Editing IPCC:AR6/SRCCL/Chapter-7 (section)

== 7.2 Climate-related risks for land-based human systems and ecosystems ==

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This section examines risks that climate change poses to selected land-based human systems and ecosystems, and then further explores how social and economic choices, as well as responses to climate change, will exacerbate or lessen risks. ‘Risk’ is defined as ''the potential for adverse consequences for human or ecological systems, recognising the diversity of values and objectives associated with such systems'' . The interacting processes of climate change, land change, and unprecedented social and technological change, pose significant risk to climate-resilient sustainable development. The pace, intensity, and scale of these sizeable risks affect the central issues in sustainable development: access to ecosystem services (ES) and resources essential to sustain people in given locations; how and where people live and work; and the means to safeguard human well-being against disruptions (Warner et al. 2019). In the context of climate change, adverse consequences can arise from the potential impacts of climate change as well as human responses to climate change. Relevant adverse consequences include those on lives, livelihoods, health and well-being, economic, social and cultural assets and investments, infrastructure, services (including ES), ecosystems and species (see Glossary). Risks result from dynamic interactions between climate-related hazards with the exposure and vulnerability of the affected human or ecological system to the hazards. Hazards, exposure and vulnerability may change over time and space as a result of socio-economic changes and human decision-making (‘risk management’). Numerous uncertainties exist in the scientific understanding of risk (Section 1.2.2).

<span id="assessing-risk"></span>
=== 7.2.1 Assessing risk ===

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This chapter applies and further improves methods used in previous IPCC reports including AR5 and the Special Report on Global Warming of 1.5°C (SR15) to assess risks. Evidence is drawn from published studies, which include observations of impacts from human-induced climate change and model projections for future climate change. Such projections are based on Integrated Assessment Models (IAMs), Earth System Models (ESMs), regional climate models and global or regional impact models examining the impact of climate change on various indicators (Cross-Chapter Box 1 in Chapter 1). Results of laboratory and field experiments that examine impacts of specific changes were also included in the review. Risks under different future socio-economic conditions were assessed using recent publications based on Shared Socio-economic Pathways (SSPs). SSPs provide storylines about future socio-economic development and can be combined with Representative Concentration Pathways RCPs (Riahi et al. 2017 <sup>[[#fn:r43|43]]</sup> ) (Cross-Chapter Box 9 in Chapters 6 and 7). Risk arising from land-based mitigation and adaptation choices is assessed using studies examining the adverse side effects of such responses (Section 7.2.3).

Burning embers figures introduced in the IPCC Third Assessment Report through to the Fifth Assessment Report, and the SR15, were developed for this report to illustrate risks at different temperature thresholds. Key components involved in desertification, land degradation and food security were identified, based on discussions with authors in Chapters 3, 4 and 5. The final list of burning embers in Figure 7.1 is not intended to be fully comprehensive, but represents processes for which sufficient literature exists to make expert judgements. Literature used in the burning embers assessment is summarised in tables in Supplementary Material. Following an approach articulated in O’Neill et al. (2017), expert judgements were made to assess thresholds of risk (O’Neill et al. 2017a <sup>[[#fn:r44|44]]</sup> ). To further strengthen replicability of the method, a predefined protocol based on a modified Delphi process was followed (Mukherjee et al. 2015 <sup>[[#fn:r45|45]]</sup> ). This included two separate anonymous rating rounds, feedback in between rounds and a group discussion to achieve consensus.

Burning embers provide ranges of a given variable (typically global mean near-surface air temperature) for which risks transitions within four categories: undetectable, moderate, high and very high. '''Moderate risk''' indicates that impacts are detectable and attributable to climate-related factors. '''High risk''' indicates widespread impacts on larger numbers or proportion of population/area, but with the potential to adapt or recover. '''Very high risk''' indicates severe and possibly irreversible impacts with limited ability of societies and ecosystems to adapt to them. Transitions between risk categories were assigned confidence levels based on the amount, and quality, of academic literature supporting judgements: L = low, M = medium, and H = high. Further details of the procedure are provided in Supplementary Material.

<span id="risks-to-land-systems-arising-from-climate-change"></span>
=== 7.2.2 Risks to land systems arising from climate change ===

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At current levels of global mean surface temperature (GMST) increase, impacts are already detectable across numerous land- related systems ( ''high confidence'' ) (Chapters 2, 3, 4 and 6). There is ''high confidence'' that unabated future climate change will result in continued changes to processes involved in desertification, land degradation and food security, including: water scarcity in drylands; soil erosion; coastal degradation; vegetation loss; fire; permafrost thaw; and access, stability, utilisation and physical availability of food (Figure 7.1). These changes will increase risks to food systems, the health of humans and ecosystems, livelihoods, the value of land, infrastructure and communities (Section 7.3). Details of the risks, and their transitions, are described in the following subsections.

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'''Figure 7.1'''

<span id="risks-to-selected-land-system-elements-as-a-function-of-global-mean-surface-temperature-increase-since-pre-industrial-times.-impacts-on-human-and-ecological-systems-include-1-economic-loss-and-declines-in-livelihoods-and-ecosystem-services-from-water-scarcity-in-drylands-2-economic-loss-and-declines-in-livelihoods-and-ecosystem-services-from-reduced-land-productivity-due"></span>
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'''Risks to selected land system elements as a function of global mean surface temperature increase since pre-industrial times. Impacts on human and ecological systems include: 1) economic loss and declines in livelihoods and ecosystem services from water scarcity in drylands, 2) economic loss and declines in livelihoods and ecosystem services from reduced land productivity due […]'''
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[[File:e0b08e4d51bd3bcb0b07a4be36750e18 7.1.jpg]]

Risks to selected land system elements as a function of global mean surface temperature increase since pre-industrial times. Impacts on human and ecological systems include: 1) economic loss and declines in livelihoods and ecosystem services from water scarcity in drylands, 2) economic loss and declines in livelihoods and ecosystem services from reduced land productivity due to soil erosion, 3) vegetation loss and shifts in vegetation structure, 4) damage to infrastructure, altered land cover, accelerated erosion and increased air pollution from fires, 5) damage to natural and built environment from permafrost thaw related ground instability, 6) changes to crop yield and food availability in low-latitude regions and 7) increased disruption of food supply stability. Risks are global (2, 3, 4, 7) and specific to certain regions (1, 5, 6). Selected components are illustrative and not intended to be fully comprehensive of factors influencing food security, land degradation and desertification. The supporting literature and methods are provided in Supplementary Material. Risk levels are estimated assuming medium exposure and vulnerability driven by moderate trends in socioeconomic conditions broadly consistent with an SSP2 pathway.

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==== 7.2.2.1 Crop yield in low latitudes ====

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There is ''high confidence'' that climate change has resulted in decreases in yield (of wheat, rice, maize, soy) and reduced food availability in low-latitude regions (IPCC, 2018 <sup>[[#fn:r46|46]]</sup> ) (Section 5.2.2). Countries in low- latitude regions are particularly vulnerable because the livelihoods of high proportions of the population are dependent on agricultural production. Even moderate temperature increases (1°C to 2°C) have negative yield impacts for major cereals, because the climate of many tropical agricultural regions is already quite close to the high-temperature thresholds for suitable production of these cereals (Rosenzweig et al. 2014 <sup>[[#fn:r47|47]]</sup> ). Thus, by 1.5°C global mean temperature GMT, or between approximately 1.6°C and approximately 2.6°C of local warming, risks to yields may already transition to ''high'' in West Africa, Southeast Asia and Central and South America (Faye et al. 2018 <sup>[[#fn:r48|48]]</sup> ) ( ''medium confidence'' ). For further information see Section 5.3.2.1. By contrast, higher latitudes may initially benefit from warming as well as well higher CO <sub>2</sub> concentrations (IPCC 2018a <sup>[[#fn:r49|49]]</sup> ). Wheat yield losses are expected to be lower for the USA (−5.5 ± 4.4% per degree Celsius) and France (−6.0 ± 4.2% per degree Celsius) compared to India (−9.1 ± 5.4% per degree Celsius) (Zhao et al. 2017 <sup>[[#fn:r50|50]]</sup> ). Very high risks to low-latitude yields may occur between 3°C and 4°C ( ''medium confidence'' ). At these temperatures, catastrophic reductions in crop

