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

== 7.5 Decision-making for climate change and land ==

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The risks posed by climate change generate considerable uncertainty and complexity for decision-makers responsible for land-use decisions ( ''robust evidence, high agreement'' ). Decision-makers balance climate ambitions, encapsulated in the NDCs, with other SDGs, which will differ considerably across different regions, sociocultural conditions and economic levels (Griggs et al. 2014 <sup>[[#fn:r985|985]]</sup> ). The interactions across SDGs also factor into decision-making processes (Nilsson et al. 2016b <sup>[[#fn:r986|986]]</sup> ). The challenge is particularly acute in least developed countries where a large share of the population is vulnerable to climate change. Matching the structure of decision-making processes to local needs while connecting to national strategies and international regimes is challenging (Nilsson and Persson 2012 <sup>[[#fn:r987|987]]</sup> ). This section explores methods of decision-making to address the risks and inter-linkages outlined in the above sections. As a result, this section outlines policy inter-linkages with SDGs and NDCs, trade-offs and synergies in specific measures, possible challenges as well as opportunities going forward.

Even in cases where uncertainty exists, there is ''medium evidence'' and ''high agreement'' in the literature that it need not present a barrier to taking action, and there are growing methodological developments and empirical applications to support decision-making. Progress has been made in identifying key sources of uncertainty and addressing them (Farber 2015 <sup>[[#fn:r988|988]]</sup> ; Lawrence et al. 2018 <sup>[[#fn:r989|989]]</sup> ; Bloemen et al. 2018 <sup>[[#fn:r990|990]]</sup> ). Many of these approaches involve principles of robustness, diversity, flexibility, learning, or choice editing (Section 7.5.2).

Since the IPCC’s Fifth Assessment Report ( ''Foundations for Decision Making'' ) chapter on Contexts for Decision-making (Jones et al. 2014 <sup>[[#fn:r994|994]]</sup> ) considerable advances have been made in decision-making under uncertainty, both conceptually and in economics (Section 7.5.2), and in the social/qualitative research areas (Sections 7.5.3 and 7.5.4). In the land sector, the degree of uncertainty varies and is particularly challenging for climate change adaptation decisions (Hallegatte 2009 <sup>[[#fn:r991|991]]</sup> ; Wilby and Dessai 2010 <sup>[[#fn:r992|992]]</sup> ). Some types of agricultural production decisions can be made in short timeframes as changes are observed, and will provide benefits in the current time period (Dittrich et al. 2017 <sup>[[#fn:r993|993]]</sup> ).

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=== 7.5.1 Formal and informal decision-making ===

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Informal decision-making facilitated by open platforms can solve problems in land and resource management by allowing evolution and adaptation, and incorporation of local knowledge ( ''medium confidence'' ) (Malogdos and Yujuico 2015a <sup>[[#fn:r995|995]]</sup> ; Vandersypen et al. 2007 <sup>[[#fn:r996|996]]</sup> ). Formal centres of decision-making are those that follow fixed procedures (written down in statutes or moulded in an organisation backed by the legal system) and structures (Onibon et al. 1999 <sup>[[#fn:r997|997]]</sup> ). Informal centres of decision-making are those following customary norms and habits based on conventions (Onibon et al. 1999 <sup>[[#fn:r998|998]]</sup> ) where problems are ill-structured and complex (Waddock 2013 <sup>[[#fn:r999|999]]</sup> ).

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==== 7.5.1.1 Formal Decision Making ====

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Formal decision-making processes can occur at all levels, including the global, regional, national and sub-national levels (Section 7.4.1). Formal decision-making support tools can be used, for example, by farmers, to answer ‘what-if’ questions as to how to respond to the effects of changing climate on soils, rainfall and other conditions (Wenkel et al. 2013 <sup>[[#fn:r1000|1000]]</sup> ).

Optimal formal decision-making is based on realistic behaviour of actors, important in land–climate systems, assessed through participatory approaches, stakeholder consultations and by incorporating results from empirical analyses. Mathematical simulations and games (Lamarque et al. 2013 <sup>[[#fn:r1001|1001]]</sup> ), behavioural models in land-based sectors (Brown et al. 2017 <sup>[[#fn:r1002|1002]]</sup> ), agent-based models and micro- simulations are examples useful to decision-makers (Bishop et al. 2013 <sup>[[#fn:r1003|1003]]</sup> ). These decision-making tools are expanded on in Section 7.5.2.

There are different ways to incorporate local knowledge, informal institutions and other contextual characteristics that capture non- deterministic elements, as well as social and cultural beliefs and systems more generally, into formal decision-making ( ''medium evidence, medium agreement'' ) (Section 7.6.4). Classic scientific methodologies now include participatory and interdisciplinary methods and approaches (Jones et al. 2014 <sup>[[#fn:r1004|1004]]</sup> ). Consequently, this broader range of approaches may capture informal and indigenous knowledge, improving the participation of indigenous peoples in decision-making processes, and thereby promote their rights to self-determination (Malogdos and Yujuico 2015b <sup>[[#fn:r1005|1005]]</sup> ) (Cross-Chapter Box 13 in Chapter 7).

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==== 7.5.1.2 Informal decision-making ====

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Informal institutions have contributed to sustainable resources management (common pool resources) through creating a suitable environment for decision-making. The role of informal institutions indecision-making can be particularly relevant for land-use decisions and practices in rural areas in the global south and north (Huisheng 2015 <sup>[[#fn:r1006|1006]]</sup> ). Understanding informal institutions is crucial for adapting to climate change, advancing technological adaptation measures, achieving comprehensive disaster management and advancing collective decision-making (Karim and Thiel 2017 <sup>[[#fn:r1007|1007]]</sup> ). Informal institutions have been found to be a crucial entry point in dealing with vulnerability of communities and exclusionary tendencies impacting on marginalised and vulnerable people (Mubaya and Mafongoya 2017 <sup>[[#fn:r1008|1008]]</sup> ).

