electricsheepafrica/africa-emissions-intensities-emissions-co2eq-ar5

Emissions intensities — Emissions (CO2eq) (AR5) | Africa (FAOSTAT)

🌍 31,780 observations · 54 Africa countries · 1961–2023 · Repackaged by Electric Sheep Africa

TL;DR

This dataset contains 31,780 observations of Climate Change data across 54 Africa countries, spanning 1961–2023, covering 14 distinct indicators.

About the source

• Source: FAOSTAT (FAO)
• Publisher: Food and Agriculture Organization of the United Nations (FAO)
• License: cc-by-4.0
• Topic: Climate Change

Geographic coverage

54 Africa countries · top rows shown below, sorted by row count:

Country	Rows	First year	Last year
Egypt	819	1961	2023
Algeria	756	1961	2023
Chad	756	1961	2023
Morocco	756	1961	2023
Niger	756	1961	2023
Kenya	756	1961	2023
Mali	756	1961	2023
Somalia	756	1961	2023
Senegal	726	1961	2023
Burkina Faso	697	1961	2023
Guinea	693	1961	2023
Mauritania	693	1961	2023
Rwanda	693	1961	2023
Tunisia	693	1961	2023
Burundi	693	1961	2023
...	39 more countries

Indicators (sample)

• Raw milk of cattle
• Meat of cattle with the bone, fresh or chilled
• Meat of goat, fresh or chilled
• Cereals excluding rice
• Meat of chickens, fresh or chilled
• Meat of sheep, fresh or chilled
• Hen eggs in shell, fresh
• Meat of pig with the bone, fresh or chilled
• Rice
• Raw milk of goats

Schema

Column	Type	Description	Example
country_name	string	—	Algeria
country_m49	int64	—	12
item	string	—	Cereals excluding rice
year	int64	—	1961
value	float64	—	180.204
unit	string	—	kt
flag	string	—	E

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-emissions-intensities-emissions-co2eq-ar5")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

kenya = df[df["country_name"] == "KEN"]

Time-series for a single indicator

sample = (df[df["item"] == "Cereals excluding rice"]
          .sort_values("year"))
sample.plot(x="year", y="value", title="Cereals excluding rice")

Pivot to country × year matrix

matrix = (df[df["item"] == "Cereals excluding rice"]
          .pivot_table(index="year", columns="country_name", values="value"))
print(matrix.tail())

Citation

@misc{africa_emissions_intensities_emissions_co2eq_ar5_2023,
  title        = {Emissions intensities — Emissions (CO2eq) (AR5) | Africa (FAOSTAT)},
  author       = {Food and Agriculture Organization of the United Nations (FAO)},
  year         = {2023},
  url          = {https://www.fao.org/faostat/en/#data/EI},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-emissions-intensities-emissions-co2eq-ar5}}
}

License

Released under cc-by-4.0.

Original data © Food and Agriculture Organization of the United Nations (FAO). 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 HuggingFace. 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 2026-06-17 via the Electric Sheep pipeline. Source URL: https://www.fao.org/faostat/en/#data/EI