NDCmitiQ v1.0.0: a tool to quantify and analyse GHG mitigation
targets

Abstract. Parties to the Paris Agreement (PA, 2015) outline their planned contributions towards achieving the PA temperature goal to hold [...] the increase in the global average temperature to well below 2 °C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 °C (Article 2.1.a, PA) in their Nationally Determined Contributions (NDCs). Most NDCs include targets to mitigate national greenhouse gas (GHG) emissions, which need quantifications to assess, i.a., whether the current NDCs collectively put us on track to reach the PA temperature goals or the gap in ambition to do so. We implemented the new open-source tool NDCmitiQ to quantify GHG mitigation targets defined in the NDCs for all countries with quantifiable targets on a disaggregated level, and to create corresponding national and global emissions pathways. In light of the five-year update cycle of NDCs and the global stocktake, the quantification of NDCs is an ongoing task for which NDCmitiQ can be used, as calculations can easily be updated upon submission of new NDCs. In this paper, we describe the methodologies behind NDCmitiQ and quantification challenges we encountered by addressing a wide range of aspects, including: target types and the input data from within NDCs; external time series of national emissions, population, and GDP; uniform approach vs. country specifics; share of national emissions covered by NDCs; how to deal with the Land Use, Land-Use Change and Forestry (LULUCF) component and the conditionality of pledges; establishing pathways from single year targets. For use in NDCmitiQ, we furthermore construct an emissions data set from the baseline emissions provided in the NDCs. Example use cases show how the tool can help to analyse targets on a national, regional, or global scale, and to quantify uncertainties caused by a lack of clarity in the NDCs. Results confirm that the conditionality of targets and assumptions on economic growth dominate uncertainty in mitigated emissions on a global scale, which are estimated as 49.2–55.7 Gt CO2 eq AR4 for 2030 (10th/90th percentiles, median: 52.4 Gt CO2 eq AR4; excl. LULUCF and bunker fuels). We estimate that 77 % of global 2017 emissions were emitted from sectors and gases covered by current NDCs (excl. the USA).



Introduction
In 2018, the Intergovernmental Panel on Climate Change (IPCC) celebrated its 30 th birthday, and in 2020 climate negotiators intended to come together for the 26 th annual Climate Change Conference (COP 26, Conference of the Parties). These numbers Table 1. GHG mitigation target types considered in NDCmitiQ, together with their abbreviations and one explanatory example per target type.

Target type
Long name Example ABS ABSolute target emissions The mitigated emissions in the target year are aimed to be 500 Mt CO2eq (net).

RBY Relative reduction compared to Base Year
The mitigated emissions in the target year are aimed to be 20% lower than our 2010 emissions.

ABU
Absolute reduction compared to Business-as-Usual The mitigated emissions in the target year are aimed to be 350 Mt CO2eq lower than our Business-as-Usual emissions in the target year.

RBU
Relative reduction compared to Business-as-Usual The mitigated emissions in the target year are aimed to be 20% lower than our Business-as-Usual emissions in the target year.

AEI Absolute Emissions Intensity target
The mitigated per-capita emissions intensity in the target year is aimed to be 2.1 t CO2eq/cap.

REI Relative reduction in
Emissions Intensity compared to a base year OR target year The mitigated per-capita emissions in the target year are aimed to be 20% lower than our 2010 per-capita emissions (comment: REI_RBY, compared to a base year).

OR
The mitigated emissions per unit of GDP in the target year are aimed to be 20% lower than our Business-as-Usual emissions per unit of GDP in the target year (comment: REI_RBU, compared to BAU -this option is similar to an RBU target).

NGT Non-GHG Target
We aim on increasing our energy efficiency by 40% (comment: nothing is calculated, baseline emissions are assumed).
single countries). In recent years, the major share of global emissions with a clear emissions increase, was caused by countries 125 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 on its withdrawal from the PA to the United Nations on the 4 th November 2019 (Pompeo, 2019), which took effect on the 4 th November 2020. Therefore, emissions from the USA are counted towards "No NDC" and the mitigation measures presented in their NDC are not considered in 130 quantifications throughout this manuscript.

