2.1 Target types

Several types of GHG mitigation targets can be found in the assessed set of NDCs. As quantifications differ between the target types, a classification of the targets is needed. All target types we differentiate in NDCmitiQ are given in Table 1. For the target types RBY and REI_RBY, reductions are compared to a historical base year, while for ABU, RBU, and REI_RBU, reductions are compared to business as usual (BAU). BAU emissions are the emissions a country would have if – starting from a certain year – no further mitigation actions were taken (inactivity scenario). Countries that indicate the year in which they plan their emissions to peak are not classified specifically, but the information is considered in the national emissions pathways (Sect. 2.4, relevant e.g. for China, who indicated an emissions peak).

Table 1GHG mitigation target types considered in NDCmitiQ, together with their abbreviations and one explanatory example per target type.

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2.1.1 Classification of target types: type_main and type_reclass

In principle the classification of target types should be simple. If a country states that “we will reduce our GHG emissions by 20 % compared to BAU”, the target classification is RBU. However, what if the country also provides a quantification of its RBU target, which could then be classified as an ABS target? To use both data, we introduce two classifications: “type_main” and “type_reclass”, which in the given example are RBU and ABS, respectively.

Possible reclassifications are shown in Fig. 1: all targets could be reclassified to ABS targets if enough information is provided in the NDC. NDCs stating actions and policies (type_main: NGT) that additionally provide estimates of the mitigation effects of their planned measures can be reclassified to absolute reductions against BAU (type_reclass: ABU). In some NDCs, targets are given as different types (e.g. relative reduction compared to BAU but also stated compared to a base year), and it is not always clear which are the “official” targets, leaving room for interpretation. This uncertainty could easily be reduced in future NDCs by clear communication. The double classification not only provides important information on which countries include quantification data in their NDCs, but is also used in NDCmitiQ to quantify the targets primarily based either on emissions data from the NDCs (use type_reclass) or external data (use type_main).

Figure 1Scheme of GHG mitigation target types and possible reclassifications. All targets can be reclassified as ABS if enough numerical information is provided in an NDC. Additionally, information on the numerical data needed for a target quantification is given.

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Figure 2World maps presenting mitigation target types as stated in NDCs (a: type_main) and reclassified target types – depending on the available numerical information in the NDCs (b: type_reclass). In brackets: number of countries with a certain target type (countries that are part of the EU NDC are counted as single countries). NDC submissions until 17 April 2020.

Figure 3Aggregated time series of national baseline (non-mitigated) emissions for all countries with a certain NDC mitigation target type (for type_main and type_reclass). Emissions based on PRIMAP-hist v2.1 and downscaled SSP2 marker scenario (data source: Gütschow et al., 2019, 2020b, see Sect. 2.2). Emissions from LULUCF and bunker fuels are excluded (bunker fuels: international shipping and aviation; not attributed to individual countries under the United Nations Framework Convention on Climate Change (UNFCCC); Gütschow et al., 2021). A vertical line indicates the last year of historical data (2017). USA: classified as “No NDC”. NDC submissions until 17 April 2020.

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On a global scale, the reclassification of target types shows a large effect: only seven countries are classified as ABS for type_main, while the reclassification based on available emissions data in the NDCs increases this number to 98 (type_reclass), with many reclassified targets for American, African, and South-East Asian countries (Fig. 2). The aggregated emissions from countries with RBY base year targets did not change much over the past years (Fig. 3; number of countries with RBY as type_main: 58 and as type_reclass: 37, including all countries considered by the NDC of the European Union, counted as single countries). In recent years, the major share of global emissions with a clear emissions increase was caused by countries with REI emissions intensity targets, mainly due to the fact that India and China chose REI targets. For NDCs with REI targets, the reclassification of target types does not noticeably impact the global emissions share, pointing towards missing numerical data in the NDCs. The United States of America submitted a formal notification of its withdrawal from the PA to the United Nations on 4 November 2019 (Pompeo, 2019), which took effect on 4 November 2020. Therefore, emissions from the USA are counted towards “No NDC”, and the mitigation measures presented in their NDC are not considered in quantifications throughout this paper unless stated otherwise.

2.1.3 Quantification input per target type and country

Based on the studied set of NDCs, we give further insight into the data needed for the quantifications by target type and country (Fig. 4 and Table A3). Some of the required information must be provided in the NDC: base year; target year; relative or absolute reduction for RBY, RBU, REI, and ABU; absolute target emissions for ABS; absolute emissions intensity for AEI. For a clearly formulated target, the information on which gases and sectors are covered and the share of covered emissions (%cov) in the base and target years should additionally be given in the NDC, otherwise the covered share of emissions relies on assumptions or one's own estimates. The assumed baseline emissions and intensity reference in the base/target year should be stated in the NDC but can also be used from “external” sources. A large number of input data requirements does not necessarily imply higher uncertainty, as can be seen for RBY targets: most of it is “easy to get”, and historical estimates generally have lower uncertainties compared to projections (e.g. BAU emissions). Nevertheless, even RBY targets can be complicated when e.g. not targeting all emissions if no emissions were recorded for the base year or if the handling of LULUCF is not clear. Based on our assessment of NDCs, Fig. 4 contains an overview of the emissions, population, and GDP data needed to quantify the targets on a country level, together with the specific years and target types (type_main). One can also conclude the chosen base and target years from this overview, with the year 2030 being the most prominent target year.

Figure 4Reference emissions, population, and GDP data needed for the assessment of current NDCs (type_main). Per country with an NDC, markers indicate that for a certain year emissions (squares), population (circles), or GDP (triangles) data are needed. Crosses indicate target years for ABS and AEI, for which no further emissions data are needed for the quantification. Black boxes indicate data needed if only parts of the national emissions are covered. Only years for which information is needed for type_main are displayed (colour coded; NGTs: shaded red area). EU target: shown for single countries (e.g. Germany). Vertical dashed lines separate countries with different initial letters. NDC submissions until 17 April 2020.

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2.2 Emissions, population, and GDP data

If an NDC provides enough numerical information, the target quantifications can be based solely on the NDC data. However, if data are missing, for comparison purposes, and for the construction of emissions pathways, “external” data are needed.

Even though the required input varies substantially between Parties (Sect. 2.1.3), in NDCmitiQ we aim at a quantification in an automatic manner. Therefore, the input is time series of national emissions, population, and GDP, spanning the period of 1990–2050 and pre-processed in a similar way for all countries.

