Editing IPCC:AR6/WGIII/Chapter-15 (section)

== 15.5 Considerations on Financing Gaps and Drivers ==

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=== 15.5.1 Definitions ===

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The analysis of financing gaps in climate action, which is used to measure implementation action and mitigation impact(FS-UNEP Centre and [[#BNEF--2019|BNEF 2019]] ) cannot be carried out as a pure demand-side challenge, in isolation from the analysis of barriers to deploy funds (e.g., [[#Ramlee--2013|Ramlee and Berma 2013]] ) and to take investment initiatives. These barriers are ‘friction that prevents socially optimal investments from being commercially attractive’ ( [[#Druce--2016|Druce et al. 2016]] ). They are at the root of the ‘microeconomic paradox’ of a deficit of infrastructure investments despite a real return between 4% and 8% (Bhattacharya et al. 2016), of the low share of carbon-saving potentials tapped by dedicated policies such as energy renovation programmes ( [[#Ürge-Vorsatz--2018|Ürge-Vorsatz et al. 2018]] ), and, more generally of a demand for climate finance lower than the volume of economically viable projects ( [[#de%20Gouvello--2010|de Gouvello and Zelenko 2010]] ; [[#Timilsina--2010|Timilsina et al. 2010]] ).

A few exercises tried assess the consequences of the perpetuation of these drivers on the magnitude of the financing gap. They suggest, comparing the evolution of the infrastructure investment trends (beyond energy) by comparison with what they should be in an optimal scenario, a cumulative deficit between 19% ( [[#Oxford%20Economics--2017|Oxford Economics 2017]] ) and 32% ( [[#Arezki--2016|Arezki et al. 2016]] ). The volume of this gap is of the same order of magnitude as the incremental infrastructure investments (energy and beyond) for meeting a 1.5°C target (2.4% of the world GDP on average) (Box 4.8 of SR1.5 ( [[#IPCC--2018|IPCC 2018]] )) calculated by exercises assuming no pre-existing investment gap. This figure is consistent with the 1.5% to 1.8% assessed by the [[#European%20Commission--2020|European Commission (2020)]] for Europe and the 2% of the [[#IMF--2021d|IMF (2021d)]] for the G20, which do not encompass many developing countries for which economic take-off is today fossil fuels dependent. For low- and middle-income economies, Rozenberg and Fay’s (2019) results suggest to increase the infrastructure investments by 2.5 to 6 percentage points of GDP to cover both the reduction of the structural investment gap and the specific additional costs for bridging it with low-carbon and climate-resilient options. These assessments indicate the challenge at stake but do not exist at very disaggregated sectoral and regional levels for sectors other than energy.

The below quantitative analysis does not differentiate between financing gaps driven by barriers within or outside the financial sector given that the IAM models as well as most other studies used do not incorporate actual risk ranges depending on policy strength and coherence and institutional capacity, low-carbon policy risks, lack of long-term capital, cross-border currency fluctuation, and pre-investment costs and barriers within the financial sector that discourage private sector financing. They comprise short-termism ( [[#UNEP%20Inquiry--2016b|UNEP Inquiry 2016b]] ), high perceived risks for mitigation-relevant technologies and/or regions (information gap through incomplete/asymmetric information, (Kempa and Moslener 2017; Clark et al. 2018)), lack of carbon pricing effects ( [[#Best--2018|Best and Burke 2018]] ), home bias (results in limited balancing for regional mismatches between current capital and needs distribution, ( [[#Boissinot--2016|Boissinot et al. 2016]] )), and perceived high opportunity and transaction costs (results from limited visibility of future pipelines and policy interventions; SME financing tickets and the missing middle, ( [[#Grubler--2016|Grubler et al. 2016]] )). In addition, barriers outside the financial sector will have to be addressed to close future financing gaps. The mix and dominance of individual barriers might vary significantly across sectors and regions and is analysed below.