Calculating GHG mitigation targets: general equations
In NDCmitiQ we use several equations to quantify GHG mitigation targets, differentiated based on the target types. The equations presented in this section provide important information on the data needed for the quantifications, and allow a first guess on possible uncertainties connected to each target type. Our general assumption is that the target emissions are the sum   year from sectors and gases that are not covered and are therefore expected to follow a Business-as-Usual pathway. Unless more detailed information is provided in an NDC, we assume similar efforts across all covered sectors and gases.
In the following, we introduce equations to calculate the target emissions for the different target types assessed in our module, starting with the very similar equations for RBY, REI, and RBU targets. The handling of emissions from LULUCF is 140 not addressed here but in Sect. 2.4. We start with the equation for a relative reduction compared to base year emissions (RBY, Eq. 1).
-refYr and tarYr are the "reference year" and the "target year". For an RBY target, the reference year is a historical base year.
-emiBL are the national baseline emissions, with emiBL COV being the share of national baseline emissions covered by plied to the covered share of emissions. emiBL notCOV stays "untouched" by the reductions and emiBL notCOVtarYr is therefore added as is.
While for RBY the reference year is a historical year, for an RBU target (relative reduction compared to BAU) the reference 155 year equals the target year, leading to Eq. 2.
emiTarget RBU = NDC %level · emiBL COVtarYr + emiBL notCOVtarYr The equation for an REI_RBY target -a relative reduction in emissions intensity compared to the emissions intensity in a historical base year -is also very similar to the RBY target. However, instead of the absolute emissions, the emissions intensity per-capita or per unit of GDP is reduced. A socio-economic growth factor has to be considered, and IntensityReftarYr IntensityRef refYr is added 160 (Eq. 3; IntensityRef refYr/tarYr : national baseline population or GDP).
emiTarget REI_RBY = IntensityRef tarYr IntensityRef refYr · NDC %level · emiBL COV refYr + emiBL notCOVtarYr The equations for the remaining target types (ABS, ABU, AEI, and NGT) are given in Eq. 4 to 7. NDC absoluteEmissions are the target emissions, NDC absoluteReduction is the absolute reduction, and NDC emissionsIntensity is the targeted emissions intensity per capita or unit of GDP, given in the NDC. The given absolute target emissions (ABS) and the absolute target emissions 165 intensity (AEI) are assumed to cover the entire national emissions, else the BAU emissions of the not-covered sectors and gases in the target year would need to be added.

Quantification input per target type and country
Based on the current set of NDCs, we give further insight into the data needed for the quantifications by target type and country.

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The various target types require different input, as presented in Table 2. 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 year should additionally be given in the NDC. Else, the covered share of emissions relies on assumptions or own estimates. The assumed baseline emissions and intensity reference 175 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, if, e.g., not targeting all emissions, if no emissions were recorded for the base year, or if the 8 https://doi.org/10.5194/gmd-2020-392 Preprint. Discussion started: 21 January 2021 c Author(s) 2021. CC BY 4.0 License.
handling of LULUCF is not clear. Based on our assessment of current NDCs, Figure 4 contains an overview of the emissions, population, and GDP data needed to quantify the targets on 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. Table 2. Input needed for the quantification of NDC GHG mitigation targets, per target type. Some information or data can be retrieved from NDCs only ("NDC"), while for some data "external" sources can be used (indicated in column "Source"). "Coverage" can be the covered sectors and gases or numerical values for the share of national emissions affected by the mitigation target (for the base and target year). "(x)" indicates that the information is only needed for Parties that do not cover all of their national base year emissions. A f g h a n is t a n A lb a n ia A lg e r ia A n d o r r a A n g o la G h a n a G r e e c e G r e n a d a G u a t e m a la G u in e a G u in e a -B is s a u G u y a n a H a it i H o n d u r a s H u n g a r y Ic e la n d In d ia In d o n e s ia Ir a n Ir a q Ir e la n d Is r a e l It a ly Ja m a ic a Ja p a n Jo r d a n K a z a k h s t a n K e n y a K ir ib a t i K u w a it K y r g y z s t a n

GDP or POP
RBU RBY REI_RBY AEI P a k is t a n P a la u P a n a m a P . N e w G u in e a P a r a g u a y P e r u P h il ip p in e s P o la n d P o r t u g a l Q a t a r R e p . C o n g o R o m a n ia R u s s ia R w a n d a S a m o a S a n M a r in o S . T o m e & P r in c .

S a u d i A r a b ia S e n e g a l S e r b ia S e y c h e ll e s S ie r r a L e o n e S in g a p o r e S lo v a k ia S lo v e n ia S o lo m o n Is . S o m a li a S o u t h A f r ic a S o u t h K o r e a S o u t h S u d a n
S p a in S r i L a n k a S t . K it t s & N e v is S t . L u c ia S t . V in c . & G r e n a d .
S u d a n S u r in a m e S w e d e n S w it z e r la n d S y r ia T a ji k is t a n T a n z a n ia T h a il a n d T im o r -L e s t e

T o g o T o n g a T r in i. & T o b a g o
T u n is ia T u r k e y T u r k m e n is t a n T u v a lu U K U S A U g a n d a U k r a in e U . A r a b E m ir a t e s U r u g u a y U z b e k is t a n V a n u a t u V e n e z u e la V ie t N a m Y e m e n Z a m b ia Z im b a b w e  Gütschow et al., 2019). We use the data set version in which country-reported data are prioritised (HISTCR: Historical Data Country Reported).
For the quantification of targets with a 100% coverage, emissions time series of national totals are sufficient. However, we also consider the covered -and not-covered -share of emissions (%cov) and test the influence on the quantification results. To 205 derive estimates of %cov, we use various time series from the PRIMAP-hist data set (for 1990-2017), which differ regarding the contributing sectors and type of emitted gas. While the main quantifications are based on national total Kyoto GHG emissions excluding LULUCF (exclLU; contributions from LULUCF treated separately), more refined time series are used to estimate the covered share of emissions. Therefore, national emissions from the main sectors "Energy", "Industrial Processes and Product Use" (IPPU), "Agriculture", "Waste", and "Other" are also used (adding up to the national totals exclLU), together with the 210 information on the respective contributions from carbon dioxide (CO 2 ), methane (CH 4 ), nitrous oxide (N 2 O), and for IPPU the basket of hydrofluorocarbons (HFCs) and perfluorocarbons (PFCs), as well as sulfur hexafluoride (SF 6 ), and nitrogen trifluoride (NF 3 ). The Kyoto GHG basket consists of all the before-mentioned gases.
As for the above described non-LULUCF emissions, the current data source in NDCmitiQ for time series of population and GDP PPP for 1990-2017 is PRIMAP-hist v2.1 (Gütschow, 2019). Historical population or GDP data are important to 215 derive the socioeconomic growth factor for REI_RBY targets. PPP stands for the Purchasing Power Parity the national GDP is adjusted by for better comparability on international levels (throughout the manuscript we will use "GDP" for GDP PPP).
Time series are complete and data are available for all UNFCCC Parties and several additional countries.

