Thank you. I guess we'll stream microphones, too. Can you hear me now? Great. Thank you. So what we do at Climate TRACE is we use satellites, AI, and other data to monitor emitting facilities. So essentially every power plant, ship, landfill, and so on in the world, and we have many different experts coming together to do this. And so the first thing I'd like you to notice about what you can do with the data is we can add it up. So if you look at that number in the top left, you will see that is the largest emissions inventory ever. And the reason for that is we can see evidence clearly from space of many emitting facilities that are not in any inventory. We can see landfills that governments don't know about. We can see cattle farms that supposedly don't exist. We can see oil fields that have not fully reported their emissions. We can do a lot more too. And so the next thing we can do is we can slice and dice the data. So for any country, state, county, large city, or increasingly even many companies, we can look at all of the facilities in that area and we can add up the emissions and then you can continue to go. So you can actually dive down into any emitting facility. So we have 745 million sources of emissions and you can get the data, and this is split up by economic sector. You can get the CO2 or the methane or the CO2 equivalent. You can see this all together. But let's talk about what happens when you really zoom in. So here we're today to talk about something new that we're doing in the preview is that we actually have extremely detailed information besides just the emissions about these facilities. So we can tell you estimates of the monthly emissions. In many cases we know who owns the facilities. We can see the technology type. In addition to the greenhouse gases, we can now see the conventional air pollutants like SO2 and PM2.5. And the reason for this is that the same facilities that are causing climate change are in almost every case the ones that are making us sick on a human level. And so it's those same pollutants that cause both effects.
Yeah. One aspect of the climate crisis that is sometimes overlooked is that the same combustion process that puts the global warming pollution into the sky and traps the heat also puts so-called conventional pollutants, PM2.5, that makes people sick. This is also a severe health threat. By the way, the WHO has identified seven of these pollutants that are particularly harmful. We have traced 100% of them back to their source. This is what it looks like in Mumbai. You can go to thousands of cities and see where the pollution streams are flowing depending on the meteorological data of that particular day. This is Tokyo. I'll just show you a few examples. As I say, there are thousands of them and soon we will have them all. And I hope on a daily basis so that perhaps in the future weather forecasters can use it along with their weather radar. This is London. This is New York City. There's the UN headquarters. And here is Bali. And you can see where we are right now. This is Manado, elsewhere not too far from here. And worldwide this conventional pollution from the burning of fossil fuels kills 8.7 million people every year. It's been out of sight, out of mind, but now we can see the plumes where it's coming from and where it's going. So Climate TRACE tracks all of this pollution all over the world and fuses it together. Here you go.
The mic. So, we fuse data from over 300 satellites from six countries, the European Union, many commercial providers. By far the largest source of data is Planet. So, I'd like to give a shout out here to our representative from Planet today. And just say a particular thank you to Will Marshall from Planet who has been so generous in providing data more than any other company or in fact country. And so what we can do with that information, we historically have focused on just describing the results but wanted to give you a sense that in the upper left you can see this is what a facility looks like when it's not emitting. This is a facility looks like when it's under construction. So our machine learning algorithms have learned to recognize what does a cattle farm actually look like when the cows are indoors. But we can actually now analyze them in much more detail. So we can tell you this facility is actually not emitting. An example of how we know that is in the upper right hand corner, the manure pond is not yet full. And so you can actually see the difference between a facility in use at different levels. And by analyzing data like this, what we can do is we can combine top-down imagery from satellites about how much methane is in an area, bottom up information about what's going on in these facilities, and we can calibrate to achieve better accuracy by mixing and matching the methods. And so it's very important to understand about Climate TRACE that we are not one organization with one data set. We're closer to Wikipedia. We are combining every single public source of information in a massive coalition of over 150 NGOs, universities, and other AI researchers working together to combine this. As an example of how it works, in the upper left, we have photos where we had on-site sensors of the exact emissions happening at that facility at that moment, and we have satellite imagery of what they looked like. And you can see with the unaided eye there's a difference. And so we can train machine learning algorithms to recognize for a certain type of technology what does a certain amount of emissions look like. And then one reason we're able to do this is we are able to calibrate it through generous donations of data from over 50,000 facilities. We have the detailed on-site sensor readings of exactly what are their emissions. And that's just one more way we can combine all this data to be more accurate. And so there are now 40 peer-reviewed papers that are either analyzing Climate TRACE methods, validating them, or applying the data, or increasingly most papers do all three.