yields may occur, of up to 60% in low latitudes (Rosenzweig et al. 2014 <sup>[[#fn:r51|51]]</sup> ) (Sections 5.2.2 and 5.2.3). Some studies report significant population displacement from the tropics related to systemic livelihood disruption in agriculture systems (Tittonell 2014 <sup>[[#fn:r59|59]]</sup> ; Montaña et al. 2016 <sup>[[#fn:r52|52]]</sup> ; Huber-Sannwald et al. 2012 <sup>[[#fn:r53|53]]</sup> ; Wise et al. 2016 <sup>[[#fn:r54|54]]</sup> ; Tanner et al. 2015 <sup>[[#fn:r55|55]]</sup> ; Mohapatra 2013 <sup>[[#fn:r56|56]]</sup> ). However, at higher temperatures of warming, all regions of the world face risks of declining yields as a result of extreme weather events and reduced heat tolerance of maize, rice, wheat and soy (Zhao et al. 2017 <sup>[[#fn:r57|57]]</sup> ; IPCC 2018a <sup>[[#fn:r58|58]]</sup> ).

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==== 7.2.2.2 Food supply instability ====

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Stability of food supply is expected to decrease as the magnitude and frequency of extreme events increase, disrupting food chains in all areas of the world ( ''medium evidence, high agreement'' ) (Wheeler and Von Braun 2013 <sup>[[#fn:r60|60]]</sup> ; Coates 2013 <sup>[[#fn:r61|61]]</sup> ; Puma et al. 2015 <sup>[[#fn:r62|62]]</sup> ; Deryng et al. 2014 <sup>[[#fn:r63|63]]</sup> ; Harvey et al. 2014b <sup>[[#fn:r64|64]]</sup> ; Iizumi et al. 2013 <sup>[[#fn:r65|65]]</sup> ; Seaman et al. 2014 <sup>[[#fn:r66|66]]</sup> ) (Sections 5.3.2, 5.3.3, 5.6.2 and 5.7.1). While international trade in food is assumed to be a key response for alleviating hunger, historical data and economic models suggest that international trade does not adequately redistribute food globally to offset yield declines or other food shortages when weather extremes reduce crop yields ( ''medium confidence'' ) (Schmitz et al. 2012 <sup>[[#fn:r67|67]]</sup> ; Chatzopoulos et al. 2019 <sup>[[#fn:r68|68]]</sup> ; Marchand et al. 2016 <sup>[[#fn:r69|69]]</sup> ; Gilbert 2010 <sup>[[#fn:r70|70]]</sup> ; Wellesley et al. 2017 <sup>[[#fn:r71|71]]</sup> ). When droughts, heat waves, floods or other extremes destroy crops, evidence has shown that exports are constrained in key producing countries contributing to price spikes and social tension in importing countries which reduce access to food ( ''medium evidence, medium agreement'' ) (von Uexkull et al. 2016 <sup>[[#fn:r72|72]]</sup> ; Gleick 2014 <sup>[[#fn:r73|73]]</sup> ; Maystadt and Ecker 2014 <sup>[[#fn:r74|74]]</sup> ; Kelley et al. 2015 <sup>[[#fn:r75|75]]</sup> ; Church et al. 2017 <sup>[[#fn:r76|76]]</sup> ; Götz et al. 2013 <sup>[[#fn:r77|77]]</sup> ; Puma et al. 2015 <sup>[[#fn:r78|78]]</sup> ; Willenbockel 2012 <sup>[[#fn:r79|79]]</sup> ; Headey 2011 <sup>[[#fn:r80|80]]</sup> ; Distefano et al. 2018 <sup>[[#fn:r81|81]]</sup> ; Brooks 2014 <sup>[[#fn:r82|82]]</sup> ). There is little understanding of how food system shocks cascade through a modern interconnected economy. Reliance on global markets may reduce some risks, but the ongoing globalisation of food trade networks exposes the world food system to new impacts that have not been seen in the past (Sections 5.1.2, 5.2.1, 5.5.2.5, 5.6.5 and 5.7.1). The global food system is vulnerable to systemic disruptions and increasingly interconnected inter-country food dependencies, and changes in the frequency and severity of extreme weather events may complicate future responses (Puma et al. 2015 <sup>[[#fn:r83|83]]</sup> ; Jones and Hiller 2017 <sup>[[#fn:r84|84]]</sup> ).

Impacts of climate change are already detectable on food supply and access as price and trade reactions have occurred in response to heatwaves, droughts and other extreme events ( ''high evidence, high agreement'' ) (Noble et al. 2014 <sup>[[#fn:r85|85]]</sup> ; O’Neill et al. 2017b <sup>[[#fn:r86|86]]</sup> ). The impact of climate change on food stability is underexplored (Schleussner et al. 2016 <sup>[[#fn:r87|87]]</sup> ; James et al. 2017 <sup>[[#fn:r88|88]]</sup> ). However, some literature assesses that by about 2035, daily maximum temperatures will exceed the 90th percentile of historical (1961–1990) temperatures on 25–30% of days (O’Neill et al. 2017b <sup>[[#fn:r89|89]]</sup> , Figures 11–17) with negative shocks to food stability and world food prices. O’Neill et al. (2017b) <sup>[[#fn:r90|90]]</sup> remark that in the future, return periods for precipitation events globally (land only) will reduce from one-in-20-year (historical) to about one-in-14- year or less by 2046–2065 in many areas of the world. Domestic efforts to insulate populations from food price spikes associated with climatic stressors in the mid-2000s have been shown to inadequately shield from poverty, and worsen poverty globally (Diffenbaugh et al. 2012 <sup>[[#fn:r91|91]]</sup> ; Meyfroidt et al. 2013 <sup>[[#fn:r92|92]]</sup> ; Hertel et al. 2010 <sup>[[#fn:r93|93]]</sup> ). The transition to high risk is estimated to occur around 1.4°C, possibly by 2035, due to changes in temperature and heavy precipitation events ( ''medium confidence)'' (O’Neill et al. 2017b <sup>[[#fn:r94|94]]</sup> ; Fritsche et al. 2017a <sup>[[#fn:r95|95]]</sup> ; Harvey et al. 2014b <sup>[[#fn:r96|96]]</sup> ). ''Very high risk'' may occur by 2.4°C ( ''medium confidence'' ) and 4°C of warming is considered catastrophic (IPCC 2018c <sup>[[#fn:r97|97]]</sup> ; Noble et al. 2014 <sup>[[#fn:r98|98]]</sup> ) for food stability and access because a combination of extreme events, compounding political and social factors, and shocks to crop yields can heavily constrain options to ensure food security in import- reliant countries.

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==== 7.2.2.3 Soil erosion ====

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Soil erosion increases risks of economic loss and declines in livelihoods due to reduced land productivity. In the EU, on-site costs of soil erosion by wind has been reported at an average of 55 USD per hectare annually, but up to 450 USD per hectare for sugar beet and oilseed rape (Middleton et al. 2017 <sup>[[#fn:r99|99]]</sup> ). Farmers in the Dapo watershed in Ethiopia lose about 220 USD per hectare of maize due to loss of nitrogen through soil erosion (Erkossa et al. 2015 <sup>[[#fn:r100|100]]</sup> ). Soil erosion not only increases crop loss but has been shown to have reduced household food supply with older farmers most vulnerable to losses from erosion (Ighodaro et al. 2016 <sup>[[#fn:r101|101]]</sup> ). Erosion also results in increased risks to human health, through air pollution from aerosols (Middleton et al. 2017 <sup>[[#fn:r102|102]]</sup> ), and brings risks of reduced ES including supporting services related to soil formation.