Scenarios (period after 2017)
For the period after 2017, we use emissions (exclLU), population, and GDP data published recently by .

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In their study, Shared Socioeconomic Pathways (SSPs, available until 2100; Riahi et al., 2017;Crespo Cuaresma, 2017;Dellink et al., 2017;Leimbach et al., 2017) were down-scaled to country-level. The SSP pathways "describe plausible major global developments that together would lead in the future to different challenges for mitigation and adaptation to climate change" (Riahi et al., 2017), and are based on five narratives (Table 3). We chose to include the five marker scenarios in NDCmitiQ, which were derived using different Integrated Assessment Models (IAMs).  .

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From , we use the data with the source names PMSSPBIE and PMSSPBIEMISC, and the scenarios named SSP1BLIMAGE, SSP2BLMESGB, SSP3BLAIMCGE, SSP4BLGCAM4, and SSP5BLREMMP (BL: baseline). These 230 are SSP IAM scenarios (emissions and socio-economic data), down-scaled using "convergence downscaling with exponential convergence of emissions intensities and convergence before transition to negative emissions", with bunkers emissions having been removed before down-scaling, and data being harmonised and combined with PRIMAP-hist v2.1 time series. The emissions data are national values, excluding LULUCF and international bunkers fuels, available for the gas baskets Kyoto GHG and F-gases (fluorinated greenhouse gases: consisting of HFCs, PFCs, SF 6 and NF 3 ), and for the individual gases CO 2 , 235 CH 4 , and N 2 O. In terms of sectoral resolution, only national totals are available. As explained in Sect. A1, pre-processing of the down-scaled SSPs is performed to fill some missing time series for countries with low emissions, population, or GDP.
Additionally, for the estimation of %cov and as not all NDCs cover all F-gases, the time series of F-gases are split into the contributing component gases by assuming recent ratios of HFCs, PFCs, SF 6 and NF 3 (see Sect. A1). The downscaled time series of the marker scenarios for SSP1-5 are generally abbreviated as dmSSP1-5 throughout the manuscript.

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, c). As it can be difficult to distinguish the anthropogenic and the natural part of the land-related 245 fluxes, estimating LULUCF emissions is more complex than for non-LULUCF sectors (Joint Research Centre). It is complicated to estimate mitigation effects by LULUCF activities, as gas fluxes depend, i.a., on the age (distribution) of trees. This distribution varies over time -a difficulty not connected to non-LULUCF emissions (Joint Research Centre). LULUCF can further work as an emissions source or sink, can have high inter-annual variability (Fyson and Jeffery, 2019), and data have a non-LULUCF emissions.
For LULUCF, emissions data availability is limited, with some data sources only providing 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

Emissions data from the NDCs
While the before mentioned data are time series from non-NDC sources, to quantify the targets our intention is to also use the emissions data provided in the NDCs when available. With NDCmitiQ, we are aiming to create national emissions pathways and global aggregates from the quantified targets. However, in NDCs, emissions are generally given as point data, not counting 280 in data visualisations from which it is often difficult to read the numbers. The external data sources serve to complement the NDC data to emissions pathways, and for comparison purposes. As output from NDCmitiQ, we intend to create mitigated emissions pathways that exclude LULUCF emissions and we therefore construct a data set of national baseline emissions time series excluding LULUCF (1990LULUCF ( -2050 that is based on the NDCs' baseline emissions, combined with PRIMAP-hist and SSP data for completeness (see Sect. A3).

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If available the following baseline emissions data were retrieved from the NDCs: excluding LULUCF ("exclLU"), including LULUCF ("inclLU"), and for LULUCF only ("onlyLU"). As it is not always clearly stated what the provided emissions stand for, some of the classifications are based on a best-guess approach. The emissions estimates are used as long as one can assume that all -or most -of a country's emissions are included.

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Emissions throughout this manuscript, and in most of the NDCs, are given as CO 2 equivalents, to make emissions from different gases comparable, and provide basket emissions. Emissions in CO 2 eq follow a certain Global Warming Potential (GWP), with all emissions in NDCmitiQ currently being based on GWPs from the IPCC Fourth Assessment Report (AR4; IPCC, 2006).
Inconsistencies can arise when using NDC emissions data (baseline emissions, and ABS, ABU, and AEI targets), which are partly based on GWPs from the IPCC Second, or Fifth Assessment Report (SAR, AR5; IPCC, 1996, 2014), or unspecified.

