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

== 15.4 Financing Needs ==

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=== 15.4.1 Definitions of Financing Needs ===

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Financing needs [[#footnote-010|7]] are discussed in various contexts, only one being international climate politics and finance. Also, financing needs are used as an indicator for required system changes (when compared to current flows and asset bases) and an indicator for near- to long-term investment opportunities from the perspective of investors and corporates. Investment needs are widely used as an indicator focusing on initial investments required to realise new infrastructure. It compares relatively well with private sector flows dominated by return-generating investments but lacks comparability and explanatory power regarding the needs in the context of international climate cooperation, where considerations on economic costs play a more substantial role. [[IPCC:Wg3:Chapter:Chapter-12|Chapter 12]] elaborates on global economic cost estimates for various technologies. This indicator includes both costs and benefits of options, of which investment-related costs make up only one component. Both analyses offer complementary insights. There are financing needs not directly related to the realisation of physical infrastructure and which are not covered in both investment and cost estimates. For instance, the needs for building institutional capacity to achieve social and economic goals and to strengthen knowledge, skills, national and international cooperation might not be significant, but an enabling environment for future investments would not be established without satisfying it. Moreover, comprehending financial needs for addressing economic losses due to climate change can hardly be measured in terms of the indicators introduced before.

Understanding the magnitude of the challenge to scale up finance in sectors and regions requires a more comprehensive (and qualitative) assessment of the needs. For finance to become an enabler of the transition, domestic and international public interventions can be needed to ensure enough supply of finance across sectors, regions and stakeholders. The location of financing needs and vicinity to capital matter given home bias ( [[#Fuchs--2017|Fuchs et al. 2017]] ; [[#OECD--2017a|OECD 2017a]] ; [[#Ito--2019|Ito and McCauley 2019]] ) (prioritising own country or regions), transaction costs and risk considerations ( [[#15.2|Section 15.2]] ). Most of the finance is mobilised domestically but the depth of capital markets is substantially greater in developed countries, increasing the challenges to mobilise substantial volumes of additional financing for many developing countries. The same applies to various stakeholders with limited connections into the financial sector. In addition, governments enabling financial market frameworks, guidelines and supportive infrastructure is crucial for inclusive finance for the bottom of the pyramid, especially disadvantaged and economically marginalised segments of society.

The attractiveness of a sector and region for capital markets depends on several factors. Some essential elements are the duration of loan and profile as long-term loans and heavily heterogeneous returns represent challenges in financing mitigation technologies and policies. After the financial crisis and restricted access to long-term debt, capital intensity of technologies and resulting long payback periods of investment opportunities for mitigation technologies have been a crucial challenge ( [[#Bertoldi--2021|Bertoldi et al. 2021]] ). Also, implicit discount rates applied during the investment decision process vary depending on the payback profile, with research mainly covering the difference between the financing of assets generating revenues versus costs ( [[#Jaffe--2004|Jaffe et al. 2004]] ; [[#Schleich--2016|Schleich et al. 2016]] ). In addition, a low correlation between the climate projects and dominating asset classes might provide an opportunity in climate action by satisfying the appetite of institutional investors, which tend to manage portfolios with consideration of the Markowitz modern portfolio theory (optimising return and risk of a portfolio through diversification) ( [[#Marinoni--2011|Marinoni et al. 2011]] ). Transaction cost is a significant barrier to the diffusion and commercialisation of low-carbon technologies and business models and adaptation action. High transaction costs, attributed to various factors, such as complexity and limited standardisation of investments, limited pipelines, complex institutional and administrative procedures, create significant opportunity costs of green investments comparing with other standard investments ( [[#IRENA--2016|IRENA 2016]] ; [[#Nelson--2016|Nelson et al. 2016]] ; [[#Feldman--2018|Feldman et al. 2018]] ). For example, transaction costs are commonly observed in small-scale, dispersed independent renewable energy systems, especially in rural areas, and energy efficiency projects ( [[#Hunecke--2019|Hunecke et al. 2019]] ). A more robust standardisation and alignment of Power Purchase Agreement (PPA) terms with best practices globally has led to a substantially increased interest in capital markets in developing countries ( [[#WBCSD--2016|WBCSD 2016]] ; [[#Schmidt--2019|Schmidt et al. 2019]] ; [[#World%20Bank--2021|]] [[#World%20Bank--2021|World Bank 2021]] ). Notably, PPA significantly increases the probability of more balanced investment and development outcomes and ultimately more sustainable independent power projects in developing countries. Therefore, lowering transaction costs would be essential for creating investor appetite. The role of intermediaries bundling demand for financing has been demonstrated to reduce transaction costs and to reach investors’ critical size. In addition, new innovative approaches, such as fintech and blockchain ( [[#15.6.8|Section 15.6.8]] ), have been discussed for providing new opportunities in the energy sector.

Economic viability of investments – ideally not relying on the pricing of positive externalities – has been a critical driver of momentum in the past. The falling technology costs and the competitiveness of renewable technologies, especially solar PV and wind, have accelerated the deployment of renewable technologies over the past years. Renewable energy technologies are now often competitive, and have even become the cheapest, in many countries, even without financial support ( [[#FS-UNEP%20Centre%20and%20BNEF--2015|FS-UNEP Centre and BNEF 2015]] , 2016, 2017, 2018, 2019; [[#IEA--2020c|IEA 2020c]] ; [[#IRENA--2020a|IRENA 2020a]] ) and without pricing of the avoided carbon emissions. In contrast, the dependency on regulatory interventions and public financial support to create financial viability has provided a source of volatile investor appetite. The annual volume of renewable investment by country is often volatile, reflecting ending and new regulations and policies ( [[#IEA--2019a|IEA 2019a]] ).

