The Climate Equity Reference Calculator database

10 October 2024, v7.4

The database for the Climate Equity Reference Calculator1 includes 197 entities: 196 of the 197 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 September 2024 (see details below). For archival purposes it is database version 7.4.4

Income (GDP)

Recent historical income (GDP)5 data comes primarily from the World Bank’s World Development Indicators Online [4], as the highest priority data source, which contains data for national income from 1960 to 2020 for almost all of the countries in the database. For most of the missing countries, data comes from the CIA World Factbook [5], as a second-order priority source. For data prior to 1960 or other missing years, data comes from the Maddison data set [6]–[8]. Where data are 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 are reported in 2010 US$, at market exchange rates (MER). For several of its calculations, the Calculator uses income adjusted for purchasing power parity (PPP). 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 2024-2029 are from the IMF’s World Economic Outlook (WEO), April 2024 [9].6 Each country’s IMF-reported growth rate is applied to the most recent World Bank national GDP data (in most cases 2020); the IMF WEO growth rate for each country for 2029 is also applied to 2030. Beyond 2030, long term estimates 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 [10].

The short term projections directly take the economic impact of the COVID-19 pandemic into account, both in terms of the economic shock in 2020/21 as well as typically lower growth rate projections through 2029. Long-term projections indirectly account for this impact by extending the same GDP projection time series beyond 2030.

CO2 emissions (excluding LULUCF)

CO2 data come from the PRIMAP-hist database [11], [12] for the period from 18507 through to 2022. PRIMAP-hist 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 [13], [14] 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 national 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 [10], combined with national GDP projections as described above.8 The EMF27-Base-FullTech scenario ensemble has been chosen since it represents a very conventional baseline scenario that does not intentionally embed preferences for any particular technologies. Another reason for choosing this scenario ensemble over more recent baseline scenarios is that contemporary baseline scenarios are typically conceptualized as “current policy” or “current effort” scenarios, whereas, conceptually, the Climate Equity Reference Calculator requires “no effort” baselines. Using “current policy” baselines in the Calculator database would disadvantage countries that are already undertaking meaningful mitigation efforts by disregarding those efforts in the effort-sharing calculations.

Non-CO2 Greenhouse Gases

Values for non-CO2 GHGs are also taken directly from the PRIMAP-hist database [11], [12]. 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).

Baseline projections for 2016 to 2035 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 ensemble by region [10].

CO2 from Land Use, Land Use Change and Forestry (LULUCF)

This database update, like every version since 7.2, does not support Climate Equity Reference Calculator calculations that include CO2 emissions from Land Use, Land Use Change, and Forestry (LULUCF). This decision has been taken for several reasons. First, LULUCF emissions data are subject to very large uncertainties and fluctuations, which in some cases are so large that for the same year and country one data source reports a land use sink while another reports a source. For example, for Annex I countries the UNFCCC data interface [15] reports removals three times as large in 2015 as the values of the PRIMAP-histdatabase, which are in turn based on FAOSTAT [16].

Further, including LULUCF emissions in a single framework together with CO2emissions from fossil fuel and industry and non-CO2 gases, presupposes the, in our view problematic, assumption that emissions from LULUCF and other sources are essentially fungible, and that emissions reductions in either space are perfectly equivalent. This has with profound implications, for example, with regard to the 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, users with specific projects where inclusion of LULUCF emissions is vital, should contact the authors to explore how this might best be facilitated.9Furthermore, even though the Climate Equity Reference Calculator does not support LULUCF anymore, the calculator database still contains up-to-date values for LULUCF emissions (for most countries, using the BLUE time series [18]as reported by the Global Carbon Project [13], [14], for Brazil using [19]). As a result, the LULUCF emissions time series will still be included in the files generated by the calculator’s “download complete Excel table” functionality, but these data should be used with appropriate caution.

Gini Coefficients

This version 7.4 includes a major update to the sourcing of Gini Coefficients for the Climate Equity Reference Calculator database.10 Recently, the World Inequality Database (WID.world) has established itself as a high-quality data source for inequality data with excellent temporal and spatial coverage [21]–[23]. WID.world now covers 173 of the 197 countries in our database for the period from 1980-2022 and provides Gini Coefficients for a number of different wealth and income inequality concepts. From among those, for the Climate Equity Reference Calculator database, we consider the inequality in post-tax incomes the most suitable metric, as it measures the distribution of income after redistribution, that is, it reflects the distributional effects of each country’s tax and social welfare systems (including in-kind welfare provision, such as government-provided health care).[24]

For the missing 24 countries,11 we turn to the World Income Inequality Database (WIID) [25] as a second-tier data source.12 The WIID utilizes different approaches, concepts, and underlying data sources for measuring income inequality than WID.world, and Gini coefficients from WIID are thus generally lower than those from WID.world. To compensate for this structural difference, we adjust the WIID-sourced values by multiplying with a constant (for each year) that is obtained by dividing the simple average across all Gini Coefficients in WID.worldfor that year by the average of the Gini Coefficients in WIID for the same countries for the same year.

