electricsheepafrica/africa-ner-climate-trace

Niger: 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: NER.

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

---

Dataset Characteristics

Domain	Climate and environment
Unit of observation	Time-series observations
Rows (total)	7,196
Columns	13 (4 numeric, 9 categorical, 0 datetime)
Train split	5,756 rows
Test split	1,439 rows
Geographic scope	NER
Publisher	Climate TRACE
HDX last updated	2026-03-30

---

Variables

Geographic — year (range 2024.0–2026.0), emissionsquantity (range 0.0–125574.1287).

Temporal — month (range 1.0–12.0).

Identifier / Metadata — full_name (Niger, Zinder Department, NER, Niamey Capital District, NER), id (NER, NER.8_1, NER.5_1), level_0_id (NER), level_1_id (NER.8_1, NER.5_1, NER.2_1), name (Niger, Zinder Department, Niamey Capital District) and 2 others.

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

---

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-ner-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%	Niger, Zinder Department, NER, Niamey Capital District, NER
id	object	0.0%	NER, NER.8_1, NER.5_1
level	int64	0.0%	0.0 – 1.0 (mean 0.8777)
level_0_id	object	0.0%	NER
level_1_id	object	12.2%	NER.8_1, NER.5_1, NER.2_1
name	object	0.0%	Niger, Zinder Department, Niamey Capital District
year	int64	0.0%	2024.0 – 2026.0 (mean 2024.606)
month	int64	0.0%	1.0 – 12.0 (mean 6.6619)
sector	object	0.0%	agriculture, waste, transportation
gas	object	0.0%	ch4
emissionsquantity	float64	0.0%	0.0 – 125574.1287 (mean 1730.6252)
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	0.8777	1.0
year	2024.0	2026.0	2024.606	2025.0
month	1.0	12.0	6.6619	7.0
emissionsquantity	0.0	125574.1287	1730.6252	1.1324

---

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. 8,122 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.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

---

Citation

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