First, we present the external data currently available in NDCmitiQ (Sect. 2.2.1 and 2.2.2) and then introduce a data set of emissions time series constructed from the baseline emissions given in NDCs (Sect. 2.2.3). This data set can then be used for target quantifications and to derive mitigated emissions pathways. The presented emissions data generally follow the IPCC 2006 sectoral categorisation (IPCC, 2006), with a few additional categories following the PRIMAP-hist v2.1 nomenclature (Gütschow et al., 2016, 2019). The current implementation of NDCmitiQ is based on the global warming potentials (GWPs) of the IPCC Fourth Assessment Report (AR4; IPCC, 2007).

2.2.2 Emissions data from LULUCF

In the previous section, only emissions data that exclude contributions from LULUCF were discussed. However, for the quantification of mitigation targets, LULUCF emissions are often needed as well. LULUCF is “A greenhouse gas inventory sector that covers emissions and removals of greenhouse gas resulting from direct human-induced land use, land-use change and forestry activities” (UNFCCC, 2020c). As it is not always possible to distinguish the anthropogenic and natural parts of the land-related fluxes, estimating LULUCF emissions is more complex than for non-LULUCF sectors (Smith et al., 2014a). It is complicated to estimate mitigation effects by LULUCF activities, as gas fluxes depend i.a. on the age (distribution) of trees, which varies over time. LULUCF can further work as an emissions source or sink and can have high inter-annual variability (Fyson and Jeffery, 2019), and data have a high uncertainty (Roman-Cuesta et al., 2016). As a consequence of the stated problems, we distinguish between LULUCF and non-LULUCF emissions.

For LULUCF, emissions data availability is limited, with some data sources only providing a few data points, and as high inter-annual fluctuations are possible in the LULUCF emissions, reasonable gap filling is difficult. PRIMAP-hist v2.1 does not contain emissions from LULUCF “due to data availability and methodological issues” (data description document for Gütschow et al., 2019). Data scarcity and fluctuations also make it complicated to combine data sets, and estimates vary strongly between data sources (PIK, 2020).

To choose external national LULUCF emissions data for the target quantifications, several data sets of LULUCF emissions are analysed for available data in the following prioritised order: CRF 2019, CRF 2018, BUR 3, BUR 2, BUR 1, UNFCCC 2019, and FAO 2019 (abbreviations: Sect. A2). For a country, CRF 2019 data are used if available, else CRF 2018, and so on. Estimates of LULUCF emissions provided by Parties are chosen when possible (similarly to e.g. PRIMAP-hist v2.1 HISTCR for non-LULUCF emissions, Gütschow et al., 2019, or Fyson and Jeffery, 2019, for LULUCF emissions). As in Fyson and Jeffery (2019), we include FAO 2019 data, which are calculated using the IPCC methodologies and are based on country-reported data. However, their definitions and data coverage differ (Tubiello et al., 2015), and they “are not directly comparable with UNFCCC data” (Fyson and Jeffery, 2019). As we intend to work with complete time series in order to have complete emissions pathways up to 2030 or 2050, gap filling and extrapolation are applied (Sect. A2), neglecting to some extent the challenges with LULUCF emissions described above. Our projections are generally based on constant extrapolation of the average 2010–2017 emissions (more details in Sect. A2).

Globally, in 2017 LULUCF was an estimated net sink of −2.1 Gt CO₂ eq. (GWP: AR4), with the estimate based on data that we chose from different sources on country level (prioritisation as above and details in Sect. A2). In Fig. 5, the distribution of global LULUCF estimates based on all possible combinations of data source prioritisations is shown for several time periods together with the global aggregates resulting from the three prioritisations implemented in NDCmitiQ. Regarding the distribution, one has to consider that estimates are biased towards country-reported data, as only one FAO version is included in this assessment. In comparison to the −2.1 Gt CO₂ eq., in all non-LULUCF sectors a total of 47.6 Gt CO₂ eq. was emitted in 2017 (based on PRIMAP-hist v2.1 HISTCR, excluding LULUCF and bunker fuels). The aggregated 2030 estimates of net LULUCF emissions (−2.2 Gt CO₂ eq.) are in line with the estimate by Fyson and Jeffery (2019) (−2.0 Gt CO₂ eq. yr⁻¹). If FAO 2019 is chosen as the primary data source, the global 2017 aggregate is an emissions source of +3.4 Gt CO₂ eq. instead of a global sink. As pointed out in Fyson and Jeffery (2019), the choice of the LULUCF data source has considerable effects on the best estimate for the year 2030 in some cases, and the higher global aggregate of +3.4 Gt CO₂ eq. (2017) when prioritising FAO data is in line with their estimate based only on FAO data (3.3 Gt CO₂ eq. yr⁻¹, 2004–2014, further comparisons in their study).

Figure 5Distribution (percentiles) of global Kyoto GHG LULUCF emissions for different orders of data source prioritisation and time periods. Lines show the percentiles of the emissions for all 5040 possible combinations of the considered data sources. Per period, the prioritisations implemented in NDCmitiQ are shown on the right (checking data availability on country level; default: “Prio CRF”). 2018–2050 estimates are similar to the 2010–2017 average per default (using a linear extrapolation of 2010-2017 averages).

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2.3 Share of emissions covered by NDCs

In the assessed set of NDCs, not all mitigation targets cover the total of national emissions. To estimate the uncertainty in target emissions resulting from different assumptions about the share of covered emissions (%cov), and for comparison purposes, two options are implemented in NDCmitiQ: use %cov=100 % or estimates of %cov that are based on the stated targeted sectors and gases, as described in the current section. Additionally, estimates of %cov indicate which countries have room to improve their coverage in an updated NDC.

With Article 4.4 of the Paris Agreement (UNFCCC, 2015), Parties to the PA agreed on the following: “Developed country Parties should continue taking the lead by undertaking economy-wide absolute emission reduction targets. Developing country Parties should continue enhancing their mitigation efforts, and are encouraged to move over time towards economy-wide emission reduction or limitation targets in the light of different national circumstances.” To reduce uncertainties and increase transparency, the targets' scope should be defined in NDCs in terms of covered sectors and gases and in numerical values of %cov in the historical base year (if needed) and the target year. It should be clear which emissions are targeted by mitigation actions or stay “untouched” and are intended to develop under a business-as-usual pathway, but not all Parties clearly communicated this information.