For example, the recent Chinese policy direction towards tougher access to and a substantial cut in feed-in-tariffs in 2018 led to a significant drop in renewable investment and new capacity addition in China (FS-UNEP Centre and [[#BNEF--2019|BNEF 2019]] ; [[#Hove--2020|Hove 2020]] ). However, the significant bouncing back of newly installed capacity (72 GW wind power and 47 GW solar power in 2020) shows the strong development of zero-carbon power generation driven by lower cost and policies to support them by energy revolution strategies in China. Investors had proven to be willing to work with transparent support mechanisms, such as with the Clean Development Mechanism (CDM), which stimulated emission reductions and allowed industrialised countries to implement emission-reduction projects in developing countries to meet their emission targets ( [[#Michaelowa--2019|Michaelowa et al. 2019]] ). However, the collapse of carbon markets and prices, especially of the EU Emissions Trading System, led to the continuous decline of Certified Emission Reductions issuances from CDM in the past years ( [[#World%20Bank%20Group--2020|World Bank Group 2020]] ). Also, the dependency on regulatory intervention to ensure fair market access only has proven to burden investor appetite.

A significant share of investment needs in heavily regulated sectors, such as electricity, public transport, and telecom, emphasises the importance of regulatory intervention, such as ownership and market access ( [[#OECD--2017b|OECD 2017b]] ). For instance, energy-system developments require effective and credible commitments and action by policymakers to ensure an efficient capital allocation aligned with climate targets ( [[#Bertram--2021|Bertram et al. 2021]] ).

There is a lot of discussion about the regulated ownership of the private sector (European Commission 2017) and the restructuring of electricity market contributed to low level of investment in baseline electricity capacity and in investment research and innovation. These changes create uncertainty of investment, and barriers to market entry and exit also potentially limit the competition in the market and restrict the entrance of new investment ( [[#Finon--2006|Finon 2006]] ; [[#Joskow--2007|Joskow 2007]] ; [[#Grubb--2018|Grubb and Newbery 2018]] ). This is also the case in developing countries ( [[#Foster--2020|Foster and Rana 2020]] ).

The positive development in the energy sector has benefitted from the evident stand-alone character of renewable energy generation projects. First movers realised these projects with investors and developers acting from conviction ( [[#Steffen--2018|Steffen et al. 2018]] ). Such action is not possible to this extent in energy efficiency with related investment rather representing an add-on component and consequently requiring the support of decision-makers used to business-as-usual projects. Despite the benefits that improvement of energy efficiency has in contributing to curbing energy consumption, mitigating greenhouse gas emissions, and providing multiple co-benefits ( [[#IEA--2014a|IEA 2014a]] ), investment in energy efficiency is a low priority for firms, and the financial environment is not favourable due to lack of awareness of energy efficiency by financial institutions, existing administrative barriers, lack of expertise to develop projects, asymmetric information, and split incentives ( [[#UNEP%20DTU--2017|UNEP DTU 2017]] ; [[#Cattaneo--2019|Cattaneo 2019]] ). While Energy Service Companies’ (ESCO) business models are expected to facilitate the investment in energy efficiency by sharing a portion of financial risk and providing expertise, there has been limited progress made with ESCO business models, and only slightly over 20% of projects used financing through ESCOs ( [[#UNEP%20DTU--2017|UNEP DTU 2017]] ).

The investment needs and existing challenges differ by sector. Each sector has different characteristics along the arguments listed above making the supply of finance by commercial investors an enabling factor or barrier. In the transport sector, transformation towards green mobility would provide significant co-benefits for human health by reducing transport-related air pollution, so the transport sector cannot achieve such transformation in isolation from other sectors. However, a considerable involvement of the public sector in many transportation infrastructure projects is given, and the absence of a standard solution increases transaction costs (including bidding package, estimating, drawing up a contract, administering the contract, corruption, and so on). Financial constraints, including access to adequate finance, pose a significant challenge in the agriculture sector, especially for SMEs and smallholder farmers. The distortion created by government failure and a lack of effective policies create barriers to financing for agriculture. The inability to manage the impact of the agriculture-related risks, such as seasonality, increases uncertainty in financial management. Moreover, inadequate infrastructure, such as electricity and telecommunication, makes it difficult for financial institutions to reach agricultural SMEs and farmers and increases transaction costs ( [[#World%20Bank--2016|World Bank 2016]] ). Low economies of scale, low bargaining power, poor connectivity to markets, and information asymmetry also lead to higher transaction costs ( [[#Pingali--2019|Pingali et al. 2019]] ). In the industrial manufacturing and residential sector, gaining energy efficiency remains one of the critical challenges. Investment in achieving energy efficiency encounters some challenges when it may not necessarily generate direct or indirect benefits, such as increase in production capacity or productivity and improvement in product quality. Also, early-stage, high upfront cost and future, stable revenue stream structure suggest the needs for a better enabling environment, such as a robust financial market, awareness of financial institutions, and regulatory frameworks (e.g., stringent building codes, incentives for ESCOs) ( [[#IEA--2014a|IEA 2014a]] ; [[#Barnsley--2015|Barnsley et al. 2015]] ).

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<span id="quantitative-assessment-of-financing-needs"></span>
=== 15.4.2 Quantitative Assessment of Financing Needs ===

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Multiple stakeholders prepare and present quantitative financing needs assessments with methodologies applied to vary significantly representing a major challenge for aggregation of needs (e.g., [[#Osama--2021|Osama et al. (2021)]] for African countries), most of them with a focus on scenarios likely to limit warming to 2°C or lower. The differences relate to the scope of the assessments regarding sectors, regions and periods, top-down versus bottom-up approaches, and methodological issues around boundaries of climate-related investment needs, particularly full vs incremental costs and the exclusion or inclusion of consumer-level investments. Information on investment needs and financing options in NDCs mirrors this challenge and is heavily heterogeneous ( [[#Zhang--2016|Zhang and Pan 2016]] ).