Many studies underline the role of local/informal traditional institutions in the management of natural resources in different parts of the world (Yami et al. 2009 <sup>[[#fn:r1009|1009]]</sup> ; Zoogah et al. 2015 <sup>[[#fn:r1010|1010]]</sup> ; Bratton 2007 <sup>[[#fn:r1011|1011]]</sup> ; Mowo et al. 2013 <sup>[[#fn:r1012|1012]]</sup> ; Grzymala-Busse 2010 <sup>[[#fn:r1013|1013]]</sup> ). Traditional systems include: traditional silvopastoral management (Iran), management of rangeland resources (South Africa), natural resource management (Ethiopia, Tanzania, Bangladesh) communal grazing land management (Ethiopia) and management of conflict over natural resources (Siddig et al. 2007 <sup>[[#fn:r1014|1014]]</sup> ; Yami et al. 2011 <sup>[[#fn:r1015|1015]]</sup> ; Valipour et al. 2014 <sup>[[#fn:r1016|1016]]</sup> ; Bennett 2013 <sup>[[#fn:r1017|1017]]</sup> ; Mowo et al. 2013 <sup>[[#fn:r1018|1018]]</sup> ).

Formal–informal institutional interaction could take different shapes such as: complementary, accommodating, competing, and substitutive. There are many examples when formal institutions might obstruct, change, and hinder informal institutions (Rahman et al. 2014 <sup>[[#fn:r1019|1019]]</sup> ; Helmke and Levitsky 2004 <sup>[[#fn:r1020|1020]]</sup> ; Bennett 2013 <sup>[[#fn:r1021|1021]]</sup> ; Osei-Tutu et al. 2014 <sup>[[#fn:r1022|1022]]</sup> ). Similarly, informal institutions can replace, undermine, and reinforce formal institutions (Grzymala-Busse 2010). In the absence of formal institutions, informal institutions gain importance, requiring focus in relation to natural resources management and rights protection (Estrin and Prevezer 2011 <sup>[[#fn:r1023|1023]]</sup> ; Helmke and Levitsky 2004 <sup>[[#fn:r1024|1024]]</sup> ; Kangalawe et al. 2014 <sup>[[#fn:r1025|1025]]</sup> ; Sauerwald and Peng 2013 <sup>[[#fn:r1026|1026]]</sup> ; Zoogah et al. 2015 <sup>[[#fn:r1027|1027]]</sup> ).

Community forestry comprises 22% of forests in tropical countries in contrast to large-scale industrial forestry (Hajjar et al. 2013 <sup>[[#fn:r1028|1028]]</sup> ) and is managed with informal institutions, ensuring a sustainable flow of forest products and income, utilising traditional ecological knowledge to determine access to resources (Singh et al. 2018 <sup>[[#fn:r1029|1029]]</sup> ). Policies that create an open platform for local debates and allow actors their own active formulation of rules strengthen informal institutions. Case studies in Zambia, Mali, Indonesia and Bolivia confirm that enabling factors for advancing the local ownership of resources and crafting durability of informal rules require recognition in laws, regulations and policies of the state (Haller et al. 2016 <sup>[[#fn:r1030|1030]]</sup> ).

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=== 7.5.2 Decision-making, timing, risk, and uncertainty ===

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This section assesses decision-making literature, concluding that advances in methods have been made in the face of conceptual risk literature and, together with a synthesis of empirical evidence, near-term decisions have significant impact on costs.

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==== 7.5.2.1 Problem structuring ====

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Structured decision-making occurs when there is scientific knowledge about cause and effect, little uncertainty, and agreement exists on values and norms relating to an issue (Hurlbert and Gupta 2016 <sup>[[#fn:r1031|1031]]</sup> ). This decision space is situated within the ‘known’ space where cause and effect is understood and predictable (although uncertainty is not quite zero) (French 2015 <sup>[[#fn:r1032|1032]]</sup> ). Figure 7.5 displays the structured problem area in the bottom left-hand corner corresponding with the ‘known’ decision-making space. Decision-making surrounding quantified risk assessment and risk management (Section 7.4.3.1) occurs within this decision-making space. Examples in the land and climate area include cost-benefit analysis surrounding implementation of irrigation projects (Batie 2008 <sup>[[#fn:r1033|1033]]</sup> ) or adopting soil erosion practices by agricultural producers based on anticipated profit (Hurlbert 2018b <sup>[[#fn:r1034|1034]]</sup> ). Comprehensive risk management also occupies this decision space (Papathoma-Köhle et al. 2016 <sup>[[#fn:r1035|1035]]</sup> ), encompassing risk assessment, reduction, transfer, retention, emergency preparedness and response, and disaster recovery by combining quantified proactive and reactive approaches (Fra.Paleo 2015 <sup>[[#fn:r1036|1036]]</sup> ) (Section 7.4.3).

