Country resolved combined emission and socio-economic pathways based on the RCP and SSP scenarios

. Climate policy analysis needs reference scenarios to assess emissions targets and current trends. When presenting their national climate policies, countries often showcase their target trajectories against ﬁctitious so-called baselines. These counterfactual scenarios are meant to present future Greenhouse Gas (GHG) emissions in the absence of climate policy. These so-called baselines presented by countries are often of limited use as they can be exaggerated and the methodology used to derive them is usually not transparent. Scenarios created by independent modeling groups using integrated assessment models 5 (IAMs) can provide different interpretations of several socio-economic storylines and can provide a more realistic backdrop against which the projected target emission trajectory can be assessed. However, the IAMs are limited in regional resolution. This resolution is further reduced in intercomparison studies as data for a common set of regions are produced by aggregating the underlying smaller regions. Thus, the data are not readily available for country-speciﬁc policy analysis. This gap is closed by downscaling regional IAM scenarios to country-level. The last of such efforts has been performed for the SRES scenarios 10 (Special Report on Emissions Scenarios), which are over a decade old by now. CMIP6 scenarios have been downscaled to a grid, however they cover only a few combinations of forcing levels and SSP storylines with only a single model per combination. Here, we provide up to date country scenarios, downscaled from the full RCP (Representative Concentration Pathways) and SSP (Shared Socio-Economic Pathways) scenario databases, using results from the SSP GDP (Gross Domestic Product) country model results as drivers for the downscaling process. The data is available at https://doi.org/10.5281/zenodo.3638137 15 (Gütschow


Introduction
In order to coordinate climate change research, different sets of joint scenarios have been developed. For example, the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES) summarized available literature and provided six illustrative marker scenarios of emissions as well as socio-economic storylines to enable cross-comparison of 20 a wide range of mitigation, adaptation, and climate change impact studies Riahi et al., 2007).
These "marker scenarios are no more or less likely than any other scenarios, but are considered by the SRES writing team as illustrative of a particular storyline" . More recently a new scenario process was started (Moss et al., 2010). The emissions scenarios used in that process are the Representative Concentration Pathways (RCPs) that have been groups (van Vuuren et al., 2011a;Meinshausen et al., 2011;van Vuuren et al., 2011b;Thomson et al., 2011;Masui et al., 2011;Riahi et al., 2011).
In a second step following the selection of concentration / emissions scenarios, five different socio-economic storylines were developed, the Shared Socio-Economic Pathways (SSPs: Nakicenovic et al., 2014), allowing mitigation and impact researchers to combine low and high emission futures with different assumptions about socio-economic development in terms of 30 population, Gross Domestic Product (GDP) and further indicators (van Vuuren et al., 2014). This is an advancement over the SRES scenarios, as in the SRES scenarios, an emissions future was often assumed to be in line with a single socio-economic development only. The exception during the SRES scenario process was the A1 scenario family that was during the plenary adoption process split out into three sub-scenarios, A1FI, A1T and A1B, indicating the importance of socio-economic assumptions below the high-level "high growth" storyline of the A1 family and their respective effect on emissions. The new SSP 35 socio-economic storylines were modeled by several independent groups to quantify them in terms of GDP Dellink et al., 2017;Crespo Cuaresma, 2017), population (KC and Lutz, 2017) and urbanization (Jiang and O'Neill, 2017) development on a country or detailed regional level. These scenario quantifications are called the SSP basic elements.
While the IAMs internally use between 11 and 26 regions, the published data are limited to a set of macro regions, namely the RC5 regions (RC: Region Categorization, see Edenhofer et al., 2014, AppendixII.2.2) for the RCPs, and the RC5.2 regions (IIASA, 2016) for the SSPs. The reasons for this limitation are manifold: decisions of the intercomparison protocols to allow a wide participation of modeling groups lead to a neglect of some regional detail; but more fundamentally, the quality of calibra-45 tion and input data for the global modeling exercises that produced the SSP GDP and population projections degrades on finer scales and hence limits the projection models. Furthermore, so far there are no official and comprehensive emissions inventories for most countries that are categorized as Non-Annex I countries, as their reporting requirements under the UNFCCC have been very limited compared to those categorized as Annex I / industrialized countries. This limitation of country-level detail can severely hamper a number of studies: climate impact assessments, quantification 50 of equity principles for effort-sharing of mitigation, or the assessment of pledges of countries against benchmark reference and mitigation scenarios. The required long-term country-level scenarios are only available based on the now over a decade old SRES scenarios (van Vuuren et al., 2007;Höhne et al., 2010).
Sector and gas resolution is limited as well. While the RCP scenarios have detailed sectoral data for some gases (e.g. CH 4 ), the resolution of CO 2 is limited to separating land use emissions from the fossil fuel and industrial emissions in the 55 publicly available database. The shared SSPv2 IAM outputs only resolve between land use and fossil / industrial emissions and hence also that coarse disaggregation is harmonized towards common historical emission levels. The RCPs resolve individual fluorinated gases, while the SSPv2 database only provides data for aggregated fluorinated gases. each year. EI · GDP(y) denotes the multiplication of the whole emission intensity time series by the GDP of year y. Emissions and emission intensities are defined for several variables (gases, pollutants), but as we are only working on one gas at a time, we do not introduce another subscript index for these variables for the sake of a simpler notation. The method could as well be used to downscale the world to regional level or country emissions to state level. We only consider downscaling from larger to 95 smaller economic or political regions, e.g. from region level to country-level and do not consider spatial downscaling of data from coarser to finer grids. However, if, e.g. GDP data are given on a finer grid than emissions data, the method described here could also be applied.
We denote the RCP scenarios and forcing levels by RCP and the downscaled RCP scenarios by RCPd. The SSP basic elements are abbreviated by SSPbe. By SSPv2 we denote the SSP IAM scenarios version 2 and by SSPv2d the downscaled 100 SSPv2 scenarios. When using just SSP we refer to the SSP storylines, e.g. RCP SSP refers to the combination of RCP forcing levels with SSP storylines.

