The database for the Climate Equity Reference Calculator1 includes 197 countries: 196 of the 196 state parties2 to the UNFCCC, plus Taiwan. Data for China, Macao, and Hong Kong, which are typically reported separately in most income and emissions databases, are combined.3 Likewise, we combine, where applicable, data for overseas dependencies of countries (e.g. we add Greenland and Faroe Island figures to Denmark). The Climate Equity Reference Calculator database is updated regularly with newly published data. The current calculator and published documentation are based primarily on data published up to March 2018 (see details below). For archival purposes it is database version 126.96.36.199
Recent historical income (GDP) data comes primarily from the World Bank’s World Development Indicators Online , as the highest priority source, which contains data for national income from 1960 to 2016 for almost all of the countries in the database. For most of the missing countries or territories, data comes from the CIA World Factbook , as a second-order priority source, For data prior to 1960 or other missing years, data comes from the Maddison data set –. Where data is available for a country for any year from a higher priority source, we use the slopes, rather than absolute values from lower priority sources to extend, or fill in, the time series. Data is reported in $2010 US, at market exchange rates (MER). Conversion to PPP (2005) is based on converting “Current US Dollars” for 2005 to PPP (Current International Dollars). The PPP conversion rate is held constant at this value.
Income (GDP) projections from 2017-2022 are from the IMF’s World Economic Outlook (October 2017  and January 2018 update ). Each country’s IMF-projected growth rate is applied to the most recent World Bank national GDP data (in most cases 2016); long term estimates (2023 to 2050) are based on the median GDP growth rates across the models in the EMF27-Base-FullTech baseline scenario ensemble as reported in the IPCC AR5 scenario database that are available for the five IPCC world regions .
CO2 emissions (excluding LULUCF)
CO2 data for the period from 18505 through to 2015 comes from the PRIMAP-hist database , , which is a well-documented, well-constructed, and well-maintained composite dataset compiled at the Potsdam Institute for Climate Impact Research (PIK). PRIMAP-hist, in turn, is based on various authoritative data sources including the UNFCCC, the Carbon Dioxide Information and Analysis Center (CDIAC), the EDGAR database and others. For Palestine, we use data from the Global Carbon Budget dataset ,  directly, and subtract those figures from Israel’s value as PRIMAP-hist reports those countries together.
Since one of the main purposes of the Climate Equity Reference Calculator is the assessment of national climate action pledges (for example, as expressed in countries’ Nationally Determined Contributions (NDCs) under the Paris Agreement), we project baseline emissions starting after 2015, the year the Paris Agreement was adopted. For CO2 emissions, exclusive of emissions from Land Use, Land Use Change, and Forestry (LULUCF), these projections after 2015 are based on the median carbon intensity changes modelled in the EMF27-Base-FullTech scenario from the IPCC AR5 scenario database for each of the five IPCC regions , combined with the GDP projections as described above. The EMF27-Base-FullTech scenario ensemble is chosen since it represents a very conventional baseline scenario that does not intentionally embed preferences for any particular technologies.
Our CO2 and non-CO2 (see below) baselines are jointly calibrated for their total to match the median baseline scenario of the UNEP Emissions Gap Report 2016  in 2030. Our calibration algorithm takes account of the different treatment of Annex I LULUCF emissions and emissions from international aviation and shipping in our database compared to the UNEP report.
Non-CO2 Greenhouse Gases
Values for non-CO2 GHGs are also taken directly from the PRIMAP-hist database , . All non-CO2 greenhouse gases are converted into CO2 equivalents (CO2eq) using the 100-year Global Warming Potential (GWP-100) values from the IPCC’s Fourth Assessment Report (AR4). This represents a shift from previous versions of this database, where we used the values from the Second Assessment Report. This change is consistent with recent trends (e.g. within UNFCCC reporting methodologies) to shift to AR4 values.
Baseline projections for 2016 to 2050 for all countries are based on the median annual absolute rates of change reported in the IPCC AR5 scenario database for the models of the EMF27-Base-FullTech scenario by region .