A moderately structured decision space is characterised as one where there is either some disagreement on norms, principles, ends and goals in defining a future state, or there is some uncertainty surrounding land and climate including land use, observations of land-use changes, early warning and decision support systems, model structures, parameterisations, inputs, or from unknown futures informing integrated assessment models and scenarios (see Chapter 1, Section 1.2.2 and Cross-Chapter Box 1 in Chapter 1). Environmental decision-making often takes place in this space where there is limited information and ability to process it, and individual stakeholders make different decisions on the best future course of action ( ''medium confidence'' ) (Waas et al. 2014 <sup>[[#fn:r1037|1037]]</sup> ; Hurlbert and Gupta 2016 <sup>[[#fn:r1038|1038]]</sup> , 2015; Hurlbert 2018b). Figure 7.5 displays the moderately structured problem space characterised by disagreement surrounding norms on the top left-hand side. This corresponds with the complex decision-making space, the realm of social sciences and qualitative knowledge, where cause and effect is difficult to relate with any confidence (French 2013 <sup>[[#fn:r1039|1039]]</sup> ).

The moderately structured decision space characterised by uncertainty surrounding land and climate on the bottom right-hand side of Figure 7.5 corresponds to the knowable decision-making space, where the realm of scientific inquiry investigates cause and effects. Here there is sufficient understanding to build models, but not enough understanding to define all parameters (French 2015 <sup>[[#fn:r1040|1040]]</sup> ).

The top right-hand corner of Figure 7.5 corresponds to the ‘unstructured’ problem or chaotic space where patterns and relationships are difficult to discern and unknown unknowns reside (French 2013 <sup>[[#fn:r1041|1041]]</sup> ). It is in the complex but knowable space, the structured and moderately structured space, that decision-making under uncertainty occurs.

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==== 7.5.2.2 Decision-making tools ====

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Decisions can be made despite uncertainty ( ''medium confidence'' ), and a wide range of possible approaches are emerging to support decision-making under uncertainty (Jones et al. 2014 <sup>[[#fn:r1042|1042]]</sup> ), applied both to adaptation and mitigation decisions.

Traditional approaches for economic appraisal, including cost- benefit analysis and cost-effectiveness analysis referred to in Section 7.5.2.1 do not handle or address uncertainty well (Hallegatte 2009 <sup>[[#fn:r1043|1043]]</sup> ; Farber 2015 <sup>[[#fn:r1044|1044]]</sup> ) and favour decisions with short-term benefits (see Cross-Chapter Box 10 in this chapter). Alternative economic decision-making approaches aim to better incorporate uncertainty while delivering adaptation goals, by selecting projects that meet their purpose across a variety of plausible futures (Hallegatte et al. 2012 <sup>[[#fn:r1045|1045]]</sup> ) – so-called ‘robust’ decision-making approaches. These are designed to be less sensitive to uncertainty about the future (Lempert and Schlesinger 2000 <sup>[[#fn:r1046|1046]]</sup> ).

Much of the research for adaptation to climate change has focused around three main economic approaches: real options analysis, portfolio analysis, and robust decision-making. Real options analysis develops flexible strategies that can be adjusted when additional climate information becomes available. It is most appropriate for large irreversible investment decisions. Applications to climate adaptation are growing quickly, with most studies addressing flood risk and sea-level rise (Gersonius et al. 2013 <sup>[[#fn:r1047|1047]]</sup> ; Woodward et al. 2014 <sup>[[#fn:r1048|1048]]</sup> ; Dan 2016 <sup>[[#fn:r1049|1049]]</sup> ), but studies in land-use decisions are also emerging, including identifying the optimal time to switch land use in a changing climate (Sanderson et al. 2016 <sup>[[#fn:r1050|1050]]</sup> ) and water storage (Sturm et al. 2017 <sup>[[#fn:r1051|1051]]</sup> ; Kim et al. 2017 <sup>[[#fn:r1052|1052]]</sup> ). Portfolio analysis aims to reduce risk by diversification, by planting multiple species rather than only one, for example, in forestry (Knoke et al. 2017 <sup>[[#fn:r1053|1053]]</sup> ) or crops (Ben-Ari and Makowski 2016 <sup>[[#fn:r1054|1054]]</sup> ), or in multiple locations. There may be a trade- off between robustness to variability and optimality (Yousefpour and Hanewinkel 2016 <sup>[[#fn:r1055|1055]]</sup> ; Ben-Ari and Makowski 2016 <sup>[[#fn:r1056|1056]]</sup> ); but this type of analysis can help identify and quantify trade-offs. Robust decision-making identifies how different strategies perform under many climate outcomes, also potentially trading off optimality for resilience (Lempert 2013 <sup>[[#fn:r1057|1057]]</sup> ).

Multi-criteria decision-making continues to be an important tool in the land-use sector, with the capacity to simultaneously consider multiple goals across different domains (e.g., economic, environmental, social) (Bausch et al. 2014 <sup>[[#fn:r1058|1058]]</sup> ; Alrø et al. 2016 <sup>[[#fn:r1059|1059]]</sup> ), and so is useful as a mitigation as well as an adaptation tool. Lifecycle assessment can also be used to evaluate emissions across a system – for example, in livestock production (McClelland et al. 2018 <sup>[[#fn:r1060|1060]]</sup> ) – and to identify areas to prioritise for reductions. Bottom-up marginal abatement cost curves calculate the most cost effective cumulative potential for mitigation across different options (Eory et al. 2018 <sup>[[#fn:r1061|1061]]</sup> ).

