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

=== 6.4.4 Opportunities for implementing integrated response options ===

<div id="section-6-4-4-1-where-can-the-response-options-be-applied"></div>

<span id="where-can-the-response-options-be-applied"></span>
==== 6.4.4.1 Where can the response options be applied? ====

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As shown in Section 6.1.3, a large part of the land area is exposed to overlapping land challenges, especially in villages, croplands and rangelands. The deployment of land management responses may vary with local exposure to land challenges. For instance, with croplands exposed to a combination of land degradation, food insecurity and climate change adaptation challenges, maximising the co-benefits of land management responses would require selecting responses having only co-benefits for these three overlapping challenges, as well as for climate change mitigation, which is a global challenge. Based on these criteria, Figure 6.6 shows the potential deployment area of land management responses across land-use types (or anthromes).

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'''Figure 6.6'''

<span id="potential-deployment-area-of-land-management-responses-see-table-6.1-across-land-use-types-or-anthromes-see-section-6.3-when-selecting-responses-having-only-co-benefits-for-local-challenges-and-for-climate-change-mitigation-and-no-large-adverse-side-effects-on-global-food-security.-see-figure-6.2-for-the-criteria-used-to-map-challenges-considered-desertification-land"></span>
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'''Potential deployment area of land management responses (see Table 6.1) across land-use types (or anthromes, see Section 6.3), when selecting responses having only co-benefits for local challenges and for climate change mitigation and no large adverse side effects on global food security. See Figure 6.2 for the criteria used to map challenges considered (desertification, land […]'''
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[[File:4fd4631001809290324c74e55f09660a Figure-6.6-1024x599.jpg]]

Potential deployment area of land management responses (see Table 6.1) across land-use types (or anthromes, see Section 6.3), when selecting responses having only co-benefits for local challenges and for climate change mitigation and no large adverse side effects on global food security. See Figure 6.2 for the criteria used to map challenges considered (desertification, land degradation, climate change adaptation, chronic undernourishment, biodiversity, groundwater stress and water quality). No response option was identified for barren lands.

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Land management responses having co-benefits across the range of challenges, including climate change mitigation, could be deployed between one land-use type (coastal wetlands, peatlands, forest management and restoration, reforestation) and five (increased soil organic carbon) or six (fire management) land-use types (Figure 6.6). Fire management and increased soil organic carbon have a large potential since they could be deployed with mostly co-benefits and few adverse effects over 76% and 58% of the ice-free land area. In contrast, other responses have a limited area-based potential due to biophysical constraints (e.g., limited extent of organic soils and of coastal wetlands for conservation and restoration responses), or due to the occurrence of adverse effects. Despite strong co-benefits for climate change mitigation, the deployment of bioenergy and BECCS would have co-benefits on only 9% of the ice-free land area (Figure 6.6), given adverse effects of this response option for food security, land degradation, climate change adaptation and desertification (Tables 6.62–6.69).

Without including the global climate change mitigation challenge, there are up to five overlapping challenges on lands that are not barren (Figure 6.7A, calculated from the overlay of individual challenges shown in Figure 6.2) and up to nine land management response options having only co-benefits for these challenges and for climate change mitigation (Figure 6.7B). Across countries, the mean number of land management response options with mostly co-benefits declines ( ''p'' &lt;0.001, Spearman rank order correlation) with the mean number of land challenges. Hence, the higher the number of land challenges per country, the fewer the land management response options having only co-benefits for the challenges encountered.

Enabling conditions (see Section 6.1.2.2) for the implementation of land management responses partly depend on human development (economics, health and education) as estimated by a country scale composite index, the Human Development Index (HDI) (UNDP 2018 <sup>[[#fn:r1045|1045]]</sup> ) (Figure 6.7C). Across countries, HDI is negatively correlated ( ''p'' &lt;0.001, Spearman rank order correlation) with the mean number of land challenges. Therefore, on a global average, the higher the number of local challenges faced, the fewer the land management responses having only co-benefits, and the lower the human development (Figure 6.7) that could favour the implementation of these responses.

