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

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

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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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