At current levels of warming, changes in erosion are already detected in many regions. Attribution to climate change is challenging as there are other powerful drivers of erosion (e.g., land use), limited global- scale studies (Li and Fang 2016a <sup>[[#fn:r103|103]]</sup> ; Vanmaercke et al. 2016a <sup>[[#fn:r104|104]]</sup> ) and the absence of formal detection and attribution studies (Section 4.2.3). However, studies have found an increase in short-duration and high-intensity precipitation, due to anthropogenic climate change, which is a causative factor for soil erosion (Lenderink and van Meijgaard 2008 <sup>[[#fn:r105|105]]</sup> ; Li and Fang 2016b <sup>[[#fn:r106|106]]</sup> ). High risks of erosion may occur between 2°C and 3.5°C ( ''low confidence'' ) as continued increases in intense precipitation are projected at these temperature thresholds (Fischer and Knutti 2015 <sup>[[#fn:r107|107]]</sup> ) in many regions. Warming also reduces soil organic matter, diminishing resistance against erosion. There is ''low confidence'' concerning the temperature threshold at which risks become very high due to large regional differences and limited global-scale studies (Li and Fang 2016b <sup>[[#fn:r108|108]]</sup> ; Vanmaercke et al. 2016b <sup>[[#fn:r109|109]]</sup> ) (Section 4.4).

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==== 7.2.2.4 Dryland water scarcity ====

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Water scarcity in drylands contributes to changes in desertification and hazards such as dust storms, increasing risks of economic loss, declines in livelihoods of communities and negative health effects ( ''high confidence'' ) (Section 3.1.3). Further information specific to costs and impacts of water scarcity and droughts is detailed in Cross- Chapter Box 5 in Chapter 3.

The IPCC AR5 report and the SR15 concluded that there is ''low confidence'' in the direction of drought trends since 1950 at the global scale. While these reports did not assess water scarcity with a specific focus on drylands, they indicated that there is ''high confidence'' in observed drought increases in some regions of the world, including in the Mediterranean and West Africa (IPCC AR5) and that there is ''medium confidence'' that anthropogenic climate change has contributed to increased drying in the Mediterranean region (including southern Europe, northern Africa and the western Asia and the Middle east) and that this tendency will continue to increase under higher levels of global warming (IPCC 2018d). Some parts of the drylands have experienced decreasing precipitation over recent decades (IPCC AR5) (Chapter 3 and Section 3.2), consistent with the fact that climate change is implicated in desertification trends in some regions (Section 3.2.2). Dust storms, linked to changes in precipitation and vegetation, appear to be occurring with greater frequency in some deserts and their margins (Goudie 2014 <sup>[[#fn:r110|110]]</sup> ) (Section 3.3.1). There is therefore ''high confidence'' that the transition from undetectable to moderate risk associated with water scarcity in drylands occurred in recent decades in the range 0.7°C to 1°C (Figure 7.1).

Between 1.5°C and 2.5°C, the risk level is expected to increase from moderate to high ( ''medium confidence'' ). Globally, at 2°C an additional 8% of the world population (of population in 2000) will be exposed to new forms of or aggravated water scarcity (IPCC 2018d). However, at 2°C, the annual warming over drylands will reach 3.2°C–4.0°C, implying about 44% more warming over drylands than humid lands (Huang et al. 2017 <sup>[[#fn:r111|111]]</sup> ), thus potentially aggravating water scarcity issues through increased evaporative demand. Byers et al. (2018a) <sup>[[#fn:r112|112]]</sup> estimate that 3–22% of the drylands population (range depending on socio-economic conditions) will be exposed and vulnerable to water stress. The Mediterranean, North Africa and the Eastern Mediterranean will be particularly vulnerable to water shortages, and expansion of desert terrain and vegetation is predicted to occur in the Mediterranean biome, an unparalleled change in the last 10,000 years ( ''medium confidence'' ) (IPCC 2018d <sup>[[#fn:r113|113]]</sup> ). At 2.5°C–3.5°C risks are expected to become very high with migration from some drylands resulting as the only adaptation option ( ''medium confidence'' ). Scarcity of water for irrigation is expected to increase, in particular in Mediterranean regions, with limited possibilities for adaptation (Haddeland et al. 2014 <sup>[[#fn:r1571|1571]]</sup> ).

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==== 7.2.2.5 Vegetation degradation ====

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There are clear links between climate change and vegetation cover changes, tree mortality, forest diseases, insect outbreaks, forest fires, forest productivity and net ecosystem biome production (Allen et al. 2010 <sup>[[#fn:r115|115]]</sup> ; Bentz et al. 2010 <sup>[[#fn:r116|116]]</sup> ; Anderegg et al. 2013 <sup>[[#fn:r117|117]]</sup> ; Hember et al. 2017 <sup>[[#fn:r118|118]]</sup> ; Song et al. 2018 <sup>[[#fn:r119|119]]</sup> ; Sturrock et al. 2011 <sup>[[#fn:r120|120]]</sup> ). Forest dieback, often a result of drought and temperature changes, not only produces risks to forest ecosystems but also to people with livelihoods dependent on forests. A 50-year study of temperate forest, dominated by beech ( ''Fagus sylvatica'' L.), documented a 33% decline in basal area and a 70% decline in juvenile tree species, possibly as a result of interacting pressures of drought, overgrazing and pathogens (Martin et al. 2015 <sup>[[#fn:r121|121]]</sup> ). There is ''high confidence'' that such dieback impacts ecosystem properties and services including soil microbial community structure (Gazol et al. 2018 <sup>[[#fn:r122|122]]</sup> ). Forest managers and users have reported negative emotional impacts from forest dieback such as pessimism about losses, hopelessness and fear (Oakes et al. 2016 <sup>[[#fn:r123|123]]</sup> ). Practices and policies such as forest classification systems, projection of growth, yield and models for timber supply are already being affected by climate change (Sturrock et al. 2011 <sup>[[#fn:r124|124]]</sup> ).

While risks to ecosystems and livelihoods from vegetation degradation are already detectable at current levels of GMT increase, risks are expected to reach ''high'' levels between 1.6°C and 2.6°C ( ''medium confidence'' ). Significant uncertainty exists due to countervailing factors: CO <sub>2</sub> fertilisation encourages forest expansion but increased drought, insect outbreaks, and fires result in dieback (Bonan 2008 <sup>[[#fn:r125|125]]</sup> ; Lindner et al. 2010 <sup>[[#fn:r126|126]]</sup> ). The combined effects of temperature and precipitation change, with CO <sub>2</sub> fertilisation, make future risks to forests very location specific. It is challenging therefore to make global estimates. However, even locally specific studies make clear that ''very high'' risks occur between 2.6°C and 4°C ( ''medium confidence'' ). Australian tropical rainforests experience significant loss of biodiversity with 3.5°C increase. At this level of increase there are no areas with greater than 30 species, and all endemics disappear from low- and mid-elevation regions (Williams et al. 2003 <sup>[[#fn:r127|127]]</sup> ). Mountain ecosystems are particularly vulnerable (Loarie et al. 2009 <sup>[[#fn:r128|128]]</sup> ).

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==== 7.2.2.6 Fire damage ====

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Increasing fires result in heightened risks to infrastructure, accelerated erosion, altered hydrology, increased air pollution, and negative mental health impacts. Fire not only destroys property but induces changes in underlying site conditions (ground cover, soil water repellency, aggregate stability and surface roughness) which amplifies runoff and erosion, increasing future risks to property and human lives during extreme rainfall events (Pierson and Williams 2016 <sup>[[#fn:r129|129]]</sup> ). Dust and ash from fires can impact air quality in a wide area. For example, a dust plume from a fire in Idaho, USA, in September 2010 was visible in MODIS satellite imagery and extended at least 100 km downwind of the source area (Wagenbrenner et al. 2013 <sup>[[#fn:r130|130]]</sup> ). Individuals can suffer from property damage or direct injury, psychological trauma, depression, and post traumatic stress disorder, and have reported negative impacts to well-being from loss of connection to landscape (Paveglio et al. 2016 <sup>[[#fn:r131|131]]</sup> ; Sharples et al. 2016a <sup>[[#fn:r132|132]]</sup> ). Costs of large wildfires in the USA can exceed 20 million USD per day (Pierson et al. 2011 <sup>[[#fn:r133|133]]</sup> ) and has been estimated at 8.5 billion USD per year in Australia (Sharples et al. 2016b <sup>[[#fn:r134|134]]</sup> ). Globally, human exposure to fire will increase due to projected population growth in fire-prone regions (Knorr et al. 2016a <sup>[[#fn:r135|135]]</sup> ).