In particular, for global approaches, modelling assumptions are often heavily standardised, focusing on technology costs. Only limited global analysis is available on incremental costs and investments, reflecting the reality of developing countries, also considering the interplay with significant infrastructure finance gaps, and can hardly serve as a robust basis for negotiations about international public climate finance. The focus on investment irrespective of uncertainty as well as other qualitative aspects of needs does not allow for a straightforward analysis of the need for public finance to leverage private sector financing and of the country heterogeneity in terms of investment risks and access to capital (Clark et al. 2018).

One source of uncertainty about the investment estimates for the power sector is the evolution of the levelised cost of technical options in the future, for example the continuation of the observed declining costs trends of renewable energy ( [[#IRENA--2020b|IRENA 2020b]] ) which has been underestimated in many modelling exercises. The learning by doing processes and economies of scale might be at least partially outweighed, in all countries and more specifically in Small Island Developing States (SIDS) and other developing countries because of different risk factors, scales of installations, accessibility, and others ( [[#Lucas--2015|Lucas et al. 2015]] ; [[#van%20der%20Zwaan--2018|van der Zwaan et al. 2018]] ). These parameters, together with transaction costs/soft costs ( [[#15.5|Section 15.5]] ), financing costs and the level of technical competences need to be better represented in the future to represent the ‘climate investment trap’ in many developing countries ( [[#García%20de%20Fonseca--2019|García de Fonseca et al. 2019]] ). This ‘climate investment trap’, as flagged by [[#Ameli--2021a|Ameli et al. (2021a)]] , is created by existing and expected physical effects of climate change, higher financing costs and resulting lower investment levels in developing countries. Applying significantly standardised assumptions can consequently not provide robust insights for specific country groups. This will require progress in the spatiotemporal granularity of the models ( [[#Collins--2016|Collins et al. 2016]] ).

Another source of uncertainty about the financing needs is the interplays between (i) the baseline economic growth rates, (ii) the link between economic growth and energy demand, including rebound effects of energy efficiency gains, (iii) the evolution of microeconomic parameters such as fossil fuel prices, interest rates, currency exchange rates (iv) the level of integration between climate policies and sectoral policies and their efficacy, and (v) the impact of climate policies on growth and the capacity of fiscal and financial policies to offset their adverse effect ( [[#IPCC--2014|IPCC 2014]] ; [[#IPCC--2018|IPCC 2018]] ). Integrated assessment models (IAMs) try to capture some of these interplays even though they typically do not capture the financial constraints and the structural causes of the infrastructure investment gap. Many of them rely on growth models with full exploitation of the means of production (labour and capital). They nevertheless provide useful indications of the orders of magnitude at play over the long run, and the determinants of their uncertainty. Global yearly average low-carbon investment needs until 2030 for electricity, transportation, AFOLU and energy efficiency measures including industry and buildings are estimated between 3% and 6% of the world’s GDP according to the analysis in [[#15.5|Section 15.5]] . The incremental costs of low-carbon options are less than that and their funding could be achieved without reducing global consumption by reallocating 1.4% to 3.9% of global savings. 2.4% on average (see Box 4.8 of SR1.5 ( [[#IPCC--2018|IPCC 2018]] )) currently flow towards real estate, land and liquid financial vehicles. For the short-term decisions, the major information they give is the uncertainty range because this is an indicator of the risks decision-makers need.

While the AR6 Scenarios Database provides good transparency with regard to technology costs for electricity generation, assumptions driving in particular investments in energy efficiency are rarely made available in both IAM-based assessments and also other studies. Taking into account the much broader range of tested and untested technologies the confidence levels, in particular for 2050 estimates, remain low but can provide an initial indication. Also, the ranges allow for a rough indication on possible ‘green’ investment volumes and respective asset allocation for financial sector stakeholders.

'''Using global scenarios assessed in [[IPCC:Wg3:Chapter:Chapter-3|Chapter 3]] for assessing investment requirements.''' Tables 15.2 and 15.3 present the analysis of investment requirements in global modelled mitigation pathways assessed in [[IPCC:Wg3:Chapter:Chapter-3|Chapter 3]] for key energy sub-sectors within modelled global pathways that limit warming to 2°C (&gt;67%) or lower. These pathways explore the energy, land-use, and climate system interactions and thus help identify required energy sector transformations to reach specific long-term climate targets. However, reporting of investment needs outside the energy sector was scarce, reducing the explanatory power of the shown total investment need in the context of overall investment needs ( [[#Ekholm--2013|Ekholm et al. 2013]] ; [[#IPCC--2018|IPCC 2018]] , Box 4.8; [[#McCollum--2018|McCollum et al. 2018]] ; [[#Bertram--2021|Bertram et al. 2021]] ). The modelling of these scenarios is done with a variation of scenario assumptions along different dimensions ( ''inter alia'' policy, socio-economic development and technology availability), as well as with different modelling tools which represent different assumptions about the structural functioning of the energy-economy-land-use system (see Annex III: ‘Scenarios and modelling methods’ for details). Tables 15.2 and 15.3 focus on the near-term (2023–2032) investment requirements in the energy sector and how these differ depending on temperature category. Figures 3.36 and 3.37 present the data for the medium term (2023–2052). The results highlight both requirements for increased investments and a shift from fossil towards renewable technologies and efficiency for more ambitious temperature categories. The substantial ranges within each category reflect multiple pathways, differentiated by socio-economic assumptions, technology, and so on. It is necessary to open up these extra dimensions and contrast them with national and sub-regional analysis to understand how investment requirements depend on particular circumstances and assumptions within a country for a specific technology. Limiting peak temperature to levels of 1.5°C–2°C requires rapid decarbonisation of the global energy systems, with the fastest relative emission reductions occurring in the power generation sector ( [[#Hirth--2016|Hirth and Steckel 2016]] ; [[#Luderer--2018|Luderer et al. 2018]] ).

'''Table 15.2 | Global average yearly investments from 2023–2032 for electricity supply in billion USD''' 2015 '''.'''

