Editing IPCC:AR6/WGIII/Chapter-4 (section)

== 4.2 Accelerating Mitigation Actions Across Scales ==

<div id="4.2.1" class="h2-container"></div>

<span id="mitigation-targets-and-measures-in-nationally-determined-contributions"></span>
=== 4.2.1 Mitigation Targets and Measures in Nationally Determined Contributions ===

<div id="h2-1-siblings" class="h2-siblings"></div>

A central instrument of the Paris Agreement is the NDCs, submitted by each country, and reflecting national efforts to reduce GHG emissions and build resilience to the impacts of climate change. Every five years, collective progress will be compared against long-term goals of the Paris Agreement. Considering the outcome of a global stocktake, countries will prepare subsequent NDCs, showing progression in their ambition and enhancing international cooperation ( [[#UNFCCC--2015a|UNFCCC 2015a]] ).

Prior to COP21, in 2015, most countries submitted their Intended Nationally Determined Contributions (INDCs), which included mitigation targets for 2025 or 2030. INDCs become first NDCs on ratification and/or after national governments’ revision, and by 11 October 2021, the official NDC registry contained 194 first NDCs with 105 new and updated NDCs from 132 Parties to the Paris Agreement, covering 53% of the total global emissions in 2019 of 52.4 GtCO 2 -eq without land use, land-use change and forestry (LULUCF), and 13 second NDCs. Most of the Parties that submitted new or updated NDCs have demonstrated increased ambition in addressing climate change. Moreover, though some countries have not submitted their updated NDCs yet, they have already announced their updated NDC goals somewhere. Countries will take the first stock in 2023 based on their progression towards achieving the objectives of Paris Agreement ( [[#UNFCCC--2015a|UNFCCC 2015a]] , 2018a; [[#SB%20Chairs--2021|SB Chairs 2021]] ) ( [[IPCC:Wg3:Chapter:Chapter-14#14.3.2.5|Section 14.3.2.5]] ).

Submitted NDCs vary in content, scope and background assumptions. First NDCs contain mitigation targets, and in many cases also provisions about adaptation. The mitigation targets range from economy-wide absolute emission reduction targets to strategies, plans and actions for low-emission development. Baseline years vary from 1990 to 2015 and in almost all NDCs the targeted time frame is 2030, with a few specified periods of until 2025, 2035, 2040 or 2050. Around 43% of the mitigation targets in first NDCs are expressed in terms of deviation below business-as-usual by a specified target year, either for the whole economy or for specific sectors, while around 35% include fixed-level targets (either reductions or limitations compared to base years), and another 22% refer to intensity targets (in terms of GHG, CO 2 or energy) or policies and measures, with an increasing number of Parties moving to absolute emission reduction targets in their new or updated NDCs ( [[#UNFCCC--2016a|UNFCCC 2016a]] , 2021). Some developing countries’ NDCs include unconditional elements, while others include conditional ones, the latter with higher ambition if finance, technology and capacity building support from developed countries is provided ( [[#UNFCCC--2016a|UNFCCC 2016a]] ). [[#footnote-004|2]] In some NDCs, the additional mitigation is quantified, in others not (Figure 14.2).

Most first NDCs cover all specific sectors, including LULUCF, and communicate specific targets for individual sub-sectors to support their overall mitigation targets. Concrete actions and priority areas are more detailed in the energy sector, with increased share of renewable energies and energy efficiency being highlighted in the majority of NDCs. Given the uncertainty behind LULUCF emission and removal accounting ( [[#Grassi--2017|Grassi et al. 2017]] ; Jian et al. 2019), several countries state that their accounting framework will only be defined in later NDCs. The GHG included and the global warming potentials (GWPs) used to aggregate emissions also vary across NDCs. Most countries only refer to carbon dioxide, methane and nitrous oxide emissions aggregated based on IPCC AR2 or AR4 metrics, while few NDCs also include [https://www.epa.gov/ghgemissions/overview-greenhouse-gases#f-gases fluorinated gases] and use IPCC AR5 GWPs. The shares of Parties that indicate possible use of at least one type of voluntary cooperation and set qualitative limits on their use have both nearly doubled in new or updated NDCs.

There is considerable literature on country-level mitigation pathways, including but not limited to NDCs. Country distribution of this literature is very unequal ( ''robust evidence'' , ''high agreement'' ). In particular, there is a growing literature on (I)NDCs, with a wide scope which includes estimate of emissions levels of NDCs (Section 4.2.2.2); alignment with sustainable development goals ( [[#Caetano--2020|Caetano et al. 2020]] ; [[#Campagnolo--2019|Campagnolo and Davide 2019]] ; [[#Fuso%20Nerini--2019|Fuso Nerini et al. 2019]] ; Antwi-Agyei et al. 2018); ambition ( [[#Höhne--2018|Höhne et al. 2018]] ; [[#Vogt-Schilb--2017|Vogt-Schilb and Hallegatte 2017]] ; [[#Hermwille--2019|Hermwille et al. 2019]] ); energy development ( [[#Scott--2018|Scott et al. 2018]] ); and the legality of downgrading NDCs ( [[#Rajamani--2017|Rajamani and Brunnée 2017]] ). Other studies note that many NDCs contain single-year mitigation targets, and suggest that a multi-year trajectory is important for more rigorous monitoring ( [[#Elliott--2017|Elliott et al. 2017]] ; [[#Dagnet--2017|Dagnet et al. 2017]] ).

The literature also points out that beyond the ‘headline numbers’, information in (I)NDCs is difficult to analyse ( [[#Pauw--2018|Pauw et al. 2018]] ). Information for ‘clarity, transparency and understanding’ is to be communicated with NDCs, although initial guidance was not specific ( [[#UNFCCC--2014|UNFCCC 2014]] ). While the adoption of the Paris rule-book provided some greater specificity ( [[#UNFCCC--2018b|UNFCCC 2018b]] ,c), the information included in the NDCs remains uneven. Many NDCs omit important mitigation sectors and do not adequately provide details on costs and financing of implementation ( [[#Pauw--2018|Pauw et al. 2018]] ). Countries are also invited to explain how their NDCs are fair and ambitious, though the way this has been done so far has been criticised as insufficiently rigorous ( [[#Winkler--2018|Winkler et al. 2018]] ).

<div id="4.2.2" class="h2-container"></div>

<span id="aggregate-effects-of-ndcs-and-other-mitigation-efforts-relative-to-long-term-mitigation-pathways"></span>
=== 4.2.2 Aggregate Effects of NDCs and Other Mitigation Efforts Relative to Long-term Mitigation Pathways ===

<div id="h2-2-siblings" class="h2-siblings"></div>

<div id="4.2.2.1" class="h3-container"></div>

<span id="introduction-1"></span>
==== 4.2.2.1 Introduction ====

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Near-term mitigation targets submitted as part of NDCs to the UNFCCC, as well as currently implemented policies, provide a basis for assessing potential emissions levels up to 2030 at the national, regional and global level. The following sections present an evaluation of the methods used for assessing projected emissions under NDCs and current policies (Section 4.2.2.2), and the results of these assessments at global, regional and national level assessing a broad available literature based on first NDC submissions from 2015/16 and pre-COVID economic projections (Section 4.2.2.3). The impacts of the COVID-19 pandemic and related government responses on emissions projections are then discussed in Section 4.2.2.4 and the implications of updated NDCs submitted in 2020/21 on emissions follow in Section 4.2.2.5. Section 4.2.2.6 presents an assessment of the so-called ‘implementation gap’ between what currently implemented policies are expected to deliver and what the ambitions laid out under the full implementation of the NDCs are projected to achieve. Finally, a comparison of ambitions across different countries or regions (Section 4.2.2.7) is presented and the uncertainties of projected emissions associated with NDCs and current policies are estimated, including a discussion of measures to reduce uncertainties in the specification of NDCs (Section 4.2.2.8).

The literature reviewed in this section includes globally comprehensive assessments of NDCs and current policies, both peer-reviewed and non-peer-reviewed (but not unpublished model results) as well as synthesis reports by the UNFCCC Secretariat, government reports and national studies.

The aggregate effects of NDCs provide information on where emissions might be in 2025/2030, working forward from their recent levels. [[IPCC:Wg3:Chapter:Chapter-3|Chapter 3]] of this report works backwards from temperature goals, defining a range of long-term global pathways consistent with 1.5°C, 2°C and higher temperature levels. By considering the two together, it is possible to assess whether NDCs are collectively consistent with 1.5°C, 2°C and other temperature pathways (Cross-Chapter Box 4 in this chapter).

<div id="4.2.2.2" class="h3-container"></div>

<span id="methods-to-project-emissions-under-ndcs-and-current-policies"></span>
==== 4.2.2.2 Methods to Project Emissions Under NDCs and Current Policies ====

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A variety of different methods are used to assess emissions implications of NDCs and current policies over the time horizon to 2025 or 2030. Some of these projections were explicitly submitted as part of an official communication to UNFCCC (e.g., Biennial Report, Biennial Update Reports or National Communications) while the majority is from independent studies.

Methods that are used in independent studies (but that can also underlie the official communications) can broadly be separated into two groups:

1. system modelling studies which analyse policies and targets in a comprehensive modelling framework such an integrated assessment, energy systems or integrated land-use model to project emissions (or other indicators) of mitigation targets in NDCs and current policies, either at the national or global scale (noting some differences in the systems); and

2. hybrid approaches that typically start out with emissions pathways as assessed by other published studies (e.g., the IEA World Energy Outlook, national emissions pathways such as those specified in some NDCs) and use these directly or apply additional modifications to them.

System modelling studies are conducted at global, regional and national scales. Global models provide an overview, are necessary for assessment of global phenomena (e.g., temperature change), can integrate climate models and trade effects. National models typically include more details on sectors, technology, behaviour and intersectoral linkages, but often use simplifying assumptions for international trade (e.g., the Armington elasticity approach). Critically, they can also better reflect local socio-economic and political conditions and their evolution (i.e., national development pathways). A variety of modelling paradigms are found, including optimisation and simulation models, myopic and with foresight, monolithic and modular (Annex III: Scenarios and Modelling Methods).

Among the hybrid approaches, three broader categories can be distinguished, (i) direct use of official emission projection as part of submitted NDC or other communication to UNFCCC, (ii) historical trend extrapolation of emissions based on inventory data, possibly disaggregated by sector and emission species, and (iii) use of Reference/Business-As-Usual pathways from an independent published study (e.g., IEA WEO). In all cases, the reductions are then estimated on top of the resulting emission trajectory. Note that globally comprehensive studies may vary the approach used depending on the country.

Beyond the method applied, studies also differ in a number of dimensions, including (i) their spatial resolution and coverage, (ii) their sectoral resolution and coverage, (iii) the GHGs that are included in the assessment, the GWPs (or other metrics) to aggregate them, the emissions inventory (official vs independent inventory data) and related accounting approaches used as a starting point for the projections, (iv) the set of scenarios analysed (Reference/Business-As-Usual, Current Policies, NDCs, etc.), and (v) the degree to which individual policies and their impact on emissions are explicitly represented (Table 4.1).

First, the studies are relevant to different spatial levels, ranging from macro-scale regions with globally comprehensive coverage to national level (Section 4.2.2.3) and sub-national and company level in a few cases (Section 4.2.3). It is important to recognise that globally comprehensive studies typically resolve a limited number of countries individually, in particular those that contribute a high share to global emissions, but have poor resolution of remaining countries or regions, which are assessed in aggregate terms. Conversely, studies with high resolution of a particular country tend to treat interactions with the global scale in a limited way. The recent literature includes attempts to provide a composite global picture from detailed national studies (Bataille et al. 2016a; [[#Deep%20Decarbonization%20Pathways%20Project--2015|Deep Decarbonization Pathways Project 2015]] ; [[#Roelfsema--2020|Roelfsema et al. 2020]] ).

A second dimension in which the studies are different is their comprehensiveness of covering different emitting sectors. Some studies focus on the contribution of a single sector, for example the agriculture, forestry and other land use (AFOLU) sector ( [[#Fyson--2019|Fyson and Jeffery 2019]] ; [[#Grassi--2017|Grassi et al. 2017]] ) or the energy system (including both energy supply and demand sectors), to emission reductions as specified in the NDC. Such studies give an indication of the importance of a given sector to achieving the NDC target of a country and can be used as a benchmark to compare to comprehensive studies, but adding sectoral contributions up represents a methodological challenge.

Third, GHG coverage is different across studies. Some focus on CO 2 only, while others take into account the full suite of Kyoto gases (CO 2 , CH 4 , N 2 O, HFCs, PFCs and SF 6 ). For the latter, different metrics for aggregating GHGs to a CO 2 -equivalent metric are being used, typically GWP 100 from different IPCC assessments (Table 4.1).

Fourth, studies typically cover a set of scenarios, though how these scenarios are defined varies widely. The literature reporting IAM results often includes ''Nationally Determined Contribution'' (NDC), which are officially communicated, and ''Current Policies'' (CP) as interpreted by modellers. Studies based on national modelling, by contrast, tend to define scenarios reflecting very different national contexts. In both cases, modellers typically include so-called ''No Policy Baseline'' scenarios (alternatively referred to as ''Reference'' or ''Business-as-Usual scenarios'' ) which do not necessarily reflect currently implemented policies and thus are not assessed as reference pathways ( [[#4.2.6.1|Section 4.2.6.1]] ). There are also various approaches to considering more ambitious action compared to the CP or NDC projections that are covered in addition.

Fifth, studies differ in the way they represent policies (current or envisioned in NDCs), depending on their internal structure. For example, a subsidy to energy efficiency in buildings may be explicitly modelled (e.g., in a sectoral model that represents household decisions relative to building insulation), represented by a proxy (e.g., by an exogenous decrease in the discount rate households use to make choices), or captured by its estimated outcome (e.g., by an exogenous decrease in the household demand for energy, say in an energy system model or in a compact CGE). Detailed representations (such as the former example) do not necessarily yield more accurate results than compact ones (the latter example), but the set of assumptions that are necessary to represent the same policy will be very different.

Finally, policy coverage strongly varies across studies with some just implementing high level targets specified in policy documents and NDCs while others represent the policies with the largest impact on emissions and some looking at very detailed measures and policies at sub-national level. In addition, in countries with rapidly evolving policy environments, slightly different cut-off dates for the policies considered in an emission projection can make a significant difference for the results ( [[#Dubash--2018|Dubash et al. 2018]] ).

The challenges described above are dealt with in the assessment of quantitative results in Section 4.2.2.3 by (i) comparing national studies with country-level results from global studies to understand systematic biases; (ii) comparing economy-wide emissions (including AFOLU) as well as energy-related emissions; (iii) using different emission metrics including CO 2 and Kyoto GHG emissions where the latter have been harmonised to using AR6 GWP100 metrics; and (iv) tracking cut-off dates of implemented policies and NDCs used in different references (Table 4.SM.1). The most notable differences in quantitative emission estimates related to current policies and NDCs relate to the COVID-19 pandemic and its implications and to the updated NDCs mostly submitted since early 2020 which are separately dealt with in Sections 4.2.2.4 and 4.2.2.5, respectively.

In addition to assessing the emissions outcomes of NDCs, some studies report development indicators, by which they mean a wide diversity of socio-economic indicators ( [[#Jiang--2013|Jiang et al. 2013]] ; [[#Chai--2014|Chai and Xu 2014]] ; [[#Delgado--2014|Delgado et al. 2014]] ; [[#La%20Rovere--2014a|La Rovere et al. 2014a]] ; [[#Zevallos--2014|Zevallos et al. 2014]] ; Benavides et al. 2015; Altieri et al. 2016; Bataille et al. 2016a; [[#Zou--2016|Zou et al. 2016]] ; [[#Paladugula--2018|Paladugula et al. 2018]] ; [[#Parikh--2018|Parikh et al. 2018]] ; [[#Yang--2021|Yang et al. 2021]] ), share of low-carbon energy (Bertram et al. 2015; [[#Riahi--2015|Riahi et al. 2015]] ), renewable energy deployment ( [[#Roelfsema--2018|Roelfsema et al. 2018]] ), production of fossil fuels ( [[#SEI--2020|SEI et al. 2020]] ) or investments into low-carbon mitigation measures ( [[#McCollum--2018|McCollum et al. 2018]] ) to track progress towards long-term temperature goals.

'''Table 4.1 | Assessment of projected 2030 emissions of current policies based on pre-COVID assumptions and original NDCs submitted in 2015/16 for 28 individual countries/regions and the world.''' The table compares projected emissions from globally comprehensive studies, national studies and, when available, official communications to UNFCCC using different emission sources (fossil fuels, AFOLU sector) and different emission metrics (CO 2 , Kyoto GHGs). The comparison allows identifying potential biases across the ranges and median estimates projected by the different sets of studies.

{| class="wikitable"
|-
! rowspan="3"| Region a

! rowspan="3"| GHG share [%] b

! rowspan="3"| Type c

! rowspan="3"| \# estimates d

! colspan="3"| Current Policies 2030 emissions

! colspan="3"| NDC 2030 emissions (conditional/unconditional)

|-
! colspan="2"| CO 2 only [GtCO 2 ] median (min–max) f

! Kyoto GHGs e [GtCO 2 -eq] median (min–max) f

! colspan="2"| CO 2 only [GtCO 2 ] median (min–max) f

! Kyoto GHGs e [GtCO 2 -eq] median (min–max) f

|-
! incl. AFOLU g

! fossil fuels

! incl. AFOLU g

! incl. AFOLU g

! fossil fuels

! incl. AFOLU g

|-
| World

| 100

| global

| 93

| 43 (38–51)

| 37 (33–45)

| 60 (54–68)

| 40 (35–45)/ 37 (35–39)

| 32 (26–39)/ 31 (27–37)

| 54 (50–60)/ 57 (49–63)

|-
| rowspan="2"| CHN

| rowspan="2"| 27

| global

| 76

| 12 (9.7–15)

| 11 (8.4–14)

| 15 (12–18)

| – /11 (9.8–13)

| – /8.8 (6.9–13)

| – /14 (13–16)

|-
| national

| 13

| 12 (12–12)

| 11 (9.2–13)

| 15 (13–15)

| – /12 (11–12)

| – /11 (10–11)

| – /15 (13–16)

|-
| rowspan="2"| USA h

| rowspan="2"| 12

| global

| 71

| 4.9 (4.4–6.6)

| 4.6 (3.5–6.5)

| 5.9 (4.9–6.6)

| – /3.8 (3.3–4.1)

| – /3.9 (3.1–5.3)

| – /4.6 (4–5.1)

|-
| national

| 5

| 4.1

| 4.5 (4.1–4.9)

| 5.9 (5.2–6.7)

| – /3.4

| – /3.5

| – /4.3

|-
| rowspan="3"| EU i

| rowspan="3"| 8.1

| global

| 24

| 2.7 (2.1–3.5)

| 2.6 (2.1–3.3)

| 3.4 (2.6–4.7)

| – /2.6 (2.1–2.8)

| – /2.4 (2.1–2.7)

| – /3.2 (2.6–3.7)

|-
| national

| 3

| 3.1

| 2.6

|
| – /2.5

|
|-
| official

| 3

|
| 3.2 (2.8–3.7)

|
|-
| rowspan="2"| IND

| rowspan="2"| 7.1

| global

| 79

| 3.7 (3–4.5)

| 3.2 (2.5–4.5)

| 4.7 (4.1–6.4)

| 3.3 (3.1–4.4)/4

| 3.3 (2.4–5.6)/3.8 (2.9–5.6)

| 5 (4.2–6.4)/5.8 (4.9–6.1)

|-
| national

| 9

| 3.4 (3.3–4)

| 3.4 (2.9–3.9)

| 5.5 (5–5.7)

| 3.4 (3.2–3.6)/3.2

| 3.4 (3.2–3.5)/2.9

| 5.1/4.9

|-
| rowspan="3"| RUS

| rowspan="3"| 4.5

| global

| 66

| 1.7 (0.84–2)

| 1.6 (1.5–2)

| 2.3 (1.6–3.3)

| – /1.7 (0.85–1.9)

| – /1.6 (1.2–1.9)

| – /2.6 (1.9–3.1)

|-
| national

| 6

|
| 1.5 (1.5–1.5)

| 2.6

|
| – /1.5 (1.5–1.5)

| – /2.5

|-
| official

| 2

|
| 2.1

|
| – /2.7

|-
| rowspan="3"| BRA

| rowspan="3"| 2.5

| global

| 69

| 1.1 (0.79–1.7)

| 0.5 (0.28–1.1)

| 1.8 (1.4–2.7)

| – /0.94 (0.52–1.5)

| – /0.38 (0.097–0.86)

| – /1.3 (1.2–2.5)

|-
| national

| 4

| 0.59

| 0.47

| 1.8

| – /0.51

| – /0.47

| – /1.2

|-
| official

| 1

|
| – /1.2

|-
| rowspan="3"| JPN

| rowspan="3"| 2.4

| global

| 66

| 1.2 (0.94–1.3)

| 1.1 (0.67–1.3)

| 1.2 (0.95–1.3)

| – /1 (0.9–1.2)

| – /0.83 (0.65–1.2)

| – /1 (0.95–1.2)

|-
| national

| 16

| 1.1 (1.1–1.6)

| 1.1 (1.1–1.5)

| 1.3 (1.2–1.7)

| – /0.93 (0.91–1.2)

| – /0.93 (0.87–1.1)

| – /1 (1–1.3)

|-
| official

| 1

|
| – /1

|-
| rowspan="2"| IDN

| rowspan="2"| 2.2

| global

| 25

| 1.1 (0.79–2)

| 0.62 (0.51–0.89)

| 1.7 (1.4–2.4)

| 0.93 (0.76–1.4)/0.99

| 0.53 (0.45–0.66)/0.68 (0.6–0.77)

| 1.8 (1.3–2.1)/2.1 (1.5–2.2)

|-
| official

| 2

|
| 1.9 (1.8–1.9)/2.2

|-
| rowspan="3"| CAN

| rowspan="3"| 1.5

| global

| 67

| 0.58 (0.4–0.8)

| 0.43 (0.38–0.72)

| 0.68 (0.51–1)

| – /0.43 (0.34–0.67)

| – /0.43 (0.31–0.64)

| – /0.53 (0.49–0.82)

|-
| national

| 2

| 0.54

|
| 0.71

| – /0.41

|
| – /0.54

|-
| official

| 2

|
| 0.67

|
|-
| rowspan="2"| MEX

| rowspan="2"| 1.5

| global

| 31

| 0.61 (0.54–1.3)

| 0.48 (0.3–0.56)

| 0.82 (0.72–1.7)

| 0.54 (0.48–1)/0.46

| 0.43 (0.27–0.54)/0.33 (0.26–0.42)

| 0.65 (0.62–1.4)/0.73 (0.63–0.79)

|-
| official

| 2

|
| 0.62/0.76

|-
| SAU

| 1.5

| global

| 6

| 0.7 (0.57–0.82)

