# _3.2.1.1 Available data on quantities and prices_

Energy related data are sourced from the IEA database “World energy balances”, which is available from the OECD (https://www.oecd-ilibrary.org/).<sup>12</sup> In the model, we aggregate energy into four categories: coal (15.2% of the energy mix in 2017), oil (40.5%), natural gas (36.9%), and “renewables” (7.5%). The “renewables” category includes nuclear power, hydro, solar and wind. While nuclear power is not renewable, all these energy sources have in common that their net carbon emissions are zero or very low. A carbon price would thus not directly affect this energy category. In addition to the 55,561 kilotons of oil equivalent (ktoe) used from the energy sector, Pakistani households also used 30,187 ktoe from biofuels in 2011. Biofuels data are not included in the model due to a lack of macroeconomic data. From a carbon accounting point of view the omission is not serious as biomass is a “carbon neutral” energy source when managed well (Favero, Daigneault, and Sohngen 2020), although it can make a significant contribution to local air pollution. Biomass first absorbs carbon from the atmosphere (when a tree grows, for example) and then releases it again (when wood is burned).

The IEA provides data on energy produced, imported and exported and the shares of fuel type in electricity generation. The data are available in kilotons of oil equivalent (ktoe). While Pakistan exports some oil products, it is a net importer in every energy category, so that we bundle exports and imports as “net imports”. We denote energy produced by Pakistan (in ktoe) as with indicating the category (coal, oil, gas, renewable). Net imports are denoted as , while energy commodities consumed in the production kk tt E E QQ kk of electricity is denoted as . MM kk,tt QQ Historical market price data for energy is sourced from the World Bank’s commodity sheets (pink sheets). kk,tt QQ These are denoted as . The units of the prices are USD per metric ton (USD/mt) for coal, USD per barrel (USD/BBL) for oil and USD per one million British thermal units (USD/MMBTU). The following UUSSDD kk,tt PP conversions are applied to make the units between prices and quantities compatible: = 1.429 ~~,~~ B s sstt = 7.4 and = 39.683 . The International Renewable Energy Agency (IRENA) provides ββ<sup>EECCbbEE</sup> ttCCEE LL bbttss data on renewable energy. Given that historically the largest energy source in the “renewable” category ββ<sup>CCiiEE</sup> ttCCEE ββ<sup>EEbbff</sup> ttCCEE is hydropower, we use the price of hydropower as a proxy for the category historically.<sup>13</sup> For local currency conversion we use the rupee- U.S. dollar exchange rate, . Electricity prices are usually denoted as cents per Kwh, however, for consistency here they are scaled to reflect the price per ton of oil equivalent. tt ffxx In recent years, fossil fuels have been sold below their respective world market prices in Pakistan. While there are plans to reduce or abolish subsidies, fuel subsidies still amounted to a total of 3.4 USD billion in 2017 or 1.14% of GDP according to IEA data. In the model, subsidies on fuel are represented as negative carbon taxes. In the modeling, it is assumed that the government is not transferring funds to energy producers, and hence it is not a direct expenditure but rather a forgone revenue that it would have collected. The fiscal accounting from a debt and overall balance perspective would have been equivalent if it was accounted for as an expenditure. Mitigation policy can take the form of increasing carbon taxes, from the current negative values to zero and then to positive values. Carbon taxes ( ) are expressed as USD per ton of CO2 (USD/t). As indicated by the subscript , carbon taxes are time-variant. The distinction kk tt ττ SS

> 12 All data used in this model can be traced to the original data through the Excel workbook “Pakistan energy and emission data.xlsx”.

> 13 The Pakistani government provides detailed data on renewable energy prices in the “Indicative Generation Capacity Expansion Plan”. We are planning to update the model with these data.

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