It is not clear how quickly, or even if, systems can recover from fires. Longevity of effects may differ depending on cover recruitment rate and soil conditions, recovering in one to two seasons or over 10 growing seasons (Pierson et al. 2011 <sup>[[#fn:r136|136]]</sup> ). In Russia, one-third of forest area affected by fires turned into unproductive areas where natural reforestation is not possible within 2–3 lifecycles of major forest forming species (i.e., 300–600 years) (Shvidenko et al. 2012 <sup>[[#fn:r137|137]]</sup> ).

Risks under current warming levels are already ''moderate'' as anthropogenic climate change has caused significant increases in fire area ( ''high confidence'' ) due to availability of detection and attribution studies) (Cross-Chapter Box 3 in Chapter 2). This has been detected and attributed regionally, notably in the western USA (Abatzoglou and Williams 2016 <sup>[[#fn:r138|138]]</sup> ; Westerling et al. 2006 <sup>[[#fn:r139|139]]</sup> ; Dennison et al. 2014 <sup>[[#fn:r1573|1573]]</sup> ), Indonesia (Fernandes et al. 2017 <sup>[[#fn:r140|140]]</sup> ) and other regions (Jolly et al. 2015 <sup>[[#fn:r141|141]]</sup> ). Regional increases have been observed despite a global- average declining trend induced by human fire-suppression strategies, especially in savannahs (Yang et al. 2014a <sup>[[#fn:r142|142]]</sup> ; Andela et al. 2017 <sup>[[#fn:r143|143]]</sup> ).

High risks of fire may occur between 1.3°C and 1.7°C ( ''medium confidence'' ). Studies note heightened risks above 1.5°C as fire, weather, and land prone to fire increase (Abatzoglou et al. 2019a <sup>[[#fn:r144|144]]</sup> ), with ''medium confidence'' in this transition, due to complex interplay between (i) global warming, (ii) CO <sub>2</sub> -fertilisation, and (iii) human/ economic factors affecting fire risk. Canada, the USA and the Mediterranean may be particularly vulnerable as the combination of increased fuel due to CO <sub>2</sub> fertilisation, and weather conditions conducive to fire increase risks to people and property. Some studies show substantial effects at 3°C (Knorr et al. 2016b <sup>[[#fn:r145|145]]</sup> ; Abatzoglou et al. 2019b <sup>[[#fn:r146|146]]</sup> ), indicating a transition to ''very high risks'' ( ''medium confidence'' ). At high warming levels, climate change may become the primary driver of fire risk in the extratropics (Knorr et al. 2016b; Abatzoglou et al. 2019b <sup>[[#fn:r147|147]]</sup> ; Yang et al. 2014b <sup>[[#fn:r148|148]]</sup> ). Pyroconvection activity may increase, in areas such as southeast Australia (Dowdy and Pepler 2018 <sup>[[#fn:r149|149]]</sup> ), posing major challenges to adaptation.

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==== 7.2.2.7 Permafrost ====

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There is a risk of damage to the natural and built environment from permafrost thaw-related ground instability. Residential, transportation, and industrial infrastructure in the pan-Arctic permafrost area are particularly at risk (Hjort et al. 2018 <sup>[[#fn:r150|150]]</sup> ). ''High risks'' already exist at low temperatures ( ''high confidence'' ). Approximately, 21–37% of Arctic permafrost is projected to thaw under a 1.5°C of warming (Hoegh-Guldberg et al. 2018 <sup>[[#fn:r151|151]]</sup> ). This increases to ''very high risk'' around 2°C (between 1.8°C and 2.3°C) of temperature increase since pre-industrial times ( ''medium confidence'' ) with 35–47% of the Arctic permafrost thawing (Hoegh-Guldberg et al. 2018 <sup>[[#fn:r152|152]]</sup> ). If climate stabilised at 2°C, still approximately 40% of permafrost area would be lost (Chadburn et al. 2017 <sup>[[#fn:r153|153]]</sup> ), leading to nearly four million people and 70% of current infrastructure in the pan-Arctic permafrost area exposed to permafrost thaw and high hazard (Hjort et al. 2018 <sup>[[#fn:r154|154]]</sup> ). Indeed between 2°C and 3°C a collapse of permafrost may occur with a drastic biome shift from tundra to boreal forest (Drijfhout et al. 2015; SR15 <sup>[[#fn:r155|155]]</sup> ). There is mixed evidence of a tipping point in permafrost collapse, leading to enhanced greenhouse gas (GHG) emission – particularly methane – between 2°C and 3°C (Hoegh-Guldberg et al. 2018 <sup>[[#fn:r156|156]]</sup> ).

<div id="section-7-2-2-8-risks-of-desertification-land-degradation-and-food-insecurity-under-different-future-development-pathways"></div>

<span id="risks-of-desertification-land-degradation-and-food-insecurity-under-different-future-development-pathways"></span>
==== 7.2.2.8 Risks of desertification, land degradation and food insecurity under different Future Development Pathways ====

<div id="section-7-2-2-8-risks-of-desertification-land-degradation-and-food-insecurity-under-different-future-development-pathways-block-1"></div>

Socio-economic developments and policy choices that govern land–climate interactions are an important driver of risk, along with climate change ( ''very high confidence'' ). Risks under two different Shared Socio-economic Pathways (SSPs) were assessed using emerging literature. SSP1 is characterised by low population growth, reduced inequalities, land-use regulation, low meat consumption, and moderate trade (Riahi et al. 2017 <sup>[[#fn:r157|157]]</sup> ; Popp et al. 2017a <sup>[[#fn:r158|158]]</sup> ). SSP3 is characterised by high population growth, higher inequalities, limited land-use regulation, resource-intensive consumption including meat-intensive diets, and constrained trade (for further details see Chapter 1 and Cross-Chapter Box 9 in Chapters 6 and 7). These two SSPs, among the set of five SSPs, were selected because they illustrate contrasting futures, ranging from low (SSP1) to high (SSP3) challenges to mitigation and adaptation. Figure 7.2 shows that for a given global mean temperature (GMT) change, risks are different under SSP1 compared to SSP3. In SSP1, global temperature change does not increase above 3°C even in the baseline case (i.e., with no additional mitigation measures) because in this pathway the combination of low population and autonomous improvements, for example, in terms of carbon intensity and/or energy intensity, effectively act as mitigation measures (Riahi et al. 2017 <sup>[[#fn:r159|159]]</sup> ). Thus Figure 7.2 does not indicate risks beyond this point in either SSP1 and SSP3. Literature based on such socio-economic and climate models is still emerging and there is a need for greater research on impacts of different pathways. There are few SSP studies exploring aspects of desertification and land degradation, but a greater number of SSP studies on food security (Supplementary Material). SSP1 reduces the vulnerability and exposure of human and natural systems and thus limits risks resulting from desertification, land degradation and food insecurity compared to SSP3 ( ''high confidence'' ).

