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

== Box 6.1 Case studies by anthrome type showing historical interlinkages between land-based challenges and the development of local responses ==

<div id="section-6-1-3-challenges-and-response-options-in-current-and-historical-interventions-block-1"></div>

'''A. Croplands. Land degradation, groundwater stress and food insecurity: Soil and water ''' '''conservation measures in the Tigray region of Ethiopia'''

In northern Ethiopia, the Tigray Region is a drought-prone area that has been subjected to severe land degradation (Frankl et al. 2013 <sup>[[#fn:r26|26]]</sup> ) and to recurrent drought and famine during 1888–1892, 1973–1974 and 1984–1985 (Gebremeskel et al. 2018 <sup>[[#fn:r27|27]]</sup> ). The prevalence of stunting and being underweight among children under five years is high (Busse et al. 2017 <sup>[[#fn:r28|28]]</sup> ) and the region was again exposed to a severe drought during the strong El Niño event of 2015–2016. Croplands are the dominant land-use type, with approximately 90% of the households relying on small-scale plough-based cultivation. Gullies affect nearly all slopes and frequently exceed 2 m in depth and 5 m in top width. Landsat imagery shows that cropland area peaked in 1984–1986, and increased erosion rates in the 1980s and 1990s caused the drainage density and volume to peak in 1994 (Frankl et al. 2013 <sup>[[#fn:r29|29]]</sup> ). Since around 2000, the large-scale implementation of soil and water conservation (SWC) measures, integrated catchment management, conservation agriculture and indigenous tree regeneration has started to yield positive effects on the vegetation cover and led to the stabilisation of about 25% of the gullies by 2010 (Frankl et al. 2013 <sup>[[#fn:r30|30]]</sup> ). Since 1991, farmers have provided labour for SWC in January as a free service for 20 consecutive working days, followed by food for work for the remaining days of the dry season. Most of the degraded landscapes have been restored, with positive impacts over the last two decades on soil fertility, water availability and crop productivity. However, misuse of fertilisers, low survival of tree seedlings and lack of income from exclosures may affect the sustainability of these land restoration measures (Gebremeskel et al. 2018 <sup>[[#fn:r31|31]]</sup> ).

'''B. Rangelands. Biodiversity hotspot, land degradation and climate change: Pasture intensification in the Cerrados of Brazil'''

Cerrados are a tropical savannah ecoregion in Brazil corresponding to a biodiversity hot spot with less than 2% of its region protected in national parks and conservation areas (Cava et al. 2018 <sup>[[#fn:r32|32]]</sup> ). Extensive cattle ranching (limited mechanisation, low use of fertiliser and seed inputs) has led to pasture expansion, including clearing forests to secure properties rights, occurring mainly over 1950–1975 (Martha et al. 2012 <sup>[[#fn:r33|33]]</sup> ). Despite observed productivity gains made over the last three decades (Martha et al. 2012 <sup>[[#fn:r34|34]]</sup> ), more than half of the pasture area is degraded to some extent, and challenges remain to reverse grassland degradation while accommodating growing demand and simultaneously avoiding the conversion of natural habitats (de Oliveira Silva et al. 2018 <sup>[[#fn:r35|35]]</sup> ). The largest share of production is on unfertilised pastures, often sown with perennial forage grasses of African origin, mainly ''Brachiaria spp'' . (Cardoso et al. 2016 <sup>[[#fn:r36|36]]</sup> ). This initial intensification era was partly at the expense of significant uncontrolled deforestation, and average animal stocking rates remained well below the potential carrying capacity (Strassburg et al. 2014 <sup>[[#fn:r37|37]]</sup> ). Changes in land use are difficult to reverse since pasture abandonment does not lead to the spontaneous restoration of old-growth savannah (Cava et al. 2018 <sup>[[#fn:r38|38]]</sup> ); moreover, pasture to crop conversion is frequent, supporting close to half of cropland expansion in Mato Grosso state over 2000–2013 (Cohn et al. 2016 <sup>[[#fn:r39|39]]</sup> ). Pasture intensification through liming, fertilisation and controlled grazing could increase soil organic carbon and reduce net GHG emission intensity per unit meat product, but only at increased investment cost per unit of area (de Oliveira Silva et al. 2017 <sup>[[#fn:r40|40]]</sup> ). Scenarios projecting a decoupling between deforestation and increased pasture intensification, provide the basis for a Nationally Determined Contribution (NDC) of Brazil that is potentially consistent with accommodating an upward trend in livestock production to meet increasing demand (de Oliveira Silva et al. 2018 <sup>[[#fn:r41|41]]</sup> ). Deforestation in Brazil has declined significantly between 2004 and 2014 in the national inventory, but recent data and analyses suggest that the decrease in deforestation and the resulting GHG emissions reductions have slowed down or even stopped (UNEP 2017 <sup>[[#fn:r42|42]]</sup> ).

