aggregated emissions (across types of vehicles) from the road transportation subsector (while preserving the breakdowns in types of fuels and GHG emissions). The emissions are calculated in general as a multiplication of fuel consumption as available from EPE’s Brazilian Energy Balance (BEN) with adjustments in vehicle usage and emission factors as published by the MCTI. The emission factors are fuel specific and those for GHG gases other than CO2 take process/activity and technology into additional consideration. SEEG allocates national emissions to subnational levels including to all 27 FUs based on actual fuel consumption for the transportation sector. Our collection starts from 2006 when data is almost complete for all FUs and fuel/vehicle types till 2021. To our knowledge, this is the most comprehensive dataset on Brazil’s GHG emissions in terms of time periods and level of disaggregation.

# V. Empirical Strategy

We focus on the relationship between fuel prices and emission intensity. Fuel prices are subject to policy intervention, such as through fuel/carbon taxes and state-owned fuel producers. To remove the effect of inflation, we will analyze deflated/real fuel prices. Emission Intensity, defined here as aggregate emissions in CO2 equivalent from road transportation divided by aggregate real income of an administrative region, could be a target of the government that represents a balanced view of economic development and climate mitigation. This emission intensity measure also serves to scale the gross emission values so that it is more comparable across our units of analysis and the heteroskedasticity of the residuals would be reduced. Based on literature and our assumptions, the emission intensity should respond to fuel prices, such as through reducing fuel consumption and substitution effects across fuels due to price differences. Given the complexity of Brazil’s fuel markets, the primary question this paper will answer is how effective fuel prices are in reducing emission intensity in Brazil. We will analyze both the effect of the general fuel price level and price differentials between fuels on emission intensity. Additionally, we will also understand the evolution and heterogeneity of emission intensity in Brazil and whether the pandemic in 2020 has an impact. As price data are timelier with higher frequency than emission and GDP data, our model could also be used to nowcast emission intensity.

# V.1. Summary Statistics

We transform the source data into our analyzed variables to start with, and **Table 2** summarizes panel statistics of our analyzed variables. We first deflate nominal income and fuel prices to obtain real income and fuel prices. The deflator was calculated from national level GDP at current and constant prices from World Development Indicators, with the deflator equals to 1 in 2010, the starting period of our analysis. We applied these year-specific deflators to the income and prices of each FU in the corresponding year. In other words, the prices and incomes used for our analysis are constant at the price level of 2010. Second, we average the fuel prices of gasoline C, diesel B, and hydrous ethanol to represent the general price level of automotive fuels that captures price information from all major fuels, as well as modelling individual fuel price. To derive the emission intensity (t CO2e per million R$), we divided the aggregate CO2 equivalent (t) emissions from road transportation by real income. The CO2 equivalent considers all GHG emissions from road transportation based on their global warming potential as equivalent to CO2, while 11