Existing downscaling methods
Several methods to downscale emissions data are found in the literature. Which methods can be used depends on available data and the choice between a simple and transparent method versus a more realistic but also more complex approach. Common to 105 all methods is the need for an auxiliary dataset called the downscaling key. Data from the downscaling key are used directly or as the basis for a model to split the regional data to country-level. It could be data for the same variable from a different source, or for a different variable with some known or assumed correlation to the variable that is to be downscaled. The data can either cover the same period of time, historical years only, or even a single year only. The basis of our work are the three groups of methods identified in van Vuuren et al. (2007) which differ in their use of the downscaling key. They specifically consider 110 cases, where country-resolved emissions data are available up to a certain year, but future projections are only available for larger regions as this is the situation given by the combination of RCP scenarios with the SSP basic elements and the SSPv2 IAM runs.
Linear downscaling This is the simplest method. The downscaling key is a dataset for the same variable as the to-bedownscaled data, e.g., both CO 2 emissions. Historical emission data for one single year y 0 (or an averaged period) 115 is used to define shares S C (y 0 ) = E C (y 0 )/E R (y 0 ) for each country C ∈ R. These shares S C (y 0 ) are used to distribute emissions from the regional pathway to individual countries: E C = S C (y 0 )E R . The relative emissions of countries within a region are thus fixed at the historical level for the whole resulting scenario. This approach was used by the MATCH group (Höhne et al., 2010) to downscale SRES scenarios from region to country-level.
While this approach is very transparent and straight forward it has the downside that it can not model differing devel-120 opments within a region. All countries in a region will have the same emission growth rates defined by the regional pathway. The method is likely to overestimate future emissions of relatively developed countries compared to those of developing countries with high economic growth within the same region. See also results Section 5.
External input based downscaling In this method a country-resolved key pathway K C for some variable is available. The shares S C = K C /K R defined by this pathway are used to downscale the regional pathway: This method can take different developments within the region into account, but only to the extent the downscaling key data K does itself. The intra-regional differentiation will be that of the existing key source, only scaled with the ratio of the regional scenario pathway to the regional key pathway. If the key data are for a different variable than the regional data to be downscaled, a systematic error is introduced if the two variables are not linearly correlated. If the correlation is known, this might be compensated, but in general this will not be the case. We use this method to downscale the SSPv2 130 socio-economic data and the PIK SSPbe data from region to country-level.
Convergence downscaling Convergence downscaling uses the assumption that a given variable converges among countries within a given region. The convergence assumption only makes sense for variables which are independent of the size of a country, e.g. emission intensity (emissions per unit of GDP) and GDP per capita but not absolute emissions or GDP. This method needs historical information for the target variable (e.g. emissions) and in case the target variable is 135 not independent of country size an auxiliary variable that can be used to create a convergence variable which does not depend on country size (e.g. GDP to create emission intensity). Furthermore, regional and country time-series for the auxiliary variable are needed for the full downscaling period.
The downscaling process begins with the creation of a temporary pathway of the convergence variable for all countries, starting from the historical values for each country and ending at a common value obtained from the given regional 140 pathway. Thus, all countries converge to the regional value in the convergence year. The convergence year can be set depending on the scenario storyline and governs if full or partial convergence is achieved within the scenario time frame. To accomplish partial convergence, the convergence year is set after the end of the scenario time frame and thus some form of extrapolation of the regional data is needed. In case we used an auxiliary variable we need to multiply the obtained pathways by the pathways of the auxiliary variable to obtain the temporary pathways for the downscaling 145 variable. The obtained temporary pathways are scaled such that their sum matches the regional pathway prescribed by the scenario for every year individually.
Convergence downscaling was employed by van Vuuren et al. (2006van Vuuren et al. ( , 2007 to downscale the SRES scenarios from region to country-level. This method employs socio-economic scenarios as the drivers of the downscaling process and is therefore a promising 150 candidate to downscale the RCP and SSPv2 scenarios using the SSP basic elements country-level data. Details are presented in Section 2.3. Figure 1 shows examples for the three methods described above. Which method is most appropriate depends on intended use and available data. If only historical data are available, linear downscaling is often the only method that can be used to derive country-level future emissions from regional emissions projections. Convergence downscaling is a good option, if the 155 variable that should be downscaled can be expressed relative to some known variable to make it comparable between different   Example results for different downscaling approaches. For the sake of simplicity a two country region is assumed. The constant relative emission intensity downscaling is a variation of the external input based downscaling where we use GDP as external input and the assumption of constant relative emissions intensities to create emissions pathways based on the GDP data (Panel b). Regional emission data are given for the whole time period while for the countries only historical data are available (Panel a). Panel c shows downscaling for increasing emissions. It is clearly visible that the constant shares downscaling does not account for the GDP development, while the convergence downscaling leads to the highest emissions of country 2 because it not only considers GDP growth, but also converging emissions intensities between the two countries. Panel d shows downscaling for a transition to negative emissions. For convergence downscaling the convergence is set to the year directly before the transition to negative emissions. The rapid reductions and early convergence lead to similar pathways for all methods before the transition to negative emissions. After the transition the effect of considering GDP is visible. The convergence year for convergence downscaling is 2150 in this example. countries, which is a prerequisite for the convergence concept to be meaningful. If emission data are available from a different source the external input method is a good option.
For our task we use a slightly modified version of the convergence downscaling which can handle negative emissions and uses the GDP data provided by the SSP basic elements and the IPAT equation to downscale the emissions of the greenhouse 160 gases included in the RCP and SSPv2 scenarios. Our method is very similar to the convergence downscaling employed in van Vuuren et al. (2006Vuuren et al. ( , 2007 (see Section 2.3).

IPAT convergence downscaling
In this section we present the details of our modified version of the IPAT based convergence downscaling introduced in van Vuuren et al. (2006,2007).

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Similar to the original approach the basis for the downscaling of emissions is given by the IPAT equation (Ehrlich and Holdren, 1971;Chertow, 2000): (1) The idea behind the equation is to decompose an environmental impact I into its drivers. The IPAT equation assumes I is linear in all three drivers: the population size P , the affluence A as a measure of consumption of goods per capita, and a technology 170 factor T which governs the environmental impact per unit of consumed goods. In our case the environmental impacts to be described are greenhouse gas emissions. As we work on an economy wide level the affluence is described by GDP per capita and the emission intensity of the GDP plays the role of the technology factor. The driver behind emissions growth is total GDP (as a measure of consumption and production) not the size of the population.

Our IPAT equation variant thus becomes
where EI C = E C /GDP C is the emission intensity of country C, the emissions per unit of GDP. The downscaling is carried out individually for each gas g (index omitted). Figure 2 gives an overview over the steps of the downscaling process, which will be described in detail in the following sections.

Convergence and target emission intensity
The year of convergence for the emission intensity within a region has to be chosen according to the SSP scenario storyline.
We assign relatively early convergence years (e.g. 2150) to scenarios with high economic integration, while scenarios with a regionalization storyline only justify partial convergence within the scenario time frame. In case convergence is achieved during the scenario time frame, all countries within a region converge to the regional emission intensity prescribed by the emission 185 scenario. In case of partial convergence we need to assume a regional emission intensity in a year after the end of the scenario.
In van Vuuren et al. (2006Vuuren et al. ( , 2007 this was created using an exponential pathway with the average growth rate of the last years  Steps of convergence downscaling of regional emissions data using the IPAT equation and country GDP data for a two country region for positive and negative regional emissions. Regional emission data are given for the whole time period while for the countries only historical data are available (Panel a). GDP data are given for both countries and the region for the whole time period (Panel b). In the first step temporary emission intensity pathways for the countries are calculated using exponential convergence from historical values (2015).
In case of completely positive regional pathways, emissions intensities converge to the regional value in a given convergence year (2150, Panel c). In case of negative emissions convergence to the regional emission intensity is in the last year before the transition to negative emissions. After that year regional emissions intensities are used. Multiplication with the given GDP time series creates temporary emissions time series (Panel d). These do not sum up to the regional values (see Panel d) and have to be scaled to the regional value (results in Panel e).
This also changes the emissions intensities (Panel f). of the scenario. We judge exponential extrapolations to be very uncertain for long periods especially when the variable to be extrapolated increases over time (as would be the case for e.g. CO 2 /GDP for e.g. the RCP 2.6 emissions scenario with SSP 4 basic elements GDP, Asia region). We therefore use the emission intensity of the last scenario year as target emission intensity 190 if the convergence year is after the end of the scenario time frame. For time series with a transition to negative emissions we have to adjust the convergence year to avoid numerical instabilities and early (before regional total) transition to negative emissions for countries with emissions intensities below the regional average. While this would make sense for countries which base their low emissions intensity on a large share of renewable energy it is not realistic for developing countries with very low emission intensities stemming from a low level of industrialization. We adjust the convergence year to be just before the 195 regional transition to negative emissions.