It is important to note that this change of Gini Coefficients, relative to prior versions of the Calculator database, has substantial impact on the results of the effort sharing calculations for individual countries. For most countries, WID.worldreports higher Gini Coefficients than the data source for Gini Coefficients that had been used in Calculator databases prior to the present version 7.4 (namely version 2 of the WIID dataset [20]). Specifically, out of the 173 countries in the WID.world database, the WID.world Gini Coefficients are lower than those in our previous database versions for 27 countries (with an average decrease in Gini Coefficients in 2020 across these countries of 9.8%), whereas the remaining 147 countries have a higher Gini Coefficient (with an average increase of 34%). Perhaps the most notable examples of the 27 countries with decreased Gini Coefficients are the United States, China, and the United Kingdom.13

This means inequality within countries is higher for most countries with the current Calculator database than with previous database versions, which in turn means higher Capacity even without any changes to GDP, because an increase in the Gini Coefficient (signifying higher inequality) shifts income upward in the income distribution, and hence a larger fraction of a country’s income could be shifted above the development threshold and/or upper threshold, depending on where these thresholds are set.14 However, since it is a country’s share of global Capacity that determines its fair share, it is the relative shift of countries’ Capacity that matters (relative to the shift of all other countries). In other words, an increase (or decrease) in Gini Coefficient does not automatically result in an increase (or decrease) in fair share. For the definitions of Capacity that were adopted by a number of studies that used the Climate Equity Reference Calculator(e.g. [27]–[31]), developing countries as a group (and most of them individually – though note the exceptions listed in the footnote) are assigned larger fair shares with the Gini Coefficients from the WID.world database than they would have with previous database versions, while the reverse is generally true for developed countries. However, for definitions of Capacity that were significantly more progressive (i.e., a higher development threshold and/or uppper threshold), this may not be the case.

Population

Current, historical and projected population for most countries are taken from the 2022 edition of the United Nations Population Division’s World Population Prospects (medium variant) [32].

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.15 The lognormal distribution has been shown to provide a reasonable approximation of measured income distributions [34]. 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, in PPP-adjusted 2005 USD; so we will use that number in the following example. For people receiving 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 if they have different Gini Coefficients. Rather, the country with the higher Gini Coefficient, i.e. the country with the less equal income distribution, will have more income above the development threshold, and thus will have greater capacity16).

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 of income 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) 2035 is the sum of responsibility calculated in this way for each year from the specified start year to 2035.

Capacity and responsibility are then expressed as a percentage of the global total, and combined into a single “Responsibility and Capacity Index,” or RCI, by taking a weighted average. In other words, a country’s RCI measures the country’s share of the combined global Capacity and Responsibility. 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 [2].