'''C. Semi-natural forests. Biodiversity hotspot, land degradation, climate change and food insecurity: Restoration and resilience of tropical forests in Indonesia'''

During the last two decades, forest cover in Indonesia declined by 150,000 km <sup>2</sup> in the period 1990–2000 (Stibig et al. 2014 <sup>[[#fn:r43|43]]</sup> ) and approximately 158,000 km <sup>2</sup> in the period 2000–2012 (Hansen et al. 2013), most of which was converted to agricultural lands (e.g., oil palm, pulpwood plantations). According to recent estimates, deforestation in Indonesia mainly concerns primary forests, including intact and degraded forests, thus leading to biodiversity loss and reduced carbon sequestration potentials (e.g., Margono et al. 2014 <sup>[[#fn:r44|44]]</sup> ). For example, Graham et al. (2017) <sup>[[#fn:r45|45]]</sup> estimated that the following strategies to reduce deforestation and forest degradation may cost-effectively increase carbon sequestration and reduce carbon emissions in 30 years: reforestation (3.54 GtCO <sub>2</sub> ), limiting the expansion of oil palm and timber plantations into forest (3.07 GtCO <sub>2</sub> and 3.05 GtCO <sub>2</sub> , respectively), reducing illegal logging (2.34 GtCO <sub>2</sub> ), and halting illegal forest loss in protected areas (1.52GtCO <sub>2</sub> ) at a total cost of 15.7USDtC <sup>-1</sup> .The importance of forest mitigation in Indonesia is indicated by the NDC, where between half and two-thirds of the 2030 emission target relative to a business-as-usual scenario is from reducing deforestation, forest degradation, peatland drainage and fires (Grassi et al. 2017 <sup>[[#fn:r46|46]]</sup> ). Avoiding deforestation and reforestation could have multiple co-benefits by improving biodiversity conservation and employment opportunities, while reducing illegal logging in protected areas. However, these options could also have adverse side effects if they deprive local communities of access to natural resources (Graham et al. 2017 <sup>[[#fn:r47|47]]</sup> ). The adoption of the Roundtable on Sustainable Palm Oil certification in oil palm plantations reduced deforestation rates by approximately 33% in the period 2001–2015 (co-benefits with mitigation), and fire rates much more than for non-certified plantations (Carlson et al. 2018 <sup>[[#fn:r48|48]]</sup> ). However, given that large-scale oil palm plantations are one of the largest drivers of deforestation in Indonesia, objective information on the baseline trajectory for land clearance for oil palm is needed to further assess commitments, regulations and transparency in plantation development (Gaveau et al. 2016 <sup>[[#fn:r49|49]]</sup> ). For adaptation options, the community forestry scheme ''Hutan Desa'' (Village Forest) in Sumatra and Kalimantan helped to avoid deforestation (co-benefits with mitigation) by between 0.6 and 0.9 ha km <sup>–2</sup> in Sumatra and 0.6 and 0.8 ha km <sup>–2</sup> in Kalimantan in the period 2012–2016; Santika et al. 2017), improve local livelihood options, and restore degraded ecosystems (positive side effects for NCP provision) (e.g., Pohnan et al. 2015). Finally, the establishment of Ecosystem Restoration Concessions in Indonesia (covering more than 5,500 km <sup>2</sup> of forests now, and 16,000 km <sup>2</sup> allocated for the future) facilitates the planting of commercial timber species (co-benefits with mitigation), while assisting natural regeneration, preserving important habitats and species, and improving local well-being and incomes (positive side effects for Nature’s Contributions to People provision), at relatively lower costs compared with timber concessions (Silalahi et al. 2017 <sup>[[#fn:r50|50]]</sup> ).