Construction of the temporary emission intensity pathways
To generate the per country temporary emission intensity pathways, we need a method to interpolate between the initial emission intensity given by historical data and the target emission intensity in the convergence year given by the regional scenario.
The methodology described in the following paragraphs is also presented graphically in Figure 2.

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Our method is based on the original approach by van Vuuren et al. (2007). The emission intensity pathway of a country is created using an exponential function that is defined by the initial emission intensity in the harmonization year and the regional emission intensity in the convergence year. The idea is that change in emission intensity is proportional to the difference of each country's emission intensity to the regional average.
The exponential convergence is modeled by the function 205 EI C (y) = a C e γy + b C , for y h < y < y c , where y h denotes the year of harmonization with historical data and y c denotes the convergence year. The decay factor γ is defined as such that the exponential function reduces the difference between country and regional emissions intensities in the harmonization year y h to EI diff (y c ) = dEI diff (y h ) in the convergence year y c , where we chose d = 0.01 to have almost complete convergence. Smaller values would lead to rapid partial convergence in the first years with only small changes in the later years. The country specific constants a C and b C are defined via In case of convergence before the end of the scenario time span we continue all country time series with the regional emission intensity: EI C (y) = EI R (y), for y ≥ y c .

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For the part of the scenario also covered by historical data we use the historical emission intensity: As an alternative to exponential convergence we also studied linear convergence of emission intensities. However we were not able to produce sensible results as the scaling step (Section 2.3.3) exhibited numerical instabilities.
The result of this step is a set of temporary emission intensity pathways EI C for every country C ∈ R.

Emission pathways and scaling
Using the IPAT equation, we generate a preliminary emissions pathway E C for every country C: Those pathways are summed up to a preliminary pathway for the region R:

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In general this pathway will differ from the regional pathway prescribed by the scenario. We create a scaling pathway The final country pathway is defined via This method does not work in case of negative emissions which are common for CO 2 pathways in low emissions scenarios 235 like RCP 2.6 or the new 1.9W/m 2 scenarios (Rogelj et al., 2018) where technologies like bio-energy with carbon capture and storage (BECCS) are assumed to remove large quantities of CO 2 from the atmosphere. So the temporary CO 2 -emission pathways (see Equation 10) of all countries in a region with negative emissions contain a transition to negative emissions and so does the regional sum pathway (Equation 11). Similarly, the regional pathway will be near zero for a few years before and after it's transition to negative emissions. Therefore, the calculation of the scaling pathway (Equation 12) is numerically unstable.

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As the country pathways are not necessarily near zero where their sum is zero, some adjusted country pathways will exhibit positive peaks in emissions, while others will contain negative emission peaks, still summing to the correct regional value.
These peaks are several years wide and can not be removed by interpolation without major changes to the country pathways.
We have investigated several different options to circumvent this problem including dynamical downscaling algorithms which downscale data year by year and can use alternate algorithms when regional emission intensity is near zero. However, fine 245 tuning the parameters to deal with the transition to negative emissions for several scenarios proved to be very complicated while the results were often very similar to the very simple solution of moving the convergence year to before the transition to negative emissions. After that year all countries follow the same emission intensity pathway. The steep reduction in emissions and emission intensity does not leave much freedom for the downscaling (see also Figure 1). All countries have to rapidly reduce emissions to meet the prescribed regional pathway. Furthermore, as described in Section 2.3.1 there are conceptual problems 250 with convergence years set to later than the transition to negative emissions. We therefore use the simple but transparent approach of early convergence. The calculation itself is not changed but y c is adjusted to be the last year before the transition to negative emissions. This is done on a per gas level. Only CO 2 pathways have negative emissions, consequently only y c of CO 2 is adjusted. The downside of this approach is that it impacts the assumptions of convergence and eliminates the possibility to define different convergence speeds for different socio-economical storylines. Figure 2 gives an overview over the steps of the 255 downscaling process.

Input data and preprocessing
This section provides an overview over the input data. It covers the RCP and SSP scenarios (Section 3.1) and their implementation including the choice of scenarios for international shipping and aviation (Section 3.1.3), the region definitions used in the models (Section 3.3), the countries covered by the datasets (Section 3.3), and the covered sectors and gases (Section 3.4).

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Furthermore, the historical data used to downscale the RCP and SSP scenarios are introduced in Section 3.2.

Scenario description
Two datasets are produced based on two sets of scenarios: RCPd, based on the RCP scenarios (van Vuuren et al., 2011a), which are downscaled using the SSP basic elements and SSPv2d, based on the SSPv2 IAM implementations of the SSP scenarios, which come with consistent socio-economic data that are used for downscaling (Riahi et al., 2017;Rogelj et al., 2018).

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Not all combinations of RCP GHG forcing scenarios and SSP storylines are meaningful, as some SSP storylines imply, e.g., emissions that lead to forcing levels below RCP 8.5 while other SSPs imply high unmitigated emissions, which are unrealistic to be mitigated to the lowest RCP forcing levels without substantially changing the socio-economic storyline. For the SSPv2 scenarios the possible combinations were determined by the IAMs: the SSP-specific baseline scenarios define the maximal forcing level for each SSP while the minimal level was found implicitly because the forcing level of low RCPs could not be 270 attained for all SSPs.

SSPv2 IAM runs (SSPv2d)
During the integrated assessment model (IAM) implementations of RCP SSP combinations (SSPv2) it was found that some combinations can not be implemented. Figure 8 in Riahi et al. (2017) illustrates the carbon prices needed to reach a certain mitigation level under a given SSP. The figure also shows that the RCP 8.5 forcing is only reached for SSP 5. All other

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SSPs have baseline emissions leading to a lower climate forcing. For SSP 1, RCP 6 is the baseline, for SSPs 2-4 the baseline forcings are between RCP 6 and RCP 8.5. Under SSP 3, the low emissions scenario RCP 2.6 can not be attained and under SSP 5 one model was unable to attain sufficiently low emissions. (see Rogelj et al., 2018, Fig. 5).
We downscale all SSPv2 runs, both marker and other. An overview is shown in Table 1 (2017), Calvin et al. (2017), and Rogelj et al. (2018). For SSP 1, RCP 6 is the baseline forcing for all models except WITCH, which has a slightly higher baseline such that RCP 6 is a mitigation scenario. "x" means that the forcing level could be attained by all models that implemented it, and "(x)" that it could be attained by at least one model but not in the marker implementation. All forcings in W/m 2 .

