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

=== 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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