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

== CCB9 Climate and land pathways ==

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Katherine Calvin (The United States of America), Edouard Davin (France/Switzerland), Margot Hurlbert (Canada), Jagdish Krishnaswamy (India), Alexander Popp (Germany), Prajal Pradhan (Nepal/Germany)

Future development of socio-economic factors and policies influence the evolution of the land–climate system, among others, in terms of the land used for agriculture and forestry. Climate mitigation policies can also have a major impact on land use, especially in scenarios consistent with the climate targets of the Paris Agreement. This includes the use of bio-energy or CDR, such as bioenergy with carbon capture and storage (BECCS) and afforestation. Land-based mitigation options have implications for GHG fluxes, desertification, land degradation, food insecurity, ecosystem services and other aspects of sustainable development.

'''Shared Socio-economic Pathways'''

The five pathways are based on the Shared Socio-economic Pathways (SSPs) (O’Neill et al. 2014 <sup>[[#fn:r1174|1174]]</sup> ; Popp et al. 2017 <sup>[[#fn:r1175|1175]]</sup> ; Riahi et al. 2017 <sup>[[#fn:r1176|1176]]</sup> ; Rogelj et al. 2018b <sup>[[#fn:r1177|1177]]</sup> ) (Cross-Chapter Box 1 in Chapter 1). SSP1 is a scenario with a broad focus on sustainability, including human development, technological development, nature conservation, globalised economy, economic convergence and early international cooperation (including moderate levels of trade). The scenario includes a peak and decline in population, relatively high agricultural yields and a move towards food produced in low-GHG emission systems (Van Vuuren et al. 2017b). Dietary change and reductions in food waste reduce agricultural demands, and effective land-use regulation enables reforestation and/or afforestation. SSP2 is a scenario in which production and consumption patterns, as well as technological development, follows historical patterns (Fricko et al. 2017 <sup>[[#fn:r1178|1178]]</sup> ). Land-based CDR is achieved through bioenergy and BECCS and, to a lesser degree, by afforestation and reforestation. SSP3 is a scenario with slow rates of technological change and limited land-use regulation. Agricultural demands are high due to material-intensive consumption and production, and barriers to trade lead to reduced flows for agricultural goods. In SSP3, forest mitigation activities and abatement of agricultural GHG emissions are limited due to major implementation barriers such as low institutional capacities in developing countries and delays as a consequence of low international cooperation (Fujimori et al. 2017 <sup>[[#fn:r1179|1179]]</sup> ). Emissions reductions are achieved primarily through the energy sector, including the use of bioenergy and BECCS.

'''Policies in the Pathways'''

SSPs are complemented by a set of shared policy assumptions (Kriegler et al. 2014 <sup>[[#fn:r1180|1180]]</sup> ), indicating the types of policies that may be implemented in each future world. Integrated Assessment Models (IAMs) represent the effect of these policies on the economy, energy system, land use and climate with the caveat that they are assumed to be effective or, in some cases, the policy goals (e.g., dietary change) are imposed rather than explicitly modelled. In the real world, there are various barriers that can make policy implementation more difficult (Section 7.4.9). These barriers will be generally higher in SSP3 than SSP1.

'''SSP1:''' A number of policies could support SSP1 in future, including: effective carbon pricing, emission trading schemes (including net CO <sub>2</sub> emissions from agriculture), carbon taxes, regulations limiting GHG emissions and air pollution, forest conservation (mix of land sharing and land sparing) through participation, incentives for ecosystem services and secure tenure, and protecting the environment, microfinance, crop and livelihood insurance, agriculture extension services, agricultural production subsidies, low export tax and import tariff rates on agricultural goods, dietary awareness campaigns, taxes on and regulations to reduce food waste, improved shelf life, sugar/fat taxes, and instruments supporting sustainable land management, including payment for ecosystem services, land-use zoning, REDD+, standards and certification for sustainable biomass production practices, legal reforms on land ownership and access, legal aid, legal education, including reframing these policies as entitlements for women and small agricultural producers (rather than sustainability) (Van Vuuren et al. 2017b; O’Neill et al. 2017 <sup>[[#fn:r1181|1181]]</sup> ) (Section 7.4).

'''SSP2:''' The same policies that support SSP1 could support SSP2 but may be less effective and only moderately successful. Policies may be challenged by adaptation limits (Section 7.4.9), inconsistency in formal and informal institutions in decision-making (Section 7.5.1) or result in maladaptation (Section 7.4.7). Moderately successful sustainable land management policies result in some land competition. Land degradation neutrality is moderately successful. Successful policies include those supporting bioenergy and BECCS (Rao et al. 2017b <sup>[[#fn:r1182|1182]]</sup> ; Fricko et al. 2017 <sup>[[#fn:r1183|1183]]</sup> ; Riahi et al. 2017 <sup>[[#fn:r1184|1184]]</sup> ) (Section 7.4.6).

