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

=== 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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