The hosts explain a 2025 paper that uses NASA's Black Marble nighttime satellite imagery to estimate which populations have access to electricity. Because a dark area could be empty land or an unlit settlement, researchers combine a decade of nighttime light data with settlement masks and population estimates to classify lighting evidence and then estimate how many people fall into each access category. The method lets researchers compare country-reported electricity access against satellite-derived estimates and map where access gaps are largest, information useful to grid planners and public agencies.
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The hosts note that comparing country-reported access to the satellite estimate shows Africa both underestimating its own access in some countries and showing the widest variance across the continent, including some countries estimated at -10%.
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Europe's reported access comes out higher than the satellite-derived estimate, while North America's reported and estimated figures are described as nearly matching.
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One host raises an unresolved question for a planned follow-up interview: whether a country's own reporting methodology might itself rely on similar satellite data, which could explain some of the discrepancies.
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A separate map in the paper shows percent of population with electricity access by country, with the largest gaps concentrated in Africa and parts of Asia and Oceania.
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1:10:16>> um I I it's it's a pretty in incredible paper that came out so now let's go to paper number four this is the fourth paper published in 2025 who has electricity >> right >> is the idea idea. It asked the question that sounds pretty straightforward until you look at it closely. Can a decade of nighttime imagery tell us where people have access to electricity? I mean, consider like two dark locations. There's one that's an empty tract of land and another one with a settlement of thousands of people and very few outdoor lights. A darkness map cannot alone tell you which is which. So these rearch so these researchers combined the
1:10:58black marble time series with the settlement mask and the population estimates that they have from other data sources and they can classify lighting evidence over time with a scale of 1 kilometer which is you know the stuff that you get from the the black marble and then they use the population layer to estimate how many people live in those areas that are assigned to all these different access categories. And the logic is that you first determine whether people plausibly live there using population data that you have from all these other sources. And then you examine whether the night signal is consistent with electric lighting for that number of people. >> And then you estimate the population affected and compare broad totals
1:11:39against these surveys. And that's kind of that's kind of the theme across the Black Marble >> papers is like how do I combine disperate data sources about humans on this planet, right? You have the data source from this satellite >> about nighttime lights, but that can't be enough, right? And and that's what the story is. >> The story is one of data aggregation and data analysis. It's it's pretty cool. It it it's also such an interesting set of questions that were not necessarily intuitive to me >> like if I if you were to give me a million years to think about ideas to think about. Um but it makes it is
1:12:20clever and makes obvious sense in retrospect now. >> Yeah. that, you know, oh, this satellite that we're using to do nighttime surveys, um, something arose out of that, which is like, oh, actually, like we could look at human activity because of light. >> Yeah. And one of the one of the cool things they did with the next figure is they looked at um what the countries reported their access was for their population and what they estimate was the >> access level >> was the level of access. Now one of the
1:13:01one of the interesting things is Africa seems to be underestimating how much access their population has to electricity but at the same time Africa has a massive variance. >> Yeah, >> this is delineated by continent and you can see that there are some countries that where the actual access according to black marble is a lot higher than what they report but then at the same time Africa has some that are like -10%. what um what what they're estimating the population has access to. All of the other continents are a little bit sort of constrained. >> Um Europe >> very interestingly is overestimating.
1:13:43>> Uh so do with that what you will. Um North America is very fairly on the zero. >> We estimate exactly how much >> American precision. >> Yeah. Yeah. Um, it could just be that like I don't know this, but like what if the estimate is also related to the satellite data? >> That's something that I'm going to ask Miguel. Actually, that's one of the questions I have. >> That's that's a part two question. >> Like, yeah, look, >> there's a little bit of uh we got to ask. >> Yeah, I got to ask, right? Like, it's it seems like a fair question. Anyway, so um another map that's useful is the
1:14:24underserved places. like where are the underserved places? This shows a percent of population with access to electricity for each country. And you can see that the highest gaps are observed in Africa and parts of Asia and Oceania. >> Yeah. >> So um the application is pretty concrete. You can have grid planners and public agencies then know where the demand is unmet for example. It's it's huge right? >> I I I mean my brain is just going in a million different places of applications for this context. M I mean you you could have a field day. >> Yeah. >> Um in terms of uh there's no shortage of agencies, organizations at local,
1:15:05municipality, state, even national levels that can use this in ways that are extremely relevant to a number of their mandates. >> Yeah. Yeah. Exactly. >> That's quite good. >> Yeah. So our final paper, paper five,
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