Editing IPCC:AR6/SRCCL/Chapter-2 (section)

== 2.6 Climate consequences of response options ==

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Response options can affect climate mitigation and adaptation simultaneously, therefore this Special Report on Climate Change and Land (SRCCL) discusses land-based response options in an integrated way (Chapter 1). In this chapter, we assess response options that that have an effect on climate. A description of the full set of response options across the SRCCL can be found in Chapter 6, including the interplay between mitigation, adaptation, desertification, land degradation, food security and other co- benefits and trade-offs. Response options specific to desertification, degradation and food security are described in more detail in Chapters 3, 4 and 5.

Some response options lead to land use change and can compete with other land uses, including other response options, while others may free-up land that can be used for further mitigation/adaptation by reducing demand for land or products (e.g., agricultural intensification, diet shifts and reduction of waste) ( ''high confidence'' ).

Some response options result in a net removal of GHGs from the atmosphere and storage in living or dead organic material, or in geological stores (IPCC SR15 <sup>[[#fn:r1488|1488]]</sup> ). Such options are frequently referred to in the literature as CO <sub>2</sub> removal (CDR), greenhouse gas removal (GGR) or negative emissions technologies (NETs). CDR options are assessed alongside emissions reduction options. Although they have a land footprint, solar and wind farms are not are not assessed here as they affect GHG flux in the energy industrial sectors with minimal effect in the land sector, but the impact of solar farms on agricultural land competition is dealt with in Chapter 7.

A number of different types of scenario approach exist for estimating climate contribution of land-based response options (Cross-Chapter Box 1 and Chapter 1). Mitigation potentials have been estimated for single and sometimes multiple response options using stylised ‘bottom-up’ scenarios. Response options are not mutually exclusive (e.g., management of soil carbon and cropland management). Different options interact with each other; they may have additive effects or compete with each other for land or other resources and thus these potentials cannot necessarily be added up. The interplay between different land-based mitigation options, as well as with mitigation options in other sectors (such as energy or transport), in contributing to specific mitigation pathways has been assessed using IAMs (Section 2.7.2). These include interactions with wider socioeconomic conditions (Cross-Chapter Box 1 and Chapter 1) and other sustainability goals (Chapter 6).

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=== 2.6.1 Climate impacts of individual response options ===

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Since AR5, there have been many new estimates of the climate impacts of single or multiple response options, summarised in Figure 2.24 and discussed in sub-sections below. Recently published syntheses of mitigation potential of land-based response options (e.g., Hawken (2017a) <sup>[[#fn:r1489|1489]]</sup> , Smith et al. (2016b) <sup>[[#fn:r1490|1490]]</sup> , Griscom et al. (2017) <sup>[[#fn:r1491|1491]]</sup> , Minx et al. (2018) <sup>[[#fn:r1492|1492]]</sup> , Fuss et al. (2018b) <sup>[[#fn:r1493|1493]]</sup> , Nemet et al. (2018) <sup>[[#fn:r1494|1494]]</sup> ) are also included in Figure 2.24. The wide range in mitigation estimates reflects differences in methodologies that may not be directly comparable, and estimates cannot be necessarily be added if they were calculated independently as they may be competing for land and other resources.

Some studies assess a ‘technical mitigation potential’ – the amount possible with current technologies. Some include resource constraints (e.g., limits to yields, limits to natural forest conversion) to assess a ‘sustainable potential’. Some assess an ‘economic potential’ mitigation at different carbon prices. Few include social and political constraints (e.g., behaviour change, enabling conditions) (Chapter 7), the biophysical climate effects (Section 2.5) or the impacts of future climate change (Section 2.3). Carbon stored in biomass and soils may be at risk of future climate change (Section 2.2), natural disturbances such as wildfire (Cross-Chapter Box 3 in this chapter) and future changes in land use or management changes that result in a net loss of carbon (Gren and Aklilu 2016 <sup>[[#fn:r1495|1495]]</sup> ).

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==== 2.6.1.1 Land management in agriculture ====

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Reducing non-CO <sub>2</sub> emissions from agriculture through cropland nutrient management, enteric fermentation, manure management, rice cultivation and fertiliser production has a total mitigation potential of 0.30–3.38 GtCO <sub>2</sub> -eq yr <sup>–1</sup> ( ''medium confidence'' ) (combined sub-category measures in Figure 2.24, details below) with a further 0.25–6.78 GtCO <sub>2</sub> -eq yr <sup>–1</sup> from soil carbon management (Section 2.6.1.3). Other literature that looks at broader categories finds mitigation potential of 1.4–2.3 GtCO <sub>2</sub> -eq yr <sup>–1</sup> from improved cropland management (Smith et al. 2008 <sup>[[#fn:r1496|1496]]</sup> , 2014 <sup>[[#fn:r1497|1497]]</sup> ; Pradhan et al., 2013 <sup>[[#fn:r1498|1498]]</sup> ); 1.4–1.8 GtCO <sub>2</sub> -eq yr <sup>–1</sup> from improved grazing land management (Conant et al. 2017 <sup>[[#fn:r1499|1499]]</sup> ; Herrero et al. 2016 <sup>[[#fn:r1500|1500]]</sup> ; Smith et al. 2008 <sup>[[#fn:r1501|1501]]</sup> , 2014 <sup>[[#fn:r1502|1502]]</sup> ) and 0.2–2.4 GtCO <sub>2</sub> -eq yr <sup>–1</sup> from improved livestock management (Smith et al. 2008 <sup>[[#fn:r1503|1503]]</sup> , 2014 <sup>[[#fn:r1504|1504]]</sup> ; Herrero et al. 2016 <sup>[[#fn:r1505|1505]]</sup> , FAO 2007 <sup>[[#fn:r1506|1506]]</sup> ). Detailed discussions of the mitigation potential of agricultural response options and their co-benefits are provided in Chapter 5 and Sections 5.5 and 5.6.

The three main measures to reduce enteric fermentation include improved animal diets (higher quality, more digestible livestock feed), supplements and additives (reduce methane by changing the microbiology of the rumen), and animal management and breeding (improve husbandry practices and genetics). Applying these measures can mitigate 0.12–1.18 GtCO <sub>2</sub> -eq yr <sup>–1</sup> ( ''medium confidence'' ) (Hristov et al. 2013 <sup>[[#fn:r1507|1507]]</sup> ; Dickie et al. 2014 <sup>[[#fn:r1508|1508]]</sup> ; Herrero et al. 2016 <sup>[[#fn:r1509|1509]]</sup> ; Griscom et al. 2017 <sup>[[#fn:r1510|1510]]</sup> ). However, these measures may have limitations such as need of crop-based feed (Pradhan et al. 2013 <sup>[[#fn:r1511|1511]]</sup> ) and associated ecological costs, toxicity and animal welfare issues related to food additives (Llonch et al. 2017 <sup>[[#fn:r1512|1512]]</sup> ). Measures to manage manure include anaerobic digestion for energy use, composting as a nutrient source, reducing storage time and changing livestock diets, and have a potential of 0.01–0.26 GtCO <sub>2</sub> -eq yr <sup>–1</sup> (Herrero et al. 2016 <sup>[[#fn:r1513|1513]]</sup> ; Dickie et al. 2014 <sup>[[#fn:r1514|1514]]</sup> ).

On croplands, there is a mitigation potential of 0.03–0.71 GtCO <sub>2</sub> -eq yr <sup>–1</sup> for cropland nutrient management (fertiliser application) ( ''medium confidence'' ) (Griscom et al. 2017 <sup>[[#fn:r1515|1515]]</sup> ; Hawken 2017 <sup>[[#fn:r1516|1516]]</sup> ; Paustian et al. 2016 <sup>[[#fn:r1517|1517]]</sup> ; Dickie et al. 2014 <sup>[[#fn:r1518|1518]]</sup> ; Beach et al. 2015 <sup>[[#fn:r1519|1519]]</sup> ). Reducing emissions from rice production through improved water management (periodic draining of flooded fields to reduce methane emissions from anaerobic decomposition) and straw residue management (applying in dry conditions instead of on flooded fields and avoiding burning to reduce methane and N2O emissions) has the potential to mitigate up to 60% of emissions (Hussain et al. 2015 <sup>[[#fn:r1520|1520]]</sup> ), or 0.08–0.87 GtCO <sub>2</sub> -eq yr <sup>–1</sup> ( ''medium confidence'' ) (Griscom et al. 2017 <sup>[[#fn:r1521|1521]]</sup> ; Hawken 2017 <sup>[[#fn:r1522|1522]]</sup> ; Paustian et al. 2016 <sup>[[#fn:r1523|1523]]</sup> ; Hussain et al. 2015 <sup>[[#fn:r1524|1524]]</sup> ; Dickie et al. 2014 <sup>[[#fn:r1525|1525]]</sup> ; Beach et al. 2015 <sup>[[#fn:r1526|1526]]</sup> ). Furthermore, sustainable intensification through the integration of crop and livestock systems can increase productivity, decrease emission intensity and act as a climate adaptation option (Section 5.5.1.4).

Agroforestry is a land management system that combines woody biomass (e.g., trees or shrubs) with crops and/or livestock). The mitigation potential from agroforestry ranges between 0.08–5.7 GtCO <sub>2</sub> yr <sup>–1</sup> , ( ''medium confidence'' ) (Griscom et al. 2017 <sup>[[#fn:r1527|1527]]</sup> ; Dickie et al. 2014 <sup>[[#fn:r1528|1528]]</sup> ; Zomer et al. 2016 <sup>[[#fn:r1529|1529]]</sup> ; Hawken 2017 <sup>[[#fn:r1530|1530]]</sup> ). The high estimate is from an optimum scenario combing four agroforestry solutions (silvopasture, tree intercropping, multistrata agroforestry and tropical staple trees) of Hawken (2017a) <sup>[[#fn:r1531|1531]]</sup> . Zomer et al. (2016) <sup>[[#fn:r1532|1532]]</sup> reported that the trees in agroforestry landscapes had increased carbon stock by 7.33 GtCO <sub>2</sub> between 2000 and 2010, or 0.7 GtCO <sub>2</sub> yr <sup>–1</sup> (Section 5.5.1.3).

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'''Mitigation potential of response options in 2020–2050, measured in GtCO2-eq yr–1, adapted from Roe et al. (2017).Mitigation potentials reflect the full range of low to high estimates from studies published after 2010, differentiated according to technical (possible with current technologies), economic (possible given economic constraints) and sustainable potential (technical or economic potential constrained by sustainability […]'''
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[[File:52abd16d45f1e34c4f41984e29be2752 Figure-2.24-820x1024.jpg]]