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<span id="figure-6.7"></span>
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'''Figure 6.7'''

<span id="global-distributions-of-a-number-of-overlapping-land-challenges-desertification-land-degradation-climate-change-adaptation-chronic-undernourishment-biodiversity-groundwater-stress-and-water-quality-figure-6.2-b-number-of-land-management-responses-providing-medium-to-large-co-benefits-and-no-adverse-side-effects-see-figure-6.6-across-challenges-c-human-development-index-hdi-by-country.-the-hdi-undp-2018"></span>
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'''Global distributions of: (a) number of overlapping land challenges (desertification, land degradation, climate change adaptation, chronic undernourishment, biodiversity, groundwater stress and water quality (Figure 6.2); (b) number of land management responses providing medium-to-large co-benefits and no adverse side effects (see Figure 6.6) across challenges; (c) Human Development Index (HDI) by country. The HDI (UNDP 2018) […]'''
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[[File:6a2f22452096530aa3c0d13de8dbfd63 Figure-6.7-512x1024.jpg]]

Global distributions of: (a) number of overlapping land challenges (desertification, land degradation, climate change adaptation, chronic undernourishment, biodiversity, groundwater stress and water quality (Figure 6.2); (b) number of land management responses providing medium-to-large co-benefits and no adverse side effects (see Figure 6.6) across challenges; (c) Human Development Index (HDI) by country. The HDI (UNDP 2018 <sup>[[#fn:r1267|1267]]</sup> ) is a country-based composite statistical index measuring average achievement in three basic dimensions of human development: a long and healthy life (estimated from life expectancy at birth), knowledge (estimated from years of schooling), and a decent standard of living (estimated from gross national income per capita).

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<div id="section-6-4-4-2-interlinkages-and-response-options-in-future-scenarios"></div>

<span id="interlinkages-and-response-options-in-future-scenarios"></span>
==== 6.4.4.2 Interlinkages and response options in future scenarios ====

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This section assesses more than 80 articles quantifying the effect of various response options in the future, covering a variety of response options and land-based challenges. These studies cover spatial scales ranging from global (Popp et al. 2017 <sup>[[#fn:r1046|1046]]</sup> ; Fujimori et al. 2019 <sup>[[#fn:r1047|1047]]</sup> ) to regional (Calvin et al. 2016a <sup>[[#fn:r1048|1048]]</sup> ; Frank et al. 2015 <sup>[[#fn:r1049|1049]]</sup> ) to country level (Gao and Bryan 2017; Pedercini et al. 2018 <sup>[[#fn:r1050|1050]]</sup> ). This section focuses on models that can quantify interlinkages between response options, including agricultural economic models, land system models, and Integrated Assessment Models (IAMs). The IAM and non-IAM literature, however, is also categorised separately to elucidate what is and is not included in global mitigation scenarios, like those included in the SR15. Results from bottom-up studies and models (e.g., Griscom et al. 2017 <sup>[[#fn:r1274|1274]]</sup> ) are assessed in Sections 6.2–6.3.

''Response options in future scenarios''

More than half of the 40 land-based response options discussed in this chapter are represented in global IAMs models used to develop and analyse future scenarios, either implicitly or explicitly (Table 6.76). For example, all IAMs include improved cropland management, either explicitly through technologies that improve nitrogen use efficiency (Humpenöder et al. 2018 <sup>[[#fn:r1051|1051]]</sup> ) or implicitly through marginal abatement cost curves that link reductions in nitrous oxide emissions from crop production to carbon prices (most other models).

However, the literature discussing the effect of these response options on land-based challenges is more limited (Table 6.76). There are 57 studies (43 IAM studies) that articulate the effect of response options on mitigation, with most including bioenergy and BECCS or a combination of reduced deforestation, reforestation, and afforestation; 37 studies (21 IAM studies) discuss the implications of response options on food security, usually using food price as a metric. While a small number of non-IAM studies examine the effects of response options on desertification (three studies) and land degradation (five studies), no IAM studies were identified. However, some studies quantify these challenges indirectly using IAMs, either via climate outputs from the representative concentration pathways (RCPs) (Huang et al. 2016 <sup>[[#fn:r1052|1052]]</sup> ) or by linking IAMs to other land and ecosystem models (Ten Brink et al. 2018 <sup>[[#fn:r1275|1275]]</sup> ; UNCCD 2017 <sup>[[#fn:r1053|1053]]</sup> ).

For many of the scenarios in the literature, land-based response options are included as part of a suite of mitigation options (Popp et al. 2017 <sup>[[#fn:r1054|1054]]</sup> ; Van Vuuren et al. 2015). As a result, it is difficult to isolate the effect of an individual option on land-related challenges. A few studies focus on specific response options (Calvin et al. 2014 <sup>[[#fn:r1055|1055]]</sup> ; Popp et al. 2014 <sup>[[#fn:r1056|1056]]</sup> ; Kreidenweis et al. 2016 <sup>[[#fn:r1057|1057]]</sup> ; Humpenöder et al. 2018 <sup>[[#fn:r1058|1058]]</sup> ), quantifying the effect of including an individual option on a variety of sustainability targets.