| 0.61 (0.48–0.74)

| 1 (0.7–1.1)

| 0.7 (0.58–0.82)/ –

| 0.62 (0.49–0.74)/ –

| 0.83 (0.7–0.96)/ –

|-
| rowspan="3"| KOR

| rowspan="3"| 1.4

| global

| 64

| 0.69 (0.55–0.76)

| 0.67 (0.42–0.91)

| 0.72 (0.68–0.81)

| – /0.57 (0.5–0.65)

| – /0.4 (0.26–0.61)

| – /0.57 (0.5–0.69)

|-
| national

| 4

| 0.78 (0.75–0.81)

| 0.73 (0.7–0.76)

| 0.86 (0.83–0.89)

| – /0.62 (0.51–0.72)

| – /0.58 (0.49–0.67)

| – /0.68 (0.56–0.8)

|-
| official

| 1

|
|-
| rowspan="3"| AUS

| rowspan="3"| 1.1

| global

| 16

| 0.42 (0.34–0.49)

| 0.34 (0.28–0.46)

| 0.54 (0.46–0.69)

| – /0.36 (0.28–0.43)

| – /0.3 (0.24–0.41)

| – /0.44 (0.39–0.52)

|-
| national

| 3

|
| 0.55

|
|-
| official

| 2

|
| 0.52 (0.51–0.52)

|
|-
| rowspan="2"| TUR

| rowspan="2"| 1.1

| global

| 18

| 0.44 (0.44–0.49)

| 0.4 (0.34–0.43)

| 0.6 (0.51–0.83)

| – /0.44 (0.44–0.49)

| – /0.4 (0.27–0.43)

| – /0.94 (0.55–1)

|-
| official

| 1

|
| – /0.93

|-
| rowspan="2"| ZAF

| rowspan="2"| 1.1

| global

| 26

| 0.49 (0.35–0.62)

| 0.36 (0.23–0.56)

| 0.64 (0.45–0.85)

| – /0.4 (0.27–0.55)

| – /0.35 (0.21–0.44)

| 0.41/0.58 (0.39–0.65)

|-
| official

| 1

|
| – /0.52 (0.41–0.64)

|-
| rowspan="2"| VNM

| rowspan="2"| 0.92

| global

| 2

|
| 0.61/0.77

|-
| national

| 4

| 0.36

| 0.28

|
| 0.32 (0.28–0.36)/0.36

| 0.26 (0.24–0.28)/0.28

|
|-
| GBR

| 0.86

| global

| 4

| 0.37

| 0.33 (0.3–0.37)

|
| – /0.37

| – /0.33 (0.3–0.37)

|
|-
| FRA

| 0.85

| global

| 4

| 0.22

| 0.32 (0.24–0.4)

|
| – /0.22

| – /0.32 (0.24–0.4)

|
|-
| rowspan="2"| THA

| rowspan="2"| 0.84

| global

| 5

|
| 0.41 (0.41–0.41)

|
| 0.44/0.47

|-
| national

| 3

| 0.43

| 0.4

| 0.58

| 0.35/0.36

| 0.32/0.34

| 0.43/0.46

|-
| rowspan="3"| ARG

| rowspan="3"| 0.76

| global

| 22

| 0.33 (0.17–0.52)

| 0.2 (0.15–0.35)

| 0.51 (0.33–0.75)

| 0.25 (0.17–0.46)/0.25

| 0.21 (0.18–0.23)/0.15 (0.14–0.16)

| 0.39 (0.32–0.69)/0.51 (0.33–0.52)

|-
| national

| 2

|
| 0.42 (0.41–0.43)

|
| – /0.19

|
|-
| official

| 2

|
| 0.4/0.52

|-
| KAZ

| 0.71

| global

| 3

|
| 0.45

|
| 0.28/0.32

|-
| UKR

| 0.52

| global

| 2

|
| 0.42 (0.42–0.42)

|
| – /0.54

|-
| PHL

| 0.48

| global

| 3

|
| 0.24

|
| 0.082/ –

|-
| COL

| 0.4

| global

| 5

|
| 0.23 (0.23–0.23)

|
| 0.26 (0.26–0.26)/0.29 (0.29–0.29)

|-
| ETH

| 0.31

| global

| 5

|
| 0.022

| 0.23 (0.19–0.27)

|
| – /0.023

| 0.16 (0.15–0.16)/ –

|-
| MAR

| 0.21

| global

| 5

|
| 0.11 (0.087–0.13)

|
| 0.13 (0.1–0.15)/0.13 (0.1–0.15)

|-
| KEN

| 0.18

| global

| 5

|
| 0.022

| 0.13 (0.11–0.14)

|
| – /0.023

| 0.11 (0.11–0.11)/ –

|-
| SWE

| 0.13

| global

| 4

| –0.012

| 0.03 (0.029–0.031)

|
| – /–0.012

| – /0.03 (0.028–0.032)

|
|-
| rowspan="2"| PRT

| rowspan="2"| 0.12

| global

| 2

| 0.045

| 0.036

|
| – /0.045

| – /0.036

|
|-
| national

| 1

|
| – /0.023

|
|-
| rowspan="2"| CHE

| rowspan="2"| 0.094

| global

| 1

|
| – /0.026

|-
| national

| 1

| 0.027

| 0.025

|
|-
| rowspan="2"| MDG

| rowspan="2"| 0.065

| global

| 1

|
| 0.033/ –

|-
| national

| 3

| 0.071

| 0.0059

|
| 0.07 (0.068–0.071)/ –

| 0.0043 (0.0026–0.0059)/ –

|
|}

Notes: a Countries are abbreviated by their ISO 3166-1 alpha-3 letter codes. EU denotes the European Union. b 2018 Share of global Kyoto GHG emissions, excluding FOLU emissions, based on 2019 GHG emissions from [[IPCC:Wg3:Chapter:Chapter-2|Chapter 2]] ( [[#Minx--2021|Minx et al. 2021]] ; [[#Crippa--2021|Crippa et al. 2021]] ). c Type distinguishes between independent globally comprehensive studies (that also provide information at the country/region level), independent national studies and official communications via Biennial Reports, Biennial Update Reports or National Communications. d Different estimates from one study (e.g., data from multiple models or minimum and maximum estimates) are counted individually, if available. e GHG emissions expressed in CO 2 -eq emission using AR6 100-year GWPs (see [[IPCC:Wg3:Chapter:Chapter-2#2.2.2|Section 2.2.2]] for a discussion of implications for historical emissions). GHG emissions from scenario data is recalculated from individual emission species using AR6 100-year GWPs. GHG emissions from studies that do provide aggregate GHG emissions using other GWPs are rescaled using 2019 GHG emissions from [[IPCC:Wg3:Chapter:Chapter-2|Chapter 2]] ( [[#Minx--2021|Minx et al. 2021]] ; [[#Crippa--2021|Crippa et al. 2021]] ). f If more than one value is available, a median is provided and the full range of estimates (in parenthesis). To avoid a bias due to multiple estimates provided by the same model, only one estimate per model, typically the most recent update, is included in the median estimate. In the full range, multiple estimates from the same model might be included, in case these reflect specific sensitivity analyses of the ‘central estimate’ (e.g., Baumstark et al. 2021; [[#Rogelj--2017|Rogelj et al. 2017]] ). g Note that AFOLU emissions from national GHG inventories and global/national land use models are generally different due to different approaches to estimate the anthropogenic CO 2 sink ( [[#Grassi--2018|Grassi et al. 2018]] , 2021) ( [[IPCC:Wg3:Chapter:Chapter-7#7.2.3|Section 7.2.3]] and Cross-Chapter Box 6 in Chapter 7). h The estimates for USA are based on the first NDC submitted prior to the withdrawal from the Paris Agreement, but not including the updated NDC submitted following its re-entry. i The EU estimates are based on the 28 member states up until 31 January 2020, i.e., including UK.

<div id="4.2.2.3" class="h3-container"></div>

<span id="projected-emissions-under-ndcs-and-current-policies-by-20252030"></span>
==== 4.2.2.3 Projected Emissions Under NDCs and Current Policies by 2025/2030 ====

<div id="h3-3-siblings" class="h3-siblings"></div>

The emissions projections presented in this section relate to the first NDCs, as communicated in 2015 and 2016, and on which an extensive literature exists. New and updated NDCs, mostly submitted since the beginning of 2020, are dealt with in Section 4.2.2.5. Similarly, the implications of COVID-19 and the related government responses on emissions projections is specifically dealt with in Section 4.2.2.4.

Table 4.1 presents the evidence base for the assessment of projected emissions of original NDCs and current policies until 2030. It covers 31 countries and regions responsible for about 82% of global GHG emission (excluding FOLU CO 2 emissions) and draws quantitative estimates from more than 40 studies (Table 4.SM.1 in the Supplementary Material to this chapter). The table allows comparing emission projections from national and globally comprehensive studies as well as official communications by countries to the UNFCCC at the national/regional level. The global aggregates presented in Table 4.1 derive from globally comprehensive studies only and are not the result of aggregating country projections up to the global level. As different studies report different emission indicators, the table includes four different indicators: CO 2 and GHG emissions, including or excluding AFOLU emissions. Where possible, multiple indicators are included per study.

<div id="Globally comprehensive studies" class="h4-container"></div>

<span id="globally-comprehensive-studies"></span>
===== Globally comprehensive studies =====

<div id="h4-1-siblings" class="h4-siblings"></div>

The UNFCCC Secretariat has assessed the aggregate effect of NDCs multiple times. The first report considered the intended NDCs in relation to 2°C ( [[#UNFCCC--2015b|UNFCCC 2015b]] ), whereas the second considered NDCs also in relation to 1.5°C ( [[#UNFCCC--2016b|UNFCCC 2016b]] ). New submissions and updates of NDCs in 2020/21 are assessed in Section 4.2.2.5. A number of globally comprehensive studies ( [[#den%20Elzen--2016|den Elzen et al. 2016]] ; [[#Luderer--2016|Luderer et al. 2016]] ; [[#Rogelj--2016|Rogelj et al. 2016]] , 2017; [[#Vandyck--2016|Vandyck et al. 2016]] ; [[#Rose--2017|Rose et al. 2017]] ; Baumstark et al. 2021) which estimate aggregate emissions outcomes of NDCs and current policies have previously been assessed in Cross-Chapter-Box 11 of IPCC SR1.5.

According to the assessment in this report, studies projecting emissions of current policies based on pre-COVID assumptions lead to median global GHG emissions of 60 GtCO 2 -eq with a full range of 54–68 by 2030 and original unconditional and conditional NDCs submitted in 2015/16 to 57 (49–63) and 54 (50–60) GtCO 2 -eq, respectively ( ''robust evidence'' , ''medium agreement'' ) (Table 4.1). Globally comprehensive and national-level studies project emissions of current policies and NDCs to 2025 and 2030 and, in general, are in good agreement about projected emissions at the country level.

These estimates are close to the ones provided by the IPCC SR1.5, Cross-Chapter-Box 11, and the UNEP emissions gap report ( [[#UNEP--2020a|UNEP 2020a]] ). [[#footnote-003|3]]

<div id="Nat" class="h4-container"></div>

<span id="national-studies"></span>
===== National studies =====

<div id="h4-2-siblings" class="h4-siblings"></div>

A large body of literature on national and regional emissions projections, including official communications of as part of the NDC submissions and independent studies exist. A subset of this literature provides quantitative estimates for the 2030 timeframe. As highlighted in Section 4.2.1, the number of independent studies varies considerably across countries with an emphasis on the largest emitting countries. This is reflected in Table 4.1 (see also Table 4.SM.1). Despite smaller differences between globally comprehensive and national studies for a few countries, there is generally good agreement between the different types of studies, providing evidence that these quantitative estimates are fairly robust.

<div id="Sectoral studies" class="h4-container"></div>

<span id="sectoral-studies"></span>
===== Sectoral studies =====

<div id="h4-3-siblings" class="h4-siblings"></div>

Sectoral studies are essential to understand the contributions of concrete measures of NDCs and current policies. For example, approximately 98% of NDCs include the energy sector in their mitigation contributions, of which nearly 50% include a specific target for the share of renewables, and about 5% aim at increasing nuclear energy production ( [[#Stephan--2016|Stephan et al. 2016]] ). Transport is covered explicitly in 75% of NDCs, although specific targets for the sector exist in only 21% of NDCs ( [[#PPMC%20and%20SLoCaT--2016|PPMC and SLoCaT 2016]] ). Measures or targets for buildings are referred to explicitly in 27% of NDCs ( [[#GIZ--2017|GIZ 2017]] ). Additionally, 36% of NDCs include targets or actions that are specific to the agriculture sector ( [[#FAO--2016|FAO 2016]] ). LULUCF (mitigation) is included in 80% of all submitted NDCs, while 59% include adaptation and 29% refer to REDD+.

Greater sectoral expertise and involvement will be critical to accomplishing development and climate goals due to enhanced availability of information and expertise on specific sectoral options, greater ease of aligning the NDCs with sectoral strategies, and greater awareness among sector-level decision-makers and stakeholders ( [[#Fekete--2015|Fekete et al. 2015]] ; [[#NDC%20Partnership--2017|NDC Partnership 2017]] ). Sector-specific studies are assessed in the sectoral Chapters (6 to 11) of this report.

<div id="4.2.2.4" class="h3-container"></div>

<span id="estimated-impact-of-covid-19-and-governmental-responses-on-emissions-projections"></span>
==== 4.2.2.4 Estimated Impact of COVID-19 and Governmental Responses on Emissions Projections ====

<div id="h3-4-siblings" class="h3-siblings"></div>

The impacts of COVID-19 and national governments’ economic recovery measures on current ( [[IPCC:Wg3:Chapter:Chapter-2#2.2.2|Section 2.2.2]] ) and projected emissions of individual countries and globally under current policies scenarios until 2030 may be significant, although estimates are highly uncertain and vary across the few available studies. The analyses published to date (October 2021) are based on limited information about how COVID-19 has affected the economy and hence GHG emissions across countries so far in 2020, and also based on assumptions about COVID-19’s longer term impact. Moreover, the comparison of pre- and post-COVID-19 projections captures the impact of COVID-19 as well as other factors such as the consideration of recently adopted policies not related to COVID-19, and methodological changes.

Across different studies ( [[#Kikstra--2021|Kikstra et al. 2021]] ; [[#IEA--2020|IEA 2020]] ; [[#Dafnomilis--2021|Dafnomilis et al. 2021]] ; [[#Pollitt--2021|Pollitt et al. 2021]] ; [[#UNEP--2020a|UNEP 2020a]] ; [[#Climate%20Action%20Tracker--2020|Climate Action Tracker 2020]] ; [[#Keramidas--2021|Keramidas et al. 2021]] ; [[#Dafnomilis--2020|Dafnomilis et al. 2020]] ), the impact of the general slowdown of the economy due to the COVID-19 pandemic and its associated policy responses would lead to a reduced estimate of global GHG emissions in 2030 of about 1 to 5 GtCO 2 -eq, equivalent to 1.5–8.5%, compared to the pre-COVID-19 estimates (Table 4.SM.2). [[#Nascimento--2021|Nascimento et al. (2021)]] analyse the impacts of COVID-19 on current policy emission projections for 26 countries and regions and find a large range of emission reduction – between –1% and –21% – across these.

As indicated by a growing number of studies at the national and global level, how large near- to mid-term emissions implications of the COVID-19 pandemic are to a large degree depends on how stimulus or recovery packages are designed ( [[#Forster--2020|Forster et al. 2020]] ; [[#Gillingham--2020|Gillingham et al. 2020]] ; [[#IEA--2020|IEA 2020]] ; [[#Le%20Quéré--2020|Le Quéré et al. 2020]] ; [[#Malliet--2020|Malliet et al. 2020]] ; [[#Wang--2020|Wang et al. 2020]] ; [[#Obergassel--2021|Obergassel et al. 2021]] ; [[#Pollitt--2021|Pollitt et al. 2021]] ; [[#UNEP--2020a|UNEP 2020a]] ).

Four studies ( [[#Climate%20Action%20Tracker--2021|Climate Action Tracker 2021]] ; [[#den%20Elzen--2021|den Elzen et al. 2021]] ; [[#JRC--2021|JRC 2021]] ; [[#Riahi--2021|Riahi et al. 2021]] ) provide an update of the current policies assessment presented in Section 4.2.2.3 by taking into account the effects of COVID-19 as well as potential updates of policies. The resulting GHG emissions in 2030 are estimated to be 57 GtCO 2 -eq with a full range of 52 to 60 GtCO 2 -eq ( [[#_idTextAnchor043|Table 4.2]] ). This is a reduction of about 3 GtCO 2 -eq or 5% compared to the pre-COVID estimates from Section [[#_idTextAnchor016|4.2.2.3]] .

'''Table 4.2 | Projected global GHG emissions of current po''' '''licies by 2030.'''

{| class="wikitable"
|-
! Study

! Cut-off date

! Kyoto GHGs a [GtCO 2 -eq] median (min–max) b

! References

|-
| Climate Action Tracker

| 8/2020

| 54 (52–56)

| [[#Climate%20Action%20Tracker--2021|Climate Action Tracker (2021)]]

|-
| PBL

| 11/2020

| 58

| [[#den%20Elzen--2021|den Elzen et al. (2021)]] ; [[#Nascimento--2021|Nascimento et al. (2021)]]

|-
| JRC – GECO

| 12/2019

| 57

| [[#JRC--2021|JRC (2021)]]

|-
| ENGAGE c

| 7/2019

| 57 (52–60)

| [[#Riahi--2021|Riahi et al. (2021)]]

|-
| Total d

|
| 57 (52–60)

|
|}

Notes: a GHG emissions expressed in CO 2 -eq emission using AR6 100-year GWPs. GHG emissions from studies that provide aggregate GHG emissions using other GWPs are rescaled using 2019 GHG emissions from [[IPCC:Wg3:Chapter:Chapter-2|Chapter 2]] ( [[#Minx--2021|Minx et al. 2021]] ; [[#Crippa--2021|Crippa et al. 2021]] ). b If a range is available from a study, a median is provided in addition to the range. c Range includes estimates from four models: GEM-E3, MESSAGEix-GLOBIOM, POLES, REMIND-MAgPIE, based on sensitivity analysis. d To avoid a bias due to multiple estimates provided by the same model, only one estimate per model, typically the most recent update, is included in the median estimate for the total.

<div id="4.2.2.5" class="h3-container"></div>

<span id="estimated-impact-of-new-and-updated-ndcs-on-emissions-projections"></span>
==== 4.2.2.5 Estimated Impact of New and Updated NDCs on Emissions Projections ====

<div id="h3-5-siblings" class="h3-siblings"></div>

The number of studies estimating the emissions implications of new and updated NDCs and announced mitigation pledges that can be used for the quantitative assessment is limited to four (Table 4.3) ( [[#Climate%20Action%20Tracker--2021|Climate Action Tracker 2021]] ; [[#den%20Elzen--2021|den Elzen et al. 2021]] ; [[#Meinshausen--2021|Meinshausen et al. 2021]] ; [[#JRC--2021|JRC 2021]] ). One other study includes a limited number of NDC updates ( [[#Riahi--2021|Riahi et al. 2021]] ) and another ( [[#UNFCCC--2021|UNFCCC 2021]] ) excludes LULUCF emissions. They are therefore not directly comparable to the other two. In addition, the UNEP Emissions Gap Report 2021 ( [[#UNEP--2021|UNEP 2021]] ) in itself is assessment of almost the same studies included here. The evidence base for the updated NDC assessment is thus considerably smaller compared to that of the assessment of emissions implications of original NDCs presented in Section 4.2.2.3. However, it is worthwhile to note that the earlier versions of the studies summarised in Table 4.2 and Table 4.3 are broadly representative for the emissions range implied by the pre-COVID-19 current policies and original NDCs of the full set of studies shown in Table 4.1, therefore building confidence in estimates.

An additional challenge lies in the fact that these studies do not all apply the same cut-off date for NDC updates, potentially leading to larger systematic deviations in the resulting emission estimates. Another complication is the fact that publicly announced mitigation pledges on global 2030 emissions that have not been officially submitted to the UNFCCC NDC registry yet, have been included in several of the studies to anticipate their impact on emission levels (see notes to [[#_idTextAnchor044|Table 4.3]] ). In addition to the updates of NDC targets, most of the new studies also include impacts of COVID-19 on future emission levels (as discussed in Section ) which may have led to considerable downward revisions of emission trends unrelated to NDCs. Table 4.3 presents the emission estimates of the four studies that form the basis of the quantitative assessment presented here and three other studies to compare with.

Comparing the emission levels implied by the new and updated NDCs as shown in Table 4.3 with those estimated by the original NDCs from the same studies (as included in Table 4.1), a downward revision of 3.8 (3.0–5.3) GtCO 2 -eq of the central unconditional NDC estimates and of 4.5 (2.7–6.3) GtCO 2 -eq of the central conditional NDC estimate emerges ( ''medium evidence'' , ''medium agreement'' ). The emissions gaps between temperature limits and new and updated NDCs are assessed in Cross-Chapter Box 4 below. New and updated unconditional NDCs reduce the median gap with emissions pathways that limit warming to 2°C (&gt;67%) in 2030 by slightly more than 20%, from a median gap of 17 GtCO 2 -eq (9–23) to 13 (10–16). New and updated conditional NDCs reduce the median gap with emissions pathways that limt warming to 2°C (&gt;67%) in 2030 by about one third, from 14 GtCO 2 -eq (10–20) to 9 (6–14). New and updated unconditional NDCs reduce the median gap with emissions pathways that limit warming to 1.5°C (&gt;50%) with no or limited overshoot in 2030 by about 15%, from a median gap of 27 GtCO 2 -eq (19–32) to 22 GtCO 2 -eq (19–26). New and updated conditional NDCs reduce the median gap with emissions pathways that limit warming to 1.5°C (&gt;50%) with no or limited overshoot in 2030 by about 20%, from a median gap of 24 GtCO 2 -eq (20–29) to 19 GtCO 2 -eq (16–23). Box 4.1 discusses the adaptation gap.

Globally, the implementation gap between projected emissions of current policies and the unconditional and conditional new and updated NDCs is estimated to be around 4 and 7 GtCO 2 -eq in 2030, respectively ( ''medium evidence'' , ''medium agreement'' ) (Tables 4.2 and 4.3), with many countries requiring additional policies and associated climate action to meet their mitigation targets as specified under the NDCs ( ''limited evidence'' ) (Section 4.2.2.6). It should be noted that the implementation gap varies considerably across countries, with some having policies in place estimated to be sufficient to achieve the emission targets their NDCs, some where additional policies may be required to be sufficient, as well as differences between the policies in place and action on the ground.

'''Table 4.3 | Projected global GHG emissions of new and update''' '''d NDCs by 2030.'''