'''D. Villages. Land degradation, groundwater overuse, climate change and food insecurity: Climate smart villages in India'''

Indian agriculture, which includes both monsoon-dependent rainfed (58%) and irrigated agriculture, is exposed to climate variability and change. Over the past years, the frequency of droughts, cyclones, and hailstorms has increased, with severe droughts in eight of 15 years between 2002 and 2017 (Srinivasa Rao et al. 2016 <sup>[[#fn:r51|51]]</sup> ; Mujumdar et al. 2017 <sup>[[#fn:r52|52]]</sup> ). Such droughts result in large yield declines for major crops like wheat in the Indo-Gangetic Plain (Zhang et al. 2017 <sup>[[#fn:r53|53]]</sup> ). The development of a submersible pump technology in the 1990s, combined with public policies that provide farmers with free electricity for groundwater irrigation, resulted in a dramatic increase in irrigated agriculture (Shah et al. 2012 <sup>[[#fn:r54|54]]</sup> ). This shift has led to increased dependence on irrigation from groundwater and induced a groundwater crisis, with large impacts on socio-ecosystems. An increasing number of farmers report bore-well failures, either due to excessive pumping of an existing well or a lack of water in new wells. The decrease in the groundwater table level has suppressed the recharge of river beds, turning permanent rivers into ephemeral streams (Srinivasan et al. 2015 <sup>[[#fn:r55|55]]</sup> ). Wells have recently been drilled in upland areas, where groundwater irrigation is also increasing (Robert et al. 2017 <sup>[[#fn:r56|56]]</sup> ). Additional challenges include declining soil organic matter and fertility under monocultures and rice/wheat systems. Unoccupied land is scarce, meaning that the potential for expanding the area farmed is very limited (Aggarwal et al. 2018 <sup>[[#fn:r57|57]]</sup> ). In rural areas, diets are deficient in protein, dietary fibre and iron, and mainly comprised of cereals and pulses grown and/or procured through welfare programmes (Vatsala et al. 2017 <sup>[[#fn:r58|58]]</sup> ). Cultivators are often indebted, and suicide rates are much higher than the national average, especially for those strongly indebted (Merriott 2016 <sup>[[#fn:r59|59]]</sup> ). Widespread use of diesel pumps for irrigation, especially for paddies, high use of inorganic fertilisers and crop residue burning lead to high GHG emissions (Aggarwal et al. 2018 <sup>[[#fn:r60|60]]</sup> ). The Climate-Smart Village (CSV) approach aims at increasing farm yield, income, input use efficiency (water, nutrients, and energy) and reducing GHG emissions (Aggarwal et al. 2018 <sup>[[#fn:r61|61]]</sup> ). Climate-smart agriculture interventions are considered in a broad sense by including practices, technologies, climate information services, insurance, institutions, policies, and finance. Options differ based on the CSV site, its agro-ecological characteristics, level of development, and the capacity and interest of farmers and the local government (Aggarwal et al. 2018 <sup>[[#fn:r62|62]]</sup> ). Selected interventions included crop diversification, conservation agriculture (minimum tillage, residue retention, laser levelling), improved varieties, weather-based insurance, agro- advisory services, precision agriculture and agroforestry. Farmers’ cooperatives were established to hire farm machinery, secure government credit for inputs, and share experiences and knowledge. Tillage practices and residue incorporation increased rice–wheat yields by 5–37%, increased income by 28–40%, reduced GHG emissions by 16–25%, and increased water-use efficiency by 30% (Jat et al. 2015 <sup>[[#fn:r63|63]]</sup> ). The resulting portfolio of options proposed by the CSV approach has been integrated with the agricultural development strategy of some states like Haryana.

'''E. Dense settlements. Climate change and food: Green infrastructures'''