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<span id="table-6.76"></span>
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'''Table 6.76'''

<span id="number-of-iam-and-non-iam-studies-including-specific-response-options-rows-and-quantifying-particular-land-challenges-columns."></span>
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'''Number of IAM and non-IAM studies including specific response options (rows) and quantifying particular land challenges (columns).'''

Thethird column shows how many IAM models include the individual response option. The remaining columns show challenges related to climate change (C), mitigation (M), adaptation (A), desertification (D), land degradation (L), food security (F), and biodiversity/ecosystem services/sustainable development (B). Additionally, counts of total (left value) and IAM-only (right value) studies are included. Some IAMs include agricultural economic models, which can also be run separately; these models are not counted as IAM literature when used on their own. Studies using a combination of IAMs and non-IAMs are included in the total only. A complete list of studies is included in the Appendix.
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''Interactions and interlinkages between response options''

The effect of response options on desertification, land degradation, food security, biodiversity, and other SDGs depends strongly on which options are included, and the extent to which they are deployed. For example, Sections 2.6 and 6.3.6, and Cross-Chapter Box 7 note that bioenergy and BECCS has a large mitigation potential, but could potentially have adverse side effects for land degradation, food security, and other SDGs. Global modelling studies demonstrate that these effects are dependent on scale. Increased use of bioenergy can result in increased mitigation (Figure 6.8, panel A) and reduced climate change, but can also lead to increased energy cropland expansion (Figure 6.8, panel B), and increased competition for land, resulting in increased food prices (Figure 6.8, panel C). However, the exact relationship between bioenergy deployment and each sustainability target depends on a number of other factors, including the feedstock used, the underlying socio-economic scenario, assumptions about technology and resource base, the inclusion of other response options, and the specific model used (Calvin et al. 2014 <sup>[[#fn:r1059|1059]]</sup> ; Clarke et al. 2014 <sup>[[#fn:r1060|1060]]</sup> ; Popp et al. 2014, 2017 <sup>[[#fn:r1061|1061]]</sup> ; Kriegler et al. 2014 <sup>[[#fn:r1062|1062]]</sup> ).

The previous sections have examined the effects of individual land-response options on multiple challenges. A number of studies using global modelling and analyses have examined interlinkages and interaction effects among land response options by incrementally adding or isolating the effects of individual options. Most of these studies focus on interactions with bioenergy and BECCS (Table 6.77). Adding response options that require land (e.g., reforestation, afforestation, reduced deforestation, avoided grassland conversion, or biodiversity conservation) results in increased food prices (Calvin et al. 2014 <sup>[[#fn:r1063|1063]]</sup> ; Humpenöder et al. 2014 <sup>[[#fn:r1064|1064]]</sup> ; Obersteiner et al. 2016 <sup>[[#fn:r1065|1065]]</sup> ; Reilly et al. 2012 <sup>[[#fn:r1066|1066]]</sup> ) and potentially increased temperature through biophysical climate effects (Jones et al. 2013 <sup>[[#fn:r1067|1067]]</sup> ). However, this combination can result in reduced water consumption (Hejazi et al. 2014b <sup>[[#fn:r1068|1068]]</sup> ), reduced cropland expansion (Calvin et al. 2014 <sup>[[#fn:r1069|1069]]</sup> ; Humpenöder et al. 2018 <sup>[[#fn:r1070|1070]]</sup> ), increased forest cover (Calvin et al. 2014 <sup>[[#fn:r1071|1071]]</sup> ; Humpenöder et al. 2018 <sup>[[#fn:r1072|1072]]</sup> ; Wise et al. 2009 <sup>[[#fn:r1073|1073]]</sup> ) and reduced biodiversity loss (Pereira et al. 2010 <sup>[[#fn:r1074|1074]]</sup> ), compared to scenarios with bioenergy and BECCS alone. While these options increase total mitigation, they reduce mitigation from bioenergy and BECCS as they compete for the same land (Wu et al. 2019 <sup>[[#fn:r1075|1075]]</sup> ; Baker et al. 2019 <sup>[[#fn:r1076|1076]]</sup> ; Calvin et al. 2014 <sup>[[#fn:r1077|1077]]</sup> ; Humpenöder et al. 2014 <sup>[[#fn:r1078|1078]]</sup> ).

The inclusion of land-sparing options (e.g., dietary change, increased food productivity, reduced food waste, management of supply chains) in addition to bioenergy and BECCS results in reduced food prices, reduced agricultural land expansion, reduced deforestation, reduced mitigation costs, reduced water use, and reduced biodiversity loss (Bertram et al. 2018 <sup>[[#fn:r1276|1276]]</sup> ; Wu et al. 2019 <sup>[[#fn:r1079|1079]]</sup> ; Obersteiner et al. 2016 <sup>[[#fn:r1080|1080]]</sup> ; Stehfest et al. 2009 <sup>[[#fn:r1081|1081]]</sup> ; Van Vuuren et al. 2018). These options can increase bioenergy potential, resulting in increased mitigation than from bioenergy and BECCS alone (Wu et al. 2019 <sup>[[#fn:r1082|1082]]</sup> ; Stehfest et al. 2009 <sup>[[#fn:r1083|1083]]</sup> ; Favero and Massetti 2014 <sup>[[#fn:r1084|1084]]</sup> ).