And so historically we have been known for measuring emissions. And what we're here to share today is we have a new capability. So by analyzing the data of what is going on in these facilities, we can actually do more. We can look for examples of facilities that have successfully decarbonized and we can find how is it that they got it done. And then we can match other facilities in similar climate zones with similar technology and we can increasingly estimate based on what other facilities successfully did what are the options for this facility to do better. So in terms of data into action let's just use an example. So Glenbrook Mill in New Zealand is a leader in applying electric arc furnace technology with renewable energy. This is really reducing their emissions. We can then find identical technology types in other facilities worldwide and estimate what would their emissions reduction be if they did that. And the big value here is instead of doing this at a vague level, we have 700 million facilities that we're able to give very specific detailed recommendations of what would work for them. You start adding up these applications and we see for example 800 million tons a year could be reduced by applying just these techniques at compatible facilities. And we can also start to have tips about when it's a better idea. So for example, steel plants, it is vastly cheaper to electrify them if you catch them at the moment they are re-lining. We increasingly know when and where that's happening. So this is finding ways to make it easier to know how to reduce emissions. To give you an example, we can compare two different facilities from the same company, Nippon Steel. We can compare their emissions factors and can start to see what are the clues of why one is cleaner than the other and what could be done about it. We can then for all steel mills in the world line up which ones are cleaner and which ones are less clean. Companies can buy from the lower emitting facilities and get advice on how they can become one of the lower emitting facilities. But the big thing is that we've received such positive feedback about doing that in a small way in steel. We're here to share today that we can now do this for every emitting sector. And so these are the emissions factors from nine representative sectors. As you can see they vary dramatically. In every sector, we see examples of those that have successfully reduced their emissions and those that have not yet successfully reduced their emissions and we can apply examples of how they can do it. An example is electricity. Everyone knows that renewable energy is a great example of reaching zero emissions factor. But what we also know is where and when is this happening. So here's an example of Nanticoke generating station, formerly the largest coal plant in North America, now a solar plant. And so we are seeing increasingly that it makes sense economically for facilities to make these changes.
I also wanted to give a shout out to Planet by the way and tell you that the CEO Will Marshall, my good good friend, is in Antarctica today. So that's why he is not here but we're glad you're here, Andrew. So, one of the reasons this solar revolution is so stunning, it's absolutely amazing, is that the cost continues to come down quite dramatically. And it's not only the panels, it's not only the modules, it's the panels, the business models, the installation, and everything else. It used 20 years ago, it took a year to install one gigawatt of solar. Now, it takes 15 hours, and it will soon take only a few hours to expand by another gigawatt. And with solar and wind and also batteries, countries face no fuel supply chain risk, no risk of fuel price volatility, no fuel cost at all. And by the way, the rapid transition away from fossil fuels as called for in COP28 will also have an effect on 40% of all shipping in the world that is presently used to lug fossil fuels from one place to another. There are so many savings that will come from using this poly-solution to the polycrisis. Of all the new electricity capacity added worldwide last year, 93% was renewable. Absolutely astonishing. And where did the money come from to build all that solar and wind? It's pretty interesting to focus on where it came from. 75% came from private investors.