Mitigation potential of response options in 2020–2050, measured in GtCO <sub>2</sub> -eq yr <sup>–1</sup> , adapted from Roe et al. (2017).Mitigation potentials reflect the full range of low to high estimates from studies published after 2010, differentiated according to technical (possible with current technologies), economic (possible given economic constraints) and sustainable potential (technical or economic potential constrained by sustainability considerations). Medians are calculated across all potentials in categories with more than four data points. We only include references that explicitly provide mitigation potential estimates in CO <sub>2</sub> -eq yr <sup>–1</sup> (or a similar derivative) by 2050. Not all options for land management potentials are additive, as some may compete for land. Estimates reflect a range of methodologies (including definitions, global warming potentials and time horizons) that may not be directly comparable or additive. Results from IAMs are shown to compare with single option ‘bottom-up’ estimates, in available categories from the 2ºC and 1.5ºC scenarios in the SSP Database (version 2.0). The models reflect land management changes, yet in some instances, can also reflect demand-side effects from carbon prices, so may not be defined exclusively as ‘supply-side’. References: 1) Griscom et al. (2017) <sup>[[#fn:r2136|2136]]</sup> , 2) Hawken (2017) <sup>[[#fn:r2137|2137]]</sup> , 3) Paustian et al. (2016) <sup>[[#fn:r2138|2138]]</sup> , 4) Beach et al. (2016) <sup>[[#fn:r2139|2139]]</sup> , 5) Dickie et al. (2014) <sup>[[#fn:r2140|2140]]</sup> , 6) Herrero et al. (2013) <sup>[[#fn:r2141|2141]]</sup> , 7) Herrero et al. (2016) <sup>[[#fn:r2142|2142]]</sup> , 8) Hussain et al. (2015) <sup>[[#fn:r2143|2143]]</sup> , 9) Hristov, et al. (2013) <sup>[[#fn:r2144|2144]]</sup> , 10) Zhang et al. (2013) <sup>[[#fn:r2145|2145]]</sup> , 11) Houghton and Nassikas (2018) <sup>[[#fn:r2146|2146]]</sup> , 12) Busch and Engelmann (2017) <sup>[[#fn:r2147|2147]]</sup> , 13) Baccini et al. (2017) <sup>[[#fn:r2148|2148]]</sup> , 14) Zarin et al. (2016) <sup>[[#fn:r2149|2149]]</sup> , 15) Houghton, et al. (2015) <sup>[[#fn:r2150|2150]]</sup> , 16) Federici et al. (2015) <sup>[[#fn:r2151|2151]]</sup> , 17) Carter et al. (2015) <sup>[[#fn:r2152|2152]]</sup> , 18) Smith et al. (2013) <sup>[[#fn:r2153|2153]]</sup> , 19) Pearson et al. (2017) <sup>[[#fn:r2154|2154]]</sup> , 20) Hooijer et al. (2010) <sup>[[#fn:r2155|2155]]</sup> , 21) Howard (2017) <sup>[[#fn:r2156|2156]]</sup> , 22) Pendleton et al. (2012) <sup>[[#fn:r2157|2157]]</sup> , 23) Fuss et al. (2018) <sup>[[#fn:r2158|2158]]</sup> , 24) Dooley and Kartha (2018) <sup>[[#fn:r2159|2159]]</sup> , 25) Kreidenweis et al. (2016) <sup>[[#fn:r2160|2160]]</sup> , 26) Yan et al. (2017) <sup>[[#fn:r2161|2161]]</sup> , 27) Sonntag et al. (2016) <sup>[[#fn:r2162|2162]]</sup> , 28) Lenton (2014) <sup>[[#fn:r2163|2163]]</sup> , 29) McLaren (2012) <sup>[[#fn:r2164|2164]]</sup> , 30) Lenton (2010) <sup>[[#fn:r2165|2165]]</sup> , 31) Sasaki et al. (2016) <sup>[[#fn:r2166|2166]]</sup> , 32) Sasaki et al. (2012) <sup>[[#fn:r2167|2167]]</sup> , 33) Zomer et al. (2016) <sup>[[#fn:r2168|2168]]</sup> , 34) Couwenberg et al. (2010) <sup>[[#fn:r2169|2169]]</sup> , 35) Conant et al. (2017) <sup>[[#fn:r2170|2170]]</sup> , 36) Sanderman et al. (2017) <sup>[[#fn:r2171|2171]]</sup> , 37) Frank et al. (2017) <sup>[[#fn:r2172|2172]]</sup> , 38) Henderson et al. (2015) <sup>[[#fn:r2173|2173]]</sup> , 39) Sommer and Bossio (2014) <sup>[[#fn:r2174|2174]]</sup> , 40. Lal (2010) <sup>[[#fn:r2175|2175]]</sup> , 41. Zomer et al. (2017) <sup>[[#fn:r2176|2176]]</sup> , 42. Smith et al. (2016) <sup>[[#fn:r2177|2177]]</sup> , 43) Poeplau and Don (2015) <sup>[[#fn:r2178|2178]]</sup> , 44. Powlson et al. (2014) <sup>[[#fn:r2179|2179]]</sup> , 45. Powell and Lenton (2012) <sup>[[#fn:r2180|2180]]</sup> , 46) Woolf et al. (2010) <sup>[[#fn:r2181|2181]]</sup> , 47) Roberts et al. (2010) <sup>[[#fn:r2182|2182]]</sup> , 48. Pratt and Moran (2010) <sup>[[#fn:r2183|2183]]</sup> , 49. Turner et al. (2018) <sup>[[#fn:r2184|2184]]</sup> , 50) Koornneef et al. (2012) <sup>[[#fn:r2185|2185]]</sup> , 51) Bajželj et al. (2014) <sup>[[#fn:r2192|2192]]</sup> , 52) Springmann et al. (2016) <sup>[[#fn:r2187|2187]]</sup> , 53) Tilman and Clark (2014) <sup>[[#fn:r2188|2188]]</sup> , 54) Hedenus et al. (2014) <sup>[[#fn:r2189|2189]]</sup> , 55) Miner (2010) <sup>[[#fn:r2190|2190]]</sup> , 56) Bailis et al. (2015) <sup>[[#fn:r2191|2191]]</sup> .

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==== 2.6.1.2 Land management in forests ====

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The mitigation potential for reducing and/or halting deforestation and degradation ranges from 0.4–5.8 GtCO <sub>2</sub> yr <sup>–1</sup> ( ''high confidence'' ) (Griscom et al. 2017 <sup>[[#fn:r1533|1533]]</sup> ; Hawken 2017 <sup>[[#fn:r1534|1534]]</sup> ; Busch and Engelmann 2017 <sup>[[#fn:r1535|1535]]</sup> ; Baccini et al. 2017 <sup>[[#fn:r1536|1536]]</sup> ; Zarin et al. 2016 <sup>[[#fn:r1537|1537]]</sup> ; Federici et al. 2015 <sup>[[#fn:r1538|1538]]</sup> ; Carter et al. 2015 <sup>[[#fn:r1539|1539]]</sup> ; Houghton et al. 2015 <sup>[[#fn:r1540|1540]]</sup> ; Smith et al. 2013a <sup>[[#fn:r1541|1541]]</sup> ; Houghton and Nassikas 2018 <sup>[[#fn:r1542|1542]]</sup> ). The higher figure represents a complete halting of land use conversion in forests and peatlands (i.e., assuming recent rates of carbon loss are saved each year). Separate estimates of degradation only range from 1.0–2.18 GtCO <sub>2</sub> yr <sup>–1</sup> . Reduced deforestation and forest degradation include conservation of existing carbon pools in vegetation and soil through protection in reserves, controlling disturbances such as fire and pest outbreaks, and changing management practices. Differences in estimates stem from varying land cover definitions, the time periods assessed and the carbon pools included (most higher estimates include belowground, dead wood, litter, soil and peat carbon). When deforestation and degradation are halted, it may take many decades to fully recover the biomass initially present in native ecosystems (Meli et al. 2017 <sup>[[#fn:r1543|1543]]</sup> ) (Section 4.8.3).

Afforestation/reforestation (A/R) and forest restoration can increase carbon sequestration in both vegetation and soils by 0.5–10.1 GtCO <sub>2</sub> yr <sup>–1</sup> ( ''medium confidence'' ) (Fuss et al. 2018 <sup>[[#fn:r1544|1544]]</sup> ; Griscom et al. 2017 <sup>[[#fn:r1545|1545]]</sup> ; Hawken 2017 <sup>[[#fn:r1546|1546]]</sup> ; Kreidenweis et al. 2016 <sup>[[#fn:r1547|1547]]</sup> ; Li et al. 2016 <sup>[[#fn:r1548|1548]]</sup> ; Huang et al. 2017 <sup>[[#fn:r1549|1549]]</sup> ; Sonntag et al. 2016 <sup>[[#fn:r1550|1550]]</sup> ; Lenton 2014 <sup>[[#fn:r1551|1551]]</sup> ; McLaren 2012 <sup>[[#fn:r1552|1552]]</sup> ; Lenton 2010 <sup>[[#fn:r1553|1553]]</sup> ; Erb et al. 2018 <sup>[[#fn:r1554|1554]]</sup> ; Dooley and Kartha 2018 <sup>[[#fn:r1555|1555]]</sup> ; Yan et al. 2017 <sup>[[#fn:r1556|1556]]</sup> ; Houghton et al. 2015 <sup>[[#fn:r1557|1557]]</sup> ; Houghton and Nassikas 2018 <sup>[[#fn:r1558|1558]]</sup> ). Afforestation is the conversion to forest of land that historically has not contained forests. Reforestation is the conversion to forest of land that has previously contained forests but that has been converted to some other use. Forest restoration refers to practices aimed at regaining ecological integrity in a deforested or degraded forest landscape. The lower estimate represents the lowest range from an ESM (Yan et al. 2017 <sup>[[#fn:r1559|1559]]</sup> ) and of sustainable global negative emissions potential (Fuss et al. 2018 <sup>[[#fn:r1560|1560]]</sup> ), and the higher estimate reforests all areas where forests are the native cover type, constrained by food security and biodiversity considerations (Griscom et al. 2017 <sup>[[#fn:r1561|1561]]</sup> ). It takes time for full carbon removal to be achieved as the forest grows. Removal occurs at faster rates in young- to medium-aged forests and declines thereafter such that older forest stands have smaller carbon removals but larger stocks, with net uptake of carbon slowing as forests reach maturity (Yao et al. 2018 <sup>[[#fn:r1562|1562]]</sup> ; Poorter et al. 2016 <sup>[[#fn:r1563|1563]]</sup> ; Tang et al. 2014 <sup>[[#fn:r1564|1564]]</sup> ). The land intensity of afforestation and reforestation has been estimated at 0.0029 km <sup>2</sup> tC <sup>–1</sup> yr <sup>–1</sup> (Smith et al. 2016a <sup>[[#fn:r1565|1565]]</sup> ). Boysen et al. (2017) <sup>[[#fn:r1566|1566]]</sup> estimated that to sequester about 100 GtC by 2100 would require 13 Mkm <sup>2</sup> of abandoned cropland and pastures (Section 4.8.3).

Forest management has the potential to mitigate 0.4–2.1 GtCO <sub>2</sub> -eq yr <sup>–1</sup> ( ''medium confidence'' ) (Sasaki et al. 2016 <sup>[[#fn:r1567|1567]]</sup> ; Griscom et al. 2017 <sup>[[#fn:r1568|1568]]</sup> ; Sasaki et al. 2012 <sup>[[#fn:r1569|1569]]</sup> ). Forest management can alter productivity, turnover rates, harvest rates carbon in soil and carbon in wood products (Erb et al. 2017 <sup>[[#fn:r1570|1570]]</sup> ; Campioli et al. 2015 <sup>[[#fn:r1571|1571]]</sup> ; Birdsey and Pan 2015 <sup>[[#fn:r1572|1572]]</sup> ; Erb et al. 2016 <sup>[[#fn:r1573|1573]]</sup> ; Noormets et al. 2015 <sup>[[#fn:r1574|1574]]</sup> ; Wäldchen et al. 2013 <sup>[[#fn:r1575|1575]]</sup> ; Malhi et al. 2015 <sup>[[#fn:r1576|1576]]</sup> ; Quesada et al. 2018 <sup>[[#fn:r1577|1577]]</sup> ; Nabuurs et al. 2017 <sup>[[#fn:r1578|1578]]</sup> ; Bosello et al. 2009 <sup>[[#fn:r1579|1579]]</sup> ) (Section 4.8.4). Fertilisation may enhance productivity but would increase N <sub>2</sub> O emissions. Preserving and enhancing carbon stocks in forests has immediate climate benefits but the sink can saturate and is vulnerable to future climate change (Seidl et al. 2017 <sup>[[#fn:r1580|1580]]</sup> ). Wood can be harvested and used for bioenergy substituting for fossil fuels (with or without carbon capture and storage) (Section 2.6.1.5), for long- lived products such as timber (see below), to be buried as biochar (Section 2.6.1.1) or for use in the wider bioeconomy, enabling areas of land to be used continuously for mitigation. This leads to initial carbon loss and lower carbon stocks but with each harvest cycle, the carbon loss (debt) can be paid back and after a parity time, result in net savings (Laganière et al. 2017 <sup>[[#fn:r1581|1581]]</sup> ; Bernier and Paré 2013 <sup>[[#fn:r1582|1582]]</sup> ; Mitchell et al. 2012 <sup>[[#fn:r1583|1583]]</sup> ; Haberl et al. 2012 <sup>[[#fn:r1584|1584]]</sup> ; Haberl 2013 <sup>[[#fn:r1585|1585]]</sup> ; Ter-Mikaelian et al. 2015 <sup>[[#fn:r1586|1586]]</sup> ; Macintosh et al. 2015 <sup>[[#fn:r1587|1587]]</sup> ). The trade-off between maximising forest carbon stocks and maximising substitution is highly dependent on the counterfactual assumption (no-use vs extrapolation of current management), initial forest conditions and site-specific contexts (such as regrowth rates and the displacement factors and efficiency of substitution), and relative differences in emissions released during extraction, transport and processing of the biomass- or fossil- based resources, as well as assumptions about emission associated with the product or energy source that is substituted (Grassi et al. 2018b <sup>[[#fn:r1588|1588]]</sup> ; Nabuurs et al. 2017 <sup>[[#fn:r1589|1589]]</sup> ; Pingoud et al. 2018 <sup>[[#fn:r1590|1590]]</sup> ; Smyth et al. 2017a <sup>[[#fn:r1591|1591]]</sup> ; Luyssaert et al. 2018 <sup>[[#fn:r1592|1592]]</sup> ; Valade et al. 2017 <sup>[[#fn:r1593|1593]]</sup> ; York 2012 <sup>[[#fn:r1594|1594]]</sup> ; Ter-Mikaelian et al. 2014 <sup>[[#fn:r1595|1595]]</sup> ; Naudts et al. 2016b <sup>[[#fn:r1596|1596]]</sup> ; Mitchell et al. 2012 <sup>[[#fn:r1597|1597]]</sup> ; Haberl et al. 2012 <sup>[[#fn:r1598|1598]]</sup> ; Macintosh et al. 2015 <sup>[[#fn:r1599|1599]]</sup> ; Laganière et al. 2017 <sup>[[#fn:r1600|1600]]</sup> ; Haberl 2013 <sup>[[#fn:r1601|1601]]</sup> ). This leads to uncertainty about optimum mitigation strategies in managed forests, while high carbon ecosystems such as primary forests would have large initial carbon losses and long pay-back times, and thus protection of stocks would be more optimal (Lemprière et al. 2013 <sup>[[#fn:r1602|1602]]</sup> ; Kurz et al. 2016 <sup>[[#fn:r1603|1603]]</sup> ; Keith et al. 2014 <sup>[[#fn:r1604|1604]]</sup> ) (Section 4.8.4).