Other combinations of land response options create synergies, alleviating land pressures. The inclusion of increased food productivity and dietary change can increase mitigation, reduce cropland use, reduce water consumption, reduce fertiliser application, and reduce biodiversity loss (Springmann et al. 2018 <sup>[[#fn:r1085|1085]]</sup> ; Obersteiner et al. 2016 <sup>[[#fn:r1086|1086]]</sup> ). Similarly, improved livestock management, combined with increased food productivity, can reduce agricultural land expansion (Weindl et al. 2017 <sup>[[#fn:r1087|1087]]</sup> ). Reducing disturbances (e.g., fire management) in combination with afforestation can increase the terrestrial carbon sink, resulting in increased mitigation potential and reduced mitigation cost (Le Page et al. 2013 <sup>[[#fn:r1088|1088]]</sup> ).

Studies including multiple land response options often find that the combined mitigation potential is not equal to the sum of individual mitigation potential as these options often share the same land. For example, including both afforestation and bioenergy and BECCS results in a cumulative reduction in GHG emissions of 1200 GtCO 2 between 2005 and 2100, which is much lower than the sum of the contributions of bioenergy (800 GtCO 2 ) and afforestation (900 GtCO 2 ) individually (Humpenöder et al. 2014 <sup>[[#fn:r1089|1089]]</sup> ). More specifically, Baker et al. (2019) <sup>[[#fn:r1090|1090]]</sup> find that woody bioenergy and afforestation are complementary in the near term, but become substitutes in the long term, as they begin to compete for the same land. Similarly, the combined effect of increased food productivity, dietary change and reduced waste on GHG emissions is less than the sum of the individual effects (Springmann et al. 2018 <sup>[[#fn:r1091|1091]]</sup> ).

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<span id="table-6.77"></span>
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'''Table 6.77'''

<span id="interlinkages-between-bioenergy-and-beccs-and-other-response-options."></span>
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'''Interlinkages between bioenergy and BECCS and other response options.'''

Table indicates the combined effects of multiple land-response options on climate change (C), mitigation (M), adaptation (A), desertification (D), land degradation (L), food security (F), and biodiversity/ecosystem services/sustainable development (O). Each cell indicates the implications of adding the option specified in the row in addition to bioenergy and BECCS. Blue colours indicate positive interactions (e.g., including the option in the second column increases mitigation, reduces cropland area, or reduces food prices relative to bioenergy and BECCS alone). Yellow indicates negative interactions; grey indicates mixed interactions (some positive, some negative). Note that only response option combinations found in the assessed literature are included in the interest of space.
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[[File:4d6d1c2debaf8ba92a8ab32e2f83baca table-6.77a.png]] [[File:74513e4d1bd4d21adea7a5f1b3a1872f table-6.77b.png]]

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Land-related response options can also interact with response options in other sectors. For example, limiting deployment of a mitigation response option will either result in increased climate change or additional mitigation in other sectors. A number of studies have examined limiting bioenergy and BECCS. Some such studies show increased emissions (Reilly et al. 2012 <sup>[[#fn:r1097|1097]]</sup> ). Other studies meet the same climate goal, but reduce emissions elsewhere ''via'' reduced energy demand (Grubler et al. 2018 <sup>[[#fn:r1098|1098]]</sup> ; Van Vuuren et al. 2018 <sup>[[#fn:r1277|1277]]</sup> ), increased fossil carbon capture and storage (CCS), nuclear energy, energy efficiency and/or renewable energy (Van Vuuren et al. 2018 <sup>[[#fn:r1278|1278]]</sup> ; Rose et al. 2014 <sup>[[#fn:r1099|1099]]</sup> ; Calvin et al. 2014 <sup>[[#fn:r1100|1100]]</sup> ; Van Vuuren et al. 2017b <sup>[[#fn:r1279|1279]]</sup> ), dietary change (Van Vuuren et al. 2018 <sup>[[#fn:r1280|1280]]</sup> ), reduced non-CO 2 emissions (Van Vuuren et al. 2018 <sup>[[#fn:r1281|1281]]</sup> ), or lower population (Van Vuuren et al. 2018 <sup>[[#fn:r1282|1282]]</sup> ). The co-benefits and adverse side effects of non-land mitigation options are discussed in SR15, Chapter 5. Limitations on bioenergy and BECCS can result in increases in the cost of mitigation (Kriegler et al. 2014 <sup>[[#fn:r1101|1101]]</sup> ; Edmonds et al. 2013 <sup>[[#fn:r1102|1102]]</sup> ). Studies have also examined limiting CDR, including reforestation, afforestation, and bioenergy and BECCS (Kriegler et al. 2018a <sup>[[#fn:r1282|1282]]</sup> ,b <sup>[[#fn:r1283|1283]]</sup> ). These studies find that limiting CDR can increase mitigation costs, increase food prices, and even preclude limiting warming to less than 1.5°C above pre-industrial levels (Kriegler et al. 2018a,b; Muratori et al. 2016 <sup>[[#fn:r1103|1103]]</sup> ).