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

! rowspan="3"| Cut-off date

! colspan="4"| Kyoto GHGs a [GtCO 2 -eq]

! rowspan="3"| References

|-
! colspan="2"| Historical

! colspan="2"| Median (min–max) b 2030

|-
! 2015

! 2019

! Unconditional NDCs

! Conditional NDCs

|-
| Climate Action Tracker c

| 5/2021

| 51

| 52

| 50

| 47

| [[#Climate%20Action%20Tracker--2021|Climate Action Tracker (2021)]]

|-
| PBL d

| 9/2021

| 52

| 54

| 53 (51–55)

| 52 (49–53)

| [[#den%20Elzen--2021|den Elzen et al. (2021)]] ; [[#Nascimento--2021|Nascimento et al. (2021)]]

|-
| JRC – GECO e

| 10/2021

| 51

|
| 48

| [[#JRC--2021|JRC (2021)]]

|-
| Meinshausen et al. f

| 10/2021

| 54

| 56

| 55 (54–57)

| 53 (52–55)

| [[#Meinshausen--2021|Meinshausen et al. (2021)]]

|-
| Total g

|
| colspan="2"|
| 53 (50–57)

| 50 (47–55)

|
|-
| colspan="6"| '''Other studies for comparison'''

|
|-
| UNEP EGR h

| 9/2021

| colspan="2"|
| 53 (50–55)

| 50 (47–53)

| [[#UNEP--2017a|UNEP (2017a)]]

|-
| UNFCCC Secretariat i

| 7/2021

| colspan="2"|
| 57 (55–58)

| 54 (52–56)

| [[#UNFCCC--2021|UNFCCC (2021)]]

|-
| ENGAGE j

| 3/2021

| colspan="2"|
|
| 51 (49–53)

| [[#Riahi--2021|Riahi et al. (2021)]]

|}

Notes: a GHG emissions expressed in CO 2 -eq emission using AR6 100-year GWPs. GHG emissions from studies that provide aggregate GHG emissions using other GWPs are rescaled using 2019 GHG emissions from [[IPCC:Wg3:Chapter:Chapter-2|Chapter 2]] ( [[#Minx--2021|Minx et al. 2021]] ; [[#Crippa--2021|Crippa et al. 2021]] ). Note that due to slightly different system boundaries across historical emission datasets as well as data uncertainties (Chapter 2, SM2.2) relative change compared to historical emissions should be calculated vis-à-vis the historical emissions data used by a particular study. b If a range is available from a study, a median is provided in addition to the range. c Announced mitigation pledges on global 2030 emissions of China and Japan included. d Announced mitigation pledges of China, Japan, Republic of Korea included. e Announced mitigation pledge of Korea not included. f Announced mitigation pledges of China and Republic of Korea not included, emissions from international aviation and shipping not included. g Ranges across four studies are calculated using the median and the full range including the minimum and maximum of studies if available. h UNEP EGR 2021 estimate listed for comparison, but since largely relying on the same studies not included in range estimate. i NDCs submitted until 30 July included, announcements not included, excluding LULUCF emissions. j NDC updates of Brazil, EU and announcement of China included as a sensitivity analysis compared to original NDCs.

<div id="4.2.2.6" class="h3-container"></div>

<span id="tracking-progress-in-implementing-and-achieving-ndcs"></span>
==== 4.2.2.6 Tracking Progress in Implementing and Achieving NDCs ====

<div id="h3-6-siblings" class="h3-siblings"></div>

Under the Enhanced Transparency Framework, countries will transition from reporting biennial reports (BRs) and biennial update reports (BURs) to reporting biennial transparency reports (BTRs) starting, at the latest, by December 2024. Each Party will be required to report information necessary to track progress made in implementing and achieving its NDC under the Paris Agreement ( [[#UNFCCC--2018b|UNFCCC 2018b]] ). Thus, no official data exists yet on tracking progress of individual NDCs.

Meanwhile, there is some literature at global and national level that aims at assessing whether countries are on track or progressing towards implementing their NDCs and to which degree the NDCs collectively are sufficient to reach the temperature targets of the Paris agreement ( [[#Rogelj--2016|Rogelj et al. 2016]] ; [[#Quéré--2018|Quéré et al. 2018]] ; [[#Höhne--2018|Höhne et al. 2018]] ; [[#Roelfsema--2020|Roelfsema et al. 2020]] ; [[#den%20Elzen--2019|den Elzen et al. 2019]] ; [[#Höhne--2020|Höhne et al. 2020]] ). Most of these studies focus on major emitters such as G20 countries and with the aim to inform countries to strengthen their ambition regularly, for example, through progress of NDCs and as part of the global stocktake ( [[#Höhne--2018|Höhne et al. 2018]] ; [[#Peters--2017|Peters et al. 2017]] ). However, a limited number of studies assess the implementation gaps of conditional NDCs in terms of finance, technology and capacity building support. Some authors conclude that finance needed to fulfil conditional NDCs exceeds available resources or the current long-term goal for finance (USD100 billion yr –1 ) (Pauw et al. 2019); others assess financial resources needed for forest-related activities ( [[#Kissinger--2019|Kissinger et al. 2019]] ) ( [[IPCC:Wg3:Chapter:Chapter-15#15.4.2|Section 15.4.2]] ). The literature suggests that consistent and harmonised approach to track progress of countries towards their NDCs would be helpful ( [[#Peters--2017|Peters et al. 2017]] ; [[#Höhne--2018|Höhne et al. 2018]] ; [[#den%20Elzen--2019|den Elzen et al. 2019]] ), and negotiations on a common tabular format are expected to conclude during COP26 in November 2021.

With an implementation gap in 2030 of 4 to 7 GtCO 2 -eq (Section 4.2.2.5), many countries will need to implement additional policies to meet their self-determined mitigation targets as specified under the NDCs. Studies that assess the level of projected emissions under current policies indicate that new policies (that have been implemented since the first assessment of the NDCs in 2015 and are thus covered in more recent projections) have reduced projections, by about two GtCO 2 -eq since the adoption of the Paris Agreement in 2015 to 2019 ( [[#Climate%20Action%20Tracker--2019|Climate Action Tracker 2019]] ; [[#UNEP--2020a|UNEP 2020a]] ; [[#den%20Elzen--2019|den Elzen et al. 2019]] ).

<div id="4.2.2.7" class="h3-container"></div>

<span id="literature-on-fairness-and-ambition-of-ndcs"></span>
==== 4.2.2.7 Literature on Fairness and Ambition of NDCs ====

<div id="h3-7-siblings" class="h3-siblings"></div>

Most countries provided information on how they consider their NDCs to be fair and ambitious in the NDCs submitted to UNFCCC and many of these NDCs refer to specific national circumstances such as social, economic and geographical factors when outlining why they are fair and ambitious. Further, several Parties provided information on specific criteria for evaluating fairness and ambition, including criteria relating to: responsibility and capability; share of emissions; development and/or technological capacity; mitigation potential; cost of mitigation actions; the degree of progression or stretching beyond the current level of effort; and the link to objectives and global goals ( [[#UNFCCC--2016a|UNFCCC 2016a]] ).

According to its Article 2.2, the Paris Agreement will be implemented to reflect equity and the principle of common but differentiated responsibilities and respective capabilities, in the light of different national circumstances, the latter clause being new, added to the UNFCCC principle ( [[#Voigt--2016|Voigt and Ferreira 2016]] ; [[#Rajamani--2017|Rajamani 2017]] ). Possible different interpretations of equity principles lead to different assessment frameworks ( [[#Lahn--2017|Lahn and Sundqvist 2017]] ; [[#Lahn--2018|Lahn 2018]] ).

Various assessment frameworks have been proposed to analyse fair share ranges for NDCs. The literature on equity frameworks including quantification of national emissions allocation is assessed in section 4.5 (Sections 13.4.2, 14.3.2 and 14.5.3). Recent literature has assessed equity, analysing how fairness is expressed in NDCs in a bottom-up manner ( [[#Mbeva--2016|Mbeva and Pauw 2016]] ; [[#Cunliffe--2019|Cunliffe et al. 2019]] ; [[#Winkler--2018|Winkler et al. 2018]] ). Some studies compare NDC ambition level with different effort sharing regimes and which principles are applied to various countries and regions ( [[#Peters--2015|Peters et al. 2015]] ; [[#Pan--2017|Pan et al. 2017]] ; [[#Robiou%20Du%20Pont--2017|Robiou Du Pont et al. 2017]] ; [[#Holz--2018|Holz et al. 2018]] ; [[#Robiou%20du%20Pont--2018|Robiou du Pont and Meinshausen 2018]] ; [[#van%20den%20Berg--2019|van den Berg et al. 2019]] ). Others propose multi-dimensional evaluation schemes for NDCs that combine a range of indicators, including the NDC targets, cost-effectiveness compared to global models, recent trends and policy implementation into consideration (Aldy et al. 2017; [[#Höhne--2018|Höhne et al. 2018]] ). Yet other literature evaluates NDC ambition against factors such as technological progress of energy efficiency and low-carbon technologies ( [[#Jiang--2017|Jiang et al. 2017]] ; [[#Kuramochi--2017|Kuramochi et al. 2017]] ; [[#Wakiyama--2017|Wakiyama and Kuramochi 2017]] ), synergies with adaptation plans ( [[#Fridahl--2017|Fridahl and Johansson 2017]] ), the obligations to deploy carbon dioxide removal technologies like bioenergy with carbon capture and storage (BECCS) in the future implied by their near-term emission reductions where they are not reflected on in the first NDCs ( [[#Peters--2017|Peters and Geden 2017]] ; [[#Fyson--2020|Fyson et al. 2020]] ; [[#Pozo--2020|Pozo et al. 2020]] ; [[#Mace--2021|Mace et al. 2021]] ). Others identify possible risks of unfairness when applying GWP* as emissions metric at national scale ( [[#Rogelj--2019|Rogelj and Schleussner 2019]] ). A recent study on national fair shares draws on principles of international environmental law, excludes approaches based on cost and grandfathering, thus narrowing the range of national fair shares previously assessed, and apply this to the quantification of national fair share emissions targets ( [[#Rajamani--2021|Rajamani et al. 2021]] ).

<div id="4.2.2.8" class="h3-container"></div>

<span id="uncertainty-in-estimates"></span>
==== 4.2.2.8 Uncertainty in Estimates ====

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There are many factors that influence the global aggregated effects of NDCs. There is limited literature on systematically analysing the impact of uncertainties on the NDC projections with some exception ( [[#Rogelj--2017|Rogelj et al. 2017]] ; Benveniste et al. 2018). The UNEP Gap Report ( [[#UNEP--2017a|UNEP 2017a]] ) discusses uncertainties of NDC estimates in some detail. The main factors include variations in overall socio-economic development; uncertainties in GHG inventories; conditionality; targets with ranges or for single years; accounting of biomass; and different GHG aggregation metrics (e.g., GWP values from different IPCC assessments). In addition, when mitigation effort in NDCs is described as measures that do only indirectly translate into emission reductions, assumptions necessary for the translation come into play ( [[#Doelle--2019|Doelle 2019]] ). For a more elaborate discussion of uncertainties in NDCs ( [[IPCC:Wg3:Chapter:Chapter-14#14.3.2|Section 14.3.2]] ).

Some studies assume successful implementation of all of the NDCs’ proposed measures, sometimes including varying assumptions to account for some of the NDC features which are subject to assumed conditions related to finance and technology transfer. Countries ‘shall pursue domestic mitigation measures’ under Article 4.2 of the Paris Agreement ( [[#UNFCCC--2015a|UNFCCC 2015a]] ), but they are not legally bound to the result of reducing emissions ( [[#Winkler--2017a|Winkler 2017a]] ). Some authors consider this to be a lack of a strong guarantee that mitigation targets in NDCs will be implemented ( [[#Nemet--2017|Nemet et al. 2017]] ). Others point to growing extent of national legislation to provide a legal basis for action ( [[#Iacobuta--2018|Iacobuta et al. 2018]] ) ( [[IPCC:Wg3:Chapter:Chapter-13#13.2|Section 13.2]] ). These factors together with incomplete information in NDCs mean there is uncertainty about the estimates of anticipated 2030 emission levels.

The aggregation of targets results in large uncertainty ( [[#Rogelj--2017|Rogelj et al. 2017]] ; Benveniste et al. 2018). In particular, clarity on the contributions from the land use sector to NDCs is needed ‘to prevent high LULUCF uncertainties from undermining the strength and clarity of mitigation in other sectors’ ( [[#Fyson--2019|Fyson and Jeffery 2019]] ). Methodological differences in the accounting of the LULUCF anthropogenic CO 2 sink between scientific studies and national GHG inventories (as submitted to UNFCCC) further complicate the comparison and aggregation of emissions of NDC implementation ( [[#Grassi--2018|Grassi et al. 2018]] , 2021) ( [[IPCC:Wg3:Chapter:Chapter-7#7.2.3|Section 7.2.3]] and Cross-Chapter Box 6 in Chapter 7). This uncertainty could be reduced with clearer guidelines for compiling future NDCs, in particular when it comes to mitigation efforts not expressed as absolute economy-wide targets ( [[#Doelle--2019|Doelle 2019]] ), and explicit specification of technical details, including energy accounting methods, harmonised emission inventories ( [[#Rogelj--2017|Rogelj et al. 2017]] ) and finally, increased transparency and comparability ( [[#Pauw--2018|Pauw et al. 2018]] ).

<div id="cross-chapter-box-4" class="h2-container box-container"></div>

<span id="cross-chapter-box-4-comparison-of-ndcs-and-current-policies-with-the-2030-ghg-emissions-from-long-term-temperature-pathways"></span>
=== Cross-Chapter Box 4 | Comparison of NDCs and current policies with the 2030 GHG Emissions from Long-term Temperature Pathways ===

<div id="h2-3-siblings" class="h2-siblings"></div>

'''Authors:''' Edward Byers (Austria/Ireland), Michel den Elzen (the Netherlands), Céline Guivarch (France), Volker Krey (Germany/Austria), Elmar Kriegler (Germany), Franck Lecocq (France), Keywan Riahi (Austria), Harald Winkler (South Africa)

Introduction

The Paris Agreement (PA) sets a long-term goal of holding the increase of global average temperature to ‘well below 2°C above pre-industrial levels’ and pursuing efforts to limit the temperature increase to 1.5°C above pre-industrial levels. This is underpinned by the ‘aim to reach global peaking of greenhouse gas emissions as soon as possible’ and ‘achieve a balance between anthropogenic emissions by sources and removals by sinks of GHG in the second half of this century’ ( [[#UNFCCC--2015a|UNFCCC 2015a]] ). The PA adopts a bottom-up approach in which countries determine their contribution to reach the PA’s long-term goal. These national targets, plans and measures are called ‘nationally determined contributions’ or NDCs.

<div id="_idContainer027" class="Boxes_Blue-Boxes_•-Box-Figure-title"></div>

[[File:a62e9a5f3b9801a531b2da5994774242 IPCC_AR6_WGIII_CCBox_4_Figure_1.png]]

'''Cross-Chapter Box 4, Figure 1:''' '''Global GHG emissions of modelled pathways (funnels in Panel a, and associated bars in Panels b, c, d) and projected emission outcomes from near-term policy assessments for 2030 (Panel b).'''

'''Panel a''' shows global GHG emissions over 2015–2050 for four types of assessed modelled global pathways:

'''–''' Trend from implemented policies: Pathways with projected near-term GHG emissions in line with policies implemented until the end of 2020 and extended with comparable ambition levels beyond 2030 (29 scenarios across categories C5–C7, Table SPM.2).

'''–''' Limit to 2°C (&gt;67%) or return warming to 1.5°C (&gt;50%) after a high overshoot, NDCs until 2030: Pathways with GHG emissions until 2030 associated with the implementation of NDCs announced prior to COP26, followed by accelerated emissions reductions likely to limit warming to 2°C (C3b, Table SPM.2) or to return warming to 1.5°C with a probability of 50% or greater after high overshoot (subset of 42 scenarios from C2, Table SPM.2).

'''–''' Limit to 2°C (&gt;67%) with immediate action: Pathways that limit warming to 2°C (&gt;67%) with immediate action after 2020 (C3a, Table SPM.2).

'''–''' Limit to 1.5°C (&gt;50%) with no or limited overshoot: Pathways limiting warming to 1.5°C with no or limited overshoot (C1, Table SPM.2 C1). All these pathways assume immediate action after 2020.

Past GHG emissions for 2010–2015 used to project global warming outcomes of the modelled pathways are shown by a black line [[#footnote-002|4]] and past global GHG emissions in 2015 and 2019 as assessed in [[IPCC:Wg3:Chapter:Chapter-2|Chapter 2]] are shown by whiskers.

'''Panels b, c and d''' show snapshots of the GHG emission ranges of the modelled pathways in 2030, 2050, and 2100, respectively. Panel b also shows projected emissions outcomes from near-term policy assessments in 2030 from Chapter 4.2 (Tables 4.2 and 4.3; median and full range). GHG emissions are in CO 2 -equivalent using GWP100 from AR6 WGI. {3.5, 4.2, Table 4.2, Table 4.3, Cross-Chapter Box 4 in Chapter 4}

The NDCs are a central instrument of the PA to achieve its long-term goal. It thus combines a global goal with a country-driven (bottom-up) instrument to a hybrid climate policy architecture to strengthen the global response to climate change. All signatory countries committed to communicating nationally determined contributions including mitigation targets, every five years. While the NDCs mostly state targets, countries are also obliged to pursue domestic mitigation measures to achieve the objectives. The literature examines the emissions outcome of the range of policies implemented to reach these targets.

Emissions gap

A comparison between the projected emission outcomes of current policies, the NDCs (which include unconditional and conditional elements, Section 4.2.1) and mitigation pathways acting immediately, i.e. from 2020 onwards, on reaching different temperature goals in the long-term ( [[IPCC:Wg3:Chapter:Chapter-3#3.3.3|Section 3.3.3]] ) allows identifying different ‘emission gaps’ in 2030 (Cross-Chapter Box 4, Figure 1). First, the implementation gap between ‘current policies’ and unconditional and conditional NDCs is estimated to be around 4 and 7 GtCO 2 -eq in 2030, respectively (Section 4.2.2 and Tables 4.2 and 4.3). Second, the comparison of unconditional (conditional) NDCs and long-term mitigation pathways that limit warming to 2°C (&gt;67%) or lower gives rise to a 2030 median emissions gap of 19–26 GtCO 2 -eq (16–23 GtCO 2 -eq) for limiting end-of-century warming to 1.5°C (&gt;50%) with no or limited overshoot and 10–16 GtCO 2 -eq (6–14 GtCO 2 -eq) for limiting warming to 2°C (&gt;67%). [[#footnote-001|5]] GHG emissions of NDCs are broadly consistent with 2030 emission levels of cost-effective long-term pathways staying below 2.5°C (scenarios category C5, Table 3.2, Chapter 3).

Other ‘gap indicators’

Beyond the quantification of different GHG emissions gaps, there is an emerging literature that identifies gaps between current policies, NDCs and long-term temperature in terms of other indicators, including for example the deployment of low-carbon energy sources, energy efficiency improvements, fossil fuel production levels or investments into mitigation measures ( [[#Roelfsema--2020|Roelfsema et al. 2020]] ; [[#McCollum--2018|McCollum et al. 2018]] ; [[#SEI--2020|SEI et al. 2020]] ).

A 2030 gap in the contribution of low-carbon energy sources to the energy mix in 2030 between current policies and cost-effective long-term temperature pathways is calculated to be around 7percentage-points (2°C) and 13percentage-points (1.5°C) by Roelfsema et al. ( [[#Roelfsema--2020|Roelfsema et al. 2020]] ). The same authors estimate an energy intensity improvement gap 10% and 18% for 2030 between current policies pathways and 2°C and 1.5°C pathways, respectively. [[#SEI--2020|SEI et al. (2020)]] estimates the ‘fossil fuel production gap’, by which they mean ‘the level of countries’ planned fossil fuel production expressed in their carbon content to be 120% and 50% higher compared to the fossil fuel production consistent with 1.5°C and 2°C pathways, respectively, as assessed in IPCC SR1.5 ( [[#Rogelj--2018a|Rogelj et al. 2018a]] ).

Cross-Chapter Box 4

The methodology used for this estimation is very similar to how emissions gaps are derived (SEI et al. 2019). The gap of global annual average investments in low-carbon energy and energy efficiency in 2030 between following current policy on the one hand and achieving the NDCs, the 2°C and 1.5°C targets on the other hand, is estimated to be approximately USD 130, 320, or 480 billion per year ( [[#McCollum--2018|McCollum et al. 2018]] ).

It is important to note that such comparisons are less straight forward as the link between long-term temperature goals and these indicators is less pronounced compared to the emission levels themselves; they are therefore associated with greater uncertainty compared to the emissions gap.

<div id="box-4.1" class="h2-container box-container"></div>

<span id="box-4.1-adaptati-on-gap-and-ndcs"></span>
=== Box 4.1 | Adaptation gap and NDCs ===

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NDCs have been an important driver of national adaptation planning, with cascading effects on sectors and sub-national action, especially in developing countries. Yet, only 40 developing countries have quantifiable adaptation targets in their current NDCs; 49 countries include quantifiable targets in their national legislation ( [[#UNEP--2018a|UNEP 2018a]] ).

Working Group II contribution to this Assessment finds that the overall extent of adaptation-related responses in human systems is low ( ''high confidence'' ) and that there is limited evidence on the extent to which adaptation-related responses in human systems are reducing climate risk (O’Neill et al. 2020). Thus there is an adaptation gap ( [[#UNEP--2018a|UNEP 2018a]] ), and bridging that gap requires enablers including institutional capacity, planning and investment ( [[#UNEP--2016|UNEP 2016]] ). Estimates of adaptation costs vary greatly across studies. Recent studies based on climate change under RCP8.5 report adaptation costs for developing countries of up to 400 billion (300 billion in RCP2.6) USD2005 in 2030 (New et al. 2020). Of the NDCs submitted in 2015, 50 countries estimated adaptation costs of USD39 billion annually. Both public and private finance for adaptation is increasing, but remains insufficient and constitutes a small fraction (4–8%) of total climate finance which is mostly aimed at mitigation. The pledge of developed countries of mobilising finance for developing countries to address adaptation needs globally as part of the Paris Agreement are insufficient. By 2030 the adaptation needs are expected to be three to six times larger than what is pledged, further increasing towards 2050 ( [[#UNEP--2016|UNEP 2016]] ; New et al. 2020).