CO2 from Land Use, Land Use Change and Forestry (LULUCF)
With this update we decided to remove support for calculations that include CO2 emissions from Land Use, Land Use Change, and Forestry (LULUCF) from the Climate Equity Reference Calculator. This decision has been taken for several reasons. First, LULUCF emissions data is subject to very large uncertainties and fluctuations, which in some cases are large enough to lead to opposite results, depending on the data source chosen. For example, for Annex I countries the UNFCCC data interface  reports removals three times as large in 2015 as the values of the PRIMAP-hist database, which are in turn based on FAOSTAT . Further, including LULUCF emissions in a single framework together with CO2 emissions from fossil fuel and industry and non-CO2 gases, presupposes the problematic view that emissions from LULUCF and other sources are essentially fungible and emissions reductions in either space perfectly equivalent, with profound implications, for example, with regards to speed of the decarbonization of the energy system and the treatment of nature primarily as a carbon sink.
We are exploring the possibility of including LULUCF emissions again in future releases, albeit only in the context of the calculation of historical responsibility. However, if you have a specific project where inclusion of LULUCF emissions is vital, please contact us and we can explore how we might be able to help. Furthermore, even though the Climate Equity Reference Calculator does not support LULUCF anymore, the calculator database still contains values for LULUCF emissions (obtained using the methodology described in the previous database description, but new source datasets), which will be included in the files generated by the calculator’s “download complete Excel table” functionality.
Gini coefficients for the majority of countries in the Equity Calculator database are taken from the World Income Inequality Database . For countries which have reliable national or supranational sources (e.g., US Census Bureau, EU Europa database) newer Ginis are used where available. For some countries other sources are used, and for those for which no published figures are available, Gini coefficients are estimated on the basis of comparable countries.
Current, historical and projected population for most countries are taken from the United Nations Population Division’s medium variant .
Calculating the RCI from the Equity Calculator dataset
The Climate Equity Reference Calculator sets a country’s fair share of the global climate effort in proportion to its Responsibility and Capacity Index, or RCI, which in turn is calculated from a country’s capacity and responsibility. Capacity for a given year is defined as the sum of the income of all individuals in the country, excluding the total income of everyone under a user-specified income level referred to as the development threshold. Central to the calculation is the commonly used assumption that national income distributions can be modeled as lognormal distributions.6 The lognormal distribution has been shown to provide a reasonable approximation of measured income distributions . With this assumption, any national income distribution can be estimated with just a Gini Coefficient and the per-capita income. The Calculator’s initial setting for the development threshold is $7,500 per capita, PPP adjusted; so we will use that number in the following example. For people making more than $7,500 annually, only their income above that threshold counts towards the national measure of capacity (as a result, two countries with the same population and with the same per capita GDP will not have equal capacity; the country with the higher Gini coefficient has more income above the development threshold, and thus will have greater capacity). Responsibility is calculated in a similar manner. Emissions are calculated from income based on a user-specified elasticity, which is initially set to one (i.e., all individuals in a country have emissions proportional to income); thus, all emissions are excluded for those whose incomes are under the development threshold, and emissions equivalent to $7,500 of consumption at the national average carbon intensity are excluded for those with income over the threshold. Unlike the calculation of capacity, however, responsibility is calculated on a cumulative basis, starting at a user-specified initial year, so that responsibility in (say) 2030 is the sum of responsibility calculated in this way for each year from the specified start year to 2030. Capacity and responsibility are then expressed as a percentage of the global total, and combined into a single “Responsibility and Capacity Index” by taking a weighted average. In the Calculator, the Responsibility and Capacity weightings are initially set to be equal, but the calculator allows this to be user-specified, all the way from 100% Responsibility to 100% Capacity, to allow users to reflect their views that determining equitable shares of the global effort based on one of these factors alone is the appropriate ethical position to take.
A more detailed technical description of the algorithms and equations involved in these calculations is available elsewhere .