RCP and SSP basic elements (RCPd)
To select sensible combinations of RCP scenarios and SSP basic elements scenarios we use the SSPv2 IAM runs as a basis.
The combination of RCP 8.5 with SSP 1 is excluded because no model reached emissions significantly above RCP 6 levels and 290 the SSP 1 storyline of a rapid sustainable development is not compatible with RCP 8.5 emission levels. The baseline forcings of SSPs 2-4 do not reach 8.5W/m 2 , however, forcings are significantly above 6W/m 2 for SSP 2 (6.5W/m 2 -7.3W/m 2 ) and SSP 3 (6.7W/m 2 -8.0W/m 2 ) (Riahi et al., 2017). Thus we include the combination of RCP 8.5 with SSPs 2 and 3. SSP 4 models a very unequal socio-economic development with low reference emissions as only a small part of the world has high consumption levels and cheap mitigation options as investment in new technologies is high. The baseline forcing of 6.4W/m 2 295  is above RCP 6 but significantly below RCP 8.5. We thus exclude the combination of SSP 4 with RCP 8.5.
In the IAM studies it was also found that the SSP 3 storyline does not allow for sufficient mitigation to reach RCP 2.6 forcing levels (Riahi et al., 2017). Consequently, we exclude this combination. The combination of SSP 5 with RCP 2.6 is included as most models used for SSPv2 can attain the necessary forcing levels. All combinations considered are shown in Table 2. Table 2. Combination of RCP scenarios with SSP basic elements country results considered in this study.

Emissions from international shipping and aviation
Emissions from international shipping and aviation (bunker fuels) are not attributed to individual countries under the UNFCCC.
Therefore they need special consideration in the downscaling process.
RCPd Emissions from international shipping and aviation are included in the RCP scenario emissions. For CO 2 and N 2 O 305 (marine only) however, they are not provided as individual emission time series, but included into the regional emissions.
As growth rates of emissions from aviation and shipping likely differ from growth rates of general fossil CO 2 emissions, the inclusion changes the growth rates of the regional emissions pathways. As there are no readily available consistent CO 2 pathways for international shipping and aviation for the original RCP scenarios, they have to be either generated or taken from other scenarios. The RCPs provide data for several gases and pollutants for aviation and shipping. One 310 approach is to try to calculate CO 2 emissions consistent with the RCP emissions from other gases using correlations between CO 2 and the other gases obtained from scenarios which cover all gases. However, using the shipping and aviation time series from Owen et al. (2010) and QUANTIFY (2010) to compute the correlations, no consistent CO 2 pathways could be generated as results based on different gases were not consistent. We therefore have to use external scenarios. We use the CMIP6 emissions scenarios from Gidden et al. (2019) which are based on the RCP forcing and 315 SSP storylines and are consistent with the RCPs on a basis of the RCP forcing targets but not the pathways to reach these targets. See Table 3 for our choice of CMIP6 bunkers scenarios for the RCPs.
The CMIP6 scenarios contain emissions for international shipping for CO 2 and CH 4 as well as aviation emissions for CO 2 . Unfortunately N 2 O emissions are only given as a national total. We thus compute a N 2 O over CO 2 factor   . RCP and SSPv2 scenarios for total Kyoto GHG emissions (AR4 GWPs) excluding LULUCF. Scenarios are not harmonized to historical data. Historical data shown are from PRIMAP-hist with bunker fuel CO2 emissions added from CDIAC data (Boden et al., 2017;Andres et al., 1999;Marland and Rotty, 1984). from historical data ( -2012( average from Smith et al., 2014 and construct scenarios from the CO 2 scenarios 320 assuming this factor is constant over time. As N 2 O emissions only contribute roughly 1% of total bunkers emissions this simplification has very limited impact. To downscale total aviation emissions to domestic and international aviation we use the shares from the historical CMIP6 emission data (Hoesly et al., 2018).

SSPv2d
The SSPv2 scenarios as presented in the SSPDB (IIASA, 2018;Riahi et al., 2017) do not include explicit bunkers emissions. As for the RCPs we use the CMIP6 scenarios which also offer implementations of the new 1.9W/m 2 and 325 3.4W/m 2 forcing targets. We base the bunkers emissions on the forcing targets only and use the same emissions time series for all SSPs. The methods are generally the same as for the RCP scenarios with a few small adjustments. External scenarios are needed for all gases including CH 4 which has explicit data in the RCP scenarios but not in SSPv2. For the SSP baseline scenarios we use the SSP 3 baseline implementation reaching 7.0W/m 2 for all SSPs. The choice of scenarios is shown in Table 3.

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In the scenarios from two of the models (IMAGE and AIM-CGE) there is a slight (< 1GtCO 2 eq) discrepancy between global emissions and the sum of the regional emissions. The discrepancy is decreasing in time towards 2100. International bunkers are a good explanation for additional global emissions. However, the discrepancies are much smaller than any bunkers estimate, especially in the future. We thus discard the global data and work with the regional data as for all other scenarios. The bunkers scenarios are shown in Figure 4.
To create scenarios excluding bunkers emissions we subtract the bunkers emissions from the regional pathways using the historical CO 2 bunkers emissions from CDIAC (Boden et al., 2017;Andres et al., 1999;Marland and Rotty, 1984) (2004 to downscale the global aviation and shipping pathway to region level. This does not take into account the development of regional emissions and regional economies, but as it is unclear how the international bunkers emissions were  calculated and assigned to regions when creating the RCP data, a more sophisticated method would not necessarily lead to better results. Bunkers emissions also depend on the socio-economic storyline, not only the emissions scenario, so selecting the bunkers pathways solely based on the RCP forcing levels and not based on the SSP storylines, which govern, e.g. trade patterns, is a simplification. There are two reasons for this: firstly, for gases that are not well mixing (i.e. all except CO 2 and N 2 O, but of 345 these we only use CH 4 here) bunkers data are already given for the RCPs; secondly, there are no bunkers scenarios available for all different RCP SSP combinations, so basing the selection of bunkers scenarios on both RCP and SSP would require several assumptions.

Historical data
Our aim is to create a set of scenarios that is directly usable for climate policy research and analysis. It is important that the 350 country specific pathways are in line with historical data for both emissions and socio-economic variables. We do not use the historical data provided with the RCP and SSP scenarios as we want to use latest historical compilation datasets .

Historical emissions data
We use the PRIMAP-hist (v2.1) historical emissions time series . It combines multiple data 355 sources into one comprehensive dataset covering all Kyoto GHGs, all sectors, all countries, and all years from 1850 to 2017.
Emissions data for some gases and sectors is interpolated for the last years. The highest priority during the combination of time-series from different sources is given to data which has been reported to the UNFCCC by countries. The dataset can be viewed on Paris Reality Check (PRIMAP, 2020) and is openly accessible . 360 We use the PRIMAP-hist historical socio-economic time series (Gütschow, 2019). It is constructed using the same methods as the PRIMAP-hist emissions time series.