The interpretation of the quantitative analysis thus needs to be performed, taking into account the qualitative needs assessment in [[#15.4.1|Section 15.4.1]] and the evolution of parameters that determine the risk-weighted relative attractiveness of low-carbon and climate-resilient investments compared to other investment opportunities. With some institutions having announced climate finance commitments and/or targets (see also Box 15.4), the actual asset allocation of commercial financial sector players including sectoral and regional focus will respond to tangible and financially viable investment opportunities available in the short term. Robust long-term pathways to create such conditions for a significant private sector involvement rarely exist and expectations on private sector involvement in some critical sectors/regions might be too high (Clark et al. 2018).

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=== 15.5.2 Identified Financing Gaps for Sector and Regions ===

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The following section compares recent climate finance flows as reported by CPI and IEA to needs derived in [[#15.4|Section 15.4]] , ignoring the slight mismatch in time horizons. The analysis ignores interlinked gaps, in particular infrastructure investment gaps and other SDG-related investment gaps, which need to be addressed in parallel to reach the LTGG but also at least partially to facilitate green investments.

Total investments in mitigation need to increase by around three and six times with significant gaps existing across sectors and regions [[#footnote-009|8]] ( ''high confidence'' ). The findings on still significant gaps and limited progress over the past few years to some extent seem to contradict the massive increase in commitments by financial institutions. As discussed in [[#15.6|Section 15.6]] , the investment gap is not due to global scarcity of funds.

However, these investment gaps have little explanatory power in terms of the magnitude of the challenge to mobilise funding. In addition to measurement challenges from different definitions and data gaps, sectors and regions offer highly divergent financial risk-return profiles, in particular due to missing or weak regulatory environments consistent with ambitions levels, and economic costs as well as limited local capital markets, limited institutional capacity to ensure safeguard, standardisation, scalability and replicability of investment opportunities and financing models, and a pipeline ready for commercial investments. Moreover, soft costs and institutional capacity for enabling environment that can be prerequisite for addressing financing gaps are ignored when focusing on investment cost needs.

'''Sectoral considerations.''' The renewable energy sector attracted the highest level of financing in absolute and relative terms with business models in generation being proven and rapidly falling technology costs driving the competitiveness of solar photovoltaic and onshore wind, even without taking account of the mitigation component (FS-UNEP Centre and [[#BNEF--2019|BNEF 2019]] ; [[#IRENA--2020a|IRENA 2020a]] ). This investment activity comes in line with the first generation of NDCs and their heavy focus on mitigation opportunities in the renewable energy sector ( [[#Pauw--2016|Pauw et al. 2016]] ; [[#Schletz--2017|Schletz et al. 2017]] ). Still, the investment gap tends to remain stable with flows over the past years not showing an upward trend.

Comparing annual average total investments in global fuel supply and the power sector of approximately USD1.5 trillion [[#footnote-008|9]] yr –1 in 2019 ( [[#IEA--2020a|IEA 2020a]] ) to the investment in the Stated Policies Scenario (approximately 1.7 trillion USD 2015 yr –1 ) and the Sustainable Development Scenario (approximately 1.8 trillion USD 2015 yr –1 ) in 2030 underlines the required shift of existing capital investment from fossil to renewables even more than the need to increase sector allocations ( [[#Granoff--2016|Granoff et al. 2016]] ; [[#McCollum--2018|McCollum et al. 2018]] ).