 E. Kemp-Benedict, T. Athanasiou, P. Baer, C. Holz, and S. Kartha, Calculations for the Climate Equity Reference Calculator (CERc). Stockholm Environment Institute; EcoEquity, 2018. https://doi.org/10.5281/zenodo.1748847
 C. Holz, S. Kartha, T. Athanasiou, P. Baer, and E. Kemp-Benedict, Climate Equity Reference Calculator database. Harvard Dataverse, 2018. https://doi.org/10.7910/DVN/O3H22Z
 World Bank, World development indicators. GDP (constant 2010 USD). NY.GDP.MKTP.KD. 2018 [Online]. http://databank.worldbank.org/data/reports.aspx?source=world-development-indicators
 CIA, The World Factbook. Central Intelligence Agency, 2017 [Online]. https://www.cia.gov/library/publications/the-world-factbook/
 A. Maddison, Statistics on world population, GDP and per capita GDP, 1-2008 AD. Groningen Growth and Development Centre, 2009 [Online]. http://www.ggdc.net/maddison/Historical_Statistics/horizontal-file_02-2010.xls
 Maddison-Project, Maddison project database. 2013 version. 2013 [Online]. http://www.ggdc.net/maddison/maddison-project/home.htm
 J. Bolt and J. L. van Zanden, “The Maddison Project: Collaborative research on historical national accounts,” The Economic History Review, Mar. 2014. https://doi.org/10.1111/1468-0289.12032
 IMF, World Economic Outlook database. October 2017 edition. International Monetary Fund, 2017 [Online]. https://www.imf.org/external/pubs/ft/weo/2017/02/weodata/index.aspx
 IMF, World Economic Outlook (WEO) Update, January 2018: Brighter prospects, optimistic markets, challenges ahead. International Monetary Fund, 2018 [Online]. http://www.imf.org/en/Publications/WEO/Issues/2018/01/11/world-economic-outlook-update-january-2018
 IPCC, AR5 scenario database. Version 1.0.2. Intergovernmental Panel on Climate Change, 2015 [Online]. https://tntcat.iiasa.ac.at/AR5DB
 T. Boden and B. Andres, Global CO2 emissions from fossil-fuel burning, cement manufacture, and gas flaring: 1751-2014. Carbon Dioxide Information Analysis Center (CDIAC), Oak Ridge National Laboratory, 2017 [Online]. https://cdiac.ess-dive.lbl.gov/ftp/ndp030/global.1751_2014.ems
 J. Gütschow, L. Jeffery, R. Gieseke, and R. Gebel, “The PRIMAP-hist national historical emissions time series (1850-2015). Version 1.2 (14 Dec 2017),” Mar. 2018. https://doi.org/10.5880/PIK.2018.003
 J. Gütschow et al., “The PRIMAP-hist national historical emissions time series,” Earth System Science Data, vol. 8, no. 2, pp. 571–603, Nov. 2016. https://doi.org/10.5194/essd-8-571-2016
 C. Le Quéré et al., “Global carbon budget 2017. National emissions v1.2,” The Global Carbon Project, 2018. https://doi.org/10.18160/GCP-2017
 C. Le Quéré et al., “Global carbon budget 2017,” Earth System Science Data, vol. 10, no. 1, pp. 405–448, Mar. 2018. https://doi.org/10.5194/essd-10-405-2018
 UNEP, The emissions gap report 2016. A unep synthesis report. United Nations Environment Programme, 2016 [Online]. http://uneplive.unep.org/media/docs/theme/13/Emissions_Gap_Report_2016.pdf
 Greenhouse gas inventory data. Time series Annex I. United Nations Framework Convention on Climate Change, 2019 [Online]. http://di.unfccc.int/time_series
 FAOSTAT database. Food; Agriculture Organization of the United Nations, 2015 [Online]. http://faostat3.fao.org/home/E
 UNU-WIDER, World income inequality database. United Nations University World Institute for Development Economics Research (UNU-WIDER).
 U. Nations, World population prospects: The 2017 revision. (Medium variant projections). United Nations Department of Economic; Social Affairs. Population Division., 2017 [Online]. https://population.un.org/wpp/DVD/
 Wikipedia, “Log-normal distribution.” 2017 [Online]. https://en.wikipedia.org/wiki/Log-normal_distribution
 H. Lopez and L. Serven, A normal relationship? Poverty, growth, and inequality. World Bank, 2006 [Online]. http://documents.worldbank.org/curated/en/620771468150322825
- The Climate Equity Reference calculator is available at https://calculator.climateequityreference.org and its open source code can be inspected at https://github.com/climateequityreferenceproject/cerc-web/.↩
- The UNFCCC has 197 parties, however, one of those is the European Union, which in our database is represented by its member states but in the calculator can also be examined as a single entity.↩
- Our methodology for combining the Gini coefficients for these entities as a weighted sum of lognormal distributions is described in more detail in .↩
- Starting with version 7.0 from 2015, all versions of the core database are available in the Havard Dataverse .↩
- Emissions before 1850 are ignored because most of the other varibales required for our calculations have no reliable, country-level data sources for prior to 1850, and since pre-1850 emissions only represent about 3% of global cumulative CO2 emissions from fossil fuels, flaring and cement .↩
- Wikipedia  offers a good technical description of the lognormal distribution↩