'''SSP3:''' Policies that exist in SSP1 may or may not exist in SSP3, and are ineffective (O’Neill et al. 2014 <sup>[[#fn:r1185|1185]]</sup> ). There are challenges to implementing these policies, as in SSP2. In addition, ineffective sustainable land management policies result in competition for land between agriculture and mitigation. Land degradation neutrality is not achieved (Riahi et al. 2017 <sup>[[#fn:r1186|1186]]</sup> ). Successful policies include those supporting bioenergy and BECCS (Kriegler et al. 2017 <sup>[[#fn:r1187|1187]]</sup> ; Fujimori et al. 2017 <sup>[[#fn:r1188|1188]]</sup> ; Rao et al. 2017b <sup>[[#fn:r1189|1189]]</sup> ) (Section 7.4.6). Demand-side food policies are absent and supply-side policies predominate. There is no success in advancing land ownership and access policies for agricultural producer livelihood (Section 7.6.5).

'''Land-use and land-cover change'''

In SSP1, sustainability in land management, agricultural intensification, production and consumption patterns result in reduced need for agricultural land, despite increases in per capita food consumption. This land can instead be used for reforestation, afforestation and bioenergy. In contrast, SSP3 has high population and strongly declining rates of crop yield growth over time, resulting in increased agricultural land area. SSP2 falls somewhere in between, with societal as well as technological development following historical patterns. Increased demand for land mitigation options such as bioenergy, reduced deforestation or afforestation decreases availability of agricultural land for food, feed and fibre. In the climate policy scenarios consistent with the Paris Agreement, bioenergy/BECCS and reforestation/afforestation play an important role in SSP1 and SSP2. The use of these options, and the impact on land, is larger in scenarios that limit radiative forcing in 2100 to 1.9 W m <sup>–2</sup> than in the 4.5 W m <sup>–2</sup> scenarios. In SSP3, the expansion of land for agricultural production implies that the use of land-related mitigation options is very limited, and the scenario is characterised by continued deforestation.

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'''Cross-Chapter Box 9 Figure 1'''

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'''Changes in agriculture land (left), bioenergy cropland (middle) and forest (right) under three different SSPs (colours) and two different warming levels (rows). Agricultural land includes both pasture and cropland. Colours indicate SSPs, with SSP1 shown in green, SSP2 in yellow, and SSP3 in red. For each pathway, the shaded areas show the range across all […]'''
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[[File:c84f26a66e05ce728644c2d4c23402b5 Cross-Chapter-Box-9-Figure-1-1024x607.jpg]]

Changes in agriculture land (left), bioenergy cropland (middle) and forest (right) under three different SSPs (colours) and two different warming levels (rows). Agricultural land includes both pasture and cropland. Colours indicate SSPs, with SSP1 shown in green, SSP2 in yellow, and SSP3 in red. For each pathway, the shaded areas show the range across all IAMs; the line indicates the median across models. There is no SSP3 in the top row, as 1.9 W m <sup>–2</sup> is infeasible in this world. Data is from an update of the Integrated Assessment Modelling Consortium (IAMC) Scenario Explorer developed for the SR15 (Huppmann et al. 2018 <sup>[[#fn:r1285|1285]]</sup> ; Rogelj et al. 2018a <sup>[[#fn:r1286|1286]]</sup> ).

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'''Implications for mitigation and other land challenges'''

The combination of baseline emissions development, technology options, and policy support makes it much easier to reach the climate targets in the SSP1 scenario than in the SSP3 scenario. As a result, carbon prices are much higher in SSP3 than in SSP1. In fact, the 1.9 W m <sup>–2</sup> target was found to be infeasible in the SSP3 world (Table 1 in Cross-Chapter Box 9). Energy system CO <sub>2</sub> emissions reductions are greater in SSP3 than in SSP1 to compensate for the higher land-based CO <sub>2</sub> emissions.

Accounting for mitigation and socio-economics alone, food prices (an indicator of food insecurity) are higher in SSP3 than in SSP1 and higher in the 1.9 W m <sup>–2</sup> target than in the 4.5 W m <sup>–2</sup> target (Table 1 in Cross-Chapter Box 9). Forest cover is higher in SSP1 than SSP3 and higher in the 1.9 W m <sup>–2</sup> target than in the 4.5 W m <sup>–2</sup> target. Water withdrawals and water scarcity are, in general, higher in SSP3 than SSP1 (Hanasaki et al. 2013 <sup>[[#fn:r1192|1192]]</sup> ; Graham et al. 2018 <sup>[[#fn:r1193|1193]]</sup> ) and higher in scenarios with more bioenergy (Hejazi et al. 2014b <sup>[[#fn:r1194|1194]]</sup> ); however, these indicators have not been quantified for the specific SSP-representative concentration pathways (RCP) combinations discussed here.

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'''CCB9, Table 1'''

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'''Quantitative indicators for the pathways.'''

Each cell shows the mean, minimum, and maximum value across IAM models for each indicator and each pathway in 2050 and 2100. All IAMs that provided results for a particular pathway are included here. Note that these indicators exclude the implications of climate change. Data is from an update of the IAMC Scenario Explorer developed for the SR15 (Huppmann et al. 2018 <sup>[[#fn:r1195|1195]]</sup> ; Rogelj et al. 2018b <sup>[[#fn:r1196|1196]]</sup> ).

