Nighttime artificial light intensity in local climate zones: A comparative study for global cities

Nighttime artificial light intensity in local climate zones: A comparative study for global cities Jan Geletiˇ c a,* , Eduardo Krüger b , Matthias Demuzere c , Michael Leuchner d , Martin Jurek e , Michal Lehnert e a Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences, Pod Vod´ arenskou vˇ eˇ zí 271/2, Praha 8, 182 00, Czech Republic b Departamento de Construç˜ ao Civil, Universidade Tecnol´ ogica Federal do Paran´ a – UTFPR/Campus Curitiba - Sede Ecoville, Rua Deputado Heitor Alencar Furtado, 4900, 81280-340, Curitiba, Brazil c B-Kode VOF, Ghent, Belgium d Physical Geography and Climatology, Institute of Geography, RWTH Aachen University, 52062, Aachen, Germany e Department of Geography, Faculty of Science, Palacký University in Olomouc, 771 46, Olomouc, Czech Republic ARTICLE INFO Keywords: Light pollution Artificial light at night Nighttime radiance Local climate zones Urban environment Metropolitan areas ABSTRACT Recent studies have proved various impacts of urban light pollution, from public health, shifted vegetated phase of trees, socio-economic consequences, to the energy budget. However, a detailed structure of urban forms and functions was not incorporated into the analysis yet. Using the concept of local climate zones LCZs) in this study, we assess the nighttime lights (NTL) of 32 urban agglomerations between the years 2012 and 2023, remotely accessed by the VIIRS instrument. We found significant statistical differences between the individual LCZs as well as various trends in the analyzed metropolitan areas. LCZ 1, typically representing densely built-up areas with skyscrapers, had the highest mean NTL of all classes. It was followed by LCZ 10 (heavy industry), LCZ 4 (openly arranged buildings tens of stories tall), and LCZ 3 (tightly packed buildings several stories tall). Lower values were identified in LCZ 9, a sparse arrangement of buildings. Significant temporal and regional trends in the NTL of built types of LCZs were detected, showing that changes in NTL cannot be explained simply by population growth. Marked differences in the contribution of individual LCZs to the overall NTL of the cities could provide a tool to focus on hot-spot areas in light pollution mitigation policies. 1. Introduction The share of the world's population residing in urban areas has increased from 29.6 % in 1950 to 56.8 % in 2020 and is projected to rise to 68.4 % by 2050 (United Nations, 2018). This continuing urbanization process is expected to exacerbate various impacts on the natural environment, such as thermal changes at the local scale, as frequently demonstrated in urban heat island studies, e.g., the issue of heat stress in densely populated urban neighborhoods (Ward et al., 2016). These changes, concurrently with global warming, will put an extra burden on the local populations (Crutzen, 2004). One of the inadvertent consequences of urbanization is the need for nighttime artificial lighting (mostly to provide visibility, for safety reasons, and additionally for ornamental illumination of monuments, buildings, parks, etc.). Nighttime lighting was publicly implemented in Europe from the 17th century onwards (M´ endez et al., 2024). Starting in the 1960s, older types of light sources outdoors were gradually replaced by high-intensity discharge (HID) lamps with increased brightness, and in the 2010s, LED technology became the new standard in street lighting installation and upgrade (Doulos et al., 2019). The topic of ‘artificial light at night’ (ALAN) is closely linked to light pollution (Linares Arroyo et al., 2024). Pauley (2004) defines light pollution as ‘light that is not targeted for a specific task, is bright and uncomfortable to the human eye, causes unsafe glare to drivers and pedestrians, harms the biological integrity of ecosystems, causes light to trespass into homes and bedrooms, and creates skyglow above cities’. Such pollution can be associated with ‘urban sky glow’, defined as the brightness of urban areas compared to reference conditions (e.g., at high sea or in rural areas). The intensity and extent of such brightness can be estimated from population density (Albers & Duriscoe, 2001), measured * Corresponding author. E-mail addresses: [email protected] (J. Geletiˇ c), [email protected] (E. Krüger), [email protected] (M. Demuzere), [email protected] (M. Leuchner), [email protected] (M. Jurek), [email protected] (M. Lehnert). Contents lists available at ScienceDirect Cities journal homepage: www.elsevier.com/locate/cities https://doi.org/10.1016/j.cities.2025.106722 Received 20 May 2025; Received in revised form 19 November 2025; Accepted 5 December 2025 Cities 170 (2026) 106722 Available online 12 December 2025 0264-2751/© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ). on-site using ground-based sensors (Mander et al., 2023), and also analyzed using remote sensing data (Cinzano et al., 2001b; Dong et al., 2025; Duriscoe et al., 2018; Levin et al., 2020; Rom´ an et al., 2018; Zheng et al., 2023, 2023b). Ye et al. (2020) and Wu et al. (2025) used satellite imagery to evaluate nighttime light pollution using a mismatch index, expressing the ratio of light supply and light demand based on population density and urban functional zones. Ye et al. (2024) used a similar approach to delineate a ‘nighttime light control area’ within a city in order to indicate the zones where excess use of ALAN should be optimized. A wide range of adverse effects of light pollution has been documented. Health research has been well aware of the phenomenon and has reported on various associated health risks, such as melatonin suppression and sleep disorders, mental illnesses, and increased risk of various types of cancer in humans (Holker et al., 2010; Nadybal et al., 2020; Zielinska-Dabkowska et al., 2023). However, the causal relationship between light pollution and impacts on human health (as discussed in Widmer et al., 2022) is unclear, as excessively lit locations at night can be associated with welfare and more affluent societies, which can provide better healthcare services and thus confound such ALAN vs. health cause-and-effect relationship. In addition, lifestyle and working schedules (e.g., nurses, flight attendants, and so on) further disturb such relationships. Therefore, health research into the adverse effects of light pollution on humans requires more long-term studies before any guidelines on safe levels of ALAN can be established and adopted into legislation or urban planning policies. Nevertheless, light pollution undoubtedly affects all ecosystems existing on the planet, both terrestrial and aquatic (Gaston et al., 2013; Gaston & S´ anchez de Miguel, 2022; M´ endez et al., 2024; Wang et al., 2025), interfering with a range of ecosystem functions and services (Anderson et al., 2024). Regarding ecological balance, nighttime artificial lighting has also drawn the attention of ecologists, concerned with the potential disruption of ecological systems related to urban sky glow (Longcore & Rich, 2004). Artificial nighttime lighting may severely impact aspects such as disorientation, attraction/repulsion, reproductive behaviour, and communication among different species. Furthermore, scientific knowledge (in this case, particularly astronomical observations – one of the aims of CIE 001–1980 ‘Guidelines for minimising urban sky glow near astronomical observatories’) and human contemplation of the universe are severely hindered by urban sky glow. Cinzano et al. (2001b) showed that a large part of humanity does not in fact experience ‘night’ or true night darkness, with one-fifth of the world population having no possibility of seeing the Milky Way Galaxy. Navara and Nelson (2007) recommend hindering urban sky glow by the use of light shields, the reduction in the number of lamps, and adjusting the colour spectrum of artificial light sources at night away from the highly disruptive (in terms of human circadian cycle) blue light. Nevertheless, there is an observed increase in light pollution worldwide. A study of sky brightness derived from citizen science stellar visibility (Kyba et al., 2023) over the period 2011–2022 indicates an increase of 9.6 % per year globally, 10.4 % in North America, 6.5 % in Europe, and 7.7 % in the rest of the world. M´ endez et al. (2024) indicate an increase in light pollution in Europe of 2–10 % per year. A comparison showing national differences between two averaging periods (2014/2015 and 2020/21) in each one of the 38 European Economic Area countries suggests that the size of a country, the density of the population, and the level of infrastructure are the factors that may highly influence the country's light emissions (Widmer et al., 2022). The