SEEG

Data Source and Methodology

SEEG emissions estimates are compiled using: - Government data from multiple agencies (MAPA, IBGE, ANP, etc.) - Satellite monitoring of deforestation and land use - International IPCC methodology standards - Peer-reviewed scientific research - Regular updates as new government data becomes available

For more information, visit SEEG Project and Observatório do Clima.

1. seeg (All Sectors Combined)

Complete greenhouse gas emissions across all sectors in one dataset.

• Coverage: All emission sources in Brazil
• Sectors included: All five (agriculture, energy, land use, industry, waste)
• Time period: 2000-2018
• Geographic levels: Country, State, Municipality
• Key variables: Total emissions (CO₂e), by sector and sub-sector
• Format: Comprehensive view of Brazil’s total emissions profile
• Note: Only available with raw_data = TRUE
• Use cases:
  • Understand overall emissions landscape
  • Identify dominant emission sources
  • Track total emissions trends over time

2. seeg_farming (Agricultural and Livestock Emissions)

Greenhouse gas emissions from agriculture and livestock activities.

• Coverage: All agricultural and livestock production
• Time period: 2000-2018
• Geographic levels: Country, State, Municipality
• Key variables: Emissions from cattle, crop production, soil management, manure
• Dominant source: Usually the largest single emissions sector in Brazil
• Components:
  • Livestock (enteric fermentation, manure)
  • Crop production and soil management
  • Agricultural land preparation
• Use cases:
  • Assess agricultural emission contributions
  • Identify highest-emission municipalities
  • Evaluate livestock and farming intensity
  • Policy targets for agricultural emissions reduction

3. seeg_energy (Energy Sector Emissions)

Emissions from energy production and consumption.

• Coverage: All energy-related emissions
• Time period: 2000-2018
• Geographic levels: Country, State, Municipality
• Key variables: Emissions from electricity, transport, heating, fuel production
• Components:
  • Energy generation and distribution
  • Transportation fuels
  • Energy consumption
  • Industrial energy use
• Use cases:
  • Understand energy sector contribution to climate change
  • Track renewable vs. fossil fuel impacts
  • Identify regional energy emission patterns

4. seeg_land (Land Use Change Emissions)

Emissions and removals from changes in forest cover and land use.

• Coverage: Deforestation, forest degradation, reforestation effects
• Time period: 2000-2018
• Geographic levels: Country, State, Municipality
• Key variables: Net emissions/removals from land use change
• Components:
  • Deforestation and forest loss
  • Forest degradation
  • Reforestation and afforestation
  • Vegetation conversion
• Importance: Often largest single contributor to Brazil’s emissions
• Use cases:
  • Analyze deforestation climate impact
  • Identify reforestation opportunities
  • Assess forest conservation value
  • Link with PRODES and DETER deforestation data

5. seeg_industry (Industrial Process Emissions)

Emissions from manufacturing and industrial processes.

• Coverage: All industrial sectors
• Time period: 2000-2018
• Geographic levels: Country, State, Municipality
• Key variables: Emissions from cement, chemicals, metals, minerals, other manufacturing
• Components:
  • Chemical production (ammonia, soda ash, etc.)
  • Metal production (iron, aluminum, others)
  • Mineral processing (cement, lime, glass)
  • Other industrial processes
• Use cases:
  • Identify industrial emission hotspots
  • Regional manufacturing impacts
  • Process-specific emission reduction opportunities

6. seeg_residuals (Waste and Residuals Emissions)

Emissions from waste management, landfills, and waste treatment.