References

[1] Ceecee Holz, Eric Kemp-Benedict, Tom Athanasiou, and Sivan Kartha, “The Climate Equity Reference Calculator,” Journal of Open Source Software, vol. 4, no. 35, p. 1273, Mar. 2019. https://doi.org/10.21105/joss.01273
[2] Eric Kemp-Benedict, Tom Athanasiou, Paul Baer, Ceecee Holz, and Sivan Kartha, Calculations for the Climate Equity Reference Calculator (CERc). Stockholm Environment Institute; EcoEquity, 2018. https://doi.org/10.5281/zenodo.1748847
[3] Ceecee Holz, Sivan Kartha, Tom Athanasiou, Paul Baer, and Eric Kemp-Benedict, Climate Equity Reference Calculator Database. Harvard Dataverse, 2018. https://doi.org/10.7910/DVN/O3H22Z
[4] World Bank, World Development Indicators. GDP (constant 2010 USD). NY.GDP.MKTP.KD. 2021 [Online]. http://databank.worldbank.org/data/reports.aspx?source=world-development-indicators
[5] CIA, The World Factbook. Central Intelligence Agency, 2017 [Online]. https://www.cia.gov/library/publications/the-world-factbook/
[6] Angus 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
[7] Maddison-Project, Maddison Project Database. 2013 Version. 2013 [Online]. http://www.ggdc.net/maddison/maddison-project/home.htm
[8] Jutta Bolt and Jan Luiten 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
[9] IMF, World Economic Outlook (WEO), April 2024: Steady but Slow: Resilience Amid Divergence. Washington, D.C.: International Monetary Fund, 2024 [Online]. https://www.imf.org/en/Publications/WEO/Issues/2024/04/16/world-economic-outlook-april-2024
[10] IPCC, AR5 Scenario Database. Version 1.0.2. Intergovernmental Panel on Climate Change, 2015 [Online]. https://tntcat.iiasa.ac.at/AR5DB
[11] Johannes Gütschow, Mika Pflüger, and Daniel Busch, “The PRIMAP-hist National Historical Emissions Time Series (1750-2022) v2.5.1.” Feb-2024. https://doi.org/10.5281/zenodo.10705513
[12] Johannes 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
[13] Global Carbon Project, “Supplemental Data of Global Carbon Budget 2023.” Global Carbon Project, Nov-2023. https://doi.org/10.18160/GCP-2023
[14] Pierre Friedlingstein et al., “Global Carbon Budget 2023,” Earth System Science Data, vol. 15, no. 12, pp. 5301–5369, Dec. 2023. https://doi.org/10.5194/essd-15-5301-2023
[15] UNFCCC, Greenhouse Gas Inventory Data. Time series Annex I. United Nations Framework Convention on Climate Change, 2019 [Online]. http://di.unfccc.int/time_series
[16] FAO, FAOSTAT database. Food and Agriculture Organization of the United Nations, 2015 [Online]. http://faostat3.fao.org/home/E
[17] David Tsai et al., Observatorio do Clima’s proposal for Brazil’s Second Nationally Determined Contribution (NDC) under the Paris Agreement (2030-2035). Climate Observatory, 2024 [Online]. https://www.oc.eco.br/observatorio-do-climas-proposal-for-brazils-second-nationally-determined-contribution-under-the-paris-agreement/
[18] Eberhard Hansis, Steven J. Davis, and Julia Pongratz, “Relevance of Methodological Choices for Accounting of Land Use Change Carbon Fluxes,” Global Biogeochemical Cycles, vol. 29, no. 8, pp. 1230–1246, Aug. 2015. https://doi.org/10.1002/2014GB004997
[19] SEEG Brazil, Total Emissions 1990-2022. System Study Greenhouse Gas Emissions Estimates (SEEG); Climate Observatory, 2024 [Online]. https://plataforma.seeg.eco.br
[20] UNU-WIDER, World income inequality database. Version 2.0c. Helsinki: United Nations University World Institute for Development Economics Research (UNU-WIDER), 2008.
[21] WID.world, World Inequality Database. WID.world. (Downloaded 2024-09-06). Paris: World Inequality Lab, 2024 [Online]. https://wid.world
[22] Lucas Chancel, Thomas Piketty, Emmanuel Saez, Gabriel Zucman, and et al., World Inequality Report 2022. 2022 [Online]. wir2022.wid.world
[23] Thomas Blanchet, Lucas Chancel, Ignacio Flores, and Marc Morgan, Distributional National Accounts Guidelines: Methods and Concepts Used in the World Inequality Database. Version February 27, 2024. WID.world, 2024 [Online]. https://wid.world/document/distributional-national-accounts-guidelines-2020-concepts-and-methods-used-in-the-world-inequality-database/
[24] WID.world, “Codes Dictionary,” WID – World Inequality Database. 2024 [Online]. https://wid.world/codes-dictionary/
[25] UNU-WIDER, World Income Inequality Database (WIID) Companion. Global Dataset. Version 28 November 2023. United Nations University World Institute for Development Economics Research (UNU-WIDER), 2023, pp. Version 28 November 2023. https://doi.org/10.35188/UNU-WIDER/WIIDcomp-281123
[26] Håkon Sælen, Vegard Tørstad, Ceecee Holz, and Tobias Dan Nielsen, “Fairness Conceptions and Self-Determined Mitigation Ambition Under the Paris Agreement: Is There a Relationship?Environmental Science & Policy, vol. 101, pp. 245–254, Nov. 2019. https://doi.org/10.1016/j.envsci.2019.08.018
[27] Civil Society Equity Review, Fair Shares: A Civil Society Equity Review of INDCs. Manila, London, Cape Town, Washington, et al.: CSO Equity Review Coalition, 2015 [Online]. https://equityreview.org/report
[28] Civil Society Equity Review, The 2023 Fair Shares Deficit: A Civil Society Equity Review of the NDCs and 2035 Mitigation Fair Shares. Manila, London, Cape Town, Washington, et al.: Civil Society Equity Review Coalition, 2023 [Online]. https://www.equityreview.org/report2023
[29] Ceecee Holz, Tom Athanasiou, and Sivan Kartha, “La juste part de la France dans la lutte contre les changements climatiques,” Serie de documents de travail de Climate Equity Reference Project, vol. WP008–FR, 2022. https://doi.org/10.5281/zenodo.2595500
[30] Ceecee Holz, Are G20 Countries Doing Their Fair Share of Global Climate Mitigation? Comparing Ambition and Fair Shares Assessments of G20 Countries’ Nationally Determined Contributions (NDCs). Oxford: Oxfam International, 2023. https://doi.org/10.21201/2023.621540
[31] Ceecee Holz, Canada’s Fair Share of 1.5°C-Consistent Global Mitigation Through 2035. Climate Equity Reference Project, 2024. https://doi.org/10.5281/zenodo.2595506
[32] United Nations, World Population Prospects 2022. (Medium Variant Projections). New York: United Nations Department of Economic; Social Affairs. Population Division., 2022 [Online]. https://population.un.org/wpp/
[33] Wikipedia, “Log-Normal Distribution.” 2017 [Online]. https://en.wikipedia.org/wiki/Log-normal_distribution
[34] Humberto Lopez and Luis Serven, A Normal Relationship? Poverty, Growth, and Inequality. World Bank, 2006 [Online]. http://documents.worldbank.org/curated/en/620771468150322825