implementation of regulations towards the control of light emissions is still developing in EU countries and scattered across different documents nationwide and across different EU states. Among the countries with national policies, the Czech Republic stands out, in 2002 becoming the first country to present a national policy to reduce light pollution (within its Clean Air Act, cf. Widmer et al., 2022). It proposes shielding of streetlamps and changes of lighting installations to reduce upward scattering of the light, later complemented with stricter regulations for the introduction of light sources in national parks to mitigate light pollution. In a recent review of existing policies to contain unnecessary light emissions, M´ endez et al. (2024) list the following measures: raising public awareness; technical improvements for reducing excess light; and changing existing policies up to now, as they have not been sufficiently effective to curb the problem in a large scale and the long run; minimizing ecological impact from light pollution. Moreover, in the context of proposed policies, ‘nighttime darkness’ is suggested as a relevant resource for sustainable development (Lapostolle & Chall´ eat, 2020). At the local scale or neighborhood level, socioeconomic status can be linked to ALAN. A study by Xiao et al. (2025) concludes that ALAN is an important environmental issue with potential public health consequences. An environmental justice study conducted by Nadybal et al. (2020) in the USA employed a cross-sectional analysis based on remote sensing and a stratified analysis of metropolitan core, suburban, and rural neighborhoods. They showed that neighborhoods predominantly inhabited by White Americans were two times less exposed to light pollution compared to socially underprivileged Black, Asian, or Hispanic communities. Findings suggest that disparities in exposure to artificial light may be related more to racial/ethnic status than to geographic context. Nadybal et al. (2020) list among the possible explanatory factors the implementation of ‘dark sky’ initiatives in privileged US communities, while there is a tendency to deploy greater artificial lighting in socially disadvantaged neighborhoods to support nighttime policing and surveillance by law enforcement authorities. On the other hand, a study conducted by Helbich et al. (2024) revealed a different pattern of sociodemographic inequalities in Bulgaria, where people with higher educational attainment experience higher light pollution exposure; they are not necessarily in the high-income group and often live in mixed-use neighborhoods and high-rise residential blocks in intermediate or peripheral areas of the cities. To understand the rather complex issue of the spatial distribution of artificial light at night and its links to population density, urban form, and function, the patterns of nighttime lights (NTL) at the local level should be studied on the grounds of a globally defined and wellrecognized classification of urban neighborhoods. Originally intended for urban climate studies, Stewart and Oke (2012) proposed the local climate zone (LCZ) classification system as a universal urban typology that categorizes individual types of urban areas into 10 built and 7 natural land cover types, moving away from the more common binary (urban versus rural) urban land cover products (Chakraborty et al., 2024; Demuzere et al., 2022). The Local Climate Zone (LCZ) scheme was initially designed to analyze local temperature variations and urban heat island effects at the local scale (Stewart & Oke, 2012). Later on, the ‘WUDAPT: An Urban Weather, Climate, and Environmental Modeling Infrastructure for the Anthropocene’ project (Ching et al., 2018) provided fast and easy-to-use LCZ mapping tools (Demuzere et al., 2021) and continental and global-scale LCZ maps (Demuzere et al., 2019, 2020, 2022). This has led to an exponential growth in applications, such as climate-sensitive design, building energy consumption, carbon emissions, quality of life, air quality, urban vegetation phenology and ecosystem patterns, functions, and dynamics, and epidemiological studies (see e.g. Demuzere et al., 2022; Huang et al., 2023; Lehnert et al., 2021). To our knowledge, however, no study has investigated the relationship between LCZs and light pollution so far. We consider it an important topic to address for several reasons. Firstly, links between urban form and function and nighttime light emissions shall be better understood to alleviate the adverse impacts of ALAN. Identifying light emission hotspot patterns as well as general trends in artificial lighting of the various types of urban neighborhoods needs to be explored. LCZs represent a suitable, detailed urban area classification, as they reflect building density and urban function typology of urban areas, based on a uniform method worldwide. It is also useful to verify whether patterns of ALAN differentiation are consistent across climate types and regions. Most importantly, by identifying trends in LCZ development as observed in the urbanization process, it J. Geletiˇ c et al. Cities 170 (2026) 106722 2 will become possible to predict their light pollution potential and provide justification for action that can be taken upfront (e.g., through building directives, urban planning guidelines, regulations for urban development). One way to do that is to follow the example of South Korea with the introduction of its Light Pollution Prevention Act in 2013. It establishes light environment management zones within a given area, ranging from E1 (green areas for conservation) to E4 (semi-industrial and commercial areas) (Kim & Kim, 2021), thereby indicating allowed luminance values for ALAN and enabling prediction methodologies to be developed (M´ endez et al., 2024). Based on the above-mentioned reasons, our study aims to answer the following research questions: (Albers & Duriscoe, 2001) Are LCZ classes a suitable urban typology proxy for estimating light pollution in the cities, derived from satellite measurements? (Anderson et al., 2024) What are the current trends in urban light pollution in individual types of LCZs worldwide? (Bar´ a & Castro-Torres, 2025) Are there regional differences in urban light pollution and its recent trends across the globe? (Barentine et al., 2018) Can a spatiotemporal analysis of nighttime radiance across LCZ assess the effects of technological innovations and implementation of light pollution-reducing policies in urban lighting? Overall, we aim to start a cross-disciplinary discussion on the methods of assessing urban light pollution on the urban neighborhood (local) level. 2. Materials and methods 2.1. Selection of metropolitan areas To provide a globally well-distributed set of large urban areas for the nighttime radiance analysis, a set of 32 metropolitan areas was selected using the population ranking for individual continents. The spatial extent of these areas was based on the Global urban and rural settlement dataset (Liu et al., 2024). The second step was evaluation using the first version of the Global Urban Polygons and Points Dataset (GUPPD) (CEISIN Columbia University and JRC European Commission, 2024). Finally, each selected polygon extent was extended manually about its surroundings, using satellite images, to consider the effects of (sub-) urbanization. Delineated polygons are available on the Zenodo repository; see the Data availability section. The annual population of urban agglomerations with more than 300,000 inhabitants from the World Urbanization Prospects, 2018 Revision (United Nations, 2018) was used to rank the five largest cities on each continent, each from a different country where applicable; in the case of Asia we selected ten cities (considering the size of Asian population), adding one city, Melbourne, to represent Australia. In the case of Europe, among its five largest cities, we selected two specific areas to represent the conurbations of Randstad (Amsterdam, Netherlands) and the Ruhr region (Germany). 