Commentary

Concern about the environmental footprint of data center infrastructure is relatively new.¹ In the early stages of the sustainability movement the volume of digital infrastructure deployed in data centers was dwarfed by the environmental impact of other industrial sectors (e.g., transport, manufacturing, agriculture etc.). Hence even as milestones such as the Paris Agreement (2015) accelerated worldwide efforts towards a sustainable economy, the Information and Communication Industry (ICT) sector received comparatively little attention.

Over the past two decades with the rapid diffusion of digital technologies billions of people changed behaviors. Almost everybody now is spending considerable time online, interacting with other people in newly invented social media. Diverse software platforms enable exchanges of all sorts of information over internet network infrastructures. Given the massive adoption there is little doubt that people derive meaningful utility from social media. At the same time a range of seriously negative impacts is becoming increasingly clear.

The standard description of financial reporting almost entirely avoids mathematical formulation (variables, equations and using abstraction as a means to step back from less relevant details). While historically there have been efforts to describe the underlying accounting systems in more rigorous language (see ¹, ²), the same does not apply to the higher level of published financial statements. The primary exception is, of-course, the famous balance sheet equation, which, while fundamental, does not remotely cover the richness of of the financial reporting machinery. Our objective in this post is to express the typical financial statements more concisely: using symbolic mathematical notation that substantially captures their numerical information content and the constraints being satisfied.

Motivation

An Open Risk Academy user recently asked if there is a real-world, end-to-end open-source project (in particular, written in the popular data science language Python) that walks through the entire (risk model) workflow: namely, collecting data, engineering features, building a risk model, iteratively optimizing it, and drawing actionable conclusions. It is a very good question. For many people learning theory in the context of real-world practice is essential and such an end-to-end example obviously helps connect all the dots of data driven risk management. But like with all good questions, the answer is not simple. In this blog post we explore some of the issues involved in a broader context.

Motivation

Sustainable Public Procurement involves significant complexity and implementation barriers as it involves diverse categories of products and services, each with their own specific sustainability and knowledge requirements. It also requires augmented organizational capacities to handle expanded information flows that provide more holistic views on market capabilities and supply chain interdependencies.

Motivation

GPP is defined as a process whereby public authorities seek to procure goods, services and works with a reduced environmental impact throughout their life-cycle when compared to goods, services and works with the same primary function that would otherwise be procured. Unpacking that definition helps pin down many important characteristics of the information gathering and processing that must take place to support this major sustainability transition process. A most striking high-level aspect is the vast scope of this process: With public procurement forming anywhere from 10% to 30% of an economy, there is vast variability in underlying economic activities, environmental impacts, market characteristics and procurement procedures. Nevertheless, the definition of GPP demands a certain degree of coherency across this vast domain, not least to ensure the cost and energy of public resources dedicated to GPP are effective in achieving their target.

Motivation

The public sector avails of significant purchasing power when contracting with the private sector for goods, works and services. This provides it with meaningful leverage across a good fraction of economic activity. Significant efforts are underway worldwide to utilize this leverage towards achieving sustainability goals. Yet implementing sustainable procurement at scale remains a task of enormous complexity that requires significant investment in different areas: in developing suitable policies, in establishing appropriate information flows and methodologies and, not least, in embedding these new practices and behaviors across swathes of economic activity.

In this blog post we provide a brief overview of how an important class of economic analysis tools termed EEIO, or environmentally extended input-output frameworks, can be utilized to support the implementation of key public procurement sustainability objectives. We sketch the nature of these tools, how they can be used to provide relevant information, and the pros and cons of such an approach.

What are the UN Open Source Principles?

The UN Open Source Principles is a set of eight high-level guiding principles that were recently adopted by the UN Chief Executive Board’s Digital Technology Network (DTN). They open a new window and provide guidelines to drive collaboration and Open Source adoption within the UN and globally. They send a strong signal of the importance of open source and how individuals and organisations need to act to benefit but also support a thriving and sustainable open source community.

