SBTi-Finance Tool for Temperature Scoring & Portfolio Coverage
Do you want to understand what drives the temperature score of your portfolio to make better engagement and investment decisions?
Based on the temperature scoring method, developed by CDP and WWF, this tool helps companies and financial institutions to assess the temperature alignment of current emission reduction targets, commitments, and investment and lending portfolios. They can for instance use this information to develop their own GHG emission reduction targets for official validation by the Science Based Targets initiative (SBTi), develop engagement strategies and help with strategic security selection and allocation decisions.
This chapter provides a non-technical introduction and overview of what the tool is for, the types of outputs it delivers, what data is required, how it works, and where you can find more information and documentation to start using the tool.
An introduction to the technical documentation
The SBTi-Finance tool has been built as an open-source, data-agnostic tool and works with input data from any data provider and in many different IT infrastructures.
As such, the SBTi-Finance Tool for Temperature Scoring & Portfolio Coverage can be used in several ways, depending on the specific preferences of the user.
If you prefer to get up and running quickly, we’ve got a no-code and a Python option:
If you are unsure whether the tool will be useful for your application and workflow, or you would first like to run some examples to get a better idea of how the tool works and what types of outputs it generates, the Analysis notebook (with abbreviated methodology) offers a quick and no-code opportunity for such testing. The notebook combines text and code to provide a testing environment for your research, to give you an understanding for how the tool can help you analyze companies’ and portfolios’ temperature scores, to aid your engagement and investment decisions.
The notebook is loaded with example data, but you can also use your own data. For your first test, you can simply run the code cells one by one in the current sequence, to get an understanding of how it works. If you are not familiar with Notebooks, please refer to this introduction.
The following diagram provides an overview of the different parts of the full toolkit and their dependencies:
As shown above, the Python code forms the core codebase of the SBTi-Finance tool. It is recommended to use the Python package if the user would like to integrate the tool in their own codebase. In turn, the second option is running the tool via the API if the user’s preference is to include the tool as a Microservice in their existing IT infrastructure in the cloud or on premise. The development project also included the creation of a simple user interface (UI), which can be used for easier user interaction in combination with the API.
The SBTi tool enables three main ways of installing and/or running the tool:
Users can integrate the Python package in their codebase. For more detailed and up-to-date information on how to run the tool via the Python package, please consult the ‘Getting Started Using Python’ section.
The tool can be included as a Microservice (containerized REST API) in any IT infrastructure (in the cloud or on premise). For more detailed and up-to-date information on how to run the tool via the API, please consult the ‘Getting Started Using REST API’ section. Optionally, the API can be run with a frontend (UI). This simple user interface makes testing by non-technical users easier. For more detailed and up-to-date information on how to use the UI as a frontend to the API, please consult the ‘Getting Started Using REST API’ section.
During the development of this tool, we have worked with several data and service providers to the financial and ESG markets, some who have or are in the process of implementing the tool and methodology into their commercial solutions. These providers include Bloomberg, CDP, ISS, MSCI, Ortec Finance, TruCost and Urgentem. Making use of their solutions can for some users be the easiest way to integrate the tool into existing infrastructure and workflow, to analyze portfolios’ and companies’ temperature scores.
Given the open source nature of the tool, the community is encouraged to make contributions (refer to Contributing section to further develop and/or update the codebase. Contributions can range from submitting a bug report, to submitting a new feature request, all the way to further enhancing the tool’s functionalities by contributing code.
- Introduction to temperature scoring and portfolio coverage methods
- Getting Started Using Python
- Getting Started Using REST API & UI
- Functional Overview
- Data Requirements
- Data Legends
- Project Links
- API Reference