{| class="wikitable"
|-
! rowspan="3"| Category

! rowspan="3"| Fossil

! rowspan="3"| Nuclear

! rowspan="3"| Storage

! rowspan="3"| Transmission and distribution

! rowspan="3"|
! colspan="3"| Non-Biomass Renewables

|-
! rowspan="2"| All

! colspan="2"| Thereof

|-
! Solar

! Wind

|-
| C1

| 53 [50]

| 127 [52]

| 221 [39]

| 549 [50]

|
| 1190 [52]

| 498 [52]

| 390 [52]

|-
| (Range)

| (34;115)

| (85;165)

| (88;295)

| (422;787)

|
| (688;1430)

| (292;603)

| (273;578)

|-
| ''C2''

| ''78 [100]''

| ''116 [92]''

| ''57 [66]''

| ''489 [81]''

|
| ''736 [96]''

| ''312 [96]''

| ''237 [96]''

|-
| ''(Range)''

| ''(50;129)''

| ''(61;150)''

| ''(37;139)''

| ''(401;620)''

|
| ''(482;848)''

| ''(181;385)''

| ''(174;328)''

|-
| ''C3''

| ''75 [221]''

| ''96 [190]''

| ''28 [129]''

| ''389 [157]''

|
| ''639 [207]''

| ''220 [207]''

| ''266 [207]''

|-
| ''(Range)''

| ''(52;129)''

| ''(50;122)''

| ''(8;155)''

| ''(326;760)''

|
| ''(432;820)''

| ''(167;345)''

| ''(137;353)''

|}

Note: Global average yearly investments from 2023–2032 (in USD 2015 ). Electricity subcomponents are not exhaustive. Hydro, geothermal, biomass and others are not shown, as these are shown to be of smaller magnitude (Chapter 3). Difference between non-biomass renewables and solar/wind represents hydro and in some scenarios geothermal, tidal, and ocean. Scenarios are grouped into common AR6 categories (vertical axis, C1–C3). The numbers represent medians across all scenarios within one category, and rounded brackets indicate inter-quartile ranges, while the numbers in squared brackets indicate number of scenarios. C6, C7, and C8 are not shown in Table 15.2. Reference C5 category for Transmission and Distribution (T&amp;D) is 364bn (294bn to 445bn) [111] used for calculation of incremental needs in Figure 15.4. Data source: AR6 Scenarios Database.

'''Table 15.3 | Regional average yearly investments from 2023–2032 for electricity supply in billion USD''' 2015 '''.'''

{| class="wikitable"
|-
!
! Africa

! East

Asia

! Europe

! South

Asia

! Latin America

! Middle

East

! North America

! Australia, Japan, and New Zealand

! East. Eur. W.C. Asia

! South East Asia

|-
| colspan="11"| '''Non-biomass renewables'''

|-
| C1

| 41 [39]

| 302 [41]

| 130 [41]

| 120 [41]

| 69 [41]

| 67 [41]

| 177 [41]

| 37 [41]

| 48 [41]

| 85 [41]

|-
| (Range)

| (36;66)

| (188;356)

| (101;150)

| (83;164)

| (55;97)

| (31;90)

| (149;222)

| (28;39)

| (35;65)

| (59;141)

|-
| ''C2''

| ''32 [77]''

| ''179 [87]''

| ''95 [87]''

| ''69 [87]''

| ''55 [87]''

| ''28 [87]''

| ''106 [87]''

| ''19 [87]''

| ''17 [87]''

| ''63 [87]''

|-
| ''(Range)''

| ''(27;42)''

| ''(124;255)''

| ''(64;104)''

| ''(35;84)''

| ''(27;73)''

| ''(19;43)''

| ''(73;134)''

| ''(12;29)''

| ''(10;37)''

| ''(35;78)''

|-
| ''C3''

| ''17 [170]''

| ''166 [185]''

| ''91 [185]''

| ''53 [182]''

| ''53 [185]''

| ''22 [182]''

| ''119 [185]''

| ''22 [179]''

| ''15 [185]''

| ''38 [182]''

|-
| ''(Range)''

| ''(12;47)''

| ''(108;200)''

| ''(42;118)''

| ''(35;80)''

| ''(25;81)''

| ''(11;32)''

| ''(71;167)''

| ''(12;30)''

| ''(11;30)''

| ''(22;67)''

|-
| colspan="11"| '''Thereof solar'''

|-
| C1

| 16 [39]

| 134 [41]

| 43 [41]

| 53 [41]

| 22 [41]

| 33 [41]

| 81 [41]

| 11 [41]

| 20 [41]

| 33 [41]

|-
| (Range)

| (8;24)

| (89;147)

| (38;55)

| (37;82)

| (14;34)

| (16;40)

| (75;95)

| (10;16)

| (10;25)

| (17;56)

|-
| ''C2''

| ''10 [77]''

| ''83 [87]''

| ''34 [87]''

| ''37 [87]''

| ''16 [87]''

| ''15 [82]''

| ''44 [87]''

| ''7 [80]''

| ''5 [81]''

| ''20 [87]''

|-
| ''(Range)''

| ''(6;14)''

| ''(54;125)''

| ''(19;47)''

| ''(17;41)''

| ''(8;21)''

| ''(10;23)''

| ''(18;69)''

| ''(4;10)''

| ''(1;12)''

| ''(9;33)''

|-
| ''C3''

| ''7 [170]''

| ''53 [185]''

| ''28 [184]''

| ''23 [182]''

| ''12 [184]''

| ''12 [164]''

| ''32 [185]''

| ''9 [157]''

| ''8 [164]''

| ''14 [182]''

|-
| ''(Range)''

| ''(3;14)''

| ''(42;83)''

| ''(17;36)''

| ''(17;39)''

| ''(5;25)''

| ''(9;20)''

| ''(21;74)''

| ''(4;11)''

| ''(3;12)''

| ''(7;27)''

|-
| colspan="11"| '''Thereof wind'''

|-
| C1

| 10 [39]

| 133 [41]