We assessed the NDCs for information on the covered sectors and Kyoto GHGs to estimate %cov, focusing on the main sectors Energy, IPPU, Agriculture, Waste, Other, and LULUCF, the single gases CO₂, CH₄, N₂O, SF₆, and NF₃, as well as the gas baskets of HFCs and PFCs. In all NDCs we could find some information on targeted sectors – not always clearly stated, however – and not all NDCs include information on the covered gases, leaving room for interpretation (unclear cases for sectors: 38 NDCs, and for gases: 27 NDCs). The rules to determine %cov for the national emissions excluding LULUCF are presented in Sect. A5 (results: Sect. 3.1). In general, for years up to 2017, %cov is derived from the PRIMAP-hist emissions data per sector and gas combination, while estimates for later years are either based on a constant extrapolation of recent %cov or on the correlation between national total and covered emissions. Regarding LULUCF emissions, the applied rule is simple: if the sector is assessed to be covered, its total emissions are assumed to be covered (not taking into account the contributions of the different gases relevant for LULUCF emissions: CO₂, CH₄, N₂O).

2.4 Target emissions and emissions pathways

In order to quantify the Parties' targets, we assessed all NDCs regarding their target types, target years, conditionality and range, covered sectors and gases, and provided emissions. National target emissions are calculated for each target year, conditionality, and range. NDCs include either or both unconditional and conditional targets (“conditionality”), where mitigation actions are conditional upon, for example, international financial support or technology transfer. Some Parties decided to give a range rather than an exact target value (e.g. “unconditional reduction of 26 %–28 %”), which we treat here as “best” and “worst”, meaning more and less ambitious.

Section 2.4.1 contains information on how we deal with targets that include contributions from LULUCF, how we derive target emissions excluding LULUCF in these cases, and why a separation into emiTarget_inclLU and emiTarget_exclLU is useful. To analyse whether the pledges put us on track to limit global warming to 1.5–2 ^∘C, regional or global emissions pathways are needed. Therefore, national emissions pathways that are consistent with the NDC targets for the single target years must be constructed and aggregated. The methodology and options for pathway creation implemented in NDCmitiQ are explained in Sect. 2.4.2.

Target includes LULUCF

• Target types ABS, AEI, and ABU: use the ABS target as emiTarget_inclLU or calculate emiTarget_inclLU from AEI (multiplication by IntensityRef_tarYr) or from ABU (reduction of the BAU emissions in the target year by the given absolute reduction).

• Target types RBY, REI, and RBU.

  • We assume the same mitigation effort in all sectors and apply the same relative reduction to all sectors unless stated differently in the NDC.

  • ${\mathrm{emiBL}}_{{\mathrm{onlyLU}}_{\mathrm{refYr}}}>\mathrm{0}$ (net source): LULUCF treated as the other covered sectors and reduced by a given relative reduction.

  • ${\mathrm{emiBL}}_{{\mathrm{onlyLU}}_{\mathrm{refYr}}}<\mathrm{0}$ (net sink): sink is left as is. We chose not to strengthen the sink (attention when choosing to strengthen the sink: applying a relative reduction to negative values would weaken the sink potential). LULUCF emissions and targets are connected to uncertainties (Fyson and Jeffery, 2019). Further, stringent non-LULUCF emissions reductions are of major importance for climate neutrality (IPCC, 2018a), and carbon sequestration in vegetation and soils comes with a time component (saturation of mitigation potential; created enhanced carbon stocks are reversible and non-permanent, Smith et al., 2014b; vegetation or tree age, Pugh et al., 2019, Köhl et al., 2017, Stephenson et al., 2014, and Carey et al., 2001).

• Calculate emiTarget_exclLU by subtracting the projected LULUCF emissions:
  ${\mathrm{emiTarget}}_{\text{exclLU}}={\mathrm{emiTarget}}_{\mathrm{inclLU}}-{\mathrm{emiBL}}_{{\mathrm{onlyLU}}_{\mathrm{tarYr}}}$.

• If for a country a resulting emiTarget_exclLU becomes negative, which could only be achieved with negative emissions technologies and reliable sequestration, we use a second approach for LULUCF.

  • Split the absolute reduction in the target year against the baseline emissions ABU_inclLU into ABU_exclLU and ABU_onlyLU, depending on the respective contributions of ${\mathrm{emiBL}}_{{\mathrm{onlyLU}}_{\mathrm{tarYr}}}$ and ${\mathrm{emiBL}}_{{\text{exclLU}}_{\mathrm{tarYr}}}({\mathrm{ABU}}_{\text{exclLU}}=({\mathrm{ABS}}_{\mathrm{inclLU}}-$
    ${\mathrm{emiBL}}_{{\mathrm{inclLU}}_{\mathrm{tarYr}}})\cdot \frac{{\mathrm{emiBL}}_{{\text{exclLU}}_{\mathrm{tarYr}}}}{{\mathrm{emiBL}}_{{\mathrm{inclLU}}_{\mathrm{tarYr}}}})$.

  • Reduce the baseline emissions ${\mathrm{emiBL}}_{{\text{exclLU}}_{\mathrm{tarYr}}}$ by the corresponding ABU_exclLU.

• ABU targets: if the absolute reduction exceeds the assumed BAU emissions ${\mathrm{emiBL}}_{{\text{exclLU}}_{\mathrm{tarYr}}}$, the then negative target is set to emiTarget_exclLU=0 Mt CO₂ eq.

For several countries, the Climate Action Tracker uses a somewhat comparable approach to derive target emissions excluding LULUCF from mitigation targets that include LULUCF – given in the NDC or calculated by applying the given reductions to the reference year emissions that include LULUCF: the projected LULUCF target year emissions are subtracted from the target emissions that include LULUCF (see e.g. CAT, 2019a, b, for Australia and Brazil).

Table 2Options for emissions pathway calculations for countries with a mitigation target but without a target for the year 2030. In this example are the country targets for a 20 % reduction compared to BAU in 2025. The relative differences from the baseline emissions from 2020 to 2025 evolve linearly from 0 % to −20 %. After 2025, either the relative difference from the baseline is kept at the level of the last target year (default: option “constant percentages”, −20.0 % in this example, in italics), the absolute difference from the baseline emissions is kept constant (option “constant difference”, here: −4.4 Mt CO₂ eq.), or the absolute emissions are kept at the level of the last target year (option “constant emissions”, here: 17.6 Mt CO₂ eq., in italics). The baseline emissions follow the chosen baseline scenario.

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When quantifying all available targets, based on the downscaled marker scenario for SSP2 (dmSSP2), with NDC emissions data prioritised if available and an assumed coverage of 100 %, the “second approach for LULUCF” is needed for seven countries, and for Tonga the ABU_exclLU exceeds the baseline emissions ${\mathrm{emiBL}}_{{\text{exclLU}}_{\mathrm{tarYr}}}$ (type_main: NGT, type_reclass: ABU).