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Climate change results in higher impacts and risks in the 4.5 W m <sup>–2</sup> world than in the 1.9 W m <sup>–2</sup> world for a given SSP and these risks are exacerbated in SSP3 compared to SSP1 and SSP2 due to the population’s higher exposure and vulnerability. For example, the risk of fire is higher in warmer worlds; in the 4.5 W m <sup>–2</sup> world, the population living in fire prone regions is higher in SSP3 (646 million) than in SSP2 (560 million) (Knorr et al. 2016 <sup>[[#fn:r1197|1197]]</sup> ). Global exposure to multi-sector risk quadruples between 1.5°C <sup>[[#fn:|]]</sup>   and 3°C and is a factor of six higher in SSP3-3°C than in SSP1–1.5°C (Byers et al. 2018 <sup>[[#fn:r1198|1198]]</sup> ). Future risks resulting from desertification, land degradation and food insecurity are lower in SSP1 compared to SSP3 at the same level of warming. For example, the transition moderate-to-high risk of food insecurity occurs between 1.3 and 1.7°C for SSP3, but not until 2.5 to 3.5°C in SSP1 (Section 7.2).

'''Summary'''

Future pathways for climate and land use include portfolios of response and policy options. Depending on the response options included, policy portfolios implemented, and other underlying socio-economic drivers, these pathways result in different land-use consequences and their contribution to climate change mitigation. Agricultural area declines by more than 5 Mkm <sup>2</sup> in one SSP but increases by as much as 5 Mkm <sup>2</sup> in another. The amount of energy cropland ranges from nearly zero to 11 Mkm <sup>2</sup> , depending on the SSP and the warming target. Forest area declines in SSP3 but increases substantially in SSP1. Subsequently, these pathways have different implications for risks related to desertification, land degradation, food insecurity, and terrestrial GHG fluxes, as well as ecosystem services, biodiversity, and other aspects of sustainable development.

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==== 7.5.6.1 Trade-offs and synergies between ecosystem services (ES) ====

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Unplanned or unintentional trade-offs and synergies between policy driven response options related to ecosystem services (ES) can happen over space (e.g., upstream-downstream, integrated watershed management, Section 3.7.5.2) or intensify over time (reduced water in future dry-season due to growing tree plantations, Section 6.4.1). Trade-offs can occur between two or more ES (land for climate mitigation vs food; Sections 6.2, 6.3, 6.4, Cross-Chapter Box 8 in Chapter 6; Cross-Chapter Box 9 in Chapters 6 and 7), and between scales, such as forest biomass-based livelihoods versus global ES carbon storage (Chhatre and Agrawal 2009 <sup>[[#fn:r1171|1171]]</sup> ) ( ''medium evidence, medium agreement'' ). Trade-offs can be reversible or irreversible (Rodríguez et al. 2006 <sup>[[#fn:r1172|1172]]</sup> ; Elmqvist et al. 2013 <sup>[[#fn:r1173|1173]]</sup> ) (for example, a soil carbon sink is reversible) (Section 6.4.1.1).

Although there is ''robust evidence'' and ''high agreement'' that ES are important for human well-being, the relationship between poverty alleviation and ES can be surprisingly complex, understudied and dependent on the political economic context; current evidence is largely about provisioning services and often ignores multiple dimensions of poverty (Suich et al. 2015 <sup>[[#fn:r1174|1174]]</sup> ; Vira et al. 2012 <sup>[[#fn:r1175|1175]]</sup> ). Spatially explicit mapping and quantification of stakeholder choices in relation to distribution of various ES can help enhance synergies and reduce trade-offs (Turkelboom et al. 2018 <sup>[[#fn:r1176|1176]]</sup> ; Locatelli et al. 2014 <sup>[[#fn:r1177|1177]]</sup> ) (Section 7.5.5).

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==== 7.5.6.2 Sustainable Development Goals (SDGs): Synergies and trade-offs ====

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The Sustainable Development Goals (SDGs) are an international persuasive policy instrument that apply to all countries, and measure sustainable and socially just development of human societies at all scales of governance (Griggs et al. 2013 <sup>[[#fn:r1178|1178]]</sup> ). The UN SDGs rest on the premise that the goals are mutually reinforcing and there are inherent linkages, synergies and trade-offs (to a greater or lesser extent) between and within the sub-goals (Fuso Nerini et al. 2018 <sup>[[#fn:r1179|1179]]</sup> ; Nilsson et al. 2016b <sup>[[#fn:r1180|1180]]</sup> ; Le Blanc 2015 <sup>[[#fn:r1181|1181]]</sup> ). There is high confidence that opportunities, trade-offs and co-benefits are context – and region-specific and depend on a variety of political, national and socio-economic factors (Nilsson et al. 2016b <sup>[[#fn:r1182|1182]]</sup> ) depending on perceived importance by decision-makers and policymakers (Figure 7.7 and Table 7.6). Aggregation of targets and indicators at the national level can mask severe biophysical and socio-economic trade-offs at local and regional scales (Wada et al. 2016 <sup>[[#fn:r1183|1183]]</sup> ).

There is ''medium evidence'' and ''high agreement'' that SDGs must not be pursued independently, but in a manner that recognises trade-offs and synergies with each other, consistent with a goal of ‘policy coherence’. Policy coherence also refers to spatial trade-offs and geopolitical implications within and between regions and countries implementing SDGs. For instance, supply-side food security initiatives of land-based agriculture are impacting on marine fisheries globally through creation of dead-zones due to agricultural run-off (Diaz and Rosenberg 2008 <sup>[[#fn:r1184|1184]]</sup> ).