| 59 [41]

| 45 [41]

| 19 [41]

| 22 [41]

| 58 [41]

| 20 [41]

| 17 [41]

| 28 [41]

|-
| (Range)

| (4;30)

| (86;164)

| (29;86)

| (23;71)

| (15;26)

| (13;39)

| (44;122)

| (12;25)

| (10;23)

| (17;52)

|-
| ''C2''

| ''5 [77]''

| ''63 [87]''

| ''41 [83]''

| ''23 [87]''

| ''15 [87]''

| ''8 [81]''

| ''31 [87]''

| ''8 [87]''

| ''4 [81]''

| ''19 [87]''

|-
| ''(Range)''

| ''(4;14)''

| ''(44;102)''

| ''(9;59)''

| ''(14;30)''

| ''(7;18)''

| ''(3;16)''

| ''(19;75)''

| ''(5;12)''

| ''(2;12)''

| ''(6;23)''

|-
| ''C3''

| ''3 [170]''

| ''64 [185]''

| ''59 [169]''

| ''21 [182]''

| ''12 [184]''

| ''10 [160]''

| ''52 [184]''

| ''10 [179]''

| ''4 [164]''

| ''10 [182]''

|-
| ''(Range)''

| ''(2;15)''

| ''(40;93)''

| ''(12;65)''

| ''(12;37)''

| ''(7;22)''

| ''(5;13)''

| ''(19;86)''

| ''(6;13)''

| ''(2;10)''

| ''(5;32)''

|-
| colspan="11"| '''Storage'''

|-
| C1

| 3 [27]

| 68 [32]

| 46 [32]

| 27 [32]

| 7 [29]

| 13 [30]

| 56 [30]

| 4 [32]

| 3 [24]

| 15 [30]

|-
| (Range)

| (0;8)

| (30;80)

| (9;54)

| (24;45)

| (2;11)

| (3;19)

| (30;62)

| (2;6)

| (0;4)

| (1;30)

|-
| ''C2''

| ''2 [36]''

| ''19 [60]''

| ''18 [52]''

| ''10 [57]''

| ''3 [42]''

| ''3 [31]''

| ''13 [44]''

| ''1 [43]''

| ''0 [20]''

| ''3 [41]''

|-
| ''(Range)''

| ''(0;4)''

| ''(6;36)''

| ''(7;35)''

| ''(4;17)''

| ''(1;8)''

| ''(0;4)''

| ''(11;34)''

| ''(1;2)''

| ''(0;0)''

| ''(2;13)''

|-
| ''C3''

| ''4 [78]''

| ''20 [106]''

| ''22 [92]''

| ''9 [107]''

| ''9 [85]''

| ''4 [78]''

| ''29 [81]''

| ''1 [90]''

| ''0 [78]''

| ''9 [83]''

|-
| ''(Range)''

| ''(0;6)''

| ''(1;33)''

| ''(3;41)''

| ''(1;21)''

| ''(0;13)''

| ''(0;9)''

| ''(2;42)''

| ''(0;2)''

| ''(0;1)''

| ''(0;16)''

|-
| colspan="11"| '''Transmission and distribution'''

|-
| C1

| 24 [39]

| 147 [39]

| 67 [39]

| 51 [39]

| 40 [39]

| 27 [39]

| 87 [39]

| 16 [39]

| 24 [39]

| 64 [39]

|-
| (Range)

| (13;39)

| (96;250)

| (61;105)

| (46;97)

| (29;62)

| (22;40)

| (70;120)

| (13;19)

| (18;35)

| (26;94)

|-
| ''C2''

| ''24 [77]''

| ''132 [77]''

| ''60 [77]''

| ''49 [77]''

| ''36 [77]''

| ''33 [77]''

| ''70 [77]''

| ''14 [77]''

| ''26 [77]''

| ''36 [77]''

|-
| ''(Range)''

| ''(14;30)''

| ''(84;175)''

| ''(48;79)''

| ''(43;56)''

| ''(28;45)''

| ''(27;37)''

| ''(53;92)''

| ''(8;19)''

| ''(17;34)''

| ''(28;61)''

|-
| ''C3''

| ''14 [150]''

| ''93 [153]''

| ''61 [153]''

| ''46 [150]''

| ''26 [153]''

| ''25 [150]''

| ''70 [153]''

| ''14 [147]''

| ''23 [153]''

| ''26 [150]''

|-
| ''(Range)''

| ''(10;37)''

| ''(74;190)''

| ''(52;86)''

| ''(38;86)''

| ''(21;62)''

| ''(17;40)''

| ''(52;90)''

| ''(11;16)''

| ''(17;27)''

| ''(17;87)''

|-
| C5

| 13 [109]

| 81 [110]

| 55 [110]

| 41 [109]

| 25 [110]

| 23 [109]

| 58 [110]

| 14 [109]

| 23 [110]

| 25 [109]

|-
| (Range)

| (9;13)

| (67;160)

| (46;59)

| (22;46)

| (19;28)

| (15;28)

| (51;67)

| (12;16)

| (16;26)

| (17;29)

|}

Note: Average yearly investments from 2023–2032 for electricity generation capacity, by aggregate regions (in billion USD 2015 ). Further notes see Table 15.2. Reference C5 category for Transmission and Distribution shown in Table 15.2 as it is used for calculation of incremental needs for Figure 15.4. Vertical axis, C4–C8 except Transmission and Distribution not shown. Data source: AR6 Scenarios Database.