In the climate adaptation literature, these tools may be used in adaptive management (Section 7.5.4), using a monitoring, research, evaluation and learning process (cycle) to improve future management strategies (Tompkins and Adger 2004 <sup>[[#fn:r1062|1062]]</sup> ). More recently these techniques have been advanced with iterative risk management (IPCC 2014a <sup>[[#fn:r1063|1063]]</sup> ) (Sections 7.4.1 and 7.4.7), adaptation pathways (Downing 2012 <sup>[[#fn:r1064|1064]]</sup> ), and dynamic adaptation pathways (Haasnoot et al. 2013 <sup>[[#fn:r1065|1065]]</sup> ) (Section 7.6.3). Decision-making tools can be selected and adapted to fit the specific land and climate problem and decision- making space. For instance, dynamic adaptation pathways processes (Haasnoot et al. 2013 <sup>[[#fn:r1066|1066]]</sup> ; Wise et al. 2014 <sup>[[#fn:r1067|1067]]</sup> ) identify and sequence potential actions based on alternative potential futures and are situated within the complex, unstructured space (see Figure 7.5). Decisions are made based on trigger points, linked to indicators and scenarios, or changing performance over time (Kwakkel et al. 2016 <sup>[[#fn:r1068|1068]]</sup> ). A key characteristic of these pathways is that, rather than making irreversible decisions now, decisions evolve over time, accounting for learning (Section 7.6.4), knowledge, and values. In New Zealand, combining dynamic adaptive pathways and a form of real options analysis with multiple-criteria decision analysis has enabled risk that changes over time to be included in the assessment of adaptation options through a participatory learning process (Lawrence et al. 2019 <sup>[[#fn:r1069|1069]]</sup> ).

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'''Figure 7.5'''

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'''Structural and uncertain decision making.'''

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[[File:c276f17cc153b8d8c7b308c663867d27 Figure-7-5.jpg]]

Structural and uncertain decision making.

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Scenario analysis is also situated within the complex, unstructured space (although, unlike adaptation pathways, it does not allow for changes in pathway over time) and is important for identifying technology and policy instruments to ensure spatial-temporal coherence of land-use allocation simulations with scenario storylines (Brown and Castellazzi 2014 <sup>[[#fn:r1070|1070]]</sup> ) and identifying technology and policy instruments for mitigation of land degradation (Fleskens et al. 2014 <sup>[[#fn:r1071|1071]]</sup> ).

While economics is usually based on the idea of a self-interested, rational agent, more recently insights from psychology are being used to understand and explain human behaviour in the field of behavioural economics (Shogren and Taylor 2008 <sup>[[#fn:r1072|1072]]</sup> ; Kesternich et al. 2017 <sup>[[#fn:r1073|1073]]</sup> ), illustrating how a range of cognitive factors and biases can affect choices (Valatin et al. 2016 <sup>[[#fn:r1074|1074]]</sup> ). These insights can be critical in supporting decision-making that will lead to more desirable outcomes relating to land and climate change. One example of this is ‘policy nudges’ (Thaler and Sunstein 2008 <sup>[[#fn:r1075|1075]]</sup> ) which can ‘shift choices in socially desirable directions’ (Valatin et al. 2016 <sup>[[#fn:r1076|1076]]</sup> ). Tools can include framing tools, binding pre-commitments, default settings, channel factors, or broad choice bracketing (Wilson et al. 2016 <sup>[[#fn:r1077|1077]]</sup> ). Although relatively few empirical examples exist in the land sector, there is evidence that nudges could be applied successfully, for example, in woodland creation (Valatin et al. 2016 <sup>[[#fn:r1078|1078]]</sup> ) and agri-environmental schemes (Kuhfuss et al. 2016 <sup>[[#fn:r1079|1079]]</sup> ) ( ''medium certainty, low evidence'' ). Consumers can be ‘nudged’ to consume less meat (Rozin et al. 2011 <sup>[[#fn:r1080|1080]]</sup> ) or to waste less food (Kallbekken and Sælen 2013 <sup>[[#fn:r1081|1081]]</sup> ).

Programmes supporting and facilitating desired practices can have success at changing behaviour, particularly if they are co-designed by the end-users (farmers, foresters, land users) ( ''medium evidence, high agreement'' ). Programmes that focus on demonstration or trials of different adaptation and mitigation measures, and facilitate interaction between farmers and industry specialists are perceived as being successful (Wreford et al. 2017 <sup>[[#fn:r1082|1082]]</sup> ; Hurlbert 2015b <sup>[[#fn:r1083|1083]]</sup> ) but systematic evaluations of their success at changing behaviour are limited (Knook et al. 2018 <sup>[[#fn:r1084|1084]]</sup> ).

Different approaches to decision-making are appropriate in different contexts. Dittrich et al. (2017) <sup>[[#fn:r1085|1085]]</sup> provide a guide to the appropriate application in different contexts for adaptation in the livestock sector in developed countries. While considerable advances have been made in theoretical approaches, a number of challenges arise when applying these in practice, and partly relate to the necessity of assigning probabilities to climate projects, and the complexity of the approaches being a prohibitive factor beyond academic exercises. Formalised expert judgement can improve how uncertainty is characterised (Kunreuther et al. 2014 <sup>[[#fn:r1086|1086]]</sup> ) and these methods have been improved utilising Bayesian belief networks to synthesise expert judgements and include fault trees and reliability block diagrams to overcome standard reliability techniques (Sigurdsson et al. 2001 <sup>[[#fn:r1087|1087]]</sup> ) as well as mechanisms incorporating transparency (Ashcroft et al. 2016 <sup>[[#fn:r1088|1088]]</sup> ).