Ensuring access to the heavily regulated electricity markets is a key driver for an accelerated private sector engagement ( [[#IFC--2016|IFC 2016]] ; [[#FS-UNEP%20Centre%20and%20BNEF--2018|FS-UNEP Centre and BNEF 2018]] ; [[#REN21--2019|REN21 2019]] ), with phasing out of support schemes and regulatory uncertainty being a major driver for reduced investment volumes in various regional markets in the past years ( [[#FS-UNEP%20Centre%20and%20BNEF--2015|FS-UNEP Centre and BNEF 2015]] , 2016, 2017, 2018, 2020). Strategic investors and corporate investments by utilities dominate the investment activity in developed countries and countries in transition ( [[#BNEF--2019|BNEF 2019]] ) based on the competitiveness of renewable energy sources. Reasonable auction results based on a substantial private-sector competition for investments have also been achieved in selected developing countries driven by rather standardised contract structures and the increased availability of risk mitigation instruments addressing political and regulatory risks and home bias constraints (FS-UNEP Centre and [[#BNEF--2019|BNEF 2019]] ; [[#IRENA--2020a|IRENA 2020a]] ). Development finance institution (DFI) climate portfolios tend to be driven by concessional loans for renewable energy generation assets with equity often being provided by (semi-) commercial investors ( [[#15.3|Section 15.3]] ) which will have to change to accelerate renewable energy investment activity.

Given the wide range of estimates on current investment flows into energy efficiency, substantial uncertainty exists with regard to the magnitude of the investment gaps. While CPI publishes investment levels of 41 billion USD 2015 in 2019 and 24 billion USD 2015 in 2020 for energy efficiency, counting majorly international flows, IEA results come in at a much higher level of around 250 billion USD 2015 annually between 2017 and 2020 ( [[#IEA--2021c|IEA 2021c]] ) and [[#IRENA--2020c|IRENA (2020c)]] estimates energy efficiency investments in buildings between 2017–2019 at an average of USD139 billion yr –1 .

Public sector investments in the transport sector have increased significantly in the past years reflecting the increased interest of capital markets in renewable energy and the efficient and corresponding reallocation of public funding. Provision of funding by capital markets for public transport infrastructure among others heavily depends on suitable financing vehicles and increased funding for development of projects with a low level of standardisation ( [[#OECD--2015a|OECD 2015a]] ).

Both IRENA and IEA include only incremental costs of EVs in their estimates on needs while CPI, when measuring actual flows, includes those at full costs. Total private flows for EVs included in CPI numbers amount to USD41 billion in 2018 ( [[#Buchner--2019|Buchner et al. 2019]] ), representing more than 80% of private sector finance into the transport sector, around one third of total public and private funding to the transport sector in 2018. This likely results in an underestimation of the financing gap – in addition to the fact that estimates for investment needs for rail infrastructure are only available for selected countries.

Current financing of land-based mitigation options is less than USD1 billion yr –1 representing only 2.5% of climate mitigation funding, significantly below the potential proportional contribution ( [[#Buchner--2019|Buchner et al. 2019]] ). A stronger focus on deforestation-free value chain, including a stronger reflection in taxonomies and financial sector investment decision processes are necessary to ''ensure'' an alignment of financial flows with the LTGG. Taking into account the specifics of land-based mitigation (in particular long investment horizons, strong dependency on the monetisation of mitigation effects, strong public sector involvement) a significant scale-up of commercial financing to the sector can hardly be expected in the absence of strong climate policies (Clark et al. 2018). Agriculture is likely to develop more potential to mobilise private finance than the forest sector given its strong linkage to food security and hunger and shorter payback periods. The significant gap in land-based mitigation finance also indicates the crucial lack of finance to the bottom of the pyramid.

Agricultural support is an important source of distortions to agricultural incentives in both rich and poor countries ( [[#Mamun--2019|Mamun et al. 2019]] ) ranging from the largest component of the support, market price supports, increased gross revenue to farmers as a result of higher prices due to market barriers created by government policies, to production payments and other support including input subsidy (e.g., fertiliser subsidy) ( [[#Searchinger--2020|Searchinger et al. 2020]] ). USD600 billion of annual governmental support for agriculture in the OECD database contributes only modestly to the related objectives of boosting crop yields and just transition ( [[#Searchinger--2020|Searchinger et al. 2020]] ). A review of NDCs of 40 developing countries which submitted a NDC to the UNFCCC Interim NDC Registry by April 2017, and include within their NDC efforts to REDD+ via support from the UN-REDD Programme and/or World Bank Forest Carbon Partnership Facility, indicates that none of the countries reviewed mention fiscal policy reform of existing finance flows to agricultural commodity production or other publicly supported programmes that affect the direct and underlying drivers of land use conversion ( [[#Kissinger--2019|Kissinger et al. 2019]] ).