In some cases, the land challenges themselves may interact with land-response options. For example, climate change could affect the production of bioenergy and BECCS. A few studies examine these effects, quantifying differences in bioenergy production (Calvin et al. 2013 <sup>[[#fn:r1104|1104]]</sup> ; Kyle et al. 2014 <sup>[[#fn:r1105|1105]]</sup> ) or carbon price (Calvin et al. 2013 <sup>[[#fn:r1106|1106]]</sup> ) as a result of climate change. Kyle et al. (2014) <sup>[[#fn:r1107|1107]]</sup> find increase in bioenergy production due to increases in bioenergy yields, while Calvin et al. (2013) <sup>[[#fn:r1108|1108]]</sup> find declines in bioenergy production and increases in carbon price due to the negative effects of climate on crop yield.

''Gaps in the literature''

Not all of the response options discussed in this chapter are included in the assessed literature, and many response options are excluded from the IAM models. The included options (e.g., bioenergy and BECCS; reforestation) are some of the largest in terms of mitigation potential (see Section 6.3). However, some of the options excluded also have large mitigation potential. For example, biochar, agroforestry, restoration/avoided conversion of coastal wetlands, and restoration/ avoided conversion of peatland all have mitigation potential of about 1 GtCO 2 yr –1 (Griscom et al. 2017 <sup>[[#fn:r1109|1109]]</sup> ). Additionally, quantifications of and response options targeting land degradation and desertification are largely excluded from the modelled studies, with a few notable exceptions (Wolff et al. 2018 <sup>[[#fn:r1110|1110]]</sup> ; Gao and Bryan 2017 <sup>[[#fn:r1111|1111]]</sup> ; Ten Brink et al. 2018 <sup>[[#fn:r1112|1112]]</sup> ; UNCCD 2017 <sup>[[#fn:r1113|1113]]</sup> ). Finally, while a large number of papers have examined interactions between bioenergy and BECCS and other response options, the literature examining other combinations of response options is more limited.

<div id="section-6-4-4-3-resolving-challenges-in-response-option-implementation"></div>

<span id="resolving-challenges-in-response-option-implementation"></span>
==== 6.4.4.3 Resolving challenges in response option implementation ====

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The 40 response options assessed in this chapter face a variety of barriers to implementation that require action across multiple actors to overcome (Section 6.4.1). Studies have noted that, while adoption of response options by individuals may depend on individual assets and motivation, larger structural and institutional factors are almost always equally important if not more so (Adimassu et al. 2016 <sup>[[#fn:r1114|1114]]</sup> ; Djenontin et al. 2018 <sup>[[#fn:r1115|1115]]</sup> ), though harder to capture in research variables (Schwilch et al. 2014 <sup>[[#fn:r1116|1116]]</sup> ). These institutional and governance factors can create an enabling environment for sustainable land management (SLM) practices, or challenges to their adoption (Adimassu et al. 2013 <sup>[[#fn:r1117|1117]]</sup> ). Governance factors include the institutions that manage rules and policies, the social norms and collective actions of participants (including civil society actors and the private sector), and the interactions between them (Ostrom 1990 <sup>[[#fn:r1118|1118]]</sup> ; Huntjens et al. 2012 <sup>[[#fn:r1119|1119]]</sup> ; Davies 2016 <sup>[[#fn:r1120|1120]]</sup> ). Many of Ostrom’s design principles for successful governance can be applied to response options for SLM; these principles are: (i) clearly defined boundaries, (ii) understanding of both benefits and costs, (iii) collective choice arrangements, (iv) monitoring, (v) graduated sanctions, (vi) conflict-resolution mechanisms, (vii) recognition of rights, and (viii) nested (multi-scale) approaches. Unfortunately, studies of many natural resources and land management policy systems – in particular, in developing countries – often show the opposite: a lack of flexibility, strong hierarchical tendencies, and a lack of local participation in institutional frameworks (Ampaire et al. 2017 <sup>[[#fn:r1121|1121]]</sup> ). Analysis of government effectiveness (GE) – defined as quality of public services, policy formulation and implementation, civil service and the degree of its independence from political pressures, as well as credibility of the government’s commitment to its policies (Kaufmann et al. 2010 <sup>[[#fn:r1122|1122]]</sup> ) – has been shown to play a key role in land management. GE mediates land-user actions on land management and investment, and government policies and laws can help land users adopt sustainable land management practices (Nkonya et al. 2016 <sup>[[#fn:r1123|1123]]</sup> ) (Figure 6.9).

