Datasets:

electricsheepafrica
/

africa-tcd-climate-trace

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 · 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.

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

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 for the publisher's own methodology notes and caveats.

Citation

@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 — Africa's ML dataset infrastructure. Lagos, Nigeria.