electricsheepafrica/africa-ghg-emissions

GHG Emissions

Publisher: Global Forest Watch · Source: OpenAfrica · License: cc-by · Updated: 2021-09-28

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Abstract

2020 GHG Emissions for 30% tree cover density

Each row in this dataset represents subnational administrative unit observations. Data was last updated on OpenAfrica on 2021-09-28. Geographic scope: Africa (multiple countries).

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Subnational administrative unit observations
Rows (total)	136
Columns	4 (1 numeric, 3 categorical, 0 datetime)
Train split	108 rows
Test split	27 rows
Geographic scope	Africa (multiple countries)
Publisher	Global Forest Watch
OpenAfrica last updated	2021-09-28

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Variables

Geographic — districts_in_uganda (Abim, Mitooma, Maracha).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-29).

Other — ghg_emmissions (range 2191.0–4477593.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-ghg-emissions")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
districts_in_uganda	object	0.0%	Abim, Mitooma, Maracha
ghg_emmissions	float64	58.8%	2191.0 – 4477593.0 (mean 642830.2143)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-29

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Numeric Summary

Column	Min	Max	Mean	Median
ghg_emmissions	2191.0	4477593.0	642830.2143	258706.0

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Curation

Raw data was downloaded from OpenAfrica 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. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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Limitations

• Data originates from Global Forest Watch and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• The following columns have >20% missing values and should be treated with caution in modelling: ghg_emmissions.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{openafrica_africa_ghg_emissions,
  title     = {GHG Emissions},
  author    = {Global Forest Watch},
  year      = {2021},
  url       = {https://open.africa/dataset/ghg-emissions},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.