IPCC:AR6/SRCCL/Chapter-1 - Revision history

Laura: /* CCB1 Scenarios and other methods to characterise the future of land */

2026-05-31T06:05:23Z

CCB1 Scenarios and other methods to characterise the future of land

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	{\| class="wikitable"		{\| class="wikitable"
	\|-		\|-
	\| Futures method		! Futures method
	\| Description and subtypes		! Description and subtypes
	\| Application domain		! Application domain
	\| Time horizon		! Time horizon
	\| Examples in this assessment		! Examples in this assessment
	\|-		\|-
	\| rowspan="6" \| Exploratory scenarios.		\| rowspan="6" \| Exploratory scenarios.

Laura: /* CCB2 Implications of large-scale conversion from non-forest to forest land */

2026-05-31T05:57:06Z

CCB2 Implications of large-scale conversion from non-forest to forest land

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	Reforestation is a mitigation measure with potential co-benefits for conservation and adaptation, including biodiversity habitat, air and water filtration, flood control, enhanced soil fertility and reversal of land degradation. Potential adverse side-effects of forest area expansion depend largely on the state of the land it displaces as well as tree species selections. Active governance and planning contribute to maximising co-benefits while minimising adverse side-effects (Laestadius et al. 2011<sup>[[#fn:r694\|694]]</sup>; Dinerstein et al. 2015<sup>[[#fn:r695\|695]]</sup>; Veldman et al. 2017<sup>[[#fn:r696\|696]]</sup>) (Section 4.8 and Chapter 7). At large spatial scales, forest expansion is expected to lead to increased competition for land, with potentially undesirable impacts on food prices, biodiversity, non-forest ecosystems and water availability (Bryan and Crossman 2013<sup>[[#fn:r697\|697]]</sup>; Boysen et al. 2017<sup>[[#fn:r698\|698]]</sup>; Kreidenweis et al. 2016<sup>[[#fn:r699\|699]]</sup>; Egginton et al. 2014<sup>[[#fn:r700\|700]]</sup>; Cao et al. 2016<sup>[[#fn:r701\|701]]</sup>; Locatelli et al. 2015a<sup>[[#fn:r702\|702]]</sup>; Smith et al. 2013<sup>[[#fn:r703\|703]]</sup>).		Reforestation is a mitigation measure with potential co-benefits for conservation and adaptation, including biodiversity habitat, air and water filtration, flood control, enhanced soil fertility and reversal of land degradation. Potential adverse side-effects of forest area expansion depend largely on the state of the land it displaces as well as tree species selections. Active governance and planning contribute to maximising co-benefits while minimising adverse side-effects (Laestadius et al. 2011<sup>[[#fn:r694\|694]]</sup>; Dinerstein et al. 2015<sup>[[#fn:r695\|695]]</sup>; Veldman et al. 2017<sup>[[#fn:r696\|696]]</sup>) (Section 4.8 and Chapter 7). At large spatial scales, forest expansion is expected to lead to increased competition for land, with potentially undesirable impacts on food prices, biodiversity, non-forest ecosystems and water availability (Bryan and Crossman 2013<sup>[[#fn:r697\|697]]</sup>; Boysen et al. 2017<sup>[[#fn:r698\|698]]</sup>; Kreidenweis et al. 2016<sup>[[#fn:r699\|699]]</sup>; Egginton et al. 2014<sup>[[#fn:r700\|700]]</sup>; Cao et al. 2016<sup>[[#fn:r701\|701]]</sup>; Locatelli et al. 2015a<sup>[[#fn:r702\|702]]</sup>; Smith et al. 2013<sup>[[#fn:r703\|703]]</sup>).
			</div>
	<span id="land-management"></span>		<span id="land-management"></span>
	=== 1.3.2 Land management ===		=== 1.3.2 Land management ===

Laura: /* 1.3.1 Targeted decarbonisation relying on large land-area need */

2026-05-31T05:56:27Z

1.3.1 Targeted decarbonisation relying on large land-area need

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	<span id="ccb2-implications-of-large-scale-conversion-from-non-forest-to-forest-land"></span>		<span id="ccb2-implications-of-large-scale-conversion-from-non-forest-to-forest-land"></span>
			<div class="container-box col-cross">

	== CCB2 Implications of large-scale conversion from non-forest to forest land ==		== CCB2 Implications of large-scale conversion from non-forest to forest land ==

