electricsheepafrica/africa-sle-climate-trace

Sierra Leone: Greenhouse Gas and Air Pollutant Emissions

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

---

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: SLE.

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

---

Dataset Characteristics

Domain	Climate and environment
Unit of observation	Time-series observations
Rows (total)	4,296
Columns	13 (4 numeric, 9 categorical, 0 datetime)
Train split	3,436 rows
Test split	859 rows
Geographic scope	SLE
Publisher	Climate TRACE
HDX last updated	2026-03-30

---

Variables

Geographic — year (range 2024.0–2026.0), emissionsquantity (range 0.0–9573.6945).

Temporal — month (range 1.0–12.0).

Identifier / Metadata — full_name (Sierra Leone, Northern Province, SLE, Western Province, SLE), id (SLE, SLE.2_1, SLE.4_1), level_0_id (SLE), level_1_id (SLE.2_1, SLE.4_1, SLE.3_1), name (Sierra Leone, Northern Province, Western Province) and 2 others.

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

---

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-sle-climate-trace")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

---

Schema

Column	Type	Null %	Range / Sample Values
full_name	object	0.0%	Sierra Leone, Northern Province, SLE, Western Province, SLE
id	object	0.0%	SLE, SLE.2_1, SLE.4_1
level	int64	0.0%	0.0 – 1.0 (mean 0.7921)
level_0_id	object	0.0%	SLE
level_1_id	object	20.8%	SLE.2_1, SLE.4_1, SLE.3_1
name	object	0.0%	Sierra Leone, Northern Province, Western Province
year	int64	0.0%	2024.0 – 2026.0 (mean 2024.6124)
month	int64	0.0%	1.0 – 12.0 (mean 6.6799)
sector	object	0.0%	agriculture, transportation, waste
gas	object	0.0%	ch4
emissionsquantity	float64	0.0%	0.0 – 9573.6945 (mean 303.5146)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-04

---

Numeric Summary

Column	Min	Max	Mean	Median
level	0.0	1.0	0.7921	1.0
year	2024.0	2026.0	2024.6124	2025.0
month	1.0	12.0	6.6799	7.0
emissionsquantity	0.0	9573.6945	303.5146	0.3878

---

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. 4,214 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.

---

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.

---

Citation

@dataset{hdx_africa_sle_climate_trace,
  title     = {Sierra Leone: Greenhouse Gas and Air Pollutant Emissions},
  author    = {Climate TRACE},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/sle-climate-trace},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

---

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