Global mitigation potential from increasing the demand of wood products to replace construction materials range from 0.25–1 GtCO <sub>2</sub> -eq yr <sup>–1</sup> ( ''medium confidence'' ) (McLaren 2012 <sup>[[#fn:r1605|1605]]</sup> ; Miner 2010 <sup>[[#fn:r1606|1606]]</sup> ), the uncertainty is determined in part by consideration of the factors described above, and is sensitive to the displacement factor, or the substitution benefit in CO <sub>2</sub> , when wood is used instead of another material, which may vary in the future as other sectors reduce emissions (and may also vary due to market factors) (Sathre and O’Connor 2010 <sup>[[#fn:r1607|1607]]</sup> ; Nabuurs et al. 2018 <sup>[[#fn:r1608|1608]]</sup> ; Iordan et al. 2018 <sup>[[#fn:r1609|1609]]</sup> ; Braun et al. 2016 <sup>[[#fn:r1610|1610]]</sup> ; Gustavsson et al. 2017 <sup>[[#fn:r1611|1611]]</sup> ; Peñaloza et al. 2018 <sup>[[#fn:r1612|1612]]</sup> ; Soimakallio et al. 2016 <sup>[[#fn:r1613|1613]]</sup> ; Grassi et al. 2018b <sup>[[#fn:r1614|1614]]</sup> ). Using harvested carbon in long-lived products (e.g., for construction) can represent a store that can sometimes be from decades to over a century, while the wood can also substitute for intensive building materials, avoiding emissions from the production of concrete and steel (Sathre and O’Connor 2010 <sup>[[#fn:r1615|1615]]</sup> ; Smyth et al. 2017b <sup>[[#fn:r1616|1616]]</sup> ; Nabuurs et al. 2007 <sup>[[#fn:r1617|1617]]</sup> ; Lemprière et al. 2013 <sup>[[#fn:r1618|1618]]</sup> ). The harvest of carbon and storage in products affects the net carbon balance of the forest sector, with the aim of sustainable forest management strategies being to optimise carbon stocks and use harvested products to generate sustained mitigation benefits (Nabuurs et al. 2007 <sup>[[#fn:r1619|1619]]</sup> ).

Biophysical effects of forest response options are variable depending on the location and scale of activity (Section 2.6). Reduced deforestation or afforestation in the tropics contributes to climate mitigation through both biogeochemical and biophysical effects. It also maintains rainfall recycling to some extent. In contrast, in higher latitude boreal areas, observational and modelling studies show that afforestation and reforestation lead to local and global warming effects, particularly in snow covered regions in the winter as the albedo is lower for forests than bare snow (Bathiany et al. 2010 <sup>[[#fn:r1620|1620]]</sup> ; Dass et al. 2013 <sup>[[#fn:r1621|1621]]</sup> ; Devaraju et al. 2018 <sup>[[#fn:r1622|1622]]</sup> ; Ganopolski et al. 2001 <sup>[[#fn:r1623|1623]]</sup> ; Snyder et al. 2004 <sup>[[#fn:r1624|1624]]</sup> ; West et al. 2011 <sup>[[#fn:r1625|1625]]</sup> ; Arora and Montenegro 2011 <sup>[[#fn:r1626|1626]]</sup> ) (Section 2.6). Management, for example, thinning practices in forestry, could increase the albedo in regions where albedo decreases with age. The length of rotation cycles in forestry affects tree height and thus roughness, and through the removal of leaf mass harvest reduces evapotranspiration (Erb et al. 2017 <sup>[[#fn:r1627|1627]]</sup> ), which could lead to increased fire susceptibility in the tropics. In temperate and boreal sites, biophysical forest management effects on surface temperature were shown to be of similar magnitude than changes in land cover (Luyssaert et al. 2014 <sup>[[#fn:r1628|1628]]</sup> ). These biophysical effects could be of a magnitude to overcompensate biogeochemical effects, for example, the sink strength of regrowing forests after past depletions (Luyssaert et al. 2018 <sup>[[#fn:r1629|1629]]</sup> ; Naudts et al. 2016b <sup>[[#fn:r1630|1630]]</sup> ), but many parameters and assumptions on counterfactual influence the account (Anderson et al. 2011 <sup>[[#fn:r1631|1631]]</sup> ; Li et al. 2015b <sup>[[#fn:r1632|1632]]</sup> ; Bright et al. 2015 <sup>[[#fn:r1633|1633]]</sup> ).

Forest cover also affects climate through reactive gases and aerosols, with ''limited evidence and medium agreement'' that the decrease in the emissions of BVOC resulting from the historical conversion of forests to cropland has resulted in a positive radiative forcing through direct and indirect aerosol effects. A negative radiative forcing through reduction in the atmospheric lifetime of CH <sub>4</sub> has increased and decreased ozone concentrations in different regions (Section 2.4).

<div id="section-2-6-1-3-land-management-of-soils"></div>

<span id="land-management-of-soils"></span>
==== 2.6.1.3 Land management of soils ====

<div id="section-2-6-1-3-land-management-of-soils-block-1"></div>

The global mitigation potential for increasing soil organic matter stocks in mineral soils is estimated to be in the range of 0.4–8.64 GtCO <sub>2</sub> yr <sup>–1</sup> ( ''high confidence'' ), though the full literature range is wider with high uncertainty related to some practices (Fuss et al. 2018 <sup>[[#fn:r1634|1634]]</sup> ; Sommer and Bossio 2014 <sup>[[#fn:r1635|1635]]</sup> ; Lal 2010 <sup>[[#fn:r1636|1636]]</sup> ; Lal et al. 2004 <sup>[[#fn:r1637|1637]]</sup> ; Conant et al. 2017 <sup>[[#fn:r1638|1638]]</sup> ; Dickie et al. 2014 <sup>[[#fn:r1639|1639]]</sup> ; Frank et al. 2017a <sup>[[#fn:r1640|1640]]</sup> ; Griscom et al. 2017 <sup>[[#fn:r1641|1641]]</sup> ; Herrero et al. 2015 <sup>[[#fn:r1642|1642]]</sup> , 2016 <sup>[[#fn:r1643|1643]]</sup> ; McLaren 2012 <sup>[[#fn:r1644|1644]]</sup> ; Paustian et al. 2016 <sup>[[#fn:r1645|1645]]</sup> ; Poeplau and Don 2015 <sup>[[#fn:r1646|1646]]</sup> ; Powlson et al. 2014 <sup>[[#fn:r1647|1647]]</sup> ; Smith et al. 2016c <sup>[[#fn:r1648|1648]]</sup> ; Zomer et al. 2017 <sup>[[#fn:r1649|1649]]</sup> ). Some studies have separate potentials for soil carbon sequestration in croplands (0.25–6.78 GtCO <sub>2</sub> yr <sup>–1</sup> ) (Griscom et al. 2017 <sup>[[#fn:r1650|1650]]</sup> ; Hawken 2017 <sup>[[#fn:r1651|1651]]</sup> ; Frank et al. 2017a <sup>[[#fn:r1652|1652]]</sup> ; Paustian et al. 2016 <sup>[[#fn:r1653|1653]]</sup> ; Herrero et al. 2016 <sup>[[#fn:r1654|1654]]</sup> ; Henderson et al. 2015 <sup>[[#fn:r1655|1655]]</sup> ; Dickie et al. 2014 <sup>[[#fn:r1656|1656]]</sup> ; Conant et al. 2017 <sup>[[#fn:r1657|1657]]</sup> ; Lal 2010 <sup>[[#fn:r1658|1658]]</sup> ) and soil carbon sequestration in grazing lands (0.13–2.56 GtCO <sub>2</sub> yr <sup>–1</sup> ) (Griscom et al. 2017 <sup>[[#fn:r1659|1659]]</sup> ; Hawken 2017 <sup>[[#fn:r1660|1660]]</sup> ; Frank et al. 2017a <sup>[[#fn:r1661|1661]]</sup> ; Paustian et al. 2016 <sup>[[#fn:r1662|1662]]</sup> ; Powlson et al. 2014 <sup>[[#fn:r1663|1663]]</sup> ; McLaren 2012 <sup>[[#fn:r1664|1664]]</sup> ; Zomer et al. 2017 <sup>[[#fn:r1665|1665]]</sup> ; Smith et al. 2015 <sup>[[#fn:r1666|1666]]</sup> ; Sommer and Bossio 2014 <sup>[[#fn:r1667|1667]]</sup> ; Lal 2010 <sup>[[#fn:r1668|1668]]</sup> ). The potential for soil carbon sequestration and storage varies considerably depending on prior and current land management approaches, soil type, resource availability, environmental conditions, microbial composition and nutrient availability among other factors (Hassink and Whitmore 1997 <sup>[[#fn:r1669|1669]]</sup> ; Smith and Dukes 2013 <sup>[[#fn:r1670|1670]]</sup> ; Palm et al. 2014 <sup>[[#fn:r1671|1671]]</sup> ; Lal 2013 <sup>[[#fn:r1672|1672]]</sup> ; Six et al. 2002 <sup>[[#fn:r1673|1673]]</sup> ; Feng et al. 2013 <sup>[[#fn:r1674|1674]]</sup> ). Soils are a finite carbon sink and sequestration rates may decline to negligible levels over as little as a couple of decades as soils reach carbon saturation (West et al. 2004 <sup>[[#fn:r1675|1675]]</sup> ; Smith and Dukes 2013 <sup>[[#fn:r1676|1676]]</sup> ). The sink is at risk of reversibility, in particular due to increased soil respiration under higher temperatures (Section 2.3).

Land management practices to increase carbon interact with agricultural and fire management practices (Cross-chapter Box 3 and Chapter 5) and include improved rotations with deeper rooting cultivars, addition of organic materials and agroforestry (Lal 2011 <sup>[[#fn:r1677|1677]]</sup> ; Smith et al. 2008 <sup>[[#fn:r1678|1678]]</sup> ; Lorenz and Pitman 2014 <sup>[[#fn:r1679|1679]]</sup> ; Lal 2013 <sup>[[#fn:r1680|1680]]</sup> ; Vermeulen et al. 2012 <sup>[[#fn:r1681|1681]]</sup> ; de Rouw et al. 2010 <sup>[[#fn:r1682|1682]]</sup> ). Adoption of green manure cover crops, while increasing cropping frequency or diversity, helps sequester SOC (Poeplau and Don 2015 <sup>[[#fn:r1683|1683]]</sup> ; Mazzoncini et al. 2011 <sup>[[#fn:r1684|1684]]</sup> ; Luo et al. 2010 <sup>[[#fn:r1685|1685]]</sup> ). Studies of the long-term SOC sequestration potential of conservation agriculture (i.e., the simultaneous adoption of minimum tillage, (cover) crop residue retention and associated soil surface coverage, and crop rotations) include results that are both positive (Powlson et al. 2016 <sup>[[#fn:r1686|1686]]</sup> ; Zhang et al. 2014 <sup>[[#fn:r1687|1687]]</sup> ) and inconclusive (Cheesman et al. 2016 <sup>[[#fn:r1688|1688]]</sup> ; Palm et al. 2014 <sup>[[#fn:r1689|1689]]</sup> ; Govaerts et al. 2009 <sup>[[#fn:r1690|1690]]</sup> ).

