---
license: cc-by-4.0
task_categories:
  - tabular-classification
  - tabular-regression
language:
  - en
tags:
  - climate
  - environment
  - africa
  - synthetic-data
  - sub-saharan-africa
  - climate-vulnerability
  - climate-adaptation
  - climate-risk
size_categories:
  - 10K<n<100K
---

# Climate Vulnerability and Exposure - Africa

A comprehensive synthetic dataset assessing climate vulnerability, exposure indices, and adaptive capacity across Sub-Saharan African countries.

## Dataset Description

This dataset provides detailed records of climate vulnerability assessments across 15 Sub-Saharan African countries. It includes exposure metrics, hazard probabilities, adaptive capacity indicators, and climate policy frameworks. The data enables analysis of climate-nutrition linkages through malnutrition burden stratification.

### Key Statistics

| Metric | Value |
|--------|-------|
| Total Records | 14,998 |
| Countries Covered | 15 |
| Time Period | 2018-2025 |
| Files | 3 (high_burden, moderate_burden, low_burden) |
| Features | 29 |

### Countries Included
DRC, Ethiopia, Ghana, Kenya, Malawi, Mali, Mozambique, Niger, Nigeria, Rwanda, Senegal, South Africa, Tanzania, Uganda, Zambia

## Column Descriptions

| Column | Type | Description |
|--------|------|-------------|
| record_id | string | Unique identifier (CLIM_XXXXXX) |
| country | string | African country name |
| year | int | Year of observation (2018-2025) |
| climate_zone | string | Climate classification |
| geographic_exposure_index | float | Geographic exposure to climate hazards (0-1) |
| coastal_proximity | int | Binary coastal proximity (0/1) |
| elevation_category | string | Elevation classification |
| temperature_anomaly_c | float | Temperature deviation from baseline (°C) |
| precipitation_variability_pct | float | Precipitation variability (%) |
| extreme_heat_days_year | int | Annual extreme heat days |
| heat_stress_index | float | Heat stress composite index |
| drought_probability | float | Probability of drought (0-1) |
| flood_probability | float | Probability of flooding (0-1) |
| climate_hazard_index | float | Composite hazard index (0-1) |
| gdp_per_capita_usd | float | GDP per capita (USD) |
| agriculture_gdp_share_pct | float | Agriculture share of GDP (%) |
| infrastructure_quality_index | float | Infrastructure quality (0-1) |
| healthcare_access_index | float | Healthcare access (0-1) |
| early_warning_coverage_pct | float | Early warning system coverage (%) |
| social_protection_coverage_pct | float | Social protection coverage (%) |
| adaptive_capacity_index | float | Overall adaptive capacity (0-1) |
| climate_policy_strength | float | Climate policy strength (0-1) |
| ndc_ambition_score | float | NDC ambition score (0-1) |
| climate_finance_access_million_usd | float | Climate finance accessed (million USD) |
| vulnerability_index | float | Composite vulnerability index (0-1) |
| exposure_score | float | Climate exposure score (0-1) |
| risk_category | string | Risk classification |
| population_at_risk_millions | float | Population at climate risk (millions) |
| potential_economic_loss_pct_gdp | float | Potential economic loss (% GDP) |

### Climate Zones
- tropical_wet, tropical_dry, semi_arid, arid, mediterranean, highland

### Risk Categories
- Low, Moderate, High, Very_High

## Usage Example

```python
import pandas as pd

# Load high burden dataset
df = pd.read_csv('climate_vulnerability_exposure_africa_high_burden.csv')

# Analyze vulnerability by country
vulnerability = df.groupby('country')['vulnerability_index'].mean()
print(vulnerability.sort_values(ascending=False))

# Filter very high risk areas
very_high_risk = df[df['risk_category'] == 'Very_High']

# Correlation analysis
correlation = df[['adaptive_capacity_index', 'vulnerability_index']].corr()
```

## Research Sources

This synthetic dataset is inspired by and aligned with data from:
- [ND-GAIN Country Index](https://gain.nd.edu/our-work/country-index/)
- [INFORM Risk Index](https://drmkc.jrc.ec.europa.eu/inform-index)
- [World Bank Climate Change Knowledge Portal](https://climateknowledgeportal.worldbank.org/)
- [IPCC Climate Reports](https://www.ipcc.ch/)