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To reduce the uncertainty resulting from emissions based on different GWPs, we apply national conversion factors to the NDC emissions data given in GWPs from SAR. Global Kyoto GHG emissions in 2017 were 46.3 Gt CO 2 eq following GWPs from SAR (excl. LULUCF and bunkers fuels), and 47.7 Gt CO 2 eq for AR4, equivalent to a 2.8% increase in their estimated forcing over a period of 100 years. The higher the national share of CO 2 emissions, the lower is the effect of a change in GWPs, as the GWP of CO 2 is 1 by definition.
A clear communication by Parties of the applied GWPs can reduce this uncertainty in emissions data retrieved from NDCs, and ultimately in the quantification of the target. We assess 50 / 35 / 5 countries to follow GWPs from SAR / AR4 / AR5, 305 representing 6.9% / 33.7% / 4.2% of global Kyoto GHG emissions (year 2017, excl. LULUCF and bunkers fuels). For the remaining countries we could not retrieve information on chosen GWPs from their NDCs and assume the given emissions to follow the GWPs from AR4.
In the Katowice climate package (Annex to decision 18/CMA.1: Modalities, procedures and guidelines for the transparency framework for action and support referred to in Article 13 of the Paris Agreement; UNFCCC, 2019), it was decided for the 310 "National inventory report of anthropogenic emissions by sources and removals by sinks of greenhouse gases" (II) that "Each Party shall use the 100-year time-horizon global warming potential (GWP) values from the IPCC Fifth Assessment Report, or 100-year time-horizon GWP values from a subsequent IPCC assessment report as agreed upon by the CMA, to report aggregate emissions and removals of GHGs, expressed in CO 2 eq" (II.D.37). Implementation of these principles would lead to increased clarity, and also applying these principles to their NDCs would further increase transparency. We assessed the NDCs for information on the covered sectors and Kyoto GHGs to estimate %cov, focusing on the main 330 sectors Energy, IPPU, Agriculture, Waste, Other, and LULUCF, and the single gases CO 2 , CH 4 , N 2 O, SF 6 , NF 3 , 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. A3.1 (results: Sect. 3.1). In general, for years up to 2017, %cov is derived from the PRIMAP-hist emissions 335 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: CO 2 , CH 4 , N 2 O).
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" & "worst", meaning 345 more & 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 350 constructed and aggregated. The methodology and options for pathway creation implemented in NDCmitiQ are explained in Sect. 2.4.2.

Target emissions: including and excluding LULUCF
LULUCF and its contributions towards a mitigation goal complicate target quantifications (e.g., Forsell et al., 2016;Fyson and Jeffery, 2019;Hargita and Rüter, 2015), which is why, for example the Climate Equity Reference (2018) "dropped support for 355 including LULUCF emissions in the assessment of the NDCs -the quality of the data and of the information in the NDCs simply wasn't good enough to do that with confidence". Reasons for the LULUCF component to be an issue are: there are large uncertainties in the LULUCF emissions data; LULUCF emissions can have high inter-annual variability; as LULUCF can be a net sink, countries can use this sector to disguise increased emissions or missing mitigation ambition in the non-LULUCF sectors; and comparability between national mitigation goals is easier when excluding LULUCF contributions. We derive target 360 emissions estimates excluding LULUCF.
In order to quantify mitigation targets excluding LULUCF, and treat the LULUCF component separately, we classified target information from the NDCs into including and excluding LULUCF (inclLU and exclLU). In principle, when LULUCF is assessed to be covered and the NDC does not indicate it otherwise, the target information is assigned to inclLU (e.g., 20% reduction vs. BAU with LULUCF being covered is "RBU inclLU"), else to exclLU ("RBU exclLU"). Unfortunately, as it is 365 not always clear, whether the NDC includes LULUCF in its mitigation target, and whether LULUCF emissions are included in provided baseline emissions, the classification sometimes relies on our judgement.
Target emissions are generally calculated based on Eq. 1 to 7 (Sect. 2.1.2), and we derive estimates both for emiTarget inclLU and emiTarget exclLU . The emissions from LULUCF are treated separately when possible, but this is not always feasible.
When, e.g., the quantification is based on NDC-data, and information on the LULUCF emissions contribution is not provided, 370 no distinction is made between a LULUCF and a non-LULUCF part. If enough data are available, however, we use the following approach to derive emiTarget inclLU and emiTarget exclLU (Table 8: example for India's REI_RBY target inclLU with LULUCF sink in the base year is assessed).
Target excludes LULUCF -emiTarget exclLU : use the given target emissions (ABS), or calculate them following Eq. 1 to 7 (LULUCF not considered 375 in these equations).
-Calculate emiTarget inclLU by adding the projected LULUCF emissions (no reduction of the LULUCF emissions):

Target includes LULUCF
-Target types ABS, AEI, and ABU: use the ABS target as emiTarget inclLU , or calculate emiTarget inclLU from AEI 380 (multiplication with 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.

