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Prepared by Fabian Valencia. The author thanks Dora Iakova, Luis Madrazo, Carlos Muñoz, Ian Parry, Glenn Sheriff, and seminar participants at the Mexican Ministry of Finance and the Central Bank of Mexico for insightful comments and suggestions and Alexander Herman for outstanding research assistance. This paper used Parry and others (2014) comprehensive calculations of optimal carbon taxes and updates it to 2015 prices.
Natural gas was exempted on account of being the cleanest fossil fuel. A 2013 Presidential Decree exempted Jet fuel from the carbon tax in adherence to international agreements signed by Mexico, which included the exemption of this fossil fuel from taxes.
The regulated retail price includes the carbon tax, excises earmarked for states, VAT, transport costs, distribution margins, and technical losses due to evaporation.
Parry and all (2014) calculate a potential reduction in deaths due to air pollution-related diseases of up to 20 percent for the case of Mexico.
This section briefly summarizes the methodology followed in Parry and all (2014) to compute Mexico’s suggested carbon tax rates. Further details and data sources can be found in Parry and all (2014) while theoretical underpinnings of fuel taxation can be found in Parry and Small (2005) and more general issues on optimal environmental taxation in the presence of other taxes in Bovenberg and Goulder (1996).
Quantifying climate change costs is subject to a great deal of uncertainty due in principal to three features of climate change. The first is the highly uncertain effect of emissions on specific climate outcomes; the second is the unknown form of human adaptation to problems building up over decades and centuries; and the third is the differences of opinion about the appropriate analytical procedure for aggregating effects occurring over long time intervals.
The population exposure uses an “intake fractions” approach, defined as the average pollution inhaled per unit of emissions released. This exposure is computed taking into account the population residing in regions within certain distance of emission sources; the change in the ambient concentration of pollution, for instance, per cubic meter; meteorological factors; and the emission rate of the pollutant in question (i.e. gasoline, diesel, coal, natural gas). Increased mortality rates from air pollution rely on empirical estimates on the link between intake fractions and mortality risks derived from pollution-related diseases. A controversial next step, but necessary to come up with a monetary cost of pollution, is to estimate how much people value mortality risk. Studies outside advanced economies are scarce. For this reason, the methodology takes an estimate of US$3 million from an OECD study (OECD, 2012) and adjusts it for differences in income levels to come up with country-specific values after updating the above benchmark to 2015 dollars. These monetary costs are ultimately expressed in terms of damage per ton of carbon emissions.
The estimates are computed in current US dollars, converted to MEX$ using purchasing power parity exchange rates, as recommended by Parry and others (2014). Purchasing power parity exchange rates take into account the local price level and thus reflect more accurately than market exchange rates people’s ability to purchase goods or pay out of their own income for risk reductions.
The 2016 budget proposed a reform to the IEPS law which encompasses substituting the variable excise component for gasoline and diesel with a fixed excise equal to the level shown in the graph. The proposed reform does not affect other fossil fuels, with natural gas continuing to be exempted.
Elasticities tend to vary across fuels, users, and even geographically; however, lacking enough granularity in projections of fuel consumption across users, regions, and fuel type, estimations are approximated using average elasticities.
The baseline projection for fuel demand is estimated using a value for the elasticity to real GDP growth of 0.3. Estimates of short-run income elasticities of fuel demand tend to be low. The chosen value of 0.3 in the construction of the baseline is within the range of values estimated in Crotte and others (2010).
For simplicity, we set the optimal tax of premium gasoline equal to the one on regular gasoline to compute estimated fiscal revenues.