'''Analysis by region and type of economy.''' The analysis of gaps by type of economy illustrates the challenge for developing countries. Estimated mitigation financing needs as a percentage of mean 2017–2020 GDP in USD 2015 comes in at around 2–4% for developed countries, and around 4-9% for developing countries ( ''high confidence'' ) (Figure 15.4). Climate finance flows have to increase by a factor of four to seven in developing countries and three to five in developed countries. This disparity is further exacerbated when considering adaptation, infrastructure and SDG-related investment needs ( ''high confidence'' ) ( [[#Hourcade--2021a|Hourcade et al. 2021a]] ). However, differences across developing countries are significant. Flows to Eastern Asia, with its annual average flows (2017–2020) of 252 billion USD 2015 being dominated by China (more than 95% of total mitigation flows to Eastern Asia), would have to increase by a factor of two to four, a comparable level to developed countries. [[#15.6.2|Section 15.6.2]] elaborates on outlooks with regard to fiscal space and ability to tap capital markets, in particular for developing countries. In particular, attention must accelerate on low-income Africa. This large continent currently contributes very little to global emissions, but its rapidly rising energy demands and renewable energy potential versus its growing reliance on fossil fuels and ‘cheap’ biomass (especially fuelwood for cooking and charcoal, with impacts on deforestation) amid fast-rising urbanisation makes it imperative that institutional investors and policymakers recognise the very large ‘leap-frog’ potential for the renewable energy transition as well as risks of lock-in effects in infrastructure more generally in Africa that is critical to hold the global temperatures rise to well below 2°C in the longer term (2020–2050). Overlooking this transition opportunity, rivalling China, India, USA and Europe, would be costly. Policies centred around the accelerated development of local capital markets for energy transitions – with support from external grants, supra-national guarantees and recognition of carbon remediation assets – are crucial options here, as in other low-income countries and regional settings. Notably, climate finance flows to African countries might have even decreased for mitigation technology deployment (stagnated for adaptation between 2017 and 2020), widening the finance gap in African countries in the recent years ( ''hig'' ''h confidence'' ) ''.''

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'''Figure 15.4 | Breakdown of recent average (downstream) mitigation investments and model-based investment requirements for 2020–2030 (USD billion) in scenarios that likely limit warming to 2°C or lower.''' Mitigation investment flows and model-based investment requirements by sector / segment (energy efficiency in buildings and industry, transport including efficiency, electricity generation, transmission and distribution including electrification, and agriculture, forestry and other land use), by type of economy, and by region (see Annex II Part I Section 1: By region is based on intermediate level (R10) classification scheme. By type of economy is based on intermediate level (R10) classification scheme, which considers ‘North America’, ‘Europe’, and ’Australia, Japan and New Zealand’ as developed countries, and the other seven regions as developing countries). Breakdown by sector / segment may differ slightly from sectoral analysis in other contexts due to the availability of investment needs data. The granularity of the models assessed in Chapter 3, and other studies, do not allow for a robust assessment of the specific investment needs of LDCs or SIDSs. Investment requirements in developing countries might be underestimated due to missing data points as well as underestimated technology costs. In modelled pathways, regional investments are projected to occur when and where they are cost cost-effective to limit global warming. The model quantifications help to identify high-priority areas for cost-effective investments, but do not provide any indication on who would finance the regional investments. Investment requirements and flows covering downstream / mitigation technology deployment only. Data includes investments with a direct mitigation effect, and in the case of electricity, additional transmission and distribution investments. See section 15.4.2 Quantitative assessment of financing needs for detailed data on investment requirements. Data on mitigation investment flows are based on a single series of reports (Climate Policy Initiative, CPI) which assembles data from multiple sources. Investment flows for energy efficiency are adjusted based on data from the International Energy Agency (IEA). Data on mitigation investments do not include technical assistance (i.e., policy and national budget support or capacity building), other non-technology deployment financing. Adaptation only flows are also excluded. Data on mitigation investment requirements for electricity are based on emission pathways C1, C2 and C3 (Table SPM.1). For electricity investment requirements, the upper end refers to the mean of C1 pathways and the lower end to the mean of C3 pathways. Data points for energy efficiency, transport and AFOLU cannot always be linked to C1–C3 scenarios. Data do not include needs for adaptation or general infrastructure investment or investment related to meeting the SDGs other than mitigation, which may be at least partially required to facilitate mitigation. The multiplication factors show the ratio of average annual model-based mitigation investment requirements (2020–2030) and most recent annual mitigation investments (averaged for 2017–2020). The lower and upper multiplication factors refer to the lower and upper ends of the range of investment needs.