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-emiBL onlyLU refYr > 0 (net source): LULUCF treated as the other covered sectors and reduced by given relative reduction. -emiBL onlyLU refYr < 0 (net sink): sink is left as is. We chose not to strengthen the sink (attention if chosing 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 390 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., 2014;vegetation or tree age, Pugh et al., 2019, Köhl et al., 2017, Stephenson et al., 2014, Carey et al., 2001.
-Calculate emiTarget exclLU by subtracting the projected LULUCF emissions: -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 emiBL onlyLUtarYr and emiBL exclLUtarYr 400 (ABU exclLU = (ABS inclLU − emiBL inclLUtarYr ) · emiBL exclLU tarYr emiBL inclLU tarYr ).
-Reduce the baseline emissions emiBL exclLUtarYr by the corresponding ABU exclLU .
-ABU targets: if the absolute reduction exceeds the assumed BAU emissions emiBL exclLUtarYr , the then negative target is set to emiTarget exclLU = 0 Mt CO 2 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).
When quantifying all available targets, based on the down-scaled marker scenario for SSP2 (dmSSP2), with NDC emissions data prioritised if available, and assumed coverage of 100%, the "second approach for LULUCF" is needed for seven countries, 410 and for Tonga the ABU exclLU exceeds the baseline emissions emiBL exclLUtarYr (type_main: NGT, type_reclass: ABU).

Emissions pathways
One of our main goals is to construct global emissions 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 415 the Kyoto Protocol period and the first year of the PA period (before 2021: baseline emissions), and a linear in-or decrease of the relative difference to the baseline is assumed between target years, while the relative difference is kept constant after the last target year (Table 5). 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 baseline. A second option for the calculation of national pathways is implemented in 420 NDCmitiQ: constant emissions after the last target year (not default). For countries that indicated an emissions peak year, the calculated pathway is used in case it declines starting in the peak year or earlier, else 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 down-scaled SSP marker scenarios (Sect. 2.2.1). Table 5. Options for emissions pathway calculations for countries with a mitigation target but without a target for the year 2030. In this example, the country targets for a 20% reduction compared to BAU in 2025. The relative difference to the baseline emissions from 2020 to 2025 evolve linearly from 0% to -20%. After 2025, either the relative difference to the baseline is kept at the level of the last target year (default: option "constant percentages"), or the absolute emissions are kept at the level of the last target year (option "constant emissions").  For countries without quantifiable mitigation target the baseline emissions are assumed as un / 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 pathways if this happens.
An option to disable this method and use the baseline as unconditional pathways nevertheless is implemented in the tool (not 435 default).
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 the Sect. A4 and consist of the following options: "targets only 440 for countries X, Y, Z", "prioritised target types", "countries without unconditional targets & what if baseline is better than the conditional targets", "set coverage to 100%", and "strengthen targets".
3 NDCmitiQ: examplary use cases Throughout Sect. 2, the methodology of NDCmitiQ to assess NDCs and quantify their mitigation targets was explained, providing information on the data sets of emissions, population, and GDP currently in use in NDCmitiQ. We presented important 445 background information needed for target calculations, and gave some insights on possible uncertainties. Now, we wish to demonstrate example use cases of the input and output data of NDCmitiQ: assessment of the covered share of emissions; baseline emissions from within NDCs compared to SSP baselines; national GHG mitigation targets: example India, with general importance of the results; and global mitigation pathways: influence of different quantification options.

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On country-level we retrieved information on the sectors and gases covered (Fig. A1), and estimated the corresponding covered share of emissions ( introduced is low.

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 7  for 2030 are +34.8% / +97.5% or +1.3 / +6.1 Gt CO 2 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.

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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,

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 of general interest and concern.
In its NDC, India presents a GHG mitigation target of a 33-35%  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. 7). There are at least three interesting aspects: 520 (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.

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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 8). When %cov increases, emiBL COV2005 and therefore the first term of the equation for emiTarget inclLU increases, while the last term (emiBL notCOV2030 ) decreases and reaches 0 Gt CO 2 eq for 100% coverage. For India's target, the rise in the first term is not 540 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 down-scaled 2030 baseline emissions were significantly higher (GDP growth factor below 1.7,or reference emissions higher than 12 Gt CO 2 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 growth factor of 1: same as RBY target).

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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 8: with a 53% / 59% reduction the target with estimated / 100% coverage, respectively, would be below baseline; and with a 78% reduction the 100%-coverage target would be below The results should not be misunderstood as a motivation not to move towards an economy-wide target and including 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 555 type overall, to ensure to ramp up the ambition rather than to lower it, 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 highest projected GDP growth, and smallest 560 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 (2019d) 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 (a) and the World Resources Institute state the "Sectors covered" as "Not specified; various 565 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  Based on quantifications under dmSSP2 and an assumed 100% coverage, India's emissions target ranges between 6.3-575 6.5 Gt CO 2 eq for emissions excluding LULUCF (6.0-6.2 Gt CO 2 eq including LULUCF; AR4). With estimated coverage of 74 / 86% for 2005 / 2030, the quantified emissions target ranges between 5.2-5.4 Gt CO 2 eq for emissions excluding LULUCF (5.0-5.1Gt CO 2 eq including LULUCF). The CAT (2019c) estimates the unconditional emissions intensity target to be in the range of 6.0-6.2 Gt CO 2 eq (excl. LULUCF, AR4). This value is a bit lower than our estimates when assuming a 100% coverage. Climate Watch (a) and the World Resources Institute give a wider range of 5.9-9.1 Gt CO 2 eq, not specifying 580 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 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).