2.4.2 Emissions pathways

One of our main goals is to construct global emission pathways up to 2030, consistent with the NDC mitigation targets. For the aggregation, rather than quantified target emissions for single years, time series are needed, defined by interpolation between target years and extrapolation after the last target year if it is before 2030. Pathway calculations start in 2021, the first year after the Kyoto Protocol period and the first year of the PA period (before 2021: baseline emissions), and a linear increase or decrease in the relative difference from the baseline is assumed between target years, while the relative difference is kept constant after the last target year (Table 2). If the baseline increases, a constant relative difference results in an increasing mitigation pathway but with a smaller growth rate. To prevent the pathway from increasing a lot, the inter-annual baseline growth rates are used if the target in the last target year is above the baseline. Second and third non-default options for the calculation of national pathways are implemented in NDCmitiQ: a constant absolute difference from the baseline emissions or constant emissions after the last target year. For countries that indicated an emissions peak year, such as China, the calculated pathway is used in case it declines starting in the peak year or earlier, otherwise the intended trajectory is approximated by keeping the national emissions constant after the peak year. Emissions baselines currently available in NDCmitiQ are either the constructed NDC emissions pathways (Sect. 2.2.3) or the downscaled SSP marker scenarios (Sect. 2.2.1).

We aim for globally aggregated emissions pathways per conditionality and range (in decreasing ambition order: unconditional best and worst, conditional best and worst). If for the current target type, conditionality, and range values for emiTarget are available for several years, all are used to construct the current pathway. Additionally, if an unconditional but no conditional target is stated for a certain year, we consider the unconditional target for the conditional pathway as well (if a target is available for X in year 20xx but not for Y, also use X for the Y pathway, with X and Y in this order: unconditional best and unconditional worst, conditional best and conditional worst, and unconditional best and conditional best and worst).

For countries without quantifiable mitigation targets, the baseline emissions are assumed to be unconditional/conditional pathways. Furthermore, if a country only has conditional targets, the baseline is used as unconditional pathways. However, in some of these cases the conditional pathway is worse than the baseline, which would result in a worse conditional than unconditional pathway. As this does not seem logical, the conditional (worst) pathway is also used as unconditional best and worst pathways if this happens. An option to disable this method and use the baseline as unconditional pathways nevertheless is implemented in the tool (not default).

Table 3Share of emissions covered by NDCs. All values exclude emissions from LULUCF and bunker fuel emissions. All values are based on PRIMAP-hist v2.1 HISTCR emissions data (GWP AR4). “NDCs (Adapt.)”: number of countries that stated (more or less explicitly) that they are covering a certain sector or gas (in brackets: adapted value based on rules given above; EU: counting single countries). The given shares represent the part of emissions per sector plus gas combination that is estimated to be covered (relative to the global emissions from this sector–gas combination) and the total per sector or gas (“Share”; e.g. an estimated 80.7 %/82.8 % of global energy/energy CO₂ emissions are covered). Countries with NGT targets that state covered sectors and gases are included in the presented numbers. Complementary information is provided in Table A6. NDC submissions until 17 April 2020.

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Figure 6(a) Share of Kyoto GHG emissions assumed to be covered by a country's NDC mitigation target (for 2017). (b) Average trend of %cov 2010–2017 in % yr⁻¹ (based on linear regression to national %cov 2010–2017). All values are based on PRIMAP-hist v2.1 HISTCR emissions, following GWP AR4 and excluding emissions from LULUCF and bunker fuels. NDC submissions until 17 April 2020.

The national pathways are finally aggregated to regional/global emissions pathways, per conditionality and range. Per country, one target type is prioritised for the aggregation, which can be type_main or type_reclass (Sect. 2.1.1). Further options to modify the target or pathway calculations are implemented in NDCmitiQ. These non-default options that can be chosen for comparison runs and sensitivity analyses are presented in Sect. A6 and consist of the following options: “targets only for countries X, Y, Z”, “prioritised target types”, “countries without unconditional targets and what if baseline is better than the conditional targets”, “countries with targets above baseline and whether to use the baseline in these cases”, “set coverage to 100 %”, and “strengthen targets”. As we do not perform policy analyses, and for comparison purposes, the option “use Climate Action Tracker estimates for countries X, Y, and Z if available” can be chosen. We gathered the target estimates provided by the CAT (estimates exclLU with GWP from AR4; source: CAT, 2020) for all countries with assessments available. From these point values, we construct national mitigated emissions pathways in the same manner as described above, which can then be used for the global aggregates instead of NDCmitiQ target quantifications.

3.1 Share of total emissions covered by NDC

On a country level we retrieved information on the sectors and gases covered (Fig. A1) and estimated the corresponding covered share of emissions (Table 3 and Fig. 6; excluding LULUCF). The Energy sector and the GHGs CO₂, CH₄, and N₂O are considered in many NDCs (mentioned by 193, 174, 157, and 147 countries, respectively, countries that are part of the EU target counted as single countries). Additionally, for a more complete picture in reference to countries' emissions, Sect. A7 includes information on sectoral and per-gas shares together with emissions and trend maps. In total, we assess 77 % of 2017 global emissions to be emissions from sectors and gases covered by the studied NDCs. An estimated 1 % was emitted by countries without an NDC plus about 14 % by the USA. This leaves 9 % of uncovered emissions from countries with an NDC. Including the USA would increase the covered share significantly to 91 %. Article 4.4 of the PA (UNFCCC, 2015) asks developed countries to implement economy-wide absolute emission reductions, which is reflected in the high %cov for developed countries; 53/75 countries are assessed to cover less than 90 %/99 % of their emissions, including China and India, which contributed 27 % and 6 % of 2017 emissions, and for 43 countries emissions from the uncovered sectors and gases have gained in importance over recent years (negative trend of %cov). The influence of %cov on India's target emissions and on a global scale is further discussed in Sect. 3.3 and 3.4.

We do not consider the covered share of emissions for ABS and AEI targets, which can introduce an uncertainty. While the 99 countries classified as ABS and AEI targets for type_reclass (absolute emissions or absolute emissions intensity, excluding the USA) are responsible for one-fourth of global emissions (2017: 24.9 %, 2030: 25.2 % following dmSSP2, excluding LULUCF and bunker fuels), the uncovered share of emissions for these countries is only 0.4 % of 2017 global emissions, and the uncertainty introduced is low.

