electricsheepafrica/africa-climate-change-opinion-survey

Climate Change Opinion Survey

Publisher: AI for Good at Meta · Source: HDX · License: other-pd-nr · Updated: 2026-03-26

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Abstract

In partnership with Yale, Meta launched a climate change opinion survey that explores public climate change knowledge, attitudes, policy preferences, and behaviors. 2023 aggregated survey responses now available.

The 2022 survey includes respondents from nearly 200 countries and territories. We are sharing country level data from this survey, providing policymakers, research institutions, and nonprofits with an international view of public climate change opinion.

For more information please see https://ai.meta.com/ai-for-good/datasets/climate-change-opinion-survey/

If you're interested in becoming a research partner and accessing record level data, please email aiforgood@meta.com.

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2026-03-26. Geographic scope: ALB, DZA, ASM, AGO, AIA, ATG, ARG, ARM, and 185 others.

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

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Dataset Characteristics

Domain	Climate and environment
Unit of observation	First-level administrative unit observations
Rows (total)	5,140
Columns	11 (4 numeric, 7 categorical, 0 datetime)
Train split	4,112 rows
Test split	1,028 rows
Geographic scope	ALB, DZA, ASM, AGO, AIA, ATG, ARG, ARM, and 185 others
Publisher	AI for Good at Meta
HDX last updated	2026-03-26

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Variables

Geographic — region (Europe, Asia, Southwest Asia & North Africa), country_code (hk, jp, no), country (Hong Kong, Japan, Norway).

Outcome / Measurement — pct (range 0.0–86.5244).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-04).

Other — response (I have not done this, I have done this, Not applicable), freq (range 0.0–2155.7138), n (range 12.4363–2836.0), prop (range 0.0–0.8652), variable (barriers_heatpump_haventadopted, barriers_ev_haventadopted, barriers_solar_haventadopted).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-climate-change-opinion-survey")
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
region	object	0.0%	Europe, Asia, Southwest Asia & North Africa
country_code	object	0.0%	hk, jp, no
country	object	0.0%	Hong Kong, Japan, Norway
response	object	0.0%	I have not done this, I have done this, Not applicable
freq	float64	0.0%	0.0 – 2155.7138 (mean 100.6589)
n	float64	0.0%	12.4363 – 2836.0 (mean 608.2886)
prop	float64	0.0%	0.0 – 0.8652 (mean 0.1557)
pct	float64	0.0%	0.0 – 86.5244 (mean 15.5693)
variable	object	0.0%	barriers_heatpump_haventadopted, barriers_ev_haventadopted, barriers_solar_haventadopted
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
freq	0.0	2155.7138	100.6589	39.2444
n	12.4363	2836.0	608.2886	551.0772
prop	0.0	0.8652	0.1557	0.0955
pct	0.0	86.5244	15.5693	9.5545

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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. 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 AI for Good at Meta and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• This dataset spans 193 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_climate_change_opinion_survey,
  title     = {Climate Change Opinion Survey},
  author    = {AI for Good at Meta},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/climate-change-opinion-survey},
  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.