Global mitigation pathways
One 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, Fig. 8 shows globally aggregated pathways resulting from a full implementation of current targets from unconditional worst to conditional best, and based on different input data and quantification options. Once, the emissions data from the NDCs are prioritised 590 (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".
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 1.6-2.7 Gt CO 2 eq 595 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, the conditionality range is 2.8-6.0 Gt CO 2 eq for all values displayed in Fig. 8 (panel b: difference between unconditional worst and conditional best), but with little difference between the conditional worst and best emissions. Reductions compared to BAU emissions in the target year will be below 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.

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The effect of different assumptions of underlying LULUCF baseline emissions on the target quantifications on global scale are shown in Fig. 8. All emissions exclude LULUCF, but in many cases the targets exclLU have to be derived from targets in-clLU (countries including LULUCF: Figure A1), and therefore the LULUCF baselines often affect exclLU targets. As default, LULUCF data from NDC, CRF, BUR, UNFCCC, and then FAO is 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 CO 2 eq if FAO has 625 the highest prioritisation, while it is a net sink of -2.2 Gt CO 2 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 CO 2 / year uncertainty in 2030, which is larger than their estimated total anthropogenic 630 land use sink of -2 Gt CO 2 / year in 2030, and larger than the influence our choice of underlying LULUCF data has on the quantified targets (0.8 Gt CO 2 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.
The effects of changing the options are 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: Depending on the quantification options and underlying dmSSP scenarios, global mitigated emissions under the NDCs in 2030 are estimated to range between 49.2 and 55.7 Gt CO 2 eq for 2030 (10 th / 90 th percentiles for unconditional worst and conditional best estimates for dmSSP1-4, with median 52.4 Gt CO 2 eq; AR4; excl. LULUCF and bunkers fuels). Both, the 6.5 Gt CO 2 eq range and absolute values are lower than the 56.8-66.5 Gt CO 2 eq / year estimates by Benveniste et al. (2018) 645 for 2030 (90% confidence interval; 9.7 Gt CO 2 eq range; Table 9). However, adding to the difference is that their estimates include emissions from bunkers 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 CO 2 eq / year for the SSP2 OECD scenario is comparable to other estimates.
found the global emissions for unconditional NDCs to be 56 Gt CO 2 eq (54-60 Gt CO 2 eq; median and 10 th /90 th percentiles; probably including LULUCF and bunkers fuels), and for conditional NDCs 54 Gt CO 2 eq (51-56 Gt CO 2 eq). Our estimates that exclude LULUCF emissions and bunkers fuels are 54.6 Gt CO 2 eq for the upper edge (52.8-56.4 Gt CO 2 eq, unconditional worst), and 50.4 Gt CO 2 eq (48.9-51.6 Gt CO 2 eq) for conditional best, representing a larger range.

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. 8, but on regional level, with refined view on target types, or 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.

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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).

Discussion
This paper shows the methodology behind NDCmitiQ and possible use cases of this newly available open-source tool to quantify and analyse national GHG mitigation targets as stated in the current set of NDCs, and construct corresponding national and global emissions pathways. NDCmitiQ is fast-running and incorporates a large amount of information retrieved from 670 NDCs. It has a uniform approach with flexible input data for comparison studies, but also provides target quantifications based on the available emissions data in NDCs whenever possible. As the presented time series of emissions, population, and GDP data currently implemented in NDCmitiQ are not intended to be exclusive, users can add other suitable time series for the quantifications. We believe that NDCmitiQ can help researchers and stakeholders for fast analyses when updated NDCs are submitted, or in the Global Stocktake.

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The 168 NDCs assessed in our study, with documents consisting of three to 83 pages and strongly differing content and clarity, often leave room for interpretation. The "clarity, transparency, and understanding" (Art

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Advantages of the presented tool are, e.g., that it can be updated easily upon submission of new NDCs, and does not only provide estimates of regional / global emissions pathways but the national contributions and pathways. Furthermore, it can be run with different data sets of national emissions and socio-economic data. Currently, for simplicity estimates of the covered share of emissions are based on the main sectors, but as some NDCs name, e.g., only the sub-sector "Electricity Generation" to be targeted and not the entire Energy sector, refinements could be implemented. Similar to Benveniste et al. (2018), targets 690 for fossil fuel shares are not included in NDCmitiQ, and the non-fossil fuel targets the large emitters China and India stated additionally to emissions intensity targets are not quantified. Estimates of the international bunkers emissions and their planned mitigation are not addressed in NDCmitiQ. We restricted our uncertainty analysis on global scale to a limited set of options, generally changing one option at a time, to be able to trace back the changes to the single options. However, this analysis can be further extended to address the interaction between the options, and quantify the resulting uncertainty range.

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NDCmitiQ is limited in its capabilities to quantify NGT targets. For countries with this target type, the assumed mitigated emissions trajectory equals the baseline pathway. Only if the reclassified target type differs from NGT, the mitigated trajectory in NDCmitiQ will differ from the reference emissions. In total, for 2017 / 2030 (dmSSP2) 5.5% / 6.1% of global emissions were emitted by countries classified as NGT (type_main). For type_reclass, the global shares are reduced to 3.5% and 3.8% for 2017 and 2030, respectively. Additional analyses and support for these NDCs would be beneficial for an improved quantification 700 of the global mitigated emissions pathways. About 1% of 2017' emissions was emitted by countries without NDC, to which one must add the contribution by the USA (approximately 14%), who withdrew from the PA (all emissions excl. LULUCF and bunkers fuels). As for Parties with NGT targets, the baseline emissions are likewise assumed as mitigated trajectories for countries without NDC.
In the Paris Agreement it was decided that all countries should move towards economy-wide targets and raise their ambition 705 over time. Based on the presented analyses, currently a total of 77% of global 2017 emissions are estimated to be covered by the NDCs (excluding LULUCF and bunkers fuels). As one of six countries, we assess that with the tested emissions and GDP scenarios, India's GHG mitigation target would show an unexpected behaviour when moving from the current estimated coverage towards a 100% coverage without simultaneously increasing the relative reduction level: it would result in a less ambitious target, with noticeable impact on global scale.