Extreme heat events have led to particularly high rates of mortality and morbidity in cities, as urban populations are pushed beyond their adaptive capacities, leading to an increase in mortality rates of 30–130% in major cities in developed countries (Norton et al. 2015 <sup>[[#fn:r64|64]]</sup> ). Increased mortality and morbidity from extreme heat events are exacerbated in urban populations by the urban heat island effect (Gabriel and Endlicher 2011 <sup>[[#fn:r65|65]]</sup> ; Schatz and Kucharik 2015 <sup>[[#fn:r66|66]]</sup> ), which can be limited by developing green infrastructure in cities. Urban green infrastructure includes public and private green spaces – such as remnant native vegetation, parks, private gardens, golf courses, street trees, urban farming – and more engineered options, such as green roofs, green walls, biofilters and raingardens (Norton et al. 2015 <sup>[[#fn:r67|67]]</sup> ). Increasing the amount of vegetation, or green infrastructure, in a city is one way to help reduce urban air temperature maxima and variation. Increasing vegetation by 10% in Melbourne, Australia was estimated to reduce daytime urban surface temperatures by approximately 1°C during extreme heat events (Coutts and Harris 2013 <sup>[[#fn:r68|68]]</sup> ). Urban farming (a type of urban green infrastructure) is largely driven by the desire to reconnect food production and consumption (Whittinghill and Rowe 2012 <sup>[[#fn:r69|69]]</sup> ) (Chapter 5). Even though urban farming can only meet a very small share of the overall urban food demand, it provides fresh and local food, especially perishable fruits and crops that are usually shipped from far and sold at high prices (Thomaier et al. 2015 <sup>[[#fn:r70|70]]</sup> ). Food-producing urban gardens and farms are often started by grassroots initiatives (Ercilla-Montserrat et al. 2019 <sup>[[#fn:r71|71]]</sup> ) that occupy vacant urban spaces. In recent years, a growing number of urban farming projects (termed Zero-Acreage farming, or Z-farming, Thomaier et al. 2015 <sup>[[#fn:r72|72]]</sup> ) were established in and on existing buildings, using rooftop spaces or abandoned buildings through contracts between food businesses and building owners. Almost all Z-farms are located in cities with more than 150,000 inhabitants, with a majority in North American cities such as New York City, Chicago and Toronto (Thomaier et al. 2015). They depend on the availability of vacant buildings and roof tops, thereby competing with other uses, such as roof-based solar systems. Urban farming, however, has potentially high levels of soil pollution and air pollutants, which may lead to crop contamination and health risks. These adverse effects could be reduced on rooftops (Harada et al. 2019 <sup>[[#fn:r73|73]]</sup> ) or in controlled environments.

<span id="challenges-represented-in-future-scenarios"></span>
=== 6.1.4 Challenges represented in future scenarios ===

<div id="section-6-1-4-challenges-represented-in-future-scenarios-block-1"></div>

In this section, the evolution of several challenges (climate change, mitigation, adaptation, desertification, land degradation, food insecurity, biodiversity and water) in the future are assessed, focusing on global analyses. The effect of response options on these land challenges in the future is discussed in Section 6.4.4. Where possible, studies quantifying these challenges in the Shared Socio-economic Pathways (SSPs) (O’Neill et al. 2014 <sup>[[#fn:r74|74]]</sup> ) (Chapter 1, Cross- Chapter Box 1, and Cross-Chapter Box 9 in this chapter), should be used to assess which future scenarios could experience multiple challenges in the future.

'''Climate change:''' Without any additional efforts to mitigate, global mean temperature rise is expected to increase by anywhere from 2°C to 7.8°C in 2100 relative to the 1850–1900 reference period (Clarke et al. 2014 <sup>[[#fn:r75|75]]</sup> ; Chapter 2). The level of warming varies, depending on the climate model (Collins et al. 2013 <sup>[[#fn:r76|76]]</sup> ), uncertainties in the Earth system (Clarke et al. 2014 <sup>[[#fn:r78|78]]</sup> ), and socio-economic/ technological assumptions (Clarke et al. 2014 <sup>[[#fn:r77|77]]</sup> ; Riahi et al. 2017 <sup>[[#fn:r79|79]]</sup> ). Warming over land is 1.2 to 1.4 times higher than global mean temperature rise; warming in the Arctic region is 2.4 to 2.6 times higher than warming in the tropics (Collins et al. 2013 <sup>[[#fn:r80|80]]</sup> ). Increases in global mean temperature are accompanied by increases in global precipitation; however, the effect varies across regions, with some regions projected to see increases in precipitation and others to see decreases (Collins et al. 2013 <sup>[[#fn:r81|81]]</sup> ) (Chapter 2). Additionally, climate change also has implications for extreme events (e.g., drought, heat waves, etc.), freshwater availability, and other aspects of the terrestrial system (Chapter 2).