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<span id="mitigation-efforts-in-sub-national-and-non-state-action-plans-and-policies"></span>

=== 4.2.3 Mitigation Efforts in Sub-national and Non-state Action Plans and Policies ===

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The decision adopting the Paris Agreement stresses the importance of ‘stronger and more ambitious climate action’ by non-government and sub-national stakeholders, ‘including civil society, the private sector, financial institutions, cities and other sub-national authorities, local communities and indigenous peoples’ ( [[#UNFCCC--2015a|UNFCCC 2015a]] ). The Marrakech Partnership for Global Action, launched in the 2016 UNFCCC Conference of Parties by two ‘high-level champions,’ further formalised the contributions of non-government and sub-national actors taking action through seven thematic areas (e.g., energy, human settlements, industry, land-use, etc.) and one cross-cutting area (resilience). Since then, non-state actors, for example, companies and civil society, and sub-national actors, such as cities and regions, have emerged to undertake a range of largely voluntary carbon mitigation actions ( [[#Hsu--2018|Hsu et al. 2018]] , 2019) both as individual non-state actors (NSAs in the following) and through national and international cooperative initiatives, or ICIs ( [[#Hsu--2018|Hsu et al. 2018]] ). ICIs take a variety of forms, ranging from those that focus solely on non-state actors to those that engage national and even local governments. They can also range in commitment level, from primarily membership-based initiatives that do not require specific actions to those that require members to tackle emissions reductions in specific sectors or aim for transformational change.

Quantification of the (potential) impact of these actions is still limited. Almost all studies estimate the potential impact of the implementation of actions by NSAs and ICIs, but do not factor in that they may not reach their targets. The main reason for this is that there is very limited data currently available from individual actors (e.g., annual GHG inventory reports) and initiatives to assess their progress towards their targets. A few studies have attempted to assess progress of initiatives by looking into the initiatives’ production of relevant outputs ( [[#Chan--2018|Chan et al. 2018]] ). Quantification does not yet cover all commitments and only a selected number of ICIs are analysed in the existing literature. Most of these studies exclude commitments that are not (self-)identified as related to climate change mitigation, those that are not connected to international networks, or those that are communicating in languages other than English.

Non state action could make significant contributions to achieving the Paris climate goals ( ''limited evidence'' , ''high agreement'' ). However, efforts to measure the extent to which non-state and sub-national actors go beyond national policy are still nascent ( [[#Hsu--2019|Hsu et al. 2019]] ; [[#Kuramochi--2020|Kuramochi et al. 2020]] ) and we do not fully understand the extent to which ambitious action by non-state actors is additional to what national governments intend to do. Sub-national and non-state climate action may also have benefits in reinforcing, implementing, or piloting national policy, in place of or in addition to achieving additional emissions reductions ( [[#Broekhoff--2015|Broekhoff et al. 2015]] ; [[#Heidrich--2016|Heidrich et al. 2016]] ; [[#Hsu--2017|Hsu et al. 2017]] ).

Quantification of commitments by individual NSAs are limited to date. Attempts to quantify aggregate effects in 2030 of commitments by individual non-state and sub-national actors are reported by ( [[#Hsu--2019|Hsu et al. 2019]] ; [[#Kuramochi--2020|Kuramochi et al. 2020]] ). [[#Kuramochi--2020|Kuramochi et al. (2020)]] estimate potential mitigation by more than 1,600 companies, around 6,000 cities and many regions (cities assessed have a collective population of 579 million, and regions 514 million). Individual commitments by these sub-national regions, cities and companies could reduce GHG emissions in 2030 by 1.2 to 2.0 GtCO 2 -eq yr –1 compared to current national policies scenario projections, reducing projected emissions by 3.8–5.5% in 2030, if commitments are fully implemented and do not lead to weaker mitigation actions by others (Figure 4.1 left). In several countries, NSA commitments could potentially help meet or exceed national mitigation targets.

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[[File:a34312bb4ea2d9a4efc4689b0fd8f78b IPCC_AR6_WGIII_Figure_4_1.png]]

'''Figure 4.1 | Emissions reduction potential for non-state and sub-national actors by 2030.''' Source: data in left panel from [[#Hsu--2020|Hsu et al. (2020)]] , right panel from Lui et al. (2020).

Quantification of potential emission reductions from international cooperative initiatives have been assessed in several studies, and recently synthesised ( [[#Hsu--2020|Hsu et al. 2020]] ; [[#Lui--2021|Lui et al. 2021]] ), with some initiatives reporting high potential. In Table 4.4 and Figure 4.1, we report estimates of the emissions reductions from 19 distinct sub-national and non-state initiatives to mitigate climate change. The table shows wide ranges of potential mitigation based on current, target or potential membership, as well as a wide diversity of actors and membership assumptions. Current membership reflects the number of non-state or sub-national actors that are presently committed to a particular initiative; while targeted or potential membership represents a membership goal (e.g., increasing from 100 to 200 members) that an initiative may seek to achieve ( [[#Kuramochi--2020|Kuramochi et al. 2020]] ). When adding up emission reduction potentials, sub-national and non-state international cooperative initiatives could reduce up to about 20 Gt of CO 2 -eq in 2030 ( ''limited evidence'' , ''medium agreement'' ). [[IPCC:Wg3:Chapter:Chapter-8|Chapter 8]] also presents data on the savings potential of cities and it suggests that these could reach 2.3 GtCO 2 -eq annually by 2030 and 4.2 GtCO 2 -eq annually for 2050.

Non-state action may be broader than assessed in the literature so far, though subject to uncertainty. The examples in Table 4.4 and Figure 4.1 do not include initiatives that target the emissions from religious organisations, colleges and universities, civic and cultural groups, and, to some extent, households, and in this sense may underestimate sub-national potential for mitigating emissions, rather than overestimate it. That said, the estimates are contingent on assumptions that sub-national and non-state actors achieve commitments – both with respect to mitigation and in some cases membership – and that these actions are not accounted for in nor lead to weakening of national actions.

Care is to be taken not to depict these efforts as additional to action within national NDCs, unless this is clearly established ( [[#Broekhoff--2015|Broekhoff et al. 2015]] ). There are potential overlaps between individual NSAs and ICIs, and across ICIs. [[#Kuramochi--2020|Kuramochi et al. (2020)]] propose partial and conservative partial effect methods to avoid double counting when comparing ambition, a matter that merits further attention. As the diversity of actions increased, the potential to count the same reductions multiple times increases.

Equally important to note here is that none of the studies reviewed in Figure 4.1 quantified the potential impact of financial sector actions, for example, divestment from emission intensive activities ( [[IPCC:Wg3:Chapter:Chapter-15#15.3|Section 15.3]] has a more detailed discussion of how financial actors and instruments are addressing climate change). Moreover, only a limited number of studies on the impact of actions by diverse actors go beyond 2050 (Table 4.4), which may reflect analysts’ recognition of the increasing uncertainties of longer time horizons. Accurate accounting methods can help to avoiding counting finance multiple times, and methods across mitigation and finance would consider counting carbon market flows and the tons reduced. As Table 4.4 and Figure 4.1 indicate, activities by businesses have potential to significantly contribute to global mitigation efforts. For example, the SBTi (Science Based Targets initiative) encourages companies to pledge to reduce their emissions at rates which according to SBTi would be compatible with global pathways to well below 2°C or 1.5°C, with various methodologies being proposed (Andersen et al. 2021; [[#Faria--2019|Faria and Labutong 2019]] ). Readers may note, however, that the link between emissions by individual actors and long-term temperature goals cannot be inferred without additional assumptions (Box 4.2). In the energy sector, some voluntary initiatives are also emerging to stop methane emissions associated with oil and gas supply chains. The Oil and Gas Methane Partnership (OGMP) is a voluntary initiative lead by the Climate and Clean Air Coalition, which has recently published a comprehensive framework for methane detection, measurement and reporting ( [[#UNEP--2020b|UNEP 2020b]] ).

Initiatives made up of cities and sub-national regions have an especially large potential to reduce emissions, due to their inclusion of many actors, across a range of different geographic regions, with ambitious emissions reduction targets, and these actors’ coverage of a large share of emissions ( [[#Kuramochi--2020|Kuramochi et al. 2020]] ). [[#Hsu--2019|Hsu et al. (2019)]] find largest potential in that area. Several sub-national regions like California and Scotland have set zero emission targets ( [[#Höhne--2019|Höhne et al. 2019]] ), supported by short- and medium-term interim goals ( [[#Scottish%20Government--2020b|Scottish Government 2020b]] ; [[#State%20of%20California--2018|State of California 2018]] ). Sharing of effort across global and sub-global scales has not been quantified, though one study suggests that non-state actors have increasingly adopted more diverse framings, including vulnerability, human rights and transformational framings of justice ( [[#Shawoo--2020|Shawoo and McDermott 2020]] ). Initiatives focused on forestry have high emissions reduction potential due to the current high deforestation rates, and due to the ambitious targets of many of these forestry initiatives, such as the New York Declaration on Forests’ goal to end deforestation by 2030 ( [[#Höhne--2019|Höhne et al. 2019]] ; [[#Lui--2021|Lui et al. 2021]] ), although the Initiative acknowledges that insufficient progress has to-date been made towards this goal ( [[#NYDF%20Assessment%20Partners--2020|NYDF Assessment Partners 2020]] ). On the other hand, uncertainties in global forest carbon emissions (and therefore potential reductions) are high and despite a multitude of initiatives in the sector, actually measured deforestation rates have not declined since the initiative was announced in 2014 (Sections 7.2 and 7.3.1). Moreover, not all initiatives are transparent about how they plan to reach their goals and may also rely on offsets.

Initiatives focused on non-CO 2 emissions, and particularly on methane, can achieve sizable reductions, in the order of multiple GtCO 2 -eq yr –1 (Table 4.4). The Global Cement and Concrete Association (formerly the Cement Sustainability Initiative), has contributed to the development of consistent energy and emissions reporting from member companies. The CSI also suggested possible approaches to balance GHG mitigation and the issues of competitiveness and leakage ( [[#Cook--2011|Cook and Ponssard 2011]] ). The member companies of the GCCA (CSI) have become better prepared for future legislation on managing GHG emissions and developed management competence to respond to climate change compared to non-member companies in the cement sector ( [[#Busch--2008|Busch et al. 2008]] ; [[#Global%20Cement%20and%20Concrete%20Association--2020|Global Cement and Concrete Association 2020]] ). Accordingly, the cement industry has developed some roadmaps to reach net zero GHG around 2050 ( [[#Sanjuán--2020|Sanjuán et al. 2020]] ).

It is also important to note that individual NSAs and ICIs that commit to GHG mitigation activities are often scarce in many crucial and ‘hard-to-abate’ sectors, such as iron and steel, cement and freight transport (Chapters 10 and 11). Sub-national and non-state action efforts could help these sectors meet an urgent need to accelerate the commercialisation and uptake of technical options to achieve low zero emissions (Bataille 2020).

'''Table 4.4 | Emissions reduction potential for sub-national and non-state international cooperative initi''' '''atives by 2030.'''

{| class="wikitable"
|-
! Sector

! Leading actor

! Name

! Scale

! Target(s)

! colspan="2"| 2030 emissions reduction potential compared to no policy, current policies or NDC baseline (GtCO 2 -eq yr –1 )

! Membership assumptions

|-
!
! ''Min''

! ''Max''

!
|-
| Energy efficiency

| Intergovernmental (UNEP)

| United for Efficiency (U4E)

| Global (focus on developing countries)

| Members to adopt policies for energy-efficient appliances and equipment

| 0.6

| 1.25

| Current membership

|-
| Energy efficiency

| Intergovernmental

| Super-efficient Equipment and Appliance Deployment (SEAD) Initiative

| Global

| Members to adopt current policy best practices for energy efficiency product standards

| 0.5

| 1.7 (excl. China)

| Current membership

|-
| Buildings

| Business

| Architecture 2030

| Global (focus on North America)

| New buildings and major renovations shall be designed to meet an energy consumption performance standard of 70% below the regional (or country) average/median for that building type and to go carbon-neutral in 2030

| 0.2

| 0.2

| Current membership

|-
| Transport

| Business (aviation sector)

| Collaborative Climate Action Across the Air Transport World (CAATW)

| Global

| Two key objectives: (i) 2% annual fuel efficiency improvement through 2050, (ii) stabilise net carbon emissions from 2020

| 0.3

| 0.6

| Current membership

|-
| Transport

| Business

| Lean and Green

| Europe

| Member companies to reduce CO 2 emissions from logistics and freight activity by at least 25% over a five-year period

| 0.02

| 0.02

| Current membership

|-
| Transport

| Hybrid

| Global Fuel Economy Initiative (GFEI)

| Global

| Halve the fuel consumption of the LDV fleet in 2050 compared to 2005

| 0.5

| 1.0

| Current membership

|-
| Transport

| Business

| Below50 LCTPi a

| Global

| Replace 10% of global transportation fossil fuel use with low-carbon transport fuels by 2030

| 0.5

| 0.5

| Scaled-up global potential

|-
| Renewable energy

| Business

| European Technology &amp; Innovation Platform Photovoltaic (ETIP PV)

| Europe

| Supply 20% of electricity from solar Photovoltaic PV technologies by 2030

| 0.2

| 0.5

| Current membership

|-
| Renewable energy

| Intergovernmental (African Union)

| Africa Renewable Energy Initiative (AREI)

| Africa

| Produce 300 gigawatt (GW) of electricity for Africa by 2030 from clean, affordable and appropriate forms of energy

| 0.3

| 0.8

| Current membership

|-
| Renewable energy

| Hybrid

| Global Geothermal Alliance (GGA)

| Global

| Achieve a five-fold growth in the installed capacity for geothermal power generation and a more than two-fold growth in geothermal heating by 2030

| 0.2

| 0.5

| Targeted capacity

|-
| Renewable energy

| Business

| REscale LCTPi a

| Global

| Support deployment of 1.5 TW of additional renewable energy capacity by 2025 in line with the IEA’s 2°C scenario

| 5

| 5

| Scaled-up global potential

|-
| Renewable energy

| Business

| RE100 initiative

| Global

| 2,000 companies commit to source 100% of their electricity from renewable sources by 2030

| 1.9

| 4

| Targeted membership

|-
| Forestry

| Hybrid

| Bonn Challenge/Governors’ Climate and Forests Task Force (GCFTF)/New York Declaration on Forests (NYDF)

| Global

| End forest loss by 2030 in member countries and restore 150 million hectares of deforested and degraded lands by 2020 and an additional 200 million hectares by 2030

| 3.8

| 8.8

| Scaled-up global potential

|-
| Non-CO 2 emissions

| Government

| Climate &amp; Clean Air Coalition (CCAC)

| Global

| Members to implement policies that will deliver substantial short-lived climate pollutants (SLCP) reductions in the near to medium-term (i.e., by 2030) for HFCs and methane

| 1.4

| 3.8

| Current membership

|-
| Non-CO 2 emissions

| Intergovernmental (World Bank)

| Zero Routine Flaring

| Global

| Eliminate routine flaring no later than 2030

| 0.4

| 0.4

| Current membership

|-
| Multisectoral

| Cities and regions

| Under2 Coalition

| Global

| Local governments (220 members) aim to limit their GHG emissions by 80 to 95% below 1990 levels by 2050

| 4.6

| 5

| Current membership

|-
| Multisectoral

| Cities and regions

| Global Covenant of Mayors for Climate &amp; Energy (GCoM)

| Global

| Member cities have a variety of targets (+9,000 members)

| 1.4

| 1.4

| Current membership

|-
| Multisectoral

| Cities and regions

| C40 Cities Climate Leadership Group (C40)

| Global

| 94 member cities have a variety of targets, aiming for 1.5°C compatibility by 2050. The network carries two explicit goals: (i) to have every C40 city develop a climate action plan before the end of 2020 (Deadline 2020), which is to ‘deliver action consistent with the objectives of the Paris Agreement’ and (ii) to have cities achieve emissions neutrality by 2050

| 1.5

| 3

| Current membership

|-
| Agriculture

| Business

| Climate Smart Agriculture (CSA) LCTPi a

| Global

| Reducing agricultural and land-use change emissions from agriculture by at least 50% by 2030 and 65% by 2050. 24 companies and 15 partners

| 3.7

| 3.7

| Scaled-up global potential

|-
| Multisectoral

| Business

| Science Based Targets initiative (SBTi)

| Global

| By 2030, 2000 companies have adopted a science-based target in line with a 2°C temperature goal

| 2.7

| 2.7

| Targeted membership

|}

Source: [[#Hsu--2020|Hsu et al. (2020)]] . Note a As of December 2020 most of the Low Carbon Technology Partnerships (LCTPi) initiatives are defunct, except the Climate Smart Agriculture programme.

<div id="4.2.4" class="h2-container"></div>

<span id="mid-century-low-emission-strategies-at-the-national-level"></span>
=== 4.2.4 Mid-century Low-emission Strategies at the National Level ===

<div id="h2-6-siblings" class="h2-siblings"></div>

An increasing amount of literature describes mitigation pathways for the mid-term (up to 2050). We assess literature reflecting on the UNFCCC process ( [[#4.2.4.1|Section 4.2.4.1]] ), other official plans and strategies ( [[#4.2.4.2|Section 4.2.4.2]] ) and academic literature on mid-century low-emission pathways at the national level ( [[#4.2.4.3|Section 4.2.4.3]] ). After the Paris Agreement and the IPCC SR1.5 Report, the number of academic papers analysing domestic emission pathways compatible with the 1.5°C limit has been increasing. Governments have developed an increasing number of mitigation strategies up to 2050. Several among these strategies aim at net zero CO 2 or net zero GHG, but it is not yet possible to draw global implications due to the limited size of sample ( ''limited evidence'' , ''limi'' ''ted agreement'' ).

<div id="box-4.2" class="h2-container box-container"></div>

<span id="box-4.2-direct-links-between-an-individual-actors-mitigation-efforts-in-the-near-term-and-global-temperature-goals-in-the-long-term-cannot-be-inferred-making-direct-links-requires-clear-distinctions-of-spatial-and-temporal-scales-robertson-2021-rogelj-et-al.-2021-and-explicit-treatment-of-ethical-judgements-made-klinsky-et-al.-2017a-holz-et-al.-2018-klinsky-and-winkler-2018-rajamani-et-al.-2021"></span>
'''Box 4.2 | Direct Links Between an Individual Actor’s Mitigation Efforts in the Near Term and Global Temperature Goals in the Long Term Cannot be Inferred: Making direct links requires clear distinctions of spatial and temporal scales ( [[#Robertson--2021|Robertson 2021]] ; [[#Rogelj--2021|Rogelj et al. 2021]] ) and explicit treatment of ethical judgements made ( [[#Klinsky--2017a|Klinsky et al. 2017a]] ; [[#Holz--2018|Holz et al. 2018]] ; [[#Klinsky--2018|Klinsky and]] [[#Winkler--2018|Winkler 2018]] ; [[#Rajamani--2021|Rajamani et al. 2021]] )'''

<div id="h2-7-siblings" class="h2-siblings"></div>

The literature frequently refers to ''national'' mitigation pathways up to 2030 or 2050 using long-term temperature limits in the Paris Agreement (i.e., ‘2°C’ or ‘1.5°C scenario’). Without additional information, such denomination is incorrect. Working Group I reaffirmed ‘with high confidence the AR5 finding that there is a near-linear relationship between cumulative anthropogenic CO 2 emissions and the global warming they cause’ (WGI SPM AR6). It is not the function of any single country’s mitigation efforts, nor any individual actor’s. Emission pathways of ''individual'' countries or sectors in the near to mid-term can only be linked to a long-term temperature with additional assumptions specifying (i) the GHG emissions and removals of other countries up the mid-term; and (ii) the GHG emissions and removals of all countries beyond the near and mid-term. For example, a national mitigation pathway can be labelled ‘2°C compatible’ if it derives from a global mitigation pathway consistent with 2°C via an explicit effort sharing scheme across countries (Sections 4.2.2.6 and 4.5).

<div id="4.2.4.1" class="h3-container"></div>

<span id="ghg-mitigation-target-under-unfccc-and-paris-agreement"></span>
==== 4.2.4.1 GHG Mitigation Target Under UNFCCC and Paris Agreement ====

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The Paris Agreement requests that Parties should strive to formulate and communicate long-term low GHG development strategies by 2020. (Note that by ‘long-term’, the UNFCCC means 2050, which is the end point of the ‘mid-term’ horizon range in the present report.) As of August 25, 2021, 31 countries and the European Union had submitted low-emissions development strategies (LEDS) (Table 4.5).

By 2018, most long-term strategies targeted 80% emissions reduction in 2050 relative to a reference (1990, 2000 or 2005). After IPCC SR1.5 was published, the number of the countries aiming at net zero CO 2 or GHG emissions has been increasing. [[#footnote-000|6]]

'''Table 4.5 | Countries having submitted long-term low-GHG emission development strategy (as of 25 August 2021).'''