2.2. Local climate zones (LCZ) Local Climate Zones (LCZs) were introduced by Stewart and Oke (2012) to provide a standardized, fine-grained typology of urban and rural landscapes—distinguished by physical form and surface properties – addressing the inadequacy of simplistic ‘urban‘ versus ‘rural‘ labels in urban heat island (UHI) research. Designed to enable consistent, comparable field observations at scales from several hundred meters to a few kilometers, the LCZ framework is now widely embraced across urban climate science. The scheme consists of 17 distinct zone types (‘built’ types 1–10 and ‘land cover’ types A-G) that are defined by structural and surface characteristics, such as building and vegetation heights, density, and imperviousness, establishing a standard, locally scaled framework to consistently categorize field sites, enabling reliable comparison of temperature observations across diverse urban and rural contexts The climatological classification of urbanized areas used herein consists of 17 standard LCZs defined by surface structure and cover, construction materials, or anthropogenic heat emissions. The standard classification is divided into ‘built types‘1–10 (originally only ‘urban‘), and ‘land cover types‘A–G (originally ‘rural‘) (Stewart & Oke, 2012). Characteristics for individual classes are shown in Appendix 7. The original idea of urban effect on air temperature measurement classification was quickly extended to a full ‘land cover’ classification of urbanized landscape, as we understand LCZs today. In general, LCZs depend on inherent physical parameters, including mainly building surface fraction (BSF), pervious surface fraction (PSF), impervious surface fraction (ISF), height of roughness elements (HRE), sky-view factor (SVF), or canyon aspect ratio. The full list of parameters can be found in Stewart and Oke (2012). Several methods for LCZ delineation exist nowadays; see, e.g., their overview in Lehnert et al. (2021), Huang et al. (2023), Han et al. (2024), or Stewart (2024). The classification process consists of categorizing all urban areas with similar characteristics into grid cells with a spatial resolution typically of 100 to 500 m. Currently used or developed methods and algorithms apply various types of smoothing functions with the purpose of including potential neighborhood effects. The present study uses the global LCZ map version 3.0.0, generated by feeding a large number of labeled training areas and earth observation images into lightweight random forest models (Demuzere et al., 2023). Classification quality was assessed using a bootstrap crossvalidation alongside a thematic benchmark for 150 selected functional urban areas using independent global and open-source data on surface cover, surface imperviousness, building height, and anthropogenic heat. A more detailed description of the input layer is available in Demuzere et al. (2022). Finally, it should be noted that the LCZ layer is here regarded as a static snapshot of urban form and surface characteristics, intended to approximate typical climate conditions over the past decade. The LCZ map (Demuzere et al., 2023) used in this study has a nominal reference year of 2018, but its delineation is derived from multiple Earth-observation datasets spanning roughly ten years. Considering the current intensive urbanization, it is practically impossible to analyze rapid urban development and land cover changes worldwide, which represents one of the limitations of this study (more details are in the Discussion). 2.3. Artificial light at night (ALAN) Remote sensing data is highly valuable for analyzing Artificial Light at Night (ALAN). For instance, the pioneering study of Cinzano et al. (2001b) used a set of radiance-calibrated high-resolution satellite data and accurate modeling of light propagation in the atmosphere in order to produce the first World Atlas of artificial night sky brightness. Before 2021, most analyses were in the resolution of hundreds of meters; the launch of the Sustainable Development Science Satellite 1 (SDGSAT-1) on 5 November 2021 enabled local-scale analysis in tens of meters. However, LCZs in Section 2.2 assume long-term and stable urbanized land cover classification. In practice, only two potential data sources offer long-term annual time series: DMSP and VIIRS. However, among these, only VIIRS provides the results with a spatial resolution, in several hundreds of meters, compatible with LCZ resolution. The data source of ALAN used in this study is the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument aboard the joint NASA/ NOAA Suomi National Polar-orbiting Partnership (Suomi NPP), NOAA20, and NOAA-21 satellites (formerly known as JPSS-1 and JPSS-2, respectively). VIIRS has a Day-Night Band (DNB) sensor that provides global daily measurements of nocturnal visible and near-infrared (NIR) light. The VIIRS DNB's ultra-sensitivity in lowlight conditions generates a new set of science-quality nighttime products that manifest substantial improvements in sensor resolution and calibration when compared to nighttime light products from the era of DMSP/OLS. These improvements allow the VIIRS DNB products to better monitor both the magnitude and signature of nighttime phenomena, and anthropogenic sources of light emissions (Rom´ an et al., 2018). A VNP46A4 nighttime lights product from the Black Marble suite (Rom´ an et al., 2018) was used J. Geletiˇ c et al. Cities 170 (2026) 106722 3 for analysis. VNP46A4 provides yearly composites generated from daily atmosphericallyand lunar-BRDF-corrected NTL radiance to remove the influence of extraneous artifacts and biases (abbreviation ‘BRDF’ means bidirectional reflectance distribution function; see Wang et al., 2022: section 2.3). The spatial resolution of the product is 15 arcsecs and contains 28 layers (e.g., the number of observations, quality, and standard deviation for multi-view zenith angle categories, their snowcovered and snow-free statuses besides land-water mask, latitude and longitude coordinate information, etc.). They also include detailed information and description of the quality flags (QF; Wang et al., 2022); in this study, only QF values with 00 were used (good-quality, and a number of observations used for the composite larger than 3). A composite ‘AllAngle_Composite_Snow_Free‘ of yearly averages used starts from 1 January 2012 and ends on 31 December 2023. For the comparison with LCZs in 100 m resolution, each composite was reprojected from the original sinusoidal projection to UTM, a specific zone depending on city location, and resampled to 100 m spatial resolution using bilinear interpolation. Despite the VIIRS data are often recalculated to, e.g., ‘artificial brightness' or ‘total brightness' (both in mcd⋅cm −2 ), we decided to use the original radiance units of nW⋅cm −2 ⋅sr −1 (note: standard SI unit 1 W⋅m −2 ⋅sr −1 =10 5 nW⋅cm −2 ⋅sr −1 ). 2.4. Proposed classification of light pollution At present, there is not enough scientific evidence to establish any guidelines for safe levels of ALAN. The human physiological response is evolutionarily adapted to the natural darkness at night and ALAN should be prevented whenever possible (Korf et al., 2025). Under ideal conditions, places without nighttime lights will have values close to 0.0 nW⋅cm −2 ⋅sr −1 . Data derived from remote sensing contain residual background noise, so NASA's VIIRS/NPP Lunar BRDF-Adjusted NTL composite values with radiance less than 0.5 nW⋅cm −2 ⋅sr −1 are set to zero. More information about sources of residuals can be found, e.g., in Wang et al. (2022). For an easier interpretation of numerical values, intervals of radiance for different levels of light pollution are suggested in Table 1. Potential limitations and methodological aspects are also briefly analyzed in the Discussion section. 3. Results The time series consisting of annual NTL between 2012 and 2023 was analyzed. While the standard LCZ classification is divided into ‘built types’ 1–10 and ‘land cover types’ A–G, most of our results are focused on built types, where the majority of inhabitants are expected. All analyzed cities' average NTL in the built LCZ types increased from 31.4 (2012) to 35.7 nW⋅cm −2 ⋅sr −1 (2023; +9.5 %), with a 12-year mean of 33.3 nW⋅cm −2 ⋅sr −1 for all metropolitan areas. However, significant regional differences were identified (see Fig. 1). Generally, the mean 12-year NTL within metropolitan areas in Fig. 1 often exceeds 20 nW⋅cm −2 ⋅sr −1 , the limit of high light pollution (see Table 1). This limit was exceeded by 24 of the 32 analyzed metropolitan areas during the analyzed period. The lowest 12-year mean occurred in the cities of the African region, Lagos (5.5 nW⋅cm −2 ⋅sr −1 ) and Dar Es Salaam (7.4 nW⋅cm −2 ⋅sr −1 ). The third city with a 12-year mean below 10 nW⋅cm −2 ⋅sr −1 was Dhaka (9.0 nW⋅cm −2 ⋅sr −1 ). The low mean value in the Ruhr region (11.7 nW⋅cm −2 ⋅sr −1 ), compared to other cities in the European region, could be explained by its specific land cover; this conurbation area comprises several larger urban centers interspersed with smaller residential areas and built-up locations that have a higher percentage of green fraction; gaps between the larger urban cores of Duisburg, Essen, Gelsenkirchen, Bochum, and Dortmund are filled by patches of small towns, villages, and agricultural land. The highest 12year mean NTL in the built LCZs was in Buenos Aires (60.4 nW⋅cm −2 ⋅sr −1 ). Madrid, Bogot´ a, Istanbul, and S˜ ao Paulo also exceeded the limit of 50 nW⋅cm −2 ⋅sr −1 for very high light