The efficacy of reduced and zero-till practices is highly context-specific; many studies demonstrate increased carbon storage (e.g., Paustian et al. (2000) <sup>[[#fn:r1691|1691]]</sup> , Six et al. (2004) <sup>[[#fn:r1692|1692]]</sup> , van Kessel et al. (2013) <sup>[[#fn:r1693|1693]]</sup> ), while others show the opposite effect (Sisti et al. 2004 <sup>[[#fn:r1694|1694]]</sup> ; Álvaro-Fuentes et al. 2008 <sup>[[#fn:r1695|1695]]</sup> ; Christopher et al. 2009 <sup>[[#fn:r1696|1696]]</sup> ). On the other hand, deep ploughing can contribute to SOC sequestration by burying soil organic matter in the subsoil where it decomposes slowly (Alcántara et al. 2016 <sup>[[#fn:r1697|1697]]</sup> ). Meta- analyses (Haddaway et al. 2017 <sup>[[#fn:r1698|1698]]</sup> ; Luo et al. 2010 <sup>[[#fn:r1699|1699]]</sup> ; Meurer et al. 2018 <sup>[[#fn:r1700|1700]]</sup> ) also show a mix of positive and negative responses, and the lack of robust comparisons of soils on an equivalent mass basis continues to be a problem for credible estimates (Wendt and Hauser 2013 <sup>[[#fn:r1701|1701]]</sup> ; Powlson et al. 2011 <sup>[[#fn:r1702|1702]]</sup> ; Powlson et al. 2014 <sup>[[#fn:r1703|1703]]</sup> ).

Soil carbon management interacts with N <sub>2</sub> O (Paustian et al. 2016 <sup>[[#fn:r1704|1704]]</sup> ). For example, Li et al. (2005) <sup>[[#fn:r1705|1705]]</sup> estimate that the management strategies required to increase carbon sequestration (reduced tillage, crop residue and manure recycling) would increase N <sub>2</sub> O emissions significantly, offsetting 75–310% of the carbon sequestered in terms of CO <sub>2</sub> equivalence, while other practices such as cover crops can reduce N <sub>2</sub> O emissions (Kaye and Quemada 2017 <sup>[[#fn:r1706|1706]]</sup> ).

The management of soil erosion could avoid a net emissions of 1.36–3.67 GtCO <sub>2</sub> yr <sup>–1</sup> and create a sink of 0.44–3.67 GtCO <sub>2</sub> yr <sup>–1</sup> ( ''low confidence'' ) (Jacinthe and Lal 2001 <sup>[[#fn:r1707|1707]]</sup> ; Lal et al. 2004 <sup>[[#fn:r1708|1708]]</sup> ; Stallard 1998 <sup>[[#fn:r1709|1709]]</sup> ; Smith et al. 2001 <sup>[[#fn:r1710|1710]]</sup> ; Van Oost et al. 2007 <sup>[[#fn:r1711|1711]]</sup> ). The overall impact of erosion control on mitigation is context-specific and uncertain at the global level and the final fate of eroded material is still debated (Hoffmann et al., 2013 <sup>[[#fn:r1712|1712]]</sup> ).

Biochar is produced by thermal decomposition of biomass in the absence of oxygen (pyrolysis) into a stable, long-lived product like charcoal that is relatively resistant to decomposition (Lehmann et al. 2015 <sup>[[#fn:r1713|1713]]</sup> ) and which can stabilise organic matter when added to soil (Weng et al. 2017 <sup>[[#fn:r1714|1714]]</sup> ). Although charcoal has been used traditionally by many cultures as a soil amendment, ‘modern biochar’, produced in facilities that control emissions, is not widely used. The range of global potential of biochar is 0.03–6.6 GtCO <sub>2</sub> -eq yr <sup>–1</sup> by 2050, including energy substitution, with 0.03–4.9 GtCO <sub>2</sub> yr <sup>–1</sup> for CDR only ( ''medium confidence'' ) (Griscom et al. 2017 <sup>[[#fn:r1715|1715]]</sup> ; Hawken 2017 <sup>[[#fn:r1716|1716]]</sup> ; Paustian et al. 2016 <sup>[[#fn:r1717|1717]]</sup> ; Fuss et al. 2018 <sup>[[#fn:r1718|1718]]</sup> ; Lenton 2014 <sup>[[#fn:r1719|1719]]</sup> , 2010 <sup>[[#fn:r1720|1720]]</sup> ; Powell and Lenton 2012 <sup>[[#fn:r1721|1721]]</sup> ; Woolf et al. 2010 <sup>[[#fn:r1722|1722]]</sup> ; Pratt and Moran 2010 <sup>[[#fn:r1723|1723]]</sup> ; Smith 2016 <sup>[[#fn:r1724|1724]]</sup> ; Roberts et al. 2010 <sup>[[#fn:r1725|1725]]</sup> ). An analysis in which biomass supply constraints were applied to protect against food insecurity, loss of habitat and land degradation, estimated technical potential abatement of 3.7–6.6 GtCO <sub>2</sub> -eq yr <sup>–1</sup> (including 2.6–4.6 GtCO <sub>2</sub> yr <sup>–1</sup> carbon stabilisation) (Woolf et al. 2010 <sup>[[#fn:r1726|1726]]</sup> ). Fuss et al. (2018) <sup>[[#fn:r1727|1727]]</sup> propose a range of 0.5–2 GtCO <sub>2</sub> -eq yr <sup>–1</sup> as the sustainable potential for negative emissions through biochar. Griscom et al. (2017) <sup>[[#fn:r1728|1728]]</sup> suggest a potential of 1.0 GtCO <sub>2</sub> yr <sup>–1</sup> based on available residues. Biochar can provide additional climate change mitigation benefits by decreasing N <sub>2</sub> O emissions from soil and reducing nitrogen fertiliser requirements in agricultural soils (Borchard et al. 2019 <sup>[[#fn:r1729|1729]]</sup> ). Application of biochar to cultivated soils can darken the surface and reduce its mitigation potential via decreases in surface albedo, but the magnitude of this effect depends on soil moisture content, biochar application method and type of land use ( ''low confidence'' ) (Verheijen et al. 2013 <sup>[[#fn:r1730|1730]]</sup> ; Bozzi et al. 2015 <sup>[[#fn:r1731|1731]]</sup> ) (Section 4.9.5).

<div id="section-2-6-1-4-land-management-in-other-ecosystems"></div>

<span id="land-management-in-other-ecosystems"></span>
==== 2.6.1.4 Land management in other ecosystems ====

<div id="section-2-6-1-4-land-management-in-other-ecosystems-block-1"></div>

Protection and restoration of wetlands, peatlands and coastal habitats reduces net carbon loss (primarily from sediment/soils) and provides continued or enhanced natural CO2 removal (Section 4.9.4). Reducing annual emissions from peatland conversion, draining and burning could mitigate 0.45–1.22 GtCO2-eq yr–1 up to 2050 (medium confidence) (Hooijer et al. 2010 <sup>[[#fn:r1732|1732]]</sup> ; Griscom et al. 2017 <sup>[[#fn:r1733|1733]]</sup> ; Hawken 2017 <sup>[[#fn:r1734|1734]]</sup> ) and peatland restoration 0.15–0.81 (low confidence) (Couwenberg et al. 2010 <sup>[[#fn:r1735|1735]]</sup> ; Griscom et al. 2017 <sup>[[#fn:r1736|1736]]</sup> ). The upper end from Griscom et al. (2017) <sup>[[#fn:r1737|1737]]</sup> represents a maximum sustainable potential (accounting for biodiversity and food security safeguards) for rewetting and biomass enhancement. Wetland drainage and rewetting was included as a flux category under the second commitment period of the Kyoto Protocol, with significant management knowledge gained over the last decade (IPCC 2013b). However, there are high uncertainties as to carbon storage and flux rates, in particular the balance between CH4 sources and CO2 sinks (Spencer et al. 2016 <sup>[[#fn:r1738|1738]]</sup> ). Peatlands are sensitive to climate change which may increase carbon uptake by vegetation and carbon emissions due to respiration, with the balance being regionally dependent (high confidence). There is low confidence about the future peatland sink globally. Some peatlands have been found to be resilient to climate change (Minayeva and Sirin 2012 <sup>[[#fn:r1739|1739]]</sup> ), but the combination of land use change and climate change may make them vulnerable to fire (Sirin et al. 2011 <sup>[[#fn:r1740|1740]]</sup> ). While models show mixed results for the future sink (Spahni et al. 2013 <sup>[[#fn:r1741|1741]]</sup> ; Chaudhary et al. 2017 <sup>[[#fn:r1742|1742]]</sup> ; Ise et al. 2008 <sup>[[#fn:r1743|1743]]</sup> ), a study that used extensive historical data sets to project change under future warming scenarios found that the current global peatland sink could increase slightly until 2100 and decline thereafter (Gallego-Sala et al. 2018 <sup>[[#fn:r1744|1744]]</sup> ).

Reducing the conversion of coastal wetlands (mangroves, seagrass and marshes) could reduce emissions by 0.11–2.25 GtCO2-eq yr–1 by 2050 (medium confidence) (Pendleton et al. 2012 <sup>[[#fn:r1745|1745]]</sup> ; Griscom et al. 2017 <sup>[[#fn:r1746|1746]]</sup> ; Howard et al. 2017 <sup>[[#fn:r1747|1747]]</sup> ; Hawken 2017 <sup>[[#fn:r1748|1748]]</sup> ). Mangrove restoration can mitigate the release of 0.07 GtCO2 yr–1 through rewetting (Crooks et al. 2011 <sup>[[#fn:r1749|1749]]</sup> ) and take up 0.02–0.84 GtCO2 yr–1 from biomass and soil enhancement (medium confidence) (Griscom et al. 2017 <sup>[[#fn:r1750|1750]]</sup> ). The ongoing benefits provided by mangroves as a natural carbon sink can be nationally-important for small island developing states (SIDS) and other countries with extensive coastlines, based on estimates of high carbon sequestration rates per unit area (McLeod et al. 2011 <sup>[[#fn:r1751|1751]]</sup> ; Duarte et al. 2013 <sup>[[#fn:r1752|1752]]</sup> ; Duarte 2017 <sup>[[#fn:r1753|1753]]</sup> ; Taillardat et al. 2018 <sup>[[#fn:r1754|1754]]</sup> ). There is only medium confidence in the effectiveness of enhanced carbon uptake using mangroves, due to the many uncertainties regarding the response of mangroves to future climate change (Jennerjahn et al. 2017 <sup>[[#fn:r1755|1755]]</sup> ), dynamic changes in distributions (Kelleway et al. 2017 <sup>[[#fn:r1756|1756]]</sup> ) and other local-scale factors affecting long-term sequestration and climatic benefits (e.g., methane release) (Dutta et al. 2017 <sup>[[#fn:r1757|1757]]</sup> ). The climate mitigation potential of coastal vegetated habitats (mangrove forests, tidal marshes and seagrasses) is considered in Chapter 5 of the IPCC Special Report on the Ocean, Cryosphere and Climate Change (SROCC), in a wider ‘blue carbon’ context.

<div id="section-2-6-1-5-bioenergy-and-bioenergy-with-carbon-capture-and-storage"></div>

<span id="bioenergy-and-bioenergy-with-carbon-capture-and-storage"></span>
==== 2.6.1.5 Bioenergy and bioenergy with carbon capture and storage ====

<div id="section-2-6-1-5-bioenergy-and-bioenergy-with-carbon-capture-and-storage-block-1"></div>

An introduction and overview of bioenergy and bioenergy with carbon capture and storage (BECCS) can be found in Cross-Chapter Boxes 7 and 12, and Chapters 6 and 7. CCS technologies are discussed in SR15. The discussion below refers to modern bioenergy only (e.g., liquid biofuels for transport and the use of solid biofuels in combined heat and power plants).

The mitigation potential of bioenergy coupled with CCS (i.e., BECCS), is estimated to be between 0.4 and 11.3 GtCO <sub>2</sub> yr <sup>–1</sup> ( ''medium confidence'' ) based on studies that directly estimate mitigation for BECCS (not bioenergy) in units of CO <sub>2</sub> (not EJ) (McLaren 2012 <sup>[[#fn:r1758|1758]]</sup> ; Lenton 2014 <sup>[[#fn:r1759|1759]]</sup> ; Fuss et al. 2018 <sup>[[#fn:r1760|1760]]</sup> ; Turner et al. 2018b <sup>[[#fn:r1761|1761]]</sup> ; Lenton 2010 <sup>[[#fn:r1762|1762]]</sup> ; Koornneef et al. 2012 <sup>[[#fn:r1763|1763]]</sup> ; Powell and Lenton 2012 <sup>[[#fn:r1764|1764]]</sup> ). SR15 reported a potential of 1–85 GtCO <sub>2</sub> yr <sup>–1</sup> which they noted could be narrowed to a range of 0.5–5 GtCO <sub>2</sub> yr <sup>–1</sup> when taking account of sustainability aims (Fuss et al. 2018 <sup>[[#fn:r1765|1765]]</sup> ). The upper end of the SR15 range is considered as a theoretical potential. Previously, the IPCC Special Report on Renewable Energy Sources concluded the technical potential of biomass supply for energy (without BECCS) could reach 100–300 EJ yr–1 by 2050, which would be 2–15 GtCO <sub>2</sub> yr <sup>–1</sup> (using conversion factors 1 EJ = 0.02–0.05 GtCO <sub>2</sub> yr <sup>–1</sup> emission reduction, SR15). A range of recent studies including sustainability or economic constraints estimate that 50–244 EJ (1–12 GtCO <sub>2</sub> yr <sup>–1</sup> using the conversion factors above) of bioenergy could be produced on 0.1–13 Mkm <sup>2</sup> of land (Fuss et al. 2018 <sup>[[#fn:r1766|1766]]</sup> ; Chan and Wu 2015 <sup>[[#fn:r1767|1767]]</sup> ; Schueler et al. 2016 <sup>[[#fn:r1767|1767]]</sup> ; Wu et al. 2013 <sup>[[#fn:r1768|1768]]</sup> ; Searle and Malins 2015 <sup>[[#fn:r1769|1769]]</sup> ; Wu et al. 2019 <sup>[[#fn:r1770|1770]]</sup> ; Heck et al. 2018 <sup>[[#fn:r1771|1771]]</sup> ; Fritz et al. 2013 <sup>[[#fn:r1772|1772]]</sup> ).

