The Climate Equity Reference Calculator database

The database for the Climate Equity Reference Calculator includes 195 countries: 193 of the 195 members of the UNFCCC (except Andorra and South Sudan), plus Taiwan and Palestine.  Data for China and Hong Kong, which are typically reported separately in most income and emissions databases, are combined.

The Climate Equity Reference Calculator database is updated regularly with newly published data.  The current calculator and published documentation are based primarily on data from 2013 (see details below).  For archival purposes it is version 6.2.3.


Recent historical income (GDP) data comes primarily from the World Bank’s World Development Indicators Online,[1] which contains data for national income from 1960 to 2012 for almost all of the 195 countries in the database.  For most of the missing countries or territories, data comes from the CIA World Factbook.[2]  For data prior to 1960 or other missing years, data comes from the Maddison data set.[3]

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 2013-2018 are from the IMF’s World Economic Outlook (April 2013).[4]  Projected growth rates are applied to the most recent World Bank national GDP data (in most cases 2012); long term estimates (to 2030) are based on the McKinsey Climate Desk 2.1 dataset.[5][6]  Convergence between IMF and McKinsey growth rates is described in detail in Definition, sourcing, and updating of emissions baselines.[7]

CO2 emissions

For Annex 1 nations, estimates through 2010 of CO2 emissions from fossil fuel use and cement manufacturing are taken from the UNFCCC,[8] based on required national reports.  For non-Annex 1 Countries, CO2 data is taken from the Carbon Dioxide Information and Analysis Center (CDIAC), and include emissions from fossil fuels and cement production, but not bunker fuels.[9]  Data from countries not in CDIAC’s data are taken from the US Energy Information Administration or the International Energy Agency (IEA).  Emissions for 2011 and 2012 are based on PBL’s estimates for growth rates by country or region.[10]  Baseline emissions are projected after 2012 based on convergence from historical rates of intensity improvement to long-term (2030) rates of intensity improvement projections from McKinsey Climate Desk 2.1 (see endnote 5), combined with GDP projections described above; the full convergence algorithm is described in the technical baselines document (see endnote 7 above).

Non-CO2 GHGs

Estimates for non-CO2 GHGs for Annex 1 countries are taken from the IPCC’s national reporting (see note 8).  Estimates for non-Annex 1 countries come from US Environmental Protection Agency[11] (2011), which contains historical data for every five years from 1990-2005 and projections for 2010-2030.  Annual data are interpolated using a spline fit.[12]

CO2 from Land Use (LUCF)

Historical emissions (1850-2012) from LUCF (Land Use Change and Forestry) in the Reference Calculator Database are generated from multiple sources, as no authoritative, country-level dataset exists.  The total global LUCF budget is constrained to match the figures published by the Global Carbon Project (GCP).[13]  Regional values 1850-2005 are constrained to meet the ten regional budgets published by CDIAC, the so-called Houghton dataset;[14] that is the relative emissions in each region are scaled such that the sum of all regions matches the global total from GCP.  National budgets are derived from the relative shares of regional totals either in the MATCH database[15] or in data reported to the UNFCCC (annual reporting for Annex 1 countries 1990-2010, National Communications for some non-Annex I countries for selected years).[16]

Scaling from relative national values to match regional totals is not a simple linear transformation, as some countries have negative estimated LUCF emissions and linear scaling would actually implausibly adjust emissions in a region with both negative and positive emissions.  Instead, where the constrained regional total is less than the sum of national estimates, countries with negative emissions are held at their reported negative emissions levels and countries with positive emissions are proportionally reduced; where the constrained regional total is larger than the sum of national estimates, countries with negative emissions are scaled towards zero (that is, their emissions become less negative) and countries with positive emissions are scaled upwards.

Gini Coefficients

Gini coefficients for the majority of countries in the Equity Calculator database are taken from the World Income Inequality Database.[17]  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.[18]

Calculating the RCI from the Equity Calculator dataset

The 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.[19]   The lognormal distribution has been shown to provide a reasonable approximation of measured income distributions.[20]  With this assumption, any national income distribution can be modeled with just a Gini Coefficient and the per-capita income.) The Calculator’s initial setting of for development threshold is $7500 per capita, PPP adjusted; so we will use that number in the following example. For people making more than $7500 annually, only their income above that threshold counts towards the national measure of 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 $7500 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) 2015 is the sum of responsibility calculated in this way for each year from the specified start year to 2015.  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.

[3] Statistics on World Population, GDP and Per Capita GDP, 1-2008 AD, available at

[5] Summary data from the McKinsey v2.1 projections are available publicly at The underlying, slightly more detailed data (21 regions or countries) is available by subscription to the Climatedesk.

[6] Some additional minor changes have been made – e.g., Switzerland and Norway have been assigned to the EU27 region for the purposes of emissions intensity projections, as opposed to “rest of OECD Europe” in the McKinsey database which is dominated by Turkey and thus has an appropriately higher emissions growth baseline.

[9] nation.1751_2010.csv, available at

[11] USEPA (2012), Global Anthropogenic Non-CO2 Greenhouse Gas Emissions: 1990-2030. Report and data available at

[12] The interpolation is done using the spline command in the R programming language with its default parameters.

[13] The data file used in this version of the Calculator database is CarbonBudget_2012-data.xls, available at

[14] Houghton, R.A. 2008. Carbon Flux to the Atmosphere from Land-Use Changes: 1850-2005. In TRENDS: A Compendium of Data on Global Change. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tenn., U.S.A. Available at

[15] The MATCH dataset is available as supplemental online material published with Höhne, Blum, Fuglestvedt, Skeie, Kurosawa, Hu, Lowe, Gohar, Matthews, Nioac de Salles, Ellermann 2011:  Contributions of individual countries’ emissions to climate change and their uncertainty, Climatic Change 106:359-391

[16] See note 8 above.

[19] A good technical description of the lognormal distribution is given on Wikipedia.

[20] Lopez, J.H, and L. Servén. 2006. A Normal Relationship? Poverty, Growth and Inequality: World Bank Policy Research Paper #3814. Available at