€œNight Light Development Index€ (NLDI) as a simple, objective, spatially explicit and globally available empirical measurement of human development derived solely from nighttime satellite imagery and population density. There is increasing recognition that the distribution of wealth and income amongst the population in a nation or region correlates strongly with both the overall happiness of that population and the environmental quality of that nation or region. Measuring the distribution of wealth and income at national and regional scales is an interesting and challenging problem. Gini coefficients derived from Lorenz curves are a well-established method of measuring income distribution. Nonetheless, there are many shortcomings of the Gini coefficient as a measure of income or wealth distribution. Gini coefficients are typically calculated using national level data on the distribution of income through the population. Such data are not available for many countries and the results are generally limited to single values representing entire countries. The NLDI measures the co-distribution of nocturnal light and people. It is derived without the use of monetary measures of wealth and is capable of providing a spatial depiction of differences in development within countries. The vintage date of this dataset is 2006. Email: ncei.info@noaa.gov

The “infrastructure index” describes the degree of development of physical facilities and networks in a certain area in 2010. The quality of infrastructure is an important measure of the relative adaptive capacity of a region. Regions with developed infrastructure systems are presumed to be better able to adapt to climatic stresses. Improved infrastructure may reduce transactions costs, and strengthen the links between labor and product markets. Moreover, improved infrastructure should encourage the formation of non-farm enterprises as a source of diversification in the short run and, eventually, a transition out of agriculture. The index results from the second cluster of the Principal Component Analysis preformed among 10 potential variables. The analysis identifies three dominant variables, namely “road density”, “road availability” and “infrastructure poverty”, assigning weights of 0.47, 0.36 and 0.17, respectively. Before to perform the analysis all variables were log transformed to shorten the extreme variation and then were score-standardized (converted to distribution with average of 0 and standard deviation of 1) in order to be comparable. A shapefile of road network was published by the Center for International Earth Science Information Network of Columbia University in 2013. The “road density” was computed by calculating the Kilometers of road per cell (size 0.5 arc-minute) and then running a focal statistic (radius of about 30 km to spread the effect of a transportation network in a neighborhood). The “road availability” is the road density divided by the logarithm of population. The 0.5 arc-minute grid “infrastructure poverty” is based on the average lights per pixel in 2010, which was produced by NOAA National Geophysical Data Center, divided by the logarithm of population. The original data was highly fragmented and at fine resolution may have contained fine-scale artifacts at urban edges due to data mismatch between the population and night-lights datasets. Thus focal statistics ran within 20 Km to calculate an average values and represents some of the extend influence of the infrastructure network for local people. The density and availability of road is a normally accepted indicator of infrastructure development degree. Moreover, developed road network facilitate the diffusion of rural products to large markets enhancing the income of rural population and sharing the risk of crisis among larger area. The average night light density per capita represents the diffusion of electricity among population and here is considered a proxy of diffusion of developed infrastructural network. This dataset has been produced in the framework of the “Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)” project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

Amidst a growing need for effective energy management, government policies increasingly rely on accurate electricity consumption forecasts to make informed decisions on renewable energy adoption. This study investigates the predictive capabilities of night light satellite imagery in forecasting electricity usage in India, aligning with Sustainable Development Goals 7 and 10. Utilizing data from the VIIRS satellite and NASA’s Black Marble product, the research employs various LSTM models to analyse electricity consumption trends. Additionally, state-wise analyses have been conducted by applying k-means clustering to capture spatial consumption variations. By demonstrating the strong correlation between night lights and electricity consumption, the study emphasizes the utility of satellite imagery for actionable insights into energy dynamics. The results emphasize the viability of night light data as a dependable indicator of electricity demand, with MAPE values below 10% and RMSE values below 20 MU. It also highlights the transformative impact of remote sensing technologies in advancing sustainable development agendas and highlights the pivotal role of night light imagery in energy forecasting initiatives.

The data interconnecting environmental disciplines of land, air, water, nighttime light intensity and socio-economics are useful for academicians, leaders and policy-makers. The land parameters are remote sensing indices and metrics used to examine vegetation health, productivity and economic system dynamics. It includes NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), GPP (Gross Primary Productivity) and NPP (Net Primary Productivity). Nighttime light intensity analyzes the brightness of artificial lights and the radiance emitted during the night. It is used as a proxy for human activity (land use), urbanization (population density), and economic development. The water parameter includes NDWI (Normalized Difference Water Index), a remote sensing index that identifies open water bodies and monitors changes in water content. The air parameters, including CO, NO2, SO2, O3, CH4 and HCHO, are examined following CPCB guidelines to ensure their concentrations align with permissible limits, prioritizing public health and welfare and adhering to international standards. The data directs the researchers to study climate change related to socio-economics and environmental parameters. The dataset analyzes the environmental and socio-economic dimensions of all the districts of India.