Given the multiple sources and lack of harmonised methodologies, the data can only be indicative of the size and pattern of investment gaps. The gap between most recent flows and required investments is only a single indicator. A more comprehensive (and qualitative) assessment is required in order to understand the magnitude of the challenge of scaling up investment in sectors and regions. The analysis also does not consider the effects of misaligned flows. {15.3, 15.4, 15.5, Table 15.2, Table 15.3, Table 15.4}

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'''Figure 15.5 | Visual abstract to address financing gaps in Se''' '''ction 15.''' '''6.'''

Over 80% of climate finance is reported to originate and stay within borders, and even higher for private climate flows (over 90%) ( [[#Boissinot--2016|Boissinot et al. 2016]] ). There are multiple reasons for such ‘home bias’ in finance – national policy support, differences in regulatory standards, exchange rate, political and governance risks, as well as information market failures. The extensive home bias means that even if national actions are announced and intended to be implemented unilaterally and voluntarily, the ability to implement them requires access to climate finance which is constrained by the relative ability of financial and capital markets at home to provide such financing, and access to global capital markets that requires supporting institutional policies in source countries. ‘Enabling’ public policies and actions locally (cities, states, countries and regions), to reduce investment risks and boost domestic climate capital markets financing, and to enlarge the pool of external climate financing sources with policy support from source capital countries thus matters at a general level. The biggest challenge in climate finance is likely to be in developing countries, even in the presence of enabling policies and quite apart from any other considerations such as equity and climate justice ( [[#Klinsky--2017|Klinsky et al. 2017]] ) or questions about the equitable allocations of future ‘climate budgets’ ( [[#Gignac--2015|Gignac and Matthews 2015]] ). The differentiation between developed and developing countries matters most on financing. Most developed countries have already achieved very high levels of incomes, have the largest pool of capital stock and financial capital (which can be more easily redeployed within these countries given the home bias of financial markets), the most well-developed financial markets and the highest sovereign credit ratings, in addition to starting with very high levels of per capita carbon consumption – factors that should allow the fastest adjustment to low-carbon investments and transition in these countries from domestic policies alone. The financial and economic circumstances are more challenging in many developing countries, even within a heterogeneity of circumstances across countries. The dilemma, however, is that the fastest rates of the expected increase in future carbon emissions are in developing countries. The biggest challenge of climate finance globally is thus likely to be the constraints to climate financing because of the opportunity costs and relative under-development of capital markets and financing constraints (and costs) at home in developing countries, and the relative availability or absence of adequate financing policy support internationally from developed countries. The Paris Agreement and commitment by developed countries to support the climate financing needs of developing countries thus continue to matter a great deal.