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Countries can use fixed baselines, which do not change over time and facilitate target and pathway quantifications (Graichen et al., 2018), but also leave room for over-or underestimation, as, contrary to dynamic baselines, the projected pathways are not adapted to parameter or methodology changes over the years. On global scale, for all historical years the baseline emissions data provided in the NDCs are lower than emissions from PRIMAP-hist, while for the year 2030 we find that they are +35 / +98% (exclLU / inclLU) higher than the middle-of-the-road scenario dmSSP2. For a total of 97 countries (excl. USA) we were 715 able to estimate targets based on NDC emissions data, and classify 77 NDCs as RBU targets (relative reduction against BAU emissions; target_orig), out of which 17 could not be quantified with NDC emissions data.
For the tested quantification options, the range of global mitigation pathways is dominated by the targets' conditionality and the underlying emissions and GDP data. Supporting findings by Benveniste et al. (2018) and Rogelj et al. (2017), we see a clear influence of the assumed GDP, dominated by the fact that India and China pledged to reduce their emissions intensity per unit 720 of GDP. In total, the analysed unconditional worst to conditional best emissions pathways differ by about 3.5-5.2 Gt CO 2 eq in 2030 (10 th / 90 th percentiles for dmSSP1-4, median: 4.3 Gt CO 2 eq). The effect of different quantification options, such as the covered share of emissions, or the evolution of emissions after the last target year (tested up to 2030), have a smaller impact on global scale. For the presented input data and quantification options, we estimate the global mitigated emissions in 2030 to range between 49.2 and 55.7 Gt CO 2 eq AR4 for dmSSP1-4 (10 th / 90 th percentiles, median: 52.4 Gt CO 2 eq; excl. LULUCF 725 and bunkers fuels).

Conclusions
Under the Paris Agreement, Parties agreed to limit global warming to 1.5-2 Code and data availability.

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We use a GitHub repository to work on the Python-based tool to quantify GHG mitigation targets and emissions pathways NDCmitiQ (https://github.com/AnnGuenther/ndc_quantifications.git). All data sets we produced with NDCmitiQ for the presented manuscript, and the code version NDCmitiQ v1.0.0 are available for download on zenodo . For each quantification (about 1min20s run time) one folder is provided, with the folder name structure being ndcs_yyyymmdd_hhss_, followed by: SSP1 to SSP5: which SSP marker scenario is chosen for the run. This information is also important if the run is based on NDC emissions 745 data (type_reclass), as not for all countries emissions data were provided, and the SSP baselines are used for the pathway construction.
typeReclass: runs with type_reclass, based on emissions data from the NDCs where possible.
pccov100: runs with an assumed coverage of 100%.

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constEmiAfterLastTar: runs with assumed constant emissions after a Party's last target year.
Without constEmiAfterLastTar: instead of the emissions, the relative difference to the baseline is kept constant after the last target year.
BLForUCAboveBL: runs using the baseline emissions as the unconditional pathways for Parties without unconditional targets, even if the baseline is better than the conditional targets.
Without BLForUCAboveBL: conditional worst pathway is used in this case instead of the baseline. Some countries only cover certain F-gases in their mitigation targets, and depending on the target type we might need scenarios of the single contributions for the calculation of the covered share of emissions. As for dmSSPs, no information is available on the contribution of HFCs, PFCs, SF 6 and NF 3 to the total basket of F-gases, we base our estimates on the historical contributions (mean over 2012-2017).