3.2 Emissions data from the NDCs vs. dmSSPs

We retrieved emissions data from all NDCs with available data and classified them as including or excluding LULUCF to use the emissions in the target quantifications. In Table 4 the emissions from NDCs are compared with external baseline emissions data (before 2017: national emissions from PRIMAP-hist v2.1 HISTCR (exclLU); after 2017: downscaled SSP2 marker scenarios (dmSSP2, exclLU); and LULUCF emissions data as described in Sect. 2.2). Table A9 additionally includes information on dmSSP1–5, the number of Parties from which we could extract emissions data from their NDC, and these countries' global emissions share (for dmSSP2). In all the historical years in Table 4, the aggregated NDC baseline emissions are lower than the comparison baselines. To some degree, lower values can be connected to a discrepancy between the sectors and gases that are included in the provided data, which are not always clearly stated in the NDCs (comparison data: national totals). Even though the estimated baseline emissions for 2025 under the NDCs are in the range of the dmSSPs, they are at the very upper edge – dmSSP5 – which is the most extreme pathway with the strongest emissions increase. For 2030, the aggregated NDC baseline emissions are even higher than dmSSP5, with data from 25 countries available for the assessment of emissions exclLU and the countries representing 5.4 % and with data from 42 countries available for the assessment of emissions inclLU and the countries representing 9.8 % of global emissions in 2030 under dmSSP2 (middle-of-the-road scenario). The emissions estimates provided in NDCs for 2030 are +34.8 %/+97.5 % or +1.3/+6.1 Gt CO₂ eq. higher than dmSSP2 for the corresponding countries (for exclLU/inclLU). Targets with reductions relative to business-as-usual emissions are higher, the higher the expected BAU emissions are. If an unrealistically strong increase in BAU emissions is assumed, it results in higher and easier-to-reach target emissions. Another incentive for countries to have high baselines is that they can reflect a strongly growing economy. Using independent, country-specific comparison data is helpful for putting national estimates into perspective. However, for the purpose of quantifying the NDCs' mitigation targets, it is most helpful to use the BAU emissions provided in the Parties' documents, if available, as this is most consistent with what the country has pledged.

Figure 7India's historical emissions 1990–2017. Panels (a)–(e): emissions per main sector, split into the contributing Kyoto GHGs (CO₂, CH₄, N₂O, and, in the case of IPPU, additionally F-gases as a total). Additionally, the share of per-sector and per-gas emissions in 2017 is presented compared to the national totals (Kyoto GHG excluding LULUCF; as text below the figure). Please note the different vertical axis limits. Data source: PRIMAP-hist v2.1 HISTCR. The raw country-reported data (UNFCCC 2019) are additionally presented as squares (no data available for the different F-gases).

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Figure 8Emissions (a) and GDP (b) time series for India. (a) Emissions from the downscaled SSP2 marker scenario (solid blue line), the corresponding covered share of emissions (dotted blue line), and LULUCF emissions (dotted green line, UNFCCC 2019; interpolated and extrapolated). NDC GHG mitigation target emissions (33 %–35 % reduction in emissions intensity per unit of GDP in 2030 compared to 2005) are shown for 2030 per SSP marker scenario. Quantifications based on an estimated coverage of 74 %/86 % in 2005/2030: “estimated coverage”. Based on an assumed coverage of 100 %: “100 % coverage”. All emissions exclude LULUCF (besides “LULUCF”). (b) GDP time series for the SSP marker scenarios (unit 2011 GK$: 2011 Geary–Khamis international dollars).

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3.3 India's emissions intensity target: quantification and challenges

As an example of national target quantifications with NDCmitiQ, we present an analysis of parts of India's NDC. We show India as an example because several points made below are not specific to India's NDC but are of general interest and concern. In its NDC, India presents a GHG mitigation target of a 33 %–35 % reduction in emissions intensity per unit of GDP, with the chosen base and target years being 2005 and 2030, respectively (Republic of India, 2016). As India has only reported emissions data to the UNFCCC for 1994, 2000, and 2010, no data were reported for the chosen base year 2005, and the 1994 data were reported before the 1996 IPCC guidelines for national GHG inventories (IPCC, 1996) were introduced. While developed countries (Annex-I Parties) are obliged to submit annual GHG inventories to the UNFCCC, India, as a developing country, is not. Under the Katowice Climate Package (UNFCCC, 2019d), however, with self-determined flexibility, “Each Party shall report a consistent annual time series starting from 1990; those developing country Parties that need flexibility in the light of their capacities with respect to this provision have the flexibility to instead report data covering, at a minimum, the reference year/period for its NDC under Article 4 of the Paris Agreement and, in addition, a consistent annual time series from at least 2020 onwards” (II.E.3.57).

Table 5Quantification of India's emissions intensity target (REI). Input data for dmSSP2 and target equations (emiTarget_inclLU and emiTarget_exclLU) for 100 % coverage and estimated coverage (2005/2030: 74 %/86 %). LULUCF baseline emissions in 2005 negative; therefore, ${\mathrm{emiBL}}_{{\mathrm{onlyLU}}_{\mathrm{2005}}}$ is not reduced or strengthened. Presented values contain rounding artefacts (results based on values with higher precision than the shown input data).

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India does not clearly state the covered gases and sectors but rather gives measures for different sectors. We assessed the covered sectors to be Energy, IPPU, Waste, and LULUCF, and as no information is provided on the considered gases, CO₂, CH₄, and N₂O are assumed to be covered, resulting in an estimated %cov of 74 %/86 % in 2005/2030 (excluding LULUCF compared to the dmSSP2 baseline emissions). India's total emissions in 2017 were 6.3 % of global Kyoto GHG emissions (excluding LULUCF and bunker fuels, based on PRIMAP-hist v2.1 HISTCR AR4). Figure 7 shows the importance of the covered CO₂ emissions from the Energy sector (71 % of India's emissions in 2017) and the steady and steep increase over recent years. In the fiscal year that ended in March 2020, India's CO₂ emissions fell by more than 1 %, for the first time in almost 4 decades, and this decline is not only caused by the Covid-19 lockdown, as it already started in early 2019 (Myllyvirta and Dahiya, 2020).

The target quantifications are based on the external data described in Sect. 2.2, as, besides an estimate of 2030 GDP, we did not find the necessary data in India's NDC. Together with the corresponding baseline emissions and GDP scenarios, quantifications based on dmSSP1–5 are compared, once for an assumed 100 % coverage and once based on the estimated %cov (Fig. 8). There are at least three interesting aspects.

• i.

  The 2030 mitigation targets lie above the baseline emissions for all dmSSPs, mainly caused by the projected growth in GDP. India would overachieve the intensity target if the assumed baseline emissions were met, and there seems to be room for a more ambitious target than a 33 %–35 % reduction in emissions intensity per unit of GDP. The GDP-based downscaling of regional SSP emissions scenarios suggests that the targets could be more stringent. For the middle-of-the-road scenario dmSSP2, India's GDP is assumed to increase by a factor of approximately 5.3 from 2005 to 2030. India provides an estimate of its 2014 and 2030 GDP at 2011–2012 prices (in trillion): USD 1.69 and 6.31 (Republic of India, 2016). This would constitute an increase by a factor of 3.7 from 2014 to 2030 and with linear approximation a 5.8 times rise from 2005 to 2030. Assumed linearity probably leads to an overestimation, and the factor is in line with the GDP growth factor of 5.3 from dmSSP2. The assumed baseline emissions also affect these findings, as, if we would assume significantly higher baseline emissions in 2030 than presented while not changing any of the remaining assumptions, the target emissions would no longer be above the increased baseline emissions.

