Dataset Viewer
Auto-converted to Parquet Duplicate
Search is not available for this dataset
Year
string
land_area_where_elevation_is_below_5_meters_of_total_land_area_
float64
renewable_energy_consumption_of_total_final_energy_consumption_
float64
total_greenhouse_gas_emissions_excluding_lulucf_per_capita_t_co2e_capita_
float64
1960-01-01
0.025395
0.2
4.149011
1961-01-01
0.025395
0.2
4.149011
1962-01-01
0.025395
0.2
4.149011
1963-01-01
0.025395
0.2
4.149011
1964-01-01
0.025395
0.2
4.149011
1965-01-01
0.025395
0.2
4.149011
1966-01-01
0.025395
0.2
4.149011
1967-01-01
0.025395
0.2
4.149011
1968-01-01
0.025395
0.2
4.149011
1969-01-01
0.025395
0.2
4.149011
1970-01-01
0.025395
0.2
4.149011
1971-01-01
0.025395
0.2
4.322496
1972-01-01
0.025395
0.2
5.760018
1973-01-01
0.025395
0.2
6.4893
1974-01-01
0.025395
0.2
5.657851
1975-01-01
0.025395
0.2
5.231591
1976-01-01
0.025395
0.2
5.880517
1977-01-01
0.025395
0.2
6.169977
1978-01-01
0.025395
0.2
6.495406
1979-01-01
0.025395
0.2
6.389877
1980-01-01
0.025395
0.2
5.965699
1981-01-01
0.025395
0.2
5.431346
1982-01-01
0.025395
0.2
5.41869
1983-01-01
0.025395
0.2
5.371173
1984-01-01
0.025395
0.2
5.4429
1985-01-01
0.025395
0.2
5.756469
1986-01-01
0.025395
0.2
5.790117
1987-01-01
0.025395
0.2
5.537634
1988-01-01
0.025395
0.2
5.392617
1989-01-01
0.025395
0.2
5.377422
1990-01-01
0.025395
0.2
5.340929
1991-01-01
0.025395
0.3
5.249811
1992-01-01
0.025395
0.3
5.50707
1993-01-01
0.025395
0.5
5.488919
1994-01-01
0.025395
0.4
5.316521
1995-01-01
0.025395
0.4
5.498565
1996-01-01
0.025395
0.4
5.363175
1997-01-01
0.025395
0.5
4.946057
1998-01-01
0.025395
0.5
4.84355
1999-01-01
0.025395
0.5
4.919215
2000-01-01
0.025395
0.4
5.123422
2001-01-01
0.025395
0.4
4.714668
2002-01-01
0.025395
0.5
4.638448
2003-01-01
0.025395
0.5
5.006813
2004-01-01
0.025395
0.4
4.880937
2005-01-01
0.025395
0.6
4.96805
2006-01-01
0.025395
0.4
5.140451
2007-01-01
0.025395
0.4
5.049144
2008-01-01
0.025395
0.3
5.192038
2009-01-01
0.025395
0.3
4.986204
2010-01-01
0.025395
0.3
5.109959
2011-01-01
0.025395
0.2
5.192842
2012-01-01
0.025395
0.2
5.523803
2013-01-01
0.025395
0.1
5.543306
2014-01-01
0.025395
0.1
5.761666
2015-01-01
0.025395
0.1
5.901709
2016-01-01
0.025395
0.1
5.730743
2017-01-01
0.025395
0.1
5.737009
2018-01-01
0.025395
0.2
5.832328
2019-01-01
0.025395
0.2
5.87613
2020-01-01
0.025395
0.1
5.475035
2021-01-01
0.025395
0.1
5.673357
2022-01-01
0.025395
0.1
5.787845
2023-01-01
0.025395
0.1
5.562579
2024-01-01
0.025395
0.1
5.562579
1960-01-01
0.160555
72.3
3.246111
1961-01-01
0.160555
72.3
3.246111
1962-01-01
0.160555
72.3
3.246111
1963-01-01
0.160555
72.3
3.246111
1964-01-01
0.160555
72.3
3.246111
1965-01-01
0.160555
72.3
3.246111
1966-01-01
0.160555
72.3
3.246111
1967-01-01
0.160555
72.3
3.246111
1968-01-01
0.160555
72.3
3.246111
1969-01-01
0.160555
72.3
3.246111
1970-01-01
0.160555
72.3
3.246111
1971-01-01
0.160555
72.3
3.14902
1972-01-01
0.160555
72.3
3.475557
1973-01-01
0.160555
72.3
3.603162
1974-01-01
0.160555
72.3
3.581443
1975-01-01
0.160555
72.3
3.290921
1976-01-01
0.160555
72.3
2.62715
1977-01-01
0.160555
72.3
3.177308
1978-01-01
0.160555
72.3
3.351919
1979-01-01
0.160555
72.3
3.302309
1980-01-01
0.160555
72.3
3.24249
1981-01-01
0.160555
72.3
2.958613
1982-01-01
0.160555
72.3
2.879743
1983-01-01
0.160555
72.3
2.962991
1984-01-01
0.160555
72.3
2.943703
1985-01-01
0.160555
72.3
3.006354
1986-01-01
0.160555
72.3
3.03766
1987-01-01
0.160555
72.3
3.111749
1988-01-01
0.160555
72.3
3.214581
1989-01-01
0.160555
72.3
3.139352
1990-01-01
0.160555
72.3
2.747205
1991-01-01
0.160555
71.9
2.634351
1992-01-01
0.160555
72.7
2.461253
1993-01-01
0.160555
71.3
2.68979
1994-01-01
0.160555
72.2
3.430121
End of preview. Expand in Data Studio