'''Soft costs/institutional capacity''' ( [[#Osama--2021|Osama et al. 2021]] ). Most funding needs assessments focus on technology costs and ignore the cascade of financing needs as outlined above. International grant funding or national budget allocations for soft costs like the creation of a regulatory environment can be a prerequisite for the supply of commercial financing for the deployment of technologies. Such critical funding needs might represent a small share of overall investment needs but current (relatively small) gaps in funding of policy reforms can hinder or delay deployment of large volumes of funding in later years. The role, as well as the approximate volumes of such required timely international grant funding or national budget allocations, appear underestimated in research. The numbers available for the creation of an enabling environment for medium-sized renewable energy (RE) projects in Uganda ( [[#GET%20FiT%20Uganda--2018|GET FiT Uganda 2018]] ) are illustrative only and cannot be transferred as assumptions to other countries without taking into account potentially varying starting points in terms of institutional readiness, pipelines, as well as the general business environment. GET FiT Uganda supported 170 MWp of medium-scale RE capacity triggering investments of USD453 million ( [[#GET%20FiT%20Uganda--2018|GET FiT Uganda 2018]] ), international results-based incremental cost support amounted to USD92 million and project preparation, technical assistance, and implementation support, required USD8 million, excluding support from national agencies.

There is strong evidence of the correlation between institutional capacity of countries and international climate finance flows towards those economies ( [[#Adenle--2017|Adenle et al. 2017]] ; [[#Stender--2019|Stender et al. 2019]] ) and a strong need for robust institutional capacity to manage the transformation in a sustainable and human rights based way ( [[#Duyck--2018|Duyck et al. 2018]] ). Oneexample to consider unaddressed social concerns is the ongoing call for feedback by the European Commission and its platform on sustainable finance. It argues for a social taxonomy, that can support the identification of financing opportunities for economic activities contributing to social objectives ( [[#European%20Commission--2021b|European Commission 2021b]] ). SEforAll has highlighted the issue of investments not going to the countries with the greatest need, also partly driven by institutional capacity levels (SEforALL and [[#CPI--2020|CPI 2020]] ). Also, most of the developing countries’ NDCs are conditional upon international support for capacity building ( [[#Pauw--2020|Pauw et al. 2020]] ). The Climate Technology Centre and Network (CTCN) was created as an operational arm of the UNFCCC Technology Mechanism with the mandate to respond to requests from developing countries. Initial evaluations of the mechanism underpin its importance and value for developing countries but stress long lead times and predictability of future international public finance to maintain operations as key challenges ( [[#UNFCCC--2017|UNFCCC 2017]] ; [[#DANIDA--2018|DANIDA 2018]] ). While limited pipelines, limited absorptive capacities as well as restricted institutional capacity of countries are often stated as challenges for an accelerated deployment of finance ( [[#Adenle--2017|Adenle et al. 2017]] ), the question remains on the role of international public climate finance to address this gap and whether a concrete current financing gap exists for patient institutional capacity building. While current short-term, mostly project-related, capacity building often fails to meet needs but alternative, well-structured patient interventions and finance could play an important role ( [[#Saldanha--2006|Saldanha 2006]] ; [[#Hope--2011|Hope 2011]] ) accepting other barriers than financing playing a role as well. One reason why international public climate finance is not sufficiently directed to such needs might be the complexity in measuring intangible, direct outcomes like improved institutional capacity (Clark et al. 2018).