pollution (see Fig. 1). Further on, it is important to emphasize that temporal trends in NTL vary across metropolitan areas of all continents. In 2012, Madrid had the highest mean annual NTL (69.7 nW⋅cm −2 ⋅sr −1 ). By 2015, however, Buenos Aires surpassed Madrid, and in 2023, Buenos Aires recorded a mean annual NTL of 62.6 nW⋅cm −2 ⋅sr −1 compared to 51.9 nW⋅cm −2 ⋅sr −1 in Madrid. Furthermore, Madrid had already been surpassed by six metropolitan areas in that year. While in 2012 only two of the analyzed metropolitan areas exceeded 50 nW⋅cm −2 ⋅sr −1 , in 2018 this value was exceeded in five areas, and in 2023 in ten areas. A statistically significant increasing trend in NTL (p-value <0.05) was identified in 20 metropolitan areas (Fig. 1). The strongest relative increase trend between the years 2012 and 2023 was in Dar Es Salaam (see Fig. 2), where mean annual radiance more than tripled (+204.9 %). Despite that, the absolute values were still very small in comparison with other regions. Large increases, to almost two times higher values, were also identified in Lima (+98.6 %) and Dhaka (+84.7 %). The increase in developed countries, typically in the European Union, North America, Australia, or Japan, was often lower (up to +30 %). On the contrary, statistically significant decreases in mean annual NTL between the years 2012 and 2023 were identified in four areas: Paris (−27.3 %), Madrid (−25.5 %), London (−22.1 %), and Chicago (−9.1 %). Surprisingly, a decrease of about −9.7 % (not statistically significant) was also observed in Cairo. The table with the mean annual NTL for all years 2012–2023 is available in Appendix 1. As shown in Fig. 2, the change in NTL within the studied cities is not spatially homogeneous; it illustrates an increase in Dar Es Salaam and a decrease in Paris. For example, the ‘hot spot’ in the city center of Dar es Salaam shows an increase of 66.6 % from 60.5 (2012) to 100.8 nW⋅cm −2 ⋅sr −1 (2023). Moreover, an increase in values occurred in most neighborhoods of the city. Nonetheless, the mean annual NTL for the whole area remains very low, increasing from 4.3 to 13.0 nW⋅cm −2 ⋅sr −1 between 2012 and 2023. In Paris, the mean annual NTL in the city center decreased by 24.7 % from 177.4 (2012) to 133.5 nW⋅cm −2 ⋅sr −1 (2023). The decrease in values over the whole area is visible, yet certain areas with low or high NTL were hardly identifiable due to their spatially scattered character; this variability in specific built-up categories can be analyzed using LCZs in more detail. Intra-urban LCZ-based differences were tested using analysis of variance (ANOVA) and the Tukey HSD test. In most cases, statistically significant differences were identified. Analysis of the delineated LCZs in the studied metropolitan areas shows that the highest 12-year mean NTL was in LCZ 1 (101.6 nW⋅cm −2 ⋅sr −1 ; see Fig. 3). However, this value strongly depends on the intra-zonal structure. Even though the average 12-year mean NTL in LCZ 1 was identified in Chicago (203.9 nW⋅cm −2 ⋅sr −1 ; Appendix 3), the maximum 12-year mean NTL in a single Table 1 Illustrative explanation of the annual nighttime radiance values (derived from a comparison of nighttime radiance images with CORINE Land Cover data for Europe, carried out by the authors during image processing). Radiance [nW⋅cm −2 ⋅sr −1 ] Level of light pollution Examples of typical areas 0.0–0.9 none Rural areas, with none or very low urbanization 1.0–2.9 very low Small villages, individual buildings 3.0–9.9 low Large villages, small cities 10.0–19.9 medium Residential areas of cities, medium-sized cities 20.0–49.9 high Densely built-up areas in the larger cities, distribution centers, small factories, and large commercial areas 50.0–99.9 very high Heavy industry, squares, railway stations, etc. 100.0–199.9 extremely high City centers, harbors, city highways, and main roads, shopping centers 200.0+bright night Large airports, greenhouse farms, and populated city centers J. Geletiˇ c et al. Cities 170 (2026) 106722 4 grid cell occurred in Moscow (1286.2 nW⋅cm −2 ⋅sr −1 ); this value is six times higher than the ‘zonal’ mean in Chicago or three times higher than the 12-year single grid cell maximum in New York or Tokyo (424.1 or 423.8 nW⋅cm −2 ⋅sr −1 , respectively). In 12 metropolitan areas, the 12year mean NTL in LCZ 1 was higher than 100 nW⋅cm −2 ⋅sr −1 (extremely high light pollution). An average increase within this period was from 97.1 in 2012 to 102.9 nW⋅cm −2 ⋅sr −1 in 2023 (+6.0 %). Although the general trend in LCZ 1 (Fig. 4) was increasing (mainly in Lima, Los Angeles, Houston, or Toronto), several metropolitan areas showed a statistically significant decrease in NTL. The highest decrease of NTL in LCZ 1 was identified in Madrid, from 166.7 (2012) to 94.1 nW⋅cm −2 ⋅sr −1 (2023; −43.5 %). A decreasing trend in LCZ 1 was also identified in Buenos Aires (from 168.2 to 124.6 nW⋅cm −2 ⋅sr −1 ; −25.9 %), Paris (from 91.1 to 74.8 nW⋅cm −2 ⋅sr −1 ; −17.9 %), and Chicago (from 201.9 to 178.0 nW⋅cm −2 ⋅sr −1 ; −11.4 %). The residential areas typically belong to LCZs 2, 3, 4, 5, 6, and 9. The highest 12-year average NTL occurred in LCZ 2 (62.6 nW⋅cm −2 ⋅sr −1 ), and the city with the highest mean NTL in LCZ 2 was Chicago (162.2 nW⋅cm −2 ⋅sr −1 ). Results for LCZ 2 are strongly variable; in 23 metropolitan areas, the 12-year mean NTL exceeded 50 nW⋅cm −2 ⋅sr −1 (very high light pollution); in 6 of them, even 100 nW⋅cm −2 ⋅sr −1 (extremely high light pollution). The majority of cities with extremely high light pollution are located in North America; aside from Chicago (mentioned above), also Houston (138.1 nW⋅cm −2 ⋅sr −1 ), Los Angeles (132.7 nW⋅cm −2 ⋅sr −1 ), and Toronto (109.3 nW⋅cm −2 ⋅sr −1 ). As to the mean annual NTL in LCT 2, only Chicago shows a significantly decreasing trend, while Los Angeles is increasing; the rest of the North American cities remain stagnant throughout the analyzed period. Asian and Australian cities typically have lower values, around 50 nW⋅cm −2 ⋅sr −1 . Similar results were also identified in LCZs 3, 4, and 5, but with lower variability of NTL; 12-year average values of NTL were 52.3 (LCZ 3), 55.3 (LCZ 4), and 46.4 nW⋅cm −2 ⋅sr −1 (LCZ 5), and limits for very high (or extremely high) light pollution were exceeded in 13 (Albers & Duriscoe, 2001), 19 (Barentine et al., 2018), and 14 (0) cases. In LCZ 4, Fig. 1. Boxplots with the mean annual nighttime lights (NTL) within the built types (Albers & Duriscoe, 2001; Anderson et al., 2024; Bar´ a & Castro-Torres, 2025; Barentine et al., 2018; Behnisch et al., 2022; CEISIN Center For International Earth Science Information Network Columbia University, JRC Joint Research Centre European Commission, 2024; Chakraborty et al., 2024; Ching et al., 2018; Cinzano et al., 2001a; Cinzano et al., 2001b) of Local Climate Zones (LCZs) for 2012–2023. The line within the box indicates the median. The bottom of the box is the first quartile, and the top is the third quartile. Whiskers indicate the lowest value still within 1.5 IQR (IQR =third quartile−first quartile) and the highest value still within 1.5 IQR. Outliers were omitted. The number in brackets is the 12-year average for metropolitan areas, increasing (+) or decreasing (−) trend, and coded statistical significance of t-test (codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘×’ 1). The yellow points represent annual means, and the blue line indicates the linear trend. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) J. Geletiˇ c et al. Cities 170 (2026) 106722 5 the highest 12-year mean NTL for an individual city was identified in Los Angeles (218.1 nW⋅cm −2 ⋅sr −1 ). This area contains only 25 grid cells covering parts of Disneyland and neighboring hotel resorts. LCZ 5 manifested very low temporal variability; the mean difference between the years 2012 and 2023 was only 0.3 nW⋅cm −2 ⋅sr −1 , but there were significant differences between the cities. Decrease in NTL was identified in most European cities; Madrid (by 47.5 nW⋅cm −2 ⋅sr −1 ; −38.4 %), Paris (by 15.5 nW⋅cm −2 ⋅sr −1 ; −27.1 %), and London (by 13.8 nW⋅cm −2 ⋅sr −1 ; −25.9 %). A high decrease, by about 24.9 nW⋅cm −2 ⋅sr −1 (−24.5 %), occurred in Chicago. An increase in values was identified in Lima (by 34.3 nW⋅cm −2 ⋅sr −1 , +86.2 %) or Ciudad de M´ exico (by 17.8 nW⋅cm −2 ⋅sr −1 ; +40.3 %). LCZ 6 was typical of lower values (see Fig. 5), with the 12-year average NTL of 23.2 nW⋅cm −2 ⋅sr −1 . In 2012, only 15 cities exceeded the limit for high light pollution; in 2023, it was 19. The lowest 12-year average NTL of 4.4 nW⋅cm −2 ⋅sr −1 occurred in LCZ 9. This large difference between LCZs 6 and 9 can be explained by the typical location of the zone; LCZ 6 often appears in centers of villages in metropolitan areas or at the peripheries of larger cities. LCZ 