• ii.

  For the different dmSSPs, the order of targets from highest to lowest is dominated by the GDP growth factor and not by the increase in baseline emissions (more details in Sect. 3.4).

• iii.

  The targets with assumed 100 % coverage are higher than with estimated coverages of 74 % in 2005 and 86 % in 2030 (details below).

The unexpected behaviour of the targets with an assumed coverage of 100 % being higher than the comparison with estimated %cov is, in a mathematical sense, a combination of two aspects: (i) the high projected GDP growth rate and (ii) the increase in the share of covered base year emissions (example for dmSSP2: equations and estimates in Table 5). When %cov increases, ${\mathrm{emiBL}}_{{\mathrm{COV}}_{\mathrm{2005}}}$ and therefore the first term of the equation for emiTarget_inclLU increase, while the last term (${\mathrm{emiBL}}_{{\mathrm{notCOV}}_{\mathrm{2030}}}$) decreases and reaches 0 Gt CO₂ eq. for 100 % coverage. For India's target, the rise in the first term is not compensated for by the decline of the last term of the equation, leading to the observed higher target emissions for 100 % coverage. However, several aspects would work against this behaviour. If the projected GDP growth rate was significantly lower or the downscaled 2030 baseline emissions were significantly higher (GDP growth factor below 1.7 or reference emissions higher than 12 Gt CO₂ eq. in this example), the behaviour would not occur, and moving towards 100 % coverage would result in lower target emissions that would lie below the 2030 baseline (REI_RBY with a growth factor of 1: same as RBY target). Furthermore, and importantly, if the target value (relative reduction in emissions intensity per unit of GDP) itself was strong enough and not weaker than the baseline assumptions, this behaviour would not occur, and at the same time the target emissions would not exceed baseline emissions (with numbers as in Table 5: with a 53 %/59 % reduction the target with estimated/100 % coverage, respectively, would be below the baseline, and with a 78 % reduction the 100 %-coverage target would be below the estimated-coverage target). No information on the part of national GDP corresponding to the different emissions sectors is included in the assessment of the covered share of emissions. Doing so can change the results, and nations should consider the emissions intensities of added sectors when updating targets to expand the scope of the pledges.

The results should not be misunderstood as a motivation not to move towards an economy-wide target and include all Kyoto GHGs and sectors in the mitigation target, as aimed for by the PA. Our findings rather show that while doing so, in some cases Parties need to assess whether they have to increase their reduction level simultaneously or move to a different target type overall to ensure the ambition is ramped up rather than lowered and point towards quantification challenges and target uncertainties. For a few other countries our results also show higher target emissions when shifting towards a 100 % coverage compared to the estimated coverage. The countries for which this happens for all five dmSSPs are India (REI), Uzbekistan (REI), Botswana (RBY), the Democratic Republic of the Congo (RBU), and Tajikistan (RBY). China's target (REI) is also higher for a 100 % coverage but only for dmSSP1 and dmSSP5, the scenarios with the highest projected GDP growth and smallest growth factor for national emissions per unit of GDP.

The coverage for India's mitigation target is prone to uncertainty, as it is not clearly communicated in the NDC and leaves room for interpretation. Based on India's NDC, we did not assess the Agriculture sector to be covered. The CAT (2019c) also assumes the Agriculture sector to be excluded based on the information on the 2020 pledges, “even though not mentioned in the NDC”, and Climate Watch (2020a) and the World Resources Institute (2020) state the “Sectors covered” as “Not specified; various sectors mentioned for mitigation and adaptation strategies such as energy, industry, transportation, agriculture, forestry, waste”. Consistent with our assessment of India's NDC, the NDC Explorer (Pauw et al., 2016) states “Not indicated” for “Mitigation focus areas: agriculture”, and for “Reducing non-CO₂ gases” it indicates “Considered (CH₄, N₂O)”. As “GHG coverage”, the World Bank (2016) states “n/a”.

Another source of uncertainty is the conditionality of the target. India's NDC states “To mobilize domestic and new & additional funds from developed countries to implement the above mitigation and adaptation actions in view of the resource required and the resource gap” (Republic of India, 2016), and we classify it as unconditional even though it is unclear to us whether parts are conditional. Contrary to our assessment and the CAT (2019d), Climate Watch (2020a) and the World Resources Institute (2020) denote India's target as conditional.

Figure 9(a, b) Global baseline emissions for dmSSP1–5. Shaded areas: emissions pathways for dmSSP2 (in Fig. 10: “default” with estimated or assumed 100 % coverage for “prio NDCs” and “prio SSPs”). (a) NDC submissions until 17 April 2020; (b) submissions until 31 December 2020. All emissions in (a) and (b) exclude LULUCF and bunker fuels. (c, d) Global pathways for the marker scenarios dmSSP1–5 baseline population and GDP. The range of mitigated 2030 emissions per SSP is given as a vertical line (0th (minimum), 10th, 50th (median), 90th, and 100th (maximum) percentiles of the data shown in Fig. 10).

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Based on quantifications under dmSSP2 and an assumed 100 % coverage, India's emissions target ranges between 6.3 and 6.5 Gt CO₂ eq. for emissions excluding LULUCF (6.0–6.2 Gt CO₂ eq. including LULUCF; AR4). With estimated coverage of 74 %/86 % for 2005/2030, the quantified emissions target ranges between 5.2 and 5.4 Gt CO₂ eq. for emissions excluding LULUCF (5.0–5.1 Gt CO₂ eq. including LULUCF). The CAT (2019d) estimates the unconditional emissions intensity target to be in the range of 6.0–6.2 Gt CO₂ eq. (excluding LULUCF, AR4). This value is a bit lower than our estimates when assuming a 100 % coverage. Climate Watch (2020a) and the World Resources Institute (2020) give a wider range of 5.9–9.1 Gt CO₂ eq., not specifying whether these emissions include or exclude LULUCF. The exact reasons for the quantification discrepancies could not be assessed, but chances are higher that differences arise from assumptions of projected data than from historical data (LULUCF and non-LULUCF emissions, GDP). In the short term, India does not plan to raise the ambition of its NDC (Prakash Javadekar, minister of environment, forests and climate change: “The raising of ambition or ratcheting-up will arise only after a global stocktake in 2023.”, Gombar, 2020).