The Gross Domestic Product per capita (gross domestic product divided by mid-year population converted to international dollars, using purchasing power parity rates) has been identified as an important determinant of susceptibility and vulnerability by different authors and used in the Disaster Risk Index 2004 (Peduzzi et al. 2009, Schneiderbauer 2007, UNDP 2004) and is commonly used as an indicator for a country’s economic development (e.g. Human Development Index). Despite some criticisms (Brooks et al. 2005) it is still considered useful to estimate a population’s susceptibility to harm, as limited monetary resources are seen as an important factor of vulnerability. However, collection of data on economic variables, especially sub-national income levels, is problematic, due to various shortcomings in the data collection process. Additionally, the informal economy is often excluded from official statistics. Night time lights satellite imagery of NOAA grid provides an alternative means for measuring economic activity. NOAA scientists developed a model for creating a world map of estimated total (formal plus informal) economic activity. Regression models were developed to calibrate the sum of lights to official measures of economic activity at the sub-national level for some target Country and at the national level for other countries of the world, and subsequently regression coefficients were derived. Multiplying the regression coefficients with the sum of lights provided estimates of total economic activity, which were spatially distributed to generate a 30 arc-second map of total economic activity (see Ghosh, T., Powell, R., Elvidge, C. D., Baugh, K. E., Sutton, P. C., & Anderson, S. (2010).Shedding light on the global distribution of economic activity. The Open Geography Journal (3), 148-161). We adjusted the GDP to the total national GDPppp amount as recorded by IMF (International Monetary Fund) for 2010 and we divided it by the population layer from Worldpop Project. Further, we ran a focal statistics analysis to determine mean values within 10 cell (5 arc-minute, about 10 Km) of each grid cell. This had a smoothing effect and represents some of the extended influence of intense economic activity for local people. Finally we apply a mask to remove the area with population below 1 people per square Km. This dataset has been produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

OLS regression results for GININTL with SPI and SPI2.

LED Road Lighting Market Outlook

As per our latest research, the global LED Road Lighting market size reached USD 9.4 billion in 2024, reflecting the widespread adoption of energy-efficient lighting solutions in urban and rural infrastructure. The market is projected to expand at a robust CAGR of 12.1% from 2025 to 2033, with the total market size anticipated to reach USD 26.3 billion by the end of 2033. This significant growth is primarily driven by increasing government initiatives for sustainable urban development, stringent regulations for energy consumption, and the ongoing replacement of conventional lighting systems with advanced LED technologies.

One of the principal growth factors for the LED Road Lighting market is the global push towards energy efficiency and sustainability. Governments and municipalities worldwide are rolling out policies and incentives to encourage the adoption of LED lighting, which consumes significantly less power compared to traditional lighting solutions such as high-pressure sodium and metal halide lamps. The longer lifespan and reduced maintenance requirements of LEDs further enhance their appeal, resulting in considerable cost savings for cities and municipalities. Additionally, the integration of smart lighting systems, which allow for remote monitoring and adaptive lighting controls, is accelerating the shift towards LED road lighting, making urban environments safer and more energy-efficient.

Another key driver is the rapid pace of urbanization and infrastructure development, particularly in emerging economies. As cities expand and new roads, highways, tunnels, and bridges are constructed, the demand for reliable and efficient road lighting solutions continues to surge. Urban planners and architects are increasingly specifying LED luminaires and lamps for new projects due to their superior illumination quality, reduced environmental impact, and compatibility with smart city technologies. Moreover, the decreasing cost of LED components, driven by technological advancements and economies of scale, is making these solutions more accessible to a broader range of end-users, further fueling market growth.

The growing awareness regarding road safety is also contributing to the expansion of the LED Road Lighting market. Enhanced visibility on roads, especially at night or during adverse weather conditions, is crucial for reducing accidents and improving security for both pedestrians and motorists. LED lighting, with its high color rendering index and uniform light distribution, significantly improves night-time visibility compared to conventional lighting systems. This has prompted many countries to prioritize LED upgrades in high-risk areas such as highways, intersections, tunnels, and pedestrian crossings, thereby boosting the demand for advanced road lighting solutions.

From a regional perspective, Asia Pacific currently leads the global LED Road Lighting market, driven by large-scale urbanization projects in China, India, and Southeast Asia. North America and Europe follow closely, supported by robust regulatory frameworks and increasing investments in smart infrastructure. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as governments in these regions ramp up efforts to modernize public lighting systems. Each region presents unique opportunities and challenges, shaping the competitive landscape and technological adoption patterns in the global market.

Product Type Analysis

The Product Type segment of the LED Road Lighting market is broadly categorized into Luminaires and Lamps. Luminaires, which encompass the complete lighting fixture including the housing, reflector, and light source, dominate the market due to their versatility, durability, and ease of integration with smart technologies. The demand for LED luminaires is particularly strong in new infrastructure projects and large-scale retrofitting initiatives, where municipalities and commercial entities seek comprehensive lighting solutions that offer both performance and longevity. The ability to incorporate advanced features such as motion sensors, dimming controls, and wireless connectivity further enhances the value proposition of LED luminaires, making them the preferred choice for modern road lighting applications.

On the other hand, the Lamps sub-segment, which refe

OLS regression results for GDDP and NTL.

OLS regression results for GININTL with QGI and QGI2.

Spatial interactive dynamic Durbin model test of night light data.

Dynamic spatial effect decomposition of night light data.