
snow/ice melt that does not evaporate or refreeze, and is not transpired.
See also Hydrological cycle.
Scenario A plausible description of how the future may develop
based on a coherent and internally consistent set of assumptions
about key driving forces (e.g., rate of technological change, prices) andrelationships. Note that scenarios are neither predictions nor forecasts,
but are used to provide a view of the implications of developments
and actions. See also Baseline scenario, Emission scenario, Mitigation
scenario and Pathways.
Scenario storyline A narrative description of a scenario (or family of
scenarios), highlighting the main scenario characteristics, relationships
between key driving forces and the dynamics of their evolution. Also
referred to as ‘narratives’ in the scenario literature. See also Narratives.
SDG-interaction score A seven-point scale (Nilsson et al., 2016)used to rate interactions between mitigation options and the SDGs.
Scores range from +3 (indivisible) to −3 (cancelling), with a zero scoreindicating ‘consistent’ but with neither a positive or negative interaction.
The scale, as applied in this report, also includes direction (whether theinteraction is uni- or bi-directional) and confidence as assessed per IPCC
guidelines.
Sea ice Ice found at the sea surface that has originated from the freezingof seawater. Sea ice may be discontinuous pieces (ice floes) moved on theocean surface by wind and currents (pack ice), or a motionless sheet
attached to the coast (land-fast ice). Sea ice concentration is the fractionof the ocean covered by ice. Sea ice less than one year old is called firstyear ice. Perennial ice is sea ice that survives at least one summer. It may
be subdivided into second-year ice and multi-year ice, where multi-year
ice has survived at least two summers.
Sea level change (sea level rise/sea level fall) Sea level canchange, both globally and locally (relative sea level change) due to (1) achange in ocean volume as a result of a change in the mass of water inthe ocean, (2) changes in ocean volume as a result of changes in ocean
water density, (3) changes in the shape of the ocean basins and changes

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Glossaryin the Earth’s gravitational and rotational fields, and (4) local subsidence
or uplift of the land. Global mean sea level change resulting from change
in the mass of the ocean is called barystatic. The amount of barystatic
sea level change due to the addition or removal of a mass of water iscalled its sea level equivalent (SLE). Sea level changes, both globally andlocally, resulting from changes in water density are called steric. Densitychanges induced by temperature changes only are called thermosteric,while density changes induced by salinity changes are called halosteric.
Barystatic and steric sea level changes do not include the effect ofchanges in the shape of ocean basins induced by the change in the ocean
mass and its distribution.
Sea surface temperature (SST) The sea surface temperature is
the subsurface bulk temperature in the top few meters of the ocean,measured by ships, buoys, and drifters. From ships, measurements ofwater samples in buckets were mostly switched in the 1940s to samples
from engine intake water. Satellite measurements of skin temperature(uppermost layer; a fraction of a millimeter thick) in the infrared or
the top centimeter or so in the microwave are also used, but must be
adjusted to be compatible with the bulk temperature.
Sendai Framework for Disaster Risk Reduction The Sendai
Framework for Disaster Risk Reduction 2015–2030 outlines seven clear
targets and four priorities for action to prevent new, and to reduce
existing, disaster risks. The voluntary, non-binding agreement recognizes
that the State has the primary role to reduce disaster risk but that
responsibility should be shared with other stakeholders, including local
government and the private sector. Its aim is to achieve ‘substantial
reduction of disaster risk and losses in lives, livelihoods and health andin the economic, physical, social, cultural and environmental assets ofpersons, businesses, communities and countries.’
Sequestration

See Uptake.

Shared Socio-economic Pathways (SSPs)

See Pathways.

Short-lived climate forcers (SLCF) Short-lived climate forcers
refers to a set of compounds that are primarily composed of those withshort lifetimes in the atmosphere compared to well-mixed greenhousegases, and are also referred to as near-term climate forcers. This setof compounds includes methane (CH4), which is also a well-mixed
greenhouse gas, as well as ozone (O3) and aerosols, or their precursors,and some halogenated species that are not well-mixed greenhouse gases.
These compounds do not accumulate in the atmosphere at decadal tocentennial time scales, and so their effect on climate is predominantlyin the first decade after their emission, although their changes can still
induce long-term climate effects such as sea level change. Their effectcan be cooling or warming. A subset of exclusively warming short-lived
climate forcers is referred to as short-lived climate pollutants. See alsoLong-lived climate forcers (LLCF).
Short-lived climate pollutants (SLCP) See Short-lived climate
forcers (SLCF).
Sink A reservoir (natural or human, in soil, ocean, and plants) where agreenhouse gas, an aerosol or a precursor of a greenhouse gas is stored.
Note that UNFCCC Article 1.8 refers to a sink as any process, activity ormechanism which removes a greenhouse gas, an aerosol or a precursor
of a greenhouse gas from the atmosphere. See also Uptake.
Small island developing states (SIDS) Small island developing
states (SIDS), as recognised by the United Nations OHRLLS (Office ofthe High Representative for the Least Developed Countries, Landlocked
Developing Countries and Small Island Developing States), are a distinctgroup of developing countries facing specific social, economic andenvironmental vulnerabilities (UN-OHRLLS, 2011). They were recognized558