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GDP data are based on purchasing power parity (PPP) adjusted data from the Penn World Table (Feenstra et al., 2015(Feenstra et al., , 2019. Missing data are filled using the 2018 Maddison Project Database (Bolt et al., 2018b, a) and WDI. Finally we fill missing historical data from a processed version of the older Maddison Project data (Geiger, 2018;Geiger and Frieler, 2017;Bolt and van Zanden, 2014;Maddison Project, 2013). See also (Gütschow, 2019).
The choice of PPP adjusted GDP has two reasons: firstly, for compatibility reasons as the SSP data are given in PPP corrected 370 form; secondly, PPP adjusted GDP is more comparable between countries than market exchange rate (MER) based GDP, which is important for the downscaling process as the process assumes convergence of emissions intensities.

Regions and country coverage
Here, we provide information on the regions used for the input data and the conditions under which countries are included in the input data and the final dataset. For a country to be available in the final time series it needs to be included in the SSP basic 375 elements GDP time series, the historical data for both emissions and GDP and for the SSPv2 scenarios it further needs to be included in the region definitions of the IAMs. Table 2 of the SI gives an overview over available countries in each scenario, the input data, and the final dataset. Each of these regions is downscaled individually and not influenced by values from other regions. Where countries are missing in the socio-economic pathways or the historical emissions data, they are ignored and the regional emissions are split among the available countries. The SSP basic elements do not cover all countries but, depending on the modeling group, leave out some smaller countries. This excludes several small states such as most of the small island states. Those states are therefore excluded from the downscaled dataset. A list of those countries can be found in the SI in Section 1.3.7. Some countries do not 390 have data for all variables and are included in the final datasets with the available variables.
The socio-economic scenarios provided by the SSPbe modeling groups contain population (KC and Lutz, 2017) and GDP (Dellink et al., 2017;Leimbach et al., 2017;Crespo Cuaresma, 2017) projections on a per-country or detailed per-region level.
Population data are only provided by the IIASA group, the other groups (OECD and PIK) use the IIASA population projections to build their GDP projections. GDP data are provided in purchasing power parity (PPP) corrected form in 2005 international 395 dollars (GKD) 1 . The PIK data are provided on a level of 32 world regions. We downscale it to individual country-level using the underlying IIASA population data and the method introduced in Section 4.2. In the SI, Section 1.2 we present the exact region definitions and list of missing countries for each modeling group.

SSPv2d
The SSPv2 IAM implementations provide both emissions and socio-economic data on the level of 5 world regions similar 400 to the regions used in the RCPs. However, the exact region definitions in terms of included countries differ from model to model. A detailed list with region definitions is available from the SSP database (IIASA, 2016). We use the model dependent region definitions to downscale both socio-economic and emissions data. GDP data are provided in PPP corrected form in 2005GKD/USD. The socioeconomic data of the SSPv2 runs is based on the IIASA country population data (KC and Lutz, 2017) and the OECD GDP data (Dellink et al., 2017). Consequently, we use these datasets to downscale the IAM data to 405 country-level using an external input based downscaling method (see Section 4.3). In the SI, Section 1.3 we present a list of missing countries for each model.

Sectors and gases
The sector and gas resolution of the historical time series is finer than the resolution of the scenarios for all sectors, gases, and countries. Thus, the resolution of the final dataset is determined by the resolution of the scenario data. In this section we are should make knowingly instead of unknowingly using our assumptions. In conclusion we exclude LULUCF data from the downscaling as done in van Vuuren et al. (2006van Vuuren et al. ( , 2007.

RCPd
The RCPs include information for the Kyoto GHGs (CO 2 , CH 4 , N 2 O, and the fluorinated gases (f-gases)) as well as several other substances (CO, SO 2 , NH 3 , NO x , Black Carbon (BC), Organic Carbon (OC), Volatile Organic Compounds (VOC), and 420 Ozone Depleting Substances (ODS)). Here we focus on the Kyoto GHGs because of their special relevance to the UNFCCC negotiations and availability of historical data. Additional substances can be added if there is demand from the scientific community and where historical data are available (for historical data see Hoesly et al., 2018;Meinshausen et al., 2017).
Fluorinated gases are treated as one gas at the moment. Historical data for fluorinated gases is available at a level of aggregate HFCs, aggregate PFCs, and SF 6 for all countries, but to be consistent with the SSPv2 scenarios which only provide aggregate 425 data for fluorinated gases we do not use this more detailed data in the downscaling process. Some substances such as black carbon need other downscaling methods as they are often co-emitted with gases like CO 2 . This correlation of emissions has to be taken into account in the downscaling.
The sectoral detail of the emissions data provided with the RCPs differs between the greenhouse gases. The data for the most important gas, CO 2 , is only resolved into emissions from land use, land use change, and forestry (LULUCF) and emissions from fossil fuels and industry. We employ the same sectoral resolution for the other Kyoto GHGs. N 2 O data are only available as national total. As LULUCF emissions only constitute a fraction of roughly 3% of global N 2 O emissions (in 2015, see Gütschow et al., 2018) we use the total emissions as a proxy for fossil fuel and industrial emissions.

SSPv2d
In principle the SSPv2 scenarios cover the same substances as the RCP scenarios. However, fluorinated gases are only avail-435 able as a global warming potential weighted aggregate time series. Therefore, fluorinated gases (f-gases) are treated as one substance. While the global warming potential (GWP) used for the f-gases basket is not explicitly given, the data are consistent with a Kyoto GHGs basket 2 created using GWPs from the IPCC's Fourth Assessment Report (AR4). Therefore, we assume that the f-gases basket has been calculated based on AR4 GWPs.
In terms of sectors the SSPv2 scenarios offer less detail than the RCPs: CO 2 , CH 4 , and N 2 O emissions are available for 440 national total and a sector called "land use" independently. For CO 2 , and for some scenarios also for CH 4 , additional time series for emissions from fossil fuels and industry are provided. However, the employed definition of the "land use" sector differs from the definition in the IPCC categorizations. The high emission levels for CH 4 and N 2 O suggest that, rather than for land use only, the time series cover emissions from the Agriculture, Forestry, and Land Use (AFOLU) sector. For CO 2 this is no practical problem as agricultural CO 2 emissions contribute less than 0.1% to total CO 2 emissions  and we use 445 the "land use" sector as a proxy for LULUCF. However, for N 2 O and CH 4 this is not possible as agricultural contributions are substantial. We thus use national total emissions as a proxy for fossil fuel and industrial emissions, as LULUCF emissions for these gases account for only 3% (N 2 O) and 4% (CH 4 ) of national total emissions (Gütschow et al., 2018). Emissions of fluorinated gases are available as national total only, which suffices as they originate from industrial sources only.

