Knowing your climate impact margin
Companies are predominantly interested in how climate change affects their business operations. Our key concept to address this question is the Climate Impact Margin (CIM). The CIM gives companies a comprehensive set of numbers how climate-related risks and opportunities transfer into components of income and cash-flows, and finally into the overall performance of the company.
The Climate Impact Margin is a framework of additive components that take key identified risks and opportunities from climate change into account. The CIM framework aligns with the recommendations of the Task Force on Climate-related Financial Disclosures. Tāmaota develops technology to address each of the shown elements, translates modelling results of each element into a Climate Impact Margin, and combines all Margins to a comprehensive, dynamic impact on company performance.
Outside-view Climate Impact Margin
Climate resilience and opportunity research on companies within their industrial and macroeconomic background. Receive
- Detailed climate risk and opportunity assessments of organizations
- Multi-scenario evaluations and forecasts
- Positioning and benchmarking of organizations within various peer groups
We combine 1000+ public and private data sources to form a holistic company view. Particular public data sources are
- Historic and current weather and climate data, future climate simulations
- Production, trade and macroeconomic information
- Financial statements, patent databases, regulatory publications
Particular proprietary data sources are
- Historic and current equity values and commodity prices
- News articles, reputation indices, social media
- Market studies and proprietary models
Insight-view Climate Impact Margin
From an insight perspective of an organization, our model facilitates deep-dived decision mining for
- Full-scale, client specific climate risk and opportunity assessment
- Dynamic adaptation through modular architecture
- formulating a corporate climate strategy
Additionally to the public and private data used on outside-view modelling, we incorporate
- The client's supply chain structure and dynamics
- The organization's value chain network (geographic distribution, vertical integration etc.)
- Quantitative measures for the organization's ability and culture to innovate and adapt
Methodology deep dive: Technology risk
Can climate-resilience be learnt from innovativeness?
In general, innovative companies can adapt to changing environments more easily.
However, just counting patents does not suffice to tell climate-resilient companies apart.
E.g., Innovation in oil and gas exploration are worthless, if fossil energy is abandoned.
Instead, innovation has to be rated according to its relevance for climate resilience.
Tamaota’s clean business innovation radar
Tamaota developed an automated scanning software to analyze patent applications from various global databases.
Based on natural language processing technology, we are going far beyond keyword search, but finding context-based, clean tech innovation.
This information delivers technology risk information for any arbitrary company.
Our client’s business case Innovation tracking over time
Any company can be dynamically tracked against peer benchmarks.
Grouping companies into new clusters beyond traditional industry segments allows for novel pattern discovery.
Forecasting of technology risk resilience is possible by a large current-state company sample and detailed temporal trend information of each company.
Business case: cocoa trade for chocolate manufacturer
Climate change impacts cocoa production and many downstream processes
Cocoa is a key crop in global tropical fruit trade.
Global warming and changes in precipitation patterns affect cocoa plantation yield.
Cocoa was studied extensively before, so benchmarks of climate impact are available.
However, impact analysis along the cocoa value chain was not pursued in appropriate depth.
Tamaota created a climate impact model on the global cocoa trade business
In order to model the cocoa value chain, we identified various parameters affecting yield, production, price and economics of cocoa.
We used a machine learning approach to discover the relationship of climate, soil, country and more features on global trade and corporate EBITs.
Our modular machine learning model is readily usable for any other tropical produce
During the entire modelling, process, we kept data formats and model architecture as open as possible.
The interfaces we use in our system allow for flexibly include new data for different tasks.
Our model is thus easily adaptable to crops like banana, which are also affected by climate change.