There is ''high confidence'' that the most important factors determining future biomass supply for energy are land availability and land productivity (Berndes et al. 2013 <sup>[[#fn:r1773|1773]]</sup> ; Creutzig et al. 2015a <sup>[[#fn:r1774|1774]]</sup> ; Woods et al. 2015 <sup>[[#fn:r1775|1775]]</sup> ; Daioglou et al. 2019 <sup>[[#fn:r1776|1776]]</sup> ). Estimates of marginal/degraded lands currently considered available for bioenergy range from 3.2–14.0 Mkm2, depending on the adopted sustainability criteria, land class definitions, soil conditions, land mapping method and environmental and economic considerations (Campbell et al. 2008 <sup>[[#fn:r1778|1778]]</sup> ; Cai et al. 2011 <sup>[[#fn:r1779|1779]]</sup> ; Lewis and Kelly 2014 <sup>[[#fn:r1780|1780]]</sup> ).

Bioenergy production systems can lead to net emissions in the short term that can be ‘paid-back’ over time, with multiple harvest cycles and fossil fuel substitution, unlike fossil carbon emissions (Campbell et al. 2008 <sup>[[#fn:r1781|1781]]</sup> ; Cai et al. 2011 <sup>[[#fn:r1782|1782]]</sup> ; Lewis and Kelly 2014 <sup>[[#fn:r1783|1783]]</sup> ; De Oliveira Bordonal et al. 2015 <sup>[[#fn:r1784|1784]]</sup> ). Stabilising bioenergy crops in previous high carbon forestland or peatland results in high emissions of carbon that may take from decades to more than a century to be re-paid in terms of net CO <sub>2</sub> emission savings from replacing fossil fuels, depending on previous forest carbon stock, bioenergy yields and displacement efficiency (Elshout et al. 2015 <sup>[[#fn:r1785|1785]]</sup> ; Harper et al. 2018 <sup>[[#fn:r1786|1786]]</sup> ; Daioglou et al. 2017 <sup>[[#fn:r1787|1787]]</sup> ). In the case of bioenergy from managed forests, the magnitude and timing of the net mitigation benefits is controversial as it varies with differences due to local climate conditions, forest management practice, fossil fuel displacement efficiency and methodological approaches (Hudiburg et al. 2011 <sup>[[#fn:r1788|1788]]</sup> ; Berndes et al. 2013 <sup>[[#fn:r1789|1789]]</sup> ; Guest et al. 2013 <sup>[[#fn:r1790|1790]]</sup> ; Lamers and Junginger 2013 <sup>[[#fn:r1791|1791]]</sup> ; Cherubini et al. 2016 <sup>[[#fn:r1792|1792]]</sup> ; Cintas et al. 2017 <sup>[[#fn:r1793|1793]]</sup> ; Laurance et al. 2018 <sup>[[#fn:r1794|1794]]</sup> ; Valade et al. 2018 <sup>[[#fn:r1795|1795]]</sup> ; Baker et al. 2019 <sup>[[#fn:r1796|1796]]</sup> ). Suitable bioenergy crops can be integrated in agricultural landscapes to reverse ecosystem carbon depletion (Creutzig et al. 2015a <sup>[[#fn:r1797|1797]]</sup> ; Robertson et al. 2017 <sup>[[#fn:r1798|1798]]</sup> ; Vaughan et al. 2018 <sup>[[#fn:r1799|1799]]</sup> ; Daioglou et al. 2017 <sup>[[#fn:r1800|1800]]</sup> ). Cultivation of short rotation woody crops and perennial grasses on degraded land or cropland previously used for annual crops typically accumulate carbon in soils due to their deep root systems (Don et al. 2012 <sup>[[#fn:r1801|1801]]</sup> ; Robertson et al. 2017 <sup>[[#fn:r1802|1802]]</sup> ). The use of residues and organic waste as bioenergy feedstock can mitigate land use change pressures associated with bioenergy deployment, but residues are limited and the removal of residues that would otherwise be left on the soil could lead soil degradation (Chum et al. 2011 <sup>[[#fn:r1803|1803]]</sup> ; Liska et al. 2014 <sup>[[#fn:r1804|1804]]</sup> ; Monforti et al. 2015 <sup>[[#fn:r1805|1805]]</sup> ; Zhao et al. 2015 <sup>[[#fn:r1806|1806]]</sup> ; Daioglou et al. 2016 <sup>[[#fn:r1807|1807]]</sup> ).

The steps required to cultivate, harvest, transport, process and use biomass for energy generate emissions of GHGs and other climate pollutants (Chum et al. 2011 <sup>[[#fn:r1808|1808]]</sup> ; Creutzig et al. 2015b <sup>[[#fn:r1809|1809]]</sup> ; Staples et al. 2017 <sup>[[#fn:r1810|1810]]</sup> ; Daioglou et al. 2019 <sup>[[#fn:r1811|1811]]</sup> ). Life-cycle GHG emissions of modern bioenergy alternatives are usually lower than those for fossil fuels ( ''robust evidence, medium agreement'' ) (Chum et al. 2011 <sup>[[#fn:r1812|1812]]</sup> ; Creutzig et al. 2015b <sup>[[#fn:r1813|1813]]</sup> ). The magnitude of these emissions largely depends on location (e.g., soil quality, climate), prior land use, feedstock used (e.g., residues, dedicated crops, algae), land use practice (e.g., soil management, fertiliser use), biomass transport (e.g., distances and transport modes) and the bioenergy conversion pathway and product (e.g., wood pellets, ethanol). Use of conventional food and feed crops as a feedstock generally provides the highest bioenergy yields per hectare, but also causes more GHG emissions per unit energy compared to agriculture residues, biomass from managed forests and lignocellulosic crops such as short-rotation coppice and perennial grasses (Chum et al. 2011 <sup>[[#fn:r1814|1814]]</sup> ; Gerbrandt et al. 2016 <sup>[[#fn:r1815|1815]]</sup> ) due to the application of fertilisers and other inputs (Oates et al. 2016 <sup>[[#fn:r1816|1816]]</sup> ; Rowe et al. 2016 <sup>[[#fn:r1817|1817]]</sup> ; Lai et al. 2017 <sup>[[#fn:r1818|1818]]</sup> ; Robertson et al. 2017 <sup>[[#fn:r1819|1819]]</sup> ).

Bioenergy from dedicated crops are in some cases held responsible for GHG emissions resulting from indirect land use change (iLUC), that is the bioenergy activity may lead to displacement of agricultural or forest activities into other locations, driven by market-mediated effects. Other mitigation options may also cause iLUC. At a global level of analysis, indirect effects are not relevant because all land-use emissions are direct. iLUC emissions are potentially more significant for crop-based feedstocks such as corn, wheat and soybean, than for advanced biofuels from lignocellulosic materials (Chum et al. 2011 <sup>[[#fn:r1820|1820]]</sup> ; Wicke et al. 2012 <sup>[[#fn:r1821|1821]]</sup> ; Valin et al. 2015 <sup>[[#fn:r1822|1822]]</sup> ; Ahlgren and Di Lucia 2014 <sup>[[#fn:r1823|1823]]</sup> ). Estimates of emissions from iLUC are inherently uncertain, widely debated in the scientific community and are highly dependent on modelling assumptions, such as supply/demand elasticities, productivity estimates, incorporation or exclusion of emission credits for coproducts and scale of biofuel deployment (Rajagopal and Plevin 2013 <sup>[[#fn:r1824|1824]]</sup> ; Finkbeiner 2014 <sup>[[#fn:r1825|1825]]</sup> ; Kim et al. 2014 <sup>[[#fn:r1826|1826]]</sup> ; Zilberman 2017 <sup>[[#fn:r1827|1827]]</sup> ). In some cases, iLUC effects are estimated to result in emission reductions. For example, market-mediated effects of bioenergy in North America showed potential for increased carbon stocks by inducing conversion of pasture or marginal land to forestland (Cintas et al. 2017 <sup>[[#fn:r1828|1828]]</sup> ; Duden et al. 2017 <sup>[[#fn:r1829|1829]]</sup> ; Dale et al. 2017 <sup>[[#fn:r1830|1830]]</sup> ; Baker et al. 2019 <sup>[[#fn:r1831|1831]]</sup> ). There is a wide range of variability in iLUC values for different types of biofuels, from –75–55 gCO <sub>2</sub> MJ <sup>–1 </sup> (Ahlgren and Di Lucia 2014 <sup>[[#fn:r1832|1832]]</sup> ; Valin et al. 2015 <sup>[[#fn:r1833|1833]]</sup> ; Plevin et al. 2015 <sup>[[#fn:r1834|1834]]</sup> ; Taheripour and Tyner 2013 <sup>[[#fn:r1835|1835]]</sup> ; Bento and Klotz 2014 <sup>[[#fn:r1836|1836]]</sup> ). There is ''low confidence'' in attribution of emissions from iLUC to bioenergy.

Bioenergy deployment can have large biophysical effects on regional climate, with the direction and magnitude of the impact depending on the type of bioenergy crop, previous land use and seasonality ( ''limited evidence, medium agreement'' ). A study of two alternative future bioenergy scenarios using 15 Mkm2 of intensively used managed land or conversion of natural areas showed a nearly neutral effect on surface temperature at global levels (considering biophysical effects and CO <sub>2</sub> and N <sub>2</sub> O fluxes from land but not substitution effects), although there were significant seasonal and regional differences (Kicklighter et al. 2013 <sup>[[#fn:r1837|1837]]</sup> ). Modelling studies on biofuels in the US found the switch from annual crops to perennial bioenergy plantations like Miscanthus could lead to regional cooling due to increases in evapotranspiration and albedo (Georgescu et al. 2011 <sup>[[#fn:r1838|1838]]</sup> ; Harding et al. 2016 <sup>[[#fn:r1839|1839]]</sup> ), with perennial bioenergy crop expansion over suitable abandoned and degraded farmlands causing near-surface cooling up to 5°C during the growing season (Wang et al. 2017b <sup>[[#fn:r1840|1840]]</sup> ). Similarly, growing sugarcane on existing cropland in Brazil cools down the local surface during daytime conditions up to –1°C, but warmer conditions occur if sugar cane is deployed at the expense of natural vegetation (Brazilian Cerrado (Loarie et al. 2011 <sup>[[#fn:r1841|1841]]</sup> ). In general, bioenergy crops (as for all crops) induce a cooling of ambient air during the growing season, but after harvest the decrease in evapotranspiration can induce warming (Harding et al. 2016 <sup>[[#fn:r1842|1842]]</sup> ; Georgescu et al. 2013 <sup>[[#fn:r1843|1843]]</sup> ; Wang et al. 2017b <sup>[[#fn:r1844|1844]]</sup> ). Bioenergy crops were found to cause increased isoprene emissions in a scenario where 0.69 Mkm2 of oil palm for biodiesel in the tropics and 0.92 Mkm2 of short rotation coppice (SRC) in the mid-latitudes were planted, but effects on global climate were negligible (Ashworth et al. 2012 <sup>[[#fn:r1845|1845]]</sup> ).

<div id="section-2-6-1-6-enhanced-weathering"></div>

<span id="enhanced-weathering"></span>
==== 2.6.1.6 Enhanced weathering ====

<div id="section-2-6-1-6-enhanced-weathering-block-1"></div>

Weathering is the natural process of rock decomposition via chemical and physical processes in which CO <sub>2</sub> is removed from the atmosphere and converted to bicarbonates and/or carbonates (IPCC 2005 <sup>[[#fn:r1846|1846]]</sup> ). Formation of calcium carbonates in the soil provides a permanent sink for mineralised organic carbon (Manning 2008 <sup>[[#fn:r1847|1847]]</sup> ; Beerling et al. 2018 <sup>[[#fn:r1848|1848]]</sup> ). Mineral weathering can be enhanced through grinding up rock material to increase the surface area, and distributing it over land to provide carbon removals of 0.5–4.0 GtCO <sub>2</sub> yr <sup>–1</sup> ( ''medium confidence'' ) (Beerling et al. 2018 <sup>[[#fn:r1849|1849]]</sup> ; Lenton 2010 <sup>[[#fn:r1850|1850]]</sup> ; Smith et al. 2016a <sup>[[#fn:r1851|1851]]</sup> ; Taylor et al. 2016 <sup>[[#fn:r1852|1852]]</sup> ). While the geochemical potential is quite large, agreement on the technical potential is low due to a variety of unknown parameters and limits, such as rates of mineral extraction, grinding, delivery and challenges with scaling and deployment.

