Datasets:

electricsheepafrica
/

africa-climate-change-opinion-survey

annotations_creators:
  - no-annotation
language_creators:
  - found
language:
  - en
license: other
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - tabular-classification
  - tabular-regression
task_ids: []
tags:
  - africa
  - humanitarian
  - hdx
  - electric-sheep-africa
  - climate-weather
  - environment
  - alb
  - dza
  - asm
  - ago
  - aia
pretty_name: Climate Change Opinion Survey
dataset_info:
  splits:
    - name: train
      num_examples: 4112
    - name: test
      num_examples: 1028

Climate Change Opinion Survey

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

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.

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

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).

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()

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

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

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.

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.

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)}
}

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