SDGs 6 (clean water and sanitation), 7 (affordable and clean energy) and 9 (industry, innovation and infrastructure) are important SDGs related to mitigation with adaptation co-benefits, but they have local trade-offs with biodiversity and competing uses of land and rivers (see Case study: Green energy: Biodiversity conservation vs global environment targets) ( ''medium evidence, high agreement'' ) (Bogardi et al. 2012 <sup>[[#fn:r1185|1185]]</sup> ; Nilsson and Berggren 2000 <sup>[[#fn:r1186|1186]]</sup> ; Hoeinghaus et al. 2009 <sup>[[#fn:r1187|1187]]</sup> ; Winemiller et al. 2016 <sup>[[#fn:r1188|1188]]</sup> ). This has occurred despite emerging knowledge about the role that rivers and riverine ecosystems play in human development and in generating global, regional and local ES (Nilsson and Berggren 2000 <sup>[[#fn:r1189|1189]]</sup> ; Hoeinghaus et al. 2009 <sup>[[#fn:r1190|1190]]</sup> ). The transformation of river ecosystems for irrigation, hydropower and water requirements of societies worldwide is the biggest threat to freshwater and estuarine biodiversity and ecosystems services (Nilsson and Berggren 2000 <sup>[[#fn:r1191|1191]]</sup> ; Vörösmarty et al. 2010 <sup>[[#fn:r1192|1192]]</sup> ). These projects address important energy and water-related demands, but their economic benefits are often overestimated in relation to trade-offs with respect to food (river capture fisheries), biodiversity and downstream ES (Winemiller et al. 2016 <sup>[[#fn:r1193|1193]]</sup> ). Some trade-offs and synergies related to SDG7 impact on aspirations of greater welfare and well-being, as well as physical and social infrastructure for sustainable development (Fuso Nerini et al. 2018 <sup>[[#fn:r1194|1194]]</sup> ) (Section 7.5.6.1, where trade-offs exist between climate mitigation and food).

There are also spatial trade-offs related to large river diversion projects and export of ‘virtual water’ through water-intensive crops produced in one region and exported to another, with implications for food security, water security and downstream ES of the exporting region (Hanasaki et al. 2010 <sup>[[#fn:r1195|1195]]</sup> ; Verma et al. 2009 <sup>[[#fn:r1196|1196]]</sup> ). Synergies include cropping adaptations that increase food system production and eliminate hunger (SDG2) (Rockström et al. 2017 <sup>[[#fn:r1197|1197]]</sup> ; Lipper et al. 2014a <sup>[[#fn:r1198|1198]]</sup> ; Neufeldt et al. 2013 <sup>[[#fn:r1199|1199]]</sup> ). Well-adapted agricultural systems are shown to have synergies, positive returns on investment and contribute to safe drinking water, health, biodiversity and equity goals (DeClerck 2016 <sup>[[#fn:r1200|1200]]</sup> ). Assessing the water footprint of different sectors at the river basin scale can provide insights for interventions and decision-making (Zeng et al. 2012 <sup>[[#fn:r1201|1201]]</sup> ).

Sometimes the trade-offs in SDGs can arise in the articulation and nested hierarchy of 17 goals and the targets under them. In terms of aquatic life and ecosystems, there is an explicit SDG for sustainable management of marine life (SDG 14, Life below water). There is no equivalent goal exclusively for freshwater ecosystems, but hidden under SDG 6 (Clean water and sanitation) out of six listed targets, the sixth target is about protecting and restoring water-related ecosystems, which suggests a lower order of global priority compared to being listed as a goal in itself (e.g., SDG 14).

There is ''limited evidence'' and ''limited agreement'' that binary evaluations of individual SDGs and synergies and trade-offs that categorise interactions as either ‘beneficial’ or ‘adverse’ may be subjective and challenged further by the fact that feedbacks can often not be assigned as unambiguously positive or negative (Blanc et al. 2017 <sup>[[#fn:r1202|1202]]</sup> ). The IPCC Special Report on Global Warming of 1.5°C (SR15) notes: ‘A reductive focus on specific SDGs in isolation may undermine the long-term achievement of sustainable climate change mitigation’ (Holden et al. 2017 <sup>[[#fn:r1203|1203]]</sup> ). Greater work is needed to tease out these relationships; studies have started that include quantitative modelling (see Karnib 2017 <sup>[[#fn:r1204|1204]]</sup> ) and nuanced scoring scales (ICSU 2017 <sup>[[#fn:r1205|1205]]</sup> ) of these relationships.

