Editing IPCC:AR6/WGIII/Chapter-12 (section)

== 12.6 Other Cross-sectoral Implications of Mitigation ==

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This section presents further cross-sectoral considerations related to GHG mitigation. Firstly, various cross-sectoral perspectives on mitigation actions are presented. Then, sectoral policy interactions are presented. Finally, implications in terms of international trade spillover effects and competitiveness, and finance flows and related spillover effects at the sectoral level, are addressed.

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=== 12.6.1 Cross-sectoral Perspectives on Mitigation Action ===

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Chapters 5 to 11 present mitigation measures applicable in individual sectors, and potential co-benefits and adverse side effects [[#footnote-000|4]] of these individual measures. This section builds on the sectoral analysis of mitigation action from a cross-sectoral perspective. Firstly, [[#12.6.1.1|Section 12.6.1.1]] brings together some of the observations presented in the sectoral chapters to show how different mitigation actions in different sectors can contribute to the same co-benefits and result in the same adverse side effects, thereby demonstrating the potential synergistic effects. The links between these co-benefits and adverse side effects and the SDGs is also demonstrated. In [[#12.6.1.2|Section 12.6.1.2]] , the focus turns from sector-specific mitigation measures to mitigation measures which have cross-sectoral implications, including measures that have application in more than one sector and measures where implementation in one sector impacts on implementation in another. Finally, [[#12.6.1.3|Section 12.6.1.3]] notes the cross-sectoral relevance of a selection of general-purpose technologies, a topic that is covered further in Chapter 16.

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==== 12.6.1.1 A Cross-sectoral Perspective on Co-benefits and Adverse Side Effects of Mitigation Measures, and Links with the SDGs ====

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A body of literature has been developed which addresses the co-benefits of climate mitigation action ( [[#Karlsson--2020|Karlsson et al. 2020]] ). Adverse side effects of mitigation are also well documented. Co-benefits and adverse side effects in individual sectors and associated with individual mitigation measures are discussed in the individual sector chapters (Sections 5.2, 6.7.7, 7.4, 7.6, 8.2, 8.4, 9.8, 10.1.1 and 11.5.3), as well as in previous IPCC General and Special Assessment reports. The term ‘co-impacts’ has been proposed to capture both the co-benefits and adverse side effects of mitigation. An alternative framing is one of multiple objectives, where climate change mitigation is placed alongside other objectives when assessing policy decisions ( [[#Ürge-Vorsatz--2014|Ürge-Vorsatz et al. 2014]] ; [[#Mayrhofer--2016|Mayrhofer and Gupta 2016]] ; [[#Cohen--2017|Cohen et al. 2017]] ; [[#Bhardwaj--2019|Bhardwaj et al. 2019]] ).

The identification and assessment of co-benefits has been argued to serve a number of functions ( [[IPCC:Wg3:Chapter:Chapter-1#1.4|Section 1.4]] ) including using them as leverage for securing financial support for implementation, providing justification of actions which provide a balance of both short- and long-term benefits and obtaining stakeholder buy-in ( ''robust evidence'' , ''low agreement'' ) ( [[#Karlsson--2020|Karlsson et al. 2020]] ). Assessment of adverse side effects has been suggested to be useful in avoiding unforeseen negative impacts of mitigation and providing policy- and decision-makers with the information required to make informed trade-offs between climate and other benefits of actions ( [[#Ürge-Vorsatz--2014|Ürge-Vorsatz et al. 2014]] ; [[#Bhardwaj--2019|Bhardwaj et al. 2019]] ; [[#Cohen--2019|Cohen et al. 2019]] ) ( ''high evidence'' , ''low agreement'' ).

Various approaches to identifying and organising co-impacts in specific contexts and across sectors have been proposed towards providing more comparable and standardised analyses. However, consistent quantification of co-impacts, including cost-benefit analysis, and the utilisation of the resulting information, remain a challenge ( [[#Ürge-Vorsatz--2014|Ürge-Vorsatz et al. 2014]] ; [[#Floater--2016|Floater et al. 2016]] ; [[#Mayrhofer--2016|Mayrhofer and Gupta 2016]] ; [[#Cohen--2019|Cohen et al. 2019]] ; [[#Karlsson--2020|Karlsson et al. 2020]] ). This challenge is further exacerbated when considering that co-impacts of a mitigation measure in one sector can either enhance or reduce the co-impacts associated with mitigation in another, or the achievement of co-benefits in one geographic location can lead to adverse side effects in another. For example, the production of lithium for batteries for energy storage has the potential to contribute to protecting water resources and reducing wastes associated with coal-fired power in many parts of the world, but mining of lithium has the potential for creating water and waste challenges if not managed properly ( [[#Agusdinata--2018|Agusdinata et al. 2018]] ; [[#Kaunda--2020|Kaunda 2020]] ).

While earlier literature has suggested that co-impacts assessments can support adoption of climate mitigation action, a more recent body of literature has suggested limitations in such framing ( [[#Ryan--2015|Ryan 2015]] ; [[#Bernauer--2016|Bernauer and McGrath 2016]] ; [[#Walker--2018|Walker et al. 2018]] ). Presenting general information on co-impacts as a component of a mitigation analysis does not always lead to increased support for climate mitigation action. Rather, the most effective framing is determined by factors relating to local context, type of mitigation action under consideration and target stakeholder group. More work has been identified to be required to bring context into planning co-impacts assessments and communication thereof ( [[#Ryan--2015|Ryan 2015]] ; [[#Bernauer--2016|Bernauer and McGrath 2016]] ; [[#Walker--2018|Walker et al. 2018]] ) ( ''low evidence'' , ''low agreement'' ).

An area where the strong link between the cross-sectoral co-impacts of mitigation action and global government policies is being clearly considered is in the achievement of the SDGs ( [[#Obergassel--2017|Obergassel et al. 2017]] ; [[#Doukas--2018|Doukas et al. 2018]] ; [[#Markkanen--2019|Markkanen and Anger-Kraavi 2019]] ; Smith et al. 2019; [[#van%20Soest--2019|van Soest et al. 2019]] ) (Chapters 1 and 17, individual sectoral chapters). Figure 12.9 demonstrates these relationships from a cross-sectoral perspective. It shows the links between sectors which give rise to emissions, the mitigation measures that can find application in the sector, and co-benefits and adverse side effects of mitigation measures and the SDGs (noting that the figure is not intended to be comprehensive). Such a framing of co-impacts from a cross-sectoral perspective in the context of the SDGs could help to further support climate mitigation action, particularly within the context of the Paris Agreement ( [[#Gomez-Echeverri--2018|Gomez-Echeverri 2018]] ) ( ''medium evidence'' , ''medium agreement'' ). Literature sources utilised in the compilation of this diagram are presented in Supplementary Material 12.SM.3.

