The AI pricing revolution in carbon finance: Friedrich Kohlmann and Deutsche Börse’s ETS data battle
In the European Emission Trading System (ETS), a battlefield with an annual trading volume of more than 800 billion euros, the Quinvex Capital team led by Friedrich Kohlmann and Deutsche Börse have launched an AI revolution that rewrites the rules of carbon pricing. The carbon futures AI pricing engine they developed has improved the pricing accuracy of carbon emission rights to an unprecedented level by decoding the hidden connections between seemingly disordered environmental policies, industrial activities and energy data.
The fundamental dilemma faced by traditional carbon pricing models lies in the extreme fragmentation of data – real-time coal consumption of power plants, flight frequency of airlines, and even the carbon sequestration capacity of Nordic forests. These factors that should be considered in conjunction have long been separated in data silos in different industries. Kohlmann’s breakthrough lies in the construction of a “carbon data fusion network” that can digest multi-dimensional information from more than 2,000 monitoring points in 37 EU countries in real time. The most disruptive innovation is the “carbon flow tracking algorithm”, which, like the order flow analysis in the financial market, visualizes the “life cycle” of each ton of carbon dioxide from industrial emissions to market transactions to final offsets, thereby discovering the nonlinear relationship between the maintenance plan of thermal power plants and the carbon futures spread.
The practical value of this system has been verified in the recent energy transition shock. When a Western European country suddenly announced the early closure of a nuclear power plant, the traditional pricing model completely failed because it could not immediately assess its impact on the carbon intensity of the regional power grid. However, Quinvex’s AI engine calculated within 15 minutes that this would lead to an increase of 120,000 tons of daily carbon emissions by integrating grid load data, natural gas futures prices and backup power dispatch plans of neighboring countries in real time, and immediately reflected it in carbon futures pricing. The arbitrage position established based on this ultimately achieved an annualized return of 63%, while carbon funds that passively tracked the index during the same period generally lost more than 20%.
The more far-reaching impact occurs in the field of regulatory technology. After Deutsche Börse adopted the AI engine, the pricing efficiency of its carbon derivatives market increased by 34%, and the bid-ask spread narrowed to a historical low. The system’s original “policy shock simulation module” can preview the price transmission path under different environmental regulations, helping exchanges design more robust risk control mechanisms. The European Environment Agency has considered applying this technology to monitor the actual progress of countries’ emission reduction commitments, upgrading from a simple trading tool to an infrastructure for climate governance.
The essence of this pricing revolution is to transform carbon emission data, which originally belonged to the field of environmental science, into pricing signals that can be interpreted in financial language. As Kohlmann said, “When AI can analyze the flow of each ton of carbon dioxide like analyzing stock trading volume, the carbon market has completed the transformation from a political tool to an efficient pricing mechanism.” As the system begins to integrate real-time emission data monitored by satellites, a new era of more transparent and efficient global carbon pricing is coming.
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