Downscaling of RCP and SSPv2 scenarios 450
The following describes the generation of the downscaled RCP and downscaled SSPv2 scenarios step by step from the preparation of input data to the combination of historical and scenario data for the final time series.
Data preparation The RCP and SSP data are processed as described in Section 4.1. Historical data does not need preprocessing at this step.
Downscaling of socio-economic data Not all socio-economic data have country resolution. The PIK GDP data need down-455 scaling to country-level (Section 4.2) and the SSPv2 socio-economic data as well (Section 4.3). After the downscaling, all socio-economic data are processed to match the country definitions of the historical emissions data.
Generation of socio-economic scenarios In this step GDP and population time series from all SSP scenarios are combined with historical data (Section 4.4). The socio-economic part of the dataset is finalized with this step and is used as input for the emissions downscaling.
Downscaling of RCP and SSPv2 emissions data RCP data are downscaled using the SSP basic elements country data (Section 4.5), while SSPv2 data are downscaled using the downscaled SSPv2 socio-economic data (Section 4.3). During the process the downscaling key is harmonized to historical data (Section 4.4).

Generation of emissions scenarios
In the final step the downscaled RCP and downscaled SSPv2 emissions scenarios are combined with and harmonized to historical emissions data. (Section 4.6).

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All operations are carried out independently per scenario, region and gas. The combination of historical and scenario data is carried out independently per scenario, country, and gas.

Preparation of RCP and SSP data
RCP and SSP data have to be preprocessed such that data are available for all sectors, gases, and years needed for the downscaling.

SSP basic elements
The SSP basic elements country-level data are first summed to the country definitions used for the historical GDP and population data. GDP data are given in PPP corrected 2005USD and have to be converted to 2011GKD (see Appendix C1 for details).

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As IIASA and OECD data cover a slightly different set of countries we create a composite GDP source which uses the OECD GDP data as the basis and fills missing countries from the IIASA data. See Table 2 of the SI for details.

SSPv2 socio-economic data
The SSPv2 socio-economic scenarios are interpolated to obtain yearly values from time series with a temporal resolution of 10 years. No further processing is done at this point.

SSPv2 emissions data
The SSPv2 emissions data are interpolated to obtain yearly values. N 2 O and CH 4 emissions excluding LULUCF are obtained from national total emissions. Existing time series are discarded because they do not include agricultural emissions. Fluorinated gases are only available as a AR4-GWP weighted sum. To create a time series for SAR GWPs, regional conversion factors from AR4 to SAR are calculated from EDGAR v4.2 data for individual gases using the years 2000 to 2012. As for CH 4 and 490 N 2 O, a copy of national total f-gases emissions is used for national total excluding LULUCF. We build Kyoto GHGs baskets for SAR and AR4 GWPs.
Time series excluding bunkers emissions are created in accordance with Section 3.1.3.

Downscaling of PIK GDP data
The PIK GDP data are not available on a per country-level, but only for 32 regions. These regions are downscaled to country-495 level using external input downscaling (see Section 2.2) with country shares from the OECD GDP data (complemented by IIASA data for missing countries).
The results of the GDP downscaling are in line with the GDP projections of the two other modeling groups. The pathways show similar developments and the spread between different scenarios is similar to the other modeling groups. This is to be expected as we use the country data from OECD and IIASA as external input to the downscaling. For results see Figures 6 and 500 7.

Downscaling of SSPv2 GDP and population data
The population and GDP data used as input to the SSPv2 IAM runs are based on the SSP basic elements results. IIASA population data (KC and Lutz, 2017) and OECD GDP data (Dellink et al., 2017) are used. In theory the data used by the IAMs should be identical to the country model data, however, different region specifications can introduce small changes in the data. 505 We thus do not take the country data directly but use it as the key in an external input based downscaling of the IAM data: the regional GDP and population time series from the IAM scenarios are downscaled to country-level using shares from the country model results. We use model specific region definitions for the downscaling (see Section 3.3.2 and SI, Section.

Harmonization
Harmonization of scenario data to historical data is used in several places throughout this study. Whenever IPAT based down-510 scaling is used, the downscaling key is harmonized to historical data. This is necessary to ensure that the concept of converging emissions intensities holds for the final scenarios, in which both socio-economic and emissions data are harmonized to and combined with historical data. Downscaling with external or constant shares does not need this harmonization step as both methods do not use socio-economic data. For the RCP and SSPv2 emissions downscaling this means that the resulting downscaled data are consistent with the harmonized GDP data, not with the raw SSPv2 GDP data. The effect of GDP harmonization 515 is shown in the SI in Section 2.3.
We also create time-series where scenario data are harmonized to historical data for socio-economic and emissions data.
The harmonization techniques and parameters are similar for socio-economic and emissions data. The harmonization year is always 2017. For the historical value we do not directly use the 2017 data, but calculate a value using a 13-year linear trend (2005 -2017) to weaken the influence of short term fluctuations in data. From this value (E h,hist ) and the 2017 scenario 520 value E h,scen = E scen (2017) a harmonization factor is calculated: f h = E h,hist /E h,scen . For socio-economic data we use this harmonization factor to harmonize the whole time series: This amounts to using the scenario growth rates to extend the historical time series. For GHG emissions data we phase out the harmonization factor f h (y) linearly until a convergence year y c = 2050. Thus, f h (y h ) = f h and f h (y c ) = 1 and linear 525 interpolation between these values gives: We phase out the harmonization factor to both keep the cumulative emissions of the scenario close to it's design and achieve a smooth transition from historical emissions to scenario emissions. Scenarios where bunkers emissions have not been removed before downscaling the post 2050 emissions include full bunkers emissions.

Downscaling of RCP and SSPv2 emissions data
RCP and SSPv2 downscaling uses the IPAT based convergence downscaling with exponential convergence of emissions intensities as introduced in Section 2.3 for all gases and sectors. The parameters are the same for RCP and SSPv2. The convergence 540 years are set for each SSP individually but with no regional variation. Convergence years are shown in Table 4.   We downscale each RCP SSP combination from Table 2 for all three GDP country model groups (IIASA, PIK and OECD) and each SSPv2 scenario from Table 1 for all available IAM implementations (see Appendix B1 for details). Each of these scenarios are available in two versions, one where the scenarios have been corrected for bunkers emissions and one where they 550 have not been corrected.

Combination with historical data
The downscaled RCP and downscaled SSPv2 data are combined with historical emissions data such that historical data takes precedence over scenario data where both are available. The last year with historical data is 2017. The scenario data are harmonized to interpolated 2017 historical data as described in Section 4.4.