It may also be beneficial to combine decision-making approaches with the precautionary principle, or the idea that lack of scientific certainty is not to postpone action when faced with serious threats or irreversible damage to the environment (Farber 2015 <sup>[[#fn:r1089|1089]]</sup> ). The precautionary principle requires cost-effective measures to address serious but uncertain risks (Farber 2015 <sup>[[#fn:r1090|1090]]</sup> ). It supports a rights-based policy instrument choice as consideration is whether actions or inactions harm others moving beyond traditional risk-management policy considerations that surround net benefits (Etkin et al. 2012 <sup>[[#fn:r1091|1091]]</sup> ). Farber, (2015) <sup>[[#fn:r1092|1092]]</sup> concludes that the principle has been successfully applied in relation to endangered species and situations where climate change is a serious enough problem to justify some response. There is ''medium confidence'' that combining the precautionary principle with integrated assessment models, risk management, and cost-benefit analysis in an integrated, holistic manner, would be a good combination of decision-making tools supporting sustainable development (Farber 2015 <sup>[[#fn:r1093|1093]]</sup> ; Etkin et al. 2012 <sup>[[#fn:r1094|1094]]</sup> ).

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==== 7.5.2.3 Cost and timing of action ====

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The Cross-Chapter Box 10 on Economic dimensions of climate change and land deals with the costs and timing of action. In terms of policies, not only is timing important, but the type of intervention itself can influence returns ( ''high evidence, high agreement'' ). Policy packages that make people more resilient – expanding financial inclusion, disaster risk and health insurance, social protection and adaptive safety nets, contingent finance and reserve funds, and universal access to early warning systems (Sections 7.4.1 and 7.6.3) – could save 100 billion USD a year, if implemented globally (Hallegatte et al. 2017 <sup>[[#fn:r1095|1095]]</sup> ). In Ethiopia, Kenya and Somalia, every 1 USD spent on safety-net/resilience programming results in net benefits of between 2.3 and 3.3 USD (Venton 2018 <sup>[[#fn:r1096|1096]]</sup> ). Investing in resilience-building activities, which increase household income by 365 to 450 USD per year in these countries, is more cost effective than providing ongoing humanitarian assistance.

There is a need to further examine returns on investment for land- based adaptation measures, both in the short and long term. Other outstanding questions include identifying specific triggers for early response. Food insecurity, for example, can occur due to a mixture of market and environmental factors (changes in food prices, animal or crop prices, rainfall patterns) (Venton 2018 <sup>[[#fn:r1097|1097]]</sup> ). The efficacy of different triggers, intervention times and modes of funding are currently being evaluated (see, for example, forecast-based finance study; Alverson and Zommers 2018 <sup>[[#fn:r1098|1098]]</sup> ). To reduce losses and maximise returns on investment, this information can be used to develop: 1) coordinated, agreed plans for action; 2) a clear, evidence-based decision-making process, and; 3) financing models to ensure that the plans for early action can be implemented (Clarke and Dercon 2016a <sup>[[#fn:r1099|1099]]</sup> ).

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=== 7.5.3 Best practices of decision-making toward sustainable land management (SLM) ===

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Sustainable land management (SLM) is a strategy and also an outcome (Waas et al. 2014 <sup>[[#fn:r1100|1100]]</sup> ) and decision-making practices are fundamental in achieving it as an outcome ( ''medium evidence, medium agreement'' ). SLM decision-making is improved ( ''medium evidence and high agreement'' ) with ecological service mapping with three characteristics: robustness (robust modelling, measurement, and stakeholder-based methods for quantification of ES supply, demand and/or flow, as well as measures of uncertainty and heterogeneity across spatial and temporal scales and resolution); transparency (to contribute to clear information-sharing and the creation of linkages with decision support processes); and relevancy to stakeholders (people-centric in which stakeholders are engaged at different stages) (Willemen et al. 2015 <sup>[[#fn:r1101|1101]]</sup> ; Ashcroft et al. 2016 <sup>[[#fn:r1102|1102]]</sup> ). Practices that advance SLM include remediation practices, as well as critical interventions that are reshaping norms and standards, joint implementation, experimentation, and integration of rural actors’ agency in analysis and approaches in decision-making (Hou and Al-Tabbaa 2014 <sup>[[#fn:r1103|1103]]</sup> ). Best practices are identified in the literature after their implementation demonstrates effectiveness at improving water quality, the environment, or reducing pollution (Rudolph et al. 2015 <sup>[[#fn:r1104|1104]]</sup> ; Lam et al. 2011 <sup>[[#fn:r1105|1105]]</sup> ).

There is ''medium evidence'' and ''medium agreement'' about what factors consistently determine the adoption of agricultural best management practices (Herendeen and Glazier 2009 <sup>[[#fn:r1106|1106]]</sup> ) and these positively correlate to education levels, income, farm size, capital, diversity, access to information, and social networks. Attending workshops for information and trust in crop consultants are also important factors in adoption of best management practices (Ulrich-Schad et al. 2017 <sup>[[#fn:r1107|1107]]</sup> ; Baumgart-Getz et al. 2012 <sup>[[#fn:r1108|1108]]</sup> ). More research is needed on the sustained adoption of these factors over time (Prokopy et al. 2008 <sup>[[#fn:r1109|1109]]</sup> ).

There is ''medium evidence'' and ''high agreement'' that SLM practices and incentives require mainstreaming into relevant policy; appropriate market-based approaches, including payment for ES and public- private partnerships, need better integration into payment schemes (Tengberg et al. 2016 <sup>[[#fn:r1110|1110]]</sup> ). There is ''medium evidenc'' e and ''high agreement'' that many of the best SLM decisions are made with the participation of stakeholders and social learning (Section 7.6.4) (Stringer and Dougill 2013 <sup>[[#fn:r1111|1111]]</sup> ). As stakeholders may not be in agreement, either practices of mediating agreement, or modelling that depicts and mediates the effects of stakeholder perceptions in decision-making may be applicable (Hou 2016 <sup>[[#fn:r1112|1112]]</sup> ; Wiggering and Steinhardt 2015 <sup>[[#fn:r1113|1113]]</sup> ).