A nexus approach is increasingly being adopted to explore synergies and trade-offs between a select subset of goals and targets (such as the interaction between water, energy and food – see for example, Yumkella and Yillia 2015 <sup>[[#fn:r1206|1206]]</sup> ; Conway et al. 2015 <sup>[[#fn:r1207|1207]]</sup> ; Ringler et al. 2015 <sup>[[#fn:r1208|1208]]</sup> ). However, even this approach ignores systemic properties and interactions across the system as a whole (Weitz et al. 2017a <sup>[[#fn:r1209|1209]]</sup> ). Pursuit of certain targets in one area can generate rippling effects across the system, and these in turn can have secondary impacts on yet other targets. Weitz et al. (2017a) <sup>[[#fn:r1210|1210]]</sup> found that SDG target 13.2 (climate change policy/planning) is influenced by actions in six other targets. SDG 13.1 (climate change adaption) and also SDG 2.4 (food production) receive the most positive influence from progression in other targets.

There is ''medium evidence'' and ''high agreement'' that, to be effective, truly sustainable, and to reduce or mitigate emerging risks, SDGs need knowledge dissemination and policy initiatives that recognise and assimilate concepts of co-production of ES in socio-ecological systems, cross-scale linkages, uncertainty, spatial and temporal trade-offs between SDGs and ES that acknowledge biophysical, social and political constraints and understand how social change occurs at various scales (Rodríguez et al. 2006 <sup>[[#fn:r1211|1211]]</sup> ; Norström et al. 2014 <sup>[[#fn:r1212|1212]]</sup> ; Palomo et al. 2016 <sup>[[#fn:r1213|1213]]</sup> ). Several methods and tools are proposed in literature to address and understand SDG interactions. Nilsson et al. (2016a) <sup>[[#fn:r1214|1214]]</sup> suggest going beyond a simplistic framing of synergies and trade-offs to understanding the various relationship dimensions, and proposing a seven-point scale to understand these interactions.

This approach, and the identification of clusters of synergy, can help indicate that government ministries work together or establish collaborations to reach their specific goals. Finally, context-specific analysis is needed. Synergies and trade-offs will depend on the natural resource base (such as land or water availability), governance arrangements, available technologies, and political ideas in a given location (Nilsson et al. 2016b <sup>[[#fn:r1215|1215]]</sup> ). Figure 7.7 shows that, at the global scale, there is less uncertainty in the evidence surrounding SDGs, but also less agreement on norms, priorities and values for SDG implementation. Although there is some agreement on the regional and local scale surrounding SDGs, there is higher certainty on the science surrounding ES.

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'''Figure 7.7'''

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'''Risks at various scales, levels of uncertainty and agreement in relation to trade-offs among SDGs and other goals.'''
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[[File:6e0a5ce500b52cb10d27750d8ced14c4 Figure-7.7.jpg]]

Risks at various scales, levels of uncertainty and agreement in relation to trade-offs among SDGs and other goals.

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==== 7.5.6.3 Forests and agriculture ====

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Retaining existing forests, restoring degraded forest and afforestation are response options for climate change mitigation with adaptation benefits (Section 6.4.1). Policies at various levels of governance that foster ownership, autonomy, and provide incentives for forest cover can reduce trade-offs between carbon sinks in forests and local livelihoods (especially when the size of forest commons is sufficiently large) (Chhatre and Agrawal 2009 <sup>[[#fn:r1216|1216]]</sup> ; Locatelli et al. 2014 <sup>[[#fn:r1217|1217]]</sup> ) (see Table 7.6 this section; Case study: Forest conservation instruments: REDD+ in the Amazon and India, Section 7.4.6).

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'''Table 7.6'''

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'''Risks at various scales, levels of uncertainty and agreement in relation to trade-offs among SDGs and other goals.'''
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[[File:d582a1c839e5a149234e9d02492e5bec table-7.6.png]]

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Forest restoration for mitigation through carbon sequestration and other ES or co-benefits (e.g., hydrologic, non-timber forest products, timber and tourism) can be passive or active (although both types largely exclude livestock). Passive restoration is more economically viable in relation to restoration costs as well as co-benefits in other ES, calculated on a net present value basis, especially under flexible carbon credits (Cantarello et al. 2010 <sup>[[#fn:r1218|1218]]</sup> ). Restoration can be more cost effective with positive socio-economic and biodiversity conservation outcomes, if costly and simplistic planting schemes are avoided (Menz et al. 2013 <sup>[[#fn:r1219|1219]]</sup> ). Passive restoration takes longer to demonstrate co-benefits and net economic gains. It can be confused with land abandonment in some regions and countries, and therefore secure land-tenure at individual or community scales is important for its success (Zahawi et al. 2014 <sup>[[#fn:r1220|1220]]</sup> ). Potential approaches include improved markets and payment schemes for ES (Tengberg et al. 2016 <sup>[[#fn:r1221|1221]]</sup> ) (Section 7.4.6).