'''Mitigation:''' Challenges to mitigation depend on the underlying emissions and ‘mitigative capacity’, including technology availability, policy institutions, and financial resources (O’Neill et al. 2014 <sup>[[#fn:r82|82]]</sup> ). Challenges to mitigation are high in SSP3 and SSP5, medium in SSP2, and low in SSP1 and SSP4 (O’Neill et al. 2014, 2017; Riahi et al. 2017 <sup>[[#fn:r83|83]]</sup> ).

'''Adaptation:''' Challenges to adaptation depend on climate risk and adaptive capacity, including technology availability, effectiveness of institutions, and financial resources (O’Neill et al. 2014 <sup>[[#fn:r84|84]]</sup> ). Challenges to adaptation are high in SSP3 and SSP4, medium in SSP2, and low in SSP1 and SSP5 (O’Neill et al. 2014, 2017; Riahi et al. 2017 <sup>[[#fn:r85|85]]</sup> ).

'''Desertification:''' The combination of climate and land-use changes can lead to decreases in soil cover in drylands (Chapter 3). Population living in drylands is expected to increase by 43% in the SSP2-Baseline, due to both population increases and an expansion of dryland area (UNCCD 2017 <sup>[[#fn:r86|86]]</sup> ).

'''Land degradation:''' Future changes in land use and climate have implications for land degradation, including impacts on soil erosion, vegetation, fire, and coastal erosion (Chapter 4; IPBES 2018 <sup>[[#fn:r87|87]]</sup> ). For example, soil organic carbon is expected to decline by 99 GtCO <sub>2</sub> e in 2050 in an SSP2-Baseline scenario, due to both land management and expansion in agricultural area (Ten Brink et al. 2018 <sup>[[#fn:r88|88]]</sup> ).

'''Food insecurity:''' Food insecurity in future scenarios varies significantly, depending on socio-economic development and study. For example, the population at risk of hunger ranges from 0 to 800 million in 2050 (Hasegawa et al. 2015a <sup>[[#fn:r89|89]]</sup> ; Ringler et al. 2016 <sup>[[#fn:r90|90]]</sup> ; Fujimori et al. 2018 <sup>[[#fn:r91|91]]</sup> ; Hasegawa et al. 2018 <sup>[[#fn:r92|92]]</sup> ; Fujimori et al. 2019 <sup>[[#fn:r93|93]]</sup> ; Baldos and Hertel 2015 <sup>[[#fn:r94|94]]</sup> ) and 0–600 million in 2100 (Hasegawa et al. 2015a). Food prices in 2100 in non-mitigation scenarios range from 0.9 to about two times their 2005 values (Hasegawa et al. 2015a; Calvin et al. 2014 <sup>[[#fn:r95|95]]</sup> ; Popp et al. 2017 <sup>[[#fn:r96|96]]</sup> ). Food insecurity depends on both income and food prices (Fujimori et al. 2018 <sup>[[#fn:r97|97]]</sup> ). Higher income (e.g., SSP1, SSP5), higher yields (e.g., SSP1, SSP5), and less meat intensive diets (e.g., SSP1) tend to result in reduced food insecurity (Hasegawa et al. 2018 <sup>[[#fn:r98|98]]</sup> ; Fujimori et al. 2018 <sup>[[#fn:r99|99]]</sup> ).

'''Biodiversity:''' Future species extinction rates vary from modest declines to 100-fold increases from 20th century rates, depending on the species (e.g., plants, vertebrates, invertebrates, birds, fish, corals), the degree of land-use change, the level of climate change, and assumptions about migration (Pereira et al. 2010 <sup>[[#fn:r100|100]]</sup> ). Mean species abundance (MSA) is also estimated to decline in the future by 10–20% in 2050 (Van Vuuren et al. 2015; Pereira et al. 2010 <sup>[[#fn:r101|101]]</sup> ). Scenarios with greater cropland expansion lead to larger declines in MSA (UNCCD 2017 <sup>[[#fn:r102|102]]</sup> ) and species richness (Newbold et al. 2015 <sup>[[#fn:r103|103]]</sup> ).