9 is more typical in rural areas, where larger spacing between buildings should be expected. Moreover, LCZ 9 grid cells typically neighbor with ‘land cover type’ grid cells, whose NTL is close to 0 nW⋅cm −2 ⋅sr −1 . Only in Ciudad de M´ exico and Moscow were the NTL values in LCZ 9 higher than 10 nW⋅cm −2 ⋅sr −1 . A surprisingly high 12-year average NTL of 22.4 nW⋅cm −2 ⋅sr −1 occurred in LCZ 7; specifically in Cairo, the average of 46 grid cells classified as LCZ 7 was 59.4 nW⋅cm −2 ⋅sr −1 . This class is typical for informal settlements and low-income residential areas, so lower values are generally expected. However, many metropolitan areas have made substantial construction, technical, and economic progress in the last decades; the situation should be different in other cities. The second-highest 12-year average NTL was in LCZ 10 (74.4 nW⋅cm −2 ⋅sr −1 ); Houston had the highest mean NTL in LCZ 10 of all cities, 168.4 nW⋅cm −2 ⋅sr −1 (Fig. 6). This value was slightly lower than in the LCZ 1 of Houston (190.3 nW⋅cm −2 ⋅sr −1 ). LCZ 10 of Cairo and Los Angeles (162.2 and 161.8 nW⋅cm −2 ⋅sr −1 , respectively) were also close to the NTL value of Houston. These values in LCZ 10 are so high that they surpass the fourth-highest mean NTL in LCZ 1 (Toronto; 151.3 nW⋅cm −2 ⋅sr −1 ). Despite that, LCZ E is a ‘land cover type’ representing rocky surfaces; in urban areas, this class may also often represent large impervious areas; typically parking lots or large traffic infrastructure, like city ring roads, harbors, or highways (note that LCZ E has limited occurrence; see Appendix 2). The 12-year average in LCZ E was 12.5 nW⋅cm −2 ⋅sr −1 . The highest values were in Shanghai (145.3), Moscow (124.7), Paris (116.2), and Madrid (110.4, all values in nW⋅cm −2 ⋅sr −1 ). This class often neighbors LCZ 8 or LCZ 10 and could be used, e.g., to analyze extending traffic infrastructure (airports, harbors, metropolitan ring roads, motorways, parking lots, etc.). For example, increasingly lighted airports are visible in NTL values in Bangkok, where the mean annual NTL in LCZ E increased by +111.7 %, from 39.4 (2012) to 83.4 nW⋅cm −2 ⋅sr −1 (2023). The opposite trend, mainly the switching-off of lights or changing of light direction, occurs, e.g., in London (City of London, Fig. 2. The examples of increasing values of annual nighttime radiance in Dar Es Salaam (first row) and decreasing values in Paris (second row) in the years 2012 (a, d), 2018 (b, e), and 2023 (c, f). Topographic map © OpenStreetMap. J. Geletiˇ c et al. Cities 170 (2026) 106722 6 2023), where the mean annual NTL decreased from 105.4 in 2012 to 61.8 nW⋅cm −2 ⋅sr −1 (−41.3 %) in 2023. Interestingly, most of the red spots in Paris (Fig. 2f) are increased radiance in LCZ E; the mean annual value in 2023 was about 12.9 nW⋅cm −2 ⋅sr −1 (+12.6 %) higher than in 2012. A relatively high average of 12-year NTL was also identified in LCZ 8 (50.3 nW⋅cm −2 ⋅sr −1 ; see Appendix 3), dominated by small factories, distribution centers, storage, or shopping malls, where light pollution is expected due to services. 4. Discussion The analysis above proves that nighttime lights (NTL) and related artificial light at night represent an actual and very complex challenge. Even though NTL is generally increasing worldwide, it comes with pronounced regional and local differences. Levin and Zhang (2017), in more than 4000 densely populated areas, explained cities' ‘brightness’ by variables at both the country and city levels. Frolking et al. (2024) noted from their analysis of more than 1550 cities worldwide that the pattern of urban growth has shifted from predominantly lateral in the 1990s towards mainly vertical in the 2010s. Analysis utilizing the LCZ classification provides several interesting outcomes. The first one is the applicability of this approach for a more detailed study of light emissions from various types of urban structures, which extends studies typically based on ‘urban’ or ‘rural’ types of landscapes. A practical benefit of spatiotemporal satellite images is a local-scale evaluation of various applied measures, such as the installation of modern lighting technologies. Our results proved that systematic change from the legacy highpressure sodium (HPS) lamps to LED technology introduced in the whole city, e.g., in the Madrid metropolitan area in 2015–2016, resulted Fig. 3. The 12-year average of nighttime radiance in the built type of Local Climate Zones (LCZs) in the analyzed metropolitan areas for the period 2012–2023. For a detailed explanation of box plots, see Fig. 1. J. Geletiˇ c et al. Cities 170 (2026) 106722 7 in major changes in sky brightness and colour (Robles et al., 2021). This effect is visible as a significant drop in light pollution in Fig. 1. However, the ‘blue blindness’ of the VIIRS instrument (technical limitations are discussed below) could cause the measured radiance values may decrease without an actual drop in the total amount of artificial light (Hung et al., 2021). Considering the various chromaticities of modern LEDs, the mix of city light sources, and the resolution of the VIIRS DNB, missing ‘blue’ represents minor uncertainty. Besides the shift in the colour of the light, street lighting has been recently influenced by the rising awareness of light pollution and the efforts to curb its adverse impacts with improved shielding, directional aiming, or dimming of street lights, which decreases the levels of upward radiance and of the resulting skyglow (Barentine et al., 2018). Despite the general trend of growth in urban populations worldwide, the comparison of nighttime radiance values between 2012 and 2023 reveals no simple correlation between city population growth and an increase in nighttime radiance (Fig. 7). This can be explained by the complex nature of urbanization, specifically the distinction between horizontal and vertical city growth. The horizontal city growth, an expansion of the city borders to their closest surroundings, can be analyzed using a longer time series of NTL in combination with land cover changes. This horizontal growth should not increase the NTL of the city by any large amount; Zheng, Huang, et al. (2023) showed that NTL decreases slowly around the city centre, then rapidly towards the suburbs, and again slowly towards the rural background. The case of vertical growth of the city is more complicated; population density can increase, but the LCZ class remains the same. Redevelopment of brownfields in urban areas often turns abandoned industrial plots into residential or commercial areas, often with a compact housing structure; the change in NTL level depends on the original lighting of the area. Frolking et al. (2024) noted from their analysis of more than 1550 cities worldwide that the pattern of urban growth has shifted from predominantly lateral in the 1990s towards mainly vertical in the 2010s. Moreover, there also exist regional socio-economic differences; an example of a related analysis for the USA can be found in Xiao et al. (2025). In the economically less developed regions of the world, the relatively higher costs of electricity may inhibit the development of street lighting despite a dynamic population growth (the example of Kinshasa). In Latin American cities, higher NTL was identified in residential LCZs 6 and 9; it is a practical impact of neighborhood safety, where streetlights serve as theft prevention (de Oliveira, 2018). Regional specifics in urban design, cultural habits, as well as technical or legislative regulations must be anticipated; case study comparisons of selected metropolitan areas might reveal influences of the prevailing surface colors and building materials (Kocifaj, 2019), local topography, street pattern shape, and density (Pan & Du, 2021). Concerning climatic regions, seasonal changes in (urban) greenery, namely trees along the streets, may decrease upward nighttime radiance. Recent studies proved that ALAN disturbed the spring bud burst and leaf development of street trees, which could negatively affect their function (Czaja & Kołton, 2022). Considering the nature of LCZ and their distribution within the urban Fig. 4. Box plots of mean annual radiance between the years 2012 and 2023 in delineated LCZ 1 (compact high-rise) of selected metropolitan areas. Yellow points represent the annual averages, and the blue line is the linear trend. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) J. Geletiˇ c et al. Cities 170 (2026) 106722 8 tissue, patches of built-up areas with high light pollution tend to occur as scattered, forming a granular pattern of higher and lower nighttime radiance within a wider area. The bright areas belong to LCZ 1, which includes the central business districts (the highest NTL in LCZ 1 was in the cities of North and South America, but also Moscow, London, and Tokyo), and LCZ 10, with heavy industry, usually located further away from the city centers. The third brightest on average is LCZ 2, typically containing the historical city cores; in the case of large urban agglomerations, they tend to appear as multiple spots of the originally separate town centers that became gradually immersed into a wider urbanized landscape. Urban planning has brought about the concept of polycentrism, providing all essential services to the city dwellers in more than one central location by creating several hubs of commercial services and offices, thus adding more bright grains into urban agglomerations (Pan et al., 2024). The horizontal growth of cities is also linked with urban sprawl (Behnisch et al., 2022; Wei & Ewing, 2018), in most of the studied agglomerations, documented by an increasing trend of NTL in LCZ 8, 9, 10, and E. Moreover, individual types of LCZ occur in unequal proportions to the overall urban area, with the lower-density and building-height LCZs taking up more space; however, they contribute relatively less to the urban nighttime radiance (see Appendices 5 and 6). Each city also has its characteristic mix of individual built-up types of LCZ; thus, using this approach can provide a more detailed background when analyzing and comparing the nature of NTL in urban areas. It could be argued that the classification of LCZs primarily serves local climate purposes; therefore, this concept should not be used beyond urban climate research (Lehnert et al., 2021). The reason is that LCZs are based on measurable physical variables and do not necessarily account for socio-economic status or development patterns, although there might be a relationship between observed density, building height, and the land-cover extent and socio-economic condition, for example, in the case of informal land use (slums, shantytowns, and informal settlements). Nevertheless, the individual classes of LCZ, to a certain extent, reflect the varying density of population and economic activities through the compactness and building-height criteria (Demuzere et al., 2020; Ma et al., 2024; Stewart & Oke, 2012), which is closely related to the concentration of traffic and pedestrian flows (where public safety calls for sufficient artificial lighting), but also additional ornamental lighting of monuments and historical heritage, retail advertising, industry, or just the support for public safety. Finally, it is worth noting that despite utilizing the most recent global LCZ map (Demuzere et al., 2023), inaccuracies can persist in certain LCZ classes. These can stem from the limited availability of training areas for specific classes like LCZ 7 (lightweight low-rise) and LCZ 1 (compact high-rise). Some LCZ classes share similar spectral and physical characteristics, such as building footprints and impervious surface areas (e.g., LCZs 4 and 5), leading to classification challenges. Additionally, LCZs representing informal settlements (LCZ 7) pose classification difficulties due to their mixed spectral signatures and lack of distinctive roughness element data. These challenges result in lower classification probabilities and accuracies for specific LCZ types (Demuzere et al., 2022). It could also be argued that using LCZ in the evaluation of light pollution is potentially biased since one of the 46 Earth observation Fig. 5. Box plots of mean annual nighttime radiance between the years 2012 and 2023 in delineated LCZ 6 (open low-rise) of selected metropolitan areas. Further description of box plots is in Fig. 1. J. Geletiˇ c et al. Cities 170 (2026) 106722 9 Appendix 7 (continued) Local climate zone (LCZ) Sky view factor 1 Aspect ratio 2 Building surface fraction 3 Impervious surface fraction 4 Pervious surface fraction 5 Height of roughness elements 6 Surface admittance 7 Anthropogenic heat flux 8 Surface albedo 9 LCZ D Low plants >0.9 <0.1 <10 <10 >90 <1 1200–1600 0 0.15–0.25 LCZ E Bare rock and paved >0.9 <0.1 <10 >90 <10 <0.25 1200–2500 0 0.15–0.30 LCZ F Bare soil and sand >0.9 <0.1 <10 <10 >90 <0.25 600–1400 0 0.20–0.35 LCZ G Water >0.9 <0.1 <10 <10 >90 –1500 0 0.02–0.10 1 Ratio of the amount of sky hemisphere visible from ground level to that of an unobstructed hemisphere. 2 Mean height-to-width ratio of street canyons (LCZs 1–7), building spacing (LCZs 8–10), and tree spacing (LCZs A–G). 3 Ratio of building plan area to total plan area (%). 4 Ratio of impervious plan area (paved, rock) to total plan area (%). 5 Ratio of pervious plan area (grass, sand) to total plan area (%). 6 Geometric average of building heights (LCZs 1–10) and tree/plant heights (LCZs A–F) (m). 7 Ability of surface to accept and release heat (J•m −2 •s –1/2 •K −1 ), varies with soil wetness and material density. 8 Mean annual heat flux density (W•m −2 ) from fuel combustion and human activity (transportation, space cooling/heating, industrial processing, human metabolism), varies significantly with latitude, season, and population density. 9 Ratio of the amount of solar radiation reflected by a surface to the amount received by it. Varies with surface colour, wetness, and roughness. Appendix 8 Total lit area [ha] with nighttime radiance higher or equal to 0.5 nW⋅cm−2⋅sr−1 for selected metropolitan areas in the period between 2012 and 2023. City 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Amsterdam 160,899 160,917 160,919 160,909 160,916 160,869 160,919 160,890 160,919 160,919 160,901 160,890 Bangkok 680,499 677,933 684,336 680,444 684,557 684,787 684,703 682,997 685,005 684,378 685,317 685,318 Bogot´ a 90,134 95,615 97,260 95,029 96,436 96,943 95,890 97,275 95,582 96,276 97,441 97,441 Buenos Aires 661,422 661,324 662,134 665,372 662,712 659,999 658,354 657,808 650,489 646,935 666,633 667,711 Cairo 1,078,824 1,079,385 1,082,921 1,081,130 1,077,585 1088,099 1,079,949 1,087,116 1,071,627 1,083,624 1,100,205 1,103,538 Chicago 1061,962 1,056,564 1,055,819 1,053,199 1,045,786 1,040,846 1,040,333 1,029,025 985,177 990,877 1,042,417 1,057,132 Ciudad de M´ exico 317,036 316,699 317,036 317,036 317,036 317,036 316,820 317,036 314,281 316,676 317,036 317,036 Dar Es Salaam 91,023 97,082 112,017 123,039 128,902 135,969 143,442 140,774 138,962 153,160 172,502 177,038 Delhi 660,788 660,788 660,788 660,788 660,788 660,788 660,788 660,788 660,788 660,788 660,788 660,788 Dhaka 122,320 122,267 122,320 122,320 122,320 122,320 122,320 122,320 122,046 122,320 122,320 122,320 Houston 1,393,288 1,402,711 1,406,468 1,397,596 1,372,379 1,395,220 1,385,712 1,385,304 1,337,512 1,373,308 1,427,442 1,431,049 Istanbul 325,877 325,811 325,765 325,877 325,877 325,877 325,877 325,877 322,967 325,877 325,877 325,877 Jakarta 479,088 478,960 485,527 485,495 483,774 478,729 485,384 484,920 475,587 483,116 487,696 487,959 Karachi 198,862 197,492 198,862 198,808 198,862 198,862 198,862 198,862 198,862 198,862 198,862 198,862 Kinshasa 111,300 114,650 109,476 115,856 107,186 112,750 114,684 112,430 100,741 110,933 117,543 122,616 Lagos 329,866 332,827 340,659 388,333 363,641 359,126 385,112 338,141 364,762 373,743 420,657 451,112 Lima 152,261 156,929 155,584 154,691 159,454 158,610 160,767 159,107 158,230 158,913 165,677 164,774 London 596,196 596,167 596,196 596,136 596,181 596,196 596,183 596,196 595,029 595,916 596,196 596,196 Los Angeles 1,226,064 1,211,533 1,246,309 1,220,725 1,226,509 1,239,567 1,249,207 1,226,774 1,183,447 1,214,543 1,273,781 1,306,410 Luanda 128,624 128,624 128,624 128,624 128,624 128,624 128,624 128,624 128,624 128,624 128,624 128,624 Madrid 385,405 385,405 385,405 385,405 385,182 385,405 385,405 385,405 381,914 384,528 385,405 385,405 Manila 228,496 230,735 245,030 244,829 242,055 243,081 248,138 244,371 239,792 244,781 251,691 255,501 Melbourne 480,356 487,559 493,721 492,566 497,035 490,426 485,472 493,937 464,571 464,231 536,300 574,882 Moscow 754,204 754,204 754,204 754,204 753,770 747,205 753,910 754,204 754,194 754,204 754,204 754,204 New York 827,729 827,729 827,729 827,729 827,729 827,729 827,397 827,729 814,897 822,435 827,694 827,724 Paris 557,979 557,979 557,979 557,979 557,979 557,979 557,979 557,979 557,979 557,666 557,979 557,979 Ruhr region 451,201 449,413 449,914 450,889 448,859 451,130 450,349 449,957 438,758 441,576 450,315 451,074 S˜ ao Paulo 538,226 539,915 545,366 543,462 542,936 539,955 543,331 535,734 523,264 529,741 545,922 545,922 Seoul 342,339 342,339 342,339 342,339 342,339 342,339 342,339 342,339 342,339 342,339 342,337 342,338 Shanghai 465,424 465,392 465,823 465,686 465,714 465,737 465,783 465,879 465,463 465,825 465,916 465,913 Tokyo 856,191 858,978 860,840 860,193 857,294 858,434 858,352 857,854 846,850 851,781 862,210 862,984 Toronto 409,679 407,755 409,668 408,632 408,501 406,387 407,200 405,655 393,207 399,260 409,709 409,674 Data availability The data that