It is simply not a matter of putting the ‘right’ institutions or policies in place, however, as governance can be undermined by inattention to power dynamics (Fabinyi et al. 2014 <sup>[[#fn:r1124|1124]]</sup> ). Power shapes how actors gain access and control over resources, and negotiate, transform and adopt certain response options or not. These variable dynamics of power between different levels and stakeholders have an impact on the ability to implement different response options. The inability of many national governments to address social exclusion in general will have an effect on the implementation of many response options. Further, response options themselves can become avenues for actors to exert power claims over others (Nightingale 2017 <sup>[[#fn:r1125|1125]]</sup> ). For example, there have been many concerns that reduced deforestation and forest degradation projects run the risk of reversing trends towards decentralisation in forest management and creating new power disparities between the state and local actors (Phelps et al. 2010 <sup>[[#fn:r1126|1126]]</sup> ). Below we assess how two important factors – the involvement of stakeholders, and the coordination of action across scales – will help in moving from response options to policy implementation, a theme Chapter 7 takes up in further detail.

''Involvement of stakeholders''

A wide range of stakeholders are necessary for successful land, agricultural and environmental policy, and implementing response options requires that a range of actors, including businesses, consumers, land managers, indigenous peoples and local communities, scientists, and policymakers work together for success. Diverse stakeholders have a particularly important role to play in defining problems, assessing knowledge and proposing solutions (Stokes et al. 2006 <sup>[[#fn:r1127|1127]]</sup> ; Phillipson et al. 2012 <sup>[[#fn:r1128|1128]]</sup> ). Lack of connection between science knowledge and on-the-ground practice has hampered adoption of many response options in the past; simply presenting ‘scientifically’ derived response options is not enough (Marques et al. 2016 <sup>[[#fn:r1129|1129]]</sup> ). For example, the importance of recognising and incorporating local knowledge and indigenous knowledge is increasingly emphasised in successful policy implementation (see Cross-Chapter Box 13 in Chapter 7), as local practices of water management, soil fertility management, improved grazing, restoration and sustainable management of forests are often well-aligned with response options assessed by scientists (Marques et al. 2016 <sup>[[#fn:r1130|1130]]</sup> ).

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'''Figure 6.9'''

<span id="relationship-between-changes-in-government-effectiveness-ge-and-changes-in-land-management.-notes-ndvi-change-in-normalized-difference-vegetation-index-baseline-year-2001-endline-year-2010.-source-of-ndvi-data-modis-goveff-change-in-ge-baseline-year-2001-endline-year-2010.-world-bank-nkonya-et-al.-2016."></span>
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'''Relationship between changes in government effectiveness (GE) and changes in land management. Notes: ∆NDVI = Change in Normalized Difference Vegetation Index (baseline year 2001, Endline year 2010). Source of NDVI data: MODIS ∆GovEff = Change in GE (baseline year 2001, endline year 2010). (World Bank; Nkonya et al. 2016).'''
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[[File:71fa138c8d6e6564c85c3e6e1e1fc811 Figure-6.9-1024x819.jpg]]

Relationship between changes in government effectiveness (GE) and changes in land management. Notes: ∆NDVI = Change in Normalized Difference Vegetation Index (baseline year 2001, Endline year 2010). Source of NDVI data: MODIS ∆GovEff = Change in GE (baseline year 2001, endline year 2010). (World Bank; Nkonya et al. 2016 <sup>[[#fn:r1284|1284]]</sup> ).