Climate Change Indicators for African Countries

This repository contains time-series datasets for key climate change indicators for 54 African countries. The data is sourced from The World Bank and has been cleaned, processed, and organized for analysis.

Each country has its own set of files, including a main CSV dataset and a corresponding datacard in Markdown format. The data covers the period from 1960 to 2024, where available.


Repository Structure

The datasets are organized by country. Each country's folder contains:

  1. A CSV file with the naming convention: {CountryName}-Climate-Change-Indicators-Dataset-{StartYear}-{EndYear}.csv
  2. A detailed datacard named datacard_Climate-Change.md.

Indicators Included

This collection includes the following climate change indicators:

  • Land area where elevation is below 5 meters (% of total land area)
  • Renewable energy consumption (% of total final energy consumption)
  • Total greenhouse gas emissions excluding LULUCF per capita (t CO2e:capita)

Countries Included

This dataset covers all 54 sovereign nations of Africa as recognized by the source data:

  • Algeria
  • Angola
  • Benin
  • Botswana
  • Burkina Faso
  • Burundi
  • Cabo Verde
  • Cameroon
  • Central African Republic
  • Chad
  • Comoros
  • Congo, Dem. Rep.
  • Congo, Rep.
  • Cote d'Ivoire
  • Djibouti
  • Egypt, Arab Rep.
  • Equatorial Guinea
  • Eritrea
  • Eswatini
  • Ethiopia
  • Gabon
  • Gambia, The
  • Ghana
  • Guinea
  • Guinea-Bissau
  • Kenya
  • Lesotho
  • Liberia
  • Libya
  • Madagascar
  • Malawi
  • Mali
  • Mauritania
  • Mauritius
  • Morocco
  • Mozambique
  • Namibia
  • Niger
  • Nigeria
  • Rwanda
  • Sao Tome and Principe
  • Senegal
  • Seychelles
  • Sierra Leone
  • Somalia
  • South Africa
  • South Sudan
  • Sudan
  • Tanzania
  • Togo
  • Tunisia
  • Uganda
  • Zambia
  • Zimbabwe

Data Preparation

The raw data from The World Bank was processed using a Python script with the following steps:

  1. Filtering: Data was filtered for each of the 54 African countries.
  2. Reshaping: Wide-format data (with years as columns) was melted into a long format.
  3. Merging: All indicator files for a country were merged into a single time-series dataset based on the 'Year' column.
  4. Cleaning: The 'Year' column was converted to a standard date format (YYYY-MM-DD).
  5. Missing Data Handling: Gaps in the time-series were filled using linear interpolation followed by a back-fill to ensure data continuity.

How to Use

You can access the data directly through the Hugging Face Hub, either by downloading individual files or by using the datasets library to load the data.

from datasets import load_dataset

# Example: Load the dataset for Nigeria
dataset = load_dataset('electricsheepafrica/Climate-Change-Indicators-For-African-Countries', data_files='Nigeria/Nigeria-Climate-Change-Indicators-Dataset-1960-2024.csv')

print(dataset)
Downloads last month
20