Summary

There are dozens upon dozes of indices that aim to measure concentration risk (and related concepts such as diversity or inequality). Why such a proliferation of alternatives? There are multiple reasons, which we will attempt to identify in this post. This enumeration prompts us to dig deeper into the nature of concentration indices and their various pros and cons.

Who Needs a New Financial Services Taxonomy?

Our age is increasingly dominated by the dual challenges and opportunities of the sustainability transition on the one hand, and digital transformation on the other. We witness emerging new financial domains with novel names such as Fintech , or TechFin, or various combinations and hues of Green and Sustainable in Sustainable Finance and we see forces that are reshaping the direction of travel for the financial industry.

June 21 2023 marks the sixth annual #ShowYourStripes Day - a time when meteorologists and other climate communicators around the world raise awareness of our warming planet by displaying colorful visuals of climate change. The warming stripe graphics are representations of the change in temperature over the past 100+ years (here we use the global average). Each stripe represents the temperature averaged over a year. The stripes typically start around the year 1900 and finish in 2022.

FOSDEM is a non-commercial, volunteer-organized, two-day conference celebrating free and open-source software development. The conference has a geographic focus on European open source ecosystems and projects. FOSDEM is primarily aimed at developers, across the entire range of software and aims to enable them to meet and discuss the status of projects.

Interest in Sustainable Finance is growing

What is “Sustainable Finance”? A working definition is: A financial system that takes into account environmental, social and governance considerations to ensure long term sustainability of the human economy.

You can imagine that with a scope and ambition that sweeping, the devil hidden in the details will be of gargantuan size. The definitions of so-called ESG factors, the incorporation of sustainability into business strategies, the governance, policies and risk management applicable to ESG Risks, the ESG and climate-related disclosures and the development of “green” financial products are all topics that combine urgency, complexity and potentially dramatic impact.

Bending the Curve - Sustainability Conceptualized as a One Dimensional Exercise

The opening of the Global Scenario Group report “Bending the Curve: Toward Global Sustainability” by Paul Raskin, Gilberto Gallopin, Pablo Gutman, Al Hammond and Rob Swart, published in 1998 goes as follows:

According to wikipedia Conflation is the merging of two or more sets of information, texts, ideas, opinions, etc., into one, often in error. This may lead to misunderstandings, as the fusion of distinct subjects might obscure analysis of relationships which are emphasized by contrasts. Why does conflation happen in the first place? There are several possible factors which in some contexts may be co-existing and overlapping:

Some form of Informal Risk Management has been practiced by individuals since time immemorial as it is essential for survival, both at the individual and group level. This is the domain of intuitive decision-making, assessing situations on the spot and taking immediate action to avoid obvious risks. It is also the domain of folk wisdom, traditional knowledge and insights passed down orally through generations, expressed in proverbs, stories, and cultural practices (think e.g., Don’t put all your eggs in one basket or, There is never only one cockroach and the long list of Risk Management One-Liners ). This know-how reflects the collective experiences and beliefs of communities, providing guidance on how to successfully handle adverse scenarios.

Course Objective

Graphs (and the related concept of Networks) have emerged from a relative mathematical and physics niches to become mainstream models for describing and interpreting various phenomena. The objective of the course is to review various important graph types as they are increasingly explored in data science and sketch their linkages and relationships (a graph of graphs!).

Data Types are a fundamental building block of data science

Data science is about data, but data are not simple and tame beasts. They have character and attitude, which can cause a lot of friction between them and the data scientist. There is a lot of sweat and tears involved when confronting data, but data scientists can do worse than know how to handle in particular Data Type quirks. Namely, a good fraction of data science involves not modelling data, not transforming data, not even cleaning data but simply goading data around the right containers, providing them with the right stage that fits their character. Getting the data type wrong is many a source of both spectacular bugs and hidden, insidious application errors.

What is the future of stress testing?

To speculate on the future of Stress Testing we need first a basic definition what stress testing is. Broadly speaking, the goal of Stress Testing is to assess how a system would behave under adverse conditions that - while not the most likely outcome with the knowledge of today - are within the realm of the plausible.