DOI: 10.5281/zenodo.12188105
title: Fair emissions allocations under various global conditions

description: Introduction and Carbon Budget Explorer

This dataset contains information on how to fairly distribute the mitigation efforts that countries need to undertake to together achieve certain climate goals. There is no single answer to this question, but we explore this topic by looking at various global emissions pathways, and subsequently allocate these emissions to countries using different effort-sharing rules. Some of these rules can be considered fair and can be used as information in the debate on just transitions. Beyond this dataset, we also published the Carbon Budget Explorer: an online interactive tool that allows users to navigate through these results, without having to download and plot the data themselves. It is free and publicly available at www.carbonbudgetexplorer.eu.

Currently, the Carbon Budget Explorer relies on a previous version of this dataset (version 0.1, unpublished, but available upon request). The Explorer will updated with version 0.2 (i.e., the version presented in this data repository) during summer 2024.

Disclaimer

The research behind this dataset is still under development and therefore this dataset is not final. A preprint of a scientific publication is being drafted and will be published in summer 2024, along with potential updates of this dataset. Subsequently, the data is subject to potential changes upon peer review of this publication. Nevertheless, because (a version of) this data is already used in the Carbon Budget Explorer and in scientific projects, we feel it should be available and versioned. Hence this release of a "version 0.2".

Data description

Default (DefaultAllocations.zip and DefaultReductions.zip)

For many users, these are the main datafiles. Per country and region, allocations and reduction targets are shown for two trajectories, which are associated with 1.5 (with slight overshoot: peak temperature 1.6) and 2.0 degree pathways, and default settings across all other dimensions. The exact parameters used in these precooked pathways are shown in Table 1 (see "Dimensions"). The reductions_default_*.csv files show data along the same structure, also using the default pathways, but contain the emission reductions with respect to 2015 rather than absolute allocations.

Global pathways (GlobalPathways.zip)

Allocating emissions to countries starts with determining global emissions pathways. The files in GlobalPathways.zip contain projected global emissions on GHG, CO2 and non-CO2 levels, constrained by various global settings (see below) such as temperature targets and derived CO2 budgets. The pathway shapes are informed by mitigation scenarios from the IPCC AR6 database. The starting values are all harmonized with 2021 historical datapoints. For convenience, the emissionspathways_default.csv datafile provides the pathways with default settings (see Table 1, column 'Default'). The complete dataset can be found in emissionspathways_all.csv.

Emission allocations (Allocations.zip -> allocations_*.nc)

The emissions from the global pathways can be divided among countries according to different allocation rules (see 'Allocation rules' for more information). Files of the format allocations_region.nc indicate allocations according to all allocation rules, parameters and global choices, for a single region. Because of the high number of parameters and dimensions, these files are shared in NetCDF (.nc) format. NetCDF files are commonly used for storing multidimensional scientific data and can be displayed, analyzed and read/written, using GIS systems (such as ArcGIS, QGIS), MATLAB funcions (such as nccreate, ncread), R (e.g. using the ncdf4 package) and Python (e.g. using the xarray package).

Input data (Inputdata.zip)

Additional input data coming from third parties, such as population and GDP data, is stored in Inputdata.zip. We prepared these input data sources in the exact same format as the rest for convenience of the user, but we would like to emphasize that the appropriate references should be cited. For further information, please check 'Input data sources'.

Allocation rules

Below you can find a summarized description of all allocation rules. More detailed information can be found in Van den Berg et al. (2020), as well as in a scientific paper (preprint) expected in summer 2024. The rules have a variety of parameters, each included as dimensions in the data. See Table 1, in "Dimensions", for details.



The (immediate) 'Per Capita' method (PC) uses a country's population share in the global population and allocates future emissions accordingly. Naturally, socio-economic conditions affect this method. Therefore, all five SSPs are used in our analysis. 

'Grandfathering' (GF) is a method that preserves current emission fractions. In other words, all countries reduce their emissions proportional to their current share. Note that this rule is controversial and is commonly not regarded as fair (see Rajamani et al. 2021). It is include here for reference only.

The 'Per Capita Convergence' (PCC) method starts as 'Grandfathering', but converges over time to a 'Per Capita' basis. An additional important parameter here is the year at which this convergence completes.

The 'Per Capita via Budget' (PCB_lin) method is a specific implementation of distributing the total CO2 budget on a per capita basis, and then drawing a linear line from current emissions down to net-zero CO2. A median non-CO2 path is added to end up with a total greenhouse gas emissions line. This is similar to, for example, Fekete et al. (2022).