'''Early stage/venture capital financing/pilot project financing.''' Early-stage companies in impact investment sectors with business solutions can contribute positively to climate impact. Figure SPM.8 highlights the need for new business models facilitating parts of the behavioural change. Also, SE4All has underpinned the need for an expansion of available business models to achieve universal access (SEforALL and [[#CPI--2020|CPI 2020]] ). Further research and development needs range from resource efficiency of proven technologies and next generation technologies but also new technologies (Chapter 16). Access to early stage financing remains critical with performance in recent years being weak ( [[#Gaddy--2016|Gaddy et al. 2016]] ). This historically weak performance of clean tech start-ups burdens the interest of investors in the sector on the one hand and discourages experienced executive talent ( [[#Wang--2020|Wang and Yee 2020]] ). Besides that, the concentration of venture capital markets in the USA, Europe and India represents a major challenge (FS-UNEP Centre and [[#BNEF--2019|BNEF 2019]] ; [[#Statistica--2021|Statistica 2021]] ). With regard to commercial-scale demonstration projects, IEA estimates a need of USD90 billion of public sector finance before 2030 having around USD25 billion already planned by governments to 2030 ( [[#IEA--2021c|IEA 2021c]] ).

'''Need for parallel rather than sequential investment decisions.''' The needs and gaps assessment does not include upstream investment needs required to facilitate the technology deployment as foreseen in the scenarios presented above. For example, for their transforming energy scenario IRENA estimates the number of EVs to increase from around 8 million units in 2019 to 269 million units in 2030 ( [[#IRENA--2020c|IRENA 2020c]] ). This would require investments in battery factories amounting to approximately USD207 billion with further investment requirements in the value chain ( [[#IRENA--2020d|IRENA 2020d]] ). This illustrates the extent of parallel investments based on goals rather than concrete regulatory interventions and/or demand and poses a problem of upfront investment risks for each industry in the chain in the absence of certainty of the presence of parallel decisions in the upstream and downstream links in the chain. This is a typical element of the ‘valley of the death’ of innovation ( [[#Scherer--2000|Scherer et al. 2000]] ; [[#Åhman--2017|Åhman et al. 2017]] ). It discourages risk-taking and slows down the learning-by-doing processes, economies of scale and increasing returns to adoption needed for lowering the costs of systemic technical change ( [[#Kahouli-Brahmi--2009|Kahouli-Brahmi 2009]] ; [[#Weiss--2010|Weiss et al. 2010]] ). Implications for risk perception, financing costs as well as investment decision-making processes and ultimately for feasibility are rarely considered.

'''Finance for adaptation and resilience.''' As explained early, the reduction of the infrastructure gap to increase societies’ resilience and the implementation of the NAPs will require more and higher levels of sustained financing. Activities mobilised for adaptation and resilience are often not marketable and their financing will continue coming from the public sector ( [[#Murphy--2020|Murphy and Parry 2020]] ) and, at the international level, from grants-based technical assistance or through budgetary support or basket finance for large projects/programmes or sector-wide approaches or multilateral finance under (Non-)UNFCCC [[#footnote-007|10]] that also anticipate supporting NAP implementation – particularly those involving incremental costs and co-benefits, which will include sectoral approaches such as water, energy, infrastructures, and food production. According to the UNFCCC, ‘in 2015–2016, 3% of international public adaptation finance flows was supplied by multilateral climate funds, while 84% came from development finance institutions and 13% from other government sources’ ( [[#UNFCCC--2019c|UNFCCC 2019c]] ). Comprehensive reporting on adaptation finance by [[#Murphy--2020|Murphy and Parry (2020)]] and [[#Buchner--2019|Buchner et al. (2019)]] argues that flows of finance for adaptation action in developing countries in 2017 and 2018 were estimated to be approximately USD30 billion; this plus an additional estimated flow of USD12 billion for dual adaptation and mitigation actions totalled USD42 billion, accounting for 7.25% of the total estimated international public and private flows of climate finance ( [[#Buchner--2019|Buchner et al. 2019]] ). They are far below the financing needs given in [[#15.4|Section 15.4]] . To date, the private sector has limited involvement in NAPs and adaptation projects and planning but can be involved through public-private partnership ( [[#15.6.2.1|Section 15.6.2.1]] ) and other incentives provided by governments ( [[#Schmidt-Traub--2015|Schmidt-Traub and Sachs 2015]] ; Druce et al. 2016; [[#Koh--2016|Koh et al. 2016]] ; [[#UNEP--2016|UNEP 2016]] ; [[#NAP%20Global%20Network--2017|NAP Global Network 2017]] ; [[#Murphy--2020|Murphy and Parry 2020]] ) and innovative private financing mechanisms such as green and blue bonds. However, adaptation financing is only about 2% of the share of green bond financing raised up to June 2019 ( [[#UNFCCC--2019c|UNFCCC 2019c]] ), [[#footnote-006|11]] whereas it is about 10% of sovereign green bonds raised ( [[#UNFCCC--2019d|UNFCCC 2019d]] ). ( [[#Tuhkanen--2020|Tuhkanen 2020]] ), in a detailed review of green bond issuance in the Environmental Finance Data base 2019, found that between March 2010 to April 2019, ‘5% of all green bonds issued were categorised as adaptation and that ‘the private sector accounts for a significant proportion of adaptation-related green bond issuances’ ( [[#Tuhkanen--2020|Tuhkanen 2020]] ). However, [[#GIZ--2017b|GIZ (2017b)]] , Nicol et al. (2017, 2018a), and [[#Tuhkanen--2020|Tuhkanen (2020)]] highlight that there is scepticism about this stream of finance for adaptation due to the factors that have thus far limited the private sector’s involvement in adaptation: lack of resilience-related revenue streams, the small scale of some adaptation projects and the overall ‘intangibility’ of financing adaptation projects ( [[#Larsen--2019|Larsen et al. 2019]] ).