Proper targeting of incentive schemes and reducing poverty through access to ES requires knowledge regarding the distribution of beneficiaries, information about those whose livelihoods are likely to be impacted, and in what manner (Nayak et al. 2014 <sup>[[#fn:r1222|1222]]</sup> ; Loaiza et al. 2015 <sup>[[#fn:r1223|1223]]</sup> ; Vira et al. 2012 <sup>[[#fn:r1224|1224]]</sup> ). Institutional arrangements to govern ecosystems are believed to synergistically influence maintenance of carbon storage and forest-based livelihoods, especially when they incorporate local knowledge and decentralised decision- making (Chhatre and Agrawal 2009 <sup>[[#fn:r1225|1225]]</sup> ). Earning carbon credits from reforestation with native trees involves the higher cost of certification and validation processes, increasing the temptation to choose fast- growing (perhaps non-native) species with consequences for native biodiversity. Strategies and policies that aggregate landowners or forest dwellers are needed to reduce the cost to individuals and payment for ecosystem services (PES) schemes can generate synergies (Bommarco et al. 2013 <sup>[[#fn:r1226|1226]]</sup> ; Chhatre and Agrawal 2009 <sup>[[#fn:r1227|1227]]</sup> ). Bundling several PES schemes that address more than one ES can increase income generated by forest restoration (Brancalion et al. 2012 <sup>[[#fn:r1228|1228]]</sup> ).

In the forestry sector, there is evidence that adaptation and mitigation can be fostered in concert. A recent assessment of the California Forestry Offset Project shows that, by compensating individuals and industries for forest conservation, such programmes can deliver mitigation and sustainability co-benefits (Anderson et al. 2017 <sup>[[#fn:r1229|1229]]</sup> ). Adaptive forest management focusing on reintroducing native tree species can provide both mitigation and adaptation benefit by reducing fire risk and increasing carbon storage (Astrup et al. 2018 <sup>[[#fn:r1230|1230]]</sup> ).

In the agricultural sector, there has been little published empirical work on interactions between adaptation and mitigation policies. Smith and Oleson (2010) <sup>[[#fn:r1231|1231]]</sup> describe potential relationships, focusing particularly on the arable sector, predominantly on mitigation efforts, and more on measures than policies. The considerable potential of the agro-forestry sector for synergies and contributing to increasing resilience of tropical farming systems is discussed in Verchot et al. (2007) <sup>[[#fn:r1232|1232]]</sup> with examples from Africa.

Climate-smart agriculture (CSA) has emerged in recent years as an approach to integrate food security and climate challenges. The three pillars of CSA are to: (1) adapt and build resilience to climate change; (2) reduce GHG emissions, and; (3) sustainably increase agricultural productivity, ultimately delivering ‘triple-wins’ (Lipper et al. 2014c). While the idea is conceptually appealing, a range of criticisms, contradictions and challenges exist in using CSA as the route to resilience in global agriculture, notably around the political economy (Newell and Taylor 2017 <sup>[[#fn:r1233|1233]]</sup> ), the vagueness of the definition, and consequent assimilation by the mainstream agricultural sector, as well as issues around monitoring, reporting and evaluation (Arakelyan et al. 2017 <sup>[[#fn:r1234|1234]]</sup> ).

Land-based mitigation is facing important trade-offs with food production, biodiversity and local biogeophysical effects (Humpenöder et al. 2017 <sup>[[#fn:r1235|1235]]</sup> ; Krause et al. 2017 <sup>[[#fn:r1235|1235]]</sup> ; Robledo-Abad et al. 2017 <sup>[[#fn:r1236|1236]]</sup> ; Boysen et al. 2016 <sup>[[#fn:r1237|1237]]</sup> , 2017a,b). Synergies between bioenergy and food security could be achieved by investing in a combination of instruments, including technology and innovations, infrastructure, pricing, flex crops, and improved communication and stakeholder engagement (Kline et al. 2017 <sup>[[#fn:r1238|1238]]</sup> ). Managing these trade-offs might also require demand-side interventions, including dietary change incentives (Section 5.7.1).

Synergies and trade-offs also result from interaction between policies (Urwin and Jordan 2008 <sup>[[#fn:r1239|1239]]</sup> ) at different levels of policy (vertical) and across different policies (horizontal) – see also Section 7.4.8. If policy mixes are designed appropriately, acknowledging and incorporating trade-offs and synergies, they are more apt to deliver an outcome such as transitioning to sustainability (Howlett and Rayner 2013 <sup>[[#fn:r1240|1240]]</sup> ; Huttunen et al. 2014 <sup>[[#fn:r1241|1241]]</sup> ) ( ''medium evidence'' and ''medium agreement'' ). However, there is ''medium 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:r1242|1242]]</sup> ).

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==== 7.5.6.4 Water, food and aquatic ecosystem services (ES) ====

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Trade-offs between some types of water use (e.g., irrigation for food security) and other ecosystem services (ES) are expected to intensify under climate change (Hanjra and Ejaz Qureshi 2010 <sup>[[#fn:r1243|1243]]</sup> ). There is an urgency to develop approaches to understand and communicate this to policymakers and decision-makers (Zheng et al. 2016 <sup>[[#fn:r1244|1244]]</sup> ). Reducing water use in agriculture (Mekonnen and Hoekstra 2016 <sup>[[#fn:r1245|1245]]</sup> ) through policies on both the supply and demand side, such as a shift to less water-intensive crops (Richter et al. 2017 <sup>[[#fn:r1246|1246]]</sup> ; Fishman et al. 2015 <sup>[[#fn:r1247|1247]]</sup> ), and a shift in diets (Springmann et al. 2016 <sup>[[#fn:r1248|1248]]</sup> ) has the potential to reduce trade-offs between food security and freshwater aquatic ES ( ''medium evidence, high agreement'' ). There is strong evidence that improved efficiency in irrigation can actually increase overall water use in agriculture, and therefore its contribution to improved flows in rivers is questionable (Ward and Pulido-Velazquez 2008 <sup>[[#fn:r1249|1249]]</sup> ).