support the findings of this study are openly available at the following URL/DOI doi:https://doi.org/10.5281/zeno do.17101954 under the Creative Commons Attribution 4.0 International license. References Albers, S., & Duriscoe, D. (2001). Modeling light pollution from population data and implications for National Park Service lands. The George Wright Forum, 18(4), 56–68. http://www.jstor.org/stable/43597774. Anderson, S. J., Kubiszewski, I., & Sutton, P. C. (2024). The ecological economics of light pollution: Impacts on ecosystem service value. Remote Sensing, 16(14), 2591. https:// doi.org/10.3390/rs16142591 Bar´ a, S., & Castro-Torres, J. J. (2025). Diverging evolution of light pollution indicators: Can the globe at night and VIIRS-DNB measurements be reconciled? Journal of J. Geletiˇ c et al. Cities 170 (2026) 106722 16 Quantitative Spectroscopy and Radiative Transfer, 335, Article 109378. https://doi. org/10.1016/j.jqsrt.2025.109378 Barentine, J. C., Walker, C. E., Kocifaj, M., Kundracik, F., Juan, A., Kanemoto, J., & Monrad, C. K. (2018). Skyglow changes over Tucson, Arizona, resulting from a municipal LED street lighting conversion. Journal of Quantitative Spectroscopy and Radiation Transfer, 212, 10–23. https://doi.org/10.1016/j.jqsrt.2018.02.038 Behnisch, M., Krüger, T., & Jaeger, J. A. G. (2022). Rapid rise in urban sprawl: Global hotspots and trends since 1990. PLOS Sustainability and Transformation, 1(11), Article e0000034. https://doi.org/10.1371/journal.pstr.0000034 CEISIN Center For International Earth Science Information Network Columbia University, JRC Joint Research Centre European Commission. (2024). Global urban polygons and points dataset (GUPPD), version 1 (version 1.00) [data set]. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/ 10.7927/BRQ1-XC29 Chakraborty, T., Venter, Z. S., Demuzere, M., Zhan, W., Gao, J., Zhao, L., & Qian, Y. (2024). Large disagreements in estimates of urban land across scales and their implications. Nature Communications, 15(1), 9165. https://doi.org/10.1038/s41467024-52241-5 Ching, J., Mills, G., Bechtel, B., See, L., Feddema, J., Wang, X., … Theeuwes, N. (2018). WUDAPT: An urban weather, climate, and environmental modeling infrastructure for the Anthropocene. Bull. Am. Meterol. Soc., 99(9), 1907–1924. https://doi.org/ 10.1175/BAMS-D-16-0236.1 Cinzano, P., Falchi, F., & Elvidge, C. D. (2001a). Naked-eye star visibility and limiting magnitude mapped from DMSP-OLS satellite data. Monthly Notices of the Royal Astronomical Society, 323(1), 34–46. https://doi.org/10.1046/j.13658711.2001.04213.x Cinzano, P., Falchi, F., & Elvidge, C. D. (2001b). The first world atlas of the artificial night sky brightness. Monthly Notices of the Royal Astronomical Society, 328(3), 689–707. https://doi.org/10.1046/j.1365-8711.2001.04882.x City of London. (2023). Light Pollution in London. Environment Committee of the City of London. https://www.london.gov.uk/sites/default/files/2023-05/Environment%20 Committee%20-%20Light%20Pollution%20Report.pdf. last access: 7 September 2025. Crutzen, P. J. (2004). New directions: The growing urban heat and pollution “island” effect–impact on chemistry and climate. Atmospheric Environment, 38(21), 3539–3540. https://doi.org/10.1016/j.atmosenv.2004.03.032 Czaja, M., & Kołton, A. (2022). How light pollution can affect spring development of urban trees and shrubs. Urban Forestry & Urban Greening, 77, Article 127753. https:// doi.org/10.1016/j.ufug.2022.127753 de Oliveira, C. A. (2018). The impact of private precautions on home burglary and robbery in Brazil. Journal of Quantitative Criminology, 34, 111–137. https://doi.org/ 10.1007/s10940-016-9325-6 Demuzere, M., Bechtel, B., Middel, A., & Mills, G. (2019). Mapping Europe into local climate zones. PLoS One, 14(4), Article e0214474. https://doi.org/10.1371/journal. pone.0214474 Demuzere, M., Hankey, S., Mills, G., Zhang, W., Lu, T., & Bechtel, B. (2020). Combining expert and crowd-sourced training data to map urban form and functions for the continental US. Scientific Data, 7, 264. https://doi.org/10.1038/s41597-020-00605-z Demuzere, M., Kittner, J., & Bechtel, B. (2021). LCZ generator: A web application to create local climate zone maps. Frontiers in Environmental Science, 9. https://doi.org/ 10.3389/fenvs.2021.637455 Demuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D., … Bechtel, B. (2022). A global map of local climate zones to support earth system modelling and urban-scale environmental science. Earth System Science Data, 14(8), 3835–3873. https://doi.org/10.5194/essd-14-3835-2022 Demuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D., … Bechtel, B. (2023). Global map of local climate zones (3.0.0) [data set]. Zenodo. https://doi.org/ 10.5281/zenodo.8419340 Dong, B., Zhang, R., Li, S., Ye, Y., & Huang, C. (2025). A meta-analysis for the nighttime light remote sensing data applied in urban research: Key topics, hotspot study areas and new trends. Science of Remote Sensing, 11, Article 100186. https://doi.org/ 10.1016/j.srs.2024.100186 Doulos, L. T., Sioutis, I., Kontaxis, P., Zissis, G., & Faidas, K. (2019). A decision support system for assessment of street lighting tenders based on energy performance indicators and environmental criteria: Overview, methodology and case study. Sustainable Cities and Society, 51, Article 101759. https://doi.org/10.1016/j. scs.2019.101759 Duriscoe, D. M., Anderson, S. J., Luginbuhl, C. B., & Baugh, K. E. (2018). A simplified model of all-sky artificial sky glow derived from VIIRS day/night band data. Journal of Quantitative Spectroscopy and Radiation Transfer, 214, 133–145. https://doi.org/ 10.1016/j.jqsrt.2018.04.028 Falchi, F., Cinzano, P., Duriscoe, D., Kyba, C. C. M., Elvidge, C. D., Baugh, K., … Furgoni, R. (2016). The new world atlas of artificial night sky brightness. Science Advances, 2, Article e1600377. https://doi.org/10.1126/sciadv.1600377 Frolking, S., Mahtta, R., Milliman, T., Esch, T., & Seto, K. C. (2024). Global urban structural growth shows a profound shift from spreading out to building up. Nature Cities, 1, 555–566. https://doi.org/10.1038/s44284-024-00100-1 Garstang, R. H. (1986). Model for artificial night-sky illumination. Publications of the Astronomical Society of the Pacific, 98, 364–375. https://doi.org/10.1086/131768 Gaston, K. J., Bennie, J., Davies, T. W., & Hopkins, J. (2013). The ecological impacts of nighttime light pollution: A mechanistic appraisal. Biological Reviews, 88(4), 912–927. https://doi.org/10.1111/brv.12036 Gaston, K. J., & S´ anchez de Miguel, A. (2022). Environmental impacts of artificial light at night. Annual Review of Environment and Resources, 47, 373–398. https://doi.org/ 10.1146/annurev-environ-112420-014438 Han, J., Mo, N., Cai, J., Ouyang, L., & Liu, Z. (2024). Advancing the local climate zones framework: A critical review of methodological progress, persisting challenges, and future research prospects. Humanit Soc Sci Commun, 11, 538. https://doi.org/ 10.1057/s41599-024-03072-8 H¨ anel, A., Posch, T., Ribas, S. J., Aub´ e, M., Duriscoe, D., Jechow, A., … Kyba, C. C. M. (2018). Measuring night sky brightness: Methods and challenges. Journal of Quantitative Spectroscopy and Radiation Transfer, 205, 278–290. https://doi.org/ 10.1016/j.jqsrt.2017.09.008 Helbich, M., Burov, A., Dimitrova, D., Markevych, I., Nieuwenhuijsen, M. J., & Dzhambov, A. M. (2024). Sociodemographic inequalities in residential nighttime light pollution in urban Bulgaria: An environmental justice analysis. Environmental Research, 262(1), Article 119803. https://doi.org/10.1016/j.envres.2024.119803 Holker, F., Moss, T., Griefahn, B., Kloas, W., Voigt, C. C., Henckel, D., Hanel, A., Kappeler, P., Volker, S., Schwope, A., Franke, S., Uhrlandt, D., Fischer, J., Klenle, R., Wolter, C., & Tockner, K. (2010). The dark side of light: a transdisciplinary research agenda for light pollution policy. Ecol. Soc., 15(4), 13. https://www.jstor.org/stable/ 26268230. Huang, F., Jiang, S., Zhan, W., Bechtel, B., Liu, Z., Demuzere, M., … Chen, J. (2023). Mapping local climate zones for cities: A large review. Remote Sensing of Environment, 292, Article 113573. https://doi.org/10.1016/j.rse.2023.113573 Hung, L.-W., Anderson, S. J., Pipkin, A., & Fristrup, K. (2021). Changes in night sky brightness after a countywide LED retrofit. Journal of Environmental Management, 292, Article 112776. https://doi.org/10.1016/j.jenvman.2021.112776 Kim, K., & Kim, G. (2021). Using simulation-based modeling to evaluate light trespass in the design stage of sports facilities. Sustainability, 13(9), 4725. https://doi.org/ 10.3390/su13094725 Kocifaj, M. (2019). Ground albedo impacts on higher-order scattering spectral radiances of night sky. Journal of Quantitative Spectroscopy and Radiation Transfer, 239, 1066670. https://doi.org/10.1016/j.jqsrt.2019.106670 Korf, H.