A2 Emissions time series for LULUCF (non-NDC data)
The LULUCF data sources included in NDCmitiQ are prioritised as follows: the details on how we choose which LULUCF emissions to use for the target quantifications are given in Table A1.  Table A2). Even though, up to 2017, the NDC data set is constructed with PRIMAP-hist emissions, the emissions given within 820 NDCs are used to quantify their targets (for type_reclass), unless it is stated otherwise, e.g., for comparison runs (type_main). are the average recent %cov, or derived from the correlation between covered and total national emissions (all for 2010-2017).
The applied rules are further clarified in Table A4, and Figure A1 contains per-country information of the covered sectors and gases. The coverage is presented as provided (more or less explicitly) in the NDCs, and as "adapted" for the use in NDCmitiQ.
Results for %cov were used in Geiges et al. (2019), with small changes in the methodology since then.
Estimates of %cov for upcoming years, needed to define the (not-)covered emissions share in the target years, are based on 830 the decisions and quantifications outlined in Figure A2. Either the average recent values of %cov are kept constant or estimates are calculated from the correlation between national total emissions and %cov (2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017). NDCmitiQ provides two options as projection preference: "correlation" (default) or "mean". The scheme presented in Figure A2 describes the steps if mean is chosen as preference. For the option "correlation" the correlation is used for each country, unless the r-value of the regression line to the correlation is below a defined limit (0.85). If the correlation is used, the estimates of %cov depend on the projected 835 national emissions and therefore on the chosen dmSSP scenario.
In Table A5, the national shares of emissions per sector / gas are given as 95 th percentiles, to reduce the influence of extreme values and missing data. Further, the number of countries assessed to cover emissions from a certain sector / gas are provided.
The information is complementary to Table 6.
A4 Options for the calculation of emissions pathways 840 Several options to modify the calculation of emissions pathways are implemented in the tool.
Targets only for countries X, Y, Z: Use quantified targets for countries X, Y, and Z, else use baseline emissions. Table A3. How we define the share of emissions covered by an NDC (%cov; excluding LULUCF). "economy-wide" stated: all sectors (LULUCF treated separately) are assumed to be covered, even if a list of covered sectors is given that is not complete. If in the NDC it becomes obvious, however, that the reduction merely applies to emissions from certain sectors, only these sectors are covered. Example on decision making from box 1+2 in Table A4 (Sect. A3.1). Table A4. Decisions on covered sectors and gases. "+" = "covered", "-" = "not-covered", and "/" = "no information available". A f g h a n is t a n A lb a n ia A lg e r ia A n d o r r a A n g o la G h a n a G r e e c e G r e n a d a G u a t e m a la G u in e a G u in e a -B is s a u G u y a n a H a it i H o n d u r a s H u n g a r y Ic e la n d In d ia In d o n e s ia Ir a n Ir a q Ir e la n d Is r a e l It a ly Ja m a ic a Ja p a n Jo r d a n K a z a k h s t a n K e n y a K ir ib a t i K u w a it K y r g y z s t a n G lo b a l s h a r e 2 0 1 7 P a k is t a n P a la u P a n a m a P . N e w G u in e a P a r a g u a y P e r u P h il ip p in e s P o la n d P o r t u g a l Q a t a r R e p . C o n g o R o m a n ia R u s s ia R w a n d a S a m o a S a n M a r in o S . T o m e & P r in c .

S a u d i A r a b ia S e n e g a l S e r b ia S e y c h e ll e s S ie r r a L e o n e S in g a p o r e S lo v a k ia S lo v e n ia S o lo m o n Is . S o m a li a S o u t h A f r ic a S o u t h K o r e a S o u t h S u d a n
S p a in S r i L a n k a

S t . K it t s & N e v is S t . L u c ia S t . V in c . & G r e n a d .
S u d a n S u r in a m e S w e d e n S w it z e r la n d S y r ia T a ji k is t a n T a n z a n ia T h a il a n d T im o r -L e s t e

T o g o T o n g a T r in i. & T o b a g o
T u n is ia T u r k e y T u r k m e n is t a n T u v a lu U K U S A U g a n d a U k r a in e U . A r a b E m ir a t e s U r u g u a y U z b e k is t a n V a n u a t u V e n e z u e la  Prioritised target types: Use prioritised target types for countries X, Y, and Z with the target types being in the order A, B, and C, else use type_reclass. type_main: use the 'main target type' (what has been stated +/-in the NDC as target type); type_reclass: use the reclassified target type (mostly ABS with the quantification based on data from the NDCs). Set coverage (exclLU) to 100%: For a set of countries or all countries.

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Strengthen targets: Strengthen targets by a certain percentage P, for countries X, Y, and Z. Either by adding P to the value given in a country's NDC, or by multiplying the reduction by 100% + P. If the resulting percentage is greater than 100%, it is set to 100%, which means a total reduction of the -covered share of -emissions. For example, for a target with 20% reduction and P = 10%, if "add" is chosen the result is − (20% + 10%) = −30%, and if "multiply" is chosen the result is −20% · 100%+10% 100% = −22%. For absolute targets (ABS, AEI, ABU), it is not distinguished between add and 855 multiply. In the case of ABS and AEI, the strengthened target is, e.g., 20 Mt CO 2 eq · 100%−10% 100% = 18 Mt CO 2 eq, or Table A5. Relative contribution of different gases and sectors to the national 2017 Kyoto GHG emissions (95 th percentiles, part a), and number of countries for which a gas / sector is covered by its NDC (part b). (a) 95 th percentiles for the national shares of emissions from a certain gas / sector (e.g., Energy-emissions: in 95% / 5% of the nations, Energy-emissions represent less / more than 91.5% of national emissions). All values exclude emissions from LULUCF and bunkers fuels emissions. All values are based on PRIMAP-hist v2.1 HISTCR emissions data (GWP AR4). (b) Coverage from within NDCs (more or less explicitly stated) and "adapted" coverage (based on the rules described in Sect. 2.3). E.g., IPPU / CO2: 123 / 174 countries are assessed to +/-clearly state in their NDCs that they cover their IPPU / CO2 emissions, with the adapted number of countries being 142 / 193. This results in 142 countries assessed to cover their CO2 IPPU emissions.
Author contributions. AG designed the study, implemented the module, carried out the analyses, and led the manuscript writing process. All authors discussed the methodology and results and contributed to the presented manuscript.