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=== 7.5.4 Adaptive management ===

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Adaptive management is an evolving approach to natural resource management founded on decision-making approaches in other fields (such as business, experimental science, and industrial ecology) (Allen et al. 2011 <sup>[[#fn:r1114|1114]]</sup> ; Williams 2011 <sup>[[#fn:r1115|1115]]</sup> ) and decision-making that overcomes management paralysis and mediates multiple stakeholder interests through use of simple steps. Adaptive governance considers a broader socio-ecological system that includes the social context that facilitates adaptive management (Chaffin et al. 2014 <sup>[[#fn:r1116|1116]]</sup> ). Adaptive management steps include evaluating a problem and integrating planning, analysis and management into a transparent process to build a road map focused on achieving fundamental objectives. Requirements of success are clearly articulated objectives, the explicit acknowledgment of uncertainty, and a transparent response to all stakeholder interests in the decision-making process (Allen et al. 2011 <sup>[[#fn:r1117|1117]]</sup> ). Adaptive management builds on this foundation by incorporating a formal iterative process, acknowledging uncertainty and achieving management objectives through a structured feedback process that includes stakeholder participation (Foxon et al. 2009 <sup>[[#fn:r1118|1118]]</sup> ) (Section 7.6.4). In the adaptive management process, the problem and desired goals are identified, evaluation criteria formulated, the system boundaries and context are ascertained, trade-offs evaluated, decisions are made regarding responses and policy instruments, which are implemented, and monitored, evaluated and adjusted (Allen et al. 2011 <sup>[[#fn:r1124|1124]]</sup> ). The implementation of policy strategies and monitoring of results occurs in a continuous management cycle of monitoring, assessment and revision (Hurlbert 2015b <sup>[[#fn:r1119|1119]]</sup> ; Newig et al. 2010 <sup>[[#fn:r1120|1120]]</sup> ; Pahl-Wostl et al. 2007 <sup>[[#fn:r1121|1121]]</sup> ), as illustrated in Figure 7.6.

<div id="section-7-5-4-adaptive-management-block-2"></div>

<span id="figure-7.6"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 7.6'''

<span id="adaptive-governance-management-and-comprehensive-iterative-risk-management.-source-adapted-from-ammann-2013-allen-et-al.-2011."></span>
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'''Adaptive governance, management and comprehensive iterative risk management. Source: Adapted from Ammann 2013; Allen et al. 2011.'''
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[[File:07e25a29d269b13178ded85b89d540e9 7-6.jpg]]

Adaptive governance, management and comprehensive iterative risk management. Source: Adapted from Ammann 2013; Allen et al. 2011.

<!-- END IMG -->
<div id="section-7-5-4-adaptive-management-block-3"></div>

A key focus on adaptive management is the identification and reduction of uncertainty (as described in Chapter 1, Section 1.2.2 and Cross-Chapter Box 1 on Scenarios) and partial controllability, whereby policies used to implement an action are only indirectly responsible (for example, setting a harvest rate) (Williams 2011 <sup>[[#fn:r1123|1123]]</sup> ). There is ''medium evidence'' and ''high agreement'' that adaptive management is an ideal method to resolve uncertainty when uncertainty and controllability (resources will respond to management) are both high (Allen et al. 2011 <sup>[[#fn:r1124|1124]]</sup> ). Where uncertainty is high, but controllability is low, developing and analysing scenarios may be more appropriate (Allen et al. 2011 <sup>[[#fn:r1125|1125]]</sup> ). Anticipatory governance has developed combining scenarios and forecasting in order to creatively design strategy to address ‘complex, fuzzy and wicked challenges’ (Ramos 2014 <sup>[[#fn:r1126|1126]]</sup> ; Quay 2010 <sup>[[#fn:r1127|1127]]</sup> ) (Section 7.5). Even where there is low controllability, such as in the case of climate change, adaptive management can help mitigate impacts, including changes in water availability and shifting distributions of plants and animals (Allen et al. 2011 <sup>[[#fn:r1128|1128]]</sup> ).

There is ''medium evidence'' and ''high agreement'' that adaptive management can help reduce anthropogenic impacts of changes of land and climate, including: species decline and habitat loss (participative identification, monitoring, and review of species at risk as well as decision-making surrounding protective measures) (Fontaine 2011 <sup>[[#fn:r1129|1129]]</sup> ; Smith 2011 <sup>[[#fn:r1130|1130]]</sup> ) including quantity and timing of harvest of animals (Johnson 2011a <sup>[[#fn:r1131|1131]]</sup> ), human participation in natural resource-based recreational activities, including selection fish harvest quotas and fishing seasons from year to year (Martin and Pope 2011 <sup>[[#fn:r1132|1132]]</sup> ), managing competing interests of land-use planners and conservationists in public lands (Moore et al. 2011 <sup>[[#fn:r1133|1133]]</sup> ), managing endangered species and minimising fire risk through land-cover management (Breininger et al. 2014 <sup>[[#fn:r1134|1134]]</sup> ), land-use change in hardwood forestry through mediation of hardwood plantation forestry companies and other stakeholders, including those interested in water, environment or farming (Leys and Vanclay 2011 <sup>[[#fn:r1135|1135]]</sup> ), and SLM protecting biodiversity, increasing carbon storage, and improving livelihoods (Cowie et al. 2011 <sup>[[#fn:r1136|1136]]</sup> ). There is ''medium evidence'' and ''medium agreement'' that, despite abundant literature and theoretical explanation, there has remained imperfect realisation of adaptive management because of several challenges: lack of clarity in definition and approach, few success stories on which to build an experiential base practitioner knowledge of adaptive management, paradigms surrounding management, policy and funding that favour reactive approaches instead of the proactive adaptive management approach, shifting objectives that do not allow for the application of the approach, and failure to acknowledge social uncertainty (Allen et al. 2011 <sup>[[#fn:r1137|1137]]</sup> ). Adaptive management includes participation (Section 7.6.4), the use of indicators (Section 7.5.5), in order to avoid maladaptation and trade-offs while maximising synergies (Section 7.5.6).

