electricsheepafrica/africa-mwi-climate-trace

Malawi: 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: MWI.

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)	25,275
Columns	13 (4 numeric, 9 categorical, 0 datetime)
Train split	20,220 rows
Test split	5,055 rows
Geographic scope	MWI
Publisher	Climate TRACE
HDX last updated	2026-03-30

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Variables

Geographic — year (range 2024.0–2026.0), emissionsquantity (range 0.0–19619.9559).

Temporal — month (range 1.0–12.0).

Identifier / Metadata — full_name (Malawi, Blantyre District, MWI, Lilongwe District, MWI), id (MWI, MWI.2_1, MWI.11_1), level_0_id (MWI), level_1_id (MWI.2_1, MWI.11_1, MWI.17_1), name (Malawi, Blantyre District, Lilongwe District) and 2 others.

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

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-mwi-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%	Malawi, Blantyre District, MWI, Lilongwe District, MWI
id	object	0.0%	MWI, MWI.2_1, MWI.11_1
level	int64	0.0%	0.0 – 1.0 (mean 0.9638)
level_0_id	object	0.0%	MWI
level_1_id	object	3.6%	MWI.2_1, MWI.11_1, MWI.17_1
name	object	0.0%	Malawi, Blantyre District, Lilongwe District
year	int64	0.0%	2024.0 – 2026.0 (mean 2024.6063)
month	int64	0.0%	1.0 – 12.0 (mean 6.7027)
sector	object	0.0%	agriculture, transportation, waste
gas	object	0.0%	ch4
emissionsquantity	float64	0.0%	0.0 – 19619.9559 (mean 110.5941)
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	0.9638	1.0
year	2024.0	2026.0	2024.6063	2025.0
month	1.0	12.0	6.7027	7.0
emissionsquantity	0.0	19619.9559	110.5941	0.2016

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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. 24,083 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.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_mwi_climate_trace,
  title     = {Malawi: Greenhouse Gas and Air Pollutant Emissions},
  author    = {Climate TRACE},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/mwi-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.