{| class="wikitable"
|-
! Country

! Date submitted

! GHG reduction target

|-
| USA

| Nov. 16, 2016

| 80% reduction of GHG in 2050 compared to 2005 level

|-
| Mexico

| Nov. 16, 2016

| 50% reduction of GHG in 2050 compared to 2000 level

|-
| Canada

| Nov. 17, 2016

| 80% reduction of GHG in 2050 compared to 2005 level

|-
| Germany

| Nov. 17, 2016

Rev. Apr. 26, 2017

Rev. May 4, 2017

| GHG neutrality by 2050

(Old target: 80–95% reduction of GHG in 2050 compared to 1990 level)

|-
| France

| Dec. 28, 2016

Rev. Apr. 18, 2017

Rev. Feb. 8, 2021

| Achieving net zero GHG emissions by 2050

(Old target: 75% reduction of GHG in 2050 compared to 1990 level)

|-
| Benin

| Dec. 12, 2016

| Resilient to climate change and low-carbon intensity by 2025

|-
| Czech Republic

| Jan. 15, 2018

| 80% reduction of GHG in 2050 compared to 1990 level

|-
| UK

| April 17, 2018

| 80% reduction of GHG in 2050 compared to 1990 level

|-
| Ukraine

| July 30, 2018

| 66–69% reduction of GHG in 2050 compared to 1990 level

|-
| Republic of the Marshall Islands

| Sept. 25, 2018

| Net zero GHG emissions by 2050

|-
| Fiji

| Feb. 25, 2019

| Net zero carbon by 2050 as central goal, and net negative emissions in 2041 under a Very High Ambition scenario

|-
| Japan

| June 26, 2019

| 80% reduction of GHG in 2050, and decarbonised society as early as possible in the 2nd half of 21st century

|-
| Portugal

| Sept. 20, 2019

| Carbon neutrality by 2050

|-
| Costa Rica

| Dec. 12, 2019

| Decarbonised economy with net zero emissions by 2050

|-
| European Union

| March 6, 2020

| Net zero GHG emissions by 2050

|-
| Slovakia

| March 30, 2020

| Climate neutrality by 2050, with decarbonisation targets implying reduction of at least 90% compared to 1990 (not taking into account removals)

|-
| Singapore

| March 31, 2020

| Halving emissions from its peak to 33 MtCO 2 -eq by 2050, with a view to achieving net zero emissions as soon as viable in the second half of the century

|-
| South Africa

| Sep. 23, 2020

| Net zero carbon economy by 2050

|-
| Finland

| Oct. 5, 2020

| Carbon neutrality by 2035; 87.5–90% reduction of GHG in 2050 to 1990 level (excluding land use sector)

|-
| Norway

| Nov. 25, 2020

| Being a low-emission society by 2050

|-
| Latvia

| Dec. 9, 2020

| Climate neutrality by 2050 (non-reducible GHG emissions are compensated by removals in the LULUCF sector)

|-
| Spain

| Dec. 10, 2020

| Climate neutrality by 2050

|-
| Belgium

| Dec. 10, 2020

| Carbon neutrality by 2050 (Walloon Region); Full climate neutrality (Flemish Region), and the European target of carbon neutrality by 2050 (Brussels-Capital Region)

|-
| Austria

| Dec. 11, 2020

| Climate-neutral by no later than 2050

|-
| Netherlands

| Dec. 11, 2020

| Reduction of GHG emissions by 95% by 2050 compared to 1990 level.

|-
| Sweden

| Dec. 11, 2020

| Zero net emissions of GHG into the atmosphere latest by 2045

|-
| Denmark

| Dec. 30, 2020

| Climate neutrality by 2050

|-
| Republic of Korea

| Dec. 30, 2020

| Carbon neutrality by 2050

|-
| Switzerland

| Jan. 28, 2021

| 2050 net zero GHG

|-
| Guatemala

| July 6, 2021

| 59% reduction of projected emissions by 2050

|-
| Indonesia

| July 22, 2021

| 540 MtCO 2 -eq by 2050, and with further exploring opportunity to rapidly progress towards net zero emission in 2060 or sooner

|-
| Slovenia

| Aug. 23, 2021

| Net zero emissions or climate neutrality by 2050

|}

‘rev.’ = ‘date revised’

<div id="4.2.4.2" class="h3-container"></div>

<span id="other-national-emission-pathways-to-mid-century"></span>
==== 4.2.4.2 Other National Emission Pathways to Mid-century ====

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At the 2019 Climate Action Summit, 77 countries indicated their aim to reach net zero CO 2 emissions by 2050, more the number of countries having submitted LEDS to the UNFCCC. Table 4.6 lists the countries that have a national net zero by 2050 target in laws, strategies or other documents ( [[#The%20Energy%20and%20Climate%20Intelligence%20Unit--2019|The Energy and Climate Intelligence Unit 2019]] ). Bhutan and Suriname already have achieved net negative emissions. France second ‘low-carbon national strategy’ adopted in 2020 has an objective of GHG neutrality by 2050. Net zero is also the basis of the recent revision of the official notional price of carbon for public investment in France ( [[#Quinet--2019|Quinet et al. 2019]] ). The Committee on Climate Change of the UK analyses sectoral options and concludes that delivering net zero GHG by 2050 is technically feasible but highly challenging ( [[#Committee%20on%20Climate%20Change--2019|Committee on Climate Change 2019]] ). For Germany, three steps to climate neutrality by 2050 are introduced: first, a 65% reduction of emissions by 2030; second, a complete switch to climate-neutral technologies, leading to a 95% cut in emissions, all relative to 1990 levels by 2050; and third balancing of residual emissions through carbon capture and storage (Prognos et al. 2020). In addition to the countries in Table 4.6, EU reported the net zero GHG emission pathways by 2050 under Green Deal ( [[#European%20Commission--2019|European Commission 2019]] ). China and South Korea, have made announcements of carbon neutrality before 2060 and net zero GHG emission by 2050, respectively ( [[#UN--2020a|UN 2020a]] ,b). In the case of Japan, the new target to net zero GHG emission by 2050 was announced in 2020 ( [[#UN--2020c|UN 2020c]] ). As of August 25, 2021, a total 121 countries participate in the ‘Climate Ambition Alliance: Net Zero 2050’, together with businesses, cities and regions.

'''Table 4.6 | Countries with a national net zero CO''' 2 '''or GHG target by 2050 (as of 2''' '''5 August 2021).'''

{| class="wikitable"
|-
! Country

! Target year

! Target status

! Source

|-
| Suriname

|
| Achieved

| Suriname INDC

|-
| Bhutan

|
| Achieved

| Royal Government of Bhutan National Environment Commission

|-
| Germany

| 2045

| In Law

| KSG

|-
| Sweden

| 2045

| In Law

| Climate Policy Framework

|-
| European Union

| 2050

| In Law

| European Climate Law

|-
| Japan

| 2050

| In Law

| Japan enshrines PM Suga’s 2050 carbon neutrality promise into law

|-
| United Kingdom

| 2050

| In Law

| The Climate Change Act

|-
| France

| 2050

| In Law

| Energy and Climate Law

|-
| Canada

| 2050

| In Law

| Canadian Net Zero Emissions Accountability Act

|-
| Spain

| 2050

| In Law

| New Law

|-
| Denmark

| 2050

| In Law

| The Climate Act

|-
| New Zealand

| 2050

| In Law

| Zero Carbon Act

|-
| Hungary

| 2050

| In Law

| Climate Ambition Alliance: Net Zero 2050

|-
| Luxembourg

| 2050

| In Law

| Climate Ambition Alliance: Net Zero 2050

|-
| South Korea

| 2050

| Proposed Legislation

| Speeches and Statements by the President

|-
| Ireland

| 2050

| Proposed Legislation

| Climate Action and Low Carbon Development (Amendment) Bill 2021

|-
| Chile

| 2050

| Proposed Legislation

| Chile charts path to greener, fairer future

|-
| Fiji

| 2050

| Proposed Legislation

| Draft Climate Law

|}

Note: In addition to the above list, the numbers of ‘In Policy Document’ and ‘Target Under discussion’ as Target status are 37 countries and 79 countries, respectively.

<div id="4.2.4.3" class="h3-container"></div>

<span id="mid-century-low-emission-strategies-at-the-national-level-in-the-academic-literature"></span>
==== 4.2.4.3 Mid-century Low Emission Strategies at the National Level in the Academic Literature ====

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Since the 2000s, an increasing number of studies have quantified the emission pathways to mid-century by using national scale models. In the early stages, the national emission pathways were mainly assessed in the developed countries such as Germany, UK, France, the Netherlands, Japan, Canada, and USA. For example, the Enquete Commission in Germany identified robust and sustainable 80% emission reduction pathways ( [[#Deutscher%20Bundestag--2002|Deutscher Bundestag 2002]] ). In Japan, 2050 Japan Low-Carbon Society scenario team (2008) assessed the 70% reduction scenarios in Japan, and summarised the necessary measures to ‘Dozen Actions towards Low-Carbon Societies’.

Among developing countries, China, India, South Africa assessed their national emission pathways. For example, detailed analysis was undertaken to analyse pathways to China’s goal for carbon neutrality ( [[#EFC--2020|EFC 2020]] ). In South Africa, a [[#Scenario%20Building%20Team--2007|Scenario Building Team (2007)]] quantified the Long Term Mitigation Scenarios for South Africa.

Prior to COP21, most of the literature on mid-century mitigation pathways at the national level was dedicated to pathways compatible with a 2°C limit (see Box 4.2 for a discussion on the relationship between national mitigation pathways and global, long-term targets). After COP21 and the IPCC SR1.5, literature increasingly explored just transition to net zero emissions around 2050. This literature reflects on low-emissions development strategies (cognate with SDPS, [[#4.3.1|Section 4.3.1]] ) and policies to get to net zero CO 2 or GHG emissions (Garg and Waisman 2021) (Cross-Chapter Box 5 in this chapter).

provides a snapshot of this literature. For a selected set of countries, it shows the mid-century emission pathways at national scale that have been registered in the International Institute for Applied Systems Analysis (IIASA) national mitigation scenario database built for the purpose of this Report (Annex III.3.3). Overall, the database contains scenarios for 50 countries. Total GHG emission are the most comprehensive information to assess the pathways on climate mitigation actions, but energy-related CO 2 emissions are the most widely populated data in the scenarios. As a result, Figure 4.2 shows energy-related CO 2 emission trajectories. Scenarios for EU countries show reduction trends even in the reference scenario, whereas developing countries and non-European developed countries such as Japan and USA show emissions increase in the reference. In most countries plotted on , studies have found that reaching net zero energy related CO 2 emissions by 2050 is feasible, although the number of such pathways is limited.

<div id="_idContainer016" class="_idGenObjectStyleOverride-1"></div>

[[File:a1bcaa26203668c0c2be238e8a637c4e IPCC_AR6_WGIII_Figure_4_2.png]]

'''Figure 4.2 | Energy related CO''' 2 '''emission pathways to mid-century from existing studies.''' Source of the historical data: Greenhouse Gas Inventory Data of UNFCCC ( https://di.unfccc.int/detailed_data_by_party )

The literature underlines the differences induced by the shift from ‘2°C scenarios’ (typically assumed to imply mitigation in 2050 around 80% relative to 1990) to ‘1.5°C scenarios’ (typically assumed to imply net zero CO 2 or GHG emissions in 2050) (Box 4.2). For Japan, [[#Oshiro--2018|Oshiro et al. (2018)]] shows the difference between the implications of a 2°C scenario (80% reduction of CO 2 in 2050) and a 1.5°C scenario (net zero CO 2 emission in 2050), suggesting that for a net zero CO 2 emission scenario, BECCS is a key technology. Their sectoral analysis aims in 2050 at negative CO 2 emissions in the energy sector, and near-zero emissions in the buildings and transport sectors, requiring energy efficiency improvement and electrification. To do so, drastic mitigation is introduced immediately, and, as a result, the mitigation target of Japan’s current NDC is considered not sufficient to achieve a 1.5°C scenario. [[#Jiang--2018|Jiang et al. (2018)]] also show the possibility of net negative emissions in the power sector in China by 2050, indicating that biomass energy with carbon capture and storage (CCS) must be adopted on a large scale by 2040. [[#Samadi--2018|Samadi et al. (2018)]] indicate the widespread use of electricity-derived synthetic fuels in end-use sectors as well as behavioural change for the 1.5°C scenario in Germany.

In addition to those analyses, [[#Vishwanathan--2018b|Vishwanathan et al. (2018b)]] , [[#Chunark--2018|Chunark and Limmeechokchai (2018)]] and [[#Pradhan--2018b|Pradhan et al. (2018b)]] build national scenarios in India, Thailand and Nepal, respectively, compatible with a global 1.5°C. Unlike the studies mentioned in the previous paragraph, they translate the 1.5°C goal by introducing in their model a carbon price trajectory estimated by global models as sufficient to achieve the 1.5°C target. Because of the high economic growth and increase of GHG emissions in the reference case, CO 2 emissions in 2050 do not reach zero. Finally, the literature also underlines that to achieve a 1.5°C target, mitigation measures relative to non-CO 2 emissions become important, especially in developing countries where the share of non-CO 2 emissions is relatively high. ( [[#La%20Rovere--2018|La Rovere et al. 2018]] ) treat mitigation actions in AFOLU sector.

[[IPCC:Wg3:Chapter:Chapter-3|Chapter 3]] reported on multi-model analyses, comparison of results using different models, of global emissions in the long term. At the national scale, multi-model analyses are still limited, though such analyses are growing as shown in Table 4.7. By comparing the results among different models and different scenarios in a country, the uncertainties on the emission pathways including the mitigation measures to achieve a given emission target can be assessed.

Another type of multi-model analysis is international, in other words, different countries join the same project and use their own national models to assess a pre-agreed joint mitigation scenario. By comparing the results of various national models, such projects help highlight specific features of each country. More robust mitigation measures can be proposed if different types of models participate. These activities can also contribute to capacity building in developing countries.

'''Table 4.7 | Examples of research projects on country-level mitigation pathways in the near to medium-term under the multi-nat''' '''ional analyses.'''

{| class="wikitable"
|-
! Project name

! Features

|-
| DDPP (Deep Decarbonisation Pathways Project)

| 16 countries participated and estimated the deep decarbonisation pathways from the viewpoint of each country’s perspective using their own models ( [[#Waisman--2019|Waisman et al. 2019]] ).

|-
| COMMIT (Climate Policy assessment and Mitigation Modelling to Integrate national and global Transition pathways)

| This research project assessed the country contributions to the target of the Paris Agreement ( [[#COMMIT--2019|COMMIT 2019]] ).

|-
| MAPS (Mitigation Action Plans and Scenarios)

| The mitigation potential and socio-economic implications in Brazil, Chile, Colombia and Peru were assessed ( [[#Delgado--2014|Delgado et al. 2014]] ; [[#Zevallos--2014|Zevallos et al. 2014]] ; Benavides et al. 2015; [[#La%20Rovere--2018|La Rovere et al. 2018]] ). The experiences of the MAPS programme suggests that co-production of knowledge by researchers and stakeholders strengthens the impact of research findings, and in depth studies of stakeholder engagement provide lessons ( [[#Boulle--2015|Boulle et al. 2015]] ; [[#Raubenheimer--2015|Raubenheimer et al. 2015]] ; [[#Kane--2018|Kane and Boulle 2018]] ), which can assist building capacity for long-term planning in other contexts ( [[#Calfucoy--2019|Calfucoy et al. 2019]] ).

|-
| CD-LINKS (Linking Climate and Development Policies – Leveraging International Networks and Knowledge Sharing)

| The complex interplay between climate action and development at both the global scale and some national perspectives were explored. The climate policies for G20 countries up to 2015 and some levels of the carbon budget are assessed for short-term and long-term, respectively ( [[#Rogelj--2017|Rogelj et al. 2017]] ).

|-
| APEC Energy Demand and Supply Outlook

| Total 21 APEC countries assessed a 2°C scenario scenario which follows the carbon emissions reduction pathway included in the IEA Energy Technology Perspectives ( [[#IEA--2017|IEA 2017]] ) by using the common framework ( [[#APERC--2019|APERC 2019]] ).

|-
| Low-Carbon Asia Research Project

| The low-carbon emission scenarios for several countries and cities in Asia were assessed by using the same framework ( [[#Matsuoka--2013|Matsuoka et al. 2013]] ). The mitigation activities were summarised into 10 actions toward Low Carbon Asia to show a guideline to plan and implement the strategies for an LCS in Asia ( [[#Low-Carbon%20Asia%20Research%20Project--2012|Low-Carbon Asia Research Project 2012]] ).

|-
| CLIMACAP–LAMP

| This is an inter-model comparison exercise that focused on energy and climate change mitigation in Latin America ( [[#Clarke--2016|Clarke et al. 2016]] ).

|-
| DDPP-LAC (Latin American Deep Decarbonisation Pathways project)

| Six countries in Latin America analysed the activities in agriculture, forestry and other land use (AFOLU) commonly (Bataille et al. 2020).

|-
| MILES (Modelling and Informing Low-Emission Strategies)

| This is an international research project which covers five countries and one region in order to build capacity and knowledge on low-emissions development strategies both at a national and global level, by investigating the concrete implications of INDCs for the low-carbon transformation by and beyond 2030 ( [[#Spencer--2015|Spencer et al. 2015]] ).

|}

<div id="4.2.5" class="h2-container"></div>

<span id="what-is-to-be-done-to-accelerate-mitigation"></span>

=== 4.2.5 What Is to Be Done to Accelerate Mitigation? ===

<div id="h2-8-siblings" class="h2-siblings"></div>

<div id="4.2.5.1" class="h3-container"></div>

<span id="overview-of-accelerated-mitigation-pathways"></span>
==== 4.2.5.1 Overview of Accelerated Mitigation Pathways ====

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The literature reports an increasing number of accelerated mitigation pathways that are beyond NDCs in different regions and countries. There is increasing understanding of the technical content of such pathways, though the literature remains limited on some dimensions, such as demand-side options, systems analysis, or mitigation of AFOLU non-CO 2 GHGs. The present section describes insights from this literature.

Overall, the literature shows that pathways considered consistent with below 2°C (&gt;67%) or 1.5°C (Box 4.2) – including inter alia 80% reduction of GHG emissions in 2050 relative to 1990 or 100% renewable electricity scenarios – are technically feasible ( [[#Lund--2009|Lund and Mathiesen 2009]] ; [[#Mathiesen--2011|Mathiesen et al. 2011]] ; [[#Esteban--2014|Esteban and Portugal-Pereira 2014]] ; [[#Young--2017|Young and Brans 2017]] ; [[#Esteban--2018|Esteban et al. 2018]] ; [[#Child--2019|Child et al. 2019]] ; [[#Hansen--2019|Hansen et al. 2019]] ). They entail increased end-use energy efficiency, significant increases in low-carbon energy, electrification, other new and transformative technologies in demand sectors, adoption of carbon capture and sequestration (CCS) to reduce gross emissions, and contribution to net negative emissions through carbon dioxide removal (CDR) and carbon sinks. For these pathways to be realised, the literature assumes higher carbon prices, combined in policy packages with a range of other policy measures.

The most recent literature also reflects on accelerated mitigation pathways aiming at reaching net zero CO 2 emissions or net zero GHG emissions by 2050 (Section 4.2.4 and Table 4.6; see Glossary entries on ‘net zero CO 2 emissions’ and ‘net zero GHG emissions’). Specific policies, measures and technologies are needed to reach such targets. These include, broadly, decarbonising electricity supply, including through low-carbon energy, radically more efficient use of energy than today; electrification of end-uses (including transport/electric vehicles); dramatically lower use of fossil fuels than today; converting other uses to low- or zero-carbon fuels (e.g., hydrogen, bioenergy, ammonia) in hard-to-decarbonise sectors; and setting ambitious targets to reduce methane and other short-lived climate forcers (SLCFs).

Accelerated mitigation pathways differ by countries, depending inter alia on sources of emissions, mitigation opportunities and economic context. In China, India, Japan and other Southeast Asian countries, more aggressive action related to climate change is also motivated by regional concerns over health and air quality related to air pollutants and SLCFs (Ashina et al. 2012; Aggarwal 2017; [[#Kuramochi--2017|Kuramochi et al. 2017]] ; [[#Xunzhang--2017|Xunzhang et al. 2017]] ; [[#Dhar--2018|Dhar et al. 2018]] ; [[#Jiang--2018|Jiang et al. 2018]] ; [[#Oshiro--2018|Oshiro et al. 2018]] ; [[#China%20National%20Renewable%20Energy%20Centre--2019|China National Renewable Energy Centre 2019]] ; [[#Energy%20Transitions%20Commission%20and%20Rocky%20Mountain%20Institute--2019|Energy Transitions Commission and Rocky Mountain Institute 2019]] ; [[#Khanna--2019|Khanna et al. 2019]] ). Studies of accelerated mitigation pathways in North America tend to focus on power sector and imported fuel decarbonisation in the US , and on electrification and demand-side reductions in Canada ( [[#Vaillancourt--2017|Vaillancourt et al. 2017]] ; [[#Hodson--2018|Hodson et al. 2018]] ; [[#Victor--2018|Victor et al. 2018]] ; [[#Bahn,%C2%A0O.%20and%C2%A0K.%20Vaillancourt--2020|Bahn and Vaillancourt 2020]] ; [[#Hammond--2020|Hammond et al. 2020]] ; [[#Jayadev--2020|Jayadev et al. 2020]] ). In Latin America, many pathways emphasise supply-side mitigation measures, finding that replacing thermal power generation and developing bioenergy (where resources are available) utilisation offers the greatest mitigation opportunities ( [[#Herreras%20Martínez--2015|Herreras Martínez et al. 2015]] ; [[#Nogueira%20de%20Oliveira--2016|Nogueira de Oliveira et al. 2016]] ; Arango-Aramburo et al. 2019; [[#Delgado--2020|Delgado et al. 2020]] ; [[#Lap--2020|Lap et al. 2020]] ). The European Union member states (EU-28) recently announced 2050 climate neutrality goal is explored by pathways that emphasise complete substitution of fossil fuels with electricity generated by low-carbon sources, particularly renewables; demand reductions through efficiency and conservation, and novel fuels and end-use technologies (Prognos et al. 2020). The limited literature so far on Africa’s future pathways suggest those could be shaped by increasing energy access and mitigating the air pollution and health effects of relying on traditional biomass use, as well as cleaner expansion of power supply alongside end-use efficiency improvements ( [[#Hamilton--2017|Hamilton and Kelly 2017]] ; [[#Oyewo--2019|Oyewo et al. 2019]] , 2020; [[#Ven--2019|Ven et al. 2019]] ; [[#Wright--2019|Wright et al. 2019]] ; [[#Forouli--2020|Forouli et al. 2020]] ).

Though they differ across countries, accelerated mitigation pathways share common characteristics as follows. First, energy efficiency, conservation, and reducing energy use in all energy demand sectors (buildings, transport, and industry) are included in nearly all literature that addresses future demand growth (Ashina et al. 2012; [[#Saveyn--2012|Saveyn et al. 2012]] ; [[#Schmid--2012|Schmid and Knopf 2012]] ; [[#Chiodi--2013|Chiodi et al. 2013]] ; [[#Deetman--2013|Deetman et al. 2013]] ; [[#Jiang--2013|Jiang et al. 2013]] ; [[#Thepkhun--2013|Thepkhun et al. 2013]] ; [[#Schiffer--2015|Schiffer 2015]] ; Altieri et al. 2016; [[#Jiang--2016|Jiang et al. 2016]] ; [[#McNeil--2016|McNeil et al. 2016]] ; [[#Nogueira%20de%20Oliveira--2016|Nogueira de Oliveira et al. 2016]] ; [[#Chilvers--2017|Chilvers et al. 2017]] ; [[#Elizondo--2017|Elizondo et al. 2017]] ; [[#Fragkos--2017|Fragkos et al. 2017]] ; [[#Jacobson--2017|Jacobson et al. 2017]] , 2019; [[#Kuramochi--2017|Kuramochi et al. 2017]] ; [[#Oshiro--2017a|Oshiro et al. 2017a]] ; [[#Ouedraogo--2017|Ouedraogo 2017]] ; [[#Shahiduzzaman--2017|Shahiduzzaman and Layton 2017]] ; [[#Vaillancourt--2017|Vaillancourt et al. 2017]] ; [[#Hanaoka--2018|Hanaoka and Masui 2018]] ; [[#Hodson--2018|Hodson et al. 2018]] ; [[#Lee--2018|Lee et al. 2018]] ; Lefèvre et al. [[#Oshiro--2018|Oshiro et al. 2018]] ; 2018; [[#Capros--2019|Capros et al. 2019]] ; [[#Dioha--2019|Dioha et al. 2019]] ; [[#Duscha--2019|Duscha et al. 2019]] ; [[#Khanna--2019|Khanna et al. 2019]] ; [[#Kato--2019|Kato and Kurosawa 2019]] ; [[#Nieves--2019|Nieves et al. 2019]] ; [[#Sugiyama--2019|Sugiyama et al. 2019]] ; [[#Zhou--2019|Zhou et al. 2019]] ; [[#Dioha--2020|Dioha and Kumar 2020]] ).

