electricsheepafrica/africa-stp-climate-trace

Sao Tome and Principe: Greenhouse Gas and Air Pollutant Emissions

Publisher: Climate TRACE · Source: HDX · License: cc-by · Updated: 2026-03-30

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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: STP.

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

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Dataset Characteristics

Domain	Climate and environment
Unit of observation	Time-series observations
Rows (total)	2,184
Columns	13 (4 numeric, 9 categorical, 0 datetime)
Train split	1,747 rows
Test split	436 rows
Geographic scope	STP
Publisher	Climate TRACE
HDX last updated	2026-03-30

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Variables

Geographic — year (range 2024.0–2026.0), emissionsquantity (range 0.0–105.3959).

Temporal — month (range 1.0–12.0).

Identifier / Metadata — full_name (Sao Tome and Principe, São Tomé Municipality, STP, Príncipe Municipality, STP), id (STP, STP.2_1, STP.1_1), level_0_id (STP), level_1_id (STP.2_1, STP.1_1), name (Sao Tome and Principe, São Tomé Municipality, Príncipe Municipality) and 2 others.

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

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-stp-climate-trace")
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
full_name	object	0.0%	Sao Tome and Principe, São Tomé Municipality, STP, Príncipe Municipality, STP
id	object	0.0%	STP, STP.2_1, STP.1_1
level	int64	0.0%	0.0 – 1.0 (mean 0.663)
level_0_id	object	0.0%	STP
level_1_id	object	33.7%	STP.2_1, STP.1_1
name	object	0.0%	Sao Tome and Principe, São Tomé Municipality, Príncipe Municipality
year	int64	0.0%	2024.0 – 2026.0 (mean 2024.6085)
month	int64	0.0%	1.0 – 12.0 (mean 6.6882)
sector	object	0.0%	transportation, waste, agriculture
gas	object	0.0%	ch4
emissionsquantity	float64	0.0%	0.0 – 105.3959 (mean 6.1657)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-04

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Numeric Summary

Column	Min	Max	Mean	Median
level	0.0	1.0	0.663	1.0
year	2024.0	2026.0	2024.6085	2025.0
month	1.0	12.0	6.6882	7.0
emissionsquantity	0.0	105.3959	6.1657	0.0071

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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. 2,922 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.

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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.
• The following columns have >20% missing values and should be treated with caution in modelling: level_1_id.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_stp_climate_trace,
  title     = {Sao Tome and Principe: Greenhouse Gas and Air Pollutant Emissions},
  author    = {Climate TRACE},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/stp-climate-trace},
  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.