  1. The Climate Equity Reference calculator [1] is available at https://calculator.climateequityreference.org. Its open source code can be inspected on Github at https://github.com/climateequityreferenceproject/cerc-web.↩︎

  2. The UNFCCC has 198 parties. However, one of those is the European Union, which in our database is represented by its member states and in the Calculator can also be examined as a single entity. The only state party to the UNFCCC that is not included in our Calculator is the Holy See, for which most of the indicators needed to construct the Calculator database are not readily available.↩︎

  3. Our methodology for combining the Gini Coefficients for these entities as a weighted sum of lognormal distributions is described in more detail in [2]. As of database version 7.4, we are not applying this methodology anymore and are instead using the Gini Coefficients for China. For all other metrics (emissions, GDP, population etc.), the figures for China include those of Hong Kong and Macao.↩︎

  4. Starting with version 7.0 from 2015, all main versions of the core database are available in the Harvard Dataverse [3].↩︎

  5. Conceptually, gross national income (GNI) would be the more appropriate principal measure to use in the Calculator’s effort sharing calculations as a measure of ability to pay, since GNI, unlike GDP, includes income earned by a country’s residents from sources outside the country while it does not include income earned in the country by non-residents. Instead, however, we are using GDP here as a proxy for GNI. The main reason is that publicly available datasets for GNI have major data gaps and are therefore not suited for a calculator that includes all countries. Importantly, GNI and GDP are extremely closely correlated, so that GDP is a suitable proxy for GNI, despite the former’s relative weakness as a measure of ability to pay.↩︎

  6. For a small number of countries, the IMF WEO does not provide a complete set of historical and projected GDP growth, typically owing to highly uncertain political and/or economic situations. For these countries (currently: Syria from 2011, Eritrea from 2020, Lebanon from 2022, Afghanistan and Sri Lanka from 2023, Palestine from 2024 and Venezuela from 2026), we use an arbitrary annual contraction rate of -10% until future IMF WEO updates resume projections. Additionally, due to the central role of GDP projections in our GHG baseline projections, for these countries, we hold GHG emissions constant from these same years onwards. Owing to this, results for these countries should be interpreted with a high degree of caution.↩︎

  7. Emissions before 1850 are ignored because most of the other variables required for our calculations have no reliable, country-level data sources for prior to 1850, and since pre-1850 emissions only represent about 0.3% of global cumulative CO2 emissions from fossil fuels, flaring and cement over the 1750-2020 period [13].↩︎

  8. For the seven countries for which the IMF WEO does not offer GDP projections (see above), and for which, therefore, our standard projection approach is unsuitable, we simply keep baseline emissions constant at the level of the last year for which the IMF WEO has data (see footnote above).↩︎

  9. For example, we contributed to Observatorio do Clima’s proposal for Brazil’s Second Nationally Determined Contribution (NDC) under the Paris Agreement (2030-2035) [17], which included LULUCF emissions in the mitigation fair shares calculations.↩︎

  10. Prior versions of the database have been using gini coefficients for income inequality from the World Income Inequality Database, maintained at the United Nations University’s World Institute for Development Economics Research (UNU-WIDER) [20].↩︎

  11. The missing countries are small island states and four European microstates.↩︎

  12. Only the Cook Islands and Niue are missing from both the WIID and WID.world datasets. For these two countries, we simply use the average across all Pacific Small Island Developing States for each year.↩︎

  13. For transparency, the other countries where this is the case are Azerbaijan, Belgium, Denmark, France, Gabon, Iceland, Ireland, Kenya, Lebanon, Lesotho, Luxembourg, Moldova, Namibia, Norway, Portugal, Sao Tome and Principe, Singapore, Slovakia, Slovenia, Spain, Switzerland, Ukraine, and Zimbabwe.↩︎

  14. For a more thorough discussion of the link between Gini Coefficients and Capacity, see Supplementary Text 3 in the supplementary materials to [26].↩︎

  15. Wikipedia [33] offers a good technical description of the lognormal distribution↩︎

  16. Again, a more thorough discussion of the effects of income inequality on the RCI, see Supplementary Text 3 in the supplementary materials to [26].↩︎