But there is an unequal distribution of access to private capital. The great science fiction writer William Gibson once wrote, 'The future's already here. It's just not evenly distributed.' Well, access to private capital for the sustainability transition is already here, but it's not evenly distributed. The entire continent of Africa has fewer solar panels than the single state of Florida. That is, in my opinion, a kind of disgrace for humanity. We need to fix this desperately. Africa has 60% of the best solar resources in the world but gets only 2% of the finance to install renewables in Africa. Now here is the emissions reduction potential in the rich developed countries. Very important, needs to accelerate. But here's the potential in the developing countries. Three times as many opportunities to reduce emissions in the developing world as in the developed world. Essentially 100% of all the increasing emissions in the years going forward are going to be in the developing world. So we have got to fix the unequal access to private capital in order to accomplish this transition because at present only 17% of global climate finance went to developing nations other than China. So we could reduce 1.6 billion tons of CO2 each year by building in the developed nations. And we could reduce 2.6 billion tons by building the same amount of solar and wind capacity in developing nations. So prioritizing what we're doing is quite important.
Some private sector companies are now taking leadership on their own using Climate TRACE data. By the way, this is a wind farm in India. You can see many other windmills behind this one in the foreground. They use Climate TRACE data to pick the locations where they will maximize the reductions of emissions. Apple is doing the same thing. Salesforce is doing the same thing. A lot of smaller companies are doing it as well. This particular wind farm will reduce 130% more emissions than it would do if it was built in Seattle in the United States next to the Amazon headquarters. So we have taken this approach with all of these sectors. We've got nine illustrative sectors. Let's look at road transport. One of the other systemic solutions to the climate crisis is electrifying the transportation system. Look at the example of success in Shanghai where 70% of new vehicle sales are now electric and they built out their charging network. If that approach was used in Tokyo, we could see a reduction of almost 50% in the emissions from transportation and a reduction of 24 million tons of CO2 each year by more fully transitioning to EVs. And if we did this globally, we could reduce 73 billion tons over the next 25 years. 2.8 billion tons per year. And by the way, we've got the momentum now. In September, 30% of all new car sales in the world were EVs, 58% in China, 98.9% in Norway. I think they can check that box. For the full year 2024, Ethiopia had 60% of all its new car sales were EVs. This is happening and it's happening in these other sectors as well. Let's look at buildings.
So buildings is just another example of where we're actually seeing the energy transition really happen. So with a few exceptions, those are mostly Iranian buildings over on the far right seem to have particularly low energy efficiency. Enormous opportunity to do better. With a few exceptions, we're seeing this start to happen everywhere. So Copenhagen is a good example that has successfully reduced their building emissions by 50% in a few years. It really worked and it was actually quite economical. And so we can do calculations like for example Toronto could benefit. And so this free open source tool can provide information. So for example, if you were in Toronto, what would be roughly the cost and the emissions impact of doing the same? And similarly, we can calculate the emissions of applying the solution elsewhere. And sort of the same idea happens. So I won't bore you with the same speed. I'll sort of go through these quickly, but in forest fires, remarkable progress has been made in Uganda. I'm very excited. We have someone on the panel with us in a moment from Uganda. But it is a country that has really demonstrated how to push back on clearing and forest fire emissions and we're able to calculate things like in certain regions of Ghana, what would be the application if you were to apply those same methods and the total savings worldwide which would be quite striking. In landfills we see similar patterns. So we can look for example before and after of landfills. It is actually quite striking visually on what happens when you cover them and successfully clean up their emissions and then we can apply them to facilities like in Indonesia what would be the equivalent savings and get the total emissions. And then one of the things we really see in the data is the significant difference between areas. So in terms of covering landfills, what we find is that typically the difference between doing nothing at all and doing something is much bigger than the difference between using a pretty good emissions reducing technology and a very good emissions reducing technology. And so by knowing where in the world has something already been done, you can figure out how to optimize how to spend fewer resources but actually cause more emissions reductions. So a quick example of that is we were asked to analyze what would be the impact of cleaning up the world's 100 largest landfills, 66 million tons. We actually figured out that for lower cost, you could reduce almost twice as much emissions by identifying the landfills where the least had been done. And the highest emitting landfills are not the same as the largest landfills. And so the basic thing that this data can do is free open-source data on how you can drive more climate action faster with the same or often even actually literally less resources. We see similar patterns by learning from Thailand's very successful emissions reducing program in rice by using smarter fertilizer. We can see that Myanmar could very successfully reduce rice emissions, a major driver of methane, and calculate that globally and so we continue that for other sectors.
