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

==== 4.2.2.2 Methods to Project Emissions Under NDCs and Current Policies ====

<div id="h3-2-siblings" class="h3-siblings"></div>

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>