electricsheepafrica/africa-eswatini-eswatini-greenhouse-gas-and-air-pollutant-emissions-f59ba83d

Eswatini: Greenhouse Gas and Air Pollutant Emissions | Africa (Eswatini official open data)

3,432 rows - 1 Africa country - 2024-2026 - Repackaged by Electric Sheep Africa

TL;DR

This dataset packages one official CSV resource from Eswatini as ML-ready Parquet. The source file is the provenance boundary; all usable indicators or tabular columns from the resource stay together in this repo.

About the source

• Source: Eswatini: Greenhouse Gas and Air Pollutant Emissions
• Publisher: Climate TRACE
• Resource: swz_ch4_city.csv
• Format: CSV
• License: CC BY 4.0
• Packaging mode: indicator_long

Geographic coverage

1 Africa country:

Country	Rows	First year	Last year	Name
SWZ	3,432	2024	2026	Eswatini

Indicators or Resource Contents

• eswatini-greenhouse-gas-and-air-pollutant-emissions-month-a9b4093e - Eswatini: Greenhouse Gas and Air Pollutant Emissions - month
• eswatini-greenhouse-gas-and-air-pollutant-emissions-emissionsquantity-115db6a1 - Eswatini: Greenhouse Gas and Air Pollutant Emissions - emissionsquantity

Schema

Column	Type	Description	Example
indicator_id	string	Stable indicator identifier.	eswatini-greenhouse-gas-and-air-pollutant-emissions-month-a9b4093e
indicator_name	string	Human-readable indicator name.	Eswatini: Greenhouse Gas and Air Pollutant Emissions - month
country_iso3	string	ISO3 country code.	SWZ
country_name	string	Country name.	Eswatini
year	Int64	Observation year.	2024
value	float64	Numeric observation value.	8.0
unit	string	Measurement unit, when available.	source_units_unspecified
dimension_id	string	Source dimension.	ghs-fua_330
dimension_name	string	Source dimension.	Manzini Urban Area, SWZ
dimension_country	string	Source dimension.	SWZ
dimension_alternatenames	string	Source dimension.	['Manzini', 'Matsapha']
dimension_sector	string	Source dimension.	agriculture
dimension_gas	string	Source dimension.	ch4
source_period_start_year	Int64	First year inferred from source resource metadata.	2015
source_period_end_year	Int64	Last year inferred from source resource metadata.	2021
source_period_label	category	Human-readable period inferred from source resource metadata.	2015-2021
source_provider	category	Publishing organization.	Climate TRACE
source_dataset	category	Source package title.	Eswatini: Greenhouse Gas and Air Pollutant Emissions
source_resource	category	Source resource title.	swz_ch4_city.csv
source_package_id	category	CKAN package UUID.	a9606158-367c-465d-946a-afd3ab199671
source_resource_id	category	CKAN resource UUID.	81cf6745-fde9-4a01-a097-af9dbebcd4c8
source_url	category	Original source resource URL.	https://data.humdata.org/dataset/a9606158-367c-465d-946a-afd3ab199671/re
license_id	category	Source license identifier.	cc-by
retrieved_at	category	UTC retrieval timestamp.	2026-08-30T05:35:43Z

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-eswatini-eswatini-greenhouse-gas-and-air-pollutant-emissions-f59ba83d")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

sample_country = df[df["country_iso3"] == "SWZ"]

Work with indicators

if "indicator_id" in df.columns:
    print(df["indicator_id"].value_counts().head())
    sample = df.sort_values([c for c in ["indicator_id", "year"] if c in df.columns])

Citation

@misc{electric_sheep_africa_africa_eswatini_eswatini_greenhouse_gas_and_air_pollutant_emissions_f59ba83d_2026,
  title        = {Eswatini: Greenhouse Gas and Air Pollutant Emissions | Africa (Eswatini official open data)},
  author       = {Climate TRACE},
  year         = {2026},
  url          = {https://data.humdata.org/dataset/swz-climate-trace},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-eswatini-eswatini-greenhouse-gas-and-air-pollutant-emissions-f59ba83d}}
}

License

Released under CC BY 4.0.

Original data (c) Climate TRACE. When using this dataset, please cite both the original source above and the Electric Sheep Africa repackaging.

About Electric Sheep

Electric Sheep Africa is part of the Electric Sheep mission: a unified, ML-ready data layer for Africa on Hugging Face. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use load_dataset() to start working in seconds.

Browse the full collection: huggingface.co/electricsheepafrica

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Provenance: ingested 2026-08-30 via the Electric Sheep pipeline. Source URL: https://data.humdata.org/dataset/a9606158-367c-465d-946a-afd3ab199671/resource/81cf6745-fde9-4a01-a097-af9dbebcd4c8/download/swz_ch4_city.csv