as a special case both for their environment and development at the
Rio Earth Summit in Brazil in 1992. Fifty-eight countries and territories
are presently classified as SIDS by the UN OHRLLS, with 38 being UN
member states and 20 being Non-UN Members or Associate Members of
the Regional Commissions (UN-OHRLLS, 2018).
Social cost of carbon (SCC) The net present value of aggregate
climate damages (with overall harmful damages expressed as a number
with positive sign) from one more tonne of carbon in the form of carbon
dioxide (CO2), conditional on a global emissions trajectory over time.
Social costs The full costs of an action in terms of social welfare
losses, including external costs associated with the impacts of this action
on the environment, the economy (GDP, employment) and on the society
as a whole.
Social-ecological systems An integrated system that includes
human societies and ecosystems, in which humans are part of nature.
The functions of such a system arise from the interactions andinterdependence of the social and ecological subsystems. The system’s
structure is characterized by reciprocal feedbacks, emphasising thathumans must be seen as a part of, not apart from, nature. This definitionbuilds from Arctic Council (2016) and Berkes and Folke (1998).
Social inclusion A process of improving the terms of participation
in society, particularly for people who are disadvantaged, throughenhancing opportunities, access to resources, and respect for rights (UNDESA, 2016).
Social justiceSee Justice.

Social learning A process of social interaction through which people
learn new behaviours, capacities, values and attitudes.
Social value of mitigation activities (SVMA) Social, economicand environmental value of mitigation activities that include, in addition
to their climate benefits, their co-benefits to adaptation and sustainable
development objectives.
Societal (social) transformation

See Transformation.

Socio-economic scenario A scenario that describes a possible
future in terms of population, gross domestic product (GDP), and othersocio-economic factors relevant to understanding the implications ofclimate change. See also Baseline scenario, Emission scenario, Mitigation
scenario and Pathways.
Socio-technical transitions Socio-technical transitions are where
technological change is associated with social systems and the two are
inextricably linked.
Soil carbon sequestration (SCS) Land management changes
which increase the soil organic carbon content, resulting in a net removalof CO2 from the atmosphere.
Soil moisture Water stored in the soil in liquid or frozen form. Rootzone soil moisture is of most relevance for plant activity.
Solar radiation management See Solar radiation modification (SRM).
Solar radiation modification (SRM) Solar radiation modificationrefers to the intentional modification of the Earth’s shortwave radiative
budget with the aim of reducing warming. Artificial injection ofstratospheric aerosols, marine cloud brightening and land surface albedo
modification are examples of proposed SRM methods. SRM does not
fall within the definitions of mitigation and adaptation (IPCC, 2012b, p.
2). Note that in the literature SRM is also referred to as solar radiation
management or albedo enhancement.

Glossary

Annex I

Stabilization (of GHG or CO2-equivalent concentration) A
state in which the atmospheric concentrations of one greenhouse gas(GHG) (e.g., carbon dioxide) or of a CO2-equivalent basket of GHGs (or acombination of GHGs and aerosols) remains constant over time.
Stranded assets Assets exposed to devaluations or conversion to‘liabilities’ because of unanticipated changes in their initially expected
revenues due to innovations and/or evolutions of the business context,
including changes in public regulations at the domestic and international
levels.
Stratosphere The highly stratified region of the atmosphere above
the troposphere extending from about 10 km (ranging from 9 km at highlatitudes to 16 km in the tropics on average) to about 50 km altitude. See
also Atmosphere, and Troposphere.
Sub-national actor Sub-national actors include state/provincial,regional, metropolitan and local/municipal governments as well as nonparty stakeholders, such as civil society, the private sector, cities and othersub-national authorities, local communities and indigenous peoples.
Substantive rights See Human rights.
Supply-side measures

See Demand- and supply-side measures.