Similarly, electrification of industrial processes (up to 50% for EU and China) and transport (e.g., 30–60% for trucks in Canada), buildings, and district heating and cooling are commonplace (Ashina et al. 2012; [[#Massetti--2012|Massetti 2012]] ; [[#Saveyn--2012|Saveyn et al. 2012]] ; [[#Chiodi--2013|Chiodi et al. 2013]] ; [[#Deetman--2013|Deetman et al. 2013]] ; [[#Fragkos--2017|Fragkos et al. 2017]] ; [[#Oshiro--2017b|Oshiro et al. 2017b]] ; [[#Vaillancourt--2017|Vaillancourt et al. 2017]] ; [[#Xunzhang--2017|Xunzhang et al. 2017]] ; [[#Jiang--2018|Jiang et al. 2018]] ; [[#Mittal--2018|Mittal et al. 2018]] ; [[#Oshiro--2018|Oshiro et al. 2018]] ; [[#Capros--2019|Capros et al. 2019]] ; [[#Zhou--2019|Zhou et al. 2019]] ; [[#Hammond--2020|Hammond et al. 2020]] ).

Third, lower emissions sources of energy, such as nuclear, renewables, and some biofuels, are seen as necessary in all pathways. However, the extent of deployment depends on resource availability. Some countries have set targets of up to 100% renewable electricity, while others such as Brazil rely on increasing biomass up to 40–45% of total or industry energy consumption by 2050.

Fourth, CCS and CDR are part of many of the national studies reviewed (Ashina et al. 2012; [[#Massetti--2012|Massetti 2012]] ; [[#Jiang--2013|Jiang et al. 2013]] ; [[#Thepkhun--2013|Thepkhun et al. 2013]] ; [[#Herreras%20Martínez--2015|Herreras Martínez et al. 2015]] ; [[#van%20der%20Zwaan--2016|van der Zwaan et al. 2016]] ; [[#Chilvers--2017|Chilvers et al. 2017]] ; [[#Solano%20Rodriguez--2017|Solano Rodriguez et al. 2017]] ; [[#Xunzhang--2017|Xunzhang et al. 2017]] ; [[#Kuramochi--2018|Kuramochi et al. 2018]] ; [[#Mittal--2018|Mittal et al. 2018]] ; [[#Oshiro--2018|Oshiro et al. 2018]] ; [[#Roberts--2018b|Roberts et al. 2018b]] ; [[#Vishwanathan--2018b|Vishwanathan et al. 2018b]] ; [[#Kato--2019|Kato and Kurosawa 2019]] ). CCS helps reduce gross emissions but does not remove CO 2 from the atmosphere, unless combined with bioenergy (BECCS). CO 2 removal from sources with no identified mitigation measures is considered necessary to help achieve economy-wide net negative emissions ( [[#Massetti--2012|Massetti 2012]] ; [[#Deetman--2013|Deetman et al. 2013]] ; [[#Solano%20Rodriguez--2017|Solano Rodriguez et al. 2017]] ).

Each option is assessed in more detail in the following sections.

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<span id="accelerated-decarbonisation-of-electricity-through-renewable-energy"></span>
==== 4.2.5.2 Accelerated Decarbonisation of Electricity Through Renewable Energy ====

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Power generation could decarbonise much faster with scaled up deployment of renewable energy and storage. Both technologies are mature, available, and fast decreasing in costs, more than for many other mitigation options. Models continuously underestimate the speed at which renewables and storage expand. Higher penetration of renewable energy in the power sector is a common theme in scenarios. Some studies provide cost optimal electricity mix under emission constraints, while others explicitly explore a 100% renewables or 100% emission free electricity sector (Box 4.3).

Figure 4.3 shows an increasing share of renewable electricity in most countries historically, with further increases projected in many decarbonisation pathways. Targets for very high shares of renewable electricity generation – up to 100% – are shown for a number of countries, with the global share projected to range from 60% to 70% for 1.5°C with no overshoot (C0) to below 2°C (C4) scenarios. Countries and states that have set 100% renewables targets include Scotland for 2020 ( [[#Scottish%20Government--2021|Scottish Government 2021]] ), Austria (2030), Denmark (2035) and California (2045) (Figure 4.3).

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[[File:b2df19443819471bc6d31425055f81cc IPCC_AR6_WGIII_Figure_4_3.png]]

'''Figure 4.3 | Historical and projected levels and targets for the share of renewables in electricity generation.''' Sources: IEA energy balances for past trends, IPCC AR6 scenario dataset including national model and regional versions in global models (10th to 90th percentile of 1.5°C with no overshoot (C0) to below 2°C (C4) scenarios), national/regional sources.

While 100% renewable electricity generation by 2050 is found to be feasible, it is not without issues. For example, ( [[#Jacobson--2017|Jacobson et al. 2017]] , 2019) find it feasible for 143 countries with only a 9% average increase in economic costs (considering all social costs) if annual electricity demand can be reduced by 57%. Others state that challenges exist with speed of expansion, ensuring sufficient supply at all times or higher costs compared to other alternatives ( [[#Clack--2017|Clack et al. 2017]] ). In-depth discussion of net zero electricity systems can be found in [[IPCC:Wg3:Chapter:Chapter-6#6.6|Section 6.6]] .

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<span id="box-4.3-examples-of-high-renewable-accelerated-miti-gation-pathways"></span>
=== Box 4.3 | Examples of High-renewable Accelerated Mitigation Pathways ===

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Many accelerated mitigation pathways include high shares of renewable energy, with national variations. In Europe, some argue that the EU 2050 net zero GHG emissions goal can be met with 100% renewable power generation, including use of renewable electricity to produce hydrogen, biofuels (including imports), and synthetic hydrocarbons, but will require significant increases in transmission capacity ( [[#Duscha--2019|Duscha et al. 2019]] ; [[#Zappa--2019|Zappa et al. 2019]] ). [[#Capros--2019|Capros et al. (2019)]] explore a 1.5°C compatible pathway that includes 85% renewable generation, with battery, pumped hydro, and chemical storage for variable renewables. High-renewable scenarios also exist for individual Member States. In France, for example, [[#Krakowski--2016|Krakowski et al. (2016)]] propose a 100% renewable power generation scenario that relies primarily on wind (62%), solar PV (26%) and oceans (12%). To reach this aim, integration into the European grid is of vital importance ( [[#Brown--2018|Brown et al. 2018]] ). While debated, incremental costs could be limited regardless of specific assumptions of future costs of individual technologies ( [[#Shirizadeh--2020|Shirizadeh et al. 2020]] ). In Germany, similarly, 100% renewable electricity systems are found feasible by numerous studies ( [[#Oei--2020|Oei et al. 2020]] ; [[#Thomas%20Klaus--2010|Thomas Klaus et al. 2010]] ; Wuppertal-Institut 2021; [[#Hansen--2019|Hansen et al. 2019]] ).

In South Africa, it is found that long-term mitigation goals could be achieved with accelerated adoption of solar PV and wind generation, if the electricity sector decarbonises by phasing-out coal entirely by 2050, even if CCS is not feasible before 2025 (Altieri et al. 2015; Beck et al. 2013). Abundant solar PV and wind potential, coupled with land availability suggest that more than 75% of power generation could ultimately originate from solar PV and wind ( [[#Oyewo--2019|Oyewo et al. 2019]] ; [[#Wright--2019|Wright et al. 2019]] ).

For the US, share of renewables in power generation in 2050 in accelerated mitigation scenarios vary widely, 40% in ( [[#Hodson--2018|Hodson et al. 2018]] ; [[#Jayadev--2020|Jayadev et al. 2020]] ), more than half renewable and nuclear in ( [[#Victor--2018|Victor et al. 2018]] ) to 100% in Jacobson et al. (2017, 2019).

Box 4.3

Under cost optimisation scenarios for Brazil, electricity generation, which is currently dominated by hydropower, could reach 100% by adding biomass ( [[#Köberle--2020|Köberle et al. 2020]] ). Other studies find that renewable energy, including biomass, could account for more than 30% of total electricity generation ( [[#Nogueira%20de%20Oliveira--2016|Nogueira de Oliveira et al. 2016]] ; Portugal- [[#Pereira--2016|Pereira et al. 2016]] ).

In Colombia, where hydropower resources are abundant and potential also exist for solar and wind, a deep decarbonisation pathway would require 57% renewable power generation by 2050 (Arango-Aramburo et al. 2019) while others find 80% would be possible ( [[#Delgado--2020|Delgado et al. 2020]] ).

In Asia, Japan could have up to 50% variable renewable electricity supply to reduce CO 2 emissions by 80% by 2050 in some of its deep mitigation scenarios ( [[#Kato--2019|Kato and Kurosawa 2019]] ; [[#Sugiyama--2019|Sugiyama et al. 2019]] ; [[#Ju--2021|Ju et al. 2021]] ; [[#Shiraki--2021|Shiraki et al. 2021]] ; [[#Silva%20Herran--2021|Silva Herran and Fujimori 2021]] ). One view of China’s 1.5°C pathway includes 59% renewable power generation by 2050 ( [[#Jiang--2018|Jiang et al. 2018]] ). One view of India’s 1.5°C pathway also includes 52% renewable power generation, and would require storage needs for 35% of generation ( [[#Parikh--2018|Parikh et al. 2018]] ).

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<span id="bioenergy-plays-significant-role-in-resource-abundant-countries-in-latin-america-and-parts-of-europe"></span>
==== 4.2.5.3 Bioenergy Plays Significant Role in Resource Abundant Countries in Latin America and Parts of Europe ====

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Bioenergy could account for up to 40% of Brazil’s total final energy consumption, and a 60% share of fuel for light-duty vehicles by 2030 ( [[#Lefèvre--2018|Lefèvre et al. 2018]] ), and is considered most cost-effective in transport and industrial applications ( [[#Lap--2020|Lap et al. 2020]] ). BECCS in the power sector is also considered cost-effective option for supply-side mitigation ( [[#Borba--2012|Borba et al. 2012]] ; [[#Herreras%20Martínez--2015|Herreras Martínez et al. 2015]] ; [[#Lucena--2016|Lucena et al. 2016]] ).

Bioenergy also plays a prominent role in some EU countries’ deep decarbonisation strategies. Domestic biomass alone can help Germany meet its 95% CO 2 reduction by 2050 goal, and biomass and CCS together are needed to reduce CO 2 by 80% by 2050 in the Netherlands ( [[#Mikova--2019|Mikova et al. 2019]] ). Studies suggest that mitigation efforts in France include biofuels and significant increases in biomass use, including up to 45% of industry energy by 2050 for its net GHG neutrality goal ( [[#Doumax-Tagliavini--2018|Doumax-Tagliavini and Sarasa 2018]] ; [[#Capros--2019|Capros et al. 2019]] ). Increased imports may be needed to meet significant increases in EU’s bioenergy use, which could affect energy security and the sustainability of bioenergy production outside of the EU ( [[#Mandley--2020|Mandley et al. 2020]] ; [[#Daioglou--2020|Daioglou et al. 2020]] ).

While BECCS is needed in multiple accelerated mitigation pathways, large-scale land-based biological CDR may not prove as effective as expected, and its large-scale deployment may result in ecological and social impacts, suggesting it may not be a viable carbon removal strategy in the next 10–20 years ( [[#Vaughan--2016|Vaughan and Gough 2016]] ; [[#Boysen--2017|Boysen et al. 2017]] ; [[#Dooley--2018|Dooley and Kartha 2018]] ). The effectiveness of BECCS could depend on local contexts, choice of biomass, fate of initial aboveground biomass and fossil-fuel emissions offsets – carbon removed through BECCS could be offset by losses due to land-use change ( [[#Harper--2018|Harper et al. 2018]] ; [[#Butnar--2020|Butnar et al. 2020]] ; [[#Calvin--2021|Calvin et al. 2021]] ). Large-scale BECCS may push planetary boundaries for freshwater use, exacerbate land-system change, significantly alter biosphere integrity and biogeochemical flows ( [[#Heck--2018|Heck et al. 2018]] ; [[#Fuhrman--2020|Fuhrman et al. 2020]] ; [[#Stenzel--2021|Stenzel et al. 2021]] ; Ai et al. 2021). (Sections 7.4 and 12.5)

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<span id="ccs-may-be-needed-to-mitigate-emissions-from-the-remaining-fossil-fuels-that-cannot-be-decarbonised-but-the-economic-feasibility-of-deployment-is-not-yet-clear"></span>
==== 4.2.5.4 CCS May Be Needed to Mitigate Emissions From the Remaining Fossil Fuels That Cannot Be Decarbonised, but the Economic Feasibility of Deployment Is Not Yet Clear ====

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CCS is present in many accelerated mitigation scenarios in the literature. In Brazil, ( [[#Nogueira%20de%20Oliveira--2016|Nogueira de Oliveira et al. 2016]] ) consider BECCS and CCS in hydrogen generation more feasible than CCS in thermal power plants, with costs ranging from USD70–100 per tCO 2 . Overall, ( [[#van%20der%20Zwaan--2016|van der Zwaan et al. 2016]] ) estimate that 33–50% of total electricity generation in Latin America could be ultimately covered by CCS. In Japan, CCS and increased bioenergy adoption plus waste-to-energy and hydrogen-reforming from fossil fuel are all considered necessary in the power sector in existing studies, with potential up to 200 MtCO 2 yr –1 (Ashina et al. 2012; [[#Oshiro--2017a|Oshiro et al. 2017a]] ; [[#Kato--2019|Kato and Kurosawa 2019]] ; [[#Sugiyama--2021|Sugiyama et al. 2021]] ). In parts of the EU, after 2030, CCS could become profitable with rising CO 2 prices ( [[#Schiffer--2015|Schiffer 2015]] ). CDR is seen as necessary in some net GHG neutrality pathways ( [[#Capros--2019|Capros et al. 2019]] ) but evidence on cost-effectiveness is scarce and uncertain ( [[#European%20Commission--2013|European Commission 2013]] ). For France and Sweden, ( [[#Millot--2020|Millot et al. 2020]] ) include CCS and BECCS to meet net zero GHG emissions by 2050. For Italy, ( [[#Massetti--2012|Massetti 2012]] ) propose a zero-emission electricity scenario with a combination of renewable and coal, natural gas, and BECCS.

In China, an analysis concluded that CCS is necessary for remaining coal and natural gas generation out to 2050 ( [[#Jiang--2018|Jiang et al. 2018]] ; [[#Energy%20Transitions%20Commission%20and%20Rocky%20Mountain%20Institute--2019|Energy Transitions Commission and Rocky Mountain Institute 2019]] ). Seven to 10 CCS projects with installed capacity of 15 GW by 2020 and total CCS investment of 105 billion RMB (2010 RMB) are projected to be needed by 2050 under a 2°C compatible pathway according to ( [[#Jiang--2013|Jiang et al. 2013]] , 2016; [[#Lee--2018|Lee et al. 2018]] ). Under 1.5°C pathway, an analysis found China would need full CCS coverage of the remaining 12% of power generation from coal and gas power and 250 GW of BECCS ( [[#Jiang--2018|Jiang et al. 2018]] ). Combined with expanded renewable and nuclear development, total estimated investment in this study is 5% of China’s total GDP in 2020, 1.3% in 2030, and 0.6% in 2050 ( [[#Jiang--2016|Jiang et al. 2016]] ).

Views regarding feasibility of CCS can vary greatly for the same country. In the case of India’s electricity sector for instance, some studies indicate that CCS would be necessary ( [[#Vishwanathan--2018a|Vishwanathan et al. 2018a]] ), while others do not – citing concerns around its feasibility due to limited potential sites and issues related to socio-political acceptance – and rather point to very ambitious increase in renewable energy, which in turn could pose significant challenges in systematically integrating renewable energy into the current energy systems ( [[#Viebahn--2014|Viebahn et al. 2014]] ; [[#Mathur--2020|Mathur and Shekhar 2020]] ). Some limitations of CCS, including uncertain costs, lifecycle and net emissions, other biophysical resource needs, and social acceptance are acknowledged in existing studies ( [[#Viebahn--2014|Viebahn et al. 2014]] ; [[#Jacobson--2019|Jacobson 2019]] ; [[#Mathur--2020|Mathur and Shekhar 2020]] ; [[#Sekera--2020|Sekera and Lichtenberger 2020]] ).

While national mitigation portfolios aiming at net zero emissions or lower will need to include some level of CDR, the choice of methods and the scale and timing of their deployment will depend on the ambition for gross emission reductions, how sustainability and feasibility constraints are managed, and how political preferences and social acceptability evolve (Cross-Chapter Box 8). Furthermore, mitigation deterrence may create further uncertainty, as anticipated future CDR could dilute incentives to reduce emissions now ( [[#Grant--2021|Grant et al. 2021]] ), and the political economy of net negative emissions has implications for equity ( [[#Mohan--2021|Mohan et al. 2021]] ).

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<span id="nuclear-power-is-considered-strategic-for-some-countries-while-others-plan-to-reach-their-mitigation-targets-without-additional-nuclear-power"></span>
==== 4.2.5.5 Nuclear Power Is Considered Strategic for Some Countries, While Others Plan to Reach Their Mitigation Targets Without Additional Nuclear Power ====

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Nuclear power generation is developed in many countries, though larger-scale national nuclear generation does not tend to associate with significantly lower carbon emissions ( [[#Sovacool--2020|Sovacool et al. 2020]] ). Unlike other energy sources such as wind and PV solar, levelised costs of nuclear power has been rising in the last decades ( [[#Grubler--2010|Grubler 2010]] ; [[#Gilbert--2017|Gilbert et al. 2017]] ; [[#Portugal-Pereira--2018|Portugal-Pereira et al. 2018]] ). This is mainly due to overrun of overnight construction costs related to delays in project approvals and construction, and more stringent passive safety measures, which increases the complexity of systems. After the Fukushima Daiichi accident in Japan, nuclear programs in several countries have been phased out or cancelled ( [[#Carrara--2020|Carrara 2020]] ; [[#Huenteler--2012|Huenteler et al. 2012]] ; [[#Kharecha--2019|Kharecha and Sato 2019]] ; [[#Hoffman--2018|Hoffman and Durlak 2018]] ). Also the compatibility of conventional prresurised water reactors and boiling water reactors with large proportion of renewable energy in the grid it is yet to be fully understood.

Accelerated mitigation scenarios offer contrasting views on the share of nuclear in power generation. In the USA, ( [[#Victor--2018|Victor et al. 2018]] ) build a scenario in which nuclear contributes 23% of CO 2 emission reductions needed to reduce GHG emissions by 80% from 2005 levels by 2050. Deep power sector decarbonisation pathways could require a two-folded increase in nuclear capacity according to ( [[#Jayadev--2020|Jayadev et al. 2020]] ) for the USA, and nearly a ten-fold increase for Canada, but may be difficult to implement ( [[#Vaillancourt--2017|Vaillancourt et al. 2017]] ). For China to meet a 1.5°C pathway or achieve carbon neutrality by 2050, nuclear may represent 14–28% of power generation in 2050 according to ( [[#Jiang--2018|Jiang et al. 2018]] ; [[#China%20National%20Renewable%20Energy%20Centre--2019|China National Renewable Energy Centre 2019]] ; [[#Energy%20Transitions%20Commission%20and%20Rocky%20Mountain%20Institute--2019|Energy Transitions Commission and Rocky Mountain Institute 2019]] ). For South Korea, [[#Hong--2014|Hong et al. (2014)]] and [[#Hong--2018|Hong and Brook (2018)]] find that increasing nuclear power can help complement renewables in decarbonising the grid. Similarly, India has put in place a three-stage nuclear programme which aims to enhance nuclear power capacity from the current level of 6 GW to 63 GW by 2032, if fuel supply is ensured (GoI 2015). Nuclear energy is also considered necessary as part of accelerated mitigation pathways in Brazil, although it is not expected to increase significantly by 2050 even under stringent low-carbon scenarios ( [[#Lucena--2016|Lucena et al. 2016]] ). France developed its nuclear strategy in response to energy security concerns after the 1970s oil crisis, but has committed to reducing nuclear’s share of power generation to 50% by 2035 ( [[#Millot--2020|Millot et al. 2020]] ). Conversely, some analysis find deep mitigation pathways, including net zero GHG emissions and 80–90% reduction from 2013 levels, feasible without additional nuclear power in EU-28 and Japan respectively, but assuming a combination of bio- and novel fuels and CCS or land-use based carbon sinks ( [[#Kato--2019|Kato and Kurosawa 2019]] ; [[#Duscha--2019|Duscha et al. 2019]] ).

Radically more efficient use of energy than today, including electricity, is a complementary set of measures, explored in the following.

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<span id="efficient-cooling-slcfs-and-co-benefits"></span>
==== 4.2.5.6 Efficient Cooling, SLCFs and Co-benefits ====

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In warmer climate regions undergoing economic transitions, improving the energy efficiency of cooling and refrigeration equipment is often important for managing peak electricity demand and can have co-benefits for climate mitigation as well as SLCF reduction, as expected in India, Africa, and Southeast Asia in the future.

Air conditioner adoption is rising significantly in low- and middle-income countries as incomes rise and average temperatures increase, including in Southeast Asian countries such as Thailand, Indonesia, Vietnam, and the Philippines, as well as Brazil, Pakistan, Bangladesh, and Nigeria ( [[#Biardeau--2020|Biardeau et al. 2020]] ). Cooling appliances are expected to increase from 3.6 billion to 9.5 billion by 2050, though up to 14 billion could be required to provide adequate cooling for all ( [[#Birmingham%20Energy%20Institute--2018|Birmingham Energy Institute 2018]] ). Current technology pathways are not sufficient to deliver universal access to cooling or meet the 2030 targets under the SDGs, but energy efficiency, including in equipment efficiency like air conditioners, can reduce this demand and help limit additional emissions that would further exacerbate climate change ( [[#Biardeau--2020|Biardeau et al. 2020]] ; [[#Dreyfus--2020|Dreyfus et al. 2020]] ; UNEP and [[#IEA--2020|IEA 2020]] ). Some countries (India, South Africa) have started to recognise the need for more efficient equipment in their mitigation strategies (Altieri et al. 2016; [[#Ouedraogo--2017|Ouedraogo 2017]] ; [[#Paladugula--2018|Paladugula et al. 2018]] ).

One possible synergy between SLCF and climate change mitigation is the simultaneous improvement in energy efficiency in refrigeration and air-conditioning equipment during the hydrofluorocarbon (HFC) phase-down, as recognised in the Kigali Amendment to the Montreal Protocol. The Kigali Amendment and related national and regional regulations are projected to reduce future radiative forcing from HFCs by about half in 2050 compared to a scenario without any HFC controls, and to reduce future global average warming in 2100 from a baseline of 0.3°C–0.5°C to less than 0.1°C, according to a recent scientific assessment of a wide literature ( [[#World%20Meteorological%20Organization--2018|World Meteorological Organization 2018]] ). If ratified by signatories, the rapid phase-down of HFCs under the Kigali Amendment is possible because of extensive replacement of high-global warming potential (GWP) HFCs with commercially available low-GWP alternatives in refrigeration and air-conditioning equipment. Each country’s choices of alternative refrigerants will likely be determined by energy efficiency, costs, and refrigerant toxicity and flammability. National and regional regulations will be needed to drive technological innovation and development ( [[#Polonara--2017|Polonara et al. 2017]] ).

