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    Academy News

    Open Risk White Paper 14: Integrated energy accounting using relational databases

    by Ad Min -
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    In this new Open Risk White Paper we demonstrate a concrete implementation of an integrated energy accounting framework using relational database technologies.

    The framework enables accounting of non-financial disclosures (such as the physical and embodied energy footprints of economic transactions) while enforcing the familiar double-entry balance constraints used to produce conventional (monetary) accounts and financial statements.

    In addition, it allows enforcing constraints associated with the flow and transformations of energy that can happen inside the organizational perimeter.

    An overview of the different elements of an integrated accounting system

    A schematic overview of the different elements that an integrated energy accounting system must (in-principle) consider:

    A schematic overview of the elements of integrated energy accounting

    The organizational boundary of a reporting entity is maybe the key element to consider first. It is something defined legally in terms of ownership and, by-and-large, it is the same as the financial reporting boundary.

    At the most basic level energy accounting would focus on the asset side of the financial balance sheet and examine its physical energy profile (for example the rate of acquisition or generation of energy for business purposes). Reporting these energy mix metrics, along with the corresponding energy intensity ratios using monetary flows from the financial reporting side comprises the essence of current reporting requirements as expressed e.g., by the European Sustainability Reporting Standards.

    Linking the energy consumption activity of an entity with its upstream and downstream value chains leads to hybrid environmentally extended input-output models which are currently available only at the macro-economic level, e.g. the EU Physical energy flow accounts (PEFA) system.

    Connecting the energy profile of the entity with financial counterparties that provide different forms of capital leads to current environmental footprint attributions models pursued by the financial industry such as PCAF.

    While material use of physical energy must in any case be accounted for, the embodied energy in various goods or services offers the most coherent representation as it ties business processes that may be disconnected in time and space yet serve the same economic purpose: providing a final product.

    Last but not least, an entity may have non-trivial interactions that are not captured under financial exchanges with economic agents. This segment includes the environmental “account” that may serve both as a source of high-quality energy and as a sink of waste energy. It also includes broader contracts with society, for example the indirect impacts of energy infrastructure on communities.

    White Paper Sections

    • Motivation and Overview: This is broadly non-technical overview that is building on the more mathematical and conceptual framework developed in WP12
    • European Sustainability Reporting Requirements: A review the energy-related reporting requirements of the newly introduced (but not yet final) European Sustainability Reporting Standards (ESRS)
    • EcoWidgetCo worked out example: We will track the activities of a fictitious company, an ambitious new green venture that builds micro-mobility widgets using recycled materials and (mostly) renewable energy. We sketch how several periods of EWC’s existence and operations could be represented in an integrated energy accounting context.
    • Postgres SQL Implementation: We describe how the framework of integrated energy accounting can be implemented using the concepts and tools of relational databases, with a focus on the schema structure and the triggers that are required to enforce generalized balance sheet equations and physical energy laws. The implementation is open source and available to explore in the Open Risk repository.

    Getting involved with the European Sustainability Reporting Standards

    by Ad Min -
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    Dear all,

    the  European Sustainability Reporting Standards (ESRS) are promising to completely reshape the business reporting landscape in Europe over the coming years. A massive amount of new, non-financial information will have to be collected and disclosed, alongside the traditional financial (monetary) considerations. In turn this will have revolutionary impact on risk management, in particular ESG risks.

    If you are involved in any aspect of sustainability, especially if based in Europe, feel free to explore and get involved with any of the resources we are putting together around the ESRS. 

    The XBRL Glossary collection at the Open Risk Manual

    by Ad Min -
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    The XBRL Glossary categoy of the Open Risk Manual collects definitions of various XBRL terms.

    The focus of the glossary is to try to clarify the technical nomenclature as it appears in the XBRL specification, which is somewhat arcane and confusing. General business reporting terms are in separate categories under the XBRL / Accounting terms categories.

    In due course the aim is to further populate the examples of the various glossary entries and potentially also include relevant snippets illustrating the XBRL format.

    Feedback, ideas or recommendations can be provided anonymously by clicking on the "Help improve this page" link that is at the bottom of every entry of the Manual.

    Getting started with Open Source: The Amazing Jupyter Universe

    by Ad Min -
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    Dear Academy users,

    In a previous module of the Getting Started with Open Source course we discussed the Linux operating system which is the bedrock and maybe most significant example of open source success.

    For our purposes the OS is but the enabling platform. We are primarily interested in running or developing applications (for example risk models and risk management tools) on top of the operating system. A large component of Quantitative Risk Management relies on data processing and quantitative tools (aka Data Science).

    In recent years open source software targeting Data Science found massive adoption in diverse applications. This open source data science spring happens across diverse language and system communities, including old workhorses such as C++, Java but also newcomers such as Rust.

    Yet a triad of ecosystems within that broader universe (namely Python, R and Julia) are distinguished by having a very important or even exclusive data science profile and impact. These three revolutionized the way we do data science today as compared to even just a decade ago.

    One nickname that has been given to this set of data science oriented language is Jupyter. Its a loose word game with the initials of "Julia, Python and R" and the planet Jupiter. The name and logo are an homage to Galileo's discovery of the moons of Jupiter, as documented in notebooks attributed to Galileo.

    In this module we undertake a side by side comparison of a wide range of aspects of the Python, Julia and R language ecosystems. The table of contents is listed below. This comparison is neither exhaustive nor conclusive. It is merely a map all of the important tools that are out there!

    A compilation of astronomical photos of planets, with Jupiter dominating.

    Getting started with Open Source: Linux module

    by Ad Min -
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    Dear Academy users, after some time we have started revamping the Getting started with Open Source course. The first new part to be released focuses on the Linux operating system. The module highlights the importance that Linux plays in the broader Open Source movement and provides a first overview of some important elements.

    This course might be for you if you are reasonably technical and comfortable with computers and have not really been into Linux much but might be tempted. You hear alot of good things about open source ecosystems based on Linux and how they can empower your work but your are not quite sure where and how to start. Maybe you are more into research and academic work and want to get closer to the "bare metal" on which to develop and productionize your models and algorithms. Maybe you are already comfortable with Windows systems but you would like to explore containers and cloud computing.

    The course table of contents is listed below. There is also an (experimental) chat facility to try out if you want to discuss things about Linux.

    Open Risk White Paper 13: Federated Credit Systems, Part II: Techniques for Federated Data Analysis

    by Ad Min -
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    In this Open Risk White Paper, the second of series focusing on Federated Credit Systems, we explore techniques for federated credit data analysis. Building on the first paper where we outlined the overall architecture, essential actors and information flows underlying various business models of credit provision, in this step we focus on the enabling arrangements and techniques for building Federated Credit Data Systems and enabling Federated Analysis.

    An illustration of a federated network of computers that perform computations locally with the help of a central node

    We start with a brief and non-technical description on privacy-preserving technologies, focusing on the special role of federated analysis within the spectrum of cryptographic approaches to multi-party computation.

    We then discuss generative processes of credit data that both motivate federated analysis uses cases and shape its specific characteristics in the context of the financial sector.

    We proceed to define the concept of a federated credit data system, with the federated master data table as an iconic outcome. Building on that layout we sketch how generic algorithms might be structured in a federated analysis context, giving examples from concentration risk analysis.

    We conclude with thoughts on the potential challenges to realize and benefit from federated systems in finance. You can find this White Paper in the usual place.  Comments, remarks, feedback etc. always welcome on our reddit sub or the Open Risk Commons

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