Results
Here, we discuss and present the data for some key countries, selected from all five regions. From the Asia regions we selected China, Afghanistan, and South Korea, to represent the diverse economical situations present in the region. Afghanistan is an extreme case not only in the Asia region but globally, as the SSP basic elements project very high GDP 3 growth rates ( Figure 6). From Latin America we select Brazil and Guatemala, which differ substantially in historical emissions intensity 560 and economical development under the SSPs. The Middle East and Africa region is represented by South Africa, a country with relatively high GDP per capita, and Ethiopia with one of the world's lowest GDP per capita values but very high economical growth in recent years. For the OECD we selected three countries: two major world economies, the USA and Great Britain, which have high GDP per capita, with the USA having twice the UK's emissions per unit of GDP; the third country is Bulgaria, with roughly half of the UK's GDP per capita. The Reforming Economies region is represented by its main economical power 565 Russia, and by Uzbekistan, which has a GDP per capita of less than one third of the Russian GDP per capita. For these countries we present selected downscaled scenarios to highlight some of the factors influencing the downscaling results.
The final GDP scenarios are displayed in Figures 6 and 7. The SSPv2d GDP pathways are very similar for all IAM groups as they are all based on the same OECD country data. The SSPbe data from IIASA and PIK vary substantially from the OECD and SSPv2d data for several countries and scenarios.
570 Figures 8 and 9 show the resulting country pathways for RCP 2.6, aggregate Kyoto GHGs, and IPAT downscaling with exponential convergence. Results for individual gases and all RCPs can be found in the SI in Section 2.4. Figures 10 and 11 compare the exponential IPAT results with other downscaling methods for RCP 2.6, SSP 2. Results for individual gases and an additional RCP SSP combination (RCP 6.0, SSP 5) can be found in the SI in Section 2.5.
Influence of the socio-economic scenarios on downscaled emissions is high where the socio-economic scenarios and / or 575 historical emissions intensity are diverse within a region. Where they are similar, the resulting country emissions pathways are similar. We have selected countries which differ in at least one of these indicators for the example plots shown, and consequently all regions show some differentiation. In relatively homogeneous regions like the OECD the differentiations are  small and comparable to the spread of scenarios from different modeling groups, while for regions with large differences in historical emissions intensity and / or GDP growth rates, the country pathways are show strong variations. The most prominent 580 example in the figures is Afghanistan, where high GDP growth and converging emissions intensities lead to 2100 negative emissions, which are more than twice the current positive emissions. Relatively developed countries in the same region have much smaller negative emissions relative to current emissions levels. We have to note here that this is a downscaling study, not an equity study. There is no implication of fairness in the resulting pathways.
The influence of the downscaling method is most prominent for regions with high economic differentiation as well. However, 585 especially for the Asia region, the differences between methods are more visible than the differences between scenarios. The major influence of the GDP growth rates is clearly visible from Figures 10 and 11: constant shares downscaling, which does not take the GDP scenarios into account, differs strongly from the other methods, which use GDP data. When comparing pathways with convergence (BIE) with pathways without convergence (BIC), the influence of convergence of emissions intensities is visible as well, but less prominent.
590 Figures 12 and 13 show the influence of the correction of scenarios for bunkers emissions on the example countries EU ( Figure 12) and Ethiopia ( Figure 13). As bunkers emissions are distributed to the regions based on historical emissions shares the influence is much larger for the EU than for Ethiopia. While the absolute emissions difference is higher for high emissions scenarios, considering bunkers emissions can be decisive for net-negative or net-positive emissions in high mitigation scenarios ( Figure 12). Figures for the other example countries can be found in the SI in Section 2.6.

6 Discussion and Limitations
The main challenge for a downscaling methodology is to produce sensible results in regions with diverse economical situations, especially in scenarios with strongly decreasing or even negative emissions. The established IPAT with exponential convergence method does not work for numerical reasons and is problematic because it produces early negative emissions for countries with low historical emissions intensities, no matter if this is due to poverty or low emissions technologies. We 600 opted to converge emissions intensities before the transition to negative emissions, which alters the concept of convergence.
Generally, a downscaling process always needs several assumptions that influence the final data. These assumptions impact both downscaling results and results of studies based on the downscaled data. Thus, downscaled data also has to be used with caution and keeping the assumptions made in mind. In the following, we list the main limitations of our approach and their impacts on the resulting emissions pathways.

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The choice of methodology is obviously a main driver of the downscaling results (Figures 10 and 11). We chose the IPAT based convergence downscaling (Section 2.3) as methodology for the main dataset, but also offer downscaled data using the IPAT equation and constant relative emissions intensities (Appendix D1) as well as simple constant share downscaling (Section D2) for reference.
In all cases, the convergence method (Section 2.3) assumes at least partial convergence of emission intensities within a   Figure 8. Results for RCP 2.6 and all SSPs for both RCPd and SSPv2d scenarios. Countries from OECD and Asia regions. For the OECD region the differences in country pathways are driven by differences in regional pathways, not by the socio-economic development leading to very similar pathways for the countries in the region. In the Asia region the socio-economic development is more diverse and strongly influences the resulting country emissions pathways. Most prominent example are the high negative emissions for Afghanistan in SSP 5 driven by the very high GDP growth rates ( Figure 6). Note: Korea is in the OECD region for the WITCH GLOBIOM model.        Bunkers influence for European Union (28) total Kyoto gases (AR4) excl. LULUCF GDP studies exist (Liddle, 2010;Markandya et al., 2006). The energy sector is a main driver of greenhouse gas emissions for most countries and therefore energy intensity of the GDP is a major input to the emission intensity of the GDP. Historical data show that the energy intensity often converges within regions (Markandya et al., 2006), however, this is not true for all regions (Liddle, 2010). 615 Several studies deal with the convergence of per capita CO 2 emissions and come to different conclusions: Stegman and McKibbin (2005) find that when a large cross section of countries is considered there is little evidence for convergence, while there is some evidence of convergence within the OECD region. This is generalized by Panopoulou and Pantelidis (2009) who find that convergence to different per capita emissions levels exist, a concept they call club convergence. Strazicich and List (2003) find that per capita CO 2 emissions levels have converged among 21 industrialized countries, which is confirmed by 620 Romero-Ávila (2008), Jobert et al. (2010), and Chang and Lee (2008). Ordás Criado and Grether (2011)  This shows that convergence of emissions intensities within regions is a sensible assumption, but it is important to note that it is an input to the downscaling process and thus the emissions intensities of the downscaled data are an input to and not a 625 result of the process. As a regional transition to negative emissions and negative emissions intensities has not yet been observed, there is no evidence if convergence is a sensible assumption for pathways with negative emissions. It is not yet clear which technologies will be used to achieve negative emissions. While for some technologies (e.g., direct air capture) a relation to GDP seems sensible, other technologies like BECCS also depend on national circumstances such as the availability of land to grow energy crops and 630 safe storage options for the captured CO 2 . More generally, the downscaling methodology considers the emissions intensity per country and gas but does not consider the reasons for high and low emissions intensities. Thus, if a region contains two countries with similar emissions intensities in the harmonization period, the algorithm will create similar emissions intensity pathways for both countries -neither considering if, e.g., a low emission intensity comes from a low development level or a high share of renewables, nor considering the potential for mitigation technologies (e.g. for BECCS). As our analysis is carried 635 out on national total emissions per gas it is also not taken into account if, e.g., high methane emissions come from agriculture or fugitive emissions from fossil fuel production and handling, which are easier to mitigate than agricultural emissions. There are several sources for uncertainty in the data presented here, e.g., the uncertainty in historical emissions and GDP data. But the most important source of uncertainty is that we are using scenarios that project GDP and emissions development 645 90 years into the future under given broad storylines. These scenarios are based on several assumptions and can only model idealized economical and technological developments. Some of the scenarios use technologies not yet proven to be applicable on large scale (BECSS), technologies deemed too dangerous to be used by several countries (nuclear), assume that we solve the problem of high variability in availability of renewable energy sources, et cetera. On the other hand the models cannot anticipate new and still unknown technologies, which might solve the problem in ways not imaginable today. Furthermore, the emissions reductions in IAMs mainly come from technological change in energy production. The energy intensity of the GDP is very similar between baseline and mitigation scenarios (see, e.g. Figure 3 of Peters et al., 2017). Thus, an important possibility for emissions reduction -the reduction of energy use -is only considered partly by IAM scenarios. The underlying population and GDP projections do not model crises as the 2008 financial crises or the 2020 global COVID-19 pandemic and their impact on lives and economy.