Surface temperature See Global mean surface temperature (GMST),Land surface air temperature, Global mean surface air temperature(GSAT) and Sea surface temperature (SST).
Sustainability A dynamic process that guarantees the persistence of
natural and human systems in an equitable manner.
Sustainable development (SD) Development that meets the needsof the present without compromising the ability of future generations tomeet their own needs (WCED, 1987) and balances social, economic andenvironmental concerns. See also Sustainable Development Goals (SDGs)
and Development pathways (under Pathways).
Sustainable Development Goals (SDGs) The 17 global goals
for development for all countries established by the United Nations
through a participatory process and elaborated in the 2030 Agenda for
Sustainable Development, including ending poverty and hunger; ensuring
health and well-being, education, gender equality, clean water andenergy, and decent work; building and ensuring resilient and sustainableinfrastructure, cities and consumption; reducing inequalities; protecting
land and water ecosystems; promoting peace, justice and partnerships;
and taking urgent action on climate change. See also Sustainable
development (SD).
Technology transfer The exchange of knowledge, hardware andassociated software, money and goods among stakeholders, whichleads to the spread of technology for adaptation or mitigation. The
term encompasses both diffusion of technologies and technologicalcooperation across and within countries.
Temperature overshoot The temporary exceedance of a specified
level of global warming, such as 1.5°C. Overshoot implies a peak followed
by a decline in global warming, achieved through anthropogenic removalof CO2 exceeding remaining CO2 emissions globally. See also Overshootpathways and Non-overshoot pathways (both under Pathways).
Tipping point A level of change in system properties beyond whicha system reorganizes, often abruptly, and does not return to the initialstate even if the drivers of the change are abated. For the climate system,
it refers to a critical threshold when global or regional climate changes
from one stable state to another stable state. See also Irreversibility.
Transformation A change in the fundamental attributes of naturaland human systems.

Societal (social) transformation
A profound and often deliberate shift initiated by communities towardsustainability, facilitated by changes in individual and collective values
and behaviours, and a fairer balance of political, cultural, and institutional
power in society.
Transformation pathways

See Pathways.

Transformational adaptation

See Adaptation.

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Transformative change A system-wide change that requires morethan technological change through consideration of social and economic
factors that, with technology, can bring about rapid change at scale.
Transient climate response

See Climate sensitivity.

Transient climate response to cumulative CO2 emissions(TCRE) The transient global average surface temperature change perunit cumulative CO2 emissions, usually 1000 GtC. TCRE combines bothinformation on the airborne fraction of cumulative CO2 emissions (thefraction of the total CO2 emitted that remains in the atmosphere, which
is determined by carbon cycle processes) and on the transient climate
response (TCR). See also Transient climate response (under Climate
sensitivity).
Transit-oriented development (TOD) An approach to urban
development that maximizes the amount of residential, business andleisure space within walking distance of efficient public transport, so as
to enhance mobility of citizens, the viability of public transport and thevalue of urban land in mutually supporting ways.
Transition The process of changing from one state or condition
to another in a given period of time. Transition can be in individuals,
firms, cities, regions and nations, and can be based on incremental ortransformative change.
Tropical cyclone The general term for a strong, cyclonic-scaledisturbance that originates over tropical oceans. Distinguished fromweaker systems (often named tropical disturbances or depressions) byexceeding a threshold wind speed. A tropical storm is a tropical cyclone
with one-minute average surface winds between 18 and 32 m s-1. Beyond
32 m s-1, a tropical cyclone is called a hurricane, typhoon, or cyclone,depending on geographic location. See also Extratropical cyclone.
Troposphere The lowest part of the atmosphere, from the surface
to about 10 km in altitude at mid-latitudes (ranging from 9 km at highlatitudes to 16 km in the tropics on average), where clouds and weather
phenomena occur. In the troposphere, temperatures generally decrease
with height. See also Atmosphere and Stratosphere.
Uncertainty A state of incomplete knowledge that can result froma lack of information or from disagreement about what is known oreven knowable. It may have many types of sources, from imprecision in
the data to ambiguously defined concepts or terminology, incomplete
understanding of critical processes, or uncertain projections of humanbehaviour. Uncertainty can therefore be represented by quantitative
measures (e.g., a probability density function) or by qualitative
statements (e.g., reflecting the judgment of a team of experts) (see Mossand Schneider, 2000; IPCC, 2004; Mastrandrea et al., 2010). See also
Confidence and Likelihood.
United Nations Framework Convention on Climate Change(UNFCCC) The UNFCCC was adopted in May 1992 and opened forsignature at the 1992 Earth Summit in Rio de Janeiro. It entered into force
in March 1994 and as of May 2018 had 197 Parties (196 States and theEuropean Union). The Convention’s ultimate objective is the ‘stabilisationof greenhouse gas concentrations in the atmosphere at a level thatwould prevent dangerous anthropogenic interference with the climate
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Glossarysystem.’ The provisions of the Convention are pursued and implemented
by two treaties: the Kyoto Protocol and the Paris Agreement. See also
Kyoto Protocol and Paris Agreement.
Uptake The addition of a substance of concern to a reservoir. See also
Carbon sequestration and Sink.
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Vulnerability The propensity or predisposition to be adversely
affected. Vulnerability encompasses a variety of concepts and elements
including sensitivity or susceptibility to harm and lack of capacity to copeand adapt. See also Exposure, Hazard and Risk.
Water cycle

See Hydrological cycle.

Well-being A state of existence that fulfils various human needs,
including material living conditions and quality of life, as well as theability to pursue one’s goals, to thrive, and feel satisfied with one’s life.
Ecosystem well-being refers to the ability of ecosystems to maintain theirdiversity and quality.
Zero emissions commitment

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See Climate change commitment.

Glossary

Annex I

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