'''Water stress:''' Changes in water supply (due to climate change) and water demand (due to socio-economic development) in the future have implications for water stress. Water withdrawals for irrigation increase from about 2500 km <sup>3</sup> yr <sup>–1</sup> in 2005 to between 2900 and 9000 km <sup>3</sup> yr <sup>–1</sup> at the end of the century (Chaturvedi et al. 2013 <sup>[[#fn:r104|104]]</sup> ; Wada and Bierkens 2014 <sup>[[#fn:r105|105]]</sup> ; Hejazi et al. 2014a <sup>[[#fn:r106|106]]</sup> ; Kim et al. 2016 <sup>[[#fn:r107|107]]</sup> ; Graham et al. 2018 <sup>[[#fn:r108|108]]</sup> ; Bonsch et al. 2015 <sup>[[#fn:r109|109]]</sup> ); total water withdrawals at the end of the century range from 5000 to 13,000 km <sup>3</sup> yr <sup>–1</sup> (Wada and Bierkens 2014 <sup>[[#fn:r110|110]]</sup> ; Hejazi et al. 2014a <sup>[[#fn:r111|111]]</sup> ; Kim et al. 2016 <sup>[[#fn:r112|112]]</sup> ; Graham et al. 2018 <sup>[[#fn:r113|113]]</sup> ). The magnitude of change in both irrigation and total water withdrawals depend on population, income, and technology (Hejazi et al. 2014a <sup>[[#fn:r114|114]]</sup> ; Graham et al. 2018 <sup>[[#fn:r115|115]]</sup> ). The combined effect of changes in water supply and water demand will lead to an increase of between 1 billion and 6 billion people living in water- stressed areas (Schlosser et al. 2014 <sup>[[#fn:r116|116]]</sup> ; Hanasaki et al. 2013 <sup>[[#fn:r117|117]]</sup> ; Hejazi et al. 2014b <sup>[[#fn:r118|118]]</sup> ). Changes in water quality are not assessed here but could be important (Liu et al. 2017 <sup>[[#fn:r119|119]]</sup> ).

'''Scenarios with multiple challenges:''' Table 6.2 summarises the challenges across the five SSP Baseline scenarios.

<div id="section-6-1-4-challenges-represented-in-future-scenarios-block-2"></div>

<span id="table-6.2"></span>
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'''Table 6.2'''

<span id="assessment-of-future-challenges-to-climate-change-mitigation-adaptation-desertification-land-degradation-food-insecurity-water-stress-and-biodiversity-in-the-ssp-baseline-scenarios."></span>
'''Assessment of future challenges to climate change, mitigation, adaptation, desertification, land degradation, food insecurity, water stress, and biodiversity in the SSP Baseline scenarios.'''

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

SSP

Summary of challenges

|-

SSP1

SSP1 (Van Vuuren et al. 2017b) has low challenges to mitigation and adaptation. The resulting Baseline scenario includes:<br />
– continued, but moderate, ''climate change'' : global mean temperature increases by 3 to 3.5°C in 2100 (Huppmann et al. 2018 <sup>[[#fn:r120|120]]</sup> ; Riahi et al. 2017 <sup>[[#fn:r121|121]]</sup> )<br />
– low levels of ''food insecurity'' : malnourishment is eliminated by 2050 (Hasegawa et al. 2015a)<br />
– declines in ''biodiversity'' : biodiversity loss increases from 34% in 2010 to 38% in 2100 (UNCCD 2017 <sup>[[#fn:r122|122]]</sup> )<br />
– high ''water stress'' : global water withdrawals decline slightly from the baseline in 2071–2100, but about 2.6 billion people live in water stressed areas (Hanasaki et al. 2013 <sup>[[#fn:r123|123]]</sup> ).

Additionally, this scenario is likely to have lower challenges related to desertification, land degradation, and biodiversity loss than SSP2 as it has lower population, lower land-use change and lower climate change (Riahi et al. 2017 <sup>[[#fn:r124|124]]</sup> ).

|-

SSP2

SSP2 (Fricko et al. 2017 <sup>[[#fn:r125|125]]</sup> ) is a scenario with medium challenges to mitigation and medium challenges to adaptation. The resulting Baseline scenario includes: – continued ''climate change'' : global mean temperature increases by 3.8°C to 4.3°C in 2100 (Fricko et al. 2017 <sup>[[#fn:r126|126]]</sup> ; Huppmann et al. 2018 <sup>[[#fn:r127|127]]</sup> ; Riahi et al. 2017 <sup>[[#fn:r128|128]]</sup> )<br />
– increased challenges related to ''desertification'' : the population living in drylands is expected to increase by 43% in 2050 (UNCCD 2017 <sup>[[#fn:r129|129]]</sup> )<br />
– increased ''land degradation'' : soil organic carbon is expected to decline by 99 GtCO <sub>2</sub> e in 2050 (Ten Brink et al. 2018)