This requires fast shifts of investment as infrastructures in the power sector generally have long lifetimes of a few decades. in global modelled pathways that limit warming to 1.5°C (&gt;50%) with no or limited overshoot, investments into non-biomass renewables (especially solar and wind, but also including hydro, geothermal, and others not shown in Table 15.2) increase to over USD1 trillion yr –1 in 2030, increasing by more than factor 3 over the values of around USD250–300 billion yr –1 that have been relatively stable over the last decade ( [[#IEA--2019a|IEA 2019a]] ). Overall, electricity generation investments increase considerably, reflecting the higher relevance of capital expenditures in decarbonised electricity systems. While decreasing technology costs have substantially reduced the challenge of high capital intensity, still remaining relative disadvantages in terms of capital intensity of low-carbon power technologies can especially create obstacles for fast decarbonisation in countries with high interest rates, which decrease the competitiveness of those technologies (Iyer et al. 2015; [[#Hirth--2016|Hirth and Steckel 2016]] ; [[#Steckel--2018|Steckel and Jakob 2018]] ; [[#Schmidt--2019|Schmidt et al. 2019]] ). CCS as well as nuclear will not drive investment needs until 2030, given considerably longer lead-times for these technologies, and the lack of a significant project pipeline currently.

What is apparent is that the bulk of investment requirements corresponds to medium- and low-income countries in Asia, Latin America, the Middle East and Africa, as these still have growing energy demand, and it is still considerably lower than the global average. This illustrates a vital opportunity to ensure the build-up of sustainable energy infrastructures in these regions and constitutes a risk of additional carbon lock-in if investments into fossil infrastructures, especially coal-fired power plants, and uncontrolled urban expansion, continue.

Investment needs in electrification derived from IAMs do not include systematically investments in end-use equipment and distribution (Box 4.8 in SR1.5 ( [[#IPCC--2018|IPCC 2018]] )). Model-based estimates of investment needs don’t have the regional granularity to single out LDCs, as model regions typically are defined based on geographic proximity and therefore aggregate LDCs and other countries. With the average electricity consumption per capita in Africa increasing to 0.68–0.87 (1.43–2.92) MWh in 2030 (2050) yr – 1 and remaining at the very low end of the global range [0.46 in Africa compared to the upper end of 12.02 in North America, MWh per capita and year in 2020], the targeted full electrification until 2030 appears unrealistic across all scenarios. SEforAll and IEA estimate assumed investment needs to decentralised end-user electrification to come in around USD40 billion on average until 2030 (SEforALL and [[#CPI--2020|CPI 2020]] ; [[#IEA--2021d|IEA 2021d]] ).

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=== Quantitative analysis of investment needs in energy generation based on IRENA and IEA data and comparison to AR6 scenario database output '''.''' ===

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According to IRENA, the government plans in place today call for investing at least USD95 trillion in energy systems over the coming three decades (2016–2050) ( [[#IRENA--2020c|IRENA 2020c]] ). Redirecting and increasing investments to ensure a climate-safe future (Transforming Energy Scenario, TES) would require reaching on average around 1 trillion USD 2015 yr –1 (average until 2030) for electricity generation as well as grids and storage, increasing to above 2 trillion USD 2015 yr –1 (average until 2030) in the 1.5 scenario ( [[#IRENA--2021|IRENA 2021]] ). IEA’s respective SDS and NZE scenarios come in at average annual investments between USD1.0 trillion yr –1 and USD1.6 trillion yr –1 (average until 2030) ( [[#IEA--2021b|IEA 2021b]] ). These additional data points for the C1 and C3 category underpin the range presented in the AR6 Scenarios Database for needs until 2032 despite the slightly varying periods.

In contrast to the IAMs, IRENA and IEA assessments do not allow for an analysis of mitigation-driven investment needs in transmission and distribution, which likely results in an overestimation of the mitigation-driven investment needs in their analysis.

It is worth highlighting that driven by technology cost assumptions, IRENA forecasts falling average annual investments needs for energy, but also energy efficiency, for the period 2030–2050 compared to 2020–2030. In the 1.5°C scenario (1.5-S) the total annual investment needs excluding fossils and nuclear decrease from 5.0 trillion USD 2015 until 2030 yr –1 to 3.8 trillion USD 2015 yr –1 for 2030–2050 ( [[#IRENA--2021|IRENA 2021]] ). In IAM scenarios of Category C1, electricity supply investments (including generation, transmission and distribution, and storage) remain flat at 2.2 trillion USD 2015 yr –1 through the coming three decades in absolute terms. Given rising GDP, the complementary methods and sources thus consistently point to a peak in electricity supply investments as a percentage of GDP in mitigation scenarios in the coming decade. This reflects the fact that the coming decade requires low-carbon power generation investments to both cover the demand increase and (partly premature) replacement of fossil generation capacities, both concentrated in emerging and developing countries. Relative investment numbers for electricity measured against GDP then decrease towards 2050, as they only need to cover natural replacement and increasing demands (which due to electrification will also pick up in developed countries), and due to further declining technology costs. Investments for low-carbon fuel supply like hydrogen and synthetic fuels, and for direct electrification equipment (heat pumps, electric vehicles (EV), etc.) scale up from much lower levels and will likely continue to grow as a share of GDP until mid-century, though uncertainties and accounting is still much more uncertain. ( [[#Bertram--2021|Bertram et al. 2021]] ).

'''Quantitative analysis of investment needs in other sectors.''' As described above, investment needs in non-energy sectors tend to be ignored in many integrated assessment models with studies for individual countries or regions providing a more fragmented picture only. However, the quality of estimates is likely not to be less robust given the drawbacks of integrated assessment models.

[[IPCC:Wg3:Chapter:Chapter-7|Chapter 7]] stresses the importance of opportunity costs for AFOLU mitigation options, in particular for afforestation and avoided deforestation projects, and derives net annual costs of around USD278 billion yr –1 in the next several decades, mostly opportunity costs. Net costs of delivering 5-6 Gt CO 2 yr –1 of forest related carbon sequestration and emission reduction around 2050 as assessed with sectoral models are estimated to reach to ~ USD400 billion yr –1 by 2050, excluding externality costs (Chapter 7.4).

'''Energy efficiency.''' Estimates on energy investment needs vary significantly with a low level of transparency with regard to underlying technology cost assumptions burdening the confidence levels.