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Stakeholder engagement is an important approach for successful environmental and climate policy and planning. Tools such as stakeholder mapping, in which affected and interested parties are identified and described in terms of their interrelationships and current or future objectives and aspirations, and scenario-based stakeholder engagement, which combines stakeholder analysis with climate scenarios, are increasingly being applied to facilitate better planning outcomes (Tompkins et al. 2008 <sup>[[#fn:r1131|1131]]</sup> ; Pomeroy and Douvere 2008 <sup>[[#fn:r1132|1132]]</sup> ; Star et al. 2016 <sup>[[#fn:r1133|1133]]</sup> ). Facilitated dialogues early in design processes have shown good success in bringing multiple and sometimes conflicting stakeholders to the table to discuss synergies and trade-offs around policy implementation (Gopnik et al. 2012 <sup>[[#fn:r1134|1134]]</sup> ). Knowledge exchange, social learning, and other concepts are also increasingly being incorporated into understanding how to facilitate sustainable land management (Djenontin et al. 2018 <sup>[[#fn:r1135|1135]]</sup> ), as evidence suggests that negotiating the complexity of socio-ecological systems (SES) requires flexible learning arrangements, in particular for multiple stakeholders (Gerlak and Heikkila 2011 <sup>[[#fn:r1136|1136]]</sup> ; Armitage et al. 2018 <sup>[[#fn:r1137|1137]]</sup> ; Heikkila and Gerlak 2018 <sup>[[#fn:r1138|1138]]</sup> ). Social learning has been defined as ‘a change in understanding and skills that becomes situated in groups of actors/ communities of practice through social interactions,’ (Albert et al. 2012 <sup>[[#fn:r1139|1139]]</sup> ), and social learning is often linked with attempts to increase levels of participation in decision-making, from consultation to more serious community control (Collins and Ison 2009 <sup>[[#fn:r1140|1140]]</sup> ; McCrum et al. 2009 <sup>[[#fn:r1141|1141]]</sup> ). Learning also facilitates responses to emerging problems and helps actors in SESs grapple with complexity. One outcome of learning can be adaptive risk management (ARM), in which ‘one takes action based on available information, monitors what happens, learns from the experience and adjusts future actions based on what has been learnt’ (Bidwell et al. 2013 <sup>[[#fn:r1142|1142]]</sup> ). Suggestions to facilitate social learning, ARM, and decision-making include extending science-policy networks and using local bridging organisations, such as extension services, for knowledge co-production (Bidwell et al. 2013 <sup>[[#fn:r1143|1143]]</sup> ; Böcher and Krott 2014 <sup>[[#fn:r1144|1144]]</sup> ; Howarth and Monasterolo 2017 <sup>[[#fn:r1145|1145]]</sup> ) (see further discussion in Chapter 7, Section 7.5).

Ensuring that women are included as key stakeholders in response option implementation is also important, as gender norms and roles affect vulnerability and access to resources, and gender inequality limits the possible range of responses for adoption by women (Lambrou and Piana 2006 <sup>[[#fn:r1146|1146]]</sup> ). For example, environmental change may increase women’s workload as their access to natural resources may decline, or they may have to take up low-wage labour if agriculture becomes unsuitable in their local areas under climate change (Nelson et al. 2002 <sup>[[#fn:r1147|1147]]</sup> ). Every response option considered in this chapter potentially has a gender dimension to it that needs to be taken into consideration (Tables 6.73–6.75 note how response options intersect with SDG 5 Gender Equality); for example, to address food security through sustainable intensification will clearly have to address female farmers in Africa (Kondylis et al. 2016 <sup>[[#fn:r1148|1148]]</sup> ; Garcia and Wanner 2017 <sup>[[#fn:r1149|1149]]</sup> ) (for further information, see Cross-Chapter Box 11 in Chapter 7).

''Challenges of coordination''

Coordinated action to implement the response options will be required across a range of actors, including business, consumers, land managers, indigenous peoples and local communities and policymakers to create enabling conditions. Conjoining response options to maximise social, climatic and environmental benefits will require framing of such actions as strong pathways to sustainable development (Ayers and Dodman 2010 <sup>[[#fn:r1150|1150]]</sup> ). As the chapter has pointed out, there are many potential options for synergies, especially among several response options that might be applied together and in coordination with one another (such as dietary change and improved land management measures). This coordination will help ensure that synergies are met and trade-offs minimised, but this will require deliberate coordination across multiple scales, actors and sectors. For example, there are a variety of response options available at different scales that could form portfolios of measures applied by different stakeholders from farm to international scales. Agricultural diversification and use of local seeds by smallholders can be particularly useful poverty eradication and biodiversity conservation measures, but are only successful when higher scales, such as national and international markets and supply chains, also value these goods in trade regimes, and consumers see the benefits of purchasing these goods. However, the land and food sectors face particular challenges of institutional fragmentation, and often suffer from a lack of engagement between stakeholders at different scales (Biermann et al. 2009 <sup>[[#fn:r1151|1151]]</sup> ; Deininger et al. 2014 <sup>[[#fn:r1152|1152]]</sup> ) (see Chapter 7, Section 7.6.2).