3.4 Global mitigation pathways

Some of the main outputs of NDCmitiQ are global emissions pathways consistent with the NDC GHG mitigation targets. Therefore, moving from example analyses of national targets and the underlying emissions data to global emissions, Figs. 9 and 10 show globally aggregated pathways resulting from a full implementation of the assessed targets from unconditional worst to conditional best and based on different input data and quantification options. First, the emissions data from the NDCs are prioritised (type_reclass), and second, the external time series are used (based on type_main). In the following, the mitigated emissions pathways under the five SSPs are named “NDCSSP”, while the baselines are named “dmSSP”.

Figure 10(a, b) Estimates of mitigated emissions for 2030 based on the studied NDCs (a: NDCs submitted until 17 April 2020, b: 31 December 2020). Vertical lines: range of unconditional/conditional best/worst targets (conditionality and range indicated by squares). Results based on the following options (altering one option per quantification): “(1) default”: LULUCF data prioritisation CRF, BUR, UNFCCC, and then FAO and constant relative difference from baseline after last target year and conditional pathway used as the unconditional pathway if the baseline is below the conditional pathway (and the country has no unconditional target) and quantified target used even if it lies above the baseline; “prio NDCs”: prioritising NDC emissions data, based on type_reclass and the emissions described in Sect. 2.2.3; “prio SSPs”: based on type_main and the dmSSPs. (2) constant absolute difference from baseline after last target year; (3) constant emissions after last target year; (4) baseline emissions used as unconditional pathway if country has no unconditional target; (5) baseline emissions used if target lies above baseline; (6) FAO as first rank of LULUCF source prioritisation; (7) CAT estimates used if available. Emissions in (a)–(d) exclude LULUCF and bunker fuels.

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First, we analyse the impact of the targets' conditionality and different scenarios for emissions, population, and GDP on the mitigation pathways. The higher aggregated emissions data from the NDCs for 2030 compared to the dmSSPs (Sect. 3.2) lead to higher global baseline emissions (difference between “NDC and SSP baselines”: dmSSP1–5 between 1.6 and 2.7 Gt CO₂ eq. AR4) and consequently result in higher quantified mitigated emissions (NDCSSP1–5).

With our tool we confirm findings by Benveniste et al. (2018) that “the main sources of uncertainty is the range of ambitions given in NDCs, and the uncertainty on the economic growth of countries who expressed their target in terms of intensity”. In the presented quantifications for submissions until 17 April 2020, the conditionality range is 2.8–6.0 Gt CO₂ eq. for all values displayed in Fig. 10 (difference between unconditional worst and conditional best; 31 December 2020: 2.9–6.1 Gt CO₂ eq.) but with little difference between the conditional worst and best emissions.

For the different dmSSP scenarios, we observe a strong influence of the projected GDP on the global mitigation results. NDCSSP5 (fossil-fuelled development) has by far higher global emissions than NDCSSP1–4, which exceeds the difference between the dmSSP1–5 baseline emissions and results from the combination of high projected emissions baselines and GDP growth. NDCSSP1–4 are approximately in the same range, with the lowest mitigated emissions for NDCSSP2 (SSP2: often used middle-of-the-road scenario) even though its emissions baseline is not the lowest, and NDCSSP3–4 have the highest quantified mitigation impacts due to the lower GDP projections. We expect that the GDP effect would be less eminent if the energy mix targets for the large emitters China and India were included in the assessment, as the CAT assessed their energy targets to be the more stringent targets (CAT, 2020b, 2019d).

Only eight countries are assessed to have REI targets (relative reduction compared to emissions intensity), but amongst them are China and India. The REI countries represented 16 % of global emissions in 1990, but their share almost doubled by 2017 (35 %) and is projected to further increase to 38 % by 2030 (dmSSP2). Only the Dominican Republic chose its population and not its GDP as an emissions intensity reference. The influence of the underlying GDP data demonstrates the importance of reasonable estimates of GDP to quantify the mitigation targets. The results are also in line with Rogelj et al. (2017), who found the dominant driver of uncertainty in estimates of NDC mitigation levels on a global scale to be the potential variation in the underlying socioeconomic assumptions.

The global aggregates for the mitigated pathways are generally below the corresponding baseline emissions scenarios. However, for NDCSSP1, with the lowest baseline emissions but one of the highest GDP projections, this is not true (for unconditional worst). Higher mitigated than baseline emissions can result from all assessed target types excluding RBU and ABU. Reductions compared to BAU emissions in the target year will be below the baseline as long as the given NDC values are real reductions. Out of the presented runs, NDCSSP1 has the highest number of countries (23–29 countries for quantifications with 100 % or estimated coverage and type_main and type_reclass) for which the worst mitigated pathways are above the countries' baseline emissions.

The effects of different assumptions of underlying LULUCF baseline emissions on the target quantifications on a global scale are shown in Fig. 10. All emissions exclude LULUCF, but in many cases the targets exclLU have to be derived from targets inclLU (countries including LULUCF: Fig. A1), and therefore the LULUCF baselines often affect exclLU targets. As a default, LULUCF data from NDC, CRF, BUR, UNFCCC, and then FAO are prioritised. Prioritising FAO over CRF data leads to lower target emissions on a global scale, even though the global LULUCF emissions estimate for 2030 is +3.4 Gt CO₂ eq. if FAO has the highest prioritisation, while it is a net sink of −2.2 Gt CO₂ eq. for prioritisation of CRF data. This behaviour is not connected to certain target types, and we could not find a general pattern in the per-country changes that leads to this decrease in target emissions on a global scale. Fyson and Jeffery (2019) focused on the LULUCF component in NDCs and studied uncertainties due to NDCs' LULUCF contributions in a more refined way. They found that the ambiguity in the emissions reductions due to land-based activities results in ∼ 3 Gt CO₂ yr⁻¹ uncertainty in 2030, which is larger than their estimated total anthropogenic land use sink of −2 Gt CO₂ yr⁻¹ in 2030 and larger than the influence our choice of underlying LULUCF data has on the quantified targets (0.8 Gt CO₂ eq. in global mitigated emissions exclLU for CRF vs. FAO).

