Green Analytics

Example of interface
Another interface

Inspiration

After considering various possibilities, we settled on an analytics toolkit to help normal people trade an up and coming alternative asset, European Carbon Emissions Allowances. Since the European carbon trading scheme was introduced, the asset is affectively a perrmit for a company to emit a tonne of TNT.

Currently, the asset is not widely traded, and our research indicates that increasing its liquidity, such as by providing tools so that the ordinary person is better placed to trade it, will be enviromentally beneficial. First, people can use it to hedge against climate risk. Second, increasing its liquidity will provide upward pressure on the price of the asset by increasing demand, thus making it more expensive for companies to pollute, thereby providing incentive to companies to reduce pollution.

What it does

We provide 3 different quantitative models to predict metrics relevant to the price of the Emission Allowance asset.

We provide short term forecasts of the Allowance price (per tonne) using a simple stochastic linear model.

We use a transfer learned GPT-3 model to provide a sentiment score based on dynamically scraped reuters news articles, which will help predict future movements in markets as public sentiment regarding climate change will influence government supply decisions.

We also made an predictor for electricity consumption based on weather. This is as higher electricity consumption demands higher electricity production, which in the current case implies that there must be higher emission of carbon dioxide. As such there would be higher demand for the emission allowance, increasing prices. Thus this will also help inform pricing decisions for consumption.