<span id="performance-indicators"></span>
=== 7.5.5 Performance indicators ===

<div id="section-7-5-5-performance-indicators-block-1"></div>

Measuring performance is important in adaptive management decision-making, policy instrument implementation and governance, and can help evaluate policy effectiveness ( ''medium evidence, high agreement'' ) (Wheaton and Kulshreshtha 2017 <sup>[[#fn:r1138|1138]]</sup> ; Bennett and Dearden 2014 <sup>[[#fn:r1139|1139]]</sup> ; Oliveira Júnior et al. 2016 <sup>[[#fn:r1140|1140]]</sup> ; Kaufmann 2009 <sup>[[#fn:r1141|1141]]</sup> ). Indicators can relate to specific policy problems (climate mitigation, land degradation), sectors (agriculture, transportation, etc.), and policy goals (SDGs, food security).

It is necessary to monitor and evaluate the effectiveness and efficiency of performing climate actions to ensure the long-term success of climate initiatives or plans. Measurable indicators are useful for climate policy development and decision-making processes since they can provide quantifiable information regarding the progress of climate actions. The Paris Agreement (UNFCCC 2015) focused on reporting the progress of implementing countries’ pledges – that is, NDCs and national adaptation needs in order to examine the aggregated results of mitigation actions that have already been implemented. For the case of measuring progress toward achieving LDN, it was suggested to use land-based indicators – that is, trends in land cover and land productivity or functioning of the land, and trends in carbon stock above and below ground (Cowie et al. 2018a <sup>[[#fn:r1142|1142]]</sup> ). There is ''medium evidence'' and ''high agreement'' that indicators for measuring biodiversity and ES in response to governance at local to international scales meet the criteria of parsimony and scale specificity, are linked to some broad social, scientific and political consensus on desirable states of ecosystems and biodiversity, and include normative aspects such as environmental justice or socially just conservation (Layke 2009 <sup>[[#fn:r1143|1143]]</sup> ; Van Oudenhoven et al. 2012 <sup>[[#fn:r1144|1144]]</sup> ; Turnhout et al. 2014 <sup>[[#fn:r1145|1145]]</sup> ; Häyhä and Franzese 2014 <sup>[[#fn:r1146|1146]]</sup> ; Guerry et al. 2015 <sup>[[#fn:r1147|1147]]</sup> ; Díaz et al. 2015 <sup>[[#fn:r1148|1148]]</sup> ).

Important in making choices of metrics and indicators is understanding that the science, linkages and dynamics in systems are complex, not amenable to be addressed by simple economic instruments, and are often unrelated to short-term management or governance scales (Naeem et al. 2015 <sup>[[#fn:r1149|1149]]</sup> ; Muradian and Rival 2012 <sup>[[#fn:r1150|1150]]</sup> ). Thus, ideally, stakeholders participate in the selection and use of indicators for biodiversity and ES and monitoring impacts of governance and management regimes on land–climate interfaces. The adoption of non-economic approaches that are part of the emerging concept of Nature’s Contributions to People (NCP) could potentially elicit support for conservation from diverse sections of civil society (Pascual et al. 2017 <sup>[[#fn:r1151|1151]]</sup> ).

Recent studies increasingly incorporate the role of stakeholders and decision-makers in the selection of indicators for land systems (Verburg et al. 2015 <sup>[[#fn:r1152|1152]]</sup> ) including sustainable agriculture (Kanter et al. 2016 <sup>[[#fn:r1153|1153]]</sup> ), bioenergy sustainability (Dale et al. 2015 <sup>[[#fn:r1154|1154]]</sup> ), desertification (Liniger et al. 2019 <sup>[[#fn:r1155|1155]]</sup> ), and vulnerability (Debortoli et al. 2018 <sup>[[#fn:r1156|1156]]</sup> ). Kanter et al. (2016) <sup>[[#fn:r1157|1157]]</sup> propose a four-step ‘cradle-to-grave’ approach for agriculture trade-off analysis, which involves co-evaluation of indicators and trade-offs with both stakeholders and decision-makers.

<span id="maximising-synergies-and-minimising-trade-offs"></span>
=== 7.5.6 Maximising synergies and minimising trade-offs ===

<div id="section-7-5-6-maximising-synergies-and-minimising-trade-offs-block-1"></div>

Synergies and trade-offs to address land and climate-related measures are identified and discussed in Chapter 6. Here we outline policies supporting Chapter 6 response options (see Table 7.5), and discuss synergies and trade-offs in policy choices and interactions among policies. Trade-offs will exist between broad policy approaches. For example, while legislative and regulatory approaches may be effective at achieving environmental goals, they may be costly and ideologically unattractive in some countries. Market-driven approaches such as carbon pricing are cost-effective ways to reduce emissions, but may not be favoured politically and economically (Section 7.4.4). Information provision involves little political risk or ideological constraints, but behavioural barriers may limit their effectiveness (Henstra 2016 <sup>[[#fn:r1158|1158]]</sup> ). This level of trade-off is often determined by the prevailing political system.