-W., Bittner, N., Caspers, S., & von Gall, Ch. (2025). Impact of artificial light at night and night shift work on brain functions and metabolism. General and Comparative Endocrinology, 373, Article 114822. https://doi.org/10.1016/j. ygcen.2025.114822 Kyba, C. C. M., Altintas, Y.¨ O., Walker, C. E., & Newhouse, M. (2023). Citizen scientists report global rapid reductions in the visibility of stars from 2011 to 2022. Science, 379(6629), 265–268. https://doi.org/10.1126/science.abq7781 Kyba, C. C. M., Kuester, T., de Miguel, A. S., Baugh, K., Jechow, A., H¨ olker, F., … Guanter, L. (2017). Artificially lit surface of earth at night increasing in radiance and extent. Science Advances, 3, Article e1701528. https://doi.org/10.1126/ sciadv.1701528 Lapostolle, D., & Chall´ eat, S. (2020). Making darkness a place-based resource: How the fight against light pollution reconfigures rural areas in France. Annals of the Association of American Geographers, 111(1), 196–215. https://doi.org/10.1080/ 24694452.2020.1747972 Lehnert, M., Savi´ c, S., Miloˇ sevi´ c, D., Dunji´ c, J., & Geletiˇ c, J. (2021). Mapping local climate zones and their applications in European urban environments: A systematic literature review and future development trends. ISPRS International Journal of GeoInformation, 10(4), 260. https://doi.org/10.3390/ijgi10040260 Levin, N., Kyba, C. C. M., Zhang, Q., S´ anchez de Miguel, A., Rom´ an, M. O., Li, X., … Elvidge, C. D. (2020). Remote sensing of night lights: A review and an outlook for the future. Remote Sensing of Environment, 237, Article 111443. https://doi.org/ 10.1016/j.rse.2019.111443 Levin, N., & Zhang, Q. (2017). A global analysis of factors controlling VIIRS nighttime light levels from densely populated areas. Remote Sensing of Environment, 190, 366–382. https://doi.org/10.1016/j.rse.2017.01.006 Linares Arroyo, H., Abascal, A., Degen, T., Aub´ e, M., Espey, B. R., Gyuk, G., … Kyba, C. C. M. (2024). Monitoring, trends and impacts of light pollution. Nature Reviews Earth and Environment, 5, 417–430. https://doi.org/10.1038/s43017-02400555-9 Liu, Z., Huang, S., Fang, C., & Liu, M. (2024). Global urban and rural settlement dataset from 2000 to 2020. Scientific Data, 11, 1359. https://doi.org/10.1038/s41597-02404195-y Longcore, T., & Rich, C. (2004). Ecological light pollution. Frontiers in Ecology and the Environment, 2, 191–198. https://doi.org/10.1890/1540-9295(2004)002[0191:ELP] 2.0.CO;2 Ma, R., Wei, Y., Zhao, Q., Zhao, S., Yang, Z., Shangguan, F., … Meng, L. (2024). Multifeature supported dam height measurement method for large hydraulic projects using high resolution remote sensing imagery. International Journal of Applied Earth Observation and Geoinformation, 129, Article 103792. https://doi.org/10.1016/j. jag.2024.103792 Mander, S., Alam, F., Lovreglio, R., & Ooi, M. (2023). How to measure light pollution—A systematic review of methods and applications. Sustainable Cities and Society, 92, Article 104465. https://doi.org/10.1016/j.scs.2023.104465 M´ endez, A., Prieto, B., Aguirre i Font, J. M., & Sanmartín, P. (2024). Better, not more, lighting: Policies in urban areas towards environmentally-sound illumination of historical stone buildings that also halts biological colonization. Science of the Total Environment, 906, Article 167560. https://doi.org/10.1016/j.scitotenv.2023.167560 Nadybal, S. M., Collins, T. W., & Grineski, S. E. (2020). Light pollution inequities in the continental United States: A distributive environmental justice analysis. Environmental Research, 189, Article 109959. https://doi.org/10.1016/j. envres.2020.109959 Navara, K. J., & Nelson, R. J. (2007). The dark side of light at night: Physiological, epidemiological, and ecological consequences. Journal of Pineal Research, 43(3), 215–224. https://doi.org/10.1111/j.1600-079X.2007.00473.x Pan, H., Yao, Y., Ming, Y., Hong, Z., & Hewings, G. (2024). Whither less is more? Understanding the contextual and configurational conditions of polycentricity to J. Geletiˇ c et al. Cities 170 (2026) 106722 17 improve urban agglomeration efficiency. Cities, 149, Article 104884. https://doi. org/10.1016/j.cities.2024.104884 Pan, W., & Du, J. (2021). Impacts of urban morphological characteristics on nocturnal outdoor lighting environment in cities: An empirical investigation in Shenzhen. Building and Environment, 192, Article 107587. https://doi.org/10.1016/j. buildenv.2021.107587 Pauley, S. M. (2004). Lighting for the human circadian clock: Recent research indicates that lighting has become a public health issue. Medical Hypotheses, 63(4), 588–596. https://doi.org/10.1016/j.mehy.2004.03.020 Robles, J., Zamorano, J., Pascual, S., S´ anchez de Miguel, A., Gallego, J., & Gaston, K. J. (2021). Evolution of brightness and color of the night sky in Madrid. Remote Sensing, 13, 1511. https://doi.org/10.3390/rs13081511 Rom´ an, M. O., Wang, Z., Sun, Q., Kalb, V., Miller, S. D., Molthan, A., … Masuoka, E. J. (2018). NASA’S black marble nighttime lights product suite. Remote Sensing of Environment, 210, 113–143. https://doi.org/10.1016/j.rse.2018.03.017 Stewart, I. D. (2024). Origins and evolution of the local climate zone classification. In R. Wang, M. Cai, C. Ren, & Y. Shi (Eds.), Local climate zone application in sustainable urban development. Cham: Springer. https://doi.org/10.1007/978-3-031-56168-9_2. Stewart, I. D., & Oke, T. R. (2012). Local climate zones for urban temperature studies. Bull. Am. Meterol. Soc., 93(12), 1879–1900. https://doi.org/10.1175/BAMS-D-1100019.1 United Nations. (2018). World urbanisation prospects: The 2018 revision. https://pop ulation.un.org/wup/. Wang, L., Meng, L., Richardson, A. D., H¨ olker, F., Li, H., Mao, J., Longcore, T., Xia, J., & She, D. (2025). Artificial light at night outweighs temperature in lengthening urban growing seasons. Nature Cities, 2, 506–517. https://doi.org/10.1038/s44284-02500258-2 Wang, Z., Rom´ an, M. O., Shrestha, R., Yao, T., & Kalb, V. (2022). Black marble user guide (version 1.3). NASA, 72 p. Available at: https://ladsweb.modaps.eosdis.nasa.gov/ap i/v2/content/archives/Document%20Archive/Science%20Data%20Product%20 Documentation/VIIRS_Black_Marble_UG_v1.3_Sep_2022.pdf [last access: 22 October 2024]. Ward, K., Lauf, S., Kleinschmit, B., & Endlicher, W. (2016). Heat waves and urban heat islands in Europe: A review of relevant drivers. Sci. Total Environ., 569, 527–539. https://doi.org/10.1016/j.scitotenv.2016.06.119 Wei, Y. D., & Ewing, R. (2018). Urban expansion, sprawl and inequality. Landscape and Urban Planning, 177, 259–265. https://doi.org/10.1016/j.landurbplan.2018.05.021 Widmer, K., Beloconi, A., Marnane, I., & Vounatsou, P. (2022). Review and assessment of available information on light pollution in Europe (Eionet report – ETC HE 2022/8), ISBN 978–82–93970-08-8, ETC HE c/o NILU, Kjeller, Norway. Wu, Y., Huang, C., Ye, Y., Mei, L., Liu, Y., Wang, D., Chen, W., & Deng, J. (2025). Identification and evaluation of nighttime light pollution in residential gathering area of megacities based on SDGSAT-1 glimmer imagery. Remote Sensing of Environment, 328, Article 114894. https://doi.org/10.1016/j.rse.2025.114894 Xiao, Q., Lyu, Y., Zhou, M., Lu, J., Zhang, K., Wang, J., & Bauer, C. (2025). Artificial light at night and social vulnerability: An environmental justice analysis in the U.S. 2012–2019. Environment International, 178, Article 108096. https://doi.org/ 10.1016/j.envint.2023.108096 Ye, Y., Tong, C., Dong, B., Huang, C., Bao, H., & Deng, J. (2024). Alleviate light pollution by recognizing urban night-time light control area based on computer vision techniques and remote sensing imagery. Ecological Indicators, 158, Article 111591. https://doi.org/10.1016/j.ecolind.2024.111591 Ye, Y., Xue, X., Huang, L., Gan, M., TOng, C., Wang, K., & Deng, J. (2020). A new perspective to map the supply and demand of artificial night light based on Loujia101 and urban big data. Journal of Cleaner Production, 276, Article 123244. https:// doi.org/10.1016/j.jclepro.2020.123244 Zheng, M., Huang, W., Xu, G., Li, X., & Jiao, L. (2023). Spatial gradients of urban land density and nighttime light intensity in 30 global megacities. Humanit. Soc. Sci. Commun., 10, 404. https://doi.org/10.1057/s41599-023-01884-8 Zheng, Q., Seto, K. C., Zhou, Y., You, S., & Weng, Q. (2023). Nighttime light remote sensing for urban applications: Progress, challenges, and prospects. ISPRS Journal of Photogrammetry and Remote Sensing, 202, 125–141. https://doi.org/10.1016/j. isprsjprs.2023.05.028 Zielinska-Dabkowska, K. M., Schernhammer, E. S., Hanifin, J. P., & Brainard, G. C. (2023). Reducing nighttime light exposure in the urban environment to benefit human health and society. Science, 380(6650), 1130–1135. https://doi.org/ 10.1126/science.adg5277 J. Geletiˇ c et al. Cities 170 (2026) 106722 18