<div id="section-7-2-2-8-risks-of-desertification-land-degradation-and-food-insecurity-under-different-future-development-pathways-block-2"></div>

<span id="figure-7.2"></span>
<!-- START IMG -->
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'''Figure 7.2'''

<span id="risks-associated-with-desertification-land-degradation-and-food-security-due-to-climate-change-and-patterns-of-socio-economic-development.increasing-risks-associated-with-desertification-include-population-exposed-and-vulnerable-to-water-scarcity-in-drylands.-risks-related-to-land-degradation-include-increased-habitat-degradation-population-exposed-to-wildfire-and-floods-and-costs-of-floods.-risks-to-food-security"></span>
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'''Risks associated with desertification, land degradation and food security due to climate change and patterns of socio-economic development.Increasing risks associated with desertification include population exposed and vulnerable to water scarcity in drylands. Risks related to land degradation include increased habitat degradation, population exposed to wildfire and floods and costs of floods. Risks to food security […]'''
<!-- IMG FILE -->
[[File:ad07eccc8c3b5d50fbfbdd75bd9d3335 7.2.jpg]]

Risks associated with desertification, land degradation and food security due to climate change and patterns of socio-economic development.Increasing risks associated with desertification include population exposed and vulnerable to water scarcity in drylands. Risks related to land degradation include increased habitat degradation, population exposed to wildfire and floods and costs of floods. Risks to food security include availability and access to food, including population at risk of hunger, food price increases and increases in disability adjusted life years attributable due to childhood underweight. The risks are assessed for two contrasted socio-economic futures (SSP1 and SSP3) under unmitigated climate change {3.6, 4.3.1.2, 5.2.2, 5.2.3, 5.2.4, 5.2.5, 6.2.4, 7.3}. Risks are not indicated beyond 3°C because SSP1 does not exceed this level of temperature change.

<!-- END IMG -->
<div id="section-7-2-2-8-risks-of-desertification-land-degradation-and-food-insecurity-under-different-future-development-pathways-block-3"></div>

Changes to the water cycle due to global warming are an essential driver of desertification and of the risks to livelihood, food production and vegetation in dryland regions. Changes in water scarcity due to climate change have already been detected in some dryland regions (Section 7.2.2.4) and therefore the transition to moderate risk occurred in recent decades ( ''high confidence'' ). IPCC (2018d) noted that in the case of risks to water resources, socio-economic drivers are expected to have a greater influence than the changes in climate ( ''medium confidence'' ). Indeed, in SSP1 there is only moderate risk even at 3°C of warming, due to the lower exposure and vulnerability of human population (Hanasaki et al. 2013a <sup>[[#fn:r160|160]]</sup> ; Arnell and Lloyd-Hughes 2014 <sup>[[#fn:r161|161]]</sup> ; Byers et al. 2018b <sup>[[#fn:r162|162]]</sup> ). Considering drylands only, Byers et al. (2018b) <sup>[[#fn:r163|163]]</sup> estimate, using a time-sampling approach for climate change and the 2050 population, that at 1.5°C, 2°C and 3°C, the dryland population exposed and vulnerable to water stress in SSP1 will be 2%, 3% and 3% respectively, thus indicating relatively stable moderate risks. In SSP3, the transition from moderate to high risk occurs in the range 1.2°C to 1.5°C ( ''medium confidence'' ) and the transition from ''high'' to very ''high risk'' is in the range 1.5°C to 2.8°C ( ''medium confidence'' ). Hanasaki et al. (2013b) <sup>[[#fn:r164|164]]</sup> found a consistent increase in water stress at higher warming levels due in large part to growth in population and demand for energy and agricultural commodities, and to a lesser extent due to hydrological changes induced by global warming. In SSP3, Byers et al. (2018b) <sup>[[#fn:r165|165]]</sup> estimate that at 1.5°C, 2°C and 3°C, the population exposed and vulnerable to water stress in drylands will steadily increase from 20% to 22% and 24% respectively, thus indicating overall much higher risks compared to SSP1 for the same global warming levels.

SSP studies relevant to land degradation assess risks such as: number of people exposed to fire; the costs of floods and coastal flooding; and loss of ES including the ability of land to sequester carbon. The risks related to permafrost melting (Section 7.2.2.7) are not considered here due to the lack of SSP studies addressing this topic. Climate change impacts on various components of land degradation have already been detected (Sections 7.2.2.3, 7.2.2.5 and 7.2.2.6) and therefore the transition from ''undetectable'' to ''moderate risk'' is in the range 0.7°C to 1°C ( ''high confidence'' ). Less than 100 million people are exposed to habitat degradation at 1.5°C under SSP1 in non-dryland regions, increasing to 257 million at 2°C (Byers et al. 2018 <sup>[[#fn:r166|166]]</sup> ). This suggests a gradual transition to high risk in the range 1.8°C to 2.8°C, but a ''low confidence'' is attributed due to the very limited evidence to constrain this transition.

By contrast in SSP3, there are already 107 million people exposed to habitat degradation at 1.5°C, increasing to 1156 million people at 3°C (Byers et al. 2018b <sup>[[#fn:r167|167]]</sup> ). Furthermore, Knorr et al. (2016b) <sup>[[#fn:r168|168]]</sup> estimate that 646 million people will be exposed to fire at 2°C warming, the main risk driver being the high population growth in SSP3 rather than increased burned area due to climate change. Exposure to extreme rainfall, a causative factor for soil erosion and flooding, also differs under SSPs. Under SSP1 up to 14% of the land and population experience five-day extreme precipitation events. Similar levels of exposure occur at lower temperatures in SSP3 (Zhang et al. 2018b <sup>[[#fn:r169|169]]</sup> ). Population exposed to coastal flooding is lowest under SSP1 and higher under SSP3 with a limited effect of enhanced protection in SSP3 already after 2°C warming (Hinkel et al. 2014 <sup>[[#fn:r170|170]]</sup> ). The transition from ''high'' to very ''high risk'' will occur at 2.2°Cto 2.8°C in SSP3 ( ''medium confidence'' ), whereas this level of risk is not expected to be reached in SSP1.

The greatest number of SSP studies explore climate change impacts relevant to food security, including population at risk of hunger, food price increases, increases in disability adjusted life years (Hasegawa et al. 2018a <sup>[[#fn:r171|171]]</sup> ; Wiebe et al. 2015a <sup>[[#fn:r172|172]]</sup> ; van Meijl et al. 2018a <sup>[[#fn:r173|173]]</sup> ; Byers et al. 2018b <sup>[[#fn:r174|174]]</sup> ). Changes in crop yields and food supply stability have already been attributed to climate change (Sections 7.2.2.1 and 7.2.2.2) and the transition from ''undetectable'' to ''moderate risk'' is placed at 0.5°C to 1°C ( ''medium confidence'' ). At 1.5°C, about two million people are exposed and vulnerable to crop yield change in SSP1 (Hasegawa et al. 2018b <sup>[[#fn:r175|175]]</sup> ; Byers et al. 2018b <sup>[[#fn:r176|176]]</sup> ), implying moderate risk. A transition from moderate to high risk is expected above 2.5°C ( ''medium confidence'' ) with population at risk of hunger of the order of 100 million (Byers et al. 2018b <sup>[[#fn:r177|177]]</sup> ). Under SSP3, high risks already exist at 1.5°C ( ''medium confidence'' ), with 20 million people exposed and vulnerable to crop yield change. By 2°C, 178 million are vulnerable and 854 million people are vulnerable at 3°C (Byers et al. 2018b <sup>[[#fn:r178|178]]</sup> ). This is supported by the higher food prices increase of up to 20% in 2050 in an RCP6.0 scenario (i.e., slightly below 2°C) in SSP3 compared to up to 5% in SSP1 (van Meijl et al. 2018 <sup>[[#fn:r179|179]]</sup> ). Furthermore in SSP3, restricted trade increase this price effect (Wiebe et al. 2015 <sup>[[#fn:r180|180]]</sup> ). In SSP3, the transition from ''high'' to ''very high'' risk is in the range 2°C to 2.7°C ( ''medium confidence'' ) while this transition is never reached in SSP1. This overall confirms that socio-economic development, by affecting exposure and vulnerability, has an even larger effect than climate change for future trends in the population at risk of hunger (O’Neill et al. 2017 <sup>[[#fn:r181|181]]</sup> , p.32). Changes can also threaten development gains ( ''medium confidence'' ). Disability adjusted life years due to childhood underweight decline in both SSP1 and SSP3 by 2030 (by 36.4 million disability adjusted life years in SSP1 and 16.2 million in SSP3). However by 2050, disability adjusted life years increase by 43.7 million in SSP3 (Ishida et al. 2014 <sup>[[#fn:r182|182]]</sup> ).