Many of the response options listed in this chapter could be potentially implemented as ‘community-based’ actions, including community-based reforestation, community-based insurance, or community-based disaster risk management. Grounding response options in community approaches aims to identify, assist and implement activities ‘that strengthen the capacity of local people to adapt to living in a riskier and less predictable climate’ (Ayers and Forsyth 2009 <sup>[[#fn:r1153|1153]]</sup> ). Research shows that people willingly come together to provide mutual aid and protection against risk, to manage natural resources, and to work cooperatively to find solutions to environmental provisioning problems. Some activities that fall under this type of collective action include the creation of institutions or rules, working cooperatively to manage a resource by restricting some activities and encouraging others, sharing information to improve public goods, or mobilising resources (such as capital) to fix a collective problem (Ostrom 2000 <sup>[[#fn:r1154|1154]]</sup> ; Poteete and Ostrom 2004 <sup>[[#fn:r1155|1155]]</sup> ), or engagement in participatory land-use planning (Bourgoin 2012 <sup>[[#fn:r1156|1156]]</sup> ; Evers and Hofmeister 2011 <sup>[[#fn:r1157|1157]]</sup> ). These participatory processes ‘are likely to lead to more beneficial environmental outcomes through better informed, sustainable decisions, and win-win solutions regarding economic and conservation objectives’ (Vente et al. 2016 <sup>[[#fn:r1158|1158]]</sup> ), and evaluations of community-based response options have been generally positive (Karim and Thiel 2017 <sup>[[#fn:r1159|1159]]</sup> ; Tompkins and Adger 2004 <sup>[[#fn:r1160|1160]]</sup> ).

Agrawal (2001) <sup>[[#fn:r1161|1161]]</sup> has identified more than 30 different indicators that have been important in understanding who undertakes collective action for the environment, including: the size of the group undertaking action; the type and distribution of the benefits from the action; the heterogeneity of the group; the dependence of the group on these benefits; the presence of leadership; presence of social capital and trust; and autonomy and independence to make and enforce rules. Alternatively, when households expect the government to undertake response actions, they have less incentive to join in collective action, as the state role has ‘crowded out’ local cooperation (Adger 2009 <sup>[[#fn:r1162|1162]]</sup> ). High levels of social trust and capital can increase willingness of farmers to engage in response options, such as improved soil management or carbon forestry (Stringer et al. 2012 <sup>[[#fn:r1163|1163]]</sup> ; Lee 2017 <sup>[[#fn:r1164|1164]]</sup> ), and social capital helps with connectivity across levels of SESs (Brondizio et al. 2009 <sup>[[#fn:r1165|1165]]</sup> ). Dietz et al. (2013) <sup>[[#fn:r1166|1166]]</sup> lay out important policy directions for more successful facilitation of collective action across scales and stakeholders. These include: providing information; dealing with conflict; inducing rule compliance; providing physical, technical or institutional infrastructure; and being prepared for change. The adoption of participatory protocols and structured processes to select response options together with stakeholders will likely lead to greater success in coordination and participation (Bautista et al. 2017 <sup>[[#fn:r1167|1167]]</sup> ; Franks 2010 <sup>[[#fn:r1168|1168]]</sup> ; Schwilch et al. 2012a <sup>[[#fn:r1169|1169]]</sup> ).

However, wider adoption of community-based approaches is potentially hampered by several factors, including the fact that most are small-scale (Forsyth 2013 <sup>[[#fn:r1170|1170]]</sup> ; Ensor et al. 2014 <sup>[[#fn:r1171|1171]]</sup> ) and it is often unclear how to assess criteria of success (Forsyth 2013 <sup>[[#fn:r1172|1172]]</sup> ). Others also caution that community-based approaches often are not able to adequately address the key drivers of vulnerability such as inequality and uneven power relations (Nagoda and Nightingale 2017 <sup>[[#fn:r1173|1173]]</sup> ).

''Moving from response options to policies''

Chapter 7 discusses in further depth the risks and challenges involved in formulating policy responses that meet the demands for sustainable land management and development outcomes, such as food security, community adaptation and poverty alleviation. Table 7.1 in Chapter 7 maps how specific response options might be turned into policies; for example, to implement a response option aimed at agricultural diversification, a range of policies from elimination of agricultural subsidies (which might favour single crops) to environmental farm programmes and agro-environmental payments (to encourage alternative crops). Oftentimes, any particular response option might have a variety of potential policy pathways that might address different scales or stakeholders or take on different aspects of coordination and integration (Section 7.6.1). Given the unique challenges of decision-making under uncertainty in future climate scenarios, Chapter 7 particularly discusses the need for flexible, iterative, and adaptive processes to turn response options into policy frameworks.

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