Laura: /* 1.3 Response options to the key challenges */

2026-05-31T05:55:46Z

1.3 Response options to the key challenges

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	{\| class="wikitable"		{\| class="wikitable"
	\|-		\|-
			\| colspan="2" \| Response options based on land management
	Response options based on land management

	\|-		\|-
			\| in agriculture
	in agriculture		\| Improved management of: cropland, grazing land, livestock; agro-forestry; avoidance of conversion of grassland to cropland; integrated water management

	Improved management of: cropland, grazing land, livestock; agro-forestry; avoidance of conversion of grassland to cropland; integrated water management

	\|-		\|-
			\| in forests
	in forests		\| Improved management of forests and forest restoration; reduced deforestation and degradation; afforestation

	Improved management of forests and forest restoration; reduced deforestation and degradation; afforestation

	\|-		\|-
			\| of soils
	of soils		\| Increased soil organic carbon content; reduced soil erosion; reduced soil salinisation

	Increased soil organic carbon content; reduced soil erosion; reduced soil salinisation

	\|-		\|-
			\| across all/other ecosystems
	across all/other ecosystems		\| Reduced landslides and natural hazards; reduced pollution including acidification; biodiversity conservation; restoration and reduced conversion of peatlands

	Reduced landslides and natural hazards; reduced pollution including acidification; biodiversity conservation; restoration and reduced conversion of peatlands

	\|-		\|-
			\| specifically for CO <sub>2</sub> removal
	specifically for CO <sub>2</sub> removal		\| Enhanced weathering of minerals; bioenergy and BECCS

	Enhanced weathering of minerals; bioenergy and BECCS

	\|-		\|-
			\| colspan="2" \| Response options based on value chain management
	Response options based on value chain management

	\|-		\|-
			\| through demand management
	through demand management		\| Dietary change; reduced post-harvest losses; reduced food waste

	Dietary change; reduced post-harvest losses; reduced food waste

	\|-		\|-
			\| through supply management
	through supply management		\| Sustainable sourcing; improved energy use in food systems; improved food processing and retailing

	Sustainable sourcing; improved energy use in food systems; improved food processing and retailing

	\|-		\|-
			\| colspan="2" \| Response options based on risk management
	Response options based on risk management

	\|-		\|-
			\| Risk management
	Risk management		\| Risk-sharing instruments; use of local seeds; disaster risk management

	Risk-sharing instruments; use of local seeds; disaster risk management

	\|}		\|}
	<!-- END TABLE -->		<!-- END TABLE -->
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	<span id="ccb2-implications-of-large-scale-conversion-from-non-forest-to-forest-land"></span>		<span id="ccb2-implications-of-large-scale-conversion-from-non-forest-to-forest-land"></span>

	== CCB2 Implications of large-scale conversion from non-forest to forest land ==		== CCB2 Implications of large-scale conversion from non-forest to forest land ==

Laura: /* CCB1 Scenarios and other methods to characterise the future of land */

2026-05-31T05:52:55Z

CCB1 Scenarios and other methods to characterise the future of land

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	\| Policy and planning scenarios		\| Policy and planning scenarios
	(including business planning)		(including business planning)
	\|. Ex ante analysis of the consequences of alternative policies or decisions based on known policy options or already implemented policy and planning measures		\| Ex ante analysis of the consequences of alternative policies or decisions based on known policy options or already implemented policy and planning measures
	\| 5–30 years		\| 5–30 years
	\| 2.6.3, 5.5.2, 5.6.2, 6.4.4		\| 2.6.3, 5.5.2, 5.6.2, 6.4.4
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	\| 1.2		\| 1.2
	\|-		\|-
	\| Normative scenarios.		\| rowspan="2" \| Normative scenarios.
	Desired futures or outcomes that are aspirational and how to achieve them		Desired futures or outcomes that are aspirational and how to achieve them
	\| Visions, goal-seeking or target-seeking scenarios		\| Visions, goal-seeking or target-seeking scenarios

Laura: /* CCB1 Scenarios and other methods to characterise the future of land */

2026-05-31T05:51:49Z

CCB1 Scenarios and other methods to characterise the future of land

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	\| 2.3, 2.6.2, 5.2.3, 6.1.4, 6.4.4, 7.2		\| 2.3, 2.6.2, 5.2.3, 6.1.4, 6.4.4, 7.2
	\|-		\|-
	\|}		\| Business-as-usual scenarios
	Business-as-usual scenarios