<span id="risks-arising-from-responses-to-climate-change"></span>
=== 7.2.3 Risks arising from responses to climate change ===

<div id="section-7-2-3-1-risk-associated-with-land-based-adaptation"></div>

<span id="risk-associated-with-land-based-adaptation"></span>
==== 7.2.3.1 Risk associated with land-based adaptation ====

<div id="section-7-2-3-1-risk-associated-with-land-based-adaptation-block-1"></div>

Land-based adaptation relates to a particular category of adaptation measures relying on land management (Sanz et al. 2017 <sup>[[#fn:r183|183]]</sup> ). While most land-based adaptation options provide co-benefits for climate mitigation and other land challenges (Chapter 6 and Section 6.4.1), in some contexts adaptation measures can have adverse side effects, thus implying a risk to socio-ecological systems.

One example of risk is the possible decrease in farmer income when applying adaptive cropland management measures. For instance, conservation agriculture including the principle of no-till farming, contributes to soil erosion management (Chapter 6 and Section 6.2). Yet, no-till management can reduce crop yields in some regions, and although this effect is minimised when no-till farming is complemented by the other two principles of conservation agriculture (residue retention and crop rotation), this could induce a risk to livelihood in vulnerable smallholder farming systems (Pittelkow et al. 2015 <sup>[[#fn:r184|184]]</sup> ).

Another example is the use of irrigation against water scarcity and drought. During the long lasting drought from 2007–2009 in California, USA, farmers adapted by relying on groundwater withdrawal and caused groundwater depletion at unsustainable levels (Christian-Smith et al. 2015 <sup>[[#fn:r185|185]]</sup> ). The long-term effects of irrigation from groundwater may cause groundwater depletion, land subsidence, aquifer overdraft, and saltwater intrusion (Tularam and Krishna 2009 <sup>[[#fn:r186|186]]</sup> ). Therefore, it is expected to increase the vulnerability of coastal aquifers to climate change due to groundwater usage (Ferguson and Gleeson 2012 <sup>[[#fn:r187|187]]</sup> ). The long-term practice of irrigation from groundwater may cause a severe combination of potential side effects and consequently irreversible results.

<div id="section-7-2-3-2-risk-associated-with-land-based-mitigation"></div>

<span id="risk-associated-with-land-based-mitigation"></span>
==== 7.2.3.2 Risk associated with land-based mitigation ====

<div id="section-7-2-3-2-risk-associated-with-land-based-mitigation-block-1"></div>

While historically land-use activities have been a net source of GHG emissions, in future decades the land sector will not only need to reduce its emissions, but also to deliver negative emissions through carbon dioxide removal (CDR) to reach the objective of limiting global warming to 2°C or below (Section 2.5).Although land-based mitigation in itself is a risk-reduction strategy aiming at abating climate change, it also entails risks to humans and ecosystems, depending on the type of measures and the scale of deployment. These risks fall broadly into two categories: risk of mitigation failure – due to uncertainties about mitigation potential, potential for sink reversal and moral hazard; and risks arising from adverse side effects – due to increased competition for land and water resources. This section focuses specifically on bioenergy and bioenergy with carbon capture and storage (BECCS) since it is one of the most prominent land-based mitigation strategies in future mitigation scenarios (along with large-scale forest expansion, which is discussed in Cross-Chapter Box 1 in Chapter 1). Bioenergy and BECCS is assessed in Chapter 6 as being, at large scales, the only response option with adverse side effects across all dimensions (adaptation, food security, land degradation and desertification) (Section 6.4.1).

'''Risk of mitigation failure.''' The mitigation potential from bioenergy and BECCS is highly uncertain, with estimates ranging from 0.4 to 11.3 GtCO <sub>2</sub> e yr <sup>–1</sup> for the technical potential, while consideration of sustainability constraints suggest an upper end around 5 GtCO <sup>2</sup> e yr <sup>–1</sup> (Chapter 2, Section 2.6). In comparison, IAM-based mitigation pathways compatible with limiting global warming at 1.5°C project bioenergy and BECCS deployment exceeding this range (Figure 2.24 in Chapter 2). There is ''medium confidence'' that IAMs currently do not reflect the lower end and exceed the upper end of bioenergy and BECCS mitigation potential estimates (Anderson and Peters 2016 <sup>[[#fn:r188|188]]</sup> ; Krause et al. 2018 <sup>[[#fn:r189|189]]</sup> ; IPCC 2018c <sup>[[#fn:r190|190]]</sup> ), with implications for the risk associated with reliance on bioenergy and BECCS deployment for climate mitigation.

In addition, land-based CDR strategies are subject to a risk of carbon sink reversal. This implies a fundamental asymmetry between mitigation achieved through fossil fuel emissions reduction compared to CDR. While carbon in fossil fuel reserves – in the case of avoided fossil fuel emissions – is locked permanently (at least over a time scale of several thousand years), carbon sequestered into the terrestrial biosphere – to compensate fossil fuel emissions – is subject to various disturbances, in particular from climate change and associated extreme events (Fuss et al. 2018 <sup>[[#fn:r191|191]]</sup> ; Dooley and Kartha 2018 <sup>[[#fn:r192|192]]</sup> ). The probability of sink reversal therefore increases with climate change, implying that the effectiveness of land-based mitigation depends on emission reductions in other sectors and can be sensitive to temperature overshoot ( ''high confidence'' ). In the case of bioenergy associated with CCS (BECCS), the issue of the long-term stability of the carbon storage is linked to technical and geological constraints, independent of climate change but presenting risks due to limited knowledge and experience (Chapter 6 and Cross-Chapter Box 7 in Chapter 6).

Another factor in the risk of mitigation failure, is the moral hazard associated with CDR technologies. There is medium evidence and medium agreement that the promise of future CDR deployment – bioenergy and BECCS in particular – can deter or delay ambitious emission reductions in other sectors (Anderson and Peters 2016 <sup>[[#fn:r193|193]]</sup> ; Markusson et al. 2018a <sup>[[#fn:r194|194]]</sup> ; Shue 2018a <sup>[[#fn:r195|195]]</sup> ). The consequences are an increased pressure on land with higher risk of mitigation failure and of temperature overshoot, and a transfer of the burden of mitigation and unabated climate change to future generations. Overall, there is therefore medium evidence and high agreement that prioritising early decarbonisation with minimal reliance on CDR decreases the risk of mitigation failure and increases intergenerational equity (Geden et al. 2019 <sup>[[#fn:r196|196]]</sup> ; Larkin et al. 2018 <sup>[[#fn:r197|197]]</sup> ; Markusson et al. 2018b <sup>[[#fn:r198|198]]</sup> ; Shue 2018b <sup>[[#fn:r199|199]]</sup> ).

'''Risk from adverse side-effects.''' At large scales, bioenergy (with or without CCS) is expected to increase competition for land, water resources and nutrients, thus exacerbating the risks of food insecurity, loss of ES and water scarcity (Chapter 6 and Cross-Chapter Box 7 in Chapter 6). Figure 7.3 shows the risk level (from undetectable to very high, aggregating risks of food insecurity, loss of ES and water scarcity) as a function of the global amount of land (million km <sup>2</sup> ) used for bioenergy, considering second generation bioenergy. Two illustrative future Socio-economic Pathways (SSP1 and SSP3; see Section 7.2.2 for more details) are depicted: in SSP3 the competition for land is exacerbated compared to SSP1 due to higher food demand resulting from larger population growth and higher consumption of meat-based products. The literature used in this assessment is based on IAM and non-IAM-based studies examining the impact of bioenergy crop deployment on various indicators, including food security (food prices or population at risk of hunger with explicit consideration of exposure and vulnerability), SDGs, ecosystem losses, transgression of various planetary boundaries and water consumption (see Supplementary Material). Since most of the assessed literature is centred around 2050, prevailing demographic and economic conditions for this year are used for the risk estimate. An aggregated risk metric including risks of food insecurity, loss of ES and water scarcity is used because there is no unique relationship between bioenergy deployment and the risk outcome for a single system. For instance, bioenergy deployment can be implemented in such a way that food security is prioritised at the expense of natural ecosystems, while the same scale of bioenergy deployment implemented with ecosystem safeguards would lead to a fundamentally different outcome in terms of food security (Boysen et al. 2017a <sup>[[#fn:r200|200]]</sup> ). Considered as a combined risk, however, the possibility of a negative outcome on either food security, ecosystems or both can be assessed with less ambiguity and independently of possible implementation choices.