Financing for resilience is limited, unpredictable, fragmented and focused on few projects or sectors and short term as opposed to programmatic and long term (10–15 years) finacing to build resilience ( [[#ISDR--2009|ISDR 2009]] , 2011; [[#Kellett--2014|Kellett and Peters 2014]] ; [[#Watson--2015|Watson et al. 2015]] ). Market-based mechanisms are available but not equally accessible to all developing countries, particularly SIDS and LDCs, and such mechanisms can undermine debt sustainability ( [[#OECD%20and%20World%20Bank--2016|OECD and]] [[#World%20Bank--2016|World Bank 2016]] ). While resilience financing is mainly grant funding, concessional loans are increasing substantially and are key sources of financing for disaster and resilience, particularly for upper-middle-income countries ( [[#OECD%20and%20World%20Bank--2016|OECD and]] [[#World%20Bank--2016|World Bank 2016]] ). The combination of these trends can contribute to greater levels of indebtedness among many developing countries, many of which are already at or approaching debt distress.

Social protection systems can be linked with a number of the instruments already considered: reserve funds, insurance and catastrophe bonds, regional risk-sharing facilities, contingent credit, in addition to traditional international aid and disaster response. [[#Hallegatte--2017|Hallegatte et al. (2017)]] recommend combining adaptive social protection with financial instruments in a consistent policy package, which includes financial instruments to deliver adequate liquidity and contingency plans for the disbursement of funds post disaster. Challenges related to financing residual climate-related losses and damages are particularly high for developing countries. Financing losses and damages from extreme events requires rapid pay-outs; the cost of financing for many developing countries is already quite high; and the expense of risk financing is expected to increase as disasters become more frequent, intense and more costly, not only due to climate change but also due to higher levels of exposure. Addressing both extreme and slow onset climate impacts requires designing adequate financial protection systems for reaching the most vulnerable. Moreover, some fraction of losses and damages, both material and non-material, are not commonly valued in monetary terms (non-economic loss) and hence financing requirements are hard to estimate. These non-market-based residual impacts include loss of cultural identity, sacred places, human health and lives ( [[#Ameli--2021a|Ameli et al. 2021a]] ; [[#Paul--2019|Paul 2019]] ; [[#Serdeczny--2019|Serdeczny 2019]] ).

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