	(including ‘outlooks’)		(including ‘outlooks’)
			\| A continuation into the future of current trends in key drivers to explore the consequences of these in the near term
	A continuation into the future of current trends~~<br />~~		\| 5–10 years, 20–30 years for outlooks
	in key drivers to explore the consequences of these in the near term		\| 1.2.1, 2.6.2, 5.3.4, 6.1.4

	5–10 years, 20–30 years for outlooks

	1.2.1, 2.6.2, 5.3.4, 6.1.4

	\|-		\|-
			\| Policy and planning scenarios
	Policy and planning scenarios

	(including business planning)		(including business planning)
			\|. Ex ante analysis of the consequences of alternative policies or decisions based on known policy options or already implemented policy and planning measures
	Ex ante analysis of the consequences of alternative policies or decisions based on known policy options or already implemented policy and planning measures		\| 5–30 years
			\| 2.6.3, 5.5.2, 5.6.2, 6.4.4
	5–30 years

	2.6.3, 5.5.2, 5.6.2, 6.4.4

	\|-		\|-
			\| Stylised scenarios (with single and multiple
	Stylised scenarios (with single and multiple ~~options)~~		\| Afforestation/reforestation areas, bioenergy areas, protected areas for conservation, consumption patterns (e.g., diets, food waste)
			\| 10–100 years
	Afforestation/reforestation areas, bioenergy areas, protected areas for conservation, consumption patterns (e.g., diets, food waste)		\| 2.6.1, 5.5.1, 5.5.2, 5.6.1, 5.6.2, 6.4.4, 7.2

	10–100 years

	2.6.1, 5.5.1, 5.5.2, 5.6.1, 5.6.2, 6.4.4, 7.2

	\|-		\|-
			\| Shock scenarios (high impact single events)
	Shock scenarios (high impact single events)		\| Food supply chain collapses, cyberattacks, pandemic diseases (humans, crops and livestock)
			\| Near-term events<br />
	Food supply chain collapses, cyberattacks, pandemic diseases (humans, crops and livestock)

	Near-term events<br />
	(up to 10 years) leading to long-term impacts (10–100 years)		(up to 10 years) leading to long-term impacts (10–100 years)
			\| 5.8.1
	5.8.1

	\|-		\|-
			\| Conditional probabilistic futures
	Conditional probabilistic futures

	ascribe probabilities to uncertain drivers that are conditional on scenario assumptions		ascribe probabilities to uncertain drivers that are conditional on scenario assumptions
			\| Where some knowledge is known about driver uncertainties, for example, population, economic growth, land-use change
	Where some knowledge is known about driver uncertainties, for example, population, economic growth, land-use change		\| 10–100 years
			\| 1.2
	10–100 years

	1.2

	\|-		\|-
			\| Normative scenarios.
	Normative scenarios.

	Desired futures or outcomes that are aspirational and how to achieve them		Desired futures or outcomes that are aspirational and how to achieve them
			\| Visions, goal-seeking or target-seeking scenarios
	Visions, goal-seeking or target-seeking scenarios		\| Environmental quality, societal development, human well-being, the Representative Concentration Pathways (RCPs,) 1.5°C scenarios
			\| 5–10 years to 10–100 years
	Environmental quality, societal development, human well-being, the Representative Concentration Pathways (RCPs,) 1.5°C scenarios		\| 2.6.2, 6.4.4, 7.2, 5.5.2

	5–10 years to 10–100 years

	2.6.2, 6.4.4, 7.2, 5.5.2

	\|-		\|-
			\| Pathways as alternative sets of choices, actions or behaviours that lead to a future vision (goal or target)
	Pathways as alternative sets~~<br />~~		\| Socio-economic systems, governance and policy actions
	of choices, actions or behaviours that lead to a future vision~~<br />~~		\| 5–10 years to 10–100 years
	(goal or target)		\| 5.5.2, 6.4.4, 7.2

	Socio-economic systems, governance and policy actions

	5–10 years to 10–100 years

	5.5.2, 6.4.4, 7.2

	\|}		\|}

Laura: /* CCB1 Scenarios and other methods to characterise the future of land */

2026-05-31T05:49:00Z

CCB1 Scenarios and other methods to characterise the future of land

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	{\| class="wikitable"		{\| class="wikitable"
	\|-		\|-
			\| Futures method
	Futures method		\| Description and subtypes
			\| Application domain
	Description and subtypes		\| Time horizon
			\| Examples in this assessment
	Application domain

	Time horizon

	Examples in this assessment

	\|-		\|-
			\| rowspan="6" \| Exploratory scenarios.
	Exploratory scenarios.