<div id="section-7-2-3-2-risk-associated-with-land-based-mitigation-block-2"></div>

<span id="figure-7.3"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 7.3'''

<span id="risks-associated-with-bioenergy-crop-deployment-as-a-land-based-mitigation-strategy-under-two-ssps-ssp1-and-ssp3.-the-assessement-is-based-on-literature-investigating-the-consequences-of-bioenergy-expansion-for-food-security-ecosystem-loss-and-water-scarcity.-these-risk-indicators-were-aggregated-as-a-single-risk-metric-in-the-figure.-in-this-context-very-high"></span>
<!-- IMG CAPTION -->
'''Risks associated with bioenergy crop deployment as a land-based mitigation strategy under two SSPs (SSP1 and SSP3). The assessement is based on literature investigating the consequences of bioenergy expansion for food security, ecosystem loss and water scarcity. These risk indicators were aggregated as a single risk metric in the figure. In this context, very high […]'''
<!-- IMG FILE -->
[[File:6c6ba145daf5e348d107df39f20323cb 7.3.jpg]]

Risks associated with bioenergy crop deployment as a land-based mitigation strategy under two SSPs (SSP1 and SSP3). The assessement is based on literature investigating the consequences of bioenergy expansion for food security, ecosystem loss and water scarcity. These risk indicators were aggregated as a single risk metric in the figure. In this context, very high risk indicates that important adverse consequences are expected for all these indicators (more than 100 million people at risk of hunger, major ecosystem losses and severe water scarcity issues). The climate scenario considered is a mitigation scenario consistent with limiting global warming at 2°C (RCP2.6), however some studies considering other scenarios (e.g., no climate change) were considered in the expert judgement as well as results from other SSPs (e.g., SSP2). The literature supporting the assessment is provided in Table SM7.3.

<!-- END IMG -->
<div id="section-7-2-3-2-risk-associated-with-land-based-mitigation-block-3"></div>

In SSP1, there is ''medium confidence'' that 1 to 4 million km <sup>2</sup> can be dedicated to bioenergy production without significant risks to food security, ES and water scarcity. At these scales of deployment, bioenergy and BECCS could have co-benefits for instance by contributing to restoration of degraded land and soils (Cross-Chapter Box 7 in Chapter 6). Although currently degraded soils (up to 20 million km <sup>2</sup> ) represent a large amount of potentially available land (Boysen et al. 2017a <sup>[[#fn:r201|201]]</sup> ), trade-offs would occur already at smaller scale due to fertiliser and water use (Hejazi et al. 2014 <sup>[[#fn:r202|202]]</sup> ; Humpenöder et al. 2017 <sup>[[#fn:r203|203]]</sup> ; Heck et al. 2018a <sup>[[#fn:r204|204]]</sup> ; Boysen et al. 2017b <sup>[[#fn:r205|205]]</sup> ). There is ''low confidence'' that the transition from moderate to high risk is in the range 6–8.7 million km <sup>2</sup> . In SSP1, (Humpenöder et al. 2017 <sup>[[#fn:r206|206]]</sup> ) found no important impacts on sustainability indicators at a level of 6.7 million km <sup>2</sup> , while (Heck et al. 2018b <sup>[[#fn:r207|207]]</sup> ) note that several planetary boundaries (biosphere integrity; land-system change; biogeochemical flows; freshwater use) would be exceeded above 8.7 million km <sup>2</sup> .

There is very ''high confidence'' that all the risk transitions occur at lower bioenergy levels in SSP3, implying higher risks associated with bioenergy deployment, due to the higher competition for land in this pathway. In SSP3, land-based mitigation is therefore strongly limited by sustainability constraints such that moderate risk occur already between 0.5 and 1.5 million km <sup>2</sup> ( ''medium confidence'' ). There is ''medium confidence'' that a bioenergy footprint beyond 4 to 8 million km <sup>2</sup> would entail very high risk with transgression of most planetary boundaries (Heck et al. 2018b <sup>[[#fn:r208|208]]</sup> ), strong decline in sustainability indicators (Humpenöder et al. 2017 <sup>[[#fn:r209|209]]</sup> ) and increase in the population at risk of hunger well above 100 million (Fujimori et al. 2018a <sup>[[#fn:r210|210]]</sup> ; Hasegawa et al. 2018b <sup>[[#fn:r211|211]]</sup> ).

<span id="risks-arising-from-hazard-exposure-and-vulnerability"></span>
=== 7.2.4 Risks arising from hazard, exposure and vulnerability ===

<div id="section-7-2-4-risks-arising-from-hazard-exposure-and-vulnerability-block-1"></div>

Table 7.1 shows hazards from land-climate-society interactions identified in previous chapters, or in other IPCC reports (with supplementary hazards appearing in the Appendix); the regions that are exposed or will be exposed to these hazards; components of the land-climate systems and societies that are vulnerable to the hazard; the risk associated with these impacts and the available indicative policy responses. The last column shows representative supporting literature.

Included are forest dieback, extreme events in multiple economic and agricultural regimes (also see Sections 7.2.2.1 and 7.2.2.2), disruption in flow regimes in river systems, climate change mitigation impacts (Section 7.2.3.2), competition for land (plastic substitution by cellulose, charcoal production), land degradation and desertification (Section 7.2.2.8), loss of carbon sinks, permafrost destabilisation (Section 7.2.2.7), and stranded assets (Section 7.3.4). Other hazards such as from failure of carbon storage, renewable energy impacts on land use, wild-fire in forest-urban transition context, extreme events effects on cultural heritage and urban air pollution from surrounding land use are covered in Table 7.1 extension in the appendix as well in Section 7.5.6.

<div id="section-7-2-4-risks-arising-from-hazard-exposure-and-vulnerability-block-2"></div>

<span id="table-7.1"></span>
<!-- START TABLE -->
'''Table 7.1'''

<span id="characterising-landclimate-risk-and-indicative-policy-responses."></span>
'''Characterising land–climate risk and indicative policy responses.'''

Table shows hazards from land–climate–society interactions identified in previous chapters or in other IPCC reports; the regions that are exposed or will be exposed to these hazards; components of the land-climate systems and societies that are vulnerable to the hazard; the risk associated with these impacts and the available policy responses and response options from Chapter 6. The last column shows representative supporting literature.

<!-- TABLE -->
{| class="wikitable"
|-

Land–climate– society interaction hazard

Exposure

Vulnerability

Risk

Policy response (indicative)

References

|-

Forest dieback

Widespread across biomes and regions

Marginalised population with insecure land tenure

– Loss of forest-based livelihoods

– Loss of identity

* –  Land rights
* –  Community-based conservation
* –  Enhanced political enfranchisement
* –  Manager–scientist partnershipsfor adaptation silviculture

Allen et al. 2010 <sup>[[#fn:r1573|1573]]</sup> ; McDowell and<br />
Allen 2015 <sup>[[#fn:r1574|1574]]</sup> ; Sunderlin et al. 2017 <sup>[[#fn:r1575|1575]]</sup> ; Belcher et al. 2005 <sup>[[#fn:r1576|1576]]</sup> ; Soizic et al. 2013 <sup>[[#fn:r1577|1577]]</sup> ; Nagel et al. 2017 <sup>[[#fn:r1578|1578]]</sup>

|-

Endangered species and ecosystems

– Extinction<br />
– Loss of ecosystem

services (ES) – Cultural loss

– Effective enforcement of protected areas and curbs on illegal trade

– Ecosystem restoration<br />
– Protection of indigenous people

Bailis et al. 2015 <sup>[[#fn:r1579|1579]]</sup> ; Cameron et al. 2016 <sup>[[#fn:r1580|1580]]</sup>

|-

Extreme events<br />
in multiple economic and agricultural regimes

Global

* –  Food-importing countries
* –  Low-income indebtedness
* –  Net food buyer

– Conflict<br />
– Migration<br />
– Food inflation<br />
– Loss of life<br />
– Disease, malnutrition – Farmer distress