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[[File:c941fa42bb9802b82a6cf08fb78d4d14 IPCC_AR6_WGIII_Figure_12_9.png]]

'''Figure 12.9 | Co-benefits and adverse side effects of mitigation actions with links to the SDGs.''' The inner circle represents the sectors in which mitigation occurs. The second circle shows different generic types of mitigation actions (A to G), with the symbols showing which sectors they are applicable to. The third circle indicates different types of climate related co-benefits (green letters) and adverse side effects (red letters) that may be observed as a result of implementing each of the mitigation actions. Here I relates to climate resilience, II-IV economic co-impacts, V-VII environmental, VIII-XII social, and XIII political and institutional. The final circle maps co-benefits and adverse side effects relevant to the SDGs. Source: re-used with permission from [[#Cohen--2021|Cohen et al. (2021)]] .

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==== 12.6.1.2 Mitigation Measures from a Cross-sectoral Perspective ====

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Three aspects of mitigation from a cross-sectoral perspective are considered, following [[#Barker--2007|Barker et al. (2007)]] :

• mitigation measures used in more than one sector;

'''•''' implications of mitigation measures for interaction and integration between sectors; and

• competition among sectors for scarce resources.

A number of mitigation measures find application in more than one sector. Renewable energy technologies such as solar and wind may be used for grid electricity supply, as embedded generation in the buildings sector and for energy supply in the agriculture sector ( [[#Shahsavari--2018|Shahsavari and Akbari 2018]] ) (Chapters 6, 7 and 8). Hydrogen and fuel cells, coupled with low-carbon energy technologies for producing the hydrogen, are being explored in transport, urban heat, industry and for balancing electricity supply ( [[#Dodds--2015|Dodds et al. 2015]] ; [[#Staffell--2019|Staffell et al. 2019]] ) (Chapters 6, 8 and 11). Electric vehicles are considered an option for balancing variable power ( [[#Kempton--2005|Kempton and Tomić 2005]] ; [[#Liu--2019|Liu and Zhong 2019]] ). Carbon capture and storage (CCS) and carbon capture and utilisation (CCU) have potential application in a number of industrial processes (cement, iron and steel, petroleum refining and pulp and paper) ( [[#Leeson--2017|Leeson et al. 2017]] ; [[#Garcia--2019|Garcia and Berghout 2019]] ) (Chapters 6 and 11) and the fossil fuel electricity sector (Chapter 6). When coupled with energy recovery from biomass, CCS can provide a carbon sink (BECCS) ( [[#12.5|Section 12.5]] ). On the demand side, energy efficiency options find application across the sectors (Chapters 6, 8, 9, 10, and 11), as do reducing demand for goods and services (Chapter 5) and improving material efficiency ( [[IPCC:Wg3:Chapter:Chapter-11#11.3.2|Section 11.3.2]] ).

A range of examples where mitigation measures result in cross-sectoral interactions and integration is identified. The mitigation potential of electric vehicles, including plug-in hybrids, is linked to the extent of decarbonisation of the electricity grid, as well as to the liquid fuel supply emissions profile ( [[#Lutsey--2015|Lutsey 2015]] ). Making buildings energy positive, where excess energy is used to charge vehicles, can increase the potential of electric and hybrid vehicles ( [[#Zhou--2019|Zhou et al. 2019]] ). Advanced process control and process optimisation in industry can reduce energy demand and material inputs ( [[IPCC:Wg3:Chapter:Chapter-11#11.3|Section 11.3]] ), which in turn can reduce emissions linked to resource extraction and manufacturing. Reductions in coal-fired power generation through replacement with renewables or nuclear power result in a reduction in coal mining and its associated emissions. Increased recycling results in a reduction in emissions from primary resource extraction. CCU can contribute to the transition to more renewable energy systems via power-to-X technologies, which enables the production of CO 2 -based fuels/e-fuels and chemicals using carbon dioxide and hydrogen ( [[#Breyer--2015|Breyer et al. 2015]] ; [[#Anwar--2020|Anwar et al. 2020]] ). Certain emissions reductions in the AFOLU sector are contingent on energy sector decarbonisation. Trees and green roofs planted to counter urban heat islands reduce the demand for energy for air conditioning and simultaneously sequester carbon ( [[#Kim--2018|Kim and Coseo 2018]] ; [[#Kuronuma--2018|Kuronuma et al. 2018]] ). Recycling of organic waste avoids methane generation if the waste would have been disposed of in landfill sites, can generate renewable energy if treated through anaerobic digestion, and can reduce requirements for synthetic fertiliser production if the nutrient value is recovered ( [[#Creutzig--2015|Creutzig et al. 2015]] ). Liquid transport biofuels link to the land, energy and transport sectors ( [[#12.5.2|Section 12.5.2]] .2).

Demand-side mitigation measures, discussed in Chapter 5, also have cross-sectoral implications which need to be taken into account when calculating mitigation potentials. Residential electrification has the potential to reduce emissions associated with lighting and heating, particularly in developing countries where these are currently met by fossil fuels and using inefficient technologies, but will increase demand for electricity (Chapters 5 and 8 and Sections 6.6.2.3 and 8.4.3.1). Many industrial processes can also be electrified in the move away from fossil reductants and direct energy carriers (Chapter 11). The impact of electrification on electricity sector emissions will depend on whether electricity generation is based on fossil fuels in the absence of CCS or low-carbon energy sources (Chapter 5).

At the same time, saving electricity in all sectors reduces the demand for electricity, thereby reducing mitigation potential of renewables and CCS. Demand-side flexibility measures and electrification of vehicle fleets are supportive of more intermittent renewable energy supply options (Sections 6.3.7, 6.4.3.1 and 10.3.4). Production of maize, wheat, rice and fresh produce requires lower energy inputs on a lifecycle basis than poultry, pork and ruminant-based meats ( [[#Clark--2017|Clark and Tilman 2017]] ) ( [[#12.4|Section 12.4]] ). It also requires less land area per kilocalorie or protein output ( [[#Clark--2017|Clark and Tilman 2017]] ; Poore and Nemecek 2018), so replacing meat with these products makes land available for sequestration, biodiversity or other societal needs. However, production of co-products of the meat industry, such as leather and wool, is reduced, resulting in a need for substitutes. Further discussion and examples of cross-sectoral implications of mitigation, with respect to cost and potentials, are presented in [[#12.2|Section 12.2]] . One final example on this topic included here is that of circular economy ( [[#_idTextAnchor014|Box 12.4]] ).