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<span id="efficient-buildings-cooler-in-summer-warmer-in-winter-towards-net-zero-energy"></span>
==== 4.2.5.7 Efficient Buildings, Cooler in Summer, Warmer in Winter, Towards Net Zero Energy ====

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Most accelerated mitigation pathway scenarios include significant increase in building energy efficiency. Countries in cold regions, in particular, often focus more on building sector GHG emissions mitigation measures such as improving building envelopes and home appliances, and electrifying space heating and water heating.

For example, scenarios for Japan project continued electrification of residential and commercial buildings to 65% and 79% respectively by 2050 to reach 70–90% CO 2 reduction from 2013 levels ( [[#Kato--2019|Kato and Kurosawa 2019]] ). Similarly, a mitigation pathway for China compatible with 1.5°C would require 58% to 70% electrification of buildings according to ( [[#Jiang--2018|Jiang et al. 2018]] ; [[#China%20National%20Renewable%20Energy%20Centre--2019|China National Renewable Energy Centre 2019]] E; nergy Transitions Commission and Rocky Mountain Institute 2019). For the EU-28 to reach net carbon neutrality, complete substitution of fossil fuels with electricity (up to 65% share), district heating, and direct use of solar and ambient heat are projected to be needed for buildings, along with increased use of solar thermal and heat pumps for heating ( [[#Duscha--2019|Duscha et al. 2019]] ). In the UK and Canada, improved insulation to reduce energy demand and efficient building appliances and heating systems are important building strategies needed to reduce emissions to zero by 2050 ( [[#Vaillancourt--2017|Vaillancourt et al. 2017]] ; [[#Chilvers--2017|Chilvers et al. 2017]] ; [[#Roberts--2018a|Roberts et al. 2018a]] ). In Ireland, achieving 80–95% emissions reduction below 1990 levels by 2050 also requires changes in building energy technology and efficiency, including improving building envelopes, fuel switching for residential buildings, and replacing service-sector coal use with gas and renewables according to ( [[#Chiodi--2013|Chiodi et al. 2013]] ). In South Africa, improving industry and building energy efficiency is also considered a key part of mitigation strategies (Altieri et al. 2016; [[#Ouedraogo--2017|Ouedraogo 2017]] ).

In addition, an increasing number of countries have set up net zero energy building targets (Table 4.8) ( [[#Höhne--2020|Höhne et al. 2020]] ). Twenty-seven countries have developed roadmap documents for NZEBs, mostly in developed countries in Europe, North America, and Asia-Pacific, focusing on energy efficiency and improved insulation and design, renewable and smart technologies ( [[#Mata--2020|Mata et al. 2020]] ). The EU, Japan and the USA (the latter for public buildings only) have set targets for shifting new buildings to 100% near-zero energy buildings by 2030, with earlier targets for public buildings. Scotland has a similar target for 2050 ( [[#Höhne--2020|Höhne et al. 2020]] ). Technologies identified as needed for achieving near-zero energy buildings vary by region, but include energy-efficient envelope components, natural ventilation, passive cooling and heating, high performance building systems, air heat recovery, smart and information and communication technologies, and changing future heating and cooling supply fuel mixes towards solar, geothermal, and biomass ( [[#Mata--2020|Mata et al. 2020]] ). Sub-national regions in Spain, USA, Germany, and Mexico have set local commitments to achieving net zero carbon new buildings by 2050, with California having the most ambitious aspirational target of zero net energy buildings for all new buildings by 2030 ( [[#Höhne--2020|Höhne et al. 2020]] ). The EU is also targeting the retrofitting of 3% of existing public buildings to zero-energy, with emphasis on greater thermal insulation of building envelopes ( [[#Höhne--2020|Höhne et al. 2020]] ; [[#Mata--2020|Mata et al. 2020]] ). China’s roadmaps have emphasised insulation of building envelope, heat recovery systems in combination with renewable energy, including solar, shallow geothermal, and air source heat pumps ( [[#Mata--2020|Mata et al. 2020]] ).

'''Table 4.8 | Targets by countries, regions, cities and businesses on decarbonising the b''' '''uilding sector.'''

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

! Sub-national Regions

! Cities

! Businesses

|-
| Shift to 100% (near-)zero energy buildings for new buildings

| 3

| 6

| &gt;28

| &gt;44

|-
| Fully decarbonise the building sector

| 1

| 6

| &gt;28

| &gt;44

|-
| Phase out fossil fuels (for example, gas) for residential heating

| 1

| –

| &gt;3

|
|-
| Increase the rate of zero-energy renovations

| 1 (public buildings)

|
|}

Source: [[#Höhne--2020|Höhne et al. (2020)]] , supplementary information. [https://newclimate.org/ambitiousactions https://newclimate.org/am bitiousactions] .

<div id="4.2.5.8" class="h3-container"></div>

<span id="electrifying-transport"></span>
==== 4.2.5.8 Electrifying Transport ====

<div id="h3-19-siblings" class="h3-siblings"></div>

Electrification of transport in tandem with power sector decarbonisation is expected to be a key strategy for deep CO 2 mitigation in many countries. Passenger transport and light duty freight can already be electrified, but electrifying heavy-duty road transport and fuel switching in aviation and shipping are much more difficult and have not been addressed in most of the recent research.

In Germany, widespread electrification of private vehicles is expected by 2030 ( [[#Schmid--2012|Schmid and Knopf 2012]] ) while for the EU-28, 50% overall transport electrification (excluding feedstock) and 75% electrification of road transport is needed to reach net carbon neutrality according to ( [[#Duscha--2019|Duscha et al. 2019]] ). In addition, novel fuels such as hydrogen, synthetic hydrocarbons and sustainable biogenic fuels are needed to decarbonise aviation and water transport to achieve net carbon neutrality ( [[#Duscha--2019|Duscha et al. 2019]] ).

In India, electrification, hydrogen, and biofuels are key to decarbonising the transport sector ( [[#Dhar--2018|Dhar et al. 2018]] ; [[#Mittal--2018|Mittal et al. 2018]] ; [[#Vishwanathan--2018b|Vishwanathan et al. 2018b]] ; [[#Mathur--2020|Mathur and Shekhar 2020]] ). Under a 1.5°C scenario, nearly half of the light-duty passenger vehicle stock needs to be electrified according to ( [[#Parikh--2018|Parikh et al. 2018]] ). In China, a 1.5°C-compatible pathway would require electrification of two-fifths of transport ( [[#Jiang--2018|Jiang et al. 2018]] ; [[#China%20National%20Renewable%20Energy%20Centre--2019|China National Renewable Energy Centre 2019]] ).

Similarly, in Canada, electrification of 59% of light-duty trucks and 23% of heavy-duty trucks are needed as part of overall strategy to reduce CO 2 emissions by 80% by 2050. In addition, hydrogen is expected to play a major role by accounting for nearly one-third of light-duty trucks, 68% of heavy-duty trucks, and 33% of rail by 2050 according to [[#Hammond--2020|Hammond et al. (2020)]] .

<div id="4.2.5.9" class="h3-container"></div>

<span id="urban-form-meets-information-technology"></span>
==== 4.2.5.9 Urban Form Meets Information Technology ====

<div id="h3-20-siblings" class="h3-siblings"></div>

Beyond technological measures, some densely populated countries including Germany, Japan, and India are exploring using information technology/internet of things (IOT) to support mode-shifting and reduce mobility demand through broader behaviour and lifestyle changes (Ashina et al. 2012; [[#Canzler--2016|Canzler and Wittowsky 2016]] ; Aggarwal 2017; [[#Dhar--2018|Dhar et al. 2018]] ; [[#Vishwanathan--2018b|Vishwanathan et al. 2018b]] ). In Japan, accelerated mitigation pathways consider the use of information technology and internet of things (IoT) to transform human behaviour and transition to a sharing economy (Ashina et al. 2012; [[#Oshiro--2017a|Oshiro et al. 2017a]] , 2018). In Germany, one study points to including electromobility information and communication technologies in the transport sector as key ( [[#Canzler--2016|Canzler and Wittowsky 2016]] ) while another emphasise shifting from road to rail transport, and reduced distances travelled as other possible transport strategies ( [[#Schmid--2012|Schmid and Knopf 2012]] ). India’s transport sector strategies also include use of information technology and the internet, a transition to a sharing economy, and increasing infrastructure investment ( [[#Dhar--2018|Dhar et al. 2018]] ; [[#Vishwanathan--2018b|Vishwanathan et al. 2018b]] ). Behaviour and lifestyle change along with stakeholder integration in decision-making are considered key to implementing new transport policies (Aggarwal 2017; [[#Dhar--2018|Dhar et al. 2018]] ).

<div id="4.2.5.10" class="h3-container"></div>

<span id="industrial-energy-efficiency"></span>
==== 4.2.5.10 Industrial Energy Efficiency ====

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Industrial energy efficiency improvements are considered in nearly all countries but for countries where industry is expected to continue to be a key sector, new and emerging technologies that require significant R&amp;D investment, such as hydrogen and CCS, make ambitious targets achievable.

In China, for example, non-conventional electrical and renewable technologies, including low-grade renewable heat, biomass use for high-temperature heat in steel and cement sectors, and additional electrification in glass, food and beverage, and paper and pulp industries, are part of scenarios that achieve 60% reduction in national CO 2 emission by 2050 ( [[#Khanna--2019|Khanna et al. 2019]] ; [[#Zhou--2019|Zhou et al. 2019]] ), in addition to increased recycled steel for electric arc furnaces and direct electrolysis or hydrogen-based direct reduction of iron and CCS utilisation in clinker and steel-making ( [[#Jiang--2018|Jiang et al. 2018]] ; [[#China%20National%20Renewable%20Energy%20Centre--2019|China National Renewable Energy Centre 2019]] ). Similarly, in India, ( [[#Vishwanathan--2020|Vishwanathan and Garg 2020]] ) point to the need for renewable energy and CCS to decarbonise the industrial sector. In EU-28, net CO 2 neutrality can only be reached with 92% reduction in industrial emissions relative to 1990, through electrification, efficiency improvement and new technologies such as hydrogen-based direct reduction of steel, low-carbon cement and recycling ( [[#Duscha--2019|Duscha et al. 2019]] ). Both China and EU see 50% of industry electrification by 2050 as needed to meet 1.5°C and net carbon neutrality pathways ( [[#Jiang--2018|Jiang et al. 2018]] ; [[#Capros--2019|Capros et al. 2019]] ).

Aggressive adoption of technology solutions for power sector decarbonisation coupled with end-use efficiency improvements and low-carbon electrification of buildings, industry and transport provides a pathway for accelerated mitigation in many key countries, but will still be insufficient to meet zero emission/1.5°C goals for all countries. Although not included in a majority of the studies related to pathways and national modelling analysis, energy demand reduction through deeper efficiency and other measures such as lifestyle changes and system solutions that go beyond components, as well as the co-benefits of the reduction of short-lived pollutants, needs to be evaluated for inclusion in future zero emission/1.5°C pathways.

<div id="4.2.5.11" class="h3-container"></div>

<span id="lowering-demand-downscaling-economies"></span>
==== 4.2.5.11 Lowering Demand, Downscaling Economies ====

<div id="h3-22-siblings" class="h3-siblings"></div>

Studies have identified socio-technological pathways to help achieve net zero CO 2 and GHG targets at national scale, that in aggregate are crucial to keeping global temperature below agreed limits. However, most of the literature focuses on supply-side options, including carbon dioxide removal mechanisms (BECCS, afforestation, and others) that are not fully commercialised (Cross-Chapter Box 8 in Chapter 12). Costs to research, deploy, and scale up these technologies are often high. Recent studies have addressed lowering demand through energy conversion efficiency improvements, but few studies have considered demand reduction through efficiency ( [[#Grubler--2018|Grubler et al. 2018]] ) and the related supply implications and mitigation measures.

Five main drivers of long-term energy demand reduction that can meet the 1.5°C target include quality of life, urbanisation, novel energy services, diversification of end-user roles, and information innovation ( [[#Grubler--2018|Grubler et al. 2018]] ). A Low Energy Demand scenario requires fundamental societal and institutional transformation from current patterns of consumption, including: decentralised services and increased granularity (small-scale, low-cost technologies to provide decentralised services), increased use value from services (multi-use vs single use), sharing economies, digitalisation, and rapid transformation driven by end-user demand. This approach to transformation differs from the status quo and current climate change policies in emphasising energy end-use and services first, with downstream effects driving intermediate and upstream structural change.

Radical low-carbon innovation involves systemic, cultural, and policy changes and acceptance of uncertainty in the beginning stages. However, the current dominant analytical perspectives are grounded in neoclassical economics and social psychology, and focus primarily on marginal changes rather than radical transformations ( [[#Geels--2018|Geels et al. 2018]] ). Some literature is beginning to focus on mitigation through behaviour and lifestyle changes, but specific policy measures for supporting such changes and their contribution to emission reductions remain unclear ( [[#4.4.2|Section 4.4.2]] and Chapter 5).

<div id="4.2.5.12" class="h3-container"></div>

<span id="ambitious-targets-to-reduce-short-lived-climate-forcers-including-methane"></span>
==== 4.2.5.12 Ambitious Targets to Reduce Short-lived Climate Forcers, Including Methane ====

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Recent research shows that temperature increases are likely to exceed 1.5°C during the 2030s and 2°C by mid-century unless both CO 2 and short-lived climate forcers (SLCFs) are reduced ( [[#Shindell--2017|Shindell et al. 2017]] ; [[#Rogelj--2018a|Rogelj et al. 2018a]] ). Because of their short lifetimes (days to a decade and a half), SLCFs can provide fast mitigation, potentially avoiding warming of up to 0.6°C at 2050 and up to 1.2°C at 2100 ( [[#Ramanathan--2010|Ramanathan and Xu 2010]] ; [[#Xu--2017|Xu and Ramanathan 2017]] ). In Asia especially, co-benefits of drastic CO 2 and air pollution mitigation measures reduce emissions of methane, black carbon, sulphur dioxide, nitrogen oxide, and fine particulate matter by approximately 23%, 63%, 73%, 27%, and 65% respectively in 2050 as compared to 2010 levels. Including the co-benefits of reduction of climate forcing adds significantly to the benefits reducing air pollutants ( [[#Hanaoka--2018|Hanaoka and Masui 2018]] ).

To achieve net zero GHG emissions implies consideration of targets for non-CO 2 gases. While methane emissions have grown less rapidly than CO 2 and F-gases since 1990 (Chapter 2), the literature urges action to bring methane back to a pathway more in line with the Paris goals ( [[#Nisbet--2020|Nisbet et al. 2020]] ). Measures to reduce methane emissions from anthropogenic sources are considered intractable – where they sustain livelihoods – but also becoming more feasible, as studies report the options for mitigation in agriculture without undermining food security ( [[#Wollenberg--2016|Wollenberg et al. 2016]] ; [[#Frank--2017|Frank et al. 2017]] ; [[#Nisbet--2020|Nisbet et al. 2020]] ). The choice of emission metrics has implications for SLCF ( [[#Cain--2019|Cain et al. 2019]] ) (Cross-Chapter Box 2 in Chapter 2). Ambitious reductions of methane are complementary to, rather than substitutes for, reductions in CO 2 ( [[#Nisbet--2020|Nisbet et al. 2020]] ).

Rapid SLCF reductions, specifically of methane, black carbon, and tropospheric ozone have immediate co-benefits including meeting sustainable development goals for reducing health burdens of household air pollution and reversing health- and crop-damaging tropospheric ozone ( [[#Jacobson--2002|Jacobson 2002]] , 2010). SLCF mitigation measures can have regional impacts, including avoiding premature deaths in Asia and Africa and warming in central and northern Asia, southern Africa, and the Mediterranean ( [[#Shindell--2012|Shindell et al. 2012]] ). Reducing outdoor air pollution could avoid 2.4 million premature deaths and 52 million tonnes of crop losses for four major staples ( [[#Haines--2017|Haines et al. 2017]] ). Existing research emphasises climate and agriculture benefits of methane mitigation measures with relatively small human health benefits ( [[#Shindell--2012|Shindell et al. 2012]] ). Research also predicts that black carbon mitigation could substantially benefit global climate and human health, but there is more uncertainty about these outcomes than about some other predictions ( [[#Shindell--2012|Shindell et al. 2012]] ). Other benefits to SLCF reduction include reducing warming in the critical near term, which will slow amplifying feedbacks, reduce the risk of non-linear changes, and reduce long-term cumulative climate impacts – like sea-level rise – and mitigation costs ( [[#Hu--2017|Hu et al. 2017]] ; [[#UNEP%20and%20WMO--2011|UNEP and WMO 2011]] ; [[#Rogelj--2018a|Rogelj et al. 2018a]] ; [[#Xu--2017|Xu and Ramanathan 2017]] ; [[#Shindell--2012|Shindell et al. 2012]] ).

<div id="4.2.5.13" class="h3-container"></div>

<span id="system-analysis-solutions-are-only-beginning-to-be-recognised-in-current-literature-on-accelerated-mitigation-pathways-and-rarely-included-in-existing-national-policies-or-strategies"></span>
==== 4.2.5.13 System Analysis Solutions Are Only Beginning to Be Recognised in Current Literature on Accelerated Mitigation Pathways, and Rarely Included in Existing National Policies or Strategies ====

<div id="h3-24-siblings" class="h3-siblings"></div>

Most models and studies fail to address system impacts of widespread new technology deployment, for example: (i) material and resources needed for hydrogen production or additional emissions and energy required to transport hydrogen; or (ii) materials, resources, grid integration, and generation capacity expansion limits of a largely decarbonised power sector and electrified transport sector. These impacts could limit regional and national scale-ups.

Systemic solutions are also not being sufficiently discussed, such as low-carbon materials; light-weighting of buildings, transport, and industrial equipment; promoting circular economy, recyclability and reusability, and addressing the food-energy-water nexus. These solutions reduce demand in multiple sectors, improve overall supply chain efficiency, and require cross-sector policies. Using fewer building materials could reduce the need for cement, steel, and other materials and thus the need for production and freight transport. Concrete can also be produced from low-carbon cement, or designed to absorb CO 2 from the atmosphere. Few regions have developed comprehensive policies or strategies for a circular economy, with the exception of the EU and China, and policies in the EU have only emerged within the last decade. While China’s circular economy policies emphasises industrial production, water, pollution and scaling-up in response to rapid economic growth and industrialisation, EU’s strategy is focused more narrowly on waste and resources and overall resource efficiency to increase economic competitiveness ( [[#McDowall--2017|McDowall et al. 2017]] ).

Increased bioenergy consumption is considered in many 1.5°C and 2°C scenarios. System thinking is needed to evaluate bioenergy’s viability because increased demand could affect land and water availability, food prices, and trade ( [[#Sharmina--2016|Sharmina et al. 2016]] ). To adequately address the water-energy-food nexus, policies and models must consider interconnections, synergies, and trade-offs among and within sectors, which is currently not the norm ( [[IPCC:Wg3:Chapter:Chapter-12#12.4|Section 12.4]] ).

A systems approach is also needed to support technological innovation. This includes recognising unintended consequences of political support mechanisms for technology adoption and restructuring current incentives to realise multi-sector benefits. It also entails assimilating knowledge from multiple sources as a basis for policy and decision-making ( [[#Hoolohan--2019|Hoolohan et al. 2019]] ).

Current literature does not explicitly consider systematic, physical drivers of inertia, such as capital and infrastructure needed to support accelerated mitigation ( [[#Pfeiffer--2018|Pfeiffer et al. 2018]] ). This makes it difficult to understand what is needed to successfully shift from current limited mitigation actions to significant transformations needed to rapidly achieve deep mitigation.

<div id="4.2.6" class="h2-container"></div>

<span id="implications-of-accelerated-mitigation-for-national-development-objectives"></span>

=== 4.2.6 Implications of Accelerated Mitigation for National Development Objectives ===

<div id="h2-10-siblings" class="h2-siblings"></div>

<div id="4.2.6.1" class="h3-container"></div>

<span id="introduction-2"></span>
==== 4.2.6.1 Introduction ====

<div id="h3-25-siblings" class="h3-siblings"></div>

This section examines how accelerated mitigation may impact the realisation of development objectives in the near- and mid-term. It focuses on three objectives discussed in the literature, sustaining economic growth ( [[#4.2.6.2|Section 4.2.6.2]] ), providing employment ( [[#4.2.6.3|Section 4.2.6.3]] ), and alleviating poverty and ensuring equity ( [[#4.2.6.4|Section 4.2.6.4]] ). It complements similar review performed at global level in [[IPCC:Wg3:Chapter:Chapter-3#3.6|Section 3.6]] . For a comprehensive survey of research on the impact of mitigation in other areas (including air quality, health, and biodiversity), see [[#Karlsson--2020|Karlsson et al. (2020)]] .

<div id="4.2.6.2" class="h3-container"></div>

<span id="mitigation-and-economic-growth-in-the-near--and-mid-term"></span>
==== 4.2.6.2 Mitigation and Economic Growth in the Near- and Mid-term ====

<div id="h3-26-siblings" class="h3-siblings"></div>

A significant part of the literature assesses the impacts of mitigation on GDP, consistent with policymakers’ interest in this variable. It must be noted upfront that computable equilibrium models, on which our assessments are mostly based, capture the impact of mitigation on GDP and other core economic variables while typically overlooking other effects that may matter (like improvements in air quality). Second, even though GDP (or better, GDP per capita) is not an indicator of welfare ( [[#Fleurbaey--2013|Fleurbaey and Blanchet 2013]] ), changes in GDP per capita across countries and over time are highly correlated with changes in welfare indicators in the areas of poverty, health, and education ( [[#Gable--2015|Gable et al. 2015]] ). The mechanisms linking mitigation to GDP outlined below would remain valid even with alternative indicators of well-being ( [[IPCC:Wg3:Chapter:Chapter-5#5.2.1|Section 5.2.1]] ). Third, another stream of literature criticises the pursuit of economic growth as a goal, instead advocating a range of alternatives and suggesting modelling of post-growth approaches to achieve rapid mitigation while improving social outcomes ( [[#Hickel--2021|Hickel et al. 2021]] ). In the language of the present chapter, these alternatives constitute alternative development pathways.