• Coverage: All waste-related emissions
• Time period: 2000-2018
• Geographic levels: Country, State, Municipality
• Key variables: Emissions from solid waste, wastewater treatment, waste treatment
• Components:
  • Landfill methane emissions
  • Wastewater treatment
  • Waste disposal and treatment
  • Municipal solid waste management
• Use cases:
  • Assess waste sector contributions
  • Identify waste management improvement opportunities
  • Evaluate circular economy potential

Collection 9 Data

The data provided is from SEEG’s Collection 9: - Time period: 2000-2018 - Methodology: Latest available when data was compiled - Quality: Peer-reviewed and validated - Revisions: May be updated in future SEEG collections as better data becomes available

Emissions Units

• Standard unit: Gigatonnes CO₂ equivalent (Gt CO₂e)
• CO₂e equivalence: Uses global warming potentials (GWP) to convert CH₄ and N₂O to CO₂ equivalent
• Consistency: Allows comparison across different gases and sectors

Download Considerations

Important: The complete SEEG dataset is quite large. When downloading: - Entire datasets are downloaded as single files; year selection is limited - A stable, high-speed internet connection is recommended - Downloads may take time depending on connection speed - Ensure sufficient disk space for storage

1. dataset

Selects which emission sector(s) to download.

dataset = "seeg"              # All sectors (raw_data = TRUE only)
dataset = "seeg_farming"      # Agriculture and livestock
dataset = "seeg_energy"       # Energy sector
dataset = "seeg_land"         # Land use changes
dataset = "seeg_industry"     # Industrial processes
dataset = "seeg_residuals"    # Waste and residuals

2. raw_data

Controls whether to download original or processed data.

• TRUE: Returns raw SEEG data format (more detailed)
• FALSE: Returns treated data with English variable names and standardized format

raw_data = FALSE  # logical

3. geo_level

Specifies geographic aggregation level.

• "country": National total
• "state": State-level emissions (27 units)
• "municipality": All 5,570+ municipalities

geo_level = "state"  # character string

4. language

Output language for variable names and labels.

• "pt": Portuguese
• "eng": English

language = "eng"  # character string

Note on timing: Downloads may take considerable time due to file size.

Example 1: All sectors combined (raw data) at the country level

# download raw SEEG data (all sectors) at the country level
# note: dataset = "seeg" only works with raw_data = TRUE
all_emissions <- load_seeg(
  dataset = "seeg",
  raw_data = TRUE,
  geo_level = "country",
  language = "eng"
)

Example 2: Agricultural emissions by state

# download treated agricultural emissions at the state level
farming <- load_seeg(
  dataset = "seeg_farming",
  raw_data = FALSE,
  geo_level = "state",
  language = "eng"
)

Example 3: Land use change emissions by state

# download treated land use change emissions at the state level
land_use <- load_seeg(
  dataset = "seeg_land",
  raw_data = FALSE,
  geo_level = "state",
  language = "eng"
)

Example 4: Energy emissions by municipality

# download treated energy emissions at the municipality level
energy <- load_seeg(
  dataset = "seeg_energy",
  raw_data = FALSE,
  geo_level = "municipality",
  language = "eng"
)

Example 5: Industrial process emissions by state

# download treated industrial process emissions at the state level
industry <- load_seeg(
  dataset = "seeg_industry",
  raw_data = FALSE,
  geo_level = "state",
  language = "eng"
)

Example 6: Waste emissions by state

# download treated waste emissions at the state level
residuals <- load_seeg(
  dataset = "seeg_residuals",
  raw_data = FALSE,
  geo_level = "state",
  language = "eng"
)

Emission Sources Included

SEEG includes all major anthropogenic emission sources: - Agriculture (livestock, crops, soil) - Energy (electricity, transport, heating) - Land use change (deforestation, afforestation) - Industrial processes (cement, chemicals, metals) - Waste (landfills, wastewater)

Methodology

Estimates follow: - IPCC guidelines for national greenhouse gas inventories - Brazilian national inventory standards - International best practices - Transparent, documented assumptions

Data Quality

• Peer-reviewed methodology
• Validated against government data
• Uncertainty ranges available in detailed products
• Regular methodology updates

Limitations

1. Fixed time period: Collection 9 covers 2000-2018 only
2. File size: Large downloads; requires good internet
3. Year aggregation: Cannot select individual years; entire dataset downloaded
4. Revisions: Methodology may change in future SEEG releases
5. Sub-national uncertainty: Municipal and state estimates have higher uncertainty than national