README.md

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- climate-weather
- environment
- points-of-interest-poi
- tcd
pretty_name: "Chad: Greenhouse Gas and Air Pollutant Emissions"
dataset_info:
  splits:
    - name: train
      num_examples: 15510
    - name: test
      num_examples: 3877
---

# Chad: Greenhouse Gas and Air Pollutant Emissions

**Publisher:** Climate TRACE · **Source:** [HDX](https://data.humdata.org/dataset/tcd-climate-trace) · **License:** `cc-by` · **Updated:** 2026-03-30

---

## Abstract

Climate TRACE is a non-profit coalition of organizations building a timely, open, and accessible inventory of exactly where greenhouse gas emissions are coming from. Climate TRACE estimates greenhouse gas (GHG) and air pollutant emissions for over 2.7 million sources (from over 744 million assets), and every single country globally.

The Climate TRACE emissions inventory includes:
- Annual country-level emissions by sub-sector and by gas beginning in 2015
- Monthly source-level emissions by sub-sector and gas beginning in 2021 and confidence
- Emissions source ownership where and when available.

Each row in this dataset represents time-series observations. Data was last updated on HDX on 2026-03-30. Geographic scope: **TCD**.

*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Climate and environment |
| **Unit of observation** | Time-series observations |
| **Rows (total)** | 19,388 |
| **Columns** | 13 (4 numeric, 9 categorical, 0 datetime) |
| **Train split** | 15,510 rows |
| **Test split** | 3,877 rows |
| **Geographic scope** | TCD |
| **Publisher** | Climate TRACE |
| **HDX last updated** | 2026-03-30 |

---

## Variables

**Geographic** — `year` (range 2024.0–2026.0), `emissionsquantity` (range 0.0–291467.866).

**Temporal** — `month` (range 1.0–12.0).

**Identifier / Metadata** — `full_name` (Chad, Ville de N'Djamena Region, TCD, Tandjilé Region, TCD), `id` (TCD, TCD.22_1, TCD.20_1), `level_0_id` (TCD), `level_1_id` (TCD.22_1, TCD.20_1, TCD.8_1), `name` (Chad, Ville de N'Djamena Region, Tandjilé Region) and 2 others.

**Other** — `level` (range 0.0–1.0), `sector` (agriculture, transportation, waste), `gas` (ch4).

---

## Quick Start

```python
from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-tcd-climate-trace")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `full_name` | object | 0.0% | Chad, Ville de N'Djamena Region, TCD, Tandjilé Region, TCD |
| `id` | object | 0.0% | TCD, TCD.22_1, TCD.20_1 |
| `level` | int64 | 0.0% | 0.0 – 1.0 (mean 0.9533) |
| `level_0_id` | object | 0.0% | TCD |
| `level_1_id` | object | 4.7% | TCD.22_1, TCD.20_1, TCD.8_1 |
| `name` | object | 0.0% | Chad, Ville de N'Djamena Region, Tandjilé Region |
| `year` | int64 | 0.0% | 2024.0 – 2026.0 (mean 2024.6113) |
| `month` | int64 | 0.0% | 1.0 – 12.0 (mean 6.6779) |
| `sector` | object | 0.0% | agriculture, transportation, waste |
| `gas` | object | 0.0% | ch4 |
| `emissionsquantity` | float64 | 0.0% | 0.0 – 291467.866 (mean 1430.8668) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-04 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `level` | 0.0 | 1.0 | 0.9533 | 1.0 |
| `year` | 2024.0 | 2026.0 | 2024.6113 | 2025.0 |
| `month` | 1.0 | 12.0 | 6.6779 | 7.0 |
| `emissionsquantity` | 0.0 | 291467.866 | 1430.8668 | 0.1738 |

---

## Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. 1 column(s) with >80% missing values were removed: `level_2_id`. 21,460 exact duplicate rows were removed. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

---

## Limitations

- Data originates from Climate TRACE and has not been independently validated by ESA.
- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/tcd-climate-trace) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_tcd_climate_trace,
  title     = {Chad: Greenhouse Gas and Air Pollutant Emissions},
  author    = {Climate TRACE},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/tcd-climate-trace},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}
```

---

*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*