Open Risk Academy

Europe's updated economic activity classification scheme (NACE Rev 2.1 - 2025) is now documented at the Open Risk Manual, with the myriad inclusions / exclusions accessible via hyperlinks.

The old classification continues to be accessible as it will be relevant for some time during the transition period. The entries of the new classification include references to the old one (which sometimes is not 1:1).

Summary

Public Procurement represents a major opportunity to catalyze the transition of economies towards more sustainable patterns but faces important challenges. 

In this new White Paper we review mechanisms that can help address key pain points through the use of open standards, open data and open source tools. In the first part we frame the overall task of Sustainable Public Procurement as an instance of portfolio management.

We outline three distinct information processing pillars that are relevant in this context. We focus here on the task of measuring and attributing greenhouse gas emissions to an existing procurement portfolio.

Structure of the paper:

• In the first section we review relevant background material. In particular we go into the measurement of Greenhouse Gas (GHG) emissions as the essential new information in GPP context.
• In the second section we discuss Sustainable Portfolio Management as an integrated practice, outline the main activities involved and their information technical requirements.
• In the third section we discuss the basics of GHG accounting as they apply in a local government procurement context. This involves the attribution of emissions to procurement contracts on an individual and then portfolio basis. Our focus is highlighting what are informative portfolio views. We discuss the attribution of direct emissions and the challenge of attributing indirect emissions. We discuss standard GHG accounting (inventory) approaches of measuring direct emissions as exemplified e.g., by the IPCC methodologies.
• In the fourth and final section we highlight interesting and relevant open data standards and open source projects in the evolving landscape of tools that can facilitate the implementation of sustainable procurement.

In this new blog post we discuss the proliferation of indices and metrics for computing concentration risk (and related diversification and inequality indices).

We go over the underlying reasons for such proliferation.

• Reason 1: Invented separately in many distinct domains
• Reason 2: Multiple names for essentially the same Index
• Reason 3: Generalized Families of Indexes
• Reason 4: The nature of the underlying data
• Reason 5: Distinct meaning and usability

In this post we elaborate on the insights we can derive when using well established Environmentally-Extended Input-Output frameworks in a slightly different manner: as the canvas for monetary random walks following the money.

In the latest Open Risk White Paper we explore how to organize Environmentally-Extended Input-Output frameworks (EEIO), and in particular their Supply and Use Table (SUT) formulation, as graphs.

Working directly with SUT systems instead of converting to symmetric IO matrices involves fewer assumptions and (in principle) higher resolution in expressing environmental impacts. We elaborate first on the representation of SUT tables as directed, weighted bipartite graphs. We discuss both closed (circular) and open system configurations, featuring source and sink nodes. These are modeled as regular and absorbing Markov Chains respectively. We outline a probabilistic random walk framework that realizes mathematically the colloquial Follow the Money concept. This enables computing a range of various existing and new metrics using the EEIO data. As an illustration, besides the standard environmental footprint metric, we introduce the concept of footprint variance or the intrinsic variability of estimates. We illustrate the overall setup using a classic numerical example from the EEIO literature.

The Data Science category of courses at the Academy has been growing, covering a wide range of current topics relevant for modern quantitative risk management and sustainable finance. In order to organize the content into more homogeneous sets we have now split this category into two sub-categories: Data Engineering and Data Science "proper".

While the precise boundary between these two categories may be a matter of semantics, there is clearly a more technology oriented side of Data Science that focuses on the underlying data flow and processing mechanisms, the API's, programming languages, diverse data formats and related issues, versus the information extraction part that assumes and builds on top of the engineering part.

The existing group of courses has thus been shifted into the corresponding sub-categories and future courses will appear under their respective buckets. The new categorization is of course available at the front page of the Academy. Alternatively you can also peruse the descriptions at our website.

Enjoy!