---
title: "SEEG"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{SEEG}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```
## Overview

SEEG (Sistema de Estimativa de Emissões e Remoções de Gases de Efeito Estufa - System of Estimates of Emissions and Removals of Greenhouse Gases) is Brazil's most comprehensive greenhouse gas emissions database developed by [Observatório do Clima](https://oc.eco.br/) (Climate Observatory).

This dataset provides:

- **Greenhouse gas emissions**: Complete estimates of all major climate-relevant gases
- **Multi-sector coverage**: Agriculture, energy, land use, industry, waste
- **Sub-sectoral detail**: Detailed breakdowns within each sector
- **Municipality and state levels**: Geographic disaggregation for regional analysis
- **Time series**: Historical data from 2000 onwards
- **Removal accounting**: Also includes carbon sequestration and removals
- **Comprehensive methodology**: Based on Brazilian national inventory standards
- **Transparent assumptions**: Well-documented methodology and data sources

SEEG is the primary tool for understanding Brazil's greenhouse gas emissions profile, tracking progress toward climate goals, identifying emission hotspots, and supporting climate policy.

### 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](https://www.seeg.org.br/) and [Observatório do Clima](https://oc.eco.br/).

***

## Available Datasets

### **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

***

## Important Data Characteristics

### 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

***

## Function Parameters

### 1. **dataset**

Selects which emission sector(s) to download.

```r
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

```r
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

```r
geo_level = "state"  # character string
```

### 4. **language**

Output language for variable names and labels.

- `"pt"`: Portuguese
- `"eng"`: English

```r
language = "eng"  # character string
```

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

***

## Examples

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

```{r eval=FALSE}
# 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

```{r eval=FALSE}
# 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

```{r eval=FALSE}
# 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

```{r eval=FALSE}
# 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

```{r eval=FALSE}
# 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

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

## Data Notes

### 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

***