Finally, in terms of competition among sectors for scarce resources, this issue is often considered in the assessments of mitigation potentials linked to bioenergy and diets (vegetable vs animal food products), land use and water ( ''robust evidence'' , ''high agreement'' ) ( [[#12.5|Section 12.5]] and Cross-Working Group Box 3 in this Chapter). It is, however, also relevant elsewhere. Constraints have been identified in the supply of indium, tellurium, silver, lithium, nickel and platinum that are required for implementation of some specific renewable energy technologies ( [[#Watari--2018|Watari et al. 2018]] ; [[#Moreau--2019|Moreau et al. 2019]] ). Other studies have shown constraints in supply of cobalt, one of the key elements used in production of lithium-ion batteries, which has been assessed for mitigation potential in energy, transport and buildings sectors ( ''medium evidence'' , ''high agreement'' ) (Jaffe 2017; [[#Olivetti--2017|Olivetti et al. 2017]] ), although alternatives to cobalt are being developed ( [[#Olivetti--2017|Olivetti et al. 2017]] ; [[#Watari--2018|Watari et al. 2018]] ).

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=== Box 12.4 | Circular Economy from a Cross-Sectoral Perspective ===

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Circular economy approaches consider the entire lifecycle of goods and services, and seek to design out waste and pollution, keep products and materials in use, and regenerate natural systems ( [[#The%20Ellen%20MacArthur%20Foundation--2013|The Ellen MacArthur Foundation 2013]] ; [[#CIRAIG--2015|CIRAIG 2015]] ). The use of circular economy for rethinking how society’s needs for goods and services is delivered in such a way as to minimise resource use and environmental impact and maximise societal benefit has been discussed elsewhere in this assessment report ( [[IPCC:Wg3:Chapter:Chapter-5|Chapter 5]] and [[IPCC:Wg3:Chapter:Chapter-5#5.3.4|Section 5.3.4]] ). A wide range of potential application areas is identified, from food systems to bio-based products to plastics to metals and minerals to manufactured goods. Circular economy approaches are implicitly cross-sectoral, impacting the energy, industrial, AFOLU, waste and other sectors. They will have climate and non-climate co-benefits and trade-offs. The scientific literature mainly investigates incremental measures claiming but not demonstrating mitigation; highest mitigation potential is found in the industry, energy, and transport sectors; mid-range potential in the waste and building sectors; and lowest mitigation gains in agriculture ( [[#Cantzler--2020|Cantzler et al. 2020]] ). Circular economy thinking has been identified to support increased resilience to the physical effects of climate change and contribute to meeting other SDGs, notably SDG 12 (responsible consumption and production) ( [[#The%20Ellen%20MacArthur%20Foundation--2019|The Ellen MacArthur Foundation 2019]] ).

Circular economy approaches to deployment of low-carbon infrastructure have been suggested to be important to optimise resource use and mitigate environmental and societal impacts caused by extraction and manufacturing of composite and critical materials as well as infrastructure decommissioning ( [[#Jensen--2018|Jensen and Skelton 2018]] ; [[#Sica--2018|Sica et al. 2018]] ; [[#Salim--2019|Salim et al. 2019]] ; [[#Watari--2019|Watari et al. 2019]] ; [[#Jensen--2020|Jensen et al. 2020]] ; [[#Mignacca--2020|Mignacca et al. 2020]] ). The circular carbon economy is an approach inspired by the circular economy principles that rely on a combination of technologies, including CCU, CCS and CDR, to enable transition pathways especially relevant in economies dependent on fossil fuel exports ( [[#Lee--2017|Lee et al. 2017]] ; [[#Alshammari--2020|Alshammari 2020]] ; [[#Morrow--2020|Morrow and Thompson 2020]] ; [[#Zakkour--2020|Zakkour et al. 2020]] ). The integration of circular economy and bioeconomy principles (Cross-Working Group Box 3 in this chapter) is conceptualised in relation to policy development ( [[#European%20Commission--2018|European Commission 2018]] ) as well as COVID-19 recovery strategies (Palahí et al. 2020), emphasising the use of renewable energy sources and sustainable management of ecosystems with transformation of biological resources into food, feed, energy and biomaterials.

At this stage, however, there is no single global agreement on how circular economy principles are best implemented, and differential government support for circular economy interventions is observed in different jurisdictions.

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==== 12.6.1.3 Cross-sectoral Considerations Relating to Emerging General-purpose Technologies ====

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General-purpose technologies (GPTs) include, but are not limited to, additive manufacturing, artificial intelligence, biotechnology, hydrogen, digitalisation, electrification, nanotechnology and robots ( [[#de%20Coninck--2018|de Coninck et al. 2018]] ). Many of the individual sectoral chapters have identified the roles that such technologies can have in supporting mitigation of GHG emissions. [[IPCC:Wg3:Chapter:Chapter-16#16.2.2.3|Section 16.2.2.3]] presents an overview of the individual technologies and specific applications thereof.

In this chapter, which focuses on cross-sectoral implications of mitigation, it is highlighted that certain of these GPTs will find application across the sectors, and there will be synergies and trade-offs when utilising these technologies in more than sector. One example here is the use of hydrogen as an energy carrier, which, when coupled with low-carbon energy, has potential for driving mitigation in energy, industry, transport, and buildings. The increased uptake of hydrogen across the economy requires establishment of hydrogen production, transport and storage infrastructure which could simultaneously support multiple sectors, although there is the potential to utilise existing infrastructure in some parts of the world ( [[#Alanne--2017|Alanne and Cao 2017]] ).

provides further details on hydrogen in the context of cross-sectoral mitigation specifically, while further details on the role of hydrogen in individual sectors are provided in Chapters 6, 8. 9, 10 and 11. In contrast, the benefits of digitalisation, which could potentially give rise to substantial energy savings across multiple sectors, need to be traded off against demand for electricity to operate consumer devices, data centres, and data networks. Measures are required to increase energy efficiency of these technologies ( [[#IEA--2017|IEA 2017]] ). [[IPCC:Wg3:Chapter:Chapter-5#5.3.4.1|Section 5.3.4.1]] of this report provides further information on energy and emissions benefits and costs of digitalisation.

With respect to co-impacts of GPTs, the other focus of this chapter, it is highlighted that assessment of the environmental, social and economic implications of such technologies is challenging and context specific, with multiple potential cross-sectoral linkages ( [[#de%20Coninck--2018|de Coninck et al. 2018]] ). Each GPT would need to be explored in context of what it is being used for, and potentially in the geographical context, in order to understand the co-impacts of its use.