* –  Insurance
* –  Social protection encouragingdiversity of sources
* –  Climate smart agriculture
* –  Land rights and tenure
* –  Adaptive public distribution systems

Fraser et al. 2005 <sup>[[#fn:r1581|1581]]</sup> ; Schmidhuber and Tubiello 2007 <sup>[[#fn:r1582|1582]]</sup> ; Lipper et al. 2014a <sup>[[#fn:r1583|1583]]</sup> ; Lunt et al. 2016 <sup>[[#fn:r1584|1584]]</sup> ; Tigchelaar et al. 2018 <sup>[[#fn:r1585|1585]]</sup> ; Casellas Connors and Janetos 2016 <sup>[[#fn:r1586|1586]]</sup>

|-

Disruption of flow regimes<br />
in river systems

– 1.5 billion people, Regional (e.g., South Asia, Australia)

– Aral sea and others

* –  Water-intensive agriculture
* –  Freshwater, estuarine and near coastal ecosystems
* –  Fishers
* –  Endangered species

and ecosystems

– Loss of livelihoods and identity

– Migration<br />
– Indebtedness

* –  Build alternative scenarios for economies and livelihoods based on non-consumptive use (e.g., wild capture fisheries)
* –  Define and maintain ecological flows in rivers for target species and ES
* –  Experiment with alternative, lesswater-consuming crops and watermanagement strategies
* –  Redefine SDGs to include freshwaterecosystems or adopt alternative metrics of sustainability Based on Nature’s Contributions to People (NCP)

Craig 2010 <sup>[[#fn:r1587|1587]]</sup> ;<br />
Di Baldassarre<br />
et al. 2013 <sup>[[#fn:r1588|1588]]</sup> ;<br />
Verma et al. 2009 <sup>[[#fn:r1589|1589]]</sup> ; Ghosh et al. 2016 <sup>[[#fn:r1590|1590]]</sup> ; Higgins et al. 2018 <sup>[[#fn:r1591|1591]]</sup> ; Hall et al. 2013 <sup>[[#fn:r1592|1592]]</sup> ; Youn et al. 2014 <sup>[[#fn:r1593|1593]]</sup>

|}
<!-- END TABLE -->

<!-- TABLE -->
{| class="wikitable"
|-

Land–climate– society interaction hazard

Exposure

Vulnerability

Risk

Policy response (indicative)

References

|-

Depletion/exhaustion of groundwater

* –  Widespread across semi-arid and humid biomes
* –  India, China and the USA
* –  Small Islands

* –  Farmers, drinking water supply
* –  Irrigation
* –  See forest note above
* –  Agriculturalproduction
* –  Urban sustainability(Phoenix, US)
* –  Reduction in dry-season river flows
* –  Sea level rise

* –  Food insecurity
* –  Water insecurity
* –  Distress migration
* –  Conflict
* –  Disease
* –  Inundation ofcoastal regions, estuaries and deltas

* –  Monitoring of emerging groundwater-climate linkages
* –  Adaptation strategies that reduce dependence on deep groundwater
* –  Regulation of groundwater use
* –  Shift to less water-intensive rainfedcrops and pasture
* –  Conjunctive use of surface and groundwater

Wada et al. 2010 <sup>[[#fn:r1594|1594]]</sup> ; Rodell et al. 2009 <sup>[[#fn:r1595|1595]]</sup> ; Taylor et al. 2013 <sup>[[#fn:r1596|1596]]</sup> ; Aeschbach-Hertig and Gleeson 2012 <sup>[[#fn:r1597|1597]]</sup>

|-

Climate change mitigation impacts

Across various biomes, especially semi-arid and aquatic, where renewable energy projects (solar, biomass, wind and small hydro) are sited

* –  Fishers and pastoralists
* –  Farmers
* –  Endangered rangerestricted species and ecosystems

* –  Extinction of species
* –  Downstreamloss of ES
* –  Loss of livelihoodsand identity of fisher/pastoralist communities
* –  Loss of regional food security

– Avoidance and informed siting in priority basins

– Mitigation of impacts – Certification

Zomer et al. 2008 <sup>[[#fn:r1598|1598]]</sup> ; Nyong et al. 2007 <sup>[[#fn:r1599|1599]]</sup> ; Pielke et al. 2002 <sup>[[#fn:r1600|1600]]</sup> ; Schmidhuber and Tubiello 2007 <sup>[[#fn:r1601|1601]]</sup> ; Jumani et al. 2017 <sup>[[#fn:r1602|1602]]</sup> ; Eldridge et al. 2011 <sup>[[#fn:r1603|1603]]</sup> ; Bryan et al. 2010 <sup>[[#fn:r1604|1604]]</sup> ; Scarlat and Dallemand 2011 <sup>[[#fn:r1605|1605]]</sup>

|-

Competition for land e.g., plastic substitution<br />
by cellulose, charcoal production

Peri-urban and rural areas in developing countries

– Rural landscapes; farmers; charcoal suppliers;<br />
small businesses

– Land degradation; loss of ES; GHG emissions; lower adaptive capacity

– Sustainability certification; producer permits; subsidies for efficient kilns

Woollen et al. 2016 <sup>[[#fn:r1606|1606]]</sup> ; Kiruki et al. 2017a <sup>[[#fn:r1607|1607]]</sup>

|-

Land degradation and desertification

Arid, semi-arid and sub-humid regions

– Farmers<br />
– Pastoralists – Biodiversity

* –  Food insecurity
* –  Drought
* –  Migration
* –  Loss of agro andwild biodiversity

* –  Restoration of ecosystems and management of invasive species
* –  Climate smart agriculture and livestock management
* –  Managing economic impacts of global and local drivers
* –  Changes in relief and rehabilitation policies
* –  Land degradation neutrality

Fleskens, Luuk, Stringer 2014 <sup>[[#fn:r1608|1608]]</sup> ; Lambin et al. 2001 <sup>[[#fn:r1609|1609]]</sup> ; Cowie et al. 2018a <sup>[[#fn:r1610|1610]]</sup> ; Few and Tebboth 2018 <sup>[[#fn:r1611|1611]]</sup> ; Sandstrom and Juhola 2017 <sup>[[#fn:r1612|1612]]</sup>

|-

Loss of carbon sinks

Widespread across biomes and regions

– Tropical forests – Boreal soils

– Feedback to global and regional climate change

– Conservation prioritisation of tropical forests – Afforestation

Barnett et al. 2005 <sup>[[#fn:r1613|1613]]</sup> ; Tribbia and Moser 2008 <sup>[[#fn:r1614|1614]]</sup>

|-

Permafrost destabilisation

Arctic and Sub-Arctic regions

– Soils<br />
– Indigenous

communities – Biodiversity

– Enhanced GHG emissions

– Enhanced carbon uptake from novel ecosystem after thaw

– Adapt to emerging wetlands

Schuur et al. 2015 <sup>[[#fn:r1615|1615]]</sup>

|-

Stranded assets

* –  Economies transitioning to low- carbon pathways
* –  Oil economies
* –  Coastal regionsfacing inundation

– Coal-based power – Oilrefineries<br />
– Plastic industry<br />
– Large dams

– Coastal infrastructure

* –  Disruption of regional economies and conflict
* –  Unemployment
* –  Pushback against renewable energy
* –  Migration

* –  Insurance and tax cuts
* –  Long-term power purchase agreements
* –  Economic and technical supportfor transitioning economies
* –  transforming oil wealth intorenewable energy leadership
* –  Redevelopment using adaptation
* –  OPEC investment in informationsharing for transition

Farfan and Breyer 2017 <sup>[[#fn:r1616|1616]]</sup> ; Ansar et al. 2013 <sup>[[#fn:r1617|1617]]</sup> ; Van de Graaf 2017 <sup>[[#fn:r1618|1618]]</sup> ; Trieb et al. 2011 <sup>[[#fn:r1619|1619]]</sup>

|}
<!-- END TABLE -->

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