DOI: 10.5281/zenodo.14018078
title: A long-term global population proportion with access to electricity dataset (SDG 7.1.1) from 1992 to 2022 based on nighttime light remote sensing

description: Introduction

In 2015, the United Nations established 17 Sustainable Development Goals (SDGs), with Goal 7 focusing on ensuring access to affordable, reliable, and sustainable modern energy for all by 2030. By 2022, approximately 760 million people, or 1 in 11 globally still lacked electricity access according to  Tracking SDG7 :The Energy Progress Report 2022, posing significant challenges to achieving this goal. Traditional survey methods for estimating the proportion of people with electricity access are often costly, infrequently updated, and hindered by the need for interpolation of historical data.

To address these challenges, this dataset employs a nighttime light remote sensing estimation framework that integrates DMSP-CCNL and NPP/VIIRS data with GlobPOP population data. This approach produces a global 0.1-degree grid and national-scale electricity access index (EAI) maps from 1992 to 2022.

The framework results' correlation coefficient (R) with World Bank survey data from 1992 to 2022 is 0.87, and the RMSE is 15.4, demonstrating its reliability at the national level. By effectively capturing geospatial changes, this dataset supports SDG 7.1.1 monitoring and offers valuable insights for policymakers to address electricity access disparities and promote sustainable energy transitions.

Data Description

1. This dataset consists of 0.1-degree grid Electricity Access Index (EAI) data in GeoTIFF format, where each pixel value represents the proportion of the population with access to electricity within that area.

Example Filename: EAI_0dot1_Deg_WGS84_F32_1992



Field 1: EAI (Proportion of people with access to electricity)

Field 2&3: Spatial resolution is 0.1 degree

Field 4: Coordinate system is WGS84

Field 5: Data type is F32 (Float32)

Field 6: Year "1992"


2. Aggregated EAI data at the national scale is provided in both Shapefile and CSV formats:



Table Filename: EAI_Level_0_1992_2022.csv





Fields include:



SOC (Country code)

Name (Country name)

National EAI data from 1992 to 2022





Shape Filename: EAI_Level_0_1992_2022.shp







Boundary data sourced from GADM (Database of Global Administrative Areas)






3. The pixel-level (30 arc-seconds) Electricity Accessed Population Density is provided in GeoTIFF format, as identified through nighttime light (NTL) data.

Example Filename: Elec_PopDen_WGS84_30arc_F32_1992



Field 1 & 2: Population Density with access to electricity (per km^2)

Field 3: Coordinate system is WGS84

Field 4: Spatial resolution is 30 arc-seconds

Field 5: Data type is F32 (Float32)

Field 6: Year "1992"


If you encounter any issues, please contact us via email at liu.luling.k2@s.mail.nagoya-u.ac.jp.

 

More Information

The source codes are publicly available at GitHub: https://github.com/lulingliu/EAI.
description: This research was supported by the National Natural Science Foundation of China (Grant No. 42371334 and No.42192584) and Open Fund of State Key Laboratory of Remote Sensing Science and Beijing Engineering Research Center for Global Land Remote Sensing Products (Grant No. OF202316). The authors gratefully acknowledge financial support from the China Scholarship Council.