<div id="section-2-6-1-7-demand-management-in-the-food-sector-diet-change-waste-reduction"></div>

<span id="demand-management-in-the-food-sector-diet-change-waste-reduction"></span>
==== 2.6.1.7 Demand management in the food sector (diet change, waste reduction) ====

<div id="section-2-6-1-7-demand-management-in-the-food-sector-diet-change-waste-reduction-block-1"></div>

Demand-side management has the potential for climate change mitigation via reducing emissions from production, switching to consumption of less emission intensive commodities and making land available for CO <sub>2</sub> removal (Section 5.5.2). Reducing food losses and waste increases the overall efficiency of food value chains (with less land and inputs needed) along the entire supply chain and has the potential to mitigate 0.8–4.5 GtCO <sub>2</sub> -eq yr <sup>–1</sup> ( ''high confidence'' ) (Bajželj et al. 2014 <sup>[[#fn:r1853|1853]]</sup> ; Dickie et al 2014 <sup>[[#fn:r1854|1854]]</sup> ; Hawken 2017 <sup>[[#fn:r1855|1855]]</sup> ; Hiç et al. 2016 <sup>[[#fn:r1856|1856]]</sup> ) (Section 5.5.2.5).

Shifting to diets that are lower in emissions-intensive foods like beef delivers a mitigation potential of 0.7–8.0 GtCO <sub>2</sub> -eq yr <sup>–1</sup> ( ''high confidence'' ) (Bajželj et al. 2014 <sup>[[#fn:r1857|1857]]</sup> ; Dickie et al. 2014 <sup>[[#fn:r1858|1858]]</sup> ; Herrero et al. 2016 <sup>[[#fn:r1859|1859]]</sup> ; Hawken 2017 <sup>[[#fn:r1860|1860]]</sup> ; Springmann et al. 2016 <sup>[[#fn:r1861|1861]]</sup> ; Tilman and Clark 2014 <sup>[[#fn:r1862|1862]]</sup> ; Hedenus et al. 2014 <sup>[[#fn:r1863|1863]]</sup> ; Stehfest et al. 2009 <sup>[[#fn:r1864|1864]]</sup> ) with most of the higher end estimates (&gt;6 GtCO <sub>2</sub> -eq yr <sup>–1</sup> ) based on veganism, vegetarianism or very low ruminant meat consumption (Section 5.5.2). In addition to direct mitigation gains, decreasing meat consumption, primarily of ruminants, and reducing wastes further reduces water use, soil degradation, pressure on forests and land used for feed potentially freeing up land for mitigation (Tilman and Clark 2014 <sup>[[#fn:r1865|1865]]</sup> ) (Chapters 5 and 6). Additionally, consumption of locally produced food, shortening the supply chain, can in some cases minimise food loss, contribute to food security and reduce GHG emissions associated with energy consumption and food loss (Section 5.5.2.6).

<span id="integrated-pathways-for-climate-change-mitigation"></span>
=== 2.6.2 Integrated pathways for climate change mitigation ===

<div id="section-2-6-2-integrated-pathways-for-climate-change-mitigation-block-1"></div>

Land-based response options have the potential to interact, resulting in additive effects (e.g., climate co-benefits) or negating each other (e.g., through competition for land). They also interact with mitigation options in other sectors (such as energy or transport) and thus they need to be assessed collectively under different climate mitigation targets and in combination with other sustainability goals (Popp et al. 2017 <sup>[[#fn:r1866|1866]]</sup> ; Obersteiner et al. 2016 <sup>[[#fn:r1867|1867]]</sup> ; Humpenöder et al. 2018 <sup>[[#fn:r1868|1868]]</sup> ). IAMs with distinctive land-use modules are the basis for the assessment of mitigation pathways as they combine insights from various disciplines in a single framework and cover the largest sources of anthropogenic GHG emissions from different sectors (see also SR15 Chapter 2 and Technical Annex for more details). IAMs consider a limited, but expanding, portfolio of land- based mitigation options. Furthermore, the inclusion and detail of a specific mitigation measure differs across IAMs and studies (see also SR15 and Chapter 6). For example, the IAM scenarios based on the shared socio-economic pathways (SSPs) (Riahi et al. 2017 <sup>[[#fn:r1869|1869]]</sup> ) (Cross-Chapter Box 1 and Chapter 1) include possible trends in agriculture and land use for five different socioeconomic futures, but cover a limited set of land-based mitigation options: dietary changes, higher efficiency in food processing (especially in livestock production systems), reduction of food waste, increasing agricultural productivity, methane reductions in rice paddies, livestock and grazing management for reduced methane emissions from enteric fermentation, manure management, improvement of N-efficiency, 1st generation biofuels, reduced deforestation, afforestation, 2nd generation bioenergy crops and BECCS (Popp et al. 2017 <sup>[[#fn:r1870|1870]]</sup> ). However, many ‘natural climate solutions’ (Griscom et al. 2017 <sup>[[#fn:r1871|1871]]</sup> ), such as forest management, rangeland management, soil carbon management or wetland management, are not included in most of these scenarios. In addition, most IAMs neglect the biophysical effects of land-use such as changes in albedo or evapotranspiration with few exceptions (Kreidenweis et al. 2016 <sup>[[#fn:r1872|1872]]</sup> ).

Mitigation pathways, based on IAMs, are typically designed to find the least cost pathway to achieve a pre-defined climate target (Riahi et al. 2017 <sup>[[#fn:r1873|1873]]</sup> ). Such cost-optimal mitigation pathways, especially in RCP2.6 (broadly a 2°C target) and 1.9 scenarios (broadly a 1.5°C target), project GHG emissions to peak early in the 21st century, 2 strict GHG emission reduction afterwards and, depending on the climate target, net CDR from the atmosphere in the second half of the century (Chapter 2 of SR15; Tavoni et al. 2015 <sup>[[#fn:r1874|1874]]</sup> ; Riahi et al. 2017 <sup>[[#fn:r1875|1875]]</sup> ). In most of these pathways, land use is of great importance because of its mitigation potential as discussed in Section 2.7.1: these pathways are based on the assumptions that (i) large-scale afforestation and reforestation removes substantial amounts of CO <sub>2</sub> from the atmosphere, (ii) biomass grown on cropland or from forestry residues can be used for energy generation or BECCS substituting fossil fuel emissions and generating CDR, and (iii) non-CO <sub>2</sub> emissions from agricultural production can be reduced, even under improved agricultural management (Popp et al. 2017 <sup>[[#fn:r1876|1876]]</sup> ; Rogelj et al. 2018a <sup>[[#fn:r1877|1877]]</sup> ; Van Vuuren et al. 2018 <sup>[[#fn:r1878|1878]]</sup> , Frank et al. 2018 <sup>[[#fn:r1879|1879]]</sup> ).

From the IAM scenarios available to this assessment, a set of feasible mitigation pathways has been identified which is illustrative of the range of possible consequences on land use and GHG emissions (presented in this chapter) and sustainable development (Chapter 6). Thus, the IAM scenarios selected here vary due to underlying socio- economic and policy assumptions, the mitigation options considered, long-term climate goals, the level of inclusion of other sustainability goals (such as land and water restrictions for biodiversity conservation or food production) and the models by which they are generated.

In the baseline case without climate change mitigation, global CO <sub>2</sub> emissions from land-use change decrease over time in most scenarios due to agricultural intensification and decreases in demand for agricultural commodities – some even turning negative by the end of the century due to abandonment of agricultural land and associated carbon uptake through vegetation regrowth. Median global CO <sub>2</sub> emissions from land-use change across 5 SSPs and 5 IAMs decrease throughout the 21st century: 3, 1.9 and –0.7 GtCO <sub>2</sub> yr <sup>–1</sup> in 2030, 2050 and 2100 respectively (Figure 2.25). In contrast, CH <sub>4</sub> and N <sub>2</sub> O emissions from agricultural production remain rather constant throughout the 21st century (CH <sub>4</sub> : 214, 231.7 and 209.1 MtCH <sub>4</sub> yr <sup>–1</sup> in 2030, 2050 and 2100 respectively; N <sub>2</sub> O: 9.1, 10.1 and 10.3 MtN <sub>2</sub> O yr <sup>–1</sup> in 2030, 2050 and 2100 respectively).

<div id="section-2-6-2-integrated-pathways-for-climate-change-mitigation-block-2"></div>

<span id="figure-2.25"></span>
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'''Figure 2.25'''

<span id="land-based-global-ghg-emissions-and-removals-in-2030-2050-and-2100-for-baseline-rcp4.5-rcp2.6-and-rcp1.9-based-on-the-ssp.-source-popp-et-al.-2017-rogelj-et-al.-2018-riahi-et-al.-2017.-data-is-from-an-update-of-the-iamc-scenario-explorer-developed-for-the-sr15-huppmann-et-al.-2018-rogelj-et-al."></span>
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'''Land-based global GHG emissions and removals in 2030, 2050 and 2100 for baseline, RCP4.5, RCP2.6 and RCP1.9 based on the SSP. Source: Popp et al. (2017), Rogelj et al. (2018), Riahi et al. (2017). Data is from an update of the IAMC Scenario Explorer developed for the SR15 (Huppmann et al. 2018; Rogelj et al. […]'''
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[[File:f2ed1ae088217867f58da644c8d6a06e Figure-2.25-1024x859.jpg]]

Land-based global GHG emissions and removals in 2030, 2050 and 2100 for baseline, RCP4.5, RCP2.6 and RCP1.9 based on the SSP. Source: Popp et al. (2017) <sup>[[#fn:r2193|2193]]</sup> , Rogelj et al. (2018) <sup>[[#fn:r2194|2194]]</sup> , Riahi et al. (2017) <sup>[[#fn:r2195|2195]]</sup> . Data is from an update of the IAMC Scenario Explorer developed for the SR15 (Huppmann et al. 2018 <sup>[[#fn:r2196|2196]]</sup> ; Rogelj et al. 2018 <sup>[[#fn:r2197|2197]]</sup> ). Boxplots (Tukey style) show median (horizontal line), interquartile range (IQR box) and the range of values within 1.5 × IQR at either end of the box (vertical lines) across 5 SSPs and across 5 IAMs. Outliers (red crosses) are values greater than 1.5 × IQR at either end of the box. The categories CO <sub>2</sub> Land, CH <sub>4</sub> Land and N <sub>2</sub> O Land include GHG emissions from land-use change and agricultural land use (including emissions related to bioenergy production). In addition, the category CO <sub>2</sub> Land includes negative emissions due to afforestation. BECCS reflects the CO <sub>2</sub> emissions captured from bioenergy use and stored in geological deposits.

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<div id="section-2-6-2-integrated-pathways-for-climate-change-mitigation-block-3"></div>

In the mitigation cases (RCP4.5, RCP2.6 and RCP1.9), most of the scenarios indicate strong reductions in CO <sub>2</sub> emissions due to (i) reduced deforestation and (ii) carbon uptake due to afforestation. However, CO <sub>2</sub> emissions from land use can occur in some mitigation scenarios as a result of weak land-use change regulation (Fujimori et al. 2017 <sup>[[#fn:r1880|1880]]</sup> ; Calvin et al. 2017 <sup>[[#fn:r1881|1881]]</sup> ) or displacement effects into pasture land caused by high bioenergy production combined with forest protection only (Popp et al. 2014 <sup>[[#fn:r1882|1882]]</sup> ). The level of CO <sub>2</sub> removal globally (median value across SSPs and IAMs) increases with the stringency of the climate target (RCP4.5, RCP2.6 and RCP1.9) for both afforestation (–1.3, –1.7 and –2.4 GtCO <sub>2</sub> yr <sup>–1</sup> in 2100) and BECCS (–6.5, –11 and –14.9 GtCO <sub>2</sub> yr <sup>–1</sup> in 2100) (Cross-Chapter Box 7 and Chapter 6). In the mitigation cases (RCP4.5, RCP2.6 and RCP1.9), CH <sub>4</sub> and N <sub>2</sub> O emissions are remarkably lower compared to the baseline case (CH4: 133.2, 108.4 and 73.5 MtCH <sub>4</sub> yr <sup>–1</sup> in 2100; N <sub>2</sub> O: 7.4, 6.1 and 4.5 MtN <sub>2</sub> O yr <sup>–1</sup> in 2100; see previous paragraph for CH <sub>4</sub> and N <sub>2</sub> O emissions in the baseline case). The reductions in the mitigation cases are mainly due to improved agricultural management such as improved nitrogen fertiliser management, improved water management in rice production, improved manure management (by, for example, covering of storages or adoption of biogas plants), better herd management and better quality of livestock through breeding and improved feeding practices. In addition, dietary shifts away from emission-intensive livestock products also lead to decreased CH <sub>4</sub> and N <sub>2</sub> O emissions especially in RCP2.6 and RCP1.9 scenarios. However, high levels of bioenergy production can result in increased N <sub>2</sub> O emissions due to nitrogen fertilisation of dedicated bioenergy crops.