Most country-level mitigation modelling studies in which GDP is an endogenous variable report negative impacts of mitigation on GDP in 2030 and 2050, relative to the reference ( ''robust evidence'' , ''high agreement'' ), for example ( [[#Nong--2017|Nong et al. 2017]] ) for Australia, ( [[#Chen--2013|Chen et al. 2013]] ) for Brazil, ( [[#Dai--2016|Dai et al. 2016]] ; [[#Li--2017|Li et al. 2017]] ; [[#Dong--2018|Dong et al. 2018]] ; [[#Mu--2018a|Mu et al. 2018a]] ; [[#Zhao--2018|Zhao et al. 2018]] ; [[#Cui--2019|Cui et al. 2019]] ) for China, (Álvarez-Espinosa et al. 2018) for Colombia, ( [[#Fragkos--2017|Fragkos et al. 2017]] ) for the EU, ( [[#Mittal--2018|Mittal et al. 2018]] ) for India, ( [[#Fujimori--2019|Fujimori et al. 2019]] ) for Japan, ( [[#Veysey--2014|Veysey et al. 2014]] ) for Mexico, ( [[#Pereira--2016|Pereira et al. 2016]] ) for Portugal, (Alton et al. 2014; [[#van%20Heerden--2016|van Heerden et al. 2016]] ) for South Africa, ( [[#Chunark--2017|Chunark et al. 2017]] ) for Thailand, ( [[#Acar,%C2%A0S.%20and%C2%A0A.E.%20Yeldan--2016|Acar and Yeldan 2016]] ) for Turkey, ( [[#Roberts--2018b|Roberts et al. 2018b]] ) for the UK, ( [[#Zhang--2017|Zhang et al. 2017]] ; [[#Chen--2019|Chen and Hafstead 2019]] ) for USA, ( [[#Nong--2018|Nong 2018]] ) for Vietnam ( ). The downward relationship between mitigation effort and emissions is strong in studies up to 2030, much weaker for studies looking farther ahead. In all reviewed studies, however, GDP continues to grow even with mitigation. It may be noted that none of the studies assessed above integrates the benefits of mitigation in terms of reduced impacts of climate change or lower adaptation costs. This is not surprising since these studies are at national or regional scale and do not extend beyond 2050, whereas the benefits depend on global emissions and primarily occur after 2050. Discussion on reduced impacts is provided in [[IPCC:Wg3:Chapter:Chapter-3#3.6.2|Section 3.6.2]] and Cross-Working Group Box 1 in Chapter 3.

<div id="_idContainer021" class="_idGenObjectStyleOverride-1"></div>

[[File:c62221328e15fd00229b8677895458e5 IPCC_AR6_WGIII_Figure_4_4.png]]

'''Figure 4.4 | GDP against emissions in country-level modelling studies, in variations relativ''' '''e to reference.'''

Two major mechanisms interplay to explain the impact of mitigation on GDP. First, the carbon constraint imposes reduced use of a production factor (fossil energy), thus reducing GDP. In the simulations, the mechanism at work is that firms and households reduce their use of GHG-intensive goods and services in response to higher prices due to reduced fossil energy use. Second, additional investment required for mitigation partially crowds out productive investment elsewhere ( [[#Fujimori--2019|Fujimori et al. 2019]] ), except in Keynesian models in which increased public investment actually boosts GDP ( [[#Pollitt--2015|Pollitt et al. 2015]] ; [[#Landa%20Rivera--2016|Landa Rivera et al. 2016]] ; [[#Bulavskaya--2018|Bulavskaya and Reynès 2018]] ). Magnitude and duration of GDP loss depend on the stringency of the carbon constraint, the degree of substitutability with less-GHG-intensive goods and services, assumptions about costs of low-carbon technologies and their evolution over time (e.g., [[#Duan--2018|Duan et al. 2018]] ; [[#van%20Meijl--2018|van Meijl et al. 2018]] ; [[#Cui--2019|Cui et al. 2019]] ) and decisions by trading partners, which influence competitiveness impacts for firms (Alton et al. 2014; [[#Fragkos--2017|Fragkos et al. 2017]] ) ( ''high evidence'' , ''h'' ''igh agreement'' ).

In the near term, presence of long-lived emissions intensive capital stock, and rigidities in the labour market ( [[#Devarajan--2011|Devarajan et al. 2011]] ) and other areas may increase impacts of mitigation on GDP. In the mid-term, on the other hand, physical and human capital, technology, institutions, skills or location of households and activities are more flexible. The development of renewable energy may help create more employment and demands for new skills, particularly in the high-skill labour market (Helgenberger, S. et al., 2019). In addition, cumulative mechanisms such as induced technical change or learning by doing on low-emissions technologies and process may reduce the impacts of mitigation on GDP.

Country-level studies find that the negative impacts of mitigation on GDP can be reduced if pre-existing economic or institutional obstacles are removed in complement to the imposition of the carbon constraint ( ''robust evidence'' , ''high agreement'' ). For example, if the carbon constraint takes the form of a carbon tax or of permits that are auctioned, the way the proceeds from the tax (or the revenues from the sales of permits) are used is critical for the overall macroeconomic impacts ( [[#Chen--2013|Chen et al. 2013]] ). (For a detailed discussion of different carbon pricing instruments, including the auctioning of permits, see [[IPCC:Wg3:Chapter:Chapter-13#13.6.3|Section 13.6.3]] ).

shows that depending on the choice of how to implement a carbon constraint, the same level of carbon constraint can yield very different outcomes for GDP. The potential for mitigating GDP implications of mitigation through fiscal reform is discussed in [[#4.4.1.8|Section 4.4.1.8]] .

<div id="_idContainer027" class="Basic-Text-Frame"></div>

[[File:4be61407bede16582e61ffcc636da0a5 IPCC_AR6_WGIII_Figure_4_5.png]]

'''Figure 4.5 | Illustrative ranges of variations in GDP relative to reference in 2030 associated with introduction of carbon constraint, depending on modality of policy implementation.''' Source: based on Alton et al. (2014); [[#Devarajan--2011|Devarajan et al. (2011)]] ; [[#Fernandez--2018|Fernandez and Daigneault (2018)]] ; [[#Glomsrød--2016|Glomsrød et al. (2016)]] ; [[#Nong--2018|Nong (2018)]] ; Asakawa et al. (2021). Stringency of carbon constraint is not comparable across the studies.

More generally, mitigation costs can be reduced by proper policy design if the economy initially is not on the efficiency frontier ( [[#Grubb--2014|Grubb 2014]] ), defined as the set of configurations within which the quality of the environment and economic activity cannot be simultaneously improved given current technologies – such improvements in policy design may include reductions in distortionary taxes. Most of the studies which find that GDP increases with mitigation in the near term precisely assume that the economy is initially not on the frontier. Making the economy more efficient – in other words, lifting the constraints that maintain the economy in an interior position – creates opportunities to simultaneously improve economic activity and reduce emissions. Table 4.9 describes the underlying assumptions in a selection of studies.

Finally, ''marginal'' costs of mitigation are not always reported in studies of national mitigation pathways. Comparing numbers across countries is not straightforward due to exchange rate fluctuations, differing assumptions by modellers in individual country studies, etc. The database of national mitigation pathways assembled for this Report – which covers only a fraction of available national mitigation studies in the literature – shows that marginal costs of mitigation are positive, with a median value of 101 USD2010 tCO 2 –1 in 2030, 244 in 2040 and 733 in 2050 for median mitigation efforts of 21%, 46% and 76% relative to business-as-usual respectively. Marginal costs increase over time along accelerated mitigation pathways, as constraints become tighter, with a non-linearity as mitigation reaches 80% of reference emissions or more. Dispersion across and within countries is high, even in the near term but increases notably in the mid-term ( ''medium evidence'' , ''med'' ''ium agreement'' ).

'''Table 4.9 | Examples of country-level modelling studies finding positive short-term outcome of mitigation on GDP relati''' '''ve to baseline.'''

{| class="wikitable"
|-
! Reference

! Country/region

! Explanation for positive outcome of mitigation on GDP

|-
| Antimiani et al. (2016)

| European Union

| GDP increases relative to reference only in the scenario with global cooperation on mitigation.

|-
| [[#Willenbockel--2017|Willenbockel et al. (2017)]]

| Kenya

| The mitigation scenario introduces cheaper (geothermal) power generation units than in BAU (in which thermal increases). Electricity prices actually decrease.

|-
| [[#Siagian--2017|Siagian et al. (2017)]]

| Indonesia

| Coal sector with low productivity is forced into BAU. Mitigation redirects investment towards sectors with higher productivity.

|-
| [[#Blazquez--2017|Blazquez et al. (2017)]]

| Saudi Arabia

| Renewable energy penetration assumed to free oil that would have been sold at publicly subsidised price on the domestic market to be sold internationally at market price.

|-
| [[#Wei--2019|Wei et al. (2019)]]

| China

| Analyse impacts of feed-in tariffs to renewables, find positive short-run impacts on GDP; public spending boost activity in the RE sector. New capital being built at faster rate than in reference increases activity more than activity decreases due to lower public spending elsewhere.

|-
| [[#Gupta--2019|Gupta et al. (2019)]]

| India

| Savings adjust to investment and fixed unemployment is considered target of public policy, thereby limiting impact of mitigation on GDP relative to other economic variables (consumption, terms of trade).

|-
| [[#Huang--2019|Huang et al. (2019)]]

| China

| Power generation plan in the baseline is assumed not cost minimising.

|}

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<span id="mitigation-and-employment-in-the-short--and-medium-term"></span>
==== 4.2.6.3 Mitigation and Employment in the Short- and Medium-term ====

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Numerous studies have analysed the potential impact of carbon pricing on labour markets. [[#Chateau--2018|Chateau et al. (2018)]] and [[#OECD--2017a|OECD (2017a)]] find that the implementation of green policies globally (defined broadly as policies that internalise environmental externalities through taxes and other tools, shifting profitability from polluting to green sectors) need not harm total employment, and that the broad skill composition (low, high- and medium-skilled jobs) of emerging and contracting sectors is very similar, with the largest shares of job creation and destruction at the lowest skill level. To smoothen the labour market transition, they conclude that it may be important to reduce labour taxes, to compensate vulnerable households, and to provide education and training programs, the latter making it easier for labour to move to new jobs. Consistent with this, other studies that simulate the impact of scenarios with more or less ambitious mitigation policies (including 100% reliance on renewable energy by 2050) find relatively small (positive or negative) impacts on aggregate global employment that are more positive if labour taxes are reduced but encompass substantial losses for sectors and regions that today are heavily dependent on fossil fuels (Arndt et al. 2013; [[#Huang--2019|Huang et al. 2019]] ; [[#Vandyck--2016|Vandyck et al. 2016]] ; [[#Jacobson--2019|Jacobson et al. 2019]] ). Among worker categories, low-skilled workers tend to suffer wage losses as they are more likely to have to reallocate, something that can come at a cost in the form of a wage cut (assuming that workers who relocate are initially less productive than those who already work in the sector). The results for alternative carbon revenue recycling schemes point to trade-offs: a reduction in labour taxes often leads to the most positive employment outcomes while lump-sum (uniform per-capita) transfers to households irrespective of income yield a more egalitarian outcome.

The results from country-level studies using CGE models tend be similar to those at global level. Aggregate employment impacts are small and may be positive especially if labour taxes are cut, see for example, [[#Telaye--2019|Telaye et al. (2019)]] for Ethiopia,( [[#Kolsuz--2017|Kolsuz and Yeldan (2017)]] for Turkey, [[#Fragkos--2017|Fragkos et al. (2017)]] for the EU, and [[#Mu--2018b|Mu et al. (2018b)]] for China. On the other hand, sectoral reallocations away from fossil-dependent sectors may be substantial, see for example, Alton et al. (2014) for South Africa or [[#Huang--2019|Huang et al. (2019)]] for China. Targeting of investment to labour-intensive green sectors may generate the strongest employment gains, see, for example, [[#Perrier--2018|Perrier and Quirion (2018)]] for France, [[#van%20Meijl--2018|van Meijl et al. (2018)]] for the Netherlands, and Patrizio et al. 2(018) for the USA. Changes in skill requirements between emerging and declining sectors appear to be quite similar, involving smaller transitions than during the IT revolution ( [[#Bowen--2018|Bowen et al. 2018]] ).

In sum, the literature suggests that the employment impact of mitigation policies tends to be limited on aggregate, but can be significant at the sectoral level ( ''medium evidence'' , ''medium agreement'' ) and that cutting labour taxes may limit adverse effects on employment ( ''limited evidence'' , ''medium agreement'' ). Labour market impacts, including job losses in certain sectors, can be mitigated by equipping workers for job changes via education and training, and by reducing labour taxes to boost overall labour demand ( [[#Stiglitz--2017|Stiglitz et al. 2017]] ) ( [[#4.5|Section 4.5]] ).

Like most of the literature on climate change, the above studies do not address gender aspects. These may be significant since the employment shares for men and women vary across sectors and countries.

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<span id="mitigation-and-equity-in-the-near-and-mid-term"></span>
==== 4.2.6.4 Mitigation and Equity in the Near and Mid-term ====

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Climate mitigation may exacerbate socio-economic pressures on poorer households ( [[#Jakob--2014|Jakob et al. 2014]] ). First, the price increase in energy-intensive goods and services – including food ( [[#Hasegawa--2018|Hasegawa et al. 2018]] ) – associated with mitigation may affect poorer households disproportionally (Bento 2013), and increase the number of energy-poor (Berry 2019). Second, the mitigation may disproportionally affect low-skilled workers (see previous section). Distributional issues have been identified not only with explicit price measures (carbon tax, emission permits system, subsidy removal), but also with subsidies for renewables ( [[#Borenstein--2016|Borenstein and Davis 2016]] ), and efficiency and emissions standards ( [[#Davis--2019|Davis and Knittel 2019]] ; [[#Bruegge--2019|Bruegge et al. 2019]] ; [[#Levinson--2019|Levinson 2019]] ; [[#Fullerton--2019|Fullerton and Muehlegger 2019]] ).

Distributional implications, however, are context specific, depending on consumption patterns (initially and ease of adjusting them in response to price changes) and asset ownership (see for example analysis of energy prices in Indonesia by Renner et al. 2019). In an analysis of the distributional impact of carbon pricing based on household expenditure data for 87 low- and middle-income countries, [[#Dorband--2019|Dorband et al. (2019)]] find that, in countries with a per-capita income of up to USD15,000 per capita (purchasing power parity (PPP) adjusted), carbon pricing has a progressive impact on income distribution and that there may be an inversely U-shaped relationship between energy expenditure shares and per-capita income, rendering carbon pricing regressive in high-income countries, in other words, in countries where the capacity to pursue compensatory policies tends to be relatively strong.

The literature finds that the detailed design of mitigation policies is critical for their distributional impacts ( ''robust evidence'' , ''high agreement'' ). For example, [[#Vogt-Schilb--2019|Vogt-Schilb et al. (2019)]] suggest to turn to cash transfer programs, established as some of the most efficient tools for poverty reduction in developing countries. In an analysis of Latin America and the Caribbean, they find that allocation of 30% of carbon revenues would suffice to compensate poor and vulnerable households on average, leaving the rest for other uses. This policy tool is not only available in countries with relatively high per-capita incomes: in Sub-Saharan Africa, where per-capita incomes are relatively low, cash transfer programs have been implemented in almost all countries (Beegle et al. 2018, p. 57), and are found central to the success of energy subsidy reforms ( [[#Rentschler--2017|Rentschler and Bazilian 2017]] ). In the same vein, Böhringer et al. (2021) finds that recycling of revenues from emissions pricing in equal amounts to every household appeals as an attractive strategy to mitigate regressive effects and thereby make stringent climate policy more acceptable on societal fairness grounds. However, distributional gains from such recycling may come at the opportunity cost of not reaping efficiency gains from reductions in the taxes that are most distortionary (Goulder et al. 2019).

Distributional concerns related to climate mitigation are also prevalent in developed countries, as demonstrated, for instance, by France’s recent yellow-vest movement, which was ignited by an increase in carbon taxes. It exemplifies the fact that, when analysing the distributional effects of carbon pricing, it is not sufficient to consider vertical redistribution (i.e., redistribution between households at different incomes levels but also horizontal redistribution (i.e., redistribution between households at similar incomes which is due to differences in terms of spending shares and elasticities for fuel consumption). Compared to vertical redistribution, it is more difficult to devise policies that effectively address horizontal redistribution (Cronin et al. 2019; [[#Pizer--2019|Pizer and Sexton 2019]] ; [[#Douenne--2020|Douenne 2020]] ). However, it has been shown ex post that transfer schemes considering income levels and location could have protected or even improved the purchasing power of the bottom half of the population ( [[#Bureau--2019|Bureau et al. 2019]] ). Investments in public transportation may reduce horizontal redistribution if it makes it easier for households to reduce fossil fuel consumption when prices increase (see Sections 4.4.1.5 and 4.4.1.9). Similarly, in relation to energy use in housing, policies that encourage investments that raise energy efficiency for low-income households may complement or be an alternative to taxes and subsidies as a means of simultaneously mitigating and reducing fuel poverty ( [[#Charlier--2019|Charlier et al. 2019]] ). From a different angle, public acceptance of the French increase in the carbon tax could also have been enhanced via a public information campaign could have raised public acceptance of the carbon tax increase ( [[#Douenne--2020|Douenne and Fabre 2020]] ). (See [[#4.4.1.8|Section 4.4.1.8]] for a discussion of this and other factors that influence public support for carbon taxation.)

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<span id="obstacles-to-accelerated-mitigation-and-how-overcoming-them-amounts-to-shifts-in-development-pathways"></span>
=== 4.2.7 Obstacles to Accelerated Mitigation and How Overcoming Them Amounts to Shifts in Development Pathways ===

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As outlined in Sections 4.2.3, 4.2.4, 4.2.5 and 4.2.6 there is improved understanding since AR5 of what accelerated mitigation would entail in the coming decades. A major finding is that accelerated mitigation pathways in the near to mid-term appear technically and economically feasible in most contexts. Chapter 4, however, cannot stop here. Section 4.2.2 has documented an important policy gap for current climate pledges, and Cross-Chapter Box 4 in this chapter shows an even larger ambition gap between current pledges and what would be needed in the near term to be on pathways consistent with below 2°C, let alone 1.5°C. In other words, while the implementation of mitigation policies to achieve updated NDC almost doubles the mitigation efforts, and notwithstanding the widespread availability of the necessary technologies, this doubling of effort merely narrows the gap to pathways consistent with 2°C by at most 20%.

Obstacles to the implementation of accelerated mitigation pathways can be grouped in four main categories (Table 4.10). The first set of arguments can be understood through the lens of cost-benefit analysis of decision-makers, as they revolve around the following question: Are costs too high relative to benefits? More precisely, are the opportunity costs – in economics terms, what is being forfeited by allocating scarce resources to mitigation – justified by the benefits for the decision-maker (whether individual, firm, or nation)? This first set of obstacles is particularly relevant because accelerated mitigation pathways imply significant effort in the short-run, while benefits in terms of limited warming accrue later and almost wholly to other actors. However, as discussed in Sections 3.6 and 4.2.6, mitigation costs for a given mitigation target are not carved in stone. They strongly depend on numerous factors, including the way mitigation policies have been designed, selected, and implemented, the processes through which markets have been shaped by market actors and institutions, and nature of socially- and culturally-determined influences on consumer preferences. Hence, mitigation choices that might be expressed straightforwardly as techno-economic decisions are, at a deeper level, strongly conditioned by underlying structures of society.

A second set of likely obstacles in the short-term to accelerated mitigation revolves around undesirable distributional consequences, within and across countries. As discussed in [[#4.2.6.3|Section 4.2.6.3]] , the distributional implications of climate policies depend strongly on their design, the way they are implemented, and on the context into which they are inserted. Distributional implications of climate policies have both ethics and equity dimensions, to determine what is desirable/acceptable by a given society in a given context, notably the relative power of different winners and losers to have their interests taken into account, or not, in the relevant decision-making processes. Like costs, distributional implications of accelerated mitigation are rooted in the underlying socio-political-institutional structures of a society.

A third set of obstacles are about technology availability and adoption. Lack of access even to existing cost-effective mitigation technologies remains an important issue, particularly for many developing countries, and even in the short-term. Though it relates most directly to techno-economic costs, technology availability raises broader issues related to the socio-technical systems within which innovation and adoption are embedded, and issues of technology availability are inherently issues of systemic failure ( [[IPCC:Wg3:Chapter:Chapter-16#16.3|Section 16.3]] ). The underlying legal, economic and social structures of the economy are central to the different stages of socio-transition processes (Cross-Chapter Box 12 in Chapter 16).

The last set of obstacles revolves around the unsuitability of existing structures to accelerated mitigation. We include here all forms of established structures, material (e.g., physical capital) or not (institutions, social norms, patterns of individual behaviour), that are potentially long-lived and limit the implementation of accelerated mitigation pathways. Typically, such structures exist for reasons other than climate change and climate mitigation, including the distribution of power among various actors. Modifying them in the name of accelerated climate mitigation thus requires to deal with other non-climate issues as well. For example, resolving the landlord-tenant dilemma, an institutional barrier to the deployment of energy efficiency in building, opens fundamental questions on private property in buildings.

Acommon thread in the discussion above is that the obstacles to accelerated mitigation are to a large degree rooted in the underlying structural features of societies. As a result, transforming those underlying structures can help to remove those obstacles, and thus facilitate the acceleration of mitigation. This remark is all the more important that accelerated mitigation pathways, while very different across countries, all share three characteristics: speed of implementation, breadth of action across all sectors of the economy, and depth of emission reduction achieving more ambitious targets. Transforming those underlying structures amounts to shifting a society’s development pathway (Figure 4.6). In the following Sections 3 and 4, we argue that it is thus necessary to recast accelerated mitigation in the broader context of shifting development pathways, and that doing so opens up additional opportunities to (i) overcome the obstacles outlined above, and also (ii) combine climate mitigation with other development objectives.

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[[File:d47d99310cc0bb9d7395361912ca4344 IPCC_AR6_WGIII_Figure_4_6.png]]

'''Figure 4.6 | Obstacles to mitigation (top panel) and measures to remove these obstacles and enable shift in development pathways''' '''(lower panel).'''

'''Table 4.10 | Objections to accelerated mitigation and where they are assessed in''' '''the WG3 report.'''

{| class="wikitable"
|-
! Category

! Main dimensions

! Location in AR6 WGIII report where objection is assessed and solutions are discussed

|-
| Costs of mitigation

| Marginal, sectoral or macroeconomic costs of mitigation too high; scarce resources could/should be used for other development priorities; mitigation benefits are not worth the costs (or even non-existent); lack of financing

| Sections 3.6, 4.2.6, 12.2; Chapter 15, Chapter 17

|-
| Distributional implications

| Risk of job losses; diminished competitiveness; inappropriate impact on poor/vulnerable people; negative impact on vested interests

| [[#4.5|Section 4.5]] ; Chapter 5, Chapter 13, Chapter 14

|-
| Lack of technology

| Lack of suitable technologies; lack of technology transfer; unfavourable socio-political environment

| [[#4.2.5|Section 4.2.5]] , Chapter 16

|-
| Unsuitable ‘structures’

| Inertia of installed capital stock; inertia of socio-technical systems; inertia to behaviour change; unsuitable institutions

| [[IPCC:Wg3:Chapter:Chapter-3#3.5|Section 3.5]] ; Chapter 5, Chapter 13

|}

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