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=== Box 12.5 | Hydrogen in the Context of Cross-sectoral Mitigation Options ===

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Interest in hydrogen as an intermediary energy carrier has grown rapidly in the years since the 5th Assessment Report of WGIII (AR5) was published. This is reflected in this WGIII assessment report, where the term ‘hydrogen’ is used more than five times more often than in AR5. In [[IPCC:Wg3:Chapter:Chapter-6|Chapter 6]] of this report, it is shown that hydrogen can be produced with low carbon impact from fossil fuels ( [[IPCC:Wg3:Chapter:Chapter-6#6.4.2.6|Section 6.4.2.6]] ), renewable electricity and nuclear energy ( [[IPCC:Wg3:Chapter:Chapter-6#6.4.5.1|Section 6.4.5.1]] ), or biomass ( [[IPCC:Wg3:Chapter:Chapter-6#6.4.2.5|Section 6.4.2.5]] ). In the energy sector, hydrogen is one of the options for storage of energy in low-carbon electricity systems (Sections 6.4.4.1 and 6.6.2.2). But, also importantly, hydrogen can be produced to be used as a fuel for sectors that are hard to decarbonise; this is possible directly in the form of hydrogen, but also in the form of ammonia or other energy carriers ( [[IPCC:Wg3:Chapter:Chapter-6#6.4.5.1|Section 6.4.5.1]] ). In the transport sector, fuel cell engines ( [[IPCC:Wg3:Chapter:Chapter-10#10.3.3|Section 10.3.3]] ) running on hydrogen can become important, especially for heavy duty vehicles ( [[IPCC:Wg3:Chapter:Chapter-10#10.4.3|Section 10.4.3]] ). In the industry sector hydrogen already plays an important role in the chemical sector (for ammonia and methanol production) (Box 11.1 in Chapter 11) and in the fuel sector (in oil refinery processes and for biofuel production) ( [[#IEA--2019b|IEA 2019b]] ). Beyond the production of ammonia and methanol for both established and novel applications, the largest potential industrial application for low-carbon hydrogen is seen in steel-making ( [[IPCC:Wg3:Chapter:Chapter-11#11.4.1.1|Section 11.4.1.1]] ). Hydrogen and hydrogen derivatives can play a further role as substitute energy carriers ( [[IPCC:Wg3:Chapter:Chapter-11#11.3.5|Section 11.3.5]] ) and for the production of intermediate chemical products such as methanol, ethanol and ethylene when combined with CCU ( [[IPCC:Wg3:Chapter:Chapter-11#11.3.6|Section 11.3.6]] ). For the building sector, the exploration of the usefulness of hydrogen is at an early stage (Box 9.4).

An overview report ( [[#IEA--2019b|IEA 2019b]] ) already sees opportunities in 2030 for buildings, road freight and passenger vehicles. This report also suggests a high potential application in iron and steel production, aviation and maritime transport, and for electricity storage. Several industry roadmaps have been published that map out a possible role for hydrogen until 2050. The most well known and ambitious is the roadmap by the [[#Hydrogen%20Council--2017|Hydrogen Council (2017)]] , which sketches a global scenario leading to 78 EJ hydrogen use in 2050, mainly for transport, industrial feedstock, industrial energy and to a lesser extent for buildings and power generation. Hydrogen makes up 18% of total final energy use in this vision. An analysis by IRENA on hydrogen from renewable sources comes to a substantially lower number: 8 EJ (excluding hydrogen use in power production and feedstock uses). On a regional level, most roadmaps and scenarios have been published for the European Union, for example by the Fuel Cell and Hydrogen Joint Undertaking (Blanco et al. 2018; [[#EC--2018|EC 2018]] ; [[#FCH--2019|FCH 2019]] ; [[#Navigant--2019|Navigant 2019]] ). All these reports have scenario variants with hydrogen share in final energy use of 10% to over 20% by 2050.

When it comes to the production of low-carbon hydrogen, the focus of the attention is on production using electricity from renewable sources via electrolysis, so-called ‘green hydrogen’. However, ‘blue hydrogen’, produced out of natural gas with CCS, is also often considered. Since a significantly increasing role for hydrogen would require considerable infrastructure investments and would affect existing trade flows in raw materials, governments have started to set up national hydrogen strategies, both potential exporting (e.g., Australia) and importing (e.g., Japan) countries ( [[#METI--2017|METI 2017]] ; [[#COAG%20Energy%20Council--2019|COAG Energy Council 2019]] ).

As already reported in [[IPCC:Wg3:Chapter:Chapter-6|Chapter 6]] ( [[IPCC:Wg3:Chapter:Chapter-6#6.2|Section 6.2]] .4.1), production costs of green hydrogen are expected to come down from the current levels of above USD100 MWh –1 . Price expectations are: EUR40–60 MWh –1 for both green and blue hydrogen production in the EU by 2050 ( [[#Navigant--2019|Navigant 2019]] ) with production costs already being lower in North Africa; 42–87 USD MWh –1 for green hydrogen in 2030 and 20–41 USD MWh –1 in 2050 ( [[#BNEF--2020|BNEF 2020]] ); EUR75 MWh –1 in 2030 ( [[#Glenk--2019|Glenk and Reichelstein 2019]] ). For fossil-based technologies combined with CCS, prices may range from USD33–80 MWh –1 (Table 6.8). Such prices can make hydrogen competitive for industrial feedstock applications, and probably for several transportation modes in combination with fuel cells, but without further incentives, not necessarily for stationary applications in the coming decades: wholesale natural gas prices are expected to range from USD7–31 MWh –1 across regions and scenarios, according to the World Energy Outlook ( [[#IEA--2020a|IEA 2020a]] ); coal prices mostly are even lower than natural gas prices (all fossil fuel prices refer to unabated technology and untaxed fuels). The evaluation of macro-economic impacts is relatively rare. A study by [[#Mayer--2019b|Mayer et al. (2019b)]] indicated that a shift to hydrogen in iron and steel production would lead to regional GDP losses in the range of 0.4–2.7% in 2050 across EU+3, with some regions making gains under a low-cost electricity scenario.

The IAM scenarios imply a modest role played by hydrogen, with some scenarios featuring higher levels of penetration. The consumption of hydrogen is projected to increase by 2050 and onwards in scenarios likely limiting global warming to 2°C or below, and the median share of hydrogen in total final energy consumption is 2.1% in 2050 and 5.1% in 2100 (Box 12.4, Figure 1) (Numbers are based on the AR6 scenarios database). There is large variety in hydrogen shares, but the values of 10% and more of final energy use that occur in many roadmaps are only rarely reached in the scenarios. Hydrogen is predominantly used in the industry and transportation sectors. In the scenarios, hydrogen is produced mostly by electrolysis and by biomass energy conversion with CCS (Box 12.5, Figure 1). Natural gas with CCS is expected to play only a modest role; here a distinct difference between the roadmaps quoted before and the IAM results is observed.

It is concluded that there is increasing confidence that hydrogen can play a significant role, especially in the transport sector and the industrial sector. However, there is much less agreement on timing and volumes, and there is also a range of perspectives on the role of the various production methods of hydrogen.

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'''Box 12.5, Figure 1 | Fraction of hydrogen (light blue) in total final energy consumption, and for each sector.''' Hinges represent the interquartile ranges and whiskers extend to 5th and 95th percentiles. Source: AR6 scenarios database.