Such high levels of CO <sub>2</sub> removal through mitigation options that require land conversion (BECCS and afforestation) shape the land system dramatically (Figure 2.26). Across the different RCPs, SSPs and IAMs, median change of global forest area throughout the 21st century ranges from about –0.2 to +7.2 Mkm <sup>2</sup> between 2010 and 2100, and agricultural land used for 2nd generation bioenergy crop production ranges from about 3.2–6.6 Mkm <sup>2</sup> in 2100 (Popp et al. 2017 <sup>[[#fn:r1883|1883]]</sup> ; Rogelj et al. 2018 <sup>[[#fn:r1884|1884]]</sup> ). Land requirements for bioenergy and afforestation for a RCP1.9 scenario are higher than for a RCP2.6 scenario and especially a RCP4.5 mitigation scenario. As a consequence of the expansion of mainly land-demanding mitigation options, global pasture land is reduced in most mitigation scenarios much more strongly than compared to baseline scenarios (median reduction of 0, 2.6, 5.1 and 7.5 Mkm <sup>2</sup> between 2010 and 2100 in baseline, RCP4.5, RCP2.6 and RCP1.9 respectively). In addition, cropland for food and feed production decreases with the stringency of the climate target (+1.2, +0.2, –1.8 and –4 Mkm <sup>2</sup> in 2100 compared to 2010 in baseline, RCP4.5, RCP2.6 and RCP1.9 respectively). These reductions in agricultural land for food and feed production are facilitated by agricultural intensification on agricultural land and in livestock production systems (Popp et al. 2017 <sup>[[#fn:r1885|1885]]</sup> ), but also by changes in consumption patterns (Fujimori et al. 2017 <sup>[[#fn:r1886|1886]]</sup> ; Frank et al. 2017b <sup>[[#fn:r1887|1887]]</sup> ).

The pace of projected land-use change over the coming decades in ambitious mitigation scenarios goes well beyond historical changes in some instances (Turner et al. (2018b) <sup>[[#fn:r2207|2207]]</sup> , see also SR15). This raises issues for societal acceptance, and distinct policy and governance for avoiding negative consequences for other sustainability goals will be required (Humpenöder et al. 2018 <sup>[[#fn:r1888|1888]]</sup> ; Obersteiner et al. 2016 <sup>[[#fn:r1889|1889]]</sup> ; Calvin et al. 2014 <sup>[[#fn:r1890|1890]]</sup> ) (Chapters 6 and 7).

Different mitigation strategies can achieve the net emissions reductions that would be required to follow a pathway that limits global warming to 2°C or 1.5°C, with very different consequences on the land system.

Figure 2.27 shows six alternative pathways (archetypes) for achieving ambitious climate targets (RCP2.6 and RCP1.9), highlighting land- based strategies and GHG emissions. All pathways are assessed by different models but are all based on the SSP2 (Riahi et al. 2017 <sup>[[#fn:r1891|1891]]</sup> ), with all based on an RCP 1.9 mitigation pathway expect for Pathway 1, which is RCP2.6. All scenarios show land-based negative emissions, but the amount varies across pathways, as do the relative contributions of different land-based CDR options, such as afforestation/reforestation and BECCS.

Pathway 1 RCP2.6 ‘Portfolio’ (Fricko et al. 2017 <sup>[[#fn:r1892|1892]]</sup> ) shows a strong near-term decrease of CO <sub>2</sub> emissions from land-use change, mainly due to reduced deforestation, as well as slightly decreasing N <sub>2</sub> O and CH4 emissions after 2050 from agricultural production due to improved agricultural management and dietary shifts away from emissions-intensive livestock products. However, in contrast to CO <sub>2</sub> emissions, which turn net-negative around 2050 due to afforestation/ reforestation, CH <sub>4</sub> and N <sub>2</sub> O emissions persist throughout the century due to difficulties of eliminating these residual emissions based on existing agricultural management methods (Stevanović et al. 2017 <sup>[[#fn:r1893|1893]]</sup> ; Frank et al. 2017b <sup>[[#fn:r1894|1894]]</sup> ). In addition to abating land related GHG emissions as well as increasing the terrestrial sink, this example also shows the importance of the land sector in providing biomass for BECCS and hence CDR in the energy sector. In this scenario, annual BECCS-based CDR is about three times higher than afforestation-based CDR in 2100 (–11.4 and –3.8 GtCO <sub>2</sub> yr <sup>–1</sup> respectively). Cumulative CDR throughout the century amounts to –395 GtCO <sub>2</sub> for BECCS and –73 GtCO <sub>2</sub> for afforestation. Based on these GHG dynamics, the land sector turns GHG emission neutral in 2100. However, accounting also for BECCS- based CDR taking place in the energy sector, but with biomass provided by the land sector, turns the land sector GHG emission neutral already in 2060, and significantly net-negative by the end of the century.

Pathway 2 RCP1.9 ‘Increased Ambition’ (Rogelj et al. 2018 <sup>[[#fn:r1895|1895]]</sup> ) has dynamics of land-based GHG emissions and removals that are very similar to those in Pathway 1 (RCP2.6) but all GHG emission reductions as well as afforestation/reforestation and BECCS-based CDR start earlier in time at a higher rate of deployment. Cumulative CDR throughout the century amounts to –466 GtCO <sub>2</sub> for BECCS and –117 GtCO <sub>2</sub> for afforestation.

Pathway 3 RCP 1.9 ‘Only BECCS’, in contrast to Pathway 2, includes only BECCS-based CDR (Kriegler et al. 2017 <sup>[[#fn:r1896|1896]]</sup> ). As a consequence, CO <sub>2</sub> emissions are persistent much longer, predominantly from indirect land-use change due to large-scale bioenergy cropland expansion into non-protected natural areas (Popp et al. 2017 <sup>[[#fn:r1897|1897]]</sup> ; Calvin et al. 2014 <sup>[[#fn:r1898|1898]]</sup> ). While annual BECCS CDR rates in 2100 are similar to Pathways 1 and 2 (–15.9 GtCO <sub>2</sub> yr <sup>–1</sup> ), cumulative BECCS-based CDR throughout the century is much larger (–944 GtCO <sub>2</sub> ).

Pathway 4 RCP1.9 ‘Early CDR’ (Bertram et al. 2018 <sup>[[#fn:r1899|1899]]</sup> ) indicates that a significant reduction in the later century in the BECCS-related CDR as well as CDR in general can be achieved with earlier and mainly terrestrial CDR, starting in 2030. In this scenario, terrestrial CDR is based on afforestation but could also be supported by soil organic carbon sequestration (Paustian et al. 2016 <sup>[[#fn:r1900|1900]]</sup> ) or other natural climate solutions, such as rangeland or forest management (Griscom et al. 2017 <sup>[[#fn:r1901|1901]]</sup> ). This scenario highlights the importance of the timing for CDR- based mitigation pathways (Obersteiner et al. 2016 <sup>[[#fn:r1902|1902]]</sup> ). As a result of near-term and mainly terrestrial CDR deployment, cumulative BECCS- based CDR throughout the century is limited to –300 GtCO <sub>2</sub> , while cumulative afforestation-based CDR amounts to –428 GtCO <sub>2</sub> .

In Pathway 5 RCP1.9 ‘Low residual emissions’ (van Vuuren et al. 2018 <sup>[[#fn:r1903|1903]]</sup> ), land-based mitigation is driven by stringent enforcement of measures and technologies to reduce end-of-pipe non-CO <sub>2</sub> emissions and by introduction of in-vitro (cultured) meat, reducing residual N <sub>2</sub> O and CH <sub>4</sub> emissions from agricultural production. In consequence, much lower amounts of CDR from afforestation and BECCS are needed with much later entry points to compensate for residual emissions. Cumulative CDR throughout the century amounts to –252 GtCO <sub>2</sub> for BECCS and –128 GtCO <sub>2</sub> for afforestation. Therefore, total cumulative land-based CDR in Pathway 5 is substantially lower compared to Pathways 2–4 (380 GtCO <sub>2</sub> ).

Finally, Pathway 6 RCP1.9 ‘Low Energy’ (Grubler et al. 2018 <sup>[[#fn:r1904|1904]]</sup> ), equivalent to Pathway LED in SR15, indicates the importance of other sectoral GHG emission reductions for the land sector. In this example, rapid and early reductions in energy demand and associated drops in energy- related CO <sub>2</sub> emissions limit overshoot and decrease the requirements for negative emissions technologies, especially for land-demanding CDR, such as biomass production for BECCS and afforestation. While BECCS is not used at all in Pathway 6, cumulative CDR throughout the century for afforestation amounts to –124 GtCO <sub>2</sub> .

Besides their consequences on mitigation pathways and land consequences, those archetypes can also affect multiple other sustainable development goals that provide both challenges and opportunities for climate action (Chapter 6).

<div id="section-2-6-2-integrated-pathways-for-climate-change-mitigation-block-4"></div>

<span id="figure-2.26"></span>
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'''Figure 2.26'''

<span id="global-change-of-major-land-cover-types-by-2030-2050-and-2100-relative-to-2010-for-baseline-rcp4.5-rcp2.6-and-rcp1.9-based-on-the-ssp.-source-popp-et-al.-2017-rogelj-et-al.-2018-riahi-et-al.-2017.-data-is-from-an-update-of-the-iamc-scenario-explorer-developed-for-the-sr15-huppmann-et-al."></span>
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'''Global change of major land cover types by 2030, 2050 and 2100 relative to 2010 for baseline, RCP4.5, RCP2.6 and RCP1.9 based on the SSP. Source: Popp et al. (2017), Rogelj et al. (2018), Riahi et al. (2017). Data is from an update of the IAMC Scenario Explorer developed for the SR15 (Huppmann et al. […]'''
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[[File:e9c49f90668c4c9fa6c372a2699331f3 Figure-2.26-1024x922.jpg]]

Global change of major land cover types by 2030, 2050 and 2100 relative to 2010 for baseline, RCP4.5, RCP2.6 and RCP1.9 based on the SSP. Source: Popp et al. (2017) <sup>[[#fn:r2208|2208]]</sup> , Rogelj et al. (2018) <sup>[[#fn:r2209|2209]]</sup> , Riahi et al. (2017) <sup>[[#fn:r2210|2210]]</sup> . Data is from an update of the IAMC Scenario Explorer developed for the SR15 (Huppmann et al. 2018 <sup>[[#fn:r2211|2211]]</sup> ; Rogelj et al. 2018 <sup>[[#fn:r2212|2212]]</sup> ). Boxplots (Tukey style) show median (horizontal line), interquartile range IQR (box) and the range of values within 1.5 × IQR at either end of the box (vertical lines) across 5 SSPs and across 5 IAMs. Outliers (red crosses) are values greater than 1.5 × IQR at either end of the box. In 2010, total land cover at global scale was estimated 15–16 Mkm <sup>2</sup> for cropland, 0–0.14 Mkm <sup>2</sup> for bioenergy, 30–35 Mkm <sup>2</sup> for pasture and 37–42 Mkm <sup>2</sup> for forest, across the IAMs that reported SSP pathways (Popp et al. 2017 <sup>[[#fn:r2198|2198]]</sup> ).

