Projection of Light Pollution and Environmental Impact Estimation Based on Population and GDP: The Case of Türkiye

Artificial Light at Night (ALAN) prediction can inform the development of strategies to minimise future light pollution and mitigate its adverse health, ecological, and environmental impacts. Population growth and GDP expansion significantly influence ALAN dynamics. This study quantifies changes in light pollution across Türkiye for 2012, 2017, and 2022, and predicts and maps potential changes in ALAN for 2027 and 2037 using the MOLUSCE plugin (MLP-ANN) in QGIS version 2.18, incorporating population and GDP as driving variables. The predicted and observed ALAN maps for 2022 show a satisfactory level of agreement, with an overall kappa coefficient of 0.63 and a general accuracy of 73.16%. The predicted and observed ALAN maps for 2022 exhibit an overall kappa coefficient of 0.63 and a general accuracy of 73.16%. Based on this validation, potential changes in ALAN according to Bortle classes were projected for 2027 and 2037. From 2022 onwards, ALAN intensity in the light suburban zone (20.1–19.1 mag/arcsec²) is projected to increase by 34,792.87 km², while the suburban–urban transition zone (19.1–18 mag/arcsec²) is expected to expand by 2,979.80 km² by 2037. The predicted spatial patterns provide critical insights into future urbanisation trends and light pollution dynamics. The contraction of rural areas and expansion of suburban zones pose notable risks to environmental sustainability. Gains and losses among Bortle classes indicate that ALAN dynamics can be managed through spatial planning and that ALAN projections can serve as early-warning and scenario-based decision-support tools. Integrating ALAN maps into Environmental Impact Assessment (EIA) processes and spatial planning policies can help preserve dark-sky areas and mitigate the impacts of light pollution.