Tech UPTechnologyThis is what poverty looks like from space

This is what poverty looks like from space

 

Almost 1 billion people are still living today without access to reliable and affordable electricity , something that not only affects their quality of life in general, but also their health, well-being and hinders sustainable development. But where exactly are all these people who do not have access to electricity on a daily basis or the infrastructure that makes it possible? A new study, published in the journal Nature Communications , tries to solve it.

 

Using satellite images

Is it possible to estimate the economic well-being of the planet with satellite images? That is the intention of the International Institute for Applied Systems Analysis (IIASA) that has presented an innovative method to do it thanks to the analysis of nocturnal satellite images.

Night lights, the researchers say, can help map problems like economic growth, poverty and inequality, especially in places where data is lacking. It’s straightforward, as in developing countries, areas that aren’t lit up at night generally indicate limited development (and more developed ones are incredibly lit, in cities like Madrid, New York, or London) where infrastructure is plentiful.

In this study, the experts focused precisely on the data from the unenlightened areas to estimate the economic well-being of the planet.

“While previous work has focused more on the relationship between lighted areas and economic development, we found that it actually works the other way around as well, and that unlighted areas are a good indicator of poverty. By identifying those unlit areas, we can target poverty alleviation interventions and places to focus on improving energy access,” explains study author and IIASA Strategic Initiatives Program Director Steffen Fritz. .

Using a geospatial richness index calculated by the Demographic and Health Surveys (DHS) program plus data from satellite images of global night lights in these countries, they found that 19% of the planet’s total settlement footprint had no radiation. associated detectable artificial.

What are the countries that stand out in poverty according to the satellite?

Most of the unlit areas were found in Africa (39%) and Asia (23%) . And if rural infrastructure without lighting was considered, these figures increased to 65% for Africa and 40% for Asia. In nearly all countries, the authors report, the results point to a clear association between increasing percentages of unlit communities in a country and declining levels of economic well-being.

“We were able to map and predict the wealth class of around 2.4 million households for 49 countries spread across Africa, Asia, and the Americas based on the percentage of unlit settlements detected using night-light satellite imagery with an overall accuracy of 87%. %”, clarifies Ian McCallum, co-author of the work.

According to the researchers, they also found unlit settlements in developed countries, particularly in Europe, although they say this could be due to conscious energy saving by homeowners, governments and industry.

The UN has stated that in sub-Saharan Africa, projections indicate that more than 300 million people will continue to live in extreme poverty by 2030 and it is even more likely that the effects of the COVID-19 pandemic will push between 88 and 115 million more people into extreme poverty by that date.

“If applied over time, the method we used in our study could offer opportunities to track well-being and progress towards these places. In policy terms, it can help better inform energy policy around the world and also “It can be useful in shaping aid policy by making sure we reach those remote rural areas that are likely to be energy poor. Also, it could be useful in spotting signs of sustainable and environmental management of lighting in the developed world.” the leader of the Transformative Social and Institutional Solutions Research Group, Shonali Pachauri.

Referencia: Ian McCallum, Christopher Conrad Maximillian Kyba, Juan Carlos Laso Bayas, Elena Moltchanova, Matt Cooper, Jesus Crespo Cuaresma, Shonali Pachauri, Linda See, Olga Danylo, Inian Moorthy, Myroslava Lesiv, Kimberly Baugh, Christopher D. Elvidge, Martin Hofer, Steffen Fritz. Estimating global economic well-being with unlit settlements. Nature Communications, 2022; 13 (1) DOI: 10.1038/s41467-022-30099-9

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