SEEG

Loads estimates of emission of greenhouse gases of Brazilian cities and states from SEEG. SEEG is the System of Estimates of Emissions and Removals of Greenhouse Gases (SEEG), an initiative of the Observatório do Clima, a network of institutions focused on climate change research in Brazil.

The data provided in SEEG’s Collection 9 is a series covering the period from 1970 to 2020, except for the Land Use Change Sector that has the series from 1990 to 2020.

Using data collected from government entities, institutes, research centers, NGOs and other institutions, the estimates are created using the methodology of the Brazilian Inventory of Anthropic Emissions and Removals of Greenhouse Gases, assembled by the Ministry of Science, Technology and Innovation (MCTI), and the directives of Intergovernmental Panel on Climate Change (IPCC)

Emissions are divided in five main sources: Agricultural and Cattle Raising, Energy, Changes in Use of Land, Industrial Processes and Residues. All greenhouse gases contained in the national inventory are considered, encompassing CO2, CH4, N2O and the HFCs, with the conversion to carbon equivalence (CO2e) also included, both in the metric of GWP (Global Warming Potential) and GTP (Global Temperature Potential).

The data is downloaded from the SEEG website in the form of one single file, so the option to select a certain range of years is not available. Also, due to the size of the file, a stable internet connection is necessary, and the function may take time to run.

Options:

  1. dataset: there are six choices:

    • "seeg": provides all sectors in a same dataframe. Only works with raw_data = TRUE
    • "seeg_farming"
    • "seeg_industry"
    • "seeg_energy"
    • "seeg_land"
    • "seeg_residuals"

  2. raw_data: there are two options:

    • TRUE: if you want the data as it is originally.
    • FALSE: if you want the treated version of the data.

  3. geo_level: "country", "state", or "municipality"

  4. language: you can choose between Portuguese ("pt") and English ("eng")

Examples:

# Download raw data (raw_data = TRUE) of greenhouse gases (dataset = "seeg") 
# by state (geo_level = "state")
data <- load_seeg(dataset = "seeg", 
                  raw_data = TRUE,
                  geo_level = "state")
  
# Download treated data (raw_data = FALSE) of industry greenhouse gases (dataset = "seeg_industry")
data <- load_seeg(dataset = "seeg_industry", 
                  raw_data = FALSE,
                  geo_level = "state")