Synergies and trade-offs also result from interaction between policies (policy interplay; Urwin and Jordan 2008 <sup>[[#fn:r1159|1159]]</sup> ) at different levels of policy (vertical) and across different policies (horizontal) (Section 7.4.8). If policy mixes are designed appropriately, acknowledging and incorporating trade-offs and synergies, they are better placed to deliver an outcome such as transitioning to sustainability (Howlett and Rayner 2013 <sup>[[#fn:r1160|1160]]</sup> ; Huttunen et al. 2014 <sup>[[#fn:r1161|1161]]</sup> ) ( ''medium evidence'' and ''medium agreement'' ). However, there is ''limited evidence'' and ''medium agreement'' that evaluating policies for coherence in responding to climate change and its impacts is not occurring, and policies are instead reviewed in a fragmented manner (Hurlbert and Gupta 2016 <sup>[[#fn:r1162|1162]]</sup> ).

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<span id="table-7.5"></span>
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'''Table 7.5'''

<span id="selection-of-policiesprogrammesinstruments-that-support-response-options."></span>
'''Selection of policies/programmes/instruments that support response options.'''

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{| class="wikitable"
|-

Category

Integrated response option Policy instrument supporting response option

|-

Land management in agriculture

Increased food productivity

Investment in agricultural research for crop and livestock improvement, agricultural technology transfer, inland capture fisheries and aquaculture {7.4.7} agricultural policy reform and trade liberalisation

|-

Improved cropland, grazing, Environmental farm programmes/agri-environment schemes, water-efficiency requirements and water and livestock management transfer {3.7.5}, extension services

|-

Agroforestry Payment for ecosystem services (ES) {7.4.6}

|-

Agricultural diversification

Elimination of agriculture subsidies {5.7.1}, environmental farm programmes, agri-environmental payments {7.4.6}, rural development programmes

|-

Reduced grassland conversion to cropland Elimination of agriculture subsidies, remove insurance incentives, ecological restoration {7.4.6}

|-

Integrated water management Integrated governance {7.6.2}, multi-level instruments {7.4.1}

|-

Land management in forests

Forest management, reduced deforestation and degradation, reforestation and forest restoration, afforestation

REDD+, forest conservation regulations, payments for ES, recognition of forest rights and land tenure {7.4.6}, adaptive management of forests {7.5.4}, land-use moratoriums, reforestation programmes and investment {4.9.1}

|-

Land management of soils

Increased soil organic carbon content, reduced soil erosion, reduced soil salinisation, reduced soil compaction, biochar addition<br />
to soil

Land degradation neutrality (LDN) {7.4.5}, drought plans, flood plans, flood zone mapping {7.4.3}, technology transfer (7.4.4}, land-use zoning {7.4.6}, ecological service mapping and stakeholder-based quantification {7.5.3}, environmental farm programmes/agri-environment schemes, water-efficiency requirements and water transfer {3.7.5}

|-

Land management in all other ecosystems

Fire management Fire suppression, prescribed fire management, mechanical treatments {7.4.3}

|-

Reduced landslides and natural hazards Land-use zoning {7.4.6}

|-

Reduced pollution – acidification Environmental regulations, climate mitigation (carbon pricing) {7.4.4}

|-

Management of invasive species/ encroachment

Invasive species regulations, trade regulations {5.7.2, 7.4.6}

|-

Restoration and reduced conversion of coastal wetlands

Flood zone mapping {7.4.3}, land-use zoning {7.4.6}

|-

Restoration and reduced conversion of peatlands

Payment for ES {7.4.6; 7.5.3}, standards and certification programmes {7.4.6}, land-use moratoriums

|-

Biodiversity conservation Conservation regulations, protected areas policies

|-

Carbon dioxide removal (CDR) land management

Enhanced weathering of minerals No data

|-

Bioenergy and bioenergy with carbon capture and storage (BECCS)

Standards and certification for sustainability of biomass and land use {7.4.6}

|-

Demand management

Dietary change

Awareness campaigns/education, changing food choices through nudges, synergies with health insurance and policy {5.7.2}

|-

Reduced post-harvest losses<br />
Reduced food waste (consumer or retailer), material substitution

Agricultural business risk programmes {7.4.8}; regulations to reduce and taxes on food waste, improved shelf life, circularising the economy to produce substitute goods, carbon pricing, sugar/fat taxes {5.7.2}

|-

Supply management

Sustainable sourcing

Food labelling, innovation to switch to food with lower environmental footprint, public procurement policies {5.7.2}, standards and certification programmes {7.4.6}

|-

Management of supply chains

Liberalised international trade {5.7.2}, food purchasing and storage policies of governments, standards and certification programmes {7.4.6}, regulations on speculation in food systems

|-

Enhanced urban food systems

Buy local policies; land-use zoning to encourage urban agriculture, nature-based solutions and green infrastructure in cities; incentives for technologies like vertical farming

|-

Improved food processing and retailing, improved energy use in food systems

Agriculture emission trading {7.4.4}; investment in R&amp;D for new technologies; certification

|-

Risk management

Management of urban sprawl Land-use zoning {7.4.6}

|-

Livelihood diversification Climate-smart agriculture policies, adaptation policies, extension services {7.5.6}

|-

Disaster risk management Disaster risk reduction {7.5.4; 7.4.3}, adaptation planning

|-

Risk-sharing instruments

Insurance, iterative risk management, CAT bonds, risk layering, contingency funds {7.4.3}, agriculture business risk portfolios {7.4.8}

|}
<!-- END TABLE -->

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<span id="ccb9-climate-and-land-pathways"></span>