To analyse their uncertainties, different options for the target and pathway calculations are implemented in NDCmitiQ. Focusing on the time period up to 2030, the effects of changing the options are generally smaller than the impact of conditionality and input data. For two options, the upper limit of the range between unconditional worst and conditional best estimates is reduced, while the lower limit is unchanged: option (4) using the baseline emissions as the unconditional pathway instead of the conditional pathway even if the baseline is lower than the conditional pathway (does not affect conditional pathways), or option (3) keeping the absolute emissions constant after a country's last target year instead of the relative difference from the baseline (only affects countries with a last target year before 2030). Especially option (3) has an increasing effect when assessing years after 2030, though, as for many countries the last target year is 2030. What has been observed for India's target in Sect. 3.3 – higher target emissions for 100 % coverage vs. estimated %cov – is seen on a global scale as well, as India and China have high national emissions and are amongst the few countries for which the target quantifications show this behaviour (China: only for dmSSP1 and 5). Option (5), in which the baseline is used instead of the quantified target if the country's mitigated pathway lies above the baseline in 2030, shows a relatively strong effect. Using this option in comparison with the default gives an impression of the countries' potential surplus emissions, an overachievement of their targets that some countries intend to sell internationally. For option (7), in which CAT estimates are used for all countries with data available instead of NDCmitiQ estimates, most of the quantification options only affect the remaining countries and the growth rates of the constructed country-level pathways, while the target estimates remain the same.

Depending on the quantification options and underlying dmSSP scenarios, global mitigated emissions under the NDCs in 2030 are estimated to range between 48.9 and 56.1 Gt CO₂ eq. for 2030 (10th/90th percentiles for unconditional worst and conditional best estimates for dmSSP1–4, with median 51.8 Gt CO₂ eq.; AR4, excluding LULUCF and bunker fuels). We do not consider dmSSP5 for these global estimates. For SSP5, the fossil fuel mobilisation in this high-emissions scenario is unprecedented (Bauer et al., 2017), and as pointed out by Hausfather and Peters (2020) from the SSPs, “SSP4-6.0 and SSP2-4.5 scenarios agree much better with near-term cumulative emissions than the SSP5-8.5 scenario” (here, the second number indicates a representative concentration pathway (RCP, van Vuuren et al., 2011) in the SSP–RCP framework). If we considered dmSSP5 as well, the range would amount to 49.0–58.0 Gt CO₂ eq. with a median of 53.1 Gt CO₂ eq. The presented estimates are based on NDCs submitted until mid-April 2020 unless stated otherwise. We recently also assessed the NDC updates up to the end of December 2020, which decreases our estimates to 45.6–54.1 Gt CO₂ eq. with median 49.6 Gt CO₂ eq. Both the 7.2 Gt CO₂ eq. range (submitted December 2020: 8.5 Gt CO₂ eq.) and absolute values are lower than the 56.8–66.5 Gt CO₂ eq. yr⁻¹ estimates by Benveniste et al. (2018) for 2030 (90 % confidence interval; 9.7 Gt CO₂ eq. range; Table 6). However, adding to the difference is that their estimates include emissions from bunker fuels and probably LULUCF emissions, with “the share of international aviation and shipping in global emissions increas[ing] from 2.3 % in 2010 to 3.0 %–3.7 % in 2030” Benveniste et al. (2018). While they noted that essentially due to a range of GDP scenarios being considered instead of a single scenario the uncertainty range is larger than previous studies, the smaller range of 6.6 Gt CO₂ eq. yr⁻¹ for the SSP2 OECD scenario is comparable to other estimates. With 4.6 Gt CO₂ eq. our median range for dmSSP2 is smaller (submission December 2020: 4.6 Gt CO₂ eq.). For 2030, the United Nations Environment Programme (2019) found the global emissions for unconditional NDCs to be 56 Gt CO₂ eq. (54–60 Gt CO₂ eq.; median and 10th/90th percentiles, probably including LULUCF and bunker fuels), and, for conditional NDCs, 54 Gt CO₂ eq. (51–56 Gt CO₂ eq.). Our estimates that exclude LULUCF emissions and bunker fuels are 54.6 Gt CO₂ eq. for the upper edge (52.2–56.7 Gt CO₂ eq., unconditional worst) and 50.3 Gt CO₂ eq. (48.1–51.7 Gt CO₂ eq.) for conditional best, representing a larger range (submitted December 2020: see Table 6).

Table 6Comparison of mitigated global emissions for the year 2030 with United Nations Environment Programme (2019) and Benveniste et al. (2018). “Current study”: estimates based on submissions until 17 April 2020 or 31 December 2020 are presented in italic or bold font, respectively. Benveniste et al. (2018) and their Supplement: “share of international aviation and shipping in global emissions increase from 2.3 % in 2010 to 3.0 to 3.7 % in 2030”; “International aviation emissions for 2030 are approximated to lie within a range of 906 to 1200 Mt CO₂ yr⁻¹ […]. International shipping emissions are based on projections […] resulting in a range of emissions of 940 to 1200 Mt CO₂ eq. yr⁻¹ in 2030”. United Nations Environment Programme (2019): “[…] with international transport (aviation and shipping) representing around 2.5 % of GHG emissions [in 2018, excluding LUC]”.

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3.5 Other possible use cases

Additional use cases of NDCmitiQ and its output data include climate change impact assessments based on the global emissions pathways, calculation of mid-century targets, analyses similar to Fig. 10 but on a regional level with a refined view of target types or by changing several calculation options at a time to estimate interactions, effect of uncertainties in historical emissions, comparisons with the allowable carbon budget for the PA temperature goals, and estimation of end-of-century temperature rise.

One area of interest is the change in a country's mitigation targets and ambition level over time. Even though many assumptions and input data are required to run NDCmitiQ, it is possible to track progress on a country level. To do so, one can keep all settings unchanged but one. For example, we can run the tool with a defined emissions baseline for a certain country and then change this baseline while keeping the remaining input and settings the same, rerun the tool, and compare this country's resulting target estimates or mitigation pathway but also the changes on a global scale. The same is possible for i.a. a change in the relative pledged reduction or the target type. Also for this purpose, NDCmitiQ holds the option of defining a certain date up to which NDC submissions should be considered (“submissions_until”). When selecting 31 December 2020, per country the most recent NDC up to this date is considered. Therefore, with NDCmitiQ it is possible to track the global mitigation progress.

To estimate the global temperatures for the year 2100 based on NDC mitigation pathways, in comparison with pre-industrial times, the aggregated emissions pathways can be used in combination with additional tools. The emissions time series can be extended to 2100 using the pathway extension by Gütschow et al. (2018), and the Kyoto GHG basket emissions can then be split into multi-gas pathways in the equal quantile walk (Meinshausen et al., 2006). These multi-gas emissions pathways are input needed to derive estimates of the probabilistic global mean temperatures by running the simple climate model “Model for the Assessment of Greenhouse Gas Induced Climate Change” (MAGICC; Meinshausen et al., 2011).