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=== 12.6.2 Sectoral Policy Interactions (Synergies and Trade-offs) ===

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A taxonomy of policy types and attributes is provided by [[IPCC:Wg3:Chapter:Chapter-13#13.6|Section 13.6]] . In addition, the sectoral chapters provide an in-depth discussion of important mitigation policy issues such as policy overlaps, policy mixes, and policy interaction as well as policy design considerations and governance. The point of departure for the assessment in this chapter is a focus on cross-sectoral perspectives aiming at maximising policy synergies and minimising policy trade-offs.

'''Synergies and trade-offs resulting from mitigation policies are not clearly discernible from either sector-level studies or global and regional top-down studies. Rather, they would require a cross-sectoral integrated policy framework''' ( [[#von%20Stechow--2015|von Stechow et al. 2015]] ; [[#Monier--2018|Monier et al. 2018]] ; [[#Pardoe--2018|Pardoe et al. 2018]] ; [[#Singh--2019|Singh et al. 2019]] ) or multiple-objective-multiple-impact policy assessment framework identifying key co-impacts and avoiding trade-offs ( ''robust evidence, high agreement'' ) ( [[#Ürge-Vorsatz--2014|Ürge-Vorsatz et al. 2014]] ) ''.''

Sectoral studies typically cover differentiated response measures while the IAM literature mostly uses uniform efficient market-based measures. This has important implications for understanding the differences in magnitude and distribution of mitigation costs and potentials of [[#12.2|Section 12.2]] ( [[#Karplus--2013|Karplus et al. 2013]] ; [[#Rausch--2014|Rausch and Karplus 2014]] ). There is a comprehensive literature on the efficiency of uniform carbon pricing compared to sector-specific mitigation approaches, but relatively less literature on the distributional impacts of carbon taxes and measures to mitigate potential adverse distributional impacts ( [[#Rausch--2014|Rausch and Karplus 2014]] ; [[#Rausch--2015|Rausch and Reilly 2015]] ; [[#Wang--2016b|Wang et al. 2016b]] ; [[#Åhman--2017|Åhman et al. 2017]] ; [[#Mu--2018|Mu et al. 2018]] ). For example, in terms of cross-sectoral distributional implications, studies find negative competitiveness impacts for the energy-intensive industries ( ''robust evidence,'' ''medium agreement'' ) ( [[#Rausch--2014|Rausch and Karplus 2014]] ; [[#Wang--2016b|Wang et al. 2016b]] ; [[#Åhman--2017|Åhman et al. 2017]] ).

Strong interdependencies and cross-sectoral linkages create both opportunities for synergies and the need to address trade-offs. This calls for coordinated sectoral approaches to climate change mitigation policies that mainstream these interactions ( [[#Pardoe--2018|Pardoe et al. 2018]] ). Such an approach is also called for in the context of cross-sectoral interactions of adaptation and mitigation measures, examples are in the agriculture, biodiversity, forests, urban, and water sectors ( [[#Arent--2014|Arent et al. 2014]] ; [[#Berry--2015|Berry et al. 2015]] ; [[#Di%20Gregorio--2017|Di Gregorio et al. 2017]] ). Integrated planning and cross-sectoral alignment of climate change policies are particularly evident in developing countries’ NDCs pledged under the Paris Agreement, where key priority sectors such as agriculture and energy are closely aligned between the proposed mitigation and adaptation actions in the context of sustainable development and the SDGs. An example is the integration between climate-smart agriculture and low-carbon energy ( ''robust evidence'' , ''high agreement'' ) ( [[#Antwi-Agyei--2018|Antwi-Agyei et al. 2018]] ; [[#England--2018|England et al. 2018]] ). Yet, there appear to be significant challenges relating to institutional capacity and resources to coordinate and implement such cross-sectoral policy alignment, particularly in developing country contexts ( [[#Antwi-Agyei--2018|Antwi-Agyei et al. 2018]] ) ''.''

Another dimension of climate change policy interactions in the literature is related to trade-offs and synergies between climate change mitigation and other societal objectives. For example, in mitigation policies related to energy, trade-offs and synergies between universal electricity access and climate change mitigation would call for complementary policies such as pro-poor tariffs, fuel subsidies, and broadly integrated policy packages (Dagnachew et al. 2018). In agriculture and forestry, research suggests that integrated policy programmes enhance mitigation potentials across the land-use-agriculture-forestry nexus and lead to synergies and positive spillovers ( [[#Galik--2019|Galik et al. 2019]] ). To maximise synergies and deal with trade-offs in such a cross-sectoral context, evidence-based/informed and holistic policy analysis approaches like nexus approaches and multi-target back-casting approaches that take into account unanticipated outcomes and indirect consequences would be needed ( ''robust evidence, high agreement'' ) ( [[#Klausbruckner--2016|Klausbruckner et al. 2016]] ; Hoff et al. 2019; [[#van%20der%20Voorn--2020|van der Voorn et al. 2020]] ) ( ) ''.''

The consequences of large-scale land-based mitigation for food security, biodiversity ( [[#Dasgupta--2021|Dasgupta 2021]] ), the state of soil, water resources, and so on can be significant, depending on many factors, such as economic development (including distributional aspects), international trade patterns, agronomic development, diets, land-use governance and policy design, and not least climate change itself ( [[#Winchester--2015|Winchester and Reilly 2015]] ; [[#Fujimori--2018|Fujimori et al. 2018]] ; [[#Hasegawa--2018|Hasegawa et al. 2018]] ; [[#Van%20Meijl--2018|Van Meijl et al. 2018]] ). Policies and regulations that address other aspects apart from climate change can indirectly influence the attractiveness of land-based mitigation options. For example, farmers may find it attractive to shift from annual food/feed crops to perennial grasses and short rotation woody crops (suitable for bioenergy) if the previous land uses become increasingly restricted due to impacts on groundwater quality and eutrophication of water bodies ( ''robust evidence'' , ''medium agreement'' ) (Sections 12.4 and 12.5).

Finally, there are knowledge gaps in the literature particularly in relation to policy scalability and the extent and magnitude of policy interactions when scaling the policy to a level consistent with low GHG emissions pathways such as 2°C and 1.5°C.

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<span id="box-12.6-case-study-sahara-forest-project-i-n-aqaba-jordan"></span>
=== Box 12.6 | Case Study: Sahara Forest Project in Aqaba, Jordan ===

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'''Nexus framing'''

Shifting to renewable (in particular solar) energy reduces dependency on fossil fuel imports and greenhouse gas emissions, which is crucial for mitigating climate change. Employing renewable energy for desalination of seawater and for cooling of greenhouses in integrated production systems can enhance water availability, increase crop productivity and generate co-products and co-benefits (e.g., algae, fish, dryland restoration, greening of the desert).

'''Nexus opportunities'''

The Sahara Forest project integrated production system uses amply available natural resources, namely solar energy and seawater, for improving water availability and agricultural/biomass production, while simultaneously providing new employment opportunities. Using hydroponic systems and humidity in the air, water needs for food production are 50% lower compared to other greenhouses.

