Global Trends in Light Pollution and Their Relationship With Socioeconomic Factors
Annals of the New York Academy of Sciences, vol.1561, no.1, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 1561 Issue: 1
- Publication Date: 2026
- Doi Number: 10.1111/nyas.70297
- Journal Name: Annals of the New York Academy of Sciences
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Abstracts in Social Gerontology, Applied Science & Technology Source, BIOSIS, Child Development & Adolescent Studies, Chimica, EMBASE, MEDLINE, Public Affairs Index, zbMATH, Zoological Record, Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Biomedical Reference Collection: Corporate Edition (EBSCO), Engineering Source (EBSCO), Health Research Premium Collection (ProQuest)
- Keywords: demographic parameters, geographic information system, light pollution, remote sensing
- Middle East Technical University Affiliated: Yes
Abstract
Artificial light at night (ALAN) is a growing environmental pressure linked to socioeconomic development. This study examines ALAN trends from 2012 to 2024 across 165 countries using harmonized VIIRS satellite data. Globally, ALAN increased at an annual rate of 3.2%. Highest radiance levels occur in developed regions (Europe, North America, and East Asia), while remote areas remain dark. A few countries, including France and Venezuela, show declines. The study extends the VIIRS time series and applies a calibrated Kaya-identity framework integrating the human development index (HDI) and Gini coefficient. It introduces radiance density (RD) as a normalized metric linked to per-capita development for cross-national comparison. ALAN shows associations with macroeconomic indicators. Absolute ALAN correlates with total GDP (log), energy consumption, and CO2 emissions ((Formula presented.) –0.81). RD and light pollution density better reflect development quality, correlating with HDI ((Formula presented.)), GDP per capita (log) ((Formula presented.)), and life expectancy ((Formula presented.)). Predictive models achieve high explanatory power ((Formula presented.) –0.89), with population and GDP per capita as the strongest determinants.