The U.S. Oak Ridge National Laboratory (ORNL) provides estimates of national carbon emissions from fossil fuel use and cement manufacturing data. Data on fuel use are drawn from the UN Statistical Division's (UNSTAT) database by ORNL, while the U.S. Department of Interior's Bureau of Mines furnishes data on cement production. The method of Marland and Rotty (1984) is applied to these data in order to convert apparent fuel consumption and cement manufacturing into carbon emissions. For each fuel type (solid, liquid, and gas), the estimated level of emissions is the product of three factors: (i) the quantity of that fuel type consumed annually,[6] (ii) the proportion of consumption which is oxidized for that fuel type, and (iii) the average carbon content for that type of fuel. Summing across all fuel types for each country generates the emissions from fossil fuels. Emissions from cement manufacturing are computed by multiplying the quantity of cement produced by a coefficient representing the average mass of carbon generated in production. Adding fossil fuel carbon emissions to those from cement production yields total emissions.[7]
The data are not ideal. They do not include effects of deforestation and land-use changes, nor the use of collected wood fuel as an energy source. Deforestation and land-use changes have been estimated to account for 17-23% of all annual anthropogenic emissions (World Resources Institute, 1996; Intergovernmental Panel on Climate Change, 1990). Data on the effects of deforestation, land-use changes, and firewood combustion on carbon emissions are not sufficiently developed to be included in this study. We suspect that these omissions would tend to lead to an underestimation of carbon emissions in poor countries, and hence an overestimation of the income elasticity of emissions. Even aside from these problems, emissions estimates also have an uncertainty of 6-10 percent at the global level, and perhaps higher at the national level.
Still, these estimates represent the best information available from a single source, and have the advantage of using a uniform estimation method for all countries. Annual data are now available for many countries back to the 1950s. However, country coverage widens considerably after 1975 (including, for example, data for the former soviet union). Also taking account of other data requirements (discussed below), we confined the analysis to the period 1975-92.
We have assembled other data from what appear to be the best available sources. The Penn World Tables (Mark 5.6) are the source of population and per capita GDP data. Per capita GDP is given in purchasing power parity (PPP) adjusted values based on 1985 U.S. dollars. GDP according to PPP has the advantage of expressing income in comparable units in terms of living standards across countries (as compared to GDP by market exchange rates). Details are provided by Summers and Heston (1995). Population data are used to convert total national emissions to per capita emissions.
The coverage of distributional data over time and countries is quite uneven, reflecting the availability of household-level survey data. The Gini index is (by far) the most widely used measure of inequality, though even then observations are sporadic.[8] We use the average Gini index for each country, averaged over all the data available for that country from the "high-quality" sub-set of the data base on Gini indices compiled by Deininger and Squire (1996). We use the average Gini index for the 1980s. This reduces the number of countries for which all data (including carbon emissions) are available to 42.[9] To test robustness to this choice, we also tried the average Gini index since 1975, the first year of the carbon emissions data; our results were affected little by this choice. The inequality data are not strictly comparable across countries, since there are underlying differences in the type of survey data; for example, some of the Gini indices are based on household incomes per person, while some are based on consumption expenditure.