There are now powerful new analytical approaches, high-resolution data and decision-making tools that help to predict cumulative impacts of dams, assess trade-offs between engineering and environmental goals, and can help funders and decision-makers compare alternative sites or designs for dam-building as well as to manage flows in regulated rivers based on experimental releases and adaptive learning. This could minimise ecological costs and maximise synergies with other development goals under climate change (Poff et al. 2003 <sup>[[#fn:r1250|1250]]</sup> ; Winemiller et al. 2016 <sup>[[#fn:r1251|1251]]</sup> ). Furthermore, the adoption of metrics based on the emerging concept of Nature’s Contributions to People (NCP) under the IPBES framework brings in non-economic instruments and values that, in combination with conventional valuation of ES approaches, could elicit greater support for non- consumptive water use of rivers for achieving SDG goals (De Groot et al. 2010 <sup>[[#fn:r1252|1252]]</sup> ; Pascual et al. 2017 <sup>[[#fn:r1253|1253]]</sup> ).

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==== 7.5.6.5 Considering synergies and trade-offs to avoid maladaptation ====

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Coherent policies that consider synergies and trade-offs can also reduce the likelihood of maladaptation, which is the opposite of sustainable adaptation (Magnan et al. 2016 <sup>[[#fn:r1254|1254]]</sup> ). Sustainable adaptation ‘contributes to socially and environmentally sustainable development pathways including both social justice and environmental integrity’ (Eriksen et al. 2011 <sup>[[#fn:r1255|1255]]</sup> ). In IPCC’s Fifth Assessment Report (AR5) there was ''medium evidence'' and ''high agreement'' that maladaptation is ‘a cause of increasing concern to adaptation planners, where intervention in one location or sector could increase the vulnerability of another location or sector, or increase the vulnerability of a group to future climate change’ (Noble et al. 2014 <sup>[[#fn:r1256|1256]]</sup> ). AR5 recognised that maladaptation arises not only from inadvertent, badly planned adaptation actions, but also from deliberate decisions where wider considerations place greater emphasis on short-term outcomes ahead of longer-term threats, or that discount, or fail to consider, the full range of interactions arising from planned actions (Noble et al. 2014 <sup>[[#fn:r1257|1257]]</sup> ).

Some maladaptations are only beginning to be recognised as we become aware of unintended consequences of decisions. An example prevalent across many countries is irrigation as an adaptation to water scarcity. During a drought from 2007–2009 in California, farmers adapted by using more groundwater, thereby depleting groundwater elevation by 15 metres. This volume of groundwater depletion is unsustainable environmentally and also emits GHG emissions during the pumping (Christian-Smith et al. 2015 <sup>[[#fn:r1258|1258]]</sup> ). Despite the three years of drought, the agricultural sector performed financially well, due to the groundwater use and crop insurance payments. Drought compensation programmes through crop insurance policies may reduce the incentive to shift to lower water-use crops, thereby perpetuating the maladaptive situation. Another example of maladaptation that may appear as adaptation to drought is pumping out groundwater and storing in surface farm ponds, with consequences for water justice, inequity and sustainability (Kale 2017 <sup>[[#fn:r1259|1259]]</sup> ). These examples highlight the potential for maladaptation from farmers’ adaptation decisions as well as the unintended consequences of policy choices; the examples illustrate the findings of Barnett and O’Neill (2010) <sup>[[#fn:r1260|1260]]</sup> that maladaptation can include: high opportunity costs (including economic, environmental, and social); reduced incentives to adapt (adaptation measures that reduce incentives to adapt by not addressing underlying causes); and path dependency or trajectories that are difficult to change.

In practice, maladaptation is a specific instance of policy incoherence, and it may be useful to develop a framework in designing policy to avoid this type of trade-off. This would specify the type, aim and target audience of an adaptation action, decision, project, plan, or policy designed initially for adaptation, but actually at high risk of inducing adverse effects, either on the system in which it was developed, or another connected system, or both. The assessment requires identifying system boundaries, including temporal and geographical scales at which the outcomes are assessed (Magnan 2014 <sup>[[#fn:r1261|1261]]</sup> ; Juhola et al. 2016 <sup>[[#fn:r1262|1262]]</sup> ). National-level institutions that cover the spectrum of sectors affected, or enhanced collaboration between relevant institutions, is expected to increase the effectiveness of policy instruments, as are joint programmes and funds (Morita and Matsumoto 2018 <sup>[[#fn:r1263|1263]]</sup> ).

As new knowledge about trade-offs and synergies amongst land- climate processes emerges regionally and globally, concerns over emerging risks and the need for planning policy responses grow. There is ''medium evidence'' and ''medium agreement'' that trade- offs currently do not figure into existing climate policies including NDCs and SDGs being vigorously pursued by some countries (Woolf et al. 2018 <sup>[[#fn:r1264|1264]]</sup> ). For instance, the biogeophysical co-benefits of reduced deforestation and re/afforestation measures (Chapter 6) are usually not accounted for in current climate policies or in the NDCs, but there is increasing scientific evidence to include them as part of the policy design (Findell et al. 2017 <sup>[[#fn:r1265|1265]]</sup> ; Hirsch et al. 2018 <sup>[[#fn:r1266|1266]]</sup> ; Bright et al. 2017 <sup>[[#fn:r1267|1267]]</sup> ).