'''Technical and economic''' '''nexus solutions'''

Several major technologies are combined in the Sahara Forest Project, namely electricity production through the use of solar power (PV or CSP), freshwater production through seawater desalination using renewable energy, seawater-cooled greenhouses for food production, and outdoor revegetation using run-off from the greenhouses.

'''Stakeh''' '''olders involved'''

The key stakeholders which benefit from such an integrated production system are from the water sector, which urgently requires an augmentation of irrigation (and other) water, and the agricultural sector, which relies on the additional desalinated water to maintain and increase agricultural production. The project also involves public and private sector partners from Jordan and abroad, with little engagement of civil society so far.

Box 12.6

'''Framework conditions'''

The Sahara Forest Project has been implemented at pilot scale so far, including the first pilot with one hectare and one greenhouse pilot in Qatar and a larger ‘launch station’ with three hectares and two greenhouses in Jordan. These pilots have been funded by international organisations such as the Norwegian Ministry of Climate and Environment, Norwegian Ministry of Foreign Affairs and the European Union. Alignment with national policies, institutions and funding, as well as upscaling of the project, is underway or planned.

'''Monitoring and evaluation''' '''and next steps'''

The multi-sectoral planning and investments that are needed to upscale the project require cooperation among the water, agriculture, and energy sectors and an active involvement of local actors, private companies, and investors. These cooperation and involvement mechanisms are currently being established in Jordan. Given the emphasis on the economic value of the project, public-private partnerships are considered as the appropriate business and governance model, when the project is upscaled. Scenarios for upscaling (seawater use primarily in low-lying areas close to the sea, to avoid energy-intensive pumping) include 50 MW of CSP, 50 hectares of greenhouses, which would produce 34,000 tonnes of vegetables annually, provide employment for over 800 people, and sequester more than 8000 tonnes of CO 2 -eq annually.

Source: SFP Foundation; Hoff et al. (2019).

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<span id="international-trade-spillover-effects-and-competitiveness"></span>

=== 12.6.3 International Trade Spillover Effects and Competitiveness ===

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International spillovers of mitigation policies are effects that carbon-abatement measures implemented in one country have on sectors in other countries. These effects include (i) carbon leakage in manufacture; (ii) the effects on energy trade flows and incomes related to fossil fuel exports from major exporters; (iii) technology and knowledge spillovers; and (iv) transfer of norms and preferences via various approaches to establish sustainability requirements on traded goods, such as EU-RED and environmental labelling systems to guide consumer choices ( ''robust evidence'' , ''medium agreement'' ) ''.'' This section focuses on cross-sectoral aspects of international spillovers related to the first two effects.

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<span id="cross-sectoral-aspects-of-carbon-leakage"></span>
==== 12.6.3.1 Cross-sectoral Aspects of Carbon Leakage ====

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Carbon leakage occurs when mitigation measures implemented in one country or sector lead to a rise in emissions in other countries or sectors. Three types of spillovers are possible: (i) domestic cross-sectoral spillovers when mitigation policy in one sector leads to the re-allocation of labour and capital towards the other sectors of the same country; (ii) international spillovers within a single sector when mitigation policy leads to substitution of domestic production of carbon-intensive goods with their imports from abroad; and (iii) international cross-sectoral spillovers when mitigation policy in one sector in one country leads to the rise in emissions in other sectors in other countries. While the first two are described in [[IPCC:Wg3:Chapter:Chapter-13#13.6|Section 13.6]] , this section focuses on the third. Though some papers address this type of leakage, there is still a significant lack of knowledge on this topic.

One possible channel of cross-sectoral international carbon leakage is through global value chains. Mitigation policy in one country not only leads to shifts in competitiveness across industries producing final goods but also across those producing raw materials and intermediary goods all over the world.

This type of leakage is especially important because the countries that provide basic materials are usually emerging or developing economies, many of which have no or limited regulation of GHG emissions. For this reason, foreign direct investment in developing economies usually leads to an increase in emissions ( [[#Kivyiro--2014|Kivyiro and Arminen 2014]] ; [[#Shahbaz--2015|Shahbaz et al. 2015]] ; [[#Bakhsh--2017|Bakhsh et al. 2017]] ): in the case of basic materials the effect of expansion of economic activity on emissions exceeds the effect of technological spillovers, while for developed countries the effect is opposite ( [[#Shahbaz--2015|Shahbaz et al. 2015]] ; [[#Pazienza--2019|Pazienza 2019]] ). [[#Meng--2018|Meng et al. (2018)]] calculated that environmental cost for generating one unit of GDP through international trade was 1.4 times higher than that through domestic production in 1995. By 2009, this difference increased to 1.8 times. Carbon leakage due to the differences in environmental regulation was the main driver of this increase.

In order to address emissions leakage through global value chains, [[#Liu--2017|Liu and Fan (2017)]] propose the value-added-based emissions accounting principle, which makes it possible to account for GHG emissions within the context of the economic benefit principle. [[#Davis--2011|Davis et al. (2011)]] notice that the analysis of value chains gives an opportunity to find the point where regulation would be the most efficient and the least vulnerable to leakage. For instance, transaction costs of global climate policy and the risks of leakage may be reduced if emissions are regulated at the extraction stage as there are far fewer agents involved in this process than in burning of fossil fuels or consumption of energy-intensive goods. [[#Li--2020|Li et al. (2020)]] calls for coordinated efforts to reduce emissions embodied in trade flows in pairs of the economies with the highest leakage, such as China and the United States, China and Germany, China and Japan, Russia and Germany.

Unfortunately, these proposals either face difficulties in collection and verification of data on emissions along value chains or require a high level of international cooperation, which is hardly achievable at the moment. [[#Neuhoff--2016|Neuhoff et al. (2016)]] and [[#Pollitt--2020|Pollitt et al. (2020)]] focus on the regulation of emissions embodied in global value chains through national policy instruments. They propose implementation of a charge on consumption of imported basic materials into the European emissions trading system. Such a charge, equivalent to around EUR80 tCO 2 –1 , could reduce the EU’s total CO 2 emissions by up to 10% by 2050 ( [[#Pollitt--2020|Pollitt et al. 2020]] ) without significant effects on competitiveness. This proposal is very close to the carbon border adjustment introduced in the EU and described in more detail in Sections 13.2 and 13.6.