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Most IAM scenarios have been created using some kind of cost optimization routine and therefore assume costs to be the driving factor of economic decision making -just steered by parameters such as a carbon tax or an emissions cap. They thus assume an idealized version of the current economical system where cost-optimal decisions are made on a rational basis.
Essentially, IAM scenarios assume no revolutionary changes (be it of technological or societal nature) but rather a continuation of our current system with some modifications to reduce GHG emissions. While IAMs are one of the main tools to generate 660 and assess socio-economic scenarios to mitigate climate change, their usefulness is not undisputed in the scientific community (see, e.g. Jewell and Anderson, 2019). Whenever the country-level scenarios presented here are used, the limitations and assumptions of the downscaling process as well as the underlying models have to be taken into account.

Conclusions
The country resolved downscaled RCP and downscaled SSPv2 scenarios we present here allow for climate policy analysis 665 in terms of RCP GHG forcing scenarios and SSP socio-economic storylines on a per country basis. While we treat the "IE" method with converging emissions intensities as our main dataset and use the others for reference, users can opt for more conservative assumptions using the datasets which employ constant shares (CS) and constant relative emissions intensities (IC) downscaling. Earlier version of the scenarios presented here have been used in several studies (Meinshausen et al., 2015;Robiou du Pont et al., 2016;du Pont et al., 2016;Robiou du Pont and Meinshausen, 2018) and are used by the climate policy 670 assessment of the Climate Action Tracker (CAT: Climate Analytics and New Climate Institute, 2020). With this paper we make the data publicly available and describe the used methodology in detail. We hope that this enables a broader use of the data.

Data availability
All datasets produced for this manuscript are available for download at https://doi.org/10.5281/zenodo.3638137 (Gütschow et al., 2020). Each dataset comes in a csv file. The file name is constructed as follows: <Source><Bunkers><Downscaling>.

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The <Source> flag indicates which input scenarios were used.
PMRCP RCP scenarios downscaled using the SSPbe: emissions and socioeconomic data; scenarios are available both harmonized to historical data and non-harmonized.
PMSSP Downscaled SSPv2 scenarios: emissions and socioeconomic data; scenarios are available both harmonized to historical data and non-harmonized. 680 the <Bunkers> flag indicates if the input emissions scenarios have been corrected for bunkers emissions before downscaling to country level or not. The flag is "B" for scenarios where emissions from bunkers have been removed before downscaling and "" (empty) where they have not been removed. We recommend using datasets where bunkers emissions have been removed before downscaling.
The <Downscaling> flag indicates the downscaling technique used. All files contain data for all countries and variables, all scenarios (RCPd or SSPv2d) both harmonized and unharmonized. 690 We recommend the use of the "BIE" dataset as a default.
More information on the data structure of the files is a available in the data description in the data repository (Gütschow et al., 2020).

Appendix A: Definitions and acronyms
In this appendix we provide tables with lists of all acronyms and definitions used in the manuscript. Acronyms regarding the 695 naming of downscaled data and files containing the data are described in Section 8. Greenhouse gas related acronyms are listed in Table A1, acronyms related to integrated assessment models in Table A2, and RCP SSP related acronyms in Table A3.
Downscaling methods are listed in Table A4 and economical acronyms in Table A5.       The same is done if the PPP05 factor is not available. This is the case for Aruba and Eswatini. If a country is not present in the PPP11 time series we use data from the Penn World Table   version 9.1 (PWT: Feenstra et al., 2019Feenstra et al., , 2015. We multiply the market exchange rate (MER, "xr" in PWT) for the country 725 with the price level of the GDP ("pl_gdpo" in PWT) to obtain a PPP time series. This is used for Djibouti and Syria. If no PPP data are available only the USD inflation is used. This is used for Cuba, French Polynesia, New Caledonia, Puerto Rico, and Somalia. For most of these countries no historical GDPPPP data are available.
This transformation is not strictly needed to create our dataset, as we only use the growth rates of the GDPPPP scenarios, however, the transformed time series give a good indication on how large the discrepancy between historical GDPPPP data and 730 the scenario data is. Except for a few small countries the discrepancy is small. To demonstrate the influence of convergence we also created a dataset in which the regional emission intensity growth rates are used for all countries of a region. To achieve that, the emission intensity is held constant at the value of the harmonization 735 year for the temporary emission intensity pathway: EI c (y) = EI c (y h ).
See also Figure D1. While methodologically very similar to the exponential convergence downscaling this is actually not convergence downscaling, but a form of external input based downscaling.

D2 Constant shares downscaling 740
As a control case for the influence of socioeconomic data on the downscaling we also created a dataset not using GDP data at all. We downscaled regional scenarios using historical country shares S C (y 0 ) = E C (y 0 )/E R (y 0 ) (D2) Steps of external input based downscaling of regional emissions data using the IPAT equation and country GDP data explained using a two country region. We use the assumption of constant relative emissions intensities to enable the use of GDP as an external input for emissions downscaling. Regional emission data are given for the whole time period while for the countries only historical data are available (Panel a). GDP data are given for both countries and the region (Panel b) for the whole time period. In the first step temporary emission intensity pathways for the countries are calculated using a constant extrapolation of historical values (2015) (Panel c). Multiplication with the given GDP time series creates temporary emissions time series. These do not sum up to the regional value (see Panel d) and have to be scaled to the regional value (results in Panel e). This also changes the emissions intensities (Panel f) Table B1. SSP IAM implementations available in the SSPv2 database. Model names are abbreviated using the first character. Illustrative marker scenarios are marked by bold italic letters. There are no RCP 8.5 scenario implementations as no SSP IAM baseline shows forcing levels above RCP 8.5. Author contributions. JG designed the study, carried out the downscaling process and led the manuscript writing process. All authors discussed the methodology and results and contributed to the manuscript.
Competing interests. We have no competing interests to declare.
Acknowledgements. JG and AG acknowledge support by the German Federal Ministry for the Environment, Nature Conservation and Nu-