	Trajectories of change in system components from the present to contrasting, alterna- tive futures based on plausible and internally consistent assumptions about the underlying drivers of change		Trajectories of change in system components from the present to contrasting, alterna- tive futures based on plausible and internally consistent assumptions about the underlying drivers of change
			\| Long-term projections quantified with models
	Long-term projections quantified with models		\| Climate system, land system and other components of the environment (e.g., biodiversity, ecosystem function- ing, water resources and quality), for example the SSPs
			\| 10–100 years
	Climate system, land system and other components of the environment (e.g., biodiversity, ecosystem function- ing, water resources and quality), for example the SSPs		\| 2.3, 2.6.2, 5.2.3, 6.1.4, 6.4.4, 7.2

	10–100 years

	2.3, 2.6.2, 5.2.3, 6.1.4, 6.4.4, 7.2

	\|-		\|-
			\|}
	Business-as-usual scenarios		Business-as-usual scenarios

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	\|}		\|}

	<!-- END TABLE -->		<!-- END TABLE -->

Laura: /* CCB1 Scenarios and other methods to characterise the future of land */

2026-05-30T08:51:07Z

CCB1 Scenarios and other methods to characterise the future of land

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	There is a small, but growing literature on quantitative pathways to achieve normative visions and their associated trade-offs (IPBES 2018a<sup>[[#fn:r515\|515]]</sup>). Whilst the visions themselves may be clearly articulated, the societal choices, behaviours and transitions needed to attain them, are not. Better accounting for human behaviour and decision-making processes in global scale land-use models would improve the capacity to quantify pathways to sustainable futures (Rounsevell et al. 2014<sup>[[#fn:r516\|516]]</sup>; Arneth et al. 2014<sup>[[#fn:r517\|517]]</sup>; Calvin and Bond-Lamberty 2018<sup>[[#fn:r518\|518]]</sup>). It is, however, difficult to understand and represent human behaviour and social interaction processes at global scales. Decision-making in global models is commonly represented through economic processes (Arneth et al. 2014<sup>[[#fn:r519\|519]]</sup>). Other important human processes for land systems including equity, fairness, land tenure and the role of institutions and governance, receive less attention, and this limits the use of global models to quantify transformative pathways, adaptation and mitigation (Arneth et al. 2014<sup>[[#fn:r520\|520]]</sup>; Rounsevell et al. 2014<sup>[[#fn:r521\|521]]</sup>; Wang et al. 2016<sup>[[#fn:r\|]]</sup> 522). No model exists at present to represent complex human behaviours at the global scale, although the need has been highlighted (Rounsevell et al. 2014<sup>[[#fn:r523\|523]]</sup>; Arneth et al. 2014<sup>[[#fn:r524\|524]]</sup>; Robinson et al. 2017<sup>[[#fn:r525\|525]]</sup>; Brown et al. 2017<sup>[[#fn:r526\|526]]</sup>; Calvin and Bond-Lamberty 2018<sup>[[#fn:r527\|527]]</sup>).		There is a small, but growing literature on quantitative pathways to achieve normative visions and their associated trade-offs (IPBES 2018a<sup>[[#fn:r515\|515]]</sup>). Whilst the visions themselves may be clearly articulated, the societal choices, behaviours and transitions needed to attain them, are not. Better accounting for human behaviour and decision-making processes in global scale land-use models would improve the capacity to quantify pathways to sustainable futures (Rounsevell et al. 2014<sup>[[#fn:r516\|516]]</sup>; Arneth et al. 2014<sup>[[#fn:r517\|517]]</sup>; Calvin and Bond-Lamberty 2018<sup>[[#fn:r518\|518]]</sup>). It is, however, difficult to understand and represent human behaviour and social interaction processes at global scales. Decision-making in global models is commonly represented through economic processes (Arneth et al. 2014<sup>[[#fn:r519\|519]]</sup>). Other important human processes for land systems including equity, fairness, land tenure and the role of institutions and governance, receive less attention, and this limits the use of global models to quantify transformative pathways, adaptation and mitigation (Arneth et al. 2014<sup>[[#fn:r520\|520]]</sup>; Rounsevell et al. 2014<sup>[[#fn:r521\|521]]</sup>; Wang et al. 2016<sup>[[#fn:r\|]]</sup> 522). No model exists at present to represent complex human behaviours at the global scale, although the need has been highlighted (Rounsevell et al. 2014<sup>[[#fn:r523\|523]]</sup>; Arneth et al. 2014<sup>[[#fn:r524\|524]]</sup>; Robinson et al. 2017<sup>[[#fn:r525\|525]]</sup>; Brown et al. 2017<sup>[[#fn:r526\|526]]</sup>; Calvin and Bond-Lamberty 2018<sup>[[#fn:r527\|527]]</sup>).
			</div>
	<div id="section-1-2-2-3-uncertainties-in-decision-making"></div>		<div id="section-1-2-2-3-uncertainties-in-decision-making"></div>