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'''Case study | Green energy: Biodiversity conservation vs global environment targets?'''

Green and renewable energy and transportation are emerging as important parts of climate change mitigation globally ( ''medium evidence, high agreement'' ) (McKinnon 2010; Zarfl et al. 2015; Creutzig et al. 2017). Evidence is, however, emerging across many biomes (from coastal to semi-arid and humid) about how green energy may have significant trade-offs with biodiversity and ecosystem services, thus demonstrating the need for closer environmental scrutiny and safeguards (Gibson et al. 2017; Hernandez et al. 2015). In most cases, the accumulated impact of pressures from decades of land use and habitat loss set the context within which the potential impacts of renewable energy generation need to be considered.

Until recently, small hydropower projects (SHPs) were considered environmentally benign compared to large dams. SHPs are poorly understood, especially since the impacts of clusters of small dams are just becoming evident (Mantel et al. 2010; Fencl et al. 2015; Kibler and Tullos 2013). SHPs (&lt;25/30 MW) are labelled ‘green’ and are often exempt from environmental scrutiny (Abbasi and Abbasi 2011; Pinho et al. 2007; Premalatha et al. 2014b; Era Consultancy 2006). Being promoted in mountainous global biodiversity hotspots, SHPs have changed the hydrology, water quality and ecology of headwater streams and neighbouring forests significantly. SHPs have created dewatered stretches of stream immediately downstream and introduced sub-daily to sub-weekly hydro-pulses that have transformed the natural dry-season flow regime. Hydrologic and ecological connectivity have been impacted, especially for endemic fish communities and forests in some sites of significant biodiversity values ( ''medium evidence, medium agreement'' ) (Jumani et al. 2017, 2018; Chhatre and Lakhanpal 2018; Anderson et al. 2006; Grumbine and Pandit 2013). In some sites, local communities have opposed SHPs due to concerns about their impact on local culture and livelihoods (Jumani et al. 2017, 2018; Chhatre and Lakhanpal 2018).

Semi-arid and arid regions are often found suitable for wind and solar farms which may impact endemic biodiversity and endangered species (Collar et al. 2015, Thaker, M, Zambre, A. Bhosale 2018). The loss of habitat for these species over the decades has been largely due to agricultural intensification driven by irrigation and bad management in designated reserves (Collar et al. 2015; Ledec, George C.; Rapp, Kennan W.; Aiello 2011) but intrusion of power lines is a major worry for highly endangered species such as the Great Indian Bustard (Great Indian Bustard (Ardeotis nigriceps) and conservation and mitigation efforts are being planned to address such concerns (Government of India 2012). In many regions around the world, wind-turbines and solar farms pose a threat to many other species especially predatory birds and insectivorous bats ( ''medium evidence, medium agreement'' ) (Thaker, M, Zambre, A. Bhosale 2018) and disrupt habitat connectivity (Northrup and Wittemyer 2013).

Additionally, conversion of rivers into waterways has emerged as a fuel-efficient (low carbon emitting) and environment- friendly alternative to surface land transport (IWAI 2016; Dharmadhikary, S., and Sandbhor 2017). India’s National Waterways seeks to cut transportation time and costs and reduce carbon emissions from road transport (Admin 2017). There is some evidence that dredging and under-water noise could impact the water quality, human health and habitat of fish species (Junior et al. 2012; Martins et al. 2012), disrupt artisanal fisheries and potentially impact species that rely on echo-location ( ''low evidence, medium agreement'' ) (Dey Mayukh 2018). Off-shore renewable energy projects in coastal zones have been known to have similar impacts on marine fauna (Gill 2005). The Government of India has decided to support studies of the impact of waterways on the endangered Gangetic dolphin in order in order to plan mitigation measures.

Responses to mitigating and reducing the negative impacts of small dams include changes in SHP operations and policies to enable the conservation of river fish diversity. These include mandatory environmental impact assessments, conserving remaining undammed headwater streams in regulated basins, maintaining adequate environmental flows, and implementing other adaptation measures based on experiments with active management of fish communities in impacted zones (Jumani et al. 2018). Location of large solar farms needs to be carefully scrutinised (Sindhu et al. 2017). For mitigating negative impacts of power lines associated with solar and wind farms in bustard habitats, suggested measures include diversion structures to prevent collision, underground cables and avoidance in core wildlife habitat, as well as incentives for maintaining low-intensity rainfed agriculture and pasture around existing reserves, and curtailing harmful infrastructure in priority areas (Collar et al. 2015). Mitigation for minimising the ecological impact of inland waterways on biodiversity and fisheries is more complicated, but may involve improved boat technology to reduce underwater noise, maintaining ecological flows and thus reduced dredging, and avoidance in key habitats (Dey Mayukh 2018).

The management of ecological trade-offs of green energy and green infrastructure and transportation projects may be crucial for long- term sustainability and acceptance of emerging low-carbon economies.

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