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<div id="section-2-6-2-integrated-pathways-for-climate-change-mitigation-block-5"></div>

<span id="figure-2.27"></span>
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<!-- IMG TITLE -->
'''Figure 2.27'''

<span id="evolution-and-breakdown-of-global-land-based-ghg-emissions-and-removals-under-six-alternative-mitigation-pathways.this-figure-illustrates-the-differences-in-timing-and-magnitude-of-land-based-mitigation-approaches-including-afforestation-and-beccs.-all-pathways-are-based-on-different-iam-realisations-of-ssp2.-pathway-1-is-based-on-rcp-2.6-while-all-other-pathways-are-based-on"></span>
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'''Evolution and breakdown of global land-based GHG emissions and removals under six alternative mitigation pathways.This figure illustrates the differences in timing and magnitude of land-based mitigation approaches including afforestation and BECCS. All pathways are based on different IAM realisations of SSP2. Pathway 1 is based on RCP 2.6, while all other pathways are based on […]'''
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[[File:732d49f4d14bd8c0f5b1861b5db42972 Figure-2.27-1024x683.jpg]]

Evolution and breakdown of global land-based GHG emissions and removals under six alternative mitigation pathways.This figure illustrates the differences in timing and magnitude of land-based mitigation approaches including afforestation and BECCS. All pathways are based on different IAM realisations of SSP2. Pathway 1 is based on RCP 2.6, while all other pathways are based on RCP 1.9. Pathway 1: MESSAGE-GLOBIOM (Fricko et al. 2017 <sup>[[#fn:r2199|2199]]</sup> ); Pathway 2: MESSAGE-GLOBIOM (Rogelj et al. 2018 <sup>[[#fn:r2200|2200]]</sup> ); Pathway 3: REMIND-MAgPIE (Kriegler et al. 2017 <sup>[[#fn:r2201|2201]]</sup> ); Pathway 4: REMIND-MAgPIE (Bertram et al. 2018 <sup>[[#fn:r2202|2202]]</sup> ); Pathway 5: IMAGE (van Vuuren et al. 2018 <sup>[[#fn:r2203|2203]]</sup> ); Pathway 6: MESSAGE-GLOBIOM (Grubler et al. 2018 <sup>[[#fn:r2204|2204]]</sup> ). Data is from an update of the IAMC Scenario Explorer developed for the SR15 (Rogelj et al. 2018 <sup>[[#fn:r2205|2205]]</sup> ). The categories CO <sub>2</sub> Land, CH <sub>4</sub> Land and N <sub>2</sub> O Land include GHG emissions from land-use change and agricultural land use (including emissions related to bioenergy production). In addition, the category CO <sub>2</sub> Land includes negative emissions due to afforestation. BECCS reflects the CO <sub>2</sub> emissions captured from bioenergy use and stored in geological deposits. Solid lines show the net effect of all land based GHG emissions and removals (CO <sub>2</sub> Land, CH <sub>4</sub> Land, N <sub>2</sub> O Land and BECCS), while dashed lines show the net effect excluding BECCS. CH <sub>4</sub> and N <sub>2</sub> O emissions are converted to CO <sub>2</sub> -eq using GWP factors of 28 and 265 respectively.

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<span id="the-contribution-of-response-options-to-the-paris-agreement"></span>
=== 2.6.3 The contribution of response options to the Paris Agreement ===

<div id="section-2-6-3-the-contribution-of-response-options-to-the-paris-agreement-block-1"></div>

The previous sections indicated how land-based response options have the potential to contribute to the Paris Agreement, not only though reducing anthropogenic emissions but also for providing anthropogenic sinks that can contribute to “…a balance between anthropogenic emissions by sources and removals by sinks of greenhouse gases in the second half of this century…” (Paris Agreement, Article 4). The balance applies globally, and relates only to GHGs, not aerosols (Section 2.4) or biophysical effects (Section 2.5).

The Paris Agreement includes an enhanced transparency framework to track countries’ progress towards achieving their individual targets (i.e., nationally determined contributions (NDCs)), and a global stocktake (every five years starting in 2023), to assess the countries’ collective progress towards the long-term goals of the Paris Agreement. The importance of robust and transparent definitions and methods (including the approach to separating anthropogenic from natural fluxes) (Fuglestvedt et al. 2018 <sup>[[#fn:r1910|1910]]</sup> ), and the needs for reconciling country GHG inventories and models (Grassi et al. 2018a <sup>[[#fn:r1911|1911]]</sup> ), was highlighted in Section 2.3 in relation to estimating emissions. Issues around estimating mitigation is also key to transparency and credibility and is part of the Paris Rulebook.

The land sector is expected to deliver up to 25% of GHG mitigation pledged by countries by 2025–2030 in their NDCs, based on early assessments of ‘Intended’ NDCs submitted ahead of the Paris Agreement and updates immediately after ( ''low confidence'' ) (Grassi et al. 2017 <sup>[[#fn:r1912|1912]]</sup> ; Forsell et al. 2016 <sup>[[#fn:r1913|1913]]</sup> ). While most NDCs submitted to date include commitments related to the land sector, they vary with how much information is given and the type of target, with more ambitious targets for developing countries often being ‘conditional’ on support and climate finance. Some do not specify the role of AFOLU but include it implicitly as part of economy-wide pledges (e.g., reducing total emission or emission intensity), a few mention multi-sectoral mitigation targets which include AFOLU in a fairly unspecified manner. Many NDCs include specific AFOLU response options, with most focused on the role of forests. A few included soil carbon sequestration or agricultural mitigation and a few explicitly mentioned bioenergy (e.g., Cambodia, Indonesia and Malaysia), but this could be implicitly included with reduced emissions in energy sectors through fuel substitution (see Cross-Chapter Box 7 and Chapter 6 for discussion on cross sector flux reporting). The countries indicating AFOLU mitigation most prominently were Brazil and Indonesia, followed by other countries focusing either on avoiding carbon emissions (e.g., Ethiopia, Gabon, Mexico, DRC, Guyana and Madagascar) or on promoting the sink through large afforestation programmes (e.g., China, India) (Grassi et al. 2017 <sup>[[#fn:r1914|1914]]</sup> ).

<div id="section-2-6-3-the-contribution-of-response-options-to-the-paris-agreement-block-2"></div>

<span id="figure-2.28"></span>
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'''Figure 2.28'''

<span id="global-lulucf-net-ghg-flux-for-the-historical-period-and-future-scenarios-based-on-analyses-of-countries-ndcs.the-lulucf-historical-data-blue-solid-line-reflect-the-following-countries-documents-in-order-of-priority-i-data-submitted-to-unfccc-ndcs-2015-ghg-inventories-and-recent-national-communications-ii-other-official-countries-documents-iii-fao-based-datasets"></span>
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'''Global LULUCF net GHG flux for the historical period and future scenarios based on analyses of countries’ NDCs.The LULUCF historical data (blue solid line) reflect the following countries’ documents (in order of priority): (i) data submitted to UNFCCC (NDCs, 2015 GHG Inventories and recent National Communications ), (ii) other official countries’ documents, (iii) FAO-based datasets […]'''
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[[File:397d59e962853b3a03e2aeffc1d6652e Figure-2.28-1024x604.jpg]]

Global LULUCF net GHG flux for the historical period and future scenarios based on analyses of countries’ NDCs.The LULUCF historical data (blue solid line) reflect the following countries’ documents (in order of priority): (i) data submitted to UNFCCC (NDCs <sup>[[#fn:4|4]]</sup> , 2015 GHG Inventories <sup>[[#fn:5|5]]</sup> and recent National Communications <sup>[[#fn:6|6]]</sup> <sup>[[#fn:7|7]]</sup> ), (ii) other official countries’ documents, (iii) FAO-based datasets (i.e., FAO-FRA for forest (Tian et al. 2015)) as elaborated by (Federici et al. 2015), and (iv) FAOSTAT for non-forest land use emissions (FAO 2015). The four future scenarios reflect official countries’ information, mostly intended NDCs or updated NDCs available at the time of the analysis (Feb 2016), complemented by Biennial Update Reports <sup>[[#fn:8|8]]</sup> and National Communications, and show (i) the BAU scenario as defined by the country, (ii) the trend based on pre-NDC levels of activity (current policies in place in countries), and (iii) the unconditional NDC and conditional NDC scenarios. The shaded area indicates the full range of countries’ available projections (min-max), expressing the available countries’ information on uncertainties beyond the specific scenarios shown. The range of historical country datasets (dotted lines) reflects differences between alternative selections of country sources, in essence, GHG inventories for developed countries complemented by FAO-based datasets (upper range) or by data in National Communications (lower range) for developing countries.

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<div id="section-2-6-3-the-contribution-of-response-options-to-the-paris-agreement-block-3"></div>

Figure 2.28 shows the CO <sub>2</sub> mitigation potential of NDCs compared to historical fluxes from LULUCF. <sup>[[#fn:3|3]]</sup> It shows future fluxes based on current policies in place and on country-stated Business As Usual (BAU) activities (these are different from current policies as many countries are already implementing polices that they do not include as part of their historical business-as-usual baseline) (Grassi et al. 2017). Under implementation of unconditional pledges, the net LULUCF flux in 2030 has been estimated to be a sink of –0.41 ± 0.68 GtCO <sub>2</sub> yr <sup>–1</sup> , which increases to –1.14 ± 0.48 GtCO <sub>2</sub> yr <sup>–1</sup> in 2030 with conditional activities. This compares to net LULUCF in 2010 calculated from the GHG Inventories of 0.01 ± 0.86 GtCO <sub>2</sub> yr <sup>–1</sup> (Grassi et al. 2017 <sup>[[#fn:r1915|1915]]</sup> ). Forsell et al. (2016) <sup>[[#fn:r1916|1916]]</sup> similarly find a reduction in 2030 compared to 2010 of 0.5 GtCO <sub>2</sub> yr <sup>–1</sup> (range: 0.2–0.8) by 2020 and 0.9 GtCO <sub>2</sub> yr <sup>–1</sup> (range: 0.5–1.3) by 2030 for unconditional and conditional cases.

The approach of countries to calculating the LULUCF contribution towards the NDC varies, with implications for comparability and transparency. For example, by following the different approaches used to include LULUCF in country NDCs, Grassi et al. (2017) <sup>[[#fn:r1917|1917]]</sup> found a three-fold difference in estimated mitigation: 1.2–1.9 GtCO <sub>2</sub> -eq yr <sup>–1</sup> when 2030 expected emissions are compared to 2005 emissions, 0.7–1.4 GtCO <sub>2</sub> -eq yr <sup>–1</sup> when 2030 emissions are compared to reference scenarios based on current policies or 2.3–3.0 GtCO <sub>2</sub> -eq yr <sup>–1</sup> when compared to BAU, and 3.0–3.8 GtCO <sub>2</sub> -eq yr <sup>–1</sup> when based on using each countries’ approach to calculation stated in the NDC (i.e., when based on a mix of country approaches, using either past years or BAU projections as reference).

In exploring the effectiveness of the NDCs, SR15 concluded “[e]stimates of global average temperature increase are 2.9°–3.4°C above preindustrial levels with a greater than 66% probability by 2100” (Roberts et al. 2006 <sup>[[#fn:r1918|1918]]</sup> ; Rogelj et al. 2016 <sup>[[#fn:r1919|1919]]</sup> ), under a full implementation of unconditional NDCs and a continuation of climate action similar to that of the NDCs. In order to achieve either the 1.5°C or 2°C pathways, this shortfall would imply the need for submission (and achievement) of more ambitious NDCs, and plan for a more rapid transformation of their national energy, industry, transport and land use sectors (Peters and Geden 2017 <sup>[[#fn:r1920|1920]]</sup> ; Millar et al. 2017 <sup>[[#fn:r1921|1921]]</sup> ; Rogelj et al. 2016 <sup>[[#fn:r1922|1922]]</sup> ).

Response options relying on the use of land could provide around a third of the additional mitigation needed in the near term (2030) to close the gap between current policy trajectories based on NDCs and what is required to achieve a 2°C (&gt;66% chance) or 1.5°C (50–66% chance) pathway according to the UNEP Emissions Gap Report (Roberts et al. 2006 <sup>[[#fn:r1923|1923]]</sup> ). The report estimates annual reduction potentials in 2030 from agriculture at 3.0 (2.3–3.7) GtCO <sub>2</sub> -eq yr <sup>–1</sup> , a combination of ‘uncertain measures’ (biochar, peat-related emission reductions and demand-side management) at 3.7 (2.6–4.8) GtCO <sub>2</sub> -eq yr <sup>–1</sup> ; forests at 5.3 (4.1–6.5) GtCO <sub>2</sub> -eq yr <sup>–1</sup> , bioenergy at 0.9 GtCO <sub>2</sub> -eq yr <sup>–1</sup> and BECCS at 0.3 (0.2–0.4) GtCO <sub>2</sub> -eq yr <sup>–1</sup> (UNEP 2017 <sup>[[#fn:r1924|1924]]</sup> ) (Table 4.1). These response options account for 35% of potential reduction (or 32% without bioenergy and BECCS) out of a total (all sector) potential of 38 (35–41) GtCO <sub>2</sub> -eq yr <sup>–1</sup> . The potentials estimated in the UNEP Emissions Gap Report are based on the technical potential of individual response options from literature including that presented in Section 2.1. CDR related to land use, while not a substitute for strong action in the energy sector, has the technical potential to balance unavoidable emissions that are difficult to eliminate with current technologies ( ''high confidence'' ), with early action avoiding deeper and more rapid action later ( ''very high confidence'' ) (Strefler et al. 2018 <sup>[[#fn:r1925|1925]]</sup> ; Elmar et al. 2018 <sup>[[#fn:r1926|1926]]</sup> ; SR15).

<span id="plant-and-soil-processes-underlying-landclimate-interactions"></span>