Cross-sectoral effects of carbon leakage also occur through the multiplier effect, when the mitigation policy in any sector in country A leads to the increase of relative competitiveness and therefore production of the same sector in country B, which automatically leads to the expansion of economic activity in other sectors of country B. This expansion may in turn lead to the rise of production and emissions in country A as a result of feedback effects. These spillovers should be taken into consideration while designing climate policy, along with potential synergies that may appear due to joint efforts. However, the scale of these effects with regards to leakage should not be overestimated. Even for intrasectoral leakage, many ''ex ante'' modelling studies generally suggest limited carbon leakage rates (Chapter 13). Intersectoral leakage should be even less significant. Interregional spillover and feedback effects are well studied in China ( [[#Zhang--2017|Zhang 2017]] ; [[#Ning--2019|Ning et al. 2019]] ). Even within a single country, interregional spillover effects are much lower than intraregional effects, and feedback effects are even less intense. Cross-sectoral spillovers across national borders as a result of mitigation policy should be even smaller, although these are less well studied. In future, if the differences in carbon price between regions increase, leakage through cross-sectoral multipliers may play a more important role.

Another important cross-sectoral aspect of carbon leakage concerns the transport sector. If mitigation policy leads to the substitution of domestic carbon-intensive production with imports, one of the side effects of this substitution is the rise of emissions from transportation of imported goods. International transport is responsible for about a third of worldwide trade-related emissions, and over 75% of emissions for major manufacturing categories ( [[#Cristea--2013|Cristea et al. 2013]] ). Carbon leakage would potentially increase the emissions from transportation significantly as the trade of major consuming economies of the EU and US would shift towards distant trading partners in East and South Asia. [[#Meng--2018|Meng et al. (2018)]] consider more distant transportation as one of the major contributors to the rise in emissions embodied in international trade from 1995 to 2009.

Emissions leakage due to international trade, investment and value chains is a significant obstacle to more ambitious climate policies in many regions. However, it does not mean that disruption of trade would reduce global emissions. Zhang et al. (2020) show that deglobalisation and the drop in international trade may result in emissions reductions in the short term, but in the longer term it will make each country build more complete industrial systems to satisfy their final demand, although they have comparative disadvantages in some production stages. As a result, emissions would increase. According to Zhang et al. (2020), for China, the decrease of the degree of global value chain participation (which ranges from 0 to 1) by 0.1 would lead to an increase in gross carbon intensity of China’s exports of 11.7%. On distributional implications, [[#Parrado--2014|Parrado and De Cian (2014)]] report that trade-driven spillover effects transmitted through imports of materials and equipment result in significant inter-sectoral distributional effects, with some sectors witnessing substantial expansion in activity and emissions and others witnessing a decline in activities and emissions.

It should also be mentioned that international trade leads to important knowledge and technology spillovers (Sections 16.3 and 16.5) and is critically important for achieving other Sustainable Development Goals ( [[#12.6.1|Section 12.6.1]] ). Any policies imposing additional barriers to international trade should therefore be implemented with great caution and require comprehensive evaluation of various economic, social and environmental effects.

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<span id="the-spillover-effects-on-the-energy-sector"></span>
==== 12.6.3.2 The Spillover Effects on the Energy Sector ====

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Cross-sectoral trade-related spillovers of mitigation policies include their effect on energy prices. Other things being equal, regulation of emissions of industrial producers decreases the demand for fossil fuels that would reduce prices and encourage the rise of fossil fuel consumption in regions with no or weaker climate policies ( ''robust evidence'' , ''med'' ''ium agreement'' ) ''.''

[[#Arroyo-Currás--2015|Arroyo-Currás et al. (2015)]] studied the energy channel of carbon leakage with the REMIND IAM of the global economy. They came to the conclusion that the leakage rate through the energy channel is less than 16% of the emissions reductions of regions who introduce climate policies first. This result did not differ much for different sizes and compositions of the early mover coalition.

[[#Bauer--2015|Bauer et al. (2015)]] built a multi-model scenario ensemble for the analysis of energy-related spillovers of mitigation policies and revealed huge uncertainty: energy-related carbon leakage rates varied from negative values to 50%, primarily depending on the trends in inter-fuel substitution.

Another kind of spillover in the energy sector concerns the ‘green paradox’: announcement of future climate policies causes an increase in production and trade in fossil fuels in the short term ( [[#Jensen--2015|Jensen et al. 2015]] ; [[#Kotlikoff--2016|Kotlikoff et al. 2016]] ). The delayed carbon tax should therefore be higher than an immediately implemented carbon tax in order to achieve the same temperature target ( [[#van%20der%20Ploeg--2016|van der Ploeg 2016]] ). Studies also make a distinction between a ‘weak’ and ‘strong’ green paradox ( [[#Gerlagh--2011|Gerlagh 2011]] ). The former refers to a short-term rise in emissions in response to climate policy, while the latter refers to rising cumulative damage.

The green paradox may work in different ways for different kinds of fossil fuels. For instance, [[#Coulomb--2018|Coulomb and Henriet (2018)]] show that climate policies in the transport and power-generation sectors increase the discounted profits of the owners of conventional oil and gas, compared to the no-regulation baseline, but will decrease these profits for coal and unconventional oil and gas producers.

Many studies also distinguish different policy measures by the scale of green paradox they provide. The immediate carbon tax is the first-best instrument from the perspective of global welfare. Delayed carbon tax leads to some green paradox but less than in the case of support for renewables ( [[#Michielsen--2014|Michielsen 2014]] ; [[#van%20der%20Ploeg--2019|van der Ploeg and Rezai 2019]] ). With respect to the latter, support for renewable electricity has a lower green paradox than support for biofuels ( [[#Michielsen--2014|Michielsen 2014]] ; [[#Gronwald--2017|Gronwald et al. 2017]] ). The existence of the green paradox is an additional argument in favour of more decisive climate policy now: any postponements will lead to additional consumption of fossil fuels and consequently the need for more ambitious and costly efforts in future.

The effect of fossil fuel production expansion as a result of anticipated climate policy may be compensated by the effect of divestment. Delayed climate policy creates incentives for investors to divest from fossil fuels. [[#Bauer--2018|Bauer et al. (2018)]] show that this divestment effect is stronger and thus announcing of climate policies leads to the reduction of energy-related emissions.

The implication of the effects of mitigation policies through the energy-related spillovers channel is of particular significance to oil-exporting countries ( ''medium evidence'' , ''medium agreement'' ). Emissions-reduction measures lead to decreasing demand for fossil fuels and consequently to the decrease in exports from major oil- and gas-exporting countries. The case of Russia is one of the most illustrative. [[#Makarov--2020|Makarov et al. (2020)]] show that the fulfilment by Paris Agreement Parties of their NDCs would lead to 25% reduction of Russia’s energy exports by 2030 with significant reduction of its economic growth rates. At the same time, the domestic consumption of fossil fuels is anticipated to increase in response to the drop in external demand that would provoke carbon leakage ( [[#Orlov--2017|Orlov and Aaheim 2017]] ). Such spillovers demonstrate the need for dialogue between exporters and importers of fossil fuels while implementing the mitigation policies.