The 'Ability to Pay' (AP) method allocates emissions inversely related to the GDP per capita of countries. Also this method is dependent on the socio-economic scenario.

The 'Equal Cumulative Per Capita' (ECPC) method computes the total budget (past plus future) that a country would obtain based on their (past plus future) population fractions, and then subtracts from this the already spent emissions in the past.

The 'Greenhouse Development Rights' (GDR) method is, in the short run, based on a Responsibility-Capability Index, and in the long run based on GDP per capita (similar to 'Ability to Pay').


Dimensions

Table 1 - Data dimensions




Name
Unit
Range
Default
Description


General
 
 
 
 


Time
Year


Past: 1850-2021

Future: 2021-2100 (yearly or 5-year increments)

All
The historic data reported here ends in 2021, and we start our analysis in 2021. Intentionally, to be able to exactly match historic and future data. The year 2021 is chosen because of limited availability of more recent data sources.


Region
ISO3 code


Country-level (ISO3)

Country groups (e.g., G20 and Umbrella)

World ('EARTH')

All
 


Global
 
 
 
 


Temperature
Degrees temperature rise with respect to pre-industrial times


1.5 - 2.4 degrees

1.6 and 2.0
Peak temperature without overshoot


Risk
Probability of reaching a temperature target (i.e., percentile of climate sensitivity)


17%, 33%, 50%, 67%, 83%



50% (for 1.6 degrees) and 33% (for 2.0 degrees)



This governs the uncertainty in climate sensitivity. Because there is still uncertainty about the exact numerical response of temperature to CO2, we have to include this. Low-risk (e.g., 0.17) indicates that we assume a high climate sensitivity: for a given amount of greenhouse gas emissions, temperature rises higher. This means that carbon budgets at a given temperature level have to be lower. Vice-versa for high-risk (e.g., 0.83).



NegEmis
Quantiles of 2100 GHG emissions among AR6 scenarios with a similar temperature target


20%, 40%, 60%, 80%



50%



Even though negative emissions (predominantly in the second-half of the century) are not very relevant for achieving a certain peak temperature, they do alter the second half of global emissions pathways.



NonCO2red
Quantiles of non-CO2 reductions in 2040 with respect to 2020 among AR6 scenarios with a similar temperature target


10%, 33%, 50%, 67%, 90%

50%
Non-CO2 reduction varies greatly among mitigation scenarios, but at the same time has a large effect on the remaining carbon budget. Hence, we vary this factor.


Timing
-


Immediate or Delayed

Immediate
The timing of mitigation action up to 2030. Either this starts immediately (2020) or only after 2030. This factor distinguishes mitigation scenarios from which the functional form of the global emissions pathways are constructed.


Parameters in allocation rules
 


 

 
 


Scenario
SSP


SSP1-5

SSP2
Shared-Socioeconomic pathway, defining population and GDP data based on a scenario of how to perceive the future world.


Convergence_year
Year


2040-2100

2050
Year of convergence for the per capita convergence rule


Discount_factor
% per year


0%, 1.6%, 2%, 2.8%

0%
Discount factor of historical emissions, counting from the startyear 2021.


Historical_startyear
Year


1850, 1950, 1990

1990
Year from which and on historical emissions are accounted for in the computation of the responsibility of countries.


Capability_threshold
-


No, PrTh, Th

Th
Implicates whether an additional development threshold should be implemented for the computation of the capability of a country to contribute to mitigation. This is used in the calculations of the Greenhouse Development Rights rule. Entries are (1) no development threshold (No), (2) a threshold of $7500 (Th) or (3) the $7500 threshold plus additional progressivity factors. For more information, see Holz et al. (2019).


RCI_weight
-


Cap, Half, Resp

Half
Distinguishes how the Responsibility-Capability Index in the Greenhouse Development Rights rule should weight capability (fully = Cap) or responsability (fully = Resp). 'Half' indicates that both factors should weigh equally.




 

Input data sources

For most important data sources, aggregated regions (e.g., G20 and the Umbrella group) are not reported in the original data sources below. We did that aggregation ourselves.



Historic population: UN population data

Future population: SSP database

Future GDP: SSP database

Historical emissions: Jones et al. (2023)

Emissions pathways (shapes): Byers, E. et al. AR6 Scenarios Database.  (2022)

NDC data: PBL NDC tool

Carbon budgets: Forster et al. (2023)

Impact of non-CO2 on carbon budgets: Rogelj et al. (2024)


Contact

We are very open to suggestions of all kinds. Feel free to contact Mark Dekker at this email address or at the contact form on this website.