Climate Analytics
Recently I have been writing a lot about how we can use machine learning to help climate change. A lot of the idea from the paper climate.ai. Which tries to bridge the gap between the machine learning community and industries dealing with climate change. One of the areas that I found interesting lots of people don’t talk much about is climate analytics. Where data is collected about the climate which is then used to make financial decisions. Due to the large scale of climate change almost all countries and most industries will be affected. So, it will make sense to make sure that people do not lose out on their investments due to climate change.
Uses of collecting climate data
All of this will require a lot of data, in which machine learning is suited for. They may be some drawbacks. But I do think they will be useful. They are lots of areas that data can help financial investments. One example is flood risk, where focusing on the long-term risk will be useful for insurance companies. So, they can avoid large payouts. Wildfire risk is very similar which can burn through rural and suburb areas with a lot of woodlands. Costing landowners at a lot of money. Especially if you use land actively to create an income. Like using the land to raise cattle.
How data can be collected
The data can be collected in many ways. One increasingly popular area is remote sensing. Where we use satellite imagery to collect data of an area. This is done as the satellite can collect data in other wavelengths that are not visible to the human eye. So, we can view gases. View vegetation and track other elements. Remote sensing can have future use of enforcing regulations. Right now, a lot of satellite data covers wide pieces of an area of each image. Something hundreds of metres per image. So, you can’t be too precise in tracking a certain area. But as technology gets better, we can be able to pinpoint areas of high emissions. And see if a company is following regulations. But this leads to some privacy concerns.
For urban areas. Tracking the amount of movement using smartphones has been very useful. As people can track the usage of public transport and other services. Using information like that we can create incentives so people can use less carbon-heavy transportation rather than cars. This can help companies to invest in new transport methods by looking at supply and demand.
Energy is the most obvious example. Right now, a lot of energy companies are going bankrupt due to unprofitable energy sources, mainly coal. Because of COVID-19, other fossil companies had to size down. As lockdowns reduced the demand of many energy products, mainly oil. This forced lots of energy companies to chart a zero-carbon future. So, they survive the transition.
