The Center for Data Innovation spoke with Adrien Firmenich, founder and CEO of Quantifying Nature, a London-based software company that identifies and quantifies the potential financial losses for companies caused by climate change and biodiversity loss. Firmenich discussed his motivation to found the company, the multiple data sources he and his team draw from, and the importance of translating climate data into financial risk analyses that finance professionals understand.
This interview has been edited.
Eva Behrens: What opportunity or challenge did you identify that led you to found Quantifying Nature, and how is your company addressing it?
Adrien Firmenich: It was while working in asset management in January 2022 that the need for Quantifying Nature became apparent to me. I had noticed that some of the fund’s assets had been suffering from decreased revenues in specific geographical areas, which were famous hotspots of climate turmoil. I hence began researching this topic in greater depth. During my research, it became apparent to me that biodiversity loss also had a material impact on companies, yet no one was talking about it at the time. Clearly, the climate-biodiversity crisis had become a financial risk to corporations, financial organizations, and governments worldwide. Yet, after researching the market, I realized that there wasn’t any market tool providing knowledge on the extent of these damages and how to reduce them to ensure both positive conservation and financial hedging outcomes. This glaring information gap drove me to take the leap of faith of quitting my job to launch Quantifying Nature in February 2022.
Quantifying Nature bridges the information gap that hinders our governments, companies, and financial institutions’ ability to make informed decisions toward effective environmental preservation interventions. At Quantifying Nature, we analyze the future monetary loss that any publicly listed company is set to lose from the climate-biodiversity crisis. This requires assessing the future losses anticipated throughout any company’s supply chain and at every one of its physical assets, such as factories, operations centers, and more. The insights generated by our AI platform bridge the information gap by providing information on which supply chain section or company assets are most vulnerable to future financial losses from the climate-biodiversity crisis. This information subsequently enables us to make targeted adaptation interventions at this location to ensure both financial hedging and conservation positive outcomes.
Behrens: What sources for your data on climate change and biodiversity loss, and what data do you mainly collect?
Firmenich: Quantifying Nature’s platform integrates many data sources, including geospatial data, earth observation data, and financial and economic data. Geospatial data includes points of interest, land cover and land use, and 3D building models. The data we use is currently mostly open source at this stage. However, our reliance on quality private data sources increases as we refine our product. These include, most notably, high-resolution satellite images from Maxar Technology and geospatial data from Echo Analytics. Although precision private data sets enable us to provide enhanced analytic precision, we recognize that the open innovation and open source data movement are doing great work. Nowadays, many open source data sets are of surprisingly high quality, and they have been of critical importance to the early stages of our product development process. As a result, we continue to use quality public data sets such as the ones from Open Street Maps and combine them with similar commercial data sets sourced from providers, Here Maps and Google Maps API.
In addition to geolocation data, we extensively rely on Earth observation data such as essential climate variables (ECV) temporal layers, synthetic-aperture radar, digital elevation models, digital terrain models from NASA and Maxar Technologies, remote sensing data, and much more. We also require ample amounts of financial and economic insights as the analysis depends on attributing a monetary value to each asset for any publicly listed company to accurately measure the future damages or losses it will incur as we expose the asset to a large variety of different climate and biodiversity models at different temporal scales. Two notable financial data sources that we have come to rely on include APIs from Refinitiv and Financial Modeling Prep (FMP).
At Quantifying Nature, we use the most advanced analytical models which process these data sources to render the precision quantitative financial analysis of the impacts of climate change and biodiversity loss on corporate. In our recommendation of adoption intervention solutions, we offer our clients to hedge these financial risks and enhance environmental conservation. Most of our adoption finance recommendations are geared towards implementing nature-based solutions (NBS), given their significant demonstrated impacts and potential. In addition, I developed the world’s first bankable nature-based solutions while working at the World Wildlife Fund (WWF) in 2017. Our work at the World WWF has laid the foundation for the International Union for Conservation of Nature (IUCN), the United Nations (UN), and other conservation initiative data on nature-based solutions. The funny anecdote here would be that most nature-based solutions data that we use comes from my work, ensuring we offer industry-leading solutions to our clients.
The data ETL (extract-transform-load) process from all data sources is performed to clean and combine data to the Quantifying Nature database using the cloud-based high-performance computing (HPC) unit.
Behrens: What are the advantages of translating the data you collect on climate and biodiversity loss into financial risk analyses?
Firmenich: We are giving financial risk insight to finance professionals and managers by identifying the areas within business and financial operations most vulnerable to the impacts of the climate-biodiversity crisis and quantifying the economic value of these risks. Moreover, the platform also recommends adaptation interventions specific to each threat to enable optimal conservation and financial hedging outcomes. In addition, our platform furthermore assists the financial and corporate sectors in cost-effectively and rapidly completing their climate and nature disclosures based on the following frameworks: Taskforce on Nature-related Financial Disclosures (TNFD) and Task Force on Climate-related Financial Disclosures (TCFD).
Behrens: What challenges do you face when translating climate risk and biodiversity loss data into financial risk analyses that finance professionals and managers can work with?
Firmenich: Climate change, biodiversity loss, and financial risks are all correlated but very complicated to quantify and different per each ecosystem type. In 2020, a total of 431 World Ecosystems were identified. Accordingly, to determine the financial impacts of climate change and biodiversity loss on any assets worldwide, we have been working non-stop on training our AI model based on an extensive database of all case studies representing various ecosystems worldwide.
Behrens: In which ways do AI algorithms help you quantify the financial risks of climate change and biodiversity loss?
Firmenich: Firstly, the AI algorithms allow hyper-automation of multiple chains of predictive climate and biodiversity analysis models at any location based on their ecosystem parameters, such as the changes of tree cover over time, land use and land cover change, hotspots of loss or gain of several ecosystems, both inland and coastal, and biodiversity stressors, among many others. Secondly, AI can automatically value any ecosystem at any desired location. It will be trained to detect vital critical features from satellite images worldwide according to the natural capital accounting system and extensive database of all case studies on each specific climate.