Laura: /* 1.2.2.2 Nature and scope of uncertainties related to land use */

2026-05-30T08:50:30Z

1.2.2.2 Nature and scope of uncertainties related to land use

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	<span id="ccb1-scenarios-and-other-methods-to-characterise-the-future-of-land"></span>		<span id="ccb1-scenarios-and-other-methods-to-characterise-the-future-of-land"></span>
			<div class="container-box col-cross">

	== CCB1 Scenarios and other methods to characterise the future of land ==		== CCB1 Scenarios and other methods to characterise the future of land ==

Laura: /* 1.1.2.3 Past and ongoing trends */

2026-05-30T08:49:34Z

1.1.2.3 Past and ongoing trends

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	[[File:500059f02cc4372eb106850ef773be66 Figure-1.3-724x1024.png]]		[[File:500059f02cc4372eb106850ef773be66 Figure-1.3-724x1024.png]]

	Status and trends in the global land system: '''A''' . Trends in area, production and trade, and drivers of change. The map shows the global pattern of land systems (combination of maps Nachtergaele (2008)<sup>[[#fn:r177\|177]]</sup>; Ellis et al. (2010)<sup>[[#fn:r178\|178]]</sup>; Potapov et al. (2017)<sup>[[#fn:r179\|179]]</sup>; FAO’s Animal Production and Health Division (2018); livestock low/high relates to low or high livestock density, respectively). The inlay figures show, for the globe and seven world regions, from left to right: (a) Cropland, permanent pastures and forest (used and unused) areas, standardised to total land area, (b) production in dry matter per year per total land area, (c) trade in dry matter in percent of total domestic production, all for 1961 to 2014 (data from FAOSTAT (2018)<sup>[[#fn:r180\|180]]</sup> and FAO (1963)<sup>[[#fn:r181\|181]]</sup> for forest area 1961). (d) drivers of cropland for food production between 1994 and 2011 (Alexander et al. 2015<sup>[[#fn:r182\|182]]</sup>). See panel “global” for legend. “Plant Produc., Animal P.”: changes in consumption of plant-based products and animal-products, respectively. '''B''' .Selected land-use pressures and impacts. The map shows the ratio between impacts on biomass stocks of land-cover conversions and of land management (changes that occur with land-cover types; only changes larger than 30 gC m <sup>–2</sup> displayed; Erb et al. 2017<sup>[[#fn:r183\|183]]</sup>), compared to the biomass stocks of the potential vegetation (vegetation that would prevail in the absence of land use, but with current climate). The inlay figures show, from left to right (e) the global Human Appropriation of Net Primary production (HANPP) in the year 2005, in gC m <sup>–2</sup> yr <sup>–1</sup> (Krausmann et al. 2013<sup>[[#fn:r184\|184]]</sup>). The sum of the three components represents the NPP of the potential vegetation and consist of: (i) NPP <sub>eco</sub> , i.e. the amount of NPP remaining in ecosystem after harvest, (ii) HANPP <sub>harv</sub> , i.e. NPP harvested or killed during harvest, and (iii) HANPP <sub>luc</sub> , i.e. NPP foregone due to land-use change. The sum of NPP <sub>eco</sub> and HANPP <sub>harv</sub> is the NPP of the actual vegetation (Haberl et al. 2014<sup>[[#fn:r185\|185]]</sup>; Krausmann et al. 2013<sup>[[#fn:r186\|186]]</sup>). The two central inlay figures show changes in land-use intensity, standardised to 2014, related to (f) cropland (yields, fertilisation, irrigated area) and (g) forestry harvest per forest area, and grazers and monogastric livestock density per agricultural area (FAOSTAT 2018). (h) Cumulative CO <sub>2</sub> fluxes between land and the atmosphere between 2000 and 2014. LUC: annual CO <sub>2</sub> land use flux due to changes in land cover and forest management; Sink <sub>land</sub> : the annual CO <sub>2</sub> land sink caused mainly by the indirect anthropogenic effects of environmental change (e.g, climate change and the fertilising effects of rising CO <sub>2</sub> and N concentrations), excluding impacts of land-use change (Le Quéré et al. 2018<sup>[[#fn:r187\|187]]</sup>) (Section 2.3)		Status and trends in the global land system: '''A'''. Trends in area, production and trade, and drivers of change. The map shows the global pattern of land systems (combination of maps Nachtergaele (2008)<sup>[[#fn:r177\|177]]</sup>; Ellis et al. (2010)<sup>[[#fn:r178\|178]]</sup>; Potapov et al. (2017)<sup>[[#fn:r179\|179]]</sup>; FAO’s Animal Production and Health Division (2018); livestock low/high relates to low or high livestock density, respectively). The inlay figures show, for the globe and seven world regions, from left to right: (a) Cropland, permanent pastures and forest (used and unused) areas, standardised to total land area, (b) production in dry matter per year per total land area, (c) trade in dry matter in percent of total domestic production, all for 1961 to 2014 (data from FAOSTAT (2018)<sup>[[#fn:r180\|180]]</sup> and FAO (1963)<sup>[[#fn:r181\|181]]</sup> for forest area 1961). (d) drivers of cropland for food production between 1994 and 2011 (Alexander et al. 2015<sup>[[#fn:r182\|182]]</sup>). See panel “global” for legend. “Plant Produc., Animal P.”: changes in consumption of plant-based products and animal-products, respectively. '''B'''. Selected land-use pressures and impacts. The map shows the ratio between impacts on biomass stocks of land-cover conversions and of land management (changes that occur with land-cover types; only changes larger than 30 gC m <sup>–2</sup> displayed; Erb et al. 2017<sup>[[#fn:r183\|183]]</sup>), compared to the biomass stocks of the potential vegetation (vegetation that would prevail in the absence of land use, but with current climate). The inlay figures show, from left to right (e) the global Human Appropriation of Net Primary production (HANPP) in the year 2005, in gC m <sup>–2</sup> yr <sup>–1</sup> (Krausmann et al. 2013<sup>[[#fn:r184\|184]]</sup>). The sum of the three components represents the NPP of the potential vegetation and consist of: (i) NPP <sub>eco</sub> , i.e. the amount of NPP remaining in ecosystem after harvest, (ii) HANPP <sub>harv</sub> , i.e. NPP harvested or killed during harvest, and (iii) HANPP <sub>luc</sub> , i.e. NPP foregone due to land-use change. The sum of NPP <sub>eco</sub> and HANPP <sub>harv</sub> is the NPP of the actual vegetation (Haberl et al. 2014<sup>[[#fn:r185\|185]]</sup>; Krausmann et al. 2013<sup>[[#fn:r186\|186]]</sup>). The two central inlay figures show changes in land-use intensity, standardised to 2014, related to (f) cropland (yields, fertilisation, irrigated area) and (g) forestry harvest per forest area, and grazers and monogastric livestock density per agricultural area (FAOSTAT 2018). (h) Cumulative CO <sub>2</sub> fluxes between land and the atmosphere between 2000 and 2014. LUC: annual CO <sub>2</sub> land use flux due to changes in land cover and forest management; Sink <sub>land</sub> : the annual CO <sub>2</sub> land sink caused mainly by the indirect anthropogenic effects of environmental change (e.g, climate change and the fertilising effects of rising CO <sub>2</sub> and N concentrations), excluding impacts of land-use change (Le Quéré et al. 2018<sup>[[#fn:r187\|187]]</sup>) (Section 2.3)

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