IRENA only selectively reports financing needs for energy efficiency in buildings and industry as separate categories. For the 1.5-S average yr –1 needs until 2050 come in at 963 billion USD 2015 for buildings, 102 billion USD 2015 for heat pumps, and 354 billion USD 2015 for industry. Applying the relative share of these categories on higher total needs until 2030, around 1.8 trillion USD 2015 yr –1 in buildings and industry are needed in the 1.5-S. For the TES cumulative energy efficiency investment needs until 2030 are stated at 29 trillion USD 2015 translating into an yearly average of around 1.7 trillion USD 2015 yr –1 , excluding transportation. IEA estimates come in at a much lower level at 0.6 and 0.8 billion USD 2015 yr –1 on average between 2026–2030 for their SDS and NZE scenarios.

'''Transportation.''' Forthe transportation sector, OECD has presented the most comprehensive assessment of financing needs in the AR6 database based on IEA data with the annual average coming in at USD2.7 trillion between 2015 and 2035 i In modelled global pathways that limit warming to 2°C (&gt;67%). The assessment comprises road, rail and airports/ports infrastructure, with only rail infrastructure being considered in this analysis.

On a regional level, [[#Oxford%20Economics--2017|Oxford Economics (2017)]] shows that annual infrastructure investments between 2016 and 2040 vary widely. For all available countries (n=50) estimates count close to 0.4 trillion USD 2015 yr –1 , including 0.217 trillion USD 2015 yr –1 for China. Based on available data for nine African countries, investments in rail infrastructure range from USD0.1 billion in Senegal to USD1.6 billion in Nigeria. [[#Osama--2021|Osama et al. (2021)]] highlight a USD4.7 billion financing gap for African countries in the transport sector. In Latin America [[#Oxford%20Economics--2017|Oxford Economics (2017)]] identifies Brazil as frontrunner of required rail investments with USD8.3 billion, followed by Peru with USD2.3 billion. In total, developed countries’ financing needs mount up to almost USD120 billion yr –1 (n=15, mean=7.97bn USD) for rail infrastructure. Financing needs in developing countries (excluding LDCs and excluding China) mount up to almost USD50 billion yr –1 (n=27, mean=1.78bn USD, excluding China). [[#Oxford%20Economics--2017|Oxford Economics (2017)]] reports rail infrastructure financing needs for China of more than USD200 billion yr –1 between 2016 and 2040.

Fisch-Romito and Guivarch (2019) show, by endogenising the impact of urban infrastructure policies on mobility needs and modal choices that transportation investment needs globally might be lower in low-carbon pathways compared with baselines, with lower investments in road and air infrastructure. This does mean that higher investments are not needed over the following two decades; this is confirmed by [[#Rozenberg--2019|Rozenberg and Fay (2019)]] that strong policy integration between urban, transportation and energy policies reduce the total investment gap.

IRENA as well as IEA have presented estimates for energy efficiency investments in the transport sector. For the 1.5-S scenario, IRENA indicates average investment needs of USD 2015 0.2 trillion yr –1 for EV infrastructure, USD 2015 0.2 trillion yr –1 for transport energy efficiency and USD 2015 0.3 trillion yr –1 for EV batteries (average until 2030) ( [[#IRENA--2020d|IRENA 2020d]] ). IEA indicates a total of around 0.6 and 0.7 trillion USD 2015 yr –1 for transport energy efficiency in the SDS and IEA scenarios for the 2026–2030 period ( [[#IEA--2021c|IEA 2021c]] ). Many investment categories relating to mitigation options, in particular with regard to behavioural change and transport mode changes (Chapter 10, Figure SPM.8), are neglected in these analyses despite their significant mitigation potential.

'''AFOLU.''' The Food and Land Use Coalition estimates additional investment needs for ten critical transitions for the global food and land use systems to achieve the long-term global goal (LTGG) and SDGs. Additional annual investment needs until 2030 add up to USD300–350 billion. Considering the change in global diets as well as the land-based nature-based solutions only, annual investment needs would come in between USD110–135 billion. [[IPCC:Wg3:Chapter:Chapter-7|Chapter 7]] stresses the importance of opportunity costs for AFOLU mitigation options, in particular for afforestation projects, and derives average yearly investment needs of around 278 billion USD 2015 yr –1 until 2030 rising to 431 billion USD 2015 yr –1 over the next several decades, including opportunity costs. The estimate is based on an assumption of emission reductions consistent with pathways C1–C4, leading to average abatement of 9.1 GtCO 2 yr –1 (median range 6.7–12.3 GtCO 2 yr –1 ) from 2020–2050 and marginal costs of USD100 per tonne CO 2 , excluding investments in bioenergy with carbon capture and storage and changes in food consumption and food waste ( [[IPCC:Wg3:Chapter:Chapter-7#7.4|Section 7.4]] ). The largest investments are projected to occur in Latin America, South-East Asia, and Africa, constituting 61% of total expenditure. The implied change of land use might trigger negative effects on other SDGs which need to be addressed to offer robust safeguards and labelling for investors.

However, given the strong interlinkage of the presented transitions and accumulated effects, climate change related investments can hardly be separated ( [[#The%20Food%20and%20Land%20Use%20Coalition--2019|The Food and Land Use Coalition 2019]] ). [[#Shakhovskoy--2019|Shakhovskoy et al. (2019)]] present an overview of financing needs of small-scale farmers globally, however, without focusing on the required climate-related investments. According to their assessment, 270 million smallholder farmers in South and South-East Asia, sub-Saharan Africa and Latin America face approximately USD240 billion of financing needs, thereof USD100 billion short-term agricultural needs, USD88 billion long-term agricultural needs and USD50 billion non-agricultural needs ( [[#Shakhovskoy--2019|Shakhovskoy et al. 2019]] ). These numbers can only provide ‘an indication of the magnitude of the climate investments required in small-scale agriculture’ ( [[#CPI--2020|CPI 2020]] ). Table 15.4 summarises the studies used as well as adjustments made to determine needs for the gap discussion in [[#15.5.2|Section 15.5.2]] .