– low levels of ''food insecurity'' : malnourishment is eliminated by 2100 (Hasegawa et al. 2015a)<br />
– declines in biodiversity: ''biodiversity'' loss increases from 34% in 2010 to 43% in 2100 (UNCCD 2017 <sup>[[#fn:r130|130]]</sup> )<br />
– high ''water stress'' : global water withdrawals nearly doubles from the baseline in 2071–2100, with about 4 billion people living in water stressed areas (Hanasaki et al. 2013 <sup>[[#fn:r131|131]]</sup> ).

|-

SSP3

SSP3 (Fujimori et al. 2017 <sup>[[#fn:r132|132]]</sup> ) is a scenario with high challenges to mitigation and high challenges to adaptation. The resulting Baseline scenario includes: – continued ''climate change'' : global mean temperature increases by 4°C to 4.8°C in 2100 (Huppmann et al. 2018 <sup>[[#fn:r133|133]]</sup> ; Riahi et al. 2017 <sup>[[#fn:r134|134]]</sup> )<br />
– high levels of ''food insecurity'' : about 600 million malnourished in 2100 (Hasegawa et al. 2015a)<br />
– declines in ''biodiversity'' : biodiversity loss increases from 34% in 2010 to 46% in 2100 (UNCCD 2017 <sup>[[#fn:r135|135]]</sup> )

– high ''water stress'' : global water withdrawals more than double from the baseline in 2071–2100, with about 5.5 billion people living in water stressed areas (Hanasaki et al. 2013 <sup>[[#fn:r136|136]]</sup> ).

Additionally, this scenario is likely to have higher challenges to desertification, land degradation, and biodiversity loss than SSP2 as it has higher population, higher land-use change and higher climate change (Riahi et al. 2017 <sup>[[#fn:r137|137]]</sup> ).

|-

SSP4

SSP4 (Calvin et al. 2017 <sup>[[#fn:r138|138]]</sup> ) has high challenges to adaptation but low challenges to mitigation. The resulting Baseline scenario includes:<br />
– continued ''climate change'' : global mean temperature increases by 3.4°C to 3.8°C in 2100 (Calvin et al. 2017 <sup>[[#fn:r139|139]]</sup> ; Huppmann et al. 2018 <sup>[[#fn:r140|140]]</sup> ; Riahi et al. 2017 <sup>[[#fn:r141|141]]</sup> ) – high levels of ''food insecurity'' : about 400 million malnourished in 2100 (Hasegawa et al. 2015a)<br />
– high ''water stress'' : about 3.5 billion people live in water stressed areas in 2100 (Hanasaki et al. 2013 <sup>[[#fn:r142|142]]</sup> ).

Additionally, this scenario is likely to have similar effects on biodiversity loss as SSP2 as it has similar land-use change and similar climate change (Riahi et al. 2017 <sup>[[#fn:r143|143]]</sup> ).

|-

SSP5

SSP5 (Kriegler et al. 2017 <sup>[[#fn:r144|144]]</sup> ) has high challenges to mitigation but low challenges to adaptation. The resulting Baseline scenario includes:<br />
– continued climate change: global mean temperature increases by 4.6°C to 5.4°C in 2100 (Kriegler et al. 2017 <sup>[[#fn:r145|145]]</sup> ; Huppmann et al. 2018 <sup>[[#fn:r146|146]]</sup> ; Riahi et al. 2017 <sup>[[#fn:r147|147]]</sup> )<br />
– low levels of ''food insecurity'' : malnourishment is eliminated by 2050 (Hasegawa et al. 2015a)<br />
– increased water use and water scarcity: global water withdrawals increase by about 80% in 2071–2100, with nearly 50% of the population living in ''water stressed'' areas

(Hanasaki et al. 2013 <sup>[[#fn:r148|148]]</sup> ).<br />
Additionally, this scenario is likely to have higher effects on biodiversity loss as SSP2 as it has similar land-use change and higher climate change (Riahi et al. 2017 <sup>[[#fn:r149|149]]</sup> ).

|}
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<span id="response-options-co-benefits-and-adverse-side-effects-across-the-land-challenges"></span>