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<span id="implications-of-finance-for-cross-sectoral-mitigation-synergies-and-trade-offs"></span>
=== 12.6.4 Implications of Finance for Cross-sectoral Mitigation Synergies and Trade-offs ===

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Finance is a principal enabler of GHG mitigation and an essential component of countries’ NDC packages submitted under the Paris Agreement ( [[#UNFCCC--2016|UNFCCC 2016]] ). The assessment of investment requirements for mitigation along with their financing at sectoral levels are addressed in detail by sectoral chapters while the assessment of financial sources, instruments, and the overall mitigation financing gap is addressed by [[IPCC:Wg3:Chapter:Chapter-15|Chapter 15]] (Sections 15.3, 15.4, and 15.5). The focus in this chapter with respect to finance is on the scope and potential for financing integrated solutions that create synergies between and among sectors.

Cross-sectoral considerations in mitigation finance are critical for the effectiveness of mitigation action as well as for balancing the often conflicting social, developmental and environmental policy goals at the sectoral level. True measures of mitigation policy impacts and hence plans for resource mobilisation that properly address costs and benefits cannot be developed in isolation from their cross-sectoral implications. Unaddressed cross-sectoral coordination and interdependency issues are identified as major constraints in raising the necessary financial resources for mitigation in a number of countries ( [[#Bazilian--2011|Bazilian et al. 2011]] ; [[#Welsch--2014|Welsch et al. 2014]] ; [[#Hoff--2019a|Hoff et al. 2019a]] ).

Integrated financial solutions to leverage synergies between sectors, as opposed to purely sector-based financing, at international, national, and local levels are needed to scale up GHG mitigation potentials. At the international level, finance from multilateral development banks (MDBs) is a major source of GHG mitigation finance in developing countries ( ''medium evidence, medium agreement'' ) ( [[#World%20Bank%20Group--2015|World Bank Group 2015]] ; [[#Ha--2016|Ha et al. 2016]] ; [[#Bhattacharya--2016|Bhattacharya et al. 2016]] ; [[#Bhattacharya--2018|Bhattacharya et al. 2018]] ) ''.'' In 2018, MDBs reported a total of USD30.165 billion in financial commitments to climate change mitigation, with 71% of total mitigation finance being committed through investment loans and the rest in the form of equity, guarantees, and other instruments. GHG reduction activities eligible for MDB finance are limited to those compatible with low-emission pathways recognising the importance of long-term structural changes, such as the shift in energy production to low-carbon energy technologies and the modal shift to low-carbon modes of transport leveraging both greenfield and energy efficiency projects. Sector-wise, the MDBs’ mitigation finance for 2018 is allocated to renewable energy (29%), transport (18%), energy efficiency (18%), lower-carbon and efficient energy generation (7%), agriculture, forestry and land use (8%), waste and wastewater (8%), and other sectors (12%) ( [[#MDB--2019|MDB 2019]] ). Unfortunately, due to institutional and incentives issues, MDB finance has mostly focused on sectoral solutions and has not been able to properly leverage cross-sectoral synergies. At the national level, applied research has shown that integrated modelling of land, energy and water resources not only has the potential to identify superior solutions, but also reveals important differences in terms of investment requirements and required financing arrangements compared to the traditional sectoral financing toolkits ( [[#Welsch--2014|Welsch et al. 2014]] ). Agriculture, forestry, nature-based solutions and other forms of land use are promising sectors for leveraging financing solutions to scale up GHG mitigation efforts ( [[IPCC:Wg3:Chapter:Chapter-15#15.4|Section 15.4]] ). Moving to more productive and resilient forms of land use is a complex task, given the cross-cutting nature of land use, which necessarily results in apparent trade-offs between mitigation, adaptation, and development objectives. Finance is one area to manage these trade-offs where there may be opportunities to redirect the hundreds of billions spent annually on land use around the world towards green activities, without sacrificing either productivity or economic development ( [[#Falconer--2015|Falconer et al. 2015]] ). Nonetheless, that would require active public support in design of land-use mitigation and adaptation strategies, coordination between public and private instruments across land use sectors, and leveraging of policy and financial instruments to redirect finance toward greener land-use practices ( ''limited evidence, medium agreement'' ) ''.'' For example, the [[#Welsch--2014|Welsch et al. (2014)]] study on Mauritius shows that the promotion of a local biofuel industry from sugar cane could be economically favourable in the absence of water constraints, leading to a reduction in petroleum imports and GHG emissions while enhancing energy security. Yet, under a water-constrained scenario as a result of climate change, the need for additional energy to expand irrigation to previously rain-fed sugar plantations and to power desalination plants yields the opposite result in terms of GHG emissions and energy costs, making biofuels a sub-optimal option, and negatively affects their economics and the prospects for financing.

At the local level, integrated planning and financing are needed to achieve more sustainable outcomes. For example, at a city level, integration is needed across sectors such as transport, energy systems, buildings, sewage and solid waste to optimise emissions footprints. How a city is designed will affect transportation demands, which makes it either more or less difficult to implement efficient public transportation, leading in turn to more or fewer emissions. Under such cases, solutions in terms of public and private investment paths and financing policies based on purely internal sector considerations are bound to cause adverse impacts on other sectors and poor overall outcomes ( [[#Gouldson--2016|Gouldson et al. 2016]] ).

Availability and access to finance are among the major barriers to GHG emissions mitigation across various sectors and technology options ( ''robust evidence, high agreement'' ) ''.'' Resource maturity mismatches and risk exposure are two main factors limiting ability of commercial banks and other private lenders to contribute to green finance ( [[#Mazzucato--2018|Mazzucato and Semieniuk 2018]] ). At all levels, mobilising the necessary resources to leverage cross-sectoral mitigation synergies would require the combination of public and private financial sources ( [[#Jensen--2018|Jensen and Dowlatabadi 2018]] ). Traditional public financing would be required to synergise mitigation across sectors where the risk-return and time profiles of investment are not sufficiently attractive for the business sector. Over the years, private development financing through public-private partnerships and other related variants has been a growing source of finance to leverage cross-sectoral synergies and manage trade-offs ( [[#Anbumozhi--2018|Anbumozhi and Timilsina 2018]] ; [[#Attridge--2019|Attridge and Engen 2019]] ; [[#Ishiwatari--2019|Ishiwatari et al. 2019]] ). Promoting such blended approaches to finance along with result-based financing architectures to strengthen delivery institutions are advocated as effective means to mainstream cross-sectoral mitigation finance ( ''limited evidence, high agreement'' ) ( [[#Attridge--2019|Attridge and Engen 2019]] ; [[#Ishiwatari--2019|Ishiwatari et al. 2019]] ). The World Bank group and the International Financial Corporation have used the blended finance results-based approach to climate financing that addresses institutional, infrastructure, and service needs across sectors targeting developing countries and marginalised communities ( [[#GPRBA--2019|GPRBA 2019]] ; [[#IDA--2019|IDA 2019]] ).

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<span id="knowledge-gaps"></span>