'''Table 15.4 | Sector studies to determine average financing needs.'''

{| class="wikitable"
|-
! Sector

! Studies

! colspan="2"| Global ranges trillion USD yr –1 ''– Confidence Level''

! colspan="2"| Regional breakdown

! Comment

|-
| Energy

| IAM database, SEforAll (SEforALL and [[#CPI--2020|CPI 2020]] ), IRENA 1.5-S and TES scenarios ( [[#IRENA--2021|IRENA 2021]] ), IEA SDS and NZE scenarios ( [[#IEA--2021b|IEA 2021b]] )

| 0.8–1.5

| ''High confidence''

| Detailed breakdown for R10 possible for IAM database and applied to the derived range

| ''Medium confidence''

| Wide ranges primarily driven by varying assumptions with regard to grid investments relating to the increased renewable energy penetration.

|-
| Energy Efficiency

| IRENA 1.5-S and TES scenarios, IEA SDS and NZE scenarios

| 0.5–1.7

| ''Medium confidence''

| Adjustments required to regional categorisation by IEA and IRENA

| ''Low-medium confidence''

| Medium confidence levels due to missing transparency with regard to underlying assumptions on technology costs. Low-to-medium confidence level on regional allocations due to required adjustments.

|-
| Transport

| OECD/IEA ( [[#OECD--2017b|OECD 2017b]] ) and [[#Oxford%20Economics--2017|Oxford Economics (2017)]] on rail investment data, IRENA 1.5-S and TES scenarios, IEA SDS and NZE scenarios for transport (energy efficiency) and electrification

| 1.0–1.1

| ''Medium confidence''

| Adjustments required to regional categorisation by IEA and IRENA

| ''Low-medium confidence''

| Needs including battery costs, not total costs, of electric vehicles, likely underestimation of needs due to missing data points on rail infrastructure.

|-
| AFOLU

| [[IPCC:Wg3:Chapter:Chapter-7|Chapter 7]] analysis, [[IPCC:Wg3:Chapter:Chapter-7#7.4|Section 7.4]] ; The Food and Land Use Coalition (Land use Coalition (2019); ( [[#Shakhovskoy--2019|Shakhovskoy et al. 2019]] )

| 0.1–0.3

| ''High confidence''

| Breakdown for R10 possible for [[IPCC:Wg3:Chapter:Chapter-7|Chapter 7]] analysis

| ''Medium confidence''

| Upper end of range includes opportunity costs as these likely increase costs of investment in land.

|}

Note: Total range USD2.3 trillion to USD4.5 trillion yr –1 .

'''Adaptation financing needs''' '''.''' Financing needs for adaptation are even more difficult to define than those of mitigation because mobilising specific adaptation investments is only part of the challenge since ultimately improving societies’ adaptive capacities depends on the SDGs’ fulfilment ( [[#Hallegatte--2016|Hallegatte et al. 2016]] ). Bridging the investment gap on irrigation, water supply, health care, energy access, and quality buildings is an essential enabling condition for adapting to climate change. The scenario analysis conducted by [[#Rozenberg--2019|Rozenberg and Fay (2019)]] show that fulfilling the SDGs to improve the adaptive capacity of low- and middle-income countries would require investments in water supply, sanitation, irrigation and flood protection that would account for about 0.5% of developing countries’ GDP in a baseline scenario to 1.85% and 1% with a strong and anticipatory policy integration (USD664 billion and 351 billion on average by 2030).

Most studies choose to assess public sector projects, ignoring household-level investments as well as private sector adaptation ( [[#UNEP--2018|UNEP 2018]] ; [[#Buchner--2019|Buchner et al. 2019]] ). UNEP’s 2020 Adaptation Gap Report estimates adaptation costs amounting to 140–300 billion USD yr –1 in 2030 and USD280–500 billion yr –1 in 2050 ( [[#UNEP--2021|UNEP 2021]] ). Over 100 countries included adaptation components in their intended NDCs (INDCs) and approximately 25% of these referenced national adaptation plans (NAPs) ( [[#GIZ--2017a|]] [[#GIZ--2017|GIZ 2017]] a ) but estimates of the financing required for NAP processes is not available. These NAPs, as formally agreed under the UNFCCC in 2010, are iterative, continuous processes that have multiple stages with a developmental phase that requires country-specific financing of primarily which comprises grants, bond issuance or debt conversion ( [[#NDC%20Partnership--2020|NDC Partnership 2020]] , [[#NAP%20Global%20Network--2017|NAP Global Network 2017]] ). At the same time, multilateral climate funds such as the Green Climate Fund and the GEF/Least Developed Countries Fund offer ‘readiness and preparatory support’ and implementation for the NAPs and adaptation planning process ( [[#GCF--2020a|GCF 2020a]] ; [[#GEF--2021a|GEF 2021a]] ,b). There has been no significant updating of adaptation cost estimates since UNEP’s ( [[#UNEP--2016|UNEP 2016]] , 2018). The Global Commission on Adaptation makes the case that investing USD1.8 trillion in early warning system, climate-resilient infrastructure, global mangrove and resilient water resources would generate about USD1.7 trillion in benefits due to avoided cost and non-monetary and social resources ( [[#Verkooijen--2019|Verkooijen 2019]] ; [[#UNEP--2021|UNEP 2021]] ).

There is increasing recognition of rising adaptation challenges and associated costs within and across developed countries. Undoubtedly many developed countries are spending more on a wide range of adaptation issues, both as preventive measures and building resilience (greening infrastructure, climate-proofing major projects and managing climate-related risks) against the impacts of climate change extreme weather events ( [[#US%20GCRP--2018a|US GCRP 2018a]] ). Developed countries’ climate change adaptation spending covers areas such as federal insurance programmes, federal, state and local property and infrastructure, supply chains, and water systems.

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